diff --git a/MODEL/desco_glip_tiny.pth b/MODEL/desco_glip_tiny.pth new file mode 100644 index 0000000000000000000000000000000000000000..6b3d6733cf476ba61f835f1e2520ea7c51696424 --- /dev/null +++ b/MODEL/desco_glip_tiny.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:199479f67b5fbd4ab5e232c8fa8df3e9ab42a96966a023524c6cd95710ea5192 +size 3707483035 diff --git a/app.py b/app.py index 446e183143e52600d900e53aebb40656e7c8fed8..4467086da18c371685be675136168b7a1410813d 100644 --- a/app.py +++ b/app.py @@ -19,9 +19,12 @@ from maskrcnn_benchmark.config import cfg from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo # Use this command for evaluate the GLIP-T model -config_file = "configs/pretrain/glip_Swin_T_O365_GoldG.yaml" +#config_file = "configs/pretrain/glip_Swin_T_O365_GoldG.yaml" #weight_file = "MODEL/glip_tiny_model_o365_goldg_cc_sbu.pth" +config_file = "configs/pretrain_new/desco_glip.yaml" +weight_file = "MODEL/desco_glip_tiny.pth" + # Use this command if you want to try the GLIP-L model # ! wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_large_model.pth -O MODEL/glip_large_model.pth # config_file = "configs/pretrain/glip_Swin_L.yaml" @@ -61,12 +64,12 @@ gr.Interface( ), ], examples=[ - ["./flickr_9472793441.jpg", "bobble heads on top of the shelf ."], - ["./flickr_9472793441.jpg", "sofa . remote . dog . person . car . sky . plane ."], + #["./flickr_9472793441.jpg", "bobble heads on top of the shelf ."], + #["./flickr_9472793441.jpg", "sofa . remote . dog . person . car . sky . plane ."], ["./coco_000000281759.jpg", "A green umbrella. A pink striped umbrella. A plain white umbrella."], ["./coco_000000281759.jpg", "a flowery top. A blue dress. An orange shirt ."], ["./coco_000000281759.jpg", "a car . An electricity box ."], - ["./flickr_7520721.jpg", "A woman figure skater in a blue costume holds her leg by the blade of her skate ."] + #["./flickr_7520721.jpg", "A woman figure skater in a blue costume holds her leg by the blade of her skate ."] ], article=Path("docs/intro.md").read_text() ).launch() diff --git a/coco_000000281759.jpg b/coco_000000281759.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a4c8ce4d4a3c89eaa0b667cdb1af7cf066442a28 --- /dev/null +++ b/coco_000000281759.jpg @@ -0,0 +1,275 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + coco_000000281759.jpg · haotiz/glip-zeroshot-demo at main + + + + + +
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just a placeholder + FCOS: + NUM_CLASSES: 8 + ROI_BOX_HEAD: + NUM_CLASSES: 8 + DYHEAD: + NUM_CLASSES: 8 +DATASETS: + REGISTER: + lvis_evaluation_mini_val: + img_dir: "coco" + ann_file: "coco/annotations/lvis_v1_minival_inserted_image_name.json" + lvis_evaluation_val: + img_dir: "coco" + ann_file: "coco/annotations/lvis_od_val.json" + TRAIN: ("lvis_evaluation_mini_val",) + TEST: ("lvis_evaluation_mini_val",) + +INPUT: + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 +DATALOADER: + SIZE_DIVISIBILITY: 32 + ASPECT_RATIO_GROUPING: False +TEST: + IMS_PER_BATCH: 8 diff --git a/configs/omnilabel/omnilabel_val_eval.yaml b/configs/omnilabel/omnilabel_val_eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aef3cac049295dfe197200624a23c53d8c17c5b5 --- /dev/null +++ b/configs/omnilabel/omnilabel_val_eval.yaml @@ -0,0 +1,18 @@ +DATASETS: + REGISTER: + omnilabel_val_lvis_minival: + img_dir: "coco/" + ann_file: "coco/annotations/lvis_v1.description_omni.json" + omnilabel_val_lvis_selected: + img_dir: "coco/" + ann_file: "coco/annotations/lvis_v1.description_omni.selected.json" + omnilabel_val_lvis_auto: + img_dir: "coco/" + ann_file: "coco/annotations/lvis_v1.description_omni.auto.json" + omnilabel_val_flickr: + img_dir: "flickr30k/flickr30k_images/val/" + ann_file: "mdetr_annotations/final_flickr_separateGT_val.v1.25-0.omnilabel.json" + TEST: ("omnilabel_val",) + # TEST: ("omnilabel_val_coco",) +DATALOADER: + ASPECT_RATIO_GROUPING: False diff --git a/configs/pretrain/_coco.yaml b/configs/pretrain/_coco.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5760b0c64b5c62370748249c8866cea0e035e2ec --- /dev/null +++ b/configs/pretrain/_coco.yaml @@ -0,0 +1,3 @@ +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val", ) \ No newline at end of file diff --git a/configs/pretrain/fiber_cc.yaml b/configs/pretrain/fiber_cc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5d9a561175f78099852ad7be6154f5344c09054a --- /dev/null +++ b/configs/pretrain/fiber_cc.yaml @@ -0,0 +1,144 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_base_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + FUSION_VERSION: "v2" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + VERSION: "fusion" + EMBED_DIM: 128 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (4, 8, 16, 32) + WINDOW_SIZE: 12 + OUT_CHANNELS: (128, 256, 512, 1024) + DROP_PATH_RATE: 0.4 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-v2" + MASK_SPECIAL: False + TOKENIZER_TYPE: "roberta-base" + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: True + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: False + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + REGISTER: + bing_caption_train: + yaml_path: "GCC/CC3M/yamls" + yaml_name: "tiny.noun.harsh" + yaml_name_no_coco: "tiny.noun.harsh" + + # PREDOWNLOAD_BING : True + # PREDOWNLOAD_WITH_AZCOPY : True + + CAPTION_CONF: 0.4 + CAPTION_AUGMENTATION_VERSION: "v3.v1" + CAPTION_VOCAB_FILE: "tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.filtered.json" + DESCRIPTION_FILE: "tools/files/o365.description.v1.json" + + TRAIN: ("mixed_train_no_coco", "flickr30k_train", "object365_dt_train", "bing_caption_train_no_coco") + # TRAIN: ("bing_caption_train", "mixed_train", "flickr30k_train", "coco_grounding_train", ) + TEST: ("coco_2017_val", ) + BING_INDEX_LIST: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + # BING_INDEX_LIST: [ 0, 1, ] + ONE_HOT: False + FLICKR_COPY: 2 + MIXED_COPY: 2 + OBJECT365_COPY: 2 + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + FURTHER_SCREEN: True + + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 235026 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: False + USE_AMP: True + MODEL_EMA: 0.999 + CHECKPOINT_PERIOD: 2500 + + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 diff --git a/configs/pretrain/fiber_tiny.yaml b/configs/pretrain/fiber_tiny.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c0e6532bdc8b4544d06a93c36e8478e8ee9fe110 --- /dev/null +++ b/configs/pretrain/fiber_tiny.yaml @@ -0,0 +1,157 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + FUSION_VERSION: "v2" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + VERSION: "fusion" + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-tiny" + MASK_SPECIAL: False + TOKENIZER_TYPE: "roberta-base" + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: False + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: False + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + TRAIN: ("mixed_train_no_coco", "flickr30k_train", "object365_dt_train", ) + TEST: ("coco_2017_val", ) + ADD_DET_PROMPT: False + ADD_DET_PROMPT_ADVANCED: False + ALTERNATIVE_TRAINING: False + BOX_THRESHOLD: 0.1 + CAPTION_CONF: 0.9 + CAPTION_FORMAT_VERSION: "v2" + CAPTION_MIN_BOX: 1 + CAPTION_NMS: 0.9 + CLASS_AGNOSTIC: False + CLASS_CONCAT: False + COCO_COPY: 1 + #CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + DISABLE_CLIP_TO_IMAGE: False + DISABLE_SHUFFLE: False + FEW_SHOT: 0 + FLICKR_COPY: 1 + FLICKR_GT_TYPE: "separate" + FULL_QUESTION_PROB: 0.5 + FURTHER_SCREEN: False + GENERAL_COPY: -1 + GENERAL_COPY_TEST: -1 + INFERENCE_CAPTION: False + IN_COPY: 1 + LOCAL_DEBUG: False + LVIS_COPY: 1 + LVIS_USE_NORMAL_AP: False + MAX_BOX: -1 + MIXED_COPY: 1 + MULTISTAGE_TRAINING: False + NEG_QUESTION_PROB: 0.8 + NO_MINUS_ONE_FOR_ONE_HOT: False + OBJECT365_COPY: 1 + OI_COPY: 1 + ONE_HOT: False + PACK_RANDOM_CAPTION_NUMBER: 0 + POS_QUESTION_PROB: 0.6 + PREDOWNLOAD_BING: False + PREDOWNLOAD_WITH_AZCOPY: False + PROMPT_LIMIT_NEG: -1 + RANDOM_SAMPLE_NEG: 85 + + REPLACE_CLEAN_LABEL: False + SAFEGUARD_POSITIVE_CAPTION: True + SEPARATION_TOKENS: ". " + SHUFFLE_SEED: 0 + TEST_DATASETNAME_SUFFIX: "" + TRAIN_DATASETNAME_SUFFIX: "" + USE_CAPTION_PROMPT: False + USE_COCO_FORMAT: False + USE_CROWD: False + USE_OD_AUG: False + USE_OVERRIDE_CATEGORY: False + USE_SUPRESS_QUERY: False + VG_COPY: 1 + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 800000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: True + USE_AMP: True + MODEL_EMA: 0.999 + CHECKPOINT_PERIOD: 2500 + + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 \ No newline at end of file diff --git a/configs/pretrain/fiber_tiny_lr.yaml b/configs/pretrain/fiber_tiny_lr.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c0e6532bdc8b4544d06a93c36e8478e8ee9fe110 --- /dev/null +++ b/configs/pretrain/fiber_tiny_lr.yaml @@ -0,0 +1,157 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + FUSION_VERSION: "v2" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + VERSION: "fusion" + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-tiny" + MASK_SPECIAL: False + TOKENIZER_TYPE: "roberta-base" + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: False + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: False + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + TRAIN: ("mixed_train_no_coco", "flickr30k_train", "object365_dt_train", ) + TEST: ("coco_2017_val", ) + ADD_DET_PROMPT: False + ADD_DET_PROMPT_ADVANCED: False + ALTERNATIVE_TRAINING: False + BOX_THRESHOLD: 0.1 + CAPTION_CONF: 0.9 + CAPTION_FORMAT_VERSION: "v2" + CAPTION_MIN_BOX: 1 + CAPTION_NMS: 0.9 + CLASS_AGNOSTIC: False + CLASS_CONCAT: False + COCO_COPY: 1 + #CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + DISABLE_CLIP_TO_IMAGE: False + DISABLE_SHUFFLE: False + FEW_SHOT: 0 + FLICKR_COPY: 1 + FLICKR_GT_TYPE: "separate" + FULL_QUESTION_PROB: 0.5 + FURTHER_SCREEN: False + GENERAL_COPY: -1 + GENERAL_COPY_TEST: -1 + INFERENCE_CAPTION: False + IN_COPY: 1 + LOCAL_DEBUG: False + LVIS_COPY: 1 + LVIS_USE_NORMAL_AP: False + MAX_BOX: -1 + MIXED_COPY: 1 + MULTISTAGE_TRAINING: False + NEG_QUESTION_PROB: 0.8 + NO_MINUS_ONE_FOR_ONE_HOT: False + OBJECT365_COPY: 1 + OI_COPY: 1 + ONE_HOT: False + PACK_RANDOM_CAPTION_NUMBER: 0 + POS_QUESTION_PROB: 0.6 + PREDOWNLOAD_BING: False + PREDOWNLOAD_WITH_AZCOPY: False + PROMPT_LIMIT_NEG: -1 + RANDOM_SAMPLE_NEG: 85 + + REPLACE_CLEAN_LABEL: False + SAFEGUARD_POSITIVE_CAPTION: True + SEPARATION_TOKENS: ". " + SHUFFLE_SEED: 0 + TEST_DATASETNAME_SUFFIX: "" + TRAIN_DATASETNAME_SUFFIX: "" + USE_CAPTION_PROMPT: False + USE_COCO_FORMAT: False + USE_CROWD: False + USE_OD_AUG: False + USE_OVERRIDE_CATEGORY: False + USE_SUPRESS_QUERY: False + VG_COPY: 1 + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 800000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: True + USE_AMP: True + MODEL_EMA: 0.999 + CHECKPOINT_PERIOD: 2500 + + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 \ No newline at end of file diff --git a/configs/pretrain/fibert_flickr_only.yaml b/configs/pretrain/fibert_flickr_only.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9d006b957feb0bb87beeae956f75a8a287307eac --- /dev/null +++ b/configs/pretrain/fibert_flickr_only.yaml @@ -0,0 +1,157 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + FUSION_VERSION: "v2" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + VERSION: "fusion" + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-tiny" + MASK_SPECIAL: False + TOKENIZER_TYPE: "roberta-base" + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: False + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: False + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + TRAIN: ("flickr30k_train", ) + TEST: ("coco_2017_val", ) + ADD_DET_PROMPT: False + ADD_DET_PROMPT_ADVANCED: False + ALTERNATIVE_TRAINING: False + BOX_THRESHOLD: 0.1 + CAPTION_CONF: 0.9 + CAPTION_FORMAT_VERSION: "v2" + CAPTION_MIN_BOX: 1 + CAPTION_NMS: 0.9 + CLASS_AGNOSTIC: False + CLASS_CONCAT: False + COCO_COPY: 1 + #CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + DISABLE_CLIP_TO_IMAGE: False + DISABLE_SHUFFLE: False + FEW_SHOT: 0 + FLICKR_COPY: 1 + FLICKR_GT_TYPE: "separate" + FULL_QUESTION_PROB: 0.5 + FURTHER_SCREEN: False + GENERAL_COPY: -1 + GENERAL_COPY_TEST: -1 + INFERENCE_CAPTION: False + IN_COPY: 1 + LOCAL_DEBUG: False + LVIS_COPY: 1 + LVIS_USE_NORMAL_AP: False + MAX_BOX: -1 + MIXED_COPY: 1 + MULTISTAGE_TRAINING: False + NEG_QUESTION_PROB: 0.8 + NO_MINUS_ONE_FOR_ONE_HOT: False + OBJECT365_COPY: 1 + OI_COPY: 1 + ONE_HOT: False + PACK_RANDOM_CAPTION_NUMBER: 0 + POS_QUESTION_PROB: 0.6 + PREDOWNLOAD_BING: False + PREDOWNLOAD_WITH_AZCOPY: False + PROMPT_LIMIT_NEG: -1 + RANDOM_SAMPLE_NEG: 85 + + REPLACE_CLEAN_LABEL: False + SAFEGUARD_POSITIVE_CAPTION: True + SEPARATION_TOKENS: ". " + SHUFFLE_SEED: 0 + TEST_DATASETNAME_SUFFIX: "" + TRAIN_DATASETNAME_SUFFIX: "" + USE_CAPTION_PROMPT: False + USE_COCO_FORMAT: False + USE_CROWD: False + USE_OD_AUG: False + USE_OVERRIDE_CATEGORY: False + USE_SUPRESS_QUERY: False + VG_COPY: 1 + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 800000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: True + USE_AMP: True + MODEL_EMA: 0.999 + CHECKPOINT_PERIOD: 2500 + + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 \ No newline at end of file diff --git a/configs/pretrain/glip_Swin_Flickr.yaml b/configs/pretrain/glip_Swin_Flickr.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8675fc522001c268bea0f300b7b3ba0d1f84af6a --- /dev/null +++ b/configs/pretrain/glip_Swin_Flickr.yaml @@ -0,0 +1,116 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + REGISTER: + bing_caption_train: + yaml_path: "GCC/CC3M/yamls" + yaml_name: "tiny" + yaml_name_no_coco: "tiny" + + # PREDOWNLOAD_BING : True + # PREDOWNLOAD_WITH_AZCOPY : True + + TRAIN: ("flickr30k_train", ) + # TRAIN: ("bing_caption_train", "mixed_train", "flickr30k_train", "coco_grounding_train", ) + TEST: ("coco_2017_val", ) + # BING_INDEX_LIST: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + # BING_INDEX_LIST: [ 0, 1, ] + ONE_HOT: False + FLICKR_COPY: 1 + MIXED_COPY: 1 + OBJECT365_COPY: 1 + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + FURTHER_SCREEN: True + CAPTION_CONF: 0.5 + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 12 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_Swin_L.yaml b/configs/pretrain/glip_Swin_L.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ee72ccee35d7a57dbb3864eae38184d49ba761bb --- /dev/null +++ b/configs/pretrain/glip_Swin_L.yaml @@ -0,0 +1,120 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_large_patch4_window12_384_22k.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + EMBED_DIM: 192 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (6, 12, 24, 48) + WINDOW_SIZE: 12 + OUT_CHANNELS: (192, 384, 768, 1536) + DROP_PATH_RATE: 0.4 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 8 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: True + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: True + EARLY_FUSE_ON: True + TYPE: "MHA-B" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + + TRAIN: ("mixed_train_no_coco",) # Place holder dataset for now. To be updated in the next version + TEST: ("coco_2017_val", ) + + ONE_HOT: False + FLICKR_COPY: 8 # 0.15 * 8 = ~1.2M + MIXED_COPY: 4 # 0.6 * 4 = ~2.4M + OBJECT365_COPY: 2 # 1.4 * 2 = ~2.8M + VG_COPY: 3 # 0.4 * 3 = ~1.2M + IN_COPY: 2 # 0.67 * 2 = ~1.33M + OI_COPY: 1 # 2M * 1 = 2M + + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + FURTHER_SCREEN: True + CAPTION_CONF: 0.5 + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 1000000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 diff --git a/configs/pretrain/glip_Swin_T_O365.yaml b/configs/pretrain/glip_Swin_T_O365.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2357f84220d27566e12800714447a1b1c381c09d --- /dev/null +++ b/configs/pretrain/glip_Swin_T_O365.yaml @@ -0,0 +1,102 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_FUSED_FEATURES_DOT_PRODUCT: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + + USE_CHECKPOINT: True + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 + +# use for grounding model +DATASETS: + TRAIN: ("object365_dt_train", ) + TEST: ("coco_2017_val", ) + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + + DESCRIPTION_FILE: "DATASET/Objects365/descriptions/o365.description.v1.json" + + SEPARATION_TOKENS: ". " + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 30 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + USE_AMP: True + MODEL_EMA: 0.999 + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_Swin_T_O365_GoldG.yaml b/configs/pretrain/glip_Swin_T_O365_GoldG.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d882bae1dfe8f506ccb6fbbfb3e74c7822c22886 --- /dev/null +++ b/configs/pretrain/glip_Swin_T_O365_GoldG.yaml @@ -0,0 +1,132 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_FUSED_FEATURES_DOT_PRODUCT: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + + USE_CHECKPOINT: True + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 + +# use for grounding model +DATASETS: + REGISTER: + mixed_train_no_coco_noun: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns.json" + mixed_train_no_coco_gpt: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_gpt.v1.new.json" + flickr30k_train_gpt: + img_folder: "flickr30k/flickr30k_images/train" + ann_file: "mdetr_annotations/final_flickr_separateGT_train_gpt.v1.json" + is_train: True + mixed_train_no_coco_noun_gpt: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns_gpt.v1.json" + mixed_train_no_coco_noun_gpt_0422: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns_gpt.0422.json" + mixed_train_no_coco_noun_gpt_0425: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns_gpt.0425.json" + flickr30k_train_gpt_0425: + img_folder: "flickr30k/flickr30k_images/train" + ann_file: "mdetr_annotations/final_flickr_separateGT_train_gpt.0425.json" + is_train: True + + TRAIN: ("object365_dt_train", "mixed_train_no_coco", "flickr30k_train", ) + TEST: ("coco_2017_val", ) + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + + DESCRIPTION_FILE: "tools/files/o365.description.v1.json" + CAPTION_VOCAB_FILE: "tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.json" + SEPARATION_TOKENS: ". " + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 30 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + USE_AMP: True + MODEL_EMA: 0.999 + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_Swin_T_O365_GoldG_description.yaml b/configs/pretrain/glip_Swin_T_O365_GoldG_description.yaml new file mode 100644 index 0000000000000000000000000000000000000000..00c1d420d124fe5ac38ff2ad045c6e36e0f6a5c8 --- /dev/null +++ b/configs/pretrain/glip_Swin_T_O365_GoldG_description.yaml @@ -0,0 +1,112 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_FUSED_FEATURES_DOT_PRODUCT: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + + USE_CHECKPOINT: True + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 + +# use for grounding model +DATASETS: + REGISTER: + mixed_train_no_coco_noun: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns.json" + + TRAIN: ("object365_dt_train", "mixed_train_no_coco", "flickr30k_train", ) + TEST: ("coco_2017_val", ) + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + + OD_TO_GROUNDING_VERSION: "description.gpt.v2.allow_zero" + CAPTION_AUGMENTATION_VERSION: "v3.v1" + CAPTION_VOCAB_FILE: "tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.json" + DESCRIPTION_FILE: "tools/files/o365.description.v1.json" + + SEPARATION_TOKENS: ". " + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 30 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + USE_AMP: True + MODEL_EMA: 0.999 + FIND_UNUSED_PARAMETERS: False + MAX_NEG_PER_BATCH: 1.0 + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_Swin_T_cc.yaml b/configs/pretrain/glip_Swin_T_cc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..90555bdba531c45a5e465d39e2ba866ad39a1074 --- /dev/null +++ b/configs/pretrain/glip_Swin_T_cc.yaml @@ -0,0 +1,116 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + REGISTER: + bing_caption_train: + yaml_path: "GCC/CC3M/yamls" + yaml_name: "tiny" + yaml_name_no_coco: "tiny" + + # PREDOWNLOAD_BING : True + # PREDOWNLOAD_WITH_AZCOPY : True + + TRAIN: ("bing_caption_train_no_coco",) + # TRAIN: ("bing_caption_train", "mixed_train", "flickr30k_train", "coco_grounding_train", ) + TEST: ("coco_2017_val", ) + BING_INDEX_LIST: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + # BING_INDEX_LIST: [ 0, 1, ] + ONE_HOT: False + FLICKR_COPY: 4 + MIXED_COPY: 4 + OBJECT365_COPY: 2 + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + FURTHER_SCREEN: True + CAPTION_CONF: 0.5 + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 12 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_Swin_T_cc_augv3.yaml b/configs/pretrain/glip_Swin_T_cc_augv3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9d0733656b81e2b7fad895d0e6cd82b40c4c7cc5 --- /dev/null +++ b/configs/pretrain/glip_Swin_T_cc_augv3.yaml @@ -0,0 +1,126 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_FUSED_FEATURES_DOT_PRODUCT: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + REGISTER: + bing_caption_train: + yaml_path: "GCC/CC3M/yamls" + yaml_name: "tiny.noun.harsh" + yaml_name_no_coco: "tiny.noun.harsh" + + # PREDOWNLOAD_BING : True + # PREDOWNLOAD_WITH_AZCOPY : True + + CAPTION_CONF: 0.4 + CAPTION_AUGMENTATION_VERSION: "v3.v1" + CAPTION_VOCAB_FILE: "tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.filtered.json" + DESCRIPTION_FILE: "tools/files/o365.description.v1.json" + + TRAIN: ("mixed_train_no_coco", "flickr30k_train", "object365_dt_train", "bing_caption_train_no_coco") + # TRAIN: ("bing_caption_train", "mixed_train", "flickr30k_train", "coco_grounding_train", ) + TEST: ("coco_2017_val", ) + BING_INDEX_LIST: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + # BING_INDEX_LIST: [ 0, 1, ] + ONE_HOT: False + FLICKR_COPY: 2 + MIXED_COPY: 2 + OBJECT365_COPY: 2 + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + FURTHER_SCREEN: True + + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + #MAX_EPOCH: 12 + MAX_ITER: 235026 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + USE_AMP: True + MODEL_EMA: 0.999 + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_Swin_T_coco.yaml b/configs/pretrain/glip_Swin_T_coco.yaml new file mode 100644 index 0000000000000000000000000000000000000000..71b3483759a638c9e3764db8128c1ca8fed31fc9 --- /dev/null +++ b/configs/pretrain/glip_Swin_T_coco.yaml @@ -0,0 +1,100 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_FUSED_FEATURES_DOT_PRODUCT: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + + USE_CHECKPOINT: True + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 + +# use for grounding model +DATASETS: + TRAIN: ("coco_2017_train", ) + TEST: ("coco_2017_val", ) + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + + SEPARATION_TOKENS: ". " + DESCRIPTION_FILE: "DATASET/coco/annotations/coco.description.v1.json" +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 30 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + USE_AMP: True + MODEL_EMA: 0.999 + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_Swing_T_flickr.yaml b/configs/pretrain/glip_Swing_T_flickr.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b0c18f0cab03c7b20d874eccbe086c9e0a8b8b8d --- /dev/null +++ b/configs/pretrain/glip_Swing_T_flickr.yaml @@ -0,0 +1,116 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + REGISTER: + bing_caption_train: + yaml_path: "GCC/CC3M/yamls" + yaml_name: "tiny" + yaml_name_no_coco: "tiny" + + # PREDOWNLOAD_BING : True + # PREDOWNLOAD_WITH_AZCOPY : True + + TRAIN: ("mixed_train_no_coco", ) #"bing_caption_train_no_coco") + # TRAIN: ("bing_caption_train", "mixed_train", "flickr30k_train", "coco_grounding_train", ) + TEST: ("coco_2017_val", ) + BING_INDEX_LIST: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + # BING_INDEX_LIST: [ 0, 1, ] + ONE_HOT: False + FLICKR_COPY: 4 + MIXED_COPY: 4 + OBJECT365_COPY: 2 + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.05, 0.05, 0.5, 0.2) + FURTHER_SCREEN: True + CAPTION_CONF: 0.5 + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 12 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/glip_large.yaml b/configs/pretrain/glip_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..77021db4f4039ebfa51d7a93dda877c5e45969cb --- /dev/null +++ b/configs/pretrain/glip_large.yaml @@ -0,0 +1,120 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_large_patch4_window12_384_22k.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + EMBED_DIM: 192 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (6, 12, 24, 48) + WINDOW_SIZE: 12 + OUT_CHANNELS: (192, 384, 768, 1536) + DROP_PATH_RATE: 0.4 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 8 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: True + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: True + EARLY_FUSE_ON: True + TYPE: "MHA-B" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + + TRAIN: ("mixed_train_no_coco",) # Place holder dataset for now. To be updated in the next version + TEST: ("coco_2017_val", ) + + ONE_HOT: False + FLICKR_COPY: 8 # 0.15 * 8 = ~1.2M + MIXED_COPY: 4 # 0.6 * 4 = ~2.4M + OBJECT365_COPY: 2 # 1.4 * 2 = ~2.8M + VG_COPY: 3 # 0.4 * 3 = ~1.2M + IN_COPY: 2 # 0.67 * 2 = ~1.33M + OI_COPY: 1 # 2M * 1 = 2M + + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + FURTHER_SCREEN: True + CAPTION_CONF: 0.5 + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 1000000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/pretrain/mixed_nococo_flickr_objects365.yaml b/configs/pretrain/mixed_nococo_flickr_objects365.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e63d289b4d213c852abf0abd9892f7ce56138604 --- /dev/null +++ b/configs/pretrain/mixed_nococo_flickr_objects365.yaml @@ -0,0 +1,162 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_base_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + FUSION_VERSION: "v2" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + VERSION: "fusion" + EMBED_DIM: 128 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (4, 8, 16, 32) + WINDOW_SIZE: 12 + OUT_CHANNELS: (128, 256, 512, 1024) + DROP_PATH_RATE: 0.4 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-v2" + MASK_SPECIAL: False + TOKENIZER_TYPE: "roberta-base" + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: True + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: False + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + TRAIN: ("mixed_train_no_coco", "flickr30k_train", "object365_dt_train" ) + TEST: ("coco_2017_val", ) + ADD_DET_PROMPT: False + ADD_DET_PROMPT_ADVANCED: False + ALTERNATIVE_TRAINING: False + BOX_THRESHOLD: 0.1 + CAPTION_CONF: 0.9 + CAPTION_FORMAT_VERSION: "v2" + CAPTION_MIN_BOX: 1 + CAPTION_NMS: 0.9 + CLASS_AGNOSTIC: False + CLASS_CONCAT: False + COCO_COPY: 1 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + DISABLE_CLIP_TO_IMAGE: False + DISABLE_SHUFFLE: False + FEW_SHOT: 0 + FLICKR_COPY: 1 + FLICKR_GT_TYPE: "separate" + FULL_QUESTION_PROB: 0.5 + FURTHER_SCREEN: False + GENERAL_COPY: -1 + GENERAL_COPY_TEST: -1 + INFERENCE_CAPTION: False + IN_COPY: 1 + LOCAL_DEBUG: False + LVIS_COPY: 1 + LVIS_USE_NORMAL_AP: False + MAX_BOX: -1 + MIXED_COPY: 1 + MULTISTAGE_TRAINING: False + NEG_QUESTION_PROB: 0.8 + NO_MINUS_ONE_FOR_ONE_HOT: False + OBJECT365_COPY: 1 + OI_COPY: 1 + ONE_HOT: False + PACK_RANDOM_CAPTION_NUMBER: 0 + POS_QUESTION_PROB: 0.6 + PREDOWNLOAD_BING: False + PREDOWNLOAD_WITH_AZCOPY: False + PROMPT_LIMIT_NEG: -1 + RANDOM_SAMPLE_NEG: 85 + + REPLACE_CLEAN_LABEL: False + SAFEGUARD_POSITIVE_CAPTION: True + SEPARATION_TOKENS: ". " + SHUFFLE_SEED: 0 + TEST_DATASETNAME_SUFFIX: "" + TRAIN_DATASETNAME_SUFFIX: "" + USE_CAPTION_PROMPT: False + USE_COCO_FORMAT: False + USE_CROWD: False + USE_OD_AUG: False + USE_OVERRIDE_CATEGORY: False + USE_SUPRESS_QUERY: False + VG_COPY: 1 + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 800000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: False + USE_AMP: True + MODEL_EMA: 0.999 + CHECKPOINT_PERIOD: 2500 + + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 diff --git a/configs/pretrain/mixed_nococo_flickr_objects365_refexpclean.yaml b/configs/pretrain/mixed_nococo_flickr_objects365_refexpclean.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2702afcc855cef4bc3804d9dd96c298ff157243e --- /dev/null +++ b/configs/pretrain/mixed_nococo_flickr_objects365_refexpclean.yaml @@ -0,0 +1,162 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_large_patch4_window12_384_22k.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + FUSION_VERSION: "v3" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + VERSION: "fusion" + EMBED_DIM: 128 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (4, 8, 16, 32) + WINDOW_SIZE: 12 + OUT_CHANNELS: (128, 256, 512, 1024) + DROP_PATH_RATE: 0.4 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-v2" + MASK_SPECIAL: False + TOKENIZER_TYPE: "roberta-base" + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: True + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: False + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + TRAIN: ("mixed_train_no_coco", "flickr30k_train", "object365_dt_train" ) + TEST: ("coco_2017_val", ) + ADD_DET_PROMPT: False + ADD_DET_PROMPT_ADVANCED: False + ALTERNATIVE_TRAINING: False + BOX_THRESHOLD: 0.1 + CAPTION_CONF: 0.9 + CAPTION_FORMAT_VERSION: "v2" + CAPTION_MIN_BOX: 1 + CAPTION_NMS: 0.9 + CLASS_AGNOSTIC: False + CLASS_CONCAT: False + COCO_COPY: 1 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + DISABLE_CLIP_TO_IMAGE: False + DISABLE_SHUFFLE: False + FEW_SHOT: 0 + FLICKR_COPY: 1 + FLICKR_GT_TYPE: "separate" + FULL_QUESTION_PROB: 0.5 + FURTHER_SCREEN: False + GENERAL_COPY: -1 + GENERAL_COPY_TEST: -1 + INFERENCE_CAPTION: False + IN_COPY: 1 + LOCAL_DEBUG: False + LVIS_COPY: 1 + LVIS_USE_NORMAL_AP: False + MAX_BOX: -1 + MIXED_COPY: 1 + MULTISTAGE_TRAINING: False + NEG_QUESTION_PROB: 0.8 + NO_MINUS_ONE_FOR_ONE_HOT: False + OBJECT365_COPY: 1 + OI_COPY: 1 + ONE_HOT: False + PACK_RANDOM_CAPTION_NUMBER: 0 + POS_QUESTION_PROB: 0.6 + PREDOWNLOAD_BING: False + PREDOWNLOAD_WITH_AZCOPY: False + PROMPT_LIMIT_NEG: -1 + RANDOM_SAMPLE_NEG: 85 + + REPLACE_CLEAN_LABEL: False + SAFEGUARD_POSITIVE_CAPTION: True + SEPARATION_TOKENS: ". " + SHUFFLE_SEED: 0 + TEST_DATASETNAME_SUFFIX: "" + TRAIN_DATASETNAME_SUFFIX: "" + USE_CAPTION_PROMPT: False + USE_COCO_FORMAT: False + USE_CROWD: False + USE_OD_AUG: False + USE_OVERRIDE_CATEGORY: False + USE_SUPRESS_QUERY: False + VG_COPY: 1 + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 800000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 5000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: False + USE_AMP: True + MODEL_EMA: 0.999 + CHECKPOINT_PERIOD: 2500 + + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 diff --git a/configs/pretrain_new/desco_fiber.yaml b/configs/pretrain_new/desco_fiber.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b09d21bafe853399deefe0b88ee365c8f5098ffe --- /dev/null +++ b/configs/pretrain_new/desco_fiber.yaml @@ -0,0 +1,168 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_base_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + FUSION_VERSION: "v2" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + SWINT: + VERSION: "fusion" + EMBED_DIM: 128 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (4, 8, 16, 32) + WINDOW_SIZE: 12 + OUT_CHANNELS: (128, 256, 512, 1024) + DROP_PATH_RATE: 0.4 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-v2" + MASK_SPECIAL: False + TOKENIZER_TYPE: "roberta-base" + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + USE_CHECKPOINT: True + FUSE_CONFIG: + USE_FUSED_FEATURES_DOT_PRODUCT: False + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +DATASETS: + REGISTER: + bing_caption_train: + yaml_path: "GCC/CC3M/yamls" + yaml_name: "tiny.noun.harsh" + yaml_name_no_coco: "tiny.noun.harsh" + mixed_train_no_coco_noun: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns.json" + mixed_train_no_coco_gpt: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_gpt.v1.new.json" + flickr30k_train_gpt: + img_folder: "flickr30k/flickr30k_images/train" + ann_file: "mdetr_annotations/final_flickr_separateGT_train_gpt.v1.json" + is_train: True + mixed_train_no_coco_noun_gpt: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns_gpt.v1.json" + mixed_train_no_coco_noun_gpt_0425: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns_gpt.0425.json" + flickr30k_train_gpt_0425: + img_folder: "flickr30k/flickr30k_images/train" + ann_file: "mdetr_annotations/final_flickr_separateGT_train_gpt.0425.json" + is_train: True + + CAPTION_CONF: 0.4 + OD_TO_GROUNDING_VERSION: "description.gpt.v10.mixed.allow_zero.v1" + CAPTION_AUGMENTATION_VERSION: "mixed.v4.8-2.drop_positive.control_pos.grouping.v1" + CC_CAPTION_AUGMENTATION_VERSION: "mixed.v3-v4.9-1.drop_positive.control_pos.grouping.v1" + CAPTION_VOCAB_FILE: "tools/files/joint_vocab.merged.v1.tmp0.davincci.json" + DESCRIPTION_FILE: "tools/files/o365.description.v1.json" + + TRAIN: ("mixed_train_no_coco_noun_gpt_0425", "flickr30k_train_gpt_0425", "object365_dt_train", ) # bing_caption_train_no_coco + TEST: ("coco_2017_val", ) + ADD_DET_PROMPT: False + ADD_DET_PROMPT_ADVANCED: False + ALTERNATIVE_TRAINING: False + BING_INDEX_LIST: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + ONE_HOT: False + FLICKR_COPY: 2 + MIXED_COPY: 2 + OBJECT365_COPY: 2 + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + FURTHER_SCREEN: True + + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.01 + WEIGHT_DECAY_SCHEDULE: True + STEPS: (0.67, 0.89) + MAX_ITER: 800000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: False + USE_AMP: True + MODEL_EMA: 0.999 + CHECKPOINT_PERIOD: 2500 + + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 diff --git a/configs/pretrain_new/desco_glip.yaml b/configs/pretrain_new/desco_glip.yaml new file mode 100644 index 0000000000000000000000000000000000000000..08858b30bdf899f2a3a9b1cde1f9cd5943aa22f0 --- /dev/null +++ b/configs/pretrain_new/desco_glip.yaml @@ -0,0 +1,134 @@ +# for final GLIP tiny, pre-trained from scratch +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "MODEL/swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + USE_CHECKPOINT: True + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_FUSED_FEATURES_DOT_PRODUCT: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + REGISTER: + bing_caption_train: + yaml_path: "GCC/CC3M/yamls" + yaml_name: "tiny.noun.harsh" + yaml_name_no_coco: "tiny.noun.harsh" + mixed_train_no_coco_noun_gpt_0425: + coco_img_dir: "coco/train2014" + vg_img_dir: "gqa/images" + ann_file: "mdetr_annotations/final_mixed_train_no_coco_with_nouns_gpt.0425.json" + flickr30k_train_gpt_0425: + img_folder: "flickr30k/flickr30k_images/train" + ann_file: "mdetr_annotations/final_flickr_separateGT_train_gpt.0425.json" + is_train: True + + CAPTION_CONF: 0.4 + + CAPTION_AUGMENTATION_VERSION: "mixed.v4-v3.5-4-1.drop_positive.control_pos.grouping.v1" # for GoldG data; used by CaptionAugmentation to determine how to perform the augmentation + OD_TO_GROUNDING_VERSION: "description.gpt.v10.mixed.allow_zero.v1" # for + CC_CAPTION_AUGMENTATION_VERSION: "mixed.v3.8-2.drop_positive.control_pos.grouping.v1" # for CC data; used by CaptionAugmentation to determine how to perform the augmentation + CAPTION_VOCAB_FILE: "tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.filtered.json" + DESCRIPTION_FILE: "tools/files/o365.description.v1.json" + + TRAIN: ("mixed_train_no_coco_noun_gpt_0425", "flickr30k_train_gpt_0425", "object365_dt_train", ) # bing_caption_train_no_coco + TEST: ("coco_2017_val", ) + BING_INDEX_LIST: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + # BING_INDEX_LIST: [ 0, 1, ] + ONE_HOT: False + FLICKR_COPY: 2 + MIXED_COPY: 2 + OBJECT365_COPY: 1 + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + FURTHER_SCREEN: True + + CAPTION_NMS: -1.0 + CAPTION_MIN_BOX: 1 + + SEPARATION_TOKENS: ". " + + PACK_RANDOM_CAPTION_NUMBER: 20 + NO_RANDOM_PACK_PROBABILITY: 0.4 + RANDOM_PACK_PROB: 0.5 + CAPTION_FORMAT_VERSION: "v2" + + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + DISTRIBUTE_CHUNK_AMONG_NODE: False + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + #MAX_EPOCH: 12 + MAX_ITER: 300000 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + USE_AMP: True + MODEL_EMA: 0.999 + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 diff --git a/configs/refcoco.yaml b/configs/refcoco.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4018f0d4f65ebc5405396ed101e5a8df25ae6555 --- /dev/null +++ b/configs/refcoco.yaml @@ -0,0 +1,116 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_base_patch4_window12_384_22k.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + ATSS: + PRE_NMS_TOP_N: 3000 + DETECTIONS_PER_IMG: 100 + INFERENCE_TH: 0.0 + + SWINT: + VERSION: "fusion" + EMBED_DIM: 128 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (4, 8, 16, 32) + WINDOW_SIZE: 12 + OUT_CHANNELS: (128, 256, 512, 1024) + DROP_PATH_RATE: 0.4 + + BACKBONE: + FUSION_VERSION: "v3" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + USE_CHECKPOINT: True + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-v2" + TOKENIZER_TYPE: "roberta-base" + LANG_DIM: 768 + MASK_SPECIAL: False + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + USE_CHECKPOINT: True + + FUSE_CONFIG: + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + TRAIN: ("refcoco_train", ) + TEST: ("refcoco_val", ) + DISABLE_SHUFFLE: True + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + FLIP_PROB_TRAIN: 0.0 # Important for refcoco esp + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.00001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 20 + IMS_PER_BATCH: 16 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: False + USE_AMP: True + MODEL_EMA: 0.999 + + CLIP_GRADIENTS: + ENABLED: False + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: True + EVAL_TASK: "grounding" + IMS_PER_BATCH: 16 + + diff --git a/configs/refcocog.yaml b/configs/refcocog.yaml new file mode 100644 index 0000000000000000000000000000000000000000..533d4e92bdb3e7776a295ccb7fa2546423b22a89 --- /dev/null +++ b/configs/refcocog.yaml @@ -0,0 +1,116 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_base_patch4_window12_384_22k.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + ATSS: + PRE_NMS_TOP_N: 3000 + DETECTIONS_PER_IMG: 100 + INFERENCE_TH: 0.0 + + SWINT: + VERSION: "fusion" + EMBED_DIM: 128 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (4, 8, 16, 32) + WINDOW_SIZE: 12 + OUT_CHANNELS: (128, 256, 512, 1024) + DROP_PATH_RATE: 0.4 + + BACKBONE: + FUSION_VERSION: "v3" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + USE_CHECKPOINT: True + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-v2" + TOKENIZER_TYPE: "roberta-base" + LANG_DIM: 768 + MASK_SPECIAL: False + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + USE_CHECKPOINT: True + + FUSE_CONFIG: + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + TRAIN: ("refcocog_train", ) + TEST: ("refcocog_val",) + DISABLE_SHUFFLE: True + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + FLIP_PROB_TRAIN: 0.0 # Important for refcoco esp + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.00001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 20 + IMS_PER_BATCH: 16 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: False + USE_AMP: True + MODEL_EMA: 0.999 + + CLIP_GRADIENTS: + ENABLED: False + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: True + EVAL_TASK: "grounding" + IMS_PER_BATCH: 16 + + diff --git a/configs/refcocoplus.yaml b/configs/refcocoplus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6928f1c2aed80ca0cff27fcdb704a643b99f4e60 --- /dev/null +++ b/configs/refcocoplus.yaml @@ -0,0 +1,116 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_base_patch4_window12_384_22k.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + ATSS: + PRE_NMS_TOP_N: 3000 + DETECTIONS_PER_IMG: 100 + INFERENCE_TH: 0.0 + + SWINT: + VERSION: "fusion" + EMBED_DIM: 128 + DEPTHS: (2, 2, 18, 2) + NUM_HEADS: (4, 8, 16, 32) + WINDOW_SIZE: 12 + OUT_CHANNELS: (128, 256, 512, 1024) + DROP_PATH_RATE: 0.4 + + BACKBONE: + FUSION_VERSION: "v3" + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + USE_CHECKPOINT: True + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "roberta-fused-v2" + TOKENIZER_TYPE: "roberta-base" + LANG_DIM: 768 + MASK_SPECIAL: False + USE_CHECKPOINT: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + USE_CHECKPOINT: True + + FUSE_CONFIG: + EARLY_FUSE_ON: False + TYPE: "NONE" # "MHA-B", "MHA-S", "FILM", "SCAN", "NONE" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + +# use for grounding model +DATASETS: + TRAIN: ("refcoco+_train", ) + TEST: ("refcoco+_val",) + DISABLE_SHUFFLE: True + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + FLIP_PROB_TRAIN: 0.0 # Important for refcoco esp + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.00001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 20 + IMS_PER_BATCH: 16 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + TEST_WITH_INFERENCE: True + FIND_UNUSED_PARAMETERS: False + USE_AMP: True + MODEL_EMA: 0.999 + + CLIP_GRADIENTS: + ENABLED: False + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 + +TEST: + DURING_TRAINING: True + EVAL_TASK: "grounding" + IMS_PER_BATCH: 16 + + diff --git a/configs/refexp/_refcoco+_testA.yaml b/configs/refexp/_refcoco+_testA.yaml new file mode 100644 index 0000000000000000000000000000000000000000..008b5159b87efa4db30c47be14101741d2d00ed0 --- /dev/null +++ b/configs/refexp/_refcoco+_testA.yaml @@ -0,0 +1,30 @@ +MODEL: + ATSS: + NUM_CLASSES: 8 + FCOS: + NUM_CLASSES: 8 + ROI_BOX_HEAD: + NUM_CLASSES: 8 + DYHEAD: + NUM_CLASSES: 8 +DATASETS: + TEST: ("refcoco+_testA", ) + FLICKR_GT_TYPE: "separate" + +INPUT: + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 +DATALOADER: + SIZE_DIVISIBILITY: 32 + ASPECT_RATIO_GROUPING: False +SOLVER: + WARMUP_ITERS: 0 + MAX_EPOCH: 12 + CHECKPOINT_PERIOD: 100 +TEST: + IMS_PER_BATCH: 8 + + +# local debug command: CUDA_VISIBLE_DEVICES=0 python tools/finetune.py --config-file configs/harold/dyhead_grounding.yaml --ft-tasks configs/odinw/_flickr.yaml --skip-train SOLVER.IMS_PER_BATCH 1 MODEL.WEIGHT OUTPUT/model_0345000.pth OUTPUT_DIR tmp TEST.IMS_PER_BATCH 1 TEST.EVAL_TASK grounding TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM 100 \ No newline at end of file diff --git a/configs/refexp/_refcoco+_testB.yaml b/configs/refexp/_refcoco+_testB.yaml new file mode 100644 index 0000000000000000000000000000000000000000..56641dc316c5d16ff66b28ab94a74544c56b7bbe --- /dev/null +++ b/configs/refexp/_refcoco+_testB.yaml @@ -0,0 +1,30 @@ +MODEL: + ATSS: + NUM_CLASSES: 8 + FCOS: + NUM_CLASSES: 8 + ROI_BOX_HEAD: + NUM_CLASSES: 8 + DYHEAD: + NUM_CLASSES: 8 +DATASETS: + TEST: ("refcoco+_testB", ) + FLICKR_GT_TYPE: "separate" + +INPUT: + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 +DATALOADER: + SIZE_DIVISIBILITY: 32 + ASPECT_RATIO_GROUPING: False +SOLVER: + WARMUP_ITERS: 0 + MAX_EPOCH: 12 + CHECKPOINT_PERIOD: 100 +TEST: + IMS_PER_BATCH: 8 + + +# local debug command: CUDA_VISIBLE_DEVICES=0 python tools/finetune.py --config-file configs/harold/dyhead_grounding.yaml --ft-tasks configs/odinw/_flickr.yaml --skip-train SOLVER.IMS_PER_BATCH 1 MODEL.WEIGHT OUTPUT/model_0345000.pth OUTPUT_DIR tmp TEST.IMS_PER_BATCH 1 TEST.EVAL_TASK grounding TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM 100 \ No newline at end of file diff --git a/configs/refexp/_refcoco_testA.yaml b/configs/refexp/_refcoco_testA.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9758ec03b8d6815cff13122188a5deda552ed600 --- /dev/null +++ b/configs/refexp/_refcoco_testA.yaml @@ -0,0 +1,30 @@ +MODEL: + ATSS: + NUM_CLASSES: 8 + FCOS: + NUM_CLASSES: 8 + ROI_BOX_HEAD: + NUM_CLASSES: 8 + DYHEAD: + NUM_CLASSES: 8 +DATASETS: + TEST: ("refcoco_testA", ) + FLICKR_GT_TYPE: "separate" + +INPUT: + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 +DATALOADER: + SIZE_DIVISIBILITY: 32 + ASPECT_RATIO_GROUPING: False +SOLVER: + WARMUP_ITERS: 0 + MAX_EPOCH: 12 + CHECKPOINT_PERIOD: 100 +TEST: + IMS_PER_BATCH: 8 + + +# local debug command: CUDA_VISIBLE_DEVICES=0 python tools/finetune.py --config-file configs/harold/dyhead_grounding.yaml --ft-tasks configs/odinw/_flickr.yaml --skip-train SOLVER.IMS_PER_BATCH 1 MODEL.WEIGHT OUTPUT/model_0345000.pth OUTPUT_DIR tmp TEST.IMS_PER_BATCH 1 TEST.EVAL_TASK grounding TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM 100 \ No newline at end of file diff --git a/configs/refexp/_refcoco_testB.yaml b/configs/refexp/_refcoco_testB.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0ac73497d800cc8bcab15a9487851e3c0e7d6ae0 --- /dev/null +++ b/configs/refexp/_refcoco_testB.yaml @@ -0,0 +1,30 @@ +MODEL: + ATSS: + NUM_CLASSES: 8 + FCOS: + NUM_CLASSES: 8 + ROI_BOX_HEAD: + NUM_CLASSES: 8 + DYHEAD: + NUM_CLASSES: 8 +DATASETS: + TEST: ("refcoco_testB", ) + FLICKR_GT_TYPE: "separate" + +INPUT: + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 +DATALOADER: + SIZE_DIVISIBILITY: 32 + ASPECT_RATIO_GROUPING: False +SOLVER: + WARMUP_ITERS: 0 + MAX_EPOCH: 12 + CHECKPOINT_PERIOD: 100 +TEST: + IMS_PER_BATCH: 8 + + +# local debug command: CUDA_VISIBLE_DEVICES=0 python tools/finetune.py --config-file configs/harold/dyhead_grounding.yaml --ft-tasks configs/odinw/_flickr.yaml --skip-train SOLVER.IMS_PER_BATCH 1 MODEL.WEIGHT OUTPUT/model_0345000.pth OUTPUT_DIR tmp TEST.IMS_PER_BATCH 1 TEST.EVAL_TASK grounding TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM 100 \ No newline at end of file diff --git a/configs/refexp/_refcocog_test.yaml b/configs/refexp/_refcocog_test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7600b61b1d3ab74a58781af931068dbfdeb6c8fd --- /dev/null +++ b/configs/refexp/_refcocog_test.yaml @@ -0,0 +1,30 @@ +MODEL: + ATSS: + NUM_CLASSES: 8 + FCOS: + NUM_CLASSES: 8 + ROI_BOX_HEAD: + NUM_CLASSES: 8 + DYHEAD: + NUM_CLASSES: 8 +DATASETS: + TEST: ("refcocog_test", ) + FLICKR_GT_TYPE: "separate" + +INPUT: + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 +DATALOADER: + SIZE_DIVISIBILITY: 32 + ASPECT_RATIO_GROUPING: False +SOLVER: + WARMUP_ITERS: 0 + MAX_EPOCH: 12 + CHECKPOINT_PERIOD: 100 +TEST: + IMS_PER_BATCH: 8 + + +# local debug command: CUDA_VISIBLE_DEVICES=0 python tools/finetune.py --config-file 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"GLIP: Grounded Language-Image Pre-training. CVPR 2022, Best Paper Finalist"

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This is the HuggingFace Gradio Demo for GLIP. The model requires an image, and a text to be the inputs. The text input can either be a natural sentence description (grounding), or a simple concatenation of some random categories (object detection).

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The paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich.

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Code: https://github.com/microsoft/GLIP

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News: We are also holding an ODinW challenge at the CV in the Wild Workshop @ ECCV 2022. We hope our open-source code encourage the community to participate in this challenge!

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+ + + + + + + + + + diff --git a/maskrcnn_benchmark/__init__.py b/maskrcnn_benchmark/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4bc96c7a6bf8379e1adfb3e4adf536107b385fa9 --- /dev/null +++ b/maskrcnn_benchmark/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. diff --git a/maskrcnn_benchmark/config/__init__.py b/maskrcnn_benchmark/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..81363c8df5f903a254414861a854116899fc8bbe --- /dev/null +++ b/maskrcnn_benchmark/config/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from .defaults import _C as cfg +from .paths_catalog import try_to_find diff --git a/maskrcnn_benchmark/config/defaults.py b/maskrcnn_benchmark/config/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..9fab60edc7dac56db4861666a951d7155c386be0 --- /dev/null +++ b/maskrcnn_benchmark/config/defaults.py @@ -0,0 +1,982 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import os + +from yacs.config import CfgNode as CN + +# ----------------------------------------------------------------------------- +# Convention about Training / Test specific parameters +# ----------------------------------------------------------------------------- +# Whenever an argument can be either used for training or for testing, the +# corresponding name will be post-fixed by a _TRAIN for a training parameter, +# or _TEST for a test-specific parameter. +# For example, the number of images during training will be +# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be +# IMAGES_PER_BATCH_TEST + +# ----------------------------------------------------------------------------- +# Config definition +# ----------------------------------------------------------------------------- + +_C = CN() + +_C.MODEL = CN() +_C.MODEL.RPN_ONLY = False +_C.MODEL.BOX_ON = True +_C.MODEL.MASK_ON = False +_C.MODEL.KEYPOINT_ON = False +_C.MODEL.DEVICE = "cuda" + +_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" + +_C.MODEL.RPN_ARCHITECTURE = "RPN" +_C.MODEL.DEBUG = False # add debug flag +_C.MODEL.ONNX = False # add onnx flag + +# If the WEIGHT starts with a catalog://, like :R-50, the code will look for +# the path in paths_catalog. Else, it will use it as the specified absolute +# path +_C.MODEL.WEIGHT = "" +_C.MODEL.PRETRAIN_NAME = "" + +# If LINEAR_PROB = True, only the last linear layers in rpn and roi_head are trainable +_C.MODEL.LINEAR_PROB = False + +# ----------------------------------------------------------------------------- +# Multitask Training / Test specific parameters +# ----------------------------------------------------------------------------- +_C.MODEL.MULTITASK = CN(new_allowed=True) + +# ----------------------------------------------------------------------------- +# INPUT +# ----------------------------------------------------------------------------- +_C.INPUT = CN() +# Size of the smallest side of the image during training +_C.INPUT.MIN_SIZE_TRAIN = 800 # (800,) +# Maximum size of the side of the image during training +_C.INPUT.MAX_SIZE_TRAIN = 1333 +# Size of the smallest side of the image during testing +_C.INPUT.MIN_SIZE_TEST = 800 +# Maximum size of the side of the image during testing +_C.INPUT.MAX_SIZE_TEST = 1333 +# Values to be used for image normalization +_C.INPUT.PIXEL_MEAN = [102.9801, 115.9465, 122.7717] +# Values to be used for image normalization +_C.INPUT.PIXEL_STD = [1.0, 1.0, 1.0] +# Convert image to BGR format (for Caffe2 models), in range 0-255 +_C.INPUT.TO_BGR255 = True +_C.INPUT.FORMAT = "" +_C.INPUT.FIX_RES = False + +# ----------------------------------------------------------------------------- +# Augmentation +# ----------------------------------------------------------------------------- +_C.AUGMENT = CN() +_C.AUGMENT.USE_RA = 0 +_C.AUGMENT.FLIP_PROB_TRAIN = 0.5 +_C.AUGMENT.VERTICAL_FLIP_PROB_TRAIN = 0.0 +_C.AUGMENT.MULT_MIN_SIZE_TRAIN = () + +_C.AUGMENT.BRIGHTNESS = 0.0 +_C.AUGMENT.CONTRAST = 0.0 +_C.AUGMENT.SATURATION = 0.0 +_C.AUGMENT.HUE = 0.0 + +_C.AUGMENT.CROP_PROB = 0.5 +_C.AUGMENT.CROP_MIN_IOUS = (0.1, 0.3, 0.5, 0.7, 0.9) +_C.AUGMENT.CROP_MIN_SIZE = 0.3 + +_C.AUGMENT.AFFINE_PROB = 0.5 +_C.AUGMENT.AFFINE_R = (-10, 10) +_C.AUGMENT.AFFINE_T = (0.1, 0.1) +_C.AUGMENT.AFFINE_S = (0.9, 1.1) +_C.AUGMENT.AFFINE_SHEAR = (-2, 2) +_C.AUGMENT.AFFINE_FILL = (127.5, 127.5, 127.5) + +_C.AUGMENT.ERASE_PROB = 0.0 +_C.AUGMENT.ERASE_L = 0.02 +_C.AUGMENT.ERASE_H = 1 / 3 +_C.AUGMENT.ERASE_MIN_ASPECT = 0.3 +_C.AUGMENT.ERASE_MODE = "const" +_C.AUGMENT.ERASE_MAX_COUNT = 1 +_C.AUGMENT.ERASE_MAX_OVERLAP = 0.6 +_C.AUGMENT.ERASE_MAX_VALUE = 255 + +_C.AUGMENT.MOSAIC_PROB = 0.0 +_C.AUGMENT.MOSAIC_SHIFT = 0.5 +_C.AUGMENT.MOSAIC_SIZE = -1 + +_C.AUGMENT.PASTE_PROB = 0.0 +_C.AUGMENT.PASTE_CAT = () +_C.AUGMENT.PASTE_NUM = 2 +# ----------------------------------------------------------------------------- +# Dataset +# ----------------------------------------------------------------------------- +_C.DATASETS = CN() +# List of the dataset names for training, as present in paths_catalog.py +_C.DATASETS.TRAIN = () +# List of the dataset names for testing, as present in paths_catalog.py +_C.DATASETS.TEST = () +# Use is_crowd label +_C.DATASETS.USE_CROWD = False +_C.DATASETS.CLASS_AGNOSTIC = False +_C.DATASETS.CLASS_CONCAT = False +_C.DATASETS.MAX_BOX = -1 +_C.DATASETS.SAMPLE_RATIO = 0.0 +_C.DATASETS.FEW_SHOT = 0 +# SHUFFLE_SEED != 0 means shuffle the dataset in the few shot setting +_C.DATASETS.SHUFFLE_SEED = 0 +_C.DATASETS.PREDEFINED_TEXT = "" +_C.DATASETS.ALTERNATIVE_TRAINING = False +_C.DATASETS.MULTISTAGE_TRAINING = False +_C.DATASETS.REGISTER = CN(new_allowed=True) +_C.DATASETS.BOX_THRESHOLD = 0.1 +# Duplicate Dataset +_C.DATASETS.COCO_COPY = 1 +_C.DATASETS.LVIS_COPY = 1 +_C.DATASETS.FLICKR_COPY = 1 +_C.DATASETS.MIXED_COPY = 1 +_C.DATASETS.OBJECT365_COPY = 1 +_C.DATASETS.VG_COPY = 1 +_C.DATASETS.OI_COPY = 1 +_C.DATASETS.IN_COPY = 1 +_C.DATASETS.MIXED_GPT_COPY = 1 + +# Duplicate Dataset +_C.DATASETS.COCO_COPY = 1 +_C.DATASETS.FLICKR_COPY = 1 +_C.DATASETS.MIXED_COPY = 1 +_C.DATASETS.OBJECT365_COPY = 1 +_C.DATASETS.VG_COPY = 1 +_C.DATASETS.OI_COPY = 1 +_C.DATASETS.IN_COPY = 1 +_C.DATASETS.REFCOCO_COPY = 1 +_C.DATASETS.GENERAL_COPY = -1 +_C.DATASETS.GENERAL_COPY_TEST = -1 + +# OD to Grounding +_C.DATASETS.RANDOM_SAMPLE_NEG = -1 +_C.DATASETS.ADD_DET_PROMPT = False +_C.DATASETS.ADD_DET_PROMPT_ADVANCED = False +_C.DATASETS.USE_OD_AUG = False +_C.DATASETS.USE_COCO_FORMAT = False +_C.DATASETS.CONTROL_PROB = () +_C.DATASETS.DISABLE_SHUFFLE = False +_C.DATASETS.PROMPT_VERSION = "" +_C.DATASETS.PROMPT_LIMIT_NEG = -1 +_C.DATASETS.POS_QUESTION_PROB = 0.6 +_C.DATASETS.NEG_QUESTION_PROB = 0.8 +_C.DATASETS.FULL_QUESTION_PROB = 0.5 +_C.DATASETS.ONE_HOT = False +_C.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT = False + +_C.DATASETS.DISABLE_CLIP_TO_IMAGE = False +_C.DATASETS.SEPARATION_TOKENS = " " + +# LVIS +_C.DATASETS.LVIS_USE_NORMAL_AP = False +_C.DATASETS.LVIS_TOPK = 10000 +_C.DATASETS.SPECIAL_SAFEGUARD_FOR_COCO_GROUNDING = False + +# Caption +_C.DATASETS.BING_INDEX_LIST = [] +_C.DATASETS.CAPTION_MIN_BOX = 1 +_C.DATASETS.REPLACE_CLEAN_LABEL = False +_C.DATASETS.FURTHER_SCREEN = False +_C.DATASETS.CAPTION_CONF = 0.9 +_C.DATASETS.CAPTION_NMS = 0.9 +_C.DATASETS.PACK_RANDOM_CAPTION_NUMBER = 0 +_C.DATASETS.INFERENCE_CAPTION = False +_C.DATASETS.SAMPLE_NEGATIVE_FOR_GROUNDING_DATA = -1.0 +_C.DATASETS.RANDOM_PACK_PROB = -1.0 +_C.DATASETS.NO_RANDOM_PACK_PROBABILITY = 0.0 +_C.DATASETS.SAFEGUARD_POSITIVE_CAPTION = True +_C.DATASETS.CAPTION_FORMAT_VERSION = "v1" +_C.DATASETS.LOCAL_DEBUG = False + + +# Od in the wild +_C.DATASETS.PREDEFINED_TEXT = None +_C.DATASETS.TRAIN_DATASETNAME_SUFFIX = "" +_C.DATASETS.TEST_DATASETNAME_SUFFIX = "" +_C.DATASETS.OVERRIDE_CATEGORY = None +_C.DATASETS.USE_OVERRIDE_CATEGORY = False +_C.DATASETS.SUPRESS_QUERY = None +_C.DATASETS.USE_SUPRESS_QUERY = False +_C.DATASETS.USE_CAPTION_PROMPT = False +_C.DATASETS.CAPTION_PROMPT = None + +_C.DATASETS.PREDOWNLOAD_BING = False +_C.DATASETS.PREDOWNLOAD_WITH_AZCOPY = False +_C.DATASETS.FLICKR_GT_TYPE = "separate" + +# PACO +_C.DATASETS.OD_TO_GROUNDING_VERSION = "legacy" + +# description +_C.DATASETS.DESCRIPTION_FILE = None +_C.DATASETS.SIMILARITY_FILE = None +_C.DATASETS.CAPTION_VOCAB_FILE = None + +# caption augmentation +_C.DATASETS.CAPTION_AUGMENTATION_VOCAB = None +_C.DATASETS.CAPTION_AUGMENTATION_VERSION = None + +_C.DATASETS.CC_CAPTION_AUGMENTATION_VERSION = None + +_C.DATASETS.KEEP_NOUN_RATIO = 0.0 + +# VQA +_C.DATASETS.DIVER_BOX_FOR_VQA = False + +# ----------------------------------------------------------------------------- +# DataLoader +# ----------------------------------------------------------------------------- +_C.DATALOADER = CN() +# Number of data loading threads +_C.DATALOADER.NUM_WORKERS = 4 +# If > 0, this enforces that each collated batch should have a size divisible +# by SIZE_DIVISIBILITY +_C.DATALOADER.SIZE_DIVISIBILITY = 0 +# If True, each batch should contain only images for which the aspect ratio +# is compatible. This groups portrait images together, and landscape images +# are not batched with portrait images. +_C.DATALOADER.ASPECT_RATIO_GROUPING = True +# Define min number of keypoints required from GT, for example 10 out of 17 +_C.DATALOADER.MIN_KPS_PER_IMS = 0 +# Use random sampler during training +_C.DATALOADER.USE_RANDOM_SEED = False + +_C.DATALOADER.DISTRIBUTE_CHUNK_AMONG_NODE = False +# ---------------------------------------------------------------------------- # +# Backbone options +# ---------------------------------------------------------------------------- # +_C.MODEL.BACKBONE = CN() + +# The backbone conv body to use +# The string must match a function that is imported in modeling.model_builder +# (e.g., 'FPN.add_fpn_ResNet101_conv5_body' to specify a ResNet-101-FPN +# backbone) +_C.MODEL.BACKBONE.CONV_BODY = "R-50-C4" + +# Add StopGrad at a specified stage so the bottom layers are frozen +_C.MODEL.BACKBONE.FREEZE_CONV_BODY_AT = 2 +_C.MODEL.BACKBONE.FREEZE = False +_C.MODEL.BACKBONE.GROUP = 1 +_C.MODEL.BACKBONE.OUT_CHANNELS = 256 * 4 +# Option to reset bn running statics +_C.MODEL.BACKBONE.RESET_BN = False +# Backbone Normalization Level +_C.MODEL.BACKBONE.NORM_LEVEL = 3 +# BN for backbone +_C.MODEL.BACKBONE.USE_BN = False +# Sync BN for backbone +_C.MODEL.BACKBONE.USE_SYNCBN = False +_C.MODEL.BACKBONE.USE_NSYNCBN = False +# GN for backbone +_C.MODEL.BACKBONE.USE_GN = False +# Evo Norm for backbone +_C.MODEL.BACKBONE.USE_EN = False +# Layers for backbone +_C.MODEL.BACKBONE.USE_DFCONV = False +_C.MODEL.BACKBONE.USE_DYRELU = False +_C.MODEL.BACKBONE.USE_SE = False +_C.MODEL.BACKBONE.LAYER_SETUP = (3, 4, 6, 3) +_C.MODEL.BACKBONE.LAYER_SEARCH = CN(new_allowed=True) +_C.MODEL.BACKBONE.OUT_FEATURES = ("stage2", "stage3", "stage4", "stage5") +_C.MODEL.BACKBONE.FPN_LAYER = () +_C.MODEL.BACKBONE.USE_CHECKPOINT = False +# Add JF efficient det cfgs +_C.MODEL.BACKBONE.EFFICIENT_DET_START_FROM = 3 +_C.MODEL.BACKBONE.EFFICIENT_DET_COMPOUND = 0 +_C.MODEL.BACKBONE.EFFICIENT_DET_BIFPN_VERSION = 0 + +_C.MODEL.BACKBONE.FUSION_VERSION = "v1" # Whether to use symmetric or non symmetric fusion + +_C.MODEL.LANGUAGE_BACKBONE = CN() +_C.MODEL.LANGUAGE_BACKBONE.WEIGHT = "" +_C.MODEL.LANGUAGE_BACKBONE.FREEZE = False +_C.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT = False +_C.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE = "bert-base-uncased" +_C.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE = "bert-base-uncased" +_C.MODEL.LANGUAGE_BACKBONE.LANG_DIM = 768 +_C.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN = 256 +_C.MODEL.LANGUAGE_BACKBONE.N_LAYERS = 1 +_C.MODEL.LANGUAGE_BACKBONE.UNUSED_TOKEN = 106 +_C.MODEL.LANGUAGE_BACKBONE.MASK_SPECIAL = False + +_C.MODEL.LANGUAGE_BACKBONE.RNN_TYPE = "lstm" +_C.MODEL.LANGUAGE_BACKBONE.VARIABLE_LENGTH = True +_C.MODEL.LANGUAGE_BACKBONE.WORD_EMBEDDING_SIZE = 512 +_C.MODEL.LANGUAGE_BACKBONE.WORD_VEC_SIZE = 512 +_C.MODEL.LANGUAGE_BACKBONE.HIDDEN_SIZE = 512 +_C.MODEL.LANGUAGE_BACKBONE.BIDIRECTIONAL = True +_C.MODEL.LANGUAGE_BACKBONE.INPUT_DROPOUT_P = 0.5 +_C.MODEL.LANGUAGE_BACKBONE.DROPOUT_P = 0.2 +_C.MODEL.LANGUAGE_BACKBONE.CORPUS_PATH = "" +_C.MODEL.LANGUAGE_BACKBONE.VOCAB_SIZE = 0 + +_C.MODEL.LANGUAGE_BACKBONE.PAD_MAX = True +# ---------------------------------------------------------------------------- # +# FPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.FPN = CN() +_C.MODEL.FPN.FREEZE = False +_C.MODEL.FPN.USE_GN = False +_C.MODEL.FPN.USE_RELU = False +_C.MODEL.FPN.USE_DYRELU = False +_C.MODEL.FPN.DROP_BLOCK = True +_C.MODEL.FPN.DROP_PROB = 0.3 +_C.MODEL.FPN.DROP_SIZE = 3 +_C.MODEL.FPN.USE_SPP = False +_C.MODEL.FPN.USE_PAN = False +_C.MODEL.FPN.USE_DYHEAD = False +_C.MODEL.FPN.RETURN_SWINT_FEATURE_BEFORE_FUSION = False +# ---------------------------------------------------------------------------- # +# BIFPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.BIFPN = CN() +_C.MODEL.BIFPN.NUM_REPEATS = 1 +_C.MODEL.BIFPN.USE_ATTENTION = True + +# ---------------------------------------------------------------------------- # +# Group Norm options +# ---------------------------------------------------------------------------- # +_C.MODEL.GROUP_NORM = CN() +# Number of dimensions per group in GroupNorm (-1 if using NUM_GROUPS) +_C.MODEL.GROUP_NORM.DIM_PER_GP = -1 +# Number of groups in GroupNorm (-1 if using DIM_PER_GP) +_C.MODEL.GROUP_NORM.NUM_GROUPS = 16 +# GroupNorm's small constant in the denominator +_C.MODEL.GROUP_NORM.EPSILON = 1e-5 + +# ---------------------------------------------------------------------------- # +# Evo Norm options +# ---------------------------------------------------------------------------- # +_C.MODEL.EVO_NORM = CN() +# Number of groups in EvoNorm (-1 if using DIM_PER_GP) +_C.MODEL.EVO_NORM.NUM_GROUPS = 8 +# EvoNorm's small constant in the denominator +_C.MODEL.EVO_NORM.EPSILON = 1e-5 + +# ---------------------------------------------------------------------------- # +# RetinaNet Options (Follow the Detectron version) +# ---------------------------------------------------------------------------- # +_C.MODEL.RETINANET = CN() +# This is the number of foreground classes and background. +_C.MODEL.RETINANET.NUM_CLASSES = 81 +# Convolutions to use in the cls and bbox tower +# NOTE: this doesn't include the last conv for logits +_C.MODEL.RETINANET.NUM_CONVS = 4 +# During inference, #locs to select based on cls score before NMS is performed +# per FPN level +_C.MODEL.RETINANET.PRE_NMS_TOP_N = 1000 +# Prior prob for the positives at the beginning of training. This is used to set +# the bias init for the logits layer +_C.MODEL.RETINANET.PRIOR_PROB = 0.01 +# Inference cls score threshold, anchors with score > INFERENCE_TH are +# considered for inference +_C.MODEL.RETINANET.INFERENCE_TH = 0.05 +# NMS threshold used in RetinaNet +_C.MODEL.RETINANET.NMS_TH = 0.4 +_C.MODEL.RETINANET.DETECTIONS_PER_IMG = 100 + +# ---------------------------------------------------------------------------- # +# Focal Loss Options (Follow the Detectron version) +# ---------------------------------------------------------------------------- # +_C.MODEL.FOCAL = CN() +# Weight for bbox_regression loss +_C.MODEL.FOCAL.BBOX_REG_WEIGHT = 4.0 +# Smooth L1 loss beta for bbox regression +_C.MODEL.FOCAL.BBOX_REG_BETA = 0.11 +# IoU overlap ratio for labeling an anchor as positive +# Anchors with >= iou overlap are labeled positive +_C.MODEL.FOCAL.FG_IOU_THRESHOLD = 0.5 +# IoU overlap ratio for labeling an anchor as negative +# Anchors with < iou overlap are labeled negative +_C.MODEL.FOCAL.BG_IOU_THRESHOLD = 0.4 +# Focal loss parameter: alpha +_C.MODEL.FOCAL.LOSS_ALPHA = 0.25 +# Focal loss parameter: gamma +_C.MODEL.FOCAL.LOSS_GAMMA = 2.0 + +# ---------------------------------------------------------------------------- # +# FCOS Options +# ---------------------------------------------------------------------------- # +_C.MODEL.FCOS = CN() +_C.MODEL.FCOS.NUM_CLASSES = 81 # the number of classes including background +_C.MODEL.FCOS.FPN_STRIDES = [8, 16, 32, 64, 128] +_C.MODEL.FCOS.PRIOR_PROB = 0.01 +_C.MODEL.FCOS.INFERENCE_TH = 0.05 +_C.MODEL.FCOS.NMS_TH = 0.6 +_C.MODEL.FCOS.PRE_NMS_TOP_N = 1000 + +# the number of convolutions used in the cls and bbox tower +_C.MODEL.FCOS.NUM_CONVS = 4 +# if use deformable conv to align features +_C.MODEL.FCOS.USE_DFCONV = False + +# if CENTER_SAMPLING_RADIUS <= 0, it will disable center sampling +_C.MODEL.FCOS.CENTER_SAMPLING_RADIUS = 0.0 +# IOU_LOSS_TYPE can be "iou", "linear_iou" or "giou" +_C.MODEL.FCOS.IOU_LOSS_TYPE = "iou" + +_C.MODEL.FCOS.NORM_REG_TARGETS = False +_C.MODEL.FCOS.CENTERNESS_ON_REG = False +_C.MODEL.FCOS.USE_GT_CENTER = False + +_C.MODEL.FCOS.DETECTIONS_PER_IMG = 100 +_C.MODEL.FCOS.USE_GN = False +_C.MODEL.FCOS.USE_BN = False + +_C.MODEL.FCOS.INFERENCE_TH_TRAIN = 0.0 +_C.MODEL.FCOS.PRE_NMS_TOP_N_TRAIN = 3000 +_C.MODEL.FCOS.POST_NMS_TOP_N_TRAIN = 1000 + +# ---------------------------------------------------------------------------- # +# ATSS Options +# ---------------------------------------------------------------------------- # +_C.MODEL.ATSS = CN() +_C.MODEL.ATSS.NUM_CLASSES = 81 # the number of classes including background +_C.MODEL.ATSS.PRIOR_PROB = 0.01 +_C.MODEL.ATSS.INFERENCE_TH = 0.05 +_C.MODEL.ATSS.NMS_TH = 0.6 +_C.MODEL.ATSS.PRE_NMS_TOP_N = 1000 + +# the number of convolutions used in the cls and bbox tower +_C.MODEL.ATSS.NUM_CONVS = 4 +# the channels of convolutions used in the cls and bbox tower +_C.MODEL.ATSS.CHANNELS = 128 +# if use deformable conv to align features +_C.MODEL.ATSS.USE_DFCONV = False + +# topk for selecting candidate positive samples from each level +_C.MODEL.ATSS.TOPK = 9 + +# Weight for bbox_regression loss +_C.MODEL.ATSS.REG_LOSS_WEIGHT = 2.0 + +_C.MODEL.ATSS.DETECTIONS_PER_IMG = 100 +_C.MODEL.ATSS.USE_GN = False +_C.MODEL.ATSS.USE_BN = False + +_C.MODEL.ATSS.USE_DYRELU = False +_C.MODEL.ATSS.USE_SE = False + +_C.MODEL.ATSS.INFERENCE_TH_TRAIN = 0.0 +_C.MODEL.ATSS.PRE_NMS_TOP_N_TRAIN = 3000 +_C.MODEL.ATSS.POST_NMS_TOP_N_TRAIN = 1000 +# ---------------------------------------------------------------------------- # +# DYHEAD Options +# ---------------------------------------------------------------------------- # +_C.MODEL.DYHEAD = CN() +_C.MODEL.DYHEAD.NUM_CLASSES = 81 # the number of classes including background +_C.MODEL.DYHEAD.PRIOR_PROB = 0.01 + +# the number of convolutions used in the cls and bbox tower +_C.MODEL.DYHEAD.NUM_CONVS = 4 +# the channels of convolutions used in the cls and bbox tower +_C.MODEL.DYHEAD.CHANNELS = 128 +_C.MODEL.DYHEAD.GROUPS = 1 +# if use deformable conv to align features +_C.MODEL.DYHEAD.USE_DFCONV = False + +# topk for selecting candidate positive samples from each level +_C.MODEL.DYHEAD.TOPK = 9 + +_C.MODEL.DYHEAD.SCORE_AGG = "MEAN" # MEAN or MAX, for binary focal loss score aggregation + +_C.MODEL.DYHEAD.LOG_SCALE = 0.0 # temperature (dot product) +_C.MODEL.DYHEAD.SHALLOW_LOG_SCALE = 0.0 # # temperature (shallow contrastive) + +_C.MODEL.DYHEAD.USE_GN = False +_C.MODEL.DYHEAD.USE_NSYNCBN = False +_C.MODEL.DYHEAD.USE_SYNCBN = False + +_C.MODEL.DYHEAD.USE_DYFUSE = False +_C.MODEL.DYHEAD.USE_DYRELU = False + +_C.MODEL.DYHEAD.CONV_FUNC = "" + +# CosineSimOutputLayers: https://github.com/ucbdrive/few-shot-object-detection/blob/master/fsdet/modeling/roi_heads/fast_rcnn.py#L448-L464 +_C.MODEL.DYHEAD.COSINE_SCALE = -1.0 + +_C.MODEL.DYHEAD.FUSE_CONFIG = CN() +_C.MODEL.DYHEAD.FUSE_CONFIG.EARLY_FUSE_ON = False +_C.MODEL.DYHEAD.FUSE_CONFIG.TYPE = "" +_C.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_SIZE = 256 +_C.MODEL.DYHEAD.FUSE_CONFIG.JOINT_OUT_SIZE = 256 +_C.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_DROPOUT = 0.1 +_C.MODEL.DYHEAD.FUSE_CONFIG.JOINT_MLP_LAYERS = 2 + +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_CLASSIFICATION_LOSS = False + +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS = False +_C.MODEL.DYHEAD.FUSE_CONFIG.TOKEN_LOSS_WEIGHT = 1.0 +_C.MODEL.DYHEAD.FUSE_CONFIG.TOKEN_GAMMA = 2.0 +_C.MODEL.DYHEAD.FUSE_CONFIG.TOKEN_ALPHA = 0.25 + +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS = False +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS = False +_C.MODEL.DYHEAD.FUSE_CONFIG.CONTRASTIVE_HIDDEN_DIM = 64 +_C.MODEL.DYHEAD.FUSE_CONFIG.CONTRASTIVE_ALIGN_LOSS_WEIGHT = 1.0 +_C.MODEL.DYHEAD.FUSE_CONFIG.DOT_PRODUCT_TOKEN_LOSS_WEIGHT = 1.0 +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_LAYER_SCALE = True +_C.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL = False +_C.MODEL.DYHEAD.FUSE_CONFIG.STABLE_SOFTMAX_2D = False + +_C.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT = False + +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_FUSED_FEATURES_DOT_PRODUCT = False + +# Controls for +_C.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW = False +_C.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW = False +_C.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MIN_FOR_UNDERFLOW = False +_C.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MAX_FOR_OVERFLOW = False +_C.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_DOT_PRODUCT = False + +# MLM Loss +_C.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS = False +_C.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS_FOR_ONLY_POSITIVES = True +_C.MODEL.DYHEAD.FUSE_CONFIG.NO_MASK_FOR_OD = False +_C.MODEL.DYHEAD.FUSE_CONFIG.NO_MASK_FOR_GOLD = False +_C.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS_COEF = 1.0 +_C.MODEL.DYHEAD.FUSE_CONFIG.MLM_OBJ_FOR_ONLY_POSITIVE = False + +# Shallow Contrastive Loss (FPN) +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS = False +_C.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_MAX_POSITIVE_ANCHORS = 100 +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_ZERO_PADS = False +_C.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_CONTRASTIVE_HIDDEN_DIM = 64 +_C.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_CONTRASTIVE_LOSS_WEIGHT = 1.0 + +# Span Loss +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_SPAN_LOSS = False # will reuse the green light span field to indicate span boundary +_C.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION = None +_C.MODEL.DYHEAD.FUSE_CONFIG.MUTE_NOOBJ_TOKEN = False + + +# Shallow Contrastive Loss (BACKBONE) +_C.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS = False + +_C.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER = False +# Mute non-essential tokens +_C.MODEL.DYHEAD.FUSE_CONFIG.MUTE_NON_ESSENTIAL_TOKENS = False +# use checkpoint to save memory +_C.MODEL.DYHEAD.USE_CHECKPOINT = False + +# ---------------------------------------------------------------------------- # +# DYDETR Options +# ---------------------------------------------------------------------------- # +_C.MODEL.DYDETR = CN() +_C.MODEL.DYDETR.NHEADS = 8 +_C.MODEL.DYDETR.DROPOUT = 0.0 +_C.MODEL.DYDETR.DIM_FEEDFORWARD = 2048 +_C.MODEL.DYDETR.ACTIVATION = "relu" +_C.MODEL.DYDETR.HIDDEN_DIM = 256 +_C.MODEL.DYDETR.NUM_CLS = 1 +_C.MODEL.DYDETR.NUM_REG = 3 +_C.MODEL.DYDETR.NUM_HEADS = 6 +_C.MODEL.DYDETR.NUM_CLASSES = 81 +_C.MODEL.DYDETR.NUM_PROPOSALS = 300 + +# Dynamic Conv. +_C.MODEL.DYDETR.NUM_DYNAMIC = 2 +_C.MODEL.DYDETR.DIM_DYNAMIC = 64 + +# Loss. +_C.MODEL.DYDETR.CLASS_WEIGHT = 2.0 +_C.MODEL.DYDETR.GIOU_WEIGHT = 2.0 +_C.MODEL.DYDETR.L1_WEIGHT = 5.0 +_C.MODEL.DYDETR.DEEP_SUPERVISION = True +_C.MODEL.DYDETR.NO_OBJECT_WEIGHT = 0.1 + +# Focal Loss. +_C.MODEL.DYDETR.USE_FOCAL = True +_C.MODEL.DYDETR.ALPHA = 0.25 +_C.MODEL.DYDETR.GAMMA = 2.0 +_C.MODEL.DYDETR.PRIOR_PROB = 0.01 + +_C.MODEL.DYDETR.APPEND_BOX = False + +# GROUNDING RELATED +_C.MODEL.DYDETR.INCLUDE_LANGUAGE_DECODER = False +_C.MODEL.DYDETR.USE_DOT_PRODUCT_TOKEN_LOSS = False +_C.MODEL.DYDETR.LOG_SCALE = 0.0 # temperature +_C.MODEL.DYDETR.RESET_PARAMETERS = True +_C.MODEL.DYDETR.USE_GROUNDING_MATCHER_SETCRITERION = False +_C.MODEL.DYDETR.MDETR_PLAIN_INFERENCE = False +_C.MODEL.DYDETR.OVERRIDE_LANGUAGE_MODEL_FOR_TOKEN_LOSS = False +_C.MODEL.DYDETR.NORMALIZE_PER_BOX = False +_C.MODEL.DYDETR.RESET_SKIP_DOT_PRODUCT_WEIGHTS = False +_C.MODEL.DYDETR.DEBUG = False +_C.MODEL.DYDETR.AGGREGATE_METHOD = "MEAN" +_C.MODEL.DYDETR.EARLY_FUSE_ON = False +_C.MODEL.DYDETR.DYTOWER_ON = False +_C.MODEL.DYDETR.USE_FUSED_LANGUAGE_FEATURES = True +# ---------------------------------------------------------------------------- # +# RPN options +# ---------------------------------------------------------------------------- # +_C.MODEL.RPN = CN() +_C.MODEL.RPN.USE_FPN = False +# Base RPN anchor sizes given in absolute pixels w.r.t. the scaled network input +_C.MODEL.RPN.ANCHOR_SIZES = (32, 64, 128, 256, 512) +# Stride of the feature map that RPN is attached. +# For FPN, number of strides should match number of scales +_C.MODEL.RPN.ANCHOR_STRIDE = (16,) +# RPN anchor aspect ratios +_C.MODEL.RPN.ASPECT_RATIOS = (0.5, 1.0, 2.0) +# Anchor shift away ration from the center for r,t,l,d +_C.MODEL.RPN.ANCHOR_SHIFT = (0.0, 0.0, 0.0, 0.0) +# Use center to decide anchor size +_C.MODEL.RPN.USE_RELATIVE_SIZE = False +# Remove RPN anchors that go outside the image by RPN_STRADDLE_THRESH pixels +# Set to -1 or a large value, e.g. 100000, to disable pruning anchors +_C.MODEL.RPN.STRADDLE_THRESH = 0 +# Anchor scales per octave for complex anchors +_C.MODEL.RPN.OCTAVE = 2.0 +_C.MODEL.RPN.SCALES_PER_OCTAVE = 3 +# Minimum overlap required between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD +# ==> positive RPN example) +_C.MODEL.RPN.FG_IOU_THRESHOLD = 0.7 +# Maximum overlap allowed between an anchor and ground-truth box for the +# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD +# ==> negative RPN example) +_C.MODEL.RPN.BG_IOU_THRESHOLD = 0.3 +# Total number of RPN examples per image +_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256 +# Target fraction of foreground (positive) examples per RPN minibatch +_C.MODEL.RPN.POSITIVE_FRACTION = 0.5 +# Number of top scoring RPN proposals to keep before applying NMS +# When FPN is used, this is *per FPN level* (not total) +_C.MODEL.RPN.PRE_NMS_TOP_N_TRAIN = 12000 +_C.MODEL.RPN.PRE_NMS_TOP_N_TEST = 6000 +# Number of top scoring RPN proposals to keep after applying NMS +_C.MODEL.RPN.POST_NMS_TOP_N_TRAIN = 2000 +_C.MODEL.RPN.POST_NMS_TOP_N_TEST = 1000 +# NMS threshold used on RPN proposals +_C.MODEL.RPN.NMS_THRESH = 0.7 +# Proposal height and width both need to be greater than RPN_MIN_SIZE +# (a the scale used during training or inference) +_C.MODEL.RPN.MIN_SIZE = 0 +# Number of top scoring RPN proposals to keep after combining proposals from +# all FPN levels +_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN = 2000 +_C.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST = 2000 +# Custom rpn head, empty to use default conv or separable conv +_C.MODEL.RPN.RPN_HEAD = "SingleConvRPNHead" +_C.MODEL.RPN.FREEZE = False +_C.MODEL.RPN.FORCE_BOXES = False +_C.MODEL.RPN.RETURN_FUSED_FEATURES = False + +# ---------------------------------------------------------------------------- # +# ROI HEADS options +# ---------------------------------------------------------------------------- # +_C.MODEL.ROI_HEADS = CN() +_C.MODEL.ROI_HEADS.USE_FPN = False +# Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD) +_C.MODEL.ROI_HEADS.FG_IOU_THRESHOLD = 0.5 +# Overlap threshold for an RoI to be considered background +# (class = 0 if overlap in [0, BG_IOU_THRESHOLD)) +_C.MODEL.ROI_HEADS.BG_IOU_THRESHOLD = 0.5 +# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets +# These are empirically chosen to approximately lead to unit variance targets +_C.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0) +# RoI minibatch size *per image* (number of regions of interest [ROIs]) +# Total number of RoIs per training minibatch = +# TRAIN.BATCH_SIZE_PER_IM * TRAIN.IMS_PER_BATCH * NUM_GPUS +# E.g., a common configuration is: 512 * 2 * 8 = 8192 +_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 +# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0) +_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25 + +# Only used on test mode + +# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to +# balance obtaining high recall with not having too many low precision +# detections that will slow down inference post processing steps (like NMS) +_C.MODEL.ROI_HEADS.SCORE_THRESH = 0.05 +# Overlap threshold used for non-maximum suppression (suppress boxes with +# IoU >= this threshold) +_C.MODEL.ROI_HEADS.NMS = 0.5 +# Maximum number of detections to return per image (100 is based on the limit +# established for the COCO dataset) +_C.MODEL.ROI_HEADS.DETECTIONS_PER_IMG = 100 + +_C.MODEL.ROI_BOX_HEAD = CN() +_C.MODEL.ROI_BOX_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor" +_C.MODEL.ROI_BOX_HEAD.PREDICTOR = "FastRCNNPredictor" +_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_BOX_HEAD.POOLER_SCALES = (1.0 / 16,) +_C.MODEL.ROI_BOX_HEAD.NUM_CLASSES = 81 +# Hidden layer dimension when using an MLP for the RoI box head +_C.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM = 1024 +# GN +_C.MODEL.ROI_BOX_HEAD.USE_GN = False +# Dilation +_C.MODEL.ROI_BOX_HEAD.DILATION = 1 +_C.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM = 256 +_C.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS = 4 +# Use D2 style ROIAlignV2 +_C.MODEL.ROI_BOX_HEAD.POOLER_ALIGNED = False + +_C.MODEL.ROI_MASK_HEAD = CN() +_C.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR = "ResNet50Conv5ROIFeatureExtractor" +_C.MODEL.ROI_MASK_HEAD.PREDICTOR = "MaskRCNNC4Predictor" +_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_MASK_HEAD.POOLER_SCALES = (1.0 / 16,) +_C.MODEL.ROI_MASK_HEAD.MLP_HEAD_DIM = 1024 +_C.MODEL.ROI_MASK_HEAD.CONV_LAYERS = (256, 256, 256, 256) +_C.MODEL.ROI_MASK_HEAD.RESOLUTION = 14 +_C.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR = True +# Whether or not resize and translate masks to the input image. +_C.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS = False +_C.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS_THRESHOLD = 0.5 +# Dilation +_C.MODEL.ROI_MASK_HEAD.DILATION = 1 +# GN +_C.MODEL.ROI_MASK_HEAD.USE_GN = False +# HG +_C.MODEL.ROI_MASK_HEAD.HG_SCALE = 1 + +_C.MODEL.ROI_KEYPOINT_HEAD = CN() +_C.MODEL.ROI_KEYPOINT_HEAD.FEATURE_EXTRACTOR = "KeypointRCNNFeatureExtractor" +_C.MODEL.ROI_KEYPOINT_HEAD.PREDICTOR = "KeypointRCNNPredictor" +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14 +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0 +_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SCALES = (1.0 / 16,) +_C.MODEL.ROI_KEYPOINT_HEAD.MLP_HEAD_DIM = 1024 +_C.MODEL.ROI_KEYPOINT_HEAD.CONV_LAYERS = tuple(512 for _ in range(8)) +_C.MODEL.ROI_KEYPOINT_HEAD.RESOLUTION = 14 +_C.MODEL.ROI_KEYPOINT_HEAD.NUM_CLASSES = 17 +_C.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME = () # If left empty, use default names +_C.MODEL.ROI_KEYPOINT_HEAD.SHARE_BOX_FEATURE_EXTRACTOR = True + +# ---------------------------------------------------------------------------- # +# ResNe[X]t options (ResNets = {ResNet, ResNeXt} +# Note that parts of a resnet may be used for both the backbone and the head +# These options apply to both +# ---------------------------------------------------------------------------- # +_C.MODEL.RESNETS = CN() + +_C.MODEL.RESNETS.USE_STEM3X3 = False +_C.MODEL.RESNETS.WITH_SE = False +_C.MODEL.RESNETS.USE_AVG_DOWN = False + +# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt +_C.MODEL.RESNETS.NUM_GROUPS = 1 + +# Baseline width of each group +_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64 + +# Place the stride 2 conv on the 1x1 filter +# Use True only for the original MSRA ResNet; use False for C2 and Torch models +_C.MODEL.RESNETS.STRIDE_IN_1X1 = True + +# Residual transformation function +_C.MODEL.RESNETS.TRANS_FUNC = "BottleneckWithFixedBatchNorm" +# ResNet's stem function (conv1 and pool1) +_C.MODEL.RESNETS.STEM_FUNC = "StemWithFixedBatchNorm" + +# Apply dilation in stage "res5" +_C.MODEL.RESNETS.RES5_DILATION = 1 + +_C.MODEL.RESNETS.BACKBONE_OUT_CHANNELS = 256 * 4 +_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256 +_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64 + +_C.MODEL.RESNETS.REVISION = "resnet_light" +# Deformable convolutions +_C.MODEL.RESNETS.STAGE_WITH_DCN = (False, False, False, False) +_C.MODEL.RESNETS.WITH_MODULATED_DCN = False +_C.MODEL.RESNETS.DEFORMABLE_GROUPS = 1 + +# ---------------------------------------------------------------------------- # +# Swin Transformer +# ---------------------------------------------------------------------------- # +_C.MODEL.SWINT = CN() +_C.MODEL.SWINT.EMBED_DIM = 96 +_C.MODEL.SWINT.OUT_CHANNELS = (96, 192, 384, 768) +_C.MODEL.SWINT.DEPTHS = (2, 2, 6, 2) +_C.MODEL.SWINT.NUM_HEADS = (3, 6, 12, 24) +_C.MODEL.SWINT.WINDOW_SIZE = 7 +_C.MODEL.SWINT.MLP_RATIO = 4 +_C.MODEL.SWINT.DROP_PATH_RATE = 0.2 +_C.MODEL.SWINT.APE = False +_C.MODEL.SWINT.VERSION = "v1" +_C.MODEL.SWINT.OUT_NORM = True +_C.MODEL.SWINT.LAYER_SCALE = 0 + +# ---------------------------------------------------------------------------- # +# CVT SPEC +# ---------------------------------------------------------------------------- # +_C.MODEL.SPEC = CN(new_allowed=True) + +# ---------------------------------------------------------------------------- # +# CLIP SPEC +# ---------------------------------------------------------------------------- # +_C.MODEL.CLIP = CN() +_C.MODEL.CLIP.CONTEXT_LENGTH = 256 # default 77 +_C.MODEL.CLIP.WIDTH = 512 +_C.MODEL.CLIP.LAYERS = 12 +_C.MODEL.CLIP.HEADS = 8 +_C.MODEL.CLIP.DROP_PATH = 0.0 +_C.MODEL.CLIP.TOKENIZER = "clip" +_C.MODEL.CLIP.VOCAB_SIZE = 49408 + +# ---------------------------------------------------------------------------- # +# SEARCH +# ---------------------------------------------------------------------------- # + +_C.SEARCH = CN() +_C.SEARCH.MAX_EPOCH = 20 +_C.SEARCH.SELECT_NUM = 20 +_C.SEARCH.POPULATION_NUM = 64 +_C.SEARCH.MUTATION_NUM = 24 +_C.SEARCH.CROSSOVER_NUM = 24 +_C.SEARCH.MUTATION_PROB = 0.1 + +# ---------------------------------------------------------------------------- # +# Solver +# ---------------------------------------------------------------------------- # +_C.SOLVER = CN() +_C.SOLVER.USE_AMP = False + +_C.SOLVER.MAX_ITER = 40000 +_C.SOLVER.MULTI_MAX_ITER = () # set different max epoch for different stage +_C.SOLVER.MAX_EPOCH = 0 # any epoch number>0 will overwrite max_iter +_C.SOLVER.MULTI_MAX_EPOCH = () # set different max epoch for different stage + +_C.SOLVER.OPTIMIZER = "SGD" # "ADAMW" + +_C.SOLVER.BASE_LR = 0.001 + +_C.SOLVER.LANG_LR = 0.00001 +_C.SOLVER.BACKBONE_BODY_LR_FACTOR = 1.0 +_C.SOLVER.FUSION_LR_FACTOR = 1.0 + + +_C.SOLVER.BIAS_LR_FACTOR = 2 +_C.SOLVER.GRAD_CLIP = 0.0 +# D2 gradient clip +_C.SOLVER.CLIP_GRADIENTS = CN() +_C.SOLVER.CLIP_GRADIENTS.ENABLED = False +_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 0.0 +_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "full_model" +_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0 +_C.SOLVER.MODEL_EMA = 0.0 + +_C.SOLVER.MOMENTUM = 0.9 + +_C.SOLVER.WEIGHT_DECAY = 0.0005 +_C.SOLVER.WEIGHT_DECAY_BIAS = 0.0 +_C.SOLVER.WEIGHT_DECAY_NORM_FACTOR = 1.0 +_C.SOLVER.WEIGHT_DECAY_HEAD_FACTOR = 1.0 + +# use cosine lr to replace default multistage +_C.SOLVER.USE_COSINE = False +_C.SOLVER.MIN_LR = 0.000001 + +_C.SOLVER.GAMMA = 0.1 +_C.SOLVER.STEPS = (30000,) + +_C.SOLVER.USE_AUTOSTEP = False +_C.SOLVER.STEP_PATIENCE = 5 + +_C.SOLVER.WARMUP_FACTOR = 1.0 / 3 +_C.SOLVER.WARMUP_ITERS = 500 +_C.SOLVER.WARMUP_METHOD = "linear" + +_C.SOLVER.CHECKPOINT_PERIOD = 2500 +_C.SOLVER.CHECKPOINT_PER_EPOCH = -1.0 +_C.SOLVER.TEST_WITH_INFERENCE = False +_C.SOLVER.AUTO_TERMINATE_PATIENCE = -1 +# Number of images per batch +# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will +# see 2 images per batch +_C.SOLVER.IMS_PER_BATCH = 16 +# This is the max negative ratio allowed per batch +_C.SOLVER.MAX_NEG_PER_BATCH = 0.1 + +_C.SOLVER.SEED = 0 +_C.SOLVER.DISABLE_OUTPUT_DISTRIBUTED = False + + +_C.SOLVER.PROMPT_PROBING_LEVEL = -1.0 +# -1 means tuning the whole model; +# 1 means tuning the whole language model; 1.5 means tuning the box head as well + +_C.SOLVER.FIND_UNUSED_PARAMETERS = True +_C.SOLVER.DATASET_LENGTH = -1 # Just for logging purpose +_C.SOLVER.TUNING_HIGHLEVEL_OVERRIDE = None +_C.SOLVER.USE_EMA_FOR_MONITOR = False + +_C.SOLVER.WEIGHT_DECAY_SCHEDULE = False +_C.SOLVER.WEIGHT_DECAY_SCHEDULE_RATIO = 0.667 +_C.SOLVER.RESUME_SKIP_SCHEDULE = False # when we resume from a checkpoint, we can skip + +# ---------------------------------------------------------------------------- # +# Specific test options +# ---------------------------------------------------------------------------- # +_C.TEST = CN() +_C.TEST.EXPECTED_RESULTS = [] +_C.TEST.EXPECTED_RESULTS_SIGMA_TOL = 4 +_C.TEST.DURING_TRAINING = False +# Number of images per batch +# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will +# see 2 images per batch +_C.TEST.IMS_PER_BATCH = 16 +# Special Test Configuration +_C.TEST.USE_MULTISCALE = False +# _C.TEST.SCALES = (400, 600, 800, 1000, 1200, 1400) +# _C.TEST.RANGES = ((96, 10000), (64, 10000), (0, 10000), (0, 10000), (0, 256), (0, 192)) +_C.TEST.SCALES = (400, 500, 600, 640, 700, 900, 1000, 1100, 1200, 1300, 1400, 1800) +_C.TEST.RANGES = ( + (96, 10000), + (96, 10000), + (64, 10000), + (64, 10000), + (64, 10000), + (0, 10000), + (0, 10000), + (0, 256), + (0, 256), + (0, 192), + (0, 192), + (0, 96), +) +_C.TEST.MAX_SIZE = 2500 +_C.TEST.FLIP = True +_C.TEST.SPECIAL_NMS = "none" # ('none', 'soft-nms', 'vote', 'soft-vote') +_C.TEST.TH = 0.6 # threshold for nms or vote +_C.TEST.PRE_NMS_TOP_N = 1000 +_C.TEST.NUM_CLASSES = 81 +_C.TEST.SELECT_CLASSES = () + +_C.TEST.EVAL_TASK = "" +_C.TEST.SUBSET = -1 +_C.TEST.CHUNKED_EVALUATION = -1 +_C.TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM = -1 +_C.TEST.CHUNK_METHOD = "random" # or similar +_C.TEST.CHUNK_INFERENCE_VERSION = "v1" # v2: modify the ATSS inference code slightly to make +# ---------------------------------------------------------------------------- # +# Misc options +# ---------------------------------------------------------------------------- # +_C.OUTPUT_DIR = "OUTPUT" + +_C.PATHS_CATALOG = os.path.join(os.path.dirname(__file__), "paths_catalog.py") + +# TensorBoard experiment location +_C.TENSORBOARD_EXP = "OUTPUT" + +_C.GLIPKNOW = CN() +_C.GLIPKNOW.KNOWLEDGE_FILE = "" +_C.GLIPKNOW.KNOWLEDGE_TYPE = "" +_C.GLIPKNOW.MAX_NUM_CLASSES_PER_BATCH_TRAIN = -1 +_C.GLIPKNOW.PARALLEL_LANGUAGE_INPUT = False +_C.GLIPKNOW.LAN_FEATURE_AGG_TYPE = "first" +_C.GLIPKNOW.GPT3_NUM = 5 +_C.GLIPKNOW.WIKI_AND_GPT3 = False \ No newline at end of file diff --git a/maskrcnn_benchmark/config/paths_catalog.py b/maskrcnn_benchmark/config/paths_catalog.py new file mode 100644 index 0000000000000000000000000000000000000000..04f128aaa53370898557d9a43e1dd9dffbc1f2ad --- /dev/null +++ b/maskrcnn_benchmark/config/paths_catalog.py @@ -0,0 +1,779 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +"""Centralized catalog of paths.""" + +import os + + +def try_to_find(file, return_dir=False, search_path=["./DATASET", "./OUTPUT", "./data", "./MODEL"]): + if not file: + return file + + if file.startswith("catalog://"): + return file + + DATASET_PATH = ["./"] + if "DATASET" in os.environ: + DATASET_PATH.append(os.environ["DATASET"]) + DATASET_PATH += search_path + + for path in DATASET_PATH: + if os.path.exists(os.path.join(path, file)): + if return_dir: + return path + else: + return os.path.join(path, file) + + print("Cannot find {} in {}".format(file, DATASET_PATH)) + exit(1) + + +class DatasetCatalog(object): + DATASETS = { + # pretrained grounding dataset + # mixed vg and coco + "mixed_train": { + "coco_img_dir": "coco/train2014", + "vg_img_dir": "gqa/images", + "ann_file": "mdetr_annotations/final_mixed_train.json", + }, + "mixed_train_no_coco": { + "coco_img_dir": "coco/train2014", + "vg_img_dir": "gqa/images", + "ann_file": "mdetr_annotations/final_mixed_train_no_coco.json", + }, + # flickr30k + "flickr30k_train": { + "img_folder": "flickr30k/flickr30k_images/train", + "ann_file": "mdetr_annotations/final_flickr_separateGT_train.json", + "is_train": True, + }, + "flickr30k_val": { + "img_folder": "flickr30k/flickr30k_images/val", + "ann_file": "mdetr_annotations/final_flickr_separateGT_val.json", + "is_train": False, + }, + "flickr30k_test": { + "img_folder": "flickr30k/flickr30k_images/test", + "ann_file": "mdetr_annotations/final_flickr_separateGT_test.json", + "is_train": False, + }, + # refcoco + "refexp_all_val": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/final_refexp_val.json", + "is_train": False, + }, + "refcoco_train": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco_train.json", + "is_train": True, + }, + "refcoco_val": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco_val.json", + "is_train": False, + }, + "refcoco_real_val": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco_val.json", + "is_train": False, + }, + "refcoco_testA": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco_testA.json", + "is_train": False, + }, + "refcoco_testB": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco_testB.json", + "is_train": False, + }, + "refcoco+_train": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco+_train.json", + "is_train": True, + }, + "refcoco+_val": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco+_val.json", + "is_train": False, + }, + "refcoco+_testA": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco+_testA.json", + "is_train": False, + }, + "refcoco+_testB": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcoco+_testB.json", + "is_train": False, + }, + "refcocog_train": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcocog_train.json", + "is_train": True, + }, + "refcocog_val": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcocog_val.json", + "is_train": False, + }, + "refcocog_test": { + "img_dir": "coco/train2014", + "ann_file": "mdetr_annotations/finetune_refcocog_test_corrected.json", + "is_train": False, + }, + # gqa + "gqa_val": {"img_dir": "gqa/images", "ann_file": "mdetr_annotations/final_gqa_val.json", "is_train": False}, + # phrasecut + "phrasecut_train": { + "img_dir": "gqa/images", + "ann_file": "mdetr_annotations/finetune_phrasecut_train.json", + "is_train": True, + }, + # caption + "bing_caption_train": { + "yaml_path": "BingData/predict_yaml", + "yaml_name": "dreamstime_com_dyhead_objvg_e39", + "yaml_name_no_coco": "dreamstime_com_Detection_Pretrain_NoCOCO_Packed125", + "is_train": True, + }, + # od to grounding + # coco tsv + "coco_dt_train": { + "dataset_file": "coco_dt", + "yaml_path": "coco_tsv/coco_obj.yaml", + "is_train": True, + }, + "COCO_odinw_train_8copy_dt_train": { + "dataset_file": "coco_odinw_dt", + "yaml_path": "coco_tsv/COCO_odinw_train_8copy.yaml", + "is_train": True, + }, + "COCO_odinw_val_dt_train": { + "dataset_file": "coco_odinw_dt", + "yaml_path": "coco_tsv/COCO_odinw_val.yaml", + "is_train": False, + }, + # lvis tsv + "lvisv1_dt_train": { + "dataset_file": "lvisv1_dt", + "yaml_path": "coco_tsv/LVIS_v1_train.yaml", + "is_train": True, + }, + "LVIS_odinw_train_8copy_dt_train": { + "dataset_file": "coco_odinw_dt", + "yaml_path": "coco_tsv/LVIS_odinw_train_8copy.yaml", + "is_train": True, + }, + # object365 tsv + "object365_dt_train": { + "dataset_file": "object365_dt", + "yaml_path": "Objects365/objects365_train_vgoiv6.cas2000.yaml", + "is_train": True, + }, + "object365_odinw_2copy_dt_train": { + "dataset_file": "object365_odinw_dt", + "yaml_path": "Objects365/objects365_train_odinw.cas2000_2copy.yaml", + "is_train": True, + }, + "objects365_odtsv_train": { + "dataset_file": "objects365_odtsv", + "yaml_path": "Objects365/train.cas2000.yaml", + "is_train": True, + }, + "objects365_odtsv_val": { + "dataset_file": "objects365_odtsv", + "yaml_path": "Objects365/val.yaml", + "is_train": False, + }, + # ImagetNet OD + "imagenetod_train_odinw_2copy_dt": { + "dataset_file": "imagenetod_odinw_dt", + "yaml_path": "imagenet_od/imagenetod_train_odinw_2copy.yaml", + "is_train": True, + }, + # OpenImage OD + "oi_train_odinw_dt": { + "dataset_file": "oi_odinw_dt", + "yaml_path": "openimages_v5c/oi_train_odinw.cas.2000.yaml", + "is_train": True, + }, + # vg tsv + "vg_dt_train": { + "dataset_file": "vg_dt", + "yaml_path": "visualgenome/train_vgoi6_clipped.yaml", + "is_train": True, + }, + "vg_odinw_clipped_8copy_dt_train": { + "dataset_file": "vg_odinw_clipped_8copy_dt", + "yaml_path": "visualgenome/train_odinw_clipped_8copy.yaml", + "is_train": True, + }, + "vg_vgoi6_clipped_8copy_dt_train": { + "dataset_file": "vg_vgoi6_clipped_8copy_dt", + "yaml_path": "visualgenome/train_vgoi6_clipped_8copy.yaml", + "is_train": True, + }, + # coco json + "coco_grounding_train": { + "img_dir": "coco/train2017", + "ann_file": "coco/annotations/instances_train2017.json", + "is_train": True, + }, + "lvis_grounding_train": {"img_dir": "coco", "ann_file": "coco/annotations/lvis_od_train.json"}, + + "lvis_evaluation_val": { + "img_dir": "lvis/coco2017", + "ann_file": "lvis/lvis_v1_minival_inserted_image_name.json", + "is_train": False, + }, + + "lvis_val": { + "img_dir": "coco", + "ann_file": "coco/annotations/lvis_od_val.json"}, + + + # legacy detection dataset + "hsd_v001": {"img_dir": "hsd/20170901_Detection_HeadShoulder.V001/RawImages", "ann_file": "hsd/HSD_V001.json"}, + "hsd_hddb": {"img_dir": "hddb/Images", "ann_file": "hddb/HDDB.json"}, + "opencoco_train": {"img_dir": "openimages/train", "ann_file": "openimages/opencoco_train.json"}, + "opencoco_val": {"img_dir": "openimages/val", "ann_file": "openimages/opencoco_val.json"}, + "opencoco_test": {"img_dir": "openimages/test", "ann_file": "openimages/opencoco_test.json"}, + "openhuman_train": {"img_dir": "openimages/train", "ann_file": "openimages/openhuman_train.json"}, + "openhuman_val": {"img_dir": "openimages/val", "ann_file": "openimages/openhuman_val.json"}, + "openhuman_test": {"img_dir": "openimages/test", "ann_file": "openimages/openhuman_test.json"}, + "opencrowd_train": {"img_dir": "openimages/train", "ann_file": "openimages/opencrowd_train.json"}, + "opencrowd_val": {"img_dir": "openimages/val", "ann_file": "openimages/opencrowd_val.json"}, + "opencrowd_test": {"img_dir": "openimages/test", "ann_file": "openimages/opencrowd_test.json"}, + "opencar_train": {"img_dir": "openimages/train", "ann_file": "openimages/opencar_train.json"}, + "opencar_val": {"img_dir": "openimages/val", "ann_file": "openimages/opencar_val.json"}, + "opencar_test": {"img_dir": "openimages/test", "ann_file": "openimages/opencar_test.json"}, + "openhumancar_train": {"img_dir": "openimages/train", "ann_file": "openimages/openhumancar_train.json"}, + "openhumancar_val": {"img_dir": "openimages/val", "ann_file": "openimages/openhumancar_val.json"}, + "openhuamncar_test": {"img_dir": "openimages/test", "ann_file": "openimages/openhumancar_test.json"}, + "open500_train": { + "img_dir": "openimages/train", + "ann_file": "openimages/openimages_challenge_2019_train_bbox.json", + }, + "open500_val": { + "img_dir": "openimages/val", + "ann_file": "openimages/openimages_challenge_2019_val_bbox.json", + }, + "openproposal_test": { + "img_dir": "openimages/test2019", + "ann_file": "openimages/proposals_test.json", + }, + "object365_train": {"img_dir": "object365/train", "ann_file": "object365/objects365_train.json"}, + "object365_val": {"img_dir": "object365/val", "ann_file": "object365/objects365_val.json"}, + "lvis_train": {"img_dir": "coco", "ann_file": "coco/annotations/lvis_od_train.json"}, + "lvis_val": {"img_dir": "coco", "ann_file": "coco/annotations/lvis_od_val.json"}, + "image200_train": {"img_dir": "imagenet-od/Data/DET/train", "ann_file": "imagenet-od/im200_train.json"}, + "image200_val": {"img_dir": "imagenet-od/Data/DET/val", "ann_file": "imagenet-od/im200_val.json"}, + "coco_2017_train": {"img_dir": "coco/train2017", "ann_file": "coco/annotations/instances_train2017.json"}, + "coco_2017_val": {"img_dir": "coco/val2017", "ann_file": "coco/annotations/instances_val2017.json"}, + "coco_2017_test": {"img_dir": "coco/test2017", "ann_file": "coco/annotations/image_info_test-dev2017.json"}, + "coco10_train": {"img_dir": "coco/train2017", "ann_file": "coco/annotations/instances_minitrain2017.json"}, + "coco_2014_train": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/instances_train2014.json"}, + "coco_2014_val": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/instances_val2014.json"}, + "coco_2014_minival": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/instances_minival2014.json"}, + "coco_2014_valminusminival": { + "img_dir": "coco/val2014", + "ann_file": "coco/annotations/instances_valminusminival2014.json", + }, + "coco_2014_train_partial": { + "img_dir": "coco/train2014", + "ann_file": "coco/annotations/partial0.2_train2014.json", + }, + "coco_2014_valminusminival_partial": { + "img_dir": "coco/val2014", + "ann_file": "coco/annotations/partial0.2_valminusminival2014.json", + }, + "coco_2014_train_few100": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/few100_train2014.json"}, + "coco_2014_train_few300": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/few300_train2014.json"}, + "coco_human_2014_train": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/humans_train2014.json"}, + "coco_human_2014_minival": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/humans_minival2014.json"}, + "coco_human_2014_valminusminival": { + "img_dir": "coco/val2014", + "ann_file": "coco/annotations/humans_valminusminival2014.json", + }, + "coco_car_2014_train": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/car_train2014.json"}, + "coco_car_2014_minival": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/car_minival2014.json"}, + "coco_car_2014_valminusminival": { + "img_dir": "coco/val2014", + "ann_file": "coco/annotations/car_valminusminival2014.json", + }, + "coco_humancar_2014_train": { + "img_dir": "coco/train2014", + "ann_file": "coco/annotations/humancar_train2014.json", + }, + "coco_humancar_2014_minival": { + "img_dir": "coco/val2014", + "ann_file": "coco/annotations/humancar_minival2014.json", + }, + "coco_humancar_2014_valminusminival": { + "img_dir": "coco/val2014", + "ann_file": "coco/annotations/humancar_valminusminival2014.json", + }, + "coco_keypoint_2017_train": { + "img_dir": "coco/train2017", + "ann_file": "coco/annotations/person_keypoints_train2017.json", + }, + "coco_keypoint_2017_val": { + "img_dir": "coco/val2017", + "ann_file": "coco/annotations/person_keypoints_val2017.json", + }, + "coco_headshoulder_2017_train": { + "img_dir": "coco/train2017", + "ann_file": "coco/annotations/headshoulder_train2017.json", + }, + "coco_headshoulder_2017_val": { + "img_dir": "coco/val2017", + "ann_file": "coco/annotations/headshoulder_val2017.json", + }, + "coco_hskeypoint_2017_train": { + "img_dir": "coco/train2017", + "ann_file": "coco/annotations/person_hskeypoints_train2017.json", + }, + "coco_hskeypoint_2017_val": { + "img_dir": "coco/val2017", + "ann_file": "coco/annotations/person_hskeypoints_val2017.json", + }, + "voc_2007_train": {"data_dir": "voc/VOC2007", "split": "train"}, + "voc_2007_train_cocostyle": { + "img_dir": "voc/VOC2007/JPEGImages", + "ann_file": "voc/VOC2007/Annotations/pascal_train2007.json", + }, + "voc_2007_val": {"data_dir": "voc/VOC2007", "split": "val"}, + "voc_2007_val_cocostyle": { + "img_dir": "voc/VOC2007/JPEGImages", + "ann_file": "voc/VOC2007/Annotations/pascal_val2007.json", + }, + "voc_2007_test": {"data_dir": "voc/VOC2007", "split": "test"}, + "voc_2007_test_cocostyle": { + "img_dir": "voc/VOC2007/JPEGImages", + "ann_file": "voc/VOC2007/Annotations/pascal_test2007.json", + }, + "voc_2012_train": {"data_dir": "voc/VOC2012", "split": "train"}, + "voc_2012_train_cocostyle": { + "img_dir": "voc/VOC2012/JPEGImages", + "ann_file": "voc/VOC2012/Annotations/pascal_train2012.json", + }, + "voc_2012_val": {"data_dir": "voc/VOC2012", "split": "val"}, + "voc_2012_val_cocostyle": { + "img_dir": "voc/VOC2012/JPEGImages", + "ann_file": "voc/VOC2012/Annotations/pascal_val2012.json", + }, + "voc_2012_test": { + "data_dir": "voc/VOC2012", + "split": "test" + # PASCAL VOC2012 doesn't made the test annotations available, so there's no json annotation + }, + "cityscapes_fine_instanceonly_seg_train_cocostyle": { + "img_dir": "cityscapes/images", + "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_train.json", + }, + "cityscapes_fine_instanceonly_seg_val_cocostyle": { + "img_dir": "cityscapes/images", + "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_val.json", + }, + "cityscapes_fine_instanceonly_seg_test_cocostyle": { + "img_dir": "cityscapes/images", + "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_test.json", + }, + "crowdhuman_train": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhuman_train.json"}, + "crowdhuman_val": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhuman_val.json"}, + "crowdhead_train": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhead_train.json"}, + "crowdhead_val": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhead_val.json"}, + "crowdfull_train": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdfull_train.json"}, + "crowdfull_val": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdfull_val.json"}, + "ternium_train": {"img_dir": "ternium/images", "ann_file": "ternium/train_annotation.json"}, + "ternium_val": {"img_dir": "ternium/images", "ann_file": "ternium/val_annotation.json"}, + "ternium_test": {"img_dir": "ternium/images", "ann_file": "ternium/test_annotation.json"}, + "ternium_test_crop": {"img_dir": "ternium/test_motion_crop", "ann_file": "ternium/test_motion_crop.json"}, + "ternium_train_aug": {"img_dir": "ternium/train_crop_aug", "ann_file": "ternium/train_crop_aug.json"}, + "ternium_test_aug": {"img_dir": "ternium/test_crop_aug", "ann_file": "ternium/test_motion_crop_aug.json"}, + "ternium_vh_train": { + "img_dir": "ternium-vehicle/train_dataset_coco/images", + "ann_file": "ternium-vehicle/train_dataset_coco/coco_annotation.json", + }, + "ternium_vh_val": { + "img_dir": "ternium-vehicle/validation_dataset_coco/images", + "ann_file": "ternium-vehicle/validation_dataset_coco/coco_annotation.json", + }, + "msra_traffic": {"img_dir": "msra-traffic/Images", "ann_file": "msra-traffic/annotation.json"}, + "msra_traffic_car": {"img_dir": "msra-traffic/Images", "ann_file": "msra-traffic/car_annotation.json"}, + "msra_traffic_humancar": { + "img_dir": "msra-traffic/Images", + "ann_file": "msra-traffic/humancar_annotation.json", + }, + "jigsaw_car_train": {"img_dir": "jigsaw", "ann_file": "jigsaw/train.json"}, + "jigsaw_car_val": {"img_dir": "jigsaw", "ann_file": "jigsaw/val.json"}, + "miotcd_train": {"img_dir": "MIO-TCD/MIO-TCD-Localization", "ann_file": "MIO-TCD/train.json"}, + "miotcd_val": {"img_dir": "MIO-TCD/MIO-TCD-Localization", "ann_file": "MIO-TCD/val.json"}, + "detrac_train": {"img_dir": "detrac/Insight-MVT_Annotation_Train", "ann_file": "detrac/train.json"}, + "detrac_val": {"img_dir": "detrac/Insight-MVT_Annotation_Train", "ann_file": "detrac/val.json"}, + "mrw": {"img_dir": "mrw/clips", "ann_file": "mrw/annotations.json"}, + "mrw_bg": {"img_dir": "mrw/bg", "ann_file": "mrw/bg_annotations.json"}, + "webmarket_bg": {"img_dir": "webmarket", "ann_file": "webmarket/bg_annotations.json"}, + "mot17_train": {"img_dir": "mot/MOT17Det", "ann_file": "mot/MOT17Det/train.json"}, + "egohands": {"img_dir": "egohands/images", "ann_file": "egohands/egohands.json"}, + "hof": {"img_dir": "hof/images_original_size", "ann_file": "hof/train.json"}, + "vlmhof": {"img_dir": "vlmhof/RGB", "ann_file": "vlmhof/train.json"}, + "vgghands_train": {"img_dir": "vgghands/training_dataset", "ann_file": "vgghands/training.json"}, + "vgghands_val": {"img_dir": "vgghands/validation_dataset", "ann_file": "vgghands/validation.json"}, + "vgghands_test": {"img_dir": "vgghands/test_dataset", "ann_file": "vgghands/test.json"}, + "od:coco_train": {"img_dir": "coco/train2017", "ann_file": "coco/annotations/od_train2017.json"}, + "od:coco_val": {"img_dir": "coco/val2017", "ann_file": "coco/annotations/od_val2017.json"}, + "od:lvis_train": {"img_dir": "coco", "ann_file": "coco/annotations/od_train-lvis.json"}, + "od:lvis_val": {"img_dir": "coco", "ann_file": "coco/annotations/od_val-lvis.json"}, + "od:o365_train": {"img_dir": "object365/train", "ann_file": "object365/od_train.json"}, + "od:o365_val": {"img_dir": "object365/val", "ann_file": "object365/od_val.json"}, + "od:oi500_train": { + "img_dir": "openimages/train", + "ann_file": "openimages/od_train2019.json", + "paste_dir": "openimages/panoptic_train_challenge_2019", + "paste_file": "openimages/panoptic_train2019.json", + }, + "od:oi500_val": { + "img_dir": "openimages/val", + "ann_file": "openimages/od_val2019.json", + "paste_dir": "openimages/panoptic_val_challenge_2019", + "paste_file": "openimages/panoptic_val2019.json", + }, + "od:im200_train": {"img_dir": "imagenet-od/Data/DET/train", "ann_file": "imagenet-od/train.json"}, + "od:im200_val": {"img_dir": "imagenet-od/Data/DET/val", "ann_file": "imagenet-od/val.json"}, + "cv:animal661_train": {"img_dir": "cvtasks/animal-661/images", "ann_file": "cvtasks/animal-661/train.json"}, + "cv:animal661_test": {"img_dir": "cvtasks/animal-661/images", "ann_file": "cvtasks/animal-661/test.json"}, + "cv:seeingai_train": {"img_dir": "cvtasks/SeeingAI/train.tsv", "ann_file": "cvtasks/SeeingAI/train.json"}, + "cv:seeingai_test": {"img_dir": "cvtasks/SeeingAI/test.tsv", "ann_file": "cvtasks/SeeingAI/test.json"}, + "cv:office_train": { + "img_dir": "cvtasks/Ping-Office-Env/train.tsv", + "ann_file": "cvtasks/Ping-Office-Env/train.json", + }, + "cv:office_test": { + "img_dir": "cvtasks/Ping-Office-Env/test.tsv", + "ann_file": "cvtasks/Ping-Office-Env/test.json", + }, + "cv:logo_train": {"img_dir": "cvtasks/Ping-Logo", "ann_file": "cvtasks/Ping-Logo/train.json"}, + "cv:logo_test": {"img_dir": "cvtasks/Ping-Logo", "ann_file": "cvtasks/Ping-Logo/test.json"}, + "cv:nba_train": {"img_dir": "cvtasks/Ping-NBA", "ann_file": "cvtasks/Ping-NBA/train.json"}, + "cv:nba_test": {"img_dir": "cvtasks/Ping-NBA", "ann_file": "cvtasks/Ping-NBA/test.json"}, + "cv:traffic_train": {"img_dir": "cvtasks/TrafficData/train.tsv", "ann_file": "cvtasks/TrafficData/train.json"}, + "cv:traffic_test": {"img_dir": "cvtasks/TrafficData/test.tsv", "ann_file": "cvtasks/TrafficData/test.json"}, + "cv:fashion5k_train": {"img_dir": "cvtasks/fashion5k", "ann_file": "cvtasks/fashion5k/train.json"}, + "cv:fashion5k_test": {"img_dir": "cvtasks/fashion5k", "ann_file": "cvtasks/fashion5k/test.json"}, + "cv:malaria_train": {"img_dir": "cvtasks/malaria", "ann_file": "cvtasks/malaria/train.json"}, + "cv:malaria_test": {"img_dir": "cvtasks/malaria", "ann_file": "cvtasks/malaria/test.json"}, + "cv:product_train": { + "img_dir": "cvtasks/product_detection", + "ann_file": "cvtasks/product_detection/train.json", + }, + "cv:product_test": {"img_dir": "cvtasks/product_detection", "ann_file": "cvtasks/product_detection/test.json"}, + "vl:vg_train": {"yaml_file": "vlp/visualgenome/train_vgoi6_clipped.yaml"}, + "vl:vg_test": {"yaml_file": "vlp/visualgenome/test_vgoi6_clipped.yaml"}, + "imagenet_train": {"img_dir": "imagenet-tsv/train.tsv", "ann_file": None}, + "imagenet_val": {"img_dir": "imagenet-tsv/val.tsv", "ann_file": None}, + + "paco_lvis_v1_train_grounding":{ + "img_dir": "coco", + "ann_file": "paco/paco_lvis_v1_train.json" + }, + + "paco_lvis_v1_val":{ + "img_dir": "coco", + "ann_file": "paco/paco_lvis_v1_val.json" + }, + "paco_lvis_v1_test": + { + "img_dir": "coco", + "ann_file": "paco/paco_lvis_v1_test.json" + }, + "omnilabel_val": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3.json"}, + "omnilabel_val_coco": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3_coco.json"}, + "omnilabel_val_o365": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3_object365.json"}, + "omnilabel_val_oi_v5": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3_openimagesv5.json"}, + "omnilabel_test": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_test_v0.1.3.json"}, + } + + @staticmethod + def set(name, info): + DatasetCatalog.DATASETS.update({name: info}) + + @staticmethod + def get(name): + + if name.endswith("_bg"): + attrs = DatasetCatalog.DATASETS[name] + data_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + root=os.path.join(data_dir, attrs["img_dir"]), + ann_file=os.path.join(data_dir, attrs["ann_file"]), + ) + return dict( + factory="Background", + args=args, + ) + else: + if "bing" in name.split("_"): + attrs = DatasetCatalog.DATASETS["bing_caption_train"] + else: + attrs = DatasetCatalog.DATASETS[name] + # if "yaml_file" in attrs: + # yaml_file = try_to_find(attrs["yaml_file"], return_dir=False) + # args = dict(yaml_file=yaml_file) + # return dict( + # factory="VGTSVDataset", + # args=args, + # ) + # elif attrs["img_dir"].endswith('tsv'): + # try: + # data_dir = try_to_find(attrs["img_dir"], return_dir=True) + # if attrs["ann_file"] is None: + # map_file = None + # elif attrs["ann_file"].startswith("./"): + # map_file = attrs["ann_file"] + # else: + # map_file = os.path.join(data_dir, attrs["ann_file"]) + # except: + # return None + # args = dict( + # tsv_file=os.path.join(data_dir, attrs["img_dir"]), + # anno_file=map_file, + # ) + # return dict( + # factory="TSVDataset", + # args=args, + # ) + if "voc" in name and "split" in attrs: + data_dir = try_to_find(attrs["data_dir"], return_dir=True) + args = dict( + data_dir=os.path.join(data_dir, attrs["data_dir"]), + split=attrs["split"], + ) + return dict( + factory="PascalVOCDataset", + args=args, + ) + elif "omnilabel" in name: + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + return dict( + factory="OmniLabelDataset", + args=args, + ) + elif "mixed" in name: + vg_img_dir = try_to_find(attrs["vg_img_dir"], return_dir=True) + coco_img_dir = try_to_find(attrs["coco_img_dir"], return_dir=True) + ann_file = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder_coco=os.path.join(coco_img_dir, attrs["coco_img_dir"]), + img_folder_vg=os.path.join(vg_img_dir, attrs["vg_img_dir"]), + ann_file=os.path.join(ann_file, attrs["ann_file"]), + ) + return dict( + factory="MixedDataset", + args=args, + ) + elif "flickr" in name: + img_dir = try_to_find(attrs["img_folder"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_folder"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + is_train=attrs["is_train"], + ) + return dict( + factory="FlickrDataset", + args=args, + ) + elif "refexp" in name or "refcoco" in name: + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + return dict( + factory="RefExpDataset", + args=args, + ) + elif "gqa" in name: + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + return dict( + factory="GQADataset", + args=args, + ) + elif "phrasecut" in name: + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + return dict( + factory="PhrasecutDetection", + args=args, + ) + elif "_caption" in name: + yaml_path = try_to_find(attrs["yaml_path"], return_dir=True) + if "no_coco" in name: + yaml_name = attrs["yaml_name_no_coco"] + else: + yaml_name = attrs["yaml_name"] + yaml_file_name = "{}.{}.yaml".format(yaml_name, name.split("_")[2]) + args = dict(yaml_file=os.path.join(yaml_path, attrs["yaml_path"], yaml_file_name)) + return dict( + factory="CaptionTSV", + args=args, + ) + elif "inferencecap" in name: + yaml_file_name = try_to_find(attrs["yaml_path"]) + args = dict(yaml_file=yaml_file_name) + return dict( + factory="CaptionTSV", + args=args, + ) + elif "pseudo_data" in name: + args = dict(yaml_file=try_to_find(attrs["yaml_path"])) + return dict( + factory="PseudoData", + args=args, + ) + elif "_dt" in name: + dataset_file = attrs["dataset_file"] + yaml_path = try_to_find(attrs["yaml_path"], return_dir=True) + args = dict( + name=dataset_file, + yaml_file=os.path.join(yaml_path, attrs["yaml_path"]), + ) + return dict( + factory="CocoDetectionTSV", + args=args, + ) + elif "_odtsv" in name: + dataset_file = attrs["dataset_file"] + yaml_path = try_to_find(attrs["yaml_path"], return_dir=True) + args = dict( + name=dataset_file, + yaml_file=os.path.join(yaml_path, attrs["yaml_path"]), + ) + return dict( + factory="ODTSVDataset", + args=args, + ) + elif "_grounding" in name: + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + return dict( + factory="CocoGrounding", + args=args, + ) + elif "lvis_evaluation" in name: + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + return dict( + factory="LvisDetection", + args=args, + ) + elif "paco" in name: + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + args = dict( + img_folder=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + return dict( + factory="PacoDetection", + args=args, + ) + else: + ann_dir = try_to_find(attrs["ann_file"], return_dir=True) + img_dir = try_to_find(attrs["img_dir"], return_dir=True) + args = dict( + root=os.path.join(img_dir, attrs["img_dir"]), + ann_file=os.path.join(ann_dir, attrs["ann_file"]), + ) + for k, v in attrs.items(): + args.update({k: os.path.join(ann_dir, v)}) + return dict( + factory="COCODataset", + args=args, + ) + + raise RuntimeError("Dataset not available: {}".format(name)) + + +class ModelCatalog(object): + S3_C2_DETECTRON_URL = "https://dl.fbaipublicfiles.com/detectron" + C2_IMAGENET_MODELS = { + "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl", + "MSRA/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl", + "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl", + "MSRA/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl", + "FAIR/20171220/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl", + "FAIR/20171220/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl", + } + + C2_DETECTRON_SUFFIX = "output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl" + C2_DETECTRON_MODELS = { + "35857197/e2e_faster_rcnn_R-50-C4_1x": "01_33_49.iAX0mXvW", + "35857345/e2e_faster_rcnn_R-50-FPN_1x": "01_36_30.cUF7QR7I", + "35857890/e2e_faster_rcnn_R-101-FPN_1x": "01_38_50.sNxI7sX7", + "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "06_31_39.5MIHi1fZ", + "35858791/e2e_mask_rcnn_R-50-C4_1x": "01_45_57.ZgkA7hPB", + "35858933/e2e_mask_rcnn_R-50-FPN_1x": "01_48_14.DzEQe4wC", + "35861795/e2e_mask_rcnn_R-101-FPN_1x": "02_31_37.KqyEK4tT", + "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "06_35_59.RZotkLKI", + } + + @staticmethod + def get(name): + if name.startswith("Caffe2Detectron/COCO"): + return ModelCatalog.get_c2_detectron_12_2017_baselines(name) + if name.startswith("ImageNetPretrained"): + return ModelCatalog.get_c2_imagenet_pretrained(name) + raise RuntimeError("model not present in the catalog {}".format(name)) + + @staticmethod + def get_c2_imagenet_pretrained(name): + prefix = ModelCatalog.S3_C2_DETECTRON_URL + name = name[len("ImageNetPretrained/") :] + name = ModelCatalog.C2_IMAGENET_MODELS[name] + url = "/".join([prefix, name]) + return url + + @staticmethod + def get_c2_detectron_12_2017_baselines(name): + # Detectron C2 models are stored following the structure + # prefix//2012_2017_baselines/.yaml./suffix + # we use as identifiers in the catalog Caffe2Detectron/COCO// + prefix = ModelCatalog.S3_C2_DETECTRON_URL + suffix = ModelCatalog.C2_DETECTRON_SUFFIX + # remove identification prefix + name = name[len("Caffe2Detectron/COCO/") :] + # split in and + model_id, model_name = name.split("/") + # parsing to make it match the url address from the Caffe2 models + model_name = "{}.yaml".format(model_name) + signature = ModelCatalog.C2_DETECTRON_MODELS[name] + unique_name = ".".join([model_name, signature]) + url = "/".join([prefix, model_id, "12_2017_baselines", unique_name, suffix]) + return url diff --git a/maskrcnn_benchmark/csrc/ROIAlign.h b/maskrcnn_benchmark/csrc/ROIAlign.h new file mode 100644 index 0000000000000000000000000000000000000000..2683dbf52e120eebb7b60bb2257cd3527c5a86c3 --- /dev/null +++ b/maskrcnn_benchmark/csrc/ROIAlign.h @@ -0,0 +1,46 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once + +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + +// Interface for Python +at::Tensor ROIAlign_forward(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return ROIAlign_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return ROIAlign_forward_cpu(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio); +} + +at::Tensor ROIAlign_backward(const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + if (grad.device().is_cuda()) { +#ifdef WITH_CUDA + return ROIAlign_backward_cuda(grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width, sampling_ratio); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + diff --git a/maskrcnn_benchmark/csrc/ROIPool.h b/maskrcnn_benchmark/csrc/ROIPool.h new file mode 100644 index 0000000000000000000000000000000000000000..9b62b2dcb8f69ac65bc1fdf0eeb5fa556539bc13 --- /dev/null +++ b/maskrcnn_benchmark/csrc/ROIPool.h @@ -0,0 +1,48 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once + +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + + +std::tuple ROIPool_forward(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width) { + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return ROIPool_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +at::Tensor ROIPool_backward(const at::Tensor& grad, + const at::Tensor& input, + const at::Tensor& rois, + const at::Tensor& argmax, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width) { + if (grad.device().is_cuda()) { +#ifdef WITH_CUDA + return ROIPool_backward_cuda(grad, input, rois, argmax, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + + + diff --git a/maskrcnn_benchmark/csrc/SigmoidFocalLoss.h b/maskrcnn_benchmark/csrc/SigmoidFocalLoss.h new file mode 100644 index 0000000000000000000000000000000000000000..e220c12ae558a176f6b4b0a6640e724358f2ecb0 --- /dev/null +++ b/maskrcnn_benchmark/csrc/SigmoidFocalLoss.h @@ -0,0 +1,41 @@ +#pragma once + +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + +// Interface for Python +at::Tensor SigmoidFocalLoss_forward( + const at::Tensor& logits, + const at::Tensor& targets, + const int num_classes, + const float gamma, + const float alpha) { + if (logits.device().is_cuda()) { +#ifdef WITH_CUDA + return SigmoidFocalLoss_forward_cuda(logits, targets, num_classes, gamma, alpha); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +at::Tensor SigmoidFocalLoss_backward( + const at::Tensor& logits, + const at::Tensor& targets, + const at::Tensor& d_losses, + const int num_classes, + const float gamma, + const float alpha) { + if (logits.device().is_cuda()) { +#ifdef WITH_CUDA + return SigmoidFocalLoss_backward_cuda(logits, targets, d_losses, num_classes, gamma, alpha); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} diff --git a/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp b/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..0c061351588df7752293ed84bba1c900768e3ab8 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp @@ -0,0 +1,257 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include "cpu/vision.h" + +// implementation taken from Caffe2 +template +struct PreCalc { + int pos1; + int pos2; + int pos3; + int pos4; + T w1; + T w2; + T w3; + T w4; +}; + +template +void pre_calc_for_bilinear_interpolate( + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int iy_upper, + const int ix_upper, + T roi_start_h, + T roi_start_w, + T bin_size_h, + T bin_size_w, + int roi_bin_grid_h, + int roi_bin_grid_w, + std::vector>& pre_calc) { + int pre_calc_index = 0; + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + for (int iy = 0; iy < iy_upper; iy++) { + const T yy = roi_start_h + ph * bin_size_h + + static_cast(iy + .5f) * bin_size_h / + static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < ix_upper; ix++) { + const T xx = roi_start_w + pw * bin_size_w + + static_cast(ix + .5f) * bin_size_w / + static_cast(roi_bin_grid_w); + + T x = xx; + T y = yy; + // deal with: inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + // empty + PreCalc pc; + pc.pos1 = 0; + pc.pos2 = 0; + pc.pos3 = 0; + pc.pos4 = 0; + pc.w1 = 0; + pc.w2 = 0; + pc.w3 = 0; + pc.w4 = 0; + pre_calc[pre_calc_index] = pc; + pre_calc_index += 1; + continue; + } + + if (y <= 0) { + y = 0; + } + if (x <= 0) { + x = 0; + } + + int y_low = (int)y; + int x_low = (int)x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T)y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T)x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + // save weights and indeces + PreCalc pc; + pc.pos1 = y_low * width + x_low; + pc.pos2 = y_low * width + x_high; + pc.pos3 = y_high * width + x_low; + pc.pos4 = y_high * width + x_high; + pc.w1 = w1; + pc.w2 = w2; + pc.w3 = w3; + pc.w4 = w4; + pre_calc[pre_calc_index] = pc; + + pre_calc_index += 1; + } + } + } + } +} + +template +void ROIAlignForward_cpu_kernel( + const int nthreads, + const T* bottom_data, + const T& spatial_scale, + const int channels, + const int height, + const int width, + const int pooled_height, + const int pooled_width, + const int sampling_ratio, + const T* bottom_rois, + //int roi_cols, + T* top_data) { + //AT_ASSERT(roi_cols == 4 || roi_cols == 5); + int roi_cols = 5; + + int n_rois = nthreads / channels / pooled_width / pooled_height; + // (n, c, ph, pw) is an element in the pooled output + // can be parallelized using omp + // #pragma omp parallel for num_threads(32) + for (int n = 0; n < n_rois; n++) { + int index_n = n * channels * pooled_width * pooled_height; + + // roi could have 4 or 5 columns + const T* offset_bottom_rois = bottom_rois + n * roi_cols; + int roi_batch_ind = 0; + if (roi_cols == 5) { + roi_batch_ind = offset_bottom_rois[0]; + offset_bottom_rois++; + } + + // Do not using rounding; this implementation detail is critical + T roi_start_w = offset_bottom_rois[0] * spatial_scale; + T roi_start_h = offset_bottom_rois[1] * spatial_scale; + T roi_end_w = offset_bottom_rois[2] * spatial_scale; + T roi_end_h = offset_bottom_rois[3] * spatial_scale; + // T roi_start_w = round(offset_bottom_rois[0] * spatial_scale); + // T roi_start_h = round(offset_bottom_rois[1] * spatial_scale); + // T roi_end_w = round(offset_bottom_rois[2] * spatial_scale); + // T roi_end_h = round(offset_bottom_rois[3] * spatial_scale); + + // Force malformed ROIs to be 1x1 + T roi_width = std::max(roi_end_w - roi_start_w, (T)1.); + T roi_height = std::max(roi_end_h - roi_start_h, (T)1.); + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) + ? sampling_ratio + : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = + (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + // we want to precalculate indeces and weights shared by all chanels, + // this is the key point of optimiation + std::vector> pre_calc( + roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height); + pre_calc_for_bilinear_interpolate( + height, + width, + pooled_height, + pooled_width, + roi_bin_grid_h, + roi_bin_grid_w, + roi_start_h, + roi_start_w, + bin_size_h, + bin_size_w, + roi_bin_grid_h, + roi_bin_grid_w, + pre_calc); + + for (int c = 0; c < channels; c++) { + int index_n_c = index_n + c * pooled_width * pooled_height; + const T* offset_bottom_data = + bottom_data + (roi_batch_ind * channels + c) * height * width; + int pre_calc_index = 0; + + for (int ph = 0; ph < pooled_height; ph++) { + for (int pw = 0; pw < pooled_width; pw++) { + int index = index_n_c + ph * pooled_width + pw; + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy++) { + for (int ix = 0; ix < roi_bin_grid_w; ix++) { + PreCalc pc = pre_calc[pre_calc_index]; + output_val += pc.w1 * offset_bottom_data[pc.pos1] + + pc.w2 * offset_bottom_data[pc.pos2] + + pc.w3 * offset_bottom_data[pc.pos3] + + pc.w4 * offset_bottom_data[pc.pos4]; + + pre_calc_index += 1; + } + } + output_val /= count; + + top_data[index] = output_val; + } // for pw + } // for ph + } // for c + } // for n +} + +at::Tensor ROIAlign_forward_cpu(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(!input.device().is_cuda(), "input must be a CPU tensor"); + AT_ASSERTM(!rois.device().is_cuda(), "rois must be a CPU tensor"); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + auto output = at::empty({num_rois, channels, pooled_height, pooled_width}, input.options()); + auto output_size = num_rois * pooled_height * pooled_width * channels; + + if (output.numel() == 0) { + return output; + } + + AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "ROIAlign_forward", [&] { + ROIAlignForward_cpu_kernel( + output_size, + input.data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois.data_ptr(), + output.data_ptr()); + }); + return output; +} diff --git a/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp b/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..11b7aa60fdca907352b334f142faadb46d662f99 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp @@ -0,0 +1,75 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include "cpu/vision.h" + + +template +at::Tensor nms_cpu_kernel(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold) { + AT_ASSERTM(!dets.device().is_cuda(), "dets must be a CPU tensor"); + AT_ASSERTM(!scores.device().is_cuda(), "scores must be a CPU tensor"); + AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); + } + + auto x1_t = dets.select(1, 0).contiguous(); + auto y1_t = dets.select(1, 1).contiguous(); + auto x2_t = dets.select(1, 2).contiguous(); + auto y2_t = dets.select(1, 3).contiguous(); + + at::Tensor areas_t = (x2_t - x1_t + 1) * (y2_t - y1_t + 1); + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + + auto ndets = dets.size(0); + at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte).device(at::kCPU)); + + auto suppressed = suppressed_t.data_ptr(); + auto order = order_t.data_ptr(); + auto x1 = x1_t.data_ptr(); + auto y1 = y1_t.data_ptr(); + auto x2 = x2_t.data_ptr(); + auto y2 = y2_t.data_ptr(); + auto areas = areas_t.data_ptr(); + + for (int64_t _i = 0; _i < ndets; _i++) { + auto i = order[_i]; + if (suppressed[i] == 1) + continue; + auto ix1 = x1[i]; + auto iy1 = y1[i]; + auto ix2 = x2[i]; + auto iy2 = y2[i]; + auto iarea = areas[i]; + + for (int64_t _j = _i + 1; _j < ndets; _j++) { + auto j = order[_j]; + if (suppressed[j] == 1) + continue; + auto xx1 = std::max(ix1, x1[j]); + auto yy1 = std::max(iy1, y1[j]); + auto xx2 = std::min(ix2, x2[j]); + auto yy2 = std::min(iy2, y2[j]); + + auto w = std::max(static_cast(0), xx2 - xx1 + 1); + auto h = std::max(static_cast(0), yy2 - yy1 + 1); + auto inter = w * h; + auto ovr = inter / (iarea + areas[j] - inter); + if (ovr >= threshold) + suppressed[j] = 1; + } + } + return at::nonzero(suppressed_t == 0).squeeze(1); +} + +at::Tensor nms_cpu(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold) { + at::Tensor result; + AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "nms", [&] { + result = nms_cpu_kernel(dets, scores, threshold); + }); + return result; +} diff --git a/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp b/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp new file mode 100644 index 0000000000000000000000000000000000000000..423941d71e29f5b9823006d57cdf0088646586ed --- /dev/null +++ b/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp @@ -0,0 +1,117 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include "cpu/vision.h" + + +template +std::pair soft_nms_cpu_kernel(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold, + const float sigma) { + AT_ASSERTM(!dets.device().is_cuda(), "dets must be a CPU tensor"); + AT_ASSERTM(!scores.device().is_cuda(), "scores must be a CPU tensor"); + AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return std::make_pair(at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)), + at::empty({0}, scores.options().dtype(at::kFloat).device(at::kCPU))); + } + + auto x1_t = dets.select(1, 0).contiguous(); + auto y1_t = dets.select(1, 1).contiguous(); + auto x2_t = dets.select(1, 2).contiguous(); + auto y2_t = dets.select(1, 3).contiguous(); + + auto scores_t = scores.clone(); + + at::Tensor areas_t = (x2_t - x1_t + 1) * (y2_t - y1_t + 1); + auto ndets = dets.size(0); + auto inds_t = at::arange(ndets, dets.options().dtype(at::kLong).device(at::kCPU)); + + auto x1 = x1_t.data_ptr(); + auto y1 = y1_t.data_ptr(); + auto x2 = x2_t.data_ptr(); + auto y2 = y2_t.data_ptr(); + auto s = scores_t.data_ptr(); + auto inds = inds_t.data_ptr(); + auto areas = areas_t.data_ptr(); + + for (int64_t i = 0; i < ndets; i++) { + + auto ix1 = x1[i]; + auto iy1 = y1[i]; + auto ix2 = x2[i]; + auto iy2 = y2[i]; + auto is = s[i]; + auto ii = inds[i]; + auto iarea = areas[i]; + + auto maxpos = scores_t.slice(0, i, ndets).argmax().item() + i; + + // add max box as a detection + x1[i] = x1[maxpos]; + y1[i] = y1[maxpos]; + x2[i] = x2[maxpos]; + y2[i] = y2[maxpos]; + s[i] = s[maxpos]; + inds[i] = inds[maxpos]; + areas[i] = areas[maxpos]; + + // swap ith box with position of max box + x1[maxpos] = ix1; + y1[maxpos] = iy1; + x2[maxpos] = ix2; + y2[maxpos] = iy2; + s[maxpos] = is; + inds[maxpos] = ii; + areas[maxpos] = iarea; + + ix1 = x1[i]; + iy1 = y1[i]; + ix2 = x2[i]; + iy2 = y2[i]; + iarea = areas[i]; + + // NMS iterations, note that ndets changes if detection boxes + // fall below threshold + for (int64_t j = i + 1; j < ndets; j++) { + auto xx1 = std::max(ix1, x1[j]); + auto yy1 = std::max(iy1, y1[j]); + auto xx2 = std::min(ix2, x2[j]); + auto yy2 = std::min(iy2, y2[j]); + + auto w = std::max(static_cast(0), xx2 - xx1 + 1); + auto h = std::max(static_cast(0), yy2 - yy1 + 1); + + auto inter = w * h; + auto ovr = inter / (iarea + areas[j] - inter); + + s[j] = s[j] * std::exp(- std::pow(ovr, 2.0) / sigma); + + // if box score falls below threshold, discard the box by + // swapping with last box update ndets + if (s[j] < threshold) { + x1[j] = x1[ndets - 1]; + y1[j] = y1[ndets - 1]; + x2[j] = x2[ndets - 1]; + y2[j] = y2[ndets - 1]; + s[j] = s[ndets - 1]; + inds[j] = inds[ndets - 1]; + areas[j] = areas[ndets - 1]; + j--; + ndets--; + } + } + } + return std::make_pair(inds_t.slice(0, 0, ndets), scores_t.slice(0, 0, ndets)); +} + +std::pair soft_nms_cpu(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold, + const float sigma) { + std::pair result; + AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "soft_nms", [&] { + result = soft_nms_cpu_kernel(dets, scores, threshold, sigma); + }); + return result; +} \ No newline at end of file diff --git a/maskrcnn_benchmark/csrc/cpu/vision.h b/maskrcnn_benchmark/csrc/cpu/vision.h new file mode 100644 index 0000000000000000000000000000000000000000..e00ef683150eb9d46d0e4f6a30f55a7230a52e93 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cpu/vision.h @@ -0,0 +1,22 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include + + +at::Tensor ROIAlign_forward_cpu(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + + +at::Tensor nms_cpu(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold); + + +std::pair soft_nms_cpu(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold, + const float sigma); \ No newline at end of file diff --git a/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu b/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..9ed1a0adfd841a17d3574dee6ac703820fcfe144 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu @@ -0,0 +1,346 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include +#include + +#include +#include +#include + +// TODO make it in a common file +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + + +template +__device__ T bilinear_interpolate(const T* bottom_data, + const int height, const int width, + T y, T x, + const int index /* index for debug only*/) { + + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + //empty + return 0; + } + + if (y <= 0) y = 0; + if (x <= 0) x = 0; + + int y_low = (int) y; + int x_low = (int) x; + int y_high; + int x_high; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T) y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T) x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + // do bilinear interpolation + T v1 = bottom_data[y_low * width + x_low]; + T v2 = bottom_data[y_low * width + x_high]; + T v3 = bottom_data[y_high * width + x_low]; + T v4 = bottom_data[y_high * width + x_high]; + T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + return val; +} + +template +__global__ void RoIAlignForward(const int nthreads, const T* bottom_data, + const T spatial_scale, const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int sampling_ratio, + const T* bottom_rois, T* top_data) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + + // Do not using rounding; this implementation detail is critical + T roi_start_w = offset_bottom_rois[1] * spatial_scale; + T roi_start_h = offset_bottom_rois[2] * spatial_scale; + T roi_end_w = offset_bottom_rois[3] * spatial_scale; + T roi_end_h = offset_bottom_rois[4] * spatial_scale; + // T roi_start_w = round(offset_bottom_rois[1] * spatial_scale); + // T roi_start_h = round(offset_bottom_rois[2] * spatial_scale); + // T roi_end_w = round(offset_bottom_rois[3] * spatial_scale); + // T roi_end_h = round(offset_bottom_rois[4] * spatial_scale); + + // Force malformed ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, (T)1.); + T roi_height = max(roi_end_h - roi_start_h, (T)1.); + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + const T* offset_bottom_data = bottom_data + (roi_batch_ind * channels + c) * height * width; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + T output_val = 0.; + for (int iy = 0; iy < roi_bin_grid_h; iy ++) // e.g., iy = 0, 1 + { + const T y = roi_start_h + ph * bin_size_h + static_cast(iy + .5f) * bin_size_h / static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix ++) + { + const T x = roi_start_w + pw * bin_size_w + static_cast(ix + .5f) * bin_size_w / static_cast(roi_bin_grid_w); + + T val = bilinear_interpolate(offset_bottom_data, height, width, y, x, index); + output_val += val; + } + } + output_val /= count; + + top_data[index] = output_val; + } +} + + +template +__device__ void bilinear_interpolate_gradient( + const int height, const int width, + T y, T x, + T & w1, T & w2, T & w3, T & w4, + int & x_low, int & x_high, int & y_low, int & y_high, + const int index /* index for debug only*/) { + + // deal with cases that inverse elements are out of feature map boundary + if (y < -1.0 || y > height || x < -1.0 || x > width) { + //empty + w1 = w2 = w3 = w4 = 0.; + x_low = x_high = y_low = y_high = -1; + return; + } + + if (y <= 0) y = 0; + if (x <= 0) x = 0; + + y_low = (int) y; + x_low = (int) x; + + if (y_low >= height - 1) { + y_high = y_low = height - 1; + y = (T) y_low; + } else { + y_high = y_low + 1; + } + + if (x_low >= width - 1) { + x_high = x_low = width - 1; + x = (T) x_low; + } else { + x_high = x_low + 1; + } + + T ly = y - y_low; + T lx = x - x_low; + T hy = 1. - ly, hx = 1. - lx; + + // reference in forward + // T v1 = bottom_data[y_low * width + x_low]; + // T v2 = bottom_data[y_low * width + x_high]; + // T v3 = bottom_data[y_high * width + x_low]; + // T v4 = bottom_data[y_high * width + x_high]; + // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + + w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; + + return; +} + +template +__global__ void RoIAlignBackwardFeature(const int nthreads, const T* top_diff, + const int num_rois, const T spatial_scale, + const int channels, const int height, const int width, + const int pooled_height, const int pooled_width, + const int sampling_ratio, + T* bottom_diff, + const T* bottom_rois) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + + // Do not using rounding; this implementation detail is critical + T roi_start_w = offset_bottom_rois[1] * spatial_scale; + T roi_start_h = offset_bottom_rois[2] * spatial_scale; + T roi_end_w = offset_bottom_rois[3] * spatial_scale; + T roi_end_h = offset_bottom_rois[4] * spatial_scale; + // T roi_start_w = round(offset_bottom_rois[1] * spatial_scale); + // T roi_start_h = round(offset_bottom_rois[2] * spatial_scale); + // T roi_end_w = round(offset_bottom_rois[3] * spatial_scale); + // T roi_end_h = round(offset_bottom_rois[4] * spatial_scale); + + // Force malformed ROIs to be 1x1 + T roi_width = max(roi_end_w - roi_start_w, (T)1.); + T roi_height = max(roi_end_h - roi_start_h, (T)1.); + T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); + + T* offset_bottom_diff = bottom_diff + (roi_batch_ind * channels + c) * height * width; + + int top_offset = (n * channels + c) * pooled_height * pooled_width; + const T* offset_top_diff = top_diff + top_offset; + const T top_diff_this_bin = offset_top_diff[ph * pooled_width + pw]; + + // We use roi_bin_grid to sample the grid and mimic integral + int roi_bin_grid_h = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height); // e.g., = 2 + int roi_bin_grid_w = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); + + // We do average (integral) pooling inside a bin + const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 + + for (int iy = 0; iy < roi_bin_grid_h; iy ++) // e.g., iy = 0, 1 + { + const T y = roi_start_h + ph * bin_size_h + static_cast(iy + .5f) * bin_size_h / static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 + for (int ix = 0; ix < roi_bin_grid_w; ix ++) + { + const T x = roi_start_w + pw * bin_size_w + static_cast(ix + .5f) * bin_size_w / static_cast(roi_bin_grid_w); + + T w1, w2, w3, w4; + int x_low, x_high, y_low, y_high; + + bilinear_interpolate_gradient(height, width, y, x, + w1, w2, w3, w4, + x_low, x_high, y_low, y_high, + index); + + T g1 = top_diff_this_bin * w1 / count; + T g2 = top_diff_this_bin * w2 / count; + T g3 = top_diff_this_bin * w3 / count; + T g4 = top_diff_this_bin * w4 / count; + + if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) + { + atomicAdd(offset_bottom_diff + y_low * width + x_low, static_cast(g1)); + atomicAdd(offset_bottom_diff + y_low * width + x_high, static_cast(g2)); + atomicAdd(offset_bottom_diff + y_high * width + x_low, static_cast(g3)); + atomicAdd(offset_bottom_diff + y_high * width + x_high, static_cast(g4)); + } // if + } // ix + } // iy + } // CUDA_1D_KERNEL_LOOP +} // RoIAlignBackward + + +at::Tensor ROIAlign_forward_cuda(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio) { + AT_ASSERTM(input.device().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + auto output = at::empty({num_rois, channels, pooled_height, pooled_width}, input.options()); + auto output_size = num_rois * pooled_height * pooled_width * channels; + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(THCCeilDiv(output_size, 512L), 4096L)); + dim3 block(512); + + if (output.numel() == 0) { + THCudaCheck(cudaGetLastError()); + return output; + } + + AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "ROIAlign_forward", [&] { + RoIAlignForward<<>>( + output_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + rois.contiguous().data_ptr(), + output.data_ptr()); + }); + THCudaCheck(cudaGetLastError()); + return output; +} + +// TODO remove the dependency on input and use instead its sizes -> save memory +at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio) { + AT_ASSERTM(grad.device().is_cuda(), "grad must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + + auto num_rois = rois.size(0); + auto grad_input = at::zeros({batch_size, channels, height, width}, grad.options()); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(THCCeilDiv(grad.numel(), 512L), 4096L)); + dim3 block(512); + + // handle possibly empty gradients + if (grad.numel() == 0) { + THCudaCheck(cudaGetLastError()); + return grad_input; + } + + AT_DISPATCH_FLOATING_TYPES(grad.scalar_type(), "ROIAlign_backward", [&] { + RoIAlignBackwardFeature<<>>( + grad.numel(), + grad.contiguous().data_ptr(), + num_rois, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + sampling_ratio, + grad_input.data_ptr(), + rois.contiguous().data_ptr()); + }); + THCudaCheck(cudaGetLastError()); + return grad_input; +} diff --git a/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu b/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..60fc9fbc55956304c7ff6b48cbf3c086029b8354 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu @@ -0,0 +1,202 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include +#include + +#include +#include +#include + + +// TODO make it in a common file +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + + +template +__global__ void RoIPoolFForward(const int nthreads, const T* bottom_data, + const T spatial_scale, const int channels, const int height, + const int width, const int pooled_height, const int pooled_width, + const T* bottom_rois, T* top_data, int* argmax_data) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + int roi_start_w = round(offset_bottom_rois[1] * spatial_scale); + int roi_start_h = round(offset_bottom_rois[2] * spatial_scale); + int roi_end_w = round(offset_bottom_rois[3] * spatial_scale); + int roi_end_h = round(offset_bottom_rois[4] * spatial_scale); + + // Force malformed ROIs to be 1x1 + int roi_width = max(roi_end_w - roi_start_w + 1, 1); + int roi_height = max(roi_end_h - roi_start_h + 1, 1); + T bin_size_h = static_cast(roi_height) + / static_cast(pooled_height); + T bin_size_w = static_cast(roi_width) + / static_cast(pooled_width); + + int hstart = static_cast(floor(static_cast(ph) + * bin_size_h)); + int wstart = static_cast(floor(static_cast(pw) + * bin_size_w)); + int hend = static_cast(ceil(static_cast(ph + 1) + * bin_size_h)); + int wend = static_cast(ceil(static_cast(pw + 1) + * bin_size_w)); + + // Add roi offsets and clip to input boundaries + hstart = min(max(hstart + roi_start_h, 0), height); + hend = min(max(hend + roi_start_h, 0), height); + wstart = min(max(wstart + roi_start_w, 0), width); + wend = min(max(wend + roi_start_w, 0), width); + bool is_empty = (hend <= hstart) || (wend <= wstart); + + // Define an empty pooling region to be zero + T maxval = is_empty ? 0 : -FLT_MAX; + // If nothing is pooled, argmax = -1 causes nothing to be backprop'd + int maxidx = -1; + const T* offset_bottom_data = + bottom_data + (roi_batch_ind * channels + c) * height * width; + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + int bottom_index = h * width + w; + if (offset_bottom_data[bottom_index] > maxval) { + maxval = offset_bottom_data[bottom_index]; + maxidx = bottom_index; + } + } + } + top_data[index] = maxval; + argmax_data[index] = maxidx; + } +} + +template +__global__ void RoIPoolFBackward(const int nthreads, const T* top_diff, + const int* argmax_data, const int num_rois, const T spatial_scale, + const int channels, const int height, const int width, + const int pooled_height, const int pooled_width, T* bottom_diff, + const T* bottom_rois) { + CUDA_1D_KERNEL_LOOP(index, nthreads) { + // (n, c, ph, pw) is an element in the pooled output + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int c = (index / pooled_width / pooled_height) % channels; + int n = index / pooled_width / pooled_height / channels; + + const T* offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + int bottom_offset = (roi_batch_ind * channels + c) * height * width; + int top_offset = (n * channels + c) * pooled_height * pooled_width; + const T* offset_top_diff = top_diff + top_offset; + T* offset_bottom_diff = bottom_diff + bottom_offset; + const int* offset_argmax_data = argmax_data + top_offset; + + int argmax = offset_argmax_data[ph * pooled_width + pw]; + if (argmax != -1) { + atomicAdd( + offset_bottom_diff + argmax, + static_cast(offset_top_diff[ph * pooled_width + pw])); + + } + } +} + +std::tuple ROIPool_forward_cuda(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width) { + AT_ASSERTM(input.device().is_cuda(), "input must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + + auto num_rois = rois.size(0); + auto channels = input.size(1); + auto height = input.size(2); + auto width = input.size(3); + + auto output = at::empty({num_rois, channels, pooled_height, pooled_width}, input.options()); + auto output_size = num_rois * pooled_height * pooled_width * channels; + auto argmax = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.options().dtype(at::kInt)); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(THCCeilDiv(output_size, 512L), 4096L)); + dim3 block(512); + + if (output.numel() == 0) { + THCudaCheck(cudaGetLastError()); + return std::make_tuple(output, argmax); + } + + AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "ROIPool_forward", [&] { + RoIPoolFForward<<>>( + output_size, + input.contiguous().data_ptr(), + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + rois.contiguous().data_ptr(), + output.data_ptr(), + argmax.data_ptr()); + }); + THCudaCheck(cudaGetLastError()); + return std::make_tuple(output, argmax); +} + +// TODO remove the dependency on input and use instead its sizes -> save memory +at::Tensor ROIPool_backward_cuda(const at::Tensor& grad, + const at::Tensor& input, + const at::Tensor& rois, + const at::Tensor& argmax, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width) { + AT_ASSERTM(grad.device().is_cuda(), "grad must be a CUDA tensor"); + AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor"); + // TODO add more checks + + auto num_rois = rois.size(0); + auto grad_input = at::zeros({batch_size, channels, height, width}, grad.options()); + + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(THCCeilDiv(grad.numel(), 512L), 4096L)); + dim3 block(512); + + // handle possibly empty gradients + if (grad.numel() == 0) { + THCudaCheck(cudaGetLastError()); + return grad_input; + } + + AT_DISPATCH_FLOATING_TYPES(grad.scalar_type(), "ROIPool_backward", [&] { + RoIPoolFBackward<<>>( + grad.numel(), + grad.contiguous().data_ptr(), + argmax.data_ptr(), + num_rois, + spatial_scale, + channels, + height, + width, + pooled_height, + pooled_width, + grad_input.data_ptr(), + rois.contiguous().data_ptr()); + }); + THCudaCheck(cudaGetLastError()); + return grad_input; +} diff --git a/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu b/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..8aeceae0f825598cd36ea99add8da613c5e2482a --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu @@ -0,0 +1,188 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +// This file is modified from https://github.com/pytorch/pytorch/blob/master/modules/detectron/sigmoid_focal_loss_op.cu +// Cheng-Yang Fu +// cyfu@cs.unc.edu +#include +#include + +#include +#include +#include + +#include + +// TODO make it in a common file +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + + +template +__global__ void SigmoidFocalLossForward(const int nthreads, + const T* logits, + const int* targets, + const int num_classes, + const float gamma, + const float alpha, + const int num, + T* losses) { + CUDA_1D_KERNEL_LOOP(i, nthreads) { + + int n = i / num_classes; + int d = i % num_classes; // current class[0~79]; + int t = targets[n]; // target class [1~80]; + + // Decide it is positive or negative case. + T c1 = (t == (d+1)); + T c2 = (t>=0 & t != (d+1)); + + T zn = (1.0 - alpha); + T zp = (alpha); + + // p = 1. / 1. + expf(-x); p = sigmoid(x) + T p = 1. / (1. + expf(-logits[i])); + + // (1-p)**gamma * log(p) where + T term1 = powf((1. - p), gamma) * logf(max(p, FLT_MIN)); + + // p**gamma * log(1-p) + T term2 = powf(p, gamma) * + (-1. * logits[i] * (logits[i] >= 0) - + logf(1. + expf(logits[i] - 2. * logits[i] * (logits[i] >= 0)))); + + losses[i] = 0.0; + losses[i] += -c1 * term1 * zp; + losses[i] += -c2 * term2 * zn; + + } // CUDA_1D_KERNEL_LOOP +} // SigmoidFocalLossForward + + +template +__global__ void SigmoidFocalLossBackward(const int nthreads, + const T* logits, + const int* targets, + const T* d_losses, + const int num_classes, + const float gamma, + const float alpha, + const int num, + T* d_logits) { + CUDA_1D_KERNEL_LOOP(i, nthreads) { + + int n = i / num_classes; + int d = i % num_classes; // current class[0~79]; + int t = targets[n]; // target class [1~80], 0 is background; + + // Decide it is positive or negative case. + T c1 = (t == (d+1)); + T c2 = (t>=0 & t != (d+1)); + + T zn = (1.0 - alpha); + T zp = (alpha); + // p = 1. / 1. + expf(-x); p = sigmoid(x) + T p = 1. / (1. + expf(-logits[i])); + + // (1-p)**g * (1 - p - g*p*log(p) + T term1 = powf((1. - p), gamma) * + (1. - p - (p * gamma * logf(max(p, FLT_MIN)))); + + // (p**g) * (g*(1-p)*log(1-p) - p) + T term2 = powf(p, gamma) * + ((-1. * logits[i] * (logits[i] >= 0) - + logf(1. + expf(logits[i] - 2. * logits[i] * (logits[i] >= 0)))) * + (1. - p) * gamma - p); + d_logits[i] = 0.0; + d_logits[i] += -c1 * term1 * zp; + d_logits[i] += -c2 * term2 * zn; + d_logits[i] = d_logits[i] * d_losses[i]; + + } // CUDA_1D_KERNEL_LOOP +} // SigmoidFocalLossBackward + + +at::Tensor SigmoidFocalLoss_forward_cuda( + const at::Tensor& logits, + const at::Tensor& targets, + const int num_classes, + const float gamma, + const float alpha) { + AT_ASSERTM(logits.device().is_cuda(), "logits must be a CUDA tensor"); + AT_ASSERTM(targets.device().is_cuda(), "targets must be a CUDA tensor"); + AT_ASSERTM(logits.dim() == 2, "logits should be NxClass"); + + const int num_samples = logits.size(0); + + auto losses = at::empty({num_samples, logits.size(1)}, logits.options()); + auto losses_size = num_samples * logits.size(1); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(THCCeilDiv(losses_size, 512L), 4096L)); + dim3 block(512); + + if (losses.numel() == 0) { + THCudaCheck(cudaGetLastError()); + return losses; + } + + AT_DISPATCH_FLOATING_TYPES(logits.scalar_type(), "SigmoidFocalLoss_forward", [&] { + SigmoidFocalLossForward<<>>( + losses_size, + logits.contiguous().data_ptr(), + targets.contiguous().data_ptr(), + num_classes, + gamma, + alpha, + num_samples, + losses.data_ptr()); + }); + THCudaCheck(cudaGetLastError()); + return losses; +} + + +at::Tensor SigmoidFocalLoss_backward_cuda( + const at::Tensor& logits, + const at::Tensor& targets, + const at::Tensor& d_losses, + const int num_classes, + const float gamma, + const float alpha) { + AT_ASSERTM(logits.device().is_cuda(), "logits must be a CUDA tensor"); + AT_ASSERTM(targets.device().is_cuda(), "targets must be a CUDA tensor"); + AT_ASSERTM(d_losses.device().is_cuda(), "d_losses must be a CUDA tensor"); + + AT_ASSERTM(logits.dim() == 2, "logits should be NxClass"); + + const int num_samples = logits.size(0); + AT_ASSERTM(logits.size(1) == num_classes, "logits.size(1) should be num_classes"); + + auto d_logits = at::zeros({num_samples, num_classes}, logits.options()); + auto d_logits_size = num_samples * logits.size(1); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + dim3 grid(std::min(THCCeilDiv(d_logits_size, 512L), 4096L)); + dim3 block(512); + + if (d_logits.numel() == 0) { + THCudaCheck(cudaGetLastError()); + return d_logits; + } + + AT_DISPATCH_FLOATING_TYPES(logits.scalar_type(), "SigmoidFocalLoss_backward", [&] { + SigmoidFocalLossBackward<<>>( + d_logits_size, + logits.contiguous().data_ptr(), + targets.contiguous().data_ptr(), + d_losses.contiguous().data_ptr(), + num_classes, + gamma, + alpha, + num_samples, + d_logits.data_ptr()); + }); + + THCudaCheck(cudaGetLastError()); + return d_logits; +} + diff --git a/maskrcnn_benchmark/csrc/cuda/deform_conv_cuda.cu b/maskrcnn_benchmark/csrc/cuda/deform_conv_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..2cdf8d61957e50d452dd230c97b5754dacd2fa0e --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/deform_conv_cuda.cu @@ -0,0 +1,691 @@ +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c + +#include +#include + +#include +#include + +#include +#include +#include + + +void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset, + const int channels, const int height, const int width, + const int ksize_h, const int ksize_w, const int pad_h, + const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, + at::Tensor data_col); + +void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset, + const int channels, const int height, const int width, + const int ksize_h, const int ksize_w, const int pad_h, + const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, + at::Tensor grad_im); + +void deformable_col2im_coord( + const at::Tensor data_col, const at::Tensor data_im, + const at::Tensor data_offset, const int channels, const int height, + const int width, const int ksize_h, const int ksize_w, const int pad_h, + const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int parallel_imgs, + const int deformable_group, at::Tensor grad_offset); + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, const at::Tensor data_offset, + const at::Tensor data_mask, const int batch_size, const int channels, + const int height_im, const int width_im, const int height_col, + const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int deformable_group, + at::Tensor data_col); + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, const at::Tensor data_offset, + const at::Tensor data_mask, const int batch_size, const int channels, + const int height_im, const int width_im, const int height_col, + const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int deformable_group, + at::Tensor grad_im); + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, const at::Tensor data_im, + const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, + const int width_im, const int height_col, const int width_col, + const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, + const int stride_h, const int stride_w, const int dilation_h, + const int dilation_w, const int deformable_group, at::Tensor grad_offset, + at::Tensor grad_mask); + +void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput, + at::Tensor weight, int kH, int kW, int dH, int dW, int padH, + int padW, int dilationH, int dilationW, int group, + int deformable_group) +{ + TORCH_CHECK(weight.ndimension() == 4, + "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " + "but got: %s", + weight.ndimension()); + + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + TORCH_CHECK(kW > 0 && kH > 0, + "kernel size should be greater than zero, but got kH: %d kW: %d", kH, + kW); + + TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW), + "kernel size should be consistent with weight, ", + "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH, + kW, weight.size(2), weight.size(3)); + + TORCH_CHECK(dW > 0 && dH > 0, + "stride should be greater than zero, but got dH: %d dW: %d", dH, dW); + + TORCH_CHECK( + dilationW > 0 && dilationH > 0, + "dilation should be greater than 0, but got dilationH: %d dilationW: %d", + dilationH, dilationW); + + int ndim = input.ndimension(); + int dimf = 0; + int dimh = 1; + int dimw = 2; + + if (ndim == 4) { + dimf++; + dimh++; + dimw++; + } + + TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s", + ndim); + + long nInputPlane = weight.size(1) * group; + long inputHeight = input.size(dimh); + long inputWidth = input.size(dimw); + long nOutputPlane = weight.size(0); + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + + TORCH_CHECK(nInputPlane % deformable_group == 0, + "input channels must divide deformable group size"); + + if (outputWidth < 1 || outputHeight < 1) + AT_ERROR( + "Given input size: (%ld x %ld x %ld). " + "Calculated output size: (%ld x %ld x %ld). Output size is too small", + nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight, + outputWidth); + + TORCH_CHECK(input.size(1) == nInputPlane, + "invalid number of input planes, expected: %d, but got: %d", + nInputPlane, input.size(1)); + + TORCH_CHECK((inputHeight >= kH && inputWidth >= kW), + "input image is smaller than kernel"); + + TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth), + "invalid spatial size of offset, expected height: %d width: %d, but " + "got height: %d width: %d", + outputHeight, outputWidth, offset.size(2), offset.size(3)); + + TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW), + "invalid number of channels of offset"); + + if (gradOutput != NULL) { + TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane, + "invalid number of gradOutput planes, expected: %d, but got: %d", + nOutputPlane, gradOutput->size(dimf)); + + TORCH_CHECK((gradOutput->size(dimh) == outputHeight && + gradOutput->size(dimw) == outputWidth), + "invalid size of gradOutput, expected height: %d width: %d , but " + "got height: %d width: %d", + outputHeight, outputWidth, gradOutput->size(dimh), + gradOutput->size(dimw)); + } +} + +int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, + at::Tensor offset, at::Tensor output, + at::Tensor columns, at::Tensor ones, int kW, + int kH, int dW, int dH, int padW, int padH, + int dilationW, int dilationH, int group, + int deformable_group, int im2col_step) +{ + // todo: resize columns to include im2col: done + // todo: add im2col_step as input + // todo: add new output buffer and transpose it to output (or directly + // transpose output) todo: possibly change data indexing because of + // parallel_imgs + + shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW, + dilationH, dilationW, group, deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input.unsqueeze_(0); + offset.unsqueeze_(0); + } + + // todo: assert batchsize dividable by im2col_step + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, + outputHeight, outputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < outputHeight * outputWidth) { + ones = at::ones({outputHeight, outputWidth}, input.options()); + } + + input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + offset = + offset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + at::Tensor output_buffer = + at::zeros({batchSize / im2col_step, nOutputPlane, + im2col_step * outputHeight, outputWidth}, + output.options()); + + output_buffer = output_buffer.view( + {output_buffer.size(0), group, output_buffer.size(1) / group, + output_buffer.size(2), output_buffer.size(3)}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, + inputWidth, kH, kW, padH, padW, dH, dW, dilationH, + dilationW, im2col_step, deformable_group, columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + + for (int g = 0; g < group; g++) { + output_buffer[elt][g] = output_buffer[elt][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output_buffer[elt][g]); + } + } + + output_buffer = output_buffer.view( + {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2), + output_buffer.size(3), output_buffer.size(4)}); + + output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, + im2col_step, outputHeight, outputWidth}); + output_buffer.transpose_(1, 2); + output.copy_(output_buffer); + output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + output = output.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, + at::Tensor gradOutput, at::Tensor gradInput, + at::Tensor gradOffset, at::Tensor weight, + at::Tensor columns, int kW, int kH, int dW, + int dH, int padW, int padH, int dilationW, + int dilationH, int group, + int deformable_group, int im2col_step) +{ + shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW, + dilationH, dilationW, group, deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view({1, input.size(0), input.size(1), input.size(2)}); + offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + // change order of grad output + gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, + nOutputPlane, outputHeight, outputWidth}); + gradOutput.transpose_(1, 2); + + gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, + outputWidth}); + offset = + offset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + // divide into groups + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), group, gradOutput.size(1) / group, + gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)}); + + for (int g = 0; g < group; g++) { + columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), + gradOutput[elt][g].flatten(1), 0.0f, 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2), + gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)}); + + deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, + inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, + dilationH, dilationW, im2col_step, deformable_group, + gradOffset[elt]); + + deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, + inputWidth, kH, kW, padH, padW, dH, dW, dilationH, + dilationW, im2col_step, deformable_group, gradInput[elt]); + } + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + gradOffset = gradOffset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + gradOffset = + gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_parameters_cuda( + at::Tensor input, at::Tensor offset, at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, + int padW, int padH, int dilationW, int dilationH, int group, + int deformable_group, float scale, int im2col_step) +{ + // todo: transpose and reshape outGrad + // todo: reshape columns + // todo: add im2col_step as input + + shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH, + padW, dilationH, dilationW, group, deformable_group); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view( + at::IntList({1, input.size(0), input.size(1), input.size(2)})); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = gradWeight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, + nOutputPlane, outputHeight, outputWidth}); + gradOutput.transpose_(1, 2); + + at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); + gradOutputBuffer = + gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, + outputHeight, outputWidth}); + gradOutputBuffer.copy_(gradOutput); + gradOutputBuffer = + gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, + im2col_step * outputHeight, outputWidth}); + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + offset = + offset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, + inputWidth, kH, kW, padH, padW, dH, dW, dilationH, + dilationW, im2col_step, deformable_group, columns); + + // divide into group + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group, + gradOutputBuffer.size(2), gradOutputBuffer.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + gradWeight = + gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1), + gradWeight.size(2), gradWeight.size(3)}); + + for (int g = 0; g < group; g++) { + gradWeight[g] = gradWeight[g] + .flatten(1) + .addmm_(gradOutputBuffer[elt][g].flatten(1), + columns[g].transpose(1, 0), 1.0, scale) + .view_as(gradWeight[g]); + } + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), + gradOutputBuffer.size(1) * gradOutputBuffer.size(2), + gradOutputBuffer.size(3), gradOutputBuffer.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), + gradWeight.size(2), gradWeight.size(3), + gradWeight.size(4)}); + } + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + } + + return 1; +} + +void modulated_deform_conv_cuda_forward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, + int kernel_h, int kernel_w, const int stride_h, const int stride_w, + const int pad_h, const int pad_w, const int dilation_h, + const int dilation_w, const int group, const int deformable_group, + const bool with_bias) +{ + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, kernel_w, kernel_h_, kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).", + channels, channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + // resize output + output = output.view({batch, channels_out, height_out, width_out}).zero_(); + // resize temporary columns + columns = + at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, + input.options()); + + output = output.view({output.size(0), group, output.size(1) / group, + output.size(2), output.size(3)}); + + for (int b = 0; b < batch; b++) { + modulated_deformable_im2col_cuda( + input[b], offset[b], mask[b], 1, channels, height, width, height_out, + width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, columns); + + // divide into group + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + + for (int g = 0; g < group; g++) { + output[b][g] = output[b][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output[b][g]); + } + + weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), + weight.size(3), weight.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + } + + output = output.view({output.size(0), output.size(1) * output.size(2), + output.size(3), output.size(4)}); + + if (with_bias) { + output += bias.view({1, bias.size(0), 1, 1}); + } +} + +void modulated_deform_conv_cuda_backward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor columns, + at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, + at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, + int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, + int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, + const bool with_bias) +{ + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).", + kernel_h_, kernel_w, kernel_h_, kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).", + channels, channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + grad_input = grad_input.view({batch, channels, height, width}); + columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out}, + input.options()); + + grad_output = + grad_output.view({grad_output.size(0), group, grad_output.size(1) / group, + grad_output.size(2), grad_output.size(3)}); + + for (int b = 0; b < batch; b++) { + // divide int group + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + + for (int g = 0; g < group; g++) { + columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), + grad_output[b][g].flatten(1), 0.0f, 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), + weight.size(3), weight.size(4)}); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda( + columns, input[b], offset[b], mask[b], 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, + stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], + grad_mask[b]); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda( + columns, offset[b], mask[b], 1, channels, height, width, height_out, + width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, grad_input[b]); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and + // group + modulated_deformable_im2col_cuda( + input[b], offset[b], mask[b], 1, channels, height, width, height_out, + width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + grad_weight = grad_weight.view({group, grad_weight.size(0) / group, + grad_weight.size(1), grad_weight.size(2), + grad_weight.size(3)}); + if (with_bias) + grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); + + for (int g = 0; g < group; g++) { + grad_weight[g] = + grad_weight[g] + .flatten(1) + .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) + .view_as(grad_weight[g]); + if (with_bias) { + grad_bias[g] = + grad_bias[g] + .view({-1, 1}) + .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) + .view(-1); + } + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), + grad_weight.size(2), grad_weight.size(3), + grad_weight.size(4)}); + if (with_bias) + grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); + } + grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), + grad_output.size(2), grad_output.size(3), + grad_output.size(4)}); +} diff --git a/maskrcnn_benchmark/csrc/cuda/deform_conv_kernel_cuda.cu b/maskrcnn_benchmark/csrc/cuda/deform_conv_kernel_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..ee15810103a4edaf213abdb222a70249d622c0f9 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/deform_conv_kernel_cuda.cu @@ -0,0 +1,874 @@ +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.cuh + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1703.06211 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng + */ + +// modify from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu + + +#include +#include +#include +#include +#include + +using namespace at; + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +const int kMaxGridNum = 65535; +inline int GET_BLOCKS(const int N) +{ + return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); +} + +/* +const int CUDA_NUM_THREADS = 1024; + +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +}*/ + +template +__device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width, + const int height, const int width, scalar_t h, scalar_t w) +{ + + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, + const int h, const int w, const int height, const int width) +{ + + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, + const int height, const int width, const scalar_t *im_data, + const int data_width, const int bp_dir) +{ + + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; + const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) + { + for (int j = 0; j < kernel_w; ++j) + { + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const scalar_t map_h = i * dilation_h + offset_h; + //const scalar_t map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val; + data_col_ptr += batch_size * height_col * width_col; + } + } + } +} + +void deformable_im2col( + const at::Tensor data_im, const at::Tensor data_offset, const int channels, + const int height, const int width, const int ksize_h, const int ksize_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int parallel_imgs, + const int deformable_group, at::Tensor data_col) +{ + // num_axes should be smaller than block size + // todo: check parallel_imgs is correctly passed in + int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "deformable_im2col_gpu", ([&] { + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + scalar_t *data_col_ = data_col.data_ptr(); + + deformable_im2col_gpu_kernel<<>>( + num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + channel_per_deformable_group, parallel_imgs, channels, deformable_group, + height_col, width_col, data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); + } +} + +template +__global__ void deformable_col2im_gpu_kernel( + const int n, const scalar_t *data_col, const scalar_t *data_offset, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + scalar_t *grad_im) +{ + CUDA_KERNEL_LOOP(index, n) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * + 2 * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index]; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +void deformable_col2im( + const at::Tensor data_col, const at::Tensor data_offset, const int channels, + const int height, const int width, const int ksize_h, + const int ksize_w, const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, + at::Tensor grad_im) +{ + + // todo: make sure parallel_imgs is passed in correctly + int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + scalar_t *grad_im_ = grad_im.data_ptr(); + + deformable_col2im_gpu_kernel<<>>( + num_kernels, data_col_, data_offset_, channels, height, width, ksize_h, + ksize_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + parallel_imgs, deformable_group, height_col, width_col, grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); + } +} + +template +__global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col, + const scalar_t *data_im, const scalar_t *data_offset, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, scalar_t *grad_offset) +{ + CUDA_KERNEL_LOOP(index, n) + { + scalar_t val = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * + batch_size * width_col * height_col; + const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * + kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + const scalar_t weight = get_coordinate_weight( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos]; + cnt += 1; + } + + grad_offset[index] = val; + } +} + +void deformable_col2im_coord( + const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, + const int channels, const int height, const int width, const int ksize_h, + const int ksize_w, const int pad_h, const int pad_w, const int stride_h, + const int stride_w, const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, at::Tensor grad_offset) +{ + + int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs; + int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + scalar_t *grad_offset_ = grad_offset.data_ptr(); + + deformable_col2im_coord_gpu_kernel<<>>( + num_kernels, data_col_, data_im_, data_offset_, channels, height, width, + ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group, + height_col, width_col, grad_offset_); + })); +} + +template +__device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width, + const int height, const int width, scalar_t h, scalar_t w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, + const int height, const int width, const scalar_t *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void modulated_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; + const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + + const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) + { + for (int j = 0; j < kernel_w; ++j) + { + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + data_col_ptr += batch_size * height_col * width_col; + //data_col_ptr += height_col * width_col; + } + } + } +} + +template +__global__ void modulated_deformable_col2im_gpu_kernel(const int n, + const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + scalar_t *grad_im) +{ + CUDA_KERNEL_LOOP(index, n) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +template +__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, + const scalar_t *data_col, const scalar_t *data_im, + const scalar_t *data_offset, const scalar_t *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + scalar_t *grad_offset, scalar_t *grad_mask) +{ + CUDA_KERNEL_LOOP(index, n) + { + scalar_t val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const scalar_t weight = dmcn_get_coordinate_weight( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, at::Tensor data_col) +{ + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + const scalar_t *data_mask_ = data_mask.data_ptr(); + scalar_t *data_col_ = data_col.data_ptr(); + + modulated_deformable_im2col_gpu_kernel<<>>( + num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, at::Tensor grad_im) +{ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + const scalar_t *data_mask_ = data_mask.data_ptr(); + scalar_t *grad_im_ = grad_im.data_ptr(); + + modulated_deformable_col2im_gpu_kernel<<>>( + num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_h, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, at::Tensor grad_mask) +{ + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + const scalar_t *data_mask_ = data_mask.data_ptr(); + scalar_t *grad_offset_ = grad_offset.data_ptr(); + scalar_t *grad_mask_ = grad_mask.data_ptr(); + + modulated_deformable_col2im_coord_gpu_kernel<<>>( + num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset_, grad_mask_); + })); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + } +} diff --git a/maskrcnn_benchmark/csrc/cuda/deform_pool_cuda.cu b/maskrcnn_benchmark/csrc/cuda/deform_pool_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..bbe22d77b49be70f174ae3f17647b09968358255 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/deform_pool_cuda.cu @@ -0,0 +1,87 @@ +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/modulated_dcn_cuda.c + +// based on +// author: Charles Shang +// https://github.com/torch/cunn/blob/master/lib/THCUNN/generic/SpatialConvolutionMM.cu + +#include +#include + +#include +#include + +#include +#include +#include + + +void DeformablePSROIPoolForward( + const at::Tensor data, const at::Tensor bbox, const at::Tensor trans, + at::Tensor out, at::Tensor top_count, const int batch, const int channels, + const int height, const int width, const int num_bbox, + const int channels_trans, const int no_trans, const float spatial_scale, + const int output_dim, const int group_size, const int pooled_size, + const int part_size, const int sample_per_part, const float trans_std); + +void DeformablePSROIPoolBackwardAcc( + const at::Tensor out_grad, const at::Tensor data, const at::Tensor bbox, + const at::Tensor trans, const at::Tensor top_count, at::Tensor in_grad, + at::Tensor trans_grad, const int batch, const int channels, + const int height, const int width, const int num_bbox, + const int channels_trans, const int no_trans, const float spatial_scale, + const int output_dim, const int group_size, const int pooled_size, + const int part_size, const int sample_per_part, const float trans_std); + +void deform_psroi_pooling_cuda_forward( + at::Tensor input, at::Tensor bbox, at::Tensor trans, at::Tensor out, + at::Tensor top_count, const int no_trans, const float spatial_scale, + const int output_dim, const int group_size, const int pooled_size, + const int part_size, const int sample_per_part, const float trans_std) +{ + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + + const int num_bbox = bbox.size(0); + if (num_bbox != out.size(0)) + AT_ERROR("Output shape and bbox number wont match: (%d vs %d).", + out.size(0), num_bbox); + + DeformablePSROIPoolForward( + input, bbox, trans, out, top_count, batch, channels, height, width, + num_bbox, channels_trans, no_trans, spatial_scale, output_dim, group_size, + pooled_size, part_size, sample_per_part, trans_std); +} + +void deform_psroi_pooling_cuda_backward( + at::Tensor out_grad, at::Tensor input, at::Tensor bbox, at::Tensor trans, + at::Tensor top_count, at::Tensor input_grad, at::Tensor trans_grad, + const int no_trans, const float spatial_scale, const int output_dim, + const int group_size, const int pooled_size, const int part_size, + const int sample_per_part, const float trans_std) +{ + TORCH_CHECK(out_grad.is_contiguous(), "out_grad tensor has to be contiguous"); + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + const int channels_trans = no_trans ? 2 : trans.size(1); + + const int num_bbox = bbox.size(0); + if (num_bbox != out_grad.size(0)) + AT_ERROR("Output shape and bbox number wont match: (%d vs %d).", + out_grad.size(0), num_bbox); + + DeformablePSROIPoolBackwardAcc( + out_grad, input, bbox, trans, top_count, input_grad, trans_grad, batch, + channels, height, width, num_bbox, channels_trans, no_trans, + spatial_scale, output_dim, group_size, pooled_size, part_size, + sample_per_part, trans_std); +} diff --git a/maskrcnn_benchmark/csrc/cuda/deform_pool_kernel_cuda.cu b/maskrcnn_benchmark/csrc/cuda/deform_pool_kernel_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..3f6c4cb22f6ecbae242e21c9530f474e709c6e90 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/deform_pool_kernel_cuda.cu @@ -0,0 +1,365 @@ +/*! + * Copyright (c) 2017 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file deformable_psroi_pooling.cu + * \brief + * \author Yi Li, Guodong Zhang, Jifeng Dai +*/ +/***************** Adapted by Charles Shang *********************/ +// modify from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/cuda/deform_psroi_pooling_cuda.cu + + +#include +#include +#include +#include +#include + +using namespace at; + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N) +{ + return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS; +} + +template +__device__ scalar_t bilinear_interp( + const scalar_t *data, + const scalar_t x, + const scalar_t y, + const int width, + const int height) +{ + int x1 = floor(x); + int x2 = ceil(x); + int y1 = floor(y); + int y2 = ceil(y); + scalar_t dist_x = (scalar_t)(x - x1); + scalar_t dist_y = (scalar_t)(y - y1); + scalar_t value11 = data[y1 * width + x1]; + scalar_t value12 = data[y2 * width + x1]; + scalar_t value21 = data[y1 * width + x2]; + scalar_t value22 = data[y2 * width + x2]; + scalar_t value = (1 - dist_x) * (1 - dist_y) * value11 + (1 - dist_x) * dist_y * value12 + dist_x * (1 - dist_y) * value21 + dist_x * dist_y * value22; + return value; +} + +template +__global__ void DeformablePSROIPoolForwardKernel( + const int count, + const scalar_t *bottom_data, + const scalar_t spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const scalar_t *bottom_rois, const scalar_t *bottom_trans, + const int no_trans, + const scalar_t trans_std, + const int sample_per_part, + const int output_dim, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class, + scalar_t *top_data, + scalar_t *top_count) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const scalar_t *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + scalar_t roi_start_w = (scalar_t)(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + scalar_t roi_start_h = (scalar_t)(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + scalar_t roi_end_w = (scalar_t)(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + scalar_t roi_end_h = (scalar_t)(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + scalar_t roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + scalar_t roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + scalar_t bin_size_h = roi_height / (scalar_t)(pooled_height); + scalar_t bin_size_w = roi_width / (scalar_t)(pooled_width); + + scalar_t sub_bin_size_h = bin_size_h / (scalar_t)(sample_per_part); + scalar_t sub_bin_size_w = bin_size_w / (scalar_t)(sample_per_part); + + int part_h = floor((scalar_t)(ph) / pooled_height * part_size); + int part_w = floor((scalar_t)(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + scalar_t trans_x = no_trans ? (scalar_t)(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * (scalar_t)trans_std; + scalar_t trans_y = no_trans ? (scalar_t)(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * (scalar_t)trans_std; + + scalar_t wstart = (scalar_t)(pw)*bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + scalar_t hstart = (scalar_t)(ph)*bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + scalar_t sum = 0; + int count = 0; + int gw = floor((scalar_t)(pw)*group_size / pooled_width); + int gh = floor((scalar_t)(ph)*group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + const scalar_t *offset_bottom_data = bottom_data + (roi_batch_ind * channels) * height * width; + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + scalar_t w = wstart + iw * sub_bin_size_w; + scalar_t h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + scalar_t val = bilinear_interp(offset_bottom_data + c * height * width, w, h, width, height); + sum += val; + count++; + } + } + top_data[index] = count == 0 ? (scalar_t)(0) : sum / count; + top_count[index] = count; + } +} + +template +__global__ void DeformablePSROIPoolBackwardAccKernel( + const int count, + const scalar_t *top_diff, + const scalar_t *top_count, + const int num_rois, + const scalar_t spatial_scale, + const int channels, + const int height, const int width, + const int pooled_height, const int pooled_width, + const int output_dim, + scalar_t *bottom_data_diff, scalar_t *bottom_trans_diff, + const scalar_t *bottom_data, + const scalar_t *bottom_rois, + const scalar_t *bottom_trans, + const int no_trans, + const scalar_t trans_std, + const int sample_per_part, + const int group_size, + const int part_size, + const int num_classes, + const int channels_each_class) +{ + CUDA_KERNEL_LOOP(index, count) + { + // The output is in order (n, ctop, ph, pw) + int pw = index % pooled_width; + int ph = (index / pooled_width) % pooled_height; + int ctop = (index / pooled_width / pooled_height) % output_dim; + int n = index / pooled_width / pooled_height / output_dim; + + // [start, end) interval for spatial sampling + const scalar_t *offset_bottom_rois = bottom_rois + n * 5; + int roi_batch_ind = offset_bottom_rois[0]; + scalar_t roi_start_w = (scalar_t)(round(offset_bottom_rois[1])) * spatial_scale - 0.5; + scalar_t roi_start_h = (scalar_t)(round(offset_bottom_rois[2])) * spatial_scale - 0.5; + scalar_t roi_end_w = (scalar_t)(round(offset_bottom_rois[3]) + 1.) * spatial_scale - 0.5; + scalar_t roi_end_h = (scalar_t)(round(offset_bottom_rois[4]) + 1.) * spatial_scale - 0.5; + + // Force too small ROIs to be 1x1 + scalar_t roi_width = max(roi_end_w - roi_start_w, 0.1); //avoid 0 + scalar_t roi_height = max(roi_end_h - roi_start_h, 0.1); + + // Compute w and h at bottom + scalar_t bin_size_h = roi_height / (scalar_t)(pooled_height); + scalar_t bin_size_w = roi_width / (scalar_t)(pooled_width); + + scalar_t sub_bin_size_h = bin_size_h / (scalar_t)(sample_per_part); + scalar_t sub_bin_size_w = bin_size_w / (scalar_t)(sample_per_part); + + int part_h = floor((scalar_t)(ph) / pooled_height * part_size); + int part_w = floor((scalar_t)(pw) / pooled_width * part_size); + int class_id = ctop / channels_each_class; + scalar_t trans_x = no_trans ? (scalar_t)(0) : bottom_trans[(((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w] * (scalar_t)trans_std; + scalar_t trans_y = no_trans ? (scalar_t)(0) : bottom_trans[(((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w] * (scalar_t)trans_std; + + scalar_t wstart = (scalar_t)(pw)*bin_size_w + roi_start_w; + wstart += trans_x * roi_width; + scalar_t hstart = (scalar_t)(ph)*bin_size_h + roi_start_h; + hstart += trans_y * roi_height; + + if (top_count[index] <= 0) + { + continue; + } + scalar_t diff_val = top_diff[index] / top_count[index]; + const scalar_t *offset_bottom_data = bottom_data + roi_batch_ind * channels * height * width; + scalar_t *offset_bottom_data_diff = bottom_data_diff + roi_batch_ind * channels * height * width; + int gw = floor((scalar_t)(pw)*group_size / pooled_width); + int gh = floor((scalar_t)(ph)*group_size / pooled_height); + gw = min(max(gw, 0), group_size - 1); + gh = min(max(gh, 0), group_size - 1); + + for (int ih = 0; ih < sample_per_part; ih++) + { + for (int iw = 0; iw < sample_per_part; iw++) + { + scalar_t w = wstart + iw * sub_bin_size_w; + scalar_t h = hstart + ih * sub_bin_size_h; + // bilinear interpolation + if (w < -0.5 || w > width - 0.5 || h < -0.5 || h > height - 0.5) + { + continue; + } + w = min(max(w, 0.), width - 1.); + h = min(max(h, 0.), height - 1.); + int c = (ctop * group_size + gh) * group_size + gw; + // backward on feature + int x0 = floor(w); + int x1 = ceil(w); + int y0 = floor(h); + int y1 = ceil(h); + scalar_t dist_x = w - x0, dist_y = h - y0; + scalar_t q00 = (1 - dist_x) * (1 - dist_y); + scalar_t q01 = (1 - dist_x) * dist_y; + scalar_t q10 = dist_x * (1 - dist_y); + scalar_t q11 = dist_x * dist_y; + int bottom_index_base = c * height * width; + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x0, q00 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x0, q01 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y0 * width + x1, q10 * diff_val); + atomicAdd(offset_bottom_data_diff + bottom_index_base + y1 * width + x1, q11 * diff_val); + + if (no_trans) + { + continue; + } + scalar_t U00 = offset_bottom_data[bottom_index_base + y0 * width + x0]; + scalar_t U01 = offset_bottom_data[bottom_index_base + y1 * width + x0]; + scalar_t U10 = offset_bottom_data[bottom_index_base + y0 * width + x1]; + scalar_t U11 = offset_bottom_data[bottom_index_base + y1 * width + x1]; + scalar_t diff_x = (U11 * dist_y + U10 * (1 - dist_y) - U01 * dist_y - U00 * (1 - dist_y)) * trans_std * diff_val; + diff_x *= roi_width; + scalar_t diff_y = (U11 * dist_x + U01 * (1 - dist_x) - U10 * dist_x - U00 * (1 - dist_x)) * trans_std * diff_val; + diff_y *= roi_height; + + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2) * part_size + part_h) * part_size + part_w, diff_x); + atomicAdd(bottom_trans_diff + (((n * num_classes + class_id) * 2 + 1) * part_size + part_h) * part_size + part_w, diff_y); + } + } + } +} + +void DeformablePSROIPoolForward(const at::Tensor data, + const at::Tensor bbox, + const at::Tensor trans, + at::Tensor out, + at::Tensor top_count, + const int batch, + const int channels, + const int height, + const int width, + const int num_bbox, + const int channels_trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + const int pooled_height = pooled_size; + const int pooled_width = pooled_size; + const int count = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data.scalar_type(), "deformable_psroi_pool_forward", ([&] { + const scalar_t *bottom_data = data.data_ptr(); + const scalar_t *bottom_rois = bbox.data_ptr(); + const scalar_t *bottom_trans = no_trans ? NULL : trans.data_ptr(); + scalar_t *top_data = out.data_ptr(); + scalar_t *top_count_data = top_count.data_ptr(); + + DeformablePSROIPoolForwardKernel<<>>( + count, bottom_data, (scalar_t)spatial_scale, channels, height, width, pooled_height, pooled_width, + bottom_rois, bottom_trans, no_trans, (scalar_t)trans_std, sample_per_part, output_dim, + group_size, part_size, num_classes, channels_each_class, top_data, top_count_data); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in DeformablePSROIPoolForward: %s\n", cudaGetErrorString(err)); + } +} + +void DeformablePSROIPoolBackwardAcc(const at::Tensor out_grad, + const at::Tensor data, + const at::Tensor bbox, + const at::Tensor trans, + const at::Tensor top_count, + at::Tensor in_grad, + at::Tensor trans_grad, + const int batch, + const int channels, + const int height, + const int width, + const int num_bbox, + const int channels_trans, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + // LOG(INFO) << "DeformablePSROIPoolBackward"; + const int num_rois = num_bbox; + const int pooled_height = pooled_size; + const int pooled_width = pooled_size; + const int count = num_bbox * output_dim * pooled_height * pooled_width; + const int num_classes = no_trans ? 1 : channels_trans / 2; + const int channels_each_class = no_trans ? output_dim : output_dim / num_classes; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + out_grad.scalar_type(), "deformable_psroi_pool_backward_acc", ([&] { + const scalar_t *top_diff = out_grad.data_ptr(); + const scalar_t *bottom_data = data.data_ptr(); + const scalar_t *bottom_rois = bbox.data_ptr(); + const scalar_t *bottom_trans = no_trans ? NULL : trans.data_ptr(); + scalar_t *bottom_data_diff = in_grad.data_ptr(); + scalar_t *bottom_trans_diff = no_trans ? NULL : trans_grad.data_ptr(); + const scalar_t *top_count_data = top_count.data_ptr(); + + DeformablePSROIPoolBackwardAccKernel<<>>( + count, top_diff, top_count_data, num_rois, (scalar_t)spatial_scale, channels, height, width, + pooled_height, pooled_width, output_dim, bottom_data_diff, bottom_trans_diff, + bottom_data, bottom_rois, bottom_trans, no_trans, (scalar_t)trans_std, sample_per_part, + group_size, part_size, num_classes, channels_each_class); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in DeformablePSROIPoolForward: %s\n", cudaGetErrorString(err)); + } +} \ No newline at end of file diff --git a/maskrcnn_benchmark/csrc/cuda/ml_nms.cu b/maskrcnn_benchmark/csrc/cuda/ml_nms.cu new file mode 100644 index 0000000000000000000000000000000000000000..cd958a0899a9e3adc69ca053170beb2b34fbd8ef --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/ml_nms.cu @@ -0,0 +1,136 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include +#include + +#include +#include + +#include +#include + +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +__device__ inline float devIoU(float const * const a, float const * const b) { + if (a[5] != b[5]) { + return 0.0; + } + float left = max(a[0], b[0]), right = min(a[2], b[2]); + float top = max(a[1], b[1]), bottom = min(a[3], b[3]); + float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); + float interS = width * height; + float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); + float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); + return interS / (Sa + Sb - interS); +} + +__global__ void ml_nms_kernel(const int n_boxes, const float nms_overlap_thresh, + const float *dev_boxes, unsigned long long *dev_mask) { + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + __shared__ float block_boxes[threadsPerBlock * 6]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 6 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 0]; + block_boxes[threadIdx.x * 6 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 1]; + block_boxes[threadIdx.x * 6 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 2]; + block_boxes[threadIdx.x * 6 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 3]; + block_boxes[threadIdx.x * 6 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 4]; + block_boxes[threadIdx.x * 6 + 5] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 5]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const float *cur_box = dev_boxes + cur_box_idx * 6; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + if (devIoU(cur_box, block_boxes + i * 6) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +// boxes is a N x 6 tensor +at::Tensor ml_nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) { + using scalar_t = float; + AT_ASSERTM(boxes.device().is_cuda(), "boxes must be a CUDA tensor"); + auto scores = boxes.select(1, 4); + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto boxes_sorted = boxes.index_select(0, order_t); + + int boxes_num = boxes.size(0); + + const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock); + + scalar_t* boxes_dev = boxes_sorted.data_ptr(); + + THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState + + unsigned long long* mask_dev = NULL; + //THCudaCheck(THCudaMalloc(state, (void**) &mask_dev, + // boxes_num * col_blocks * sizeof(unsigned long long))); + + mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long)); + + dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock), + THCCeilDiv(boxes_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); + ml_nms_kernel<<>>(boxes_num, + nms_overlap_thresh, + boxes_dev, + mask_dev); + + std::vector mask_host(boxes_num * col_blocks); + THCudaCheck(cudaMemcpy(&mask_host[0], + mask_dev, + sizeof(unsigned long long) * boxes_num * col_blocks, + cudaMemcpyDeviceToHost)); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < boxes_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + THCudaFree(state, mask_dev); + // TODO improve this part + return std::get<0>(order_t.index({ + keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to( + order_t.device(), keep.scalar_type()) + }).sort(0, false)); +} diff --git a/maskrcnn_benchmark/csrc/cuda/nms.cu b/maskrcnn_benchmark/csrc/cuda/nms.cu new file mode 100644 index 0000000000000000000000000000000000000000..d6221b85fa8f6b40cf498b76d6dbfc3c8438e25e --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/nms.cu @@ -0,0 +1,131 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include +#include + +#include +#include + +#include +#include + +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +__device__ inline float devIoU(float const * const a, float const * const b) { + float left = max(a[0], b[0]), right = min(a[2], b[2]); + float top = max(a[1], b[1]), bottom = min(a[3], b[3]); + float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f); + float interS = width * height; + float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1); + float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1); + return interS / (Sa + Sb - interS); +} + +__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh, + const float *dev_boxes, unsigned long long *dev_mask) { + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + __shared__ float block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const float *cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +// boxes is a N x 5 tensor +at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) { + using scalar_t = float; + AT_ASSERTM(boxes.device().is_cuda(), "boxes must be a CUDA tensor"); + auto scores = boxes.select(1, 4); + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto boxes_sorted = boxes.index_select(0, order_t); + + int boxes_num = boxes.size(0); + + const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock); + + scalar_t* boxes_dev = boxes_sorted.data_ptr(); + + THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState + + unsigned long long* mask_dev = NULL; + //THCudaCheck(THCudaMalloc(state, (void**) &mask_dev, + // boxes_num * col_blocks * sizeof(unsigned long long))); + + mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long)); + + dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock), + THCCeilDiv(boxes_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); + nms_kernel<<>>(boxes_num, + nms_overlap_thresh, + boxes_dev, + mask_dev); + + std::vector mask_host(boxes_num * col_blocks); + THCudaCheck(cudaMemcpy(&mask_host[0], + mask_dev, + sizeof(unsigned long long) * boxes_num * col_blocks, + cudaMemcpyDeviceToHost)); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < boxes_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + THCudaFree(state, mask_dev); + // TODO improve this part + return std::get<0>(order_t.index({ + keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to( + order_t.device(), keep.scalar_type()) + }).sort(0, false)); +} diff --git a/maskrcnn_benchmark/csrc/cuda/vision.h b/maskrcnn_benchmark/csrc/cuda/vision.h new file mode 100644 index 0000000000000000000000000000000000000000..16a7f644ed5798d1917d32cda0590161b6da8c64 --- /dev/null +++ b/maskrcnn_benchmark/csrc/cuda/vision.h @@ -0,0 +1,116 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include + + +at::Tensor SigmoidFocalLoss_forward_cuda( + const at::Tensor& logits, + const at::Tensor& targets, + const int num_classes, + const float gamma, + const float alpha); + +at::Tensor SigmoidFocalLoss_backward_cuda( + const at::Tensor& logits, + const at::Tensor& targets, + const at::Tensor& d_losses, + const int num_classes, + const float gamma, + const float alpha); + +at::Tensor ROIAlign_forward_cuda(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int sampling_ratio); + +at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width, + const int sampling_ratio); + + +std::tuple ROIPool_forward_cuda(const at::Tensor& input, + const at::Tensor& rois, + const float spatial_scale, + const int pooled_height, + const int pooled_width); + +at::Tensor ROIPool_backward_cuda(const at::Tensor& grad, + const at::Tensor& input, + const at::Tensor& rois, + const at::Tensor& argmax, + const float spatial_scale, + const int pooled_height, + const int pooled_width, + const int batch_size, + const int channels, + const int height, + const int width); + +at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh); +at::Tensor ml_nms_cuda(const at::Tensor boxes, float nms_overlap_thresh); + +int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, + at::Tensor offset, at::Tensor output, + at::Tensor columns, at::Tensor ones, int kW, + int kH, int dW, int dH, int padW, int padH, + int dilationW, int dilationH, int group, + int deformable_group, int im2col_step); + +int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, + at::Tensor gradOutput, at::Tensor gradInput, + at::Tensor gradOffset, at::Tensor weight, + at::Tensor columns, int kW, int kH, int dW, + int dH, int padW, int padH, int dilationW, + int dilationH, int group, + int deformable_group, int im2col_step); + +int deform_conv_backward_parameters_cuda( + at::Tensor input, at::Tensor offset, at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, + int padW, int padH, int dilationW, int dilationH, int group, + int deformable_group, float scale, int im2col_step); + +void modulated_deform_conv_cuda_forward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, + int kernel_h, int kernel_w, const int stride_h, const int stride_w, + const int pad_h, const int pad_w, const int dilation_h, + const int dilation_w, const int group, const int deformable_group, + const bool with_bias); + +void modulated_deform_conv_cuda_backward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor columns, + at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, + at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, + int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, + int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, + const bool with_bias); + +void deform_psroi_pooling_cuda_forward( + at::Tensor input, at::Tensor bbox, at::Tensor trans, at::Tensor out, + at::Tensor top_count, const int no_trans, const float spatial_scale, + const int output_dim, const int group_size, const int pooled_size, + const int part_size, const int sample_per_part, const float trans_std); + +void deform_psroi_pooling_cuda_backward( + at::Tensor out_grad, at::Tensor input, at::Tensor bbox, at::Tensor trans, + at::Tensor top_count, at::Tensor input_grad, at::Tensor trans_grad, + const int no_trans, const float spatial_scale, const int output_dim, + const int group_size, const int pooled_size, const int part_size, + const int sample_per_part, const float trans_std); + + +at::Tensor compute_flow_cuda(const at::Tensor& boxes, + const int height, + const int width); diff --git a/maskrcnn_benchmark/csrc/deform_conv.h b/maskrcnn_benchmark/csrc/deform_conv.h new file mode 100644 index 0000000000000000000000000000000000000000..56452c18cb8677ed964ca08c9e6e68b368da39a6 --- /dev/null +++ b/maskrcnn_benchmark/csrc/deform_conv.h @@ -0,0 +1,191 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + + +// Interface for Python +int deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor offset, + at::Tensor output, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) +{ + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_conv_forward_cuda( + input, weight, offset, output, columns, ones, + kW, kH, dW, dH, padW, padH, dilationW, dilationH, + group, deformable_group, im2col_step + ); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + + +int deform_conv_backward_input( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradInput, + at::Tensor gradOffset, + at::Tensor weight, + at::Tensor columns, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + int im2col_step) +{ + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_conv_backward_input_cuda( + input, offset, gradOutput, gradInput, gradOffset, weight, columns, + kW, kH, dW, dH, padW, padH, dilationW, dilationH, + group, deformable_group, im2col_step + ); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + + +int deform_conv_backward_parameters( + at::Tensor input, + at::Tensor offset, + at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, + at::Tensor ones, + int kW, + int kH, + int dW, + int dH, + int padW, + int padH, + int dilationW, + int dilationH, + int group, + int deformable_group, + float scale, + int im2col_step) +{ + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_conv_backward_parameters_cuda( + input, offset, gradOutput, gradWeight, columns, ones, + kW, kH, dW, dH, padW, padH, dilationW, dilationH, + group, deformable_group, scale, im2col_step + ); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + + +void modulated_deform_conv_forward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor output, + at::Tensor columns, + int kernel_h, + int kernel_w, + const int stride_h, + const int stride_w, + const int pad_h, + const int pad_w, + const int dilation_h, + const int dilation_w, + const int group, + const int deformable_group, + const bool with_bias) +{ + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return modulated_deform_conv_cuda_forward( + input, weight, bias, ones, offset, mask, output, columns, + kernel_h, kernel_w, stride_h, stride_w, + pad_h, pad_w, dilation_h, dilation_w, + group, deformable_group, with_bias + ); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + + +void modulated_deform_conv_backward( + at::Tensor input, + at::Tensor weight, + at::Tensor bias, + at::Tensor ones, + at::Tensor offset, + at::Tensor mask, + at::Tensor columns, + at::Tensor grad_input, + at::Tensor grad_weight, + at::Tensor grad_bias, + at::Tensor grad_offset, + at::Tensor grad_mask, + at::Tensor grad_output, + int kernel_h, + int kernel_w, + int stride_h, + int stride_w, + int pad_h, + int pad_w, + int dilation_h, + int dilation_w, + int group, + int deformable_group, + const bool with_bias) +{ + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return modulated_deform_conv_cuda_backward( + input, weight, bias, ones, offset, mask, columns, + grad_input, grad_weight, grad_bias, grad_offset, grad_mask, grad_output, + kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, + group, deformable_group, with_bias + ); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} \ No newline at end of file diff --git a/maskrcnn_benchmark/csrc/deform_pool.h b/maskrcnn_benchmark/csrc/deform_pool.h new file mode 100644 index 0000000000000000000000000000000000000000..b3379e205caa43d854447ba896ce5848ccd65c89 --- /dev/null +++ b/maskrcnn_benchmark/csrc/deform_pool.h @@ -0,0 +1,70 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + + +// Interface for Python +void deform_psroi_pooling_forward( + at::Tensor input, + at::Tensor bbox, + at::Tensor trans, + at::Tensor out, + at::Tensor top_count, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_psroi_pooling_cuda_forward( + input, bbox, trans, out, top_count, + no_trans, spatial_scale, output_dim, group_size, + pooled_size, part_size, sample_per_part, trans_std + ); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + + +void deform_psroi_pooling_backward( + at::Tensor out_grad, + at::Tensor input, + at::Tensor bbox, + at::Tensor trans, + at::Tensor top_count, + at::Tensor input_grad, + at::Tensor trans_grad, + const int no_trans, + const float spatial_scale, + const int output_dim, + const int group_size, + const int pooled_size, + const int part_size, + const int sample_per_part, + const float trans_std) +{ + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_psroi_pooling_cuda_backward( + out_grad, input, bbox, trans, top_count, input_grad, trans_grad, + no_trans, spatial_scale, output_dim, group_size, pooled_size, + part_size, sample_per_part, trans_std + ); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} diff --git a/maskrcnn_benchmark/csrc/ml_nms.h b/maskrcnn_benchmark/csrc/ml_nms.h new file mode 100644 index 0000000000000000000000000000000000000000..bb4370d0576a3280b324ae69257f41789dd2416d --- /dev/null +++ b/maskrcnn_benchmark/csrc/ml_nms.h @@ -0,0 +1,27 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + + +at::Tensor ml_nms(const at::Tensor& dets, + const at::Tensor& scores, + const at::Tensor& labels, + const float threshold) { + + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + // TODO raise error if not compiled with CUDA + if (dets.numel() == 0) + return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); + auto b = at::cat({dets, scores.unsqueeze(1), labels.unsqueeze(1)}, 1); + return ml_nms_cuda(b, threshold); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("CPU version not implemented"); +} diff --git a/maskrcnn_benchmark/csrc/nms.h b/maskrcnn_benchmark/csrc/nms.h new file mode 100644 index 0000000000000000000000000000000000000000..cb86028949747e215a8f5c74d768ece8937f4f81 --- /dev/null +++ b/maskrcnn_benchmark/csrc/nms.h @@ -0,0 +1,45 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#pragma once +#include "cpu/vision.h" + +#ifdef WITH_CUDA +#include "cuda/vision.h" +#endif + + +at::Tensor nms(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold) { + + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + // TODO raise error if not compiled with CUDA + if (dets.numel() == 0) + return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); + auto b = at::cat({dets, scores.unsqueeze(1)}, 1); + return nms_cuda(b, threshold); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + + at::Tensor result = nms_cpu(dets, scores, threshold); + return result; +} + + +std::pair soft_nms(const at::Tensor& dets, + const at::Tensor& scores, + const float threshold, + const float sigma) { + + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + AT_ERROR("Soft NMS Does Not have GPU support"); +#endif + } + + std::pair result = soft_nms_cpu(dets, scores, threshold, sigma); + + return result; +} \ No newline at end of file diff --git a/maskrcnn_benchmark/csrc/vision.cpp b/maskrcnn_benchmark/csrc/vision.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a5bd4751b67aa35f7649dd3f5b733982e38088d1 --- /dev/null +++ b/maskrcnn_benchmark/csrc/vision.cpp @@ -0,0 +1,27 @@ +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#include "nms.h" +#include "ml_nms.h" +#include "ROIAlign.h" +#include "ROIPool.h" +#include "SigmoidFocalLoss.h" +#include "deform_conv.h" +#include "deform_pool.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("nms", &nms, "non-maximum suppression"); + m.def("ml_nms", &ml_nms, "multi-label non-maximum suppression"); + m.def("soft_nms", &soft_nms, "soft non-maximum suppression"); + m.def("roi_align_forward", &ROIAlign_forward, "ROIAlign_forward"); + m.def("roi_align_backward", &ROIAlign_backward, "ROIAlign_backward"); + m.def("roi_pool_forward", &ROIPool_forward, "ROIPool_forward"); + m.def("roi_pool_backward", &ROIPool_backward, "ROIPool_backward"); + m.def("sigmoid_focalloss_forward", &SigmoidFocalLoss_forward, "SigmoidFocalLoss_forward"); + m.def("sigmoid_focalloss_backward", &SigmoidFocalLoss_backward, "SigmoidFocalLoss_backward"); + m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward"); + m.def("deform_conv_backward_input", &deform_conv_backward_input, "deform_conv_backward_input"); + m.def("deform_conv_backward_parameters", &deform_conv_backward_parameters, "deform_conv_backward_parameters"); + m.def("modulated_deform_conv_forward", &modulated_deform_conv_forward, "modulated_deform_conv_forward"); + m.def("modulated_deform_conv_backward", &modulated_deform_conv_backward, "modulated_deform_conv_backward"); + m.def("deform_psroi_pooling_forward", &deform_psroi_pooling_forward, "deform_psroi_pooling_forward"); + m.def("deform_psroi_pooling_backward", &deform_psroi_pooling_backward, "deform_psroi_pooling_backward"); +} diff --git a/maskrcnn_benchmark/data/__init__.py b/maskrcnn_benchmark/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae0210bc1653fd56b4fcea06e22f185ffaa57e06 --- /dev/null +++ b/maskrcnn_benchmark/data/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from .build import make_data_loader diff --git a/maskrcnn_benchmark/data/build.py b/maskrcnn_benchmark/data/build.py new file mode 100644 index 0000000000000000000000000000000000000000..07d183d3cf0c97f5f383d67c2a061900a1487fb1 --- /dev/null +++ b/maskrcnn_benchmark/data/build.py @@ -0,0 +1,529 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import bisect +import copy +import logging +import os + +import torch.utils.data +import torch.distributed as dist +from maskrcnn_benchmark.utils.comm import get_world_size +from maskrcnn_benchmark.utils.imports import import_file + +from . import datasets as D +from . import samplers + +from .collate_batch import BatchCollator, BBoxAugCollator +from .transforms import build_transforms + +from transformers import AutoTokenizer +from .datasets.duplicate_dataset import create_duplicate_dataset + + +def build_dataset(cfg, dataset_list, transforms, dataset_catalog, is_train=True, class_concat=False, extra_args={}): + """ + Arguments: + dataset_list (list[str]): Contains the names of the datasets, i.e., + coco_2014_trian, coco_2014_val, etc + transforms (callable): transforms to apply to each (image, target) sample + dataset_catalog (DatasetCatalog): contains the information on how to + construct a dataset. + is_train (bool): whether to setup the dataset for training or testing + """ + if not isinstance(dataset_list, (list, tuple)): + raise RuntimeError("dataset_list should be a list of strings, got {}".format(dataset_list)) + datasets = [] + num_category = 1 + for dataset_id, dataset_name in enumerate(dataset_list, 1): + if is_train: + dataset_name = dataset_name + cfg.DATASETS.TRAIN_DATASETNAME_SUFFIX + else: + dataset_name = dataset_name + cfg.DATASETS.TEST_DATASETNAME_SUFFIX + data = dataset_catalog.get(dataset_name) + factory = getattr(D, data["factory"]) + args = data["args"] + # for COCODataset, we want to remove images without annotations + # during training + if data["factory"] == "COCODataset": + args["remove_images_without_annotations"] = is_train + + if data["factory"] == "PascalVOCDataset": + args["use_difficult"] = not is_train + if data["factory"] in ["VGTSVDataset", "CocoDetectionTSV", "ODTSVDataset"]: + args["extra_fields"] = ["class"] + if cfg.MODEL.MASK_ON: + args["extra_fields"].append("mask") + + if data["factory"] in [ + "CocoGrounding", + "CocoDetectionTSV", + "CaptionTSV", + "MixedDataset", + "FlickrDataset", + "RefExpDataset", + "GQADataset", + "PseudoData", + "PhrasecutDetection", + ]: + # args["return_masks"] = False + args["return_masks"] = cfg.MODEL.MASK_ON + args["return_tokens"] = True + args["max_num_labels"] = cfg.TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM + args["max_query_len"] = cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN + + args["transforms"] = transforms + args.update(extra_args) + + if "flickr30k_train" in dataset_name: #dataset_name == "flickr30k_train": + copy = cfg.DATASETS.FLICKR_COPY + elif "mixed_train" in dataset_name: #dataset_name in ["mixed_train", "mixed_train_no_coco"]: + copy = cfg.DATASETS.MIXED_COPY + elif dataset_name in ["COCO_odinw_train_8copy_dt_train", "coco_dt_train", "coco_grounding_train"]: + copy = cfg.DATASETS.COCO_COPY + elif dataset_name in ["LVIS_odinw_train_8copy_dt_train", "lvisv1_dt_train", "lvis_grounding_train"]: + copy = cfg.DATASETS.LVIS_COPY + elif dataset_name in ["object365_odinw_2copy_dt_train", "object365_dt_train"]: + copy = cfg.DATASETS.OBJECT365_COPY + elif dataset_name == "vg_odinw_clipped_8copy_dt_train": + copy = cfg.DATASETS.VG_COPY + elif dataset_name == "vg_vgoi6_clipped_8copy_dt_train": + copy = cfg.DATASETS.VG_COPY + elif dataset_name == "imagenetod_train_odinw_2copy_dt": + copy = cfg.DATASETS.IN_COPY + elif dataset_name == "oi_train_odinw_dt": + copy = cfg.DATASETS.OI_COPY + elif "refcoco" in dataset_name: + copy = cfg.DATASETS.REFCOCO_COPY + elif is_train: + copy = cfg.DATASETS.GENERAL_COPY + elif not is_train: + copy = cfg.DATASETS.GENERAL_COPY_TEST + else: + copy = -1 # do not ever copy test + + if copy != -1 and copy != 1: + new_factory = create_duplicate_dataset(factory) + dataset = new_factory(copy=copy, **args) + else: + # make dataset from factory + dataset = factory(**args) + + print(dataset_name, "has the {} data points".format(len(dataset)), data["factory"]) + + if class_concat: + category = list(dataset.contiguous_category_id_to_json_id.values()) + dataset.contiguous_category_id_to_json_id = {} + dataset.json_category_id_to_contiguous_id = {} + for id, cat in enumerate(category, start=num_category): + dataset.json_category_id_to_contiguous_id[cat] = id + dataset.contiguous_category_id_to_json_id[id] = cat + num_category += len(category) + print("Found {} #category after group {}, concating ...".format(num_category, dataset_id)) + datasets.append(dataset) + + # for testing, return a list of datasets + if not is_train: + return datasets + + # for training, concatenate all datasets into a single one + dataset = datasets[0] + if len(datasets) > 1: + dataset = D.ConcatDataset(datasets) + + return [dataset] + + +def build_dataset_by_group( + dataset_list, transforms, dataset_catalog, is_train=True, class_by_group=True, class_concat=False, extra_args={} +): + """ + Arguments: + dataset_list (list[str]): Contains the names of the datasets, i.e., + coco_2014_trian, coco_2014_val, etc + transforms (callable): transforms to apply to each (image, target) sample + dataset_catalog (DatasetCatalog): contains the information on how to + construct a dataset. + is_train (bool): whether to setup the dataset for training or testing + """ + if not isinstance(dataset_list, (list, tuple)): + raise RuntimeError("dataset_list should be a list of strings, got {}".format(dataset_list)) + + num_category = 1 + grouped_datasets = [] + for group_id, group in enumerate(dataset_list, 1): + datasets = [] + for dataset_name in group: + data = dataset_catalog.get(dataset_name) + factory = getattr(D, data["factory"]) + args = data["args"] + # for COCODataset, we want to remove images without annotations + # during training + if data["factory"] == "COCODataset": + args["remove_images_without_annotations"] = is_train + if data["factory"] == "PascalVOCDataset": + args["use_difficult"] = not is_train + args["transforms"] = transforms + args.update(extra_args) + # make dataset from factory + dataset = factory(**args) + + # check if dataset is grouped by task, assume one class per task + if class_by_group and data["factory"] != "Background": + category = dataset.contiguous_category_id_to_json_id[1] + del dataset.contiguous_category_id_to_json_id[1] + dataset.json_category_id_to_contiguous_id[category] = group_id + dataset.contiguous_category_id_to_json_id[group_id] = category + + datasets.append(dataset) + + if class_concat: + for dataset in datasets: + category = list(dataset.contiguous_category_id_to_json_id.values()) + dataset.contiguous_category_id_to_json_id = {} + dataset.json_category_id_to_contiguous_id = {} + for id, cat in enumerate(category, start=num_category): + dataset.json_category_id_to_contiguous_id[cat] = id + dataset.contiguous_category_id_to_json_id[id] = cat + num_category += len(category) + print("Found {} #category after group {}, concating ...".format(num_category, group_id)) + + if is_train: + datasets = D.ConcatDataset(datasets) + + grouped_datasets.append(datasets) + + # for testing, return a list of datasets + if not is_train: + datasets = [dataset for group in grouped_datasets for dataset in group] + return datasets + if class_concat: + grouped_datasets = D.ConcatDataset(grouped_datasets) + return [grouped_datasets] + + # for training, concatenate all datasets into a single one + return grouped_datasets + + +def make_data_sampler(dataset, shuffle, distributed, num_replicas=None, rank=None, use_random_seed=True): + if distributed: + return samplers.DistributedSampler( + dataset, shuffle=shuffle, num_replicas=num_replicas, rank=rank, use_random=use_random_seed + ) + if shuffle: + sampler = torch.utils.data.sampler.RandomSampler(dataset) + else: + sampler = torch.utils.data.sampler.SequentialSampler(dataset) + return sampler + + +def _quantize(x, bins): + bins = copy.copy(bins) + bins = sorted(bins) + quantized = list(map(lambda y: bisect.bisect_right(bins, y), x)) + return quantized + + +def _compute_aspect_ratios(dataset): + aspect_ratios = [] + for i in range(len(dataset)): + img_info = dataset.get_img_info(i) + aspect_ratio = float(img_info["height"]) / float(img_info["width"]) + aspect_ratios.append(aspect_ratio) + return aspect_ratios + + +def make_batch_data_sampler( + dataset, sampler, aspect_grouping, images_per_batch, num_iters=None, start_iter=0, drop_last=False +): + if aspect_grouping: + if not isinstance(aspect_grouping, (list, tuple)): + aspect_grouping = [aspect_grouping] + aspect_ratios = _compute_aspect_ratios(dataset) + group_ids = _quantize(aspect_ratios, aspect_grouping) + batch_sampler = samplers.GroupedBatchSampler(sampler, group_ids, images_per_batch, drop_uneven=drop_last) + else: + batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, images_per_batch, drop_last=drop_last) + if num_iters is not None: + batch_sampler = samplers.IterationBasedBatchSampler(batch_sampler, num_iters, start_iter) + return batch_sampler + + +def make_data_loader(cfg, is_train=True, is_distributed=False, num_replicas=None, rank=None, start_iter=0): + num_gpus = num_replicas or get_world_size() + + if is_train: + images_per_batch = cfg.SOLVER.IMS_PER_BATCH + assert images_per_batch % num_gpus == 0, "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number " + "of GPUs ({}) used.".format(images_per_batch, num_gpus) + images_per_gpu = images_per_batch // num_gpus + shuffle = True + num_iters = cfg.SOLVER.MAX_ITER + else: + images_per_batch = cfg.TEST.IMS_PER_BATCH + assert images_per_batch % num_gpus == 0, "TEST.IMS_PER_BATCH ({}) must be divisible by the number " + "of GPUs ({}) used.".format(images_per_batch, num_gpus) + images_per_gpu = images_per_batch // num_gpus + shuffle = False if not is_distributed else True + num_iters = None + start_iter = 0 + + if images_per_gpu > 1: + logger = logging.getLogger(__name__) + logger.warning( + "When using more than one image per GPU you may encounter " + "an out-of-memory (OOM) error if your GPU does not have " + "sufficient memory. If this happens, you can reduce " + "SOLVER.IMS_PER_BATCH (for training) or " + "TEST.IMS_PER_BATCH (for inference). For training, you must " + "also adjust the learning rate and schedule length according " + "to the linear scaling rule. See for example: " + "https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14" + ) + + # group images which have similar aspect ratio. In this case, we only + # group in two cases: those with width / height > 1, and the other way around, + # but the code supports more general grouping strategy + aspect_grouping = [1] if cfg.DATALOADER.ASPECT_RATIO_GROUPING else [] + + paths_catalog = import_file("maskrcnn_benchmark.config.paths_catalog", cfg.PATHS_CATALOG, True) + + DatasetCatalog = paths_catalog.DatasetCatalog + if len(cfg.DATASETS.REGISTER) > 0: + for new_dataset in cfg.DATASETS.REGISTER: + # img_dir = cfg.DATASETS.REGISTER[new_dataset]["img_dir"] + # if "ann_file" in cfg.DATASETS.REGISTER[new_dataset]: + # ann_file = cfg.DATASETS.REGISTER[new_dataset]["ann_file"] + # else: + # ann_file = None + attrs = dict(cfg.DATASETS.REGISTER[new_dataset]) + if is_train: + new_dataset = new_dataset + cfg.DATASETS.TRAIN_DATASETNAME_SUFFIX + else: + new_dataset = new_dataset + cfg.DATASETS.TEST_DATASETNAME_SUFFIX + DatasetCatalog.set(new_dataset, attrs) + + dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST + + # Haotian: expand bing dataset + if "bing_caption_train" in dataset_list and len(cfg.DATASETS.BING_INDEX_LIST) > 0: + dataset_list = list(dataset_list) + dataset_list.remove("bing_caption_train") + for bing_index in cfg.DATASETS.BING_INDEX_LIST: + dataset_list.insert(len(dataset_list), "bing_caption_{}_train".format(bing_index)) + dataset_list = tuple(dataset_list) + + if "bing_caption_train_no_coco" in dataset_list and len(cfg.DATASETS.BING_INDEX_LIST) > 0: + dataset_list = list(dataset_list) + dataset_list.remove("bing_caption_train_no_coco") + for bing_index in cfg.DATASETS.BING_INDEX_LIST: + dataset_list.insert(len(dataset_list), "bing_caption_{}_train_no_coco".format(bing_index)) + dataset_list = tuple(dataset_list) + + print("The combined datasets are: {}.".format(dataset_list)) + + transforms = None if not is_train and cfg.TEST.USE_MULTISCALE else build_transforms(cfg, is_train) + + extra_args = {} + if is_train and cfg.DATASETS.USE_CROWD: + extra_args["ignore_crowd"] = False + if is_train and cfg.DATASETS.MAX_BOX > 0: + extra_args["max_box"] = cfg.DATASETS.MAX_BOX + if is_train and cfg.DATASETS.FEW_SHOT > 0: + extra_args["few_shot"] = cfg.DATASETS.FEW_SHOT + if is_train and cfg.DATASETS.SHUFFLE_SEED != 0: + extra_args["shuffle_seed"] = cfg.DATASETS.SHUFFLE_SEED + if is_train and cfg.AUGMENT.MOSAIC_PROB > 0: + extra_args["mosaic_prob"] = cfg.AUGMENT.MOSAIC_PROB + extra_args["mosaic_shift"] = cfg.AUGMENT.MOSAIC_SHIFT + extra_args["mosaic_size"] = cfg.AUGMENT.MOSAIC_SIZE + if is_train and cfg.AUGMENT.PASTE_PROB > 0: + extra_args["paste_prob"] = cfg.AUGMENT.PASTE_PROB + extra_args["paste_cat"] = cfg.AUGMENT.PASTE_CAT + extra_args["paste_num"] = cfg.AUGMENT.PASTE_NUM + + # od to grounding + if is_train and cfg.DATASETS.RANDOM_SAMPLE_NEG > 0: + extra_args["random_sample_negative"] = cfg.DATASETS.RANDOM_SAMPLE_NEG + if is_train and cfg.DATASETS.ADD_DET_PROMPT: + extra_args["add_detection_prompt"] = True + if is_train and cfg.DATASETS.USE_OD_AUG: + extra_args["use_od_data_aug"] = True + if is_train and cfg.DATASETS.USE_COCO_FORMAT: + extra_args["use_coco_format"] = True + if is_train and cfg.DATASETS.DISABLE_SHUFFLE: + extra_args["disable_shuffle"] = True + if cfg.DATASETS.ONE_HOT: + extra_args["one_hot"] = True + if is_train and len(cfg.DATASETS.PROMPT_VERSION) > 0: + extra_args["prompt_engineer_version"] = cfg.DATASETS.PROMPT_VERSION + if is_train and len(cfg.DATASETS.CONTROL_PROB) == 4: + extra_args["control_probabilities"] = cfg.DATASETS.CONTROL_PROB + if is_train and cfg.DATASETS.DISABLE_CLIP_TO_IMAGE: + extra_args["disable_clip_to_image"] = cfg.DATASETS.DISABLE_CLIP_TO_IMAGE + if is_train and cfg.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT: + extra_args["no_minus_one_for_one_hot"] = cfg.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT + if is_train: + extra_args["separation_tokens"] = cfg.DATASETS.SEPARATION_TOKENS + # caption + if is_train and cfg.DATASETS.CAPTION_MIN_BOX > 0: + extra_args["caption_min_box"] = cfg.DATASETS.CAPTION_MIN_BOX + if is_train and cfg.DATASETS.REPLACE_CLEAN_LABEL: + extra_args["replace_clean_label"] = True + if is_train and cfg.DATASETS.FURTHER_SCREEN: + extra_args["further_screen"] = True + if is_train and cfg.DATASETS.CAPTION_CONF > 0.0: + extra_args["caption_conf"] = cfg.DATASETS.CAPTION_CONF + if is_train: + extra_args["caption_nms"] = cfg.DATASETS.CAPTION_NMS + if is_train and cfg.DATASETS.PACK_RANDOM_CAPTION_NUMBER > 0: + extra_args["pack_random_caption_number"] = cfg.DATASETS.PACK_RANDOM_CAPTION_NUMBER + if is_train and cfg.DATASETS.INFERENCE_CAPTION: + extra_args["inference_caption"] = True + if is_train and cfg.DATASETS.SAMPLE_NEGATIVE_FOR_GROUNDING_DATA > 0: + extra_args["sample_negative_for_grounding_data"] = cfg.DATASETS.SAMPLE_NEGATIVE_FOR_GROUNDING_DATA + if is_train and cfg.DATASETS.RANDOM_PACK_PROB > 0: + extra_args["random_pack_prob"] = cfg.DATASETS.RANDOM_PACK_PROB + if is_train and cfg.DATASETS.NO_RANDOM_PACK_PROBABILITY > 0: + extra_args["no_random_pack_probability"] = cfg.DATASETS.NO_RANDOM_PACK_PROBABILITY + if is_train: + extra_args["safeguard_positive_caption"] = cfg.DATASETS.SAFEGUARD_POSITIVE_CAPTION + if is_train: + extra_args["local_debug"] = cfg.DATASETS.LOCAL_DEBUG + if is_train: + extra_args["no_mask_for_od"] = cfg.MODEL.DYHEAD.FUSE_CONFIG.NO_MASK_FOR_OD + if is_train: + extra_args["no_mask_for_gold"] = cfg.MODEL.DYHEAD.FUSE_CONFIG.NO_MASK_FOR_GOLD + if is_train: + extra_args["mlm_obj_for_only_positive"] = cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_OBJ_FOR_ONLY_POSITIVE + if cfg.DATASETS.OVERRIDE_CATEGORY and cfg.DATASETS.USE_OVERRIDE_CATEGORY: + extra_args["override_category"] = cfg.DATASETS.OVERRIDE_CATEGORY + if is_train: + extra_args["caption_format_version"] = cfg.DATASETS.CAPTION_FORMAT_VERSION + if is_train: + extra_args["special_safeguard_for_coco_grounding"] = cfg.DATASETS.SPECIAL_SAFEGUARD_FOR_COCO_GROUNDING + if is_train: + extra_args["diver_box_for_vqa"] = cfg.DATASETS.DIVER_BOX_FOR_VQA + + extra_args["od_to_grounding_version"] = cfg.DATASETS.OD_TO_GROUNDING_VERSION + extra_args["caption_prompt"] = cfg.DATASETS.CAPTION_PROMPT + extra_args["use_caption_prompt"] = cfg.DATASETS.USE_CAPTION_PROMPT + extra_args["description_file"] = cfg.DATASETS.DESCRIPTION_FILE + extra_args["similarity_file"] = cfg.DATASETS.SIMILARITY_FILE + extra_args["caption_vocab_file"] = cfg.DATASETS.CAPTION_VOCAB_FILE + extra_args["caption_augmentation_version"] = cfg.DATASETS.CAPTION_AUGMENTATION_VERSION + extra_args["cc_caption_augmentation_version"] = cfg.DATASETS.CC_CAPTION_AUGMENTATION_VERSION + # extra_args['tokenizer'] = AutoTokenizer.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE) + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + # extra_args['tokenizer'] = build_tokenizer("clip") + from transformers import CLIPTokenizerFast + + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + extra_args["tokenizer"] = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + extra_args["tokenizer"] = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + else: + extra_args["tokenizer"] = AutoTokenizer.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE) + + if isinstance(dataset_list[0], (tuple, list)): + datasets = build_dataset_by_group( + dataset_list, + transforms, + DatasetCatalog, + is_train, + class_by_group=cfg.DATASETS.ALTERNATIVE_TRAINING, + class_concat=cfg.DATASETS.CLASS_CONCAT, + extra_args=extra_args, + ) + else: + datasets = build_dataset( + cfg, + dataset_list, + transforms, + DatasetCatalog, + is_train, + class_concat=cfg.DATASETS.CLASS_CONCAT, + extra_args=extra_args, + ) + + data_loaders = [] + for di, dataset in enumerate(datasets): + if is_train and cfg.SOLVER.MAX_EPOCH > 0: + num_iters = cfg.SOLVER.MAX_EPOCH * len(dataset) // cfg.SOLVER.IMS_PER_BATCH + print("Number of iterations are {}".format(num_iters)) + cfg.defrost() + cfg.SOLVER.MAX_ITER = num_iters + cfg.SOLVER.DATASET_LENGTH = len(dataset) + cfg.freeze() + if is_train and cfg.SOLVER.MULTI_MAX_EPOCH: + num_iters = None + cfg.defrost() + cfg.SOLVER.MULTI_MAX_ITER += (cfg.SOLVER.MULTI_MAX_EPOCH[di] * len(dataset) // cfg.SOLVER.IMS_PER_BATCH,) + cfg.freeze() + + if is_train and cfg.DATALOADER.DISTRIBUTE_CHUNK_AMONG_NODE: + from .datasets.custom_distributed_sampler import DistributedSamplerChunkByNode + + chunk_or_not = [] + for i in dataset_list: + if "bing_caption" in i: + chunk_or_not.append(True) + else: + chunk_or_not.append(False) + assert len(chunk_or_not) == len(dataset.datasets) + """ + If we are training on 4 nodes, each with 8 GPUs + """ + num_nodes = int(os.getenv("NODE_COUNT", os.getenv("OMPI_COMM_WORLD_SIZE", 1))) + local_size = cfg.num_gpus // num_nodes + node_rank = int(os.getenv("NODE_RANK", os.getenv("OMPI_COMM_WORLD_RANK", 0))) + local_rank = cfg.local_rank + sampler = DistributedSamplerChunkByNode( + dataset=dataset, + all_datasets=dataset.datasets, # Assumming dataset is a ConcateDataset instance, + chunk_or_not=chunk_or_not, + num_replicas=cfg.num_gpus, # total GPU number, e.g., 32 + rank=dist.get_rank(), # Global Rank, e.g., 0~31 + node_rank=node_rank, # Node Rank, e.g., 0~3 + node_number=num_nodes, # how many node e.g., 4 + process_num_per_node=local_size, # e.g., 8 + rank_within_local_node=local_rank, # e.g., 0~7 + ) + else: + sampler = make_data_sampler( + dataset, + shuffle, + is_distributed, + num_replicas=num_replicas, + rank=rank, + use_random_seed=cfg.DATALOADER.USE_RANDOM_SEED, + ) + batch_sampler = make_batch_data_sampler( + dataset, sampler, aspect_grouping, images_per_gpu, num_iters, start_iter, drop_last=is_train + ) + collator = ( + BBoxAugCollator() + if not is_train and cfg.TEST.USE_MULTISCALE + else BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY) + ) + num_workers = cfg.DATALOADER.NUM_WORKERS + data_loader = torch.utils.data.DataLoader( + dataset, + num_workers=num_workers, + batch_sampler=batch_sampler, + collate_fn=collator, + ) + data_loaders.append(data_loader) + if is_train and cfg.SOLVER.MULTI_MAX_EPOCH: + cfg.defrost() + cfg.SOLVER.MULTI_MAX_ITER += ( + cfg.SOLVER.MULTI_MAX_EPOCH[-1] * min([len(dataset) // cfg.SOLVER.IMS_PER_BATCH for dataset in datasets]), + ) + cfg.freeze() + + if is_train and not cfg.DATASETS.ALTERNATIVE_TRAINING and not cfg.DATASETS.MULTISTAGE_TRAINING: + # during training, a single (possibly concatenated) data_loader is returned + assert len(data_loaders) == 1 + return data_loaders[0] + + return data_loaders diff --git a/maskrcnn_benchmark/data/collate_batch.py b/maskrcnn_benchmark/data/collate_batch.py new file mode 100644 index 0000000000000000000000000000000000000000..8d57536829f3abd695d8d18045a643176ca6bad4 --- /dev/null +++ b/maskrcnn_benchmark/data/collate_batch.py @@ -0,0 +1,89 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from maskrcnn_benchmark.structures.image_list import to_image_list + +import pdb + + +class BatchCollator(object): + """ + From a list of samples from the dataset, + returns the batched images and targets. + This should be passed to the DataLoader + """ + + def __init__(self, size_divisible=0): + self.size_divisible = size_divisible + + def __call__(self, batch): + transposed_batch = list(zip(*batch)) + + images = to_image_list(transposed_batch[0], self.size_divisible) + targets = transposed_batch[1] + img_ids = transposed_batch[2] + positive_map = None + positive_map_eval = None + greenlight_map = None + + if isinstance(targets[0], dict): + return images, targets, img_ids, positive_map, positive_map_eval + + if "greenlight_map" in transposed_batch[1][0].fields(): + greenlight_map = torch.stack([i.get_field("greenlight_map") for i in transposed_batch[1]], dim=0) + + if "positive_map" in transposed_batch[1][0].fields(): + # we batch the positive maps here + # Since in general each batch element will have a different number of boxes, + # we collapse a single batch dimension to avoid padding. This is sufficient for our purposes. + max_len = max([v.get_field("positive_map").shape[1] for v in transposed_batch[1]]) + nb_boxes = sum([v.get_field("positive_map").shape[0] for v in transposed_batch[1]]) + batched_pos_map = torch.zeros((nb_boxes, max_len), dtype=torch.bool) + cur_count = 0 + for v in transposed_batch[1]: + cur_pos = v.get_field("positive_map") + batched_pos_map[cur_count : cur_count + len(cur_pos), : cur_pos.shape[1]] = cur_pos + cur_count += len(cur_pos) + + assert cur_count == len(batched_pos_map) + positive_map = batched_pos_map.float() + + if "positive_map_eval" in transposed_batch[1][0].fields(): + # we batch the positive maps here + # Since in general each batch element will have a different number of boxes, + # we collapse a single batch dimension to avoid padding. This is sufficient for our purposes. + max_len = max([v.get_field("positive_map_eval").shape[1] for v in transposed_batch[1]]) + nb_boxes = sum([v.get_field("positive_map_eval").shape[0] for v in transposed_batch[1]]) + batched_pos_map = torch.zeros((nb_boxes, max_len), dtype=torch.bool) + cur_count = 0 + for v in transposed_batch[1]: + cur_pos = v.get_field("positive_map_eval") + batched_pos_map[cur_count : cur_count + len(cur_pos), : cur_pos.shape[1]] = cur_pos + cur_count += len(cur_pos) + + assert cur_count == len(batched_pos_map) + # assert batched_pos_map.sum().item() == sum([v["positive_map"].sum().item() for v in batch[1]]) + positive_map_eval = batched_pos_map.float() + return images, targets, img_ids, positive_map, positive_map_eval, greenlight_map + + +class BBoxAugCollator(object): + """ + From a list of samples from the dataset, + returns the images and targets. + Images should be converted to batched images in `im_detect_bbox_aug` + """ + + def __call__(self, batch): + # return list(zip(*batch)) + transposed_batch = list(zip(*batch)) + + images = transposed_batch[0] + targets = transposed_batch[1] + img_ids = transposed_batch[2] + positive_map = None + positive_map_eval = None + + if isinstance(targets[0], dict): + return images, targets, img_ids, positive_map, positive_map_eval + + return images, targets, img_ids, positive_map, positive_map_eval diff --git a/maskrcnn_benchmark/data/datasets/__init__.py b/maskrcnn_benchmark/data/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..daedc7c3390d3970f2441e450bac80e43409724f --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/__init__.py @@ -0,0 +1,43 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from .coco import COCODataset +from .voc import PascalVOCDataset +from .concat_dataset import ConcatDataset +from .background import Background +from .tsv import TSVDataset, ODTSVDataset + +from .modulated_coco import ModulatedDataset, CocoDetection, CocoGrounding +from .flickr import FlickrDataset +from .refexp import RefExpDataset +from .mixed import MixedDataset +from .gqa import GQADataset + +from .coco_dt import CocoDetectionTSV +from .caption import CaptionTSV +from .lvis import LvisDetection +from .pseudo_data import PseudoData +from .phrasecut import PhrasecutDetection +try: + from .omnilabel import OmniLabelDataset +except: + pass +__all__ = [ + "COCODataset", + "TSVDataset", + "ODTSVDataset", + "ConcatDataset", + "PascalVOCDataset", + "Background", + "ModulatedDataset", + "MixedDataset", + "CocoDetection", + "FlickrDataset", + "RefExpDataset", + "GQADataset", + "CocoDetectionTSV", + "CocoGrounding", + "CaptionTSV", + "LvisDetection", + "PseudoData", + "PhrasecutDetection", + "OmniLabelDataset", +] diff --git a/maskrcnn_benchmark/data/datasets/_caption_aug.py b/maskrcnn_benchmark/data/datasets/_caption_aug.py new file mode 100644 index 0000000000000000000000000000000000000000..8a2c78cb7a2ccc4936cdafecf802d9f37258dce6 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/_caption_aug.py @@ -0,0 +1,992 @@ +# Utilities for converting object detection data into grounding data +import numpy as np +import torch +import pdb, json, random, re +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file +from collections import defaultdict +import json +import json +import nltk +from collections import Counter +from tqdm import tqdm +import random +import pdb +from copy import deepcopy +from nltk.corpus import stopwords +from nltk.tokenize import word_tokenize +from maskrcnn_benchmark.data.datasets.parse_gpt import GPTOutputParser +def find_only_noun(caption: str): + caption = caption.lower() + tokens = nltk.word_tokenize(caption) + pos_tags = nltk.pos_tag(tokens) + + grammar = "NP: {+}" + #grammar = "NP: {
?*+}" + cp = nltk.RegexpParser(grammar) + result = cp.parse(pos_tags) + + noun_phrases = list() + for subtree in result.subtrees(): + if subtree.label() == "NP": + noun_phrases.append(" ".join(t[0] for t in subtree.leaves())) + + return noun_phrases + +def find_jj_noun(caption: str): + caption = caption.lower() + tokens = nltk.word_tokenize(caption) + pos_tags = nltk.pos_tag(tokens) + + grammar = "NP: {++}" + cp = nltk.RegexpParser(grammar) + result = cp.parse(pos_tags) + + noun_phrases = list() + for subtree in result.subtrees(): + if subtree.label() == "NP": + noun_phrases.append(" ".join(t[0] for t in subtree.leaves())) + + return noun_phrases + +def remove_stop_words(caption, stop_words): + + word_tokens = caption.split(" ") + # converts the words in word_tokens to lower case and then checks whether + # they are present in stop_words or not + filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words] + # with no lower case conversion + filtered_sentence = [] + + for w in word_tokens: + if w not in stop_words: + filtered_sentence.append(w) + + return " ".join(filtered_sentence) +def rand_element(dic): + ind = random.randint(0, len(dic) - 1) + return list(dic.keys())[ind] + + +def replace_word(w, voc): + new_w = rand_element(voc) + while new_w == w: + new_w = rand_element(voc) + return new_w + +def replace_pos(tags, l, vocab): + if len(l) == 0: + return '', '' + ind = random.randint(0, len(l) - 1) + ind = l[ind] + word, tag = tags[ind] + new_word = replace_word(word, vocab[tag]) + return word, new_word + +noun_pos = set(['NN', 'NNS', 'NNP', 'NNPS']) +verb_pos = set(['VB', 'VBG', 'VBD', 'VBN', 'VBP', 'VBZ']) +adj_pos = set(['JJ', 'JJR', 'JJS']) + + +class CaptionAugmentation(): + def __init__(self, caption_augmentation_version, tokenizer = None, caption_vocab_file = None): + self.caption_augmentation_version = caption_augmentation_version + self.tokenizer = tokenizer + # v1 and v2 are legacy experimental versions so we remove them from the code + if self.caption_augmentation_version.startswith("v3"): + self.augmentation = AugmentationV3(self.caption_augmentation_version, self.tokenizer, caption_vocab_file) + elif self.caption_augmentation_version.startswith("v4"): + self.augmentation = AugmentationV4(self.caption_augmentation_version, self.tokenizer, caption_vocab_file) + elif self.caption_augmentation_version.startswith("mixed"): + # format: mixed.v4-v3.4-4-2.content.v1 + self.augmentations = [] + self.rations = [] + versions = self.caption_augmentation_version.split(".")[1] + ratios = self.caption_augmentation_version.split(".")[2] + suffix = ".".join(self.caption_augmentation_version.split(".")[3:]) + for version in versions.split("-"): + self.augmentations.append(CaptionAugmentation(version + "." + suffix, self.tokenizer, caption_vocab_file)) + for ratio in ratios.split("-"): + self.rations.append(float(ratio) * 0.1) + print(self.rations) + print(self.augmentations) + else: + raise NotImplementedError + + def __call__(self, caption, target, **kwargs): + if self.caption_augmentation_version.startswith("mixed"): + # do a mixed augmentation + random_prob = random.random() + for augmentation, ratio in zip(self.augmentations, self.rations): + if random_prob < ratio: + return augmentation(caption, target, **kwargs) + random_prob -= ratio + + return caption, target, None # this is the vanilla case + + else: + return self.augmentation(caption, target, **kwargs) + +class NegativeCaptionGenerator(): + def __init__(self, caption_augmentation_version, **kwargs): + self.caption_augmentation_version = caption_augmentation_version + if self.caption_augmentation_version.endswith("v1"): + self.generator = NegativeCaptionGeneratorV1(self.caption_augmentation_version, **kwargs) + elif self.caption_augmentation_version.endswith("v2"): + self.generator = NegativeCaptionGeneratorV2(self.caption_augmentation_version, **kwargs) + else: + raise NotImplementedError + + def __call__(self, caption, **kwargs): + return self.generator(caption, **kwargs) + + +class NegativeCaptionGeneratorV1(): + def __init__(self, caption_augmentation_version, caption_vocab_file=None): + self.caption_augmentation_version = caption_augmentation_version + self.caption_vocab_file = caption_vocab_file + self.vocab = json.load(open('tools/data_process/image_caption/vocab.json')) + for tag in self.vocab: + most_common = 1000 + self.vocab[tag] = dict(Counter(self.vocab[tag]).most_common(1000)) + min_cnt = 5 + self.vocab[tag] = {x: cnt for x, cnt in self.vocab[tag].items() if cnt >= min_cnt} + + def __call__(self, caption, num_negative_caption=4): + tokens = nltk.word_tokenize(caption) + tags = nltk.pos_tag(tokens) + nouns = [] + verbs = [] + adjs = [] + for ind, (word, tag) in enumerate(tags): + if tag in noun_pos: + nouns.append(ind) + elif tag in verb_pos: + verbs.append(ind) + elif tag in adj_pos: + adjs.append(ind) + negative_caption = [] + + for i in range(random.randint(0, num_negative_caption)): + replace_atoms = random.choice([nouns, verbs, adjs]) + word, new_word = replace_pos(tags, replace_atoms, self.vocab) + if word == '': + continue + new_caption = caption.replace(word, new_word) + negative_caption.append(new_caption) + return negative_caption + +class NegativeCaptionGeneratorV2(): + def __init__(self, caption_augmentation_version, tokenizer = None, caption_vocab_file="tools/files/llm_10K_noun_freq_mixed.json"): + self.caption_augmentation_version = caption_augmentation_version + self.stop_words = set(stopwords.words('english')) + self.tokenizer = tokenizer + with open(caption_vocab_file, 'r') as f: + self.vocab = json.load(f) + + def parse_info(self, noun): + # given a noun, return the category and other info + ''' + "chrome faucet": ["Yes. 'Chrome faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'chrome faucet' but are not 'chrome faucet' are:\tbrushed nickel faucet\tstainless steel faucet\tchrome showerhead\tchrome soap dispenser\nThere are several useful visual features to tell there is 'chrome faucet' and not similar things in a photo:\tchrome finish\ton/off handles\tspout for water flow\tsingle or double handled faucet\tmounted on a sink or countertop", 57] + ''' + noun = remove_stop_words(noun, self.stop_words) + if noun not in self.vocab: + return 0, [], [], "" + info = self.vocab[noun] + + # check the format of type of thing + if "has a tangible appearance and is" in info[0]: + type_of_thing = info[0].split(" has a tangible appearance and is ")[-1].split(".")[0] + elif "has a tangible appearance" in info[0]: + type_of_thing = info[0].split(" has a tangible appearance ")[-1].split(".")[0] + else: + #print(info[0], "type of thing not found") + type_of_thing = "" + + if " are:\t" in info[0]: + similar_things = info[0].split(" are:\t")[-1].split("\nThere are several useful visual features to tell")[0].split("\t") + similar_things = [i for i in similar_things if i.strip() != ""] + else: + #print(info[0], "similar things not found") + similar_things = [] + + if " and not similar things in a photo:\t" in info[0]: + visual_feature_descriptions = info[0].split(" and not similar things in a photo:\t")[-1].split("\t") + visual_feature_descriptions = [i for i in visual_feature_descriptions if i.strip() != ""] + else: + #print(info[0], "visual feature descriptions not found") + visual_feature_descriptions = [] + + + return info[1], visual_feature_descriptions, similar_things, type_of_thing + + def __call__(self, caption, num_negative_caption=4): + nouns = set(caption.split(" ")) #find_only_noun(caption) + negative_captions = [] + for noun in nouns: + freq, visual_feature_descriptions, similar_things, type_of_thing = self.parse_info(noun) + if freq > 20000: + continue + # print(freq, noun, visual_feature_descriptions, similar_things, type_of_thing) + + if len(visual_feature_descriptions) == 0 or len(similar_things) == 0 or type_of_thing == "Yes": + continue # did not find the noun in the vocab + + negative_captions.append(caption.replace(noun, random.choice(similar_things))) + + return negative_captions + +class AugmentationV3(): + ''' + Extract the noun entity; get descriptions and confusable entities; form the new query; throw away the original caption + ''' + def __init__(self, caption_augmentation_version, tokenizer = None, caption_vocab_file="tools/files/llm_10K_noun_freq_mixed.json"): + self.caption_augmentation_version = caption_augmentation_version + self.tokenizer = tokenizer + with open(caption_vocab_file, 'r') as f: + self.vocab = json.load(f) + self.vocab_keys = list(self.vocab.keys()) + self.stop_words = set(stopwords.words('english')) + self.do_augment_prob = 1.0 + self.include_name_prob = 0.5 + self.include_only_description_prob = 0.0 + self.length_limit = 800 if "span" in caption_augmentation_version else 180 + self.gpt_parser = GPTOutputParser(caption_augmentation_version.split(".")[-1]) + + def parse_info(self, noun): + # given a noun, return the category and other info + ''' + {'type': 'human', 'description': 'female; could have long hair; could wear dresses', 'similar objects': ['girl', 'lady', 'mother']} + ''' + noun = remove_stop_words(noun, self.stop_words) + if noun not in self.vocab: + return 0, [], [], "" + info = self.vocab[noun] + descriptions = self.gpt_parser(info[0]) + + return info[1], descriptions["description"], descriptions["similar objects"], descriptions["type"] + + def get_freq(self, noun): + noun = remove_stop_words(noun, self.stop_words) + if noun not in self.vocab: + return 0 + info = self.vocab[noun] + return info[1] + + def get_similar_things(self, noun): + noun = remove_stop_words(noun, self.stop_words) + if noun not in self.vocab: + return [] + info = self.vocab[noun] + descriptions = self.gpt_parser(info[0]) + return descriptions["similar objects"] + + def form_span(self, noun): + noun = remove_stop_words(noun, self.stop_words) + info = self.vocab[noun] + description = info[0] + if random.random() < self.include_name_prob: + #postive_span = "{}, {}".format(noun, type_of_thing) + #final_span = "{}, {}, {}".format(noun, type_of_thing, ", ".join(similar_visual_feature_descriptions)) + final_span, end_index, spans, *_ = self.gpt_parser.form_span(noun, description, type = "vanilla_span", positive_range = "partial") + else: + final_span, end_index, spans, *_ = self.gpt_parser.form_span(noun, description, type = "remove_noun_span", positive_range = "partial") + return final_span, end_index, spans + + def __call__(self, caption, target, **kwargs): + # 1. get the categories mentioned in the caption + original_str_spans = [] + original_nouns = defaultdict(list) + for box_index, box in enumerate(target): + for start, end in box["tokens_positive"]: + original_str_spans.append(caption[start:end]) + if "nouns" in box: + original_nouns[caption[start:end]] = box["nouns"] + original_str_spans = set(original_str_spans) + + #### Important structures + positive_text_pieces = {} # mapping from positive text pieces to the original text span + positive_text_pieces_reverse = {} + positive_text_pieces_center_length = {} + all_pieces = [] + text_pieces_to_spans = {} # mapping from text pieces to the spans + all_spans = [] # all the spans, noun_num x span_num_each_noun x 2 + ##### + length_limit = self.length_limit + original_str_spans = list(original_str_spans) + # shuffle + random.shuffle(original_str_spans) + + for text_span in original_str_spans: + if len(original_nouns[text_span]) == 0: + nouns = text_span.split(" ") #[text_span] #find_only_noun(text_span) + else: + nouns = original_nouns[text_span] + + for noun in nouns: + frequency = self.get_freq(noun) + if frequency > 10000 or frequency == 0: + continue + + positive_span, centern_noun_lenghth, span_locations = self.form_span(noun) + length_limit -= len(positive_span.split(" ")) + if length_limit < 0: + break + text_pieces_to_spans[positive_span] = span_locations + positive_text_pieces[positive_span] = text_span + positive_text_pieces_reverse[text_span] = positive_span + positive_text_pieces_center_length[positive_span] = centern_noun_lenghth + all_pieces.append(positive_span) + + # do the augmentation + if "no_similar" in self.caption_augmentation_version: + continue # skip the similar things + + for similar_thing in self.get_similar_things(noun): + frequency = self.get_freq(similar_thing) + if frequency > 10000 or frequency == 0: + continue # did not find the noun in the vocab + negative_span, _, span_locations = self.form_span(similar_thing) + length_limit -= len(negative_span.split(" ")) + if length_limit < 0: + break + all_pieces.append(negative_span) + text_pieces_to_spans[negative_span] = span_locations # record the span mapping + + # randomly sample some negatives + + if len(all_pieces) == 0: + return caption, target, None + + if random.random() > self.do_augment_prob: # + return caption, target, None + + # if we have some space left, sample more descriptions + while length_limit > 0: + random_noun = random.choice(self.vocab_keys) + frequency = self.get_freq(random_noun) + if frequency > 10000 or frequency == 0: + continue + + negative_span, _, span_locations = self.form_span(random_noun,) + length_limit -= len(negative_span.split(" ")) + if length_limit < 0: + break + all_pieces.append(negative_span) # add the negative span + text_pieces_to_spans[negative_span] = span_locations # record the span mapping + + + # 2. randomly assemble the caption + new_target = deepcopy(target) + random.shuffle(all_pieces) + final_caption = "" + + # create the mapping from "text_span" to "tokens_positive" + text_span_to_tokens_positive = {} + for text_piece in all_pieces: + if text_piece in positive_text_pieces: + text_span_to_tokens_positive[positive_text_pieces[text_piece]] = (len(final_caption), len(final_caption) + positive_text_pieces_center_length[text_piece]) # only mark the centern noun as positive + + # update the spans + cur_length = len(final_caption) + + for span in text_pieces_to_spans[text_piece]: + span[0] = span[0] + cur_length + span[1] = span[1] + cur_length + + final_caption += text_piece + + # update the target + new_target = [] + for box in target: + new_tokens_positive = [] + new_spans = [] + for start, end in box["tokens_positive"]: + if caption[start:end] in text_span_to_tokens_positive: + new_tokens_positive.append(text_span_to_tokens_positive[caption[start:end]]) + new_spans.extend(text_pieces_to_spans[positive_text_pieces_reverse[caption[start:end]]]) + if len(new_tokens_positive) != 0: + _box = deepcopy(box) + _box["tokens_positive"] = new_tokens_positive + _box["spans_positive"] = new_spans + new_target.append(_box) + + ''' + For using span representation, all that needs done is to give: spans, and spans_positive for each box + ''' + + all_spans = list(text_pieces_to_spans.values()) + all_spans = sorted(all_spans, key=lambda x: x[0][0]) + + + #print("V3 Augmented caption: ", final_caption) + # Need to provide the spans + return final_caption, new_target, all_spans + +class AugmentationV4(): + def __init__(self, caption_augmentation_version, tokenizer, caption_vocab_file): + self.caption_augmentation_version = caption_augmentation_version + self.stop_words = set(stopwords.words('english')) + self.tokenizer = tokenizer + self.do_augment_prob = 0.9 + self.include_name_prob = 0.5 + self.include_only_description_prob = 0.0 + self.length_limit = 800 if "span" in caption_augmentation_version else 180 + self.gpt_parser = GPTOutputParser(caption_augmentation_version.split(".")[-1]) + + with open(caption_vocab_file, 'r') as f: + self.vocab = json.load(f) + self.vocab_keys = list(self.vocab.keys()) + self.include_v3_augmentation = "include_v3" in caption_augmentation_version + + # do a stat + from ._pos_rate import PosRateController, PosRateControllerLength, PosRateControllerV2 + self.pos_rate_controller = PosRateControllerV2(max_length=35, center_length = 20) + + def parse_info(self, noun): + # given a noun, return the category and other info + ''' + {'type': 'human', 'description': 'female; could have long hair; could wear dresses', 'similar objects': ['girl', 'lady', 'mother']} + ''' + noun = remove_stop_words(noun, self.stop_words) + if noun not in self.vocab: + return 0, [], [], "" + info = self.vocab[noun] + descriptions = self.gpt_parser(info[0]) + + return info[1], descriptions["description"], descriptions["similar objects"], descriptions["type"] + + def get_freq(self, noun): + noun = remove_stop_words(noun, self.stop_words) + if noun not in self.vocab: + return 0 + info = self.vocab[noun] + return info[1] + + def get_similar_things(self, noun): + noun = remove_stop_words(noun, self.stop_words) + if noun not in self.vocab: + return [] + info = self.vocab[noun] + descriptions = self.gpt_parser(info[0]) + return descriptions["similar objects"] + + def form_span(self, noun): + noun = remove_stop_words(noun, self.stop_words) + info = self.vocab[noun] + description = info[0] + if random.random() < self.include_name_prob: + #postive_span = "{}, {}".format(noun, type_of_thing) + #final_span = "{}, {}, {}".format(noun, type_of_thing, ", ".join(similar_visual_feature_descriptions)) + final_span, end_index, spans, *_ = self.gpt_parser.form_span(noun, description, type = "vanilla_span") + else: + final_span, end_index, spans, *_ = self.gpt_parser.form_span(noun, description, type = "remove_noun_span") + return final_span, end_index, spans + + def simple_gpt_parser(self, gpt_output): + ''' + Visually concrete phrases and their visual descriptions: {"beans": "a kind of vegetable, small, round, usually greeen"} + Negative visual phrases and their visual descriptions: {"coffee beans": "a kind of vegetable, small, round, brown and dark", "beeds": "a kind of decorations, small, round, colorful"} + Negative captions: ["the beans in the green silver cup.", "the apples in the red silicone cup.", "the beans in the red porcelain cup."] + ''' + try: + if "\n" not in gpt_output: + pos_description = gpt_output[gpt_output.find("1. Visually concrete objects and descriptions:") : gpt_output.find(" 2. Objects that can be confused with the above objects:")].replace("1. Visually concrete objects and descriptions:", "").strip() + pos_description = json.loads(pos_description) + neg_description = gpt_output[gpt_output.find(" 2. Objects that can be confused with the above objects:") : gpt_output.find(" 3. Negative captions:")].replace(" 2. Objects that can be confused with the above objects:", "").strip() + neg_description = json.loads(neg_description) + neg_captions = gpt_output[gpt_output.find(" 3. Negative captions:") : ].replace(" 3. Negative captions:", "").strip().replace("", "").replace("", "") + neg_captions = json.loads(neg_captions) + else: + pos_description = gpt_output.split("\n")[0].split("descriptions: ")[1].strip() + pos_description = json.loads(pos_description) + + try: + neg_description = gpt_output.split("\n")[1].split("descriptions: ")[1].strip() + neg_description = json.loads(neg_description) + except: + neg_description = gpt_output.split("\n")[1].split("objects: ")[1].strip() + neg_description = json.loads(neg_description) + + neg_captions = gpt_output.split("\n")[2].split("captions: ")[1].strip() + neg_captions = json.loads(neg_captions) + + return { + "pos_description": pos_description, + "neg_description": neg_description, + "neg_captions": neg_captions + } + except: + return { + "pos_description": {}, + "neg_description": {}, + "neg_captions": [] + } + + @staticmethod + def randomly_assemble_pieces_while_maintaining_spans_locations( + caption, # the original caption + all_pieces, # a list of text strings that will form the final caption + positive_text_pieces, # a mapping from the positive text pieces to the original text piece + positive_text_pieces_reverse, # reversed mapping + positive_text_pieces_center_length, # the length of the center noun + text_pieces_to_spans, # record the mapping from text pieces to their spans + target, # a list of boxes, each box has a "tokens_positive" field + ): + final_caption = "" + + # create the mapping from "text_span" to "tokens_positive" + text_span_to_tokens_positive = {} + for text_piece in all_pieces: + if text_piece in positive_text_pieces: + text_span_to_tokens_positive[positive_text_pieces[text_piece]] = (len(final_caption), len(final_caption) + positive_text_pieces_center_length[text_piece]) # only mark the centern noun as positive + + # update the spans + cur_length = len(final_caption) + + for span in text_pieces_to_spans[text_piece]: + span[0] = span[0] + cur_length + span[1] = span[1] + cur_length + + final_caption += text_piece + + # update the target + new_target = [] + for box in target: + new_tokens_positive = [] + new_spans = [] + for start, end in box["tokens_positive"]: + if caption[start:end] in text_span_to_tokens_positive: + new_tokens_positive.append(text_span_to_tokens_positive[caption[start:end]]) + new_spans.extend(text_pieces_to_spans[positive_text_pieces_reverse[caption[start:end]]]) + if len(new_tokens_positive) != 0: + _box = deepcopy(box) + _box["tokens_positive"] = new_tokens_positive + _box["spans_positive"] = new_spans + new_target.append(_box) + + ''' + For using span representation, all that needs done is to give: spans, and spans_positive for each box + ''' + + all_spans = list(text_pieces_to_spans.values()) + all_spans = sorted(all_spans, key=lambda x: x[0][0]) + return final_caption, new_target, all_spans + + + def merge_token_posivie(self, tokens_positive): + previous_end = -5 + current_start = -5 + new_tokens_positive = [] + for token_positive in tokens_positive: + # try to merge tokens positive if they are continuous + if current_start == -5: # this is the start + current_start = token_positive[0] + previous_end = token_positive[1] + continue + + if token_positive[0] == previous_end + 1: # continus + previous_end = token_positive[1] + else: + new_tokens_positive.append((current_start, previous_end)) + current_start = token_positive[0] + previous_end = token_positive[1] + new_tokens_positive.append((current_start, previous_end)) + return new_tokens_positive + + def _change_target(self, start_original_span, end_original_span, description, target, caption, centern_noun_lenghth): + subcaptions = [] + # find if there is a match + matched_i = False + for box_index, box in enumerate(target): + for start, end in box["tokens_positive"]: + # if the tokens_positive is within the span or it contains the span + if (start_original_span <= start and end <= end_original_span) or (start <= start_original_span and end_original_span <= end): + # add the description to the positive_text_pieces + # mark the matching between this box and this new subcaption # need to think later + # TODO: support partial match + box['tokens_positive'].append((len(caption), len(caption) + centern_noun_lenghth)) + matched_i = True + + if matched_i: + # add the description to the caption + caption += description + subcaptions.append(description) + #negative_captions.extend(list(gpt_result["neg_description"].values())) + return caption, subcaptions, target + + def __call__(self, caption, target, gpt3_outputs = None,): + if gpt3_outputs is None: + return caption, target, None # skip this augmentation + + #### + negative_captions = [] + subcaptions = [] + original_subcaptions = [] + grouping_subcaptions = defaultdict(list) + #### + probablity = random.random() + # 40% chance to only include original subcaptions and neg captions + # 20% chance to include only v3 captions + # 10% chance to include only v4 descriptions + # 20% chance to include all kinds of stuff + # 10% chance to return original + if probablity < 0.2: + include_v3 = False + include_v4_des = False + include_original = True + elif probablity < 1.0: + include_v3 = False + include_v4_des = True + include_original = True + else: + return caption, target, None + + # 1. do somme preprocessing; extract the subcaptions + original_caption = deepcopy(caption) + original_target = deepcopy(target) + # parse the GPT outputstart_index = 0 + start_index = 0 + for i in range(len(caption)): + if caption[i] == "." or caption[i] == "?": + subcaption_i = caption[start_index:i+1] + subcaptions.append(subcaption_i) + start_index = i + 1 + if start_index != len(caption): + # some remaining stuff + subcaption_i = caption[start_index:] + if subcaption_i.strip() != "": + subcaptions.append(subcaption_i) + + original_subcaptions = deepcopy(subcaptions) # keep a copy of the original subcaptions + for box in target: + box['tokens_positive'] = self.merge_token_posivie(box['tokens_positive']) # merge the tokens_positive if they happen to be continuous + + if self.include_v3_augmentation and include_v3: + # 1. get the categories mentioned in the caption + all_nouns = [] + for box_index, box in enumerate(target): + for start, end in box["tokens_positive"]: + if "nouns" in box: + all_nouns.extend(box["nouns"]) # if we pre-extract the nouns, we can use them + else: + all_nouns.extend(caption[start:end].split(" ")) # otherwise, we just use the tokens_positive and do a split by " " + all_nouns = list(set(all_nouns)) + + ##### + # shuffle + random.shuffle(all_nouns) + + for noun in all_nouns: + frequency = self.get_freq(noun) + if frequency > 10000 or frequency == 0: + continue + + positive_span, centern_noun_lenghth, span_locations = self.form_span(noun) + + # find the noun in the caption + start_i = original_caption.find(noun) + end_i = start_i + len(noun) + + # add the positive span to the caption + caption, subcaptions_noun, target = self._change_target( + start_original_span = start_i, + end_original_span = end_i, + description = positive_span, + target = target, + caption = caption, + centern_noun_lenghth=centern_noun_lenghth) + + if len(subcaptions_noun) != 0: + subcaptions.extend(subcaptions_noun) + # do the augmentation + _tmp_negs = [] + for similar_thing in self.get_similar_things(noun): + frequency = self.get_freq(similar_thing) + if frequency > 10000 or frequency == 0: + continue # did not find the noun in the vocab + negative_span, _, span_locations = self.form_span(similar_thing) + negative_captions.append(negative_span) + _tmp_negs.append(negative_span) + + grouping_subcaptions["v3"].append((positive_span, _tmp_negs)) + + if gpt3_outputs is None: + gpt3_outputs = {} + + ban_list = ['man', "woman", "child", "men", "women", "children", "people", "person"] + for key, value in gpt3_outputs.items(): + try: + gpt_result = self.simple_gpt_parser(value) + for key_phrase, description_i in gpt_result["pos_description"].items(): + # find the location of the span + start_i = caption.find(key_phrase) + end_i = start_i + len(key_phrase) + description_i = description_i + ". " if description_i[-1] != "." else description_i + if random.random() < 0.5: + description_i = key_phrase + ", " + description_i + center_ = 2 + else: + center_ = 1 + # find the center noun + center_length = len(",".join(description_i.split(",")[:center_])) + # else: + # center_length = len(description_i) + + # find if there is a match + matched_i = False + skip_i = False + for ban_noun in ban_list: + if ban_noun in key_phrase: + skip_i = True + break + if skip_i: + continue + + for box_index, box in enumerate(target): + for start, end in box["tokens_positive"]: + # if the tokens_positive is within the span or it contains the span + if (start_i <= start and end <= end_i) or (start <= start_i and end_i <= end): + # add the description to the positive_text_pieces + # mark the matching between this box and this new subcaption # need to think later + # TODO: support partial match + box['tokens_positive'].append((len(caption), len(caption) + center_length)) + matched_i = True + + if matched_i and include_v4_des: + # add the description to the caption + caption += description_i + subcaptions.append(description_i) + negative_captions.extend(list(gpt_result["neg_description"].values())) + grouping_subcaptions["v4_des"].append((description_i, list(gpt_result["neg_description"].values()))) + + # the rest are negative captions + negative_captions.extend(gpt_result["neg_captions"]) + grouping_subcaptions["original"].append((key, gpt_result["neg_captions"])) + except: + pass + + for i in range(len(negative_captions)): + if negative_captions[i].endswith(".") or negative_captions[i].endswith("?"): + negative_captions[i] = negative_captions[i] + " " + elif negative_captions[i].endswith(". ") or negative_captions[i].endswith("? "): + pass + else: + negative_captions[i] = negative_captions[i] + ". " + for value in grouping_subcaptions.values(): + for caps in value: + for index in range(len(caps[1])): + if caps[1][index].endswith(".") or caps[1][index].endswith("?"): + caps[1][index] = caps[1][index] + " " + elif caps[1][index].endswith(". ") or caps[1][index].endswith("? "): + pass + else: + caps[1][index] = caps[1][index] + ". " + + if "drop_positive" in self.caption_augmentation_version: + drop_positive_rate = 0.5 + if random.random() < 0.1: # 10% drop all the positive + drop_positive_rate = 1.0 + drop_negative_rate = 0.0 + else: + drop_positive_rate = 0.0 + drop_negative_rate = 0.0 + + if len(subcaptions) == 0 and len(negative_captions) == 0: + return original_caption, original_target, None + + if "control_pos" in self.caption_augmentation_version: + # calculate on average how many captions we can afford here + sub_cap_mean_length = np.mean([len(i.split(" ")) for i in subcaptions]) + neg_cap_mean_length = np.mean([len(i.split(" ")) for i in negative_captions]) + mean_length = (sub_cap_mean_length * len(subcaptions) + neg_cap_mean_length * len(negative_captions)) / (len(subcaptions) + len(negative_captions)) + if sub_cap_mean_length * len(subcaptions) + neg_cap_mean_length * len(negative_captions) > 200: + # need to drop some of the captions + max_cap_num = 180 // mean_length + else: + max_cap_num = -1 + if "grouping" in self.caption_augmentation_version: + # dynamically determine the number of positive and negative captions + final_included_groups = [] + if include_v3: + final_included_groups.extend(grouping_subcaptions["v3"]) + if include_v4_des: + final_included_groups.extend(grouping_subcaptions["v4_des"]) + if include_original: + final_included_groups.extend(grouping_subcaptions["original"]) + # negative captions + grouped_positive_num = len(final_included_groups) + grouped_negative_num = sum([len(i[1]) for i in final_included_groups]) + else: + grouped_positive_num = len(subcaptions) + grouped_negative_num = len(negative_captions) + + + # prefered captions + pos_num, neg_num = self.pos_rate_controller(grouped_positive_num, grouped_negative_num, max_cap_num=max_cap_num) + + if "grouping" in self.caption_augmentation_version: + # do the preselection + preselected_captions = set() + preselected_captions_neg = set() + neg_counter = 0 + # let's see if we need to drop some negative; do a preselection of negative captions + random.shuffle(final_included_groups) + for i in range(pos_num): + preselected_captions.add(final_included_groups[i][0]) + if neg_counter < neg_num: + _tmp = random.randint(0, len(final_included_groups[i][1])) + preselected_captions_neg.update(final_included_groups[i][1][:_tmp]) + neg_counter += _tmp + + if neg_counter < neg_num: + random.shuffle(negative_captions) + preselected_captions_neg.update(negative_captions[:neg_num - neg_counter]) + + # print(include_v3, include_v4_des, include_original) + # print(preselected_captions) + # print(preselected_captions_neg) + # print(pos_num, neg_num) + # print("grouped", grouped_positive_num, grouped_negative_num) + # print("original", len(subcaptions), len(negative_captions)) + + preselected_captions.update(preselected_captions_neg) + else: + preselected_captions = None + + augmented_caption, location_mapping, final_pos_num, final_neg_num = random_resemble_captions( subcaptions, negative_captions, pos_num, neg_num, tokenizer = self.tokenizer, preselected_captions= preselected_captions) + + self.pos_rate_controller.update_true_pos_rate(final_pos_num, final_pos_num + final_neg_num) + + # update the target + new_target = [] + for box in target: + new_tokens_positive = [] + for start, end in box["tokens_positive"]: + if start in location_mapping and end - 1 in location_mapping: + new_tokens_positive.append([location_mapping[start], location_mapping[end - 1] + 1]) # location of the character in the new string + + if len(new_tokens_positive) > 0: # possible the caption was dropped + _box = deepcopy(box) + _box["tokens_positive"] = new_tokens_positive + new_target.append(_box) + + original_spans = [] + for box in target: + for start, end in box["tokens_positive"]: + original_spans.append(caption[start:end]) + + augmented_spans = [] + for box in new_target: + for start, end in box["tokens_positive"]: + augmented_spans.append(augmented_caption[start:end]) + + if len(augmented_caption) == 0: + return original_caption, original_target, None + return augmented_caption, new_target, None + + +def find_noun_phrases(caption: str): + caption = caption.lower() + tokens = nltk.word_tokenize(caption) + pos_tags = nltk.pos_tag(tokens) + + grammar = "NP: {
?*+}" + cp = nltk.RegexpParser(grammar) + result = cp.parse(pos_tags) + + noun_phrases = list() + for subtree in result.subtrees(): + if subtree.label() == "NP": + noun_phrases.append(" ".join(t[0] for t in subtree.leaves())) + + return noun_phrases + +def random_resemble_captions( + captions, additional_captions, sub_sample_pos_num = -1, sub_sample_neg_num = -1, preselected_captions = None, tokenizer=None): + location_mapping = {} + indexes = list(range(len(captions) + len(additional_captions))) + all_captions = captions + additional_captions + random.shuffle(indexes) + # create a mapping between the original location and the new location + + # 1. create a mapping from original index to their character location + original_index_to_location = defaultdict(list) + current_caption = '' + for i, caption in enumerate(captions): + current_len = len(current_caption) + for j in range(len(caption)): + original_index_to_location[i].append(current_len + j) # location of the character in the original string + current_caption += caption + #current_caption += '. ' + + # determind the kept indexes + if sub_sample_pos_num != -1: + pos_indexes = list(range(len(captions))) + if preselected_captions is not None: + pos_indexes = [i for i in pos_indexes if all_captions[i] in preselected_captions] + + random.shuffle(pos_indexes) + kept_pos_indexes = set(pos_indexes[:sub_sample_pos_num]) + else: + kept_pos_indexes = set(range(len(captions))) + + if sub_sample_neg_num != -1: + neg_indexes = list(range(len(captions), len(captions) + len(additional_captions))) + if preselected_captions is not None: + neg_indexes = [i for i in neg_indexes if all_captions[i] in preselected_captions] + random.shuffle(neg_indexes) + kept_neg_indexes = set(neg_indexes[:sub_sample_neg_num]) + else: + kept_neg_indexes = set(range(len(captions), len(captions) + len(additional_captions))) + + kep_indexes = kept_pos_indexes | kept_neg_indexes + + + final_kept_positive = [] + final_kept_negative = [] + + final_kep_indexes = [] + # 2. create a mapping from original locations + length_limit = 254 + current_caption = "" + # need to avoid calling the tokenizer too many times + + for i in range(len(indexes)): + caption = all_captions[indexes[i]] + + if indexes[i] not in kep_indexes: # will not be kept + continue + + tokenized = tokenizer.tokenize(caption) + #tokenized = caption.split(" ") + + length_limit -= len(tokenized) + if length_limit < 0: + break # we have reached the length limit + + # if not caption.startswith(" "): + # current_caption += " " + + current_len = len(current_caption) + if indexes[i] < len(captions): # means it is one of the original caption and we need to record location + for j in range(len(caption)): + location_mapping[ original_index_to_location[indexes[i]][j] ] = current_len + j # location of the character in the new string + + current_caption += caption + + if current_caption.endswith("."): + current_caption += ' ' + elif current_caption.endswith("?"): + current_caption += ' ' + elif current_caption.endswith(". ") or current_caption.endswith("? "): + pass + else: + current_caption += '. ' + + if indexes[i] in kept_pos_indexes: + final_kept_positive.append(caption) + else: + final_kept_negative.append(caption) + + return current_caption, location_mapping, len(final_kept_positive), len(final_kept_negative) diff --git a/maskrcnn_benchmark/data/datasets/_od_to_description.py b/maskrcnn_benchmark/data/datasets/_od_to_description.py new file mode 100644 index 0000000000000000000000000000000000000000..67506e95c26a1abcb8c5cf99cb1752d2b7bf93ed --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/_od_to_description.py @@ -0,0 +1,520 @@ +# Utilities for converting object detection data into grounding data +import numpy as np +import torch +import pdb, json, random, re +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file +from collections import defaultdict +from tqdm import tqdm +from maskrcnn_benchmark.data.datasets.parse_gpt import GPTOutputParser +from ._pos_rate import PosRateController, PosRateControllerLength, PosRateControllerV2 +def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + all_ = [] + for i in range(0, len(lst), n): + data_index = lst[i:i + n] + all_.append(data_index) + counter = 0 + for i in all_: + counter += len(i) + assert(counter == len(lst)) + + return all_ + +def clean_name(name): + + def _clean_name(name): + name = re.sub(r"\(.*\)", "", name) + name = re.sub(r"_", " ", name) + name = re.sub(r" ", " ", name) + return name + + if ":" in name: + obj_name, part_name = name.split(":") + obj_name = _clean_name(obj_name) + part_name = _clean_name(part_name) + return part_name + " of " + obj_name + else: + return _clean_name(name) + +def clean_string(input_string): + # remove leading and trailing spaces + input_string = input_string.strip() + # remove trailing ";" and "." + input_string = re.sub(r";$", "", input_string) + input_string = re.sub(r"\.$", "", input_string) + return input_string + + +class DetectionToGrounding(): + ''' + Convert detection data into grounding data; + Construct prompts for training and inference; + ''' + def __init__(self, version): + pass + + +class DescriptionConverter(): + def __init__( + self, + description_file, + od_to_grounding_version, + categories, + ind_to_class, + similarity_file = None,): + self.description_file = description_file + self.od_to_grounding_version = od_to_grounding_version + self.categories = categories + self.name_to_def = {} + for cat in self.categories: + try: + self.name_to_def[cat["name"]] = cat["def"] + except: + pass + if description_file is not None: + with open(description_file, "r") as f: + self.description_list = json.load(f) + + self.gpt_parser = GPTOutputParser(od_to_grounding_version.split(".")[-1]) + #self.preparse_descriptions() + + self.category_name_to_description = {} + for i in self.description_list: + # {'object': 'aerosol_can', 'object_id': 1, 'gpt3_output': '"\n{\"type\": \"vegetable\", \n\"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \n\"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}'} + self.category_name_to_description[i["object"]] = i + + # stats to print warning + self.drop_label_count = 0 + self.all_count = 0 + + self.ind_to_class = ind_to_class + + if similarity_file is not None: + with open(similarity_file, "r") as f: + self.category_name_to_similarity = json.load(f) + + if "control_pos" in od_to_grounding_version: + self.pos_rate_controller = PosRateControllerLength(max_length = 9, center_length=8) + + self.pos_rates = [] + + def inference_od_to_grounding(self, dataset, cfg, negative_label=None, negative_index=None): + categories = dataset.categories() + + labels = [] + label_list = [] + keys = list(categories.keys()) + keys.sort() + if negative_label is not None: + labels.append(negative_label) + label_list.append(categories[negative_label]) + else: + for i in keys: + labels.append(i) + label_list.append(categories[i]) + + if cfg.TEST.CHUNKED_EVALUATION != -1: + labels = chunks(labels, cfg.TEST.CHUNKED_EVALUATION) + label_list = chunks(label_list, cfg.TEST.CHUNKED_EVALUATION) + else: + labels = [labels] + label_list = [label_list] + + all_queries = [] + all_positive_map_label_to_token = [] + + from transformers import AutoTokenizer + # tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenizer = AutoTokenizer.from_pretrained("roberta-base") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + from transformers import CLIPTokenizerFast + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", + from_slow=True, mask_token='ðŁĴij') + else: + tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", + from_slow=True) + else: + tokenizer = None + raise NotImplementedError + + for i in tqdm(range(len(labels))): + labels_i = labels[i] + label_list_i = label_list[i] + query_i, positive_map_label_to_token_i = self._create_queries_and_maps( + labels_i, label_list_i, additional_labels = cfg.DATASETS.SUPRESS_QUERY if cfg.DATASETS.USE_SUPRESS_QUERY else None, cfg = cfg, tokenizer = tokenizer, negative_label=negative_label, negative_index=negative_index) + + all_queries.append(query_i) + all_positive_map_label_to_token.append(positive_map_label_to_token_i) + print("All queries", all_queries) + return all_queries, all_positive_map_label_to_token + + def _create_queries_and_maps(self, labels, label_list, additional_labels = None, cfg = None, tokenizer = None, negative_label=None, negative_index=None): + + label_to_positions, objects_query, label_to_spans, label_to_positive_spans = self._generate_senetence_given_labels(labels, self.ind_to_class, disable_shuffle=True, negative_label=negative_label, negative_index=negative_index) + tokens_positive = [[label_to_positions[i]] for i in labels] + print(objects_query) + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased" or cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenized = tokenizer(objects_query, return_tensors="pt") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + tokenized = tokenizer(objects_query, + max_length=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + truncation=True, + return_tensors="pt") + else: + raise NotImplementedError + # Create the mapping between tokenized sentence and the original label + positive_map_token_to_label, positive_map_label_to_token = self._infer_create_positive_dict( + tokenized, + tokens_positive, + labels=labels) # from token position to original label + + # Create the spans, and the span maps + if cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION is not None: + if "sep_span" in self.od_to_grounding_version: + all_spans = [] + for k, v in label_to_spans.items(): + all_spans.append(v) + all_spans = sorted(all_spans, key=lambda x: x[0][0]) + all_spans_flattered = [] + for i in all_spans: + all_spans_flattered += i + + else: + all_spans = [] + for k, v in label_to_spans.items(): + all_spans += v + # sort the spans based on the start index + all_spans = sorted(all_spans, key=lambda x: x[0]) + all_spans_flattered = all_spans + + span_map = self._infer_create_span_map(all_spans_flattered, label_to_positive_spans) + positive_map_label_to_token = (positive_map_label_to_token, span_map, all_spans) + + return objects_query, positive_map_label_to_token + + + def _infer_create_positive_dict(self, tokenized, tokens_positive, labels): + """construct a dictionary such that positive_map[i] = j, iff token i is mapped to j label""" + positive_map = defaultdict(int) + + # Additionally, have positive_map_label_to_tokens + positive_map_label_to_token = defaultdict(list) + + for j, tok_list in enumerate(tokens_positive): + for (beg, end) in tok_list: + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + for i in range(beg_pos, end_pos + 1): + positive_map[i] = labels[j] # because the labels starts from 1 + positive_map_label_to_token[labels[j]].append(i) + # positive_map[j, beg_pos : end_pos + 1].fill_(1) + return positive_map, positive_map_label_to_token # / (positive_map.sum(-1)[:, None] + 1e-6) + + def _infer_create_span_map(self, all_spans, label_to_positive_spans): + # input: boxes, num_box to spans mapping + # output: boxes, spans, num_box to spans mapping + index_spans = {} + for i, span in enumerate(all_spans): + index_spans[tuple(span)] = i + + span_map = defaultdict(list) + for label, spans in label_to_positive_spans.items(): + span_map[label].extend([index_spans[tuple(span)] for span in spans]) + return span_map + + + def train_od_to_grounding(self, + target, + image_id, + ind_to_class, + tokenizer, + random_sample_negative=8): + + ''' + 1. _random_label_selection: select which labels to include in the caption + 2. _generate_senetence_given_labels: generate a caption given the selected labels + 3. _create_new_target: create the new target (optionally drop the boxes if positive label is missing) + ''' + + separation_tokens = ". " + max_num_labels = 8 + if "description.gpt" in self.od_to_grounding_version: + max_num_labels = 8 + if "description.baseline" in self.od_to_grounding_version: + max_num_labels = 8 + + max_seq_length = 254 + if "sep_span" in self.od_to_grounding_version: + max_num_labels = random_sample_negative # + if random_sample_negative == 8: + max_seq_length = 254 # hacky to reproduce the results + else: + max_seq_length = int(254 * random_sample_negative / 8) # hacky to maintain the results + + screened_label_list = self._random_label_selection( + all_labels = list(ind_to_class.keys()), + ind_to_class = ind_to_class, + max_seq_length = max_seq_length, + max_num_labels = max_num_labels, + tokenizer = tokenizer, + positive_label_set = set(target.extra_fields["labels"].tolist()), + ) + label_to_positions, pheso_caption, label_to_spans, label_to_positive_spans = self._generate_senetence_given_labels( + label_list=screened_label_list, + ind_to_class=ind_to_class,) + + new_target, greenlight_span_for_masked_lm_objective, new_target_boxlist = self._create_new_target(target, image_id, label_to_positions, label_to_spans) + return new_target, pheso_caption, greenlight_span_for_masked_lm_objective, label_to_positions, new_target_boxlist + + def _random_label_selection(self, all_labels, ind_to_class, max_seq_length, max_num_labels, tokenizer, positive_label_set): + + if "complete_random" in self.od_to_grounding_version: + random_label_num = np.random.choice(max_num_labels + 1) + shuffle_label_list = [i for i in all_labels] + random.shuffle(shuffle_label_list) + screened_label_list = shuffle_label_list[:random_label_num] + return screened_label_list + + full_positive = len(positive_label_set) + full_negative = max_num_labels - full_positive + + outer_prob = random.random() + + if "control_pos" in self.od_to_grounding_version: + num_positives, num_negatives = self.pos_rate_controller(full_positive, len(all_labels)) + + elif "allow_zero" in self.od_to_grounding_version: + if outer_prob < 0.5: + num_negatives = full_negative + num_positives = full_positive + elif outer_prob < 0.6: + num_negatives = np.random.choice(max(1, full_negative + 1)) # mininum 1 + num_positives = full_positive + else: + num_positives = np.random.choice(max(1, full_positive + 1)) # mininum 1 + num_negatives = full_negative + elif "keep_all" in self.od_to_grounding_version: + num_positives = full_positive + num_negatives = full_negative + else: + if outer_prob < 0.5: + num_negatives = full_negative + num_positives = full_positive + elif outer_prob < 0.6: + num_negatives = np.random.choice(max(1, full_negative)) + 1 # mininum 1 + num_positives = full_positive + else: + num_positives = np.random.choice(max(1, full_positive)) + 1 # mininum 1 + num_negatives = full_negative + + # Keep some negatives + negative_label_list = [label for label in all_labels if label not in positive_label_set] + random.shuffle(negative_label_list) + negative_label_list = negative_label_list[:num_negatives] + + # Keep some positives + positive_label_list = list(positive_label_set) + random.shuffle(positive_label_list) + positive_label_list = positive_label_list[:num_positives] + + selected_label_list = positive_label_list + negative_label_list + screened_label_list = self._label_drop_with_length_limit(selected_label_list, ind_to_class, max_seq_length, tokenizer) + + # calculate the current positive rate + _screened_label_list = set(screened_label_list) + _pos_label_list = set(positive_label_list).intersection(_screened_label_list) + if "control_pos" in self.od_to_grounding_version: + self.pos_rate_controller.update_true_pos_rate(len(_pos_label_list), max(len(screened_label_list), 1.0)) + + return screened_label_list + + def _generate_sentence(self, label, ind_to_class, pheso_caption = "", force_mode = None, negative_label=None, negative_index=None): + start_index = len(pheso_caption) + category_name = ind_to_class[label] + clean_category_name = clean_name(category_name) + + # generate_version + od_to_grounding_version = ".".join(self.od_to_grounding_version.split(".")[:3]) + range_version = "partial" + + if od_to_grounding_version == "description.gpt.v10": + if negative_label is not None: + if negative_index == 0: + description = self.category_name_to_description[category_name]["gpt3_output"] + else: + from copy import deepcopy + description = deepcopy(self.category_name_to_description[category_name]["gpt3_output"]) + try: + neg_desc = self.category_name_to_description[category_name]['chatgpt_negatives'].split('\n')[negative_index-1] + except: + neg_desc = self.category_name_to_description[category_name]['chatgpt_negatives'].split('\n')[-1] + description = json.loads(description) + description['description'] = neg_desc + description = json.dumps(description) + else: + description = self.category_name_to_description[category_name]["gpt3_output"] + if "infer" in self.od_to_grounding_version: + prob = 0.0 + else: + prob = random.random() + + if "independent" in self.od_to_grounding_version: + func = self.gpt_parser.form_span_independent + else: + func = self.gpt_parser.form_span + + if prob < 0.5: + des_caption_i, end_index, spans, positive_spans = func( + noun=clean_category_name, + description=description, + type = "vanilla_span", + start_index = start_index, + positive_range = range_version, + od_to_grounding_version=self.od_to_grounding_version) + else: + des_caption_i, end_index, spans, positive_spans = func( + noun=clean_category_name, + description=description, + type = "remove_noun_span", + start_index = start_index, + positive_range = range_version, + od_to_grounding_version=self.od_to_grounding_version) + end_index = len(pheso_caption) + end_index + pheso_caption += des_caption_i + return pheso_caption, (start_index, end_index), spans, positive_spans + + else: + raise NotImplementedError + + + return pheso_caption, (start_index, end_index), None, None + + def _generate_senetence_given_labels( + self, + label_list, + ind_to_class, + disable_shuffle=False, + negative_label=None, + negative_index=None, + ): + ''' + given a label list, generate a caption (with descriptions) + also generate a label_to_positions dictionary + ''' + + label_to_positions = {} + label_to_spans = {} + label_to_positive_spans = {} # + if not disable_shuffle: + random.shuffle(label_list) + + pheso_caption = "Detect: " + + for index, label in enumerate(label_list): + + pheso_caption, (start_index, end_index), spans, positive_spans = self._generate_sentence(label, ind_to_class, pheso_caption, negative_label=negative_label, negative_index=negative_index) + + # need to record the spans + + label_to_positions[label] = (start_index, end_index) + label_to_spans[label] = spans + label_to_positive_spans[label] = positive_spans + return label_to_positions, pheso_caption, label_to_spans, label_to_positive_spans + + def _create_new_target(self, target, image_id, label_to_positions, label_to_spans = None, label_to_positive_spans = None): + new_target = [] + areas = target.area() + #greenlight_span_for_masked_lm_objective = [] + for i in range(len(target)): + new_target_i = {} + new_target_i["area"] = areas[i] + new_target_i["iscrowd"] = 0 + new_target_i["image_id"] = image_id + new_target_i["category_id"] = target.extra_fields["labels"][i].item() + new_target_i["id"] = None + new_target_i['bbox'] = target.bbox[i].numpy().tolist() + + label_i = target.extra_fields["labels"][i].item() + new_target_i["original_od_label"] = label_i + + if label_i in label_to_positions: # NOTE: Only add labels that actually appear in the final caption + new_target_i["tokens_positive"] = [label_to_positions[label_i]] + + if label_to_positive_spans is not None: # NOTE: Use label_to_positive_spans instead of label_to_spans; as certain spans can be negative + new_target_i["spans_positive"] = label_to_positive_spans[label_i] + new_target.append(new_target_i) + #greenlight_span_for_masked_lm_objective.append(label_to_positions[label_i]) + + if "sep_span" in self.od_to_grounding_version: + all_spans = [] + for k, v in label_to_spans.items(): # NOTE: Use the label_to_spans to get all the spans + all_spans.append(v) + all_spans = sorted(all_spans, key=lambda x: x[0][0]) + + # max_spans_per_seq = max([len(i) for i in all_spans]) + # all_spans_tensor = torch.ones((len(all_spans), max_spans_per_seq, 2), dtype=torch.long) * -1 + # for i, spans in enumerate(all_spans): + # for j, span in enumerate(spans): + # all_spans_tensor[i, j, :] = torch.as_tensor(span) + + elif "span" in self.od_to_grounding_version: + all_spans = [] + for k, v in label_to_spans.items(): + all_spans += v + # sort the spans based on the start index + all_spans = sorted(all_spans, key=lambda x: x[0]) + all_spans = torch.as_tensor(all_spans) + else: + all_spans = None + + # reconstruct the target + new_target_boxlist = BoxList(torch.as_tensor([i['bbox'] for i in new_target]).reshape(-1, 4), target.size, mode="xyxy") + new_target_boxlist.add_field("labels", torch.as_tensor([i['category_id'] for i in new_target])) + if all_spans is not None: + new_target_boxlist.add_field("spans", all_spans) + greenlight_span_for_masked_lm_objective = [value for value in label_to_positions.values()] + return new_target, greenlight_span_for_masked_lm_objective, new_target_boxlist + + def _label_drop_with_length_limit(self, label_list, ind_to_class, length_limit, tokenizer): + screened_label_list = [] + random.shuffle(label_list) # randomly drop labels + for label in label_list: + pheso_caption, *_ = self._generate_sentence(label, ind_to_class, "") + tokenized = tokenizer.tokenize(pheso_caption) + + length_limit -= len(tokenized) + if length_limit > 0: + screened_label_list.append(label) # keep this label + else: + break + self.all_count += 1 + if len(screened_label_list) < len(label_list): + self.drop_label_count += 1 + + if self.drop_label_count / self.all_count > 0.3: + print("Warning: {} of {} examples have dropped labels".format(self.drop_label_count, self.all_count)) + + return screened_label_list diff --git a/maskrcnn_benchmark/data/datasets/_pos_rate.py b/maskrcnn_benchmark/data/datasets/_pos_rate.py new file mode 100644 index 0000000000000000000000000000000000000000..6cbf942d2a7042879e2b6c23d453b2b99a11eba4 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/_pos_rate.py @@ -0,0 +1,208 @@ +import random +import pdb +from collections import defaultdict +import numpy +import numpy as np +import math +class PosRateControllerLength(): + def __init__(self, max_length = 9, center_length = 8): + self.leng_to_controller = [PosRateController() for i in range(max_length + 1)] + self.max_length = max_length + self.center_length = center_length + self.pos_rates = [] + self.lengths = [] + def __call__(self, pos_num, neg_num): + # first sample the query length + length = numpy.random.normal(self.center_length, 5.0) + # cap to 1 and max_length + length = max(1, min(self.max_length, length)) + length = round(length) + length = min(pos_num + neg_num, length) + + pos_num, neg_num = self.leng_to_controller[length](pos_num, neg_num, desired_length = length) + return pos_num, neg_num + + def update_true_pos_rate(self, pos_num, total_num): + if total_num == 0: + return + self.pos_rates.append(pos_num / total_num) + self.lengths.append(total_num) + total_num = int(min(total_num, self.max_length)) + self.leng_to_controller[total_num].update_true_pos_rate(pos_num, total_num) + + # if len(self.pos_rates) % 1000 == 0: + # print(self.pos_rates) + # print(self.lengths) + # for i in range(len(self.leng_to_controller)): + # print("length: ", i) + # print("overall pos rate: ", sum(self.leng_to_controller[i].pos_rates) / max(1.0, len(self.leng_to_controller[i].pos_rates))) + +class PosRateController(): + def __init__(self, bin_num = 10, adhoc_bin_weights = {}, control_length = -1): + self.bins = [1.0 / bin_num * i for i in range(bin_num + 1)] + self.bin_counter = [0 for i in range(bin_num + 1)] + + self.adhoc_bin_weights = adhoc_bin_weights # this is a list of weights for each bin + self.slack = 20 # we can allow some slack for the pos rate control + self.pos_rates = [] + self.lengths = [] + + def _find_closest_bin(self, pos_rate, valid_bins): + valid_bins_rate = [self.bins[i] for i in valid_bins] + # determine the pos rate is in which bin + # find the closes bin to the current pos rate + bin_index = valid_bins[0] + min_diff = abs(pos_rate - valid_bins_rate[0]) + + for i in range(1, len(valid_bins)): + diff = abs(pos_rate - valid_bins_rate[i]) + if diff < min_diff: + bin_index = valid_bins[i] + min_diff = diff + if diff == min_diff and random.random() > 0.5: + bin_index = valid_bins[i] + min_diff = diff + return bin_index + + def __call__(self, pos_num, neg_num, desired_length = -1): + if pos_num == 0 and neg_num == 0: + return 0, 0 + if pos_num == 1 and neg_num == 0: + return 1, 0 + + pos_now = pos_num / (pos_num + neg_num) + + min_bin_counter = min([self.bin_counter[i] * self.adhoc_bin_weights.get(i, 1.0) for i in range(len(self.bin_counter)) ]) + valid_bins = [i for i in range(len(self.bin_counter)) if self.bin_counter[i] * self.adhoc_bin_weights.get(i, 1.0) <= min_bin_counter + self.slack] # these are the bins this example could go to + bin_index = random.choice(valid_bins) + #self._find_closest_bin(pos_now, valid_bins) + + if desired_length > 0: + # control to the desired length + desired_pos = round(desired_length * self.bins[bin_index]) + pos_num = min(pos_num, desired_pos) + if self.bins[bin_index] == 0: + neg_num = min(neg_num, desired_length) + else: + neg_num = min(neg_num, round(pos_num / self.bins[bin_index] * (1 - self.bins[bin_index]))) + else: + # let's control the pos_rate to the desired rate + if pos_now == self.bins[bin_index]: + pass + elif pos_now < self.bins[bin_index]: + # this means we need to drop some negative examples + neg_num = round(pos_num / self.bins[bin_index] - pos_num) + else: + # this means we need to drop some positive examples + pos_num = round(neg_num * self.bins[bin_index] / (1 - self.bins[bin_index])) + + # new_bin_index = self._find_closest_bin(pos_num / (pos_num + neg_num), list(range(len(self.bins)))) + # if new_bin_index != bin_index and len(self.pos_rates) > 1000: + # pdb.set_trace() + + # self.bin_counter[new_bin_index] += 1 + # self.pos_rates.append(pos_num / (pos_num + neg_num)) + # make sure we don't have all 0s + if pos_num == 0 and neg_num == 0: + pos_num = 1 + neg_num = 0 + + return pos_num, neg_num + + def update_true_pos_rate(self, pos_num, total_num): + if total_num == 0: # ignore + return + pos_rate = pos_num / total_num + bin_index = self._find_closest_bin(pos_rate, list(range(len(self.bins)))) + self.bin_counter[bin_index] += 1 + self.pos_rates.append(pos_rate) + self.lengths.append(total_num) + # if len(self.pos_rates) % 1000 == 0: + # print(self.pos_rates) + # for i in self.pos_rate_by_lengths: + # print(i, len(self.pos_rate_by_lengths[i]), sum(self.pos_rate_by_lengths[i]) / len(self.pos_rate_by_lengths[i])) + def report(self,): + #print(self.lengths) + print(np.mean(self.lengths), self.bin_counter) +from scipy.stats import norm +class PosRateControllerV2(): + def __init__(self, max_length, center_length, scale = 4.0): + self.max_length = max_length + self.center_length = center_length + self.bins = defaultdict(int) + for i in range(1, max_length + 1): + for j in range(0, i + 1): + self.bins[(i, j)] = 0 + + # calculate the weights according to a normal distribution centered on center_length + dis = norm(loc = center_length, scale = scale) + + self.weights = {} + for i in range(1, max_length+1): + self.weights[i] = dis.cdf(i + 0.5) - dis.cdf(i - 0.5) + # print(self.weights) + # renormalize the weights + total_weight = sum(self.weights.values()) + for i in self.weights: + self.weights[i] /= total_weight + + self.weights_pos_rate = {} + + # do a slight reweight + self.pos_rates = [] + + self.slack = 10 + + def __call__(self, pos_num, neg_num, max_cap_num = -1): + # find the most good matching bin + + valid_keys = [] + for key in self.bins: + if key[0] <= pos_num + neg_num and key[1] <= pos_num and key[0] - key[1] <= neg_num and (max_cap_num == -1 or key[0] <= max_cap_num): + valid_keys.append(key) + # find the min count in the valid keys + if len(valid_keys) == 0: + print(pos_num, neg_num) + return pos_num, neg_num + min_counter = min([self.bins[key] / self.weights[key[0]] for key in valid_keys]) + valid_keys = [key for key in valid_keys if self.bins[key] / self.weights[key[0]] <= min_counter + self.slack] # rescreened + + # find the counter where we drop the minimal number of examples + closest_key = None + min_diff = 100 + for key in valid_keys: + diff = abs(key[1] - pos_num) + if diff < min_diff: + closest_key = key + min_diff = diff + + if closest_key is None: + return pos_num, neg_num + + return closest_key[1], closest_key[0] - closest_key[1] + + def update_true_pos_rate(self, pos_num, total_num): + if total_num == 0: + return + self.bins[(total_num, pos_num)] += 1 + self.pos_rates.append(pos_num / total_num) + + def report(self): + if len(self.pos_rates) % 1000 != 0: + return + + for i in range(1, self.max_length + 1): + print("length", i, sum([self.bins[(i, j)] for j in range(0, i + 1)])) + for j in range(0, i + 1): + print(" pos", j, " ", self.bins[(i, j)]) + print("\n\n") + + +''' +import matplotlib.pyplot as plt +# drop a histogram +plt.hist(data, bins = 10) +plt.show() +''' + + diff --git a/maskrcnn_benchmark/data/datasets/background.py b/maskrcnn_benchmark/data/datasets/background.py new file mode 100644 index 0000000000000000000000000000000000000000..09fab0d522455568f25f7cc68bb112e852986a43 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/background.py @@ -0,0 +1,54 @@ +import os +import os.path +import json +from PIL import Image + +import torch +import torchvision +import torch.utils.data as data +from maskrcnn_benchmark.structures.bounding_box import BoxList + + +class Background(data.Dataset): + """Background + + Args: + root (string): Root directory where images are downloaded to. + annFile (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.ToTensor`` + """ + + def __init__(self, ann_file, root, remove_images_without_annotations=None, transforms=None): + self.root = root + + with open(ann_file, "r") as f: + self.ids = json.load(f)["images"] + self.transform = transforms + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``. + """ + im_info = self.ids[index] + path = im_info["file_name"] + fp = os.path.join(self.root, path) + + img = Image.open(fp).convert("RGB") + if self.transform is not None: + img, _ = self.transform(img, None) + null_target = BoxList(torch.zeros((0, 4)), (img.shape[-1], img.shape[-2])) + null_target.add_field("labels", torch.zeros(0)) + + return img, null_target, index + + def __len__(self): + return len(self.ids) + + def get_img_info(self, index): + im_info = self.ids[index] + return im_info diff --git a/maskrcnn_benchmark/data/datasets/box_label_loader.py b/maskrcnn_benchmark/data/datasets/box_label_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..1a19e40d82d1a1881c35466f8cd84cae2d485ce1 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/box_label_loader.py @@ -0,0 +1,254 @@ +import torch +import numpy as np +import math +import base64 +import collections +import pycocotools.mask as mask_utils + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask + + +class LabelLoader(object): + def __init__( + self, + labelmap, + extra_fields=(), + filter_duplicate_relations=False, + ignore_attr=None, + ignore_rel=None, + mask_mode="poly", + ): + self.labelmap = labelmap + self.extra_fields = extra_fields + self.supported_fields = ["class", "conf", "attributes", "scores_all", "boxes_all", "feature", "mask"] + self.filter_duplicate_relations = filter_duplicate_relations + self.ignore_attr = set(ignore_attr) if ignore_attr != None else set() + self.ignore_rel = set(ignore_rel) if ignore_rel != None else set() + assert mask_mode == "poly" or mask_mode == "mask" + self.mask_mode = mask_mode + + def __call__(self, annotations, img_size, remove_empty=False, load_fields=None): + boxes = [obj["rect"] for obj in annotations] + boxes = torch.as_tensor(boxes).reshape(-1, 4) + target = BoxList(boxes, img_size, mode="xyxy") + + if load_fields is None: + load_fields = self.extra_fields + + for field in load_fields: + assert field in self.supported_fields, "Unsupported field {}".format(field) + if field == "class": + classes = self.add_classes(annotations) + target.add_field("labels", classes) + elif field == "conf": + confidences = self.add_confidences(annotations) + target.add_field("scores", confidences) + elif field == "attributes": + attributes = self.add_attributes(annotations) + target.add_field("attributes", attributes) + elif field == "scores_all": + scores_all = self.add_scores_all(annotations) + target.add_field("scores_all", scores_all) + elif field == "boxes_all": + boxes_all = self.add_boxes_all(annotations) + target.add_field("boxes_all", boxes_all) + elif field == "feature": + features = self.add_features(annotations) + target.add_field("box_features", features) + elif field == "mask": + masks, is_box_mask = self.add_masks(annotations, img_size) + target.add_field("masks", masks) + target.add_field("is_box_mask", is_box_mask) + + target = target.clip_to_image(remove_empty=remove_empty) + return target + + def get_box_mask(self, rect, img_size): + x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3] + if self.mask_mode == "poly": + return [[x1, y1, x1, y2, x2, y2, x2, y1]] + elif self.mask_mode == "mask": + # note the order of height/width order in mask is opposite to image + mask = np.zeros([img_size[1], img_size[0]], dtype=np.uint8) + mask[math.floor(y1) : math.ceil(y2), math.floor(x1) : math.ceil(x2)] = 255 + encoded_mask = mask_utils.encode(np.asfortranarray(mask)) + encoded_mask["counts"] = encoded_mask["counts"].decode("utf-8") + return encoded_mask + + def add_masks(self, annotations, img_size): + masks = [] + is_box_mask = [] + for obj in annotations: + if "mask" in obj: + masks.append(obj["mask"]) + is_box_mask.append(0) + else: + masks.append(self.get_box_mask(obj["rect"], img_size)) + is_box_mask.append(1) + masks = SegmentationMask(masks, img_size, mode=self.mask_mode) + is_box_mask = torch.tensor(is_box_mask) + return masks, is_box_mask + + def add_classes(self, annotations): + class_names = [obj["class"] for obj in annotations] + classes = [None] * len(class_names) + for i in range(len(class_names)): + classes[i] = self.labelmap["class_to_ind"][class_names[i]] + return torch.tensor(classes) + + def add_confidences(self, annotations): + confidences = [] + for obj in annotations: + if "conf" in obj: + confidences.append(obj["conf"]) + else: + confidences.append(1.0) + return torch.tensor(confidences) + + def add_attributes(self, annotations): + # the maximal number of attributes per object is 16 + attributes = [[0] * 16 for _ in range(len(annotations))] + for i, obj in enumerate(annotations): + for j, attr in enumerate(obj["attributes"]): + attributes[i][j] = self.labelmap["attribute_to_ind"][attr] + return torch.tensor(attributes) + + def add_features(self, annotations): + features = [] + for obj in annotations: + features.append(np.frombuffer(base64.b64decode(obj["feature"]), np.float32)) + return torch.tensor(features) + + def add_scores_all(self, annotations): + scores_all = [] + for obj in annotations: + scores_all.append(np.frombuffer(base64.b64decode(obj["scores_all"]), np.float32)) + return torch.tensor(scores_all) + + def add_boxes_all(self, annotations): + boxes_all = [] + for obj in annotations: + boxes_all.append(np.frombuffer(base64.b64decode(obj["boxes_all"]), np.float32).reshape(-1, 4)) + return torch.tensor(boxes_all) + + def relation_loader(self, relation_annos, target): + if self.filter_duplicate_relations: + # Filter out dupes! + all_rel_sets = collections.defaultdict(list) + for triplet in relation_annos: + all_rel_sets[(triplet["subj_id"], triplet["obj_id"])].append(triplet) + relation_annos = [np.random.choice(v) for v in all_rel_sets.values()] + + # get M*M pred_labels + relation_triplets = [] + relations = torch.zeros([len(target), len(target)], dtype=torch.int64) + for i in range(len(relation_annos)): + if len(self.ignore_rel) != 0 and relation_annos[i]["class"] in self.ignore_rel: + continue + subj_id = relation_annos[i]["subj_id"] + obj_id = relation_annos[i]["obj_id"] + predicate = self.labelmap["relation_to_ind"][relation_annos[i]["class"]] + relations[subj_id, obj_id] = predicate + relation_triplets.append([subj_id, obj_id, predicate]) + + relation_triplets = torch.tensor(relation_triplets) + target.add_field("relation_labels", relation_triplets) + target.add_field("pred_labels", relations) + return target + + +class BoxLabelLoader(object): + def __init__(self, labelmap, extra_fields=(), ignore_attrs=(), mask_mode="poly"): + self.labelmap = labelmap + self.extra_fields = extra_fields + self.ignore_attrs = ignore_attrs + assert mask_mode == "poly" or mask_mode == "mask" + self.mask_mode = mask_mode + self.all_fields = ["class", "mask", "confidence", "attributes_encode", "IsGroupOf", "IsProposal"] + + def __call__(self, annotations, img_size, remove_empty=True): + boxes = [obj["rect"] for obj in annotations] + boxes = torch.as_tensor(boxes).reshape(-1, 4) + target = BoxList(boxes, img_size, mode="xyxy") + + for field in self.extra_fields: + assert field in self.all_fields, "Unsupported field {}".format(field) + if field == "class": + classes = self.add_classes_with_ignore(annotations) + target.add_field("labels", classes) + elif field == "mask": + masks, is_box_mask = self.add_masks(annotations, img_size) + target.add_field("masks", masks) + target.add_field("is_box_mask", is_box_mask) + elif field == "confidence": + confidences = self.add_confidences(annotations) + target.add_field("confidences", confidences) + elif field == "attributes_encode": + attributes = self.add_attributes(annotations) + target.add_field("attributes", attributes) + elif field == "IsGroupOf": + is_group = [1 if "IsGroupOf" in obj and obj["IsGroupOf"] == 1 else 0 for obj in annotations] + target.add_field("IsGroupOf", torch.tensor(is_group)) + elif field == "IsProposal": + is_proposal = [1 if "IsProposal" in obj and obj["IsProposal"] == 1 else 0 for obj in annotations] + target.add_field("IsProposal", torch.tensor(is_proposal)) + + target = target.clip_to_image(remove_empty=remove_empty) + return target + + def add_classes_with_ignore(self, annotations): + class_names = [obj["class"] for obj in annotations] + classes = [None] * len(class_names) + if self.ignore_attrs: + for i, obj in enumerate(annotations): + if any([obj[attr] for attr in self.ignore_attrs if attr in obj]): + classes[i] = -1 + for i, cls in enumerate(classes): + if cls != -1: + classes[i] = self.labelmap[class_names[i]] + 1 # 0 is saved for background + return torch.tensor(classes) + + def add_masks(self, annotations, img_size): + masks = [] + is_box_mask = [] + for obj in annotations: + if "mask" in obj: + masks.append(obj["mask"]) + is_box_mask.append(0) + else: + masks.append(self.get_box_mask(obj["rect"], img_size)) + is_box_mask.append(1) + masks = SegmentationMask(masks, img_size, mode=self.mask_mode) + is_box_mask = torch.tensor(is_box_mask) + return masks, is_box_mask + + def get_box_mask(self, rect, img_size): + x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3] + if self.mask_mode == "poly": + return [[x1, y1, x1, y2, x2, y2, x2, y1]] + elif self.mask_mode == "mask": + # note the order of height/width order in mask is opposite to image + mask = np.zeros([img_size[1], img_size[0]], dtype=np.uint8) + mask[math.floor(y1) : math.ceil(y2), math.floor(x1) : math.ceil(x2)] = 255 + encoded_mask = mask_utils.encode(np.asfortranarray(mask)) + encoded_mask["counts"] = encoded_mask["counts"].decode("utf-8") + return encoded_mask + + def add_confidences(self, annotations): + confidences = [] + for obj in annotations: + if "confidence" in obj: + confidences.append(obj["confidence"]) + elif "conf" in obj: + confidences.append(obj["conf"]) + else: + confidences.append(1.0) + return torch.tensor(confidences) + + def add_attributes(self, annotations): + # we know that the maximal number of attributes per object is 16 + attributes = [[0] * 16 for _ in range(len(annotations))] + for i, obj in enumerate(annotations): + attributes[i][: len(obj["attributes_encode"])] = obj["attributes_encode"] + return torch.tensor(attributes) diff --git a/maskrcnn_benchmark/data/datasets/caption.py b/maskrcnn_benchmark/data/datasets/caption.py new file mode 100644 index 0000000000000000000000000000000000000000..2b9239100f7cf3ef55dc1910a6b05804006b063f --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/caption.py @@ -0,0 +1,337 @@ +import torch +import torch.distributed as dist +import time +from torchvision.ops import nms +import random +import numpy as np +from PIL import Image, ImageDraw +import pdb +from maskrcnn_benchmark.structures.bounding_box import BoxList +from .modulated_coco import ConvertCocoPolysToMask +from .tsv import ODTSVDataset, TSVYamlDataset +from .od_to_grounding import sanity_check_target_after_processing +from maskrcnn_benchmark.data.datasets._caption_aug import CaptionAugmentation +from collections import defaultdict + +class CaptionTSV(TSVYamlDataset): + def __init__( + self, + yaml_file, + transforms, + return_tokens, + return_masks, + tokenizer, + caption_min_box=1, + replace_clean_label=False, + further_screen=False, + caption_conf=0.5, + caption_nms=-1, + pack_random_caption_number=0, + inference_caption=False, + sample_negative_for_grounding_data=-1, + random_pack_prob=-1.0, + no_random_pack_probability=0.0, + safeguard_positive_caption=True, + mlm_obj_for_only_positive=False, + caption_format_version="v1", + local_debug=False, + max_query_len=256, + cc_caption_augmentation_version=None, + caption_vocab_file=None, + **kwargs + ): + super(CaptionTSV, self).__init__(yaml_file, None, replace_clean_label) + self.yaml_file = yaml_file + self._transforms = transforms + self.max_query_len = 225 + self.prepare = ConvertCocoPolysToMask( + return_masks=return_masks, return_tokens=return_tokens, tokenizer=tokenizer, max_query_len=max_query_len + ) + self.tokenizer = tokenizer + self.caption_min_box = caption_min_box + self.replace_clean_label = replace_clean_label + self.further_screen = further_screen + self.pack_random_caption_number = pack_random_caption_number + self.caption_format_version = caption_format_version + + self.caption_conf = caption_conf + self.caption_nms = caption_nms + self.inference_caption = inference_caption + self.sample_negative_for_grounding_data = sample_negative_for_grounding_data + self.random_pack_prob = random_pack_prob + self.no_random_pack_probability = no_random_pack_probability + self.safeguard_positive_caption = safeguard_positive_caption + self.mlm_obj_for_only_positive = mlm_obj_for_only_positive + try: + self.rank = dist.get_rank() + except: + self.rank = 0 + self.caption_augmentation_version = cc_caption_augmentation_version + if self.caption_augmentation_version is not None: + self.caption_augmentation = CaptionAugmentation( + self.caption_augmentation_version, + tokenizer, + caption_vocab_file=caption_vocab_file + ) + + def __len__(self): + return super(CaptionTSV, self).__len__() + + def pack_caption(self, positive_caption, negative_captions, original_tokens_positive): + if len(negative_captions) == 0: + return positive_caption, original_tokens_positive, [(0, len(positive_caption))] + if self.safeguard_positive_caption: + length_of_each_caption = [] + for caption in negative_captions + [positive_caption]: + tokenized = self.tokenizer(caption, return_tensors="pt") + length_of_each_caption.append(tokenized.input_ids.size(-1)) + max_length = self.max_query_len - length_of_each_caption[-1] + indexes = list(range(len(negative_captions))) + random.shuffle(indexes) + new_caption_list = [positive_caption] + for i in indexes: + if length_of_each_caption[i] < max_length: + new_caption_list.append(negative_captions[i]) + max_length -= length_of_each_caption[i] + else: + new_caption_list = [positive_caption] + negative_captions + random.shuffle(new_caption_list) + + new_caption = "" + + for i in new_caption_list: + if i == positive_caption: + start_position = len(new_caption) + new_caption += i + if not i.endswith("."): + new_caption += "." + new_caption += " " + + # shift the token positions the boxes are aligned to + for index, i in enumerate(original_tokens_positive): + original_tokens_positive[index] = [tuple(j) for j in i] + for i in original_tokens_positive: + for index, j in enumerate(i): + i[index] = (j[0] + start_position, j[1] + start_position) + + return new_caption, original_tokens_positive, [(start_position, start_position + len(positive_caption))] + + def __get_negative_captions__(self, idx, negative_size=7): + negative_captions = [] + for i in range(negative_size): + img, anno, _, scale = super(CaptionTSV, self).__getitem__(np.random.choice(len(self))) + caption = anno["caption"] + negative_captions.append(caption) + + return negative_captions + + def target_transpose_in(self, anno): + # for the target from "caption", we need to transpose to box format + new_target = [] + for box in range(len(anno["bboxes"])): + new_box = {} + new_box["tokens_positive"] = anno["tokens_positive"][box] + new_box["nouns"] = anno["all_nounds_in_vocab"][box] + new_box["bbox"] = anno["bboxes"][box] + new_target.append(new_box) + return new_target + + def target_transpose_out(self, target): + # for the target from "caption", we need to transpose to box format + new_target = defaultdict(list) + + for box in target: + new_target["bboxes"].append(box["bbox"]) + new_target["tokens_positive"].append(box["tokens_positive"]) + if "spans_positive" in box: + new_target["spans_positive"].append(box["spans_positive"]) + return new_target + + def __getitem__(self, idx): + try: + img, anno, _, scale = super(CaptionTSV, self).__getitem__(idx) + if self.inference_caption: + caption = None + if isinstance(anno, list): + caption = anno[0]["caption"] # inference mode for bing + anno = [] + elif len(anno) == 1: + caption = anno["caption"] # inference mode for googlecc + anno = [] + else: + caption = " ".join(anno["captions"]) + anno = [] + else: + """ + An example + {'img_h': 1154, 'img_w': 1600, 'caption': 'xxx', 'tokens_positive': [[[47, 50], [51, 53], [54, 59]], [[32, 35], [36, 41]], [[32, 35], [36, 41]], [[0, 3], [3, 6], [6, 10], [11, 16], [17, 19], [20, 23]], [[32, 35], [36, 41]], [[32, 35], [36, 41]]], 'bboxes': [[7.344961166381836, 10.479412078857422, 1592.2679443359375, 1090.0028076171875], [950.32861328125, 346.572021484375, 1333.2373046875, 679.3215942382812], [927.44140625, 342.7712707519531, 1389.833984375, 719.5758666992188], [90.48786163330078, 363.67572021484375, 1381.8631591796875, 1078.687744140625], [122.84217071533203, 422.6786193847656, 507.845703125, 667.2651977539062], [80.62384033203125, 416.500244140625, 563.1666259765625, 734.603271484375]], 'scores': [0.7966700196266174, 0.8952182531356812, 0.8186006546020508, 0.9995516538619995, 0.8021856546401978, 0.8923134803771973]} + """ + if len(anno["bboxes"]) < self.caption_min_box: # Retry triggered! + return self[np.random.choice(len(self))] + if self.caption_format_version == "v2": + anno = self.convert_anno_from_v2_to_v1(anno) + + if self.further_screen: + conf = self.caption_conf + nms_thre = self.caption_nms + + bboxes = torch.as_tensor(anno["bboxes"]).float() + scores = torch.as_tensor(anno["scores"]) + tokens_positive = anno["tokens_positive"] + if "all_nounds_in_vocab" in anno: + all_nounds_in_vocab = anno["all_nounds_in_vocab"] + else: + all_nounds_in_vocab = [] + # print("\n\n\n\n tokens_positive in original data", tokens_positive) + + keep = scores > conf + scores = scores[keep] + bboxes = bboxes[keep] + tokens_positive = [i for index, i in enumerate(tokens_positive) if keep[index]] + all_nounds_in_vocab = [i for index, i in enumerate(all_nounds_in_vocab) if keep[index]] + + assert len(tokens_positive) == len(bboxes) == len(scores) + + if len(bboxes) < self.caption_min_box: # Retry triggered! + return self[np.random.choice(len(self))] + + if nms_thre > 0: + keep = nms(boxes=bboxes, scores=scores, iou_threshold=nms_thre) + scores = scores[keep] + bboxes = bboxes[keep] + tokens_positive = [tokens_positive[i] for i in keep] + assert len(tokens_positive) == len(bboxes) == len(scores) + + # Write back + anno["bboxes"] = bboxes.tolist() + anno["scores"] = scores.tolist() + anno["tokens_positive"] = tokens_positive + anno["all_nounds_in_vocab"] = all_nounds_in_vocab + + if len(anno["bboxes"]) < self.caption_min_box: # Retry triggered! + return self[np.random.choice(len(self))] + + if self.caption_augmentation_version is not None: + caption, new_anno, spans = self.caption_augmentation( + anno["caption"], + self.target_transpose_in(anno), + gpt3_outputs = anno.get("gpt3_outputs", None)) + anno.update(self.target_transpose_out(new_anno)) + anno["caption"] = caption + do_neg_aug = False + else: + do_neg_aug = True + spans = None + + boxes = torch.as_tensor(anno["bboxes"]) + caption = anno["caption"] + target = BoxList(boxes, (anno["img_w"], anno["img_h"]), mode="xyxy") + target = target.clip_to_image(remove_empty=True) + if spans is not None: + target.add_field("spans", spans) # add spans to target + #pdb.set_trace() + # print("original caption", caption) + empty_everything = False + if self.sample_negative_for_grounding_data != -1: + if random.random() < self.sample_negative_for_grounding_data: + empty_everything = True + + if empty_everything: + caption = self.__get_negative_captions__(idx, negative_size=1)[0] + + if self.pack_random_caption_number != 0 and do_neg_aug: + if self.random_pack_prob != -1.0: + if random.random() < self.no_random_pack_probability: + negative_pack_number = 0 + elif random.random() < self.random_pack_prob: + negative_pack_number = self.pack_random_caption_number + else: + negative_pack_number = np.random.choice(self.pack_random_caption_number) + else: + negative_pack_number = self.pack_random_caption_number + + negative_captions = self.__get_negative_captions__(idx, negative_size=negative_pack_number) + + caption, anno["tokens_positive"], greenlight_span_for_masked_lm_objective = self.pack_caption( + caption, negative_captions, anno["tokens_positive"] + ) + else: + greenlight_span_for_masked_lm_objective = [(0, len(caption))] + + if not self.mlm_obj_for_only_positive: + greenlight_span_for_masked_lm_objective = [(0, len(caption))] + + new_anno = [] + areas = target.area() + for i in range(len(target)): + new_anno_i = {} + new_anno_i["area"] = areas[i] + new_anno_i["iscrowd"] = 0 + new_anno_i["image_id"] = idx + new_anno_i["category_id"] = 1 # following vg and others + new_anno_i["id"] = None + new_anno_i["bbox"] = target.bbox[i].numpy().tolist() + new_anno_i["tokens_positive"] = anno["tokens_positive"][i] + if "spans_positive" in anno: + new_anno_i["spans_positive"] = anno["spans_positive"][i] + new_anno.append(new_anno_i) + + # except: + # return self[np.random.choice(len(self))] + + anno = new_anno + if empty_everything: + anno = [] + + annotations = {"image_id": idx, "annotations": anno, "caption": caption} + annotations["greenlight_span_for_masked_lm_objective"] = greenlight_span_for_masked_lm_objective + if "spans" in target.extra_fields: + annotations["spans"] = target.extra_fields["spans"] + if not isinstance(annotations["spans"], list): + annotations["spans"] = annotations["spans"].tolist() + img, annotations = self.prepare(img, annotations, box_format="xyxy") + if self._transforms is not None: + img, target = self._transforms(img, target) + + # add additional property + for ann in annotations: + target.add_field(ann, annotations[ann]) + except: + print("Outter Retry triggered!!") + return self[np.random.choice(len(self))] + + return img, target, idx + + def convert_anno_from_v2_to_v1(self, anno): + flatterned_bboxes = [] + flatterned_tokens_positive = [] + flatterned_bboxes_scores = [] + flatterned_nouns = [] + for i in range(len(anno["bboxes"])): + # i is the index for entity + for j in range(len(anno["bboxes"][i])): + # j is the index for each box + flatterned_bboxes.append(anno["bboxes"][i][j]) + flatterned_tokens_positive.append( + anno["tokens_positive"][i] + ) # Assume this box corresponds to all the token_spans for this entity + if "all_nounds_in_vocab" in anno: + flatterned_nouns.append(anno["all_nounds_in_vocab"][i]) + flatterned_bboxes_scores.append(anno["scores"][i][j]) + anno["bboxes"] = flatterned_bboxes + anno["tokens_positive"] = flatterned_tokens_positive + anno["scores"] = flatterned_bboxes_scores + if "all_nounds_in_vocab" in anno: + anno["all_nounds_in_vocab"] = flatterned_nouns + return anno + + def get_raw_image(self, idx): + image, *_ = super(CaptionTSV, self).__getitem__(idx) + return image + + def get_img_id(self, idx): + line_no = self.get_line_no(idx) + if self.label_tsv is not None: + row = self.label_tsv.seek(line_no) + img_id = row[0] + return img_id diff --git a/maskrcnn_benchmark/data/datasets/coco.py b/maskrcnn_benchmark/data/datasets/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..e61b5db7175a65597f1aeb2acd73f3a3060b9cf3 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/coco.py @@ -0,0 +1,275 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import os +import os.path +import math +from PIL import Image, ImageDraw + +import random +import numpy as np + +import torch +import torchvision +import torch.utils.data as data + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask +from maskrcnn_benchmark.structures.keypoint import PersonKeypoints +from maskrcnn_benchmark.config import cfg +import pdb + + +def _count_visible_keypoints(anno): + return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno) + + +def _has_only_empty_bbox(anno): + return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno) + + +def has_valid_annotation(anno): + # if it's empty, there is no annotation + if len(anno) == 0: + return False + # if all boxes have close to zero area, there is no annotation + if _has_only_empty_bbox(anno): + return False + # keypoints task have a slight different critera for considering + # if an annotation is valid + if "keypoints" not in anno[0]: + return True + # for keypoint detection tasks, only consider valid images those + # containing at least min_keypoints_per_image + if _count_visible_keypoints(anno) >= cfg.DATALOADER.MIN_KPS_PER_IMS: + return True + return False + + +def pil_loader(path, retry=5): + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + ri = 0 + while ri < retry: + try: + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + except: + ri += 1 + + +def rgb2id(color): + if isinstance(color, np.ndarray) and len(color.shape) == 3: + if color.dtype == np.uint8: + color = color.astype(np.int32) + return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2] + return int(color[0] + 256 * color[1] + 256 * 256 * color[2]) + + +class CocoDetection(data.Dataset): + """`MS Coco Detection `_ Dataset. + + Args: + root (string): Root directory where images are downloaded to. + annFile (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.ToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + def __init__(self, root, annFile, transform=None, target_transform=None): + from pycocotools.coco import COCO + + self.root = root + self.coco = COCO(annFile) + self.ids = list(self.coco.imgs.keys()) + self.transform = transform + self.target_transform = target_transform + + def __getitem__(self, index, return_meta=False): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``. + """ + coco = self.coco + img_id = self.ids[index] + if isinstance(img_id, str): + img_id = [img_id] + ann_ids = coco.getAnnIds(imgIds=img_id) + target = coco.loadAnns(ann_ids) + + meta = coco.loadImgs(img_id)[0] + path = meta["file_name"] + img = pil_loader(os.path.join(self.root, path)) + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + if return_meta: + return img, target, meta + else: + return img, target + + def __len__(self): + return len(self.ids) + + def __repr__(self): + fmt_str = "Dataset " + self.__class__.__name__ + "\n" + fmt_str += " Number of datapoints: {}\n".format(self.__len__()) + fmt_str += " Root Location: {}\n".format(self.root) + tmp = " Transforms (if any): " + fmt_str += "{0}{1}\n".format(tmp, self.transform.__repr__().replace("\n", "\n" + " " * len(tmp))) + tmp = " Target Transforms (if any): " + fmt_str += "{0}{1}".format(tmp, self.target_transform.__repr__().replace("\n", "\n" + " " * len(tmp))) + return fmt_str + + +class COCODataset(CocoDetection): + def __init__( + self, + ann_file, + root, + remove_images_without_annotations, + transforms=None, + ignore_crowd=True, + max_box=-1, + few_shot=0, + one_hot=False, + override_category=None, + **kwargs + ): + super(COCODataset, self).__init__(root, ann_file) + # sort indices for reproducible results + self.ids = sorted(self.ids) + + # filter images without detection annotations + if remove_images_without_annotations: + ids = [] + for img_id in self.ids: + if isinstance(img_id, str): + ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None) + else: + ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = self.coco.loadAnns(ann_ids) + if has_valid_annotation(anno): + ids.append(img_id) + self.ids = ids + + if few_shot: + ids = [] + cats_freq = [few_shot] * len(self.coco.cats.keys()) + if "shuffle_seed" in kwargs and kwargs["shuffle_seed"] != 0: + import random + + random.Random(kwargs["shuffle_seed"]).shuffle(self.ids) + print("Shuffle the dataset with random seed: ", kwargs["shuffle_seed"]) + for img_id in self.ids: + if isinstance(img_id, str): + ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None) + else: + ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = self.coco.loadAnns(ann_ids) + cat = set([ann["category_id"] for ann in anno]) # set/tuple corresponde to instance/image level + is_needed = sum([cats_freq[c - 1] > 0 for c in cat]) + if is_needed: + ids.append(img_id) + for c in cat: + cats_freq[c - 1] -= 1 + # print(cat, cats_freq) + self.ids = ids + + if override_category is not None: + self.coco.dataset["categories"] = override_category + print("Override category: ", override_category) + + self.json_category_id_to_contiguous_id = {v: i + 1 for i, v in enumerate(self.coco.getCatIds())} + self.contiguous_category_id_to_json_id = {v: k for k, v in self.json_category_id_to_contiguous_id.items()} + self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} + self.transforms = transforms + self.ignore_crowd = ignore_crowd + self.max_box = max_box + self.one_hot = one_hot + + def categories(self, no_background=True): + categories = self.coco.dataset["categories"] + label_list = {} + for index, i in enumerate(categories): + if not no_background or (i["name"] != "__background__" and i["id"] != 0): + label_list[self.json_category_id_to_contiguous_id[i["id"]]] = i["name"] + return label_list + + def __getitem__(self, idx): + + img, anno = super(COCODataset, self).__getitem__(idx) + + # filter crowd annotations + if self.ignore_crowd: + anno = [obj for obj in anno if obj["iscrowd"] == 0] + + boxes = [obj["bbox"] for obj in anno] + boxes = torch.as_tensor(boxes).reshape(-1, 4) # guard against no boxes + if self.max_box > 0 and len(boxes) > self.max_box: + rand_idx = torch.randperm(self.max_box) + boxes = boxes[rand_idx, :] + else: + rand_idx = None + target = BoxList(boxes, img.size, mode="xywh").convert("xyxy") + + classes = [obj["category_id"] for obj in anno] + classes = [self.json_category_id_to_contiguous_id[c] for c in classes] + classes = torch.tensor(classes) + + if rand_idx is not None: + classes = classes[rand_idx] + if cfg.DATASETS.CLASS_AGNOSTIC: + classes = torch.ones_like(classes) + target.add_field("labels", classes) + + if anno and "segmentation" in anno[0]: + masks = [obj["segmentation"] for obj in anno] + masks = SegmentationMask(masks, img.size, mode="poly") + target.add_field("masks", masks) + + if anno and "cbox" in anno[0]: + cboxes = [obj["cbox"] for obj in anno] + cboxes = torch.as_tensor(cboxes).reshape(-1, 4) # guard against no boxes + cboxes = BoxList(cboxes, img.size, mode="xywh").convert("xyxy") + target.add_field("cbox", cboxes) + + if anno and "keypoints" in anno[0]: + keypoints = [] + gt_keypoint = self.coco.cats[1]["keypoints"] # a better way to get keypoint description + use_keypoint = cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME + for obj in anno: + if len(use_keypoint) > 0: + kps = [] + for name in use_keypoint: + kp_idx = slice(3 * gt_keypoint.index(name), 3 * gt_keypoint.index(name) + 3) + kps += obj["keypoints"][kp_idx] + keypoints.append(kps) + else: + keypoints.append(obj["keypoints"]) + keypoints = PersonKeypoints(keypoints, img.size) + target.add_field("keypoints", keypoints) + + target = target.clip_to_image(remove_empty=True) + + if self.transforms is not None: + img, target = self.transforms(img, target) + + if cfg.DATASETS.SAMPLE_RATIO != 0.0: + ratio = cfg.DATASETS.SAMPLE_RATIO + num_sample_target = math.ceil(len(target) * ratio) if ratio > 0 else math.ceil(-ratio) + sample_idx = torch.randperm(len(target))[:num_sample_target] + target = target[sample_idx] + return img, target, idx + + def get_img_info(self, index): + img_id = self.id_to_img_map[index] + img_data = self.coco.imgs[img_id] + return img_data diff --git a/maskrcnn_benchmark/data/datasets/coco_dt.py b/maskrcnn_benchmark/data/datasets/coco_dt.py new file mode 100644 index 0000000000000000000000000000000000000000..4fc65a16fd3d7be457eb881ab5242107a5f486fa --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/coco_dt.py @@ -0,0 +1,224 @@ +""" +COCO dataset which returns image_id for evaluation. + +Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py +""" + +import torch +import json +from PIL import Image, ImageDraw + +from .modulated_coco import ConvertCocoPolysToMask +from .tsv import ODTSVDataset +from pycocotools.coco import COCO +from maskrcnn_benchmark.structures.bounding_box import BoxList +import random +from .od_to_grounding import convert_object_detection_to_grounding_optimized_for_od, check_for_positive_overflow, sanity_check_target_after_processing, od_to_grounding_optimized_streamlined +from ._od_to_description import DescriptionConverter +import pdb +from collections import defaultdict + +class CocoDetectionTSV(ODTSVDataset): + def __init__( + self, + name, + yaml_file, + transforms, + return_tokens, + tokenizer, + extra_fields, + random_sample_negative=-1, + add_detection_prompt=False, + add_detection_prompt_advanced=False, + use_od_data_aug=False, + control_probabilities={}, + disable_shuffle=False, + prompt_engineer_version="v2", + prompt_limit_negative=-1, + positive_question_probability=0.6, + negative_question_probability=0.8, + full_question_probability=0.5, + disable_clip_to_image=False, + separation_tokens=" ", + no_mask_for_od=False, + max_num_labels=-1, + max_query_len=256, + od_to_grounding_version="legacy", + description_file = None, + similarity_file = None, + **kwargs + ): + super(CocoDetectionTSV, self).__init__(yaml_file, extra_fields, **kwargs) + + self._transforms = transforms + self.name = name + self.max_query_len = max_query_len + self.prepare = ConvertCocoPolysToMask( + return_masks=False, return_tokens=return_tokens, tokenizer=tokenizer, max_query_len=max_query_len + ) + self.tokenizer = tokenizer + + self.control_probabilities = control_probabilities + self.random_sample_negative = random_sample_negative + self.add_detection_prompt = add_detection_prompt + self.add_detection_prompt_advanced = add_detection_prompt_advanced + self.use_od_data_aug = use_od_data_aug + + self.prompt_engineer_version = prompt_engineer_version + self.prompt_limit_negative = prompt_limit_negative + self.positive_question_probability = positive_question_probability + self.negative_question_probability = negative_question_probability + self.full_question_probability = full_question_probability + self.separation_tokens = separation_tokens + self.disable_clip_to_image = disable_clip_to_image + self.disable_shuffle = disable_shuffle + self.no_mask_for_od = no_mask_for_od + self.max_num_labels = max_num_labels + + self.od_to_grounding_version = od_to_grounding_version + self.description_file = description_file + self.similarity_file = similarity_file + if "description" in self.od_to_grounding_version: + self.od_grounding_converter = DescriptionConverter( + self.description_file, + self.od_to_grounding_version, + [], + self.ind_to_class, + self.similarity_file,) + + ### stat + self.pos_rate = defaultdict(list) + + def __len__(self): + return super(CocoDetectionTSV, self).__len__() + + def categories(self, no_background=True): + categories = self.coco.dataset["categories"] + label_list = {} + for index, i in enumerate(categories): + # assert(index + 1 == i["id"]) + if not no_background or (i["name"] != "__background__" and i["id"] != 0): + label_list[i["id"]] = i["name"] + return label_list + + def __getitem__(self, idx): + # tgt is a BoxList + img, target, _, scale = super(CocoDetectionTSV, self).__getitem__(idx) + image_id = self.get_img_id(idx) + restricted_negative_list = None + + if not self.disable_clip_to_image: + target = target.clip_to_image(remove_empty=True) + + original_box_num = len(target) + + target, positive_caption_length = check_for_positive_overflow( + target, self.ind_to_class, self.tokenizer, self.max_query_len - 2 + ) # leave some space for the special tokens + + if len(target) < original_box_num: + print("WARNING: removed {} boxes due to positive caption overflow".format(original_box_num - len(target))) + + if "mixed" in self.od_to_grounding_version: # 70% v.s. 30% + if random.random() < 0.7: + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = self.od_grounding_converter.train_od_to_grounding( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + tokenizer=self.tokenizer, + random_sample_negative=self.random_sample_negative, + ) + else: + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions = convert_object_detection_to_grounding_optimized_for_od( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + disable_shuffle=self.disable_shuffle, + add_detection_prompt=self.add_detection_prompt, + add_detection_prompt_advanced=self.add_detection_prompt_advanced, + random_sample_negative=self.random_sample_negative, + control_probabilities=self.control_probabilities, + restricted_negative_list=restricted_negative_list, + separation_tokens=self.separation_tokens, + max_num_labels=self.max_num_labels, + positive_caption_length=positive_caption_length, + tokenizer=self.tokenizer, + max_seq_length=self.max_query_len - 2, + ) + elif "description" in self.od_to_grounding_version: + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = self.od_grounding_converter.train_od_to_grounding( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + tokenizer=self.tokenizer, + random_sample_negative=self.random_sample_negative, + ) + elif self.od_to_grounding_version != "legacy": + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = od_to_grounding_optimized_streamlined( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + tokenizer=self.tokenizer, + od_to_grounding_version=self.od_to_grounding_version, + ) + else: + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions = convert_object_detection_to_grounding_optimized_for_od( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + disable_shuffle=self.disable_shuffle, + add_detection_prompt=self.add_detection_prompt, + add_detection_prompt_advanced=self.add_detection_prompt_advanced, + random_sample_negative=self.random_sample_negative, + control_probabilities=self.control_probabilities, + restricted_negative_list=restricted_negative_list, + separation_tokens=self.separation_tokens, + max_num_labels=self.max_num_labels, + positive_caption_length=positive_caption_length, + tokenizer=self.tokenizer, + max_seq_length=self.max_query_len - 2, + ) + + # assert(len(self.tokenizer.tokenize(caption)) <= self.max_query_len-2) + anno = { + "image_id": image_id, + "annotations": annotations, + "caption": caption, + "label_to_positions": label_to_positions, + } + if "spans" in target.extra_fields: + anno["spans"] = target.extra_fields["spans"] + if not isinstance(anno["spans"], list): + anno["spans"] = anno["spans"].tolist() + + anno["greenlight_span_for_masked_lm_objective"] = greenlight_span_for_masked_lm_objective + + if self.no_mask_for_od: + anno["greenlight_span_for_masked_lm_objective"].append((-1, -1, -1)) + + img, anno = self.prepare(img, anno, box_format="xyxy") + + if self._transforms is not None: + img, target = self._transforms(img, target) + + # add additional property + for ann in anno: + target.add_field(ann, anno[ann]) + + # sanity_check_target_after_processing(target) + + return img, target, idx + + def get_raw_image(self, idx): + image, *_ = super(CocoDetectionTSV, self).__getitem__(idx) + return image + + def get_img_id(self, idx): + line_no = self.get_line_no(idx) + if self.label_tsv is not None: + row = self.label_tsv.seek(line_no) + img_id = row[0] + try: + return int(img_id) + except: + return idx diff --git a/maskrcnn_benchmark/data/datasets/concat_dataset.py b/maskrcnn_benchmark/data/datasets/concat_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5cb6c0d96f906056c0b6d0d001db00c6eac2a5ae --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/concat_dataset.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import bisect + +from torch.utils.data.dataset import ConcatDataset as _ConcatDataset + + +class ConcatDataset(_ConcatDataset): + """ + Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra + method for querying the sizes of the image + """ + + def get_idxs(self, idx): + dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx) + if dataset_idx == 0: + sample_idx = idx + else: + sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] + return dataset_idx, sample_idx + + def get_img_info(self, idx): + dataset_idx, sample_idx = self.get_idxs(idx) + return self.datasets[dataset_idx].get_img_info(sample_idx) diff --git a/maskrcnn_benchmark/data/datasets/custom_distributed_sampler.py b/maskrcnn_benchmark/data/datasets/custom_distributed_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..b28436c14ba841032454bbd201142a0398d4ab52 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/custom_distributed_sampler.py @@ -0,0 +1,191 @@ +import math +from typing import TypeVar, Optional, Iterator + +import torch +from torch.utils.data import Sampler, Dataset +import torch.distributed as dist +import random +import numpy as np +import torch + + +class DistributedSamplerChunkByNode(torch.utils.data.Sampler): + def __init__( + self, + dataset, + all_datasets, + chunk_or_not, + num_replicas: Optional[int] = None, + rank: Optional[int] = None, + shuffle: bool = True, + seed: int = 0, + drop_last: bool = False, + node_rank=0, + node_number=1, + process_num_per_node=1, + rank_within_local_node=0, + ) -> None: + if num_replicas is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + num_replicas = dist.get_world_size() + if rank is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + rank = dist.get_rank() + if rank >= num_replicas or rank < 0: + raise ValueError( + "Invalid rank {}, rank should be in the interval" " [0, {}]".format(rank, num_replicas - 1) + ) + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.node_number = node_number + self.node_rank = node_rank + self.chunk_or_not = chunk_or_not + self.process_num_per_node = process_num_per_node + self.rank_within_local_node = rank_within_local_node + + assert self.process_num_per_node * self.node_number == self.num_replicas + + # 1. divide the datasets into two parts + normal_datasets = [] + chunked_datasets = [] + for dataset_i, chunk_i in zip(all_datasets, chunk_or_not): + if chunk_i: + chunked_datasets.append(dataset_i) + else: + normal_datasets.append(dataset_i) + + # 2. calculate dataset sizes: + self.normal_dataset_size = sum( + [len(i) for i in normal_datasets] + ) # this part we follow the conventional distributed sampler + + # 3. Divide + self.current_node_start_range = -1 + self.current_node_end_range = -1 + assert len(chunked_datasets) >= self.node_number + chunk_size = len(chunked_datasets) // self.node_number + current_example_num = self.normal_dataset_size + + for index in range(len(chunked_datasets)): + if index == self.node_rank * chunk_size: + self.current_node_start_range = current_example_num + current_example_num += len(chunked_datasets[index]) + if index == (self.node_rank + 1) * chunk_size - 1: + self.current_node_end_range = current_example_num + + if self.current_node_end_range == -1: # boundary + self.current_node_end_range = current_example_num + + self.drop_last = drop_last + # If the dataset length is evenly divisible by # of replicas, then there + # is no need to drop any data, since the dataset will be split equally. + if self.drop_last and len(self.dataset) % self.num_replicas != 0: # type: ignore[arg-type] + # Split to nearest available length that is evenly divisible. + # This is to ensure each rank receives the same amount of data when + # using this Sampler. + self.num_samples = math.ceil( + # `type:ignore` is required because Dataset cannot provide a default __len__ + # see NOTE in pytorch/torch/utils/data/sampler.py + (len(self.dataset) - self.num_replicas) + / self.num_replicas # type: ignore[arg-type] + ) + else: + self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) # type: ignore[arg-type] + self.total_size = self.num_samples * self.num_replicas + self.shuffle = shuffle + self.seed = seed + + def __iter__(self): + indices = self.generate_indices_within_range_with_rank( + seed=self.seed, + epoch=self.epoch, + # NOTE: Distribute among all processes + process_num=self.num_replicas, + rank=self.rank, + generate_length=-1, + valid_indices=list(range(self.normal_dataset_size)), + prefix="Normal ", + ) + + addition_indices = self.generate_indices_within_range_with_rank( + seed=self.seed, + epoch=self.epoch, + # NOTE : very important arguments, distribute among local nodes + process_num=self.process_num_per_node, + rank=self.rank_within_local_node, + generate_length=self.num_samples - len(indices), + valid_indices=list(range(self.current_node_start_range, self.current_node_end_range)), + prefix="Distribute ", + ) + + indices.extend(addition_indices) + random.seed(self.seed + self.epoch + 10 * self.rank) # Set the seed to maximize randomness + random.shuffle(indices) # Reshuffle + assert len(indices) == self.num_samples + return iter(indices) + + def generate_indices_within_range_with_rank( + self, seed, epoch, process_num, generate_length, valid_indices, rank=-1, shuffle=True, prefix="" + ): + """ + Use scenario : we want to sample 2500 examples from 10000 examples, while not sampling overlapping examples with other three process. + Modified from DistributedSampler + """ + dataset_size = len(valid_indices) + if shuffle: + # deterministically shuffle based on epoch and seed + g = torch.Generator() + g.manual_seed(seed + epoch) + indices = torch.randperm(dataset_size, generator=g).tolist() # type: ignore[arg-type] + else: + indices = list(range(dataset_size)) # type: ignore[arg-type] + + indices = [valid_indices[i] for i in indices] + + num_samples_normal = math.ceil((dataset_size - process_num) / process_num) # type: ignore[arg-type] + # remove tail of data to make it evenly divisible. + indices = indices[: num_samples_normal * process_num] + + print("\n") + print( + prefix, + "Global Rank {} Local Rank {} generate_length {} valid_indices {} process_num {} indices_before_subsample {} {}".format( + self.rank, rank, generate_length, len(valid_indices), process_num, len(indices), indices[:10] + ), + ) + + # subsample + indices = indices[rank : num_samples_normal * process_num : process_num] + + print( + prefix, + "Global Rank {} Local Rank {} generate_length {} valid_indices {} process_num {} indices_after_subsample {} {}".format( + self.rank, rank, generate_length, len(valid_indices), process_num, len(indices), indices[:10] + ), + ) + print("\n") + + if generate_length != -1: + if len(indices) > generate_length: + indices = indices[:generate_length] + else: + indices.extend(np.random.choice(valid_indices, generate_length - len(indices)).tolist()) + return indices + + def __len__(self) -> int: + return self.num_samples + + def set_epoch(self, epoch: int) -> None: + r""" + Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas + use a different random ordering for each epoch. Otherwise, the next iteration of this + sampler will yield the same ordering. + + Args: + epoch (int): Epoch number. + """ + self.epoch = epoch diff --git a/maskrcnn_benchmark/data/datasets/duplicate_dataset.py b/maskrcnn_benchmark/data/datasets/duplicate_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5c7ff0fe0fa8bff2007d2d1e79ee7fb311b3bd06 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/duplicate_dataset.py @@ -0,0 +1,30 @@ +import math +from typing import TypeVar, Optional, Iterator + +import torch +from torch.utils.data import Sampler, Dataset +import torch.distributed as dist +import random +import numpy as np + + +def create_duplicate_dataset(DatasetBaseClass): + class DupDataset(DatasetBaseClass): + def __init__(self, copy, **kwargs): + super(DupDataset, self).__init__(**kwargs) + + self.copy = copy + self.length = super(DupDataset, self).__len__() + + def __len__(self): + return self.copy * self.length + + def __getitem__(self, index): + true_index = index % self.length + return super(DupDataset, self).__getitem__(true_index) + + def get_img_info(self, index): + true_index = index % self.length + return super(DupDataset, self).get_img_info(true_index) + + return DupDataset diff --git a/maskrcnn_benchmark/data/datasets/evaluation/__init__.py b/maskrcnn_benchmark/data/datasets/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..970294a35de679a7dac1914ab80b4d9f4f6f044c --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/__init__.py @@ -0,0 +1,52 @@ +from maskrcnn_benchmark.data import datasets + +from .coco import coco_evaluation +from .voc import voc_evaluation +from .vg import vg_evaluation +from .box_aug import im_detect_bbox_aug +from .od_to_grounding import od_to_grounding_evaluation + + +def evaluate(dataset, predictions, output_folder, **kwargs): + """evaluate dataset using different methods based on dataset type. + Args: + dataset: Dataset object + predictions(list[BoxList]): each item in the list represents the + prediction results for one image. + output_folder: output folder, to save evaluation files or results. + **kwargs: other args. + Returns: + evaluation result + """ + args = dict(dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs) + if isinstance(dataset, datasets.COCODataset) or isinstance(dataset, datasets.TSVDataset): + return coco_evaluation(**args) + # elif isinstance(dataset, datasets.VGTSVDataset): + # return vg_evaluation(**args) + elif isinstance(dataset, datasets.PascalVOCDataset): + return voc_evaluation(**args) + elif isinstance(dataset, datasets.CocoDetectionTSV): + return od_to_grounding_evaluation(**args) + elif isinstance(dataset, datasets.LvisDetection): + pass + else: + dataset_name = dataset.__class__.__name__ + raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name)) + + +def evaluate_mdetr(dataset, predictions, output_folder, cfg): + + args = dict(dataset=dataset, predictions=predictions, output_folder=output_folder, **kwargs) + if isinstance(dataset, datasets.COCODataset) or isinstance(dataset, datasets.TSVDataset): + return coco_evaluation(**args) + # elif isinstance(dataset, datasets.VGTSVDataset): + # return vg_evaluation(**args) + elif isinstance(dataset, datasets.PascalVOCDataset): + return voc_evaluation(**args) + elif isinstance(dataset, datasets.CocoDetectionTSV): + return od_to_grounding_evaluation(**args) + elif isinstance(dataset, datasets.LvisDetection): + pass + else: + dataset_name = dataset.__class__.__name__ + raise NotImplementedError("Unsupported dataset type {}.".format(dataset_name)) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/box_aug.py b/maskrcnn_benchmark/data/datasets/evaluation/box_aug.py new file mode 100644 index 0000000000000000000000000000000000000000..f077e2e3e8822aa0f52e775a8c6e7d17e1a8c45f --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/box_aug.py @@ -0,0 +1,357 @@ +import torch +import numpy as np + +from maskrcnn_benchmark.config import cfg +from maskrcnn_benchmark.data import transforms as T +from maskrcnn_benchmark.structures.image_list import to_image_list +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.layers import nms, soft_nms + + +def im_detect_bbox_aug(model, images, device, captions=None, positive_map_label_to_token=None): + # Collect detections computed under different transformations + boxlists_ts = [] + for _ in range(len(images)): + boxlists_ts.append([]) + + def add_preds_t(boxlists_t): + for i, boxlist_t in enumerate(boxlists_t): + # Resize the boxlist as the first one + boxlists_ts[i].append(boxlist_t.resize(images[i].size)) + + # Compute detections at different scales + if len(cfg.TEST.RANGES) == len(cfg.TEST.SCALES): + keep_ranges = cfg.TEST.RANGES + else: + keep_ranges = [None for _ in cfg.TEST.SCALES] + + for scale, keep_range in zip(cfg.TEST.SCALES, keep_ranges): + max_size = cfg.TEST.MAX_SIZE + boxlists_scl = im_detect_bbox_scale( + model, + images, + scale, + max_size, + device, + captions=captions, + positive_map_label_to_token=positive_map_label_to_token, + ) + if keep_range is not None: + boxlists_scl = remove_boxes(boxlists_scl, *keep_range) + add_preds_t(boxlists_scl) + + if cfg.TEST.FLIP: + boxlists_scl_hf = im_detect_bbox_scale( + model, + images, + scale, + max_size, + device, + captions=captions, + positive_map_label_to_token=positive_map_label_to_token, + hflip=True, + ) + if keep_range is not None: + boxlists_scl_hf = remove_boxes(boxlists_scl_hf, *keep_range) + add_preds_t(boxlists_scl_hf) + + # Merge boxlists detected by different bbox aug params + boxlists = [] + for i, boxlist_ts in enumerate(boxlists_ts): + bbox = torch.cat([boxlist_t.bbox for boxlist_t in boxlist_ts]) + scores = torch.cat([boxlist_t.get_field("scores") for boxlist_t in boxlist_ts]) + labels = torch.cat([boxlist_t.get_field("labels") for boxlist_t in boxlist_ts]) + boxlist = BoxList(bbox, boxlist_ts[0].size, boxlist_ts[0].mode) + boxlist.add_field("scores", scores) + boxlist.add_field("labels", labels) + boxlists.append(boxlist) + results = merge_result_from_multi_scales(boxlists) + return results + + +def im_detect_bbox( + model, images, target_scale, target_max_size, device, captions=None, positive_map_label_to_token=None +): + """ + Performs bbox detection on the original image. + """ + if cfg.INPUT.FORMAT != "": + input_format = cfg.INPUT.FORMAT + elif cfg.INPUT.TO_BGR255: + input_format = "bgr255" + transform = T.Compose( + [ + T.Resize(target_scale, target_max_size), + T.ToTensor(), + T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, format=input_format), + ] + ) + images = [transform(image) for image in images] + images = to_image_list(images, cfg.DATALOADER.SIZE_DIVISIBILITY) + if captions is None: + return model(images.to(device)) + else: + return model(images.to(device), captions=captions, positive_map=positive_map_label_to_token) + + +def im_detect_bbox_hflip( + model, images, target_scale, target_max_size, device, captions=None, positive_map_label_to_token=None +): + """ + Performs bbox detection on the horizontally flipped image. + Function signature is the same as for im_detect_bbox. + """ + if cfg.INPUT.FORMAT != "": + input_format = cfg.INPUT.FORMAT + elif cfg.INPUT.TO_BGR255: + input_format = "bgr255" + transform = T.Compose( + [ + T.Resize(target_scale, target_max_size), + T.RandomHorizontalFlip(1.0), + T.ToTensor(), + T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, format=input_format), + ] + ) + images = [transform(image) for image in images] + images = to_image_list(images, cfg.DATALOADER.SIZE_DIVISIBILITY) + if captions is None: + boxlists = model(images.to(device)) + else: + boxlists = model(images.to(device), captions=captions, positive_map=positive_map_label_to_token) + + # Invert the detections computed on the flipped image + boxlists_inv = [boxlist.transpose(0) for boxlist in boxlists] + return boxlists_inv + + +def im_detect_bbox_scale( + model, images, target_scale, target_max_size, device, captions=None, positive_map_label_to_token=None, hflip=False +): + """ + Computes bbox detections at the given scale. + Returns predictions in the scaled image space. + """ + if hflip: + boxlists_scl = im_detect_bbox_hflip( + model, + images, + target_scale, + target_max_size, + device, + captions=captions, + positive_map_label_to_token=positive_map_label_to_token, + ) + else: + boxlists_scl = im_detect_bbox( + model, + images, + target_scale, + target_max_size, + device, + captions=captions, + positive_map_label_to_token=positive_map_label_to_token, + ) + return boxlists_scl + + +def remove_boxes(boxlist_ts, min_scale, max_scale): + new_boxlist_ts = [] + for _, boxlist_t in enumerate(boxlist_ts): + mode = boxlist_t.mode + boxlist_t = boxlist_t.convert("xyxy") + boxes = boxlist_t.bbox + keep = [] + for j, box in enumerate(boxes): + w = box[2] - box[0] + 1 + h = box[3] - box[1] + 1 + if (w * h > min_scale * min_scale) and (w * h < max_scale * max_scale): + keep.append(j) + new_boxlist_ts.append(boxlist_t[keep].convert(mode)) + return new_boxlist_ts + + +def merge_result_from_multi_scales(boxlists): + num_images = len(boxlists) + results = [] + for i in range(num_images): + scores = boxlists[i].get_field("scores") + labels = boxlists[i].get_field("labels") + boxes = boxlists[i].bbox + boxlist = boxlists[i] + result = [] + # test on classes + if len(cfg.TEST.SELECT_CLASSES): + class_list = cfg.TEST.SELECT_CLASSES + else: + class_list = range(1, cfg.TEST.NUM_CLASSES) + for j in class_list: + inds = (labels == j).nonzero().view(-1) + + scores_j = scores[inds] + boxes_j = boxes[inds, :].view(-1, 4) + boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy") + boxlist_for_class.add_field("scores", scores_j) + boxlist_for_class = boxlist_nms( + boxlist_for_class, cfg.TEST.TH, score_field="scores", nms_type=cfg.TEST.SPECIAL_NMS + ) + num_labels = len(boxlist_for_class) + boxlist_for_class.add_field("labels", torch.full((num_labels,), j, dtype=torch.int64, device=scores.device)) + result.append(boxlist_for_class) + + result = cat_boxlist(result) + number_of_detections = len(result) + + # Limit to max_per_image detections **over all classes** + if number_of_detections > cfg.TEST.PRE_NMS_TOP_N > 0: + cls_scores = result.get_field("scores") + image_thresh, _ = torch.kthvalue(cls_scores.cpu(), number_of_detections - cfg.TEST.PRE_NMS_TOP_N + 1) + keep = cls_scores >= image_thresh.item() + keep = torch.nonzero(keep).squeeze(1) + result = result[keep] + results.append(result) + return results + + +def boxlist_nms(boxlist, thresh, max_proposals=-1, score_field="scores", nms_type="nms"): + if thresh <= 0: + return boxlist + mode = boxlist.mode + boxlist = boxlist.convert("xyxy") + boxes = boxlist.bbox + score = boxlist.get_field(score_field) + + if nms_type == "vote": + boxes_vote, scores_vote = bbox_vote(boxes, score, thresh) + if len(boxes_vote) > 0: + boxlist.bbox = boxes_vote + boxlist.extra_fields["scores"] = scores_vote + elif nms_type == "soft-vote": + boxes_vote, scores_vote = soft_bbox_vote(boxes, score, thresh) + if len(boxes_vote) > 0: + boxlist.bbox = boxes_vote + boxlist.extra_fields["scores"] = scores_vote + elif nms_type == "soft-nms": + keep, new_score = soft_nms(boxes.cpu(), score.cpu(), thresh, 0.95) + if max_proposals > 0: + keep = keep[:max_proposals] + boxlist = boxlist[keep] + boxlist.extra_fields["scores"] = new_score + else: + keep = nms(boxes, score, thresh) + if max_proposals > 0: + keep = keep[:max_proposals] + boxlist = boxlist[keep] + return boxlist.convert(mode) + + +def bbox_vote(boxes, scores, vote_thresh): + boxes = boxes.cpu().numpy() + scores = scores.cpu().numpy().reshape(-1, 1) + det = np.concatenate((boxes, scores), axis=1) + if det.shape[0] <= 1: + return np.zeros((0, 5)), np.zeros((0, 1)) + order = det[:, 4].ravel().argsort()[::-1] + det = det[order, :] + dets = [] + while det.shape[0] > 0: + # IOU + area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1) + xx1 = np.maximum(det[0, 0], det[:, 0]) + yy1 = np.maximum(det[0, 1], det[:, 1]) + xx2 = np.minimum(det[0, 2], det[:, 2]) + yy2 = np.minimum(det[0, 3], det[:, 3]) + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + o = inter / (area[0] + area[:] - inter) + + # get needed merge det and delete these det + merge_index = np.where(o >= vote_thresh)[0] + det_accu = det[merge_index, :] + det = np.delete(det, merge_index, 0) + + if merge_index.shape[0] <= 1: + try: + dets = np.row_stack((dets, det_accu)) + except: + dets = det_accu + continue + else: + det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4)) + max_score = np.max(det_accu[:, 4]) + det_accu_sum = np.zeros((1, 5)) + det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:]) + det_accu_sum[:, 4] = max_score + try: + dets = np.row_stack((dets, det_accu_sum)) + except: + dets = det_accu_sum + + boxes = torch.from_numpy(dets[:, :4]).float().cuda() + scores = torch.from_numpy(dets[:, 4]).float().cuda() + + return boxes, scores + + +def soft_bbox_vote(boxes, scores, vote_thresh): + boxes = boxes.cpu().numpy() + scores = scores.cpu().numpy().reshape(-1, 1) + det = np.concatenate((boxes, scores), axis=1) + if det.shape[0] <= 1: + return np.zeros((0, 5)), np.zeros((0, 1)) + order = det[:, 4].ravel().argsort()[::-1] + det = det[order, :] + dets = [] + while det.shape[0] > 0: + # IOU + area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1) + xx1 = np.maximum(det[0, 0], det[:, 0]) + yy1 = np.maximum(det[0, 1], det[:, 1]) + xx2 = np.minimum(det[0, 2], det[:, 2]) + yy2 = np.minimum(det[0, 3], det[:, 3]) + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + o = inter / (area[0] + area[:] - inter) + + # get needed merge det and delete these det + merge_index = np.where(o >= vote_thresh)[0] + det_accu = det[merge_index, :] + det_accu_iou = o[merge_index] + det = np.delete(det, merge_index, 0) + + if merge_index.shape[0] <= 1: + try: + dets = np.row_stack((dets, det_accu)) + except: + dets = det_accu + continue + else: + soft_det_accu = det_accu.copy() + soft_det_accu[:, 4] = soft_det_accu[:, 4] * (1 - det_accu_iou) + soft_index = np.where(soft_det_accu[:, 4] >= cfg.MODEL.RETINANET.INFERENCE_TH)[0] + soft_det_accu = soft_det_accu[soft_index, :] + + det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4)) + max_score = np.max(det_accu[:, 4]) + det_accu_sum = np.zeros((1, 5)) + det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:]) + det_accu_sum[:, 4] = max_score + + if soft_det_accu.shape[0] > 0: + det_accu_sum = np.row_stack((det_accu_sum, soft_det_accu)) + + try: + dets = np.row_stack((dets, det_accu_sum)) + except: + dets = det_accu_sum + + order = dets[:, 4].ravel().argsort()[::-1] + dets = dets[order, :] + + boxes = torch.from_numpy(dets[:, :4]).float().cuda() + scores = torch.from_numpy(dets[:, 4]).float().cuda() + + return boxes, scores diff --git a/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py b/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6a25c9b536e131b4d8bfd8e7ceb24c783d8d97cd --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/coco/__init__.py @@ -0,0 +1,21 @@ +from .coco_eval import do_coco_evaluation + + +def coco_evaluation( + dataset, + predictions, + output_folder, + box_only=False, + iou_types=("bbox",), + expected_results=(), + expected_results_sigma_tol=4, +): + return do_coco_evaluation( + dataset=dataset, + predictions=predictions, + box_only=box_only, + output_folder=output_folder, + iou_types=iou_types, + expected_results=expected_results, + expected_results_sigma_tol=expected_results_sigma_tol, + ) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py b/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..a69f94feeda4595fe5024acccbf6cb4d8cf1357e --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/coco/coco_eval.py @@ -0,0 +1,517 @@ +import logging +import tempfile +import os +import torch +import numpy as np +import json + +from collections import OrderedDict +from tqdm import tqdm + +from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou + + +def do_coco_evaluation( + dataset, + predictions, + box_only, + output_folder, + iou_types, + expected_results, + expected_results_sigma_tol, +): + logger = logging.getLogger("maskrcnn_benchmark.inference") + + if box_only: + logger.info("Evaluating bbox proposals") + if dataset.coco is None and output_folder: + json_results = prepare_for_tsv_detection(predictions, dataset) + with open(os.path.join(output_folder, "box_proposals.json"), "w") as f: + json.dump(json_results, f) + return None + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + res = COCOResults("box_proposal") + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = evaluate_box_proposals(predictions, dataset, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res.results["box_proposal"][key] = stats["ar"].item() + logger.info(res) + check_expected_results(res, expected_results, expected_results_sigma_tol) + if output_folder: + torch.save(res, os.path.join(output_folder, "box_proposals.pth")) + return res, predictions + logger.info("Preparing results for COCO format") + coco_results = {} + if "bbox" in iou_types: + logger.info("Preparing bbox results") + if dataset.coco is None: + coco_results["bbox"] = prepare_for_tsv_detection(predictions, dataset) + else: + coco_results["bbox"] = prepare_for_coco_detection(predictions, dataset) + if "segm" in iou_types: + logger.info("Preparing segm results") + coco_results["segm"] = prepare_for_coco_segmentation(predictions, dataset) + if "keypoints" in iou_types: + logger.info("Preparing keypoints results") + coco_results["keypoints"] = prepare_for_coco_keypoint(predictions, dataset) + + results = COCOResults(*iou_types) + logger.info("Evaluating predictions") + for iou_type in iou_types: + with tempfile.NamedTemporaryFile() as f: + file_path = f.name + if output_folder: + file_path = os.path.join(output_folder, iou_type + ".json") + if dataset.coco: + res = evaluate_predictions_on_coco(dataset.coco, coco_results[iou_type], file_path, iou_type) + results.update(res) + elif output_folder: + with open(file_path, "w") as f: + json.dump(coco_results[iou_type], f) + + logger.info(results) + check_expected_results(results, expected_results, expected_results_sigma_tol) + if output_folder: + torch.save(results, os.path.join(output_folder, "coco_results.pth")) + return results, coco_results + + +def prepare_for_tsv_detection(predictions, dataset): + # assert isinstance(dataset, COCODataset) + proposal_results = [] + image_list = [] + for im_id, prediction in enumerate(predictions): + image_info = dataset.get_img_info(im_id) + if len(prediction) == 0: + continue + + # TODO replace with get_img_info? + image_id = image_info["id"] + image_width = image_info["width"] + image_height = image_info["height"] + prediction = prediction.resize((image_width, image_height)) + prediction = prediction.convert("xywh") + + boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + if prediction.has_field("centers"): + centers = prediction.get_field("centers") + else: + centers = None + + for k, box in enumerate(boxes): + proposal = { + "image_id": image_id, + "category_id": labels[k], + "bbox": box, + "score": scores[k], + "area": image_width * image_height, + "iscrowd": 0, + } + if centers is not None: + proposal.update(center=centers[k].tolist()) + proposal_results.append(proposal) + + image_list.append(image_info) + + # categories = [{'supercategory': 'proposal', 'id': 0, 'name': 'proposal'}] + return dict(images=image_list, annotations=proposal_results) + + +def prepare_for_coco_detection(predictions, dataset): + # assert isinstance(dataset, COCODataset) + coco_results = [] + for image_id, prediction in enumerate(predictions): + original_id = dataset.id_to_img_map[image_id] + if len(prediction) == 0: + continue + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + prediction = prediction.convert("xywh") + + boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + + for k, box in enumerate(boxes): + if labels[k] in dataset.contiguous_category_id_to_json_id: + coco_results.append( + { + "image_id": original_id, + "category_id": dataset.contiguous_category_id_to_json_id[labels[k]], + "bbox": box, + "score": scores[k], + } + ) + + return coco_results + + +def prepare_for_coco_segmentation(predictions, dataset): + import pycocotools.mask as mask_util + import numpy as np + + masker = Masker(threshold=0.5, padding=1) + # assert isinstance(dataset, COCODataset) + coco_results = [] + for image_id, prediction in tqdm(enumerate(predictions)): + original_id = dataset.id_to_img_map[image_id] + if len(prediction) == 0: + continue + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + masks = prediction.get_field("mask") + # t = time.time() + # Masker is necessary only if masks haven't been already resized. + if list(masks.shape[-2:]) != [image_height, image_width]: + masks = masker(masks.expand(1, -1, -1, -1, -1), prediction) + masks = masks[0] + # logger.info('Time mask: {}'.format(time.time() - t)) + # prediction = prediction.convert('xywh') + + # boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + + # rles = prediction.get_field('mask') + + rles = [mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0] for mask in masks] + for rle in rles: + rle["counts"] = rle["counts"].decode("utf-8") + + mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels] + + coco_results.extend( + [ + { + "image_id": original_id, + "category_id": mapped_labels[k], + "segmentation": rle, + "score": scores[k], + } + for k, rle in enumerate(rles) + ] + ) + return coco_results + + +def prepare_for_coco_keypoint(predictions, dataset): + # assert isinstance(dataset, COCODataset) + coco_results = [] + for image_id, prediction in enumerate(predictions): + original_id = dataset.id_to_img_map[image_id] + if len(prediction.bbox) == 0: + continue + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + prediction = prediction.convert("xywh") + + boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + keypoints = prediction.get_field("keypoints") + keypoints = keypoints.resize((image_width, image_height)) + keypoints = keypoints.to_coco_format() + + mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels] + + coco_results.extend( + [ + {"image_id": original_id, "category_id": mapped_labels[k], "keypoints": keypoint, "score": scores[k]} + for k, keypoint in enumerate(keypoints) + ] + ) + return coco_results + + +# inspired from Detectron +def evaluate_box_proposals(predictions, dataset, thresholds=None, area="all", limit=None): + """Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for image_id, prediction in enumerate(predictions): + original_id = dataset.id_to_img_map[image_id] + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + if prediction.has_field("objectness"): + inds = prediction.get_field("objectness").sort(descending=True)[1] + else: + inds = prediction.get_field("scores").sort(descending=True)[1] + prediction = prediction[inds] + + ann_ids = dataset.coco.getAnnIds(imgIds=original_id) + anno = dataset.coco.loadAnns(ann_ids) + gt_boxes = [obj["bbox"] for obj in anno if obj["iscrowd"] == 0] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = BoxList(gt_boxes, (image_width, image_height), mode="xywh").convert("xyxy") + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if len(prediction) == 0: + continue + + if limit is not None and len(prediction) > limit: + prediction = prediction[:limit] + + overlaps = boxlist_iou(prediction, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(prediction), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + + if len(gt_overlaps) == 0: + return { + "ar": torch.zeros(1), + "recalls": torch.zeros(1), + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + gt_overlaps = torch.cat(gt_overlaps, dim=0) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def evaluate_predictions_on_coco(coco_gt, coco_results, json_result_file, iou_type="bbox"): + import json + + with open(json_result_file, "w") as f: + json.dump(coco_results, f) + + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + coco_dt = coco_gt.loadRes(str(json_result_file)) if coco_results else COCO() + + # coco_dt = coco_gt.loadRes(coco_results) + if iou_type == "keypoints": + coco_gt = filter_valid_keypoints(coco_gt, coco_dt) + coco_eval = COCOeval(coco_gt, coco_dt, iou_type) + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + if iou_type == "bbox": + summarize_per_category(coco_eval, json_result_file.replace(".json", ".csv")) + return coco_eval + + +def summarize_per_category(coco_eval, csv_output=None): + """ + Compute and display summary metrics for evaluation results. + Note this functin can *only* be applied on the default parameter setting + """ + + def _summarize(iouThr=None, areaRng="all", maxDets=100): + p = coco_eval.params + titleStr = "Average Precision" + typeStr = "(AP)" + iouStr = "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) if iouThr is None else "{:0.2f}".format(iouThr) + result_str = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ], ".format( + titleStr, typeStr, iouStr, areaRng, maxDets + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + + # dimension of precision: [TxRxKxAxM] + s = coco_eval.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + # cacluate AP(average precision) for each category + num_classes = len(p.catIds) + avg_ap = 0.0 + for i in range(0, num_classes): + result_str += "{}, ".format(np.mean(s[:, :, i, :])) + avg_ap += np.mean(s[:, :, i, :]) + result_str += "{} \n".format(avg_ap / num_classes) + return result_str + + id2name = {} + for _, cat in coco_eval.cocoGt.cats.items(): + id2name[cat["id"]] = cat["name"] + title_str = "metric, " + for cid in coco_eval.params.catIds: + title_str += "{}, ".format(id2name[cid]) + title_str += "avg \n" + + results = [title_str] + results.append(_summarize()) + results.append(_summarize(iouThr=0.5, maxDets=coco_eval.params.maxDets[2])) + results.append(_summarize(areaRng="small", maxDets=coco_eval.params.maxDets[2])) + results.append(_summarize(areaRng="medium", maxDets=coco_eval.params.maxDets[2])) + results.append(_summarize(areaRng="large", maxDets=coco_eval.params.maxDets[2])) + + with open(csv_output, "w") as f: + for result in results: + f.writelines(result) + + +def filter_valid_keypoints(coco_gt, coco_dt): + kps = coco_dt.anns[1]["keypoints"] + for id, ann in coco_gt.anns.items(): + ann["keypoints"][2::3] = [a * b for a, b in zip(ann["keypoints"][2::3], kps[2::3])] + ann["num_keypoints"] = sum(ann["keypoints"][2::3]) + return coco_gt + + +class COCOResults(object): + METRICS = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "box_proposal": [ + "AR@100", + "ARs@100", + "ARm@100", + "ARl@100", + "AR@1000", + "ARs@1000", + "ARm@1000", + "ARl@1000", + ], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + } + + def __init__(self, *iou_types): + allowed_types = ("box_proposal", "bbox", "segm", "keypoints") + assert all(iou_type in allowed_types for iou_type in iou_types) + results = OrderedDict() + for iou_type in iou_types: + results[iou_type] = OrderedDict([(metric, -1) for metric in COCOResults.METRICS[iou_type]]) + self.results = results + + def update(self, coco_eval): + if coco_eval is None: + return + from pycocotools.cocoeval import COCOeval + + assert isinstance(coco_eval, COCOeval) + s = coco_eval.stats + iou_type = coco_eval.params.iouType + res = self.results[iou_type] + metrics = COCOResults.METRICS[iou_type] + for idx, metric in enumerate(metrics): + res[metric] = s[idx] + + def __repr__(self): + # TODO make it pretty + return repr(self.results) + + +def check_expected_results(results, expected_results, sigma_tol): + if not expected_results: + return + + logger = logging.getLogger("maskrcnn_benchmark.inference") + for task, metric, (mean, std) in expected_results: + actual_val = results.results[task][metric] + lo = mean - sigma_tol * std + hi = mean + sigma_tol * std + ok = (lo < actual_val) and (actual_val < hi) + msg = ( + "{} > {} sanity check (actual vs. expected): " "{:.3f} vs. mean={:.4f}, std={:.4}, range=({:.4f}, {:.4f})" + ).format(task, metric, actual_val, mean, std, lo, hi) + if not ok: + msg = "FAIL: " + msg + logger.error(msg) + else: + msg = "PASS: " + msg + logger.info(msg) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/flickr/__init__.py b/maskrcnn_benchmark/data/datasets/evaluation/flickr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cd063073c837183ac09aee7c6bbc4d8ad9dd47ef --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/flickr/__init__.py @@ -0,0 +1 @@ +from .flickr_eval import FlickrEvaluator diff --git a/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py b/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..4dab1251db38cbec0226fde3411e6656e4866726 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py @@ -0,0 +1,443 @@ +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.structures.bounding_box import BoxList +import json +import numpy as np +import os.path as osp +import os +from prettytable import PrettyTable + +import xml.etree.ElementTree as ET +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union + +import maskrcnn_benchmark.utils.mdetr_dist as dist + +#### The following loading utilities are imported from +#### https://github.com/BryanPlummer/flickr30k_entities/blob/68b3d6f12d1d710f96233f6bd2b6de799d6f4e5b/flickr30k_entities_utils.py +# Changelog: +# - Added typing information +# - Completed docstrings + + +def get_sentence_data(filename) -> List[Dict[str, Any]]: + """ + Parses a sentence file from the Flickr30K Entities dataset + + input: + filename - full file path to the sentence file to parse + + output: + a list of dictionaries for each sentence with the following fields: + sentence - the original sentence + phrases - a list of dictionaries for each phrase with the + following fields: + phrase - the text of the annotated phrase + first_word_index - the position of the first word of + the phrase in the sentence + phrase_id - an identifier for this phrase + phrase_type - a list of the coarse categories this + phrase belongs to + + """ + with open(filename, "r") as f: + sentences = f.read().split("\n") + + annotations = [] + for sentence in sentences: + if not sentence: + continue + + first_word = [] + phrases = [] + phrase_id = [] + phrase_type = [] + words = [] + current_phrase = [] + add_to_phrase = False + for token in sentence.split(): + if add_to_phrase: + if token[-1] == "]": + add_to_phrase = False + token = token[:-1] + current_phrase.append(token) + phrases.append(" ".join(current_phrase)) + current_phrase = [] + else: + current_phrase.append(token) + + words.append(token) + else: + if token[0] == "[": + add_to_phrase = True + first_word.append(len(words)) + parts = token.split("/") + phrase_id.append(parts[1][3:]) + phrase_type.append(parts[2:]) + else: + words.append(token) + + sentence_data = {"sentence": " ".join(words), "phrases": []} + for index, phrase, p_id, p_type in zip(first_word, phrases, phrase_id, phrase_type): + sentence_data["phrases"].append( + {"first_word_index": index, "phrase": phrase, "phrase_id": p_id, "phrase_type": p_type} + ) + + annotations.append(sentence_data) + + return annotations + + +def get_annotations(filename) -> Dict[str, Union[int, List[str], Dict[str, List[List[int]]]]]: + """ + Parses the xml files in the Flickr30K Entities dataset + + input: + filename - full file path to the annotations file to parse + + output: + dictionary with the following fields: + scene - list of identifiers which were annotated as + pertaining to the whole scene + nobox - list of identifiers which were annotated as + not being visible in the image + boxes - a dictionary where the fields are identifiers + and the values are its list of boxes in the + [xmin ymin xmax ymax] format + height - int representing the height of the image + width - int representing the width of the image + depth - int representing the depth of the image + """ + tree = ET.parse(filename) + root = tree.getroot() + size_container = root.findall("size")[0] + anno_info: Dict[str, Union[int, List[str], Dict[str, List[List[int]]]]] = {} + all_boxes: Dict[str, List[List[int]]] = {} + all_noboxes: List[str] = [] + all_scenes: List[str] = [] + for size_element in size_container: + assert size_element.text + anno_info[size_element.tag] = int(size_element.text) + + for object_container in root.findall("object"): + for names in object_container.findall("name"): + box_id = names.text + assert box_id + box_container = object_container.findall("bndbox") + if len(box_container) > 0: + if box_id not in all_boxes: + all_boxes[box_id] = [] + xmin = int(box_container[0].findall("xmin")[0].text) + ymin = int(box_container[0].findall("ymin")[0].text) + xmax = int(box_container[0].findall("xmax")[0].text) + ymax = int(box_container[0].findall("ymax")[0].text) + all_boxes[box_id].append([xmin, ymin, xmax, ymax]) + else: + nobndbox = int(object_container.findall("nobndbox")[0].text) + if nobndbox > 0: + all_noboxes.append(box_id) + + scene = int(object_container.findall("scene")[0].text) + if scene > 0: + all_scenes.append(box_id) + anno_info["boxes"] = all_boxes + anno_info["nobox"] = all_noboxes + anno_info["scene"] = all_scenes + + return anno_info + + +#### END of import from flickr30k_entities + + +#### Bounding box utilities imported from torchvision and converted to numpy +def box_area(boxes: np.array) -> np.array: + """ + Computes the area of a set of bounding boxes, which are specified by its + (x1, y1, x2, y2) coordinates. + + Args: + boxes (Tensor[N, 4]): boxes for which the area will be computed. They + are expected to be in (x1, y1, x2, y2) format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + + Returns: + area (Tensor[N]): area for each box + """ + assert boxes.ndim == 2 and boxes.shape[-1] == 4 + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py +# with slight modifications +def _box_inter_union(boxes1: np.array, boxes2: np.array) -> Tuple[np.array, np.array]: + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = np.maximum(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = np.minimum(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clip(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + return inter, union + + +def box_iou(boxes1: np.array, boxes2: np.array) -> np.array: + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + + Args: + boxes1 (Tensor[N, 4]) + boxes2 (Tensor[M, 4]) + + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2 + """ + inter, union = _box_inter_union(boxes1, boxes2) + iou = inter / union + return iou + + +#### End of import of box utilities + + +def _merge_boxes(boxes: List[List[int]]) -> List[List[int]]: + """ + Return the boxes corresponding to the smallest enclosing box containing all the provided boxes + The boxes are expected in [x1, y1, x2, y2] format + """ + if len(boxes) == 1: + return boxes + + np_boxes = np.asarray(boxes) + + return [[np_boxes[:, 0].min(), np_boxes[:, 1].min(), np_boxes[:, 2].max(), np_boxes[:, 3].max()]] + + +class RecallTracker: + """Utility class to track recall@k for various k, split by categories""" + + def __init__(self, topk: Sequence[int]): + """ + Parameters: + - topk : tuple of ints corresponding to the recalls being tracked (eg, recall@1, recall@10, ...) + """ + + self.total_byk_bycat: Dict[int, Dict[str, int]] = {k: defaultdict(int) for k in topk} + self.positives_byk_bycat: Dict[int, Dict[str, int]] = {k: defaultdict(int) for k in topk} + + def add_positive(self, k: int, category: str): + """Log a positive hit @k for given category""" + if k not in self.total_byk_bycat: + raise RuntimeError(f"{k} is not a valid recall threshold") + self.total_byk_bycat[k][category] += 1 + self.positives_byk_bycat[k][category] += 1 + + def add_negative(self, k: int, category: str): + """Log a negative hit @k for given category""" + if k not in self.total_byk_bycat: + raise RuntimeError(f"{k} is not a valid recall threshold") + self.total_byk_bycat[k][category] += 1 + + def report(self) -> Dict[int, Dict[str, float]]: + """Return a condensed report of the results as a dict of dict. + report[k][cat] is the recall@k for the given category + """ + report: Dict[int, Dict[str, float]] = {} + for k in self.total_byk_bycat: + assert k in self.positives_byk_bycat + report[k] = { + cat: self.positives_byk_bycat[k][cat] / self.total_byk_bycat[k][cat] for cat in self.total_byk_bycat[k] + } + return report + + +class Flickr30kEntitiesRecallEvaluator: + def __init__( + self, + flickr_path: str, + subset: str = "test", + topk: Sequence[int] = (1, 5, 10, -1), + iou_thresh: float = 0.5, + merge_boxes: bool = False, + verbose: bool = True, + ): + assert subset in ["train", "test", "val"], f"Wrong flickr subset {subset}" + + self.topk = topk + self.iou_thresh = iou_thresh + + flickr_path = Path(flickr_path) + + # We load the image ids corresponding to the current subset + with open(flickr_path / f"{subset}.txt") as file_d: + self.img_ids = [line.strip() for line in file_d] + + if verbose: + print(f"Flickr subset contains {len(self.img_ids)} images") + + # Read the box annotations for all the images + self.imgid2boxes: Dict[str, Dict[str, List[List[int]]]] = {} + + if verbose: + print("Loading annotations...") + + for img_id in self.img_ids: + anno_info = get_annotations(flickr_path / "Annotations" / f"{img_id}.xml")["boxes"] + if merge_boxes: + merged = {} + for phrase_id, boxes in anno_info.items(): + merged[phrase_id] = _merge_boxes(boxes) + anno_info = merged + self.imgid2boxes[img_id] = anno_info + + # Read the sentences annotations + self.imgid2sentences: Dict[str, List[List[Optional[Dict]]]] = {} + + if verbose: + print("Loading annotations...") + + self.all_ids: List[str] = [] + tot_phrases = 0 + for img_id in self.img_ids: + sentence_info = get_sentence_data(flickr_path / "Sentences" / f"{img_id}.txt") + self.imgid2sentences[img_id] = [None for _ in range(len(sentence_info))] + + # Some phrases don't have boxes, we filter them. + for sent_id, sentence in enumerate(sentence_info): + phrases = [phrase for phrase in sentence["phrases"] if phrase["phrase_id"] in self.imgid2boxes[img_id]] + if len(phrases) > 0: + self.imgid2sentences[img_id][sent_id] = phrases + tot_phrases += len(phrases) + + self.all_ids += [ + f"{img_id}_{k}" for k in range(len(sentence_info)) if self.imgid2sentences[img_id][k] is not None + ] + + if verbose: + print(f"There are {tot_phrases} phrases in {len(self.all_ids)} sentences to evaluate") + + def evaluate(self, predictions: List[Dict]): + evaluated_ids = set() + + recall_tracker = RecallTracker(self.topk) + + for pred in predictions: + cur_id = f"{pred['image_id']}_{pred['sentence_id']}" + if cur_id in evaluated_ids: + print( + "Warning, multiple predictions found for sentence" + f"{pred['sentence_id']} in image {pred['image_id']}" + ) + continue + + # Skip the sentences with no valid phrase + if cur_id not in self.all_ids: + if len(pred["boxes"]) != 0: + print( + f"Warning, in image {pred['image_id']} we were not expecting predictions " + f"for sentence {pred['sentence_id']}. Ignoring them." + ) + continue + + evaluated_ids.add(cur_id) + + pred_boxes = pred["boxes"] + if str(pred["image_id"]) not in self.imgid2sentences: + raise RuntimeError(f"Unknown image id {pred['image_id']}") + if not 0 <= int(pred["sentence_id"]) < len(self.imgid2sentences[str(pred["image_id"])]): + raise RuntimeError(f"Unknown sentence id {pred['sentence_id']}" f" in image {pred['image_id']}") + target_sentence = self.imgid2sentences[str(pred["image_id"])][int(pred["sentence_id"])] + + phrases = self.imgid2sentences[str(pred["image_id"])][int(pred["sentence_id"])] + if len(pred_boxes) != len(phrases): + raise RuntimeError( + f"Error, got {len(pred_boxes)} predictions, expected {len(phrases)} " + f"for sentence {pred['sentence_id']} in image {pred['image_id']}" + ) + + for cur_boxes, phrase in zip(pred_boxes, phrases): + target_boxes = self.imgid2boxes[str(pred["image_id"])][phrase["phrase_id"]] + + ious = box_iou(np.asarray(cur_boxes), np.asarray(target_boxes)) + for k in self.topk: + maxi = 0 + if k == -1: + maxi = ious.max() + else: + assert k > 0 + maxi = ious[:k].max() + if maxi >= self.iou_thresh: + recall_tracker.add_positive(k, "all") + for phrase_type in phrase["phrase_type"]: + recall_tracker.add_positive(k, phrase_type) + else: + recall_tracker.add_negative(k, "all") + for phrase_type in phrase["phrase_type"]: + recall_tracker.add_negative(k, phrase_type) + + if len(evaluated_ids) != len(self.all_ids): + print("ERROR, the number of evaluated sentence doesn't match. Missing predictions:") + un_processed = set(self.all_ids) - evaluated_ids + for missing in un_processed: + img_id, sent_id = missing.split("_") + print(f"\t sentence {sent_id} in image {img_id}") + raise RuntimeError("Missing predictions") + + return recall_tracker.report() + + +class FlickrEvaluator(object): + def __init__( + self, + flickr_path, + subset, + top_k=(1, 5, 10, -1), + iou_thresh=0.5, + merge_boxes=False, + ): + assert isinstance(top_k, (list, tuple)) + + self.evaluator = Flickr30kEntitiesRecallEvaluator( + flickr_path, subset=subset, topk=top_k, iou_thresh=iou_thresh, merge_boxes=merge_boxes, verbose=False + ) + self.predictions = [] + self.results = None + + def accumulate(self): + pass + + def update(self, predictions): + self.predictions += predictions + + def synchronize_between_processes(self): + all_predictions = dist.all_gather(self.predictions) + self.predictions = sum(all_predictions, []) + + def summarize(self): + if dist.is_main_process(): + self.results = self.evaluator.evaluate(self.predictions) + table = PrettyTable() + all_cat = sorted(list(self.results.values())[0].keys()) + table.field_names = ["Recall@k"] + all_cat + + score = {} + for k, v in self.results.items(): + cur_results = [v[cat] for cat in all_cat] + header = "Upper_bound" if k == -1 else f"Recall@{k}" + + for cat in all_cat: + score[f"{header}_{cat}"] = v[cat] + table.add_row([header] + cur_results) + + print(table) + + return score + + return None, None diff --git a/maskrcnn_benchmark/data/datasets/evaluation/lvis/_change_lvis_annotation.py b/maskrcnn_benchmark/data/datasets/evaluation/lvis/_change_lvis_annotation.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0045ed611931973f65d8df4262205c586c65ea --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/lvis/_change_lvis_annotation.py @@ -0,0 +1,11 @@ +path = "DATASET/coco/annotations/lvis_v1_minival.json" +import json + +with open(path) as f: + all = json.load(f) + +for i in all["images"]: + i["file_name"] = "/".join(i["coco_url"].split("/")[-2:]) + +with open("DATASET/coco/annotations/lvis_v1_minival_inserted_image_name.json", "w") as f: + json.dump(all, f) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py b/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py new file mode 100644 index 0000000000000000000000000000000000000000..c67c24a9b887f39342668565babe9c0838d26cbc --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis.py @@ -0,0 +1,205 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import json +import os +import time +from collections import defaultdict + +import pycocotools.mask as mask_utils +import torchvision +from PIL import Image + + +def _isArrayLike(obj): + return hasattr(obj, "__iter__") and hasattr(obj, "__len__") + + +class LVIS: + def __init__(self, annotation_path=None): + """Class for reading and visualizing annotations. + Args: + annotation_path (str): location of annotation file + """ + self.anns = {} + self.cats = {} + self.imgs = {} + self.img_ann_map = defaultdict(list) + self.cat_img_map = defaultdict(list) + self.dataset = {} + + if annotation_path is not None: + print("Loading annotations.") + + tic = time.time() + self.dataset = self._load_json(annotation_path) + print("Done (t={:0.2f}s)".format(time.time() - tic)) + + assert type(self.dataset) == dict, "Annotation file format {} not supported.".format(type(self.dataset)) + self._create_index() + + def _load_json(self, path): + with open(path, "r") as f: + return json.load(f) + + def _create_index(self): + print("Creating index.") + + self.img_ann_map = defaultdict(list) + self.cat_img_map = defaultdict(list) + + self.anns = {} + self.cats = {} + self.imgs = {} + + for ann in self.dataset["annotations"]: + self.img_ann_map[ann["image_id"]].append(ann) + self.anns[ann["id"]] = ann + + for img in self.dataset["images"]: + self.imgs[img["id"]] = img + + for cat in self.dataset["categories"]: + self.cats[cat["id"]] = cat + + for ann in self.dataset["annotations"]: + self.cat_img_map[ann["category_id"]].append(ann["image_id"]) + + print("Index created.") + + def get_ann_ids(self, img_ids=None, cat_ids=None, area_rng=None): + """Get ann ids that satisfy given filter conditions. + Args: + img_ids (int array): get anns for given imgs + cat_ids (int array): get anns for given cats + area_rng (float array): get anns for a given area range. e.g [0, inf] + Returns: + ids (int array): integer array of ann ids + """ + if img_ids is not None: + img_ids = img_ids if _isArrayLike(img_ids) else [img_ids] + if cat_ids is not None: + cat_ids = cat_ids if _isArrayLike(cat_ids) else [cat_ids] + anns = [] + if img_ids is not None: + for img_id in img_ids: + anns.extend(self.img_ann_map[img_id]) + else: + anns = self.dataset["annotations"] + + # return early if no more filtering required + if cat_ids is None and area_rng is None: + return [_ann["id"] for _ann in anns] + + cat_ids = set(cat_ids) + + if area_rng is None: + area_rng = [0, float("inf")] + + ann_ids = [ + _ann["id"] + for _ann in anns + if _ann["category_id"] in cat_ids and _ann["area"] > area_rng[0] and _ann["area"] < area_rng[1] + ] + return ann_ids + + def get_cat_ids(self): + """Get all category ids. + Returns: + ids (int array): integer array of category ids + """ + return list(self.cats.keys()) + + def get_img_ids(self): + """Get all img ids. + Returns: + ids (int array): integer array of image ids + """ + return list(self.imgs.keys()) + + def _load_helper(self, _dict, ids): + if ids is None: + return list(_dict.values()) + elif _isArrayLike(ids): + return [_dict[id] for id in ids] + else: + return [_dict[ids]] + + def load_anns(self, ids=None): + """Load anns with the specified ids. If ids=None load all anns. + Args: + ids (int array): integer array of annotation ids + Returns: + anns (dict array) : loaded annotation objects + """ + return self._load_helper(self.anns, ids) + + def load_cats(self, ids): + """Load categories with the specified ids. If ids=None load all + categories. + Args: + ids (int array): integer array of category ids + Returns: + cats (dict array) : loaded category dicts + """ + return self._load_helper(self.cats, ids) + + def load_imgs(self, ids): + """Load categories with the specified ids. If ids=None load all images. + Args: + ids (int array): integer array of image ids + Returns: + imgs (dict array) : loaded image dicts + """ + return self._load_helper(self.imgs, ids) + + def download(self, save_dir, img_ids=None): + """Download images from mscoco.org server. + Args: + save_dir (str): dir to save downloaded images + img_ids (int array): img ids of images to download + """ + imgs = self.load_imgs(img_ids) + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + for img in imgs: + file_name = os.path.join(save_dir, img["file_name"]) + if not os.path.exists(file_name): + from urllib.request import urlretrieve + + urlretrieve(img["coco_url"], file_name) + + def ann_to_rle(self, ann): + """Convert annotation which can be polygons, uncompressed RLE to RLE. + Args: + ann (dict) : annotation object + Returns: + ann (rle) + """ + img_data = self.imgs[ann["image_id"]] + h, w = img_data["height"], img_data["width"] + segm = ann["segmentation"] + if isinstance(segm, list): + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = mask_utils.frPyObjects(segm, h, w) + rle = mask_utils.merge(rles) + elif isinstance(segm["counts"], list): + # uncompressed RLE + rle = mask_utils.frPyObjects(segm, h, w) + else: + # rle + rle = ann["segmentation"] + return rle + + def ann_to_mask(self, ann): + """Convert annotation which can be polygons, uncompressed RLE, or RLE + to binary mask. + Args: + ann (dict) : annotation object + Returns: + binary mask (numpy 2D array) + """ + rle = self.ann_to_rle(ann) + return mask_utils.decode(rle) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py b/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..738d0fac452449449f7aeeadb49f1e798ca60585 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/lvis/lvis_eval.py @@ -0,0 +1,1050 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import copy +import datetime +import json +import os +from collections import OrderedDict, defaultdict + +import numpy as np +import pycocotools.mask as mask_util +import torch +import torch._six + +import maskrcnn_benchmark.utils.mdetr_dist as dist + +from maskrcnn_benchmark.utils.mdetr_dist import all_gather + + +from .lvis import LVIS + +def merge(img_ids, eval_imgs): + all_img_ids = all_gather(img_ids) + all_eval_imgs = all_gather(eval_imgs) + + merged_img_ids = [] + for p in all_img_ids: + merged_img_ids.extend(p) + + merged_eval_imgs = [] + for p in all_eval_imgs: + merged_eval_imgs.append(p) + + merged_img_ids = np.array(merged_img_ids) + merged_eval_imgs = np.concatenate(merged_eval_imgs, 2) + + # keep only unique (and in sorted order) images + merged_img_ids, idx = np.unique(merged_img_ids, return_index=True) + merged_eval_imgs = merged_eval_imgs[..., idx] + + return merged_img_ids, merged_eval_imgs + + +################################################################# +# From LVIS, with following changes: +# * fixed LVISEval constructor to accept empty dt +# * Removed logger +# * LVIS results supports numpy inputs +################################################################# + + +class Params: + def __init__(self, iou_type): + """Params for LVIS evaluation API.""" + self.img_ids = [] + self.cat_ids = [] + # np.arange causes trouble. the data point on arange is slightly + # larger than the true value + self.iou_thrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True) + self.rec_thrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True) + self.max_dets = 300 + self.area_rng = [ + [0 ** 2, 1e5 ** 2], + [0 ** 2, 32 ** 2], + [32 ** 2, 96 ** 2], + [96 ** 2, 1e5 ** 2], + ] + self.area_rng_lbl = ["all", "small", "medium", "large"] + self.use_cats = 1 + # We bin categories in three bins based how many images of the training + # set the category is present in. + # r: Rare : < 10 + # c: Common : >= 10 and < 100 + # f: Frequent: >= 100 + self.img_count_lbl = ["r", "c", "f"] + self.iou_type = iou_type + + +class LVISResults(LVIS): + def __init__(self, lvis_gt, results, max_dets=300): + """Constructor for LVIS results. + Args: + lvis_gt (LVIS class instance, or str containing path of + annotation file) + results (str containing path of result file or a list of dicts) + max_dets (int): max number of detections per image. The official + value of max_dets for LVIS is 300. + """ + super(LVISResults, self).__init__() + assert isinstance(lvis_gt, LVIS) + self.dataset["images"] = [img for img in lvis_gt.dataset["images"]] + + if isinstance(results, str): + result_anns = self._load_json(results) + elif type(results) == np.ndarray: + result_anns = self.loadNumpyAnnotations(results) + else: + result_anns = results + + if max_dets >= 0: + result_anns = self.limit_dets_per_image(result_anns, max_dets) + + if len(result_anns) > 0 and "bbox" in result_anns[0]: + self.dataset["categories"] = copy.deepcopy(lvis_gt.dataset["categories"]) + for id, ann in enumerate(result_anns): + x1, y1, w, h = ann["bbox"] + x2 = x1 + w + y2 = y1 + h + + if "segmentation" not in ann: + ann["segmentation"] = [[x1, y1, x1, y2, x2, y2, x2, y1]] + + ann["area"] = w * h + ann["id"] = id + 1 + + elif len(result_anns) > 0 and "segmentation" in result_anns[0]: + self.dataset["categories"] = copy.deepcopy(lvis_gt.dataset["categories"]) + for id, ann in enumerate(result_anns): + # Only support compressed RLE format as segmentation results + ann["area"] = mask_util.area(ann["segmentation"]) + + if "bbox" not in ann: + ann["bbox"] = mask_util.toBbox(ann["segmentation"]) + + ann["id"] = id + 1 + self.dataset["annotations"] = result_anns + self._create_index() + + # #FIXME: disabling this check for now + # img_ids_in_result = [ann["image_id"] for ann in result_anns] + + # assert set(img_ids_in_result) == ( + # set(img_ids_in_result) & set(self.get_img_ids()) + # ), "Results do not correspond to current LVIS set." + + def limit_dets_per_image(self, anns, max_dets): + img_ann = defaultdict(list) + for ann in anns: + img_ann[ann["image_id"]].append(ann) + + for img_id, _anns in img_ann.items(): + if len(_anns) <= max_dets: + continue + _anns = sorted(_anns, key=lambda ann: ann["score"], reverse=True) + img_ann[img_id] = _anns[:max_dets] + + return [ann for anns in img_ann.values() for ann in anns] + + def get_top_results(self, img_id, score_thrs): + ann_ids = self.get_ann_ids(img_ids=[img_id]) + anns = self.load_anns(ann_ids) + return list(filter(lambda ann: ann["score"] > score_thrs, anns)) + + +class LVISEval: + def __init__(self, lvis_gt, lvis_dt=None, iou_type="segm"): + """Constructor for LVISEval. + Args: + lvis_gt (LVIS class instance, or str containing path of annotation file) + lvis_dt (LVISResult class instance, or str containing path of result file, + or list of dict) + iou_type (str): segm or bbox evaluation + """ + + if iou_type not in ["bbox", "segm"]: + raise ValueError("iou_type: {} is not supported.".format(iou_type)) + + if isinstance(lvis_gt, LVIS): + self.lvis_gt = lvis_gt + elif isinstance(lvis_gt, str): + self.lvis_gt = LVIS(lvis_gt) + else: + raise TypeError("Unsupported type {} of lvis_gt.".format(lvis_gt)) + + if isinstance(lvis_dt, LVISResults): + self.lvis_dt = lvis_dt + elif isinstance(lvis_dt, (str, list)): + self.lvis_dt = LVISResults(self.lvis_gt, lvis_dt) + elif lvis_dt is not None: + raise TypeError("Unsupported type {} of lvis_dt.".format(lvis_dt)) + + # per-image per-category evaluation results + self.eval_imgs = defaultdict(list) + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iou_type=iou_type) # parameters + self.results = OrderedDict() + self.stats = [] + self.ious = {} # ious between all gts and dts + + self.params.img_ids = sorted(self.lvis_gt.get_img_ids()) + self.params.cat_ids = sorted(self.lvis_gt.get_cat_ids()) + + def _to_mask(self, anns, lvis): + for ann in anns: + rle = lvis.ann_to_rle(ann) + ann["segmentation"] = rle + + def _prepare(self): + """Prepare self._gts and self._dts for evaluation based on params.""" + + cat_ids = self.params.cat_ids if self.params.cat_ids else None + + gts = self.lvis_gt.load_anns(self.lvis_gt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids)) + dts = self.lvis_dt.load_anns(self.lvis_dt.get_ann_ids(img_ids=self.params.img_ids, cat_ids=cat_ids)) + # convert ground truth to mask if iou_type == 'segm' + if self.params.iou_type == "segm": + self._to_mask(gts, self.lvis_gt) + self._to_mask(dts, self.lvis_dt) + + # set ignore flag + for gt in gts: + if "ignore" not in gt: + gt["ignore"] = 0 + + for gt in gts: + self._gts[gt["image_id"], gt["category_id"]].append(gt) + + # For federated dataset evaluation we will filter out all dt for an + # image which belong to categories not present in gt and not present in + # the negative list for an image. In other words detector is not penalized + # for categories about which we don't have gt information about their + # presence or absence in an image. + img_data = self.lvis_gt.load_imgs(ids=self.params.img_ids) + # per image map of categories not present in image + img_nl = {d["id"]: d["neg_category_ids"] for d in img_data} + # per image list of categories present in image + img_pl = defaultdict(set) + for ann in gts: + img_pl[ann["image_id"]].add(ann["category_id"]) + # per image map of categoires which have missing gt. For these + # categories we don't penalize the detector for flase positives. + self.img_nel = {d["id"]: d["not_exhaustive_category_ids"] for d in img_data} + + for dt in dts: + img_id, cat_id = dt["image_id"], dt["category_id"] + if cat_id not in img_nl[img_id] and cat_id not in img_pl[img_id]: + continue + self._dts[img_id, cat_id].append(dt) + + self.freq_groups = self._prepare_freq_group() + + def _prepare_freq_group(self): + freq_groups = [[] for _ in self.params.img_count_lbl] + cat_data = self.lvis_gt.load_cats(self.params.cat_ids) + for idx, _cat_data in enumerate(cat_data): + frequency = _cat_data["frequency"] + freq_groups[self.params.img_count_lbl.index(frequency)].append(idx) + return freq_groups + + def evaluate(self): + """ + Run per image evaluation on given images and store results + (a list of dict) in self.eval_imgs. + """ + + self.params.img_ids = list(np.unique(self.params.img_ids)) + + if self.params.use_cats: + cat_ids = self.params.cat_ids + else: + cat_ids = [-1] + + self._prepare() + + self.ious = { + (img_id, cat_id): self.compute_iou(img_id, cat_id) for img_id in self.params.img_ids for cat_id in cat_ids + } + + # loop through images, area range, max detection number + self.eval_imgs = [ + self.evaluate_img(img_id, cat_id, area_rng) + for cat_id in cat_ids + for area_rng in self.params.area_rng + for img_id in self.params.img_ids + ] + + def _get_gt_dt(self, img_id, cat_id): + """Create gt, dt which are list of anns/dets. If use_cats is true + only anns/dets corresponding to tuple (img_id, cat_id) will be + used. Else, all anns/dets in image are used and cat_id is not used. + """ + if self.params.use_cats: + gt = self._gts[img_id, cat_id] + dt = self._dts[img_id, cat_id] + else: + gt = [_ann for _cat_id in self.params.cat_ids for _ann in self._gts[img_id, cat_id]] + dt = [_ann for _cat_id in self.params.cat_ids for _ann in self._dts[img_id, cat_id]] + return gt, dt + + def compute_iou(self, img_id, cat_id): + gt, dt = self._get_gt_dt(img_id, cat_id) + + if len(gt) == 0 and len(dt) == 0: + return [] + + # Sort detections in decreasing order of score. + idx = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in idx] + + iscrowd = [int(False)] * len(gt) + + if self.params.iou_type == "segm": + ann_type = "segmentation" + elif self.params.iou_type == "bbox": + ann_type = "bbox" + else: + raise ValueError("Unknown iou_type for iou computation.") + gt = [g[ann_type] for g in gt] + dt = [d[ann_type] for d in dt] + + # compute iou between each dt and gt region + # will return array of shape len(dt), len(gt) + ious = mask_util.iou(dt, gt, iscrowd) + return ious + + def evaluate_img(self, img_id, cat_id, area_rng): + """Perform evaluation for single category and image.""" + gt, dt = self._get_gt_dt(img_id, cat_id) + + if len(gt) == 0 and len(dt) == 0: + return None + + # Add another filed _ignore to only consider anns based on area range. + for g in gt: + if g["ignore"] or (g["area"] < area_rng[0] or g["area"] > area_rng[1]): + g["_ignore"] = 1 + else: + g["_ignore"] = 0 + + # Sort gt ignore last + gt_idx = np.argsort([g["_ignore"] for g in gt], kind="mergesort") + gt = [gt[i] for i in gt_idx] + + # Sort dt highest score first + dt_idx = np.argsort([-d["score"] for d in dt], kind="mergesort") + dt = [dt[i] for i in dt_idx] + + # load computed ious + ious = self.ious[img_id, cat_id][:, gt_idx] if len(self.ious[img_id, cat_id]) > 0 else self.ious[img_id, cat_id] + + num_thrs = len(self.params.iou_thrs) + num_gt = len(gt) + num_dt = len(dt) + + # Array to store the "id" of the matched dt/gt + gt_m = np.zeros((num_thrs, num_gt)) + dt_m = np.zeros((num_thrs, num_dt)) + + gt_ig = np.array([g["_ignore"] for g in gt]) + dt_ig = np.zeros((num_thrs, num_dt)) + + for iou_thr_idx, iou_thr in enumerate(self.params.iou_thrs): + if len(ious) == 0: + break + + for dt_idx, _dt in enumerate(dt): + iou = min([iou_thr, 1 - 1e-10]) + # information about best match so far (m=-1 -> unmatched) + # store the gt_idx which matched for _dt + m = -1 + for gt_idx, _ in enumerate(gt): + # if this gt already matched continue + if gt_m[iou_thr_idx, gt_idx] > 0: + continue + # if _dt matched to reg gt, and on ignore gt, stop + if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1: + break + # continue to next gt unless better match made + if ious[dt_idx, gt_idx] < iou: + continue + # if match successful and best so far, store appropriately + iou = ious[dt_idx, gt_idx] + m = gt_idx + + # No match found for _dt, go to next _dt + if m == -1: + continue + + # if gt to ignore for some reason update dt_ig. + # Should not be used in evaluation. + dt_ig[iou_thr_idx, dt_idx] = gt_ig[m] + # _dt match found, update gt_m, and dt_m with "id" + dt_m[iou_thr_idx, dt_idx] = gt[m]["id"] + gt_m[iou_thr_idx, m] = _dt["id"] + + # For LVIS we will ignore any unmatched detection if that category was + # not exhaustively annotated in gt. + dt_ig_mask = [ + d["area"] < area_rng[0] or d["area"] > area_rng[1] or d["category_id"] in self.img_nel[d["image_id"]] + for d in dt + ] + dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt)) # 1 X num_dt + dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0) # num_thrs X num_dt + # Based on dt_ig_mask ignore any unmatched detection by updating dt_ig + dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == 0, dt_ig_mask)) + # store results for given image and category + return { + "image_id": img_id, + "category_id": cat_id, + "area_rng": area_rng, + "dt_ids": [d["id"] for d in dt], + "gt_ids": [g["id"] for g in gt], + "dt_matches": dt_m, + "gt_matches": gt_m, + "dt_scores": [d["score"] for d in dt], + "gt_ignore": gt_ig, + "dt_ignore": dt_ig, + } + + def accumulate(self): + """Accumulate per image evaluation results and store the result in + self.eval. + """ + + if not self.eval_imgs: + print("Warning: Please run evaluate first.") + + if self.params.use_cats: + cat_ids = self.params.cat_ids + else: + cat_ids = [-1] + + num_thrs = len(self.params.iou_thrs) + num_recalls = len(self.params.rec_thrs) + num_cats = len(cat_ids) + num_area_rngs = len(self.params.area_rng) + num_imgs = len(self.params.img_ids) + + # -1 for absent categories + precision = -np.ones((num_thrs, num_recalls, num_cats, num_area_rngs)) + recall = -np.ones((num_thrs, num_cats, num_area_rngs)) + + # Initialize dt_pointers + dt_pointers = {} + for cat_idx in range(num_cats): + dt_pointers[cat_idx] = {} + for area_idx in range(num_area_rngs): + dt_pointers[cat_idx][area_idx] = {} + + # Per category evaluation + for cat_idx in range(num_cats): + Nk = cat_idx * num_area_rngs * num_imgs + for area_idx in range(num_area_rngs): + Na = area_idx * num_imgs + E = [self.eval_imgs[Nk + Na + img_idx] for img_idx in range(num_imgs)] + # Remove elements which are None + E = [e for e in E if e is not None] + if len(E) == 0: + continue + + # Append all scores: shape (N,) + dt_scores = np.concatenate([e["dt_scores"] for e in E], axis=0) + dt_ids = np.concatenate([e["dt_ids"] for e in E], axis=0) + + dt_idx = np.argsort(-dt_scores, kind="mergesort") + dt_scores = dt_scores[dt_idx] + dt_ids = dt_ids[dt_idx] + + dt_m = np.concatenate([e["dt_matches"] for e in E], axis=1)[:, dt_idx] + dt_ig = np.concatenate([e["dt_ignore"] for e in E], axis=1)[:, dt_idx] + + gt_ig = np.concatenate([e["gt_ignore"] for e in E]) + # num gt anns to consider + num_gt = np.count_nonzero(gt_ig == 0) + + if num_gt == 0: + continue + + tps = np.logical_and(dt_m, np.logical_not(dt_ig)) + fps = np.logical_and(np.logical_not(dt_m), np.logical_not(dt_ig)) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + + dt_pointers[cat_idx][area_idx] = { + "dt_ids": dt_ids, + "tps": tps, + "fps": fps, + } + + for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + num_tp = len(tp) + rc = tp / num_gt + if num_tp: + recall[iou_thr_idx, cat_idx, area_idx] = rc[-1] + else: + recall[iou_thr_idx, cat_idx, area_idx] = 0 + + # np.spacing(1) ~= eps + pr = tp / (fp + tp + np.spacing(1)) + pr = pr.tolist() + + # Replace each precision value with the maximum precision + # value to the right of that recall level. This ensures + # that the calculated AP value will be less suspectable + # to small variations in the ranking. + for i in range(num_tp - 1, 0, -1): + if pr[i] > pr[i - 1]: + pr[i - 1] = pr[i] + + rec_thrs_insert_idx = np.searchsorted(rc, self.params.rec_thrs, side="left") + + pr_at_recall = [0.0] * num_recalls + + try: + for _idx, pr_idx in enumerate(rec_thrs_insert_idx): + pr_at_recall[_idx] = pr[pr_idx] + except Exception: + pass + precision[iou_thr_idx, :, cat_idx, area_idx] = np.array(pr_at_recall) + + self.eval = { + "params": self.params, + "counts": [num_thrs, num_recalls, num_cats, num_area_rngs], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "dt_pointers": dt_pointers, + } + + def _summarize(self, summary_type, iou_thr=None, area_rng="all", freq_group_idx=None): + aidx = [idx for idx, _area_rng in enumerate(self.params.area_rng_lbl) if _area_rng == area_rng] + + if summary_type == "ap": + s = self.eval["precision"] + if iou_thr is not None: + tidx = np.where(iou_thr == self.params.iou_thrs)[0] + s = s[tidx] + if freq_group_idx is not None: + s = s[:, :, self.freq_groups[freq_group_idx], aidx] + else: + s = s[:, :, :, aidx] + else: + s = self.eval["recall"] + if iou_thr is not None: + tidx = np.where(iou_thr == self.params.iou_thrs)[0] + s = s[tidx] + s = s[:, :, aidx] + + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + return mean_s + + def summarize(self): + """Compute and display summary metrics for evaluation results.""" + if not self.eval: + raise RuntimeError("Please run accumulate() first.") + + max_dets = self.params.max_dets + + self.results["AP"] = self._summarize("ap") + self.results["AP50"] = self._summarize("ap", iou_thr=0.50) + self.results["AP75"] = self._summarize("ap", iou_thr=0.75) + self.results["APs"] = self._summarize("ap", area_rng="small") + self.results["APm"] = self._summarize("ap", area_rng="medium") + self.results["APl"] = self._summarize("ap", area_rng="large") + self.results["APr"] = self._summarize("ap", freq_group_idx=0) + self.results["APc"] = self._summarize("ap", freq_group_idx=1) + self.results["APf"] = self._summarize("ap", freq_group_idx=2) + + self.stats = np.zeros((9,)) + self.stats[0] = self._summarize("ap") + self.stats[1] = self._summarize("ap", iou_thr=0.50) + self.stats[2] = self._summarize("ap", iou_thr=0.75) + self.stats[3] = self._summarize("ap", area_rng="small") + self.stats[4] = self._summarize("ap", area_rng="medium") + self.stats[5] = self._summarize("ap", area_rng="large") + self.stats[6] = self._summarize("ap", freq_group_idx=0) + self.stats[7] = self._summarize("ap", freq_group_idx=1) + self.stats[8] = self._summarize("ap", freq_group_idx=2) + + key = "AR@{}".format(max_dets) + self.results[key] = self._summarize("ar") + + for area_rng in ["small", "medium", "large"]: + key = "AR{}@{}".format(area_rng[0], max_dets) + self.results[key] = self._summarize("ar", area_rng=area_rng) + _returned = self.print_results() + return _returned + + def run(self): + """Wrapper function which calculates the results.""" + self.evaluate() + self.accumulate() + self.summarize() + + def print_results(self): + template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}" + out_strings = [] + for key, value in self.results.items(): + max_dets = self.params.max_dets + if "AP" in key: + title = "Average Precision" + _type = "(AP)" + else: + title = "Average Recall" + _type = "(AR)" + + if len(key) > 2 and key[2].isdigit(): + iou_thr = float(key[2:]) / 100 + iou = "{:0.2f}".format(iou_thr) + else: + iou = "{:0.2f}:{:0.2f}".format(self.params.iou_thrs[0], self.params.iou_thrs[-1]) + + if len(key) > 2 and key[2] in ["r", "c", "f"]: + cat_group_name = key[2] + else: + cat_group_name = "all" + + if len(key) > 2 and key[2] in ["s", "m", "l"]: + area_rng = key[2] + else: + area_rng = "all" + + print(template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value)) + out_strings.append(template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value)) + return out_strings + + def get_results(self): + if not self.results: + print("Warning: results is empty. Call run().") + return self.results + + +################################################################# +# end of straight copy from lvis, just fixing constructor +################################################################# + + +class LvisEvaluator(object): + def __init__(self, lvis_gt, iou_types): + assert isinstance(iou_types, (list, tuple)) + # lvis_gt = copy.deepcopy(lvis_gt) + self.lvis_gt = lvis_gt + + self.iou_types = iou_types + self.coco_eval = {} + for iou_type in iou_types: + self.coco_eval[iou_type] = LVISEval(lvis_gt, iou_type=iou_type) + + self.img_ids = [] + self.eval_imgs = {k: [] for k in iou_types} + + def update(self, predictions): + img_ids = list(np.unique(list(predictions.keys()))) + self.img_ids.extend(img_ids) + + for iou_type in self.iou_types: + results = self.prepare(predictions, iou_type) + lvis_dt = LVISResults(self.lvis_gt, results) + lvis_eval = self.coco_eval[iou_type] + + lvis_eval.lvis_dt = lvis_dt + lvis_eval.params.img_ids = list(img_ids) + lvis_eval.evaluate() + eval_imgs = lvis_eval.eval_imgs + eval_imgs = np.asarray(eval_imgs).reshape( + len(lvis_eval.params.cat_ids), len(lvis_eval.params.area_rng), len(lvis_eval.params.img_ids) + ) + + self.eval_imgs[iou_type].append(eval_imgs) + + def synchronize_between_processes(self): + for iou_type in self.iou_types: + self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2) + create_common_lvis_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type]) + + def accumulate(self): + for lvis_eval in self.coco_eval.values(): + lvis_eval.accumulate() + + def summarize(self): + for iou_type, lvis_eval in self.coco_eval.items(): + print("IoU metric: {}".format(iou_type)) + lvis_eval.summarize() + + def prepare(self, predictions, iou_type): + if iou_type == "bbox": + return self.prepare_for_lvis_detection(predictions) + elif iou_type == "segm": + return self.prepare_for_lvis_segmentation(predictions) + elif iou_type == "keypoints": + return self.prepare_for_lvis_keypoint(predictions) + else: + raise ValueError("Unknown iou type {}".format(iou_type)) + + def prepare_for_lvis_detection(self, predictions): + lvis_results = [] + for original_id, prediction in predictions.items(): + if len(prediction) == 0: + continue + + boxes = prediction["boxes"] + boxes = convert_to_xywh(boxes).tolist() + scores = prediction["scores"].tolist() + labels = prediction["labels"].tolist() + + lvis_results.extend( + [ + { + "image_id": original_id, + "category_id": labels[k], + "bbox": box, + "score": scores[k], + } + for k, box in enumerate(boxes) + ] + ) + return lvis_results + + def prepare_for_lvis_segmentation(self, predictions): + lvis_results = [] + for original_id, prediction in predictions.items(): + if len(prediction) == 0: + continue + + scores = prediction["scores"] + labels = prediction["labels"] + masks = prediction["masks"] + + masks = masks > 0.5 + + scores = prediction["scores"].tolist() + labels = prediction["labels"].tolist() + + rles = [ + mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0] for mask in masks + ] + for rle in rles: + rle["counts"] = rle["counts"].decode("utf-8") + + lvis_results.extend( + [ + { + "image_id": original_id, + "category_id": labels[k], + "segmentation": rle, + "score": scores[k], + } + for k, rle in enumerate(rles) + ] + ) + return lvis_results + + +def _merge_lists(listA, listB, maxN, key): + result = [] + indA, indB = 0, 0 + while (indA < len(listA) or indB < len(listB)) and len(result) < maxN: + if (indB < len(listB)) and (indA >= len(listA) or key(listA[indA]) < key(listB[indB])): + result.append(listB[indB]) + indB += 1 + else: + result.append(listA[indA]) + indA += 1 + return result + + +# Adapted from https://github.com/achalddave/large-vocab-devil/blob/9aaddc15b00e6e0d370b16743233e40d973cd53f/scripts/evaluate_ap_fixed.py +class LvisEvaluatorFixedAP(object): + def __init__(self, gt: LVIS, topk=10000, fixed_ap=True): + + self.results = [] + self.by_cat = {} + self.gt = gt + self.topk = topk + self.fixed_ap = fixed_ap + + + def update(self, predictions): + cur_results = self.prepare(predictions) + if self.fixed_ap: + by_cat = defaultdict(list) + for ann in cur_results: + by_cat[ann["category_id"]].append(ann) + + for cat, cat_anns in by_cat.items(): + if cat not in self.by_cat: + self.by_cat[cat] = [] + + cur = sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk] + self.by_cat[cat] = _merge_lists(self.by_cat[cat], cur, self.topk, key=lambda x: x["score"]) + else: + by_id = defaultdict(list) + for ann in cur_results: + by_id[ann["image_id"]].append(ann) + + for id_anns in by_id.values(): + self.results.extend(sorted(id_anns, key=lambda x: x["score"], reverse=True)[:300]) + + def synchronize_between_processes(self): + if self.fixed_ap: + all_cats = dist.all_gather(self.by_cat) + self.by_cat = defaultdict(list) + for cats in all_cats: + for cat, cat_anns in cats.items(): + self.by_cat[cat].extend(cat_anns) + else: + self.results = sum(dist.all_gather(self.results), []) + + def prepare(self, predictions): + lvis_results = [] + for original_id, prediction in predictions: + if len(prediction) == 0: + continue + + boxes = prediction["boxes"] + boxes = convert_to_xywh(boxes).tolist() + scores = prediction["scores"].tolist() + labels = prediction["labels"].tolist() + + lvis_results.extend( + [ + { + "image_id": original_id, + "category_id": labels[k], + "bbox": box, + "score": scores[k], + } + for k, box in enumerate(boxes) + ] + ) + return lvis_results + + def summarize(self): + if not dist.is_main_process(): + return + + if self.fixed_ap: + return self._summarize_fixed() + else: + return self._summarize_standard() + + def _summarize_standard(self): + results = LVISResults(self.gt, self.results) + lvis_eval = LVISEval(self.gt, results, iou_type="bbox") + lvis_eval.run() + lvis_eval.print_results() + + def _summarize_fixed(self): + results = [] + + missing_dets_cats = set() + for cat, cat_anns in self.by_cat.items(): + if len(cat_anns) < self.topk: + missing_dets_cats.add(cat) + results.extend(sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk]) + if missing_dets_cats: + print( + f"\n===\n" + f"{len(missing_dets_cats)} classes had less than {self.topk} detections!\n" + f"Outputting {self.topk} detections for each class will improve AP further.\n" + f"If using detectron2, please use the lvdevil/infer_topk.py script to " + f"output a results file with {self.topk} detections for each class.\n" + f"===" + ) + + results = LVISResults(self.gt, results, max_dets=-1) + lvis_eval = LVISEval(self.gt, results, iou_type="bbox") + params = lvis_eval.params + params.max_dets = -1 # No limit on detections per image. + lvis_eval.run() + scores = lvis_eval.print_results() + metrics = {k: v for k, v in lvis_eval.results.items() if k.startswith("AP")} + + try: + obj_cat_ids = [] + # 200 semantic part classes to object-part cats + part_id_to_obj_part_ids = defaultdict(list) + id2name = {} + for x in self.gt.dataset["categories"]: + if ":" in x["name"]: + part_id_to_obj_part_ids[x["name"].split(":")[-1]].append(x["id"]) + else: + obj_cat_ids.append(x["id"]) + id2name[x['id']] = x['name'] + sorted_cats = sorted(self.gt.dataset["categories"], key=lambda x: x["id"]) + obj_cats_to_cont_id_eval = {cat["id"]: _i for _i, cat in enumerate(sorted_cats)} + + def get_mean_AP(aps): + aps = np.array(aps) + return np.mean(aps[aps > -1]) + def get_AP_from_precisions(precisions, idx): + precision = precisions[:, :, idx, 0] + precision = precision[precision > -1] + return np.mean(precision) if precision.size else float("nan") + precisions = lvis_eval.eval["precision"] + #print(precisions) + #1/0 + results_processed = {} + obj_results = [] + obj_results_per_class = {} + for obj_cat in obj_cat_ids: + idx = obj_cats_to_cont_id_eval[obj_cat] + ap = get_AP_from_precisions(precisions, idx) + obj_results.append(float(ap * 100)) + obj_results_per_class[id2name[obj_cat]] = ap * 100 + results_processed["obj-AP"] = get_mean_AP(list(obj_results_per_class.values())) + results_processed["per-obj-AP"] = obj_results_per_class + part_results_per_class = {} + for part, obj_part_ids in part_id_to_obj_part_ids.items(): + results_for_part = [] + for _id in obj_part_ids: + idx = obj_cats_to_cont_id_eval[_id] + ap = get_AP_from_precisions(precisions, idx) + results_for_part.append(float(ap * 100)) + part_results_per_class[part] = get_mean_AP(results_for_part) + + overall_part_res = np.array(list(part_results_per_class.values())) + results_processed["obj-part-AP-heirarchical"] = np.mean( + overall_part_res[overall_part_res > -1] + ) + results_processed["per-part-AP"] = part_results_per_class + print(results_processed) + except: + print("no part evaluation") + print("copypaste: %s,%s", ",".join(map(str, metrics.keys())), "path") + return scores, results_processed + + +class LvisDumper(object): + def __init__(self, topk=10000, fixed_ap=True, out_path="lvis_eval"): + + self.results = [] + self.by_cat = {} + self.topk = topk + self.fixed_ap = fixed_ap + self.out_path = out_path + if dist.is_main_process(): + if not os.path.exists(self.out_path): + os.mkdir(self.out_path) + + def update(self, predictions): + cur_results = self.prepare(predictions) + if self.fixed_ap: + by_cat = defaultdict(list) + for ann in cur_results: + by_cat[ann["category_id"]].append(ann) + + for cat, cat_anns in by_cat.items(): + if cat not in self.by_cat: + self.by_cat[cat] = [] + + cur = sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk] + self.by_cat[cat] = _merge_lists(self.by_cat[cat], cur, self.topk, key=lambda x: x["score"]) + else: + by_id = defaultdict(list) + for ann in cur_results: + by_id[ann["image_id"]].append(ann) + + for id_anns in by_id.values(): + self.results.extend(sorted(id_anns, key=lambda x: x["score"], reverse=True)[:300]) + + def synchronize_between_processes(self): + if self.fixed_ap: + all_cats = dist.all_gather(self.by_cat) + self.by_cat = defaultdict(list) + for cats in all_cats: + for cat, cat_anns in cats.items(): + self.by_cat[cat].extend(cat_anns) + else: + self.results = sum(dist.all_gather(self.results), []) + + def prepare(self, predictions): + lvis_results = [] + for original_id, prediction in predictions: + if len(prediction) == 0: + continue + + boxes = prediction["boxes"] + boxes = convert_to_xywh(boxes).tolist() + scores = prediction["scores"].tolist() + labels = prediction["labels"].tolist() + + lvis_results.extend( + [ + { + "image_id": original_id, + "category_id": labels[k], + "bbox": box, + "score": scores[k], + } + for k, box in enumerate(boxes) + ] + ) + return lvis_results + + def summarize(self): + if not dist.is_main_process(): + return + + if self.fixed_ap: + self._summarize_fixed() + else: + self._summarize_standard() + + def _summarize_standard(self): + json_path = os.path.join(self.out_path, "results.json") + print("dumping to ", json_path) + with open(json_path, "w") as f: + json.dump(self.results, f) + + print("dumped") + + def _summarize_fixed(self): + results = [] + + missing_dets_cats = set() + for cat, cat_anns in self.by_cat.items(): + if len(cat_anns) < self.topk: + missing_dets_cats.add(cat) + results.extend(sorted(cat_anns, key=lambda x: x["score"], reverse=True)[: self.topk]) + if missing_dets_cats: + print( + f"\n===\n" + f"{len(missing_dets_cats)} classes had less than {self.topk} detections!\n" + f"Outputting {self.topk} detections for each class will improve AP further.\n" + f"If using detectron2, please use the lvdevil/infer_topk.py script to " + f"output a results file with {self.topk} detections for each class.\n" + f"===" + ) + + json_path = os.path.join(self.out_path, "results.json") + print("dumping to ", json_path) + with open(json_path, "w") as f: + json.dump(results, f) + + print("dumped") + + +def convert_to_xywh(boxes): + xmin, ymin, xmax, ymax = boxes.unbind(1) + return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1) + + +def create_common_lvis_eval(lvis_eval, img_ids, eval_imgs): + img_ids, eval_imgs = merge(img_ids, eval_imgs) + img_ids = list(img_ids) + eval_imgs = list(eval_imgs.flatten()) + + lvis_eval.eval_imgs = eval_imgs + lvis_eval.params.img_ids = img_ids + +def lvis_evaluation(): + pass diff --git a/maskrcnn_benchmark/data/datasets/evaluation/od_eval.py b/maskrcnn_benchmark/data/datasets/evaluation/od_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/__init__.py b/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..267b7ac2cdfd1cc0147899c6ce51449c815afd7a --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/__init__.py @@ -0,0 +1,21 @@ +from .od_eval import do_od_evaluation + + +def od_to_grounding_evaluation( + dataset, + predictions, + output_folder, + box_only=False, + iou_types=("bbox",), + expected_results=(), + expected_results_sigma_tol=4, +): + return do_od_evaluation( + dataset=dataset, + predictions=predictions, + box_only=box_only, + output_folder=output_folder, + iou_types=iou_types, + expected_results=expected_results, + expected_results_sigma_tol=expected_results_sigma_tol, + ) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/od_eval.py b/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/od_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..121b624e71410d0d00d5016684b645dadc8d63b6 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/od_to_grounding/od_eval.py @@ -0,0 +1,517 @@ +import logging +import tempfile +import os +import torch +import numpy as np +import json + +from collections import OrderedDict +from tqdm import tqdm + +from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou + + +def do_od_evaluation( + dataset, + predictions, + box_only, + output_folder, + iou_types, + expected_results, + expected_results_sigma_tol, +): + logger = logging.getLogger("maskrcnn_benchmark.inference") + + if box_only: + logger.info("Evaluating bbox proposals") + if dataset.coco is None and output_folder: + json_results = prepare_for_tsv_detection(predictions, dataset) + with open(os.path.join(output_folder, "box_proposals.json"), "w") as f: + json.dump(json_results, f) + return None + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + res = COCOResults("box_proposal") + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = evaluate_box_proposals(predictions, dataset, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res.results["box_proposal"][key] = stats["ar"].item() + logger.info(res) + check_expected_results(res, expected_results, expected_results_sigma_tol) + if output_folder: + torch.save(res, os.path.join(output_folder, "box_proposals.pth")) + return res, predictions + logger.info("Preparing results for COCO format") + coco_results = {} + if "bbox" in iou_types: + logger.info("Preparing bbox results") + if dataset.coco is None: + coco_results["bbox"] = prepare_for_tsv_detection(predictions, dataset) + else: + coco_results["bbox"] = prepare_for_coco_detection(predictions, dataset) + if "segm" in iou_types: + logger.info("Preparing segm results") + coco_results["segm"] = prepare_for_coco_segmentation(predictions, dataset) + if "keypoints" in iou_types: + logger.info("Preparing keypoints results") + coco_results["keypoints"] = prepare_for_coco_keypoint(predictions, dataset) + + results = COCOResults(*iou_types) + logger.info("Evaluating predictions") + for iou_type in iou_types: + with tempfile.NamedTemporaryFile() as f: + file_path = f.name + if output_folder: + file_path = os.path.join(output_folder, iou_type + ".json") + if dataset.coco: + res = evaluate_predictions_on_coco(dataset.coco, coco_results[iou_type], file_path, iou_type) + results.update(res) + elif output_folder: + with open(file_path, "w") as f: + json.dump(coco_results[iou_type], f) + + logger.info(results) + check_expected_results(results, expected_results, expected_results_sigma_tol) + if output_folder: + torch.save(results, os.path.join(output_folder, "coco_results.pth")) + return results, coco_results + + +def prepare_for_tsv_detection(predictions, dataset): + # assert isinstance(dataset, COCODataset) + proposal_results = [] + image_list = [] + for im_id, prediction in enumerate(predictions): + image_info = dataset.get_img_info(im_id) + if len(prediction) == 0: + continue + + # TODO replace with get_img_info? + image_id = image_info["id"] + image_width = image_info["width"] + image_height = image_info["height"] + prediction = prediction.resize((image_width, image_height)) + prediction = prediction.convert("xywh") + + boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + if prediction.has_field("centers"): + centers = prediction.get_field("centers") + else: + centers = None + + for k, box in enumerate(boxes): + proposal = { + "image_id": image_id, + "category_id": labels[k], + "bbox": box, + "score": scores[k], + "area": image_width * image_height, + "iscrowd": 0, + } + if centers is not None: + proposal.update(center=centers[k].tolist()) + proposal_results.append(proposal) + + image_list.append(image_info) + + # categories = [{'supercategory': 'proposal', 'id': 0, 'name': 'proposal'}] + return dict(images=image_list, annotations=proposal_results) + + +def prepare_for_coco_detection(predictions, dataset): + # assert isinstance(dataset, COCODataset) + coco_results = [] + for image_id, prediction in enumerate(predictions): + original_id = dataset.id_to_img_map[image_id] + if len(prediction) == 0: + continue + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + prediction = prediction.convert("xywh") + + boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + + for k, box in enumerate(boxes): + if labels[k] in dataset.contiguous_category_id_to_json_id: + coco_results.append( + { + "image_id": original_id, + "category_id": dataset.contiguous_category_id_to_json_id[labels[k]], + "bbox": box, + "score": scores[k], + } + ) + + return coco_results + + +def prepare_for_coco_segmentation(predictions, dataset): + import pycocotools.mask as mask_util + import numpy as np + + masker = Masker(threshold=0.5, padding=1) + # assert isinstance(dataset, COCODataset) + coco_results = [] + for image_id, prediction in tqdm(enumerate(predictions)): + original_id = dataset.id_to_img_map[image_id] + if len(prediction) == 0: + continue + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + masks = prediction.get_field("mask") + # t = time.time() + # Masker is necessary only if masks haven't been already resized. + if list(masks.shape[-2:]) != [image_height, image_width]: + masks = masker(masks.expand(1, -1, -1, -1, -1), prediction) + masks = masks[0] + # logger.info('Time mask: {}'.format(time.time() - t)) + # prediction = prediction.convert('xywh') + + # boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + + # rles = prediction.get_field('mask') + + rles = [mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0] for mask in masks] + for rle in rles: + rle["counts"] = rle["counts"].decode("utf-8") + + mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels] + + coco_results.extend( + [ + { + "image_id": original_id, + "category_id": mapped_labels[k], + "segmentation": rle, + "score": scores[k], + } + for k, rle in enumerate(rles) + ] + ) + return coco_results + + +def prepare_for_coco_keypoint(predictions, dataset): + # assert isinstance(dataset, COCODataset) + coco_results = [] + for image_id, prediction in enumerate(predictions): + original_id = dataset.id_to_img_map[image_id] + if len(prediction.bbox) == 0: + continue + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + prediction = prediction.convert("xywh") + + boxes = prediction.bbox.tolist() + scores = prediction.get_field("scores").tolist() + labels = prediction.get_field("labels").tolist() + keypoints = prediction.get_field("keypoints") + keypoints = keypoints.resize((image_width, image_height)) + keypoints = keypoints.to_coco_format() + + mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels] + + coco_results.extend( + [ + {"image_id": original_id, "category_id": mapped_labels[k], "keypoints": keypoint, "score": scores[k]} + for k, keypoint in enumerate(keypoints) + ] + ) + return coco_results + + +# inspired from Detectron +def evaluate_box_proposals(predictions, dataset, thresholds=None, area="all", limit=None): + """Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for image_id, prediction in enumerate(predictions): + original_id = dataset.id_to_img_map[image_id] + + # TODO replace with get_img_info? + image_width = dataset.coco.imgs[original_id]["width"] + image_height = dataset.coco.imgs[original_id]["height"] + prediction = prediction.resize((image_width, image_height)) + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + if prediction.has_field("objectness"): + inds = prediction.get_field("objectness").sort(descending=True)[1] + else: + inds = prediction.get_field("scores").sort(descending=True)[1] + prediction = prediction[inds] + + ann_ids = dataset.coco.getAnnIds(imgIds=original_id) + anno = dataset.coco.loadAnns(ann_ids) + gt_boxes = [obj["bbox"] for obj in anno if obj["iscrowd"] == 0] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = BoxList(gt_boxes, (image_width, image_height), mode="xywh").convert("xyxy") + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if len(prediction) == 0: + continue + + if limit is not None and len(prediction) > limit: + prediction = prediction[:limit] + + overlaps = boxlist_iou(prediction, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(prediction), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + + if len(gt_overlaps) == 0: + return { + "ar": torch.zeros(1), + "recalls": torch.zeros(1), + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + gt_overlaps = torch.cat(gt_overlaps, dim=0) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def evaluate_predictions_on_coco(coco_gt, coco_results, json_result_file, iou_type="bbox"): + import json + + with open(json_result_file, "w") as f: + json.dump(coco_results, f) + + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + coco_dt = coco_gt.loadRes(str(json_result_file)) if coco_results else COCO() + + # coco_dt = coco_gt.loadRes(coco_results) + if iou_type == "keypoints": + coco_gt = filter_valid_keypoints(coco_gt, coco_dt) + coco_eval = COCOeval(coco_gt, coco_dt, iou_type) + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + if iou_type == "bbox": + summarize_per_category(coco_eval, json_result_file.replace(".json", ".csv")) + return coco_eval + + +def summarize_per_category(coco_eval, csv_output=None): + """ + Compute and display summary metrics for evaluation results. + Note this functin can *only* be applied on the default parameter setting + """ + + def _summarize(iouThr=None, areaRng="all", maxDets=100): + p = coco_eval.params + titleStr = "Average Precision" + typeStr = "(AP)" + iouStr = "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) if iouThr is None else "{:0.2f}".format(iouThr) + result_str = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ], ".format( + titleStr, typeStr, iouStr, areaRng, maxDets + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + + # dimension of precision: [TxRxKxAxM] + s = coco_eval.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + # cacluate AP(average precision) for each category + num_classes = len(p.catIds) + avg_ap = 0.0 + for i in range(0, num_classes): + result_str += "{}, ".format(np.mean(s[:, :, i, :])) + avg_ap += np.mean(s[:, :, i, :]) + result_str += "{} \n".format(avg_ap / num_classes) + return result_str + + id2name = {} + for _, cat in coco_eval.cocoGt.cats.items(): + id2name[cat["id"]] = cat["name"] + title_str = "metric, " + for cid in coco_eval.params.catIds: + title_str += "{}, ".format(id2name[cid]) + title_str += "avg \n" + + results = [title_str] + results.append(_summarize()) + results.append(_summarize(iouThr=0.5, maxDets=coco_eval.params.maxDets[2])) + results.append(_summarize(areaRng="small", maxDets=coco_eval.params.maxDets[2])) + results.append(_summarize(areaRng="medium", maxDets=coco_eval.params.maxDets[2])) + results.append(_summarize(areaRng="large", maxDets=coco_eval.params.maxDets[2])) + + with open(csv_output, "w") as f: + for result in results: + f.writelines(result) + + +def filter_valid_keypoints(coco_gt, coco_dt): + kps = coco_dt.anns[1]["keypoints"] + for id, ann in coco_gt.anns.items(): + ann["keypoints"][2::3] = [a * b for a, b in zip(ann["keypoints"][2::3], kps[2::3])] + ann["num_keypoints"] = sum(ann["keypoints"][2::3]) + return coco_gt + + +class COCOResults(object): + METRICS = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "box_proposal": [ + "AR@100", + "ARs@100", + "ARm@100", + "ARl@100", + "AR@1000", + "ARs@1000", + "ARm@1000", + "ARl@1000", + ], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + } + + def __init__(self, *iou_types): + allowed_types = ("box_proposal", "bbox", "segm", "keypoints") + assert all(iou_type in allowed_types for iou_type in iou_types) + results = OrderedDict() + for iou_type in iou_types: + results[iou_type] = OrderedDict([(metric, -1) for metric in COCOResults.METRICS[iou_type]]) + self.results = results + + def update(self, coco_eval): + if coco_eval is None: + return + from pycocotools.cocoeval import COCOeval + + assert isinstance(coco_eval, COCOeval) + s = coco_eval.stats + iou_type = coco_eval.params.iouType + res = self.results[iou_type] + metrics = COCOResults.METRICS[iou_type] + for idx, metric in enumerate(metrics): + res[metric] = s[idx] + + def __repr__(self): + # TODO make it pretty + return repr(self.results) + + +def check_expected_results(results, expected_results, sigma_tol): + if not expected_results: + return + + logger = logging.getLogger("maskrcnn_benchmark.inference") + for task, metric, (mean, std) in expected_results: + actual_val = results.results[task][metric] + lo = mean - sigma_tol * std + hi = mean + sigma_tol * std + ok = (lo < actual_val) and (actual_val < hi) + msg = ( + "{} > {} sanity check (actual vs. expected): " "{:.3f} vs. mean={:.4f}, std={:.4}, range=({:.4f}, {:.4f})" + ).format(task, metric, actual_val, mean, std, lo, hi) + if not ok: + msg = "FAIL: " + msg + logger.error(msg) + else: + msg = "PASS: " + msg + logger.info(msg) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/vg/__init__.py b/maskrcnn_benchmark/data/datasets/evaluation/vg/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ef18b3e5e9b007018fd7c839c7d053c48c2984d3 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/vg/__init__.py @@ -0,0 +1,16 @@ +import logging + +from .vg_eval import do_vg_evaluation + + +def vg_evaluation(dataset, predictions, output_folder, box_only, eval_attributes=False, **_): + logger = logging.getLogger("maskrcnn_benchmark.inference") + logger.info("performing vg evaluation, ignored iou_types.") + return do_vg_evaluation( + dataset=dataset, + predictions=predictions, + output_folder=output_folder, + box_only=box_only, + eval_attributes=eval_attributes, + logger=logger, + ) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/vg/vg_eval.py b/maskrcnn_benchmark/data/datasets/evaluation/vg/vg_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..08fa2505e4f4e69992a4acf7ea94c07ed7f89c26 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/vg/vg_eval.py @@ -0,0 +1,671 @@ +# A modification version from chainercv repository. +# (See https://github.com/chainer/chainercv/blob/master/chainercv/evaluations/eval_detection_voc.py) +from __future__ import division + +import os +from collections import OrderedDict +import numpy as np +import torch +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou, getUnionBBox + + +# inspired from Detectron +def evaluate_box_proposals(predictions, dataset, thresholds=None, area="all", limit=None): + """Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for image_id, prediction in enumerate(predictions): + img_info = dataset.get_img_info(image_id) + image_width = img_info["width"] + image_height = img_info["height"] + prediction = prediction.resize((image_width, image_height)) + + # deal with ground truth + gt_boxes = dataset.get_groundtruth(image_id) + # filter out the field "relations" + gt_boxes = gt_boxes.copy_with_fields(["attributes", "labels"]) + gt_areas = gt_boxes.area() + + if len(gt_boxes) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + _gt_overlaps = torch.zeros(len(gt_boxes)) + if len(prediction) == 0: + gt_overlaps.append(_gt_overlaps) + continue + if "objectness" in prediction.extra_fields: + inds = prediction.get_field("objectness").sort(descending=True)[1] + elif "scores" in prediction.extra_fields: + inds = prediction.get_field("scores").sort(descending=True)[1] + else: + raise ValueError("Neither objectness nor scores is in the extra_fields!") + prediction = prediction[inds] + + if limit is not None and len(prediction) > limit: + prediction = prediction[:limit] + + overlaps = boxlist_iou(prediction, gt_boxes) + + for j in range(min(len(prediction), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = torch.cat(gt_overlaps, dim=0) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +class VGResults(object): + METRICS = { + "bbox": [ + "AP", + ], + "segm": [ + "AP", + ], + "box_proposal": [ + "AR@100", + ], + } + + def __init__(self, iou_type, value): + allowed_types = ("box_proposal", "bbox", "segm", "keypoints") + assert iou_type in allowed_types + results = OrderedDict() + results[iou_type] = OrderedDict([(metric, value) for metric in VGResults.METRICS[iou_type]]) + self.results = results + + +def do_vg_evaluation(dataset, predictions, output_folder, box_only, eval_attributes, logger, save_predictions=True): + # TODO need to make the use_07_metric format available + # for the user to choose + # we use int for box_only. 0: False, 1: box for RPN, 2: box for object detection, + if box_only: + if box_only == 1: + limits = [100, 1000] + elif box_only == 2: + limits = [36, 99] + else: + raise ValueError("box_only can be either 0/1/2, but get {0}".format(box_only)) + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + result = {} + for area, suffix in areas.items(): + for limit in limits: + logger.info("Evaluating bbox proposals@{:d}".format(limit)) + stats = evaluate_box_proposals(predictions, dataset, area=area, limit=limit) + key_ar = "AR{}@{:d}".format(suffix, limit) + key_num_pos = "num_pos{}@{:d}".format(suffix, limit) + result[key_num_pos] = stats["num_pos"] + result[key_ar] = stats["ar"].item() + key_recalls = "Recalls{}@{:d}".format(suffix, limit) + # result[key_recalls] = stats["recalls"] + print(key_recalls, stats["recalls"]) + print(key_ar, "ar={:.4f}".format(result[key_ar])) + print(key_num_pos, "num_pos={:d}".format(result[key_num_pos])) + if limit != 1000 and dataset.relation_on: + # if True: + # relation @ 1000 (all and large) takes about 2 hs to compute + # relation pair evaluation + logger.info("Evaluating relation proposals@{:d}".format(limit)) + stats = evaluate_box_proposals_for_relation(predictions, dataset, area=area, limit=limit) + key_ar = "AR{}@{:d}_for_relation".format(suffix, limit) + key_num_pos = "num_pos{}@{:d}_for_relation".format(suffix, limit) + result[key_num_pos] = stats["num_pos"] + result[key_ar] = stats["ar"].item() + # key_recalls = "Recalls{}@{:d}_for_relation".format(suffix, limit) + # result[key_recalls] = stats["recalls"] + print(key_ar, "ar={:.4f}".format(result[key_ar])) + print(key_num_pos, "num_pos={:d}".format(result[key_num_pos])) + logger.info(result) + # check_expected_results(result, expected_results, expected_results_sigma_tol) + if output_folder and save_predictions: + if box_only == 1: + torch.save(result, os.path.join(output_folder, "rpn_proposals.pth")) + elif box_only == 2: + torch.save(result, os.path.join(output_folder, "box_proposals.pth")) + else: + raise ValueError("box_only can be either 0/1/2, but get {0}".format(box_only)) + return VGResults("box_proposal", result["AR@100"]), {"box_proposal": result} + + pred_boxlists = [] + gt_boxlists = [] + for image_id, prediction in enumerate(predictions): + img_info = dataset.get_img_info(image_id) + if len(prediction) == 0: + continue + image_width = img_info["width"] + image_height = img_info["height"] + prediction = prediction.resize((image_width, image_height)) + pred_boxlists.append(prediction) + + gt_boxlist = dataset.get_groundtruth(image_id) + gt_boxlists.append(gt_boxlist) + if eval_attributes: + classes = dataset.attributes + else: + classes = dataset.classes + result = eval_detection_voc( + pred_boxlists=pred_boxlists, + gt_boxlists=gt_boxlists, + classes=classes, + iou_thresh=0.5, + eval_attributes=eval_attributes, + use_07_metric=False, + ) + result_str = "mAP: {:.4f}\n".format(result["map"]) + logger.info(result_str) + for i, ap in enumerate(result["ap"]): + # if i == 0: # skip background + # continue + # we skipped background in result['ap'], so we need to use i+1 + if eval_attributes: + result_str += "{:<16}: {:.4f}\n".format(dataset.map_attribute_id_to_attribute_name(i + 1), ap) + else: + result_str += "{:<16}: {:.4f}\n".format(dataset.map_class_id_to_class_name(i + 1), ap) + # return mAP and weighted mAP + vg_result = VGResults("bbox", result["map"]) + if eval_attributes: + if output_folder and save_predictions: + with open(os.path.join(output_folder, "result_attr.txt"), "w") as fid: + fid.write(result_str) + return vg_result, {"attr": {"map": result["map"], "weighted map": result["weighted map"]}} + else: + if output_folder and save_predictions: + with open(os.path.join(output_folder, "result_obj.txt"), "w") as fid: + fid.write(result_str) + return ( + vg_result, + {"obj": {"map": result["map"], "weighted map": result["weighted map"]}}, + ) + + +def eval_detection_voc(pred_boxlists, gt_boxlists, classes, iou_thresh=0.5, eval_attributes=False, use_07_metric=False): + """Evaluate on voc dataset. + Args: + pred_boxlists(list[BoxList]): pred boxlist, has labels and scores fields. + gt_boxlists(list[BoxList]): ground truth boxlist, has labels field. + iou_thresh: iou thresh + use_07_metric: boolean + Returns: + dict represents the results + """ + assert len(gt_boxlists) == len(pred_boxlists), "Length of gt and pred lists need to be same." + + aps = [] + nposs = [] + thresh = [] + + for i, classname in enumerate(classes): + if classname == "__background__" or classname == "__no_attribute__": + continue + rec, prec, ap, scores, npos = calc_detection_voc_prec_rec( + pred_boxlists=pred_boxlists, + gt_boxlists=gt_boxlists, + classindex=i, + iou_thresh=iou_thresh, + eval_attributes=eval_attributes, + use_07_metric=use_07_metric, + ) + # Determine per class detection thresholds that maximise f score + # if npos > 1: + if npos > 1 and type(scores) != np.int: + f = np.nan_to_num((prec * rec) / (prec + rec)) + thresh += [scores[np.argmax(f)]] + else: + thresh += [0] + aps += [ap] + nposs += [float(npos)] + # print('AP for {} = {:.4f} (npos={:,})'.format(classname, ap, npos)) + # if pickle: + # with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f: + # cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap, + # 'scores': scores, 'npos':npos}, f) + + # Set thresh to mean for classes with poor results + thresh = np.array(thresh) + avg_thresh = np.mean(thresh[thresh != 0]) + thresh[thresh == 0] = avg_thresh + # if eval_attributes: + # filename = 'attribute_thresholds_' + self._image_set + '.txt' + # else: + # filename = 'object_thresholds_' + self._image_set + '.txt' + # path = os.path.join(output_dir, filename) + # with open(path, 'wt') as f: + # for i, cls in enumerate(classes[1:]): + # f.write('{:s} {:.3f}\n'.format(cls, thresh[i])) + + weights = np.array(nposs) + weights /= weights.sum() + # print('Mean AP = {:.4f}'.format(np.mean(aps))) + # print('Weighted Mean AP = {:.4f}'.format(np.average(aps, weights=weights))) + # print('Mean Detection Threshold = {:.3f}'.format(avg_thresh)) + # print('~~~~~~~~') + # print('Results:') + # for ap, npos in zip(aps, nposs): + # print('{:.3f}\t{:.3f}'.format(ap, npos)) + # print('{:.3f}'.format(np.mean(aps))) + # print('~~~~~~~~') + # print('') + # print('--------------------------------------------------------------') + # print('Results computed with the **unofficial** PASCAL VOC Python eval code.') + # print('--------------------------------------------------------------') + + # pdb.set_trace() + return {"ap": aps, "map": np.mean(aps), "weighted map": np.average(aps, weights=weights)} + + +def calc_detection_voc_prec_rec( + pred_boxlists, gt_boxlists, classindex, iou_thresh=0.5, eval_attributes=False, use_07_metric=False +): + """Calculate precision and recall based on evaluation code of PASCAL VOC. + This function calculates precision and recall of + predicted bounding boxes obtained from a dataset which has :math:`N` + images. + The code is based on the evaluation code used in PASCAL VOC Challenge. + """ + class_recs = {} + npos = 0 + image_ids = [] + confidence = [] + BB = [] + for image_index, (gt_boxlist, pred_boxlist) in enumerate(zip(gt_boxlists, pred_boxlists)): + pred_bbox = pred_boxlist.bbox.numpy() + gt_bbox = gt_boxlist.bbox.numpy() + if eval_attributes: + gt_label = gt_boxlist.get_field("attributes").numpy() + pred_label = pred_boxlist.get_field("attr_labels").numpy() + pred_score = pred_boxlist.get_field("attr_scores").numpy() + else: + gt_label = gt_boxlist.get_field("labels").numpy() + pred_label = pred_boxlist.get_field("labels").numpy() + pred_score = pred_boxlist.get_field("scores").numpy() + + # get the ground truth information for this class + if eval_attributes: + gt_mask_l = np.array([classindex in i for i in gt_label]) + else: + gt_mask_l = gt_label == classindex + gt_bbox_l = gt_bbox[gt_mask_l] + gt_difficult_l = np.zeros(gt_bbox_l.shape[0], dtype=bool) + det = [False] * gt_bbox_l.shape[0] + npos = npos + sum(~gt_difficult_l) + class_recs[image_index] = {"bbox": gt_bbox_l, "difficult": gt_difficult_l, "det": det} + + # prediction output for each class + # pdb.set_trace() + if eval_attributes: + pred_mask_l = np.logical_and(pred_label == classindex, np.not_equal(pred_score, 0.0)).nonzero() + pred_bbox_l = pred_bbox[pred_mask_l[0]] + pred_score_l = pred_score[pred_mask_l] + else: + pred_mask_l = pred_label == classindex + pred_bbox_l = pred_bbox[pred_mask_l] + pred_score_l = pred_score[pred_mask_l] + + for bbox_tmp, score_tmp in zip(pred_bbox_l, pred_score_l): + image_ids.append(image_index) + confidence.append(float(score_tmp)) + BB.append([float(z) for z in bbox_tmp]) + + if npos == 0: + # No ground truth examples + return 0, 0, 0, 0, npos + + if len(confidence) == 0: + # No detection examples + return 0, 0, 0, 0, npos + + confidence = np.array(confidence) + BB = np.array(BB) + + # sort by confidence + sorted_ind = np.argsort(-confidence) + sorted_scores = -np.sort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R["bbox"].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1.0, 0.0) + ih = np.maximum(iymax - iymin + 1.0, 0.0) + inters = iw * ih + + # union + uni = ( + (bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0) + + (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0) + - inters + ) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > iou_thresh: + if not R["difficult"][jmax]: + if not R["det"][jmax]: + tp[d] = 1.0 + R["det"][jmax] = 1 + else: + fp[d] = 1.0 + else: + fp[d] = 1.0 + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap, sorted_scores, npos + + +def voc_ap(rec, prec, use_07_metric=False): + """ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0.0 + for t in np.arange(0.0, 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11.0 + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.0], rec, [1.0])) + mpre = np.concatenate(([0.0], prec, [0.0])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def calc_detection_voc_ap(prec, rec, use_07_metric=False): + """Calculate average precisions based on evaluation code of PASCAL VOC. + This function calculates average precisions + from given precisions and recalls. + The code is based on the evaluation code used in PASCAL VOC Challenge. + Args: + prec (list of numpy.array): A list of arrays. + :obj:`prec[l]` indicates precision for class :math:`l`. + If :obj:`prec[l]` is :obj:`None`, this function returns + :obj:`numpy.nan` for class :math:`l`. + rec (list of numpy.array): A list of arrays. + :obj:`rec[l]` indicates recall for class :math:`l`. + If :obj:`rec[l]` is :obj:`None`, this function returns + :obj:`numpy.nan` for class :math:`l`. + use_07_metric (bool): Whether to use PASCAL VOC 2007 evaluation metric + for calculating average precision. The default value is + :obj:`False`. + Returns: + ~numpy.ndarray: + This function returns an array of average precisions. + The :math:`l`-th value corresponds to the average precision + for class :math:`l`. If :obj:`prec[l]` or :obj:`rec[l]` is + :obj:`None`, the corresponding value is set to :obj:`numpy.nan`. + """ + + n_fg_class = len(prec) + ap = np.empty(n_fg_class) + for l in range(n_fg_class): + if prec[l] is None or rec[l] is None: + ap[l] = np.nan + continue + + if use_07_metric: + # 11 point metric + ap[l] = 0 + for t in np.arange(0.0, 1.1, 0.1): + if np.sum(rec[l] >= t) == 0: + p = 0 + else: + p = np.max(np.nan_to_num(prec[l])[rec[l] >= t]) + ap[l] += p / 11 + else: + # correct AP calculation + # first append sentinel values at the end + mpre = np.concatenate(([0], np.nan_to_num(prec[l]), [0])) + mrec = np.concatenate(([0], rec[l], [1])) + + mpre = np.maximum.accumulate(mpre[::-1])[::-1] + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap[l] = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + + return ap + + +# inspired from Detectron +def evaluate_box_proposals_for_relation(predictions, dataset, thresholds=None, area="all", limit=None): + """Evaluate how many relation pairs can be captured by the proposed boxes.""" + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for image_id, prediction in enumerate(predictions): + img_info = dataset.get_img_info(image_id) + image_width = img_info["width"] + image_height = img_info["height"] + prediction = prediction.resize((image_width, image_height)) + + # deal with ground truth + gt_boxes = dataset.get_groundtruth(image_id) + # filter out the field "relation_labels" + gt_triplets = gt_boxes.get_field("relation_labels") + if len(gt_triplets) == 0: + continue + gt_boxes = gt_boxes.copy_with_fields(["attributes", "labels"]) + # get union bounding boxes (the box that cover both) + gt_relations = getUnionBBox(gt_boxes[gt_triplets[:, 0]], gt_boxes[gt_triplets[:, 1]], margin=0) + gt_relations.add_field("rel_classes", gt_triplets[:, 2]) + # focus on the range interested + gt_relation_areas = gt_relations.area() + valid_gt_inds = (gt_relation_areas >= area_range[0]) & (gt_relation_areas <= area_range[1]) + gt_relations = gt_relations[valid_gt_inds] + + num_pos += len(gt_relations) + + if len(gt_relations) == 0: + continue + + # sort predictions in descending order and limit to the number we specify + # TODO maybe remove this and make it explicit in the documentation + _gt_overlaps = torch.zeros(len(gt_relations)) + if len(prediction) == 0: + gt_overlaps.append(_gt_overlaps) + continue + if "objectness" in prediction.extra_fields: + inds = prediction.get_field("objectness").sort(descending=True)[1] + elif "scores" in prediction.extra_fields: + inds = prediction.get_field("scores").sort(descending=True)[1] + else: + raise ValueError("Neither objectness nor scores is in the extra_fields!") + prediction = prediction[inds] + if limit is not None and len(prediction) > limit: + prediction = prediction[:limit] + # get the predicted relation pairs + N = len(prediction) + map_x = np.arange(N) + map_y = np.arange(N) + map_x_g, map_y_g = np.meshgrid(map_x, map_y) + anchor_pairs = torch.from_numpy(np.vstack((map_y_g.ravel(), map_x_g.ravel())).transpose()) + # remove diagonal pairs + keep = anchor_pairs[:, 0] != anchor_pairs[:, 1] + anchor_pairs = anchor_pairs[keep] + # get anchor_relations + # anchor_relations = getUnionBBox(prediction[anchor_pairs[:,0]], prediction[anchor_pairs[:,1]], margin=0) + if len(anchor_pairs) == 0: + continue + + overlaps_sub = boxlist_iou(prediction[anchor_pairs[:, 0]], gt_boxes[gt_triplets[valid_gt_inds, 0]]) + overlaps_obj = boxlist_iou(prediction[anchor_pairs[:, 1]], gt_boxes[gt_triplets[valid_gt_inds, 1]]) + overlaps = torch.min(overlaps_sub, overlaps_obj) + + for j in range(min(len(anchor_pairs), len(gt_relations))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal pair that covers the best covered gt pair + pair_ind = argmax_overlaps[gt_ind] + # record the co-iou coverage of this gt pair + _gt_overlaps[j] = overlaps[pair_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal pair and the gt pair as used + overlaps[pair_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = torch.cat(gt_overlaps, dim=0) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } diff --git a/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py b/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7c26048b361ddd41b6e82d4bb9d5ead745f6bb07 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/voc/__init__.py @@ -0,0 +1,16 @@ +import logging + +from .voc_eval import do_voc_evaluation + + +def voc_evaluation(dataset, predictions, output_folder, box_only, **_): + logger = logging.getLogger("maskrcnn_benchmark.inference") + if box_only: + logger.warning("voc evaluation doesn't support box_only, ignored.") + logger.info("performing voc evaluation, ignored iou_types.") + return do_voc_evaluation( + dataset=dataset, + predictions=predictions, + output_folder=output_folder, + logger=logger, + ) diff --git a/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py b/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..dd5026cf4ee15d584e170c8018a1941617ce583a --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/evaluation/voc/voc_eval.py @@ -0,0 +1,210 @@ +# A modification version from chainercv repository. +# (See https://github.com/chainer/chainercv/blob/master/chainercv/evaluations/eval_detection_voc.py) +from __future__ import division + +import os +from collections import defaultdict +import numpy as np +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou + + +def do_voc_evaluation(dataset, predictions, output_folder, logger): + # TODO need to make the use_07_metric format available + # for the user to choose + pred_boxlists = [] + gt_boxlists = [] + for image_id, prediction in enumerate(predictions): + img_info = dataset.get_img_info(image_id) + if len(prediction) == 0: + continue + image_width = img_info["width"] + image_height = img_info["height"] + prediction = prediction.resize((image_width, image_height)) + pred_boxlists.append(prediction) + + gt_boxlist = dataset.get_groundtruth(image_id) + gt_boxlists.append(gt_boxlist) + result = eval_detection_voc( + pred_boxlists=pred_boxlists, + gt_boxlists=gt_boxlists, + iou_thresh=0.5, + use_07_metric=True, + ) + result_str = "mAP: {:.4f}\n".format(result["map"]) + for i, ap in enumerate(result["ap"]): + if i == 0: # skip background + continue + result_str += "{:<16}: {:.4f}\n".format(dataset.map_class_id_to_class_name(i), ap) + logger.info(result_str) + if output_folder: + with open(os.path.join(output_folder, "result.txt"), "w") as fid: + fid.write(result_str) + return result + + +def eval_detection_voc(pred_boxlists, gt_boxlists, iou_thresh=0.5, use_07_metric=False): + """Evaluate on voc dataset. + Args: + pred_boxlists(list[BoxList]): pred boxlist, has labels and scores fields. + gt_boxlists(list[BoxList]): ground truth boxlist, has labels field. + iou_thresh: iou thresh + use_07_metric: boolean + Returns: + dict represents the results + """ + assert len(gt_boxlists) == len(pred_boxlists), "Length of gt and pred lists need to be same." + prec, rec = calc_detection_voc_prec_rec(pred_boxlists=pred_boxlists, gt_boxlists=gt_boxlists, iou_thresh=iou_thresh) + ap = calc_detection_voc_ap(prec, rec, use_07_metric=use_07_metric) + return {"ap": ap, "map": np.nanmean(ap)} + + +def calc_detection_voc_prec_rec(gt_boxlists, pred_boxlists, iou_thresh=0.5): + """Calculate precision and recall based on evaluation code of PASCAL VOC. + This function calculates precision and recall of + predicted bounding boxes obtained from a dataset which has :math:`N` + images. + The code is based on the evaluation code used in PASCAL VOC Challenge. + """ + n_pos = defaultdict(int) + score = defaultdict(list) + match = defaultdict(list) + for gt_boxlist, pred_boxlist in zip(gt_boxlists, pred_boxlists): + pred_bbox = pred_boxlist.bbox.numpy() + pred_label = pred_boxlist.get_field("labels").numpy() + pred_score = pred_boxlist.get_field("scores").numpy() + gt_bbox = gt_boxlist.bbox.numpy() + gt_label = gt_boxlist.get_field("labels").numpy() + gt_difficult = gt_boxlist.get_field("difficult").numpy() + + for l in np.unique(np.concatenate((pred_label, gt_label)).astype(int)): + pred_mask_l = pred_label == l + pred_bbox_l = pred_bbox[pred_mask_l] + pred_score_l = pred_score[pred_mask_l] + # sort by score + order = pred_score_l.argsort()[::-1] + pred_bbox_l = pred_bbox_l[order] + pred_score_l = pred_score_l[order] + + gt_mask_l = gt_label == l + gt_bbox_l = gt_bbox[gt_mask_l] + gt_difficult_l = gt_difficult[gt_mask_l] + + n_pos[l] += np.logical_not(gt_difficult_l).sum() + score[l].extend(pred_score_l) + + if len(pred_bbox_l) == 0: + continue + if len(gt_bbox_l) == 0: + match[l].extend((0,) * pred_bbox_l.shape[0]) + continue + + # VOC evaluation follows integer typed bounding boxes. + pred_bbox_l = pred_bbox_l.copy() + pred_bbox_l[:, 2:] += 1 + gt_bbox_l = gt_bbox_l.copy() + gt_bbox_l[:, 2:] += 1 + iou = boxlist_iou( + BoxList(pred_bbox_l, gt_boxlist.size), + BoxList(gt_bbox_l, gt_boxlist.size), + ).numpy() + gt_index = iou.argmax(axis=1) + # set -1 if there is no matching ground truth + gt_index[iou.max(axis=1) < iou_thresh] = -1 + del iou + + selec = np.zeros(gt_bbox_l.shape[0], dtype=bool) + for gt_idx in gt_index: + if gt_idx >= 0: + if gt_difficult_l[gt_idx]: + match[l].append(-1) + else: + if not selec[gt_idx]: + match[l].append(1) + else: + match[l].append(0) + selec[gt_idx] = True + else: + match[l].append(0) + + n_fg_class = max(n_pos.keys()) + 1 + prec = [None] * n_fg_class + rec = [None] * n_fg_class + + for l in n_pos.keys(): + score_l = np.array(score[l]) + match_l = np.array(match[l], dtype=np.int8) + + order = score_l.argsort()[::-1] + match_l = match_l[order] + + tp = np.cumsum(match_l == 1) + fp = np.cumsum(match_l == 0) + + # If an element of fp + tp is 0, + # the corresponding element of prec[l] is nan. + prec[l] = tp / (fp + tp) + # If n_pos[l] is 0, rec[l] is None. + if n_pos[l] > 0: + rec[l] = tp / n_pos[l] + + return prec, rec + + +def calc_detection_voc_ap(prec, rec, use_07_metric=False): + """Calculate average precisions based on evaluation code of PASCAL VOC. + This function calculates average precisions + from given precisions and recalls. + The code is based on the evaluation code used in PASCAL VOC Challenge. + Args: + prec (list of numpy.array): A list of arrays. + :obj:`prec[l]` indicates precision for class :math:`l`. + If :obj:`prec[l]` is :obj:`None`, this function returns + :obj:`numpy.nan` for class :math:`l`. + rec (list of numpy.array): A list of arrays. + :obj:`rec[l]` indicates recall for class :math:`l`. + If :obj:`rec[l]` is :obj:`None`, this function returns + :obj:`numpy.nan` for class :math:`l`. + use_07_metric (bool): Whether to use PASCAL VOC 2007 evaluation metric + for calculating average precision. The default value is + :obj:`False`. + Returns: + ~numpy.ndarray: + This function returns an array of average precisions. + The :math:`l`-th value corresponds to the average precision + for class :math:`l`. If :obj:`prec[l]` or :obj:`rec[l]` is + :obj:`None`, the corresponding value is set to :obj:`numpy.nan`. + """ + + n_fg_class = len(prec) + ap = np.empty(n_fg_class) + for l in range(n_fg_class): + if prec[l] is None or rec[l] is None: + ap[l] = np.nan + continue + + if use_07_metric: + # 11 point metric + ap[l] = 0 + for t in np.arange(0.0, 1.1, 0.1): + if np.sum(rec[l] >= t) == 0: + p = 0 + else: + p = np.max(np.nan_to_num(prec[l])[rec[l] >= t]) + ap[l] += p / 11 + else: + # correct AP calculation + # first append sentinel values at the end + mpre = np.concatenate(([0], np.nan_to_num(prec[l]), [0])) + mrec = np.concatenate(([0], rec[l], [1])) + + mpre = np.maximum.accumulate(mpre[::-1])[::-1] + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap[l] = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + + return ap diff --git a/maskrcnn_benchmark/data/datasets/flickr.py b/maskrcnn_benchmark/data/datasets/flickr.py new file mode 100644 index 0000000000000000000000000000000000000000..fe71a932182f0cb88385e990c7f0c22342ef5fbf --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/flickr.py @@ -0,0 +1,8 @@ +import torch +import torchvision +import torch.utils.data as data +from maskrcnn_benchmark.data.datasets.modulated_coco import ModulatedDataset + + +class FlickrDataset(ModulatedDataset): + pass diff --git a/maskrcnn_benchmark/data/datasets/gqa.py b/maskrcnn_benchmark/data/datasets/gqa.py new file mode 100644 index 0000000000000000000000000000000000000000..98d906cf9c9cb7e4d5d2ad17923398b25f11d9f6 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/gqa.py @@ -0,0 +1,91 @@ +import json +from pathlib import Path + +import torch +import torchvision + +from .modulated_coco import ConvertCocoPolysToMask, ModulatedDataset + + +class GQADataset(ModulatedDataset): + pass + + +class GQAQuestionAnswering(torchvision.datasets.CocoDetection): + def __init__(self, img_folder, ann_file, transforms, return_masks, return_tokens, tokenizer, ann_folder): + super(GQAQuestionAnswering, self).__init__(img_folder, ann_file) + self._transforms = transforms + self.prepare = ConvertCocoPolysToMask(return_masks, return_tokens, tokenizer=tokenizer) + with open(ann_folder / "gqa_answer2id.json", "r") as f: + self.answer2id = json.load(f) + with open(ann_folder / "gqa_answer2id_by_type.json", "r") as f: + self.answer2id_by_type = json.load(f) + self.type2id = {"obj": 0, "attr": 1, "rel": 2, "global": 3, "cat": 4} + + def __getitem__(self, idx): + img, target = super(GQAQuestionAnswering, self).__getitem__(idx) + image_id = self.ids[idx] + coco_img = self.coco.loadImgs(image_id)[0] + caption = coco_img["caption"] + dataset_name = coco_img["dataset_name"] + questionId = coco_img["questionId"] + target = {"image_id": image_id, "annotations": target, "caption": caption} + img, target = self.prepare(img, target) + if self._transforms is not None: + img, target = self._transforms(img, target) + target["dataset_name"] = dataset_name + target["questionId"] = questionId + + if coco_img["answer"] not in self.answer2id: + answer = "unknown" + else: + answer = coco_img["answer"] + + target["answer"] = torch.as_tensor(self.answer2id[answer], dtype=torch.long) + target["answer_type"] = torch.as_tensor(self.type2id[coco_img["question_type"]], dtype=torch.long) + + if coco_img["answer"] not in self.answer2id_by_type["answer_attr"]: + answer = "unknown" + else: + answer = coco_img["answer"] + target["answer_attr"] = torch.as_tensor( + self.answer2id_by_type["answer_attr"][answer] if coco_img["question_type"] == "attr" else -100, + dtype=torch.long, + ) + + if coco_img["answer"] not in self.answer2id_by_type["answer_global"]: + answer = "unknown" + else: + answer = coco_img["answer"] + target["answer_global"] = torch.as_tensor( + self.answer2id_by_type["answer_global"][answer] if coco_img["question_type"] == "global" else -100, + dtype=torch.long, + ) + + if coco_img["answer"] not in self.answer2id_by_type["answer_rel"]: + answer = "unknown" + else: + answer = coco_img["answer"] + target["answer_rel"] = torch.as_tensor( + self.answer2id_by_type["answer_rel"][answer] if coco_img["question_type"] == "rel" else -100, + dtype=torch.long, + ) + + if coco_img["answer"] not in self.answer2id_by_type["answer_cat"]: + answer = "unknown" + else: + answer = coco_img["answer"] + target["answer_cat"] = torch.as_tensor( + self.answer2id_by_type["answer_cat"][answer] if coco_img["question_type"] == "cat" else -100, + dtype=torch.long, + ) + + if coco_img["answer"] not in self.answer2id_by_type["answer_obj"]: + answer = "unknown" + else: + answer = coco_img["answer"] + target["answer_obj"] = torch.as_tensor( + self.answer2id_by_type["answer_obj"][answer] if coco_img["question_type"] == "obj" else -100, + dtype=torch.long, + ) + return img, target diff --git a/maskrcnn_benchmark/data/datasets/imagenet.py b/maskrcnn_benchmark/data/datasets/imagenet.py new file mode 100644 index 0000000000000000000000000000000000000000..5d9c13dd55e14f24d3614423dff65591dfb4c380 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/imagenet.py @@ -0,0 +1,64 @@ +import os +import os.path +import json +from PIL import Image + +import torch.utils.data as data + + +def pil_loader(path): + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + + +class ImageNet(data.Dataset): + """ImageNet + + Args: + root (string): Root directory where images are downloaded to. + annFile (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.ToTensor`` + """ + + def __init__(self, ann_file, root, remove_images_without_annotations=None, transforms=None): + + self.root = root + self.transform = transforms + + meta_file = os.path.join(root, ann_file) + assert os.path.exists(meta_file), "meta file %s under root %s not found" % (os.path.basename(meta_file), root) + + with open(meta_file, "r") as f: + meta = json.load(f) + + self.classes = meta["classes"] + self.class_to_idx = meta["class_to_idx"] + self.samples = meta["samples"] + self.num_sample = len(self.samples) + self.allsamples = self.samples + + def select_class(self, cls): + new_samples = [sample for sample in self.allsamples if sample[-1] in cls] + self.samples = new_samples + self.num_sample = len(self.samples) + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: (sample, target) where target is class_index of the target class. + """ + img_path, target = self.samples[index] + sample = pil_loader(self.root + "/" + img_path) + if self.transform is not None: + sample = self.transform(sample) + + return sample, target, index + + def __len__(self): + return len(self.samples) diff --git a/maskrcnn_benchmark/data/datasets/list_dataset.py b/maskrcnn_benchmark/data/datasets/list_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..a2a4f47fc08c8317ade1a762cf4070b6d16a3edf --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/list_dataset.py @@ -0,0 +1,36 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +Simple dataset class that wraps a list of path names +""" + +from PIL import Image + +from maskrcnn_benchmark.structures.bounding_box import BoxList + + +class ListDataset(object): + def __init__(self, image_lists, transforms=None): + self.image_lists = image_lists + self.transforms = transforms + + def __getitem__(self, item): + img = Image.open(self.image_lists[item]).convert("RGB") + + # dummy target + w, h = img.size + target = BoxList([[0, 0, w, h]], img.size, mode="xyxy") + + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target + + def __len__(self): + return len(self.image_lists) + + def get_img_info(self, item): + """ + Return the image dimensions for the image, without + loading and pre-processing it + """ + pass diff --git a/maskrcnn_benchmark/data/datasets/lvis.py b/maskrcnn_benchmark/data/datasets/lvis.py new file mode 100644 index 0000000000000000000000000000000000000000..60298db70912b09b2e4149b4e7c75b795409e9fd --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/lvis.py @@ -0,0 +1,267 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import json +import os +import time +from collections import defaultdict + +import pycocotools.mask as mask_utils +import torchvision +from PIL import Image + +# from .coco import ConvertCocoPolysToMask, make_coco_transforms +from .modulated_coco import ConvertCocoPolysToMask + + +def _isArrayLike(obj): + return hasattr(obj, "__iter__") and hasattr(obj, "__len__") + + +class LVIS: + def __init__(self, annotation_path=None): + """Class for reading and visualizing annotations. + Args: + annotation_path (str): location of annotation file + """ + self.anns = {} + self.cats = {} + self.imgs = {} + self.img_ann_map = defaultdict(list) + self.cat_img_map = defaultdict(list) + self.dataset = {} + + if annotation_path is not None: + print("Loading annotations.") + + tic = time.time() + self.dataset = self._load_json(annotation_path) + print("Done (t={:0.2f}s)".format(time.time() - tic)) + + assert type(self.dataset) == dict, "Annotation file format {} not supported.".format(type(self.dataset)) + self._create_index() + + def _load_json(self, path): + with open(path, "r") as f: + return json.load(f) + + def _create_index(self): + print("Creating index.") + + self.img_ann_map = defaultdict(list) + self.cat_img_map = defaultdict(list) + + self.anns = {} + self.cats = {} + self.imgs = {} + + for ann in self.dataset["annotations"]: + self.img_ann_map[ann["image_id"]].append(ann) + self.anns[ann["id"]] = ann + + for img in self.dataset["images"]: + self.imgs[img["id"]] = img + + for cat in self.dataset["categories"]: + self.cats[cat["id"]] = cat + + for ann in self.dataset["annotations"]: + self.cat_img_map[ann["category_id"]].append(ann["image_id"]) + + print("Index created.") + + def get_ann_ids(self, img_ids=None, cat_ids=None, area_rng=None): + """Get ann ids that satisfy given filter conditions. + Args: + img_ids (int array): get anns for given imgs + cat_ids (int array): get anns for given cats + area_rng (float array): get anns for a given area range. e.g [0, inf] + Returns: + ids (int array): integer array of ann ids + """ + if img_ids is not None: + img_ids = img_ids if _isArrayLike(img_ids) else [img_ids] + if cat_ids is not None: + cat_ids = cat_ids if _isArrayLike(cat_ids) else [cat_ids] + anns = [] + if img_ids is not None: + for img_id in img_ids: + anns.extend(self.img_ann_map[img_id]) + else: + anns = self.dataset["annotations"] + + # return early if no more filtering required + if cat_ids is None and area_rng is None: + return [_ann["id"] for _ann in anns] + + cat_ids = set(cat_ids) + + if area_rng is None: + area_rng = [0, float("inf")] + + ann_ids = [ + _ann["id"] + for _ann in anns + if _ann["category_id"] in cat_ids and _ann["area"] > area_rng[0] and _ann["area"] < area_rng[1] + ] + return ann_ids + + def get_cat_ids(self): + """Get all category ids. + Returns: + ids (int array): integer array of category ids + """ + return list(self.cats.keys()) + + def get_img_ids(self): + """Get all img ids. + Returns: + ids (int array): integer array of image ids + """ + return list(self.imgs.keys()) + + def _load_helper(self, _dict, ids): + if ids is None: + return list(_dict.values()) + elif _isArrayLike(ids): + return [_dict[id] for id in ids] + else: + return [_dict[ids]] + + def load_anns(self, ids=None): + """Load anns with the specified ids. If ids=None load all anns. + Args: + ids (int array): integer array of annotation ids + Returns: + anns (dict array) : loaded annotation objects + """ + return self._load_helper(self.anns, ids) + + def load_cats(self, ids): + """Load categories with the specified ids. If ids=None load all + categories. + Args: + ids (int array): integer array of category ids + Returns: + cats (dict array) : loaded category dicts + """ + return self._load_helper(self.cats, ids) + + def load_imgs(self, ids): + """Load categories with the specified ids. If ids=None load all images. + Args: + ids (int array): integer array of image ids + Returns: + imgs (dict array) : loaded image dicts + """ + return self._load_helper(self.imgs, ids) + + def download(self, save_dir, img_ids=None): + """Download images from mscoco.org server. + Args: + save_dir (str): dir to save downloaded images + img_ids (int array): img ids of images to download + """ + imgs = self.load_imgs(img_ids) + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + for img in imgs: + file_name = os.path.join(save_dir, img["file_name"]) + if not os.path.exists(file_name): + from urllib.request import urlretrieve + + urlretrieve(img["coco_url"], file_name) + + def ann_to_rle(self, ann): + """Convert annotation which can be polygons, uncompressed RLE to RLE. + Args: + ann (dict) : annotation object + Returns: + ann (rle) + """ + img_data = self.imgs[ann["image_id"]] + h, w = img_data["height"], img_data["width"] + segm = ann["segmentation"] + if isinstance(segm, list): + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = mask_utils.frPyObjects(segm, h, w) + rle = mask_utils.merge(rles) + elif isinstance(segm["counts"], list): + # uncompressed RLE + rle = mask_utils.frPyObjects(segm, h, w) + else: + # rle + rle = ann["segmentation"] + return rle + + def ann_to_mask(self, ann): + """Convert annotation which can be polygons, uncompressed RLE, or RLE + to binary mask. + Args: + ann (dict) : annotation object + Returns: + binary mask (numpy 2D array) + """ + rle = self.ann_to_rle(ann) + return mask_utils.decode(rle) + + +class LvisDetectionBase(torchvision.datasets.VisionDataset): + def __init__(self, root, annFile, transform=None, target_transform=None, transforms=None): + super(LvisDetectionBase, self).__init__(root, transforms, transform, target_transform) + self.lvis = LVIS(annFile) + self.ids = list(sorted(self.lvis.imgs.keys())) + + def __getitem__(self, index): + """ + Args: + index (int): Index + Returns: + tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``. + """ + lvis = self.lvis + img_id = self.ids[index] + ann_ids = lvis.get_ann_ids(img_ids=img_id) + target = lvis.load_anns(ann_ids) + + path = "/".join(self.lvis.load_imgs(img_id)[0]["coco_url"].split("/")[-2:]) + + img = Image.open(os.path.join(self.root, path)).convert("RGB") + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target + + def __len__(self): + return len(self.ids) + + +class LvisDetection(LvisDetectionBase): + def __init__(self, img_folder, ann_file, transforms, return_masks=False, **kwargs): + super(LvisDetection, self).__init__(img_folder, ann_file) + self.ann_file = ann_file + self._transforms = transforms + self.prepare = ConvertCocoPolysToMask(return_masks) + + def __getitem__(self, idx): + img, target = super(LvisDetection, self).__getitem__(idx) + image_id = self.ids[idx] + target = {"image_id": image_id, "annotations": target} + img, target = self.prepare(img, target) + if self._transforms is not None: + img = self._transforms(img) + return img, target, idx + + def get_raw_image(self, idx): + img, target = super(LvisDetection, self).__getitem__(idx) + return img + + def categories(self): + id2cat = {c["id"]: c for c in self.lvis.dataset["categories"]} + all_cats = sorted(list(id2cat.keys())) + categories = {} + for l in list(all_cats): + categories[l] = id2cat[l]["name"] + return categories diff --git a/maskrcnn_benchmark/data/datasets/mixed.py b/maskrcnn_benchmark/data/datasets/mixed.py new file mode 100644 index 0000000000000000000000000000000000000000..c811e10f406f4efa3a75d10b20f3bb9087e769f8 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/mixed.py @@ -0,0 +1,173 @@ +import os +import os.path +from pathlib import Path +from typing import Any, Callable, Optional, Tuple + +import torch +from maskrcnn_benchmark.structures.bounding_box import BoxList +import pdb +from PIL import Image, ImageDraw +from torchvision.datasets.vision import VisionDataset + +from .modulated_coco import ConvertCocoPolysToMask, has_valid_annotation +from maskrcnn_benchmark.data.datasets._caption_aug import CaptionAugmentation +import numpy as np + +class CustomCocoDetection(VisionDataset): + """Coco-style dataset imported from TorchVision. + It is modified to handle several image sources + + Args: + root_coco (string): Path to the coco images + root_vg (string): Path to the vg images + annFile (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.ToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + transforms (callable, optional): A function/transform that takes input sample and its target as entry + and returns a transformed version. + """ + + def __init__( + self, + root_coco: str, + root_vg: str, + annFile: str, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + transforms: Optional[Callable] = None, + ) -> None: + super(CustomCocoDetection, self).__init__(root_coco, transforms, transform, target_transform) + from pycocotools.coco import COCO + + self.coco = COCO(annFile) + self.ids = list(sorted(self.coco.imgs.keys())) + + ids = [] + for img_id in self.ids: + if isinstance(img_id, str): + ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None) + else: + ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = self.coco.loadAnns(ann_ids) + if has_valid_annotation(anno): + ids.append(img_id) + self.ids = ids + + self.root_coco = root_coco + self.root_vg = root_vg + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``. + """ + coco = self.coco + img_id = self.ids[index] + ann_ids = coco.getAnnIds(imgIds=img_id) + target = coco.loadAnns(ann_ids) + + img_info = coco.loadImgs(img_id)[0] + path = img_info["file_name"] + dataset = img_info["data_source"] + + cur_root = self.root_coco if dataset == "coco" else self.root_vg + img = Image.open(os.path.join(cur_root, path)).convert("RGB") + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target + + def __len__(self): + return len(self.ids) + + +class MixedDataset(CustomCocoDetection): + """Same as the modulated detection dataset, except with multiple img sources""" + + def __init__( + self, + img_folder_coco, + img_folder_vg, + ann_file, + transforms, + return_masks, + return_tokens, + tokenizer=None, + disable_clip_to_image=False, + no_mask_for_gold=False, + max_query_len=256, + caption_augmentation_version=None, + caption_vocab_file=None, + **kwargs + ): + super(MixedDataset, self).__init__(img_folder_coco, img_folder_vg, ann_file) + self._transforms = transforms + self.max_query_len = max_query_len + self.prepare = ConvertCocoPolysToMask( + return_masks, return_tokens, tokenizer=tokenizer, max_query_len=max_query_len + ) + self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} + self.disable_clip_to_image = disable_clip_to_image + self.no_mask_for_gold = no_mask_for_gold + self.caption_augmentation_version = caption_augmentation_version + if self.caption_augmentation_version is not None: + self.caption_augmentation = CaptionAugmentation( + self.caption_augmentation_version, + tokenizer, + caption_vocab_file=caption_vocab_file + ) + def __getitem__(self, idx): + #try: + img, target = super(MixedDataset, self).__getitem__(idx) + + image_id = self.ids[idx] + __anno = self.coco.loadImgs(image_id)[0] + caption = __anno["caption"] + + if self.caption_augmentation_version is not None: + caption, target, spans = self.caption_augmentation(caption, target, gpt3_outputs = __anno.get("gpt3_outputs", None)) + # print("augmented caption: ", caption) + # print("\n") + else: + spans = None + + anno = {"image_id": image_id, "annotations": target, "caption": caption} + anno["greenlight_span_for_masked_lm_objective"] = [(0, len(caption))] + if self.no_mask_for_gold: + anno["greenlight_span_for_masked_lm_objective"].append((-1, -1, -1)) + + img, anno = self.prepare(img, anno) + + # convert to BoxList (bboxes, labels) + boxes = torch.as_tensor(anno["boxes"]).reshape(-1, 4) # guard against no boxes + target = BoxList(boxes, img.size, mode="xyxy") + classes = anno["labels"] + target.add_field("labels", classes) + # if spans is not None: + # target.add_field("spans", spans) # add spans to target + + if not self.disable_clip_to_image: + num_boxes = len(boxes) + target = target.clip_to_image(remove_empty=True) + assert len(target.bbox) == num_boxes, "Box removed in MixedDataset!!!" + + if self._transforms is not None: + img, target = self._transforms(img, target) + + # add additional property + for ann in anno: + target.add_field(ann, anno[ann]) + return img, target, idx + # except: + # print("error in __getitem__ in mixed", idx) + # return self[np.random.choice(len(self))] + + def get_img_info(self, index): + img_id = self.id_to_img_map[index] + img_data = self.coco.imgs[img_id] + return img_data diff --git a/maskrcnn_benchmark/data/datasets/mixup.py b/maskrcnn_benchmark/data/datasets/mixup.py new file mode 100644 index 0000000000000000000000000000000000000000..95fc6d6b03da2fe7d4a86f76ff3d289204e3bc0f --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/mixup.py @@ -0,0 +1,125 @@ +"""Mixup detection dataset wrapper.""" +from __future__ import absolute_import +import numpy as np +import torch +import torch.utils.data as data + + +class MixupDetection(data.Dataset): + """Detection dataset wrapper that performs mixup for normal dataset. + Parameters + ---------- + dataset : mx.gluon.data.Dataset + Gluon dataset object. + mixup : callable random generator, e.g. np.random.uniform + A random mixup ratio sampler, preferably a random generator from numpy.random + A random float will be sampled each time with mixup(*args). + Use None to disable. + *args : list + Additional arguments for mixup random sampler. + """ + + def __init__(self, dataset, mixup=None, preproc=None, *args): + super().__init__(dataset.input_dim) + self._dataset = dataset + self.preproc = preproc + self._mixup = mixup + self._mixup_args = args + + def set_mixup(self, mixup=None, *args): + """Set mixup random sampler, use None to disable. + Parameters + ---------- + mixup : callable random generator, e.g. np.random.uniform + A random mixup ratio sampler, preferably a random generator from numpy.random + A random float will be sampled each time with mixup(*args) + *args : list + Additional arguments for mixup random sampler. + """ + self._mixup = mixup + self._mixup_args = args + + def __len__(self): + return len(self._dataset) + + @Dataset.resize_getitem + def __getitem__(self, idx): + self._dataset._input_dim = self.input_dim + # first image + img1, label1, _, _ = self._dataset.pull_item(idx) + lambd = 1 + + # draw a random lambda ratio from distribution + if self._mixup is not None: + lambd = max(0, min(1, self._mixup(*self._mixup_args))) + + if lambd >= 1: + weights1 = np.ones((label1.shape[0], 1)) + label1 = np.hstack((label1, weights1)) + height, width, _ = img1.shape + img_info = (width, height) + if self.preproc is not None: + img_o, target_o = self.preproc(img1, label1, self.input_dim) + return img_o, target_o, img_info, idx + + # second image + idx2 = int(np.random.choice(np.delete(np.arange(len(self)), idx))) + img2, label2, _, _ = self._dataset.pull_item(idx2) + + # mixup two images + height = max(img1.shape[0], img2.shape[0]) + width = max(img1.shape[1], img2.shape[1]) + mix_img = np.zeros((height, width, 3), dtype=np.float32) + mix_img[: img1.shape[0], : img1.shape[1], :] = img1.astype(np.float32) * lambd + mix_img[: img2.shape[0], : img2.shape[1], :] += img2.astype(np.float32) * (1.0 - lambd) + mix_img = mix_img.astype(np.uint8) + + y1 = np.hstack((label1, np.full((label1.shape[0], 1), lambd))) + y2 = np.hstack((label2, np.full((label2.shape[0], 1), 1.0 - lambd))) + mix_label = np.vstack((y1, y2)) + if self.preproc is not None: + mix_img, padded_labels = self.preproc(mix_img, mix_label, self.input_dim) + + img_info = (width, height) + + return mix_img, padded_labels, img_info, idx + + def pull_item(self, idx): + self._dataset._input_dim = self.input_dim + # first image + img1, label1, _, _ = self._dataset.pull_item(idx) + lambd = 1 + + # draw a random lambda ratio from distribution + if self._mixup is not None: + lambd = max(0, min(1, self._mixup(*self._mixup_args))) + + if lambd >= 1: + weights1 = np.ones((label1.shape[0], 1)) + label1 = np.hstack((label1, weights1)) + height, width, _ = img1.shape + img_info = (width, height) + if self.preproc is not None: + img_o, target_o = self.preproc(img1, label1, self.input_dim) + return img_o, target_o, img_info, idx + + # second image + idx2 = int(np.random.choice(np.delete(np.arange(len(self)), idx))) + img2, label2 = self._dataset.pull_item(idx2) + + # mixup two images + height = max(img1.shape[0], img2.shape[0]) + width = max(img1.shape[1], img2.shape[1]) + mix_img = np.zeros((height, width, 3), dtype=np.float32) + mix_img[: img1.shape[0], : img1.shape[1], :] = img1.astype(np.float32) * lambd + mix_img[: img2.shape[0], : img2.shape[1], :] += img2.astype(np.float32) * (1.0 - lambd) + mix_img = mix_img.astype(np.uint8) + + y1 = np.hstack((label1, np.full((label1.shape[0], 1), lambd))) + y2 = np.hstack((label2, np.full((label2.shape[0], 1), 1.0 - lambd))) + mix_label = np.vstack((y1, y2)) + if self.preproc is not None: + mix_img, padded_labels = self.preproc(mix_img, mix_label, self.input_dim) + + img_info = (width, height) + return mix_img, padded_labels, img_info, idx diff --git a/maskrcnn_benchmark/data/datasets/modulated_coco.py b/maskrcnn_benchmark/data/datasets/modulated_coco.py new file mode 100644 index 0000000000000000000000000000000000000000..6586a9883dc20ec1e1c54a206273b7231514b4ef --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/modulated_coco.py @@ -0,0 +1,734 @@ +import logging +import os +import os.path +import math +from PIL import Image, ImageDraw + +import random +import numpy as np + +import torch +import torchvision +import torch.utils.data as data +from pycocotools import mask as coco_mask + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask +from maskrcnn_benchmark.data.datasets.coco import has_valid_annotation +from .od_to_grounding import convert_od_to_grounding_simple, check_for_positive_overflow, sanity_check_target_after_processing, convert_object_detection_to_grounding_optimized_for_od, od_to_grounding_optimized_streamlined +from ._od_to_description import DescriptionConverter +import pdb +import json +from maskrcnn_benchmark.data.datasets._caption_aug import CaptionAugmentation + +class CocoGrounding(torchvision.datasets.CocoDetection): + def __init__( + self, + img_folder, + ann_file, + transforms, + return_masks, + return_tokens, + is_train=False, + tokenizer=None, + disable_shuffle=False, + add_detection_prompt=False, + one_hot=False, + disable_clip_to_image=False, + no_minus_one_for_one_hot=False, + separation_tokens=" ", + few_shot=0, + no_mask_for_od=False, + override_category=None, + use_caption_prompt=False, + caption_prompt=None, + max_query_len=256, + special_safeguard_for_coco_grounding=False, + random_sample_negative=-1, + od_to_grounding_version="legacy", + description_file = None, + similarity_file = None, + **kwargs + ): + super(CocoGrounding, self).__init__(img_folder, ann_file) + self.ids = sorted(self.ids) + + ids = [] + for img_id in self.ids: + if isinstance(img_id, str): + ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None) + else: + ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = self.coco.loadAnns(ann_ids) + if has_valid_annotation(anno): + ids.append(img_id) + + self.ids = ids + + if few_shot: + ids = [] + # cats_freq = [few_shot]*len(self.coco.cats.keys()) + cats_freq = [few_shot] * max(list(self.coco.cats.keys())) + for img_id in self.ids: + if isinstance(img_id, str): + ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None) + else: + ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = self.coco.loadAnns(ann_ids) + cat = set([ann["category_id"] for ann in anno]) # set/tuple corresponde to instance/image level + is_needed = sum([cats_freq[c - 1] > 0 for c in cat]) + if is_needed: + ids.append(img_id) + for c in cat: + cats_freq[c - 1] -= 1 + # print(cat, cats_freq) + self.ids = ids + + self.json_category_id_to_contiguous_id = {v: i + 1 for i, v in enumerate(self.coco.getCatIds())} + self.contiguous_category_id_to_json_id = {v: k for k, v in self.json_category_id_to_contiguous_id.items()} + + if override_category is not None: + self.coco.dataset["categories"] = override_category + self.use_caption_prompt = use_caption_prompt + self.caption_prompt = caption_prompt + self.special_safeguard_for_coco_grounding = special_safeguard_for_coco_grounding + self.random_sample_negative = random_sample_negative + self.ind_to_class = self.categories(no_background=False) + self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} + self._transforms = transforms + self.max_query_len = max_query_len + self.prepare = ConvertCocoPolysToMask(False, return_tokens, tokenizer=tokenizer, max_query_len=max_query_len) + self.tokenizer = tokenizer + self.is_train = is_train + + self.ind_to_class = self.categories(no_background=False) + + self.disable_shuffle = disable_shuffle + self.add_detection_prompt = add_detection_prompt + self.one_hot = one_hot + self.no_minus_one_for_one_hot = no_minus_one_for_one_hot + + self.disable_clip_to_image = disable_clip_to_image + self.separation_tokens = separation_tokens + self.no_mask_for_od = no_mask_for_od + self.return_masks = return_masks + self.od_to_grounding_version = od_to_grounding_version + self.description_file = description_file + self.similarity_file = similarity_file + if "description" in self.od_to_grounding_version: + self.od_grounding_converter = DescriptionConverter( + self.description_file, + self.od_to_grounding_version, + self.coco.dataset["categories"], + self.ind_to_class, + self.similarity_file,) + + def categories(self, no_background=True): + categories = self.coco.dataset["categories"] + label_list = {} + for index, i in enumerate(categories): + # assert(index + 1 == i["id"]) + if not no_background or (i["name"] != "__background__" and i["id"] != 0): + label_list[self.json_category_id_to_contiguous_id[i["id"]]] = i["name"] + return label_list + + def get_box_mask(self, rect, img_size, mode="poly"): + assert mode == "poly", "Only support poly mask right now!" + x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3] + return [[x1, y1, x1, y2, x2, y2, x2, y1]] + + def __getitem__(self, idx): + img, tgt = super(CocoGrounding, self).__getitem__(idx) + image_id = self.ids[idx] + tgt = [obj for obj in tgt if "iscrowd" not in obj or obj["iscrowd"] == 0] + boxes = [obj["bbox"] for obj in tgt] + boxes = torch.as_tensor(boxes).reshape(-1, 4) # guard against no boxes + target = BoxList(boxes, img.size, mode="xywh").convert("xyxy") + classes = [obj["category_id"] for obj in tgt] + classes = [self.json_category_id_to_contiguous_id[c] for c in classes] + classes = torch.tensor(classes) + target.add_field("labels", classes) + + if self.return_masks: + masks = [] + is_box_mask = [] + for obj, bbox in zip(tgt, target.bbox): + if "segmentation" in obj: + masks.append(obj["segmentation"]) + is_box_mask.append(0) + else: + masks.append(self.get_box_mask(bbox, img.size, mode="poly")) + is_box_mask.append(1) + masks = SegmentationMask(masks, img.size, mode="poly") + is_box_mask = torch.tensor(is_box_mask) + target.add_field("masks", masks) + target.add_field("is_box_mask", is_box_mask) + + if not self.disable_clip_to_image: + target = target.clip_to_image(remove_empty=True) + + if "description" in self.od_to_grounding_version: + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = self.od_grounding_converter.train_od_to_grounding( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + tokenizer=self.tokenizer, + ) + elif self.od_to_grounding_version != "legacy": # new and more streamlined version of od to grounding conversion + # NOTE: target is rewritten here + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions, target = od_to_grounding_optimized_streamlined( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + tokenizer=self.tokenizer, + od_to_grounding_version=self.od_to_grounding_version, + ) + elif self.special_safeguard_for_coco_grounding: + # Intended for LVIS + assert(not self.use_caption_prompt) + + original_box_num = len(target) + target, positive_caption_length = check_for_positive_overflow(target, self.ind_to_class, self.tokenizer, self.max_query_len-2) # leave some space for the special tokens + if len(target) < original_box_num: + print("WARNING: removed {} boxes due to positive caption overflow".format(original_box_num - len(target))) + + annotations, caption, greenlight_span_for_masked_lm_objective, label_to_positions = convert_object_detection_to_grounding_optimized_for_od( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + disable_shuffle=self.disable_shuffle, + add_detection_prompt=False, + add_detection_prompt_advanced=False, + random_sample_negative=self.random_sample_negative, + control_probabilities=(0.0, 0.0, 1.0, 0.0), # always try to add a lot of negatives + restricted_negative_list=None, + separation_tokens=self.separation_tokens, + max_num_labels=-1, + positive_caption_length=positive_caption_length, + tokenizer=self.tokenizer, + max_seq_length=self.max_query_len - 2, + od_to_grounding_version=self.od_to_grounding_version, + ) + else: + # Intended for COCO / ODinW + annotations, caption, greenlight_span_for_masked_lm_objective = convert_od_to_grounding_simple( + target=target, + image_id=image_id, + ind_to_class=self.ind_to_class, + disable_shuffle=self.disable_shuffle, + add_detection_prompt=self.add_detection_prompt, + separation_tokens=self.separation_tokens, + caption_prompt=self.caption_prompt if self.use_caption_prompt else None, + ) + + anno = {"image_id": image_id, "annotations": annotations, "caption": caption} + anno["greenlight_span_for_masked_lm_objective"] = greenlight_span_for_masked_lm_objective + if self.no_mask_for_od: + anno["greenlight_span_for_masked_lm_objective"].append((-1, -1, -1)) + img, anno = self.prepare(img, anno, box_format="xyxy") + + # for equivalence check + if self.one_hot: + logging.info("using one hot for equivalence check.") + one_hot_map = torch.zeros_like(anno["positive_map"], dtype=torch.float) + text_mask = torch.zeros(anno["positive_map"].shape[1], dtype=torch.int64) + # create one hot mapping + for ii, cls in enumerate(classes): + if self.no_minus_one_for_one_hot: + one_hot_map[ii, cls] = 1.0 + else: + one_hot_map[ii, cls - 1] = 1.0 + if self.no_minus_one_for_one_hot: + text_mask[:] = 1 + else: + text_mask[: len(self.ind_to_class)] = 1 + anno["positive_map"] = one_hot_map + anno["text_mask"] = text_mask + + if self._transforms is not None: + img, target = self._transforms(img, target) + + # add additional property + for ann in anno: + target.add_field(ann, anno[ann]) + + # sanity_check_target_after_processing(target) + + return img, target, idx + + def get_img_info(self, index): + img_id = self.id_to_img_map[index] + img_data = self.coco.imgs[img_id] + return img_data + + +class ModulatedDataset(torchvision.datasets.CocoDetection): + def __init__( + self, + img_folder, + ann_file, + transforms, + return_masks, + return_tokens, + is_train=False, + tokenizer=None, + disable_clip_to_image=False, + no_mask_for_gold=False, + max_query_len=256, + caption_augmentation_version=None, + caption_vocab_file=None, + **kwargs + ): + super(ModulatedDataset, self).__init__(img_folder, ann_file) + self.ids = sorted(self.ids) + + ids = [] + for img_id in self.ids: + if isinstance(img_id, str): + ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None) + else: + ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None) + anno = self.coco.loadAnns(ann_ids) + if has_valid_annotation(anno): + ids.append(img_id) + self.ids = ids + + self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} + self._transforms = transforms + self.max_query_len = max_query_len + self.prepare = ConvertCocoPolysToMask( + return_masks, return_tokens, tokenizer=tokenizer, max_query_len=max_query_len + ) + self.is_train = is_train + self.disable_clip_to_image = disable_clip_to_image + self.no_mask_for_gold = no_mask_for_gold + self.caption_augmentation_version = caption_augmentation_version + + if self.caption_augmentation_version is not None: + self.caption_augmentation = CaptionAugmentation( + self.caption_augmentation_version, + tokenizer, + caption_vocab_file=caption_vocab_file + ) + def __getitem__(self, idx): + + img, target = super(ModulatedDataset, self).__getitem__(idx) + image_id = self.ids[idx] + coco_img = self.coco.loadImgs(image_id)[0] + caption = coco_img["caption"] + dataset_name = coco_img["dataset_name"] if "dataset_name" in coco_img else None + # print("original caption: ", caption) + if self.caption_augmentation_version is not None: + caption, target, spans = self.caption_augmentation(caption, target, gpt3_outputs = coco_img.get("gpt3_outputs", None)) + else: + spans = None + + anno = {"image_id": image_id, "annotations": target, "caption": caption} + # This dataset is used for Flickr & Mixed, so the sequence is maskable + anno["greenlight_span_for_masked_lm_objective"] = [(0, len(caption))] + if self.no_mask_for_gold: + anno["greenlight_span_for_masked_lm_objective"].append((-1, -1, -1)) + img, anno = self.prepare(img, anno) + + # convert to BoxList (bboxes, labels) + boxes = torch.as_tensor(anno["boxes"]).reshape(-1, 4) # guard against no boxes + target = BoxList(boxes, img.size, mode="xyxy") + classes = anno["labels"] + target.add_field("labels", classes) + if self.prepare.return_masks: + target.add_field("masks", anno.pop("masks")) + target.add_field("is_box_mask", anno.pop("is_box_mask")) + if not self.disable_clip_to_image: + num_boxes = len(target.bbox) + target = target.clip_to_image(remove_empty=True) + assert num_boxes == len(target.bbox), "Box got removed in MixedDataset!!!" + + if self._transforms is not None: + img, target = self._transforms(img, target) + + # add additional property + for ann in anno: + target.add_field(ann, anno[ann]) + + target.add_field("dataset_name", dataset_name) + for extra_key in ["sentence_id", "original_img_id", "original_id", "task_id"]: + if extra_key in coco_img: + target.add_field(extra_key, coco_img[extra_key]) + + if "tokens_positive_eval" in coco_img and not self.is_train: + tokenized = self.prepare.tokenizer(caption, return_tensors="pt") + target.add_field("positive_map_eval", create_positive_map(tokenized, coco_img["tokens_positive_eval"])) + target.add_field("nb_eval", len(target.get_field("positive_map_eval"))) + #sanity_check_target_after_processing(target) + return img, target, idx + + + def get_img_info(self, index): + img_id = self.id_to_img_map[index] + img_data = self.coco.imgs[img_id] + return img_data + + +class CocoDetection(data.Dataset): + """`MS Coco Detection `_ Dataset. + + Args: + root (string): Root directory where images are downloaded to. + annFile (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.ToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + def __init__(self, root, annFile, transform=None, target_transform=None): + from pycocotools.coco import COCO + + self.root = root + self.coco = COCO(annFile) + self.ids = list(self.coco.imgs.keys()) + self.transform = transform + self.target_transform = target_transform + + def __getitem__(self, index, return_meta=False): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``. + """ + coco = self.coco + img_id = self.ids[index] + if isinstance(img_id, str): + img_id = [img_id] + ann_ids = coco.getAnnIds(imgIds=img_id) + target = coco.loadAnns(ann_ids) + + meta = coco.loadImgs(img_id)[0] + path = meta["file_name"] + img = pil_loader(os.path.join(self.root, path)) + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + if return_meta: + return img, target, meta + else: + return img, target + + def __len__(self): + return len(self.ids) + + def __repr__(self): + fmt_str = "Dataset " + self.__class__.__name__ + "\n" + fmt_str += " Number of datapoints: {}\n".format(self.__len__()) + fmt_str += " Root Location: {}\n".format(self.root) + tmp = " Transforms (if any): " + fmt_str += "{0}{1}\n".format(tmp, self.transform.__repr__().replace("\n", "\n" + " " * len(tmp))) + tmp = " Target Transforms (if any): " + fmt_str += "{0}{1}".format(tmp, self.target_transform.__repr__().replace("\n", "\n" + " " * len(tmp))) + return fmt_str + + +class ConvertCocoPolysToMask(object): + def __init__(self, return_masks=False, return_tokens=False, tokenizer=None, max_query_len=256): + self.return_masks = return_masks + self.return_tokens = return_tokens + self.tokenizer = tokenizer + self.max_query_len = max_query_len + + def get_box_mask(self, rect, img_size, mode="poly"): + assert mode == "poly", "Only support poly mask right now!" + x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3] + return [[x1, y1, x1, y2, x2, y2, x2, y1]] + + def __call__(self, image, target, ignore_box_screen=False, box_format="xywh"): + w, h = image.size + + image_id = target["image_id"] + image_id = torch.tensor([image_id]) + + anno = target["annotations"] + caption = target["caption"] if "caption" in target else None + label_to_positions = target.get("label_to_positions", {}) + spans = target.get("spans", []) + greenlight_span_for_masked_lm_objective = target.get("greenlight_span_for_masked_lm_objective", None) + + anno = [obj for obj in anno if "iscrowd" not in obj or obj["iscrowd"] == 0] + + boxes = [obj["bbox"] for obj in anno] + # guard against no boxes via resizing + boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4) + if box_format == "xywh": + boxes[:, 2:] += boxes[:, :2] - 1 # TO_REMOVE = 1 + boxes[:, 0::2].clamp_(min=0, max=w - 1) # TO_REMOVE = 1 + boxes[:, 1::2].clamp_(min=0, max=h - 1) # TO_REMOVE = 1 + + classes = [obj["category_id"] for obj in anno] + classes = torch.tensor(classes, dtype=torch.int64) + + if self.return_masks: + masks = [] + is_box_mask = [] + for obj, bbox in zip(anno, boxes): + if "segmentation" in obj: + masks.append(obj["segmentation"]) + is_box_mask.append(0) + else: + masks.append(self.get_box_mask(bbox, image.size, mode="poly")) + is_box_mask.append(1) + masks = SegmentationMask(masks, image.size, mode="poly") + is_box_mask = torch.tensor(is_box_mask) + + keypoints = None + if anno and "keypoints" in anno[0]: + keypoints = [obj["keypoints"] for obj in anno] + keypoints = torch.as_tensor(keypoints, dtype=torch.float32) + num_keypoints = keypoints.shape[0] + if num_keypoints: + keypoints = keypoints.view(num_keypoints, -1, 3) + + isfinal = None + if anno and "isfinal" in anno[0]: + isfinal = torch.as_tensor([obj["isfinal"] for obj in anno], dtype=torch.float) + + tokens_positive = [] if self.return_tokens else None + if self.return_tokens and anno and "tokens" in anno[0]: + tokens_positive = [obj["tokens"] for obj in anno] + elif self.return_tokens and anno and "tokens_positive" in anno[0]: + tokens_positive = [obj["tokens_positive"] for obj in anno] + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + boxes = boxes[keep] + classes = classes[keep] + if self.return_masks: + masks = masks[keep] + is_box_mask = is_box_mask[keep] + if keypoints is not None: + keypoints = keypoints[keep] + + target = {} + target["boxes"] = boxes + target["labels"] = classes + if caption is not None: + target["caption"] = caption + if self.return_masks: + target["masks"] = masks + target["is_box_mask"] = is_box_mask + target["image_id"] = image_id + if keypoints is not None: + target["keypoints"] = keypoints + + if tokens_positive is not None: + target["tokens_positive"] = [] + + for i, k in enumerate(keep): + if k or ignore_box_screen: + target["tokens_positive"].append(tokens_positive[i]) + + if isfinal is not None: + target["isfinal"] = isfinal + + # for conversion to coco api + area = torch.tensor([obj["area"] for obj in anno]) + iscrowd = torch.tensor([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno]) + target["area"] = area[keep] + target["iscrowd"] = iscrowd[keep] + + target["orig_size"] = torch.as_tensor([int(h), int(w)]) + target["size"] = torch.as_tensor([int(h), int(w)]) + + if self.return_tokens and self.tokenizer is not None: + if not ignore_box_screen: + assert len(target["boxes"]) == len(target["tokens_positive"]) + tokenized = self.tokenizer(caption, return_tensors="pt", max_length=self.max_query_len, truncation=True) + target["positive_map"] = create_positive_map(tokenized, target["tokens_positive"]) + target["greenlight_map"] = create_greenlight_map(greenlight_span_for_masked_lm_objective, tokenized) + target["positive_map_for_od_labels"] = create_positive_map_for_od_labels(tokenized, label_to_positions) + if len(anno) > 0 and "spans_positive" in anno[0]: + try: + target["span_map"] = transfer_token_mapping_to_span_mapping(spans, [i['spans_positive'] for i in anno]) + except: + pass + # create another field called the span boundaries + # target["span_boundaries"] = create_span_boundaries(tokenized) + + original_od_label = [] + for obj in anno: + original_od_label.append( + obj.get("original_od_label", -10) + ) # NOTE: The padding value has to be not the same as -1 or -100 + target["original_od_label"] = torch.as_tensor(original_od_label) + + return image, target + +def create_default_span(tokenized, positive_map, captions): + ''' + This is used when we don't need the span; we need to create fake spans, where each span is just a subword + ''' + # desired: each token now is a span + token_length = tokenized.input_ids.shape[1] + spans = [] + for i in range(token_length): + print(tokenized.token_to_chars(i)) + pass + +def transfer_token_mapping_to_span_mapping(all_spans, spans_positive): + # input: boxes, num_box to spans mapping + # output: boxes, spans, num_box to spans mapping + + # flattern all_spans + all_spans_new = [] + for spans in all_spans: + all_spans_new += spans + all_spans = all_spans_new + + index_spans = {} + for i, span in enumerate(all_spans): + index_spans[tuple(span)] = i + + positive_map = torch.zeros((len(spans_positive), len(all_spans)), dtype=torch.float) + for box_i, spans_box_i in enumerate(spans_positive): + for span in spans_box_i: + positive_map[box_i, index_spans[tuple(span)]] = 1 + + return positive_map + +def create_greenlight_map(tok_list, tokenized): + # An example tok_list: + # [(0, 5), (10, 13), (-1, -1, -1)] + # The last one is a special indicator.. + + greenlight_map = torch.zeros(256, dtype=torch.float) + for item in tok_list: + if len(item) != 2: + assert len(item) == 3 + # Make everything unmakable + greenlight_map[:] = -1 + break + + beg, end = item + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + greenlight_map[beg_pos : end_pos + 1].fill_(1) + return greenlight_map + + +def create_positive_map_for_od_labels(tokenized, label_to_positions): + """construct a map such that positive_map[i] = j, where j is the object detection label of the token i""" + """ + {3: [1: 5)} + 256 : -1 3 3 3 3 -1 .. 8 8 .. + the woman in the garden + -1 -1 -1 -1 -1 + """ + positive_map = torch.ones(256, dtype=torch.float) * -1 # -1 means no match + keys = list(label_to_positions.keys()) + for j, key in enumerate(keys): + tok_list = label_to_positions[key] + # one label only mapps to one location + beg, end = tok_list + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + assert beg_pos is not None and end_pos is not None + positive_map[beg_pos : end_pos + 1].fill_(key) + return positive_map + + +def convert_coco_poly_to_mask(segmentations, height, width): + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = torch.as_tensor(mask, dtype=torch.uint8) + mask = mask.any(dim=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, dim=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8) + return masks + + +def create_positive_map(tokenized, tokens_positive): + """construct a map such that positive_map[i,j] = True iff box i is associated to token j""" + positive_map = torch.zeros((len(tokens_positive), 256), dtype=torch.float) + + for j, tok_list in enumerate(tokens_positive): + for (beg, end) in tok_list: + if beg < 0 or end < 0: + continue + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + positive_map[j, beg_pos : end_pos + 1].fill_(1) + return positive_map / (positive_map.sum(-1)[:, None] + 1e-6) + + +def pil_loader(path, retry=5): + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + ri = 0 + while ri < retry: + try: + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + except: + ri += 1 diff --git a/maskrcnn_benchmark/data/datasets/object365.py b/maskrcnn_benchmark/data/datasets/object365.py new file mode 100644 index 0000000000000000000000000000000000000000..aa9bb4aabe13237b9fad229b310be8b50e31727b --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/object365.py @@ -0,0 +1,8 @@ +import torch +import torchvision +import torch.utils.data as data +from maskrcnn_benchmark.data.datasets.coco_dt import CocoDetectionTSV + + +class Object365DetectionTSV(CocoDetectionTSV): + pass diff --git a/maskrcnn_benchmark/data/datasets/od_to_grounding.py b/maskrcnn_benchmark/data/datasets/od_to_grounding.py new file mode 100644 index 0000000000000000000000000000000000000000..43412d4f747b941899e4b3405e8df1e02bdf0dd3 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/od_to_grounding.py @@ -0,0 +1,589 @@ +# Utilities for converting object detection data into grounding data +import numpy as np +import torch +import pdb, json, random, re +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file +from collections import defaultdict +from tqdm import tqdm +from maskrcnn_benchmark.data.datasets.parse_gpt import GPTOutputParser + +def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + all_ = [] + for i in range(0, len(lst), n): + data_index = lst[i:i + n] + all_.append(data_index) + counter = 0 + for i in all_: + counter += len(i) + assert(counter == len(lst)) + + return all_ + +def clean_name(name): + + def _clean_name(name): + name = re.sub(r"\(.*\)", "", name) + name = re.sub(r"_", " ", name) + name = re.sub(r" ", " ", name) + return name + + if ":" in name: + obj_name, part_name = name.split(":") + obj_name = _clean_name(obj_name) + part_name = _clean_name(part_name) + return part_name + " of " + obj_name + else: + return _clean_name(name) + +def clean_string(input_string): + # remove leading and trailing spaces + input_string = input_string.strip() + # remove trailing ";" and "." + input_string = re.sub(r";$", "", input_string) + input_string = re.sub(r"\.$", "", input_string) + return input_string + + +class DetectionToGrounding(): + ''' + Convert detection data into grounding data; + Construct prompts for training and inference; + ''' + def __init__(self, version): + pass + +def sanity_check_target_after_processing(target): + assert(len(target.bbox) == len(target.extra_fields["boxes"])) + + +def convert_od_to_grounding_simple( + target, + image_id, + ind_to_class, + disable_shuffle=True, + add_detection_prompt=False, + separation_tokens=" ", + caption_prompt=None): + """ + Convert object detection data into grounding data format, on the fly. + ind_to_class: {0: "__background__", 1 : "person" ...}, contiguous id + """ + + def generate_sentence_from_labels(positive_label_list, negative_label_list, disable_shuffle=True): + label_to_positions = {} + label_list = negative_label_list + positive_label_list + if not disable_shuffle: + random.shuffle(label_list) + assert (caption_prompt is None), "Should not specify caption_prompt when shuffle is enabled!!" # avoid potential bug + + if add_detection_prompt: + pheso_caption = "object detection : " + else: + pheso_caption = "" + + for index, label in enumerate(label_list): + if caption_prompt is not None: + pheso_caption += caption_prompt[index]['prefix'] + + start_index = len(pheso_caption) + if caption_prompt is not None: + pheso_caption += clean_name(caption_prompt[index]['name']) + else: + pheso_caption += clean_name(ind_to_class[label]) # NOTE: slight change... + end_index = len(pheso_caption) + + if caption_prompt is not None: + pheso_caption += caption_prompt[index]['suffix'] + + # e.g.: pheso_caption = "cat dog", where cat is label 4, and dog is label 17 + # label_to_positions: {4: (0, 3), 17: (4, 7)} + label_to_positions[label] = [start_index, end_index] + + if index != len(label_list) - 1: + pheso_caption += separation_tokens + + return label_to_positions, pheso_caption + + label_list = list(sorted(ind_to_class.keys())) # do not include the background + label_to_positions, pheso_caption = generate_sentence_from_labels( + positive_label_list=label_list, + negative_label_list=[], + disable_shuffle=disable_shuffle + ) + + new_target = [] + + ''' + Convert into: + {'area': 10506.0, 'iscrowd': 0, 'image_id': 571335, 'category_id': 1, 'id': 2999421, 'bbox': [221, 319, 103, 102], 'tokens_positive': [[0, 3]]} + tokens_positive is the char position + ''' + areas = target.area() + greenlight_span_for_masked_lm_objective = [] + for i in range(len(target)): + new_target_i = {} + new_target_i["area"] = areas[i] + new_target_i["iscrowd"] = 0 + new_target_i["image_id"] = image_id + new_target_i["category_id"] = target.extra_fields["labels"][i].item() + new_target_i["id"] = None + new_target_i['bbox'] = target.bbox[i].numpy().tolist() + + label_i = target.extra_fields["labels"][i].item() + + if label_i in label_to_positions: # NOTE: Only add those that actually appear in the final caption + new_target_i["tokens_positive"] = [label_to_positions[label_i]] + new_target.append(new_target_i) + greenlight_span_for_masked_lm_objective.append(label_to_positions[label_i]) + + return new_target, pheso_caption, greenlight_span_for_masked_lm_objective + + +def check_for_positive_overflow(target, ind_to_class, tokenizer, max_seq_length=256): + # NOTE: Only call this function for OD data; DO NOT USE IT FOR GROUNDING DATA + # NOTE: called only in coco_dt + + # Check if we have too many positive labels + # generate a caption by appending the positive labels + positive_label_set = set() + for i in range(len(target)): + label_i = target.extra_fields["labels"][i].item() + positive_label_set.add(label_i) + positive_label_list = list(positive_label_set) + + # random shuffule so we can sample different annotations at different epochs + random.shuffle(positive_label_list) + + kept_lables = [] + length = 0 + + for index, label in enumerate(positive_label_list): + + label_text = clean_name(ind_to_class[label]) + ". " # "dog. " + + tokenized = tokenizer.tokenize(label_text) + + length += len(tokenized) + + if length > max_seq_length: + break + else: + kept_lables.append(label) + + ## filter boxes + keep_box_index = [] + for i in range(len(target)): + label_i = target.extra_fields["labels"][i].item() + if label_i in kept_lables: + keep_box_index.append(i) + + keep_box_index = torch.LongTensor(keep_box_index) + + target = target[keep_box_index] ## filter boxes + + return target, length + + + +def _label_drop_with_length_limit(label_list, ind_to_class, length_limit, tokenizer): + screened_label_list = [] + random.shuffle(label_list) # randomly drop labels + for label in label_list: + label_text = clean_name(ind_to_class[label]) + ". " # "dog. " + + tokenized = tokenizer.tokenize(label_text) + + length_limit -= len(tokenized) + + if length_limit > 0: + screened_label_list.append(label) # keep this label + else: + break + return screened_label_list + +def _randomv1_od_to_grounding(all_labels, ind_to_class, max_seq_length, max_num_labels, tokenizer): + + label_num = np.random.randint(1, max_num_labels) + selected_label_list = np.random.choice(all_labels, label_num, replace=False) + screened_label_list = _label_drop_with_length_limit(selected_label_list, ind_to_class, max_seq_length, tokenizer) + + return screened_label_list + +def _randomv2_od_to_grounding(all_labels, ind_to_class, max_seq_length, max_num_labels, tokenizer, positive_label_set): + + full_positive = len(positive_label_set) + full_negative = max_num_labels - full_positive + + outer_prob = random.random() + + if outer_prob < 0.8: + num_negatives = full_negative + num_positives = full_positive + elif outer_prob < 0.9: + num_negatives = np.random.choice(max(1, full_negative)) + 1 # mininum 1 + num_positives = full_positive + else: + num_positives = np.random.choice(max(1, full_positive)) + 1 # mininum 1 + num_negatives = full_negative + + # Keep some negatives + negative_label_list = [label for label in all_labels if label not in positive_label_set] + random.shuffle(negative_label_list) + negative_label_list = negative_label_list[:num_negatives] + + # Keep some positives + positive_label_list = list(positive_label_set) + random.shuffle(positive_label_list) + positive_label_list = positive_label_list[:num_positives] + + selected_label_list = positive_label_list + negative_label_list + screened_label_list = _label_drop_with_length_limit(selected_label_list, ind_to_class, max_seq_length, tokenizer) + return screened_label_list + +def od_to_grounding_optimized_streamlined( + target, + image_id, + ind_to_class, + tokenizer, + od_to_grounding_version, + ): + + if od_to_grounding_version == "random.v1": + separation_tokens = ". " + max_num_labels = 85 + max_seq_length = 254 + elif od_to_grounding_version == "random.v2": + separation_tokens = ". " + max_num_labels = 60 + max_seq_length = 254 + + def generate_senetence_given_labels( + label_list, + disable_shuffle=False, + ): + label_to_positions = {} + if not disable_shuffle: + random.shuffle(label_list) + + pheso_caption = "" + + for index, label in enumerate(label_list): + + start_index = len(pheso_caption) + pheso_caption += clean_name(ind_to_class[label]) # NOTE: slight change... + end_index = len(pheso_caption) + + # e.g.: pheso_caption = "cat dog", where cat is label 4, and dog is label 17 + # label_to_positions: {4: (0, 3), 17: (4, 7)} + label_to_positions[label] = [start_index, end_index] + + if index != len(label_list) - 1: + pheso_caption += separation_tokens + + return label_to_positions, pheso_caption + + + if od_to_grounding_version == "random.v1": + # all_labels, ind_to_class, max_seq_length, max_num_labels, tokenizer + screened_label_list = _randomv1_od_to_grounding( + all_labels = list(ind_to_class.keys()), + ind_to_class = ind_to_class, + max_seq_length = max_seq_length, + max_num_labels = max_num_labels, + tokenizer = tokenizer, + ) + label_to_positions, pheso_caption = generate_senetence_given_labels( + label_list=screened_label_list, ) + elif od_to_grounding_version == "random.v2": + screened_label_list = _randomv2_od_to_grounding( + all_labels = list(ind_to_class.keys()), + ind_to_class = ind_to_class, + max_seq_length = max_seq_length, + max_num_labels = max_num_labels, + tokenizer = tokenizer, + positive_label_set = set(target.extra_fields["labels"].tolist()), + ) + label_to_positions, pheso_caption = generate_senetence_given_labels( + label_list=screened_label_list, ) + else: + raise NotImplementedError + + new_target = [] + + ''' + Convert into: + {'area': 10506.0, 'iscrowd': 0, 'image_id': 571335, 'category_id': 1, 'id': 2999421, 'bbox': [221, 319, 103, 102], 'tokens_positive': [[0, 3]]} + tokens_positive is the char position + ''' + areas = target.area() + greenlight_span_for_masked_lm_objective = [] + for i in range(len(target)): + new_target_i = {} + new_target_i["area"] = areas[i] + new_target_i["iscrowd"] = 0 + new_target_i["image_id"] = image_id + new_target_i["category_id"] = target.extra_fields["labels"][i].item() + new_target_i["id"] = None + new_target_i['bbox'] = target.bbox[i].numpy().tolist() + + label_i = target.extra_fields["labels"][i].item() + new_target_i["original_od_label"] = label_i + + if label_i in label_to_positions: # NOTE: Only add labels that actually appear in the final caption + new_target_i["tokens_positive"] = [label_to_positions[label_i]] + new_target.append(new_target_i) + greenlight_span_for_masked_lm_objective.append(label_to_positions[label_i]) + + # reconstruct the target + new_target_boxlist = BoxList(torch.as_tensor([i['bbox'] for i in new_target]).reshape(-1, 4), target.size, mode="xyxy") + new_target_boxlist.add_field("labels", torch.as_tensor([i['category_id'] for i in new_target])) + + return new_target, pheso_caption, greenlight_span_for_masked_lm_objective, label_to_positions, new_target_boxlist + + + +def convert_object_detection_to_grounding_optimized_for_od( + target, + image_id, + ind_to_class, + disable_shuffle, + add_detection_prompt, + add_detection_prompt_advanced, + random_sample_negative, + control_probabilities, + restricted_negative_list=None, + separation_tokens=" ", + max_num_labels=-1, + max_seq_length=256, + tokenizer=None, + positive_caption_length=0, + od_to_grounding_version = "vanilla", +): + ''' + ind_to_class: {0: "__background__", 1 : "person" ...} + target: + + restricted_negative_list : for datasets with restricted negatives, sample only the negatives + + Convert object detection data into grounding data format, on the fly. + + Control options: + 1. add_detection_prompt: add "object detection : " to the front of the prompt + 2. num_negatives: randomly sampled negative classes + 3. num_positives: how many positives to keep (-1 means do not cut any) + + Probabilities to generate the control options: + + a. probability_one_negative: only give one negative class to mimic evaluation + b. probability_one_positive: only give one positive class to mimic evaluation + c. probability_full: add both all positive and all negatives + d. other: + randomly sample some negatives and some positives + The below control options are independent of each other: + - probability_random_negative: probability of randomly sample X negatives + - probability_random_positive: probability of randomly sample some positives + + + NEW: control version; we will have a few pre-defined control versions; and we only need to sepecify the version instead of all the detailed paratmeters + ''' + def generate_senetence_given_labels( + positive_label_list, + negative_label_list, + prompt_engineer_version="v2", + disable_shuffle=False, + positive_question_probability=0.6, + negative_question_probability=0.8, + full_question_probability=0.5): + + ''' + v3: with simple prompt such as "there are", "are there?" + v4: try to merge some are there / there are together, to avoid sequence being too long + ''' + + label_to_positions = {} + + assert (prompt_engineer_version == "v2") + num_negatives = len(negative_label_list) + num_positives = len(positive_label_list) + label_list = negative_label_list + positive_label_list + if not disable_shuffle: + random.shuffle(label_list) + + if add_detection_prompt: + if add_detection_prompt_advanced and (num_negatives == 0 or num_positives == 0) and not disable_shuffle: + pheso_caption = "object detection query : " + else: + pheso_caption = "object detection : " + else: + pheso_caption = "" + + for index, label in enumerate(label_list): + + start_index = len(pheso_caption) + + pheso_caption += clean_name(ind_to_class[label]) # NOTE: slight change... + end_index = len(pheso_caption) + + # e.g.: pheso_caption = "cat dog", where cat is label 4, and dog is label 17 + # label_to_positions: {4: (0, 3), 17: (4, 7)} + label_to_positions[label] = [start_index, end_index] + + if index != len(label_list) - 1: + pheso_caption += separation_tokens + + return label_to_positions, pheso_caption + + + positive_label_set = set() + for i in range(len(target)): + label_i = target.extra_fields["labels"][i].item() + positive_label_set.add(label_i) + + if restricted_negative_list is None: + valid_negative_indexes = list(ind_to_class.keys()) + else: + valid_negative_indexes = restricted_negative_list + + all_vailable_labels = positive_label_set | set(valid_negative_indexes) + + if disable_shuffle: + label_list = list(sorted(ind_to_class.keys()))[1:] # do not include the background + label_to_positions, pheso_caption = generate_senetence_given_labels( + positive_label_list=label_list, + negative_label_list=[], + disable_shuffle=True) + elif od_to_grounding_version == "random": + # all_labels, ind_to_class, max_seq_length, max_num_labels, tokenizer + screened_label_list = _random_od_to_grounding( + all_labels = all_vailable_labels, + ind_to_class = ind_to_class, + max_seq_length = max_seq_length, + max_num_labels = max_num_labels, + tokenizer = tokenizer, + ) + label_to_positions, pheso_caption = generate_senetence_given_labels( + positive_label_list=screened_label_list) + else: + full_positive = len(positive_label_set) + if max_num_labels <= 0: + full_negative = random_sample_negative + else: + full_negative = max(min(max_num_labels-full_positive, random_sample_negative), 0) + + if full_negative > len(valid_negative_indexes): + full_negative = len(valid_negative_indexes) + + num_negatives, num_positives = generate_control_options_given_probabilities( + control_probabilities=control_probabilities, + full_positive=full_positive, + full_negative=full_negative) + # num_positives not used + + # Keep some negatives + negative_label_list = set() + if num_negatives != -1: + if num_negatives > len(valid_negative_indexes): + num_negatives = len(valid_negative_indexes) + for i in np.random.choice(valid_negative_indexes, size=num_negatives, replace=False): + # label_sets.add(i) + if i not in positive_label_set: + negative_label_list.add(i) + + # Keep all positives; ignoring num_positives + positive_label_list = list(positive_label_set) + random.shuffle(positive_label_list) + + negative_label_list = list(negative_label_list) # e.g.: [17, 1, 13] where each number is the class name + random.shuffle(negative_label_list) + + # Do a pre-screen. If we cannot afford this many negatives, we will sample less + negative_max_length = max_seq_length - positive_caption_length + screened_negative_label_list = [] + for negative_label in negative_label_list: + label_text = clean_name(ind_to_class[negative_label]) + ". " # "dog. " + + tokenized = tokenizer.tokenize(label_text) + + negative_max_length -= len(tokenized) + + if negative_max_length > 0: + screened_negative_label_list.append(negative_label) # keep this negative + else: + break + negative_label_list = screened_negative_label_list + + label_to_positions, pheso_caption = generate_senetence_given_labels( + positive_label_list=positive_label_list, + negative_label_list=negative_label_list) + + new_target = [] + + ''' + Convert into: + {'area': 10506.0, 'iscrowd': 0, 'image_id': 571335, 'category_id': 1, 'id': 2999421, 'bbox': [221, 319, 103, 102], 'tokens_positive': [[0, 3]]} + tokens_positive is the char position + ''' + areas = target.area() + greenlight_span_for_masked_lm_objective = [] + for i in range(len(target)): + new_target_i = {} + new_target_i["area"] = areas[i] + new_target_i["iscrowd"] = 0 + new_target_i["image_id"] = image_id + new_target_i["category_id"] = target.extra_fields["labels"][i].item() + new_target_i["id"] = None + new_target_i['bbox'] = target.bbox[i].numpy().tolist() + + label_i = target.extra_fields["labels"][i].item() + new_target_i["original_od_label"] = label_i + + if label_i in label_to_positions: # NOTE: Only add those that actually appear in the final caption + new_target_i["tokens_positive"] = [label_to_positions[label_i]] + new_target.append(new_target_i) + greenlight_span_for_masked_lm_objective.append(label_to_positions[label_i]) + + return new_target, pheso_caption, greenlight_span_for_masked_lm_objective, label_to_positions + + +def generate_control_options_given_probabilities( + control_probabilities, + full_positive, + full_negative): + + # The function was originally designed to perform data augmentation by randomly dropping negative and positive classes. Later, we decided to only consider dropping negative classes. So the returned 'num_positives' by this function will be ignored. + + outer_prob = random.random() + + probability_one_negative = control_probabilities[0] + probability_one_positive = control_probabilities[1] + probability_full = control_probabilities[2] + probability_drop_positive = control_probabilities[3] + + assert(probability_drop_positive == 0) + + if outer_prob < probability_one_negative: + # a. probability_one_negative: only give one negative class to mimic evaluation (10%) + num_negatives = 1 + num_positives = 0 + elif outer_prob < probability_one_positive + probability_one_negative: + # b. probability_one_positive: only give one positive class to mimic evaluation (10%) + num_negatives = 0 + num_positives = 1 + elif outer_prob < probability_full + probability_one_positive + probability_one_negative: + # c. probability_full: add both all positive and all negatives (20%) + num_negatives = full_negative + num_positives = full_positive + else: + if random.random() < 1.0: # - probability_random_negative: probability of randomly sample X negatives (100%) + num_negatives = np.random.choice(max(1, full_negative)) + 1 # mininum 1 + else: + num_negatives = full_negative # Full + + if random.random() < probability_drop_positive: # + num_positives = np.random.choice(max(1, full_positive)) + 1 + else: + num_positives = full_positive # Full + + return num_negatives, num_positives diff --git a/maskrcnn_benchmark/data/datasets/omnilabel.py b/maskrcnn_benchmark/data/datasets/omnilabel.py new file mode 100644 index 0000000000000000000000000000000000000000..00c0fe5983d536db310840004c93f0a27f96a4e4 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/omnilabel.py @@ -0,0 +1,103 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import os +import os.path +import math +from PIL import Image + +import random +import numpy as np + +import torch +import torchvision +import torch.utils.data as data + +import omnilabeltools as olt +from maskrcnn_benchmark.structures.bounding_box import BoxList +# from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask +# from maskrcnn_benchmark.structures.keypoint import PersonKeypoints +# from maskrcnn_benchmark.config import cfg +import pdb + + +def pil_loader(path, retry=5): + # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) + ri = 0 + while ri < retry: + try: + with open(path, "rb") as f: + img = Image.open(f) + return img.convert("RGB") + except: + ri += 1 + +def load_omnilabel_json(path_json: str, path_imgs: str): + assert isinstance(path_json, str) + + ol = olt.OmniLabel(path_json) + dataset_dicts = [] + for img_id in ol.image_ids: + img_sample = ol.get_image_sample(img_id) + dataset_dicts.append({ + "image_id": img_sample["id"], + "file_name": os.path.join(path_imgs, img_sample["file_name"]), + "inference_obj_descriptions": [od["text"] for od in img_sample["labelspace"]], + "inference_obj_description_ids": [od["id"] for od in img_sample["labelspace"]], + "tokens_positive":[od['anno_info'].get("tokens_positive", None) for od in img_sample["labelspace"]], + }) + return dataset_dicts + +class OmniLabelDataset(data.Dataset): + """`MS Coco Detection `_ Dataset. + + Args: + img_folder (string): Root directory where images are downloaded to. + ann_file (string): Path to json annotation file. + transform (callable, optional): A function/transform that takes in an PIL image + and returns a transformed version. E.g, ``transforms.ToTensor`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + """ + + def __init__(self, img_folder, ann_file, transforms=None, **kwargs): + self.img_folder = img_folder + self.transforms = transforms + self.dataset_dicts = load_omnilabel_json(ann_file, img_folder) + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``. + """ + data_dict = self.dataset_dicts[index] + img_id = data_dict["image_id"] + + path = data_dict["file_name"] + img = pil_loader(path) + + # only support test. No box here + target = BoxList(torch.Tensor(0,4), img.size, mode="xywh").convert("xyxy") + target.add_field("inference_obj_descriptions", data_dict["inference_obj_descriptions"]) + target.add_field("inference_obj_description_ids", data_dict["inference_obj_description_ids"]) + target.add_field("tokens_positive", data_dict["tokens_positive"]) + + if self.transforms is not None: + img = self.transforms(img) + + return img, target, img_id + + def __len__(self): + return len(self.dataset_dicts) + + def __repr__(self): + fmt_str = "Dataset " + self.__class__.__name__ + "\n" + fmt_str += " Number of datapoints: {}\n".format(self.__len__()) + fmt_str += " Root Location: {}\n".format(self.img_folder) + return fmt_str + + # def get_img_info(self, index): + # img_id = self.id_to_img_map[index] + # img_data = self.coco.imgs[img_id] + # return img_data \ No newline at end of file diff --git a/maskrcnn_benchmark/data/datasets/paco.py b/maskrcnn_benchmark/data/datasets/paco.py new file mode 100644 index 0000000000000000000000000000000000000000..545f88baf90ce193bb29e16950ce73a90e329bf3 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/paco.py @@ -0,0 +1,70 @@ +# Following LVIS dataset +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import json +import os +import time +from collections import defaultdict + +import pdb +import pycocotools.mask as mask_utils +import torchvision +from PIL import Image +import torch +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask +from maskrcnn_benchmark.structures.keypoint import PersonKeypoints +from maskrcnn_benchmark.config import cfg +# from .coco import ConvertCocoPolysToMask, make_coco_transforms +from .modulated_coco import ConvertCocoPolysToMask + +from .lvis import LVIS, LvisDetectionBase + + +class PacoDetection(LvisDetectionBase): + def __init__(self, img_folder, ann_file, transforms, return_masks=False, **kwargs): + super(PacoDetection, self).__init__(img_folder, ann_file) + self.ann_file = ann_file + self._transforms = transforms + self.ids = sorted(list(self.lvis.imgs.keys())) + self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} + self.prepare = ConvertCocoPolysToMask(return_masks) + + def categories(self): + id2cat = {c["id"]: c for c in self.lvis.dataset["categories"]} + all_cats = sorted(list(id2cat.keys())) + categories = {} + for l in list(all_cats): + categories[l] = id2cat[l]['name'] + return categories + + def __getitem__(self, idx): + pdb.set_trace() + img, target = super(PacoDetection, self).__getitem__(idx) + image_id = self.ids[idx] + target = {"image_id": image_id, "annotations": target} + img, target = self.prepare(img, target) + if self._transforms is not None: + img = self._transforms(img) + return img, target, idx + + + def convert_dict_anno_to_box(self, annos): + pass + + def get_raw_image(self, idx): + img, target = super(PacoDetection, self).__getitem__(idx) + return img + + def categories(self): + id2cat = {c["id"]: c for c in self.lvis.dataset["categories"]} + all_cats = sorted(list(id2cat.keys())) + categories = {} + for l in list(all_cats): + categories[l] = id2cat[l]['name'] + return categories + + def get_img_info(self, index): + img_id = self.id_to_img_map[index] + img_data = self.lvis.imgs[img_id] + return img_data \ No newline at end of file diff --git a/maskrcnn_benchmark/data/datasets/paco_query.py b/maskrcnn_benchmark/data/datasets/paco_query.py new file mode 100644 index 0000000000000000000000000000000000000000..27e09d9253fd4cfee043280df961fa220bd5c8cd --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/paco_query.py @@ -0,0 +1,61 @@ +# Following LVIS dataset +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import json +import os +import time +from collections import defaultdict + +import pdb +import pycocotools.mask as mask_utils +import torchvision +from PIL import Image +import torch +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask +from maskrcnn_benchmark.structures.keypoint import PersonKeypoints +from maskrcnn_benchmark.config import cfg +# from .coco import ConvertCocoPolysToMask, make_coco_transforms +from .modulated_coco import ConvertCocoPolysToMask + + + +class PacoDetection(): + def __init__(self, img_folder, ann_file, transforms, return_masks=False, **kwargs): + super(PacoDetection, self).__init__(img_folder, ann_file) + self.ann_file = ann_file + self._transforms = transforms + self.ids = sorted(list(self.lvis.imgs.keys())) + self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} + self.prepare = ConvertCocoPolysToMask(return_masks) + + def __getitem__(self, idx): + pdb.set_trace() + img, target = super(PacoDetection, self).__getitem__(idx) + image_id = self.ids[idx] + target = {"image_id": image_id, "annotations": target} + img, target = self.prepare(img, target) + if self._transforms is not None: + img = self._transforms(img) + return img, target, idx + + + def convert_dict_anno_to_box(self, annos): + pass + + def get_raw_image(self, idx): + img, target = super(PacoDetection, self).__getitem__(idx) + return img + + def categories(self): + id2cat = {c["id"]: c for c in self.lvis.dataset["categories"]} + all_cats = sorted(list(id2cat.keys())) + categories = {} + for l in list(all_cats): + categories[l] = id2cat[l]['name'] + return categories + + def get_img_info(self, index): + img_id = self.id_to_img_map[index] + img_data = self.lvis.imgs[img_id] + return img_data \ No newline at end of file diff --git a/maskrcnn_benchmark/data/datasets/parse_gpt.py b/maskrcnn_benchmark/data/datasets/parse_gpt.py new file mode 100644 index 0000000000000000000000000000000000000000..3c4658acd797b10c3967994c0a24d243a537a0c5 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/parse_gpt.py @@ -0,0 +1,218 @@ +import json +import re +from copy import deepcopy +import random +def clean_string(input_string): + # remove leading and trailing spaces + input_string = input_string.strip() + # remove trailing ";" and "." + input_string = re.sub(r";$", "", input_string) + input_string = re.sub(r"\.$", "", input_string) + return input_string + +class GPTOutputParser(): + def __init__(self, version): + self.version = version + + def __call__(self, description): + if self.version == "v1": + try: + description = json.loads(description.strip("\n")) + description['description'] = description['description'].split("; ") + description['type'] = "a kind of {}".format(description['type']) + except: + description = { + "type": "object", + "description": [], + "similar objects": [] + } + return description + if self.version == "v5": + info = description + # check the format of type of thing + if "has a tangible appearance and is" in info[0]: + type_of_thing = info[0].split(" has a tangible appearance and is ")[-1].split(".")[0] + elif "has a tangible appearance" in info[0]: + type_of_thing = info[0].split(" has a tangible appearance ")[-1].split(".")[0] + if "and refers to" in type_of_thing: + type_of_thing = type_of_thing.split("and refers to ")[-1] + else: + #print(info[0], "type of thing not found") + type_of_thing = "" + + if " are:\t" in info[0]: + similar_things = info[0].split(" are:\t")[-1].split("\nThere are several useful visual features to tell")[0].split("\t") + similar_things = [i for i in similar_things if i.strip() != ""] # remove empty strings + else: + #print(info[0], "similar things not found") + similar_things = [] + + if " and not similar things in a photo:\t" in info[0]: + visual_feature_descriptions = info[0].split(" and not similar things in a photo:\t")[-1].split("\t") + visual_feature_descriptions = [i for i in visual_feature_descriptions if i.strip() != ""] # remove empty strings + else: + #print(info[0], "visual feature descriptions not found") + visual_feature_descriptions = [] + return { + "type": type_of_thing, + "description": "; ".join(visual_feature_descriptions), + "similar_things": similar_things + } + if self.version == "v6": + description = description.lower() + type_ = re.findall(r"type: (.*)", description)[0] + # description + + visual_description = re.findall(r"visual description.*:(.*)similar objects", description, re.DOTALL)[0] + + # fine substrings with leading 1. 2. + visual_description = re.findall(r"[(\d\.)(-\.)]\ (.*)", visual_description) + + # similar objects + similar_objects = re.findall(r"similar objects:(.*)", description, re.DOTALL)[0] + similar_objects = re.findall(r"[(\d\.)(-\.)]\ (.*)", similar_objects) + + visual_description = [clean_string(i) for i in visual_description] + visual_description = [i for i in visual_description if i != ""] + + similar_objects = [clean_string(i) for i in similar_objects] + similar_objects = [i for i in similar_objects if i != ""] + + final_description = { + "type": type_, + "description": "; ".join(visual_description), + "similar objects": similar_objects, + } + return final_description + # except: + # print(description_dict) + # pdb.set_trace() + if self.version == "v7": + ''' + "- plumbing fixture\n- white or off-white\n- a bowl-shaped basin\n- a drain at the bottom\n- a water supply line\n- a flush handle or button\n- a splash guard\n- a wall-mounted or floor-mounted design" + ''' + description = description.lower() + description = [des.replace("- ", "") for des in description.split("\n")] + final_description = { + "type": description[0], + "description": description[1:], + "similar objects": [], + } + return final_description + + assert False, "version not supported" + + def form_span(self, + noun, + description, + type = "vanilla", + positive_range = "partial", + start_index = 0, + od_to_grounding_version = ''): + ''' + Given the parsed description, form the span + ''' + + if "random_origin" in od_to_grounding_version and random.random() < 0.1: + # directly use the noun it self + return noun, len(noun), None, None # forget about span + + description = self(description) + type_of_thing = description['type'] + + pheso_caption = "" + spans = [] + + start_index_rolling = start_index + # the first substring is the name + if "remove_noun" in type: + pass + else: + sub_descripion = "{}, ".format(noun) + pheso_caption += sub_descripion + spans.append([start_index_rolling, len(pheso_caption) + start_index]) + + start_index_rolling = len(pheso_caption) + start_index + if "skip_des" in type: + descriptions = [type_of_thing] + else: + descriptions = [type_of_thing] + description['description'] + + for index, description_i in enumerate(descriptions): + if index != len(descriptions) - 1: + _suffix = ", " + else: + _suffix = ". " + + sub_descripion = "{}{}".format(description_i, _suffix) + pheso_caption += sub_descripion + spans.append([start_index_rolling, len(pheso_caption) + start_index]) + start_index_rolling = len(pheso_caption) + start_index + + if index == 0: # pheso_caption: cat, a kind of animal + if positive_range == "partial": # when + end_index = len(pheso_caption) + positive_spans = deepcopy(spans) + + if positive_range == "all": + end_index = len(pheso_caption) + positive_spans = deepcopy(spans) + return pheso_caption, end_index, spans, positive_spans + + def form_span_independent(self, + noun, + description, + type = "vanilla", + positive_range = "partial", + start_index = 0, + od_to_grounding_version = None): + ''' + Given the parsed description, form the span + ''' + if "infer" in od_to_grounding_version: + use_random = False + else: + use_random = True + + description = self(description) + type_of_thing = description['type'] + + pheso_caption = "" + spans = [] + + start_index_rolling = start_index + # the first substring is the name + if use_random and random.random() < 0.5: + drop_noun = True + else: + drop_noun = False + + if not drop_noun: + sub_descripion = "{}, {}. ".format(noun, type_of_thing) + pheso_caption += sub_descripion + spans.append([start_index_rolling, len(pheso_caption) + start_index]) + + start_index_rolling = len(pheso_caption) + start_index + descriptions = description['description'] + + for index, description_i in enumerate(descriptions): + _suffix = ". " + if use_random and random.random() < 0.5: + leading_word = type_of_thing + else: + leading_word = noun + + sub_descripion = "{}, {}{}".format(leading_word, description_i, _suffix) + pheso_caption += sub_descripion + spans.append([start_index_rolling, len(pheso_caption) + start_index]) + start_index_rolling = len(pheso_caption) + start_index + + if index == 0: # pheso_caption: cat, a kind of animal + if positive_range == "partial": # when + end_index = len(pheso_caption) + positive_spans = deepcopy(spans) + + if positive_range == "all": + end_index = len(pheso_caption) + positive_spans = deepcopy(spans) + return pheso_caption, end_index, spans, positive_spans \ No newline at end of file diff --git a/maskrcnn_benchmark/data/datasets/phrasecut.py b/maskrcnn_benchmark/data/datasets/phrasecut.py new file mode 100644 index 0000000000000000000000000000000000000000..2a68262d2372c69ba9e64535014770ce4be98189 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/phrasecut.py @@ -0,0 +1,8 @@ +import torch +import torchvision +import torch.utils.data as data +from maskrcnn_benchmark.data.datasets.modulated_coco import ModulatedDataset + + +class PhrasecutDetection(ModulatedDataset): + pass diff --git a/maskrcnn_benchmark/data/datasets/pseudo_data.py b/maskrcnn_benchmark/data/datasets/pseudo_data.py new file mode 100644 index 0000000000000000000000000000000000000000..a4d8de9bf1ba9052664c426c6ddfa847d992acdc --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/pseudo_data.py @@ -0,0 +1,230 @@ +import torch +import torch.distributed as dist +import time +from torchvision.ops import nms +import random +import numpy as np +from PIL import Image, ImageDraw +import pdb +from maskrcnn_benchmark.structures.bounding_box import BoxList +from .modulated_coco import ConvertCocoPolysToMask +from .tsv import ODTSVDataset, TSVYamlDataset +from .od_to_grounding import sanity_check_target_after_processing +from copy import deepcopy + + +class PseudoData(TSVYamlDataset): + def __init__( + self, + yaml_file, + transforms, + return_tokens, + return_masks, + tokenizer, + caption_min_box=1, + replace_clean_label=False, + further_screen=False, + caption_conf=0.5, + caption_nms=-1, + pack_random_caption_number=0, + inference_caption=False, + sample_negative_for_grounding_data=-1, + random_pack_prob=-1.0, + no_random_pack_probability=0.0, + safeguard_positive_caption=True, + mlm_obj_for_only_positive=False, + caption_format_version="v1", + local_debug=False, + max_query_len=256, + diver_box_for_vqa=False, + **kwargs + ): + super(PseudoData, self).__init__(yaml_file, None, replace_clean_label) + self.yaml_file = yaml_file + self._transforms = transforms + self.max_query_len = max_query_len + self.prepare = ConvertCocoPolysToMask( + return_masks=return_masks, return_tokens=return_tokens, tokenizer=tokenizer, max_query_len=max_query_len + ) + self.diver_box_for_vqa = diver_box_for_vqa + if "qa" in self.yaml_file: + assert self.diver_box_for_vqa # must diver box + self.tokenizer = tokenizer + self.caption_min_box = caption_min_box + self.replace_clean_label = replace_clean_label + self.further_screen = further_screen + self.pack_random_caption_number = pack_random_caption_number + self.caption_format_version = caption_format_version + + self.caption_conf = caption_conf + self.caption_nms = caption_nms + self.inference_caption = inference_caption + self.sample_negative_for_grounding_data = sample_negative_for_grounding_data + self.random_pack_prob = random_pack_prob + self.no_random_pack_probability = no_random_pack_probability + self.safeguard_positive_caption = safeguard_positive_caption + self.mlm_obj_for_only_positive = mlm_obj_for_only_positive + self.local_debug = local_debug + try: + self.rank = dist.get_rank() + except: + self.rank = 0 + + def __len__(self): + return super(PseudoData, self).__len__() + + @staticmethod + def check_for_overlap(range1, range2): + if range1[0] > range2[1] or range2[0] > range1[1]: + return False + return True + + def divert_boxes(self, anno): + # first get answer start and end + answer_start = len(anno["text"]) + 1 # +1 for the space + answer_end = len(anno["caption"]) + + question = anno["caption"][:answer_start] # get the question + + mask_start = len(question) + # add the mask token + mask_token = self.tokenizer.mask_token + if mask_token is None: + mask_token = "answer" + question += mask_token + mask_end = len(question) + + # divert the box + for i in range(len(anno["bboxes"])): + # check over lap + for j in range(len(anno["tokens_positive"][i])): + if self.check_for_overlap(anno["tokens_positive"][i][j], [answer_start, answer_end]): + # if overlap, then divert the box to the mask token + anno["tokens_positive"][i][j] = [mask_start, mask_end] + + anno["caption"] = question + return question, anno + + def __getitem__(self, idx): + img, anno, _, scale = super(PseudoData, self).__getitem__(idx) + if self.inference_caption: + caption = None + if isinstance(anno, list): + caption = anno[0]["caption"] # inference mode for bing + anno = [] + elif len(anno) == 1: + caption = anno["caption"] # inference mode for googlecc + anno = [] + else: + caption = " ".join(anno["captions"]) + anno = [] + else: + if self.caption_format_version == "v2": + anno = self.convert_anno_from_yiling_to_ours(anno) + + if self.further_screen: + conf = self.caption_conf + nms_thre = self.caption_nms + + bboxes = torch.as_tensor(anno["bboxes"]).float() + scores = torch.as_tensor(anno["scores"]) + tokens_positive = anno["tokens_positive"] + + keep = scores > conf + scores = scores[keep] + bboxes = bboxes[keep] + tokens_positive = [i for index, i in enumerate(tokens_positive) if keep[index]] + + assert len(tokens_positive) == len(bboxes) == len(scores) + + if len(bboxes) < self.caption_min_box: # Retry triggered! + return self[np.random.choice(len(self))] + + if nms_thre > 0: + keep = nms(boxes=bboxes, scores=scores, iou_threshold=nms_thre) + scores = scores[keep] + bboxes = bboxes[keep] + tokens_positive = [tokens_positive[i] for i in keep] + assert len(tokens_positive) == len(bboxes) == len(scores) + + # Write back + anno["bboxes"] = bboxes.tolist() + anno["scores"] = scores.tolist() + anno["tokens_positive"] = tokens_positive + + boxes = torch.as_tensor(anno["bboxes"]) + + if len(boxes) < self.caption_min_box: # Retry triggered! + return self[np.random.choice(len(self))] + + target = BoxList(boxes, (anno["img_w"], anno["img_h"]), mode="xyxy") + target = target.clip_to_image(remove_empty=True) + + if self.diver_box_for_vqa: + caption, anno = self.divert_boxes(anno=anno) # will change caption and "tokens_positive" + + caption = anno["caption"] + + greenlight_span_for_masked_lm_objective = [(0, len(caption))] + + new_anno = [] + areas = target.area() + for i in range(len(target)): + new_anno_i = {} + new_anno_i["area"] = areas[i] + new_anno_i["iscrowd"] = 0 + new_anno_i["image_id"] = idx + new_anno_i["category_id"] = 1 # following vg and others + new_anno_i["id"] = None + new_anno_i["bbox"] = target.bbox[i].numpy().tolist() + new_anno_i["tokens_positive"] = anno["tokens_positive"][i] + new_anno.append(new_anno_i) + anno = new_anno + + annotations = {"image_id": idx, "annotations": anno, "caption": caption} + annotations["greenlight_span_for_masked_lm_objective"] = greenlight_span_for_masked_lm_objective + img, annotations = self.prepare(img, annotations, box_format="xyxy") + + if self._transforms is not None: + img, target = self._transforms(img, target) + + # add additional property + for ann in annotations: + target.add_field(ann, annotations[ann]) + + # This is the real image_id + image_id = self.get_img_id(idx) + # Can insert additional field into target if needed + + sanity_check_target_after_processing(target) + + return img, target, idx + + def convert_anno_from_yiling_to_ours(self, anno): + flatterned_bboxes = [] + flatterned_tokens_positive = [] + flatterned_bboxes_scores = [] + for i in range(len(anno["bboxes"])): + # i is the index for entity + for j in range(len(anno["bboxes"][i])): + # j is the index for each box + flatterned_bboxes.append(anno["bboxes"][i][j]) + flatterned_tokens_positive.append( + anno["tokens_positive"][i] + ) # Assume this box corresponds to all the token_spans for this entity + flatterned_bboxes_scores.append(anno["scores"][i][j]) + anno["bboxes"] = flatterned_bboxes + anno["tokens_positive"] = flatterned_tokens_positive + anno["scores"] = flatterned_bboxes_scores + return anno + + def get_raw_image(self, idx): + image, *_ = super(PseudoData, self).__getitem__(idx) + return image + + def get_img_id(self, idx): + line_no = self.get_line_no(idx) + if self.label_tsv is not None: + row = self.label_tsv.seek(line_no) + img_id = row[0] + return img_id diff --git a/maskrcnn_benchmark/data/datasets/refexp.py b/maskrcnn_benchmark/data/datasets/refexp.py new file mode 100644 index 0000000000000000000000000000000000000000..a63015aff6919f1c2ea97382bc319f92b742f76a --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/refexp.py @@ -0,0 +1,88 @@ +import copy +from collections import defaultdict +from pathlib import Path + +import torch +import torch.utils.data + +import maskrcnn_benchmark.utils.dist as dist +from maskrcnn_benchmark.layers.set_loss import generalized_box_iou + +from .modulated_coco import ModulatedDataset + + +class RefExpDataset(ModulatedDataset): + pass + + +class RefExpEvaluator(object): + def __init__(self, refexp_gt, iou_types, k=(1, 5, 10), thresh_iou=0.5): + assert isinstance(k, (list, tuple)) + refexp_gt = copy.deepcopy(refexp_gt) + self.refexp_gt = refexp_gt + self.iou_types = iou_types + self.img_ids = self.refexp_gt.imgs.keys() + self.predictions = {} + self.k = k + self.thresh_iou = thresh_iou + + def accumulate(self): + pass + + def update(self, predictions): + self.predictions.update(predictions) + + def synchronize_between_processes(self): + all_predictions = dist.all_gather(self.predictions) + merged_predictions = {} + for p in all_predictions: + merged_predictions.update(p) + self.predictions = merged_predictions + + def summarize(self): + if dist.is_main_process(): + dataset2score = { + "refcoco": {k: 0.0 for k in self.k}, + "refcoco+": {k: 0.0 for k in self.k}, + "refcocog": {k: 0.0 for k in self.k}, + } + dataset2count = {"refcoco": 0.0, "refcoco+": 0.0, "refcocog": 0.0} + for image_id in self.img_ids: + ann_ids = self.refexp_gt.getAnnIds(imgIds=image_id) + assert len(ann_ids) == 1 + img_info = self.refexp_gt.loadImgs(image_id)[0] + + target = self.refexp_gt.loadAnns(ann_ids[0]) + prediction = self.predictions[image_id] + assert prediction is not None + sorted_scores_boxes = sorted( + zip(prediction["scores"].tolist(), prediction["boxes"].tolist()), reverse=True + ) + sorted_scores, sorted_boxes = zip(*sorted_scores_boxes) + sorted_boxes = torch.cat([torch.as_tensor(x).view(1, 4) for x in sorted_boxes]) + target_bbox = target[0]["bbox"] + converted_bbox = [ + target_bbox[0], + target_bbox[1], + target_bbox[2] + target_bbox[0], + target_bbox[3] + target_bbox[1], + ] + giou = generalized_box_iou(sorted_boxes, torch.as_tensor(converted_bbox).view(-1, 4)) + for k in self.k: + if max(giou[:k]) >= self.thresh_iou: + dataset2score[img_info["dataset_name"]][k] += 1.0 + dataset2count[img_info["dataset_name"]] += 1.0 + + for key, value in dataset2score.items(): + for k in self.k: + try: + value[k] /= dataset2count[key] + except: + pass + results = {} + for key, value in dataset2score.items(): + results[key] = sorted([v for k, v in value.items()]) + print(f" Dataset: {key} - Precision @ 1, 5, 10: {results[key]} \n") + + return results + return None diff --git a/maskrcnn_benchmark/data/datasets/tsv.py b/maskrcnn_benchmark/data/datasets/tsv.py new file mode 100644 index 0000000000000000000000000000000000000000..d1f3c94b1d33028ad49470c9383eed7afc488b18 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/tsv.py @@ -0,0 +1,421 @@ +import os +import os.path as op +import json + +# import logging +import base64 +import yaml +import errno +import io +import math +from PIL import Image, ImageDraw + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from .box_label_loader import LabelLoader + + +def load_linelist_file(linelist_file): + if linelist_file is not None: + line_list = [] + with open(linelist_file, "r") as fp: + for i in fp: + line_list.append(int(i.strip())) + return line_list + + +def img_from_base64(imagestring): + try: + img = Image.open(io.BytesIO(base64.b64decode(imagestring))) + return img.convert("RGB") + except ValueError: + return None + + +def load_from_yaml_file(yaml_file): + with open(yaml_file, "r") as fp: + return yaml.load(fp, Loader=yaml.CLoader) + + +def find_file_path_in_yaml(fname, root): + if fname is not None: + found_file = None + if op.isfile(fname): + found_file = fname + elif op.isfile(op.join(root, fname)): + found_file = op.join(root, fname) + else: + # be a bit more flexible and try to find the file in the root recursively + try_time = 3 + while try_time > 0: + try_time -= 1 + root = os.path.dirname(root) + if op.isfile(op.join(root, fname)): + found_file = op.join(root, fname) + break + if found_file is None: + raise FileNotFoundError( + errno.ENOENT, os.strerror(errno.ENOENT), op.join(root, fname) + ) + print('found file: {}'.format(found_file)) + return found_file + +def create_lineidx(filein, idxout): + idxout_tmp = idxout + ".tmp" + with open(filein, "r") as tsvin, open(idxout_tmp, "w") as tsvout: + fsize = os.fstat(tsvin.fileno()).st_size + fpos = 0 + while fpos != fsize: + tsvout.write(str(fpos) + "\n") + tsvin.readline() + fpos = tsvin.tell() + os.rename(idxout_tmp, idxout) + + +def read_to_character(fp, c): + result = [] + while True: + s = fp.read(32) + assert s != "" + if c in s: + result.append(s[: s.index(c)]) + break + else: + result.append(s) + return "".join(result) + + +class TSVFile(object): + def __init__(self, tsv_file, generate_lineidx=False): + self.tsv_file = tsv_file + self.lineidx = op.splitext(tsv_file)[0] + ".lineidx" + self._fp = None + self._lineidx = None + # the process always keeps the process which opens the file. + # If the pid is not equal to the currrent pid, we will re-open the file. + self.pid = None + # generate lineidx if not exist + if not op.isfile(self.lineidx) and generate_lineidx: + create_lineidx(self.tsv_file, self.lineidx) + + def __del__(self): + if self._fp: + self._fp.close() + + def __str__(self): + return "TSVFile(tsv_file='{}')".format(self.tsv_file) + + def __repr__(self): + return str(self) + + def num_rows(self): + self._ensure_lineidx_loaded() + return len(self._lineidx) + + def seek(self, idx): + self._ensure_tsv_opened() + self._ensure_lineidx_loaded() + try: + pos = self._lineidx[idx] + except: + # logging.info('{}-{}'.format(self.tsv_file, idx)) + raise + self._fp.seek(pos) + return [s.strip() for s in self._fp.readline().split("\t")] + + def seek_first_column(self, idx): + self._ensure_tsv_opened() + self._ensure_lineidx_loaded() + pos = self._lineidx[idx] + self._fp.seek(pos) + return read_to_character(self._fp, "\t") + + def get_key(self, idx): + return self.seek_first_column(idx) + + def __getitem__(self, index): + return self.seek(index) + + def __len__(self): + return self.num_rows() + + def _ensure_lineidx_loaded(self): + if self._lineidx is None: + # logging.info('loading lineidx: {}'.format(self.lineidx)) + with open(self.lineidx, "r") as fp: + self._lineidx = [int(i.strip()) for i in fp.readlines()] + + def _ensure_tsv_opened(self): + if self._fp is None: + self._fp = open(self.tsv_file, "r") + self.pid = os.getpid() + + if self.pid != os.getpid(): + # logging.info('re-open {} because the process id changed'.format(self.tsv_file)) + self._fp = open(self.tsv_file, "r") + self.pid = os.getpid() + + +class CompositeTSVFile: + def __init__(self, file_list, seq_file, root="."): + if isinstance(file_list, str): + self.file_list = load_list_file(file_list) + else: + assert isinstance(file_list, list) + self.file_list = file_list + + self.seq_file = seq_file + self.root = root + self.initialized = False + self.initialize() + + def get_key(self, index): + idx_source, idx_row = self.seq[index] + k = self.tsvs[idx_source].get_key(idx_row) + return "_".join([self.file_list[idx_source], k]) + + def num_rows(self): + return len(self.seq) + + def __getitem__(self, index): + idx_source, idx_row = self.seq[index] + return self.tsvs[idx_source].seek(idx_row) + + def __len__(self): + return len(self.seq) + + def initialize(self): + """ + this function has to be called in init function if cache_policy is + enabled. Thus, let's always call it in init funciton to make it simple. + """ + if self.initialized: + return + self.seq = [] + with open(self.seq_file, "r") as fp: + for line in fp: + parts = line.strip().split("\t") + self.seq.append([int(parts[0]), int(parts[1])]) + self.tsvs = [TSVFile(op.join(self.root, f)) for f in self.file_list] + self.initialized = True + + +def load_list_file(fname): + with open(fname, "r") as fp: + lines = fp.readlines() + result = [line.strip() for line in lines] + if len(result) > 0 and result[-1] == "": + result = result[:-1] + return result + + +class TSVDataset(object): + def __init__(self, img_file, label_file=None, hw_file=None, linelist_file=None, imageid2idx_file=None): + """Constructor. + Args: + img_file: Image file with image key and base64 encoded image str. + label_file: An optional label file with image key and label information. + A label_file is required for training and optional for testing. + hw_file: An optional file with image key and image height/width info. + linelist_file: An optional file with a list of line indexes to load samples. + It is useful to select a subset of samples or duplicate samples. + """ + self.img_file = img_file + self.label_file = label_file + self.hw_file = hw_file + self.linelist_file = linelist_file + + self.img_tsv = TSVFile(img_file) + self.label_tsv = None if label_file is None else TSVFile(label_file, generate_lineidx=True) + self.hw_tsv = None if hw_file is None else TSVFile(hw_file) + self.line_list = load_linelist_file(linelist_file) + self.imageid2idx = None + if imageid2idx_file is not None: + self.imageid2idx = json.load(open(imageid2idx_file, "r")) + + self.transforms = None + + def __len__(self): + if self.line_list is None: + if self.imageid2idx is not None: + assert self.label_tsv is not None, "label_tsv is None!!!" + return self.label_tsv.num_rows() + return self.img_tsv.num_rows() + else: + return len(self.line_list) + + def __getitem__(self, idx): + img = self.get_image(idx) + img_size = img.size # w, h + annotations = self.get_annotations(idx) + # print(idx, annotations) + target = self.get_target_from_annotations(annotations, img_size, idx) + img, target = self.apply_transforms(img, target) + + if self.transforms is None: + return img, target, idx, 1.0 + else: + new_img_size = img.shape[1:] + scale = math.sqrt(float(new_img_size[0] * new_img_size[1]) / float(img_size[0] * img_size[1])) + return img, target, idx, scale + + def get_line_no(self, idx): + return idx if self.line_list is None else self.line_list[idx] + + def get_image(self, idx): + line_no = self.get_line_no(idx) + if self.imageid2idx is not None: + assert self.label_tsv is not None, "label_tsv is None!!!" + row = self.label_tsv.seek(line_no) + annotations = json.loads(row[1]) + imageid = annotations["img_id"] + line_no = self.imageid2idx[imageid] + row = self.img_tsv.seek(line_no) + # use -1 to support old format with multiple columns. + img = img_from_base64(row[-1]) + return img + + def get_annotations(self, idx): + line_no = self.get_line_no(idx) + if self.label_tsv is not None: + row = self.label_tsv.seek(line_no) + annotations = json.loads(row[1]) + return annotations + else: + return [] + + def get_target_from_annotations(self, annotations, img_size, idx): + # This function will be overwritten by each dataset to + # decode the labels to specific formats for each task. + return annotations + + def apply_transforms(self, image, target=None): + # This function will be overwritten by each dataset to + # apply transforms to image and targets. + return image, target + + def get_img_info(self, idx): + if self.imageid2idx is not None: + assert self.label_tsv is not None, "label_tsv is None!!!" + line_no = self.get_line_no(idx) + row = self.label_tsv.seek(line_no) + annotations = json.loads(row[1]) + return {"height": int(annotations["img_w"]), "width": int(annotations["img_w"])} + + if self.hw_tsv is not None: + line_no = self.get_line_no(idx) + row = self.hw_tsv.seek(line_no) + try: + # json string format with "height" and "width" being the keys + data = json.loads(row[1]) + if type(data) == list: + return data[0] + elif type(data) == dict: + return data + except ValueError: + # list of strings representing height and width in order + hw_str = row[1].split(" ") + hw_dict = {"height": int(hw_str[0]), "width": int(hw_str[1])} + return hw_dict + + def get_img_key(self, idx): + line_no = self.get_line_no(idx) + # based on the overhead of reading each row. + if self.imageid2idx is not None: + assert self.label_tsv is not None, "label_tsv is None!!!" + row = self.label_tsv.seek(line_no) + annotations = json.loads(row[1]) + return annotations["img_id"] + + if self.hw_tsv: + return self.hw_tsv.seek(line_no)[0] + elif self.label_tsv: + return self.label_tsv.seek(line_no)[0] + else: + return self.img_tsv.seek(line_no)[0] + + +class TSVYamlDataset(TSVDataset): + """TSVDataset taking a Yaml file for easy function call""" + + def __init__(self, yaml_file, root=None, replace_clean_label=False): + print("Reading {}".format(yaml_file)) + self.cfg = load_from_yaml_file(yaml_file) + if root: + self.root = root + else: + self.root = op.dirname(yaml_file) + img_file = find_file_path_in_yaml(self.cfg["img"], self.root) + label_file = find_file_path_in_yaml(self.cfg.get("label", None), self.root) + hw_file = find_file_path_in_yaml(self.cfg.get("hw", None), self.root) + linelist_file = find_file_path_in_yaml(self.cfg.get("linelist", None), self.root) + imageid2idx_file = find_file_path_in_yaml(self.cfg.get("imageid2idx", None), self.root) + + if replace_clean_label: + assert "raw_label" in label_file + label_file = label_file.replace("raw_label", "clean_label") + + super(TSVYamlDataset, self).__init__(img_file, label_file, hw_file, linelist_file, imageid2idx_file) + + +class ODTSVDataset(TSVYamlDataset): + """ + Generic TSV dataset format for Object Detection. + """ + + def __init__(self, yaml_file, extra_fields=(), transforms=None, is_load_label=True, **kwargs): + if yaml_file is None: + return + super(ODTSVDataset, self).__init__(yaml_file) + + self.transforms = transforms + self.is_load_label = is_load_label + self.attribute_on = False + # self.attribute_on = kwargs['args'].MODEL.ATTRIBUTE_ON if "args" in kwargs else False + + if self.is_load_label: + # construct maps + jsondict_file = find_file_path_in_yaml(self.cfg.get("labelmap", None), self.root) + if jsondict_file is None: + jsondict_file = find_file_path_in_yaml(self.cfg.get("jsondict", None), self.root) + if "json" in jsondict_file: + jsondict = json.load(open(jsondict_file, "r")) + if "label_to_idx" not in jsondict: + jsondict = {"label_to_idx": jsondict} + elif "tsv" in jsondict_file: + label_to_idx = {} + counter = 1 + with open(jsondict_file) as f: + for line in f: + label_to_idx[line.strip()] = counter + counter += 1 + jsondict = {"label_to_idx": label_to_idx} + else: + assert 0 + + self.labelmap = {} + self.class_to_ind = jsondict["label_to_idx"] + self.class_to_ind["__background__"] = 0 + self.ind_to_class = {v: k for k, v in self.class_to_ind.items()} + self.labelmap["class_to_ind"] = self.class_to_ind + + if self.attribute_on: + self.attribute_to_ind = jsondict["attribute_to_idx"] + self.attribute_to_ind["__no_attribute__"] = 0 + self.ind_to_attribute = {v: k for k, v in self.attribute_to_ind.items()} + self.labelmap["attribute_to_ind"] = self.attribute_to_ind + + self.label_loader = LabelLoader( + labelmap=self.labelmap, + extra_fields=extra_fields, + ) + + def get_target_from_annotations(self, annotations, img_size, idx): + if isinstance(annotations, list): + annotations = {"objects": annotations} + if self.is_load_label: + return self.label_loader(annotations["objects"], img_size) + + def apply_transforms(self, img, target=None): + if self.transforms is not None: + img, target = self.transforms(img, target) + return img, target diff --git a/maskrcnn_benchmark/data/datasets/vg.py b/maskrcnn_benchmark/data/datasets/vg.py new file mode 100644 index 0000000000000000000000000000000000000000..468ce80363cb5124e23356e22d62e67f88004a90 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/vg.py @@ -0,0 +1,270 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import collections +import json +import os.path as op + +import numpy as np +import torch + +from .tsv import TSVYamlDataset, find_file_path_in_yaml +from .box_label_loader import BoxLabelLoader +from maskrcnn_benchmark.data.datasets.coco_dt import CocoDetectionTSV + + +class VGDetectionTSV(CocoDetectionTSV): + pass + + +def sort_key_by_val(dic): + sorted_dic = sorted(dic.items(), key=lambda kv: kv[1]) + return [kv[0] for kv in sorted_dic] + + +def bbox_overlaps(anchors, gt_boxes): + """ + anchors: (N, 4) ndarray of float + gt_boxes: (K, 4) ndarray of float + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + N = anchors.size(0) + K = gt_boxes.size(0) + + gt_boxes_area = ((gt_boxes[:, 2] - gt_boxes[:, 0] + 1) * (gt_boxes[:, 3] - gt_boxes[:, 1] + 1)).view(1, K) + + anchors_area = ((anchors[:, 2] - anchors[:, 0] + 1) * (anchors[:, 3] - anchors[:, 1] + 1)).view(N, 1) + + boxes = anchors.view(N, 1, 4).expand(N, K, 4) + query_boxes = gt_boxes.view(1, K, 4).expand(N, K, 4) + + iw = torch.min(boxes[:, :, 2], query_boxes[:, :, 2]) - torch.max(boxes[:, :, 0], query_boxes[:, :, 0]) + 1 + iw[iw < 0] = 0 + + ih = torch.min(boxes[:, :, 3], query_boxes[:, :, 3]) - torch.max(boxes[:, :, 1], query_boxes[:, :, 1]) + 1 + ih[ih < 0] = 0 + + ua = anchors_area + gt_boxes_area - (iw * ih) + overlaps = iw * ih / ua + + return overlaps + + +# VG data loader for Danfei Xu's Scene graph focused format. +# todo: if ordering of classes, attributes, relations changed +# todo make sure to re-write the obj_classes.txt/rel_classes.txt files + + +def _box_filter(boxes, must_overlap=False): + """Only include boxes that overlap as possible relations. + If no overlapping boxes, use all of them.""" + overlaps = bbox_overlaps(boxes, boxes).numpy() > 0 + np.fill_diagonal(overlaps, 0) + + all_possib = np.ones_like(overlaps, dtype=np.bool) + np.fill_diagonal(all_possib, 0) + + if must_overlap: + possible_boxes = np.column_stack(np.where(overlaps)) + + if possible_boxes.size == 0: + possible_boxes = np.column_stack(np.where(all_possib)) + else: + possible_boxes = np.column_stack(np.where(all_possib)) + return possible_boxes + + +class VGTSVDataset(TSVYamlDataset): + """ + Generic TSV dataset format for Object Detection. + """ + + def __init__( + self, + yaml_file, + extra_fields=None, + transforms=None, + is_load_label=True, + filter_duplicate_rels=True, + relation_on=False, + cv2_output=False, + **kwargs + ): + if extra_fields is None: + extra_fields = [] + self.transforms = transforms + self.is_load_label = is_load_label + self.relation_on = relation_on + super(VGTSVDataset, self).__init__(yaml_file, cv2_output=cv2_output) + + ignore_attrs = self.cfg.get("ignore_attrs", None) + # construct those maps + jsondict_file = find_file_path_in_yaml(self.cfg.get("jsondict", None), self.root) + jsondict = json.load(open(jsondict_file, "r")) + + # self.linelist_file + if "train" in op.basename(self.linelist_file): + self.split = "train" + elif ( + "test" in op.basename(self.linelist_file) + or "val" in op.basename(self.linelist_file) + or "valid" in op.basename(self.linelist_file) + ): + self.split = "test" + else: + raise ValueError("Split must be one of [train, test], but get {}!".format(self.linelist_file)) + self.filter_duplicate_rels = filter_duplicate_rels and self.split == "train" + + self.class_to_ind = jsondict["label_to_idx"] + self.ind_to_class = jsondict["idx_to_label"] + self.class_to_ind["__background__"] = 0 + self.ind_to_class["0"] = "__background__" + self.classes = sort_key_by_val(self.class_to_ind) + assert all([self.classes[i] == self.ind_to_class[str(i)] for i in range(len(self.classes))]) + + # writing obj classes to disk for Neural Motif model building. + obj_classes_out_fn = op.splitext(self.label_file)[0] + ".obj_classes.txt" + if not op.isfile(obj_classes_out_fn): + with open(obj_classes_out_fn, "w") as f: + for item in self.classes: + f.write("%s\n" % item) + + self.attribute_to_ind = jsondict["attribute_to_idx"] + self.ind_to_attribute = jsondict["idx_to_attribute"] + self.attribute_to_ind["__no_attribute__"] = 0 + self.ind_to_attribute["0"] = "__no_attribute__" + self.attributes = sort_key_by_val(self.attribute_to_ind) + assert all([self.attributes[i] == self.ind_to_attribute[str(i)] for i in range(len(self.attributes))]) + + self.relation_to_ind = jsondict["predicate_to_idx"] + self.ind_to_relation = jsondict["idx_to_predicate"] + self.relation_to_ind["__no_relation__"] = 0 + self.ind_to_relation["0"] = "__no_relation__" + self.relations = sort_key_by_val(self.relation_to_ind) + assert all([self.relations[i] == self.ind_to_relation[str(i)] for i in range(len(self.relations))]) + + # writing rel classes to disk for Neural Motif Model building. + rel_classes_out_fn = op.splitext(self.label_file)[0] + ".rel_classes.txt" + if not op.isfile(rel_classes_out_fn): + with open(rel_classes_out_fn, "w") as f: + for item in self.relations: + f.write("%s\n" % item) + + # label map: minus one because we will add one in BoxLabelLoader + self.labelmap = {key: val - 1 for key, val in self.class_to_ind.items()} + labelmap_file = find_file_path_in_yaml(self.cfg.get("labelmap_dec"), self.root) + # self.labelmap_dec = load_labelmap_file(labelmap_file) + if self.is_load_label: + self.label_loader = BoxLabelLoader( + labelmap=self.labelmap, extra_fields=extra_fields, ignore_attrs=ignore_attrs + ) + + # get frequency prior for relations + if self.relation_on: + self.freq_prior_file = op.splitext(self.label_file)[0] + ".freq_prior.npy" + if self.split == "train" and not op.exists(self.freq_prior_file): + print("Computing frequency prior matrix...") + fg_matrix, bg_matrix = self._get_freq_prior() + prob_matrix = fg_matrix.astype(np.float32) + prob_matrix[:, :, 0] = bg_matrix + prob_matrix[:, :, 0] += 1 + prob_matrix /= np.sum(prob_matrix, 2)[:, :, None] + np.save(self.freq_prior_file, prob_matrix) + + def _get_freq_prior(self, must_overlap=False): + fg_matrix = np.zeros((len(self.classes), len(self.classes), len(self.relations)), dtype=np.int64) + + bg_matrix = np.zeros( + ( + len(self.classes), + len(self.classes), + ), + dtype=np.int64, + ) + + for ex_ind in range(self.__len__()): + target = self.get_groundtruth(ex_ind) + gt_classes = target.get_field("labels").numpy() + gt_relations = target.get_field("relation_labels").numpy() + gt_boxes = target.bbox + + # For the foreground, we'll just look at everything + try: + o1o2 = gt_classes[gt_relations[:, :2]] + for (o1, o2), gtr in zip(o1o2, gt_relations[:, 2]): + fg_matrix[o1, o2, gtr] += 1 + + # For the background, get all of the things that overlap. + o1o2_total = gt_classes[np.array(_box_filter(gt_boxes, must_overlap=must_overlap), dtype=int)] + for (o1, o2) in o1o2_total: + bg_matrix[o1, o2] += 1 + except IndexError as e: + assert len(gt_relations) == 0 + + if ex_ind % 20 == 0: + print("processing {}/{}".format(ex_ind, self.__len__())) + + return fg_matrix, bg_matrix + + def relation_loader(self, relation_triplets, target): + # relation_triplets [list of tuples]: M*3 + # target: BoxList from label_loader + if self.filter_duplicate_rels: + # Filter out dupes! + assert self.split == "train" + all_rel_sets = collections.defaultdict(list) + for (o0, o1, r) in relation_triplets: + all_rel_sets[(o0, o1)].append(r) + relation_triplets = [(k[0], k[1], np.random.choice(v)) for k, v in all_rel_sets.items()] + + # get M*M pred_labels + relations = torch.zeros([len(target), len(target)], dtype=torch.int64) + for i in range(len(relation_triplets)): + subj_id = relation_triplets[i][0] + obj_id = relation_triplets[i][1] + pred = relation_triplets[i][2] + relations[subj_id, obj_id] = int(pred) + + relation_triplets = torch.tensor(relation_triplets) + target.add_field("relation_labels", relation_triplets) + target.add_field("pred_labels", relations) + return target + + def get_target_from_annotations(self, annotations, img_size, idx): + if self.is_load_label and annotations: + target = self.label_loader(annotations["objects"], img_size) + # make sure no boxes are removed + assert len(annotations["objects"]) == len(target) + if self.split in ["val", "test"]: + # add the difficult field + target.add_field("difficult", torch.zeros(len(target), dtype=torch.int32)) + # load relations + if self.relation_on: + target = self.relation_loader(annotations["relations"], target) + return target + + def get_groundtruth(self, idx, call=False): + # similar to __getitem__ but without transform + img = self.get_image(idx) + if self.cv2_output: + img_size = img.shape[:2][::-1] # h, w -> w, h + else: + img_size = img.size # w, h + annotations = self.get_annotations(idx) + target = self.get_target_from_annotations(annotations, img_size, idx) + if call: + return img, target, annotations + else: + return target + + def apply_transforms(self, img, target=None): + if self.transforms is not None: + img, target = self.transforms(img, target) + return img, target + + def map_class_id_to_class_name(self, class_id): + return self.classes[class_id] + + def map_attribute_id_to_attribute_name(self, attribute_id): + return self.attributes[attribute_id] + + def map_relation_id_to_relation_name(self, relation_id): + return self.relations[relation_id] diff --git a/maskrcnn_benchmark/data/datasets/voc.py b/maskrcnn_benchmark/data/datasets/voc.py new file mode 100644 index 0000000000000000000000000000000000000000..b1a0a98f640ce9eedaeccca23003c3eddfdb5b58 --- /dev/null +++ b/maskrcnn_benchmark/data/datasets/voc.py @@ -0,0 +1,132 @@ +import os + +import torch +import torch.utils.data +from PIL import Image +import sys + +if sys.version_info[0] == 2: + import xml.etree.cElementTree as ET +else: + import xml.etree.ElementTree as ET + + +from maskrcnn_benchmark.structures.bounding_box import BoxList + + +class PascalVOCDataset(torch.utils.data.Dataset): + + CLASSES = ( + "__background__ ", + "aeroplane", + "bicycle", + "bird", + "boat", + "bottle", + "bus", + "car", + "cat", + "chair", + "cow", + "diningtable", + "dog", + "horse", + "motorbike", + "person", + "pottedplant", + "sheep", + "sofa", + "train", + "tvmonitor", + ) + + def __init__(self, data_dir, split, use_difficult=False, transforms=None): + self.root = data_dir + self.image_set = split + self.keep_difficult = use_difficult + self.transforms = transforms + + self._annopath = os.path.join(self.root, "Annotations", "%s.xml") + self._imgpath = os.path.join(self.root, "JPEGImages", "%s.jpg") + self._imgsetpath = os.path.join(self.root, "ImageSets", "Main", "%s.txt") + + with open(self._imgsetpath % self.image_set) as f: + self.ids = f.readlines() + self.ids = [x.strip("\n") for x in self.ids] + self.id_to_img_map = {k: v for k, v in enumerate(self.ids)} + + cls = PascalVOCDataset.CLASSES + self.class_to_ind = dict(zip(cls, range(len(cls)))) + + def __getitem__(self, index): + img_id = self.ids[index] + img = Image.open(self._imgpath % img_id).convert("RGB") + + target = self.get_groundtruth(index) + target = target.clip_to_image(remove_empty=True) + + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target, index + + def __len__(self): + return len(self.ids) + + def get_groundtruth(self, index): + img_id = self.ids[index] + anno = ET.parse(self._annopath % img_id).getroot() + anno = self._preprocess_annotation(anno) + + height, width = anno["im_info"] + target = BoxList(anno["boxes"], (width, height), mode="xyxy") + target.add_field("labels", anno["labels"]) + target.add_field("difficult", anno["difficult"]) + return target + + def _preprocess_annotation(self, target): + boxes = [] + gt_classes = [] + difficult_boxes = [] + TO_REMOVE = 1 + + for obj in target.iter("object"): + difficult = int(obj.find("difficult").text) == 1 + if not self.keep_difficult and difficult: + continue + name = obj.find("name").text.lower().strip() + bb = obj.find("bndbox") + # Make pixel indexes 0-based + # Refer to "https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/pascal_voc.py#L208-L211" + box = [ + bb.find("xmin").text, + bb.find("ymin").text, + bb.find("xmax").text, + bb.find("ymax").text, + ] + bndbox = tuple(map(lambda x: x - TO_REMOVE, list(map(int, box)))) + + boxes.append(bndbox) + gt_classes.append(self.class_to_ind[name]) + difficult_boxes.append(difficult) + + size = target.find("size") + im_info = tuple(map(int, (size.find("height").text, size.find("width").text))) + + res = { + "boxes": torch.tensor(boxes, dtype=torch.float32), + "labels": torch.tensor(gt_classes), + "difficult": torch.tensor(difficult_boxes), + "im_info": im_info, + } + return res + + def get_img_info(self, index): + img_id = self.ids[index] + anno = ET.parse(self._annopath % img_id).getroot() + size = anno.find("size") + im_info = tuple(map(int, (size.find("height").text, size.find("width").text))) + return {"height": im_info[0], "width": im_info[1]} + + def map_class_id_to_class_name(self, class_id): + return PascalVOCDataset.CLASSES[class_id] diff --git a/maskrcnn_benchmark/data/samplers/__init__.py b/maskrcnn_benchmark/data/samplers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f891498f3d66c08a4840de0b12fb03b6834ba4c8 --- /dev/null +++ b/maskrcnn_benchmark/data/samplers/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from .distributed import DistributedSampler +from .grouped_batch_sampler import GroupedBatchSampler +from .iteration_based_batch_sampler import IterationBasedBatchSampler + +__all__ = ["DistributedSampler", "GroupedBatchSampler", "IterationBasedBatchSampler"] diff --git a/maskrcnn_benchmark/data/samplers/distributed.py b/maskrcnn_benchmark/data/samplers/distributed.py new file mode 100644 index 0000000000000000000000000000000000000000..0b2aa926f61243e77a9e959ef36826c854467fc5 --- /dev/null +++ b/maskrcnn_benchmark/data/samplers/distributed.py @@ -0,0 +1,72 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# Code is copy-pasted exactly as in torch.utils.data.distributed. +# FIXME remove this once c10d fixes the bug it has +import math +import torch +import torch.distributed as dist +from torch.utils.data.sampler import Sampler + +from maskrcnn_benchmark.utils.comm import shared_random_seed + + +class DistributedSampler(Sampler): + """Sampler that restricts data loading to a subset of the dataset. + It is especially useful in conjunction with + :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each + process can pass a DistributedSampler instance as a DataLoader sampler, + and load a subset of the original dataset that is exclusive to it. + .. note:: + Dataset is assumed to be of constant size. + Arguments: + dataset: Dataset used for sampling. + num_replicas (optional): Number of processes participating in + distributed training. + rank (optional): Rank of the current process within num_replicas. + """ + + def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True, use_random=False): + if num_replicas is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + num_replicas = dist.get_world_size() + if rank is None: + if not dist.is_available(): + raise RuntimeError("Requires distributed package to be available") + rank = dist.get_rank() + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas)) + self.total_size = self.num_samples * self.num_replicas + self.shuffle = shuffle + self.use_random = use_random + + def __iter__(self): + if self.shuffle: + # deterministically shuffle based on epoch + _seed = self.epoch + if self.use_random: + _seed = int(shared_random_seed()) + g = torch.Generator() + g.manual_seed(_seed) + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = torch.arange(len(self.dataset)).tolist() + + # add extra samples to make it evenly divisible + indices += indices[: (self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + offset = self.num_samples * self.rank + indices = indices[offset : offset + self.num_samples] + assert len(indices) == self.num_samples + + return iter(indices) + + def __len__(self): + return self.num_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py b/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..40e7f896c45717fc9b9ac8709c86eebbca56a401 --- /dev/null +++ b/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py @@ -0,0 +1,112 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import itertools + +import torch +from torch.utils.data.sampler import BatchSampler +from torch.utils.data.sampler import Sampler + + +class GroupedBatchSampler(BatchSampler): + """ + Wraps another sampler to yield a mini-batch of indices. + It enforces that elements from the same group should appear in groups of batch_size. + It also tries to provide mini-batches which follows an ordering which is + as close as possible to the ordering from the original sampler. + + Arguments: + sampler (Sampler): Base sampler. + batch_size (int): Size of mini-batch. + drop_uneven (bool): If ``True``, the sampler will drop the batches whose + size is less than ``batch_size`` + + """ + + def __init__(self, sampler, group_ids, batch_size, drop_uneven=False): + if not isinstance(sampler, Sampler): + raise ValueError( + "sampler should be an instance of " "torch.utils.data.Sampler, but got sampler={}".format(sampler) + ) + self.sampler = sampler + self.group_ids = torch.as_tensor(group_ids) + assert self.group_ids.dim() == 1 + self.batch_size = batch_size + self.drop_uneven = drop_uneven + + self.groups = torch.unique(self.group_ids).sort(0)[0] + + self._can_reuse_batches = False + + def _prepare_batches(self): + dataset_size = len(self.group_ids) + # get the sampled indices from the sampler + sampled_ids = torch.as_tensor(list(self.sampler)) + # potentially not all elements of the dataset were sampled + # by the sampler (e.g., DistributedSampler). + # construct a tensor which contains -1 if the element was + # not sampled, and a non-negative number indicating the + # order where the element was sampled. + # for example. if sampled_ids = [3, 1] and dataset_size = 5, + # the order is [-1, 1, -1, 0, -1] + order = torch.full((dataset_size,), -1, dtype=torch.int64) + order[sampled_ids] = torch.arange(len(sampled_ids)) + + # get a mask with the elements that were sampled + mask = order >= 0 + + # find the elements that belong to each individual cluster + clusters = [(self.group_ids == i) & mask for i in self.groups] + # get relative order of the elements inside each cluster + # that follows the order from the sampler + relative_order = [order[cluster] for cluster in clusters] + # with the relative order, find the absolute order in the + # sampled space + permutation_ids = [s[s.sort()[1]] for s in relative_order] + # permute each cluster so that they follow the order from + # the sampler + permuted_clusters = [sampled_ids[idx] for idx in permutation_ids] + + # splits each cluster in batch_size, and merge as a list of tensors + splits = [c.split(self.batch_size) for c in permuted_clusters] + merged = tuple(itertools.chain.from_iterable(splits)) + + # now each batch internally has the right order, but + # they are grouped by clusters. Find the permutation between + # different batches that brings them as close as possible to + # the order that we have in the sampler. For that, we will consider the + # ordering as coming from the first element of each batch, and sort + # correspondingly + first_element_of_batch = [t[0].item() for t in merged] + # get and inverse mapping from sampled indices and the position where + # they occur (as returned by the sampler) + inv_sampled_ids_map = {v: k for k, v in enumerate(sampled_ids.tolist())} + # from the first element in each batch, get a relative ordering + first_index_of_batch = torch.as_tensor([inv_sampled_ids_map[s] for s in first_element_of_batch]) + + # permute the batches so that they approximately follow the order + # from the sampler + permutation_order = first_index_of_batch.sort(0)[1].tolist() + # finally, permute the batches + batches = [merged[i].tolist() for i in permutation_order] + + if self.drop_uneven: + kept = [] + for batch in batches: + if len(batch) == self.batch_size: + kept.append(batch) + batches = kept + return batches + + def __iter__(self): + if self._can_reuse_batches: + batches = self._batches + self._can_reuse_batches = False + else: + batches = self._prepare_batches() + self._batches = batches + return iter(batches) + + def __len__(self): + if not hasattr(self, "_batches"): + self._batches = self._prepare_batches() + self._can_reuse_batches = True + return len(self._batches) diff --git a/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py b/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..431693eecd2e474dacdbc9eb805dbe2b092234cc --- /dev/null +++ b/maskrcnn_benchmark/data/samplers/iteration_based_batch_sampler.py @@ -0,0 +1,31 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from torch.utils.data.sampler import BatchSampler + + +class IterationBasedBatchSampler(BatchSampler): + """ + Wraps a BatchSampler, resampling from it until + a specified number of iterations have been sampled + """ + + def __init__(self, batch_sampler, num_iterations, start_iter=0): + self.batch_sampler = batch_sampler + self.num_iterations = num_iterations + self.start_iter = start_iter + + def __iter__(self): + iteration = self.start_iter + while iteration <= self.num_iterations: + # if the underlying sampler has a set_epoch method, like + # DistributedSampler, used for making each process see + # a different split of the dataset, then set it + if hasattr(self.batch_sampler.sampler, "set_epoch"): + self.batch_sampler.sampler.set_epoch(iteration) + for batch in self.batch_sampler: + iteration += 1 + if iteration > self.num_iterations: + break + yield batch + + def __len__(self): + return self.num_iterations diff --git a/maskrcnn_benchmark/data/transforms/__init__.py b/maskrcnn_benchmark/data/transforms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94ce850056fdd7ed45f416bc4ead90f3f7da0073 --- /dev/null +++ b/maskrcnn_benchmark/data/transforms/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from .transforms import Compose +from .transforms import Resize +from .transforms import RandomHorizontalFlip +from .transforms import ToTensor +from .transforms import Normalize + +from .build import build_transforms diff --git a/maskrcnn_benchmark/data/transforms/build.py b/maskrcnn_benchmark/data/transforms/build.py new file mode 100644 index 0000000000000000000000000000000000000000..6ecea5ee0cec3913df7cd1c98746b79a1561c55d --- /dev/null +++ b/maskrcnn_benchmark/data/transforms/build.py @@ -0,0 +1,43 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from . import transforms as T + + +def build_transforms(cfg, is_train=True): + if is_train: + if len(cfg.AUGMENT.MULT_MIN_SIZE_TRAIN) > 0: + min_size = cfg.AUGMENT.MULT_MIN_SIZE_TRAIN + else: + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + flip_horizontal_prob = cfg.AUGMENT.FLIP_PROB_TRAIN + flip_vertical_prob = cfg.AUGMENT.VERTICAL_FLIP_PROB_TRAIN + brightness = cfg.AUGMENT.BRIGHTNESS + contrast = cfg.AUGMENT.CONTRAST + saturation = cfg.AUGMENT.SATURATION + hue = cfg.AUGMENT.HUE + + crop_prob = cfg.AUGMENT.CROP_PROB + min_ious = cfg.AUGMENT.CROP_MIN_IOUS + min_crop_size = cfg.AUGMENT.CROP_MIN_SIZE + + else: + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + flip_horizontal_prob = 0.0 + + fix_res = cfg.INPUT.FIX_RES + if cfg.INPUT.FORMAT != "": + input_format = cfg.INPUT.FORMAT + elif cfg.INPUT.TO_BGR255: + input_format = "bgr255" + normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, format=input_format) + + transform = T.Compose( + [ + T.Resize(min_size, max_size, restrict=fix_res), + T.RandomHorizontalFlip(flip_horizontal_prob), + T.ToTensor(), + normalize_transform, + ] + ) + return transform diff --git a/maskrcnn_benchmark/data/transforms/transforms.py b/maskrcnn_benchmark/data/transforms/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..7c5c7cd4e73c3b716683cac6bc5698380c751071 --- /dev/null +++ b/maskrcnn_benchmark/data/transforms/transforms.py @@ -0,0 +1,414 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import cv2 +import random +import numpy as np +import math +import torch +import torchvision +from torchvision.transforms import functional as F + +from maskrcnn_benchmark.structures.bounding_box import BoxList + + +def matrix_iou(a, b, relative=False): + """ + return iou of a and b, numpy version for data augenmentation + """ + lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) + rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) + + area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) + area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) + area_b = np.prod(b[:, 2:] - b[:, :2], axis=1) + if relative: + ious = area_i / (area_b[:, np.newaxis] + 1e-12) + else: + ious = area_i / (area_a[:, np.newaxis] + area_b - area_i + 1e-12) + return ious + + +class RACompose(object): + def __init__(self, pre_transforms, rand_transforms, post_transforms, concurrent=2): + self.preprocess = pre_transforms + self.transforms = post_transforms + self.rand_transforms = rand_transforms + self.concurrent = concurrent + + def __call__(self, image, target): + for t in self.preprocess: + image, target = t(image, target) + for t in random.choices(self.rand_transforms, k=self.concurrent): + image = np.array(image) + image, target = t(image, target) + for t in self.transforms: + image, target = t(image, target) + + return image, target + + def __repr__(self): + format_string = self.__class__.__name__ + "(" + for t in self.preprocess: + format_string += "\n" + format_string += " {0}".format(t) + format_string += "\nRandom select {0} from: (".format(self.concurrent) + for t in self.rand_transforms: + format_string += "\n" + format_string += " {0}".format(t) + format_string += ")\nThen, apply:" + for t in self.transforms: + format_string += "\n" + format_string += " {0}".format(t) + format_string += "\n)" + return format_string + + +class Compose(object): + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, image, target=None): + for t in self.transforms: + image, target = t(image, target) + if target is None: + return image + return image, target + + def __repr__(self): + format_string = self.__class__.__name__ + "(" + for t in self.transforms: + format_string += "\n" + format_string += " {0}".format(t) + format_string += "\n)" + return format_string + + +class Resize(object): + def __init__(self, min_size, max_size, restrict=False): + if not isinstance(min_size, (list, tuple)): + min_size = (min_size,) + self.min_size = min_size + self.max_size = max_size + self.restrict = restrict + + # modified from torchvision to add support for max size + def get_size(self, image_size): + w, h = image_size + size = random.choice(self.min_size) + max_size = self.max_size + if self.restrict: + return (size, max_size) + if max_size is not None: + min_original_size = float(min((w, h))) + max_original_size = float(max((w, h))) + if max_original_size / min_original_size * size > max_size: + size = int(round(max_size * min_original_size / max_original_size)) + + if (w <= h and w == size) or (h <= w and h == size): + return (h, w) + + if w < h: + ow = size + oh = int(size * h / w) + else: + oh = size + ow = int(size * w / h) + + return (oh, ow) + + def __call__(self, image, target): + if isinstance(image, np.ndarray): + image_size = self.get_size(image.shape[:2]) + image = cv2.resize(image, image_size) + new_size = image_size + else: + image = F.resize(image, self.get_size(image.size)) + new_size = image.size + if target is not None: + target = target.resize(new_size) + return image, target + + +class RandomHorizontalFlip(object): + def __init__(self, prob=0.5): + self.prob = prob + + def __call__(self, image, target): + if random.random() < self.prob: + if isinstance(image, np.ndarray): + image = np.fliplr(image) + else: + image = F.hflip(image) + if target is not None: + target = target.transpose(0) + return image, target + + +class RandomVerticalFlip(object): + def __init__(self, prob=0.5): + self.prob = prob + + def __call__(self, image, target): + if random.random() < self.prob: + if isinstance(image, np.ndarray): + image = np.flipud(image) + else: + image = F.vflip(image) + target = target.transpose(1) + return image, target + + +class ToTensor(object): + def __call__(self, image, target): + return F.to_tensor(image), target + + +class Normalize(object): + def __init__(self, mean, std, format="rgb"): + self.mean = mean + self.std = std + self.format = format.lower() + + def __call__(self, image, target): + if "bgr" in self.format: + image = image[[2, 1, 0]] + if "255" in self.format: + image = image * 255 + image = F.normalize(image, mean=self.mean, std=self.std) + return image, target + + +class ColorJitter(object): + def __init__( + self, + brightness=0.0, + contrast=0.0, + saturation=0.0, + hue=0.0, + ): + self.color_jitter = torchvision.transforms.ColorJitter( + brightness=brightness, + contrast=contrast, + saturation=saturation, + hue=hue, + ) + + def __call__(self, image, target): + image = self.color_jitter(image) + return image, target + + +class RandomCrop(object): + def __init__(self, prob=0.5, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3): + # 1: return ori img + self.prob = prob + self.sample_mode = (1, *min_ious, 0) + self.min_crop_size = min_crop_size + + def __call__(self, img, target): + if random.random() > self.prob: + return img, target + + h, w, c = img.shape + boxes = target.bbox.numpy() + labels = target.get_field("labels") + + while True: + mode = random.choice(self.sample_mode) + if mode == 1: + return img, target + + min_iou = mode + + new_w = random.uniform(self.min_crop_size * w, w) + new_h = random.uniform(self.min_crop_size * h, h) + + # h / w in [0.5, 2] + if new_h / new_w < 0.5 or new_h / new_w > 2: + continue + + left = random.uniform(0, w - new_w) + top = random.uniform(0, h - new_h) + + patch = np.array([left, top, left + new_w, top + new_h]) + overlaps = matrix_iou(patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1) + if overlaps.min() < min_iou: + continue + + # center of boxes should inside the crop img + center = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask = ( + (center[:, 0] > patch[0]) + * (center[:, 1] > patch[1]) + * (center[:, 0] < patch[2]) + * (center[:, 1] < patch[3]) + ) + if not mask.any(): + continue + + boxes = boxes[mask] + labels = labels[mask] + + # adjust boxes + img = img[int(patch[1]) : int(patch[3]), int(patch[0]) : int(patch[2])] + + boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:]) + boxes[:, :2] = boxes[:, :2].clip(min=patch[:2]) + boxes -= np.tile(patch[:2], 2) + + new_target = BoxList(boxes, (img.shape[1], img.shape[0]), mode="xyxy") + new_target.add_field("labels", labels) + return img, new_target + + +class RandomAffine(object): + def __init__( + self, + prob=0.5, + degrees=(-10, 10), + translate=(0.1, 0.1), + scale=(0.9, 1.1), + shear=(-2, 2), + borderValue=(127.5, 127.5, 127.5), + ): + self.prob = prob + self.degrees = degrees + self.translate = translate + self.scale = scale + self.shear = shear + self.borderValue = borderValue + + def __call__(self, img, targets=None): + if random.random() > self.prob: + return img, targets + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4 + + border = 0 # width of added border (optional) + # height = max(img.shape[0], img.shape[1]) + border * 2 + height, width, _ = img.shape + bbox = targets.bbox + + # Rotation and Scale + R = np.eye(3) + a = random.random() * (self.degrees[1] - self.degrees[0]) + self.degrees[0] + # a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations + s = random.random() * (self.scale[1] - self.scale[0]) + self.scale[0] + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s) + + # Translation + T = np.eye(3) + T[0, 2] = (random.random() * 2 - 1) * self.translate[0] * img.shape[0] + border # x translation (pixels) + T[1, 2] = (random.random() * 2 - 1) * self.translate[1] * img.shape[1] + border # y translation (pixels) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan( + (random.random() * (self.shear[1] - self.shear[0]) + self.shear[0]) * math.pi / 180 + ) # x shear (deg) + S[1, 0] = math.tan( + (random.random() * (self.shear[1] - self.shear[0]) + self.shear[0]) * math.pi / 180 + ) # y shear (deg) + + M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!! + imw = cv2.warpPerspective( + img, M, dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=self.borderValue + ) # BGR order borderValue + + # Return warped points also + if targets: + n = bbox.shape[0] + points = bbox[:, 0:4] + area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1]) + + # warp points + xy = np.ones((n * 4, 3)) + xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = (xy @ M.T)[:, :2].reshape(n, 8) + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # apply angle-based reduction + radians = a * math.pi / 180 + reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 + x = (xy[:, 2] + xy[:, 0]) / 2 + y = (xy[:, 3] + xy[:, 1]) / 2 + w = (xy[:, 2] - xy[:, 0]) * reduction + h = (xy[:, 3] - xy[:, 1]) * reduction + xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T + + # reject warped points outside of image + x1 = np.clip(xy[:, 0], 0, width) + y1 = np.clip(xy[:, 1], 0, height) + x2 = np.clip(xy[:, 2], 0, width) + y2 = np.clip(xy[:, 3], 0, height) + new_bbox = np.concatenate((x1, y1, x2, y2)).reshape(4, n).T + targets.bbox = torch.as_tensor(new_bbox, dtype=torch.float32) + + return imw, targets + + +class RandomErasing: + def __init__( + self, + prob=0.5, + era_l=0.02, + era_h=1 / 3, + min_aspect=0.3, + mode="const", + max_count=1, + max_overlap=0.3, + max_value=255, + ): + self.prob = prob + self.era_l = era_l + self.era_h = era_h + self.min_aspect = min_aspect + self.min_count = 1 + self.max_count = max_count + self.max_overlap = max_overlap + self.max_value = max_value + self.mode = mode.lower() + assert self.mode in ["const", "rand", "pixel"], "invalid erase mode: %s" % self.mode + + def _get_pixels(self, patch_size): + if self.mode == "pixel": + return np.random.random(patch_size) * self.max_value + elif self.mode == "rand": + return np.random.random((1, 1, patch_size[-1])) * self.max_value + else: + return np.zeros((1, 1, patch_size[-1])) + + def __call__(self, image, target): + if random.random() > self.prob: + return image, target + ih, iw, ic = image.shape + ia = ih * iw + count = self.min_count if self.min_count == self.max_count else random.randint(self.min_count, self.max_count) + erase_boxes = [] + for _ in range(count): + for try_idx in range(10): + erase_area = random.uniform(self.era_l, self.era_h) * ia / count + aspect_ratio = math.exp(random.uniform(math.log(self.min_aspect), math.log(1 / self.min_aspect))) + eh = int(round(math.sqrt(erase_area * aspect_ratio))) + ew = int(round(math.sqrt(erase_area / aspect_ratio))) + if eh < ih and ew < iw: + x = random.randint(0, iw - ew) + y = random.randint(0, ih - eh) + image[y : y + eh, x : x + ew, :] = self._get_pixels((eh, ew, ic)) + erase_boxes.append([x, y, x + ew, y + eh]) + break + + if target is not None and len(erase_boxes) > 0: + boxes = target.bbox.numpy() + labels = target.get_field("labels") + overlap = matrix_iou(np.array(erase_boxes), boxes, relative=True) + mask = overlap.max(axis=0) < self.max_overlap + boxes = boxes[mask] + labels = labels[mask] + target.bbox = torch.as_tensor(boxes, dtype=torch.float32) + target.add_field("labels", labels) + + return image, target diff --git a/maskrcnn_benchmark/engine/__init__.py b/maskrcnn_benchmark/engine/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4bc96c7a6bf8379e1adfb3e4adf536107b385fa9 --- /dev/null +++ b/maskrcnn_benchmark/engine/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. diff --git a/maskrcnn_benchmark/engine/alter_trainer.py b/maskrcnn_benchmark/engine/alter_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..5f7c90c67b69c38641773539fdaa95e82820cf66 --- /dev/null +++ b/maskrcnn_benchmark/engine/alter_trainer.py @@ -0,0 +1,123 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import datetime +import logging +import time + +import torch +import torch.distributed as dist + +from maskrcnn_benchmark.utils.comm import get_world_size +from maskrcnn_benchmark.utils.metric_logger import MetricLogger + + +def reduce_loss_dict(all_loss_dict): + """ + Reduce the loss dictionary from all processes so that process with rank + 0 has the averaged results. Returns a dict with the same fields as + loss_dict, after reduction. + """ + world_size = get_world_size() + with torch.no_grad(): + loss_names = [] + all_losses = [] + for loss_dict in all_loss_dict: + for k in sorted(loss_dict.keys()): + loss_names.append(k) + all_losses.append(loss_dict[k]) + all_losses = torch.stack(all_losses, dim=0) + if world_size > 1: + dist.reduce(all_losses, dst=0) + if dist.get_rank() == 0: + # only main process gets accumulated, so only divide by + # world_size in this case + all_losses /= world_size + + reduced_losses = {} + for k, v in zip(loss_names, all_losses): + if k not in reduced_losses: + reduced_losses[k] = v / len(all_loss_dict) + reduced_losses[k] += v / len(all_loss_dict) + + return reduced_losses + + +def do_train( + model, + data_loader, + optimizer, + scheduler, + checkpointer, + device, + checkpoint_period, + arguments, +): + logger = logging.getLogger("maskrcnn_benchmark.trainer") + logger.info("Start training") + meters = MetricLogger(delimiter=" ") + max_iter = min(len(task_loader) for task_loader in data_loader) + start_iter = arguments["iteration"] + model.train() + start_training_time = time.time() + end = time.time() + for iteration, task_loader in enumerate(zip(*data_loader), start_iter): + data_time = time.time() - end + iteration = iteration + 1 + arguments["iteration"] = iteration + + all_task_loss_dict = [] + for task, (images, targets, _) in enumerate(task_loader, 1): + if all(len(target) < 1 for target in targets): + logger.warning("Sampled all negative batches, skip") + continue + + images = images.to(device) + targets = [target.to(device) for target in targets] + + loss_dict = model(images, targets, task) + all_task_loss_dict.append(loss_dict) + + losses = sum(loss for loss_dict in all_task_loss_dict for loss in loss_dict.values()) + + # reduce losses over all GPUs for logging purposes + loss_dict_reduced = reduce_loss_dict(all_task_loss_dict) + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + meters.update(loss=losses_reduced, **loss_dict_reduced) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + scheduler.step() + + batch_time = time.time() - end + end = time.time() + meters.update(time=batch_time, data=data_time) + + eta_seconds = meters.time.global_avg * (max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + + if iteration % 20 == 0 or iteration == max_iter: + logger.info( + meters.delimiter.join( + [ + "eta: {eta}", + "iter: {iter}", + "{meters}", + "lr: {lr:.6f}", + "max mem: {memory:.0f}", + ] + ).format( + eta=eta_string, + iter=iteration, + meters=str(meters), + lr=optimizer.param_groups[0]["lr"], + memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0, + ) + ) + if iteration % checkpoint_period == 0: + checkpointer.save("model_{:07d}".format(iteration), **arguments) + if iteration == max_iter: + checkpointer.save("model_final", **arguments) + + total_training_time = time.time() - start_training_time + total_time_str = str(datetime.timedelta(seconds=total_training_time)) + logger.info("Total training time: {} ({:.4f} s / it)".format(total_time_str, total_training_time / (max_iter))) diff --git a/maskrcnn_benchmark/engine/evolution.py b/maskrcnn_benchmark/engine/evolution.py new file mode 100644 index 0000000000000000000000000000000000000000..9ebc2f9a2b190763f010fae7d7ebfadb8621a9ea --- /dev/null +++ b/maskrcnn_benchmark/engine/evolution.py @@ -0,0 +1,354 @@ +import time +import pickle +import logging +import os +import numpy as np +import torch +import torch.nn as nn + + +from collections import OrderedDict +from yaml import safe_dump +from yacs.config import load_cfg, CfgNode # , _to_dict +from maskrcnn_benchmark.config import cfg +from maskrcnn_benchmark.engine.inference import _accumulate_predictions_from_multiple_gpus +from maskrcnn_benchmark.modeling.backbone.nas import get_layer_name +from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process, get_world_size, all_gather +from maskrcnn_benchmark.data.datasets.evaluation import evaluate +from maskrcnn_benchmark.utils.flops import profile + + +choice = lambda x: x[np.random.randint(len(x))] if isinstance(x, tuple) else choice(tuple(x)) + + +def gather_candidates(all_candidates): + all_candidates = all_gather(all_candidates) + all_candidates = [cand for candidates in all_candidates for cand in candidates] + return list(set(all_candidates)) + + +def gather_stats(all_candidates): + all_candidates = all_gather(all_candidates) + reduced_statcs = {} + for candidates in all_candidates: + reduced_statcs.update(candidates) # will replace the existing key with last value if more than one exists + return reduced_statcs + + +def compute_on_dataset(model, rngs, data_loader, device=cfg.MODEL.DEVICE): + model.eval() + results_dict = {} + cpu_device = torch.device("cpu") + for _, batch in enumerate(data_loader): + images, targets, image_ids = batch + with torch.no_grad(): + output = model(images.to(device), rngs=rngs) + output = [o.to(cpu_device) for o in output] + results_dict.update({img_id: result for img_id, result in zip(image_ids, output)}) + return results_dict + + +def bn_statistic(model, rngs, data_loader, device=cfg.MODEL.DEVICE, max_iter=500): + for name, param in model.named_buffers(): + if "running_mean" in name: + nn.init.constant_(param, 0) + if "running_var" in name: + nn.init.constant_(param, 1) + + model.train() + for iteration, (images, targets, _) in enumerate(data_loader, 1): + images = images.to(device) + targets = [target.to(device) for target in targets] + with torch.no_grad(): + loss_dict = model(images, targets, rngs) + if iteration >= max_iter: + break + + return model + + +def inference( + model, + rngs, + data_loader, + iou_types=("bbox",), + box_only=False, + device="cuda", + expected_results=(), + expected_results_sigma_tol=4, + output_folder=None, +): + + # convert to a torch.device for efficiency + device = torch.device(device) + dataset = data_loader.dataset + predictions = compute_on_dataset(model, rngs, data_loader, device) + # wait for all processes to complete before measuring the time + synchronize() + + predictions = _accumulate_predictions_from_multiple_gpus(predictions) + if not is_main_process(): + return + + extra_args = dict( + box_only=box_only, + iou_types=iou_types, + expected_results=expected_results, + expected_results_sigma_tol=expected_results_sigma_tol, + ) + + return evaluate(dataset=dataset, predictions=predictions, output_folder=output_folder, **extra_args) + + +def fitness(cfg, model, rngs, val_loaders): + iou_types = ("bbox",) + if cfg.MODEL.MASK_ON: + iou_types = iou_types + ("segm",) + for data_loader_val in val_loaders: + results = inference( + model, + rngs, + data_loader_val, + iou_types=iou_types, + box_only=False, + device=cfg.MODEL.DEVICE, + expected_results=cfg.TEST.EXPECTED_RESULTS, + expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL, + ) + synchronize() + + return results + + +class EvolutionTrainer(object): + def __init__(self, cfg, model, flops_limit=None, is_distributed=True): + + self.log_dir = cfg.OUTPUT_DIR + self.checkpoint_name = os.path.join(self.log_dir, "evolution.pth") + self.is_distributed = is_distributed + + self.states = model.module.mix_nums if is_distributed else model.mix_nums + self.supernet_state_dict = pickle.loads(pickle.dumps(model.state_dict())) + self.flops_limit = flops_limit + self.model = model + + self.candidates = [] + self.vis_dict = {} + + self.max_epochs = cfg.SEARCH.MAX_EPOCH + self.select_num = cfg.SEARCH.SELECT_NUM + self.population_num = cfg.SEARCH.POPULATION_NUM / get_world_size() + self.mutation_num = cfg.SEARCH.MUTATION_NUM / get_world_size() + self.crossover_num = cfg.SEARCH.CROSSOVER_NUM / get_world_size() + self.mutation_prob = cfg.SEARCH.MUTATION_PROB / get_world_size() + + self.keep_top_k = {self.select_num: [], 50: []} + self.epoch = 0 + self.cfg = cfg + + def save_checkpoint(self): + if not is_main_process(): + return + if not os.path.exists(self.log_dir): + os.makedirs(self.log_dir) + info = {} + info["candidates"] = self.candidates + info["vis_dict"] = self.vis_dict + info["keep_top_k"] = self.keep_top_k + info["epoch"] = self.epoch + torch.save(info, self.checkpoint_name) + print("Save checkpoint to", self.checkpoint_name) + + def load_checkpoint(self): + if not os.path.exists(self.checkpoint_name): + return False + info = torch.load(self.checkpoint_name) + self.candidates = info["candidates"] + self.vis_dict = info["vis_dict"] + self.keep_top_k = info["keep_top_k"] + self.epoch = info["epoch"] + print("Load checkpoint from", self.checkpoint_name) + return True + + def legal(self, cand): + assert isinstance(cand, tuple) and len(cand) == len(self.states) + if cand in self.vis_dict: + return False + + if self.flops_limit is not None: + net = self.model.module.backbone if self.is_distributed else self.model.backbone + inp = (1, 3, 224, 224) + flops, params = profile(net, inp, extra_args={"paths": list(cand)}) + flops = flops / 1e6 + print("flops:", flops) + if flops > self.flops_limit: + return False + + return True + + def update_top_k(self, candidates, *, k, key, reverse=False): + assert k in self.keep_top_k + # print('select ......') + t = self.keep_top_k[k] + t += candidates + t.sort(key=key, reverse=reverse) + self.keep_top_k[k] = t[:k] + + def eval_candidates(self, train_loader, val_loader): + for cand in self.candidates: + t0 = time.time() + + # load back supernet state dict + self.model.load_state_dict(self.supernet_state_dict) + # bn_statistic + model = bn_statistic(self.model, list(cand), train_loader) + # fitness + evals = fitness(cfg, model, list(cand), val_loader) + + if is_main_process(): + acc = evals[0].results["bbox"]["AP"] + self.vis_dict[cand] = acc + print("candiate ", cand) + print("time: {}s".format(time.time() - t0)) + print("acc ", acc) + + def stack_random_cand(self, random_func, *, batchsize=10): + while True: + cands = [random_func() for _ in range(batchsize)] + for cand in cands: + yield cand + + def random_can(self, num): + # print('random select ........') + candidates = [] + cand_iter = self.stack_random_cand(lambda: tuple(np.random.randint(i) for i in self.states)) + while len(candidates) < num: + cand = next(cand_iter) + + if not self.legal(cand): + continue + candidates.append(cand) + # print('random {}/{}'.format(len(candidates),num)) + + # print('random_num = {}'.format(len(candidates))) + return candidates + + def get_mutation(self, k, mutation_num, m_prob): + assert k in self.keep_top_k + # print('mutation ......') + res = [] + iter = 0 + max_iters = mutation_num * 10 + + def random_func(): + cand = list(choice(self.keep_top_k[k])) + for i in range(len(self.states)): + if np.random.random_sample() < m_prob: + cand[i] = np.random.randint(self.states[i]) + return tuple(cand) + + cand_iter = self.stack_random_cand(random_func) + while len(res) < mutation_num and max_iters > 0: + cand = next(cand_iter) + if not self.legal(cand): + continue + res.append(cand) + # print('mutation {}/{}'.format(len(res),mutation_num)) + max_iters -= 1 + + # print('mutation_num = {}'.format(len(res))) + return res + + def get_crossover(self, k, crossover_num): + assert k in self.keep_top_k + # print('crossover ......') + res = [] + iter = 0 + max_iters = 10 * crossover_num + + def random_func(): + p1 = choice(self.keep_top_k[k]) + p2 = choice(self.keep_top_k[k]) + return tuple(choice([i, j]) for i, j in zip(p1, p2)) + + cand_iter = self.stack_random_cand(random_func) + while len(res) < crossover_num and max_iters > 0: + cand = next(cand_iter) + if not self.legal(cand): + continue + res.append(cand) + # print('crossover {}/{}'.format(len(res),crossover_num)) + max_iters -= 1 + + # print('crossover_num = {}'.format(len(res))) + return res + + def train(self, train_loader, val_loader): + logger = logging.getLogger("maskrcnn_benchmark.evolution") + + if not self.load_checkpoint(): + self.candidates = gather_candidates(self.random_can(self.population_num)) + + while self.epoch < self.max_epochs: + self.eval_candidates(train_loader, val_loader) + self.vis_dict = gather_stats(self.vis_dict) + + self.update_top_k(self.candidates, k=self.select_num, key=lambda x: 1 - self.vis_dict[x]) + self.update_top_k(self.candidates, k=50, key=lambda x: 1 - self.vis_dict[x]) + + if is_main_process(): + logger.info("Epoch {} : top {} result".format(self.epoch + 1, len(self.keep_top_k[self.select_num]))) + for i, cand in enumerate(self.keep_top_k[self.select_num]): + logger.info(" No.{} {} perf = {}".format(i + 1, cand, self.vis_dict[cand])) + + mutation = gather_candidates(self.get_mutation(self.select_num, self.mutation_num, self.mutation_prob)) + crossover = gather_candidates(self.get_crossover(self.select_num, self.crossover_num)) + rand = gather_candidates(self.random_can(self.population_num - len(mutation) - len(crossover))) + + self.candidates = mutation + crossover + rand + + self.epoch += 1 + self.save_checkpoint() + + def save_candidates(self, cand, template): + paths = self.keep_top_k[self.select_num][cand - 1] + + with open(template, "r") as f: + super_cfg = load_cfg(f) + + search_spaces = {} + for mix_ops in super_cfg.MODEL.BACKBONE.LAYER_SEARCH: + search_spaces[mix_ops] = super_cfg.MODEL.BACKBONE.LAYER_SEARCH[mix_ops] + search_layers = super_cfg.MODEL.BACKBONE.LAYER_SETUP + + layer_setup = [] + for i, layer in enumerate(search_layers): + name, setup = get_layer_name(layer, search_spaces) + if not isinstance(name, list): + name = [name] + name = name[paths[i]] + + layer_setup.append("('{}', {})".format(name, str(setup)[1:-1])) + super_cfg.MODEL.BACKBONE.LAYER_SETUP = layer_setup + + cand_cfg = _to_dict(super_cfg) + del cand_cfg["MODEL"]["BACKBONE"]["LAYER_SEARCH"] + with open( + os.path.join(self.cfg.OUTPUT_DIR, os.path.basename(template)).replace(".yaml", "_cand{}.yaml".format(cand)), + "w", + ) as f: + f.writelines(safe_dump(cand_cfg)) + + super_weight = self.supernet_state_dict + cand_weight = OrderedDict() + cand_keys = ["layers.{}.ops.{}".format(i, c) for i, c in enumerate(paths)] + + for key, val in super_weight.items(): + if "ops" in key: + for ck in cand_keys: + if ck in key: + cand_weight[key.replace(ck, ck.split(".ops.")[0])] = val + else: + cand_weight[key] = val + + torch.save({"model": cand_weight}, os.path.join(self.cfg.OUTPUT_DIR, "init_cand{}.pth".format(cand))) diff --git a/maskrcnn_benchmark/engine/inference.py b/maskrcnn_benchmark/engine/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..cbc38e05e09ce74a6ecbd74b1294a7f72415797f --- /dev/null +++ b/maskrcnn_benchmark/engine/inference.py @@ -0,0 +1,900 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import datetime +import logging +import time +import os +import re + +import torch +from tqdm import tqdm +from collections import defaultdict +import torch.distributed as dist + +from maskrcnn_benchmark.data.datasets.evaluation import evaluate, im_detect_bbox_aug +from ..utils.comm import is_main_process +from ..utils.comm import all_gather +from ..utils.comm import synchronize +from .tsv_saver import TSVResultWriter +import pdb +from maskrcnn_benchmark.data.datasets.evaluation.flickr.flickr_eval import FlickrEvaluator + +from maskrcnn_benchmark.data.datasets.refexp import RefExpEvaluator +from maskrcnn_benchmark.structures.bounding_box import BoxList +import matplotlib.pyplot as plt +import matplotlib.pylab as pylab +from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file +from sentence_transformers import SentenceTransformer +from numpy.random import RandomState +import fastcluster +import collections +import scipy +import numpy as np +import scipy.cluster +import sklearn +import base64 +import cv2, json +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.data.datasets.od_to_grounding import clean_name +from maskrcnn_benchmark.data.datasets._od_to_description import DescriptionConverter + +from copy import deepcopy +from pprint import pprint +import wandb +def imshow(img, file_name = "tmp.jpg"): + plt.imshow(img[:, :, [2, 1, 0]]) + plt.axis("off") + #plt.figtext(0.5, 0.09, "test", wrap=True, horizontalalignment='center', fontsize=20) + plt.savefig(file_name) +def load(url_or_file_name): + try: + response = requests.get(url_or_file_name) + except: + response = None + if response is None: + pil_image = Image.open(url_or_file_name).convert("RGB") + else: + pil_image = Image.open(BytesIO(response.content)).convert("RGB") + # convert to BGR format + image = np.array(pil_image)[:, :, [2, 1, 0]] + return image + +def inference_default( + model, + data_loader, + dataset_name, + iou_types=("bbox",), + box_only=False, + device="cuda", + expected_results=(), + expected_results_sigma_tol=4, + output_folder=None, + cfg=None, +): + # convert to a torch.device for efficiency + device = torch.device(device) + num_devices = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1 + logger = logging.getLogger("maskrcnn_benchmark.inference") + dataset = data_loader.dataset + logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset))) + start_time = time.time() + + model.eval() + results_dict = {} + cpu_device = torch.device("cpu") + for i, batch in enumerate(tqdm(data_loader)): + images, targets, image_ids, *_ = batch + with torch.no_grad(): + if cfg.TEST.USE_MULTISCALE: + output = im_detect_bbox_aug(model, images, device) + else: + output = model(images.to(device)) + output = [o.to(cpu_device) for o in output] + results_dict.update({img_id: result for img_id, result in zip(image_ids, output)}) + predictions = results_dict + # wait for all processes to complete before measuring the time + synchronize() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + logger.info( + "Total inference time: {} ({} s / img per device, on {} devices)".format( + total_time_str, total_time * num_devices / len(dataset), num_devices + ) + ) + + predictions = _accumulate_predictions_from_multiple_gpus(predictions) + if not is_main_process(): + return None + + if output_folder: + torch.save(predictions, os.path.join(output_folder, "predictions.pth")) + + extra_args = dict( + box_only=box_only, + iou_types=iou_types, + expected_results=expected_results, + expected_results_sigma_tol=expected_results_sigma_tol, + ) + return evaluate(dataset=dataset, predictions=predictions, output_folder=output_folder, **extra_args) + + +def clean_name(name): + name = re.sub(r"\(.*\)", "", name) + name = re.sub(r"_", " ", name) + name = re.sub(r" ", " ", name) + return name + + +def create_one_hot_dict(labels, no_minus_one_for_one_hot=False): + positive_map_token_to_label = defaultdict(int) + positive_map_label_to_token = defaultdict(int) + + for i in range(len(labels)): + positive_map_token_to_label[i] = labels[i] + positive_map_label_to_token[labels[i]] = i + + if no_minus_one_for_one_hot: + positive_map_token_to_label = defaultdict(int) + positive_map_label_to_token = defaultdict(int) + + for i in range(len(labels)): + positive_map_token_to_label[i + 1] = labels[i] + positive_map_label_to_token[labels[i]] = i + 1 + + return positive_map_token_to_label, positive_map_label_to_token + + +def create_positive_dict(tokenized, tokens_positive, labels): + """construct a dictionary such that positive_map[i] = j, iff token i is mapped to j label""" + positive_map = defaultdict(int) + + # Additionally, have positive_map_label_to_tokens + positive_map_label_to_token = defaultdict(list) + + for j, tok_list in enumerate(tokens_positive): + for (beg, end) in tok_list: + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + for i in range(beg_pos, end_pos + 1): + positive_map[i] = labels[j] # because the labels starts from 1 + positive_map_label_to_token[labels[j]].append(i) + # positive_map[j, beg_pos : end_pos + 1].fill_(1) + return positive_map, positive_map_label_to_token # / (positive_map.sum(-1)[:, None] + 1e-6) + + +def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + all_ = [] + for i in range(0, len(lst), n): + data_index = lst[i : i + n] + all_.append(data_index) + counter = 0 + for i in all_: + counter += len(i) + assert counter == len(lst) + + return all_ + + +sbert_model = None +def _get_sbert_model(): + global sbert_model + if not sbert_model: + sbert_model = SentenceTransformer('paraphrase-MiniLM-L6-v2') + return sbert_model + +def semantic_deduplicate_captions(captions, + label_list, + keep_p=.8, + must_keep_idxs=None, + seed=1, verbose=False, + return_features=False, + force_exact=False): + ''' + keep_p can be a proportion to keep, e.g., .5, or it can be an int representing the number to keep, like 10. + ''' + original_captions = deepcopy(captions) + captions = ["This is " + c for c in captions] + prng = RandomState(seed) + + must_keep_idxs = set(must_keep_idxs) if must_keep_idxs is not None else set() + + sbert =_get_sbert_model() + features = sbert.encode([c for c in captions], show_progress_bar=verbose) + pdists = sklearn.metrics.pairwise_distances(features, metric='cosine') + # numerical issues... + np.fill_diagonal(pdists, 0.0) + pdists = (pdists + pdists.transpose()) / 2 + pdists = scipy.spatial.distance.squareform(pdists) + res = fastcluster.linkage(pdists, method='average', preserve_input=False) + del pdists + if keep_p < 1: + n_keep_from_cluster = int(np.round(keep_p*(len(captions)))) + else: + n_keep_from_cluster = min(keep_p, len(captions)) + print('going to keep {} out of {} captions'.format(n_keep_from_cluster, len(captions))) + clusters = scipy.cluster.hierarchy.fcluster(res, n_keep_from_cluster, criterion='maxclust') + + cluster2idxs = collections.defaultdict(list) + for idx, cluster in enumerate(clusters): + cluster2idxs[cluster].append(idx) + + # algo: + # 1. go through clusters with must includes, add must keeps. + # 2. for each cluster without a must keep, add it to candidate list, shuffle candidate list + # 3. loop over each candidate in the candidate list until the return set is the correct size. + + chunked_labels = [] + chunked_label_list = [] + for c, idxs in cluster2idxs.items(): + chunked_labels.append([original_captions[i] for i in idxs]) + chunked_label_list.append([label_list[i] for i in idxs]) + print("size of each prompt:", [len(i) for i in chunked_labels]) + return chunked_labels, chunked_label_list + + +def create_queries_and_maps_from_dataset(dataset, cfg): + categories = dataset.categories() + # one_hot = dataset.one_hot + + labels = [] + label_list = [] + keys = list(categories.keys()) + keys.sort() + for i in keys: + labels.append(i) + label_list.append(categories[i]) + + if cfg.TEST.CHUNKED_EVALUATION != -1: + if cfg.TEST.CHUNK_METHOD == "similar": + label_list, labels = semantic_deduplicate_captions( + label_list, labels, keep_p=len(labels) // cfg.TEST.CHUNKED_EVALUATION,) + else: + labels = chunks(labels, cfg.TEST.CHUNKED_EVALUATION) + label_list = chunks(label_list, cfg.TEST.CHUNKED_EVALUATION) + else: + labels = [labels] + label_list = [label_list] + + all_queries = [] + all_positive_map_label_to_token = [] + + for i in range(len(labels)): + labels_i = labels[i] + label_list_i = label_list[i] + query_i, positive_map_label_to_token_i = create_queries_and_maps( + labels_i, + label_list_i, + additional_labels=cfg.DATASETS.SUPRESS_QUERY if cfg.DATASETS.USE_SUPRESS_QUERY else None, + cfg=cfg, + ) + + all_queries.append(query_i) + all_positive_map_label_to_token.append(positive_map_label_to_token_i) + print("All queries", all_queries) + return all_queries, all_positive_map_label_to_token + +def create_queries_and_maps(labels, label_list, additional_labels=None, cfg=None): + + # Clean label list + label_list = [clean_name(i) for i in label_list] + # Form the query and get the mapping + tokens_positive = [] + start_i = 0 + end_i = 0 + objects_query = "" + + # sep between tokens, follow training + separation_tokens = cfg.DATASETS.SEPARATION_TOKENS + + caption_prompt = cfg.DATASETS.CAPTION_PROMPT + if caption_prompt is not None and isinstance(caption_prompt, str): + caption_prompt = load_from_yaml_file(caption_prompt) + use_caption_prompt = cfg.DATASETS.USE_CAPTION_PROMPT and caption_prompt is not None + for _index, label in enumerate(label_list): + if use_caption_prompt: + objects_query += caption_prompt[_index]["prefix"] + + start_i = len(objects_query) + + if use_caption_prompt: + objects_query += caption_prompt[_index]["name"] + else: + objects_query += label + + end_i = len(objects_query) + tokens_positive.append([(start_i, end_i)]) # Every label has a [(start, end)] + + if use_caption_prompt: + objects_query += caption_prompt[_index]["suffix"] + + if _index != len(label_list) - 1: + objects_query += separation_tokens + + if additional_labels is not None: + objects_query += separation_tokens + for _index, label in enumerate(additional_labels): + objects_query += label + if _index != len(additional_labels) - 1: + objects_query += separation_tokens + + print(objects_query) + + from transformers import AutoTokenizer + + # tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + tokenized = tokenizer(objects_query, return_tensors="pt") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenizer = AutoTokenizer.from_pretrained("roberta-base") + tokenized = tokenizer(objects_query, return_tensors="pt") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + from transformers import CLIPTokenizerFast + + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + tokenizer = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + tokenized = tokenizer( + objects_query, max_length=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, truncation=True, return_tensors="pt" + ) + else: + tokenizer = None + raise NotImplementedError + + # Create the mapping between tokenized sentence and the original label + # if one_hot: + # positive_map_token_to_label, positive_map_label_to_token = create_one_hot_dict(labels, no_minus_one_for_one_hot=cfg.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT) + # else: + positive_map_token_to_label, positive_map_label_to_token = create_positive_dict( + tokenized, tokens_positive, labels=labels + ) # from token position to original label + return objects_query, positive_map_label_to_token + + +def create_positive_map_label_to_token_from_positive_map(positive_map, plus=0): + positive_map_label_to_token = {} + for i in range(len(positive_map)): + positive_map_label_to_token[i + plus] = torch.nonzero(positive_map[i], as_tuple=True)[0].tolist() + return positive_map_label_to_token + + +def _accumulate_predictions_from_multiple_gpus(predictions_per_gpu): + all_predictions = all_gather(predictions_per_gpu) + if not is_main_process(): + return + # merge the list of dicts + predictions = {} + for p in all_predictions: + predictions.update(p) + # convert a dict where the key is the index in a list + image_ids = list(sorted(predictions.keys())) + if len(image_ids) != image_ids[-1] + 1: + logger = logging.getLogger("maskrcnn_benchmark.inference") + logger.warning( + "Number of images that were gathered from multiple processes is not " + "a contiguous set. Some images might be missing from the evaluation" + ) + + # convert to a list + predictions = [predictions[i] for i in image_ids] + return predictions + + +def resize_box(output, targets): + if isinstance(targets[0], dict): + orig_target_sizes = targets[0]["orig_size"].unsqueeze(0) + else: + orig_target_sizes = torch.stack([targets[0].extra_fields["orig_size"] for _ in range(1)], dim=0) + img_h, img_w = orig_target_sizes.unbind(1) + return output.resize((img_w, img_h)) + + +def flickr_post_process(output, targets, positive_map_label_to_token, plus): + raw_boxes = deepcopy(output.bbox) + output = resize_box(output, targets) + scores, indices = torch.topk(output.extra_fields["scores"], k=len(output.extra_fields["scores"]), sorted=True) + boxes = output.bbox.tolist() + boxes = [boxes[i] for i in indices] + labels = [output.extra_fields["labels"][i] for i in indices] + output_boxes = [[] for i in range(len(positive_map_label_to_token))] + output_scores = [[] for i in range(len(positive_map_label_to_token))] + for i in range(len(boxes)): + output_boxes[labels[i] - plus].append(boxes[i]) + output_scores[labels[i] - plus].append(scores[i]) + for i in output_boxes: + i.append([0.0, 0.0, 0.0, 0.0]) + image_ids = [t.extra_fields["original_img_id"] for t in targets] + sentence_ids = [t.extra_fields["sentence_id"] for t in targets] + + return {"image_id": image_ids[0], "sentence_id": sentence_ids[0], "boxes": output_boxes, "scores": output_scores, "raw_boxes": raw_boxes} + +def post_process(dataset_name, output, targets, positive_map_label_to_token, plus, categories = None, captions = None): + ''' + Transfer the output from the model to appropriate formats for evaluation + ''' + if "flickr" in dataset_name: + output = output[0] + raw_boxes = deepcopy(output.bbox) + new_output = flickr_post_process( + output, targets, positive_map_label_to_token, plus # This is only used in Flickr + ) + visualization_output = ( + new_output["image_id"], + { + "boxes": new_output["boxes"], + "scores": new_output["scores"], + "raw_boxes": raw_boxes, + } + ) + elif "lvis" in dataset_name: + output = output[0] + raw_boxes = deepcopy(output.bbox) + output = resize_box(output, targets) + scores = output.extra_fields["scores"] + labels = output.extra_fields["labels"] + boxes = output.bbox + new_output = (targets[0]["image_id"].item(), {"scores": scores, "labels": labels, "boxes": boxes, "raw_boxes": raw_boxes, "labels_text": [categories[cat_id.item()] for cat_id in labels]}) + visualization_output = new_output[1] + elif "refcoco" in dataset_name: + output = output[0] + output = resize_box(output, targets) + scores = output.extra_fields["scores"] + boxes = output.bbox + image_id = [t.extra_fields["image_id"] for t in targets][0].item() + new_output = {image_id: {"scores": scores, "boxes": boxes}} + visualization_output = {"scores": scores, "boxes": boxes} + else: + new_output = output + visualization_output = output + return new_output, visualization_output + +def process_for_vis(dataset_name, image_ids, visualization_outputs): + ''' + Transfer the output from the model to appropriate formats for visualization + ''' + if "lvis" in dataset_name: + assert(len(image_ids) == 1) + # merge the visualization_outputs + visualization_output = {} + for key in visualization_outputs[0].keys(): + if key == "labels_text": + _labels_text = [v[key] for v in visualization_outputs] + visualization_output[key] = [item for sublist in _labels_text for item in sublist] + else: + visualization_output[key] = torch.cat([v[key] for v in visualization_outputs], dim=0) + visualization_output = [(image_ids[0], visualization_output)] # + return visualization_output + +def write_to_wandb_log(score, dataset_name, weight_iter, history): + all_results = defaultdict(dict) + exclude_keys = ['_step', '_runtime', '_timestamp'] + if history is not None: + for stat in history: + all_results[stat['_step']].update({k: v for k, v in stat.items() if k not in exclude_keys}) + if "lvis" in dataset_name.lower(): + mAP_all = float(score[0].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds=all] = ")[-1]) + mAP_rare = float(score[6].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds= r] = ")[-1]) + mAP_common = float(score[7].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds= c] = ")[-1]) + mAP_frequent = float(score[8].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds= f] = ")[-1]) + #wandb.log({f"{dataset_name}_mAP_all": mAP_all, f"{dataset_name}_mAP_rare": mAP_rare, f"{dataset_name}_mAP_common": mAP_common, f"{dataset_name}_mAP_frequent": mAP_frequent}, step = weight_iter) + all_results[weight_iter].update({f"{dataset_name}_mAP_all": mAP_all, f"{dataset_name}_mAP_rare": mAP_rare, f"{dataset_name}_mAP_common": mAP_common, f"{dataset_name}_mAP_frequent": mAP_frequent}) + elif "flickr" in dataset_name.lower(): + recall_1 = score["Recall@1_all"] + recall_5 = score["Recall@5_all"] + recall_10 = score["Recall@10_all"] + # wandb.log( + # {f"{dataset_name}_recall@1": recall_1, f"{dataset_name}_recall@5": recall_5, f"{dataset_name}_recall@10": recall_10}, step = weight_iter + # ) + all_results[weight_iter].update({f"{dataset_name}_recall@1": recall_1, f"{dataset_name}_recall@5": recall_5, f"{dataset_name}_recall@10": recall_10}) + elif "coco" in dataset_name.lower(): + all_results[weight_iter].update({f"{dataset_name}_mAP": score[0].results['bbox']['AP']}) + + # sort all results + max_key = max(all_results.keys()) + for i in range(max_key + 1): + if i in all_results: + wandb.log(all_results[i], step = i) + else: + wandb.log({}, step = i) + # for k in sorted(all_results.keys()): + # # need to do consecutive logging + # wandb.log(all_results[k], step = k) + + +def build_flickr_evaluator(cfg): + evaluator = FlickrEvaluator( + "DATASET/flickr30k/flickr30k/", # Hard written!! + subset="test" if "test" in cfg.DATASETS.TEST[0] else "val", + merge_boxes=cfg.DATASETS.FLICKR_GT_TYPE == "merged", + ) + return evaluator + + +def build_refexp_evaluator(dataset): + from maskrcnn_benchmark.data.datasets.refexp import RefExpDataset + + evaluator = RefExpEvaluator(dataset.coco, ("bbox")) + return evaluator + + +def build_lvis_evaluator(ann_file, topk, fixed_ap=True): + from maskrcnn_benchmark.data.datasets.evaluation.lvis.lvis import LVIS + from maskrcnn_benchmark.data.datasets.evaluation.lvis.lvis_eval import LvisEvaluatorFixedAP, LvisEvaluator + evaluator = LvisEvaluatorFixedAP(LVIS(ann_file), topk = topk, fixed_ap=fixed_ap) # topk + #evaluator = LvisEvaluator(LVIS(ann_file), iou_types=['segm', 'bbox']) + return evaluator + + +def write_lvis_results(results, output_file_name): + if isinstance(results, dict): + output_file_name = output_file_name.replace("bbox.csv", "coco_results.pth") + torch.save(results, output_file_name) + return + + lines = [] + lines.append("metric, avg ") + for each_result in results: + metric_string = " ".join(each_result.split(" ")[:-2]) + number = each_result.split(" ")[-1] + each_result = metric_string + ", " + number + " " + lines.append(each_result) + + string_to_write = "\n".join(lines) + "\n" + with open(output_file_name, "w") as f: + f.write(string_to_write) + return + + +def write_flickr_results(results, output_file_name): + lines = [] + lines.append("metric, avg ") + for each_metric, number in results.items(): + each_result = each_metric + ", " + str(number) + " " + lines.append(each_result) + + string_to_write = "\n".join(lines) + "\n" + with open(output_file_name, "w") as f: + f.write(string_to_write) + return + + +def write_refexp_results(results, output_file_name): + lines = [] + lines.append("metric, avg ") + for each_metric, recall_list in results.items(): + for i, recall in zip( + [1, 5, 10], + recall_list, + ): + each_result = each_metric + ": " + f"Recall@{i} = " + str(recall) + " " + lines.append(each_result) + + string_to_write = "\n".join(lines) + "\n" + with open(output_file_name, "w") as f: + f.write(string_to_write) + return + + +def inference( + model, + data_loader, + dataset_name, + iou_types=("bbox",), + box_only=False, + device="cuda", + expected_results=(), + expected_results_sigma_tol=4, + output_folder=None, + cfg=None, + verbose=True, + weight_iter = None, + wandb_run=None, + history=None +): + # convert to a torch.device for efficiency + try: + device = torch.device(device) + except: + device = device + num_devices = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1 + logger = logging.getLogger("maskrcnn_benchmark.inference") + dataset = data_loader.dataset + if verbose: + logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset))) + start_time = time.time() + + task = cfg.TEST.EVAL_TASK + + if not task: + return inference_default( + model, + data_loader, + dataset_name, + iou_types, + box_only, + device, + expected_results, + expected_results_sigma_tol, + output_folder, + cfg, + ) + + if task == "detection": + if "description" in cfg.DATASETS.OD_TO_GROUNDING_VERSION: + try: + descriptions = dataset.lvis.dataset["categories"] + except: + descriptions = dataset.coco.dataset["categories"] + od_grounding_converter = DescriptionConverter( + cfg.DATASETS.DESCRIPTION_FILE, + cfg.DATASETS.OD_TO_GROUNDING_VERSION, + descriptions, + dataset.categories()) # the last parameters is a bit ad-hoc + all_queries, all_positive_map_label_to_token = od_grounding_converter.inference_od_to_grounding(dataset, cfg) + else: + all_queries, all_positive_map_label_to_token = create_queries_and_maps_from_dataset(dataset, cfg) + elif task == "grounding": + all_queries = [None] + all_positive_map_label_to_token = [None] + else: + assert 0 + + """ + Build Dataset Sepecific Evaluator + """ + if "flickr" in cfg.DATASETS.TEST[0]: + evaluator = build_flickr_evaluator(cfg) + elif "lvis" in cfg.DATASETS.TEST[0]: + evaluator = build_lvis_evaluator(dataset.ann_file, topk=cfg.DATASETS.LVIS_TOPK, fixed_ap=not cfg.DATASETS.LVIS_USE_NORMAL_AP) + elif "refcoco" in cfg.DATASETS.TEST[0]: + evaluator = build_refexp_evaluator(dataset) + else: + evaluator = None + + model.eval() + results_dict = {} + cpu_device = torch.device("cpu") + if verbose: + _iterator = tqdm(data_loader) + else: + _iterator = data_loader + # save the visualization results + max_visualize_num = 1000 + try: + gold_data_tsv = TSVResultWriter( + max_visualize_num=max_visualize_num, + file_name=os.path.join(output_folder, "gold_{}/test.tsv").format(torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, + write_freq=10) + ) + prediction_data_tsv = TSVResultWriter( + max_visualize_num=max_visualize_num, + file_name=os.path.join(output_folder, "prediction_{}/test.tsv").format(torch.distributed.get_rank() if torch.distributed.is_initialized() else 0), + write_freq=10) + except: + pass + + + try: + categories = dataset.categories() + raw_categories = dataset.lvis.dataset["categories"] + raw_categories = {c["id"]: c for c in raw_categories} + except: + categories = None + raw_categories = None + for i, batch in enumerate(_iterator): + if i == cfg.TEST.SUBSET: + break + images, targets, image_ids, *_ = batch + try: + gold_data_tsv.update_gold_od_data(images, targets, raw_categories) + except: + pass + + all_output = [] + mdetr_style_output = [] + visualization_outputs = [] + + with torch.no_grad(): + if cfg.TEST.USE_MULTISCALE: + query_time = len(all_queries) + for query_i in range(query_time): + if task == "detection": + captions = [all_queries[query_i] for ii in range(len(targets))] + positive_map_label_to_token = all_positive_map_label_to_token[query_i] + else: + captions = None + positive_map_label_to_token = None + + output = im_detect_bbox_aug(model, images, device, captions, positive_map_label_to_token) + output = [o.to(cpu_device) for o in output] + all_output.append(output) + else: + images = images.to(device) + query_time = len(all_queries) + + output_for_one_image = [] + for query_i in range(query_time): + if not isinstance(targets[0], dict): # For LVIS dataset and datasets directly copied from MDETR + targets = [target.to(device) for target in targets] + """ + different datasets seem to have different data format... For LVIS dataset, the target is a dictionary, while for modulatedDataset such as COCO/Flickr, the target is a BoxList + """ + + if task == "detection": + captions = [all_queries[query_i] for ii in range(len(targets))] + positive_map_label_to_token = all_positive_map_label_to_token[query_i] + if cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION is not None: + positive_map_label_to_token, span_map, spans = positive_map_label_to_token + spans = [spans] # Let's just use one image per batch + else: + span_map = None + spans = None + elif task == "grounding": + captions = [t.get_field("caption") for t in targets] + positive_map_eval = [ + t.get_field("positive_map_eval") + if t.has_field("positive_map_eval") + else t.get_field("positive_map") + for t in targets + ] + if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + assert len(positive_map_eval) == 1 # Let's just use one image per batch + positive_map_eval = positive_map_eval[0] + positive_map_label_to_token = create_positive_map_label_to_token_from_positive_map( + positive_map_eval, plus=plus + ) + span_map = None + spans = None + output = model(images, captions=captions, positive_map=positive_map_label_to_token, spans = spans, span_map=span_map) + if cfg.TEST.CHUNK_INFERENCE_VERSION == "v2": + assert(len(output) == 1) + output_for_one_image.append(output[0]) + else: + output = [o.to(cpu_device) for o in output] + if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + output, visualization_output = post_process( + cfg.DATASETS.TEST[0], + output, targets, positive_map_label_to_token, plus=plus, categories=categories, captions=captions) + if evaluator is not None: + mdetr_style_output.append(output) + else: + all_output.append(output) + visualization_outputs.append(visualization_output) + + if cfg.TEST.CHUNK_INFERENCE_VERSION == "v2": + # merge boxes + output = cat_boxlist(output_for_one_image) + output = model.rpn.box_selector_test.select_over_all_levels([output]) + output = [o.to(cpu_device) for o in output] + if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + output, visualization_output = post_process( + output, targets, positive_map_label_to_token, plus=plus, categories=categories,) + + if evaluator is not None: + mdetr_style_output.append(output) + else: + all_output.append(output) + visualization_outputs.append(visualization_output) + + + try: + prediction_data_tsv.update(images, process_for_vis(cfg.DATASETS.TEST[0], image_ids, visualization_outputs)) # write the prediction data to tsv file + except: + pass + + if evaluator is not None: + try: + evaluator.update(mdetr_style_output) + except: + evaluator.update(mdetr_style_output[0]) + else: + output = [[row[_i] for row in all_output] for _i in range(len(all_output[0]))] + for index, i in enumerate(output): + output[index] = i[0].concate_box_list(i) + + results_dict.update({img_id: result for img_id, result in zip(image_ids, output)}) + + if evaluator is not None: + evaluator.synchronize_between_processes() + try: + evaluator.accumulate() + except: + print("Evaluator has no accumulation, skipped...") + + try: + score, results_processed = evaluator.summarize() + pprint(results_processed) + except: + score = evaluator.summarize() + results_processed = None + + if is_main_process(): + if wandb_run is not None: + # + dataset_name = cfg.DATASETS.TEST[0] + write_to_wandb_log(score, dataset_name, weight_iter, history) + + with open("{}/detailed.json".format(output_folder), "w") as f: + json.dump(results_processed, f) + wandb_run.save("{}/detailed.json".format(output_folder)) + + pprint(score) + import maskrcnn_benchmark.utils.mdetr_dist as dist + if is_main_process(): + if "flickr" in cfg.DATASETS.TEST[0]: + write_flickr_results(score, output_file_name=os.path.join(output_folder, "bbox.csv")) + elif "lvis" in cfg.DATASETS.TEST[0]: + write_lvis_results(score, output_file_name=os.path.join(output_folder, "bbox.csv")) + elif "refcoco" in cfg.DATASETS.TEST[0] and output_folder is not None: + write_refexp_results(score, output_file_name=os.path.join(output_folder, "Recall_results.csv")) + try: + torch.distributed.barrier() + except: + print("Default process group is not initialized") + return + + if evaluator is not None: + predictions = mdetr_style_output + else: + predictions = results_dict + # wait for all processes to complete before measuring the time + synchronize() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + logger.info( + "Total inference time: {} ({} s / img per device, on {} devices)".format( + total_time_str, total_time * num_devices / len(dataset), num_devices + ) + ) + + predictions = _accumulate_predictions_from_multiple_gpus(predictions) + print("Accumulated results") + if not is_main_process(): + return None + + if output_folder: + torch.save(predictions, os.path.join(output_folder, "predictions.pth")) + + extra_args = dict( + box_only=box_only, + iou_types=iou_types, + expected_results=expected_results, + expected_results_sigma_tol=expected_results_sigma_tol, + ) + results = evaluate(dataset=dataset, predictions=predictions, output_folder=output_folder, **extra_args) + + if is_main_process(): + if wandb_run is not None: + dataset_name = cfg.DATASETS.TEST[0] + write_to_wandb_log(results, dataset_name, weight_iter, history) + return results diff --git a/maskrcnn_benchmark/engine/inference_contrastive.py b/maskrcnn_benchmark/engine/inference_contrastive.py new file mode 100644 index 0000000000000000000000000000000000000000..3b57b72caeb58ac0c1ca672b7af80df52f44cc80 --- /dev/null +++ b/maskrcnn_benchmark/engine/inference_contrastive.py @@ -0,0 +1,906 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import datetime +import logging +import time +import os +import re + +import torch +from tqdm import tqdm +from collections import defaultdict +import torch.distributed as dist + +from maskrcnn_benchmark.data.datasets.evaluation import evaluate, im_detect_bbox_aug +from ..utils.comm import is_main_process +from ..utils.comm import all_gather +from ..utils.comm import synchronize +from .tsv_saver import TSVResultWriter +import pdb +from maskrcnn_benchmark.data.datasets.evaluation.flickr.flickr_eval import FlickrEvaluator + +from maskrcnn_benchmark.data.datasets.refexp import RefExpEvaluator +from maskrcnn_benchmark.structures.bounding_box import BoxList +import matplotlib.pyplot as plt +import matplotlib.pylab as pylab +from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file +from sentence_transformers import SentenceTransformer +from numpy.random import RandomState +import fastcluster +import collections +import scipy +import numpy as np +import scipy.cluster +import sklearn +import base64 +import cv2, json +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.data.datasets.od_to_grounding import clean_name +from maskrcnn_benchmark.data.datasets._od_to_description import DescriptionConverter + +from copy import deepcopy +from pprint import pprint +import wandb +def imshow(img, file_name = "tmp.jpg"): + plt.imshow(img[:, :, [2, 1, 0]]) + plt.axis("off") + #plt.figtext(0.5, 0.09, "test", wrap=True, horizontalalignment='center', fontsize=20) + plt.savefig(file_name) +def load(url_or_file_name): + try: + response = requests.get(url_or_file_name) + except: + response = None + if response is None: + pil_image = Image.open(url_or_file_name).convert("RGB") + else: + pil_image = Image.open(BytesIO(response.content)).convert("RGB") + # convert to BGR format + image = np.array(pil_image)[:, :, [2, 1, 0]] + return image + +def inference_default( + model, + data_loader, + dataset_name, + iou_types=("bbox",), + box_only=False, + device="cuda", + expected_results=(), + expected_results_sigma_tol=4, + output_folder=None, + cfg=None, +): + # convert to a torch.device for efficiency + device = torch.device(device) + num_devices = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1 + logger = logging.getLogger("maskrcnn_benchmark.inference") + dataset = data_loader.dataset + logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset))) + start_time = time.time() + + model.eval() + results_dict = {} + cpu_device = torch.device("cpu") + for i, batch in enumerate(tqdm(data_loader)): + images, targets, image_ids, *_ = batch + with torch.no_grad(): + if cfg.TEST.USE_MULTISCALE: + output = im_detect_bbox_aug(model, images, device) + else: + output = model(images.to(device)) + output = [o.to(cpu_device) for o in output] + results_dict.update({img_id: result for img_id, result in zip(image_ids, output)}) + predictions = results_dict + # wait for all processes to complete before measuring the time + synchronize() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + logger.info( + "Total inference time: {} ({} s / img per device, on {} devices)".format( + total_time_str, total_time * num_devices / len(dataset), num_devices + ) + ) + + predictions = _accumulate_predictions_from_multiple_gpus(predictions) + if not is_main_process(): + return None + + if output_folder: + torch.save(predictions, os.path.join(output_folder, "predictions.pth")) + + extra_args = dict( + box_only=box_only, + iou_types=iou_types, + expected_results=expected_results, + expected_results_sigma_tol=expected_results_sigma_tol, + ) + return evaluate(dataset=dataset, predictions=predictions, output_folder=output_folder, **extra_args) + + +def clean_name(name): + name = re.sub(r"\(.*\)", "", name) + name = re.sub(r"_", " ", name) + name = re.sub(r" ", " ", name) + return name + + +def create_one_hot_dict(labels, no_minus_one_for_one_hot=False): + positive_map_token_to_label = defaultdict(int) + positive_map_label_to_token = defaultdict(int) + + for i in range(len(labels)): + positive_map_token_to_label[i] = labels[i] + positive_map_label_to_token[labels[i]] = i + + if no_minus_one_for_one_hot: + positive_map_token_to_label = defaultdict(int) + positive_map_label_to_token = defaultdict(int) + + for i in range(len(labels)): + positive_map_token_to_label[i + 1] = labels[i] + positive_map_label_to_token[labels[i]] = i + 1 + + return positive_map_token_to_label, positive_map_label_to_token + + +def create_positive_dict(tokenized, tokens_positive, labels): + """construct a dictionary such that positive_map[i] = j, iff token i is mapped to j label""" + positive_map = defaultdict(int) + + # Additionally, have positive_map_label_to_tokens + positive_map_label_to_token = defaultdict(list) + + for j, tok_list in enumerate(tokens_positive): + for (beg, end) in tok_list: + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + for i in range(beg_pos, end_pos + 1): + positive_map[i] = labels[j] # because the labels starts from 1 + positive_map_label_to_token[labels[j]].append(i) + # positive_map[j, beg_pos : end_pos + 1].fill_(1) + return positive_map, positive_map_label_to_token # / (positive_map.sum(-1)[:, None] + 1e-6) + + +def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + all_ = [] + for i in range(0, len(lst), n): + data_index = lst[i : i + n] + all_.append(data_index) + counter = 0 + for i in all_: + counter += len(i) + assert counter == len(lst) + + return all_ + + +sbert_model = None +def _get_sbert_model(): + global sbert_model + if not sbert_model: + sbert_model = SentenceTransformer('paraphrase-MiniLM-L6-v2') + return sbert_model + +def semantic_deduplicate_captions(captions, + label_list, + keep_p=.8, + must_keep_idxs=None, + seed=1, verbose=False, + return_features=False, + force_exact=False): + ''' + keep_p can be a proportion to keep, e.g., .5, or it can be an int representing the number to keep, like 10. + ''' + original_captions = deepcopy(captions) + captions = ["This is " + c for c in captions] + prng = RandomState(seed) + + must_keep_idxs = set(must_keep_idxs) if must_keep_idxs is not None else set() + + sbert =_get_sbert_model() + features = sbert.encode([c for c in captions], show_progress_bar=verbose) + pdists = sklearn.metrics.pairwise_distances(features, metric='cosine') + # numerical issues... + np.fill_diagonal(pdists, 0.0) + pdists = (pdists + pdists.transpose()) / 2 + pdists = scipy.spatial.distance.squareform(pdists) + res = fastcluster.linkage(pdists, method='average', preserve_input=False) + del pdists + if keep_p < 1: + n_keep_from_cluster = int(np.round(keep_p*(len(captions)))) + else: + n_keep_from_cluster = min(keep_p, len(captions)) + print('going to keep {} out of {} captions'.format(n_keep_from_cluster, len(captions))) + clusters = scipy.cluster.hierarchy.fcluster(res, n_keep_from_cluster, criterion='maxclust') + + cluster2idxs = collections.defaultdict(list) + for idx, cluster in enumerate(clusters): + cluster2idxs[cluster].append(idx) + + # algo: + # 1. go through clusters with must includes, add must keeps. + # 2. for each cluster without a must keep, add it to candidate list, shuffle candidate list + # 3. loop over each candidate in the candidate list until the return set is the correct size. + + chunked_labels = [] + chunked_label_list = [] + for c, idxs in cluster2idxs.items(): + chunked_labels.append([original_captions[i] for i in idxs]) + chunked_label_list.append([label_list[i] for i in idxs]) + print("size of each prompt:", [len(i) for i in chunked_labels]) + return chunked_labels, chunked_label_list + + +def create_queries_and_maps_from_dataset(dataset, cfg): + categories = dataset.categories() + # one_hot = dataset.one_hot + + labels = [] + label_list = [] + keys = list(categories.keys()) + keys.sort() + for i in keys: + labels.append(i) + label_list.append(categories[i]) + + if cfg.TEST.CHUNKED_EVALUATION != -1: + if cfg.TEST.CHUNK_METHOD == "similar": + label_list, labels = semantic_deduplicate_captions( + label_list, labels, keep_p=len(labels) // cfg.TEST.CHUNKED_EVALUATION,) + else: + labels = chunks(labels, cfg.TEST.CHUNKED_EVALUATION) + label_list = chunks(label_list, cfg.TEST.CHUNKED_EVALUATION) + else: + labels = [labels] + label_list = [label_list] + + all_queries = [] + all_positive_map_label_to_token = [] + + for i in range(len(labels)): + labels_i = labels[i] + label_list_i = label_list[i] + query_i, positive_map_label_to_token_i = create_queries_and_maps( + labels_i, + label_list_i, + additional_labels=cfg.DATASETS.SUPRESS_QUERY if cfg.DATASETS.USE_SUPRESS_QUERY else None, + cfg=cfg, + ) + + all_queries.append(query_i) + all_positive_map_label_to_token.append(positive_map_label_to_token_i) + print("All queries", all_queries) + return all_queries, all_positive_map_label_to_token + +def create_queries_and_maps(labels, label_list, additional_labels=None, cfg=None): + + # Clean label list + label_list = [clean_name(i) for i in label_list] + # Form the query and get the mapping + tokens_positive = [] + start_i = 0 + end_i = 0 + objects_query = "" + + # sep between tokens, follow training + separation_tokens = cfg.DATASETS.SEPARATION_TOKENS + + caption_prompt = cfg.DATASETS.CAPTION_PROMPT + if caption_prompt is not None and isinstance(caption_prompt, str): + caption_prompt = load_from_yaml_file(caption_prompt) + use_caption_prompt = cfg.DATASETS.USE_CAPTION_PROMPT and caption_prompt is not None + for _index, label in enumerate(label_list): + if use_caption_prompt: + objects_query += caption_prompt[_index]["prefix"] + + start_i = len(objects_query) + + if use_caption_prompt: + objects_query += caption_prompt[_index]["name"] + else: + objects_query += label + + end_i = len(objects_query) + tokens_positive.append([(start_i, end_i)]) # Every label has a [(start, end)] + + if use_caption_prompt: + objects_query += caption_prompt[_index]["suffix"] + + if _index != len(label_list) - 1: + objects_query += separation_tokens + + if additional_labels is not None: + objects_query += separation_tokens + for _index, label in enumerate(additional_labels): + objects_query += label + if _index != len(additional_labels) - 1: + objects_query += separation_tokens + + print(objects_query) + + from transformers import AutoTokenizer + + # tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + tokenized = tokenizer(objects_query, return_tensors="pt") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenizer = AutoTokenizer.from_pretrained("roberta-base") + tokenized = tokenizer(objects_query, return_tensors="pt") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + from transformers import CLIPTokenizerFast + + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + tokenizer = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + tokenized = tokenizer( + objects_query, max_length=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, truncation=True, return_tensors="pt" + ) + else: + tokenizer = None + raise NotImplementedError + + # Create the mapping between tokenized sentence and the original label + # if one_hot: + # positive_map_token_to_label, positive_map_label_to_token = create_one_hot_dict(labels, no_minus_one_for_one_hot=cfg.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT) + # else: + positive_map_token_to_label, positive_map_label_to_token = create_positive_dict( + tokenized, tokens_positive, labels=labels + ) # from token position to original label + return objects_query, positive_map_label_to_token + + +def create_positive_map_label_to_token_from_positive_map(positive_map, plus=0): + positive_map_label_to_token = {} + for i in range(len(positive_map)): + positive_map_label_to_token[i + plus] = torch.nonzero(positive_map[i], as_tuple=True)[0].tolist() + return positive_map_label_to_token + + +def _accumulate_predictions_from_multiple_gpus(predictions_per_gpu): + all_predictions = all_gather(predictions_per_gpu) + if not is_main_process(): + return + # merge the list of dicts + predictions = {} + for p in all_predictions: + predictions.update(p) + # convert a dict where the key is the index in a list + image_ids = list(sorted(predictions.keys())) + if len(image_ids) != image_ids[-1] + 1: + logger = logging.getLogger("maskrcnn_benchmark.inference") + logger.warning( + "Number of images that were gathered from multiple processes is not " + "a contiguous set. Some images might be missing from the evaluation" + ) + + # convert to a list + predictions = [predictions[i] for i in image_ids] + return predictions + + +def resize_box(output, targets): + if isinstance(targets[0], dict): + orig_target_sizes = targets[0]["orig_size"].unsqueeze(0) + else: + orig_target_sizes = torch.stack([targets[0].extra_fields["orig_size"] for _ in range(1)], dim=0) + img_h, img_w = orig_target_sizes.unbind(1) + return output.resize((img_w, img_h)) + + +def flickr_post_process(output, targets, positive_map_label_to_token, plus): + raw_boxes = deepcopy(output.bbox) + output = resize_box(output, targets) + scores, indices = torch.topk(output.extra_fields["scores"], k=len(output.extra_fields["scores"]), sorted=True) + boxes = output.bbox.tolist() + boxes = [boxes[i] for i in indices] + labels = [output.extra_fields["labels"][i] for i in indices] + output_boxes = [[] for i in range(len(positive_map_label_to_token))] + output_scores = [[] for i in range(len(positive_map_label_to_token))] + for i in range(len(boxes)): + output_boxes[labels[i] - plus].append(boxes[i]) + output_scores[labels[i] - plus].append(scores[i]) + for i in output_boxes: + i.append([0.0, 0.0, 0.0, 0.0]) + image_ids = [t.extra_fields["original_img_id"] for t in targets] + sentence_ids = [t.extra_fields["sentence_id"] for t in targets] + + return {"image_id": image_ids[0], "sentence_id": sentence_ids[0], "boxes": output_boxes, "scores": output_scores, "raw_boxes": raw_boxes} + +def post_process(dataset_name, output, targets, positive_map_label_to_token, plus, categories = None, captions = None): + ''' + Transfer the output from the model to appropriate formats for evaluation + ''' + if "flickr" in dataset_name: + output = output[0] + raw_boxes = deepcopy(output.bbox) + new_output = flickr_post_process( + output, targets, positive_map_label_to_token, plus # This is only used in Flickr + ) + visualization_output = ( + new_output["image_id"], + { + "boxes": new_output["boxes"], + "scores": new_output["scores"], + "raw_boxes": raw_boxes, + } + ) + elif "lvis" in dataset_name: + output = output[0] + raw_boxes = deepcopy(output.bbox) + output = resize_box(output, targets) + scores = output.extra_fields["scores"] + labels = output.extra_fields["labels"] + boxes = output.bbox + new_output = (targets[0]["image_id"].item(), {"scores": scores, "labels": labels, "boxes": boxes, "raw_boxes": raw_boxes, "labels_text": [categories[cat_id.item()] for cat_id in labels]}) + visualization_output = new_output[1] + elif "refcoco" in dataset_name: + output = output[0] + output = resize_box(output, targets) + scores = output.extra_fields["scores"] + boxes = output.bbox + image_id = [t.extra_fields["image_id"] for t in targets][0].item() + new_output = {image_id: {"scores": scores, "boxes": boxes}} + visualization_output = {"scores": scores, "boxes": boxes} + else: + new_output = output + visualization_output = output + return new_output, visualization_output + +def process_for_vis(dataset_name, image_ids, visualization_outputs): + ''' + Transfer the output from the model to appropriate formats for visualization + ''' + if "lvis" in dataset_name: + assert(len(image_ids) == 1) + # merge the visualization_outputs + visualization_output = {} + if len(visualization_outputs) > 0: + for key in visualization_outputs[0].keys(): + if key == "labels_text": + _labels_text = [v[key] for v in visualization_outputs] + visualization_output[key] = [item for sublist in _labels_text for item in sublist] + else: + visualization_output[key] = torch.cat([v[key] for v in visualization_outputs], dim=0) + visualization_output = [(image_ids[0], visualization_output)] # + return visualization_output + +def write_to_wandb_log(score, dataset_name, weight_iter, history): + all_results = defaultdict(dict) + exclude_keys = ['_step', '_runtime', '_timestamp'] + if history is not None: + for stat in history: + all_results[stat['_step']].update({k: v for k, v in stat.items() if k not in exclude_keys}) + if "lvis" in dataset_name.lower(): + mAP_all = float(score[0].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds=all] = ")[-1]) + mAP_rare = float(score[6].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds= r] = ")[-1]) + mAP_common = float(score[7].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds= c] = ")[-1]) + mAP_frequent = float(score[8].split("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= -1 catIds= f] = ")[-1]) + #wandb.log({f"{dataset_name}_mAP_all": mAP_all, f"{dataset_name}_mAP_rare": mAP_rare, f"{dataset_name}_mAP_common": mAP_common, f"{dataset_name}_mAP_frequent": mAP_frequent}, step = weight_iter) + all_results[weight_iter].update({f"{dataset_name}_mAP_all": mAP_all, f"{dataset_name}_mAP_rare": mAP_rare, f"{dataset_name}_mAP_common": mAP_common, f"{dataset_name}_mAP_frequent": mAP_frequent}) + elif "flickr" in dataset_name.lower(): + recall_1 = score["Recall@1_all"] + recall_5 = score["Recall@5_all"] + recall_10 = score["Recall@10_all"] + # wandb.log( + # {f"{dataset_name}_recall@1": recall_1, f"{dataset_name}_recall@5": recall_5, f"{dataset_name}_recall@10": recall_10}, step = weight_iter + # ) + all_results[weight_iter].update({f"{dataset_name}_recall@1": recall_1, f"{dataset_name}_recall@5": recall_5, f"{dataset_name}_recall@10": recall_10}) + elif "coco" in dataset_name.lower(): + all_results[weight_iter].update({f"{dataset_name}_mAP": score[0].results['bbox']['AP']}) + + # sort all results + max_key = max(all_results.keys()) + for i in range(max_key + 1): + if i in all_results: + wandb.log(all_results[i], step = i) + else: + wandb.log({}, step = i) + # for k in sorted(all_results.keys()): + # # need to do consecutive logging + # wandb.log(all_results[k], step = k) + + +def build_flickr_evaluator(cfg): + evaluator = FlickrEvaluator( + "DATASET/flickr30k/flickr30k/", # Hard written!! + subset="test" if "test" in cfg.DATASETS.TEST[0] else "val", + merge_boxes=cfg.DATASETS.FLICKR_GT_TYPE == "merged", + ) + return evaluator + + +def build_refexp_evaluator(dataset): + from maskrcnn_benchmark.data.datasets.refexp import RefExpDataset + + evaluator = RefExpEvaluator(dataset.coco, ("bbox")) + return evaluator + + +def build_lvis_evaluator(ann_file, topk, fixed_ap=True): + from maskrcnn_benchmark.data.datasets.evaluation.lvis.lvis import LVIS + from maskrcnn_benchmark.data.datasets.evaluation.lvis.lvis_eval import LvisEvaluatorFixedAP, LvisEvaluator + evaluator = LvisEvaluatorFixedAP(LVIS(ann_file), topk = topk, fixed_ap=fixed_ap) # topk + #evaluator = LvisEvaluator(LVIS(ann_file), iou_types=['segm', 'bbox']) + return evaluator + + +def write_lvis_results(results, output_file_name): + if isinstance(results, dict): + output_file_name = output_file_name.replace("bbox.csv", "coco_results.pth") + torch.save(results, output_file_name) + return + + lines = [] + lines.append("metric, avg ") + for each_result in results: + metric_string = " ".join(each_result.split(" ")[:-2]) + number = each_result.split(" ")[-1] + each_result = metric_string + ", " + number + " " + lines.append(each_result) + + string_to_write = "\n".join(lines) + "\n" + with open(output_file_name, "w") as f: + f.write(string_to_write) + return + + +def write_flickr_results(results, output_file_name): + lines = [] + lines.append("metric, avg ") + for each_metric, number in results.items(): + each_result = each_metric + ", " + str(number) + " " + lines.append(each_result) + + string_to_write = "\n".join(lines) + "\n" + with open(output_file_name, "w") as f: + f.write(string_to_write) + return + + +def write_refexp_results(results, output_file_name): + lines = [] + lines.append("metric, avg ") + for each_metric, recall_list in results.items(): + for i, recall in zip( + [1, 5, 10], + recall_list, + ): + each_result = each_metric + ": " + f"Recall@{i} = " + str(recall) + " " + lines.append(each_result) + + string_to_write = "\n".join(lines) + "\n" + with open(output_file_name, "w") as f: + f.write(string_to_write) + return + + +def inference( + model, + data_loader, + dataset_name, + iou_types=("bbox",), + box_only=False, + device="cuda", + expected_results=(), + expected_results_sigma_tol=4, + output_folder=None, + cfg=None, + verbose=True, + weight_iter = None, + wandb_run=None, + history=None +): + # convert to a torch.device for efficiency + try: + device = torch.device(device) + except: + device = device + num_devices = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1 + logger = logging.getLogger("maskrcnn_benchmark.inference") + dataset = data_loader.dataset + if verbose: + logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset))) + start_time = time.time() + + task = cfg.TEST.EVAL_TASK + + if not task: + return inference_default( + model, + data_loader, + dataset_name, + iou_types, + box_only, + device, + expected_results, + expected_results_sigma_tol, + output_folder, + cfg, + ) + + if task == "detection": + if "description" in cfg.DATASETS.OD_TO_GROUNDING_VERSION: + try: + descriptions = dataset.lvis.dataset["categories"] + except: + descriptions = dataset.coco.dataset["categories"] + od_grounding_converter = DescriptionConverter( + cfg.DATASETS.DESCRIPTION_FILE, + cfg.DATASETS.OD_TO_GROUNDING_VERSION, + descriptions, + dataset.categories()) # the last parameters is a bit ad-hoc + all_queries, all_positive_map_label_to_token = od_grounding_converter.inference_od_to_grounding(dataset, cfg) + else: + all_queries, all_positive_map_label_to_token = create_queries_and_maps_from_dataset(dataset, cfg) + elif task == "grounding": + all_queries = [None] + all_positive_map_label_to_token = [None] + else: + assert 0 + + """ + Build Dataset Sepecific Evaluator + """ + if "flickr" in cfg.DATASETS.TEST[0]: + evaluator = build_flickr_evaluator(cfg) + elif "lvis" in cfg.DATASETS.TEST[0]: + evaluator = build_lvis_evaluator(dataset.ann_file, topk=cfg.DATASETS.LVIS_TOPK, fixed_ap=not cfg.DATASETS.LVIS_USE_NORMAL_AP) + elif "refcoco" in cfg.DATASETS.TEST[0]: + evaluator = build_refexp_evaluator(dataset) + else: + evaluator = None + + model.eval() + results_dict = {} + cpu_device = torch.device("cpu") + if verbose: + _iterator = tqdm(data_loader) + else: + _iterator = data_loader + # save the visualization results + max_visualize_num = 1000 + gold_data_tsv = TSVResultWriter( + max_visualize_num=max_visualize_num, + file_name=os.path.join(output_folder, "gold_{}/test.tsv").format(torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, + write_freq=10) + ) + prediction_data_tsv = TSVResultWriter( + max_visualize_num=max_visualize_num, + file_name=os.path.join(output_folder, "prediction_{}/test.tsv").format(torch.distributed.get_rank() if torch.distributed.is_initialized() else 0), + write_freq=10) + + + + try: + categories = dataset.categories() + raw_categories = dataset.lvis.dataset["categories"] + raw_categories = {c["id"]: c for c in raw_categories} + except: + categories = None + raw_categories = None + for i, batch in enumerate(_iterator): + if i == cfg.TEST.SUBSET: + break + images, targets, image_ids, *_ = batch + gold_data_tsv.update_gold_od_data(images, targets, raw_categories) + + all_output = [] + mdetr_style_output = [] + visualization_outputs = [] + + all_labels = set() + assert len(targets) == 1 + for l in targets[0]['labels']: #assert len(targets) == 1 + all_labels.add(int(l)) + + negative_index = 4 #0:pos, 1-5: neg + for cur_label in all_labels: + with torch.no_grad(): + all_queries, all_positive_map_label_to_token = od_grounding_converter.inference_od_to_grounding(dataset, cfg, negative_label=cur_label, negative_index=negative_index) + if cfg.TEST.USE_MULTISCALE: + query_time = len(all_queries) + for query_i in range(query_time): + if task == "detection": + captions = [all_queries[query_i] for ii in range(len(targets))] + positive_map_label_to_token = all_positive_map_label_to_token[query_i] + else: + captions = None + positive_map_label_to_token = None + + output = im_detect_bbox_aug(model, images, device, captions, positive_map_label_to_token) + output = [o.to(cpu_device) for o in output] + all_output.append(output) + else: + images = images.to(device) + query_time = len(all_queries) + + output_for_one_image = [] + for query_i in range(query_time): + if not isinstance(targets[0], dict): # For LVIS dataset and datasets directly copied from MDETR + targets = [target.to(device) for target in targets] + """ + different datasets seem to have different data format... For LVIS dataset, the target is a dictionary, while for modulatedDataset such as COCO/Flickr, the target is a BoxList + """ + + if task == "detection": + captions = [all_queries[query_i] for ii in range(len(targets))] + positive_map_label_to_token = all_positive_map_label_to_token[query_i] + if cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION is not None: + positive_map_label_to_token, span_map, spans = positive_map_label_to_token + spans = [spans] # Let's just use one image per batch + else: + span_map = None + spans = None + elif task == "grounding": + captions = [t.get_field("caption") for t in targets] + positive_map_eval = [ + t.get_field("positive_map_eval") + if t.has_field("positive_map_eval") + else t.get_field("positive_map") + for t in targets + ] + if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + assert len(positive_map_eval) == 1 # Let's just use one image per batch + positive_map_eval = positive_map_eval[0] + positive_map_label_to_token = create_positive_map_label_to_token_from_positive_map( + positive_map_eval, plus=plus + ) + span_map = None + spans = None + output = model(images, captions=captions, positive_map=positive_map_label_to_token, spans = spans, span_map=span_map) + if cfg.TEST.CHUNK_INFERENCE_VERSION == "v2": + assert(len(output) == 1) + output_for_one_image.append(output[0]) + else: + output = [o.to(cpu_device) for o in output] + if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + output, visualization_output = post_process( + cfg.DATASETS.TEST[0], + output, targets, positive_map_label_to_token, plus=plus, categories=categories, captions=captions) + if evaluator is not None: + mdetr_style_output.append(output) + else: + all_output.append(output) + visualization_outputs.append(visualization_output) + + if cfg.TEST.CHUNK_INFERENCE_VERSION == "v2": + # merge boxes + output = cat_boxlist(output_for_one_image) + output = model.rpn.box_selector_test.select_over_all_levels([output]) + output = [o.to(cpu_device) for o in output] + if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + output, visualization_output = post_process( + output, targets, positive_map_label_to_token, plus=plus, categories=categories,) + + if evaluator is not None: + mdetr_style_output.append(output) + else: + all_output.append(output) + visualization_outputs.append(visualization_output) + + + + prediction_data_tsv.update(images, process_for_vis(cfg.DATASETS.TEST[0], image_ids, visualization_outputs)) # write the prediction data to tsv file + + if evaluator is not None: + try: + evaluator.update(mdetr_style_output) + except: + evaluator.update(mdetr_style_output[0]) + else: + output = [[row[_i] for row in all_output] for _i in range(len(all_output[0]))] + for index, i in enumerate(output): + output[index] = i[0].concate_box_list(i) + + results_dict.update({img_id: result for img_id, result in zip(image_ids, output)}) + + if evaluator is not None: + evaluator.synchronize_between_processes() + try: + evaluator.accumulate() + except: + print("Evaluator has no accumulation, skipped...") + + try: + score, results_processed = evaluator.summarize() + pprint(results_processed) + except: + score = evaluator.summarize() + results_processed = None + + if is_main_process(): + if wandb_run is not None: + # + dataset_name = cfg.DATASETS.TEST[0] + write_to_wandb_log(score, dataset_name, weight_iter, history) + + with open("{}/detailed.json".format(output_folder), "w") as f: + json.dump(results_processed, f) + wandb_run.save("{}/detailed.json".format(output_folder)) + + pprint(score) + import maskrcnn_benchmark.utils.mdetr_dist as dist + if is_main_process(): + if "flickr" in cfg.DATASETS.TEST[0]: + write_flickr_results(score, output_file_name=os.path.join(output_folder, "bbox.csv")) + elif "lvis" in cfg.DATASETS.TEST[0]: + write_lvis_results(score, output_file_name=os.path.join(output_folder, "bbox.csv")) + elif "refcoco" in cfg.DATASETS.TEST[0] and output_folder is not None: + write_refexp_results(score, output_file_name=os.path.join(output_folder, "Recall_results.csv")) + try: + torch.distributed.barrier() + except: + print("Default process group is not initialized") + return + + if evaluator is not None: + predictions = mdetr_style_output + else: + predictions = results_dict + # wait for all processes to complete before measuring the time + synchronize() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + logger.info( + "Total inference time: {} ({} s / img per device, on {} devices)".format( + total_time_str, total_time * num_devices / len(dataset), num_devices + ) + ) + + predictions = _accumulate_predictions_from_multiple_gpus(predictions) + print("Accumulated results") + if not is_main_process(): + return None + + if output_folder: + torch.save(predictions, os.path.join(output_folder, "predictions.pth")) + + extra_args = dict( + box_only=box_only, + iou_types=iou_types, + expected_results=expected_results, + expected_results_sigma_tol=expected_results_sigma_tol, + ) + results = evaluate(dataset=dataset, predictions=predictions, output_folder=output_folder, **extra_args) + + if is_main_process(): + if wandb_run is not None: + dataset_name = cfg.DATASETS.TEST[0] + write_to_wandb_log(results, dataset_name, weight_iter, history) + + # with open("{}/detailed.json".format(output_folder), "w") as f: + # json.dump(results, f) + # wandb_run.save("{}/detailed.json".format(output_folder)) + return results \ No newline at end of file diff --git a/maskrcnn_benchmark/engine/predictor.py b/maskrcnn_benchmark/engine/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3548f76fa667a55c03bcab1c9fb0dbc845b1cc --- /dev/null +++ b/maskrcnn_benchmark/engine/predictor.py @@ -0,0 +1,568 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import cv2 +import torch +import numpy as np +from torchvision import transforms as T + +from maskrcnn_benchmark.modeling.detector import build_detection_model +from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer +from maskrcnn_benchmark.structures.image_list import to_image_list +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker +from maskrcnn_benchmark import layers as L +from maskrcnn_benchmark.utils import cv2_util + + +import timeit + + +class COCODemo(object): + # COCO categories for pretty print + CATEGORIES = [ + "__background", + "person", + "bicycle", + "car", + "motorcycle", + "airplane", + "bus", + "train", + "truck", + "boat", + "traffic light", + "fire hydrant", + "stop sign", + "parking meter", + "bench", + "bird", + "cat", + "dog", + "horse", + "sheep", + "cow", + "elephant", + "bear", + "zebra", + "giraffe", + "backpack", + "umbrella", + "handbag", + "tie", + "suitcase", + "frisbee", + "skis", + "snowboard", + "sports ball", + "kite", + "baseball bat", + "baseball glove", + "skateboard", + "surfboard", + "tennis racket", + "bottle", + "wine glass", + "cup", + "fork", + "knife", + "spoon", + "bowl", + "banana", + "apple", + "sandwich", + "orange", + "broccoli", + "carrot", + "hot dog", + "pizza", + "donut", + "cake", + "chair", + "couch", + "potted plant", + "bed", + "dining table", + "toilet", + "tv", + "laptop", + "mouse", + "remote", + "keyboard", + "cell phone", + "microwave", + "oven", + "toaster", + "sink", + "refrigerator", + "book", + "clock", + "vase", + "scissors", + "teddy bear", + "hair drier", + "toothbrush", + ] + + def __init__( + self, + cfg, + confidence_threshold=0.7, + show_mask_heatmaps=False, + masks_per_dim=2, + min_image_size=None, + exclude_region=None, + ): + self.cfg = cfg.clone() + self.model = build_detection_model(cfg) + self.model.eval() + self.device = torch.device(cfg.MODEL.DEVICE) + self.model.to(self.device) + self.min_image_size = min_image_size + + save_dir = cfg.OUTPUT_DIR + checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir) + _ = checkpointer.load(cfg.MODEL.WEIGHT) + + self.transforms = self.build_transform() + + mask_threshold = -1 if show_mask_heatmaps else 0.5 + self.masker = Masker(threshold=mask_threshold, padding=1) + + # used to make colors for each class + self.palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1]) + + self.cpu_device = torch.device("cpu") + self.confidence_threshold = confidence_threshold + self.show_mask_heatmaps = show_mask_heatmaps + self.masks_per_dim = masks_per_dim + self.exclude_region = exclude_region + + def build_transform(self): + """ + Creates a basic transformation that was used to train the models + """ + cfg = self.cfg + + # we are loading images with OpenCV, so we don't need to convert them + # to BGR, they are already! So all we need to do is to normalize + # by 255 if we want to convert to BGR255 format, or flip the channels + # if we want it to be in RGB in [0-1] range. + if cfg.INPUT.TO_BGR255: + to_bgr_transform = T.Lambda(lambda x: x * 255) + else: + to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]]) + + normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD) + + transform = T.Compose( + [ + T.ToPILImage(), + T.Resize(self.min_image_size) if self.min_image_size is not None else lambda x: x, + T.ToTensor(), + to_bgr_transform, + normalize_transform, + ] + ) + return transform + + def inference(self, image, debug=False): + """ + Arguments: + image (np.ndarray): an image as returned by OpenCV + + Returns: + prediction (BoxList): the detected objects. Additional information + of the detection properties can be found in the fields of + the BoxList via `prediction.fields()` + """ + predictions, debug_info = self.compute_prediction(image) + top_predictions = self.select_top_predictions(predictions) + + if debug: + return top_predictions, debug_info + else: + return top_predictions + + def run_on_opencv_image(self, image): + """ + Arguments: + image (np.ndarray): an image as returned by OpenCV + + Returns: + prediction (BoxList): the detected objects. Additional information + of the detection properties can be found in the fields of + the BoxList via `prediction.fields()` + """ + predictions, debug_info = self.compute_prediction(image) + top_predictions = self.select_top_predictions(predictions) + + result = image.copy() + if self.show_mask_heatmaps: + return self.create_mask_montage(result, top_predictions) + result = self.overlay_boxes(result, top_predictions) + if self.cfg.MODEL.MASK_ON: + result = self.overlay_mask(result, top_predictions) + if self.cfg.MODEL.KEYPOINT_ON: + result = self.overlay_keypoints(result, top_predictions) + result = self.overlay_class_names(result, top_predictions) + + return result, debug_info, top_predictions + + def compute_prediction(self, original_image): + """ + Arguments: + original_image (np.ndarray): an image as returned by OpenCV + + Returns: + prediction (BoxList): the detected objects. Additional information + of the detection properties can be found in the fields of + the BoxList via `prediction.fields()` + """ + # apply pre-processing to image + # if self.exclude_region: + # for region in self.exclude_region: + # original_image[region[1]:region[3], region[0]:region[2], :] = 255 + image = self.transforms(original_image) + + # convert to an ImageList, padded so that it is divisible by + # cfg.DATALOADER.SIZE_DIVISIBILITY + image_list = to_image_list(image, self.cfg.DATALOADER.SIZE_DIVISIBILITY) + image_list = image_list.to(self.device) + tic = timeit.time.perf_counter() + + # compute predictions + with torch.no_grad(): + predictions, debug_info = self.model(image_list) + predictions = [o.to(self.cpu_device) for o in predictions] + debug_info["total_time"] = timeit.time.perf_counter() - tic + + # always single image is passed at a time + prediction = predictions[0] + + # reshape prediction (a BoxList) into the original image size + height, width = original_image.shape[:-1] + prediction = prediction.resize((width, height)) + + if prediction.has_field("mask"): + # if we have masks, paste the masks in the right position + # in the image, as defined by the bounding boxes + masks = prediction.get_field("mask") + # always single image is passed at a time + masks = self.masker([masks], [prediction])[0] + prediction.add_field("mask", masks) + + return prediction, debug_info + + def select_top_predictions(self, predictions): + """ + Select only predictions which have a `score` > self.confidence_threshold, + and returns the predictions in descending order of score + + Arguments: + predictions (BoxList): the result of the computation by the model. + It should contain the field `scores`. + + Returns: + prediction (BoxList): the detected objects. Additional information + of the detection properties can be found in the fields of + the BoxList via `prediction.fields()` + """ + + scores = predictions.get_field("scores") + labels = predictions.get_field("labels").tolist() + thresh = scores.clone() + for i, lb in enumerate(labels): + if isinstance(self.confidence_threshold, float): + thresh[i] = self.confidence_threshold + elif len(self.confidence_threshold) == 1: + thresh[i] = self.confidence_threshold[0] + else: + thresh[i] = self.confidence_threshold[lb - 1] + keep = torch.nonzero(scores > thresh).squeeze(1) + predictions = predictions[keep] + + if self.exclude_region: + exlude = BoxList(self.exclude_region, predictions.size) + iou = boxlist_iou(exlude, predictions) + keep = torch.nonzero(torch.sum(iou > 0.5, dim=0) == 0).squeeze(1) + if len(keep) > 0: + predictions = predictions[keep] + + scores = predictions.get_field("scores") + _, idx = scores.sort(0, descending=True) + return predictions[idx] + + def compute_colors_for_labels(self, labels): + """ + Simple function that adds fixed colors depending on the class + """ + colors = (30 * (labels[:, None] - 1) + 1) * self.palette + colors = (colors % 255).numpy().astype("uint8") + return colors + + def overlay_boxes(self, image, predictions): + """ + Adds the predicted boxes on top of the image + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `labels`. + """ + labels = predictions.get_field("labels") + boxes = predictions.bbox + + colors = self.compute_colors_for_labels(labels).tolist() + + for box, color in zip(boxes, colors): + box = box.to(torch.int64) + top_left, bottom_right = box[:2].tolist(), box[2:].tolist() + image = cv2.rectangle(image, tuple(top_left), tuple(bottom_right), tuple(color), 2) + + return image + + def overlay_scores(self, image, predictions): + """ + Adds the predicted boxes on top of the image + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `labels`. + """ + scores = predictions.get_field("scores") + boxes = predictions.bbox + + for box, score in zip(boxes, scores): + box = box.to(torch.int64) + image = cv2.putText( + image, + "%.3f" % score, + (box[0], (box[1] + box[3]) / 2), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + (255, 255, 255), + 1, + ) + + return image + + def overlay_cboxes(self, image, predictions): + """ + Adds the predicted boxes on top of the image + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `labels`. + """ + scores = predictions.get_field("scores") + boxes = predictions.bbox + for box, score in zip(boxes, scores): + box = box.to(torch.int64) + top_left, bottom_right = box[:2].tolist(), box[2:].tolist() + image = cv2.rectangle(image, tuple(top_left), tuple(bottom_right), (255, 0, 0), 2) + image = cv2.putText( + image, "%.3f" % score, (box[0], (box[1] + box[3]) / 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1 + ) + return image + + def overlay_centers(self, image, predictions): + """ + Adds the predicted boxes on top of the image + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `labels`. + """ + centers = predictions.get_field("centers") + + for cord in centers: + cord = cord.to(torch.int64) + image = cv2.circle(image, (cord[0].item(), cord[1].item()), 2, (255, 0, 0), 20) + + return image + + def overlay_count(self, image, predictions): + """ + Adds the predicted boxes on top of the image + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `labels`. + """ + if isinstance(predictions, int): + count = predictions + else: + count = len(predictions) + image = cv2.putText(image, "Count: %d" % count, (0, 100), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 3) + + return image + + def overlay_mask(self, image, predictions): + """ + Adds the instances contours for each predicted object. + Each label has a different color. + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `mask` and `labels`. + """ + masks = predictions.get_field("mask").numpy() + labels = predictions.get_field("labels") + + colors = self.compute_colors_for_labels(labels).tolist() + + for mask, color in zip(masks, colors): + thresh = mask[0, :, :, None].astype(np.uint8) + contours, hierarchy = cv2_util.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + image = cv2.drawContours(image, contours, -1, color, 3) + + composite = image + + return composite + + def overlay_keypoints(self, image, predictions): + keypoints = predictions.get_field("keypoints") + kps = keypoints.keypoints + scores = keypoints.get_field("logits") + kps = torch.cat((kps[:, :, 0:2], scores[:, :, None]), dim=2).numpy() + for region in kps: + image = vis_keypoints( + image, region.transpose((1, 0)), names=keypoints.NAMES, connections=keypoints.CONNECTIONS + ) + return image + + def create_mask_montage(self, image, predictions): + """ + Create a montage showing the probability heatmaps for each one one of the + detected objects + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `mask`. + """ + masks = predictions.get_field("mask") + masks_per_dim = self.masks_per_dim + masks = L.interpolate(masks.float(), scale_factor=1 / masks_per_dim).byte() + height, width = masks.shape[-2:] + max_masks = masks_per_dim**2 + masks = masks[:max_masks] + # handle case where we have less detections than max_masks + if len(masks) < max_masks: + masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8) + masks_padded[: len(masks)] = masks + masks = masks_padded + masks = masks.reshape(masks_per_dim, masks_per_dim, height, width) + result = torch.zeros((masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8) + for y in range(masks_per_dim): + start_y = y * height + end_y = (y + 1) * height + for x in range(masks_per_dim): + start_x = x * width + end_x = (x + 1) * width + result[start_y:end_y, start_x:end_x] = masks[y, x] + return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET) + + def overlay_class_names(self, image, predictions, names=None): + """ + Adds detected class names and scores in the positions defined by the + top-left corner of the predicted bounding box + + Arguments: + image (np.ndarray): an image as returned by OpenCV + predictions (BoxList): the result of the computation by the model. + It should contain the field `scores` and `labels`. + """ + scores = predictions.get_field("scores").tolist() + labels = predictions.get_field("labels").tolist() + if names: + labels = [names[i - 1] for i in labels] + else: + labels = [self.CATEGORIES[i] for i in labels] + boxes = predictions.bbox + + template = "{}: {:.2f}" + for box, score, label in zip(boxes, scores, labels): + x, y = box[:2] + s = template.format(label, score) + cv2.putText(image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + return image + + +def vis_keypoints(img, kps, kp_thresh=0, alpha=0.7, names=None, connections=None): + """Visualizes keypoints (adapted from vis_one_image). + kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob). + """ + + dataset_keypoints = names + kp_lines = connections + + # simple rainbow color map implementation + blue_red_ratio = 0.8 + gx = lambda x: (6 - 2 * blue_red_ratio) * x + blue_red_ratio + colors = [ + [ + 256 * max(0, (3 - abs(gx(i) - 4) - abs(gx(i) - 5)) / 2), + 256 * max(0, (3 - abs(gx(i) - 2) - abs(gx(i) - 4)) / 2), + 256 * max(0, (3 - abs(gx(i) - 1) - abs(gx(i) - 2)) / 2), + ] + for i in np.linspace(0, 1, len(kp_lines) + 2) + ] + + # Perform the drawing on a copy of the image, to allow for blending. + kp_mask = np.copy(img) + + # Draw mid shoulder / mid hip first for better visualization. + mid_shoulder = ( + kps[:2, dataset_keypoints.index("right_shoulder")] + kps[:2, dataset_keypoints.index("left_shoulder")] + ) / 2.0 + sc_mid_shoulder = np.minimum( + kps[2, dataset_keypoints.index("right_shoulder")], kps[2, dataset_keypoints.index("left_shoulder")] + ) + nose_idx = dataset_keypoints.index("nose") + if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: + cv2.line( + kp_mask, + tuple(mid_shoulder), + tuple(kps[:2, nose_idx]), + color=colors[len(kp_lines)], + thickness=2, + lineType=cv2.LINE_AA, + ) + + if "right_hip" in names and "left_hip" in names: + mid_hip = (kps[:2, dataset_keypoints.index("right_hip")] + kps[:2, dataset_keypoints.index("left_hip")]) / 2.0 + sc_mid_hip = np.minimum( + kps[2, dataset_keypoints.index("right_hip")], kps[2, dataset_keypoints.index("left_hip")] + ) + if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: + cv2.line( + kp_mask, + tuple(mid_shoulder), + tuple(mid_hip), + color=colors[len(kp_lines) + 1], + thickness=2, + lineType=cv2.LINE_AA, + ) + + # Draw the keypoints. + for l in range(len(kp_lines)): + i1 = kp_lines[l][0] + i2 = kp_lines[l][1] + p1 = kps[0, i1], kps[1, i1] + p2 = kps[0, i2], kps[1, i2] + if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: + cv2.line(kp_mask, p1, p2, color=colors[l], thickness=2, lineType=cv2.LINE_AA) + if kps[2, i1] > kp_thresh: + cv2.circle(kp_mask, p1, radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + if kps[2, i2] > kp_thresh: + cv2.circle(kp_mask, p2, radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) + + # Blend the keypoints. + return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) diff --git a/maskrcnn_benchmark/engine/predictor_FIBER.py b/maskrcnn_benchmark/engine/predictor_FIBER.py new file mode 100644 index 0000000000000000000000000000000000000000..ad28e04aae5db80ae13baf1eb40bf58f1fc112db --- /dev/null +++ b/maskrcnn_benchmark/engine/predictor_FIBER.py @@ -0,0 +1,426 @@ +import cv2 +import torch +import re +import numpy as np +from typing import List, Union +import nltk +import inflect +from transformers import AutoTokenizer +from torchvision import transforms as T + +from maskrcnn_benchmark.modeling.detector import build_detection_model +from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer +from maskrcnn_benchmark.structures.image_list import to_image_list +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark import layers as L +from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker +from maskrcnn_benchmark.utils import cv2_util + +engine = inflect.engine() +nltk.download("punkt") +nltk.download("averaged_perceptron_tagger") + +import timeit + + +class GLIPDemo(object): + def __init__( + self, + cfg, + confidence_threshold=0.7, + min_image_size=None, + show_mask_heatmaps=False, + masks_per_dim=5, + ): + self.cfg = cfg.clone() + self.model = build_detection_model(cfg) + self.model.eval() + self.device = torch.device(cfg.MODEL.DEVICE) + self.model.to(self.device) + self.min_image_size = min_image_size + self.show_mask_heatmaps = show_mask_heatmaps + self.masks_per_dim = masks_per_dim + + save_dir = cfg.OUTPUT_DIR + checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir) + _ = checkpointer.load(cfg.MODEL.WEIGHT) + + self.transforms = self.build_transform() + + # used to make colors for each tokens + mask_threshold = -1 if show_mask_heatmaps else 0.5 + self.masker = Masker(threshold=mask_threshold, padding=1) + self.palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1]) + self.cpu_device = torch.device("cpu") + self.confidence_threshold = confidence_threshold + + self.tokenizer = self.build_tokenizer() + + def build_transform(self): + """ + Creates a basic transformation that was used to train the models + """ + cfg = self.cfg + + # we are loading images with OpenCV, so we don't need to convert them + # to BGR, they are already! So all we need to do is to normalize + # by 255 if we want to convert to BGR255 format, or flip the channels + # if we want it to be in RGB in [0-1] range. + if cfg.INPUT.TO_BGR255: + to_bgr_transform = T.Lambda(lambda x: x * 255) + else: + to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]]) + + normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD) + + transform = T.Compose( + [ + T.ToPILImage(), + T.Resize(self.min_image_size) if self.min_image_size is not None else lambda x: x, + T.ToTensor(), + to_bgr_transform, + normalize_transform, + ] + ) + return transform + + def build_tokenizer(self): + cfg = self.cfg + tokenizer = None + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenizer = AutoTokenizer.from_pretrained("roberta-base") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + from transformers import CLIPTokenizerFast + + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + tokenizer = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + return tokenizer + + def run_ner(self, caption, refexp_mode=False): + noun_phrases = find_noun_phrases(caption) + noun_phrases = [remove_punctuation(phrase) for phrase in noun_phrases] + noun_phrases = [phrase for phrase in noun_phrases if phrase != ""] + relevant_phrases = noun_phrases + if refexp_mode: + noun_phrases = [caption] + relevant_phrases = [caption] + + labels = noun_phrases + self.entities = labels + + tokens_positive = [] + + for entity, label in zip(relevant_phrases, labels): + try: + # search all occurrences and mark them as different entities + for m in re.finditer(entity, caption.lower()): + tokens_positive.append([[m.start(), m.end()]]) + except: + print("noun entities:", noun_phrases) + print("entity:", entity) + print("caption:", caption.lower()) + + return tokens_positive + + def inference(self, original_image, original_caption, refexp_mode=False): + predictions = self.compute_prediction(original_image, original_caption, refexp_mode) + top_predictions = self._post_process_fixed_thresh(predictions) + return top_predictions + + def run_on_web_image(self, original_image, original_caption, thresh=0.5, refexp_mode=False): + predictions = self.compute_prediction(original_image, original_caption, refexp_mode) + top_predictions = self._post_process(predictions, thresh) + + result = original_image.copy() + if self.show_mask_heatmaps: + return self.create_mask_montage(result, top_predictions) + result = self.overlay_boxes(result, top_predictions) + result = self.overlay_entity_names(result, top_predictions) + if self.cfg.MODEL.MASK_ON: + result = self.overlay_mask(result, top_predictions) + + return result, top_predictions + + def compute_prediction(self, original_image, original_caption, refexp_mode=False): + # image + image = self.transforms(original_image) + image_list = to_image_list(image, self.cfg.DATALOADER.SIZE_DIVISIBILITY) + image_list = image_list.to(self.device) + # caption + tokenized = self.tokenizer([original_caption], return_tensors="pt") + tokens_positive = self.run_ner(original_caption, refexp_mode) + # process positive map + positive_map = create_positive_map(tokenized, tokens_positive) + if self.cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + + positive_map_label_to_token = create_positive_map_label_to_token_from_positive_map(positive_map, plus=plus) + self.plus = plus + self.positive_map_label_to_token = positive_map_label_to_token + tic = timeit.time.perf_counter() + + # compute predictions + with torch.no_grad(): + predictions = self.model(image_list, captions=[original_caption], positive_map=positive_map_label_to_token) + predictions = [o.to(self.cpu_device) for o in predictions] + print("inference time per image: {}".format(timeit.time.perf_counter() - tic)) + + # always single image is passed at a time + prediction = predictions[0] + + # reshape prediction (a BoxList) into the original image size + height, width = original_image.shape[:-1] + prediction = prediction.resize((width, height)) + + if prediction.has_field("mask"): + # if we have masks, paste the masks in the right position + # in the image, as defined by the bounding boxes + masks = prediction.get_field("mask") + # always single image is passed at a time + masks = self.masker([masks], [prediction])[0] + prediction.add_field("mask", masks) + + return prediction + + def _post_process_fixed_thresh(self, predictions): + scores = predictions.get_field("scores") + labels = predictions.get_field("labels").tolist() + thresh = scores.clone() + for i, lb in enumerate(labels): + if isinstance(self.confidence_threshold, float): + thresh[i] = self.confidence_threshold + elif len(self.confidence_threshold) == 1: + thresh[i] = self.confidence_threshold[0] + else: + thresh[i] = self.confidence_threshold[lb - 1] + keep = torch.nonzero(scores > thresh).squeeze(1) + predictions = predictions[keep] + + scores = predictions.get_field("scores") + _, idx = scores.sort(0, descending=True) + return predictions[idx] + + def _post_process(self, predictions, threshold=0.5): + scores = predictions.get_field("scores") + labels = predictions.get_field("labels").tolist() + thresh = scores.clone() + for i, lb in enumerate(labels): + if isinstance(self.confidence_threshold, float): + thresh[i] = threshold + elif len(self.confidence_threshold) == 1: + thresh[i] = threshold + else: + thresh[i] = self.confidence_threshold[lb - 1] + keep = torch.nonzero(scores > thresh).squeeze(1) + predictions = predictions[keep] + + scores = predictions.get_field("scores") + _, idx = scores.sort(0, descending=True) + return predictions[idx] + + def compute_colors_for_labels(self, labels): + """ + Simple function that adds fixed colors depending on the class + """ + colors = (30 * (labels[:, None] - 1) + 1) * self.palette + colors = (colors % 255).numpy().astype("uint8") + return colors + + def overlay_boxes(self, image, predictions): + labels = predictions.get_field("labels") + boxes = predictions.bbox + + colors = self.compute_colors_for_labels(labels).tolist() + + for box, color in zip(boxes, colors): + box = box.to(torch.int64) + top_left, bottom_right = box[:2].tolist(), box[2:].tolist() + image = cv2.rectangle(image, tuple(top_left), tuple(bottom_right), tuple(color), 2) + + return image + + def overlay_scores(self, image, predictions): + scores = predictions.get_field("scores") + boxes = predictions.bbox + + for box, score in zip(boxes, scores): + box = box.to(torch.int64) + image = cv2.putText( + image, + "%.3f" % score, + (int(box[0]), int((box[1] + box[3]) / 2)), + cv2.FONT_HERSHEY_DUPLEX , + 0.3, + (255, 255, 255), + 1, + ) + + return image + + def overlay_entity_names(self, image, predictions, names=None): + scores = predictions.get_field("scores").tolist() + labels = predictions.get_field("labels").tolist() + new_labels = [] + if self.entities and self.plus: + for i in labels: + if i <= len(self.entities): + new_labels.append(self.entities[i - self.plus]) + else: + new_labels.append("object") + # labels = [self.entities[i - self.plus] for i in labels ] + else: + new_labels = ["object" for i in labels] + boxes = predictions.bbox + + template = "{}: {:.2f}" + for box, score, label in zip(boxes, scores, new_labels): + x, y = box[:2] + s = template.format(label, score) + cv2.putText(image, s, (int(x), int(y + 10)), cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255), 1) + + return image + + def overlay_mask(self, image, predictions): + masks = predictions.get_field("mask").numpy() + labels = predictions.get_field("labels") + + colors = self.compute_colors_for_labels(labels).tolist() + + # import pdb + # pdb.set_trace() + # masks = masks > 0.1 + + for mask, color in zip(masks, colors): + thresh = mask[0, :, :, None].astype(np.uint8) + contours, hierarchy = cv2_util.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + image = cv2.drawContours(image, contours, -1, color, 2) + + composite = image + + return composite + + def create_mask_montage(self, image, predictions): + masks = predictions.get_field("mask") + masks_per_dim = self.masks_per_dim + masks = L.interpolate(masks.float(), scale_factor=1 / masks_per_dim).byte() + height, width = masks.shape[-2:] + max_masks = masks_per_dim**2 + masks = masks[:max_masks] + # handle case where we have less detections than max_masks + if len(masks) < max_masks: + masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8) + masks_padded[: len(masks)] = masks + masks = masks_padded + masks = masks.reshape(masks_per_dim, masks_per_dim, height, width) + result = torch.zeros((masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8) + for y in range(masks_per_dim): + start_y = y * height + end_y = (y + 1) * height + for x in range(masks_per_dim): + start_x = x * width + end_x = (x + 1) * width + result[start_y:end_y, start_x:end_x] = masks[y, x] + + return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET), None + + +def create_positive_map_label_to_token_from_positive_map(positive_map, plus=0): + positive_map_label_to_token = {} + for i in range(len(positive_map)): + positive_map_label_to_token[i + plus] = torch.nonzero(positive_map[i], as_tuple=True)[0].tolist() + return positive_map_label_to_token + + +def create_positive_map(tokenized, tokens_positive): + """construct a map such that positive_map[i,j] = True iff box i is associated to token j""" + positive_map = torch.zeros((len(tokens_positive), 256), dtype=torch.float) + + for j, tok_list in enumerate(tokens_positive): + for (beg, end) in tok_list: + try: + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + except Exception as e: + print("beg:", beg, "end:", end) + print("token_positive:", tokens_positive) + # print("beg_pos:", beg_pos, "end_pos:", end_pos) + raise e + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + positive_map[j, beg_pos : end_pos + 1].fill_(1) + return positive_map / (positive_map.sum(-1)[:, None] + 1e-6) + + +def find_noun_phrases(caption: str) -> List[str]: + caption = caption.lower() + tokens = nltk.word_tokenize(caption) + pos_tags = nltk.pos_tag(tokens) + + grammar = "NP: {
?*+}" + cp = nltk.RegexpParser(grammar) + result = cp.parse(pos_tags) + + noun_phrases = list() + for subtree in result.subtrees(): + if subtree.label() == "NP": + noun_phrases.append(" ".join(t[0] for t in subtree.leaves())) + + return noun_phrases + + +def remove_punctuation(text: str) -> str: + punct = [ + "|", + ":", + ";", + "@", + "(", + ")", + "[", + "]", + "{", + "}", + "^", + "'", + '"', + "’", + "`", + "?", + "$", + "%", + "#", + "!", + "&", + "*", + "+", + ",", + ".", + ] + for p in punct: + text = text.replace(p, "") + return text.strip() diff --git a/maskrcnn_benchmark/engine/predictor_glip.py b/maskrcnn_benchmark/engine/predictor_glip.py new file mode 100644 index 0000000000000000000000000000000000000000..9d3b5cfa0c2f6d8bec557d16dff6f9b29f82c19b --- /dev/null +++ b/maskrcnn_benchmark/engine/predictor_glip.py @@ -0,0 +1,474 @@ +import cv2 +import torch +import re +import numpy as np +from typing import List, Union +import nltk +import inflect +from transformers import AutoTokenizer +from torchvision import transforms as T +import pdb +from maskrcnn_benchmark.modeling.detector import build_detection_model +from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer +from maskrcnn_benchmark.structures.image_list import to_image_list +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark import layers as L +from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker +from maskrcnn_benchmark.utils import cv2_util + +engine = inflect.engine() +nltk.download("punkt") +nltk.download("averaged_perceptron_tagger") + +import timeit + + +class GLIPDemo(object): + def __init__( + self, + cfg, + confidence_threshold=0.7, + min_image_size=None, + show_mask_heatmaps=False, + masks_per_dim=5, + ): + self.cfg = cfg.clone() + self.model = build_detection_model(cfg) + self.model.eval() + self.device = torch.device(cfg.MODEL.DEVICE) + self.model.to(self.device) + self.min_image_size = min_image_size + self.show_mask_heatmaps = show_mask_heatmaps + self.masks_per_dim = masks_per_dim + + save_dir = cfg.OUTPUT_DIR + checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir) + _ = checkpointer.load(cfg.MODEL.WEIGHT) + + self.transforms = self.build_transform() + + # used to make colors for each tokens + mask_threshold = -1 if show_mask_heatmaps else 0.5 + self.masker = Masker(threshold=mask_threshold, padding=1) + self.palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1]) + self.cpu_device = torch.device("cpu") + self.confidence_threshold = confidence_threshold + + self.tokenizer = self.build_tokenizer() + + def build_transform(self): + """ + Creates a basic transformation that was used to train the models + """ + cfg = self.cfg + + # we are loading images with OpenCV, so we don't need to convert them + # to BGR, they are already! So all we need to do is to normalize + # by 255 if we want to convert to BGR255 format, or flip the channels + # if we want it to be in RGB in [0-1] range. + if cfg.INPUT.TO_BGR255: + to_bgr_transform = T.Lambda(lambda x: x * 255) + else: + to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]]) + + normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD) + + transform = T.Compose( + [ + T.ToPILImage(), + T.Resize(self.min_image_size) if self.min_image_size is not None else lambda x: x, + T.ToTensor(), + to_bgr_transform, + normalize_transform, + ] + ) + return transform + + def build_tokenizer(self): + cfg = self.cfg + tokenizer = None + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenizer = AutoTokenizer.from_pretrained("roberta-base") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + from transformers import CLIPTokenizerFast + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + tokenizer = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + + return tokenizer + + def run_ner(self, caption, specified_tokens=None): + if specified_tokens is None: + noun_phrases = find_noun_phrases(caption) + noun_phrases = [remove_punctuation(phrase) for phrase in noun_phrases] + noun_phrases = [phrase for phrase in noun_phrases if phrase != ""] + else: + noun_phrases = specified_tokens + relevant_phrases = noun_phrases + labels = noun_phrases + self.entities = labels + + tokens_positive = [] + + for entity, label in zip(relevant_phrases, labels): + try: + # search all occurrences and mark them as different entities + for m in re.finditer(entity, caption.lower()): + tokens_positive.append([[m.start(), m.end()]]) + except: + print("noun entities:", noun_phrases) + print("entity:", entity) + print("caption:", caption.lower()) + return tokens_positive + + def inference(self, original_image, original_caption): + predictions = self.compute_prediction(original_image, original_caption) + top_predictions = self._post_process_fixed_thresh(predictions) + return top_predictions + + def run_on_web_image(self, + original_image, + original_caption, + thresh=0.5, + specified_tokens = None, + **kwargs): + original_caption = original_caption.lower() + specified_tokens = [token.lower() for token in specified_tokens] + + predictions = self.compute_prediction(original_image, original_caption, specified_tokens=specified_tokens) + top_predictions = self._post_process(predictions, thresh) + + result = original_image.copy() + if self.show_mask_heatmaps: + return self.create_mask_montage(result, top_predictions) + + result = self.overlay_boxes(result, + top_predictions, + **kwargs) + result = self.overlay_entity_names(result, top_predictions,**kwargs) + if self.cfg.MODEL.MASK_ON: + result = self.overlay_mask(result, top_predictions) + + return result, top_predictions + + def compute_prediction(self, original_image, original_caption, specified_tokens = None): + # image + image = self.transforms(original_image) + image_list = to_image_list(image, self.cfg.DATALOADER.SIZE_DIVISIBILITY) + image_list = image_list.to(self.device) + # caption + tokenized = self.tokenizer([original_caption], return_tensors="pt") + tokens_positive = self.run_ner(original_caption, specified_tokens=specified_tokens) + # process positive map + positive_map = create_positive_map(tokenized, tokens_positive) + + if self.cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + plus = 1 + else: + plus = 0 + + positive_map_label_to_token = create_positive_map_label_to_token_from_positive_map(positive_map, plus=plus) + self.plus = plus + self.positive_map_label_to_token = positive_map_label_to_token + tic = timeit.time.perf_counter() + + # compute predictions + with torch.no_grad(): + predictions = self.model(image_list, captions=[original_caption], positive_map=positive_map_label_to_token) + predictions = [o.to(self.cpu_device) for o in predictions] + print("inference time per image: {}".format(timeit.time.perf_counter() - tic)) + + # always single image is passed at a time + prediction = predictions[0] + + # reshape prediction (a BoxList) into the original image size + height, width = original_image.shape[:-1] + prediction = prediction.resize((width, height)) + + if prediction.has_field("mask"): + # if we have masks, paste the masks in the right position + # in the image, as defined by the bounding boxes + masks = prediction.get_field("mask") + # always single image is passed at a time + masks = self.masker([masks], [prediction])[0] + prediction.add_field("mask", masks) + + return prediction + + def _post_process_fixed_thresh(self, predictions): + scores = predictions.get_field("scores") + labels = predictions.get_field("labels").tolist() + thresh = scores.clone() + for i, lb in enumerate(labels): + if isinstance(self.confidence_threshold, float): + thresh[i] = self.confidence_threshold + elif len(self.confidence_threshold) == 1: + thresh[i] = self.confidence_threshold[0] + else: + thresh[i] = self.confidence_threshold[lb - 1] + keep = torch.nonzero(scores > thresh).squeeze(1) + predictions = predictions[keep] + + scores = predictions.get_field("scores") + _, idx = scores.sort(0, descending=True) + return predictions[idx] + + def _post_process(self, predictions, threshold=0.5): + scores = predictions.get_field("scores") + labels = predictions.get_field("labels").tolist() + thresh = scores.clone() + for i, lb in enumerate(labels): + if isinstance(self.confidence_threshold, float): + thresh[i] = threshold + elif len(self.confidence_threshold) == 1: + thresh[i] = threshold + else: + thresh[i] = self.confidence_threshold[lb - 1] + keep = torch.nonzero(scores > thresh).squeeze(1) + predictions = predictions[keep] + + scores = predictions.get_field("scores") + _, idx = scores.sort(0, descending=True) + return predictions[idx] + + def compute_colors_for_labels(self, labels): + """ + Simple function that adds fixed colors depending on the class + """ + colors = (30 * (labels[:, None] - 1) + 1) * self.palette + colors = (colors % 255).numpy().astype("uint8") + return colors + + def overlay_boxes(self, + image, + predictions, + box_alpha=0.5, + override_color = None, + box_pixel = 2, + **kwargs): + labels = predictions.get_field("labels") + boxes = predictions.bbox + + colors = self.compute_colors_for_labels(labels).tolist() + new_image = image.copy() + for box, color in zip(boxes, colors): + box = box.to(torch.int64) + top_left, bottom_right = box[:2].tolist(), box[2:].tolist() + new_image = cv2.rectangle(new_image, tuple(top_left), tuple(bottom_right), tuple(color) if override_color is None else tuple(override_color), box_pixel) + + image = cv2.addWeighted(new_image, box_alpha, image, 1 - box_alpha, 0) + return image + + def overlay_scores(self, image, predictions): + scores = predictions.get_field("scores") + boxes = predictions.bbox + labels = predictions.get_field("labels") + colors = self.compute_colors_for_labels(labels).tolist() + + for box, score, color in zip(boxes, scores, colors): + box = box.to(torch.int64) + image = cv2.putText( + image, + "%.3f" % score, + (int(box[0]), int((box[1] + box[3]) / 2)), + cv2.FONT_HERSHEY_SIMPLEX, + 0.3, + tuple(color), + 1, + ) + + return image + + def overlay_entity_names(self, + image, + predictions, + text_size=1.0, + text_pixel=2, + text_offset = 10, + text_offset_original = 4, + override_color = None, + skip_name = False, + **kwargs): + + scores = predictions.get_field("scores").tolist() + labels = predictions.get_field("labels") + colors = self.compute_colors_for_labels(labels).tolist() + + new_labels = [] + if self.entities and self.plus: + for i in labels: + if i <= len(self.entities): + new_labels.append(self.entities[i - self.plus]) + else: + new_labels.append("object") + # labels = [self.entities[i - self.plus] for i in labels ] + else: + new_labels = ["object" for i in labels] + boxes = predictions.bbox + + previous_locations = [] + + for box, score, label, color in zip(boxes, scores, new_labels, colors): + x, y = box[:2] + if skip_name: + s = "{:.2f}".format(score) + else: + s = "{}: {:.2f}".format(label, score) + print(s) + for x_prev, y_prev in previous_locations: + if abs(x - x_prev) < abs(text_offset) and abs(y - y_prev) < abs(text_offset): + y -= text_offset + + if int(y) - text_offset_original < 20: + y += 50 + cv2.putText( + image, s, (int(x), int(y)-text_offset_original), + cv2.FONT_HERSHEY_SIMPLEX, text_size, + tuple(color) if override_color is None else tuple(override_color), + text_pixel, cv2.LINE_AA + ) + previous_locations.append((int(x), int(y))) + + return image + + def overlay_mask(self, image, predictions): + masks = predictions.get_field("mask").numpy() + labels = predictions.get_field("labels") + + colors = self.compute_colors_for_labels(labels).tolist() + + # import pdb + # pdb.set_trace() + # masks = masks > 0.1 + + for mask, color in zip(masks, colors): + thresh = mask[0, :, :, None].astype(np.uint8) + contours, hierarchy = cv2_util.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + image = cv2.drawContours(image, contours, -1, color, 2) + + composite = image + + return composite + + def create_mask_montage(self, image, predictions): + masks = predictions.get_field("mask") + masks_per_dim = self.masks_per_dim + masks = L.interpolate(masks.float(), scale_factor=1 / masks_per_dim).byte() + height, width = masks.shape[-2:] + max_masks = masks_per_dim**2 + masks = masks[:max_masks] + # handle case where we have less detections than max_masks + if len(masks) < max_masks: + masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8) + masks_padded[: len(masks)] = masks + masks = masks_padded + masks = masks.reshape(masks_per_dim, masks_per_dim, height, width) + result = torch.zeros((masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8) + for y in range(masks_per_dim): + start_y = y * height + end_y = (y + 1) * height + for x in range(masks_per_dim): + start_x = x * width + end_x = (x + 1) * width + result[start_y:end_y, start_x:end_x] = masks[y, x] + + return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET), None + + +def create_positive_map_label_to_token_from_positive_map(positive_map, plus=0): + positive_map_label_to_token = {} + for i in range(len(positive_map)): + positive_map_label_to_token[i + plus] = torch.nonzero(positive_map[i], as_tuple=True)[0].tolist() + return positive_map_label_to_token + + +def create_positive_map(tokenized, tokens_positive): + """construct a map such that positive_map[i,j] = True iff box i is associated to token j""" + positive_map = torch.zeros((len(tokens_positive), 256), dtype=torch.float) + + for j, tok_list in enumerate(tokens_positive): + for (beg, end) in tok_list: + try: + beg_pos = tokenized.char_to_token(beg) + end_pos = tokenized.char_to_token(end - 1) + except Exception as e: + print("beg:", beg, "end:", end) + print("token_positive:", tokens_positive) + # print("beg_pos:", beg_pos, "end_pos:", end_pos) + raise e + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + positive_map[j, beg_pos : end_pos + 1].fill_(1) + return positive_map / (positive_map.sum(-1)[:, None] + 1e-6) + + +def find_noun_phrases(caption: str) -> List[str]: + caption = caption.lower() + tokens = nltk.word_tokenize(caption) + pos_tags = nltk.pos_tag(tokens) + + grammar = "NP: {
?*+}" + cp = nltk.RegexpParser(grammar) + result = cp.parse(pos_tags) + + noun_phrases = list() + for subtree in result.subtrees(): + if subtree.label() == "NP": + noun_phrases.append(" ".join(t[0] for t in subtree.leaves())) + + return noun_phrases + + +def remove_punctuation(text: str) -> str: + punct = [ + "|", + ":", + ";", + "@", + "(", + ")", + "[", + "]", + "{", + "}", + "^", + "'", + '"', + "’", + "`", + "?", + "$", + "%", + "#", + "!", + "&", + "*", + "+", + ",", + ".", + ] + for p in punct: + text = text.replace(p, "") + return text.strip() diff --git a/maskrcnn_benchmark/engine/singlepath_trainer.py b/maskrcnn_benchmark/engine/singlepath_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6734acb3d7c6dfd2d27069874d8805efb25c9381 --- /dev/null +++ b/maskrcnn_benchmark/engine/singlepath_trainer.py @@ -0,0 +1,130 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import datetime +import logging +import time +import random +import torch +import torch.distributed as dist +from maskrcnn_benchmark.utils.comm import get_world_size, synchronize, broadcast_data +from maskrcnn_benchmark.utils.metric_logger import MetricLogger +from maskrcnn_benchmark.utils.ema import ModelEma + + +def reduce_loss_dict(loss_dict): + """ + Reduce the loss dictionary from all processes so that process with rank + 0 has the averaged results. Returns a dict with the same fields as + loss_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return loss_dict + with torch.no_grad(): + loss_names = [] + all_losses = [] + for k in sorted(loss_dict.keys()): + loss_names.append(k) + all_losses.append(loss_dict[k]) + all_losses = torch.stack(all_losses, dim=0) + dist.reduce(all_losses, dst=0) + if dist.get_rank() == 0: + # only main process gets accumulated, so only divide by + # world_size in this case + all_losses /= world_size + reduced_losses = {k: v for k, v in zip(loss_names, all_losses)} + return reduced_losses + + +def do_train( + cfg, model, data_loader, optimizer, scheduler, checkpointer, device, checkpoint_period, arguments, rngs=None +): + logger = logging.getLogger("maskrcnn_benchmark.trainer") + logger.info("Start training") + meters = MetricLogger(delimiter=" ") + max_iter = len(data_loader) + start_iter = arguments["iteration"] + model.train() + model_ema = None + if cfg.SOLVER.MODEL_EMA > 0: + model_ema = ModelEma(model, decay=cfg.SOLVER.MODEL_EMA) + start_training_time = time.time() + end = time.time() + + for iteration, (images, targets, _) in enumerate(data_loader, start_iter): + + if any(len(target) < 1 for target in targets): + logger.error( + "Iteration={iteration + 1} || Image Ids used for training {_} || targets Length={[len(target) for target in targets]}" + ) + continue + data_time = time.time() - end + iteration = iteration + 1 + arguments["iteration"] = iteration + + images = images.to(device) + targets = [target.to(device) for target in targets] + + # synchronize rngs + if rngs is None: + if isinstance(model, torch.nn.parallel.DistributedDataParallel): + mix_nums = model.module.mix_nums + else: + mix_nums = model.mix_nums + rngs = [random.randint(0, mix - 1) for mix in mix_nums] + rngs = broadcast_data(rngs) + + for param in model.parameters(): + param.requires_grad = False + loss_dict = model(images, targets, rngs) + + losses = sum(loss for loss in loss_dict.values()) + + # reduce losses over all GPUs for logging purposes + loss_dict_reduced = reduce_loss_dict(loss_dict) + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + meters.update(loss=losses_reduced, **loss_dict_reduced) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + scheduler.step() + + if model_ema is not None: + model_ema.update(model) + arguments["model_ema"] = model_ema.state_dict() + + batch_time = time.time() - end + end = time.time() + meters.update(time=batch_time, data=data_time) + + eta_seconds = meters.time.global_avg * (max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + + if iteration % 20 == 0 or iteration == max_iter: + logger.info( + meters.delimiter.join( + [ + "eta: {eta}", + "iter: {iter}", + "{meters}", + "lr: {lr:.6f}", + "max mem: {memory:.0f}", + ] + ).format( + eta=eta_string, + iter=iteration, + meters=str(meters), + lr=optimizer.param_groups[0]["lr"], + memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0, + ) + ) + if iteration % checkpoint_period == 0: + checkpointer.save("model_{:07d}".format(iteration), **arguments) + if iteration == max_iter: + if model_ema is not None: + model.load_state_dict(model_ema.state_dict()) + checkpointer.save("model_final", **arguments) + + total_training_time = time.time() - start_training_time + total_time_str = str(datetime.timedelta(seconds=total_training_time)) + logger.info("Total training time: {} ({:.4f} s / it)".format(total_time_str, total_training_time / (max_iter))) diff --git a/maskrcnn_benchmark/engine/stage_trainer.py b/maskrcnn_benchmark/engine/stage_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..c048092a9d7124c60b26dc99713d6229c61f892a --- /dev/null +++ b/maskrcnn_benchmark/engine/stage_trainer.py @@ -0,0 +1,180 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import datetime +import logging +import time + +import torch +import torch.distributed as dist + +from maskrcnn_benchmark.utils.comm import get_world_size +from maskrcnn_benchmark.utils.metric_logger import MetricLogger + + +def reduce_loss_dict(all_loss_dict): + """ + Reduce the loss dictionary from all processes so that process with rank + 0 has the averaged results. Returns a dict with the same fields as + loss_dict, after reduction. + """ + world_size = get_world_size() + with torch.no_grad(): + loss_names = [] + all_losses = [] + for loss_dict in all_loss_dict: + for k in sorted(loss_dict.keys()): + loss_names.append(k) + all_losses.append(loss_dict[k]) + all_losses = torch.stack(all_losses, dim=0) + if world_size > 1: + dist.reduce(all_losses, dst=0) + if dist.get_rank() == 0: + # only main process gets accumulated, so only divide by + # world_size in this case + all_losses /= world_size + + reduced_losses = {} + for k, v in zip(loss_names, all_losses): + if k not in reduced_losses: + reduced_losses[k] = v / len(all_loss_dict) + reduced_losses[k] += v / len(all_loss_dict) + + return reduced_losses + + +def do_train( + model, + data_loader, + optimizer, + scheduler, + checkpointer, + device, + checkpoint_period, + arguments, +): + logger = logging.getLogger("maskrcnn_benchmark.trainer") + logger.info("Start training") + meters = MetricLogger(delimiter=" ") + epoch_per_stage = arguments["epoch_per_stage"] + max_iter = sum(len(stage_loader) * epoch_per_stage[si] for si, stage_loader in enumerate(data_loader)) + max_iter += epoch_per_stage[-1] * min(len(stage_loader) for stage_loader in data_loader) + model.train() + start_training_time = time.time() + end = time.time() + + for stage_i, stage_loader in enumerate(data_loader): + for ep in range(epoch_per_stage[stage_i]): + start_iter = arguments["iteration"] + for iteration, (images, targets, _) in enumerate(stage_loader, start_iter): + data_time = time.time() - end + iteration = iteration + 1 + arguments["iteration"] = iteration + + scheduler[stage_i].step() + + all_stage_loss_dict = [] + images = images.to(device) + targets = [target.to(device) for target in targets] + loss_dict = model(images, targets, stage_i) + all_stage_loss_dict.append(loss_dict) + + losses = sum(loss for loss_dict in all_stage_loss_dict for loss in loss_dict.values()) + + # reduce losses over all GPUs for logging purposes + loss_dict_reduced = reduce_loss_dict(all_stage_loss_dict) + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + meters.update(loss=losses_reduced, **loss_dict_reduced) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + + batch_time = time.time() - end + end = time.time() + meters.update(time=batch_time, data=data_time) + + eta_seconds = meters.time.global_avg * (max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + + if iteration % 20 == 0 or iteration == max_iter: + logger.info( + meters.delimiter.join( + [ + "eta: {eta}", + "iter: {iter}", + "{meters}", + "lr: {lr:.6f}", + "max mem: {memory:.0f}", + ] + ).format( + eta=eta_string, + iter=iteration, + meters=str(meters), + lr=optimizer.param_groups[0]["lr"], + memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0, + ) + ) + if iteration % checkpoint_period == 0: + checkpointer.save("model_{:07d}".format(iteration), **arguments) + if iteration == max_iter: + checkpointer.save("model_final", **arguments) + + for ep in range(epoch_per_stage[-1]): + start_iter = arguments["iteration"] + for iteration, stage_loader in enumerate(zip(*data_loader), start_iter): + data_time = time.time() - end + iteration = iteration + 1 + arguments["iteration"] = iteration + + scheduler[-1].step() + + all_task_loss_dict = [] + for stage_i, (images, targets, _) in enumerate(stage_loader): + images = images.to(device) + targets = [target.to(device) for target in targets] + loss_dict = model(images, targets, stage_i) + all_task_loss_dict.append(loss_dict) + + losses = sum(loss for loss_dict in all_task_loss_dict for loss in loss_dict.values()) + + # reduce losses over all GPUs for logging purposes + loss_dict_reduced = reduce_loss_dict(all_task_loss_dict) + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + meters.update(loss=losses_reduced, **loss_dict_reduced) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + + batch_time = time.time() - end + end = time.time() + meters.update(time=batch_time, data=data_time) + + eta_seconds = meters.time.global_avg * (max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + + if iteration % 20 == 0 or iteration == max_iter: + logger.info( + meters.delimiter.join( + [ + "eta: {eta}", + "iter: {iter}", + "{meters}", + "lr: {lr:.6f}", + "max mem: {memory:.0f}", + ] + ).format( + eta=eta_string, + iter=iteration, + meters=str(meters), + lr=optimizer.param_groups[0]["lr"], + memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0, + ) + ) + if iteration % checkpoint_period == 0: + checkpointer.save("model_{:07d}".format(iteration), **arguments) + if iteration == max_iter: + checkpointer.save("model_final", **arguments) + + total_training_time = time.time() - start_training_time + total_time_str = str(datetime.timedelta(seconds=total_training_time)) + logger.info("Total training time: {} ({:.4f} s / it)".format(total_time_str, total_training_time / (max_iter))) diff --git a/maskrcnn_benchmark/engine/trainer.py b/maskrcnn_benchmark/engine/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..f3e37e10e2a224996b62038a8321157156f86f17 --- /dev/null +++ b/maskrcnn_benchmark/engine/trainer.py @@ -0,0 +1,375 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import datetime +import logging +import sys +import os +import math +import time + +import torch +import torch.distributed as dist + +from maskrcnn_benchmark.utils.comm import get_world_size, all_gather, is_main_process, broadcast_data, get_rank +from maskrcnn_benchmark.utils.metric_logger import MetricLogger +from maskrcnn_benchmark.utils.ema import ModelEma +from maskrcnn_benchmark.utils.amp import autocast, GradScaler +from maskrcnn_benchmark.data.datasets.evaluation import evaluate +from .inference import inference +from .tsv_saver import TSVResultWriter +import wandb +import pdb, random + + +def reduce_loss_dict(loss_dict): + """ + Reduce the loss dictionary from all processes so that process with rank + 0 has the averaged results. Returns a dict with the same fields as + loss_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return loss_dict + with torch.no_grad(): + loss_names = [] + all_losses = [] + for k in sorted(loss_dict.keys()): + loss_names.append(k) + all_losses.append(loss_dict[k]) + all_losses = torch.stack(all_losses, dim=0) + dist.reduce(all_losses, dst=0) + if dist.get_rank() == 0: + # only main process gets accumulated, so only divide by + # world_size in this case + all_losses /= world_size + reduced_losses = {k: v for k, v in zip(loss_names, all_losses)} + return reduced_losses + + +def do_train( + cfg, + model, + data_loader, + optimizer, + scheduler, + checkpointer, + device, + checkpoint_period, + arguments, + val_data_loader=None, + meters=None, + use_wandb=False +): + logger = logging.getLogger("maskrcnn_benchmark.trainer") + logger.info("Start training") + # meters = MetricLogger(delimiter=" ") + max_iter = len(data_loader) + start_iter = arguments["iteration"] + model.train() + model_ema = None + if cfg.SOLVER.MODEL_EMA > 0: + model_ema = ModelEma(model, decay=cfg.SOLVER.MODEL_EMA) + start_training_time = time.time() + end = time.time() + + if cfg.SOLVER.USE_AMP: + scaler = GradScaler() + + global_rank = get_rank() + + if cfg.SOLVER.CHECKPOINT_PER_EPOCH != -1 and cfg.SOLVER.MAX_EPOCH >= 1: + checkpoint_period = len(data_loader) * cfg.SOLVER.CHECKPOINT_PER_EPOCH // cfg.SOLVER.MAX_EPOCH + + if global_rank <= 0 and cfg.SOLVER.MAX_EPOCH >= 1: + print("Iter per epoch ", len(data_loader) // cfg.SOLVER.MAX_EPOCH) + + if cfg.SOLVER.AUTO_TERMINATE_PATIENCE != -1: + patience_counter = 0 + previous_best = 0.0 + + # Adapt the weight decay + if cfg.SOLVER.WEIGHT_DECAY_SCHEDULE and hasattr(scheduler, "milestones"): + milestone_target = 0 + for i, milstone in enumerate(list(scheduler.milestones)): + if scheduler.last_epoch >= milstone * cfg.SOLVER.WEIGHT_DECAY_SCHEDULE_RATIO: + milestone_target = i + 1 + # try to visualize the training data + ### get the tokenizer + if hasattr(model, "module"): + tokenizer = model.module.tokenizer + else: + tokenizer = model.tokenizer + + tsv_visualizer = TSVResultWriter( + tokenizer=tokenizer, + max_visualize_num=1000, + file_name=cfg.OUTPUT_DIR + "/train_visualize/train.tsv", write_freq=100) + for iteration, (images, targets, idxs, positive_map, positive_map_eval, greenlight_map) in enumerate( + data_loader, start_iter + ): + + nnegative = sum(len(target) < 1 for target in targets) + nsample = len(targets) + if nnegative > nsample * cfg.SOLVER.MAX_NEG_PER_BATCH: + logger.info( + "[WARNING] Sampled {} negative in {} in a batch, greater the allowed ratio {}, skip".format( + nnegative, nsample, cfg.SOLVER.MAX_NEG_PER_BATCH + ) + ) + continue + + data_time = time.time() - end + iteration = iteration + 1 + arguments["iteration"] = iteration + + images = images.to(device) + captions = None + try: + targets = [target.to(device) for target in targets] + captions = [t.get_field("caption") for t in targets if "caption" in t.fields()] + except: + pass + # Freeze language backbone + if cfg.MODEL.LANGUAGE_BACKBONE.FREEZE: + if hasattr(model, "module"): + if hasattr(model.module, "fusion_backbone"): + model.module.fusion_backbone.language_backbone.eval() + else: + model.module.language_backbone.eval() + else: + if hasattr(model, "fusion_backbone"): + model.fusion_backbone.language_backbone.eval() + else: + model.language_backbone.eval() + if is_main_process(): # only visualize for the main process + tsv_visualizer.update_train_data(images, targets) + if cfg.SOLVER.USE_AMP: + with autocast(): + if len(captions) > 0: + loss_dict = model(images, targets, captions, positive_map, greenlight_map=greenlight_map) + else: + loss_dict = model(images, targets) + losses = sum(loss for loss in loss_dict.values()) + + # save checkpoints for further debug if nan happens + loss_value = losses.item() + if torch.isnan(losses) or torch.isinf(losses): + logging.error("NaN encountered, ignoring") + losses[losses != losses] = 0 + # if loss is too large, ignore it + if loss_value > 10 and iteration > 10000: + losses[losses == losses] = 0 # this is a bad example + print("Loss is too large, ignore it, loss: ", loss_value) + + optimizer.zero_grad() + scaler.scale(losses).backward() + scaler.step(optimizer) + scaler.update() + scheduler.step() + else: + if len(captions) > 0: + loss_dict = model(images, targets, captions, positive_map) + else: + loss_dict = model(images, targets) + losses = sum(loss for loss in loss_dict.values()) + + # save checkpoints for further debug if nan happens + loss_value = losses.item() + if not math.isfinite(loss_value): + logging.error(f"=> loss is {loss_value}, stopping training") + time_str = time.strftime("%Y-%m-%d-%H-%M") + fname = os.path.join(checkpointer.save_dir, f"{time_str}_states.pth") + logging.info(f"=> save error state to {fname}") + dict_to_save = { + "x": images, + "y": targets, + "loss": losses, + "states": model.module.state_dict() if hasattr(model, "module") else model.state_dict(), + } + if len(captions) > 0: + dict_to_save["captions"] = captions + dict_to_save["positive_map"] = positive_map + torch.save(dict_to_save, fname) + sys.exit(-1) + + if torch.isnan(losses) or torch.isinf(losses): + losses[losses != losses] = 0 + # if loss is too large, ignore it + # if loss_value > 10 and iteration > 10000: + # losses[losses == losses] = 0 # this is a bad example + # print("Loss is too large, ignore it, loss: ", loss_value) + optimizer.zero_grad() + losses.backward() + optimizer.step() + scheduler.step() + + + # Adapt the weight decay: only support multiStepLR + if cfg.SOLVER.WEIGHT_DECAY_SCHEDULE and hasattr(scheduler, "milestones"): + if milestone_target < len(scheduler.milestones): + next_milestone = list(scheduler.milestones)[milestone_target] + else: + next_milestone = float("inf") + if scheduler.last_epoch >= next_milestone * cfg.SOLVER.WEIGHT_DECAY_SCHEDULE_RATIO: + gamma = scheduler.gamma + logger.info("Drop the weight decay by {}!".format(gamma)) + for param in optimizer.param_groups: + if "weight_decay" in param: + param["weight_decay"] *= gamma + # move the target forward + milestone_target += 1 + + # reduce losses over all GPUs for logging purposes + loss_dict_reduced = reduce_loss_dict(loss_dict) + losses_reduced = sum(loss for loss in loss_dict_reduced.values()) + meters.update(loss=losses_reduced, **loss_dict_reduced) + if model_ema is not None: + model_ema.update(model) + arguments["model_ema"] = model_ema.state_dict() + + batch_time = time.time() - end + end = time.time() + meters.update(time=batch_time, data=data_time) + eta_seconds = meters.time.global_avg * (max_iter - iteration) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + + if iteration % 20 == 0 or iteration == max_iter: + # if iteration % 1 == 0 or iteration == max_iter: + # logger.info( + if global_rank <= 0: + print( + meters.delimiter.join( + [ + "eta: {eta}", + "iter: {iter}", + "{meters}", + "lr: {lr:.6f}", + "wd: {wd:.6f}", + "max mem: {memory:.0f}", + ] + ).format( + eta=eta_string, + iter=iteration, + meters=str(meters), + lr=optimizer.param_groups[0]["lr"], + wd=optimizer.param_groups[0]["weight_decay"], + memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0, + ) + ) + if use_wandb and is_main_process(): + wandb.log({"train_loss": losses_reduced, "lr": optimizer.param_groups[0]["lr"], "wd": optimizer.param_groups[0]["weight_decay"], **loss_dict_reduced}) + if val_data_loader and (iteration % checkpoint_period == 0 or iteration == max_iter): + if is_main_process(): + print("Evaluating") + eval_result = 0.0 + model.eval() + if cfg.SOLVER.TEST_WITH_INFERENCE: + with torch.no_grad(): + try: + _model = model.module + except: + _model = model + _result = inference( + model=_model, + data_loader=val_data_loader, + dataset_name="val", + device=device, + expected_results=cfg.TEST.EXPECTED_RESULTS, + expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL, + output_folder=None, + cfg=cfg, + verbose=False, + ) + if is_main_process(): + try: + eval_result = _result[0].results["bbox"]["AP"] + except: + pass + else: + results_dict = {} + cpu_device = torch.device("cpu") + for i, batch in enumerate(val_data_loader): + images, targets, image_ids, positive_map, *_ = batch + with torch.no_grad(): + images = images.to(device) + if positive_map is None: + output = model(images) + else: + captions = [t.get_field("caption") for t in targets if "caption" in t.fields()] + output = model(images, captions, positive_map) + output = [o.to(cpu_device) for o in output] + results_dict.update({img_id: result for img_id, result in zip(image_ids, output)}) + all_predictions = all_gather(results_dict) + if is_main_process(): + predictions = {} + for p in all_predictions: + predictions.update(p) + predictions = [predictions[i] for i in list(sorted(predictions.keys()))] + eval_result, _ = evaluate( + val_data_loader.dataset, predictions, output_folder=None, box_only=cfg.DATASETS.CLASS_AGNOSTIC + ) + if cfg.DATASETS.CLASS_AGNOSTIC: + eval_result = eval_result.results["box_proposal"]["AR@100"] + else: + eval_result = eval_result.results["bbox"]["AP"] + model.train() + + if model_ema is not None and cfg.SOLVER.USE_EMA_FOR_MONITOR: + model_ema.ema.eval() + results_dict = {} + cpu_device = torch.device("cpu") + for i, batch in enumerate(val_data_loader): + images, targets, image_ids, positive_map, positive_map_eval = batch + with torch.no_grad(): + images = images.to(device) + if positive_map is None: + output = model_ema.ema(images) + else: + captions = [t.get_field("caption") for t in targets if "caption" in t.fields()] + output = model_ema.ema(images, captions, positive_map) + output = [o.to(cpu_device) for o in output] + results_dict.update({img_id: result for img_id, result in zip(image_ids, output)}) + all_predictions = all_gather(results_dict) + if is_main_process(): + predictions = {} + for p in all_predictions: + predictions.update(p) + predictions = [predictions[i] for i in list(sorted(predictions.keys()))] + eval_result, _ = evaluate( + val_data_loader.dataset, predictions, output_folder=None, box_only=cfg.DATASETS.CLASS_AGNOSTIC + ) + if cfg.DATASETS.CLASS_AGNOSTIC: + eval_result = eval_result.results["box_proposal"]["AR@100"] + else: + eval_result = eval_result.results["bbox"]["AP"] + + if eval_result is not None: + arguments.update(eval_result=eval_result) + + if cfg.SOLVER.USE_AUTOSTEP: + assert eval_result is not None + eval_result = all_gather(eval_result)[0] # broadcast_data([eval_result])[0] + # print("Rank {} eval result gathered".format(cfg.local_rank), eval_result) + scheduler.step(eval_result) + + if cfg.SOLVER.AUTO_TERMINATE_PATIENCE != -1: + if eval_result < previous_best: + patience_counter += 1 + else: + patience_counter = 0 + previous_best = eval_result + checkpointer.save("model_best", **arguments) + print("Previous Best", previous_best, "Patience Counter", patience_counter, "Eval Result", eval_result) + if patience_counter >= cfg.SOLVER.AUTO_TERMINATE_PATIENCE: + if is_main_process(): + print("\n\n\n\nAuto Termination at {}, current best {}\n\n\n".format(iteration, previous_best)) + break + + if iteration % checkpoint_period == 0 and checkpoint_period > 100: + checkpointer.save("model_{:07d}".format(iteration), **arguments) + if iteration == max_iter: + checkpointer.save("model_final", **arguments) + break + + total_training_time = time.time() - start_training_time + total_time_str = str(datetime.timedelta(seconds=total_training_time)) + logger.info("Total training time: {} ({:.4f} s / it)".format(total_time_str, total_training_time / (max_iter))) diff --git a/maskrcnn_benchmark/engine/tsv_saver.py b/maskrcnn_benchmark/engine/tsv_saver.py new file mode 100644 index 0000000000000000000000000000000000000000..490e28496cef0bf70bec2bc11b110fd90233476d --- /dev/null +++ b/maskrcnn_benchmark/engine/tsv_saver.py @@ -0,0 +1,168 @@ +import os +import torch +from tqdm import tqdm +from collections import defaultdict +import collections +import numpy as np +import cv2, json, base64 +import pdb +from copy import deepcopy +from pprint import pprint +import os.path as op + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.data.datasets.tsv import load_from_yaml_file +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.data.datasets.od_to_grounding import clean_name + +def ensure_file(file_name): + # if the directory does not exist, create it + if not os.path.exists(os.path.dirname(file_name)): + os.makedirs(os.path.dirname(file_name)) + ensure_file(os.path.dirname(file_name)) + +class TSVResultWriter(object): + def __init__(self, tokenizer = None, max_visualize_num=-1, dataset_length=-1, threshold = -1.0, in_order = True, write_freq = 100, file_name = None): + self.tokenizer = tokenizer + self.max_visualize_num = max_visualize_num + self.dataset_length = dataset_length + self.threshold = threshold + self.in_order = in_order + self.file_name = file_name + self.write_freq = write_freq + self.predictions = [] + if not self.in_order: + assert(0) + + @staticmethod + def imagelist_to_b64(imgs): + imgs = imgs.tensors.permute(0, 2, 3, 1).cpu().numpy() + # the last dimension is BGR, convert to RGB + imgs = ((imgs * [0.225, 0.224, 0.229] + [0.406, 0.456, 0.485]) * 255).astype(np.uint8) + # imgs = [cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for img in imgs] + imgs = [base64.b64encode(cv2.imencode('.jpg', image)[1]) for image in imgs] + return imgs + + def update(self, imgs, results): + if self.max_visualize_num > 0 and len(self.predictions) >= self.max_visualize_num: + return + + imgs = self.imagelist_to_b64(imgs) + + for img_encoded_str, result in zip(imgs, results): + # result: (img_id, {"scores": scores, "labels": labels, "boxes": boxes}) + annotations = result[1] + # img_encoded_str = image #base64.b64encode(cv2.imencode('.jpg', image)[1]) + # convert boxes + boxes = annotations["raw_boxes"] #box_cxcywh_to_xyxy(annotations["boxes"]) + pred = {} + pred["objects"] = [] + + # pred["caption"] = "" + for s, rect, l in zip(annotations["scores"], boxes.tolist(), annotations["labels_text"]): + pred["num_boxes"] = len(rect) + pred["objects"].append({"rect": rect, + "class": l, + "conf": float(s) + #"caption": captions[0] + }) + if "caption" in annotations: + pred['objects'][0]["caption"] = annotations["caption"] # record the caption in the first object; a workaround for the tsvviewer + + pred["predicates"] = [] + pred["relations"] = [] + pred = [str(result[0]), json.dumps(pred, sort_keys=False), img_encoded_str] + self.predictions.append(pred) + + if len(self.predictions) % self.write_freq == 0 or len(self.predictions) >= self.max_visualize_num: + self.tsv_writer(self.predictions, self.file_name) + + + def update_train_data(self, imgs, targets): + if self.max_visualize_num > 0 and len(self.predictions) >= self.max_visualize_num: + return + + imgs = self.imagelist_to_b64(imgs) + for img_encoded_str, target in zip(imgs, targets): + boxes = target.bbox + pred = {} + pred["objects"] = [] + pred["caption"] = [target.extra_fields["caption"]] + caption_tokenized = self.tokenizer.tokenize(target.extra_fields["caption"]) + for rect, positive_map in zip(boxes.tolist(), target.extra_fields["positive_map"]): + pred["num_boxes"] = len(rect) + non_zero_indexes = positive_map.nonzero().squeeze(1).tolist() + label = [caption_tokenized[i-1] for i in non_zero_indexes] + label = " ".join(label).replace(" ##", "") + pred["objects"].append({"rect": rect, + "class": label, + "conf": 1.0, + #"caption": target.extra_fields["caption"] + }) + try: + pred['objects'][0]["caption"] = target.extra_fields["caption"] # record the caption in the first object; a workaround for the tsvviewer + except: + pass + pred["predicates"] = [] + pred["relations"] = [] + pred = [str(0), json.dumps(pred, sort_keys=False), img_encoded_str] + self.predictions.append(pred) + if len(self.predictions) % self.write_freq == 0 or len(self.predictions) >= self.max_visualize_num: + ensure_file(self.file_name) + self.tsv_writer(self.predictions, self.file_name) + + def update_gold_od_data(self, imgs, targets, categories): + if self.max_visualize_num > 0 and len(self.predictions) >= self.max_visualize_num: + return + + imgs = self.imagelist_to_b64(imgs) + for img_encoded_str, target in zip(imgs, targets): + boxes = target["boxes"] + pred = {} + pred["objects"] = [] + + for rect, label in zip(boxes.tolist(), target["labels"].tolist()): + pred["num_boxes"] = len(rect) + cat = categories[label] + label_text = "{}_{}".format(cat["name"], cat["frequency"]) + pred["objects"].append({"rect": rect, + "class": label_text, + "conf": 1.0, + #"caption": target.extra_fields["caption"] + }) + pred["predicates"] = [] + pred["relations"] = [] + pred = [str(0), json.dumps(pred, sort_keys=False), img_encoded_str] + self.predictions.append(pred) + + if len(self.predictions) % self.write_freq == 0 or len(self.predictions) >= self.max_visualize_num: + ensure_file(self.file_name) + print("Writing to {}".format(self.file_name)) + self.tsv_writer(self.predictions, self.file_name) + + @staticmethod + def tsv_writer(values, tsv_file, sep='\t'): + try: + os.makedirs(op.dirname(tsv_file)) + except: + pass + lineidx_file = op.splitext(tsv_file)[0] + '.lineidx' + idx = 0 + tsv_file_tmp = tsv_file + '.tmp' + lineidx_file_tmp = lineidx_file + '.tmp' + with open(tsv_file_tmp, 'w') as fp, open(lineidx_file_tmp, 'w') as fpidx: + assert values is not None + for value in values: + assert value is not None + # this step makes sure python2 and python3 encoded img string are the same. + # for python2 encoded image string, it is a str class starts with "/". + # for python3 encoded image string, it is a bytes class starts with "b'/". + # v.decode('utf-8') converts bytes to str so the content is the same. + # v.decode('utf-8') should only be applied to bytes class type. + value = [v if type(v)!=bytes else v.decode('utf-8') for v in value] + v = '{0}\n'.format(sep.join(map(str, value))) + fp.write(v) + fpidx.write(str(idx) + '\n') + idx = idx + len(v) + os.rename(tsv_file_tmp, tsv_file) + os.rename(lineidx_file_tmp, lineidx_file) diff --git a/maskrcnn_benchmark/layers/__init__.py b/maskrcnn_benchmark/layers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..34c373af0af96c95a4cf251ae3b21ea911ed3e4b --- /dev/null +++ b/maskrcnn_benchmark/layers/__init__.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + +from .batch_norm import FrozenBatchNorm2d, NaiveSyncBatchNorm2d +from .misc import Conv2d, _NewEmptyTensorOp +from .misc import ConvTranspose2d +from .misc import DFConv2d +from .misc import interpolate +from .misc import Scale +from .nms import nms +from .nms import ml_nms +from .nms import soft_nms +from .roi_align import ROIAlign +from .roi_align import roi_align +from .roi_align import ROIAlignV2 +from .roi_pool import ROIPool +from .roi_pool import roi_pool +from .smooth_l1_loss import smooth_l1_loss +from .sigmoid_focal_loss import SigmoidFocalLoss, TokenSigmoidFocalLoss +from .iou_loss import IOULoss, IOUWHLoss +from .deform_conv import DeformConv, ModulatedDeformConv +from .dropblock import DropBlock2D, DropBlock3D +from .evonorm import EvoNorm2d +from .dyrelu import DYReLU, swish +from .se import SELayer, SEBlock +from .dyhead import DyHead +from .set_loss import HungarianMatcher, SetCriterion + +__all__ = [ + "nms", + "ml_nms", + "soft_nms", + "roi_align", + "ROIAlign", + "roi_pool", + "ROIPool", + "smooth_l1_loss", + "Conv2d", + "ConvTranspose2d", + "interpolate", + "swish", + "FrozenBatchNorm2d", + "NaiveSyncBatchNorm2d", + "SigmoidFocalLoss", + "TokenSigmoidFocalLoss", + "IOULoss", + "IOUWHLoss", + "Scale", + "DeformConv", + "ModulatedDeformConv", + "DyHead", + "DropBlock2D", + "DropBlock3D", + "EvoNorm2d", + "DYReLU", + "SELayer", + "SEBlock", + "HungarianMatcher", + "SetCriterion", + "ROIAlignV2", + "_NewEmptyTensorOp", +] diff --git a/maskrcnn_benchmark/layers/batch_norm.py b/maskrcnn_benchmark/layers/batch_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..1f62abe583b87cb052942b14203b9316f8b08ccc --- /dev/null +++ b/maskrcnn_benchmark/layers/batch_norm.py @@ -0,0 +1,116 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch import nn + +import torch.distributed as dist +import maskrcnn_benchmark.utils.comm as comm +from torch.autograd.function import Function + + +class FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters + are fixed + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def forward(self, x): + scale = self.weight * self.running_var.rsqrt() + bias = self.bias - self.running_mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + return x * scale + bias + + +class AllReduce(Function): + @staticmethod + def forward(ctx, input): + input_list = [torch.zeros_like(input) for k in range(dist.get_world_size())] + # Use allgather instead of allreduce since I don't trust in-place operations .. + dist.all_gather(input_list, input, async_op=False) + inputs = torch.stack(input_list, dim=0) + return torch.sum(inputs, dim=0) + + @staticmethod + def backward(ctx, grad_output): + dist.all_reduce(grad_output, async_op=False) + return grad_output + + +class NaiveSyncBatchNorm2d(nn.BatchNorm2d): + """ + In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient + when the batch size on each worker is different. + (e.g., when scale augmentation is used, or when it is applied to mask head). + + This is a slower but correct alternative to `nn.SyncBatchNorm`. + + Note: + There isn't a single definition of Sync BatchNorm. + + When ``stats_mode==""``, this module computes overall statistics by using + statistics of each worker with equal weight. The result is true statistics + of all samples (as if they are all on one worker) only when all workers + have the same (N, H, W). This mode does not support inputs with zero batch size. + + When ``stats_mode=="N"``, this module computes overall statistics by weighting + the statistics of each worker by their ``N``. The result is true statistics + of all samples (as if they are all on one worker) only when all workers + have the same (H, W). It is slower than ``stats_mode==""``. + + Even though the result of this module may not be the true statistics of all samples, + it may still be reasonable because it might be preferrable to assign equal weights + to all workers, regardless of their (H, W) dimension, instead of putting larger weight + on larger images. From preliminary experiments, little difference is found between such + a simplified implementation and an accurate computation of overall mean & variance. + """ + + def __init__(self, *args, stats_mode="", **kwargs): + super().__init__(*args, **kwargs) + assert stats_mode in ["", "N"] + self._stats_mode = stats_mode + + def forward(self, input): + if comm.get_world_size() == 1 or not self.training: + return super().forward(input) + + B, C = input.shape[0], input.shape[1] + + mean = torch.mean(input, dim=[0, 2, 3]) + meansqr = torch.mean(input * input, dim=[0, 2, 3]) + + if self._stats_mode == "": + assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.' + vec = torch.cat([mean, meansqr], dim=0) + vec = AllReduce.apply(vec) * (1.0 / dist.get_world_size()) + mean, meansqr = torch.split(vec, C) + momentum = self.momentum + else: + if B == 0: + vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype) + vec = vec + input.sum() # make sure there is gradient w.r.t input + else: + vec = torch.cat([mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0) + vec = AllReduce.apply(vec * B) + + total_batch = vec[-1].detach() + momentum = total_batch.clamp(max=1) * self.momentum # no update if total_batch is 0 + total_batch = torch.max(total_batch, torch.ones_like(total_batch)) # avoid div-by-zero + mean, meansqr, _ = torch.split(vec / total_batch, C) + + var = meansqr - mean * mean + invstd = torch.rsqrt(var + self.eps) + scale = self.weight * invstd + bias = self.bias - mean * scale + scale = scale.reshape(1, -1, 1, 1) + bias = bias.reshape(1, -1, 1, 1) + + self.running_mean += momentum * (mean.detach() - self.running_mean) + self.running_var += momentum * (var.detach() - self.running_var) + return input * scale + bias diff --git a/maskrcnn_benchmark/layers/deform_conv.py b/maskrcnn_benchmark/layers/deform_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..e1937df2b54cc4e35c2e0c40ce73d7613d737aa0 --- /dev/null +++ b/maskrcnn_benchmark/layers/deform_conv.py @@ -0,0 +1,421 @@ +import torch +import math +from torch import nn +from torch.nn import init +from torch.nn.modules.utils import _pair +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd + +from maskrcnn_benchmark import _C + + +class DeformConvFunction(Function): + @staticmethod + def forward( + ctx, input, offset, weight, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1, im2col_step=64 + ): + if input is not None and input.dim() != 4: + raise ValueError("Expected 4D tensor as input, got {}D tensor instead.".format(input.dim())) + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.im2col_step = im2col_step + + ctx.save_for_backward(input, offset, weight) + + output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride)) + + ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones + + if not input.is_cuda: + raise NotImplementedError + else: + cur_im2col_step = min(ctx.im2col_step, input.shape[0]) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + _C.deform_conv_forward( + input, + weight, + offset, + output, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, weight = ctx.saved_tensors + + grad_input = grad_offset = grad_weight = None + + if not grad_output.is_cuda: + raise NotImplementedError + else: + cur_im2col_step = min(ctx.im2col_step, input.shape[0]) + assert (input.shape[0] % cur_im2col_step) == 0, "im2col step must divide batchsize" + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + _C.deform_conv_backward_input( + input, + offset, + grad_output, + grad_input, + grad_offset, + weight, + ctx.bufs_[0], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + cur_im2col_step, + ) + + if ctx.needs_input_grad[2]: + grad_weight = torch.zeros_like(weight) + _C.deform_conv_backward_parameters( + input, + offset, + grad_output, + grad_weight, + ctx.bufs_[0], + ctx.bufs_[1], + weight.size(3), + weight.size(2), + ctx.stride[1], + ctx.stride[0], + ctx.padding[1], + ctx.padding[0], + ctx.dilation[1], + ctx.dilation[0], + ctx.groups, + ctx.deformable_groups, + 1, + cur_im2col_step, + ) + + return (grad_input, grad_offset, grad_weight, None, None, None, None, None) + + @staticmethod + def _output_size(input, weight, padding, dilation, stride): + channels = weight.size(0) + output_size = (input.size(0), channels) + for d in range(input.dim() - 2): + in_size = input.size(d + 2) + pad = padding[d] + kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 + stride_ = stride[d] + output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1,) + if not all(map(lambda s: s > 0, output_size)): + raise ValueError( + "convolution input is too small (output would be {})".format("x".join(map(str, output_size))) + ) + return output_size + + +class ModulatedDeformConvFunction(Function): + @staticmethod + def forward( + ctx, input, offset, mask, weight, bias=None, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1 + ): + ctx.stride = stride + ctx.padding = padding + ctx.dilation = dilation + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.with_bias = bias is not None + if not ctx.with_bias: + bias = input.new_empty(1) # fake tensor + if not input.is_cuda: + raise NotImplementedError + if weight.requires_grad or mask.requires_grad or offset.requires_grad or input.requires_grad: + ctx.save_for_backward(input, offset, mask, weight, bias) + output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight)) + ctx._bufs = [input.new_empty(0), input.new_empty(0)] + _C.modulated_deform_conv_forward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + output, + ctx._bufs[1], + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + if not grad_output.is_cuda: + raise NotImplementedError + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + grad_mask = torch.zeros_like(mask) + grad_weight = torch.zeros_like(weight) + grad_bias = torch.zeros_like(bias) + _C.modulated_deform_conv_backward( + input, + weight, + bias, + ctx._bufs[0], + offset, + mask, + ctx._bufs[1], + grad_input, + grad_weight, + grad_bias, + grad_offset, + grad_mask, + grad_output, + weight.shape[2], + weight.shape[3], + ctx.stride, + ctx.stride, + ctx.padding, + ctx.padding, + ctx.dilation, + ctx.dilation, + ctx.groups, + ctx.deformable_groups, + ctx.with_bias, + ) + if not ctx.with_bias: + grad_bias = None + + return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None) + + @staticmethod + def _infer_shape(ctx, input, weight): + n = input.size(0) + channels_out = weight.size(0) + height, width = input.shape[2:4] + kernel_h, kernel_w = weight.shape[2:4] + height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1 + width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1 + return n, channels_out, height_out, width_out + + +deform_conv = DeformConvFunction.apply +modulated_deform_conv = ModulatedDeformConvFunction.apply + + +class DeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=False, + ): + assert not bias + super(DeformConv, self).__init__() + self.with_bias = bias + + assert in_channels % groups == 0, "in_channels {} cannot be divisible by groups {}".format(in_channels, groups) + assert out_channels % groups == 0, "out_channels {} cannot be divisible by groups {}".format( + out_channels, groups + ) + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.deformable_groups = deformable_groups + + self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size)) + + self.reset_parameters() + + def reset_parameters(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1.0 / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + + @custom_fwd(cast_inputs=torch.float32) + def forward(self, input, offset): + return deform_conv( + input, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups + ) + + def __repr__(self): + return "".join( + [ + "{}(".format(self.__class__.__name__), + "in_channels={}, ".format(self.in_channels), + "out_channels={}, ".format(self.out_channels), + "kernel_size={}, ".format(self.kernel_size), + "stride={}, ".format(self.stride), + "dilation={}, ".format(self.dilation), + "padding={}, ".format(self.padding), + "groups={}, ".format(self.groups), + "deformable_groups={}, ".format(self.deformable_groups), + "bias={})".format(self.with_bias), + ] + ) + + +class ModulatedDeformConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=True, + ): + super(ModulatedDeformConv, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.deformable_groups = deformable_groups + self.with_bias = bias + + self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter("bias", None) + self.reset_parameters() + + def reset_parameters(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1.0 / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + if self.bias is not None: + self.bias.data.zero_() + + @custom_fwd(cast_inputs=torch.float32) + def forward(self, input, offset, mask): + return modulated_deform_conv( + input, + offset, + mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) + + def __repr__(self): + return "".join( + [ + "{}(".format(self.__class__.__name__), + "in_channels={}, ".format(self.in_channels), + "out_channels={}, ".format(self.out_channels), + "kernel_size={}, ".format(self.kernel_size), + "stride={}, ".format(self.stride), + "dilation={}, ".format(self.dilation), + "padding={}, ".format(self.padding), + "groups={}, ".format(self.groups), + "deformable_groups={}, ".format(self.deformable_groups), + "bias={})".format(self.with_bias), + ] + ) + + +class ModulatedDeformConvPack(ModulatedDeformConv): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=True, + ): + super(ModulatedDeformConvPack, self).__init__( + in_channels, out_channels, kernel_size, stride, padding, dilation, groups, deformable_groups, bias + ) + + self.conv_offset_mask = nn.Conv2d( + self.in_channels // self.groups, + self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1], + kernel_size=self.kernel_size, + stride=_pair(self.stride), + padding=_pair(self.padding), + bias=True, + ) + self.init_offset() + + def init_offset(self): + self.conv_offset_mask.weight.data.zero_() + self.conv_offset_mask.bias.data.zero_() + + @custom_fwd(cast_inputs=torch.float32) + def forward(self, input): + out = self.conv_offset_mask(input) + o1, o2, mask = torch.chunk(out, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + return modulated_deform_conv( + input, + offset, + mask, + self.weight, + self.bias, + self.stride, + self.padding, + self.dilation, + self.groups, + self.deformable_groups, + ) diff --git a/maskrcnn_benchmark/layers/deform_pool.py b/maskrcnn_benchmark/layers/deform_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..8e32d49eebb3c65d3d52caca779c2b6c8e68ed93 --- /dev/null +++ b/maskrcnn_benchmark/layers/deform_pool.py @@ -0,0 +1,450 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .deform_conv import DeformConv2d + + +def add_conv(in_ch, out_ch, ksize, stride, leaky=True): + """ + Add a conv2d / batchnorm / leaky ReLU block. + Args: + in_ch (int): number of input channels of the convolution layer. + out_ch (int): number of output channels of the convolution layer. + ksize (int): kernel size of the convolution layer. + stride (int): stride of the convolution layer. + Returns: + stage (Sequential) : Sequential layers composing a convolution block. + """ + stage = nn.Sequential() + pad = (ksize - 1) // 2 + stage.add_module( + "conv", + nn.Conv2d(in_channels=in_ch, out_channels=out_ch, kernel_size=ksize, stride=stride, padding=pad, bias=False), + ) + stage.add_module("batch_norm", nn.BatchNorm2d(out_ch)) + if leaky: + stage.add_module("leaky", nn.LeakyReLU(0.1)) + else: + stage.add_module("relu6", nn.ReLU6(inplace=True)) + return stage + + +class upsample(nn.Module): + __constants__ = ["size", "scale_factor", "mode", "align_corners", "name"] + + def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None): + super(upsample, self).__init__() + self.name = type(self).__name__ + self.size = size + self.scale_factor = scale_factor + self.mode = mode + self.align_corners = align_corners + + def forward(self, input): + return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners) + + def extra_repr(self): + if self.scale_factor is not None: + info = "scale_factor=" + str(self.scale_factor) + else: + info = "size=" + str(self.size) + info += ", mode=" + self.mode + return info + + +class SPPLayer(nn.Module): + def __init__(self): + super(SPPLayer, self).__init__() + + def forward(self, x): + x_1 = x + x_2 = F.max_pool2d(x, 5, stride=1, padding=2) + x_3 = F.max_pool2d(x, 9, stride=1, padding=4) + x_4 = F.max_pool2d(x, 13, stride=1, padding=6) + out = torch.cat((x_1, x_2, x_3, x_4), dim=1) + return out + + +class DropBlock(nn.Module): + def __init__(self, block_size=7, keep_prob=0.9): + super(DropBlock, self).__init__() + self.block_size = block_size + self.keep_prob = keep_prob + self.gamma = None + self.kernel_size = (block_size, block_size) + self.stride = (1, 1) + self.padding = (block_size // 2, block_size // 2) + + def reset(self, block_size, keep_prob): + self.block_size = block_size + self.keep_prob = keep_prob + self.gamma = None + self.kernel_size = (block_size, block_size) + self.stride = (1, 1) + self.padding = (block_size // 2, block_size // 2) + + def calculate_gamma(self, x): + return ( + (1 - self.keep_prob) * x.shape[-1] ** 2 / (self.block_size**2 * (x.shape[-1] - self.block_size + 1) ** 2) + ) + + def forward(self, x): + if not self.training or self.keep_prob == 1: # set keep_prob=1 to turn off dropblock + return x + if self.gamma is None: + self.gamma = self.calculate_gamma(x) + if x.type() == "torch.cuda.HalfTensor": # TODO: not fully support for FP16 now + FP16 = True + x = x.float() + else: + FP16 = False + p = torch.ones_like(x) * (self.gamma) + mask = 1 - torch.nn.functional.max_pool2d(torch.bernoulli(p), self.kernel_size, self.stride, self.padding) + + out = mask * x * (mask.numel() / mask.sum()) + + if FP16: + out = out.half() + return out + + +class resblock(nn.Module): + """ + Sequential residual blocks each of which consists of \ + two convolution layers. + Args: + ch (int): number of input and output channels. + nblocks (int): number of residual blocks. + shortcut (bool): if True, residual tensor addition is enabled. + """ + + def __init__(self, ch, nblocks=1, shortcut=True): + + super().__init__() + self.shortcut = shortcut + self.module_list = nn.ModuleList() + for i in range(nblocks): + resblock_one = nn.ModuleList() + resblock_one.append(add_conv(ch, ch // 2, 1, 1)) + resblock_one.append(add_conv(ch // 2, ch, 3, 1)) + self.module_list.append(resblock_one) + + def forward(self, x): + for module in self.module_list: + h = x + for res in module: + h = res(h) + x = x + h if self.shortcut else h + return x + + +class RFBblock(nn.Module): + def __init__(self, in_ch, residual=False): + super(RFBblock, self).__init__() + inter_c = in_ch // 4 + self.branch_0 = nn.Sequential( + nn.Conv2d(in_channels=in_ch, out_channels=inter_c, kernel_size=1, stride=1, padding=0), + ) + self.branch_1 = nn.Sequential( + nn.Conv2d(in_channels=in_ch, out_channels=inter_c, kernel_size=1, stride=1, padding=0), + nn.Conv2d(in_channels=inter_c, out_channels=inter_c, kernel_size=3, stride=1, padding=1), + ) + self.branch_2 = nn.Sequential( + nn.Conv2d(in_channels=in_ch, out_channels=inter_c, kernel_size=1, stride=1, padding=0), + nn.Conv2d(in_channels=inter_c, out_channels=inter_c, kernel_size=3, stride=1, padding=1), + nn.Conv2d(in_channels=inter_c, out_channels=inter_c, kernel_size=3, stride=1, dilation=2, padding=2), + ) + self.branch_3 = nn.Sequential( + nn.Conv2d(in_channels=in_ch, out_channels=inter_c, kernel_size=1, stride=1, padding=0), + nn.Conv2d(in_channels=inter_c, out_channels=inter_c, kernel_size=5, stride=1, padding=2), + nn.Conv2d(in_channels=inter_c, out_channels=inter_c, kernel_size=3, stride=1, dilation=3, padding=3), + ) + self.residual = residual + + def forward(self, x): + x_0 = self.branch_0(x) + x_1 = self.branch_1(x) + x_2 = self.branch_2(x) + x_3 = self.branch_3(x) + out = torch.cat((x_0, x_1, x_2, x_3), 1) + if self.residual: + out += x + return out + + +class FeatureAdaption(nn.Module): + def __init__(self, in_ch, out_ch, n_anchors, rfb=False, sep=False): + super(FeatureAdaption, self).__init__() + if sep: + self.sep = True + else: + self.sep = False + self.conv_offset = nn.Conv2d( + in_channels=2 * n_anchors, + out_channels=2 * 9 * n_anchors, + groups=n_anchors, + kernel_size=1, + stride=1, + padding=0, + ) + self.dconv = DeformConv2d( + in_channels=in_ch, out_channels=out_ch, kernel_size=3, stride=1, padding=1, deformable_groups=n_anchors + ) + self.rfb = None + if rfb: + self.rfb = RFBblock(out_ch) + + def forward(self, input, wh_pred): + # The RFB block is added behind FeatureAdaption + # For mobilenet, we currently don't support rfb and FeatureAdaption + if self.sep: + return input + if self.rfb is not None: + input = self.rfb(input) + wh_pred_new = wh_pred.detach() + offset = self.conv_offset(wh_pred_new) + out = self.dconv(input, offset) + return out + + +class ASFFmobile(nn.Module): + def __init__(self, level, rfb=False, vis=False): + super(ASFFmobile, self).__init__() + self.level = level + self.dim = [512, 256, 128] + self.inter_dim = self.dim[self.level] + if level == 0: + self.stride_level_1 = add_conv(256, self.inter_dim, 3, 2, leaky=False) + self.stride_level_2 = add_conv(128, self.inter_dim, 3, 2, leaky=False) + self.expand = add_conv(self.inter_dim, 1024, 3, 1, leaky=False) + elif level == 1: + self.compress_level_0 = add_conv(512, self.inter_dim, 1, 1, leaky=False) + self.stride_level_2 = add_conv(128, self.inter_dim, 3, 2, leaky=False) + self.expand = add_conv(self.inter_dim, 512, 3, 1, leaky=False) + elif level == 2: + self.compress_level_0 = add_conv(512, self.inter_dim, 1, 1, leaky=False) + self.compress_level_1 = add_conv(256, self.inter_dim, 1, 1, leaky=False) + self.expand = add_conv(self.inter_dim, 256, 3, 1, leaky=False) + + compress_c = 8 if rfb else 16 # when adding rfb, we use half number of channels to save memory + + self.weight_level_0 = add_conv(self.inter_dim, compress_c, 1, 1, leaky=False) + self.weight_level_1 = add_conv(self.inter_dim, compress_c, 1, 1, leaky=False) + self.weight_level_2 = add_conv(self.inter_dim, compress_c, 1, 1, leaky=False) + + self.weight_levels = nn.Conv2d(compress_c * 3, 3, kernel_size=1, stride=1, padding=0) + self.vis = vis + + def forward(self, x_level_0, x_level_1, x_level_2): + if self.level == 0: + level_0_resized = x_level_0 + level_1_resized = self.stride_level_1(x_level_1) + + level_2_downsampled_inter = F.max_pool2d(x_level_2, 3, stride=2, padding=1) + level_2_resized = self.stride_level_2(level_2_downsampled_inter) + + elif self.level == 1: + level_0_compressed = self.compress_level_0(x_level_0) + level_0_resized = F.interpolate(level_0_compressed, scale_factor=2, mode="nearest") + level_1_resized = x_level_1 + level_2_resized = self.stride_level_2(x_level_2) + elif self.level == 2: + level_0_compressed = self.compress_level_0(x_level_0) + level_0_resized = F.interpolate(level_0_compressed, scale_factor=4, mode="nearest") + level_1_compressed = self.compress_level_1(x_level_1) + level_1_resized = F.interpolate(level_1_compressed, scale_factor=2, mode="nearest") + level_2_resized = x_level_2 + + level_0_weight_v = self.weight_level_0(level_0_resized) + level_1_weight_v = self.weight_level_1(level_1_resized) + level_2_weight_v = self.weight_level_2(level_2_resized) + levels_weight_v = torch.cat((level_0_weight_v, level_1_weight_v, level_2_weight_v), 1) + levels_weight = self.weight_levels(levels_weight_v) + levels_weight = F.softmax(levels_weight, dim=1) + + fused_out_reduced = ( + level_0_resized * levels_weight[:, 0:1, :, :] + + level_1_resized * levels_weight[:, 1:2, :, :] + + level_2_resized * levels_weight[:, 2:, :, :] + ) + + out = self.expand(fused_out_reduced) + + if self.vis: + return out, levels_weight, fused_out_reduced.sum(dim=1) + else: + return out + + +class ASFF(nn.Module): + def __init__(self, level, rfb=False, vis=False): + super(ASFF, self).__init__() + self.level = level + self.dim = [512, 256, 256] + self.inter_dim = self.dim[self.level] + if level == 0: + self.stride_level_1 = add_conv(256, self.inter_dim, 3, 2) + self.stride_level_2 = add_conv(256, self.inter_dim, 3, 2) + self.expand = add_conv(self.inter_dim, 1024, 3, 1) + elif level == 1: + self.compress_level_0 = add_conv(512, self.inter_dim, 1, 1) + self.stride_level_2 = add_conv(256, self.inter_dim, 3, 2) + self.expand = add_conv(self.inter_dim, 512, 3, 1) + elif level == 2: + self.compress_level_0 = add_conv(512, self.inter_dim, 1, 1) + self.expand = add_conv(self.inter_dim, 256, 3, 1) + + compress_c = 8 if rfb else 16 # when adding rfb, we use half number of channels to save memory + + self.weight_level_0 = add_conv(self.inter_dim, compress_c, 1, 1) + self.weight_level_1 = add_conv(self.inter_dim, compress_c, 1, 1) + self.weight_level_2 = add_conv(self.inter_dim, compress_c, 1, 1) + + self.weight_levels = nn.Conv2d(compress_c * 3, 3, kernel_size=1, stride=1, padding=0) + self.vis = vis + + def forward(self, x_level_0, x_level_1, x_level_2): + if self.level == 0: + level_0_resized = x_level_0 + level_1_resized = self.stride_level_1(x_level_1) + + level_2_downsampled_inter = F.max_pool2d(x_level_2, 3, stride=2, padding=1) + level_2_resized = self.stride_level_2(level_2_downsampled_inter) + + elif self.level == 1: + level_0_compressed = self.compress_level_0(x_level_0) + level_0_resized = F.interpolate(level_0_compressed, scale_factor=2, mode="nearest") + level_1_resized = x_level_1 + level_2_resized = self.stride_level_2(x_level_2) + elif self.level == 2: + level_0_compressed = self.compress_level_0(x_level_0) + level_0_resized = F.interpolate(level_0_compressed, scale_factor=4, mode="nearest") + level_1_resized = F.interpolate(x_level_1, scale_factor=2, mode="nearest") + level_2_resized = x_level_2 + + level_0_weight_v = self.weight_level_0(level_0_resized) + level_1_weight_v = self.weight_level_1(level_1_resized) + level_2_weight_v = self.weight_level_2(level_2_resized) + levels_weight_v = torch.cat((level_0_weight_v, level_1_weight_v, level_2_weight_v), 1) + levels_weight = self.weight_levels(levels_weight_v) + levels_weight = F.softmax(levels_weight, dim=1) + + fused_out_reduced = ( + level_0_resized * levels_weight[:, 0:1, :, :] + + level_1_resized * levels_weight[:, 1:2, :, :] + + level_2_resized * levels_weight[:, 2:, :, :] + ) + + out = self.expand(fused_out_reduced) + + if self.vis: + return out, levels_weight, fused_out_reduced.sum(dim=1) + else: + return out + + +def make_divisible(v, divisor, min_value=None): + """ + This function is taken from the original tf repo. + It ensures that all layers have a channel number that is divisible by 8 + It can be seen here: + https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py + :param v: + :param divisor: + :param min_value: + :return: + """ + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +class ConvBNReLU(nn.Sequential): + def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1): + padding = (kernel_size - 1) // 2 + super(ConvBNReLU, self).__init__( + nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False), + nn.BatchNorm2d(out_planes), + nn.ReLU6(inplace=True), + ) + + +def add_sepconv(in_ch, out_ch, ksize, stride): + + stage = nn.Sequential() + pad = (ksize - 1) // 2 + stage.add_module( + "sepconv", + nn.Conv2d( + in_channels=in_ch, + out_channels=in_ch, + kernel_size=ksize, + stride=stride, + padding=pad, + groups=in_ch, + bias=False, + ), + ) + stage.add_module("sepbn", nn.BatchNorm2d(in_ch)) + stage.add_module("seprelu6", nn.ReLU6(inplace=True)) + stage.add_module("ptconv", nn.Conv2d(in_ch, out_ch, 1, 1, 0, bias=False)) + stage.add_module("ptbn", nn.BatchNorm2d(out_ch)) + stage.add_module("ptrelu6", nn.ReLU6(inplace=True)) + return stage + + +class InvertedResidual(nn.Module): + def __init__(self, inp, oup, stride, expand_ratio): + super(InvertedResidual, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = int(round(inp * expand_ratio)) + self.use_res_connect = self.stride == 1 and inp == oup + + layers = [] + if expand_ratio != 1: + # pw + layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1)) + layers.extend( + [ + # dw + ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ] + ) + self.conv = nn.Sequential(*layers) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +class ressepblock(nn.Module): + def __init__(self, ch, out_ch, in_ch=None, shortcut=True): + + super().__init__() + self.shortcut = shortcut + self.module_list = nn.ModuleList() + in_ch = ch // 2 if in_ch == None else in_ch + resblock_one = nn.ModuleList() + resblock_one.append(add_conv(ch, in_ch, 1, 1, leaky=False)) + resblock_one.append(add_conv(in_ch, out_ch, 3, 1, leaky=False)) + self.module_list.append(resblock_one) + + def forward(self, x): + for module in self.module_list: + h = x + for res in module: + h = res(h) + x = x + h if self.shortcut else h + return x diff --git a/maskrcnn_benchmark/layers/dropblock.py b/maskrcnn_benchmark/layers/dropblock.py new file mode 100644 index 0000000000000000000000000000000000000000..f89e845150c350e889da9438613ceac0fb35c582 --- /dev/null +++ b/maskrcnn_benchmark/layers/dropblock.py @@ -0,0 +1,148 @@ +import torch +import torch.nn.functional as F +from torch import nn + + +class DropBlock2D(nn.Module): + r"""Randomly zeroes 2D spatial blocks of the input tensor. + + As described in the paper + `DropBlock: A regularization method for convolutional networks`_ , + dropping whole blocks of feature map allows to remove semantic + information as compared to regular dropout. + + Args: + drop_prob (float): probability of an element to be dropped. + block_size (int): size of the block to drop + + Shape: + - Input: `(N, C, H, W)` + - Output: `(N, C, H, W)` + + .. _DropBlock: A regularization method for convolutional networks: + https://arxiv.org/abs/1810.12890 + + """ + + def __init__(self, drop_prob, block_size): + super(DropBlock2D, self).__init__() + + self.drop_prob = drop_prob + self.block_size = block_size + + def forward(self, x): + # shape: (bsize, channels, height, width) + + assert x.dim() == 4, "Expected input with 4 dimensions (bsize, channels, height, width)" + + if not self.training or self.drop_prob == 0.0: + return x + else: + # get gamma value + gamma = self._compute_gamma(x) + + # sample mask + mask = (torch.rand(x.shape[0], *x.shape[2:]) < gamma).float() + + # place mask on input device + mask = mask.to(x.device) + + # compute block mask + block_mask = self._compute_block_mask(mask) + + # apply block mask + out = x * block_mask[:, None, :, :] + + # scale output + out = out * block_mask.numel() / block_mask.sum() + + return out + + def _compute_block_mask(self, mask): + block_mask = F.max_pool2d( + input=mask[:, None, :, :], + kernel_size=(self.block_size, self.block_size), + stride=(1, 1), + padding=self.block_size // 2, + ) + + if self.block_size % 2 == 0: + block_mask = block_mask[:, :, :-1, :-1] + + block_mask = 1 - block_mask.squeeze(1) + + return block_mask + + def _compute_gamma(self, x): + return self.drop_prob / (self.block_size**2) + + +class DropBlock3D(DropBlock2D): + r"""Randomly zeroes 3D spatial blocks of the input tensor. + + An extension to the concept described in the paper + `DropBlock: A regularization method for convolutional networks`_ , + dropping whole blocks of feature map allows to remove semantic + information as compared to regular dropout. + + Args: + drop_prob (float): probability of an element to be dropped. + block_size (int): size of the block to drop + + Shape: + - Input: `(N, C, D, H, W)` + - Output: `(N, C, D, H, W)` + + .. _DropBlock: A regularization method for convolutional networks: + https://arxiv.org/abs/1810.12890 + + """ + + def __init__(self, drop_prob, block_size): + super(DropBlock3D, self).__init__(drop_prob, block_size) + + def forward(self, x): + # shape: (bsize, channels, depth, height, width) + + assert x.dim() == 5, "Expected input with 5 dimensions (bsize, channels, depth, height, width)" + + if not self.training or self.drop_prob == 0.0: + return x + else: + # get gamma value + gamma = self._compute_gamma(x) + + # sample mask + mask = (torch.rand(x.shape[0], *x.shape[2:]) < gamma).float() + + # place mask on input device + mask = mask.to(x.device) + + # compute block mask + block_mask = self._compute_block_mask(mask) + + # apply block mask + out = x * block_mask[:, None, :, :, :] + + # scale output + out = out * block_mask.numel() / block_mask.sum() + + return out + + def _compute_block_mask(self, mask): + block_mask = F.max_pool3d( + input=mask[:, None, :, :, :], + kernel_size=(self.block_size, self.block_size, self.block_size), + stride=(1, 1, 1), + padding=self.block_size // 2, + ) + + if self.block_size % 2 == 0: + block_mask = block_mask[:, :, :-1, :-1, :-1] + + block_mask = 1 - block_mask.squeeze(1) + + return block_mask + + def _compute_gamma(self, x): + return self.drop_prob / (self.block_size**3) diff --git a/maskrcnn_benchmark/layers/dyhead.py b/maskrcnn_benchmark/layers/dyhead.py new file mode 100644 index 0000000000000000000000000000000000000000..9331f6fc76d49124446905b40a84991019e943f0 --- /dev/null +++ b/maskrcnn_benchmark/layers/dyhead.py @@ -0,0 +1,149 @@ +import torch +import torch.nn.functional as F +from torch import nn + +from .deform_conv import ModulatedDeformConv +from .dyrelu import h_sigmoid, DYReLU + + +class Conv3x3Norm(torch.nn.Module): + def __init__(self, in_channels, out_channels, stride, deformable=False, use_gn=False): + super(Conv3x3Norm, self).__init__() + + if deformable: + self.conv = ModulatedDeformConv(in_channels, out_channels, kernel_size=3, stride=stride, padding=1) + else: + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1) + + if use_gn: + self.bn = nn.GroupNorm(num_groups=16, num_channels=out_channels) + else: + self.bn = None + + def forward(self, input, **kwargs): + x = self.conv(input, **kwargs) + if self.bn: + x = self.bn(x) + return x + + +class DyConv(nn.Module): + def __init__( + self, + in_channels=256, + out_channels=256, + conv_func=Conv3x3Norm, + use_dyfuse=True, + use_dyrelu=False, + use_deform=False, + ): + super(DyConv, self).__init__() + + self.DyConv = nn.ModuleList() + self.DyConv.append(conv_func(in_channels, out_channels, 1)) + self.DyConv.append(conv_func(in_channels, out_channels, 1)) + self.DyConv.append(conv_func(in_channels, out_channels, 2)) + + if use_dyfuse: + self.AttnConv = nn.Sequential( + nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_channels, 1, kernel_size=1), nn.ReLU(inplace=True) + ) + self.h_sigmoid = h_sigmoid() + else: + self.AttnConv = None + + if use_dyrelu: + self.relu = DYReLU(in_channels, out_channels) + else: + self.relu = nn.ReLU() + + if use_deform: + self.offset = nn.Conv2d(in_channels, 27, kernel_size=3, stride=1, padding=1) + else: + self.offset = None + + self.init_weights() + + def init_weights(self): + for m in self.DyConv.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight.data, 0, 0.01) + if m.bias is not None: + m.bias.data.zero_() + if self.AttnConv is not None: + for m in self.AttnConv.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight.data, 0, 0.01) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x): + next_x = [] + for level, feature in enumerate(x): + + conv_args = dict() + if self.offset is not None: + offset_mask = self.offset(feature) + offset = offset_mask[:, :18, :, :] + mask = offset_mask[:, 18:, :, :].sigmoid() + conv_args = dict(offset=offset, mask=mask) + + temp_fea = [self.DyConv[1](feature, **conv_args)] + + if level > 0: + temp_fea.append(self.DyConv[2](x[level - 1], **conv_args)) + if level < len(x) - 1: + temp_fea.append( + F.upsample_bilinear( + self.DyConv[0](x[level + 1], **conv_args), size=[feature.size(2), feature.size(3)] + ) + ) + mean_fea = torch.mean(torch.stack(temp_fea), dim=0, keepdim=False) + + if self.AttnConv is not None: + attn_fea = [] + res_fea = [] + for fea in temp_fea: + res_fea.append(fea) + attn_fea.append(self.AttnConv(fea)) + + res_fea = torch.stack(res_fea) + spa_pyr_attn = self.h_sigmoid(torch.stack(attn_fea)) + + mean_fea = torch.mean(res_fea * spa_pyr_attn, dim=0, keepdim=False) + + next_x.append(self.relu(mean_fea)) + + return next_x + + +class DyHead(nn.Module): + def __init__(self, cfg, in_channels): + super(DyHead, self).__init__() + self.cfg = cfg + channels = cfg.MODEL.DYHEAD.CHANNELS + use_gn = cfg.MODEL.DYHEAD.USE_GN + use_dyrelu = cfg.MODEL.DYHEAD.USE_DYRELU + use_dyfuse = cfg.MODEL.DYHEAD.USE_DYFUSE + use_deform = cfg.MODEL.DYHEAD.USE_DFCONV + + conv_func = lambda i, o, s: Conv3x3Norm(i, o, s, deformable=use_deform, use_gn=use_gn) + + dyhead_tower = [] + for i in range(cfg.MODEL.DYHEAD.NUM_CONVS): + dyhead_tower.append( + DyConv( + in_channels if i == 0 else channels, + channels, + conv_func=conv_func, + use_dyrelu=use_dyrelu, + use_dyfuse=use_dyfuse, + use_deform=use_deform, + ) + ) + + self.add_module("dyhead_tower", nn.Sequential(*dyhead_tower)) + + def forward(self, x): + dyhead_tower = self.dyhead_tower(x) + return dyhead_tower diff --git a/maskrcnn_benchmark/layers/dyrelu.py b/maskrcnn_benchmark/layers/dyrelu.py new file mode 100644 index 0000000000000000000000000000000000000000..a2cbeb85c563d2a1a4bda5a45d6f5bd8ee1a8205 --- /dev/null +++ b/maskrcnn_benchmark/layers/dyrelu.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def _make_divisible(v, divisor, min_value=None): + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +class swish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class h_swish(nn.Module): + def __init__(self, inplace=False): + super(h_swish, self).__init__() + self.inplace = inplace + + def forward(self, x): + return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0 + + +class h_sigmoid(nn.Module): + def __init__(self, inplace=True, h_max=1): + super(h_sigmoid, self).__init__() + self.relu = nn.ReLU6(inplace=inplace) + self.h_max = h_max + + def forward(self, x): + return self.relu(x + 3) * self.h_max / 6 + + +class DYReLU(nn.Module): + def __init__( + self, + inp, + oup, + reduction=4, + lambda_a=1.0, + K2=True, + use_bias=True, + use_spatial=False, + init_a=[1.0, 0.0], + init_b=[0.0, 0.0], + ): + super(DYReLU, self).__init__() + self.oup = oup + self.lambda_a = lambda_a * 2 + self.K2 = K2 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + + self.use_bias = use_bias + if K2: + self.exp = 4 if use_bias else 2 + else: + self.exp = 2 if use_bias else 1 + self.init_a = init_a + self.init_b = init_b + + # determine squeeze + if reduction == 4: + squeeze = inp // reduction + else: + squeeze = _make_divisible(inp // reduction, 4) + # print('reduction: {}, squeeze: {}/{}'.format(reduction, inp, squeeze)) + # print('init_a: {}, init_b: {}'.format(self.init_a, self.init_b)) + + self.fc = nn.Sequential( + nn.Linear(inp, squeeze), nn.ReLU(inplace=True), nn.Linear(squeeze, oup * self.exp), h_sigmoid() + ) + if use_spatial: + self.spa = nn.Sequential( + nn.Conv2d(inp, 1, kernel_size=1), + nn.BatchNorm2d(1), + ) + else: + self.spa = None + + def forward(self, x): + if isinstance(x, list): + x_in = x[0] + x_out = x[1] + else: + x_in = x + x_out = x + b, c, h, w = x_in.size() + y = self.avg_pool(x_in).view(b, c) + y = self.fc(y).view(b, self.oup * self.exp, 1, 1) + if self.exp == 4: + a1, b1, a2, b2 = torch.split(y, self.oup, dim=1) + a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0] # 1.0 + a2 = (a2 - 0.5) * self.lambda_a + self.init_a[1] + + b1 = b1 - 0.5 + self.init_b[0] + b2 = b2 - 0.5 + self.init_b[1] + out = torch.max(x_out * a1 + b1, x_out * a2 + b2) + elif self.exp == 2: + if self.use_bias: # bias but not PL + a1, b1 = torch.split(y, self.oup, dim=1) + a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0] # 1.0 + b1 = b1 - 0.5 + self.init_b[0] + out = x_out * a1 + b1 + + else: + a1, a2 = torch.split(y, self.oup, dim=1) + a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0] # 1.0 + a2 = (a2 - 0.5) * self.lambda_a + self.init_a[1] + out = torch.max(x_out * a1, x_out * a2) + + elif self.exp == 1: + a1 = y + a1 = (a1 - 0.5) * self.lambda_a + self.init_a[0] # 1.0 + out = x_out * a1 + + if self.spa: + ys = self.spa(x_in).view(b, -1) + ys = F.softmax(ys, dim=1).view(b, 1, h, w) * h * w + ys = F.hardtanh(ys, 0, 3, inplace=True) / 3 + out = out * ys + + return out diff --git a/maskrcnn_benchmark/layers/evonorm.py b/maskrcnn_benchmark/layers/evonorm.py new file mode 100644 index 0000000000000000000000000000000000000000..b37e636ef38591c283a7f3d1d0d79ee3c5fbdaa8 --- /dev/null +++ b/maskrcnn_benchmark/layers/evonorm.py @@ -0,0 +1,40 @@ +import torch +import torch.nn as nn + + +class EvoNorm2d(nn.Module): + __constants__ = ["num_features", "eps", "nonlinearity"] + + def __init__(self, num_features, eps=1e-5, nonlinearity=True, group=32): + super(EvoNorm2d, self).__init__() + + self.num_features = num_features + self.eps = eps + self.nonlinearity = nonlinearity + self.group = group + + self.weight = nn.Parameter(torch.Tensor(1, num_features, 1, 1)) + self.bias = nn.Parameter(torch.Tensor(1, num_features, 1, 1)) + if self.nonlinearity: + self.v = nn.Parameter(torch.Tensor(1, num_features, 1, 1)) + + self.reset_parameters() + + def reset_parameters(self): + nn.init.ones_(self.weight) + nn.init.zeros_(self.bias) + if self.nonlinearity: + nn.init.ones_(self.v) + + def group_std(self, x, groups=32): + N, C, H, W = x.shape + x = torch.reshape(x, (N, groups, C // groups, H, W)) + std = torch.std(x, (3, 4), keepdim=True) + return torch.reshape(std + self.eps, (N, C, 1, 1)) + + def forward(self, x): + if self.nonlinearity: + num = x * torch.sigmoid(self.v * x) + return num / self.group_std(x, self.group) * self.weight + self.bias + else: + return x * self.weight + self.bias diff --git a/maskrcnn_benchmark/layers/iou_loss.py b/maskrcnn_benchmark/layers/iou_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..a5c1f132adcf0c7a6d2c9a0a69e71be90f1bd074 --- /dev/null +++ b/maskrcnn_benchmark/layers/iou_loss.py @@ -0,0 +1,77 @@ +import torch +from torch import nn + + +class IOULoss(nn.Module): + def __init__(self, loss_type="iou"): + super(IOULoss, self).__init__() + self.loss_type = loss_type + + def forward(self, pred, target, weight=None): + pred_left = pred[:, 0] + pred_top = pred[:, 1] + pred_right = pred[:, 2] + pred_bottom = pred[:, 3] + + target_left = target[:, 0] + target_top = target[:, 1] + target_right = target[:, 2] + target_bottom = target[:, 3] + + target_area = (target_left + target_right) * (target_top + target_bottom) + pred_area = (pred_left + pred_right) * (pred_top + pred_bottom) + + w_intersect = torch.min(pred_left, target_left) + torch.min(pred_right, target_right) + g_w_intersect = torch.max(pred_left, target_left) + torch.max(pred_right, target_right) + h_intersect = torch.min(pred_bottom, target_bottom) + torch.min(pred_top, target_top) + g_h_intersect = torch.max(pred_bottom, target_bottom) + torch.max(pred_top, target_top) + ac_uion = g_w_intersect * g_h_intersect + 1e-7 + area_intersect = w_intersect * h_intersect + area_union = target_area + pred_area - area_intersect + ious = (area_intersect + 1.0) / (area_union + 1.0) + gious = ious - (ac_uion - area_union) / ac_uion + if self.loss_type == "iou": + losses = -torch.log(ious) + elif self.loss_type == "linear_iou": + losses = 1 - ious + elif self.loss_type == "giou": + losses = 1 - gious + else: + raise NotImplementedError + + if weight is not None and weight.sum() > 0: + return (losses * weight).sum() + else: + assert losses.numel() != 0 + return losses.sum() + + +class IOUWHLoss(nn.Module): # used for anchor guiding + def __init__(self, reduction="none"): + super(IOUWHLoss, self).__init__() + self.reduction = reduction + + def forward(self, pred, target): + orig_shape = pred.shape + pred = pred.view(-1, 4) + target = target.view(-1, 4) + target[:, :2] = 0 + tl = torch.max((target[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2)) + + br = torch.min((target[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2)) + + area_p = torch.prod(pred[:, 2:], 1) + area_g = torch.prod(target[:, 2:], 1) + + en = (tl < br).type(tl.type()).prod(dim=1) + area_i = torch.prod(br - tl, 1) * en + U = area_p + area_g - area_i + 1e-16 + iou = area_i / U + + loss = 1 - iou**2 + if self.reduction == "mean": + loss = loss.mean() + elif self.reduction == "sum": + loss = loss.sum() + + return loss diff --git a/maskrcnn_benchmark/layers/misc.py b/maskrcnn_benchmark/layers/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..edf3465f334a4137bf343eb10dcc308ecd79f679 --- /dev/null +++ b/maskrcnn_benchmark/layers/misc.py @@ -0,0 +1,192 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +helper class that supports empty tensors on some nn functions. + +Ideally, add support directly in PyTorch to empty tensors in +those functions. + +This can be removed once https://github.com/pytorch/pytorch/issues/12013 +is implemented +""" + +import math +import torch +from torch.nn.modules.utils import _ntuple + + +class _NewEmptyTensorOp(torch.autograd.Function): + @staticmethod + def forward(ctx, x, new_shape): + ctx.shape = x.shape + return x.new_empty(new_shape) + + @staticmethod + def backward(ctx, grad): + shape = ctx.shape + return _NewEmptyTensorOp.apply(grad, shape), None + + +class Conv2d(torch.nn.Conv2d): + def forward(self, x): + if x.numel() > 0: + return super(Conv2d, self).forward(x) + # get output shape + + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // d + 1 + for i, p, di, k, d in zip(x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride) + ] + output_shape = [x.shape[0], self.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + +class ConvTranspose2d(torch.nn.ConvTranspose2d): + def forward(self, x): + if x.numel() > 0: + return super(ConvTranspose2d, self).forward(x) + # get output shape + + output_shape = [ + (i - 1) * d - 2 * p + (di * (k - 1) + 1) + op + for i, p, di, k, d, op in zip( + x.shape[-2:], + self.padding, + self.dilation, + self.kernel_size, + self.stride, + self.output_padding, + ) + ] + output_shape = [x.shape[0], self.bias.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + +class BatchNorm2d(torch.nn.BatchNorm2d): + def forward(self, x): + if x.numel() > 0: + return super(BatchNorm2d, self).forward(x) + # get output shape + output_shape = x.shape + return _NewEmptyTensorOp.apply(x, output_shape) + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + if input.numel() > 0: + return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners) + + def _check_size_scale_factor(dim): + if size is None and scale_factor is None: + raise ValueError("either size or scale_factor should be defined") + if size is not None and scale_factor is not None: + raise ValueError("only one of size or scale_factor should be defined") + if scale_factor is not None and isinstance(scale_factor, tuple) and len(scale_factor) != dim: + raise ValueError( + "scale_factor shape must match input shape. " + "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor)) + ) + + def _output_size(dim): + _check_size_scale_factor(dim) + if size is not None: + return size + scale_factors = _ntuple(dim)(scale_factor) + # math.floor might return float in py2.7 + return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)] + + output_shape = tuple(_output_size(2)) + output_shape = input.shape[:-2] + output_shape + return _NewEmptyTensorOp.apply(input, output_shape) + + +class Scale(torch.nn.Module): + def __init__(self, init_value=1.0): + super(Scale, self).__init__() + self.scale = torch.nn.Parameter(torch.FloatTensor([init_value])) + + def forward(self, input): + return input * self.scale + + +class DFConv2d(torch.nn.Module): + """Deformable convolutional layer""" + + def __init__( + self, + in_channels, + out_channels, + with_modulated_dcn=True, + kernel_size=3, + stride=1, + groups=1, + padding=1, + dilation=1, + deformable_groups=1, + bias=False, + ): + super(DFConv2d, self).__init__() + if isinstance(kernel_size, (list, tuple)): + assert len(kernel_size) == 2 + offset_base_channels = kernel_size[0] * kernel_size[1] + else: + offset_base_channels = kernel_size * kernel_size + if with_modulated_dcn: + from maskrcnn_benchmark.layers import ModulatedDeformConv + + offset_channels = offset_base_channels * 3 # default: 27 + conv_block = ModulatedDeformConv + else: + from maskrcnn_benchmark.layers import DeformConv + + offset_channels = offset_base_channels * 2 # default: 18 + conv_block = DeformConv + self.offset = Conv2d( + in_channels, + deformable_groups * offset_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=1, + dilation=dilation, + ) + for l in [ + self.offset, + ]: + torch.nn.init.kaiming_uniform_(l.weight, a=1) + torch.nn.init.constant_(l.bias, 0.0) + self.conv = conv_block( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + deformable_groups=deformable_groups, + bias=bias, + ) + self.with_modulated_dcn = with_modulated_dcn + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.offset_base_channels = offset_base_channels + + def forward(self, x): + if x.numel() > 0: + if not self.with_modulated_dcn: + offset = self.offset(x) + x = self.conv(x, offset) + else: + offset_mask = self.offset(x) + split_point = self.offset_base_channels * 2 + offset = offset_mask[:, :split_point, :, :] + mask = offset_mask[:, split_point:, :, :].sigmoid() + x = self.conv(x, offset, mask) + return x + # get output shape + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // d + 1 + for i, p, di, k, d in zip(x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride) + ] + output_shape = [x.shape[0], self.conv.weight.shape[0]] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) diff --git a/maskrcnn_benchmark/layers/nms.py b/maskrcnn_benchmark/layers/nms.py new file mode 100644 index 0000000000000000000000000000000000000000..12e81ad4a2183b5fca497d33f0d34d5fcc0d4ea1 --- /dev/null +++ b/maskrcnn_benchmark/layers/nms.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from maskrcnn_benchmark import _C + +try: + import torchvision + from torchvision.ops import nms +except: + nms = _C.nms + +ml_nms = _C.ml_nms +soft_nms = _C.soft_nms + +# nms.__doc__ = """ +# This function performs Non-maximum suppresion""" diff --git a/maskrcnn_benchmark/layers/roi_align.py b/maskrcnn_benchmark/layers/roi_align.py new file mode 100644 index 0000000000000000000000000000000000000000..378e7062aeaf36a96d5f757b0c0a461c62d648dd --- /dev/null +++ b/maskrcnn_benchmark/layers/roi_align.py @@ -0,0 +1,89 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from maskrcnn_benchmark import _C + + +class _ROIAlign(Function): + @staticmethod + def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio): + ctx.save_for_backward(roi) + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.sampling_ratio = sampling_ratio + ctx.input_shape = input.size() + output = _C.roi_align_forward(input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + (rois,) = ctx.saved_tensors + output_size = ctx.output_size + spatial_scale = ctx.spatial_scale + sampling_ratio = ctx.sampling_ratio + bs, ch, h, w = ctx.input_shape + grad_input = _C.roi_align_backward( + grad_output, + rois, + spatial_scale, + output_size[0], + output_size[1], + bs, + ch, + h, + w, + sampling_ratio, + ) + return grad_input, None, None, None, None + + +try: + import torchvision + from torchvision.ops import roi_align +except: + roi_align = _ROIAlign.apply + + +class ROIAlign(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio): + super(ROIAlign, self).__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + + def forward(self, input, rois): + return roi_align(input, rois, self.output_size, self.spatial_scale, self.sampling_ratio) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ")" + return tmpstr + + +class ROIAlignV2(nn.Module): + def __init__(self, output_size, spatial_scale, sampling_ratio): + super(ROIAlignV2, self).__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + self.sampling_ratio = sampling_ratio + + def forward(self, input, rois): + return torchvision.ops.roi_align( + input, rois, self.output_size, self.spatial_scale, self.sampling_ratio, aligned=True + ) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ", sampling_ratio=" + str(self.sampling_ratio) + tmpstr += ")" + return tmpstr diff --git a/maskrcnn_benchmark/layers/roi_pool.py b/maskrcnn_benchmark/layers/roi_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..10b2e01f9a3e62f7f2069824e65da6948c905539 --- /dev/null +++ b/maskrcnn_benchmark/layers/roi_pool.py @@ -0,0 +1,61 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch import nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn.modules.utils import _pair + +from maskrcnn_benchmark import _C + + +class _ROIPool(Function): + @staticmethod + def forward(ctx, input, roi, output_size, spatial_scale): + ctx.output_size = _pair(output_size) + ctx.spatial_scale = spatial_scale + ctx.input_shape = input.size() + output, argmax = _C.roi_pool_forward(input, roi, spatial_scale, output_size[0], output_size[1]) + ctx.save_for_backward(input, roi, argmax) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, rois, argmax = ctx.saved_tensors + output_size = ctx.output_size + spatial_scale = ctx.spatial_scale + bs, ch, h, w = ctx.input_shape + grad_input = _C.roi_pool_backward( + grad_output, + input, + rois, + argmax, + spatial_scale, + output_size[0], + output_size[1], + bs, + ch, + h, + w, + ) + return grad_input, None, None, None + + +roi_pool = _ROIPool.apply + + +class ROIPool(nn.Module): + def __init__(self, output_size, spatial_scale): + super(ROIPool, self).__init__() + self.output_size = output_size + self.spatial_scale = spatial_scale + + def forward(self, input, rois): + return roi_pool(input, rois, self.output_size, self.spatial_scale) + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "output_size=" + str(self.output_size) + tmpstr += ", spatial_scale=" + str(self.spatial_scale) + tmpstr += ")" + return tmpstr diff --git a/maskrcnn_benchmark/layers/se.py b/maskrcnn_benchmark/layers/se.py new file mode 100644 index 0000000000000000000000000000000000000000..b18964a741b8df3f3efdfaa10253e2783db6a92c --- /dev/null +++ b/maskrcnn_benchmark/layers/se.py @@ -0,0 +1,53 @@ +from torch import nn + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=16): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid(), + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y.expand_as(x) + + +class SEBlock(nn.Module): + def __init__( + self, channels, reduction=16, use_conv=True, mid_activation=nn.ReLU(inplace=True), out_activation=nn.Sigmoid() + ): + super(SEBlock, self).__init__() + self.use_conv = use_conv + mid_channels = channels // reduction + + self.pool = nn.AdaptiveAvgPool2d(output_size=1) + if use_conv: + self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, bias=True) + else: + self.fc1 = nn.Linear(channels, mid_channels) + self.activ = mid_activation + if use_conv: + self.conv2 = nn.Conv2d(mid_channels, channels, kernel_size=1, bias=True) + else: + self.fc2 = nn.Linear(mid_channels, channels) + self.sigmoid = out_activation + + def forward(self, x): + w = self.pool(x) + if not self.use_conv: + w = w.view(x.size(0), -1) + w = self.conv1(w) if self.use_conv else self.fc1(w) + w = self.activ(w) + w = self.conv2(w) if self.use_conv else self.fc2(w) + w = self.sigmoid(w) + if not self.use_conv: + w = w.unsqueeze(2).unsqueeze(3) + x = x * w + return x diff --git a/maskrcnn_benchmark/layers/set_loss.py b/maskrcnn_benchmark/layers/set_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..aba039d3f01b099315bdf8316785286ca8e4b28e --- /dev/null +++ b/maskrcnn_benchmark/layers/set_loss.py @@ -0,0 +1,389 @@ +import torch +import torch.nn.functional as F +import torch.distributed as dist +from torch import nn + +from scipy.optimize import linear_sum_assignment +from torch.cuda.amp import custom_fwd, custom_bwd + + +def box_area(boxes): + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# modified from torchvision to also return the union +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + # assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + # assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / area + + +def dice_loss(inputs, targets, num_boxes): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_boxes + + +def sigmoid_focal_loss( + inputs: torch.Tensor, targets: torch.Tensor, alpha: float = -1, gamma: float = 2, reduction: str = "none" +): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha: (optional) Weighting factor in range (0,1) to balance + positive vs negative examples. Default = -1 (no weighting). + gamma: Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. + reduction: 'none' | 'mean' | 'sum' + 'none': No reduction will be applied to the output. + 'mean': The output will be averaged. + 'sum': The output will be summed. + Returns: + Loss tensor with the reduction option applied. + """ + p = torch.sigmoid(inputs) + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = p * targets + (1 - p) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + if reduction == "mean": + loss = loss.mean() + elif reduction == "sum": + loss = loss.sum() + + return loss + + +sigmoid_focal_loss_jit = torch.jit.script(sigmoid_focal_loss) # type: torch.jit.ScriptModule + + +class HungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + def __init__( + self, + cost_class: float = 1, + cost_bbox: float = 1, + cost_giou: float = 1, + use_focal: bool = False, + focal_loss_alpha: float = 0.25, + focal_loss_gamma: float = 2.0, + **kwargs, + ): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost + cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_bbox = cost_bbox + self.cost_giou = cost_giou + self.use_focal = use_focal + if self.use_focal: + self.focal_loss_alpha = focal_loss_alpha + self.focal_loss_gamma = focal_loss_gamma + assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0" + + @torch.no_grad() + @custom_fwd(cast_inputs=torch.float32) + def forward(self, outputs, targets): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + bs, num_queries = outputs["pred_logits"].shape[:2] + + # We flatten to compute the cost matrices in a batch + if self.use_focal: + out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid() # [batch_size * num_queries, num_classes] + out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] + else: + out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] + out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] + + # Also concat the target labels and boxes + tgt_ids = torch.cat([v["labels"] for v in targets]) + tgt_bbox = torch.cat([v["boxes_xyxy"] for v in targets]) + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + if self.use_focal: + # Compute the classification cost. + alpha = self.focal_loss_alpha + gamma = self.focal_loss_gamma + neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log()) + pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log()) + cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids] + else: + cost_class = -out_prob[:, tgt_ids] + + # Compute the L1 cost between boxes + image_size_out = torch.cat([v["image_size_xyxy"].unsqueeze(0) for v in targets]) + image_size_out = image_size_out.unsqueeze(1).repeat(1, num_queries, 1).flatten(0, 1) + image_size_tgt = torch.cat([v["image_size_xyxy_tgt"] for v in targets]) + + out_bbox_ = out_bbox / image_size_out + tgt_bbox_ = tgt_bbox / image_size_tgt + cost_bbox = torch.cdist(out_bbox_, tgt_bbox_, p=1) + + # Compute the giou cost betwen boxes + # cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)) + cost_giou = -generalized_box_iou(out_bbox, tgt_bbox) + + # Final cost matrix + C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou + C = C.view(bs, num_queries, -1).cpu() + + C[torch.isnan(C)] = 0.0 + C[torch.isinf(C)] = 0.0 + + sizes = [len(v["boxes"]) for v in targets] + indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] + return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] + + +class SetCriterion(nn.Module): + """ + The process happens in two steps: + 1) we compute hungarian assignment between ground truth boxes and the outputs of the model + 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) + """ + + def __init__( + self, + num_classes, + matcher, + weight_dict, + eos_coef, + losses, + use_focal, + focal_loss_alpha=0.25, + focal_loss_gamma=2.0, + ): + """Create the criterion. + Parameters: + num_classes: number of object categories, omitting the special no-object category + matcher: module able to compute a matching between targets and proposals + weight_dict: dict containing as key the names of the losses and as values their relative weight. + eos_coef: relative classification weight applied to the no-object category + losses: list of all the losses to be applied. See get_loss for list of available losses. + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + self.losses = losses + self.use_focal = use_focal + if self.use_focal: + self.focal_loss_alpha = focal_loss_alpha + self.focal_loss_gamma = focal_loss_gamma + else: + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + + def loss_labels(self, outputs, targets, indices, num_boxes, log=False): + """Classification loss (NLL) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"] + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full(src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device) + target_classes[idx] = target_classes_o + + if self.use_focal: + src_logits = src_logits.flatten(0, 1) + # prepare one_hot target. + target_classes = target_classes.flatten(0, 1) + pos_inds = torch.nonzero(target_classes != self.num_classes, as_tuple=True)[0] + labels = torch.zeros_like(src_logits) + labels[pos_inds, target_classes[pos_inds]] = 1 + # comp focal loss. + class_loss = ( + sigmoid_focal_loss_jit( + src_logits, + labels, + alpha=self.focal_loss_alpha, + gamma=self.focal_loss_gamma, + reduction="sum", + ) + / num_boxes + ) + losses = {"loss_ce": class_loss} + else: + loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) + losses = {"loss_ce": loss_ce} + + return losses + + def loss_boxes(self, outputs, targets, indices, num_boxes): + """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss + targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] + The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + assert "pred_boxes" in outputs + idx = self._get_src_permutation_idx(indices) + src_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes_xyxy"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + losses = {} + loss_giou = 1 - torch.diag(generalized_box_iou(src_boxes, target_boxes)) + losses["loss_giou"] = loss_giou.sum() / num_boxes + + image_size = torch.cat([v["image_size_xyxy_tgt"] for v in targets]) + src_boxes_ = src_boxes / image_size + target_boxes_ = target_boxes / image_size + + loss_bbox = F.l1_loss(src_boxes_, target_boxes_, reduction="none") + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs): + loss_map = { + "labels": self.loss_labels, + "boxes": self.loss_boxes, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) + + @custom_fwd(cast_inputs=torch.float32) + def forward(self, outputs, targets, *argrs, **kwargs): + """This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes accross all nodes, for normalization purposes + num_boxes = sum(len(t["labels"]) for t in targets) + num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device) + if dist.is_available() and dist.is_initialized(): + torch.distributed.all_reduce(num_boxes) + word_size = dist.get_world_size() + else: + word_size = 1 + num_boxes = torch.clamp(num_boxes / word_size, min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + if loss == "masks": + # Intermediate masks losses are too costly to compute, we ignore them. + continue + kwargs = {} + if loss == "labels": + # Logging is enabled only for the last layer + kwargs = {"log": False} + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses diff --git a/maskrcnn_benchmark/layers/sigmoid_focal_loss.py b/maskrcnn_benchmark/layers/sigmoid_focal_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0a0d4ec1c3cbe5f859764b8c679da9006c79b0af --- /dev/null +++ b/maskrcnn_benchmark/layers/sigmoid_focal_loss.py @@ -0,0 +1,204 @@ +import torch +from torch import nn +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable + +from maskrcnn_benchmark import _C + + +# TODO: Use JIT to replace CUDA implementation in the future. +class _SigmoidFocalLoss(Function): + @staticmethod + def forward(ctx, logits, targets, gamma, alpha): + ctx.save_for_backward(logits, targets) + num_classes = logits.shape[1] + ctx.num_classes = num_classes + ctx.gamma = gamma + ctx.alpha = alpha + + losses = _C.sigmoid_focalloss_forward(logits, targets, num_classes, gamma, alpha) + return losses + + @staticmethod + @once_differentiable + def backward(ctx, d_loss): + logits, targets = ctx.saved_tensors + num_classes = ctx.num_classes + gamma = ctx.gamma + alpha = ctx.alpha + d_loss = d_loss.contiguous() + d_logits = _C.sigmoid_focalloss_backward(logits, targets, d_loss, num_classes, gamma, alpha) + return d_logits, None, None, None, None + + +sigmoid_focal_loss_cuda = _SigmoidFocalLoss.apply + + +def sigmoid_focal_loss_cpu(logits, targets, gamma, alpha): + num_classes = logits.shape[1] + dtype = targets.dtype + device = targets.device + class_range = torch.arange(1, num_classes + 1, dtype=dtype, device=device).unsqueeze(0) + + t = targets.unsqueeze(1) + p = torch.sigmoid(logits) + term1 = (1 - p) ** gamma * torch.log(p) + term2 = p**gamma * torch.log(1 - p) + return -(t == class_range).float() * term1 * alpha - ((t != class_range) * (t >= 0)).float() * term2 * (1 - alpha) + + +class SigmoidFocalLoss(nn.Module): + def __init__(self, gamma, alpha): + super(SigmoidFocalLoss, self).__init__() + self.gamma = gamma + self.alpha = alpha + + def forward(self, logits, targets): + # Protect for the case where there are no boxes! + if targets.nelement() == 0: + return torch.as_tensor(0, device=logits.device) + + if logits.is_cuda: + loss_func = sigmoid_focal_loss_cuda + else: + loss_func = sigmoid_focal_loss_cpu + + loss = loss_func(logits, targets, self.gamma, self.alpha) + return loss.sum() + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "gamma=" + str(self.gamma) + tmpstr += ", alpha=" + str(self.alpha) + tmpstr += ")" + return tmpstr + + +def token_sigmoid_softmax_focal_loss(pred_logits, targets, alpha, gamma, text_mask=None): + # Another modification is that because we use the cross entropy version, there is no frequent or not frequent class. + # So we temporarily retired the design of alpha. + + assert targets.dim() == 3 + assert pred_logits.dim() == 3 # batch x from x to + + # reprocess target to become probability map ready for softmax + targets = targets.float() + target_num = targets.sum(-1) + 1e-8 # numerical stability + targets = targets / target_num.unsqueeze(-1) # T(x) + + if text_mask is not None: + # reserve the last token for non object + assert text_mask.dim() == 2 + text_mask[:, -1] = 1 + text_mask = (text_mask > 0).unsqueeze(1).repeat(1, pred_logits.size(1), 1) # copy along the image channel + pred_logits = pred_logits.masked_fill(~text_mask, -1000000) # softmax + + out_prob = pred_logits.softmax(-1) + + filled_targets = targets.clone() + filled_targets[filled_targets == 0] = 1.0 + + weight = torch.clamp(targets - out_prob, min=0.001) / filled_targets + weight = torch.pow(weight, gamma) # weight = torch.pow(torch.clamp(target - out_prob, min=0.01), gamma) + + loss_ce = ( + -targets * weight * pred_logits.log_softmax(-1) + ) # only those positives with positive target_sim will have losses. + return loss_ce + + +def token_sigmoid_binary_focal_loss_v2(pred_logits, targets, alpha, gamma, text_mask=None): + assert targets.dim() == 3 + assert pred_logits.dim() == 3 # batch x from x to + + if text_mask is not None: + assert text_mask.dim() == 2 + + # We convert everything into binary + out_prob = pred_logits.sigmoid() + out_prob_neg_pos = torch.stack([1 - out_prob, out_prob], dim=-1) + 1e-8 # batch x boxes x 256 x 2 + weight = torch.pow(-out_prob_neg_pos + 1.0, gamma) + + focal_zero = -weight[:, :, :, 0] * torch.log(out_prob_neg_pos[:, :, :, 0]) * (1 - alpha) # negative class + focal_one = -weight[:, :, :, 1] * torch.log(out_prob_neg_pos[:, :, :, 1]) * alpha # positive class + focal = torch.stack([focal_zero, focal_one], dim=-1) + loss_ce = torch.gather(focal, index=targets.long().unsqueeze(-1), dim=-1) + return loss_ce + + +def token_sigmoid_binary_focal_loss(pred_logits, targets, alpha, gamma, text_mask=None): + # binary version of focal loss + # copied from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha: (optional) Weighting factor in range (0,1) to balance + positive vs negative examples. Default = -1 (no weighting). + gamma: Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. + Returns: + Loss tensor with the reduction option applied. + """ + assert targets.dim() == 3 + assert pred_logits.dim() == 3 # batch x from x to + + bs, n, _ = pred_logits.shape + if text_mask is not None: + assert text_mask.dim() == 2 + text_mask = (text_mask > 0).unsqueeze(1) + text_mask = text_mask.repeat(1, pred_logits.size(1), 1) # copy along the image channel dimension + pred_logits = torch.masked_select(pred_logits, text_mask) + if targets.size(-1) > text_mask.size(-1): + targets = targets[:, :, : text_mask.size(-1)] + targets = torch.masked_select(targets, text_mask) + + # print(pred_logits.shape) + # print(targets.shape) + + p = torch.sigmoid(pred_logits) + ce_loss = F.binary_cross_entropy_with_logits(pred_logits, targets, reduction="none") + p_t = p * targets + (1 - p) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + return loss + + +class TokenSigmoidFocalLoss(nn.Module): + def __init__(self, alpha, gamma): + super(TokenSigmoidFocalLoss, self).__init__() + self.alpha = alpha + self.gamma = gamma + + def forward(self, logits, targets, text_masks=None, version="binary", **kwargs): + + # Protect for the case where there are no boxes! + if targets.nelement() == 0: + return torch.as_tensor(0, device=logits.device) + + if version == "binary": + loss_func = token_sigmoid_binary_focal_loss + elif version == "softmax": + loss_func = token_sigmoid_softmax_focal_loss + elif version == "binaryv2": + loss_func = token_sigmoid_binary_focal_loss_v2 + else: + raise NotImplementedError + loss = loss_func(logits, targets, self.alpha, self.gamma, text_masks, **kwargs) + return loss.sum() + + def __repr__(self): + tmpstr = self.__class__.__name__ + "(" + tmpstr += "gamma=" + str(self.gamma) + tmpstr += ", alpha=" + str(self.alpha) + tmpstr += ")" + return tmpstr diff --git a/maskrcnn_benchmark/layers/smooth_l1_loss.py b/maskrcnn_benchmark/layers/smooth_l1_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..6efe58b4b3088e1c657f533f1fad46163507d323 --- /dev/null +++ b/maskrcnn_benchmark/layers/smooth_l1_loss.py @@ -0,0 +1,16 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + + +# TODO maybe push this to nn? +def smooth_l1_loss(input, target, beta=1.0 / 9, size_average=True): + """ + very similar to the smooth_l1_loss from pytorch, but with + the extra beta parameter + """ + n = torch.abs(input - target) + cond = n < beta + loss = torch.where(cond, 0.5 * n**2 / beta, n - 0.5 * beta) + if size_average: + return loss.mean() + return loss.sum() diff --git a/maskrcnn_benchmark/modeling/__init__.py b/maskrcnn_benchmark/modeling/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/maskrcnn_benchmark/modeling/backbone/__init__.py b/maskrcnn_benchmark/modeling/backbone/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f7dd090ddd49ea11e1f99f3080a823488ac595be --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/__init__.py @@ -0,0 +1,466 @@ +from collections import OrderedDict + +from torch import nn + +from maskrcnn_benchmark.modeling import registry +from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform +from maskrcnn_benchmark.layers import DropBlock2D, DyHead +from . import fpn as fpn_module +from . import bifpn +from . import resnet +from . import efficientnet +from . import efficientdet +from . import swint +from . import swint_v2 +from . import swint_vl +from . import swint_v2_vl +from . import fusion_swin_transformer +from . import fusion_swin_transformer_v2 +from . import fusion_swin_transformer_v3 + + +@registry.BACKBONES.register("R-50-C4") +@registry.BACKBONES.register("R-50-C5") +@registry.BACKBONES.register("R-101-C4") +@registry.BACKBONES.register("R-101-C5") +def build_resnet_backbone(cfg): + body = resnet.ResNet(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.BACKBONES.register("R-50-RETINANET") +@registry.BACKBONES.register("R-101-RETINANET") +def build_resnet_c5_backbone(cfg): + body = resnet.ResNet(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.BACKBONES.register("R-34-v2-RETINANET") +@registry.BACKBONES.register("R-34-v2-FCOS") +def build_mobile_backbone(cfg): + body = resnet_light_v2.ResNetC5(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.BACKBONES.register("R-34-v2-FPN") +def build_resnet_fpn_backbone(cfg): + body = resnet_light_v2.ResNet(cfg) + in_channels_stage2 = 64 + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + fpn = fpn_module.FPN( + in_channels_list=[ + in_channels_stage2, + in_channels_stage2 * 2, + in_channels_stage2 * 4, + in_channels_stage2 * 8, + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU, cfg.MODEL.FPN.USE_DYRELU), + top_blocks=fpn_module.LastLevelMaxPool(), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("R-34-v2-FPN-RETINANET") +@registry.BACKBONES.register("R-34-v2-FPN-FCOS") +def build_resnet_light_fpn_p6p7_backbone(cfg): + body = resnet_light_v2.ResNet(cfg) + in_channels_stage2 = 64 + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + in_channels_p6p7 = out_channels + fpn = fpn_module.FPN( + in_channels_list=[ + 0, + in_channels_stage2 * 2, + in_channels_stage2 * 4, + in_channels_stage2 * 8, + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU, cfg.MODEL.FPN.USE_DYRELU), + top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("R-50-FPN") +@registry.BACKBONES.register("R-101-FPN") +def build_resnet_fpn_backbone(cfg): + body = resnet_evo.ResNet(cfg) if cfg.MODEL.BACKBONE.USE_EN else resnet.ResNet(cfg) + in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + fpn = fpn_module.FPN( + in_channels_list=[ + in_channels_stage2, + in_channels_stage2 * 2, + in_channels_stage2 * 4, + in_channels_stage2 * 8, + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelMaxPool(), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + if cfg.MODEL.FPN.USE_DYHEAD: + dyhead = DyHead(cfg, out_channels) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)])) + else: + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("R-50-FPN-RETINANET") +@registry.BACKBONES.register("R-101-FPN-RETINANET") +@registry.BACKBONES.register("R-50-FPN-FCOS") +@registry.BACKBONES.register("R-101-FPN-FCOS") +def build_resnet_fpn_p6p7_backbone(cfg): + body = resnet_evo.ResNet(cfg) if cfg.MODEL.BACKBONE.USE_EN else resnet.ResNet(cfg) + in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + in_channels_p6p7 = out_channels + fpn = fpn_module.FPN( + in_channels_list=[ + 0, + in_channels_stage2 * 2, + in_channels_stage2 * 4, + in_channels_stage2 * 8, + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("SWINT-FPN-RETINANET") +def build_retinanet_swint_fpn_backbone(cfg): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + if cfg.MODEL.SWINT.VERSION == "v1": + body = swint.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "v2": + body = swint_v2.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "vl": + body = swint_vl.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "v2_vl": + body = swint_v2_vl.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "fusion" and cfg.MODEL.BACKBONE.FUSION_VERSION == "v1": + body = fusion_swin_transformer.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "fusion" and cfg.MODEL.BACKBONE.FUSION_VERSION == "v2": + body = fusion_swin_transformer_v2.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "fusion" and cfg.MODEL.BACKBONE.FUSION_VERSION == "v3": + body = fusion_swin_transformer_v3.build_swint_backbone(cfg) + + in_channels_stages = cfg.MODEL.SWINT.OUT_CHANNELS + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + in_channels_p6p7 = out_channels + fpn = fpn_module.FPN( + in_channels_list=[ + 0, + in_channels_stages[-3], + in_channels_stages[-2], + in_channels_stages[-1], + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + return_swint_feature_before_fusion=cfg.MODEL.FPN.RETURN_SWINT_FEATURE_BEFORE_FUSION, + ) + if cfg.MODEL.FPN.USE_DYHEAD: + dyhead = DyHead(cfg, out_channels) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)])) + else: + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("SWINT-FPN") +def build_swint_fpn_backbone(cfg): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + if cfg.MODEL.SWINT.VERSION == "v1": + body = swint.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "v2": + body = swint_v2.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "vl": + body = swint_vl.build_swint_backbone(cfg) + elif cfg.MODEL.SWINT.VERSION == "v2_vl": + body = swint_v2_vl.build_swint_backbone(cfg) + + in_channels_stages = cfg.MODEL.SWINT.OUT_CHANNELS + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + fpn = fpn_module.FPN( + in_channels_list=[ + in_channels_stages[-4], + in_channels_stages[-3], + in_channels_stages[-2], + in_channels_stages[-1], + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelMaxPool(), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + if cfg.MODEL.FPN.USE_DYHEAD: + dyhead = DyHead(cfg, out_channels) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)])) + else: + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("CVT-FPN-RETINANET") +def build_retinanet_cvt_fpn_backbone(cfg): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + body = cvt.build_cvt_backbone(cfg) + in_channels_stages = cfg.MODEL.SPEC.DIM_EMBED + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + in_channels_p6p7 = out_channels + fpn = fpn_module.FPN( + in_channels_list=[ + 0, + in_channels_stages[-3], + in_channels_stages[-2], + in_channels_stages[-1], + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + if cfg.MODEL.FPN.USE_DYHEAD: + dyhead = DyHead(cfg, out_channels) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)])) + else: + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("CVT-FPN") +def build_cvt_fpn_backbone(cfg): + """ + Args: + cfg: a detectron2 CfgNode + + Returns: + backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. + """ + body = cvt.build_cvt_backbone(cfg) + in_channels_stages = cfg.MODEL.SPEC.DIM_EMBED + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + fpn = fpn_module.FPN( + in_channels_list=[ + in_channels_stages[-4], + in_channels_stages[-3], + in_channels_stages[-2], + in_channels_stages[-1], + ], + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelMaxPool(), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + if cfg.MODEL.FPN.USE_DYHEAD: + dyhead = DyHead(cfg, out_channels) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn), ("dyhead", dyhead)])) + else: + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("BigR50x3-FPN-RETINANET") +@registry.BACKBONES.register("BigR50x3-FPN-FCOS") +@registry.BACKBONES.register("BigR101x3-FPN-RETINANET") +@registry.BACKBONES.register("BigR101x3-FPN-FCOS") +@registry.BACKBONES.register("BigR152x4-FPN-RETINANET") +@registry.BACKBONES.register("BigR152x4-FPN-FCOS") +def build_resnet_fpn_p6p7_backbone(cfg): + version = cfg.MODEL.BACKBONE.CONV_BODY.split("-")[0] + body = resnet_big.BIG_MODELS[version](cfg) + in_channels_stage = body.out_channels + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + in_channels_p6p7 = out_channels + in_channels_stage[0] = 0 + fpn = fpn_module.FPN( + in_channels_list=in_channels_stage, + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("NAS-RETINANET") +def build_nas_backbone(cfg): + body = nas.Net(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.BACKBONES.register("NAS-FPN") +def build_nas_backbone(cfg): + body = nas.SingpathSupernet(cfg) if cfg.MODEL.META_ARCHITECTURE == "SupernetRCNN" else nas.Net(cfg) + in_channels_stage = body.out_channels + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + fpn = fpn_module.FPN( + in_channels_list=in_channels_stage, + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelMaxPool(), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("NAS-FPN-RETINANET") +@registry.BACKBONES.register("NAS-FPN-FCOS") +def build_resnet_light_fpn_p6p7_backbone(cfg): + body = nas.SingpathSupernet(cfg) if cfg.MODEL.META_ARCHITECTURE == "SupernetRCNN" else nas.Net(cfg) + in_channels_stage = body.out_channels + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + in_channels_p6p7 = out_channels + in_channels_stage[0] = 0 + fpn = fpn_module.FPN( + in_channels_list=in_channels_stage, + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("EFFICIENT7-FPN-RETINANET") +@registry.BACKBONES.register("EFFICIENT7-FPN-FCOS") +@registry.BACKBONES.register("EFFICIENT5-FPN-RETINANET") +@registry.BACKBONES.register("EFFICIENT5-FPN-FCOS") +@registry.BACKBONES.register("EFFICIENT3-FPN-RETINANET") +@registry.BACKBONES.register("EFFICIENT3-FPN-FCOS") +def build_eff_fpn_p6p7_backbone(cfg): + version = cfg.MODEL.BACKBONE.CONV_BODY.split("-")[0] + version = version.replace("EFFICIENT", "b") + body = efficientnet.get_efficientnet(cfg, version) + in_channels_stage = body.out_channels + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + in_channels_p6p7 = out_channels + in_channels_stage[0] = 0 + fpn = fpn_module.FPN( + in_channels_list=in_channels_stage, + out_channels=out_channels, + conv_block=conv_with_kaiming_uniform(cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU), + top_blocks=fpn_module.LastLevelP6P7(in_channels_p6p7, out_channels), + drop_block=DropBlock2D(cfg.MODEL.FPN.DROP_PROB, cfg.MODEL.FPN.DROP_SIZE) if cfg.MODEL.FPN.DROP_BLOCK else None, + use_spp=cfg.MODEL.FPN.USE_SPP, + use_pan=cfg.MODEL.FPN.USE_PAN, + ) + model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)])) + return model + + +@registry.BACKBONES.register("EFFICIENT7-BIFPN-RETINANET") +@registry.BACKBONES.register("EFFICIENT7-BIFPN-FCOS") +@registry.BACKBONES.register("EFFICIENT5-BIFPN-RETINANET") +@registry.BACKBONES.register("EFFICIENT5-BIFPN-FCOS") +@registry.BACKBONES.register("EFFICIENT3-BIFPN-RETINANET") +@registry.BACKBONES.register("EFFICIENT3-BIFPN-FCOS") +def build_eff_fpn_p6p7_backbone(cfg): + version = cfg.MODEL.BACKBONE.CONV_BODY.split("-")[0] + version = version.replace("EFFICIENT", "b") + body = efficientnet.get_efficientnet(cfg, version) + in_channels_stage = body.out_channels + out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + bifpns = nn.ModuleList() + for i in range(cfg.MODEL.BIFPN.NUM_REPEATS): + first_time = i == 0 + fpn = bifpn.BiFPN( + in_channels_list=in_channels_stage[1:], + out_channels=out_channels, + first_time=first_time, + attention=cfg.MODEL.BIFPN.USE_ATTENTION, + ) + bifpns.append(fpn) + model = nn.Sequential(OrderedDict([("body", body), ("bifpn", bifpns)])) + return model + + +@registry.BACKBONES.register("EFFICIENT-DET") +def build_efficientdet_backbone(cfg): + efficientdet.g_simple_padding = True + compound = cfg.MODEL.BACKBONE.EFFICIENT_DET_COMPOUND + start_from = cfg.MODEL.BACKBONE.EFFICIENT_DET_START_FROM + model = efficientdet.EffNetFPN( + compound_coef=compound, + start_from=start_from, + ) + if cfg.MODEL.BACKBONE.USE_SYNCBN: + import torch + + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) + return model + + +def build_backbone(cfg): + assert ( + cfg.MODEL.BACKBONE.CONV_BODY in registry.BACKBONES + ), "cfg.MODEL.BACKBONE.CONV_BODY: {} are not registered in registry".format(cfg.MODEL.BACKBONE.CONV_BODY) + return registry.BACKBONES[cfg.MODEL.BACKBONE.CONV_BODY](cfg) + + +def build_fusion_backbone(vision_backbone, language_backbone, version, add_linear_layer): + if version == "v1": + return fusion_swin_transformer.build_combined_backbone(vision_backbone, language_backbone, add_linear_layer) + elif version == "v2": + return fusion_swin_transformer_v2.build_combined_backbone(vision_backbone, language_backbone, add_linear_layer) + elif version == "v3": + return fusion_swin_transformer_v3.build_combined_backbone(vision_backbone, language_backbone, add_linear_layer) diff --git a/maskrcnn_benchmark/modeling/backbone/bifpn.py b/maskrcnn_benchmark/modeling/backbone/bifpn.py new file mode 100644 index 0000000000000000000000000000000000000000..fd87072152a9bb18056fee5fc1f841e76529d519 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/bifpn.py @@ -0,0 +1,271 @@ +import torch.nn as nn +import torch + +from maskrcnn_benchmark.layers import swish + + +class BiFPN(nn.Module): + def __init__(self, in_channels_list, out_channels, first_time=False, epsilon=1e-4, attention=True): + super(BiFPN, self).__init__() + self.epsilon = epsilon + # Conv layers + self.conv6_up = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.conv5_up = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.conv4_up = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.conv3_up = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.conv4_down = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.conv5_down = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.conv6_down = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.conv7_down = nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, groups=out_channels, bias=False), + nn.Conv2d(out_channels, out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + + # Feature scaling layers + self.p6_upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.p5_upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.p4_upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.p3_upsample = nn.Upsample(scale_factor=2, mode="nearest") + + self.p4_downsample = nn.MaxPool2d(3, 2) + self.p5_downsample = nn.MaxPool2d(3, 2) + self.p6_downsample = nn.MaxPool2d(3, 2) + self.p7_downsample = nn.MaxPool2d(3, 2) + + self.swish = swish() + + self.first_time = first_time + if self.first_time: + self.p5_down_channel = nn.Sequential( + nn.Conv2d(in_channels_list[2], out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.p4_down_channel = nn.Sequential( + nn.Conv2d(in_channels_list[1], out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.p3_down_channel = nn.Sequential( + nn.Conv2d(in_channels_list[0], out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + + self.p5_to_p6 = nn.Sequential( + nn.Conv2d(in_channels_list[2], out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + nn.MaxPool2d(3, 2), + ) + self.p6_to_p7 = nn.Sequential(nn.MaxPool2d(3, 2)) + + self.p4_down_channel_2 = nn.Sequential( + nn.Conv2d(in_channels_list[1], out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + self.p5_down_channel_2 = nn.Sequential( + nn.Conv2d(in_channels_list[2], out_channels, 1), + nn.BatchNorm2d(out_channels, momentum=0.01, eps=1e-3), + ) + + # Weight + self.p6_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p6_w1_relu = nn.ReLU() + self.p5_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p5_w1_relu = nn.ReLU() + self.p4_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p4_w1_relu = nn.ReLU() + self.p3_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p3_w1_relu = nn.ReLU() + + self.p4_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p4_w2_relu = nn.ReLU() + self.p5_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p5_w2_relu = nn.ReLU() + self.p6_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p6_w2_relu = nn.ReLU() + self.p7_w2 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p7_w2_relu = nn.ReLU() + + self.attention = attention + + def forward(self, inputs): + """ + illustration of a minimal bifpn unit + P7_0 -------------------------> P7_2 --------> + |-------------| ↑ + ↓ | + P6_0 ---------> P6_1 ---------> P6_2 --------> + |-------------|--------------↑ ↑ + ↓ | + P5_0 ---------> P5_1 ---------> P5_2 --------> + |-------------|--------------↑ ↑ + ↓ | + P4_0 ---------> P4_1 ---------> P4_2 --------> + |-------------|--------------↑ ↑ + |--------------↓ | + P3_0 -------------------------> P3_2 --------> + """ + + # downsample channels using same-padding conv2d to target phase's if not the same + # judge: same phase as target, + # if same, pass; + # elif earlier phase, downsample to target phase's by pooling + # elif later phase, upsample to target phase's by nearest interpolation + + if self.attention: + p3_out, p4_out, p5_out, p6_out, p7_out = self._forward_fast_attention(inputs) + else: + p3_out, p4_out, p5_out, p6_out, p7_out = self._forward(inputs) + + return p3_out, p4_out, p5_out, p6_out, p7_out + + def _forward_fast_attention(self, inputs): + if self.first_time: + p3, p4, p5 = inputs[-3:] + + p6_in = self.p5_to_p6(p5) + p7_in = self.p6_to_p7(p6_in) + + p3_in = self.p3_down_channel(p3) + p4_in = self.p4_down_channel(p4) + p5_in = self.p5_down_channel(p5) + + else: + # P3_0, P4_0, P5_0, P6_0 and P7_0 + p3_in, p4_in, p5_in, p6_in, p7_in = inputs + + # P7_0 to P7_2 + + # Weights for P6_0 and P7_0 to P6_1 + p6_w1 = self.p6_w1_relu(self.p6_w1) + weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon) + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_up = self.conv6_up(self.swish(weight[0] * p6_in + weight[1] * self.p6_upsample(p7_in))) + + # Weights for P5_0 and P6_1 to P5_1 + p5_w1 = self.p5_w1_relu(self.p5_w1) + weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon) + # Connections for P5_0 and P6_1 to P5_1 respectively + p5_up = self.conv5_up(self.swish(weight[0] * p5_in + weight[1] * self.p5_upsample(p6_up))) + + # Weights for P4_0 and P5_1 to P4_1 + p4_w1 = self.p4_w1_relu(self.p4_w1) + weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon) + # Connections for P4_0 and P5_1 to P4_1 respectively + p4_up = self.conv4_up(self.swish(weight[0] * p4_in + weight[1] * self.p4_upsample(p5_up))) + + # Weights for P3_0 and P4_1 to P3_2 + p3_w1 = self.p3_w1_relu(self.p3_w1) + weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon) + # Connections for P3_0 and P4_1 to P3_2 respectively + p3_out = self.conv3_up(self.swish(weight[0] * p3_in + weight[1] * self.p3_upsample(p4_up))) + + if self.first_time: + p4_in = self.p4_down_channel_2(p4) + p5_in = self.p5_down_channel_2(p5) + + # Weights for P4_0, P4_1 and P3_2 to P4_2 + p4_w2 = self.p4_w2_relu(self.p4_w2) + weight = p4_w2 / (torch.sum(p4_w2, dim=0) + self.epsilon) + # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively + p4_out = self.conv4_down( + self.swish(weight[0] * p4_in + weight[1] * p4_up + weight[2] * self.p4_downsample(p3_out)) + ) + + # Weights for P5_0, P5_1 and P4_2 to P5_2 + p5_w2 = self.p5_w2_relu(self.p5_w2) + weight = p5_w2 / (torch.sum(p5_w2, dim=0) + self.epsilon) + # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively + p5_out = self.conv5_down( + self.swish(weight[0] * p5_in + weight[1] * p5_up + weight[2] * self.p5_downsample(p4_out)) + ) + + # Weights for P6_0, P6_1 and P5_2 to P6_2 + p6_w2 = self.p6_w2_relu(self.p6_w2) + weight = p6_w2 / (torch.sum(p6_w2, dim=0) + self.epsilon) + # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively + p6_out = self.conv6_down( + self.swish(weight[0] * p6_in + weight[1] * p6_up + weight[2] * self.p6_downsample(p5_out)) + ) + + # Weights for P7_0 and P6_2 to P7_2 + p7_w2 = self.p7_w2_relu(self.p7_w2) + weight = p7_w2 / (torch.sum(p7_w2, dim=0) + self.epsilon) + # Connections for P7_0 and P6_2 to P7_2 + p7_out = self.conv7_down(self.swish(weight[0] * p7_in + weight[1] * self.p7_downsample(p6_out))) + + return p3_out, p4_out, p5_out, p6_out, p7_out + + def _forward(self, inputs): + if self.first_time: + p3, p4, p5 = inputs + + p6_in = self.p5_to_p6(p5) + p7_in = self.p6_to_p7(p6_in) + + p3_in = self.p3_down_channel(p3) + p4_in = self.p4_down_channel(p4) + p5_in = self.p5_down_channel(p5) + + else: + # P3_0, P4_0, P5_0, P6_0 and P7_0 + p3_in, p4_in, p5_in, p6_in, p7_in = inputs + + # P7_0 to P7_2 + + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_in))) + + # Connections for P5_0 and P6_1 to P5_1 respectively + p5_up = self.conv5_up(self.swish(p5_in + self.p5_upsample(p6_up))) + + # Connections for P4_0 and P5_1 to P4_1 respectively + p4_up = self.conv4_up(self.swish(p4_in + self.p4_upsample(p5_up))) + + # Connections for P3_0 and P4_1 to P3_2 respectively + p3_out = self.conv3_up(self.swish(p3_in + self.p3_upsample(p4_up))) + + if self.first_time: + p4_in = self.p4_down_channel_2(p4) + p5_in = self.p5_down_channel_2(p5) + + # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively + p4_out = self.conv4_down(self.swish(p4_in + p4_up + self.p4_downsample(p3_out))) + + # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively + p5_out = self.conv5_down(self.swish(p5_in + p5_up + self.p5_downsample(p4_out))) + + # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively + p6_out = self.conv6_down(self.swish(p6_in + p6_up + self.p6_downsample(p5_out))) + + # Connections for P7_0 and P6_2 to P7_2 + p7_out = self.conv7_down(self.swish(p7_in + self.p7_downsample(p6_out))) + + return p3_out, p4_out, p5_out, p6_out, p7_out diff --git a/maskrcnn_benchmark/modeling/backbone/blocks.py b/maskrcnn_benchmark/modeling/backbone/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..8a5f797f2e8598b14a1de093f5726f0abea88d9b --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/blocks.py @@ -0,0 +1,266 @@ +import torch.nn as nn +from .ops import * + + +class stem(nn.Module): + num_layer = 1 + + def __init__(self, conv, inplanes, planes, stride=1, norm_layer=nn.BatchNorm2d): + super(stem, self).__init__() + + self.conv1 = conv(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + return out + + +class basic(nn.Module): + expansion = 1 + num_layer = 2 + + def __init__(self, conv, inplanes, planes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d): + super(basic, self).__init__() + midplanes = planes if midplanes is None else midplanes + self.conv1 = conv(inplanes, midplanes, stride) + self.bn1 = norm_layer(midplanes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv(midplanes, planes) + self.bn2 = norm_layer(planes) + if stride != 1 or inplanes != planes * self.expansion: + self.downsample = nn.Sequential( + conv1x1(inplanes, planes, stride), + norm_layer(planes), + ) + else: + self.downsample = None + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class bottleneck(nn.Module): + expansion = 4 + num_layer = 3 + + def __init__(self, conv, inplanes, planes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d): + super(bottleneck, self).__init__() + midplanes = planes if midplanes is None else midplanes + self.conv1 = conv1x1(inplanes, midplanes) + self.bn1 = norm_layer(midplanes) + self.conv2 = conv(midplanes, midplanes, stride) + self.bn2 = norm_layer(midplanes) + self.conv3 = conv1x1(midplanes, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + if stride != 1 or inplanes != planes * self.expansion: + self.downsample = nn.Sequential( + conv1x1(inplanes, planes * self.expansion, stride), + norm_layer(planes * self.expansion), + ) + else: + self.downsample = None + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class invert(nn.Module): + def __init__(self, conv, inp, oup, stride=1, expand_ratio=1, norm_layer=nn.BatchNorm2d): + super(invert, self).__init__() + self.stride = stride + assert stride in [1, 2] + + hidden_dim = round(inp * expand_ratio) + self.use_res_connect = self.stride == 1 and inp == oup + + if expand_ratio == 1: + self.conv = nn.Sequential( + # dw + conv(hidden_dim, hidden_dim, stride), + norm_layer(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + norm_layer(oup), + ) + else: + self.conv = nn.Sequential( + # pw + nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), + norm_layer(hidden_dim), + nn.ReLU6(inplace=True), + # dw + conv(hidden_dim, hidden_dim, stride), + norm_layer(hidden_dim), + nn.ReLU6(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + norm_layer(oup), + ) + + def forward(self, x): + if self.use_res_connect: + return x + self.conv(x) + else: + return self.conv(x) + + +invert2 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=2, **kwargs) +invert3 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=3, **kwargs) +invert4 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=4, **kwargs) +invert6 = lambda op, inp, outp, stride, **kwargs: invert(op, inp, outp, stride, expand_ratio=6, **kwargs) + + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + # reshape + x = x.view(batchsize, groups, channels_per_group, height, width) + x = torch.transpose(x, 1, 2).contiguous() + # flatten + x = x.view(batchsize, -1, height, width) + return x + + +class shuffle(nn.Module): + expansion = 1 + num_layer = 3 + + def __init__(self, conv, inplanes, outplanes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d): + super(shuffle, self).__init__() + inplanes = inplanes // 2 if stride == 1 else inplanes + midplanes = outplanes // 2 if midplanes is None else midplanes + rightoutplanes = outplanes - inplanes + if stride == 2: + self.left_branch = nn.Sequential( + # dw + conv(inplanes, inplanes, stride), + norm_layer(inplanes), + # pw-linear + conv1x1(inplanes, inplanes), + norm_layer(inplanes), + nn.ReLU(inplace=True), + ) + + self.right_branch = nn.Sequential( + # pw + conv1x1(inplanes, midplanes), + norm_layer(midplanes), + nn.ReLU(inplace=True), + # dw + conv(midplanes, midplanes, stride), + norm_layer(midplanes), + # pw-linear + conv1x1(midplanes, rightoutplanes), + norm_layer(rightoutplanes), + nn.ReLU(inplace=True), + ) + + self.reduce = stride == 2 + + def forward(self, x): + if self.reduce: + out = torch.cat((self.left_branch(x), self.right_branch(x)), 1) + else: + x1 = x[:, : (x.shape[1] // 2), :, :] + x2 = x[:, (x.shape[1] // 2) :, :, :] + out = torch.cat((x1, self.right_branch(x2)), 1) + + return channel_shuffle(out, 2) + + +class shufflex(nn.Module): + expansion = 1 + num_layer = 3 + + def __init__(self, conv, inplanes, outplanes, stride=1, midplanes=None, norm_layer=nn.BatchNorm2d): + super(shufflex, self).__init__() + inplanes = inplanes // 2 if stride == 1 else inplanes + midplanes = outplanes // 2 if midplanes is None else midplanes + rightoutplanes = outplanes - inplanes + if stride == 2: + self.left_branch = nn.Sequential( + # dw + conv(inplanes, inplanes, stride), + norm_layer(inplanes), + # pw-linear + conv1x1(inplanes, inplanes), + norm_layer(inplanes), + nn.ReLU(inplace=True), + ) + + self.right_branch = nn.Sequential( + # dw + conv(inplanes, inplanes, stride), + norm_layer(inplanes), + # pw-linear + conv1x1(inplanes, midplanes), + norm_layer(midplanes), + nn.ReLU(inplace=True), + # dw + conv(midplanes, midplanes, 1), + norm_layer(midplanes), + # pw-linear + conv1x1(midplanes, midplanes), + norm_layer(midplanes), + nn.ReLU(inplace=True), + # dw + conv(midplanes, midplanes, 1), + norm_layer(midplanes), + # pw-linear + conv1x1(midplanes, rightoutplanes), + norm_layer(rightoutplanes), + nn.ReLU(inplace=True), + ) + + self.reduce = stride == 2 + + def forward(self, x): + if self.reduce: + out = torch.cat((self.left_branch(x), self.right_branch(x)), 1) + else: + x1 = x[:, : (x.shape[1] // 2), :, :] + x2 = x[:, (x.shape[1] // 2) :, :, :] + out = torch.cat((x1, self.right_branch(x2)), 1) + + return channel_shuffle(out, 2) diff --git a/maskrcnn_benchmark/modeling/backbone/efficientdet.py b/maskrcnn_benchmark/modeling/backbone/efficientdet.py new file mode 100644 index 0000000000000000000000000000000000000000..08334424b00707059f7b19a85ed2d5a5558ae203 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/efficientdet.py @@ -0,0 +1,2007 @@ +import torch +import re +import numpy as np +import torch.nn as nn +import torch.nn.functional as F +import logging +import cv2 +import math +import itertools +import collections +from torchvision.ops import nms + + +GlobalParams = collections.namedtuple( + "GlobalParams", + [ + "batch_norm_momentum", + "batch_norm_epsilon", + "dropout_rate", + "num_classes", + "width_coefficient", + "depth_coefficient", + "depth_divisor", + "min_depth", + "drop_connect_rate", + "image_size", + ], +) + +# Parameters for an individual model block +BlockArgs = collections.namedtuple( + "BlockArgs", + ["kernel_size", "num_repeat", "input_filters", "output_filters", "expand_ratio", "id_skip", "stride", "se_ratio"], +) + +# https://stackoverflow.com/a/18348004 +# Change namedtuple defaults +GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields) +BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields) + +# in the old version, g_simple_padding = False, which tries to align +# tensorflow's implementation, which is not required here. +g_simple_padding = True + + +class MaxPool2dStaticSamePadding(nn.Module): + """ + created by Zylo117 + The real keras/tensorflow MaxPool2d with same padding + """ + + def __init__(self, kernel_size, stride): + super().__init__() + if g_simple_padding: + self.pool = nn.MaxPool2d(kernel_size, stride, padding=(kernel_size - 1) // 2) + else: + assert ValueError() + self.pool = nn.MaxPool2d(kernel_size, stride) + self.stride = self.pool.stride + self.kernel_size = self.pool.kernel_size + + if isinstance(self.stride, int): + self.stride = [self.stride] * 2 + elif len(self.stride) == 1: + self.stride = [self.stride[0]] * 2 + + if isinstance(self.kernel_size, int): + self.kernel_size = [self.kernel_size] * 2 + elif len(self.kernel_size) == 1: + self.kernel_size = [self.kernel_size[0]] * 2 + + def forward(self, x): + if g_simple_padding: + return self.pool(x) + else: + assert ValueError() + h, w = x.shape[-2:] + + h_step = math.ceil(w / self.stride[1]) + v_step = math.ceil(h / self.stride[0]) + h_cover_len = self.stride[1] * (h_step - 1) + 1 + (self.kernel_size[1] - 1) + v_cover_len = self.stride[0] * (v_step - 1) + 1 + (self.kernel_size[0] - 1) + + extra_h = h_cover_len - w + extra_v = v_cover_len - h + + left = extra_h // 2 + right = extra_h - left + top = extra_v // 2 + bottom = extra_v - top + + x = F.pad(x, [left, right, top, bottom]) + + x = self.pool(x) + return x + + +class Conv2dStaticSamePadding(nn.Module): + """ + created by Zylo117 + The real keras/tensorflow conv2d with same padding + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, groups=1, dilation=1, **kwargs): + super().__init__() + if g_simple_padding: + assert kernel_size % 2 == 1 + assert dilation == 1 + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + bias=bias, + groups=groups, + padding=(kernel_size - 1) // 2, + ) + self.stride = self.conv.stride + if isinstance(self.stride, int): + self.stride = [self.stride] * 2 + elif len(self.stride) == 1: + self.stride = [self.stride[0]] * 2 + else: + self.stride = list(self.stride) + else: + assert ValueError() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, bias=bias, groups=groups) + self.stride = self.conv.stride + self.kernel_size = self.conv.kernel_size + self.dilation = self.conv.dilation + + if isinstance(self.stride, int): + self.stride = [self.stride] * 2 + elif len(self.stride) == 1: + self.stride = [self.stride[0]] * 2 + + if isinstance(self.kernel_size, int): + self.kernel_size = [self.kernel_size] * 2 + elif len(self.kernel_size) == 1: + self.kernel_size = [self.kernel_size[0]] * 2 + + def forward(self, x): + if g_simple_padding: + return self.conv(x) + else: + assert ValueError() + h, w = x.shape[-2:] + + h_step = math.ceil(w / self.stride[1]) + v_step = math.ceil(h / self.stride[0]) + h_cover_len = self.stride[1] * (h_step - 1) + 1 + (self.kernel_size[1] - 1) + v_cover_len = self.stride[0] * (v_step - 1) + 1 + (self.kernel_size[0] - 1) + + extra_h = h_cover_len - w + extra_v = v_cover_len - h + + left = extra_h // 2 + right = extra_h - left + top = extra_v // 2 + bottom = extra_v - top + + x = F.pad(x, [left, right, top, bottom]) + + x = self.conv(x) + return x + + +class SeparableConvBlock(nn.Module): + """ + created by Zylo117 + """ + + def __init__(self, in_channels, out_channels=None, norm=True, activation=False, onnx_export=False): + super(SeparableConvBlock, self).__init__() + if out_channels is None: + out_channels = in_channels + + # Q: whether separate conv + # share bias between depthwise_conv and pointwise_conv + # or just pointwise_conv apply bias. + # A: Confirmed, just pointwise_conv applies bias, depthwise_conv has no bias. + + self.depthwise_conv = Conv2dStaticSamePadding( + in_channels, in_channels, kernel_size=3, stride=1, groups=in_channels, bias=False + ) + self.pointwise_conv = Conv2dStaticSamePadding(in_channels, out_channels, kernel_size=1, stride=1) + + self.norm = norm + if self.norm: + # Warning: pytorch momentum is different from tensorflow's, momentum_pytorch = 1 - momentum_tensorflow + self.bn = nn.BatchNorm2d(num_features=out_channels, momentum=0.01, eps=1e-3) + + self.activation = activation + if self.activation: + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + def forward(self, x): + x = self.depthwise_conv(x) + x = self.pointwise_conv(x) + + if self.norm: + x = self.bn(x) + + if self.activation: + x = self.swish(x) + + return x + + +class BiFPN(nn.Module): + """ + modified by Zylo117 + """ + + def __init__( + self, + num_channels, + conv_channels, + first_time=False, + epsilon=1e-4, + onnx_export=False, + attention=True, + adaptive_up=False, + ): + """ + + Args: + num_channels: + conv_channels: + first_time: whether the input comes directly from the efficientnet, + if True, downchannel it first, and downsample P5 to generate P6 then P7 + epsilon: epsilon of fast weighted attention sum of BiFPN, not the BN's epsilon + onnx_export: if True, use Swish instead of MemoryEfficientSwish + """ + super(BiFPN, self).__init__() + self.epsilon = epsilon + # Conv layers + self.conv6_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv5_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv4_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv3_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv4_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv5_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv6_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv7_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + + # Feature scaling layers + self.p6_upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.p5_upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.p4_upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.p3_upsample = nn.Upsample(scale_factor=2, mode="nearest") + + self.adaptive_up = adaptive_up + + self.p4_downsample = MaxPool2dStaticSamePadding(3, 2) + self.p5_downsample = MaxPool2dStaticSamePadding(3, 2) + self.p6_downsample = MaxPool2dStaticSamePadding(3, 2) + self.p7_downsample = MaxPool2dStaticSamePadding(3, 2) + + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + self.first_time = first_time + if self.first_time: + self.p5_down_channel = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[2], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + self.p4_down_channel = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[1], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + self.p3_down_channel = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[0], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + + if len(conv_channels) == 3: + self.p5_to_p6 = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[2], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + MaxPool2dStaticSamePadding(3, 2), + ) + else: + assert len(conv_channels) == 4 + self.p6_down_channel = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[3], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + + self.p6_to_p7 = nn.Sequential(MaxPool2dStaticSamePadding(3, 2)) + + self.p4_down_channel_2 = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[1], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + self.p5_down_channel_2 = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[2], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + + # Weight + self.p6_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p6_w1_relu = nn.ReLU() + self.p5_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p5_w1_relu = nn.ReLU() + self.p4_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p4_w1_relu = nn.ReLU() + self.p3_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p3_w1_relu = nn.ReLU() + + self.p4_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p4_w2_relu = nn.ReLU() + self.p5_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p5_w2_relu = nn.ReLU() + self.p6_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p6_w2_relu = nn.ReLU() + self.p7_w2 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p7_w2_relu = nn.ReLU() + + self.attention = attention + + def forward(self, inputs): + """ + illustration of a minimal bifpn unit + P7_0 -------------------------> P7_2 --------> + |-------------| ↑ + ↓ | + P6_0 ---------> P6_1 ---------> P6_2 --------> + |-------------|--------------↑ ↑ + ↓ | + P5_0 ---------> P5_1 ---------> P5_2 --------> + |-------------|--------------↑ ↑ + ↓ | + P4_0 ---------> P4_1 ---------> P4_2 --------> + |-------------|--------------↑ ↑ + |--------------↓ | + P3_0 -------------------------> P3_2 --------> + """ + + # downsample channels using same-padding conv2d to target phase's if not the same + # judge: same phase as target, + # if same, pass; + # elif earlier phase, downsample to target phase's by pooling + # elif later phase, upsample to target phase's by nearest interpolation + if self.attention: + p3_out, p4_out, p5_out, p6_out, p7_out = self._forward_fast_attention(inputs) + else: + p3_out, p4_out, p5_out, p6_out, p7_out = self._forward(inputs) + + return p3_out, p4_out, p5_out, p6_out, p7_out + + def _forward_fast_attention(self, inputs): + if self.first_time: + if len(inputs) == 3: + p3, p4, p5 = inputs + p6_in = self.p5_to_p6(p5) + else: + p3, p4, p5, p6 = inputs + p6_in = self.p6_down_channel(p6) + + p7_in = self.p6_to_p7(p6_in) + + p3_in = self.p3_down_channel(p3) + p4_in = self.p4_down_channel(p4) + p5_in = self.p5_down_channel(p5) + else: + # P3_0, P4_0, P5_0, P6_0 and P7_0 + p3_in, p4_in, p5_in, p6_in, p7_in = inputs + + # P7_0 to P7_2 + + if not self.adaptive_up: + # Weights for P6_0 and P7_0 to P6_1 + p6_w1 = self.p6_w1_relu(self.p6_w1) + weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon) + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_up = self.conv6_up(self.swish(weight[0] * p6_in + weight[1] * self.p6_upsample(p7_in))) + + # Weights for P5_0 and P6_0 to P5_1 + p5_w1 = self.p5_w1_relu(self.p5_w1) + weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon) + # Connections for P5_0 and P6_0 to P5_1 respectively + p5_up = self.conv5_up(self.swish(weight[0] * p5_in + weight[1] * self.p5_upsample(p6_up))) + + # Weights for P4_0 and P5_0 to P4_1 + p4_w1 = self.p4_w1_relu(self.p4_w1) + weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon) + # Connections for P4_0 and P5_0 to P4_1 respectively + p4_up = self.conv4_up(self.swish(weight[0] * p4_in + weight[1] * self.p4_upsample(p5_up))) + + # Weights for P3_0 and P4_1 to P3_2 + p3_w1 = self.p3_w1_relu(self.p3_w1) + weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon) + # Connections for P3_0 and P4_1 to P3_2 respectively + p3_out = self.conv3_up(self.swish(weight[0] * p3_in + weight[1] * self.p3_upsample(p4_up))) + else: + # Weights for P6_0 and P7_0 to P6_1 + p6_w1 = self.p6_w1_relu(self.p6_w1) + weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon) + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_upsample = nn.Upsample(size=p6_in.shape[-2:]) + p6_up = self.conv6_up(self.swish(weight[0] * p6_in + weight[1] * p6_upsample(p7_in))) + + # Weights for P5_0 and P6_0 to P5_1 + p5_w1 = self.p5_w1_relu(self.p5_w1) + weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon) + # Connections for P5_0 and P6_0 to P5_1 respectively + p5_upsample = nn.Upsample(size=p5_in.shape[-2:]) + p5_up = self.conv5_up(self.swish(weight[0] * p5_in + weight[1] * p5_upsample(p6_up))) + + # Weights for P4_0 and P5_0 to P4_1 + p4_w1 = self.p4_w1_relu(self.p4_w1) + weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon) + # Connections for P4_0 and P5_0 to P4_1 respectively + p4_upsample = nn.Upsample(size=p4_in.shape[-2:]) + p4_up = self.conv4_up(self.swish(weight[0] * p4_in + weight[1] * p4_upsample(p5_up))) + + # Weights for P3_0 and P4_1 to P3_2 + p3_w1 = self.p3_w1_relu(self.p3_w1) + weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon) + p3_upsample = nn.Upsample(size=p3_in.shape[-2:]) + # Connections for P3_0 and P4_1 to P3_2 respectively + p3_out = self.conv3_up(self.swish(weight[0] * p3_in + weight[1] * p3_upsample(p4_up))) + + if self.first_time: + p4_in = self.p4_down_channel_2(p4) + p5_in = self.p5_down_channel_2(p5) + + # Weights for P4_0, P4_1 and P3_2 to P4_2 + p4_w2 = self.p4_w2_relu(self.p4_w2) + weight = p4_w2 / (torch.sum(p4_w2, dim=0) + self.epsilon) + # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively + p4_out = self.conv4_down( + self.swish(weight[0] * p4_in + weight[1] * p4_up + weight[2] * self.p4_downsample(p3_out)) + ) + + # Weights for P5_0, P5_1 and P4_2 to P5_2 + p5_w2 = self.p5_w2_relu(self.p5_w2) + weight = p5_w2 / (torch.sum(p5_w2, dim=0) + self.epsilon) + # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively + p5_out = self.conv5_down( + self.swish(weight[0] * p5_in + weight[1] * p5_up + weight[2] * self.p5_downsample(p4_out)) + ) + + # Weights for P6_0, P6_1 and P5_2 to P6_2 + p6_w2 = self.p6_w2_relu(self.p6_w2) + weight = p6_w2 / (torch.sum(p6_w2, dim=0) + self.epsilon) + # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively + p6_out = self.conv6_down( + self.swish(weight[0] * p6_in + weight[1] * p6_up + weight[2] * self.p6_downsample(p5_out)) + ) + + # Weights for P7_0 and P6_2 to P7_2 + p7_w2 = self.p7_w2_relu(self.p7_w2) + weight = p7_w2 / (torch.sum(p7_w2, dim=0) + self.epsilon) + # Connections for P7_0 and P6_2 to P7_2 + p7_out = self.conv7_down(self.swish(weight[0] * p7_in + weight[1] * self.p7_downsample(p6_out))) + + return p3_out, p4_out, p5_out, p6_out, p7_out + + def _forward(self, inputs): + if self.first_time: + p3, p4, p5 = inputs + + p6_in = self.p5_to_p6(p5) + p7_in = self.p6_to_p7(p6_in) + + p3_in = self.p3_down_channel(p3) + p4_in = self.p4_down_channel(p4) + p5_in = self.p5_down_channel(p5) + + else: + # P3_0, P4_0, P5_0, P6_0 and P7_0 + p3_in, p4_in, p5_in, p6_in, p7_in = inputs + + # P7_0 to P7_2 + + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_in))) + + # Connections for P5_0 and P6_0 to P5_1 respectively + p5_up = self.conv5_up(self.swish(p5_in + self.p5_upsample(p6_up))) + + # Connections for P4_0 and P5_0 to P4_1 respectively + p4_up = self.conv4_up(self.swish(p4_in + self.p4_upsample(p5_up))) + + # Connections for P3_0 and P4_1 to P3_2 respectively + p3_out = self.conv3_up(self.swish(p3_in + self.p3_upsample(p4_up))) + + if self.first_time: + p4_in = self.p4_down_channel_2(p4) + p5_in = self.p5_down_channel_2(p5) + + # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively + p4_out = self.conv4_down(self.swish(p4_in + p4_up + self.p4_downsample(p3_out))) + + # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively + p5_out = self.conv5_down(self.swish(p5_in + p5_up + self.p5_downsample(p4_out))) + + # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively + p6_out = self.conv6_down(self.swish(p6_in + p6_up + self.p6_downsample(p5_out))) + + # Connections for P7_0 and P6_2 to P7_2 + p7_out = self.conv7_down(self.swish(p7_in + self.p7_downsample(p6_out))) + + return p3_out, p4_out, p5_out, p6_out, p7_out + + +class Regressor(nn.Module): + """ + modified by Zylo117 + """ + + def __init__(self, in_channels, num_anchors, num_layers, onnx_export=False): + super(Regressor, self).__init__() + self.num_layers = num_layers + self.num_layers = num_layers + + self.conv_list = nn.ModuleList( + [SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)] + ) + self.bn_list = nn.ModuleList( + [ + nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)]) + for j in range(5) + ] + ) + self.header = SeparableConvBlock(in_channels, num_anchors * 4, norm=False, activation=False) + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + def forward(self, inputs): + feats = [] + for feat, bn_list in zip(inputs, self.bn_list): + for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list): + feat = conv(feat) + feat = bn(feat) + feat = self.swish(feat) + feat = self.header(feat) + feat = feat.permute(0, 2, 3, 1) + feat = feat.contiguous().view(feat.shape[0], -1, 4) + + feats.append(feat) + + feats = torch.cat(feats, dim=1) + + return feats + + +class SwishImplementation(torch.autograd.Function): + @staticmethod + def forward(ctx, i): + result = i * torch.sigmoid(i) + ctx.save_for_backward(i) + return result + + @staticmethod + def backward(ctx, grad_output): + i = ctx.saved_variables[0] + sigmoid_i = torch.sigmoid(i) + return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i))) + + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + if torch._C._get_tracing_state(): + return x * torch.sigmoid(x) + return SwishImplementation.apply(x) + + +class Swish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +class Classifier(nn.Module): + """ + modified by Zylo117 + """ + + def __init__(self, in_channels, num_anchors, num_classes, num_layers, onnx_export=False, prior_prob=0.01): + super(Classifier, self).__init__() + self.num_anchors = num_anchors + self.num_classes = num_classes + self.num_layers = num_layers + self.conv_list = nn.ModuleList( + [SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)] + ) + self.bn_list = nn.ModuleList( + [ + nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)]) + for j in range(5) + ] + ) + self.header = SeparableConvBlock(in_channels, num_anchors * num_classes, norm=False, activation=False) + + prior_prob = prior_prob + bias_value = -math.log((1 - prior_prob) / prior_prob) + torch.nn.init.normal_(self.header.pointwise_conv.conv.weight, std=0.01) + torch.nn.init.constant_(self.header.pointwise_conv.conv.bias, bias_value) + + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + def forward(self, inputs): + feats = [] + for feat, bn_list in zip(inputs, self.bn_list): + for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list): + feat = conv(feat) + feat = bn(feat) + feat = self.swish(feat) + feat = self.header(feat) + + feat = feat.permute(0, 2, 3, 1) + feat = feat.contiguous().view( + feat.shape[0], feat.shape[1], feat.shape[2], self.num_anchors, self.num_classes + ) + feat = feat.contiguous().view(feat.shape[0], -1, self.num_classes) + + feats.append(feat) + + feats = torch.cat(feats, dim=1) + # feats = feats.sigmoid() + + return feats + + +class Conv2dDynamicSamePadding(nn.Conv2d): + """2D Convolutions like TensorFlow, for a dynamic image size""" + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True): + super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) + raise ValueError("tend to be deprecated") + self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2 + + def forward(self, x): + ih, iw = x.size()[-2:] + kh, kw = self.weight.size()[-2:] + sh, sw = self.stride + oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) + pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) + pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) + if pad_h > 0 or pad_w > 0: + x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) + return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + + +# TODO: it seems like the standard conv layer is good enough with proper padding +# parameters. +def get_same_padding_conv2d(image_size=None): + """Chooses static padding if you have specified an image size, and dynamic padding otherwise. + Static padding is necessary for ONNX exporting of models.""" + if image_size is None: + raise ValueError("not validated") + return Conv2dDynamicSamePadding + else: + from functools import partial + + return partial(Conv2dStaticSamePadding, image_size=image_size) + + +def round_filters(filters, global_params): + """Calculate and round number of filters based on depth multiplier.""" + multiplier = global_params.width_coefficient + if not multiplier: + return filters + divisor = global_params.depth_divisor + min_depth = global_params.min_depth + filters *= multiplier + min_depth = min_depth or divisor + new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor) + if new_filters < 0.9 * filters: # prevent rounding by more than 10% + new_filters += divisor + return int(new_filters) + + +def round_repeats(repeats, global_params): + """Round number of filters based on depth multiplier.""" + multiplier = global_params.depth_coefficient + if not multiplier: + return repeats + return int(math.ceil(multiplier * repeats)) + + +def drop_connect(inputs, p, training): + """Drop connect.""" + if not training: + return inputs + batch_size = inputs.shape[0] + keep_prob = 1 - p + random_tensor = keep_prob + random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device) + binary_tensor = torch.floor(random_tensor) + output = inputs / keep_prob * binary_tensor + return output + + +class MBConvBlock(nn.Module): + """ + Mobile Inverted Residual Bottleneck Block + + Args: + block_args (namedtuple): BlockArgs, see above + global_params (namedtuple): GlobalParam, see above + + Attributes: + has_se (bool): Whether the block contains a Squeeze and Excitation layer. + """ + + def __init__(self, block_args, global_params): + super().__init__() + self._block_args = block_args + self._bn_mom = 1 - global_params.batch_norm_momentum + self._bn_eps = global_params.batch_norm_epsilon + self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1) + self.id_skip = block_args.id_skip # skip connection and drop connect + + # Get static or dynamic convolution depending on image size + Conv2d = get_same_padding_conv2d(image_size=global_params.image_size) + + # Expansion phase + inp = self._block_args.input_filters # number of input channels + oup = self._block_args.input_filters * self._block_args.expand_ratio # number of output channels + if self._block_args.expand_ratio != 1: + self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False) + self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + + # Depthwise convolution phase + k = self._block_args.kernel_size + s = self._block_args.stride + if isinstance(s, (tuple, list)) and all([s0 == s[0] for s0 in s]): + s = s[0] + self._depthwise_conv = Conv2d( + in_channels=oup, + out_channels=oup, + groups=oup, # groups makes it depthwise + kernel_size=k, + stride=s, + bias=False, + ) + self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + + # Squeeze and Excitation layer, if desired + if self.has_se: + num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio)) + self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1) + self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1) + + # Output phase + final_oup = self._block_args.output_filters + self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False) + self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps) + self._swish = MemoryEfficientSwish() + + def forward(self, inputs, drop_connect_rate=None): + """ + :param inputs: input tensor + :param drop_connect_rate: drop connect rate (float, between 0 and 1) + :return: output of block + """ + + # Expansion and Depthwise Convolution + x = inputs + if self._block_args.expand_ratio != 1: + x = self._expand_conv(inputs) + x = self._bn0(x) + x = self._swish(x) + + x = self._depthwise_conv(x) + x = self._bn1(x) + x = self._swish(x) + + # Squeeze and Excitation + if self.has_se: + x_squeezed = F.adaptive_avg_pool2d(x, 1) + x_squeezed = self._se_reduce(x_squeezed) + x_squeezed = self._swish(x_squeezed) + x_squeezed = self._se_expand(x_squeezed) + x = torch.sigmoid(x_squeezed) * x + + x = self._project_conv(x) + x = self._bn2(x) + + # Skip connection and drop connect + input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters + if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters: + if drop_connect_rate: + x = drop_connect(x, p=drop_connect_rate, training=self.training) + x = x + inputs # skip connection + return x + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export)""" + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + + +class BlockDecoder(object): + """Block Decoder for readability, straight from the official TensorFlow repository""" + + @staticmethod + def _decode_block_string(block_string): + """Gets a block through a string notation of arguments.""" + assert isinstance(block_string, str) + + ops = block_string.split("_") + options = {} + for op in ops: + splits = re.split(r"(\d.*)", op) + if len(splits) >= 2: + key, value = splits[:2] + options[key] = value + + # Check stride + assert ("s" in options and len(options["s"]) == 1) or ( + len(options["s"]) == 2 and options["s"][0] == options["s"][1] + ) + + return BlockArgs( + kernel_size=int(options["k"]), + num_repeat=int(options["r"]), + input_filters=int(options["i"]), + output_filters=int(options["o"]), + expand_ratio=int(options["e"]), + id_skip=("noskip" not in block_string), + se_ratio=float(options["se"]) if "se" in options else None, + stride=[int(options["s"][0])], + ) + + @staticmethod + def _encode_block_string(block): + """Encodes a block to a string.""" + args = [ + "r%d" % block.num_repeat, + "k%d" % block.kernel_size, + "s%d%d" % (block.strides[0], block.strides[1]), + "e%s" % block.expand_ratio, + "i%d" % block.input_filters, + "o%d" % block.output_filters, + ] + if 0 < block.se_ratio <= 1: + args.append("se%s" % block.se_ratio) + if block.id_skip is False: + args.append("noskip") + return "_".join(args) + + @staticmethod + def decode(string_list): + """ + Decodes a list of string notations to specify blocks inside the network. + + :param string_list: a list of strings, each string is a notation of block + :return: a list of BlockArgs namedtuples of block args + """ + assert isinstance(string_list, list) + blocks_args = [] + for block_string in string_list: + blocks_args.append(BlockDecoder._decode_block_string(block_string)) + return blocks_args + + @staticmethod + def encode(blocks_args): + """ + Encodes a list of BlockArgs to a list of strings. + + :param blocks_args: a list of BlockArgs namedtuples of block args + :return: a list of strings, each string is a notation of block + """ + block_strings = [] + for block in blocks_args: + block_strings.append(BlockDecoder._encode_block_string(block)) + return block_strings + + +def efficientnet( + width_coefficient=None, + depth_coefficient=None, + dropout_rate=0.2, + drop_connect_rate=0.2, + image_size=None, + num_classes=1000, +): + """Creates a efficientnet model.""" + + blocks_args = [ + "r1_k3_s11_e1_i32_o16_se0.25", + "r2_k3_s22_e6_i16_o24_se0.25", + "r2_k5_s22_e6_i24_o40_se0.25", + "r3_k3_s22_e6_i40_o80_se0.25", + "r3_k5_s11_e6_i80_o112_se0.25", + "r4_k5_s22_e6_i112_o192_se0.25", + "r1_k3_s11_e6_i192_o320_se0.25", + ] + blocks_args = BlockDecoder.decode(blocks_args) + + global_params = GlobalParams( + batch_norm_momentum=0.99, + batch_norm_epsilon=1e-3, + dropout_rate=dropout_rate, + drop_connect_rate=drop_connect_rate, + # data_format='channels_last', # removed, this is always true in PyTorch + num_classes=num_classes, + width_coefficient=width_coefficient, + depth_coefficient=depth_coefficient, + depth_divisor=8, + min_depth=None, + image_size=image_size, + ) + + return blocks_args, global_params + + +def efficientnet_params(model_name): + """Map EfficientNet model name to parameter coefficients.""" + params_dict = { + # Coefficients: width,depth,res,dropout + "efficientnet-b0": (1.0, 1.0, 224, 0.2), + "efficientnet-b1": (1.0, 1.1, 240, 0.2), + "efficientnet-b2": (1.1, 1.2, 260, 0.3), + "efficientnet-b3": (1.2, 1.4, 300, 0.3), + "efficientnet-b4": (1.4, 1.8, 380, 0.4), + "efficientnet-b5": (1.6, 2.2, 456, 0.4), + "efficientnet-b6": (1.8, 2.6, 528, 0.5), + "efficientnet-b7": (2.0, 3.1, 600, 0.5), + "efficientnet-b8": (2.2, 3.6, 672, 0.5), + "efficientnet-l2": (4.3, 5.3, 800, 0.5), + } + return params_dict[model_name] + + +def get_model_params(model_name, override_params): + """Get the block args and global params for a given model""" + if model_name.startswith("efficientnet"): + w, d, s, p = efficientnet_params(model_name) + # note: all models have drop connect rate = 0.2 + blocks_args, global_params = efficientnet( + width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s + ) + else: + raise NotImplementedError("model name is not pre-defined: %s" % model_name) + if override_params: + # ValueError will be raised here if override_params has fields not included in global_params. + global_params = global_params._replace(**override_params) + return blocks_args, global_params + + +url_map = { + "efficientnet-b0": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b0-355c32eb.pth", + "efficientnet-b1": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b1-f1951068.pth", + "efficientnet-b2": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b2-8bb594d6.pth", + "efficientnet-b3": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b3-5fb5a3c3.pth", + "efficientnet-b4": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b4-6ed6700e.pth", + "efficientnet-b5": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b5-b6417697.pth", + "efficientnet-b6": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b6-c76e70fd.pth", + "efficientnet-b7": "https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b7-dcc49843.pth", +} + +url_map_advprop = { + "efficientnet-b0": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b0-b64d5a18.pth", + "efficientnet-b1": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b1-0f3ce85a.pth", + "efficientnet-b2": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b2-6e9d97e5.pth", + "efficientnet-b3": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b3-cdd7c0f4.pth", + "efficientnet-b4": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b4-44fb3a87.pth", + "efficientnet-b5": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b5-86493f6b.pth", + "efficientnet-b6": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b6-ac80338e.pth", + "efficientnet-b7": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b7-4652b6dd.pth", + "efficientnet-b8": "https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b8-22a8fe65.pth", +} + + +def load_pretrained_weights(model, model_name, load_fc=True, advprop=False): + """Loads pretrained weights, and downloads if loading for the first time.""" + # AutoAugment or Advprop (different preprocessing) + url_map_ = url_map_advprop if advprop else url_map + from torch.utils import model_zoo + + state_dict = model_zoo.load_url(url_map_[model_name], map_location=torch.device("cpu")) + # state_dict = torch.load('../../weights/backbone_efficientnetb0.pth') + if load_fc: + ret = model.load_state_dict(state_dict, strict=False) + print(ret) + else: + state_dict.pop("_fc.weight") + state_dict.pop("_fc.bias") + res = model.load_state_dict(state_dict, strict=False) + assert set(res.missing_keys) == set(["_fc.weight", "_fc.bias"]), "issue loading pretrained weights" + print("Loaded pretrained weights for {}".format(model_name)) + + +class EfficientNet(nn.Module): + """ + An EfficientNet model. Most easily loaded with the .from_name or .from_pretrained methods + + Args: + blocks_args (list): A list of BlockArgs to construct blocks + global_params (namedtuple): A set of GlobalParams shared between blocks + + Example: + model = EfficientNet.from_pretrained('efficientnet-b0') + + """ + + def __init__(self, blocks_args=None, global_params=None): + super().__init__() + assert isinstance(blocks_args, list), "blocks_args should be a list" + assert len(blocks_args) > 0, "block args must be greater than 0" + self._global_params = global_params + self._blocks_args = blocks_args + + # Get static or dynamic convolution depending on image size + Conv2d = get_same_padding_conv2d(image_size=global_params.image_size) + + # Batch norm parameters + bn_mom = 1 - self._global_params.batch_norm_momentum + bn_eps = self._global_params.batch_norm_epsilon + + # Stem + in_channels = 3 # rgb + out_channels = round_filters(32, self._global_params) # number of output channels + self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) + self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + + # Build blocks + self._blocks = nn.ModuleList([]) + for block_args in self._blocks_args: + + # Update block input and output filters based on depth multiplier. + block_args = block_args._replace( + input_filters=round_filters(block_args.input_filters, self._global_params), + output_filters=round_filters(block_args.output_filters, self._global_params), + num_repeat=round_repeats(block_args.num_repeat, self._global_params), + ) + + # The first block needs to take care of stride and filter size increase. + self._blocks.append(MBConvBlock(block_args, self._global_params)) + if block_args.num_repeat > 1: + block_args = block_args._replace(input_filters=block_args.output_filters, stride=1) + for _ in range(block_args.num_repeat - 1): + self._blocks.append(MBConvBlock(block_args, self._global_params)) + + # Head + in_channels = block_args.output_filters # output of final block + out_channels = round_filters(1280, self._global_params) + self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + + # Final linear layer + self._avg_pooling = nn.AdaptiveAvgPool2d(1) + self._dropout = nn.Dropout(self._global_params.dropout_rate) + self._fc = nn.Linear(out_channels, self._global_params.num_classes) + self._swish = MemoryEfficientSwish() + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export)""" + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + for block in self._blocks: + block.set_swish(memory_efficient) + + def extract_features(self, inputs): + """Returns output of the final convolution layer""" + + # Stem + x = self._swish(self._bn0(self._conv_stem(inputs))) + + # Blocks + for idx, block in enumerate(self._blocks): + drop_connect_rate = self._global_params.drop_connect_rate + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self._blocks) + x = block(x, drop_connect_rate=drop_connect_rate) + # Head + x = self._swish(self._bn1(self._conv_head(x))) + + return x + + def forward(self, inputs): + """Calls extract_features to extract features, applies final linear layer, and returns logits.""" + bs = inputs.size(0) + # Convolution layers + x = self.extract_features(inputs) + + # Pooling and final linear layer + x = self._avg_pooling(x) + x = x.view(bs, -1) + x = self._dropout(x) + x = self._fc(x) + return x + + @classmethod + def from_name(cls, model_name, override_params=None): + cls._check_model_name_is_valid(model_name) + blocks_args, global_params = get_model_params(model_name, override_params) + return cls(blocks_args, global_params) + + @classmethod + def from_pretrained(cls, model_name, load_weights=True, advprop=True, num_classes=1000, in_channels=3): + model = cls.from_name(model_name, override_params={"num_classes": num_classes}) + if load_weights: + load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000), advprop=advprop) + if in_channels != 3: + Conv2d = get_same_padding_conv2d(image_size=model._global_params.image_size) + out_channels = round_filters(32, model._global_params) + model._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) + return model + + @classmethod + def get_image_size(cls, model_name): + cls._check_model_name_is_valid(model_name) + _, _, res, _ = efficientnet_params(model_name) + return res + + @classmethod + def _check_model_name_is_valid(cls, model_name): + """Validates model name.""" + valid_models = ["efficientnet-b" + str(i) for i in range(9)] + if model_name not in valid_models: + raise ValueError("model_name should be one of: " + ", ".join(valid_models)) + + +class EfficientNetD(nn.Module): + """ + modified by Zylo117 + """ + + def __init__(self, compound_coef, load_weights=False): + super().__init__() + model = EfficientNet.from_pretrained(f"efficientnet-b{compound_coef}", load_weights) + del model._conv_head + del model._bn1 + del model._avg_pooling + del model._dropout + del model._fc + self.model = model + + def forward(self, x): + x = self.model._conv_stem(x) + x = self.model._bn0(x) + x = self.model._swish(x) + feature_maps = [] + + # TODO: temporarily storing extra tensor last_x and del it later might not be a good idea, + # try recording stride changing when creating efficientnet, + # and then apply it here. + last_x = None + for idx, block in enumerate(self.model._blocks): + drop_connect_rate = self.model._global_params.drop_connect_rate + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self.model._blocks) + x = block(x, drop_connect_rate=drop_connect_rate) + + if tuple(block._depthwise_conv.stride) == (2, 2): + feature_maps.append(last_x) + elif idx == len(self.model._blocks) - 1: + feature_maps.append(x) + last_x = x + del last_x + return feature_maps[1:] + + +class Anchors(nn.Module): + """ + adapted and modified from https://github.com/google/automl/blob/master/efficientdet/anchors.py by Zylo117 + """ + + def __init__(self, anchor_scale=4.0, pyramid_levels=None, **kwargs): + super().__init__() + from qd.qd_common import print_frame_info + + print_frame_info() + self.anchor_scale = anchor_scale + + if pyramid_levels is None: + self.pyramid_levels = [3, 4, 5, 6, 7] + + self.strides = kwargs.get("strides", [2**x for x in self.pyramid_levels]) + self.scales = np.array(kwargs.get("scales", [2**0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])) + self.ratios = kwargs.get("ratios", [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]) + + self.buffer = {} + + @torch.no_grad() + def forward(self, image, dtype=torch.float32, features=None): + """Generates multiscale anchor boxes. + + Args: + image_size: integer number of input image size. The input image has the + same dimension for width and height. The image_size should be divided by + the largest feature stride 2^max_level. + anchor_scale: float number representing the scale of size of the base + anchor to the feature stride 2^level. + anchor_configs: a dictionary with keys as the levels of anchors and + values as a list of anchor configuration. + + Returns: + anchor_boxes: a numpy array with shape [N, 4], which stacks anchors on all + feature levels. + Raises: + ValueError: input size must be the multiple of largest feature stride. + """ + image_shape = image.shape[2:] + anchor_key = self.get_key("anchor", image_shape) + stride_idx_key = self.get_key("anchor_stride_index", image_shape) + + if anchor_key in self.buffer: + return {"stride_idx": self.buffer[stride_idx_key].detach(), "anchor": self.buffer[anchor_key].detach()} + + if dtype == torch.float16: + dtype = np.float16 + else: + dtype = np.float32 + + boxes_all = [] + all_idx_strides = [] + for idx_stride, stride in enumerate(self.strides): + boxes_level = [] + for scale, ratio in itertools.product(self.scales, self.ratios): + if features is not None: + f_h, f_w = features[idx_stride].shape[-2:] + x = np.arange(stride / 2, stride * f_w, stride) + y = np.arange(stride / 2, stride * f_h, stride) + else: + if image_shape[1] % stride != 0: + x_max = stride * ((image_shape[1] + stride - 1) // stride) + y_max = stride * ((image_shape[0] + stride - 1) // stride) + else: + x_max = image_shape[1] + y_max = image_shape[0] + x = np.arange(stride / 2, x_max, stride) + y = np.arange(stride / 2, y_max, stride) + xv, yv = np.meshgrid(x, y) + xv = xv.reshape(-1) + yv = yv.reshape(-1) + + base_anchor_size = self.anchor_scale * stride * scale + anchor_size_x_2 = base_anchor_size * ratio[0] / 2.0 + anchor_size_y_2 = base_anchor_size * ratio[1] / 2.0 + # y1,x1,y2,x2 + boxes = np.vstack( + (yv - anchor_size_y_2, xv - anchor_size_x_2, yv + anchor_size_y_2, xv + anchor_size_x_2) + ) + boxes = np.swapaxes(boxes, 0, 1) + boxes_level.append(np.expand_dims(boxes, axis=1)) + # concat anchors on the same level to the reshape NxAx4 + boxes_level = np.concatenate(boxes_level, axis=1) + boxes_level = boxes_level.reshape([-1, 4]) + idx_strides = torch.tensor([idx_stride] * len(boxes_level)) + all_idx_strides.append(idx_strides) + boxes_all.append(boxes_level) + + anchor_boxes = np.vstack(boxes_all) + anchor_stride_indices = torch.cat(all_idx_strides).to(image.device) + + self.buffer[stride_idx_key] = anchor_stride_indices + + anchor_boxes = torch.from_numpy(anchor_boxes.astype(dtype)).to(image.device) + anchor_boxes = anchor_boxes.unsqueeze(0) + + # save it for later use to reduce overhead + self.buffer[anchor_key] = anchor_boxes + + return {"stride_idx": self.buffer[stride_idx_key], "anchor": self.buffer[anchor_key]} + + def get_key(self, hint, image_shape): + return "{}_{}".format(hint, "_".join(map(str, image_shape))) + + +class EffNetFPN(nn.Module): + def __init__(self, compound_coef=0, start_from=3): + super().__init__() + + self.backbone_net = EfficientNetD( + EfficientDetBackbone.backbone_compound_coef[compound_coef], load_weights=False + ) + if start_from == 3: + conv_channel_coef = EfficientDetBackbone.conv_channel_coef[compound_coef] + else: + conv_channel_coef = EfficientDetBackbone.conv_channel_coef2345[compound_coef] + self.bifpn = nn.Sequential( + *[ + BiFPN( + EfficientDetBackbone.fpn_num_filters[compound_coef], + conv_channel_coef, + True if _ == 0 else False, + attention=True if compound_coef < 6 else False, + adaptive_up=True, + ) + for _ in range(EfficientDetBackbone.fpn_cell_repeats[compound_coef]) + ] + ) + + self.out_channels = EfficientDetBackbone.fpn_num_filters[compound_coef] + + self.start_from = start_from + assert self.start_from in [2, 3] + + def forward(self, inputs): + if self.start_from == 3: + _, p3, p4, p5 = self.backbone_net(inputs) + + features = (p3, p4, p5) + features = self.bifpn(features) + return features + else: + p2, p3, p4, p5 = self.backbone_net(inputs) + features = (p2, p3, p4, p5) + features = self.bifpn(features) + return features + + +class EfficientDetBackbone(nn.Module): + backbone_compound_coef = [0, 1, 2, 3, 4, 5, 6, 6] + fpn_num_filters = [64, 88, 112, 160, 224, 288, 384, 384] + conv_channel_coef = { + # the channels of P3/P4/P5. + 0: [40, 112, 320], + 1: [40, 112, 320], + 2: [48, 120, 352], + 3: [48, 136, 384], + 4: [56, 160, 448], + 5: [64, 176, 512], + 6: [72, 200, 576], + 7: [72, 200, 576], + } + conv_channel_coef2345 = { + # the channels of P2/P3/P4/P5. + 0: [24, 40, 112, 320], + # to be determined for the following + 1: [24, 40, 112, 320], + 2: [24, 48, 120, 352], + 3: [32, 48, 136, 384], + 4: [32, 56, 160, 448], + 5: [40, 64, 176, 512], + 6: [72, 200], + 7: [72, 200], + } + fpn_cell_repeats = [3, 4, 5, 6, 7, 7, 8, 8] + + def __init__(self, num_classes=80, compound_coef=0, load_weights=False, prior_prob=0.01, **kwargs): + super(EfficientDetBackbone, self).__init__() + self.compound_coef = compound_coef + + self.input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536] + self.box_class_repeats = [3, 3, 3, 4, 4, 4, 5, 5] + self.anchor_scale = [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0] + self.aspect_ratios = kwargs.get("ratios", [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]) + self.num_scales = len(kwargs.get("scales", [2**0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])) + + num_anchors = len(self.aspect_ratios) * self.num_scales + + self.bifpn = nn.Sequential( + *[ + BiFPN( + self.fpn_num_filters[self.compound_coef], + self.conv_channel_coef[compound_coef], + True if _ == 0 else False, + attention=True if compound_coef < 6 else False, + adaptive_up=kwargs.get("adaptive_up"), + ) + for _ in range(self.fpn_cell_repeats[compound_coef]) + ] + ) + + self.num_classes = num_classes + self.regressor = Regressor( + in_channels=self.fpn_num_filters[self.compound_coef], + num_anchors=num_anchors, + num_layers=self.box_class_repeats[self.compound_coef], + ) + self.classifier = Classifier( + in_channels=self.fpn_num_filters[self.compound_coef], + num_anchors=num_anchors, + num_classes=num_classes, + num_layers=self.box_class_repeats[self.compound_coef], + prior_prob=prior_prob, + ) + anchor_scale = self.anchor_scale[compound_coef] + if kwargs.get("anchor_scale"): + anchor_scale = kwargs.pop("anchor_scale") + if "anchor_scale" in kwargs: + del kwargs["anchor_scale"] + self.anchors = Anchors(anchor_scale=anchor_scale, **kwargs) + + self.backbone_net = EfficientNetD(self.backbone_compound_coef[compound_coef], load_weights) + + def freeze_bn(self): + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() + + def forward(self, inputs): + _, p3, p4, p5 = self.backbone_net(inputs) + + features = (p3, p4, p5) + features = self.bifpn(features) + + regression = self.regressor(features) + classification = self.classifier(features) + anchors = self.anchors(inputs, inputs.dtype, features=features) + + return features, regression, classification, anchors + + def init_backbone(self, path): + state_dict = torch.load(path) + try: + ret = self.load_state_dict(state_dict, strict=False) + print(ret) + except RuntimeError as e: + print("Ignoring " + str(e) + '"') + + +def init_weights(model): + for name, module in model.named_modules(): + is_conv_layer = isinstance(module, nn.Conv2d) + + if is_conv_layer: + nn.init.kaiming_uniform_(module.weight.data) + + if module.bias is not None: + module.bias.data.zero_() + + +def calc_iou(a, b): + # a(anchor) [boxes, (y1, x1, y2, x2)] + # b(gt, coco-style) [boxes, (x1, y1, x2, y2)] + + area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1]) + iw = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 0]) + ih = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 1]) + iw = torch.clamp(iw, min=0) + ih = torch.clamp(ih, min=0) + ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih + ua = torch.clamp(ua, min=1e-8) + intersection = iw * ih + IoU = intersection / ua + + return IoU + + +class BBoxTransform(nn.Module): + def forward(self, anchors, regression): + """ + decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py + + Args: + anchors: [batchsize, boxes, (y1, x1, y2, x2)] + regression: [batchsize, boxes, (dy, dx, dh, dw)] + + Returns: + + """ + y_centers_a = (anchors[..., 0] + anchors[..., 2]) / 2 + x_centers_a = (anchors[..., 1] + anchors[..., 3]) / 2 + ha = anchors[..., 2] - anchors[..., 0] + wa = anchors[..., 3] - anchors[..., 1] + + w = regression[..., 3].exp() * wa + h = regression[..., 2].exp() * ha + + y_centers = regression[..., 0] * ha + y_centers_a + x_centers = regression[..., 1] * wa + x_centers_a + + ymin = y_centers - h / 2.0 + xmin = x_centers - w / 2.0 + ymax = y_centers + h / 2.0 + xmax = x_centers + w / 2.0 + if len(anchors.shape) == 3: + return torch.stack([xmin, ymin, xmax, ymax], dim=2) + else: + return torch.stack([xmin, ymin, xmax, ymax], dim=1) + + +class ClipBoxes(nn.Module): + def __init__(self): + super(ClipBoxes, self).__init__() + + def forward(self, boxes, img): + batch_size, num_channels, height, width = img.shape + + boxes[:, :, 0] = torch.clamp(boxes[:, :, 0], min=0) + boxes[:, :, 1] = torch.clamp(boxes[:, :, 1], min=0) + + boxes[:, :, 2] = torch.clamp(boxes[:, :, 2], max=width - 1) + boxes[:, :, 3] = torch.clamp(boxes[:, :, 3], max=height - 1) + + return boxes + + +def postprocess2(x, anchors, regression, classification, transformed_anchors, threshold, iou_threshold, max_box): + anchors = anchors["anchor"] + all_above_th = classification > threshold + out = [] + num_image = x.shape[0] + num_class = classification.shape[-1] + + # classification = classification.cpu() + # transformed_anchors = transformed_anchors.cpu() + # all_above_th = all_above_th.cpu() + max_box_pre_nms = 1000 + for i in range(num_image): + all_rois = [] + all_class_ids = [] + all_scores = [] + for c in range(num_class): + above_th = all_above_th[i, :, c].nonzero() + if len(above_th) == 0: + continue + above_prob = classification[i, above_th, c].squeeze(1) + if len(above_th) > max_box_pre_nms: + _, idx = above_prob.topk(max_box_pre_nms) + above_th = above_th[idx] + above_prob = above_prob[idx] + transformed_anchors_per = transformed_anchors[i, above_th, :].squeeze(dim=1) + from torchvision.ops import nms + + nms_idx = nms(transformed_anchors_per, above_prob, iou_threshold=iou_threshold) + if len(nms_idx) > 0: + all_rois.append(transformed_anchors_per[nms_idx]) + ids = torch.tensor([c] * len(nms_idx)) + all_class_ids.append(ids) + all_scores.append(above_prob[nms_idx]) + + if len(all_rois) > 0: + rois = torch.cat(all_rois) + class_ids = torch.cat(all_class_ids) + scores = torch.cat(all_scores) + if len(scores) > max_box: + _, idx = torch.topk(scores, max_box) + rois = rois[idx, :] + class_ids = class_ids[idx] + scores = scores[idx] + out.append( + { + "rois": rois, + "class_ids": class_ids, + "scores": scores, + } + ) + else: + out.append( + { + "rois": [], + "class_ids": [], + "scores": [], + } + ) + + return out + + +def postprocess(x, anchors, regression, classification, regressBoxes, clipBoxes, threshold, iou_threshold): + anchors = anchors["anchor"] + transformed_anchors = regressBoxes(anchors, regression) + transformed_anchors = clipBoxes(transformed_anchors, x) + scores = torch.max(classification, dim=2, keepdim=True)[0] + scores_over_thresh = (scores > threshold)[:, :, 0] + out = [] + for i in range(x.shape[0]): + if scores_over_thresh.sum() == 0: + out.append( + { + "rois": [], + "class_ids": [], + "scores": [], + } + ) + continue + + classification_per = classification[i, scores_over_thresh[i, :], ...].permute(1, 0) + transformed_anchors_per = transformed_anchors[i, scores_over_thresh[i, :], ...] + scores_per = scores[i, scores_over_thresh[i, :], ...] + from torchvision.ops import nms + + anchors_nms_idx = nms(transformed_anchors_per, scores_per[:, 0], iou_threshold=iou_threshold) + + if anchors_nms_idx.shape[0] != 0: + scores_, classes_ = classification_per[:, anchors_nms_idx].max(dim=0) + boxes_ = transformed_anchors_per[anchors_nms_idx, :] + + out.append( + { + "rois": boxes_, + "class_ids": classes_, + "scores": scores_, + } + ) + else: + out.append( + { + "rois": [], + "class_ids": [], + "scores": [], + } + ) + + return out + + +def display(preds, imgs, obj_list, imshow=True, imwrite=False): + for i in range(len(imgs)): + if len(preds[i]["rois"]) == 0: + continue + + for j in range(len(preds[i]["rois"])): + (x1, y1, x2, y2) = preds[i]["rois"][j].detach().cpu().numpy().astype(np.int) + logging.info((x1, y1, x2, y2)) + cv2.rectangle(imgs[i], (x1, y1), (x2, y2), (255, 255, 0), 2) + # obj = obj_list[preds[i]['class_ids'][j]] + # score = float(preds[i]['scores'][j]) + + # cv2.putText(imgs[i], '{}, {:.3f}'.format(obj, score), + # (x1, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, + # (255, 255, 0), 1) + # break + if imshow: + cv2.imshow("image", imgs[i]) + cv2.waitKey(0) + + +def calculate_focal_loss2(classification, target_list, alpha, gamma): + from maskrcnn_benchmark.layers.sigmoid_focal_loss import sigmoid_focal_loss_cuda + + cls_loss = sigmoid_focal_loss_cuda(classification, target_list.int(), gamma, alpha) + return cls_loss + + +def calculate_focal_loss(classification, targets, alpha, gamma): + classification = classification.sigmoid() + device = classification.device + alpha_factor = torch.ones_like(targets) * alpha + alpha_factor = alpha_factor.to(device) + + alpha_factor = torch.where(torch.eq(targets, 1.0), alpha_factor, 1.0 - alpha_factor) + focal_weight = torch.where(torch.eq(targets, 1.0), 1.0 - classification, classification) + focal_weight = alpha_factor * torch.pow(focal_weight, gamma) + + bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification)) + + cls_loss = focal_weight * bce + + zeros = torch.zeros_like(cls_loss) + zeros = zeros.to(device) + cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, zeros) + return cls_loss.mean() + + +def calculate_giou(pred, gt): + ax1, ay1, ax2, ay2 = pred[:, 0], pred[:, 1], pred[:, 2], pred[:, 3] + bx1, by1, bx2, by2 = gt[:, 0], gt[:, 1], gt[:, 2], gt[:, 3] + a = (ax2 - ax1) * (ay2 - ay1) + b = (bx2 - bx1) * (by2 - by1) + max_x1, _ = torch.max(torch.stack([ax1, bx1], dim=1), dim=1) + max_y1, _ = torch.max(torch.stack([ay1, by1], dim=1), dim=1) + min_x2, _ = torch.min(torch.stack([ax2, bx2], dim=1), dim=1) + min_y2, _ = torch.min(torch.stack([ay2, by2], dim=1), dim=1) + inter = (min_x2 > max_x1) * (min_y2 > max_y1) + inter = inter * (min_x2 - max_x1) * (min_y2 - max_y1) + + min_x1, _ = torch.min(torch.stack([ax1, bx1], dim=1), dim=1) + min_y1, _ = torch.min(torch.stack([ay1, by1], dim=1), dim=1) + max_x2, _ = torch.max(torch.stack([ax2, bx2], dim=1), dim=1) + max_y2, _ = torch.max(torch.stack([ay2, by2], dim=1), dim=1) + cover = (max_x2 - min_x1) * (max_y2 - min_y1) + union = a + b - inter + iou = inter / (union + 1e-5) + giou = iou - (cover - union) / (cover + 1e-5) + return giou + + +class FocalLoss(nn.Module): + def __init__( + self, + alpha=0.25, + gamma=2.0, + cls_loss_type="FL", + smooth_bce_pos=0.99, + smooth_bce_neg=0.01, + reg_loss_type="L1", + at_least_1_assgin=False, + neg_iou_th=0.4, + pos_iou_th=0.5, + cls_weight=1.0, + reg_weight=1.0, + ): + super(FocalLoss, self).__init__() + from qd.qd_common import print_frame_info + + print_frame_info() + self.iter = 0 + self.reg_loss_type = reg_loss_type + self.regressBoxes = BBoxTransform() + if cls_loss_type == "FL": + from qd.layers.loss import FocalLossWithLogitsNegLoss + + self.cls_loss = FocalLossWithLogitsNegLoss(alpha, gamma) + elif cls_loss_type == "BCE": + from qd.qd_pytorch import BCEWithLogitsNegLoss + + self.cls_loss = BCEWithLogitsNegLoss(reduction="sum") + elif cls_loss_type == "SmoothBCE": + from qd.layers.loss import SmoothBCEWithLogitsNegLoss + + self.cls_loss = SmoothBCEWithLogitsNegLoss(pos=smooth_bce_pos, neg=smooth_bce_neg) + elif cls_loss_type == "SmoothFL": + from qd.layers.loss import FocalSmoothBCEWithLogitsNegLoss + + self.cls_loss = FocalSmoothBCEWithLogitsNegLoss( + alpha=alpha, gamma=2.0, pos=smooth_bce_pos, neg=smooth_bce_neg + ) + else: + raise NotImplementedError(cls_loss_type) + self.at_least_1_assgin = at_least_1_assgin + + self.gt_total = 0 + self.gt_saved_by_at_least = 0 + + self.neg_iou_th = neg_iou_th + self.pos_iou_th = pos_iou_th + + self.cls_weight = cls_weight + self.reg_weight = reg_weight + + self.buf = {} + + def forward(self, classifications, regressions, anchor_info, annotations, **kwargs): + debug = (self.iter % 100) == 0 + self.iter += 1 + if debug: + from collections import defaultdict + + debug_info = defaultdict(list) + + batch_size = classifications.shape[0] + classification_losses = [] + regression_losses = [] + anchors = anchor_info["anchor"] + anchor = anchors[0, :, :] # assuming all image sizes are the same, which it is + dtype = anchors.dtype + + anchor_widths = anchor[:, 3] - anchor[:, 1] + anchor_heights = anchor[:, 2] - anchor[:, 0] + anchor_ctr_x = anchor[:, 1] + 0.5 * anchor_widths + anchor_ctr_y = anchor[:, 0] + 0.5 * anchor_heights + + # anchor_widths = anchor[:, 2] - anchor[:, 0] + # anchor_heights = anchor[:, 3] - anchor[:, 1] + # anchor_ctr_x = anchor[:, 0] + 0.5 * anchor_widths + # anchor_ctr_y = anchor[:, 1] + 0.5 * anchor_heights + device = classifications.device + + for j in range(batch_size): + + classification = classifications[j, :, :] + regression = regressions[j, :, :] + + bbox_annotation = annotations[j] + bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1] + + # classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4) + + if bbox_annotation.shape[0] == 0: + # cls_loss = calculate_focal_loss2(classification, + # torch.zeros(len(classification)), alpha, + # gamma) + # cls_loss = cls_loss.mean() + cls_loss = torch.tensor(0).to(dtype).to(device) + regression_losses.append(torch.tensor(0).to(dtype).to(device)) + classification_losses.append(cls_loss) + continue + + IoU = calc_iou(anchor[:, :], bbox_annotation[:, :4]) + + IoU_max, IoU_argmax = torch.max(IoU, dim=1) + if self.at_least_1_assgin: + iou_max_gt, iou_argmax_gt = torch.max(IoU, dim=0) + curr_saved = (iou_max_gt < self.pos_iou_th).sum() + self.gt_saved_by_at_least += curr_saved + self.gt_total += len(iou_argmax_gt) + IoU_max[iou_argmax_gt] = 1.0 + IoU_argmax[iou_argmax_gt] = torch.arange(len(iou_argmax_gt)).to(device) + + # compute the loss for classification + targets = torch.ones_like(classification) * -1 + targets = targets.to(device) + + targets[torch.lt(IoU_max, self.neg_iou_th), :] = 0 + + positive_indices = torch.ge(IoU_max, self.pos_iou_th) + + num_positive_anchors = positive_indices.sum() + + assigned_annotations = bbox_annotation[IoU_argmax, :] + + targets[positive_indices, :] = 0 + targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1 + + if debug: + if num_positive_anchors > 0: + debug_info["pos_conf"].append( + classification[positive_indices, assigned_annotations[positive_indices, 4].long()].mean() + ) + debug_info["neg_conf"].append(classification[targets == 0].mean()) + stride_idx = anchor_info["stride_idx"] + positive_stride_idx = stride_idx[positive_indices] + pos_count_each_stride = torch.tensor([(positive_stride_idx == i).sum() for i in range(5)]) + if "cum_pos_count_each_stride" not in self.buf: + self.buf["cum_pos_count_each_stride"] = pos_count_each_stride + else: + cum_pos_count_each_stride = self.buf["cum_pos_count_each_stride"] + cum_pos_count_each_stride += pos_count_each_stride + self.buf["cum_pos_count_each_stride"] = cum_pos_count_each_stride + + # cls_loss = calculate_focal_loss(classification, targets, alpha, + # gamma) + cls_loss = self.cls_loss(classification, targets) + + cls_loss = cls_loss.sum() / torch.clamp(num_positive_anchors.to(dtype), min=1.0) + assert cls_loss == cls_loss + classification_losses.append(cls_loss) + + if positive_indices.sum() > 0: + assigned_annotations = assigned_annotations[positive_indices, :] + if self.reg_loss_type == "L1": + anchor_widths_pi = anchor_widths[positive_indices] + anchor_heights_pi = anchor_heights[positive_indices] + anchor_ctr_x_pi = anchor_ctr_x[positive_indices] + anchor_ctr_y_pi = anchor_ctr_y[positive_indices] + + gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0] + gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1] + gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths + gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights + + # efficientdet style + gt_widths = torch.clamp(gt_widths, min=1) + gt_heights = torch.clamp(gt_heights, min=1) + + targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi + targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi + targets_dw = torch.log(gt_widths / anchor_widths_pi) + targets_dh = torch.log(gt_heights / anchor_heights_pi) + + targets = torch.stack((targets_dy, targets_dx, targets_dh, targets_dw)) + targets = targets.t() + + regression_diff = torch.abs(targets - regression[positive_indices, :]) + + regression_loss = torch.where( + torch.le(regression_diff, 1.0 / 9.0), + 0.5 * 9.0 * torch.pow(regression_diff, 2), + regression_diff - 0.5 / 9.0, + ).mean() + elif self.reg_loss_type == "GIOU": + curr_regression = regression[positive_indices, :] + curr_anchors = anchor[positive_indices] + curr_pred_xyxy = self.regressBoxes(curr_anchors, curr_regression) + regression_loss = 1.0 - calculate_giou(curr_pred_xyxy, assigned_annotations) + regression_loss = regression_loss.mean() + assert regression_loss == regression_loss + else: + raise NotImplementedError + regression_losses.append(regression_loss) + else: + if torch.cuda.is_available(): + regression_losses.append(torch.tensor(0).to(dtype).cuda()) + else: + regression_losses.append(torch.tensor(0).to(dtype)) + if debug: + if len(debug_info) > 0: + logging.info( + "pos = {}; neg = {}, saved_ratio = {}/{}={:.1f}, " + "stride_info = {}".format( + torch.tensor(debug_info["pos_conf"]).mean(), + torch.tensor(debug_info["neg_conf"]).mean(), + self.gt_saved_by_at_least, + self.gt_total, + 1.0 * self.gt_saved_by_at_least / self.gt_total, + self.buf["cum_pos_count_each_stride"], + ) + ) + return self.cls_weight * torch.stack(classification_losses).mean( + dim=0, keepdim=True + ), self.reg_weight * torch.stack(regression_losses).mean(dim=0, keepdim=True) + + +class ModelWithLoss(nn.Module): + def __init__(self, model, criterion): + super().__init__() + self.criterion = criterion + self.module = model + + def forward(self, *args): + if len(args) == 2: + imgs, annotations = args + elif len(args) == 1: + imgs, annotations = args[0][:2] + _, regression, classification, anchors = self.module(imgs) + cls_loss, reg_loss = self.criterion(classification, regression, anchors, annotations) + return {"cls_loss": cls_loss, "reg_loss": reg_loss} + + +class TorchVisionNMS(nn.Module): + def __init__(self, iou_threshold): + super().__init__() + self.iou_threshold = iou_threshold + + def forward(self, box, prob): + nms_idx = nms(box, prob, iou_threshold=self.iou_threshold) + return nms_idx + + +class PostProcess(nn.Module): + def __init__(self, iou_threshold): + super().__init__() + self.nms = TorchVisionNMS(iou_threshold) + + def forward(self, x, anchors, regression, classification, transformed_anchors, threshold, max_box): + all_above_th = classification > threshold + out = [] + num_image = x.shape[0] + num_class = classification.shape[-1] + + # classification = classification.cpu() + # transformed_anchors = transformed_anchors.cpu() + # all_above_th = all_above_th.cpu() + max_box_pre_nms = 1000 + for i in range(num_image): + all_rois = [] + all_class_ids = [] + all_scores = [] + for c in range(num_class): + above_th = all_above_th[i, :, c].nonzero() + if len(above_th) == 0: + continue + above_prob = classification[i, above_th, c].squeeze(1) + if len(above_th) > max_box_pre_nms: + _, idx = above_prob.topk(max_box_pre_nms) + above_th = above_th[idx] + above_prob = above_prob[idx] + transformed_anchors_per = transformed_anchors[i, above_th, :].squeeze(dim=1) + nms_idx = self.nms(transformed_anchors_per, above_prob) + if len(nms_idx) > 0: + all_rois.append(transformed_anchors_per[nms_idx]) + ids = torch.tensor([c] * len(nms_idx)) + all_class_ids.append(ids) + all_scores.append(above_prob[nms_idx]) + + if len(all_rois) > 0: + rois = torch.cat(all_rois) + class_ids = torch.cat(all_class_ids) + scores = torch.cat(all_scores) + if len(scores) > max_box: + _, idx = torch.topk(scores, max_box) + rois = rois[idx, :] + class_ids = class_ids[idx] + scores = scores[idx] + out.append( + { + "rois": rois, + "class_ids": class_ids, + "scores": scores, + } + ) + else: + out.append( + { + "rois": [], + "class_ids": [], + "scores": [], + } + ) + + return out + + +class InferenceModel(nn.Module): + def __init__(self, model): + super().__init__() + self.module = model + + self.regressBoxes = BBoxTransform() + self.clipBoxes = ClipBoxes() + self.threshold = 0.01 + self.nms_threshold = 0.5 + self.max_box = 100 + self.debug = False + self.post_process = PostProcess(self.nms_threshold) + + def forward(self, sample): + features, regression, classification, anchor_info = self.module(sample["image"]) + anchors = anchor_info["anchor"] + classification = classification.sigmoid() + transformed_anchors = self.regressBoxes(anchors, regression) + transformed_anchors = self.clipBoxes(transformed_anchors, sample["image"]) + + preds = self.post_process( + sample["image"], anchors, regression, classification, transformed_anchors, self.threshold, self.max_box + ) + + if self.debug: + logging.info("debugging") + imgs = sample["image"] + imgs = imgs.permute(0, 2, 3, 1).cpu().numpy() + imgs = ((imgs * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]) * 255).astype(np.uint8) + imgs = [cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for img in imgs] + display(preds, imgs, list(map(str, range(80)))) + + for p, s in zip(preds, sample["scale"]): + if len(p["rois"]) > 0: + p["rois"] /= s + return preds diff --git a/maskrcnn_benchmark/modeling/backbone/efficientnet.py b/maskrcnn_benchmark/modeling/backbone/efficientnet.py new file mode 100644 index 0000000000000000000000000000000000000000..2bbc0512206e608b90452693661dae08f02aa100 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/efficientnet.py @@ -0,0 +1,698 @@ +""" + EfficientNet for ImageNet-1K, implemented in PyTorch. + Original papers: + - 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,' https://arxiv.org/abs/1905.11946, + - 'Adversarial Examples Improve Image Recognition,' https://arxiv.org/abs/1911.09665. +""" + +import os +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from maskrcnn_benchmark.layers import SEBlock, swish + + +def round_channels(channels, divisor=8): + """ + Round weighted channel number (make divisible operation). + + Parameters: + ---------- + channels : int or float + Original number of channels. + divisor : int, default 8 + Alignment value. + + Returns + ------- + int + Weighted number of channels. + """ + rounded_channels = max(int(channels + divisor / 2.0) // divisor * divisor, divisor) + if float(rounded_channels) < 0.9 * channels: + rounded_channels += divisor + return rounded_channels + + +def calc_tf_padding(x, kernel_size, stride=1, dilation=1): + """ + Calculate TF-same like padding size. + + Parameters: + ---------- + x : tensor + Input tensor. + kernel_size : int + Convolution window size. + stride : int, default 1 + Strides of the convolution. + dilation : int, default 1 + Dilation value for convolution layer. + + Returns + ------- + tuple of 4 int + The size of the padding. + """ + height, width = x.size()[2:] + oh = math.ceil(height / stride) + ow = math.ceil(width / stride) + pad_h = max((oh - 1) * stride + (kernel_size - 1) * dilation + 1 - height, 0) + pad_w = max((ow - 1) * stride + (kernel_size - 1) * dilation + 1 - width, 0) + return pad_h // 2, pad_h - pad_h // 2, pad_w // 2, pad_w - pad_w // 2 + + +class ConvBlock(nn.Module): + """ + Standard convolution block with Batch normalization and activation. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + kernel_size : int or tuple/list of 2 int + Convolution window size. + stride : int or tuple/list of 2 int + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + groups : int, default 1 + Number of groups. + bias : bool, default False + Whether the layer uses a bias vector. + use_bn : bool, default True + Whether to use BatchNorm layer. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU(inplace=True) + Activation function or name of activation function. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation=1, + groups=1, + bias=False, + use_bn=True, + bn_eps=1e-5, + activation=nn.ReLU(inplace=True), + ): + super(ConvBlock, self).__init__() + self.activate = activation is not None + self.use_bn = use_bn + self.use_pad = isinstance(padding, (list, tuple)) and (len(padding) == 4) + + if self.use_pad: + self.pad = nn.ZeroPad2d(padding=padding) + padding = 0 + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + ) + if self.use_bn: + self.bn = nn.BatchNorm2d(num_features=out_channels, eps=bn_eps) + if self.activate: + self.activ = activation + + def forward(self, x): + if self.use_pad: + x = self.pad(x) + x = self.conv(x) + if self.use_bn: + x = self.bn(x) + if self.activate: + x = self.activ(x) + return x + + +def conv1x1_block( + in_channels, + out_channels, + stride=1, + padding=0, + groups=1, + bias=False, + use_bn=True, + bn_eps=1e-5, + activation=nn.ReLU(inplace=True), +): + """ + 1x1 version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 0 + Padding value for convolution layer. + groups : int, default 1 + Number of groups. + bias : bool, default False + Whether the layer uses a bias vector. + use_bn : bool, default True + Whether to use BatchNorm layer. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU(inplace=True) + Activation function or name of activation function. + """ + return ConvBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=stride, + padding=padding, + groups=groups, + bias=bias, + use_bn=use_bn, + bn_eps=bn_eps, + activation=activation, + ) + + +def conv3x3_block( + in_channels, + out_channels, + stride=1, + padding=1, + dilation=1, + groups=1, + bias=False, + use_bn=True, + bn_eps=1e-5, + activation=nn.ReLU(inplace=True), +): + """ + 3x3 version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 1 + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + groups : int, default 1 + Number of groups. + bias : bool, default False + Whether the layer uses a bias vector. + use_bn : bool, default True + Whether to use BatchNorm layer. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU(inplace=True) + Activation function or name of activation function. + """ + return ConvBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + use_bn=use_bn, + bn_eps=bn_eps, + activation=activation, + ) + + +def dwconv3x3_block( + in_channels, + out_channels, + stride=1, + padding=1, + dilation=1, + bias=False, + bn_eps=1e-5, + activation=nn.ReLU(inplace=True), +): + """ + 3x3 depthwise version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 1 + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + bias : bool, default False + Whether the layer uses a bias vector. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU(inplace=True) + Activation function or name of activation function. + """ + return ConvBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + padding=padding, + dilation=dilation, + groups=out_channels, + bias=bias, + use_bn=True, + bn_eps=bn_eps, + activation=activation, + ) + + +def dwconv5x5_block( + in_channels, + out_channels, + stride=1, + padding=2, + dilation=1, + bias=False, + bn_eps=1e-5, + activation=nn.ReLU(inplace=True), +): + """ + 5x5 depthwise version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 2 + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + bias : bool, default False + Whether the layer uses a bias vector. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU(inplace=True) + Activation function or name of activation function. + """ + return ConvBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=5, + stride=stride, + padding=padding, + dilation=dilation, + groups=out_channels, + bias=bias, + use_bn=True, + bn_eps=bn_eps, + activation=activation, + ) + + +class EffiDwsConvUnit(nn.Module): + """ + EfficientNet specific depthwise separable convolution block/unit with BatchNorms and activations at each convolution + layers. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int + Strides of the second convolution layer. + bn_eps : float + Small float added to variance in Batch norm. + activation : str + Name of activation function. + tf_mode : bool + Whether to use TF-like mode. + """ + + def __init__(self, in_channels, out_channels, stride, bn_eps, activation, tf_mode): + super(EffiDwsConvUnit, self).__init__() + self.tf_mode = tf_mode + self.residual = (in_channels == out_channels) and (stride == 1) + + self.dw_conv = dwconv3x3_block( + in_channels=in_channels, + out_channels=in_channels, + padding=(0 if tf_mode else 1), + bn_eps=bn_eps, + activation=activation, + ) + self.se = SEBlock(channels=in_channels, reduction=4, mid_activation=activation) + self.pw_conv = conv1x1_block(in_channels=in_channels, out_channels=out_channels, bn_eps=bn_eps, activation=None) + + def forward(self, x): + if self.residual: + identity = x + if self.tf_mode: + x = F.pad(x, pad=calc_tf_padding(x, kernel_size=3)) + x = self.dw_conv(x) + x = self.se(x) + x = self.pw_conv(x) + if self.residual: + x = x + identity + return x + + +class EffiInvResUnit(nn.Module): + """ + EfficientNet inverted residual unit. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + kernel_size : int or tuple/list of 2 int + Convolution window size. + stride : int or tuple/list of 2 int + Strides of the second convolution layer. + exp_factor : int + Factor for expansion of channels. + se_factor : int + SE reduction factor for each unit. + bn_eps : float + Small float added to variance in Batch norm. + activation : str + Name of activation function. + tf_mode : bool + Whether to use TF-like mode. + """ + + def __init__( + self, in_channels, out_channels, kernel_size, stride, exp_factor, se_factor, bn_eps, activation, tf_mode + ): + super(EffiInvResUnit, self).__init__() + self.kernel_size = kernel_size + self.stride = stride + self.tf_mode = tf_mode + self.residual = (in_channels == out_channels) and (stride == 1) + self.use_se = se_factor > 0 + mid_channels = in_channels * exp_factor + dwconv_block_fn = dwconv3x3_block if kernel_size == 3 else (dwconv5x5_block if kernel_size == 5 else None) + + self.conv1 = conv1x1_block( + in_channels=in_channels, out_channels=mid_channels, bn_eps=bn_eps, activation=activation + ) + self.conv2 = dwconv_block_fn( + in_channels=mid_channels, + out_channels=mid_channels, + stride=stride, + padding=(0 if tf_mode else (kernel_size // 2)), + bn_eps=bn_eps, + activation=activation, + ) + if self.use_se: + self.se = SEBlock(channels=mid_channels, reduction=(exp_factor * se_factor), mid_activation=activation) + self.conv3 = conv1x1_block(in_channels=mid_channels, out_channels=out_channels, bn_eps=bn_eps, activation=None) + + def forward(self, x): + if self.residual: + identity = x + x = self.conv1(x) + if self.tf_mode: + x = F.pad(x, pad=calc_tf_padding(x, kernel_size=self.kernel_size, stride=self.stride)) + x = self.conv2(x) + if self.use_se: + x = self.se(x) + x = self.conv3(x) + if self.residual: + x = x + identity + return x + + +class EffiInitBlock(nn.Module): + """ + EfficientNet specific initial block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + bn_eps : float + Small float added to variance in Batch norm. + activation : str + Name of activation function. + tf_mode : bool + Whether to use TF-like mode. + """ + + def __init__(self, in_channels, out_channels, bn_eps, activation, tf_mode): + super(EffiInitBlock, self).__init__() + self.tf_mode = tf_mode + + self.conv = conv3x3_block( + in_channels=in_channels, + out_channels=out_channels, + stride=2, + padding=(0 if tf_mode else 1), + bn_eps=bn_eps, + activation=activation, + ) + + def forward(self, x): + if self.tf_mode: + x = F.pad(x, pad=calc_tf_padding(x, kernel_size=3, stride=2)) + x = self.conv(x) + return x + + +class EfficientNet(nn.Module): + """ + EfficientNet model from 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,' + https://arxiv.org/abs/1905.11946. + + Parameters: + ---------- + channels : list of list of int + Number of output channels for each unit. + init_block_channels : int + Number of output channels for initial unit. + final_block_channels : int + Number of output channels for the final block of the feature extractor. + kernel_sizes : list of list of int + Number of kernel sizes for each unit. + strides_per_stage : list int + Stride value for the first unit of each stage. + expansion_factors : list of list of int + Number of expansion factors for each unit. + dropout_rate : float, default 0.2 + Fraction of the input units to drop. Must be a number between 0 and 1. + tf_mode : bool, default False + Whether to use TF-like mode. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + in_channels : int, default 3 + Number of input channels. + in_size : tuple of two ints, default (224, 224) + Spatial size of the expected input image. + num_classes : int, default 1000 + Number of classification classes. + """ + + def __init__( + self, + cfg, + channels, + init_block_channels, + kernel_sizes, + strides_per_stage, + expansion_factors, + tf_mode=False, + bn_eps=1e-5, + in_channels=3, + ): + super(EfficientNet, self).__init__() + activation = swish() + + self.out_channels = [] + self.features = nn.Sequential() + self.stages = [] + stem = EffiInitBlock( + in_channels=in_channels, + out_channels=init_block_channels, + bn_eps=bn_eps, + activation=activation, + tf_mode=tf_mode, + ) + self.features.add_module("init_block", stem) + self.stages.append(stem) + + in_channels = init_block_channels + for i, channels_per_stage in enumerate(channels): + kernel_sizes_per_stage = kernel_sizes[i] + expansion_factors_per_stage = expansion_factors[i] + stage = nn.Sequential() + for j, out_channels in enumerate(channels_per_stage): + kernel_size = kernel_sizes_per_stage[j] + expansion_factor = expansion_factors_per_stage[j] + stride = strides_per_stage[i] if (j == 0) else 1 + if i == 0: + stage.add_module( + "unit{}".format(j + 1), + EffiDwsConvUnit( + in_channels=in_channels, + out_channels=out_channels, + stride=stride, + bn_eps=bn_eps, + activation=activation, + tf_mode=tf_mode, + ), + ) + else: + stage.add_module( + "unit{}".format(j + 1), + EffiInvResUnit( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + exp_factor=expansion_factor, + se_factor=4, + bn_eps=bn_eps, + activation=activation, + tf_mode=tf_mode, + ), + ) + in_channels = out_channels + if i > 0: + self.out_channels.append(out_channels) + self.features.add_module("stage{}".format(i + 1), stage) + self.stages.append(stage) + # Optionally freeze (requires_grad=False) parts of the backbone + self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT) + + def _freeze_backbone(self, freeze_at): + if freeze_at < 0: + return + for stage_index in range(freeze_at): + m = self.stages[stage_index] + for p in m.parameters(): + p.requires_grad = False + + def forward(self, x): + res = [] + for i, stage in enumerate(self.stages): + x = stage(x) + if i > 1: + res.append(x) + return res + + +def get_efficientnet(cfg, version, tf_mode=True, bn_eps=1e-5, **kwargs): + if version == "b0": + depth_factor = 1.0 + width_factor = 1.0 + elif version == "b1": + depth_factor = 1.1 + width_factor = 1.0 + elif version == "b2": + depth_factor = 1.2 + width_factor = 1.1 + elif version == "b3": + depth_factor = 1.4 + width_factor = 1.2 + elif version == "b4": + depth_factor = 1.8 + width_factor = 1.4 + elif version == "b5": + depth_factor = 2.2 + width_factor = 1.6 + elif version == "b6": + depth_factor = 2.6 + width_factor = 1.8 + elif version == "b7": + depth_factor = 3.1 + width_factor = 2.0 + elif version == "b8": + depth_factor = 3.6 + width_factor = 2.2 + else: + raise ValueError("Unsupported EfficientNet version {}".format(version)) + + init_block_channels = 32 + layers = [1, 2, 2, 3, 3, 4, 1] + downsample = [1, 1, 1, 1, 0, 1, 0] + channels_per_layers = [16, 24, 40, 80, 112, 192, 320] + expansion_factors_per_layers = [1, 6, 6, 6, 6, 6, 6] + kernel_sizes_per_layers = [3, 3, 5, 3, 5, 5, 3] + strides_per_stage = [1, 2, 2, 2, 1, 2, 1] + + layers = [int(math.ceil(li * depth_factor)) for li in layers] + channels_per_layers = [round_channels(ci * width_factor) for ci in channels_per_layers] + + from functools import reduce + + channels = reduce( + lambda x, y: x + [[y[0]] * y[1]] if y[2] != 0 else x[:-1] + [x[-1] + [y[0]] * y[1]], + zip(channels_per_layers, layers, downsample), + [], + ) + kernel_sizes = reduce( + lambda x, y: x + [[y[0]] * y[1]] if y[2] != 0 else x[:-1] + [x[-1] + [y[0]] * y[1]], + zip(kernel_sizes_per_layers, layers, downsample), + [], + ) + expansion_factors = reduce( + lambda x, y: x + [[y[0]] * y[1]] if y[2] != 0 else x[:-1] + [x[-1] + [y[0]] * y[1]], + zip(expansion_factors_per_layers, layers, downsample), + [], + ) + strides_per_stage = reduce( + lambda x, y: x + [[y[0]] * y[1]] if y[2] != 0 else x[:-1] + [x[-1] + [y[0]] * y[1]], + zip(strides_per_stage, layers, downsample), + [], + ) + strides_per_stage = [si[0] for si in strides_per_stage] + + init_block_channels = round_channels(init_block_channels * width_factor) + + net = EfficientNet( + cfg, + channels=channels, + init_block_channels=init_block_channels, + kernel_sizes=kernel_sizes, + strides_per_stage=strides_per_stage, + expansion_factors=expansion_factors, + tf_mode=tf_mode, + bn_eps=bn_eps, + **kwargs + ) + + return net diff --git a/maskrcnn_benchmark/modeling/backbone/fbnet.py b/maskrcnn_benchmark/modeling/backbone/fbnet.py new file mode 100644 index 0000000000000000000000000000000000000000..1156efbdd4552ae66d0a7f2cd528d7401f94c0a0 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/fbnet.py @@ -0,0 +1,434 @@ +""" +FBNet model builder +""" + +from __future__ import absolute_import, division, print_function, unicode_literals + +import copy +import logging +import math +from collections import OrderedDict + +import torch +import torch.nn as nn +from torch.nn import BatchNorm2d, SyncBatchNorm +from maskrcnn_benchmark.layers import Conv2d, interpolate +from maskrcnn_benchmark.layers import NaiveSyncBatchNorm2d, FrozenBatchNorm2d +from maskrcnn_benchmark.layers.misc import _NewEmptyTensorOp + + +logger = logging.getLogger(__name__) + + +def _py2_round(x): + return math.floor(x + 0.5) if x >= 0.0 else math.ceil(x - 0.5) + + +def _get_divisible_by(num, divisible_by, min_val): + ret = int(num) + if divisible_by > 0 and num % divisible_by != 0: + ret = int((_py2_round(num / divisible_by) or min_val) * divisible_by) + return ret + + +class Identity(nn.Module): + def __init__(self, C_in, C_out, stride): + super(Identity, self).__init__() + self.conv = ( + ConvBNRelu( + C_in, + C_out, + kernel=1, + stride=stride, + pad=0, + no_bias=1, + use_relu="relu", + bn_type="bn", + ) + if C_in != C_out or stride != 1 + else None + ) + + def forward(self, x): + if self.conv: + out = self.conv(x) + else: + out = x + return out + + +class CascadeConv3x3(nn.Sequential): + def __init__(self, C_in, C_out, stride): + assert stride in [1, 2] + ops = [ + Conv2d(C_in, C_in, 3, stride, 1, bias=False), + BatchNorm2d(C_in), + nn.ReLU(inplace=True), + Conv2d(C_in, C_out, 3, 1, 1, bias=False), + BatchNorm2d(C_out), + ] + super(CascadeConv3x3, self).__init__(*ops) + self.res_connect = (stride == 1) and (C_in == C_out) + + def forward(self, x): + y = super(CascadeConv3x3, self).forward(x) + if self.res_connect: + y += x + return y + + +class Shift(nn.Module): + def __init__(self, C, kernel_size, stride, padding): + super(Shift, self).__init__() + self.C = C + kernel = torch.zeros((C, 1, kernel_size, kernel_size), dtype=torch.float32) + ch_idx = 0 + + assert stride in [1, 2] + self.stride = stride + self.padding = padding + self.kernel_size = kernel_size + self.dilation = 1 + + hks = kernel_size // 2 + ksq = kernel_size**2 + + for i in range(kernel_size): + for j in range(kernel_size): + if i == hks and j == hks: + num_ch = C // ksq + C % ksq + else: + num_ch = C // ksq + kernel[ch_idx : ch_idx + num_ch, 0, i, j] = 1 + ch_idx += num_ch + + self.register_parameter("bias", None) + self.kernel = nn.Parameter(kernel, requires_grad=False) + + def forward(self, x): + if x.numel() > 0: + return nn.functional.conv2d( + x, + self.kernel, + self.bias, + (self.stride, self.stride), + (self.padding, self.padding), + self.dilation, + self.C, # groups + ) + + output_shape = [ + (i + 2 * p - (di * (k - 1) + 1)) // d + 1 + for i, p, di, k, d in zip( + x.shape[-2:], + (self.padding, self.dilation), + (self.dilation, self.dilation), + (self.kernel_size, self.kernel_size), + (self.stride, self.stride), + ) + ] + output_shape = [x.shape[0], self.C] + output_shape + return _NewEmptyTensorOp.apply(x, output_shape) + + +class ShiftBlock5x5(nn.Sequential): + def __init__(self, C_in, C_out, expansion, stride): + assert stride in [1, 2] + self.res_connect = (stride == 1) and (C_in == C_out) + + C_mid = _get_divisible_by(C_in * expansion, 8, 8) + + ops = [ + # pw + Conv2d(C_in, C_mid, 1, 1, 0, bias=False), + BatchNorm2d(C_mid), + nn.ReLU(inplace=True), + # shift + Shift(C_mid, 5, stride, 2), + # pw-linear + Conv2d(C_mid, C_out, 1, 1, 0, bias=False), + BatchNorm2d(C_out), + ] + super(ShiftBlock5x5, self).__init__(*ops) + + def forward(self, x): + y = super(ShiftBlock5x5, self).forward(x) + if self.res_connect: + y += x + return y + + +class ChannelShuffle(nn.Module): + def __init__(self, groups): + super(ChannelShuffle, self).__init__() + self.groups = groups + + def forward(self, x): + """Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]""" + N, C, H, W = x.size() + g = self.groups + assert C % g == 0, "Incompatible group size {} for input channel {}".format(g, C) + return x.view(N, g, int(C / g), H, W).permute(0, 2, 1, 3, 4).contiguous().view(N, C, H, W) + + +class ConvBNRelu(nn.Sequential): + def __init__( + self, input_depth, output_depth, kernel, stride, pad, no_bias, use_relu, bn_type, group=1, *args, **kwargs + ): + super(ConvBNRelu, self).__init__() + + assert use_relu in ["relu", None] + if isinstance(bn_type, (list, tuple)): + assert len(bn_type) == 2 + assert bn_type[0] == "gn" + gn_group = bn_type[1] + bn_type = bn_type[0] + assert bn_type in ["bn", "nsbn", "sbn", "af", "gn", None] + assert stride in [1, 2, 4] + + op = Conv2d( + input_depth, + output_depth, + kernel_size=kernel, + stride=stride, + padding=pad, + bias=not no_bias, + groups=group, + *args, + **kwargs + ) + nn.init.kaiming_normal_(op.weight, mode="fan_out", nonlinearity="relu") + if op.bias is not None: + nn.init.constant_(op.bias, 0.0) + self.add_module("conv", op) + + if bn_type == "bn": + bn_op = BatchNorm2d(output_depth) + elif bn_type == "sbn": + bn_op = SyncBatchNorm(output_depth) + elif bn_type == "nsbn": + bn_op = NaiveSyncBatchNorm2d(output_depth) + elif bn_type == "gn": + bn_op = nn.GroupNorm(num_groups=gn_group, num_channels=output_depth) + elif bn_type == "af": + bn_op = FrozenBatchNorm2d(output_depth) + if bn_type is not None: + self.add_module("bn", bn_op) + + if use_relu == "relu": + self.add_module("relu", nn.ReLU(inplace=True)) + + +class SEModule(nn.Module): + reduction = 4 + + def __init__(self, C): + super(SEModule, self).__init__() + mid = max(C // self.reduction, 8) + conv1 = Conv2d(C, mid, 1, 1, 0) + conv2 = Conv2d(mid, C, 1, 1, 0) + + self.op = nn.Sequential(nn.AdaptiveAvgPool2d(1), conv1, nn.ReLU(inplace=True), conv2, nn.Sigmoid()) + + def forward(self, x): + return x * self.op(x) + + +class Upsample(nn.Module): + def __init__(self, scale_factor, mode, align_corners=None): + super(Upsample, self).__init__() + self.scale = scale_factor + self.mode = mode + self.align_corners = align_corners + + def forward(self, x): + return interpolate(x, scale_factor=self.scale, mode=self.mode, align_corners=self.align_corners) + + +def _get_upsample_op(stride): + assert ( + stride in [1, 2, 4] + or stride in [-1, -2, -4] + or (isinstance(stride, tuple) and all(x in [-1, -2, -4] for x in stride)) + ) + + scales = stride + ret = None + if isinstance(stride, tuple) or stride < 0: + scales = [-x for x in stride] if isinstance(stride, tuple) else -stride + stride = 1 + ret = Upsample(scale_factor=scales, mode="nearest", align_corners=None) + + return ret, stride + + +class IRFBlock(nn.Module): + def __init__( + self, + input_depth, + output_depth, + expansion, + stride, + bn_type="bn", + kernel=3, + width_divisor=1, + shuffle_type=None, + pw_group=1, + se=False, + cdw=False, + dw_skip_bn=False, + dw_skip_relu=False, + ): + super(IRFBlock, self).__init__() + + assert kernel in [1, 3, 5, 7], kernel + + self.use_res_connect = stride == 1 and input_depth == output_depth + self.output_depth = output_depth + + mid_depth = int(input_depth * expansion) + mid_depth = _get_divisible_by(mid_depth, width_divisor, width_divisor) + + # pw + self.pw = ConvBNRelu( + input_depth, + mid_depth, + kernel=1, + stride=1, + pad=0, + no_bias=1, + use_relu="relu", + bn_type=bn_type, + group=pw_group, + ) + + # negative stride to do upsampling + self.upscale, stride = _get_upsample_op(stride) + + # dw + if kernel == 1: + self.dw = nn.Sequential() + elif cdw: + dw1 = ConvBNRelu( + mid_depth, + mid_depth, + kernel=kernel, + stride=stride, + pad=(kernel // 2), + group=mid_depth, + no_bias=1, + use_relu="relu", + bn_type=bn_type, + ) + dw2 = ConvBNRelu( + mid_depth, + mid_depth, + kernel=kernel, + stride=1, + pad=(kernel // 2), + group=mid_depth, + no_bias=1, + use_relu="relu" if not dw_skip_relu else None, + bn_type=bn_type if not dw_skip_bn else None, + ) + self.dw = nn.Sequential(OrderedDict([("dw1", dw1), ("dw2", dw2)])) + else: + self.dw = ConvBNRelu( + mid_depth, + mid_depth, + kernel=kernel, + stride=stride, + pad=(kernel // 2), + group=mid_depth, + no_bias=1, + use_relu="relu" if not dw_skip_relu else None, + bn_type=bn_type if not dw_skip_bn else None, + ) + + # pw-linear + self.pwl = ConvBNRelu( + mid_depth, + output_depth, + kernel=1, + stride=1, + pad=0, + no_bias=1, + use_relu=None, + bn_type=bn_type, + group=pw_group, + ) + + self.shuffle_type = shuffle_type + if shuffle_type is not None: + self.shuffle = ChannelShuffle(pw_group) + + self.se4 = SEModule(output_depth) if se else nn.Sequential() + + self.output_depth = output_depth + + def forward(self, x): + y = self.pw(x) + if self.shuffle_type == "mid": + y = self.shuffle(y) + if self.upscale is not None: + y = self.upscale(y) + y = self.dw(y) + y = self.pwl(y) + if self.use_res_connect: + y += x + y = self.se4(y) + return y + + +skip = lambda C_in, C_out, stride, **kwargs: Identity(C_in, C_out, stride) +basic_block = lambda C_in, C_out, stride, **kwargs: CascadeConv3x3(C_in, C_out, stride) +# layer search 2 +ir_k3_e1 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 1, stride, kernel=3, **kwargs) +ir_k3_e3 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 3, stride, kernel=3, **kwargs) +ir_k3_e6 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 6, stride, kernel=3, **kwargs) +ir_k3_s4 = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 4, stride, kernel=3, shuffle_type="mid", pw_group=4, **kwargs +) +ir_k5_e1 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 1, stride, kernel=5, **kwargs) +ir_k5_e3 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 3, stride, kernel=5, **kwargs) +ir_k5_e6 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 6, stride, kernel=5, **kwargs) +ir_k5_s4 = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 4, stride, kernel=5, shuffle_type="mid", pw_group=4, **kwargs +) +# layer search se +ir_k3_e1_se = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 1, stride, kernel=3, se=True, **kwargs) +ir_k3_e3_se = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 3, stride, kernel=3, se=True, **kwargs) +ir_k3_e6_se = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 6, stride, kernel=3, se=True, **kwargs) +ir_k3_s4_se = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 4, stride, kernel=3, shuffle_type=mid, pw_group=4, se=True, **kwargs +) +ir_k5_e1_se = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 1, stride, kernel=5, se=True, **kwargs) +ir_k5_e3_se = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 3, stride, kernel=5, se=True, **kwargs) +ir_k5_e6_se = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 6, stride, kernel=5, se=True, **kwargs) +ir_k5_s4_se = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 4, stride, kernel=5, shuffle_type="mid", pw_group=4, se=True, **kwargs +) +# layer search 3 (in addition to layer search 2) +ir_k3_s2 = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 1, stride, kernel=3, shuffle_type="mid", pw_group=2, **kwargs +) +ir_k5_s2 = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 1, stride, kernel=5, shuffle_type="mid", pw_group=2, **kwargs +) +ir_k3_s2_se = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 1, stride, kernel=3, shuffle_type="mid", pw_group=2, se=True, **kwargs +) +ir_k5_s2_se = lambda C_in, C_out, stride, **kwargs: IRFBlock( + C_in, C_out, 1, stride, kernel=5, shuffle_type="mid", pw_group=2, se=True, **kwargs +) +# layer search 4 (in addition to layer search 3) +ir_k33_e1 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 1, stride, kernel=3, cdw=True, **kwargs) +ir_k33_e3 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 3, stride, kernel=3, cdw=True, **kwargs) +ir_k33_e6 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 6, stride, kernel=3, cdw=True, **kwargs) +# layer search 5 (in addition to layer search 4) +ir_k7_e1 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 1, stride, kernel=7, **kwargs) +ir_k7_e3 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 3, stride, kernel=7, **kwargs) +ir_k7_e6 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 6, stride, kernel=7, **kwargs) +ir_k7_sep_e1 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 1, stride, kernel=7, cdw=True, **kwargs) +ir_k7_sep_e3 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 3, stride, kernel=7, cdw=True, **kwargs) +ir_k7_sep_e6 = lambda C_in, C_out, stride, **kwargs: IRFBlock(C_in, C_out, 6, stride, kernel=7, cdw=True, **kwargs) diff --git a/maskrcnn_benchmark/modeling/backbone/fpn.py b/maskrcnn_benchmark/modeling/backbone/fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..f3197869bd801d2a3b6e900b5311d0a986625be4 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/fpn.py @@ -0,0 +1,176 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +import torch.nn.functional as F +from torch import nn + + +class FPN(nn.Module): + """ + Module that adds FPN on top of a list of feature maps. + The feature maps are currently supposed to be in increasing depth + order, and must be consecutive + """ + + def __init__( + self, + in_channels_list, + out_channels, + conv_block, + top_blocks=None, + drop_block=None, + use_spp=False, + use_pan=False, + return_swint_feature_before_fusion=False, + ): + """ + Arguments: + in_channels_list (list[int]): number of channels for each feature map that + will be fed + out_channels (int): number of channels of the FPN representation + top_blocks (nn.Module or None): if provided, an extra operation will + be performed on the output of the last (smallest resolution) + FPN output, and the result will extend the result list + """ + super(FPN, self).__init__() + self.inner_blocks = [] + self.layer_blocks = [] + self.pan_blocks = [] if use_pan else None + self.spp_block = SPPLayer() if use_spp else None + self.return_swint_feature_before_fusion = return_swint_feature_before_fusion + for idx, in_channels in enumerate(in_channels_list, 1): + inner_block = "fpn_inner{}".format(idx) + layer_block = "fpn_layer{}".format(idx) + + if in_channels == 0: + continue + if idx == len(in_channels_list) and use_spp: + in_channels = in_channels * 4 + inner_block_module = conv_block(in_channels, out_channels, 1) + layer_block_module = conv_block(out_channels, out_channels, 3, 1) + self.add_module(inner_block, inner_block_module) + self.add_module(layer_block, layer_block_module) + self.inner_blocks.append(inner_block) + self.layer_blocks.append(layer_block) + + if use_pan: + pan_in_block = "pan_in_layer{}".format(idx) + pan_in_block_module = conv_block(out_channels, out_channels, 3, 2) + self.add_module(pan_in_block, pan_in_block_module) + pan_out_block = "pan_out_layer{}".format(idx) + pan_out_block_module = conv_block(out_channels, out_channels, 3, 1) + self.add_module(pan_out_block, pan_out_block_module) + self.pan_blocks.append([pan_in_block, pan_out_block]) + + self.top_blocks = top_blocks + self.drop_block = drop_block + + def forward(self, x): + """ + Arguments: + x (list[Tensor]): feature maps for each feature level. + Returns: + results (tuple[Tensor]): feature maps after FPN layers. + They are ordered from highest resolution first. + """ + if type(x) is tuple: + # for the case of VL backbone + x, x_text = x[0], x[1] + # print([v.shape for v in x]) + swint_feature_c4 = None + if self.return_swint_feature_before_fusion: + # TODO: here we only return last single scale feature map before the backbone fusion, should be more flexible + swint_feature_c4 = x[-2] + + if self.spp_block: + last_inner = getattr(self, self.inner_blocks[-1])(self.spp_block(x[-1])) + else: + last_inner = getattr(self, self.inner_blocks[-1])(x[-1]) + results = [] + results.append(getattr(self, self.layer_blocks[-1])(last_inner)) + for feature, inner_block, layer_block in zip( + x[:-1][::-1], self.inner_blocks[:-1][::-1], self.layer_blocks[:-1][::-1] + ): + if not inner_block: + continue + inner_lateral = getattr(self, inner_block)(feature) + + if inner_lateral.shape[-2:] != last_inner.shape[-2:]: + # TODO: could also give size instead of + inner_top_down = F.interpolate(last_inner, size=inner_lateral.shape[-2:], mode="nearest") + else: + inner_top_down = last_inner + + # TODO use size instead of scale to make it robust to different sizes + # inner_top_down = F.upsample(last_inner, size=inner_lateral.shape[-2:], + # mode='bilinear', align_corners=False) + last_inner = inner_lateral + inner_top_down + if self.drop_block and self.training: + results.insert(0, getattr(self, layer_block)(self.drop_block(last_inner))) + else: + results.insert(0, getattr(self, layer_block)(last_inner)) + + if self.pan_blocks: + pan_results = [] + last_outer = results[0] + pan_results.append(last_outer) + for outer_top_down, pan_block in zip(results[1:], self.pan_blocks): + + if self.drop_block and self.training: + pan_lateral = getattr(self, pan_block[0])(self.drop_block(last_outer)) + else: + pan_lateral = getattr(self, pan_block[0])(last_outer) + + last_outer = getattr(self, pan_block[1])(pan_lateral + outer_top_down) + pan_results.append(last_outer) + results = pan_results + + if isinstance(self.top_blocks, LastLevelP6P7): + last_results = self.top_blocks(x[-1], results[-1]) + results.extend(last_results) + elif isinstance(self.top_blocks, LastLevelMaxPool): + last_results = self.top_blocks(results[-1]) + results.extend(last_results) + + try: + return tuple(results), x_text, swint_feature_c4 + except NameError as e: + return tuple(results) + + +class LastLevelMaxPool(nn.Module): + def forward(self, x): + return [F.max_pool2d(x, 1, 2, 0)] + + +class LastLevelP6P7(nn.Module): + """ + This module is used in RetinaNet to generate extra layers, P6 and P7. + """ + + def __init__(self, in_channels, out_channels): + super(LastLevelP6P7, self).__init__() + self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) + self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) + for module in [self.p6, self.p7]: + nn.init.kaiming_uniform_(module.weight, a=1) + nn.init.constant_(module.bias, 0) + self.use_P5 = in_channels == out_channels + + def forward(self, c5, p5): + x = p5 if self.use_P5 else c5 + p6 = self.p6(x) + p7 = self.p7(F.relu(p6)) + return [p6, p7] + + +class SPPLayer(nn.Module): + def __init__(self): + super(SPPLayer, self).__init__() + + def forward(self, x): + x_1 = x + x_2 = F.max_pool2d(x, 5, stride=1, padding=2) + x_3 = F.max_pool2d(x, 9, stride=1, padding=4) + x_4 = F.max_pool2d(x, 13, stride=1, padding=6) + out = torch.cat((x_1, x_2, x_3, x_4), dim=1) + return out diff --git a/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer.py b/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..044aaf4890f8d4a168de15677f8fc8bab3b53b2b --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer.py @@ -0,0 +1,978 @@ +""" Swin Transformer +A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` + - https://arxiv.org/pdf/2103.14030 +Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below +""" +# -------------------------------------------------------- +# Swin Transformer +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu +# -------------------------------------------------------- +import logging +import math +from copy import deepcopy +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, dim_text=None + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + # dim_text = 768 + if dim_text is not None: + self.qkv_text_i2t = nn.Linear(dim_text, dim * 2, bias=qkv_bias) + self.qkv_i2t = nn.Linear(dim, dim, bias=qkv_bias) + self.attn_drop_i2t = nn.Dropout(attn_drop) + self.proj_i2t = nn.Linear(dim, dim) + self.proj_drop_i2t = nn.Dropout(proj_drop) + # self.proj_i2t = nn.Linear(dim, dim) + + # self.gate_i2t = nn.Linear(2*dim, 1) + # self.gate_i2t = nn.Linear(2*dim, dim) + # self.sigmoid_i2t = nn.Sigmoid() + + """self.i2t_relative_position_bias = nn.Parameter( + torch.zeros(2, num_heads, ntext)) # (2, nH, ntext) + self.t2t_relative_position_bias = nn.Parameter( + torch.zeros(num_heads, ntext, ntext)) # (nH, ntext, ntext) + trunc_normal_(self.i2t_relative_position_bias, std=.02) + trunc_normal_(self.t2t_relative_position_bias, std=.02)#""" + + def forward(self, x, mask: Optional[torch.Tensor] = None, y=None, y_mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + + attn = self.softmax(attn) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + if y is not None: + B_text, N_text, C_text = y.shape + nW = B_ // B_text # number of windows + assert B_text * nW == B_, "B_ is not a multiplier of B_text in window attention" + # notice that after qkv_text, the hidden dimension is C instead of C_text + qkv_text = ( + self.qkv_text_i2t(y) + .reshape(B_text, N_text, 2, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + k_text, v_text = qkv_text[0], qkv_text[1] + + k_text = torch.repeat_interleave(k_text, nW, dim=0) + v_text = torch.repeat_interleave(v_text, nW, dim=0) + # TODO: remove q_text + q_i2t = self.qkv_i2t(x).reshape(B_, N, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q_i2t = q_i2t[0] + + # image to text attention + # attn_i2t = (q_i2t @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1)) # B_, nH, N, N_text + # print(q_i2t.size()) + # print(k_text.size()) + # torch.Size([4096, 4, 49, 32]) + # torch.Size([4096, 4, 50, 32]) + text_scale = k_text.size(-1) ** -0.5 + q_i2t = q_i2t * text_scale + attn_i2t = q_i2t @ k_text.transpose(-2, -1) # B_, nH, N, N_text + # add image to text bias and text_mask + if y_mask is not None: + mask_and_i2t_bias = y_mask.view( + B_text, 1, 1, N_text + ) # + self.i2t_relative_position_bias[:1].expand(B_text, -1, -1).unsqueeze(-2) # B_text, nH, 1, N_text + attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0) + + attn_i2t = self.softmax(attn_i2t) + attn_i2t = self.attn_drop_i2t(attn_i2t) + # torch.Size([4096, 4, 49, 50]) + # torch.Size([64, 4, 50, 32]) + # print(attn_i2t.size()) + # print(v_text.size()) + # 1/0 + y = (attn_i2t @ v_text).transpose(1, 2).reshape(B_, N, C) + y = self.proj_i2t(y) + y = self.proj_drop_i2t(y) + + # g = torch.cat([x, y], dim=-1) + # g = (self.gate_i2t(g)) + # g = self.sigmoid_i2t(self.gate_i2t(g)) + # x = x+g*y + + x = x + y + + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + dim_text=None, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + dim_text=dim_text, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix, x_text=None, mask_text=None): + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn( + x_windows, mask=attn_mask, y=x_text, y_mask=mask_text + ) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + # TODO: Keep? + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + # TODO: Keep? + # def extra_repr(self) -> str: + # return f"input_resolution={self.input_resolution}, dim={self.dim}" + # + # def flops(self): + # H, W = self.input_resolution + # flops = H * W * self.dim + # flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + # return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + dim_text=None, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + dim_text=(768 if i >= 9 else dim_text), + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def get_attention_mask(self, H, W, device): + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x, H, W, x_text=None, mask_text=None): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + x_text: input text features with shape of (B_text, N_text, C_text) + mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; + """ + attn_mask = self.get_attention_mask(H, W, x.device) + + for blk in self.blocks: + blk.H, blk.W = H, W + if not torch.jit.is_scripting() and self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text) + else: + x = blk(x, mask_matrix=attn_mask, x_text=x_text, mask_text=mask_text) + # print(x.size()) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + # TODO: Keep? + # def extra_repr(self) -> str: + # return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + frozen_stages=-1, + use_checkpoint=False, + out_features=["stage2", "stage3", "stage4", "stage5"], + backbone_arch="SWINT-FPN-RETINANET", + max_query_len=None, + lang_dim=None, + ): + super(SwinTransformer, self).__init__() + + print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint) + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + + self.out_features = out_features + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + self._out_feature_strides = {} + self._out_feature_channels = {} + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1, + dim_text=(768 if i_layer == 3 else None), + ) # TODO: Make this general : lang_dim not 768 + self.layers.append(layer) + + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + self._out_feature_channels[stage] = embed_dim * 2**i_layer + self._out_feature_strides[stage] = 4 * 2**i_layer + + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + self.num_features = num_features + + # TODO : need this? + # assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '') + # head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0. + # if weight_init.startswith('jax'): + # for n, m in self.named_modules(): + # _init_vit_weights(m, n, head_bias=head_bias, jax_impl=True) + # else: + # self.apply(_init_vit_weights) + + # add a norm layer for each output + for i_layer in range(self.num_layers): + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + if i_layer == 0 and backbone_arch.endswith("RETINANET"): + layer = nn.Identity() + else: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def forward(self, inputs): + """Forward function.""" + x = inputs["img"] + language_dict_features = inputs["lang"] + + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + x_text = language_dict_features["hidden"] + if "masks" in language_dict_features: + mask_text = 1.0 - language_dict_features["masks"] # (B, N_text) 0 means not to be masked out + mask_text.masked_fill_(mask_text.bool(), -float("inf")) + else: + mask_text = None + + outs = [] + for layer_i, layer in enumerate(self.layers): + # if layer_i > 1: + # if layer_i > 2: + if layer_i > -1: + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text) + else: + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=None, mask_text=None) + name = f"stage{layer_i + 2}" + if name in self.out_features: + norm_layer = getattr(self, f"norm{layer_i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.num_features[layer_i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + # Here the text features are just combined directly with the image features, so language_dict_features is unchanged + return outs, language_dict_features + + @torch.jit.ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +class FusionSwinTransformer(nn.Module): + def __init__(self, vision_backbone, language_backbone, add_linear_layer=False): + super().__init__() + self.backbone = vision_backbone + self.language_backbone = language_backbone + self.cross_modal_image_transform2 = nn.Linear(1024, 768) + self.cross_modal_image_transform3 = nn.Linear(1024, 768) + self.add_linear_layer = add_linear_layer + if self.add_linear_layer: + self.tunable_linear = torch.nn.Linear( + self.language_backbone.body.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, 1000, bias=False + ) + self.tunable_linear.weight.data.fill_(0.0) + + def forward( + self, + tokenizer_input, + images, + ): + + # Fusion in the backbone forward - interleaves the passed through the langauge and image backbone. + x = images.tensors + + # Embed the image + x = self.backbone.body.patch_embed(x) + Wh, Ww = x.size(2), x.size(3) + + if self.backbone.body.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.backbone.body.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + image_embeds = self.backbone.body.pos_drop(x) + + # Embed the text + text_embeds = self.language_backbone.body.model.embeddings(input_ids=tokenizer_input["input_ids"]) + input_shape = tokenizer_input["attention_mask"].size() + extended_text_masks = self.language_backbone.body.model.get_extended_attention_mask( + tokenizer_input["attention_mask"], input_shape, device=tokenizer_input["attention_mask"].device + ) + + if self.add_linear_layer: + text_embeds = self.tunable_linear.weight[: text_embeds.size(1), :].unsqueeze(0) + text_embeds + + outs = [] + # Pass the text through the first 10 layers + num_pre_text = 10 + for layer_i, layer in enumerate(self.language_backbone.body.model.encoder.layer[:num_pre_text]): + text_embeds = layer(text_embeds, extended_text_masks)[0] + + # Pass through first 2 image backbone layers + num_pre_vision = 2 + for layer_i, layer in enumerate(self.backbone.body.layers[:num_pre_vision]): + x_out, H, W, image_embeds, Wh, Ww = layer(image_embeds, Wh, Ww, x_text=None, mask_text=None) + name = f"stage{layer_i + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{layer_i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.backbone.body.num_features[layer_i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + num_pre_block = 9 + # Get the attention mask for the third layer: + attn_mask = self.backbone.body.layers[num_pre_vision].get_attention_mask(Wh, Ww, image_embeds.device) + for blk_cnt, blk in enumerate(self.backbone.body.layers[num_pre_vision].blocks): + blk.H, blk.W = Wh, Ww + if blk_cnt < num_pre_block: + if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint: + image_embeds = checkpoint.checkpoint(blk, image_embeds, attn_mask) + else: + image_embeds = blk(image_embeds, attn_mask) + else: + if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint: + image_embeds = checkpoint.checkpoint(blk, image_embeds, attn_mask, text_embeds, extended_text_masks) + else: + image_embeds = blk(image_embeds, attn_mask, text_embeds, extended_text_masks) + + # Apply layer norm after 3rd layer and take output + name = f"stage{num_pre_vision + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision}") + x_out = norm_layer(image_embeds) + out = ( + x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision]).permute(0, 3, 1, 2).contiguous() + ) + outs.append(out) + + # Apply downsampling if we need to at the output of third layer for input to next layer + if self.backbone.body.layers[num_pre_vision].downsample is not None: + image_embeds = self.backbone.body.layers[num_pre_vision].downsample(image_embeds, Wh, Ww) + Wh, Ww = (Wh + 1) // 2, (Ww + 1) // 2 + + # Final layer + + # Get attention mask for 4th layer + attn_mask = self.backbone.body.layers[num_pre_vision + 1].get_attention_mask(Wh, Ww, image_embeds.device) + blk = self.backbone.body.layers[num_pre_vision + 1].blocks[0] + blk.H, blk.W = Wh, Ww + + fuse_image_embeds = blk( + x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks + ) + fuse_text_embeds = self.language_backbone.body.model.encoder.layer[num_pre_text]( + text_embeds, extended_text_masks, encoder_hidden_states=self.cross_modal_image_transform2(image_embeds) + )[0] + text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds + + blk = self.backbone.body.layers[num_pre_vision + 1].blocks[1] + blk.H, blk.W = Wh, Ww + fuse_image_embeds = self.backbone.body.layers[num_pre_vision + 1].blocks[1]( + x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks + ) + fuse_text_embeds = self.language_backbone.body.model.encoder.layer[num_pre_text + 1]( + text_embeds, extended_text_masks, encoder_hidden_states=self.cross_modal_image_transform3(image_embeds) + )[0] + text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds + + # Apply layer norm after 4th layer and take output + name = f"stage{num_pre_vision + 1 + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision + 1}") + x_out = norm_layer(image_embeds) + out = ( + x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision + 1]) + .permute(0, 3, 1, 2) + .contiguous() + ) + outs.append(out) + + language_dict_features = self.language_backbone.body.get_aggregated_output( + text_embeds, tokenizer_input["input_ids"], tokenizer_input["attention_mask"] + ) + + # Apply fpn + visual_features = self.backbone.fpn(outs) + + # None for now, need to add if we want to add shallow contrastive loss? + swint_feature_c4 = None + + return visual_features, language_dict_features, swint_feature_c4 + + +def build_swint_backbone(cfg): + """ + Create a SwinT instance from config. + + Returns: + VoVNet: a :class:`VoVNet` instance. + """ + return SwinTransformer( + patch_size=4, + in_chans=3, + embed_dim=cfg.MODEL.SWINT.EMBED_DIM, + depths=cfg.MODEL.SWINT.DEPTHS, + num_heads=cfg.MODEL.SWINT.NUM_HEADS, + window_size=cfg.MODEL.SWINT.WINDOW_SIZE, + mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE, + norm_layer=nn.LayerNorm, + ape=cfg.MODEL.SWINT.APE, + patch_norm=True, + frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT, + backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY, + use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT, + out_features=cfg.MODEL.BACKBONE.OUT_FEATURES, + max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, + ) + + +def build_combined_backbone(vision_backbone, language_backbone, add_linear_layer=False): + return FusionSwinTransformer(vision_backbone, language_backbone, add_linear_layer=add_linear_layer) diff --git a/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer_v2.py b/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..4441668112a7e469c29ce737a66c544c89aef7a2 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer_v2.py @@ -0,0 +1,987 @@ +""" Swin Transformer +A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` + - https://arxiv.org/pdf/2103.14030 +Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below +""" +# -------------------------------------------------------- +# Swin Transformer +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu +# -------------------------------------------------------- +import logging +import math +from copy import deepcopy +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, dim_text=None + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + # dim_text = 768 + if dim_text is not None: + self.qkv_text_i2t = nn.Linear(dim_text, dim * 2, bias=qkv_bias) + self.qkv_i2t = nn.Linear(dim, dim, bias=qkv_bias) + self.attn_drop_i2t = nn.Dropout(attn_drop) + self.proj_i2t = nn.Linear(dim, dim) + self.proj_drop_i2t = nn.Dropout(proj_drop) + # self.proj_i2t = nn.Linear(dim, dim) + self.alpha_i2t = nn.Parameter(torch.Tensor([0])) + + # self.gate_i2t = nn.Linear(2*dim, 1) + # self.gate_i2t = nn.Linear(2*dim, dim) + # self.sigmoid_i2t = nn.Sigmoid() + + """self.i2t_relative_position_bias = nn.Parameter( + torch.zeros(2, num_heads, ntext)) # (2, nH, ntext) + self.t2t_relative_position_bias = nn.Parameter( + torch.zeros(num_heads, ntext, ntext)) # (nH, ntext, ntext) + trunc_normal_(self.i2t_relative_position_bias, std=.02) + trunc_normal_(self.t2t_relative_position_bias, std=.02)#""" + + def forward(self, x, mask: Optional[torch.Tensor] = None, y=None, y_mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + + attn = self.softmax(attn) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + if y is not None: + B_text, N_text, C_text = y.shape + nW = B_ // B_text # number of windows + assert B_text * nW == B_, "B_ is not a multiplier of B_text in window attention" + # notice that after qkv_text, the hidden dimension is C instead of C_text + qkv_text = ( + self.qkv_text_i2t(y) + .reshape(B_text, N_text, 2, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + k_text, v_text = qkv_text[0], qkv_text[1] + + k_text = torch.repeat_interleave(k_text, nW, dim=0) + v_text = torch.repeat_interleave(v_text, nW, dim=0) + # TODO: remove q_text + q_i2t = self.qkv_i2t(x).reshape(B_, N, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q_i2t = q_i2t[0] + + # image to text attention + # attn_i2t = (q_i2t @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1)) # B_, nH, N, N_text + # print(q_i2t.size()) + # print(k_text.size()) + # torch.Size([4096, 4, 49, 32]) + # torch.Size([4096, 4, 50, 32]) + text_scale = k_text.size(-1) ** -0.5 + q_i2t = q_i2t * text_scale + attn_i2t = q_i2t @ k_text.transpose(-2, -1) # B_, nH, N, N_text + # add image to text bias and text_mask + if y_mask is not None: + mask_and_i2t_bias = y_mask.view( + B_text, 1, 1, N_text + ) # + self.i2t_relative_position_bias[:1].expand(B_text, -1, -1).unsqueeze(-2) # B_text, nH, 1, N_text + attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0) + + attn_i2t = self.softmax(attn_i2t) + attn_i2t = self.attn_drop_i2t(attn_i2t) + # torch.Size([4096, 4, 49, 50]) + # torch.Size([64, 4, 50, 32]) + # print(attn_i2t.size()) + # print(v_text.size()) + # 1/0 + y = (attn_i2t @ v_text).transpose(1, 2).reshape(B_, N, C) + y = self.proj_i2t(y) + y = self.proj_drop_i2t(y) + + # g = torch.cat([x, y], dim=-1) + # g = (self.gate_i2t(g)) + # g = self.sigmoid_i2t(self.gate_i2t(g)) + # x = x+g*y + + x = x + self.alpha_i2t * y + + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + dim_text=None, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + dim_text=dim_text, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix, x_text=None, mask_text=None): + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn( + x_windows, mask=attn_mask, y=x_text, y_mask=mask_text + ) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + # TODO: Keep? + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + # TODO: Keep? + # def extra_repr(self) -> str: + # return f"input_resolution={self.input_resolution}, dim={self.dim}" + # + # def flops(self): + # H, W = self.input_resolution + # flops = H * W * self.dim + # flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + # return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + dim_text=None, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + dim_text=(768 if i >= 14 else dim_text), + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def get_attention_mask(self, H, W, device): + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x, H, W, x_text=None, mask_text=None): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + x_text: input text features with shape of (B_text, N_text, C_text) + mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; + """ + attn_mask = self.get_attention_mask(H, W, x.device) + + for blk in self.blocks: + blk.H, blk.W = H, W + if not torch.jit.is_scripting() and self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text) + else: + x = blk(x, mask_matrix=attn_mask, x_text=x_text, mask_text=mask_text) + # print(x.size()) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + # TODO: Keep? + # def extra_repr(self) -> str: + # return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + frozen_stages=-1, + use_checkpoint=False, + out_features=["stage2", "stage3", "stage4", "stage5"], + backbone_arch="SWINT-FPN-RETINANET", + max_query_len=None, + lang_dim=None, + ): + super(SwinTransformer, self).__init__() + + print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint) + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + + self.out_features = out_features + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + self._out_feature_strides = {} + self._out_feature_channels = {} + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1, + dim_text=(768 if i_layer == 3 else None), + ) # TODO: Make this general : lang_dim not 768 + self.layers.append(layer) + + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + self._out_feature_channels[stage] = embed_dim * 2**i_layer + self._out_feature_strides[stage] = 4 * 2**i_layer + + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + self.num_features = num_features + + # TODO : need this? + # assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '') + # head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0. + # if weight_init.startswith('jax'): + # for n, m in self.named_modules(): + # _init_vit_weights(m, n, head_bias=head_bias, jax_impl=True) + # else: + # self.apply(_init_vit_weights) + + # add a norm layer for each output + for i_layer in range(self.num_layers): + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + if i_layer == 0 and backbone_arch.endswith("RETINANET"): + layer = nn.Identity() + else: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def forward(self, inputs): + """Forward function.""" + x = inputs["img"] + language_dict_features = inputs["lang"] + + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + x_text = language_dict_features["hidden"] + if "masks" in language_dict_features: + mask_text = 1.0 - language_dict_features["masks"] # (B, N_text) 0 means not to be masked out + mask_text.masked_fill_(mask_text.bool(), -float("inf")) + else: + mask_text = None + + outs = [] + for layer_i, layer in enumerate(self.layers): + # if layer_i > 1: + # if layer_i > 2: + if layer_i > -1: + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text) + else: + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=None, mask_text=None) + name = f"stage{layer_i + 2}" + if name in self.out_features: + norm_layer = getattr(self, f"norm{layer_i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.num_features[layer_i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + # Here the text features are just combined directly with the image features, so language_dict_features is unchanged + return outs, language_dict_features + + @torch.jit.ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +class FusionSwinTransformer(nn.Module): + def __init__(self, vision_backbone, language_backbone, add_linear_layer=False): + super().__init__() + self.backbone = vision_backbone + self.language_backbone = language_backbone + # self.cross_modal_image_transform2 = nn.Linear(1024, 768) + # self.cross_modal_image_transform3 = nn.Linear(1024, 768) + self.add_linear_layer = add_linear_layer + if self.add_linear_layer: + self.tunable_linear = torch.nn.Linear( + self.language_backbone.body.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, 1000, bias=False + ) + self.tunable_linear.weight.data.fill_(0.0) + + def forward( + self, + tokenizer_input, + images, + ): + + # Fusion in the backbone forward - interleaves the passed through the langauge and image backbone. + x = images.tensors + + # Embed the image + x = self.backbone.body.patch_embed(x) + Wh, Ww = x.size(2), x.size(3) + + if self.backbone.body.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.backbone.body.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + image_embeds = self.backbone.body.pos_drop(x) + + # Embed the text + text_embeds = self.language_backbone.body.model.embeddings(input_ids=tokenizer_input["input_ids"]) + input_shape = tokenizer_input["attention_mask"].size() + extended_text_masks = self.language_backbone.body.model.get_extended_attention_mask( + tokenizer_input["attention_mask"], input_shape, device=tokenizer_input["attention_mask"].device + ) + + if self.add_linear_layer: + text_embeds = self.tunable_linear.weight[: text_embeds.size(1), :].unsqueeze(0) + text_embeds + + outs = [] + # Pass the text through the first 10 layers + num_pre_text = 6 + for layer_i, layer in enumerate(self.language_backbone.body.model.encoder.layer[:num_pre_text]): + text_embeds = layer(text_embeds, extended_text_masks)[0] + + # Pass through first 2 image backbone layers + num_pre_vision = 2 + for layer_i, layer in enumerate(self.backbone.body.layers[:num_pre_vision]): + x_out, H, W, image_embeds, Wh, Ww = layer(image_embeds, Wh, Ww, x_text=None, mask_text=None) + name = f"stage{layer_i + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{layer_i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.backbone.body.num_features[layer_i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + num_pre_block = 14 + # Get the attention mask for the third layer: + attn_mask = self.backbone.body.layers[num_pre_vision].get_attention_mask(Wh, Ww, image_embeds.device) + for blk_cnt, blk in enumerate(self.backbone.body.layers[num_pre_vision].blocks): + blk.H, blk.W = Wh, Ww + if blk_cnt < num_pre_block: + if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint: + image_embeds = checkpoint.checkpoint(blk, image_embeds, attn_mask) + else: + image_embeds = blk(image_embeds, attn_mask) + else: + if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint: + fused_image_embeds = checkpoint.checkpoint( + blk, image_embeds, attn_mask, text_embeds, extended_text_masks + ) + else: + fused_image_embeds = blk(image_embeds, attn_mask, text_embeds, extended_text_masks) + text_embeds = self.language_backbone.body.model.encoder.layer[blk_cnt - num_pre_block + num_pre_text]( + text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds) + )[0] + image_embeds = fused_image_embeds + + # Apply layer norm after 3rd layer and take output + name = f"stage{num_pre_vision + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision}") + x_out = norm_layer(image_embeds) + out = ( + x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision]).permute(0, 3, 1, 2).contiguous() + ) + outs.append(out) + + # Apply downsampling if we need to at the output of third layer for input to next layer + if self.backbone.body.layers[num_pre_vision].downsample is not None: + image_embeds = self.backbone.body.layers[num_pre_vision].downsample(image_embeds, Wh, Ww) + Wh, Ww = (Wh + 1) // 2, (Ww + 1) // 2 + + # Final layer + + # Get attention mask for 4th layer + attn_mask = self.backbone.body.layers[num_pre_vision + 1].get_attention_mask(Wh, Ww, image_embeds.device) + blk = self.backbone.body.layers[num_pre_vision + 1].blocks[0] + blk.H, blk.W = Wh, Ww + + fuse_image_embeds = blk( + x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks + ) + + fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-2]( + text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds) + )[0] + text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds + + blk = self.backbone.body.layers[num_pre_vision + 1].blocks[1] + blk.H, blk.W = Wh, Ww + fuse_image_embeds = self.backbone.body.layers[num_pre_vision + 1].blocks[1]( + x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks + ) + + fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-1]( + text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds) + )[0] + text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds + + # Apply layer norm after 4th layer and take output + name = f"stage{num_pre_vision + 1 + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision + 1}") + x_out = norm_layer(image_embeds) + out = ( + x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision + 1]) + .permute(0, 3, 1, 2) + .contiguous() + ) + outs.append(out) + + language_dict_features = self.language_backbone.body.get_aggregated_output( + text_embeds, tokenizer_input["input_ids"], tokenizer_input["attention_mask"] + ) + + # Apply fpn + visual_features = self.backbone.fpn(outs) + + # None for now, need to add if we want to add shallow contrastive loss? + swint_feature_c4 = None + + return visual_features, language_dict_features, swint_feature_c4 + + +def build_swint_backbone(cfg): + """ + Create a SwinT instance from config. + + Returns: + VoVNet: a :class:`VoVNet` instance. + """ + return SwinTransformer( + patch_size=4, + in_chans=3, + embed_dim=cfg.MODEL.SWINT.EMBED_DIM, + depths=cfg.MODEL.SWINT.DEPTHS, + num_heads=cfg.MODEL.SWINT.NUM_HEADS, + window_size=cfg.MODEL.SWINT.WINDOW_SIZE, + mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE, + norm_layer=nn.LayerNorm, + ape=cfg.MODEL.SWINT.APE, + patch_norm=True, + frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT, + backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY, + use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT, + out_features=cfg.MODEL.BACKBONE.OUT_FEATURES, + max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, + ) + + +def build_combined_backbone(vision_backbone, language_backbone, add_linear_layer=False): + return FusionSwinTransformer(vision_backbone, language_backbone, add_linear_layer=add_linear_layer) diff --git a/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer_v3.py b/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..2965cd232b40c5f3e964efa18a868c03da5ae9df --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/fusion_swin_transformer_v3.py @@ -0,0 +1,992 @@ +""" Swin Transformer +A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` + - https://arxiv.org/pdf/2103.14030 +Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below +""" +# -------------------------------------------------------- +# Swin Transformer +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu +# -------------------------------------------------------- +import logging +import math +from copy import deepcopy +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, dim_text=None + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + # dim_text = 768 + if dim_text is not None: + self.qkv_text_i2t = nn.Linear(dim_text, dim * 2, bias=qkv_bias) + self.qkv_i2t = nn.Linear(dim, dim, bias=qkv_bias) + self.attn_drop_i2t = nn.Dropout(attn_drop) + self.proj_i2t = nn.Linear(dim, dim) + self.proj_drop_i2t = nn.Dropout(proj_drop) + # self.proj_i2t = nn.Linear(dim, dim) + self.alpha_i2t = nn.Parameter(torch.Tensor([0])) + self.norm_i2t_i = nn.LayerNorm(dim) + + # self.gate_i2t = nn.Linear(2*dim, 1) + # self.gate_i2t = nn.Linear(2*dim, dim) + # self.sigmoid_i2t = nn.Sigmoid() + + """self.i2t_relative_position_bias = nn.Parameter( + torch.zeros(2, num_heads, ntext)) # (2, nH, ntext) + self.t2t_relative_position_bias = nn.Parameter( + torch.zeros(num_heads, ntext, ntext)) # (nH, ntext, ntext) + trunc_normal_(self.i2t_relative_position_bias, std=.02) + trunc_normal_(self.t2t_relative_position_bias, std=.02)#""" + + def forward(self, x, mask: Optional[torch.Tensor] = None, y=None, y_mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + + attn = self.softmax(attn) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + if y is not None: + B_text, N_text, C_text = y.shape + nW = B_ // B_text # number of windows + assert B_text * nW == B_, "B_ is not a multiplier of B_text in window attention" + # notice that after qkv_text, the hidden dimension is C instead of C_text + qkv_text = ( + self.qkv_text_i2t(y) + .reshape(B_text, N_text, 2, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + k_text, v_text = qkv_text[0], qkv_text[1] + + k_text = torch.repeat_interleave(k_text, nW, dim=0) + v_text = torch.repeat_interleave(v_text, nW, dim=0) + # TODO: remove q_text + q_i2t = ( + self.qkv_i2t(self.norm_i2t_i(x)) + .reshape(B_, N, 1, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q_i2t = q_i2t[0] + + # image to text attention + # attn_i2t = (q_i2t @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1)) # B_, nH, N, N_text + # print(q_i2t.size()) + # print(k_text.size()) + # torch.Size([4096, 4, 49, 32]) + # torch.Size([4096, 4, 50, 32]) + text_scale = k_text.size(-1) ** -0.5 + q_i2t = q_i2t * text_scale + attn_i2t = q_i2t @ k_text.transpose(-2, -1) # B_, nH, N, N_text + # add image to text bias and text_mask + if y_mask is not None: + mask_and_i2t_bias = y_mask.view( + B_text, 1, 1, N_text + ) # + self.i2t_relative_position_bias[:1].expand(B_text, -1, -1).unsqueeze(-2) # B_text, nH, 1, N_text + attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0) + + attn_i2t = self.softmax(attn_i2t) + attn_i2t = self.attn_drop_i2t(attn_i2t) + # torch.Size([4096, 4, 49, 50]) + # torch.Size([64, 4, 50, 32]) + # print(attn_i2t.size()) + # print(v_text.size()) + # 1/0 + y = (attn_i2t @ v_text).transpose(1, 2).reshape(B_, N, C) + y = self.proj_i2t(y) + y = self.proj_drop_i2t(y) + + # g = torch.cat([x, y], dim=-1) + # g = (self.gate_i2t(g)) + # g = self.sigmoid_i2t(self.gate_i2t(g)) + # x = x+g*y + + x = x + self.alpha_i2t * y + + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + dim_text=None, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + dim_text=dim_text, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix, x_text=None, mask_text=None): + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn( + x_windows, mask=attn_mask, y=x_text, y_mask=mask_text + ) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + # TODO: Keep? + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + # TODO: Keep? + # def extra_repr(self) -> str: + # return f"input_resolution={self.input_resolution}, dim={self.dim}" + # + # def flops(self): + # H, W = self.input_resolution + # flops = H * W * self.dim + # flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + # return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + dim_text=None, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + dim_text=(768 if i >= 14 else dim_text), + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def get_attention_mask(self, H, W, device): + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x, H, W, x_text=None, mask_text=None): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + x_text: input text features with shape of (B_text, N_text, C_text) + mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; + """ + attn_mask = self.get_attention_mask(H, W, x.device) + + for blk in self.blocks: + blk.H, blk.W = H, W + if not torch.jit.is_scripting() and self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text) + else: + x = blk(x, mask_matrix=attn_mask, x_text=x_text, mask_text=mask_text) + # print(x.size()) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + # TODO: Keep? + # def extra_repr(self) -> str: + # return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + frozen_stages=-1, + use_checkpoint=False, + out_features=["stage2", "stage3", "stage4", "stage5"], + backbone_arch="SWINT-FPN-RETINANET", + max_query_len=None, + lang_dim=None, + ): + super(SwinTransformer, self).__init__() + + print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint) + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + + self.out_features = out_features + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + self._out_feature_strides = {} + self._out_feature_channels = {} + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1, + dim_text=(768 if i_layer == 3 else None), + ) # TODO: Make this general : lang_dim not 768 + self.layers.append(layer) + + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + self._out_feature_channels[stage] = embed_dim * 2**i_layer + self._out_feature_strides[stage] = 4 * 2**i_layer + + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + self.num_features = num_features + + # TODO : need this? + # assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '') + # head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0. + # if weight_init.startswith('jax'): + # for n, m in self.named_modules(): + # _init_vit_weights(m, n, head_bias=head_bias, jax_impl=True) + # else: + # self.apply(_init_vit_weights) + + # add a norm layer for each output + for i_layer in range(self.num_layers): + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + if i_layer == 0 and backbone_arch.endswith("RETINANET"): + layer = nn.Identity() + else: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def forward(self, inputs): + """Forward function.""" + x = inputs["img"] + language_dict_features = inputs["lang"] + + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + x_text = language_dict_features["hidden"] + if "masks" in language_dict_features: + mask_text = 1.0 - language_dict_features["masks"] # (B, N_text) 0 means not to be masked out + mask_text.masked_fill_(mask_text.bool(), -float("inf")) + else: + mask_text = None + + outs = [] + for layer_i, layer in enumerate(self.layers): + # if layer_i > 1: + # if layer_i > 2: + if layer_i > -1: + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text) + else: + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=None, mask_text=None) + name = f"stage{layer_i + 2}" + if name in self.out_features: + norm_layer = getattr(self, f"norm{layer_i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.num_features[layer_i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + # Here the text features are just combined directly with the image features, so language_dict_features is unchanged + return outs, language_dict_features + + @torch.jit.ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +class FusionSwinTransformer(nn.Module): + def __init__(self, vision_backbone, language_backbone, add_linear_layer=False): + super().__init__() + self.backbone = vision_backbone + self.language_backbone = language_backbone + # self.cross_modal_image_transform2 = nn.Linear(1024, 768) + # self.cross_modal_image_transform3 = nn.Linear(1024, 768) + self.add_linear_layer = add_linear_layer + if self.add_linear_layer: + self.tunable_linear = torch.nn.Linear( + self.language_backbone.body.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, 1000, bias=False + ) + self.tunable_linear.weight.data.fill_(0.0) + + def forward( + self, + tokenizer_input, + images, + ): + + # Fusion in the backbone forward - interleaves the passed through the langauge and image backbone. + x = images.tensors + + # Embed the image + x = self.backbone.body.patch_embed(x) + Wh, Ww = x.size(2), x.size(3) + + if self.backbone.body.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.backbone.body.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + image_embeds = self.backbone.body.pos_drop(x) + + # Embed the text + text_embeds = self.language_backbone.body.model.embeddings(input_ids=tokenizer_input["input_ids"]) + input_shape = tokenizer_input["attention_mask"].size() + extended_text_masks = self.language_backbone.body.model.get_extended_attention_mask( + tokenizer_input["attention_mask"], input_shape, device=tokenizer_input["attention_mask"].device + ) + + if self.add_linear_layer: + text_embeds = self.tunable_linear.weight[: text_embeds.size(1), :].unsqueeze(0) + text_embeds + + outs = [] + # Pass the text through the first 10 layers + num_pre_text = 6 + for layer_i, layer in enumerate(self.language_backbone.body.model.encoder.layer[:num_pre_text]): + text_embeds = layer(text_embeds, extended_text_masks)[0] + + # Pass through first 2 image backbone layers + num_pre_vision = 2 + for layer_i, layer in enumerate(self.backbone.body.layers[:num_pre_vision]): + x_out, H, W, image_embeds, Wh, Ww = layer(image_embeds, Wh, Ww, x_text=None, mask_text=None) + name = f"stage{layer_i + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{layer_i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.backbone.body.num_features[layer_i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + num_pre_block = 14 + # Get the attention mask for the third layer: + attn_mask = self.backbone.body.layers[num_pre_vision].get_attention_mask(Wh, Ww, image_embeds.device) + for blk_cnt, blk in enumerate(self.backbone.body.layers[num_pre_vision].blocks): + blk.H, blk.W = Wh, Ww + if blk_cnt < num_pre_block: + if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint: + image_embeds = checkpoint.checkpoint(blk, image_embeds, attn_mask) + else: + image_embeds = blk(image_embeds, attn_mask) + else: + if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint: + fused_image_embeds = checkpoint.checkpoint( + blk, image_embeds, attn_mask, text_embeds, extended_text_masks + ) + else: + fused_image_embeds = blk(image_embeds, attn_mask, text_embeds, extended_text_masks) + text_embeds = self.language_backbone.body.model.encoder.layer[blk_cnt - num_pre_block + num_pre_text]( + text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds) + )[0] + image_embeds = fused_image_embeds + + # Apply layer norm after 3rd layer and take output + name = f"stage{num_pre_vision + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision}") + x_out = norm_layer(image_embeds) + out = ( + x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision]).permute(0, 3, 1, 2).contiguous() + ) + outs.append(out) + + # Apply downsampling if we need to at the output of third layer for input to next layer + if self.backbone.body.layers[num_pre_vision].downsample is not None: + image_embeds = self.backbone.body.layers[num_pre_vision].downsample(image_embeds, Wh, Ww) + Wh, Ww = (Wh + 1) // 2, (Ww + 1) // 2 + + # Final layer + + # Get attention mask for 4th layer + attn_mask = self.backbone.body.layers[num_pre_vision + 1].get_attention_mask(Wh, Ww, image_embeds.device) + blk = self.backbone.body.layers[num_pre_vision + 1].blocks[0] + blk.H, blk.W = Wh, Ww + + fuse_image_embeds = blk( + x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks + ) + + fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-2]( + text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds) + )[0] + text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds + + blk = self.backbone.body.layers[num_pre_vision + 1].blocks[1] + blk.H, blk.W = Wh, Ww + fuse_image_embeds = self.backbone.body.layers[num_pre_vision + 1].blocks[1]( + x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks + ) + + fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-1]( + text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds) + )[0] + text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds + + # Apply layer norm after 4th layer and take output + name = f"stage{num_pre_vision + 1 + 2}" + if name in self.backbone.body.out_features: + norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision + 1}") + x_out = norm_layer(image_embeds) + out = ( + x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision + 1]) + .permute(0, 3, 1, 2) + .contiguous() + ) + outs.append(out) + + language_dict_features = self.language_backbone.body.get_aggregated_output( + text_embeds, tokenizer_input["input_ids"], tokenizer_input["attention_mask"] + ) + + # Apply fpn + visual_features = self.backbone.fpn(outs) + + # None for now, need to add if we want to add shallow contrastive loss? + swint_feature_c4 = None + + return visual_features, language_dict_features, swint_feature_c4 + + +def build_swint_backbone(cfg): + """ + Create a SwinT instance from config. + + Returns: + VoVNet: a :class:`VoVNet` instance. + """ + return SwinTransformer( + patch_size=4, + in_chans=3, + embed_dim=cfg.MODEL.SWINT.EMBED_DIM, + depths=cfg.MODEL.SWINT.DEPTHS, + num_heads=cfg.MODEL.SWINT.NUM_HEADS, + window_size=cfg.MODEL.SWINT.WINDOW_SIZE, + mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE, + norm_layer=nn.LayerNorm, + ape=cfg.MODEL.SWINT.APE, + patch_norm=True, + frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT, + backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY, + use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT, + out_features=cfg.MODEL.BACKBONE.OUT_FEATURES, + max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, + ) + + +def build_combined_backbone(vision_backbone, language_backbone, add_linear_layer=False): + return FusionSwinTransformer(vision_backbone, language_backbone, add_linear_layer=add_linear_layer) diff --git a/maskrcnn_benchmark/modeling/backbone/mixer.py b/maskrcnn_benchmark/modeling/backbone/mixer.py new file mode 100644 index 0000000000000000000000000000000000000000..91c558ef4b35f49abd8337062c4b375dafa0025e --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/mixer.py @@ -0,0 +1,25 @@ +import torch +from torch import nn + + +class MixedOperationRandom(nn.Module): + def __init__(self, search_ops): + super(MixedOperationRandom, self).__init__() + self.ops = nn.ModuleList(search_ops) + self.num_ops = len(search_ops) + + def forward(self, x, x_path=None): + if x_path is None: + output = sum(op(x) for op in self.ops) / self.num_ops + else: + assert isinstance(x_path, (int, float)) and 0 <= x_path < self.num_ops or isinstance(x_path, torch.Tensor) + if isinstance(x_path, (int, float)): + x_path = int(x_path) + assert 0 <= x_path < self.num_ops + output = self.ops[x_path](x) + elif isinstance(x_path, torch.Tensor): + assert x_path.size(0) == x.size(0), "batch_size should match length of y_idx" + output = torch.cat( + [self.ops[int(x_path[i].item())](x.narrow(0, i, 1)) for i in range(x.size(0))], dim=0 + ) + return output diff --git a/maskrcnn_benchmark/modeling/backbone/ops.py b/maskrcnn_benchmark/modeling/backbone/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..95f124642848f2048fd0980bffa4313ff9c7b022 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/ops.py @@ -0,0 +1,96 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def conv7x7(in_planes, out_planes, stride=1, groups=1, dilation=1): + """7x7 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=7, + stride=stride, + padding=3 * dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1): + """5x5 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=5, + stride=stride, + padding=2 * dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +def maxpool(**kwargs): + return nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + +def avgpool(**kwargs): + return nn.AvgPool2d(kernel_size=3, stride=2, padding=1) + + +def dropout(prob): + return nn.Dropout(prob) + + +conv3x3sep = lambda i, o, s=1: conv3x3(i, o, s, groups=i) +conv3x3g2 = lambda i, o, s=1: conv3x3(i, o, s, groups=2) +conv3x3g4 = lambda i, o, s=1: conv3x3(i, o, s, groups=4) +conv3x3g8 = lambda i, o, s=1: conv3x3(i, o, s, groups=8) +conv3x3dw = lambda i, o, s=1: conv3x3(i, o, s, groups=i) + +conv3x3d2 = lambda i, o, s=1: conv3x3(i, o, s, dilation=2) +conv3x3d3 = lambda i, o, s=1: conv3x3(i, o, s, dilation=3) +conv3x3d4 = lambda i, o, s=1: conv3x3(i, o, s, dilation=4) + + +conv5x5sep = lambda i, o, s=1: conv5x5(i, o, s, groups=i) +conv5x5g2 = lambda i, o, s=1: conv5x5(i, o, s, groups=2) +conv5x5g4 = lambda i, o, s=1: conv5x5(i, o, s, groups=4) +conv5x5g8 = lambda i, o, s=1: conv5x5(i, o, s, groups=8) +conv5x5dw = lambda i, o, s=1: conv5x5(i, o, s, groups=i) + + +conv5x5d2 = lambda i, o, s=1: conv5x5(i, o, s, dilation=2) +conv5x5d3 = lambda i, o, s=1: conv5x5(i, o, s, dilation=3) +conv5x5d4 = lambda i, o, s=1: conv5x5(i, o, s, dilation=4) + +conv7x7sep = lambda i, o, s=1: conv7x7(i, o, s, groups=i) +conv7x7g2 = lambda i, o, s=1: conv7x7(i, o, s, groups=2) +conv7x7g4 = lambda i, o, s=1: conv7x7(i, o, s, groups=4) +conv7x7g8 = lambda i, o, s=1: conv7x7(i, o, s, groups=8) +conv7x7dw = lambda i, o, s=1: conv7x7(i, o, s, groups=i) + +conv7x7d2 = lambda i, o, s=1: conv7x7(i, o, s, dilation=2) +conv7x7d3 = lambda i, o, s=1: conv7x7(i, o, s, dilation=3) +conv7x7d4 = lambda i, o, s=1: conv7x7(i, o, s, dilation=4) diff --git a/maskrcnn_benchmark/modeling/backbone/resnet.py b/maskrcnn_benchmark/modeling/backbone/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..15232220cb1220f0cc459a0281544be40ea67c74 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/resnet.py @@ -0,0 +1,630 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +Variant of the resnet module that takes cfg as an argument. +Example usage. Strings may be specified in the config file. + model = ResNet( + "StemWithFixedBatchNorm", + "BottleneckWithFixedBatchNorm", + "ResNet50StagesTo4", + ) +OR: + model = ResNet( + "StemWithGN", + "BottleneckWithGN", + "ResNet50StagesTo4", + ) +Custom implementations may be written in user code and hooked in via the +`register_*` functions. +""" +from collections import namedtuple + +import torch +import torch.nn.functional as F +from torch import nn +from torch.nn import BatchNorm2d, SyncBatchNorm + +from maskrcnn_benchmark.layers import FrozenBatchNorm2d, NaiveSyncBatchNorm2d +from maskrcnn_benchmark.layers import Conv2d, DFConv2d, SELayer +from maskrcnn_benchmark.modeling.make_layers import group_norm +from maskrcnn_benchmark.utils.registry import Registry + + +# ResNet stage specification +StageSpec = namedtuple( + "StageSpec", + [ + "index", # Index of the stage, eg 1, 2, ..,. 5 + "block_count", # Number of residual blocks in the stage + "return_features", # True => return the last feature map from this stage + ], +) + +# ----------------------------------------------------------------------------- +# Standard ResNet models +# ----------------------------------------------------------------------------- +# ResNet-50 (including all stages) +ResNet50StagesTo5 = tuple( + StageSpec(index=i, block_count=c, return_features=r) + for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, False), (4, 3, True)) +) +# ResNet-50 up to stage 4 (excludes stage 5) +ResNet50StagesTo4 = tuple( + StageSpec(index=i, block_count=c, return_features=r) for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 6, True)) +) +# ResNet-101 (including all stages) +ResNet101StagesTo5 = tuple( + StageSpec(index=i, block_count=c, return_features=r) + for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, False), (4, 3, True)) +) +# ResNet-101 up to stage 4 (excludes stage 5) +ResNet101StagesTo4 = tuple( + StageSpec(index=i, block_count=c, return_features=r) for (i, c, r) in ((1, 3, False), (2, 4, False), (3, 23, True)) +) +# ResNet-50-FPN (including all stages) +ResNet50FPNStagesTo5 = tuple( + StageSpec(index=i, block_count=c, return_features=r) + for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 6, True), (4, 3, True)) +) +# ResNet-101-FPN (including all stages) +ResNet101FPNStagesTo5 = tuple( + StageSpec(index=i, block_count=c, return_features=r) + for (i, c, r) in ((1, 3, True), (2, 4, True), (3, 23, True), (4, 3, True)) +) +# ResNet-152-FPN (including all stages) +ResNet152FPNStagesTo5 = tuple( + StageSpec(index=i, block_count=c, return_features=r) + for (i, c, r) in ((1, 3, True), (2, 8, True), (3, 36, True), (4, 3, True)) +) + + +class ResNet(nn.Module): + def __init__(self, cfg): + super(ResNet, self).__init__() + + # If we want to use the cfg in forward(), then we should make a copy + # of it and store it for later use: + # self.cfg = cfg.clone() + + # Translate string names to implementations + norm_level = None + stem_module = _STEM_MODULES[cfg.MODEL.RESNETS.STEM_FUNC] + stage_specs = _STAGE_SPECS[cfg.MODEL.BACKBONE.CONV_BODY] + transformation_module = _TRANSFORMATION_MODULES[cfg.MODEL.RESNETS.TRANS_FUNC] + + if cfg.MODEL.BACKBONE.USE_BN: + stem_module = StemWithBatchNorm + transformation_module = BottleneckWithBatchNorm + norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL + elif cfg.MODEL.BACKBONE.USE_NSYNCBN: + stem_module = StemWithNaiveSyncBatchNorm + transformation_module = BottleneckWithNaiveSyncBatchNorm + norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL + elif cfg.MODEL.BACKBONE.USE_SYNCBN: + stem_module = StemWithSyncBatchNorm + transformation_module = BottleneckWithSyncBatchNorm + norm_level = cfg.MODEL.BACKBONE.NORM_LEVEL + + # Construct the stem module + self.stem = stem_module(cfg) + + # Constuct the specified ResNet stages + num_groups = cfg.MODEL.RESNETS.NUM_GROUPS + width_per_group = cfg.MODEL.RESNETS.WIDTH_PER_GROUP + in_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS + stage2_bottleneck_channels = num_groups * width_per_group + stage2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + with_se = cfg.MODEL.RESNETS.WITH_SE + + self.stages = [] + self.out_channels = [] + self.return_features = {} + for stage_spec in stage_specs: + name = "layer" + str(stage_spec.index) + stage2_relative_factor = 2 ** (stage_spec.index - 1) + bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor + out_channels = stage2_out_channels * stage2_relative_factor + stage_with_dcn = cfg.MODEL.RESNETS.STAGE_WITH_DCN[stage_spec.index - 1] + if cfg.MODEL.RESNETS.USE_AVG_DOWN: + avg_down_stride = 1 if stage_spec.index == 1 else 2 + else: + avg_down_stride = 0 + module = _make_stage( + transformation_module, + in_channels, + bottleneck_channels, + out_channels, + stage_spec.block_count, + num_groups, + cfg.MODEL.RESNETS.STRIDE_IN_1X1, + first_stride=int(stage_spec.index > 1) + 1, + dcn_config={ + "stage_with_dcn": stage_with_dcn, + "with_modulated_dcn": cfg.MODEL.RESNETS.WITH_MODULATED_DCN, + "deformable_groups": cfg.MODEL.RESNETS.DEFORMABLE_GROUPS, + }, + norm_level=norm_level, + with_se=with_se, + avg_down_stride=avg_down_stride, + ) + in_channels = out_channels + self.add_module(name, module) + self.stages.append(name) + self.out_channels.append(out_channels) + self.return_features[name] = stage_spec.return_features + + # Optionally freeze (requires_grad=False) parts of the backbone + self._freeze_backbone(cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT) + + def _freeze_backbone(self, freeze_at): + if freeze_at < 0: + return + for stage_index in range(freeze_at): + if stage_index == 0: + m = self.stem # stage 0 is the stem + else: + m = getattr(self, "layer" + str(stage_index)) + for p in m.parameters(): + p.requires_grad = False + + def forward(self, x): + outputs = [] + x = self.stem(x) + for stage_name in self.stages: + x = getattr(self, stage_name)(x) + if self.return_features[stage_name]: + outputs.append(x) + return outputs + + +class ResNetHead(nn.Module): + def __init__( + self, + block_module, + stages, + num_groups=1, + width_per_group=64, + stride_in_1x1=True, + stride_init=None, + res2_out_channels=256, + dilation=1, + dcn_config=None, + ): + super(ResNetHead, self).__init__() + + stage2_relative_factor = 2 ** (stages[0].index - 1) + stage2_bottleneck_channels = num_groups * width_per_group + out_channels = res2_out_channels * stage2_relative_factor + in_channels = out_channels // 2 + bottleneck_channels = stage2_bottleneck_channels * stage2_relative_factor + + block_module = _TRANSFORMATION_MODULES[block_module] + + self.stages = [] + stride = stride_init + for stage in stages: + name = "layer" + str(stage.index) + if not stride: + stride = int(stage.index > 1) + 1 + module = _make_stage( + block_module, + in_channels, + bottleneck_channels, + out_channels, + stage.block_count, + num_groups, + stride_in_1x1, + first_stride=stride, + dilation=dilation, + dcn_config=dcn_config, + ) + stride = None + self.add_module(name, module) + self.stages.append(name) + self.out_channels = out_channels + + def forward(self, x): + for stage in self.stages: + x = getattr(self, stage)(x) + return x + + +def _make_stage( + transformation_module, + in_channels, + bottleneck_channels, + out_channels, + block_count, + num_groups, + stride_in_1x1, + first_stride, + dilation=1, + dcn_config=None, + norm_level=None, + **kwargs +): + blocks = [] + stride = first_stride + for li in range(block_count): + if norm_level is not None: + layer_module = BottleneckWithFixedBatchNorm + if norm_level >= 1 and li == 0: + layer_module = transformation_module + if norm_level >= 2 and li == block_count - 1: + layer_module = transformation_module + if norm_level >= 3: + layer_module = transformation_module + else: + layer_module = transformation_module + + blocks.append( + layer_module( + in_channels, + bottleneck_channels, + out_channels, + num_groups, + stride_in_1x1, + stride, + dilation=dilation, + dcn_config=dcn_config, + **kwargs + ) + ) + stride = 1 + in_channels = out_channels + return nn.Sequential(*blocks) + + +class Bottleneck(nn.Module): + def __init__( + self, + in_channels, + bottleneck_channels, + out_channels, + num_groups, + stride_in_1x1, + stride, + dilation, + norm_func, + dcn_config, + with_se=False, + avg_down_stride=0, + ): + super(Bottleneck, self).__init__() + + self.downsample = None + if in_channels != out_channels: + down_stride = stride if dilation == 1 else 1 + if avg_down_stride > 0: + self.downsample = nn.Sequential( + nn.AvgPool2d( + kernel_size=avg_down_stride, stride=avg_down_stride, ceil_mode=True, count_include_pad=False + ), + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), + norm_func(out_channels), + ) + else: + self.downsample = nn.Sequential( + Conv2d(in_channels, out_channels, kernel_size=1, stride=down_stride, bias=False), + norm_func(out_channels), + ) + for modules in [ + self.downsample, + ]: + for l in modules.modules(): + if isinstance(l, Conv2d): + nn.init.kaiming_uniform_(l.weight, a=1) + + if dilation > 1: + stride = 1 # reset to be 1 + + # The original MSRA ResNet models have stride in the first 1x1 conv + # The subsequent fb.torch.resnet and Caffe2 ResNe[X]t implementations have + # stride in the 3x3 conv + stride_1x1, stride_3x3 = (stride, 1) if stride_in_1x1 else (1, stride) + + self.conv1 = Conv2d( + in_channels, + bottleneck_channels, + kernel_size=1, + stride=stride_1x1, + bias=False, + ) + self.bn1 = norm_func(bottleneck_channels) + # TODO: specify init for the above + with_dcn = dcn_config.get("stage_with_dcn", False) + if with_dcn: + deformable_groups = dcn_config.get("deformable_groups", 1) + with_modulated_dcn = dcn_config.get("with_modulated_dcn", False) + self.conv2 = DFConv2d( + bottleneck_channels, + bottleneck_channels, + with_modulated_dcn=with_modulated_dcn, + kernel_size=3, + stride=stride_3x3, + groups=num_groups, + dilation=dilation, + deformable_groups=deformable_groups, + bias=False, + ) + else: + self.conv2 = Conv2d( + bottleneck_channels, + bottleneck_channels, + kernel_size=3, + stride=stride_3x3, + padding=dilation, + bias=False, + groups=num_groups, + dilation=dilation, + ) + nn.init.kaiming_uniform_(self.conv2.weight, a=1) + + self.bn2 = norm_func(bottleneck_channels) + + self.conv3 = Conv2d(bottleneck_channels, out_channels, kernel_size=1, bias=False) + self.bn3 = norm_func(out_channels) + + self.se = SELayer(out_channels) if with_se and not with_dcn else None + + for l in [ + self.conv1, + self.conv3, + ]: + nn.init.kaiming_uniform_(l.weight, a=1) + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = F.relu_(out) + + out = self.conv2(out) + out = self.bn2(out) + out = F.relu_(out) + + out0 = self.conv3(out) + out = self.bn3(out0) + + if self.se: + out = self.se(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = F.relu_(out) + + return out + + +class BaseStem(nn.Module): + def __init__(self, cfg, norm_func): + super(BaseStem, self).__init__() + + out_channels = cfg.MODEL.RESNETS.STEM_OUT_CHANNELS + self.stem_3x3 = cfg.MODEL.RESNETS.USE_STEM3X3 + + if self.stem_3x3: + self.conv1 = Conv2d(3, out_channels, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = norm_func(out_channels) + self.conv2 = Conv2d(out_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False) + self.bn2 = norm_func(out_channels) + for l in [self.conv1, self.conv2]: + nn.init.kaiming_uniform_(l.weight, a=1) + else: + self.conv1 = Conv2d(3, out_channels, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = norm_func(out_channels) + + for l in [ + self.conv1, + ]: + nn.init.kaiming_uniform_(l.weight, a=1) + + def forward(self, x): + if self.stem_3x3: + x = self.conv1(x) + x = self.bn1(x) + x = F.relu_(x) + x = self.conv2(x) + x = self.bn2(x) + x = F.relu_(x) + else: + x = self.conv1(x) + x = self.bn1(x) + x = F.relu_(x) + x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) + return x + + +class BottleneckWithFixedBatchNorm(Bottleneck): + def __init__( + self, + in_channels, + bottleneck_channels, + out_channels, + num_groups=1, + stride_in_1x1=True, + stride=1, + dilation=1, + dcn_config=None, + **kwargs + ): + super(BottleneckWithFixedBatchNorm, self).__init__( + in_channels=in_channels, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + stride_in_1x1=stride_in_1x1, + stride=stride, + dilation=dilation, + norm_func=FrozenBatchNorm2d, + dcn_config=dcn_config, + **kwargs + ) + + +class StemWithFixedBatchNorm(BaseStem): + def __init__(self, cfg): + super(StemWithFixedBatchNorm, self).__init__(cfg, norm_func=FrozenBatchNorm2d) + + +class BottleneckWithBatchNorm(Bottleneck): + def __init__( + self, + in_channels, + bottleneck_channels, + out_channels, + num_groups=1, + stride_in_1x1=True, + stride=1, + dilation=1, + dcn_config=None, + **kwargs + ): + super(BottleneckWithBatchNorm, self).__init__( + in_channels=in_channels, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + stride_in_1x1=stride_in_1x1, + stride=stride, + dilation=dilation, + norm_func=BatchNorm2d, + dcn_config=dcn_config, + **kwargs + ) + + +class StemWithBatchNorm(BaseStem): + def __init__(self, cfg): + super(StemWithBatchNorm, self).__init__(cfg, norm_func=BatchNorm2d) + + +class BottleneckWithNaiveSyncBatchNorm(Bottleneck): + def __init__( + self, + in_channels, + bottleneck_channels, + out_channels, + num_groups=1, + stride_in_1x1=True, + stride=1, + dilation=1, + dcn_config=None, + **kwargs + ): + super(BottleneckWithNaiveSyncBatchNorm, self).__init__( + in_channels=in_channels, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + stride_in_1x1=stride_in_1x1, + stride=stride, + dilation=dilation, + norm_func=NaiveSyncBatchNorm2d, + dcn_config=dcn_config, + **kwargs + ) + + +class StemWithNaiveSyncBatchNorm(BaseStem): + def __init__(self, cfg): + super(StemWithNaiveSyncBatchNorm, self).__init__(cfg, norm_func=NaiveSyncBatchNorm2d) + + +class BottleneckWithSyncBatchNorm(Bottleneck): + def __init__( + self, + in_channels, + bottleneck_channels, + out_channels, + num_groups=1, + stride_in_1x1=True, + stride=1, + dilation=1, + dcn_config=None, + **kwargs + ): + super(BottleneckWithSyncBatchNorm, self).__init__( + in_channels=in_channels, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + stride_in_1x1=stride_in_1x1, + stride=stride, + dilation=dilation, + norm_func=SyncBatchNorm, + dcn_config=dcn_config, + **kwargs + ) + + +class StemWithSyncBatchNorm(BaseStem): + def __init__(self, cfg): + super(StemWithSyncBatchNorm, self).__init__(cfg, norm_func=SyncBatchNorm) + + +class BottleneckWithGN(Bottleneck): + def __init__( + self, + in_channels, + bottleneck_channels, + out_channels, + num_groups=1, + stride_in_1x1=True, + stride=1, + dilation=1, + dcn_config=None, + **kwargs + ): + super(BottleneckWithGN, self).__init__( + in_channels=in_channels, + bottleneck_channels=bottleneck_channels, + out_channels=out_channels, + num_groups=num_groups, + stride_in_1x1=stride_in_1x1, + stride=stride, + dilation=dilation, + norm_func=group_norm, + dcn_config=dcn_config, + **kwargs + ) + + +class StemWithGN(BaseStem): + def __init__(self, cfg): + super(StemWithGN, self).__init__(cfg, norm_func=group_norm) + + +_TRANSFORMATION_MODULES = Registry( + { + "BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm, + "BottleneckWithGN": BottleneckWithGN, + } +) + +_STEM_MODULES = Registry( + { + "StemWithFixedBatchNorm": StemWithFixedBatchNorm, + "StemWithGN": StemWithGN, + } +) + +_STAGE_SPECS = Registry( + { + "R-50-C4": ResNet50StagesTo4, + "R-50-C5": ResNet50StagesTo5, + "R-50-RETINANET": ResNet50StagesTo5, + "R-101-C4": ResNet101StagesTo4, + "R-101-C5": ResNet101StagesTo5, + "R-101-RETINANET": ResNet101StagesTo5, + "R-50-FPN": ResNet50FPNStagesTo5, + "R-50-FPN-RETINANET": ResNet50FPNStagesTo5, + "R-50-FPN-FCOS": ResNet50FPNStagesTo5, + "R-101-FPN": ResNet101FPNStagesTo5, + "R-101-FPN-RETINANET": ResNet101FPNStagesTo5, + "R-101-FPN-FCOS": ResNet101FPNStagesTo5, + "R-152-FPN": ResNet152FPNStagesTo5, + } +) diff --git a/maskrcnn_benchmark/modeling/backbone/swint.py b/maskrcnn_benchmark/modeling/backbone/swint.py new file mode 100644 index 0000000000000000000000000000000000000000..a33f405a7ec275fd9a67493a73c2a2363ea17c7c --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/swint.py @@ -0,0 +1,688 @@ +# -------------------------------------------------------- +# Swin Transformer +# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py +# -------------------------------------------------------- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + frozen_stages=-1, + use_checkpoint=False, + out_features=["stage2", "stage3", "stage4", "stage5"], + backbone_arch="SWINT-FPN-RETINANET", + ): + super(SwinTransformer, self).__init__() + + print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint) + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + + self.out_features = out_features + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + self._out_feature_strides = {} + self._out_feature_channels = {} + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1, + ) + self.layers.append(layer) + + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + self._out_feature_channels[stage] = embed_dim * 2**i_layer + self._out_feature_strides[stage] = 4 * 2**i_layer + + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in range(self.num_layers): + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + if i_layer == 0 and backbone_arch.endswith("RETINANET"): + layer = nn.Identity() + else: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def forward(self, x): + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + name = f"stage{i + 2}" + if name in self.out_features: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + return outs + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +def build_swint_backbone(cfg): + """ + Create a SwinT instance from config. + + Returns: + VoVNet: a :class:`VoVNet` instance. + """ + return SwinTransformer( + patch_size=4, + in_chans=3, + embed_dim=cfg.MODEL.SWINT.EMBED_DIM, + depths=cfg.MODEL.SWINT.DEPTHS, + num_heads=cfg.MODEL.SWINT.NUM_HEADS, + window_size=cfg.MODEL.SWINT.WINDOW_SIZE, + mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE, + norm_layer=nn.LayerNorm, + ape=cfg.MODEL.SWINT.APE, + patch_norm=True, + frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT, + backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY, + use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT, + out_features=cfg.MODEL.BACKBONE.OUT_FEATURES, + ) diff --git a/maskrcnn_benchmark/modeling/backbone/swint_v2.py b/maskrcnn_benchmark/modeling/backbone/swint_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..4f28380ffaf7753c37ae904dc4bfe6122eb4812f --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/swint_v2.py @@ -0,0 +1,749 @@ +# -------------------------------------------------------- +# Swin Transformer +# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py +# -------------------------------------------------------- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from einops import rearrange +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_scale=False, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + self.gamma = 1.0 + if layer_scale: + self.gamma = nn.Parameter(1e-4 * torch.ones(dim), requires_grad=True) + + def forward(self, x, mask_matrix): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(self.gamma * x) + x = x + self.drop_path(self.gamma * self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + layer_scale=False, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + layer_scale=layer_scale, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + patch_size=3, in_chans=dim, embed_dim=dim * 2, stride=2, padding=1, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +# class PatchEmbed(nn.Module): +# """ Image to Patch Embedding +# Args: +# patch_size (int): Patch token size. Default: 4. +# in_chans (int): Number of input image channels. Default: 3. +# embed_dim (int): Number of linear projection output channels. Default: 96. +# norm_layer (nn.Module, optional): Normalization layer. Default: None +# """ +# +# def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): +# super().__init__() +# patch_size = to_2tuple(patch_size) +# self.patch_size = patch_size +# +# self.in_chans = in_chans +# self.embed_dim = embed_dim +# +# self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) +# if norm_layer is not None: +# self.norm = norm_layer(embed_dim) +# else: +# self.norm = None +# +# def forward(self, x): +# """Forward function.""" +# # padding +# _, _, H, W = x.size() +# if W % self.patch_size[1] != 0: +# x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) +# if H % self.patch_size[0] != 0: +# x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) +# +# x = self.proj(x) # B C Wh Ww +# if self.norm is not None: +# Wh, Ww = x.size(2), x.size(3) +# x = x.flatten(2).transpose(1, 2) +# x = self.norm(x) +# x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) +# +# return x + + +class ConvEmbed(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, patch_size=7, in_chans=3, embed_dim=64, stride=4, padding=2, norm_layer=None): + super().__init__() + self.patch_size = patch_size + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding) + self.norm = norm_layer(embed_dim) if norm_layer else None + + def forward(self, x, H=None, W=None): + restore_hw = False + if H is None and W is None and len(x.size()) == 4: + _, _, H, W = x.size() + if W % self.patch_size != 0: + x = F.pad(x, (0, self.patch_size - W % self.patch_size)) + if H % self.patch_size != 0: + x = F.pad(x, (0, 0, 0, self.patch_size - H % self.patch_size)) + restore_hw = True + + if len(x.size()) == 3: + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + x = self.proj(x) # B C Wh Ww + B, C, Wh, Ww = x.shape + x = rearrange(x, "b c h w -> b (h w) c") + if self.norm: + x = self.norm(x) + + if restore_hw: + x = rearrange(x, "b (h w) c -> b c h w", h=Wh, w=Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=7, + patch_padding=2, + patch_stride=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + frozen_stages=-1, + use_checkpoint=False, + layer_scale=False, + out_features=["stage2", "stage3", "stage4", "stage5"], + out_norm=True, + backbone_arch="SWINT-FPN-RETINANET", + ): + super(SwinTransformer, self).__init__() + + print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint) + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + + self.out_features = out_features + self.out_norm = out_norm + + # split image into non-overlapping patches + # self.patch_embed = PatchEmbed( + # patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + # norm_layer=norm_layer if self.patch_norm else None) + self.patch_embed = ConvEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + padding=patch_padding, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + self._out_feature_strides = {} + self._out_feature_channels = {} + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=ConvEmbed if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1, + layer_scale=layer_scale, + ) + self.layers.append(layer) + + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + self._out_feature_channels[stage] = embed_dim * 2**i_layer + self._out_feature_strides[stage] = 4 * 2**i_layer + + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + if self.out_norm: + for i_layer in range(self.num_layers): + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + if i_layer == 0 and backbone_arch.endswith("RETINANET"): + layer = nn.Identity() + else: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def forward(self, x): + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + name = f"stage{i + 2}" + if name in self.out_features: + if self.out_norm: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + return outs + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +def build_swint_backbone(cfg): + """ + Create a SwinT instance from config. + + Returns: + VoVNet: a :class:`VoVNet` instance. + """ + return SwinTransformer( + patch_size=7, + patch_padding=2, + patch_stride=4, + in_chans=3, + embed_dim=cfg.MODEL.SWINT.EMBED_DIM, + depths=cfg.MODEL.SWINT.DEPTHS, + num_heads=cfg.MODEL.SWINT.NUM_HEADS, + window_size=cfg.MODEL.SWINT.WINDOW_SIZE, + mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE, + norm_layer=nn.LayerNorm, + ape=cfg.MODEL.SWINT.APE, + patch_norm=True, + frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT, + backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY, + use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT, + layer_scale=cfg.MODEL.SWINT.LAYER_SCALE, + out_features=cfg.MODEL.BACKBONE.OUT_FEATURES, + out_norm=cfg.MODEL.SWINT.OUT_NORM, + ) diff --git a/maskrcnn_benchmark/modeling/backbone/swint_v2_vl.py b/maskrcnn_benchmark/modeling/backbone/swint_v2_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..bb092a7c9ceba88f49aaa337c24819a3e8461565 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/swint_v2_vl.py @@ -0,0 +1,895 @@ +# -------------------------------------------------------- +# Swin Transformer +# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py +# -------------------------------------------------------- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from einops import rearrange +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ntext=None, + dim_text=None, + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + if ntext is not None: + self.qkv_text = nn.Linear(dim_text, dim * 3, bias=qkv_bias) + self.proj_text = nn.Linear(dim, dim_text) + + self.i2t_relative_position_bias = nn.Parameter(torch.zeros(2, num_heads, ntext)) # (2, nH, ntext) + self.t2t_relative_position_bias = nn.Parameter(torch.zeros(num_heads, ntext, ntext)) # (nH, ntext, ntext) + trunc_normal_(self.i2t_relative_position_bias, std=0.02) + trunc_normal_(self.t2t_relative_position_bias, std=0.02) + + def forward(self, x, mask=None, x_text=None, mask_text=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + x_text: input text features with shape of (B_text, N_text, C_text) + mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; TODO: support casual mask + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + + if x_text is not None: + B_text, N_text, C_text = x_text.shape + nW = B_ // B_text # number of windows + assert B_text * nW == B_, "B_ is not a multiplier of B_text in window attention" + # notice that after qkv_text, the hidden dimension is C instead of C_text + qkv_text = ( + self.qkv_text(x_text) + .reshape(B_text, N_text, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q_text, k_text, v_text = ( + qkv_text[0], + qkv_text[1], + qkv_text[2], + ) # make torchscript happy (cannot use tensor as tuple) + + # image to text attention + attn_i2t = q @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1) # B_, nH, N, N_text + # add image to text bias and text_mask + if mask_text is not None: + mask_and_i2t_bias = mask_text.view(B_text, 1, 1, N_text) + self.i2t_relative_position_bias[:1].expand( + B_text, -1, -1 + ).unsqueeze( + -2 + ) # B_text, nH, 1, N_text + else: + mask_and_i2t_bias = ( + self.i2t_relative_position_bias[:1].expand(B_text, -1, -1).unsqueeze(-2) + ) # B_text, nH, 1, N_text + attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0) + + attn = torch.cat((attn, attn_i2t), dim=-1) # B_, nH, N, N+N_text + + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + if x_text is None: + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + else: + x = ( + (attn @ torch.cat((v, torch.repeat_interleave(v_text, nW, dim=0)), dim=-2)) + .transpose(1, 2) + .reshape(B_, N, C) + ) + + # compute attn_t2i + q_text = q_text * self.scale + + kv = qkv[1:].reshape(2, B_text, nW, self.num_heads, N, C // self.num_heads).transpose(2, 3) + k, v = kv[0].reshape(B_text, self.num_heads, nW * N, -1), kv[1].reshape(B_text, self.num_heads, nW * N, -1) + attn_t2i = q_text @ k.transpose(-2, -1) + mask_t2i = self.i2t_relative_position_bias[1:].expand(B_text, -1, -1).unsqueeze(-1) # B_text, nH, N_text, 1 + attn_t2i = attn_t2i + mask_t2i + + attn_t2t = q_text @ k_text.transpose(-2, -1) + # add relative positional bias + attn_t2t = attn_t2t + self.t2t_relative_position_bias.unsqueeze(0) + if mask_text is not None: + attn_t2t = attn_t2t + mask_text.view(B_text, 1, 1, N_text) + + attn_t = torch.cat((attn_t2i, attn_t2t), dim=-1) # B_text, nH, N_text, N+N_text + attn_t = self.softmax(attn_t) + attn_t = self.attn_drop(attn_t) + + x_text = (attn_t @ torch.cat((v, v_text), dim=-2)).transpose(1, 2).reshape(B_text, N_text, C) + + x_text = self.proj_text(x_text) + x_text = self.proj_drop(x_text) + + x = self.proj(x) + x = self.proj_drop(x) + return x, x_text + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_scale=False, + ntext=None, + dim_text=None, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ntext=ntext, + dim_text=dim_text, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + self.gamma = 1.0 + if layer_scale: + self.gamma = nn.Parameter(1e-4 * torch.ones(dim), requires_grad=True) + + if dim_text is not None: + self.norm1_text = norm_layer(dim_text) + self.norm2_text = norm_layer(dim_text) + mlp_hidden_dim_text = int(dim_text * mlp_ratio) + self.mlp_text = Mlp( + in_features=dim_text, hidden_features=mlp_hidden_dim_text, act_layer=act_layer, drop=drop + ) + self.gamma_text = 1.0 + if layer_scale: + self.gamma_text = nn.Parameter(1e-4 * torch.ones(dim_text), requires_grad=True) + + def forward(self, x, mask_matrix, x_text, mask_text): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + x_text: Input text feature, tensor size (B, L_text, C_text). L_text: Number of text tokens. + mask_text: text mask (vector right now). + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + if x_text is not None: + B, L_text, C_text = x_text.shape + shortcut_text = x_text + x_text = self.norm1_text(x_text) + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows, x_text = self.attn( + x_windows, mask=attn_mask, x_text=x_text, mask_text=mask_text + ) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(self.gamma * x) + x = x + self.drop_path(self.gamma * self.mlp(self.norm2(x))) + + if x_text is not None: + x_text = shortcut_text + self.drop_path(self.gamma_text * x_text) + x_text = x_text + self.drop_path(self.gamma_text * self.mlp_text(self.norm2_text(x_text))) + + return x, x_text + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + layer_scale=False, + ntext=None, + dim_text=None, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + layer_scale=layer_scale, + ntext=ntext, + dim_text=dim_text, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + patch_size=3, in_chans=dim, embed_dim=dim * 2, stride=2, padding=1, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, H, W, x_text=None, mask_text=None): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + x_text: input text features with shape of (B_text, N_text, C_text) + mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x, x_text = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text) + else: + x, x_text = blk(x, attn_mask, x_text, mask_text) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww, x_text + else: + return x, H, W, x, H, W, x_text + + +# class PatchEmbed(nn.Module): +# """ Image to Patch Embedding +# Args: +# patch_size (int): Patch token size. Default: 4. +# in_chans (int): Number of input image channels. Default: 3. +# embed_dim (int): Number of linear projection output channels. Default: 96. +# norm_layer (nn.Module, optional): Normalization layer. Default: None +# """ +# +# def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): +# super().__init__() +# patch_size = to_2tuple(patch_size) +# self.patch_size = patch_size +# +# self.in_chans = in_chans +# self.embed_dim = embed_dim +# +# self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) +# if norm_layer is not None: +# self.norm = norm_layer(embed_dim) +# else: +# self.norm = None +# +# def forward(self, x): +# """Forward function.""" +# # padding +# _, _, H, W = x.size() +# if W % self.patch_size[1] != 0: +# x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) +# if H % self.patch_size[0] != 0: +# x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) +# +# x = self.proj(x) # B C Wh Ww +# if self.norm is not None: +# Wh, Ww = x.size(2), x.size(3) +# x = x.flatten(2).transpose(1, 2) +# x = self.norm(x) +# x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) +# +# return x + + +class ConvEmbed(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, patch_size=7, in_chans=3, embed_dim=64, stride=4, padding=2, norm_layer=None): + super().__init__() + self.patch_size = patch_size + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding) + self.norm = norm_layer(embed_dim) if norm_layer else None + + def forward(self, x, H=None, W=None): + restore_hw = False + if H is None and W is None and len(x.size()) == 4: + _, _, H, W = x.size() + if W % self.patch_size != 0: + x = F.pad(x, (0, self.patch_size - W % self.patch_size)) + if H % self.patch_size != 0: + x = F.pad(x, (0, 0, 0, self.patch_size - H % self.patch_size)) + restore_hw = True + + if len(x.size()) == 3: + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + x = self.proj(x) # B C Wh Ww + B, C, Wh, Ww = x.shape + x = rearrange(x, "b c h w -> b (h w) c") + if self.norm: + x = self.norm(x) + + if restore_hw: + x = rearrange(x, "b (h w) c -> b c h w", h=Wh, w=Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=7, + patch_padding=2, + patch_stride=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + frozen_stages=-1, + use_checkpoint=False, + layer_scale=False, + out_features=["stage2", "stage3", "stage4", "stage5"], + out_norm=True, + backbone_arch="SWINT-FPN-RETINANET", + max_query_len=None, + lang_dim=None, + ): + super(SwinTransformer, self).__init__() + + print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint) + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + + self.out_features = out_features + self.out_norm = out_norm + + # split image into non-overlapping patches + # self.patch_embed = PatchEmbed( + # patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + # norm_layer=norm_layer if self.patch_norm else None) + self.patch_embed = ConvEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + padding=patch_padding, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + self._out_feature_strides = {} + self._out_feature_channels = {} + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + if i_layer < self.num_layers - 1: + ntext, dim_text = None, None + else: + ntext, dim_text = max_query_len, lang_dim + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=ConvEmbed if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1, + layer_scale=layer_scale, + ntext=ntext, + dim_text=dim_text, + ) + self.layers.append(layer) + + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + self._out_feature_channels[stage] = embed_dim * 2**i_layer + self._out_feature_strides[stage] = 4 * 2**i_layer + + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + if self.out_norm: + for i_layer in range(self.num_layers): + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + if i_layer == 0 and backbone_arch.endswith("RETINANET"): + layer = nn.Identity() + else: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def forward(self, inputs): + """Forward function.""" + x = inputs["img"] + language_dict_features = inputs["lang"] + + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + x_text = language_dict_features["hidden"] + if "masks" in language_dict_features: + mask_text = 1.0 - language_dict_features["masks"] # (B, N_text) 0 means not to be masked out + mask_text.masked_fill_(mask_text.bool(), -float("inf")) + else: + mask_text = None + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + if i < self.num_layers - 1: + x_out, H, W, x, Wh, Ww, _ = layer(x, Wh, Ww, x_text=None, mask_text=None) + else: + x_out, H, W, x, Wh, Ww, x_text = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text) + name = f"stage{i + 2}" + if name in self.out_features: + if self.out_norm: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + # the backbone only update the "hidden" field, currently + language_dict_features["hidden"] = x_text + + return outs, language_dict_features + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +def build_swint_backbone(cfg): + """ + Create a SwinT instance from config. + + Returns: + VoVNet: a :class:`VoVNet` instance. + """ + return SwinTransformer( + patch_size=7, + patch_padding=2, + patch_stride=4, + in_chans=3, + embed_dim=cfg.MODEL.SWINT.EMBED_DIM, + depths=cfg.MODEL.SWINT.DEPTHS, + num_heads=cfg.MODEL.SWINT.NUM_HEADS, + window_size=cfg.MODEL.SWINT.WINDOW_SIZE, + mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE, + norm_layer=nn.LayerNorm, + ape=cfg.MODEL.SWINT.APE, + patch_norm=True, + frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT, + backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY, + use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT, + layer_scale=cfg.MODEL.SWINT.LAYER_SCALE, + out_features=cfg.MODEL.BACKBONE.OUT_FEATURES, + out_norm=cfg.MODEL.SWINT.OUT_NORM, + max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, + ) diff --git a/maskrcnn_benchmark/modeling/backbone/swint_vl.py b/maskrcnn_benchmark/modeling/backbone/swint_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..891b1f61839ca37d0decdaac5cb8acce4f4709e0 --- /dev/null +++ b/maskrcnn_benchmark/modeling/backbone/swint_vl.py @@ -0,0 +1,831 @@ +# -------------------------------------------------------- +# Swin Transformer +# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py +# -------------------------------------------------------- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import numpy as np +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ntext=None, + dim_text=None, + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + if ntext is not None: + self.qkv_text = nn.Linear(dim_text, dim * 3, bias=qkv_bias) + self.proj_text = nn.Linear(dim, dim_text) + + self.i2t_relative_position_bias = nn.Parameter(torch.zeros(2, num_heads, ntext)) # (2, nH, ntext) + self.t2t_relative_position_bias = nn.Parameter(torch.zeros(num_heads, ntext, ntext)) # (nH, ntext, ntext) + trunc_normal_(self.i2t_relative_position_bias, std=0.02) + trunc_normal_(self.t2t_relative_position_bias, std=0.02) + + def forward(self, x, mask=None, x_text=None, mask_text=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + x_text: input text features with shape of (B_text, N_text, C_text) + mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; TODO: support casual mask + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + + if x_text is not None: + B_text, N_text, C_text = x_text.shape + nW = B_ // B_text # number of windows + assert B_text * nW == B_, "B_ is not a multiplier of B_text in window attention" + # notice that after qkv_text, the hidden dimension is C instead of C_text + qkv_text = ( + self.qkv_text(x_text) + .reshape(B_text, N_text, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q_text, k_text, v_text = ( + qkv_text[0], + qkv_text[1], + qkv_text[2], + ) # make torchscript happy (cannot use tensor as tuple) + + # image to text attention + attn_i2t = q @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1) # B_, nH, N, N_text + # add image to text bias and text_mask + if mask_text is not None: + mask_and_i2t_bias = mask_text.view(B_text, 1, 1, N_text) + self.i2t_relative_position_bias[:1].expand( + B_text, -1, -1 + ).unsqueeze( + -2 + ) # B_text, nH, 1, N_text + else: + mask_and_i2t_bias = ( + self.i2t_relative_position_bias[:1].expand(B_text, -1, -1).unsqueeze(-2) + ) # B_text, nH, 1, N_text + attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0) + + attn = torch.cat((attn, attn_i2t), dim=-1) # B_, nH, N, N+N_text + + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + if x_text is None: + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + else: + x = ( + (attn @ torch.cat((v, torch.repeat_interleave(v_text, nW, dim=0)), dim=-2)) + .transpose(1, 2) + .reshape(B_, N, C) + ) + + # compute attn_t2i + q_text = q_text * self.scale + + kv = qkv[1:].reshape(2, B_text, nW, self.num_heads, N, C // self.num_heads).transpose(2, 3) + k, v = kv[0].reshape(B_text, self.num_heads, nW * N, -1), kv[1].reshape(B_text, self.num_heads, nW * N, -1) + attn_t2i = q_text @ k.transpose(-2, -1) + mask_t2i = self.i2t_relative_position_bias[1:].expand(B_text, -1, -1).unsqueeze(-1) # B_text, nH, N_text, 1 + attn_t2i = attn_t2i + mask_t2i + + attn_t2t = q_text @ k_text.transpose(-2, -1) + # add relative positional bias + attn_t2t = attn_t2t + self.t2t_relative_position_bias.unsqueeze(0) + if mask_text is not None: + attn_t2t = attn_t2t + mask_text.view(B_text, 1, 1, N_text) + + attn_t = torch.cat((attn_t2i, attn_t2t), dim=-1) # B_text, nH, N_text, N+N_text + attn_t = self.softmax(attn_t) + attn_t = self.attn_drop(attn_t) + + x_text = (attn_t @ torch.cat((v, v_text), dim=-2)).transpose(1, 2).reshape(B_text, N_text, C) + + x_text = self.proj_text(x_text) + x_text = self.proj_drop(x_text) + + x = self.proj(x) + x = self.proj_drop(x) + return x, x_text + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ntext=None, + dim_text=None, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ntext=ntext, + dim_text=dim_text, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + self.H = None + self.W = None + + if dim_text is not None: + self.norm1_text = norm_layer(dim_text) + self.norm2_text = norm_layer(dim_text) + mlp_hidden_dim_text = int(dim_text * mlp_ratio) + self.mlp_text = Mlp( + in_features=dim_text, hidden_features=mlp_hidden_dim_text, act_layer=act_layer, drop=drop + ) + + def forward(self, x, mask_matrix, x_text, mask_text): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + x_text: Input text feature, tensor size (B, L_text, C_text). L_text: Number of text tokens. + mask_text: text mask (vector right now). + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + if x_text is not None: + B, L_text, C_text = x_text.shape + shortcut_text = x_text + x_text = self.norm1_text(x_text) + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C + x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows, x_text = self.attn( + x_windows, mask=attn_mask, x_text=x_text, mask_text=mask_text + ) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + if x_text is not None: + x_text = shortcut_text + self.drop_path(x_text) + x_text = x_text + self.drop_path(self.mlp_text(self.norm2_text(x_text))) + + return x, x_text + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ntext=None, + dim_text=None, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + ntext=ntext, + dim_text=dim_text, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W, x_text=None, mask_text=None): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + x_text: input text features with shape of (B_text, N_text, C_text) + mask_text: (0/-inf) mask with shape of (B_text, N_text) or None; + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x, x_text = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text) + else: + x, x_text = blk(x, attn_mask, x_text, mask_text) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww, x_text + else: + return x, H, W, x, H, W, x_text + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + frozen_stages=-1, + use_checkpoint=False, + out_features=["stage2", "stage3", "stage4", "stage5"], + backbone_arch="SWINT-FPN-RETINANET", + max_query_len=None, + lang_dim=None, + ): + super(SwinTransformer, self).__init__() + + print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint) + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + + self.out_features = out_features + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + self._out_feature_strides = {} + self._out_feature_channels = {} + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + if i_layer < self.num_layers - 1: + ntext, dim_text = None, None + else: + ntext, dim_text = max_query_len, lang_dim + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1, + ntext=ntext, + dim_text=dim_text, + ) + self.layers.append(layer) + + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + self._out_feature_channels[stage] = embed_dim * 2**i_layer + self._out_feature_strides[stage] = 4 * 2**i_layer + + num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in range(self.num_layers): + stage = f"stage{i_layer + 2}" + if stage in self.out_features: + if i_layer == 0 and backbone_arch.endswith("RETINANET"): + layer = nn.Identity() + else: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def forward(self, inputs): + """Forward function.""" + x = inputs["img"] + language_dict_features = inputs["lang"] + + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic") + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + x_text = language_dict_features["hidden"] + if "masks" in language_dict_features: + mask_text = 1.0 - language_dict_features["masks"] # (B, N_text) 0 means not to be masked out + mask_text.masked_fill_(mask_text.bool(), -float("inf")) + else: + mask_text = None + + outs = [] + for i in range(self.num_layers): + layer = self.layers[i] + if i < self.num_layers - 1: + x_out, H, W, x, Wh, Ww, _ = layer(x, Wh, Ww, x_text=None, mask_text=None) + else: + x_out, H, W, x, Wh, Ww, x_text = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text) + name = f"stage{i + 2}" + if name in self.out_features: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + # the backbone only update the "hidden" field, currently + language_dict_features["hidden"] = x_text + + return outs, language_dict_features + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +def build_swint_backbone(cfg): + """ + Create a SwinT instance from config. + + Returns: + VoVNet: a :class:`VoVNet` instance. + """ + return SwinTransformer( + patch_size=4, + in_chans=3, + embed_dim=cfg.MODEL.SWINT.EMBED_DIM, + depths=cfg.MODEL.SWINT.DEPTHS, + num_heads=cfg.MODEL.SWINT.NUM_HEADS, + window_size=cfg.MODEL.SWINT.WINDOW_SIZE, + mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE, + norm_layer=nn.LayerNorm, + ape=cfg.MODEL.SWINT.APE, + patch_norm=True, + frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT, + backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY, + use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT, + out_features=cfg.MODEL.BACKBONE.OUT_FEATURES, + max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, + ) diff --git a/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py b/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..50ecf54e8c645d8a7581a19767e0ab3d75759c3c --- /dev/null +++ b/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + + +class BalancedPositiveNegativeSampler(object): + """ + This class samples batches, ensuring that they contain a fixed proportion of positives + """ + + def __init__(self, batch_size_per_image, positive_fraction): + """ + Arguments: + batch_size_per_image (int): number of elements to be selected per image + positive_fraction (float): percentace of positive elements per batch + """ + self.batch_size_per_image = batch_size_per_image + self.positive_fraction = positive_fraction + + def __call__(self, matched_idxs): + """ + Arguments: + matched idxs: list of tensors containing -1, 0 or positive values. + Each tensor corresponds to a specific image. + -1 values are ignored, 0 are considered as negatives and > 0 as + positives. + + Returns: + pos_idx (list[tensor]) + neg_idx (list[tensor]) + + Returns two lists of binary masks for each image. + The first list contains the positive elements that were selected, + and the second list the negative example. + """ + pos_idx = [] + neg_idx = [] + for matched_idxs_per_image in matched_idxs: + positive = torch.nonzero(matched_idxs_per_image >= 1).squeeze(1) + negative = torch.nonzero(matched_idxs_per_image == 0).squeeze(1) + + num_pos = int(self.batch_size_per_image * self.positive_fraction) + # protect against not enough positive examples + num_pos = min(positive.numel(), num_pos) + num_neg = self.batch_size_per_image - num_pos + # protect against not enough negative examples + num_neg = min(negative.numel(), num_neg) + + # randomly select positive and negative examples + perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] + perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] + + pos_idx_per_image = positive[perm1] + neg_idx_per_image = negative[perm2] + + # create binary mask from indices + pos_idx_per_image_mask = torch.zeros_like(matched_idxs_per_image, dtype=torch.bool) + neg_idx_per_image_mask = torch.zeros_like(matched_idxs_per_image, dtype=torch.bool) + pos_idx_per_image_mask[pos_idx_per_image] = 1 + neg_idx_per_image_mask[neg_idx_per_image] = 1 + + pos_idx.append(pos_idx_per_image_mask) + neg_idx.append(neg_idx_per_image_mask) + + return pos_idx, neg_idx diff --git a/maskrcnn_benchmark/modeling/box_coder.py b/maskrcnn_benchmark/modeling/box_coder.py new file mode 100644 index 0000000000000000000000000000000000000000..cde175804b19fff49eaa2349a345d18b888d7934 --- /dev/null +++ b/maskrcnn_benchmark/modeling/box_coder.py @@ -0,0 +1,95 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import math + +import torch + + +class BoxCoder(object): + """ + This class encodes and decodes a set of bounding boxes into + the representation used for training the regressors. + """ + + def __init__(self, weights, bbox_xform_clip=math.log(1000.0 / 16)): + """ + Arguments: + weights (4-element tuple) + bbox_xform_clip (float) + """ + self.weights = weights + self.bbox_xform_clip = bbox_xform_clip + + def encode(self, reference_boxes, proposals): + """ + Encode a set of proposals with respect to some + reference boxes + + Arguments: + reference_boxes (Tensor): reference boxes + proposals (Tensor): boxes to be encoded + """ + + TO_REMOVE = 1 # TODO remove + ex_widths = proposals[:, 2] - proposals[:, 0] + TO_REMOVE + ex_heights = proposals[:, 3] - proposals[:, 1] + TO_REMOVE + ex_ctr_x = proposals[:, 0] + 0.5 * ex_widths + ex_ctr_y = proposals[:, 1] + 0.5 * ex_heights + + gt_widths = reference_boxes[:, 2] - reference_boxes[:, 0] + TO_REMOVE + gt_heights = reference_boxes[:, 3] - reference_boxes[:, 1] + TO_REMOVE + gt_ctr_x = reference_boxes[:, 0] + 0.5 * gt_widths + gt_ctr_y = reference_boxes[:, 1] + 0.5 * gt_heights + + wx, wy, ww, wh = self.weights + targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths + targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights + targets_dw = ww * torch.log(gt_widths / ex_widths) + targets_dh = wh * torch.log(gt_heights / ex_heights) + + targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1) + return targets + + def decode(self, rel_codes, boxes): + """ + From a set of original boxes and encoded relative box offsets, + get the decoded boxes. + + Arguments: + rel_codes (Tensor): encoded boxes + boxes (Tensor): reference boxes. + """ + + boxes = boxes.to(rel_codes.dtype) + + TO_REMOVE = 1 # TODO remove + widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE + heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE + ctr_x = boxes[:, 0] + 0.5 * widths + ctr_y = boxes[:, 1] + 0.5 * heights + + wx, wy, ww, wh = self.weights + dx = rel_codes[:, 0::4] / wx + dy = rel_codes[:, 1::4] / wy + dw = rel_codes[:, 2::4] / ww + dh = rel_codes[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=self.bbox_xform_clip) + dh = torch.clamp(dh, max=self.bbox_xform_clip) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + pred_boxes = torch.zeros_like(rel_codes) + # x1 + pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w + # y1 + pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h + # x2 (note: "- 1" is correct; don't be fooled by the asymmetry) + pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1 + # y2 (note: "- 1" is correct; don't be fooled by the asymmetry) + pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1 + + return pred_boxes diff --git a/maskrcnn_benchmark/modeling/detector/__init__.py b/maskrcnn_benchmark/modeling/detector/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b82ac5564be5219ea2fe9caa973153f7e2f4cde7 --- /dev/null +++ b/maskrcnn_benchmark/modeling/detector/__init__.py @@ -0,0 +1,9 @@ +from .generalized_rcnn import GeneralizedRCNN +from .generalized_vl_rcnn import GeneralizedVLRCNN + +_DETECTION_META_ARCHITECTURES = {"GeneralizedRCNN": GeneralizedRCNN, "GeneralizedVLRCNN": GeneralizedVLRCNN} + + +def build_detection_model(cfg): + meta_arch = _DETECTION_META_ARCHITECTURES[cfg.MODEL.META_ARCHITECTURE] + return meta_arch(cfg) diff --git a/maskrcnn_benchmark/modeling/detector/fuse_helper.py b/maskrcnn_benchmark/modeling/detector/fuse_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..ca63dbfdeeef385872b55a267eb52e7d2d285fbd --- /dev/null +++ b/maskrcnn_benchmark/modeling/detector/fuse_helper.py @@ -0,0 +1,181 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class FeatureResizer(nn.Module): + """ + This class takes as input a set of embeddings of dimension C1 and outputs a set of + embedding of dimension C2, after a linear transformation, dropout and normalization (LN). + """ + + def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): + super().__init__() + self.do_ln = do_ln + # Object feature encoding + self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) + self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) + self.dropout = nn.Dropout(dropout) + + def forward(self, encoder_features): + x = self.fc(encoder_features) + if self.do_ln: + x = self.layer_norm(x) + output = self.dropout(x) + return output + + +def _make_conv(input_dim, output_dim, k, stride=1): + pad = (k - 1) // 2 + return nn.Sequential( + nn.Conv2d(input_dim, output_dim, (k, k), padding=(pad, pad), stride=(stride, stride)), + nn.BatchNorm2d(output_dim), + nn.ReLU(inplace=True), + ) + + +def _make_mlp(input_dim, output_dim, drop): + return nn.Sequential( + nn.Linear(input_dim, output_dim), + nn.BatchNorm1d(output_dim), + nn.ReLU(inplace=True), + nn.Dropout(drop), + nn.Linear(output_dim, output_dim), + nn.BatchNorm1d(output_dim), + nn.ReLU(inplace=True), + ) + + +def _make_coord(batch, height, width): + # relative position encoding + xv, yv = torch.meshgrid([torch.arange(0, height), torch.arange(0, width)]) + xv_min = (xv.float() * 2 - width) / width + yv_min = (yv.float() * 2 - height) / height + xv_max = ((xv + 1).float() * 2 - width) / width + yv_max = ((yv + 1).float() * 2 - height) / height + xv_ctr = (xv_min + xv_max) / 2 + yv_ctr = (yv_min + yv_max) / 2 + hmap = torch.ones(height, width) * (1.0 / height) + wmap = torch.ones(height, width) * (1.0 / width) + coord = torch.autograd.Variable( + torch.cat( + [ + xv_min.unsqueeze(0), + yv_min.unsqueeze(0), + xv_max.unsqueeze(0), + yv_max.unsqueeze(0), + xv_ctr.unsqueeze(0), + yv_ctr.unsqueeze(0), + hmap.unsqueeze(0), + wmap.unsqueeze(0), + ], + dim=0, + ) + ) + coord = coord.unsqueeze(0).repeat(batch, 1, 1, 1) + return coord + + +def l1norm(X, dim, eps=1e-8): + """L1-normalize columns of X""" + norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps + X = torch.div(X, norm) + return X + + +def l2norm(X, dim, eps=1e-8): + """L2-normalize columns of X""" + norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps + X = torch.div(X, norm) + return X + + +def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8): + """ + query: (n_context, queryL, d) + context: (n_context, sourceL, d) + """ + batch_size_q, queryL = query.size(0), query.size(1) + batch_size, sourceL = context.size(0), context.size(1) + + # Get attention + # --> (batch, d, queryL) + queryT = torch.transpose(query, 1, 2) + + # (batch, sourceL, d)(batch, d, queryL) + # --> (batch, sourceL, queryL) + attn = torch.bmm(context, queryT) + if raw_feature_norm == "softmax": + # --> (batch*sourceL, queryL) + attn = attn.view(batch_size * sourceL, queryL) + attn = nn.Softmax()(attn) + # --> (batch, sourceL, queryL) + attn = attn.view(batch_size, sourceL, queryL) + elif raw_feature_norm == "l2norm": + attn = l2norm(attn, 2) + elif raw_feature_norm == "clipped_l2norm": + attn = nn.LeakyReLU(0.1)(attn) + attn = l2norm(attn, 2) + else: + raise ValueError("unknown first norm type:", raw_feature_norm) + # --> (batch, queryL, sourceL) + attn = torch.transpose(attn, 1, 2).contiguous() + # --> (batch*queryL, sourceL) + attn = attn.view(batch_size * queryL, sourceL) + attn = nn.Softmax()(attn * smooth) + # --> (batch, queryL, sourceL) + attn = attn.view(batch_size, queryL, sourceL) + # --> (batch, sourceL, queryL) + attnT = torch.transpose(attn, 1, 2).contiguous() + + # --> (batch, d, sourceL) + contextT = torch.transpose(context, 1, 2) + # (batch x d x sourceL)(batch x sourceL x queryL) + # --> (batch, d, queryL) + weightedContext = torch.bmm(contextT, attnT) + # --> (batch, queryL, d) + weightedContext = torch.transpose(weightedContext, 1, 2) + + return weightedContext, attnT + + +class MultiHeadAttention(nn.Module): + """Multi-head attention module for both image and text""" + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + super(MultiHeadAttention, self).__init__() + + self.n_head = n_head + self.d_k = d_k + self.d_v = d_v + + self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False) + self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False) + self.fc = nn.Linear(n_head * d_v, d_model, bias=False) + + self.dropout = nn.Dropout(dropout) + self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) + + def forward(self, q, k, v): + d_k, d_v, n_head = self.d_k, self.d_v, self.n_head + sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1) + + # Pass through the pre-attention projection: b x lq x (n*dv) + # Separate different heads: b x lq x n x dv + q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) + k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) + v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) + + # Transpose for attention dot product: b x n x lq x dv + q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) + attn = torch.matmul(q, k.transpose(2, 3)) + attn = self.dropout(F.softmax(attn, dim=-1)) + q = torch.matmul(attn, v) + + # Transpose to move the head dimension back: b x lq x n x dv + # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv) + q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1) + q = self.dropout(self.fc(q)) + + return q, attn diff --git a/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py b/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..5f5f39f5b8288041369a2f2a5f75b9e42fd1fbb9 --- /dev/null +++ b/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py @@ -0,0 +1,137 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +Implements the Generalized R-CNN framework +""" + +import torch +from torch import nn + +from maskrcnn_benchmark.structures.image_list import to_image_list + +from ..backbone import build_backbone +from ..rpn import build_rpn +from ..roi_heads import build_roi_heads + +import timeit + + +class GeneralizedRCNN(nn.Module): + """ + Main class for Generalized R-CNN. Currently supports boxes and masks. + It consists of three main parts: + - backbone + - rpn + - heads: takes the features + the proposals from the RPN and computes + detections / masks from it. + """ + + def __init__(self, cfg): + super(GeneralizedRCNN, self).__init__() + + self.backbone = build_backbone(cfg) + self.rpn = build_rpn(cfg) + self.roi_heads = build_roi_heads(cfg) + self.DEBUG = cfg.MODEL.DEBUG + self.ONNX = cfg.MODEL.ONNX + self.freeze_backbone = cfg.MODEL.BACKBONE.FREEZE + self.freeze_fpn = cfg.MODEL.FPN.FREEZE + self.freeze_rpn = cfg.MODEL.RPN.FREEZE + + if cfg.MODEL.LINEAR_PROB: + assert cfg.MODEL.BACKBONE.FREEZE, "For linear probing, backbone should be frozen!" + if hasattr(self.backbone, "fpn"): + assert cfg.MODEL.FPN.FREEZE, "For linear probing, FPN should be frozen!" + self.linear_prob = cfg.MODEL.LINEAR_PROB + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(GeneralizedRCNN, self).train(mode) + if self.freeze_backbone: + self.backbone.body.eval() + for p in self.backbone.body.parameters(): + p.requires_grad = False + if self.freeze_fpn: + self.backbone.fpn.eval() + for p in self.backbone.fpn.parameters(): + p.requires_grad = False + if self.freeze_rpn: + self.rpn.eval() + for p in self.rpn.parameters(): + p.requires_grad = False + if self.linear_prob: + if self.rpn is not None: + for key, value in self.rpn.named_parameters(): + if not ("bbox_pred" in key or "cls_logits" in key or "centerness" in key or "cosine_scale" in key): + value.requires_grad = False + if self.roi_heads is not None: + for key, value in self.roi_heads.named_parameters(): + if not ("bbox_pred" in key or "cls_logits" in key or "centerness" in key or "cosine_scale" in key): + value.requires_grad = False + + def forward(self, images, targets=None): + """ + Arguments: + images (list[Tensor] or ImageList): images to be processed + targets (list[BoxList]): ground-truth boxes present in the image (optional) + + Returns: + result (list[BoxList] or dict[Tensor]): the output from the model. + During training, it returns a dict[Tensor] which contains the losses. + During testing, it returns list[BoxList] contains additional fields + like `scores`, `labels` and `mask` (for Mask R-CNN models). + + """ + if self.training and targets is None: + raise ValueError("In training mode, targets should be passed") + + if self.DEBUG: + debug_info = {} + if self.DEBUG: + debug_info["input_size"] = images[0].size() + if self.DEBUG: + tic = timeit.time.perf_counter() + + if self.ONNX: + features = self.backbone(images) + else: + images = to_image_list(images) + features = self.backbone(images.tensors) + + if self.DEBUG: + debug_info["feat_time"] = timeit.time.perf_counter() - tic + if self.DEBUG: + debug_info["feat_size"] = [feat.size() for feat in features] + if self.DEBUG: + tic = timeit.time.perf_counter() + + proposals, proposal_losses = self.rpn(images, features, targets) + + if self.DEBUG: + debug_info["rpn_time"] = timeit.time.perf_counter() - tic + if self.DEBUG: + debug_info["#rpn"] = [prop for prop in proposals] + if self.DEBUG: + tic = timeit.time.perf_counter() + + if self.roi_heads: + x, result, detector_losses = self.roi_heads(features, proposals, targets) + else: + # RPN-only models don't have roi_heads + x = features + result = proposals + detector_losses = {} + + if self.DEBUG: + debug_info["rcnn_time"] = timeit.time.perf_counter() - tic + if self.DEBUG: + debug_info["#rcnn"] = result + if self.DEBUG: + return result, debug_info + + if self.training: + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + return losses + + return result diff --git a/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py b/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..8bf40dcc2aa734fb4d60f9a8b530027f29be69cc --- /dev/null +++ b/maskrcnn_benchmark/modeling/detector/generalized_vl_rcnn.py @@ -0,0 +1,600 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +Implements the Generalized VL R-CNN framework +""" + +import torch +from torch import nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from maskrcnn_benchmark.structures.image_list import to_image_list +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist + +from ..backbone import build_backbone, build_fusion_backbone +from ..rpn import build_rpn +from ..roi_heads import build_roi_heads + +from ..language_backbone import build_language_backbone +from transformers import AutoTokenizer + +import random +import timeit +import pdb +from copy import deepcopy + + +def random_word(input_ids, mask_token_id, vocabs, padding_token_id, greenlight_map): + """ + greenlight_map, batch_size x 256 (seq_len): + 0 means this location cannot be calculated in the MLM loss + -1 means this location cannot be masked!! + 1 means this location can be masked and can be calculated in the MLM loss + """ + output_label = deepcopy(input_ids) + for j in range(input_ids.size(0)): + for i in range(input_ids.size(1)): + prob = random.random() + # mask token with probability + ratio = 0.15 + if greenlight_map is not None and greenlight_map[j, i] == -1: + output_label[j, i] = -100 + continue + + if (not input_ids[j, i] == padding_token_id) and prob < ratio: + prob /= ratio + + # 80% randomly change token to mask token + if prob < 0.8: + input_ids[j, i] = mask_token_id + + # 10% randomly change token to random token + elif prob < 0.9: + input_ids[j, i] = random.choice(vocabs) + + else: + # no masking token (will be ignored by loss function later) + output_label[j, i] = -100 + + if greenlight_map is not None and greenlight_map[j, i] != 1: + output_label[j, i] = -100 # If this location should not be masked + return input_ids, output_label + +def get_char_token_with_relaxation(tokenized, beg, end, batch_index = None): + beg_pos = tokenized.char_to_token(batch_index, beg) + end_pos = tokenized.char_to_token(batch_index, end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(batch_index, beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(batch_index, beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(batch_index, end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(batch_index, end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + return None, None + return beg_pos, end_pos + 1 + +class GeneralizedVLRCNN(nn.Module): + """ + Main class for Generalized R-CNN. Currently supports boxes and masks. + It consists of three main parts: + - backbone + - rpn + - heads: takes the features + the proposals from the RPN and computes + detections / masks from it. + """ + + def __init__(self, cfg): + super(GeneralizedVLRCNN, self).__init__() + self.cfg = cfg + self.fusion_in_backbone = cfg.MODEL.SWINT.VERSION == "fusion" + + # visual encoder + backbone = build_backbone(cfg) + + # language encoder + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + # self.tokenizer = build_tokenizer("clip") + from transformers import CLIPTokenizerFast + + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + print("Reuse token 'ðŁĴij' (token_id = 49404) for mask token!") + self.tokenizer = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + self.tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + else: + self.tokenizer = AutoTokenizer.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE) + self.tokenizer_vocab = self.tokenizer.get_vocab() + self.tokenizer_vocab_ids = [item for key, item in self.tokenizer_vocab.items()] + + # if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + # self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") + # elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + # self.tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base") + # else: + # raise NotImplementedError + # self.tokenizer = AutoTokenizer.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE) + + language_backbone = build_language_backbone(cfg) + + if self.fusion_in_backbone: + self.fusion_backbone = build_fusion_backbone( + backbone, + language_backbone, + cfg.MODEL.BACKBONE.FUSION_VERSION, + add_linear_layer=cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER, + ) + else: + self.backbone = backbone + self.language_backbone = language_backbone + + self.rpn = build_rpn(cfg) + self.roi_heads = build_roi_heads(cfg) + self.DEBUG = cfg.MODEL.DEBUG + + self.freeze_backbone = cfg.MODEL.BACKBONE.FREEZE + self.freeze_fpn = cfg.MODEL.FPN.FREEZE + self.freeze_rpn = cfg.MODEL.RPN.FREEZE + self.add_linear_layer = cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER + + self.force_boxes = cfg.MODEL.RPN.FORCE_BOXES + + if cfg.MODEL.LINEAR_PROB: + assert cfg.MODEL.BACKBONE.FREEZE, "For linear probing, backbone should be frozen!" + if self.fusion_in_backbone: + if hasattr(self.fusion_backbone.backbone, "fpn"): + assert cfg.MODEL.FPN.FREEZE, "For linear probing, FPN should be frozen!" + else: + if hasattr(self.backbone, "fpn"): + assert cfg.MODEL.FPN.FREEZE, "For linear probing, FPN should be frozen!" + self.linear_prob = cfg.MODEL.LINEAR_PROB + self.freeze_cls_logits = cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS + if cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + # disable cls_logits + if hasattr(self.rpn.head, "cls_logits"): + for p in self.rpn.head.cls_logits.parameters(): + p.requires_grad = False + + self.freeze_language_backbone = self.cfg.MODEL.LANGUAGE_BACKBONE.FREEZE + if self.cfg.MODEL.LANGUAGE_BACKBONE.FREEZE: + if self.fusion_in_backbone: + for p in self.fusion_backbone.language_backbone.parameters(): + p.requires_grad = False + else: + for p in self.language_backbone.parameters(): + p.requires_grad = False + + self.use_mlm_loss = cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS + self.mlm_loss_for_only_positives = cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS_FOR_ONLY_POSITIVES + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER and not self.fusion_in_backbone: + self.tunable_linear = torch.nn.Linear(cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, 1000, bias=False) + self.tunable_linear.weight.data.fill_(0.0) + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(GeneralizedVLRCNN, self).train(mode) + if self.freeze_backbone: + if self.fusion_in_backbone: + self.fusion_backbone.backbone.body.eval() + for p in self.fusion_backbone.backbone.body.parameters(): + p.requires_grad = False + else: + self.backbone.body.eval() + for p in self.backbone.body.parameters(): + p.requires_grad = False + if self.freeze_fpn: + if self.fusion_in_backbone: + self.fusion_backbone.backbone.fpn.eval() + for p in self.fusion_backbone.backbone.fpn.parameters(): + p.requires_grad = False + else: + self.backbone.fpn.eval() + for p in self.backbone.fpn.parameters(): + p.requires_grad = False + if self.freeze_rpn: + if hasattr(self.rpn, "head"): + self.rpn.head.eval() + for p in self.rpn.parameters(): + p.requires_grad = False + if self.linear_prob: + if self.rpn is not None: + for key, value in self.rpn.named_parameters(): + if not ( + "bbox_pred" in key + or "cls_logits" in key + or "centerness" in key + or "cosine_scale" in key + or "dot_product_projection_text" in key + or "head.log_scale" in key + or "head.bias_lang" in key + or "head.bias0" in key + ): + value.requires_grad = False + if self.roi_heads is not None: + for key, value in self.roi_heads.named_parameters(): + if not ( + "bbox_pred" in key + or "cls_logits" in key + or "centerness" in key + or "cosine_scale" in key + or "dot_product_projection_text" in key + or "head.log_scale" in key + or "head.bias_lang" in key + or "head.bias0" in key + ): + value.requires_grad = False + if self.freeze_cls_logits: + if hasattr(self.rpn.head, "cls_logits"): + self.rpn.head.cls_logits.eval() + for p in self.rpn.head.cls_logits.parameters(): + p.requires_grad = False + if self.add_linear_layer: + if not self.fusion_in_backbone: + if self.rpn is not None: + for key, p in self.rpn.named_parameters(): + if "tunable_linear" in key: + p.requires_grad = True + else: + for key, p in self.fusion_backbone.named_parameters(): + if "tunable_linear" in key: + p.requires_grad = True + + if self.freeze_language_backbone: + if self.fusion_in_backbone: + self.fusion_backbone.language_backbone.eval() + for p in self.fusion_backbone.language_backbone.parameters(): + p.requires_grad = False + else: + self.language_backbone.eval() + for p in self.language_backbone.parameters(): + p.requires_grad = False + + def forward(self, images, targets=None, captions=None, positive_map=None, greenlight_map=None, spans = None, span_map = None): + """ + Arguments: + images (list[Tensor] or ImageList): images to be processed + targets (list[BoxList]): ground-truth boxes present in the image (optional) + + mask_black_list: batch x 256, indicates whether or not a certain token is maskable or not + + Returns: + result (list[BoxList] or dict[Tensor]): the output from the model. + During training, it returns a dict[Tensor] which contains the losses. + During testing, it returns list[BoxList] contains additional fields + like `scores`, `labels` and `mask` (for Mask R-CNN models). + + """ + if self.training and targets is None: + raise ValueError("In training mode, targets should be passed") + + images = to_image_list(images) + # batch_size = images.tensors.shape[0] + device = images.tensors.device + + # if we use the advanced span prediction version, we need to do both preprocessing and postprocessing + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION is not None and self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION.startswith("v2"): + if spans is None: + spans = [i.extra_fields['spans'] if "spans" in i.extra_fields else [] for i in targets] # if we did not pass the spans explicitly + assert(len(spans) == len(captions)) + new_captions = [] + mapping_batch_span_to_caption_num = {} # (batch_num, start, end) -> caption_num + mapping_batch_to_caption_num = {} + corrected_spans = deepcopy(spans) + + for i in range(len(captions)): + if len(spans[i]) == 0: # if this instance does not have span + mapping_batch_to_caption_num[i] = len(new_captions) + new_captions.append(captions[i]) + continue + + for j in range(len(spans[i])): + ''' spans[i][j]: + [[230, 241], + [241, 254], + [254, 269], + [269, 298],] + ''' + valid_spans = [k for k in spans[i][j] if k[0] != -1] + if "independent" in self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION: + for k, span_i_j_k in enumerate(valid_spans): + mapping_batch_span_to_caption_num[(i, span_i_j_k[0], span_i_j_k[1])] = len(new_captions) + corrected_spans[i][j][k] = (0, span_i_j_k[1] - span_i_j_k[0]) + new_captions.append(captions[i][span_i_j_k[0]:span_i_j_k[1]]) + else: + start = valid_spans[0][0] + end = valid_spans[-1][-1] + # rewrite the spans !! + corrected_spans[i][j] = [(k[0] - start, k[1] - start) for k in spans[i][j]] + + for k in valid_spans: + mapping_batch_span_to_caption_num[(i, k[0], k[1])] = len(new_captions) + #mapping_batch_to_caption_num[i] = len(new_captions) + new_captions.append(captions[i][start:end]) + captions = new_captions + padding_method = "longest" + #print(new_captions) + else: + mapping_batch_span_to_caption_num = None + padding_method = "max_length" if self.cfg.MODEL.LANGUAGE_BACKBONE.PAD_MAX else "longest" + + # language embedding + language_dict_features = {} + if captions is not None: + # print(captions[0]) + tokenized = self.tokenizer.batch_encode_plus( + captions, + max_length=self.cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + padding=padding_method, + return_special_tokens_mask=True, + return_tensors="pt", + truncation=True, + ).to(device) + if self.use_mlm_loss: + if not self.mlm_loss_for_only_positives: + greenlight_map = None + input_ids, mlm_labels = random_word( + input_ids=tokenized.input_ids, + mask_token_id=self.tokenizer.mask_token_id, + vocabs=self.tokenizer_vocab_ids, + padding_token_id=self.tokenizer.pad_token_id, + greenlight_map=greenlight_map, + ) + else: + input_ids = tokenized.input_ids + mlm_labels = None + + tokenizer_input = {"input_ids": input_ids, "attention_mask": tokenized.attention_mask} + + if not self.fusion_in_backbone: + if self.cfg.MODEL.LANGUAGE_BACKBONE.FREEZE: + with torch.no_grad(): + language_dict_features = self.language_backbone(tokenizer_input) + else: + language_dict_features = self.language_backbone(tokenizer_input) + + # ONE HOT + if self.cfg.DATASETS.ONE_HOT: + new_masks = torch.zeros_like( + language_dict_features["masks"], device=language_dict_features["masks"].device + ) + new_masks[:, : self.cfg.MODEL.DYHEAD.NUM_CLASSES] = 1 + language_dict_features["masks"] = new_masks + + # MASK ALL SPECIAL TOKENS + if self.cfg.MODEL.LANGUAGE_BACKBONE.MASK_SPECIAL: + language_dict_features["masks"] = 1 - tokenized.special_tokens_mask + + language_dict_features["mlm_labels"] = mlm_labels + + if not self.fusion_in_backbone: + # visual embedding + swint_feature_c4 = None + if "vl" in self.cfg.MODEL.SWINT.VERSION: + # the backbone only updates the "hidden" field in language_dict_features + inputs = {"img": images.tensors, "lang": language_dict_features} + visual_features, language_dict_features, swint_feature_c4 = self.backbone(inputs) + else: + visual_features = self.backbone(images.tensors) + + else: + visual_features, language_dict_features, swint_feature_c4 = self.fusion_backbone(tokenizer_input, images) + language_dict_features["mlm_labels"] = mlm_labels + + # add the prompt tuning linear layer if not fusion, for fusion do it inside the backbone + if not self.fusion_in_backbone: + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER: + embedding = language_dict_features["embedded"] + embedding = self.tunable_linear.weight[: embedding.size(1), :].unsqueeze(0) + embedding + language_dict_features["embedded"] = embedding + language_dict_features["hidden"] = ( + self.tunable_linear.weight[: embedding.size(1), :].unsqueeze(0) + language_dict_features["hidden"] + ) + + # if we do span prediction + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION is not None: + loss_version = self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION.split(".")[0] + pooling_version = self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SPAN_VERSION.split(".")[-1] + + # will just override everything + + # Step 1. get the spans + embedding = language_dict_features["hidden"] + if spans is None: + spans = [i.extra_fields['spans'] if "spans" in i.extra_fields else [] for i in targets] # if we did not pass the spans explicitly + + if mapping_batch_span_to_caption_num is not None: # need to do a remapping + flatterned_spans = [] + for i in spans: + _ = [] + for j in i: + _.extend(j) + flatterned_spans.append(_) + spans = flatterned_spans + + # flattern corrected spans + flatterned_corrected_spans = [] + for i in corrected_spans: + _ = [] + for j in i: + _.extend(j) + flatterned_corrected_spans.append(_) + corrected_spans = flatterned_corrected_spans + + max_span_num = max([len(i) for i in spans]) + + # go over the batch, see if there is an instance without spans; if so, we override the span_num to the token_num of that instance + for i, spans_i in enumerate(spans): + if len(spans_i) == 0: # no spans + text_length = sum(tokenized.attention_mask[mapping_batch_to_caption_num[i]]) + max_span_num = max(text_length, max_span_num) # override + + # Step 2. Get the Masks + span_masks = torch.zeros((len(spans), max_span_num), device=embedding.device, dtype=torch.long) + for i, spans_i in enumerate(spans): + if len(spans_i) == 0: + # this would be the text masks + text_mask_i = tokenized.attention_mask[mapping_batch_to_caption_num[i]] + text_length = sum(text_mask_i) + span_masks[i, : text_length] = text_mask_i[:text_length] + else: + span_masks[i, : len(spans_i)] = 1 + + # Step 3. get the span features + span_features = torch.zeros((len(spans), max_span_num, embedding.size(2)), device=embedding.device, dtype=embedding.dtype) + # the complexity is just batch x span_num; should be begign for a foor loop + for i, spans_i in enumerate(spans): + if len(spans_i) == 0: + # directly override with the embedding + __len = min(max_span_num, embedding[mapping_batch_to_caption_num[i]].size(0)) + span_features[i, :__len, :] = embedding[mapping_batch_to_caption_num[i], :__len, :] + else: + for j, span in enumerate(spans_i): + # first need to get the correct tokenized version + mapped_sentence_index = mapping_batch_span_to_caption_num[(i, span[0], span[1])] # here we use the original span + + start, end = get_char_token_with_relaxation(tokenized, corrected_spans[i][j][0], corrected_spans[i][j][1], batch_index = mapped_sentence_index) # here use the span location after we have partitioned the sentence + if start is None or end is None: + span_masks[i, j] = 0 # mark this span as invalid + + if pooling_version == "mean": + span_rep_i_j = torch.mean(embedding[mapped_sentence_index, start:end, :], dim=0) + elif pooling_version == "max": + span_rep_i_j = torch.max(embedding[mapped_sentence_index, start:end, :], dim=0)[0] + span_features[i, j, :] = span_rep_i_j + else: + assert(0) + # max_span_num = max([len(i) for i in spans]) + + # # Step 2. Get the Masks + # span_masks = torch.zeros((len(spans), max_span_num), device=embedding.device, dtype=torch.long) + # for i, spans_i in enumerate(spans): + # span_masks[i, : len(spans_i)] = 1 + + # # Step 3. get the span features + # span_features = torch.zeros((len(spans), max_span_num, embedding.size(2)), device=embedding.device, dtype=embedding.dtype) + # # the complexity is just batch x span_num; should be begign for a foor loop + # for i, spans in enumerate(spans): + # for j, span in enumerate(spans): + # # span records the char location; needs to convert to token location first + # start, end = get_char_token_with_relaxation(tokenized, span[0], span[1], batch_index = i) + + # if start is None or end is None: + # span_masks[i, j] = 0 # mark this span as invalid + + # if pooling_version == "mean": + # span_rep_i_j = torch.mean(embedding[i, start:end, :], dim=0) + # elif pooling_version == "max": + # span_rep_i_j = torch.max(embedding[i, start:end, :], dim=0)[0] + # span_features[i, j, :] = span_rep_i_j + + # Step 4. Rewrite the labels (?) + # we need to rewrite targets, positive_map, text_masks, text_embeddings + if span_map is None: + span_map = torch.zeros((positive_map.size(0), max_span_num), device=embedding.device, dtype=torch.float) + _all_span_map_flattern = [] # box_num x span_num + + for target_i in targets: + if "span_map" in target_i.extra_fields: + _all_span_map_flattern.extend([j for j in target_i.extra_fields["span_map"]]) + else: + # if not, create a list of empty lists + num_box = target_i.bbox.size(0) + _all_span_map_flattern.extend([[]] * num_box) # very important + + assert(len(_all_span_map_flattern) == positive_map.size(0)) + for i, span_map_i in enumerate(_all_span_map_flattern): + if len(span_map_i) == 0: + seq_len = min(max_span_num, positive_map.size(1)) + span_map[i, :seq_len] = positive_map[i, :seq_len] # use the original positive map in this case! + else: + span_map[i, :len(span_map_i)] = span_map_i + + + # Step 5. Override + positive_map = span_map + language_dict_features["masks"] = span_masks + language_dict_features["embedded"] = span_features + language_dict_features["hidden"] = span_features + if targets is not None: + for i in targets: + if "span_map" in i.extra_fields: + i.extra_fields["positive_map"] = i.extra_fields["span_map"] # override if span + + # rpn force boxes + if targets: + targets = [target.to(device) for target in targets if target is not None] + + if self.force_boxes: + proposals = [] + for t in targets: + tb = t.copy_with_fields(["labels"]) + tb.add_field("scores", torch.ones(tb.bbox.shape[0], dtype=torch.bool, device=tb.bbox.device)) + proposals.append(tb) + if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES: + _, proposal_losses, fused_visual_features = self.rpn( + images, visual_features, targets, language_dict_features, positive_map, captions, swint_feature_c4 + ) + elif self.training: + null_loss = 0 + for key, param in self.rpn.named_parameters(): + null_loss += 0.0 * param.sum() + proposal_losses = {("rpn_null_loss", null_loss)} + else: + proposals, proposal_losses, fused_visual_features = self.rpn( + images, visual_features, targets, language_dict_features, positive_map, captions, swint_feature_c4 + ) + + if self.roi_heads: + if not self.training: + assert len(proposals) == 1, "Evaluation batch size per GPU should be 1!" + if len(proposals[0]) == 0: + return proposals + if self.cfg.MODEL.ROI_MASK_HEAD.PREDICTOR.startswith("VL"): + if self.training: + # "Only support VL mask head right now!!" + assert len(targets) == 1 and len(targets[0]) == len( + positive_map + ), "shape match assert for mask head!!" + # Not necessary but as a safe guard: + # use the binary 0/1 positive map to replace the normalized positive map + targets[0].add_field("positive_map", positive_map) + # TODO: make sure that this use of language_dict_features is correct!! Its content should be changed in self.rpn + if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES: + x, result, detector_losses = self.roi_heads( + fused_visual_features, + proposals, + targets, + language_dict_features=language_dict_features, + positive_map_label_to_token=positive_map if not self.training else None, + ) + else: + x, result, detector_losses = self.roi_heads( + visual_features, + proposals, + targets, + language_dict_features=language_dict_features, + positive_map_label_to_token=positive_map if not self.training else None, + ) + else: + # RPN-only models don't have roi_heads + x = visual_features + result = proposals + detector_losses = {} + + if self.training: + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + return losses + + return result diff --git a/maskrcnn_benchmark/modeling/language_backbone/__init__.py b/maskrcnn_benchmark/modeling/language_backbone/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f78d6ab1d5b2d59007bb4c042d0fc1a5a06253da --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/__init__.py @@ -0,0 +1,6 @@ +from .backbone import build_backbone as build_language_backbone +from .build import build_tokenizer + +from .hfpt_tokenizer import HFPTTokenizer +from .simple_tokenizer import SimpleTokenizer +from .clip_model import CLIPTransformer diff --git a/maskrcnn_benchmark/modeling/language_backbone/backbone.py b/maskrcnn_benchmark/modeling/language_backbone/backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..1a94da67952c5d0df07a25b0275037827f8b7cfe --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/backbone.py @@ -0,0 +1,67 @@ +from collections import OrderedDict +import torch +from torch import nn + +from maskrcnn_benchmark.modeling import registry +from . import bert_model +from . import rnn_model +from . import clip_model +from . import word_utils +from . import roberta_fused_model +from . import roberta_fused_model_v2 +from . import roberta_fused_model_tiny + +@registry.LANGUAGE_BACKBONES.register("bert-base-uncased") +def build_bert_backbone(cfg): + body = bert_model.BertEncoder(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.LANGUAGE_BACKBONES.register("roberta-base") +def build_bert_backbone(cfg): + body = bert_model.BertEncoder(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.LANGUAGE_BACKBONES.register("rnn") +def build_rnn_backbone(cfg): + body = rnn_model.RNNEnoder(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.LANGUAGE_BACKBONES.register("clip") +def build_clip_backbone(cfg): + body = clip_model.CLIPTransformer(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.LANGUAGE_BACKBONES.register("roberta-fused") +def build_clip_backbone(cfg): + body = roberta_fused_model.RobertaFusedEncoder(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + + +@registry.LANGUAGE_BACKBONES.register("roberta-fused-v2") +def build_clip_backbone(cfg): + body = roberta_fused_model_v2.RobertaFusedEncoder(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + +@registry.LANGUAGE_BACKBONES.register("roberta-fused-tiny") +def build_clip_backbone(cfg): + body = roberta_fused_model_tiny.RobertaFusedEncoder(cfg) + model = nn.Sequential(OrderedDict([("body", body)])) + return model + +def build_backbone(cfg): + assert ( + cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE in registry.LANGUAGE_BACKBONES + ), "cfg.MODEL.LANGUAGE_BACKBONE.TYPE: {} is not registered in registry".format( + cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE + ) + return registry.LANGUAGE_BACKBONES[cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE](cfg) diff --git a/maskrcnn_benchmark/modeling/language_backbone/bert_model.py b/maskrcnn_benchmark/modeling/language_backbone/bert_model.py new file mode 100644 index 0000000000000000000000000000000000000000..86db31a897e1a2d2d0f47d8f51329a83acceda28 --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/bert_model.py @@ -0,0 +1,74 @@ +from copy import deepcopy +import numpy as np +import torch +from torch import nn + +# from pytorch_pretrained_bert.modeling import BertModel +from transformers import BertConfig, RobertaConfig, RobertaModel, BertModel + + +class BertEncoder(nn.Module): + def __init__(self, cfg): + super(BertEncoder, self).__init__() + self.cfg = cfg + self.bert_name = cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE + print("LANGUAGE BACKBONE USE GRADIENT CHECKPOINTING: ", self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT) + + if self.bert_name == "bert-base-uncased": + config = BertConfig.from_pretrained(self.bert_name) + config.gradient_checkpointing = self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT + self.model = BertModel.from_pretrained(self.bert_name, add_pooling_layer=False, config=config) + self.language_dim = 768 + elif self.bert_name == "roberta-base": + config = RobertaConfig.from_pretrained(self.bert_name) + config.gradient_checkpointing = self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT + self.model = RobertaModel.from_pretrained(self.bert_name, add_pooling_layer=False, config=config) + self.language_dim = 768 + else: + raise NotImplementedError + + self.num_layers = cfg.MODEL.LANGUAGE_BACKBONE.N_LAYERS + + def forward(self, x): + input = x["input_ids"] + mask = x["attention_mask"] + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + # with padding, always 256 + outputs = self.model( + input_ids=input, + attention_mask=mask, + output_hidden_states=True, + ) + # outputs has 13 layers, 1 input layer and 12 hidden layers + encoded_layers = outputs.hidden_states[1:] + features = None + features = torch.stack(encoded_layers[-self.num_layers :], 1).mean(1) + + # language embedding has shape [len(phrase), seq_len, language_dim] + features = features / self.num_layers + + embedded = features * mask.unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + else: + # without padding, only consider positive_tokens + max_len = (input != 0).sum(1).max().item() + outputs = self.model( + input_ids=input[:, :max_len], + attention_mask=mask[:, :max_len], + output_hidden_states=True, + ) + # outputs has 13 layers, 1 input layer and 12 hidden layers + encoded_layers = outputs.hidden_states[1:] + + features = None + features = torch.stack(encoded_layers[-self.num_layers :], 1).mean(1) + # language embedding has shape [len(phrase), seq_len, language_dim] + features = features / self.num_layers + + embedded = features * mask[:, :max_len].unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + ret = {"aggregate": aggregate, "embedded": embedded, "masks": mask, "hidden": encoded_layers[-1]} + return ret diff --git a/maskrcnn_benchmark/modeling/language_backbone/bpe_simple_vocab_16e6.txt.gz b/maskrcnn_benchmark/modeling/language_backbone/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000000000000000000000000000000000000..e74ad860329b14ff6b53f3ae0b007bec308cc5af --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/bpe_simple_vocab_16e6.txt.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc496842c2d4b6e40b2bd1207a5ded6e425e6a7cf9c16afa86caa5d7d12df233 +size 1355337 diff --git a/maskrcnn_benchmark/modeling/language_backbone/build.py b/maskrcnn_benchmark/modeling/language_backbone/build.py new file mode 100644 index 0000000000000000000000000000000000000000..627767dde4b95cb6cbae2a2e64398efa7b16d0c9 --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/build.py @@ -0,0 +1,19 @@ +from .simple_tokenizer import SimpleTokenizer + + +def build_tokenizer(tokenizer_name): + tokenizer = None + if tokenizer_name == "clip": + tokenizer = SimpleTokenizer() + elif "hf_" in tokenizer_name: + from .hfpt_tokenizer import HFPTTokenizer + + tokenizer = HFPTTokenizer(pt_name=tokenizer_name[3:]) + elif "hfc_" in tokenizer_name: + from .hfpt_tokenizer import HFPTTokenizer + + tokenizer = HFPTTokenizer(pt_name=tokenizer_name[4:]) + else: + raise ValueError("Unknown tokenizer") + + return tokenizer diff --git a/maskrcnn_benchmark/modeling/language_backbone/clip_model.py b/maskrcnn_benchmark/modeling/language_backbone/clip_model.py new file mode 100644 index 0000000000000000000000000000000000000000..ce01cd34840d06490003067f3074ae430f859ae8 --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/clip_model.py @@ -0,0 +1,185 @@ +from collections import OrderedDict +import logging +import os + +import torch +from torch import nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from maskrcnn_benchmark.config import try_to_find + +from timm.models.layers import DropPath, trunc_normal_ + +logger = logging.getLogger(__name__) + + +class LayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-12): + """Construct a layernorm module in the TF style (epsilon inside the square root).""" + super(LayerNorm, self).__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.bias = nn.Parameter(torch.zeros(hidden_size)) + self.variance_epsilon = eps + + def forward(self, x): + pdtype = x.dtype + x = x.float() + u = x.mean(-1, keepdim=True) + s = (x - u).pow(2).mean(-1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.variance_epsilon) + return self.weight * x.to(pdtype) + self.bias + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, drop_path: float = 0.0): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict( + [ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)), + ] + ) + ) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0] + + def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None): + x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask)) + x = x + self.drop_path(self.mlp(self.ln_2(x))) + return x + + +class CLIPTransformer(nn.Module): + def __init__(self, cfg): + super().__init__() + + self.cfg = cfg + self.use_checkpoint = cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT + print("LANGUAGE BACKBONE USE GRADIENT CHECKPOINTING: ", self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT) + + self.context_length = self.cfg.MODEL.CLIP.CONTEXT_LENGTH + self.width = self.cfg.MODEL.CLIP.WIDTH + self.layers = self.cfg.MODEL.CLIP.LAYERS + self.heads = self.cfg.MODEL.CLIP.HEADS + self.drop_path = self.cfg.MODEL.CLIP.DROP_PATH + self.vocab_size = self.cfg.MODEL.CLIP.VOCAB_SIZE + + self.token_embedding = nn.Embedding(self.vocab_size, self.width) + + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, self.width)) + + # attn_mask = self.build_attention_mask() + attn_mask = None + + dpr = [x.item() for x in torch.linspace(0, self.drop_path, self.layers)] # stochastic depth decay rule + self.resblocks = nn.ModuleList( + [ResidualAttentionBlock(self.width, self.heads, attn_mask, dpr[i]) for i in range(self.layers)] + ) + + self.ln_final = LayerNorm(self.width) + + trunc_normal_(self.positional_embedding, std=0.02) + # nn.init.normal_(self.token_embedding, std=.02) + trunc_normal_(self.token_embedding.weight, std=0.02) + self.apply(self._init_weights) + + # loading pre-trained weight from our CLIP models + if len(self.cfg.MODEL.LANGUAGE_BACKBONE.WEIGHT) > 0: + self.init_weights(pretrained=try_to_find(self.cfg.MODEL.LANGUAGE_BACKBONE.WEIGHT), pretrained_layers=["*"]) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def _init_weights(self, m): + if isinstance(m, (nn.Linear, nn.Conv2d)): + trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)): + nn.init.constant_(m.bias, 0) + + def resize_pos_embed_1d(self, posemb, shape_new): + # rescale the grid of position embeddings when loading from state_dict + ntok_old = posemb.shape[0] + if ntok_old > 1: + ntok_new = shape_new[0] + posemb_grid = posemb.unsqueeze(dim=0).permute(0, 2, 1).unsqueeze(dim=-1) + posemb_grid = F.interpolate(posemb_grid, size=[ntok_new, 1], mode="bilinear") + posemb_grid = posemb_grid.squeeze(dim=-1).permute(0, 2, 1).squeeze(dim=0) + posemb = posemb_grid + return posemb + + def init_weights(self, pretrained="", pretrained_layers=[], verbose=False): + if os.path.isfile(pretrained): + pretrained_dict = torch.load(pretrained, map_location="cpu") + logger.info(f"=> loading pretrained clip text model {pretrained}") + model_dict = self.state_dict() + + need_init_state_dict = {} + for k, v in pretrained_dict.items(): + need_init = k.split(".")[0] in pretrained_layers or pretrained_layers[0] is "*" + if need_init: + if k.startswith("text.") and k[5:] in model_dict.keys(): + need_init_state_dict[k[5:]] = v + + # notice the context length now changes from 77 to 256, so we need to resize the positional embedding + if "positional_embedding" in need_init_state_dict.keys(): + old_pos_embed = need_init_state_dict["positional_embedding"].float() + new_pos_embed = self.resize_pos_embed_1d( + old_pos_embed, (self.cfg.MODEL.CLIP.CONTEXT_LENGTH, old_pos_embed.shape[1]) + ) + need_init_state_dict["positional_embedding"] = new_pos_embed + self.load_state_dict(need_init_state_dict, strict=True) + + @torch.jit.ignore + def no_weight_decay(self): + return { + "positional_embedding", + "token_embedding", + } + + def forward(self, text): + input = text["input_ids"] + mask = text["attention_mask"] + # get extended attention mask for nn.MultiHeadAttention + key_padding_mask = (1.0 - mask).to(torch.bool) + + x = self.token_embedding(input) # [batch_size, n_ctx, d_model] + x = x + self.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + + for resblock in self.resblocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(resblock, x, key_padding_mask) + else: + x = resblock(x, key_padding_mask) + + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_final(x) + + # x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] + + ret = {"aggregate": x, "embedded": x, "masks": mask, "hidden": x} + + return ret diff --git a/maskrcnn_benchmark/modeling/language_backbone/hfpt_tokenizer.py b/maskrcnn_benchmark/modeling/language_backbone/hfpt_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..260ad3de1763ce44d133a5a7ad7d46767f9da5c5 --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/hfpt_tokenizer.py @@ -0,0 +1,95 @@ +from typing import Union, List + +from transformers import AutoTokenizer +import torch + + +class HFPTTokenizer(object): + def __init__(self, pt_name=None): + + self.pt_name = pt_name + self.added_sep_token = 0 + self.added_cls_token = 0 + self.enable_add_tokens = False + self.gpt_special_case = (not self.enable_add_tokens) and ("gpt" in self.pt_name) + + if pt_name is None: + self.tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") + else: + self.tokenizer = AutoTokenizer.from_pretrained(pt_name) + + # Adding tokens to GPT causing NaN training loss. + # Disable for now until further investigation. + if self.enable_add_tokens: + if self.tokenizer.sep_token is None: + self.tokenizer.add_special_tokens({"sep_token": ""}) + self.added_sep_token = 1 + + if self.tokenizer.cls_token is None: + self.tokenizer.add_special_tokens({"cls_token": ""}) + self.added_cls_token = 1 + + if self.gpt_special_case: + self.tokenizer.pad_token = self.tokenizer.eos_token + self.tokenizer.sep_token = self.tokenizer.eos_token + + def get_eot_token(self): + return self.tokenizer.encode(self.tokenizer.sep_token, add_special_tokens=False)[0] + + def get_sot_token(self): + return self.tokenizer.encode(self.tokenizer.cls_token, add_special_tokens=False)[0] + + def get_eot_token_list(self): + return self.tokenizer.encode(self.tokenizer.sep_token, add_special_tokens=False) + + def get_sot_token_list(self): + return self.tokenizer.encode(self.tokenizer.cls_token, add_special_tokens=False) + + def get_tokenizer_obj(self): + return self.tokenizer + + # Language model needs to know if new tokens + # were added to the dictionary. + def check_added_tokens(self): + return self.added_sep_token + self.added_cls_token + + def tokenize(self, texts: Union[str, List[str]], context_length: int = 77): + if isinstance(texts, str): + texts = [texts] + + padding = "max_length" + + seqstart = [] + seqtok = [] + seqend = [] + + max_length = context_length + + if self.added_cls_token > 0: + seqstart = self.get_sot_token_list() + max_length = max_length - 1 + + if self.added_sep_token > 0: + seqend = self.get_eot_token_list() + max_length = max_length - 1 + + tokens = self.tokenizer(texts, padding=padding, truncation=True, max_length=max_length)["input_ids"] + + for i in range(len(tokens)): + tokens[i] = seqstart + tokens[i] + seqend + + if self.gpt_special_case: + for i in range(len(tokens)): + tokens[i][-1] = self.get_eot_token() + + # print(str(tokens)) + + result = torch.Tensor(tokens).type(torch.LongTensor) + + return result + + def get_vocab_size(self): + return self.tokenizer.vocab_size + + def __call__(self, texts: Union[str, List[str]], context_length: int = 77): + return self.tokenize(texts, context_length) diff --git a/maskrcnn_benchmark/modeling/language_backbone/rnn_model.py b/maskrcnn_benchmark/modeling/language_backbone/rnn_model.py new file mode 100644 index 0000000000000000000000000000000000000000..5e8104eb01bf0849d122ae506e57e72e1bd6e1eb --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/rnn_model.py @@ -0,0 +1,117 @@ +from copy import deepcopy +import numpy as np +import torch +from torch import nn + + +class RNNEnoder(nn.Module): + def __init__(self, cfg): + super(RNNEnoder, self).__init__() + self.cfg = cfg + + self.rnn_type = cfg.MODEL.LANGUAGE_BACKBONE.RNN_TYPE + self.variable_length = cfg.MODEL.LANGUAGE_BACKBONE.VARIABLE_LENGTH + self.word_embedding_size = cfg.MODEL.LANGUAGE_BACKBONE.WORD_EMBEDDING_SIZE + self.word_vec_size = cfg.MODEL.LANGUAGE_BACKBONE.WORD_VEC_SIZE + self.hidden_size = cfg.MODEL.LANGUAGE_BACKBONE.HIDDEN_SIZE + self.bidirectional = cfg.MODEL.LANGUAGE_BACKBONE.BIDIRECTIONAL + self.input_dropout_p = cfg.MODEL.LANGUAGE_BACKBONE.INPUT_DROPOUT_P + self.dropout_p = cfg.MODEL.LANGUAGE_BACKBONE.DROPOUT_P + self.n_layers = cfg.MODEL.LANGUAGE_BACKBONE.N_LAYERS + self.corpus_path = cfg.MODEL.LANGUAGE_BACKBONE.CORPUS_PATH + self.vocab_size = cfg.MODEL.LANGUAGE_BACKBONE.VOCAB_SIZE + + # language encoder + self.embedding = nn.Embedding(self.vocab_size, self.word_embedding_size) + self.input_dropout = nn.Dropout(self.input_dropout_p) + self.mlp = nn.Sequential(nn.Linear(self.word_embedding_size, self.word_vec_size), nn.ReLU()) + self.rnn = getattr(nn, self.rnn_type.upper())( + self.word_vec_size, + self.hidden_size, + self.n_layers, + batch_first=True, + bidirectional=self.bidirectional, + dropout=self.dropout_p, + ) + self.num_dirs = 2 if self.bidirectional else 1 + + def forward(self, input, mask=None): + word_id = input + max_len = (word_id != 0).sum(1).max().item() + word_id = word_id[:, :max_len] # mask zero + # embedding + output, hidden, embedded, final_output = self.RNNEncode(word_id) + return { + "hidden": hidden, + "output": output, + "embedded": embedded, + "final_output": final_output, + } + + def encode(self, input_labels): + """ + Inputs: + - input_labels: Variable long (batch, seq_len) + Outputs: + - output : Variable float (batch, max_len, hidden_size * num_dirs) + - hidden : Variable float (batch, num_layers * num_dirs * hidden_size) + - embedded: Variable float (batch, max_len, word_vec_size) + """ + device = input_labels.device + if self.variable_length: + input_lengths_list, sorted_lengths_list, sort_idxs, recover_idxs = self.sort_inputs(input_labels) + input_labels = input_labels[sort_idxs] + + embedded = self.embedding(input_labels) # (n, seq_len, word_embedding_size) + embedded = self.input_dropout(embedded) # (n, seq_len, word_embedding_size) + embedded = self.mlp(embedded) # (n, seq_len, word_vec_size) + + if self.variable_length: + if self.variable_length: + embedded = nn.utils.rnn.pack_padded_sequence(embedded, sorted_lengths_list, batch_first=True) + # forward rnn + self.rnn.flatten_parameters() + output, hidden = self.rnn(embedded) + + # recover + if self.variable_length: + # recover embedded + embedded, _ = nn.utils.rnn.pad_packed_sequence( + embedded, batch_first=True + ) # (batch, max_len, word_vec_size) + embedded = embedded[recover_idxs] + + # recover output + output, _ = nn.utils.rnn.pad_packed_sequence( + output, batch_first=True + ) # (batch, max_len, hidden_size * num_dir) + output = output[recover_idxs] + + # recover hidden + if self.rnn_type == "lstm": + hidden = hidden[0] # hidden state + hidden = hidden[:, recover_idxs, :] # (num_layers * num_dirs, batch, hidden_size) + hidden = hidden.transpose(0, 1).contiguous() # (batch, num_layers * num_dirs, hidden_size) + hidden = hidden.view(hidden.size(0), -1) # (batch, num_layers * num_dirs * hidden_size) + + # final output + finnal_output = [] + for ii in range(output.shape[0]): + finnal_output.append(output[ii, int(input_lengths_list[ii] - 1), :]) + finnal_output = torch.stack(finnal_output, dim=0) # (batch, number_dirs * hidden_size) + + return output, hidden, embedded, finnal_output + + def sort_inputs(self, input_labels): # sort input labels by descending + device = input_labels.device + input_lengths = (input_labels != 0).sum(1) + input_lengths_list = input_lengths.data.cpu().numpy().tolist() + sorted_input_lengths_list = np.sort(input_lengths_list)[::-1].tolist() # list of sorted input_lengths + sort_idxs = np.argsort(input_lengths_list)[::-1].tolist() + s2r = {s: r for r, s in enumerate(sort_idxs)} + recover_idxs = [s2r[s] for s in range(len(input_lengths_list))] + assert max(input_lengths_list) == input_labels.size(1) + # move to long tensor + sort_idxs = input_labels.data.new(sort_idxs).long().to(device) # Variable long + recover_idxs = input_labels.data.new(recover_idxs).long().to(device) # Variable long + return input_lengths_list, sorted_input_lengths_list, sort_idxs, recover_idxs diff --git a/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model.py b/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model.py new file mode 100644 index 0000000000000000000000000000000000000000..697d6d7c261937b118647041ee97a2c6e0ca7b54 --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model.py @@ -0,0 +1,848 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RoBERTa model. """ + +import math + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN, gelu +from transformers.file_utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + replace_return_docstrings, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.roberta.configuration_roberta import RobertaConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "roberta-base" +_CONFIG_FOR_DOC = "RobertaConfig" +_TOKENIZER_FOR_DOC = "RobertaTokenizer" + +ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "roberta-base", + "roberta-large", + "roberta-large-mnli", + "distilroberta-base", + "roberta-base-openai-detector", + "roberta-large-openai-detector", + # See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class RobertaFusedEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.bert_name = "roberta-base" + print("LANGUAGE BACKBONE USE GRADIENT CHECKPOINTING: ", self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT) + + if self.bert_name == "roberta-base": + config = RobertaConfig.from_pretrained(self.bert_name) + config.gradient_checkpointing = self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT + self.model = RobertaModel.from_pretrained(self.bert_name, add_pooling_layer=False, config=config) + self.language_dim = 768 + else: + raise NotImplementedError + + self.num_layers = cfg.MODEL.LANGUAGE_BACKBONE.N_LAYERS + + def get_aggregated_output(self, features, input_ids, mask): + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + embedded = features * mask.unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + else: + # without padding, only consider positive_tokens + max_len = (input_ids != 0).sum(1).max().item() + + embedded = features * mask[:, :max_len].unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + ret = {"aggregate": aggregate, "embedded": embedded, "masks": mask, "hidden": features} + return ret + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + def forward( + self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ).to(input_ids.device) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + Args: + inputs_embeds: torch.Tensor + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if encoder_hidden_states is not None: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor, cross=False): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + # if not cross: + # hidden_states = self.LayerNorm(hidden_states + input_tensor) + # else: + # hidden_states = self.LayerNorm(hidden_states) + input_tensor + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, cross=False): + super().__init__() + self.self = RobertaSelfAttention(config) + self.output = RobertaSelfOutput(config) + if cross: + # We do not use this LayerNorm, need to protect for DDP with find_unused_parameters=False + self.output.LayerNorm = nn.Identity() + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + cross=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states, cross=cross) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config, add_cross=False): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if add_cross: + self.crossattention_t2i = RobertaAttention(config, cross=True) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + # dim = config.hidden_size + # self.gate_t2i = nn.Linear(2*dim, 1) + # self.gate_t2i = nn.Linear(2*dim, dim) + # self.sigmoid_t2i = nn.Sigmoid() + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if encoder_hidden_states is not None: + assert hasattr( + self, "crossattention_t2i" + ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + cross_attention_outputs = self.crossattention_t2i( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + cross=True, + ) + attention_output = cross_attention_outputs[0] + attention_output + # g = self.gate_t2i(torch.cat([cross_attention_outputs[0], attention_output], dim=-1)) + # attention_output = g*cross_attention_outputs[0] + attention_output + + attention_output = self.attention.output.LayerNorm(attention_output + hidden_states) + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config, add_cross=(layer_i >= 10)) for layer_i in range(config.num_hidden_layers)] + ) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if getattr(self.config, "gradient_checkpointing", False) and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " + "`use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +ROBERTA_START_DOCSTRING = r""" + This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic + methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, + pruning heads etc.) + This model is also a PyTorch `torch.nn.Module `__ + subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to + general usage and behavior. + Parameters: + config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model + weights. +""" + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`): + Indices of input sequence tokens in the vocabulary. + Indices can be obtained using :class:`~transformers.RobertaTokenizer`. See + :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for + details. + `What are input IDs? <../glossary.html#input-ids>`__ + attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`): + Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + `What are attention masks? <../glossary.html#attention-mask>`__ + token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, + 1]``: + - 0 corresponds to a `sentence A` token, + - 1 corresponds to a `sentence B` token. + `What are token type IDs? <../glossary.html#token-type-ids>`_ + position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, + config.max_position_embeddings - 1]``. + `What are position IDs? <../glossary.html#position-ids>`_ + head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`): + Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`): + Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert :obj:`input_ids` indices into associated + vectors than the model's internal embedding lookup matrix. + output_attentions (:obj:`bool`, `optional`): + Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned + tensors for more detail. + output_hidden_states (:obj:`bool`, `optional`): + Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for + more detail. + return_dict (:obj:`bool`, `optional`): + Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", + ROBERTA_START_DOCSTRING, +) +class RobertaModel(RobertaPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration + set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder` + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + .. _`Attention is all you need`: https://arxiv.org/abs/1706.03762 + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + self.init_weights() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) + @add_code_sample_docstrings( + tokenizer_class=_TOKENIZER_FOR_DOC, + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPoolingAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + Args: + x: torch.Tensor x: + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx diff --git a/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model_tiny.py b/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model_tiny.py new file mode 100644 index 0000000000000000000000000000000000000000..77602332ec1c508ce117d64cea0766f4b1111d1a --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model_tiny.py @@ -0,0 +1,858 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RoBERTa model. """ + +import math + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN, gelu +from transformers.file_utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + replace_return_docstrings, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.roberta.configuration_roberta import RobertaConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "roberta-base" +_CONFIG_FOR_DOC = "RobertaConfig" +_TOKENIZER_FOR_DOC = "RobertaTokenizer" + +ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "roberta-base", + "roberta-large", + "roberta-large-mnli", + "distilroberta-base", + "roberta-base-openai-detector", + "roberta-large-openai-detector", + # See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class RobertaFusedEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.bert_name = "roberta-base" + print("LANGUAGE BACKBONE USE GRADIENT CHECKPOINTING: ", self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT) + + if self.bert_name == "roberta-base": + config = RobertaConfig.from_pretrained(self.bert_name) + config.gradient_checkpointing = self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT + self.model = RobertaModel.from_pretrained(self.bert_name, add_pooling_layer=False, config=config) + self.language_dim = 768 + else: + raise NotImplementedError + + self.num_layers = cfg.MODEL.LANGUAGE_BACKBONE.N_LAYERS + + def get_aggregated_output(self, features, input_ids, mask): + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + embedded = features * mask.unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + else: + # without padding, only consider positive_tokens + max_len = (input_ids != 0).sum(1).max().item() + + embedded = features * mask[:, :max_len].unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + ret = {"aggregate": aggregate, "embedded": embedded, "masks": mask, "hidden": features} + return ret + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + def forward( + self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ).to(input_ids.device) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + Args: + inputs_embeds: torch.Tensor + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, cross=False, layer_index=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if layer_index is None: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + else: + if layer_index < 10: + self.key = nn.Linear(512, self.all_head_size) + self.value = nn.Linear(512, self.all_head_size) + else: + self.key = nn.Linear(768, self.all_head_size) + self.value = nn.Linear(768, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if encoder_hidden_states is not None: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor, cross=False): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + # if not cross: + # hidden_states = self.LayerNorm(hidden_states + input_tensor) + # else: + # hidden_states = self.LayerNorm(hidden_states) + input_tensor + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, cross=False, layer_index=None): + super().__init__() + self.self = RobertaSelfAttention(config, cross=cross, layer_index=layer_index) + self.output = RobertaSelfOutput(config) + if cross: + # We do not use this LayerNorm, need to protect for DDP with find_unused_parameters=False + self.output.LayerNorm = nn.Identity() + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + cross=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states, cross=cross) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config, add_cross=False, layer_index=None): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if add_cross: + self.crossattention_t2i = RobertaAttention(config, cross=True, layer_index=layer_index) + self.alpha_t2i = nn.Parameter(torch.Tensor([0])) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + # dim = config.hidden_size + # self.gate_t2i = nn.Linear(2*dim, 1) + # self.gate_t2i = nn.Linear(2*dim, dim) + # self.sigmoid_t2i = nn.Sigmoid() + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if encoder_hidden_states is not None: + assert hasattr( + self, "crossattention_t2i" + ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + cross_attention_outputs = self.crossattention_t2i( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + cross=True, + ) + attention_output = self.alpha_t2i * cross_attention_outputs[0] + attention_output + + attention_output = self.attention.output.LayerNorm(attention_output + hidden_states) + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [ + RobertaLayer(config, add_cross=(layer_i >= 6), layer_index=layer_i) + for layer_i in range(config.num_hidden_layers) + ] + ) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if getattr(self.config, "gradient_checkpointing", False) and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " + "`use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +ROBERTA_START_DOCSTRING = r""" + This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic + methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, + pruning heads etc.) + This model is also a PyTorch `torch.nn.Module `__ + subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to + general usage and behavior. + Parameters: + config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model + weights. +""" + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`): + Indices of input sequence tokens in the vocabulary. + Indices can be obtained using :class:`~transformers.RobertaTokenizer`. See + :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for + details. + `What are input IDs? <../glossary.html#input-ids>`__ + attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`): + Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + `What are attention masks? <../glossary.html#attention-mask>`__ + token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, + 1]``: + - 0 corresponds to a `sentence A` token, + - 1 corresponds to a `sentence B` token. + `What are token type IDs? <../glossary.html#token-type-ids>`_ + position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, + config.max_position_embeddings - 1]``. + `What are position IDs? <../glossary.html#position-ids>`_ + head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`): + Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`): + Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert :obj:`input_ids` indices into associated + vectors than the model's internal embedding lookup matrix. + output_attentions (:obj:`bool`, `optional`): + Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned + tensors for more detail. + output_hidden_states (:obj:`bool`, `optional`): + Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for + more detail. + return_dict (:obj:`bool`, `optional`): + Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", + ROBERTA_START_DOCSTRING, +) +class RobertaModel(RobertaPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration + set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder` + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + .. _`Attention is all you need`: https://arxiv.org/abs/1706.03762 + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + self.init_weights() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) + @add_code_sample_docstrings( + tokenizer_class=_TOKENIZER_FOR_DOC, + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPoolingAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + Args: + x: torch.Tensor x: + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx diff --git a/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model_v2.py b/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..8b14befe8003c8966532ca848bf7951903064b55 --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/roberta_fused_model_v2.py @@ -0,0 +1,858 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RoBERTa model. """ + +import math + +import torch +import torch.nn as nn +import torch.utils.checkpoint +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN, gelu +from transformers.file_utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + replace_return_docstrings, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.roberta.configuration_roberta import RobertaConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "roberta-base" +_CONFIG_FOR_DOC = "RobertaConfig" +_TOKENIZER_FOR_DOC = "RobertaTokenizer" + +ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "roberta-base", + "roberta-large", + "roberta-large-mnli", + "distilroberta-base", + "roberta-base-openai-detector", + "roberta-large-openai-detector", + # See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class RobertaFusedEncoder(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.bert_name = "roberta-base" + print("LANGUAGE BACKBONE USE GRADIENT CHECKPOINTING: ", self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT) + + if self.bert_name == "roberta-base": + config = RobertaConfig.from_pretrained(self.bert_name) + config.gradient_checkpointing = self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT + self.model = RobertaModel.from_pretrained(self.bert_name, add_pooling_layer=False, config=config) + self.language_dim = 768 + else: + raise NotImplementedError + + self.num_layers = cfg.MODEL.LANGUAGE_BACKBONE.N_LAYERS + + def get_aggregated_output(self, features, input_ids, mask): + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + embedded = features * mask.unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + else: + # without padding, only consider positive_tokens + max_len = (input_ids != 0).sum(1).max().item() + + embedded = features * mask[:, :max_len].unsqueeze(-1).float() + aggregate = embedded.sum(1) / (mask.sum(-1).unsqueeze(-1).float()) + + ret = {"aggregate": aggregate, "embedded": embedded, "masks": mask, "hidden": features} + return ret + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + def forward( + self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ).to(input_ids.device) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + Args: + inputs_embeds: torch.Tensor + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, cross=False, layer_index=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if layer_index is None: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + else: + if layer_index < 10: + self.key = nn.Linear(512, self.all_head_size) + self.value = nn.Linear(512, self.all_head_size) + else: + self.key = nn.Linear(1024, self.all_head_size) + self.value = nn.Linear(1024, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if encoder_hidden_states is not None: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor, cross=False): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + # if not cross: + # hidden_states = self.LayerNorm(hidden_states + input_tensor) + # else: + # hidden_states = self.LayerNorm(hidden_states) + input_tensor + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, cross=False, layer_index=None): + super().__init__() + self.self = RobertaSelfAttention(config, cross=cross, layer_index=layer_index) + self.output = RobertaSelfOutput(config) + if cross: + # We do not use this LayerNorm, need to protect for DDP with find_unused_parameters=False + self.output.LayerNorm = nn.Identity() + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + cross=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states, cross=cross) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config, add_cross=False, layer_index=None): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if add_cross: + self.crossattention_t2i = RobertaAttention(config, cross=True, layer_index=layer_index) + self.alpha_t2i = nn.Parameter(torch.Tensor([0])) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + # dim = config.hidden_size + # self.gate_t2i = nn.Linear(2*dim, 1) + # self.gate_t2i = nn.Linear(2*dim, dim) + # self.sigmoid_t2i = nn.Sigmoid() + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if encoder_hidden_states is not None: + assert hasattr( + self, "crossattention_t2i" + ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None + cross_attention_outputs = self.crossattention_t2i( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + cross=True, + ) + attention_output = self.alpha_t2i * cross_attention_outputs[0] + attention_output + + attention_output = self.attention.output.LayerNorm(attention_output + hidden_states) + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [ + RobertaLayer(config, add_cross=(layer_i >= 6), layer_index=layer_i) + for layer_i in range(config.num_hidden_layers) + ] + ) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if getattr(self.config, "gradient_checkpointing", False) and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting " + "`use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +ROBERTA_START_DOCSTRING = r""" + This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic + methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, + pruning heads etc.) + This model is also a PyTorch `torch.nn.Module `__ + subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to + general usage and behavior. + Parameters: + config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model + weights. +""" + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`): + Indices of input sequence tokens in the vocabulary. + Indices can be obtained using :class:`~transformers.RobertaTokenizer`. See + :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for + details. + `What are input IDs? <../glossary.html#input-ids>`__ + attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`): + Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + `What are attention masks? <../glossary.html#attention-mask>`__ + token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0, + 1]``: + - 0 corresponds to a `sentence A` token, + - 1 corresponds to a `sentence B` token. + `What are token type IDs? <../glossary.html#token-type-ids>`_ + position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, + config.max_position_embeddings - 1]``. + `What are position IDs? <../glossary.html#position-ids>`_ + head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`): + Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`): + Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert :obj:`input_ids` indices into associated + vectors than the model's internal embedding lookup matrix. + output_attentions (:obj:`bool`, `optional`): + Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned + tensors for more detail. + output_hidden_states (:obj:`bool`, `optional`): + Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for + more detail. + return_dict (:obj:`bool`, `optional`): + Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", + ROBERTA_START_DOCSTRING, +) +class RobertaModel(RobertaPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration + set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder` + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + .. _`Attention is all you need`: https://arxiv.org/abs/1706.03762 + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + self.init_weights() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) + @add_code_sample_docstrings( + tokenizer_class=_TOKENIZER_FOR_DOC, + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPoolingAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + Args: + x: torch.Tensor x: + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx diff --git a/maskrcnn_benchmark/modeling/language_backbone/simple_tokenizer.py b/maskrcnn_benchmark/modeling/language_backbone/simple_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..add1ba41addd0552e738b03e9a6b429baea6964f --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/simple_tokenizer.py @@ -0,0 +1,174 @@ +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re +from typing import Union, List + +import torch + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r"\s+", " ", text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split("\n") + merges = merges[1 : 49152 - 256 - 2 + 1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v + "" for v in vocab] + for merge in merges: + vocab.append("".join(merge)) + vocab.extend(["<|startoftext|>", "<|endoftext|>"]) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"} + self.pat = re.compile( + r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", + re.IGNORECASE, + ) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + (token[-1] + "",) + pairs = get_pairs(word) + + if not pairs: + return token + "" + + while True: + bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word) - 1 and word[i + 1] == second: + new_word.append(first + second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = " ".join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")) + return bpe_tokens + + def decode(self, tokens): + text = "".join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors="replace").replace("", " ") + return text + + def get_vocab_size(self): + return 49408 + + def get_eot_token(self): + return self.encoder["<|endoftext|>"] + + def get_sot_token(self): + return self.encoder["<|startoftext|>"] + + def check_added_tokens(self): + return 0 + + def get_tokenizer_obj(self): + return None + + def tokenize(self, texts: Union[str, List[str]], context_length: int = 77): + if isinstance(texts, str): + texts = [texts] + + sot_token = self.encoder["<|startoftext|>"] + eot_token = self.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] + # raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") + + result[i, : len(tokens)] = torch.tensor(tokens) + + return result + + def __call__(self, texts: Union[str, List[str]], context_length: int = 77): + return self.tokenize(texts, context_length) diff --git a/maskrcnn_benchmark/modeling/language_backbone/test_clip_tokenizer.py b/maskrcnn_benchmark/modeling/language_backbone/test_clip_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..734ae64f5feb8b3f4730bd52b2da0d16210dc66e --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/test_clip_tokenizer.py @@ -0,0 +1,9 @@ +from maskrcnn_benchmark.modeling.language_backbone import build_tokenizer + +if __name__ == "__main__": + + tokenizer2 = build_tokenizer("clip") + tokenized2 = tokenizer2( + ["Detectest : fishid. jellyfishioasod. penguinasd. puffin.asd shark. starfish. round stingray"] + ) + print(tokenized2) diff --git a/maskrcnn_benchmark/modeling/language_backbone/word_utils.py b/maskrcnn_benchmark/modeling/language_backbone/word_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4225ab359e970aabf0101a6484915975e80b6b3c --- /dev/null +++ b/maskrcnn_benchmark/modeling/language_backbone/word_utils.py @@ -0,0 +1,104 @@ +""" +Language-related data loading helper functions and class wrappers. +""" + +import re +import torch +import codecs + +UNK_TOKEN = "" +PAD_TOKEN = "" +END_TOKEN = "" +SENTENCE_SPLIT_REGEX = re.compile(r"(\W+)") + + +class Dictionary(object): + def __init__(self): + self.word2idx = {} + self.idx2word = [] + + def add_word(self, word): + if word not in self.word2idx: + self.idx2word.append(word) + self.word2idx[word] = len(self.idx2word) - 1 + return self.word2idx[word] + + def __len__(self): + return len(self.idx2word) + + def __getitem__(self, a): + if isinstance(a, int): + return self.idx2word[a] + elif isinstance(a, list): + return [self.idx2word[x] for x in a] + elif isinstance(a, str): + return self.word2idx[a] + else: + raise TypeError("Query word/index argument must be int or str") + + def __contains__(self, word): + return word in self.word2idx + + +class Corpus(object): + def __init__(self): + self.dictionary = Dictionary() + + def set_max_len(self, value): + self.max_len = value + + def load_file(self, filename): + with codecs.open(filename, "r", "utf-8") as f: + for line in f: + line = line.strip() + self.add_to_corpus(line) + self.dictionary.add_word(UNK_TOKEN) + self.dictionary.add_word(PAD_TOKEN) + + def add_to_corpus(self, line): + """Tokenizes a text line.""" + # Add words to the dictionary + words = line.split() + # tokens = len(words) + for word in words: + word = word.lower() + self.dictionary.add_word(word) + + def tokenize(self, line, max_len=20): + # Tokenize line contents + words = SENTENCE_SPLIT_REGEX.split(line.strip()) + # words = [w.lower() for w in words if len(w) > 0] + words = [w.lower() for w in words if (len(w) > 0 and w != " ")] ## do not include space as a token + + if words[-1] == ".": + words = words[:-1] + + if max_len > 0: + if len(words) > max_len: + words = words[:max_len] + elif len(words) < max_len: + # words = [PAD_TOKEN] * (max_len - len(words)) + words + words = words + [END_TOKEN] + [PAD_TOKEN] * (max_len - len(words) - 1) + + tokens = len(words) ## for end token + ids = torch.LongTensor(tokens) + token = 0 + for word in words: + if word not in self.dictionary: + word = UNK_TOKEN + # print(word, type(word), word.encode('ascii','ignore').decode('ascii'), type(word.encode('ascii','ignore').decode('ascii'))) + if type(word) != type("a"): + print( + word, + type(word), + word.encode("ascii", "ignore").decode("ascii"), + type(word.encode("ascii", "ignore").decode("ascii")), + ) + word = word.encode("ascii", "ignore").decode("ascii") + ids[token] = self.dictionary[word] + token += 1 + # ids[token] = self.dictionary[END_TOKEN] + return ids + + def __len__(self): + return len(self.dictionary) diff --git a/maskrcnn_benchmark/modeling/make_layers.py b/maskrcnn_benchmark/modeling/make_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..6505b8a89c2a8df00801e73a6f171b1f09094357 --- /dev/null +++ b/maskrcnn_benchmark/modeling/make_layers.py @@ -0,0 +1,108 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +Miscellaneous utility functions +""" + +import torch +from torch import nn +from torch.nn import functional as F +from maskrcnn_benchmark.config import cfg +from maskrcnn_benchmark.layers import Conv2d, DYReLU +from maskrcnn_benchmark.modeling.poolers import Pooler + + +def get_group_gn(dim, dim_per_gp, num_groups): + """get number of groups used by GroupNorm, based on number of channels.""" + assert dim_per_gp == -1 or num_groups == -1, "GroupNorm: can only specify G or C/G." + + if dim_per_gp > 0: + assert dim % dim_per_gp == 0, "dim: {}, dim_per_gp: {}".format(dim, dim_per_gp) + group_gn = dim // dim_per_gp + else: + assert dim % num_groups == 0, "dim: {}, num_groups: {}".format(dim, num_groups) + group_gn = num_groups + + return group_gn + + +def group_norm(out_channels, affine=True, divisor=1): + out_channels = out_channels // divisor + dim_per_gp = cfg.MODEL.GROUP_NORM.DIM_PER_GP // divisor + num_groups = cfg.MODEL.GROUP_NORM.NUM_GROUPS // divisor + eps = cfg.MODEL.GROUP_NORM.EPSILON # default: 1e-5 + return torch.nn.GroupNorm(get_group_gn(out_channels, dim_per_gp, num_groups), out_channels, eps, affine) + + +def make_conv3x3(in_channels, out_channels, dilation=1, stride=1, use_gn=False, use_relu=False, kaiming_init=True): + conv = Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=stride, + padding=dilation, + dilation=dilation, + bias=False if use_gn else True, + ) + if kaiming_init: + nn.init.kaiming_normal_(conv.weight, mode="fan_out", nonlinearity="relu") + else: + torch.nn.init.normal_(conv.weight, std=0.01) + if not use_gn: + nn.init.constant_(conv.bias, 0) + module = [ + conv, + ] + if use_gn: + module.append(group_norm(out_channels)) + if use_relu: + module.append(nn.ReLU(inplace=True)) + if len(module) > 1: + return nn.Sequential(*module) + return conv + + +def make_fc(dim_in, hidden_dim, use_gn=False): + """ + Caffe2 implementation uses XavierFill, which in fact + corresponds to kaiming_uniform_ in PyTorch + """ + if use_gn: + fc = nn.Linear(dim_in, hidden_dim, bias=False) + nn.init.kaiming_uniform_(fc.weight, a=1) + return nn.Sequential(fc, group_norm(hidden_dim)) + fc = nn.Linear(dim_in, hidden_dim) + nn.init.kaiming_uniform_(fc.weight, a=1) + nn.init.constant_(fc.bias, 0) + return fc + + +def conv_with_kaiming_uniform(use_gn=False, use_relu=False, use_dyrelu=False): + def make_conv(in_channels, out_channels, kernel_size, stride=1, dilation=1): + conv = Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=dilation * (kernel_size - 1) // 2, + dilation=dilation, + bias=False if use_gn else True, + ) + # Caffe2 implementation uses XavierFill, which in fact + # corresponds to kaiming_uniform_ in PyTorch + nn.init.kaiming_uniform_(conv.weight, a=1) + if not use_gn: + nn.init.constant_(conv.bias, 0) + module = [ + conv, + ] + if use_gn: + module.append(group_norm(out_channels)) + if use_relu: + module.append(nn.ReLU(inplace=True)) + if use_dyrelu: + module.append(DYReLU(out_channels, out_channels, use_spatial=True)) + if len(module) > 1: + return nn.Sequential(*module) + return conv + + return make_conv diff --git a/maskrcnn_benchmark/modeling/matcher.py b/maskrcnn_benchmark/modeling/matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..c5efa0529ec24b6cd34a0353fc101653a367f2d2 --- /dev/null +++ b/maskrcnn_benchmark/modeling/matcher.py @@ -0,0 +1,109 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + + +class Matcher(object): + """ + This class assigns to each predicted "element" (e.g., a box) a ground-truth + element. Each predicted element will have exactly zero or one matches; each + ground-truth element may be assigned to zero or more predicted elements. + + Matching is based on the MxN match_quality_matrix, that characterizes how well + each (ground-truth, predicted)-pair match. For example, if the elements are + boxes, the matrix may contain box IoU overlap values. + + The matcher returns a tensor of size N containing the index of the ground-truth + element m that matches to prediction n. If there is no match, a negative value + is returned. + """ + + BELOW_LOW_THRESHOLD = -1 + BETWEEN_THRESHOLDS = -2 + + def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False): + """ + Args: + high_threshold (float): quality values greater than or equal to + this value are candidate matches. + low_threshold (float): a lower quality threshold used to stratify + matches into three levels: + 1) matches >= high_threshold + 2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold) + 3) BELOW_LOW_THRESHOLD matches in [0, low_threshold) + allow_low_quality_matches (bool): if True, produce additional matches + for predictions that have only low-quality match candidates. See + set_low_quality_matches_ for more details. + """ + assert low_threshold <= high_threshold + self.high_threshold = high_threshold + self.low_threshold = low_threshold + self.allow_low_quality_matches = allow_low_quality_matches + + def __call__(self, match_quality_matrix): + """ + Args: + match_quality_matrix (Tensor[float]): an MxN tensor, containing the + pairwise quality between M ground-truth elements and N predicted elements. + + Returns: + matches (Tensor[int64]): an N tensor where N[i] is a matched gt in + [0, M - 1] or a negative value indicating that prediction i could not + be matched. + """ + if match_quality_matrix.numel() == 0: + # empty targets or proposals not supported during training + if match_quality_matrix.shape[0] == 0: + # raise ValueError( + # "No ground-truth boxes available for one of the images " + # "during training") + length = match_quality_matrix.size(1) + device = match_quality_matrix.device + return torch.ones(length, dtype=torch.int64, device=device) * -1 + else: + raise ValueError("No proposal boxes available for one of the images " "during training") + + # match_quality_matrix is M (gt) x N (predicted) + # Max over gt elements (dim 0) to find best gt candidate for each prediction + matched_vals, matches = match_quality_matrix.max(dim=0) + if self.allow_low_quality_matches: + all_matches = matches.clone() + + # Assign candidate matches with low quality to negative (unassigned) values + below_low_threshold = matched_vals < self.low_threshold + between_thresholds = (matched_vals >= self.low_threshold) & (matched_vals < self.high_threshold) + matches[below_low_threshold] = Matcher.BELOW_LOW_THRESHOLD + matches[between_thresholds] = Matcher.BETWEEN_THRESHOLDS + + if self.allow_low_quality_matches: + self.set_low_quality_matches_(matches, all_matches, match_quality_matrix) + + return matches + + def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix): + """ + Produce additional matches for predictions that have only low-quality matches. + Specifically, for each ground-truth find the set of predictions that have + maximum overlap with it (including ties); for each prediction in that set, if + it is unmatched, then match it to the ground-truth with which it has the highest + quality value. + """ + # For each gt, find the prediction with which it has highest quality + highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) + # Find highest quality match available, even if it is low, including ties + gt_pred_pairs_of_highest_quality = torch.nonzero(match_quality_matrix == highest_quality_foreach_gt[:, None]) + # Example gt_pred_pairs_of_highest_quality: + # tensor([[ 0, 39796], + # [ 1, 32055], + # [ 1, 32070], + # [ 2, 39190], + # [ 2, 40255], + # [ 3, 40390], + # [ 3, 41455], + # [ 4, 45470], + # [ 5, 45325], + # [ 5, 46390]]) + # Each row is a (gt index, prediction index) + # Note how gt items 1, 2, 3, and 5 each have two ties + + pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1] + matches[pred_inds_to_update] = all_matches[pred_inds_to_update] diff --git a/maskrcnn_benchmark/modeling/poolers.py b/maskrcnn_benchmark/modeling/poolers.py new file mode 100644 index 0000000000000000000000000000000000000000..f2d4bd9d97a4ef7a5c0943527719a2ba00ed6d47 --- /dev/null +++ b/maskrcnn_benchmark/modeling/poolers.py @@ -0,0 +1,118 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +import torch.nn.functional as F +from torch import nn + +from maskrcnn_benchmark.layers import ROIAlign, ROIAlignV2 + +from .utils import cat + + +class LevelMapper(object): + """Determine which FPN level each RoI in a set of RoIs should map to based + on the heuristic in the FPN paper. + """ + + def __init__(self, k_min, k_max, canonical_scale=224, canonical_level=4, eps=1e-6): + """ + Arguments: + k_min (int) + k_max (int) + canonical_scale (int) + canonical_level (int) + eps (float) + """ + self.k_min = k_min + self.k_max = k_max + self.s0 = canonical_scale + self.lvl0 = canonical_level + self.eps = eps + + def __call__(self, boxlists): + """ + Arguments: + boxlists (list[BoxList]) + """ + # Compute level ids + s = torch.sqrt(cat([boxlist.area() for boxlist in boxlists])) + + # Eqn.(1) in FPN paper + target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0 + self.eps)) + target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max) + return target_lvls.to(torch.int64) - self.k_min + + +class Pooler(nn.Module): + """ + Pooler for Detection with or without FPN. + It currently hard-code ROIAlign in the implementation, + but that can be made more generic later on. + Also, the requirement of passing the scales is not strictly necessary, as they + can be inferred from the size of the feature map / size of original image, + which is available thanks to the BoxList. + """ + + def __init__(self, output_size, scales, sampling_ratio, use_v2=False): + """ + Arguments: + output_size (list[tuple[int]] or list[int]): output size for the pooled region + scales (list[float]): scales for each Pooler + sampling_ratio (int): sampling ratio for ROIAlign + """ + super(Pooler, self).__init__() + poolers = [] + for scale in scales: + poolers.append( + ROIAlignV2(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) + if use_v2 + else ROIAlign(output_size, spatial_scale=scale, sampling_ratio=sampling_ratio) + ) + self.poolers = nn.ModuleList(poolers) + self.output_size = output_size + # get the levels in the feature map by leveraging the fact that the network always + # downsamples by a factor of 2 at each level. + lvl_min = -torch.log2(torch.tensor(scales[0], dtype=torch.float32)).item() + lvl_max = -torch.log2(torch.tensor(scales[-1], dtype=torch.float32)).item() + self.map_levels = LevelMapper(lvl_min, lvl_max) + + def convert_to_roi_format(self, boxes): + concat_boxes = cat([b.bbox for b in boxes], dim=0) + device, dtype = concat_boxes.device, concat_boxes.dtype + ids = cat( + [torch.full((len(b), 1), i, dtype=dtype, device=device) for i, b in enumerate(boxes)], + dim=0, + ) + rois = torch.cat([ids, concat_boxes], dim=1) + return rois + + def forward(self, x, boxes): + """ + Arguments: + x (list[Tensor]): feature maps for each level + boxes (list[BoxList]): boxes to be used to perform the pooling operation. + Returns: + result (Tensor) + """ + num_levels = len(self.poolers) + rois = self.convert_to_roi_format(boxes) + if num_levels == 1: + return self.poolers[0](x[0], rois) + + levels = self.map_levels(boxes) + + num_rois = len(rois) + num_channels = x[0].shape[1] + output_size = self.output_size[0] + + dtype, device = x[0].dtype, x[0].device + result = torch.zeros( + (num_rois, num_channels, output_size, output_size), + dtype=dtype, + device=device, + ) + for level, (per_level_feature, pooler) in enumerate(zip(x, self.poolers)): + idx_in_level = torch.nonzero(levels == level).squeeze(1) + rois_per_level = rois[idx_in_level] + result[idx_in_level] = pooler(per_level_feature, rois_per_level) + + return result diff --git a/maskrcnn_benchmark/modeling/registry.py b/maskrcnn_benchmark/modeling/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..3d828cdbb550a242a2b2a944fc1c7efccbe9da90 --- /dev/null +++ b/maskrcnn_benchmark/modeling/registry.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + +from maskrcnn_benchmark.utils.registry import Registry + +BACKBONES = Registry() + +LANGUAGE_BACKBONES = Registry() + +ROI_BOX_FEATURE_EXTRACTORS = Registry() +RPN_HEADS = Registry() diff --git a/maskrcnn_benchmark/modeling/roi_heads/__init__.py b/maskrcnn_benchmark/modeling/roi_heads/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2d9f2a055a2d8ac82326181c75cf495aea79c831 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/__init__.py @@ -0,0 +1,81 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + +from .box_head.box_head import build_roi_box_head +from .mask_head.mask_head import build_roi_mask_head +from .keypoint_head.keypoint_head import build_roi_keypoint_head + + +class CombinedROIHeads(torch.nn.ModuleDict): + """ + Combines a set of individual heads (for box prediction or masks) into a single + head. + """ + + def __init__(self, cfg, heads): + super(CombinedROIHeads, self).__init__(heads) + self.cfg = cfg.clone() + if cfg.MODEL.MASK_ON and cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR: + self.mask.feature_extractor = self.box.feature_extractor + if cfg.MODEL.KEYPOINT_ON and cfg.MODEL.ROI_KEYPOINT_HEAD.SHARE_BOX_FEATURE_EXTRACTOR: + self.keypoint.feature_extractor = self.box.feature_extractor + + def forward(self, features, proposals, targets=None, language_dict_features=None, positive_map_label_to_token=None): + losses = {} + detections = proposals + if self.cfg.MODEL.BOX_ON: + # TODO rename x to roi_box_features, if it doesn't increase memory consumption + x, detections, loss_box = self.box(features, proposals, targets) + losses.update(loss_box) + + if self.cfg.MODEL.MASK_ON: + mask_features = features + # optimization: during training, if we share the feature extractor between + # the box and the mask heads, then we can reuse the features already computed + if self.training and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR: + mask_features = x + # During training, self.box() will return the unaltered proposals as "detections" + # this makes the API consistent during training and testing + x, detections, loss_mask = self.mask( + mask_features, + detections, + targets, + language_dict_features=language_dict_features, + positive_map_label_to_token=positive_map_label_to_token, + ) + losses.update(loss_mask) + + if self.cfg.MODEL.KEYPOINT_ON: + keypoint_features = features + # optimization: during training, if we share the feature extractor between + # the box and the mask heads, then we can reuse the features already computed + if self.training and self.cfg.MODEL.ROI_KEYPOINT_HEAD.SHARE_BOX_FEATURE_EXTRACTOR: + keypoint_features = x + # During training, self.box() will return the unaltered proposals as "detections" + # this makes the API consistent during training and testing + x, detections, loss_keypoint = self.keypoint(keypoint_features, detections, targets) + losses.update(loss_keypoint) + return x, detections, losses + + +def build_roi_heads(cfg): + # individually create the heads, that will be combined together + # afterwards + # if cfg.MODEL.RPN_ONLY: + # return None + + roi_heads = [] + if cfg.MODEL.BOX_ON and not cfg.MODEL.RPN_ONLY: + roi_heads.append(("box", build_roi_box_head(cfg))) + if cfg.MODEL.MASK_ON: + roi_heads.append(("mask", build_roi_mask_head(cfg))) + if cfg.MODEL.KEYPOINT_ON: + roi_heads.append(("keypoint", build_roi_keypoint_head(cfg))) + + # combine individual heads in a single module + if roi_heads: + roi_heads = CombinedROIHeads(cfg, roi_heads) + else: + roi_heads = None + + return roi_heads diff --git a/maskrcnn_benchmark/modeling/roi_heads/box_head/__init__.py b/maskrcnn_benchmark/modeling/roi_heads/box_head/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py b/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py new file mode 100644 index 0000000000000000000000000000000000000000..17ba94e41019b9b8a8e72c41677a0ee7d5a0a25b --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py @@ -0,0 +1,74 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch import nn + +from .roi_box_feature_extractors import make_roi_box_feature_extractor +from .roi_box_predictors import make_roi_box_predictor +from .inference import make_roi_box_post_processor +from .loss import make_roi_box_loss_evaluator +from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd + + +class ROIBoxHead(torch.nn.Module): + """ + Generic Box Head class. + """ + + def __init__(self, cfg): + super(ROIBoxHead, self).__init__() + self.feature_extractor = make_roi_box_feature_extractor(cfg) + self.predictor = make_roi_box_predictor(cfg) + self.post_processor = make_roi_box_post_processor(cfg) + self.loss_evaluator = make_roi_box_loss_evaluator(cfg) + self.onnx = cfg.MODEL.ONNX + + @custom_fwd(cast_inputs=torch.float32) + def forward(self, features, proposals, targets=None): + """ + Arguments: + features (list[Tensor]): feature-maps from possibly several levels + proposals (list[BoxList]): proposal boxes + targets (list[BoxList], optional): the ground-truth targets. + + Returns: + x (Tensor): the result of the feature extractor + proposals (list[BoxList]): during training, the subsampled proposals + are returned. During testing, the predicted boxlists are returned + losses (dict[Tensor]): During training, returns the losses for the + head. During testing, returns an empty dict. + """ + + if self.training: + # Faster R-CNN subsamples during training the proposals with a fixed + # positive / negative ratio + with torch.no_grad(): + proposals = self.loss_evaluator.subsample(proposals, targets) + + # extract features that will be fed to the final classifier. The + # feature_extractor generally corresponds to the pooler + heads + x = self.feature_extractor(features, proposals) + # final classifier that converts the features into predictions + class_logits, box_regression = self.predictor(x) + + if self.onnx: + return x, (class_logits, box_regression, [box.bbox for box in proposals]), {} + + if not self.training: + result = self.post_processor((class_logits, box_regression), proposals) + return x, result, {} + + loss_classifier, loss_box_reg = self.loss_evaluator([class_logits], [box_regression]) + return ( + x, + proposals, + dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg), + ) + + +def build_roi_box_head(cfg): + """ + Constructs a new box head. + By default, uses ROIBoxHead, but if it turns out not to be enough, just register a new class + and make it a parameter in the config + """ + return ROIBoxHead(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py b/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..6f4d2d1beaf0e49c8285ebee50d100e45cfab49c --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py @@ -0,0 +1,164 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +import torch.nn.functional as F +from torch import nn + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.modeling.box_coder import BoxCoder +from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd + + +class PostProcessor(nn.Module): + """ + From a set of classification scores, box regression and proposals, + computes the post-processed boxes, and applies NMS to obtain the + final results + """ + + def __init__(self, score_thresh=0.05, nms=0.5, detections_per_img=100, box_coder=None): + """ + Arguments: + score_thresh (float) + nms (float) + detections_per_img (int) + box_coder (BoxCoder) + """ + super(PostProcessor, self).__init__() + self.score_thresh = score_thresh + self.nms = nms + self.detections_per_img = detections_per_img + if box_coder is None: + box_coder = BoxCoder(weights=(10.0, 10.0, 5.0, 5.0)) + self.box_coder = box_coder + + @custom_fwd(cast_inputs=torch.float32) + def forward(self, x, boxes): + """ + Arguments: + x (tuple[tensor, tensor]): x contains the class logits + and the box_regression from the model. + boxes (list[BoxList]): bounding boxes that are used as + reference, one for ech image + + Returns: + results (list[BoxList]): one BoxList for each image, containing + the extra fields labels and scores + """ + class_logits, box_regression = x + class_prob = F.softmax(class_logits, -1) + + # TODO think about a representation of batch of boxes + image_shapes = [box.size for box in boxes] + boxes_per_image = [len(box) for box in boxes] + concat_boxes = torch.cat([a.bbox for a in boxes], dim=0) + + extra_fields = [{} for box in boxes] + if boxes[0].has_field("cbox"): + concat_cboxes = torch.cat([a.get_field("cbox").bbox for a in boxes], dim=0) + concat_cscores = torch.cat([a.get_field("cbox").get_field("scores") for a in boxes], dim=0) + for cbox, cscore, extra_field in zip( + concat_cboxes.split(boxes_per_image, dim=0), concat_cscores.split(boxes_per_image, dim=0), extra_fields + ): + extra_field["cbox"] = cbox + extra_field["cscore"] = cscore + + proposals = self.box_coder.decode(box_regression.view(sum(boxes_per_image), -1), concat_boxes) + + num_classes = class_prob.shape[1] + + proposals = proposals.split(boxes_per_image, dim=0) + class_prob = class_prob.split(boxes_per_image, dim=0) + + results = [] + for prob, boxes_per_img, image_shape, extra_field in zip(class_prob, proposals, image_shapes, extra_fields): + boxlist = self.prepare_boxlist(boxes_per_img, prob, image_shape, extra_field) + boxlist = boxlist.clip_to_image(remove_empty=False) + boxlist = self.filter_results(boxlist, num_classes) + results.append(boxlist) + return results + + def prepare_boxlist(self, boxes, scores, image_shape, extra_field={}): + """ + Returns BoxList from `boxes` and adds probability scores information + as an extra field + `boxes` has shape (#detections, 4 * #classes), where each row represents + a list of predicted bounding boxes for each of the object classes in the + dataset (including the background class). The detections in each row + originate from the same object proposal. + `scores` has shape (#detection, #classes), where each row represents a list + of object detection confidence scores for each of the object classes in the + dataset (including the background class). `scores[i, j]`` corresponds to the + box at `boxes[i, j * 4:(j + 1) * 4]`. + """ + boxes = boxes.reshape(-1, 4) + scores = scores.reshape(-1) + boxlist = BoxList(boxes, image_shape, mode="xyxy") + boxlist.add_field("scores", scores) + for key, val in extra_field.items(): + boxlist.add_field(key, val) + return boxlist + + def filter_results(self, boxlist, num_classes): + """Returns bounding-box detection results by thresholding on scores and + applying non-maximum suppression (NMS). + """ + # unwrap the boxlist to avoid additional overhead. + # if we had multi-class NMS, we could perform this directly on the boxlist + boxes = boxlist.bbox.reshape(-1, num_classes * 4) + scores = boxlist.get_field("scores").reshape(-1, num_classes) + if boxlist.has_field("cbox"): + cboxes = boxlist.get_field("cbox").reshape(-1, 4) + cscores = boxlist.get_field("cscore") + else: + cboxes = None + + device = scores.device + result = [] + # Apply threshold on detection probabilities and apply NMS + # Skip j = 0, because it's the background class + inds_all = scores > self.score_thresh + for j in range(1, num_classes): + inds = inds_all[:, j].nonzero().squeeze(1) + scores_j = scores[inds, j] + boxes_j = boxes[inds, j * 4 : (j + 1) * 4] + boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy") + boxlist_for_class.add_field("scores", scores_j) + if cboxes is not None: + cboxes_j = cboxes[inds, :] + cscores_j = cscores[inds] + cbox_boxlist = BoxList(cboxes_j, boxlist.size, mode="xyxy") + cbox_boxlist.add_field("scores", cscores_j) + boxlist_for_class.add_field("cbox", cbox_boxlist) + + boxlist_for_class = boxlist_nms(boxlist_for_class, self.nms, score_field="scores") + num_labels = len(boxlist_for_class) + boxlist_for_class.add_field("labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)) + result.append(boxlist_for_class) + + result = cat_boxlist(result) + number_of_detections = len(result) + + # Limit to max_per_image detections **over all classes** + if number_of_detections > self.detections_per_img > 0: + cls_scores = result.get_field("scores") + image_thresh, _ = torch.kthvalue(cls_scores.cpu(), number_of_detections - self.detections_per_img + 1) + keep = cls_scores >= image_thresh.item() + keep = torch.nonzero(keep).squeeze(1) + result = result[keep] + return result + + +def make_roi_box_post_processor(cfg): + use_fpn = cfg.MODEL.ROI_HEADS.USE_FPN + + bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS + box_coder = BoxCoder(weights=bbox_reg_weights) + + score_thresh = cfg.MODEL.ROI_HEADS.SCORE_THRESH + nms_thresh = cfg.MODEL.ROI_HEADS.NMS + detections_per_img = cfg.MODEL.ROI_HEADS.DETECTIONS_PER_IMG + + postprocessor = PostProcessor(score_thresh, nms_thresh, detections_per_img, box_coder) + return postprocessor diff --git a/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py b/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..8ace3ea9bdce65210bf865bfcc8ab3d6f972793a --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py @@ -0,0 +1,178 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch.nn import functional as F + +from maskrcnn_benchmark.layers import smooth_l1_loss +from maskrcnn_benchmark.modeling.box_coder import BoxCoder +from maskrcnn_benchmark.modeling.matcher import Matcher +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import BalancedPositiveNegativeSampler +from maskrcnn_benchmark.modeling.utils import cat +from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd + + +class FastRCNNLossComputation(object): + """ + Computes the loss for Faster R-CNN. + Also supports FPN + """ + + def __init__(self, proposal_matcher, fg_bg_sampler, box_coder): + """ + Arguments: + proposal_matcher (Matcher) + fg_bg_sampler (BalancedPositiveNegativeSampler) + box_coder (BoxCoder) + """ + self.proposal_matcher = proposal_matcher + self.fg_bg_sampler = fg_bg_sampler + self.box_coder = box_coder + + def match_targets_to_proposals(self, proposal, target): + match_quality_matrix = boxlist_iou(target, proposal) + matched_idxs = self.proposal_matcher(match_quality_matrix) + # Fast RCNN only need "labels" field for selecting the targets + target = target.copy_with_fields("labels") + # get the targets corresponding GT for each proposal + # NB: need to clamp the indices because we can have a single + # GT in the image, and matched_idxs can be -2, which goes + # out of bounds + + if len(target): + matched_targets = target[matched_idxs.clamp(min=0)] + else: + device = target.get_field("labels").device + dtype = target.get_field("labels").dtype + labels = torch.zeros_like(matched_idxs, dtype=dtype, device=device) + matched_targets = target + matched_targets.add_field("labels", labels) + + matched_targets.add_field("matched_idxs", matched_idxs) + return matched_targets + + def prepare_targets(self, proposals, targets): + labels = [] + regression_targets = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + matched_targets = self.match_targets_to_proposals(proposals_per_image, targets_per_image) + matched_idxs = matched_targets.get_field("matched_idxs") + + labels_per_image = matched_targets.get_field("labels") + labels_per_image = labels_per_image.to(dtype=torch.int64) + + # Label background (below the low threshold) + bg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD + labels_per_image[bg_inds] = 0 + + # Label ignore proposals (between low and high thresholds) + ignore_inds = matched_idxs == Matcher.BETWEEN_THRESHOLDS + labels_per_image[ignore_inds] = -1 # -1 is ignored by sampler + + # compute regression targets + if not matched_targets.bbox.shape[0]: + zeros = torch.zeros_like(labels_per_image, dtype=torch.float32) + regression_targets_per_image = torch.stack((zeros, zeros, zeros, zeros), dim=1) + else: + regression_targets_per_image = self.box_coder.encode(matched_targets.bbox, proposals_per_image.bbox) + + labels.append(labels_per_image) + regression_targets.append(regression_targets_per_image) + + return labels, regression_targets + + def subsample(self, proposals, targets): + """ + This method performs the positive/negative sampling, and return + the sampled proposals. + Note: this function keeps a state. + + Arguments: + proposals (list[BoxList]) + targets (list[BoxList]) + """ + + labels, regression_targets = self.prepare_targets(proposals, targets) + sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels) + + proposals = list(proposals) + # add corresponding label and regression_targets information to the bounding boxes + for labels_per_image, regression_targets_per_image, proposals_per_image in zip( + labels, regression_targets, proposals + ): + proposals_per_image.add_field("labels", labels_per_image) + proposals_per_image.add_field("regression_targets", regression_targets_per_image) + + # distributed sampled proposals, that were obtained on all feature maps + # concatenated via the fg_bg_sampler, into individual feature map levels + for img_idx, (pos_inds_img, neg_inds_img) in enumerate(zip(sampled_pos_inds, sampled_neg_inds)): + img_sampled_inds = torch.nonzero(pos_inds_img | neg_inds_img).squeeze(1) + proposals_per_image = proposals[img_idx][img_sampled_inds] + proposals[img_idx] = proposals_per_image + + self._proposals = proposals + return proposals + + @custom_fwd(cast_inputs=torch.float32) + def __call__(self, class_logits, box_regression): + """ + Computes the loss for Faster R-CNN. + This requires that the subsample method has been called beforehand. + + Arguments: + class_logits (list[Tensor]) + box_regression (list[Tensor]) + + Returns: + classification_loss (Tensor) + box_loss (Tensor) + """ + + class_logits = cat(class_logits, dim=0) + box_regression = cat(box_regression, dim=0) + device = class_logits.device + + if not hasattr(self, "_proposals"): + raise RuntimeError("subsample needs to be called before") + + proposals = self._proposals + + labels = cat([proposal.get_field("labels") for proposal in proposals], dim=0) + regression_targets = cat([proposal.get_field("regression_targets") for proposal in proposals], dim=0) + + classification_loss = F.cross_entropy(class_logits, labels) + + # get indices that correspond to the regression targets for + # the corresponding ground truth labels, to be used with + # advanced indexing + sampled_pos_inds_subset = torch.nonzero(labels > 0).squeeze(1) + labels_pos = labels[sampled_pos_inds_subset] + map_inds = 4 * labels_pos[:, None] + torch.tensor([0, 1, 2, 3], device=device) + + box_loss = smooth_l1_loss( + box_regression[sampled_pos_inds_subset[:, None], map_inds], + regression_targets[sampled_pos_inds_subset], + size_average=False, + beta=1, + ) + box_loss = box_loss / labels.numel() + + return classification_loss, box_loss + + +def make_roi_box_loss_evaluator(cfg): + matcher = Matcher( + cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD, + cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD, + allow_low_quality_matches=False, + ) + + bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS + box_coder = BoxCoder(weights=bbox_reg_weights) + + fg_bg_sampler = BalancedPositiveNegativeSampler( + cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION + ) + + loss_evaluator = FastRCNNLossComputation(matcher, fg_bg_sampler, box_coder) + + return loss_evaluator diff --git a/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py b/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py new file mode 100644 index 0000000000000000000000000000000000000000..76fdf5740d6fca437dfd8f94bbba6ce4a7dc9031 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py @@ -0,0 +1,199 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch import nn +from torch.nn import functional as F + +from maskrcnn_benchmark.modeling import registry +from maskrcnn_benchmark.modeling.backbone import resnet +from maskrcnn_benchmark.modeling.poolers import Pooler +from maskrcnn_benchmark.modeling.make_layers import group_norm +from maskrcnn_benchmark.modeling.make_layers import make_fc + + +@registry.ROI_BOX_FEATURE_EXTRACTORS.register("LightheadFeatureExtractor") +class LightheadFeatureExtractor(nn.Module): + def __init__(self, cfg): + super(LightheadFeatureExtractor, self).__init__() + + resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + input_size = 10 * resolution**2 + representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM + use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN + + C_in, C_mid, C_out = cfg.MODEL.BACKBONE.OUT_CHANNELS, 256, input_size + self.separable_conv_11 = nn.Conv2d(C_in, C_mid, (15, 1), 1, (7, 0)) + self.separable_conv_12 = nn.Conv2d(C_mid, C_out, (1, 15), 1, (0, 7)) + self.separable_conv_21 = nn.Conv2d(C_in, C_mid, (15, 1), 1, (7, 0)) + self.separable_conv_22 = nn.Conv2d(C_mid, C_out, (1, 15), 1, (0, 7)) + + for module in [self.separable_conv_11, self.separable_conv_12, self.separable_conv_21, self.separable_conv_22]: + # Caffe2 implementation uses XavierFill, which in fact + # corresponds to kaiming_uniform_ in PyTorch + nn.init.kaiming_uniform_(module.weight, a=1) + + self.pooler = pooler + self.fc6 = make_fc( + input_size * resolution**2, representation_size, use_gn + ) # wait official repo to support psroi + + def forward(self, x, proposals): + light = [] + for feat in x: + sc11 = self.separable_conv_11(feat) + sc12 = self.separable_conv_12(sc11) + sc21 = self.separable_conv_21(feat) + sc22 = self.separable_conv_22(sc21) + out = sc12 + sc22 + light.append(out) + + x = self.pooler(light, proposals) + x = x.view(x.size(0), -1) + x = F.relu(self.fc6(x)) + + return x + + +@registry.ROI_BOX_FEATURE_EXTRACTORS.register("ResNet50Conv5ROIFeatureExtractor") +class ResNet50Conv5ROIFeatureExtractor(nn.Module): + def __init__(self, config): + super(ResNet50Conv5ROIFeatureExtractor, self).__init__() + + resolution = config.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + scales = config.MODEL.ROI_BOX_HEAD.POOLER_SCALES + sampling_ratio = config.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + + stage = resnet.StageSpec(index=4, block_count=3, return_features=False) + head = resnet.ResNetHead( + block_module=config.MODEL.RESNETS.TRANS_FUNC, + stages=(stage,), + num_groups=config.MODEL.RESNETS.NUM_GROUPS, + width_per_group=config.MODEL.RESNETS.WIDTH_PER_GROUP, + stride_in_1x1=config.MODEL.RESNETS.STRIDE_IN_1X1, + stride_init=None, + res2_out_channels=config.MODEL.RESNETS.RES2_OUT_CHANNELS, + dilation=config.MODEL.RESNETS.RES5_DILATION, + ) + + self.pooler = pooler + self.head = head + + def forward(self, x, proposals): + x = self.pooler(x, proposals) + x = self.head(x) + return x + + +@registry.ROI_BOX_FEATURE_EXTRACTORS.register("FPN2MLPFeatureExtractor") +class FPN2MLPFeatureExtractor(nn.Module): + """ + Heads for FPN for classification + """ + + def __init__(self, cfg): + super(FPN2MLPFeatureExtractor, self).__init__() + + resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS * resolution**2 + representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM + use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN + self.pooler = pooler + self.fc6 = make_fc(input_size, representation_size, use_gn) + self.fc7 = make_fc(representation_size, representation_size, use_gn) + + def forward(self, x, proposals): + x = self.pooler(x, proposals) + x = x.view(x.size(0), -1) + + x = F.relu(self.fc6(x)) + x = F.relu(self.fc7(x)) + + return x + + +@registry.ROI_BOX_FEATURE_EXTRACTORS.register("FPNXconv1fcFeatureExtractor") +class FPNXconv1fcFeatureExtractor(nn.Module): + """ + Heads for FPN for classification + """ + + def __init__(self, cfg): + super(FPNXconv1fcFeatureExtractor, self).__init__() + + resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION + scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES + sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + self.pooler = pooler + + use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN + in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + conv_head_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM + num_stacked_convs = cfg.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS + dilation = cfg.MODEL.ROI_BOX_HEAD.DILATION + + xconvs = [] + for ix in range(num_stacked_convs): + xconvs.append( + nn.Conv2d( + in_channels, + conv_head_dim, + kernel_size=3, + stride=1, + padding=dilation, + dilation=dilation, + bias=False if use_gn else True, + ) + ) + in_channels = conv_head_dim + if use_gn: + xconvs.append(group_norm(in_channels)) + xconvs.append(nn.ReLU(inplace=True)) + + self.add_module("xconvs", nn.Sequential(*xconvs)) + for modules in [ + self.xconvs, + ]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + if not use_gn: + torch.nn.init.constant_(l.bias, 0) + + input_size = conv_head_dim * resolution**2 + representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM + self.fc6 = make_fc(input_size, representation_size, use_gn=False) + + def forward(self, x, proposals): + x = self.pooler(x, proposals) + x = self.xconvs(x) + x = x.view(x.size(0), -1) + x = F.relu(self.fc6(x)) + return x + + +def make_roi_box_feature_extractor(cfg): + func = registry.ROI_BOX_FEATURE_EXTRACTORS[cfg.MODEL.ROI_BOX_HEAD.FEATURE_EXTRACTOR] + return func(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py b/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py new file mode 100644 index 0000000000000000000000000000000000000000..ac03cfaece2e47900fc04b58e173f6dea6423caa --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py @@ -0,0 +1,62 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from torch import nn + + +class FastRCNNPredictor(nn.Module): + def __init__(self, config, pretrained=None): + super(FastRCNNPredictor, self).__init__() + + stage_index = 4 + stage2_relative_factor = 2 ** (stage_index - 1) + res2_out_channels = config.MODEL.RESNETS.RES2_OUT_CHANNELS + num_inputs = res2_out_channels * stage2_relative_factor + + num_classes = config.MODEL.ROI_BOX_HEAD.NUM_CLASSES + self.avgpool = nn.AvgPool2d(kernel_size=7, stride=7) + self.cls_score = nn.Linear(num_inputs, num_classes) + self.bbox_pred = nn.Linear(num_inputs, num_classes * 4) + + nn.init.normal_(self.cls_score.weight, mean=0, std=0.01) + nn.init.constant_(self.cls_score.bias, 0) + + nn.init.normal_(self.bbox_pred.weight, mean=0, std=0.001) + nn.init.constant_(self.bbox_pred.bias, 0) + + def forward(self, x): + x = self.avgpool(x) + x = x.view(x.size(0), -1) + cls_logit = self.cls_score(x) + bbox_pred = self.bbox_pred(x) + return cls_logit, bbox_pred + + +class FPNPredictor(nn.Module): + def __init__(self, cfg): + super(FPNPredictor, self).__init__() + num_classes = cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES + representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM + + self.cls_score = nn.Linear(representation_size, num_classes) + self.bbox_pred = nn.Linear(representation_size, num_classes * 4) + + nn.init.normal_(self.cls_score.weight, std=0.01) + nn.init.normal_(self.bbox_pred.weight, std=0.001) + for l in [self.cls_score, self.bbox_pred]: + nn.init.constant_(l.bias, 0) + + def forward(self, x): + scores = self.cls_score(x) + bbox_deltas = self.bbox_pred(x) + + return scores, bbox_deltas + + +_ROI_BOX_PREDICTOR = { + "FastRCNNPredictor": FastRCNNPredictor, + "FPNPredictor": FPNPredictor, +} + + +def make_roi_box_predictor(cfg): + func = _ROI_BOX_PREDICTOR[cfg.MODEL.ROI_BOX_HEAD.PREDICTOR] + return func(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f681e0ca64380362347ba9525a54115e0d4762d8 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py @@ -0,0 +1,117 @@ +import cv2 +import numpy as np +import torch +from torch import nn + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.keypoint import PersonKeypoints + + +class KeypointPostProcessor(nn.Module): + def __init__(self, keypointer=None): + super(KeypointPostProcessor, self).__init__() + self.keypointer = keypointer + + def forward(self, x, boxes): + mask_prob = x + + scores = None + if self.keypointer: + mask_prob, scores = self.keypointer(x, boxes) + + assert len(boxes) == 1, "Only non-batched inference supported for now" + boxes_per_image = [box.bbox.size(0) for box in boxes] + mask_prob = mask_prob.split(boxes_per_image, dim=0) + scores = scores.split(boxes_per_image, dim=0) + + results = [] + for prob, box, score in zip(mask_prob, boxes, scores): + bbox = BoxList(box.bbox, box.size, mode="xyxy") + for field in box.fields(): + bbox.add_field(field, box.get_field(field)) + prob = PersonKeypoints(prob, box.size) + prob.add_field("logits", score) + bbox.add_field("keypoints", prob) + results.append(bbox) + + return results + + +def heatmaps_to_keypoints(maps, rois): + """Extract predicted keypoint locations from heatmaps. Output has shape + (#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob) + for each keypoint. + """ + # This function converts a discrete image coordinate in a HEATMAP_SIZE x + # HEATMAP_SIZE image to a continuous keypoint coordinate. We maintain + # consistency with keypoints_to_heatmap_labels by using the conversion from + # Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a + # continuous coordinate. + offset_x = rois[:, 0] + offset_y = rois[:, 1] + + widths = rois[:, 2] - rois[:, 0] + heights = rois[:, 3] - rois[:, 1] + widths = np.maximum(widths, 1) + heights = np.maximum(heights, 1) + widths_ceil = np.ceil(widths) + heights_ceil = np.ceil(heights) + + # NCHW to NHWC for use with OpenCV + maps = np.transpose(maps, [0, 2, 3, 1]) + min_size = 0 # cfg.KRCNN.INFERENCE_MIN_SIZE + num_keypoints = maps.shape[3] + xy_preds = np.zeros((len(rois), 3, num_keypoints), dtype=np.float32) + end_scores = np.zeros((len(rois), num_keypoints), dtype=np.float32) + for i in range(len(rois)): + if min_size > 0: + roi_map_width = int(np.maximum(widths_ceil[i], min_size)) + roi_map_height = int(np.maximum(heights_ceil[i], min_size)) + else: + roi_map_width = widths_ceil[i] + roi_map_height = heights_ceil[i] + width_correction = widths[i] / roi_map_width + height_correction = heights[i] / roi_map_height + roi_map = cv2.resize(maps[i], (roi_map_width, roi_map_height), interpolation=cv2.INTER_CUBIC) + # Bring back to CHW + roi_map = np.transpose(roi_map, [2, 0, 1]) + # roi_map_probs = scores_to_probs(roi_map.copy()) + w = roi_map.shape[2] + pos = roi_map.reshape(num_keypoints, -1).argmax(axis=1) + x_int = pos % w + y_int = (pos - x_int) // w + # assert (roi_map_probs[k, y_int, x_int] == + # roi_map_probs[k, :, :].max()) + x = (x_int + 0.5) * width_correction + y = (y_int + 0.5) * height_correction + xy_preds[i, 0, :] = x + offset_x[i] + xy_preds[i, 1, :] = y + offset_y[i] + xy_preds[i, 2, :] = 1 + end_scores[i, :] = roi_map[np.arange(num_keypoints), y_int, x_int] + + return np.transpose(xy_preds, [0, 2, 1]), end_scores + + +class Keypointer(object): + """ + Projects a set of masks in an image on the locations + specified by the bounding boxes + """ + + def __init__(self, padding=0): + self.padding = padding + + def __call__(self, masks, boxes): + # TODO do this properly + if isinstance(boxes, BoxList): + boxes = [boxes] + assert len(boxes) == 1 + + result, scores = heatmaps_to_keypoints(masks.detach().cpu().numpy(), boxes[0].bbox.cpu().numpy()) + return torch.from_numpy(result).to(masks.device), torch.as_tensor(scores, device=masks.device) + + +def make_roi_keypoint_post_processor(cfg): + keypointer = Keypointer() + keypoint_post_processor = KeypointPostProcessor(keypointer) + return keypoint_post_processor diff --git a/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py new file mode 100644 index 0000000000000000000000000000000000000000..d7270838a5393821e5b58cc74ba9ad4056a2b52c --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/keypoint_head.py @@ -0,0 +1,50 @@ +import torch + +from .roi_keypoint_feature_extractors import make_roi_keypoint_feature_extractor +from .roi_keypoint_predictors import make_roi_keypoint_predictor +from .inference import make_roi_keypoint_post_processor +from .loss import make_roi_keypoint_loss_evaluator + + +class ROIKeypointHead(torch.nn.Module): + def __init__(self, cfg): + super(ROIKeypointHead, self).__init__() + self.cfg = cfg.clone() + self.feature_extractor = make_roi_keypoint_feature_extractor(cfg) + self.predictor = make_roi_keypoint_predictor(cfg) + self.post_processor = make_roi_keypoint_post_processor(cfg) + self.loss_evaluator = make_roi_keypoint_loss_evaluator(cfg) + + def forward(self, features, proposals, targets=None): + """ + Arguments: + features (list[Tensor]): feature-maps from possibly several levels + proposals (list[BoxList]): proposal boxes + targets (list[BoxList], optional): the ground-truth targets. + + Returns: + x (Tensor): the result of the feature extractor + proposals (list[BoxList]): during training, the original proposals + are returned. During testing, the predicted boxlists are returned + with the `mask` field set + losses (dict[Tensor]): During training, returns the losses for the + head. During testing, returns an empty dict. + """ + if self.training: + with torch.no_grad(): + proposals = self.loss_evaluator.subsample(proposals, targets) + + x = self.feature_extractor(features, proposals) + kp_logits = self.predictor(x) + + if not self.training: + result = self.post_processor(kp_logits, proposals) + return x, result, {} + + loss_kp = self.loss_evaluator(proposals, kp_logits) + + return x, proposals, dict(loss_kp=loss_kp) + + +def build_roi_keypoint_head(cfg): + return ROIKeypointHead(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..510565691c0bb55a3c28d7584c021015a4f83360 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py @@ -0,0 +1,169 @@ +import torch +from torch.nn import functional as F + +from maskrcnn_benchmark.modeling.matcher import Matcher + +from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import ( + BalancedPositiveNegativeSampler, +) +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.modeling.utils import cat +from maskrcnn_benchmark.layers import smooth_l1_loss +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist + +from maskrcnn_benchmark.structures.keypoint import keypoints_to_heat_map + + +def project_keypoints_to_heatmap(keypoints, proposals, discretization_size): + proposals = proposals.convert("xyxy") + return keypoints_to_heat_map(keypoints.keypoints, proposals.bbox, discretization_size) + + +def cat_boxlist_with_keypoints(boxlists): + assert all(boxlist.has_field("keypoints") for boxlist in boxlists) + + kp = [boxlist.get_field("keypoints").keypoints for boxlist in boxlists] + kp = cat(kp, 0) + + fields = boxlists[0].get_fields() + fields = [field for field in fields if field != "keypoints"] + + boxlists = [boxlist.copy_with_fields(fields) for boxlist in boxlists] + boxlists = cat_boxlist(boxlists) + boxlists.add_field("keypoints", kp) + return boxlists + + +def _within_box(points, boxes): + """Validate which keypoints are contained inside a given box. + points: NxKx2 + boxes: Nx4 + output: NxK + """ + x_within = (points[..., 0] >= boxes[:, 0, None]) & (points[..., 0] <= boxes[:, 2, None]) + y_within = (points[..., 1] >= boxes[:, 1, None]) & (points[..., 1] <= boxes[:, 3, None]) + return x_within & y_within + + +class KeypointRCNNLossComputation(object): + def __init__(self, proposal_matcher, fg_bg_sampler, discretization_size): + """ + Arguments: + proposal_matcher (Matcher) + fg_bg_sampler (BalancedPositiveNegativeSampler) + discretization_size (int) + """ + self.proposal_matcher = proposal_matcher + self.fg_bg_sampler = fg_bg_sampler + self.discretization_size = discretization_size + + def match_targets_to_proposals(self, proposal, target): + match_quality_matrix = boxlist_iou(target, proposal) + matched_idxs = self.proposal_matcher(match_quality_matrix) + # Keypoint RCNN needs "labels" and "keypoints "fields for creating the targets + target = target.copy_with_fields(["labels", "keypoints"]) + # get the targets corresponding GT for each proposal + # NB: need to clamp the indices because we can have a single + # GT in the image, and matched_idxs can be -2, which goes + # out of bounds + matched_targets = target[matched_idxs.clamp(min=0)] + matched_targets.add_field("matched_idxs", matched_idxs) + return matched_targets + + def prepare_targets(self, proposals, targets): + labels = [] + keypoints = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + matched_targets = self.match_targets_to_proposals(proposals_per_image, targets_per_image) + matched_idxs = matched_targets.get_field("matched_idxs") + + labels_per_image = matched_targets.get_field("labels") + labels_per_image = labels_per_image.to(dtype=torch.int64) + + # this can probably be removed, but is left here for clarity + # and completeness + # TODO check if this is the right one, as BELOW_THRESHOLD + neg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD + labels_per_image[neg_inds] = 0 + + keypoints_per_image = matched_targets.get_field("keypoints") + within_box = _within_box(keypoints_per_image.keypoints, matched_targets.bbox) + vis_kp = keypoints_per_image.keypoints[..., 2] > 0 + is_visible = (within_box & vis_kp).sum(1) > 0 + + labels_per_image[~is_visible] = -1 + + labels.append(labels_per_image) + keypoints.append(keypoints_per_image) + + return labels, keypoints + + def subsample(self, proposals, targets): + """ + This method performs the positive/negative sampling, and return + the sampled proposals. + Note: this function keeps a state. + + Arguments: + proposals (list[BoxList]) + targets (list[BoxList]) + """ + + labels, keypoints = self.prepare_targets(proposals, targets) + sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels) + + proposals = list(proposals) + # add corresponding label and regression_targets information to the bounding boxes + for labels_per_image, keypoints_per_image, proposals_per_image in zip(labels, keypoints, proposals): + proposals_per_image.add_field("labels", labels_per_image) + proposals_per_image.add_field("keypoints", keypoints_per_image) + + # distributed sampled proposals, that were obtained on all feature maps + # concatenated via the fg_bg_sampler, into individual feature map levels + for img_idx, (pos_inds_img, neg_inds_img) in enumerate(zip(sampled_pos_inds, sampled_neg_inds)): + img_sampled_inds = torch.nonzero(pos_inds_img).squeeze(1) + proposals_per_image = proposals[img_idx][img_sampled_inds] + proposals[img_idx] = proposals_per_image + + self._proposals = proposals + return proposals + + def __call__(self, proposals, keypoint_logits): + heatmaps = [] + valid = [] + for proposals_per_image in proposals: + kp = proposals_per_image.get_field("keypoints") + heatmaps_per_image, valid_per_image = project_keypoints_to_heatmap( + kp, proposals_per_image, self.discretization_size + ) + heatmaps.append(heatmaps_per_image.view(-1)) + valid.append(valid_per_image.view(-1)) + + keypoint_targets = cat(heatmaps, dim=0) + valid = cat(valid, dim=0).to(dtype=torch.bool) + valid = torch.nonzero(valid).squeeze(1) + + # torch.mean (in binary_cross_entropy_with_logits) does'nt + # accept empty tensors, so handle it sepaartely + if keypoint_targets.numel() == 0 or len(valid) == 0: + return keypoint_logits.sum() * 0 + + N, K, H, W = keypoint_logits.shape + keypoint_logits = keypoint_logits.view(N * K, H * W) + + keypoint_loss = F.cross_entropy(keypoint_logits[valid], keypoint_targets[valid]) + return keypoint_loss + + +def make_roi_keypoint_loss_evaluator(cfg): + matcher = Matcher( + cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD, + cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD, + allow_low_quality_matches=False, + ) + fg_bg_sampler = BalancedPositiveNegativeSampler( + cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION + ) + resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.RESOLUTION + loss_evaluator = KeypointRCNNLossComputation(matcher, fg_bg_sampler, resolution) + return loss_evaluator diff --git a/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py new file mode 100644 index 0000000000000000000000000000000000000000..9e4f458974fbc7e551cd8569ea307fec0cc7a4dd --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_feature_extractors.py @@ -0,0 +1,96 @@ +from torch import nn +from torch.nn import functional as F + +from maskrcnn_benchmark.modeling.poolers import Pooler + +from maskrcnn_benchmark.layers import Conv2d +from maskrcnn_benchmark.layers import ConvTranspose2d + + +class KeypointRCNNFeatureExtractor(nn.Module): + def __init__(self, cfg): + super(KeypointRCNNFeatureExtractor, self).__init__() + + resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION + scales = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SCALES + sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + self.pooler = pooler + + input_features = cfg.MODEL.BACKBONE.OUT_CHANNELS + layers = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_LAYERS + next_feature = input_features + self.blocks = [] + for layer_idx, layer_features in enumerate(layers, 1): + layer_name = "conv_fcn{}".format(layer_idx) + module = Conv2d(next_feature, layer_features, 3, stride=1, padding=1) + nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + nn.init.constant_(module.bias, 0) + self.add_module(layer_name, module) + next_feature = layer_features + self.blocks.append(layer_name) + + def forward(self, x, proposals): + x = self.pooler(x, proposals) + for layer_name in self.blocks: + x = F.relu(getattr(self, layer_name)(x)) + return x + + +class KeypointRCNNFeature2XZoomExtractor(nn.Module): + def __init__(self, cfg): + super(KeypointRCNNFeature2XZoomExtractor, self).__init__() + + resolution = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION + scales = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SCALES + sampling_ratio = cfg.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + self.pooler = pooler + + input_features = cfg.MODEL.BACKBONE.OUT_CHANNELS + layers = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_LAYERS + next_feature = input_features + self.blocks = [] + for layer_idx, layer_features in enumerate(layers, 1): + layer_name = "conv_fcn{}".format(layer_idx) + module = Conv2d(next_feature, layer_features, 3, stride=1, padding=1) + nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + nn.init.constant_(module.bias, 0) + self.add_module(layer_name, module) + if layer_idx == len(layers) // 2: + deconv_kernel = 4 + kps_upsacle = ConvTranspose2d( + layer_features, layer_features, deconv_kernel, stride=2, padding=deconv_kernel // 2 - 1 + ) + nn.init.kaiming_normal_(kps_upsacle.weight, mode="fan_out", nonlinearity="relu") + nn.init.constant_(kps_upsacle.bias, 0) + self.add_module("conv_fcn_upscale", kps_upsacle) + self.blocks.append("conv_fcn_upscale") + + next_feature = layer_features + self.blocks.append(layer_name) + + def forward(self, x, proposals): + x = self.pooler(x, proposals) + for layer_name in self.blocks: + x = F.relu(getattr(self, layer_name)(x)) + return x + + +_ROI_KEYPOINT_FEATURE_EXTRACTORS = { + "KeypointRCNNFeatureExtractor": KeypointRCNNFeatureExtractor, + "KeypointRCNNFeature2XZoomExtractor": KeypointRCNNFeature2XZoomExtractor, +} + + +def make_roi_keypoint_feature_extractor(cfg): + func = _ROI_KEYPOINT_FEATURE_EXTRACTORS[cfg.MODEL.ROI_KEYPOINT_HEAD.FEATURE_EXTRACTOR] + return func(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_predictors.py b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_predictors.py new file mode 100644 index 0000000000000000000000000000000000000000..42094a1421fb3f3538f3d350c935e35b20317162 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/roi_keypoint_predictors.py @@ -0,0 +1,35 @@ +from torch import nn +from torch.nn import functional as F + +from maskrcnn_benchmark import layers + + +class KeypointRCNNPredictor(nn.Module): + def __init__(self, cfg): + super(KeypointRCNNPredictor, self).__init__() + input_features = cfg.MODEL.ROI_KEYPOINT_HEAD.CONV_LAYERS[-1] + num_keypoints = cfg.MODEL.ROI_KEYPOINT_HEAD.NUM_CLASSES + deconv_kernel = 4 + self.kps_score_lowres = layers.ConvTranspose2d( + input_features, + num_keypoints, + deconv_kernel, + stride=2, + padding=deconv_kernel // 2 - 1, + ) + nn.init.kaiming_normal_(self.kps_score_lowres.weight, mode="fan_out", nonlinearity="relu") + nn.init.constant_(self.kps_score_lowres.bias, 0) + self.up_scale = 2 + + def forward(self, x): + x = self.kps_score_lowres(x) + x = layers.interpolate(x, scale_factor=self.up_scale, mode="bilinear", align_corners=False) + return x + + +_ROI_KEYPOINT_PREDICTOR = {"KeypointRCNNPredictor": KeypointRCNNPredictor} + + +def make_roi_keypoint_predictor(cfg): + func = _ROI_KEYPOINT_PREDICTOR[cfg.MODEL.ROI_KEYPOINT_HEAD.PREDICTOR] + return func(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/mask_head/__init__.py b/maskrcnn_benchmark/modeling/roi_heads/mask_head/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py b/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py new file mode 100644 index 0000000000000000000000000000000000000000..234149ed66615cfa24048e57bc0762141476a95a --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/mask_head/hourglass.py @@ -0,0 +1,65 @@ +from torch import nn + +from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 + + +class Residual(nn.Module): + def __init__(self, inp_dim, out_dim, use_gn=False): + super(Residual, self).__init__() + self.relu = nn.ReLU() + # self.bn1 = nn.BatchNorm2d(inp_dim) + self.conv1 = make_conv3x3(inp_dim, int(out_dim / 2), 1, use_relu=False, use_gn=use_gn) + # self.bn2 = nn.BatchNorm2d(int(out_dim / 2)) + self.conv2 = make_conv3x3(int(out_dim / 2), int(out_dim / 2), 3, use_relu=False, use_gn=use_gn) + # self.bn3 = nn.BatchNorm2d(int(out_dim / 2)) + self.conv3 = make_conv3x3(int(out_dim / 2), out_dim, 1, use_relu=False, use_gn=use_gn) + if inp_dim == out_dim: + self.need_skip = False + else: + self.need_skip = True + self.skip_layer = make_conv3x3(inp_dim, out_dim, 1, use_relu=False, use_gn=False) + + def forward(self, x): + if self.need_skip: + residual = self.skip_layer(x) + else: + residual = x + out = x + # out = self.bn1(out) + out = self.relu(out) + out = self.conv1(out) + # out = self.bn2(out) + out = self.relu(out) + out = self.conv2(out) + # out = self.bn3(out) + out = self.relu(out) + out = self.conv3(out) + out += residual + return out + + +class Hourglass(nn.Module): + def __init__(self, n, f, gn=False, increase=0): + super(Hourglass, self).__init__() + nf = f + increase + self.up1 = Residual(f, f) + # Lower branch + self.pool1 = nn.MaxPool2d(2, 2) + self.low1 = Residual(f, nf) + self.n = n + # Recursive hourglass + if self.n > 1: + self.low2 = Hourglass(n - 1, nf, gn=gn) + else: + self.low2 = Residual(nf, nf, gn) + self.low3 = Residual(nf, f, gn) + self.up2 = nn.Upsample(scale_factor=2, mode="nearest") + + def forward(self, x): + up1 = self.up1(x) + pool1 = self.pool1(x) + low1 = self.low1(pool1) + low2 = self.low2(low1) + low3 = self.low3(low2) + up2 = self.up2(low3) + return up1 + up2 diff --git a/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py b/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..edf67a1eac7c22ab1df80ec030347a2f241f1834 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py @@ -0,0 +1,221 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F + +from maskrcnn_benchmark.structures.bounding_box import BoxList + + +def convert_mask_grounding_to_od_logits(logits, positive_map_label_to_token, num_classes): + od_logits = torch.zeros(logits.shape[0], num_classes + 1, logits.shape[2], logits.shape[3]).to(logits.device) + for label_j in positive_map_label_to_token: + od_logits[:, label_j, :, :] = logits[:, torch.LongTensor(positive_map_label_to_token[label_j]), :, :].mean(1) + mask_prob = od_logits.sigmoid() + return mask_prob + + +# TODO check if want to return a single BoxList or a composite +# object +class MaskPostProcessor(nn.Module): + """ + From the results of the CNN, post process the masks + by taking the mask corresponding to the class with max + probability (which are of fixed size and directly output + by the CNN) and return the masks in the mask field of the BoxList. + + If a masker object is passed, it will additionally + project the masks in the image according to the locations in boxes, + """ + + def __init__(self, masker=None, mdetr_style_aggregate_class_num=None, vl_version=None): + super(MaskPostProcessor, self).__init__() + self.masker = masker + self.mdetr_style_aggregate_class_num = mdetr_style_aggregate_class_num + self.vl_version = vl_version + + def forward(self, x, boxes, positive_map_label_to_token=None): + """ + Arguments: + x (Tensor): the mask logits + boxes (list[BoxList]): bounding boxes that are used as + reference, one for ech image + + Returns: + results (list[BoxList]): one BoxList for each image, containing + the extra field mask + """ + if self.vl_version: + mask_prob = convert_mask_grounding_to_od_logits( + x, positive_map_label_to_token, self.mdetr_style_aggregate_class_num + ) + else: + mask_prob = x.sigmoid() + + # select masks coresponding to the predicted classes + num_masks = x.shape[0] + labels = [bbox.get_field("labels") for bbox in boxes] + labels = torch.cat(labels) + if not self.vl_version: + # TODO: a hack for binary mask head + labels = (labels > 0).to(dtype=torch.int64) + + index = torch.arange(num_masks, device=labels.device) + mask_prob = mask_prob[index, labels][:, None] + + boxes_per_image = [len(box) for box in boxes] + mask_prob = mask_prob.split(boxes_per_image, dim=0) + + if self.masker: + mask_prob = self.masker(mask_prob, boxes) + + results = [] + for prob, box in zip(mask_prob, boxes): + bbox = BoxList(box.bbox, box.size, mode="xyxy") + for field in box.fields(): + bbox.add_field(field, box.get_field(field)) + bbox.add_field("mask", prob) + results.append(bbox) + + return results + + +class MaskPostProcessorCOCOFormat(MaskPostProcessor): + """ + From the results of the CNN, post process the results + so that the masks are pasted in the image, and + additionally convert the results to COCO format. + """ + + def forward(self, x, boxes, positive_map_label_to_token=None, vl_version=None): + import pycocotools.mask as mask_util + import numpy as np + + results = super(MaskPostProcessorCOCOFormat, self).forward(x, boxes) + for result in results: + masks = result.get_field("mask").cpu() + rles = [mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0] for mask in masks] + for rle in rles: + rle["counts"] = rle["counts"].decode("utf-8") + result.add_field("mask", rles) + return results + + +# the next two functions should be merged inside Masker +# but are kept here for the moment while we need them +# temporarily gor paste_mask_in_image +def expand_boxes(boxes, scale): + w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5 + h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5 + x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5 + y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5 + + w_half *= scale + h_half *= scale + + boxes_exp = torch.zeros_like(boxes) + boxes_exp[:, 0] = x_c - w_half + boxes_exp[:, 2] = x_c + w_half + boxes_exp[:, 1] = y_c - h_half + boxes_exp[:, 3] = y_c + h_half + return boxes_exp + + +def expand_masks(mask, padding): + N = mask.shape[0] + M = mask.shape[-1] + pad2 = 2 * padding + scale = float(M + pad2) / M + padded_mask = mask.new_zeros((N, 1, M + pad2, M + pad2)) + padded_mask[:, :, padding:-padding, padding:-padding] = mask + return padded_mask, scale + + +def paste_mask_in_image(mask, box, im_h, im_w, thresh=0.5, padding=1): + padded_mask, scale = expand_masks(mask[None], padding=padding) + mask = padded_mask[0, 0] + box = expand_boxes(box[None], scale)[0] + box = box.to(dtype=torch.int32) + + TO_REMOVE = 1 + w = int(box[2] - box[0] + TO_REMOVE) + h = int(box[3] - box[1] + TO_REMOVE) + w = max(w, 1) + h = max(h, 1) + + # Set shape to [batchxCxHxW] + mask = mask.expand((1, 1, -1, -1)) + + # Resize mask + mask = mask.to(torch.float32) + mask = F.interpolate(mask, size=(h, w), mode="bilinear", align_corners=False) + mask = mask[0][0] + + if thresh >= 0: + mask = mask > thresh + else: + # for visualization and debugging, we also + # allow it to return an unmodified mask + mask = (mask * 255).to(torch.bool) + + im_mask = torch.zeros((im_h, im_w), dtype=torch.bool) + x_0 = max(box[0], 0) + x_1 = min(box[2] + 1, im_w) + y_0 = max(box[1], 0) + y_1 = min(box[3] + 1, im_h) + + im_mask[y_0:y_1, x_0:x_1] = mask[(y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])] + return im_mask + + +class Masker(object): + """ + Projects a set of masks in an image on the locations + specified by the bounding boxes + """ + + def __init__(self, threshold=0.5, padding=1): + self.threshold = threshold + self.padding = padding + + def forward_single_image(self, masks, boxes): + boxes = boxes.convert("xyxy") + im_w, im_h = boxes.size + res = [ + paste_mask_in_image(mask[0], box, im_h, im_w, self.threshold, self.padding) + for mask, box in zip(masks, boxes.bbox) + ] + if len(res) > 0: + res = torch.stack(res, dim=0)[:, None] + else: + res = masks.new_empty((0, 1, masks.shape[-2], masks.shape[-1])) + return res + + def __call__(self, masks, boxes): + if isinstance(boxes, BoxList): + boxes = [boxes] + + # Make some sanity check + assert len(boxes) == len(masks), "Masks and boxes should have the same length." + + # TODO: Is this JIT compatible? + # If not we should make it compatible. + results = [] + for mask, box in zip(masks, boxes): + assert mask.shape[0] == len(box), "Number of objects should be the same." + result = self.forward_single_image(mask, box) + results.append(result) + return results + + +def make_roi_mask_post_processor(cfg): + if cfg.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS: + mask_threshold = cfg.MODEL.ROI_MASK_HEAD.POSTPROCESS_MASKS_THRESHOLD + masker = Masker(threshold=mask_threshold, padding=1) + else: + masker = None + mdetr_style_aggregate_class_num = cfg.TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM + mask_post_processor = MaskPostProcessor( + masker, mdetr_style_aggregate_class_num, vl_version=cfg.MODEL.ROI_MASK_HEAD.PREDICTOR.startswith("VL") + ) + return mask_post_processor diff --git a/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py b/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..edd6654f30d553ea34bd2ee36911a420634f32b6 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch.nn import functional as F + +from maskrcnn_benchmark.layers import smooth_l1_loss +from maskrcnn_benchmark.modeling.matcher import Matcher +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.modeling.utils import cat + + +def project_masks_on_boxes(segmentation_masks, proposals, discretization_size): + """ + Given segmentation masks and the bounding boxes corresponding + to the location of the masks in the image, this function + crops and resizes the masks in the position defined by the + boxes. This prepares the masks for them to be fed to the + loss computation as the targets. + + Arguments: + segmentation_masks: an instance of SegmentationMask + proposals: an instance of BoxList + """ + masks = [] + M = discretization_size + device = proposals.bbox.device + proposals = proposals.convert("xyxy") + assert segmentation_masks.size == proposals.size, "{}, {}".format(segmentation_masks, proposals) + # TODO put the proposals on the CPU, as the representation for the + # masks is not efficient GPU-wise (possibly several small tensors for + # representing a single instance mask) + proposals = proposals.bbox.to(torch.device("cpu")) + for segmentation_mask, proposal in zip(segmentation_masks, proposals): + # crop the masks, resize them to the desired resolution and + # then convert them to the tensor representation, + # instead of the list representation that was used + cropped_mask = segmentation_mask.crop(proposal) + scaled_mask = cropped_mask.resize((M, M)) + mask = scaled_mask.convert(mode="mask") + masks.append(mask) + if len(masks) == 0: + return torch.empty(0, dtype=torch.float32, device=device) + return torch.stack(masks, dim=0).to(device, dtype=torch.float32) + + +class MaskRCNNLossComputation(object): + def __init__(self, proposal_matcher, discretization_size, vl_version=False): + """ + Arguments: + proposal_matcher (Matcher) + discretization_size (int) + """ + self.proposal_matcher = proposal_matcher + self.discretization_size = discretization_size + self.vl_version = vl_version + + def match_targets_to_proposals(self, proposal, target): + match_quality_matrix = boxlist_iou(target, proposal) + matched_idxs = self.proposal_matcher(match_quality_matrix) + # Mask RCNN needs "labels" and "masks "fields for creating the targets + if self.vl_version: + target = target.copy_with_fields(["positive_map", "masks"]) + else: + target = target.copy_with_fields(["labels", "masks"]) + # get the targets corresponding GT for each proposal + # NB: need to clamp the indices because we can have a single + # GT in the image, and matched_idxs can be -2, which goes + # out of bounds + matched_targets = target[matched_idxs.clamp(min=0)] + matched_targets.add_field("matched_idxs", matched_idxs) + return matched_targets + + def prepare_targets(self, proposals, targets): + labels = [] + masks = [] + positive_maps = [] + for proposals_per_image, targets_per_image in zip(proposals, targets): + matched_targets = self.match_targets_to_proposals(proposals_per_image, targets_per_image) + matched_idxs = matched_targets.get_field("matched_idxs") + + if self.vl_version: + positive_maps_per_image = matched_targets.get_field("positive_map") + + # this can probably be removed, but is left here for clarity + # and completeness + neg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD + positive_maps_per_image[neg_inds, :] = 0 + + positive_maps.append(positive_maps_per_image) + + # TODO: make sure for the softmax [NoObj] case + labels_per_image = positive_maps_per_image.sum(dim=-1) + labels_per_image = labels_per_image.to(dtype=torch.int64) + else: + labels_per_image = matched_targets.get_field("labels") + labels_per_image = labels_per_image.to(dtype=torch.int64) + + # this can probably be removed, but is left here for clarity + # and completeness + neg_inds = matched_idxs == Matcher.BELOW_LOW_THRESHOLD + labels_per_image[neg_inds] = 0 + + # mask scores are only computed on positive samples + positive_inds = torch.nonzero(labels_per_image > 0).squeeze(1) + + segmentation_masks = matched_targets.get_field("masks") + segmentation_masks = segmentation_masks[positive_inds] + + positive_proposals = proposals_per_image[positive_inds] + + masks_per_image = project_masks_on_boxes(segmentation_masks, positive_proposals, self.discretization_size) + + labels.append(labels_per_image) + masks.append(masks_per_image) + + return labels, masks, positive_maps + + def __call__(self, proposals, mask_logits, targets): + """ + Arguments: + proposals (list[BoxList]) + mask_logits (Tensor) + targets (list[BoxList]) + + Return: + mask_loss (Tensor): scalar tensor containing the loss + """ + labels, mask_targets, positive_maps = self.prepare_targets(proposals, targets) + + labels = cat(labels, dim=0) + mask_targets = cat(mask_targets, dim=0) + + positive_inds = torch.nonzero(labels > 0).squeeze(1) + labels_pos = labels[positive_inds] + # TODO: a hack for binary mask head + labels_pos = (labels_pos > 0).to(dtype=torch.int64) + + # torch.mean (in binary_cross_entropy_with_logits) doesn't + # accept empty tensors, so handle it separately + if mask_targets.numel() == 0: + return mask_logits.sum() * 0 + + if self.vl_version: + positive_maps = cat(positive_maps, dim=0) + mask_logits_pos = [] + for positive_ind in positive_inds: + positive_map = positive_maps[positive_ind] + # TODO: make sure for the softmax [NoObj] case + mask_logit_pos = mask_logits[positive_ind][torch.nonzero(positive_map).squeeze(1)].mean( + dim=0, keepdim=True + ) + mask_logits_pos.append(mask_logit_pos) + mask_logits_pos = cat(mask_logits_pos, dim=0) + mask_loss = F.binary_cross_entropy_with_logits(mask_logits_pos, mask_targets) + else: + mask_loss = F.binary_cross_entropy_with_logits(mask_logits[positive_inds, labels_pos], mask_targets) + return mask_loss + + +def make_roi_mask_loss_evaluator(cfg): + matcher = Matcher( + cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD, + cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD, + allow_low_quality_matches=False, + ) + + loss_evaluator = MaskRCNNLossComputation( + matcher, cfg.MODEL.ROI_MASK_HEAD.RESOLUTION, vl_version=cfg.MODEL.ROI_MASK_HEAD.PREDICTOR.startswith("VL") + ) + + return loss_evaluator diff --git a/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py b/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py new file mode 100644 index 0000000000000000000000000000000000000000..af468653ff46c5dc47e56dcb85ae36bc1de20243 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py @@ -0,0 +1,85 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch import nn + +from maskrcnn_benchmark.structures.bounding_box import BoxList + +from .roi_mask_feature_extractors import make_roi_mask_feature_extractor +from .roi_mask_predictors import make_roi_mask_predictor +from .inference import make_roi_mask_post_processor +from .loss import make_roi_mask_loss_evaluator + + +def keep_only_positive_boxes(boxes): + """ + Given a set of BoxList containing the `labels` field, + return a set of BoxList for which `labels > 0`. + + Arguments: + boxes (list of BoxList) + """ + assert isinstance(boxes, (list, tuple)) + assert isinstance(boxes[0], BoxList) + assert boxes[0].has_field("labels") + positive_boxes = [] + positive_inds = [] + num_boxes = 0 + for boxes_per_image in boxes: + labels = boxes_per_image.get_field("labels") + inds_mask = labels > 0 + inds = inds_mask.nonzero().squeeze(1) + positive_boxes.append(boxes_per_image[inds]) + positive_inds.append(inds_mask) + return positive_boxes, positive_inds + + +class ROIMaskHead(torch.nn.Module): + def __init__(self, cfg): + super(ROIMaskHead, self).__init__() + self.cfg = cfg.clone() + self.feature_extractor = make_roi_mask_feature_extractor(cfg) + self.predictor = make_roi_mask_predictor(cfg) + self.post_processor = make_roi_mask_post_processor(cfg) + self.loss_evaluator = make_roi_mask_loss_evaluator(cfg) + + def forward(self, features, proposals, targets=None, language_dict_features=None, positive_map_label_to_token=None): + """ + Arguments: + features (list[Tensor]): feature-maps from possibly several levels + proposals (list[BoxList]): proposal boxes + targets (list[BoxList], optional): the ground-truth targets. + language_dict_features: language features: hidden, embedding, mask, ... + + Returns: + x (Tensor): the result of the feature extractor + proposals (list[BoxList]): during training, the original proposals + are returned. During testing, the predicted boxlists are returned + with the `mask` field set + losses (dict[Tensor]): During training, returns the losses for the + head. During testing, returns an empty dict. + """ + if self.training: + # during training, only focus on positive boxes + all_proposals = proposals + proposals, positive_inds = keep_only_positive_boxes(proposals) + if self.training and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR: + x = features + x = x[torch.cat(positive_inds, dim=0)] + else: + x = self.feature_extractor(features, proposals) + if self.cfg.MODEL.ROI_MASK_HEAD.PREDICTOR.startswith("VL"): + mask_logits = self.predictor(x, language_dict_features) + else: + mask_logits = self.predictor(x) + + if not self.training: + result = self.post_processor(mask_logits, proposals, positive_map_label_to_token) + return x, result, {} + + loss_mask = self.loss_evaluator(proposals, mask_logits, targets) + + return x, all_proposals, dict(loss_mask=loss_mask) + + +def build_roi_mask_head(cfg): + return ROIMaskHead(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py b/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py new file mode 100644 index 0000000000000000000000000000000000000000..486eac0526080f12526ed13112f58e9e271480b3 --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py @@ -0,0 +1,114 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from torch import nn +from torch.nn import functional as F + +from .hourglass import Hourglass +from ..box_head.roi_box_feature_extractors import ResNet50Conv5ROIFeatureExtractor +from maskrcnn_benchmark.modeling.poolers import Pooler +from maskrcnn_benchmark.layers import Conv2d +from maskrcnn_benchmark.modeling.make_layers import make_conv3x3 + + +class MaskRCNNFPNFeatureExtractor(nn.Module): + """ + Heads for FPN for classification + """ + + def __init__(self, cfg): + """ + Arguments: + num_classes (int): number of output classes + input_size (int): number of channels of the input once it's flattened + representation_size (int): size of the intermediate representation + """ + super(MaskRCNNFPNFeatureExtractor, self).__init__() + + resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION + scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES + sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS + self.pooler = pooler + + use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN + layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS + dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION + + next_feature = input_size + self.blocks = [] + for layer_idx, layer_features in enumerate(layers, 1): + layer_name = "mask_fcn{}".format(layer_idx) + module = make_conv3x3(next_feature, layer_features, dilation=dilation, stride=1, use_gn=use_gn) + self.add_module(layer_name, module) + next_feature = layer_features + self.blocks.append(layer_name) + + def forward(self, x, proposals): + x = self.pooler(x, proposals) + + for layer_name in self.blocks: + x = F.relu(getattr(self, layer_name)(x)) + + return x + + +class HourglassFPNFeatureExtractor(nn.Module): + """ + Heads for FPN for classification + """ + + def __init__(self, cfg): + """ + Arguments: + num_classes (int): number of output classes + input_size (int): number of channels of the input once it's flattened + representation_size (int): size of the intermediate representation + """ + super(HourglassFPNFeatureExtractor, self).__init__() + + resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION + scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES + sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO + pooler = Pooler( + output_size=(resolution, resolution), + scales=scales, + sampling_ratio=sampling_ratio, + ) + input_size = cfg.MODEL.BACKBONE.OUT_CHANNELS + self.pooler = pooler + + use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN + layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS + scale = cfg.MODEL.ROI_MASK_HEAD.HG_SCALE + + assert input_size == layers[0] + self.blocks = [] + for layer_idx, layer_features in enumerate(layers, 1): + layer_name = "mask_hg{}".format(layer_idx) + module = Hourglass(scale, layer_features, gn=use_gn) + self.add_module(layer_name, module) + self.blocks.append(layer_name) + + def forward(self, x, proposals): + x = self.pooler(x, proposals) + + for layer_name in self.blocks: + x = F.relu(getattr(self, layer_name)(x)) + + return x + + +_ROI_MASK_FEATURE_EXTRACTORS = { + "ResNet50Conv5ROIFeatureExtractor": ResNet50Conv5ROIFeatureExtractor, + "MaskRCNNFPNFeatureExtractor": MaskRCNNFPNFeatureExtractor, + "HourglassFPNFeatureExtractor": HourglassFPNFeatureExtractor, +} + + +def make_roi_mask_feature_extractor(cfg): + func = _ROI_MASK_FEATURE_EXTRACTORS[cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR] + return func(cfg) diff --git a/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py b/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py new file mode 100644 index 0000000000000000000000000000000000000000..86aed08f425a2668c83fc7e2c86c04bbe52c06bc --- /dev/null +++ b/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py @@ -0,0 +1,109 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +from torch import nn +from torch.nn import functional as F + +from maskrcnn_benchmark.layers import Conv2d, _NewEmptyTensorOp +from maskrcnn_benchmark.layers import ConvTranspose2d +from ...utils import permute_and_flatten + + +class MaskRCNNC4Predictor(nn.Module): + def __init__(self, cfg): + super(MaskRCNNC4Predictor, self).__init__() + # TODO: a hack for binary mask head + # num_classes = cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES + num_classes = 2 + dim_reduced = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS[-1] + + if cfg.MODEL.ROI_HEADS.USE_FPN: + num_inputs = dim_reduced + else: + stage_index = 4 + stage2_relative_factor = 2 ** (stage_index - 1) + res2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + num_inputs = res2_out_channels * stage2_relative_factor + + self.conv5_mask = ConvTranspose2d(num_inputs, dim_reduced, 2, 2, 0) + self.mask_fcn_logits = Conv2d(dim_reduced, num_classes, 1, 1, 0) + + for name, param in self.named_parameters(): + if "bias" in name: + nn.init.constant_(param, 0) + elif "weight" in name: + # Caffe2 implementation uses MSRAFill, which in fact + # corresponds to kaiming_normal_ in PyTorch + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") + + def forward(self, x): + x = F.relu(self.conv5_mask(x)) + return self.mask_fcn_logits(x) + + +class VLMaskRCNNC4Predictor(nn.Module): + def __init__(self, cfg): + super(VLMaskRCNNC4Predictor, self).__init__() + dim_reduced = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS[-1] + + if cfg.MODEL.ROI_HEADS.USE_FPN: + num_inputs = dim_reduced + else: + stage_index = 4 + stage2_relative_factor = 2 ** (stage_index - 1) + res2_out_channels = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS + num_inputs = res2_out_channels * stage2_relative_factor + + self.conv5_mask = ConvTranspose2d(num_inputs, dim_reduced, 2, 2, 0) + + # self.mask_fcn_logits = Conv2d(dim_reduced, num_classes, 1, 1, 0) + log_scale = cfg.MODEL.DYHEAD.LOG_SCALE + self.out_dim = cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN + self.dot_product_projection_image = nn.Identity() + self.dot_product_projection_text = nn.Linear(cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, dim_reduced, bias=True) + self.log_scale = nn.Parameter(torch.Tensor([log_scale]), requires_grad=True) + self.bias_lang = nn.Parameter(torch.zeros(cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM), requires_grad=True) + + for name, param in self.named_parameters(): + if "bias" in name: + nn.init.constant_(param, 0) + elif "weight" in name: + # Caffe2 implementation uses MSRAFill, which in fact + # corresponds to kaiming_normal_ in PyTorch + nn.init.kaiming_normal_(param, mode="fan_out", nonlinearity="relu") + + def forward(self, x, language_dict_features): + x = F.relu(self.conv5_mask(x)) + if x.numel() <= 0: + output_shape = [x.shape[0], self.out_dim] + list(x.shape[-2:]) + return _NewEmptyTensorOp.apply(x, output_shape) + + embedding = language_dict_features["hidden"] + # norm + embedding = F.normalize(embedding, p=2, dim=-1) + dot_product_proj_tokens = self.dot_product_projection_text(embedding / 2.0) + dot_product_proj_tokens_bias = torch.matmul(embedding, self.bias_lang) + + B, C, H, W = x.shape + # add bias (language) + dot_product_proj_queries = self.dot_product_projection_image(x) + dot_product_proj_queries = permute_and_flatten(dot_product_proj_queries, B, -1, C, H, W) + A = dot_product_proj_queries.shape[1] + bias = dot_product_proj_tokens_bias.unsqueeze(1).repeat(1, A, 1) + + # dot product + dot_product_logit = ( + torch.matmul(dot_product_proj_queries, dot_product_proj_tokens.transpose(-1, -2)) / self.log_scale.exp() + ) + bias + # clamp for stability + dot_product_logit = torch.clamp(dot_product_logit, max=50000) + dot_product_logit = torch.clamp(dot_product_logit, min=-50000) + dot_product_logit = dot_product_logit.view(B, H, W, self.out_dim).permute(0, 3, 1, 2) + return dot_product_logit + + +_ROI_MASK_PREDICTOR = {"MaskRCNNC4Predictor": MaskRCNNC4Predictor, "VLMaskRCNNC4Predictor": VLMaskRCNNC4Predictor} + + +def make_roi_mask_predictor(cfg): + func = _ROI_MASK_PREDICTOR[cfg.MODEL.ROI_MASK_HEAD.PREDICTOR] + return func(cfg) diff --git a/maskrcnn_benchmark/modeling/rpn/__init__.py b/maskrcnn_benchmark/modeling/rpn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..09d77d5adf9bed8fa81b778c7452527b91853b09 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/__init__.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# from .rpn import build_rpn +from .rpn import RPNModule +from .retina import RetinaNetModule +from .fcos import FCOSModule +from .atss import ATSSModule +from .dyhead import DyHeadModule +from .vldyhead import VLDyHeadModule + +_RPN_META_ARCHITECTURES = { + "RPN": RPNModule, + "RETINA": RetinaNetModule, + "FCOS": FCOSModule, + "ATSS": ATSSModule, + "DYHEAD": DyHeadModule, + "VLDYHEAD": VLDyHeadModule, +} + + +def build_rpn(cfg): + """ + This gives the gist of it. Not super important because it doesn't change as much + """ + rpn_arch = _RPN_META_ARCHITECTURES[cfg.MODEL.RPN_ARCHITECTURE] + return rpn_arch(cfg) diff --git a/maskrcnn_benchmark/modeling/rpn/anchor_generator.py b/maskrcnn_benchmark/modeling/rpn/anchor_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..5c76cf65cd2d2fbe876af2007bb9d21d85c40bc8 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/anchor_generator.py @@ -0,0 +1,401 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import math + +import numpy as np +import torch +from torch import nn + +from maskrcnn_benchmark.structures.bounding_box import BoxList +from maskrcnn_benchmark.structures.image_list import ImageList +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist + + +class BufferList(nn.Module): + """ + Similar to nn.ParameterList, but for buffers + """ + + def __init__(self, buffers=None): + super(BufferList, self).__init__() + if buffers is not None: + self.extend(buffers) + + def extend(self, buffers): + offset = len(self) + for i, buffer in enumerate(buffers): + self.register_buffer(str(offset + i), buffer) + return self + + def __len__(self): + return len(self._buffers) + + def __iter__(self): + return iter(self._buffers.values()) + + +class AnchorGenerator(nn.Module): + """ + For a set of image sizes and feature maps, computes a set + of anchors + """ + + def __init__( + self, + sizes=(128, 256, 512), + aspect_ratios=(0.5, 1.0, 2.0), + anchor_strides=(8, 16, 32), + straddle_thresh=0, + ): + super(AnchorGenerator, self).__init__() + + if len(anchor_strides) == 1: + anchor_stride = anchor_strides[0] + cell_anchors = [generate_anchors(anchor_stride, sizes, aspect_ratios).float()] + else: + if len(anchor_strides) != len(sizes): + raise RuntimeError("FPN should have #anchor_strides == #sizes") + cell_anchors = [ + generate_anchors( + anchor_stride, size if isinstance(size, (tuple, list)) else (size,), aspect_ratios + ).float() + for anchor_stride, size in zip(anchor_strides, sizes) + ] + self.strides = anchor_strides + self.cell_anchors = BufferList(cell_anchors) + self.straddle_thresh = straddle_thresh + + def num_anchors_per_location(self): + return [len(cell_anchors) for cell_anchors in self.cell_anchors] + + def grid_anchors(self, grid_sizes): + anchors = [] + for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors): + grid_height, grid_width = size + device = base_anchors.device + shifts_x = torch.arange(0, grid_width * stride, step=stride, dtype=torch.float32, device=device) + shifts_y = torch.arange(0, grid_height * stride, step=stride, dtype=torch.float32, device=device) + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1) + + anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)) + + return anchors + + def add_visibility_to(self, boxlist): + image_width, image_height = boxlist.size + anchors = boxlist.bbox + if self.straddle_thresh >= 0: + inds_inside = ( + (anchors[..., 0] >= -self.straddle_thresh) + & (anchors[..., 1] >= -self.straddle_thresh) + & (anchors[..., 2] < image_width + self.straddle_thresh) + & (anchors[..., 3] < image_height + self.straddle_thresh) + ) + else: + device = anchors.device + inds_inside = torch.ones(anchors.shape[0], dtype=torch.bool, device=device) + boxlist.add_field("visibility", inds_inside) + + def forward(self, image_list, feature_maps): + grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps] + anchors_over_all_feature_maps = self.grid_anchors(grid_sizes) + anchors = [] + if isinstance(image_list, ImageList): + for i, (image_height, image_width) in enumerate(image_list.image_sizes): + anchors_in_image = [] + for anchors_per_feature_map in anchors_over_all_feature_maps: + boxlist = BoxList(anchors_per_feature_map, (image_width, image_height), mode="xyxy") + self.add_visibility_to(boxlist) + anchors_in_image.append(boxlist) + anchors.append(anchors_in_image) + else: + image_height, image_width = [int(x) for x in image_list.size()[-2:]] + anchors_in_image = [] + for anchors_per_feature_map in anchors_over_all_feature_maps: + boxlist = BoxList(anchors_per_feature_map, (image_width, image_height), mode="xyxy") + self.add_visibility_to(boxlist) + anchors_in_image.append(boxlist) + anchors.append(anchors_in_image) + return anchors + + +def make_anchor_generator(config): + anchor_sizes = config.MODEL.RPN.ANCHOR_SIZES + aspect_ratios = config.MODEL.RPN.ASPECT_RATIOS + anchor_stride = config.MODEL.RPN.ANCHOR_STRIDE + straddle_thresh = config.MODEL.RPN.STRADDLE_THRESH + + if config.MODEL.RPN.USE_FPN: + assert len(anchor_stride) == len(anchor_sizes), "FPN should have len(ANCHOR_STRIDE) == len(ANCHOR_SIZES)" + else: + assert len(anchor_stride) == 1, "Non-FPN should have a single ANCHOR_STRIDE" + anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios, anchor_stride, straddle_thresh) + return anchor_generator + + +def make_anchor_generator_complex(config): + anchor_sizes = config.MODEL.RPN.ANCHOR_SIZES + aspect_ratios = config.MODEL.RPN.ASPECT_RATIOS + anchor_strides = config.MODEL.RPN.ANCHOR_STRIDE + straddle_thresh = config.MODEL.RPN.STRADDLE_THRESH + octave = config.MODEL.RPN.OCTAVE + scales_per_octave = config.MODEL.RPN.SCALES_PER_OCTAVE + + if config.MODEL.RPN.USE_FPN: + assert len(anchor_strides) == len(anchor_sizes), "Only support FPN now" + new_anchor_sizes = [] + for size in anchor_sizes: + per_layer_anchor_sizes = [] + for scale_per_octave in range(scales_per_octave): + octave_scale = octave ** (scale_per_octave / float(scales_per_octave)) + per_layer_anchor_sizes.append(octave_scale * size) + new_anchor_sizes.append(tuple(per_layer_anchor_sizes)) + else: + assert len(anchor_strides) == 1, "Non-FPN should have a single ANCHOR_STRIDE" + new_anchor_sizes = anchor_sizes + + anchor_generator = AnchorGenerator(tuple(new_anchor_sizes), aspect_ratios, anchor_strides, straddle_thresh) + return anchor_generator + + +class CenterAnchorGenerator(nn.Module): + """ + For a set of image sizes and feature maps, computes a set + of anchors + """ + + def __init__( + self, + sizes=(128, 256, 512), + aspect_ratios=(0.5, 1.0, 2.0), + anchor_strides=(8, 16, 32), + straddle_thresh=0, + anchor_shift=(0.0, 0.0, 0.0, 0.0), + use_relative=False, + ): + super(CenterAnchorGenerator, self).__init__() + + self.sizes = sizes + self.aspect_ratios = aspect_ratios + self.strides = anchor_strides + self.straddle_thresh = straddle_thresh + self.anchor_shift = anchor_shift + self.use_relative = use_relative + + def add_visibility_to(self, boxlist): + image_width, image_height = boxlist.size + anchors = boxlist.bbox + if self.straddle_thresh >= 0: + inds_inside = ( + (anchors[..., 0] >= -self.straddle_thresh) + & (anchors[..., 1] >= -self.straddle_thresh) + & (anchors[..., 2] < image_width + self.straddle_thresh) + & (anchors[..., 3] < image_height + self.straddle_thresh) + ) + else: + device = anchors.device + inds_inside = torch.ones(anchors.shape[0], dtype=torch.uint8, device=device) + boxlist.add_field("visibility", inds_inside) + + def forward(self, centers, image_sizes, feature_maps): + shift_left, shift_top, shift_right, shift_down = self.anchor_shift + grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps] + anchors = [] + for i, ((image_height, image_width), center_bbox) in enumerate(zip(image_sizes, centers)): + center = center_bbox.get_field("centers") + boxlist_per_level = [] + for size, fsize in zip(self.sizes, grid_sizes): + for ratios in self.aspect_ratios: + + size_ratios = size * size / ratios + ws = np.round(np.sqrt(size_ratios)) + hs = np.round(ws * ratios) + + anchors_per_level = torch.cat( + ( + center[:, 0, None] - 0.5 * (1 + shift_left) * (ws - 1), + center[:, 1, None] - 0.5 * (1 + shift_top) * (hs - 1), + center[:, 0, None] + 0.5 * (1 + shift_right) * (ws - 1), + center[:, 1, None] + 0.5 * (1 + shift_down) * (hs - 1), + ), + dim=1, + ) + boxlist = BoxList(anchors_per_level, (image_width, image_height), mode="xyxy") + boxlist.add_field("cbox", center_bbox) + self.add_visibility_to(boxlist) + boxlist_per_level.append(boxlist) + if self.use_relative: + area = center_bbox.area() + for ratios in self.aspect_ratios: + + size_ratios = area / ratios + ws = torch.round(torch.sqrt(size_ratios)) + hs = torch.round(ws * ratios) + + anchors_per_level = torch.stack( + ( + center[:, 0] - (1 + shift_left) * ws, + center[:, 1] - (1 + shift_top) * hs, + center[:, 0] + (1 + shift_right) * ws, + center[:, 1] + (1 + shift_down) * hs, + ), + dim=1, + ) + boxlist = BoxList(anchors_per_level, (image_width, image_height), mode="xyxy") + boxlist.add_field("cbox", center_bbox) + self.add_visibility_to(boxlist) + boxlist_per_level.append(boxlist) + anchors_in_image = cat_boxlist(boxlist_per_level) + anchors.append(anchors_in_image) + return anchors + + +def make_center_anchor_generator(config): + anchor_sizes = config.MODEL.RPN.ANCHOR_SIZES + aspect_ratios = config.MODEL.RPN.ASPECT_RATIOS + anchor_strides = config.MODEL.RPN.ANCHOR_STRIDE + straddle_thresh = config.MODEL.RPN.STRADDLE_THRESH + octave = config.MODEL.RPN.OCTAVE + scales_per_octave = config.MODEL.RPN.SCALES_PER_OCTAVE + anchor_shift = config.MODEL.RPN.ANCHOR_SHIFT + use_relative = config.MODEL.RPN.USE_RELATIVE_SIZE + + if config.MODEL.RPN.USE_FPN: + assert len(anchor_strides) == len(anchor_sizes), "Only support FPN now" + new_anchor_sizes = [] + for size in anchor_sizes: + per_layer_anchor_sizes = [] + for scale_per_octave in range(scales_per_octave): + octave_scale = octave ** (scale_per_octave / float(scales_per_octave)) + per_layer_anchor_sizes.append(octave_scale * size) + new_anchor_sizes.append(tuple(per_layer_anchor_sizes)) + else: + assert len(anchor_strides) == 1, "Non-FPN should have a single ANCHOR_STRIDE" + new_anchor_sizes = anchor_sizes + + anchor_generator = CenterAnchorGenerator( + tuple(new_anchor_sizes), aspect_ratios, anchor_strides, straddle_thresh, anchor_shift, use_relative + ) + return anchor_generator + + +# Copyright (c) 2017-present, Facebook, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################## +# +# Based on: +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Sean Bell +# -------------------------------------------------------- + + +# Verify that we compute the same anchors as Shaoqing's matlab implementation: +# +# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat +# >> anchors +# +# anchors = +# +# -83 -39 100 56 +# -175 -87 192 104 +# -359 -183 376 200 +# -55 -55 72 72 +# -119 -119 136 136 +# -247 -247 264 264 +# -35 -79 52 96 +# -79 -167 96 184 +# -167 -343 184 360 + +# array([[ -83., -39., 100., 56.], +# [-175., -87., 192., 104.], +# [-359., -183., 376., 200.], +# [ -55., -55., 72., 72.], +# [-119., -119., 136., 136.], +# [-247., -247., 264., 264.], +# [ -35., -79., 52., 96.], +# [ -79., -167., 96., 184.], +# [-167., -343., 184., 360.]]) + + +def generate_anchors(stride=16, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)): + """Generates a matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors + are centered on stride / 2, have (approximate) sqrt areas of the specified + sizes, and aspect ratios as given. + """ + return _generate_anchors( + stride, + np.array(sizes, dtype=np.float) / stride, + np.array(aspect_ratios, dtype=np.float), + ) + + +def _generate_anchors(base_size, scales, aspect_ratios): + """Generate anchor (reference) windows by enumerating aspect ratios X + scales wrt a reference (0, 0, base_size - 1, base_size - 1) window. + """ + anchor = np.array([1, 1, base_size, base_size], dtype=np.float) - 1 + anchors = _ratio_enum(anchor, aspect_ratios) + anchors = np.vstack([_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])]) + return torch.from_numpy(anchors) + + +def _whctrs(anchor): + """Return width, height, x center, and y center for an anchor (window).""" + w = anchor[2] - anchor[0] + 1 + h = anchor[3] - anchor[1] + 1 + x_ctr = anchor[0] + 0.5 * (w - 1) + y_ctr = anchor[1] + 0.5 * (h - 1) + return w, h, x_ctr, y_ctr + + +def _mkanchors(ws, hs, x_ctr, y_ctr): + """Given a vector of widths (ws) and heights (hs) around a center + (x_ctr, y_ctr), output a set of anchors (windows). + """ + ws = ws[:, np.newaxis] + hs = hs[:, np.newaxis] + anchors = np.hstack( + ( + x_ctr - 0.5 * (ws - 1), + y_ctr - 0.5 * (hs - 1), + x_ctr + 0.5 * (ws - 1), + y_ctr + 0.5 * (hs - 1), + ) + ) + return anchors + + +def _ratio_enum(anchor, ratios): + """Enumerate a set of anchors for each aspect ratio wrt an anchor.""" + w, h, x_ctr, y_ctr = _whctrs(anchor) + size = w * h + size_ratios = size / ratios + ws = np.round(np.sqrt(size_ratios)) + hs = np.round(ws * ratios) + anchors = _mkanchors(ws, hs, x_ctr, y_ctr) + return anchors + + +def _scale_enum(anchor, scales): + """Enumerate a set of anchors for each scale wrt an anchor.""" + w, h, x_ctr, y_ctr = _whctrs(anchor) + ws = w * scales + hs = h * scales + anchors = _mkanchors(ws, hs, x_ctr, y_ctr) + return anchors diff --git a/maskrcnn_benchmark/modeling/rpn/atss.py b/maskrcnn_benchmark/modeling/rpn/atss.py new file mode 100644 index 0000000000000000000000000000000000000000..c29a2bf3f6f4ea13d082f0868582261c20dc00dc --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/atss.py @@ -0,0 +1,201 @@ +import math +import torch +import torch.nn.functional as F +from torch import nn + +from .inference import make_atss_postprocessor +from .loss import make_atss_loss_evaluator + +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.layers import Scale, DFConv2d, DYReLU, SELayer +from .anchor_generator import make_anchor_generator_complex + + +class BoxCoder(object): + def __init__(self, cfg): + self.cfg = cfg + + def encode(self, gt_boxes, anchors): + + TO_REMOVE = 1 # TODO remove + ex_widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE + ex_heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE + ex_ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2 + ex_ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2 + + gt_widths = gt_boxes[:, 2] - gt_boxes[:, 0] + TO_REMOVE + gt_heights = gt_boxes[:, 3] - gt_boxes[:, 1] + TO_REMOVE + gt_ctr_x = (gt_boxes[:, 2] + gt_boxes[:, 0]) / 2 + gt_ctr_y = (gt_boxes[:, 3] + gt_boxes[:, 1]) / 2 + + wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0) + targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths + targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights + targets_dw = ww * torch.log(gt_widths / ex_widths) + targets_dh = wh * torch.log(gt_heights / ex_heights) + targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1) + + return targets + + def decode(self, preds, anchors): + + anchors = anchors.to(preds.dtype) + + TO_REMOVE = 1 # TODO remove + widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE + heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE + ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2 + ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2 + + wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0) + dx = preds[:, 0::4] / wx + dy = preds[:, 1::4] / wy + dw = preds[:, 2::4] / ww + dh = preds[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=math.log(1000.0 / 16)) + dh = torch.clamp(dh, max=math.log(1000.0 / 16)) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + pred_boxes = torch.zeros_like(preds) + pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * (pred_w - 1) + pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * (pred_h - 1) + pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * (pred_w - 1) + pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * (pred_h - 1) + + return pred_boxes + + +class ATSSHead(torch.nn.Module): + def __init__(self, cfg): + super(ATSSHead, self).__init__() + self.cfg = cfg + num_classes = cfg.MODEL.ATSS.NUM_CLASSES - 1 + num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * cfg.MODEL.RPN.SCALES_PER_OCTAVE + in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + channels = cfg.MODEL.ATSS.CHANNELS + use_gn = cfg.MODEL.ATSS.USE_GN + use_bn = cfg.MODEL.ATSS.USE_BN + use_dcn_in_tower = cfg.MODEL.ATSS.USE_DFCONV + use_dyrelu = cfg.MODEL.ATSS.USE_DYRELU + use_se = cfg.MODEL.ATSS.USE_SE + + cls_tower = [] + bbox_tower = [] + for i in range(cfg.MODEL.ATSS.NUM_CONVS): + if use_dcn_in_tower and i == cfg.MODEL.ATSS.NUM_CONVS - 1: + conv_func = DFConv2d + else: + conv_func = nn.Conv2d + + cls_tower.append( + conv_func(in_channels if i == 0 else channels, channels, kernel_size=3, stride=1, padding=1, bias=True) + ) + if use_gn: + cls_tower.append(nn.GroupNorm(32, channels)) + if use_bn: + cls_tower.append(nn.BatchNorm2d(channels)) + if use_se: + cls_tower.append(SELayer(channels)) + if use_dyrelu: + cls_tower.append(DYReLU(channels, channels)) + else: + cls_tower.append(nn.ReLU()) + + bbox_tower.append( + conv_func(in_channels if i == 0 else channels, channels, kernel_size=3, stride=1, padding=1, bias=True) + ) + if use_gn: + bbox_tower.append(nn.GroupNorm(32, channels)) + if use_bn: + bbox_tower.append(nn.BatchNorm2d(channels)) + if use_se: + bbox_tower.append(SELayer(channels)) + if use_dyrelu: + bbox_tower.append(DYReLU(channels, channels)) + else: + bbox_tower.append(nn.ReLU()) + + self.add_module("cls_tower", nn.Sequential(*cls_tower)) + self.add_module("bbox_tower", nn.Sequential(*bbox_tower)) + self.cls_logits = nn.Conv2d(channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1) + self.bbox_pred = nn.Conv2d(channels, num_anchors * 4, kernel_size=3, stride=1, padding=1) + self.centerness = nn.Conv2d(channels, num_anchors * 1, kernel_size=3, stride=1, padding=1) + + # initialization + for modules in [self.cls_tower, self.bbox_tower, self.cls_logits, self.bbox_pred, self.centerness]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + # initialize the bias for focal loss + prior_prob = cfg.MODEL.ATSS.PRIOR_PROB + bias_value = -math.log((1 - prior_prob) / prior_prob) + torch.nn.init.constant_(self.cls_logits.bias, bias_value) + + self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)]) + + def forward(self, x): + logits = [] + bbox_reg = [] + centerness = [] + for l, feature in enumerate(x): + cls_tower = self.cls_tower(feature) + box_tower = self.bbox_tower(feature) + + logits.append(self.cls_logits(cls_tower)) + + bbox_pred = self.scales[l](self.bbox_pred(box_tower)) + bbox_reg.append(bbox_pred) + + centerness.append(self.centerness(box_tower)) + return logits, bbox_reg, centerness + + +class ATSSModule(torch.nn.Module): + def __init__(self, cfg): + super(ATSSModule, self).__init__() + self.cfg = cfg + self.head = ATSSHead(cfg) + box_coder = BoxCoder(cfg) + self.loss_evaluator = make_atss_loss_evaluator(cfg, box_coder) + self.box_selector_train = make_atss_postprocessor(cfg, box_coder, is_train=True) + self.box_selector_test = make_atss_postprocessor(cfg, box_coder, is_train=False) + self.anchor_generator = make_anchor_generator_complex(cfg) + + def forward(self, images, features, targets=None): + box_cls, box_regression, centerness = self.head(features) + anchors = self.anchor_generator(images, features) + + if self.training: + return self._forward_train(box_cls, box_regression, centerness, targets, anchors) + else: + return self._forward_test(box_cls, box_regression, centerness, anchors) + + def _forward_train(self, box_cls, box_regression, centerness, targets, anchors): + loss_box_cls, loss_box_reg, loss_centerness = self.loss_evaluator( + box_cls, box_regression, centerness, targets, anchors + ) + losses = {"loss_cls": loss_box_cls, "loss_reg": loss_box_reg, "loss_centerness": loss_centerness} + if self.cfg.MODEL.RPN_ONLY: + return None, losses + else: + boxes = self.box_selector_train(box_cls, box_regression, centerness, anchors) + train_boxes = [] + for b, a in zip(boxes, anchors): + a = cat_boxlist(a) + b.add_field("visibility", torch.ones(b.bbox.shape[0], dtype=torch.bool, device=b.bbox.device)) + del b.extra_fields["scores"] + del b.extra_fields["labels"] + train_boxes.append(cat_boxlist([b, a])) + return train_boxes, losses + + def _forward_test(self, box_cls, box_regression, centerness, anchors): + boxes = self.box_selector_test(box_cls, box_regression, centerness, anchors) + return boxes, {} diff --git a/maskrcnn_benchmark/modeling/rpn/dyhead.py b/maskrcnn_benchmark/modeling/rpn/dyhead.py new file mode 100644 index 0000000000000000000000000000000000000000..82e0a65663821684598e1827982310baf63cb772 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/dyhead.py @@ -0,0 +1,365 @@ +import math +import torch +import torch.nn.functional as F +from torch import nn + +from .inference import make_atss_postprocessor +from .loss import make_atss_loss_evaluator +from .anchor_generator import make_anchor_generator_complex + +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.layers import Scale, DYReLU, SELayer, ModulatedDeformConv +from maskrcnn_benchmark.layers import NaiveSyncBatchNorm2d, FrozenBatchNorm2d +from maskrcnn_benchmark.modeling.backbone.fbnet import * + + +class h_sigmoid(nn.Module): + def __init__(self, inplace=True, h_max=1): + super(h_sigmoid, self).__init__() + self.relu = nn.ReLU6(inplace=inplace) + self.h_max = h_max + + def forward(self, x): + return self.relu(x + 3) * self.h_max / 6 + + +class BoxCoder(object): + def __init__(self, cfg): + self.cfg = cfg + + def encode(self, gt_boxes, anchors): + TO_REMOVE = 1 # TODO remove + ex_widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE + ex_heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE + ex_ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2 + ex_ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2 + + gt_widths = gt_boxes[:, 2] - gt_boxes[:, 0] + TO_REMOVE + gt_heights = gt_boxes[:, 3] - gt_boxes[:, 1] + TO_REMOVE + gt_ctr_x = (gt_boxes[:, 2] + gt_boxes[:, 0]) / 2 + gt_ctr_y = (gt_boxes[:, 3] + gt_boxes[:, 1]) / 2 + + wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0) + targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths + targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights + targets_dw = ww * torch.log(gt_widths / ex_widths) + targets_dh = wh * torch.log(gt_heights / ex_heights) + targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1) + + return targets + + def decode(self, preds, anchors): + anchors = anchors.to(preds.dtype) + + TO_REMOVE = 1 # TODO remove + widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE + heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE + ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2 + ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2 + + wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0) + dx = preds[:, 0::4] / wx + dy = preds[:, 1::4] / wy + dw = preds[:, 2::4] / ww + dh = preds[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=math.log(1000.0 / 16)) + dh = torch.clamp(dh, max=math.log(1000.0 / 16)) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + pred_boxes = torch.zeros_like(preds) + pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * (pred_w - 1) + pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * (pred_h - 1) + pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * (pred_w - 1) + pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * (pred_h - 1) + + return pred_boxes + + +class Conv3x3Norm(torch.nn.Module): + def __init__(self, in_channels, out_channels, stride, groups=1, deformable=False, bn_type=None): + super(Conv3x3Norm, self).__init__() + + if deformable: + self.conv = ModulatedDeformConv( + in_channels, out_channels, kernel_size=3, stride=stride, padding=1, groups=groups + ) + else: + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, groups=groups) + + if isinstance(bn_type, (list, tuple)): + assert len(bn_type) == 2 + assert bn_type[0] == "gn" + gn_group = bn_type[1] + bn_type = bn_type[0] + + if bn_type == "bn": + bn_op = nn.BatchNorm2d(out_channels) + elif bn_type == "sbn": + bn_op = nn.SyncBatchNorm(out_channels) + elif bn_type == "nsbn": + bn_op = NaiveSyncBatchNorm2d(out_channels) + elif bn_type == "gn": + bn_op = nn.GroupNorm(num_groups=gn_group, num_channels=out_channels) + elif bn_type == "af": + bn_op = FrozenBatchNorm2d(out_channels) + if bn_type is not None: + self.bn = bn_op + else: + self.bn = None + + def forward(self, input, **kwargs): + x = self.conv(input, **kwargs) + if self.bn: + x = self.bn(x) + return x + + +class DyConv(torch.nn.Module): + def __init__( + self, + in_channels=256, + out_channels=256, + conv_func=nn.Conv2d, + use_dyfuse=True, + use_dyrelu=False, + use_deform=False, + ): + super(DyConv, self).__init__() + + self.DyConv = nn.ModuleList() + self.DyConv.append(conv_func(in_channels, out_channels, 1)) + self.DyConv.append(conv_func(in_channels, out_channels, 1)) + self.DyConv.append(conv_func(in_channels, out_channels, 2)) + + if use_dyfuse: + self.AttnConv = nn.Sequential( + nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_channels, 1, kernel_size=1), nn.ReLU(inplace=True) + ) + self.h_sigmoid = h_sigmoid() + else: + self.AttnConv = None + + if use_dyrelu: + self.relu = DYReLU(in_channels, out_channels) + else: + self.relu = nn.ReLU() + + if use_deform: + self.offset = nn.Conv2d(in_channels, 27, kernel_size=3, stride=1, padding=1) + else: + self.offset = None + + self.init_weights() + + def init_weights(self): + for m in self.DyConv.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight.data, 0, 0.01) + if m.bias is not None: + m.bias.data.zero_() + if self.AttnConv is not None: + for m in self.AttnConv.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight.data, 0, 0.01) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, x): + next_x = [] + for level, feature in enumerate(x): + + conv_args = dict() + if self.offset is not None: + offset_mask = self.offset(feature) + offset = offset_mask[:, :18, :, :] + mask = offset_mask[:, 18:, :, :].sigmoid() + conv_args = dict(offset=offset, mask=mask) + + temp_fea = [self.DyConv[1](feature, **conv_args)] + + if level > 0: + temp_fea.append(self.DyConv[2](x[level - 1], **conv_args)) + if level < len(x) - 1: + temp_fea.append( + F.upsample_bilinear( + self.DyConv[0](x[level + 1], **conv_args), size=[feature.size(2), feature.size(3)] + ) + ) + mean_fea = torch.mean(torch.stack(temp_fea), dim=0, keepdim=False) + + if self.AttnConv is not None: + attn_fea = [] + res_fea = [] + for fea in temp_fea: + res_fea.append(fea) + attn_fea.append(self.AttnConv(fea)) + + res_fea = torch.stack(res_fea) + spa_pyr_attn = self.h_sigmoid(torch.stack(attn_fea)) + + mean_fea = torch.mean(res_fea * spa_pyr_attn, dim=0, keepdim=False) + + next_x.append(mean_fea) + + next_x = [self.relu(item) for item in next_x] + return next_x + + +class DyHead(torch.nn.Module): + def __init__(self, cfg): + super(DyHead, self).__init__() + self.cfg = cfg + num_classes = cfg.MODEL.DYHEAD.NUM_CLASSES - 1 + num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * cfg.MODEL.RPN.SCALES_PER_OCTAVE + in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + channels = cfg.MODEL.DYHEAD.CHANNELS + if cfg.MODEL.DYHEAD.USE_GN: + bn_type = ["gn", cfg.MODEL.GROUP_NORM.NUM_GROUPS] + elif cfg.MODEL.DYHEAD.USE_NSYNCBN: + bn_type = "nsbn" + elif cfg.MODEL.DYHEAD.USE_SYNCBN: + bn_type = "sbn" + else: + bn_type = None + + use_dyrelu = cfg.MODEL.DYHEAD.USE_DYRELU + use_dyfuse = cfg.MODEL.DYHEAD.USE_DYFUSE + use_deform = cfg.MODEL.DYHEAD.USE_DFCONV + + if cfg.MODEL.DYHEAD.CONV_FUNC: + conv_func = lambda i, o, s: eval(cfg.MODEL.DYHEAD.CONV_FUNC)(i, o, s, bn_type=bn_type) + else: + conv_func = lambda i, o, s: Conv3x3Norm(i, o, s, deformable=use_deform, bn_type=bn_type) + + dyhead_tower = [] + for i in range(cfg.MODEL.DYHEAD.NUM_CONVS): + dyhead_tower.append( + DyConv( + in_channels if i == 0 else channels, + channels, + conv_func=conv_func, + use_dyrelu=(use_dyrelu and in_channels == channels) if i == 0 else use_dyrelu, + use_dyfuse=(use_dyfuse and in_channels == channels) if i == 0 else use_dyfuse, + use_deform=(use_deform and in_channels == channels) if i == 0 else use_deform, + ) + ) + + self.add_module("dyhead_tower", nn.Sequential(*dyhead_tower)) + if cfg.MODEL.DYHEAD.COSINE_SCALE <= 0: + self.cls_logits = nn.Conv2d(channels, num_anchors * num_classes, kernel_size=1) + self.cls_logits_bias = None + else: + self.cls_logits = nn.Conv2d(channels, num_anchors * num_classes, kernel_size=1, bias=False) + self.cls_logits_bias = nn.Parameter(torch.zeros(num_anchors * num_classes, requires_grad=True)) + self.cosine_scale = nn.Parameter(torch.ones(1) * cfg.MODEL.DYHEAD.COSINE_SCALE) + self.bbox_pred = nn.Conv2d(channels, num_anchors * 4, kernel_size=1) + self.centerness = nn.Conv2d(channels, num_anchors * 1, kernel_size=1) + + # initialization + for modules in [self.cls_logits, self.bbox_pred, self.centerness]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + if hasattr(l, "bias") and l.bias is not None: + torch.nn.init.constant_(l.bias, 0) + + # initialize the bias for focal loss + prior_prob = cfg.MODEL.DYHEAD.PRIOR_PROB + bias_value = -math.log((1 - prior_prob) / prior_prob) + if self.cls_logits_bias is None: + torch.nn.init.constant_(self.cls_logits.bias, bias_value) + else: + torch.nn.init.constant_(self.cls_logits_bias, bias_value) + + self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)]) + + def extract_feature(self, x): + output = [] + for i in range(len(self.dyhead_tower)): + x = self.dyhead_tower[i](x) + output.append(x) + return output + + def forward(self, x): + logits = [] + bbox_reg = [] + centerness = [] + + dyhead_tower = self.dyhead_tower(x) + + for l, feature in enumerate(x): + if self.cls_logits_bias is None: + logit = self.cls_logits(dyhead_tower[l]) + else: + # CosineSimOutputLayers: https://github.com/ucbdrive/few-shot-object-detection/blob/master/fsdet/modeling/roi_heads/fast_rcnn.py#L448-L464 + # normalize the input x along the `channel` dimension + x_norm = torch.norm(dyhead_tower[l], p=2, dim=1, keepdim=True).expand_as(dyhead_tower[l]) + x_normalized = dyhead_tower[l].div(x_norm + 1e-5) + # normalize weight + temp_norm = torch.norm(self.cls_logits.weight.data, p=2, dim=1, keepdim=True).expand_as( + self.cls_logits.weight.data + ) + self.cls_logits.weight.data = self.cls_logits.weight.data.div(temp_norm + 1e-5) + cos_dist = self.cls_logits(x_normalized) + logit = self.cosine_scale * cos_dist + self.cls_logits_bias.reshape(1, len(self.cls_logits_bias), 1, 1) + logits.append(logit) + + bbox_pred = self.scales[l](self.bbox_pred(dyhead_tower[l])) + bbox_reg.append(bbox_pred) + + centerness.append(self.centerness(dyhead_tower[l])) + return logits, bbox_reg, centerness + + +class DyHeadModule(torch.nn.Module): + def __init__(self, cfg): + super(DyHeadModule, self).__init__() + self.cfg = cfg + self.head = DyHead(cfg) + box_coder = BoxCoder(cfg) + self.loss_evaluator = make_atss_loss_evaluator(cfg, box_coder) + self.box_selector_train = make_atss_postprocessor(cfg, box_coder, is_train=True) + self.box_selector_test = make_atss_postprocessor(cfg, box_coder, is_train=False) + self.anchor_generator = make_anchor_generator_complex(cfg) + + def forward(self, images, features, targets=None): + box_cls, box_regression, centerness = self.head(features) + anchors = self.anchor_generator(images, features) + + if self.training: + return self._forward_train(box_cls, box_regression, centerness, targets, anchors) + else: + return self._forward_test(box_cls, box_regression, centerness, anchors) + + def _forward_train(self, box_cls, box_regression, centerness, targets, anchors): + loss_box_cls, loss_box_reg, loss_centerness, _, _, _, _ = self.loss_evaluator( + box_cls, box_regression, centerness, targets, anchors + ) + losses = {"loss_cls": loss_box_cls, "loss_reg": loss_box_reg, "loss_centerness": loss_centerness} + if self.cfg.MODEL.RPN_ONLY: + return None, losses + else: + # boxes = self.box_selector_train(box_cls, box_regression, centerness, anchors) + boxes = self.box_selector_train(box_regression, centerness, anchors, box_cls) + train_boxes = [] + # for b, a in zip(boxes, anchors): + # a = cat_boxlist(a) + # b.add_field("visibility", torch.ones(b.bbox.shape[0], dtype=torch.bool, device=b.bbox.device)) + # del b.extra_fields['scores'] + # del b.extra_fields['labels'] + # train_boxes.append(cat_boxlist([b, a])) + for b, t in zip(boxes, targets): + tb = t.copy_with_fields(["labels"]) + tb.add_field("scores", torch.ones(tb.bbox.shape[0], dtype=torch.bool, device=tb.bbox.device)) + train_boxes.append(cat_boxlist([b, tb])) + return train_boxes, losses + + def _forward_test(self, box_cls, box_regression, centerness, anchors): + boxes = self.box_selector_test(box_regression, centerness, anchors, box_cls) + return boxes, {} diff --git a/maskrcnn_benchmark/modeling/rpn/fcos.py b/maskrcnn_benchmark/modeling/rpn/fcos.py new file mode 100644 index 0000000000000000000000000000000000000000..aa858bb21c951ceb7c15e023ea4c2c588e795272 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/fcos.py @@ -0,0 +1,176 @@ +import math +import torch +import torch.nn.functional as F +from torch import nn + +from maskrcnn_benchmark.modeling import registry +from maskrcnn_benchmark.layers import Scale, DFConv2d +from .loss import make_fcos_loss_evaluator +from .anchor_generator import make_center_anchor_generator +from .inference import make_fcos_postprocessor + + +@registry.RPN_HEADS.register("FCOSHead") +class FCOSHead(torch.nn.Module): + def __init__(self, cfg): + + super(FCOSHead, self).__init__() + # TODO: Implement the sigmoid version first. + num_classes = cfg.MODEL.FCOS.NUM_CLASSES - 1 + in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + use_gn = cfg.MODEL.FCOS.USE_GN + use_bn = cfg.MODEL.FCOS.USE_BN + use_dcn_in_tower = cfg.MODEL.FCOS.USE_DFCONV + self.fpn_strides = cfg.MODEL.FCOS.FPN_STRIDES + self.norm_reg_targets = cfg.MODEL.FCOS.NORM_REG_TARGETS + self.centerness_on_reg = cfg.MODEL.FCOS.CENTERNESS_ON_REG + + cls_tower = [] + bbox_tower = [] + for i in range(cfg.MODEL.FCOS.NUM_CONVS): + if use_dcn_in_tower and i == cfg.MODEL.FCOS.NUM_CONVS - 1: + conv_func = DFConv2d + else: + conv_func = nn.Conv2d + + cls_tower.append(conv_func(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=True)) + if use_gn: + cls_tower.append(nn.GroupNorm(32, in_channels)) + if use_bn: + cls_tower.append(nn.BatchNorm2d(in_channels)) + cls_tower.append(nn.ReLU()) + + bbox_tower.append(conv_func(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=True)) + if use_gn: + bbox_tower.append(nn.GroupNorm(32, in_channels)) + if use_bn: + bbox_tower.append(nn.BatchNorm2d(in_channels)) + bbox_tower.append(nn.ReLU()) + + self.add_module("cls_tower", nn.Sequential(*cls_tower)) + self.add_module("bbox_tower", nn.Sequential(*bbox_tower)) + self.cls_logits = nn.Conv2d(in_channels, num_classes, kernel_size=3, stride=1, padding=1) + self.bbox_pred = nn.Conv2d(in_channels, 4, kernel_size=3, stride=1, padding=1) + self.centerness = nn.Conv2d(in_channels, 1, kernel_size=3, stride=1, padding=1) + + # initialization + for modules in [self.cls_tower, self.bbox_tower, self.cls_logits, self.bbox_pred, self.centerness]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + # initialize the bias for focal loss + prior_prob = cfg.MODEL.FCOS.PRIOR_PROB + bias_value = -math.log((1 - prior_prob) / prior_prob) + torch.nn.init.constant_(self.cls_logits.bias, bias_value) + + self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)]) + + def forward(self, x): + logits = [] + bbox_reg = [] + centerness = [] + for l, feature in enumerate(x): + cls_tower = self.cls_tower(feature) + box_tower = self.bbox_tower(feature) + + logits.append(self.cls_logits(cls_tower)) + if self.centerness_on_reg: + centerness.append(self.centerness(box_tower)) + else: + centerness.append(self.centerness(cls_tower)) + + bbox_pred = self.scales[l](self.bbox_pred(box_tower)) + if self.norm_reg_targets: + bbox_pred = F.relu(bbox_pred) + if self.training: + bbox_reg.append(bbox_pred) + else: + bbox_reg.append(bbox_pred * self.fpn_strides[l]) + else: + bbox_reg.append(torch.exp(bbox_pred)) + return logits, bbox_reg, centerness + + +class FCOSModule(torch.nn.Module): + """ + Module for FCOS computation. Takes feature maps from the backbone and + FCOS outputs and losses. Only Test on FPN now. + """ + + def __init__(self, cfg): + super(FCOSModule, self).__init__() + + head = FCOSHead(cfg) + + box_selector_train = make_fcos_postprocessor(cfg, is_train=True) + box_selector_test = make_fcos_postprocessor(cfg, is_train=False) + + loss_evaluator = make_fcos_loss_evaluator(cfg) + + self.cfg = cfg + self.head = head + self.box_selector_train = box_selector_train + self.box_selector_test = box_selector_test + self.loss_evaluator = loss_evaluator + self.fpn_strides = cfg.MODEL.FCOS.FPN_STRIDES + if not cfg.MODEL.RPN_ONLY: + self.anchor_generator = make_center_anchor_generator(cfg) + + def forward(self, images, features, targets=None): + """ + Arguments: + images (ImageList): images for which we want to compute the predictions + features (list[Tensor]): features computed from the images that are + used for computing the predictions. Each tensor in the list + correspond to different feature levels + targets (list[BoxList): ground-truth boxes present in the image (optional) + + Returns: + boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per + image. + losses (dict[Tensor]): the losses for the model during training. During + testing, it is an empty dict. + """ + box_cls, box_regression, centerness = self.head(features) + locations = self.compute_locations(features) + if self.training and targets is not None: + return self._forward_train(locations, box_cls, box_regression, centerness, targets, images.image_sizes) + else: + return self._forward_test(locations, box_cls, box_regression, centerness, images.image_sizes) + + def _forward_train(self, locations, box_cls, box_regression, centerness, targets, image_sizes=None): + loss_box_cls, loss_box_reg, loss_centerness = self.loss_evaluator( + locations, box_cls, box_regression, centerness, targets + ) + losses = {"loss_cls": loss_box_cls, "loss_reg": loss_box_reg, "loss_centerness": loss_centerness} + if self.cfg.MODEL.RPN_ONLY: + return None, losses + else: + boxes = self.box_selector_train(locations, box_cls, box_regression, centerness, image_sizes) + proposals = self.anchor_generator(boxes, image_sizes, centerness) + return proposals, losses + + def _forward_test(self, locations, box_cls, box_regression, centerness, image_sizes): + boxes = self.box_selector_test(locations, box_cls, box_regression, centerness, image_sizes) + if not self.cfg.MODEL.RPN_ONLY: + boxes = self.anchor_generator(boxes, image_sizes, centerness) + return boxes, {} + + def compute_locations(self, features): + locations = [] + for level, feature in enumerate(features): + h, w = feature.size()[-2:] + locations_per_level = self.compute_locations_per_level(h, w, self.fpn_strides[level], feature.device) + locations.append(locations_per_level) + return locations + + def compute_locations_per_level(self, h, w, stride, device): + shifts_x = torch.arange(0, w * stride, step=stride, dtype=torch.float32, device=device) + shifts_y = torch.arange(0, h * stride, step=stride, dtype=torch.float32, device=device) + shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) + shift_x = shift_x.reshape(-1) + shift_y = shift_y.reshape(-1) + locations = torch.stack((shift_x, shift_y), dim=1) + stride // 2 + return locations diff --git a/maskrcnn_benchmark/modeling/rpn/inference.py b/maskrcnn_benchmark/modeling/rpn/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..02e13673bf769dccc7e7914feba4c4a3d54f2cad --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/inference.py @@ -0,0 +1,823 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import logging + +import torch + +from maskrcnn_benchmark.modeling.box_coder import BoxCoder +from maskrcnn_benchmark.structures.bounding_box import BoxList, _onnx_clip_boxes_to_image +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_ml_nms +from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes + +from ..utils import permute_and_flatten +import pdb + + +class RPNPostProcessor(torch.nn.Module): + """ + Performs post-processing on the outputs of the RPN boxes, before feeding the + proposals to the heads + """ + + def __init__( + self, pre_nms_top_n, post_nms_top_n, nms_thresh, min_size, box_coder=None, fpn_post_nms_top_n=None, onnx=False + ): + """ + Arguments: + pre_nms_top_n (int) + post_nms_top_n (int) + nms_thresh (float) + min_size (int) + box_coder (BoxCoder) + fpn_post_nms_top_n (int) + """ + super(RPNPostProcessor, self).__init__() + self.pre_nms_top_n = pre_nms_top_n + self.post_nms_top_n = post_nms_top_n + self.nms_thresh = nms_thresh + self.min_size = min_size + self.onnx = onnx + + if box_coder is None: + box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) + self.box_coder = box_coder + + if fpn_post_nms_top_n is None: + fpn_post_nms_top_n = post_nms_top_n + self.fpn_post_nms_top_n = fpn_post_nms_top_n + + def add_gt_proposals(self, proposals, targets): + """ + Arguments: + proposals: list[BoxList] + targets: list[BoxList] + """ + # Get the device we're operating on + device = proposals[0].bbox.device + + gt_boxes = [target.copy_with_fields([]) for target in targets] + + # later cat of bbox requires all fields to be present for all bbox + # so we need to add a dummy for objectness that's missing + for gt_box in gt_boxes: + gt_box.add_field("objectness", torch.ones(len(gt_box), device=device)) + + proposals = [cat_boxlist((proposal, gt_box)) for proposal, gt_box in zip(proposals, gt_boxes)] + + return proposals + + def forward_for_single_feature_map(self, anchors, objectness, box_regression): + """ + Arguments: + anchors: list[BoxList] + objectness: tensor of size N, A, H, W + box_regression: tensor of size N, A * 4, H, W + """ + device = objectness.device + N, A, H, W = objectness.shape + + # put in the same format as anchors + objectness = objectness.permute(0, 2, 3, 1).reshape(N, -1) + objectness = objectness.sigmoid() + box_regression = box_regression.view(N, -1, 4, H, W).permute(0, 3, 4, 1, 2) + box_regression = box_regression.reshape(N, -1, 4) + + num_anchors = A * H * W + + pre_nms_top_n = min(self.pre_nms_top_n, num_anchors) + objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True) + + batch_idx = torch.arange(N, device=device)[:, None] + box_regression = box_regression[batch_idx, topk_idx] + + image_shapes = [box.size for box in anchors] + concat_anchors = torch.cat([a.bbox for a in anchors], dim=0) + concat_anchors = concat_anchors.reshape(N, -1, 4)[batch_idx, topk_idx] + + proposals = self.box_coder.decode(box_regression.view(-1, 4), concat_anchors.view(-1, 4)) + + proposals = proposals.view(N, -1, 4) + + result = [] + for proposal, score, im_shape in zip(proposals, objectness, image_shapes): + if self.onnx: + proposal = _onnx_clip_boxes_to_image(proposal, im_shape) + boxlist = BoxList(proposal, im_shape, mode="xyxy") + else: + boxlist = BoxList(proposal, im_shape, mode="xyxy") + boxlist = boxlist.clip_to_image(remove_empty=False) + + boxlist.add_field("objectness", score) + boxlist = remove_small_boxes(boxlist, self.min_size) + boxlist = boxlist_nms( + boxlist, + self.nms_thresh, + max_proposals=self.post_nms_top_n, + score_field="objectness", + ) + result.append(boxlist) + return result + + def forward(self, anchors, objectness, box_regression, targets=None): + """ + Arguments: + anchors: list[list[BoxList]] + objectness: list[tensor] + box_regression: list[tensor] + + Returns: + boxlists (list[BoxList]): the post-processed anchors, after + applying box decoding and NMS + """ + sampled_boxes = [] + num_levels = len(objectness) + anchors = list(zip(*anchors)) + for a, o, b in zip(anchors, objectness, box_regression): + sampled_boxes.append(self.forward_for_single_feature_map(a, o, b)) + + boxlists = list(zip(*sampled_boxes)) + boxlists = [cat_boxlist(boxlist) for boxlist in boxlists] + + if num_levels > 1: + boxlists = self.select_over_all_levels(boxlists) + + # append ground-truth bboxes to proposals + if self.training and targets is not None: + boxlists = self.add_gt_proposals(boxlists, targets) + + return boxlists + + def select_over_all_levels(self, boxlists): + num_images = len(boxlists) + # different behavior during training and during testing: + # during training, post_nms_top_n is over *all* the proposals combined, while + # during testing, it is over the proposals for each image + # TODO resolve this difference and make it consistent. It should be per image, + # and not per batch + if self.training: + objectness = torch.cat([boxlist.get_field("objectness") for boxlist in boxlists], dim=0) + box_sizes = [len(boxlist) for boxlist in boxlists] + post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness)) + _, inds_sorted = torch.topk(objectness, post_nms_top_n, dim=0, sorted=True) + inds_mask = torch.zeros_like(objectness, dtype=torch.bool) + inds_mask[inds_sorted] = 1 + inds_mask = inds_mask.split(box_sizes) + for i in range(num_images): + boxlists[i] = boxlists[i][inds_mask[i]] + else: + for i in range(num_images): + objectness = boxlists[i].get_field("objectness") + post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness)) + _, inds_sorted = torch.topk(objectness, post_nms_top_n, dim=0, sorted=True) + boxlists[i] = boxlists[i][inds_sorted] + return boxlists + + +def make_rpn_postprocessor(config, rpn_box_coder, is_train): + fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN + if not is_train: + fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST + + pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TRAIN + post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TRAIN + if not is_train: + pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TEST + post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TEST + nms_thresh = config.MODEL.RPN.NMS_THRESH + min_size = config.MODEL.RPN.MIN_SIZE + onnx = config.MODEL.ONNX + box_selector = RPNPostProcessor( + pre_nms_top_n=pre_nms_top_n, + post_nms_top_n=post_nms_top_n, + nms_thresh=nms_thresh, + min_size=min_size, + box_coder=rpn_box_coder, + fpn_post_nms_top_n=fpn_post_nms_top_n, + onnx=onnx, + ) + return box_selector + + +class RetinaPostProcessor(torch.nn.Module): + """ + Performs post-processing on the outputs of the RetinaNet boxes. + This is only used in the testing. + """ + + def __init__( + self, + pre_nms_thresh, + pre_nms_top_n, + nms_thresh, + fpn_post_nms_top_n, + min_size, + num_classes, + box_coder=None, + ): + """ + Arguments: + pre_nms_thresh (float) + pre_nms_top_n (int) + nms_thresh (float) + fpn_post_nms_top_n (int) + min_size (int) + num_classes (int) + box_coder (BoxCoder) + """ + super(RetinaPostProcessor, self).__init__() + self.pre_nms_thresh = pre_nms_thresh + self.pre_nms_top_n = pre_nms_top_n + self.nms_thresh = nms_thresh + self.fpn_post_nms_top_n = fpn_post_nms_top_n + self.min_size = min_size + self.num_classes = num_classes + + if box_coder is None: + box_coder = BoxCoder(weights=(10.0, 10.0, 5.0, 5.0)) + self.box_coder = box_coder + + def forward_for_single_feature_map(self, anchors, box_cls, box_regression): + """ + Arguments: + anchors: list[BoxList] + box_cls: tensor of size N, A * C, H, W + box_regression: tensor of size N, A * 4, H, W + """ + device = box_cls.device + N, _, H, W = box_cls.shape + A = box_regression.size(1) // 4 + C = box_cls.size(1) // A + + # put in the same format as anchors + box_cls = permute_and_flatten(box_cls, N, A, C, H, W) + box_cls = box_cls.sigmoid() + + box_regression = permute_and_flatten(box_regression, N, A, 4, H, W) + box_regression = box_regression.reshape(N, -1, 4) + + num_anchors = A * H * W + + candidate_inds = box_cls > self.pre_nms_thresh + + pre_nms_top_n = candidate_inds.view(N, -1).sum(1) + pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_top_n) + + results = [] + for per_box_cls, per_box_regression, per_pre_nms_top_n, per_candidate_inds, per_anchors in zip( + box_cls, box_regression, pre_nms_top_n, candidate_inds, anchors + ): + # Sort and select TopN + # TODO most of this can be made out of the loop for + # all images. + # TODO:Yang: Not easy to do. Because the numbers of detections are + # different in each image. Therefore, this part needs to be done + # per image. + per_box_cls = per_box_cls[per_candidate_inds] + + per_box_cls, top_k_indices = per_box_cls.topk(per_pre_nms_top_n, sorted=False) + + per_candidate_nonzeros = per_candidate_inds.nonzero()[top_k_indices, :] + + per_box_loc = per_candidate_nonzeros[:, 0] + per_class = per_candidate_nonzeros[:, 1] + per_class += 1 + + detections = self.box_coder.decode( + per_box_regression[per_box_loc, :].view(-1, 4), per_anchors.bbox[per_box_loc, :].view(-1, 4) + ) + + boxlist = BoxList(detections, per_anchors.size, mode="xyxy") + boxlist.add_field("labels", per_class) + boxlist.add_field("scores", per_box_cls) + boxlist = boxlist.clip_to_image(remove_empty=False) + boxlist = remove_small_boxes(boxlist, self.min_size) + results.append(boxlist) + + return results + + # TODO very similar to filter_results from PostProcessor + # but filter_results is per image + # TODO Yang: solve this issue in the future. No good solution + # right now. + def select_over_all_levels(self, boxlists): + num_images = len(boxlists) + results = [] + for i in range(num_images): + scores = boxlists[i].get_field("scores") + labels = boxlists[i].get_field("labels") + boxes = boxlists[i].bbox + boxlist = boxlists[i] + result = [] + # skip the background + for j in range(1, self.num_classes): + inds = (labels == j).nonzero().view(-1) + + scores_j = scores[inds] + boxes_j = boxes[inds, :].view(-1, 4) + boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy") + boxlist_for_class.add_field("scores", scores_j) + boxlist_for_class = boxlist_nms(boxlist_for_class, self.nms_thresh, score_field="scores") + num_labels = len(boxlist_for_class) + boxlist_for_class.add_field( + "labels", torch.full((num_labels,), j, dtype=torch.int64, device=scores.device) + ) + result.append(boxlist_for_class) + + result = cat_boxlist(result) + number_of_detections = len(result) + + # Limit to max_per_image detections **over all classes** + if number_of_detections > self.fpn_post_nms_top_n > 0: + cls_scores = result.get_field("scores") + image_thresh, _ = torch.kthvalue(cls_scores.cpu(), number_of_detections - self.fpn_post_nms_top_n + 1) + keep = cls_scores >= image_thresh.item() + keep = torch.nonzero(keep).squeeze(1) + result = result[keep] + results.append(result) + return results + + def forward(self, anchors, objectness, box_regression, targets=None): + """ + Arguments: + anchors: list[list[BoxList]] + objectness: list[tensor] + box_regression: list[tensor] + + Returns: + boxlists (list[BoxList]): the post-processed anchors, after + applying box decoding and NMS + """ + sampled_boxes = [] + anchors = list(zip(*anchors)) + for a, o, b in zip(anchors, objectness, box_regression): + sampled_boxes.append(self.forward_for_single_feature_map(a, o, b)) + + boxlists = list(zip(*sampled_boxes)) + boxlists = [cat_boxlist(boxlist) for boxlist in boxlists] + + boxlists = self.select_over_all_levels(boxlists) + + return boxlists + + +def make_retina_postprocessor(config, rpn_box_coder, is_train): + pre_nms_thresh = config.MODEL.RETINANET.INFERENCE_TH + pre_nms_top_n = config.MODEL.RETINANET.PRE_NMS_TOP_N + nms_thresh = config.MODEL.RETINANET.NMS_TH + fpn_post_nms_top_n = config.MODEL.RETINANET.DETECTIONS_PER_IMG + min_size = 0 + + box_selector = RetinaPostProcessor( + pre_nms_thresh=pre_nms_thresh, + pre_nms_top_n=pre_nms_top_n, + nms_thresh=nms_thresh, + fpn_post_nms_top_n=fpn_post_nms_top_n, + min_size=min_size, + num_classes=config.MODEL.RETINANET.NUM_CLASSES, + box_coder=rpn_box_coder, + ) + + return box_selector + + +class FCOSPostProcessor(torch.nn.Module): + """ + Performs post-processing on the outputs of the RetinaNet boxes. + This is only used in the testing. + """ + + def __init__( + self, + pre_nms_thresh, + pre_nms_top_n, + nms_thresh, + fpn_post_nms_top_n, + min_size, + num_classes, + bbox_aug_enabled=False, + ): + """ + Arguments: + pre_nms_thresh (float) + pre_nms_top_n (int) + nms_thresh (float) + fpn_post_nms_top_n (int) + min_size (int) + num_classes (int) + box_coder (BoxCoder) + """ + super(FCOSPostProcessor, self).__init__() + self.pre_nms_thresh = pre_nms_thresh + self.pre_nms_top_n = pre_nms_top_n + self.nms_thresh = nms_thresh + self.fpn_post_nms_top_n = fpn_post_nms_top_n + self.min_size = min_size + self.num_classes = num_classes + self.bbox_aug_enabled = bbox_aug_enabled + + def forward_for_single_feature_map(self, locations, box_cls, box_regression, centerness, image_sizes): + """ + Arguments: + anchors: list[BoxList] + box_cls: tensor of size N, A * C, H, W + box_regression: tensor of size N, A * 4, H, W + """ + N, C, H, W = box_cls.shape + + # put in the same format as locations + box_cls = box_cls.view(N, C, H, W).permute(0, 2, 3, 1) + box_cls = box_cls.reshape(N, -1, C).sigmoid() + box_regression = box_regression.view(N, 4, H, W).permute(0, 2, 3, 1) + box_regression = box_regression.reshape(N, -1, 4) + centerness = centerness.view(N, 1, H, W).permute(0, 2, 3, 1) + centerness = centerness.reshape(N, -1).sigmoid() + + candidate_inds = box_cls > self.pre_nms_thresh + pre_nms_top_n = candidate_inds.reshape(N, -1).sum(1) + pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_top_n) + + # multiply the classification scores with centerness scores + box_cls = box_cls * centerness[:, :, None] + + results = [] + for i in range(N): + per_box_cls = box_cls[i] + per_candidate_inds = candidate_inds[i] + per_box_cls = per_box_cls[per_candidate_inds] + + per_candidate_nonzeros = per_candidate_inds.nonzero() + per_box_loc = per_candidate_nonzeros[:, 0] + per_class = per_candidate_nonzeros[:, 1] + 1 + + per_box_regression = box_regression[i] + per_box_regression = per_box_regression[per_box_loc] + per_locations = locations[per_box_loc] + + per_pre_nms_top_n = pre_nms_top_n[i] + + if per_candidate_inds.sum().item() > per_pre_nms_top_n.item(): + per_box_cls, top_k_indices = per_box_cls.topk(per_pre_nms_top_n, sorted=False) + per_class = per_class[top_k_indices] + per_box_regression = per_box_regression[top_k_indices] + per_locations = per_locations[top_k_indices] + + detections = torch.stack( + [ + per_locations[:, 0] - per_box_regression[:, 0], + per_locations[:, 1] - per_box_regression[:, 1], + per_locations[:, 0] + per_box_regression[:, 2], + per_locations[:, 1] + per_box_regression[:, 3], + ], + dim=1, + ) + + h, w = image_sizes[i] + boxlist = BoxList(detections, (int(w), int(h)), mode="xyxy") + boxlist.add_field("centers", per_locations) + boxlist.add_field("labels", per_class) + boxlist.add_field("scores", torch.sqrt(per_box_cls)) + boxlist = boxlist.clip_to_image(remove_empty=False) + boxlist = remove_small_boxes(boxlist, self.min_size) + results.append(boxlist) + + return results + + def forward(self, locations, box_cls, box_regression, centerness, image_sizes): + """ + Arguments: + anchors: list[list[BoxList]] + box_cls: list[tensor] + box_regression: list[tensor] + image_sizes: list[(h, w)] + Returns: + boxlists (list[BoxList]): the post-processed anchors, after + applying box decoding and NMS + """ + sampled_boxes = [] + for _, (l, o, b, c) in enumerate(zip(locations, box_cls, box_regression, centerness)): + sampled_boxes.append(self.forward_for_single_feature_map(l, o, b, c, image_sizes)) + + boxlists = list(zip(*sampled_boxes)) + boxlists = [cat_boxlist(boxlist) for boxlist in boxlists] + if not self.bbox_aug_enabled: + boxlists = self.select_over_all_levels(boxlists) + + return boxlists + + # TODO very similar to filter_results from PostProcessor + # but filter_results is per image + # TODO Yang: solve this issue in the future. No good solution + # right now. + def select_over_all_levels(self, boxlists): + num_images = len(boxlists) + results = [] + for i in range(num_images): + # multiclass nms + result = boxlist_ml_nms(boxlists[i], self.nms_thresh) + number_of_detections = len(result) + + # Limit to max_per_image detections **over all classes** + if number_of_detections > self.fpn_post_nms_top_n > 0: + cls_scores = result.get_field("scores") + image_thresh, _ = torch.kthvalue(cls_scores.cpu(), number_of_detections - self.fpn_post_nms_top_n + 1) + keep = cls_scores >= image_thresh.item() + keep = torch.nonzero(keep).squeeze(1) + result = result[keep] + results.append(result) + return results + + +def make_fcos_postprocessor(config, is_train=False): + pre_nms_thresh = config.MODEL.FCOS.INFERENCE_TH + if is_train: + pre_nms_thresh = config.MODEL.FCOS.INFERENCE_TH_TRAIN + pre_nms_top_n = config.MODEL.FCOS.PRE_NMS_TOP_N + fpn_post_nms_top_n = config.MODEL.FCOS.DETECTIONS_PER_IMG + if is_train: + pre_nms_top_n = config.MODEL.FCOS.PRE_NMS_TOP_N_TRAIN + fpn_post_nms_top_n = config.MODEL.FCOS.POST_NMS_TOP_N_TRAIN + nms_thresh = config.MODEL.FCOS.NMS_TH + + box_selector = FCOSPostProcessor( + pre_nms_thresh=pre_nms_thresh, + pre_nms_top_n=pre_nms_top_n, + nms_thresh=nms_thresh, + fpn_post_nms_top_n=fpn_post_nms_top_n, + min_size=0, + num_classes=config.MODEL.FCOS.NUM_CLASSES, + ) + + return box_selector + + +class ATSSPostProcessor(torch.nn.Module): + def __init__( + self, + pre_nms_thresh, + pre_nms_top_n, + nms_thresh, + fpn_post_nms_top_n, + min_size, + num_classes, + box_coder, + bbox_aug_enabled=False, + bbox_aug_vote=False, + score_agg="MEAN", + mdetr_style_aggregate_class_num=-1, + ): + super(ATSSPostProcessor, self).__init__() + self.pre_nms_thresh = pre_nms_thresh + self.pre_nms_top_n = pre_nms_top_n + self.nms_thresh = nms_thresh + self.fpn_post_nms_top_n = fpn_post_nms_top_n + self.min_size = min_size + self.num_classes = num_classes + self.bbox_aug_enabled = bbox_aug_enabled + self.box_coder = box_coder + self.bbox_aug_vote = bbox_aug_vote + self.score_agg = score_agg + self.mdetr_style_aggregate_class_num = mdetr_style_aggregate_class_num + + def forward_for_single_feature_map( + self, + box_regression, + centerness, + anchors, + box_cls=None, + token_logits=None, + dot_product_logits=None, + positive_map=None, + ): + + N, _, H, W = box_regression.shape + + A = box_regression.size(1) // 4 + + if box_cls is not None: + C = box_cls.size(1) // A + + if token_logits is not None: + T = token_logits.size(1) // A + + # put in the same format as anchors + if box_cls is not None: + # print('Classification.') + box_cls = permute_and_flatten(box_cls, N, A, C, H, W) + box_cls = box_cls.sigmoid() + + # binary focal loss version + if token_logits is not None: + # print('Token.') + token_logits = permute_and_flatten(token_logits, N, A, T, H, W) + token_logits = token_logits.sigmoid() + # turn back to original classes + scores = convert_grounding_to_od_logits( + logits=token_logits, box_cls=box_cls, positive_map=positive_map, score_agg=self.score_agg + ) + box_cls = scores + + # binary dot product focal version + if dot_product_logits is not None: + # print('Dot Product.') + dot_product_logits = dot_product_logits.sigmoid() + if self.mdetr_style_aggregate_class_num != -1: + scores = convert_grounding_to_od_logits_v2( + logits=dot_product_logits, + num_class=self.mdetr_style_aggregate_class_num, + positive_map=positive_map, + score_agg=self.score_agg, + disable_minus_one=False, + ) + else: + scores = convert_grounding_to_od_logits( + logits=dot_product_logits, box_cls=box_cls, positive_map=positive_map, score_agg=self.score_agg + ) + box_cls = scores + + box_regression = permute_and_flatten(box_regression, N, A, 4, H, W) + box_regression = box_regression.reshape(N, -1, 4) + + candidate_inds = box_cls > self.pre_nms_thresh + pre_nms_top_n = candidate_inds.reshape(N, -1).sum(1) + pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_top_n) + + centerness = permute_and_flatten(centerness, N, A, 1, H, W) + centerness = centerness.reshape(N, -1).sigmoid() + + # multiply the classification scores with centerness scores + + box_cls = box_cls * centerness[:, :, None] + + results = [] + + for per_box_cls, per_box_regression, per_pre_nms_top_n, per_candidate_inds, per_anchors in zip( + box_cls, box_regression, pre_nms_top_n, candidate_inds, anchors + ): + per_box_cls = per_box_cls[per_candidate_inds] + + per_box_cls, top_k_indices = per_box_cls.topk(per_pre_nms_top_n, sorted=False) + + per_candidate_nonzeros = per_candidate_inds.nonzero()[top_k_indices, :] + + per_box_loc = per_candidate_nonzeros[:, 0] + per_class = per_candidate_nonzeros[:, 1] + 1 + + # print(per_class) + + detections = self.box_coder.decode( + per_box_regression[per_box_loc, :].view(-1, 4), per_anchors.bbox[per_box_loc, :].view(-1, 4) + ) + + boxlist = BoxList(detections, per_anchors.size, mode="xyxy") + boxlist.add_field("labels", per_class) + boxlist.add_field("scores", torch.sqrt(per_box_cls)) + boxlist = boxlist.clip_to_image(remove_empty=False) + boxlist = remove_small_boxes(boxlist, self.min_size) + results.append(boxlist) + + return results + + def forward( + self, + box_regression, + centerness, + anchors, + box_cls=None, + token_logits=None, + dot_product_logits=None, + positive_map=None, + ): + sampled_boxes = [] + anchors = list(zip(*anchors)) + for idx, (b, c, a) in enumerate(zip(box_regression, centerness, anchors)): + o = None + t = None + d = None + if box_cls is not None: + o = box_cls[idx] + if token_logits is not None: + t = token_logits[idx] + if dot_product_logits is not None: + d = dot_product_logits[idx] + + sampled_boxes.append(self.forward_for_single_feature_map(b, c, a, o, t, d, positive_map)) + + boxlists = list(zip(*sampled_boxes)) + boxlists = [cat_boxlist(boxlist) for boxlist in boxlists] + if not (self.bbox_aug_enabled and not self.bbox_aug_vote): + boxlists = self.select_over_all_levels(boxlists) + + return boxlists + + # TODO very similar to filter_results from PostProcessor + # but filter_results is per image + # TODO Yang: solve this issue in the future. No good solution + # right now. + def select_over_all_levels(self, boxlists): + num_images = len(boxlists) + results = [] + for i in range(num_images): + # multiclass nms + result = boxlist_ml_nms(boxlists[i], self.nms_thresh) + number_of_detections = len(result) + + # Limit to max_per_image detections **over all classes** + if number_of_detections > self.fpn_post_nms_top_n > 0: + cls_scores = result.get_field("scores") + image_thresh, _ = torch.kthvalue( + # TODO: confirm with Pengchuan and Xiyang, torch.kthvalue is not implemented for 'Half' + # cls_scores.cpu(), + cls_scores.cpu().float(), + number_of_detections - self.fpn_post_nms_top_n + 1, + ) + keep = cls_scores >= image_thresh.item() + keep = torch.nonzero(keep).squeeze(1) + result = result[keep] + results.append(result) + return results + + +def convert_grounding_to_od_logits(logits, box_cls, positive_map, score_agg=None): + scores = torch.zeros(logits.shape[0], logits.shape[1], box_cls.shape[2]).to(logits.device) + # 256 -> 80, average for each class + if positive_map is not None: + # score aggregation method + if score_agg == "MEAN": + for label_j in positive_map: + scores[:, :, label_j - 1] = logits[:, :, torch.LongTensor(positive_map[label_j])].mean(-1) + elif score_agg == "MAX": + # torch.max() returns (values, indices) + for label_j in positive_map: + scores[:, :, label_j - 1] = logits[:, :, torch.LongTensor(positive_map[label_j])].max(-1)[0] + elif score_agg == "ONEHOT": + # one hot + scores = logits[:, :, : len(positive_map)] + else: + raise NotImplementedError + return scores + + +def convert_grounding_to_od_logits_v2(logits, num_class, positive_map, score_agg=None, disable_minus_one=True): + + scores = torch.zeros(logits.shape[0], logits.shape[1], num_class).to(logits.device) + # 256 -> 80, average for each class + if positive_map is not None: + # score aggregation method + if score_agg == "MEAN": + for label_j in positive_map: + locations_label_j = positive_map[label_j] + if isinstance(locations_label_j, int): + locations_label_j = [locations_label_j] + scores[:, :, label_j if disable_minus_one else label_j - 1] = logits[ + :, :, torch.LongTensor(locations_label_j) + ].mean(-1) + elif score_agg == "POWER": + for label_j in positive_map: + locations_label_j = positive_map[label_j] + if isinstance(locations_label_j, int): + locations_label_j = [locations_label_j] + + probability = torch.prod(logits[:, :, torch.LongTensor(locations_label_j)], dim=-1).squeeze(-1) + probability = torch.pow(probability, 1 / len(locations_label_j)) + scores[:, :, label_j if disable_minus_one else label_j - 1] = probability + elif score_agg == "MAX": + # torch.max() returns (values, indices) + for label_j in positive_map: + scores[:, :, label_j if disable_minus_one else label_j - 1] = logits[ + :, :, torch.LongTensor(positive_map[label_j]) + ].max(-1)[0] + elif score_agg == "ONEHOT": + # one hot + scores = logits[:, :, : len(positive_map)] + else: + raise NotImplementedError + return scores + + +def make_atss_postprocessor(config, box_coder, is_train=False): + pre_nms_thresh = config.MODEL.ATSS.INFERENCE_TH + if is_train: + pre_nms_thresh = config.MODEL.ATSS.INFERENCE_TH_TRAIN + pre_nms_top_n = config.MODEL.ATSS.PRE_NMS_TOP_N + fpn_post_nms_top_n = config.MODEL.ATSS.DETECTIONS_PER_IMG + if is_train: + pre_nms_top_n = config.MODEL.ATSS.PRE_NMS_TOP_N_TRAIN + fpn_post_nms_top_n = config.MODEL.ATSS.POST_NMS_TOP_N_TRAIN + nms_thresh = config.MODEL.ATSS.NMS_TH + score_agg = config.MODEL.DYHEAD.SCORE_AGG + + box_selector = ATSSPostProcessor( + pre_nms_thresh=pre_nms_thresh, + pre_nms_top_n=pre_nms_top_n, + nms_thresh=nms_thresh, + fpn_post_nms_top_n=fpn_post_nms_top_n, + min_size=0, + num_classes=config.MODEL.ATSS.NUM_CLASSES, + box_coder=box_coder, + bbox_aug_enabled=config.TEST.USE_MULTISCALE, + score_agg=score_agg, + mdetr_style_aggregate_class_num=config.TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM, + ) + + return box_selector diff --git a/maskrcnn_benchmark/modeling/rpn/loss.py b/maskrcnn_benchmark/modeling/rpn/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..fc3633729b31738e35ab9f3a83c31d4fcede62fe --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/loss.py @@ -0,0 +1,1315 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +This file contains specific functions for computing losses on the RPN +file +""" + +import torch +from torch import nn +from torch.nn import functional as F + +from ..balanced_positive_negative_sampler import BalancedPositiveNegativeSampler +from ..utils import cat, concat_box_prediction_layers + +from maskrcnn_benchmark.layers import smooth_l1_loss +from maskrcnn_benchmark.modeling.matcher import Matcher +from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.layers import SigmoidFocalLoss, IOULoss, TokenSigmoidFocalLoss +from maskrcnn_benchmark.utils.comm import get_world_size, reduce_sum +from maskrcnn_benchmark.utils.amp import custom_fwd, custom_bwd +from maskrcnn_benchmark.utils.shallow_contrastive_loss_helper import * +import pdb +from transformers import AutoTokenizer + +INF = 1e8 + + +class RPNLossComputation(object): + """ + This class computes the RPN loss. + """ + + def __init__(self, proposal_matcher, fg_bg_sampler, box_coder): + """ + Arguments: + proposal_matcher (Matcher) + fg_bg_sampler (BalancedPositiveNegativeSampler) + box_coder (BoxCoder) + """ + # self.target_preparator = target_preparator + self.proposal_matcher = proposal_matcher + self.fg_bg_sampler = fg_bg_sampler + self.box_coder = box_coder + + def match_targets_to_anchors(self, anchor, target): + match_quality_matrix = boxlist_iou(target, anchor) + matched_idxs = self.proposal_matcher(match_quality_matrix) + # RPN doesn't need any fields from target + # for creating the labels, so clear them all + target = target.copy_with_fields([]) + # get the targets corresponding GT for each anchor + # NB: need to clamp the indices because we can have a single + # GT in the image, and matched_idxs can be -2, which goes + # out of bounds + + if len(target): + matched_targets = target[matched_idxs.clamp(min=0)] + else: + matched_targets = target + + matched_targets.add_field("matched_idxs", matched_idxs) + return matched_targets + + def prepare_targets(self, anchors, targets): + labels = [] + regression_targets = [] + for anchors_per_image, targets_per_image in zip(anchors, targets): + matched_targets = self.match_targets_to_anchors(anchors_per_image, targets_per_image) + + matched_idxs = matched_targets.get_field("matched_idxs") + labels_per_image = matched_idxs >= 0 + labels_per_image = labels_per_image.to(dtype=torch.float32) + # discard anchors that go out of the boundaries of the image + labels_per_image[~anchors_per_image.get_field("visibility")] = -1 + + # discard indices that are between thresholds + inds_to_discard = matched_idxs == Matcher.BETWEEN_THRESHOLDS + labels_per_image[inds_to_discard] = -1 + + # compute regression targets + if not matched_targets.bbox.shape[0]: + zeros = torch.zeros_like(labels_per_image) + regression_targets_per_image = torch.stack((zeros, zeros, zeros, zeros), dim=1) + else: + regression_targets_per_image = self.box_coder.encode(matched_targets.bbox, anchors_per_image.bbox) + + labels.append(labels_per_image) + regression_targets.append(regression_targets_per_image) + + return labels, regression_targets + + @custom_fwd(cast_inputs=torch.float32) + def __call__(self, anchors, objectness, box_regression, targets): + """ + Arguments: + anchors (list[BoxList]) + objectness (list[Tensor]) + box_regression (list[Tensor]) + targets (list[BoxList]) + + Returns: + objectness_loss (Tensor) + box_loss (Tensor + """ + anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors] + labels, regression_targets = self.prepare_targets(anchors, targets) + sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels) + sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0)).squeeze(1) + sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0)).squeeze(1) + + sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0) + + objectness_flattened = [] + box_regression_flattened = [] + # for each feature level, permute the outputs to make them be in the + # same format as the labels. Note that the labels are computed for + # all feature levels concatenated, so we keep the same representation + # for the objectness and the box_regression + for objectness_per_level, box_regression_per_level in zip(objectness, box_regression): + N, A, H, W = objectness_per_level.shape + objectness_per_level = objectness_per_level.permute(0, 2, 3, 1).reshape(N, -1) + box_regression_per_level = box_regression_per_level.view(N, -1, 4, H, W) + box_regression_per_level = box_regression_per_level.permute(0, 3, 4, 1, 2) + box_regression_per_level = box_regression_per_level.reshape(N, -1, 4) + objectness_flattened.append(objectness_per_level) + box_regression_flattened.append(box_regression_per_level) + # concatenate on the first dimension (representing the feature levels), to + # take into account the way the labels were generated (with all feature maps + # being concatenated as well) + objectness = cat(objectness_flattened, dim=1).reshape(-1) + box_regression = cat(box_regression_flattened, dim=1).reshape(-1, 4) + + labels = torch.cat(labels, dim=0) + regression_targets = torch.cat(regression_targets, dim=0) + + box_loss = smooth_l1_loss( + box_regression[sampled_pos_inds], + regression_targets[sampled_pos_inds], + beta=1.0 / 9, + size_average=False, + ) / (sampled_inds.numel()) + + objectness_loss = F.binary_cross_entropy_with_logits(objectness[sampled_inds], labels[sampled_inds]) + + return objectness_loss, box_loss + + +class FocalLossComputation(object): + """ + This class computes the RetinaNet loss. + """ + + def __init__( + self, + proposal_matcher, + box_coder, + generate_labels_func, + sigmoid_focal_loss, + bbox_reg_beta=0.11, + regress_norm=1.0, + ): + """ + Arguments: + proposal_matcher (Matcher) + box_coder (BoxCoder) + """ + self.proposal_matcher = proposal_matcher + self.box_coder = box_coder + self.box_cls_loss_func = sigmoid_focal_loss + self.bbox_reg_beta = bbox_reg_beta + self.copied_fields = ["labels"] + self.generate_labels_func = generate_labels_func + self.discard_cases = ["between_thresholds"] + self.regress_norm = regress_norm + + def match_targets_to_anchors(self, anchor, target, copied_fields=[]): + match_quality_matrix = boxlist_iou(target, anchor) + matched_idxs = self.proposal_matcher(match_quality_matrix) + # RPN doesn't need any fields from target + # for creating the labels, so clear them all + target = target.copy_with_fields(copied_fields) + # get the targets corresponding GT for each anchor + # NB: need to clamp the indices because we can have a single + # GT in the image, and matched_idxs can be -2, which goes + # out of bounds + matched_targets = target[matched_idxs.clamp(min=0)] + matched_targets.add_field("matched_idxs", matched_idxs) + return matched_targets + + def prepare_targets(self, anchors, targets): + labels = [] + regression_targets = [] + for anchors_per_image, targets_per_image in zip(anchors, targets): + matched_targets = self.match_targets_to_anchors(anchors_per_image, targets_per_image, self.copied_fields) + + matched_idxs = matched_targets.get_field("matched_idxs") + labels_per_image = self.generate_labels_func(matched_targets) + labels_per_image = labels_per_image.to(dtype=torch.float32) + + # Background (negative examples) + bg_indices = matched_idxs == Matcher.BELOW_LOW_THRESHOLD + labels_per_image[bg_indices] = 0 + + # discard anchors that go out of the boundaries of the image + if "not_visibility" in self.discard_cases: + labels_per_image[~anchors_per_image.get_field("visibility")] = -1 + + # discard indices that are between thresholds + if "between_thresholds" in self.discard_cases: + inds_to_discard = matched_idxs == Matcher.BETWEEN_THRESHOLDS + labels_per_image[inds_to_discard] = -1 + + # compute regression targets + regression_targets_per_image = self.box_coder.encode(matched_targets.bbox, anchors_per_image.bbox) + + labels.append(labels_per_image) + regression_targets.append(regression_targets_per_image) + + return labels, regression_targets + + @custom_fwd(cast_inputs=torch.float32) + def __call__(self, anchors, box_cls, box_regression, targets): + """ + Arguments: + anchors (list[BoxList]) + box_cls (list[Tensor]) + box_regression (list[Tensor]) + targets (list[BoxList]) + + Returns: + retinanet_cls_loss (Tensor) + retinanet_regression_loss (Tensor + """ + anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors] + labels, regression_targets = self.prepare_targets(anchors, targets) + + N = len(labels) + box_cls, box_regression = concat_box_prediction_layers(box_cls, box_regression) + + labels = torch.cat(labels, dim=0) + regression_targets = torch.cat(regression_targets, dim=0) + pos_inds = torch.nonzero(labels > 0).squeeze(1) + + retinanet_regression_loss = smooth_l1_loss( + box_regression[pos_inds], + regression_targets[pos_inds], + beta=self.bbox_reg_beta, + size_average=False, + ) / (max(1, pos_inds.numel() * self.regress_norm)) + + labels = labels.int() + + retinanet_cls_loss = self.box_cls_loss_func(box_cls, labels) / (pos_inds.numel() + N) + + return retinanet_cls_loss, retinanet_regression_loss + + +class FCOSLossComputation(object): + """ + This class computes the FCOS losses. + """ + + def __init__(self, cfg): + self.cls_loss_func = SigmoidFocalLoss(cfg.MODEL.FOCAL.LOSS_GAMMA, cfg.MODEL.FOCAL.LOSS_ALPHA) + self.fpn_strides = cfg.MODEL.FCOS.FPN_STRIDES + self.center_sampling_radius = cfg.MODEL.FCOS.CENTER_SAMPLING_RADIUS + self.iou_loss_type = cfg.MODEL.FCOS.IOU_LOSS_TYPE + self.norm_reg_targets = cfg.MODEL.FCOS.NORM_REG_TARGETS + self.use_gt_center = cfg.MODEL.FCOS.USE_GT_CENTER + + # we make use of IOU Loss for bounding boxes regression, + # but we found that L1 in log scale can yield a similar performance + self.box_reg_loss_func = IOULoss(self.iou_loss_type) + self.centerness_loss_func = torch.nn.BCEWithLogitsLoss(reduction="sum") + + def get_sample_region(self, gt, strides, num_points_per, gt_xs, gt_ys, radius=1.0): + """ + This code is from + https://github.com/yqyao/FCOS_PLUS/blob/0d20ba34ccc316650d8c30febb2eb40cb6eaae37/ + maskrcnn_benchmark/modeling/rpn/fcos/loss.py#L42 + """ + num_gts = gt.shape[0] + K = len(gt_xs) + gt = gt[None].expand(K, num_gts, 4) + center_x = (gt[..., 0] + gt[..., 2]) / 2 + center_y = (gt[..., 1] + gt[..., 3]) / 2 + center_gt = gt.new_zeros(gt.shape) + # no gt + if center_x[..., 0].sum() == 0: + return gt_xs.new_zeros(gt_xs.shape, dtype=torch.uint8) + beg = 0 + for level, n_p in enumerate(num_points_per): + end = beg + n_p + stride = strides[level] * radius + xmin = center_x[beg:end] - stride + ymin = center_y[beg:end] - stride + xmax = center_x[beg:end] + stride + ymax = center_y[beg:end] + stride + # limit sample region in gt + center_gt[beg:end, :, 0] = torch.where(xmin > gt[beg:end, :, 0], xmin, gt[beg:end, :, 0]) + center_gt[beg:end, :, 1] = torch.where(ymin > gt[beg:end, :, 1], ymin, gt[beg:end, :, 1]) + center_gt[beg:end, :, 2] = torch.where(xmax > gt[beg:end, :, 2], gt[beg:end, :, 2], xmax) + center_gt[beg:end, :, 3] = torch.where(ymax > gt[beg:end, :, 3], gt[beg:end, :, 3], ymax) + beg = end + left = gt_xs[:, None] - center_gt[..., 0] + right = center_gt[..., 2] - gt_xs[:, None] + top = gt_ys[:, None] - center_gt[..., 1] + bottom = center_gt[..., 3] - gt_ys[:, None] + center_bbox = torch.stack((left, top, right, bottom), -1) + inside_gt_bbox_mask = center_bbox.min(-1)[0] > 0 + return inside_gt_bbox_mask + + def prepare_targets(self, points, targets): + object_sizes_of_interest = [ + [-1, 64], + [64, 128], + [128, 256], + [256, 512], + [512, INF], + ] + expanded_object_sizes_of_interest = [] + for l, points_per_level in enumerate(points): + object_sizes_of_interest_per_level = points_per_level.new_tensor(object_sizes_of_interest[l]) + expanded_object_sizes_of_interest.append( + object_sizes_of_interest_per_level[None].expand(len(points_per_level), -1) + ) + + expanded_object_sizes_of_interest = torch.cat(expanded_object_sizes_of_interest, dim=0) + num_points_per_level = [len(points_per_level) for points_per_level in points] + self.num_points_per_level = num_points_per_level + points_all_level = torch.cat(points, dim=0) + labels, reg_targets = self.compute_targets_for_locations( + points_all_level, targets, expanded_object_sizes_of_interest + ) + + for i in range(len(labels)): + labels[i] = torch.split(labels[i], num_points_per_level, dim=0) + reg_targets[i] = torch.split(reg_targets[i], num_points_per_level, dim=0) + + labels_level_first = [] + reg_targets_level_first = [] + for level in range(len(points)): + labels_level_first.append(torch.cat([labels_per_im[level] for labels_per_im in labels], dim=0)) + + reg_targets_per_level = torch.cat([reg_targets_per_im[level] for reg_targets_per_im in reg_targets], dim=0) + + if self.norm_reg_targets: + reg_targets_per_level = reg_targets_per_level / self.fpn_strides[level] + reg_targets_level_first.append(reg_targets_per_level) + + return labels_level_first, reg_targets_level_first + + def compute_targets_for_locations(self, locations, targets, object_sizes_of_interest): + labels = [] + reg_targets = [] + xs, ys = locations[:, 0], locations[:, 1] + + for im_i in range(len(targets)): + targets_per_im = targets[im_i] + assert targets_per_im.mode == "xyxy" + + if self.use_gt_center: + center = targets_per_im.get_field("cbox") + bboxes = center.bbox + area = center.area() + else: + bboxes = targets_per_im.bbox + area = targets_per_im.area() + labels_per_im = targets_per_im.get_field("labels") + + l = xs[:, None] - bboxes[:, 0][None] + t = ys[:, None] - bboxes[:, 1][None] + r = bboxes[:, 2][None] - xs[:, None] + b = bboxes[:, 3][None] - ys[:, None] + reg_targets_per_im = torch.stack([l, t, r, b], dim=2) + + if self.center_sampling_radius > 0: + is_in_boxes = self.get_sample_region( + bboxes, self.fpn_strides, self.num_points_per_level, xs, ys, radius=self.center_sampling_radius + ) + else: + # no center sampling, it will use all the locations within a ground-truth box + is_in_boxes = reg_targets_per_im.min(dim=2)[0] > 0 + + max_reg_targets_per_im = reg_targets_per_im.max(dim=2)[0] + # limit the regression range for each location + is_cared_in_the_level = (max_reg_targets_per_im >= object_sizes_of_interest[:, [0]]) & ( + max_reg_targets_per_im <= object_sizes_of_interest[:, [1]] + ) + + locations_to_gt_area = area[None].repeat(len(locations), 1) + locations_to_gt_area[is_in_boxes == 0] = INF + locations_to_gt_area[is_cared_in_the_level == 0] = INF + + # if there are still more than one objects for a location, + # we choose the one with minimal area + locations_to_min_area, locations_to_gt_inds = locations_to_gt_area.min(dim=1) + + reg_targets_per_im = reg_targets_per_im[range(len(locations)), locations_to_gt_inds] + labels_per_im = labels_per_im[locations_to_gt_inds] + labels_per_im[locations_to_min_area == INF] = 0 + + labels.append(labels_per_im) + reg_targets.append(reg_targets_per_im) + + return labels, reg_targets + + def compute_centerness_targets(self, reg_targets): + left_right = reg_targets[:, [0, 2]] + top_bottom = reg_targets[:, [1, 3]] + centerness = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * ( + top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0] + ) + return torch.sqrt(centerness) + + @custom_fwd(cast_inputs=torch.float32) + def __call__(self, locations, box_cls, box_regression, centerness, targets): + """ + Arguments: + locations (list[BoxList]) + box_cls (list[Tensor]) + box_regression (list[Tensor]) + centerness (list[Tensor]) + targets (list[BoxList]) + + Returns: + cls_loss (Tensor) + reg_loss (Tensor) + centerness_loss (Tensor) + """ + N = box_cls[0].size(0) + num_classes = box_cls[0].size(1) + labels, reg_targets = self.prepare_targets(locations, targets) + + box_cls_flatten = [] + box_regression_flatten = [] + centerness_flatten = [] + labels_flatten = [] + reg_targets_flatten = [] + for l in range(len(labels)): + box_cls_flatten.append(box_cls[l].permute(0, 2, 3, 1).reshape(-1, num_classes)) + box_regression_flatten.append(box_regression[l].permute(0, 2, 3, 1).reshape(-1, 4)) + labels_flatten.append(labels[l].reshape(-1)) + reg_targets_flatten.append(reg_targets[l].reshape(-1, 4)) + centerness_flatten.append(centerness[l].reshape(-1)) + + box_cls_flatten = torch.cat(box_cls_flatten, dim=0) + box_regression_flatten = torch.cat(box_regression_flatten, dim=0) + centerness_flatten = torch.cat(centerness_flatten, dim=0) + labels_flatten = torch.cat(labels_flatten, dim=0) + reg_targets_flatten = torch.cat(reg_targets_flatten, dim=0) + + pos_inds = torch.nonzero(labels_flatten > 0).squeeze(1) + + box_regression_flatten = box_regression_flatten[pos_inds] + reg_targets_flatten = reg_targets_flatten[pos_inds] + centerness_flatten = centerness_flatten[pos_inds] + + cls_loss = self.cls_loss_func(box_cls_flatten, labels_flatten.int()) / max(pos_inds.numel(), 1.0) + + if pos_inds.numel() > 0: + centerness_targets = self.compute_centerness_targets(reg_targets_flatten) + + reg_loss = ( + self.box_reg_loss_func(box_regression_flatten, reg_targets_flatten, centerness_targets) + / centerness_targets.sum() + ) + centerness_loss = self.centerness_loss_func(centerness_flatten, centerness_targets) / max( + pos_inds.numel(), 1.0 + ) + else: + reg_loss = box_regression_flatten.sum() + centerness_loss = centerness_flatten.sum() + + return cls_loss, reg_loss, centerness_loss + + +# class ATSSLossComputation(object): +class ATSSLossComputation(torch.nn.Module): + def __init__(self, cfg, box_coder): + super(ATSSLossComputation, self).__init__() + + self.cfg = cfg + self.cls_loss_func = SigmoidFocalLoss(cfg.MODEL.FOCAL.LOSS_GAMMA, cfg.MODEL.FOCAL.LOSS_ALPHA) + self.centerness_loss_func = torch.nn.BCEWithLogitsLoss(reduction="sum") + self.matcher = Matcher(cfg.MODEL.FOCAL.FG_IOU_THRESHOLD, cfg.MODEL.FOCAL.BG_IOU_THRESHOLD, True) + self.box_coder = box_coder + + if ( + self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS + or self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS + ): + self.token_loss_func = TokenSigmoidFocalLoss( + cfg.MODEL.DYHEAD.FUSE_CONFIG.TOKEN_ALPHA, cfg.MODEL.DYHEAD.FUSE_CONFIG.TOKEN_GAMMA + ) + + if cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE in ["roberta-fused", "roberta-fused-v2", "roberta-fused-tiny"]: + self.lang = "roberta-base" + else: + self.lang = cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE + + # self.tokenizer = AutoTokenizer.from_pretrained(self.lang) + if self.cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + from transformers import CLIPTokenizerFast + + # self.tokenizer = build_tokenizer(self.cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE) + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + print("Reuse token 'ðŁĴij' (token_id = 49404) for mask token!") + self.tokenizer = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + self.tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + else: + self.tokenizer = AutoTokenizer.from_pretrained(self.lang) + + # if use shallow contrastive loss + if ( + self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS + or self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS + ): + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS: + assert self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS == False + channels = cfg.MODEL.DYHEAD.CHANNELS + num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * cfg.MODEL.RPN.SCALES_PER_OCTAVE + shallow_input_dim = channels * num_anchors + elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS: + assert self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS == False + shallow_input_dim = cfg.MODEL.SWINT.OUT_CHANNELS[-2] + + shallow_log_scale = self.cfg.MODEL.DYHEAD.SHALLOW_LOG_SCALE + shallow_contrastive_hdim = cfg.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_CONTRASTIVE_HIDDEN_DIM + # self.shallow_contrastive_projection_image = nn.Conv2d(channels, num_anchors * shallow_contrastive_hdim, + # kernel_size=1) + self.shallow_contrastive_projection_image = nn.Linear( + shallow_input_dim, shallow_contrastive_hdim, bias=True + ) + self.shallow_contrastive_projection_text = nn.Linear( + self.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, shallow_contrastive_hdim, bias=True + ) + self.shallow_log_scale = nn.Parameter(torch.Tensor([shallow_log_scale]), requires_grad=True) + + # (initialization) if use shallow contrastive loss + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS: + for modules in [self.shallow_contrastive_projection_image, self.shallow_contrastive_projection_text]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + if isinstance(l, nn.Linear): + torch.nn.init.xavier_uniform_(l.weight) + l.bias.data.fill_(0) + + def NllSoftMaxLoss(self, logits, target): + loss_ce = -target * logits.log_softmax( + -1 + ) # basically, only the those positives with positive target_sim will have losses + return loss_ce + + def ContrastiveAlignLoss(self, logits, positive_map): + positive_logits = -logits.masked_fill(~positive_map, 0) + negative_logits = logits # .masked_fill(positive_map, -1000000) + + boxes_with_pos = positive_map.any(2) + pos_term = positive_logits.sum(2) + neg_term = negative_logits.logsumexp(2) + + nb_pos = positive_map.sum(2) + 1e-6 + + box_to_token_loss = ((pos_term / nb_pos + neg_term)).masked_fill(~boxes_with_pos, 0).sum() + + tokens_with_pos = positive_map.any(1) + pos_term = positive_logits.sum(1) + neg_term = negative_logits.logsumexp(1) + + nb_pos = positive_map.sum(1) + 1e-6 + + tokens_to_boxes_loss = ((pos_term / nb_pos + neg_term)).masked_fill(~tokens_with_pos, 0).sum() + tot_loss = (box_to_token_loss + tokens_to_boxes_loss) / 2 + + return tot_loss + + def GIoULoss(self, pred, target, anchor, weight=None): + pred_boxes = self.box_coder.decode(pred.view(-1, 4), anchor.view(-1, 4)) + pred_x1 = pred_boxes[:, 0] + pred_y1 = pred_boxes[:, 1] + pred_x2 = pred_boxes[:, 2] + pred_y2 = pred_boxes[:, 3] + pred_x2 = torch.max(pred_x1, pred_x2) + pred_y2 = torch.max(pred_y1, pred_y2) + pred_area = (pred_x2 - pred_x1) * (pred_y2 - pred_y1) + + gt_boxes = self.box_coder.decode(target.view(-1, 4), anchor.view(-1, 4)) + target_x1 = gt_boxes[:, 0] + target_y1 = gt_boxes[:, 1] + target_x2 = gt_boxes[:, 2] + target_y2 = gt_boxes[:, 3] + target_area = (target_x2 - target_x1) * (target_y2 - target_y1) + + x1_intersect = torch.max(pred_x1, target_x1) + y1_intersect = torch.max(pred_y1, target_y1) + x2_intersect = torch.min(pred_x2, target_x2) + y2_intersect = torch.min(pred_y2, target_y2) + area_intersect = torch.zeros(pred_x1.size()).to(pred) + mask = (y2_intersect > y1_intersect) * (x2_intersect > x1_intersect) + area_intersect[mask] = (x2_intersect[mask] - x1_intersect[mask]) * (y2_intersect[mask] - y1_intersect[mask]) + + x1_enclosing = torch.min(pred_x1, target_x1) + y1_enclosing = torch.min(pred_y1, target_y1) + x2_enclosing = torch.max(pred_x2, target_x2) + y2_enclosing = torch.max(pred_y2, target_y2) + area_enclosing = (x2_enclosing - x1_enclosing) * (y2_enclosing - y1_enclosing) + 1e-7 + + area_union = pred_area + target_area - area_intersect + 1e-7 + ious = area_intersect / area_union + gious = ious - (area_enclosing - area_union) / area_enclosing + + losses = 1 - gious + + if weight is not None and weight.sum() > 0: + return (losses * weight).sum() + else: + assert losses.numel() != 0 + return losses.sum() + + def prepare_targets(self, targets, anchors, tokenized=None, positive_map=None, proj_tokens=None): + cls_labels = [] + reg_targets = [] + token_labels = [] + map_labels = [] + + gold_box_od_labels = [] + od_label_of_tokens_labels = [] + positive_indices = [] + + offset = 0 + + for im_i in range(len(targets)): + targets_per_im = targets[im_i] + assert targets_per_im.mode == "xyxy" + # bboxes_per_im = targets_per_im.get_field("boxes") + bboxes_per_im = targets_per_im.bbox + labels_per_im = targets_per_im.get_field("labels") + num_gt = len(bboxes_per_im) + + if positive_map is not None: + token_per_im = positive_map[offset : offset + num_gt, :] + offset += num_gt + + # shallow contrastive + if "original_od_label" in targets_per_im.fields(): + gold_box_od_label = targets_per_im.get_field("original_od_label") + if "positive_map_for_od_labels" in targets_per_im.fields(): + od_label_of_token_per_im = targets_per_im.get_field("positive_map_for_od_labels") + + # print(gold_box_od_label) + # print(od_label_of_token_per_im) + + if positive_map is not None and proj_tokens is not None: + if "tokens_positive" in targets_per_im.fields(): + cur_tokens = targets_per_im.get_field("tokens_positive") + else: + cur_tokens = targets_per_im.get_field("tokens") + map = torch.zeros((len(cur_tokens), proj_tokens.shape[1]), dtype=torch.bool) + for j, tok_list in enumerate(cur_tokens): + for (beg, end) in tok_list: + beg_pos = tokenized.char_to_token(im_i, beg) + end_pos = tokenized.char_to_token(im_i, end - 1) + if beg_pos is None: + try: + beg_pos = tokenized.char_to_token(im_i, beg + 1) + if beg_pos is None: + beg_pos = tokenized.char_to_token(im_i, beg + 2) + except: + beg_pos = None + if end_pos is None: + try: + end_pos = tokenized.char_to_token(im_i, end - 2) + if end_pos is None: + end_pos = tokenized.char_to_token(im_i, end - 3) + except: + end_pos = None + if beg_pos is None or end_pos is None: + continue + + assert beg_pos is not None and end_pos is not None + map[j, beg_pos : end_pos + 1].fill_(True) + + anchors_per_im = cat_boxlist(anchors[im_i]) + + num_anchors_per_loc = len(self.cfg.MODEL.RPN.ASPECT_RATIOS) * self.cfg.MODEL.RPN.SCALES_PER_OCTAVE + num_anchors_per_level = [len(anchors_per_level.bbox) for anchors_per_level in anchors[im_i]] + ious = boxlist_iou(anchors_per_im, targets_per_im) + + gt_cx = (bboxes_per_im[:, 2] + bboxes_per_im[:, 0]) / 2.0 + gt_cy = (bboxes_per_im[:, 3] + bboxes_per_im[:, 1]) / 2.0 + gt_points = torch.stack((gt_cx, gt_cy), dim=1) + + anchors_cx_per_im = (anchors_per_im.bbox[:, 2] + anchors_per_im.bbox[:, 0]) / 2.0 + anchors_cy_per_im = (anchors_per_im.bbox[:, 3] + anchors_per_im.bbox[:, 1]) / 2.0 + anchor_points = torch.stack((anchors_cx_per_im, anchors_cy_per_im), dim=1) + + distances = (anchor_points[:, None, :] - gt_points[None, :, :]).pow(2).sum(-1).sqrt() + + # Selecting candidates based on the center distance between anchor box and object + candidate_idxs = [] + star_idx = 0 + for level, anchors_per_level in enumerate(anchors[im_i]): + end_idx = star_idx + num_anchors_per_level[level] + distances_per_level = distances[star_idx:end_idx, :] + topk = min(self.cfg.MODEL.ATSS.TOPK * num_anchors_per_loc, num_anchors_per_level[level]) + _, topk_idxs_per_level = distances_per_level.topk(topk, dim=0, largest=False) + candidate_idxs.append(topk_idxs_per_level + star_idx) + star_idx = end_idx + candidate_idxs = torch.cat(candidate_idxs, dim=0) + + # Using the sum of mean and standard deviation as the IoU threshold to select final positive samples + candidate_ious = ious[candidate_idxs, torch.arange(num_gt)] + iou_mean_per_gt = candidate_ious.mean(0) + iou_std_per_gt = candidate_ious.std(0) + iou_thresh_per_gt = iou_mean_per_gt + iou_std_per_gt + is_pos = candidate_ious >= iou_thresh_per_gt[None, :] + + # Limiting the final positive samples’ center to object + anchor_num = anchors_cx_per_im.shape[0] + for ng in range(num_gt): + candidate_idxs[:, ng] += ng * anchor_num + e_anchors_cx = anchors_cx_per_im.view(1, -1).expand(num_gt, anchor_num).contiguous().view(-1) + e_anchors_cy = anchors_cy_per_im.view(1, -1).expand(num_gt, anchor_num).contiguous().view(-1) + candidate_idxs = candidate_idxs.view(-1) + if num_gt == 0: + l = e_anchors_cx[candidate_idxs] - bboxes_per_im[:, 0] + t = e_anchors_cy[candidate_idxs] - bboxes_per_im[:, 1] + r = bboxes_per_im[:, 2] - e_anchors_cx[candidate_idxs] + b = bboxes_per_im[:, 3] - e_anchors_cy[candidate_idxs] + else: + l = e_anchors_cx[candidate_idxs].view(-1, num_gt) - bboxes_per_im[:, 0] + t = e_anchors_cy[candidate_idxs].view(-1, num_gt) - bboxes_per_im[:, 1] + r = bboxes_per_im[:, 2] - e_anchors_cx[candidate_idxs].view(-1, num_gt) + b = bboxes_per_im[:, 3] - e_anchors_cy[candidate_idxs].view(-1, num_gt) + is_in_gts = torch.stack([l, t, r, b], dim=1).min(dim=1)[0] > 0.01 + is_pos = is_pos & is_in_gts + + # if an anchor box is assigned to multiple gts, the one with the highest IoU will be selected. + ious_inf = torch.full_like(ious, -INF).t().contiguous().view(-1) + index = candidate_idxs.view(-1)[is_pos.view(-1)] + ious_inf[index] = ious.t().contiguous().view(-1)[index] + + if num_gt > 0: + ious_inf = ious_inf.view(num_gt, -1).t() + anchors_to_gt_values, anchors_to_gt_indexs = ious_inf.max(dim=1) + # get positive anchors index from ATSS + positive_index = [i[0].item() for i in torch.nonzero(anchors_to_gt_indexs)] + cls_labels_per_im = labels_per_im[anchors_to_gt_indexs] + cls_labels_per_im[anchors_to_gt_values == -INF] = 0 + else: + cls_labels_per_im = torch.zeros((ious.size(0)), device=labels_per_im.device) + anchors_to_gt_values, anchors_to_gt_indexs = [], [] + + if positive_map is not None: + if num_gt > 0: + token_labels_per_im = token_per_im[anchors_to_gt_indexs] + unmatched_labels = torch.zeros(token_labels_per_im.shape[1], device=token_labels_per_im.device) + if not self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MUTE_NOOBJ_TOKEN: + unmatched_labels[-1] = 1 # token: none object - > 256 + token_labels_per_im[anchors_to_gt_values == -INF] = unmatched_labels + # move from cpu to gpu + token_labels_per_im = token_labels_per_im.to(cls_labels_per_im.device) + else: + unmatched_labels = torch.zeros(token_per_im.size(1), device=token_per_im.device) + if not self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MUTE_NOOBJ_TOKEN: + unmatched_labels[-1] = 1 # token: none object - > 256 + token_labels_per_im = unmatched_labels.unsqueeze(0).repeat(ious.size(0), 1) + token_labels_per_im = token_labels_per_im.to(cls_labels_per_im.device) + + if positive_map is not None and proj_tokens is not None: # TODO fix this for no box case + map_labels_per_im = map[anchors_to_gt_indexs] + unmatched_labels = torch.zeros( + map_labels_per_im.shape[1], dtype=torch.bool, device=map_labels_per_im.device + ) # map: none False + map_labels_per_im[anchors_to_gt_values == -INF] = unmatched_labels + # move from cpu to gpu + map_labels_per_im = map_labels_per_im.to(cls_labels_per_im.device) + + # print(map_labels_per_im[anchors_to_gt_values == -INF].shape) + # print(map_labels_per_im[anchors_to_gt_values != -INF][0]) + + if positive_map is not None and proj_tokens is not None: + gold_box_od_label_per_im = gold_box_od_label[anchors_to_gt_indexs] + gold_box_od_label_per_im[anchors_to_gt_values == -INF] = -100 + # move from cpu to gpu + gold_box_od_label_per_im = gold_box_od_label_per_im.to(cls_labels_per_im.device) + + # print(gold_box_od_label_per_im[anchors_to_gt_values != -INF]) + + matched_gts = bboxes_per_im[anchors_to_gt_indexs] + + reg_targets_per_im = self.box_coder.encode(matched_gts, anchors_per_im.bbox) + cls_labels.append(cls_labels_per_im) + reg_targets.append(reg_targets_per_im) + + if positive_map is not None: + token_labels.append(token_labels_per_im) + + if positive_map is not None and proj_tokens is not None: + map_labels.append(map_labels_per_im) + gold_box_od_labels.append(gold_box_od_label_per_im) + od_label_of_tokens_labels.append(od_label_of_token_per_im) + positive_indices.append(positive_index) + + # print([len(x) for x in positive_indices]) + + return ( + cls_labels, + reg_targets, + token_labels, + map_labels, + gold_box_od_labels, + od_label_of_tokens_labels, + positive_indices, + ) + + def compute_centerness_targets(self, reg_targets, anchors): + gts = self.box_coder.decode(reg_targets, anchors) + anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2 + anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2 + l = anchors_cx - gts[:, 0] + t = anchors_cy - gts[:, 1] + r = gts[:, 2] - anchors_cx + b = gts[:, 3] - anchors_cy + left_right = torch.stack([l, r], dim=1) + top_bottom = torch.stack([t, b], dim=1) + centerness = torch.sqrt( + (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) + * (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) + ) + assert not torch.isnan(centerness).any() + return centerness + + @custom_fwd(cast_inputs=torch.float32) + def __call__( + self, + box_cls, + box_regression, + centerness, + targets, + anchors, + captions=None, + positive_map=None, + token_logits=None, + proj_tokens=None, + contrastive_logits=None, + dot_product_logits=None, + text_masks=None, + shallow_img_emb_feats=None, + ): + + tokenized = None + if captions is not None: + # tokenized = self.tokenizer.batch_encode_plus(captions, padding="longest", return_tensors="pt") + if self.cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + tokenized = self.tokenizer.batch_encode_plus( + captions, + max_length=self.cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, + padding="max_length" if self.cfg.MODEL.LANGUAGE_BACKBONE.PAD_MAX else "longest", + return_tensors="pt", + truncation=True, + ) + else: + tokenized = self.tokenizer.batch_encode_plus(captions, padding="longest", return_tensors="pt") + + ( + labels, + reg_targets, + token_labels, + map_labels, + gold_box_od_labels, + od_label_of_tokens_labels, + positive_indices, + ) = self.prepare_targets(targets, anchors, tokenized, positive_map, proj_tokens) + + N = len(labels) + box_regression_flatten, box_cls_flatten, token_logits_stacked = concat_box_prediction_layers( + box_regression, + box_cls, + token_logits, + ) + + # contrastive logits # TODO: fix no box case here + if positive_map is not None and contrastive_logits is not None: + contrastive_logits = torch.cat(contrastive_logits, dim=1) + + # dot product soft token logits + if dot_product_logits is not None: + dot_product_logits = torch.cat(dot_product_logits, dim=1) + + centerness_flatten = [ct.permute(0, 2, 3, 1).reshape(N, -1, 1) for ct in centerness] + centerness_flatten = torch.cat(centerness_flatten, dim=1).reshape(-1) + + labels_flatten = torch.cat(labels, dim=0) + reg_targets_flatten = torch.cat(reg_targets, dim=0) + anchors_flatten = torch.cat([cat_boxlist(anchors_per_image).bbox for anchors_per_image in anchors], dim=0) + + if positive_map is not None: + token_labels_stacked = torch.stack(token_labels, dim=0) + + if positive_map is not None and proj_tokens is not None: # TODO: fix no box case here + proj_map = torch.stack(map_labels, dim=0) + + if positive_map is not None and proj_tokens is not None: + positive_map_box_to_self_text = None + shallow_positive_map = None + bs = proj_tokens.shape[0] + device = proj_tokens.device + + # NOTE: 0. setup env + if dist.is_dist_avail_and_initialized(): + world_size = dist.get_world_size() + rank = torch.distributed.get_rank() + else: + world_size = 1 + rank = 0 + + if contrastive_logits is not None: + positive_map_box_to_self_text = torch.stack(map_labels, dim=0) + + if shallow_img_emb_feats is not None: + """ + Ultimate: + N*B*(max_anchor_num) x N*B*T + Final Goal: + F = B x (max_anchor_num) x N*B*T + X: B x (max_anchor_num) od_labels : [0, 20, 30, ..] + Y: N*B*T: which denotes the od_label of every token + F[i,j] = A[i] == B[j] + """ + with torch.no_grad(): + # NOTE: 1. get X (predicted_box_od_label), which the detection label of every predicted boxes + # predicted_box_od_label: B x A + + # check memory limitation: prevent # of positive >= # of max_positive + new_positive_indices = [] + # print([len(positive_index) for positive_index in positive_indices]) + for positive_index in positive_indices: + if len(positive_index) >= self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_MAX_POSITIVE_ANCHORS: + import random + + positive_index = sorted( + random.sample( + positive_index, self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_MAX_POSITIVE_ANCHORS + ) + ) + new_positive_indices.append(positive_index) + # print([len(positive_index) for positive_index in positive_indices]) + + max_len = max([len(positive_index) for positive_index in new_positive_indices]) + max_anchor_num = max_len + + if world_size > 1: + num_anchors = torch.tensor(max_len, device=positive_map.device) + num_anchors_full = [torch.zeros_like(num_anchors) for _ in range(world_size)] + torch.distributed.all_gather(num_anchors_full, num_anchors) + max_anchor_num = max([anchor.item() for anchor in num_anchors_full]) + + new_negative_pad_indices = [] + # if not PAD_ZEROS, select random negative paddings + if not self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_ZERO_PADS: + for (positive_index, old_positive_index) in zip(new_positive_indices, positive_indices): + negative_index = [ + i for i in range(len(cat_boxlist(anchors[0]))) if i not in old_positive_index + ] + import random + + negative_pad_index = sorted( + random.sample(negative_index, max_anchor_num - len(positive_index)) + ) + new_negative_pad_indices.append(negative_pad_index) + + predicted_box_od_label = [] + for i in range(bs): + predicted_box_od_label.append( + pad_tensor_given_dim_length( + gold_box_od_labels[i][new_positive_indices[i]], + dim=0, + length=max_anchor_num, + padding_value=-100, + batch_first=False, + ) + ) + predicted_box_od_label = torch.stack(predicted_box_od_label, dim=0) + + # if padding, need to create image masks to filter out the paddings + image_masks = None + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_ZERO_PADS: + image_masks = torch.zeros((bs, max_anchor_num), dtype=torch.long).to(text_masks.device) + for i in range(bs): + image_masks[i, : len(new_positive_indices[i])] = 1 + + # NOTE: 2. Get Y (od_label_of_tokens) + # od_label_of_tokens: N x B x T + od_label_of_tokens = torch.stack(od_label_of_tokens_labels, dim=0).long() + od_label_of_tokens = gather_tensors(od_label_of_tokens) + + # NOTE: 3. get F + # F: B*A x N*B*T + mapping_predicted_box_to_all_text = predicted_box_od_label.view(-1).unsqueeze( + 1 + ) == od_label_of_tokens.view(-1).unsqueeze(0) + + # NOTE: 4. we still need to calculate the mapping between predicted box to its corresponding text's mapping + # positive_map_box_to_self_text: B x A x T, leave this for vanilla contrastive alignment loss + positive_map_box_to_self_text = [] + for i in range(bs): + positive_map_box_to_self_text.append( + pad_tensor_given_dim_length( + map_labels[i][new_positive_indices[i]], + dim=0, + length=max_anchor_num, + padding_value=False, + batch_first=False, + ) + ) + positive_map_box_to_self_text = torch.stack(positive_map_box_to_self_text, dim=0) + + # change the corresponding place in our batch + for i in range(bs): + mapping_predicted_box_to_all_text[ + i * max_anchor_num : (i + 1) * max_anchor_num, + (rank * bs + i) * 256 : (rank * bs + i + 1) * 256, + ] = positive_map_box_to_self_text[i] + + # NOTE: 5. communicate and get positive map + # mapping_predicted_box_to_all_text: N*B*A x N*B*T + mapping_predicted_box_to_all_text = gather_tensors(mapping_predicted_box_to_all_text).view( + -1, mapping_predicted_box_to_all_text.size(-1) + ) + shallow_positive_map = mapping_predicted_box_to_all_text # This is the true positive map + shallow_positive_map = shallow_positive_map.unsqueeze(0) + + # Get text attention masks + text_attention_mask = torch.zeros((bs, 256), dtype=torch.long) # B x 256 + for i in range(bs): + text_attention_mask[i, : len(text_masks[i])] = text_masks[i] + text_attention_mask = gather_tensors(text_attention_mask.bool().to(device)) # N x B x 256 + + # if PAD_ZEROS, get image masks + if image_masks is not None: + image_attention_mask = torch.zeros((bs, max_anchor_num), dtype=torch.long) # B x max_anchor + for i in range(bs): + image_attention_mask[i, : len(image_masks[i])] = image_masks[i] + image_attention_mask = gather_tensors( + image_attention_mask.bool().to(device) + ) # N x B x max_anchor + + # NOTE: 6. calculate shallow contrastive logits + shallow_proj_tokens = F.normalize(self.shallow_contrastive_projection_text(proj_tokens), p=2, dim=-1) + + shallow_normalized_img_embs = [] + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS: + # choice 1：use features from SWINT backbone layer (c4) before vl fusion + from maskrcnn_benchmark.layers.roi_align import ROIAlignV2 + + pooler = ROIAlignV2((1, 1), 1.0 / 16, 0) + # get positive features + for i in range(bs): + rois = convert_to_roi_format(cat_boxlist(anchors[i])[new_positive_indices[i]]) + roi_feature = pooler(shallow_img_emb_feats[i].unsqueeze(0), rois) + roi_feature = roi_feature.squeeze(-1).squeeze(-1) + shallow_contrastive_proj_queries = self.shallow_contrastive_projection_image(roi_feature) + shallow_normalized_img_emb = F.normalize(shallow_contrastive_proj_queries, p=2, dim=-1) + if image_masks is not None: + # pad zeros + shallow_normalized_img_embs.append( + pad_tensor_given_dim_length( + shallow_normalized_img_emb, + dim=0, + length=max_anchor_num, + padding_value=0.0, + batch_first=False, + ) + ) + else: + # pad negatives + negative_rois = convert_to_roi_format(cat_boxlist(anchors[i])[new_negative_pad_indices[i]]) + negative_roi_feature = pooler(shallow_img_emb_feats[i].unsqueeze(0), negative_rois) + negative_roi_feature = negative_roi_feature.squeeze(-1).squeeze(-1) + negative_shallow_contrastive_proj_queries = self.shallow_contrastive_projection_image( + negative_roi_feature + ) + negative_shallow_normalized_img_emb = F.normalize( + negative_shallow_contrastive_proj_queries, p=2, dim=-1 + ) + shallow_normalized_img_embs.append( + pad_random_negative_tensor_given_length( + shallow_normalized_img_emb, + negative_shallow_normalized_img_emb, + length=max_anchor_num, + ) + ) + elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS: + # choice 2：use features after FPN + shallow_img_embs = torch.cat(shallow_img_emb_feats, dim=1) + # get positive features + for i in range(bs): + shallow_contrastive_proj_queries = self.shallow_contrastive_projection_image( + shallow_img_embs[i, new_positive_indices[i], :] + ) + shallow_normalized_img_emb = F.normalize(shallow_contrastive_proj_queries, p=2, dim=-1) + if image_masks is not None: + # pad zeros + shallow_normalized_img_embs.append( + pad_tensor_given_dim_length( + shallow_normalized_img_emb, + dim=0, + length=max_anchor_num, + padding_value=0.0, + batch_first=False, + ) + ) + else: + # pad negatives + negative_shallow_contrastive_proj_queries = self.shallow_contrastive_projection_image( + shallow_img_embs[i, new_negative_pad_indices[i], :] + ) + negative_shallow_normalized_img_emb = F.normalize( + negative_shallow_contrastive_proj_queries, p=2, dim=-1 + ) + shallow_normalized_img_embs.append( + pad_random_negative_tensor_given_length( + shallow_normalized_img_emb, + negative_shallow_normalized_img_emb, + length=max_anchor_num, + ) + ) + + shallow_normalized_img_embs = torch.stack(shallow_normalized_img_embs, dim=0) + shallow_normalized_text_emb = shallow_proj_tokens + shallow_normalized_text_emb = pad_tensor_given_dim_length( + shallow_normalized_text_emb, dim=1, length=256, padding_value=0.0 + ) + + gathered_shallow_normalized_img_emb = gather_tensors(shallow_normalized_img_embs) + gathered_shallow_normalized_text_emb = gather_tensors(shallow_normalized_text_emb) + gathered_shallow_normalized_img_emb = gathered_shallow_normalized_img_emb.view( + -1, gathered_shallow_normalized_img_emb.size(-1) + ) + gathered_shallow_normalized_text_emb = gathered_shallow_normalized_text_emb.view( + -1, gathered_shallow_normalized_text_emb.size(-1) + ) + shallow_contrastive_logits = ( + torch.matmul( + gathered_shallow_normalized_img_emb, gathered_shallow_normalized_text_emb.transpose(-1, -2) + ) + / self.shallow_log_scale.exp() + ) + shallow_contrastive_logits = shallow_contrastive_logits.unsqueeze(0) + + # apply text mask + text_attention_mask = text_attention_mask.view(-1).unsqueeze(0).unsqueeze(0) + text_attention_mask = text_attention_mask.repeat( + 1, shallow_contrastive_logits.size(1), 1 + ) # copy along the image feature dimension + shallow_contrastive_logits = shallow_contrastive_logits.masked_fill(~text_attention_mask, -1000000) + + # if PAD ZEROS, apply image mask + if image_masks is not None: + image_attention_mask = image_attention_mask.view(-1).unsqueeze(0).unsqueeze(-1) + image_attention_mask = image_attention_mask.repeat( + 1, 1, shallow_contrastive_logits.size(2) + ) # copy along the text feature dimension + shallow_contrastive_logits = shallow_contrastive_logits.masked_fill(~image_attention_mask, -1000000) + + # Note: 7. calculate image and text logits and maps + shallow_image_logits = shallow_contrastive_logits[ + :, (rank * bs) * max_anchor_num : (rank * bs + bs) * max_anchor_num, : + ] + shallow_image_positive_map = normalized_positive_map( + shallow_positive_map[:, (rank * bs) * max_anchor_num : (rank * bs + bs) * max_anchor_num, :] + ) + + shallow_text_logits = shallow_contrastive_logits[ + :, :, (rank * bs) * 256 : (rank * bs + bs) * 256 + ].transpose(1, 2) + shallow_text_positive_map = normalized_positive_map( + shallow_positive_map[:, :, (rank * bs) * 256 : (rank * bs + bs) * 256].transpose(1, 2) + ) + + pos_inds = torch.nonzero(labels_flatten > 0).squeeze(1) + + num_gpus = get_world_size() + total_num_pos = reduce_sum(pos_inds.new_tensor([pos_inds.numel()])).item() + num_pos_avg_per_gpu = max(total_num_pos / float(num_gpus), 1.0) + + # TODO: Do we want to have this loss for the no box case? currently all labels are set to 0 which might not be the current way forward + # I set it to zero since thats what is chosen in prepare_targets : cls_labels_per_im[anchors_to_gt_values == -INF] = 0 + cls_loss = self.cls_loss_func(box_cls_flatten, labels_flatten.int()) / num_pos_avg_per_gpu + + token_logits_loss = None + contrastive_align_loss = None + dot_product_token_loss = None + shallow_contrastive_loss = None + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MUTE_NON_ESSENTIAL_TOKENS: + # we mask out the non_essential tokens + greenlight_map = [i.get_field("greenlight_map") for i in targets] + greenlight_map = torch.stack(greenlight_map, dim=0) + # make sure the greenlight map is the same size as the text masks + assert(greenlight_map.size(0) == text_masks.size(0)) + assert(greenlight_map.size(1) == text_masks.size(1)) + _text_masks_for_loss = greenlight_map + else: + _text_masks_for_loss = text_masks + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS: + token_logits_loss = ( + self.token_loss_func( + token_logits_stacked, token_labels_stacked, text_masks=text_masks, version="binary" + ) + / num_pos_avg_per_gpu + ) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + contrastive_align_loss = ( + self.ContrastiveAlignLoss(contrastive_logits, positive_map_box_to_self_text) / num_pos_avg_per_gpu + ) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + dot_product_token_loss = ( + self.token_loss_func(dot_product_logits, token_labels_stacked, text_masks=_text_masks_for_loss, version="binary") + / num_pos_avg_per_gpu + ) + + if ( + self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS + or self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS + ): + box_to_token_loss = self.NllSoftMaxLoss(shallow_image_logits, shallow_image_positive_map).sum() + token_to_box_loss = self.NllSoftMaxLoss(shallow_text_logits, shallow_text_positive_map).sum() + tot_loss = (box_to_token_loss + token_to_box_loss) / 2 + shallow_contrastive_loss = tot_loss / num_pos_avg_per_gpu + + box_regression_flatten = box_regression_flatten[pos_inds] + reg_targets_flatten = reg_targets_flatten[pos_inds] + anchors_flatten = anchors_flatten[pos_inds] + centerness_flatten = centerness_flatten[pos_inds] + + if pos_inds.numel() > 0: + centerness_targets = self.compute_centerness_targets(reg_targets_flatten, anchors_flatten) + + sum_centerness_targets_avg_per_gpu = reduce_sum(centerness_targets.sum()).item() / float(num_gpus) + reg_loss = ( + self.GIoULoss(box_regression_flatten, reg_targets_flatten, anchors_flatten, weight=centerness_targets) + / sum_centerness_targets_avg_per_gpu + ) + centerness_loss = self.centerness_loss_func(centerness_flatten, centerness_targets) / num_pos_avg_per_gpu + else: + reg_loss = box_regression_flatten.sum() + reduce_sum(centerness_flatten.new_tensor([0.0])) + centerness_loss = centerness_flatten.sum() + + return ( + cls_loss, + reg_loss * self.cfg.MODEL.ATSS.REG_LOSS_WEIGHT, + centerness_loss, + token_logits_loss, + contrastive_align_loss, + dot_product_token_loss, + shallow_contrastive_loss, + ) + + +def generate_anchor_labels(matched_targets): + labels_per_image = matched_targets.get_field("labels") + return labels_per_image + + +def make_focal_loss_evaluator(cfg, box_coder): + matcher = Matcher( + cfg.MODEL.FOCAL.FG_IOU_THRESHOLD, + cfg.MODEL.FOCAL.BG_IOU_THRESHOLD, + allow_low_quality_matches=True, + ) + sigmoid_focal_loss = SigmoidFocalLoss(cfg.MODEL.FOCAL.LOSS_GAMMA, cfg.MODEL.FOCAL.LOSS_ALPHA) + + loss_evaluator = FocalLossComputation( + matcher, + box_coder, + generate_anchor_labels, + sigmoid_focal_loss, + bbox_reg_beta=cfg.MODEL.FOCAL.BBOX_REG_BETA, + regress_norm=cfg.MODEL.FOCAL.BBOX_REG_WEIGHT, + ) + return loss_evaluator + + +def make_rpn_loss_evaluator(cfg, box_coder): + matcher = Matcher( + cfg.MODEL.RPN.FG_IOU_THRESHOLD, + cfg.MODEL.RPN.BG_IOU_THRESHOLD, + allow_low_quality_matches=True, + ) + + fg_bg_sampler = BalancedPositiveNegativeSampler(cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE, cfg.MODEL.RPN.POSITIVE_FRACTION) + + loss_evaluator = RPNLossComputation(matcher, fg_bg_sampler, box_coder) + return loss_evaluator + + +def make_fcos_loss_evaluator(cfg): + loss_evaluator = FCOSLossComputation(cfg) + return loss_evaluator + + +def make_atss_loss_evaluator(cfg, box_coder): + loss_evaluator = ATSSLossComputation(cfg, box_coder) + return loss_evaluator diff --git a/maskrcnn_benchmark/modeling/rpn/matcher.py b/maskrcnn_benchmark/modeling/rpn/matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..3926f7cbda07af30af833674bfe381715e7b5720 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/matcher.py @@ -0,0 +1,134 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Modules to compute the matching cost and solve the corresponding LSAP. +""" +import torch +from scipy.optimize import linear_sum_assignment +from torch import nn +import pdb +from maskrcnn_benchmark.layers.set_loss import generalized_box_iou, box_iou + +class HungarianMatcherCustom(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1, special = False): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost + cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_bbox = cost_bbox + self.cost_giou = cost_giou + self.norm = nn.Softmax(-1) + self.special = special + assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0" + + @torch.no_grad() + def forward(self, outputs, targets): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + bs, num_queries = outputs["pred_logits"].shape[:2] + + # We flatten to compute the cost matrices in a batch + out_prob = outputs["pred_logits"].flatten(0, 1) # [batch_size * num_queries, num_classes] + # out_prob_bg = 1 - out_prob + # out_prob = torch.cat([out_prob_bg, out_prob], dim = 1) + + out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] + + # Also concat the target labels and boxes + tgt_bbox = targets["pred_boxes"].flatten(0, 1) # [batch_size * num_target_boxes, 4] + tgt_prob = targets["pred_logits"].flatten(0, 1) # [batch_size * num_target_boxes, num_classes] + # tgt_prob_bg = 1 - tgt_prob + # tgt_prob = torch.cat([tgt_prob_bg, tgt_prob], dim = 1) + + # Compute the soft-cross entropy between the predicted token alignment and the GT one for each box + # import pdb + + cost_class = out_prob - tgt_prob.transpose(0,1) + cost_class = cost_class.abs() + + + # Compute the L1 cost between boxes + cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1) + + # Compute the giou cost betwen boxes + # cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox)) + cost_giou, _ = box_iou(out_bbox, tgt_bbox) + cost_giou = -cost_giou + + # Final cost matrix + C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou + C = C.view(bs, num_queries, -1).cpu() + + C_class = cost_class + C_class = C_class.view(bs, num_queries, -1).cpu() + + C_bbox = cost_bbox + C_bbox = C_bbox.view(bs, num_queries, -1).cpu() + #C[torch.isnan(C)] = 0.0 + #C[torch.isinf(C)] = 0.0 + #print(C) + + sizes = [tgt_bbox.size(0)] # assum b = 1 + indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))] + + + assignment = [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] + + # calculate the total cost; + assignment = assignment[0] + C = C[0] + C_class = C_class[0] + C_bbox = C_bbox[0] + + cost = 0 + selected_entries = [] + cost_class = 0 + cost_bbox = 0 + cost_matched_box = 0 + + + if self.special: # calculate the difference between boxes + for first_index, second_index in zip(assignment[0], assignment[1]): + if -C[first_index, second_index] > 0.5: + cost += C_class[first_index, second_index] + selected_entries.append(C[first_index, second_index]) + cost_class += C_class[first_index, second_index] + cost_bbox += C_bbox[first_index, second_index] + else: + for first_index, second_index in zip(assignment[0], assignment[1]): + cost += C[first_index, second_index] + selected_entries.append(C[first_index, second_index]) + cost_class += C_class[first_index, second_index] + cost_bbox += C_bbox[first_index, second_index] + print(selected_entries, cost) + + return cost, len(selected_entries), selected_entries, cost_class, cost_bbox \ No newline at end of file diff --git a/maskrcnn_benchmark/modeling/rpn/modeling_bert.py b/maskrcnn_benchmark/modeling/rpn/modeling_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..94fe5b1afdf371fb720c98b8ba32a8065dd4ac00 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/modeling_bert.py @@ -0,0 +1,276 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch BERT model. """ + + +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss +from transformers.activations import ACT2FN +import pdb +from transformers.modeling_utils import find_pruneable_heads_and_indices, prune_linear_layer + + +def clamp_values(vector, min_val=-50000, max_val=50000): + vector = torch.clamp(vector, min=min_val, max=max_val) + return vector + + +class BertSelfAttention(nn.Module): + def __init__(self, config, clamp_min_for_underflow=False, clamp_max_for_overflow=False): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.clamp_min_for_underflow = clamp_min_for_underflow + self.clamp_max_for_overflow = clamp_max_for_overflow + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + if self.clamp_min_for_underflow: + attention_scores = torch.clamp( + attention_scores, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attention_scores = torch.clamp( + attention_scores, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # if math.isnan(attention_probs.sum().item()): + # for i in range(attention_probs.size(1)): + # for j in range(attention_probs.size(2)): + # if math.isnan(attention_probs[0, i, j].sum().item()): + # print(i, j) + # pdb.set_trace() + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, clamp_min_for_underflow=False, clamp_max_for_overflow=False): + super().__init__() + self.self = BertSelfAttention(config, clamp_min_for_underflow, clamp_max_for_overflow) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = clamp_values(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = clamp_values(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = clamp_values(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + hidden_states = clamp_values(hidden_states) + return hidden_states diff --git a/maskrcnn_benchmark/modeling/rpn/retina.py b/maskrcnn_benchmark/modeling/rpn/retina.py new file mode 100644 index 0000000000000000000000000000000000000000..1e650c76e217ad7ecf996dc2672f1c694145f712 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/retina.py @@ -0,0 +1,128 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import math +import torch +import torch.nn.functional as F +from torch import nn + +from maskrcnn_benchmark.modeling import registry +from maskrcnn_benchmark.modeling.box_coder import BoxCoder +from .loss import make_focal_loss_evaluator +from .anchor_generator import make_anchor_generator_complex +from .inference import make_retina_postprocessor + + +@registry.RPN_HEADS.register("RetinaNetHead") +class RetinaNetHead(torch.nn.Module): + """ + Adds a RetinNet head with classification and regression heads + """ + + def __init__(self, cfg): + """ + Arguments: + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + """ + super(RetinaNetHead, self).__init__() + # TODO: Implement the sigmoid version first. + num_classes = cfg.MODEL.RETINANET.NUM_CLASSES - 1 + in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + if cfg.MODEL.RPN.USE_FPN: + num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * cfg.MODEL.RPN.SCALES_PER_OCTAVE + else: + num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * len(cfg.MODEL.RPN.ANCHOR_SIZES) + + cls_tower = [] + bbox_tower = [] + for i in range(cfg.MODEL.RETINANET.NUM_CONVS): + cls_tower.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) + cls_tower.append(nn.ReLU()) + bbox_tower.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) + bbox_tower.append(nn.ReLU()) + + self.add_module("cls_tower", nn.Sequential(*cls_tower)) + self.add_module("bbox_tower", nn.Sequential(*bbox_tower)) + self.cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1) + self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1) + + # Initialization + for modules in [self.cls_tower, self.bbox_tower, self.cls_logits, self.bbox_pred]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + # retinanet_bias_init + prior_prob = cfg.MODEL.RETINANET.PRIOR_PROB + bias_value = -math.log((1 - prior_prob) / prior_prob) + torch.nn.init.constant_(self.cls_logits.bias, bias_value) + + def forward(self, x): + logits = [] + bbox_reg = [] + for feature in x: + logits.append(self.cls_logits(self.cls_tower(feature))) + bbox_reg.append(self.bbox_pred(self.bbox_tower(feature))) + return logits, bbox_reg + + +class RetinaNetModule(torch.nn.Module): + """ + Module for RetinaNet computation. Takes feature maps from the backbone and + RetinaNet outputs and losses. Only Test on FPN now. + """ + + def __init__(self, cfg): + super(RetinaNetModule, self).__init__() + + self.cfg = cfg.clone() + + anchor_generator = make_anchor_generator_complex(cfg) + head = RetinaNetHead(cfg) + + box_coder = BoxCoder(weights=(10.0, 10.0, 5.0, 5.0)) + + box_selector_test = make_retina_postprocessor(cfg, box_coder, is_train=False) + + loss_evaluator = make_focal_loss_evaluator(cfg, box_coder) + + self.anchor_generator = anchor_generator + self.head = head + self.box_selector_test = box_selector_test + self.loss_evaluator = loss_evaluator + + def forward(self, images, features, targets=None): + """ + Arguments: + images (ImageList): images for which we want to compute the predictions + features (list[Tensor]): features computed from the images that are + used for computing the predictions. Each tensor in the list + correspond to different feature levels + targets (list[BoxList): ground-truth boxes present in the image (optional) + + Returns: + boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per + image. + losses (dict[Tensor]): the losses for the model during training. During + testing, it is an empty dict. + """ + box_cls, box_regression = self.head(features) + anchors = self.anchor_generator(images, features) + + if self.training: + return self._forward_train(anchors, box_cls, box_regression, targets) + else: + return self._forward_test(anchors, box_cls, box_regression) + + def _forward_train(self, anchors, box_cls, box_regression, targets): + + loss_box_cls, loss_box_reg = self.loss_evaluator(anchors, box_cls, box_regression, targets) + losses = { + "loss_retina_cls": loss_box_cls, + "loss_retina_reg": loss_box_reg, + } + return anchors, losses + + def _forward_test(self, anchors, box_cls, box_regression): + boxes = self.box_selector_test(anchors, box_cls, box_regression) + return boxes, {} diff --git a/maskrcnn_benchmark/modeling/rpn/rpn.py b/maskrcnn_benchmark/modeling/rpn/rpn.py new file mode 100644 index 0000000000000000000000000000000000000000..ea6cdba8d29d9defbd4c349cb9fe47f58a7b4028 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/rpn.py @@ -0,0 +1,157 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +import torch.nn.functional as F +from torch import nn + +from maskrcnn_benchmark.modeling import registry +from maskrcnn_benchmark.modeling.box_coder import BoxCoder +from .loss import make_rpn_loss_evaluator +from .anchor_generator import make_anchor_generator +from .inference import make_rpn_postprocessor + + +@registry.RPN_HEADS.register("SimpleRPNHead") +class mRPNHead(nn.Module): + """ + Adds a simple RPN Head with classification and regression heads + """ + + def __init__(self, cfg, in_channels, num_anchors): + """ + Arguments: + cfg : config + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + """ + super(mRPNHead, self).__init__() + self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1) + self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1) + + for l in [self.cls_logits, self.bbox_pred]: + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + def forward(self, x): + logits = [] + bbox_reg = [] + for feature in x: + t = F.relu(feature) + logits.append(self.cls_logits(t)) + bbox_reg.append(self.bbox_pred(t)) + return logits, bbox_reg + + +@registry.RPN_HEADS.register("SingleConvRPNHead") +class RPNHead(nn.Module): + """ + Adds a simple RPN Head with classification and regression heads + """ + + def __init__(self, cfg, in_channels, num_anchors): + """ + Arguments: + cfg : config + in_channels (int): number of channels of the input feature + num_anchors (int): number of anchors to be predicted + """ + super(RPNHead, self).__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1) + self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1) + + for l in [self.conv, self.cls_logits, self.bbox_pred]: + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + def forward(self, x): + logits = [] + bbox_reg = [] + for feature in x: + t = F.relu(self.conv(feature)) + logits.append(self.cls_logits(t)) + bbox_reg.append(self.bbox_pred(t)) + return logits, bbox_reg + + +class RPNModule(torch.nn.Module): + """ + Module for RPN computation. Takes feature maps from the backbone and RPN + proposals and losses. Works for both FPN and non-FPN. + """ + + def __init__(self, cfg): + super(RPNModule, self).__init__() + + self.cfg = cfg.clone() + + anchor_generator = make_anchor_generator(cfg) + + in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + rpn_head = registry.RPN_HEADS[cfg.MODEL.RPN.RPN_HEAD] + head = rpn_head(cfg, in_channels, anchor_generator.num_anchors_per_location()[0]) + + rpn_box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) + + box_selector_train = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=True) + box_selector_test = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=False) + + loss_evaluator = make_rpn_loss_evaluator(cfg, rpn_box_coder) + + self.anchor_generator = anchor_generator + self.head = head + self.box_selector_train = box_selector_train + self.box_selector_test = box_selector_test + self.loss_evaluator = loss_evaluator + + def forward(self, images, features, targets=None): + """ + Arguments: + images (ImageList): images for which we want to compute the predictions + features (list[Tensor]): features computed from the images that are + used for computing the predictions. Each tensor in the list + correspond to different feature levels + targets (list[BoxList): ground-truth boxes present in the image (optional) + + Returns: + boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per + image. + losses (dict[Tensor]): the losses for the model during training. During + testing, it is an empty dict. + """ + objectness, rpn_box_regression = self.head(features) + anchors = self.anchor_generator(images, features) + + if self.training: + return self._forward_train(anchors, objectness, rpn_box_regression, targets) + else: + return self._forward_test(anchors, objectness, rpn_box_regression) + + def _forward_train(self, anchors, objectness, rpn_box_regression, targets): + if self.cfg.MODEL.RPN_ONLY: + # When training an RPN-only model, the loss is determined by the + # predicted objectness and rpn_box_regression values and there is + # no need to transform the anchors into predicted boxes; this is an + # optimization that avoids the unnecessary transformation. + boxes = anchors + else: + # For end-to-end models, anchors must be transformed into boxes and + # sampled into a training batch. + with torch.no_grad(): + boxes = self.box_selector_train(anchors, objectness, rpn_box_regression, targets) + loss_objectness, loss_rpn_box_reg = self.loss_evaluator(anchors, objectness, rpn_box_regression, targets) + losses = { + "loss_objectness": loss_objectness, + "loss_rpn_box_reg": loss_rpn_box_reg, + } + return boxes, losses + + def _forward_test(self, anchors, objectness, rpn_box_regression): + boxes = self.box_selector_test(anchors, objectness, rpn_box_regression) + if self.cfg.MODEL.RPN_ONLY: + # For end-to-end models, the RPN proposals are an intermediate state + # and don't bother to sort them in decreasing score order. For RPN-only + # models, the proposals are the final output and we return them in + # high-to-low confidence order. + inds = [box.get_field("objectness").sort(descending=True)[1] for box in boxes] + boxes = [box[ind] for box, ind in zip(boxes, inds)] + return boxes, {} diff --git a/maskrcnn_benchmark/modeling/rpn/transformer.py b/maskrcnn_benchmark/modeling/rpn/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..797cd081944e1e830ef30b08b611a914ed84d815 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/transformer.py @@ -0,0 +1,48 @@ +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +import copy +from typing import Optional, List + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + + +class TransformerEncoderLayer(nn.Module): + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False): + super(TransformerEncoderLayer, self).__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def forward(self, src, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None): + src2 = self.self_attn(src, src, src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src diff --git a/maskrcnn_benchmark/modeling/rpn/vldyhead.py b/maskrcnn_benchmark/modeling/rpn/vldyhead.py new file mode 100644 index 0000000000000000000000000000000000000000..56d7c64e188c1514e7ecff2e965323496ffead17 --- /dev/null +++ b/maskrcnn_benchmark/modeling/rpn/vldyhead.py @@ -0,0 +1,1154 @@ +import torch +import torch.nn.functional as F +from torch import nn +from collections import defaultdict + +from .inference import make_atss_postprocessor +from .loss import make_atss_loss_evaluator +from .anchor_generator import make_anchor_generator_complex + +from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist +from maskrcnn_benchmark.layers import Scale, DYReLU, SELayer, ModulatedDeformConv +from maskrcnn_benchmark.layers import NaiveSyncBatchNorm2d, FrozenBatchNorm2d +from maskrcnn_benchmark.modeling.backbone.fbnet import * +from maskrcnn_benchmark.engine.inference import create_positive_map_label_to_token_from_positive_map +from ..utils import cat, concat_box_prediction_layers, permute_and_flatten + +from maskrcnn_benchmark.utils.fuse_helper import ( + FeatureResizer, + func_attention, + _make_mlp, + _make_conv, + _make_coord, + BiAttentionBlock, + AttentionT2I, + BiAttentionBlockForCheckpoint, + BertLMPredictionHead, +) +from transformers.models.bert.modeling_bert import ( + BertConfig, + BertAttention, + BertIntermediate, + BertOutput, + BertPreTrainedModel, +) +from transformers.models.roberta.configuration_roberta import RobertaConfig +from transformers.modeling_utils import apply_chunking_to_forward +import torch.utils.checkpoint as checkpoint +import pdb + +from maskrcnn_benchmark.modeling.language_backbone.clip_model import QuickGELU, LayerNorm, DropPath +from timm.models.layers import DropPath, trunc_normal_ + + +class h_sigmoid(nn.Module): + def __init__(self, inplace=True, h_max=1): + super(h_sigmoid, self).__init__() + self.relu = nn.ReLU6(inplace=inplace) + self.h_max = h_max + + def forward(self, x): + return self.relu(x + 3) * self.h_max / 6 + + +class BoxCoder(object): + def __init__(self, cfg): + self.cfg = cfg + + def encode(self, gt_boxes, anchors): + TO_REMOVE = 1 # TODO remove + ex_widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE + ex_heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE + ex_ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2 + ex_ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2 + + gt_widths = gt_boxes[:, 2] - gt_boxes[:, 0] + TO_REMOVE + gt_heights = gt_boxes[:, 3] - gt_boxes[:, 1] + TO_REMOVE + gt_ctr_x = (gt_boxes[:, 2] + gt_boxes[:, 0]) / 2 + gt_ctr_y = (gt_boxes[:, 3] + gt_boxes[:, 1]) / 2 + + wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0) + if gt_ctr_x.nelement() == 0: + targets_dx = torch.zeros_like(ex_ctr_x) + targets_dy = torch.zeros_like(ex_ctr_y) + targets_dw = torch.zeros_like(ex_widths) + targets_dh = torch.zeros_like(ex_heights) + else: + targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths + targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights + targets_dw = ww * torch.log(gt_widths / ex_widths) + targets_dh = wh * torch.log(gt_heights / ex_heights) + targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1) + + return targets + + def decode(self, preds, anchors): + anchors = anchors.to(preds.dtype) + + TO_REMOVE = 1 # TODO remove + widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE + heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE + ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2 + ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2 + + wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0) + dx = preds[:, 0::4] / wx + dy = preds[:, 1::4] / wy + dw = preds[:, 2::4] / ww + dh = preds[:, 3::4] / wh + + # Prevent sending too large values into torch.exp() + dw = torch.clamp(dw, max=math.log(1000.0 / 16)) + dh = torch.clamp(dh, max=math.log(1000.0 / 16)) + + pred_ctr_x = dx * widths[:, None] + ctr_x[:, None] + pred_ctr_y = dy * heights[:, None] + ctr_y[:, None] + pred_w = torch.exp(dw) * widths[:, None] + pred_h = torch.exp(dh) * heights[:, None] + + pred_boxes = torch.zeros_like(preds) + pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * (pred_w - 1) + pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * (pred_h - 1) + pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * (pred_w - 1) + pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * (pred_h - 1) + + return pred_boxes + + +class Conv3x3Norm(torch.nn.Module): + def __init__(self, in_channels, out_channels, stride, groups=1, deformable=False, bn_type=None): + super(Conv3x3Norm, self).__init__() + + if deformable: + self.conv = ModulatedDeformConv( + in_channels, out_channels, kernel_size=3, stride=stride, padding=1, groups=groups + ) + else: + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, groups=groups) + + if isinstance(bn_type, (list, tuple)): + assert len(bn_type) == 2 + assert bn_type[0] == "gn" + gn_group = bn_type[1] + bn_type = bn_type[0] + + if bn_type == "bn": + bn_op = nn.BatchNorm2d(out_channels) + elif bn_type == "sbn": + bn_op = nn.SyncBatchNorm(out_channels) + elif bn_type == "nsbn": + bn_op = NaiveSyncBatchNorm2d(out_channels) + elif bn_type == "gn": + bn_op = nn.GroupNorm(num_groups=gn_group, num_channels=out_channels) + elif bn_type == "af": + bn_op = FrozenBatchNorm2d(out_channels) + if bn_type is not None: + self.bn = bn_op + else: + self.bn = None + + def forward(self, input, **kwargs): + x = self.conv(input, **kwargs) + if self.bn: + x = self.bn(x) + return x + + +class DyConv(torch.nn.Module): + def __init__( + self, + in_channels=256, + out_channels=256, + conv_func=nn.Conv2d, + use_dyfuse=True, + use_dyrelu=False, + use_deform=False, + ): + super(DyConv, self).__init__() + + self.DyConv = nn.ModuleList() + self.DyConv.append(conv_func(in_channels, out_channels, 1)) + self.DyConv.append(conv_func(in_channels, out_channels, 1)) + self.DyConv.append(conv_func(in_channels, out_channels, 2)) + + if use_dyfuse: + self.AttnConv = nn.Sequential( + nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_channels, 1, kernel_size=1), nn.ReLU(inplace=True) + ) + self.h_sigmoid = h_sigmoid() + else: + self.AttnConv = None + + if use_dyrelu: + self.relu = DYReLU(in_channels, out_channels) + else: + self.relu = nn.ReLU() + + if use_deform: + self.offset = nn.Conv2d(in_channels, 27, kernel_size=3, stride=1, padding=1) + else: + self.offset = None + + self.init_weights() + + def init_weights(self): + for m in self.DyConv.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight.data, 0, 0.01) + if m.bias is not None: + m.bias.data.zero_() + if self.AttnConv is not None: + for m in self.AttnConv.modules(): + if isinstance(m, nn.Conv2d): + nn.init.normal_(m.weight.data, 0, 0.01) + if m.bias is not None: + m.bias.data.zero_() + + def forward(self, inputs): + visual_feats = inputs["visual"] + language_dict_features = inputs["lang"] + + next_x = [] + for level, feature in enumerate(visual_feats): + + conv_args = dict() + if self.offset is not None: + offset_mask = self.offset(feature) + offset = offset_mask[:, :18, :, :] + mask = offset_mask[:, 18:, :, :].sigmoid() + conv_args = dict(offset=offset, mask=mask) + + temp_fea = [self.DyConv[1](feature, **conv_args)] + + if level > 0: + temp_fea.append(self.DyConv[2](visual_feats[level - 1], **conv_args)) + if level < len(visual_feats) - 1: + temp_fea.append( + F.upsample_bilinear( + self.DyConv[0](visual_feats[level + 1], **conv_args), size=[feature.size(2), feature.size(3)] + ) + ) + mean_fea = torch.mean(torch.stack(temp_fea), dim=0, keepdim=False) + + if self.AttnConv is not None: + attn_fea = [] + res_fea = [] + for fea in temp_fea: + res_fea.append(fea) + attn_fea.append(self.AttnConv(fea)) + + res_fea = torch.stack(res_fea) + spa_pyr_attn = self.h_sigmoid(torch.stack(attn_fea)) + + mean_fea = torch.mean(res_fea * spa_pyr_attn, dim=0, keepdim=False) + + next_x.append(mean_fea) + + next_x = [self.relu(item) for item in next_x] + + features_dict = {"visual": next_x, "lang": language_dict_features} + + return features_dict + + +class BertEncoderLayer(BertPreTrainedModel): + def __init__(self, config, clamp_min_for_underflow=False, clamp_max_for_overflow=False): + super().__init__(config) + self.config = config + + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + + from maskrcnn_benchmark.modeling.rpn.modeling_bert import BertAttention, BertIntermediate, BertOutput + + self.attention = BertAttention(config, clamp_min_for_underflow, clamp_max_for_overflow) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward(self, inputs): + language_dict_features = inputs["lang"] + hidden_states = language_dict_features["hidden"] + attention_mask = language_dict_features["masks"] + + device = hidden_states.device + input_shape = hidden_states.size()[:-1] + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device) + + self_attention_outputs = self.attention( + hidden_states, + extended_attention_mask, + None, + output_attentions=False, + past_key_value=None, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + hidden_states = outputs[0] + + language_dict_features["hidden"] = hidden_states + + features_dict = {"visual": inputs["visual"], "lang": language_dict_features} + + return features_dict + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class CLIPTransformerLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + d_model = self.config.MODEL.CLIP.WIDTH + n_head = self.config.MODEL.CLIP.HEADS + drop_path = self.config.MODEL.CLIP.DROP_PATH + self.context_length = self.config.MODEL.CLIP.CONTEXT_LENGTH + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict( + [ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)), + ] + ) + ) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = None + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, (nn.Linear, nn.Conv2d)): + trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)): + nn.init.constant_(m.bias, 0) + + def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0] + + def forward(self, inputs): + language_dict_features = inputs["lang"] + x = language_dict_features["hidden"] + mask = language_dict_features["masks"] + # get extended attention mask for nn.MultiHeadAttention + key_padding_mask = (1.0 - mask).to(torch.bool) + + x = x.permute(1, 0, 2) + x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask)) + x = x + self.drop_path(self.mlp(self.ln_2(x))) + x = x.permute(1, 0, 2) + + language_dict_features["hidden"] = x + features_dict = {"visual": inputs["visual"], "lang": language_dict_features} + return features_dict + + +class DummyLayer(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, inputs): + return inputs + + +class VLFuse(torch.nn.Module): + """ + Early Fusion Module + """ + + def __init__(self, cfg): + super(VLFuse, self).__init__() + self.init_configs(cfg) + self.cfg = cfg + + self.use_checkpoint = False + if hasattr(cfg.MODEL.DYHEAD, "USE_CHECKPOINT"): + self.use_checkpoint = cfg.MODEL.DYHEAD.USE_CHECKPOINT + self.dummy_tensor = torch.ones(1, dtype=torch.float32, requires_grad=True) + + # early fusion module + print("EARLY FUSION ON, USING {}".format(cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE)) + if cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-S": + # single-direction (text->image) + # text -> image + self.t2i_attn = AttentionT2I( + q_dim=self.joint_embedding_size, + k_dim=self.lang_dim, + embed_dim=self.embed_dim, + num_heads=self.n_head, + hidden_dim=self.t2i_hidden_dim, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / cfg.MODEL.DYHEAD.NUM_CONVS, + mode="t2i", + use_layer_scale=cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_LAYER_SCALE, + clamp_min_for_underflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW, + clamp_max_for_overflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW, + ) + + elif cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-B": + # bi-direction (text->image, image->text) + self.b_attn = BiAttentionBlockForCheckpoint( + v_dim=self.joint_embedding_size, + l_dim=self.lang_dim, + embed_dim=self.embed_dim, + num_heads=self.n_head, + hidden_dim=self.i2t_hidden_dim, + dropout=0.1, + drop_path=0.0, + init_values=1.0 / cfg.MODEL.DYHEAD.NUM_CONVS, + cfg=cfg, + ) + if ( + self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL + and self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT + ): + self.shrink_lang = FeatureResizer(self.lang_dim * 5, self.lang_dim, 0.1) + + elif cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "SCAN": + # single-direction (text->image) + self.mapping_lang = _make_mlp(self.lang_dim, self.joint_embedding_size, self.joint_embedding_dropout) + self.joint_fusion = nn.ModuleList([_make_conv(self.joint_inp_dim, self.joint_out_dim, 1) for _ in range(5)]) + + elif cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "FILM": + # single-direction (text->image) + self.mapping_lang = _make_mlp(self.lang_dim, self.joint_embedding_size, self.joint_embedding_dropout) + self.gamma = nn.ModuleList(nn.Linear(self.joint_embedding_size, self.joint_inp_dim) for _ in range(5)) + self.beta = nn.ModuleList(nn.Linear(self.joint_embedding_size, self.joint_inp_dim) for _ in range(5)) + + self.joint_fusion = nn.ModuleList([_make_conv(self.joint_inp_dim, self.joint_out_dim, 1) for _ in range(5)]) + + else: + print("NO FUSION INVOLVED.") + + def init_configs(self, cfg): + # common params + self.lang_model = cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE + self.joint_embedding_size = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_SIZE + self.joint_embedding_dropout = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_DROPOUT + self.joint_mlp_layers = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_MLP_LAYERS + + self.max_query_len = cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN + self.n_layers = cfg.MODEL.LANGUAGE_BACKBONE.N_LAYERS + self.coord_dim = 8 + self.joint_inp_dim = self.coord_dim + self.joint_embedding_size + self.joint_out_dim = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_OUT_SIZE + + # mha params + self.n_head = 8 + self.embed_dim = 2048 + self.t2i_hidden_dim = 1024 # 256 * 4 + self.i2t_hidden_dim = 3072 # 768 * 4 + + if self.lang_model in ["bert-base-uncased", "roberta-base", "clip", "roberta-fused", "roberta-fused-v2", "roberta-fused-tiny"]: + self.lang_dim = cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM + else: + self.lang_dim = 1024 + + def forward(self, x): + visual_features = x["visual"] + language_dict_features = x["lang"] + + batch_size = visual_features[0].shape[0] + device = visual_features[0].device + + fused_visual_features = None + fused_language_dict_features = None + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-S": + language_feature = language_dict_features["hidden"] + mask = language_dict_features["masks"] + # text -> image + if self.use_checkpoint: + q0, q1, q2, q3, q4 = checkpoint.checkpoint( + self.t2i_attn, + visual_features[0], + visual_features[1], + visual_features[2], + visual_features[3], + visual_features[4], + language_feature, + language_feature, + mask, + self.dummy_tensor, + ) + else: + q0, q1, q2, q3, q4 = self.t2i_attn( + visual_features[0], + visual_features[1], + visual_features[2], + visual_features[3], + visual_features[4], + language_feature, + language_feature, + attention_mask=mask, + ) + + fused_visual_features = [q0, q1, q2, q3, q4] + fused_language_dict_features = language_dict_features + + elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-B": + if self.use_checkpoint: + q0, q1, q2, q3, q4, l0, l1, l2, l3, l4 = checkpoint.checkpoint( + self.b_attn, + visual_features[0], + visual_features[1], + visual_features[2], + visual_features[3], + visual_features[4], + language_dict_features["hidden"], + language_dict_features["masks"], + self.dummy_tensor, + ) + else: + q0, q1, q2, q3, q4, l0, l1, l2, l3, l4 = self.b_attn( + visual_features[0], + visual_features[1], + visual_features[2], + visual_features[3], + visual_features[4], + language_dict_features["hidden"], + language_dict_features["masks"], + self.dummy_tensor, + ) + + fused_visual_features = [q0, q1, q2, q3, q4] + if ( + self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL + and self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT + ): + language_features = self.shrink_lang(torch.cat([l0, l1, l2, l3, l4], dim=-1)) + else: + language_features = l0 + + language_dict_features["hidden"] = language_features + fused_language_dict_features = language_dict_features + + elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "SCAN": + # text -> image + language_feature = language_dict_features["aggregate"] + language_feature = self.mapping_lang(language_feature) + visu_feat = [] + for ii, feat in enumerate(visual_features): + attn_feat = func_attention(feat, language_feature, smooth=1, raw_feature_norm="softmax") + visu_feat.append(attn_feat) + + fused_visual_features = [fusion(feat) for feat, fusion in zip(visu_feat, self.joint_fusion)] + fused_language_dict_features = language_dict_features + + elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "FILM": + # text -> image + # relative position embedding + coord_feats = [_make_coord(batch_size, x.shape[2], x.shape[3]) for x in visual_features] + # I only use a global representation of language + # you can also use more complex modeling using word-level representations + # Usage: lang_feat = lang_feat['words'] shape [seq_len, dim] + language_feature = language_dict_features["aggregate"] + language_feature = self.mapping_lang(language_feature) + + # attention mechanism for fusion + gamma = [F.tanh(gamma(language_feature)) for gamma in self.gamma] + beta = [F.tanh(beta(language_feature)) for beta in self.beta] + + visu_feat = [] + for ii, feat in enumerate(visual_features): + coord_feat = coord_feats[ii].to(device) + feat = torch.cat([feat, coord_feat], dim=1) + b = beta[ii].view(batch_size, -1, 1, 1).expand_as(feat) + g = gamma[ii].view(batch_size, -1, 1, 1).expand_as(feat) + feat = F.relu(g * feat + b) + visu_feat.append(feat) + + fused_visual_features = [fusion(feat) for feat, fusion in zip(visu_feat, self.joint_fusion)] + fused_language_dict_features = language_dict_features + + else: + fused_visual_features = visual_features + fused_language_dict_features = language_dict_features + + features_dict = {"visual": fused_visual_features, "lang": fused_language_dict_features} + + return features_dict + + +class VLDyHead(torch.nn.Module): + def __init__(self, cfg): + super(VLDyHead, self).__init__() + self.cfg = cfg + # bert_cfg = BertConfig.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE) + if cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE in ["bert-base-uncased", "roberta-base"]: + lang_cfg = BertConfig.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE) + elif cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "clip": + lang_cfg = cfg + elif cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE in ["roberta-fused", "roberta-fused-v2", "roberta-fused-tiny"]: + lang_cfg = RobertaConfig.from_pretrained("roberta-base") + else: + lang_cfg = None + raise NotImplementedError + + num_classes = cfg.MODEL.DYHEAD.NUM_CLASSES - 1 + num_tokens = cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN + num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * cfg.MODEL.RPN.SCALES_PER_OCTAVE + in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS + channels = cfg.MODEL.DYHEAD.CHANNELS + + if cfg.MODEL.DYHEAD.USE_GN: + bn_type = ["gn", cfg.MODEL.GROUP_NORM.NUM_GROUPS] + elif cfg.MODEL.DYHEAD.USE_NSYNCBN: + bn_type = "nsbn" + elif cfg.MODEL.DYHEAD.USE_SYNCBN: + bn_type = "sbn" + else: + bn_type = None + + use_dyrelu = cfg.MODEL.DYHEAD.USE_DYRELU + use_dyfuse = cfg.MODEL.DYHEAD.USE_DYFUSE + use_deform = cfg.MODEL.DYHEAD.USE_DFCONV + + if cfg.MODEL.DYHEAD.CONV_FUNC: + conv_func = lambda i, o, s: eval(cfg.MODEL.DYHEAD.CONV_FUNC)(i, o, s, bn_type=bn_type) + else: + conv_func = lambda i, o, s: Conv3x3Norm(i, o, s, deformable=use_deform, bn_type=bn_type) + + dyhead_tower = [] + for i in range(cfg.MODEL.DYHEAD.NUM_CONVS): + if cfg.MODEL.DYHEAD.FUSE_CONFIG.EARLY_FUSE_ON: + # cross-modality fusion + dyhead_tower.append(VLFuse(cfg)) + # self language path + if i < cfg.MODEL.DYHEAD.NUM_CONVS - 1 or cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_FUSED_FEATURES_DOT_PRODUCT: + # dyhead_tower.append( + # BertEncoderLayer( + # bert_cfg, + # clamp_min_for_underflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MIN_FOR_UNDERFLOW, + # clamp_max_for_overflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MAX_FOR_OVERFLOW) + # ) + if cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE in [ + "bert-base-uncased", + "roberta-fused", + "roberta-fused-v2", + "roberta-fused-tiny", + "roberta-base", + ]: + dyhead_tower.append( + BertEncoderLayer( + lang_cfg, + clamp_min_for_underflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MIN_FOR_UNDERFLOW, + clamp_max_for_overflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MAX_FOR_OVERFLOW, + ) + ) + elif cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "clip": + dyhead_tower.append(CLIPTransformerLayer(lang_cfg)) + else: + raise NotImplementedError + + else: + dyhead_tower.append(DummyLayer()) + + # self vision path + dyhead_tower.append( + DyConv( + in_channels if i == 0 else channels, + channels, + conv_func=conv_func, + use_dyrelu=(use_dyrelu and in_channels == channels) if i == 0 else use_dyrelu, + use_dyfuse=(use_dyfuse and in_channels == channels) if i == 0 else use_dyfuse, + use_deform=(use_deform and in_channels == channels) if i == 0 else use_deform, + ) + ) + + self.add_module("dyhead_tower", nn.Sequential(*dyhead_tower)) + + self.cls_logits = nn.Conv2d(channels, num_anchors * num_classes, kernel_size=1) + self.bbox_pred = nn.Conv2d(channels, num_anchors * 4, kernel_size=1) + self.centerness = nn.Conv2d(channels, num_anchors * 1, kernel_size=1) + + # initialize the bias for focal loss + prior_prob = cfg.MODEL.DYHEAD.PRIOR_PROB + bias_value = -math.log((1 - prior_prob) / prior_prob) + + log_scale = self.cfg.MODEL.DYHEAD.LOG_SCALE + + # soft token head + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS: + self.token_logits = nn.Conv2d(channels, num_anchors * num_tokens, kernel_size=1) + # ABLATION + # self.token_logits = nn.Conv2d(channels, num_anchors * num_tokens, kernel_size=1, bias=False) + # self.bias = nn.Parameter(torch.zeros(channels), requires_grad=True) + # self.bias0 = nn.Parameter(torch.Tensor([bias_value]), requires_grad=True) + + # contrastive alignment head + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + assert self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS == False + contrastive_hdim = cfg.MODEL.DYHEAD.FUSE_CONFIG.CONTRASTIVE_HIDDEN_DIM + self.contrastive_align_projection_image = nn.Conv2d(channels, num_anchors * contrastive_hdim, kernel_size=1) + self.contrastive_align_projection_text = nn.Linear(channels, contrastive_hdim, bias=True) + self.log_scale = nn.Parameter(torch.Tensor([log_scale]), requires_grad=True) + + # dot product soft token head + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + assert self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS == False + self.dot_product_projection_image = nn.Identity() + self.dot_product_projection_text = nn.Linear( + self.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, num_anchors * channels, bias=True + ) + self.log_scale = nn.Parameter(torch.Tensor([log_scale]), requires_grad=True) + # DEBUG + # self.bias = nn.Parameter(torch.zeros(channels), requires_grad=True) + self.bias_lang = nn.Parameter(torch.zeros(self.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM), requires_grad=True) + self.bias0 = nn.Parameter(torch.Tensor([bias_value]), requires_grad=True) + + # initialization + for modules in [self.cls_logits, self.bbox_pred, self.centerness]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)]) + + torch.nn.init.constant_(self.cls_logits.bias, bias_value) + + # if use soft token loss + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS: + for modules in [self.token_logits]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + torch.nn.init.constant_(self.token_logits.bias, bias_value) + # print(torch.norm(self.token_logits.weight)) + + # if use contrastive loss + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + for modules in [self.contrastive_align_projection_image]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, 0) + + # if use dot product token loss + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + for modules in [self.dot_product_projection_image]: + for l in modules.modules(): + if isinstance(l, nn.Conv2d): + torch.nn.init.normal_(l.weight, std=0.01) + torch.nn.init.constant_(l.bias, bias_value) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + if cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "clip": + lang_cfg = BertConfig.from_pretrained("bert-base-uncased") + lang_cfg.hidden_size = cfg.MODEL.CLIP.WIDTH + lang_cfg.vocab_size = cfg.MODEL.CLIP.VOCAB_SIZE + self.mlm_head = BertLMPredictionHead(lang_cfg) # nn.Linear(hidden_size, config.vocab_size, bias=False) + + def forward(self, x, language_dict_features=None, embedding=None, swint_feature_c4=None): + logits = [] + bbox_reg = [] + centerness = [] + + feat_inputs = {"visual": x, "lang": language_dict_features} + + dyhead_tower = self.dyhead_tower(feat_inputs) + + # soft token + t_logits = None + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS: + t_logits = [] + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_FUSED_FEATURES_DOT_PRODUCT: + embedding = dyhead_tower["lang"]["hidden"] + + # MLM loss + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + mlm_logits = self.mlm_head(embedding) + else: + mlm_logits = None + + # contrastive + contrastive_logits = None + proj_tokens = None + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + contrastive_logits = [] + # follow MDETR's way + proj_tokens = F.normalize(self.contrastive_align_projection_text(embedding), p=2, dim=-1) + + # dot product soft token + dot_product_logits = None + dot_product_proj_tokens = None + dot_product_proj_tokens_bias = None + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + dot_product_logits = [] + # norm + embedding = F.normalize(embedding, p=2, dim=-1) + dot_product_proj_tokens = self.dot_product_projection_text(embedding / 2.0) + # w/o norm + # dot_product_proj_tokens = self.dot_product_projection_text(embedding / 28.0) + + dot_product_proj_tokens_bias = torch.matmul(embedding, self.bias_lang) + self.bias0 + + # shallow contrastive (original feature from image & text encoder) + shallow_img_emb_feats = None + shallow_text_emb = None + if ( + self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS + or self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS + ): + shallow_img_emb_feats = [] + shallow_text_emb = embedding + + # print([v.shape for v in x]) + # shallow contrastive: use the feature from swint backbone + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS: + for b, feature in enumerate(swint_feature_c4): + # BF, CF, HF, WF = feat.shape + # shallow_img_emb = permute_and_flatten(feat, BF, -1, CF, HF, WF) + shallow_img_emb_feats.append(feature) + + fused_visual_features = None + if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES: + fused_visual_features = [] + + # use the feature from FPN + for l, feature in enumerate(x): + logits.append(self.cls_logits(dyhead_tower["visual"][l])) + + bbox_pred = self.scales[l](self.bbox_pred(dyhead_tower["visual"][l])) + bbox_reg.append(bbox_pred) + + centerness.append(self.centerness(dyhead_tower["visual"][l])) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS: + t_logits.append(self.token_logits(dyhead_tower["visual"][l])) + + # ABLATION + # b = self.bias.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) + # x = dyhead_tower["visual"][l] + # B, C, H, W = x.shape + # bias = b.repeat(B, 1, H, W) + # t_logits.append(self.token_logits(dyhead_tower["visual"][l] + bias) + self.bias0) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + x = dyhead_tower["visual"][l] + B, _, H, W = x.shape + C = proj_tokens.shape[2] + proj_queries = self.contrastive_align_projection_image(dyhead_tower["visual"][l]) + proj_queries = permute_and_flatten(proj_queries, B, -1, C, H, W) + normalized_img_emb = F.normalize(proj_queries, p=2, dim=-1) + normalized_text_emb = proj_tokens + contrastive_logit = ( + torch.matmul(normalized_img_emb, normalized_text_emb.transpose(-1, -2)) / self.log_scale.exp() + ) + contrastive_logits.append(contrastive_logit) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + x = dyhead_tower["visual"][l] + if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES: + fused_visual_features.append(x) + B, C, H, W = x.shape + + # add bias (language) + dot_product_proj_queries = self.dot_product_projection_image(x) + dot_product_proj_queries = permute_and_flatten(dot_product_proj_queries, B, -1, C, H, W) + + A = dot_product_proj_queries.shape[1] + bias = dot_product_proj_tokens_bias.unsqueeze(1).repeat(1, A, 1) + + # add bias (vision) + # b = self.bias.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) + # tensor.repeat() is supposed to cost more memory, bias = b.repeat(B, 1, H, W) + # here we replace it with tensor.expand() + # bias = b.repeat(B, 1, H, W) + # dot_product_proj_queries = self.dot_product_projection_image(x) + bias + + # print(torch.norm(dot_product_proj_tokens)) + # exit() + + dot_product_logit = ( + torch.matmul(dot_product_proj_queries, dot_product_proj_tokens.transpose(-1, -2)) + / self.log_scale.exp() + ) + bias + + # dot_product_logit = (torch.matmul(dot_product_proj_queries, + # dot_product_proj_tokens.transpose(-1, + # -2)) / self.log_scale.exp()) + self.bias0 + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_DOT_PRODUCT: + dot_product_logit = torch.clamp(dot_product_logit, max=50000) + dot_product_logit = torch.clamp(dot_product_logit, min=-50000) + dot_product_logits.append(dot_product_logit) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS: + feat = feature + BF, CF, HF, WF = feat.shape + shallow_img_emb = permute_and_flatten(feat, BF, -1, CF, HF, WF) + shallow_img_emb_feats.append(shallow_img_emb) + + # no matter the feature is from backboone or from fpn, we use shallow_img_embs all the time + if shallow_img_emb_feats is not None and shallow_text_emb is not None: + # shallow_img_embs = torch.cat(shallow_img_embs, dim=1) + proj_tokens = shallow_text_emb + + return ( + logits, + bbox_reg, + centerness, + t_logits, + proj_tokens, + contrastive_logits, + dot_product_logits, + mlm_logits, + shallow_img_emb_feats, + fused_visual_features, + ) + + +class VLDyHeadModule(torch.nn.Module): + def __init__(self, cfg): + super(VLDyHeadModule, self).__init__() + self.cfg = cfg + self.head = VLDyHead(cfg) + box_coder = BoxCoder(cfg) + self.loss_evaluator = make_atss_loss_evaluator(cfg, box_coder) + self.box_selector_train = make_atss_postprocessor(cfg, box_coder, is_train=True) + self.box_selector_test = make_atss_postprocessor(cfg, box_coder, is_train=False) + self.anchor_generator = make_anchor_generator_complex(cfg) + + self.lang_model = cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE + self.joint_embedding_size = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_SIZE + self.joint_embedding_dropout = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_DROPOUT + if self.lang_model in ["bert-base-uncased", "roberta-base", "clip", "roberta-fused", "roberta-fused-v2", "roberta-fused-tiny"]: + self.lang_dim = cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM + else: + self.lang_dim = 1024 + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + self.resizer = FeatureResizer( + input_feat_size=self.lang_dim, + output_feat_size=self.joint_embedding_size, + dropout=self.joint_embedding_dropout, + ) + # if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER: + # self.tunable_linear = torch.nn.Linear(self.lang_dim, 1000, bias=False) + # self.tunable_linear.weight.data.fill_(0.0) + + def forward( + self, + images, + features, + targets=None, + language_dict_features=None, + positive_map=None, + captions=None, + swint_feature_c4=None, + ): + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + # resizer needed + embedding = language_dict_features["embedded"] + embedding = self.resizer(embedding) + elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + # no resizer needed + embedding = language_dict_features["embedded"] + # print(captions) + # print(embedding) + else: + embedding = None + + if "masks" in language_dict_features: + text_masks = language_dict_features["masks"] + else: + text_masks = None + + # if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER: + # embedding = self.tunable_linear.weight[:embedding.size(1), :].unsqueeze(0) + embedding + # language_dict_features['embedded'] = embedding + # language_dict_features['hidden'] = self.tunable_linear.weight[:embedding.size(1), :].unsqueeze(0) + language_dict_features['hidden'] + + ( + box_cls, + box_regression, + centerness, + token_logits, + proj_tokens, + contrastive_logits, + dot_product_logits, + mlm_logits, + shallow_img_emb_feats, + fused_visual_features, + ) = self.head(features, language_dict_features, embedding, swint_feature_c4) + anchors = self.anchor_generator(images, features) + + if self.training: + return self._forward_train( + box_cls, + box_regression, + centerness, + targets, + anchors, + captions, + positive_map, + token_logits, + proj_tokens, + contrastive_logits, + dot_product_logits, + text_masks, + mlm_logits=mlm_logits, + mlm_labels=language_dict_features["mlm_labels"], + shallow_img_emb_feats=shallow_img_emb_feats, + fused_visual_features=fused_visual_features, + ) + else: + return self._forward_test( + box_regression, + centerness, + anchors, + box_cls, + token_logits, + dot_product_logits, + positive_map, + fused_visual_features=fused_visual_features, + ) + + def _forward_train( + self, + box_cls, + box_regression, + centerness, + targets, + anchors, + captions=None, + positive_map=None, + token_logits=None, + proj_tokens=None, + contrastive_logits=None, + dot_product_logits=None, + text_masks=None, + mlm_logits=None, + mlm_labels=None, + shallow_img_emb_feats=None, + fused_visual_features=None, + ): + + ( + loss_box_cls, + loss_box_reg, + loss_centerness, + loss_token, + loss_contrastive_align, + loss_dot_product_token, + loss_shallow_contrastive, + ) = self.loss_evaluator( + box_cls, + box_regression, + centerness, + targets, + anchors, + captions, + positive_map, + token_logits, + proj_tokens, + contrastive_logits, + dot_product_logits, + text_masks, + shallow_img_emb_feats, + ) + + losses = { + # "loss_cls": loss_box_cls, + "loss_reg": loss_box_reg, + "loss_centerness": loss_centerness, + } + + if mlm_labels is not None and mlm_logits is not None: + losses["mlm_loss"] = ( + nn.CrossEntropyLoss(ignore_index=-100)(mlm_logits.view(-1, mlm_logits.size(-1)), mlm_labels.view(-1)) + * self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS_COEF + ) + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CLASSIFICATION_LOSS: + losses["loss_cls"] = loss_box_cls + else: + losses["loss_cls"] = 0.0 * loss_box_cls + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS: + losses["loss_token"] = loss_token * self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TOKEN_LOSS_WEIGHT + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS: + losses["loss_contrastive_align"] = ( + loss_contrastive_align * self.cfg.MODEL.DYHEAD.FUSE_CONFIG.CONTRASTIVE_ALIGN_LOSS_WEIGHT + ) + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS: + losses["loss_dot_product_token"] = ( + loss_dot_product_token * self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DOT_PRODUCT_TOKEN_LOSS_WEIGHT + ) + if ( + self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS + or self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS + ): + losses["loss_shallow_contrastive"] = ( + loss_shallow_contrastive * self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_CONTRASTIVE_LOSS_WEIGHT + ) + + if self.cfg.MODEL.RPN_ONLY: + return None, losses, None + else: + # Let's just use one image per batch + assert (box_regression[0].shape[0]) == 1 + positive_map_label_to_token = create_positive_map_label_to_token_from_positive_map(positive_map, plus=1) + boxes = self.box_selector_train( + box_regression, + centerness, + anchors, + box_cls, + token_logits, + dot_product_logits, + positive_map=positive_map_label_to_token, + ) + train_boxes = [] + # for b, a in zip(boxes, anchors): + # a = cat_boxlist(a) + # b.add_field("visibility", torch.ones(b.bbox.shape[0], dtype=torch.bool, device=b.bbox.device)) + # del b.extra_fields['scores'] + # del b.extra_fields['labels'] + # train_boxes.append(cat_boxlist([b, a])) + for b, t in zip(boxes, targets): + tb = t.copy_with_fields(["labels"]) + tb.add_field("scores", torch.ones(tb.bbox.shape[0], dtype=torch.bool, device=tb.bbox.device)) + train_boxes.append(cat_boxlist([b, tb])) + return train_boxes, losses, fused_visual_features + + def _forward_test( + self, + box_regression, + centerness, + anchors, + box_cls=None, + token_logits=None, + dot_product_logits=None, + positive_map=None, + fused_visual_features=None, + ): + + boxes = self.box_selector_test( + box_regression, + centerness, + anchors, + box_cls, + token_logits, + dot_product_logits, + positive_map, + ) + return boxes, {}, fused_visual_features diff --git a/maskrcnn_benchmark/modeling/utils.py b/maskrcnn_benchmark/modeling/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..531720a08d7ada3923a376b77dbf69addaac3f7e --- /dev/null +++ b/maskrcnn_benchmark/modeling/utils.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +""" +Miscellaneous utility functions +""" + +import torch + + +def cat(tensors, dim=0): + """ + Efficient version of torch.cat that avoids a copy if there is only a single element in a list + """ + assert isinstance(tensors, (list, tuple)) + if len(tensors) == 1: + return tensors[0] + return torch.cat(tensors, dim) + + +def permute_and_flatten(layer, N, A, C, H, W): + layer = layer.view(N, -1, C, H, W) + layer = layer.permute(0, 3, 4, 1, 2) + layer = layer.reshape(N, -1, C) + return layer + + +def concat_box_prediction_layers(box_regression, box_cls=None, token_logits=None): + box_regression_flattened = [] + box_cls_flattened = [] + token_logit_flattened = [] + + # for each feature level, permute the outputs to make them be in the + # same format as the labels. Note that the labels are computed for + # all feature levels concatenated, so we keep the same representation + # for the objectness and the box_regression + for box_cls_per_level, box_regression_per_level in zip(box_cls, box_regression): + N, AxC, H, W = box_cls_per_level.shape + Ax4 = box_regression_per_level.shape[1] + A = Ax4 // 4 + C = AxC // A + box_cls_per_level = permute_and_flatten(box_cls_per_level, N, A, C, H, W) + box_cls_flattened.append(box_cls_per_level) + + box_regression_per_level = permute_and_flatten(box_regression_per_level, N, A, 4, H, W) + box_regression_flattened.append(box_regression_per_level) + + if token_logits is not None: + for token_logit_per_level in token_logits: + N, AXT, H, W = token_logit_per_level.shape + T = AXT // A + token_logit_per_level = permute_and_flatten(token_logit_per_level, N, A, T, H, W) + token_logit_flattened.append(token_logit_per_level) + + # concatenate on the first dimension (representing the feature levels), to + # take into account the way the labels were generated (with all feature maps + # being concatenated as well) + box_cls = cat(box_cls_flattened, dim=1).reshape(-1, C) + box_regression = cat(box_regression_flattened, dim=1).reshape(-1, 4) + + token_logits_stacked = None + if token_logits is not None: + # stacked + token_logits_stacked = cat(token_logit_flattened, dim=1) + + return box_regression, box_cls, token_logits_stacked + + +def round_channels(channels, divisor=8): + rounded_channels = max(int(channels + divisor / 2.0) // divisor * divisor, divisor) + if float(rounded_channels) < 0.9 * channels: + rounded_channels += divisor + return rounded_channels diff --git a/maskrcnn_benchmark/solver/__init__.py b/maskrcnn_benchmark/solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..927668ea6f35aedcff25f779e85a8b8c27a8c797 --- /dev/null +++ b/maskrcnn_benchmark/solver/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from .build import make_optimizer +from .build import make_lr_scheduler +from .lr_scheduler import WarmupMultiStepLR diff --git a/maskrcnn_benchmark/solver/build.py b/maskrcnn_benchmark/solver/build.py new file mode 100644 index 0000000000000000000000000000000000000000..f84724b13f031d511f01647f9303ff877cb1bf35 --- /dev/null +++ b/maskrcnn_benchmark/solver/build.py @@ -0,0 +1,123 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch +import itertools + +from .lr_scheduler import WarmupMultiStepLR, WarmupCosineAnnealingLR, WarmupReduceLROnPlateau + + +def make_optimizer(cfg, model): + def maybe_add_full_model_gradient_clipping(optim): # optim: the optimizer class + # detectron2 doesn't have full model gradient clipping now + clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE + enable = ( + cfg.SOLVER.CLIP_GRADIENTS.ENABLED + and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model" + and clip_norm_val > 0.0 + ) + + class FullModelGradientClippingOptimizer(optim): + def step(self, closure=None): + all_params = itertools.chain(*[x["params"] for x in self.param_groups]) + torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val) + super().step(closure=closure) + + return FullModelGradientClippingOptimizer if enable else optim + + params = [] + for key, value in model.named_parameters(): + if not value.requires_grad: + print(key, "no grad") + continue + lr = cfg.SOLVER.BASE_LR + weight_decay = cfg.SOLVER.WEIGHT_DECAY + + # different lr schedule + if "language_backbone" in key: + lr = cfg.SOLVER.LANG_LR + + if "backbone.body" in key and "language_backbone.body" not in key: + lr = cfg.SOLVER.BASE_LR * cfg.SOLVER.BACKBONE_BODY_LR_FACTOR + + if "t2i" in key: + lr = lr * cfg.SOLVER.FUSION_LR_FACTOR + + if "bias" in key: + lr *= cfg.SOLVER.BIAS_LR_FACTOR + weight_decay = cfg.SOLVER.WEIGHT_DECAY_BIAS + + if "norm" in key or "Norm" in key: + weight_decay *= cfg.SOLVER.WEIGHT_DECAY_NORM_FACTOR + print("Setting weight decay of {} to {}".format(key, weight_decay)) + print(key, lr, ) + params += [{"params": [value], "lr": lr, "weight_decay": weight_decay}] + + if cfg.SOLVER.OPTIMIZER == "SGD": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(params, lr, momentum=cfg.SOLVER.MOMENTUM) + elif cfg.SOLVER.OPTIMIZER == "ADAMW": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(params, lr) + + return optimizer + + +def make_lr_scheduler(cfg, optimizer): + if cfg.SOLVER.MULTI_MAX_EPOCH: + assert len(cfg.SOLVER.MULTI_MAX_EPOCH) == len(cfg.SOLVER.STEPS) + lr_scheduler = [] + + for stage_step, stage_max_epoch in zip(cfg.SOLVER.STEPS, cfg.SOLVER.MULTI_MAX_ITER): + milestones = [] + for step in stage_step: + milestones.append(round(step * stage_max_epoch)) + lr_scheduler.append( + WarmupMultiStepLR( + optimizer, + milestones, + cfg.SOLVER.GAMMA, + warmup_factor=cfg.SOLVER.WARMUP_FACTOR, + warmup_iters=cfg.SOLVER.WARMUP_ITERS, + warmup_method=cfg.SOLVER.WARMUP_METHOD, + ) + ) + return lr_scheduler + + elif cfg.SOLVER.USE_COSINE: + max_iters = cfg.SOLVER.MAX_ITER + return WarmupCosineAnnealingLR( + optimizer, + max_iters, + cfg.SOLVER.GAMMA, + warmup_factor=cfg.SOLVER.WARMUP_FACTOR, + warmup_iters=cfg.SOLVER.WARMUP_ITERS, + warmup_method=cfg.SOLVER.WARMUP_METHOD, + eta_min=cfg.SOLVER.MIN_LR, + ) + + elif cfg.SOLVER.USE_AUTOSTEP: + max_iters = cfg.SOLVER.MAX_ITER + return WarmupReduceLROnPlateau( + optimizer, + max_iters, + cfg.SOLVER.GAMMA, + warmup_factor=cfg.SOLVER.WARMUP_FACTOR, + warmup_iters=cfg.SOLVER.WARMUP_ITERS, + warmup_method=cfg.SOLVER.WARMUP_METHOD, + eta_min=cfg.SOLVER.MIN_LR, + patience=cfg.SOLVER.STEP_PATIENCE, + verbose=True, + ) + + else: + milestones = [] + for step in cfg.SOLVER.STEPS: + if step < 1: + milestones.append(round(step * cfg.SOLVER.MAX_ITER)) + else: + milestones.append(step) + return WarmupMultiStepLR( + optimizer, + milestones, + cfg.SOLVER.GAMMA, + warmup_factor=cfg.SOLVER.WARMUP_FACTOR, + warmup_iters=cfg.SOLVER.WARMUP_ITERS, + warmup_method=cfg.SOLVER.WARMUP_METHOD, + ) diff --git a/maskrcnn_benchmark/solver/lr_scheduler.py b/maskrcnn_benchmark/solver/lr_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..a248e57be4ee7e850e6b040f4aa4065ddaee853a --- /dev/null +++ b/maskrcnn_benchmark/solver/lr_scheduler.py @@ -0,0 +1,151 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from bisect import bisect_right + +import math +import torch + + +# FIXME ideally this would be achieved with a CombinedLRScheduler, +# separating MultiStepLR with WarmupLR +# but the current LRScheduler design doesn't allow it +class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer, + milestones, + gamma=0.1, + warmup_factor=1.0 / 3, + warmup_iters=500, + warmup_method="linear", + last_epoch=-1, + ): + if not list(milestones) == sorted(milestones): + raise ValueError( + "Milestones should be a list of" " increasing integers. Got {}", + milestones, + ) + + if warmup_method not in ("constant", "linear"): + raise ValueError("Only 'constant' or 'linear' warmup_method accepted" "got {}".format(warmup_method)) + self.milestones = milestones + self.gamma = gamma + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch) + + def get_lr(self): + warmup_factor = 1 + if self.last_epoch < self.warmup_iters: + if self.warmup_method == "constant": + warmup_factor = self.warmup_factor + elif self.warmup_method == "linear": + alpha = float(self.last_epoch) / self.warmup_iters + warmup_factor = self.warmup_factor * (1 - alpha) + alpha + return [ + base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch) + for base_lr in self.base_lrs + ] + + +class WarmupCosineAnnealingLR(torch.optim.lr_scheduler._LRScheduler): + def __init__( + self, + optimizer, + max_iters, + gamma=0.1, + warmup_factor=1.0 / 3, + warmup_iters=500, + warmup_method="linear", + eta_min=0, + last_epoch=-1, + ): + + if warmup_method not in ("constant", "linear"): + raise ValueError("Only 'constant' or 'linear' warmup_method accepted" "got {}".format(warmup_method)) + self.max_iters = max_iters + self.gamma = gamma + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + self.eta_min = eta_min + super(WarmupCosineAnnealingLR, self).__init__(optimizer, last_epoch) + + def get_lr(self): + warmup_factor = 1 + + if self.last_epoch < self.warmup_iters: + if self.warmup_method == "constant": + warmup_factor = self.warmup_factor + elif self.warmup_method == "linear": + alpha = float(self.last_epoch) / self.warmup_iters + warmup_factor = self.warmup_factor * (1 - alpha) + alpha + return [base_lr * warmup_factor for base_lr in self.base_lrs] + else: + return [ + self.eta_min + + (base_lr - self.eta_min) + * (1 + math.cos(math.pi * (self.last_epoch - self.warmup_iters) / self.max_iters)) + / 2 + for base_lr in self.base_lrs + ] + + +class WarmupReduceLROnPlateau(torch.optim.lr_scheduler.ReduceLROnPlateau): + def __init__( + self, + optimizer, + max_iters, + gamma=0.1, + warmup_factor=1.0 / 3, + warmup_iters=500, + warmup_method="linear", + eta_min=0, + last_epoch=-1, + patience=5, + verbose=False, + ): + + if warmup_method not in ("constant", "linear"): + raise ValueError("Only 'constant' or 'linear' warmup_method accepted" "got {}".format(warmup_method)) + self.warmup_factor = warmup_factor + self.warmup_iters = warmup_iters + self.warmup_method = warmup_method + self.eta_min = eta_min + + if last_epoch == -1: + for group in optimizer.param_groups: + group.setdefault("initial_lr", group["lr"]) + else: + for i, group in enumerate(optimizer.param_groups): + if "initial_lr" not in group: + raise KeyError( + "param 'initial_lr' is not specified " + "in param_groups[{}] when resuming an optimizer".format(i) + ) + self.base_lrs = list(map(lambda group: group["initial_lr"], optimizer.param_groups)) + super(WarmupReduceLROnPlateau, self).__init__( + optimizer, factor=gamma, patience=patience, mode="max", min_lr=eta_min, verbose=verbose + ) + + def step(self, metrics=None): + warmup_factor = 1 + + if self.last_epoch < self.warmup_iters: + if self.warmup_method == "constant": + warmup_factor = self.warmup_factor + elif self.warmup_method == "linear": + alpha = float(self.last_epoch) / self.warmup_iters + warmup_factor = self.warmup_factor * (1 - alpha) + alpha + + if self.last_epoch >= self.warmup_iters - 1: + warmup_factor = 1.0 + + warmup_lrs = [base_lr * warmup_factor for base_lr in self.base_lrs] + + for param_group, lr in zip(self.optimizer.param_groups, warmup_lrs): + param_group["lr"] = lr + + self.last_epoch += 1 + elif metrics: + super().step(metrics) diff --git a/maskrcnn_benchmark/structures/__init__.py b/maskrcnn_benchmark/structures/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/maskrcnn_benchmark/structures/bounding_box.py b/maskrcnn_benchmark/structures/bounding_box.py new file mode 100644 index 0000000000000000000000000000000000000000..fabe1c0528d467b1dba8bb9ca2b32c269909e8d4 --- /dev/null +++ b/maskrcnn_benchmark/structures/bounding_box.py @@ -0,0 +1,306 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + +# transpose +FLIP_LEFT_RIGHT = 0 +FLIP_TOP_BOTTOM = 1 + + +class BoxList(object): + """ + This class represents a set of bounding boxes. + The bounding boxes are represented as a Nx4 Tensor. + In order to uniquely determine the bounding boxes with respect + to an image, we also store the corresponding image dimensions. + They can contain extra information that is specific to each bounding box, such as + labels. + """ + + def __init__(self, bbox, image_size, mode="xyxy"): + device = bbox.device if isinstance(bbox, torch.Tensor) else torch.device("cpu") + # only do as_tensor if isn't a "no-op", because it hurts JIT tracing + if not isinstance(bbox, torch.Tensor) or bbox.dtype != torch.float32 or bbox.device != device: + bbox = torch.as_tensor(bbox, dtype=torch.float32, device=device) + if bbox.ndimension() != 2: + raise ValueError("bbox should have 2 dimensions, got {}".format(bbox.ndimension())) + if bbox.size(-1) != 4: + raise ValueError("last dimenion of bbox should have a " "size of 4, got {}".format(bbox.size(-1))) + if mode not in ("xyxy", "xywh"): + raise ValueError("mode should be 'xyxy' or 'xywh'") + + self.bbox = bbox + self.size = image_size # (image_width, image_height) + self.mode = mode + self.extra_fields = {} + + # note: _jit_wrap/_jit_unwrap only work if the keys and the sizes don't change in between + def _jit_unwrap(self): + return (self.bbox,) + tuple( + f for f in (self.get_field(field) for field in sorted(self.fields())) if isinstance(f, torch.Tensor) + ) + + def _jit_wrap(self, input_stream): + self.bbox = input_stream[0] + num_consumed = 1 + for f in sorted(self.fields()): + if isinstance(self.extra_fields[f], torch.Tensor): + self.extra_fields[f] = input_stream[num_consumed] + num_consumed += 1 + return self, input_stream[num_consumed:] + + def add_field(self, field, field_data): + self.extra_fields[field] = field_data + + def get_field(self, field): + return self.extra_fields[field] + + def has_field(self, field): + return field in self.extra_fields + + def fields(self): + return list(self.extra_fields.keys()) + + def _copy_extra_fields(self, bbox): + for k, v in bbox.extra_fields.items(): + self.extra_fields[k] = v + + def convert(self, mode): + if mode not in ("xyxy", "xywh"): + raise ValueError("mode should be 'xyxy' or 'xywh'") + if mode == self.mode: + return self + # we only have two modes, so don't need to check + # self.mode + xmin, ymin, xmax, ymax = self._split_into_xyxy() + if mode == "xyxy": + bbox = torch.cat((xmin, ymin, xmax, ymax), dim=-1) + bbox = BoxList(bbox, self.size, mode=mode) + else: + TO_REMOVE = 1 + # NOTE: explicitly specify dim to avoid tracing error in GPU + bbox = torch.cat((xmin, ymin, xmax - xmin + TO_REMOVE, ymax - ymin + TO_REMOVE), dim=1) + bbox = BoxList(bbox, self.size, mode=mode) + bbox._copy_extra_fields(self) + return bbox + + def _split_into_xyxy(self): + if self.mode == "xyxy": + xmin, ymin, xmax, ymax = self.bbox.split(1, dim=-1) + return xmin, ymin, xmax, ymax + elif self.mode == "xywh": + TO_REMOVE = 1 + xmin, ymin, w, h = self.bbox.split(1, dim=-1) + return ( + xmin, + ymin, + xmin + (w - TO_REMOVE).clamp(min=0), + ymin + (h - TO_REMOVE).clamp(min=0), + ) + else: + raise RuntimeError("Should not be here") + + def resize(self, size, *args, **kwargs): + """ + Returns a resized copy of this bounding box + + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + """ + + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size)) + if ratios[0] == ratios[1]: + ratio = ratios[0] + scaled_box = self.bbox * ratio + bbox = BoxList(scaled_box, size, mode=self.mode) + # bbox._copy_extra_fields(self) + for k, v in self.extra_fields.items(): + if not isinstance(v, torch.Tensor) and not isinstance(v, list): + v = v.resize(size, *args, **kwargs) + bbox.add_field(k, v) + return bbox + + ratio_width, ratio_height = ratios + xmin, ymin, xmax, ymax = self._split_into_xyxy() + scaled_xmin = xmin * ratio_width + scaled_xmax = xmax * ratio_width + scaled_ymin = ymin * ratio_height + scaled_ymax = ymax * ratio_height + scaled_box = torch.cat((scaled_xmin, scaled_ymin, scaled_xmax, scaled_ymax), dim=-1) + bbox = BoxList(scaled_box, size, mode="xyxy") + # bbox._copy_extra_fields(self) + for k, v in self.extra_fields.items(): + if not isinstance(v, torch.Tensor) and not isinstance(v, list): + v = v.resize(size, *args, **kwargs) + bbox.add_field(k, v) + + return bbox.convert(self.mode) + + def transpose(self, method): + """ + Transpose bounding box (flip or rotate in 90 degree steps) + :param method: One of :py:attr:`PIL.Image.FLIP_LEFT_RIGHT`, + :py:attr:`PIL.Image.FLIP_TOP_BOTTOM`, :py:attr:`PIL.Image.ROTATE_90`, + :py:attr:`PIL.Image.ROTATE_180`, :py:attr:`PIL.Image.ROTATE_270`, + :py:attr:`PIL.Image.TRANSPOSE` or :py:attr:`PIL.Image.TRANSVERSE`. + """ + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError("Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented") + + image_width, image_height = self.size + xmin, ymin, xmax, ymax = self._split_into_xyxy() + if method == FLIP_LEFT_RIGHT: + TO_REMOVE = 1 + transposed_xmin = image_width - xmax - TO_REMOVE + transposed_xmax = image_width - xmin - TO_REMOVE + transposed_ymin = ymin + transposed_ymax = ymax + elif method == FLIP_TOP_BOTTOM: + transposed_xmin = xmin + transposed_xmax = xmax + transposed_ymin = image_height - ymax + transposed_ymax = image_height - ymin + + transposed_boxes = torch.cat((transposed_xmin, transposed_ymin, transposed_xmax, transposed_ymax), dim=-1) + bbox = BoxList(transposed_boxes, self.size, mode="xyxy") + # bbox._copy_extra_fields(self) + for k, v in self.extra_fields.items(): + if not isinstance(v, torch.Tensor) and not isinstance(v, list): + v = v.transpose(method) + bbox.add_field(k, v) + return bbox.convert(self.mode) + + def crop(self, box): + """ + Cropss a rectangular region from this bounding box. The box is a + 4-tuple defining the left, upper, right, and lower pixel + coordinate. + """ + xmin, ymin, xmax, ymax = self._split_into_xyxy() + w, h = box[2] - box[0], box[3] - box[1] + cropped_xmin = (xmin - box[0]).clamp(min=0, max=w) + cropped_ymin = (ymin - box[1]).clamp(min=0, max=h) + cropped_xmax = (xmax - box[0]).clamp(min=0, max=w) + cropped_ymax = (ymax - box[1]).clamp(min=0, max=h) + + # TODO should I filter empty boxes here? + cropped_box = torch.cat((cropped_xmin, cropped_ymin, cropped_xmax, cropped_ymax), dim=-1) + bbox = BoxList(cropped_box, (w, h), mode="xyxy") + # bbox._copy_extra_fields(self) + for k, v in self.extra_fields.items(): + if not isinstance(v, torch.Tensor) and not isinstance(v, list): + v = v.crop(box) + bbox.add_field(k, v) + return bbox.convert(self.mode) + + # Tensor-like methods + + def to(self, device): + bbox = BoxList(self.bbox.to(device), self.size, self.mode) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(device) + bbox.add_field(k, v) + return bbox + + def __getitem__(self, item): + bbox = BoxList(self.bbox[item], self.size, self.mode) + for k, v in self.extra_fields.items(): + bbox.add_field(k, v[item]) + return bbox + + def __len__(self): + return self.bbox.shape[0] + + def clip_to_image(self, remove_empty=True): + TO_REMOVE = 1 + x1s = self.bbox[:, 0].clamp(min=0, max=self.size[0] - TO_REMOVE) + y1s = self.bbox[:, 1].clamp(min=0, max=self.size[1] - TO_REMOVE) + x2s = self.bbox[:, 2].clamp(min=0, max=self.size[0] - TO_REMOVE) + y2s = self.bbox[:, 3].clamp(min=0, max=self.size[1] - TO_REMOVE) + self.bbox = torch.stack((x1s, y1s, x2s, y2s), dim=-1) + if remove_empty: + box = self.bbox + keep = (box[:, 3] > box[:, 1]) & (box[:, 2] > box[:, 0]) + return self[keep] + return self + + def area(self): + if self.mode == "xyxy": + TO_REMOVE = 1 + box = self.bbox + area = (box[:, 2] - box[:, 0] + TO_REMOVE) * (box[:, 3] - box[:, 1] + TO_REMOVE) + elif self.mode == "xywh": + box = self.bbox + area = box[:, 2] * box[:, 3] + else: + raise RuntimeError("Should not be here") + + return area + + def copy_with_fields(self, fields): + bbox = BoxList(self.bbox, self.size, self.mode) + if not isinstance(fields, (list, tuple)): + fields = [fields] + for field in fields: + bbox.add_field(field, self.get_field(field)) + return bbox + + def __repr__(self): + s = self.__class__.__name__ + "(" + s += "num_boxes={}, ".format(len(self)) + s += "image_width={}, ".format(self.size[0]) + s += "image_height={}, ".format(self.size[1]) + s += "mode={})".format(self.mode) + return s + + @staticmethod + def concate_box_list(list_of_boxes): + boxes = torch.cat([i.bbox for i in list_of_boxes], dim=0) + extra_fields_keys = list(list_of_boxes[0].extra_fields.keys()) + extra_fields = {} + for key in extra_fields_keys: + extra_fields[key] = torch.cat([i.extra_fields[key] for i in list_of_boxes], dim=0) + + final = list_of_boxes[0].copy_with_fields(extra_fields_keys) + + final.bbox = boxes + final.extra_fields = extra_fields + return final + + +@torch.jit.unused +def _onnx_clip_boxes_to_image(boxes, size): + # type: (Tensor, Tuple[int, int]) + """ + Clip boxes so that they lie inside an image of size `size`. + Clip's min max are traced as constants. Use torch.min/max to WAR this issue + Arguments: + boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format + size (Tuple[height, width]): size of the image + Returns: + clipped_boxes (Tensor[N, 4]) + """ + TO_REMOVE = 1 + device = boxes.device + dim = boxes.dim() + boxes_x = boxes[..., 0::2] + boxes_y = boxes[..., 1::2] + + boxes_x = torch.max(boxes_x, torch.tensor(0.0, dtype=torch.float).to(device)) + boxes_x = torch.min(boxes_x, torch.tensor(size[1] - TO_REMOVE, dtype=torch.float).to(device)) + boxes_y = torch.max(boxes_y, torch.tensor(0.0, dtype=torch.float).to(device)) + boxes_y = torch.min(boxes_y, torch.tensor(size[0] - TO_REMOVE, dtype=torch.float).to(device)) + + clipped_boxes = torch.stack((boxes_x, boxes_y), dim=dim) + return clipped_boxes.reshape(boxes.shape) + + +if __name__ == "__main__": + bbox = BoxList([[0, 0, 10, 10], [0, 0, 5, 5]], (10, 10)) + s_bbox = bbox.resize((5, 5)) + print(s_bbox) + print(s_bbox.bbox) + + t_bbox = bbox.transpose(0) + print(t_bbox) + print(t_bbox.bbox) diff --git a/maskrcnn_benchmark/structures/boxlist_ops.py b/maskrcnn_benchmark/structures/boxlist_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..f4febf65a7394c9c33d972ba6488512c119af25a --- /dev/null +++ b/maskrcnn_benchmark/structures/boxlist_ops.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + +from .bounding_box import BoxList + +from maskrcnn_benchmark.layers import nms as _box_nms +from maskrcnn_benchmark.layers import ml_nms as _box_ml_nms + + +def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="score"): + """ + Performs non-maximum suppression on a boxlist, with scores specified + in a boxlist field via score_field. + + Arguments: + boxlist(BoxList) + nms_thresh (float) + max_proposals (int): if > 0, then only the top max_proposals are kept + after non-maxium suppression + score_field (str) + """ + if nms_thresh <= 0: + return boxlist + mode = boxlist.mode + boxlist = boxlist.convert("xyxy") + boxes = boxlist.bbox + score = boxlist.get_field(score_field) + keep = _box_nms(boxes, score, nms_thresh) + if max_proposals > 0: + keep = keep[:max_proposals] + boxlist = boxlist[keep] + return boxlist.convert(mode) + + +def boxlist_ml_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores", label_field="labels"): + """ + Performs non-maximum suppression on a boxlist, with scores specified + in a boxlist field via score_field. + + Arguments: + boxlist(BoxList) + nms_thresh (float) + max_proposals (int): if > 0, then only the top max_proposals are kept + after non-maximum suppression + score_field (str) + """ + if nms_thresh <= 0: + return boxlist + mode = boxlist.mode + boxlist = boxlist.convert("xyxy") + boxes = boxlist.bbox + scores = boxlist.get_field(score_field) + labels = boxlist.get_field(label_field) + + if boxes.device == torch.device("cpu"): + keep = [] + unique_labels = torch.unique(labels) + print(unique_labels) + for j in unique_labels: + inds = (labels == j).nonzero().view(-1) + + scores_j = scores[inds] + boxes_j = boxes[inds, :].view(-1, 4) + keep_j = _box_nms(boxes_j, scores_j, nms_thresh) + + keep += keep_j + else: + keep = _box_ml_nms(boxes, scores, labels.float(), nms_thresh) + + if max_proposals > 0: + keep = keep[:max_proposals] + boxlist = boxlist[keep] + + return boxlist.convert(mode) + + +def remove_small_boxes(boxlist, min_size): + """ + Only keep boxes with both sides >= min_size + + Arguments: + boxlist (Boxlist) + min_size (int) + """ + # WORK AROUND: work around unbind using split + squeeze. + xywh_boxes = boxlist.convert("xywh").bbox + _, _, ws, hs = xywh_boxes.split(1, dim=1) + ws = ws.squeeze(1) + hs = hs.squeeze(1) + keep = ((ws >= min_size) & (hs >= min_size)).nonzero().squeeze(1) + return boxlist[keep] + + +# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py +# with slight modifications +def boxlist_iou(boxlist1, boxlist2): + """Compute the intersection over union of two set of boxes. + The box order must be (xmin, ymin, xmax, ymax). + + Arguments: + box1: (BoxList) bounding boxes, sized [N,4]. + box2: (BoxList) bounding boxes, sized [M,4]. + + Returns: + (tensor) iou, sized [N,M]. + + Reference: + https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py + """ + if boxlist1.size != boxlist2.size: + raise RuntimeError("boxlists should have same image size, got {}, {}".format(boxlist1, boxlist2)) + + N = len(boxlist1) + M = len(boxlist2) + + area1 = boxlist1.area() + area2 = boxlist2.area() + + box1, box2 = boxlist1.bbox, boxlist2.bbox + + lt = torch.max(box1[:, None, :2], box2[:, :2]) # [N,M,2] + rb = torch.min(box1[:, None, 2:], box2[:, 2:]) # [N,M,2] + + TO_REMOVE = 1 + + wh = (rb - lt + TO_REMOVE).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + iou = inter / (area1[:, None] + area2 - inter) + return iou + + +# TODO redundant, remove +def _cat(tensors, dim=0): + """ + Efficient version of torch.cat that avoids a copy if there is only a single element in a list + """ + assert isinstance(tensors, (list, tuple)) + if len(tensors) == 1: + return tensors[0] + if isinstance(tensors[0], torch.Tensor): + return torch.cat(tensors, dim) + else: + return cat_boxlist(tensors) + + +def cat_boxlist(bboxes): + """ + Concatenates a list of BoxList (having the same image size) into a + single BoxList + + Arguments: + bboxes (list[BoxList]) + """ + assert isinstance(bboxes, (list, tuple)) + assert all(isinstance(bbox, BoxList) for bbox in bboxes) + + size = bboxes[0].size + assert all(bbox.size == size for bbox in bboxes) + + mode = bboxes[0].mode + assert all(bbox.mode == mode for bbox in bboxes) + + fields = set(bboxes[0].fields()) + assert all(set(bbox.fields()) == fields for bbox in bboxes) + + cat_boxes = BoxList(_cat([bbox.bbox for bbox in bboxes], dim=0), size, mode) + + for field in fields: + data = _cat([bbox.get_field(field) for bbox in bboxes], dim=0) + cat_boxes.add_field(field, data) + + return cat_boxes + + +def getUnionBBox(aBB, bBB, margin=10): + assert aBB.size == bBB.size + assert aBB.mode == bBB.mode + ih, iw = aBB.size + union_boxes = torch.cat( + [ + (torch.min(aBB.bbox[:, [0, 1]], bBB.bbox[:, [0, 1]]) - margin).clamp(min=0), + (torch.max(aBB.bbox[:, [2]], bBB.bbox[:, [2]]) + margin).clamp(max=iw), + (torch.max(aBB.bbox[:, [3]], bBB.bbox[:, [3]]) + margin).clamp(max=ih), + ], + dim=1, + ) + return BoxList(union_boxes, aBB.size, mode=aBB.mode) diff --git a/maskrcnn_benchmark/structures/image_list.py b/maskrcnn_benchmark/structures/image_list.py new file mode 100644 index 0000000000000000000000000000000000000000..e24df46e95ba39476fdce9f748c0e0f4fb94be98 --- /dev/null +++ b/maskrcnn_benchmark/structures/image_list.py @@ -0,0 +1,70 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from __future__ import division + +import torch + + +class ImageList(object): + """ + Structure that holds a list of images (of possibly + varying sizes) as a single tensor. + This works by padding the images to the same size, + and storing in a field the original sizes of each image + """ + + def __init__(self, tensors, image_sizes): + """ + Arguments: + tensors (tensor) + image_sizes (list[tuple[int, int]]) + """ + self.tensors = tensors + self.image_sizes = image_sizes + + def to(self, *args, **kwargs): + cast_tensor = self.tensors.to(*args, **kwargs) + return ImageList(cast_tensor, self.image_sizes) + + +def to_image_list(tensors, size_divisible=0): + """ + tensors can be an ImageList, a torch.Tensor or + an iterable of Tensors. It can't be a numpy array. + When tensors is an iterable of Tensors, it pads + the Tensors with zeros so that they have the same + shape + """ + if isinstance(tensors, torch.Tensor) and size_divisible > 0: + tensors = [tensors] + + if isinstance(tensors, ImageList): + return tensors + elif isinstance(tensors, torch.Tensor): + # single tensor shape can be inferred + assert tensors.dim() == 4 + image_sizes = [tensor.shape[-2:] for tensor in tensors] + return ImageList(tensors, image_sizes) + elif isinstance(tensors, (tuple, list)): + max_size = tuple(max(s) for s in zip(*[img.shape for img in tensors])) + + # TODO Ideally, just remove this and let me model handle arbitrary + # input sizs + if size_divisible > 0: + import math + + stride = size_divisible + max_size = list(max_size) + max_size[1] = int(math.ceil(max_size[1] / stride) * stride) + max_size[2] = int(math.ceil(max_size[2] / stride) * stride) + max_size = tuple(max_size) + + batch_shape = (len(tensors),) + max_size + batched_imgs = tensors[0].new(*batch_shape).zero_() + for img, pad_img in zip(tensors, batched_imgs): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + + image_sizes = [im.shape[-2:] for im in tensors] + + return ImageList(batched_imgs, image_sizes) + else: + raise TypeError("Unsupported type for to_image_list: {}".format(type(tensors))) diff --git a/maskrcnn_benchmark/structures/keypoint.py b/maskrcnn_benchmark/structures/keypoint.py new file mode 100644 index 0000000000000000000000000000000000000000..874471b88111fc3ddce208fcdadc9145c7533707 --- /dev/null +++ b/maskrcnn_benchmark/structures/keypoint.py @@ -0,0 +1,214 @@ +import torch +from maskrcnn_benchmark.config import cfg + +# transpose +FLIP_LEFT_RIGHT = 0 +FLIP_TOP_BOTTOM = 1 + + +class Keypoints(object): + def __init__(self, keypoints, size, mode=None): + # FIXME remove check once we have better integration with device + # in my version this would consistently return a CPU tensor + device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu") + keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device) + num_keypoints = keypoints.shape[0] + if num_keypoints: + keypoints = keypoints.view(num_keypoints, -1, 3) + + # TODO should I split them? + # self.visibility = keypoints[..., 2] + self.keypoints = keypoints # [..., :2] + + self.size = size + self.mode = mode + self.extra_fields = {} + + def crop(self, box): + raise NotImplementedError() + + def resize(self, size, *args, **kwargs): + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size)) + ratio_w, ratio_h = ratios + resized_data = self.keypoints.clone() + resized_data[..., 0] *= ratio_w + resized_data[..., 1] *= ratio_h + keypoints = type(self)(resized_data, size, self.mode) + for k, v in self.extra_fields.items(): + keypoints.add_field(k, v) + return keypoints + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT,): + raise NotImplementedError("Only FLIP_LEFT_RIGHT implemented") + + flip_inds = self.FLIP_INDS + flipped_data = self.keypoints[:, flip_inds] + width = self.size[0] + TO_REMOVE = 1 + # Flip x coordinates + flipped_data[..., 0] = width - flipped_data[..., 0] - TO_REMOVE + + # Maintain COCO convention that if visibility == 0, then x, y = 0 + inds = flipped_data[..., 2] == 0 + flipped_data[inds] = 0 + + keypoints = type(self)(flipped_data, self.size, self.mode) + for k, v in self.extra_fields.items(): + keypoints.add_field(k, v) + return keypoints + + def to(self, *args, **kwargs): + keypoints = type(self)(self.keypoints.to(*args, **kwargs), self.size, self.mode) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + keypoints.add_field(k, v) + return keypoints + + def __getitem__(self, item): + keypoints = type(self)(self.keypoints[item], self.size, self.mode) + for k, v in self.extra_fields.items(): + keypoints.add_field(k, v[item]) + return keypoints + + def add_field(self, field, field_data): + self.extra_fields[field] = field_data + + def get_field(self, field): + return self.extra_fields[field] + + def __repr__(self): + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self.keypoints)) + s += "image_width={}, ".format(self.size[0]) + s += "image_height={})".format(self.size[1]) + return s + + +class PersonKeypoints(Keypoints): + _NAMES = [ + "nose", + "left_eye", + "right_eye", + "left_ear", + "right_ear", + "left_shoulder", + "right_shoulder", + "left_elbow", + "right_elbow", + "left_wrist", + "right_wrist", + "left_hip", + "right_hip", + "left_knee", + "right_knee", + "left_ankle", + "right_ankle", + ] + _FLIP_MAP = { + "left_eye": "right_eye", + "left_ear": "right_ear", + "left_shoulder": "right_shoulder", + "left_elbow": "right_elbow", + "left_wrist": "right_wrist", + "left_hip": "right_hip", + "left_knee": "right_knee", + "left_ankle": "right_ankle", + } + + def __init__(self, *args, **kwargs): + super(PersonKeypoints, self).__init__(*args, **kwargs) + if len(cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME) > 0: + self.NAMES = cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME + self.FLIP_MAP = { + l: r for l, r in PersonKeypoints._FLIP_MAP.items() if l in cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME + } + else: + self.NAMES = PersonKeypoints._NAMES + self.FLIP_MAP = PersonKeypoints._FLIP_MAP + + self.FLIP_INDS = self._create_flip_indices(self.NAMES, self.FLIP_MAP) + self.CONNECTIONS = self._kp_connections(self.NAMES) + + def to_coco_format(self): + coco_result = [] + for i in range(self.keypoints.shape[0]): + coco_kps = [0] * len(PersonKeypoints._NAMES) * 3 + for ki, name in enumerate(self.NAMES): + coco_kps[3 * PersonKeypoints._NAMES.index(name)] = self.keypoints[i, ki, 0].item() + coco_kps[3 * PersonKeypoints._NAMES.index(name) + 1] = self.keypoints[i, ki, 1].item() + coco_kps[3 * PersonKeypoints._NAMES.index(name) + 2] = self.keypoints[i, ki, 2].item() + coco_result.append(coco_kps) + return coco_result + + def _create_flip_indices(self, names, flip_map): + full_flip_map = flip_map.copy() + full_flip_map.update({v: k for k, v in flip_map.items()}) + flipped_names = [i if i not in full_flip_map else full_flip_map[i] for i in names] + flip_indices = [names.index(i) for i in flipped_names] + return torch.tensor(flip_indices) + + def _kp_connections(self, keypoints): + CONNECTIONS = [ + ["left_eye", "right_eye"], + ["left_eye", "nose"], + ["right_eye", "nose"], + ["right_eye", "right_ear"], + ["left_eye", "left_ear"], + ["right_shoulder", "right_elbow"], + ["right_elbow", "right_wrist"], + ["left_shoulder", "left_elbow"], + ["left_elbow", "left_wrist"], + ["right_hip", "right_knee"], + ["right_knee", "right_ankle"], + ["left_hip", "left_knee"], + ["left_knee", "left_ankle"], + ["right_shoulder", "left_shoulder"], + ["right_hip", "left_hip"], + ] + + kp_lines = [ + [keypoints.index(conn[0]), keypoints.index(conn[1])] + for conn in CONNECTIONS + if conn[0] in self.NAMES and conn[1] in self.NAMES + ] + return kp_lines + + +# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop) +def keypoints_to_heat_map(keypoints, rois, heatmap_size): + if rois.numel() == 0: + return rois.new().long(), rois.new().long() + offset_x = rois[:, 0] + offset_y = rois[:, 1] + scale_x = heatmap_size / (rois[:, 2] - rois[:, 0]) + scale_y = heatmap_size / (rois[:, 3] - rois[:, 1]) + + offset_x = offset_x[:, None] + offset_y = offset_y[:, None] + scale_x = scale_x[:, None] + scale_y = scale_y[:, None] + + x = keypoints[..., 0] + y = keypoints[..., 1] + + x_boundary_inds = x == rois[:, 2][:, None] + y_boundary_inds = y == rois[:, 3][:, None] + + x = (x - offset_x) * scale_x + x = x.floor().long() + y = (y - offset_y) * scale_y + y = y.floor().long() + + x[x_boundary_inds] = heatmap_size - 1 + y[y_boundary_inds] = heatmap_size - 1 + + valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size) + vis = keypoints[..., 2] > 0 + valid = (valid_loc & vis).long() + + lin_ind = y * heatmap_size + x + heatmaps = lin_ind * valid + + return heatmaps, valid diff --git a/maskrcnn_benchmark/structures/segmentation_mask.py b/maskrcnn_benchmark/structures/segmentation_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..6d5eb89c69ced0fe2226d500e76c321fd7339a89 --- /dev/null +++ b/maskrcnn_benchmark/structures/segmentation_mask.py @@ -0,0 +1,206 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + +import pycocotools.mask as mask_utils + +# transpose +FLIP_LEFT_RIGHT = 0 +FLIP_TOP_BOTTOM = 1 + + +class Mask(object): + """ + This class is unfinished and not meant for use yet + It is supposed to contain the mask for an object as + a 2d tensor + """ + + def __init__(self, masks, size, mode): + self.masks = masks + self.size = size + self.mode = mode + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError("Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented") + + width, height = self.size + if method == FLIP_LEFT_RIGHT: + dim = width + idx = 2 + elif method == FLIP_TOP_BOTTOM: + dim = height + idx = 1 + + flip_idx = list(range(dim)[::-1]) + flipped_masks = self.masks.index_select(dim, flip_idx) + return Mask(flipped_masks, self.size, self.mode) + + def crop(self, box): + w, h = box[2] - box[0], box[3] - box[1] + + cropped_masks = self.masks[:, box[1] : box[3], box[0] : box[2]] + return Mask(cropped_masks, size=(w, h), mode=self.mode) + + def resize(self, size, *args, **kwargs): + pass + + +class Polygons(object): + """ + This class holds a set of polygons that represents a single instance + of an object mask. The object can be represented as a set of + polygons + """ + + def __init__(self, polygons, size, mode): + # assert isinstance(polygons, list), '{}'.format(polygons) + if isinstance(polygons, list): + polygons = [torch.as_tensor(p, dtype=torch.float32) for p in polygons] + elif isinstance(polygons, Polygons): + polygons = polygons.polygons + + self.polygons = polygons + self.size = size + self.mode = mode + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError("Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented") + + flipped_polygons = [] + width, height = self.size + if method == FLIP_LEFT_RIGHT: + dim = width + idx = 0 + elif method == FLIP_TOP_BOTTOM: + dim = height + idx = 1 + + for poly in self.polygons: + p = poly.clone() + TO_REMOVE = 1 + p[idx::2] = dim - poly[idx::2] - TO_REMOVE + flipped_polygons.append(p) + + return Polygons(flipped_polygons, size=self.size, mode=self.mode) + + def crop(self, box): + w, h = box[2] - box[0], box[3] - box[1] + + # TODO chck if necessary + w = max(w, 1) + h = max(h, 1) + + cropped_polygons = [] + for poly in self.polygons: + p = poly.clone() + p[0::2] = p[0::2] - box[0] # .clamp(min=0, max=w) + p[1::2] = p[1::2] - box[1] # .clamp(min=0, max=h) + cropped_polygons.append(p) + + return Polygons(cropped_polygons, size=(w, h), mode=self.mode) + + def resize(self, size, *args, **kwargs): + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size)) + if ratios[0] == ratios[1]: + ratio = ratios[0] + scaled_polys = [p * ratio for p in self.polygons] + return Polygons(scaled_polys, size, mode=self.mode) + + ratio_w, ratio_h = ratios + scaled_polygons = [] + for poly in self.polygons: + p = poly.clone() + p[0::2] *= ratio_w + p[1::2] *= ratio_h + scaled_polygons.append(p) + + return Polygons(scaled_polygons, size=size, mode=self.mode) + + def convert(self, mode): + width, height = self.size + if mode == "mask": + rles = mask_utils.frPyObjects([p.detach().numpy() for p in self.polygons], height, width) + rle = mask_utils.merge(rles) + mask = mask_utils.decode(rle) + mask = torch.from_numpy(mask) + # TODO add squeeze? + return mask + + def __repr__(self): + s = self.__class__.__name__ + "(" + s += "num_polygons={}, ".format(len(self.polygons)) + s += "image_width={}, ".format(self.size[0]) + s += "image_height={}, ".format(self.size[1]) + s += "mode={})".format(self.mode) + return s + + +class SegmentationMask(object): + """ + This class stores the segmentations for all objects in the image + """ + + def __init__(self, polygons, size, mode=None): + """ + Arguments: + polygons: a list of list of lists of numbers. The first + level of the list correspond to individual instances, + the second level to all the polygons that compose the + object, and the third level to the polygon coordinates. + """ + assert isinstance(polygons, list) + + self.polygons = [Polygons(p, size, mode) for p in polygons] + self.size = size + self.mode = mode + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError("Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented") + + flipped = [] + for polygon in self.polygons: + flipped.append(polygon.transpose(method)) + return SegmentationMask(flipped, size=self.size, mode=self.mode) + + def crop(self, box): + w, h = box[2] - box[0], box[3] - box[1] + cropped = [] + for polygon in self.polygons: + cropped.append(polygon.crop(box)) + return SegmentationMask(cropped, size=(w, h), mode=self.mode) + + def resize(self, size, *args, **kwargs): + scaled = [] + for polygon in self.polygons: + scaled.append(polygon.resize(size, *args, **kwargs)) + return SegmentationMask(scaled, size=size, mode=self.mode) + + def to(self, *args, **kwargs): + return self + + def __getitem__(self, item): + if isinstance(item, (int, slice)): + selected_polygons = [self.polygons[item]] + else: + # advanced indexing on a single dimension + selected_polygons = [] + if isinstance(item, torch.Tensor) and item.dtype == torch.bool: + item = item.nonzero() + item = item.squeeze(1) if item.numel() > 0 else item + item = item.tolist() + for i in item: + selected_polygons.append(self.polygons[i]) + return SegmentationMask(selected_polygons, size=self.size, mode=self.mode) + + def __iter__(self): + return iter(self.polygons) + + def __repr__(self): + s = self.__class__.__name__ + "(" + s += "num_instances={}, ".format(len(self.polygons)) + s += "image_width={}, ".format(self.size[0]) + s += "image_height={})".format(self.size[1]) + return s diff --git a/maskrcnn_benchmark/utils/README.md b/maskrcnn_benchmark/utils/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3c35e560d1b3e3fb6cfc5e5a5653a283b1c603e3 --- /dev/null +++ b/maskrcnn_benchmark/utils/README.md @@ -0,0 +1,5 @@ +# Utility functions + +This folder contain utility functions that are not used in the +core library, but are useful for building models or training +code using the config system. diff --git a/maskrcnn_benchmark/utils/__init__.py b/maskrcnn_benchmark/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/maskrcnn_benchmark/utils/amp.py b/maskrcnn_benchmark/utils/amp.py new file mode 100644 index 0000000000000000000000000000000000000000..c1ccbec135b7ab38fb9a008e4f0a2cf751a07dd8 --- /dev/null +++ b/maskrcnn_benchmark/utils/amp.py @@ -0,0 +1,16 @@ +from contextlib import contextmanager + + +@contextmanager +def nullcontext(enter_result=None, **kwargs): + yield enter_result + + +try: + from torch.cuda.amp import autocast, GradScaler, custom_fwd, custom_bwd +except: + print("[Warning] Library for automatic mixed precision is not found, AMP is disabled!!") + GradScaler = nullcontext + autocast = nullcontext + custom_fwd = nullcontext + custom_bwd = nullcontext diff --git a/maskrcnn_benchmark/utils/big_model_loading.py b/maskrcnn_benchmark/utils/big_model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..9bc4890b64419b42f858c5f0b43f2afd51da74ab --- /dev/null +++ b/maskrcnn_benchmark/utils/big_model_loading.py @@ -0,0 +1,77 @@ +import numpy as np +import torch +import torch.nn as nn + +from collections import OrderedDict + + +def tf2th(conv_weights): + """Possibly convert HWIO to OIHW.""" + if conv_weights.ndim == 4: + conv_weights = conv_weights.transpose([3, 2, 0, 1]) + return torch.from_numpy(conv_weights) + + +def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg): + import re + + layer_keys = sorted(state_dict.keys()) + for ix, stage_with_dcn in enumerate(cfg.MODEL.RESNETS.STAGE_WITH_DCN, 1): + if not stage_with_dcn: + continue + for old_key in layer_keys: + pattern = ".*block{}.*conv2.*".format(ix) + r = re.match(pattern, old_key) + if r is None: + continue + for param in ["weight", "bias"]: + if old_key.find(param) is -1: + continue + if "unit01" in old_key: + continue + new_key = old_key.replace("conv2.{}".format(param), "conv2.conv.{}".format(param)) + print("pattern: {}, old_key: {}, new_key: {}".format(pattern, old_key, new_key)) + # Calculate SD conv weight + w = state_dict[old_key] + v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False) + w = (w - m) / torch.sqrt(v + 1e-10) + + state_dict[new_key] = w + del state_dict[old_key] + return state_dict + + +def load_big_format(cfg, f): + model = OrderedDict() + weights = np.load(f) + + cmap = {"a": 1, "b": 2, "c": 3} + for key, val in weights.items(): + old_key = key.replace("resnet/", "") + if "root_block" in old_key: + new_key = "root.conv.weight" + elif "/proj/standardized_conv2d/kernel" in old_key: + key_pattern = old_key.replace("/proj/standardized_conv2d/kernel", "").replace("resnet/", "") + bname, uname, cidx = key_pattern.split("/") + new_key = "{}.downsample.{}.conv{}.weight".format(bname, uname, cmap[cidx]) + elif "/standardized_conv2d/kernel" in old_key: + key_pattern = old_key.replace("/standardized_conv2d/kernel", "").replace("resnet/", "") + bname, uname, cidx = key_pattern.split("/") + new_key = "{}.{}.conv{}.weight".format(bname, uname, cmap[cidx]) + elif "/group_norm/gamma" in old_key: + key_pattern = old_key.replace("/group_norm/gamma", "").replace("resnet/", "") + bname, uname, cidx = key_pattern.split("/") + new_key = "{}.{}.gn{}.weight".format(bname, uname, cmap[cidx]) + elif "/group_norm/beta" in old_key: + key_pattern = old_key.replace("/group_norm/beta", "").replace("resnet/", "") + bname, uname, cidx = key_pattern.split("/") + new_key = "{}.{}.gn{}.bias".format(bname, uname, cmap[cidx]) + else: + print("Unknown key {}".format(old_key)) + continue + print("Map {} -> {}".format(key, new_key)) + model[new_key] = tf2th(val) + + model = _rename_conv_weights_for_deformable_conv_layers(model, cfg) + + return dict(model=model) diff --git a/maskrcnn_benchmark/utils/c2_model_loading.py b/maskrcnn_benchmark/utils/c2_model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..c343b607034f54ba1d348ef502a1eb95059d7d60 --- /dev/null +++ b/maskrcnn_benchmark/utils/c2_model_loading.py @@ -0,0 +1,209 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import logging +import pickle +from collections import OrderedDict + +import torch + +from maskrcnn_benchmark.utils.model_serialization import load_state_dict +from maskrcnn_benchmark.utils.registry import Registry + + +def _rename_basic_resnet_weights(layer_keys): + layer_keys = [k.replace("_", ".") for k in layer_keys] + layer_keys = [k.replace(".w", ".weight") for k in layer_keys] + layer_keys = [k.replace(".bn", "_bn") for k in layer_keys] + layer_keys = [k.replace(".b", ".bias") for k in layer_keys] + layer_keys = [k.replace("_bn.s", "_bn.scale") for k in layer_keys] + layer_keys = [k.replace(".biasranch", ".branch") for k in layer_keys] + layer_keys = [k.replace("bbox.pred", "bbox_pred") for k in layer_keys] + layer_keys = [k.replace("cls.score", "cls_score") for k in layer_keys] + layer_keys = [k.replace("res.conv1_", "conv1_") for k in layer_keys] + + # RPN / Faster RCNN + layer_keys = [k.replace(".biasbox", ".bbox") for k in layer_keys] + layer_keys = [k.replace("conv.rpn", "rpn.conv") for k in layer_keys] + layer_keys = [k.replace("rpn.bbox.pred", "rpn.bbox_pred") for k in layer_keys] + layer_keys = [k.replace("rpn.cls.logits", "rpn.cls_logits") for k in layer_keys] + + # Affine-Channel -> BatchNorm enaming + layer_keys = [k.replace("_bn.scale", "_bn.weight") for k in layer_keys] + + # Make torchvision-compatible + layer_keys = [k.replace("conv1_bn.", "bn1.") for k in layer_keys] + + layer_keys = [k.replace("res2.", "layer1.") for k in layer_keys] + layer_keys = [k.replace("res3.", "layer2.") for k in layer_keys] + layer_keys = [k.replace("res4.", "layer3.") for k in layer_keys] + layer_keys = [k.replace("res5.", "layer4.") for k in layer_keys] + + layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys] + layer_keys = [k.replace(".branch2a_bn.", ".bn1.") for k in layer_keys] + layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys] + layer_keys = [k.replace(".branch2b_bn.", ".bn2.") for k in layer_keys] + layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys] + layer_keys = [k.replace(".branch2c_bn.", ".bn3.") for k in layer_keys] + + layer_keys = [k.replace(".branch1.", ".downsample.0.") for k in layer_keys] + layer_keys = [k.replace(".branch1_bn.", ".downsample.1.") for k in layer_keys] + + # GroupNorm + layer_keys = [k.replace("conv1.gn.s", "bn1.weight") for k in layer_keys] + layer_keys = [k.replace("conv1.gn.bias", "bn1.bias") for k in layer_keys] + layer_keys = [k.replace("conv2.gn.s", "bn2.weight") for k in layer_keys] + layer_keys = [k.replace("conv2.gn.bias", "bn2.bias") for k in layer_keys] + layer_keys = [k.replace("conv3.gn.s", "bn3.weight") for k in layer_keys] + layer_keys = [k.replace("conv3.gn.bias", "bn3.bias") for k in layer_keys] + layer_keys = [k.replace("downsample.0.gn.s", "downsample.1.weight") for k in layer_keys] + layer_keys = [k.replace("downsample.0.gn.bias", "downsample.1.bias") for k in layer_keys] + + return layer_keys + + +def _rename_fpn_weights(layer_keys, stage_names): + for mapped_idx, stage_name in enumerate(stage_names, 1): + suffix = "" + if mapped_idx < 4: + suffix = ".lateral" + layer_keys = [ + k.replace("fpn.inner.layer{}.sum{}".format(stage_name, suffix), "fpn_inner{}".format(mapped_idx)) + for k in layer_keys + ] + layer_keys = [ + k.replace("fpn.layer{}.sum".format(stage_name), "fpn_layer{}".format(mapped_idx)) for k in layer_keys + ] + + layer_keys = [k.replace("rpn.conv.fpn2", "rpn.conv") for k in layer_keys] + layer_keys = [k.replace("rpn.bbox_pred.fpn2", "rpn.bbox_pred") for k in layer_keys] + layer_keys = [k.replace("rpn.cls_logits.fpn2", "rpn.cls_logits") for k in layer_keys] + + return layer_keys + + +def _rename_weights_for_resnet(weights, stage_names): + original_keys = sorted(weights.keys()) + layer_keys = sorted(weights.keys()) + + # for X-101, rename output to fc1000 to avoid conflicts afterwards + layer_keys = [k if k != "pred_b" else "fc1000_b" for k in layer_keys] + layer_keys = [k if k != "pred_w" else "fc1000_w" for k in layer_keys] + + # performs basic renaming: _ -> . , etc + layer_keys = _rename_basic_resnet_weights(layer_keys) + + # FPN + layer_keys = _rename_fpn_weights(layer_keys, stage_names) + + # Mask R-CNN + layer_keys = [k.replace("mask.fcn.logits", "mask_fcn_logits") for k in layer_keys] + layer_keys = [k.replace(".[mask].fcn", "mask_fcn") for k in layer_keys] + layer_keys = [k.replace("conv5.mask", "conv5_mask") for k in layer_keys] + + # Keypoint R-CNN + layer_keys = [k.replace("kps.score.lowres", "kps_score_lowres") for k in layer_keys] + layer_keys = [k.replace("kps.score", "kps_score") for k in layer_keys] + layer_keys = [k.replace("conv.fcn", "conv_fcn") for k in layer_keys] + + # Rename for our RPN structure + layer_keys = [k.replace("rpn.", "rpn.head.") for k in layer_keys] + + key_map = {k: v for k, v in zip(original_keys, layer_keys)} + + logger = logging.getLogger(__name__) + logger.info("Remapping C2 weights") + max_c2_key_size = max([len(k) for k in original_keys if "_momentum" not in k]) + + new_weights = OrderedDict() + for k in original_keys: + v = weights[k] + if "_momentum" in k: + continue + if "weight_order" in k: + continue + # if 'fc1000' in k: + # continue + w = torch.from_numpy(v) + # if "bn" in k: + # w = w.view(1, -1, 1, 1) + logger.info("C2 name: {: <{}} mapped name: {}".format(k, max_c2_key_size, key_map[k])) + new_weights[key_map[k]] = w + + return new_weights + + +def _load_c2_pickled_weights(file_path): + with open(file_path, "rb") as f: + if torch._six.PY3: + data = pickle.load(f, encoding="latin1") + else: + data = pickle.load(f) + if "blobs" in data: + weights = data["blobs"] + else: + weights = data + return weights + + +def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg): + import re + + logger = logging.getLogger(__name__) + logger.info("Remapping conv weights for deformable conv weights") + layer_keys = sorted(state_dict.keys()) + for ix, stage_with_dcn in enumerate(cfg.MODEL.RESNETS.STAGE_WITH_DCN, 1): + if not stage_with_dcn: + continue + for old_key in layer_keys: + pattern = ".*layer{}.*conv2.*".format(ix) + r = re.match(pattern, old_key) + if r is None: + continue + for param in ["weight", "bias"]: + if old_key.find(param) is -1: + continue + new_key = old_key.replace("conv2.{}".format(param), "conv2.conv.{}".format(param)) + logger.info("pattern: {}, old_key: {}, new_key: {}".format(pattern, old_key, new_key)) + state_dict[new_key] = state_dict[old_key] + del state_dict[old_key] + return state_dict + + +_C2_STAGE_NAMES = { + "R-50": ["1.2", "2.3", "3.5", "4.2"], + "R-101": ["1.2", "2.3", "3.22", "4.2"], +} + +C2_FORMAT_LOADER = Registry() + + +@C2_FORMAT_LOADER.register("R-50-C4") +@C2_FORMAT_LOADER.register("R-50-C5") +@C2_FORMAT_LOADER.register("R-101-C4") +@C2_FORMAT_LOADER.register("R-101-C5") +@C2_FORMAT_LOADER.register("R-50-FPN") +@C2_FORMAT_LOADER.register("R-50-FPN-RETINANET") +@C2_FORMAT_LOADER.register("R-50-FPN-FCOS") +@C2_FORMAT_LOADER.register("R-101-FPN") +@C2_FORMAT_LOADER.register("R-101-FPN-RETINANET") +@C2_FORMAT_LOADER.register("R-101-FPN-FCOS") +def load_resnet_c2_format(cfg, f): + state_dict = _load_c2_pickled_weights(f) + conv_body = cfg.MODEL.BACKBONE.CONV_BODY + arch = ( + conv_body.replace("-C4", "") + .replace("-C5", "") + .replace("-FPN", "") + .replace("-RETINANET", "") + .replace("-FCOS", "") + ) + stages = _C2_STAGE_NAMES[arch] + state_dict = _rename_weights_for_resnet(state_dict, stages) + # *********************************** + # for deformable convolutional layer + state_dict = _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg) + # *********************************** + return dict(model=state_dict) + + +def load_c2_format(cfg, f): + return C2_FORMAT_LOADER[cfg.MODEL.BACKBONE.CONV_BODY](cfg, f) diff --git a/maskrcnn_benchmark/utils/checkpoint.py b/maskrcnn_benchmark/utils/checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..505d806978ab40058c2002268c19bd805c947bc2 --- /dev/null +++ b/maskrcnn_benchmark/utils/checkpoint.py @@ -0,0 +1,160 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import logging +import os + +import torch + +from maskrcnn_benchmark.utils.model_serialization import load_state_dict +from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format +from maskrcnn_benchmark.utils.big_model_loading import load_big_format +from maskrcnn_benchmark.utils.pretrain_model_loading import load_pretrain_format +from maskrcnn_benchmark.utils.imports import import_file +from maskrcnn_benchmark.utils.model_zoo import cache_url + + +class Checkpointer(object): + def __init__( + self, + model, + optimizer=None, + scheduler=None, + save_dir="", + save_to_disk=None, + logger=None, + ): + self.model = model + self.optimizer = optimizer + self.scheduler = scheduler + self.save_dir = save_dir + self.save_to_disk = save_to_disk + if logger is None: + logger = logging.getLogger(__name__) + self.logger = logger + + def save(self, name, **kwargs): + if not self.save_dir: + return + + if not self.save_to_disk: + return + + data = {} + data["model"] = self.model.state_dict() + if self.optimizer is not None: + data["optimizer"] = self.optimizer.state_dict() + if self.scheduler is not None: + if isinstance(self.scheduler, list): + data["scheduler"] = [scheduler.state_dict() for scheduler in self.scheduler] + else: + data["scheduler"] = self.scheduler.state_dict() + data.update(kwargs) + + save_file = os.path.join(self.save_dir, "{}.pth".format(name)) + self.logger.info("Saving checkpoint to {}".format(save_file)) + torch.save(data, save_file) + # self.tag_last_checkpoint(save_file) + # use relative path name to save the checkpoint + self.tag_last_checkpoint("{}.pth".format(name)) + + def load(self, f=None, force=False, keyword="model", skip_optimizer=False, skip_scheduler=False): + resume = False + if self.has_checkpoint() and not force: + # override argument with existing checkpoint + f = self.get_checkpoint_file() + # get the absolute path + f = os.path.join(self.save_dir, f) + resume = True + if not f: + # no checkpoint could be found + self.logger.info("No checkpoint found. Initializing model from scratch") + return {} + self.logger.info("Loading checkpoint from {}".format(f)) + checkpoint = self._load_file(f) + self._load_model(checkpoint, keyword=keyword) + # if resume training, load optimizer and scheduler, + # otherwise use the specified LR in config yaml for fine-tuning + if resume and not skip_optimizer: + if "optimizer" in checkpoint and self.optimizer: + self.logger.info("Loading optimizer from {}".format(f)) + self.optimizer.load_state_dict(checkpoint.pop("optimizer")) + if "scheduler" in checkpoint and self.scheduler and not skip_scheduler: + self.logger.info("Loading scheduler from {}".format(f)) + if isinstance(self.scheduler, list): + for scheduler, state_dict in zip(self.scheduler, checkpoint.pop("scheduler")): + scheduler.load_state_dict(state_dict) + else: + self.scheduler.load_state_dict(checkpoint.pop("scheduler")) + + # print("Scheduler", {k:v for k,v in self.scheduler.state_dict() if k != "base_lrs"}) + # return any further checkpoint data + return checkpoint + else: + return {} + + def has_checkpoint(self): + save_file = os.path.join(self.save_dir, "last_checkpoint") + return os.path.exists(save_file) + + def get_checkpoint_file(self): + save_file = os.path.join(self.save_dir, "last_checkpoint") + try: + with open(save_file, "r") as f: + last_saved = f.read() + last_saved = last_saved.strip() + except IOError: + # if file doesn't exist, maybe because it has just been + # deleted by a separate process + last_saved = "" + return last_saved + + def tag_last_checkpoint(self, last_filename): + save_file = os.path.join(self.save_dir, "last_checkpoint") + with open(save_file, "w") as f: + f.write(last_filename) + + def _load_file(self, f): + return torch.load(f, map_location=torch.device("cpu")) + + def _load_model(self, checkpoint, keyword="model"): + load_state_dict(self.model, checkpoint.pop(keyword)) + + +class DetectronCheckpointer(Checkpointer): + def __init__( + self, + cfg, + model, + optimizer=None, + scheduler=None, + save_dir="", + save_to_disk=None, + logger=None, + ): + super(DetectronCheckpointer, self).__init__(model, optimizer, scheduler, save_dir, save_to_disk, logger) + self.cfg = cfg.clone() + + def _load_file(self, f): + # catalog lookup + if f.startswith("catalog://"): + paths_catalog = import_file("maskrcnn_benchmark.config.paths_catalog", self.cfg.PATHS_CATALOG, True) + catalog_f = paths_catalog.ModelCatalog.get(f[len("catalog://") :]) + self.logger.info("{} points to {}".format(f, catalog_f)) + f = catalog_f + # download url files + if f.startswith("http"): + # if the file is a url path, download it and cache it + cached_f = cache_url(f) + self.logger.info("url {} cached in {}".format(f, cached_f)) + f = cached_f + # convert Caffe2 checkpoint from pkl + if f.endswith(".pkl"): + return load_c2_format(self.cfg, f) + if f.endswith(".big"): + return load_big_format(self.cfg, f) + if f.endswith(".pretrain"): + return load_pretrain_format(self.cfg, f) + # load native detectron.pytorch checkpoint + loaded = super(DetectronCheckpointer, self)._load_file(f) + if "model" not in loaded: + loaded = dict(model=loaded) + return loaded diff --git a/maskrcnn_benchmark/utils/collect_env.py b/maskrcnn_benchmark/utils/collect_env.py new file mode 100644 index 0000000000000000000000000000000000000000..d93d6164aed31b783c58581cc85c183e1f1805be --- /dev/null +++ b/maskrcnn_benchmark/utils/collect_env.py @@ -0,0 +1,14 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import PIL + +from torch.utils.collect_env import get_pretty_env_info + + +def get_pil_version(): + return "\n Pillow ({})".format(PIL.__version__) + + +def collect_env_info(): + env_str = get_pretty_env_info() + env_str += get_pil_version() + return env_str diff --git a/maskrcnn_benchmark/utils/comm.py b/maskrcnn_benchmark/utils/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..c6fb41719ecef89f7054ab567075424eb1077d5b --- /dev/null +++ b/maskrcnn_benchmark/utils/comm.py @@ -0,0 +1,157 @@ +""" +This file contains primitives for multi-gpu communication. +This is useful when doing distributed training. +""" + +import pickle +import time +import functools +import logging +import torch +import torch.distributed as dist +import numpy as np + + +def get_world_size(): + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + dist.barrier() + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.LongTensor([tensor.numel()]).to("cuda") + size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda")) + if local_size != max_size: + padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that process with rank + 0 has the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +def broadcast_data(data): + if not torch.distributed.is_initialized(): + return data + rank = dist.get_rank() + if rank == 0: + data_tensor = torch.tensor(data + [0], device="cuda") + else: + data_tensor = torch.tensor(data + [1], device="cuda") + torch.distributed.broadcast(data_tensor, 0) + while data_tensor.cpu().numpy()[-1] == 1: + time.sleep(1) + + return data_tensor.cpu().numpy().tolist()[:-1] + + +def reduce_sum(tensor): + if get_world_size() <= 1: + return tensor + + tensor = tensor.clone() + dist.all_reduce(tensor, op=dist.ReduceOp.SUM) + return tensor + + +def shared_random_seed(): + """ + Returns: + int: a random number that is the same across all workers. + If workers need a shared RNG, they can use this shared seed to + create one. + + All workers must call this function, otherwise it will deadlock. + """ + ints = np.random.randint(2**31) + all_ints = all_gather(ints) + return all_ints[0] diff --git a/maskrcnn_benchmark/utils/cv2_util.py b/maskrcnn_benchmark/utils/cv2_util.py new file mode 100644 index 0000000000000000000000000000000000000000..9eff71e4166ba57dcdb3eea59d94c3a9edb55af0 --- /dev/null +++ b/maskrcnn_benchmark/utils/cv2_util.py @@ -0,0 +1,23 @@ +""" +Module for cv2 utility functions and maintaining version compatibility +between 3.x and 4.x +""" +import cv2 + + +def findContours(*args, **kwargs): + """ + Wraps cv2.findContours to maintain compatiblity between versions + 3 and 4 + + Returns: + contours, hierarchy + """ + if cv2.__version__.startswith("4"): + contours, hierarchy = cv2.findContours(*args, **kwargs) + elif cv2.__version__.startswith("3"): + _, contours, hierarchy = cv2.findContours(*args, **kwargs) + else: + raise AssertionError("cv2 must be either version 3 or 4 to call this method") + + return contours, hierarchy diff --git a/maskrcnn_benchmark/utils/dist.py b/maskrcnn_benchmark/utils/dist.py new file mode 100644 index 0000000000000000000000000000000000000000..de7ac00c0eed1acc723df95f79367af82f79ddb0 --- /dev/null +++ b/maskrcnn_benchmark/utils/dist.py @@ -0,0 +1,228 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Utilities related to distributed mode. + +By default, the reduce of metrics and such are done on GPU, since it's more straightforward (we reuse the NCCL backend) +If you want to reduce on CPU instead (required for big datasets like GQA), use the env variable MDETR_CPU_REDUCE=1 +""" +import functools +import io +import os + +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + + return dist.group.WORLD + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + + world_size = get_world_size() + if world_size == 1: + return [data] + + cpu_group = None + if os.getenv("MDETR_CPU_REDUCE") == "1": + cpu_group = _get_global_gloo_group() + + buffer = io.BytesIO() + torch.save(data, buffer) + data_view = buffer.getbuffer() + device = "cuda" if cpu_group is None else "cpu" + tensor = torch.ByteTensor(data_view).to(device) + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long) + size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)] + if cpu_group is None: + dist.all_gather(size_list, local_size) + else: + print("gathering on cpu") + dist.all_gather(size_list, local_size, group=cpu_group) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + assert isinstance(local_size.item(), int) + local_size = int(local_size.item()) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device)) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device) + tensor = torch.cat((tensor, padding), dim=0) + if cpu_group is None: + dist.all_gather(tensor_list, tensor) + else: + dist.all_gather(tensor_list, tensor, group=cpu_group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + tensor = torch.split(tensor, [size, max_size - size], dim=0)[0] + buffer = io.BytesIO(tensor.cpu().numpy()) + obj = torch.load(buffer) + data_list.append(obj) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + """ + Returns: + True if distributed training is enabled + """ + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + """ + Returns: + The number of processes in the process group + """ + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + """ + Returns: + The rank of the current process within the global process group. + """ + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert _LOCAL_PROCESS_GROUP is not None + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process(): + """Return true if the current process is the main one""" + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + """Utility function to save only from the main process""" + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + """Initialize distributed training, if appropriate""" + if "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + + dist.init_process_group( + backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank + ) + dist.barrier() + setup_for_distributed(args.rank == 0) diff --git a/maskrcnn_benchmark/utils/ema.py b/maskrcnn_benchmark/utils/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..a4d4c326ed551bcae1250f39436d5b2c04a383f7 --- /dev/null +++ b/maskrcnn_benchmark/utils/ema.py @@ -0,0 +1,45 @@ +from copy import deepcopy +from collections import OrderedDict +import torch + + +class ModelEma: + def __init__(self, model, decay=0.9999, device=""): + self.ema = deepcopy(model) + self.ema.eval() + self.decay = decay + self.device = device + if device: + self.ema.to(device=device) + self.ema_is_dp = hasattr(self.ema, "module") + for p in self.ema.parameters(): + p.requires_grad_(False) + + def load_checkpoint(self, checkpoint): + if isinstance(checkpoint, str): + checkpoint = torch.load(checkpoint) + + assert isinstance(checkpoint, dict) + if "model_ema" in checkpoint: + new_state_dict = OrderedDict() + for k, v in checkpoint["model_ema"].items(): + if self.ema_is_dp: + name = k if k.startswith("module") else "module." + k + else: + name = k.replace("module.", "") if k.startswith("module") else k + new_state_dict[name] = v + self.ema.load_state_dict(new_state_dict) + + def state_dict(self): + return self.ema.state_dict() + + def update(self, model): + pre_module = hasattr(model, "module") and not self.ema_is_dp + with torch.no_grad(): + curr_msd = model.state_dict() + for k, ema_v in self.ema.state_dict().items(): + k = "module." + k if pre_module else k + model_v = curr_msd[k].detach() + if self.device: + model_v = model_v.to(device=self.device) + ema_v.copy_(ema_v * self.decay + (1.0 - self.decay) * model_v) diff --git a/maskrcnn_benchmark/utils/env.py b/maskrcnn_benchmark/utils/env.py new file mode 100644 index 0000000000000000000000000000000000000000..688a6f055763743915b987bf4d4a4c58581036f1 --- /dev/null +++ b/maskrcnn_benchmark/utils/env.py @@ -0,0 +1,32 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import os + +from maskrcnn_benchmark.utils.imports import import_file + + +def setup_environment(): + """Perform environment setup work. The default setup is a no-op, but this + function allows the user to specify a Python source file that performs + custom setup work that may be necessary to their computing environment. + """ + custom_module_path = os.environ.get("TORCH_DETECTRON_ENV_MODULE") + if custom_module_path: + setup_custom_environment(custom_module_path) + else: + # The default setup is a no-op + pass + + +def setup_custom_environment(custom_module_path): + """Load custom environment setup from a Python source file and run the setup + function. + """ + module = import_file("maskrcnn_benchmark.utils.env.custom_module", custom_module_path) + assert hasattr(module, "setup_environment") and callable(module.setup_environment), ( + "Custom environment module defined in {} does not have the " "required callable attribute 'setup_environment'." + ).format(custom_module_path) + module.setup_environment() + + +# Force environment setup when this module is imported +setup_environment() diff --git a/maskrcnn_benchmark/utils/flops.py b/maskrcnn_benchmark/utils/flops.py new file mode 100644 index 0000000000000000000000000000000000000000..0ceb9ae5433c4fe41f885a65045acf1b6141083d --- /dev/null +++ b/maskrcnn_benchmark/utils/flops.py @@ -0,0 +1,247 @@ +import argparse +import logging +import torch +import torch.nn as nn +import timeit + +from maskrcnn_benchmark.layers import * +from maskrcnn_benchmark.modeling.backbone.resnet_big import StdConv2d +from maskrcnn_benchmark.modeling.backbone.fpn import * +from maskrcnn_benchmark.modeling.rpn.inference import * +from maskrcnn_benchmark.modeling.roi_heads.box_head.inference import PostProcessor +from maskrcnn_benchmark.modeling.rpn.anchor_generator import BufferList + + +def profile(model, input_size, custom_ops={}, device="cpu", verbose=False, extra_args={}, return_time=False): + handler_collection = [] + + def add_hooks(m): + if len(list(m.children())) > 0: + return + + m.register_buffer("total_ops", torch.zeros(1)) + m.register_buffer("total_params", torch.zeros(1)) + + for p in m.parameters(): + m.total_params += torch.Tensor([p.numel()]) + + m_type = type(m) + fn = None + + if m_type in custom_ops: + fn = custom_ops[m_type] + elif m_type in register_hooks: + fn = register_hooks[m_type] + else: + print("Not implemented for ", m) + + if fn is not None: + if verbose: + print("Register FLOP counter for module %s" % str(m)) + handler = m.register_forward_hook(fn) + handler_collection.append(handler) + + original_device = model.parameters().__next__().device + training = model.training + + model.eval().to(device) + model.apply(add_hooks) + + x = torch.zeros(input_size).to(device) + with torch.no_grad(): + tic = timeit.time.perf_counter() + model(x, **extra_args) + toc = timeit.time.perf_counter() + total_time = toc - tic + + total_ops = 0 + total_params = 0 + for m in model.modules(): + if len(list(m.children())) > 0: # skip for non-leaf module + continue + total_ops += m.total_ops + total_params += m.total_params + + total_ops = total_ops.item() + total_params = total_params.item() + + model.train(training).to(original_device) + for handler in handler_collection: + handler.remove() + + if return_time: + return total_ops, total_params, total_time + else: + return total_ops, total_params + + +multiply_adds = 1 + + +def count_conv2d(m, x, y): + x = x[0] + cin = m.in_channels + cout = m.out_channels + kh, kw = m.kernel_size + batch_size = x.size()[0] + out_h = y.size(2) + out_w = y.size(3) + # ops per output element + # kernel_mul = kh * kw * cin + # kernel_add = kh * kw * cin - 1 + kernel_ops = multiply_adds * kh * kw * cin // m.groups + bias_ops = 1 if m.bias is not None else 0 + ops_per_element = kernel_ops + bias_ops + # total ops + # num_out_elements = y.numel() + output_elements = batch_size * out_w * out_h * cout + total_ops = output_elements * ops_per_element + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_convtranspose2d(m, x, y): + x = x[0] + cin = m.in_channels + cout = m.out_channels + kh, kw = m.kernel_size + batch_size = x.size()[0] + out_h = y.size(2) + out_w = y.size(3) + # ops per output element + # kernel_mul = kh * kw * cin + # kernel_add = kh * kw * cin - 1 + kernel_ops = multiply_adds * kh * kw * cin // m.groups + bias_ops = 1 if m.bias is not None else 0 + ops_per_element = kernel_ops + bias_ops + # total ops + # num_out_elements = y.numel() + # output_elements = batch_size * out_w * out_h * cout + ops_per_element = m.weight.nelement() + output_elements = y.nelement() + total_ops = output_elements * ops_per_element + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_bn(m, x, y): + x = x[0] + nelements = x.numel() + # subtract, divide, gamma, beta + total_ops = 4 * nelements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_relu(m, x, y): + x = x[0] + nelements = x.numel() + total_ops = nelements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_softmax(m, x, y): + x = x[0] + batch_size, nfeatures = x.size() + total_exp = nfeatures + total_add = nfeatures - 1 + total_div = nfeatures + total_ops = batch_size * (total_exp + total_add + total_div) + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_maxpool(m, x, y): + kernel_ops = torch.prod(torch.Tensor([m.kernel_size])) + num_elements = y.numel() + total_ops = kernel_ops * num_elements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_adap_maxpool(m, x, y): + kernel = torch.Tensor([*(x[0].shape[2:])]) // torch.Tensor(list((m.output_size,))).squeeze() + kernel_ops = torch.prod(kernel) + num_elements = y.numel() + total_ops = kernel_ops * num_elements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_avgpool(m, x, y): + total_add = torch.prod(torch.Tensor([m.kernel_size])) + total_div = 1 + kernel_ops = total_add + total_div + num_elements = y.numel() + total_ops = kernel_ops * num_elements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_adap_avgpool(m, x, y): + kernel = torch.Tensor([*(x[0].shape[2:])]) // torch.Tensor(list((m.output_size,))).squeeze() + total_add = torch.prod(kernel) + total_div = 1 + kernel_ops = total_add + total_div + num_elements = y.numel() + total_ops = kernel_ops * num_elements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_linear(m, x, y): + # per output element + total_mul = m.in_features + total_add = m.in_features - 1 + num_elements = y.numel() + total_ops = (total_mul + total_add) * num_elements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_LastLevelMaxPool(m, x, y): + num_elements = y[-1].numel() + total_ops = num_elements + m.total_ops = torch.Tensor([int(total_ops)]) + + +def count_ROIAlign(m, x, y): + num_elements = y.numel() + total_ops = num_elements * 4 + m.total_ops = torch.Tensor([int(total_ops)]) + + +register_hooks = { + Scale: None, + Conv2d: count_conv2d, + nn.Conv2d: count_conv2d, + ModulatedDeformConv: count_conv2d, + StdConv2d: count_conv2d, + nn.BatchNorm1d: count_bn, + nn.BatchNorm2d: count_bn, + nn.BatchNorm3d: count_bn, + FrozenBatchNorm2d: count_bn, + nn.GroupNorm: count_bn, + NaiveSyncBatchNorm2d: count_bn, + nn.ReLU: count_relu, + nn.ReLU6: count_relu, + swish: None, + nn.ConstantPad2d: None, + SPPLayer: count_LastLevelMaxPool, + LastLevelMaxPool: count_LastLevelMaxPool, + nn.MaxPool1d: count_maxpool, + nn.MaxPool2d: count_maxpool, + nn.MaxPool3d: count_maxpool, + nn.AdaptiveMaxPool1d: count_adap_maxpool, + nn.AdaptiveMaxPool2d: count_adap_maxpool, + nn.AdaptiveMaxPool3d: count_adap_maxpool, + nn.AvgPool1d: count_avgpool, + nn.AvgPool2d: count_avgpool, + nn.AvgPool3d: count_avgpool, + nn.AdaptiveAvgPool1d: count_adap_avgpool, + nn.AdaptiveAvgPool2d: count_adap_avgpool, + nn.AdaptiveAvgPool3d: count_adap_avgpool, + nn.Linear: count_linear, + nn.Upsample: None, + nn.Dropout: None, + nn.Sigmoid: None, + DropBlock2D: None, + ROIAlign: count_ROIAlign, + RPNPostProcessor: None, + PostProcessor: None, + BufferList: None, + RetinaPostProcessor: None, + FCOSPostProcessor: None, + ATSSPostProcessor: None, +} diff --git a/maskrcnn_benchmark/utils/fuse_helper.py b/maskrcnn_benchmark/utils/fuse_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..4e34b88d9e084bbc3827bb2d856e3cbf804cb0af --- /dev/null +++ b/maskrcnn_benchmark/utils/fuse_helper.py @@ -0,0 +1,672 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import pdb +import math +from maskrcnn_benchmark.modeling.utils import cat, concat_box_prediction_layers, permute_and_flatten +from timm.models.layers import DropPath + +from transformers.activations import ACT2FN + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class FeatureResizer(nn.Module): + """ + This class takes as input a set of embeddings of dimension C1 and outputs a set of + embedding of dimension C2, after a linear transformation, dropout and normalization (LN). + """ + + def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True): + super().__init__() + self.do_ln = do_ln + # Object feature encoding + self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True) + self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12) + self.dropout = nn.Dropout(dropout) + + def forward(self, encoder_features): + x = self.fc(encoder_features) + if self.do_ln: + x = self.layer_norm(x) + output = self.dropout(x) + return output + + +def _make_conv(input_dim, output_dim, k, stride=1): + pad = (k - 1) // 2 + return nn.Sequential( + nn.Conv2d(input_dim, output_dim, (k, k), padding=(pad, pad), stride=(stride, stride)), + nn.BatchNorm2d(output_dim), + nn.ReLU(inplace=True), + ) + + +def _make_mlp(input_dim, output_dim, drop): + return nn.Sequential( + nn.Linear(input_dim, output_dim), + nn.BatchNorm1d(output_dim), + nn.ReLU(inplace=True), + nn.Dropout(drop), + nn.Linear(output_dim, output_dim), + nn.BatchNorm1d(output_dim), + nn.ReLU(inplace=True), + ) + + +def _make_coord(batch, height, width): + # relative position encoding + xv, yv = torch.meshgrid([torch.arange(0, height), torch.arange(0, width)]) + xv_min = (xv.float() * 2 - width) / width + yv_min = (yv.float() * 2 - height) / height + xv_max = ((xv + 1).float() * 2 - width) / width + yv_max = ((yv + 1).float() * 2 - height) / height + xv_ctr = (xv_min + xv_max) / 2 + yv_ctr = (yv_min + yv_max) / 2 + hmap = torch.ones(height, width) * (1.0 / height) + wmap = torch.ones(height, width) * (1.0 / width) + coord = torch.autograd.Variable( + torch.cat( + [ + xv_min.unsqueeze(0), + yv_min.unsqueeze(0), + xv_max.unsqueeze(0), + yv_max.unsqueeze(0), + xv_ctr.unsqueeze(0), + yv_ctr.unsqueeze(0), + hmap.unsqueeze(0), + wmap.unsqueeze(0), + ], + dim=0, + ) + ) + coord = coord.unsqueeze(0).repeat(batch, 1, 1, 1) + return coord + + +def l1norm(X, dim, eps=1e-8): + """L1-normalize columns of X""" + norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps + X = torch.div(X, norm) + return X + + +def l2norm(X, dim, eps=1e-8): + """L2-normalize columns of X""" + norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps + X = torch.div(X, norm) + return X + + +def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8): + """ + query: (n_context, queryL, d) + context: (n_context, sourceL, d) + """ + batch_size_q, queryL = query.size(0), query.size(1) + batch_size, sourceL = context.size(0), context.size(1) + + # Get attention + # --> (batch, d, queryL) + queryT = torch.transpose(query, 1, 2) + + # (batch, sourceL, d)(batch, d, queryL) + # --> (batch, sourceL, queryL) + attn = torch.bmm(context, queryT) + if raw_feature_norm == "softmax": + # --> (batch*sourceL, queryL) + attn = attn.view(batch_size * sourceL, queryL) + attn = nn.Softmax()(attn) + # --> (batch, sourceL, queryL) + attn = attn.view(batch_size, sourceL, queryL) + elif raw_feature_norm == "l2norm": + attn = l2norm(attn, 2) + elif raw_feature_norm == "clipped_l2norm": + attn = nn.LeakyReLU(0.1)(attn) + attn = l2norm(attn, 2) + else: + raise ValueError("unknown first norm type:", raw_feature_norm) + # --> (batch, queryL, sourceL) + attn = torch.transpose(attn, 1, 2).contiguous() + # --> (batch*queryL, sourceL) + attn = attn.view(batch_size * queryL, sourceL) + attn = nn.Softmax()(attn * smooth) + # --> (batch, queryL, sourceL) + attn = attn.view(batch_size, queryL, sourceL) + # --> (batch, sourceL, queryL) + attnT = torch.transpose(attn, 1, 2).contiguous() + + # --> (batch, d, sourceL) + contextT = torch.transpose(context, 1, 2) + # (batch x d x sourceL)(batch x sourceL x queryL) + # --> (batch, d, queryL) + weightedContext = torch.bmm(contextT, attnT) + # --> (batch, queryL, d) + weightedContext = torch.transpose(weightedContext, 1, 2) + + return weightedContext, attnT + + +class BiMultiHeadAttention(nn.Module): + def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None): + super(BiMultiHeadAttention, self).__init__() + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.v_dim = v_dim + self.l_dim = l_dim + + assert ( + self.head_dim * self.num_heads == self.embed_dim + ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + self.scale = self.head_dim ** (-0.5) + self.dropout = dropout + + self.v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.l_proj = nn.Linear(self.l_dim, self.embed_dim) + self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim) + self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim) + + self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim) + self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim) + + self.stable_softmax_2d = cfg.MODEL.DYHEAD.FUSE_CONFIG.STABLE_SOFTMAX_2D + self.clamp_min_for_underflow = cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW + self.clamp_max_for_overflow = cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW + + self._reset_parameters() + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def _reset_parameters(self): + nn.init.xavier_uniform_(self.v_proj.weight) + self.v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.l_proj.weight) + self.l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_v_proj.weight) + self.values_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.values_l_proj.weight) + self.values_l_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_v_proj.weight) + self.out_v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_l_proj.weight) + self.out_l_proj.bias.data.fill_(0) + + def forward(self, v, l, attention_mask_l=None): + bsz, tgt_len, embed_dim = v.size() + + query_states = self.v_proj(v) * self.scale + key_states = self._shape(self.l_proj(l), -1, bsz) + value_v_states = self._shape(self.values_v_proj(v), -1, bsz) + value_l_states = self._shape(self.values_l_proj(l), -1, bsz) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_v_states = value_v_states.view(*proj_shape) + value_l_states = value_l_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" + ) + + # attn_weights_l = nn.functional.softmax(attn_weights.transpose(1, 2), dim=-1) + + if self.stable_softmax_2d: + attn_weights = attn_weights - attn_weights.max() + + if self.clamp_min_for_underflow: + attn_weights = torch.clamp( + attn_weights, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights = torch.clamp( + attn_weights, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + attn_weights_T = attn_weights.transpose(1, 2) + attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0] + if self.clamp_min_for_underflow: + attn_weights_l = torch.clamp( + attn_weights_l, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights_l = torch.clamp( + attn_weights_l, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + attn_weights_l = attn_weights_l.softmax(dim=-1) + + if attention_mask_l is not None: + assert attention_mask_l.dim() == 2 + attention_mask = attention_mask_l.unsqueeze(1).unsqueeze(1) + attention_mask = attention_mask.expand(bsz, 1, tgt_len, src_len) + attention_mask = attention_mask.masked_fill(attention_mask == 0, -9e15) + + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}") + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights_v = nn.functional.softmax(attn_weights, dim=-1) + + attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training) + attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training) + + attn_output_v = torch.bmm(attn_probs_v, value_l_states) + attn_output_l = torch.bmm(attn_probs_l, value_v_states) + + if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}" + ) + + if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim): + raise ValueError( + f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}" + ) + + attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output_v = attn_output_v.transpose(1, 2) + attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim) + + attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim) + attn_output_l = attn_output_l.transpose(1, 2) + attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim) + + attn_output_v = self.out_v_proj(attn_output_v) + attn_output_l = self.out_l_proj(attn_output_l) + + return attn_output_v, attn_output_l + + +# Bi-Direction MHA (text->image, image->text) +class BiAttentionBlock(nn.Module): + def __init__( + self, + v_dim, + l_dim, + embed_dim, + num_heads, + hidden_dim=None, + dropout=0.1, + drop_path=0.0, + init_values=1e-4, + cfg=None, + ): + """ + Inputs: + embed_dim - Dimensionality of input and attention feature vectors + hidden_dim - Dimensionality of hidden layer in feed-forward network + (usually 2-4x larger than embed_dim) + num_heads - Number of heads to use in the Multi-Head Attention block + dropout - Amount of dropout to apply in the feed-forward network + """ + super(BiAttentionBlock, self).__init__() + + # pre layer norm + self.layer_norm_v = nn.LayerNorm(v_dim) + self.layer_norm_l = nn.LayerNorm(l_dim) + self.attn = BiMultiHeadAttention( + v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout, cfg=cfg + ) + + # add layer scale for training stability + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True) + self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True) + + def forward(self, v, l, attention_mask_l=None, dummy_tensor=None): + v = self.layer_norm_v(v) + l = self.layer_norm_l(l) + delta_v, delta_l = self.attn(v, l, attention_mask_l=attention_mask_l) + # v, l = v + delta_v, l + delta_l + v = v + self.drop_path(self.gamma_v * delta_v) + l = l + self.drop_path(self.gamma_l * delta_l) + return v, l + + +class BiAttentionBlockForCheckpoint(nn.Module): + def __init__( + self, + v_dim, + l_dim, + embed_dim, + num_heads, + hidden_dim=None, + dropout=0.1, + drop_path=0.0, + init_values=1e-4, + cfg=None, + ): + """ + Inputs: + embed_dim - Dimensionality of input and attention feature vectors + hidden_dim - Dimensionality of hidden layer in feed-forward network + (usually 2-4x larger than embed_dim) + num_heads - Number of heads to use in the Multi-Head Attention block + dropout - Amount of dropout to apply in the feed-forward network + """ + super(BiAttentionBlockForCheckpoint, self).__init__() + + # pre layer norm + self.layer_norm_v = nn.LayerNorm(v_dim) + self.layer_norm_l = nn.LayerNorm(l_dim) + self.attn = BiMultiHeadAttention( + v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout, cfg=cfg + ) + + # add layer scale for training stability + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True) + self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True) + + self.cfg = cfg + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL: + if not self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT: + self.shrink_lang = FeatureResizer(l_dim * 5, l_dim, 0.1) + + def forward(self, q0, q1, q2, q3, q4, l, attention_mask_l=None, dummy_tensor=None): + + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL: + visu_feat = [] + lang_feat = [] + for ii, feat in enumerate([q0, q1, q2, q3, q4]): + bs, _, h, w = feat.shape + q = feat.flatten(2).transpose(1, 2) + + new_v, new_l = self.single_attention_call(q, l, attention_mask_l=attention_mask_l) + new_v = new_v.transpose(1, 2).contiguous().view(bs, -1, h, w) + lang_feat.append(new_l) + visu_feat.append(new_v) + if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT: + pass + else: + lang_feat = self.shrink_lang(torch.cat(lang_feat, dim=-1)) # From multiple dimensions + lang_feat = [lang_feat, None, None, None, None] + else: + visu_feat = [] + size_per_level, visual_features_flatten = [], [] + for ii, feat_per_level in enumerate([q0, q1, q2, q3, q4]): + bs, c, h, w = feat_per_level.shape + size_per_level.append([h, w]) + feat = permute_and_flatten(feat_per_level, bs, 1, c, h, w) + visual_features_flatten.append(feat) + visual_features_flatten = cat(visual_features_flatten, dim=1) + new_v, new_l = self.single_attention_call(visual_features_flatten, l, attention_mask_l=attention_mask_l) + # [bs, N, C] -> [bs, C, N] + new_v = new_v.transpose(1, 2).contiguous() + + start = 0 + for (h, w) in size_per_level: + new_v_per_level = new_v[:, :, start : start + h * w].view(bs, -1, h, w).contiguous() + visu_feat.append(new_v_per_level) + start += h * w + + lang_feat = [new_l, None, None, None, None] + + return ( + visu_feat[0], + visu_feat[1], + visu_feat[2], + visu_feat[3], + visu_feat[4], + lang_feat[0], + lang_feat[1], + lang_feat[2], + lang_feat[3], + lang_feat[4], + ) + + def single_attention_call(self, v, l, attention_mask_l=None, dummy_tensor=None): + v = self.layer_norm_v(v) + l = self.layer_norm_l(l) + delta_v, delta_l = self.attn(v, l, attention_mask_l=attention_mask_l) + # v, l = v + delta_v, l + delta_l + v = v + self.drop_path(self.gamma_v * delta_v) + l = l + self.drop_path(self.gamma_l * delta_l) + return v, l + + +# Single Direction MHA +class MultiHeadAttention(nn.Module): + """ + Multi-head attention module for both image and text + """ + + def __init__( + self, + q_dim, + k_dim, + embed_dim, + num_heads, + dropout=0.1, + clamp_min_for_underflow=False, + clamp_max_for_overflow=False, + ): + super(MultiHeadAttention, self).__init__() + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.q_dim = q_dim + self.k_dim = k_dim + + assert ( + self.head_dim * self.num_heads == self.embed_dim + ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + self.scale = self.head_dim ** (-0.5) + self.dropout = dropout + + self.q_proj = nn.Linear(self.q_dim, self.embed_dim) + self.k_proj = nn.Linear(self.k_dim, self.embed_dim) + self.v_proj = nn.Linear(self.k_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.q_dim) + self.clamp_min_for_underflow = clamp_min_for_underflow + self.clamp_max_for_overflow = clamp_max_for_overflow + + self._reset_parameters() + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def _reset_parameters(self): + nn.init.xavier_uniform_(self.q_proj.weight) + self.q_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.k_proj.weight) + self.k_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.v_proj.weight) + self.v_proj.bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_proj.weight) + self.out_proj.bias.data.fill_(0) + + def forward(self, q, k, v, attention_mask=None, return_attention=False): + bsz, tgt_len, embed_dim = q.size() + + query_states = self.q_proj(q) * self.scale + key_states = self._shape(self.k_proj(k), -1, bsz) + value_states = self._shape(self.v_proj(v), -1, bsz) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}" + ) + + if self.clamp_min_for_underflow: + attn_weights = torch.clamp( + attn_weights, min=-50000 + ) # Do not increase -50000, data type half has quite limited range + if self.clamp_max_for_overflow: + attn_weights = torch.clamp( + attn_weights, max=50000 + ) # Do not increase 50000, data type half has quite limited range + + if attention_mask is not None: + # [bsz, src_len] + assert attention_mask.dim() == 2 + attention_mask = attention_mask.unsqueeze(1).unsqueeze(1) + attention_mask = attention_mask.expand(bsz, 1, tgt_len, src_len) + attention_mask = attention_mask.masked_fill(attention_mask == 0, -9e15) + + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}") + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if return_attention: + # this operation is a bit akward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class AttentionMLP(nn.Module): + def __init__(self, q_dim, hidden_dim, dropout=0.1): + super(AttentionMLP, self).__init__() + self.hidden_dim = hidden_dim + self.activation_fn = nn.GELU() + self.fc1 = nn.Linear(q_dim, hidden_dim) + self.fc2 = nn.Linear(hidden_dim, q_dim) + self.dropout = nn.Dropout(dropout) + + def forward(self, hidden_states): + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class AttentionT2I(nn.Module): + def __init__( + self, + q_dim, + k_dim, + embed_dim, + num_heads, + hidden_dim=None, + dropout=0.1, + drop_path=0.0, + init_values=1e-4, + mode="i2t", + use_layer_scale=False, + clamp_min_for_underflow=False, + clamp_max_for_overflow=False, + ): + """ + Inputs: + embed_dim - Dimensionality of input and attention feature vectors + hidden_dim - Dimensionality of hidden layer in feed-forward network + (usually 2-4x larger than embed_dim) + num_heads - Number of heads to use in the Multi-Head Attention block + dropout - Amount of dropout to apply in the feed-forward network + """ + super(AttentionT2I, self).__init__() + + # pre_layer norm + self.layer_norm_q_1 = nn.LayerNorm(q_dim) + self.layer_norm_k_1 = nn.LayerNorm(k_dim) + self.attn = MultiHeadAttention( + q_dim=q_dim, + k_dim=k_dim, + embed_dim=embed_dim, + num_heads=num_heads, + clamp_min_for_underflow=clamp_min_for_underflow, + clamp_max_for_overflow=clamp_max_for_overflow, + ) + self.mode = mode + + # add layer scale for training stability + self.use_layer_scale = use_layer_scale + if self.use_layer_scale: + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.gamma = nn.Parameter(init_values * torch.ones((q_dim)), requires_grad=True) + + def forward(self, q0, q1, q2, q3, q4, k, v, attention_mask, dummy_arg=None): + qs = [] + for q_index, q in enumerate([q0, q1, q2, q3, q4]): + bs, _, h, w = q.shape + # (batch, seq_len, embed_size) + q = q.flatten(2).transpose(1, 2) + q = self.layer_norm_q_1(q) + k, v = self.layer_norm_k_1(k), self.layer_norm_k_1(v) + delta_q = self.attn(q, k, v, attention_mask=attention_mask)[0] + if self.use_layer_scale: + q = q + self.drop_path(self.gamma * delta_q) + else: + q = q + delta_q + q = q.transpose(1, 2).contiguous().view(bs, -1, h, w) + qs.append(q) + + return qs[0], qs[1], qs[2], qs[3], qs[4] diff --git a/maskrcnn_benchmark/utils/imports.py b/maskrcnn_benchmark/utils/imports.py new file mode 100644 index 0000000000000000000000000000000000000000..0532b12a275ea12f7b59c377c286f18656c20af8 --- /dev/null +++ b/maskrcnn_benchmark/utils/imports.py @@ -0,0 +1,23 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import torch + +if torch._six.PY37: + import importlib + import importlib.util + import sys + + # from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa + def import_file(module_name, file_path, make_importable=False): + spec = importlib.util.spec_from_file_location(module_name, file_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + if make_importable: + sys.modules[module_name] = module + return module + +else: + import imp + + def import_file(module_name, file_path, make_importable=None): + module = imp.load_source(module_name, file_path) + return module diff --git a/maskrcnn_benchmark/utils/logger.py b/maskrcnn_benchmark/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..a30fa8d49a67111cb7d8d47e7db1ece98134aa8e --- /dev/null +++ b/maskrcnn_benchmark/utils/logger.py @@ -0,0 +1,25 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import logging +import os +import sys + + +def setup_logger(name, save_dir, distributed_rank): + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + # don't log results for the non-master process + if distributed_rank > 0: + return logger + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s: %(message)s") + ch.setFormatter(formatter) + logger.addHandler(ch) + + if save_dir: + fh = logging.FileHandler(os.path.join(save_dir, "log.txt")) + fh.setLevel(logging.DEBUG) + fh.setFormatter(formatter) + logger.addHandler(fh) + + return logger diff --git a/maskrcnn_benchmark/utils/mdetr_dist.py b/maskrcnn_benchmark/utils/mdetr_dist.py new file mode 100644 index 0000000000000000000000000000000000000000..221757340def5c46b333360e0bedd533498e1dfb --- /dev/null +++ b/maskrcnn_benchmark/utils/mdetr_dist.py @@ -0,0 +1,233 @@ +# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Utilities related to distributed mode. + +By default, the reduce of metrics and such are done on GPU, since it's more straightforward (we reuse the NCCL backend) +If you want to reduce on CPU instead (required for big datasets like GQA), use the env variable MDETR_CPU_REDUCE=1 +""" +import functools +import io +import os +import datetime + +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + + return dist.group.WORLD + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + + world_size = get_world_size() + if world_size == 1: + return [data] + + cpu_group = None + if os.getenv("MDETR_CPU_REDUCE") == "1": + cpu_group = _get_global_gloo_group() + + buffer = io.BytesIO() + torch.save(data, buffer) + data_view = buffer.getbuffer() + device = "cuda" if cpu_group is None else "cpu" + tensor = torch.ByteTensor(data_view).to(device) + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long) + size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)] + if cpu_group is None: + dist.all_gather(size_list, local_size) + else: + print("gathering on cpu") + dist.all_gather(size_list, local_size, group=cpu_group) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + assert isinstance(local_size.item(), int) + local_size = int(local_size.item()) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device)) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device) + tensor = torch.cat((tensor, padding), dim=0) + if cpu_group is None: + dist.all_gather(tensor_list, tensor) + else: + dist.all_gather(tensor_list, tensor, group=cpu_group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + tensor = torch.split(tensor, [size, max_size - size], dim=0)[0] + buffer = io.BytesIO(tensor.cpu().numpy()) + obj = torch.load(buffer) + data_list.append(obj) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + """ + Returns: + True if distributed training is enabled + """ + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + """ + Returns: + The number of processes in the process group + """ + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + """ + Returns: + The rank of the current process within the global process group. + """ + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert _LOCAL_PROCESS_GROUP is not None + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process(): + """Return true if the current process is the main one""" + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + """Utility function to save only from the main process""" + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + """Initialize distributed training, if appropriate""" + if "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + + dist.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + timeout=datetime.timedelta(0, 7200), + ) + dist.barrier() + setup_for_distributed(args.debug or args.rank == 0) diff --git a/maskrcnn_benchmark/utils/metric_logger.py b/maskrcnn_benchmark/utils/metric_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..507128ceaece6355c236e9bb78269b690adb63eb --- /dev/null +++ b/maskrcnn_benchmark/utils/metric_logger.py @@ -0,0 +1,122 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from collections import defaultdict +from collections import deque + +import torch +import time +from datetime import datetime +from .comm import is_main_process + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20): + self.deque = deque(maxlen=window_size) + # self.series = [] + self.total = 0.0 + self.count = 0 + + def update(self, value): + self.deque.append(value) + # self.series.append(value) + self.count += 1 + if value != value: + value = 0 + self.total += value + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque)) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + +class AverageMeter(object): + """Computes and stores the average and current value""" + + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append("{}: {:.4f} ({:.4f})".format(name, meter.median, meter.global_avg)) + return self.delimiter.join(loss_str) + + +# haotian added tensorboard support +class TensorboardLogger(MetricLogger): + def __init__(self, log_dir, start_iter=0, delimiter="\t"): + super(TensorboardLogger, self).__init__(delimiter) + self.iteration = start_iter + self.writer = self._get_tensorboard_writer(log_dir) + + @staticmethod + def _get_tensorboard_writer(log_dir): + try: + from tensorboardX import SummaryWriter + except ImportError: + raise ImportError( + "To use tensorboard please install tensorboardX " "[ pip install tensorflow tensorboardX ]." + ) + + if is_main_process(): + # timestamp = datetime.fromtimestamp(time.time()).strftime('%Y%m%d-%H:%M') + tb_logger = SummaryWriter("{}".format(log_dir)) + return tb_logger + else: + return None + + def update(self, **kwargs): + super(TensorboardLogger, self).update(**kwargs) + if self.writer: + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.writer.add_scalar(k, v, self.iteration) + + self.iteration += 1 diff --git a/maskrcnn_benchmark/utils/miscellaneous.py b/maskrcnn_benchmark/utils/miscellaneous.py new file mode 100644 index 0000000000000000000000000000000000000000..721c49994e2285fafaf7f99b7ff376392b4b75b6 --- /dev/null +++ b/maskrcnn_benchmark/utils/miscellaneous.py @@ -0,0 +1,18 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import errno +import os +from .comm import is_main_process + + +def mkdir(path): + try: + os.makedirs(path) + except OSError as e: + if e.errno != errno.EEXIST: + raise + + +def save_config(cfg, path): + if is_main_process(): + with open(path, "w") as f: + f.write(cfg.dump()) diff --git a/maskrcnn_benchmark/utils/model_serialization.py b/maskrcnn_benchmark/utils/model_serialization.py new file mode 100644 index 0000000000000000000000000000000000000000..1b81828d686c7b9d502d7def7740304ec44c2e7a --- /dev/null +++ b/maskrcnn_benchmark/utils/model_serialization.py @@ -0,0 +1,245 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +from collections import OrderedDict, defaultdict +import logging +import math +import torch + +from maskrcnn_benchmark.utils.imports import import_file + + +def resize_2d(posemb, shape_new): + # Rescale the grid of position embeddings when loading from state_dict. Adapted from + # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 + ntok_new = shape_new[0] + gs_old = int(math.sqrt(len(posemb))) # 2 * w - 1 + gs_new = int(math.sqrt(ntok_new)) # 2 * w - 1 + posemb_grid = posemb.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) + posemb_grid = torch.nn.functional.interpolate(posemb_grid, size=(gs_new, gs_new), mode="bilinear") + posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(gs_new * gs_new, -1) + return posemb_grid + + +def align_and_update_state_dicts( + model_state_dict, loaded_state_dict, reshape_keys=["pos_bias_table"], use_weightmap=False +): + """ + Strategy: suppose that the models that we will create will have prefixes appended + to each of its keys, for example due to an extra level of nesting that the original + pre-trained weights from ImageNet won't contain. For example, model.state_dict() + might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains + res2.conv1.weight. We thus want to match both parameters together. + For that, we look for each model weight, look among all loaded keys if there is one + that is a suffix of the current weight name, and use it if that's the case. + If multiple matches exist, take the one with longest size + of the corresponding name. For example, for the same model as before, the pretrained + weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case, + we want to match backbone[0].body.conv1.weight to conv1.weight, and + backbone[0].body.res2.conv1.weight to res2.conv1.weight. + """ + current_keys = sorted(list(model_state_dict.keys())) + + new_loaded_state_dict = dict() + for key in loaded_state_dict.keys(): + new_loaded_state_dict[key.replace("text_transformer.", "").replace("vit_model.", "")] = loaded_state_dict[key] + + loaded_state_dict = new_loaded_state_dict + loaded_keys = sorted(list(loaded_state_dict.keys())) + + # get a matrix of string matches, where each (i, j) entry correspond to the size of the + # loaded_key string, if it matches + match_matrix = [len(j) if i.endswith(j) else 0 for i in current_keys for j in loaded_keys] + match_matrix = torch.as_tensor(match_matrix).view(len(current_keys), len(loaded_keys)) + max_match_size, idxs = match_matrix.max(1) + # remove indices that correspond to no-match + idxs[max_match_size == 0] = -1 + + matched_keys = [] + # used for logging + max_size = max([len(key) for key in current_keys]) if current_keys else 1 + max_size_loaded = max([len(key) for key in loaded_keys]) if loaded_keys else 1 + log_str_template = "{: <{}} loaded from {: <{}} of shape {}" + logger = logging.getLogger(__name__) + for idx_new, idx_old in enumerate(idxs.tolist()): + if idx_old == -1: + continue + key = current_keys[idx_new] + key_old = loaded_keys[idx_old] + if model_state_dict[key].shape != loaded_state_dict[key_old].shape: + if any([k in key_old for k in reshape_keys]): + new_shape = model_state_dict[key].shape + logger.warning("Reshaping {} -> {}. \n".format(key_old, key)) + model_state_dict[key] = resize_2d(loaded_state_dict[key_old], new_shape) + elif use_weightmap and "cls_logits" in key: + coco_in_objects365_inds = [ + 227, + 26, + 55, + 202, + 2, + 44, + 338, + 346, + 32, + 336, + 118, + 299, + 218, + 25, + 361, + 59, + 95, + 161, + 278, + 82, + 110, + 22, + 364, + 134, + 9, + 350, + 152, + 323, + 304, + 130, + 285, + 289, + 16, + 172, + 17, + 18, + 283, + 305, + 321, + 35, + 362, + 88, + 127, + 174, + 292, + 37, + 11, + 6, + 267, + 212, + 41, + 58, + 162, + 237, + 98, + 48, + 63, + 81, + 247, + 23, + 94, + 326, + 349, + 178, + 203, + 259, + 171, + 60, + 198, + 213, + 325, + 282, + 258, + 33, + 71, + 353, + 273, + 318, + 148, + 330, + ] + logger.info( + "Use coco_in_objects365_inds labelmap for COCO detection because of size mis-match, " + "Reshaping {} -> {}. \n".format(key_old, key) + ) + new_shape = model_state_dict[key].shape + assert new_shape[0] == len(coco_in_objects365_inds) + weight_inds_old = torch.as_tensor(coco_in_objects365_inds).to(loaded_state_dict[key_old].device) + model_state_dict[key] = loaded_state_dict[key_old][weight_inds_old].to(model_state_dict[key].device) + else: + logger.info("Skip due to size mismatch: {} -> {}. \n".format(key_old, key)) + continue + else: + model_state_dict[key] = loaded_state_dict[key_old] + matched_keys.append(key) + logger.info( + log_str_template.format( + key, + max_size, + key_old, + max_size_loaded, + tuple(loaded_state_dict[key_old].shape), + ) + ) + missing_keys = set(current_keys) - set(matched_keys) + if len(missing_keys): + groups = _group_checkpoint_keys(missing_keys) + msg_per_group = sorted(k + _group_to_str(v) for k, v in groups.items()) + msg = "\n".join(sorted(msg_per_group)) + logger.warning("Some layers unloaded with pre-trained weight: \n" + msg) + + +def strip_prefix_if_present(state_dict, prefix): + keys = sorted(state_dict.keys()) + if not all(key.startswith(prefix) for key in keys): + return state_dict + stripped_state_dict = OrderedDict() + for key, value in state_dict.items(): + stripped_state_dict[key.replace(prefix, "", 1)] = value + return stripped_state_dict + + +def load_state_dict(model, loaded_state_dict): + model_state_dict = model.state_dict() + # if the state_dict comes from a model that was wrapped in a + # DataParallel or DistributedDataParallel during serialization, + # remove the "module" prefix before performing the matching + if "state_dict" in loaded_state_dict: + loaded_state_dict = loaded_state_dict["state_dict"] + loaded_state_dict = strip_prefix_if_present(loaded_state_dict, prefix="module.") + align_and_update_state_dicts(model_state_dict, loaded_state_dict) + + # use strict loading + model.load_state_dict(model_state_dict) + + +def _group_checkpoint_keys(keys): + """ + Group keys based on common prefixes. A prefix is the string up to the final + "." in each key. + Args: + keys (list[str]): list of parameter names, i.e. keys in the model + checkpoint dict. + Returns: + dict[list]: keys with common prefixes are grouped into lists. + """ + groups = defaultdict(list) + for key in keys: + pos = key.rfind(".") + if pos >= 0: + head, tail = key[:pos], [key[pos + 1 :]] + else: + head, tail = key, [] + groups[head].extend(tail) + return groups + + +def _group_to_str(group): + """ + Format a group of parameter name suffixes into a loggable string. + Args: + group (list[str]): list of parameter name suffixes. + Returns: + str: formated string. + """ + if len(group) == 0: + return "" + + if len(group) == 1: + return "." + group[0] + + return ".{" + ", ".join(sorted(group)) + "}" diff --git a/maskrcnn_benchmark/utils/model_zoo.py b/maskrcnn_benchmark/utils/model_zoo.py new file mode 100644 index 0000000000000000000000000000000000000000..000f869e7c04e8de6d5895881ec1549d9e055d14 --- /dev/null +++ b/maskrcnn_benchmark/utils/model_zoo.py @@ -0,0 +1,61 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +import os +import sys + +try: + from torch.hub import _download_url_to_file + from torch.hub import urlparse + from torch.hub import HASH_REGEX +except ImportError: + from torch.utils.model_zoo import _download_url_to_file + from torch.utils.model_zoo import urlparse + from torch.utils.model_zoo import HASH_REGEX + +from maskrcnn_benchmark.utils.comm import is_main_process +from maskrcnn_benchmark.utils.comm import synchronize + + +# very similar to https://github.com/pytorch/pytorch/blob/master/torch/utils/model_zoo.py +# but with a few improvements and modifications +def cache_url(url, model_dir="model", progress=True): + r"""Loads the Torch serialized object at the given URL. + If the object is already present in `model_dir`, it's deserialized and + returned. The filename part of the URL should follow the naming convention + ``filename-.ext`` where ```` is the first eight or more + digits of the SHA256 hash of the contents of the file. The hash is used to + ensure unique names and to verify the contents of the file. + The default value of `model_dir` is ``$TORCH_HOME/models`` where + ``$TORCH_HOME`` defaults to ``~/.torch``. The default directory can be + overridden with the ``$TORCH_MODEL_ZOO`` environment variable. + Args: + url (string): URL of the object to download + model_dir (string, optional): directory in which to save the object + progress (bool, optional): whether or not to display a progress bar to stderr + Example: + >>> cached_file = maskrcnn_benchmark.utils.model_zoo.cache_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth') + """ + if model_dir is None: + torch_home = os.path.expanduser(os.getenv("TORCH_HOME", "~/.torch")) + model_dir = os.getenv("TORCH_MODEL_ZOO", os.path.join(torch_home, "models")) + if not os.path.exists(model_dir): + os.makedirs(model_dir, exist_ok=True) + parts = urlparse(url) + filename = os.path.basename(parts.path) + if filename == "model_final.pkl": + # workaround as pre-trained Caffe2 models from Detectron have all the same filename + # so make the full path the filename by replacing / with _ + filename = parts.path.replace("/", "_") + cached_file = os.path.join(model_dir, filename) + if not os.path.exists(cached_file): + sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) + hash_prefix = HASH_REGEX.search(filename) + if hash_prefix is not None: + hash_prefix = hash_prefix.group(1) + # workaround: Caffe2 models don't have a hash, but follow the R-50 convention, + # which matches the hash PyTorch uses. So we skip the hash matching + # if the hash_prefix is less than 6 characters + if len(hash_prefix) < 6: + hash_prefix = None + _download_url_to_file(url, cached_file, hash_prefix, progress=progress) + synchronize() + return cached_file diff --git a/maskrcnn_benchmark/utils/pretrain_model_loading.py b/maskrcnn_benchmark/utils/pretrain_model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..b23e86cea6803ad545f52f71695dcd21b646e74a --- /dev/null +++ b/maskrcnn_benchmark/utils/pretrain_model_loading.py @@ -0,0 +1,48 @@ +import numpy as np +import torch +import torch.nn as nn + +from collections import OrderedDict + + +def _remove_bn_statics(state_dict): + layer_keys = sorted(state_dict.keys()) + remove_list = [] + for key in layer_keys: + if "running_mean" in key or "running_var" in key or "num_batches_tracked" in key: + remove_list.append(key) + for key in remove_list: + del state_dict[key] + return state_dict + + +def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg): + import re + + layer_keys = sorted(state_dict.keys()) + for ix, stage_with_dcn in enumerate(cfg.MODEL.RESNETS.STAGE_WITH_DCN, 1): + if not stage_with_dcn: + continue + for old_key in layer_keys: + pattern = ".*layer{}.*conv2.*".format(ix) + r = re.match(pattern, old_key) + if r is None: + continue + for param in ["weight", "bias"]: + if old_key.find(param) is -1: + continue + if "unit01" in old_key: + continue + new_key = old_key.replace("conv2.{}".format(param), "conv2.conv.{}".format(param)) + print("pattern: {}, old_key: {}, new_key: {}".format(pattern, old_key, new_key)) + state_dict[new_key] = state_dict[old_key] + del state_dict[old_key] + return state_dict + + +def load_pretrain_format(cfg, f): + model = torch.load(f) + model = _remove_bn_statics(model) + model = _rename_conv_weights_for_deformable_conv_layers(model, cfg) + + return dict(model=model) diff --git a/maskrcnn_benchmark/utils/registry.py b/maskrcnn_benchmark/utils/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..5cdd05b4baba1a6da9b837f3068c36e5c357bc0a --- /dev/null +++ b/maskrcnn_benchmark/utils/registry.py @@ -0,0 +1,46 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + + +def _register_generic(module_dict, module_name, module): + assert module_name not in module_dict + module_dict[module_name] = module + + +class Registry(dict): + """ + A helper class for managing registering modules, it extends a dictionary + and provides a register functions. + + Eg. creeting a registry: + some_registry = Registry({"default": default_module}) + + There're two ways of registering new modules: + 1): normal way is just calling register function: + def foo(): + ... + some_registry.register("foo_module", foo) + 2): used as decorator when declaring the module: + @some_registry.register("foo_module") + @some_registry.register("foo_modeul_nickname") + def foo(): + ... + + Access of module is just like using a dictionary, eg: + f = some_registry["foo_modeul"] + """ + + def __init__(self, *args, **kwargs): + super(Registry, self).__init__(*args, **kwargs) + + def register(self, module_name, module=None): + # used as function call + if module is not None: + _register_generic(self, module_name, module) + return + + # used as decorator + def register_fn(fn): + _register_generic(self, module_name, fn) + return fn + + return register_fn diff --git a/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py b/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..9f14a0bd2d4ee61a9c305af8c73399a12a4d3c2f --- /dev/null +++ b/maskrcnn_benchmark/utils/shallow_contrastive_loss_helper.py @@ -0,0 +1,55 @@ +import torch +import maskrcnn_benchmark.utils.dist as dist + + +def normalized_positive_map(positive_map): + positive_map = positive_map.float() + positive_map_num_pos = positive_map.sum(2) + positive_map_num_pos[positive_map_num_pos == 0] = 1e-6 + positive_map = positive_map / positive_map_num_pos.unsqueeze(-1) + return positive_map + + +def pad_tensor_given_dim_length(tensor, dim, length, padding_value=0, batch_first=True): + new_size = list(tensor.size()[:dim]) + [length] + list(tensor.size()[dim + 1 :]) + out_tensor = tensor.data.new(*new_size).fill_(padding_value) + if batch_first: + out_tensor[:, : tensor.size(1), ...] = tensor + else: + out_tensor[: tensor.size(0), ...] = tensor + return out_tensor + + +def pad_random_negative_tensor_given_length(positive_tensor, negative_padding_tensor, length=None): + assert positive_tensor.shape[0] + negative_padding_tensor.shape[0] == length + return torch.cat((positive_tensor, negative_padding_tensor), dim=0) + + +def gather_tensors(tensor): + """ + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + if not dist.is_dist_avail_and_initialized(): + return torch.stack([tensor], dim=0) + + total = dist.get_world_size() + rank = torch.distributed.get_rank() + # gathered_normalized_img_emb = [torch.zeros_like(normalized_img_emb) for _ in range(total)] + # torch.distributed.all_gather(gathered_normalized_img_emb, normalized_img_emb) + + tensors_gather = [torch.zeros_like(tensor) for _ in range(total)] + torch.distributed.all_gather(tensors_gather, tensor, async_op=False) + + # need to do this to restore propagation of the gradients + tensors_gather[rank] = tensor + output = torch.stack(tensors_gather, dim=0) + return output + + +def convert_to_roi_format(boxes): + concat_boxes = boxes.bbox + device, dtype = concat_boxes.device, concat_boxes.dtype + ids = torch.full((len(boxes), 1), 0, dtype=dtype, device=device) + rois = torch.cat([ids, concat_boxes], dim=1) + return rois diff --git a/maskrcnn_benchmark/utils/stats.py b/maskrcnn_benchmark/utils/stats.py new file mode 100644 index 0000000000000000000000000000000000000000..5990cf1ea2362706689f240529b7dcb0c5abcb83 --- /dev/null +++ b/maskrcnn_benchmark/utils/stats.py @@ -0,0 +1,513 @@ +""" +Copyright (C) 2019 Sovrasov V. - All Rights Reserved + * You may use, distribute and modify this code under the + * terms of the MIT license. + * You should have received a copy of the MIT license with + * this file. If not visit https://opensource.org/licenses/MIT +""" + +import sys +from functools import partial + +import numpy as np +import torch +import torch.nn as nn + +from maskrcnn_benchmark.layers import * + + +def get_model_complexity_info( + model, + input_res, + print_per_layer_stat=True, + as_strings=True, + input_constructor=None, + ost=sys.stdout, + verbose=False, + ignore_modules=[], + custom_modules_hooks={}, +): + assert type(input_res) is tuple + assert len(input_res) >= 1 + assert isinstance(model, nn.Module) + global CUSTOM_MODULES_MAPPING + CUSTOM_MODULES_MAPPING = custom_modules_hooks + flops_model = add_flops_counting_methods(model) + flops_model.eval() + flops_model.start_flops_count(ost=ost, verbose=verbose, ignore_list=ignore_modules) + if input_constructor: + input = input_constructor(input_res) + _ = flops_model(**input) + else: + try: + batch = torch.ones(()).new_empty( + (1, *input_res), + dtype=next(flops_model.parameters()).dtype, + device=next(flops_model.parameters()).device, + ) + except StopIteration: + batch = torch.ones(()).new_empty((1, *input_res)) + + _ = flops_model(batch) + + flops_count, params_count = flops_model.compute_average_flops_cost() + if print_per_layer_stat: + print_model_with_flops(flops_model, flops_count, params_count, ost=ost) + flops_model.stop_flops_count() + CUSTOM_MODULES_MAPPING = {} + + if as_strings: + return flops_to_string(flops_count), params_to_string(params_count) + + return flops_count, params_count + + +def flops_to_string(flops, units="GMac", precision=2): + if units is None: + if flops // 10**9 > 0: + return str(round(flops / 10.0**9, precision)) + " GMac" + elif flops // 10**6 > 0: + return str(round(flops / 10.0**6, precision)) + " MMac" + elif flops // 10**3 > 0: + return str(round(flops / 10.0**3, precision)) + " KMac" + else: + return str(flops) + " Mac" + else: + if units == "GMac": + return str(round(flops / 10.0**9, precision)) + " " + units + elif units == "MMac": + return str(round(flops / 10.0**6, precision)) + " " + units + elif units == "KMac": + return str(round(flops / 10.0**3, precision)) + " " + units + else: + return str(flops) + " Mac" + + +def params_to_string(params_num, units=None, precision=2): + if units is None: + if params_num // 10**6 > 0: + return str(round(params_num / 10**6, 2)) + " M" + elif params_num // 10**3: + return str(round(params_num / 10**3, 2)) + " k" + else: + return str(params_num) + else: + if units == "M": + return str(round(params_num / 10.0**6, precision)) + " " + units + elif units == "K": + return str(round(params_num / 10.0**3, precision)) + " " + units + else: + return str(params_num) + + +def accumulate_flops(self): + if is_supported_instance(self): + return self.__flops__ + else: + sum = 0 + for m in self.children(): + sum += m.accumulate_flops() + return sum + + +def print_model_with_flops(model, total_flops, total_params, units="GMac", precision=3, ost=sys.stdout): + def accumulate_params(self): + if is_supported_instance(self): + return self.__params__ + else: + sum = 0 + for m in self.children(): + sum += m.accumulate_params() + return sum + + def flops_repr(self): + accumulated_params_num = self.accumulate_params() + accumulated_flops_cost = self.accumulate_flops() / model.__batch_counter__ + return ", ".join( + [ + params_to_string(accumulated_params_num, units="M", precision=precision), + "{:.3%} Params".format(accumulated_params_num / total_params), + flops_to_string(accumulated_flops_cost, units=units, precision=precision), + "{:.3%} MACs".format(accumulated_flops_cost / total_flops), + self.original_extra_repr(), + ] + ) + + def add_extra_repr(m): + m.accumulate_flops = accumulate_flops.__get__(m) + m.accumulate_params = accumulate_params.__get__(m) + flops_extra_repr = flops_repr.__get__(m) + if m.extra_repr != flops_extra_repr: + m.original_extra_repr = m.extra_repr + m.extra_repr = flops_extra_repr + assert m.extra_repr != m.original_extra_repr + + def del_extra_repr(m): + if hasattr(m, "original_extra_repr"): + m.extra_repr = m.original_extra_repr + del m.original_extra_repr + if hasattr(m, "accumulate_flops"): + del m.accumulate_flops + + model.apply(add_extra_repr) + print(repr(model), file=ost) + model.apply(del_extra_repr) + + +def get_model_parameters_number(model): + params_num = sum(p.numel() for p in model.parameters() if p.requires_grad) + return params_num + + +def add_flops_counting_methods(net_main_module): + # adding additional methods to the existing module object, + # this is done this way so that each function has access to self object + net_main_module.start_flops_count = start_flops_count.__get__(net_main_module) + net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module) + net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module) + net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module) + + net_main_module.reset_flops_count() + + return net_main_module + + +def compute_average_flops_cost(self): + """ + A method that will be available after add_flops_counting_methods() is called + on a desired net object. + + Returns current mean flops consumption per image. + + """ + + for m in self.modules(): + m.accumulate_flops = accumulate_flops.__get__(m) + + flops_sum = self.accumulate_flops() + + for m in self.modules(): + if hasattr(m, "accumulate_flops"): + del m.accumulate_flops + + params_sum = get_model_parameters_number(self) + return flops_sum / self.__batch_counter__, params_sum + + +def start_flops_count(self, **kwargs): + """ + A method that will be available after add_flops_counting_methods() is called + on a desired net object. + + Activates the computation of mean flops consumption per image. + Call it before you run the network. + + """ + add_batch_counter_hook_function(self) + + seen_types = set() + + def add_flops_counter_hook_function(module, ost, verbose, ignore_list): + if type(module) in ignore_list: + seen_types.add(type(module)) + if is_supported_instance(module): + module.__params__ = 0 + elif is_supported_instance(module): + if hasattr(module, "__flops_handle__"): + return + if type(module) in CUSTOM_MODULES_MAPPING: + handle = module.register_forward_hook(CUSTOM_MODULES_MAPPING[type(module)]) + elif getattr(module, "compute_macs", False): + handle = module.register_forward_hook(module.compute_macs) + else: + handle = module.register_forward_hook(MODULES_MAPPING[type(module)]) + module.__flops_handle__ = handle + seen_types.add(type(module)) + else: + if verbose and not type(module) in (nn.Sequential, nn.ModuleList) and not type(module) in seen_types: + print("Warning: module " + type(module).__name__ + " is treated as a zero-op.", file=ost) + seen_types.add(type(module)) + + self.apply(partial(add_flops_counter_hook_function, **kwargs)) + + +def stop_flops_count(self): + """ + A method that will be available after add_flops_counting_methods() is called + on a desired net object. + + Stops computing the mean flops consumption per image. + Call whenever you want to pause the computation. + + """ + remove_batch_counter_hook_function(self) + self.apply(remove_flops_counter_hook_function) + + +def reset_flops_count(self): + """ + A method that will be available after add_flops_counting_methods() is called + on a desired net object. + + Resets statistics computed so far. + + """ + add_batch_counter_variables_or_reset(self) + self.apply(add_flops_counter_variable_or_reset) + + +# ---- Internal functions +def empty_flops_counter_hook(module, input, output): + module.__flops__ += 0 + + +def upsample_flops_counter_hook(module, input, output): + output_size = output[0] + batch_size = output_size.shape[0] + output_elements_count = batch_size + for val in output_size.shape[1:]: + output_elements_count *= val + module.__flops__ += int(output_elements_count) + + +def relu_flops_counter_hook(module, input, output): + active_elements_count = output.numel() + module.__flops__ += int(active_elements_count) + + +def linear_flops_counter_hook(module, input, output): + input = input[0] + # pytorch checks dimensions, so here we don't care much + output_last_dim = output.shape[-1] + bias_flops = output_last_dim if module.bias is not None else 0 + module.__flops__ += int(np.prod(input.shape) * output_last_dim + bias_flops) + + +def pool_flops_counter_hook(module, input, output): + input = input[0] + module.__flops__ += int(np.prod(input.shape)) + + +def bn_flops_counter_hook(module, input, output): + input = input[0] + + batch_flops = np.prod(input.shape) + if module.affine: + batch_flops *= 2 + module.__flops__ += int(batch_flops) + + +def conv_flops_counter_hook(conv_module, input, output): + # Can have multiple inputs, getting the first one + input = input[0] + + batch_size = input.shape[0] + output_dims = list(output.shape[2:]) + + kernel_dims = list(conv_module.kernel_size) + in_channels = conv_module.in_channels + out_channels = conv_module.out_channels + groups = conv_module.groups + + filters_per_channel = out_channels // groups + conv_per_position_flops = int(np.prod(kernel_dims)) * in_channels * filters_per_channel + + active_elements_count = batch_size * int(np.prod(output_dims)) + + overall_conv_flops = conv_per_position_flops * active_elements_count + + bias_flops = 0 + + if conv_module.bias is not None: + + bias_flops = out_channels * active_elements_count + + overall_flops = overall_conv_flops + bias_flops + + conv_module.__flops__ += int(overall_flops) + + +def batch_counter_hook(module, input, output): + batch_size = 1 + if len(input) > 0: + # Can have multiple inputs, getting the first one + input = input[0] + batch_size = len(input) + else: + pass + print("Warning! No positional inputs found for a module," " assuming batch size is 1.") + module.__batch_counter__ += batch_size + + +def rnn_flops(flops, rnn_module, w_ih, w_hh, input_size): + # matrix matrix mult ih state and internal state + flops += w_ih.shape[0] * w_ih.shape[1] + # matrix matrix mult hh state and internal state + flops += w_hh.shape[0] * w_hh.shape[1] + if isinstance(rnn_module, (nn.RNN, nn.RNNCell)): + # add both operations + flops += rnn_module.hidden_size + elif isinstance(rnn_module, (nn.GRU, nn.GRUCell)): + # hadamard of r + flops += rnn_module.hidden_size + # adding operations from both states + flops += rnn_module.hidden_size * 3 + # last two hadamard product and add + flops += rnn_module.hidden_size * 3 + elif isinstance(rnn_module, (nn.LSTM, nn.LSTMCell)): + # adding operations from both states + flops += rnn_module.hidden_size * 4 + # two hadamard product and add for C state + flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size + # final hadamard + flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size + return flops + + +def rnn_flops_counter_hook(rnn_module, input, output): + """ + Takes into account batch goes at first position, contrary + to pytorch common rule (but actually it doesn't matter). + IF sigmoid and tanh are made hard, only a comparison FLOPS should be accurate + """ + flops = 0 + # input is a tuple containing a sequence to process and (optionally) hidden state + inp = input[0] + batch_size = inp.shape[0] + seq_length = inp.shape[1] + num_layers = rnn_module.num_layers + + for i in range(num_layers): + w_ih = rnn_module.__getattr__("weight_ih_l" + str(i)) + w_hh = rnn_module.__getattr__("weight_hh_l" + str(i)) + if i == 0: + input_size = rnn_module.input_size + else: + input_size = rnn_module.hidden_size + flops = rnn_flops(flops, rnn_module, w_ih, w_hh, input_size) + if rnn_module.bias: + b_ih = rnn_module.__getattr__("bias_ih_l" + str(i)) + b_hh = rnn_module.__getattr__("bias_hh_l" + str(i)) + flops += b_ih.shape[0] + b_hh.shape[0] + + flops *= batch_size + flops *= seq_length + if rnn_module.bidirectional: + flops *= 2 + rnn_module.__flops__ += int(flops) + + +def rnn_cell_flops_counter_hook(rnn_cell_module, input, output): + flops = 0 + inp = input[0] + batch_size = inp.shape[0] + w_ih = rnn_cell_module.__getattr__("weight_ih") + w_hh = rnn_cell_module.__getattr__("weight_hh") + input_size = inp.shape[1] + flops = rnn_flops(flops, rnn_cell_module, w_ih, w_hh, input_size) + if rnn_cell_module.bias: + b_ih = rnn_cell_module.__getattr__("bias_ih") + b_hh = rnn_cell_module.__getattr__("bias_hh") + flops += b_ih.shape[0] + b_hh.shape[0] + + flops *= batch_size + rnn_cell_module.__flops__ += int(flops) + + +def add_batch_counter_variables_or_reset(module): + + module.__batch_counter__ = 0 + + +def add_batch_counter_hook_function(module): + if hasattr(module, "__batch_counter_handle__"): + return + + handle = module.register_forward_hook(batch_counter_hook) + module.__batch_counter_handle__ = handle + + +def remove_batch_counter_hook_function(module): + if hasattr(module, "__batch_counter_handle__"): + module.__batch_counter_handle__.remove() + del module.__batch_counter_handle__ + + +def add_flops_counter_variable_or_reset(module): + if is_supported_instance(module): + if hasattr(module, "__flops__") or hasattr(module, "__params__"): + print( + "Warning: variables __flops__ or __params__ are already " + "defined for the module" + type(module).__name__ + " ptflops can affect your code!" + ) + module.__flops__ = 0 + module.__params__ = get_model_parameters_number(module) + + +CUSTOM_MODULES_MAPPING = {} + +MODULES_MAPPING = { + # convolutions + nn.Conv1d: conv_flops_counter_hook, + nn.Conv2d: conv_flops_counter_hook, + nn.Conv3d: conv_flops_counter_hook, + Conv2d: conv_flops_counter_hook, + ModulatedDeformConv: conv_flops_counter_hook, + # activations + nn.ReLU: relu_flops_counter_hook, + nn.PReLU: relu_flops_counter_hook, + nn.ELU: relu_flops_counter_hook, + nn.LeakyReLU: relu_flops_counter_hook, + nn.ReLU6: relu_flops_counter_hook, + # poolings + nn.MaxPool1d: pool_flops_counter_hook, + nn.AvgPool1d: pool_flops_counter_hook, + nn.AvgPool2d: pool_flops_counter_hook, + nn.MaxPool2d: pool_flops_counter_hook, + nn.MaxPool3d: pool_flops_counter_hook, + nn.AvgPool3d: pool_flops_counter_hook, + nn.AdaptiveMaxPool1d: pool_flops_counter_hook, + nn.AdaptiveAvgPool1d: pool_flops_counter_hook, + nn.AdaptiveMaxPool2d: pool_flops_counter_hook, + nn.AdaptiveAvgPool2d: pool_flops_counter_hook, + nn.AdaptiveMaxPool3d: pool_flops_counter_hook, + nn.AdaptiveAvgPool3d: pool_flops_counter_hook, + # BNs + nn.BatchNorm1d: bn_flops_counter_hook, + nn.BatchNorm2d: bn_flops_counter_hook, + nn.BatchNorm3d: bn_flops_counter_hook, + nn.GroupNorm: bn_flops_counter_hook, + # FC + nn.Linear: linear_flops_counter_hook, + # Upscale + nn.Upsample: upsample_flops_counter_hook, + # Deconvolution + nn.ConvTranspose1d: conv_flops_counter_hook, + nn.ConvTranspose2d: conv_flops_counter_hook, + nn.ConvTranspose3d: conv_flops_counter_hook, + ConvTranspose2d: conv_flops_counter_hook, + # RNN + nn.RNN: rnn_flops_counter_hook, + nn.GRU: rnn_flops_counter_hook, + nn.LSTM: rnn_flops_counter_hook, + nn.RNNCell: rnn_cell_flops_counter_hook, + nn.LSTMCell: rnn_cell_flops_counter_hook, + nn.GRUCell: rnn_cell_flops_counter_hook, +} + + +def is_supported_instance(module): + if ( + type(module) in MODULES_MAPPING + or type(module) in CUSTOM_MODULES_MAPPING + or getattr(module, "compute_macs", False) + ): + return True + return False + + +def remove_flops_counter_hook_function(module): + if is_supported_instance(module): + if hasattr(module, "__flops_handle__"): + module.__flops_handle__.remove() + del module.__flops_handle__ diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..86e4f1cdeda23173beba616a86304b529690b230 --- /dev/null +++ b/setup.py @@ -0,0 +1,71 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +#!/usr/bin/env python + +import glob +import os + +import torch +from setuptools import find_packages +from setuptools import setup +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import CppExtension +from torch.utils.cpp_extension import CUDAExtension + +requirements = ["torch", "torchvision"] + + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "maskrcnn_benchmark", "csrc") + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + + sources = main_file + source_cpu + extension = CppExtension + + extra_compile_args = {"cxx": []} + define_macros = [] + + if torch.cuda.is_available() and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + + sources = [os.path.join(extensions_dir, s) for s in sources] + + include_dirs = [extensions_dir] + + ext_modules = [ + extension( + "maskrcnn_benchmark._C", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + + return ext_modules + + +setup( + name="maskrcnn_benchmark", + description="object detection in pytorch", + packages=find_packages( + exclude=( + "configs", + "tests", + ) + ), + # install_requires=requirements, + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension.with_options(use_ninja=False)}, +) diff --git a/tools/.DS_Store b/tools/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..c883765fd0c9b9542e5b074d29441a76469004af Binary files /dev/null and b/tools/.DS_Store differ diff --git a/tools/files/.DS_Store b/tools/files/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..d900787e1b579c0cd6ae55d52a7a51f1b2fe450f Binary files /dev/null and b/tools/files/.DS_Store differ diff --git a/tools/files/llm_10K_noun_freq_mixed.json b/tools/files/llm_10K_noun_freq_mixed.json new file mode 100644 index 0000000000000000000000000000000000000000..f7d0a331d589fee819739c54e61814f1ee9d9285 --- /dev/null +++ b/tools/files/llm_10K_noun_freq_mixed.json @@ -0,0 +1 @@ +{"man": ["Yes. 'Man' has a tangible appearance and is a type of human being.\nA few things that are visually similar to 'man' but are not 'man' are:\twoman\tchild\telderly person\tcartoon character\nThere are several useful visual features to tell there is 'man' and not similar things in a photo:\thair and beard on face\tprominent jawline\tmuscular or athletic build\tfacial hair\tbody hair\tdressed in masculine clothing", 130119], "woman": ["Yes. 'Woman' has a tangible appearance and is a female human being.\nA few things that are visually similar to 'woman' but are not 'woman' are:\tmannequin\ttransgender person\tdrag queen\twax statue\nThere are several useful visual features to tell there is 'woman' and not similar things in a photo:\tsoft facial features\tbreasts on the chest\thourglass or pear-shaped body\tcurves on the hips and buttocks\tslimmer waistline\tlong hair or styled haircuts\tmakeup and accessories worn by women", 59721], "person": ["Yes. 'Person' has a tangible appearance and is a living being.\nA few things that are visually similar to 'person' but are not 'person' are:\tmannequin\tsculpture\tanimal with human-like features\nThere are several useful visual features to tell there is 'person' and not similar things in a photo:\ttwo legs, two arms, and a head\thuman-like facial features, such as eyes, nose, and mouth\tvariety of skin colors and textures\tnatural movement and gestures", 52037], "shirt": ["Yes. 'Shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'shirt' but are not 'shirt' are:\tblouse\tt-shirt\ttunic\t\nThere are several useful visual features to tell there is 'shirt' and not similar things in a photo:\tcollared or button-down front\twearable on the upper body\tlong sleeves or short sleeves\tpockets or no pockets", 33477], "sign": ["Yes. 'Sign' has a tangible appearance and is an object with words or symbols conveying information or instructions.\nA few things that are visually similar to 'sign' but are not 'sign' are:\tposters\tartwork\tadvertisements\tpaintings\nThere are several useful visual features to tell there is 'sign' and not similar things in a photo:\twords or symbols\tconveying information or instructions\tmounted or affixed to a surface\tclear and easy to read\tcolorful (depending on the type of sign)", 27835], "grass": ["Yes. 'Grass' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'grass' but are not 'grass' are:\tweeds\tmoss\tferns\talgae\nThere are several useful visual features to tell there is 'grass' and not similar things in a photo: \tlong, thin leaves\tgreen or brown color\tgrowing from the ground\tin tufts or patches", 26770], "table": ["Yes. 'Table' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'table' but are not 'table' are:\tdesk\tcounter\tbench\tshelf\nThere are several useful visual features to tell there is 'table' and not similar things in a photo:\ta flat surface\tlegs or a base to support it\tvariety of shapes and sizes\tchairs or seating around it", 24745], "wall": ["Yes. 'Wall' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'wall' but are not 'wall' are:\tfence\tpartition\tscreen\nThere are several useful visual features to tell there is 'wall' and not similar things in a photo:\tvertical and flat surface\tmade of brick, stone, concrete, or wood\tmay have texture, patterns or color variations\tmay have decorations or openings for doors and windows.", 23911], "water": ["Yes. 'Water' has a tangible appearance and can be seen as a liquid or in other forms.\nA few things that are visually similar to 'water' but are not 'water' are:\tmirrors\tglass\tice crystals\tgel or jello\nThere are several useful visual features to tell there is 'water' and not similar things in a photo:\tin a liquid state (flowing or still)\tsurface tension and waves\ttransparency or blue-green color\tglint or sparkles in the light\twhen frozen, forms a solid mass or appears cloudy.", 23792], "people": ["Yes. 'People' has a tangible appearance and typically refers to human beings.\nA few things that are visually similar to 'people' but are not 'people' are: statues, mannequins, animals standing upright, robots.\nThere are several useful visual features to tell there are 'people' and not similar things in a photo: skin color, hair, clothing, facial features, body shape, movement, and poses.", 23194], "plate": ["Yes. 'Plate' has a tangible appearance and is a flat dish for holding food. \nA few things that are visually similar to 'plate' but are not 'plate' are:\tplatter\tbowl\ttray\tplacemat\nThere are several useful visual features to tell there is 'plate' and not similar things in a photo:\tflat and circular or oval in shape\twith a raised edge to keep food from spilling\toutlines for dividing food into sections\tmade of ceramic, glass, or metal.", 22978], "sky": ["Yes. 'Sky' has a tangible appearance and refers to the expanse of space above the earth's surface where clouds, the sun, and other celestial objects can be seen.\nA few things that are visually similar to 'sky' but are not 'sky' are:\tocean\twalls\tpaintings\tdesk\nThere are several useful visual features to tell there is 'sky' and not similar things in a photo:\tblue or dark colors\tsun, moon or stars\tclusters of white or grey clouds\tdaytime or nighttime gradients of colors\topen space with no visible obstacles or boundaries", 22819], "building": ["Yes. 'Building' has a tangible appearance and is a physical structure.\nA few things that are visually similar to 'building' but are not 'building' are:\tbridge\ttower\twall\tpillar\nThere are several useful visual features to tell there is 'building' and not similar things in a photo:\troof\twindows\tand doors\twalls\tmultiple levels or stories\tstructural materials, such as bricks or concrete.", 21658], "dog": ["Yes. 'Dog' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'dog' but are not 'dog' are:\tcat\tfox\twolf\tcoyote\tfennec\nThere are several useful visual features to tell there is 'dog' and not similar things in a photo:\tfour-legged\tfurry body\tpointed ears\twagging tail\tsnout with a wet nose\tvariety of breeds and colors", 20206], "boy": ["Yes. 'Boy' has a tangible appearance and is a gender-specific term for a male child.\nA few things that are visually similar to 'boy' but are not 'boy' are:\tgirl\tadult man\tpuppet\tfigurine\nThere are several useful visual features to tell there is 'boy' and not similar things in a photo:\tshort height and stature compared to adults\tsmaller build generally than adult men\tshort hair or medium length hair with side part\tor no hair\tboyish clothes like t-shirts, jeans, and sneakers", 19966], "window": ["Yes. 'Window' has a tangible appearance and is a type of opening in a building that allows for light and air to come through.\nA few things that are visually similar to 'window' but are not 'window' are:\tpicture frame\tmirror\ttelevision\tscreen\nThere are several useful visual features to tell there is 'window' and not similar things in a photo:\tframe around the opening\tperspectives looking through the glass\ttransparency or translucency to let light pass through the opening", 19399], "tree": ["Yes. 'Tree' has a tangible appearance and is a perennial plant with a single stem or trunk, supporting branches and leaves.\nA few things that are visually similar to 'tree' but are not 'tree' are:\tbush\tshrub\tgrass\thedge\nThere are several useful visual features to tell there is 'tree' and not similar things in a photo:\tsingle stem or trunk\tsupporting branches and leaves\ttaller than a bush or a shrub\twooden bark\tat least several meters in height or taller.", 19280], "animal": ["Yes. 'Animal' has a tangible appearance and refers to living organisms that can move, breathe, and sense their surroundings.\nA few things that are visually similar to 'animal' but are not 'animal' are:\tplants\trocks\thuman-made objects\t\nThere are several useful visual features to distinguish 'animal' from similar things in a photo:\tlegs or appendages for movement\tdistinguishing body features, such as wings, horns, or fins\tfacial features, such as eyes, nose, and mouth\tthe ability to move on their own and interact with the environment.", 18802], "trees": ["Yes. 'Trees' has a tangible and distinctive appearance.\nA few things that are visually similar to 'trees' but are not 'trees' are:\tbushes\tshrubs\tgrass\nThere are several useful visual features to tell there is 'trees' and not similar things in a photo:\twoody\ttall with a single trunk or several branches\tcrown of leaves\tor needles that grow in either an upward or downward orientation, alternate or opposite on the branch", 18707], "girl": ["Yes. 'Girl' has a tangible appearance and is a young female human.\nA few things that are visually similar to 'girl' but are not 'girl' are:\tboy\twoman\tfemale animal\nThere are several useful visual features to tell there is 'girl' and not similar things in a photo:\tshorter height\tless muscular body\tslender frame\tsmaller hands and feet\tfeminine facial features, such as delicate jawline and fuller lips", 18145], "fence": ["Yes. 'Fence' has a tangible appearance and is a physical barrier.\nA few things that are visually similar to 'fence' but are not 'fence' are:\twall\thedge\tline of trees\trope barrier\nThere are several useful visual features to tell there is 'fence' and not similar things in a photo:\tseries of vertical or horizontal posts\twith a continuous surface\tto indicate property or mark a boundary\tcan have pickets, boards, wires, or rails", 17244], "hair": ["Yes. 'Hair' has a tangible appearance and grows on the body of humans and animals.\nA few things that are visually similar to 'hair' but are not 'hair' are:\tfur, feathers, scales, leaves\nThere are several useful visual features to tell there is 'hair' and distinguish it from similar things in a photo:\tthinner and more flexible than fur\tdoes not cover the whole body like feathers or scales\tis attached to the skin and grows from roots in the scalp or body of animals or humans\tis usually styled or cut in a variety of ways.", 17215], "car": ["Yes. 'Car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'car' but are not 'car' are:\ttruck\tmotorcycle\tbicycle\tscooter\nThere are several useful visual features to tell there is 'car' and not similar things in a photo:\tfour wheels\telectric, gas, or diesel engine\tenclosed passenger compartment and trunk\tdoors and windows\trear-view mirrors and windshield\twindshield wipers and headlights\tgrill and bumper", 17115], "part": ["No. 'Part' is too vague or abstract to be distinguished in a photo.", 16852], "cat": ["Yes. 'Cat' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'cat' but are not 'cat' are:\tlynx\tleopard\tlion\ttiger\tbobcat\tocelot\tcheetah\nThere are several useful visual features to tell there is 'cat' and not similar things in a photo:\tsmall to medium size\tprick ears, whiskers, pointy noses\tsharp claws\tretractable claws\tfurry coat in various colors\tslit-shaped pupils", 16822], "train": ["Yes. 'Train' has a tangible appearance and is a type of transportation.\nA few things that are visually similar to 'train' but are not 'train' are:\tbus\tsubway\ttractor\ttruck\nThere are several useful visual features to tell there is a 'train' and not similar things in a photo:\tlong, connected carriages on metal tracks\tsmokestack\tlong nose and big headlights\tonboard cables and wires\tpassenger or cargo carriages.", 16670], "chair": ["Yes. 'Chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'chair' but are not 'chair' are:\tstool\tbench\tcouch\tottoman\nThere are several useful visual features to tell there is 'chair' and not similar things in a photo:\tbackrest\tarmrests\tseat\tcapable of supporting a person's weight\tand legs to keep the seat off the ground.", 15511], "head": ["Yes. 'Head' has a tangible appearance and is a visible part of the body.\nA few things that are visually similar to 'head' but are not 'head' are:\thelmet\tball\tmask\tsculpture\nThere are several useful visual features to tell there is a 'head' and not similar things in a photo:\tfacial features such as eyes, nose, mouth\tears\thair\ttop of the neck and bottom of the chin\thead shape and size", 14960], "ground": ["Yes. 'Ground' has a tangible appearance and refers to the surface of the earth or land.\nA few things that are visually similar to 'ground' but are not 'ground' are:\twall\tpavement\tfloor\tceiling\nThere are several useful visual features to tell there is 'ground' and not similar things in a photo:\thorizontal surface\tcovered in grass, dirt, sand, or other natural materials\tunobstructed view of the surrounding area\torienteering markers or stakes to indicate a route or trail", 14600], "pole": ["Yes. 'Pole' has a tangible appearance as it is a long, cylindrical object.\nA few things that are visually similar to 'pole' but are not 'pole' are:\trod\tstick\tmast\tpillar\nThere are several useful visual features to tell there is 'pole' and not similar things in a photo:\tlong and cylindrical shape\tslim and straight surface\ttop and bottom ends are usually tapered or thin\tMounted vertically on the ground, wall, or another surface.", 14491], "bus": ["Yes. 'Bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'bus' but are not 'bus' are:\ttruck\tvan\tambulance\tjeep\nThere are several useful visual features to tell there is 'bus' and not similar things in a photo:\tlarge size\tlong body\tmany windows\ttwo-axle or three-axle\tlayout of seats\tsymbol or name of a bus company on the side of the vehicle", 14383], "food": ["Yes. 'Food' has a tangible appearance and is something that is eaten to provide nutrition.\nA few things that are visually similar to 'food' but are not 'food' are:\tdecorative items\tpotpourri\tartificial fruits and vegetables\tplastic toys\nThere are several useful visual features to tell there is 'food' and not similar things in a photo:\tvarious colors, textures, and shapes\tsome may be packaged or covered in wrappers\tor plated and served on a dish or bowl\tmay be accompanied by utensils, such as forks, spoons, or knives", 13657], "vehicle": ["Yes. 'Vehicle' has a tangible appearance and refers to a mode of transportation.\nA few things that are visually similar to 'vehicle' but are not 'vehicle' are:\tbicycle\tscooter\troller skates\twheelchair\nThere are several useful visual features to tell there is 'vehicle' and not similar things in a photo:\tengine\tor motor\tfour or more wheels\tdoors and windows\tforward-facing seats\tor driver's seat\tand steering wheel\tfor ground, water, or air transportation", 13523], "hand": ["Yes. 'Hand' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'hand' but are not 'hand' are:\tpaw\tclaw\thoof\twebbed Feet\nThere are several useful visual features to tell there is 'hand' and not similar things in a photo:\tfive digits or fingers\tpalm and back of the hand\twrinkles or lines on the skin\tjoints and nails", 13091], "giraffe": ["Yes. 'Giraffe' has a tangible appearance and is a tall, long-necked mammal.\nA few things that are visually similar to 'giraffe' but are not 'giraffe' are:\tzebra\tokapi\telongated neck sculptures\nThere are several useful visual features to tell there is 'giraffe' and not similar things in a photo:\tlong neck\tsmall horns\tunique spotted pattern on their fur\tbrownish-yellow or reddish-brown coat", 12537], "door": ["Yes. 'Door' has a tangible appearance and is a type of opening in a wall or a structure.\nA few things that are visually similar to 'door' but are not 'door' are:\twindow\thatch\tgate\nThere are several useful visual features to tell there is 'door' and not similar things in a photo:\trectangle or square shape\thinges\thandle or knob\ton a wall or structure\tthat can be opened and closed", 12415], "bear": ["Yes. 'Bear' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'bear' but are not 'bear' are:\tdog\tcat\tbigfoot\tyeti\nThere are several useful visual features to tell there is 'bear' and not similar things in a photo:\thuge body size\tshaggy fur\tpointy snouts\tshort tails\tcurved claws\tv-shaped ears\ttop-heavy body shape\tprominent shoulder humps(lowland gorilla)\tbulky and muscular appearance (grizzly bear)\tsleek and streamlined shape (polar bear)", 12317], "horse": ["Yes. 'Horse' has a tangible appearance and is a type of four-legged mammal.\nA few things that are visually similar to 'horse' but are not 'horse' are:\tzebra\tdonkey\tgiraffe\tantelope\nThere are several useful visual features to tell there is 'horse' and not similar things in a photo:\tlarge body with muscular legs\tand a long tail\thair on the tail\tthat is beautiful and long\tmane and tail can be different colors from the horse's coat\tcolors that are commonly brown or black", 12237], "light": ["Yes. 'Light' has a tangible appearance and can be seen in photos.\nA few things that are visually similar to 'light' but are not 'light' are:\treflection\tfire\tdust\tparticles\nThere are several useful visual features to tell there is 'light' and not similar things in a photo:\temitting light source\tbright and luminous\tfaint or bright in color\tcasting shadows or reflections", 11982], "plane": ["Yes. 'Plane' has a tangible appearance and is a type of flying object.\nA few things that are visually similar to 'plane' but are not 'plane' are:\tbird\thelicopter\tdrone\tinsect\nThere are several useful visual features to tell there is 'plane' and not similar things in a photo:\tfixed wings\tengine(s) or propulsion system\tcockpit\twith or without tail\tno visible legs or appendages\texhaust trail or contrail\tif commercial plane, a company logo is visible on the tail", 11528], "bag": ["Yes. 'Bag' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'bag' but are not 'bag' are:\tbox\tpurse\tbackpack\tbasket\tsuitcase\tenvelope\t\nThere are several useful visual features to tell there is 'bag' and not similar things in a photo:\tfabric or leather material\twith handles or straps\tzipper, clasps or a closure at the top\tof varying sizes and shapes.", 11293], "snow": ["Yes. 'Snow' has a tangible appearance and is a form of precipitation.\nA few things that are visually similar to 'snow' but are not 'snow' are:\twhite sand\tcotton\tfog\tfoam\nThere are several useful visual features to tell there is 'snow' and not similar things in a photo:\twhite, powdery flakes\tcold environment\tlots of snow covering the ground or other surfaces\tmelting or dripping when it starts to warm up", 11257], "pants": ["Yes. 'Pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pants' but are not 'pants' are:\tskirts\tshorts\ttights\tleggings\nThere are several useful visual features to tell there is 'pants' and not similar things in a photo:\tseparate coverings for each leg\twith or without pockets\tzippers or buttons at the front or sides\tbelt loops around the waist", 10837], "hat": ["Yes. 'Hat' has a tangible appearance and is a type of accessory worn on the head.\nA few things that are visually similar to 'hat' but are not 'hat' are:\thelmet\tcap\tturban\theadband\tbeanie\nThere are several useful visual features to tell there is 'hat' and not similar things in a photo:\tbrim or bill\tformed crown or top\tunique style or design\tmade from fabric, leather or other materials\tsized to fit head correctly", 10778], "bench": ["Yes. 'Bench' has a tangible appearance and is a type of sitting furniture.\nA few things that are visually similar to 'bench' but are not 'bench' are: chair sofa stool ottoman\nThere are several useful visual features to tell there is 'bench' and not similar things in a photo: flat and elongated seat supported by legs may or may not have a backrest", 10729], "clock": ["Yes. 'Clock' has a tangible appearance and is a type of timepiece.\nA few things that are visually similar to 'clock' but are not 'clock' are:\twatch\twall decor\tcompass\tsundial\nThere are several useful visual features to tell there is 'clock' and not similar things in a photo:\tcircular or rectangular shape\tnumbers or symbols to indicate hours and minutes\thands or pointers to show time\ton a stand or mounted on a wall may have sound", 10719], "elephant": ["Yes. 'Elephant' has a tangible appearance and is an animal.\nA few things that are visually similar to 'elephant' but are not 'elephant' are:\thippopotamus\trhino\tbuffalo\t mammoth\nThere are several useful visual features to tell there is 'elephant' and not similar things in a photo:\tlong trunk\tbig tusks (in males)\tlarge, floppy ears\tgray skin\twith a short tail", 10655], "pizza": ["Yes. 'Pizza' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'pizza' but are not 'pizza' are:\tfocaccia\tbread\tcake\tpie\nThere are several useful visual features to tell there is 'pizza' and not similar things in a photo:\tcircular or rectangular shape\ttomato sauce\tcheese and toppings\tslices cut in pieces.", 10521], "floor": ["Yes. 'Floor' has a tangible appearance and refers to the surface of a room or building.\nA few things that are visually similar to 'floor' but are not 'floor' are:\tcarpet\trug\ttile\tmat\nThere are several useful visual features to tell there is 'floor' and not similar things in a photo:\tlevel surface that people walk on\tcan be made of wood, concrete, or other materials\tmay have patterns or designs, but generally flat and plain in appearance\tmay have objects on top of it, such as furniture or rugs.", 10430], "furniture": ["Yes. 'Furniture' has a tangible appearance and refers to items used to make a room comfortable and functional.\nA few things that are visually similar to 'furniture' but are not 'furniture' are:\tdecorations\tpillows\trugs\tblinds\nThere are several useful visual features to tell there is 'furniture' and not similar things in a photo:\tdesigned to sit or lie on\tdesigned for storage or organization\tmade of wood, metal, plastic, or fabric\tusually found indoors or in a specific space, like a bedroom or living room\thas specific functions, like sitting or sleeping.", 10188], "blue": ["Yes. 'Blue' has a tangible appearance and is a distinct color on the visible spectrum.\nA few things that are visually similar to 'blue' but are not 'blue' are:\tpurple\taqua\tturquoise\tnavy\nThere are several useful visual features to tell there is 'blue' and not similar things in a photo:\tdistinct color hue\trecognizable shade of blue", 10118], "device": ["No. 'Device' is too vague or abstract to be distinguished in a photo.", 10107], "bird": ["Yes. 'Bird' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'bird' but are not 'bird' are:\tplane\tbat\tpaper airplane\tdragonfly\nThere are several useful visual features to tell there is 'bird' and not similar things in a photo:\tcovered in feathers\thas wings and a beak\ttwo legs and two wings (or two arms)\tflying in the air (or capable of flying)\thas a tail (usually)\texhibits behaviors that are common for birds (e.g., perching, flapping wings)", 10027], "truck": ["Yes. 'Truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'truck' but are not 'truck' are:\tvan\tbus\ttractor\ttrailer\nThere are several useful visual features to tell there is 'truck' and not similar things in a photo:\theavy four-wheeled vehicle\twith an enclosed cab or cargo area\tlarge flatbed for carrying goods or materials\ttaller than a regular car or sedan\tusually painted in solid colors or with a logo\ton the back of the vehicle there may be doors, a tailgate, or a liftgate.", 9951], "jacket": ["Yes. 'Jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'jacket' but are not 'jacket' are:\tsweater\thoodie\tshirt\tblouse\t\nThere are several useful visual features to tell there is 'jacket' and not similar things in a photo:\touterwear designed to cover the torso\tzips or buttons up\tthe sleeves are separate from the body of the jacket\tmay have a collar and pockets", 9854], "zebra": ["Yes. 'Zebra' has a tangible appearance and is a kind of animal.\nA few things that are visually similar to 'zebra' but are not 'zebra' are:\thorse\tdonkey\tcow\nThere are several useful visual features to tell there is 'zebra' and not similar things in a photo:\tblack and white striped pattern\tlarge and sturdy build\twither mane and tail\tnarrow head and muzzle\tno hump or dewlap on the neck", 9796], "helmet": ["Yes. 'Helmet' has a tangible appearance and is a type of headgear.\nA few things that are visually similar to 'helmet' but are not 'helmet' are:\tHat\tCap\tBeanie\tHelmet-shaped decorative object\nThere are several useful visual features to tell there is 'helmet' and not similar things in a photo:\thard, protective outer layer\tcovers and protects the head and sometimes the neck\tusually has straps to secure it in place\treinforced areas for impact protection, such as a visor or padding.", 9679], "boat": ["Yes. 'Boat' has a tangible appearance and is a watercraft designed to navigate, float or travel on water.\nA few things that are visually similar to 'boat' but are not 'boat' are:\traft\tkayak\tcanoe\tferry\tyacht\nThere are several useful visual features to tell there is 'boat' and not similar things in a photo:\thull or body that floats on water\tmovable rudder or steering mechanism\toars or propulsion device to move through water\tseating or standing areas for passengers or crew", 9640], "umbrella": ["Yes. 'Umbrella' has a tangible appearance and is a type of device used for protection against rain or sunlight.\nA few things that are visually similar to 'umbrella' but are not 'umbrella' are:\ttent\tcanopy\tshade\tsunhat\t\nThere are several useful visual features to tell there is 'umbrella' and not similar things in a photo:\tCan be folded and carried by hand\tcircular or semi-circular shape\thas a pole or handle for support\tmade of waterproof or UV-resistant material", 9483], "bowl": ["Yes. 'Bowl' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'bowl' but are not 'bowl' are:\tplate\tcan\tmug\tpot\nThere are several useful visual features to tell there is 'bowl' and not similar things in a photo:\thollow in shape\trounded at the bottom\twide opening\tlarger than a cup or a mug, but not as large as a pot.", 9448], "windows": ["Yes. 'Windows' has a tangible appearance and is an architectural element.\nA few things that are visually similar to 'windows' but are not 'windows' are:\tpaintings\tframes\tmirrors\tdoors\nThere are several useful visual features to tell there are 'windows' and not similar things in a photo:\ttransparent or translucent rectangular shapes\tframes\tthat permit light and air to enter a building\tor keep it out, with shutters or blinds\twall-mounted, usually horizontal\tlocation and shape vary depending on architectural style", 9332], "road": ["Yes. 'Road' has a tangible appearance and is a type of pathway used for transportation.\nA few things that are visually similar to 'road' but are not 'road' are:\tsidewalk\ttrail\tpath\tparking lot\trunway\nThere are several useful visual features to tell there is 'road' and not similar things in a photo:\tdesigned for vehicles to travel on\toften marked with lines or arrows\tasphalt or concrete surface\tsigns or traffic signals present in the surroundings\tsurrounded by buildings, trees, or other structures", 9238], "leaves": ["Yes. 'Leaves' has a tangible appearance and is often found on plants and trees.\nA few things that are visually similar to 'leaves' but are not 'leaves' are:\tgrass\tpetals\tseeds\tfeathers\nThere are several useful visual features to tell there are 'leaves' and not similar things in a photo:\tattached to stems or branches\tveins on the leaf\tserrated or smooth edges varying shades of green, yellow, orange, or red depending on season.", 9017], "clouds": ["Yes. 'Clouds' have a tangible appearance and are visible natural phenomena.\nA few things that are visually similar to 'clouds' but are not 'clouds' are:\tsmoke\tfog\tdust\tpollen\tsteam\t\nThere are several useful visual features to tell there are 'clouds' and not similar things in a photo:\tpuffy or fluffy appearance\twhite, gray or black color\tvarious shapes like cumulus, cirrus or stratus\tmoving in the sky or obscuring the sun or the moon", 8839], "street": ["Yes. 'Street' has a tangible appearance and is a public road in a city, town, or village.\nA few things that are visually similar to 'street' but are not 'street' are: sidewalk, park trail, bike path, hiking trail, dirt road, parking lot\nThere are several useful visual features to tell there is 'street' and not similar things in a photo:\troad markings such as lines and arrows\tcars, buses, or other vehicles\tpedestrians and street signs\tsurrounded by buildings\tor street lights", 8730], "ear": ["Yes. 'Ear' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'ear' but are not 'ear' are:\tflower\tpetal\tshell\nThere are several useful visual features to tell there is 'ear' and not similar things in a photo:\tlocated on the side of the head\tcurved shape\tvisible opening for sound to enter", 8691], "tail": ["Yes. 'Tail' has a tangible appearance and is a body part of animals.\nA few things that are visually similar to 'tail' but are not 'tail' are:\ttrunk\tbranch\those\tpipe\tcord\nThere are several useful visual features to tell there is 'tail' and not similar things in a photo:\tattached to an animal's body\thair, fur or scale-covered\tvaries in length and shape (depending on the animal species)", 8486], "bottle": ["Yes. 'Bottle' has a tangible appearance and is a container used to store liquids.\nA few things that are visually similar to 'bottle' but are not 'bottle' are:\tjar\tvase\tcan\tthermos\nThere are several useful visual features to tell there is 'bottle' and not similar things in a photo:\tnarrow neck or spout\tcylindrical or rounded shape\ttransparency or translucency\tcap or cork on the top", 8173], "shorts": ["Yes. 'Shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'shorts' but are not 'shorts' are:\tskirt\tpants\tunderwear\tswimsuit\nThere are several useful visual features to tell there are 'shorts' and not similar things in a photo:\tshort length (above the knee)\ttwo leg openings\twaistband and zipper or elastic opening\tfor warmer weather", 7908], "field": ["Yes. 'Field' has a tangible appearance and refers to an open stretch of land.\nA few things that are visually similar to 'field' but are not 'field' are:\tmeadow\tpasture\tprairie\tplain\nThere are several useful visual features to tell there is 'field' and not similar things in a photo:\tplots of land with crops, plants or grasses\tgenerally flat or gently sloping ground\tusually surrounded by trees, bushes or other barriers", 7729], "bed": ["Yes. 'Bed' has a tangible appearance and is a piece of furniture used for sleeping.\nA few things that are visually similar to 'bed' but are not 'bed' are:\tcouch\tfuton\tmattress\tchair\nThere are several useful visual features to tell there is 'bed' and not similar things in a photo:\ta raised platform with a horizontal surface for sleeping\tmattress with pillows and blankets\theadboard and footboard (optionally)\tside rails and bedposts (optionally)", 7708], "motorcycle": ["Yes. 'Motorcycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motorcycle' but are not 'motorcycle' are:\tbicycle\tscooter\tmoped\ttricycle\nThere are several useful visual features to tell there is 'motorcycle' and not similar things in a photo:\ttwo wheels\tengine\thandlebars\tsaddle or seat\tframe and body design", 7540], "cup": ["Yes. 'Cup' has a tangible appearance and is a common object used for drinking.\nA few things that are visually similar to 'cup' but are not 'cup' are:\tglass\tbottle\tmug\tjar\tbowl\nThere are several useful visual features to tell there is 'cup' and not similar things in a photo:\thandles or no handles\tusually made of ceramic, porcelain, or plastic\tround or cylindrical shape\toften with a saucer or a lid\tfor drinking, not for pouring or storing liquid", 7425], "picture": ["Yes. 'Picture' has a tangible appearance and refers to a visual representation of something.\nA few things that are visually similar to 'picture' but are not 'picture' are:\tmirror\treflection\twindow\tscreen\nThere are several useful visual features to tell there is 'picture' and not similar things in a photo:\tframed or mounted on a wall\tpaint, brushstrokes, or pixels representing a scene, object, or person\tmay contain text, but the visual representation is the focus of the image", 7381], "eye": ["Yes. 'Eye' has a tangible appearance and is a part of the human or animal body.\nA few things that are visually similar to 'eye' but are not 'eye' are:\tball\tmarble\tbutton\tpebble\nThere are several useful visual features to tell there is 'eye' and not similar things in a photo:\torb-shaped\tobject surrounded by eyelids or eye sockets\twith a visible pupil and iris\tvarious colors and hues, depending on the species\tand with eyelashes (in case of human eye)", 7379], "pair": ["No. 'Pair' is too vague or abstract to be visually distinguished in a photo. It refers to a set of two similar or complementary items. \nThere are no things visually similar to 'pair' but not 'pair'.", 7271], "cow": ["Yes. 'Cow' has a tangible appearance and is a kind of animal.\nA few things that are visually similar to 'cow' but are not 'cow' are:\thorse, deer, sheep, goat, bison\nThere are several useful visual features to tell there is 'cow' and not similar things in a photo:\tbroad, flat nose\trounded ears\tcud-chewing animal\thorns, if they are present\tat least two noticeable teats\ton average, cow has spots or patches of color, usually black and white or brown and white.", 7128], "glass": ["Yes. 'Glass' has a tangible appearance and is a material.\nA few things that are visually similar to 'glass' but are not 'glass' are:\tcrystals\tice\tdiamonds\tplastic\t\nThere are several useful visual features to tell there is 'glass' and not similar things in a photo:\ttranslucent or transparent material\treflective surface\thard and brittle material\tlight passes through the object clearly\tand can be used to contain liquid or preserve items.", 6964], "glasses": ["Yes. 'Glasses' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'glasses' but are not 'glasses' are:\tsunglasses\tbinoculars\tmonocle\ttelescope\nThere are several useful visual features to tell there is 'glasses' and not similar things in a photo:\ttypically sits on the nose\tframe with arms to hold the lenses\tupturned or downturned edges where the arms meet the lenses\tlenses made of clear or prescription glass or plastic.", 6907], "line": ["Yes. 'Line' has a tangible appearance and is a type of mark or shape that has length but no width.\nA few things that are visually similar to 'line' but are not 'line' are:\trectangle\tcircle\ttriangle\nThere are no visual features to distinguish between a line, a rectangle, a circle, or a triangle because they all have specific shapes and appearances. However, lines can be distinguished by their length, orientation, and thickness. For example, a vertical line is longer in height than width, a diagonal line leans at a specific angle, and a thick line appears more bold and visible.", 6873], "surfboard": ["Yes, 'surfboard' has a tangible appearance and is a type of sports equipment used in surfing.\nA few things that are visually similar to 'surfboard' but are not 'surfboard' are:\tpaddleboard\twakeboard\tkayak\tbodyboard\nThere are several useful visual features to tell there is 'surfboard' and not similar things in a photo:\tlong and narrow shape\tcurved at the nose and tail\tpointed tip\tdecorative artwork or branding on the board\tfloats on the water", 6790], "skateboard": ["Yes. 'Skateboard' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'skateboard' but are not 'skateboard' are:\tsurfboard\tlongboard\tsnowboard\tbicycle\tboard game pieces\nThere are several useful visual features to tell there is 'skateboard' and not similar things in a photo:\trectangular shape with rounded edges and nose and tail\tcustomizable graphics on the bottom\tfour small wheels with trucks connecting them\tgriptape on the top to provide traction for the rider", 6528], "kite": ["Yes. 'Kite' has a tangible appearance and is a toy that can fly in the air.\nA few things that are visually similar to 'kite' but are not 'kite' are:\tflags\tstreamers\tballoons\nThere are several useful visual features to tell there is 'kite' and not similar things in a photo:\tdiamond or triangle shape\ttail or ribbons\ttogether with a tether to control the direction\tand lines attached to control the elevation\tand made from thin and light materials such as paper or plastic", 6454], "sheep": ["Yes. 'Sheep' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'sheep' but are not 'sheep' are:\tgoats\tcows\tdeers\talpacas\nThere are several useful visual features to tell there is 'sheep' and not similar things in a photo:\tfour-legged mammal\tcurly wooly or fluffy fur\tlong droopy ears\tnarrow, pointed snout\ttail pointing downwards\tsheared wool (in some cases)", 6429], "lady": ["No. 'Lady' is too vague or abstract to be distinguished in a photo. However, the concept of a 'lady' may include certain visual cues or characteristics such as clothing, hair, makeup, or behavior, but these may differ depending on cultural or personal perspectives. \n\nIt is not appropriate to name things that are similar to 'lady' as it objectifies and degrades individuals based on their gender identity.", 6398], "toilet": ["Yes. 'Toilet' has a tangible appearance and is a kind of fixture used for sanitation.\nA few things that are visually similar to 'toilet' but are not 'toilet' are:\tsink\tbathtub\tshower\turinal\nThere are several useful visual features to tell there is 'toilet' and not similar things in a photo:\tbowl-shaped fixture\twith a seat or a lid\tfor flushing waste\twater tank or a flushometer\tbutton, lever, or chain to operate the flush", 6370], "flowers": ["Yes. 'Flowers' has a tangible appearance and refers to the reproductive part of a plant.\nA few things that are visually similar to 'flowers' but are not 'flowers' are:\tleaves\tbutterflies\tsnowflakes\tfireworks\nThere are several useful visual features to tell there is 'flowers' and not similar things in a photo:\tpetals arranged in a circular shape\tbrightly colored (pink, red, yellow, purple, etc)\thave a distinct fragrance\tpollen in the center of the flower\topened or bloomed shape.", 6314], "sidewalk": ["Yes. 'Sidewalk' has a tangible appearance and is a type of pedestrian walkway.\nA few things that are visually similar to 'sidewalk' but are not 'sidewalk' are:\tdriveway\tpatio\tparking lot\nThere are several useful visual features to tell there is 'sidewalk' and not similar things in a photo:\tlocated at the side of a street\tsmooth flat surface\toften made of concrete or pavement\traised from road level\tcurb or gutter on one or both sides", 6311], "child": ["Yes. 'Child' has a tangible appearance and is a young human.\nA few things that are visually similar to 'child' but are not 'child' are:\tadults\tdwarfs\tmidgets\tteenagers\t\nThere are several useful visual features to distinguish 'child' from the listed similar things in a photo:\tsmaller in size (usually less than 4 feet tall)\tshorter limbs compared to the torso\tsofter facial features, including larger eyes and a smaller nose and mouth\tplayful or innocent facial expressions\tlighter or brighter clothing compared to adults", 6284], "men": ["Yes. 'Men' has a tangible appearance and refers to adult human males.\nA few things that are visually similar to 'men' but are not 'men' are:\twomen\tchildren\tdolls\twax figures\nThere are several useful visual features to tell there is 'men' and not similar things in a photo:\thaving facial hair (beard or mustache)\tmuscular physique\tdeep voice\tshort hair, often parted to the side\twearing men's clothing (like a suit)", 6276], "box": ["Yes. 'Box' has a tangible appearance and is a container made of cardboard, paper, plastic, or wood.\nA few things that are visually similar to 'box' but are not 'box' are:\tenvelope\tbag\tcrate\tsuitcase\nThere are several useful visual features to tell there is 'box' and not similar things in a photo:\trectangular or square shape\twith or without a lid\tsides, top, and bottom made of cardboard, paper, plastic, or wood\tcan be unfolded or flattened out\tif opened, may contain something inside", 6273], "laptop": ["Yes. 'Laptop' has a tangible appearance and is a type of computer.\nA few things that are visually similar to 'laptop' but are not 'laptop' are:\ttablet\tsmartphone\tnotepad\tbinder\nThere are several useful visual features to tell there is 'laptop' and not similar things in a photo:\thinged screen and keyboard\tlarge rectangular screen\torbit touchpad\ttypically has a logo of the manufacturer on the lid\tthe screen lid typically can be opened or closed to change the angle of the screen", 6215], "mirror": ["Yes. 'Mirror' has a tangible appearance and is a reflective object.\nA few things that are visually similar to 'mirror' but are not 'mirror' are:\twindow\tpuddle\tphone screen\nThere are several useful visual features to tell there is 'mirror' and not similar things in a photo:\trectangular shape\tflat and smooth surface\tclear and reflective appearance\timage reflects in the surface", 6135], "airplane": ["Yes. 'Airplane' has a tangible appearance and is a type of flying machine.\nA few things that are visually similar to 'airplane' but are not 'airplane' are:\thelicopter\tbird\trocket\tglider\nThere are several useful visual features to tell there is 'airplane' and not similar things in a photo:\tfixed wings\tengines (or turbojet pods)\tat least one cockpit\tvertical stabilizer (or a vertical fin)\thorizontal stabilizer (or a horizontal tail)\ta fuselage (or body) for crew and/or cargo\thigh speed and altitude", 6064], "bike": ["Yes. 'Bike' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'bike' but are not 'bike' are:\tmotorcycle\tscooter\tskateboard\troller skates\nThere are several useful visual features to tell there is 'bike' and not similar things in a photo:\ttwo wheels\tpedals\thandlebars\tforward-leaning frame and riding position\tsaddle or seat on top of the frame", 5953], "leg": ["Yes. 'Leg' has a tangible appearance and is a human or animal body part.\nA few things that are visually similar to 'leg' but are not 'leg' are:\ttree\ttrunk\tpillar\tchair\tbranch\tcrutch\nThere are several useful visual features to tell there is 'leg' and not similar things in a photo:\tattached to a torso\tor a hip\thas a knee and an ankle\tjointed\thas toes or claws\tmuscular and fleshy", 5902], "number": ["No. 'Number' is too vague or abstract to be distinguished in a photo.", 5779], "edge": ["Yes. 'Edge' has a tangible appearance and refers to the boundary or limit of something.\nA few things that are visually similar to 'edge' but are not 'edge' are:\tline\tborder\trim\tmargin\nThere are several useful visual features to tell there is 'edge' and not similar things in a photo:\tthe location where one surface ends and another surface begins\tthe point where the two surfaces meet\tor abrupt or gradual change in textures or colors\tsignificant enough to define the two surfaces meet", 5726], "shadow": ["Yes. 'Shadow' has a tangible appearance and is a dark area or shape produced by something that blocks light.\nA few things that are visually similar to 'shadow' but are not 'shadow' are:\treflection\tdark-colored object\tsilhouette\t\nThere are several useful visual features to tell there is 'shadow' and not similar things in a photo:\tproduced by the blocking of light\tdark area or shape on a surface\tthe outline of the object causing the shadow is visible\tthe size and shape of the shadow changes with the position of the light source", 5659], "face": ["Yes. 'Face' has a tangible appearance and is the front part of a person's head where the eyes, nose, and mouth are located.\nA few things that are visually similar to 'face' but are not 'face' are:\tmask\tsculpture\tportrait\tdoll\nThere are several useful visual features to tell there is a 'face' and not similar things in a photo:\tpairs of eyes\tnose\tmouth\tfacial hair or makeup\tfacial expressions\tfacial contours and bone structure.", 5619], "shoes": ["Yes. 'Shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'shoes' but are not 'shoes' are:\tboots\tsandals\tslippers\tsocks\nThere are several useful visual features to tell there are 'shoes' and not similar things in a photo:\tclosed-toe or open-toe design\twith or without laces/straps\tmade of leather, fabric, or other materials\tsoles and heels for grip and support\tvarious colors and patterns.", 5598], "vase": ["Yes. 'Vase' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'vase' but are not 'vase' are:\tjar\turn\tbottle\tpot\tcup\nThere are several useful visual features to tell there is 'vase' and not similar things in a photo:\tusually made of glass or ceramic\ttall and narrow at the neck\twider at the base\tmay have decorative patterns or shapes\ton a flat surface, like a table or the ground, rather than hanging or being held", 5579], "group": ["No. 'Group' is too vague or abstract to be distinguished in a photo.", 5554], "brown": ["Yes. 'Brown' has a tangible appearance and is a color.\nA few things that are visually similar to 'brown' but are not 'brown' are:\tbeige\ttan\tsepia\nThere are no visual features for distinguishing between different shades of brown, but brown can be distinguished from other colors based on its specific hue and saturation.", 5539], "piece": ["No. 'Piece' is too vague or abstract to be distinguished in a photo.", 5320], "arm": ["Yes. 'Arm' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'arm' but are not 'arm' are:\tleg\tcrooked tree branch\tsnake tail\nThere are several useful visual features to tell there is 'arm' and not similar things in a photo:\tattached to the shoulder\tjoint at the elbow\tbent at the wrist\thand at the end\tfingers with nails", 5302], "pillow": ["Yes. 'Pillow' has a tangible appearance and is an object used for comfort and support.\nA few things that are visually similar to 'pillow' but are not 'pillow' are:\tcushion\tstuffed animal\tbolster\tpouf\nThere are several useful visual features to tell there is 'pillow' and not similar things in a photo:\trectangular or square shape\tsoft and fluffy texture\tsolid color or patterned fabric\tthat it is placed on a bed or couch", 5298], "lamp": ["Yes. 'Lamp' has a tangible appearance as an object that provides light.\nA few things that are visually similar to 'lamp' but are not 'lamp' are:\tflashlight\tcandle\tlightbulb\tchandelier\nThere are several useful visual features to tell there is 'lamp' and not similar things in a photo:\ta visible lampshade\ta stand or a base\tswitch or a cord\ttwo or more lightbulbs", 5223], "blue sky": ["Yes. 'Blue sky' has a tangible appearance and is a natural atmospheric phenomenon.\nA few things that are visually similar to 'blue sky' but are not 'blue sky' are:\tblue painting\tblue curtain\t\nThere are no useful visual features to distinguish 'blue sky' from the listed similar things in a photo, except the context of the image.", 5151], "house": ["Yes. 'House' has a tangible appearance and is a kind of building where people live.\nA few things that are visually similar to 'house' but are not 'house' are:\tapartment\tbuilding\toffice\tstore\tbarn\nThere are several useful visual features to tell there is 'house' and not similar things in a photo:\tsingle-family dwelling\twith a front door and a chimney\tmostly made of wood or brick\ta pitched roof with shingles or tiles\twindows and shutters\ta small yard or garden in front of or behind it.", 5142], "trunk": ["Yes. 'Trunk' has a tangible appearance and refers to the main stem of a tree, or a large, strong case or box.\nA few things that are visually similar to 'trunk' but are not 'trunk' are:\tsuitcase\tbox\tchest\tbarrel\nThere are several useful visual features to tell there is 'trunk' and not similar things in a photo:\n- Bark on the outside if referring to a tree trunk\n- Distinct ridges or rings if referring to a tree trunk\n- Branches or leaves attached if referring to a tree trunk\n- Square or rectangular shape if referring to a storage trunk\n- Hinges, latches or locks if referring to a storage trunk", 5134], "frisbee": ["Yes. 'Frisbee' has a tangible appearance and is a kind of plastic disc used for playing sports.\nA few things that are visually similar to 'frisbee' but are not 'frisbee' are:\tpizza\tbaseball\tbasketball\tflying saucer\nThere are several useful visual features to tell there is 'frisbee' and not similar things in a photo:\tround\tplastic\tflat\twith a curved edge in the shape of a ring\tcommonly in bright colors", 5124], "shelf": ["Yes. 'Shelf' has a tangible appearance and is a piece of furniture used to store things.\nA few things that are visually similar to 'shelf' but are not 'shelf' are:\ttable\tbench\tcounter\tmantel\nThere are several useful visual features to tell there is 'shelf' and not similar things in a photo:\t\nflat surface attached to a wall or other structure\t\nhas one or more horizontal surfaces for displaying or storing objects\t\nmay have brackets or supports for additional stability\t\nusually rectangular or square in shape", 5113], "cake": ["Yes. 'Cake' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'cake' but are not 'cake' are:\tpie\ttart\tbread\tmuffin\nThere are several useful visual features to tell there is 'cake' and not similar things in a photo:\tround shape\tlayered with frosting or cream\tmulti-colored designs or patterns\tcandles on top\tdecorations such as flowers, fruits or sprinkles", 5080], "desk": ["Yes. 'Desk' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'desk' but are not 'desk' are:\ttable\tworkbench\tcounter\tshelf\nThere are several useful visual features to tell there is 'desk' and not similar things in a photo:\tflat surface for writing or working\tdrawers or shelves for storage\tchair or seat nearby\tspecific height and size for sitting or standing", 5053], "flag": ["Yes. 'Flag' has a tangible appearance and is an emblematic representation of a country or organization.\nA few things that are visually similar to 'flag' but are not 'flag' are:\tbanners\tribbons\ttapestries\t\nThere are several useful visual features to tell there is 'flag' and not similar things in a photo:\tusually rectangular in shape\twith specific colors or patterns\tsymbolic elements (e.g. stars, stripes, emblem)\tcan be seen on a flagpole or carried by a person in a parade\tor waving in the wind.", 5046], "lights": ["Yes. 'Lights' has a tangible appearance and can be seen in various forms.\nA few things that are visually similar to 'lights' but are not 'lights' are:\treflections\tglitter\tfireflies\tstars\nThere are several useful visual features to tell there is 'lights' and not similar things in a photo:\tbright illumination\tof different colors\tstringed together\torbs that light up\tbulbs or LEDs that emit light\tlight fixtures or lamps with visible wires or cords", 5036], "beach": ["Yes. 'Beach' has a tangible appearance and is a type of natural landscape.\nA few things that are visually similar to 'beach' but are not 'beach' are:\tdunes\tdesert\tlake\tshoreline\triverbed\nThere are several useful visual features to tell there is 'beach' and not similar things in a photo:\tcoastline with sand\tor pebbles\twaves or sea\tshells\ttourists or beach-goers\tbeach umbrellas or chairs", 5032], "reflection": ["No. 'Reflection' is too abstract to be distinguished in a photo. However, the visual effect of a reflection can be captured in a photo.\nA few things that are visually similar to 'reflection' but are not 'reflection' are: shadow, silhouette, echo, mirage, illusion.\nThere are several useful visual features to tell there is a reflection in a photo: mirror-like appearance, mirrored image of the object or scene, sharpness and clarity of the reflection, reflection appears in a different orientation than the object it reflects.", 4978], "jeans": ["Yes. 'Jeans' has a tangible appearance and refers to a specific type of pants.\nA few things that are visually similar to 'jeans' but are not 'jeans' are:\ttrousers\tchinos\tkhakis\tjeggings\nThere are several useful visual features to tell there is 'jeans' and not similar things in a photo:\tdenim fabric\tblue color (although jeans can come in other colors)\tfive-pocket design\twith a waistband and belt loops", 4954], "vegetable": ["Yes. 'Vegetable' has a tangible appearance and refers to edible plant parts.\nA few things that are visually similar to 'vegetable' but are not 'vegetable' are:\tFruits\tFlowers\tLeaves\tStems\nThere are several useful visual features to tell there is 'vegetable' and not similar things in a photo:\tusually green, but can be other colors as well\tedible\tfleshy or leafy appearance (depending on the type of vegetable)", 4928], "cap": ["Yes. 'Cap' has a tangible appearance and is a kind of headwear.\nA few things that are visually similar to 'cap' but are not 'cap' are:\that\tvisor\tbonnet\tbalaclava\thood\nThere are several useful visual features to tell there is 'cap' and not similar things in a photo:\tstructured headwear\twith a brim or peak\tto cover the top of the head and part of the forehead\tmay have a strap or adjuster at the back or sides\tfor casual wear or sport activities.", 4906], "legs": ["Yes. 'Legs' has a tangible appearance.\nA few things that are visually similar to 'legs' but are not 'legs' are: tree trunks\tpillow rolls\tcolumns\nThere are several useful visual features to tell there is 'legs' and not similar things in a photo:\tconnected to a torso or a body\tcapable of movement\tjointed, with knees\tand feet or paws", 4878], "orange": ["Yes. 'Orange' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'orange' but are not 'orange' are:\ttangerine\tmandarin\tpumpkin\tbaby carrot\nThere are several useful visual features to tell there is 'orange' and not similar things in a photo:\tround\torange color\twith a little green stem or nub\ton a tree or in piles at a store or market\tcitrus smell when cut open", 4834], "guy": ["Yes. 'Guy' has a tangible appearance and refers to a person, especially a man.\nA few things that are visually similar to 'guy' but are not 'guy' are:\tmannequin\twax figure\trobot\thuman-like statue\nThere are several useful visual features to tell there is a 'guy' and not similar things in a photo:\thuman-like features\tfacial hair, such as mustache or beard\tclothing that is typically worn by men\tposture, gesture, expression that is specific to men's behavior and mannerisms.", 4830], "sink": ["Yes. 'Sink' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'sink' but are not 'sink' are:\tbathtub\ttoilet\tfountain\twashbasin\nThere are several useful visual features to tell there is 'sink' and not similar things in a photo:\tlocated in a kitchen or a bathroom\thas a faucet and handles\tfor washing hands, dishes, or utensils\tbowl-shaped basin made of ceramic or metal\tdrain at the bottom", 4760], "shoe": ["Yes. 'Shoe' has a tangible appearance and is a type of footgear.\nA few things that are visually similar to 'shoe' but are not 'shoe' are:\tsocks\tsandals\tboots\tslippers\nThere are several useful visual features to tell there is 'shoe' and not similar things in a photo:\tsole\theel\ttongue\tlaces or straps\tenclosed upper part\tpadded inner part (in most cases)", 4706], "player": ["No. 'Player' is too vague or abstract to be distinguished in a photo. It could refer to a person playing a game, a musical instrument, or a role in a movie, among other things.\nHowever, a few things that could be visually similar in certain contexts are: audience member, spectator, team member, coach.\nThere are no specific visual features that distinguish a 'player' in all contexts. The features would depend on the specific activity or situation in which the term is being used. For example, in sports, a player might be identified by a numbered jersey or uniform, while in a band, a player might be holding or playing a specific instrument.", 4693], "lines": ["Yes. 'Lines' has a tangible appearance and is a visible path or mark with length and direction.\nA few things that are visually similar to 'lines' but are not 'lines' are:\tsquiggles\tshadows\ttexture\tseams\nThere are several useful visual features to tell there are 'lines' and not similar things in a photo:\tstraightness or curvature of the path\tsharpness or bluntness of the edges\tconsistency or irregularity in thickness, color, or texture\tdirection and orientation in space", 4690], "container": ["Yes. 'Container' has a tangible appearance and is a type of object used to hold or store things.\nA few things that are visually similar to 'container' but are not 'container' are:\tbasket\tbox\tbag\tjar\tbottle\t\nThere are several useful visual features to tell there is 'container' and not similar things in a photo:\thollow and enclosed shape\twith or without lid or cover\tmade of various materials such as plastic, metal, or glass\tdifferent sizes and shapes for different purposes, such as storage or transportation.", 4568], "eyes": ["Yes. 'Eyes' has a tangible appearance and is a part of the human or animal body.\nA few things that are visually similar to 'eyes' but are not 'eyes' are:\tcircles\tballs\tbubbles\tholes\tbuttons\nThere are several useful visual features to distinguish 'eyes' from the listed similar things in a photo:\tusually two in numbers\tpupil (dark center)\tiris (colored part) and sclera (white part)\teyelashes and eyebrows around them\texpression and emotions reflected through them located on the face or the head", 4524], "top": ["Yes. 'Top' has a tangible appearance and is a spinning toy.\nA few things that are visually similar to 'top' but are not 'top' are:\tfidget spinner\tspinning wheel\tdreidel\tyo-yo\nThere are several useful visual features to tell there is 'top' and not similar things in a photo:\tconical or pointed shape\tsmooth surface with patterns or colors\tpointed stem on the bottom\tthat spins when twisted or flicked", 4515], "wheel": ["Yes. 'Wheel' has a tangible appearance and is a circular object used for transportation or machinery.\nA few things that are visually similar to 'wheel' but are not 'wheel' are:\tclock\tfrisbee\tcircular button\torbit\nThere are several useful visual features to tell there is 'wheel' and not similar things in a photo:\tcircular shape\taxle\trim\ttread\thub\tcenter bore or hole", 4472], "roof": ["Yes. 'Roof' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'roof' but are not 'roof' are:\tsidewalk\tfloor\tceiling\tground\nThere are several useful visual features to tell there is 'roof' and not similar things in a photo:\tlocated at the top of a building\tsloping or flat shape\tvarious materials being used (tiles, shingles, metal, etc.)", 4472], "board": ["Yes. 'Board' has a tangible appearance and is a flat piece of material used for a variety of purposes.\nA few things that are visually similar to 'board' but are not 'board' are:\tcard\ttable\tsurf\titem\t\nThere are several useful visual features to tell there is 'board' and not similar things in a photo:\trectangular or square in shape\tflat and even surface\tmade of wood or other solid material\tthick and sturdy", 4458], "sand": ["Yes. 'Sand' has a tangible appearance and is a granular material.\nA few things that are visually similar to 'sand' but are not 'sand' are:\tpebbles, rocks\tor gravel\tsalt or sugar crystals\tashes or dust\nThere are several useful visual features to tell there is 'sand' and not similar things in a photo:\tfine-grained and small particles\tusually light beige, yellow or white color\tfound on a beach or near water\tbody has a grainy texture when touched", 4393], "logo": ["Yes. 'Logo' has a tangible appearance and is a graphic symbol or emblem used to represent a company or organization.\nA few things that are visually similar to 'logo' but are not 'logo' are:\tsymbols\tsigns\tletters\tgraphics\nThere are several useful visual features to tell there is 'logo' and not similar things in a photo:\tdistinctive design or text\tspecific colors or font\ttypeface or font\tunique shape or arrangement", 4355], "counter": ["Yes. 'Counter' has a tangible appearance and is a type of horizontal surface typically used for business transactions or food preparation.\nA few things that are visually similar to 'counter' but are not 'counter' are:\ttable\tdesk\tshelf\tbench\nThere are several useful visual features to tell there is 'counter' and not similar things in a photo:\theight and width appropriate for standing or working\tspace underneath for storage or seating\tlevel surface for business transactions or food preparation\tfrequently made of durable materials like wood or stone", 4348], "ball": ["Yes. 'Ball' has a tangible appearance and can refer to several objects with a rounded shape.\nA few things that are visually similar to 'ball' but are not 'ball' are:\torange\tsphere\tpellet\tglobe\tapple\nThere are several useful visual features to tell there is 'ball' and not similar things in a photo:\tcircular\tsolid or hollow\twith a smooth surface\tsymmetrical\tin various sizes\tand often used in sports/games.", 4334], "mouth": ["Yes. 'Mouth' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'mouth' but are not 'mouth' are:\teyes\tnose\tears\tcheeks\tlips\nThere are several useful visual features to tell there is 'mouth' and not similar things in a photo:\ta space with lips at the bottom of the face\tfor opening and closing\tmight be open or closed\tlots of teeth visible might be present", 4310], "couch": ["Yes. 'Couch' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'couch' but are not 'couch' are:\tsofa\tbed\tchair\tottoman\nThere are several useful visual features to tell there is 'couch' and not similar things in a photo:\tlong upholstered seat\twith or without a backrest\tand armrests\tvarying number of cushions\tsitting low to the ground", 4307], "dirt": ["Yes. 'Dirt' has a tangible appearance and is a type of soil or earth.\nA few things that are visually similar to 'dirt' but are not 'dirt' are:\tmud\tsand\tgravel\tcement\nThere are several useful visual features to tell there is 'dirt' and not similar things in a photo:\tdark or brown color\tsoft and crumbly texture\tcontains organic matter such as plant roots or worms\tfound in natural environments such as forests or fields", 4295], "cars": ["Yes. 'Cars' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'cars' but are not 'cars' are:\ttrucks\tbuses\tmotorcycles\tbicycles\tscooters\nThere are several useful visual features to tell there is 'car' and not similar things in a photo:\tfour wheels\ton-road vehicle\tin-tact roof and doors\tseating for passengers and driver\theadlights and taillights", 4249], "letters": ["Yes. 'Letters' has a tangible appearance and refers to the individual characters used in writing or printing.\nA few things that are visually similar to 'letters' but are not 'letters' are: shapes, symbols, and drawings.\nThere are several useful visual features to tell there are 'letters' and not similar things in a photo: rectangular shapes with distinct corners, the presence of curves or straight lines, varied sizes and styles, and letter arrangements that form words that are easily recognizable or legible.", 4170], "keyboard": ["Yes. 'Keyboard' has a tangible appearance and is an input device.\nA few things that are visually similar to 'keyboard' but are not 'keyboard' are:\tpiano\torgan\ttypewriter\tsynthesizer\nThere are several useful visual features to tell there is 'keyboard' and not similar things in a photo:\trectangular shape\twith keys in a specific pattern\tvarious keys have letters, numbers, symbols, or functions\tconnected to a computer or electronic device via wire or Bluetooth", 4104], "towel": ["Yes. 'Towel' has a tangible appearance and is a piece of cloth used for drying.\nA few things that are visually similar to 'towel' but are not 'towel' are:\tnapkin\trag\tbathmat\tcarpet\nThere are several useful visual features to tell there is 'towel' and not similar things in a photo:\trectangular or square shape\tsmall to large size\tfor drying or wiping moisture from the body or surfaces\toften made of absorbent material, such as cotton.", 4054], "fruit": ["Yes. 'Fruit' has a tangible appearance and refers to the edible part of a plant that typically has seeds.\nA few things that are visually similar to 'fruit' but are not 'fruit' are:\tvegetables\tnuts\tseeds\nThere are several useful visual features to tell there is 'fruit' and not similar things in a photo:\tvarious colors and shapes\tpulp around seeds\tfleshy or juicy texture\tgrowing on trees or plants", 4035], "yellow": ["Yes. 'Yellow' has a tangible appearance and is a color.\nA few things that are visually similar to 'yellow' but are not 'yellow' are:\torange\tgold\tmustard\tyellow-green\nThere are several useful visual features to tell there is 'yellow' and not similar things in a photo:\tbright and vibrant color\tlighter than orange and darker than white\tno visible hint of green or brown\tpure, clear and consistent tone.", 3997], "paper": ["Yes. 'Paper' has a tangible appearance and is a sheet material.\nA few things that are visually similar to 'paper' but are not 'paper' are:\tcardboard\tfabric\tmetal\twood\tplastic\nThere are several useful visual features to tell there is 'paper' and not similar things in a photo:\tthin and flat\thas a texture that ranges from smooth to slightly coarse\tcan be different colors but is usually white or beige\tmade from pulp or fibers, sometimes with visible grain or threads", 3945], "coat": ["Yes. 'Coat' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'coat' but are not 'coat' are:\tjacket\tblazer\tcardigan\tsweater\nThere are several useful visual features to tell there is 'coat' and not similar things in a photo:\tlong sleeves\tbutton or zipper closure\tlapel collar or hood\tmade of wool, leather, or other heavy fabric\ttypically worn over other clothes for warmth or style", 3941], "appliance": ["Yes. 'Appliance' has a tangible appearance and refers to a device or machine used for household tasks or activities.\nA few things that are visually similar to 'appliance' but are not 'appliance' are:\tfurniture\tutensils\ttools\tdecorations\nThere are several useful visual features to tell there is 'appliance' and not similar things in a photo:\tmade of metal, plastic or a combination of both\thas buttons, knobs or screens\tconnected to a power source\thas specific functions or purposes for household tasks or activities.", 3896], "rocks": ["Yes. 'Rocks' has a tangible appearance and is a type of geological formation.\nA few things that are visually similar to 'rocks' but are not 'rocks' are: boulders, pebbles, fossils, minerals, dirt.\nThere are several useful visual features to tell there are 'rocks' and not similar things in a photo: hardness, rough surface, weight, natural formation, visible layers or patterns, presence of minerals or crystals.", 3857], "knife": ["Yes. 'Knife' has a tangible appearance and is a cutting tool.\nA few things that are visually similar to 'knife' but are not 'knife' are:\tspoon\tfork\trazor\tscissors\nThere are several useful visual features to tell there is 'knife' and not similar things in a photo:\tsharp edge\tfor cutting purposes\tblade\tgrip\thandle", 3826], "fork": ["Yes. 'Fork' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'fork' but are not 'fork' are:\tspoon\tknife\ttuning fork\nThere are several useful visual features to tell there is 'fork' and not similar things in a photo:\tfour-pronged or tined head\thandle for gripping\tdimensions and angles of the prongs and handle", 3782], "tennis racket": ["Yes. 'Tennis racket' has a tangible appearance and is a piece of sports equipment.\nA few things that are visually similar to 'tennis racket' but are not 'tennis racket' are:\tbadminton racket\tsquash racket\tpaddle\tracquetball racket\nThere are several useful visual features to tell there is 'tennis racket' and not similar things in a photo:\toblong shape\tlarge head with strings\thandle with grip\tstrung with synthetic material and cross pattern\tnet-like pattern of the strings", 3740], "patch": ["Yes. 'Patch' has a tangible appearance and can be a small piece of material used to cover or mend a hole or a damaged area.\nA few things that are visually similar to 'patch' but are not 'patch' are:\tsticker\tband-aid\tpainted area\nThere are several useful visual features to tell there is 'patch' and not similar things in a photo:\tirregular shape\tmaterial with a different color, pattern, or texture\tplaced over a damaged area\tintended to repair or reinforce\tthe material of the patch matches the surrounding material", 3729], "background": ["No. 'Background' is too vague or abstract to be distinguished in a photo. It refers to the part of an image that is not the main focus.\nThere are no things that are visually similar to 'background' but are not 'background'.\nUseful visual features for distinguishing 'background' from the main subject in a photo are: lack of detail, blurred or out of focus, monotone or muted color scheme.", 3707], "row": ["Yes. 'Row' has a tangible appearance and refers to a linear arrangement of objects or people.\nA few things that are visually similar to 'row' but are not 'row' are:\tqueue\tline\tcolumn\tarray\nThere are several useful visual features to tell there is a 'row' and not similar things in a photo:\tobjects or people arranged linearly\tsimilar spacing and alignment between objects or people in the row.", 3691], "bicycle": ["Yes. 'Bicycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'bicycle' but are not 'bicycle' are:\ttricycle\tmotorcycle\tscooter\ttruck\nThere are several useful visual features to tell there is 'bicycle' and not similar things in a photo:\ttwo wheels\tpedals\thandlebars\tframe with a saddle\tforward-leaning rider\tpositioned between the wheels", 3623], "clothing": ["Yes. 'Clothing' has a tangible appearance and is an item worn on the body.\nA few things that are visually similar to 'clothing' but are not 'clothing' are:\ttowels\trugs\tcurtains\tblankets\nThere are several useful visual features to tell there is 'clothing' and not similar things in a photo: worn on the body\tcovering a part of the body, such as the torso or legs\tmade of fabric or material that is commonly used for clothing, such as cotton, wool or silk\tvariability in style and color, indicating it is not just a functional item", 3613], "wheels": ["Yes. 'Wheels' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'wheels' but are not 'wheels' are:\tcircles\tgears\trecords\tfrisbees\nThere are several useful visual features to tell there are 'wheels' and not similar things in a photo:\tcircular shape\trubber or metal material\thubcap or spokes\taxle_attachment to a vehicle", 3590], "blanket": ["Yes. 'Blanket' has a tangible appearance and is a type of cloth used for warmth or comfort.\nA few things that are visually similar to 'blanket' but are not 'blanket' are:\ttowels\trugs\tcarpets\tnapkins\nThere are several useful visual features to tell there is 'blanket' and not similar things in a photo:\tsoft and fluffy fabric\tsquare or rectangular shape\twrapping around a person or an object \twarm colors like red, yellow, or orange", 3577], "computer": ["Yes. 'Computer' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'computer' but are not 'computer' are:\ttelevision\ttablet\tsmartphone\tradio\nThere are several useful visual features to tell there is 'computer' and not similar things in a photo:\tscreen or monitor with a visual interface\tfor keyboard or set of buttons\ta CPU or main body that stores data and runs programs\ta power cord or battery for energy supply\toften accompanied by a mouse or touchpad for navigation purposes.", 3556], "tie": ["Yes. 'Tie' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'tie' but are not 'tie' are:\tscarf\tbow tie\tcravat\tnecklace\nThere are several useful visual features to tell there is 'tie' and not similar things in a photo:\tlong strip of cloth\tdraped around the neck and tied in a knot or bow\tmay have patterns or stripes\tmay have a sheen or shine", 3546], "area": ["No. 'Area' is too vague or abstract to be visually concrete or distinguished in a photo.", 3522], "cheese": ["Yes. 'Cheese' has a tangible appearance and there are many varieties with different textures, colors, and shapes.\nA few things that are visually similar to 'cheese' but are not 'cheese' are:\tbutter\tsoap\twax\tcandle\nThere are several useful visual features to tell there is 'cheese' and not similar things in a photo:\tdifferent textures and colors, such as yellow, white, or blue\tholes or cracks in the surface\tvarious shapes, such as blocks, wedges, or slices\tmade from milk products", 3502], "backpack": ["Yes. 'backpack' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'backpack' but are not 'backpack' are:\tpurse\ttote bag\tduffle bag\tmessenger bag\nThere are several useful visual features to tell there is 'backpack' and not similar things in a photo:\ttwo shoulder straps\tfor carrying items on the back\tzippers\tand pockets\tfor organization and storage\tLarge enough to hold books and other items typically carried by students", 3499], "suitcase": ["Yes. 'Suitcase' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'suitcase' but are not 'suitcase' are:\tbackpack\tbriefcase\tpurse\ttravel bag\nThere are several useful visual features to tell there is 'suitcase' and not similar things in a photo:\trectangular or box-shaped\thinged lid\thandles\ton wheels or not\tzippers or clasps\tto store clothes and personal belongings for traveling", 3469], "phone": ["Yes. 'Phone' has a tangible appearance and typically refers to a handheld device used for communication.\nA few things that are visually similar to 'phone' but are not 'phone' are:\ttablet\tlaptop\tcamera\tremote control\nThere are several useful visual features to tell there is 'phone' and not similar things in a photo:\trectangular shape\twith a screen and buttons or touchpad\tfor making and receiving calls and messages\table to connect to the internet or cellular network", 3463], "napkin": ["Yes. 'Napkin' has a tangible appearance and is a piece of cloth or paper used for wiping one's mouth or hands.\nA few things that are visually similar to 'napkin' but are not 'napkin' are:\tpaper towel\ttissue\ttablecloth\thandkerchief\nThere are several useful visual features to tell there is 'napkin' and not similar things in a photo:\tsquare or rectangular in shape\tmade of cloth or paper\tfolded or rolled pattern\tmay have a decorative design or pattern.", 3426], "sandwich": ["Yes. 'Sandwich' has a tangible appearance and consists of two slices of bread with fillings in between.\nA few things that are visually similar to 'sandwich' but are not 'sandwich' are:\tburger\tkebab\twrap\tpizza\tburrito\nThere are several useful visual features to tell there is 'sandwich' and not similar things in a photo:\ttwo slices of bread\tpossible fillings visible or peeking out (such as lettuce, tomato, meat, cheese, etc.)\tcut in half or in wedge shape\tcan be held in hand or on a plate.", 3420], "plant": ["Yes. 'Plant' has a tangible appearance and refers to a living organism.\nA few things that are visually similar to 'plant' but are not 'plant' are:\tweed\tflower\tbush\ttree\nThere are several useful visual features to tell there is 'plant' and not similar things in a photo:\tliving organism\twith leaves, stems and roots\tvariety of colors and texturesphotosynthesis process", 3376], "cabinet": ["Yes. 'Cabinet' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'cabinet' but are not 'cabinet' are:\tbox\tshelf\tlocker\nThere are several useful visual features to distinguish 'cabinet' from the listed similar things in a photo:\tclosed doors and drawers\twith or without shelves\tsolid or glass doors\ttall and narrow, or short and wide\ttypes of material (wood, metal, plastic, etc.)", 3372], "book": ["Yes. 'Book' has a tangible appearance and is an object used for reading or containing information.\nA few things that are visually similar to 'book' but are not 'book' are:\tmagazine\tnewspaper\tnotepad\tbinder\nThere are several useful visual features to tell there is 'book' and not similar things in a photo:\tthick covers\tbinding\tvisible pages\thas a title or author name\tcan be opened and closed\twith text and images inside", 3372], "photo": ["Yes. 'Photo' has a tangible appearance and is a picture or image produced by capturing light on a sensitive surface.\nA few things that are visually similar to 'photo' but are not 'photo' are:\tpainting\tdrawings\tmurals\tsketches\tgraphs\nThere are several useful visual features to tell there is 'photo' and not similar things in a photo:\trealistic and detailed images\tsharp and clear\tdifferent tones of black and white or color\tcaptures a moment in time or a scene in real life", 3350], "meat": ["Yes. 'Meat' has a tangible appearance and refers to animal flesh that is eaten as food.\nA few things that are visually similar to 'meat' but are not 'meat' are:\tfruit\tvegetables\tsponge\nThere are several useful visual features to tell there is 'meat' and not similar things in a photo:\tred, pink, white or brown in color\traw or cooked texture\tmuscular fiber texture", 3345], "grey": ["Yes. 'Grey' has a tangible appearance and is a specific shade of color.\nA few things that are visually similar to 'grey' but are not 'grey' are:\twhite\tblack\tsilver\nThere are no visual features that can be used to distinguish between different shades of grey. However, the context in which the shade of grey is presented might give some idea about its relative lightness or darkness.", 3341], "women": ["Yes. 'Women' has a tangible appearance and is a gender classification.\nA few things that are visually similar to 'women' but are not 'women' are: men, mannequins, statues, anime/manga characters, some animals (i.e. female monkeys or birds)\nThere are several useful visual features to tell there is 'women' and not similar things in a photo:\tfeminine body shape and curves\thair length, style and color\tfeminine clothing or accessories\tmake-up and jewelry", 3325], "street sign": ["Yes. 'Street sign' has a tangible appearance and is a type of sign used to give information about streets.\nA few things that are visually similar to 'street sign' but are not 'street sign' are:\tadvertising billboard\tdirection sign\ttraffic cone\tbanner\nThere are several useful visual features to tell there is 'street sign' and not similar things in a photo:\trectangular shape\tborder and background color indicating the type of information (e.g. blue for guidance, red for stop)\ttext indicating the street name, direction, or restriction\tpictograms showing parking, pedestrian crossing, or bicycle lane.", 3315], "item furniture": ["No. 'Item furniture' is too vague or abstract to be distinguished in a photo.\n\nNote: This phrase does not make sense grammatically. \"Item\" and \"furniture\" are redundant, as furniture is already a collection of items. Perhaps if you specified a certain type of furniture, such as \"sofa\" or \"chair,\" it would be a concrete concept that could be distinguished visually.", 3260], "body": ["Yes. 'Body' has a tangible appearance and refers to the physical structure of a human or an animal.\nA few things that are visually similar to 'body' but are not 'body' are:\tclothing\tdolls\tmannequins\tstatues\t\nThere are several useful visual features to tell there is a 'body' and not similar things in a photo:\thead\tlimbs\ttorso\tvisible organs or bones\thair or fur (in the case of animals)", 3256], "skier": ["Yes. 'Skier' has a tangible appearance and generally refers to a person engaged in the sport of skiing.\nA few things that are visually similar to 'skier' but are not 'skier' are:\tice skater\tsnowboarder snowshoer\nThere are several useful visual features to tell there is 'skier' and not similar things in a photo:\twearing ski boots, ski poles, and skis\tleaning forward\twith skis pointing forward or to the side\tmaneuvering around snow-covered terrain or mountains in a skiing stance.", 3203], "wave": ["Yes. 'Wave' has a tangible appearance and is a water movement.\nA few things that are visually similar to 'wave' but are not 'wave' are:\tripple\tcurrent\tseaweed\tfoam\nThere are several useful visual features to tell there is 'wave' and not similar things in a photo:\tlarge body of water\tcrest or peak\tforeward motion or in a curling action\trepeating pattern or sequence\tvarying shades of blue or green\tcolor contrast between the wave and the water around it.", 3198], "piece furniture": ["No. 'Piece furniture' is too vague or abstract to be distinguished in a photo. It is a general term used to refer to any type of furniture. \n\nIt is not possible to list things that are visually similar to 'piece furniture' but are not 'piece furniture' since the concept is too broad and encompasses a wide range of items. \n\nHowever, some useful visual features for identifying a piece of furniture in a photo could include: size, shape, material, style, and function. For example, a table could be distinguished from a chair because of its flat surface and the fact that it is meant for sitting around rather than sitting on.", 3188], "rock": ["Yes. 'Rock' has a tangible appearance and is a type of solid material.\nA few things that are visually similar to 'rock' but are not 'rock' are:\tboulder\tconcrete\tcliff\tbrick\nThere are several useful visual features to tell there is 'rock' and not similar things in a photo:\tnatural material\tthat is composed of minerals uneven texture\tcan be different colors and sizes", 3184], "tray": ["Yes. 'Tray' has a tangible appearance and is a type of flat surface used for carrying or holding things.\nA few things that are visually similar to 'tray' but are not 'tray' are:\tplank\tboard\tshelf\ttablecloth\nThere are several useful visual features to tell there is 'tray' and not similar things in a photo:\tFlat surface\twith raised edges or sides\thandles for carrying things", 3182], "bananas": ["Yes. 'Bananas' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'bananas' but are not 'bananas' are:\tplantains\tyellow cucumbers\tyellow zucchinis\nThere are several useful visual features to tell there is 'bananas' and not similar things in a photo:\telongated shape\tbright yellow color\tcurved appearance\ttapered ends\tdark spots or streaks on the skin", 3181], "side": ["No. 'Side' is too vague or abstract to be distinguished in a photo.", 3180], "handle": ["Yes. 'Handle' has a tangible appearance and is a physical part of an object that is meant to be held.\nA few things that are visually similar to 'handle' but are not 'handle' are:\tlever\tknob\tbutton\tswitch\nThere are several useful visual features to tell there is 'handle' and not similar things in a photo:\tattached or integrated into an object\tmeant to be held or grasped\telongated shape\tfor pulling or pushing", 3179], "buildings": ["Yes. 'Buildings' has a tangible appearance and refers to man-made structures used for various purposes.\nA few things that are visually similar to 'buildings' but are not 'buildings' are:\trock formations\tnatural landmarks\tbridges\tmountains\nThere are several useful visual features to tell there are 'buildings' and not similar things in a photo:\tman-made structure\tdesigned for human habitation or use\tconsists of walls and roof\tgenerally rectangular or purpose-specific in shape\tmay have windows or doors, and may be made of a variety of materials.", 3167], "tire": ["Yes. 'Tire' has a tangible appearance and is a component of a vehicle.\nA few things that are visually similar to 'tire' but are not 'tire' are:\tsteering wheel\tdoor handle\tbrake pad\nThere are several useful visual features to tell there is 'tire' and not similar things in a photo:\tcircular shape with a hole in the center\ttread on the outer surface\trubber or synthetic material\tfire tread pattern and width, depending on the vehicle and tire type.", 3154], "giraffes": ["Yes. 'Giraffes' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'giraffes' but are not 'giraffes' are:\tzebra\tokapi\tcamel\tlama\tsome species of deer\nThere are several useful visual features to tell there is 'giraffes' and not similar things in a photo:\tTall neck with distinctive spotted pattern\ton average 18 feet in height\tlong legs relative to the body size\ttuft of hair on their tail\tossicones (horns) on the top of their head\tsmall ears in proportion to their head size.", 3119], "neck": ["Yes. 'Neck' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'neck' but are not 'neck' are:\ttube\tcollar\tpillow\tsnake\nThere are several useful visual features to tell there is 'neck' and not similar things in a photo:\tconnects the head to the body\thas skin and visible muscles\tmay have a visible Adam's apple in males.", 3113], "hands": ["Yes. 'Hands' has a tangible appearance and is a body part.\nA few things that are visually similar to 'hands' but are not 'hands' are:\tpaws\thooves\tclaws\tfeet\nThere are several useful visual features to tell there are 'hands' and not similar things in a photo:\tfive digits (fingers) including an opposable thumb\tpalm with lines and creases\tvariations in skin color, texture, and hairiness\tvariations in size and shape, depending on age and gender", 3092], "foot": ["Yes. 'Foot' has a tangible appearance and is a body part.\nA few things that are visually similar to 'foot' but are not 'foot' are:\thand\tpaw\tclaw\thoof\nThere are several useful visual features to tell there is 'foot' and not similar things in a photo:\ttoes, including the big toe\tan arch or a curve on the sole\tthe top of foot that usually has a hairless surface and bony landmarks like the ankle, instep, and toes.", 3078], "zebras": ["Yes. 'Zebras' has a tangible appearance and is a type of striped mammal.\nA few things that are visually similar to 'zebras' but are not 'zebras' are:\ttigers\tdonkeys\thorses\tpainted horses\nThere are several useful visual features to tell there is 'zebras' and not similar things in a photo:\twhite fur with black stripes (or vice versa)\thorse-like body shape\tshort mane\tblack ears and nose", 3075], "banana": ["Yes. 'Banana' has a tangible appearance and is a type of fruit. \nA few things that are visually similar to 'banana' but are not 'banana' are: plantain, cucumber, zucchini.\nThere are several useful visual features to tell there is 'banana' and not similar things in a photo:\tcurved elongated shape\tyellow skin or green when unripe\tthick skin with a bumpy texture \tdivided into sections\twhen peeled, soft flesh with small black seeds at the center", 3053], "waves": ["Yes. 'Waves' has a tangible appearance and is a physical phenomenon.\nA few things that are visually similar to 'waves' but are not 'waves' are:\tcurls\tfabric folds\tlines of traffic\tclouds\nThere are several useful visual features to tell there is 'waves' and not similar things in a photo:\tundulating pattern\tsuggestive of water, like a beach or an ocean\tdiffraction patterns, which makes overlapping waves with multiple crests and troughs.", 3034], "stripes": ["Yes. 'Stripes' has a tangible appearance and is a pattern characterized by a sequence of parallel lines.\nA few things that are visually similar to 'stripes' but are not 'stripes' are:\tchecks\thoundstooth polka dots\trectangles\nThere are several useful visual features to tell there are 'stripes' and not similar things in a photo:\tsequence of parallel lines\tregular width and spacing of the lines\talternating colors or tones of the lines\tthe lines do not intersect or cross over each other\tuniform or consistent pattern", 2989], "post": ["Yes. 'Post' has a tangible appearance and can refer to various objects, such as a fence post, letter post, or social media post.\nA few things that are visually similar to 'post' but are not 'post' are:\tpillar\tpole\tcolumn\tpile of mail or envelopes\nThere are several useful visual features to distinguish 'post' from the listed similar things in a photo, depending on the specific type of post:\t\n\n- Fence post: wooden or metal material, usually pointed at one end for sticking into the ground, and has a flat surface on top for attaching objects like signs or wires.\n- Letter post: a rectangular or square-shaped box attached to a wall or a freestanding pole with a slot or opening for mail, often adorned with a red or blue flag to indicate outgoing mail.\n- Social media post: typically displayed on a digital device such as a computer, phone, or tablet, with various visual elements such as text, images, or videos, and associated with a specific account or platform.", 2955], "chairs": ["Yes. 'Chairs' has a tangible appearance and is a piece of furniture used for sitting.\nA few things that are visually similar to 'chairs' but are not 'chairs' are:\tstools\tbenches\tcouches\tottomans\nThere are several useful visual features to tell there is 'chairs' and not similar things in a photo:\thave a backrest\tintended for one person\thave legs to raise them off the ground\toften have armrests or cushions", 2931], "bread": ["Yes. 'Bread' has a tangible appearance and can be visually distinguished.\nA few things that are visually similar to bread but are not bread are:\tCake, chips, crackers, cereal, pastry.\nThere are several useful visual features to tell there is 'bread' and not similar things in a photo:\tRound, oval or rectangular shape, brown color, baked crust on the outer layer, visible air pockets or grains, the density of the body.", 2927], "racket": ["Yes. 'Racket' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'racket' but are not 'racket' are:\tpaddle\tbat\tspatula\ttool\nThere are several useful visual features to tell there is 'racket' and not similar things in a photo:\tlong handle\tflat, oval-shaped head\tstrung or netted surface\tfor use in sports (especially tennis, badminton, and squash)", 2922], "van": ["Yes. 'Van' has a tangible appearance and is a kind of transportation vehicle.\nA few things that are visually similar to 'van' but are not 'van' are:\ttruck\tsuv\tcar\tjeep\t\nThere are several useful visual features to tell there is 'van' and not similar things in a photo:\tbox-like shape\tlarge and spacious\tbody style with a rear cargo area\tor side and/or rear windows, often covered with curtains or blinds\tpassenger and driver's doors on either side.", 2908], "metal": ["Yes. 'Metal' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'metal' but are not 'metal' are:\tstone\tplastic\tglass\twood\nThere are several useful visual features to tell there is 'metal' and not similar things in a photo:\tmetallic shine\thard\tand heavy\ttypically gray, silver or bronze\tno visible grain or texture", 2895], "elephants": ["Yes. 'Elephants' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'elephants' but are not 'elephants' are:\thippopotamus\trhino\tpig\tdonkey\nThere are several useful visual features to tell there is 'elephants' and not similar things in a photo:\tlarge ears and tusks\tsnout-like nose with two nostrils\thighly wrinkled gray skin\tlong trunk\tthick legs and wide feet\tdistinctive curved shape with a hump on their back", 2890], "tracks": ["Yes. 'Tracks' has a tangible appearance and refers to marks left by something that has passed over a surface.\nA few things that are visually similar to 'tracks' but are not 'tracks' are:\tshadows\tcracks in the ground\tpaint marks\nThere are several useful visual features to tell there are 'tracks' and not similar things in a photo:\trepeating pattern of marks\tleft by something that has passed over a surface\tdifferent shapes depending on the type of track, such as pawprints or tire tracks\tmay be accompanied by other evidence of the thing that left them", 2875], "sunglasses": ["Yes. 'Sunglasses' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'sunglasses' but are not 'sunglasses' are:\tglasses\tgoggles\tshades\nThere are several useful visual features to tell there is 'sunglasses' and not similar things in a photo:\tdark or tinted lenses\tframe around the lenses\tarms that rest on the ears\tprotect the eyes from the sun's ultraviolet (UV) rays\tRESTING ON A NOSE", 2859], "flower": ["Yes. 'Flower' has a tangible appearance and is a type of plant structure.\nA few things that are visually similar to 'flower' but are not 'flower' are:\tumbrella\tsculpture\tfirework\t\nThere are several useful visual features to tell there is 'flower' and not similar things in a photo:\n\n- Vibrant colors: Flowers come in a variety of colors, and their petals usually have vibrant shades that make them stand out from other objects.\n- Petal shape: Flowers have a distinctive petal shape that sets them apart from other objects.\n- Stem and leaves: Flowers are attached to a stem with leaves.\n- Fragility: Flowers are fragile and delicate, which further distinguishes them from non-living objects.\n- Blossoming: Flowers can be seen blooming which is unique to them.", 2843], "ocean": ["Yes. 'Ocean' has a tangible appearance and is a large water body.\nA few things that are visually similar to 'ocean' but are not 'ocean' are:\tlake\triver\tswimming pool\tpond\nThere are several useful visual features to tell there is 'ocean' and not similar things in a photo:\tcontinuous body of water\tsaltwater\tseashells\tbig waves\tblue or green water color\tunlimited horizon", 2830], "catcher": ["Yes. 'Catcher' has a tangible appearance and typically refers to a baseball catcher.\nA few things that are visually similar to 'catcher' but are not 'catcher' are:\tfielder\tpitcher\tbatter\tumpire\nThere are several useful visual features to tell there is a 'catcher' and not similar things in a photo:\twearing a helmet and mask\twearing a chest protector and shin guards\tsquatting behind the home plate\twith a mitt to catch the ball.", 2818], "wood": ["Yes. 'Wood' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'wood' but are not 'wood' are:\tcork\tcardboard\tbark\tstone\nThere are several useful visual features to tell there is 'wood' and not similar things in a photo:\tfibrous texture\tgrain pattern\thard and solid appearance\tbrownish-yellow or reddish-brown color\ttypically used for construction or furniture", 2800], "glove": ["Yes. 'Glove' has a tangible appearance and is a type of handwear.\nA few things that are visually similar to 'glove' but are not 'glove' are:\tsocks\tmittens\tpuppet\nThere are several useful visual features to tell there is 'glove' and not similar things in a photo:\tfull-fingered handwear\twithout separate holes for each finger\twrist extension covering part of the arm\tparticularly designed for winter and outdoor activities", 2791], "seat": ["Yes. 'Seat' has a tangible appearance and is a structure or object upon which a person can sit.\nA few things that are visually similar to 'seat' but are not 'seat' are:\tbench\ttable\tcouch\tchair\tstool\nThere are several useful visual features to tell there is 'seat' and not similar things in a photo:\tseat has a flat surface to sit on\tseat has a backrest\tfor chairs and some benches: seat has four legs\"\"\",\n", 2789], "horses": ["Yes. 'Horses' has a tangible appearance and is a kind of animal.\nA few things that are visually similar to 'horses' but are not 'horses' are:\tdonkeys\tzebras\tcows\tantelopes\nThere are several useful visual features to tell there is 'horses' and not similar things in a photo:\tlong mane and tail\thooves\tgrazing in a pasture or corral\tvariety of coat colors and patterns, such as brown, black, white, or spotted.\tlean and athletic body shape.", 2769], "surfer": ["Yes. 'Surfer' has a tangible appearance and is a person who rides waves on a surfboard.\nA few things that are visually similar to 'surfer' but are not 'surfer' are:\tswimmer\tboogie boarder\twater skier\tkayaker\t\nThere are several useful visual features to tell there is 'surfer' and not similar things in a photo:\tstanding on a surfboard\triding a wave or waiting for one\twearing a wetsuit or board shorts\tcarrying a surfboard or paddling with one's arms\tpotentially performing tricks or turns on the board.", 2753], "screen": ["Yes. 'Screen' has a tangible appearance and is typically rectangular in shape.\nA few things that are visually similar to 'screen' but are not 'screen' are:\tpaper\tsheet\tmirror\ttablet\ttv\nThere are several useful visual features to tell there is 'screen' and not similar things in a photo:\trectangular shape\tglowing or illuminated surface\tpixels or grid-like texture\tsymbols or images displayed on the surface\tframe or casing surrounding the surface.", 2745], "tower": ["Yes. 'Tower' has a tangible appearance and is a tall structure.\nA few things that are visually similar to 'tower' but are not 'tower' are:\tbuilding\tchimney\tpillar\nThere are several useful visual features to tell there is 'tower' and not similar things in a photo:\ttall and narrow shape\twith a pointed top\tor with a dome\ton top\tof a lower structure, like a castle or a bridge.", 2733], "curtain": ["Yes. 'Curtain' has a tangible appearance and is a kind of fabric used for covering windows or doors.\nA few things that are visually similar to 'curtain' but are not 'curtain' are:\tdrapes\tblinds\tshades\ttapestries\nThere are several useful visual features to tell there is 'curtain' and not similar things in a photo:\thanging from a rod or a rail\tcovering a window, doorway, or stage\tmade of lightweight or medium-weight fabric\tcan be opened or closed", 2723], "ears": ["Yes. 'Ears' has a tangible appearance and is a body part.\nA few things that are visually similar to 'ears' but are not 'ears' are:\tshells\tfunnels\thorns\tvarious types of headphones\nThere are several useful visual features to tell there is 'ears' and not similar things in a photo:\tlocated on the side of the head\tof fleshy texture\twith a visible opening to canal the sound", 2721], "traffic light": ["Yes. 'Traffic light' has a tangible appearance and is a device used to control traffic.\nA few things that are visually similar to 'traffic light' but are not 'traffic light' are:\tstreet light\tspotlight\tneon sign\nThere are several useful visual features to tell there is 'traffic light' and not similar things in a photo:\tthree colored lights (red, yellow, green)\tcircular shape\tmounted on a pole or a mast\thas arrows or symbols to indicate direction of traffic\thas an electronic timer display", 2704], "bush": ["Yes. 'Bush' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'bush' but are not 'bush' are:\ttree\tgrass\tshrub\nThere are several useful visual features to tell there is 'bush' and not similar things in a photo:\tmultiple branches and leaves\tbunched foliage\ton the ground or low to the ground\tshorter than a tree or a shrub, but taller than grass\tgreen leaves or needles (depending on the type of bush)", 2691], "letter": ["Yes. 'Letter' has a tangible appearance and refers to a written or printed message on a piece of paper.\nA few things that are visually similar to 'letter' but are not 'letter' are:\tenvelope\tmemo\tnote\tcard\nThere are several useful visual features to tell there is 'letter' and not similar things in a photo:\ta piece of paper with a message on it\twritten or printed text addressed to a person or organization\tpostage stamp or mark indicating it was sent through mail.", 2689], "skis": ["Yes. 'Skis' have a tangible appearance and are a type of winter sports equipment.\nA few things that are visually similar to 'skis' but are not 'skis' are:\tsnowboards\tsleds\tboogie boards\nThere are several useful visual features to tell there are 'skis' and not similar things in a photo:\tlong and narrow\tplanks or boards with curved tips\tfor use on snow or slopes\twith poles\tusually worn with ski boots\tand often used in pairs", 2652], "kid": ["Yes. 'Kid' has a tangible appearance and is a term used to describe a young human.\nA few things that are visually similar to 'kid' but are not 'kid' are:\tyoung animals\tdolls\tstatues\tautomatons\nThere are several useful visual features to tell there is a 'kid' and not similar things in a photo:\thuman facial features\tfontanelles on the head\tgeneral size compared to other objects in the photo\tpudgy hands and feet\tshort stature, usually less than 4.5 feet (1.4 meters) tall", 2631], "pot": ["Yes. 'Pot' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'pot' but are not 'pot' are:\tpan\tbowl\tbasket\tjar\nThere are several useful visual features to tell there is 'pot' and not similar things in a photo:\trounded or cylindrical shape\twith or without handles\tmade of ceramic, clay, glass, or metal\tused for cooking or plants", 2622], "bushes": ["Yes. 'Bushes' has a tangible appearance and refers to a dense plant-growth.\nA few things that are visually similar to 'bushes' but are not 'bushes' are:\tgrass\thedges\tweeds\ttrees\nThere are several useful visual features to tell there is 'bushes' and not similar things in a photo:\tdense, leafy branches\tgrowing low to the ground\tcloser to the ground than trees\tnot as tall as trees\tmany stems or branches coming from a single point.", 2605], "baby": ["Yes. 'Baby' has a tangible appearance and refers to a young human.\nA few things that are visually similar to 'baby' but are not 'baby' are:\tpuppy\tkitten\tdoll\tchimpanzee\nThere are several useful visual features to tell there is 'baby' and not similar things in a photo:\tsmall size\tround head and chubby cheeks\tsoft, smooth skin\tlack of developed teeth and hair\tbright, wide eyes", 2604], "section": ["No. 'Section' is too abstract to have a visually concrete concept. \n\nHowever, if we consider a physical section, then it has a tangible appearance. In that case, some things that are visually similar to a 'section' but are not 'section' could include: a slice of bread, a chunk of ice, a piece of lumber, a portion of a cake or pie.\n\nUseful visual features for distinguishing a 'section' from the listed similar things in a photo could include: \n- defining edges or boundaries \n- uniform thickness or shape \n- possible labeling or numbering.", 2590], "bat": ["Yes. 'Bat' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'bat' but are not 'bat' are:\tbird\tmouse\tcat\nThere are several useful visual features to tell there is 'bat' and not similar things in a photo:\twings\tmembrane-like structure for flying\tfurry black or brown body\tpointy ears\tflying at night\tcraving for blood", 2567], "ceiling": ["Yes. 'Ceiling' has a tangible appearance and is the upper surface of a room.\nA few things that are visually similar to 'ceiling' but are not 'ceiling' are:\tsky roof\tawning\tbridge \nThere are several useful visual features to tell there is 'ceiling' and not similar things in a photo:\tinside a room or building above the head\tmatching with walls or floor\tcovered with paint, plaster, wood or any other material on the upper surface\theight from the ground level.", 2564], "spoon": ["Yes. 'Spoon' has a tangible appearance and is a kind of utensil.\nA few things that are visually similar to 'spoon' but are not 'spoon' are:\tfork\tknife\tchopsticks\tspork\nThere are several useful visual features to tell there is 'spoon' and not similar things in a photo:\ta concave and oval-shaped bowl portion\tfor scooping or holding food\ta handle at one end for holding or manipulating\tthe bowl is deeper than a fork, and there is no sharp edge like a knife", 2539], "broccoli": ["Yes. 'Broccoli' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'broccoli' but are not 'broccoli' are:\tcauliflower\tspinach\tkale\tcabbage\nThere are several useful visual features to tell there is 'broccoli' and not similar things in a photo:\tgreen color\ttight clusters of small buds on a thick stem\tslightly rounded or dome-shaped head", 2522], "feet": ["Yes. 'Feet' has a tangible appearance and is a body part.\nA few things that are visually similar to 'feet' but are not 'feet' are:\thooves\tpaws\ttentacles\t\nThere are several useful visual features to tell there are 'feet' and not similar things in a photo:\tlocated at the end of the legs\tfive toes, unless wearing socks or shoes\tvarious shapes and sizes depending on the animal/human they belong to\tcan be hairy or smooth depending on the species", 2519], "basket": ["Yes. 'Basket' has a tangible appearance and is a container made of interwoven materials.\nA few things that are visually similar to 'basket' but are not 'basket' are:\tbox\tbag\tdrawer\tcrate\nThere are several useful visual features to tell there is 'basket' and not similar things in a photo:\tinterwoven materials (such as cane, bamboo, or wicker)\thandle or handles\topen top", 2500], "bunch": ["No. 'Bunch' is too vague or abstract to be distinguished in a photo.", 2497], "animals": ["Yes. 'Animals' has a tangible appearance and refers to living organisms of different species.\nA few things that are visually similar to 'animals' but are not 'animals' are:\tplants\tinanimate objects\t\nThere are several useful visual features to tell there are 'animals' and not similar things in a photo:\torganism with cellular structure\twith eyes, mouth, nose, and ears\tbody covered by skin or fur\tor feathers\twalking on feet or legs\tdevoid of leaves and stems (in contrast to plants)", 2487], "doors": ["Yes. 'Doors' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'doors' but are not 'doors' are:\twindows\tshutters\tgates\troofs\nThere are several useful visual features to tell there is 'doors' and not similar things in a photo:\trectangular shaped\tentryway\thinged or sliding mechanism\thandle, knob or latch\tdivisions or frames within the door", 2485], "mountain": ["Yes. 'Mountain' has a tangible appearance and refers to a large landform that rises steeply above the surrounding area.\nA few things that are visually similar to 'mountain' but are not 'mountain' are:\thill\trock formation\tdune\nThere are several useful visual features to tell there is 'mountain' and not similar things in a photo:\tgreat height or altitude\tsloping sides or steep cliffs\ttowering over the surrounding terrain\tmay have snow caps or glaciers at the top", 2480], "television": ["Yes. 'Television' has a tangible appearance and is an electronic device that displays images and sound.\nA few things that are visually similar to 'television' but are not 'television' are: computer monitor, projector, tablet, smartphone.\nThere are several useful visual features to tell there is 'television' and not similar things in a photo:\tcathode-ray tube or flat-screen display\tspeakers or soundbar\tantenna, cable or satellite connection (TV signal) physical buttons or remote control.", 2477], "pillows": ["Yes. 'Pillows' has a tangible appearance and is a soft object used for resting the head or body.\nA few things that are visually similar to 'pillows' but are not 'pillows' are:\tcushion\tstuffed animals\tmattress\ttopper\tblanket\nThere are several useful visual features to tell there is 'pillows' and not similar things in a photo:\trectangular or square shape\tsoft and fluffy\tfabric covering\theadrest or lumbar support\tfor sleeping or resting on top of them.", 2473], "baseball player": ["Yes. 'Baseball player' has a tangible appearance and refers to a person who plays baseball.\nA few things that are visually similar to 'baseball player' but are not 'baseball player' are:\tfootball player\tbasketball player\ttennis player\tcoach\tumpire\nThere are several useful visual features to tell there is 'baseball player' and not similar things in a photo:\twearing a baseball uniform, including a cap\tcarrying a baseball bat or a glove\tstanding on a baseball field or diamond\tengaged in a baseball-related activity, such as hitting or catching the ball.", 2471], "stripe": ["Yes. 'Stripe' has a tangible appearance and refers to a line or band of color or texture.\nA few things that are visually similar to 'stripe' but are not 'stripe' are:\tsash\tcard\tvoucher\nThere are several useful visual features to tell there is 'stripe' and not similar things in a photo:\tsingle, elongated shape\tconsistent width\tbanding of color or pattern\trepetitive pattern or texture on a surface.", 2463], "camera": ["Yes. 'Camera' has a tangible appearance and is a type of device used for taking photographs or recording videos.\nA few things that are visually similar to 'camera' but are not 'camera' are:\tmicrophone\tflashlight\tbinoculars\tsmartphone\tlaptop\nThere are several useful visual features to tell there is 'camera' and not similar things in a photo:\tlens\tshutter\tbutton or screen for taking photos or videos\tflash\tor other light source for illuminating the subject\tbody shape similar to a rectangle or a square", 2461], "hill": ["Yes. 'Hill' has a tangible appearance and refers to a naturally formed raised area of land.\nA few things that are visually similar to 'hill' but are not 'hill' are:\tmountain\tmound\tcliff\tplateau\nThere are some useful visual features to tell there is a 'hill' and not similar things in a photo:\tgently sloping sides\ta summit that is lower than a mountain's summit\tgrassy, rocky, or wooded surface", 2453], "front": ["No. 'Front' is too vague or abstract to be distinguished in a photo.", 2441], "stove": ["Yes. 'Stove' has a tangible appearance and is a type of home appliance used for cooking.\nA few things that are visually similar to 'stove' but are not 'stove' are:\tfireplace\tbarbecue\theater\nThere are several useful visual features to tell there is 'stove' and not similar things in a photo:\tflat surface for cooking\tburners, elements or plates\tgrill or oven component\tmetallic appearance\tdials or buttons to control temperature", 2406], "numbers": ["No. 'Numbers' are too vague or abstract to be distinguished in a photo.", 2403], "collar": ["Yes. 'Collar' has a tangible appearance and is usually worn around the neck.\nA few things that are visually similar to 'collar' but are not 'collar' are:\tnecklace\tchoker\tbib\tcowl\nThere are several useful visual features to tell there is 'collar' and not similar things in a photo:\tflattened band of fabric or leather\tsits around the neck often with a buckle, button or snap closure\tmay have a tag or an attached leash", 2353], "bridge": ["Yes. 'Bridge' has a tangible appearance and is a structure used for crossing over an obstacle, such as a body of water or a roadway.\nA few things that are visually similar to 'bridge' but are not 'bridge' are:\tviaduct\tdam\toverpass\ttunnel\nThere are several useful visual features to tell there is 'bridge' and not similar things in a photo:\tsupporting piers or columns\tconnecting two banks or two land areas\tdeck for people or vehicles to travel on\trailing or fence to prevent falling into the water or off the bridge", 2339], "snowboard": ["Yes. 'Snowboard' has a tangible appearance and is a type of winter sports equipment.\nA few things that are visually similar to 'snowboard' but are not 'snowboard' are:\tskateboard\tsled\tski\tsurfboard\nThere are several useful visual features to tell there is 'snowboard' and not similar things in a photo:\trectangular shape\twith or without bindings\tfor use on snow or ice\toften has a graphic design on the underside", 2309], "books": ["Yes. 'Books' has a tangible appearance and is an object made up of pages bound together.\nA few things that are visually similar to 'books' but are not 'books' are:\tmagazines\tbinders\tjournals\tnotebooks\tnewspapers\nThere are several useful visual features to tell there is 'books' and not similar things in a photo:\tpages bound together\thard or soft cover\ttitle and/or author printed on cover\tspines with text and graphics\tpaper pages with text or images", 2307], "tennis ball": ["Yes. 'Tennis ball' has a tangible appearance and is a specific type of ball.\nA few things that are visually similar to 'tennis ball' but are not 'tennis ball' are:\tsoftball\tbaseball\tfoam ball\thandball\nThere are several useful visual features to tell there is 'tennis ball' and not similar things in a photo:\tyellow or green felt cover\tsmooth surface\twith a white line looped over\tit is smaller and less heavy than a softball", 2304], "rug": ["Yes. 'Rug' has a tangible appearance and is a type of floor covering.\nA few things that are visually similar to 'rug' but are not 'rug' are:\tcarpet\tmat\tgrass\nThere are several useful visual features to tell there is 'rug' and not similar things in a photo:\tsoft and plush texture\tdecorative patterns or designs\tusually smaller than a carpet, but larger than a mat or a doormat\ttypically used indoors on hard surfaces, such as wood or tile floors.", 2303], "cows": ["Yes. 'Cows' has a tangible appearance and is a type of domesticated animal.\nA few things that are visually similar to 'cows' but are not 'cows' are:\tbuffalo\thorses\tzebras\t\nThere are several useful visual features to tell there is 'cows' and not similar things in a photo:\tlarge, sturdy body\thooves\ton-pointed horns or ears\tblunt snout as their nose\tlong tails\tbrown or white coat color\tdistinguishable black patches on white coat color", 2301], "mountains": ["Yes. 'Mountains' has a tangible appearance and usually refers to a high and rocky landform.\nA few things that are visually similar to 'mountains' but are not 'mountains' are:\thills\tcliffs\tmounds\trock formations\nThere are several useful visual features to tell there are 'mountains' and not similar things in a photo:\ttall and high landforms\twith jagged or rocky peaks\tand steep slopes\toften covered in snow or vegetation\tusually seen with a skyline", 2293], "bathroom": ["Yes. 'Bathroom' has a tangible appearance and is a type of room used for personal hygiene.\nA few things that are visually similar to 'bathroom' but are not 'bathroom' are:\tkitchen\tlaundry room\tpowder room\tcloset\nThere are several useful visual features to tell there is 'bathroom' and not similar things in a photo:\ttoilet\tshower or bathtub\tsink or basin\tmirror\tsoap dispenser, hand towel or tissue box on a counter or shelf.", 2285], "windshield": ["Yes. 'Windshield' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'windshield' but are not 'windshield' are:\twindow\tglass door\tscreen\nThere are several useful visual features to tell there is 'windshield' and not similar things in a photo:\tlocated at the front of a vehicle\tcurved or slightly angled shape\tlarger and more sloped than other windows in the vehicle\twipers attached near the bottom of the windshield.", 2284], "dress": ["Yes. 'Dress' has a tangible appearance and is a type of clothing worn typically by women.\nA few things that are visually similar to 'dress' but are not 'dress' are:\tskirt\tjumpsuit\tromper\ttunic\nThere are several useful visual features to tell there is 'dress' and not similar things in a photo:\tfull-length garment\tthat covers both the top and the bottom of the body\tspecific styles, such as ballgown, cocktail, formal, or sundress\tcan be sleeveless, short-sleeved, or long-sleeved\tcan be made of a variety of materials, such as silk, cotton, polyester, or lace.", 2253], "sweater": ["Yes. 'Sweater' has a tangible appearance and is a knitted garment.\nA few things that are visually similar to 'sweater' but are not 'sweater' are:\tshirt\tjacket\thoodie\tcardigan\nThere are several useful visual features to tell there is 'sweater' and not similar things in a photo:\tknitted fabric\tclose-fitted on the upper body\tlong-sleeved\tno collar or lapels", 2251], "tv": ["Yes. 'TV' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'TV' but are not 'TV' are:\tcomputer monitor\tprojector\tscreen\nThere are several useful visual features to tell there is 'TV' and not similar things in a photo:\trectangular or square shape\tblinking or moving images\tantenna or cable connection\tbuttons or remote control to change channels or volume\tspeaker or sound bar", 2217], "design": ["No. 'Design' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually related to design, but not design itself, could be sketches, drawings or blueprints. They are similar in that they are often used in the process of creating a design but do not represent the design itself.\n\nUseful visual features for distinguishing design itself in a photo could be the final product - for example, the appearance of a building or a piece of furniture. However, even then, design is more of a subjective concept and may not have specific visual features that can be used to identify it definitively.", 2210], "microwave": ["Yes. 'Microwave' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'microwave' but are not 'microwave' are: \toven\ttoaster\tconvection oven\tcoffee maker\nThere are several useful visual features to tell there is 'microwave' and not similar things in a photo:\tdoor with window\tbuttons or touch screen\tdisplay for time and settings\tvent or exhaust fan\ton a countertop, not built-in or standalone like an oven.", 2203], "trash": ["Yes. 'Trash' has a tangible appearance and refers to waste material.\nA few things that are visually similar to 'trash' but are not 'trash' are:\tleaves\tdirt\tpaper\tcluttered objects\tsand\nThere are several useful visual features to tell there is 'trash' and not similar things in a photo:\tman-made objects\tfood waste\tdisposable materials, such as plastic bottles, cans, and wrappers\tempty containers\trubbish lying on the ground or in a bin", 2199], "apple": ["Yes. 'Apple' has a tangible appearance, and it is a fruit.\nA few things that are visually similar to 'apple' but are not 'apple' are: \tpomegranate \tpear \tquince \ttomato\nThere are several useful visual features to tell there is 'apple' and not similar things in a photo: \tround or oval shape \tsmooth and shiny skin \tvarious shades of reds, greens, or yellows \ta star-shaped core \ta stem and leaf on top.", 2191], "license plate": ["Yes. 'License plate' has a tangible appearance and is a type of vehicle identification.\nA few things that are visually similar to 'license plate' but are not 'license plate' are:\tstreet sign\tadvertisement\tsignature\tsticker\nThere are several useful visual features to tell there is 'license plate' and not similar things in a photo:\trectangular shape\twith letters and numbers\ton the front and back of a vehicle", 2185], "dark": ["No. 'Dark' is too vague or abstract to be distinguished in a photo.", 2180], "slice": ["Yes. 'Slice' has a tangible appearance and refers to a portion of something that has been cut.\nA few things that are visually similar to 'slice' but are not 'slice' are:\tcube\twedge\tpiece\tchunk\nThere are several useful visual features to tell there is 'slice' and not similar things in a photo:\tflat and thin piece\tof uniform thickness and shape\twith straight edges and smooth surfaces", 2166], "gloves": ["Yes. 'Gloves' has a tangible appearance and is a type of clothing. \nA few things that are visually similar to 'gloves' but are not 'gloves' are:\tmittens\tsocks\tbeauty gloves\twristbands \nThere are several useful visual features to tell there is 'gloves' and not similar things in a photo:\thave five-finger slots or two padded sections for fingers and thumb\tusually come in pairs, one for the left hand and one for the right\tmade of fabric, leather, latex, or rubber\tworn to keep hands warm or protect hands from harm or injury.", 2166], "pile": ["Yes. 'Pile' has a tangible appearance and refers to a collection of objects stacked on top of each other in a disordered or orderly manner.\nA few things that are visually similar to 'pile' but are not 'pile' are:\tstack\theap\tmound\tpyramid\nThere are several useful visual features to tell there is a 'pile' and not similar things in a photo:\tobjects stacked on top of each other\tdisordered or orderly arrangement\tof similar objects or of different objects.", 2159], "rope": ["Yes. 'Rope' has a tangible appearance and is typically made of fibers twisted or braided together.\nA few things that are visually similar to 'rope' but are not 'rope' are:\tcord\ttwine\thair\tivy\tvine\nThere are several useful visual features to tell there is 'rope' and not similar things in a photo:\tlong and flexible\tsturdy-looking\twith a braided or twisted texture\ttypically made of natural fibers\tlight or dark in color", 2158], "batter": ["Yes. 'Batter' has a tangible appearance and is a mixture used in cooking.\nA few things that are visually similar to 'batter' but are not 'batter' are:\tdough\tcement\tpaint\tsauce\nThere are several useful visual features to tell there is 'batter' and not similar things in a photo:\tthick consistency\twet, sticky texture\tusually found in a mixing bowl or poured onto a griddle or frying pan\tmade up of flour, eggs, milk, and other ingredients depending on type and cuisine", 2154], "birds": ["Yes. 'Birds' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'birds' but are not 'birds' are:\tplanes\tdrones\tbutterflies\tinsects\tflying squirrels\t\nThere are several useful visual features to tell there is 'birds' and not similar things in a photo:\tbeaks\tfeathers\twings\ttwo legs\ttwo wings\tbeady eyes\thollow bones\tfor some species, bright plumage or distinctive patterns.", 2141], "tennis player": ["Yes. 'Tennis player' has a tangible appearance and is a type of athlete.\nA few things that are visually similar to 'tennis player' but are not 'tennis player' are:\tgolfer\tbaseball player\tsoccer player\tbasketball player\nThere are several useful visual features to tell there is 'tennis player' and not similar things in a photo:\tusing a racket to hit a ball\twearing tennis shoes and clothes\thaving a net and lines marking the court", 2135], "vegetables": ["Yes. 'Vegetables' have a tangible appearance and are edible plants.\nA few things that are visually similar to 'vegetables' but are not 'vegetables' are:\tfruits\therbs\tflowers\t\nThere are several useful visual features to tell there are 'vegetables' and not similar things in a photo:\tgrow from the ground\tvariety of colors, mainly green, red, and orange\tvariety of shapes, sizes, and textures\tedible leaves, stems, and roots", 2133], "umpire": ["Yes. 'Umpire' has a tangible appearance and is a person who makes decisions in sports games.\nA few things that are visually similar to 'umpire' but are not 'umpire' are:\tcoach\treferee\tplayer\tspectator\nThere are several useful visual features to tell there is 'umpire' and not similar things in a photo:\twearing a uniform\tbatting helmet or hat\tcarrying a mask and a home plate brush\thigh socks and plate shoes\tstylish chest protector and shin guards\tmaking hand gestures and signals to communicate decisions during the game", 2113], "cloud": ["Yes. 'Cloud' has a tangible appearance and is a visible mass of condensed water vapor floating in the atmosphere.\nA few things that are visually similar to 'cloud' but are not 'cloud' are:\tsteam\tsmoke\tfog\tdust\nThere are several useful visual features to tell there is 'cloud' and not similar things in a photo:\twhite or grey\tcotton-like appearance\tclearly defined edges or shapes\tpresent in the sky", 2094], "utensil": ["Yes. 'Utensil' has a tangible appearance and is a type of tool used in cooking or eating.\nA few things that are visually similar to 'utensil' but are not 'utensil' are:\ttools\tinstruments\nThere are several useful visual features to tell there is 'utensil' and not similar things in a photo:\tsteel or metal material\tspecific shapes (spoon, fork, knife, etc.) \thandle for holding\tsmall enough for hand use.", 2090], "street light": ["Yes, 'street light' has a tangible appearance and it is a type of lighting.\nA few things that are visually similar to 'street light' but are not 'street light' are:\ttraffic cone\tflashing beacon\toutdoor spotlight\nThere are several useful visual features to tell there is 'street light' and not similar things in a photo:\ttall post or structure\twith a light source at the top\tlight directed downwards\tspherical, cylindrical or lantern-shaped enclosure or cover\tdark environment, such as street or highway.", 2082], "leaf": ["Yes. 'Leaf' has a tangible appearance and is a part of a plant.\nA few things that are visually similar to 'leaf' but are not 'leaf' are:\tgrass\tpetals\tmoss\talgae\nThere are several useful visual features to tell there is 'leaf' and not similar things in a photo:\tgreen or brown in color\tveins or patterns through the middle of the leaf\tattached to a stem\tor a branch of a tree\toften flat and thin rectangular shape or round shape.", 2067], "plates": ["Yes. 'Plates' has a tangible appearance and is a flat dish for serving or eating food.\nA few things that are visually similar to 'plates' but are not 'plates' are:\tbowls\ttrays\tplacemats\tfrisbees\nThere are several useful visual features to tell there is 'plates' and not similar things in a photo:\tflat surface\tcircular, square, or rectangular shape\traised edges or rim\tfor holding, serving or eating food made of ceramic, porcelain, or plastic material.", 2059], "socks": ["Yes. 'Socks' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'socks' but are not 'socks' are:\tpantyhose\tleggings\ttights\tstockings\nThere are several useful visual features to tell there is 'socks' and not similar things in a photo:\ttube-shaped covering for feet and legs\thave a separate compartment for each toe or one compartment for all toes\thave elastic tops to hold them in place\tmade of cotton, wool, or synthetic materials", 2022], "silver": ["Yes. 'Silver' has a tangible appearance and is a type of metal.\nA few things that are visually similar to 'silver' but are not 'silver' are:\tsteel\taluminum\tchrome\tzinc\nThere are several useful visual features to tell there is 'silver' and not similar things in a photo: shiny, reflective surface \tmetallic appearance \tlight grey color \tspecifically used in jewelry or decor \tassociated with luxury or value", 2016], "lid": ["Yes. 'Lid' has a tangible appearance and is an object that covers or seals an opening on a container.\nA few things that are visually similar to 'lid' but are not 'lid' are:\tbutton\tcap\tplug\tdoor\nThere are several useful visual features that can help distinguish 'lid' from the listed similar objects in a photo:\tround or square shape\tflat surface with an edge either all around or just on one side\tattached via hinges, screws or other fasteners to the object it covers or seals.", 1983], "plants": ["Yes. 'Plants' has a tangible appearance and refers to living organisms.\nA few things that are visually similar to 'plants' but are not 'plants' are:\tartificial plants\tfurniture\tdecorations\nThere are several useful visual features to tell there is 'plants' and not similar things in a photo:\tchlorophyll (green color in leaves)\tleaves or stems\tgrowing from the ground or a planter\tfor outdoor photos, visible soil or terrain around the plants", 1966], "refrigerator": ["Yes. 'Refrigerator' has a tangible appearance and is a type of appliance.\nA few things that are visually similar to 'refrigerator' but are not 'refrigerator' are:\toven\tdishwasher\tkitchen cabinet\nThere are several useful visual features to tell there is 'refrigerator' and not similar things in a photo:\tbox-shaped\tappliance with a door\thandles or knobs\ton the upper part, there should be a freezer, and on the lower part, a fridge box\tcan have a variety of colors (depending on design and model) \twith shelves, drawers, and compartments inside for food and drinks storage", 1965], "carpet": ["Yes. 'Carpet' has a tangible appearance and is a type of floor covering.\nA few things that are visually similar to 'carpet' but are not 'carpet' are:\trug\tmat\tgrass\nThere are several useful visual features to tell there is 'carpet' and not similar things in a photo:\tsoft and plush texture\toften made of wool or synthetic fibers\tpatterned or solid color\tcovering the entire floor surface of a room", 1961], "nose": ["Yes. 'Nose' has a tangible appearance and is a facial feature.\nA few things that are visually similar to 'nose' but are not 'nose' are:\tmouth\tchin\tforehead\t\nThere are several useful visual features to tell there is 'nose' and not similar things in a photo:\tprotruding from the center of the face\ttwo nostrils\tbridge connecting nostrils\tdividing the face into two parts\tridged surface\ttip pointing downward\tor upward", 1948], "gray": ["Yes. 'Gray' has a tangible appearance and is a specific color.\nA few things that are visually similar to 'gray' but are not 'gray' are:\tsilver\twhite\trust\nThere are no useful visual features to distinguish 'gray' from similar colors like silver, white, or rust in a photo. However, gray can have different shades, from light gray to dark gray, and this can help to distinguish it from other colors that might be similar in hue but not in tone.", 1945], "wires": ["Yes. 'Wires' has a tangible appearance and is a cord-like object used to transmit electricity or signal.\nA few things that are visually similar to 'wires' but are not 'wires' are:\tshoelaces\tribbons\tstrings\thoses\nThere are several useful visual features to differentiate 'wires' from the listed similar things in a photo:\n- Metallic or plastic appearance\n- Connected to a power source or electronic device\n- Conducting electricity or signal", 1936], "arrow": ["Yes. 'Arrow' has a tangible appearance and is a kind of pointed and directed symbol.\nA few things that are visually similar to 'arrow' but are not 'arrow' are:\ttriangle\tpyramid\tv-shaped figure\nThere are several useful visual features to tell there is 'arrow' and not similar things in a photo, such as:\tpointed and straight shape\tdirection or indication of movement, either in a line, curve, or angle.", 1928], "round": ["Yes. 'Round' has a visually concrete concept and describes an object's shape.\nA few things that are visually similar to 'round' but are not 'round' are: oval, circular, ball-shaped but not completely circular, cylindrical.\nThere are several useful visual features to tell there is 'round' and not similar things in a photo: \n-Edges are continuous and curve equally in all directions \n-No hard angles or corners \n-There is a clear and visible curvature along the perimeter of the object \n-Its diameter is consistent from every angle of view", 1921], "statue": ["Yes. 'Statue' has a tangible appearance and is a three-dimensional art piece.\nA few things that are visually similar to 'statue' but are not 'statue' are:\tsculpture\tfountain\tmonument\tbuilding\nThere are several useful visual features to tell there is 'statue' and not similar things in a photo:\tthree-dimensional\thumanoid or animal-like form\tmade of stone, metal, or other materials\tstanding on a pedestal or a base in a public place.", 1919], "dogs": ["Yes. 'Dogs' has a tangible appearance and is a type of four-legged mammal.\nA few things that are visually similar to 'dogs' but are not 'dogs' are:\tcats\tfoxes\twolves\tdingoes\thyenas\nThere are several useful visual features to tell there is 'dogs' and not similar things in a photo:\tfurry or hairy body\tfour legs\tand a tail\twet noses and drooling habits\tdifferent sizes and colors\tpointed or floppy ears\tlicking behavior\tbarking or whining voices.", 1909], "spot": ["No. 'Spot' is too vague or abstract to be distinguished in a photo since it can refer to a variety of things like a mark, a stain, a location or a dog name. \n\nHowever, a few things that are visually similar to 'spot' but are not 'spot' are:\tdots\tcircles\tstains\tpatches\n\nUseful visual features for distinguishing 'spot' from the listed similar things in a photo would depend on the context and the type of spot being referred to. For instance, if it is a spot on a dog's fur, useful visual features would be the color, texture and size of the spot, as well as the shape of the dog. If it is a spot on a shirt, useful visual features would be the color, shape and location of the spot, as well as the pattern of the shirt.", 1892], "purse": ["Yes. 'Purse' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'purse' but are not 'purse' are:\tbriefcase\tbackpack\tgym bag\tclutch\thandbag\nThere are several useful visual features to tell there is 'purse' and not similar things in a photo:\tsmall size\theld by hand or over the shoulder\ttop zipper or clasp compartment\tpockets or divisions inside for storage of cards, cash, cell phone or keys.", 1891], "sofa": ["Yes. 'Sofa' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'sofa' but are not 'sofa' are:\tchair\tbed\tbench\tottoman\nThere are several useful visual features to tell there is 'sofa' and not similar things in a photo:\thave armrests and backrests\tfor seating\tpadded with cushions or upholstery\tcan accommodate multiple people.", 1889], "suit": ["Yes. 'Suit' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'suit' but are not 'suit' are:\ttuxedo\tcoat\tjacket\tblazer\nThere are several useful visual features to tell there is 'suit' and not similar things in a photo:\tconsists of a jacket and a matching pair of trousers\tmaterial is often wool or cotton\tdark in color coordinated shoes and belt\tbutton-up or collared shirt is typically worn underneath", 1885], "fur": ["Yes. 'Fur' has a tangible appearance and is a type of mammalian hair.\nA few things that are visually similar to 'fur' but are not 'fur' are:\twool\tfeathers\tbristles\nThere are several useful visual features to tell there is 'fur' and not similar things in a photo:\tthick and dense\thairy\tlayered\tsoft to the touch\tfound on mammals like dogs, cats, foxes, etc.", 1859], "signs": ["Yes. 'Signs' has a tangible appearance and is a type of visual communication tool.\nA few things that are visually similar to 'signs' but are not 'signs' are:\tpaintings\tposters\tpictures\tbanners\tdecorations\nThere are several useful visual features to tell there are 'signs' and not similar things in a photo:\twords or symbols used to communicate a message\tclearly visible lettering or imagery\tmounted on a surface in a public space", 1859], "stack": ["Yes. 'Stack' has a tangible appearance and refers to a pile of objects.\nA few things that are visually similar to 'stack' but are not 'stack' are:\theap\tpile\tmound\ttower\nThere are several useful visual features to tell there is 'stack' and not similar things in a photo:\tobjects arranged in a vertical or horizontal manner\teach object is flush with the one next to it\teach object is typically uniform in shape and size\tmay be leaning to one side", 1858], "fire": ["Yes. 'Fire' has a tangible appearance and produces a flame.\nA few things that are visually similar to 'fire' but are not 'fire' are: lava torch candle electric light sunlight reflection \nThere are several useful visual features to tell there is 'fire' and not similar things in a photo: bright orange or red color flickering flame smoke or steam producing heat or light consuming fuel shimmering, wavering movements", 1856], "spots": ["Yes. 'Spots' has a tangible appearance and is a pattern or mark on a surface.\nA few things that are visually similar to 'spots' but are not 'spots' are:\tfreckles\tstains\tpatterns of light and shadow\tonion rings\nThere are several useful visual features to distinguish 'spots' from the listed similar things in a photo:\tcircular or oval shape\tclear edges or borders\tcontrast in color or brightness\twithin a larger pattern or context, such as animal fur or fabric", 1852], "bottom": ["No. 'Bottom' is too vague or abstract to be distinguished in a photo.", 1851], "kitchen": ["Yes. 'Kitchen' has a tangible appearance and is a type of room.\nA few things that are visually similar to 'kitchen' but are not 'kitchen' are:\tliving room\tdining room\toffice\tbathroom\nThere are several useful visual features to tell there is 'kitchen' and not similar things in a photo:\noven, stove or cooktop\tcountertops and cabinets\tsink and faucet\trefrigerator\tor other appliances like a toaster or blender\tkitchen utensils and cookware\ttable and chairs for dining or eating\tin general, a kitchen will have more appliances, utensils, and tools for cooking and preparing food than other rooms.", 1850], "sun": ["Yes. 'Sun' has a tangible appearance, it is a star and is visible in the sky during the day.\nThere are no things that are visually similar to 'sun' as it is a unique astronomical object.\nThere is not any visual feature that is useful to distinguish 'sun' from similar things in a photo as there are no similar things to 'sun'.", 1845], "scene": ["No. 'Scene' is too vague or abstract to be distinguished in a photo.", 1839], "drink": ["Yes. 'Drink' has a tangible appearance and is a liquid that is consumed.\nA few things that are visually similar to 'drink' but are not 'drink' are:\tliquid soap\tpaint\twater in a pool, lake or ocean\nThere are several useful visual features to tell there is 'drink' and not similar things in a photo:\tcontainer, such as a glass or a bottle\tvisible liquid, sometimes with ice or a straw\tlabeled with a brand name, such as soda or beer", 1816], "curtains": ["Yes. 'Curtains' has a tangible appearance and is a kind of window covering.\nA few things that are visually similar to 'curtains' but are not 'curtains' are:\tblinds\tshades\tdrapes\ttapestries\nThere are several useful visual features to tell there is 'curtains' and not similar things in a photo:\tfabric\tdraped or gathered material\thanging from a rod or rail covering a window or a door\ttypically solid-colored or patterned", 1813], "tile": ["Yes. 'Tile' has a tangible appearance and is a type of flat, usually square, piece of material used for flooring or walls.\nA few things that are visually similar to 'tile' but are not 'tile' are:\tbricks\tpavers\tlinoleum\tlaminate\nThere are several useful visual features to tell there is 'tile' and not similar things in a photo:\tflat and level surface\tsquare or rectangular shape\thard and durable material\tpatterns or designs in the surface\tglazed or unglazed surface", 1805], "pink": ["Yes. 'Pink' has a tangible appearance and is a specific hue on the color spectrum.\nA few things that are visually similar to 'pink' but are not 'pink' are:\tlight red\tsalmon\tcoral\tmagenta\nThere are several useful visual features to tell there is 'pink' and not similar things in a photo:\ta hue between red and white\twith a cool, blue tint or a warm, yellow tint\tlighter than red, but darker than white", 1792], "words": ["No. 'Words' is too vague or abstract to be visually concrete.\nThere are no things that are visually similar to 'words' but are not 'words'.\nUseful visual features for identifying words in a photo depend on the context in which they appear. For example, words on a sign or a book will have distinct shapes, sizes, and colors, while words in a speech bubble in a comic strip will have a distinctive font and style.", 1791], "tennis court": ["Yes. 'Tennis court' has a tangible appearance and is a kind of sports venue.\nA few things that are visually similar to 'tennis court' but are not 'tennis court' are:\tbasketball court\tvolleyball court\tbadminton court\tpickleball court\nThere are several useful visual features to tell there is 'tennis court' and not similar things in a photo:\trectangular shape\twith a net at the center\tmarked with white lines on green or clay surface", 1791], "tag": ["Yes. 'Tag' has a tangible appearance and is a small piece of paper or cardboard attached to an object for identification.\nA few things that are visually similar to 'tag' but are not 'tag' are:\tsticker\tlabel\tstamp\tticket\nThere are several useful visual features to tell there is 'tag' and not similar things in a photo:\trectangular or square shape\thanging or attached to an object\twith specific information, such as names, prices, or descriptions\tmade of paper or cardboard", 1778], "cell phone": ["Yes. 'Cell phone' has a tangible appearance and is a handheld electronic device.\nA few things that are visually similar to 'cell phone' but are not 'cell phone' are: calculator, remote control, iPod, handheld gaming device.\nThere are several useful visual features to tell there is 'cell phone' and not similar things in a photo:\tscreen\tdisplay with icons or text\tkeyboard or touch screen for input\tcamera\tfunctional buttons for navigation, volume, and power.", 1775], "dish": ["Yes. 'Dish' has a tangible appearance and can refer to a plate, bowl or other type of container used for serving food.\nA few things that are visually similar to 'dish' but are not 'dish' are:\tcup\tbucket\tvase\tbasket\tpot\nThere are several useful visual features to tell there is 'dish' and not similar things in a photo:\tflat or concave surface\twith or without a rim or edges\tintended for food or liquid serving or mixing\tmade of ceramic, glass, metal, or plastic", 1769], "watch": ["Yes. 'Watch' has a tangible appearance and is a type of timekeeping device worn on the wrist.\nA few things that are visually similar to 'watch' but are not 'watch' are:\tbracelet\tfitness tracker\tjewelry\nThere are several useful visual features to tell there is 'watch' and not similar things in a photo:\ta small clock face\ton a band\tor a strap around the wrist\thands or digital display indicating time\tbuttons or knobs to adjust the time or other functions.", 1761], "boats": ["Yes. 'Boats' has a tangible appearance and is a mode of transportation on water.\nA few things that are visually similar to 'boats' but are not 'boats' are:\traft\tcanoe\tkayak\tbarge\nThere are several useful visual features to tell there is 'boats' and not similar things in a photo:\tfloats on water\thull, deck, and superstructure\trudder and keel propulsion engine or sails", 1755], "cabinets": ["Yes. 'Cabinets' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'cabinets' but are not 'cabinets' are:\tshelves\tdrawers\tdesks\t\nThere are several useful visual features to tell there is 'cabinets' and not similar things in a photo:\tdoors or panels\thandles or knobs\tfor holding things, such as dishes or clothes\tmultiple shelves or compartments\tmay be attached to a wall or stand on its own", 1751], "boots": ["Yes. 'Boots' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'boots' but are not 'boots' are:\tshoes\tsandals\tslippers\tsocks\t\nThere are several useful visual features to tell there is 'boots' and not similar things in a photo:\tcovers the foot and ankle\tmay have laces or zippers\tmay have a heel\tsole made of rubber or sturdy material\tintended for outdoor or heavy-duty use", 1731], "mane": ["Yes. 'Mane' has a tangible appearance and is the long hair growing along the neck of certain animals, such as horses and lions.\nA few things that are visually similar to 'mane' but are not 'mane' are:\tfur\thair\tWool\t\nThere are several useful visual features to tell there is 'mane' and not similar things in a photo:\tlonger hair along the back of the animal's neck\thaving a larger and more abundant hair along the neck, compared to the rest of the body\thaving a specific color and texture, such as being wavy or curly.", 1720], "mouse": ["Yes. 'Mouse' has a tangible appearance and is a type of small rodent.\nA few things that are visually similar to 'mouse' but are not 'mouse' are:\thamster\trat\tvole\tshrew\nThere are several useful visual features to tell there is 'mouse' and not similar things in a photo:\tsmall size\tupright, oval-shaped ears\tpointy snout and teeth\tshort, hairy tail in proportion to the body\tfur-covered body, usually grey or brown in color", 1716], "button": ["Yes. 'Button' has a tangible appearance and is an object with a specific function.\nA few things that are visually similar to 'button' but are not 'button' are:\tbeads\tdots\tblinds\tkeypads\nThere are several useful visual features to tell there is 'button' and not similar things in a photo:\tcircular or square-shaped\tobject with an indentation in the middle and a raised edge\tfor clothing, made of plastic or metal, often with holes or a shank on the back\tfor electronic devices, often with letters or symbols printed on top\tof a different color or material than the surrounding surface.", 1715], "sauce": ["Yes. 'Sauce' has a tangible appearance and is a type of food or seasoning.\nA few things that are visually similar to 'sauce' but are not 'sauce' are:\tliquid\tdressing\toil\tliquid soap\nThere are several useful visual features to tell there is 'sauce' and not similar things in a photo:\tthicker than water\tpourable\tfood related\tcolorful\tcomes in a container like a jar or bottle", 1715], "donut": ["Yes. 'Donut' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'donut' but are not 'donut' are:\tbagel\tchurro\tcruller\tpastry\nThere are several useful visual features to tell there is 'donut' and not similar things in a photo:\tcircular shape\twith a hole in the center or without hole\tglazed or sprinkled in sugar or other decorations\tfried and chewy", 1714], "skateboarder": ["Yes. 'Skateboarder' has a tangible appearance and refers to a person riding a skateboard.\nA few things that are visually similar to 'skateboarder' but are not 'skateboarder' are:\tsnowboarder\tsurfer\tbiker\troller skater\nThere are several useful visual features to tell there is 'skateboarder' and not similar things in a photo:\triding a skateboard\tperforming skateboarding tricks\tusing a ramp or a skatepark\twearing skateboard shoes and protective gear\tskateboard visible in the photo.", 1703], "view": ["No. 'View' is too vague or abstract to be distinguished in a photo.", 1674], "bottles": ["Yes. 'Bottles' has a tangible appearance and is a container made of glass or plastic for liquids.\nA few things that are visually similar to 'bottles' but are not 'bottles' are:\tcan\tjar\tcup\tglass\nThere are several useful visual features to tell there are 'bottles' and not similar things in a photo:\tpartially or fully cylindrical shape\tnarrow neck with a cap or a cork\tbody made of glass or plastic\tclear or colored liquid visible inside\tthe shape of the bottle reflects the type of beverage contained", 1674], "t-shirt": ["Yes. 'T-shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 't-shirt' but are not 't-shirt' are:\tshirt\tpolo-shirt\ttank top\tHenley shirt\nThere are several useful visual features to tell there is 't-shirt' and not similar things in a photo:\tshort sleeves\tround neckline\tsoft fabric\tloose-fitting shape", 1671], "engine": ["Yes. 'Engine' has a tangible appearance and is a mechanical device that produces power.\nA few things that are visually similar to 'engine' but are not 'engine' are:\tmachine\tmotor\tmechanism\tgenerator\nThere are several useful visual features to tell there is 'engine' and not similar things in a photo:\tmetallic and mechanical in appearance\tpistons and cylinders\twires, hoses, and tubes\tcooling fans or fins\tlarge exhaust pipes\tconnected to a vehicle or other machinery", 1632], "belt": ["Yes. 'Belt' has a tangible appearance and is a clothing accessory.\nA few things that are visually similar to 'belt' but are not 'belt' are:\tscarf\ttie\tband\tsash\nThere are several useful visual features to tell there is 'belt' and not similar things in a photo:\tstrip of material worn around the waist\tmetal or plastic buckle\tfor men, generally made of leather or other sturdy material, but for women, it can be made from a variety of materials and decorated with different designs and colors.", 1603], "bears": ["Yes. 'Bears' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'bears' but are not 'bears' are:\tdogs\tcats\tcoyotes\twolves\t\nThere are several useful visual features to tell there is 'bears' and not similar things in a photo:\tvery large and heavy animal\tfurry or shaggy appearance\tsharp claws and teeth\thunched-over shoulders, giving them a distinctive loping gait\tbroad head and small ears", 1597], "sticker": ["Yes. 'Sticker' has a tangible appearance and is a type of adhesive label.\nA few things that are visually similar to 'sticker' but are not 'sticker' are:\ttape\tbookmarks\tpost-it notes\nThere are several useful visual features to tell there is 'sticker' and not similar things in a photo:\tpeelable backing layer\tadhesive side used to stick to a surface\tvariety of designs, shapes, and sizes", 1590], "air": ["No. 'Air' is too vague or abstract to be distinguished in a photo.", 1590], "tiles": ["Yes. 'Tiles' has a tangible appearance and is a kind of surface covering.\nA few things that are visually similar to 'tiles' but are not 'tiles' are:\thardwood floor\tcarpet \tmarble counter\ttop\ticon tiles\tlinoleum\nThere are several useful visual features to tell there is 'tiles' and not similar things in a photo:\tsquare or rectangular shapes\tsmooth and flat surface\tvarious colors or patterns\tarranged in a grid\tor always touching at least one other tile.", 1583], "wire": ["Yes. 'Wire' has a tangible appearance and is a type of thin, flexible, and elongated metal.\nA few things that are visually similar to 'wire' but are not 'wire' are:\trope\thair\tsprings\those\tbranches\nThere are several useful visual features to tell there is 'wire' and not similar things in a photo:\tthin and flexible\telongated shape\tmetallic appearance\tsmooth surface\tand it is used for electrical conductivity, fencing, or craft purposes", 1579], "branches": ["Yes. 'Branches' has a tangible appearance and refers to the parts of a tree that grow out from the trunk.\nA few things that are visually similar to 'branches' but are not 'branches' are: twigs, roots, vines, wires, antennas.\nThere are several useful visual features to tell there are 'branches' and not similar things in a photo: they grow from the trunk of a tree, they are thicker than a twig or a vine, they have small woody outgrowths, they usually have leaves attached to them.", 1578], "sneakers": ["Yes. 'Sneakers' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'sneakers' but are not 'sneakers' are: \tloafers\tboots\tsandals\nThere are several useful visual features to tell there is 'sneakers' and not similar things in a photo:\tlow-cut shoes\tlaces or straps\tcomfortable and casual style\tcushioned sole and breathable material.", 1576], "horns": ["Yes. 'Horns' has a tangible appearance and is a type of animal feature.\nA few things that are visually similar to 'horns' but are not 'horns' are:\tantlers\tspikes/cones/hardened growths on plants\t\nThere are several useful visual features to tell there are 'horns' and not similar things in a photo:\tprotrude from the head of an animal\tsymmetrical\tcurved or straight shape\tpointed or blunt tip\tgenerally found in pairs in mammals", 1565], "cloth": ["Yes. 'Cloth' has a tangible appearance and refers to a material made of fibers or fabrics.\nA few things that are visually similar to 'cloth' but are not 'cloth' are:\tpaper\ttowel\tbag\tpillowcase\nThere are several useful visual features to tell there is 'cloth' and not similar things in a photo:\tsoft and flexible texture\tvariety of colors and patterns\tweaving and stitching patterns\tfrayed edges\tor folded layers", 1561], "paint": ["Yes, 'paint' has a tangible appearance and is a form of liquid or substance that can be applied to a surface.\nA few things that are visually similar to 'paint' but are not 'paint' are: ink, dye, varnish, enamel, stain.\nThere are several useful visual features to tell there is 'paint' and not similar things in a photo: liquid consistency, often in a can, can be applied with a brush, roller or spray, comes in a variety of colors, dries to a solid or semi-solid state, often used for decorative or protective purposes.", 1559], "word": ["No. 'Word' is too vague or abstract to be distinguished in a photo.", 1557], "couple": ["No. 'Couple' is too vague or abstract to be distinguished in a photo.", 1554], "vest": ["Yes. 'Vest' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'vest' but are not 'vest' are:\tjacket\tblazer\tsweater\tshirt\nThere are several useful visual features to tell there is 'vest' and not similar things in a photo:\tsleeveless\tcollarless\tbuttoned down\tthe top comes up to the chest", 1538], "monitor": ["Yes. 'Monitor' has a tangible appearance and is a kind of electronic display device.\nA few things that are visually similar to 'monitor' but are not 'monitor' are:\tTV\tLaptop\tscreen\tphone\nThere are several useful visual features to tell there is 'monitor' and not similar things in a photo:\tretangular in shape\thave a stand at the back or mount on a wall\tthin and flat\tdevice to output video or graphics\tpassive display (most monitors do not have a TV tuner or speakers built in)", 1522], "room": ["Yes. 'Room' has a tangible appearance and is an enclosed space within a building.\nA few things that are visually similar to 'room' but are not 'room' are:\thallway\tcorridor\tatrium\topen floor plan area\nThere are several useful visual features to tell there is 'room' and not similar things in a photo:\tdoor and/or window\tfour walls\tceiling and floor\tdecorative elements or furniture indicating its function (e.g. bed, desk, dining table)", 1507], "toy": ["Yes. 'Toy' has a tangible appearance and is an object intended for play.\nA few things that are visually similar to 'toy' but are not 'toy' are:\tdecorative figurine\tsculpture\ttool\thousehold item\nThere are several useful visual features to tell there is 'toy' and not similar things in a photo:\tbright colors\tunusual shapes or forms\tdepiction of cartoon or animal characters\tsmall size\thandles or buttons for interaction or manipulation", 1499], "platform": ["Yes. 'Platform' has a tangible appearance and can refer to a variety of structures.\nA few things that are visually similar to 'platform' but are not 'platform' are:\trostrum\tstage\tlanding\tdock\tpodium\nThere are several useful visual features to tell there is 'platform' and not similar things in a photo:\ta flat, elevated surface\tusually horizontal\tlarger than the ground it's built on\tcan support heavy weight, such as people or equipment\tmay have railings or other safety features", 1497], "color": ["Yes. 'Color' has a tangible appearance and refers to the various shades of colors.\nThere are no things similar to 'color' that are not 'color'.\nThere are no visual features to distinguish 'color' from similar things in a photo.", 1493], "frame": ["Yes, 'frame' has a tangible appearance and usually refers to a structure that surrounds or encloses something.\nA few things that are visually similar to 'frame' but are not 'frame' are:\tborder\tedge\toutline\tcarving\nThere are several useful visual features to distinguish 'frame' from the listed similar things in a photo:\tenclosing or surrounding something\trectangular or square shape\tthickness or depth\tcorners or joints around the edges\thanging on a wall or standing on a surface.", 1491], "skirt": ["Yes. 'Skirt' has a tangible appearance and is a type of garment worn by women.\nA few things that are visually similar to 'skirt' but are not 'skirt' are:\tshorts\ttrousers\tpants\tleggings\nThere are several useful visual features to tell there is 'skirt' and not similar things in a photo:\twaistband\tdraping fabric around the hips or waist\tmostly covering the lower half of the body", 1489], "cord": ["Yes. 'Cord' has a tangible appearance and refers to a flexible string or rope-like object.\nA few things that are visually similar to 'cord' but are not 'cord' are:\tthread\twire\tshoelaces\thair\nThere are several useful visual features to tell there is 'cord' and not similar things in a photo:\tthick and flexible\tmade of fibers or strands\tobject attached to or hanging from the cord (e.g. plug, knot, hook)", 1478], "goggles": ["Yes. 'Goggles' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'goggles' but are not 'goggles' are:\tglasses\tsunglasses\tswim masks\tvirtual reality headsets\nThere are several useful visual features to tell there are 'goggles' and not similar things in a photo:\teye-hugging design\tadjustable strap\tto protect the eyes from hazards such as water, wind, snow, sand, or dust\tcylindrical or spherical lenses\twith or without vents or anti-fog coating.", 1467], "tomato": ["Yes. 'Tomato' has a tangible appearance and is a kind of fruit.\nA few things that are visually similar to 'tomato' but are not 'tomato' are:\tapple\tpepper\tpomegranate\nThere are several useful visual features to tell there is 'tomato' and not similar things in a photo:\tround or oblong shape\tsmooth surface\tglossy or matte skin\tbright red or yellow color\tgreen stem at the top\tfleshy interior with small seeds inside", 1463], "uniform": ["Yes. 'Uniform' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'uniform' but are not 'uniform' are: casual clothes, formal wear, costumes, workwear\nThere are several useful visual features to tell there is 'uniform' and not similar things in a photo:\tconsistent colors, patterns, or designs worn by a group of people, often related to a specific organization or profession (e.g. military, police, airline staff, etc.)\tthe uniform may include specific accessories or equipment (e.g. badge, hat, helmet, boots, etc.)", 1461], "poles": ["Yes. 'Poles' has a tangible appearance and can refer to a variety of long, thin objects.\nA few things that are visually similar to 'poles' but are not 'poles' are:\tposts\tpillars\trods\tbars\nThere are several useful visual features to tell there is 'poles' and not similar things in a photo, including:\tstanding upright\tlong, thin shape\tcylindrical form\tmade of wood, metal, plastic or other materials.", 1460], "end": ["No. 'End' is too vague or abstract to be distinguished in a photo.", 1459], "surface": ["Yes. 'Surface' has a tangible appearance and refers to the outer layer of an object.\nA few things that are visually similar to 'surface' but are not 'surface' are:\tlayer\tskin\ttexture\tpaint\tcolor\nThere are no particular visual features to distinguish 'surface' as it depends on the context and the specific object on which the surface is being discussed. However, some general distinguishing features may include:\tthe material from which the surface is made\tthe texture, color, and pattern of the surface\tthe position of the surface in relation to other parts of the object", 1458], "object": ["No. 'Object' is too vague or abstract to be distinguished in a photo.", 1450], "base": ["No. 'Base' is too abstract to be visually distinguished in a photo, as it can refer to a range of different objects or concepts, such as military bases, baseball bases, or chemical bases. \nHowever, some things that may be visually similar to a specific type of 'base' but are not that type of 'base' include:\t\n- Home plate in baseball: this is similar in shape to a military base or a chemical base, but serves a different purpose in a different context.\n- A pedestal or stand: these are also sometimes referred to as a 'base', but may look different from other types of bases.\n- A socket or plug: for example, the base of a light bulb, which is similar in shape to a chemical base or a military base, but is designed for a different purpose.\n\nUseful visual features for distinguishing a specific type of 'base' from visually similar things in a photo will vary depending on the type of base being referred to. In general, some possible visual features that could help distinguish one type of base from others might include:\n- The presence of military equipment or structures, such as walls or guards, in the case of a military base\n- The presence of markers or lines on the ground indicating the dimensions of the base, in the case of a baseball or softball base\n- The presence of chemical or laboratory equipment, such as beakers, in the case of a chemical base", 1450], "tip": ["No. 'Tip' is too vague or abstract to be distinguished in a photo. However, 'tip' can refer to the end point or a small portion of something.\nA few things that are visually similar to 'tip' but are not 'tip' are: end, point, edge, corner, extremity.\nThere are several useful visual features to tell there is a 'tip' and not similar things in a photo:\ta small portion at the end of something\toften pointed or tapered in shape\tmay be a different color or texture from the rest of the object\tmay indicate direction or orientation", 1448], "corner": ["Yes. 'Corner' has a tangible appearance and refers to the point where two edges meet.\nA few things that are visually similar to 'corner' but are not 'corner' are:\tedge\tline\tangle\nThere are several useful visual features to tell there is a 'corner' and not similar things in a photo:\ttwo edges meeting at a sharp angle\tcreating a 90 degree angle or a rounded corner\tmeeting at the intersection of two walls, floors, or ceilings", 1440], "metal pole": ["Yes. 'Metal pole' has a tangible appearance and is a kind of cylindrical support structure.\nA few things that are visually similar to 'metal pole' but are not 'metal pole' are:\twooden pole\tmetal rod\tlamppost\tfence post\nThere are several useful visual features to tell there is 'metal pole' and not similar things in a photo:\tcylindrical shape\tmetal material\tsolid structure\tno visible branching or bending", 1433], "blinds": ["Yes. 'Blinds' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'blinds' but are not 'blinds' are: curtains, shades, shutters.\nThere are several useful visual features to tell there are 'blinds' and not similar things in a photo: slatted or venetian appearance, mounted horizontally or vertically.", 1430], "trousers": ["Yes. 'Trousers' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'trousers' but are not 'trousers' are:\tleggings\tjeans\tskirts\tshorts\tpants\nThere are several useful visual features to tell there is 'trousers' and not similar things in a photo:\ttwo separate tubes of fabric\tthat cover each leg\tentirely\twaistband to keep them up\tzippers, buttons, or other closures", 1427], "scarf": ["Yes. 'Scarf' has a tangible appearance and is an item of clothing.\nA few things that are visually similar to 'scarf' but are not 'scarf' are:\tshawl\tponcho\tcape\tsnood\nThere are several useful visual features to tell there is 'scarf' and not similar things in a photo:\tlong and narrow shape\tfolded or wrapped around the neck\tor draping off the shoulders\tvariety of colors and patterns\ttypically made of a warm material such as wool, cashmere or silk.", 1418], "beak": ["Yes. 'Beak' has a tangible appearance and is a characteristic part of a bird's anatomy.\nA few things that are visually similar to 'beak' but are not 'beak' are:\tteeth\tmouths\tsnouts\tfaces\nThere are several useful visual features to tell there is 'beak' and not similar things in a photo:\thard and pointy\tattached to the head of a bird\thas two parts (upper and lower)\tthat are used for eating, grooming and defense/dominance\thas distinctive shape and size for different bird species.", 1404], "bags": ["Yes. 'Bags' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'bags' but are not 'bags' are:\tboxes\tpouches\tenvelopes\tbaskets\nThere are several useful visual features to tell there is 'bags' and not similar things in a photo:\tsoft material\tfor carrying things\thandles or straps\tfor containing an object in a secure way\tclosing mechanism (zipper, buttons, drawstrings, etc.)", 1401], "cart": ["Yes. 'Cart' has a tangible appearance and is a wheeled vehicle for transporting goods or people.\nA few things that are visually similar to 'cart' but are not 'cart' are:\twagon\tbike\troller skate\tshopping trolley\nThere are several useful visual features to tell there is 'cart' and not similar things in a photo:\twheels\thandles or shafts\tbasket, tray or other container\tstructure made of wood or metal", 1400], "speaker": ["Yes. 'Speaker' has a tangible appearance and refers to a device that produces sound.\nA few things that are visually similar to 'speaker' but are not 'speaker' are:\tmicrophone\theadphone\talarm clock\tmegaphone\nThere are several useful visual features to tell there is 'speaker' and not similar things in a photo:\tusually two units, one for high frequency and one for low frequency\tsound waves emanating from it\tcone-shaped or box-shaped with an opening in the front\tgrilles or holes on the surface for sound to come out\tvolume control or other buttons on the surface", 1393], "fire hydrant": ["Yes. 'Fire hydrant' has a tangible appearance and is a type of street furniture.\nA few things that are visually similar to 'fire hydrant' but are not 'fire hydrant' are:\tbollard\ttraffic cone\tlamppost\tmanhole cover\tpostbox\nThere are several useful visual features to tell there is 'fire hydrant' and not similar things in a photo:\tupright post\twith water valve\ton top\twith a hose attachment\tdifferent colors from surrounding objects, usually red or yellow\tMarked with reflective tape or paint", 1391], "tall": ["No. 'Tall' is too vague or abstract to be distinguished in a photo.", 1388], "branch": ["Yes. 'Branch' has a tangible appearance and is a part of a tree.\nA few things that are visually similar to 'branch' but are not 'branch' are:\tstick\tlogs\troots\nThere are several useful visual features to tell there is 'branch' and not similar things in a photo:\twide and flat\twrinkled and rough texture\tbifurcates from a trunk or a stem\tcovered in leaves\tbends outwards or downwards", 1386], "writing": ["Yes. 'Writing' has a tangible appearance and is a physical representation of language.\nA few things that are visually similar to 'writing' but are not 'writing' are:\tartwork\tcalligraphy\tdoodles\tgraphs\ttextures\nThere are several useful visual features to tell there is 'writing' and not similar things in a photo:\twords or letters arranged in a specific order\tmeaningful language\tlegible or recognizable characters\tpaper or surface used to record the writing tool used to create the writing", 1384], "umbrellas": ["Yes. 'Umbrellas' has a tangible appearance and is an object used for protection from rain or sun.\nA few things that are visually similar to 'umbrellas' but are not 'umbrellas' are:\tcanes\twalking sticks\tlarge plants\nThere are several useful visual features to tell there is 'umbrellas' and not similar things in a photo:\tcircular or dome shape\tcanopy made of fabric or other waterproof material\tmetal or plastic frame\thandle where a person can hold it", 1382], "baseball": ["Yes. 'Baseball' has a tangible appearance and is a type of ball used in a sport.\nA few things that are visually similar to 'baseball' but are not 'baseball' are:\ttennis ball\tcricket ball\tfield hockey ball\trubber ball\nThere are several useful visual features to tell there is 'baseball' and not similar things in a photo:\n-White with red stitching\n-Rough and hard to touch\n-Nine-inch circumference\n-Used for playing baseball", 1379], "pavement": ["Yes. 'Pavement' has a tangible appearance and refers to the surface of a road or sidewalk.\nA few things that are visually similar to 'pavement' but are not 'pavement' are:\tgrass\tconcrete tile\tflooring\tbrick\nThere are several useful visual features to tell there is 'pavement' and not similar things in a photo:\tflat and even surface\tasphalt or concrete construction\tdifferent lanes or directions for pedestrians and vehicles.", 1378], "name": ["No. 'Name' is too vague or abstract to be distinguished in a photo.", 1376], "wine": ["Yes. 'Wine' has a tangible appearance and is a type of alcoholic beverage.\nA few things that are visually similar to 'wine' but are not 'wine' are:\tjuice\tvinegar\tlipstick\tred-colored water\tmedicine\nThere are several useful visual features to tell there is 'wine' and not similar things in a photo:\tliquid contained in a glass or a bottle\ttypically red or white in color\ttranslucent\twhen poured or swirled, it leaves a trail called 'legs' or 'tears'\tthat visually differs from juice or colored water", 1349], "boys": ["Yes. 'Boys' has a tangible appearance and is a reference to male children.\nA few things that are visually similar to 'boys' but are not 'boys' are:\tmen\tpuppets\tmale animals\taction figures\nThere are several useful visual features to tell there is 'boys' and not similar things in a photo:\tborderline juvenile appearance\tshort hair or a brightly-colored haircut\tbold or plain clothing preferred over floral, bright-colored, or patterned prints\ttypically more active or rough in games or activities", 1344], "headlights": ["Yes. 'Headlights' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'headlights' but are not 'headlights' are:\temergency vehicle lights\tflashlight\twall sconce\nThere are several useful visual features to tell there is 'headlights' and not similar things in a photo:\tlocated in the front of a vehicle\tcircular or oval shape\twarm, white light\tprojected forward to illuminate the road\tmay have a reflective or colored trim around the perimeter.", 1339], "bucket": ["Yes. 'Bucket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'bucket' but are not 'bucket' are:\tbasket\tpail\tbarrel\tbowl\nThere are several useful visual features to tell there is 'bucket' and not similar things in a photo:\tround or cylindrical shape\twith a handle or bail\tfor carrying liquids or solids\tmade of metal or plastic\tbottom is flat", 1338], "snowboarder": ["Yes. 'Snowboarder' has a tangible appearance and is a person who engages in the activity of snowboarding.\nA few things that are visually similar to 'snowboarder' but are not 'snowboarder' are:\tskier\tbobsledder\tice skater\nThere are several useful visual features to tell there is 'snowboarder' and not similar things in a photo:\twearing snow gear, including boots, jacket, and pants\tsnowboard strapped to their boots\tbending knees and leaning forward on the snowboard\tjumping or doing tricks on the snowboard", 1336], "front wheel": ["Yes. 'Front wheel' has a tangible appearance and is a component of a vehicle.\nA few things that are visually similar to 'front wheel' but are not 'front wheel' are:\trear wheel\tbicycle handlebar\tdoor knob\twagon wheel\nThere are several useful visual features to tell there is 'front wheel' and not similar things in a photo:\tpositioned at the front of the vehicle\thub, spokes and rim\tattachment to the suspension system of the vehicle\tlarger than the rear wheel on some vehicles", 1329], "brick building": ["Yes. 'Brick building' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'brick building' but are not 'brick building' are:\tstone building\tcement building\t\nThere are several useful visual features to tell there is 'brick building' and not similar things in a photo:\tflat, rectangular-shaped bricks\tbrown, red or grey in color\tchimneys or smokestacks\tmortar between the bricks\trectangular or square-shaped windows and doors", 1322], "shadows": ["Yes. 'Shadows' has a tangible appearance and is a dark area produced by an object coming between rays of light and a surface.\nA few things that are visually similar to 'shadows' but are not 'shadows' are:\treflection\tmirage\tillusion\nThere are several useful visual features to tell there is 'shadows' and not similar things in a photo:\tdark area produced by an object blocking light\tshape that corresponds to the object's outline\tlight source casting the shadows gives an idea of the location and direction of the light\tsource of the light can sometimes be seen in the photo.", 1321], "metal fence": ["Yes. 'Metal fence' has a tangible appearance and is used for barriers or enclosures.\nA few things that are visually similar to 'metal fence' but are not 'metal fence' are:\tchain-link fence\tbarbed wire\tfishing net\twire mesh\nThere are several useful visual features to tell there is 'metal fence' and not similar things in a photo:\tmetal material\tvertical or horizontal bars or wires\tmostly solid or opaque structure\twith or without pointed tips or decoration.", 1317], "towels": ["Yes. 'Towels' has a tangible appearance and is a piece of cloth used for drying oneself or things.\nA few things that are visually similar to 'towels' but are not 'towels' are:\n- Cloths\n- Rugs\n- Mats\n- Tablecloths\nThere are several useful visual features that can help to distinguish 'towels' from the listed similar things in a photo:\n- Absorbent texture\n- Rectangular or square shape\n- Typically has a loop for hanging \n- Often brightly colored or patterned", 1308], "tent": ["Yes. 'Tent' has a tangible appearance and is a kind of shelter.\nA few things that are visually similar to 'tent' but are not 'tent' are:\tcanopy\ttarpaulin\tawning\nThere are several useful visual features to tell there is 'tent' and not similar things in a photo:\tpyramid or dome shape\tfabric walls and roof\tpoles and stakes to hold it up\ta flap or entrance", 1306], "lettuce": ["Yes. 'Lettuce' has a tangible appearance and is a type of leafy green vegetable.\nA few things that are visually similar to 'lettuce' but are not 'lettuce' are:\tspinach\tkale\tarugula\tswiss chard\nThere are several useful visual features to tell there is 'lettuce' and not similar things in a photo:\tcrisp green leaves\tround or oval shape\tsmooth or wrinkled appearance\tplanted in rows in a garden or in a container at a store.", 1304], "luggage": ["Yes. 'Luggage' has a tangible appearance and refers to the bags and suitcases used for travel.\nA few things that are visually similar to 'luggage' but are not 'luggage' are:\tbackpacks\tbriefcases\tpurses\tshopping bags\tplastic bags\nThere are several useful visual features to tell there is 'luggage' and not similar things in a photo:\thard or soft-sided bags with zippers\tor locks\twheels or handles\tfor travel purposes\tvariety of sizes and shapes", 1303], "fences": ["Yes. 'Fences' has a tangible appearance and is a barrier made of wood, metal, or other materials.\nA few things that are visually similar to 'fences' but are not 'fences' are:\twalls\thedges\tbarricades\ttraffic cones\nThere are several useful visual features to tell there is 'fences' and not similar things in a photo:\tstraight or curved lines\thorizontal or vertical planks\tsolid or see-through surface\tgaps or openings at the bottom or the top", 1300], "wooden table": ["Yes. 'Wooden table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden table' but are not 'wooden table' are:\tdresser\tdesk\tworkbench\tbar counter\nThere are several useful visual features to tell there is 'wooden table' and not similar things in a photo:\tflat surface for placing things on\tfour legs (or a pedestal)\tmade of wood or wooden-looking material (e.g. MDF)", 1297], "bun": ["Yes. 'Bun' has a tangible appearance and is a type of bread.\nA few things that are visually similar to 'bun' but are not 'bun' are:\tcupcake\tmuffin\tbiscuit\tcookie\nThere are several useful visual features to tell there is 'bun' and not similar things in a photo:\tround or oval-shaped\tsoft and fluffy\ttexture of bread\tbrown color on top or all around\tbaked, not fried (in the case of donuts or empanadas)", 1295], "wrist": ["Yes. 'Wrist' has a tangible appearance and is a body part.\nA few things that are visually similar to 'wrist' but are not 'wrist' are:\tankle\tneck\tknee\telbow\nThere are several useful visual features to tell there is 'wrist' and not similar things in a photo:\twhere the hand joins the arm\tcan be seen when the hand is bent\tor when jewelry is worn (watch, bracelet)", 1294], "set": ["No. 'Set' is too vague or abstract to be distinguished in a photo. It is a term used to describe a collection of items or a group of things that belong together.", 1290], "tablecloth": ["Yes. 'Tablecloth' has a tangible appearance and is a fabric covering used on a table.\nA few things that are visually similar to 'tablecloth' but are not 'tablecloth' are:\trug\ttowel\tblanket\tcurtain\nThere are several useful visual features to tell there is 'tablecloth' and not similar things in a photo:\tsquare or rectangular piece of fabric\tdraped over a table\tsmooth or textured surface\tfringe or tassels on the edges\tdecorative patterns or colors", 1289], "vehicles": ["Yes. 'Vehicles' has a tangible appearance and is a machine used for transportation.\nA few things that are visually similar to 'vehicles' but are not 'vehicles' are:\ttraffic cones\tbicycles\tskateboards\tshopping carts\nThere are several useful visual features to tell there is 'vehicles' and not similar things in a photo:\tengine or motor\twheels or tracks\tfor carrying people or cargo\tdoors or openings for entering and exiting\tthe shape and size might vary depending on the type of vehicle", 1287], "pan": ["Yes. 'Pan' has a tangible appearance and is a container to cook food in.\nA few things that are visually similar to 'pan' but are not 'pan' are:\tpot\twok\tkettle\tpancake\tplate\nThere are several useful visual features to tell there is 'pan' and not similar things in a photo:\tflat or slightly curved bottom\tsidewalls\tof metal material\twith handle(s) on one side", 1276], "buttons": ["Yes. 'Buttons' has a tangible appearance and is a small disc or knob sewn onto clothing to fasten it.\nA few things that are visually similar to 'buttons' but are not 'buttons' are:\tbeads\tjewels\tdots\tcaps\nThere are several useful visual features to tell there is 'buttons' and not similar things in a photo:\tround or square in shape\tflat or slightly raised center\ta hole or shank in the center to attach to fabric or garment variety of colors or designs made from plastic, metal, or other materials.", 1273], "drawer": ["Yes. 'Drawer' has a tangible appearance and is a storage unit with a particular mechanism.\nA few things that are visually similar to 'drawer' but are not 'drawer' are:\tbox\tshelf\tcabinet\tcupboard\nThere are several useful visual features to tell there is 'drawer' and not similar things in a photo:\thandle or knob to open and close\tslides in or out of a larger piece of furniture\tusually rectangular or square\tshallow height compared to width and depth", 1270], "train tracks": ["Yes. 'Train tracks' has a tangible appearance and refers to the parallel rails on which trains run.\nA few things that are visually similar to 'train tracks' but are not 'train tracks' are: tram tracks, roller coaster tracks, bicycle tracks, ski tracks, footsteps in the sand.\nThere are several useful visual features to tell there are 'train tracks' and not similar things in a photo:\tmetal rails\tparallel lines\tsleepers or ties supporting the rails\tstraight or curved lines\tballasts or gravel between the rails and the ties", 1265], "apples": ["Yes. 'Apples' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'apples' but are not 'apples' are:\toranges\tlemons\ttomatoes\tpomegranates\nThere are several useful visual features to tell there is 'apples' and not similar things in a photo:\tround or oval shape\tvarious shades of red, green, or yellow\tsmooth or dimpled skin\twith or without stem\tand leaves\tfirm or soft to the touch\twhite or off-white flesh.", 1263], "headlight": ["Yes, 'headlight' is a visually concrete concept with a tangible appearance, typically used in vehicles for illumination.\nA few things that are visually similar to 'headlight' but are not 'headlight' are: \tlamps\tbulbs\tflashlights\nThere are several useful visual features to tell there is a 'headlight' and not similar things in a photo: \tlocated on the front of a vehicle\tcircular or oval shape\tusually two headlights for each vehicle\tcovered with a transparent material, such as plastic or glass\tbright and focused beam of light", 1262], "paw": ["Yes, 'paw' has a tangible appearance and is a physical feature of animals with claws or nails.\nA few things that are visually similar to 'paw' but are not 'paw' are:\thuman hand\thorse hoof\tbear paw\tprint\tmonkey hand\nThere are several useful visual features to tell there is 'paw' and not similar things in a photo:\twebbed toes or not\tnumber of toes\tnail or claw\tshape and texture of the paw's pads\thair or fur-covered", 1249], "fruits": ["Yes. 'Fruits' has a tangible appearance and is a type of edible food.\nA few things that are visually similar to 'fruits' but are not 'fruits' are:\tvegetables\tleaves\tflowers\tbranches\nThere are several useful visual features to tell there are 'fruits' and not similar things in a photo:\tvarious shapes, colors, and sizes\tcontain seeds or pits\tjuicy or fleshy flesh", 1247], "fridge": ["Yes. 'Fridge' has a tangible appearance and is a household appliance.\nA few things that are visually similar to 'fridge' but are not 'fridge' are:\tfreezer\tpantry\tshelving unit\nThere are several useful visual features to tell there is 'fridge' and not similar things in a photo:\tdoor(s)\thandle(s)\tshelves\tfor storing food and drinks\tusually in a kitchen or dining area", 1246], "stem": ["Yes. 'Stem' has a tangible appearance and is a part of a plant.\nA few things that are visually similar to 'stem' but are not 'stem' are:\tbranches\tstalks\troots\ttrunks\nThere are several useful visual features to tell there is 'stem' and not similar things in a photo:\tthin and cylindrical\tpart of a plant\tusually green, brown, or gray\tsupports leaves, flowers, and fruitsMay have buds or smaller branches coming out.", 1240], "hydrant": ["Yes. 'Hydrant' has a tangible appearance and is a type of structure or equipment.\nA few things that are visually similar to 'hydrant' but are not 'hydrant' are:\tpost\tbollard\tpillar\nThere are several useful visual features to tell there is 'hydrant' and not similar things in a photo:\tvertical and pipe-like structure\twith a valve and a nozzle\tusually painted red, white or yellow\thoses or pipes attached to it", 1239], "candle": ["Yes. 'Candle' has a tangible appearance and is a type of wax object that produces light.\nA few things that are visually similar to 'candle' but are not 'candle' are:\tlamp\tlight bulb\ttorch\nThere are several useful visual features to tell there is 'candle' and not similar things in a photo:\t \nwax structure with wick for burning \nburning flame \nmay come in a holder or stand to catch wax droppings", 1238], "ring": ["Yes. 'Ring' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'ring' but are not 'ring' are:\tferris wheel\tcircular door handle\tbracelet\tearring\nThere are several useful visual features to tell there is 'ring' and not similar things in a photo:\tcircular shape\tmetal or precious stone\tfitted to a finger", 1233], "cats": ["Yes. 'Cats' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'cats' but are not 'cats' are:\tpanthers\tlions\ttigers\tleopards\t\nThere are several useful visual features to tell there is a 'cat' and not similar things in a photo:\tshort to medium fur\tsemipointed ears\tsharp teeth\tand whiskers\toval-shaped paws\tslender bodies", 1226], "pieces": ["No. 'Pieces' is too vague or abstract to be distinguished in a photo. It could refer to a variety of things such as puzzle pieces, game pieces, broken pieces, etc.", 1224], "gravel": ["Yes. 'Gravel' has a tangible appearance and refers to small stones.\nA few things that are visually similar to 'gravel' but are not 'gravel' are:\tsand\tsoil\tcrushed shells\tconcrete rubble\nThere are several useful visual features to tell there is 'gravel' and not similar things in a photo:\tirregular shapes\trange of sizes\tusually grey, brown or beige\tcolors\tdry appearance", 1224], "bricks": ["Yes. 'Bricks' has a tangible appearance and is a building material.\nA few things that are visually similar to 'bricks' but are not 'bricks' are:\trocks\tblocks\tstones\tpavers\nThere are several useful visual features to tell there are 'bricks' and not similar things in a photo:\trectangular shape\thard, solid material\tred or brown color\tridged texture\tno visible natural formations", 1223], "kites": ["Yes. 'Kites' has a tangible appearance and is a specific type of flying toy.\nA few things that are visually similar to 'kites' but are not 'kites' are:\tballoons\tplanes\tbirds\thang gliders\nThere are several useful visual features to tell there is 'kites' and not similar things in a photo:\tflat and geometric shapes\ttails or streamers\tattached to a string or line\tmade of lightweight material\tflying in a stationary or looping pattern\twith a handle for guidance and control", 1223], "carrots": ["Yes. 'Carrots' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'carrots' but are not 'carrots' are:\tsweet potatoes\tginger\tradish\nThere are several useful visual features to tell there is 'carrots' and not similar things in a photo:\torange color\tconical or cylindrical shape\twith a green leafy top when not peeled\tsmooth surface when peeled", 1220], "scissors": ["Yes. 'Scissors' has a tangible appearance and is a kind of cutting tool.\nA few things that are visually similar to 'scissors' but are not 'scissors' are:\tknife\tnail clipper\tpaintbrush\tpaper clip\nThere are a few useful visual features to tell there is 'scissors' and not similar things in a photo:\ttwo blades for cutting\tfinger holes for holding and controlling the blades", 1220], "pitcher": ["Yes. 'Pitcher' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'pitcher' but are not 'pitcher' are:\tcarafe\tteapot\tvase\turn\nThere are several useful visual features to tell there is 'pitcher' and not similar things in a photo:\twide base with a narrower neck and a spout\thandle on the opposite side to the spout\thollow construction for holding liquid\tcontaining liquid, such as water or juice", 1216], "jar": ["Yes. 'Jar' has a tangible appearance and is a container.\nA few things that are visually similar to 'jar' but are not 'jar' are:\tbottle\tcan\twine glass\ttumbler\nThere are several useful visual features to tell that there is 'jar' and not similar things in a photo:\tcylindrical shape\twith straight or slightly curved sides\ta wide mouth for putting things inside\ta lid or cover\tfor storing food, drinks or other items", 1216], "graffiti": ["Yes. 'Graffiti' has a tangible appearance and is a type of art or writing on a surface in a public space.\nA few things that are visually similar to 'graffiti' but are not 'graffiti' are:\tmurals\tmosaics\tadvertisements\tsigns\nThere are several useful visual features to tell there is 'graffiti' and not similar things in a photo:\tillicit, unauthorized or unsanctioned art or writing\tbold or expressive lettering or images\tspray paint or markers as medium\tpainted on walls, buildings, or other public surfaces", 1212], "mug": ["Yes. 'Mug' has a tangible appearance and is a type of vessel used for drinking.\nA few things that are visually similar to 'mug' but are not 'mug' are:\tcup\tglass\tbowl\t\nThere are several useful visual features to tell there is 'mug' and not similar things in a photo:\thandles\tcurved or cylindrical shape\tthick walls or base\tdesigned to hold hot beverages\ttypically made of ceramic, porcelain, or glass (but sometimes metal or plastic)", 1211], "brick": ["Yes. 'Brick' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'brick' but are not 'brick' are:\tstone\tblock\tcement\nThere are several useful visual features to tell there is 'brick' and not similar things in a photo:\trectangular in shape\tmade of clay or other materials with a rough texture\tred or brown color\thas visible cracks and lines in its surface", 1207], "clothing item": ["Yes. 'Clothing item' has a tangible appearance.\nA few things that are visually similar to 'clothing item' but are not 'clothing item' are:\ttowel, blanket, scarf, rug\nThere are several useful visual features to tell there is 'clothing item' and not similar things in a photo:\n- designed and tailored for wearing on the body \n- made of fabric or other textile materials \n- includes garments such as shirts, pants, dresses, as well as accessories such as hats, belts, and gloves \n- may have buttons, zippers, or other fasteners to close or adjust the garment.", 1201], "gate": ["Yes. 'Gate' has a tangible appearance and is an entryway or an exitway.\nA few things that are visually similar to 'gate' but are not 'gate' are:\tdoor\tfence\twall\tarchway\nThere are several useful visual features to tell there is 'gate' and not similar things in a photo:\ttwo vertical posts\ta horizontal bar or bars\table to swing\topen or close to allow entry or exit\tmutex or latch for locking and unlocking\tintegrated with a fence or wall.", 1200], "faucet": ["Yes. 'Faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'faucet' but are not 'faucet' are:\tknob\tbutton\thandle\tswitch\nThere are several useful visual features to tell there is 'faucet' and not similar things in a photo:\twater spout\tlevers, knobs or handles to turn on and off\tthe base and metal pipe that supports the faucet", 1185], "net": ["Yes. 'Net' has a tangible appearance and is a type of web-like material.\nA few things that are visually similar to 'net' but are not 'net' are:\tfabric\tcurtain\tcobweb\nThere are several useful visual features to tell there is 'net' and not similar things in a photo:\tcrisscross grid pattern holes or gaps in the material\ttranslucent or see-through material\tlightweight and flexible material used in sports or fishing.", 1179], "computer mouse": ["Yes. 'computer mouse' has a tangible appearance and is an input device.\nA few things that are visually similar to 'computer mouse' but are not 'computer mouse' are:\tsmall handheld device\twith buttons\tand arrow keys\tremote control\nThere are several useful visual features to tell there is 'computer mouse' and not similar things in a photo:\toval or oblong shape\tscroll wheel or button\tat least two buttons on top for clicking and scrolling\toptical light or ball underneath for movement\tconnects to a computer via a wire or wireless technology", 1177], "coffee table": ["Yes. 'Coffee table' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'coffee table' but are not 'coffee table' are:\tend table\tdining table\tdesk\t\nThere are several useful visual features to tell there is 'coffee table' and not similar things in a photo:\tlow height compared to other tables\tpositioned in a living room or seating area\tsize (usually smaller than dining tables)\tsimple construction or design \tsquare or rectangular shape.", 1177], "stand": ["Yes. 'Stand' has a tangible appearance and is a supportive structure to hold or display something.\nA few things that are visually similar to 'stand' but are not 'stand' are:\tchair\ttable\tcounter\tpodium\tshelf\nThere are several useful visual features to tell there is 'stand' and not similar things in a photo:\tupright position\tfor holding or displaying something\tsupportive structure\tfor stand-alone or tabletop use", 1166], "ski": ["Yes. 'Ski' has a tangible appearance and is an object used for skiing.\nA few things that are visually similar to 'ski' but are not 'ski' are:\tsled\tsnowboard\tice skate\troller skate\nThere are several useful visual features to tell there is 'ski' and not similar things in a photo:\tlong thin shape\tflat base in the middle\ttapered ends\tbinding for attaching to boots\tsuitable for sliding on snow", 1166], "brick wall": ["Yes. 'Brick wall' has a tangible appearance and is a type of constructed barrier made of bricks.\nA few things that are visually similar to 'brick wall' but are not 'brick wall' are:\tstone wall\twooden wall\tcement wall\t\t\nThere are several useful visual features to tell there is 'brick wall' and not similar things in a photo:\trectangular-shaped bricks in a repeating pattern,\tred or tan color,\trough texture,\ttypically used for exterior or interior walls of buildings.", 1159], "painting": ["Yes. 'Painting' has a tangible appearance and is a form of visual art.\nA few things that are visually similar to 'painting' but are not 'painting' are:\tphotography \tdrawing \tengraving \tposter\nThere are several useful visual features to tell there is 'painting' and not similar things in a photo:\tuse of paint or other pigments\ton a canvas or other surface\tshows a subject or image\tcolored brushstrokes or other mark-making techniques", 1155], "chain": ["Yes. 'Chain' has a tangible appearance and is composed of connected metal links.\nA few things that are visually similar to 'chain' but are not 'chain' are:\tnecklace\twire\tfencing\tbarbed wire\tbungee cord\nThere are several useful visual features to tell there is 'chain' and not similar things in a photo:\tmetal links\tcontiguous links\tthat are looped around\tor interlocked with each other\tcreating a flexible and strong bond.", 1155], "strap": ["Yes. 'Strap' has a tangible appearance and is a narrow piece of material used for fastening or holding something.\nA few things that are visually similar to 'strap' but are not 'strap' are:\tbelt\trope\tband\tribbon\ttape\nThere are several useful visual features to tell there is 'strap' and not similar things in a photo:\tnarrow shape\tbuckles or loops on the ends\tfor fastening or holding something together\tmade of leather, fabric, or other materials\tthat is adjustable in length", 1148], "pepper": ["Yes. 'Pepper' has a tangible appearance and is a spicy seasoning.\nA few things that are visually similar to 'pepper' but are not 'pepper' are:\tsalt\tcumin\tcinnamon\tpaprika\nThere are several useful visual features to tell there is 'pepper' and not similar things in a photo:\tsmall and round\tdark color\tgrainy texture", 1146], "court": ["Yes. 'Court' has a tangible appearance and is a place where legal proceedings take place.\nA few things that are visually similar to 'court' but are not 'court' are:\ttennis court\tbasketball court\tplayground court\nThere are a few useful visual features to tell there is 'court' and not similar things in a photo:\tsymmetrical layout\tspectators or a jury present\tjudge's bench or desk\toficial seals or flags\tbarricades\texplicit signage indicating it is a court of law.", 1144], "roll": ["Yes. 'Roll' has a tangible appearance and is a type of cylindrical shape.\nA few things that are visually similar to 'roll' but are not 'roll' are:\tcylinder\ttube\tlog\tcircular stack of papers\tor circular stack of clothes\tor circular stack of plates\nThere are several useful visual features to tell there is 'roll' and not similar things in a photo:\tcylindrical shape\twith rounded ends\tcan be made of paper, fabric, or food\tcan be sliced or cut into sections.", 1143], "tables": ["Yes. 'Tables' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'tables' but are not 'tables' are:\tdesk\tbench\tcountertop\tshelf\nThere are several useful visual features to tell there is 'tables' and not similar things in a photo:\tlegs or pedestal base\thorizontal surface, usually rectangular, circular or square\tin various materials (e.g. wood, metal, glass, plastic)\tcan be used for eating or working purposes.", 1135], "circle": ["Yes. 'Circle' has a visually concrete concept as it is a 2D shape with a distinct appearance.\nA few things that are visually similar to 'circle' but are not 'circle' are:\tSphere\tCoin\tButton\nThere are several useful visual features to tell there is a circle or not in a photo:\tclosed, curved shape\tall points on the edge of the shape are equidistant from the center\tsymmetrical in shape\tno edges or corners in the shape", 1135], "baseball bat": ["Yes. 'Baseball bat' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'baseball bat' but are not 'baseball bat' are:\thockey stick\tgolf club\twooden plank\t\nThere are several useful visual features to tell there is 'baseball bat' and not similar things in a photo:\tcylindrical shape\tthick body\twith a tapered handle\tmade of wood or metal\tdimpled or textured surface at the hitting end", 1131], "crowd": ["Yes. 'Crowd' has a tangible appearance and refers to a large group of people.\nA few things that are visually similar to 'crowd' but are not 'crowd' are:\tline\tqueue\trow\tgaggle\nThere are several useful visual features to tell there is 'crowd' and not similar things in a photo:\tlarge group of people\tclose proximity to each other\tdifferent shapes, sizes, ages, and colors of people\tmoving or gathered together in a specific location", 1131], "river": ["Yes. 'River' has a tangible appearance and is a natural flowing body of water.\nA few things that are visually similar to 'river' but are not 'river' are:\tstream\tcanal\tdrainage ditch\tswimming pool\nThere are several useful visual features to tell there is 'river' and not similar things in a photo:\tnatural flow of water\tmay have rapids or waterfalls\tbanks or shorelines\twith trees or vegetation along the sides\tmay have boats or watercrafts flowing along it\tusually wider and deeper than a stream or canal", 1129], "portion": ["No. 'Portion' is too vague or abstract to be distinguished in a photo.", 1117], "hole": ["Yes. 'Hole' has a tangible appearance and is an empty space or opening.\nA few things that are visually similar to 'hole' but are not 'hole' are:\tshadows\tdents\tcracks\tbumps\nThere are several useful visual features to tell there is 'hole' and not similar things in a photo:\tempty space\tor opening through a surface\tor object\tcircular or irregularly shaped\tlack of material or substance within the hole\tvisible depth, suggesting a path or opening through the surface or object.", 1115], "carrot": ["Yes. 'Carrot' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'carrot' but are not 'carrot' are:\tparsnip\tsweet potato\tginger\tbeets\nThere are several useful visual features to tell there is 'carrot' and not similar things in a photo: long, thin, and cone-shaped bright orange (or sometimes purple, yellow, or white) pointed at one end and rounded at the other with a slightly rough, hairy texture.", 1112], "papers": ["Yes. 'Papers' has a tangible appearance and is a material used for writing, printing, or drawing.\nA few things that are visually similar to 'papers' but are not 'papers' are:\tmoney\ttissue\twrapping paper\nThere are several useful visual features to tell there is 'papers' and not similar things in a photo:\tthinness\tflexibility\tblank or printed surface\tvariety of colors and textures", 1110], "water bottle": ["Yes. 'Water bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'water bottle' but are not 'water bottle' are:\tthermos\tcanteen\tcoffee mug\tplastic container\t\nThere are several useful visual features to tell there is 'water bottle' and not similar things in a photo:\ttall and cylindrical shape\twith a cap or lid\tfor holding liquid, usually water\tclear or opaque plastic or glass material\tsportive design for outdoor activities", 1107], "stairs": ["Yes. 'Stairs' has a tangible appearance and is a type of structure used for going up or down.\nA few things that are visually similar to 'stairs' but are not 'stairs' are:\tramps, escalators, ladders, slides.\nThere are several useful visual features to tell there is 'stairs' and not similar things in a photo:\theight and depth of steps\tparallel steps with handrails\tup or down direction\tsymmetrical design of the steps\tand the surrounding structure", 1104], "toothbrush": ["Yes. 'Toothbrush' has a tangible appearance and is an instrument used for cleaning teeth.\nA few things that are visually similar to 'toothbrush' but are not 'toothbrush' are:\thairbrush\tnailbrush\tpaintbrush\tcleaning brush\nThere are several useful visual features to tell there is 'toothbrush' and not similar things in a photo:\tlong, narrow handle\ta head with bristles\tbristles should look plastic or nylon\toften has a specific brand name or logo printed on the handle", 1102], "clock tower": ["Yes. 'Clock tower' has a tangible appearance and is a type of tower with a clock on it.\nA few things that are visually similar to 'clock tower' but are not 'clock tower' are:\tbell tower\tminaret\twatchtower\tchimney\tsmokestack\nThere are several useful visual features to tell there is 'clock tower' and not similar things in a photo:\ttall tower structure\twith a clock\tdial and hour markers\tclock hands\tbell or chime at the top (sometimes)", 1101], "jersey": ["Yes. 'Jersey' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'jersey' but are not 'jersey' are:\tsweater\tt-shirt\tpolo shirt\thenley shirt\nThere are several useful visual features to tell there is 'jersey' and not similar things in a photo:\tplain or printed design\tlightweight and stretchy material\tlong sleeves and sometimes a collar\toften worn by sports teams", 1099], "wine glass": ["Yes. 'Wine glass' has a tangible appearance and is a type of glassware.\nA few things that are visually similar to 'wine glass' but are not 'wine glass' are:\ttumbler\tcocktail glass\tflute\tchampagne glass\nThere are several useful visual features to tell there is 'wine glass' and not similar things in a photo:\ttall stem to hold the bowl with the wine\tnarrow bowl designed to concentrate aromas\tround or tulip-shaped bowl that helps with swirling\tthe bowl opening is not as wide as the base", 1090], "finger": ["Yes. 'Finger' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'finger' but are not 'finger' are:\ttoes\tcarrots\ttwigs\nThere are several useful visual features to tell there is 'finger' and not similar things in a photo:\tlong, slender shape\twith fingernail on one end\tand flesh on the other\tend able to bend\tand move\twith a joint", 1086], "surf board": ["Yes. 'Surfboard' has a tangible appearance and is a type of board used for surfing.\nA few things that are visually similar to 'surf board' but are not 'surf board' are:\tskateboard\tsnowboard\tpaddle board\nThere are several useful visual features to tell there is 'surf board' and not similar things in a photo:\tlong and narrow board rounded at one end and pointed at the other\tboard made of foam or fiberglass with a smooth, shiny finish\ttether or leash attached to the ankle of a surfer\tboard held or used in the water", 1077], "girls": ["Yes. 'Girls' has a tangible appearance and is a gender-specific descriptor for female humans.\nA few things that are visually similar to 'girls' but are not 'girls' are:\tboys\twomen\tfeminine clothing or accessories\tfeminine hairstyles\nThere are several useful visual features to tell there are 'girls' and not similar things in a photo:\tfemale\tyoung or adolescent age features\tgender-specific markers, such as breasts or feminine facial features", 1073], "banner": ["Yes. 'Banner' has a tangible appearance and is a type of sign or flag.\nA few things that are visually similar to 'banner' but are not 'banner' are:\tposters\tflags\tribbons\nThere are several useful visual features to tell there is 'banner' and not similar things in a photo:\tlong and narrow\tflattened shape\twith text or graphics\thanging or suspended from a higher point", 1069], "onion": ["Yes. 'Onion' has a tangible appearance and is a type of edible plant.\nA few things that are visually similar to 'onion' but are not 'onion' are:\tshallot\tgarlic\tleek\tscallion\nThere are several useful visual features to tell there is 'onion' and not similar things in a photo:\tbulb-shaped\twrapped in multiple layers of paper-thin skin\twhite, yellow, or purple in color\thas a distinct, pungent smell\twhen cut, releases a strong-smelling, tear-inducing compound called syn-propanethial-S-oxide.", 1061], "toilet seat": ["Yes. 'Toilet seat' has a tangible appearance and is a commonly used object in bathrooms.\nA few things that are visually similar to 'toilet seat' but are not 'toilet seat' are:\tchair\tlid\tbench\nThere are several useful visual features to tell there is 'toilet seat' and not similar things in a photo:\toval or round shape\thinged in the back\tfor use in a bathroom or restroom\tmade of plastic or porcelain\thave a space for the toilet bowl to fit inside", 1061], "flags": ["Yes. 'Flags' has a tangible appearance and is a type of cloth used for signaling or symbolizing something.\nA few things that are visually similar to 'flags' but are not 'flags' are:\ttapestry\tcurvilinear motifs\tbanners\tpennants\tribbons\tbunting\nThere are several useful visual features to tell there is 'flags' and not similar things in a photo:\trectangle or square shape\twith or without emblems, symbols or patterns\tattached to a pole or a flagstaff\twaving or fluttering in the wind", 1060], "computer monitor": ["Yes. 'Computer monitor' has a tangible appearance and is a device used to display information from a computer.\nA few things that are visually similar to 'computer monitor' but are not 'computer monitor' are:\ttelevisions\tprojectors\tscreens\nThere are several useful visual features to tell there is 'computer monitor' and not similar things in a photo:\trectangular or square shape\tthick or thin frame\tdesktop stand or wall-mountable\tscreen displaying computer graphics or text.", 1060], "sock": ["Yes. 'Sock' has a tangible appearance and is a kind of clothing.\nA few things that are visually similar to 'sock' but are not 'sock' are:\tglove\tstocking\twristband\tleg warmer\nThere are several useful visual features to tell there is 'sock' and not similar things in a photo:\tcovering the foot and ankle\tsnug fitting shape\tvariety of colors and patterns\topening at the top for the foot to slide in\tmesh or knit texture on the fabric", 1057], "lettering": ["Yes. 'Lettering' has a tangible appearance and refers to the art or process of creating written words or designs.\nA few things that are visually similar to 'lettering' but are not 'lettering' are: handwriting graffiti calligraphy typography\nThere are several useful visual features to tell there is 'lettering' and not similar things in a photo: clear and deliberate markings uniformity in size and style used for artistic or communicative purposes", 1052], "pipe": ["Yes. 'Pipe' has a tangible appearance and is a cylindrical hollow object used for conveying water, gas, oil, or other fluid substances.\nA few things that are visually similar to 'pipe' but are not 'pipe' are:\tcigar\tdrainage system\texhaust pipe\tfunnel\nThere are several useful visual features to tell there is 'pipe' and not similar things in a photo:\tcylindrical or tubular shape\thollow structure with an opening at both ends\tmetallic or plastic material\twith or without valves or joints", 1049], "jet": ["Yes. 'Jet' has a tangible appearance and is a kind of aircraft that uses jet engines.\nA few things that are visually similar to 'jet' but are not 'jet' are:\tplane\thelicopter\trocket\nThere are several useful visual features to tell there is 'jet' and not similar things in a photo:\tlong, pointed nose\tcone-shaped engines\ttriangle-shaped wings\twith or without tail fins\tsmoke or vapor trails\tfrom commercial airlines to military planes\tvarious color schemes and logos depending on the airline or military branch", 1049], "path": ["Yes. 'Path' has a tangible appearance and refers to a way or route.\nA few things that are visually similar to 'path' but are not 'path' are: river, road, railway lines, wall\nThere are several useful visual features to tell there is 'path' and not similar things in a photo: narrow\tstretching from one place to another\tmarked with stones, tiles, or bricks\tfootprints or wheels\tsigns or arrows pointing in a certain direction", 1044], "children": ["Yes. 'Children' has a tangible appearance and refers to young human beings.\nA few things that are visually similar to 'children' but are not 'children' are:\tadults\tdolls\tpets\nThere are several useful visual features to tell there are 'children' and not similar things in a photo:\tsmall size\tshorter limbs and torso in proportion to their heads\tfacial features resembling those of human juveniles\tplayful and energetic demeanor\tinteracting with toys or other children.", 1036], "train car": ["Yes. 'Train car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'train car' but are not 'train car' are:\tbus\ttruck\tcar\ttram\nThere are several useful visual features to tell there is 'train car' and not similar things in a photo:\tlong and narrow\tcarries goods or passengers\tmultiple wheels or bogies\tconnected to other train cars or locomotive", 1032], "baseball cap": ["Yes. 'Baseball cap' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'baseball cap' but are not 'baseball cap' are:\tvisor\tsun hat\tbeanie\tfedora\nThere are several useful visual features to tell there is 'baseball cap' and not similar things in a photo:\ta rounded crown\ta flat brim\tan adjustable back strap\tsport team logo or design on the front panel", 1029], "tan": ["Yes. 'Tan' has a tangible appearance and refers to a light brown color.\nA few things that are visually similar to 'tan' but are not 'tan' are: beige, cream, ivory, and khaki.\nThere are no useful visual features to distinguish a color like 'tan' from the other colors in a photo, as it depends on the shades and lighting conditions.", 1026], "necklace": ["Yes. 'Necklace' has a tangible appearance and is a kind of jewelry.\nA few things that are visually similar to 'necklace' but are not 'necklace' are:\tbracelet\tchoker\tlariat / lasso\tscarf\nThere are several useful visual features to tell there is 'necklace' and not similar things in a photo:\tworn around the neck\thas beads, gems, or other decorative elements\thas a clasp or closure to secure it in place\tdangles or rests on the chest or collarbone area", 1026], "onions": ["Yes. 'Onions' has a tangible appearance and is a kind of vegetable.\nA few things that are visually similar to 'onions' but are not 'onions' are:\tshallots\tgarlic\tbulbs\nThere are several useful visual features to tell there are 'onions' and not similar things in a photo:\tpapery and layered skin\tbulbous round shape\tinternal layers when the onion is cut in half\tshort green stems at the top of the bulbs", 1025], "cellphone": ["Yes. 'Cellphone' has a tangible appearance and is an electronic device used for communication.\nA few things that are visually similar to 'cellphone' but are not 'cellphone' are:\tlaptop\tcomputer\ttablet\tradio\nThere are several useful visual features to tell there is 'cellphone' and not similar things in a photo:\trectangular shape\twith a touchscreen or keypad\tfor making calls\tor sending messages\thave a camera and other apps\tsmall enough to be held in one hand.", 1022], "park": ["Yes. 'Park' has a tangible appearance and is a type of outdoor space.\nA few things that are visually similar to 'park' but are not 'park' are:\tgarden\tpatio\tbackyard\tcourtyard\nThere are several useful visual features to tell there is 'park' and not similar things in a photo:\tlarge open space with grass, trees, or flowers\tpaved walkways or bike paths\tbenches or picnic tables\tplayground equipment or sports courts\tfountains or water features\tpark signs or maps", 1016], "sheet": ["Yes. 'Sheet' has a tangible appearance and refers to a piece of material used as bedding or covering.\nA few things that are visually similar to 'sheet' but are not 'sheet' are:\ttarpaulin\twallpaper\tbanner\ttowel\tcurtain\t\nThere are several useful visual features to tell there is 'sheet' and not similar things in a photo:\n\n- Rectangular or square shape\n- Thin and flexible\n- Designed for covering or lying on top of something (such as a bed)\n- Often made of cotton or linen materials\n- May have a pattern or design on it, such as stripes or polka dots.", 1011], "motorcycles": ["Yes. 'Motorcycles' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motorcycles' but are not 'motorcycles' are:\tbicycles\tscooters\tmopeds\tATVs\nThere are several useful visual features to tell there is 'motorcycles' and not similar things in a photo:\tone or two-wheeled vehicle\tengine\tor motor\thandlebars\tand throttle\tleather seats or coverings for the engine\tusually ridden by one or two people.", 1005], "buses": ["Yes. 'Buses' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'buses' but are not 'buses' are:\ttrucks\tvans\tlimousines\nThere are several useful visual features to tell there is 'buses' and not similar things in a photo:\t\nlong, rectangular-shaped vehicle\tmultiple rows of seats\tforward-facing windows\tbus-specific features such as a retractable stop sign and bike rack.", 1004], "track": ["Yes. 'Track' has a tangible appearance and can refer to a physical trail or path.\nA few things that are visually similar to 'track' but are not 'track' are:\tsidewalks\troads\ttrails in the woods\tpipelines\nThere are several useful visual features to tell there is 'track' and not similar things in a photo:\tnarrow and confined\tpathway made of dirt, gravel, or other materials\tfootprints, tire tracks or other markers left behind by repetitive use\tintended for walking, hiking, biking, or vehicles\tsurrounded by nature or other landmarks", 999], "benches": ["Yes. 'Benches' has a tangible appearance and is a piece of furniture designed for sitting on.\nA few things that are visually similar to 'benches' but are not 'benches' are:\tchairs\tstools\tcouches\tottomans\nThere are several useful visual features to tell there is 'benches' and not similar things in a photo:\tlong and narrow surface\tusually made of wood or metal\ta backrest or armrest may or may not be present\tintended for multiple people to sit on at once", 995], "stick": ["Yes. 'Stick' has a tangible appearance and is a kind of object.\nA few things that are visually similar to 'stick' but are not 'stick' are:\tbranch\tlog\tpole\tdowel\nThere are several useful visual features to tell there is 'stick' and not similar things in a photo:\tlong and narrow\tcylindrical shape\tmade of wood, metal, or plastic\tsmooth or rough surface", 991], "motorbike": ["Yes. 'Motorbike' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motorbike' but are not 'motorbike' are:\tbicycle\tscooter\tmoped\tmotorized wheelchair\nThere are several useful visual features to tell there is 'motorbike' and not similar things in a photo:\ttwo wheels\tengine\tand exhaust pipes\thandlebars\tand handgrips\tpedals\tand footrests\tseat\tand saddlebags\theadlight\tand taillight\tfenders\tand mudflaps", 983], "ladder": ["Yes. 'Ladder' has a tangible appearance and is a type of equipment.\nA few things that are visually similar to 'ladder' but are not 'ladder' are:\tstaircase\tstepladder\tshelves\tbookcase\nThere are several useful visual features to tell there is 'ladder' and not similar things in a photo:\tvertical beams or rungs\tfor climbing or reaching high places\tleaned against a surface, such as a wall or a tree\tfreestanding or portable design\tno solid risers or back support.", 980], "trains": ["Yes. 'Trains' has a tangible appearance and is a mode of transportation.\nA few things that are visually similar to 'trains' but are not 'trains' are:\tbuses\ttrucks\ttrolleys\nThere are several useful visual features to tell there is 'trains' and not similar things in a photo:\tlong and narrow shape\tmultiple cars connected to each other\ttrain tracks underneath the train\tsmokestack or steam\tplumes of smoke or steam\tborders or numbers printed on each car.", 976], "donuts": ["Yes. 'Donuts' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'donuts' but are not 'donuts' are:\tbagels\tfritters\tchurros\tcroissants\nThere are several useful visual features to tell there is 'donuts' and not similar things in a photo:\thole in the center\tcircular shape\tglazed or powdered on top\tsometimes filled with cream or jam or jelly", 974], "steps": ["Yes. 'Steps' has a tangible appearance and refers to a set of stairs or a series of surfaces used for people to go from one level to another.\nA few things that are visually similar to 'steps' but are not 'steps' are: ramp, escalator, slope, hill\nThere are several useful visual features to tell there are 'steps' and not similar things in a photo: level platforms arranged vertically with a clear vertical inclination, even height and length of each step, handrail, and treads to stand on.", 973], "pictures": ["Yes. 'Pictures' has a tangible appearance and can refer to photographs or paintings.\nA few things that are visually similar to 'pictures' but are not 'pictures' are:\tmirrors\twindows\tclocks\nThere are several useful visual features to tell there is 'pictures' and not similar things in a photo:\tflat surface with an image\tpainted or printed on paper or canvas\thanging on a wall or standing on a table\tmultiple colors or shades\tresemblance to real objects or scenes", 967], "fries": ["Yes. 'Fries' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'fries' but are not 'fries' are:\tonion rings\tmozzarella sticks\tchicken nuggets\nThere are several useful visual features to tell there is 'fries' and not similar things in a photo:\tthin, elongated shape\tgolden or brown color\tcrispy exterior\telongated cuts of potatoes\tserved in a paper or cardboard container", 965], "bikes": ["Yes. 'Bikes' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'bikes' but are not 'bikes' are:\tscooters\tmopeds\tmotorcycles\tbicycles\nThere are several useful visual features to tell there is 'bikes' and not similar things in a photo:\ttwo-wheeled vehicle\tpedals\thandlebars\tbrakes and gears\tframe and wheels", 959], "planes": ["Yes. 'Planes' have a tangible appearance and are a type of vehicle that flies in the air.\nA few things that are visually similar to 'planes' but are not 'planes' are:\thelicopters\tdrones\tbirds\tbutterflies\nThere are several useful visual features to tell there is 'planes' and not similar things in a photo:\tfixed wings\tjet or propeller engine\tcabin windows\tand tail or tail fin on the back of the plane\thorizontal and vertical stabilizers", 959], "front tire": ["Yes. 'Front tire' has a tangible appearance and is a component of a vehicle.\nA few things that are visually similar to 'front tire' but are not 'front tire' are:\trear tire\tbicycle tire\tmotorcycle tire\ttractor tire\nThere are several useful visual features to tell there is 'front tire' and not similar things in a photo:\tlocated in front of the vehicle\tusually larger than the rear tire\tsmooth and streamlined surface\ttread and grooves for traction\t connected to the front fork through an axle.", 956], "shelves": ["Yes. 'Shelves' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'shelves' but are not 'shelves' are:\tdrawers\tcabinets\tcountertops\tdesks\nThere are several useful visual features to tell there is 'shelves' and not similar things in a photo:\tboards or planks connected by brackets or supports\table to hold and display items such as books, decorations, or dishes\tlevel and flat surface to place things on", 956], "weeds": ["Yes. 'Weeds' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'weeds' but are not 'weeds' are:\tflowers\tgrasses\tferns\nThere are several useful visual features to tell there is 'weeds' and not similar things in a photo:\trapid growth in undesirable places\tunattractive appearance to gardeners\tdistinctive leaves and stems\tcomposition of small flowers", 952], "arms": ["Yes. 'Arms' has a tangible appearance and refers to the upper limbs of the human body.\nA few things that are visually similar to 'arms' but are not 'arms' are:\ttree limbs\torangutan arms\nThere are no useful visual features for distinguishing arms from tree limbs or orangutan arms since they are anatomically similar. However, in a photo context, typically the context and location make it clear whether the subject is arms or another kind of limb.", 947], "salad": ["Yes. 'Salad' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'salad' but are not 'salad' are:\tfruit bowl\tfruit salad\tvegetable platter\tsnack mix\ttrail mix\nThere are several useful visual features to tell there is 'salad' and not similar things in a photo:\tmixture of greens or vegetables\tdressing or sauce\tutensils, like a fork or a spoon\tpresented in a bowl or a plate\twith other food items on the table, such as dinner plates or glasses", 944], "boxes": ["Yes. 'Boxes' has a tangible appearance and is a container used for storage or transport.\nA few things that are visually similar to 'boxes' but are not 'boxes' are:\tbags\tbaskets\tenvelopes\tpackages\nThere are several useful visual features to tell there is 'boxes' and not similar things in a photo:\trectangular shape\twith lids or flaps\tto indicate size and weight\tof varying materials such as cardboard, wood, or plastic.", 943], "telephone": ["Yes. 'Telephone' has a tangible appearance and is a device used for communication.\nA few things that are visually similar to 'telephone' but are not 'telephone' are:\tmicrophone\tloudspeaker\tmegaphone\twalkie-talkies\nThere are several useful visual features to tell there is 'telephone' and not similar things in a photo:\thandheld or corded device\twith a dialing pad\tor with a digital screen\treceiver and transmitter parts", 942], "cloudy sky": ["Yes. 'Cloudy sky' has a tangible appearance and is a type of weather phenomenon.\nA few things that are visually similar to 'cloudy sky' but are not 'cloudy sky' are:\tsunset\tsunrise\tsmoke\tfog\nThere are several useful visual features to tell there is 'cloudy sky' and not similar things in a photo:\tvisible clouds covering a significant portion of the sky\tgray, white, or off-white colors\tvarious cloud shapes and sizes\tsunlight partially or completely blocked by clouds", 941], "label": ["Yes. 'Label' has a tangible appearance and is a piece of paper, fabric or other material affixed to an object.\nA few things that are visually similar to 'label' but are not 'label' are:\tsticker\tsign\ttag\tposter\nThere are several useful visual features to tell there is 'label' and not similar things in a photo:\tattached to an object\tinformational content, such as text or images\tcleanly affixed to the object, often with adhesive backingeasily peeled off or removed\tfrom brand, price, or content information.", 940], "whiskers": ["Yes. 'Whiskers' has a tangible appearance and are a type of hair on some animals' faces.\nA few things that are visually similar to 'whiskers' but are not 'whiskers' are:\thair\tfur\tbristles\nThere are several useful visual features to tell there are 'whiskers' and not similar things in a photo:\thair-like strands growing near the mouth\tarea around the nose or mouth\tlonger than fur, often thicker\tbend or curve outward from the face of an animal", 940], "purple": ["Yes. 'Purple' has a tangible appearance and is a hue on the visible color spectrum.\nA few things that are visually similar to 'purple' but are not 'purple' are:\tblue\tviolet\tmagenta\nThere are several useful visual features to tell there is 'purple' and not similar things in a photo:\tcombining blue and red\tcolors with a dominant hue of bluish or reddish\ttending toward cool and calming tones", 939], "mans": ["No. 'Mans' is not a correct English word, so it does not have a visual appearance. It is possible that you meant 'man', in which case the answer would be 'yes'. ", 938], "toilet paper": ["Yes. 'Toilet paper' has a tangible appearance and is a kind of paper that is used to clean after using the toilet.\nA few things that are visually similar to 'toilet paper' but are not 'toilet paper' are:\tkitchen paper\ttissues\twipes\thand towels\nThere are several useful visual features to tell there is 'toilet paper' and not similar things in a photo:\tthin\tsoft\tmultiple layers\ton a roll or folded\tpink, white or beige in color\tin the bathroom or toilet area", 938], "clocks": ["Yes. 'Clocks' has a tangible appearance and is a type of time-keeping device.\nA few things that are visually similar to 'clocks' but are not 'clocks' are:\twatches\tthermometers\tbarometers\t\nThere are several useful visual features to tell there is 'clocks' and not similar things in a photo:\tcircular or rectangular shape\tnumerical or other clock-related markings\thands or digital display\tticking mechanism or other signs of movement\tcan be found on a wall, desk or another surface.", 937], "crust": ["Yes. 'Crust' has a tangible appearance and is a type of outer layer.\nA few things that are visually similar to 'crust' but are not 'crust' are:\touter layer of bread\tmud on shoes\tbrownish layer on a surface\touter layer of skin\nThere are several useful visual features to tell there is 'crust' and not similar things in a photo:\thardened layer\tdry and scaly texture\tformed on a liquid or a solid surface, like food\tor rock.", 929], "ramp": ["Yes. 'Ramp' has a tangible appearance and is a structure used to connect different levels.\nA few things that are visually similar to 'ramp' but are not 'ramp' are:\tstaircase\tescalator\tslide\tbridge\nThere are several useful visual features to tell there is 'ramp' and not similar things in a photo:\tslope gradual\tinclination structure\tdesigned for wheelchairs, bicycles, or skateboarding", 922], "tomatoes": ["Yes. 'Tomatoes' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'tomatoes' but are not 'tomatoes' are:\tapples\toranges\tpeppers\tpumpkins\tplums\nThere are several useful visual features to tell there are 'tomatoes' and not similar things in a photo:\tround or oval shape\tsmooth and shiny surface\tbright red or yellowish-orange color\tsmall green leafy extensions at the top.", 921], "garbage": ["Yes. 'Garbage' has a tangible appearance and refers to waste or discarded objects.\nA few things that are visually similar to 'garbage' but are not 'garbage' are:\trecycling items\tcompost\tpiles of debris\tdirty or soiled objects\nThere are several useful visual features to tell there is 'garbage' and not similar things in a photo:\tunpleasant or foul smells\ttrash bags or bins\tpiles of discarded objects\trotting food or other organic materials\tunorganized and messy appearance", 921], "suv": ["Yes. 'SUV' has a tangible appearance and is a type of car.\nA few things that are visually similar to 'suv' but are not 'suv' are:\tsedan\tpickup truck\tminivan\tcrossover\nThere are several useful visual features to tell there is 'suv' and not similar things in a photo:\ttall and boxy body shape\tlarge size and spacious interior\thigh ground clearance\theavy-duty wheels\tand chassis\tSUV specific design elements such as roof racks and tow hitches.", 919], "curb": ["Yes. 'Curb' has a tangible appearance and is a physical border or edge of a sidewalk or street.\nA few things that are visually similar to 'curb' but are not 'curb' are:\tmedian\tbarrier\twall\nThere are several useful visual features to tell there is 'curb' and not similar things in a photo:\traised edge or border of a sidewalk or street\tcolor contrasting with the road and/or the sidewalk\tendif with a sloping or vertical face to guide vehicles and pedestrians.", 918], "traffic lights": ["Yes. 'Traffic lights' has a tangible appearance and is a kind of signaling device.\nA few things that are visually similar to 'traffic lights' but are not 'traffic lights' are:\tstreet lamps\tbillboards\tadvertisement panels\nThere are several useful visual features to tell there are 'traffic lights' and not similar things in a photo:\tconsisting of three lights\twith red, yellow, and green lenses\tlocated at an intersection\tor near pedestrian crossings", 917], "stone": ["Yes. 'Stone' has a tangible appearance and is a hard, naturally occurring solid material.\nA few things that are visually similar to 'stone' but are not 'stone' are:\tbrick\tconcrete\tmetal\twood\nThere are several useful visual features to tell there is 'stone' and not similar things in a photo:\tnatural-looking surface\trough, grainy or porous texture\tnatural colors like gray or brown\tsolid and heavy appearance\tgenetic patterns or markings like veins, speckles or pits.", 913], "walls": ["Yes. 'Walls' has a tangible appearance and is a part of architecture or construction.\nA few things that are visually similar to 'walls' but are not 'walls' are:\tceilings\tfloors\tpartitions\tdividers\nThere are several useful visual features to tell there is 'walls' and not similar things in a photo:\tupright surface vertical to the ground that divides space\ttexture, color or material that indicates it is a vertical surface in contrast to floors or ceilings", 906], "dessert": ["Yes. 'Dessert' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'dessert' but are not 'dessert' are:\tdecorative candles\tscented wax\ttowels\tfancy soap\nThere are several useful visual features to tell there is 'dessert' and not similar things in a photo:\tsweet looking\tfluffy or creamy texture\twith berries or chocolate on top\tin a bowl or a plate, not a holder for other things.", 898], "home plate": ["Yes. 'Home plate' has a tangible appearance and is a specific component of a baseball field.\nA few things that are visually similar to 'home plate' but are not 'home plate' are:\tdinner plates\tname plates\tdecorative plates\nThere are several useful visual features to tell there is 'home plate' and not similar things in a photo:\tfive-sided shape\twith one corner cut off\twhite\twith black lines and markings\tin the center of a baseball field", 898], "tee shirt": ["Yes. 'Tee shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tee shirt' but are not 'tee shirt' are:\ttank top\tpolo shirt\tblouse\tsweatshirt\nThere are several useful visual features to tell there is 'tee shirt' and not similar things in a photo:\tshort sleeves\tround neckline\tcotton fabric\tplain or printed design", 894], "palm tree": ["Yes, 'palm tree' has a tangible appearance and is a type of tree with a unique morphology.\nA few things that are visually similar to 'palm tree' but are not 'palm tree' are: pine tree, ferns, eucalyptus.\nThere are several useful visual features to tell there is 'palm tree' and not similar things in a photo: \tsingle, unbranched stem\twith a crown of large, spiky leaves\tleaves arranged in a spiral form\ttrunk with rough texture.", 893], "runway": ["Yes. 'Runway' has a tangible appearance and is a type of surface for landing and taking off aeroplanes.\nA few things that are visually similar to 'runway' but are not 'runway' are:\troad\tparking lot\tfield\nThere are several useful visual features to tell there is 'runway' and not similar things in a photo:\tlong and straight surface\tmarked with stripes and numbers\tfor use by aircraft only\twith lights for night operations", 891], "plastic": ["Yes. 'Plastic' has a tangible appearance and is a material.\nA few things that are visually similar to 'plastic' but are not 'plastic' are: glass, crystal, metal, rubber.\nThere are several useful visual features to tell there is 'plastic' and not similar things in a photo: a slightly glossy or matte surface texture, the ability to bend and flex without breaking, visible seams or molding lines, and a relatively low weight compared to similar objects made of other materials.", 891], "feathers": ["Yes. 'Feathers' has a tangible appearance and is a natural object.\nA few things that are visually similar to 'feathers' but are not 'feathers' are:\thair\tleaves\tthin fabrics\nThere are several useful visual features to tell there are 'feathers' and not similar things in a photo:\telongated and thin structures\twith vanes that form branches called barbs\tbarbs that contain barbules on either side of the shaft\tbarbs that are zipped together centrally\twith veins running through the length of each barb giving it strength and support.", 890], "text": ["Yes. 'Text' has a tangible appearance and is a collection of written or printed words.\nA few things that are visually similar to 'text' but are not 'text' are:\tpatterns\tdrawings\tsymbols\timages\nThere are several useful visual features to tell there is 'text' and not similar things in a photo:\tarrangement of letters and words\ttypical font style or handwriting\tblack or colored ink on white paper or background\ttypical spacing between characters\tand lines.", 886], "cups": ["Yes. 'Cups' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'cups' but are not 'cups' are:\tglasses\tmugs\tbowls\tjars\turns\nThere are several useful visual features to tell there is 'cups' and not similar things in a photo:\tcylindrical or conical shape\twith a handle for grasping\ttypically smaller than a mug or a bowl\tcan sit on a flat surface", 885], "fingers": ["Yes. 'Fingers' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'fingers' but are not 'fingers' are:\tbranches\tsnakes\t\nThere are several useful visual features to tell there is 'fingers' and not similar things in a photo:\tattached to a hand\tfive digits\twith nails and knuckles", 885], "poster": ["Yes. 'Poster' has a tangible appearance and is a type of printed material used for advertising, decoration, or information display.\nA few things that are visually similar to 'poster' but are not 'poster' are:\tpainting\tsign\tbanner\tmural\nThere are several useful visual features to tell there is 'poster' and not similar things in a photo:\tpaper material\tlarge size\tinformational or decorative\tcontent with text and/or images\twall-mounted or displayed", 885], "doorway": ["Yes. 'Doorway' has a tangible appearance and refers to an entryway or opening that allows people to pass through a wall or other barrier.\nA few things that are visually similar to 'doorway' but are not 'doorway' are:\twindow\tarch\tbridge\tgate\nThere are several useful visual features to tell there is 'doorway' and not similar things in a photo:\ta rectangular or arched shape\tthat connects two rooms or spaces\twith a door or portal\tfor people to pass through\tsigificant change in light or color on either side of it", 881], "boot": ["Yes. 'Boot' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'boot' but are not 'boot' are:\tshoe\tsandal\tslipper\theel\train boots\nThere are several useful visual features to tell there is 'boot' and not similar things in a photo:\tcovers the foot and extends above the ankle or calf\toften has a heel or a thick sole usually made of rubber or leather\tcan be made from various materials like leather, suede or rubber\tlaces, zippers or buckles to fasten the boot to the foot\tor not", 877], "outlet": ["Yes. 'Outlet' has a tangible appearance and is a type of electrical device.\nA few things that are visually similar to 'outlet' but are not 'outlet' are:\tswitch\tpanel\tsocket\tjack\nThere are several useful visual features to tell there is 'outlet' and not similar things in a photo:\ttwo or three holes for prongs\tone or two holes for screws\tmetallic or plastic plate\tembedded in a wall or a floor\telectricity signs or labels", 877], "front legs": ["Yes. 'Front legs' has a tangible appearance and is a part of an animal's body.\nA few things that are visually similar to 'front legs' but are not 'front legs' are:\tback legs\tarms\ttrunks\nThere are several useful visual features to tell there are 'front legs' and not similar things in a photo:\tpositioned in the front of the animal\tbody attached to the legs\tsupporting the animal's weight\tjointed with a knee or elbow-like bend", 874], "distance": ["No. 'Distance' is too vague or abstract to be distinguished in a photo.", 874], "oranges": ["Yes. 'Oranges' has a tangible appearance and is a citrus fruit.\nA few things that are visually similar to 'oranges' but are not 'oranges' are:\tlemons\tgrapefruits\ttangerines\tclementines\nThere are several useful visual features to tell there are 'oranges' and not similar things in a photo:\tround or oblong shape\torange color\twith or without leaves\ttextured skin (pebbled or dimpled)\tthicker skin than lemons or limes\tjuicy flesh and segments in the interior.", 873], "drawers": ["Yes. 'Drawers' have a tangible appearance and refer to a piece of furniture with a set of sliding or pull-out compartments.\nA few things that are visually similar to 'drawers' but are not 'drawers' are:\tdoors\tshelves\tbookcases\tchests\nThere are several useful visual features to tell there are 'drawers' and not similar things in a photo:\tRectangular or square in shape\tHaving handles or pulls to open them\tSliding or pull-out compartments used for storage\tBuilt into or attached to a larger piece of furniture.", 872], "clothes": ["Yes. 'Clothes' has a tangible appearance and refers to garments worn on the body.\nA few things that are visually similar to 'clothes' but are not 'clothes' are: blankets, towels, curtains, rugs.\nThere are several useful visual features to tell there is 'clothes' and not similar things in a photo: textile materials, buttons, zippers or other fasteners, collar, sleeves, pants or skirts, designed to be worn on the body.", 867], "spectators": ["Yes. 'Spectators' has a tangible appearance and refers to people who are watching an event or performance.\nA few things that are visually similar to 'spectators' but are not 'spectators' are:\tpedestrians\tpassersby\tcrowd\tprotesters\nThere are several useful visual features to tell there are 'spectators' and not similar things in a photo:\tfacing an event or performance\tstanding or seated\tlooking towards the event or performance\treaction to the event or performance (applause, cheering, etc.)", 867], "plastic bag": ["Yes. 'Plastic bag' has a tangible appearance.\nA few things that are visually similar to 'plastic bag' but are not 'plastic bag' are:\tpaper bag\tbasket\twrapped cloth\tgarbage bag\nThere are several useful visual features to tell there is 'plastic bag' and not similar things in a photo:\tclear or translucent color\tplastic material\thandle on top\topen at the top to put things inside", 865], "wings": ["Yes. 'Wings' has a tangible appearance and is a type of organ used by birds, insects, and some mammals to achieve flight.\nA few things that are visually similar to 'wings' but are not 'wings' are:\tleaves\tarms\tairplanes\nThere are several useful visual features to tell there are 'wings' and not similar things in a photo:\tattached to the body\tthin membrane structure\tfeathers or scales on the surface\tfan-like shape\tor elongated triangular shape\tthat allows for flight\tmay contain patterns or colors for camouflage or display purposes", 865], "helmets": ["Yes. 'Helmets' has a tangible appearance and is a type of protective headgear.\nA few things that are visually similar to 'helmets' but are not 'helmets' are:\thats\tcaps\twigs\tmasks\nThere are several useful visual features to tell there is 'helmets' and not similar things in a photo:\thard and durable material\tchinstrap or buckle\tprotective face guard or visor\tunusual shape or design\tfor a specific activity (e.g. biking, riding, skiing)", 858], "ripples": ["Yes. 'Ripples' has a tangible appearance and is a type of pattern or motion in a body of water or a similar substance.\nA few things that are visually similar to 'ripples' but are not 'ripples' are:\twaves\twrinkles\ton a fabric\ton a sand dune\nThere are several useful visual features to tell there are 'ripples' and not similar things in a photo:\tcircular or elliptical\twavy motion\tvisible disturbance of the surface of the water or substance.", 856], "players": ["No. 'Players' is too vague or abstract to be distinguished in a photo. It needs to be clarified whether it refers to sports players or game players or any other type of players.", 853], "chain link fence": ["Yes. 'Chain link fence' has a tangible appearance and is a type of fence.\nA few things that are visually similar to 'chain link fence' but are not 'chain link fence' are:\tpicket fence\twire mesh fence\twooden fence\tbarbed wire fence\nThere are several useful visual features to tell there is 'chain link fence' and not similar things in a photo:\tsmall diamond-shaped openings in the fence\tchain-link pattern of horizontal and vertical wires\tgrey or silver metal appearance", 853], "rice": ["Yes. 'Rice' has a tangible appearance and is a type of grain used as food.\nA few things that are visually similar to 'rice' but are not 'rice' are:\tcouscous\tquinoa\tpearl barley\nThere are several useful visual features to tell there is 'rice' and not similar things in a photo:\tsmall, oblong grains\twhite, brown or black in color\ttypically served in a bowl or on a plate", 851], "beard": ["Yes. 'Beard' has a tangible appearance and is a facial hair grown on the chin and cheeks.\nA few things that are visually similar to 'beard' but are not 'beard' are:\tmustache\thair mask\tnecklace\nThere are several useful visual features to tell there is 'beard' and not similar things in a photo:\tfacial hair grown on the chin and cheeks\tcan vary in length and thickness\tcan be groomed or ungroomed\tmay or may not be accompanied by a mustache", 850], "wetsuit": ["Yes. 'Wetsuit' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'wetsuit' but are not 'wetsuit' are:\tdrysuit\tneoprene top\torca body suit\t\nThere are several useful visual features to tell there is 'wetsuit' and not similar things in a photo:\ttight-fitting\tbody-hugging\t\ncovers most of the body\texcept for the face, hands, and feet\tmade of neoprene or similar synthetic rubber material\tzips up the back or front\twith long or short sleeves and legs, depending on the style and purpose\tintended to keep the wearer warm and dry while in the water.", 850], "log": ["Yes. 'Log' has a tangible appearance and typically refers to a wooden trunk of a tree that has been cut.\nA few things that are visually similar to 'log' but are not 'log' are:\twood plank\tsandbag\tboulder\tpillar\nThere are several useful visual features to tell there is 'log' and not similar things in a photo:\tlong and cylindrical shape\tbark texture\tvisible growth rings\tknots or branches attached at the ends", 848], "sleeve": ["Yes. 'Sleeve' has a tangible appearance and refers to the covering of an arm or leg of a garment.\nA few things that are visually similar to 'sleeve' but are not 'sleeve' are:\tpant leg\tstocking\tsock\tglove\nThere are several useful visual features to tell there is 'sleeve' and not similar things in a photo:\tcylindrical shape\tfits the arm or leg of a person\tconnects to the collar or the shoulder of a garment\tvarious lengths and styles (short, long, puffy, fitted, etc.)", 844], "half": ["Yes. 'Half' has a tangible appearance and refers to a part of a whole object that has been divided into two equal parts.\nA few things that are visually similar to 'half' but are not 'half' are:\tquarter\tpieces\twedges\tportions\nThere are several useful visual features to tell there is 'half' and not similar things in a photo:\tthe object should have a symmetrical division in two equal parts.", 838], "parking meter": ["Yes. 'Parking meter' has a tangible appearance and is a device for collecting money for parking.\nA few things that are visually similar to 'parking meter' but are not 'parking meter' are:\ttraffic lights\tpostbox\tbinoculars\tadvertisement stand\nThere are several useful visual features to tell there is 'parking meter' and not similar things in a photo:\ttall and slender\tpole-shaped with a box on top\tmeter display for time or money inserted\tkeys or buttons for operation\tparking information sign next to it", 837], "smoke": ["Yes. 'Smoke' has a tangible appearance and is a visible suspension of particles in the air.\nA few things that are visually similar to 'smoke' but are not 'smoke' are:\tsteam\tfog\tmist\tdust\nThere are several useful visual features to tell there is 'smoke' and not similar things in a photo:\tdarker color or tint\tsmell of burning or fire\toriginating from a fire source (e.g. chimney, building) tend to rise up in the air in a wavy pattern.", 833], "slices": ["Yes. 'Slices' has a tangible appearance and commonly refers to pieces of food, such as fruits and vegetables.\nA few things that are visually similar to 'slices' but are not 'slices' are:\tdices\twhole pieces\tchunks\tshreds\nThere are several useful visual features to tell there are 'slices' and not similar things in a photo:\tthin and flat\tpieces of food\tcircular or oblong shape\twith or without seeds or pits\tvarious colors, depending on the type of food", 830], "doughnut": ["Yes. 'doughnut' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'doughnut' but are not 'doughnut' are:\tbagel\tbunt cake\ttire\tlife-saver candy\nThere are several useful visual features to tell there is 'doughnut' and not similar things in a photo:\tcircular in shape\twith a hole in the center\tor solid and filled with jam, cream, or chocolate\tfried or baked\toften coated with sugar or glaze or sprinkles", 828], "mat": ["Yes. 'Mat' has a tangible appearance and is a kind of flat object used for protecting or decorating floors.\nA few things that are visually similar to 'mat' but are not 'mat' are: carpet, rug, blanket, towel\nThere are several useful visual features to tell there is 'mat' and not similar things in a photo:\tflat and thin\tcome in various sizes and shapes\tsimilar texture and color as the floor where it's placed\tcould have patterns or decorative designs", 827], "bracelet": ["Yes. 'Bracelet' has a tangible appearance and is a piece of jewelry worn on the wrist.\nA few things that are visually similar to 'bracelet' but are not 'bracelet' are:\twatch\tanklet\thair tie \nThere are several useful visual features to tell there is 'bracelet' and not similar things in a photo:\tworn on the wrist\tvarious materials such as metal, leather, beads, or plastic\tfits closely to the wrist or loose and dangly\thave a closure or continuous loop shape.", 827], "shore": ["Yes. 'Shore' has a tangible appearance and refers to the land along the edge of a body of water.\nA few things that are visually similar to 'shore' but are not 'shore' are: pier, beach, dock, wharf, marina, and port.\nThere are several useful visual features to tell there is 'shore' and not similar things in a photo: sandy or rocky terrain, waves of the water touching the land, or vegetation growing on the ground.", 826], "houses": ["Yes. 'Houses' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'houses' but are not 'houses' are:\toffice buildings\tapartment buildings\tchurches\tshops\nThere are several useful visual features to tell there is 'houses' and not similar things in a photo:\tfreestanding or attached to other houses\tone or more stories\tresidential character\t typical features such as doors, windows, roofs, and chimneys.", 822], "control": ["No. 'Control' is too vague or abstract to be distinguished in a photo.", 821], "tank top": ["Yes. 'Tank top' has a tangible appearance and is a type of shirt.\nA few things that are visually similar to 'tank top' but are not 'tank top' are:\tblouse\tV-neck shirts\tcropped top\tsleeveless shirt\nThere are several useful visual features to tell there is 'tank top' and not similar things in a photo:\ttwo shoulder straps\twith a scoop neckline or a racerback\tdoes not cover the stomach", 815], "knee": ["Yes. 'Knee' has a tangible appearance and is a joint in the human body.\nA few things that are visually similar to 'knee' but are not 'knee' are:\telbow\tankle\twrist\thinge\t\nThere are several useful visual features to tell there is 'knee' and not similar things in a photo:\tbony protrusion on the leg, below the thigh and above the calf\tmore rounded than the elbow or ankle\tcovered by skin and hair\tpatella (kneecap) visible in the front of the knee", 814], "silver car": ["Yes. 'Silver car' has a visually concrete appearance and can be distinguished from other types of cars based on its color.\nA few things that are visually similar to 'silver car' but are not 'silver car' are:\tgrey car\tchrome car\taluminum car\nThe useful visual features for distinguishing 'silver car' from similar things in a photo are:\treflection or shine that shows a metallic appearance\tspecific shade of metallic grey that appears similar to silver.", 806], "string": ["Yes. 'String' has a tangible appearance and is a thin length of cord, rope, or twine.\nA few things that are visually similar to 'string' but are not 'string' are:\twire\tthread\thair\nThere are several useful visual features to tell there is 'string' and not similar things in a photo:\tthin and cylindrical\tmade of fibrous materials\ttexture is different than wire or hair\tpotentially tied in knots or bows.", 805], "bathtub": ["Yes. 'Bathtub' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'bathtub' but are not 'bathtub' are:\tshower\ttub\tsink\tpool\nThere are several useful visual features to tell there is 'bathtub' and not similar things in a photo:\tflat bottom with sloping sides\toval or rectangular shape\tattached faucet or showerhead\tporcelain or acrylic material\tdrain on one end of the tub", 804], "egg": ["Yes. 'Egg' has a tangible appearance and is a real object.\nA few things that are visually similar to 'egg' but are not 'egg' are:\tavocado\tpotato\tbilliard ball\tpearl\nThere are several useful visual features to tell there is 'egg' and not similar things in a photo:\toval shape\tsmooth, hard shell\twhite or brown color\tyolk inside visible through a cracked shell", 798], "balcony": ["Yes. 'Balcony' has a tangible appearance and is a kind of architectural feature.\nA few things that are visually similar to 'balcony' but are not 'balcony' are:\tpatio\tterrace\tdeck\tveranda\nThere are several useful visual features to tell there is 'balcony' and not similar things in a photo:\tplatform protruding from the side of a building\twith a railing or a balustrade\tmay be enclosed or open to the elements\tsmall in size compared to the building it's attached to.", 797], "soccer ball": ["Yes. 'Soccer ball' has a tangible appearance and is a type of ball used in soccer.\nA few things that are visually similar to 'soccer ball' but are not 'soccer ball' are:\tbasketball\tvolleyball\tbowling ball\tbeach ball\tpumpkin\nThere are several useful visual features to tell there is 'soccer ball' and not similar things in a photo:\tblack and white pentagonal pattern\t32 panels\tinterlocking segments", 797], "image": ["No. 'Image' is too vague or abstract to be distinguished in a photo. It can refer to a mental representation or a digital file.\nThere are no things that are visually similar to 'image' as it is an intangible concept.\nTherefore, there are no useful visual features for distinguishing 'image' from other visual things in a photo as it is an abstract concept.", 795], "teeth": ["Yes. 'Teeth' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'teeth' but are not 'teeth' are:\tpearls\twhite stones\tseashells\nThere are several useful visual features to tell there are 'teeth' and not similar things in a photo:\tlocated in the mouth\tjagged edges\tcone-shaped\thard, opaque and white in color (usually)\twith roots\tin groups of different sizes and shapes.", 795], "bar": ["Yes. 'Bar' has a tangible appearance and is a type of establishment.\nA few things that are visually similar to 'bar' but are not 'bar' are:\trestaurant\tcafe\tclub\tpub\nThere are several useful visual features to tell there is 'bar' and not similar things in a photo:\tbar counter or a bar table\tbartender or servers\talcoholic drinks or beverages\tspecial bar glasses or utensils\tsignage, logos, or advertisements that indicate the establishment is predominantly serving alcoholic beverages", 790], "remote": ["Yes. 'Remote' has a tangible appearance and is an electronic device used to control other devices.\nA few things that are visually similar to 'remote' but are not 'remote' are:\tmobile phone\ttablet\tcamera\twalkie-talkie\nThere are several useful visual features to tell there is 'remote' and not similar things in a photo:\trectangular or square shape\tvisible buttons or touch screen\tarrow or circular navigation pad\tinfrared or wireless connection to other devices", 788], "power lines": ["Yes. 'Power lines' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'power lines' but are not 'power lines' are:\ttelephone lines\tcable lines\tfishing lines\tdrying clothes rope\nThere are several useful visual features to tell there is 'power lines' and not similar things in a photo:\tlines are attached to tall poles or towers\tlines are organized in a grid or network\tlines are usually thick and heavy-looking\tlines are connected by insulators\tlines carry electricity", 788], "pattern": ["Yes. 'Pattern' has a tangible appearance and is a repetition of elements or designs.\nA few things that are visually similar to 'pattern' but are not 'pattern' are:\ttexture\tarrangement\tmotif\tshape\tline\nThere are several useful visual features to tell there is 'pattern' and not similar things in a photo:\trepetition of elements or designs\tsymmetry or regularity\tsimilar shapes or colors", 787], "hills": ["Yes. 'Hills' has a tangible appearance and is a natural formation of land.\nA few things that are visually similar to 'hills' but are not 'hills' are:\tmountains\tdunes\tridges\tcliffs\nThere are several useful visual features to tell there is 'hills' and not similar things in a photo:\tsmall size in comparison to mountains and cliffs\trounded shape, without a sharp or steep peak\ttypically abundant with vegetation, such as trees and grasses\tvarious shades of green or brown, depending on the season and crops present", 783], "tennis shoes": ["Yes. 'Tennis shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'tennis shoes' but are not 'tennis shoes' are: running shoes, basketball shoes, boots, sandals, flip flops.\nThere are several useful visual features to tell there are 'tennis shoes' and not similar things in a photo: flat sole, round toe, low-cut design, comfortable fit, laces or straps, breathable and flexible material, specific colors and patterns commonly used for tennis shoes.", 783], "scooter": ["Yes. 'Scooter' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'scooter' but are not 'scooter' are:\tmotorcycle\tbicycle\tsegway\trollerblades\nThere are several useful visual features to tell there is 'scooter' and not similar things in a photo:\tstep-through frame design\tlarge wheels, small frame\tflat footrest\tarea for a rider to place his feet when riding upright\thandlebars for steering and braking", 783], "advertisement": ["No. 'Advertisement' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are referring to a physical advertisement such as a billboard or a printed ad, then 'advertisement' would have a tangible appearance.\n\nA few things that are visually similar to a physical 'advertisement' but are not 'advertisement' are:\tposter\tartwork\tsignboard\n\nThere are several useful visual features to tell there is a physical 'advertisement' and not similar things in a photo:\tbrand logos or names\tproduct images or descriptions\tsales or promotional language\trelevant contact information or website addresses\tclear and legible text and imagery", 781], "straw": ["Yes. 'Straw' has a tangible appearance and is a type of dried plant material.\nA few things that are visually similar to 'straw' but are not 'straw' are:\thay, bamboo, reeds, grass\nThere are several useful visual features to tell there is 'straw' and not similar things in a photo:\tyellow or golden color\tfine, thin, and dry\tdisorganized or knotted bundles arranged linearly, often with ends frayed or uneven", 779], "outfit": ["Yes. 'Outfit' has a tangible appearance and refers to a set of clothing worn together.\nA few things that are visually similar to 'outfit' but are not 'outfit' are:\tsingle clothing item\tuniform\tcostume\taccessory\nThere are several useful visual features to tell there is 'outfit' and not similar things in a photo:\tcombination of multiple clothing items\tcoordinated colors, patterns or styles\tworn together as a complete look\ton a person's body or displayed together", 779], "rail": ["Yes. 'Rail' has a tangible appearance and refers to a track or a bar used for support or transportation.\nA few things that are visually similar to 'rail' but are not 'rail' are:\tfence\tpole\tbar\nThere are several useful visual features to tell there is 'rail' and not similar things in a photo:\tlong, narrow shape\tmetallic surface\tsymmetrical grooves or ridges\tattached to the ground or another structure", 776], "rack": ["Yes. 'Rack' has a tangible appearance and is a piece of furniture used to store or display things.\nA few things that are visually similar to 'rack' but are not 'rack' are:\ttable\tcounter\tshelf\tstand\nThere are several useful visual features to tell there is 'rack' and not similar things in a photo:\thorizontal surfaces\twith multiple levels or shelves\tmade to hold specific items (such as clothes, tools, shoes or wine)", 770], "hats": ["Yes. 'Hats' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'hats' but are not 'hats' are:\tbeanies\tbandanas\thair accessories\thelmets\nThere are several useful visual features to tell there is 'hats' and not similar things in a photo:\tcrown-like shape\tbill or brim\tvisible headband or strap\tunique material or texture\tdifferent styles or shapes based on the occasion", 761], "peppers": ["Yes. 'Peppers' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'peppers' but are not 'peppers' are:\ttomatoes,\tcherries,\tberries\nThere are several useful visual features to tell there is 'peppers' and not similar things in a photo:\thollow inside\tedible\tflesh or skin can be red, green, yellow, or orange in color\ttypically elongated shape with a pointed end", 761], "coffee cup": ["Yes. 'Coffee cup' has a tangible appearance and is a type of drinking vessel.\nA few things that are visually similar to 'coffee cup' but are not 'coffee cup' are:\tmug\tteapot\tglass\ttumbler\nThere are several useful visual features to tell there is 'coffee cup' and not similar things in a photo:\thandle\trounded shape\ttapered top\trim around the top\tfor hot beverages or coffee in particular", 760], "cone": ["Yes. 'Cone' has a tangible appearance and is a type of three-dimensional shape.\nA few things that are visually similar to 'cone' but are not 'cone' are:\ttriangle\tpyramid\ttepee\tvolcano\nThere are several useful visual features to tell there is 'cone' and not similar things in a photo:\tcircular base\tpointed or tapered end\tconical shape\tuniform or smooth surface", 755], "street signs": ["Yes. 'Street signs' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'street signs' but are not 'street signs' are:\tbillboards\tshop signs\tdirection signs\tadvertising signs\nThere are several useful visual features to tell there is 'street signs' and not similar things in a photo:\trectangular or square shape\twith clear and bold white or yellow letters\tand usually a recognizable symbol or icon indicating the type of information conveyed\tslightly elevated and attached to a pole or a wall along the street.", 755], "glass window": ["Yes. 'Glass window' has a tangible appearance and is a type of window made of glass.\nA few things that are visually similar to 'glass window' but are not 'glass window' are:\tmirrors\tglass doors\tphoto frames\tfish tanks\nThere are several useful visual features to tell there is 'glass window' and not similar things in a photo:\ttransparent or translucent\tglass panes\taffixed to a wall or frame\tallows light or outside view to enter the room or building", 754], "tennis shoe": ["Yes. 'Tennis shoe' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'tennis shoe' but are not 'tennis shoe' are:\thiking boots\trunning shoes\tsandals\tloafers\nThere are several useful visual features to tell there is 'tennis shoe' and not similar things in a photo:\tflexible sole for quick lateral movements\tlow-cut design\tpadded collar and tongue for comfort\treinforcements near the toe\tbox for protection and durability\tlaces or velcro for secure fit.", 753], "silver fork": ["Yes. 'Silver fork' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'silver fork' but are not 'silver fork' are:\tspoon\tknife\tchopstick\ttweezer\nThere are several useful visual features to tell there is 'silver fork' and not similar things in a photo:\tmetallic material\tsilver color\tfour prongs or tines\thandles", 751], "street lamp": ["Yes. 'Street lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'street lamp' but are not 'street lamp' are: garden lamp, porch light, traffic light.\nThere are several useful visual features to tell there is 'street lamp' and not similar things in a photo:\ttall pole with a light on top\tsurrounded by a cage or a glass cover\tproviding illumination for a street or a path\tin an outdoor setting.", 743], "traffic sign": ["Yes. 'Traffic sign' has a tangible appearance and is a kind of visual communication device used for traffic regulation.\nA few things that are visually similar to 'traffic sign' but are not 'traffic sign' are:\tbillboards\tposters\twindows\tbanners\nThere are several useful visual features to tell there is 'traffic sign' and not similar things in a photo:\tshape (usually circular, triangular, or rectangular)\twith specific text or images (such as arrows, speed limits, or warnings)\tplaced on or near a road or area with traffic\tcontrol the behavior or actions of drivers and pedestrians", 743], "wristband": ["Yes. 'Wristband' has a tangible appearance and is a type of wearable item.\nA few things that are visually similar to 'wristband' but are not 'wristband' are:\twatch\tbracelet\tankleband\nThere are several useful visual features to tell there is 'wristband' and not similar things in a photo:\tstrip of fabric or material worn around the wrist\tvariety of colors, patterns, or designs\tcan be plain or have writing or images\ton the smaller side of wearable items", 742], "watercraft": ["Yes. 'Watercraft' has a tangible appearance and refers to any kind of vehicle designed to travel on water.\nA few things that are visually similar to 'watercraft' but are not 'watercraft' are:\tswimming animals (dolphins, whales)\tinflatable pool toys (floaties)\trocks or debris floating in water\nThere are several useful visual features to tell there is 'watercraft' and not similar things in a photo:\tvarious shapes and sizes\thulls to displace water\tvisible propellers, sails, or oars\tmoving across the water's surface", 738], "parking lot": ["Yes, 'parking lot' has a tangible appearance and is a space designed for parking cars.\nA few things that are visually similar to 'parking lot' but are not 'parking lot' are:\tempty field\tstreet without parked cars\thighway\trest area\nThere are several useful visual features to tell there is 'parking lot' and not similar things in a photo:\trectangular or square shaped\tlined parking spaces\tasphalt or concrete surface\tparked cars or visible tire tracks\tparking signs or barriers", 737], "aircraft": ["Yes. 'Aircraft' has a tangible appearance and is a kind of machine used for flying.\nA few things that are visually similar to 'aircraft' but are not 'aircraft' are:\tbirds\tballoons\tgliders\tdrones\nThere are several useful visual features to tell there is 'aircraft' and not similar things in a photo:\twings or rotors\tengine\texhaust\ttrail or contrail\tlanding gears", 736], "coffee": ["Yes. 'Coffee' has a tangible appearance and is a type of beverage.\nA few things that are visually similar to 'coffee' but are not 'coffee' are:\ttea\tbrown soda\tcocoa\thot chocolate\nThere are several useful visual features to tell there is 'coffee' and not similar things in a photo:\tdark brown or black color\ttranslucent or transparent surface\twhite steam rising from the surface\tcup or mug-shaped container with a handle\tthe cup or mug is often marked with a logo or design", 735], "duck": ["Yes. 'Duck' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'duck' but are not 'duck' are:\tgoose\tswan\tpenguin\nThere are several useful visual features to tell there is 'duck' and not similar things in a photo:\trounded body, slender neck\twebbed feet\tyellow or orange beak\twater-repellent feathers\tquacking sound\twhen the duck is flying, the wings can show a distinctive color pattern.", 732], "knob": ["Yes. 'Knob' has a tangible appearance and is a type of handle or switch.\nA few things that are visually similar to 'knob' but are not 'knob' are:\tbutton\tdial\tlever\thinge\tlatch\nThere are several useful visual features to tell there is 'knob' and not similar things in a photo:\tcircular or spherical shape\tprotruding from a surface\tridged or textured surface\tfor grasping or turning\tcontrol function for a device or furniture piece", 730], "lot": ["No. 'Lot' is too vague or abstract to be distinguished in a photo.", 730], "kids": ["Yes. 'Kids' has a tangible appearance and refers to human children.\nA few things that are visually similar to 'kids' but are not 'kids' are:\tadults\tdwarfs\tpuppets\tmidgets\nThere are several useful visual features to tell there are 'kids' and not similar things in a photo:\tshorter height\tlively, playful expressions\tchildlike clothing, such as school uniforms or casual wear\ttoys or school supplies in their hands or proximity\ttoothless smile or missing teeth", 728], "liquid": ["Yes. 'Liquid' has a tangible appearance and is a state of matter that can flow and take the shape of its container.\nA few things that are visually similar to 'liquid' but are not 'liquid' are:\tglass\tmirror\tgel\tice\nThere are several useful visual features to tell there is 'liquid' and not similar things in a photo:\ttakes the shape of its container\tfluid\tmoveable or flowing surface\twet appearance\tif colored, translucent or transparent", 727], "fireplace": ["Yes. 'Fireplace' has a tangible appearance and is a structure used for heating.\nA few things that are visually similar to 'fireplace' but are not 'fireplace' are:\tchimney\tsmokestack\toven\nThere are several useful visual features to tell there is 'fireplace' and not similar things in a photo:\tstructure made of bricks or stones\thearth where the fire burns\tmantel on top of the fireplace\tfirewood inside (if visible)\tabsence of smoke or particles coming out (unlike the chimney)", 721], "hay": ["Yes. 'Hay' has a tangible appearance and is a kind of agricultural product.\nA few things that are visually similar to 'hay' but are not 'hay' are:\tstraw\tdried grass\tdried leaves\tdried flowers\nThere are several useful visual features to tell there is 'hay' and not similar things in a photo:\tdried and cured grasses\tbaled or stacked\tin rectangular or cylindrical shapes\tbrownish-yellow color", 721], "shade": ["Yes. 'Shade' has a tangible appearance and refers to an area where direct sunlight is blocked or reduced.\nA few things that are visually similar to 'shade' but are not 'shade' are:\treflections\tdark areas\tcast shadows\nThere are several useful visual features to tell there is 'shade' and not similar things in a photo:\tpartially or fully covered area by an object or a structure\tdiminished sunlight or light intensity in the area\ttemperature difference between shaded and unshaded areas", 720], "clock face": ["Yes. 'Clock face' has a tangible appearance and refers to the visible part of the clock that displays the time.\nA few things that are visually similar to 'clock face' but are not 'clock face' are:\twatches\twallpapers\tsun dial\tcompass\nThere are several useful visual features to tell there is 'clock face' and not similar things in a photo:\tnumeral indicators\thour, minute, and second hands\tcenter of the clock's face\tcircular shape\twith or without a bezel\tthe presence of the words \"hour,\" \"minute,\" or \"second\"", 719], "tusk": ["Yes. 'Tusk' has a tangible appearance and is a long, pointed tooth typically found in animals like elephants and walruses.\nA few things that are visually similar to 'tusk' but are not 'tusk' are:\thorn\tantler\tnail\tcone\ticicle\nThere are several useful visual features to tell there is 'tusk' and not similar things in a photo:\tlong and curved\tivory or bone texture\tfound in the mouth area of certain animals\tpointed and sharp\ttapered at one end and wider at the other\tend coming out of a trunk or a mouth", 717], "traffic signal": ["Yes. 'Traffic signal' has a tangible appearance and is a type of road signage.\nA few things that are visually similar to 'traffic signal' but are not 'traffic signal' are:\tstreet signs\tbillboards\tbanners\tparking meter\nThere are several useful visual features to tell there is 'traffic signal' and not similar things in a photo:\tthree signal lights in red, yellow, and green\thanging on a pole\tor attached to a wall\twith arrows or letters indicating the direction of the lanes.", 712], "lake": ["Yes. 'Lake' has a tangible appearance and is a large body of water surrounded by land.\nA few things that are visually similar to 'lake' but are not 'lake' are:\tpond\treservoir\tocean\t\nThere are several useful visual features to tell there is 'lake' and not similar things in a photo:\tlarge body of water\tfreshwater\tsource from river or melting snow\tsurrounded by land and trees or mountains\tvisible shoreline and underwater ground", 712], "band": ["No. 'Band' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are referring to a 'musical band', then the answer is yes.\nA few things that are visually similar to a 'musical band' but are not 'band' are:\tgroup of people standing together\tdancers\taudience\tsports team\nThere are several useful visual features to tell there is a 'musical band' and not similar things in a photo: musicians playing musical instruments\tmicrophones or other audio equipment\tmusic stands or sheet music\tstage or performance area", 711], "dresser": ["Yes. 'Dresser' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'dresser' but are not 'dresser' are:\tchest of drawers\tbuffet\tsideboard\nThere are several useful visual features to tell there is a 'dresser' and not similar things in a photo:\tupright storage furniture with multiple drawers\tdrawers are typically stacked in two columns on each other\thas a flat top surface\tfor clothing and personal items\tsometimes has a mirror attached to the top", 707], "beer": ["Yes. 'Beer' has a tangible appearance and is a kind of beverage.\nA few things that are visually similar to 'beer' but are not 'beer' are:\tcolored water\tcarbonated drink\ticed tea\tapple juice\nThere are several useful visual features to tell there is 'beer' and not similar things in a photo:\tgolden or amber color\tfrothy head\ton a pint glass or a beer mug\tlabel with beer brand logo\tbubbles inside\tthe thickness of the drink", 707], "sunlight": ["Yes. 'Sunlight' has a tangible appearance and refers to the visible light and heat that comes from the sun.\nA few things that are visually similar to 'sunlight' but are not 'sunlight' are:\tlight bulbs\tflashes of lightning\tfireworks\t\nThere are several useful visual features to tell there is 'sunlight' and not similar things in a photo:\tcomes from a visible sun\tsource of warmth and light\toutdoor setting\tshadows cast by objects and people\tdirection and angle of the light's rays.", 707], "pocket": ["Yes. 'Pocket' has a tangible appearance and is a type of opening on a garment.\nA few things that are visually similar to 'pocket' but are not 'pocket' are:\tButtonhole\tZippers\tRuffles\nThere are several useful visual features to tell there is 'pocket' and not similar things in a photo:\tAn opening in fabric\tAble to hold small objects\tA flat or slightly curved shape. Often rectangle or circle.\tSame material as the garment.", 704], "cream": ["Yes. 'Cream' has a tangible appearance and is a type of dairy product.\nA few things that are visually similar to 'cream' but are not 'cream' are: milk, yogurt, lotion, shampoo\nThere are several useful visual features to tell there is 'cream' and not similar things in a photo: a thick and white-colored liquid, often poured from a container or spoon, typically used as a topping for food or beverages.", 697], "star": ["Yes. 'Star' has a tangible appearance and is a celestial object.\nA few things that are visually similar to 'star' but are not 'star' are:\tasterisk\tsnowflake\tsea star\tpentagon\nThere are several useful visual features to tell there is 'star' and not similar things in a photo:\tpointed shape\tbright light or illumination\tin the sky, often against a dark background\tsurrounding by other stars\ttoo bright to look at directly, may require a filter or special lens to view", 689], "forest": ["Yes. 'Forest' has a tangible appearance and is a type of vegetation.\nA few things that are visually similar to 'forest' but are not 'forest' are:\tjungle\tpark\tgarden\nThere are several useful visual features to tell there is 'forest' and not similar things in a photo:\ttrees with a dense growth pattern\ta variety of types of trees and plants\tlimited visibility between trees and plants\ta natural setting (not manicured or cultivated)", 689], "fixture": ["No. 'Fixture' is too vague or abstract to be distinguished in a photo. It can refer to a wide range of permanent or semi-permanent elements that are typically attached to a building or a structure.\nWithout more context, it's hard to come up with things that are visually similar to 'fixture' but not 'fixture'. However, some things that could potentially be confused with a fixture are:\tfurniture\tdecorations\tart pieces\tappliances\nAgain, without more context, it's hard to provide useful visual features for distinguishing 'fixture' from other things. However, some general characteristics of fixtures that could be helpful in identifying them are:\t\n- They are usually attached to a building or a structure in a permanent or semi-permanent way.\n- They serve a functional purpose, such as providing lighting, plumbing, or ventilation.\n- They are often designed to blend in with the surroundings and have a utilitarian look rather than an artistic or decorative one.\n- They are typically installed by professionals rather than being moved around or placed by individuals.", 688], "coffee mug": ["Yes. 'Coffee mug' has a tangible appearance and is a type of cup.\nA few things that are visually similar to 'coffee mug' but are not 'coffee mug' are:\ttea cup\tjuice glass\tbeer stein\twine glass\nThere are several useful visual features to tell there is 'coffee mug' and not similar things in a photo:\tcylindrical or curved shape\thandle\ton the larger side with a wide mouth\tused for hot beverages such as coffee or tea\tdecorated with patterns such as text or images", 687], "herd": ["Yes. 'Herd' has a tangible appearance and is a group of animals.\nA few things that are visually similar to 'herd' but are not 'herd' are:\tcrowd\tswarm\tpack\tteam\nThere are several useful visual features to tell there is 'herd' and not similar things in a photo:\tgroups of animals\tsame or similar types of animals\tclose proximity to each other\tmoving together or in the same direction", 686], "strip": ["Yes. 'Strip' has a tangible appearance and refers to a long and narrow piece of something.\nA few things that are visually similar to 'strip' but are not 'strip' are:\tline\tribbon\tband\tpiece\nThere are several useful visual features to tell there is 'strip' and not similar things in a photo:\tlong and narrow shape\twith uniform width and thickness\tusually straight and flat\tcan have patterns or colors on it.", 683], "headboard": ["Yes. 'Headboard' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'headboard' but are not 'headboard' are:\tfootboard\twall\tpanel\tdoor\nThere are several useful visual features to tell there is 'headboard' and not similar things in a photo:\tattached to a bed\tfancy designs, shapes or materials\tvertical and rectangular in shape\tvaries in height from the bed", 681], "propeller": ["Yes. 'Propeller' has a tangible appearance and is a type of rotating blade.\nA few things that are visually similar to 'propeller' but are not 'propeller' are:\tceiling fan\thelicopter rotor\twind turbine blades\tfidget spinner\nThere are several useful visual features to tell there is 'propeller' and not similar things in a photo:\ttwo or more rotating blades\tattached to a motor or engine\tnarrow at the base, wide at the tip\tmade of metal or composite material\tspinning fast to create thrust or lift\tno support structure or safety guard around the blades", 681], "stones": ["Yes. 'Stones' has a tangible appearance and refers to a type of rock.\nA few things that are visually similar to 'stones' but are not 'stones' are: \tpebbles\tsand\tmarble\twood\nThere are several useful visual features to tell there is 'stones' and not similar things in a photo: \thard and solid surface\tnatural or man-made surface\tvariety of colors and textures\trough or smooth texture", 680], "trucks": ["Yes. 'Trucks' has a tangible appearance and refers to a specific kind of vehicle.\nA few things that are visually similar to 'trucks' but are not 'trucks' are:\tcars\tbuses\tvans\ttrailers\nThere are several useful visual features to tell there is 'trucks' and not similar things in a photo:\tlarge size and weight\t2-3 axles\tand more wheels\tfor carrying goods\tand materials\tusually have an open-bed section or container-backed compartment on its rear end.", 676], "thumb": ["Yes. 'Thumb' has a tangible appearance and is a part of the hand.\nA few things that are visually similar to 'thumb' but are not 'thumb' are:\tfingers\ttoes\troots\nThere are several useful visual features to tell there is 'thumb' and not similar things in a photo:\tthe shortest digit of the hand\topposable to the other fingers\tfat and round shape with a visible knuckle", 675], "walkway": ["Yes. 'Walkway' has a tangible appearance and is a structure designed for pedestrians to walk on.\nA few things that are visually similar to 'walkway' but are not 'walkway' are: roads, bike paths, hiking trails, sidewalks, staircases\nThere are several useful visual features to tell there is 'walkway' and not similar things in a photo:\tnarrow path designed for walking\tsurface level to the ground or slightly elevated\tconnected to a larger building or structure\tsurrounded by landscaping or greenery with no cars or park structures in sight", 675], "planter": ["Yes. 'Planter' has a tangible appearance and is a container for growing plants.\nA few things that are visually similar to 'planter' but are not 'planter' are:\tpot\tvase\tbowl\turn\nThere are several useful visual features to tell there is 'planter' and not similar things in a photo:\thole or holes in the bottom for drainage\tsoil and plants\tdecorative designs or patterns\tfor outdoor or indoor use\thandles for easy transportation", 670], "tires": ["Yes. 'Tires' has a tangible appearance and is a type of rubber object.\nA few things that are visually similar to 'tires' but are not 'tires' are:\tfrisbee\thula hoop\tsports ball\tdonut-shaped foam cushion\nThere are several useful visual features to tell there is 'tires' and not similar things in a photo:\tround and flat\tshaped like a donut or a ring\tmade of rubber\ttread on the surface usually in black color.", 669], "hood": ["Yes. 'Hood' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'hood' but are not 'hood' are:\that\tcowl\thelmet\twig\nThere are several useful visual features to tell there is 'hood' and not similar things in a photo:\tattached to a sweatshirt, jacket, or coat\tinverted U-shaped\tdraped over the head and neck partially or fully\tno visor or brim", 668], "items": ["No. 'Items' is too vague or abstract to be distinguished in a photo. It's a general term for any physical object.", 665], "case": ["Yes. 'Case' has a tangible appearance and can refer to various types of containers or enclosures.\nA few things that are visually similar to 'case' but are not 'case' are:\tbox\tbag\ttrunk\tsuitcase\nThere are several useful visual features to tell there is 'case' and not similar things in a photo:\thard or rigid structure\thinged or latched opening\thandle for carrying or transport\tvarying sizes or shapes, such as briefcases or computer cases", 665], "blue car": ["Yes. 'Blue car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'blue car' but are not 'blue car' are:\tblue truck\tblue motorcycle\tblue bicycle\nThere are several useful visual features to tell there is a 'blue car' and not similar things in a photo:\tfour wheels\tand a windshield\ttop part of the car is one color (blue)\tside and bottom may have other colors\tdoor handles\tand mirrors.", 665], "ship": ["Yes. 'Ship' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'ship' but are not 'ship' are:\tboat\tyacht\tcruise ship\tkayak\nThere are several useful visual features to tell there is 'ship' and not similar things in a photo:\thuge size\thigh deck\tmultiple masts or chimneys\tforward-facing bow and stern\tdistinctive hull shape and color", 659], "chicken": ["Yes. 'Chicken' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'chicken' but are not 'chicken' are:\tduck\tturkey\tgoose\tpigeon\nThere are several useful visual features to tell there is 'chicken' and not similar things in a photo:\tbeak and comb at the top of the head\tsmall wings and a plump body\ttwo legs with claws or talons\tfeathers in shades of brown, white, or black", 658], "tall building": ["Yes. 'Tall building' has a tangible appearance and is a kind of structure.\nA few things that are visually similar to 'tall building' but are not 'tall building' are:\ttower\tchimney\t\nThere are several useful visual features to tell there is 'tall building' and not similar things in a photo:\tmultiple stories\tor levels\tvisible windows or balconies\tthe structure looks man-made or designed by humans", 658], "driver": ["Yes. 'Driver' has a tangible appearance and is a person who operates a vehicle.\nA few things that are visually similar to 'driver' but are not 'driver' are:\tpassenger\tpedestrian\tcyclist\tmotorcyclist\nThere are several useful visual features to tell there is 'driver' and not similar things in a photo:\tsitting behind the steering wheel\thands on the wheel\torients the vehicle's direction\tofte worn seatbelt\tfocused on the road ahead", 658], "baby elephant": ["Yes. 'Baby elephant' has a tangible appearance and is a young offspring of an elephant.\nA few things that are visually similar to 'baby elephant' but are not 'baby elephant' are:\tadult elephant\thippopotamus\trhinoceros\nThere are several useful visual features to tell there is 'baby elephant' and not similar things in a photo:\tsmall size compared to adults\tflappy ears\tshort trunk\tstill have wobbly legs\tcute and adorable appearance\tcomparatively thinner and delicate looking skin", 656], "chimney": ["Yes. 'Chimney' has a tangible appearance and is a type of architectural feature.\nA few things that are visually similar to 'chimney' but are not 'chimney' are:\tvent\tpillar\tpipe\nThere are several useful visual features to tell there is 'chimney' and not similar things in a photo:\ttall and narrow\tstacked bricks or stones\tsmoke coming out of the top", 655], "soup": ["Yes. 'Soup' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'soup' but are not 'soup' are:\tstew\tcasserole\tsauce\nThere are several useful visual features to tell there is 'soup' and not similar things in a photo:\tliquid consistency\tmixture of meat, vegetables, and spices\tin a bowl or pot\tserved with a spoon", 652], "eyeglasses": ["Yes. 'Eyeglasses' has a tangible appearance and is a kind of vision aid.\nA few things that are visually similar to 'eyeglasses' but are not 'eyeglasses' are:\tsunglasses\tgoggles\t3D glasses\tsafety glasses\nThere are several useful visual features to tell there is 'eyeglasses' and not similar things in a photo:\tclear lenses\tthat rest on the bridge of the nose\tarms that go over the ears\tadjustable nose pads for comfort", 649], "palm trees": ["Yes. 'Palm trees' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'palm trees' but are not 'palm trees' are:\tBanana trees\tDracena\tYucca trees\nThere are several useful visual features to tell there is 'palm trees' and not similar things in a photo:\tlong, slender trunk with a usually narrower base and wider top\tfan-like or feather-like fronds or leaves\tgrooved bark\tgrowing in warm and tropical places.", 646], "bicycles": ["Yes. 'Bicycles' has a tangible appearance and is a type of transportation.\nA few things that are visually similar to 'bicycles' but are not 'bicycles' are:\tmotorcycles\tscooters\tsegways\nThere are several useful visual features to tell there is 'bicycles' and not similar things in a photo:\ttwo wheels\tpedals\thandlebars\tframe\tthat may have a chain and gears\tfor human-powered use only", 644], "blender": ["Yes. 'Blender' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'blender' but are not 'blender' are:\tfood processor\tmixer\tgrinder\tjuicer\nThere are several useful visual features to tell there is 'blender' and not similar things in a photo:\ttransparent or semi-transparent container with blades at the bottom\ttall or vertical shape\tpour spout on the container\tblades or cutting edges inside the container\tcontrol panel or buttons for adjusting speed and intensity", 641], "counter top": ["Yes. 'Counter top' has a tangible appearance and refers to a flat surface in a kitchen or bathroom for preparing food or other activities.\nA few things that are visually similar to 'counter top' but are not 'counter top' are:\tdesk\tshelf\ttable\tcabinet\nThere are several useful visual features to tell there is 'counter top' and not similar things in a photo:\tflat and smooth surface\tmade of stone, wood, or other materials\tcommonly found in a kitchen or bathroom\tpart of a larger section of cabinetry or shelving.", 641], "lots": ["No. 'Lots' is too vague or abstract to be distinguished in a photo.", 640], "decker bus": ["Yes. 'Decker bus' has a tangible appearance and is a type of bus with two levels.\nA few things that are visually similar to 'decker bus' but are not 'decker bus' are:\tregular bus\ttruck\ttrain\tboat\nThere are several useful visual features to tell there is 'decker bus' and not similar things in a photo:\ttwo levels\topen top on the second deck\twindows on the second deck\tlong and narrow shape\twith or without stairs, depending on the design\tand wheels located towards the back of the vehicle.", 639], "skater": ["Yes. 'Skater' has a tangible appearance and is a person who skates.\nA few things that are visually similar to 'skater' but are not 'skater' are:\trunner\thockey player\tsnowboarder\tsurfer\nThere are several useful visual features to tell there is 'skater' and not similar things in a photo:\tskates, skateboarding or roller-skating attire or gear\tmoving on a board, wheels or snowboard\tbalance on a board or wheels\taction or performing tricks on a skate or board", 637], "structure": ["No. 'Structure' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider 'structure' as a physical object that has been built, a few things that are visually similar to 'structure' but are not 'structure' are: pile of rocks, mound of sand, heap of garbage, and random assortment of objects.\n\nUseful visual features that distinguish 'structure' from these similar things could include:\n- Clearly defined and intentional organization or arrangement of materials \n- Symmetry or regularity in the design or construction \n- A roof, walls or some kind of enclosure \n- Evidence of human design, like straight lines or uniformity", 636], "controller": ["Yes. 'Controller' has a tangible appearance and refers to a device used to operate electronic devices.\nA few things that are visually similar to 'controller' but are not 'controller' are:\tremote\tcontrol panel\tkeyboard\tvideo game joystick\nThere are several useful visual features to tell there is 'controller' and not similar things in a photo:\thandheld device\twith buttons or keys\tfor operating electronic devices, such as TV, stereo, game console\ttriggers\tor joysticks\tfor games or volume, channel, and power controls\toften black or another bold color with distinct lettering or logos.", 633], "skiers": ["Yes. 'Skiers' has a tangible appearance and refers to people who ski.\nA few things that are visually similar to 'skiers' but are not 'skiers' are:\tSnowboarders\tIce skaters\tHikers\nThere are several useful visual features to tell there are 'skiers' and not similar things in a photo:\tPeople wearing ski boots, skis, and ski poles\tSkiers using ski lifts, gondolas, or other ski-specific transportation\tSkiers holding poles and leaning forward while skiing\tthe presence of snow and ski tracks", 629], "store": ["Yes. 'Store' has a tangible appearance and is a building or establishment where goods are sold.\nA few things that are visually similar to 'store' but are not 'store' are:\thouse\tchurch\tcar garage\tpolice station\nThere are several useful visual features to tell there is 'store' and not similar things in a photo:\tsignage or banners containing the name of a business or its logo\tdisplay windows\twith a variety of products inside\ta cash register or checkout area\tshelves stacked with merchandise or inventory", 627], "fan": ["Yes. 'Fan' has a tangible appearance and is an electric or manual appliance used for moving air.\nA few things that are visually similar to 'fan' but are not 'fan' are:\tclock\tradio\tspeaker\tair conditioner\t\nThere are several useful visual features to tell there is 'fan' and not similar things in a photo:\tblades or propellers in circular motion\tstand or clip to hold the appliance\tadjustable speed settings\ton/off switch", 627], "gear": ["Yes. 'Gear' has a tangible appearance and is a mechanical component used in machinery.\nA few things that are visually similar to 'gear' but are not 'gear' are:\tdecorative wheels\tclockwork wheels\tbicycle chain wheels\tfan blades\nThere are several useful visual features to tell there is 'gear' and not similar things in a photo:\tteeth or cogs\tmade of metal or plastic\tpaired with other gears as part of a machine or mechanical system.", 626], "someone": ["No. 'Someone' is too vague or abstract to be distinguished in a photo.", 625], "brush": ["Yes. 'Brush' has a tangible appearance and is a tool used for cleaning, painting, or grooming.\nA few things that are visually similar to 'brush' but are not 'brush' are: comb, broom, mop, sponge, cloth\nThere are several useful visual features to tell there is 'brush' and not similar things in a photo:\tbristles or fibers\thandles of different sizes and shapes\tstraight or curved shape\tmultiple rows of bristles or a single section of bristles.", 624], "headband": ["Yes. 'Headband' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'headband' but are not 'headband' are:\tscarf\thair clip\that\tbandana\nThere are several useful visual features to tell there is 'headband' and not similar things in a photo:\tworn on the head and goes across the forehead or crown of the head\tnarrow or wide\tin a range of colors and patterns\tmade from fabric or plastic/stiff materials\tcan have embellishments like bows, beads or jewels", 624], "apron": ["Yes. 'Apron' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'apron' but are not 'apron' are:\tshirts\tdresses\tjackets\nThere are several useful visual features to tell there is 'apron' and not similar things in a photo:\tcovering the front of the body\tfrom neck to waist or knees\tties or straps at the back\tor around the waist\tmade from cloth or plastic", 623], "symbol": ["No. 'Symbol' is too vague or abstract to be represented by a tangible appearance.\nA few things that are visually similar to 'symbol' but are not 'symbol' are:\tletter\tnumber\tpicture\temoticon\tlogo\nThere are no useful visual features for distinguishing 'symbol' from these similar things because 'symbol' is a broader concept that encompasses all of them as types of representation. However, specific symbols may be distinguished based on their unique visual features, such as color, shape, and pattern.", 622], "canopy": ["Yes. 'Canopy' has a tangible appearance and refers to a type of natural or artificial covering.\nA few things that are visually similar to 'canopy' but are not 'canopy' are:\tumbrella\ttarp\tawning\tshade cloth\nThere are several useful visual features to distinguish 'canopy' from the listed similar things in a photo:\tmost commonly refers to the upper covering of trees or plants, forming a thick shade or shelter.\tIn architecture, a canopy is typically a suspended cover supported by posts and used to provide shade, shelter, or decoration.", 622], "stop sign": ["Yes. 'Stop sign' has a tangible appearance and is a kind of traffic sign.\nA few things that are visually similar to 'stop sign' but are not 'stop sign' are:\tyield sign\tcrosswalk sign\tspeed limit sign\nThere are several useful visual features to tell there is 'stop sign' and not similar things in a photo:\toctagonal shape\tred background and white letters\tspecific word 'STOP' written in the center of the sign", 621], "ribbon": ["Yes. 'Ribbon' has a tangible appearance and is a thin band used to decorate or wrap.\nA few things that are visually similar to 'ribbon' but are not 'ribbon' are:\tstrip of paper\tbandage\ttape\nThere are several useful visual features to tell there is 'ribbon' and not similar things in a photo:\tslim\twavy or straight\tlong enough to be tied or wrapped\tusually made of fabric or satin", 621], "cover": ["No. 'Cover' is too vague or abstract to be distinguished in a photo without additional context or information. \n\nHowever, if we were talking specifically about book covers or album covers, then the answer would be different. \n\nA few things that are visually similar to 'book covers' but are not 'book covers' are:\tbinders\tdust jackets\tenvelopes\nA few things that are visually similar to 'album covers' but are not 'album covers' are:\tposters\tframed artwork\tcalendars\n\nUseful visual features for distinguishing 'book covers' from the listed similar things in a photo are:\trectangular\tshiny or matte surface\tdesigned with images or text that relate to the content of the book\t\nUseful visual features for distinguishing 'album covers' from the listed similar things in a photo are:\tsquare or rectangular\tsize and shape of a vinyl record or CD\tshiny or matte surface\tdesigned with images or text that relate to the content of the album", 620], "potatoes": ["Yes. 'Potatoes' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'potatoes' but are not 'potatoes' are:\tturnips\tyams\tbeets\tonions\nThere are several useful visual features to tell there is 'potatoes' and not similar things in a photo:\tbrown, yellow or white skin\tsmooth or bumpy texture\tround or oblong shape\tsprouts or eyes on the surface\tchopped or sliced for cooking", 612], "lamps": ["Yes. 'Lamps' has a tangible appearance and is a type of lighting device.\nA few things that are visually similar to 'lamps' but are not 'lamps' are:\tcandles\tflashlights\tbulbs\nThere are several useful visual features to tell there is 'lamps' and not similar things in a photo:\ta structure that holds a lightbulb\tbase or stand\tfor lampshade or bulb\tswitch or knob to turn on/off or adjust the light\tfocused beam of light ", 607], "wing": ["Yes. 'Wing' has a tangible appearance and is a part of animal or aircraft anatomy.\nA few things that are visually similar to 'wing' but are not 'wing' are:\tfin\ttail\tmoss\tfoliage\nThere are several useful visual features to tell there is 'wing' and not similar things in a photo:\tflattened surface\tattached to body or aircraft\tusually paired (left and right)\tfeathers on birds", 605], "grill": ["Yes. 'Grill' has a tangible appearance and is a device used for cooking food.\nA few things that are visually similar to 'grill' but are not 'grill' are:\tfireplace\tbbq pit\ttoaster\toven\nThere are several useful visual features to tell there is 'grill' and not similar things in a photo:\tmetallic structure\tgrids or bars\tfor cooking outside or on a stovetop", 602], "rider": ["Yes. 'Rider' has a tangible appearance and refers to a person riding something such as an animal or a vehicle.\nA few things that are visually similar to 'rider' but are not 'rider' are:\tpedestrian\tjogger\tpassenger\tcyclist\nThere are several useful visual features to tell there is 'rider' and not similar things in a photo:\tsitting or standing on top of something\thands holding reins or handlebars\tfacing the same direction as the animal or vehicle", 600], "candles": ["Yes. 'Candles' have a tangible appearance and are a type of flame-producing decorative item.\nA few things that are visually similar to 'candles' but not 'candles' are:\tLED lights\tlamps\tfireplaces\toil lamps\nThere are several useful visual features to tell there are 'candles' and not similar things in a photo:\ttapered shape\twick at the top\twax residue dripping down\twax melted from the flame\twarm glowing light\tflame visible at the top", 600], "gold": ["Yes. 'Gold' has a tangible appearance and is a precious metal.\nA few things that are visually similar to 'gold' but are not 'gold' are:\tbrass\tcopper\tpyrite\tbronze\nThere are several useful visual features to tell there is 'gold' and not similar things in a photo:\tyellow or golden color\tshiny or reflective appearance\tmalleable and ductile properties\texpensive or luxurious appearance", 599], "pepperoni": ["Yes. 'Pepperoni' has a tangible appearance and is a type of sausage.\nA few things that are visually similar to 'pepperoni' but are not 'pepperoni' are:\tsalami\tchorizo\tgarlic sausage\t\nThere are several useful visual features to tell there is 'pepperoni' and not similar things in a photo:\tred coloration\ttubular shape\tsliced into thin rounds\tcontaining visible specks of fat\tspicy aroma", 599], "foam": ["Yes. 'Foam' has a tangible appearance and is a type of substance with a specific texture.\nA few things that are visually similar to 'foam' but are not 'foam' are:\tbubbles\tsoap\tsuds\tclouds\tmilk\nThere are several useful visual features to tell there is 'foam' and not similar things in a photo:\tlight and airy texture\tfrothy appearance\tmade of many small bubbles\toranges, whites, yellows, or browns in color", 599], "tub": ["Yes. 'Tub' has a tangible appearance and is a container for holding water or other liquids.\nA few things that are visually similar to 'tub' but are not 'tub' are:\tsink\tbucket\tpool\tbasin\tbarrel\nThere are several useful visual features to tell there is 'tub' and not similar things in a photo:\toval or rectangular shape\tporcelain or plastic material\tdrain hole\tsides that are high enough to contain water in the tub", 599], "display": ["No. 'Display' is too vague or abstract to be distinguished in a photo.", 597], "goat": ["Yes. 'Goat' has a tangible appearance and is a type of farm animal.\nA few things that are visually similar to 'goat' but are not 'goat' are:\tsheep\tcow\tdeer\tantelope\nThere are several useful visual features to tell there is 'goat' and not similar things in a photo:\tcurved and hollow horns\tbeard\tvertical pupils\tslim and agile body\tbrown, black, or white coat\tswift and erratic movements", 597], "countertop": ["Yes. 'Countertop' has a tangible appearance and is a horizontal surface used as a work area in a kitchen, bathroom, or other room.\nA few things that are visually similar to 'countertop' but are not 'countertop' are:\ttable\tshelves\tdresser\tdesk\nThere are several useful visual features to distinguish 'countertop' from the listed similar things in a photo:\tusually made of stone, wood, or laminate, and designed to withstand moisture and wear\tlocated in a kitchen, bathroom, or other area built for tasks such as food preparation, grooming, or work\twith built-in sinks, faucets, or cooking appliances (in the case of kitchen countertops)", 594], "napkins": ["Yes. 'Napkins' has a tangible appearance and is a kind of cloth or paper used for wiping your mouth or hands.\nA few things that are visually similar to 'napkins' but are not 'napkins' are:\ttissues\ttoilet paper\tpaper towels\thandkerchiefs\nThere are several useful visual features to tell there is 'napkins' and not similar things in a photo:\tusually rectangular in shape\tcan have various colors, patterns or prints\toften found on a table or next to a plate\tcan be made of paper or cloth materials", 594], "stone wall": ["Yes. 'Stone wall' has a tangible appearance and is a type of construction.\nA few things that are visually similar to 'stone wall' but are not 'stone wall' are:\tbrick wall\tconcrete wall\twooden fence\tmetal fence\nThere are several useful visual features to tell there is 'stone wall' and not similar things in a photo:\tmade of stones or rocks\tvaried textures and colors\thorizontal or vertical pattern", 592], "sandals": ["Yes. 'sandals' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'sandals' but are not 'sandals' are:\tboots\tflip-flops\tshoes\tslippers\nThere are several useful visual features to tell there are 'sandals' and not similar things in a photo:\topen-toe or open-heel footwear\tstraps or bands attaching to the sole\tof any material or pattern\toffset heel or wedge", 592], "saucer": ["Yes. 'Saucer' has a tangible appearance and is a kind of dish.\nA few things that are visually similar to 'saucer' but are not 'saucer' are:\tplate\tbowl\tlid\tforce field\nThere are several useful visual features to tell there is 'saucer' and not similar things in a photo:\tround or oval shape\tshallow depth\tthinner than a plate or bowl usually\twith a small circular indentation in the center\tfor serving tea or coffee, sometimes with a handle.", 591], "concrete": ["Yes. 'Concrete' has a tangible appearance and refers to a specific construction material.\nA few things that are visually similar to 'concrete' but are not 'concrete' are:\tstone\tmarble\tgranite\tbrick\nThere are several useful visual features to tell there is 'concrete' and not similar things in a photo:\tgrey color\tsolid appearance\tsmooth texture\tif cracked, it may have a visible aggregate inside", 591], "skies": ["Yes. 'Skies' has a tangible appearance and is the expanse of air above the surface of the earth.\nA few things that are visually similar to 'skies' but are not 'skies' are:\tocean\ttrees\tbuildings\t\nThere are no specific visual features to distinguish 'skies' from these things, as they are vastly different from 'skies'. However, some useful visual features to describe 'skies' could include:\tcolor of the sky (blue, grey, orange, etc.)\tclouds or fog\tbirds or other flying objects (like planes)\twithin a landscape or horizon\tline where the sky meets the earth or objects (horizon line)", 591], "pine tree": ["Yes. 'Pine tree' has a tangible appearance and is a type of evergreen tree.\nA few things that are visually similar to 'pine tree' but are not 'pine tree' are:\tcypress\ttree fern Juniperus communis\tholly\t\nThere are several useful visual features to tell there is 'pine tree' and not similar things in a photo:\tneedle-shaped leaves\tcone-shaped\tmostly green, sometimes blue or yellow\tbark with distinctive flakey plates\torangish-brown color \thard, woody texture", 591], "bookshelf": ["Yes. 'Bookshelf' has a tangible appearance and is a piece of furniture used for storing books.\nA few things that are visually similar to 'bookshelf' but are not 'bookshelf' are:\ttv stand\tconsole table\tstorage cabinet\nThere are several useful visual features to tell there is 'bookshelf' and not similar things in a photo:\thorizontal shelves\tfor storing books or other items\teasily recognizable rows for book placement\ttypically found against a wall or freestanding.", 590], "baseball glove": ["Yes. 'Baseball glove' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'baseball glove' but are not 'baseball glove' are:\toven mitts\tgardening gloves\tsnow gloves\tboxing gloves\nThere are several useful visual features to tell there is 'baseball glove' and not similar things in a photo:\tlarge and round\twith fingers slots\tin leather or synthetic materials\tfor the left or right hand\tused to catch a ball\twhile it is being tossed or hit.", 589], "wrist band": ["Yes. 'Wrist band' has a tangible appearance and is a type of accessory worn on the wrist.\nA few things that are visually similar to 'wrist band' but are not 'wrist band' are:\twatches\tbracelets\thair ties\nThere are several useful visual features to tell there is 'wrist band' and not similar things in a photo:\tband-shaped\tfits tightly around the wrist is usually made of rubber, plastic or fabric\toften printed with a message or a logo", 588], "elbow": ["Yes. 'Elbow' has a tangible appearance and is a joint in the human body.\nA few things that are visually similar to 'elbow' but are not 'elbow' are:\tknee\tankle\twrist\tshoulder\nThere are several useful visual features to tell there is 'elbow' and not similar things in a photo:\tbend in the arm located between the upper arm bone and the forearm bone\tpointed boe shape\twhen the arm is in the flexed position, the wrinkles at the joint form a crease.", 588], "containers": ["Yes. 'Containers' has a tangible appearance.\nA few things that are visually similar to 'containers' but are not 'containers' are:\tbaskets\tbowls\tbuckets\tbags\nThere are several useful visual features to tell there is 'containers' and not similar things in a photo:\ta designated opening for placing or removing items\tregular or uniform shape\tclosed all around or having a lid", 585], "carriage": ["Yes. 'Carriage' has a tangible appearance and is a means of transportation.\nA few things that are visually similar to 'carriage' but are not 'carriage' are:\tcart\tbicycle\tscooter\ttrolley\thorseback riding\nThere are several useful visual features to tell there is 'carriage' and not similar things in a photo:\tlarge wheels\thorse or motor-powered\tcovered seating area\tfor passengers or goods", 585], "airplanes": ["Yes. 'Airplanes' has a tangible appearance and is a type of flying vehicle.\nA few things that are visually similar to 'airplanes' but are not 'airplanes' are:\thelicopters\tballoons\tdrones\tbirds\tinsects\nThere are several useful visual features to tell there is 'airplanes' and not similar things in a photo:\tfuselage\twith wings and engines\tcockpit\tlanding gear\tor retractable wheels\ttail and rudder\tsingle or multiple engines\tno visible propellers or rotors.", 585], "trailer": ["Yes. 'Trailer' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'trailer' but are not 'trailer' are:\tcargo container\ttruck warehouse\ttrain car\nThere are several useful visual features to tell there is 'trailer' and not similar things in a photo:\thitched to a truck or car\tlarge, rectangular shape with wheels\tusually made of metal or fiberglass\twith windows or a door at one or both ends", 584], "handles": ["Yes. 'Handles' has a tangible appearance and is the part of an object that is held to operate or move it.\nA few things that are visually similar to 'handles' but are not 'handles' are:\tknobs\tswitches\tbuttons\tpulls\nThere are several useful visual features to tell there are 'handles' and not similar things in a photo:\telongated shape that accommodates a grip\tusually attached to the side, top or bottom of an object\tMost often associated with doors, cabinets, drawers, and utensils.", 584], "keys": ["Yes. 'Keys' has a tangible appearance and is an object used to open locks.\nA few things that are visually similar to 'keys' but are not 'keys' are:\tnails\tbolts\tscrews\tclips\nThere are several useful visual features to tell there is 'keys' and not similar things in a photo:\tmetallic object\twith ridges or grooves to fit into a lock\thaving a loop at one end\tfor unlocking doors or other locked items of equipment", 583], "knobs": ["Yes. 'Knobs' has a tangible appearance and is a type of handle.\nA few things that are visually similar to 'knobs' but are not 'knobs' are:\tbuttons\tswitches\tdials\tdrawer pulls\nThere are several useful visual features to tell there is 'knobs' and not similar things in a photo:\trounded shape\tprotruding from a surface\tsometimes has ridges or texture\tfor turning or pulling", 582], "comforter": ["Yes. 'Comforter' has a tangible appearance and is a type of blanket.\nA few things that are visually similar to 'comforter' but are not 'comforter' are:\tduvet cover\tquilt\tthrow blanket\nThere are several useful visual features to tell there is 'comforter' and not similar things in a photo:\tthick and fluffy\tbulky\tmade of soft fabric, often with filling\tusually covering the entire bed", 581], "mirrors": ["Yes. 'Mirrors' has a tangible appearance and is a type of reflective surface.\nA few things that are visually similar to 'mirrors' but are not 'mirrors' are:\tglass windows\tpuddles\tof water\tshiny metal surfaces\nThere are several useful visual features to tell there is 'mirrors' and not similar things in a photo:\trectangular or circular shape\treflective surface\tmay have a frame or edge\tmay be handheld or mounted on a wall", 581], "nightstand": ["Yes. 'Nightstand' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'nightstand' but are not 'nightstand' are:\tside table\tend table\tdresser\tcoffee table\nThere are several useful visual features to tell there is 'nightstand' and not similar things in a photo:\tsmall table or cabinet\tbeside a bed or couch\thas drawers or shelves", 581], "pots": ["Yes. 'Pots' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'pots' but are not 'pots' are:\tpans\tbowls\tbuckets\tvases\tmugs\nThere are several useful visual features to tell there is 'pots' and not similar things in a photo:\thave a wide opening\thave a narrow base\ttapered shape\thandles or grips\tmade of clay or ceramics", 579], "crate": ["Yes. 'Crate' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'crate' but are not 'crate' are:\tbox\tbarrel\tbasket\tbag\nThere are several useful visual features to tell there is 'crate' and not similar things in a photo:\twooden or plastic rectangular shape\twith or without a lid\thandles on the sides\toranges, apples or vegetables inside (in case of a fruit crate)", 575], "stuffed animal": ["Yes. 'Stuffed animal' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'stuffed animal' but are not 'stuffed animal' are:\treal animal\tplush pillow\t\nThere are several useful visual features to tell there is 'stuffed animal' and not similar things in a photo:\tfurry or plush material\tstuffed with filling\tanthropomorphic features (such as eyes, mouth, nose, and limbs)\tdesigned to resemble a specific animal or character", 572], "tongue": ["Yes. 'Tongue' has a tangible appearance and is a body part.\nA few things that are visually similar to 'tongue' but are not 'tongue' are:\tleaf\tsnake\trubber spatula\nThere are several useful visual features to tell there is 'tongue' and not similar things in a photo:\tpink or red\tfleshy, muscular organ\tinside the mouth\twet, shiny surface\tmuscle movements", 572], "bumper": ["Yes. 'Bumper' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'bumper' but are not 'bumper' are:\tmetal bars\tgrills\tlicense plates\nThere are several useful visual features to tell there is 'bumper' and not similar things in a photo:\thorizontal bar on the front or back of a car\tbumps, ridges or grooves for better protection\tfrom automotive plastic or metal\tcolored in a way to match the car's style and design", 571], "tall trees": ["Yes. 'Tall trees' has a tangible appearance and refers to a specific type of plant. \nA few things that are visually similar to 'tall trees' but are not 'tall trees' are:\ttowers\tbuildings\tcliffs\nThere are several useful visual features for distinguishing 'tall trees' from the listed similar things in a photo:\twooden trunks\twith or without foliage\theight\tcomparatively thin structures\tforrest or woods", 571], "mound": ["Yes. 'Mound' has a tangible appearance and is a natural or artificial elevation of earth.\nA few things that are visually similar to 'mound' but are not 'mound' are:\thills\tbumps\tpiles\nThere are several useful visual features to tell there is 'mound' and not similar things in a photo:\tartificially or naturally created\thigher than the surrounding terrain\tsmooth or uneven surface\tconical or dome shape", 569], "shrubs": ["Yes. 'Shrubs' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'shrubs' but are not 'shrubs' are:\ttrees\tbushes\tgrass\tweeds\nThere are several useful visual features to tell there is 'shrubs' and not similar things in a photo:\tlow-growing woody plant\twith several stems arising from or near the base of the plant\tleaves are generally small and narrow\tbranches arise from the base of the plant, not from a trunk", 568], "surfboards": ["Yes. 'Surfboards' has a tangible appearance and is a type of board used in surfing.\nA few things that are visually similar to 'surfboards' but are not 'surfboards' are:\tsnowboards\twakeboards\tpaddleboards\tskateboards\nThere are several useful visual features to tell there is 'surfboards' and not similar things in a photo:\tlong and narrow\tboard shape\tsharp or rounded nose\tpointed or rounded tail\tone or more fins on the underside\tfor use in water or waves.", 567], "pack": ["No. 'Pack' is too vague or abstract to be distinguished in a photo without additional context. \nHowever, here are a few things that are commonly described as a 'pack' in certain contexts:\nA few things that are visually similar to 'pack' but are not 'pack' are: group of dogs, deck of cards, backpack, pack of wolves. \nUseful visual features for distinguishing 'pack' from the listed similar things in a photo would depend on the specific context. For example, a group of dogs in a pack might have a similar body posture and similar fur color, while a deck of cards in a pack might have the familiar patterns and colors of a standard deck.", 566], "side mirror": ["Yes. 'Side mirror' has a tangible appearance and is a type of car mirror.\nA few things that are visually similar to 'side mirror' but are not 'side mirror' are:\tbathroom mirror\tdressing table mirror\thand-held mirror\nThere are several useful visual features to tell there is 'side mirror' and not similar things in a photo:\tattached to the side of a car\tconvex mirror shape\treflective surface\tlocated outside of the car\tbody color matching the car's color", 566], "mask": ["Yes. 'Mask' has a tangible appearance and is a type of face cover.\nA few things that are visually similar to 'mask' but are not 'mask' are:\tsunglasses\tbandanas\thats\tveils\nThere are several useful visual features to tell there is 'mask' and not similar things in a photo:\tcovering most of the face or at least the nose and mouth\tties or straps to secure it in place\tdifferent shapes and styles depending on cultural or functional purposes (e.g. medical masks, masquerade masks)\tmade out of fabric, plastic, or other materials", 565], "trunks": ["Yes. 'Trunks' has a tangible appearance and can refer to the main stem of a tree or a large and strong box.\nA few things that are visually similar to 'trunks' but are not 'trunks' are:\tchests\tsuitcases\ttorsos\nThere are several useful visual features to tell there is 'trunks' and not similar things in a photo:\nTree Trunk:\n- Rough textured bark\n- Rings that show its age\n- Branches or leaves may be visible\n- Growing out of the ground\nBox Trunk:\n- Rectangular shape\n- Hinged lid\n- Metal or wooden material\n- Handles on the sides", 563], "cockpit": ["Yes. 'Cockpit' has a tangible appearance and is a compartment in the front of an aircraft.\nA few things that are visually similar to 'cockpit' but are not 'cockpit' are:\tdriver's seat\tinstrument panel\tcommand center\nThere are several useful visual features to tell there is 'cockpit' and not similar things in a photo:\ta compartment or enclosed space\tin front of a plane or aircraft\tpilot or co-pilot seats\tmultiple instrument panels or gauges\tcontrol yokes or sticks.", 562], "sneaker": ["Yes. 'Sneaker' has a tangible appearance and is a type of shoe.\nA few things that are visually similar to 'sneaker' but are not 'sneaker' are:\tloafer\tsandal\tboot\theel\nThere are several useful visual features to tell there is 'sneaker' and not similar things in a photo:\tlow-cut or high-top athletic shoe\tlace-up or slip-on design\tflexible rubber sole\tvariety of colors and patterns\tsignature brand logos and designs", 561], "hotdog": ["Yes. 'Hotdog' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'hotdog' but are not 'hotdog' are:\tsausage\tkebab\tsandwich\tbaguette\nThere are several useful visual features to tell there is 'hotdog' and not similar things in a photo:\tlong, narrow sausage-shaped meat\tpartially embedded in a bun\thorizontal grill marks on the meat\ttoppings like ketchup and mustard may be visible", 560], "vent": ["Yes. 'Vent' has a tangible appearance and is a kind of opening or passage for air or gas to escape.\nA few things that are visually similar to 'vent' but are not 'vent' are:\tpipe\tdrain\torifice\tobstruction\nThere are several useful visual features to tell there is 'vent' and not similar things in a photo:\topen or partially open\tpassage for air or gas, often with a grate or cover\tcylindrical or rectangular shape usually mounted in a wall or ceiling.", 560], "shoulder": ["Yes. 'Shoulder' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'shoulder' but are not 'shoulder' are:\tbuttocks\tchest\thip\tknee\nThere are several useful visual features to tell there is 'shoulder' and not similar things in a photo:\t\nlocated between the neck and the upper arm\thas a ball and socket joint, allowing for a wide range of motion\twhen wearing clothing, typically covered by a sleeve", 559], "ski poles": ["Yes. 'Ski poles' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'ski poles' but are not 'ski poles' are:\thiking poles\ttrekking poles\tnordic walking poles\tumbrellas\nThere are several useful visual features to tell there is 'ski poles' and not similar things in a photo:\tthin and lightweight\tpaired\tfive to six feet long\thave grips and straps\tat the top, there are baskets for the snow", 558], "horn": ["Yes. 'Horn' has a tangible appearance and is a part of an animal's body.\nA few things that are visually similar to 'horn' but are not 'horn' are:\tantler\ttusk\tconch shell\tscrewdriver\nThere are several useful visual features to tell there is 'horn' and not similar things in a photo:\tsolid curved structure\tattached to the animal's head\tsmooth or ridged texture\tpointed or rounded tip\tGrow out of the skin", 557], "sweatshirt": ["Yes. 'Sweatshirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sweatshirt' but are not 'sweatshirt' are:\tshirt\tjacket\thoodie\tcardigan\nThere are several useful visual features to tell there is 'sweatshirt' and not similar things in a photo:\tloose and casual\tfabric is usually made of cotton or fleece\thave long sleeves and a hood\thave a front pocket", 555], "airport": ["Yes. 'Airport' has a tangible appearance and is a place where airplanes take off and land.\nA few things that are visually similar to 'airport' but are not 'airport' are:\tbus station\ttrain station\tharbor\tparking lot\nThere are several useful visual features to tell there is 'airport' and not similar things in a photo:\trunways\ttower\twith planes on the ground\tor in the air\tbuildings with airport logos or signage\tor airplanes parked nearby.", 553], "picture frame": ["Yes. 'Picture frame' has a tangible appearance and is used to display photos or artwork.\nA few things that are visually similar to 'picture frame' but are not 'picture frame' are:\tmirror\twindow\tframe\ton the wall\nThere are several useful visual features to tell there is 'picture frame' and not similar things in a photo:\trectangle or square shape\tenclosing a photo or artwork\tglass or plastic cover\tto hang on a wall\tor stand on a surface\tsuitable for indoor decoration.", 551], "city": ["Yes. 'City' has a tangible appearance and is a place with a high population density and mixed land use.\nA few things that are visually similar to 'city' but are not 'city' are:\ttown\tvillage\tmetropolis\turban area\nThere are several useful visual features to tell there is 'city' and not similar things in a photo:\thigh-rise buildings or skyscrapers\trailway tracks or trains\tbusy streets or highways\tparks or recreational areas\tbridges or overpasses", 548], "paws": ["Yes. 'Paws' has a tangible appearance and is a part of an animal's anatomy.\nA few things that are visually similar to 'paws' but are not 'paws' are:\tclaws\ttoes\tshells\tpads\nThere are several useful visual features to tell there is 'paws' and not similar things in a photo:\thairy or furry surface\tfive toes, often with claws\tpinky finger-like appendage on the inner side\tfor larger animals, such as bears or lions, it is evident that they are part of a paw and not a human hand or foot", 547], "skin": ["Yes. 'Skin' has a tangible appearance and is the outer layer of an animal's body.\nA few things that are visually similar to 'skin' but are not 'skin' are:\tfur\tleather\tfabric\tpaint\nThere are several useful visual features to tell there is 'skin' and not similar things in a photo:\tmeets hair or feathers at the edges\tvarious shades and textures\tpores and hair follicles\tblood vessels and veins visible through it", 542], "cardboard box": ["Yes. 'Cardboard box' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'cardboard box' but are not 'cardboard box' are:\tcardboard folder\tpaper bag\twooden crate\tmetal container\nThere are several useful visual features to tell there is 'cardboard box' and not similar things in a photo:\tquadrilateral shape\tmade of corrugated cardboard\tbrown or tan color\tfoldable and easy to assemble", 541], "bowls": ["Yes. 'Bowls' has a tangible appearance and is a type of vessel used for holding food or liquids.\nA few things that are visually similar to 'bowls' but are not 'bowls' are:\tplates\tmugs\tbuckets\tvases\thats\nThere are several useful visual features to tell there is 'bowls' and not similar things in a photo:\tround or curved shape\twith or without a rim\tsmooth or textured surface\tcapable of holding food or liquids", 539], "sheets": ["Yes. 'Sheets' has a tangible appearance and refers to bed linens.\nA few things that are visually similar to 'sheets' but are not 'sheets' are:\tblankets\ttablecloths\ttowels\trugs\tcurtains\nThere are several useful visual features to tell there is 'sheets' and not similar things in a photo:\tthin and flat fabric, usually made of cotton, linen or silk\tsized to fit a bed shape and size\tpatterned or solid colors\tcan be folded or draped over a bed", 539], "decoration": ["Yes. 'Decoration' has a tangible appearance and refers to objects used to enhance the visual appeal of a space or object.\nA few things that are visually similar to 'decoration' but are not 'decoration' are:\tfurniture\ttoys\ttools\tstationery\nThere are several useful visual features to distinguish 'decoration' from the listed similar things in a photo:\tornamental or aesthetic purpose\tbright or contrasting colors\tintricate or detailed design\tused to enhance a space or object", 537], "fabric": ["Yes. 'Fabric' has a tangible appearance and refers to a material woven or knitted together to form a cloth.\nA few things that are visually similar to 'fabric' but are not 'fabric' are:\tpaper\tleather\tplastic\twood\nThere are several useful visual features to tell there is 'fabric' and not similar things in a photo:\ttextured surface\tfoldable\tand can drape_soft\tto the touch\tcan be woven or knitted\ttogether to form a cloth", 533], "tree branches": ["Yes. 'Tree branches' has a tangible appearance and is a part of a tree.\nA few things that are visually similar to 'tree branches' but are not 'tree branches' are:\troots\tleaves\ttrunks\tvines\tskyscrapers\thuman arms\nThere are several useful visual features to tell there are 'tree branches' and not similar things in a photo:\tbranching structure\twoody texture\tand buds or leaves (if they are visible)\tgrowing from a trunk or main stem of a tree.", 532], "computer keyboard": ["Yes. 'Computer keyboard' has a tangible appearance and is a device designed for typing.\nA few things that are visually similar to 'computer keyboard' but are not 'computer keyboard' are:\ttypewriter\tkeyboard of a piano\tor instrument\tpanel of buttons on a machine\nThere are several useful visual features to tell there is 'computer keyboard' and not similar things in a photo:\trectangle with rounded edges\tkeys with letters, numbers, and symbols\twired or wireless connection to a computer or mobile device\ttyping marks or signs on the keys", 529], "lamp post": ["Yes. 'Lamp post' has a tangible appearance and is a type of street furniture.\nA few things that are visually similar to 'lamp post' but are not 'lamp post' are:\ttraffic light\tsign\tpost\tbox\nThere are several useful visual features to tell there is 'lamp post' and not similar things in a photo:\ttall thin pole\tattached lamp/s\tconcrete or metal base\tdiagonal braces\tcurved or ornamented top", 528], "front window": ["Yes. 'Front window' has a tangible appearance and refers to the main window in the front of a building or a vehicle.\nA few things that are visually similar to 'front window' but are not 'front window' are:\tside window\tdisplay window\tpicture frame\nThere are several useful visual features to tell there is 'front window' and not similar things in a photo:\tlarger than other windows in a building or a vehicle\tdirectly facing the front of a building or a vehicle\twithin the frame of a building or a vehicle", 526], "patches": ["Yes. 'Patches' have a tangible appearance and are a type of cloth material used for mending or decoration.\nA few things that are visually similar to 'patches' but are not 'patches' are:\tstickers\tband-aids\ttattoos\temblem\nThere are several useful visual features to tell there is 'patches' and not similar things in a photo:\tattached to a piece of clothing or fabric\tdesigned differently than surrounding fabric\tmay have visible stitching or edges\tdifferent color or pattern from surrounding fabric.", 526], "vegetation": ["Yes. 'Vegetation' has a tangible appearance and refers to plants in general.\nA few things that are visually similar to 'vegetation' but are not 'vegetation' are: paintings, wallpapers, and backdrops depicting plants.\nThere are several useful visual features to tell there is 'vegetation' and not similar things in a photo:\tleaves\tbranches\troots\tpetals\tstems\tbuds and flowers\tfruits\torbs of different shapes and sizes\tmulti-colored images of nature", 525], "square": ["Yes. 'Square' has a tangible appearance and is a four-sided polygon with four right angles.\nA few things that are visually similar to 'square' but are not 'square' are:\trectangle\tdiamond\tcube\tcard\nThere are several useful visual features to tell there is 'square' and not similar things in a photo:\tfour sides of equal length\tall four angles are right angles\topposite sides are parallel", 525], "teddy": ["Yes. 'Teddy' has a tangible appearance and is a type of stuffed animal toy.\nA few things that are visually similar to 'teddy' but are not 'teddy' are:\tother stuffed animals\tpillows\tdolls\nThere are several useful visual features to tell there is 'teddy' and not similar things in a photo:\tbear-like shape\tfur or plush covering\tcute or childlike features, such as big eyes or a smile\tcommon color schemes like brown or beige\tpaw pads\ton the belly can be a heart or a message", 524], "train engine": ["Yes. 'Train engine' has a tangible appearance and is a type of locomotive.\nA few things that are visually similar to 'train engine' but are not 'train engine' are:\ttruck\ttractor\tairplane\tboat\nThere are several useful visual features to tell there is 'train engine' and not similar things in a photo:\tlong and rectangular shape\tmultiple wheels and axles\tsmokestack on top of the engine\tcoupling mechanism between the engine and the cars or carriages\tpaint or markings indicating it belongs to a specific train company", 521], "toilet bowl": ["Yes. 'Toilet bowl' has a tangible appearance and is a fixture in a bathroom.\nA few things that are visually similar to 'toilet bowl' but are not 'toilet bowl' are:\tsink\tbathtub\turinal\tbidet\nThere are several useful visual features to tell there is 'toilet bowl' and not similar things in a photo:\toval or round shape\tporcelain or ceramic material\twater in the bowl\tflush mechanism\tlocated next to a toilet tank or on the floor", 521], "soap": ["Yes. 'Soap' has a tangible appearance and is a kind of cleaning product.\nA few things that are visually similar to 'soap' but are not 'soap' are:\tcandles\twax\trocks\t\nThere are several useful visual features to tell there is 'soap' and not similar things in a photo:\trectangular or oval-shaped\tbar or liquid\tform\tlathers or foams when wet\thas a clean, fresh scent", 521], "plastic container": ["Yes. 'Plastic container' has a tangible appearance and is a type of container made of plastic.\nA few things that are visually similar to 'plastic container' but are not 'plastic container' are:\tglass container\ttin can\tpaper bag\nThere are several useful visual features to tell there is 'plastic container' and not similar things in a photo:\tplastic material\tclear or opaque material\tlid or cover\tcylindrical or rectangular shape\tmultiple compartments or dividers.", 520], "pizzas": ["Yes. 'Pizzas' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pizzas' but are not 'pizzas' are:\ttarts\tpies\tflatbreads\tcakes\nThere are several useful visual features to tell there is 'pizzas' and not similar things in a photo:\tcircular or rectangular shape\tflat dough with toppings, usually tomato sauce, cheese, and meat or vegetables\tvariety of toppings like mushrooms, pepperoni, olives, peppers, etc.\tbrowned and slightly crispy crust", 518], "switch": ["Yes. 'Switch' has a tangible appearance and is a type of mechanical device.\nA few things that are visually similar to 'switch' but are not 'switch' are:\tbutton\tknob\thandle\nThere are several useful visual features to tell there is 'switch' and not similar things in a photo:\tlever\tforward and backward position\tor on and off position\telectrical wires or connections\tembedded in a wall or on a machine", 516], "day": ["No. 'Day' is too vague or abstract and cannot be visually distinguished in a photo.", 515], "hillside": ["Yes, 'hillside' has a visually concrete concept and refers to the natural slope of a hill.\nA few things that are visually similar to 'hillside' but are not 'hillside' are:\tsloping roof\thill of sand or dirt\tcascading waterfall\nThere are several useful visual features to tell there is 'hillside' and not similar things in a photo:\tnatural terrain or landscape\tmountain or hill shape\tconsists of grass, shrubs, trees or rocks\tsloping downwards towards the bottom of the hill or mountain.", 514], "lamb": ["Yes. 'Lamb' has a tangible appearance and is a kind of young sheep.\nA few things that are visually similar to 'lamb' but are not 'lamb' are:\tsheep\tram\tgoat\nThere are several useful visual features to tell there is 'lamb' and not similar things in a photo:\tsmall and young\tspecific wool patterns\tshort wool\tfur-covered\tmultiple shapes and sizes of multicolored ears", 513], "back": ["Yes. 'Back' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'back' but are not 'back' are:\tshirt\tfabric\tclothing\tseat\nThere are several useful visual features to tell there is 'back' and not similar things in a photo:\tpart of the body behind the torso\thas a spine and ribs\tmay have tattoos or scars\tmuscular or fleshy appearance.", 512], "cross": ["Yes. 'Cross' has a tangible appearance and is a symbol or religious object.\nA few things that are visually similar to 'cross' but are not 'cross' are:\tplus sign\tt-shape shape\nThere are several useful visual features to tell there is 'cross' and not similar things in a photo: upright shape\ttwo intersecting lines, one vertical and one horizontal\tno other lines or shapes in the same location or configuration", 510], "doll": ["Yes. 'Doll' has a tangible appearance and is a toy or model of a human or an animal.\nA few things that are visually similar to 'doll' but are not 'doll' are:\tpuppet\tstatue\tmannequin\nThere are several useful visual features to tell there is 'doll' and not similar things in a photo:\tsoft body made of cloth or plastic\thuman-like face\twith or without hair\tjointed limbs\tfor children to play with or as collectibles", 509], "stroller": ["Yes. 'Stroller' has a tangible appearance and is a type of wheeled baby carriage.\nA few things that are visually similar to 'stroller' but are not 'stroller' are:\twheelbarrow\tbike\ttricycle\trollator\nThere are several useful visual features to tell there is 'stroller' and not similar things in a photo:\tcanopy for sun protection\thandlebar for pushing\tthe seat\trestraint system for securing the child\twheels for easy movement\tfoldable mechanism for storage and transport", 507], "print": ["No. 'Print' is too vague or abstract to be distinguished in a photo.", 507], "tap": ["Yes. 'Tap' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'tap' but are not 'tap' are:\tfaucet\tshowerhead\tknob\nThere are several useful visual features to tell there is 'tap' and not similar things in a photo:\ta spout for dispensing water or other liquids\ta handle for turning water on and off\ta base or plate for attaching it to a sink or wall", 506], "shadow ground": ["No. 'Shadow ground' is too vague or abstract to be distinguished in a photo.", 505], "baseball field": ["Yes. 'Baseball field' has a tangible appearance and is a type of sports field.\nA few things that are visually similar to 'baseball field' but are not 'baseball field' are:\tsoccer field\tfootball field\tpark\tgolf course\t\nThere are several useful visual features to tell there is 'baseball field' and not similar things in a photo:\tdiamond shape\twhite bases\tinfield and outfield areas\tpitcher's mound\tbackstop fence", 503], "cushion": ["Yes. 'Cushion' has a tangible appearance and is a type of soft furnishing.\nA few things that are visually similar to 'cushion' but are not 'cushion' are:\tpillow\tmattress\tpouf\tbeanbag\tottoman\nThere are several useful visual features to tell there is 'cushion' and not similar things in a photo:\toften square or rectangular in shape\tcovered with fabric\tsoft and fluffy\tor firm and supportive\tdesign and pattern on the cover\tcan come in various colors and sizes\tplaced on a sofa or a bed for comfort or decoration", 503], "dock": ["Yes. 'Dock' has a tangible appearance and is a platform extending from a shore over water.\nA few things that are visually similar to 'dock' but are not 'dock' are:\tpier\tjetty\twharf\tbridge\nThere are several useful visual features to tell there is 'dock' and not similar things in a photo:\tplatform or structure extending from shore or coastline\tpilings or posts to support the structure or platform\tsituated over water or a body of water\tcan accommodate boats, ships or other watercraft with mooring facilities.", 501], "plastic bottle": ["Yes. 'Plastic bottle' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'plastic bottle' but are not 'plastic bottle' are:\tglass bottle\tjar\tmetal can\tthermos\nThere are several useful visual features to tell there is 'plastic bottle' and not similar things in a photo:\ttranslucent and lightweight material\tcurved neck with a cap or a lid\tridged or smooth surface\tcylindrical shape with a tapered bottom\tsizes and shapes that vary depending on their contents", 501], "magazine": ["Yes. 'Magazine' has a tangible appearance and is a type of printed publication.\nA few things that are visually similar to 'magazine' but are not 'magazine' are:\tbook\tnewspaper\tcatalog\tbrochure\nThere are several useful visual features to tell there is 'magazine' and not similar things in a photo:\tthin and lightweight\thas glossy pages\thas a headline or title on the cover\thas a mixture of text, images, and advertisements\tpages are bound together with staples or glue", 501], "ketchup": ["Yes. 'Ketchup' has a tangible appearance and is a type of sauce.\nA few things that are visually similar to 'ketchup' but are not 'ketchup' are:\tsalsa\tbarbecue sauce\tmarinara sauce\thot sauce\nThere are several useful visual features to tell there is 'ketchup' and not similar things in a photo:\tthick consistency\tbright red color\tin a squeezable or pourable container\twith a label that says \"ketchup\" or \"tomato ketchup\" or with a recognizable brand logo\tsmall seeds or particles in the sauce", 499], "visor": ["Yes. 'Visor' has a tangible appearance and is a type of hat or shield.\nA few things that are visually similar to 'visor' but are not 'visor' are:\tcap\thelmet\tgoggles\tsunglasses\nThere are several useful visual features to tell there is 'visor' and not similar things in a photo:\ta flat or curved brim that sticks out\tfits on the head and covers the eyes and/or face\tmay be attached to a larger hat or helmet\tmay have transparent material that allows for visibility while blocking sun/glare\tmay have adjustable straps or fasteners to secure it to the head.", 498], "border": ["Yes. 'Border' has a tangible appearance and refers to a line or boundary between two areas.\nA few things that are visually similar to 'border' but are not 'border' are:\tshadows\tlines\ton the edge of a page or picture\nThere are several useful visual features to tell there is 'border' and not similar things in a photo:\ta marked or drawn line\tseparation between two distinct areas\toften contrasting color or pattern to the surrounding area", 496], "eggs": ["Yes. 'Eggs' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'eggs' but are not 'eggs' are:\tballs of white yarn\tmarbles\tavocado\nThere are several useful visual features to tell there is 'eggs' and not similar things in a photo:\tovoid shape\tsmooth surface\tsome texture and color variety on the surface (if not pure white)\tone pointed end and one round end.", 494], "street lights": ["Yes. 'Street lights' have a tangible, visible appearance.\nA few things that are visually similar to 'street lights' but are not 'street lights' are:\tcar headlights\tlanterns\ttorches\tfireflies\nThere are several useful visual features to distinguish 'street lights' from the listed similar things in a photo: mounted on a pole, located at regular intervals along a roadway, emitting a consistent, artificial light source, typically in the color range of yellow to orange, and specifically designed for the illumination of the surrounding street or pavement.", 493], "posts": ["Yes. 'Posts' has a tangible appearance and refers to vertical structures meant to hold something up or mark a boundary.\nA few things that are visually similar to 'posts' but are not 'posts' are:\tpillars\tcolumns\ttrees\tfences\tsigns\nThere are several useful visual features to tell there are 'posts' and not similar things in a photo:\ttall and vertical\tman-made structure\tusually made of wood, metal, or concrete\tsimple and unadorned\tfunctional purpose (such as supporting a structure or marking a boundary)", 493], "sail": ["Yes. 'Sail' has a tangible appearance and is a type of fabric used for boats.\nA few things that are visually similar to 'sail' but are not 'sail' are:\tawning\ttent\tflag\tcurtain\nThere are several useful visual features to tell there is 'sail' and not similar things in a photo:\ttriangular, rectangular or square shape\tmade of a lightweight, water-resistant material\tattached to a mast or pole used for propulsion\tflap or bulge when catching wind", 492], "shrub": ["Yes. 'Shrub' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'shrub' but are not 'shrub' are:\ttree\tbush\tweed\tgrass\nThere are several useful visual features to tell there is 'shrub' and not similar things in a photo:\tlow-lying plant\twith woody stems and branches\tmultiple small stems arising from the base\tbunches of leaves or flowers\tat most 6 meters tall (smaller than a tree)\tthick foliage\tclose to the ground", 491], "cords": ["Yes. 'Cords' has a tangible appearance and refers to a variety of wire-like materials.\nA few things that are visually similar to 'cords' but are not 'cords' are:\tropes\twires\thoses\tchains\tstrings\nThere are several useful visual features to tell there is 'cords' and not similar things in a photo:\tlong and thin shape\tsolid color or a striped pattern\tconnecting two or more electronic devices\tor attached to a power source\tplugged in at one or both ends", 491], "view mirror": ["Yes. 'View mirror' has a tangible appearance and is a type of mirror used in vehicles.\nA few things that are visually similar to 'view mirror' but are not 'view mirror' are:\tbathroom mirror\thand mirror\tmakeup mirror\ttelescope mirror\nThere are several useful visual features to tell there is 'view mirror' and not similar things in a photo:\trectangular or oval shape\tattached to a vehicle\tprovides a reflection of the rear or side view of the vehicle\thas an adjustable angle or position to aid in driving", 490], "tape": ["Yes. 'Tape' has a tangible appearance and is a type of adhesive material.\nA few things that are visually similar to 'tape' but are not 'tape' are:\tribbon\tstripe\tsticker\tbandage\t\nThere are several useful visual features to tell there is 'tape' and not similar things in a photo:\tlong and narrow shape\ttranslucent or opaque material\tsticky or adhesive surface\tutilitarian or office-oriented appearance\tvisible dispenser or roll", 488], "headphones": ["Yes. 'Headphones' has a tangible appearance and is a type of audio accessory.\nA few things that are visually similar to 'headphones' but are not 'headphones' are:\tearbuds\thairbands\ton-ear earmuffs\nThere are several useful visual features to tell there are 'headphones' and not similar things in a photo:\ttwo separate ear cups connected by a headband\tor neckband\tpadded ear cups\taudio cable with a jack or wireless connectivity\tmicrophone for phone calls or recording\tsound controls (volume, playback, etc.)", 486], "dishwasher": ["Yes. 'Dishwasher' has a tangible appearance and is a home appliance.\nA few things that are visually similar to 'dishwasher' but are not 'dishwasher' are:\twashing machine\tdryer\trefrigerator\nThere are several useful visual features to tell there is 'dishwasher' and not similar things in a photo:\tdoor for loading and unloading dishes\tracks or compartments for dishes and utensils\tcontrol panel or buttons for operating\ttimer or display screen\tdrain at the bottom of the machine", 486], "slope": ["Yes. 'Slope' has a tangible appearance and refers to the inclination of a surface or terrain.\nA few things that are visually similar to 'slope' but are not 'slope' are:\thill\tstairs\tramp\nThere are several useful visual features to tell there is 'slope' and not similar things in a photo:\tan inclined surface, terrain, or pathway\tuneven ground or surface\tangle of incline, steepness or gradients\tdirection of the slope or the rise and fall of the surface", 484], "freezer": ["Yes. 'Freezer' has a tangible appearance and is a household appliance.\nA few things that are visually similar to 'freezer' but are not 'freezer' are:\trefrigerator\toven\tmicrowave\tdishwasher\tstorage cabinet\nThere are several useful visual features to tell there is 'freezer' and not similar things in a photo:\ttypically a standalone appliance or a section in a refrigerator\tdoor handles\tforcing mechanism for door closure\ttypical shelving and racks for storage of frozen goods", 484], "sausage": ["Yes. 'Sausage' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'sausage' but are not 'sausage' are:\thot dogs\tchorizo\tbologna\tjerky\nThere are several useful visual features to tell there is 'sausage' and not similar things in a photo:\tlong and cylindrical shape\tvariations in color and texture\tcasing or no casing\tgrilled, boiled or fried appearance", 484], "silver spoon": ["Yes. 'Silver spoon' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'silver spoon' but are not 'silver spoon' are:\tmetal spoon\tplastic spoon\twooden spoon\nThere are several useful visual features to distinguish 'silver spoon' from the listed similar things in a photo:\t\nmade of high-quality metal-like silver\tshiny surface with the reflection of the surrounding objects\tslender and elongated shape\tluxurious appearance and texture", 484], "bars": ["Yes. 'Bars' has a tangible appearance and refers to horizontal or vertical lines or rods. \nA few things that are visually similar to 'bars' but are not 'bars' are:\tLines\tRods\tScaffolding\tPoles\t\nThere are several useful visual features to tell there are 'bars' and not similar things in a photo:\tRectangular shape with defined edges\tMetallic surface of silver or gold colors\tUniform size and spacing in between them.", 483], "chest": ["Yes. 'Chest' has a tangible appearance and refers to a part of the body or a type of storage furniture.\nA few things that are visually similar to 'chest' but are not 'chest' are:\ttrunk\tbox\tcabinet\nThere are several useful visual features to tell there is 'chest' and not similar things in a photo:\ta part of the body: located between the neck and the abdomen, with ribs and breastbone visible\teither male or female\tbody hair or cleavage might be visible\tif it is furniture, a rectangular or square-shaped box made of wood or other materials, with hinges on the back side and a lid on top.", 483], "necktie": ["Yes. 'Necktie' has a tangible appearance and is a type of clothing.\n\nA few things that are visually similar to 'necktie' but are not 'necktie' are:\tscarfs, headbands, ribbons, belts.\n\nThere are several useful visual features to tell there is 'necktie' and not similar things in a photo:\tlong and narrow piece of fabric\tusually worn around the neck\tfolds or knot in the front area\tmore formal and professional look than similar accessories.", 483], "utensils": ["Yes. 'Utensils' has a tangible appearance and refers to tools used for eating or cooking.\nA few things that are visually similar to 'utensils' but are not 'utensils' are:\ttools\tcutlery\tkitchenware\tappliances\nThere are several useful visual features to tell there are 'utensils' and not similar things in a photo:\tspecific shapes for spoons, forks, and knives\tmetal or plastic material\tsize and weight for cooking utensils (e.g., spatulas, ladles, tongs)", 482], "cable": ["Yes. 'Cable' has a tangible appearance and refers to a physical wire or cord.\nA few things that are visually similar to 'cable' but are not 'cable' are:\trope\tcord\those\twire\nThere are several useful visual features to tell there is 'cable' and not similar things in a photo:\tthick and cylindrical\tmade of plastic or metal\tcolors such as black, white, or grey\twith connectors at the ends\tbe used for electrical or data transmission", 480], "bacon": ["Yes. 'Bacon' has a tangible appearance and is a type of meat.\nA few things that are visually similar to 'bacon' but are not 'bacon' are:\tprosciutto\tham\tjerky\nThere are several useful visual features to tell there is 'bacon' and not similar things in a photo:\tlong, thin slices of meat\tpink or reddish-brown color\tfatty streaks or layers\tcrisp or slightly crispy texture", 478], "wood table": ["Yes. 'Wood table' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wood table' but are not 'wood table' are:\tchair\tbed\tbench\tcounter\nThere are several useful visual features to tell there is 'wood table' and not similar things in a photo:\ta flat surface\ttable legs or base\tmade of wood material\tsupport for chairs or other objects", 476], "knives": ["Yes. 'Knives' has a tangible appearance and is a type of cutting tool.\nA few things that are visually similar to 'knives' but are not 'knives' are:\trazor blades\tscissors\tbox cutters\tswords\nThere are several useful visual features to tell there are 'knives' and not similar things in a photo:\tsharp blade\tmetallic or reflective surface\thandles for gripping\tcutting edge with a pointed tip\tor serrated or straight edges.", 474], "seats": ["Yes. 'Seats' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'seats' but are not 'seats' are:\ttables\tbookcases\tstands\tcabinets\nThere are several useful visual features to tell there is 'seats' and not similar things in a photo:\thave a place to sit, such as a cushion or a seat pad\tbackrest or armrests\tflanked by legs or a base\tcan be made of materials like wood, metal, or plastic\tcan come in various shapes, sizes, and colors.", 474], "tank": ["Yes. 'Tank' has a tangible appearance and refers to an armored military vehicle.\nA few things that are visually similar to 'tank' but are not 'tank' are:\ttruck\tbulldozer\tarmored car\nThere are several useful visual features to tell there is a 'tank' and not similar things in a photo:\tlarge and heavy vehicle\twith armored plating\tcrawling on caterpillar tracks\thaving a turret with a large-caliber gun and other weapons.", 473], "land": ["Yes. 'Land' has a tangible appearance and refers to the physical surface of the Earth that is not covered by water.\nA few things that are visually similar to 'land' but are not 'land' are:\tocean\tdesert\ticeberg\tclouds\nThere are several useful visual features to tell there is 'land' and not similar things in a photo:\tflat, solid surface\tcovered with vegetation, buildings, or other structures\tvarious textures such as dirt, sand, or rocks\tmight have water bodies or rivers running through it", 473], "guys": ["No. 'Guys' is too vague or abstract to have a tangible appearance and cannot be distinguished in a photo.", 469], "heart": ["Yes. 'Heart' has a tangible appearance and is an organ in the human body.\nA few things that are visually similar to 'heart' but are not 'heart' are:\tfruit (e.g. strawberry)\tleaf\tValentine's Day symbol\nThere are several useful visual features to tell there is 'heart' and not similar things in a photo:\ttwo lobes at the top\tslight point at the bottom\tveins and arteries attached to it in the image\tpink or red color (in the case of a human heart)", 469], "tusks": ["Yes. 'Tusks' has a tangible appearance and refers to the elongated teeth of an animal.\nA few things that are visually similar to 'tusks' but are not 'tusks' are: horns, antlers, fangs, teeth, spines.\nThere are several useful visual features to distinguish 'tusks' from the similar things in a photo: generally ivory-colored and elongated in shape, usually protruding from the mouth rather than the head or back, often curved, smooth surface with a slight taper towards the end, unique size and shape depending on the animal species.", 469], "brown table": ["Yes. 'Brown table' has a tangible appearance and refers to a specific object.\nA few things that are visually similar to 'brown table' but are not 'brown table' are:\tchair\tdesk\tshelf\tcounter\nThere are several useful visual features to tell there is 'brown table' and not similar things in a photo:\tflat and solid surface\tfor placing objects to be used for various activities, such as eating or working\tbrown-colored or with a brown tablecloth or runner\tlegs or base to stand on", 464], "fish": ["Yes. 'Fish' has a tangible appearance and is a type of aquatic animal.\nA few things that are visually similar to 'fish' but are not 'fish' are:\tdolphins\twhales\tseals\tsharks\tjellyfish\nThere are several useful visual features to tell there is 'fish' and not similar things in a photo:\tgills\tfins\tscales\tstreamlined body shape\tno legs or arms (most of the time)\tswimming in water (most of the time)", 464], "pine trees": ["Yes. 'Pine trees' has a tangible appearance and is a type of coniferous tree.\nA few things that are visually similar to 'pine trees' but are not 'pine trees' are:\tcypress trees\tcedars\tfir trees\tspruce trees\nThere are several useful visual features to tell there is 'pine trees' and not similar things in a photo:\tneedle-like leaves\tgreen or bluish-green color\tconical shape\tflaky or scaly bark\tpinecones present on the branches.", 464], "beans": ["Yes. 'Beans' has a tangible appearance and is a type of legume.\nA few things that are visually similar to 'beans' but are not 'beans' are:\tpebbles\tchocolate pieces\trocks\tdried beetles\nThere are several useful visual features to tell there is 'beans' and not similar things in a photo:\toval or kidney-shaped\tusually brown or green\tin edible or seed form, not worn smooth or rounded by natural forces\tmay have a seam or split", 463], "wine bottle": ["Yes. 'Wine bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'wine bottle' but are not 'wine bottle' are:\tbeer bottle\tsoda bottle\tolive oil bottle\tperfume bottle\nThere are several useful visual features to tell there is 'wine bottle' and not similar things in a photo:\tlong cylindrical shape\twith a long neck and narrow opening\tcork or screw-top closure\tdark green, clear or brown glass material\tlabels or markings with the name of the wine or vineyard", 462], "telephone pole": ["Yes. 'Telephone pole' has a tangible appearance and is a type of utility pole.\nA few things that are visually similar to 'telephone pole' but are not 'telephone pole' are:\tfence\tpost\tsign\tpost\tlight post\ttree\nThere are several useful visual features to tell there is 'telephone pole' and not similar things in a photo:\ttall and vertical\twooden or metal\tpower lines or cables attached to it\ttop of the pole has a transformer or insulators", 460], "panel": ["Yes. 'Panel' has a tangible appearance and can refer to various types of flat surfaces used for different purposes.\nA few things that are visually similar to 'panel' but are not 'panel' are:\tpainting\tcanvas\twall\tboard\nThere are several useful visual features to tell there is 'panel' and not similar things in a photo:\trectangular shape\tflat surface\tvisible screws, bolts, or rivets\tattached to another surface, such as a wall or ceiling.", 460], "bull": ["Yes. 'Bull' has a tangible appearance and is a male cow.\nA few things that are visually similar to 'bull' but are not 'bull' are:\tcow\tbuffalo\tyak\nThere are several useful visual features to tell there is 'bull' and not similar things in a photo:\thorns\ta muscular body\tbroad shoulders\tand a large head.", 459], "game": ["No. 'Game' is too vague or abstract to be distinguished in a photo. It could refer to various forms of play or competition.\nA few things that are visually similar to different types of games but aren't games themselves might include: board game boxes, video game controllers, dice, sports equipment, playing cards, etc.\nSince 'game' can refer to many different things, there are no specific visual features that would distinguish it from similar items in a photo. Rather, the context and setting would need to be considered to determine if the depicted activity or object is a game or something else.", 458], "front leg": ["Yes. 'Front leg' has a tangible appearance and refers to the limbs of an animal's front portion.\nA few things that are visually similar to 'front leg' but are not 'front leg' are:\tback leg, arm, branch, pole\nThere are several useful visual features to tell there is 'front leg' and not similar things in a photo:\n- Attached to the animal's front body\n- Positioned opposite to the rear legs\n- Ends in a paw or hoof, depending on the animal species\n- Generally more muscular and thicker than the animal's back legs", 456], "wet suit": ["Yes. 'Wet suit' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'wet suit' but are not 'wet suit' are:\tdry suit\tscuba gear\twaders\tdiving suit\nThere are several useful visual features to tell there is a 'wet suit' and not similar things in a photo:\ttight-fitting and stretchy material\tzippers around the torso and limbs\tneoprene or another type of rubber-like material\tbright colors or bold patterns (often used in water sports)", 454], "meal": ["No. 'Meal' is too vague or abstract to be distinguished in a photo.", 452], "leash": ["Yes. 'Leash' has a tangible appearance and is an object used to control an animal.\nA few things that are visually similar to 'leash' but are not 'leash' are:\trope\tlanyard\tcord\tchain\nThere are several useful visual features to tell there is 'leash' and not similar things in a photo:\tclasp or clip on one end\ttoy or animal on the other end\tmade of fabric or leather\tintended for use in animal control or training.", 452], "glass bottle": ["Yes. 'Glass bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'glass bottle' but are not 'glass bottle' are:\tjar\tvase\tthermos\tbowl\ttumbler\nThere are several useful visual features to tell there is 'glass bottle' and not similar things in a photo:\tclear or colored glass material\tnarrow neck and wider base\twith or without a cap or a cork\ttypically used for storing liquids or beverages, such as wine, beer, or soda.", 451], "tree branch": ["Yes. 'Tree branch' has a tangible appearance and refers to a part of a tree.\nA few things that are visually similar to 'tree branch' but are not 'tree branch' are:\tsticks\tlogs\troots\ttrunks\nThere are several useful visual features to tell there is 'tree branch' and not similar things in a photo:\toriginates from a tree or a bush\tconnected to the main trunk or stem of a plant\tbranching structure in smaller nodes\tleaves, flowers, or fruits growing from it (depending on the season)", 451], "chocolate": ["Yes. 'Chocolate' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'chocolate' but are not 'chocolate' are:\tmud\tfertilizer\tcocoa powder\nThere are several useful visual features to tell there is 'chocolate' and not similar things in a photo:\tbrown color\tsmooth and glossy texture\tirregular shape or rectangular bars\twrapping or packaging indicating chocolate brand or type", 450], "toddler": ["Yes. 'Toddler' has a tangible appearance and is a young child who has just started walking.\nA few things that are visually similar to 'toddler' but are not 'toddler' are:\tbaby\tchild\tadult\tdoll\nThere are several useful visual features to tell there is 'toddler' and not similar things in a photo:\tshorter than an adult\tbigger than a baby\twobbly gait\tless coordinated\tface expressions that represents innocence and curiosity", 449], "markings": ["Yes. 'Markings' has a tangible appearance and refers to patterns or designs on a surface.\nA few things that are visually similar to 'markings' but are not 'markings' are:\tscratches\tstains\tcracks\ttextures\nThere are several useful visual features to tell there are 'markings' and not similar things in a photo:\tdistinguishable patterns or designs\tintricate or complex shapes\tnot caused by natural wear and tear or damage\tto distinguish a marking from a texture, look for repeated or symmetrical designs.", 449], "mitt": ["Yes. 'Mitt' has a tangible appearance and refers to a type of glove.\nA few things that are visually similar to 'mitt' but are not 'mitt' are:\tgloves\tsleeves\tpuppet hands\nThere are several useful visual features to tell there is 'mitt' and not similar things in a photo:\topen at the fingers\tcovering the wrist as well\tcushion-y inside\tmade of leather or fabric", 448], "male": ["Yes. 'Male' has a tangible appearance and refers to the biological sex of a person or animal.\nA few things that are visually similar to 'male' but are not 'male' are:\tfemale\tchildren\tanimals of a different sex\nThere are several useful visual features to tell there is 'male' and not similar things in a photo:\tbroad shoulders\tthick neck\tmuscular build\tfacial hair\tgenitalia", 448], "skateboards": ["Yes. 'Skateboards' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'skateboards' but are not 'skateboards' are:\tlongboards\tsnowboards\tsurfboards\nThere are several useful visual features to tell there is 'skateboards' and not similar things in a photo:\trectangular and narrow\tboard made of wood or plastic\tfour small wheels on the bottom of the board\tforward-leaning rider\twith or without a handle", 447], "trim": ["Yes. 'Trim' has a tangible appearance and usually refers to a decorative or finishing detail applied to something.\nA few things that are visually similar to 'trim' but are not 'trim' are:\tmolding\tframe\taccent\tpattern\nThere are several useful visual features to tell there is 'trim' and not similar things in a photo:\tnarrow in width\tcut in a decorative or ornamental design\tused for finishing edges, borders, or seams\tapplications to clothing, furniture, or home d\u00e9cor", 447], "taxi": ["Yes. 'Taxi' has a tangible appearance and is a type of vehicle for hire.\nA few things that are visually similar to 'taxi' but are not 'taxi' are:\tUber cars\tprivate cars\tlimousines\nThere are several useful visual features to tell there is 'taxi' and not similar things in a photo:\tyellow, black, or another bright color\twith a TAXI sign on the roof or on the windshield\tsmall sign with the car's license number on the roof\ttop light on when available\tfor hire sign on the rear or side of the car.", 444], "foliage": ["Yes. 'Foliage' has a tangible appearance and refers to the leaves and branches of plants and trees.\nA few things that are visually similar to 'foliage' but are not 'foliage' are:\tgrass\tweeds\tmoss\talgae\nThere are several useful visual features to tell there is 'foliage' and not similar things in a photo:\tleaves attached to branches or stems\tbunches of leaves forming a canopy or screen\tvarious shapes and sizes of leaves depending on the plant or tree\ttype of plant or tree (deciduous or evergreen)", 444], "trash bin": ["Yes. 'Trash bin' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'trash bin' but are not 'trash bin' are: \tcontainer\tbasket\tpot\nThere are several useful visual features to tell there is 'trash bin' and not similar things in a photo:\tlarge\tcylindrical or rectangular in shape\thave a lid\tlabeled as 'trash', 'garbage', or 'recycling'", 442], "machine": ["Yes. 'Machine' has a tangible appearance and is a man-made mechanical device.\nA few things that are visually similar to 'machine' but are not 'machine' are:\ttools\tvehicles\tappliances\tequipment\nThere are several useful visual features to tell there is 'machine' and not similar things in a photo:\thas moving parts\tmechanical or electronic components\tmay have buttons or levers\tmay have a power source or engine\thas a functional purpose or task", 442], "lips": ["Yes. 'Lips' have a tangible appearance and are a part of the human body.\nA few things that are visually similar to 'lips' but are not 'lips' are:\tfruit (e.g. strawberries, cherries)\tflowers\tlips-shaped toys or decorations\nThere are several useful visual features to tell there are 'lips' and not similar things in a photo:\tlocated on the lower part of the face\tsoft, fleshy, and plump in appearance\tpink or red in color\thave defined edges and a central indentation\tmeet at the corners of the mouth", 441], "pastry": ["Yes. 'Pastry' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'pastry' but are not 'pastry' are:\tbread\tcake\tcookies\tpudding\nThere are several useful visual features to tell there is 'pastry' and not similar things in a photo:\tflaky or crispy texture\tdough folded into layers or twisted into shapes\tfilling inside (such as fruit, custard, or meat)\tgolden brown color on the surface\tglaze, icing, or powdered sugar dusted on top", 440], "forks": ["Yes. 'Forks' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'forks' but are not 'forks' are:\tspoons\tknives\tchopsticks\ttridents\nThere are several useful visual features to tell there is 'forks' and not similar things in a photo:\tlong handle with tines (prongs) on one end\ttines (prongs)\tat least three in number made of metal or plastic", 440], "crosswalk": ["Yes. 'Crosswalk' has a tangible appearance and refers to a designated pedestrian pathway or strip across a street.\nA few things that are visually similar to 'crosswalk' but are not 'crosswalk' are: pedestrian path, pavement or sidewalk, bike lane.\nThere are several useful visual features to tell there is 'crosswalk' and not similar things in a photo: white painted strips or lines on the roadway, markings of a human or walking figure, indication signs or signals.", 439], "stool": ["Yes. 'Stool' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'stool' but are not 'stool' are:\tchairs\tbenches\tottomans\tfootstools\nThere are several useful visual features to tell there is 'stool' and not similar things in a photo:\tno backrest or armrests\thas a flat or round seat\tmight have legs or a single central pillar\tfor seating or stepping on\tto be used indoors or outdoors\theight might vary depending on its function and usage", 439], "church": ["Yes. 'Church' has a tangible appearance and is a type of building used for religious gatherings.\nA few things that are visually similar to 'church' but are not 'church' are:\tschool\thall\ttheater\tmuseum\nThere are several useful visual features to distinguish 'church' from the listed similar things in a photo:\tcross on top or somewhere on the building\treligious symbols or iconography\tspire or bell tower\tstained glass windows\tpews or rows of seating area\tfor religious gatherings", 438], "tail light": ["Yes. 'Tail light' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'tail light' but are not 'tail light' are:\tbrake light\theadlight\treflector\nThere are several useful visual features to tell there is 'tail light' and not similar things in a photo:\tred light\tlocation at the back of the vehicle\tcircular or oblong shape\tif illuminated, activated by the car's brake pedal", 438], "lawn": ["Yes. 'Lawn' has a tangible appearance and is a type of land used for a garden or sports area.\nA few things that are visually similar to 'lawn' but are not 'lawn' are:\tfield\tpark\tgolf course\nThere are several useful visual features to tell there is 'lawn' and not similar things in a photo:\tgreen grass\tmanicured, even surface\tmowed or trimmed edges\tsquare or rectangular shape", 438], "newspaper": ["Yes. 'Newspaper' has a tangible appearance and is a type of printed media.\nA few things that are visually similar to 'newspaper' but are not 'newspaper' are:\tmagazine\tjournal\tbook\tbrochure\nThere are several useful visual features to tell there is 'newspaper' and not similar things in a photo:\tlarge sheets of paper\tfolded or tabloid format\twith news, articles, and ads\tin black and white or color\twith headlines in bold letters", 438], "pillar": ["Yes. 'Pillar' has a tangible appearance and is a kind of architectural element.\nA few things that are visually similar to 'pillar' but are not 'pillar' are:\tcolumn\tpost\tpole\tstack\nThere are several useful visual features to tell there is 'pillar' and not similar things in a photo:\tvertical support structure\twith a circular or rectangular cross-section\tcan be made of various materials (e.g. stone, wood, concrete)\tsupport a structure from the ground up", 438], "ties": ["Yes. 'Ties' has a tangible appearance and is a kind of clothing accessory.\nA few things that are visually similar to 'ties' but are not 'ties' are:\tscarves\tbelts\tnecklaces\t\nThere are several useful visual features to tell there is 'ties' and not similar things in a photo:\tnarrow\tsymmetrical\tlarge variety of colors and patterns\thanging around the neck or fastened to clothes with a knot at the front", 437], "beds": ["Yes. 'Beds' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'beds' but are not 'beds' are:\tcouches\tchairs\tottomans\tmattresses\nThere are several useful visual features to tell there is 'beds' and not similar things in a photo:\trectangular shape\tbed frame\theadboard and footboard\tpillows\tsheets and blankets\tmattress\tframe and legs", 437], "holes": ["Yes. 'Holes' has a tangible appearance and is a space that extends through a surface or material.\nA few things that are visually similar to 'holes' but are not 'holes' are: \tshadows\tpits\tcracks\tdents\nThere are several useful visual features to tell there is 'holes' and not similar things in a photo: a space that extends completely or partially through a surface or material\tround, oval, or irregularly shaped\topenings that allow things to pass through or be seen from the other side\tdifferent depths or sizes of the spaces.", 436], "toaster": ["Yes. 'Toaster' has a tangible appearance and is a household appliance used to toast bread.\nA few things that are visually similar to 'toaster' but are not 'toaster' are:\tmicrowave\toven\tgrill\nThere are several useful visual features to tell there is 'toaster' and not similar things in a photo:\trectangular shape and upright position\tslots for bread slices\tor a single opening at the top\tdial or lever to adjust toastiness\tpush-button to lift the bread\twhen in use, emits toasting heat or light", 436], "shower": ["Yes. 'Shower' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'shower' but are not 'shower' are:\tbathtub\tfountain\twaterfall\tgarden sprinkler\nThere are several useful visual features to tell there is 'shower' and not similar things in a photo:\tmetallic or chrome finish\tnozzle or head for water spray\tconnected to a wall or ceiling\tdrains for water to run out from\tthe presence of shower curtains or doors.", 436], "hooves": ["Yes. 'Hooves' has a tangible appearance and is a part of an animal's foot.\nA few things that are visually similar to 'hooves' but are not 'hooves' are: claws, paws, talons, nails\nThere are several useful visual features to tell there is 'hooves' and not similar things in a photo: hard, keratin-covered structure\tthat surrounds the tips of the toes of certain ungulate mammals, such as horses, cows, and deer.", 433], "laptops": ["Yes. 'Laptops' has a tangible appearance and is a type of portable computer.\nA few things that are visually similar to 'laptops' but are not 'laptops' are: tablets, smartphones, calculators, digital notepads\nThere are several useful visual features to tell there is 'laptops' and not similar things in a photo:\thinged design with a screen on top and a keyboard on the bottom\tscreen is larger than a smartphone, tablet or calculator\tattached touchpad or mouse\tUSB or other ports on the sides or back", 431], "orange cone": ["Yes. 'Orange cone' has a tangible appearance and is a kind of traffic cone.\nA few things that are visually similar to 'orange cone' but are not 'orange cone' are:\tpylon\tconical hat\tvolcano\nThere are several useful visual features to tell there is 'orange cone' and not similar things in a photo:\tcone-shaped\tbright orange or yellow\tcolorful stripes or bands on the cone\ttop or bottom cut off\thollow center with an opening at the top.", 429], "chin": ["Yes. 'Chin' has a tangible appearance and is a facial feature. \nA few things that are visually similar to 'chin' but are not 'chin' are:\tjawline\tneck\tfacial hair\tcollarbone\nThere are several useful visual features to tell there is 'chin' and not similar things in a photo:\tbony prominence at the bottom of the face\tdivides the face from the neck\tcan be pointed, round, square, or cleft\tvaries in size and shape depending on the individual's bone structure and weight", 427], "soda": ["Yes. 'Soda' has a tangible appearance and is a type of beverage.\nA few things that are visually similar to 'soda' but are not 'soda' are:\tjuice\twater\tice tea\tlemonade\tmilkshake\nThere are several useful visual features to tell there is 'soda' and not similar things in a photo:\tfizzy bubbles\tcarbonation\ta can or bottle of soda\tdifferent colors and flavors than other drinks\tsound of bubbles when it is poured or opened.", 427], "microphone": ["Yes. 'Microphone' has a tangible appearance and is a kind of audio equipment.\nA few things that are visually similar to 'microphone' but are not 'microphone' are: hairbrush, paintbrush, stick, dildo, flashlight\nThere are several useful visual features to tell there is 'microphone' and not similar things in a photo: cylindrical shape, rounded top, metal or plastic body, mesh top, cable to connect to a sound system.", 426], "wooden": ["Yes. 'Wooden' has a tangible appearance and refers to objects made from wood.\nA few things that are visually similar to 'wooden' but are not 'wooden' are:\tstone\tbrick\tceramic\tplastic\nThere are several useful visual features to tell there is 'wooden' and not similar things in a photo:\tgrain patterns or lines\ton the surface\tearthy colors (brown, beige or tan)\tnatural-looking texture or knots", 425], "pool": ["Yes. 'Pool' has a tangible appearance and typically refers to a body of water for swimming.\nA few things that are visually similar to 'pool' but are not 'pool' are:\tpond\tlake\tocean\thot tub\tfountain\nThere are many useful visual features to tell there is 'pool' and not similar things in a photo:\trelatively small body of water with shallow and deep ends\tclear blue water\tladder or stairs for easy entry\tconcrete or tiled borders\tdiving board or slide for additional fun\tplants or chairs around the pool.", 424], "item": ["No. 'Item' is too vague or abstract to be visually concrete or tangible.", 423], "juice": ["Yes. 'Juice' has a tangible appearance and is a liquid.\nA few things that are visually similar to 'juice' but are not 'juice' are:\twater\tsoda\ttea\tsoup\nThere are several useful visual features to tell there is 'juice' and not similar things in a photo:\tcolorful or opaque\tusually in a glass or a bottle\twith pulp or without pulp\tusually associated with fruits or vegetables (e.g. orange juice, carrot juice)", 421], "tennis racquet": ["Yes. 'Tennis racquet' has a tangible appearance and is a specific sports equipment.\nA few things that are visually similar to 'tennis racquet' but are not 'tennis racquet' are:\tracquetball racquet\tsquash racquet\tbadminton racquet\tpaddle\nThere are several useful visual features to tell there is 'tennis racquet' and not similar things in a photo:\tconcave and flat hitting surface\tcorded strands linking the hitting surface\toval-shaped head\tframe with a handle\tgrip on the handle", 420], "railing": ["Yes. 'Railing' has a tangible appearance and is a kind of fence or barrier.\nA few things that are visually similar to 'railing' but are not 'railing' are:\tfence\tguardrail\tdivider\tbarricade\nThere are several useful visual features to tell there is 'railing' and not similar things in a photo:\tparallel bars\tthat provide support\tlines or bars\tthat are vertical or horizontal\tmay have spaces or gaps between each bar.", 419], "column": ["Yes. 'Column' has a tangible appearance and is a structural element.\nA few things that are visually similar to 'column' but are not 'column' are:\tpillar\tpost\tpole\tbeam\nThere are several useful visual features to tell there is 'column' and not similar things in a photo:\ttall and vertical\tcylindrical or rectangular in shape\thas a base and a capital\tmay have decorative elements such as carvings or fluting", 419], "bathroom sink": ["Yes. 'Bathroom sink' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'bathroom sink' but are not 'bathroom sink' are:\tkitchen sink\tfountain\turinal\ttrough sink\nThere are several useful visual features to tell there is 'bathroom sink' and not similar things in a photo: \tporcelain or ceramic material\tbasin shape\tfaucet with hot and cold water controls\tdrain with stopper or plug\tmounted on a vanity or pedestal.", 418], "blankets": ["Yes. 'Blankets' has a tangible appearance and refers to a cloth used for warmth or comfort.\nA few things that are visually similar to 'blankets' but are not 'blankets' are:\tduvets\tcarpets\trobes\ttowels\nThere are several useful visual features to tell there is 'blankets' and not similar things in a photo:\tflat or folded layer of cloth\tsquared or rectangular shape\tvarious colors or patterns\tcan be draped over furniture or put on a bed or a person for insulation.", 417], "lamp shade": ["Yes. 'Lamp shade' has a tangible appearance and is typically used as part of a lamp.\nA few things that are visually similar to 'lamp shade' but are not 'lamp shade' are:\that\tumbrella\tparasol\tceiling fan covers\nThere are several useful visual features to tell there is 'lamp shade' and not similar things in a photo:\tcylindrical or conical shape\tfabric or paper material that covers a light bulb\tfitting on top or bottom that attaches to a lamp body\tdecorative patterns or designs", 417], "shower curtain": ["Yes. 'Shower curtain' has a tangible appearance and is a type of curtain.\nA few things that are visually similar to 'shower curtain' but are not 'shower curtain' are:\twindow curtain\troom divider\tclothing\tdrapery\tstage curtain\nThere are several useful visual features to tell there is 'shower curtain' and not similar things in a photo:\tlong and rectangular shape\tplastic or waterproof material\thangs from a rod\tin a bathroom or shower area\ttranslucent or opaque", 417], "hose": ["Yes. 'Hose' has a tangible appearance and is a flexible tube used for conveying liquids or gases.\nA few things that are visually similar to 'hose' but are not 'hose' are:\tcable\ttube\tpipe\trope\tbelt\nThere are several useful visual features to tell there is 'hose' and not similar things in a photo:\tflexibility\tinlet and outlet\tflexible and bendable\tmade of rubber or plastic\tend connectors\tfor conveying liquids or gases", 416], "light post": ["Yes. 'Light post' has a tangible appearance and is a type of street fixture.\nA few things that are visually similar to 'light post' but are not 'light post' are:\tflag pole\tsign pole\ttent pole\tumbrella post\nThere are several useful visual features to tell there is 'light post' and not similar things in a photo:\ttall and slender\tpainted or metal surface\tbulb or light source at the top\tmay have additional features such as a crossbar or banner", 416], "tarmac": ["Yes. 'Tarmac' has a tangible appearance and refers to a type of surface.\nA few things that are visually similar to 'tarmac' but are not 'tarmac' are:\tconcrete\tasphalt\tpavement\nThere are several useful visual features to tell there is 'tarmac' and not similar things in a photo:\tdark grey or black color\tsmooth, flat surface\tsmall stones embedded in the surface", 414], "paper plate": ["Yes. 'Paper plate' has a tangible appearance and is a kind of dishware.\nA few things that are visually similar to 'paper plate' but are not 'paper plate' are:\tplastic plate\tceramic plate\tbowl\nThere are several useful visual features to tell there is 'paper plate' and not similar things in a photo:\tmade of paper or cardboard\tdisposable\thas a rim around the edge\tmay be coated with polyethylene or wax to prevent leaks", 414], "balloon": ["Yes. 'Balloon' has a tangible appearance and is a type of inflatable object.\nA few things that are visually similar to 'balloon' but are not 'balloon' are:\tairship\tbeach ball\tbouncy castle\tsphere\nThere are several useful visual features to tell there is 'balloon' and not similar things in a photo:\tinflatable\tobject filled with gas or air\tbright colors or patterns\toften tied to a string or ribbon\tcircular or oblong shape", 414], "cage": ["Yes. 'Cage' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'cage' but are not 'cage' are:\tcrate\tbox\tpen\ttrap\nThere are several useful visual features to tell there is 'cage' and not similar things in a photo:\tmade of bars or wire\tenclosing an animal or object\thas a door for entry and exit.", 412], "rim": ["Yes. 'Rim' has a tangible appearance and refers to the outer edge of an object.\nA few things that are visually similar to 'rim' but are not 'rim' are: \tlip\tedge\tborder\tframe\nThere are several useful visual features to tell 'rim' from similar things in a photo: \tcurvature of the object\tthe outermost edge of the object\tis generally thinner than the rest of the object\tthe surface texture or color may differ from the rest of the object", 412], "bolt": ["Yes. 'Bolt' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'bolt' but are not 'bolt' are:\tscrew\tnail\tpin\twire\nThere are several useful visual features to tell there is 'bolt' and not similar things in a photo:\tthick, cylindrical or oval shape\ttwo flat ends or one flat and one rounded end\tspiral threads\ton the end, hexagonal, square or flat head\tfor connecting two objects using a nut", 412], "lemon": ["Yes. 'Lemon' has a tangible appearance and is a kind of citrus fruit.\nA few things that are visually similar to 'lemon' but are not 'lemon' are:\toranges\tgrapefruits\tlimes\nThere are several useful visual features to tell there is 'lemon' and not similar things in a photo:\tyellow color\toval shape\tsmooth and shiny skin\tpointed end opposite the stem", 410], "tile floor": ["Yes. 'Tile floor' has a tangible appearance and is a kind of flooring.\nA few things that are visually similar to 'tile floor' but are not 'tile floor' are:\twooden floor\tcarpet\tlaminate flooring\tstone floor\nThere are several useful visual features to tell there is 'tile floor' and not similar things in a photo:\trectangular or square shaped\ttessellating pattern\tvisible grout lines\tmade of ceramic, porcelain, or natural stone materials\tshiny or glossy surface", 408], "stickers": ["Yes. 'Stickers' has a tangible appearance and is a type of adhesive label.\nA few things that are visually similar to 'stickers' but are not 'stickers' are:\tpost-it notes\ttape\tlabels\tprice tags\nThere are several useful visual features to tell there is 'stickers' and not similar things in a photo:\tadhesive\tbacking paper\tvariety of designs and shapes\tsticked on surfaces (e.g. laptop, walls, notebooks)", 408], "wire fence": ["Yes. 'Wire fence' has a tangible appearance and is a kind of barrier.\nA few things that are visually similar to 'wire fence' but are not 'wire fence' are:\twooden fence\tconcrete barrier\tbarbed wire\tmesh netting\nThere are several useful visual features to tell there is 'wire fence' and not similar things in a photo:\tmade of metal wires\tcrosshatch pattern\tthat allows visibility and airflow\tsupport posts at regular intervals", 407], "puddle": ["Yes. 'Puddle' has a tangible appearance and is a body of water that has collected on a surface.\nA few things that are visually similar to 'puddle' but are not 'puddle' are:\tlake\tpool\tocean\tsnow\nThere are several useful visual features to tell there is 'puddle' and not similar things in a photo:\trelatively small in size \tlocated on a flat surface\treflective surface\tin a depressed area or low spot\ton the ground or pavement, not floating in air or space.", 407], "sleeve shirt": ["Yes. 'Sleeve shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sleeve shirt' but are not 'sleeve shirt' are:\ttank top\tcrop top\tbodysuit\tt-shirt\tpolo shirt\nThere are several useful visual features to tell there is 'sleeve shirt' and not similar things in a photo:\ta collar\ttwo or more sleeves\tbutton-down or pullover style\tfabric extending to the wrist or beyond", 406], "bow": ["Yes. 'Bow' has a tangible appearance and has multiple meanings like a tied ribbon or a weapon for shooting arrows.\nA few things that are visually similar to 'bow' but are not 'bow' are:\tribbon\tknot\ttie\tarc\nThere are several useful visual features to tell there is 'bow' and not similar things in a photo:\n- The presence of an arrow and the bend in the weapon for shooting arrows (in case of the weapon 'bow')\n- The shape of tied ribbon or fabric loops (in case of 'bow' meaning tied ribbon)", 406], "passenger train": ["Yes. 'Passenger train' has a tangible appearance and is a type of mode of transportation.\nA few things that are visually similar to 'passenger train' but are not 'passenger train' are:\tfreight train\tsubway\ttram\ttrolley\tbus\nThere are several useful visual features to tell there is 'passenger train' and not similar things in a photo:\tconnected train cars\twith windows and doors\tfor passengers\tseparate locomotive car on the front tracks for navigating", 405], "calf": ["Yes. 'Calf' has a tangible appearance and refers to a young bovine.\nA few things that are visually similar to 'calf' but are not 'calf' are:\tcow\tbull\tbuffalo\thorse\nThere are several useful visual features to tell there is 'calf' and not similar things in a photo:\tsmaller size than adult cows\tbaby-like features (large eyes, round body)\tless-developed horns or absence of horns (depending on breed)\tsmooth, fuzzy or short hair (depending on breed)", 405], "mountain range": ["Yes. 'Mountain range' has a tangible appearance and is a series of connected mountains.\nA few things that are visually similar to 'mountain range' but are not 'mountain range' are:\tmultiple hills\tbuildings and skyscrapers\ta row of trees\toranges stacked together\nThere are several useful visual features to tell there is 'mountain range' and not similar things in a photo:\ta series of mountain peaks\twith valleys or lowlands\tin a natural setting\twithout human-made structures or shapes seen\tfrom a distance.", 403], "cupcake": ["Yes. 'Cupcake' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'cupcake' but are not 'cupcake' are:\tmuffin\tbun\tdoughnut\tpastry\nThere are several useful visual features to tell there is 'cupcake' and not similar things in a photo:\tcake-like texture\tsmall size\tfrosted top and sprinkles\tpaper cupcake liner at the bottom\tdifferent flavors and colors of frosting and batter", 403], "side walk": ["Yes, 'side walk' has a tangible appearance and is a type of pavement.\nA few things that are visually similar to 'side walk' but are not 'side walk' are:\tparking lot pavement, cobblestone road, driveway, bike lane.\nThere are several useful visual features to tell there is 'side walk' and not similar things in a photo:\tpredominantly flat surface, adjacent to a road or pedestrian path, often concrete or asphalt material, often marked with some kind of line or divider.", 402], "notebook": ["Yes. 'Notebook' has a tangible appearance and is a type of book.\nA few things that are visually similar to 'notebook' but are not 'notebook' are:\tjournal\tdiary\tbinder\tplanner\nThere are several useful visual features to tell there is 'notebook' and not similar things in a photo:\tpaper pages\tbound or spiral binding\thard or soft cover\truled or blank pages\tvarious sizes and colors", 399], "tennis net": ["Yes. 'Tennis net' has a tangible appearance and is a kind of sports equipment.\nA few things that are visually similar to 'tennis net' but are not 'tennis net' are:\tvolleyball net\tbadminton net\tsoccer goal net\nThere are several useful visual features to tell there is 'tennis net' and not similar things in a photo:\theight\twidth\tdiamond-shaped holes\tdark green or black color\twire cable running through the net's edges\tgaps on the top and bottom of the net that provide access to the posts.", 399], "tennis": ["Yes. 'Tennis' has a tangible appearance and involves a specific set of equipment and rules.\nA few things that are visually similar to 'tennis' but are not 'tennis' are:\tbadminton\ttable tennis\tpickleball\tracquetball\nThere are several useful visual features to tell there is 'tennis' and not similar things in a photo:\tsingles or doubles matches\tracquets\tball\tnet\tcourt with white lines", 398], "mark": ["No. 'Mark' is too vague or abstract to be distinguished in a photo.", 397], "mushroom": ["Yes. 'Mushroom' has a tangible appearance and is a type of fungus.\nA few things that are visually similar to 'mushroom' but are not 'mushroom' are:\tFungi, such as toadstools, puffballs, or truffles\tFruit, such as apples, oranges, or grapes\tVegetables, such as broccoli or cauliflowers\nThere are several useful visual features to tell there is 'mushroom' and not similar things in a photo:\tCap and stem or stalk\tCap may be smooth, flat, or have bumps or ridges\tGills or pores under the cap\tRanges in color from white to brown to red to blue to black\tMushrooms typically grow from the ground, wood, or other organic material", 396], "potato": ["Yes. 'Potato' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'potato' but are not 'potato' are:\tYam\tGinger\tCarrot\tJicama\nThere are several useful visual features to tell there is 'potato' and not similar things in a photo:\tBrown or golden skin\tcolor: white, yellow, or purple\tshape: oval or round\teyes or indentations on the surface", 392], "cement": ["Yes. 'Cement' has a tangible appearance and is a building material.\nA few things that are visually similar to 'cement' but are not 'cement' are:\tconcrete\tsandstone\tgravel\tplaster\nThere are several useful visual features to tell there is 'cement' and not similar things in a photo:\tgray color\tpowdery texture\twet or dry\tcement mixer or bags of cement\tin construction sites or buildings", 392], "bookcase": ["Yes. 'Bookcase' has a tangible appearance and is a piece of furniture used to store books.\nA few things that are visually similar to 'bookcase' but are not 'bookcase' are:\tshelves\tdisplay case\tcloset\twardrobe\nThere are several useful visual features to tell there is 'bookcase' and not similar things in a photo:\thas multiple shelves or compartments specifically designed for books\ttypically has a rectangular shape and straight lines\tmay have doors or drawers\tto be placed against a wall or a corner\twithout any clothes or other items on its shelves.", 391], "moss": ["Yes. 'Moss' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'moss' but are not 'moss' are:\tliverwort\talgae\tlichen\tmold\nThere are several useful visual features to tell there is 'moss' and not similar things in a photo:\tsoft and fuzzy texture\tgreen color\tgrowing on rocks or soil\tin a dense and carpet-like formation\tbunches of small leaves growing closely together", 391], "stop": ["No. 'Stop' is too vague or abstract to be distinguished in a photo.\n \nNote: The concept of 'stop' is more of a command or instruction rather than a tangible object that can be visually distinguished. However, 'Stop' signs (which convey the instruction to stop) can be considered visually concrete and have specific visual features that distinguish them from other similar things such as: \n\nA few things that are visually similar to 'Stop signs' but are not 'Stop signs' are: \nyield signs, construction signs, speed limit signs\n\nUseful visual features for distinguishing 'Stop signs' from similar things in a photo can include: \nOctagonal shape, bright red color, white letters spelling \"STOP\", reflective materials, and a distinct border or band of white or yellow.", 388], "pasture": ["Yes. 'Pasture' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'pasture' but are not 'pasture' are:\tfield\tgarden\tforest\tpark\nThere are several useful visual features to tell there is 'pasture' and not similar things in a photo:\tlarge area covered with grass or other vegetation\tlivestock grazing or visible evidence of grazing, such as bare patches of land or manure\ta fence or other boundary around the area\tgrassy ground covering the majority of terrain", 388], "windshield wiper": ["Yes. 'Windshield wiper' has a tangible appearance and is a device used to clean a car windshield.\nA few things that are visually similar to 'windshield wiper' but are not 'windshield wiper' are:\tantennas\tcar mirrors\thood ornaments\tsnow scraper\nThere are several useful visual features to tell there is 'windshield wiper' and not similar things in a photo:\tthin, flat blade attached to an arm\tmoving back and forth across the windshield\twater or fluid being sprayed onto the windshield\twiping away rain, snow, or other debris\tfrom inside a car", 388], "footprints": ["Yes. 'Footprints' has a tangible appearance and is an impression of the foot on a surface.\nA few things that are visually similar to 'footprints' but are not 'footprints' are:\tpaw prints\tbike tire tracks\ttire marks\tdecorative stamp imprints\nThere are several useful visual features to tell there are 'footprints' and not similar things in a photo:\timpression of a foot\tvisible shoe patterns or sole outlines\tvariations in size and shape\tdirectional indication (i.e., following a path or not)\tdepth of the impression in the surface material (i.e., deeper for heavy people or animals)", 388], "zipper": ["Yes. 'Zipper' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'zipper' but are not 'zipper' are:\tbuttons\tsnaps\tvelcro\thook and eye closures\nThere are several useful visual features to tell there is 'zipper' and not similar things in a photo:\ttwo flexible strips with interlocking projections or teeth\tpull tab\tfor closing or opening clothes", 386], "rackets": ["Yes. 'Rackets' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'rackets' but are not 'rackets' are:\tpaddles\tcanes\thockey sticks\tgolf clubs\nThere are several useful visual features to tell there is 'rackets' and not similar things in a photo:\tflat head and a long handle\toval or round shape\tpattern of strings on the head\tfor use in games like tennis, badminton, or squash.", 386], "cupboard": ["Yes. 'Cupboard' has a tangible appearance and is a piece of furniture used for storage.\nA few things that are visually similar to 'cupboard' but are not 'cupboard' are:\tshelves\tdresser\twardrobe\tcounter\ttop cabinet\nThere are several useful visual features to tell there is 'cupboard' and not similar things in a photo:\tdoor(s)\tfor holding dishes, food, or other items\tin a kitchen or pantry", 385], "strings": ["Yes. 'Strings' has a tangible appearance and is a thin and flexible piece of material.\nA few things that are visually similar to 'strings' but are not 'strings' are:\thair\tcables\tribbons\trope\t\nThere are several useful visual features to tell there is 'strings' and not similar things in a photo:\tthin and flexible\ttranslucent or transparent\ttexture or pattern\trecurring length or pattern.", 385], "mushrooms": ["Yes. 'Mushrooms' has a tangible appearance and is a type of fungus.\nA few things that are visually similar to 'mushrooms' but are not 'mushrooms' are:\ttoadstools\tumbrellas\tgolf balls\nThere are several useful visual features to tell there is 'mushrooms' and not similar things in a photo:\tstem cap or cap-like top\tgills or other spore-producing structures on the underside\tvariety of colors, including brown, white, and red", 384], "floor lamp": ["Yes. 'Floor lamp' has a tangible appearance and is a common household item.\nA few things that are visually similar to 'floor lamp' but are not 'floor lamp' are:\ttable lamp\ttorchere lamp\tcandle holder\tchandelier\nThere are several useful visual features to tell there is 'floor lamp' and not similar things in a photo:\ttall and freestanding\tusually has a long and thin shaft\tsupports a lamp shade\tbright enough to light up an entire room\thas a power cord attached to it", 384], "palm": ["Yes. 'Palm' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'palm' but are not 'palm' are:\toak\ttree fern\tbirch\tmaple\nThere are several useful visual features to tell there is 'palm' and not similar things in a photo:\ttall and slender trunk with no branches at the bottom\tlong and wide leaves that are blade-shaped and fan-like\tgrowing in tropical or subtropical areas", 384], "pink flowers": ["Yes. 'Pink flowers' has a tangible appearance and is a kind of plant.\nA few things that are visually similar to 'pink flowers' but are not 'pink flowers' are:\tred flowers\tpink roses\tfake flowers\tcherry blossoms\nThere are several useful visual features to tell there is 'pink flowers' and not similar things in a photo:\tpink-colored petals\tgreen stem\tand leaves\tthat can be either thin or broad\tblossoming", 383], "sculpture": ["Yes. 'Sculpture' has a tangible appearance and is a three-dimensional art form.\nA few things that are visually similar to 'sculpture' but are not 'sculpture' are:\tdecorative items\tarchitecture\tfurniture\nThere are several useful visual features to tell there is 'sculpture' and not similar things in a photo:\tthree-dimensional\tobject made by carving, modeling, or molding\tmade of materials such as stone, clay, metal, or wood\tdecoration or art piece intended to be viewed from multiple angles", 382], "traffic": ["No. 'Traffic' is too vague or abstract to be distinguished in a photo. \n\nHowever, I can provide a few things that are visually similar to 'a traffic jam' in a photo but are not 'traffic':\n- A group of people crowded together in a small space\n- A cluster of parked cars in a small area\n- A group of pedestrians all trying to cross a small street at the same time\n\nUseful visual features for distinguishing a traffic jam are:\n- Cars in close proximity to each other, with little room to maneuver \n- Vehicles visibly stopped or moving slowly \n- Possible presence of traffic lights, traffic signs or police officers directing traffic.", 381], "spectator": ["No. 'Spectator' is too vague or abstract to be distinguished in a photo.", 381], "windshield wipers": ["Yes. 'Windshield wipers' has a tangible appearance and is a kind of car accessory.\nA few things that are visually similar to 'windshield wipers' but are not 'windshield wipers' are:\trubber strips\tflexible plastic blades\tpaint brushes\tbroom bristles\nThere are several useful visual features to tell there is 'windshield wipers' and not similar things in a photo:\tmetallic arm attached to the windscreen\tblades made of rubber or high-grade plastic\tthat moves back and forth to clean the windscreen\tfrom the front of the car", 380], "cooler": ["Yes. 'Cooler' has a tangible appearance and is used to store food or drinks.\nA few things that are visually similar to 'cooler' but are not 'cooler' are:\trefrigerator\tlunchbox\tthermos\tice bucket\nThere are several useful visual features to tell there is 'cooler' and not similar things in a photo:\tportable and can be carried by a handle\tor with a strap\tinsulated surface to keep the inside temperature cool\toften made of plastic with a lid on top", 380], "snout": ["Yes. 'Snout' has a tangible appearance and is a part of an animal's face.\nA few things that are visually similar to 'snout' but are not 'snout' are:\tmouth\tbeak\ttrunk\tnose\nThere are several useful visual features to tell there is 'snout' and not similar things in a photo:\tpart of an animal's facial structure, such as a pig or a dog\tcylindrical or elongated in shape\toften protrudes from the rest of the face\tsometimes has nostrils or other features such as whiskers, depending on the animal", 380], "station": ["Yes. 'Station' has a tangible appearance and refers to a building or site where transport vehicles stop or start.\nA few things that are visually similar to 'station' but are not 'station' are:\tparking lot\tbus stop\trest area\tgas station\nThere are several useful visual features to tell there is 'station' and not similar things in a photo:\ta building or structure for transportation vehicles\ttrains, buses, or airplanes\thigh traffic of humans and vehicles\thigh visibility signage or logos\tdocks or platforms for vehicle boarding or disembarking.", 377], "roman numerals": ["Yes. 'Roman numerals' has a tangible appearance and is a system of numerical notation using letters.\nA few things that are visually similar to 'roman numerals' but are not 'roman numerals' are:\tregular alphabet letters\thieroglyphics\tcuneiform\tChinese characters\nThere are several useful visual features to tell there is 'roman numerals' and not similar things in a photo:\tletters are typically capitalized\tthe letters used are I, V, X, L, C, D, and M\tthey are often used in a sequential order\tfrom left to right, I represents 1, V represents 5, X represents 10, L represents 50, C represents 100, D represents 500, and M represents 1,000", 377], "crack": ["Yes. 'Crack' has a tangible appearance and is a type of opening or fracture.\nA few things that are visually similar to 'crack' but are not 'crack' are:\tline\tcrevice\tshadow\tfold\tvein\nThere are several useful visual features to tell there is 'crack' and not similar things in a photo:\tnarrow opening or fracture in a surface\tstraight or zigzag shape\tdepth of the opening may vary\tcolor may be different from the surrounding surface\tlight may reflect differently from the surrounding surface.", 377], "multi": ["No. 'Multi' is too vague or abstract to be distinguished in a photo.", 377], "cluster": ["Yes. 'Cluster' has a tangible appearance and refers to a group of items close together.\nA few things that are visually similar to 'cluster' but are not 'cluster' are:\tscatter\tlining up\ta single object\tisolated\nThere are several useful visual features to tell there is 'cluster' and not similar things in a photo:\tmultiple objects in close proximity\ttogether in a compact group\tdistinct separation from surrounding objects or negative space", 376], "time": ["No. 'Time' is too vague or abstract to be distinguished in a photo.", 376], "fence post": ["Yes. 'Fence post' has a tangible appearance and is a type of wooden or metal pole used for fencing.\nA few things that are visually similar to 'fence post' but are not 'fence post' are:\ttree\ttrunk\tpole\tflagpole\nThere are several useful visual features to tell there is 'fence post' and not similar things in a photo:\tvertical pole\tstripped or painted in white or brown or gray\tburied partially in the ground\tfor supporting wiring, mesh or a railing", 375], "olives": ["Yes. 'Olives' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'olives' but are not 'olives' are:\tgrapes\tblueberries\tcherry tomatoes\nThere are several useful visual features to tell there are 'olives' and not similar things in a photo:\tsmall ovoid or rounded shape\twith a hard seed in the center\tgreen or black color depending on ripeness or variety\tof a similar size to the tip of a thumb", 373], "blouse": ["Yes. 'Blouse' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blouse' but are not 'blouse' are:\tshirt\tsweater\tt-shirt\ttank top\nThere are several useful visual features to tell there is 'blouse' and not similar things in a photo:\t\nfitted to the upper body\t\nbutton-front\t\ncollared\t\nshort or long-sleeved\t\nmade with lightweight fabrics\t\nmay have patterns or decorative elements", 371], "ottoman": ["Yes. 'Ottoman' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'ottoman' but are not 'ottoman' are:\tfootstool\tpouf\tbench\nThere are several useful visual features to tell there is 'ottoman' and not similar things in a photo:\tlow height\tcushioned top\tupholstered surface\tfour legs or no legs at all(square or cylindrical)", 371], "silver handle": ["Yes. 'Silver handle' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'silver handle' but are not 'silver handle' are:\tdoorknob\thinges\tkitchen faucet\tdrawer pull\tlatch\nThere are several useful visual features to tell there is 'silver handle' and not similar things in a photo:\ta singular object apart from a larger piece of furniture\tsilver color\tattached to a door or drawer for opening and closing purposes", 370], "wrinkles": ["Yes. 'Wrinkles' has a tangible appearance and refers to folds or creases on skin, fabric, or other surfaces.\nA few things that are visually similar to 'wrinkles' but are not 'wrinkles' are:\tcreases\tfolds\tcracks\tveins\nThere are several useful visual features to tell there is 'wrinkles' and not similar things in a photo:\tfine or deep lines on a surface\tusually on skin, clothes or paper\tsmooth surface in surrounding\tarea\tentire surface appears smooth except for wrinkles.", 370], "menu": ["Yes. 'Menu' has a tangible appearance and is a written or printed list of dishes to be served.\nA few things that are visually similar to 'menu' but are not 'menu' are:\tsigns\tbrochures\tnewspapers\tbooks\nThere are several useful visual features to tell there is 'menu' and not similar things in a photo:\twritten or printed list of dishes or drinks\tfont or typography\tcolor scheme\tglossy or matte paper finish\tdistinctive layout and organization for dishes and drinks", 369], "spatula": ["Yes. 'Spatula' has a tangible appearance and is a kind of kitchen tool.\nA few things that are visually similar to 'spatula' but are not 'spatula' are:\tturner\tflipper\ttongs\tspoon\nThere are several useful visual features to tell there is 'spatula' and not similar things in a photo:\tflat and thin with a rounded end\theld by a handle\tmade of metal or plastic\tused for flipping or lifting food while cooking", 368], "blonde hair": ["Yes. 'Blonde hair' has a tangible appearance and refers to a type of hair color.\nA few things that are visually similar to 'blonde hair' but are not 'blonde hair' are:\tlight-colored wig\tcolor-treated hair\tsun-bleached hair\tfur of an animal\nThere are several useful visual features to tell there is 'blonde hair' and not similar things in a photo:\tlight-colored hair in shades from pale yellow to light brown\toften found on people with fair complexion and light eye color\tnormally straight or slightly wavy\thair texture and growth patterns.", 368], "rows": ["Yes. 'Rows' has a tangible appearance and refers to a linear arrangement of objects.\nA few things that are visually similar to 'rows' but are not 'rows' are:\tpiles\theaps\tlines\nThere are several useful visual features to tell there are 'rows' and not similar things in a photo:\tlinear arrangement\tequidistant spacing\tsimilar or identical objects in each row", 368], "deck": ["Yes. 'Deck' has a tangible appearance and refers to a raised flat surface outside a building.\nA few things that are visually similar to 'deck' but are not 'deck' are:\tpatio\tveranda\tterrace\tbalcony\nThere are several useful visual features to tell there is 'deck' and not similar things in a photo:\televated from the ground\tflat or slightly inclined\toutdoor space made of wood, concrete, etc.\twith chairs, tables, or other furniture\tfor leisure, entertainment, or dining purposes.", 368], "vases": ["Yes. 'Vases' has a tangible appearance and is a type of container for flowers or decorative items.\nA few things that are visually similar to 'vases' but are not 'vases' are:\turns\tjars\tpots\tcups\tbowls\nThere are several useful visual features to tell there is 'vases' and not similar things in a photo:\ttall and narrow neck\tround or oval body\tbase\tfor holding flowers or other decorative items\tvariety of materials and shapes\tfor indoor or outdoor use", 368], "baseball players": ["Yes. 'Baseball players' has a tangible appearance and is a type of athlete who plays baseball.\nA few things that are visually similar to 'baseball players' but are not 'baseball players' are:\tfootball players\tbasketball players\thockey players\ttennis players\nThere are several useful visual features to tell there is 'baseball players' and not similar things in a photo:\twearing baseball uniform\tholding baseball bat\tor a baseball in hand\tplay baseball in a baseball field with a diamond-shape pattern grass\tfielding gloves\tpitcher throwing a ball to a batter.", 366], "tree trunk": ["Yes. 'Tree trunk' has a tangible appearance and is a part of a tree.\nA few things that are visually similar to 'tree trunk' but are not 'tree trunk' are:\tpillar\tcolumn\tlog\tstone\nThere are several useful visual features to tell there is 'tree trunk' and not similar things in a photo:\tbark\ttextured or smooth surface\twith branches or roots attached\tcylindrical or conical shape\tbrown or grey in color\twith visible growth rings (if cut)", 365], "bouquet": ["Yes. 'Bouquet' has a tangible appearance and usually refers to a collection of flowers.\nA few things that are visually similar to 'bouquet' but are not 'bouquet' are:\tplants\tgardens\tflower arrangements\nThere are several useful visual features to tell there is 'bouquet' and not similar things in a photo:\tcollection of flowers\tbunched together\twith stems or ribbon wrapped around them\tusually held by someone or placed in a vase.", 364], "sticks": ["Yes. 'Sticks' has a tangible appearance and is a common natural object.\nA few things that are visually similar to 'sticks' but are not 'sticks' are: branches, logs, pencils, straws, toothpicks.\nThere are several useful visual features to tell there are 'sticks' and not similar things in a photo: straight or curved shape, tapered ends, bark texture or visible wood grain, rough or textured surface, natural colors.", 362], "tshirt": ["Yes. 'Tshirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tshirt' but are not 'tshirt' are:\tshirt\tblouse\ttank top\t\nThere are several useful visual features to tell there is 'tshirt' and not similar things in a photo:\tshort-sleeved or sleeveless\tcrew neck or V-neck\tplain or with print\tfitted or loose\ttraditionally made of cotton or jersey material", 361], "entrance": ["Yes. 'Entrance' has a tangible appearance and is a point of entry or access.\nA few things that are visually similar to 'entrance' but are not 'entrance' are:\texit\tdecorative archway\twindow\t\nThere are several useful visual features to tell there is 'entrance' and not similar things in a photo:\tdoorway\tpassageway\tthreshold\tsign indicating entry and exit\tpoints towards the interior or exterior of a building\tor it can also be a natural entrance like a cave opening.", 361], "outdoors": ["No. 'Outdoors' is too vague or abstract to be distinguished in a photo.", 360], "surfers": ["Yes. 'Surfers' has a tangible appearance and refers to people engaged in the activity of surfing.\nA few things that are visually similar to 'surfers' but are not 'surfers' are:\tswimmers\tbodyboarders\tsnorkelers\t\nThere are several useful visual features to tell there are 'surfers' and not similar things in a photo:\triding a surfboard or a wave\twearing wetsuits or rashguards\thair wet and combed back\tsurfboard under the arm, or in front of them or under their torso", 360], "passengers": ["Yes. 'Passengers' has a tangible appearance and refers to people who are traveling in a vehicle.\nA few things that are visually similar to 'passengers' but are not 'passengers' are:\tpedestrians\tcrowds\tstationary people\nThere are several useful visual features to tell there are 'passengers' and not similar things in a photo:\tseated in a vehicle\tseat belts or safety restraints on\tlooking out of windows or reading", 360], "holder": ["No. 'Holder' is too vague or abstract to be distinguished in a photo. \nHowever, if we are talking about specific types of holders, such as a candle holder or a phone holder, then the answer would be yes.", 359], "door handle": ["Yes. 'Door handle' has a tangible appearance and is a type of handle used for opening and closing doors.\nA few things that are visually similar to 'door handle' but are not 'door handle' are:\tknob\tdrawer handle\tcupboard handle\tswitch button\nThere are several useful visual features to tell there is 'door handle' and not similar things in a photo:\tattached to a door\thorizontal or vertical handle shape\tmade of metal, plastic, or wood\ttypically located at waist height (for interior doors) or shoulder height (for exterior doors)", 359], "round plate": ["Yes. 'Round plate' has a tangible appearance and is a kind of dishware.\nA few things that are visually similar to 'round plate' but are not 'round plate' are:\tBowl\tFrisbee\tCoin\tWheels\nThere are several useful visual features to tell there is 'round plate' and not similar things in a photo:\tflat surface\tbottom surface\tcurved edges or rim\tcircular shape\tno thickness or depth.", 357], "skate board": ["Yes. 'Skate board' has a tangible appearance and is a type of board used for skateboarding.\nA few things that are visually similar to 'skate board' but are not 'skate board' are:\tsnowboard\tsurfboard\tcarving board\tlongboard\nThere are several useful visual features to tell there is 'skateboard' and not similar things in a photo:\trectangular board with rounded edges and a slightly curved shape for maneuverability\twheels at each end\tfor skateboard: a smaller, more maneuverable version of a longboard", 356], "silver knife": ["Yes. 'Silver knife' has a tangible appearance and is an object made of metal.\nA few things that are visually similar to 'silver knife' but are not 'silver knife' are:\tsilver spoon\tsilver fork\tletter opener\tpaper knife\nThere are several useful visual features to tell there is 'silver knife' and not similar things in a photo:\tserrated or sharp edge\tmetallic or reflective surface\tlarge or small size\tpointed tip with a handle", 355], "sets": ["No. 'Sets' is too vague or abstract to be distinguished in a photo. \n\nHowever, if the term refers to physical sets used in theater or film production, then the answer would be \"yes\". \n\nA few things that are visually similar to physical sets but are not 'sets' could be:\tbackgrounds, backdrops, or scenography\n\nUseful visual features for distinguishing physical sets from similar things in a photo could include:\t\n- 3-dimensional structures \n- design elements specific to the production (e.g. a medieval castle) \n- stage lighting and props that complement the set \n- the presence of actors or performers interacting with the set.", 355], "toppings": ["Yes, 'toppings' has a visually concrete concept and is a kind of food-related item.\nA few things that are visually similar to 'toppings' but are not 'toppings' are:\tingredients\tspices\tgarnish\nThere are several useful visual features to tell there are 'toppings' and not similar things in a photo:\tcovering the top of the food\tmultiple colors and textures\tsprinkled or spread evenly over the food.", 353], "zoo": ["Yes. 'Zoo' has a tangible appearance and refers to a place where animals are kept for public exhibition and entertainment.\nA few things that are visually similar to 'zoo' but are not 'zoo' are:\tpark\tnature reserve\tfarm\tsafari\nThere are several useful visual features to tell there is a 'zoo' and not similar things in a photo:\tenclosures or cages for animals\tsigns or labels identifying the animals\tand buildings for visitors, such as ticket booths, restaurants, and gift shops.", 353], "meter": ["No. 'Meter' is too abstract to be distinguished in a photo. It is a unit of measurement for length, distance, or quantity.\nTherefore, it does not have a tangible appearance and cannot be visually recognized.\nThere are no things that are visually similar to 'meter' but are not 'meter'.", 353], "doughnuts": ["Yes. 'Doughnuts' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'doughnuts' but are not 'doughnuts' are:\tbagels\tchurros\ttwisted pretzels\tfunnel cakes\nThere are several useful visual features to tell there is 'doughnuts' and not similar things in a photo:\tring or round-shaped\tpuffy texture\twith a hole in the middle\toften covered in glaze or sprinkles", 353], "photograph": ["Yes. 'Photograph' has a tangible appearance and refers to an image captured with a camera.\nA few things that are visually similar to 'photograph' but are not 'photograph' are:\tpainting\tdrawing\tillustration\tprint\nThere are several useful visual features to tell there is 'photograph' and not similar things in a photo:\trealistic, high-resolution image\taccurate representation of the subject matter\tspecific details and textures\tthat make it seem like a captured moment in time.", 352], "stands": ["Yes. 'Stands' has a tangible appearance and refers to a support structure.\nA few things that are visually similar to 'stands' but are not 'stands' are:\tchairs\ttables\tshelves\tpedestals\nThere are several useful visual features to tell there is 'stands' and not similar things in a photo:\tupright position\tbase or legs to support something\ttop or a surface to put something on\tvertical or slanted orientation.", 352], "restaurant": ["Yes. 'Restaurant' has a tangible appearance and is a type of business establishment that serves food.\nA few things that are visually similar to 'restaurant' but are not 'restaurant' are:\tcafe\tbar\tpub\tdiner\nThere are several useful visual features to tell there is 'restaurant' and not similar things in a photo:\ttables and chairs\tmenu board\tor plates and cutlery\tcounter or cash register\tkitchen or food preparation area\twaitstaff or customers", 350], "computer screen": ["Yes. 'Computer screen' has a tangible appearance and refers to the display of a computer or other digital device.\nA few things that are visually similar to 'computer screen' but are not 'computer screen' are:\tTV screen\tprojector screen\tsmartphone screen\ttablet screen\nThere are several useful visual features to tell there is 'computer screen' and not similar things in a photo:\trectangular shape\tself-luminous or backlit\tdisplaying digital information, images, or videos\tpixels or small dots that make up the image\tglossy or matte finish\tcables or ports for connecting to a computer or device.", 350], "bell": ["Yes. 'Bell' has a tangible appearance and is a type of musical instrument/object.\nA few things that are visually similar to 'bell' but are not 'bell' are:\tcup\tMetal dome\tSmall tower\tdecorative ornament\nThere are several useful visual features to tell there is 'bell' and not similar things in a photo:\thollow object\tbottom wider than the top\tSuspended by a clapper or other mechanism\tdome-shaped\tbody made of metal or glass", 349], "glass vase": ["Yes. 'Glass vase' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'glass vase' but are not 'glass vase' are:\twine glass\tcandle holder\tflower pot\tfishbowl\nThere are several useful visual features to tell there is 'glass vase' and not similar things in a photo:\ttall and narrow shape\tmade of transparent or translucent glass\tcylindrical or bulbous body\tnarrow or flared opening at the top.", 348], "pond": ["Yes. 'Pond' has a tangible appearance and is a type of small body of water.\nA few things that are visually similar to 'pond' but are not 'pond' are:\tlake\tpool\tfountain\tswimming hole\nThere are several useful visual features to tell there is 'pond' and not similar things in a photo:\tshallow depth\tstationary water aquatic plants or algae\tmay feature fish or frogs\tsurrounded by land or vegetation.", 347], "crane": ["Yes. 'Crane' has a tangible appearance and can refer to either a type of bird or a type of machinery.\nA few things that are visually similar to 'crane' but are not 'crane' are:\theron\tstork\tconstruction equipment\t\nThere are several useful visual features to tell there is 'crane' and not similar things in a photo:\tlong neck or arm\thoisted object or material\tfor construction equipment, a movable arm and a hook\tfor birds, a distinctive beak and/or feather pattern", 347], "ice": ["Yes. 'Ice' has a tangible appearance in its solid form.\nA few things that are visually similar to 'ice' but are not 'ice' are:\tclear glass\tquartz crystals\tdiamonds\tplastic objects\nThere are several useful visual features to tell there is 'ice' and not similar things in a photo:\ttranslucent or transparent appearance\tcold temperature\tfrosty or frozen texture\twet or slippery surface\treflection or refraction of light when light passes through\tit can melt in warmer temperatures", 346], "mattress": ["Yes. 'Mattress' has a tangible appearance and is a type of bed accessory.\nA few things that are visually similar to 'mattress' but are not 'mattress' are:\tpillow\tsofa cushion\tyoga mat\tfuton\nThere are several useful visual features to tell there is 'mattress' and not similar things in a photo:\trectangle or square-shaped\tthick and cushioned\tcovered in fabric, usually white or patterned\tlarger and thicker than pillows or cushions", 346], "pineapple": ["Yes. 'Pineapple' has a tangible appearance and is a fruit.\nA few things that are visually similar to 'pineapple' but are not 'pineapple' are:\tdurian\tjackfruit\tartichoke\nThere are several useful visual features to tell there is 'pineapple' and not similar things in a photo:\tspiky green or brown exterior\tyellow flesh\twith a crown of green leaves on top\trough texture on the exterior\tand cylindrical or conical shape.", 345], "police officer": ["Yes. 'Police officer' has a tangible appearance and is a type of profession.\nA few things that are visually similar to 'police officer' but are not 'police officer' are:\tsecurity guard\tmilitary officer\tprivate investigator\t\nThere are several useful visual features to tell there is 'police officer' and not similar things in a photo:\tuniform\twith a badge or emblem\ton patrol with a vehicle or on foot\tcarrying a firearm or other equipment\tspecific insignias or identifiers\tthat represent a specific law enforcement agency", 344], "side view mirror": ["Yes. 'Side view mirror' has a tangible appearance and is a type of car accessory.\nA few things that are visually similar to 'side view mirror' but are not 'side view mirror' are:\tdecorative car accessories\tbicycle side mirrors\twall mirrors\nThere are several useful visual features to tell there is 'side view mirror' and not similar things in a photo:\trectangular or oval shape\tattached to a car exterior\tconvex or concave reflective surface\tfor passenger side or driver side", 344], "grapes": ["Yes. 'Grapes' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'grapes' but are not 'grapes' are:\tblueberries\tsnow peas\tpomegranates\nThere are several useful visual features to tell there is 'grapes' and not similar things in a photo:\ttightly clustered round or oval-shaped fruit\tsmooth or textured skin\tcolors can range from green, red, black or purple (depending on the variety)\tstems sticking out of the top of the bunch.", 344], "sea": ["Yes. 'Sea' has a tangible appearance and is a large body of saltwater.\nA few things that are visually similar to 'sea' but are not 'sea' are:\tpond\tlake\triver\tocean\nThere are several useful visual features to tell there is 'sea' and not similar things in a photo:\tsaltwater\thuge water body\tsurfing waves\tsea creatures such as fish, sharks or dolphins\tbeach or coastline bordering the sea", 344], "barrier": ["Yes. 'Barrier' has a tangible appearance and refers to physical structures that limit or prevent movement.\nA few things that are visually similar to 'barrier' but are not 'barrier' are:\twall\tfence\trope\tline\nThere are several useful visual features to tell there is 'barrier' and not similar things in a photo:\tcan be solid object or several objects placed together\thigh enough to prevent passage\tvisible signs or words indicating restriction or warning\tdefining a boundary, like a sidewalk or road barrier.", 343], "table cloth": ["Yes. 'Table cloth' has a tangible appearance and is a type of cloth used to cover a table.\nA few things that are visually similar to 'table cloth' but are not 'table cloth' are:\tnapkins\tplacemats\tscarves\tbed sheets\nThere are several useful visual features to tell there is 'table cloth' and not similar things in a photo:\tcovers a table\tlarge enough to hang off the table on all sides\toften made of fabric or plastic\tsolid color or patterned\tmay have decorative edges or fringe", 343], "spinach": ["Yes. 'Spinach' is a visually concrete concept and is a type of leafy green vegetable.\nA few things that are visually similar to 'spinach' but are not 'spinach' are:\tkale lettuces collard greens\nThere are several useful visual features to tell there is 'spinach' and not similar things in a photo:\twrinkled, dark green leaves with pointy tips\tand rounded bottoms. Flat leaves in a rosette shape. The leaves should have a slightly rubbery texture when rubbed or touched.", 343], "wagon": ["Yes. 'Wagon' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'wagon' but are not 'wagon' are:\ttruck\tcar\tcart\ttrailer\nThere are several useful visual features to tell there is 'wagon' and not similar things in a photo:\tfour-wheeled vehicle\tlarge, box-like body\twooden or metal surface\tusually with an open top\thitches or ropes for pulling by animals or vehicles", 343], "pier": ["Yes. 'Pier' has a tangible appearance and is a structure built over water for boats to dock.\nA few things that are visually similar to 'pier' but are not 'pier' are:\tjetty\tdock\twharf\tboardwalk\nThere are several useful visual features to tell there is 'pier' and not similar things in a photo:\tprotruding from the land into the water\tsupport beams\tpilings\tor pillars\tdeck\tplanks or boards for walking", 342], "seagull": ["Yes. 'Seagull' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'seagull' but are not 'seagull' are:\tduck\tpelican\tosprey\ttern\nThere are several useful visual features to tell there is 'seagull' and not similar things in a photo:\twhite or grey feathers\twith a hooked beak\twingspan up to 5 ft\tvaried diet (fish, insects, etc.)\tfound near water (seaside, lake, river)", 342], "ledge": ["Yes. 'Ledge' has a tangible appearance and refers to a narrow horizontal surface projecting from a vertical surface such as a cliff or a building.\nA few things that are visually similar to 'ledge' but are not 'ledge' are:\tshelf\tmolding\trim\t\nThere are several useful visual features to tell there is 'ledge' and not similar things in a photo:\tnarrow\thorizontal\tprojecting from a vertical surface\tcan be on a building or a natural formation (such as a rock or a mountain)", 340], "barn": ["Yes. 'Barn' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'barn' but are not 'barn' are:\twarehouse\tgarage\tshed\tstable\nThere are several useful visual features to tell there is 'barn' and not similar things in a photo:\tred or brown wooden exterior with white trim\tsloping roof with shingles or metal panels\tdoor or doors that open wide\tenough for large farm equipment\tto be stored inside\toften flanked by silos, fields, or pastures", 340], "cab": ["Yes. 'Cab' has a tangible appearance and refers to a particular type of vehicle.\nA few things that are visually similar to 'cab' but are not 'cab' are:\tbus\ttruck\tvan\tcar\tjeep\nThere are several useful visual features to tell there is 'cab' and not similar things in a photo:\tyellow or black and white color\tsmall size compared to other vehicles\ttaxi sign on the roof, indicating a specific company\ttaximeter on the dashboard, indicating the fare\tnumber on the roof and sides with taxi identification or license plate\tnumber and shape of doors and windows", 340], "thick": ["No. 'Thick' is too vague or abstract to be distinguished in a photo. It is a description of the degree of density of an object, which cannot be observed visually.", 339], "fire truck": ["Yes. 'Fire truck' has a tangible appearance and is a kind of emergency vehicle.\nA few things that are visually similar to 'fire truck' but are not 'fire truck' are:\tambulance\tpolice car\ttruck\nThere are several useful visual features to tell there is 'fire truck' and not similar things in a photo:\tbright red color\tlarge ladder on top\tflashing lights and sirens\t\"Fire Department\" written on the side\thoses and other firefighting equipment", 339], "salt": ["Yes. 'Salt' has a tangible appearance and is a type of seasoning.\nA few things that are visually similar to 'salt' but are not 'salt' are:\tsugar\tbaking soda\tcocaine\nThere are several useful visual features to tell there is 'salt' and not similar things in a photo:\twhite, small crystals\tor fine powder\thas a salty taste\tif used to season, found in a shaker or grinder", 338], "hoof": ["Yes. 'Hoof' has a tangible appearance and is a part of an animal's foot.\nA few things that are visually similar to 'hoof' but are not 'hoof' are: paw, talon, foot, claw.\nThere are several useful visual features to tell there is 'hoof' and not similar things in a photo: hard and keratinized material\tclover-shaped\tprint in the ground or snow\tassociated with animals like horses, cows, and deer.", 338], "handbag": ["Yes. 'Handbag' has a tangible appearance and is a kind of fashion accessory.\nA few things that are visually similar to 'handbag' but are not 'handbag' are:\tbriefcase\tbackpack\tluggage\ttote\nThere are several useful visual features to tell there is 'handbag' and not similar things in a photo:\trelatively small in size\thandles or straps for carrying\tpockets or compartments\tfor women's use mostly\tdifferent styles and designs", 336], "dome": ["Yes. 'Dome' has a tangible appearance and refers to a rounded vault in architecture.\nA few things that are visually similar to 'dome' but are not 'dome' are:\tcupola\tbulbous roof\ttent\tigloo\nThere are several useful visual features to tell there is 'dome' and not similar things in a photo:\tcircular or polygonal shape\tcurved, rounded structure\torbs or rounded forms on a building's roof or ceiling", 335], "figure": ["No. 'Figure' is too vague or abstract to be distinguished in a photo. \n\nHowever, if you are referring specifically to a physical representation of a person or object, 'figure' can be considered a visually concrete concept. In that case: \n\nA few things that are visually similar to 'figure' but are not 'figure' are:\tmannequin\tsculpture\tstatue\tdoll\n\nThere are several useful visual features to tell there is a 'figure' and not similar things in a photo:\thumanoid form or resembling an object or animal\tsolid shape and form, not flat or 2D\tpainted or textured to appear realistic\tor stylized in a recognizable manner\tstanding upright or in a pose.", 335], "pipes": ["Yes. 'Pipes' has a tangible appearance and is a type of cylindrical object used for conveying fluids or gases.\nA few things that are visually similar to 'pipes' but are not 'pipes' are:\tcans\ttubes\tpencils\tstraws\thollow branches\nThere are several useful visual features to tell there is 'pipes' and not similar things in a photo:\tcylindrical shape\tconstructed of metal, plastic or other materials\tlong and usually narrow shape\twith or without visible connectors or valves\tvisible fluid or gas flow", 334], "wood fence": ["Yes. 'Wood fence' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'wood fence' but are not 'wood fence' are:\tbrick wall\thedge\tstone fence\twire fence\nThere are several useful visual features to tell there is 'wood fence' and not similar things in a photo:\tmade of wood, with vertical or horizontal planks\tspaced evenly or close to each other\thave support beams or posts\tbrown or tan coloring", 333], "mud": ["Yes. 'Mud' has a tangible appearance and is a mixture of water and soil or clay.\nA few things that are visually similar to 'mud' but are not 'mud' are:\twet sand\tsoft snow\twet soil\nThere are several useful visual features to tell there is 'mud' and not similar things in a photo:\twet and sticky consistency\tdark brown or grey color\tvisible foot or handprints\tmay have plant material or debris mixed in", 333], "sailboat": ["Yes, 'sailboat' is a visually concrete concept and refers to a boat with sails as its primary means of propulsion.\nA few things that are visually similar to 'sailboat' but are not 'sailboat' are:\tkayak\tcanoe\tspeedboat\tferry\nThere are several useful visual features to distinguish 'sailboat' from the listed similar things in a photo:\tlarge and tall sails\ta mast to hold the sails\tan open deck to control the sails\tusing wind power for propulsion", 332], "cakes": ["Yes. 'Cakes' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'cakes' but are not 'cakes' are:\tpies\ttarts\tbreads\tcupcakes\tdonuts\nThere are several useful visual features to tell there is 'cakes' and not similar things in a photo:\tlayered structure\tfrosting or icing decorated on top\tcandles on top\tfor special occasions such as birthdays or weddings", 330], "tarp": ["Yes. 'Tarp' has a tangible appearance and is a type of protective covering.\nA few things that are visually similar to 'tarp' but are not 'tarp' are:\ttablecloth\tbed sheet\tbanner\tflags\nThere are several useful visual features to tell there is 'tarp' and not similar things in a photo:\tthick and durable material\tgrommets or metal rings along the edges\tdark blue, brown, or green colors\ttypically used for covering or protecting outdoor items or areas", 330], "jeep": ["Yes. 'Jeep' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'jeep' but are not 'jeep' are:\ttruck\tSUV\tATV\tbuggy\nThere are several useful visual features to tell there is 'jeep' and not similar things in a photo:\trectangular shape with a front grille,two front headlights, one on each side of the grille,\troom for four to five passengers or cargo in the back,\toff-road tires,\tlarge wheelbase,\tusually convertible or has a removable top", 330], "yard": ["Yes. 'Yard' has a tangible appearance and is a specific outdoor area around a house.\nA few things that are visually similar to 'yard' but are not 'yard' are: park, garden, field, forest.\nThere are several useful visual features to tell there is 'yard' and not similar things in a photo:\tit is adjacent to a house or a building, and most likely has a driveway or street visible beside it.\tIt is typically a well-manicured lawn with neatly trimmed vegetation.\tIt may have outdoor furniture, a grill, or other backyard items visible.", 329], "silver faucet": ["Yes. 'Silver faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'silver faucet' but are not 'silver faucet' are:\tShowerhead\tTap knob\tMetal hook\tDoor handle\nThere are several useful visual features to tell there is 'silver faucet' and not similar things in a photo:\tSilver color\tL-shaped or curved body\tHandle at the top that turns to control water flow and temperature\tSpout at the bottom where water comes out.", 328], "straps": ["Yes. 'Straps' has a tangible appearance and refers to a narrow piece of material or band used to fasten or secure something.\nA few things that are visually similar to 'straps' but are not 'straps' are:\tbelts\tribbons\tchains\tbands\nThere are several useful visual features to tell there are 'straps' and not similar things in a photo:\tnarrow\twidth is almost the same along its length\tmay have buckles or fasteners\tto be attached to something or around something", 328], "balls": ["Yes. 'Balls' has a tangible appearance and can come in various shapes and sizes.\nA few things that are visually similar to 'balls' but are not 'balls' are:\tround fruits like apples or oranges\tround candy or chocolates\tspherical vases or jars\tdecorative spheres\nThere are several useful visual features to tell there are 'balls' and not similar things in a photo:\tspherical or circular shape\tridged or smooth surface\tvarious colors and patterns\tmade of materials like rubber, plastic, or leather", 326], "bedroom": ["Yes. 'Bedroom' has a tangible appearance and is a type of room.\nA few things that are visually similar to 'bedroom' but are not 'bedroom' are:\tliving room\tdining room\tbathroom\tkitchen\nThere are several useful visual features to tell there is 'bedroom' and not similar things in a photo:\ta bed\tfurniture such as nightstands, dressers\ta closet or wardrobe\tsleeping pillows and bedding\tan alarm clock or bedside lamp on the nightstand", 326], "trail": ["Yes. 'Trail' has a tangible appearance and is a visible path or track.\nA few things that are visually similar to 'trail' but are not 'trail' are:\tcracks in the pavement\trivers or streams\truts in the mud\tor any other linear features caused by natural or artificial means.\nThere are several useful visual features to tell there is 'trail' and not similar things in a photo:\tdirt or gravel path\tclearly defined pathway\tfootprints, bike tracks, or any other indications of use.", 326], "train station": ["Yes. 'Train station' has a tangible appearance and is a kind of building for trains.\nA few things that are visually similar to 'train station' but are not 'train station' are:\tbus station\tsubway station\tairport\toffice building\nSome useful visual features for distinguishing 'train station' from the listed similar things in a photo are: platforms for trains or tracks, overhead wires or poles for electric trains, signs for arrivals and departures, locomotives and train cars, ticket machines or counters, and railroad crossings with gates.", 325], "blue jacket": ["Yes. 'Blue jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blue jacket' but are not 'blue jacket' are:\tblue shirt\tblue sweater\tblue coat\tblue hoodie\nThere are several useful visual features to tell there is 'blue jacket' and not similar things in a photo:\tzipper or buttons\tsleeves\tpockets\tusually made of a thicker material like denim or leather", 325], "ham": ["Yes. 'Ham' has a tangible appearance and is a type of meat.\nA few things that are visually similar to 'ham' but are not 'ham' are:\tbeef\tpork\tturkey\tchicken\nThere are several useful visual features to tell there is 'ham' and not similar things in a photo:\tpink or brown meat\tcured or smoked texture\tfatty marbling in the meat\tlong, cylindrical shape", 325], "speakers": ["Yes. 'Speakers' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'speakers' but are not 'speakers' are:\tdecorative boxes\tloudspeakers\tmegaphones\talarm systems\nThere are several useful visual features to tell there is 'speakers' and not similar things in a photo:\tusually two speakers working together\tto reproduce sound or music\tcylindrical or rectangular shapes\tsound grills\tfrequently connected to an electronic device, such as a computer or a music player", 325], "key": ["Yes. 'Key' has a tangible appearance and is a tool used to open locks.\nA few things that are visually similar to 'key' but are not 'key' are:\tspoon\tnail\tneedle\nThere are several useful visual features to tell there is 'key' and not similar things in a photo:\tmetallic or plastic material\tuniquely shaped with protrusions and indentations\tfor opening a lock or a door", 325], "shirts": ["Yes. 'Shirts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'shirts' but are not 'shirts' are: t-shirts, blouses, sweaters, tank tops, and dresses.\nThere are several useful visual features to tell there is 'shirts' and not similar things in a photo: a collar, buttons, sleeves, and a distinct separation between the upper and lower parts of the garment.", 324], "dots": ["Yes. 'Dots' has a tangible appearance and is a small round-shaped object.\nA few things that are visually similar to 'dots' but are not 'dots' are:\tcircles\tholes\tpebbles\tjewels\nThere are several useful visual features to tell there are 'dots' and not similar things in a photo:\tsmall\tperfectly round\tevenly spaced or arranged\tdifferent colors or shades", 324], "dishes": ["Yes. 'Dishes' has a tangible appearance and refers to objects used for serving and eating food.\nA few things that are visually similar to 'dishes' but are not 'dishes' are:\tcups\tbowls\tpots\tand pans\nThere are several useful visual features to distinguish 'dishes' from similar things in a photo:\tflat or concave surface for holding food\tmultiple dishes arranged together\ton a table or counter, ready to be used\tfor serving or eating food.", 323], "rear wheel": ["Yes. 'Rear wheel' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'rear wheel' but are not 'rear wheel' are:\tfront wheel\tbicycle wheel\tstroller wheel\trollerblade wheel\nThere are several useful visual features to tell there is 'rear wheel' and not similar things in a photo:\tlocation near the back of a vehicle\tsmaller than the front wheel\thas a chain or belt connecting it to the motor (if it is a motorized vehicle)\tmay have a brake disc attached to it\thas a tire with a tread pattern designed for specific terrain (e.g. road, off-road)", 323], "spoons": ["Yes. 'Spoons' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'spoons' but are not 'spoons' are: knifes, forks, chopsticks, sporks.\nThere are several useful visual features to tell there is 'spoons' and not similar things in a photo:\tsmall bowl-shaped end\tfor holding and scooping food\tintended to be used by hand or with other utensils\ttypically made of metal or plastic", 323], "mobile phone": ["Yes. 'Mobile phone' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'mobile phone' but are not 'mobile phone' are:\tlandline phone\tmp3 player\tspeaker\twalkie-talkie\nThere are several useful visual features to tell there is 'mobile phone' and not similar things in a photo:\tthin and flat screen with touch capability\tbuilt-in camera\tforward-facing speaker\tand microphone\tbuttons or switches for volume or power\tport for charging\tcalling or messaging apps are visible on the screen", 322], "park bench": ["Yes. 'Park bench' has a tangible appearance and is a type of outdoor seating.\nA few things that are visually similar to 'park bench' but are not 'park bench' are:\trock\tbale of hay\tlog\nThere are several useful visual features to tell there is 'park bench' and not similar things in a photo:\tlong wooden or metal seat\tbuilt with armrests and/or a backrest\tlegs or supports of the bench\tfixed to the ground and not movable", 321], "train cars": ["Yes. 'Train cars' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'train cars' but are not 'train cars' are:\ttrucks\ttractors\ttrailers\tcarriages\nThere are several useful visual features to tell there is 'train cars' and not similar things in a photo:\tconnected to each other\twith rail or wheels\telongated shape\tmetallic surface (usually)", 320], "sandwiches": ["Yes. 'Sandwiches' has a tangible appearance and is a type of food/cuisine.\nA few things that are visually similar to 'sandwiches' but are not 'sandwiches' are:\tburgers\twraps\tpizza\ttacos\tsubmarine sandwiches\nThere are several useful visual features to tell there is 'sandwiches' and not similar things in a photo:\tsliced bread layers\twith various fillings (meat, cheese, vegetables, etc.)\texample of sandwiches include BLT, Club, or PB&J\tsquare, rectangular, or triangular shape", 320], "suitcases": ["Yes. 'Suitcases' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'suitcases' but are not 'suitcases' are:\tbackpacks\tpurses\tbriefcases\tduffle bags\nThere are several useful visual features to tell there is 'suitcases' and not similar things in a photo:\trectangular or box-shaped\thinged on one side\thandle or strap for carrying\thard or soft exterior shell\twith or without wheels or feet\tzippers or clasps for opening and closing.", 319], "platter": ["Yes. 'Platter' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'platter' but are not 'platter' are:\tplate\ttray\tbowl\tpan\nThere are several useful visual features to tell there is 'platter' and not similar things in a photo:\tlarge and flat surface\tfor serving food or drinks\tmay have a raised edge or rim\tmade of various materials, such as ceramic, glass, or metal.", 318], "beverage": ["Yes. 'Beverage' has a tangible appearance and is a drink that people consume.\nA few things that are visually similar to 'beverage' but are not 'beverage' are:\tliquid soap\thand sanitizer\tmouthwash\tperfume\nThere are several useful visual features to tell there is 'beverage' and not similar things in a photo:\tcontainer for liquid\tdrinking straw or cup\tbeading on the surface to indicate cold drink\tcolor differences to indicate different types of beverage (e.g., coffee, tea, soda, juice)", 318], "road sign": ["Yes. 'Road sign' has a tangible appearance and is a specific type of sign used on roads for signaling purposes.\nA few things that are visually similar to 'road sign' but are not 'road sign' are:\tstore signs\tdirection signs\tadvertising banners\nThere are several useful visual features to tell there is 'road sign' and not similar things in a photo:\tlocated along a roadway\tusually has a specific shape, like a rectangle, circle, or triangle\tcontains specific symbols, text, or numbers for traffic guidance or regulation\tmay be reflective or illuminated for visibility at night", 318], "salt shaker": ["Yes. 'Salt shaker' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'salt shaker' but are not 'salt shaker' are:\tpepper shaker\tsugar dispenser\tspice jar\tmason jar\nThere are several useful visual features to tell there is 'salt shaker' and not similar things in a photo:\tsmall and handheld\tsize and shape of the holes on top\tlabel or sign indicating 'salt'\tcylindrical or spherical shape\twith or without a handle", 317], "thing": ["No. 'Thing' is too vague and abstract to be distinguished in a photo. It could refer to virtually anything and therefore lacks a tangible appearance. \nAs for things that are visually similar to 'thing,' it's impossible to answer because it's too broad and non-specific.", 317], "island": ["Yes. 'Island' has a tangible appearance and is a piece of land surrounded by water.\nA few things that are visually similar to 'island' but are not 'island' are:\tpeninsula\tatoll\treef\tsandbar\nThere are several useful visual features to tell there is 'island' and not similar things in a photo:\tground covered with vegetation or trees\tsurrounded by water\tdistance from the mainland or other surrounding islands\tmay have a beach or shore", 317], "guitar": ["Yes. 'Guitar' has a tangible appearance and is a musical instrument.\nA few things that are visually similar to 'guitar' but are not 'guitar' are:\tukulele\tbanjo\tviolin\tcello\nThere are several useful visual features to tell there is 'guitar' and not similar things in a photo:\tlong neck, fretted or fretless\tbody with a sound hole\tsix or twelve strings\tcurved or angled shape", 317], "mustard": ["Yes. 'Mustard' has a tangible appearance and is a type of condiment.\nA few things that are visually similar to 'mustard' but are not 'mustard' are:\thoney\tmayonnaise\tcustard\nThere are several useful visual features to tell there is 'mustard' and not similar things in a photo:\tyellow or brown color\tthick or creamy consistency\tpouring from a bottle or a jar\ttypically served with hot dogs, sandwiches, or burgers", 316], "oven": ["Yes. 'Oven' has a tangible appearance and is a household cooking appliance.\nA few things that are visually similar to 'oven' but are not 'oven' are:\tmicrowave\tBBQ grill\tfireplace\twith oven-like shape\nThere are several useful visual features to tell there is 'oven' and not similar things in a photo:\tdoor\thandle\tracks and trays\tdisplay and control panel\ttemperature gauge", 315], "saddle": ["Yes. 'Saddle' has a tangible appearance and is an equipment used for riding horses.\nA few things that are visually similar to 'saddle' but are not 'saddle' are:\tblanket\tchair\tpack\tof cards\nThere are several useful visual features to tell there is 'saddle' and not similar things in a photo:\tcushioned seat\tfor a horse to carry rider and other equipment\tleather or synthetic straps\tfor attaching to a horse's back\tfront and back flaps\tthat curve upwards to provide support for the rider's legs\tsmall stirrups\tfor the rider's feet to rest in\twhile riding.", 315], "strawberries": ["Yes. 'Strawberries' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'strawberries' but are not 'strawberries' are:\traspberries\ttomatoes\tcherry\trose hips\nThere are several useful visual features to tell there is 'strawberries' and not similar things in a photo:\tred\tpinkish\ttexture with tiny seeds on the surface\thave a green stem at the top", 315], "tube": ["Yes. 'Tube' has a tangible appearance and is a type of cylindrical object.\nA few things that are visually similar to 'tube' but are not 'tube' are:\tpipes\tstraws\tbottles\tcans\nThere are several useful visual features to tell there is 'tube' and not similar things in a photo:\tcylindrical shape\thollow and open at both ends\tmade of plastic, metal or glass\ttextures such as ridges or lines\tcan be flexible or rigid", 315], "veggies": ["No. 'Veggies' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider specific types of vegetables, the answer could be different.\n\nFor example:\nYes. 'Carrots', 'tomatoes' or 'broccoli' as individual vegetables have a tangible appearance.\n\nA few things that are visually similar to 'carrots' but are not 'carrots' are:\torange popsicles, candy corns. \nA few things that are visually similar to 'tomatoes' but are not 'tomatoes' are:\tred balls, cherries. \nA few things that are visually similar to 'broccoli' but are not 'broccoli' are:\tcauliflower, shrubs. \n\nThere are several useful visual features to tell there is a specific vegetable and not similar things in a photo:\n- Carrots: elongated shape, tapered edges, orange color, rough texture on the skin.\n- Tomatoes: round shape, red color, smooth and shiny surface.\n- Broccoli: green color, multiple branches with small flower-like buds.", 314], "emblem": ["Yes. 'Emblem' has a tangible appearance and is a visual symbol that represents a concept or organization.\nA few things that are visually similar to 'emblem' but are not 'emblem' are:\tlogo\tbadge\tinsignia\nThere are several useful visual features to tell there is 'emblem' and not similar things in a photo:\tunique design or pattern\trecognizable symbol\tor color scheme\tspecific imagery that represents a concept or organization", 314], "wood chair": ["Yes. 'Wood chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood chair' but are not 'wood chair' are:\tstool\tbench\tsofa\t\nThere are several useful visual features to tell there is 'wood chair' and not similar things in a photo:\thave legs or support structures for sitting\thave a backrest\tmade of wood or a combination of wood and other materials\tspecific shapes and designs to form seat and backrest\tcan have cushions or padding for extra comfort", 313], "cones": ["Yes. 'Cones' has a tangible appearance and is a three-dimensional shape.\nA few things that are visually similar to 'cones' but are not 'cones' are:\tice cream cone\tpine tree\ttraffic cone\nThere are several useful visual features to tell there is 'cones' and not similar things in a photo:\tcircular base\tpointed vertex\tsmooth, rounded surface, tapering to a point\tat least one curved side if it is a non-right circular cone.", 313], "step": ["Yes. 'Step' has a tangible appearance and is a physical structure used for stepping on.\nA few things that are visually similar to 'step' but are not 'step' are:\tbench\tstool\trock\tboulder\nThere are several useful visual features to tell there is 'step' and not similar things in a photo:\t Generally rectangular or squarish in shape\tEven or flat surface\tvisible lower and higher surface/level or steps.", 313], "horizon": ["Yes. 'Horizon' has a tangible appearance and is the line where the sky meets the land or sea.\nA few things that are visually similar to 'horizon' but are not 'horizon' are:\tsky\tline of trees\tbuildings\tmountains\nThere are several useful visual features to tell there is 'horizon' and not similar things in a photo:\ta straight or curved line\twhere the sky and land/sea meets\tunobstructed view\tof the sky and the land/sea", 313], "woods": ["Yes. 'Woods' has a tangible appearance and is a type of vegetation.\nA few things that are visually similar to 'woods' but are not 'woods' are:\tforest\tjungle\tpark\tzoo\tgrassy area\nThere are several useful visual features to tell there are 'woods' and not similar things in a photo:\ttrees\tunderbrush and/or canopy\tlimited visibility\traise ground level\tdappled sunlight\tleaves on the ground.", 313], "strawberry": ["Yes. 'Strawberry' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'strawberry' but are not 'strawberry' are:\traspberry\tcranberry\tcherry\ttomato\nThere are several useful visual features to tell there is 'strawberry' and not similar things in a photo:\theart-shaped\tred, pink or white surface\ttextured surface with small seeds or bumps\twhite seeds on the surface\tsmall, green stem at the top", 313], "birthday": ["No. 'Birthday' is too vague or abstract to be distinguished in a photo.", 312], "ladies": ["No. 'Ladies' is too vague or abstract to be distinguished in a photo. It refers to women in general, but there is no specific tangible appearance for it.\nTherefore, there are no things that are visually similar to 'ladies' but are not 'ladies'.\nNo visual features can distinguish 'ladies' from things that are visually similar or related to it because it is a broad, inclusive term for women.", 311], "beige": ["Yes. 'Beige' has a tangible appearance and is a color.\nA few things that are visually similar to 'beige' but are not 'beige' are:\tcream\toff-white\tlight yellow\tlight brown\nThere are several useful visual features to tell there is 'beige' and not similar things in a photo:\tlight brown with a hint of yellow or gray\tmoderate saturation level (not too bright or too dull)\tfairly uniform color (without many variations or patterns)", 310], "snow board": ["Yes. 'Snow board' has a tangible appearance and is a type of winter sports equipment.\nA few things that are visually similar to 'snow board' but are not 'snow board' are:\tsleds\tskis\ttoboggans\tice skates\nThere are several useful visual features to tell there is 'snow board' and not similar things in a photo:\trectangular or hourglass shape\twith or without bindings\tsingle board for both feet\twith or without boots\tused for riding down a snowy slope or performing tricks\tin some cases, featuring a colorful design or pattern on the underside.", 310], "wicker basket": ["Yes. 'Wicker basket' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'wicker basket' but are not 'wicker basket' are:\tbackpack\tpurse\tlaundry hamper\tpicnic blanket\nThere are several useful visual features to tell there is 'wicker basket' and not similar things in a photo:\twoven material\twith a handle\tmade of natural materials, such as willow or bamboo\tclumped together in a bunch", 310], "railroad tracks": ["Yes. 'Railroad tracks' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'railroad tracks' but are not 'railroad tracks' are:\ttram tracks\tbike lane\tfootpath\tgrooves in pavement\nThere are several useful visual features to tell there is 'railroad tracks' and not similar things in a photo:\ttwo parallel metal tracks\twooden or concrete sleepers\tballast or gravel in between tracks\tcurving or straight line shape\tcrossing signs or gates", 310], "train track": ["Yes. 'Train track' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'train track' but are not 'train track' are:\trailway bridge\thighway\triver\tcurved pavement\nThere are several useful visual features to tell there is 'train track' and not similar things in a photo:\tparallel rails\twith ties and ballasts\tiron or steel tracks\tcrossbeams with bolts\tinherent straightness or slight curves\tfrequent power poles or electricity wires\tnext to stations or platforms.", 309], "grey elephant": ["Yes. 'Grey elephant' has a tangible appearance and is a kind of mammal.\nA few things that are visually similar to 'grey elephant' but are not 'grey elephant' are:\thippopotamus\trhino\tbuffalo\nThere are several useful visual features to tell there is 'grey elephant' and not similar things in a photo:\tenormous size\tlong trunk\ttusks\trounded ears\tthick grey skin\tfour legs, toenails\ttrunk is able to grasp and pick things up.", 308], "milk": ["Yes. 'Milk' has a visually concrete concept and is a liquid.\nA few things that are visually similar to 'milk' but are not 'milk' are:\tcream\thot chocolate\twater\twith white paint\nThere are several useful visual features to tell there is 'milk' and not similar things in a photo:\topaque\tcolor is usually white or cream\tusually comes in a container (e.g. glass, carton, jug)", 308], "jets": ["Yes. 'Jets' has a tangible appearance and is a kind of aircraft.\nA few things that are visually similar to 'jets' but are not 'jets' are:\thelicopters\tplanes\tdrones\nThere are several useful visual features to tell there is 'jets' and not similar things in a photo:\tlong and narrow body\twith wings and flaps\tat least one jet engine or turbine\ton the side of the aircraft\table to reach very high speeds and altitudes.", 308], "cables": ["Yes. 'Cables' has a tangible appearance and refers to electrical cords or wires.\nA few things that are visually similar to 'cables' but are not 'cables' are:\trope\ttwine\tshoelaces\tribbons\nThere are several useful visual features to tell there is 'cables' and not similar things in a photo:\telectrical wires or cords\tplugged into a device or outlet\tmetallic or plastic outer layer\tdifferent colors or thicknesses", 307], "staircase": ["Yes. 'Staircase' has a tangible appearance and is a structure used for going up or down between different levels.\nA few things that are visually similar to 'staircase' but are not 'staircase' are:\tramp\tescalator\tladder\tslide\nThere are several useful visual features to tell there is 'staircase' and not similar things in a photo:\tmultiple steps or levels\tvertical or diagonal orientation\thandrails for support\tconnected to floors or landings", 307], "placemat": ["Yes. 'Placemat' has a tangible appearance and is a type of table setting.\nA few things that are visually similar to 'placemat' but are not 'placemat' are:\ttablecloth\tnapkin\ttray\tbasket\t\nThere are several useful visual features to tell there is 'placemat' and not similar things in a photo:\tindividual mat for each guest\tplaced under each dinner plate\tmade of fabric, paper or other materials\tdesigned with patterns, colors or images that complement the table setting", 307], "knees": ["Yes. 'Knees' have a tangible appearance and are specific body parts.\nA few things that are visually similar to 'knees' but are not 'knees' are: elbows, ankles, wrists, hips, shoulders.\nThere are several useful visual features to tell there is 'knees' and not similar things in a photo:\tbend in the middle of the leg,\tyou can see the patella (kneecap),\tfleshy, round shape on the front of the leg", 306], "drain": ["Yes. 'Drain' has a tangible appearance and is a type of pipe or opening used to remove water or other liquids.\nA few things that are visually similar to 'drain' but are not 'drain' are:\tpipe\tmanhole cover\tgrate\tsewer\nThere are several useful visual features to tell there is 'drain' and not similar things in a photo:\tcircular or square opening\tin the ground, wall, or floor\tusually covered with a grate\tor other decorative covering\tdark, wet, or slimy appearance\tif flowing, with small amounts of water or other liquids around the edge.", 306], "end table": ["Yes. 'End table' has a tangible appearance and refers to a type of furniture.\nA few things that are visually similar to 'end table' but are not 'end table' are:\tcoffee table\tnightstand\tsideboard\nThere are several useful visual features to tell there is 'end table' and not similar things in a photo:\tsmall in size\tplaced near a sofa or chair\tlevel with or just below the arms of a chair or sofa used as support for beverages, remote controls, magazines or books\thas a flat surface on top and a base or legs to support it", 305], "dispenser": ["Yes. 'Dispenser' has a tangible appearance and is a device that dispenses something.\nA few things that are visually similar to 'dispenser' but are not 'dispenser' are:\tbottle\tvase\tcan\tjug\nThere are several useful visual features to tell there is 'dispenser' and not similar things in a photo:\tan opening or spout where the contents come out\ta pump or button to release the contents\ta container for holding the contents (such as soap, water, or candy)", 305], "picnic table": ["Yes. 'Picnic table' has a tangible appearance and is a type of outdoor seating.\nA few things that are visually similar to 'picnic table' but are not 'picnic table' are:\tdining table\tcoffee table\tpatio furniture\tbenches\nThere are several useful visual features to tell there is 'picnic table' and not similar things in a photo:\tattached benches or seats\twooden or metal construction\trectangular or square shape\toutdoor location (e.g. park, campground, backyard)", 305], "pad": ["No. 'Pad' is too vague or abstract to be distinguished in a photo without further context. \nHowever, if we assume 'pad' to mean a flat cushion or writing pad, then:\nA few things that are visually similar to 'pad' but are not 'pad' are:\tmouse pad\tcotton pad\tnotepad\tlily pad\nThere are several useful visual features to tell there is a 'pad' cushion in a photo:\tflat and rectangular or circular shape\twith a thickness of a few inches\tsoft or cushioned material used for sitting or sleeping on\nFor a writing or drawing pad, useful visual features would be:\ta flat surface to write or draw on\tpaper or paper-like material\tbound or stapled together in a notepad\tformally divided into separate pages", 303], "mouse pad": ["Yes. 'Mouse pad' has a tangible appearance and is a kind of computer accessory.\nA few things that are visually similar to 'mouse pad' but are not 'mouse pad' are:\tcarpet\tcoaster\tplace mat\tdesk pad\nThere are several useful visual features to tell there is 'mouse pad' and not similar things in a photo:\trectangular shape\tpadded or cushioned surface\tsmooth or textured surface\twith a graphic or design to track computer mouse movement", 303], "objects": ["No. 'Objects' is too vague or abstract to be distinguished in a photo.", 303], "soap dispenser": ["Yes. 'Soap dispenser' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'soap dispenser' but are not 'soap dispenser' are:\tshampoo bottle\thand lotion bottle\tdish soap bottle\tmouthwash bottle\nThere are several useful visual features to tell there is 'soap dispenser' and not similar things in a photo:\tpump or spout at the top\tdedicated for soap with labeling or branding\tcylindrical or rectangular shape\tclear or opaque body to show the soap level", 303], "package": ["Yes. 'Package' has a tangible appearance and is a kind of object used to contain or transport things.\nA few things that are visually similar to 'package' but are not 'package' are:\tbox\tenvelope\tbag\tsuitcase\nThere are several useful visual features to tell there is 'package' and not similar things in a photo:\trectangular or square shape\tclosed with tape or string\tlabel or address\tprinting or branding on the surface\tvarying sizes or materials based on its content", 302], "frisbees": ["Yes. 'Frisbees' has a tangible appearance and is a type of flying object.\nA few things that are visually similar to 'Frisbees' but are not 'Frisbees' are:\tplastic plates\tflying saucers\tcircular boomerangs\tbeach balls\nThere are several useful visual features to tell there is 'Frisbees' and not similar things in a photo:\tdisc-shaped\tflat\tbody with curved edges\tmade of plastic or rubber\tbright colors or patterns", 301], "bandana": ["Yes. 'Bandana' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'bandana' but are not 'bandana' are:\thandkerchief\tscarf\tneckerchief\nThere are several useful visual features to tell there is 'bandana' and not similar things in a photo:\tsquare-shaped piece of cloth\tworn around the neck or head\tbright and bold colors\tpatterns, such as paisley or floral motifs.", 301], "engines": ["Yes. 'Engines' has a tangible appearance and is an essential component of many machines.\nA few things that are visually similar to 'engines' but are not 'engines' are:\tfans\tcompressors\tturbines\tcylinders\nThere are several useful visual features to tell there are 'engines' and not similar things in a photo:\tcombustion chamber\tpistons\texhaust pipes\tmetallic parts\tvisible moving parts, such as gears and belts", 301], "wine glasses": ["Yes. 'Wine glasses' has a tangible appearance and is a kind of glassware.\nA few things that are visually similar to 'wine glasses' but are not 'wine glasses' are:\ttumbler glass\tcup\tvase\tbowl\nThere are several useful visual features to tell there is 'wine glasses' and not similar things in a photo:\ttall with a stem and a base\tcurved and narrow opening for holding wine\tclear or colored glass", 301], "grass area": ["Yes. 'Grass area' has a tangible appearance and refers to an open space covered in grass.\nA few things that are visually similar to 'grass area' but are not 'grass area' are:\tfarmland\tparkway\tside of the road\tgolf course\nThere are several useful visual features to tell there is 'grass area' and not similar things in a photo:\tgreen grass in various shades\tgrass blades that have different length and thickness\tirregular shapes or patterns natural elements like flowers, bushes or trees might be part of it", 301], "wood floor": ["Yes. 'Wood floor' has a tangible appearance and refers to a type of flooring made of wood.\nA few things that are visually similar to 'wood floor' but are not 'wood floor' are:\tlaminate flooring\tvinyl flooring\ttile flooring\nThere are several useful visual features to tell there is 'wood floor' and not similar things in a photo:\tgrain patterns and knots in the wood\tnatural wood color and texture\tsmooth finish without visible seams or grout lines", 300], "wrist watch": ["Yes. 'Wrist watch' has a tangible appearance and is a type of timepiece worn on the wrist.\nA few things that are visually similar to 'wrist watch' but are not 'wrist watch' are:\tbracelet\tfitness tracker\tsmartwatch\nThere are several useful visual features to tell there is 'wrist watch' and not similar things in a photo:\tround or square face with numbers or markers\tone or more hands pointing to the time\ta wristband that fits around the wrist\tbuckles or clasps to fasten the wristband the hands move in a clockwise direction.", 299], "splash": ["Yes. 'Splash' has a tangible appearance and is a type of water movement.\nA few things that are visually similar to 'splash' but are not 'splash' are: wave, ripple, foam, spray\nThere are several useful visual features to tell there is 'splash' and not similar things in a photo: droplets flying in the air, disruptive or turbulent water surface, circular or ovular shape.", 299], "family": ["No. 'Family' is too vague or abstract to be distinguished in a photo.", 296], "round clock": ["Yes. 'Round clock' has a tangible appearance and is a kind of time measuring device.\nA few things that are visually similar to 'round clock' but are not 'round clock' are:\twatch\ttimer\tcompass\nThere are several useful visual features to tell there is 'round clock' and not similar things in a photo:\tcircular shape\twith numbers or marks for hours and minutes\thands pointing to the time\ton a wall or table", 296], "mustache": ["Yes. 'Mustache' has a tangible appearance and is a facial hair.\nA few things that are visually similar to 'mustache' but are not 'mustache' are:\tbeard\tsideburns\teyebrows\thair\nThere are several useful visual features to tell there is 'mustache' and not similar things in a photo:\thair grown above the upper lip\tusually smaller than a beard\tcan be curly, straight, or pointed\tvarious colors and textures", 296], "sugar": ["Yes. 'Sugar' has a tangible appearance and is a type of granulated sweetener.\nA few things that are visually similar to 'sugar' but are not 'sugar' are:\tsalt\tbaking soda\tcooking powder\tcocaine\nThere are several useful visual features to tell there is 'sugar' and not similar things in a photo:\tfine, granulated texture\twhite or light brown color\tcrystal-like appearance\tsweet scent and taste", 296], "pasta": ["Yes. 'Pasta' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pasta' but are not 'pasta' are:\tnoodles\tstraws\tribbons\nThere are several useful visual features to tell there is 'pasta' and not similar things in a photo:\tvarious shapes (e.g., penne, spaghetti, fusilli, etc.)\tcooked or uncooked\tgrainy or smooth texture\tusually served with sauce", 296], "pink flower": ["Yes. 'Pink flower' has a tangible appearance and is a kind of plant.\nA few things that are visually similar to 'pink flower' but are not 'pink flower' are:\trose\tcarnation\tcherry blossom\thibiscus\nThere are several useful visual features to tell there is 'pink flower' and not similar things in a photo:\tpink petals\tgreen stem and leaves\tblooming or in bud shape\tfluffy or smooth texture of the petals", 295], "reflections": ["Yes. 'Reflections' has a tangible appearance and refers to the visible image of an object or scene reflected in a surface.\nA few things that are visually similar to 'reflections' but are not 'reflections' are:\trefractions\tshadows\tmirages\nThere are several useful visual features to tell there are 'reflections' and not similar things in a photo:\tan inverted or reversed image\ta mirrored copy of the original object or scene\tthe presence of a reflective surface, such as water or a mirror", 295], "card": ["Yes. 'Card' has a tangible appearance and can refer to various types of rectangular or square-shaped paper.\nA few things that are visually similar to 'card' but are not 'card' are:\tpaper\tmoney\tboard\tgame pieces\nThere are several useful visual features to tell there is 'card' and not similar things in a photo:\trectangular or square shape\tthin but sturdy paper or cardboard texture\twith designs, numbers, or letters\tfor greeting, identification, or game purposes", 295], "concrete wall": ["Yes. 'Concrete wall' has a tangible and recognizable appearance.\nA few things that are visually similar to 'concrete wall' but are not 'concrete wall' are:\tcement blocks\tbricks\tstone walls\tdrywall\nThere are several useful visual features to tell there is 'concrete wall' and not similar things in a photo:\tsolid and thick\tgray or light gray in color\tsmooth or rough texture\tcan have visible seams or cracks.", 295], "traffic cone": ["Yes. 'Traffic cone' has a tangible appearance and is a type of road safety equipment.\nA few things that are visually similar to 'traffic cone' but are not 'traffic cone' are:\tpylons\troad barriers\thard hats\nThere are several useful visual features to tell there is 'traffic cone' and not similar things in a photo:\tcone-shaped\tbright orange or yellow\tcolorful stripes\thollow on the inside\tvarious sizes from small to large \ttapered at the top", 294], "persons": ["Yes. 'Persons' has a tangible appearance and refers to human beings.\nThere are no things that are visually similar to 'persons' but are not 'persons'.\nUseful visual features for distinguishing 'persons' from each other in a photo are: differences in clothing, hairstyles, facial features, skin color, age, and body shape.", 294], "rails": ["Yes. 'Rails' has a tangible appearance and is a kind of mechanical structure.\nA few things that are visually similar to 'rails' but are not 'rails' are:\tbars\tfences\twires\tchains\nThere are several useful visual features to tell there is 'rails' and not similar things in a photo:\tparallel lines\thorizontal orientation\tmetal material\tjoint connection with each other.", 293], "bin": ["Yes. 'Bin' has a tangible appearance and is a container for waste or recycling material.\nA few things that are visually similar to 'bin' but are not 'bin' are:\tbox\tbasket\ttray\tpot\nThere are several useful visual features to tell there is 'bin' and not similar things in a photo:\tplaced on the ground or mounted on a wall\tdesignated for waste or recycling material\tlabeled with words or symbols for waste or recycling\teasily accessible with an opening for depositing material\ttop lid or cover", 292], "claws": ["Yes. 'Claws' has a tangible appearance and is a part of the body of some animals.\nA few things that are visually similar to 'claws' but are not 'claws' are:\tfingernails\ttoenails\tsharp tools\tpaws\nThere are several useful visual features to tell there are 'claws' and not similar things in a photo:\tpointed shape\tcurved tips\tbony or hard material\tlocated at the end of toes or fingers", 292], "crumbs": ["Yes. 'Crumbs' has a tangible appearance and refers to small, broken pieces of food or other substances.\nA few things that are visually similar to 'crumbs' but are not 'crumbs' are:\tpowder\tsand\tshavings\tashes\nThere are several useful visual features to tell there are 'crumbs' and not similar things in a photo:\tsmall pieces or fragments\tof food, bread, biscuits, or cake\tcrunchy or crispy texture\tmessy or scattered appearance", 292], "center": ["No. 'Center' is too vague or abstract and does not have a tangible appearance.", 292], "items furniture": ["No. 'Items furniture' is an abstract term that does not have a concrete visual appearance. It encompasses various types of furniture objects rather than a specific category or style of furniture.\nTherefore, there are no similar things that can visually resemble 'items furniture'.\nThe useful visual features to distinguish furniture objects from each other vary by the type of furniture being examined. For example, a chair can be distinguished by its backrest, armrest, leg design, and seat shape, while a table can be identified by its top, legs, base, and shape.", 291], "building background": ["Yes. 'Building background' has a tangible appearance and usually includes the background of a photo or video showing urban buildings.\nA few things that are visually similar to 'building background' but are not 'building background' are:\tlandscape\tbackground cityscape\nThere are several useful visual features to tell there is 'building background' and not similar things in a photo:\ttall buildings\turban skyline\tconcrete structures, glass and metal structures in frame, or classic buildings visible\tbuildings should not be the primary focus of the photo. It should simply serve as a visual backdrop.", 291], "colors": ["No. 'Colors' are too vague or abstract to be distinguished in a photo.\nThere are no things similar to 'colors' but not 'colors'.", 290], "asphalt": ["Yes. 'Asphalt' has a tangible appearance and refers to a specific material used for paving roads and other surfaces.\nA few things that are visually similar to 'asphalt' but are not 'asphalt' are:\tconcrete\tpavement\ttar\tmud\nThere are several useful visual features to tell there is 'asphalt' and not similar things in a photo:\tdark grey or black in color\tsmooth surface\tlaid in distinct sections or pattern", 290], "bark": ["Yes. 'Bark' has a tangible appearance and is the outer layer of a tree's trunk.\nA few things that are visually similar to 'bark' but are not 'bark' are:\twood\tleaves\tgrass\tmoss\tlichen\nThere are several useful visual features to tell there is 'bark' and not similar things in a photo:\tgrooves or ridges\trough or smooth texture\tspecific color patterns (e.g. white and black stripes for birch bark)\tlocation on a tree trunk (versus on branches or leaves)", 289], "computers": ["Yes. 'Computers' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'computers' but are not 'computers' are: TV monitors, calculators, typewriters, cash registers.\nThere are several useful visual features to tell there is 'computers' and not similar things in a photo:\tscreen or monitor\tkeyboard or input device\tcpu or box-shaped computer body\twires and cables", 289], "wii controller": ["Yes. 'Wii controller' has a tangible appearance and is a specific type of controller used for playing games on the Wii console.\nA few things that are visually similar to 'wii controller' but are not 'wii controller' are:\tPlayStation controller\tXbox controller\tPC controller\tJoystick\nThere are several useful visual features to tell there is 'wii controller' and not similar things in a photo:\tRectangular shape\tWhite or black color\tVery few buttons compared to other types of controllers\tInfrared sensor bar at the top of the controller\tWii logo on the front", 289], "garbage bin": ["Yes. 'Garbage bin' has a tangible appearance and is an object used for waste disposal.\nA few things that are visually similar to 'garbage bin' but are not 'garbage bin' are:\trecycling bin\tclothes hamper\tpaper shredder\ttrash bag\nThere are several useful visual features to tell there is 'garbage bin' and not similar things in a photo:\tlid for opening and closing\tthe words 'garbage' or 'trash' on it\ta plastic or metal exterior a shape that can hold a trash bag", 288], "leafy": ["Yes. 'Leafy' has a tangible appearance and is used to describe something that has or is covered in leaves.\nA few things that are visually similar to 'leafy' but are not 'leafy' are:\tfuzzy\tfurry\tshaggy\nThere are several useful visual features to tell there is 'leafy' and not similar things in a photo:\tcollection of leaves or having many leaves\trough or smooth texture\tgreen or other earth tone colors", 287], "magazines": ["Yes. 'Magazines' has a tangible appearance and is a type of printed publication.\nA few things that are visually similar to 'magazines' but are not 'magazines' are:\tbooks\tcatalogs\tbrochures\tnewspapers\t\nThere are several useful visual features to tell there is 'magazines' and not similar things in a photo:\tthin and flat\tprinted pages\tstaples or binding along the spine\tvarious articles and pictures\ton newsstands or in store shelves", 287], "lift": ["Yes. 'Lift' has a tangible appearance and is a mechanical device used for vertical transportation.\nA few things that are visually similar to 'lift' but are not 'lift' are:\tescalator\tstairs\tcrane\televator\nThere are several useful visual features to tell there is 'lift' and not similar things in a photo:\tvertical transportation device\twith doors\tmetal construction\tbuttons or a touch screen panel inside\tthe ability to move both up and down", 287], "pie": ["Yes. 'Pie' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pie' but are not 'pie' are:\tcake\ttart\tquiche\tpizza\nThere are several useful visual features to tell there is 'pie' and not similar things in a photo:\tcrust on the edges\tsliced or with a pie cutter\tusually served in a dish or pie pan\twith a filling (fruit, custard, savory, etc.)", 287], "billboard": ["Yes. 'Billboard' has a tangible appearance and is a type of advertising display.\nA few things that are visually similar to 'billboard' but are not 'billboard' are:\tsigns\tposters\tdecorations\tbuildings\nThere are several useful visual features to tell there is 'billboard' and not similar things in a photo:\tlarge size\tdisplaying advertisements or messages\tmounted on a pole or a wall\tunable to be missed by pedestrians or drivers", 286], "passenger": ["Yes. 'Passenger' has a tangible appearance and refers to a person traveling in a vehicle.\nA few things that are visually similar to 'passenger' but are not 'passenger' are:\tdriver\tpedestrian\tcyclist\ttraveler\tonlooker\nThere are several useful visual features to tell there is a 'passenger' and not similar things in a photo:\tsitting in a vehicle, such as a car, a plane, a train, or a bus\tlooking out of the window\tor wearing a seatbelt\tor holding a bag or luggage", 286], "toilet lid": ["Yes. 'Toilet lid' has a tangible appearance and is a part of a toilet.\nA few things that are visually similar to 'toilet lid' but are not 'toilet lid' are:\tfaucet handle\tpan lid\nThere are several useful visual features to tell there is 'toilet lid' and not similar things in a photo:\toval or round shape\thinged attachment to the toilet bowl\tsimilar color and material to the toilet bowl", 283], "bedspread": ["Yes. 'Bedspread' is a visually concrete concept and is a decorative covering for a bed.\nA few things that are visually similar to 'bedspread' but are not 'bedspread' are:\tduvet cover\tquilt\tblanket\ttapestry\nThere are several useful visual features to tell there is 'bedspread' and not similar things in a photo:\tcovering the entire surface of the bed\tmatching the room's decor\thaving a specific pattern or design", 283], "blonde woman": ["Yes. 'Blonde woman' has a tangible appearance and is a person with blonde hair.\nA few things that are visually similar to 'blonde woman' but are not 'blonde woman' are: people with other hair colors, such as brunettes or redheads; wigs or hair extensions; cartoon characters or illustrations of women with blonde hair.\nThere are several useful visual features to tell there is a 'blonde woman' and not similar things in a photo:\tblonde or light-colored hair\tfeminine features such as makeup or jewelry facial features such as eye or nose shape that are characteristic of a woman.", 282], "middle": ["No. 'Middle' is too vague or abstract to be distinguished in a photo.", 282], "butter": ["Yes. 'Butter' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'butter' but are not 'butter' are:\tmargarine\tshortening\tyogurt\tcream cheese\nThere are several useful visual features to tell there is 'butter' and not similar things in a photo:\tyellow or pale color\tsolid but spreadable texture\tglossy and smooth surface\ttranslucent appearance in thin slices or when melted", 281], "printer": ["Yes. 'Printer' has a tangible appearance and is an electronic device used for printing.\nA few things that are visually similar to 'printer' but are not 'printer' are:\tscanner\tfax machine\tphotocopy machine\t\nThere are several useful visual features to tell there is a 'printer' and not similar things in a photo:\tpaper trays\tprinter cartridge or toner cartridges\tdisplay screen\tprinter cable or USB cable for connecting to a computer.", 280], "noodles": ["Yes. 'Noodles' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'noodles' but are not 'noodles' are:\tspaghetti\tvermicelli\trice\tcapellini\nThere are several useful visual features to tell there is 'noodles' and not similar things in a photo:\tlong and thin shape\tsoft and flexible texture\tcooked in boiling water\tserved with sauce, soup or broth", 280], "cleats": ["Yes. 'Cleats' has a tangible appearance and are typically used for sports.\nA few things that are visually similar to 'cleats' but are not 'cleats' are:\tregular sneakers\thiking boots\tmountaineering boots\tspikes\nThere are several useful visual features to tell there are 'cleats' and not similar things in a photo:\tmetal studs or spikes on the sole\tspecific design for the sport\thigh ankle support\tmade of synthetic or leather materials", 279], "cracks": ["Yes. 'Cracks' has a tangible appearance and can be observed on surfaces or materials.\nA few things that are visually similar to 'cracks' but are not 'cracks' are:\tlines\tborders\tveins\nThere are several useful visual features to tell there is 'cracks' and not similar things in a photo:\tirregular patterns along surfaces\tor openings in surfaces\toften an indication of wear or damage in surfaces such as walls, floors, or pottery.", 279], "grass field": ["Yes, 'grass field' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'grass field' but are not 'grass field' are:\twheat field\tcorn field\tgolf course\tforest\tjungle\nThere are several useful visual features to tell there is 'grass field' and not similar things in a photo:\tprimary vegetation is tall grasses\tpossibly contains wildflowers or weeds\tgenerally larger than a lawn or yard\tflat or rolling terrain", 279], "marks": ["No. 'Marks' is too vague or abstract to be distinguished in a photo.", 279], "kettle": ["Yes. 'Kettle' has a tangible appearance and is a type of kitchen appliance used for heating water.\nA few things that are visually similar to 'kettle' but are not 'kettle' are:\tcoffee pot\tteapot\tpitcher\t\nThere are several useful visual features to tell there is 'kettle' and not similar things in a photo:\tdomed lid\twith a handle\ton a stove or a heating element\tsteam coming out of the spout\tmetal constriction or shiny surface", 278], "tissue": ["Yes. 'Tissue' has a tangible appearance and is a soft and thin paper-like material used for wiping.\nA few things that are visually similar to 'tissue' but are not 'tissue' are:\ttoilet paper\tpaper towels\tnapkins\thandkerchiefs\nThere are several useful visual features to tell there is 'tissue' and not similar things in a photo:\tthin and delicate material\twhite or pastel color\tsquare or rectangular shape\tobvious softness and flexibility in material.", 278], "mother": ["No. 'Mother' is too vague or abstract to be distinguished in a photo.", 278], "table lamp": ["Yes. 'Table lamp' has a tangible appearance and is an object used for providing light.\nA few things that are visually similar to 'table lamp' but are not 'table lamp' are:\tfloor lamp\tdesk lamp\tflashlight\tceiling light\nThere are several useful visual features to tell there is 'table lamp' and not similar things in a photo:\tsitting on a table or flat surface\tshade covering the light source\tcord or switch for turning the lamp on and off\tbase holding the light source in place", 277], "coffee maker": ["Yes. 'Coffee maker' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'coffee maker' but are not 'coffee maker' are:\ttea kettle\thot water dispenser\tespresso machine\tthermos\nThere are several useful visual features to tell there is 'coffee maker' and not similar things in a photo:\tfiltering mechanism or basket for ground coffee beans\twater tank or reservoir\ta hot plate or a heating element to keep the coffee warm\thandle and spout for pouring the coffee out\tdials or buttons to control the brewing process\talpha-numeric display for showing the brewing status or the time", 276], "dugout": ["Yes. 'Dugout' has a tangible appearance and is a type of shelter.\nA few things that are visually similar to 'dugout' but are not 'dugout' are:\tcave\ttunnel\tcellar\tbasement\tbunker\nThere are several useful visual features to tell there is 'dugout' and not similar things in a photo:\tshelter dug into the ground\tearthen walls and roof of the structure\tsmooth, rounded interior surface of the structure\tno visible entrance or exit\tfrom the outside, the structure may blend in with the surrounding terrain or appear naturally formed", 276], "lighthouse": ["Yes. 'Lighthouse' has a tangible appearance and is a tall structure by the shore to guide ships.\nA few things that are visually similar to 'lighthouse' but are not 'lighthouse' are:\twatchtower\tobservation tower\twind turbine\tfire tower\nThere are several useful visual features to tell there is 'lighthouse' and not similar things in a photo:\ttall cylinder or tower shape\twith a light source\tat the top of the tower\tpointing towards the sea, lake, or river\t striped color or pattern\ton the tower or adjacent buildings", 276], "ponytail": ["Yes. 'Ponytail' has a tangible appearance and is a hairstyle where the hair is gathered and tied at the back of the head.\nA few things that are visually similar to 'ponytail' but are not 'ponytail' are:\tman-bun\thigh-bun\tpigtails\tsingle braids\tknot hairstyle\nThere are several useful visual features to tell there is 'ponytail' and not similar things in a photo:\thair tied at the back of the head\thair is hanging down from the tie or the elastic band\tthe ponytail is usually in the middle or lower part of the head", 275], "cucumber": ["Yes. 'Cucumber' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'cucumber' but are not 'cucumber' are:\tzucchini\tsquash\tpickle\tgourd\nThere are several useful visual features to tell there is 'cucumber' and not similar things in a photo:\telongated shape\tgreen or yellow color\tsmooth surface\tridged exterior\ttapered ends\twhite seeds in the center when cut open ", 275], "sprinkles": ["Yes. 'Sprinkles' has a tangible appearance and is a type of topping for food.\nA few things that are visually similar to 'sprinkles' but are not 'sprinkles' are:\tsequins\tglitter\tsalt\tcrystals\tconfetti\nThere are several useful visual features to tell there is 'sprinkles' and not similar things in a photo:\tsmall and thin\tcylindrical or spherical vibrant colors or shades usually used as a topping for desserts or baked goods.", 275], "toys": ["Yes. 'Toys' has a tangible appearance and refers to playthings for children or even adults.\nA few things that are visually similar to 'toys' but are not 'toys' are:\tdecorative figurines\thousehold items\tpet toys\tsport equipment\nThere are several useful visual features to tell there is 'toys' and not similar things in a photo:\tmade of plastic, metal, wood, or fabric\tcolorful and playful in design\tpresent in a child's play area or in a toy store\tdesigned for playing, rather than functional use", 274], "bride": ["Yes. 'Bride' has a tangible appearance and refers to a woman on her wedding day.\nA few things that are visually similar to 'bride' but are not 'bride' are:\tmaid of honor\tguests in formal gowns\tmodels in bridal shoots\tactresses in wedding scenes\nThere are several useful visual features to tell there is 'bride' and not similar things in a photo:\twearing a white wedding gown\tveil or headpiece\tflowers or bouquet\tjewelry or accessories\tmake-up and styled hair\tgroom or wedding party members in the photo.", 274], "paper towels": ["Yes. 'Paper towels' has a tangible appearance and is a type of disposable towel made of paper.\nA few things that are visually similar to 'paper towels' but are not 'paper towels' are:\ttoilet paper\tnapkins\ttissues\tdishcloths\nThere are several useful visual features to tell there is 'paper towels' and not similar things in a photo:\trolls or sheets of white or brown paper\tthicker and more absorbent than regular tissues or napkins\tcommonly used for cleaning or wiping surfaces.", 273], "art": ["No. 'Art' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to 'art' but are not 'art' are: graffiti, random splashes of color, or unintentional designs (like patterns on a sidewalk). \n\nTo distinguish 'art' from these things in a photo, one might look for visual cues such as the intentionality, creativity, and skill of the work, as well as whether or not it is presented in a way that is meant to be appreciated or displayed. Art may also be associated with a certain level of historical, cultural, or personal significance that sets it apart from other visually similar things.", 273], "boulder": ["Yes. 'Boulder' has a tangible appearance and refers to a large rock.\nA few things that are visually similar to 'boulder' but are not 'boulder' are:\tpebble\tstone\tcliff\thill\nThere are several useful visual features to tell there is 'boulder' and not similar things in a photo:\tvery large\tsize greater than a person\tflat or rounded surface\toften found in natural, outdoor settings", 272], "rock wall": ["Yes. 'Rock wall' has a tangible appearance and refers to a wall made of natural rock.\nA few things that are visually similar to 'rock wall' but are not 'rock wall' are: buildings made of stonework, sculptures made of stone, concrete wall made to look like a rock.\nThere are several useful visual features to tell there is 'rock wall' and not similar things in a photo: natural rocks, stones compounding the wall, bumpy and uneven surface, appearance of rock strata, irregular shapes of the rocks making up the wall, possible climbing equipment or ropes attached to the wall.", 271], "office chair": ["Yes. 'Office chair' has a tangible appearance and is a type of chair specifically designed for use in an office.\nA few things that are visually similar to 'office chair' but are not 'office chair' are:\tdining chair\tarmchair\tclubs chair\t\nThere are several useful visual features to tell there is 'office chair' and not similar things in a photo:\tswivel base\tand adjustable height\tbackrest and seat with padding\tor mesh material for better breathability\twheels for maneuverability\toften has armrests\tfor comfort during long periods of sitting.", 271], "screen tv": ["Yes. 'Screen TV' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'screen tv' but are not 'screen tv' are:\tcomputer monitor\tprojector\tmovie screen\nThere are several useful visual features to tell there is 'screen tv' and not similar things in a photo:\trectangular shape\tsoundbar or speakers attached\tscreen displaying moving images or videos\tcontrol buttons or remote control visible.", 271], "paddle": ["Yes. 'Paddle' has a tangible appearance and is a tool used for propelling a boat or navigating water.\nA few things that are visually similar to 'paddle' but are not 'paddle' are:\toar\ttablet\twithout buttons or keyboard\nThere are several useful visual features to tell there is 'paddle' and not similar things in a photo:\tlong, narrow shape\twith handles or grips\ton or in close proximity to a body of water, such as a boat or a kayak.", 270], "bald man": ["Yes. 'Bald man' has a tangible appearance and is a physical trait of a person.\nA few things that are visually similar to 'bald man' but are not 'bald man' are:\tbald woman\tperson wearing a hat or a wig\temoticon with no hair\nThere are several useful visual features to tell there is a 'bald man' and not similar things in a photo:\tman's facial features\tgleaming scalp\tno hair or very little hair on the head", 270], "lock": ["Yes. 'Lock' has a tangible appearance and is a device used for securing something.\nA few things that are visually similar to 'lock' but are not 'lock' are:\thook\tclasp\tknob\tbolt\nThere are several useful visual features to tell there is 'lock' and not similar things in a photo:\tmetallic and rigid construction\tconsisting of a latch or a bolt\tthat engages with a counterpart fixed to the edge of the unit\twhen the key is turned, it operates a mechanism that keeps the bolt in place", 270], "laces": ["Yes. 'Laces' has a tangible appearance and refers to a part of footwear.\nA few things that are visually similar to 'laces' but are not 'laces' are:\tstitching\tribbons\tstraps\tbelts\nThere are several useful visual features to tell there are 'laces' and not similar things in a photo:\tlong and thin\tcrossing over each other\tpassing through eyelets\ton shoes or boots\tmade of a material like cotton or nylon", 269], "glass door": ["Yes. 'Glass door' has a tangible appearance and is a specific type of door.\nA few things that are visually similar to 'glass door' but are not 'glass door' are:\tmetal door\twooden door\tscreen door\nThere are several useful visual features to tell there is 'glass door' and not similar things in a photo:\tglass panel or panels\tinvisible hinges\tframeless or minimal frame construction\texterior hardware with a lever or knob for opening and closing\tthe transparency of the glass", 269], "block": ["Yes. 'Block' has a tangible appearance and is a three-dimensional object.\nA few things that are visually similar to 'block' but are not 'block' are:\tbrick\tcube\ttile\tbox\tdice\nThere are several useful visual features to tell there is 'block' and not similar things in a photo:\trectangular or square shape\tequal dimensions on all sides\tflat surfaces on the top and bottom of the object\tcan be made of various materials, such as wood or plastic.", 269], "ropes": ["Yes. 'Ropes' has a tangible appearance and refers to a type of cordage made from fibers, yarns, or strands twisted together.\nA few things that are visually similar to 'ropes' but are not 'ropes' are:\tcables\thoses\tlicorice\tstrands of hair\nThere are several useful visual features to tell there is 'ropes' and not similar things in a photo:\tfibrous construction\twith knots or loops\tmostly brown, tan or grey\tcolorful (in some cases)\tsometimes frayed or damaged in certain areas.", 269], "phones": ["Yes. 'Phones' has a tangible appearance and is a type of electronic device used for communication.\nA few things that are visually similar to 'phones' but are not 'phones' are:\tkeyboards\tcameras\ttablets\tpagers\tmusic players\nThere are several useful visual features to tell there is 'phone' and not similar things in a photo:\tscreen\tdisplay\tkeypad\tor touchscreen\tspeakers/microphone\tfor apps\tand internet connectivity\tfor making calls or sending messages.", 269], "antenna": ["Yes. 'Antenna' has a tangible appearance and is a type of device used for transmitting or receiving electromagnetic signals.\nA few things that are visually similar to 'antenna' but are not 'antenna' are:\tflagpole\tlamppost\texclamation mark\nThere are several useful visual features to tell there is 'antenna' and not similar things in a photo:\tthin and elongated structure\tmounted on a building or a tower\twire or rods sticking out from the top\tdirectionality or aim", 269], "harness": ["Yes. 'Harness' has a tangible appearance and is a device used to secure or control something or someone.\nA few things that are visually similar to 'harness' but are not 'harness' are:\tbelts\tstraps\tleashes\nThere are several useful visual features to tell there is 'harness' and not similar things in a photo:\tmade of leather or nylon\thas buckles or clasps\tmultiple straps or loops to secure to a person or an object\tdesigned to distribute force or weight evenly", 268], "tattoo": ["Yes. 'Tattoo' has a tangible appearance and is a form of body art.\nA few things that are visually similar to 'tattoo' but are not 'tattoo' are:\tpainting\tmakeup\tbruise\thenna\nThere are several useful visual features to tell there is 'tattoo' and not similar things in a photo:\tpermanent or semi-permanent ink\ton the skin\tor on a fake skin patch\tpatterns, pictures, or words", 268], "logs": ["Yes. 'Logs' has a tangible appearance and is a type of wood.\n\nA few things that are visually similar to 'logs' but are not 'logs' are:\n- Sticks\n- Branches\n- Lumber\n\nThere are several useful visual features to distinguish 'logs' from these similar things in a photo:\n- Cut ends\n- Smooth texture\n- Uniform size and shape\n- Wood grain pattern visible on the surface.", 268], "pieces furniture": ["Yes. 'Pieces of furniture' has a tangible appearance.\nA few things that are visually similar to 'pieces of furniture' but are not 'pieces of furniture' are:\twood planks\tpallets\tcrates\tblocks\nThere are several useful visual features to tell there are 'pieces of furniture' and not similar things in a photo:\tdifferent shapes and sizes\tspecific purpose or function, such as a chair or table\tmade of various materials like wood or metal\tcan be arranged and used together as a cohesive set\u6216", 267], "paper bag": ["Yes. 'Paper bag' has a tangible appearance and is a type of bag made of paper.\nA few things that are visually similar to 'paper bag' but are not 'paper bag' are:\tplastic bag\ttote bag\tbackpack\tenvelope\nThere are several useful visual features to tell there is 'paper bag' and not similar things in a photo:\tmade of paper\tbrown or white color\trectangular shape\twith handles or a fold-over top\topen at the top", 267], "eyebrow": ["Yes. 'Eyebrow' has a tangible appearance and is a part of the human face.\nA few things that are visually similar to 'eyebrow' but are not 'eyebrow' are:\thair\tline\tworm\tinsect\nThere are several useful visual features to tell there are 'eyebrows' and not similar things in a photo:\tlocated above the eyes\thairy and slightly curved\tfollowing the shape of the eye sockets\tdarker than the skin tone around them.", 267], "adult": ["No. 'Adult' is too vague or abstract to be distinguished in a photo.", 266], "smile": ["Yes. 'Smile' has a tangible appearance and is facial expression.\nA few things that are visually similar to 'smile' but are not 'smile' are:\tGrimace\tpout\tfrown\nThere are several useful visual features to tell there is 'smile' and not similar things in a photo:\tup-turned mouth\tcrinkles around the eyes\texpression of happiness or joy", 266], "wool": ["Yes. 'Wool' has a tangible appearance and is a type of fabric.\nA few things that are visually similar to 'wool' but are not 'wool' are:\tfur\tcotton\tlinen\tsilk\nThere are several useful visual features to tell there is 'wool' and not similar things in a photo:\tthick and fluffy texture\tcrimped or curly fibers\ttypically cream, white, or black color\tslightly shiny appearance when in good condition\tin a farm or being processed in a textile factory", 265], "cookie": ["Yes. 'Cookie' has a tangible appearance and is a type of baked food.\nA few things that are visually similar to 'cookie' but are not 'cookie' are:\tcake\tmuffin\tbread\tpie\tpastry\nThere are several useful visual features to tell there is 'cookie' and not similar things in a photo:\tflat and round or rectangular in shape\tbaked or cooked appearance\tgolden or brown color with a crispy texture often with small craters on the surface\tmay contain chocolate chips, nuts, or bits of candy as toppings", 264], "panda": ["Yes. 'Panda' has a tangible appearance and is a type of bear.\nA few things that are visually similar to 'panda' but are not 'panda' are:\traccoon\tkoala\tstuffed animals\nThere are several useful visual features to tell there is 'panda' and not similar things in a photo:\tdistinctive black and white fur\tcolor blocks around the eyes\trounded ears\tflat face and nose\tchubby body\tclawed paws", 263], "ducks": ["Yes. 'Ducks' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'ducks' but are not 'ducks' are:\tgeese\tswans\tpelicans\tseagulls\nThere are several useful visual features to tell there is 'ducks' and not similar things in a photo:\tbeak-like bills\twebbed feet\twaterproof feathers\tfound near bodies of water and marshlands\tshort neck and stout body\tflattened broad bill.", 263], "artwork": ["Yes. 'Artwork' has a tangible appearance and refers to creative works of art.\nA few things that are visually similar to 'artwork' but are not 'artwork' are:\tphotographs\tdecorations\tposters\tadvertisements\tcrafts\nThere are several useful visual features to tell there is 'artwork' and not similar things in a photo:\tunique and original imagery\tskillful use of color, texture, and composition\thand-drawn or painted by an artist\tor an original digital creation\tframe or display in a museum or gallery context", 262], "orange cat": ["Yes. 'Orange cat' has a tangible appearance and is a type of feline.\nA few things that are visually similar to 'orange cat' but are not 'orange cat' are:\torange tabby kittens\torange stuffed animals\torange raccoons\nThere are several useful visual features to tell there is 'orange cat' and not similar things in a photo:\torange fur\tcolor is dominantly orange, with stripes on body\tand/or legs\tyellow or green eyes\twhiskers\tpointed ears\tfurry tail", 262], "eye glasses": ["Yes. 'Eye glasses' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'eye glasses' but are not 'eye glasses' are:\tsunglasses\tbinoculars\tmonocles\tsafety goggles\nThere are several useful visual features to tell there is 'eye glasses' and not similar things in a photo:\ttwo lenses attached to a frame\tworn on the face, specifically on the nose and ears\tlenses can be transparent, translucent, or tinted\tframe can be made of various materials (plastic, metal, wood, etc.)", 261], "pen": ["Yes. 'Pen' has a tangible appearance and is a type of writing instrument.\nA few things that are visually similar to 'pen' but are not 'pen' are:\tpencil\tmarker\tcrayon\thighlighter\nThere are several useful visual features to tell there is 'pen' and not similar things in a photo:\tthin cylindrical shape\twith a point at one end and an ink reservoir\tat the other end\tthe ability to write in ink", 261], "tail feathers": ["Yes. 'Tail feathers' have a tangible appearance and are a part of a bird's anatomy.\nA few things that are visually similar to 'tail feathers' but are not 'tail feathers' are:\tleaves\thair\tfish scales\nThere are several useful visual features to tell there is 'tail feathers' and not similar things in a photo:\tthin and elongated appearance\ttufted appearance\tpoints or spikes at the ends\tof varying lengths and colors\tfound on the lower back of a bird", 260], "trouser": ["Yes. 'Trouser' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'trouser' but are not 'trouser' are:\tleggings\tjeans\tskirt\tpantyhose\nThere are several useful visual features to tell there is 'trouser' and not similar things in a photo:\ttwo legs\tsewn crotch\tarea\tpart that covers the hips and legs\tfastened with a zipper or buttons\tpockets may be present.", 259], "corn": ["Yes. 'Corn' has a tangible appearance and is a type of crop.\nA few things that are visually similar to 'corn' but are not 'corn' are:\twheat\tbarley\trye\toats\nThere are several useful visual features to tell there is 'corn' and not similar things in a photo:\tlarge cob with kernels in rows\ttall stalks\twith or without leaves\tyellow, white or black kernels\tjagged, pointed leaves.", 259], "cabin": ["Yes. 'Cabin' has a tangible appearance and is a small, typically wooden house in a rural area.\nA few things that are visually similar to 'cabin' but are not 'cabin' are:\tshed\touthouse\tlarge tents\ttrailers\nThere are several useful visual features to tell there is 'cabin' and not similar things in a photo:\tlog or wooden construction\topen porch or balcony\troof protruding over the porch or balcony\twintry surroundings\tchimney or smokestack", 259], "columns": ["Yes. 'Columns' has a tangible appearance and refers to a type of architectural element.\nA few things that are visually similar to 'columns' but are not 'columns' are:\tpillars\tpoles\ttowers\tchimneys\nThere are several useful visual features to tell there are 'columns' and not similar things in a photo:\ttall and vertical supporting structure\tbase, shaft, and capital (head)\tdifferent styles and orders (e.g. Doric, Ionic, Corinthian)\tcylindrical or rectangular shape\tcolumns are always found in a series, whereas pillars, towers, poles, and chimneys may stand alone.", 258], "blade": ["Yes. 'Blade' has a tangible appearance and is a sharp metal tool or weapon.\nA few things that are visually similar to 'blade' but are not 'blade' are:\tknife\tsaw\tscissors\taxe\t\nThere are several useful visual features to tell there is 'blade' and not similar things in a photo:\tthin and sharp edge\tsymmetrical shape\tsimilar thickness throughout\tthe ability to cut through solid objects", 258], "school bus": ["Yes, 'school bus' is a visually concrete concept and it is a type of bus.\nA few things that are visually similar to 'school bus' but are not 'school bus' are:\tcity bus\tshuttle bus\tminivan\tambulance\nThere are several useful visual features to distinguish 'school bus' from these similar things in a photo:\t\n- Large, rectangular shape with distinct front and rear sections\n- Yellow color with black stripes along the sides\n- Flashing red lights and stop sign arm on the sides and rear for student safety\n- Lettering on the side indicating the school district and/or specific school\n- Large windows for visibility and natural light inside the bus", 258], "game controller": ["Yes. 'Game controller' has a tangible appearance and is a kind of input device.\nA few things that are visually similar to 'game controller' but are not 'game controller' are:\ttv remote\tcontrol panel\tmouse\tkeyboard\nThere are several useful visual features to tell there is 'game controller' and not similar things in a photo:\thandheld device\tbuttons or joysticks\tfor gaming purposes", 257], "metal post": ["Yes. 'Metal post' has a tangible appearance and is a type of support structure.\nA few things that are visually similar to 'metal post' but are not 'metal post' are:\twooden post\tpillar\tcolumn\tsignpost\nThere are several useful visual features to tell there is 'metal post' and not similar things in a photo:\tmade of metal\tstraight and cylindrical shape\tno visible grain or knots in the material\tmay have bolts or screws visible for attaching additional structures", 257], "awning": ["Yes. 'Awning' has a tangible appearance and is a kind of shelter.\nA few things that are visually similar to 'awning' but are not 'awning' are:\tcanopy\tparasol\tsunshade\ttent\tshade sail\nThere are several useful visual features to tell there is 'awning' and not similar things in a photo:\toverhanging cover\tattached to a building or storefront\tbold solid color or stripes\tusually made of fabric or canvas\thangs over a window, door or outdoor area\t\n", 257], "blond hair": ["Yes. 'Blond hair' has a tangible appearance and refers to a hair color.\nA few things that are visually similar to 'blond hair' but are not 'blond hair' are:\twhite hair\tlight brown hair\nThere are several useful visual features to tell there is 'blond hair' and not similar things in a photo:\tlight yellow or gold hair color\tthe hair is not white, silver, or light brown.", 256], "day time picture": ["No. 'Day time picture' is too vague or abstract to be distinguished in a photo.", 256], "side table": ["Yes. 'Side table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'side table' but are not 'side table' are:\tcoffee table\tnightstand\tdresser\tbench\nThere are several useful visual features to distinguish 'side table' from the listed similar things in a photo:\tsmall in size\tplaced next to a sofa or bed\tor between two armchairs\tcan have shelves or drawers for storage\tmay have a lamp or decor on top of it.", 256], "jet engine": ["Yes. 'Jet engine' has a tangible appearance and is a type of propulsion mechanism for aircraft.\nA few things that are visually similar to 'jet engine' but are not 'jet engine' are:\tturbine engine\trocket engine\tgas turbine\tindustrial fan\nThere are several useful visual features to tell there is 'jet engine' and not similar things in a photo:\tcircular or cylindrical shape\twith fan blades or turbine blades\twith exhaust nozzle or tailpipe\tmetallic or shiny appearance\tin an aircraft or on a runway", 256], "stars": ["Yes. 'Stars' has a tangible appearance and is a celestial object.\nA few things that are visually similar to 'stars' but are not 'stars' are:\tLED lights\tbright dots in a picture\tsnowflakes\torbs or circles\nThere are several useful visual features to tell there is 'stars' and not similar things in a photo:\tsmall and twinkling\tin the night sky\twhite or yellow light\tpointy shapes (like an asterisk)\tinconstancy in their intensity or brightness", 256], "womans": ["No. 'Womans' is not a grammatically correct or meaningful term. It should either be \"woman\" in the singular form or \"women\" in the plural form.", 256], "hardwood floor": ["Yes. 'Hardwood floor' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'hardwood floor' but are not 'hardwood floor' are: laminate flooring, vinyl flooring, tile flooring, carpet flooring, concrete flooring \nThere are several useful visual features to tell there is 'hardwood floor' and not similar things in a photo: natural wood grain pattern, varying shades of brown, individual planks fitted together to form a larger surface", 255], "butter knife": ["Yes. 'Butter knife' has a tangible appearance and is a type of knife.\nA few things that are visually similar to 'butter knife' but are not 'butter knife' are:\ttable knife\tspoon\tfork\nThere are several useful visual features to tell there is 'butter knife' and not similar things in a photo:\tshort and blunt blade with a rounded tip\tno serrated edge - blade is smooth\tusually has a flat edge on one side and a slightly curved edge on the other side", 255], "front wheels": ["Yes. 'Front wheels' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'front wheels' but are not 'front wheels' are:\tbike handlebars\troller skate wheels\tluggage wheels\tchair wheels\nThere are several useful visual features to tell there is 'front wheels' and not similar things in a photo:\tpart of a larger vehicle, such as a car or motorcycle\thub, tire, and spokes in a circular shape\tpositioned at the front of the vehicle, usually near the engine or handlebars", 255], "stump": ["Yes. 'Stump' has a tangible appearance and is a part of a tree trunk left after it has been cut down.\nA few things that are visually similar to 'stump' but are not 'stump' are:\trock\tboulder\tmushroom\tstone\t\nThere are several useful visual features to tell there is 'stump' and not similar things in a photo:\tusually a tree ring at the top\tor split wood marks on the surface\theight and width proportions of a tree trunk\tno visible signs of a natural formation of a rock or a boulder", 255], "front windows": ["Yes. 'Front windows' has a tangible appearance and typically refers to the windows on the front of a house or building.\nA few things that are visually similar to 'front windows' but are not 'front windows' are:\tsidelight windows\tskylight windows\tsunroof car windows\tshowcase windows\nThere are several useful visual features to tell there are 'front windows' and not similar things in a photo:\tpositioned at the front of the building\tratio or proportion to the rest of the building's facade\tsquare or rectangular shape\twithin frames or trim\tthat can be opened and closed\tfrom which people can look out onto the street or sidewalk.", 255], "utility pole": ["Yes. 'Utility pole' has a tangible appearance and is a type of pole used for power or communication lines.\nA few things that are visually similar to 'utility pole' but are not 'utility pole' are:\ttree\ttraffic sign\tstreet light\tflagpole\nThere are several useful visual features to tell there is 'utility pole' and not similar things in a photo:\tpole-like shape\tseveral cables or wires attached to the top\tsometimes crossbars or transformers attached to the pole.", 254], "bare trees": ["Yes. 'Bare trees' has a tangible appearance and refers to trees without leaves or foliage.\nA few things that are visually similar to 'bare trees' but are not 'bare trees' are:\tdead trees\tflashlight beams\tfallen branches\tsilhouettes\nThere are several useful visual features to tell there are 'bare trees' and not similar things in a photo:\tno leaves or foliage\tbare branches visible\ttrunks visible against the sky or background\tno greenery visible in the surrounding landscape", 254], "city street": ["Yes. 'City street' has a tangible appearance and is a type of road.\nA few things that are visually similar to 'city street' but are not 'city street' are:\tcountry road\tparking lot\tsidewalk\tcorridor\nThere are several useful visual features to tell there is 'city street' and not similar things in a photo:\tmultiple lanes\tfor pedestrians and vehicles\tbuildings and shops nearby\tsidewalks along the road\tsigns and traffic signals", 254], "blue wall": ["Yes. 'Blue wall' has a tangible appearance and is a type of wall color.\nA few things that are visually similar to 'blue wall' but are not 'blue wall' are:\tblue sky\tblue ocean\tblue car\tblue clothes\nThere are several useful visual features to tell there is 'blue wall' and not similar things in a photo:\tman-made surface with a flat or textured finish\tpainted a shade of blue\tvariations in tone or hue due to lighting or shadow", 254], "arrows": ["Yes. 'Arrows' has a tangible appearance and is a kind of directional symbol.\nA few things that are visually similar to 'arrows' but are not 'arrows' are:\tlines\tpipes\tsticks\tflags\nThere are several useful visual features to tell there is 'arrows' and not similar things in a photo:\tpointed end\tcurved shape\tdirectional indications (up, down, left, right)", 254], "leather": ["Yes. 'Leather' has a tangible appearance and is a material made from animal hide.\nA few things that are visually similar to 'leather' but are not 'leather' are:\tpleather\tvinyl\tfaux leather\nThere are several useful visual features to tell there is 'leather' and not similar things in a photo: \tglossy or matte surface\tfrom animal hides (cow, sheep, etc.)\tpores and creases of the animal hide grain pattern\tsupple and durable texture", 253], "dress shirt": ["Yes. 'Dress shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'dress shirt' but are not 'dress shirt' are:\tt-shirt\tblouse\tpolo shirt\tsweater\nThere are several useful visual features to tell there is 'dress shirt' and not similar things in a photo:\tcollared shirt\tbuttons down the front\tlong sleeves\tusually made of dressier fabric such as cotton or silk", 253], "blue stripe": ["Yes. 'Blue stripe' has a tangible appearance and is a specific color and pattern.\nA few things that are visually similar to 'blue stripe' but are not 'blue stripe' are:\tblue ribbon\tbluetit bird's feather\tstripey shirt\tblue paint\nThere are several useful visual features to tell there is 'blue stripe' and not similar things in a photo:\tline or lines of blue color\tspecific width and spacing\tof uniform thickness\tand patterns\trepeating across a surface or object", 253], "round table": ["Yes. 'Round table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'round table' but are not 'round table' are:\tsquare table\trectangular table\toval table\nThere are several useful visual features to distinguish 'round table' from the listed similar things in a photo:\t\n- circular or round shape\n- absence of sharp corners or edges\n- centerpiece in the middle of the table (if visible)", 253], "streetlight": ["Yes. 'Streetlight' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'streetlight' but are not 'streetlight' are:\tlamp post\ttall building antenna\tcell tower\nThere are several useful visual features to tell there is 'streetlight' and not similar things in a photo:\tvertical pole with a light fixture on top\tillumination of the surrounding area\tconical or spherical shape\tlight directed downwards\ttall enough to light up the street\tor sidewalk", 253], "place": ["No. 'Place' is too vague or abstract to be distinguished in a photo.", 253], "boarder": ["No. 'Boarder' is too vague or abstract to be distinguished in a photo.\nHowever, if you meant \"border\" instead of \"boarder\":\nYes. 'Border' has a tangible appearance and is a visible boundary.\nA few things that are visually similar to 'border' but are not 'border' are:\tshadows\tedges\thorizons\toutlines\nThere are several useful visual features to tell there is 'border' and not similar things in a photo:\ta visible line separating two areas of different colors, textures, or materials\ta distinct contrast between the two areas with different features", 253], "dirt road": ["Yes. 'Dirt road' has a tangible appearance and refers to a type of road surface.\nA few things that are visually similar to 'dirt road' but are not 'dirt road' are:\tgravel road\tpath or trail\tasphalt road\tbeach\tsidewalk\nThere are several useful visual features to tell there is 'dirt road' and not similar things in a photo:\t\n\tmade of compacted dirt, mud, or sand\tsurrounded by dirt or vegetation\tmay have ruts, bumps or potholes", 253], "nostril": ["Yes. 'Nostril' has a tangible appearance and is a part of human or animal anatomy.\nA few things that are visually similar to 'nostril' but are not 'nostril' are:\tfreckles\tpores\tdimples\tspots\nThere are several useful visual features to tell there is 'nostril' and not similar things in a photo:\toval or circular shape\tdifferent color or texture from surrounding skin or fur\tlocation on the nose or snout", 252], "bow tie": ["Yes. 'Bow tie' has a tangible appearance and is a type of neckwear.\nA few things that are visually similar to 'bow tie' but are not 'bow tie' are:\ttie\tscarf\tribbon\nThere are several useful visual features to tell there is 'bow tie' and not similar things in a photo:\ttwo loops knotted around a narrow strip of fabric\tworn around the neckline in a symmetrical way\tcommonly made of silk, cotton or polyester\thas a distinct butterfly or batwing shape\thas two tails hanging down in equal length with pointed or rounded ends.", 251], "opening": ["No. 'Opening' is too vague or abstract to be distinguished in a photo.", 251], "debris": ["Yes. 'Debris' has a tangible appearance and is a type of scattered material leftover from something.\nA few things that are visually similar to 'debris' but are not 'debris' are:\trocks\tleaves\tsnow\nThere are several useful visual features to tell there is 'debris' and not similar things in a photo:\tscattered and random arrangement\tmultiple types or colors\tbroken or unrecognizable pieces of something else.", 250], "ski tracks": ["Yes. 'Ski tracks' has a tangible appearance and refers to the marks left in the snow by skis.\nA few things that are visually similar to 'ski tracks' but are not 'ski tracks' are:\tanimal tracks\tsnowmobile tracks\thiking tracks\t\nThere are several useful visual features to tell there are 'ski tracks' and not similar things in a photo:\ttypically parallel lines\tcrisp and clean edges\tregularly spaced intervals\tcan be seen leading down a slope or mountain\tresemble train tracks", 250], "designs": ["No. 'Designs' are too abstract and can refer to anything visual, including physical and digital creations, and can vary widely in appearance and style, so it is not a visually concrete concept. \n\nIt is not applicable to list things that are similar to 'designs' but not 'designs' since 'designs' is a generic term encompassing a wide range of visual representations.\n\nInstead, some useful visual features for identifying a specific design in a photo could include: the use of specific colors, the repetition of a particular shape or symbol, the presence of unique patterns or textures, or the arrangement of elements in a particular composition or layout. However, these features will depend on the specific design and cannot be generalized for all designs.", 249], "enclosure": ["Yes. 'Enclosure' has a tangible appearance and refers to a physical area or structure that is designed to keep something inside or outside of it.\nA few things that are visually similar to 'enclosure' but are not 'enclosure' are:\tfence\twall\tcage\tnetting\nThere are several useful visual features to tell there is 'enclosure' and not similar things in a photo:\tclear boundaries or borders\tphysical barriers that prevent entry or exit\tdoors or gates\tthat are designed to keep something inside or outside of it.", 249], "pickup truck": ["Yes. 'Pickup truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'pickup truck' but are not 'pickup truck' are:\tsuv\tvan\ttrailer\nThere are several useful visual features to tell there is 'pickup truck' and not similar things in a photo:\ttwo-axle vehicle\twith an open cargo area at the rear\tback side seats enclosed in a cabin area\tseparate area for hauling cargo and passengers", 248], "silverware": ["Yes. 'Silverware' has a tangible appearance and refers to utensils used for dining and serving food.\nA few things that are visually similar to 'silverware' but are not 'silverware' are:\tregular cutlery\tplastic utensils\ttools\nThere are several useful visual features to tell there is 'silverware' and not similar things in a photo:\tmetal material\tpolished or shiny appearance\telegant design, often with ornate patterns and details\tvarious utensils for specific purposes (e.g. fork, knife, spoon)", 247], "tunnel": ["Yes. 'Tunnel' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'tunnel' but are not 'tunnel' are:\tbridge\tcave\tditch\tpipe\nThere are several useful visual features to tell there is 'tunnel' and not similar things in a photo:\tnarrow and enclosed passage\tusually made of brick or stone\tdark interior with some sources of light\televated or excavated passage through a hill or mountain", 247], "daytime sky": ["Yes. 'Daytime sky' has a tangible appearance and refers to the atmospheric conditions and the celestial objects visible during the day.\nA few things that are visually similar to 'daytime sky' but are not 'daytime sky' are:\twater surface\tblue painted walls\tfog\tsmoke\nThere are several useful visual features to tell there is 'daytime sky' and not similar things in a photo:\tblue sky\tsun\tclouds\tbirds or airplanes", 247], "coats": ["Yes. 'Coats' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'coats' but are not 'coats' are:\tjackets\tshirts\thoodies\tponchos\tcardigans\nThere are several useful visual features to tell there is 'coats' and not similar things in a photo:\tlong or short outerwear usually with sleeves\tfor colder weather or fashion purposes\tvarious colors, materials and patterns\tzippers, buttons or belts to fasten", 246], "toast": ["Yes. 'Toast' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'toast' but are not 'toast' are: bread, crackers, biscuits, waffles\nThere are several useful visual features to tell there is 'toast' and not similar things in a photo: crispy surface, toasty brown color, hot and steamy from toasting, potentially with visible grill marks or a toaster in the photo.", 246], "bare tree": ["Yes. 'Bare tree' has a tangible appearance and is a type of tree without leaves or foliage.\nA few things that are visually similar to 'bare tree' but are not 'bare tree' are:\ttree with leaves\tbush\tfence\nThere are several useful visual features to tell there is 'bare tree' and not similar things in a photo:\tno leaves or foliage\tbare branches with twigs and buds\ttrunk and roots visible\tno signs of growth\tor flowering", 246], "living room": ["Yes. 'Living room' has a tangible appearance and is a common space in a house.\nA few things that are visually similar to 'living room' but are not 'living room' are:\tlibrary\tbedroom\tkitchen\tdining room\nThere are several useful visual features to tell there is 'living room' and not similar things in a photo:\tspace for seating furniture, such as sofas or armchairs\ta coffee table or an end table\trugs or carpets\ton the first floor or level of a house\ta TV or a media center", 246], "canoe": ["Yes. 'Canoe' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'canoe' but are not 'canoe' are:\tkayak\trowboat\traft\tpaddleboat\nThere are several useful visual features to tell there is 'canoe' and not similar things in a photo:\tnarrow and pointed at both ends\topen-topped and usually made from wood or fiberglass\tseats one or more people\trowed with a paddle or paddles, not oars.", 245], "shutters": ["Yes. 'Shutters' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'shutters' but are not 'shutters' are:\tblinds\tcurtains\tscreens\tawnings\nThere are several useful visual features to tell there is 'shutters' and not similar things in a photo:\trectangular or square-shaped\tattached to the window's exterior\thinged to open or close\tconsist of horizontal or vertical slats\tornamental features, such as cutouts or louvers.", 245], "spray": ["Yes. 'Spray' has a tangible appearance and refers to the dispersal of liquid or particles in a misty form.\nA few things that are visually similar to 'spray' but are not 'spray' are:\tfog\tsmoke\train mist\t\nThere are several useful visual features to tell there is 'spray' and not similar things in a photo:\taerosol can or spray bottle\tsource of liquid particles such as a fountain or wave\tmultiple fine droplets visible in the air misty appearance", 245], "bleachers": ["Yes. 'Bleachers' has a tangible appearance and is a type of seating usually found in stadiums or sports arenas.\nA few things that are visually similar to 'bleachers' but are not 'bleachers' are:\tstairs\tatop a building\torchestral seating\nThere are several useful visual features to tell there is 'bleachers' and not similar things in a photo:\tlong rows of elevated seats, usually made of metal or wood\tarranged in tiers or steps\tto provide better view of the event happening in front of them", 244], "cloudy blue sky": ["Yes. 'Cloudy blue sky' has a tangible appearance and is a type of atmospheric condition.\nA few things that are visually similar to 'cloudy blue sky' but are not 'cloudy blue sky' are:\tclear blue sky\tsunset\tsunrise\nThere are several useful visual features to tell there is 'cloudy blue sky' and not similar things in a photo:\tblue hue with gray or white clouds covering some or most of the sky\tthick or thin clouds covering some or most of the sky\tno rain or lightning visible", 243], "blue hat": ["Yes. 'Blue hat' has a tangible appearance and is a head covering of a specific color.\nA few things that are visually similar to 'blue hat' but are not 'blue hat' are:\tpurple hat\tsnorkeling cap\tbaseball cap\twoolen cap\nThere are several useful visual features to tell there is 'blue hat' and not similar things in a photo:\tfull cap covering the head\tblue color\tno logos, text, or designs on the hat.", 243], "racquet": ["Yes. 'Racquet' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'racquet' but are not 'racquet' are:\tbat\tpaddle\tgolf club\thockey stick\nThere are several useful visual features to tell there is 'racquet' and not similar things in a photo:\toval or round head\twith strings or netting\tthin handle, usually made of wood or plastic\tused for games like tennis, badminton or squash.", 243], "clothing items": ["Yes. 'Clothing items' has a tangible appearance and refers to various garments.\nA few things that are visually similar to 'clothing items' but are not 'clothing items' are:\ttowels\trugs\tcurtains\ttablecloths\nThere are several useful visual features to tell there are 'clothing items' and not similar things in a photo:\tfabric\tmade to fit the body\tworn on the body by a person\tzippers, buttons, pockets, or other fasteners\tdesigns or patterns specific to clothing (e.g., collars, sleeves, hems)", 243], "dirty": ["Yes. 'Dirty' has a tangible appearance and refers to the presence of dirt or grime on a surface.\nA few things that are visually similar to 'dirty' but are not 'dirty' are:\tshadow\tmessy\ttexture\tdistressed\n\nThere are several useful visual features to tell there is 'dirty' and not similar things in a photo:\tbrown or gray discoloration\tspeckles or streaks on a surface\ttextured or roughened appearance\tunwanted materials or substances on a surface (such as dust, mud, grease or stains)", 243], "wooden bench": ["Yes. 'Wooden bench' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden bench' but are not 'wooden bench' are:\tchair\tswing\tsofa\ttable\nThere are several useful visual features to tell there is 'wooden bench' and not similar things in a photo:\tlong, flat sitting surface\tmade of wood or wood-like material\twith or without backrest and armrests\tpair of legs or a base for support", 243], "chandelier": ["Yes. 'Chandelier' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'chandelier' but are not 'chandelier' are:\tpendant light\tcandle holder\tanimatronic fixture\t\nThere are several useful visual features to tell there is 'chandelier' and not similar things in a photo:\tmultiple arms or branches\thanging crystals or glass pieces\telaborate or ornate design\tfixed to the ceiling", 242], "cans": ["Yes. 'Cans' has a tangible appearance and is a type of container generally made of metal or aluminium.\nA few things that are visually similar to 'cans' but are not 'cans' are:\tcontainers\tbottles\tcartons\tbags\nThere are several useful visual features to tell there is 'cans' and not similar things in a photo:\tcylindrical shape\tmade of metal or aluminium\tridged top and bottom\tusually with a pull-tab or pop-top lid", 242], "blue shorts": ["Yes. 'Blue shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blue shorts' but are not 'blue shorts' are:\tblue pants\tblue skirt\tblue swim trunks\nThere are several useful visual features to tell there are 'blue shorts' and not similar things in a photo:\tshort in length\thave leg openings\tmade of fabric\tor shorts have buttons or zippers\ttop waistband\tis usually elastic.", 241], "home": ["No. 'Home' is too vague or abstract to be distinguished in a photo.", 241], "cookies": ["Yes. 'Cookies' has a tangible appearance and is a kind of baked food.\nA few things that are visually similar to 'cookies' but are not 'cookies' are:\tcrackers\tbiscuits\tcandy\tbread\nThere are several useful visual features to tell there are 'cookies' and not similar things in a photo:\tcircular or square-shaped\tbrown or golden color\tvariety of sizes and shapes\tchocolate chips or other added ingredients\ttexture and crumbliness based on the type of cookie", 241], "toilet brush": ["Yes. 'Toilet brush' has a tangible appearance and is a cleaning tool.\nA few things that are visually similar to 'toilet brush' but are not 'toilet brush' are:\thairbrush\tpaintbrush\tvegetable brush\tbroom\nThere are several useful visual features to tell there is 'toilet brush' and not similar things in a photo:\tlong handle\twith bristles or fibers at the end\tdesigned to clean toilets or bathroom surfaces", 241], "beer bottle": ["Yes, 'beer bottle' has a visually concrete concept.\nA few things that are visually similar to 'beer bottle' but are not 'beer bottle' are:\twine bottle\tjuice bottle\tsoda bottle\twater bottle\nSome useful visual features for distinguishing 'beer bottle' from similar things in a photo could be:\ta stubby and rounded shape\twith a long and narrow neck and thin mouth\ta label containing the brand name and product informations\tcontaining beer or having some foam coming out the top when opened", 241], "flower pot": ["Yes. 'Flower pot' has a tangible appearance and is a container used for growing plants.\nA few things that are visually similar to 'flower pot' but are not 'flower pot' are:\tvase\tbowl\ttrash can\tcup\nThere are several useful visual features to tell there is 'flower pot' and not similar things in a photo:\tcylinder or conical shape\tcontaining soil or other growing materials\thave a drainage hole at the bottom\tmay have plants or flowers growing out of it.", 241], "radiator": ["Yes. 'Radiator' has a tangible appearance and is a kind of heating device.\nA few things that are visually similar to 'radiator' but are not 'radiator' are:\tair conditioner\tventilation system\tbattery\nThere are several useful visual features to tell there is 'radiator' and not similar things in a photo:\tmetallic\tfin design\thot to the touch\thanging on a wall or standing on the floor", 240], "cloudless sky": ["Yes. 'Cloudless sky' has a tangible appearance and is a type of sky.\nA few things that are visually similar to 'cloudless sky' but are not 'cloudless sky' are:\tcloudy sky\tsunset\tsunrise\ta painted blue background\nThere are several useful visual features to tell there is 'cloudless sky' and not similar things in a photo:\tno visible clouds\tor any other visible particles\tno objects blocking the sky\tview of the sky with a blue color tone", 240], "policeman": ["Yes. 'Policeman' has a tangible appearance and is a type of profession.\nA few things that are visually similar to 'policeman' but are not 'policeman' are:\tsecurity guard\tmilitary soldier\tfirefighter\tconstruction worker\tdoctor\nThere are several useful visual features to tell there is 'policeman' and not similar things in a photo:\tuniform with a badge or insignia\temblem of the police department\ton-duty equipment like a radio, handcuffs or a gun\tpatrol car with a siren\tor badge number.", 239], "signal": ["No. 'Signal' is too vague or abstract to be distinguished in a photo without context.\n\nHowever, a few things that are visually similar to 'signal' but are not 'signal' could include: flags, banners, billboards, signs, and hand gestures. \n\nSome useful visual features for distinguishing a 'signal' from these similar things in a photo might include: a specific color or pattern (e.g. red and green for traffic lights), a recognizable symbol or icon (such as a lightning bolt for a WiFi signal), or visual cues such as a flashing light or movement (such as a waving hand signal). Contextual clues, such as the presence of people actively using or responding to the signal, might also be helpful in identifying it as a signal.", 239], "something": ["No. 'Something' is too vague or abstract to be distinguished in a photo.", 238], "spokes": ["Yes. 'Spokes' has a tangible appearance and typically refers to the radiating elements of a wheel.\nA few things that are visually similar to 'spokes' but are not 'spokes' are:\trays of a sun\tfingers of a hand\tstruts of an umbrella frame\nThere are several useful visual features to tell there are 'spokes' and not similar things in a photo:\tradiating from a center\tpointed ends\tequally spaced apart\trim or tire visible\tnext to other spokes in a circular pattern", 238], "knee pads": ["Yes. 'Knee pads' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'knee pads' but are not 'knee pads' are: elbow pads\tshin guards\tankle protectors\nThere are several useful visual features to tell there is 'knee pads' and not similar things in a photo:\tprotective gear for knees\tusually worn by athletes or workers\tmade of foam, plastic or other cushioning materials\tstraps to secure around the leg", 238], "motorcycle helmet": ["Yes. 'Motorcycle helmet' has a tangible appearance and is a headgear worn by motorcycle riders.\nA few things that are visually similar to 'motorcycle helmet' but are not 'motorcycle helmet' are:\tbicycle helmet\tskateboard helmet\tconstruction hat\tfirefighter helmet\nThere are several useful visual features to tell there is 'motorcycle helmet' and not similar things in a photo:\thard and durable outer shell\tdense interior foam lining\tcircular eye visor\tstrap or chin buckle for securing the helmet to the head", 238], "knot": ["Yes. 'Knot' has a tangible appearance and is a type of entanglement.\nA few things that are visually similar to 'knot' but are not 'knot' are:\tcoil\ttangle\ttwist\tloop\tsnarl\nThere are several useful visual features to tell there is 'knot' and not similar things in a photo:\tropes or strings\tentanglement of one or more strands or segments\tcrossing-over of strands\tor tying of multiple strands together\thas at least one end, or could be a loop.", 238], "couches": ["Yes. 'Couches' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'couches' but are not 'couches' are:\tchairs\tsofas\tbenches\tstools\nThere are several useful visual features to tell there is 'couches' and not similar things in a photo:\tlong cushioned seating for multiple people\tbackrests for support\tpadded armrests or sides\tvarious colors and patterns\tof appropriate size for lounging or sitting", 238], "goats": ["Yes. 'Goats' has a tangible appearance and is a type of domesticated animal.\nA few things that are visually similar to 'goats' but are not 'goats' are:\tsheep\tllamas\tdeer\nThere are several useful visual features to tell there is 'goats' and not similar things in a photo:\tcurved or spiral horns\tbeard or chin hair\tvertical, slit-shaped pupils\tmost commonly white, black or brown fur, sometimes with spots or patches.", 238], "handlebars": ["Yes, 'handlebars' has a tangible appearance and is a part of a vehicle or bicycle.\nA few things that are visually similar to 'handlebars' but are not 'handlebars' are:\tguitar necks\tfaucet handles\tkitchen cabinet handles\tumbrella handles\nThere are several useful visual features to tell there is 'handlebars' and not similar things in a photo:\tusually mounted on a vehicle or bicycle\ttwo parallel bars or tubes for grip\tcurved or straight lengths\tfor bicycles, connected to the front wheel and fork of the bike", 236], "baseball helmet": ["Yes. 'Baseball helmet' has a tangible appearance and is a type of headgear.\nA few things that are visually similar to 'baseball helmet' but are not 'baseball helmet' are:\tbiking helmet\tmotorcycle helmet\thardhat\nThere are several useful visual features to tell there is 'baseball helmet' and not similar things in a photo:\trounded shape\tprotective shell\tcushioned interior\tear holes\tface guard strap", 235], "chocolate cake": ["Yes. 'Chocolate cake' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'chocolate cake' but are not 'chocolate cake' are:\tbrownie\tfudge\tpudding\ttruffle\t\nThere are several useful visual features to tell there is 'chocolate cake' and not similar things in a photo:\tmultiple layers or tiers\tchocolate frosting or ganache\tchocolate cake texture or crumb\tchocolate shavings or sprinkles on top", 235], "rear tire": ["Yes. 'Rear tire' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'rear tire' but are not 'rear tire' are:\tfrisbee\tbicycle wheel\tsteering wheel\thula hoop\nThere are several useful visual features to tell there is 'rear tire' and not similar things in a photo:\tlocated at the back of a vehicle\ttread pattern\trubber material\tconnected to a wheel rim", 234], "neck tie": ["Yes. 'Neck tie' has a tangible appearance and is a type of clothing accessory worn around the neck.\nA few things that are visually similar to 'neck tie' but are not 'neck tie' are:\tscarf\tcrutches\tbelt\t\nThere are several useful visual features to tell there is 'neck tie' and not similar things in a photo:\tnarrow and long\tpatterns or solid colors\tworn with a suit or dress shirt\ttied in a knot at the front of the neck", 234], "wallpaper": ["Yes. 'Wallpaper' has a tangible appearance and refers to a material used to cover and decorate walls.\nA few things that are visually similar to 'wallpaper' but are not 'wallpaper' are: painted walls, textured walls, murals, tapestries.\nThere are several useful visual features to tell there is 'wallpaper' and not similar things in a photo: \n- patterns or designs printed or embossed on the material\n- repeating patterns or motifs that cover a large area\n- paper-like texture or appearance\n- seams or joints where individual strips of wallpaper meet.", 233], "stomach": ["Yes. 'Stomach' has a tangible appearance and is a part of the digestive system.\nA few things that are visually similar to 'stomach' but are not 'stomach' are:\tballs\tfruit\tair balloon\nThere are several useful visual features to tell there is 'stomach' and not similar things in a photo:\tj-shaped organ in the upper abdomen\tpinkish or purplish hue\tsome fleshy folds on the inside wall of the organ.", 233], "keyboards": ["Yes. 'Keyboards' has a tangible appearance and refers to a musical instrument or a computer peripheral.\nA few things that are visually similar to 'keyboards' but are not 'keyboards' are:\tpianos\torgans\tsynthesizers\ttypewriters\nThere are several useful visual features to tell there is 'keyboards' and not similar things in a photo:\trectangular shape rows of keys, either white or black or both\tmay have additional buttons or controls on the sides\tsome may have wires or cables coming out", 233], "railroad": ["Yes. 'Railroad' has a tangible appearance and refers to a system of tracks and trains.\nA few things that are visually similar to 'railroad' but are not 'railroad' are:\tcrane\tsystem of pipes and tubes\nThere are several useful visual features to tell there is 'railroad' and not similar things in a photo:\ttwo parallel tracks\ttrain cars or locomotives\tpower lines or signals along the tracks\ttrain stations, platforms, or bridges", 233], "marker": ["Yes. 'Marker' has a tangible appearance and is a writing and coloring tool.\nA few things that are visually similar to 'marker' but are not 'marker' are:\tpen\thighlighter\tcrayon\tcolored pencil\nThere are several useful visual features to tell there is 'marker' and not similar things in a photo:\tpen-shaped body\trectangular or circular tip\tvibrant ink colors\tslender body with a cap to protect the tip\tfrom dry erase to permanent ink options.", 232], "wooden desk": ["Yes. 'Wooden desk' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden desk' but are not 'wooden desk' are:\ttable\tbench\tcounter\tshelf\nThere are several useful visual features to tell there is 'wooden desk' and not similar things in a photo:\tmade primarily of wood, with wooden legs and top\tdesktop surface for working or studying\tdrawers or compartments for storage of office supplies or personal items\tergonomic design for comfortable use", 232], "cowboy hat": ["Yes. 'Cowboy hat' has a tangible appearance and is a specific style of hat.\nA few things that are visually similar to 'cowboy hat' but are not 'cowboy hat' are:\tsombrero\tfedora\tbeanie\tbaseball cap\nThere are several useful visual features to tell there is 'cowboy hat' and not similar things in a photo:\twide brim\tstiff material\trounded crown\tno visor or bill typically\tband around the base of the crown\tcontoured shape\tforward bend on the front part of the brim.", 231], "alarm clock": ["Yes. 'Alarm clock' has a tangible appearance and is a type of clock.\nA few things that are visually similar to 'alarm clock' but are not 'alarm clock' are:\twall clock\twatch\tauthentic classic alarm clock\tphone\ttimer\nThere are several useful visual features to tell there is 'alarm clock' and not similar things in a photo:\t\ndigital or analog display\thour and minute hands or digits\talarm setting\tfunctional on/off button or switch\tsound or vibration alarm option\ttypically placed on a nightstand or bedside table.", 231], "concrete sidewalk": ["Yes. 'Concrete sidewalk' has a tangible appearance and is a specific type of pavement.\nA few things that are visually similar to 'concrete sidewalk' but are not 'concrete sidewalk' are:\tasphalt pavement\tbrick walkway\tstone path\nThere are several useful visual features to tell there is 'concrete sidewalk' and not similar things in a photo:\tgrey or light tan color\trectangular, flat slabs\ttrowel or brush marks on the surface\tstamped or engraved patterns", 231], "bunches": ["Yes. 'Bunches' has a tangible appearance and refers to a collection of objects tied together.\nA few things that are visually similar to 'bunches' but are not 'bunches' are:\tpiles\tclusters\tgroups\tarrangements\nThere are several useful visual features to tell there are 'bunches' and not similar things in a photo:\tobjects tied or gathered together\tevenly spaced\toutlined by a tie or wrapping\tvariety of colors or shapes", 231], "door knob": ["Yes. 'Door knob' has a tangible appearance and is a type of handle used to open doors.\nA few things that are visually similar to 'door knob' but are not 'door knob' are:\tdrawer knob\tcabinet handle\t\nThere are several useful visual features to tell there is 'door knob' and not similar things in a photo:\tcircular or cylindrical shape\tattached to a door\tlocated near the edge of a door\tmay have a keyhole or lock mechanism\tmade of metal, plastic, or other materials", 231], "hook": ["Yes. 'Hook' has a tangible appearance and is a tool used for attaching or holding things.\nA few things that are visually similar to 'hook' but are not 'hook' are:\tnail\tpin\tclamp\tpeg\nThere are several useful visual features to tell there is 'hook' and not similar things in a photo:\tcurved or bent shape\tpointed end\thollow or open end\tintended for hanging or suspending things\tmade of metal, plastic or wood, etc.", 231], "grassy field": ["Yes. 'Grassy field' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'grassy field' but are not 'grassy field' are:\tforest\tpark\tmeadow\tgarden\nThere are several useful visual features to tell there is 'grassy field' and not similar things in a photo:\tgrass covering the majority of the photo\tmaybe a few trees or bushes\tmaybe some clouds in the background\tmaybe some animals running around.", 230], "sleeves": ["Yes. 'Sleeves' has a tangible appearance and is a part of clothing that covers the arms.\nA few things that are visually similar to 'sleeves' but are not 'sleeves' are:\tgloves\tarm warmers\tshawls\t\nThere are several useful visual features to tell there are 'sleeves' and not similar things in a photo:\tattached to a shirt or a dress\tcovering the entire arm or part of it\tdifferent colors and patterns than the rest of the clothing \thas a different texture than the rest of the clothing.", 230], "decorations": ["Yes. 'Decorations' has a tangible appearance and refers to various objects used for decoration purposes.\nA few things that are visually similar to 'decorations' but are not 'decorations' are:\ttools\tpaintings\tclothes\tbooks\nThere are several useful visual features to tell there is 'decorations' and not similar things in a photo:\taesthetic purpose\tcolorful\tornamental patterns used for celebrations or events such as balloons, streamers, flowers, ribbons, etc.", 230], "pink shirt": ["Yes. 'Pink shirt' has a tangible appearance and is a kind of clothing.\nA few things that are visually similar to 'pink shirt' but are not 'pink shirt' are:\tpink dress\tpink tie\tpink blouse\nThere are several useful visual features to tell there is 'pink shirt' and not similar things in a photo:\ta shirt with buttons and a collar\tmade of fabric\tpink color", 229], "material": ["No. 'Material' is too vague or abstract to be distinguished in a photo. However, materials themselves can have tangible appearances and can be visually concrete concepts. \n\nA few things that are visually similar to 'material' but are not 'material' are:\tobjects\tmatter\tsubstances\telements\n\nVisual features for distinguishing a specific material from other materials in a photo could include:\ttexture, color, pattern, shine, transparency, opacity, weight.", 229], "rear": ["No. 'Rear' is too vague or abstract to be distinguished in a photo. It can refer to the back side or hindquarters of something, which could have various visual appearances depending on the object in question.", 229], "wood bench": ["Yes. 'Wood bench' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood bench' but are not 'wood bench' are:\tchair\tstool\tcouch\ttable\nThere are several useful visual features to tell there is 'wood bench' and not similar things in a photo:\telongated seat\tsupported by legs or a pedestal\tmade of wood or wood-like material\tbackrest or armrest (optional)", 229], "pillars": ["Yes. 'Pillars' has a tangible appearance and is a type of vertical support structure.\nA few things that are visually similar to 'pillars' but are not 'pillars' are:\ttrees\tcolumns\tobelisks\tstatues\nThere are several useful visual features to tell there is 'pillars' and not similar things in a photo:\tvertical and tall\tcylindrical shape\tsupporting a roof or structure\tmade of brick, stone, or concrete", 228], "lemons": ["Yes. 'Lemons' has a tangible appearance and is a type of citrus fruit.\nA few things that are visually similar to 'lemons' but are not 'lemons' are:\toranges\tgrapefruits\tlimes\nThere are several useful visual features to tell there are 'lemons' and not similar things in a photo:\tbright yellow color\telongated oval shape\tsmooth and shiny skin\twith a small point on one end", 228], "leafy tree": ["Yes. 'Leafy tree' has a tangible appearance.\nA few things that are visually similar to 'leafy tree' but are not 'leafy tree' are:\tpalm tree\tcactus\tbush\nThere are several useful visual features to tell there is 'leafy tree' and not similar things in a photo:\tabove-ground stem or trunk\tbranches and leaves\tfoliage or dense leaves \twithout spikes or thorns (in most cases)", 228], "passenger bus": ["Yes. 'Passenger bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'passenger bus' but are not 'passenger bus' are:\ttruck\tambulance\tvan\tmotorhome\nThere are several useful visual features to tell there is 'passenger bus' and not similar things in a photo:\tlong and rectangular shape\tmultiple rows of seats\tside and rear doors\tspecial markings or insignia indicating it is a bus", 228], "briefcase": ["Yes. 'Briefcase' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'briefcase' but are not 'briefcase' are:\tbackpack\tsatchel\tpurse\tlaptop bag\nThere are several useful visual features to tell there is 'briefcase' and not similar things in a photo:\trectangular shape\thard or firm material\thandle for carrying\ton the smaller side compared to other bags\topening at the top with a zipper, latch, or buckle", 227], "steeple": ["Yes. 'Steeple' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'steeple' but are not 'steeple' are:\tchimney\ttower\tpyramid\tspire\tminaret\nThere are several useful visual features to tell there is 'steeple' and not similar things in a photo:\tlocated on the top of a building\tpointed shape\ttaller than the rest of the roof\tdecorative elements like crosses or weather vanes", 226], "bit": ["No. 'Bit' is too vague or abstract to be distinguished in a photo. \n\nAs a language model AI, I do not have personal preferences or beliefs, therefore, I will not be able to provide any response beyond what is asked of me.", 226], "landscape": ["Yes. 'Landscape' has a tangible appearance and refers to the visual features of an area of land.\nA few things that are visually similar to 'landscape' but are not 'landscape' are:\tpainting\tposter\tphotograph\t\nThere are several useful visual features to tell there is 'landscape' and not similar things in a photo:\tthe features of a natural scene, e.g. mountains, hills, rivers, trees, and sky\tthe composition of the scene, e.g. foreground, middle ground, and background\tthe feeling of depth created by perspective\tand the quality of light and color in the scene.", 226], "rust": ["Yes. 'Rust' has a tangible appearance as it refers to the reddish-brown coating that forms on iron or steel as a result of the oxidation process.\nA few things that are visually similar to 'rust' but are not 'rust' are:\tred paint\tmud\tclay\trust-colored objects\t\nThere are several useful visual features to distinguish 'rust' from the listed similar things in a photo:\t\n-Texture and appearance - Rust has a rough and flaky texture, while other materials would have a smoother texture.\n-Location - Rust would typically be seen around metal objects or surfaces, while other materials may not.\n-Color - Rust has a distinctive reddish-brown color, which is different from the shades of red seen in paint, blood, or clay. \n-Formation - Rust occurs as a result of a chemical reaction and would be distinct from naturally occurring substances like mud.", 226], "nails": ["Yes. 'Nails' has a tangible appearance and is a type of metal object used for fastening.\nA few things that are visually similar to 'nails' but are not 'nails' are:\tscrews\tpins\ttacks\tneedles\nThere are several useful visual features to tell there are 'nails' and not similar things in a photo:\tpointed on one end and flat on the other\tmetallic appearance\tused for fastening or attaching materials to each other\ttypically sold in groups or containers of various sizes and lengths", 225], "silhouette": ["Yes. 'Silhouette' has a tangible appearance and refers to the dark outline or shape of an object or person against a lighter background.\nA few things that are visually similar to 'silhouette' but are not 'silhouette' are:\tshadow\treflection\tghost\t\nThere are several useful visual features to tell there is 'silhouette' and not similar things in a photo:\tcontrasting colors, usually black against a light background\toutline or shape without details or texture\tno visible facial features or other distinguishing characteristics", 225], "stream": ["Yes. 'Stream' has a tangible appearance and is a type of flowing water body.\nA few things that are visually similar to 'stream' but are not 'stream' are:\tRiver\tCreek\tCanal\tWaterfall\tFountain\nThere are several useful visual features to tell there is 'stream' and not similar things in a photo: A small and narrow flowing water body, making noise while flowing, surrounded by rocks, pebbles, and plants.", 224], "pens": ["Yes. 'Pens' has a tangible appearance and is a writing tool.\nA few things that are visually similar to 'pens' but are not 'pens' are:\tpencils\tmarkers\tcrayons\nThere are several useful visual features to tell there is 'pens' and not similar things in a photo:\tlong and cylindrical shape\twith a cap or retractable tip\tfor writing or drawing\tthe ink comes in different colors (e.g., black, blue, red)", 224], "christmas": ["No. 'Christmas' is too vague or abstract to be distinguished in a photo.", 224], "motorcyclist": ["Yes. 'Motorcyclist' has a tangible appearance and refers to a person riding a motorcycle.\nA few things that are visually similar to 'motorcyclist' but are not 'motorcyclist' are:\tbicyclist\tskateboarder\tscooter rider\tdirt bike rider\nThere are several useful visual features to tell there is 'motorcyclist' and not similar things in a photo:\tperson wearing a helmet\tperson wearing protective riding gear\tmotorcycle with two or three wheels\tengine or exhaust visible in the photo", 224], "drapes": ["Yes. 'Drapes' has a tangible appearance and refers to a kind of curtain that hangs from a rod.\nA few things that are visually similar to 'drapes' but are not 'drapes' are:\tblinds\tshades\tawnings\tcloth\nThere are several useful visual features to tell there is 'drapes' and not similar things in a photo:\t\nheavy and long pieces of fabric, often made of velvet, silk, or linen\thanging from a rod, with pleats or gathers\tat times, held back by tie-backs or decorative hooks", 223], "sign post": ["Yes. 'Sign post' has a tangible appearance and is a type of outdoor direction indicator.\nA few things that are visually similar to 'sign post' but are not 'sign post' are:\ttree\tpostbox\tlamppost\tbollard\nThere are several useful visual features to tell there is 'sign post' and not similar things in a photo:\ttall and slender\tpainted or labeled with information\tpointing direction and distance", 223], "tops": ["Yes. 'Tops' has a tangible appearance and is a kind of toy.\nA few things that are visually similar to 'tops' but are not 'tops' are:\tcoins\tfrisbees\tbutton covers\tbottle caps\nThere are several useful visual features to tell there is 'tops' and not similar things in a photo:\tcone-shaped object\twith a pointed end\tspinning or rotating in place\tmade of plastic or wood\tbrightly colored or patterned\ton a flat surface or a stick/top base.", 223], "city bus": ["Yes. 'City bus' has a tangible appearance and is a type of public transportation.\nA few things that are visually similar to 'city bus' but are not 'city bus' are:\tCoach\tbus stop\ttruck\nThere are several useful visual features to tell there is 'city bus' and not similar things in a photo:\tDual doors for entry and exit\tonboard seating for passengers\tLarge windows for visibility\tRoof-mounted luggage racks or bike racks\tSide panels with route numbers or destinations.", 222], "burger": ["Yes. 'Burger' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'burger' but are not 'burger' are:\tveggie burger\tsandwich\thot dog\tsausage\nThere are several useful visual features to tell there is 'burger' and not similar things in a photo:\ttwo buns\ton or between the buns there should be meat/veggie patty, lettuce or tomatoes, cheese, and sauces.\tSometimes other ingredients are added as well, such as bacon or onion rings.", 221], "pizza crust": ["Yes. 'Pizza crust' has a tangible appearance and is a part of pizza.\nA few things that are visually similar to 'pizza crust' but are not 'pizza crust' are:\tbread\tborder\ttortilla\nThere are several useful visual features to tell there is 'pizza crust' and not similar things in a photo:\tround or rectangular shape\ta golden, crispy texture\tshorter and more rigid than the rest of the pizza.", 221], "stainless steel": ["Yes. 'Stainless steel' has a tangible appearance and is a type of metal.\nA few things that are visually similar to 'stainless steel' but are not 'stainless steel' are: chrome, silver, aluminum or tin plated objects, polished surfaces.\nThere are several useful visual features to tell there is 'stainless steel' and not similar things in a photo:\tnon-corrosive, non-rusting characteristic\tsilvery or grayish finish\tdull or shiny texture\thard and durable surface\tsmooth or brushed surface finish.", 221], "bath tub": ["Yes. 'Bath tub' has a tangible appearance and is a fixture commonly found in bathrooms.\nA few things that are visually similar to 'bath tub' but are not 'bath tub' are:\twhirlpool\tpool\tsofa with curved corners\nThere are several useful visual features to tell there is 'bath tub' and not similar things in a photo:\toval or rectangular shape\twith or without claw feet, depending on the design\tsolid surface or surface with textured patterns\tfaucet and drain on the side or in the middle\tsimilar features found in bathroom decor", 221], "garden": ["Yes. 'Garden' has a tangible appearance and is defined by a space with plants and usually surrounded by walls or fences.\nA few things that are visually similar to 'garden' but are not 'garden' are:\tpark\tforest\tfarm\tbackyard\nThere are several useful visual features to tell there is 'garden' and not similar things in a photo:\tman-made structures like fountains, benches, or statues\tarrangement of plants and flowers\tintricate geometries of plants, hedges, or sections\twalls or fences around the perimeter of the space.", 221], "cabinet door": ["Yes. 'Cabinet door' has a tangible appearance and is a functional part of a cabinet. \nA few things that are visually similar to 'cabinet door' but are not 'cabinet door' are:\tdrawer front\tkitchen counter\tbathroom vanity door\tfiling cabinet\nThere are several useful visual features to tell there is 'cabinet door' and not similar things in a photo:\trectangular or square shape\thinged to a cabinet\tframe or no frame\thandle or knob to open it", 221], "blue shirt": ["Yes. 'Blue shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blue shirt' but are not 'blue shirt' are:\tblue dress\tblue jacket\tblue sweater\tblue blouse\nThere are several useful visual features to tell there is a 'blue shirt' and not similar things in a photo:\t\nbutton-down or collared shirt\t\nshort or long-sleeved\t\nsolid or patterned\t\nspecific shade of blue", 220], "grasses": ["Yes. 'Grasses' have a tangible appearance and are a type of plant.\nA few things that are visually similar to 'grasses' but are not 'grasses' are:\tsedges\tbamboo\treeds\tcattails\nThere are several useful visual features to tell there are 'grasses' and not similar things in a photo:\tlong and narrow leaves\tthat can have sharp edges or be soft\ttoothed margins or edges\tfibrous roots\tgrow in clumps or dense groupings\tcan have seed heads at the top", 220], "arch": ["Yes. 'Arch' has a tangible appearance and is a curved architectural element.\nA few things that are visually similar to 'arch' but are not 'arch' are:\tdome\tcurved bridge\trainbow\tbent tree branches\nThere are several useful visual features to tell there is 'arch' and not similar things in a photo:\tcurved shape that resembles a semi-circle or a horseshoe\tmade of stone or brick\tframed by columns or pillars\tthat it is part of a building or a structure", 220], "plaque": ["Yes. 'Plaque' has a tangible appearance and can refer to different things, such as a dental plaque or a commemorative plaque.\nA few things that are visually similar to 'plaque' but are not 'plaque' are:\tmetal plates\tcertificates\tsigns\tpaintings\tdecorative tiles\nThere are several useful visual features to tell there is 'plaque' and not similar things in a photo:\tflat surface with text or images\tdense, sticky material on teeth (for dental plaque)\tmetallic, stone or plastic material (depending on the type of plaque)\tfixed to a wall or other object to commemorate an event or a person.", 220], "soccer player": ["Yes. 'Soccer player' has a tangible appearance and refers to a person playing soccer.\nA few things that are visually similar to 'soccer player' but are not 'soccer player' are:\tbasketball player\tfootball player\ttennis player\tathlete\nThere are several useful visual features to tell there is 'soccer player' and not similar things in a photo:\twearing soccer jersey and shorts\tplaying soccer using a soccer ball\ton a soccer field or pitch\tusing their feet to control or pass the ball.", 220], "beef": ["Yes. 'Beef' has a tangible appearance and refers to a specific type of meat.\nA few things that are visually similar to 'beef' but are not 'beef' are:\tpork\tlamb\tveal\nThere are several useful visual features to tell there is 'beef' and not similar things in a photo:\tred or dark pink color\tmarbled fat distributed throughout\tthe texture of the meat\tinformal cuts like steak or minced meat\tface of a cow or bull", 219], "jockey": ["Yes. 'Jockey' has a tangible appearance and is a person who rides horses in races.\nA few things that are visually similar to 'jockey' but are not 'jockey' are:\thorseback rider\tcowboy\tpolo player\trodeo competitor\nThere are several useful visual features to tell there is 'jockey' and not similar things in a photo:\twearing colorful silks with numbers\tcarrying a whipn\thelmet with a chinstrap\triding a horse on a racecourse over jumps\tor on a flat track", 219], "blue skies": ["Yes. 'Blue skies' has a tangible appearance and refers to the clear, unobstructed atmosphere during the day.\nA few things that are visually similar to 'blue skies' but are not 'blue skies' are:\tcloudy skies\tmorning sky\tsunset or sunrise\tsky with birds\nThere are several useful visual features to tell there are 'blue skies' and not similar things in a photo:\tan absence of clouds\tor a few scattered clouds\tpure blue color a gradient from light to dark blue (if the photo is in daylight), from black to dark blue (if the photo is at night)", 219], "devices": ["No. 'Devices' is too vague or abstract to be visually concrete.\nA few things that are visually similar to 'devices' but are not 'devices' are: appliances, gadgets, equipment, tools.\nThere are no specific visual features to distinguish 'devices' since the term encompasses a wide range of objects with different shapes, sizes, and purposes. However, some commonly used devices may have distinguishing visual features, such as a screen for a smartphone or a keyboard for a computer.", 219], "seeds": ["Yes. 'Seeds' has a tangible appearance and is a reproductive part of a plant.\nA few things that are visually similar to 'seeds' but are not 'seeds' are:\tgrains\tbulbs\tbeans\tpills\nThere are several useful visual features to tell there are 'seeds' and not similar things in a photo:\tsmall\tsize\toften oval or round in shape\tvariety of colors\tand textures\tfrequently attached to other plant materials or emanate from a plant", 219], "tea kettle": ["Yes. 'Tea kettle' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'tea kettle' but are not 'tea kettle' are:\tcoffee pot\tsaucepan\twater jug\nThere are several useful visual features to tell there is 'tea kettle' and not similar things in a photo:\tSpout\tLid\tHandle\tKettle shape\tSteam rising from the spout", 219], "silver pole": ["Yes. 'Silver pole' has a tangible appearance and is a kind of object.\nA few things that are visually similar to 'silver pole' but are not 'silver pole' are:\tchrome rod\tsilver pipe\tsilver flagpole\t\nThere are several useful visual features to tell there is 'silver pole' and not similar things in a photo:\tlong, thin and cylindrical shape\tbright silver or shiny surface\tno visible bends or curves\tfree-standing or attached to a structure", 219], "drinking glass": ["Yes. 'Drinking glass' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'drinking glass' but are not 'drinking glass' are:\tvase\tjar\ttumbler\thurricane\nThere are several useful visual features to tell there is 'drinking glass' and not similar things in a photo:\tcylindrical or conical shape\ttransparency or translucency\twide and flat base\tnarrow opening to drink from\tcan hold liquid", 218], "hour hand": ["Yes. 'Hour hand' has a tangible appearance and is a part of a clock.\nA few things that are visually similar to 'hour hand' but are not 'hour hand' are:\tminute hand\tsecond hand\nThere are several useful visual features to tell there is 'hour hand' and not similar things in a photo:\tshortest hand on the clock\t'hour' usually written on it\tthe hand that moves most slowly", 218], "plastic fork": ["Yes. 'Plastic fork' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'plastic fork' but are not 'plastic fork' are:\tmetal fork\twooden fork\tplastic spoon\nThere are several useful visual features to tell there is 'plastic fork' and not similar things in a photo:\ttines or prongs for picking up food\thandle\tfor use with meals and food service\ttranslucent or opaque color typically in white or black\tmade from synthetic material", 217], "figurine": ["Yes. 'Figurine' has a tangible appearance and refers to a small statue or sculpture of a human, animal or other character.\nA few things that are visually similar to 'figurine' but are not 'figurine' are:\taction figure\tstatue\tbust\tsculpture\tornament\nThere are several useful visual features to tell there is 'figurine' and not similar things in a photo:\tsmall and usually handheld\tsize, no more than a few inches\ttoys, decorative or cultural purposes_human, animal or cartoon characters, typically with details or a base.", 217], "metal gate": ["Yes. 'metal gate' has a tangible appearance and is a physical object used for security or decoration.\nA few things that are visually similar to 'metal gate' but are not 'metal gate' are: Fence, wire mesh, railing\nThere are several useful visual features to tell there is 'metal gate' and not similar things in a photo: made up of metal bars or metal panels, has hinges or sliding mechanism, may have locks or latches, often used to restrict access or for decoration.", 217], "stop light": ["Yes. 'Stop light' has a tangible appearance and is a kind of traffic signal.\nA few things that are visually similar to 'stop light' but are not 'stop light' are:\ttraffic sign\tbike light\tflashlight\tlantern\nThere are several useful visual features to tell there is 'stop light' and not similar things in a photo:\tthree colored lights\tred on top, yellow in the middle, and green on the bottom\tlocated on a pole or overhead\tpost or signal box next to it\tsquare or triangular shape.", 217], "power line": ["Yes. 'Power line' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'power line' but are not 'power line' are:\tcable wire\ttrolley wire\trailroad track\nThere are several useful visual features to tell there is 'power line' and not similar things in a photo:\ttall poles or towers\tsuspended wires or cables\tcarrying electrical current\tcrossing over roads or rivers", 217], "photographer": ["Yes. 'Photographer' has a tangible appearance and typically refers to a person who takes photographs.\nA few things that are visually similar to 'photographer' but are not 'photographer' are:\tmodels\tfilm crew\ttourists\nThere are several useful visual features to tell there is 'photographer' and not similar things in a photo:\tholding a camera or other photography equipment\tfocusing on a subject\tadjusting camera settings or lens\tpositioned to take a photograph\tframing or composing a shot", 217], "toothpaste": ["Yes. 'Toothpaste' has a visually concrete appearance and is a kind of paste or gel used for dental hygiene.\nA few things that are visually similar to 'toothpaste' but are not 'toothpaste' are:\tcream\tshampoo\tconditioner\nThere are several useful visual features to tell there is 'toothpaste' and not similar things in a photo:\ttube-shaped\tcontainer with a cap or nozzle\tpaste or gel consistency\tinformation about dental hygiene on the packaging", 217], "coffee pot": ["Yes. 'Coffee pot' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'coffee pot' but are not 'coffee pot' are:\tteapot\tkettle\tthermos\tmug\nThere are several useful visual features to tell there is 'coffee pot' and not similar things in a photo:\tcylindrical shape\twith a spout and a handle\ttop lid\tfor brewing coffee or keeping it hot", 216], "way sign": ["Yes. 'Way sign' has a tangible appearance and is a type of signpost used for direction.\nA few things that are visually similar to 'way sign' but are not 'way sign' are:\tadvertisements\tbillboards\twarning signs\nThere are several useful visual features to tell there is 'way sign' and not similar things in a photo:\trectangular or triangular shape\twith arrows or text indicating directions\tcommonly seen at intersections\tor airports, train stations, or other transportation hubs\tbold and contrasting colors, such as white on a blue background or black on a yellow background.", 216], "minivan": ["Yes. 'Minivan' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'minivan' but are not 'minivan' are:\tSUVs\tpickup trucks\tcoaches\tvans\nThere are several useful visual features to tell there is 'minivan' and not similar things in a photo:\tsmaller than a regular van\tboxy shape\tsliding doors for rear passengers\tlower to the ground cabin than other vans or trucks\tlarge windows on the sides and rear of the vehicle", 216], "baby giraffe": ["Yes. 'Baby giraffe' has a tangible appearance and is a young offspring of a giraffe.\nA few things that are visually similar to 'baby giraffe' but are not 'baby giraffe' are:\tfawn\tdeer\tantelope\tcamel\tadult giraffe\nThere are several useful visual features to tell there is 'baby giraffe' and not similar things in a photo:\ttall and lanky legs\tunique spotted coat pattern\tlong neck\tdark, protruding eyes\tundersized and delicate in comparison to the adult giraffe presence of ossicones (horn-like bumps) on the head indicates a young giraffe", 216], "baseball catcher": ["Yes. 'Baseball catcher' has a tangible appearance and is a person who plays a specific role in the sport of baseball.\nA few things that are visually similar to 'baseball catcher' but are not 'baseball catcher' are:\tfootball linebacker\thockey goalie\tlong snapper in football\nThere are several useful visual features to tell there is 'baseball catcher' and not similar things in a photo, such as:\t\n- Wearing a baseball uniform, including a chest protector and shin guards, along with a baseball mitt\n- Positioned in a squatting stance behind home plate\n- Catching the ball thrown by the pitcher\n- Working closely with the pitcher and other members of the infield team.", 216], "toilet tank": ["Yes. 'Toilet tank' has a tangible appearance and is a part of a toilet.\nA few things that are visually similar to 'toilet tank' but are not 'toilet tank' are:\twater storage container\tair conditioning unit\tbidet\thumidifier\nThere are several useful visual features to tell there is 'toilet tank' and not similar things in a photo:\tattached to a toilet bowl\tlocated behind or above the toilet bowl\tsimilar color and material to the toilet bowl\tnear the water supply and flushing mechanism.", 216], "metal bar": ["Yes. 'Metal bar' has a tangible appearance and is a long, narrow piece of metal.\nA few things that are visually similar to 'metal bar' but are not 'metal bar' are:\tmetal pipe\tmetal rod\tmetal wire\teyeglasses\nThere are several useful visual features to tell there is 'metal bar' and not similar things in a photo:\telongated rectangular shape\tsolid, dense appearance\tmetallic texture, color, or shine\tstraight, uniform edges and corners", 216], "bank": ["Yes. 'Bank' has a tangible appearance and can be a building or an institution that deals with financial matters.\nA few things that are visually similar to 'bank' but are not 'bank' are:\toffice building\tapartment building\tpost office\tbusiness center\nThere are several useful visual features to tell a 'bank' from the listed similar things in a photo:\tofficial bank logo on the building or wall\tATM machines or teller windows visible\tmoney, coins or bills in the image\tpeople inside conducting financial transactions.", 215], "skateboard ramp": ["Yes. 'Skateboard ramp' has a tangible appearance and is a structure used for skateboarding.\nA few things that are visually similar to 'skateboard ramp' but are not 'skateboard ramp' are:\thalf pipe\tski jump\tplayground slide\nThere are several useful visual features to tell there is 'skateboard ramp' and not similar things in a photo:\t\ncurved shape\t\nheight\t\nangled incline\t\ncovered in skating surface, such as wood or metal\t\nusually found in a skatepark or outdoor area", 214], "hedge": ["Yes. 'Hedge' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'hedge' but are not 'hedge' are:\tfence\tbush\tshrub\ttree\tgrassy field\nThere are several useful visual features to tell there is 'hedge' and not similar things in a photo:\tlining a path or a boundary relatively in a straight line\tdensely packed foliage of bushes or shrubs\tusually pruned or trimmed to a specific shape like a rectangle or a sphere.", 214], "table top": ["Yes. 'Table top' has a tangible appearance and is a horizontal surface for placing objects.\nA few things that are visually similar to 'table top' but are not 'table top' are:\tcounter\ttop of a bookshelf\tdesk\tfloor\nThere are several useful visual features to tell there is 'table top' and not similar things in a photo:\thorizontal surface\tflat and level\tunobstructed and clear\tfor placing objects or activities to be carried out on top of it.", 214], "passenger windows": ["Yes. 'Passenger windows' has a tangible appearance and is a type of window in a vehicle for the passengers.\nA few things that are visually similar to 'passenger windows' but are not 'passenger windows' are:\tdriver's window\tsunroof\tdoors\nThere are several useful visual features to tell there is 'passenger windows' and not similar things in a photo:\tlocated on the side of the vehicle\trectangular or square shape\ttinted glass\tfor letting light and air into the vehicle.", 214], "remote control": ["Yes. 'Remote control' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'remote control' but are not 'remote control' are:\tcell phone\tpager\ttablet\tgame controller\nThere are several useful visual features to tell there is 'remote control' and not similar things in a photo:\trectangular shape\twith buttons to control different functions\toften has a directional pad and navigation buttons\tusually has an 'on' and 'off' button May have a visual display\tscreen or led lights.", 214], "catchers": ["No. 'Catchers' is too vague or abstract to be distinguished in a photo. \"Catchers\" could refer to various things such as sports players or objects used for catching things. Please specify the context for a more accurate answer.", 214], "baseball hat": ["Yes. 'Baseball hat' has a tangible appearance and is a kind of headgear.\nA few things that are visually similar to 'baseball hat' but are not 'baseball hat' are:\tvisor\tcap\tbeanie\tcowboy hat\nThere are several useful visual features to tell there is 'baseball hat' and not similar things in a photo:\trounded cap shape\twith a brim or visor\tsnap closure at the back of the head\ttop button or loop in the center of the crown\temblem or logo of a baseball team\ton the front or side panels", 214], "fans": ["Yes. 'Fans' has a tangible appearance and is a kind of mechanical device.\nA few things that are visually similar to 'fans' but are not 'fans' are:\twind turbine\tventilation duct\tceiling light\tchopper blades\nThere are several useful visual features to tell there is 'fans' and not similar things in a photo:\tblades that rotate in a circular motion\tconnected to a motor or power source\tcan be portable or mounted on a ceiling\tor placed on a desk or floor.", 214], "pigeon": ["Yes. 'Pigeon' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'pigeon' but are not 'pigeon' are:\tdove\tsparrow\trobin\tfinch\nThere are several useful visual features to tell there is 'pigeon' and not similar things in a photo:\tlarger than a sparrow or finch\tsmaller than a dove or robin\tiridescent feathers on the neck\tarea around the eyes appears almost bare (no feathers)\ttwo-tone coloring on the wings, usually gray and black, with white markings on the tips of the wings\tgray or blueish color on the back and head, and some may have green, red, or purple feathers on the neck and chest.", 214], "mannequin": ["Yes. 'Mannequin' has a tangible appearance and typically refers to a life-sized doll used for display.\nA few things that are visually similar to 'mannequin' but are not 'mannequin' are:\tstatue\thuman person\tdoll\tpuppet\nThere are several useful visual features to tell there is 'mannequin' and not similar things in a photo:\tlife-size\thumanoid shape and proportions\tmade of plastic or composite material\tspecialized for displaying clothes or products", 214], "porch": ["Yes. 'Porch' has a tangible appearance and is a structure attached to a building.\nA few things that are visually similar to 'porch' but are not 'porch' are:\tdeck\tbalcony\tpatio\tveranda\nThere are several useful visual features to tell there is 'porch' and not similar things in a photo:\tcovered, attached structure\twith a roof\tor an overhang\tmay have columns or railings\tmay have a door or windows leading to the inside of a building.", 214], "bracelets": ["Yes. 'Bracelets' has a tangible appearance and is a type of jewelry worn around the wrist.\nA few things that are visually similar to 'bracelets' but are not 'bracelets' are:\twatches\thair ties\tanklets\nThere are several useful visual features to tell there is 'bracelets' and not similar things in a photo:\tworn around the wrist\tdecorative design\tclosure to fasten around the wrist\tvariety of materials (metal, fabric, leather, beads, etc.)", 213], "greenery": ["Yes. 'Greenery' has a tangible appearance and refers to the plants and foliage in an area.\nA few things that are visually similar to 'greenery' but are not 'greenery' are:\tpaintings\twith green color\tsynthetic leaves\tartificial turf\nThere are several useful visual features to tell there is 'greenery' and not similar things in a photo:\tlush and full leaves\tvaried shades of green\tnatural texture and patterns\tgrowing out of the ground or a container.", 213], "garage door": ["Yes. 'Garage door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'garage door' but are not 'garage door' are:\tfront door\tshed door\tfence gate\tbarn door\nThere are several useful visual features to tell there is 'garage door' and not similar things in a photo:\trectangular in shape\thinged or rolls up and down\ttwo or more panels or sections\tmade of wood, metal, or plastic\thas a handle and a lock or opener system.", 213], "minute hand": ["Yes. 'Minute hand' has a tangible appearance and is a component of a clock or watch.\nA few things that are visually similar to 'minute hand' but are not 'minute hand' are:\thour hand\tsecond hand\tcalendar hand\nThere are several useful visual features to tell there is 'minute hand' and not similar things in a photo:\tlongest and thinnest of the three hands on a clock or watch\textends from the center of the clock or watch\tdifferent length or color from the other hands usually has a pointed tip to indicate minutes", 213], "baseball game": ["Yes. 'Baseball game' has a tangible appearance and implies a set of rules, equipment, and a playing field.\nA few things that are visually similar to 'baseball game' but are not 'baseball game' are:\tsoftball game\tcricket game\ttennis game\tfrisbee game\nThere are several useful visual features to tell there is 'baseball game' and not similar things in a photo:\t\n- Large grassy field with a diamond shape\n- Players that wear uniforms, gloves, and hats\n- A pitcher standing on a mound\n- Batters waiting in a specific area\n- A ball that is hit with a bat\n- Umpires wearing different uniforms and standing behind the batter and first base\n- A scoreboard showing the score and inning.", 212], "polo shirt": ["Yes. 'Polo shirt' has a tangible appearance.\nA few things that are visually similar to 'polo shirt' but are not 'polo shirt' are:\tt-shirt\tdress shirt\thenley shirt\tcrew neck shirt\nThere are several useful visual features to tell there is 'polo shirt' and not similar things in a photo:\tcollared shirt\ttwo or three-button placket\tshort sleeves\ttrademark ribbed collar and sleeve cuffs", 212], "toilets": ["Yes. 'Toilets' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'toilets' but are not 'toilets' are:\tbidet\turinal\tsink\tshower\tfountain\nThere are several useful visual features to tell there is 'toilets' and not similar things in a photo:\tbowl-shaped device with a seat\tforbidden to flush tampons, wipes or other objects\twith a handle or button to flush\twater supply and drainage system", 212], "shoreline": ["Yes. 'Shoreline' has a tangible appearance and is the line where the land meets the sea or a lake.\nA few things that are visually similar to 'shoreline' but are not 'shoreline' are:\tsand\tdune\thill\tcoastline\tbeach\tshore\nThere are several useful visual features to tell there is 'shoreline' and not similar things in a photo:\twater meeting land or sand clearly visible\trocks or sand visible on the ground\tarea between water and land clearly defined by a change in color or texture in the ground or the presence of vegetation or human-made structures such as buildings or docks.", 212], "clump": ["Yes. 'Clump' has a tangible appearance and is a grouping of objects.\nA few things that are visually similar to 'clump' but are not 'clump' are:\tpile\tgroup\tcollection\nThere are several useful visual features to tell there is 'clump' and not similar things in a photo:\tobjects are grouped closely together\tobjects are similar in shape or size\tthere is a clear boundary around the group\tof a different color or texture than the surrounding area", 212], "tents": ["Yes. 'Tents' has a tangible appearance and is a kind of temporary shelter.\nA few things that are visually similar to 'tents' but are not 'tents' are:\tumbrellas\tpavilions\tcanopies\tbeach shades\nThere are several useful visual features to tell there is 'tents' and not similar things in a photo:\tfabric or canvas material\tpegs and ropes for support\tand a pole or frame\tfor structure\tzipped doors\tand windows\tfor ventilation and entry/exit.", 211], "steam": ["Yes. 'Steam' has a tangible appearance and is a type of gas.\nA few things that are visually similar to 'steam' but are not 'steam' are:\tsmoke\tmist\tfog\tdust\nThere are several useful visual features to tell there is 'steam' and not similar things in a photo:\trising from a hot surface, like a kettle or a geyser\ttranslucent\twhite and fluffy\ttypically very hot or warm in temperature", 210], "tan building": ["Yes. 'Tan building' has a tangible appearance and refers to a building with a tan color or hue.\nA few things that are visually similar to 'tan building' but are not 'tan building' are:\tother colored buildings\tsand-colored rock formations\tmountains with a tan hue\nThere are several useful visual features to tell there is 'tan building' and not similar things in a photo:\trectangular or square-shaped structure\twith windows and doors\tman-made\tbuildings can have different textures or patterns except for the tan hue that distinguishes them.", 210], "rain": ["Yes. 'Rain' has a tangible appearance as it is a type of precipitation.\nA few things that are visually similar to 'rain' but are not 'rain' are:\tsnow\thail\tdew\tmist\tfog\nThere are several useful visual features to tell there is 'rain' and not similar things in a photo:\tvertical streaks of water\tdark or grey skies\tfalling from clouds\tin motion or falling down in a diagonal or straight pattern", 210], "name tag": ["Yes. 'Name tag' has a tangible appearance and is a recognizable object that usually has someone's name written on it.\nA few things that are visually similar to 'name tag' but are not 'name tag' are:\tbutton\tpin\tbadge\tlogo\tsticker\nThere are several useful visual features to tell there is 'name tag' and not similar things in a photo:\trectangular or oval shape\tlanyard or clip attachment\tname written on it\tclearly visible and legible\tfont and color contrast with the background", 210], "stacks": ["Yes. 'Stacks' has a tangible appearance and refers to a pile of objects.\nA few things that are visually similar to 'stacks' but are not 'stacks' are: pile of leaves, mound of dirt, heap of rocks, bundle of papers\nThere are several useful visual features to tell there is 'stacks' and not similar things in a photo:\tvertical arrangement of objects\tevenly spaced objects\tstaggered or overlapping objects\tvarying color or size of objects\tinanimate objects, not natural formations", 210], "apple logo": ["Yes. 'Apple logo' has a tangible appearance and is a symbol/logo.\nA few things that are visually similar to 'apple logo' but are not 'apple logo' are:\tpineapple crown logocherry logopear logosun with leaf shapes logo\nThere are several useful visual features to tell there is 'Apple logo' and not similar things in a photo:\n- a white or silver apple silhouette with a bite mark on the right side\n- the apple is facing to the right\n- the logo is often on a black, white, or grey background\n- usually with the word \"apple\" written beside or below it", 210], "heads": ["Yes. 'Heads' has a tangible appearance and is a body part.\nA few things that are visually similar to 'heads' but are not 'heads' are: statues, balloons, rocks, hats, mannequins\nThere are several useful visual features to tell there is 'heads' and not similar things in a photo:\tfacial features (eyes, nose, mouth)\thair or lack of hair\tneck and shoulders\tskin color and texture\theight (usually attached to a body)", 210], "baskets": ["Yes. 'Baskets' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'baskets' but are not 'baskets' are:\tbags\tbins\tcrates\tbuckets\nThere are several useful visual features to tell there is 'baskets' and not similar things in a photo:\twoven or mesh-like material\thandles\tfor carrying and storing different items\tvarious shapes and sizes.", 209], "glass bowl": ["Yes. 'Glass bowl' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'glass bowl' but are not 'glass bowl' are:\tvases\tfish tanks\tdecorative orbs\tcandy dishes\nThere are several useful visual features to tell there is a 'glass bowl' and not similar things in a photo:\tclear and transparent material\tusing for food or liquid holding\ta round shape\twith a flat base and curved sides\tsmooth surface\twith no seams or cracks", 209], "balloons": ["Yes. 'Balloons' has a tangible appearance and is a type of inflatable object.\nA few things that are visually similar to 'balloons' but are not 'balloons' are:\tbubbles\tbeach balls\tfloating toys\tinflatable Christmas decorations\nThere are several useful visual features to tell there is 'balloons' and not similar things in a photo:\toval shape\tinflatable\tpartially transparent\tbrightly colored\twith a string or ribbon attached\tto be used for decoration or celebration.", 209], "cupcakes": ["Yes. 'Cupcakes' has a tangible appearance and is a kind of baked dessert.\nA few things that are visually similar to 'cupcakes' but are not 'cupcakes' are:\tmuffins\tscones\tbiscuits\tdonuts\nThere are several useful visual features to tell there is 'cupcakes' and not similar things in a photo:\tsmall size\tcupcake liner on the bottom\tfrosting or sprinkles on top (optional)", 209], "intersection": ["Yes. 'Intersection' has a tangible appearance and is a place where two or more roads meet.\nA few things that are visually similar to 'intersection' but are not 'intersection' are:\tcrosswalk\troundabout\ttunnel\tbridge\nThere are several useful visual features to tell there is 'intersection' and not similar things in a photo:\troad signs\tand painted road markings\ttwo or more roads meeting at an angle or point\tcars, bicycles or pedestrians traveling in different directions", 208], "plastic cup": ["Yes. 'Plastic cup' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'plastic cup' but are not 'plastic cup' are:\tglass cup\tmetal cup\tpaper cup\tceramic cup\nThere are several useful visual features to tell there is 'plastic cup' and not similar things in a photo:\tplastic material\tclear or opaque appearance\tcylindrical or cone-like shape\twith or without a handle and a lid", 208], "collection": ["No. 'Collection' is too vague or abstract to have a tangible appearance that could be distinguished in a photo.", 208], "brick sidewalk": ["Yes. 'Brick sidewalk' has a tangible appearance and is a kind of pedestrian pavement.\nA few things that are visually similar to 'brick sidewalk' but are not 'brick sidewalk' are:\tconcrete sidewalk\tpaver sidewalk\tcobblestone sidewalk\nThere are several useful visual features to tell there is 'brick sidewalk' and not similar things in a photo:\tsquares or rectangular shapes\tred or brown color\ttextured surface\tpattern created by the laying of the bricks", 207], "bolts": ["Yes. 'Bolts' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'bolts' but are not 'bolts' are:\tscrews\tpins\tnails\trivets\nThere are several useful visual features to tell there is 'bolts' and not similar things in a photo:\trod-shaped\thead at one end\tthreads along the shaft\toften used with a nut", 207], "chains": ["Yes. 'Chains' has a tangible appearance and is a type of metal link or restriction.\nA few things that are visually similar to 'chains' but are not 'chains' are:\tnecklaces\tbelts\tzippers\tcables\nThere are several useful visual features to tell there is 'chains' and not similar things in a photo:\tmetal links or loops\tchunky or heavy appearance\tshiny or rusty surface\tlooped or intertwined shapes", 207], "exit sign": ["Yes. 'Exit sign' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'exit sign' but are not 'exit sign' are:\tno exit sign\temergency door signs\tdirection signs\nThere are several useful visual features to tell there is 'exit sign' and not similar things in a photo:\trectangular shape with rounded corners\tbright green or red background with white letters\tor white with green or red letters\tword \"EXIT\" written in bold letters\tpictogram of a person running towards a door or an arrow pointing towards an exit.", 207], "biker": ["Yes. 'Biker' has a tangible appearance and refers to a person who rides a motorcycle.\nA few things that are visually similar to 'biker' but are not 'biker' are:\tmotorcycle rider\tbicycle rider\troller skater\t\nThere are several useful visual features to tell there is 'biker' and not similar things in a photo:\twearing leather clothing and boots\triding a motorcycle or a chopper\thelmet with spikes\tor a bandana around the head\tpossibly carrying motorcycle gloves\tor a chain or a wallet chain", 207], "blond woman": ["Yes. 'Blond woman' has a tangible appearance and refers to a person with specific physical characteristics.\nA few things that are visually similar to 'blond woman' but are not 'blond woman' are:\tbrunette woman\tredheaded woman\tblonde wig\tman\nThere are several useful visual features to tell there is a 'blond woman' and not similar things in a photo:\tblonde hair and eyebrows\tlight skin-tone and complexion\tsmiling face and facial features typically associated with female gender: full lips, arched eyebrows, long lashes.", 206], "plug": ["Yes. 'Plug' has a tangible appearance and is a type of connector.\nA few things that are visually similar to 'plug' but are not 'plug' are:\tslot\tscrew\tUSB cable\theadphone\nThere are several useful visual features to tell there is 'plug' and not similar things in a photo:\tthree-pronged or two-pronged shape\tmetallic or plastic material\tholes or slots for wires or cables\tElectrical cord or wire attached on one end", 206], "piano": ["Yes. 'Piano' has a tangible appearance and is a type of musical instrument.\nA few things that are visually similar to 'piano' but are not 'piano' are:\torgan\tharpsichord\tkeyboard\tmarimba\nThere are several useful visual features to tell there is 'piano' and not similar things in a photo:\tblack and white keys\t88 keys\thorizontal rectangle shape\twith or without pedals\ttypically made of wood and metal\thinged lid on top", 206], "chef": ["Yes. 'Chef' has a tangible appearance and is a person who cooks professionally.\nA few things that are visually similar to 'chef' but are not 'chef' are:\tcook\twaiter\tbaker\thome cook\nThere are several useful visual features to tell there is 'chef' and not similar things in a photo:\twearing a chef's hat and coat\twielding a knife or a cooking tool in hand\tbusy in preparing dishes\tin a commercial kitchen or restaurant setting", 206], "wallet": ["Yes. 'Wallet' has a tangible appearance and is a personal item used to carry money and cards.\nA few things that are visually similar to 'wallet' but are not 'wallet' are:\tpurses\tclutches\tcoin pouches\tpassport holders\nThere are several useful visual features to tell there is 'wallet' and not similar things in a photo:\trectangular or square shape\tsoft or hard material\tfor men: usually in dark colors such as black or brown, made of leather or fabric, may have a folding design, often with a button or a zipper; for women: usually in brighter colors or patterns, may have multiple compartments or zippers, can be made of leather, fabric, or synthetic materials.", 206], "plaid shirt": ["Yes. 'Plaid shirt' has a tangible appearance and is a kind of clothing.\nA few things that are visually similar to 'plaid shirt' but are not 'plaid shirt' are:\tstriped shirt\thoundstooth shirt\tcheckered shirt\nThere are several useful visual features to tell there is 'plaid shirt' and not similar things in a photo:\tpattern consisting of multiple colored stripes or blocks often crossing each other\tshirt with buttons usually worn as casual attire\ttypically made of cotton or flannel material", 206], "side view": ["Yes. 'Side view' has a tangible appearance and refers to a specific angle of viewing an object from its side.\nThere are no things that are visually similar to 'side view' but are not 'side view'.\nThere are no useful visual features to distinguish 'side view' from anything else in a photo because it is a specific angle of viewing and not a distinct object or feature.", 205], "side window": ["Yes. 'Side window' has a tangible appearance and is a part of a vehicle or building.\nA few things that are visually similar to 'side window' but are not 'side window' are:\tdoors\twalls\tdecorative frames\nThere are several useful visual features to tell there is 'side window' and not similar things in a photo:\ttransparent or tinted\tglass or acrylic material\trectangular or square shape\tattached to the side of a vehicle or building", 205], "bare": ["No. 'Bare' is too vague or abstract to be distinguished in a photo. It is an adjective used to describe something that lacks a usual or expected covering or adornment. \n\nIt is not possible to name things that are visually similar to 'bare' as it is a descriptive term, not a physical object.\n\nHowever, useful visual features for capturing the concept of 'bare' in a photo would be: absence of clothing, absence of leaves or foliage, lack of equipment or tools, lack of any covering or layer.", 205], "metal bench": ["Yes. 'Metal bench' has a tangible appearance as it's a type of outdoor furniture.\nA few things that are visually similar to 'metal bench' but are not 'metal bench' are:\tchair\tstool\ttable\nThere are several useful visual features to tell there is 'metal bench' and not similar things in a photo:\tmade of metal\tlong seat with a backrest\tnot designed for indoor use\tcan seat multiple people\tstructure of its legs is unique to a bench", 205], "overcast sky": ["Yes. 'Overcast sky' has a tangible appearance and is a meteorological phenomenon.\nA few things that are visually similar to 'overcast sky' but are not 'overcast sky' are:\tfog\tsmoke\tdusk\thaze\tcloud formations\nThere are several useful visual features to tell there is an 'overcast sky' and not similar things in a photo:\tgrey or dark clouds covering most or all parts of the sky\tno visible sun or blue sky\thumid or damp-looking atmosphere", 204], "twigs": ["Yes. 'Twigs' has a tangible appearance and refers to small branches or sticks.\nA few things that are visually similar to 'twigs' but are not 'twigs' are:\tleaves\troots\tgrasses\tweeds\nThere are several useful visual features to tell there are 'twigs' and not similar things in a photo:\tthin and small branches\toften with buds or leaves attached\tvariations in color, from green to brown, depending on the season and the environment.", 204], "tomato sauce": ["Yes. 'Tomato sauce' has a tangible appearance and is a type of sauce made from tomatoes.\nA few things that are visually similar to 'tomato sauce' but are not 'tomato sauce' are:\tketchup\tpasta sauce\tchili sauce\tbbq sauce\tsoy sauce\nThere are several useful visual features to tell there is 'tomato sauce' and not similar things in a photo:\tthick, liquid consistency\tdark red color\tmade from tomatoes\thigh shine on a smooth surface", 203], "left ear": ["Yes. 'Left ear' has a tangible appearance and is a body part.\nThere are no things that are visually similar to 'left ear' but are not 'left ear'.\nThere are no useful visual features for distinguishing 'left ear' from similar things in a photo because there are no similar things to a left ear.", 203], "garage": ["Yes. 'Garage' has a tangible appearance and is a type of building or structure.\nA few things that are visually similar to 'garage' but are not 'garage' are:\tshed\tbarn\twarehouse\tparking lot\nThere are several useful visual features to tell there is 'garage' and not similar things in a photo:\tattached or detached from a house\toverhead or side door for vehicle entry\trectangular or square shape with a roof and walls\tmay have windows or other features but this depends on the specific design of the garage.", 203], "brown leaves": ["Yes. 'Brown leaves' has a tangible appearance and is a type of foliage.\nA few things that are visually similar to 'brown leaves' but are not 'brown leaves' are:\tdry leaves\tdead flowers\tbark\nThere are several useful visual features to tell there are 'brown leaves' and not similar things in a photo:\tbrown or tan\tcolor of dead leaves\tthin and papery texture of dead leaves\tfall foliage on the ground, with recognizable leaf shape and veins.", 203], "parrot": ["Yes. 'Parrot' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'parrot' but are not 'parrot' are:\tcockatoo\tmacaw\ttoucan\tpigeon\nThere are several useful visual features to tell there is 'parrot' and not similar things in a photo:\tlarge, curved beaks\tbright and colorful feathers\toften green, blue, and red\tfour toes, two pointing forward and two pointing backward\tzygodactyl feet\tcapable of mimicking sounds and talking", 203], "pickle": ["Yes. 'Pickle' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pickle' but are not 'pickle' are:\tcucumber\tzucchini\tcelery\tcabbage\nThere are several useful visual features to tell there is 'pickle' and not similar things in a photo:\tpreserved in vinegar or brine\tgreen or yellow\tcolorful spices and herbs\tridged texture of the skin or flesh.", 202], "backpacks": ["Yes. 'Backpacks' has a tangible appearance and is a type of bag that is worn on the back.\nA few things that are visually similar to 'backpacks' but are not 'backpacks' are:\tpurse\tmessenger bag\tbriefcase\tduffle bag\tlaptop bag\nThere are several useful visual features to tell there is 'backpacks' and not similar things in a photo:\tshoulder straps for carrying on the back\ttop-loading design with a zipper or drawstring\tcompartments for storage", 202], "hamburger": ["Yes. 'Hamburger' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'hamburger' but are not 'hamburger' are:\tveggie burger\tsandwich\tsausage\tburrito\nThere are several useful visual features to tell there is 'hamburger' and not similar things in a photo:\tmeal between two buns\twith meat\tpatties\tslices of cheese\tlettuce, tomato, and maybe pickle or onions\tsauces like ketchup or mayo might be visible", 202], "ski jacket": ["Yes, 'ski jacket' has a tangible appearance and is a type of jacket designed for skiing.\nA few things that are visually similar to 'ski jacket' but are not 'ski jacket' are:\twinter coat\tparka\tpuffer jacket\twindbreaker\nThere are several useful visual features to tell there is 'ski jacket' and not similar things in a photo:\tbright colors, often with bold prints or patterns\tinsulated to keep skiers warm\tbuilt-in snow skirt to prevent snow from entering\tthe presence of specialized features like ski pass pockets, ventilation zips, and removable hoods", 201], "space": ["No. 'Space' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to the idea of 'space' are: dark night sky, starry sky, and foggy weather. \n\nUseful visual features that may distinguish space from similar things in a photo could include: \n\n1. A clear and dark sky that is not obstructed by clouds or pollution\n2. Stars or celestial bodies that are visible in the photo \n3. The curvature of the Earth, which may imply that the photo was taken from outer space or from a high altitude\n4. Elements of space exploration that convey a sense of technological advancement, such as a space shuttle or space station.", 201], "forehead": ["Yes. 'Forehead' has a tangible appearance and is a part of the human face.\nA few things that are visually similar to 'forehead' but are not 'forehead' are:\tchin\tcheek\tneck\tshoulder\nThere are several useful visual features to tell there is 'forehead' and not similar things in a photo:\tflat area above the eyebrows\tcurvature of the skull\tthat connects the hairline with the eyebrows.", 201], "screw": ["Yes. 'Screw' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'screw' but are not 'screw' are:\tnail\tbolt\trivet\tpin\nThere are several useful visual features to tell there is 'screw' and not similar things in a photo:\tspiral-shaped\tbody with ridges or threads at the top and a flat or pointed tip at the bottom\tmay have a slotted or cross-shaped head\tfor fastening or attaching objects with rotational force", 201], "pilot": ["Yes. 'Pilot' has a tangible appearance and is a person who operates an aircraft.\nA few things that are visually similar to 'pilot' but are not 'pilot' are: cabin crew, passengers, air traffic controllers, ground crew.\nThere are several useful visual features to tell there is 'pilot' and not similar things in a photo: wearing a flight suit, a cap and/or sunglasses; in the cockpit operating an aircraft, holding a flight control stick; communicating over headset with air traffic controllers; checking instruments, maps, or displays.", 201], "eyebrows": ["Yes. 'Eyebrows' has a tangible appearance and is a part of the human face.\nA few things that are visually similar to 'eyebrows' but are not 'eyebrows' are:\thair\tfur\teyeglasses\thats\nThere are several useful visual features to tell there is 'eyebrows' and not similar things in a photo:\tarched shape\thair-like texture\tabove the eyes\tsymmetrical to each other\tin a similar color to the hair", 199], "burner": ["Yes. 'Burner' has a tangible appearance and is a type of heating element.\nA few things that are visually similar to 'burner' but are not 'burner' are:\tfireplace\tstove\tcampfire\theated coil\nThere are several useful visual features to tell there is 'burner' and not similar things in a photo:\tflame coming from a gas or oil source\tcircular or rectangular shape\twith knobs or dials to adjust temperature embedded in a stove top or an oven", 199], "pepper shaker": ["Yes. 'Pepper shaker' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'pepper shaker' but are not 'pepper shaker' are:\tsalt shaker\tsugar shaker\tspice jar\tseasoning container\nThere are several useful visual features to tell there is 'pepper shaker' and not similar things in a photo:\tholes on top\toften has 'P' printed on top\tcylindrical shape can be made of glass, plastic or metal.", 198], "orange light": ["Yes. 'Orange light' has a tangible appearance and is a type of light.\nA few things that are visually similar to 'orange light' but are not 'orange light' are:\tyellow light\tred light\tsunrise\tsunset\tfire\nThere are several useful visual features to tell there is 'orange light' and not similar things in a photo:\thues of orange\tcolor temperature around 2000-3000K\tmight create a warm, cozy atmosphere", 198], "shrubbery": ["Yes. 'Shrubbery' has a tangible appearance and refers to a collection of bushes or small trees.\nA few things that are visually similar to 'shrubbery' but are not 'shrubbery' are:\tgrass lawn\thedges\tvines\tflower beds\nThere are several useful visual features to tell there is 'shrubbery' and not similar things in a photo:\tbushy and leafy plants\twith woody or green stems\tvaried heights and shapes\toften found in groups or clusters.", 198], "door frame": ["Yes. 'Door frame' has a tangible appearance and is a part of a door structure.\nA few things that are visually similar to 'door frame' but are not 'door frame' are:\twindow frame\twall corner\tshelf edge\tbookcase frame\nThere are several useful visual features to tell there is 'door frame' and not similar things in a photo:\tlocated around the perimeter of the door frame\tsquared shape or rectangular shape\tcould be made of wood, metal, or other material\tdistinguished from the other frames in the room", 198], "grey sky": ["Yes. 'Grey sky' has a tangible appearance.\nA few things that are visually similar to 'grey sky' but are not 'grey sky' are:\tsmoke\tfog\tclouds\nThere are several useful visual features to tell there is 'grey sky' and not similar things in a photo:\tUniform grey coloring\tCovering the entire sky\tLack of visible sun or stars\tFamiliar outdoor settings such as trees, buildings or streets", 198], "ceiling light": ["Yes. 'Ceiling light' has a tangible appearance and is a kind of lighting fixture.\nA few things that are visually similar to 'ceiling light' but are not 'ceiling light' are:\ttable lamp\tfloor lamp\tcandles\tsunlight\nThere are several useful visual features to tell there is 'ceiling light' and not similar things in a photo:\tattached to the ceiling\tfixed or adjustable direction\tdiffuse, directional or angled light\tbulb or light source visible", 198], "pebbles": ["Yes. 'Pebbles' has a tangible appearance and is a small, smooth, rounded rock.\nA few things that are visually similar to 'pebbles' but are not 'pebbles' are:\tgravel\tsand\tpumice stone\tsea glass\nThere are several useful visual features to tell there is 'pebbles' and not similar things in a photo:\tsmall size\tsmooth surface\trounded shape\tvariety of colors\ttypically found near water or in natural settings", 198], "blue bus": ["Yes. 'Blue bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'blue bus' but are not 'blue bus' are:\ttrucks\tother colored buses\ttaxis\nThere are several useful visual features to tell there is 'blue bus' and not similar things in a photo:\tblue color\tlarge size\tlong shape\twindows\twheel covers\tdoors", 197], "wristwatch": ["Yes. 'Wristwatch' has a tangible appearance and is a kind of timepiece.\nA few things that are visually similar to 'wristwatch' but are not 'wristwatch' are:\tbracelet\tactivity tracker\talarm clock\tpocket watch\nThere are several useful visual features to tell there is 'wristwatch' and not similar things in a photo:\tstrapped to wrist or arm\tdisplaying digital or analog time\tusually small enough to fit on wrist\tdials or buttons to adjust time or features", 197], "paper towel": ["Yes. 'Paper towel' has a tangible appearance and is a type of absorbent paper.\nA few things that are visually similar to 'paper towel' but are not 'paper towel' are:\tnotebook paper\ttissues\ttoilet paper\tnapkins\tdisposable wipes\nThere are several useful visual features to tell there is 'paper towel' and not similar things in a photo:\tthicker and more absorbent than notebook paper\tnot in a roll like toilet paper\tnot as soft as tissues with lotion\ttypically used for cleaning and spills, not for drying hands.", 197], "slats": ["Yes. 'Slats' has a tangible appearance and is a sequence of parallel long, thin, narrow pieces of wood, metal, or plastic that are spaced apart.\nA few things that are visually similar to 'slats' but are not 'slats' are:\tboards\tfences\tshutters\t\nThere are several useful visual features to distinguish 'slats' from the listed similar things in a photo:\tparallel and evenly spaced boards or pieces\tused for ventilation or shading purposes\tcan be movable or fixed", 197], "pastries": ["Yes. 'Pastries' has a tangible appearance and refers to a type of baked goods.\nA few things that are visually similar to 'pastries' but are not 'pastries' are:\tbread\tcakes\tdo-nuts\tbagels\nThere are several useful visual features to tell there is 'pastries' and not similar things in a photo:\tflaky or crispy texture\tvariety of shapes, such as croissants, turnovers, or danishes\tsweet or savory fillings\tdough made with butter or lard.", 197], "entertainment center": ["Yes. 'Entertainment center' has a tangible appearance and is a piece of furniture used to hold electronic devices and media.\nA few things that are visually similar to 'entertainment center' but are not 'entertainment center' are:\tbookshelf\tshelves\tdesks\ttv stand\nThere are several useful visual features to tell there is 'entertainment center' and not similar things in a photo:\troom for TV\tmultiple shelves or compartments\tfor storing electronics\tand media\tusually placed against a wall or in a corner.", 197], "pedestrians": ["Yes. 'Pedestrians' has a tangible appearance and is a type of person who is walking on foot.\nA few things that are visually similar to 'pedestrians' but are not 'pedestrians' are:\tcyclists\tmotorcyclists\tjoggers\tpets\tscooters\nThere are several useful visual features to tell there is 'pedestrians' and not similar things in a photo:\tpeople walking on their feet\tsidewalks or crosswalks in the background\tpeople carrying bags, backpacks, or purses\tfacial features indicating walking (e.g. mid-stride, stepping, etc.)", 197], "radio": ["Yes. 'Radio' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'radio' but are not 'radio' are:\talarm clock\tBluetooth speaker\tsound system\twalkie-talkie\nThere are several useful visual features to tell there is 'radio' and not similar things in a photo:\tfrequency dial\ttuner knobs or buttons\tspeaker grille or grill\tan antenna or aerial power cord or battery compartment\tpush buttons or toggle switches\tlabels with AM/FM or other radio bands", 196], "pencil": ["Yes. 'Pencil' has a tangible appearance and is a type of writing tool.\nA few things that are visually similar to 'pencil' but are not 'pencil' are:\tpen\tcrayon\tmarker\tchalk\nThere are several useful visual features to tell there is 'pencil' and not similar things in a photo:\tslim cylindrical shape\twooden or plastic casing\tyellow exterior\tsharpened tip\tlead in the center", 196], "highway": ["Yes. 'Highway' has a tangible appearance and is a type of road for high-speed traffic.\nA few things that are visually similar to 'highway' but are not 'highway' are:\tstreets\talleys\tparking lots\tside roads\nThere are several useful visual features to tell there is 'highway' and not similar things in a photo:\tmultiple lanes\tfor high-speed traffic\thigh volume of vehicles\tseparated from other roads by barriers or wide medians\tinterchanges and exits for entering or leaving the road.", 196], "statues": ["Yes. 'Statues' has a tangible appearance and refers to a three-dimensional representation of a person, animal, or object made of various materials.\nA few things that are visually similar to 'statues' but are not 'statues' are:\tmannequins\tfigurines\ttrophies\tbusts\nThere are several useful visual features to tell there is 'statues' and not similar things in a photo:\tlife-size or larger\tscale and proportion to a real person or object\tdetails such as facial expressions, clothing, texture, and pose\tstand or base supporting the statue", 195], "paper napkin": ["Yes. 'Paper napkin' has a tangible appearance and is a type of napkin made of paper.\nA few things that are visually similar to 'paper napkin' but are not 'paper napkin' are:\tcloth napkin\thandkerchief\ttissue\twipes\tpaper towel\nThere are several useful visual features to tell there is 'paper napkin' and not similar things in a photo:\tmade of paper or fiber material\tfolded or rolled into a smaller size\tused for wiping mouth or hands\tdisposable after a single use\tpotential holiday or decorative prints", 194], "cabbage": ["Yes. 'Cabbage' has a tangible appearance and is a type of leafy vegetable.\nA few things that are visually similar to 'cabbage' but are not 'cabbage' are:\tlettuce\tkale\tspinach\tbroccoli\tcauliflower\nThere are several useful visual features to tell there is 'cabbage' and not similar things in a photo:\t\nlarge, round or oval head of tightly packed leaves\tcrisscrossing, wavy or smooth appearance\tof green, red or white color\tthick stem toward the base", 194], "sheeps": ["Yes. 'Sheeps' has a tangible appearance and is a kind of domestic animal.\nA few things that are visually similar to 'sheeps' but are not 'sheeps' are:\tgoats\tdeer\tcows\talpacas\nThere are several useful visual features to tell there is 'sheeps' and not similar things in a photo:\twoolly appearance\tcurved horns (if male)\tsplit hooves (two toes on each foot)\teaten grasses or other vegetation alongside other sheep\tbroad, flat face", 194], "televisions": ["Yes. 'Televisions' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'televisions' but are not 'televisions' are:\tcomputer monitor\tprojector\tscreen\nThere are several useful visual features to tell there is 'televisions' and not similar things in a photo:\tthin, flat panel display\tremote control\tor buttons and knobs\ton-screen images, videos or shows\tan antenna or cable connection", 194], "barrel": ["Yes, 'barrel' has a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'barrel' but are not 'barrel' are:\tTub\tCrate\tCask\tCylinder\nSome useful visual features for distinguishing 'barrel' from the listed similar things in a photo are:\tcylindrical shape\tbulging midsection\tmetal or wooden material\twith metal hoops\tasymmetrical opening or bung hole at the top or side.", 194], "stems": ["Yes. 'Stems' has a tangible appearance and is a part of a plant.\nA few things that are visually similar to 'stems' but are not 'stems' are:\tbranches\ttrunks\tstalks\tof fruits or vegetables\nThere are several useful visual features to tell there is 'stems' and not similar things in a photo:\tcylindrical shape\tgreen color\tconnected to leaves or flowers", 193], "iron fence": ["Yes. 'Iron fence' has a tangible appearance and is a type of barrier.\nA few things that are visually similar to 'iron fence' but are not 'iron fence' are:\twire fence\twooden fence\tchain-link fence\thedge wall\nThere are several useful visual features to tell there is 'iron fence' and not similar things in a photo:\tmade of metal bars or rods\thorizontal or vertical bars, sometimes with decorative ornaments\tsymmetrical and evenly spaced bars or rods", 193], "caps": ["Yes. 'Caps' have a tangible appearance and refer to headwear.\nA few things that are visually similar to 'caps' but are not 'caps' are:\thats\tbonnets\thelmets\theadscarves\nThere are several useful visual features to tell there are 'caps' and not similar things in a photo: brim at the front a visor or peak, and can be adjustable or have straps to secure them on the head, they can either be curved or flat or baseball-style caps usually come with a small button on top of the cap.", 193], "nuts": ["Yes. 'Nuts' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'nuts' but are not 'nuts' are:\trocks\tseeds\tbolts\nThere are several useful visual features to tell there are 'nuts' and not similar things in a photo:\tvaried shapes and sizes\thard exterior and soft interior\tbrown, tan, or beige colors\tridged or textured surface\tshells or husks that can be opened to reveal the nut inside.", 193], "plastic chair": ["Yes. 'Plastic chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'plastic chair' but are not 'plastic chair' are:\twooden chair\tmetal chair\ttire\tswimming pool float\nThere are several useful visual features to tell there is 'plastic chair' and not similar things in a photo:\tmade of plastic\tsolid back and seat\tsimilar leg shape\tfor indoor or outdoor use (patio or garden)", 193], "article clothing": ["No. 'Article clothing' is too vague or abstract to be distinguished in a photo. \n\nInstead, let's use 'shirt' as a visually concrete concept. \n\nA few things that are visually similar to 'shirt' but are not 'shirt' are:\tt-shirt\tblouse\tsweater\tjacket\n\nThere are several useful visual features to tell there is 'shirt' and not similar things in a photo:\tcollar\tbutton-down\tfront pocket\tlong-sleeved or short-sleeved", 193], "kickstand": ["Yes. 'Kickstand' has a tangible appearance and is a mechanical device attached to a bike or motorcycle.\nA few things that are visually similar to 'kickstand' but are not 'kickstand' are:\tparking meters\tsignposts\tlampposts\tbollards\nThere are several useful visual features to tell there is 'kickstand' and not similar things in a photo:\tmetallic or plastic structure\tattaching to a bike or a motorcycle\tfolds up and down\televating the bike or the motorcycle for stationary use", 193], "sandal": ["Yes. 'Sandal' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'sandal' but are not 'sandal' are:\tslipper\tshoe\t\nThere are several useful visual features to tell there is 'sandal' and not similar things in a photo:\tconsist of sole and straps\theel-less\tcovering toes and leaving other parts of the foot open\tusually worn in the summer or in warm weather.", 192], "porcelain toilet": ["Yes. 'Porcelain toilet' has a tangible appearance and is a specific type of plumbing fixture.\nA few things that are visually similar to 'porcelain toilet' but are not 'porcelain toilet' are:\tsink\twith basin and drain\tbathtub\tfountain\tpool\nThere are several useful visual features to tell there is 'porcelain toilet' and not similar things in a photo:\toval-shaped bowl\twith a seat and lid\ton a pedestal or a tank\twith a flushing mechanism or button", 192], "item clothing": ["No. 'Item clothing' is too vague or abstract to be distinguished in a photo. \n\nInstead, here are some answers for \"clothing items\":\nA few things that are visually similar to 'clothing items' but are not 'clothing items' are:\tpillows\ttowels\tblankets\trugs\nThere are several useful visual features to tell there is 'clothing items' and not similar things in a photo:\n- identifiable garment shapes such as shirts, dresses, pants, etc.\n- visible textures or materials of clothing (e.g. denim, cotton, leather, etc.)\n- detailing such as buttons, zippers, hems, and collars\n- hanging from hangers or displayed on mannequins or racks.", 192], "ice cream": ["Yes. 'Ice cream' has a tangible appearance and is a frozen dessert made of milk or cream.\nA few things that are visually similar to 'ice cream' but are not 'ice cream' are:\tfrozen yogurt\tgelato\tsorbet\twhipped cream\nThere are several useful visual features to tell there is 'ice cream' and not similar things in a photo:\tcreamy texture\tmelting\twhen in a cone or a bowl with a spoon\tvariety of flavors\tcold appearance and environment topped with syrups or toppings", 192], "mast": ["Yes. 'Mast' has a tangible appearance and is a vertical pole on a boat or a ship that supports sails.\nA few things that are visually similar to 'mast' but are not 'mast' are:\ttree\tlamppost\tbuilding tower\tsmokestack\nThere are several useful visual features to tell there is 'mast' and not similar things in a photo:\tlocated on the deck or the hull of a boat or a ship\tsupporting a sail or a flag\ttapered shape\tropes, lines, or rigging attached to it.", 192], "cliff": ["Yes. 'Cliff' has a tangible appearance and is a steep rock face.\n\nA few things that are visually similar to 'cliff' but are not 'cliff' are:\n- Slopes\n- Hills\n- Mountains\n\nThere are several useful visual features to distinguish 'cliff' from the listed similar things in a photo:\n- A vertical drop or steep incline\n- A rocky or sheer face\n- No vegetation on the face of the rock\n- Often positioned at the edge of a body of water", 192], "fender": ["Yes. 'Fender' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'fender' but are not 'fender' are:\tbumper\thubcap\tmirror\tdoor\thandle\nThere are several useful visual features to tell there is 'fender' and not similar things in a photo:\tcurved shape\tattached to the side of a vehicle\tmade of metal or plastic\tmeant to protect the vehicle from damage or debris", 192], "sunset": ["Yes. 'Sunset' has a tangible appearance and is a natural event when the sun descends below the horizon.\nA few things that are visually similar to 'sunset' but are not 'sunset' are:\tsunrise\teclipse\tclouds with red and yellow hues\nThere are several useful visual features to tell there is 'sunset' and not similar things in a photo:\torange, pink, purple, and red colors in the sky\ta flat horizon with the sun receding below it\tlong shadows being cast\taccompanying reflections on water or windows", 192], "food item": ["Yes. 'Food item' has a tangible appearance and refers to any type of food.\nA few things that are visually similar to 'food item' but are not 'food item' are:\tplastic fruit models\tpotpourri\tdecorative pie pillows\twax food models\nThere are several useful visual features to tell there is 'food item' and not similar things in a photo:\tedible and safe for consumption\trecognizable type of food (e.g., fruit, sandwich, pizza)\tvarious textures, colors, and shapes\twell-prepared or presented for consumption (e.g., cooked, served on a plate)", 191], "writings": ["No. 'Writings' are too vague or abstract to be distinguished in a photo.", 191], "urinals": ["Yes. 'Urinals' have a tangible appearance and are a type of bathroom fixtures.\nA few things that are visually similar to 'urinals' but are not 'urinals' are:\ttoilets\tfloor drains\tsinks\nThere are several useful visual features to tell there are 'urinals' and not similar things in a photo:\t\nvertical mounted ceramic bowls\t\nlong and narrow shape\t\nlow to the ground\t\ntypically have a privacy divider between adjacent units.", 191], "dinner plate": ["Yes. 'Dinner plate' has a tangible appearance and is a type of tableware.\nA few things that are visually similar to 'dinner plate' but are not 'dinner plate' are:\tbowls\tplacemats\ttrays\nThere are several useful visual features to tell there is 'dinner plate' and not similar things in a photo:\tcircular or oval shape\tflat base\twith or without a rim\torally used for serving food or eating from\tmade of ceramic, glass or plastic materials.", 190], "bed sheet": ["Yes. 'Bed sheet' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'bed sheet' but are not 'bed sheet' are:\ttablecloth\ttowel\tshower curtain\tblanket\nThere are several useful visual features to tell there is 'bed sheet' and not similar things in a photo:\tthin and lightweight\trectangular or square shape\tflat or slightly folded\tsolid color or simple patterned used to cover a bed", 190], "wii": ["Yes. 'Wii' has a tangible appearance and is a type of video game console.\nA few things that are visually similar to 'wii' but are not 'wii' are:\tXbox\tPlayStation\tNintendo Switch\nThere are several useful visual features to tell there is 'wii' and not similar things in a photo:\trectangular box-shaped object\twith 'Wii' branding\twhite color, though black color also exists\tconnected to a TV or screen\tusing a wireless remote controller", 190], "officer": ["Yes. 'Officer' has a tangible appearance.\nA few things that are visually similar to 'officer' but are not 'officer' are:\tsecurity guard\tpolice mannequin\tactor playing a police officer\nThere are several useful visual features to tell there is 'officer' and not similar things in a photo:\tuniform with a badge or emblem\that or cap with a badge or emblem\trank insignia on the collar or shoulder\tepaulettes on the shoulder or sleeve\tbelt with a holster for a weapon", 190], "mousepad": ["Yes. 'Mousepad' has a tangible appearance and is a type of computer accessory.\nA few things that are visually similar to 'mousepad' but are not 'mousepad' are:\tcoasters\ttable placemats\tbookmarks\nThere are several useful visual features to tell there is 'mousepad' and not similar things in a photo:\trectangle-shaped\tthick or padded surface\twith a printed or textured design\ton a desk or tabletop\tnext to a computer mouse", 190], "brand": ["No. 'Brand' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we're talking about the visual representation of a brand, then yes, it can be a visually concrete concept. \n\nA few things that are visually similar to the visual representation of a 'brand' but are not 'brand' are:\tgraphics\tlogos\ttypography\t\n\nThere are several useful visual features to tell there is a 'brand' and not similar things in a photo:\t\n- Consistency in the visual elements used across various brand assets \n- Use of specific brand colors \n- Incorporation of brand name or logo \n- Use of a particular type of imagery or photography style \n- Use of specific typography or font styles", 190], "desks": ["Yes. 'Desks' has a tangible appearance and is a piece of furniture used for working or studying.\nA few things that are visually similar to 'desks' but are not 'desks' are:\ttables\tworkbenches\tshelves\tbookcases\nThere are several useful visual features to tell there is 'desks' and not similar things in a photo:\tflat surface\tfor writing or typing\tdrawers, compartments or shelves\tfor keeping supplies or documents, or other objects\tdesigned for sitting at or standing near\twhile working.", 190], "blue light": ["Yes. 'Blue light' has a tangible appearance and is a kind of light with a blue hue.\nA few things that are visually similar to 'blue light' but are not 'blue light' are:\tblue sky\tblue water\tblue paint\tblue eyes\nThere are several useful visual features to tell there is 'blue light' and not similar things in a photo:\tbluish hue\temit light or glow\tlight source\treflection or refraction of light in a blue color", 190], "tissue box": ["Yes. 'Tissue box' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'tissue box' but are not 'tissue box' are:\tshoe box\tstorage box\tkitchen tissue holder\tdrawer\nThere are several useful visual features to tell there is 'tissue box' and not similar things in a photo:\trectangular shape\tcardboard or plastic material\topening on top\tfor tissues or paper mementos\thollow center to hold a stack of tissues.", 190], "cell": ["Yes. 'Cell' has a tangible appearance and is a microscopic structure.\nA few things that are visually similar to 'cell' but are not 'cell' are:\tdots\tbubbles\tpores\tbeads\nThere are several useful visual features to tell there is 'cell' and not similar things in a photo:\tvery small, typically microscopic\tsize and shape vary depending on the type of cell\tcertain cells have a nucleus, cytoplasm and cell membrane\tassembled in groups to create tissues, organs and organisms", 190], "breads": ["Yes. 'Breads' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'breads' but are not 'breads' are:\tcakes\tpastries\tbiscuits\tmuffins\nThere are several useful visual features to distinguish 'breads' from the listed similar things in a photo:\tloaf-shaped\tusually brown on the outside\tairy or slightly dense texture\tcrusty or soft surface\tcan have seeds, nuts, or grains\ton bread slices, there may be visible crumb texture.", 190], "dot": ["Yes. 'Dot' has a tangible appearance and is a small mark or point.\nA few things that are visually similar to 'dot' but are not 'dot' are:\tcircle\tspot\tbubble\nThere are no significant visual features to distinguish 'dot' from the listed similar things in a photo. However, a dot is often a small, solid circle, whereas a spot or bubble may have a blurred or fuzzy edge.", 190], "thin": ["No. 'Thin' is too vague or abstract to be distinguished in a photo.", 189], "bus driver": ["Yes, 'bus driver' has a tangible appearance and is a person driving a bus.\nA few things that are visually similar to 'bus driver' but are not 'bus driver' are:\tDelivery driver\tTaxi driver\tUber/Lyft driver\tPolice officer\nThere are several useful visual features to tell there is 'bus driver' and not similar things in a photo:\tWearing uniform or ID badge\tDriving a bus or standing next to a bus\tChecking tickets or driving controls\tCommunicating with passengers", 189], "mailbox": ["Yes. 'Mailbox' has a tangible appearance and is a kind of container for receiving mail.\nA few things that are visually similar to 'mailbox' but are not 'mailbox' are:\tgarbage bin\tnewspaper box\tutility box\nThere are several useful visual features to tell there is 'mailbox' and not similar things in a photo:\trectangular or cylindrical shape\twith a flag or symbol indicating it is for mail collection\topening on the front or top\tof metal or sturdy plastic material", 189], "skyscraper": ["Yes. 'Skyscraper' has a tangible appearance and is a kind of tall building.\nA few things that are visually similar to 'skyscraper' but are not 'skyscraper' are:\ttower\tchimney\tbridge\nThere are several useful visual features to tell there is 'skyscraper' and not similar things in a photo:\ta tall building with more than 40 floors or more than 150 meters high\tsymmetrical structure\tbuildings that located in the city center or financial buildings\twith glass or reflective exterior panels", 189], "brake light": ["Yes. 'Brake light' has a tangible appearance and is a type of signal light on a vehicle.\nA few things that are visually similar to 'brake light' but are not 'brake light' are:\ttaillight\tsignal light\theadlight\tLED light bar\nThere are several useful visual features to tell there is 'brake light' that differentiate it from similar things in a photo:\tbright red color\tlocated at the rear of the vehicle\tilluminates when the brake pedal is pressed.", 188], "tablet": ["Yes. 'Tablet' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'tablet' but are not 'tablet' are:\tlaptop\te-reader\tsmartphone\t\nThere are several useful visual features to tell there is 'tablet' and not similar things in a photo:\trectangular shape\tscreen in the center\tof the device\twithout a keyboard or physical buttons\tcan be held in one hand\twith touch screen interface", 188], "knee pad": ["Yes. 'Knee pad' has a tangible appearance and is a kind of protective gear.\nA few things that are visually similar to 'knee pad' but are not 'knee pad' are:\telbow pad\tshoulder pad\tshin guard\nThere are several useful visual features to tell there is 'knee pad' and not similar things in a photo:\tworn on the knee\tarea of cushioning\tfor sports or manual work\tpadded with foam or plastic material\tsoft or hard shell on outside\tloop or strap to secure on the leg", 188], "hinge": ["Yes. 'Hinge' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'hinge' but are not 'hinge' are:\tknobs\thandles\tlatches\tclasps\nThere are several useful visual features to tell there is 'hinge' and not similar things in a photo:\ta flat metal piece with two or more plates\tattached to a door, window or lid\table to rotate or pivot to allow movement or closing\toften exposed and visible", 188], "orange shirt": ["Yes. 'Orange shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'orange shirt' but are not 'orange shirt' are:\torange dress\torange jacket\nThere are several useful visual features to tell there is 'orange shirt' and not similar things in a photo:\tshirt design\tshort or long sleeves\tcollar or no collar\tbuttons or zippers on the front\tfabric texture and pattern", 188], "head lights": ["Yes. 'Head lights' has a tangible appearance and refers to the front lights on a vehicle.\nA few things that are visually similar to 'head lights' but are not 'head lights' are:\tstreet lamps\tflashlights\tlanterns\tbike lights\nThere are several useful visual features to tell there is 'head lights' and not similar things in a photo:\tlocated on the front of a vehicle\tusually in a pair\tcircular or oval shape\tbright light beam\tprojecting forward from a vehicle", 188], "screen television": ["Yes. 'Screen television' has a tangible appearance and is a device for displaying audio and video content.\nA few things that are visually similar to 'screen television' but are not 'screen television' are: computer monitor, projector, tablet.\nThere are several useful visual features to tell there is 'screen television' and not similar things in a photo: rectangular shape, built-in speakers, a stand or wall mount, attached to a cable or satellite box, remote control.", 188], "lampshade": ["Yes. 'Lampshade' has a tangible appearance and is a type of lighting accessory.\nA few things that are visually similar to 'lampshade' but are not 'lampshade' are:\thats\tbowls\tvases\tcups\nThere are several useful visual features to tell there is 'lampshade' and not similar things in a photo:\tfits over a lamp or light bulb\thas an open top and bottom\tcylindrical, conical, or drum-shaped\tformal or casual designs\tfabric, glass, or paper material", 188], "window frame": ["Yes. 'Window frame' has a tangible appearance and is a structure surrounding a window. \nA few things that are visually similar to 'window frame' but are not 'window frame' are:\tpicture frame\tdoor frame\tshower frame\nThere are several useful visual features to tell there is 'window frame' and not similar things in a photo:\topen space to see through\tsquare or rectangular shape\tframe corners that meet at 90-degree angles\thorizontal or vertical panels or bars surrounding the opening.", 188], "sedan": ["Yes. 'Sedan' has a tangible appearance and is a type of car.\nA few things that are visually similar to 'sedan' but are not 'sedan' are:\tCoupe\tSUV\tRV\tTruck\nThere are several useful visual features to tell there is 'sedan' and not similar things in a photo:\tfour doors\tthree-box style\tlower roofline\tfits up to five passengers\ttwo rows of seats", 186], "zebra grazing": ["Yes. 'Zebra grazing' has a tangible appearance and depicts a zebra eating grass.\nA few things that are visually similar to 'zebra grazing' but are not 'zebra grazing' are: horse running, horse grazing, deer grazing, antelope grazing.\nThere are several useful visual features to tell there is 'zebra grazing' and not similar things in a photo: black and white stripes, a grassy background, a long and slender muzzle, erect ears, grazing posture.", 186], "roadway": ["Yes. 'Roadway' has a tangible appearance.\nA few things that are visually similar to 'roadway' but are not 'roadway' are: \tpathways\tsidewalks\tparking lots\ttrails\nThere are several useful visual features to tell there is 'roadway' and not similar things in a photo:\tstraight or winding path\tfor vehicles or transportation\tpainted lines or markings on the surface\tmay have signs or signals for controlling traffic\tasphalt, concrete, or gravel surface with markings for different lanes", 186], "stuffed": ["No. 'Stuffed' is too vague or abstract to be distinguished in a photo. It could refer to many different things like stuffed animals, stuffed toys or even stuffed food, and each would have its own set of visual features to distinguish from other things. \n\nAs for things visually similar to 'stuffed,' it would depend on the context. For example, if 'stuffed' referred to a type of food, things that could be visually similar but not 'stuffed' might include: regular cooked meat, vegetables or fruit, bread, etc. \n\nTherefore, there are no useful visual features to distinguish 'stuffed' from similar things in a photo unless the context is specified.", 186], "weather": ["No. 'Weather' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to certain weather conditions but are not 'weather' are:\n\n- A spray bottle mist can resemble fog or mist from a distance but is not actual weather.\n- A smoke or fog machine can create a cloud-like effect, but it is artificial and not the result of actual weather conditions.\n- A humidifier can create a moist atmosphere, but this is not the same as a humid day caused by weather. \n\nThere are no particular visual features that can distinguish 'weather' from the above-mentioned objects, as they are not actual weather conditions.", 186], "stabilizer": ["Yes. 'Stabilizer' has a tangible appearance and is a device used to stabilize or balance something.\nA few things that are visually similar to 'stabilizer' but are not 'stabilizer' are:\tshock absorber\tpillow\tjack stand\nThere are several useful visual features to tell there is 'stabilizer' and not similar things in a photo:\tadjustable\tmounted or attached to a larger object\thas moving parts or mechanisms (such as springs and hinges)\tdesigned to prevent or reduce movement or shaking", 186], "chunk": ["No. 'Chunk' is too vague and abstract to be visualized.", 186], "berries": ["Yes. 'Berries' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'berries' but are not 'berries' are: \tgrapes\tcherry tomatoes\tpoisonous berries\tpeas\tNerf Vortex footballs\nThere are several useful visual features to tell there are 'berries' and not similar things in a photo: \tsmall, fleshy fruit with seeds bright and vivid colors, such as red, blue, purple, black or orange. They are found in bunches on plants or trees, and they may be round or oblong in shape. Berries will have stems, whereas some other similarly looking things won't have them.", 186], "pans": ["Yes. 'Pans' has a tangible appearance and is a kind of cookware.\nA few things that are visually similar to 'pans' but are not 'pans' are:\tplates\tbowls\tlids\n\t\nThere are several useful visual features to tell there is 'pans' and not similar things in a photo:\tmetallic material\tcircular or oval shape\twith a handle or handles\t\n\nNote: When distinguishing 'pans' from lids, it can be useful to check if there is a handle or if the object has an indentation for it.", 185], "blonde": ["Yes. 'Blonde' has a tangible appearance and refers to a hair color.\nA few things that are visually similar to 'blonde' but are not 'blonde' are:\tlight brown hair\tdirty blonde hair\tplatinum blonde hair\tsilver or grey hair\nThere are several useful visual features to tell there is 'blonde' and not similar things in a photo:\tvery light hair color\tyellow or golden tones in the hair color\tlighter than brown but not as light as platinum or silver", 185], "place mat": ["Yes. 'Place mat' has a tangible appearance and is a type of table setting.\nA few things that are visually similar to 'place mat' but are not 'place mat' are:\ttable cloth\tnapkin\tpaper towel\twrapping paper\nThere are several useful visual features to tell there is 'place mat' and not similar things in a photo:\trectangular or square shape\tlarger than a napkin but smaller than a tablecloth\tmade of fabric or natural materials, such as bamboo or straw\tplaced under a plate or a table setting", 185], "glass jar": ["Yes. 'Glass jar' has a tangible appearance and is a type of container made of glass.\nA few things that are visually similar to 'glass jar' but are not 'glass jar' are:\tbottle\tcan\ttumbler\tcup\nThere are several useful visual features to tell there is 'glass jar' and not similar things in a photo:\tcylindrical or rounded shape\tmade of clear glass\tor colored glass\twith a metal lid or closure\tno handle or a small handle", 185], "tv stand": ["Yes. 'TV stand' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'TV stand' but are not 'TV stand' are:\tshelves\tdresser\tbookcase\ttable\nThere are several useful visual features to tell there is 'TV stand' and not similar things in a photo:\tdesigned to hold a television set or monitor\thorizontal surface at the appropriate height for comfortable viewing\tshelves or drawers for media components or storage", 185], "oar": ["Yes. 'Oar' has a tangible appearance and is a type of tool used for rowing boats.\nA few things that are visually similar to 'oar' but are not 'oar' are:\tPaddle\tStick\tRod\tSword\nThere are several useful visual features to tell there is 'oar' and not similar things in a photo:\tLong and flat\tBlade at one end\tHandle or grip at the other end\tUsed for rowing boats or similar watercraft", 185], "dirt path": ["Yes. 'Dirt path' has a tangible appearance and can easily be identified in a photo.\nA few things that are visually similar to 'dirt path' but are not 'dirt path' are:\tmud track\tanimal trail\tsidewalk\tbeach sand\nThere are several useful visual features to tell there is 'dirt path' and not similar things in a photo:\tthe color of dirt and soil\tis surrounded by a natural environment\tfootprints of human or animal on it\tis narrower than a road", 185], "stuffed animals": ["Yes. 'Stuffed animals' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'stuffed animals' but are not 'stuffed animals' are:\treal animals\tplush pillows\nThere are several useful visual features to tell there is 'stuffed animals' and not similar things in a photo:\tsoft and plush\tfurry or fluffy\tmade to look like a specific animal or creature\thave seams and stuffing visible\tglass or plastic eyes and nose (not real ones)", 184], "instrument": ["Yes. 'Instrument' has a tangible appearance and is a tool for making music.\nA few things that are visually similar to 'instrument' but are not 'instrument' are:\ttools \tmachines \telectronics \nThere are several useful visual features to tell there is 'instrument' and not similar things in a photo:\tunique shapes and sizes\tstrings or keys\tbutton panels\tsound holes or hollow body", 184], "monkey": ["Yes. 'monkey' has a tangible appearance and is a type of primate.\nA few things that are visually similar to 'monkey' but are not 'monkey' are:\tlemur\tgibbon\tchimpanzee\torangutan\nThere are several useful visual features to tell there is 'monkey' and not similar things in a photo:\tfour-limbed primate\ttail (in most species)\tround face\twith hair or fur covering the body\tand uniquely shaped ears, eyes, and nose.", 184], "stoplight": ["Yes. 'Stoplight' has a tangible appearance and is a type of traffic signal.\nA few things that are visually similar to 'stoplight' but are not 'stoplight' are:\tstreet signs\tparking meters\tconstruction barriers\t\nThere are several useful visual features to tell there is 'stoplight' and not similar things in a photo:\ttraffic signal with red, yellow, and green circles\thanging above a street or intersection\tlight cones pointing in different directions", 184], "quilt": ["Yes. 'Quilt' has a tangible appearance and is a type of bedding or decorative textile.\nA few things that are visually similar to 'quilt' but are not 'quilt' are:\tcomforter\tduvet cover\tblanket\t\nThere are several useful visual features to tell there is 'quilt' and not similar things in a photo:\tpatchwork or stitched together\tfrom scraps or different fabrics\tcommonly has a repeating pattern or design\tlayered with batting or fill\thas distinct borders or edging.", 184], "trays": ["Yes. 'Trays' has a tangible appearance and is an object used for carrying or holding items.\nA few things that are visually similar to 'trays' but are not 'trays' are:\tplanks\ttables\tshelves\tcountertops\nThere are several useful visual features to tell there is 'trays' and not similar things in a photo:\tflat surface with raised edges\thandles or grips\tfor carrying or holding items\tmay have compartments or divisions.", 184], "blades": ["Yes. 'Blades' has a tangible appearance and generally refers to thin and sharp edges.\nA few things that are visually similar to 'blades' but are not 'blades' are:\tknives\tsaws\tskates\tscissors\nThere are several useful visual features to tell there is 'blades' and not similar things in a photo:\tthin and sharp edge\tno handle or hilt\tsingle or double-edged\tmay be straight or curved.", 184], "snowboards": ["Yes. 'Snowboards' has a tangible appearance and is a kind of winter sports equipment.\nA few things that are visually similar to 'snowboards' but are not 'snowboards' are:\tskis\tsleds\tskates\nThere are several useful visual features to tell there is 'snowboards' and not similar things in a photo:\tboard-shaped piece of equipment with bindings for feet, designed for sliding down a snow-covered slope.\tFlatter and wider, with just one board for the feet.\tThe snowboard has no poles or blades.", 183], "shopping bag": ["Yes. 'Shopping bag' has a tangible appearance and is a type of bag used for carrying purchased items.\nA few things that are visually similar to 'shopping bag' but are not 'shopping bag' are:\tpurse\tbackpack\ttrash bag\tgift bag\nThere are several useful visual features to tell there is 'shopping bag' and not similar things in a photo:\tpaper or plastic material\thandles\tfor carrying goods, often with a store logo or branding design printed on it.", 183], "towel rack": ["Yes. 'Towel rack' has a tangible appearance and is a type of household item.\nA few things that are visually similar to 'towel rack' but are not 'towel rack' are:\tshower rod\thandrail\tclothesline\tbar counter\nThere are several useful visual features to tell there is 'towel rack' and not similar things in a photo:\tmetallic or plastic\tattached to the wall or door\thorizontal or vertical bars\tfor holding towels or clothes", 183], "bathroom mirror": ["Yes. 'Bathroom mirror' has a tangible appearance and is an object used for reflection in a bathroom.\nA few things that are visually similar to 'bathroom mirror' but are not 'bathroom mirror' are:\tregular mirror\tmedicine cabinet\twindow\nThere are several useful visual features to tell there is 'bathroom mirror' and not similar things in a photo:\tusually attached to a wall or a cabinet\toften above a sink or a countertop\trectangular or square shape\twith or without a frame\tfoggy or steamy from a hot shower or bath\tgrime or toothpaste stains from daily use", 183], "beach chair": ["Yes. 'Beach chair' has a tangible appearance and is a kind of chair.\nA few things that are visually similar to 'beach chair' but are not 'beach chair' are:\tadirondack chair\trecliner\tlawn chair\tpool chair\nThere are several useful visual features to tell there is 'beach chair' and not similar things in a photo:\tlow to the ground\tlightweight\tfoldable\tbackrest\tand seat made of fabric or mesh\tsometimes includes an umbrella", 183], "soap dish": ["Yes. 'Soap dish' has a tangible appearance and is a container for holding soap.\nA few things that are visually similar to 'soap dish' but are not 'soap dish' are:\ttrinket dish\tcandy dish\tashtray\tbowl\tsaucer\nThere are several useful visual features to tell there is 'soap dish' and not similar things in a photo:\trectangular or oval shape\twith raised edges\tto hold soap\tgrids to allow water to drain out.", 183], "patio": ["Yes. 'Patio' is a visually concrete concept and is an outdoor living space.\nA few things that are visually similar to 'patio' but are not 'patio' are:\tdeck\tbalcony\tcourtyard\nThere are several useful visual features to tell there is 'patio' and not similar things in a photo:\toutdoor living space\tflat floor surface\toutdoor furniture and decor (chairs, tables, planters, etc.)\tuse of pavers, stones or bricks as flooring shade structure such as an umbrella or pergola", 183], "equipment": ["No. 'Equipment' is too vague or abstract to be distinguished in a photo.", 182], "blue blanket": ["Yes. 'Blue blanket' has a tangible appearance and is a specific kind of cloth.\nA few things that are visually similar to 'blue blanket' but are not 'blue blanket' are:\tblue towel\tblue shirt\tblue bedsheet\tblue carpet\nThere are several useful visual features to tell there is 'blue blanket' and not similar things in a photo:\ta rectangular shape\ta soft and fuzzy texture\themmed edges for finishing\ta size appropriate for covering a human body.", 182], "triangle": ["Yes. 'Triangle' has a tangible appearance and is a closed, three-sided shape.\nA few things that are visually similar to 'triangle' but are not 'triangle' are:\tdiamond\tpyramid\tpennant\tcone\tsail\nThere are several useful visual features to tell there is 'triangle' and not similar things in a photo:\thas three sides\tand three angles\tsum of three internal angles equals 180 degrees\tstraight lines and sharp corners on each side.", 182], "toilet paper roll": ["Yes. 'Toilet paper roll' has a tangible appearance and is a household item.\nA few things that are visually similar to 'toilet paper roll' but are not 'toilet paper roll' are:\tpaper towels\twrapping paper tubes\taluminum foil tubes\nThere are several useful visual features to tell there is 'toilet paper roll' and not similar things in a photo:\twhite or beige\tcylindrical shape\twith perforations on it\tjust the right size to fit on a toilet paper holder", 182], "side windows": ["Yes. 'Side windows' has a tangible appearance and refers to the windows on the side of a vehicle or building.\nA few things that are visually similar to 'side windows' but are not 'side windows' are:\tshop windows\tsunglasses\teyeglasses\tmirrors\nThere are several useful visual features to tell there are 'side windows' and not similar things in a photo:\tlocated on the sides of a vehicle or a building\trectangular or square in shape\ttranslucent or transparent\tallows light to pass through\tit has a frame that surrounds it.", 182], "squash": ["Yes. 'Squash' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'squash' but are not 'squash' are:\tcucumber\tzucchini\tpumpkin\tmelon\nThere are several useful visual features to tell there is 'squash' and not similar things in a photo:\tvarious shapes and sizes (round, oblong, crooked)\tsmooth or bumpy skin\tusually yellow or green\tcolorful internal pulp and seeds (when cut open)", 181], "leafless tree": ["Yes. 'Leafless tree' has a tangible appearance and is a type of tree that has lost its leaves.\nA few things that are visually similar to 'leafless tree' but are not 'leafless tree' are: tree covered in snow, tree without flowers, tree with a sparse amount of leaves\nThere are several useful visual features to tell there is 'leafless tree' and not similar things in a photo:\tno leaves on branches\tdry or brown branches with small twigs\tno green foliage or flowers in bloom", 181], "tea pot": ["Yes. 'Teapot' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'teapot' but are not 'teapot' are:\tkettle\tmug\tcup\tjug\nThere are several useful visual features to tell there is 'teapot' and not similar things in a photo:\tspout\thinged lid\thandle-rounded body\tpartition for striking up tea", 181], "shades": ["Yes. 'Shades' has a tangible appearance and refers to different types of eyewear.\nA few things that are visually similar to 'shades' but are not 'shades' are: glasses\tsunglasses\tgoggles\tmonocle\nThere are several useful visual features to tell there are 'shades' and not similar things in a photo:\tdark lenses\tthat cover the eyes or parts of the face\ta frame or a structure to hold the lenses\ti.e. aviators have wire frames, wayfarers have thick frames, etc.", 181], "ram": ["Yes. 'Ram' has a tangible appearance and is a type of male sheep.\nA few things that are visually similar to 'ram' but are not 'ram' are:\tsheep\tgoat\tmouflon\tdeer\nThere are several useful visual features to tell there is 'ram' and not similar things in a photo:\tlong, curved horns\tno beard or goatee\thair on its hindquarters and legs is thicker and coarser than on its body\twhite muzzle and rump", 181], "robe": ["Yes. 'Robe' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'robe' but are not 'robe' are:\tdress\tcoat\tcape\ttunic\nThere are several useful visual features to tell there is 'robe' and not similar things in a photo:\tloose-fitting garment\tthat covers the body\tfrom the shoulders to the feet or knees\twith or without a hood\ttypically tied around the waist with a belt or sash\tmade with soft, comfortable fabric such as cotton, silk or satin", 180], "kitchen cabinets": ["Yes. 'Kitchen cabinets' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'kitchen cabinets' but are not 'kitchen cabinets' are:\tbookshelves\tdressers\tshoe racks\tfiling cabinets\nThere are several useful visual features to tell there is 'kitchen cabinets' and not similar things in a photo:\tattached to a wall\tclosed doors\tand shelves\tfor storing kitchen items (such as utensils, dishes, glasses, and food)", 180], "metal fork": ["Yes. 'Metal fork' has a tangible appearance.\nA few things that are visually similar to 'metal fork' but are not 'metal fork' are:\tmetal spoon\tplastic fork\twooden fork\nThere are several useful visual features to tell there is 'metal fork' and not similar things in a photo:\tlong thin handle\twith usually 4 long and thin prongs at the end\tmade of metal\tshiny or reflective surface", 180], "cattle": ["Yes. 'Cattle' has a tangible appearance and is a type of livestock.\nA few things that are visually similar to 'cattle' but are not 'cattle' are:\tbison\tyak\tmoose\nThere are several useful visual features for distinguishing 'cattle' from the listed similar things in a photo:\tbranded or tagged ears\trounded horns\tupright ears\tmilk or beef-producing body types", 180], "foil": ["Yes. 'Foil' has a tangible appearance and is a kind of shiny metal.\nA few things that are visually similar to 'foil' but are not 'foil' are:\tmetallic paper\tshiny plastic cover\tpewter\tcopper sheet\nThere are several useful visual features to tell there is 'foil' and not similar things in a photo:\tshiny\tmetallic appearance\tlight reflecting surface\tmalleable/can be easily bent", 180], "clock hands": ["Yes. 'Clock hands' have a tangible appearance and is an essential component of a clock.\nA few things that are visually similar to 'clock hands' but are not 'clock hands' are:\thourglass timer\tdigital clock\tclock face diagram\nThere are several useful visual features to tell there are 'clock hands' and not similar things in a photo:\tstick-like in shape\tone short and one long pointing like an arrow\tmoving in circles on the clock face\tdifferent shapes and lengths for the hour and minute hands", 180], "bridle": ["Yes. 'Bridle' has a tangible appearance and is a piece of horse tack used to control a horse.\nA few things that are visually similar to 'bridle' but are not 'bridle' are:\tcollar\tharness\trope\nThere are several useful visual features to tell there is 'bridle' and not similar things in a photo:\tconsists of a headstall and reins for controlling a horse\tvaries in color and design\tmay have additional decorative elements such as beads or crystals\tfits around the horse's head and mouth", 179], "glass cup": ["Yes. 'Glass cup' has a tangible appearance and is a type of drinkware.\nA few things that are visually similar to 'glass cup' but are not 'glass cup' are:\tglass jar\tvase\tmug\twine glass\nThere are several useful visual features to tell there is 'glass cup' and not similar things in a photo:\tcylindrical shape\twith a handle\tclear or translucent\tmade from glass or similar material\tcan hold hot or cold drinks", 179], "boulders": ["Yes. 'Boulders' has a tangible appearance and is a type of rock.\nA few things that are visually similar to 'boulders' but are not 'boulders' are:\tpebbles\tsandbags\tmountains\tclouds\nThere are several useful visual features to tell there is 'boulders' and not similar things in a photo:\tlarge size\trough texture\tnatural irregular shapes\tdifferent shades and hues of grey or brown\tcolors and patterns caused by moss or lichen on the surface", 179], "driveway": ["Yes. 'Driveway' has a tangible appearance and is a specific type of road designed for vehicles leading to a building.\nA few things that are visually similar to 'driveway' but are not 'driveway' are:\troad\tparking lot\twalkway\ttrail\nThere are several useful visual features to tell there is 'driveway' and not similar things in a photo:\tconnected to a street or main road\tleads to a building or house\tpaved or made of concrete or asphalt\tcurbed or lined with plants or stones\tmay have a gate or fence at the beginning", 179], "piece clothing": ["No. 'Piece clothing' is too vague or abstract to be distinguished in a photo. Could you please clarify or provide a more specific term to work with?", 179], "front headlight": ["Yes. 'Front headlight' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'front headlight' but are not 'front headlight' are:\tmotorcycle headlight\tbicycle headlight\tflashlight\tlantern\nThere are several useful visual features to tell there is 'front headlight' and not similar things in a photo:\tpositioned in the front of a vehicle\tmounted on the grill or the front fender\tof a car, has a lens and a bulb or LED\tlight beam projected forward from the headlight.", 178], "pink nose": ["Yes. 'Pink nose' has a tangible appearance and is a specific feature of some animals.\nA few things that are visually similar to 'pink nose' but are not 'pink nose':\tred nose\tblush\tmake-up\tonion\tgum\nThere are several useful visual features to tell there is 'pink nose' and not similar things in a photo:\tpink or light brown color\tslightly wet surface\tlocation on the face of an animal (usually a dog, a cat, or a pig)", 178], "safety helmet": ["Yes. 'Safety helmet' has a tangible appearance and is a kind of protective headgear.\nA few things that are visually similar to 'safety helmet' but are not 'safety helmet' are:\tbaseball cap\thard hat\tmotorcycle helmet\nThere are several useful visual features to tell there is 'safety helmet' and not similar things in a photo:\trigid plastic or metal shell\tinner lining\tfastened under the chin\twithstanding impact from falling objects, debris, or electric shock\tbright colors that increase visibility in low light or high traffic environments.", 178], "area rug": ["Yes. 'Area rug' has a tangible appearance and is a type of floor covering.\nA few things that are visually similar to 'area rug' but are not 'area rug' are:\tcarpet\ttile floor\tblanket\tlinoleum\nThere are several useful visual features to tell there is 'area rug' and not similar things in a photo:\tusually covers only a portion of the floor\tmay have fringed edges or decorative patterns\tmay be made of natural or synthetic materials\tsits on top of the floor rather than being attached to it", 178], "steel": ["Yes. 'Steel' has a tangible appearance and is a type of metal.\nA few things that are visually similar to 'steel' but are not 'steel' are:\tiron\taluminum\tchrome\tsilver\nThere are several useful visual features to tell there is 'steel' and not similar things in a photo:\tgrey or silver color\tsmooth surface\tbright, but not too shiny appearance\tdense and heavy material", 178], "butterfly": ["Yes. 'Butterfly' has a tangible appearance and is a type of insect.\nA few things that are visually similar to 'butterfly' but are not 'butterfly' are:\tmoth\tgrasshopper\tcaterpillar\tbee\nThere are several useful visual features to tell there is 'butterfly' and not similar things in a photo:\tthin, curved antennae\ttwo pairs of wings\tclub-shaped endings on antennae\tbrightly colored wings with intricate patterns\tlong, thin legs\tsix legs (as an adult)\tv-shaped wing when resting", 178], "desk lamp": ["Yes. 'Desk lamp' has a tangible appearance and is a type of lighting device.\nA few things that are visually similar to 'desk lamp' but are not 'desk lamp' are:\tfloor lamp\ttable lamp\tceiling lamp\tled light desk pad\nThere are several useful visual features to tell there is 'desk lamp' and not similar things in a photo:\tsmall size\tadjustable neck\tdirectionality of light\tsource of light on the top of the pole\tor attached to a clamp\tthat can be positioned on a desk, table or other flat surface.", 178], "cigarette": ["Yes. 'Cigarette' has a tangible appearance and is a product made of tobacco.\nA few things that are visually similar to 'cigarette' but are not 'cigarette' are:\tcigarillo\tjoint\tvape pen\nThere are several useful visual features to tell there is 'cigarette' and not similar things in a photo:\tthin, cylindrical shape\twhite paper wrapped around one end\tbrown or yellow tobacco inside\tfilters or tips\tonflammable and emits smoke", 178], "wine bottles": ["Yes. 'Wine bottles' has a tangible appearance and is a type of bottle used for wine storage.\nA few things that are visually similar to 'wine bottles' but are not 'wine bottles' are:\tolive oil bottles\tvinegar bottles\tbeer bottles\tsoda bottles\nThere are several useful visual features to tell there is 'wine bottles' and not similar things in a photo:\tlong necks\tcork stoppers or screw caps\ttransparent or tinted glass\tlabels with wine-related information\tsome sediment at the bottom of the bottle (for old or unfiltered wine)", 177], "rod": ["Yes. 'Rod' has a tangible appearance and can refer to different types of objects, such as a fishing rod or a metal rod.\nA few things that are visually similar to 'rod' but are not 'rod' are:\tstick\tbar\tdowel\tpole\t\nThere are several useful visual features to tell there is 'rod' and not similar things in a photo, which depend on the specific type of rod. For example:\n- Fishing rod: long, thin, flexible, with a reel and a hook at the end.\n- Curtain rod: long, straight, usually made of metal or wood, with brackets holding it to the wall.\n- Metal rod: long, thin, cylindrical, made of metal, used for reinforcement or construction purposes.", 177], "deer": ["Yes. 'Deer' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'deer' but are not 'deer' are: \tantelope\tmoose\tcow\thorse\nThere are several useful visual features to tell there is 'deer' and not similar things in a photo:\tmedium-sized animal\twith antlers (in males) or without (in females)\tbrown fur in most species\thigh, pointed ears\tlong snout\tslender legs and hooves", 177], "cell phones": ["Yes. 'Cell phones' has a tangible appearance and is a kind of electronic device.\nA few things that are visually similar to 'cell phones' but are not 'cell phones' are:\tcameras\tpagers\tmusic players\tremote controls\twatches\nThere are several useful visual features to tell there is 'cell phones' and not similar things in a photo:\trectangular shape\twith a screen and buttons\tor with a touch screen\tand a menu of apps\tand cellular service signals", 176], "jars": ["Yes. 'Jars' have a tangible appearance and are vessels used for storage or preserving.\nA few things that are visually similar to 'jars' but are not 'jars' are:\tbottles\tvases\tcans\turns\nThere are several useful visual features to tell there is 'jars' and not similar things in a photo:\tcylindrical or rounded shape\twith a lid or cover\tmade of glass, plastic, or ceramic\tclear or transparent material\tlabelled with words like 'preserve', 'jam', or 'sauce' for kitchen jars.", 176], "armchair": ["Yes. 'Armchair' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'armchair' but are not 'armchair' are:\tcouch\tsofa\tchaise lounge\trecliner\tdining chair\nThere are several useful visual features to tell there is 'armchair' and not similar things in a photo:\tupholstered with a back and a seat\thave armrests\tmight have cushions or decorative elements\ton four legs or a pedestal", 176], "bus stop": ["Yes. 'Bus stop' has a tangible appearance and is a physical location for public transportation.\nA few things that are visually similar to 'bus stop' but are not 'bus stop' are:\tadvertisement billboard\tbench over the sidewalk\ta telephone booth\tparking spot\nThere are several useful visual features to tell there is 'bus stop' and not similar things in a photo:\tsign with information about routes and schedules\tof a certain height and width\twith a roof to protect from the elements\tmarking on the pavement to indicate where the bus stops", 176], "brown couch": ["Yes. 'Brown couch' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'brown couch' but are not 'brown couch' are:\tchair\tottoman\tloveseat\tbench\nThere are several useful visual features to tell there is 'brown couch' and not similar things in a photo:\tlarge and upholstered\tpadded back and arms\tstraight or curved lines\tbrown or shades of brown in color", 175], "tea": ["Yes. 'Tea' has a tangible appearance and is a type of beverage.\nA few things that are visually similar to 'tea' but are not 'tea' are:\tcoffee\tjuice\tcola soup\therbal infusion\nThere are several useful visual features to tell there is 'tea' and not similar things in a photo:\tliquid in a cup or a teapot\tsteaming hot\tlight brown or amber in color\tis frequently accompanied by a tea bag, sugar, honey, or milk.", 175], "earrings": ["Yes. 'Earrings' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'earrings' but are not 'earrings' are:\tpiercings\tear cuffs\tclip-ons\thair accessories\nThere are several useful visual features to tell there is 'earrings' and not similar things in a photo:\tjewelry hanging from earlobes\tvariety of shapes, sizes, and designs\tmetallic, gemstone or beaded material\tattaching to earlobes with posts, wires or clips", 175], "computer desk": ["Yes. 'Computer desk' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'computer desk' but are not 'computer desk' are:\twriting desk\tworkbench\tkitchen counter\nThere are several useful visual features to tell there is 'computer desk' and not similar things in a photo:\ttypically rectangular or L-shaped\thave a dedicated space for a computer or laptop\tinclude a keyboard tray\tor a sliding shelf for a mouse and keyboard\tmay have holes or slots for cords and wires\tto power the computer via the desk's built-in power strip or separate wall outlet.", 174], "plain": ["No. 'Plain' is too vague or abstract to be distinguished in a photo. \n\nNote for clarification: 'Plain' has multiple definitions that can be both concrete and abstract, so it depends on the context of the question. If the question refers to a wide, flat area of land, then 'plain' has a tangible appearance and can be visually concrete. But if the question refers to a simple or unadorned quality, then 'plain' is abstract and not visually concrete.", 174], "taxi cab": ["Yes. 'Taxi cab' has a tangible appearance and is a type of car used for transportation.\nA few things that are visually similar to 'taxi cab' but are not 'taxi cab' are:\tbuses\tlimousines\tregular cars\nThere are several useful visual features to tell there is 'taxi cab' and not similar things in a photo:\t\"taxi\" sign on top of the vehicle\tyellow color (in some countries)\tblack and white checkered pattern on the side of the car\ttaxi meter inside the car\tdoor numbers for identification", 174], "brown sand": ["Yes. 'Brown sand' has a tangible appearance and is a type of soil.\nA few things that are visually similar to 'brown sand' but are not 'brown sand' are:\tmud\tsoil\tdirt\tpebbles\nThere are several useful visual features to tell there is 'brown sand' and not similar things in a photo:\tfine grains\tloose and easily deformed\ttexture that reminds of sugary crystals\tbrown or pale yellow-brown color", 174], "sinks": ["Yes. 'Sinks' has a tangible appearance and is a type of fixture used for water storage.\nA few things that are visually similar to 'sinks' but are not 'sinks' are:\tbathtubs\tcontainers\tbowls\tfountains\tpools\nThere are several useful visual features to tell there is 'sinks' and not similar things in a photo:\tusually installed in a countertop or a cabinet\thas a faucet\tfor hand-washing and cleaning\tfeatures a drain and overflow hole\thas one or more basins or bowls for water", 174], "remote controls": ["Yes. 'Remote controls' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'remote controls' but are not 'remote controls' are:\tsmartphone\ttablets\tMP3 players\tcamera\nThere are several useful visual features to tell there is 'remote controls' and not similar things in a photo:\trectangular shape\twith buttons or keys\tfor controlling electronic devices or appliances\tmight have a screen\tor display\tfor channels and volume control", 173], "night stand": ["Yes. 'Night stand' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'night stand' but are not 'night stand' are:\tend table\tside table\tcoffee table\nThere are several useful visual features to tell there is 'night stand' and not similar things in a photo:\tusually located next to a bed\thas a drawer or shelves\tfor holding lamps, books, glasses, or other items\theight of around 24-30 inches", 173], "air conditioner": ["Yes. 'Air conditioner' has a tangible appearance and is a type of machine.\nA few things that are visually similar to 'air conditioner' but are not 'air conditioner' are: fans, dehumidifiers, heaters, refrigerators.\nThere are several useful visual features to tell there is 'air conditioner' and not similar things in a photo:\tsquare, rectangular or cylindrical shape, often with a grille or vent\tsometimes has digital display or control panel\trelease of cool air through vents or a hose or pipe.", 173], "tips": ["No. 'Tips' is too vague or abstract to be distinguished in a photo. The meaning of the word depends on the context in which it is used.", 173], "tags": ["Yes. 'Tags' has a tangible appearance and refers to small pieces of paper or other materials used for labeling or attaching to something.\nA few things that are visually similar to 'tags' but are not 'tags' are:\tstickers\tbusiness cards\tpostcards\tpaper clips\nThere are several useful visual features to tell there is 'tags' and not similar things in a photo:\n\tUsually made of paper, cardboard, or plastic\n\tAttached to objects\n\tContain information or labels\n\tCome in various shapes, sizes, and colors \n\tCommonly seen on retail merchandise or luggage", 173], "fountain": ["Yes. 'Fountain' has a tangible appearance and is a structure that dispenses water.\nA few things that are visually similar to 'fountain' but are not 'fountain' are:\twater feature\tgarden ornament\tswimming pool\twaterfall\nThere are several useful visual features to tell there is 'fountain' and not similar things in a photo:\twater shooting up into the air from a central point\tstructure or basin that contains the water\tcascading water pattern\tSCULPTURAL \t\tornaments or designs on the structure around the fountain.", 173], "paper cup": ["Yes. 'Paper cup' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'paper cup' but are not 'paper cup' are:\tmug\tglass\ttumbler\tbottle\nThere are several useful visual features to tell there is 'paper cup' and not similar things in a photo:\tcylindrical shape\ttapered design\twith or without handle\tmade of paper or cardboard\tdisposable and recyclable\toften with printed designs on the surface", 172], "kayak": ["Yes. 'Kayak' has a tangible appearance and is a type of small boat.\nA few things that are visually similar to 'kayak' but are not 'kayak' are:\tcanoe\trowboat\tpaddleboard\t\nThere are several useful visual features to tell there is 'kayak' and not similar things in a photo:\tnarrow\tand\thigh\tpointed bow and stern (front and back)\tdecks\tusually covered with a spray skirt\tsmall opening for a paddler to sit inside\tnumerous seating positions, depending on the size and model of the kayak.", 172], "twig": ["Yes. 'Twig' has a tangible appearance and is a small, thin branch of a tree or bush.\nA few things that are visually similar to 'twig' but are not 'twig' are:\tdry leaves\tsticks\nThere are several useful visual features to tell there is 'twig' and not similar things in a photo:\tthin and long shape\tsmaller than a branch and larger than a stem\tbrown or grey color\tmay have smaller branches or leaves growing from it", 172], "waters": ["Yes. 'Waters' has a tangible appearance and refers to bodies of water such as oceans, rivers, lakes, etc.\nA few things that are visually similar to 'waters' but are not 'waters' are:\tsand\tdirt\trocks\tsnow\nThere are several useful visual features to tell there is 'waters' and not similar things in a photo:\tblue or green color\twavy or rippled texture\treflected light or glare\tmotion or movement of the water surface\twater plants and sea creatures visible\tif it's a photo of a river, you can see a riverbank or surrounding landscape", 172], "lap": ["No. 'Lap' is too vague or abstract to be distinguished in a photo. It is a body part that is used for sitting or holding objects in it. \nThere are no things that are visually similar to 'lap'.", 172], "female": ["Yes. 'Female' has a tangible appearance and refers to the biological sex characterized by reproductive organs that produce eggs.\nA few things that are visually similar to 'female' but are not 'female' are:\tmale\tchildren\tanimals\nThere are several useful visual features to tell there is 'female' and not similar things in a photo:\tbreasts\thourglass shape\tsofter facial features\tandrogynous clothing", 172], "aprt": ["I'm sorry, 'aprt' is not a recognized word in the English language, so it is not possible to determine whether it is a visually concrete concept or not. Can you please rephrase or provide more context?", 171], "head light": ["Yes. 'Head light' has a tangible appearance and is a type of vehicle equipment.\nA few things that are visually similar to 'head light' but are not 'head light' are: brake light, turn signal light, fog light, interior light\nThere are several useful visual features to tell there is 'head light' and not similar things in a photo:\tlocated at the front of a vehicle\tusually two lights, one on each side of the front of the vehicle\tare white or yellow in color\toften have a circular or rectangular shape\tbright and highly focused beam\tof a size and shape suitable for a vehicle\theadlights can be turned on and off from inside the vehicle", 171], "market": ["Yes. 'Market' has a tangible appearance and is a type of commercial establishment.\nA few things that are visually similar to 'market' but are not 'market' are:\tshop\tstore\tsupermarket\tbazaar\nThere are several useful visual features to tell there is 'market' and not similar things in a photo:\toutdoor stands or indoor stalls \tvendors and shoppers\tselling goods and products like fruits, vegetables, clothing and accessories.", 171], "glass windows": ["Yes. 'Glass windows' has a tangible appearance and refers to the transparent or translucent material used to form a window pane. \nA few things that are visually similar to 'glass windows' but are not 'glass windows' are:\tplexiglass\tsunglasses\teyeglasses\nThere are several useful visual features to tell there are 'glass windows' and not similar things in a photo:\ttranslucent or transparent material\tused to form window panes\tclosely fitted together\twith frames, either wooden or metal", 171], "unit": ["No. 'Unit' is too vague or abstract to be considered as visually concrete concept.", 171], "parking": ["Yes. 'Parking' has a tangible appearance and refers to a designated area for vehicles to park.\nA few things that are visually similar to 'parking' but are not 'parking' are:\ttraffic\tjam\tcar on the street\tcar on a highway\nThere are several useful visual features to tell there is 'parking' and not similar things in a photo:\tdesignated space\twith lines or markings for each car\tright outside a building or business\tsigns indicating rules and regulations for the area.", 171], "magnets": ["Yes. 'Magnets' has a tangible appearance and is an object with magnetic properties.\nA few things that are visually similar to 'magnets' but are not 'magnets' are: metal pins, metallic bolts or screws, metallic coins\nThere are several useful visual features to tell there is 'magnets' and not similar things in a photo:\t\ntwo poles, north and south\t\nattracting or repelling other magnetic objects\t\nusually small and cylindrical or rectangular in shape", 171], "candy": ["Yes. 'Candy' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'candy' but are not 'candy' are:\tgems\tbeads\tstones\t\nThere are several useful visual features to tell there is 'candy' and not similar things in a photo:\tColorful and bright appearance\tSmooth and glossy texture\tSmall and bite-sized shape\tCandy wrappers or packaging\t Sugar or chocolate-based composition", 171], "mirror wall": ["Yes. 'Mirror wall' has a tangible appearance and refers to a wall covered by mirrors.\nA few things that are visually similar to 'mirror wall' but are not 'mirror wall' are:\twindow\twater surface\treflection in a puddle\tstainless steel\nThere are several useful visual features to tell there is 'mirror wall' and not similar things in a photo:\t\nwall covered with reflective surfaces, usually made of mirrors\tthe reflection of the surrounding environment\tshowing multiple reflections of the viewer's image", 171], "shop": ["Yes. 'Shop' has a tangible appearance and is a place where goods or services are sold.\nA few things that are visually similar to 'shop' but are not 'shop' are:\thouse\thospital\tlibrary\tmuseum\tschool\nThere are several useful visual features to tell there is 'shop' and not similar things in a photo:\tsigns or banners with names or logos of businesses\tshelves or display cases with products\tcash registers or payment centers\tstorefront windows or entrances with open doors or windows\tsalespeople or customers in the store", 170], "drinks": ["Yes. 'Drinks' has a tangible appearance and can be seen in a photo.\nA few things that are visually similar to 'drinks' but are not 'drinks' are:\tdecorations\tcondiments\tice cubes\tjelly cubes\nThere are several useful visual features to tell there is 'drinks' and not similar things in a photo:\tliquid or semi-liquid consistency\tglass, cup or bottle container\tstraw or spoon for drinking\tcolor and texture or bubbles or foam.", 170], "helicopter": ["Yes. 'Helicopter' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'helicopter' but are not 'helicopter' are:\tplane\tdrone\tballoon\tairship\nThere are several useful visual features to tell there is 'helicopter' and not similar things in a photo:\tmain rotor blades\ttail rotor\tpilot's cockpit\tlanding gear\twindow\tforward-facing propeller\tor jet engines.", 170], "fire hydrants": ["Yes. 'Fire hydrants' has a tangible appearance and is an object.\nA few things that are visually similar to 'fire hydrants' but are not 'fire hydrants' are:\tbollards\ttraffic cones\twater pipes\tmailboxes\nThere are several useful visual features to tell there is 'fire hydrants' and not similar things in a photo:\tupright post with a nozzle\tportable\tred color\tmetallic material\thandles on the side", 169], "toilet paper holder": ["Yes. 'Toilet paper holder' has a tangible appearance and is a bathroom fixture.\nA few things that are visually similar to 'toilet paper holder' but are not 'toilet paper holder' are:\ttowel rack\tshower curtain rod\tgrab bar\nThere are several useful visual features to tell there is 'toilet paper holder' and not similar things in a photo:\tcylindrical shape\tmounted on the wall or standing on the floor\thorizontal bar for holding toilet paper roll.", 169], "ceiling fan": ["Yes. 'Ceiling fan' has a tangible appearance and is a type of appliance.\nA few things that are visually similar to 'ceiling fan' but are not 'ceiling fan' are:\tchandelier\tlight fixture\tventilation system\nThere are several useful visual features to tell there is 'ceiling fan' and not similar things in a photo:\trotating blades\tsuspended from a ceiling\tmotor or chain mechanism to control speed and direction\tof various shapes and sizes", 169], "costume": ["Yes. 'Costume' has a tangible appearance and refers to a set of clothing or accessories worn for a particular occasion or event.\nA few things that are visually similar to 'costume' but are not 'costume' are:\tordinary clothes\tuniforms\tdisguises\taccessories\nThere are several useful visual features to tell there is 'costume' and not similar things in a photo:\tcoordinated clothing, especially with a particular theme or design\tbright or unusual colors or patterns\thistorical, cultural, or fictional references\taccessories like wigs, hats, or props", 168], "hand towel": ["Yes. 'Hand towel' has a tangible appearance and is a type of towel.\nA few things that are visually similar to 'hand towel' but are not 'hand towel' are:\tbath towel\twashcloth\trug\tdish towel\nThere are several useful visual features to tell there is 'hand towel' and not similar things in a photo:\tsmall in size\trectangle or square shape\tused for drying hands or face\tusually hung on a rack, hook or loop.", 168], "nail": ["Yes. 'Nail' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'nail' but are not 'nail' are:\tscrew\thook\tbolt\tlatch\nThere are several useful visual features to tell there is 'nail' and not similar things in a photo:\tlarge head\tpointed and tapered shank\tsmooth or ridged surface\tmetallic material\ttypically used for fastening or attaching one object to another", 168], "weed": ["Yes. 'Weed' has a tangible appearance and refers to plants that grow wild and unwanted in gardens and fields.\nA few things that are visually similar to 'weed' but are not 'weed' are:\tflowers\therbs\tgrasses\nThere are several useful visual features to tell there is 'weed' and not similar things in a photo:\tragged edges of the leaves or flowers\tgrowing in an uncontrolled and disorderly manner\tdifferent colors and textures compared to surrounding plants\tno specific pattern or design in the growth of the plant", 168], "way": ["No. 'Way' is too vague or abstract to be distinguished in a photo.", 168], "gas tank": ["Yes. 'Gas tank' has a tangible appearance and is a container for fuel.\nA few things that are visually similar to 'gas tank' but are not 'gas tank' are:\toil tank\twater tank\tpropane tank\nThere are several useful visual features to tell there is 'gas tank' and not similar things in a photo:\tattached to a vehicle or machinery (e.g., car, boat, generator)\tmetallic or plastic material\twith a fuel cap or pump\those connected to it\tor fuel level gauge visible.", 167], "cloudy": ["Yes. 'Cloudy' has a tangible appearance and refers to the appearance of the sky.\nA few things that are visually similar to 'cloudy' but are not 'cloudy' are:\tfoggy\tsmoky\thazy\tdusty\nThere are several useful visual features to tell there is 'cloudy' and not similar things in a photo:\tpuffy or wispy white or grey formations\tin the sky or above the horizon\tmay partially or completely obscure the sun or sky behind them.", 167], "hoodie": ["Yes. 'Hoodie' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'hoodie' but are not 'hoodie' are:\tsweatshirt\tjacket\tcoat\tblouse\tcardigan\nThere are several useful visual features to tell there is 'hoodie' and not similar things in a photo:\tdrawstrings at the hood\tlong sleeves\tpockets at the front\trelaxed fit\tsoft and cozy fabric\thanging loosely on the body", 167], "circles": ["Yes. 'Circles' has a visually concrete concept and has a tangible appearance as a geometric shape.\nA few things that are visually similar to 'circles' but are not 'circles' are:\tovals\tdots\tspirals\twheels\tbubbles\nThere are several useful visual features to tell there are 'circles' and not similar things in a photo:\tperfectly round shape\tno distinct endpoints\tor angles\tuniform thickness or width.", 167], "cucumbers": ["Yes. 'Cucumbers' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'cucumbers' but are not 'cucumbers' are:\tzucchinis\tsquashes\tmelons\tgourds\nThere are several useful visual features to tell there is 'cucumbers' and not similar things in a photo:\tcylindrical or elongated shape\twith a ridged or bumpy surface\tgreen or yellowish color\tno fur or spines\ton the inside: seeds and wet flesh", 167], "flock": ["Yes. 'Flock' has a tangible appearance and refers to a group of animals.\nA few things that are visually similar to 'flock' but are not 'flock' are:\therd\tswarm\tgroup\tteam\nThere are several useful visual features to tell there is 'flock' and not similar things in a photo:\ta group of birds or sheep or other animals\tanimals are close together and moving as a unit\tor they are gathered together in a common area.", 167], "pony": ["Yes. 'Pony' has a tangible appearance and is a type of small horse.\nA few things that are visually similar to 'pony' but are not 'pony' are:\tdonkey\tdeer\tgazelle\tantelope\nThere are several useful visual features to tell there is 'pony' and not similar things in a photo:\tsmall horse (<14.2 hands tall)\tshort legs and ears\tthick mane and tail\tstocky body\tbuild and proportions resembling a horse, but with shorter legs and thicker bodies.", 167], "buoy": ["Yes. 'Buoy' has a tangible appearance and is a type of floating device.\nA few things that are visually similar to 'buoy' but are not 'buoy' are:\tfishing float\tbeach ball\tlifebuoy\tdock floater\nThere are several useful visual features to tell there is 'buoy' and not similar things in a photo:\tcylindrical or spherical shape\tbright colors or reflective surface\tattached to a rope or chain\tfor marine navigation or marking hazards.", 166], "muzzle": ["Yes. 'Muzzle' has a tangible appearance and is a part of an animal's face.\nA few things that are visually similar to 'muzzle' but are not 'muzzle' are:\tnose\tmouth\tface\nThere are several useful visual features to tell there is 'muzzle' and not similar things in a photo:\textends beyond the animal's forehead and eyes\thas nostrils\tfor carnivorous animals, has sharp teeth", 166], "orange sign": ["Yes. 'Orange sign' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'orange sign' but are not 'orange sign' are:\tyellow sign\tred sign\tgreen sign\nThere are several useful visual features to tell there is 'orange sign' and not similar things in a photo:\torange background\tblack text\tbold and clear text\tpictograms or symbols used for road safety or information\tsign appears on, near, or extending over a highway", 166], "manhole cover": ["Yes. 'Manhole cover' has a tangible appearance and is a type of cover for an underground sewer. \nA few things that are visually similar to 'manhole cover' but are not 'manhole cover' are:\tdrainage grates\tbolted metal covers\ton-road utility boxes\t\nThere are several useful visual features to tell there is 'manhole cover' and not similar things in a photo:\tcircular or square-shaped\thole in the middle of the plate\twording, such as 'sewer' or 'drain'\tmade of cast iron or metal-heavy identify raised patterns or design", 166], "feeder": ["Yes. 'Feeder' has a tangible appearance and is a device used for providing food to birds or animals.\nA few things that are visually similar to 'feeder' but are not 'feeder' are:\tplant pot\tcandle holder\ttrash bin\tvase\thumidifier\nThere are several useful visual features to tell there is 'feeder' and not similar things in a photo:\thanging or standing device\twith holes, compartments, or troughs\tfor holding seeds, fruits, or other food items\tvisitors (birds or animals) nearby", 166], "roses": ["Yes. 'Roses' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'roses' but are not 'roses' are:\tTulips\tPoppies\tLilies\tSunflowers\nThere are several useful visual features to tell there is 'roses' and not similar things in a photo:\t5-petaled flowers\twith thorns\ton long stems\twith varying shades of red, pink, and white\tfragrant scent.", 166], "worker": ["No. 'Worker' is too vague or abstract to be distinguished in a photo.", 166], "crown": ["Yes. 'Crown' has a tangible appearance and is a type of headgear.\nA few things that are visually similar to 'crown' but are not 'crown' are:\thats\ttiaras\theaddresses\thelmets\nThere are several useful visual features to tell there is 'crown' and not similar things in a photo:\tworn on the head\tasymmetrical, with a central high point\tjeweled or metallic decoration\ttop made of a series of curved points\tsymbolic or ceremonial significance.", 166], "baby zebra": ["Yes. 'Baby zebra' has a tangible appearance and is a young zebra.\nA few things that are visually similar to 'baby zebra' but are not 'baby zebra' are:\tbaby horse\tdalmatian puppy\tcow\tpig\nThere are several useful visual features to tell there is 'baby zebra' and not similar things in a photo:\tdistinctive black and white stripes\telongated ears\tslim tail\twith a tuft of black hair at the end", 166], "dog collar": ["Yes. 'Dog collar' has a tangible appearance and is a type of pet accessory.\nA few things that are visually similar to 'dog collar' but are not 'dog collar' are:\tchoker necklace leather belt\tnecklace\tbracelet\tleash\nThere are several useful visual features to tell there is 'dog collar' and not similar things in a photo:\tadjustable and fastened around the neck of a dog\tbuckle or clasp to fasten\tthe presence of a metal tag with the dog's identification information\tthe presence of a leash attached", 165], "beach umbrella": ["Yes. 'Beach umbrella' has a tangible appearance and is a type of shade structure.\nA few things that are visually similar to 'beach umbrella' but are not 'beach umbrella' are:\tparasol\ttent\tcanopy\nThere are several useful visual features to tell there is 'beach umbrella' and not similar things in a photo:\tround or octagonal canopy\tattached to a long pole\tsmall size compared to other structures on the beach\tbright, colorful fabric or design\tsand anchor at the base.", 165], "evergreen trees": ["Yes. 'Evergreen trees' has a tangible appearance and is a type of tree that retains its green leaves throughout the year.\nA few things that are visually similar to 'evergreen trees' but are not 'evergreen trees' are:\tdeciduous trees\tbushes\thedges\tferns\nThere are several useful visual features to tell there are 'evergreen trees' and not similar things in a photo:\tretaining green leaves throughout the year (not losing leaves in winter)\tconical or pyramidal shape\tneedle-shaped leaves or scale-like leaves", 165], "grape": ["Yes. 'Grape' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'grape' but are not 'grape' are:\tcherry\tblueberry\tplum\tolive\nThere are several useful visual features that can help to distinguish 'grape' from the listed similar things in a photo:\tround or oval shape\twith a stem and green leaves\tpurple or green color\tsmooth surface\twith a translucent flesh and small seeds inside", 165], "bright": ["No. 'Bright' is too vague or abstract to be distinguished in a photo.", 165], "shrimp": ["Yes. 'Shrimp' has a tangible appearance and is a type of seafood.\nA few things that are visually similar to 'shrimp' but are not 'shrimp' are:\tcrawfish\tlobster\tcrabs\nThere are several useful visual features to tell there is 'shrimp' and not similar things in a photo:\telongated body shape\ttapered tail\tcurved legs\tand antenna\tpaired leaf-like gills on their bodies.", 164], "parasail": ["Yes. 'Parasail' has a tangible appearance and is a type of recreational activity.\nA few things that are visually similar to 'parasail' but are not 'parasail' are:\tkite\thang glider\tballoon\nThere are several useful visual features to tell there is 'parasail' and not similar things in a photo:\ta fabric canopy or sail\tharness or straps attached to the sail\ta person or people suspended in the air by the sail\ta boat or other watercraft towing the sail", 164], "leaves ground": ["Yes. 'Leaves ground' has a tangible appearance.\nA few things that are visually similar to 'leaves ground' but are not 'leaves ground' are:\tgrass\tdirt\tsand\trocks\nThere are several useful visual features to tell there is 'leaves ground' and not similar things in a photo:\tmany small, flat, and thin pieces on the ground\tvibrant colors of brown, yellow, orange, and red\tnatural shapes and textures of fallen leaves\tpossibly fallen sticks and twigs mixed in with the leaves", 164], "tractor": ["Yes. 'Tractor' has a tangible appearance and is a type of agricultural vehicle.\nA few things that are visually similar to 'tractor' but are not 'tractor' are:\tcar\ttruck\tbus\theavy machinery\nThere are several useful visual features to tell there is 'tractor' and not similar things in a photo:\tlarge wheels or tracks\tattached equipment for farming, such as plows or cultivators\tdual tires on the back\tand open operator's compartment at the front or back.", 164], "stone building": ["Yes. 'Stone building' has a tangible appearance and refers to a building made of stone.\nA few things that are visually similar to 'stone building' but are not 'stone building' are: brick buildings, concrete buildings, wooden buildings, metal buildings, glass buildings.\nThere are several useful visual features to tell there is 'stone building' and not similar things in a photo, such as:\tclearly visible stones on walls\tnatural rugged textures of stone\tnon-uniform shape and size of stones\tarchitectural details and ornaments characteristic of stone buildings", 164], "petals": ["Yes. 'Petals' has a tangible appearance and is the colorful part of a flower.\nA few things that are visually similar to 'petals' but are not 'petals' are:\tleaves\twings\tfruits\t\nThere are several useful visual features to tell there are 'petals' and not similar things in a photo:\tthin and delicate structure\tbright and vibrant colors\tvarious shapes and sizes\tattached to the base of a flower", 163], "propellers": ["Yes. 'Propellers' has a tangible appearance and is a type of rotating blades used for propulsion.\nA few things that are visually similar to 'propellers' but are not 'propellers' are:\tfans\tceiling fans\twind turbines\trotating ornaments\n\nThere are several useful visual features to tell there is 'propellers' and not similar things in a photo:\ttwo or more rotating blades\tattached to a motor or engine\taerofoil or curved blade shape\tspinning rapidly for propulsion or lift", 163], "sail boat": ["Yes. 'Sail boat' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'sail boat' but are not 'sail boat' are:\tspeed boat\tkayak\tcanoe\tyacht\t\nThere are several useful visual features to tell there is 'sail boat' and not similar things in a photo:\tsails\tmast\trudder\thull\twater\tline and shape of boat", 162], "wrapper": ["Yes, 'wrapper' has a visually concrete concept that refers to a material that wraps or covers something.\nA few things that are visually similar to 'wrapper' but are not 'wrapper' are:\trug\ttapestry\tbanner\nSome useful visual features to distinguish 'wrapper' from the listed similar things in a photo are:\t\n- It has a fold in the middle so that it can wrap objects or items inside.\n- Its outer surface is often smooth or printed with patterns or logos.\n- It is often made of paper, plastic, or similar materials.\n- It is sometimes secured with tape or ribbon to hold it in place.", 162], "squares": ["Yes. 'Squares' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'squares' but are not 'squares' are:\trectangle\tdiamond\tcube\tlayered cake\nThere are several useful visual features to tell there is 'square' and not similar things in a photo:\tequal sides and angles\tstraight sides and angles\tno curves or rounded edges", 162], "wake": ["Yes. 'Wake' has a tangible appearance and is a visible track left by a moving object on a body of water.\nA few things that are visually similar to 'wake' but are not 'wake' are:\tripples in the water caused by wind or rain\tswimming pool lanes\tunderwater current\tboat reflection on water surface\nThere are several useful visual features to tell there is 'wake' and not similar things in a photo:\tnarrow and elongated shape\tv-shaped wave pattern\tcaused by a boat or a ship\tmoving away from the source of the wave", 162], "bare feet": ["Yes. 'Bare feet' has a tangible appearance and is a type of body part.\nA few things that are visually similar to 'bare feet' but are not 'bare feet' are:\tshoes\tpaws\thands\nThere are several useful visual features to tell there are 'bare feet' and not similar things in a photo:\tno shoes\ttouching the ground or a surface\tskin is visible on soles and tops of feet\ttoes are visible and not covered by shoes or fur.", 162], "audience": ["No. 'Audience' is too vague or abstract to be distinguished in a photo.", 162], "wet sand": ["Yes. 'Wet sand' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'wet sand' but are not 'wet sand' are:\tdry sand\tcement\tconcrete\tpowdered sugar\nThere are several useful visual features to tell there is 'wet sand' and not similar things in a photo:\tdark color\tbumpy texture\twaterlogged appearance\tclings to objects\twhen pressed, leaves a wet imprint", 161], "basil": ["Yes. 'Basil' has a tangible appearance and is a type of herb.\nA few things that are visually similar to 'basil' but are not 'basil' are:\tmint\tcilantro\tparsley\tthyme\nThere are several useful visual features to tell there is 'basil' and not similar things in a photo:\tlarge green leaves\twith serrated edges and pointed tips\tgrowing on stems\twith purple flowers\tor white flowers\tin a bunch or a pot", 161], "cement wall": ["Yes. 'Cement wall' has a tangible appearance and is a type of wall.\nA few things that are visually similar to 'cement wall' but are not 'cement wall' are:\tbrick wall\tstucco wall\tstone wall\twooden wall\tiron sheet wall\nThere are several useful visual features to tell there is 'cement wall' and not similar things in a photo:\tfractured surface\tgrey color\tsmooth or rough texture\tshadows created by the wall", 161], "tail fin": ["Yes. 'Tail fin' has a tangible appearance and is a part of a fish or a watercraft.\nA few things that are visually similar to 'tail fin' but are not 'tail fin' are:\twings\trudders\tflap\nThere are several useful visual features to tell there is 'tail fin' and not similar things in a photo:\tflattened and triangular or fan-shaped\ttop of the fish or watercraft, located at the back\thelps to balance or propel the fish or watercraft through the water", 161], "ball cap": ["Yes. 'Ball cap' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'ball cap' but are not 'ball cap' are:\tsun hat\tberet\tbaseball helmet\tbeanie\tfedora\nThere are several useful visual features to tell there is 'ball cap' and not similar things in a photo:\t\nrounded crown\t\ncurved visor\t\nusually made of cotton or wool\t\noften has a logo or design on the front\t\nadjustable strap or plastic snap closure at the back to adjust the size", 160], "pink umbrella": ["Yes. 'Pink umbrella' has a tangible appearance and is a specific type of umbrella with a pink color.\nA few things that are visually similar to 'pink umbrella' but are not 'pink umbrella' are:\tumbrellas of different colors\tparasols\tbeach umbrellas\t\nThere are several useful visual features to tell there is 'pink umbrella' and not similar things in a photo:\tpink color\tmetal or plastic frame\twith a handle and spokes\tfor protection against rain or sun\tfolding and portable", 160], "beanie": ["Yes. 'Beanie' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'beanie' but are not 'beanie' are:\tCap\tBalaclava\tHeadband\nThere are several useful visual features to tell there is 'beanie' and not similar things in a photo:\twoolen\tclosely fitting to the head\tno visor\tat times with a pom-pom on the top or tassels.", 160], "tail wing": ["Yes. 'Tail wing' has a tangible appearance and is a part of an airplane.\nA few things that are visually similar to 'tail wing' but are not 'tail wing' are:\tmain wing\tfuselage\tempennage\thorizontal stabilizer\nThere are several useful visual features to tell there is 'tail wing' and not similar things in a photo:\tlocated at the back of the airplane\thorizontal surface positioned behind the main wing and fuselage\tmade up of a vertical stabilizer and a horizontal stabilizer with attached control surfaces", 160], "chopsticks": ["Yes. 'Chopsticks' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'chopsticks' but are not 'chopsticks' are:\tpencil\tpaintbrush\ttweezers\nThere are several useful visual features to tell there is 'chopsticks' and not similar things in a photo:\tlong and thin\tsticks or rods\ttwo similar pieces\theld between fingers or placed on a table for eating or cooking", 160], "swan": ["Yes. 'Swan' is a visually concrete concept and a type of bird.\nA few things that are visually similar to 'swan' but are not 'swan' are:\tduck\tgoose\tpelican\tcygnet\nThere are several useful visual features to distinguish 'swan' from the listed similar things in a photo:\t\nlong and graceful neck\t\ndistinctive white feather\t\norange beak\t\nblack and rounded eyes\t\nlarge wingspan", 160], "pedal": ["Yes. 'Pedal' has a tangible appearance and is a type of lever that is used to control a machine or a vehicle.\nA few things that are visually similar to 'pedal' but are not 'pedal' are:\tbuttons\tswitches\tknobs\t\nThere are several useful visual features to tell there is 'pedal' and not similar things in a photo:\tflat or slightly curved surface\tfor feet to rest or push on\tmounted on a machine or a vehicle", 160], "cardboard": ["Yes. 'Cardboard' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'cardboard' but are not 'cardboard' are:\tpaper\twood\tplastic\tmetal\tfabric\nThere are several useful visual features to tell there is 'cardboard' and not similar things in a photo:\tlight brown or beige color\tpapery texture\twith visible corrugations or ridges\tstiff and flat yet flexible\tused for packaging boxes or folder materials", 160], "shakers": ["Yes. 'Shakers' has a tangible appearance and refers to a type of container used for shaking something out.\nA few things that are visually similar to 'shakers' but are not 'shakers' are: salt container, pepper container, hourglass.\nThere are several useful visual features to tell there is 'shakers' and not similar things in a photo:\tholes or perforations on one end of the container\tcap on the other end of the container\tcontainer is tilted or upside down to shake out the contents of the container.", 159], "life jacket": ["Yes. 'Life jacket' has a tangible appearance and is a type of personal flotation device worn to prevent drowning.\nA few things that are visually similar to 'life jacket' but are not 'life jacket' are:\tswimming pool floats\tboating cushions\tinflatable toys\nThere are several useful visual features to tell there is 'life jacket' and not similar things in a photo:\torange or red color\tbulky and padded\twith straps and buckles\tto be worn around the chest and torso\tarea to keep the head above water when floating", 159], "tissue paper": ["Yes. 'Tissue paper' has a tangible appearance and is a kind of paper.\nA few things that are visually similar to 'tissue paper' but are not 'tissue paper' are:\ttoilet paper\tnewspaper\twrapping paper\tnotebook paper\nThere are several useful visual features to tell there is 'tissue paper' and not similar things in a photo:\tthin and delicate texture\ttranslucent appearance\tcommonly used for gift wrapping or stuffing\tpastel or bright colors", 159], "disc": ["Yes. 'Disc' has a tangible appearance and is a flat circular object.\nA few things that are visually similar to 'disc' but are not 'disc' are:\twheel\tfrisbee\tpizza\tcoin\nThere are several useful visual features to tell there is 'disc' and not similar things in a photo:\tthin and flat\tcircular shape\tvisible edge\torbits or rotates around a central point ", 159], "parsley": ["Yes. 'Parsley' has a tangible appearance and is a type of herb.\nA few things that are visually similar to 'parsley' but are not 'parsley' are:\tcilantro\tbasil\tdill\tmint\nThere are several useful visual features to tell there is 'parsley' and not similar things in a photo:\tbright green color\ttriangular leaves\tcurly or flat leaves\tserrated edges\tpetite size", 159], "orange carrot": ["Yes. 'Orange carrot' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'orange carrot' but are not 'orange carrot' are:\tparsnips\tpumpkins\tsweet potatoes\toranges\nThere are several useful visual features to tell there is 'orange carrot' and not similar things in a photo: \tlong, tapered shape\tsmooth skin in shades of orange or red-orange\tgreen leaves at the top\tno visible segments inside", 158], "weather vane": ["Yes. 'Weather vane' has a tangible appearance and is a device used to determine the direction of the wind.\nA few things that are visually similar to 'weather vane' but are not 'weather vane' are:\tflag\tpinwheel\tturbine\tfan\nThere are several useful visual features to tell there is 'weather vane' and not similar things in a photo:\tarrow or pointer\trooster, horse, or other animal as a ornament\tattached to the top of a building or a structure\table to rotate with the wind\tdirection indicators (N, S, E, W)", 158], "hedges": ["Yes. 'Hedges' has a tangible appearance and is a type of greenery.\nA few things that are visually similar to 'hedges' but are not 'hedges' are:\tbush\tshrub\tfence\tgrass\nThere are several useful visual features to tell there is 'hedges' and not similar things in a photo:\ttall, dense plants arranged in a row or a pattern\thave leaves, stems, and branches\tcreate a natural barrier or boundary in a landscape\tsurround a garden or a property.", 158], "cd": ["Yes. 'CD' has a tangible appearance as a physical disc.\nA few things that are visually similar to 'CD' but are not 'CD' are:\tdvd\trecord\tplayer\tpizza\nThere are several useful visual features to tell there is 'CD' and not similar things in a photo:\tcircular shape\twith a small hole in the center\tshiny or reflective surface\tcontaining small text or images on the surface\treadable by a CD player or computer drive.", 157], "calendar": ["Yes. 'Calendar' has a tangible appearance and is a visual chart that shows days, weeks, and months of a year.\nA few things that are visually similar to 'calendar' but are not 'calendar' are:\twatch\tclock\tplanner\tnotebook\nThere are several useful visual features to distinguish 'calendar' from the listed similar things in a photo:\tmonth and year clearly displayed\tday and date squares arranged in rows and columns\tsymbols for holidays or special events recurring regularly\tweeks starting with Monday or Sunday", 157], "shelter": ["Yes. 'Shelter' has a tangible appearance and is a structure that protects from weather or danger.\nA few things that are visually similar to 'shelter' but are not 'shelter' are:\thut\thouse\tantenna\ttent\twall\nThere are several useful visual features to tell there is 'shelter' and not similar things in a photo:\troof or cover\twalls or enclosure\tentrance or opening\tdesigned to provide protection or safety from the elements or danger.", 157], "watch man": ["Yes. 'Watch man' has a tangible appearance and refers to a security guard or someone who keeps an eye on things.\nA few things that are visually similar to 'watch man' but are not 'watch man' are:\tpolice officer\tworker on a surveillance camera\tautomated security camera\nThere are several useful visual features to tell there is 'watch man' and not similar things in a photo:\twearing a uniform or specific clothing\tcarrying a flashlight or walkie-talkie\tstanding or walking around\ta human appearing figure", 157], "pant": ["Yes. 'Pant' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'pant' but are not 'pant' are:\tshorts\tskirts\tleggings\ttights\t\nThere are several useful visual features to tell there is 'pant' and not similar things in a photo:\ttwo separate and distinct parts for each leg\tfabric covering both legs, usually up to the waist or hip area\tzipper or buttons at the front or back pockets for storing items\tbelt loops or waistband for tightening around the waist.", 157], "power pole": ["Yes. 'Power pole' has a tangible appearance and is a type of utility infrastructure.\nA few things that are visually similar to 'power pole' but are not 'power pole' are:\ttelephone pole\tstreet light post\tfence post\ttree trunk\nThere are several useful visual features to tell there is 'power pole' and not similar things in a photo:\ttall, vertical pole\tmetallic or wooden material\tinsulators and wires attached on the pole\tmay have a transformer box or meter attached at the base", 156], "brown nose": ["No. 'Brown nose' is too vague or abstract to be distinguished in a photo.", 156], "metal bars": ["Yes. 'Metal bars' has a tangible appearance and is a type of material used in construction, manufacturing, or security.\nA few things that are visually similar to 'metal bars' but are not 'metal bars' are:\twooden bars\tpipes\trails\tfences\nThere are several useful visual features to tell there are 'metal bars' and not similar things in a photo:\tmade of metal\tsolid and rigid\tregularly spaced rectangular or cylindrical shape\tshiny or matte surface\ttexture and color of metal", 156], "pickles": ["Yes. 'Pickles' has a tangible appearance and is a type of preserved food.\nA few things that are visually similar to 'pickles' but are not 'pickles' are:\tolives\tgreen beans\tbrussels sprouts\tpeppers\nThere are several useful visual features to tell there is 'pickles' and not similar things in a photo:\toblong or round shape\tgreen or yellow color\ttypically speckled or bumpy skin\toften found in a clear or opaque jar\twith a label specifying the type of vegetable or fruit", 156], "tongs": ["Yes. 'Tongs' has a tangible appearance and is a kitchen tool used for grasping or lifting hot objects.\nA few things that are visually similar to 'tongs' but are not 'tongs' are:\tspider\tcrab\tclaw\tpincers\nThere are several useful visual features to tell there is 'tongs' and not similar things in a photo:\thandles\ton opposing sides or ends of the tool\thinged or spring-loaded metal arms with a pivot\tpointed or ridged ends\tfor picking up and holding hot or difficult-to-handle objects", 156], "grey car": ["Yes. 'Grey car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'grey car' but are not 'grey car' are:\ttruck\tmotorcycle\tbike\tbus\nThere are several useful visual features to tell there is 'grey car' and not similar things in a photo:\tfour wheels\t2-4 doors\tfor passenger transportation\tgrey metal body", 156], "baseball mitt": ["Yes. 'Baseball mitt' has a tangible appearance and is a type of glove used in the game of baseball.\nA few things that are visually similar to 'baseball mitt' but are not 'baseball mitt' are:\tgardening gloves\tbiker gloves\twork gloves\twinter gloves\nThere are several useful visual features to tell there is 'baseball mitt' and not similar things in a photo:\thollowed webbing between fingers and thumb\tthick padding in the palm area\twrist strap to secure it to hand\tlight-colored leather or synthetic material\tbaseball stitching on the surface of the mitt", 156], "silver pot": ["Yes. 'Silver pot' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'silver pot' but are not 'silver pot' are:\tsilver vase\tsilver cup\tsilver candlestick\tsilver trophy\nThere are several useful visual features to tell there is 'silver pot' and not similar things in a photo:\thandles on each side\tlarge enough to cook food\tsilver or metallic appearance\tpot-shaped with a curved bottom and a round lid", 156], "orange cones": ["Yes. 'Orange cones' has a tangible appearance and is a type of traffic control device.\nA few things that are visually similar to 'orange cones' but are not 'orange cones' are:\ttraffic barrels\tpylons\tbollards\nThere are several useful visual features to tell there is 'orange cones' and not similar things in a photo:\tcone-shaped\tobject\torange color\tusually made of plastic or rubber\thas a reflective striping at the top to be visible at night.", 156], "panels": ["Yes. 'Panels' has a tangible appearance and can refer to various types of flat, rectangular surfaces.\nA few things that are visually similar to 'panels' but are not 'panels' are:\tboards\twalls\ttiles\tcanvas\nThere are several useful visual features to tell there is 'panels' and not similar things in a photo:\trectangular or square shape\tflat surface\tsimilar size and shape in a repeating pattern\tor an adjacent sequence of surfaces", 155], "head band": ["Yes. 'Head band' has a tangible appearance and is an accessory worn around the head.\nA few things that are visually similar to 'head band' but are not 'head band' are:\that\thair clip\tsweatband\tbandana\nThere are several useful visual features to tell there is 'head band' and not similar things in a photo:\tnarrow band\tworn on the forehead or on top of the head\tmade of a stretchy or flexible material (such as elastic or cloth)\tcan have decorative features (such as a bow or a flower)", 155], "head board": ["Yes. 'Head board' has a tangible appearance and is a furniture piece.\nA few things that are visually similar to 'head board' but are not 'head board' are:\twall art\tpillowbackrest\tcushion\nThere are several useful visual features to tell there is 'head board' and not similar things in a photo:\tattached to a bed\tframe for the head of the bed\tvertical board situated at the head of the bed\tpadded or upholstered often", 155], "fire extinguisher": ["Yes. 'Fire extinguisher' has a tangible appearance and is an object used for fire safety.\nA few things that are visually similar to 'fire extinguisher' but are not 'fire extinguisher' are:\tpaint can\tgas tank\tpropane tank\tair compressor\nThere are several useful visual features to tell there is 'fire extinguisher' and not similar things in a photo:\tbright red color\tcylindrical shape\tpressure gauge\tor large nozzle\those or tube on top\tsafety pin or tamper seal at the handle\tarea labeled RECHARGE or INSPECTION", 155], "kitchen sink": ["Yes. 'Kitchen sink' has a tangible appearance and is a type of fixture.\nA few things that are visually similar to 'kitchen sink' but are not 'kitchen sink' are:\tbathroom sink\twashbasin\ttrough\tfountain\nThere are several useful visual features to tell there is 'kitchen sink' and not similar things in a photo:\tfaucet with hot and cold water handles\tdouble bowls or single bowl\tdrainboard\tnext to a kitchen counter or cabinet", 155], "skiier": ["Yes. 'Skier' has a tangible appearance and is a person who skis.\nA few things that are visually similar to 'skier' but are not 'skier' are:\tsnowboarder\tice skater\tcross-country skier\nThere are several useful visual features to tell there is 'skier' and not similar things in a photo:\twearing ski boots, skis, and poles\tsliding down a snowy slope\tin a bent-forward position\twith arms slightly bent and in front of the body", 155], "water hose": ["Yes. 'Water hose' has a tangible appearance and is an object used for watering plants or cleaning.\nA few things that are visually similar to 'water hose' but are not 'water hose' are:\tpool noodle\trope\textension cord\t\nThere are several useful visual features to tell there is 'water hose' and not similar things in a photo:\tlong plastic or rubber tube\tattached nozzle at one end\toften green or blue in color\tMay have a ribbed or smooth texture.", 155], "owl": ["Yes. 'Owl' has a tangible appearance and is a kind of bird.\nA few things that are visually similar to 'owl' but are not 'owl' are:\teagle\tpigeon\tsparrow\tvulture\nThere are several useful visual features to tell there is 'owl' and not similar things in a photo:\tlarge head in proportion to body\tbig round eyes\tfeathers around the face and beak\tsharp curved beak\tability to turn its head almost 360 degrees\tnighttime hunting behavior", 155], "metal rail": ["Yes. 'Metal rail' has a tangible appearance and is a type of structural element.\nA few things that are visually similar to 'metal rail' but are not 'metal rail' are:\tfence\twire railing\tpipe\thandrail\nThere are several useful visual features to tell there is 'metal rail' and not similar things in a photo:\tsolid metal construction\tsleek and straight design\thorizontal or vertical orientation\tused for support or safety purposes", 154], "fluffy clouds": ["Yes. 'Fluffy clouds' has a tangible appearance and is a natural phenomenon.\nA few things that are visually similar to 'fluffy clouds' but are not 'fluffy clouds' are:\tfog\tsmoke\tsteam\t\nThere are several useful visual features to tell there is 'fluffy clouds' and not similar things in a photo:\twhite or light grey in color\tsoft and fluffy appearance\tfloating in the sky, not near the ground\tvariety of shapes, such as cumulus or cirrus clouds.", 154], "reflector": ["Yes. 'Reflector' has a tangible appearance and is used to redirect light.\nA few things that are visually similar to 'reflector' but are not 'reflector' are:\tMirror\tPrism\tTranslucent Film\tMylar\tBalloon\nThere are several useful visual features to tell there is 'reflector' and not similar things in a photo:\t\ncircular or rectangular shape\nthin and not opaque\nusually situated behind the light source\nnot reflecting the full image, but redirecting the light in a particular direction.", 154], "pine": ["Yes. 'Pine' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'pine' but are not 'pine' are:\tfir\trainforest tree\tcactus\nThere are several useful visual features to tell there is 'pine' and not similar things in a photo:\tneedle-shaped leaves\tgroups of needles growing in bundles\tfrom 2 to 5 needles per bundle\tcone-shaped fruit or pine cones\thigh and conical crown with a thick and dense foliage\tlayered bark in different colors - red, grey, or brown", 154], "metal poles": ["Yes. 'Metal poles' has a tangible appearance and refers to cylindrical metal objects.\nA few things that are visually similar to 'metal poles' but are not 'metal poles' are:\ttrees\tlampposts\tflagpoles\nThere are several useful visual features to tell there is 'metal poles' and not similar things in a photo:\tcylindrical shape\tmade of metal\tpainted in a metallic color\tno branches, leaves, or lights on them.", 154], "bikini": ["Yes. 'Bikini' has a tangible appearance and is a type of swimwear.\nA few things that are visually similar to 'bikini' but are not 'bikini' are:\tone-piece swimsuit\tsports bra\tpanties\tlingerie\nThere are several useful visual features to tell there is 'bikini' and not similar things in a photo:\ttwo-pieces\tswimsuit\ttop and bottom in different colors and/or patterns\tit reveals more skin and shows more torso than other types of swimsuits", 154], "bulb": ["Yes. 'Bulb' has a tangible appearance and is a type of object used for producing light.\nA few things that are visually similar to 'bulb' but are not 'bulb' are:\tpear\tfruit\tlightning bug\tglass bottle\nThere are several useful visual features to tell there is 'bulb' and not similar things in a photo:\tglass or plastic shell\tsmall metal or plastic base\tinternal filament or gas that produces light\tscrew or bayonet mount for attaching to a socket\tbright when turned on\tdull when turned off", 154], "trash bag": ["Yes. 'Trash bag' has a tangible appearance and is a kind of bag used for holding waste material.\nA few things that are visually similar to 'trash bag' but are not 'trash bag' are:\tstring bag\tshopping bag\tbackpack\tsatchel\nThere are several useful visual features to tell there is 'trash bag' and not similar things in a photo:\tthick and durable material\tdark color\ttrash or waste inside\ttied at the top or cinched with a drawstring", 153], "surf": ["Yes. 'Surf' has a tangible appearance and is a physical phenomenon in the ocean.\nA few things that are visually similar to 'surf' but are not 'surf' are:\twaves\twhirlpools\tcurrents\ttides\nThere are several useful visual features to tell there is 'surf' and not similar things in a photo:\tbreaking waves near the shore\tspray or foam\ton top of the waves\tsurfboards or surfers riding the waves\tthe sound of crashing waves.", 153], "looks": ["No. 'Looks' is too vague or abstract to be distinguished in a photo.", 153], "exhaust pipe": ["Yes. 'Exhaust pipe' has a tangible appearance and is a part of a vehicle or a machine.\nA few things that are visually similar to 'exhaust pipe' but are not 'exhaust pipe' are:\tpipes\ttubes\tchimneys\tcylinders\nThere are several useful visual features to tell there is 'exhaust pipe' and not similar things in a photo:\tpart of a vehicle or a machine\tcylindrical shape\tmounted to the rear or the side of a vehicle\tdark, sooty or rusty appearance\toften has a muffler attached at the end", 153], "teapot": ["Yes. 'Teapot' has a tangible appearance and is a type of container used for making and serving tea.\nA few things that are visually similar to 'teapot' but are not 'teapot' are:\tkettle\tcoffee pot\tpitcher\tdecorative vase\nThere are several useful visual features to tell there is 'teapot' and not similar things in a photo:\tshort spout on the top\tround middle handle\tlid on the top with a knob or handle\tusually made of ceramic or metal\tspecific shapes and designs for different cultures and time periods.", 153], "wet": ["Yes. 'Wet' has a tangible appearance and refers to a surface covered with liquid.\nA few things that are visually similar to 'wet' but are not 'wet' are:\tshiny\toily\tglossy\twaxed\nThere are several useful visual features to tell there is 'wet' and not similar things in a photo:\tglistening or shiny appearance\twater droplets visible\ton non-shiny surfaces, a change in tone or color\tfrom dry to water-saturated appearance", 153], "canister": ["Yes. 'Canister' has a tangible appearance and is a cylindrical container.\nA few things that are visually similar to 'canister' but are not 'canister' are:\ttin\tcylinder\tjar\tbottle\tcan\nThere are several useful visual features to tell there is 'canister' and not similar things in a photo:\tgenerally made of metal or plastic\tclosed with a lid\tcylindrical shape\twithout handles or spouts", 152], "cupboards": ["Yes. 'Cupboards' has a tangible appearance and is a type of furniture used for storage.\nA few things that are visually similar to 'cupboards' but are not 'cupboards' are:\tshelves\tbenches\tdesks\t\nThere are several useful visual features to tell there is 'cupboards' and not similar things in a photo:\tdoors or drawers\tfor storing\tusually found in a kitchen or pantry or bedroom\tmay have shelves or compartments inside\tmay have handles or knobs for opening and closing.", 152], "tail lights": ["Yes. 'Tail lights' has a tangible appearance and refers to the red lights on the back of a vehicle.\nA few things that are visually similar to 'tail lights' but are not 'tail lights' are:\theadlights\tstop signs\ttailpipes\tbicycle lights\nThere are several useful visual features to tell there is 'tail lights' and not similar things in a photo:\tred color\tlocated on the back of a vehicle\tin a distinct pattern or shape (usually elongated)\tlighting up when the vehicle is braking", 152], "right eye": ["Yes. 'Right eye' has a tangible appearance and is a part of the human face.\nA few things that are visually similar to 'right eye' but are not 'right eye' are:\tLeft eye \tglass eye \tbutton.\nThere are several useful visual features to tell there is 'right eye' and not similar things in a photo:\tlocated on the right side of the face\teyeball surrounded by white sclera, colored iris, and black pupil\teyelids and eyelashes that can be opened and closed\tbrow ridge and wrinkles around the eye socket.", 152], "carton": ["Yes. 'Carton' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'carton' but are not 'carton' are:\tpackage\tbox\tenvelope\tcrate\tbag\tpouch\nThere are several useful visual features to tell there is 'carton' and not similar things in a photo:\trectangular shape\tflap or lid for closure\tcardboard or paper material\tprinted labels for identification", 152], "apartment building": ["Yes. 'Apartment building' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'apartment building' but are not 'apartment building' are:\thotel\tdormitory\toffice building\tshopping mall\nThere are several useful visual features to tell there is 'apartment building' and not similar things in a photo:\tmultiple floors\tcontaining many separate units or apartments\tbalconies or terraces\tfor residential use", 152], "stuff": ["No. 'Stuff' is too vague or abstract to be distinguished in a photo.", 152], "dirt ground": ["Yes. 'Dirt ground' has a tangible appearance and is a type of terrestrial surface.\nA few things that are visually similar to 'dirt ground' but are not 'dirt ground' are:\tpebbles\tgravel\tsand\tconcrete\nThere are several useful visual features to tell there is 'dirt ground' and not similar things in a photo:\tbrown or reddish color\tuneven texture or surface\tmay contain small rocks or twigs\tno visible human-made structures (e.g. pavement)", 152], "foreground": ["No. 'Foreground' is too vague or abstract to be distinguished in a photo. It is a relative position in an image depends on the viewer's perspective.", 152], "water glass": ["Yes. 'Water glass' has a tangible appearance and is a type of glass.\nA few things that are visually similar to 'water glass' but are not 'water glass' are:\twine glass\tchampagne flute\tmartini glass\tshot glass\nThere are several useful visual features to tell there is 'water glass' and not similar things in a photo:\tlarge enough to hold water\tstraight sides\ta flat or slightly curved bottom\ttapered or smooth edges\tno stem or handle (unlike wine glass or mug)", 151], "tennis skirt": ["Yes. 'Tennis skirt' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'tennis skirt' but are not 'tennis skirt' are:\tgolf skirt\trunning skirt\tballet skirt\nThere are several useful visual features to tell there is 'tennis skirt' and not similar things in a photo:\tshort length\tflowy and flared design\tbuilt-in shorts or compression leggings\tworn by female tennis players in matches or practice sessions", 151], "round mirror": ["Yes. 'Round mirror' has a tangible appearance and is a type of reflective surface.\nA few things that are visually similar to 'round mirror' but are not 'round mirror' are:\tserving tray\tdome\tcircular window\tfrisbee\nThere are several useful visual features to tell there is 'round mirror' and not similar things in a photo:\tround or circular shape\treflection of objects or surroundings\tframe or edging around the mirror\tsupporting chain, stand or hanger behind it (if applicable)", 151], "nostrils": ["Yes. 'Nostrils' has a tangible appearance and is a part of the nose.\nA few things that are visually similar to 'nostrils' but are not 'nostrils' are:\tfacial pores\tdimples\t\nThere are no useful visual features to distinguish 'nostrils' from the listed similar things in a photo, as the structure of the nostrils is unique and easily recognizable.", 151], "tool": ["Yes. 'Tool' has a tangible appearance and is an object used to carry out a particular function.\nA few things that are visually similar to 'tool' but are not 'tool' are:\tkitchen utensils\tnails and screws\tpens and pencils\tcraft supplies\nThere are several useful visual features to tell there is 'tool' and not similar things in a photo:\thandles or grips\tparts for specific functions or tasks\tmetallic or durable materials\tshapes to perform actions such as cutting, drilling, or grasping.", 151], "scale": ["Yes. 'Scale' has a tangible appearance and refers to a measuring device or a proportion.\nA few things that are visually similar to 'scale' but are not 'scale' are:\tthermometer\truler\tbalance or weighing machine\nThere are several useful visual features to tell there is 'scale' and not similar things in a photo:\n\nFor a measuring device:\nclearly marked increments or units of measurement, such as centimeters or Fahrenheit degrees; used to measure the length, weight, or temperature of an object.\n\nFor a proportion:\nrelativity between the different sizes, quantities, or dimensions in the picture; such as a person standing next to a building to provide a sense of scale.", 151], "toothbrushes": ["Yes. 'Toothbrushes' has a tangible appearance and is an object for cleaning teeth.\nA few things that are visually similar to 'toothbrushes' but are not 'toothbrushes' are:\thairbrushes\tcleaning brushes\tcombs\tpaintbrushes\nThere are several useful visual features to tell there is 'toothbrushes' and not similar things in a photo:\thandle\twith bristles\tusually with a small head\tdesigned for the mouth area and teeth\tcome in different colors and sizes", 151], "baseball uniform": ["Yes. 'baseball uniform' has a tangible appearance and is a type of sports attire.\nA few things that are visually similar to 'baseball uniform' but are not 'baseball uniform' are:\tfootball uniform\tbasketball uniform\tsoccer uniform\tathletic clothing\nThere are several useful visual features to tell there is 'baseball uniform' and not similar things in a photo:\tpinstripes or solid colors\tbuttons or zipper closure\tbaseball cap\tJersey number on the back of the shirt", 151], "rolls": ["Yes. 'Rolls' has a tangible appearance and can refer to different types of rolled-up objects like bread rolls or rolls of paper.\nA few things that are visually similar to 'rolls' but are not 'rolls' are:\tcinnamon rolls\ttoilet paper roll\tsushi rolls\tcigarette rolls\nThere are several useful visual features to tell there is 'rolls' and not similar things in a photo:\tround or cylindrical shape\tsoft texture (if bread) or thin texture (if paper)\tslight curling at the edges (if paper)\tor criss-cross pattern on the top (if bread)", 150], "spices": ["Yes. 'Spices' has a tangible appearance and refers to dried plant products used to flavor food.\nA few things that are visually similar to 'spices' but are not 'spices' are:\therbs\tdirt\tdebris\tashes\nThere are several useful visual features to tell there is 'spices' and not similar things in a photo:\tcolorful mixture of powders or solids\tin small containers or bags\taromatic smell\traw, whole plant parts generally aren't considered spices (such as a 'bundle of cilantro')", 150], "lion": ["Yes. 'Lion' has a tangible appearance and is a carnivorous feline.\nA few things that are visually similar to 'lion' but are not 'lion' are:\ttiger\tleopard\tcheetah\thouse cat\tbobcat\nThere are several useful visual features to tell there is 'lion' and not similar things in a photo:\ttawny coloring with white underparts\tlong tail\twith a black ball at the end\tmane around the neck of the male lion\topen mouth with visible sharp teeth\tround ears\tflattened nose\tshort fur", 150], "crates": ["Yes. 'Crates' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'crates' but are not 'crates' are:\tboxes\tbins\tbaskets\tdrawers\ttrays\nThere are several useful visual features to tell there is 'crates' and not similar things in a photo:\trectangular shape\tmade of wood, plastic or metal\thas gaps between the slats or holes for ventilation\tdesigned to be stacked on top of each other\tno lid or cover, or a removable lid or cover.", 150], "silver ring": ["Yes. 'Silver ring' has a tangible appearance and is a piece of jewelry.\nA few things that are visually similar to 'silver ring' but are not 'silver ring' are:\tgold ring\tbracelet\tnecklace\twatch\nThere are several useful visual features to tell there is 'silver ring' and not similar things in a photo:\ta circular band worn as an ornament\ton a finger\twide variety of designs and styles\tsilver in color or made of silver material", 150], "undershirt": ["Yes. 'Undershirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'undershirt' but are not 'undershirt' are:\tregular shirt\ttank top\tundershorts\tswimsuit\nThere are several useful visual features to tell there is 'undershirt' and not similar things in a photo:\ta lightweight, sleeveless or short-sleeved garment worn under a shirt or blouse\tto be worn on the upper body\tclose-fitting and made of soft or breathable fabricusually white or off-white color", 150], "socket": ["Yes. 'Socket' has a tangible appearance and is a part of an electrical system or a device.\nA few things that are visually similar to 'socket' but are not 'socket' are:\tswitch\tplug\toutlet\tjack\nThere are several useful visual features to tell there is 'socket' and not similar things in a photo:\treceptacle for a bulb, a power cord, a USB cable or an audio connector\trectangular or circular shape\tmetallic or plastic material\tvisible prongs or holes for connection or insertion", 150], "bubbles": ["Yes. 'Bubbles' has a tangible appearance and are light, spherical objects with a thin film of soap.\nA few things that are visually similar to 'bubbles' but are not 'bubbles' are:\traindrops\tfoam\twater droplets\tair balloons\nThere are several useful visual features to tell there is 'bubbles' and not similar things in a photo:\tspherical or circular shape\tthin and iridescent film\ttranslucent or transparent\tfloats in the air or water\tpop when touched or burst after a few seconds.", 150], "burners": ["Yes. 'Burners' has a tangible appearance and refers to objects that produce flame or heat for cooking or heating.\nA few things that are visually similar to 'burners' but are not 'burners' are:\tcandles\tfireplaces\tgrills\tovens\nThere are several useful visual features to tell there are 'burners' and not similar things in a photo:\t\ncircular or rectangular shape\tgas or electric power\tsource of flame or heat\tgrates, coils, or burners for holding pots or pans", 149], "ovens": ["Yes. 'Ovens' has a tangible appearance and is a household appliance used for baking or roasting.\nA few things that are visually similar to 'ovens' but are not 'ovens' are:\tstoves\tmicrowaves\tfireplace\tgrills\nThere are several useful visual features to tell there is 'ovens' and not similar things in a photo:\tenclosed space with a door\tracks or shelves inside\tdials, buttons, or touch screens to control temperature and settings\texhaust or ventilation system to release heat and smoke\tbaking or roasting items inside", 149], "curve": ["Yes. 'Curve' has a tangible appearance and is a type of shape.\nA few things that are visually similar to 'curve' but are not 'curve' are:\tline\tbend\tarch\tspiral\nThere are several useful visual features to distinguish 'curve' from the listed similar things in a photo:\tIt changes direction smoothly\tIt has no straight lines or angles\tIt looks like a part of a circle or an S-shape\tIt appears to be flowing or bending", 149], "soil": ["Yes. 'Soil' has a tangible appearance and is a mixture of minerals, organic matter, and other materials.\nA few things that are visually similar to 'soil' but are not 'soil' are:\tsand\trocks\tclay\tpebbles\nThere are several useful visual features to tell there is 'soil' and not similar things in a photo:\tdark or light brown color\tgrainy or powdery texture\tvisible organic matter or debris\tplants growing from it", 149], "point": ["No. 'Point' is too vague or abstract to be distinguished in a photo.", 149], "ostrich": ["Yes. 'Ostrich' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'ostrich' but are not 'ostrich' are:\temus\trheas\tcassowaries\nThere are several useful visual features to tell there is 'ostrich' and not similar things in a photo:\tlarge size\tfluffy feathers\tblack and white feathers\tlong neck and legs\tbulging eyes\twith or without wings", 149], "brown basket": ["Yes. 'Brown basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'brown basket' but are not 'brown basket' are:\twooden crate\twoven tray\tbamboo box\t\nThere are several useful visual features to tell there is 'brown basket' and not similar things in a photo:\tmade of brown material, such as wood or wicker\thandles for carrying\tridged or woven texture\ta hollow center for holding items.", 148], "shirtless man": ["Yes, 'shirtless man' has a tangible appearance.\nA few things that are visually similar to 'shirtless man' but are not 'shirtless man' are:\tswimsuit\tundershirt\tbathing suit\tstatue\nThere are several useful visual features to tell there is 'shirtless man' and not similar things in a photo:\tvisible chest, torso, and arms\tabsence of a shirt or any upper body clothing", 148], "meters": ["No. 'Meters' is too abstract to have a tangible appearance.\nThere are no things that are visually similar to 'meters' but are not 'meters,' as it is a unit of measurement.\nN/A", 148], "dark clouds": ["Yes. 'Dark clouds' has a tangible appearance and refers to a specific type of cloud formation.\nA few things that are visually similar to 'dark clouds' but are not 'dark clouds' are:\tsmoke\tfog\tdust storm\t\nThere are several useful visual features to tell there are 'dark clouds' and not similar things in a photo:\tdark or grey color\toften associated with stormy weather or rain\ttowering or puffy shape\tlower altitude than other types of clouds like cirrus or stratus.", 148], "layer": ["Yes. 'Layer' has a tangible appearance and can be seen physically.\nA few things that are visually similar to 'layer' but are not 'layer' are:\tcoating\tstratum\tfilm\tveneer\nThere are several useful visual features to tell there is 'layer' and not similar things in a photo:\tan object with multiple visible and separated sections\tcould be transparent or opaque, uniform or uneven in thickness\tmight have different colors or patterns\tin some cases, could be peeled off or stripped away with hands or tools", 148], "brown box": ["Yes. 'Brown box' has a tangible appearance and refers to a specific type of container.\nA few things that are visually similar to 'brown box' but are not 'brown box' are:\twooden crate\tbrown bag\tpaper parcel\tshipping container\nThere are several useful visual features to tell there is 'brown box' and not similar things in a photo:\trectangular shape\tcorrugated cardboard material\tbrown color\tflapping lid or cover", 147], "range": ["No. 'Range' is too vague or abstract to be distinguished in a photo. \n\nNote: Without context, \"range\" can have various interpretations (e.g., a range of mountains, a range of music notes, a range of products in a store, etc.), which may or may not be visually concrete.", 147], "closet": ["Yes. 'Closet' has a tangible appearance and is a type of storage unit.\nA few things that are visually similar to 'closet' but are not 'closet' are:\tpantry\tcabinet\tshelves\twardrobe\nThere are several useful visual features to tell there is 'closet' and not similar things in a photo:\ta door or a curtain for access\tshelves or hanging bars for clothing or storage\twithin a room or a walk-in space\tclean and organized interior", 147], "moon": ["Yes. 'Moon' has a tangible appearance and is a natural satellite.\nA few things that are visually similar to 'moon' but are not 'moon' are:\tplanet\tstar\tlight bulb\treflective surfaces\t\nThere are several useful visual features to tell there is 'moon' and not similar things in a photo:\tcircular shape\tcrusty texture grey or white color\tdark spots on its surface that resemble a face (in some cases)\tcan be seen in the night sky", 147], "variety": ["No. 'Variety' is too vague or abstract to be distinguished in a photo.", 146], "chips": ["Yes. 'Chips' has a tangible appearance and refers to thin slices of potato or other vegetables that are deep-fried or baked.\nA few things that are visually similar to 'chips' but are not 'chips' are:\tfrench fries\tcrisps\tvegetable sticks\nThere are several useful visual features to tell there is 'chips' and not similar things in a photo:\tsliced and thin of potatoes or vegetables\tfried or baked\tgolden or brown crispy outside\tsalty as a seasoning", 146], "sweatband": ["Yes. 'Sweatband' has a tangible appearance and is a kind of sport accessory.\nA few things that are visually similar to 'sweatband' but are not 'sweatband' are:\theadbands\tbracelets\twristbands\tbandanas\thair ties\nThere are several useful visual features to tell there is 'sweatband' and not similar things in a photo:\tnarrow band made of absorbent material\tworn around the forehead or wrist\tplaced to prevent sweat from getting in the eyes\tcommonly worn during physical activities or sports", 146], "blueberries": ["Yes. 'Blueberries' has a tangible appearance and is a type of small, round fruit.\nA few things that are visually similar to 'blueberries' but are not 'blueberries' are:\traisins\tgrapes\tcranberries\nThere are several useful visual features to tell there is 'blueberries' and not similar things in a photo:\tsmall\tround\tpurplish-blue color or blue-black color\tsmooth skin or texture\tdark, green stems on each berry.", 146], "cereal": ["Yes. 'Cereal' has a tangible appearance and refers to a type of food made of grains.\nA few things that are visually similar to 'cereal' but are not 'cereal' are:\tpellets\tforage\tpelts\nThere are several useful visual features to tell there is 'cereal' and not similar things in a photo:\tflakes, puffs or granular in texture\tvariety of colors and shapes\tmay be in a bowl or a spoon\tin a box or a bag with a cereal brand name on it.", 146], "motor": ["Yes. 'Motor' has a tangible appearance and is a kind of machine.\nA few things that are visually similar to 'motor' but are not 'motor' are:\tmachine\tengine\tfan\tpump\nThere are several useful visual features to tell there is 'motor' and not similar things in a photo:\tcylinder-shaped object\tmetallic surfaces\twires or cables attached to it\tsparks or flames visible from the engine\texposed moving parts to generate power or motion.", 146], "throw pillow": ["Yes. 'Throw pillow' has a tangible appearance and is a kind of cushion.\nA few things that are visually similar to 'throw pillow' but are not 'throw pillow' are:\tbed pillow\tstuffed animal\tpouf\tchair cushion\nThere are several useful visual features to tell there is 'throw pillow' and not similar things in a photo:\tsquare or rectangular shape\tdecorative patterns or designs\trelatively small\tsize (usually smaller than bed pillow)\tbright colors\tor contrasting colors\tplaced on a couch or a chair", 146], "blue letters": ["Yes. 'Blue letters' has a tangible appearance and is a type of writing or text.\nA few things that are visually similar to 'blue letters' but are not 'blue letters' are:\tblack letters\tcolored shapes\tpieces of fabric\twatercolor paintings\nThere are several useful visual features to tell there are 'blue letters' and not similar things in a photo:\twritten characters or symbols\tbluish hue or tint\tset against a contrasting background, such as white or black", 146], "boards": ["Yes. 'Boards' have a tangible appearance and can refer to different types of flat, rectangular pieces of material, such as wood or plastic.\nA few things that are visually similar to 'boards' but are not 'boards' are:\tblocks\tcubes\ttablets\tpaper sheets\nThere are several useful visual features to tell there is 'boards' and not similar things in a photo:\t\nrectangular in shape\na thicker, flat piece of material\nvisible wood grain or texture\nmay have screws, nails, or other fasteners visible on their surface", 146], "sponge": ["Yes. 'Sponge' has a tangible appearance and is a type of porous material.\nA few things that are visually similar to 'sponge' but are not 'sponge' are:\tfoam\tcheese\trock with holes\tcoral\nThere are several useful visual features to tell there is 'sponge' and not similar things in a photo:\tporous texture\twith holes, dimples, or channels\tsoft and flexible\twhen wet, can hold water or soap bubbles", 145], "railings": ["Yes. 'Railings' have a tangible appearance and are physical barriers or protectors.\nA few things that are visually similar to 'railings' but are not 'railings' are:\tfences\tbarricades\tgates\tpedestrian boundaries\nThere are several useful visual features to tell there is 'railings' and not similar things in a photo:\tparallel bars or rods\tattached to a wall or surface\tused for support or safety purposes\tusually found on stairs, balconies or bridges.", 145], "office": ["Yes. 'Office' has a tangible appearance and is a workspace.\nA few things that are visually similar to 'office' but are not 'office' are:\tclassroom, library, laboratory, hospital, restaurant\nThere are several useful visual features to tell there is 'office' and not similar things in a photo:\toffice desk and chair\tcomputer and monitor\tfiling cabinets\ttelephone\tdocument, pens, and paper\twhiteboard or bulletin board\tartificial lighting", 145], "metal chair": ["Yes. 'Metal chair' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'metal chair' but are not 'metal chair' are:\twooden chair\tplastic chair\tbench\tstool\t\nThere are several useful visual features to tell there is 'metal chair' and not similar things in a photo:\tmetallic or shiny surface\thard and cold to the touch\tmade entirely of metal\tarmrests and backrests", 145], "lambs": ["Yes. 'Lambs' has a tangible appearance and is a young sheep.\nA few things that are visually similar to 'lambs' but are not 'lambs' are:\tadult sheep\tgoats\tdeer\tcalves\nThere are several useful visual features to tell there is 'lambs' and not similar things in a photo:\tsmaller size\twooly white or black coat\twith or without horns\tshort and straight tails\tthat generally remain with their mother and flock", 145], "groom": ["Yes. 'Groom' has a tangible appearance and is a person who is dressed up for a formal event, usually a wedding.\nA few things that are visually similar to 'groom' but are not 'groom' are:\tbusinessman\twaiter\tdoorman\tmodel\nThere are several useful visual features to tell there is 'groom' and not similar things in a photo:\twearing a suit or formal attire\twearing a boutonniere\tclean-shaven\tstanding next to a bride or a wedding party", 145], "grassy hill": ["Yes. 'Grassy hill' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'grassy hill' but are not 'grassy hill' are:\tmountains\trocky hills\tdunes\nThere are several useful visual features to tell there is 'grassy hill' and not similar things in a photo:\tcovered with green grass or foliage\tround or sloping shape\tusually not too high\tsmooth or slightly bumpy texture", 145], "cut": ["No. 'Cut' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to a physical 'cut' (such as a cut on the skin) but are not a 'cut' can be: \t\na red line or mark drawn on the skin with a pen or marker;\na bruise or other skin injury that changes the color of the skin.\n\nUseful visual features to distinguish a physical 'cut' from these similar things in a photo would be: \na visible opening or split in the skin; \na visible amount of bleeding coming from the wound.", 144], "pancakes": ["Yes. 'Pancakes' has a tangible appearance and is a type of food item.\nA few things that are visually similar to 'pancakes' but are not 'pancakes' are:\twaffles\tcrepes\tflatbreads\tEnglish muffins\nThere are several useful visual features to tell there is 'pancakes' and not similar things in a photo:\tcircular shape\tstacked on top of each other\tbrowned or golden surface\tcovered in syrup or toppings", 144], "vents": ["Yes. 'Vents' has a tangible appearance and is an opening that allows air or gas to escape or enter a space.\nA few things that are visually similar to 'vents' but are not 'vents' are:\tdoors\twindows\tgrates\t\nThere are several useful visual features to tell there are 'vents' and not similar things in a photo:\trectangular or circular shape\tplaced on walls, ceilings or floors\t\nslats or grilles covering the opening\tvisible air or gas flow", 144], "parachute": ["Yes. 'Parachute' has a tangible appearance and is a type of device used for aerial descent.\nA few things that are visually similar to 'parachute' but are not 'parachute' are:\tumbrella\tkite\tballoon\tglider\nThere are several useful visual features to tell there is 'parachute' and not similar things in a photo:\n\tnon-rigid canopy made of fabric or other materials\n\tcords attached to the canopy and to a harness\n\tperson wearing a harness attached to the cords\n\tdescent or landing is imminent", 144], "hotdogs": ["Yes. 'Hotdogs' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'hotdogs' but are not 'hotdogs' are:\tsausages\tbratwursts\tkielbasa\nThere are several useful visual features to tell there is 'hotdogs' and not similar things in a photo:\telongated shape\twith or without bun or toppings\tskin or casing on the outside", 143], "chrome": ["Yes. 'Chrome' has a tangible appearance and is a type of metal finish.\nA few things that are visually similar to 'chrome' but are not 'chrome' are:\tstainless steel\tsilver\taluminum\nThere are few useful visual features to tell there is 'chrome' and not similar things in a photo:\tmirrored finish\tbright and reflective surface\tblue-ish tint\twhen scratched, doesn't turn brown or yellow.", 143], "jug": ["Yes. 'Jug' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'jug' but are not 'jug' are:\tpitcher\tbottle\tvase\turn\nThere are several useful visual features to tell there is 'jug' and not similar things in a photo:\thandle\ton top of the container (not at the neck)\twith a spout\tfor pouring liquid out of the spout", 143], "bathroom toilet": ["Yes. 'Bathroom toilet' is a visually concrete concept and is a type of bathroom fixture.\nA few things that are visually similar to 'bathroom toilet' but are not 'bathroom toilet' are:\tbidet\tportable toilet\turinal\nThere are several useful visual features to tell there is 'bathroom toilet' and not similar things in a photo:\tbowl-shaped porcelain or ceramic fixture\twith a hinged seat\tfor the purpose of human waste disposal\twith water tank or flushing mechanism\tconnected to a plumbing system", 143], "bathroom floor": ["Yes. 'Bathroom floor' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'bathroom floor' but are not 'bathroom floor' are: kitchen floor, bedroom floor, living room floor.\nThere are several useful visual features to tell there is 'bathroom floor' and not similar things in a photo:\ttile, stone or linoleum surface\twater stains or puddles\tdrain\tor sink nearby\tbathtub/shower nearby", 143], "rubber tire": ["Yes. 'Rubber tire' has a tangible appearance and is part of a vehicle.\nA few things that are visually similar to 'rubber tire' but are not 'rubber tire' are:\tinner tube\tbicycle tire\tbouncy ball\tsoccer ball\nThere are several useful visual features to tell there is 'rubber tire' and not similar things in a photo:\tcircular shape\ttread pattern\trubber material\thole in the center\tside walls", 143], "silver bowl": ["Yes. 'Silver bowl' is a visually concrete concept and is a type of container.\nA few things that are visually similar to 'silver bowl' but are not 'silver bowl' are:\tglass bowl\tstainless steel bowl\twhite porcelain bowl\tcopper bowl\tAluminum bowl\nThere are several useful visual features to tell there is 'silver bowl' and not similar things in a photo:\tmade of silver or a silver-like material\tshiny and reflective\tmetallic appearance\tbowl-shaped with a round base and sloping sides\tcan hold food or other items.", 143], "olive": ["Yes. 'Olive' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'olive' but are not 'olive' are:\tgrapes\tplums\tcherries\tcranberries\nThere are several useful visual features to tell there is 'olive' and not similar things in a photo:\tsmall oval shape\tsmooth texture\tshiny green, purple, brown or black\tcolor hard pit in the center.", 143], "leafy trees": ["Yes. 'Leafy trees' has a tangible appearance and is a type of tree with broad, flat leaves.\nA few things that are visually similar to 'leafy trees' but are not 'leafy trees' are:\tpalm trees\tcacti\tconifers\tartificial trees\nThere are several useful visual features to tell there are 'leafy trees' and not similar things in a photo:\tbroad, flat leaves\tbushy and dense canopy\tvarious shades of green in the leaves\tand the overall shape or silhouette of the tree", 143], "bus number": ["No. 'Bus number' is too vague or abstract to be distinguished in a photo. It is a numerical identification for a bus and doesn't have tangible appearance itself. \n\nTherefore, there aren't really any things that are visually similar to 'bus number' but are not 'bus number'.", 143], "amount": ["No. 'Amount' is too vague or abstract to be distinguished in a photo.", 143], "foot prints": ["Yes. 'Foot prints' has a tangible appearance and is a mark left behind by a foot on a surface.\nA few things that are visually similar to 'foot prints' but are not 'foot prints' are:\tpaw prints\ttire tracks\tskid marks\tstains\ttraces\nThere are several useful visual features to tell there is 'foot prints' and not similar things in a photo:\ttwo distinct ovals for each foot\tarch shape\theel mark\ttoe impression\tdirection of the foot prints\tdistance between footsteps\tsize of the prints", 143], "map": ["Yes. 'Map' has a tangible appearance and is a visual representation of an area.\nA few things that are visually similar to 'map' but are not 'map' are:\tchart\tgraph\tdiagram\tblueprint\nThere are several useful visual features to tell there is 'map' and not similar things in a photo:\tgeographical data\tlines or symbols that show streets, features, or topography\tkey or legend that explains symbols\ttitle or heading that describes the contents", 143], "stadium": ["Yes. 'Stadium' has a tangible appearance and is a type of sports facility.\nA few things that are visually similar to 'stadium' but are not 'stadium' are:\tarena\ttheater\tconcert hall\tcoliseum\nThere are several useful visual features to tell there is 'stadium' and not similar things in a photo:\tlarge seating capacity\tfield or court\tfor sports events or concerts\topen-air or with a retractable roof\ttall structure for better visibility from a distance", 142], "oval": ["Yes. 'Oval' has a tangible appearance and refers to a specific shape.\nA few things that are visually similar to 'oval' but are not 'oval' are:\tcircle\tegg\tteardrop\tbean\nThere are several useful visual features to tell there is 'oval' and not similar things in a photo:\telongated\trounded at both ends\tsymmetric\tcentered", 142], "sack": ["Yes. 'Sack' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'sack' but are not 'sack' are:\tbackpack\tpurse\tpouch\tbasket\t\nThere are several useful visual features to tell there is 'sack' and not similar things in a photo:\trectangular or cylindrical shape\tmade of burlap or other coarse material\tdrawstring or other closure at the top\tof a certain size and shape used for carrying and storing goods.", 142], "blue body": ["Yes. 'Blue body' has a tangible appearance but it is still quite vague and could refer to any object or creature that is blue and has a body.\nA few things that are visually similar to 'blue body' but are not 'blue body' are:\tblue car\tblue dress\tblue ocean\tblueberry\nThere are no clear or specific visual features to distinguish 'blue body' from the listed similar things in a photo, as it depends on the context and the purpose of the image.", 142], "wh": ["No. 'wh' is not a visually concrete concept, it is a combination of letters used in the English language for building words (e.g., 'what', 'when', 'where', 'who', 'why').", 141], "wipers": ["Yes. 'Wipers' have a tangible appearance and are a part of a car's windshield wiper system.\nA few things that are visually similar to 'wipers' but are not 'wipers' are:\tantennas\tdecorative stripes\tstickers\ttrims\troof racks\nThere are several useful visual features to tell there are 'wipers' and not similar things in a photo:\tattached to the windshield\tof rubber or silicone material\trectangular or curved shape\tmaking back and forth motion to clean the windshield", 141], "puffy clouds": ["Yes. 'Puffy clouds' has a tangible appearance and refers to cumulus clouds that are fluffy and resemble cotton balls.\nA few things that are visually similar to 'puffy clouds' but are not 'puffy clouds' are:\tSmoke\tMist\tFog\t\nThere are several useful visual features that distinguish 'puffy clouds' from the listed similar things in a photo:\n\n- Puffy clouds are typically whiter and brighter in color than smoke or fog.\n- Puffy clouds have more defined shapes and edges than mist or fog.\n- Puffy clouds are often seen in a blue sky, while smoke and fog are typically seen in gray or hazy conditions.\n- Puffy clouds can appear at varying elevations in the atmosphere, while fog and mist tend to be closer to the ground.", 141], "cheek": ["Yes. 'Cheek' has a tangible appearance and is a part of the face.\nA few things that are visually similar to 'cheek' but are not 'cheek' are:\tFruit cheeks (part of a fruit)\tButtocks (part of the body)\tCheekpieces (part of a horse\u2019s bridle)\nThere are several useful visual features to tell there is 'cheek' and not similar things in a photo:\tpart of the face\thighly vascularized and fleshy\tsurrounds the mouth and extends up to the ear\thas a natural rounded contour and curves slightly outward\tfrom the side of the nose to the side of the mouth", 141], "gun": ["Yes. 'Gun' has a tangible appearance and is a type of weapon.\nA few things that are visually similar to 'gun' but are not 'gun' are:\ttoy gun\tcigarette lighter\tmetal pipe\twith some similar features attached to it, like barrel, trigger, etc.\nThere are several useful visual features to tell there is 'gun' and not similar things in a photo:\t\nbarrel\tcylinder\tcontoured handle or grip\ttrigger", 141], "lime": ["Yes. 'Lime' has a tangible appearance and is a type of citrus fruit.\nA few things that are visually similar to 'lime' but are not 'lime' are:\tlemon\tgrapefruit\tkiwi\tavocado\nThere are several useful visual features to tell there is 'lime' and not similar things in a photo:\tbright green color\twith a small size round shape and smooth texture, with a small nipple on one end\tthe inside is green and juicy with a sour taste.", 140], "male tennis player": ["Yes. 'Male tennis player' has a tangible appearance and refers to a person playing tennis.\nA few things that are visually similar to 'male tennis player' but are not 'male tennis player' are:\tmale golfer\tmale basketball player\tmale soccer player\tmale runner\nThere are several useful visual features to tell there is 'male tennis player' and not similar things in a photo:\twearing tennis shoes and shorts or pants\tcarrying a tennis racquet\thitting or about to hit a tennis ball\tplaying on a tennis court or surface with a net in the middle", 140], "pizza slice": ["Yes. 'Pizza slice' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'pizza slice' but are not 'pizza slice' are:\tpie slice\tcheese wedge\tcake triangle\nThere are several useful visual features to tell there is 'pizza slice' and not similar things in a photo:\tcircular\tdoughy crust\tmelted cheese and sauce on top\tsliced into triangular pieces\tadditional toppings such as pepperoni, mushrooms, onions, etc.", 140], "blue pillow": ["Yes. 'Blue pillow' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'blue pillow' but are not 'blue pillow' are:\tblue cushion\tblue stuffed toy\tblue blanket\nThere are several useful visual features to tell there is 'blue pillow' and not similar things in a photo:\trectangular or square shape\tsoft and fluffy texture\tsolid blue color\tpillowcase or cover with visible seams, zippers or buttons.", 140], "cushions": ["Yes. 'Cushions' has a tangible appearance and is a type of soft accessory for furniture.\nA few things that are visually similar to 'cushions' but are not 'cushions' are:\tpillows\tstuffed animals\tpoufs\nThere are several useful visual features to tell there is 'cushions' and not similar things in a photo:\tsquare or rectangular shape\tvariety of colors and patterns\tsoft and plump appearance\tused to accessorize furniture like sofas or chairs.", 140], "feather": ["Yes. 'Feather' has a tangible appearance and is a type of natural object.\nA few things that are visually similar to 'feather' but are not 'feather' are:\tleaves\tpetals\tfur\thair\nThere are several useful visual features to tell there is 'feather' and not similar things in a photo:\tlight and delicate weight\tthin and long shape\tscaly appearance\thaving a quill or a stem in the middle\tV-shaped strands in different colors", 140], "beam": ["Yes. 'Beam' has a tangible appearance and generally refers to a long and sturdy piece of wood or metal.\nA few things that are visually similar to 'beam' but are not 'beam' are:\tpole\tbar\trod\twire\nThere are several useful visual features to tell there is 'beam' and not similar things in a photo:\tlong and rectangular in shape, can also be rounded or cylindrical\tsolid and sturdy-looking\tsupporting weight or structure, such as a ceiling or a building", 140], "bats": ["Yes. 'Bats' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'bats' but are not 'bats' are:\tbirds\tmoths\tinsects\nThere are several useful visual features to tell there is 'bats' and not similar things in a photo:\tfurry bodies\twings with a web-like structure\tears that are almost as large as their body\tsharp claws on their feet and elongated fingers on their wings\tdark or brownish coloration\tthat bats typically sleep upside down", 139], "spire": ["Yes. 'Spire' has a tangible appearance and refers to a tall, pointed structure on the roof of a building.\nA few things that are visually similar to 'spire' but are not 'spire' are:\ttower\tmast\tminaret\tpile\nThere are several useful visual features to tell there is 'spire' and not similar things in a photo:\ttall and pointed\ton the roof of a building or a church(mostly)\tnarrower at the top than the bottom", 139], "blue building": ["Yes. 'Blue building' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'blue building' but are not 'blue building' are:\tblue car\tblue billboard\tblue tent\nThere are several useful visual features to tell there is a 'blue building' and not similar things in a photo:\thas walls and roof\thas windows and doors\tpermanent or semi-permanent structure\tfixed location or foundation", 139], "note": ["No. 'Note' is too vague or abstract to be distinguished in a photo. \nHowever, a few things that are visually similar to 'note' but are not 'note' are:\tpaper\tclipping\tpicture\tmessage", 139], "panda bear": ["Yes. 'Panda bear' has a tangible appearance as a type of bear with a distinct black and white pattern.\nA few things that are visually similar to 'panda bear' but are not 'panda bear' are:\traccoon\tskunk\tbadger\tpenguin\nThere are several useful visual features to tell there is 'panda bear' and not similar things in a photo:\tdistinct black and white fur pattern\tround face and ears\tdark circles around the eyes\tdark limbs and shoulders with white torso\tand its overall bear-like appearance.", 139], "terrain": ["Yes. 'Terrain' has a tangible appearance and is a physical landscape or surface.\nA few things that are visually similar to 'terrain' but are not 'terrain' are:\tpainting\tmap\ttexture\nThere are several useful visual features to tell there is 'terrain' and not similar things in a photo:\tvaried elevations, such as hills, mountains, or valleys\trocks, boulders, or cliffs\tvegetation, such as trees, grass, or bushes\tbodies of water, such as rivers, lakes, or oceans\tman-made structures, such as buildings, roads, or bridges.", 139], "face mask": ["Yes. 'Face mask' has a tangible appearance and is a kind of protective equipment.\nA few things that are visually similar to 'face mask' but are not 'face mask' are:\tbandana\tscarf\tdust mask\tski mask\nThere are several useful visual features to tell there is 'face mask' and not similar things in a photo:\tcovering nose and mouth\tfitting snugly against the face\tear loops or ties\tfor medical or public health purposes", 139], "desk chair": ["Yes. 'Desk chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'desk chair' but are not 'desk chair' are:\tsofa\tstool\tbench\tlounge chair\nThere are several useful visual features to tell there is 'desk chair' and not similar things in a photo:\thas a backrest\thas armrests\thas wheels or a swivel base\tadjustable height or angle\tpadded seat and backrest.", 139], "pears": ["Yes. 'Pears' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'pears' but are not 'pears' are:\tapples\tguavas\tquinces\nThere are several useful visual features to tell there is 'pears' and not similar things in a photo:\tpear-shaped\tfleshy with a thin skin\ttapering at the stem end and usually wider towards the base\tyellow, green, brown, or red in color", 139], "stage": ["Yes, 'stage' has a tangible appearance and is usually elevated platform used for performances.\nA few things that are visually similar to 'stage' but are not 'stage' are:\tpodium, platform, altar\torator's platform\tramp\nThere are several useful visual features to tell there is 'stage' and not similar things in a photo:\televated platform\twith curtains or a backdrop\tmicrophones, instruments or other performance props\ton a stage, there is usually a focus on a performance or presentation happening.", 139], "strips": ["Yes. 'Strips' has a tangible appearance and refers to thin, long pieces or lines that are usually flat or rectangular.\nA few things that are visually similar to 'strips' but are not 'strips' are:\tribbons\tpieces of paper\tlines on a page\tof piece of fabric\nThere are several useful visual features to tell there is 'strips' and not similar things in a photo:\tthin and elongated\trectangular or flat shape\tuniform width and length", 139], "magnet": ["Yes. 'Magnet' has a tangible appearance and is a type of metal object.\nA few things that are visually similar to 'magnet' but are not 'magnet' are:\tmetal paper clip\tbolt or nut\tmetal coin\tmetal badge\nThere are several useful visual features to tell there is 'magnet' and not similar things in a photo:\tattracting metal objects\tusually small and handheld\trectangular or circular shape\thas a north and south pole, with opposite charges on each end\tcommonly made of iron or other magnetic materials", 139], "flag pole": ["Yes. 'Flag pole' has a tangible appearance and is a type of pole.\nA few things that are visually similar to 'flag pole' but are not 'flag pole' are:\tsign post\tlamppost\ttelephone pole\tumbrella\nThere are several useful visual features to tell there is 'flag pole' and not similar things in a photo:\ttall and slender\tpainted white or metal color\twith a pulley mechanism\tfor holding a flag at the top\tpart of a building or standing alone in an open field", 139], "cauliflower": ["Yes. 'Cauliflower' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'cauliflower' but are not 'cauliflower' are:\tbroccoli\tcabbage\tbrussels sprouts\tromanesco broccoli\nThere are several useful visual features to tell there is 'cauliflower' and not similar things in a photo:\twhite or off-white florets\tin a compact, round head\torchart, leafy structures around each floret\tno stem or only a short stem.", 138], "company logo": ["Yes. 'Company logo' has a tangible appearance and is a design or symbol used by a company to represent their brand.\nA few things that are visually similar to 'company logo' but are not 'company logo' are:\tgraphic design\tillustration\tsignature\tstamp\nThere are several useful visual features to tell there is 'company logo' and not similar things in a photo:\tunique design or symbol\tassociated with a particular brand or company\tdistinct color scheme or typography", 138], "blue box": ["Yes. 'Blue box' has a tangible appearance and is a type of box.\nA few things that are visually similar to 'blue box' but are not 'blue box' are:\tblue suitcase\tblue bag\tblue crate\tblue package\nThere are several useful visual features to tell there is 'blue box' and not similar things in a photo:\trectangular shape\thigher than it is wide\thaving an open lid or a closed lid\tthe material it is made of may vary, but the color should be blue", 138], "cleat": ["Yes. 'Cleat' is a visually concrete concept and is a type of shoe accessory.\nA few things that are visually similar to 'cleat' but are not 'cleat' are:\tspike\theel\tshoelace\teylet\nThere are several useful visual features to tell there is 'cleat' and not similar things in a photo:\t\n- metal or rubber protrusion from the sole of a shoe\n- used for added grip and traction on certain surfaces (such as sports fields)\n- often shaped like a rectangular or triangular bar with raised bumps or ridges \n- can be detachable or integrated into the shoe design.", 138], "vines": ["Yes. 'Vines' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'vines' but are not 'vines' are:\ttendrils\troots\ttree branches\nThere are several useful visual features to tell there is 'vines' and not similar things in a photo:\telongated and thin stems\twith leaves and flowers that grow upward or climb surfaces\tcan form tangled and twisted structures can be found in gardens, forests or climbing on walls/ buildings", 137], "silver watch": ["Yes. 'Silver watch' has a tangible appearance and is a kind of timepiece.\nA few things that are visually similar to 'silver watch' but are not 'silver watch' are:\tbracelets\tother metal watches\tjewelry\tclocks\nThere are several useful visual features to tell there is 'silver watch' and not similar things in a photo:\tstainless steel or silver color\tmetal wristband\tanalog or digital display\tfor displaying time or date", 137], "pear": ["Yes. 'Pear' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'pear' but are not 'pear' are:\tgreen apple\tavocado\tgreen bell pepper\tguava\nThere are several useful visual features to tell there is 'pear' and not similar things in a photo:\tpear-shaped\tfuzzy skin at the top of the fruit\tsmooth skin on the bottom of the fruit\tgreenish-yellow or brownish-green skin\tflesh in the center of the fruit that is white or creamy with a texture that is juicy but not mushy or watery.", 137], "dough": ["Yes. 'Dough' has a tangible appearance and is a mixture of flour and water.\nA few things that are visually similar to 'dough' but are not 'dough' are:\tbatter\tclay\tcement\nThere are several useful visual features to tell there is 'dough' and not similar things in a photo:\tsoft and malleable\ttexture and consistency of wet sand or clay\twhite or beige color\tsticky or stretchy when pulled or kneaded", 137], "handlebar": ["Yes. 'Handlebar' has a tangible appearance and is a part of a bike or motorcycle.\nA few things that are visually similar to 'handlebar' but are not 'handlebar' are:\tdoorknob\tshowerhead\tcrutch handle\twalking stick handle\trod\nThere are several useful visual features to tell there is 'handlebar' and not similar things in a photo:\tcylindrical shape\twith grips or tape to provide traction\tfor bikes and motorcycles (with brake and gear lines visible)", 137], "brick chimney": ["Yes. 'Brick chimney' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'brick chimney' but are not 'brick chimney' are:\ttree\ttrumpet\tbuilding pillar\nThere are several useful visual features to tell there is 'brick chimney' and not similar things in a photo:\tbrick construction\tcylindrical shape\tsmoke coming out of the top\tnear a roof or a fireplace", 137], "trolley": ["Yes. 'Trolley' has a tangible appearance and is a wheeled vehicle used for transporting goods.\nA few things that are visually similar to 'trolley' but are not 'trolley' are:\tcart\tbicycle\ttruck\tstroller\nThere are several useful visual features to tell there is 'trolley' and not similar things in a photo:\twheeled platform or cart\thandles for pushing or pulling\toften used in retail stores or airports\tfor carrying goods or luggage", 137], "hind legs": ["Yes. 'Hind legs' has a tangible appearance and is a physical part of an animal's body.\nThere are no things that are visually similar to 'hind legs' but are not 'hind legs' since hind legs are a specific body part in animals.\nUseful visual features for distinguishing 'hind legs' from other body parts in a photo are:\tlocated at the back of the animal's body\tjointed and bendable\tthick and muscular\tbears weight during standing or jumping", 136], "police": ["Yes. 'Police' has a tangible appearance and refers to law enforcement officers.\nA few things that are visually similar to 'police' but are not 'police' are:\tsecurity guards\tmilitary personnel\tfirefighters\tparamedics\nThere are several useful visual features to tell there is 'police' and not similar things in a photo:\tuniform with badge or name tag\tbaton, gun, or handcuffs\tpatrol car or motorcycle\tpolice station or logo on clothing or vehicle", 136], "mantle": ["Yes. 'Mantle' has a tangible appearance and is a type of shelf over a fireplace.\nA few things that are visually similar to 'mantle' but are not 'mantle' are:\tshelf\tcabinet\tcounter\ttop of a dresser\nThere are several useful visual features to tell there is 'mantle' and not similar things in a photo:\tlocated above a fireplace\tmade of wood, stone, or marble\tdecorated with candles, vases, or picture frames", 136], "flip flops": ["Yes. 'Flip flops' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'flip flops' but are not 'flip flops' are:\tsandals\tslippers\tclogs\t\nThere are several useful visual features to tell there is 'flip flops' and not similar things in a photo:\tonly have a thong strap that goes between the big and second toe\thave a flat sole that can be made of rubber or plastic\tcasual and informal style\topen design that doesn't cover the heel or the toes.", 136], "refrigerators": ["Yes. 'Refrigerators' has a tangible appearance and is a type of appliance.\nA few things that are visually similar to 'refrigerators' but are not 'refrigerators' are:\tovens\twashing machines\tdishwashers\tcabinets\tchests\nThere are several useful visual features to tell there is 'refrigerators' and not similar things in a photo:\tvertical shape\tmetallic or plastic exterior\thorizontal handle or handles\tand most importantly the cold air coming from the inside part of the refrigerator.", 136], "orange carrots": ["Yes. 'Orange carrots' has a tangible appearance and refers to a specific variety of carrots.\nA few things that are visually similar to 'orange carrots' but are not 'orange carrots' are:\tred carrots\tpurple carrots\tyellow carrots\nThere are several useful visual features to tell there is 'orange carrots' and not similar things in a photo:\torange color\ttapered shape\tgreen stem on top\tlong and thin size\tcrunchy texture when eaten.", 136], "baseball umpire": ["Yes. 'Baseball umpire' has a tangible appearance and is an official in a baseball game.\nA few things that are visually similar to 'baseball umpire' but are not 'baseball umpire' are:\tcoach\treferee\tplayer\tball boy\tsecurity guard\nThere are several useful visual features to tell there is 'baseball umpire' and not similar things in a photo:\twearing a black or navy blue shirt and pants\twearing a chest protector\tand leg guards\tcarrying a mask or helmet and a counter for tracking balls and strikes\tmaking hand signals or gestures to call the game", 136], "stalk": ["Yes. 'Stalk' has a tangible appearance as it refers to the stem of a plant.\nA few things that are visually similar to 'stalk' but are not 'stalk' are:\ttrunk of a tree\tleg of an animal\tpipe\nThere are several useful visual features to tell there is 'stalk' and not similar things in a photo:\tslender shape\twith leaves attached at intervals\tvariations in color or texture\toften with flowers or fruits at the top", 136], "neon sign": ["Yes. 'Neon sign' has a tangible appearance and refers to a type of sign made of glass tubes filled with neon gas.\nA few things that are visually similar to 'neon sign' but are not 'neon sign' are:\tLED sign\tlightbulb\tsignboard\tneon-like illustrations\nThere are several useful visual features to tell there is 'neon sign' and not similar things in a photo:\tlight source created by gas in glass tubes\tbright and vibrant colors\ttypically seen beaming out from storefront windows or on building facades\twriting or images spelled out in cursive or block lettering", 136], "type": ["No. 'Type' is too vague or abstract to be distinguished in a photo.", 136], "charger": ["Yes. 'Charger' has a tangible appearance and is a device used to power electronic devices.\nA few things that are visually similar to 'charger' but are not 'charger' are:\tpower cord\tadapter\tbattery pack\nThere are several useful visual features to tell there is 'charger' and not similar things in a photo:\tspecific port or connector for the device\ttoo small to be a battery to power the device\tthe device is plugged into it", 136], "round pizza": ["Yes. 'Round pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'round pizza' but are not 'round pizza' are:\tpancake\ttortilla\tpie\tcheese wheel\nThere are several useful visual features to tell there is 'round pizza' and not similar things in a photo:\tcircular shape\ttomato sauce and cheese on top\tvariety of toppings\tslices cut from the center outwards", 136], "piles": ["Yes. 'Piles' has a tangible appearance and refers to a collection of objects stacked on top of each other.\nA few things that are visually similar to 'piles' but are not 'piles' are:\theaps\tmounds\thills\ttowers\nThere are several useful visual features to tell there is 'piles' and not similar things in a photo:\tstacked objects\tin a vertical or tilted position\tsimilar objects in each pile\teach pile consisting of more than one object", 136], "lanyard": ["Yes. 'Lanyard' has a tangible appearance and is a kind of strap used for carrying things.\nA few things that are visually similar to 'lanyard' but are not 'lanyard' are:\tstraps\tbelts\tribbons\tbungee cords\nThere are several useful visual features to tell there is 'lanyard' and not similar things in a photo:\tattached to an ID badge, keychain, or electronic device\ttypically made of flat fabric or woven material\toften has a clip or hook for attaching to an object\tworn around the neck or wrist.", 136], "guard": ["Yes. 'Guard' has a tangible appearance and is a person or object that protects someone/something.\nA few things that are visually similar to 'guard' but are not 'guard' are:\tsoldiers\tpolice officers\tbouncers\tbarriers\nThere are several useful visual features to tell there is 'guard' and not similar things in a photo:\twearing a uniform or specific clothing\thaving a badge or emblem\tcarrying a weapon or tool\tpositioned in front of or around a specific area or object\thaving a focused or attentive posture.", 136], "metal train tracks": ["Yes, 'metal train tracks' is a visually concrete concept.\nA few things that are visually similar to 'metal train tracks' but are not 'metal train tracks' are:\troad tracks\troller coaster tracks\thiking trail\nThere are several useful visual features to tell there are 'metal train tracks' and not similar things in a photo:\tstraight or gently curved lines\tmade of metal or steel\tpieces of tracks connected by bolts or ties\ttwo parallel lines with equal distance between them", 136], "shape": ["No. 'Shape' is too vague and abstract to be visually concrete or tangible.\nThere are no things that are visually similar to 'shape' but are not 'shape'.\nAs 'shape' is an abstract concept, there are no useful visual features that can distinguish it from other things in a photo. Instead, we recognize shapes in photos by their visual characteristics, such as curves, straight lines, corners, angles, or other geometric patterns.", 136], "passenger plane": ["Yes. 'Passenger plane' has a tangible appearance and is a type of aircraft used to transport people.\nA few things that are visually similar to 'passenger plane' but are not 'passenger plane' are:\thelicopter\tglider\tblimp\tjet fighter\nThere are several useful visual features to tell there is 'passenger plane' and not similar things in a photo:\tlarge body and wings\ttwo or more engines\tpassenger windows\ton the ground or in the air\tcommercial airliner branding", 136], "series": ["No. 'Series' is too vague or abstract to be distinguished in a photo.", 136], "binder": ["Yes. 'Binder' has a tangible appearance and is a type of stationary item used to hold papers together.\nA few things that are visually similar to 'binder' but are not 'binder' are:\tfolder\tportable file box\tnotebook\tbriefcase\nThere are several useful visual features to tell there is 'binder' and not similar things in a photo:\tthree-ring binder clasp\tbinder dividers\tbinder cover and spine\ttabbed dividers or tabs\tstructured and rectangular shape.", 135], "sails": ["Yes. 'Sails' has a tangible appearance and is a part of a boat.\nA few things that are visually similar to 'sails' but are not 'sails' are:\tcurtains\tblinds\tflags\ttarps\nThere are several useful visual features to tell there are 'sails' and not similar things in a photo:\t\ntriangular, rectangular or trapezoidal shape\t\nmade of fabric or other lightweight materials\t\nattached to a mast or a boom\t\nused for propulsion or steering the boat", 135], "edges": ["Yes. 'Edges' has a tangible appearance and refers to the borders or boundaries of an object.\nA few things that are visually similar to 'edges' but are not 'edges' are:\tlines\tshadows\tcontours\t\nThere are several useful visual features to tell there are 'edges' and not similar things in a photo:\tdefined borders or boundaries\tmarked contrast between two surfaces\twhere two different surfaces meet or intersect", 135], "ad": ["No. 'Ad' is too vague or abstract to have a tangible appearance. \n\nHowever, a few things that are visually similar to 'ad' but are not 'ad' are:\n- Posters\n- Banners\n- Flyers\n\nUseful visual features for distinguishing 'ad' from the listed similar things in a photo could include:\n- The presence of recognizable brand logos or product images\n- A clear call-to-action message (such as \"Buy now\" or \"Visit our website\")\n- A slogan or branding statement associated with a particular product or service.", 135], "purple flower": ["Yes. 'Purple flower' has a tangible appearance and is a specific type of flower.\nA few things that are visually similar to 'purple flower' but are not 'purple flower' are:\tblue flower\tviolet flower\tpink flower\tmagenta flower\nThere are several useful visual features to tell there is 'purple flower' and not similar things in a photo:\tpurple petals\tgreen stem\tpetal arrangement (e.g., how many petals)\tpollen center\tcolor and texture of the leaves", 135], "w": ["No. 'w' is too abstract to be a visually concrete concept. It is a written symbol that does not have a tangible appearance. \n\nTherefore, there is nothing visually similar to 'w' that is not 'w', and there are no useful visual features for distinguishing 'w' from anything else in a photo.", 135], "faucets": ["Yes. 'Faucets' has a tangible appearance and is a kind of plumbing fixture used for controlling the flow of water.\nA few things that are visually similar to 'faucets' but are not 'faucets' are:\tknobs\tbuttons\tdoorknobs\nThere are several useful visual features to tell there is 'faucets' and not similar things in a photo:\tmetallic material\twater flowing from the spout\tattached to a sink or a bathtub\tlevers or handles for controlling water flow and temperature", 134], "kind": ["No. 'Kind' is too vague or abstract to be distinguished in a photo. It is a personality trait that refers to being compassionate, caring or generous towards others.\nThere are no things that are visually similar to 'kind' as it is a non-visual concept.", 134], "safety cone": ["Yes. 'Safety cone' has a tangible appearance and is a type of traffic control device.\nA few things that are visually similar to 'safety cone' but are not 'safety cone' are:\ttraffic pylons\tbarrels\tbollards\nThere are several useful visual features to tell there is 'safety cone' and not similar things in a photo:\tcone-shaped\tbright orange color\twith reflective stripes\tor with printed text\tsmall holes at the top\tfor use in traffic control or road work", 134], "oil": ["Yes. 'Oil' has a tangible appearance and is a type of liquid that can be extracted from the ground.\nA few things that are visually similar to 'oil' but are not 'oil' are:\twater\tpaints\tliquor\tsoy sauce\nThere are several useful visual features to tell there is 'oil' and not similar things in a photo:\tthick, black, or brown liquid\tviscous or sticky consistency\tshiny or reflective surface\tcan be found in oil wells or oil spills", 134], "towers": ["Yes. 'Towers' have a tangible appearance and are a kind of building.\nA few things that are visually similar to 'towers' but are not 'towers' are:\tchimneys\tpoles\tstatues\tmasts\tlighthouses\nThere are several useful visual features to tell there is 'towers' and not similar things in a photo:\tvertical structure\ttaller than surrounding buildings or structures\tnarrower than the base\theight that is significantly greater than the width of its base.", 134], "pin": ["Yes. 'Pin' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'pin' but are not 'pin' are:\tneedle\tstaple\tpaper clip\nThere are several useful visual features to tell there is 'pin' and not similar things in a photo:\tsharp point\ttwo heads, one used for gripping\tmetallic appearance\tused for attaching things to fabric or a surface", 134], "left eye": ["Yes. 'Left eye' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'left eye' but are not 'left eye' are:\tright eye\tcameras\tbinoculars\tballs with a black circle printed on them\nThere are several useful visual features to tell there is 'left eye' and not similar things in a photo:\tpositioned on the left side of a person's face\tshape of the eye socket\tposition and shape of the iris and pupil.", 134], "date": ["No. 'Date' is too vague or abstract to be distinguished in a photo. However, if we're talking about the fruit 'date', then yes, it has a tangible appearance.\nA few things that are visually similar to the fruit 'date' but are not 'date' are:\tplum\tfig\tprune\tblush\nThere are several useful visual features to tell there is the fruit 'date' and not similar things in a photo:\toblong shape\tdark brown color\tslightly wrinkled skin\tlong pit in the middle", 134], "food truck": ["Yes. 'Food truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'food truck' but are not 'food truck' are:\ttrailer\tbus\tvan\nThere are several useful visual features to tell there is 'food truck' and not similar things in a photo:\tdecorated with the name and menu of the restaurant\tserving window or counter\twith some chairs or stools around\tfor preparing and selling food on the go", 134], "wood door": ["Yes. 'Wood door' has a tangible appearance and is a type of door made of wood.\nA few things that are visually similar to 'wood door' but are not 'wood door' are:\tmetal door\tglass door\tgate\nThere are several useful visual features to tell there is 'wood door' and not similar things in a photo:\tmade of wood\tvisible wood grain\ttexture and color of the wood\thandle and hinges\tframes and moldings around the door", 133], "lunch": ["No. 'Lunch' is too vague or abstract to be distinguished in a photo.", 133], "brown crust": ["Yes. 'Brown crust' has a tangible appearance and is a type of food component.\nA few things that are visually similar to 'brown crust' but are not 'brown crust' are:\tburnt food hand\tsoil\nThere are several useful visual features to tell there is 'brown crust' and not similar things in a photo:\tdark brown color\tuneven texture\tformed on the surface of baked or roasted food items", 133], "broom": ["Yes. 'Broom' has a tangible appearance and is a cleaning tool.\nA few things that are visually similar to 'broom' but are not 'broom' are:\tmop\tduster\tswiffer\tvacuum cleaner\nThere are several useful visual features to tell there is 'broom' and not similar things in a photo:\tlong wooden, plastic or metal handle\tbristles or fibers on one end\tcylindrical or triangular shape\twithout a motor or electric cord", 133], "chunks": ["Yes. 'Chunks' has a tangible appearance and refers to a particular shape of objects.\nA few things that are visually similar to 'chunks' but are not 'chunks' are:\tpieces\tcubes\tblocks segments\nThere are several useful visual features to tell there is 'chunks' and not similar things in a photo:\tirregular and non-uniform shapes\tvarious sizes and dimensions\tcan be made of different materials, such as food or rocks", 133], "winter coat": ["Yes. 'Winter coat' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'winter coat' but are not 'winter coat' are:\tjacket\tvest\tsweater\thoodie\nThere are several useful visual features to tell there is 'winter coat' and not similar things in a photo:\tthick and insulated fabric or material\tdark or neutral color\tzipped or buttoned front (as opposed to pullover style)\tlong and covers the hips or thighs to keep warm\tpockets at the front.", 133], "hairs": ["Yes. 'Hairs' has a tangible appearance and are thin, string-like structures that grow from the skin of animals.\nA few things that are visually similar to 'hairs' but are not 'hairs' are:\tthreads of fabric\tstrands of spaghetti\tgrains of rice\nThere are several useful visual features to tell there is 'hairs' and not similar things in a photo:\tgrowing from skin\tbeing part of a living organism\tflexible and bendable\tdifferent colors and textures, depending on the animal species.", 133], "pumpkin": ["Yes. 'Pumpkin' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'pumpkin' but are not 'pumpkin' are:\tsquash\twatermelon\tcantaloupe\tgourd\t\nThere are several useful visual features to tell there is 'pumpkin' and not similar things in a photo:\torangish or yellowish color\twith a thick stem on top\tcircular or oblong shape\twith vertical ribs or segmentation\tsmooth or slightly bumpy skin\twith a hollow interior and seeds\toften used as a decorative item or in cooking for dishes like pies or soups", 133], "rings": ["Yes. 'Rings' has a tangible appearance and refers to circular objects or shapes.\nA few things that are visually similar to 'rings' but are not 'rings' are:\tbangles\tcircles\thalos\thula hoops\nThere are several useful visual features to tell there are 'rings' and not similar things in a photo:\tcircular shape\tusually made of metal\tdiamonds or other gems may be present\tworn on fingers or necklaces", 132], "balconies": ["Yes. 'Balconies' has a tangible appearance and is a kind of architectural structure.\nA few things that are visually similar to 'balconies' but are not 'balconies' are:\tterraces\tverandas\tpatios\tplatforms\nThere are several useful visual features to tell there is 'balconies' and not similar things in a photo:\tan elevated platform attached to a building\tenclosed or semi-enclosed by a railing or balustrade\tjutting outwards from the building's facade or wall\tcan be accessed from an upper floor of the building", 132], "tennis players": ["Yes. 'Tennis players' has a tangible appearance and refers to people playing tennis.\nA few things that are visually similar to 'tennis players' but are not 'tennis players' are:\tgymnasts\trunners\tswimmers\tbaseball players\nThere are several useful visual features to tell there is 'tennis players' and not similar things in a photo:\tholding tennis rackets\twearing tennis shoes\tand sportswear\tplay with a tennis ball\tand net", 132], "blue train": ["Yes. 'Blue train' has a tangible appearance and is a specific type of train.\nA few things that are visually similar to 'blue train' but are not 'blue train' are:\tred train\tlong bus\tblue car\nThere are several useful visual features to tell there is 'blue train' and not similar things in a photo:\ttrain tracks\tin a train station or on-the-go\tround wheels\twith carriages or compartments\tblue color\tdesignated name or logo 'Blue Train'", 132], "signboard": ["Yes. 'Signboard' has a tangible appearance and is a type of directional or informative board.\nA few things that are visually similar to 'signboard' but are not 'signboard' are: billboards, posters, art installations, traffic cones.\nThere are several useful visual features to tell there is 'signboard' and not similar things in a photo: clear text with information or directions, usually rectangular or square in shape, mounted on walls or stands, with contrasting colors or graphics to attract attention.", 132], "thigh": ["Yes. 'Thigh' has a tangible appearance and is a part of the leg.\nA few things that are visually similar to 'thigh' but are not 'thigh':\tknee\tcalf\tfoot\tarm\nThere are several useful visual features to tell there is 'thigh' and not similar things in a photo:\tpositioned between the hip and knee\tthicker and larger than the calf\tmuscular appearance compared to the knee\tor the calf.", 131], "herbs": ["Yes. 'Herbs' has a tangible appearance and refers to plant leaves used for seasoning or medicine.\nA few things that are visually similar to 'herbs' but are not 'herbs' are:\tgrass\tweeds\tleaves\nThere are several useful visual features to tell there are 'herbs' and not similar things in a photo:\taromatic or fragrant leaves\ttypically small in size\tgreen in color\tgrowing in a garden or a pot\tbunches of leaves sold in stores as seasoning or tea.", 131], "flower vase": ["Yes. 'Flower vase' has a tangible appearance and is a container for holding flowers.\nA few things that are visually similar to 'flower vase' but are not 'flower vase' are:\tmason jars\tmugs\tglasses\tpots\nThere are several useful visual features to tell there is 'flower vase' and not similar things in a photo:\tclear or translucent material\tsmooth surface\ttapered or narrow neck\twide opening at the top", 131], "pizza box": ["Yes. 'Pizza box' has a tangible appearance and is a type of cardboard container.\nA few things that are visually similar to 'pizza box' but are not 'pizza box' are:\tcake box\tdelivery box\tshoe box\tbriefcase\nThere are several useful visual features to tell there is 'pizza box' and not similar things in a photo:\trectangular shape\twith a pizza inside\tor greasy stains from pizza\thave a pizza brand logo on top\thave ventilation holes on top", 131], "fog": ["Yes. 'Fog' has a tangible appearance and is a weather phenomenon.\nA few things that are visually similar to 'fog' but are not 'fog' are:\tsmoke\tsteam\tdust\thaze\nThere are several useful visual features to tell there is 'fog' and not similar things in a photo:\tthick cloud of water droplets or ice crystals\thazy appearance\treduces visibility\thangs low in the air\tcan occur over bodies of water or on land", 131], "outline": ["Yes. 'Outline' has a tangible appearance as it is the exterior line or shape of something. \nA few things that are visually similar to 'outline' but are not 'outline' are:\tshadow\tcontour\tsilhouette\tborder\ttemplate\nThere are several useful visual features to tell there is 'outline' and not similar things in a photo:\tthe outer edge of a shape or object\tdefined and visible\tline thickness and consistency\tmay or may not be filled in with color or texture.", 131], "silver metal": ["Yes. 'Silver metal' has a tangible appearance, just like any other metal with a silver or greyish color.\nA few things that are visually similar to 'silver metal' but are not 'silver metal' are:\tgray plastic\taluminum foil\tchrome\tplatinum\nThere are several useful visual features to tell there is 'silver metal' and not similar things in a photo:\ta metallic shine or reflection\tthe ability to conduct heat and electricity", 130], "lush": ["No. 'Lush' is too vague or abstract to have a tangible appearance that can be distinguished in a photo.", 130], "hand rail": ["Yes. 'Hand rail' has a tangible appearance and is a type of railing for support.\nA few things that are visually similar to 'hand rail' but are not 'hand rail' are:\tfence\tbanister\tguardrail\nThere are several useful visual features to tell there is 'hand rail' and not similar things in a photo:\thorizontal or inclined rail\tmounted on walls or posts\tsuitable for grasping with one's hand\tdesignated for providing stability or protection in stairs, walkways or balconies.", 130], "clean": ["No. 'Clean' is too vague or abstract to be distinguished in a photo.", 130], "power cord": ["Yes. 'Power cord' has a tangible appearance and is a type of electric wire.\nA few things that are visually similar to 'power cord' but are not 'power cord' are: headsets, earphones and charging cables\nThere are several useful visual features to tell there is 'power cord' and not similar things in a photo:\tthree-pronged or two-pronged plug\tthin cord or cable\tpower indicator light", 130], "crest": ["Yes. 'Crest' has a tangible appearance and generally refers to a decorative emblem atop a helmet or shield.\nA few things that are visually similar to 'crest' but are not 'crest' are:\tcomb\tof a rooster\theadband\t\nThere are several useful visual features to tell there is 'crest' and not similar things in a photo:\telaborate and decorative design\tsits atop a helmet or shield\tmay include animals, symbols, or words\tin a medieval or modern style", 130], "tissues": ["Yes. 'Tissues' has a tangible appearance and is a kind of paper product.\nA few things that are visually similar to 'tissues' but are not 'tissues' are:\ttoilet paper\twipes\tpaper towels\thandkerchiefs\nThere are several useful visual features to tell there are 'tissues' and not similar things in a photo:\tsmall and rectangular or square shape\tsoft and thin texture\tdisposable and meant for single use\tfolded or in a box packaging", 130], "photos": ["Yes. 'Photos' has a tangible appearance and refers to images captured with a camera.\nA few things that are visually similar to 'photos' but are not 'photos' are:\tpaintings\tdrawings\tprints\tposters\nThere are several useful visual features to tell there are 'photos' and not similar things in a photo: \tsharp, defined lines and edges\tthe presence of small, rectangular or square shapes\tresemblance to real-life scenes or objects\tdepth and perspective", 130], "police car": ["Yes. 'Police car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'police car' but are not 'police car' are:\tambulance\tfire engine\ttaxi\tsecurity car\nThere are several useful visual features to tell there is 'police car' and not similar things in a photo:\tblue and white or black and white color scheme\twith \"POLICE\" or \"SHERIFF\" written on the side\tor has a siren and flashing lights\ton patrol in urban or suburban areas\twith law enforcement officers operating the vehicle", 130], "moped": ["Yes. 'Moped' has a tangible appearance and is a type of motorized vehicle.\nA few things that are visually similar to 'moped' but are not 'moped' are:\tmotorbike\tscooter\tbicycle\tgolf cart\nThere are several useful visual features to tell there is 'moped' and not similar things in a photo:\tfootrest for the rider\tlow-power engine\tonly two wheels\tdistinguished from a bicycle or a motorbike by its size, speed, and power", 130], "coach": ["Yes. 'Coach' has a tangible appearance and is a type of vehicle used for transportation.\nA few things that are visually similar to 'coach' but are not 'coach' are:\tbus\tvan\ttruck\tcar\nThere are several useful visual features to tell there is 'coach' and not similar things in a photo:\tlarge size\tseparate compartments for passengers\topen sides with seats inside\ttwo or more wheels\tonboard storage for luggage or gear", 129], "wood cabinet": ["Yes. 'Wood cabinet' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood cabinet' but are not 'wood cabinet' are:\tbookshelf\ttv stand\tdrawers\tshelving unit\nThere are several useful visual features to tell there is 'wood cabinet' and not similar things in a photo:\tconstructed from wood or wood veneer\tone or more doors or drawers\thandles or knobs\tfor storing or displaying items, such as dishes, glasses, books, or clothes.", 129], "watermark": ["Yes. 'Watermark' has a tangible appearance and is a type of image or text used to protect or identify a document or image.\nA few things that are visually similar to 'watermark' but are not 'watermark' are:\tshadow\treflection\tembossing\t\nThere are several useful visual features to tell there is 'watermark' and not similar things in a photo:\tfaint image or text in the background of a document or image\trepeating pattern or logo\ttransparency\teffectively placed over the main image or text", 129], "skateboarders": ["Yes. 'Skateboarders' has a tangible appearance and refers to people who ride skateboards.\nA few things that are visually similar to 'skateboarders' but are not 'skateboarders' are:\tcyclists\tscooter riders\trollerskaters\tchildren on toys\twith someone who is being pushed or pulled\nThere are several useful visual features to tell there is 'skateboarders' and not similar things in a photo:\triding a skateboard or performing tricks\twearing helmets, knee pads or elbow pads\tstreet or skate park scenery\tboard with four wheels and a flat surface\tshowing balance and control on the skateboard", 129], "packet": ["Yes. 'Packet' has a tangible appearance and is a small container or package.\nA few things that are visually similar to 'packet' but are not 'packet' are:\tenvelope\tbox\tbag\tpouch\nThere are several useful visual features to tell there is 'packet' and not similar things in a photo:\tflat and folded\tsquare or rectangular shape\thas a label or a sticker\twith logos or text\tthat can be opened and closed.", 129], "turn sign": ["Yes. 'Turn sign' has a tangible appearance and is a type of traffic signs.\nA few things that are visually similar to 'turn sign' but are not 'turn sign' are:\tspeed limit sign\tstop sign\tyield sign\nThere are several useful visual features to tell there is 'turn sign' and not similar things in a photo:\tarrow-shaped symbol indicating a direction\tusually green with white or black lettering, or white with black or green lettering\tplacement in an intersection or just before a turn", 129], "brick house": ["Yes. 'Brick house' has a tangible appearance and refers to a house made of bricks.\nA few things that are visually similar to 'brick house' but are not 'brick house' are:\twooden house\tstucco house\tstone house\nThere are several useful visual features to tell there is 'brick house' and not similar things in a photo:\tred, brown, or grey bricks clearly visible on the exterior of the house\trectangular or square-shaped bricks arranged in a pattern", 129], "blue tarp": ["Yes. 'Blue tarp' has a tangible appearance and is a type of covering material.\nA few things that are visually similar to 'blue tarp' but are not 'blue tarp' are:\tplastic wrap\ttent cover\tblue blanket\nThere are several useful visual features to tell there is 'blue tarp' and not similar things in a photo:\tblue color made of durable plastic material\twith or without grommets or eyelets\tto cover or protect an outdoor area\tor act as a temporary roof", 129], "tails": ["Yes. 'Tails' has a tangible appearance and is a part of an animal's anatomy.\nA few things that are visually similar to 'tails' but are not 'tails' are:\trope\tbranches\those\tcurly hair\nThere are several useful visual features to tell there is 'tails' and not similar things in a photo:\tattached to an animal's body\tfurry or scaly end\tvarious shapes and lengths, depending on the animal's species", 129], "computer mice": ["Yes. 'Computer mice' has a tangible appearance and is a kind of device.\nA few things that are visually similar to 'computer mice' but are not 'computer mice' are: TV remote control, gaming controller, wireless presenter, stylus.\nThere are several useful visual features to tell there is 'computer mice' and not similar things in a photo:\toval or egg-shaped\tobject\tdragged on a flat surface\tplastic surface\twith buttons or touchpad\ton a cable or wireless communication.", 129], "story building": ["Yes. 'Story building' has a tangible appearance and refers to a building with multiple floors.\nA few things that are visually similar to 'story building' but are not 'story building' are:\thouses\twarehouses\ttowers\tparks\nThere are several useful visual features to tell there is 'story building' and not similar things in a photo:\tmultiple floors clearly visible or implied in the design of the building\tconsistent window spacing on each floor", 129], "avocado": ["Yes. 'Avocado' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'avocado' but are not 'avocado' are:\tkiwi\tpear\tguava\tolive\nThere are several useful visual features to tell there is 'avocado' and not similar things in a photo:\tpear-shaped\tflesh color ranging from pale green to almost black\tpit or seed in the center\tthick and bumpy skin with a pebble-like texture", 129], "hind leg": ["Yes. 'Hind leg' has a tangible appearance and is a part of an animal's body.\nA few things that are visually similar to 'hind leg' but are not 'hind leg' are:\tfront leg\ttail\tbranch\nThere are several useful visual features to tell there is 'hind leg' and not similar things in a photo:\tattached to the animal's rear end\tbends in the opposite direction of the animal's front legs\thas a hip joint\thas a knee or ankle joint\tcan end in a foot or paw", 129], "store sign": ["Yes. 'Store sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'store sign' but are not 'store sign' are:\tstreet signs\tbillboards\tdirectional signs\twarning signs\nThere are several useful visual features to tell there is 'store sign' and not similar things in a photo:\tlocated on or near a building\tbears the name or logo of a store\tor business\teasily readable to pedestrians and drivers\tdefines the owner or business of the establishment.", 128], "window pane": ["Yes. 'Window pane' has a tangible appearance and refers to the individual sheets of glass in a window.\nA few things that are visually similar to 'window pane' but are not 'window pane' are:\tmirrors\tpicture frames\tdoors\twhiteboards\nThere are several useful visual features to tell there is 'window pane' and not similar things in a photo:\trectangular shape\tclear, transparent glass or material\tthickness or depth of the glass\tframing or casing around the edges of the pane", 128], "rectangle": ["Yes. 'Rectangle' has a tangible appearance and is a geometrical shape.\nA few things that are visually similar to 'rectangle' but are not 'rectangle' are:\tsquare\tparallelogram\trhombus\ttrapezoid\nThere are several useful visual features to tell there is 'rectangle' and not similar things in a photo:\tfour sides\ttwo sets of parallel lines\tright angles (90-degree angles)\topposite sides of equal length", 128], "flour": ["Yes. 'Flour' has a tangible appearance and is a kind of powder.\nA few things that are visually similar to 'flour' but are not 'flour' are:\tbaking soda\tcornstarch\tpowdered sugar\nThere are several useful visual features to tell there is 'flour' and not similar things in a photo:\twhite or off-white powder\tfine consistency or grain size\tfrequently used in baking or cooking processes.", 128], "male surfer": ["Yes. 'Male surfer' has a tangible appearance and is a person who engages in surfing.\nA few things that are visually similar to 'male surfer' but are not 'male surfer' are:\tswimmer\tjet skier\twater polo player\nThere are several useful visual features to tell there is 'male surfer' and not similar things in a photo:\tstanding on a surfboard\tholding or using a surfboard\twearing a wetsuit or swimming shorts\tchallenging or catching waves in the sea", 128], "bits": ["No. 'Bits' is too vague or abstract to be distinguished in a visual way.\nHowever, there are a few things that are visually similar to the concept of 'data bits' but are not really 'bits' themselves, such as:\tdots\tpixels\tletters\tnumbers.\nUseful visual features for distinguishing 'bits' from the listed similar things in a photo would depend on the context of the 'bits' being referred to, as the term can have different meanings in various fields such as computing, telecommunication, or digital electronics. In general, bits are a binary unit of data representing a 0 or 1, so looking for patterns of 0s and 1s could be a useful visual feature to distinguish bits in a photo.", 127], "shadow man": ["No. 'Shadow man' is too vague or abstract to be distinguished in a photo.", 127], "calm": ["No. 'Calm' is too vague or abstract to be distinguished in a photo.", 127], "pockets": ["Yes. 'Pockets' has a tangible appearance and can be found in clothing.\nA few things that are visually similar to 'pockets' but are not 'pockets' are:\tbuttons\tzippers\tfolds\tintricate stitching\nThere are several useful visual features to tell there is 'pockets' and not similar things in a photo:\ta separate and enclosed area of fabric\ton the surface of a garment\tor inside a garment\tdesigned for holding small objects (such as wallets, phones or keys)", 127], "watermelon": ["Yes. 'Watermelon' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'watermelon' but are not 'watermelon' are:\thoneydew melon\tcantaloupe\tmuskmelon\tcoconut\nThere are several useful visual features to tell there is 'watermelon' and not similar things in a photo:\tlarge and round shape\tgreen skin with dark green stripes\thollow sound when tapped\tdark seeds inside\tpink or red juicy flesh", 127], "grey rock": ["Yes. 'Grey rock' has a tangible appearance and it's a type of geological formation.\nA few things that are visually similar to 'grey rock' but are not 'grey rock' are:\tconcrete\tpavement\tasphalt\tmetal\t\nThere are several useful visual features to tell there is 'grey rock' and not similar things in a photo:\tnatural formation\tporous surface\trough or jagged edges\tspecimen-like texture", 127], "cherry": ["Yes. 'Cherry' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'cherry' but are not 'cherry' are:\tplum\tgrape\tcrabapple\tlychee\nThere are several useful visual features to tell there is 'cherry' and not similar things in a photo:\tround and small size\tbright red color or sometimes yellow or purple\tglossy skin with a small indented line on one side\tthe fruit has a hard seed in the center", 127], "suit case": ["Yes. 'Suit case' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'suit case' but are not 'suit case' are:\tbackpack\thandbag\tbriefcase\tduffel bag\nThere are several useful visual features to tell there is 'suit case' and not similar things in a photo:\trectangular shape\thinged lid\thandle for carrying\thard or soft shell\tfor holding clothes and personal items.", 126], "light switch": ["Yes. 'Light switch' has a tangible appearance and is a device used to turn lights on and off.\nA few things that are visually similar to 'light switch' but are not 'light switch' are:\toutlet\tswitches on appliances\tlight dimmers\nThere are several useful visual features to tell there is 'light switch' and not similar things in a photo:\tusually mounted on a wall\tswitches or buttons\tfor lighting purposes", 126], "life vest": ["Yes. 'Life vest' has a tangible appearance and is a type of safety gear.\nA few things that are visually similar to 'life vest' but are not 'life vest' are:\tflotation devices\tswim noodles\tinner tubes\nThere are several useful visual features to tell there is 'life vest' and not similar things in a photo:\tbulky\tpuffy\toranges or bright colors\tstraps and buckles for secure fit\tshould have labels indicating it is a personal flotation device \u2013 PFD.", 126], "screws": ["Yes. 'Screws' has a tangible appearance and is a type of fastener or hardware.\nA few things that are visually similar to 'screws' but are not 'screws' are:\tnails\tbolts\tpins\ttacks\nThere are several useful visual features to tell there is 'screws' and not similar things in a photo:\tspiral-shaped thread\tmultiple grooves or ridges on the shaft\trounded or flat head\tvariation in size, length, and thickness", 126], "heels": ["Yes. 'Heels' has a tangible appearance and is a type of shoe.\nA few things that are visually similar to 'heels' but are not 'heels' are:\tboots\tsandals\tflats\tslippers\nThere are several useful visual features to tell there is 'heels' and not similar things in a photo:\thigh heel portion at the back of the shoe\tpointed toe\tboxy or round toe\tankle strap or no strap", 126], "gentleman": ["No. 'Gentleman' is too vague or abstract to be distinguished in a photo.", 126], "ipod": ["Yes. 'iPod' has a tangible appearance and is a kind of portable media player.\nA few things that are visually similar to 'iPod' but are not 'iPod' are:\tother brands of portable media players\tsmartphones\torphaned media players\nThere are several useful visual features to tell there is 'iPod' and not similar things in a photo:\ticonic circular click wheel screen made of glass or plastic\twith or without buttons metallic back with logo of apple company", 126], "envelope": ["Yes. 'Envelope' has a tangible appearance.\nA few things that are visually similar to 'envelope' but are not 'envelope' are:\tbooklet\tleaflet\tshirt\tcloth\tfolder\nThere are several useful visual features to tell there is 'envelope' and not similar things in a photo:\tretangular shape\tpaper material\tfolded flaps intended for holding documents or letters", 126], "metal structure": ["Yes. 'metal structure' has a tangible appearance and refers to a construction composed primarily of metal.\nA few things that are visually similar to 'metal structure' but are not 'metal structure' are:\tbridge\ttower\tantenna\tferris wheel\nThere are several useful visual features to tell there is 'metal structure' and not similar things in a photo:\thighly reflective surface, often with a shiny or polished finish\tstraight and sharp edges\tframework composed of beams, columns, and supports\trigid and geometric shapes", 126], "pathway": ["Yes, 'pathway' has a visually concrete concept and can be distinguished in a photo.\nA few things that are visually similar to 'pathway' but are not 'pathway' are: road, river, railway tracks, trail.\nUseful visual features for distinguishing 'pathway' from the listed similar things in a photo are: the presence of clear and defined tracks, the surrounding vegetation, and the pedestrian or bike-friendly nature of the path.", 126], "microwaves": ["No. 'Microwaves' are a type of electromagnetic radiation and are not visible to the human eye, so it is not visually concrete.\nIt is not possible to identify objects that look like 'microwaves' as they are not visible.\nThere are no useful visual features for distinguishing 'microwaves' from similar things in a photo because they are not visually perceptible.", 126], "lit": ["No. 'Lit' is too vague or abstract to be seen in a photo.\nHowever, a few things that are visually similar to 'lit' but are not 'lit' are:\tBright colors\tLight reflecting off surfaces\tSunrise or sunset\nThere aren't any visual features to distinguish 'lit' from the listed similar things in a photo, as 'lit' implies the presence of light and cannot be visually identified on its own.", 126], "shaker": ["Yes. 'Shaker' has a tangible appearance and is a kind of tool or container.\nA few things that are visually similar to 'shaker' but are not 'shaker' are:\tsalt shaker\tpepper shaker\tmusical shaker (maracas)\tvibrator\nThere are several useful visual features to tell there is 'shaker' and not similar things in a photo:\thollow container\twith perforated top (or sides)\tfor shaking or dispensing small objects (such as salt, pepper, or spices)\tor for creating a rhythmic sound (musical shaker)", 125], "tile wall": ["Yes. 'Tile wall' has a tangible appearance and refers to a wall surface made of tiles.\nA few things that are visually similar to 'tile wall' but are not 'tile wall' are:\tbrick wall\tconcrete wall\twooden wall\tpainted wall\nThere are several useful visual features to tell there is 'tile wall' and not similar things in a photo:\tsmall, square or rectangular pieces\tmultiple colors or patterns\tregularly spaced grout\tlines between the tiles", 125], "suit jacket": ["Yes. 'Suit jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'suit jacket' but are not 'suit jacket' are:\tblazer\tsport coat\tcardigan\thoodie\nThere are several useful visual features to tell there is 'suit jacket' and not similar things in a photo:\tnot inherently casual\tslim-fitting\tformal structure (lapels, buttons, pockets)\tmade from wool, cotton, or linen materials.", 125], "rearview mirror": ["Yes. 'Rearview mirror' has a tangible appearance and is a kind of mirror used in vehicles to see what's behind.\nA few things that are visually similar to 'rearview mirror' but are not 'rearview mirror' are:\tside mirror\tbathroom mirror\tfloor-length mirror\tmake-up mirror\nThere are several useful visual features to tell there is 'rearview mirror' and not similar things in a photo:\tfound inside a vehicle\tmounted on the windshield\tor, mounted on the top of the dashboard\tprovides a view of the back of the vehicle, often through the rear window.", 125], "floor tile": ["Yes. 'Floor tile' has a tangible appearance and is a type of flooring material.\nA few things that are visually similar to 'floor tile' but are not 'floor tile' are:\twooden planks\tvinyl flooring\tmarble slab\tcarpets\nThere are several useful visual features to tell there is 'floor tile' and not similar things in a photo:\tsquare or rectangular in shape\teven surface\ttexture or patterns imprinted\ton the surface\tdifferent colors or shades\tcan be found in groups or rows", 125], "homes": ["Yes. 'Homes' has a tangible appearance and refers to a place where people live.\nA few things that are visually similar to 'homes' but are not 'homes' are:\toffice buildings\thotels\tapartment complexes\tshopping malls\nThere are several useful visual features to tell there is 'homes' and not similar things in a photo:\tresidential looking\tdifferent architectural styles\tdoors and windows\tfront yards or gardens\tparking areas.", 125], "seam": ["Yes. 'Seam' has a tangible appearance and it refers to where two pieces of material are joined together.\nA few things that are visually similar to 'seam' but are not 'seam' are:\ttextures\tlines\tshadows\nThere are several useful visual features to tell there is 'seam' and not similar things in a photo:\ta visible line between two pieces of material\tstitches or thread holding the pieces of material together\ta change in texture or pattern at the point where the pieces of material are joined", 125], "front light": ["Yes. 'Front light' has a tangible appearance and is a type of lighting device.\nA few things that are visually similar to 'front light' but are not 'front light' are:\tfloodlight\theadlight\tstage light\tsearchlight\nThere are several useful visual features to tell there is 'front light' and not similar things in a photo:\tlocated at the front of a vehicle or a stage\tusually white or yellow\tin a closed casing or lens\twith adjustable brightness and direction\tmounted on a wall, ceiling or vehicle", 125], "tines": ["Yes. 'Tines' has a tangible appearance and refers to the pointed prongs on a fork, rake or other tool.\nA few things that are visually similar to 'tines' but are not 'tines' are:\tnails\tpins\tneedles\tteeth\nThere are several useful visual features to tell there are 'tines' and not similar things in a photo:\tpointed\tprong or spike-shaped\tpart of a fork or rake\thandle or shaft attached for holding or wielding", 124], "batting helmet": ["Yes. 'Batting helmet' has a tangible appearance and is a type of helmet worn in baseball or softball.\nA few things that are visually similar to 'batting helmet' but are not 'batting helmet' are:\tbicycle helmet\tmotorcycle helmet\tskateboarding helmet\nThere are several useful visual features to tell there is 'batting helmet' and not similar things in a photo:\tworn by a baseball or softball player\ttwo earflaps on the sides\ta faceguard in the front\tvarious colors and designs\twith logos or numbers of the team.", 124], "support": ["No. 'Support' is too vague or abstract to be distinguished in a photo.", 124], "pizza cutter": ["Yes. 'Pizza cutter' has a tangible appearance and is a type of kitchen utensil.\nA few things that are visually similar to 'pizza cutter' but are not 'pizza cutter' are:\tknife\tscissors\twheel\tchopper\nThere are several useful visual features to tell there is 'pizza cutter' and not similar things in a photo:\tcircular blade or wheel\tusually smaller than a knife\thandle attached to the blade\tspecifically designed for cutting pizza.", 124], "ski pole": ["Yes. 'Ski pole' has a tangible appearance and is a type of equipment.\nA few things that are visually similar to 'ski pole' but are not 'ski pole' are:\thiking pole\twalking stick\tfishing rod\nThere are several useful visual features to tell there is 'ski pole' and not similar things in a photo:\tlong and thin cylinder-shaped object\twith a grip on one end\tand a pointy tip on the other\tend\tmade of sturdy materials\tsold in pairs, designed to be used while skiing", 124], "team": ["No. 'Team' is too vague or abstract to be distinguished in a photo.", 124], "waist": ["Yes. 'Waist' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'waist' but are not 'waist' are:\thips\tbelly\tbutton\tribcage\nThere are several useful visual features to tell there is 'waist' and not similar things in a photo:\tthe narrower part of the torso between the ribs and hips\tcurves inward\tif a belt is worn, it separates the waist from the upper or lower parts of the body.", 124], "bristles": ["Yes. 'Bristles' has a tangible appearance and refers to stiff hairs.\nA few things that are visually similar to 'bristles' but are not 'bristles' are:\tcilia\tfur\thair\tleaves\nThere are several useful visual features to tell there are 'bristles' and not similar things in a photo:\tstiff hairs\ttapered at the end\tclustered together\ton a brush or a tool", 124], "blazer": ["Yes. 'Blazer' has a tangible appearance and is a type of jacket.\nA few things that are visually similar to 'blazer' but are not 'blazer' are:\tsuit jacket\tcoat\tcardigan\nThere are several useful visual features to tell there is 'blazer' and not similar things in a photo:\tfitted style\tnotched lapels or collar\tbuttons at the front\tpockets on the sides or chest\tmade of solid color or patterned fabric", 124], "monument": ["Yes. 'Monument' has a tangible appearance and refers to a type of structure or statue built to commemorate a person or event.\nA few things that are visually similar to 'monument' but are not 'monument' are:\tbuildings\tstatues\tmemorials\ttowers\nThere are several useful visual features to tell there is 'monument' and not similar things in a photo:\tcommemorates a person or event\thas a plaque or inscription\tsculpted figures or symbols\thistorical or cultural significance\tdifferent from surrounding buildings or structures.", 123], "monitors": ["Yes. 'Monitors' has a tangible appearance and refers to a display screen.\nA few things that are visually similar to 'monitors' but are not 'monitors' are:\ttelevisions\tlaptops\ttablets\tphones\nThere are several useful visual features to tell there is 'monitors' and not similar things in a photo:\trectangular shape\tthin and flat\tdisplaying images or text at high resolution\thave buttons or control panels on the front or bottom", 123], "control panel": ["Yes. 'Control panel' has a tangible appearance and is a device or a board that it has buttons or switches to operate a machine or system.\nA few things that are visually similar to 'control panel' but are not 'control panel' are:\tkeyboard\tsound mixer\tboard game\nThere are several useful visual features to tell there is 'control panel' and not similar things in a photo:\tlayout of buttons or switches\twires or cables leading to other machinery or system\tdisplay screen\tmarking or labels indicating its function or purpose", 123], "skateboard wheels": ["Yes. 'Skateboard wheels' has a tangible appearance and is a component of a skateboard.\nA few things that are visually similar to 'skateboard wheels' but are not 'skateboard wheels' are:\troller-skate wheels\tbicycle wheels\tscooter wheels\nThere are several useful visual features to tell there is 'skateboard wheels' and not similar things in a photo:\tsmall and round shape\thard and durable material\tvarious colors and designs\tbearings for smooth rotation\tnarrow profile specifically designed for a skateboard", 123], "toes": ["Yes. 'Toes' has a tangible appearance and refers to the digits on the feet of human beings and many animals.\nA few things that are visually similar to 'toes' but are not 'toes' are:\tfingers\tclaws\tpaws\nThere are several useful visual features to tell there are 'toes' and not similar things in a photo:\tlocated on the foot rather than hand\tsmaller and shorter than fingers\tjointed and flexible\tbear nails or toenails", 123], "pointy ears": ["Yes. 'Pointy ears' has a tangible appearance and is a physical feature of some animals.\nA few things that are visually similar to 'pointy ears' but are not 'pointy ears' are:\tsmaller triangles\thorns\tand antlers\tvegetation\nThere are several useful visual features to tell there are 'pointy ears' and not similar things in a photo:\tprotrude from the head\tnarrow at the base\twith a sharp and tapered end\toften on the head of an animal or a mythical creature", 123], "shoulder bag": ["Yes. 'Shoulder bag' has a tangible appearance and is a kind of bag.\nA few things that are visually similar to 'shoulder bag' but are not 'shoulder bag' are:\ttote bag\tbackpack\tpurse\tclutch\nThere are several useful visual features to tell there is 'shoulder bag' and not similar things in a photo:\tworn over the shoulder\twith a strap or handle for support\tmedium to large in size\trectangular or curved in shape\tzips, snaps, or fastens at the top", 123], "zucchini": ["Yes. 'Zucchini' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'zucchini' but are not 'zucchini' are:\tcucumber\tsquash\teggplant\tgourd\nThere are several useful visual features to tell there is 'zucchini' and not similar things in a photo:\tlong and cylindrical shape\tsmooth and shiny skin\tcolor gradient from green to light green\tor from green to yellow\ta white or off-white interior\tfive prominent longitudinal ridges on the fruit's surface.", 123], "stains": ["Yes. 'Stains' has a tangible appearance and can be seen as discoloration or marks on a surface.\nA few things that are visually similar to 'stains' but are not 'stains' are:\tshadows\treflections\tpatterns\tdirt\nThere are several useful visual features to tell there is 'stains' and not similar things in a photo:\tdiscoloration or marks on a surface\tunintentional or irregularly shaped\tcould be caused by spills or other accidents\tcan take on various colors depending on the source of the stain", 123], "overpass": ["Yes. 'Overpass' has a tangible appearance and is a type of bridge.\nA few things that are visually similar to 'overpass' but are not 'overpass' are:\tarchbridge\twalkway\tferry\nThere are several useful visual features to tell there is 'overpass' and not similar things in a photo:\tbridge that goes over another road or waterway\ttwo levels of roadway or railway on separate decks or levels\tpillars or abutments supporting the structure", 123], "wooden shelf": ["Yes. 'Wooden shelf' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden shelf' but are not 'wooden shelf' are:\twooden table\twooden cabinet\twooden crate\twooden pallet\nThere are several useful visual features to tell there is 'wooden shelf' and not similar things in a photo:\tlevel platforms supported by brackets, columns, or a frame\tused for storing or displaying objects or books\tattached to a wall or free-standing\tmade of wood or wood-like material.", 123], "metal pipe": ["Yes. 'Metal pipe' has a tangible appearance and is a type of cylindrical object.\nA few things that are visually similar to 'metal pipe' but are not 'metal pipe' are:\tpvc pipe\tdrainage pipe\tchimney\twooden cylinder\nThere are several useful visual features to tell there is 'metal pipe' and not similar things in a photo:\tmade of metal\tcylindrical shape\tsmooth surface with visible seams or joints in the metal\tdiameter larger than length", 123], "planks": ["Yes. 'Planks' has a tangible appearance and refers to long, flat pieces of wood.\nA few things that are visually similar to 'planks' but are not 'planks' are:\tboards\ttiles\tbricks\tpanes of glass\nThere are several useful visual features to tell there are 'planks' and not similar things in a photo:\tlong, rectangular shape\tsmooth or rough wood texture\tvisible wood grains, knots, or cracks", 122], "asphalt road": ["Yes. 'Asphalt road' has a tangible appearance and is a type of road surface.\nA few things that are visually similar to 'asphalt road' but are not 'asphalt road' are:\tconcrete road\tdirt road\tbrick road\tcobblestone road\nThere are several useful visual features to tell there is 'asphalt road' and not similar things in a photo:\tsmooth and black surface\tvisible tar\tor seams in between the asphalt\tpainted lines down the middle or along the edges\tcars or other vehicles driving on the surface", 122], "brake lights": ["Yes. 'Brake lights' has a tangible appearance and is a type of car component.\nA few things that are visually similar to 'brake lights' but are not 'brake lights' are:\tturn signals\theadlights\ttail lights\nThere are several useful visual features to tell there are 'brake lights' and not similar things in a photo:\tbright red color\tlocated at the rear of the car\tilluminated when the brakes are applied\tcircular or rectangular shape", 122], "dvd player": ["Yes. 'DVD player' has a tangible appearance and is an electronic device used for playing DVDs.\nA few things that are visually similar to 'dvd player' but are not 'dvd player' are:\tblu-ray player\tVHS player\tset-top box\tremote control\nThere are several useful visual features to tell there is 'dvd player' and not similar things in a photo:\tflat, rectangular shape\tdisc tray on the front panel\tdisplay screen on the front panel\tcontrol buttons on the front or top panel\tDVD logo or label on the front panel or disc tray", 122], "dinner": ["No. 'Dinner' is too vague or abstract to be distinguished in a photo.", 122], "grip": ["No. 'Grip' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are referring to the physical act of holding or grasping something firmly, then 'grip' can be considered a visually concrete concept.\n\nA few things that are visually similar to the physical act of 'grip' but are not 'grip' are:\t\n- Touching \n- Holding gently\n- Tapping \n\nThere are several useful visual features to tell there is 'grip' and not similar things in a photo:\n- Fingers tightly wrapped around an object \n- Strong pressure being applied to hold the object \n- Object appears to be securely held in place.", 122], "globe": ["Yes. 'Globe' has a tangible appearance and refers to a three-dimensional representation of the earth.\nA few things that are visually similar to 'globe' but are not 'globe' are:\tmap\tatlas\tplanisphere\tsatellite image\nThere are several useful visual features to tell there is 'globe' and not similar things in a photo:\tspherical shape\tcontinents and oceans represented\tdetailed topography and features\ttilted on an axis with longitude and latitude markings", 122], "turkey": ["Yes. 'Turkey' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'turkey' but are not 'turkey' are:\tchicken\tpheasant\tpeacock\tvulture\nThere are several useful visual features to tell there is 'turkey' and not similar things in a photo:\tbig bird\twith plumage mostly brown or black, with metallic-like tones on its feathers\twattle\ton its head is an unusual fleshy protuberance called a snood, which goes from the base of its beak to the nape of its neck.", 122], "steak": ["Yes. 'Steak' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'steak' but are not 'steak' are: \tchicken breast \tpork chops \ttofu \toven-roasted vegetables\nThere are several useful visual features to tell there is 'steak' and not similar things in a photo: \tbrown or charred exterior \tpink or red interior \tjuicy appearance \ttypical marbling or fat \ttypically flat and rectangular or oval", 121], "asparagus": ["Yes. 'Asparagus' has a tangible appearance and is a type of green vegetable.\nA few things that are visually similar to 'asparagus' but are not 'asparagus' are: broccoli, green beans, sugar snap peas, okra\nThere are several useful visual features to tell there is 'asparagus' and not similar things in a photo: long, slim stalks; small, leafy tips; vibrant green color. Asparagus have a distinctive shape with a rounded base that tapers to a point at the tip. The spears have a slightly rough texture and are often accompanied by small scales along the stem.", 121], "leggings": ["Yes. 'Leggings' has a tangible appearance and is a type of clothing worn on the legs.\nA few things that are visually similar to 'leggings' but are not 'leggings' are:\tpants\ttights\tstockings\tjeans\nThere are several useful visual features to tell there are 'leggings' and not similar things in a photo:\ttight-fitting\tcotton, spandex or nylon\tsmooth texture\tworn as pants or under a skirt or dress\tno visible seams or pockets", 121], "greens": ["Yes. 'Greens' have a tangible appearance and refer to leafy vegetables.\nA few things that are visually similar to 'greens' but are not 'greens' are:\therbs\tlawn grass\tweeds\ttree leaves\nThere are several useful visual features to tell there is 'greens' and not similar things in a photo:\tleafy and green\tedible\tusually served raw or cooked, as a side or in a salad.", 121], "iron": ["Yes. 'Iron' has a tangible appearance and is a metallic element.\nA few things that are visually similar to 'iron' but are not 'iron' are:\tsteel\taluminum\tcopper\tbronze\nThere are several useful visual features to tell there is 'iron' and not similar things in a photo:\tdark grey or silver color\tmetallic sheen\tdense and heavy\tmagnetic\tproperties such as rusting and magnetic response.", 121], "train door": ["Yes. 'Train door' has a tangible appearance and is a type of door located on a train.\nA few things that are visually similar to 'train door' but are not 'train door' are:\tcar door\televator door\tplane door\nThere are several useful visual features to tell there is 'train door' and not similar things in a photo:\tvertical or horizontal sliding door with handles or buttons\tinset glass windows\tside markings or stickers indicating the train's route or destination\tlarge size compared to other doors in the vicinity.", 121], "orange traffic cone": [" Yes. 'Orange traffic cone' has a tangible appearance and is a kind of road safety equipment.\nA few things that are visually similar to 'orange traffic cone' but are not 'orange traffic cone' are:\torange plastic bucket\toranges\t\nThere are several useful visual features to tell there is 'orange traffic cone' and not similar things in a photo:\tcone-shaped\torange\tcolor\twhite stripes on the top\tusually placed on a road or a construction site", 121], "cloud cover": ["Yes. 'Cloud cover' has a tangible appearance and refers to the extent to which clouds obscure the sky.\nA few things that are visually similar to 'cloud cover' but are not 'cloud cover' are:\tfog\tsmoke\thaze\tdust\nThere are several useful visual features to tell there is 'cloud cover' and not similar things in a photo:\tvarious forms and shapes (fluffy, wispy, stratus, cumulus, etc.)\twhite or grey color\tmotion or direction in the sky (depending on wind and weather conditions)\theight of the cloud base and the total covering of the sky", 121], "blurry": ["Yes. 'Blurry' has a tangible appearance and refers to an image that is not clearly visible.\nA few things that are visually similar to 'blurry' but are not 'blurry' are:\tclear\tsharp\tfocused\nThere is no need for useful visual features to distinguish 'blurry' from similar things in a photo as it is an opposite of sharp and clear, which can be determined by the lack of sharpness and clarity in the photo.", 121], "skiing": ["Yes. 'Skiing' has a tangible appearance and is a winter sport.\nA few things that are visually similar to 'skiing' but are not 'skiing' are:\tsnowboarding\tsledging\tor skating\thiking on mountains\nThere are several useful visual features to tell there is 'skiing' and not similar things in a photo:\twearing skis\twearing special boots\twearing goggles or helmet\tsnow on the ground\tski poles in hands\tsliding down a slope", 120], "back legs": ["Yes. 'Back legs' has a tangible appearance and refers to the hind legs of an animal.\nThere are no things that are visually similar to 'back legs' and are not 'back legs'.\nUseful visual features for distinguishing 'back legs' in a photo would be:\tpositioned behind the front legs\tjointed\tknee-like structure\tfeet or paws at the bottom of the legs\ttypically longer and more muscular than front legs.", 120], "buns": ["Yes. 'Buns' has a tangible appearance and refers to a kind of food item.\nA few things that are visually similar to 'buns' but are not 'buns' are:\tdonuts\tbagels\tcroissants\trolls\tpastries\nThere are several useful visual features to tell there is 'buns' and not similar things in a photo:\tdome or circular shape\tbrowned crust on the top and bottom\tsoft and fluffy texture\thollow in the center (when applicable)", 119], "pepperoni pizza": ["Yes. 'Pepperoni pizza' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'pepperoni pizza' but are not 'pepperoni pizza' are:\tcheese pizza\tmushroom pizza\tsausage pizza\tonion pizza\nThere are several useful visual features to tell there is 'pepperoni pizza' and not similar things in a photo:\tcircular shape\ttomato sauce base\tshredded mozzarella cheese\tsliced pepperoni sausage\tspices and herbs on top", 119], "beach sand": ["Yes. 'Beach sand' has a tangible appearance and can be identified based on its properties.\nA few things that are visually similar to 'beach sand' but are not 'beach sand' are:\tground coffee powder\tdirt\tcement\tdry flour\nThere are several useful visual features to tell there is 'beach sand' and not similar things in a photo:\tlight-colored granules\tvisible grains/course texture\twhen photographed on the beach there are shells or rocks visible within it\tWet sand might leave traces of water", 119], "front paw": ["Yes. 'Front paw' has a tangible appearance and is a part of an animal's body.\nA few things that are visually similar to 'front paw' but are not 'front paw' are:\tback paw\thuman hand\tpuppet hand\nThere are several useful visual features to tell there is 'front paw' and not similar things in a photo:\tattached to an animal's front leg\tfurry\tclawed or pawed\tmay be raised in the air or resting on a surface", 119], "muffin": ["Yes. 'Muffin' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'muffin' but are not 'muffin' are:\tcupcake\tbread roll\tdonut\tbiscuit\nThere are several useful visual features to tell there is 'muffin' and not similar things in a photo:\tdomed top\tridged or crinkled sides\tbaked in a muffin tin\ttop may be sprinkled with sugar or crumb topping", 119], "fingernail": [" Yes. 'Fingernail' has a tangible appearance and is part of the human body.\nA few things that are visually similar to 'fingernail' but are not 'fingernail' are:\ttoenail\tclaw\thoof\tshell\nThere are several useful visual features to tell there is 'fingernail' and not similar things in a photo:\tlocated at the tip of the finger or toe\tthin\tand flat shape\ttranslucent and colorless, with a pinkish-white color at the base\thard texture that can be painted or polished.", 119], "stairway": ["Yes. 'Stairway' has a tangible appearance and is a type of structure used for going up or down floors.\nA few things that are visually similar to 'stairway' but are not 'stairway' are:\thill\tslope\tladder\tramp\nThere are several useful visual features to tell there is 'stairway' and not similar things in a photo:\tmultiple steps or levels\thandrail or banister\tindoor or outdoor\tuse with a building or structure", 119], "turf": ["Yes. 'Turf' has a tangible appearance and refers to grass and the surface layer of soil.\nA few things that are visually similar to 'turf' but are not 'turf' are:\tgrass\tpile of hay or straw\tgreen carpet\tor any other synthetic ground covering material\nThere are several useful visual features to tell there is 'turf' and not similar things in a photo:\tthick green grass\tlayer of soil\troots and earth clumps\tusually laid in rolls or mats", 119], "neon": ["Yes. 'Neon' has a tangible appearance and refers to a type of gas that produces vibrant colors when electrified.\nA few things that are visually similar to 'neon' but are not 'neon' are: LED lights, glow-in-the-dark stickers, fluorescent lights.\nThere are several useful visual features to tell there is 'neon' and not similar things in a photo: brightly colored, usually pink, blue, or green, has a tube-like shape, often used in signs or art. The illuminated tubes also have a consistent glow throughout the entire length of the tube, which sets them apart from other lighting sources. Additionally, neon lighting often has a distinctive hum or buzz.", 119], "website": ["No. 'Website' is too vague or abstract to be distinguished by visuals alone.\nThere are no things that are visually similar to 'website' but are not 'website'.\nAll visual features of a website are useful for distinguishing it from non-websites, such as: layout, text, images, logos, buttons, links, and menus. However, these features can also be present on non-websites, so additional information is needed beyond just visuals to confirm if a given concept is a website or not.", 119], "wooden handle": ["Yes. 'Wooden handle' has a tangible appearance and is a type of tool or object.\nA few things that are visually similar to 'wooden handle' but are not 'wooden handle' are:\tmetal handle\tplastic handle\trubber grip\tleather strap\nThere are several useful visual features to tell there is 'wooden handle' and not similar things in a photo:\tmade of wood\tpolished or rough texture\tcylindrical or shaped like a specific tool or object\tattached to a blade or tool", 119], "hallway": ["Yes. 'Hallway' has a tangible appearance and is a narrow passage in a building.\nA few things that are visually similar to 'hallway' but are not 'hallway' are:\tstreet\tcorridor\taisle\tpathway\nThere are several useful visual features to tell there is 'hallway' and not similar things in a photo:\tinside a building\tor enclosed space\tlong and narrow space\twith walls and a ceiling\tfloor and ceiling with tiles or carpeting\tmultiple doors or rooms branching off from it", 119], "shutter": ["Yes. 'Shutter' has a tangible appearance and is a device to control light entering a camera.\nA few things that are visually similar to 'shutter' but are not 'shutter' are:\tblinds\tcurtains\tdoors\nThere are several useful visual features to tell there is 'shutter' and not similar things in a photo:\trectangular or circular shape\tmetallic, plastic or rubber surface\ta button, dial, or lever to control it\tfound on the front or the top of the camera.", 119], "controllers": ["Yes. 'Controllers' has a tangible appearance and is a device used to operate an electronic system.\nA few things that are visually similar to 'controllers' but are not 'controllers' are:\tremote controls\tkeyboards\tjosticks\ttool handles\nThere are several useful visual features to tell there is 'controllers' and not similar things in a photo:\tbutton inputs\tanalog sticks\tdirection pads\twired or wireless connectivity-specific branding or labeling for popular game consoles", 119], "stretch": ["No. 'Stretch' is too vague or abstract to have a tangible appearance.", 119], "skate park": ["Yes. 'Skate park' has a tangible appearance and is typically an outdoor park designed for skateboarding.\nA few things that are visually similar to 'skate park' but are not 'skate park' are:\tprivate driveways\toutdoor basketball courts\tparking lots\nThere are several useful visual features to tell there is 'skate park' and not similar things in a photo:\tcement or wooden ramps and obstacles\tbowl or halfpipe structures\tskaters in the area\tgraffiti or murals on the walls or ramps\tsafety equipment (helmets, pads)", 119], "brown elephant": ["Yes. 'Brown elephant' has a tangible appearance.\nA few things that are visually similar to 'brown elephant' but are not 'brown elephant' are:\trock\tbig pig\thippopotamus\tgiraffe\nThere are several useful visual features to tell there is 'brown elephant' and not similar things in a photo:\tbig size\ttrunk\twith tusks\tthick legs\tgray skin\tdefinitely not resembling any other animal.", 119], "female tennis player": ["Yes. 'Female tennis player' has a tangible appearance.\nA few things that are visually similar to 'female tennis player' but are not 'female tennis player' are:\tfemale athlete\tfemale golfer\tyoga practitioner\nThere are several useful visual features to tell there is 'female tennis player' and not similar things in a photo:\tholding a tennis racket\twearing tennis clothes\tplaying on a tennis court\twith a tennis ball\tin an athletic position\tswinging a tennis racket to hit the ball.", 119], "dust": ["Yes. 'Dust' has a tangible appearance and is made up of tiny particles.\nA few things that are visually similar to 'dust' but are not 'dust' are:\tsmoke\tpollen\tsand\tsugar\nThere are several useful visual features to tell there is 'dust' and not similar things in a photo:\tvery small size-floating in the air\tmade up of tiny particles\tmight be motile\tif visible, usually appears as a thin layer on a surface\tor as motes in a beam of light.", 119], "level": ["No. 'Level' is too vague or abstract to have a tangible appearance. It is a concept used to describe the horizontal or even position of something.\n\nThere are no things that are visually similar to 'level' but are not 'level'.\n\nThere are no useful visual features to distinguish 'level' from things, as it depends on measurements and precision in placement rather than appearance.", 119], "comb": ["Yes. 'Comb' has a tangible appearance and is a type of hair-styling tool.\nA few things that are visually similar to 'comb' but are not 'comb' are:\tbrush\thair pick\tfork\nThere are several useful visual features to tell there is 'comb' and not similar things in a photo:\t\nlong, thin teeth arranged in a row\t\nflat or slightly curved shape\t\nsmall enough to be held with one hand\t\nmade of plastic or metal", 119], "heater": ["Yes. 'Heater' has a tangible appearance and is an electronic or mechanical device used for warming.\nA few things that are visually similar to 'heater' but are not 'heater' are:\tair conditioner\tfan\tdehumidifier\nThere are several useful visual features to tell there is 'heater' and not similar things in a photo:\tthermostat or temperature control\tswitch or power button\tgrill or vent to release hot air or steam\tcasing or outer shell often made of metal or plastic", 118], "pairs": ["No. 'Pairs' is too vague or abstract to have a tangible appearance, it is a numerical concept representing a grouping of two things.\nThere are no things that are visually similar to 'pairs' as it is a numerical concept.\nTherefore, there are no useful visual features to distinguish 'pairs' from similar things in a photo.", 118], "stain": ["Yes. 'Stain' has a tangible appearance.\nA few things that are visually similar to 'stain' but are not 'stain' are:\tshadow\tspill\tpattern\tsmudge\nThere are several useful visual features to tell there is 'stain' and not similar things in a photo:\tdiscoloration on a surface\tdifficult to remove\tirregular shape\tdifferent texture than the surface it is on", 118], "baseball pitcher": ["Yes. 'Baseball pitcher' has a tangible appearance and is a person who throws the ball in baseball game.\nA few things that are visually similar to 'baseball pitcher' but are not 'baseball pitcher' are:\ttennis player\tcricket bowler\tbowling player\nThere are several useful visual features to tell there is 'baseball pitcher' and not similar things in a photo:\twearing a baseball uniform\twearing a baseball cap or a helmet\tstanding on a pitcher's mound\twhen throwing, holding a baseball in hand\tand going through a pitching motion", 118], "water bottles": ["Yes. 'Water bottles' has a tangible appearance and is a container for holding water.\nA few things that are visually similar to 'water bottles' but are not 'water bottles' are:\tjuice bottles\tsoft drink bottles\tperfume bottles\nThere are several useful visual features to tell there is 'water bottles' and not similar things in a photo:\ttranslucent or clear material\ta lid or cap\tfor storing water or other liquids\ta shape designed for holding with a hand\tor a built-in straw or cap for drinking", 118], "green": ["Yes. 'Green' has a tangible appearance and is a color.\nA few things that are visually similar to 'green' but are not 'green' are:\ttrees\tgrass\tbrussels sprouts\tcacti\nThere are no useful visual features to tell what is not 'green' since 'green' is a specific color. However, visual features that could describe 'green' are: medium to dark hue, without any tinge of blue or yellow.", 118], "gray clouds": ["Yes. 'Gray clouds' has a tangible appearance and refers to clouds that are colored in different shades of gray.\nA few things that are visually similar to 'gray clouds' but are not 'gray clouds' are:\tsmoke\tfog\tdust\tstormy sea\nThere are several useful visual features to tell there is 'gray clouds' and not similar things in a photo:\tmass of condensed water droplets\tgray or dark color\thaving visible layers or tufts in the cloud structure\tassociated with precipitation or thunderstorms", 118], "arches": ["Yes. 'Arches' has a tangible appearance and refers to a curved structure.\nA few things that are visually similar to 'arches' but are not 'arches' are:\tbridges\trainbows\tcaves\nThere are several useful visual features to tell there are 'arches' and not similar things in a photo:\tcurved structure with a semicircular shape\ttwo pillars or supports on both sides of the arch\thollow space or tunnel between the pillars", 118], "swimsuit": ["Yes. 'Swimsuit' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'swimsuit' but are not 'swimsuit' are:\tunderwear\tleotard\tbodysuit\nThere are several useful visual features to tell there is 'swimsuit' and not similar things in a photo:\tmade of lightweight, quick-drying material\tbikini, one-piece, or trunks design\tbright colors or patterns\tused for swimming or sunbathing at the beach or a pool", 118], "goose": ["Yes. 'Goose' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'goose' but are not 'goose' are:\tduck\tswans\tgeese in general\nThere are several useful visual features to tell there is 'goose' and not similar things in a photo:\tlarge bird\twith a long neck and a broad, flat beak\tdark feathers on the head and neck area, white feathers on the rest of the body\tcurved beak\tlong neck compared to other birds of similar size.", 118], "skull": ["Yes. 'Skull' has a tangible appearance and refers to the bony structure of the head in vertebrates.\nA few things that are visually similar to 'skull' but are not 'skull' are:\tcoconut shells\trock formation\tanimal tracks\tdried fruits\nThere are several useful visual features to tell there is 'skull' and not similar things in a photo:\tbony and hard\thollow eye sockets\tnasal cavity\tjaw or teeth\tsutures along the bones\tmidline sagittal suture", 117], "brand name": ["No. 'Brand name' is too abstract and does not have a tangible appearance.\nThere are no things that are visually similar to 'brand name'. \nTherefore, there are no useful visual features to distinguish 'brand name' from similar things in a photo.", 117], "rubber": ["Yes. 'Rubber' has a tangible appearance and is a type of synthetic material.\nA few things that are visually similar to 'rubber' but are not 'rubber' are:\tplastic\tlatex\tsilicone\tpolyurethane\tleather\nThere are several useful visual features to tell there is 'rubber' and not similar things in a photo:\tstretchy or elastic texture\tsmooth and shiny surface\tmay be translucent or opaque\ttendency to bounce when dropped or compressed\tcan be molded into various shapes or products (such as tires or erasers)", 117], "bill": ["Yes. 'Bill' has a tangible appearance and refers to a piece of paper currency or an anatomical structure in some animals.\nA few things that are visually similar to 'bill' but are not 'bill' are:\tinvoices\treceipts\tduck beaks\tpelican beaks\nThere are several useful visual features to tell there is 'bill' and not similar things in a photo:\tpaper material with specific denominations or symbols\tbeak-like structure with nostrils and mouth\topen and close motion when breathing or eating", 117], "jet plane": ["Yes. 'Jet plane' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'jet plane' but are not 'jet plane' are:\thelicopter\tglider\tballoon\tfighter plane\nThere are several useful visual features to tell there is 'jet plane' and not similar things in a photo:\tlong, slender body\ttwo large wings\tsharp nose for cutting through the air\tengines mounted under each wing\tangled tail section for stability\tsleek, aerodynamic design.", 117], "cycle": ["Yes. 'Cycle' has a tangible appearance and refers to a bicycle, motorcycle, or other two-wheeled vehicle.\nA few things that are visually similar to 'cycle' but are not 'cycle' are:\tcar\tscooter\tskateboard\t\nThere are several useful visual features to tell there is 'cycle' and not similar things in a photo:\ttwo wheels\tpedals or an engine\thandlebars\tsaddle or seat\tframe or chassis\tbrakes or brakes lights", 117], "aeroplane": ["Yes. 'Aeroplane' has a tangible appearance and refers to a type of aircraft.\nA few things that are visually similar to 'aeroplane' but are not 'aeroplane' are:\thelicopter\tglider\trocket\tballoon\nThere are several useful visual features to tell there is 'aeroplane' and not similar things in a photo:\tfixed wings\tpropellers\tor jet engines\ton-board cockpit and fuselage\ttail fin and rudder\temitting contrails in the sky", 117], "skillet": ["Yes. 'Skillet' has a tangible appearance and is a type of cooking pan.\nA few things that are visually similar to 'skillet' but are not 'skillet' are:\tsaucepan\tfrying pan\twok\tgrill pan\nThere are several useful visual features to tell there is 'skillet' and not similar things in a photo:\twide, flat cooking surface\traised edges or walls\thandle for gripping or hanging\tdiameter smaller than saucepan and larger than frying pan.", 116], "pineapples": ["Yes. 'Pineapples' has a tangible appearance and is a type of tropical fruit.\nA few things that are visually similar to 'pineapples' but are not 'pineapples' are:\tartichokes\tsucculents\tpine cones\tcacti\nThere are several useful visual features to tell there is 'pineapples' and not similar things in a photo:\ttall, cylindrical shape with a round base\tpattern of hexagonal scales on the exterior\trough, spiky texture on the leaves and skin\tyellow or green color with brown patches on the exterior", 116], "roofs": ["Yes. 'Roofs' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'roofs' but are not 'roofs' are:\tcanopies\ttents\ttarps\nThere are several useful visual features to tell there are 'roofs' and not similar things in a photo:\tridges and slopes\tshingles or tiles\tattached to a building\ttop view of a building", 116], "grey building": ["Yes. 'Grey building' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'grey building' but are not 'grey building' are:\tbridge\troad\tparking lot\tconcrete wall\nThere are several useful visual features to tell there is 'grey building' and not similar things in a photo:\tmade of bricks, concrete, or other building material\thas windows, doors, or other openings\thas a roof or other distinctive architectural features such as a spire, dome or tower.", 116], "mugs": ["Yes. 'Mugs' has a tangible appearance and is a type of cup.\nA few things that are visually similar to 'mugs' but are not 'mugs' are:\tglasses\ttumbler\tteapot\ttravel mug\nThere are several useful visual features to tell there is 'mugs' and not similar things in a photo:\thandles\ta round vessel shape\tdesigned to hold hot beverages (coffee, tea, etc.)\tmay have a saucer or a lid\tmade of ceramic, porcelain, glass or metal", 116], "winter jacket": ["Yes. 'Winter jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'winter jacket' but are not 'winter jacket' are:\tcoat\tparka\tvest\tsweater\nThere are several useful visual features to tell there is 'winter jacket' and not similar things in a photo:\tthick and padded\thooded or with a collar\tlarge buttons or a zipper\tdark or neutral colors (such as black, gray, or navy)\tdesigned for cold weather", 116], "shield": ["Yes. 'Shield' has a tangible appearance and is a type of defensive gear.\nA few things that are visually similar to 'shield' but are not 'shield' are:\tplates\tfrisbee\tturtle's shell\nThere are several useful visual features to tell there is 'shield' and not similar things in a photo:\tround or curved shape\thand-held or strapped to the arm\tmade of metal, wood, or other durable materials\temblazoned with a symbol or design\tfor use in combat or defense", 116], "floors": ["Yes. 'Floors' has a tangible appearance and refers to the surface of a room or building.\nA few things that are visually similar to 'floors' but are not 'floors' are:\tsidewalks\tdriveways\tpavements\ttiles\nThere are several useful visual features to tell there is 'floors' and not similar things in a photo:\tinside a building or a room\tcovered by materials such as wood, carpet, tile, or concrete\tleveled with the ground of the surrounding area", 115], "price tag": ["Yes. 'Price tag' has a tangible appearance.\nA few things that are visually similar to 'price tag' but are not 'price tag' are:\tstickers\tlabels\tpackaging tags\nThere are several useful visual features to tell there is 'price tag' and not similar things in a photo:\tdisplaying the cost of a product\tpricing information written or printed on it\tstuck or attached to an item for sale", 115], "wooden wall": ["Yes. 'Wooden wall' has a tangible appearance and is a type of wall made of wood.\nA few things that are visually similar to 'wooden wall' but are not 'wooden wall' are:\tconcrete wall\tbrick wall\tstone wall\twallpaper\nThere are several useful visual features to tell there is 'wooden wall' and not similar things in a photo:\tmade of wood\tgrain patterns\ttextured surface\tbrown, beige, or other natural wood tones.", 115], "blue umbrella": ["Yes. 'Blue umbrella' has a tangible appearance and is a type of umbrella.\nA few things that are visually similar to 'blue umbrella' but are not 'blue umbrella' are: red umbrella, yellow umbrella, green umbrella, black umbrella.\nThere are several useful visual features to tell there is 'blue umbrella' and not similar things in a photo:\tcolor - specifically a shade of blue\tumbrella shape - canopy attached to a central frame\thandle - specifically a straight handle with a curved end.", 115], "sole": ["Yes. 'Sole' has a tangible appearance and is a part of the foot.\nA few things that are visually similar to 'sole' but are not 'sole' are:\tshoe\tinsert\tcushion\tmat\nThere are several useful visual features to tell there is 'sole' and not similar things in a photo:\tbottom part of the foot\tcurved arch\tpink skin\tpattern of wrinkles or lines at the bottom of the foot.", 115], "clay": ["Yes. 'Clay' has a tangible appearance and is a malleable material.\nA few things that are visually similar to 'clay' but are not 'clay' are:\tPlay-Doh\tmud\tdough\nThere are several useful visual features to tell there is 'clay' and not similar things in a photo:\tearthy colors\tmoldable texture\tpottery or sculptures made out of it\twet or dry appearance.", 115], "sad": ["No. 'Sad' is too vague or abstract to be depicted in a photo.", 115], "fins": ["Yes. 'Fins' has a tangible appearance and is a body part or a part of an object used to create propulsion in water or air.\nA few things that are visually similar to 'fins' but are not 'fins' are:\twings\tflaps\tleaves\nThere are several useful visual features to tell there is 'fins' and not similar things in a photo:\telongated and tapered shape\tlocated on the back, sides, or bottom of an object or body\tridged or smooth surface\ttexture or color that matches the object or body", 115], "banners": ["Yes. 'Banners' has a tangible appearance and is a type of sign usually made of cloth, paper, or vinyl.\nA few things that are visually similar to 'banners' but are not 'banners' are:\tflags\tstreamers\tribbons\tcurtains\nThere are several useful visual features to tell there is 'banners' and not similar things in a photo:\tusually rectangular or square\tinformative or decorative\ttext or graphics\tmounted or hanging from a wall, ceiling, or pole", 115], "chimneys": ["Yes. 'Chimneys' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'chimneys' but are not 'chimneys' are:\tmasts\tsmokestacks\ttowers\nThere are several useful visual features to tell there are 'chimneys' and not similar things in a photo:\tbrick or stone structure\ton the top of a roof\tsmoke coming out of them\tcylinder or rectangular-shaped structure.", 115], "advertisements": ["No. 'Advertisements' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that may be visually similar (in terms of their format) to advertisements but are not advertisements could be: flyers, posters, banners, and signs. \n\nUseful visual features for distinguishing 'advertisements' from these similar things may include elements such as branding or logos, promotional messaging or calls-to-action, and product or service imagery. Advertisements may also be found in various mediums such as television, radio, print and online as well.", 115], "air vent": ["Yes. 'Air vent' has a tangible appearance and is a type of opening for air to pass through.\nA few things that are visually similar to 'air vent' but are not 'air vent' are:\tdecorative grilles\tdecorative concrete blocks\tlattice or trellis panels\nThere are several useful visual features to tell there is 'air vent' and not similar things in a photo:\tretangular, square, or circular shape\tgrids or slats to allow the air through\tattached to a wall, floor, or ceiling", 115], "nut": ["Yes. 'Nut' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'nut' but are not 'nut' are:\tstone\tseeds\tbeads\tpebbles\t\nThere are several useful visual features to tell there is 'nut' and not similar things in a photo:\thard shell or outer coating\tridged or bumpy surface\toblong or circular shape\toften brown, but can be other colors\tsometimes with a stem or stalk attached\tto be found in nature, such as attached to a tree or lying on the ground.", 115], "tuft": ["Yes. 'tuft' has a tangible appearance and refers to a clump or a small bundle of something, such as hair or grass.\nA few things that are visually similar to 'tuft' but are not 'tuft' are:\ttwigs\tstraws\tbristles\nThere are several useful visual features to distinguish 'tuft' from the listed similar things in a photo:\tclumped together at one end\tfluffy or bushy appearance\tsimilar in color or texture to the larger object it is attached to (e.g., a tuft of grass)", 115], "bib": ["Yes. 'Bib' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'bib' but are not 'bib' are:\tnapkin\tapron\tshirt\tscarf\nThere are several useful visual features to tell there is 'bib' and not similar things in a photo:\tworn around the neck\tcovers the chest and lap\tarea around the mouth is covered\tdoes not extend below the waist\tback fastening strap or tie", 115], "pads": ["No. 'Pads' is too vague or abstract to be distinguished visually. Can you please provide more context or details about what type of pads you are referring to?", 114], "grey clouds": ["Yes. 'Grey clouds' has a tangible appearance and is a type of weather phenomenon.\nA few things that are visually similar to 'grey clouds' but are not 'grey clouds' are:\tsmoke\tfog\tdust\nThere are several useful visual features to tell there is 'grey clouds' and not similar things in a photo:\tfluffy or billowy appearance\tgrey or dark color\tcovering much of the sky\tcasting shadows on the ground or surrounding objects\thaving visible rain or thunderstorm cells", 114], "book shelf": ["Yes. 'Book shelf' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'book shelf' but are not 'book shelf' are:\tchina cabinet\tfiling cabinet\ttv stand\tkitchen cabinets\nThere are several useful visual features to tell there is 'book shelf' and not similar things in a photo:\tconsist of horizontal shelves for books and other items\tusually made of wood or metal\tmay have doors or be open", 114], "guard rail": ["Yes. 'Guard rail' has a tangible appearance and is a type of safety barrier.\nA few things that are visually similar to 'guard rail' but are not 'guard rail' are:\tfence\tbarrier\twall\t\nThere are several useful visual features to tell there is 'guard rail' and not similar things in a photo:\thorizontal metal or wooden bars\tplaced along the edge of a road, bridge, or balcony\tmeant to prevent accidents or falls.", 114], "hotel": ["Yes. 'Hotel' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'hotel' but not 'hotel' are:\tapartment building\tmotel\tdormitory\thostel\tresort\nThere are several useful visual features to tell there is 'hotel' and not similar things in a photo:\tsignage with the word \"hotel\" or the name of a hotel\tbuilding with many rooms and floors\tlobby or reception desk\twith furniture like chairs, tables, or couches\tbedding and pillows inside the rooms", 114], "notepad": ["Yes. 'Notepad' has a tangible appearance and is a type of stationery.\nA few things that are visually similar to 'notepad' but are not 'notepad' are:\tstack of paper\tsticky notes\tclipboard\tjournal or diary\nThere are several useful visual features to tell there is 'notepad' and not similar things in a photo:\trectangular shape\tbinding at the top or the side\tlines or grids on the paper\tspiral or wire binding on the side", 114], "lip": ["Yes. 'Lip' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'lip' but are not 'lip' are:\t\npaintbrush stroke\t\ntire\t\nsausage\nThere are several useful visual features to tell there is 'lip' and not similar things in a photo:\t\nfleshy\t\nsurrounding the mouth\t\nvermillion border\t\npink or brownish-gray color\t\nridged or textured surface\t\ncapable of puckering or stretching", 114], "hazy sky": ["Yes. 'Hazy sky' has a tangible appearance and is a meteorological phenomenon.\nA few things that are visually similar to 'hazy sky' but are not 'hazy sky' are:\tfog\tsmog\tclouds\nThere are several useful visual features to tell there is 'hazy sky' and not similar things in a photo:\tatmospheric conditions that cause a decrease in visibility\thigher concentration of particulate matter in the air\tdiminished contrast and sharpness in the photograph compared to normal", 114], "sweat band": ["Yes. 'Sweat band' has a tangible appearance and is a type of athletic accessory.\nA few things that are visually similar to 'sweat band' but are not 'sweat band' are:\theadbands\twristbands\tbracelets\tankle bands\nThere are several useful visual features to tell there is 'sweat band' and not similar things in a photo:\tmade of absorbent fabric\tworn on the forehead, wrist or arm\toften plain or with a simple design or logo\tdifferent width but usually thinner than a standard headband\tor thicker than a standard bracelet.", 113], "mixer": ["Yes. 'Mixer' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'mixer' but are not 'mixer' are:\tblender\tfood processor\thand whisk\tbeater\nThere are several useful visual features to tell there is 'mixer' and not similar things in a photo:\tbowl attached to the base\trotating beaters or whisk\tpower cord and control panel\thandles for lifting and pouring the mixture", 113], "shingles": ["Yes. 'Shingles' has a tangible appearance and refers to a type of roofing material.\nA few things that are visually similar to 'shingles' but are not 'shingles' are:\twood planks,\troofing tiles,\tmetal sheets,\tthatch roofs\t\nThere are several useful visual features to tell there are 'shingles' and not similar things in a photo:\trectangular or square shapes, overlapping, layered pattern, texture and color of the material (usually asphalt or fiberglass, and dark grey or black in color)", 113], "pillow bed": ["Yes, 'pillow bed' has a visually concrete concept.\nA few things that are visually similar to 'pillow bed' but are not 'pillow bed' are:\tmattress\tfuton\tcouch\tpile of pillows\nThere are several useful visual features to tell there is 'pillow bed' and not similar things in a photo:\thorizontal arrangement of pillows\ton the floor or a flat surface\tno frame or support structure\tpillows are usually the same size and color.", 113], "cooker": ["Yes. 'Cooker' has a tangible appearance and is a kind of kitchen equipment.\nA few things that are visually similar to 'cooker' but are not 'cooker' are:\tmicrowave\toven\ttoaster\tgrill\nThere are several useful visual features to tell there is 'cooker' and not similar things in a photo:\tmultiple burners or heat sources\tpots or pans on the burners\tknobs or buttons for temperature control\ta ventilation hood above the cooking area", 113], "peas": ["Yes. 'Peas' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'peas' but are not 'peas' are:\tbeans\tlentils\tmarbles\tbeads\nThere are several useful visual features to tell there are 'peas' and not similar things in a photo:\tgreen color\tround shape\tsmooth texture\tpea pod attached\tto grow in clusters", 113], "posters": ["Yes. 'Posters' has a tangible appearance and is a printed display used for advertisement or decoration.\nA few things that are visually similar to 'posters' but are not 'posters' are:\tphotographs\tpaintings\tsigns\tbanners\nThere are several useful visual features to tell there is 'posters' and not similar things in a photo:\tpaper or cardboard material\tdisplaying printed text or graphics\tfixed or hung on a wall\tadvertising or promoting a product, event or idea\tlarge enough to be easily recognized from a distance", 113], "brown trees": ["Yes. 'Brown trees' has a tangible appearance and refers to trees that have turned brown, usually due to seasonal changes or disease.\nA few things that are visually similar to 'brown trees' but are not 'brown trees' are:\tdead trees\tautumn leaves\tdry bushes\nThere are several useful visual features to tell there are 'brown trees' and not similar things in a photo:\ttall structures with a distinct trunk and branches\tleaves or needles (even if they are brown)\tno movement", 113], "harbor": ["Yes. 'Harbor' has a tangible appearance and is a type of port for ships.\nA few things that are visually similar to 'harbor' but are not 'harbor' are:\tbeach\tbay\testuary\nThere are several useful visual features to tell there is 'harbor' and not similar things in a photo:\tdocks and piers\tfor ships or boats to moor and be tied up, often against wharves, quays, or bulkheads\tbreakwaters, stone walls, or jetties to protect boats from the effects of waves or currents, or to confine them in a particular area\tbuildings, facilities, or infrastructure for the processing of cargo, passengers, or other goods.", 113], "licence plate": ["Yes. 'Licence plate' has a tangible appearance and is a piece of metal with numbers and letters used to identify a vehicle.\nA few things that are visually similar to 'licence plate' but are not 'licence plate' are:\tbumper\tsticker\tmetal plaque\nThere are several useful visual features to tell there is 'licence plate' and not similar things in a photo:\trectangular shape\twith numbers and letters\tstate or country name at the top or bottom\tmetal or reflective surface", 112], "purple shirt": ["Yes. 'Purple shirt' has a tangible appearance and is a piece of clothing.\nA few things that are visually similar to 'purple shirt' but are not 'purple shirt' are:\tblue shirt\tviolet shirt\tmagenta shirt\ttank top\tsweater\nThere are several useful visual features to tell there is 'purple shirt' and not similar things in a photo:\tpurple color\tfabric texture (cotton, silk, etc.)\tbuttons, collar, cuffs, and sleeves as appropriate to the style of the shirt", 112], "helmet man": ["Yes. 'Helmet man' has a tangible appearance and describes a person wearing a helmet.\nA few things that are visually similar to 'helmet man' but are not 'helmet man' are:\tperson wearing a hat\tperson wearing a cap\tperson wearing a crown\tperson wearing a wig\nThere are several useful visual features to tell there is 'helmet man' and not similar things in a photo:\thelmet-shaped headgear, covering the top and sides of the head\tface visible, excluding the surrounding helmet\tarea around the ears is also covered\twith or without a visor or face shield\ttypically worn for safety or protection", 112], "horse grazing": ["Yes. 'Horse grazing' has a tangible appearance and is a horse eating grass.\nA few things that are visually similar to 'horse grazing' but are not 'horse grazing' are: \tcow grazing\tsheep grazing\tdeer grazing\nThere are several useful visual features to tell there is 'horse grazing' and not similar things in a photo:\tlong face\tmane and tail\teating grass or plants\tsaddle and bridle (in domesticated horses)\tmuscular build\tbrown, black, white, or mixed coat\tcolor", 112], "window blinds": ["Yes. 'Window blinds' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'window blinds' but are not 'window blinds' are:\tshades\tdrapes\tshutters\nThere are several useful visual features to distinguish 'window blinds' from the listed similar things in a photo:\thorizontal or vertical slats\tadjustable\tlouvers or vanes\tto be placed inside or outside the window\tframe or casing", 112], "heel": ["Yes. 'Heel' has a tangible appearance and it is a part of a shoe.\nA few things that are visually similar to 'heel' but are not 'heel' are: sole, toe box, upper, tongue, laces.\nUseful visual features for distinguishing 'heel' from the listed similar things in a photo are: being located at the back of the shoe, angle at which it extends upward, and it is typically thicker and more substantial than the other parts of the shoe.", 112], "jackson mingus": ["No. 'Jackson Mingus' is a proper noun and does not have a tangible appearance.", 112], "bicycle seat": ["Yes. 'Bicycle seat' has a tangible appearance and is a part of a bicycle.\nA few things that are visually similar to 'bicycle seat' but are not 'bicycle seat' are:\toffice chair seat\tmotorcycle seat\tstool seat\nThere are several useful visual features to tell there is 'bicycle seat' and not similar things in a photo:\tnarrow and elongated shape\tattached to a bicycle\tframe\tforward or backward tilt\tpadding or cushioning for comfort\tbolt or clamp for attachment to bicycle", 112], "things": ["No. 'Things' is too vague or abstract to be distinguished in a photo.", 112], "brown building": ["Yes. 'Brown building' has a tangible appearance and refers to a building that is colored brown.\nA few things that are visually similar to 'brown building' but are not 'brown building' are:\tgarage\tshed\tbarn\tfactory\nThere are several useful visual features to tell there is 'brown building' and not similar things in a photo:\trectangular shape\tmultiple stories or levels\troof and walls made of different materials, such as tiles or bricks\tbrown color\tpresence of windows, doors, and other architectural details.", 112], "adult elephant": ["Yes. 'Adult elephant' has a tangible appearance and is a kind of large land animal.\nA few things that are visually similar to 'adult elephant' but are not 'adult elephant' are: hippopotamus, rhinoceros, buffalo\nThere are several useful visual features to tell there is 'adult elephant' and not similar things in a photo:\t\nlong trunk\t\nlarge ears\t\ngray skin\t\ntwo tusks\t\nprominent forehead\t\nlarge size compared to other land animals", 112], "baseball jersey": ["Yes. 'Baseball jersey' has a tangible appearance and is a specific type of sports clothing.\nA few things that are visually similar to 'baseball jersey' but are not 'baseball jersey' are:\tt-shirt\tpolo shirt\tjacket\tvest\nThere are several useful visual features to tell there is 'baseball jersey' and not similar things in a photo:\tbutton-up front or zipper\ta distinctive logo, number or name on the back and/or front\tshort sleeves\tstripe or solid color pattern", 112], "riders": ["Yes. 'Riders' has a tangible appearance and refers to people who ride something.\nA few things that are visually similar to 'riders' but are not 'riders' are:\tpassengers\tpedestrians\tbikers\tathletes\nThere are several useful visual features to tell there are 'riders' and not similar things in a photo:\triding on horses, bicycles, motorcycles, or similar vehicles\twearing helmets and protective gear\tmotion and speed\tbody posture and position of limbs", 112], "flooring": ["Yes. 'Flooring' has a tangible appearance and refers to the material used to cover a floor.\nA few things that are visually similar to 'flooring' but are not 'flooring' are:\ttiles\tcarpet\trugs\tlinoleum\nThere are several useful visual features to tell there is 'flooring' and not similar things in a photo:\tcovering the entire floor\tlevel with the ground\tdifferent colors or patterns from the wall or other surfaces", 112], "wall outlet": ["Yes. 'Wall outlet' has a tangible appearance and is a type of electrical component.\nA few things that are visually similar to 'wall outlet' but are not 'wall outlet' are:\tlight switch\tthermostat\tcoaxial cable outlet\tphone charger\tportable power bank\nThere are several useful visual features to tell there is 'wall outlet' and not similar things in a photo:\tusually white or beige\tcolor-coded screw terminals\ton the wall or flush-mounted\tslots for prongs or pins\tof 2 or 3 holes, arranged in a line or a triangle", 112], "denim jeans": ["Yes. 'Denim jeans' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'denim jeans' but are not 'denim jeans' are:\tblue pants\tjeggings\ttights\twork pants\nThere are several useful visual features to tell there is 'denim jeans' and not similar things in a photo:\tcotton fabric with a particular blue color\tpockets on the back and front\torangish stitching on the seams\tbutton and zipper on the front", 111], "fin": ["Yes. 'Fin' has a tangible appearance and typically refers to the appendages of aquatic animals.\nA few things that are visually similar to 'fin' but are not 'fin' are:\tleaves\tkite\tbird wings\t\nThere are several useful visual features to tell there is 'fin' and not similar things in a photo:\tfound on the body of aquatic animals, such as fish and whales\tfan or triangular-shaped\tscaled, smooth or rough texture\tflexible and able to move in water", 111], "backside": ["Yes. 'Backside' has a tangible appearance and refers to the rear or posterior part of something.\nA few things that are visually similar to 'backside' but are not 'backside' are:\tfrontside\ttop\tside\nThere are several useful visual features to tell there is 'backside' and not similar things in a photo:\tcurvature\tof the spine or a body part\tbutt muscles (glutes)\twaistline\tor hip structure\trear end\tposition and orientation (e.g., facing away from the camera)", 111], "grate": ["Yes. 'Grate' has a tangible appearance and is a kind of metal mesh covering.\nA few things that are visually similar to 'grate' but are not 'grate' are:\tfence\tpatterned fabric\tscreen\twindow\nThere are several useful visual features to tell there is 'grate' and not similar things in a photo:\tmetal mesh pattern\tusually found in a floor or a fireplace\tdark color and metallic luster\tvarying sizes of holes or slits", 111], "hanger": ["Yes. 'Hanger' has a tangible appearance and is an object for holding clothes.\nA few things that are visually similar to 'hanger' but are not 'hanger' are: hook, nail, peg, clamp\nThere are several useful visual features to tell there is 'hanger' and not similar things in a photo: it has notches or curves for holding clothing, it is usually made of metal or plastic, it has a hook on top for hanging on a rod or in a closet.", 111], "metal trash": ["Yes. 'Metal trash' has a tangible appearance and refers to waste made of metal materials.\nA few things that are visually similar to 'metal trash' but are not 'metal trash' are:\ttools and equipment\tscrap metal\tartwork made from metal\nThere are several useful visual features to tell there is 'metal trash' and not similar things in a photo:\tdented or crushed metal objects\trusty or discolored metal objects\tobjects that appear to be discarded or forgotten", 111], "shiny": ["Yes. 'Shiny' has a tangible appearance and refers to the reflective or polished surface of an object.\nA few things that are visually similar to 'shiny' but are not 'shiny' are:\tmatte\tmetallic\tpolished\tcrystal\tclear\nThere are several useful visual features to tell there is 'shiny' and not similar things in a photo:\treflective surface\tlight reflecting off of an object\tpolished or smooth appearance\tbright and clear", 111], "orange frisbee": ["Yes. 'Orange frisbee' has a tangible appearance and is a kind of sports equipment.\nA few things that are visually similar to 'orange frisbee' but are not 'orange frisbee' are:\tflying disc\tbeach ball\ttoy saucer\nThere are several useful visual features to tell there is 'orange frisbee' and not similar things in a photo:\tround\tor disc-shaped\tobject\tflat in the middle, curved or sloping upwards at the edges\tbright orange color\ttextured surface\twith a lip or rim around the edge", 111], "chalkboard": ["Yes. 'Chalkboard' has a tangible appearance and is a flat writing surface.\nA few things that are visually similar to 'chalkboard' but are not 'chalkboard' are:\twhiteboard\tpaper\tcanvas\nThere are several useful visual features to tell there is 'chalkboard' and not similar things in a photo:\tdark surface\twriting or drawing with chalk\tvisible chalk dust or eraser marks\ton a wall or easel", 111], "seagulls": ["Yes. 'Seagulls' has a tangible appearance and is a kind of bird.\nA few things that are visually similar to 'seagulls' but are not 'seagulls' are:\tpigeons\tdoves\tsparrows\tblackbirds\tcrows\nThere are several useful visual features to tell there is 'seagulls' and not similar things in a photo:\twhite and grey feathers\tlong wingspan\tpointed beak\tflattened head\twebbed feet\tand they are usually found near the shore or bodies of water.", 111], "clock building": ["Yes. 'Clock building' has a tangible appearance and refers to buildings that have clocks on them.\nA few things that are visually similar to 'clock building' but are not 'clock building' are:\tregular building with a clock tower\tclock without building\ttall building\nThere are several useful visual features to tell there is 'clock building' and not similar things in a photo: building with a clock\tspecific architectural style\tsymbolic clock face or design\tclearly visible from a distance", 110], "faucet sink": ["Yes. 'Faucet sink' has a tangible appearance and is a specific type of sink with a water tap or faucet.\nA few things that are visually similar to 'faucet sink' but are not 'faucet sink' are: Baking tray water dispenser, washing machine, bathtub\nThere are several useful visual features to tell there is 'faucet sink' and not similar things in a photo:\toval or rectangular-shaped basin\tmetal or ceramic material\twater faucet or tap attached to the basin\tdrain at the bottom of the basin.", 110], "kitty": ["Yes. 'Kitty' has a tangible appearance and is a kind of small domesticated feline.\nA few things that are visually similar to 'kitty' but are not 'kitty' are:\tjaguar\tlynx\tlioness\tcheetah\nThere are several useful visual features to tell there is 'kitty' and not similar things in a photo:\tsoft fur\tpointy ears\tsmall size\tnarrow face\twith or without whiskers\tgentle eyes\tdomesticated", 110], "conductor": ["No. 'Conductor' is too abstract to be distinguished in a photo. It refers to a person who directs an orchestra or choir, or a material that allows the flow of electricity.\nHowever, a few things that are visually similar to a conductor as in directing an orchestra are:\tmusician, director, actor. These may all hold a similar pose or gesture to direct attention, but they wouldn't be called 'conductors'.\nA few things that are visually similar to a conductor as in a material include:\twire, cable, pipeline. These may all conduct electricity or other substances, but they wouldn't be called 'conductors' in the musical sense.\nNote: Although 'conductor' is not visually concrete, it can be a tangible role, material or object to a certain extent.", 110], "loaf": ["Yes. 'Loaf' has a tangible appearance and generally refers to a bread.\nA few things that are visually similar to 'loaf' but are not 'loaf' are:\tbrick\tcake\tsliced bread\tsculpture\tpiece of wood\nThere are several useful visual features to tell there is 'loaf' and not similar things in a photo:\tbread-like shape\tand brownish color\tuneven texture\ttop may have slice marks or decorative patterns", 110], "peach": ["Yes. 'Peach' has a tangible appearance and is a kind of fruit.\nA few things that are visually similar to 'peach' but are not 'peach' are:\tapricot\tmango\torange\tbell pepper\nThere are several useful visual features to tell there is 'peach' and not similar things in a photo:\tround or oblong shape, with a slightly pointed end on one side\tfuzzy skin that is usually orange or pink\tflesh that is yellow or white with a large pit in the center", 110], "sun glasses": ["Yes. 'Sun glasses' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'sun glasses' but are not 'sun glasses' are:\tregular glasses\tsafety glasses\tgoggles\tshooting glasses\nThere are several useful visual features to tell there is 'sun glasses' and not similar things in a photo:\tdark or tinted lenses\tframes that go around the ears\tframes that go around the nose and above the ears\treduce glare or UV rays from the sun", 110], "mom": ["No. 'Mom' is too vague or abstract to have a tangible appearance.\nThere aren't any things that are visually similar to 'mom'.\nSince 'mom' is a human relationship rather than a physical object, there are no useful visual features to distinguish it from other things in a photo.", 110], "vine": ["Yes. 'Vine' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'vine' but are not 'vine' are:\tshrub\ttree\tgrass\tweed\nThere are several useful visual features to tell there is 'vine' and not similar things in a photo:\ttrailing or climbing stem\ttwisted or woody appearance\ttendrils or leaves that wrap around objects\tgrape-like clusters of fruit or flowers", 110], "business sign": ["Yes. 'Business sign' has a tangible appearance and is a type of signage used to identify a business.\nA few things that are visually similar to 'business sign' but are not 'business sign' are:\ttraffic sign\tbillboard\tstreet address\tnumber\nThere are several useful visual features to tell there is 'business sign' and not similar things in a photo:\tdisplaying the name or logo of a business\tlocated outside or above a physical store or office\tinformative, indicating services offered or hours of operation\tcolored, often brightly so it can be seen from afar.", 110], "fencing": ["Yes. 'Fencing' has a tangible appearance and is a sport that involves swords and protective gear.\nA few things that are visually similar to 'fencing' but are not 'fencing' are:\tsword fighting\tfight scenes in movies and TV shows\tswordplay in theatrical performances\tcosplay\nThere are several useful visual features to tell there is 'fencing' and not similar things in a photo:\ttwo opponents facing each other with swords\telectric scoring equipment\tmask, jacket, glove, and fencing pants for protection\ten garde (ready) position with one arm extended and feet in a specific position", 110], "metal container": ["Yes. 'Metal container' has a tangible appearance and is a type of storage or transportation vessel.\nA few things that are visually similar to 'metal container' but are not 'metal container' are:\ttin\tbox\tcan\ttrash can\nThere are several useful visual features to tell there is 'metal container' and not similar things in a photo:\tmade of metal\tdurable and sturdy\thave a lid or closing mechanism\tdesigned for storing or transporting goods.", 110], "train platform": ["Yes. 'Train platform' has a tangible appearance.\nA few things that are visually similar to 'train platform' but are not 'train platform' are:\tbus stops\tsubway platforms\tairport boarding gates\nThere are several useful visual features to tell there is 'train platform' and not similar things in a photo:\traised area alongside train tracks\tsafety lines or barriers\tplatform numbers or signage\tbenches or seats for waiting\tpassengers boarding or disembarking trains", 110], "light glare": ["Yes. 'Light glare' has a tangible appearance and refers to the reflection of light that causes a harsh, bright spot or area.\nA few things that are visually similar to 'light glare' but are not 'light glare' are:\tlight source\tshine\tflash\tcamera flash\treflection\nThere are several useful visual features to tell there is 'light glare' and not similar things in a photo:\tharsh, bright spot or area\tcaused by reflection\tof a light source\tor shiny surface\toften unwanted or distracting in a photograph\tor any reflective surface", 110], "cane": ["Yes. 'Cane' has a tangible appearance and is a type of walking stick.\nA few things that are visually similar to 'cane' but are not 'cane' are:\twand\tstaff\tcrutch\tbaton\nThere are several useful visual features to tell there is 'cane' and not similar things in a photo:\tslim and straight\tstick made of wood or metal\twith a curved handle at the top\tto aid in walking or for decorative purposes", 110], "fry": ["Yes. 'Fry' has a tangible appearance and refers to a type of food that has been fried.\nA few things that are visually similar to 'fry' but are not 'fry' are:\tchips\tcrisps\ttempura\tbattered foods\nThere are several useful visual features to tell there is 'fry' and not similar things in a photo:\tgolden or brown color\tcrunchy or crispy texture\tgreasy appearance\toblong or irregular shape, depending on the food\titem is usually small in size or cut into small pieces.", 109], "hardwood floors": ["Yes. 'Hardwood floors' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'hardwood floors' but are not 'hardwood floors' are:\tlaminate floors\ttile floors\tconcrete floors\tcarpet\nThere are several useful visual features to tell there is 'hardwood floors' and not similar things in a photo:\tbeautiful wood grain\tnatural colors and patterns\tno grout lines or seams\tplanks of wood running across the floor", 109], "dog bun": ["No. 'Dog bun' is too vague or abstract to be a tangible concept.\nThere are no things that are similar to 'dog bun' but not 'dog bun' as the term 'dog bun' does not have a clear definition.\nTherefore, there are no visual features to distinguish 'dog bun' from anything else in a photo.", 109], "father": ["No. 'Father' is too vague or abstract to be distinguished in a photo.", 109], "outlets": ["Yes. 'Outlets' has a tangible appearance and refers to the points on a wall where electrical devices can be plugged in.\nA few things that are visually similar to 'outlets' but are not 'outlets' are:\tlight switches\tventilation grills\tspeaker covers\nThere are several useful visual features to tell there is 'outlets' and not similar things in a photo:\tusually contain two or three holes\tfor prongs of plug\tconnected to wires in the wall.", 109], "video game": ["No. 'Video game' is too vague or abstract to be distinguished in a photo.\nHowever, some things that are visually similar to 'video game' but are not 'video game' are:\tboard game\tcard game\tpuzzle app\nUseful visual features to distinguish 'video game' from these similar things are not available as the concept is not visually concrete.", 109], "baseboard": ["Yes. 'Baseboard' has a tangible appearance and is a part of the interior design of a room.\nA few things that are visually similar to 'baseboard' but are not 'baseboard' are:\tcrown molding\tchair rail\ttrim\tworktop\nThere are several useful visual features to tell there is 'baseboard' and not similar things in a photo:\tpaneling along the bottom of a wall, connecting it to the floor\tusually painted white, but can be any color\trectangular or wedge-shaped that transitions the wall to the floor", 109], "cement sidewalk": ["Yes. 'Cement sidewalk' has a tangible appearance and is a type of pedestrian pathway.\nA few things that are visually similar to 'cement sidewalk' but are not 'cement sidewalk' are:\tbrick sidewalk\tasphalt pavement\tpaved road\nThere are several useful visual features to tell there is 'cement sidewalk' and not similar things in a photo:\tgrey color\trectangular or square shape\thard and flat surface\tno visible cracks or unevenness", 109], "index finger": ["Yes. 'Index finger' has a tangible appearance and is a part of the hand.\nA few things that are visually similar to 'index finger' but are not 'index finger' are:\tthumb\tpinky finger\tmiddle finger\tpointer\nThere are several useful visual features to tell there is 'index finger' and not similar things in a photo:\tsecond from the thumb\tlonger than the pinky finger\tpointing forward\tor touching something with the tip", 109], "cloudy skies": ["Yes. 'Cloudy skies' has a tangible appearance and is a weather condition.\nA few things that are visually similar to 'cloudy skies' but are not 'cloudy skies' are:\tsunrise/sunset\tstarry sky\tsmoke/fog\tdusty atmosphere\nThere are several useful visual features to tell there are 'cloudy skies' and not similar things in a photo:\tmultiple layers of clouds\tvariety of shapes and textures\tgrey or white color\tdark or shadowy appearance", 109], "antelope": ["Yes. 'Antelope' has a tangible appearance and is a part of the Bovidae family.\nA few things that are visually similar to 'antelope' but are not 'antelope' are:\tdeer\tgoat\tsheep\tkangaroo\nThere are several useful visual features to tell there is 'antelope' and not similar things in a photo:\tlong, slender legs\tbrown or beige fur\tsharp, pointed horns\torbits on the side of the head", 109], "skateboard wheel": ["Yes. 'Skateboard wheel' has a tangible appearance and is a kind of wheel.\nA few things that are visually similar to 'skateboard wheel' but are not 'skateboard wheel' are:\tbike wheel\trollerblade wheel\nThere are several useful visual features to tell there is 'skateboard wheel' and not similar things in a photo:\tsmall size\thard exterior\ttexture on the surface\tusually white or colored with a graphic\tdifferent wheel size comparing to other vehicles.", 109], "caution sign": ["Yes. 'Caution sign' has a tangible appearance and is a type of warning sign.\nA few things that are visually similar to 'caution sign' but are not 'caution sign' are:\tstop sign\tno entry sign\tspeed limit sign\tpedestrian crossing sign\nThere are several useful visual features to tell there is 'caution sign' and not similar things in a photo:\tyellow or orange background with black lettering\tor yellow and black diagonal stripes\tpictogram of a person falling or other hazard\tnormally shaped as an equilateral triangle", 109], "cloudless blue sky": ["Yes. 'Cloudless blue sky' has a tangible appearance and is a type of sky.\nA few things that are visually similar to 'cloudless blue sky' but are not 'cloudless blue sky' are:\tblue ocean\tor blue desert\tvarious shades of blue paint\tblue clothes\nThere are several useful visual features to tell there is 'cloudless blue sky' and not similar things in a photo:\tno clouds at all\tbright or light blue color gradient\tno objects or features obscuring the sky (like trees or buildings)", 109], "cabinet doors": ["Yes. 'Cabinet doors' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'cabinet doors' but are not 'cabinet doors' are:\tdrawers\tshelves\tbookcases\troom dividers\nThere are several useful visual features to tell there are 'cabinet doors' and not similar things in a photo:\trectangular or square shape\thandles or knobs\thinges\tattached to a cabinet frame\tor furniture piece.", 108], "snowboarders": ["Yes. 'Snowboarders' has a tangible appearance and refers to people who engage in the sport of snowboarding.\nA few things that are visually similar to 'snowboarders' but are not 'snowboarders' are: \tskiers \thikers \tshovelers \tsledders\nThere are several useful visual features to tell there are 'snowboarders' and not similar things in a photo: \triding a snowboard \tperforming tricks or jumps \twearing snow gear and boots \tboard strapped to feet \ton a snow-covered slope or park", 108], "hair dryer": ["Yes. 'Hair dryer' has a tangible appearance and is an electrical appliance.\nA few things that are visually similar to 'hair dryer' but are not 'hair dryer' are:\tvacuum cleaner\telectric fan\tportable heater\nThere are several useful visual features to tell there is 'hair dryer' and not similar things in a photo:\tnozzle\tfor blowing hot air\thand-held\tsize and shape\tof the device\tlength of cord (if visible)\tonClick=\"showAnswer()\">", 108], "ambulance": ["Yes. 'Ambulance' has a tangible appearance and is a type of emergency vehicle.\nA few things that are visually similar to 'ambulance' but are not 'ambulance' are:\tfiretruck\tpolice car\tdelivery van\ttaxi\nThere are several useful visual features to tell there is 'ambulance' and not similar things in a photo:\tflashy red, white and yellow design on the body and top of the vehicle\temergency lights and sirens\ta large compartment in the back for carrying medical equipment and stretchers", 108], "cameras": ["Yes. 'Cameras' has a tangible appearance and refers to a device used to take photographs or record videos.\nA few things that are visually similar to 'cameras' but are not 'cameras' are:\tsmartphone\ttablet\tdigital recorder\tbinoculars\nThere are several useful visual features to tell there is 'cameras' and not similar things in a photo:\tlens\tviewfinder\tshutter button\tflash or strobe\tgrid of buttons or menu\tscreen on the back of the device.", 108], "rest": ["No. 'Rest' is too vague or abstract to be distinguished in a photo.", 108], "grass brown": ["Yes. 'Grass brown' has a tangible appearance and refers to a specific color of grass.\nA few things that are visually similar to 'grass brown' but are not 'grass brown' are:\tbark of a tree\tdesert sand\tcoffee stain\tonion skin\nThere are no useful visual features to tell there is 'grass brown' and not similar things in a photo except to examine the context and surrounding objects.", 108], "street lamps": ["Yes. 'Street lamps' has a tangible appearance and refers to lighting fixtures used for illuminating public roads.\nA few things that are visually similar to 'street lamps' but are not 'street lamps' are:\tporch lights\ttraffic lights\tfloodlights\tgarden lights\nThere are several useful visual features to tell there is 'street lamps' and not similar things in a photo:\ttall metal pole\twith a visible light bulb\tor curved shade\tpointed down toward the ground\toften seen in a row or a grid pattern\talight at night", 108], "kitten": ["Yes. 'Kitten' has a tangible appearance and is a young cat.\nA few things that are visually similar to 'kitten' but are not 'kitten' are:\tpuppy\thamster\tbunny\tferret\nThere are several useful visual features to tell there is 'kitten' and not similar things in a photo:\tbig ears, high on the head\twhiskers\tplump and furry body\teyes large in relation to the head\tpointed ears\twith sharp claws and teeth", 108], "celery": ["Yes. 'Celery' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'celery' but are not 'celery' are:\tgreen onions\tasparagus\tleeks\nThere are several useful visual features to tell there is 'celery' and not similar things in a photo:\tlong and skinny stalks\tlight green color\tridged texture on the stalks\tand leafy, green tops", 108], "plank": ["Yes. 'Plank' has a tangible appearance and is a rectangular piece of wood.\nA few things that are visually similar to 'plank' but are not 'plank' are:\tbeam\tlog\tboard\nThere are several useful visual features to tell there is 'plank' and not similar things in a photo:\trectangular shape\tthin and flat\twooden texture\tcan be used as a walking surface\tor as a building material", 108], "letter t": ["Yes. 'Letter t' has a tangible appearance and is a specific symbol in the alphabet.\nA few things that are visually similar to 'letter t' but are not 'letter t' are:\t+ symbol\tI symbol\tlowercase l\nThere are no other distinguishing visual features for ensuring that a letter is a 'letter t' in a photo, as its unique characteristics are its specific shape and orientation, with a vertical line and a horizontal line intersecting it perpendicularly.", 108], "exhaust": ["Yes. 'Exhaust' has a tangible appearance and refers to the gas emitted from an engine.\nA few things that are visually similar to 'exhaust' but are not 'exhaust' are:\tsmoke\tsteam\tfire\t\nThere are several useful visual features to tell there is 'exhaust' and not similar things in a photo:\tcoming from a vehicle's tail pipe\tdark and opaque\tchemically smelling", 107], "aluminum": ["Yes. 'Aluminum' is a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'aluminum' but are not 'aluminum' are:\tsilver\tplatinum\tsteel\tchrome\nThere are several useful visual features to tell there is 'aluminum' and not similar things in a photo:\tsilvery-grey color\tmetallic surface that reflects light\tlightweight\tmalleable and ductile\tcorrosion-resistant, but matte and not as shiny or reflective as chrome", 107], "blue handle": ["Yes. 'Blue handle' has a tangible appearance and is a specific feature of an object.\nA few things that are visually similar to 'blue handle' but are not 'blue handle' are:\tred handle\tgreen handle\tyellow handle\tblack handle\nThere are no visually similar things that would be challenging to distinguish from a 'blue handle.' However, some useful visual features for distinguishing a blue handle from differently-colored handles in a photo are:\ta handle that is entirely blue or has distinct blue markings\tor consists of the blue color as a majority of its design.", 107], "kitchen counter": ["Yes. 'Kitchen counter' has a tangible appearance and refers to a flat surface in a kitchen for food preparation.\nA few things that are visually similar to 'kitchen counter' but are not 'kitchen counter' are:\tdresser\ttable\tbench\nThere are several useful visual features to tell there is 'kitchen counter' and not similar things in a photo:\tlocated in a kitchen\trectangular shape\tflat surface\theight about the waist of a person\tspace for appliances or utensils.", 107], "interior": ["Yes. 'Interior' has a tangible appearance and refers to the inside of a building or space.\nA few things that are visually similar to 'interior' but are not 'interior' are:\texterior\tlandscape\tcityscape\nThere are several useful visual features to tell there is 'interior' and not similar things in a photo:\tfurniture\twalls\tceiling\tfloor\tlighting\tdecorations\tor any other visual signifiers of an indoor space", 107], "condiments": ["Yes. 'Condiments' has a tangible appearance and refers to various sauces and seasonings used to flavor food.\nA few things that are visually similar to 'condiments' but are not 'condiments' are:\tdecorative items\ttableware\tcandles\nThere are several useful visual features to tell there is 'condiments' and not similar things in a photo:\tvisibly stored in containers, bottles or jars\tmixture or layers of sauces and seasonings on food\ttypically found on or next to food in a dining context\tinclude things like ketchup, mustard, salt, pepper, and hot sauce.", 107], "scoreboard": ["Yes. 'Scoreboard' has a tangible appearance and is a device used to display the score in a game.\nA few things that are visually similar to 'scoreboard' but are not 'scoreboard' are:\tthermometer\tclock\tcounter\nThere are several useful visual features to tell there is 'scoreboard' and not similar things in a photo:\tdivided into sections for each team's score and time easily visible from a distance or angle\tnumbers or digits indicating the score and time\tbackground graphics or colors related to the sport or event being scored", 107], "bathroom wall": ["Yes. 'Bathroom wall' has a tangible appearance and is a part of a room.\nA few things that are visually similar to 'bathroom wall' but are not 'bathroom wall' are:\tkitchen wall\tbedroom wall\tliving room wall\toffice wall\nThere are several useful visual features to tell there is 'bathroom wall' and not similar things in a photo:\ttiles\tmirrors\tshower\tcabinets\tshelves\ttowel racks", 107], "armrest": ["Yes. 'Armrest' has a tangible appearance and is a part of furniture.\nA few things that are visually similar to 'armrest' but are not 'armrest' are:\tpillow\tcushion\theadrest\nThere are several useful visual features to tell there is 'armrest' and not similar things in a photo:\trectangular or square shape\tattached to a seat or a sofa level with the seat surface\tpadded or cushioned\tfor placing an arm for comfort during sitting", 107], "parking sign": ["Yes. 'Parking sign' has a tangible appearance and is a type of traffic sign.\nA few things that are visually similar to 'parking sign' but are not 'parking sign' are:\tstop sign\tyield sign\tspeed limit sign\tcrossing sign\nThere are several useful visual features to tell there is 'parking sign' and not similar things in a photo:\twhite or blue with red markings\tthe word 'parking' or a letter 'P' indicating parking availability\tdirections for parking, such as 'no parking' or 'parking allowed'", 107], "zebra tail": ["Yes. 'Zebra tail' has a tangible appearance and is a body part of the zebra.\nA few things that are visually similar to 'zebra tail' but are not 'zebra tail' are:\thorse tail\tdonkey tail\tpony tail\tlion tail\nThere are several useful visual features to tell there is 'zebra tail' and not similar things in a photo:\tpatterned with black and white stripes\tgrowing from the back end of the zebra\tfur-like texture\tsimilar shape and size to a horse tail, but with distinct stripes", 107], "taillight": ["Yes. 'Taillight' has a tangible appearance and refers to the backlight of a vehicle.\nA few things that are visually similar to 'taillight' but are not 'taillight' are:\theadlights\tbicycle lights\treflector stickers\ttraffic cones\nThere are several useful visual features to tell there is 'taillight' and not similar things in a photo:\tlocated on the back of a vehicle\tred in color, sometimes with a hint of orange\tcould be circular, rectangular, or irregular in shape\tmay have multiple bulbs or sections", 107], "podium": ["Yes. 'Podium' has a tangible appearance and refers to a raised platform.\nA few things that are visually similar to 'podium' but are not 'podium' are:\tstage\triser\taltar\tplatform\nThere are several useful visual features to tell there is 'podium' and not similar things in a photo:\traised platform\tfor speakers or performers\twith a microphone or lectern on it\tfront and center in a room or space.", 107], "paintings": ["Yes. 'Paintings' has a tangible appearance and is a form of art.\nA few things that are visually similar to 'paintings' but are not 'paintings' are:\tphotographs\tposters\ttapestries\tdrawings\nThere are several useful visual features to tell there are 'paintings' and not similar things in a photo:\tcolored surfaces\ton canvas, paper or other materials\tbrushstrokes, texture\tor other marks made by the artist\tintended for decorative or aesthetic purposes.", 107], "torso": ["Yes. 'Torso' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'torso' but are not 'torso' are:\tlegs\tarms\thead\tbutt\nThere are several useful visual features to tell there is 'torso' and not similar things in a photo:\tthe main part of the body between the neck and the hips\thas the chest, abdomen, and waist as its parts", 107], "medium": ["No. 'Medium' is too vague or abstract to have a tangible appearance or be distinguished in a photo. \n\nNote: \"Medium\" can refer to several things, such as a psychic medium, a medium of creative expression (like paint, clay, or film), or a means of communication (like television or radio). However, for each of these meanings, the concept is not visually concrete, but rather an abstract idea or skill.", 106], "tabby cat": ["Yes. 'Tabby cat' has a tangible appearance and is a specific breed of cat.\nA few things that are visually similar to 'tabby cat' but are not 'tabby cat' are:\tleopard\tcheetah\tjaguar\tocelot\nThere are several useful visual features to tell there is 'tabby cat' and not similar things in a photo:\tmedium-sized\tcat-like features, such as pointed ears and whiskers\tpatterned coat with stripes, dots or swirling lines, usually in brown or grey colors\tm-shaped patch on the forehead", 106], "town": ["Yes. 'Town' has a tangible appearance and is a place where people live and work.\nA few things that are visually similar to 'town' but are not 'town' are:\tvillage\tcity\tsuburb\tfactory\tindustrial park\nThere are several useful visual features to tell there is 'town' and not similar things in a photo:\tcluster of buildings\thouses and commercial buildings in close proximity\tstreet lights, sidewalks, and roads\thuman activity such as cars or people on streets", 106], "leather jacket": ["Yes. 'Leather jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'leather jacket' but are not 'leather jacket' are:\tleather coat\tleather vest\tfaux leather jacket\tbiker jacket\nThere are several useful visual features to tell there is 'leather jacket' and not similar things in a photo:\tshort length\tfront zipper or buttons\twide lapels or collar\tslanted pockets\tmade of leather or suede\tdark colors (usually black or brown)", 106], "types": ["No. 'Types' is too vague or abstract to be distinguished in a photo. \n\nNote: The term 'types' lacks a tangible appearance and refers to a classification system or a way of categorizing things.", 106], "i": ["No. 'i' is too vague or abstract to be visually concrete. It refers to the self-referential pronoun used to refer to oneself.\nThere are no things that are visually similar to 'i'. \nThus, there are no useful visual features for distinguishing 'i' from visually similar things in a photo.", 106], "ripple": ["Yes. 'Ripple' has a tangible appearance and is a visible wave or pattern on a surface of water or any other material.\nA few things that are visually similar to 'ripple' but are not 'ripple' are:\tcrinkles\twrinkles\tfolds\tlines\nThere are several useful visual features to tell there is 'ripple' and not similar things in a photo:\twave-like pattern\tconcentric circles originating from a central point\tdisturbance or movement on the surface of water or any other material varying in size and intensity\tdiffraction and refraction effects\tlight reflection and shadow changes.", 106], "fence posts": ["Yes. 'Fence posts' has a tangible appearance and is a type of wooden or metal post used to support a fence.\nA few things that are visually similar to 'fence posts' but are not 'fence posts' are:\tflagpole\tsign pole\tutility pole\ttree stump\nThere are several useful visual features to tell there is 'fence posts' and not similar things in a photo:\tstanding vertically\tin a line\tconnected by fence rails or wires\tpointed or round at the top\tmade of wood or metal", 106], "vans": ["Yes. 'Vans' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'vans' but are not 'vans' are:\tsneakers\tloafers\tslippers\tboots\nThere are several useful visual features to tell there are 'vans' and not similar things in a photo:\tlow-top\tslip-on or lace-up\tstyle with canvas upper rubber sole in block white color\tsquared toe box with a stripe on the side of the shoe.", 106], "crackers": ["Yes. 'Crackers' has a tangible appearance and is a kind of snack.\nA few things that are visually similar to 'crackers' but are not 'crackers' are:\tcrisps\tchips\tcookies\twafers\nThere are several useful visual features to tell there is 'crackers' and not similar things in a photo:\tflat and crispy\tobject with an irregular shape, often with perforations\tsavory or salty flavor", 106], "sailboats": ["Yes. 'Sailboats' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'sailboats' but are not 'sailboats' are:\tpowerboats\tkayaks\trafts\tships\nThere are several useful visual features to tell there is 'sailboats' and not similar things in a photo:\tlarge mast(s) with sails\tsail(s) attached to the mast(s)\trudder\thull that is wider at the bottom than at the top", 106], "glass doors": ["Yes. 'Glass doors' has a tangible appearance and is a type of door made of glass.\nA few things that are visually similar to 'glass doors' but are not 'glass doors' are:\twindows\tdisplay cases\tshower doors\nThere are several useful visual features to tell there is 'glass doors' and not similar things in a photo:\tattached to a frame or hinges\ttranslucent or transparent\tused for entry or exit to a room or building\thas a handle or latch to open or close.", 106], "wind": ["No. 'Wind' is too vague or abstract to be distinguished in a photo.", 106], "frames": ["Yes. 'Frames' has a tangible appearance and is a physical structure used to hold and display pictures or artwork.\nA few things that are visually similar to 'frames' but are not 'frames' are:\twindows\tdoors\tmirrors\tglasses\nThere are several useful visual features to tell there is 'frames' and not similar things in a photo:\trectangular or square shape\thollow interior\twith or without a stand\thooks for hanging on a wall\tvariety of materials such as wood, metal, plastic, or glass\tframe border around the picture or artwork inside.", 105], "metal chain link fence": ["Yes. 'Metal chain link fence' has a tangible appearance and is a type of barrier or enclosure.\nA few things that are visually similar to 'metal chain link fence' but are not 'metal chain link fence' are:\tmetal mesh\tnetting\twire fencing\nThere are several useful visual features to tell there is 'metal chain link fence' and not similar things in a photo:\tdistinct diamond or rhombus shapes made by the chain links\tsilver or grey color\tmetallic or reflective surface\tinterlocking links in a continuous pattern", 105], "shoelaces": ["Yes, 'shoelaces' has a tangible appearance.\nA few things that are visually similar to 'shoelaces' but are not 'shoelaces' are:\tzippers, straps, cords, ropes, bands.\nThere are several useful visual features to distinguish 'shoelaces' from the listed similar things in a photo: long, flat, and thin in shape, specifically designed for securing shoes. The ends are usually sealed with plastic or metal tips, and they form loops on top of the shoes' surface.", 105], "straw hat": ["Yes. 'Straw hat' has a tangible appearance and is a kind of hat.\nA few things that are visually similar to 'straw hat' but are not 'straw hat' are:\tcowboy hat\tfedora\tbowler hat\tberet\nThere are several useful visual features to tell there is 'straw hat' and not similar things in a photo:\tstraw or woven material\tbrimmed hat with a wide brim\tlight-colored or natural brown\tcolorful ribbons or bands on hat", 105], "dumpster": ["Yes. 'Dumpster' has a tangible appearance and refers to a large metal waste container.\nA few things that are visually similar to 'dumpster' but are not 'dumpster' are:\ttrash can\tpetroleum tank\tshipping container\nThere are several useful visual features to tell there is 'dumpster' and not similar things in a photo:\tlarge metal container\twith two lids\topening for throwing trash away\tsquare or rectangular shape\twith wheels on the bottom", 105], "glare": ["Yes. 'Glare' has a tangible appearance and is a kind of bright or harsh light.\nA few things that are visually similar to 'glare' but are not 'glare' are:\treflection\tshadow\thighlights\tbrightness\nThere are several useful visual features to tell there is 'glare' and not similar things in a photo:\tintense brightness\torangish or yellowish tint\tobscures details in a photo or in real life\tspecific angles of reflection or incidence\tlight source outside of the field of view.", 105], "desert": ["Yes. 'Desert' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'desert' but are not 'desert' are:\tbeach\trocky terrain\tsnowy tundra\nThere are several useful visual features to tell there is 'desert' and not similar things in a photo:\textreme dryness and lack of humidity\tlimited vegetation, often consisting of cacti and succulents\tsandy or rocky terrain\twith large and vast dunes, rocks or mesas.", 105], "coaster": ["Yes. 'Coaster' has a tangible appearance and is a type of object that is used to protect surfaces from liquids or heat damage.\nA few things that are visually similar to 'coaster' but are not 'coaster' are:\tdecorative tiles\tdrink lid\tdecorative plate\tpotholder\nThere are several useful visual features to tell there is 'coaster' and not similar things in a photo:\tcircular shape\tsmall size\tmade of cork or absorbent material\tintended to be placed under a drink glass or mug to protect surfaces from moisture or heat damage.", 105], "colorful": ["No. 'Colorful' is too vague or abstract to be distinguished in a photo. It is a subjective concept that depends on individual perception.\nThere are no things that are visually similar to 'colorful' but are not 'colorful'.\nTherefore, there are no useful visual features to distinguish 'colorful' from other things in a photo.", 105], "railway line": ["Yes. 'Railway line' has a tangible appearance.\nA few things that are visually similar to 'railway line' but are not 'railway line' are:\troad\tpower lines\thiking trail\tcanals\nThere are several useful visual features to tell there is 'railway line' and not similar things in a photo:\tpair of metal rails\twooden or concrete sleepers\tballast rocks or gravel around the sleepers\tparallel straight tracks", 105], "meadow": ["Yes. 'Meadow' has a tangible appearance and is a type of grassland.\nA few things that are visually similar to 'meadow' but are not 'meadow' are:\tfarmland\tpark\tlawn\tsavannah\nThere are several useful visual features to tell there is 'meadow' and not similar things in a photo:\twide-open area\tcovered with grass, wildflowers and herbs\tgently rolling terrain\twithout trees or with scattered trees and shrubs", 105], "elbow pad": ["Yes. 'Elbow pad' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'elbow pad' but are not 'elbow pad' are:\tknee pad\tshin guard\tgloves\nThere are several useful visual features to tell there is 'elbow pad' and not similar things in a photo:\tcircular or oval shape\tfits snugly around the elbow\tpadded surface\tfor sports or safety use", 105], "reins": ["Yes. 'Reins' has a tangible appearance and refers to the straps used to control a horse or other animal.\nA few things that are visually similar to 'reins' but are not 'reins' are:\tleash\tbelt\tluggage strap\tcamera strap\nThere are several useful visual features to tell there are 'reins' and not similar things in a photo:\tconnected to a bit in the horse's mouth\theld by a rider\trequire two pieces, not one\tmade of leather or synthetic material", 104], "tennis balls": ["Yes. 'Tennis balls' has a tangible appearance and is a type of ball used in the sport of tennis.\nA few things that are visually similar to 'tennis balls' but are not 'tennis balls' are:\tjuggling balls\tbouncy balls\tdodgeball balls\nThere are several useful visual features to tell there is 'tennis balls' and not similar things in a photo:\tyellow or green felt cover\tslightly fuzzy surface\ta size between 2.5 and 2.7 inches (6.4 and 6.9 cm) in diameter\thollow rubber core\twith a distinctive seam around the equator", 104], "handle bars": ["Yes. 'Handle bars' has a tangible appearance and is a part of a bicycle or motorcycle.\nA few things that are visually similar to 'handle bars' but are not 'handle bars' are:\tdoor handles\tcabinet handles\thandrails\nThere are several useful visual features to tell there are 'handle bars' and not similar things in a photo:\ttwo distinct grips\ttogether, creating a shape like the letter 'U'\tor a shape like the letter 'Y'\tattached to a bicycle or motorcycle.", 104], "bundle": ["Yes. 'Bundle' has a tangible appearance and refers to a collection of items gathered together.\nA few things that are visually similar to 'bundle' but are not 'bundle' are:\tpile\tstack\tgroup\tpack\t\nThere are several useful visual features to tell there is 'bundle' and not similar things in a photo:\titems tied or wrapped together with string, rope, or paper\tbulky or awkward shape\tuniformity in size and shape of objects in the collection", 104], "slab": ["Yes. 'Slab' has a tangible appearance and is a flat and thick piece of material.\nA few things that are visually similar to 'slab' but are not 'slab' are:\ttile\tpaver\tbrick\tcounter\nThere are several useful visual features to tell there is 'slab' and not similar things in a photo:\tvery thick and heavy flat piece\toften made of concrete or stone\tsquare or rectangular shape", 104], "night sky": ["Yes. 'Night sky' has a tangible appearance and refers to the appearance of the sky at night.\nA few things that are visually similar to 'night sky' but are not 'night sky' are:\tblue ocean with waves\treflection on a dark surface\nThere are several useful visual features to tell there is 'night sky' and not similar things in a photo:\tdark background\tstars\torbiting planets\tmoon(s)\tcosmic phenomena (comets, meteor showers, etc.)", 104], "p": ["No. 'p' is too vague or abstract to have a tangible appearance that can be distinguished visually. \n\nHowever, for letters that have a visually concrete concept like 'A', 'B', 'C', etc. some things that are similar but not the letter can be:\tO \u2013 0,\tD \u2013 O,\tZ \u2013 2\n\nSome useful visual features for distinguishing letters are:\tthe number and shape of lines and curves used to make up the letter, if it is uppercase or lowercase, the font used, and the surrounding context.", 104], "claw": ["Yes. 'Claw' has a tangible appearance and is a type of appendage.\nA few things that are visually similar to 'claw' but are not 'claw' are:\tfingers\ttalons\tpaws\thands\nThere are several useful visual features to tell there is 'claw' and not similar things in a photo:\tsharp, curved tips\thinged or jointed structure, like a crab or lobster claw\tassociated with an animal that has a distinct body plan or shape, like a bird or a reptile", 104], "berry": ["Yes. 'Berry' has a tangible appearance and is a small, fleshy fruit.\nA few things that are visually similar to 'berry' but are not 'berry' are:\tcherry\tgrape\ttomato\nThere are several useful visual features to tell there is 'berry' and not similar things in a photo:\tsmall size\tround or oblong shape\tfleshy texture\tcontains seeds or small pits\tgrows on bushes, vines or trees\tranges in color from red, blue, purple, black and green.", 104], "divider": ["Yes. 'Divider' has a tangible appearance and is an object used to separate or divide space.\nA few things that are visually similar to 'divider' but are not 'divider' are:\tshelves\tbookcases\twalls\tscreens\nThere are several useful visual features to tell there is 'divider' and not similar things in a photo:\tupright position\trelatively thin in width or depth\tstanding alone\tstylistic design or pattern used for decoration", 104], "racks": ["Yes. 'Racks' has a tangible appearance and is a kind of furniture or structure.\nA few things that are visually similar to 'racks' but are not 'racks' are:\tshelves\thangers\tstands\tbars\nThere are several useful visual features to tell there is 'racks' and not similar things in a photo:\tlevelled surfaces\tfor holding or storing things\toften made of wood or metal", 103], "medicine cabinet": ["Yes. 'Medicine cabinet' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'medicine cabinet' but are not 'medicine cabinet' are:\tbathroom cabinet\tkitchen cabinet\tshoe cabinet\tfile cabinet\nThere are several useful visual features to tell there is 'medicine cabinet' and not similar things in a photo:\tcontains shelves or compartments for storing medicine\tor personal care items\tmirrored door\tor sides\toften mounted on a wall\tin a bathroom\tor near a sink \tor other personal care areas", 103], "ripe": ["No. 'Ripe' is too vague or abstract to be distinguished in a photo. It is a state of maturity or readiness that may not have specific visual characteristics. \n\nHowever, some things that are visually similar to 'ripe' but are not 'ripe' could be:\tunripe fruit\tgreen leaves\tnot-yet-blooming flowers", 103], "metal spoon": ["Yes. 'Metal spoon' has a tangible appearance and is a type of kitchen utensil.\nA few things that are visually similar to 'metal spoon' but are not 'metal spoon' are:\tfork\tknife\tteaspoon\ttray\nThere are several useful visual features to tell there is 'metal spoon' and not similar things in a photo:\thave a concave part to hold food\tlong and narrow\tsmall enough to fit in a mouth\tbe made from metal or have a metallic surface", 103], "jackets": ["Yes. 'Jackets' has a tangible appearance and is a type of clothing worn on the upper body.\nA few things that are visually similar to 'jackets' but are not 'jackets' are:\tshirts\tt-shirts\tblouses\tsweaters\tcoats\t\nThere are several useful visual features to tell there is 'jackets' and not similar things in a photo:\thave distinct sleeves\tzipper, buttons, or snaps\tsignificantly thicker than a shirt\ttends to be shorter in length\ttypically worn over other clothing items like shirts or sweaters", 103], "sea foam": ["Yes. 'Sea foam' has a tangible appearance and is the white, bubbly substance that forms on the surface of the ocean waves.\nA few things that are visually similar to 'sea foam' but are not 'sea foam' are:\tbubbles\tclouds\tsoap suds\tmilk froth\t\nThere are several useful visual features to tell there is 'sea foam' and not similar things in a photo:\tfound on beach or ocean surface\twhite, sometimes with yellow or brown tinges\tlightweight and airy\tmade up of small bubbles or spheres.", 103], "tire tracks": ["Yes. 'Tire tracks' has a tangible appearance and refers to the marks left by the tires on a surface.\nA few things that are visually similar to 'tire tracks' but are not 'tire tracks' are:\tprints left by a shoe or a footprints left by an animal\ttracks made by a bicycle or a motorcycle\nThere are several useful visual features to tell there are 'tire tracks' and not similar things in a photo:\trepeating patterns of lines or shapes\ttreads or patterns the tires make in the tracks\tstraight or curved lines in the tracks\tdistance between the tracks can help identify the type of vehicle that created it (e.g. car, truck, bike)", 103], "rectangular": ["Yes. 'Rectangular' has a visually concrete concept and refers to a shape that has four straight sides and four right angles.\nA few things that are visually similar to 'rectangular' but are not 'rectangular' are: square; parallelogram; trapezoid.\nThere are useful visual features to tell there is 'rectangular' and not similar things in a photo: long sides; a pair of opposite short sides; all corners are at right angles.", 102], "water faucet": ["Yes. 'Water faucet' has a tangible appearance and is a type of plumbing fixture. \nA few things that are visually similar to 'water faucet' but are not 'water faucet' are:\tsoap dispenser\tair freshener dispenser\tdispenser pump\n\nThere are several useful visual features to tell there is 'water faucet' and not similar things in a photo:\tlocated above a sink or basin\tmetallic or chrome finish\tstraight or curved spout\tknob or lever for controlling water flow", 102], "scarves": ["Yes. 'Scarves' has a tangible appearance and is a type of clothing/accessory.\nA few things that are visually similar to 'scarves' but not 'scarves' are: \t shawls\t wraps\t stoles\t blankets\nThere are several useful visual features to distinguish 'scarves' from the listed similar things in a photo: \t worn around the neck\t narrow and long\t made of lightweight fabric like silk, wool or cotton\t often patterned or colorful", 102], "controls": ["No. 'Controls' is too vague or abstract to have a tangible appearance in a photo.", 102], "sign board": ["Yes. 'Sign board' has a tangible appearance and is a type of board with a message or information displayed on it.\nA few things that are visually similar to 'sign board' but are not 'sign board' are: billboards, blackboards, notice boards, whiteboards\nThere are several useful visual features to tell there is 'sign board' and not similar things in a photo:\tusually rectangular or square\tsharing information, message, promotion or warning\tmaybe lit or unlit\tplaced in public places like streets, hotels, malls, or theaters, etc.", 102], "stainless steel refrigerator": ["Yes, 'stainless steel refrigerator' has a visually concrete concept and is a type of home appliance.\nA few things that are visually similar to 'stainless steel refrigerator' but are not 'stainless steel refrigerator' are:\tpainted metal refrigerator\tplastic refrigerator\twooden cupboard\t\nThere are several useful visual features to tell there is 'stainless steel refrigerator' and not similar things in a photo:\tsilvery metallic look\thandles, hinges, and locks\tinvisible cooling mechanism\tboxy shape with a door or multiple doors\tglass shelves or containers inside", 102], "front paws": ["Yes. 'Front paws' has a tangible appearance and is a part of an animal's body.\nA few things that are visually similar to 'front paws' but are not 'front paws' are:\tback paws\thands\tfeet\nThere are several useful visual features to tell there is 'front paws' and not similar things in a photo:\tshort and sturdy limbs\twith sharp claws or nails\tpads on the bottom of the paws\thair or fur on top of the paws\ttheir position at the front of an animal's body, close to the head.", 102], "latch": ["Yes. 'Latch' has a tangible appearance and is a type of locking mechanism.\nA few things that are visually similar to 'latch' but are not 'latch' are:\tlock\thandle\tknob\tbolt\thook\nThere are several useful visual features to tell there is 'latch' and not similar things in a photo:\tMetallic or plastic material, rectangular or round in shape, with a mechanism that can be pulled or turned to secure a door or gate.", 102], "screens": ["Yes. 'Screens' has a tangible appearance and refers to electronic displays or partitions.\nA few things that are visually similar to 'screens' but are not 'screens' are:\twindows\tdoors\tpartitions\tprojectors\nThere are several useful visual features to tell there is 'screens' and not similar things in a photo:\tlit up or displaying an image or video\trectangular shape\tflat surface\tphysical interface for interacting with a device", 102], "rock formation": ["Yes. 'Rock formation' has a tangible appearance and refers to a naturally occurring arrangement of rocks.\nA few things that are visually similar to 'rock formation' but are not 'rock formation' are:\tpile of rocks\tartificial stone wall\tconcrete blocks\tstack of books\nThere are several useful visual features to tell there is 'rock formation' and not similar things in a photo:\tnatural-looking arrangement of rocks\tin a mountain or hillside context\tdifferent shapes, sizes, and colors of rocks\tuniformity in the rock types and their arrangement.", 102], "signpost": ["Yes. 'Signpost' has a tangible appearance and is a type of guide or direction signage.\nA few things that are visually similar to 'signpost' but are not 'signpost' are:\ttree branch\tpost\tpole\tlamp post\nThere are several useful visual features to tell there is 'signpost' and not similar things in a photo:\tarrow-shaped sign\tdirectional information or words, such as \"exit\" or \"rest area\"\ttall post or structure\tusually found on the side of a road or a trail", 102], "brown dog": ["Yes. 'Brown dog' has a tangible appearance and is a specific type of animal.\nA few things that are visually similar to 'brown dog' but are not 'brown dog' are:\tbrown bear\tkangaroo\tmouse\trabbit\nThere are several useful visual features to tell there is 'brown dog' and not similar things in a photo:\tfour legs\tfurry body\tpointed ears\tsloping back\tbrown fur\tsnout and wet nose\twith or without tail (if there's a tail, it's usually wagging)", 102], "cuff": ["Yes. 'Cuff' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'cuff' but are not 'cuff' are:\tsleeve\them\tband\tbracelet\twatch strap\nThere are several useful visual features to tell there is 'cuff' and not similar things in a photo:\tstrip of fabric at the end of a sleeve or pant leg\tfolded or layered fabric\tsame material and color as the garment\tit may have buttons or decorative details", 102], "doorknob": ["Yes. 'Doorknob' has a tangible appearance and is a type of handle on a door.\nA few things that are visually similar to 'doorknob' but are not 'doorknob' are:\tdrawer knob\tcabinet handle\tshower faucet\tkitchen faucet\nThere are several useful visual features to tell there is 'doorknob' and not similar things in a photo:\tattached to a door\trounded shape\trotates to open the door\tfinish or color matches door or hardware", 101], "sign pole": ["Yes. 'Sign pole' has a tangible appearance and is a type of pole that holds signs.\nA few things that are visually similar to 'sign pole' but are not 'sign pole' are:\tstreet lamp\tparking meter\tbollards\tflag pole\nThere are several useful visual features to tell there is 'sign pole' and not similar things in a photo:\ttall and thin\tcylindrical shape\tmultiple signs attached to it\tarrow or directional signs on top of it\tusually placed at street corners, intersections or sidewalks.", 101], "tools": ["Yes. 'Tools' have a tangible appearance and are objects used for tasks or mechanical work.\nA few things that are visually similar to 'tools' but are not 'tools' are:\tcutlery\thousehold appliances\tgadgets\tmechanical toys\nThere are several useful visual features to tell there are 'tools' and not similar things in a photo:\tmetallic or sturdy material\tsharp or pointed edges or tips\thandle for grip and control\tvariety of shapes and sizes\tcommonly used in construction or repair work", 101], "kiwi": ["Yes. 'Kiwi' has a tangible appearance and is a kind of fruit.\nA few things that are visually similar to 'kiwi' but are not 'kiwi' are:\tlime\tgreen apple\tpapaya\tavocado\nThere are several useful visual features to tell there is 'kiwi' and not similar things in a photo:\tbrown, hairy exterior\toval shape\tgreen interior\twith small, black, edible seeds inside.", 101], "flip phone": ["Yes. 'Flip phone' has a tangible appearance and is a kind of mobile phone.\nA few things that are visually similar to 'flip phone' but are not 'flip phone' are:\tsmartphone\tcordless phone\tpager\twalkie-talkie\nThere are several useful visual features to tell there is 'flip phone' and not similar things in a photo:\tcompact and lightweight\tflip design with a hinge\ttwo halves of the phone screen when open\tor keypad on the bottom half\twhen closed, the screen and keypad are protected by the top half", 101], "round light": ["Yes. 'Round light' has a tangible appearance and is a specific type of light fixture.\nA few things that are visually similar to 'round light' but are not 'round light' are:\tlight bulb\tmoon\tsun\tlight disc\tplastic ball\nThere are several useful visual features to tell there is 'round light' and not similar things in a photo:\tcircular shape\tlight source in center\thalo or aura of light around center\tdiffused or direct light coming from the fixture", 101], "peak": ["Yes. 'Peak' has a tangible appearance and refers to the pointed top of a mountain or hill.\nA few things that are visually similar to 'peak' but are not 'peak' are:\tslope\thill\tridge\nThere are several useful visual features to tell there is 'peak' and not similar things in a photo:\tvery pointed or sharp top\thigher than the surrounding area\tsnow-covered or rocky\tsteep, sheer sides or cliffs", 101], "referee": ["Yes. 'Referee' has a tangible appearance and is a person who officiates in a sport.\nA few things that are visually similar to 'referee' but are not 'referee' are:\tcoach\tplayer\tstaff\nThere are several useful visual features to tell there is 'referee' and not similar things in a photo:\tstriped black-and-white shirt\twhistle\tflags or hand gestures to indicate calls and decisions\tstanding on the playing field or court.", 101], "wood desk": ["Yes. 'Wood desk' has a tangible appearance.\nA few things that are visually similar to 'wood desk' but are not 'wood desk' are:\ttable\tworkbench\tcounter\nThere are several useful visual features to tell there is 'wood desk' and not similar things in a photo:\tusually rectangular in shape\tmade of wood or with a wooden veneer\ttop surface for writing or working\tdrawers or shelves for storage\tcarved details or decorative embellishments", 101], "clock wall": ["Yes. 'Clock wall' has a tangible appearance and refers to a wall with multiple clocks as a decoration.\nA few things that are visually similar to 'clock wall' but are not 'clock wall' are:\tpicture wall\tshelving unit\tmirror wall\twith different objects mounted on it\nThere are several useful visual features to tell there is 'clock wall' and not similar things in a photo:\tmultiple clocks\thung on a wall\tin a row or a pattern\tfacing towards the viewer\twith varying styles and sizes", 101], "brown eye": ["Yes. 'brown eye' has a tangible appearance and is a specific type of eye color.\nThere are no things that are visually similar to 'brown eye' but are not 'brown eye'.\nThere are no useful visual features to distinguish 'brown eye' from anything else because it is a characteristic of eyes that can only be identified by the color of the iris.", 101], "shot": ["No. 'Shot' is too vague or abstract to have a visually concrete concept. It could refer to a photograph, a firing of a weapon, a small amount of liquid or medication injection. \nWithout proper context, it's impossible to distinguish 'shot' from visually similar things in a photo.", 101], "ivy": ["Yes. 'Ivy' has a tangible appearance and is a type of climbing plant.\nA few things that are visually similar to 'ivy' but are not 'ivy' are:\tcreeping fig\tpoison ivy\tjasmine\tvines\nThere are several useful visual features to tell there is 'ivy' and not similar things in a photo:\theart-shaped leaves\twith serrated edges\talternating leaves on the stem\tclimbing or creeping along a wall or tree\tusually green or variegated colors", 101], "dragon": ["Yes. 'Dragon' has a tangible appearance and is a mythological creature.\nA few things that are visually similar to 'dragon' but are not 'dragon' are:\tlizard\tsnake\tcrocodile\tdinosaur\tiguana\nThere are several useful visual features to tell there is 'dragon' and not similar things in a photo:\tfour legs or two legs and wings\tbreathing fire or smoke\tscales or armored skin\tpointy tail and sharp claws or talons\thorns or spikes on the head", 101], "basin": ["Yes. 'Basin' has a tangible appearance and is a type of container for holding water or other liquids.\nA few things that are visually similar to 'basin' but are not 'basin' are:\tbucket\tbowl\tsink\tpool\ttub\nThere are several useful visual features to tell there is 'basin' and not similar things in a photo:\twide and round shape\twith or without handles\tusually made of ceramic, metal, or plastic\tintended for holding water or other liquids\tdoes not have an outlet for draining the water on its own.", 101], "castle": ["Yes. 'Castle' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'castle' but are not 'castle' are:\tpalace\tmansion\tfortress\tmanor\tobservatory\nThere are several useful visual features to tell there is 'castle' and not similar things in a photo:\ttall walls or towers\tmoats or drawbridges\tcrenellations or battlements\tturrets, spires, or domes\thistorical or medieval aesthetic", 101], "round window": ["Yes. 'Round window' has a tangible appearance and is a kind of window.\nA few things that are visually similar to 'round window' but are not 'round window' are:\tcircular mirror\tcircular light fixture\tport-hole\teyes\nThere are several useful visual features to tell there is 'round window' and not similar things in a photo:\tcircular shape\tframe around the glass\twindow-like structure, with glass and surrounding material\tin a building or structure, not a standalone object", 100], "snowsuit": ["Yes. 'Snowsuit' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'snowsuit' but are not 'snowsuit' are:\tjumpsuit\tpajamas\twetsuit\tski jacket\nThere are several useful visual features to tell there is 'snowsuit' and not similar things in a photo:\tthick and padded material\tbuilt-in hood\tmatching pants and jacket\tbright colors (often red, blue or green)\tzippers and snaps to close tightly around the body\twaterproof or water-resistant material", 100], "diamond": ["Yes. 'Diamond' has a tangible appearance and is a type of precious stone.\nA few things that are visually similar to 'diamond' but are not 'diamond' are:\tquartz crystal\trock salt\tglass\nThere are several useful visual features to tell there is 'diamond' and not similar things in a photo:\tclear or translucent\thard and durable with a high Mohs hardness score\tbrilliant, sparkling and reflective appearance\twith a distinctive shape of a four-sided pyramid, flat top, and sharp edges", 100], "company name": ["No. 'Company name' is too vague or abstract to be distinguished in a photo.", 100], "earring": ["Yes. 'Earring' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'earring' but are not 'earring' are:\thairpin\tpiercing stud\tbobby pin\nThere are several useful visual features to tell there is 'earring' and not similar things in a photo:\thanging from the earlobe\tmetal or precious stones in the middle or dangling from the bottom\tclasp or post at the back of the earring.", 100], "ankle": ["Yes. 'Ankle' has a tangible appearance and is a joint between the foot and the leg.\nA few things that are visually similar to 'ankle' but are not 'ankle' are:\twrist\telbow\tknee\tshoulder\thip\nThere are several useful visual features to tell there is 'ankle' and not similar things in a photo:\tthe joint connecting the lower leg (tibia and fibula) and the foot\tbony protrusions on either side of the ankle\tGently curved shape of the bone and the heel", 100], "metal handle": ["Yes. 'Metal handle' has a tangible appearance and is a kind of object.\nA few things that are visually similar to 'metal handle' but are not 'metal handle' are:\tKnob\tShell Door Handle\tPull Handle\tWooden Handle\nThere are several useful visual features to tell there is 'metal handle' and not similar things in a photo:\tMetallic\tconspicuous\tShiny\tGrip\tLength and shape of the handle.", 99], "metal fencing": ["Yes. 'Metal fencing' has a tangible appearance and is a type of barrier.\nA few things that are visually similar to 'metal fencing' but are not 'metal fencing' are:\twooden fence\tbamboo fence\tnetting\tbarbed wire\nThere are several useful visual features to tell there is 'metal fencing' and not similar things in a photo:\tmade of metal\tvisible bars or wires\tregular or rectangular patterns\tsilver or gray color", 99], "bottom half": ["Yes. 'Bottom half' has a tangible appearance and typically refers to the lower part of the human body.\nA few things that are visually similar to 'bottom half' but are not 'bottom half' are:\tupper half of the body\thalf of an object\ta close-up of someone's legs or shoes\ta cropped or zoomed-in photo of a person's torso\nThere are several useful visual features to tell there is 'bottom half' and not similar things in a photo:\tthe lower half of a person's body, including hips, legs, and feet\tthe clothing or shoes worn in the lower half of the body\tthe shape and size of the lower half of the body compared to the upper half of the body the posture, stance, or physical activity depicted in the photo", 99], "glass container": ["Yes. 'Glass container' has a tangible appearance and is a type of container made of glass.\nA few things that are visually similar to 'glass container' but are not 'glass container' are:\tplastic container\tmetal container\tceramic container\twater bottle\nThere are several useful visual features to tell there is 'glass container' and not similar things in a photo:\tclear or transparent material\tsmooth and shiny surface\thard and brittle material\tlack of visible seams or joints\ttypically cylindrical or rectangular in shape \thas a lid or cap", 99], "remotes": ["Yes. 'Remotes' has a tangible appearance and is an electronic device used to control a device from a distance.\nA few things that are visually similar to 'remotes' but are not 'remotes' are:\tphones\twatches\ttablet computers\tlaptops\nThere are several useful visual features to tell there is 'remotes' and not similar things in a photo:\tbutton controls\tinfrared window\tdisplay screen size and shape\tdevice-specific buttons and labels (e.g. 'volume', 'channel')", 99], "characters": ["No. 'Characters' is too vague or abstract to be distinguished in a photo. However, if referring to characters as in fictional or recognizable individuals, then the answer would be yes. \n\nA few things that are visually similar to fictional characters but are not them could be: people in costumes, cosplayers, or individuals wearing theatrical makeup. \n\nUseful visual features to distinguish fictional characters from similar things in a photo would depend on the character's unique traits and appearance, such as specific clothing or accessories, distinct physical features, and recognizable expressions or poses.", 99], "flops": ["Yes. 'Flops' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'flops' but are not 'flops' are:\tslippers\tsandals\tshoes\nThere are several useful visual features to tell there is 'flops' and not similar things in a photo:\tthin and flat sole with no heel\tstrap that goes over the foot and between the big toe and the second toe\topen toe design\tflexibility and lightweight material (such as rubber or foam)", 99], "pigeons": ["Yes. 'Pigeons' has a tangible appearance and is a kind of bird.\nA few things that are visually similar to 'pigeons' but are not 'pigeons' are:\tdoves\tsparrows\tfinches\troadrunners\nThere are several useful visual features to tell there is 'pigeons' and not similar things in a photo:\tlarger than a sparrow, but smaller than a crow\tsmooth feathers in shades of blue, grey, green, and brown\tbold, bright eyes and beaks\tthat pigeon-like bobbing walk", 99], "tattoos": ["Yes. 'Tattoos' has a tangible appearance and refers to permanent ink designs on the skin.\nA few things that are visually similar to 'tattoos' but are not 'tattoos' are:\tstickers\tmakeup\tpaint\nThere are several useful visual features to tell there is 'tattoos' and not similar things in a photo:\tpermanent ink design on skin\tusually black or colored\tdetailed and intricate designs\tcan cover a large or small area of skin\tvariety of styles and symbols", 99], "orange flowers": ["Yes. 'Orange flowers' has a tangible appearance and is a specific type of flower.\nA few things that are visually similar to 'orange flowers' but are not 'orange flowers' are:\tyellow flowers\tred flowers\tmulticolored flowers\torange fruit\nThere are several useful visual features to tell there is 'orange flowers' and not similar things in a photo:\torange petals\tgreen stems and leaves\tpetal arrangement (ex. daisy, rose, tulip)\tpetal shape (ex. round, pointed, curved)", 99], "left": ["No. 'Left' is too vague or abstract to be distinguished in a photo. It is a relative directional concept and can vary depending on one's orientation and position.", 98], "baseball pants": ["Yes. 'Baseball pants' has a tangible appearance and is a type of sportswear.\nA few things that are visually similar to 'baseball pants' but are not 'baseball pants' are:\tyoga pants\tleggings\tjeans\ttrousers\t\nThere are several useful visual features to tell there is 'baseball pants' and not similar things in a photo:\tworn by baseball players\tmade of lightweight, breathable fabric\ttapered design with elastic or zippered cuffs\tmay have stripes or other decorative elements", 98], "business": ["No. 'Business' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to 'business' but are not 'business' are: \n- Group of people working together \n- People in formal attire \n- A busy office setting \n\nUseful visual features for distinguishing 'business' from the listed similar things in a photo would not be applicable as 'business' itself cannot be visually identified.", 98], "cds": ["Yes. 'CDs' has a tangible appearance and is a type of disc used for storing digital data.\nA few things that are visually similar to 'CDs' but are not 'CDs' are: DVDs, Blu-rays, vinyl records, frisbees.\nThere are several useful visual features to tell there is 'CDs' and not similar things in a photo:\tcircular shape\tsmall size\tsilver or gold reflective surface\tnon-existent grooves or tracks\ton a spindle or stored in a jewel case or paper sleeve.", 98], "blue bucket": ["Yes. 'Blue bucket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'blue bucket' but are not 'blue bucket' are:\tblue trash can\tblue vase\tblue cooler\tblue stool\nThere are several useful visual features to tell there is 'blue bucket' and not similar things in a photo:\tcylindrical shape\twith a handle and a spout or protrusion\tblue color\tplastic or metal material", 98], "hearts": ["Yes. 'Hearts' has a tangible appearance and is a symbol representing love and affection.\nA few things that are visually similar to 'hearts' but are not 'hearts' are:\tdiamonds\tspades\tclubs\tmushrooms\nThere are several useful visual features to tell there is 'hearts' and not similar things in a photo:\tround shape\twith a cleft at the top\tbulging sides\tcurved base\tcolor red or pink\temotional and romantic connotation.", 98], "gray wall": ["Yes. 'Gray wall' has a tangible appearance and is a physical structure.\nA few things that are visually similar to 'gray wall' but are not 'gray wall' are:\tgray surface painted on a canvas\tor a sheet of paper\nThere are several useful visual features to tell there is 'gray wall' and not similar things in a photo:\tsolid, three-dimensional structure\tmade of real building materials, such as bricks or stones\thas texture and depth, not just a flat surface.", 98], "litter": ["Yes. 'Litter' has a tangible appearance and refers to waste or trash that is improperly disposed of.\nA few things that are visually similar to 'litter' but are not 'litter' are: fallen leaves, sticks or debris on the ground.\nThere are several useful visual features to tell there is 'litter' and not similar things in a photo: man-made objects and materials (e.g. plastic bags, paper cups), items that are out of place in the environment (e.g. chewing gum on the sidewalk), multiple pieces of trash in a concentrated area.", 98], "fluffy": ["No. 'Fluffy' is too vague or abstract to be visually distinguished in a photo. \n\nHowever, here are a few things that might be described as 'fluffy':\n- cotton candy\n- a feather boa\n- a teddy bear\n\nUseful visual features for distinguishing these things might include:\n- The light, airy texture of cotton candy\n- The long, slender shape of a feather boa\n- The cuddly, stuffed appearance of a teddy bear", 98], "traffic signals": ["Yes. 'Traffic signals' has a tangible appearance and refers to the devices that control traffic at intersections.\nA few things that are visually similar to 'traffic signals' but are not 'traffic signals' are:\tstreet lights\tcrosswalk signals\tbuilding signs\nThere are several useful visual features to tell there are 'traffic signals' and not similar things in a photo:\t\nthree lights - red, yellow and green (vertical or horizontal arrangement)\tblack and gray metal pole\tbrightness of the colors\tlighted numbers for countdown\ttimer display to cross intersections\tfor pedestrian or vehicular traffic control", 98], "skyline": ["Yes. 'Skyline' has a tangible appearance and refers to the outline of buildings and structures against the sky.\nA few things that are visually similar to 'skyline' but are not 'skyline' are:\tmountains\thills\tforests\t\nThere are several useful visual features to tell there is 'skyline' and not similar things in a photo:\tgroups of buildings or structures\tsilhouettes against a sky background\tman-made structures (skyscrapers, bridges, monuments, etc.)", 98], "television screen": ["Yes, 'television screen' has a visually concrete concept, it refers to the physical display screen of a television set.\nA few things that are visually similar to 'television screen' but are not 'television screen' are:\tmobile phone screen, laptop/PC screen, digital billboard, A/V projector screen.\nThere are several useful visual features that can be used to distinguish a 'television screen' from these similar objects in a photo: rectilinear shape, aspect ratio, bezel size, and physical buttons located around the screen.", 98], "flame": ["Yes. 'Flame' has a tangible appearance and is a type of fire.\nA few things that are visually similar to 'flame' but are not 'flame' are:\tsunrise\tsunset\tcampfires\tlava\tlights\nThere are several useful visual features to tell there is 'flame' and not similar things in a photo:\ttapering shape\tof different colors (yellow, orange, red)\tflickering or dancing movement\twarmth or heat emitting from the object", 98], "metal grate": ["Yes. 'Metal grate' has a tangible appearance and is a type of metal mesh or grid.\nA few things that are visually similar to 'metal grate' but are not 'metal grate' are:\twire mesh\tfence\tscreen door\nThere are several useful visual features to tell there is 'metal grate' and not similar things in a photo:\tsquare or rectangular shapes\tmetallic or gray color\tgrid-like pattern with regularly spaced bars\tnarrow openings or gaps between bars", 98], "tall tower": ["Yes. 'Tall tower' has a tangible appearance and can refer to a high-rise building, a tower or a mast.\nA few things that are visually similar to 'tall tower' but are not 'tall tower' are:\tbridge\tchimney\tpillar\nThere are several useful visual features to tell there is 'tall tower' and not similar things in a photo:\t\n- Height: A tall tower is considerably taller than other structures in the surrounding area.\n- Symmetry: Many tall towers are designed with a distinct symmetry, such as identical floors, windows, or other design elements that repeat throughout the structure.\n- Function: Tall towers often have a specific function, such as broadcasting signals or providing living or office space.", 97], "dryer": ["Yes. 'Dryer' has a tangible appearance and is a common household appliance.\nA few things that are visually similar to 'dryer' but are not 'dryer' are:\twashing machine\tdishwasher\toven\tmicrowave\nThere are several useful visual features to tell there is 'dryer' and not similar things in a photo:\tcylindrical shape\twith a door near the front\tcontrols for heat and time\tsettings for different fabrics\tand a vent on the back or side.", 97], "parts": ["No. 'Parts' is too vague or abstract to be distinguished in a photo. It could mean components of different things, and each part may have a unique appearance. \n\nTherefore, it is difficult to name something visually similar to 'parts' but not 'parts,' as it depends on the context in which the term is used.", 97], "peaches": ["Yes. 'Peaches' has a tangible appearance, and they are a type of fruit.\nA few things that are visually similar to 'peaches' but are not 'peaches' are:\tapricots\tplums\toranges\tmangoes\nThere are several useful visual features to tell there is 'peaches' and not similar things in a photo:\tround shape\twith a crease on one side\tfuzzy skin\tlight orange to pinkish color with a red blush\tfuzzy stem\tsoft when ripe", 97], "picket fence": ["Yes. 'Picket fence' has a tangible appearance and refers to a type of fence made of wooden stakes.\nA few things that are visually similar to 'picket fence' but are not 'picket fence' are:\tiron fence\thedge\tstone wall\nThere are several useful visual features to tell there is 'picket fence' and not similar things in a photo:\twhite or light-colored\tflat-topped, pointed stakes with a gap between them.", 97], "athlete": ["Yes. 'Athlete' has a tangible appearance and is a person who participates in sports or athletic activities.\nA few things that are visually similar to 'athlete' but are not 'athlete' are:\tspectator\tcoach\tsports equipment\tstadium\nThere are several useful visual features to tell there is 'athlete' and not similar things in a photo:\tathletic build and physique\twearing sports clothing, such as shorts or jersey\tperforming physical activity or sport\tin various locations, such as a track, field, or court", 97], "lace": ["Yes. 'Lace' has a tangible appearance and is a type of delicate fabric.\nA few things that are visually similar to 'lace' but are not 'lace' are:\tcrochet\tdoily\tembroidery\tknitting\ttulle\nThere are several useful visual features to tell there is 'lace' and not similar things in a photo:\tdelicate fabric with intricate patterns or web-like designs\tusually made of cotton, silk, or linen\toften used in clothing or as a decoration on furniture or for weddings/European dresses\tsheer or transparent appearance.", 97], "flower design": ["Yes. 'Flower design' has a tangible appearance and is a pattern featuring floral elements.\nA few things that are visually similar to 'flower design' but are not 'flower design' are:\tFoliage design\tFruit design\tAnimal design\tGeometric design\nThere are several useful visual features to tell there is 'flower design' and not similar things in a photo:\tdepiction of flowers or floral patterns\tintricate and highly detailed surface design\tsymmetrical design\tsimilar colors to actual flowers (pink, yellow, red, etc.)", 97], "wineglass": ["Yes, 'wineglass' is a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'wineglass' but are not 'wineglass' are:\tbeer mug\tvase\tjar\tbottle\tteacup\nThere are several useful visual features to tell there is 'wineglass' and not similar things in a photo:\t\nnarrow stem\t\nbowl-shaped\t\nopen-mouth\t\nclear glass\t\ndesigned to hold wine", 97], "blind": ["No. 'Blind' is too vague or abstract to be distinguished in a photo. However, some objects related to blindness can be visually concrete, such as a white cane or a guide dog.\nA few things that are visually similar to 'blind' but are not 'blind' are:\tclosed eyes\tdark glasses\ttinted windows\nThere are several useful visual features to tell there is 'blind' and not similar things in a photo:\tusing a white cane or a guide dog\thaving a sighted guide\twhile walking\thaving a Braille book or other special tools to access information about the environment", 97], "bed cover": ["Yes. 'Bed cover' has a tangible appearance and refers to any material that is placed over a bed for decoration or comfort.\nA few things that are visually similar to 'bed cover' but are not 'bed cover' are:\tthrow pillows\tblankets\tsheets\tquilts\nThere are several useful visual features to distinguish 'bed cover' from similar things in a photo:\tcovers the entire bed\tmatches the decor of the room\tusually made of a different material than sheets or blankets may have a decorative pattern or design", 97], "trick": ["No. 'Trick' is too vague or abstract to be distinguished in a photo. \n\nNote: 'Trick' can be a physical action or a mental concept, but it cannot be visually captured on its own without further context or explanation.", 97], "beams": ["Yes. 'Beams' has a tangible appearance and refers to a long, sturdy piece of wood or metal used for support.\nA few things that are visually similar to 'beams' but are not 'beams' are:\tplanks\tpoles\tscaffolding\nThere are several useful visual features to tell there is 'beams' and not similar things in a photo:\tlong and thick\tsturdy material\tfixed horizontally or vertically\tto provide support for structures or buildings.", 97], "trash cans": ["Yes. 'Trash cans' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'trash cans' but are not 'trash cans' are:\tbuckets\tplastic storage containers\tbarrels\nThere are several useful visual features to tell there is 'trash cans' and not similar things in a photo:\tmade of metal or plastic\tRectangular or circular opening\ton wheels or stationary\thandle on the top or sides\tlabelled with trash or recycling", 97], "trashcan": ["Yes. 'Trashcan' has a tangible appearance and is an object for waste disposal.\nA few things that are visually similar to 'trashcan' but are not 'trashcan' are:\tbarrel\tbucket\tbasket\tvase\nThere are several useful visual features to tell there is 'trashcan' and not similar things in a photo:\tlarge enough to hold garbage\toften made of plastic or metal\ta lid or a cover for closing\tthe word \"trash\" or \"garbage\" written on it", 97], "dirt brown": ["Yes. 'Dirt brown' has a tangible appearance and is a color.\nA few things that are visually similar to 'dirt brown' but are not 'dirt brown' are:\twood\tbark\tfur\thair\tleather\tchocolate\nThere are several useful visual features to tell there is 'dirt brown' and not similar things in a photo:\tearthy brown color\tgritty or rough texture\tsimilar look to soil or mud", 97], "square window": ["Yes. 'Square window' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'square window' but are not 'square window' are:\trectangular window\tcircular window\tarched window\tpicture frame\nThere are several useful visual features to distinguish 'square window' from the listed similar things in a photo:\tequal length and width on all sides of the frame\t90-degree angles at the corners\tclarity or transparency of the glass or material used for the window.", 96], "orange fruit": ["Yes. 'Orange fruit' has a tangible appearance and is a type of citrus fruit.\nA few things that are visually similar to 'orange fruit' but are not 'orange fruit' are:\tlemons\tlimes\tgrapefruits\tpumpkins\nThere are several useful visual features to tell there is 'orange fruit' and not similar things in a photo:\tround orange fruit\twith a dimple at the top\tsometimes with a green leaf\ton an orange tree", 96], "donkey": ["Yes. 'Donkey' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'donkey' but are not 'donkey' are:\thorse\tzebra\tmule\nThere are several useful visual features to tell there is 'donkey' and not similar things in a photo:\tpointy ears with long fur at the tip\tgray or brown fur\tthick body and short legs\tmane and tail that are less voluminous than horses' \thave a cross at their back shoulder area called the dorsal stripe.", 96], "plastic bags": ["Yes. 'Plastic bags' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'plastic bags' but are not 'plastic bags' are:\tpaper bags\tfabric bags\tenvelopes\twrappers\nThere are several useful visual features to tell there is 'plastic bags' and not similar things in a photo:\tthin and transparent or opaque material\tplastic texture\thandles on top\tlarge opening", 96], "traffic signs": ["Yes. 'Traffic signs' have a tangible appearance and are a type of sign used for regulating traffic.\nA few things that are visually similar to 'traffic signs' but are not 'traffic signs' are:\tbillboards\tstore signs\tstreet names\tneon signs\nThere are several useful visual features to tell there is 'traffic signs' and not similar things in a photo:\tgeometric shapes and symbols\tstandardized colors (red, yellow, green, white, blue, black)\tclear and concise messages (e.g. stop, yield, speed limit)\tmounted on poles\tor affixed to buildings or traffic lights", 96], "metal utensil": ["Yes. 'Metal utensil' has a tangible appearance and is a kind of kitchen tool.\nA few things that are visually similar to 'metal utensil' but are not 'metal utensil' are:\tplastic utensils\twooden spoons\tglass stirrers\nThere are several useful visual features to tell there is 'metal utensil' and not similar things in a photo:\tmade of metal(silver, stainless steel or iron)\tspoon, fork or knife-shaped\tserving or cooking food\tpresent in a kitchen", 96], "leafless trees": ["Yes. 'Leafless trees' has a tangible appearance and refers to trees without any leaves.\nA few things that are visually similar to 'leafless trees' but are not 'leafless trees' are: Dead trees, thin trees with few leaves, trees in winter.\nThere are several useful visual features to tell there is 'leafless trees' and not similar things in a photo:\tthick branches and trunks without leaves\tdifferent branch patterns\tand bark\tcolor (usually brown and gray)", 96], "lawn chair": ["Yes. 'Lawn chair' has a tangible appearance and is a type of outdoor furniture.\nA few things that are visually similar to 'lawn chair' but are not 'lawn chair' are:\tdeck chair\tfolding chair\tcamping chair\tstool\nThere are several useful visual features to tell there is 'lawn chair' and not similar things in a photo:\tconsists of a long seat and a backrest\tmade of lightweight materials, such as aluminum or plastic\toften foldable, easy to store and transport\tusually has armrests and/or a cup holder\tsuitable for outdoor use, such as in a garden or at the beach.", 96], "wood post": ["Yes. 'Wood post' has a tangible appearance and is a type of wooden object.\nA few things that are visually similar to 'wood post' but are not 'wood post' are:\ttree trunk\tfence\tpost-it notes\tcolumn\nThere are several useful visual features to tell there is 'wood post' and not similar things in a photo:\tvertical wooden beam\tpointed or squared top\tpossibly with horizontal planks\tnatural wood grain or texture", 96], "tan sand": ["Yes. 'Tan sand' has a tangible appearance and is a type of sand with a tan color.\nA few things that are visually similar to 'tan sand' but are not 'tan sand' are:\tdirt,\tpebbles,\tconcrete,\tfloor tiles\nThere are several useful visual features to tell there is 'tan sand' and not similar things in a photo:\tsoft and fine-grained\tloose and shifting texture\tlight brown or beige\ttend to be found on beaches or deserts", 96], "back wheel": ["Yes. 'Back wheel' has a tangible appearance and is a specific part of a bike or a vehicle.\nA few things that are visually similar to 'back wheel' but are not 'back wheel' are:\tfront wheel\tpulley or gear\ttire\nThere are several useful visual features to tell there is 'back wheel' and not similar things in a photo:\tlocated at the back of a bike or a vehicle\tlarger than the front wheel\tthicker and heavier than a pulley or a gear\tattached to a chain or a transmission system", 96], "brown rock": ["Yes. 'Brown rock' has a tangible appearance.\nA few things that are visually similar to 'brown rock' but are not 'brown rock' are:\tdead leaves\tbark of a tree\tdry soil\nThere are several useful visual features to tell there is 'brown rock' and not similar things in a photo:\tsolid and hard texture\tuniform color\tgeometric or organic shape", 96], "roots": ["Yes. 'Roots' has a tangible appearance and is a part of a plant.\nA few things that are visually similar to 'roots' but are not 'roots' are: branches, leaves, stems, flowers\nThere are several useful visual features to tell there are 'roots' and not similar things in a photo: underground or partially underground elongated structures, often narrow in shape, web-like structures, different color or texture compared to above-ground plant parts, no leaves or buds on them.", 96], "wiper": ["Yes. 'Wiper' has a tangible appearance and is a mechanical device used for cleaning.\nA few things that are visually similar to 'wiper' but are not 'wiper' are:\tbrush\tsponge\tcloth\tbroom\nThere are several useful visual features to tell there is 'wiper' and not similar things in a photo:\trectangular or curved shape\tmetallic or rubbery material\ttwo sections for wiping and clearing\twiping and clearing motions seen in action", 96], "pig": ["Yes. 'Pig' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'pig' but are not 'pig' are:\thog\twarthog\tjavelina\tboar\nThere are several useful visual features to tell there is 'pig' and not similar things in a photo:\tround body shape\twith a snout\tfour legs\thooves\tpink or brown skin\tcurled tail\tEars with long hairs.", 96], "dry": ["No. 'Dry' is too vague or abstract to be distinguished in a photo.", 96], "side door": ["Yes. 'Side door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'side door' but are not 'side door' are:\tmain door\tgarage door\tshed door\nThere are several useful visual features to tell there is 'side door' and not similar things in a photo:\tsmaller than a main door located on the side of a building\thinge on one side of the door\thandles and locks next to each other", 96], "year": ["No. 'Year' is too vague or abstract to be distinguished in a photo.", 96], "spectacle": ["No. 'Spectacle' is too vague or abstract to be distinguished in a photo.", 95], "tan wall": ["Yes. 'Tan wall' has a tangible appearance and is a kind of wall with a specific color.\nA few things that are visually similar to 'tan wall' but are not 'tan wall' are:\tsandstone\tmasonry\tgolden beach sand\twood paneling\nThere are several useful visual features to tell there is 'tan wall' and not similar things in a photo:\ttan, beige or light brown color\tsmooth surface or textured surface that looks like a wall\trectangular shape that extends from the floor to the ceiling", 95], "water body": ["Yes. 'Water body' has a tangible appearance and refers to any significant accumulation of water.\nA few things that are visually similar to 'water body' but are not 'water body' are:\tpond\tlake\tocean\tswimming pool\tdecorative fountain\nThere are several useful visual features to tell there is 'water body' and not similar things in a photo:\treflective surface\twaves or ripples\tshoreline or a surrounding area\tnatural or artificial source of water\tclear or blue color of the water", 95], "bath towel": ["Yes. 'Bath towel' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'bath towel' but are not 'bath towel' are:\thand towel\tdish towel\twashcloth\tnapkin\t\nThere are several useful visual features to tell there is 'bath towel' and not similar things in a photo:\tlarge size compared to hand towels or washcloths\trectangular shape\tsolid or patterned designs\tfluffy or absorbent texture\tmay be folded or draped over a bar or a hook.", 95], "shoulders": ["Yes. 'Shoulders' has a tangible appearance and is a part of human body.\nA few things that are visually similar to 'shoulders' but are not 'shoulders' are:\thills\tmountains\tbumps\tboulders\nThere are several useful visual features to tell there are 'shoulders' and not similar things in a photo:\thuman arms and the area where they meet the torso\tcan be bare or covered with clothing or accessories, such as a shoulder pad", 95], "wall clock": ["Yes. 'Wall clock' has a tangible appearance and is a type of clock designed to hang on a wall.\nA few things that are visually similar to 'wall clock' but are not 'wall clock' are:\twrist watch\talarm clock\tcountdown timer\nThere are several useful visual features to tell there is 'wall clock' and not similar things in a photo:\tcircular shape\thands\tdigits or numbers representing time\thanging on a wall or mounted on a surface", 95], "street pole": ["Yes. 'Street pole' has a tangible appearance and is an object that can be found on a street.\nA few things that are visually similar to 'street pole' but are not 'street pole' are:\tsignpost\tflagpole\tlamp post\tparking meter\nThere are several useful visual features to tell there is 'street pole' and not similar things in a photo:\ttall, slender metal pole\tsquare or round base\twith one or multiple arms or fixtures\tinstalled on or alongside street or sidewalk", 95], "batters": ["Yes. 'Batters' has a tangible appearance and refers to the players in the game of baseball who are hitting the ball.\nA few things that are visually similar to 'batters' but are not 'batters' are:\tfielders\tpitchers\tumpires\tcrowd\nThere are several useful visual features to tell there are 'batters' and not similar things in a photo:\twearing a uniform\tholding a bat in their hands\tstanding near home plate, ready to hit the ball\tfacing the pitcher", 95], "cape": ["Yes. 'Cape' has a tangible appearance and refers to a type of clothing.\nA few things that are visually similar to 'cape' but are not 'cape' are:\tponcho\tshawl\thooded cloak\tjacket\nThere are several useful visual features to tell there is 'cape' and not similar things in a photo:\topen in the front\tattached to the neck area\tlong fabric that hangs down on the back or sides\tmay have a hood or be sleeveless", 95], "lipstick": ["Yes. 'Lipstick' has a tangible appearance and is a kind of makeup product.\nA few things that are visually similar to 'lipstick' but are not 'lipstick' are:\tchapstick\tlip balm\tlip gloss\tcrushed berries\nThere are several useful visual features to tell there is 'lipstick' and not similar things in a photo:\tsleek, cylindrical shape\tvibrant colors\tmatte or shiny finish\tapplication marks on lips", 95], "metal chain": ["Yes. 'Metal chain' has a tangible appearance and is a type of fastening device.\nA few things that are visually similar to 'metal chain' but are not 'metal chain' are:\t\nwire\t\nrope\t\nnecklace\t\nbelt\t\nzipper\t\nThere are several useful visual features to tell there is 'metal chain' and not similar things in a photo:\tmade of metal links or rings\tjointed or connected to form a continuous line\tused for fastening or securing objects together.", 95], "cowboy": ["Yes. 'Cowboy' has a tangible appearance and is a type of person.\nA few things that are visually similar to 'cowboy' but are not 'cowboy' are:\t farmer\trancher\tbiker\nThere are several useful visual features to tell there is 'cowboy' and not similar things in a photo:\t cowboy hat\tboots\twith spurs\tdenim or leather clothing \twestern-style belt\tbandana\tlooped lasso in the hand\tor ride a horse", 95], "wrinkle": ["Yes. 'Wrinkle' has a tangible appearance and refers to a crease, line or fold in the skin or fabric.\nA few things that are visually similar to 'wrinkle' but are not 'wrinkle' are:\tshadows\tlines\tpatterns\ton fabric or paper\nThere are several useful visual features to tell there is 'wrinkle' and not similar things in a photo:\tcrease or fold in the skin or fabric\tvisible texture\tor roughness\tinconsistent or uneven surface\tformed by pressure, age or movement.", 95], "bushy": ["Yes. 'Bushy' has a tangible appearance and refers to something that is thick and covered in a lot of foliage.\nA few things that are visually similar to 'bushy' but are not 'bushy' are:\thairy\tfuzzy\tfluffy\nThere are several useful visual features to tell there is 'bushy' and not similar things in a photo:\tlots of foliage\tthick and dense appearance\tbulky and voluminous appearance\ttangled and unkempt appearance", 95], "canal": ["Yes. 'Canal' has a tangible appearance and typically refers to a man-made waterway.\nA few things that are visually similar to 'canal' but are not 'canal' are:\triver\tstream\tditch\tchannel\nThere are several useful visual features to tell there is 'canal' and not similar things in a photo:\tman-made\twater is calm or slow-moving\tbanked by walls or paths\tusually straight (not winding like a river)\tmay contain boats or barges.", 95], "eyelashes": ["Yes. 'Eyelashes' has a tangible appearance and is a part of the human eye.\nA few things that are visually similar to 'eyelashes' but are not 'eyelashes' are:\thair\tfur\tbristles\nThere are several useful visual features to tell there are 'eyelashes' and not similar things in a photo:\tlocated on the edge of eyelids\tthinner and shorter than other types of hair\tcurved shape\tdarker color\tcombed appearance when styled", 95], "rainbow": ["Yes. 'Rainbow' has a tangible appearance and is a meteorological phenomenon.\nA few things that are visually similar to 'rainbow' but are not 'rainbow' are:\tcolorful paint stripes\tmulticolored tapestry\nThere are several useful visual features to tell there is 'rainbow' and not similar things in a photo:\tarc-shaped\tbands of color in the order of red, orange, yellow, green, blue, indigo, and violet\ttranslucent appearance\twith an outline in the sky following a rain shower or other precipitation event.", 95], "turn": ["No. 'Turn' is too vague or abstract to be distinguished in a photo.", 95], "brown shoes": ["Yes. 'Brown shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'brown shoes' but are not 'brown shoes' are:\tlblack shoes\tflip flops\trunning shoes\tboots\nThere are several useful visual features to tell there is 'brown shoes' and not similar things in a photo:\tshoe shape with a closed toe and heel\tleather or suede material\tbrown or tan color\tclose-to-the-ground appearance\tlaces or straps for securing to the foot", 95], "rear window": ["Yes. 'Rear window' has a tangible appearance and refers to the back window of a vehicle or a building.\nA few things that are visually similar to 'rear window' but are not 'rear window' are:\tfront window\tside window\tsliding door\tmirror\nThere are several useful visual features to distinguish 'rear window' from the listed similar things in a photo:\tpositioned in the back of a vehicle or building\tlarger than a regular side window\tcould have a defrost mechanism\tor a wiper\tfor a building, could be adjacent to a fire escape", 95], "knife block": ["Yes. 'Knife block' has a tangible appearance and is a type of kitchen accessory.\nA few things that are visually similar to 'knife block' but are not 'knife block' are:\tcutlery holder\tpen holder\tutensil holder\nThere are several useful visual features to tell there is 'knife block' and not similar things in a photo:\trectangular or square shape\tvertical slots for knives\tvariety of slot sizes\tcapacity to hold multiple knives\toften made of wood or plastic", 94], "light poles": ["Yes. 'Light poles' has a tangible appearance and is a physical object used for illumination.\nA few things that are visually similar to 'light poles' but are not 'light poles' are:\ttrees\tsign posts\ttraffic cones\tflag poles\nThere are several useful visual features to tell there are 'light poles' and not similar things in a photo:\ttall and cylindrical shape\tpower cables or fixtures\thanging lights or lamp bulbs\tmounted on a base or platform", 94], "computer speaker": ["Yes. 'Computer speaker' has a tangible appearance and is an electronic device used for producing sound from a computer.\nA few things that are visually similar to 'computer speaker' but are not 'computer speaker' are:\tbluetooth speaker\tportable speaker\tsoundbar\nThere are several useful visual features to tell there is 'computer speaker' and not similar things in a photo:\tdesigned to be used with a computer\tsystem speakers with cords or wireless capability\tsound control buttons or sliding bars", 94], "model": ["No. 'Model' is too vague or abstract to be distinguished in a photo. It could refer to a person, a scale replica of a building, or even a mathematical model. \nTherefore, there aren't things that are visually similar to 'model' but are not 'model' that can be listed. \nHowever, some useful visual features for identifying a person as a 'model' in a photo could include: a poised and confident posture, fashionable clothing or accessories, and professional makeup or styling. For identifying a scale replica of a building as a 'model', useful features could include: a high level of detail, a small size, and being placed alongside blueprints or architectural drawings. For identifying a mathematical model, useful features could include: graphs, charts, and formulas.", 94], "orange beak": ["Yes. 'Orange beak' has a tangible appearance and refers to the beak or bill of a bird which is predominantly orange in color.\nA few things that are visually similar to 'orange beak' but are not 'orange beak' are:\tyellow beak\tred beak\tblack beak\tbrown beak\nThere are several useful visual features to tell there is 'orange beak' and not similar things in a photo:\tbeak or bill of a bird\tpredominant orange color\thue & saturation of the orange color in the beak shape and size of the beak", 94], "orange stripe": ["Yes. 'Orange stripe' has a tangible appearance and is a visible mark or line of the color orange.\nA few things that are visually similar to 'orange stripe' but are not 'orange stripe' are:\torange ribbon\torange rope\torange wire\torange paint\nThere are several useful visual features to tell there is 'orange stripe' and not similar things in a photo:\ta thin, straight line\tsymmetric\twell-defined\tbordering areas of different colors or materials", 94], "television set": ["Yes. 'Television set' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'television set' but are not 'television set' are:\tcomputer monitor\tprojector\tscreen\t\nThere are several useful visual features to tell there is 'television set' and not similar things in a photo:\trectangular or square shape\twide screen display\tbuttons or knobs for controlling brightness, contrast or volume\tspeakers or small holes for sound\toutput ports\tfor cables or antennas", 94], "drum": ["Yes. 'Drum' has a tangible appearance and is a type of percussion instrument.\nA few things that are visually similar to 'drum' but are not 'drum' are:\tbucket\tbarrel\tcan\thollow log\nThere are several useful visual features to tell there is 'drum' and not similar things in a photo:\tcylindrical shape\tmade of wood or metal\tmembrane stretched over the top to create a vibrating surface\tbeaters or drumsticks nearby to play it", 94], "buckets": ["Yes. 'Buckets' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'buckets' but are not 'buckets' are:\tbaskets\tbasins\tpots\tcans\nThere are several useful visual features to tell there is 'buckets' and not similar things in a photo:\tcylindrical or rounded shape\twith a handle\tmade of metal or plastic\thas a spout or a lid (sometimes) primarily used for carrying liquids.", 94], "arm band": ["Yes. 'Arm band' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'arm band' but are not 'arm band' are:\twristwatch\tbracelet\thair tie\tanklet\nThere are several useful visual features to tell there is 'arm band' and not similar things in a photo:\tworn on the upper arm\ttight fitting\tcylindrical, flat or curved shape\tmade of cloth or plastic\tsporting team or event logo", 94], "blue logo": ["Yes. 'Blue logo' has a tangible appearance and refers to a logo that is primarily blue in color.\nA few things that are visually similar to 'blue logo' but are not 'blue logo' are:\tlogos with blue accents\tblue patterns on a non-logo design\nThere are several useful visual features to tell there is 'blue logo' and not similar things in a photo:\tpredominantly blue color scheme\tdistinctive font or graphic design that identifies a brand or company", 94], "metal box": ["Yes. 'Metal box' has a tangible appearance and is a container made of metal.\nA few things that are visually similar to 'metal box' but are not 'metal box' are:\tcage\tlocker\tcrate\ttrash can\nThere are several useful visual features to tell there is 'metal box' and not similar things in a photo:\trectangular or square shape\thinged lid or top that can be removed or opened\tmade of metal or metal-like material\tsolid structure or frame with no holes or openings for visibility", 94], "grey shirt": ["Yes. 'Grey shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'grey shirt' but are not 'grey shirt' are:\tgrey sweatshirt\tgrey sweater\tgrey jacket\tgrey dress\nThere are several useful visual features to tell there is 'grey shirt' and not similar things in a photo:\tshirt collar\tcuff sleeves symmetrically buttoned on the front\tchest pockets (if any)\tfabric texture and material\ttype of collar (e.g. round neck, v-neck, etc.)", 94], "bald": ["Yes. 'Bald' has a tangible appearance and refers to lack of hair on the head.\nA few things that are visually similar to 'bald' but are not 'bald' are:\tshort hair\tbuzz cut\thead scarf\nThere are several useful visual features to tell there is 'bald' and not similar things in a photo:\tno hair on the head\tsmooth, shiny scalp\tno bangs or longer hair visible no hairline visible", 94], "toilet paper dispenser": ["Yes. 'Toilet paper dispenser' has a tangible appearance and is a common fixture in restrooms.\nA few things that are visually similar to 'toilet paper dispenser' but are not 'toilet paper dispenser' are:\ttowel dispenser\tsoap dispenser\tautomatic hand dryer\tbathroom cabinet\nThere are several useful visual features to tell there is 'toilet paper dispenser' and not similar things in a photo:\trectangular in shape\twith a hinged cover or a flap\ttoilet paper visible or partially visible\tfrom the wall or on a stand near the toilet", 94], "metal plate": ["Yes. 'Metal plate' has a tangible appearance and refers to a flat piece of metal typically used for various purposes.\nA few things that are visually similar to 'metal plate' but are not 'metal plate' are:\twood plank\tconcrete slab\tceramic tile\nThere are several useful visual features to tell there is 'metal plate' and not similar things in a photo:\tmetallic appearance\tsilver or grey color\tsmooth surface with uniform thickness\tand often with visible screws or bolts", 94], "seaweed": ["Yes. 'Seaweed' has a tangible appearance and is a type of aquatic plant.\nA few things that are visually similar to 'seaweed' but are not 'seaweed' are:\tcoral shells\tmarine animals\nThere are several useful visual features to tell there is 'seaweed' and not similar things in a photo:\tgreen or brown color\twavy or stringy textures\tgrowing in water or near the shoreline\tbushy or feathery shapes.", 94], "cooking utensils": ["Yes. 'Cooking utensils' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'cooking utensils' but are not 'cooking utensils' are:\tdishes\tcutlery\tglassware\tpots and pans\nThere are several useful visual features to tell there is 'cooking utensils' and not similar things in a photo:\tmetallic or wooden utensils\tfor cooking and serving a variety of foods\tincluding spatulas, spoons, forks, tongs, ladles, whisks, and more.", 93], "tortilla": ["Yes. 'Tortilla' has a tangible appearance and is a type of flatbread.\nA few things that are visually similar to 'tortilla' but are not 'tortilla' are:\tpita bread\tnaan bread\tcrepe\twrap\nThere are several useful visual features to tell there is 'tortilla' and not similar things in a photo:\tthin and flat\tcircular shape\tcrispy or soft texture\tbrowned spots on the surface\tcorn or wheat-based dough", 93], "waste basket": ["Yes. 'Waste basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'waste basket' but are not 'waste basket' are:\ttrash can\trecycling bin\tclothes hamper\tbucket\nThere are several useful visual features to tell there is 'waste basket' and not similar things in a photo:\topen top\twithout a lid\toften made of plastic or metal\tcan be found in offices or homes\tdesigned to hold trash or waste products.", 93], "power button": ["Yes, 'power button' has a tangible appearance and is a physical button on electronic devices used to turn it on or off.\nA few things that are visually similar to 'power button' but are not 'power button' are: volume button, camera button, home button, play/pause button, mute button.\nThere are several useful visual features to tell there is 'power button' and not similar things in a photo: rectangular or circular shape, usually labeled with the universal power symbol consisting of a circle with a vertical line through it, located near the edge or corner of the electronic device.", 93], "pink tongue": ["Yes. 'Pink tongue' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'pink tongue' but are not 'pink tongue' are:\tpink gum\tpink candy\tpink tissue paper\tpink balloon\nThere are several useful visual features to tell there is 'pink tongue' and not similar things in a photo:\tfleshy, muscular organ\tlocated inside a mouth\tranges from light pink to bright red\tdifferent texture than gums or candy", 93], "runner": ["Yes. 'Runner' has a tangible appearance and refers to a person who runs.\nA few things that are visually similar to 'runner' but are not 'runner' are:\twalker\tjogger\tcyclist\thiker\nThere are several useful visual features to tell there is 'runner' and not similar things in a photo:\twearing athletic clothing and shoes\trunning shoes with support and cushioning\tbalanced posture and steady arm movements\twhile running in mid-stride", 93], "babies": ["Yes. 'Babies' has a tangible appearance and refers to very young humans.\nA few things that are visually similar to 'babies' but are not 'babies' are:\tdolls\tpets\ttoddler mannequins\nThere are several useful visual features to tell there is 'babies' and not similar things in a photo:\tsmall size\tsoft skin\tlarge head in proportion to the body\tshort arms and legs\tbig eyes\tcan be held in arms or hands", 93], "tv screen": ["Yes. 'Tv screen' has a tangible appearance and is a type of electronic display.\nA few things that are visually similar to 'tv screen' but are not 'tv screen' are:\tcomputer monitor\tprojector\tscreen door\twindow\nThere are several useful visual features to tell there is 'tv screen' and not similar things in a photo:\trectangular shape\twith a frame\tor bracketed to a wall or stand\tdisplaying moving or still images\temit light or color\thave buttons or controls for powering on or off, changing channels, adjusting volume, or adjusting settings.", 93], "orange bag": ["Yes. 'Orange bag' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'orange bag' but are not 'orange bag' are:\torange purse\torange backpack\tplastic orange bag\t\nThere are several useful visual features to tell there is 'orange bag' and not similar things in a photo:\torange color\thandles or straps for carrying\tthe shape of the bag, i.e., it could be a tote bag, a shopping bag or a duffel bag, etc.", 93], "blocks": ["Yes. 'Blocks' has a tangible appearance and can refer to a variety of solid, rectangular toys or construction materials.\n\nA few things that are visually similar to 'blocks' but are not 'blocks' are:\n\n- Rectangular wooden planks used for building\n- Concrete or cement bricks used for construction\n- Rectangular metal bars or rods used in welding or industrial work\n\nSome useful visual features for distinguishing 'blocks' from these similar things in a photo are:\n\n- Blocks will typically have a bright or colorful appearance, while other materials may be more muted in color\n- Blocks used for construction (like wooden or cement blocks) will typically have a rougher, less polished appearance than building blocks used as toys\n- Building blocks used as toys may have letters, numbers, or other designs printed on them to differentiate them from other block-like materials.", 93], "toothpick": ["Yes. 'Toothpick' has a tangible appearance and is a small stick used to remove food particles from teeth.\nA few things that are visually similar to 'toothpick' but are not 'toothpick' are:\tmatchstick\tcocktail stick\tbarbecue stick\t\nThere are several useful visual features to tell there is 'toothpick' and not similar things in a photo:\tpointed at one end\tsmooth and straight\trounded and thicker at the other end\ttoothpicks often come in a container or a package", 93], "right": ["No. 'Right' is too vague or abstract to be distinguished in a photo.", 93], "beads": ["Yes. 'Beads' has a tangible appearance and is a type of jewelry or decoration.\nA few things that are visually similar to 'beads' but are not 'beads' are: \tmarbles\tcandy\tpebbles\nThere are several useful visual features to tell there is 'beads' and not similar things in a photo: \tsmall and round\tshiny or colorful\tsymmetrical and evenly spaced(when used for jewelry)\thaving a hole in the center(when used for decoration)", 93], "round sign": ["Yes. 'Round sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'round sign' but are not 'round sign' are:\tcircular clock\tfrisbee\tbarrel\ttop of a silo\nThere are several useful visual features to tell there is 'round sign' and not similar things in a photo:\tusually made of metal or plastic\tbold text or graphics\tspecific shapes or colors (such as red and white for a stop sign)\tmounted on a post or a wall\tfor informational or regulatory purposes", 92], "blue bike": ["Yes. 'Blue bike' has a tangible appearance and is a specific type of bicycle.\nA few things that are visually similar to 'blue bike' but are not 'blue bike' are:\tgreen bike\tred bike\tbicycles with a basket or a bell\nThere are several useful visual features to tell there is 'blue bike' and not similar things in a photo:\tblue-colored frame\ttwo wheels\tpedals\thandlebars\tbrakes and gears", 92], "dials": ["Yes. 'Dials' has a tangible appearance and refers to round indicators used to show measurements or settings.\nA few things that are visually similar to 'dials' but are not 'dials' are:\tknobs\twheels\tbuttons\tlevers\nThere are several useful visual features to tell there are 'dials' and not similar things in a photo:\tround shape\twith numbers or markings\tneedle or indicator pointing to a measurement or setting", 92], "safety vest": ["Yes. 'Safety vest' has a tangible appearance and is a type of clothing worn for safety purposes.\nA few things that are visually similar to 'safety vest' but are not 'safety vest' are:\tfishing vest\thunting vest\tmilitary vest\tphotography vest\nThere are several useful visual features to tell there is 'safety vest' and not similar things in a photo:\tbright, fluorescent colors such as yellow, orange or lime green\treflective strips or patches around the chest and back\tadjustable, fastening closures at the front\tV-shaped neckline on the back of the collar\tarea around the chest and waist is tightly fitted and free of extra material", 92], "vanity": ["No. 'Vanity' is too abstract to have a tangible appearance and cannot be distinguished in a photo. \nHowever, if the context is a piece of furniture, then 'vanity' could be a visually concrete concept. In this case, similar things that are not 'vanity' could be:\tdesk\tdressing table\tworkstation\tcabinet. \nUseful visual features for distinguishing 'vanity' from the listed similar things may include:\tdecorative design, such as ornate carvings or decorative legs\tmirrors on or above the table\tdrawers or compartments for storing beauty products or accessories.", 92], "hinges": ["Yes. 'Hinges' has a tangible appearance and is a type of hardware used as a joint or bearing between two objects.\nA few things that are visually similar to 'hinges' but are not 'hinges' are:\tknobs\thandles\tlocks\tscrews\tbolts\nThere are several useful visual features to tell there is 'hinges' and not similar things in a photo:\ttwo flat plates connected by a pin\tor cylinder\tvisible screw holes\tor pivot points\table to rotate around an axis\tor pivot point", 92], "assortment": ["No. 'Assortment' is too vague or abstract to be distinguished in a photo.", 92], "dinosaur": ["Yes. 'Dinosaur' has a tangible appearance and is a type of extinct reptile.\nA few things that are visually similar to 'dinosaur' but are not 'dinosaur' are:\talligator\tcrocodile\tiguana\tlizard\tkomodo dragon\nThere are several useful visual features to tell there is 'dinosaur' and not similar things in a photo:\tlarge size\tsharp teeth and claws\treptilian scales\tlong tail and neck\tbipedal posture (walking on two legs)\tno visible ears or external genitals\texistence only in fossil records or imaginings", 92], "ski pants": ["Yes. 'Ski pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'ski pants' but are not 'ski pants' are:\tleggings\ttrousers\tyoga pants\t\nThere are several useful visual features to tell there is 'ski pants' and not similar things in a photo:\tthick and insulated material\twaterproof or water-resistant\tdark color or patterns\tpockets\ton adjustable suspenders or a belt\tzippers at the bottom of the legs for easy access to ski boots.", 92], "peel": ["Yes. 'Peel' has a tangible appearance and refers to the outer layer of some fruits and vegetables.\nA few things that are visually similar to 'peel' but are not 'peel' are:\tcrust\tbark\tshell\nThere are several useful visual features to tell there is 'peel' and not similar things in a photo:\tthinner than the rest of the fruit or vegetable\tsmooth or rough textures\tvarious colors depending on the type of fruit or vegetable", 92], "baby sheep": ["Yes. 'Baby sheep' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'baby sheep' but are not 'baby sheep' are:\tgoat\tbaby cow\tlamb\nThere are several useful visual features to tell there is 'baby sheep' and not similar things in a photo:\tfloppy ears\tcurly woolly fur\t4 legs and hooves\tshort tail'small size in comparison to fully grown sheep", 92], "butt": ["Yes. 'Butt' has a tangible appearance and refers to the backside of a person or animal.\nA few things that are visually similar to 'butt' but are not 'butt' are:\tpillow\tcushion\tball\nThere are several useful visual features to tell there is 'butt' and not similar things in a photo:\ttwo fleshy mounds\tcurves on the lower back and upper legs\tcleft visible between the cheeks", 92], "passenger car": ["Yes. 'Passenger car' has a tangible appearance and is a type of vehicle used for transportation.\nA few things that are visually similar to 'passenger car' but are not 'passenger car' are:\ttruck\tbus\tmotorcycle\nThere are several useful visual features to tell there is 'passenger car' and not similar things in a photo:\ttwo- or four-door\tcompact size\tseating for up to five people\twindows on all sides\theadlights and tail lights license plate at the rear\tend", 92], "projector": ["Yes. 'Projector' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'projector' but are not 'projector' are:\ttv\tscreen\tpainting\tslide viewer\nThere are several useful visual features to tell there is 'projector' and not similar things in a photo:\tportable and easy to move or set up\tprojecting an image or video onto a surface\tcords or cables for connectivity to other devices\tlens for focusing and adjusting the image size\tand control buttons or remote for managing the image", 92], "stapler": ["Yes. 'Stapler' has a tangible appearance and is an office tool.\nA few things that are visually similar to 'stapler' but are not 'stapler' are:\thole punch\tscissors\tpen holder\ttape dispenser\nThere are several useful visual features to tell there is 'stapler' and not similar things in a photo:\tmetallic device\ttwo parallel bars for holding staples,\tarm for pressing\tstaples loaded on the back\tside slot for refilling\tstaples on paper", 92], "story": ["No. 'Story' is too vague or abstract to be distinguished in a photo.", 92], "shin guards": ["Yes. 'Shin guards' has a tangible appearance and is a type of protective equipment worn during sports or physical activities.\nA few things that are visually similar to 'shin guards' but are not 'shin guards' are:\tleggings\tstockings\tbraces\tgreaves\nThere are several useful visual features to tell there is 'shin guards' and not similar things in a photo:\tprotective pad covering the front of the shin\thard outer shell\tcushioned interior\tsynthetic strapping to hold in place", 91], "kitchen table": ["Yes. 'Kitchen table' has a tangible appearance and is a type of piece of furniture.\nA few things that are visually similar to 'kitchen table' but are not 'kitchen table' are:\tdinning table\tcoffee table\tdesk\tpicnic table\nThere are several useful visual features to tell there is 'kitchen table' and not similar things in a photo:\toften located in or near a kitchen area\tchairs or benches are accompanying it rectangular or round-shaped\ta flat horizontal surface\tfor preparing or eating food", 91], "elephants trunk": ["Yes. 'Elephant's trunk' has a tangible appearance and is an elongated and flexible nose-like organ.\nA few things that are visually similar to 'elephants trunk' but are not 'elephants trunk' are:\those\tsnake\ttrumpet\nThere are several useful visual features to tell there is 'elephants trunk' and not similar things in a photo:\tthick and muscular\tlong and flexible\tprone to wrinkles and folds at the base and tip\thas two finger-like protrusions at the tip.", 91], "bottle cap": ["Yes. 'Bottle cap' has a tangible appearance and is a small round cap used to seal bottles.\nA few things that are visually similar to 'bottle cap' but are not 'bottle cap' are:\tlids\tcoins\tbuttons\tscrews\tnuts\nThere are several useful visual features to tell there is 'bottle cap' and not similar things in a photo:\tround or circular\tmetallic or plastic material\twith ridges or grooves\ton top of the bottle opening", 91], "tooth": ["Yes. 'Tooth' has a tangible appearance and is a type of body part.\nA few things that are visually similar to 'tooth' but are not 'tooth' are:\tbone\tpennies\tshell\nThere are several useful visual features to tell there is 'tooth' and not similar things in a photo:\twhite or off-white color\thard texture\tpointed or flat shape\troot at one end or flat at the bottom\tvisible gaps between teeth", 91], "gas stove": ["Yes. 'Gas stove' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'gas stove' but are not 'gas stove' are:\telectric stove\toven\tgrill\tportable gas burner out of the kitchen\nThere are several useful visual features to tell there is 'gas stove' and not similar things in a photo:\tmetallic surface with burners or gas rings\tadjustable knobs for controlling the flame\tsizably fitted countertop space lying next to the cabinets and the sink\tconnected to a gas pipeline or cylinder with a tube.", 91], "desserts": ["Yes. 'Desserts' have a tangible appearance and are a type of food.\nA few things that are visually similar to 'desserts' but are not 'desserts' are:\tmain dishes\tappetizers\tsnacks\tbread\nThere are several useful visual features to tell there is 'desserts' and not similar things in a photo:\tsweet\ttreats\tsugary\tfluffy or creamy textures\tdifferent colors\tand shapes\tserved in small portions\twith toppings or decorations such as fruits, whipped cream or chocolate.", 91], "bathroom door": ["Yes. 'Bathroom door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'bathroom door' but are not 'bathroom door' are: bedroom door, closet door, kitchen door.\nThere are several useful visual features to tell there is 'bathroom door' and not similar things in a photo: bathroom symbol on the door handle, a lock on the door, made of waterproof or moisture-resistant material, tile or painted in a color that matches the bathroom.", 91], "restroom": ["Yes. 'Restroom' has a tangible appearance and is a type of room.\nA few things that are visually similar to 'restroom' but are not 'restroom' are:\tkitchen\tliving room\tbathroom\tbedroom\tcloset\nThere are several useful visual features to tell there is 'restroom' and not similar things in a photo:\tsigns with the word 'restroom' or symbols depicting a toilet or a man/woman\turinals or toilet bowls\tin the men's/women's/mixed-gender section\tfaucets, sinks, soap dispensers, or hand dryers\ttiled surfaces and mirrors", 91], "zebra head": ["Yes. 'Zebra head' has a tangible appearance and is a part of an animal body.\nA few things that are visually similar to 'zebra head' but are not 'zebra head' are:\thorse head\tdonkey head\tantelope head\nThere are several useful visual features to tell there is 'zebra head' and not similar things in a photo:\tdistinctive black and white stripes\tlarge, round ears\tlong, narrow snout with black nose and white lips\teyes situated on the side of the head", 91], "flames": ["Yes. 'Flames' has a tangible appearance and refers to the visible, glowing gas created during combustion.\nA few things that are visually similar to 'flames' but are not 'flames' are:\tsunrise/sunset\tsparklers\tlava\tflashing lights\tfireworks\nThere are several useful visual features to differentiate 'flames' from the listed similar things in a photo:\tcrackling fire\tsound of burning\tbuilding or object on fire\tvarious colors such as orange, red, and yellow\tin motion and flickering\tcan produce smoke", 91], "fringe": ["Yes. 'Fringe' has a tangible appearance and refers to decorative trims or edgings.\nA few things that are visually similar to 'fringe' but are not 'fringe' are:\ttassels\tlace\tpiping\tbeads\nThere are several useful visual features to tell there is 'fringe' and not similar things in a photo:\tlong and thin strips of material\thanging or attached to the edge of a garment or a piece of furniture\thas a decorative or ornamental purpose\tvaried colors or textures", 91], "valley": ["Yes. 'Valley' has a tangible appearance and is a low area of land between hills or mountains.\nA few things that are visually similar to 'valley' but are not 'valley' are:\tcanyon\tgully\travine\tdepression\tpit\nThere are several useful visual features to tell there is 'valley' and not similar things in a photo:\tgentle slopes or gradients\thills or mountains on either side\tcurved or U-shape appearance\trivers or streams running through it\tlush vegetation or trees in the valley floor", 91], "shoe laces": ["Yes. 'Shoe laces' has a tangible appearance.\nA few things that are visually similar to 'shoe laces' but are not 'shoe laces' are:\tribbons\tcords\ttwine\t\nThere are several useful visual features to tell there is 'shoe laces' and not similar things in a photo:\tthin and flat\tshoelace aglets\tconnected to shoes.", 90], "bear nose": ["Yes. 'Bear nose' has a tangible appearance and is a body part of a bear.\nA few things that are visually similar to 'bear nose' but are not 'bear nose' are:\tdog nose\tcat nose\twolf nose\thuman nose\nThere are several useful visual features to tell there is 'bear nose' and not similar things in a photo:\tblack or brown\tcolor\tsmooth surface with nostrils\tSnout-like appearance\tBig and wide in size.", 90], "home base": ["Yes. 'Home base' has a tangible appearance and is a part of a sports field.\nA few things that are visually similar to 'home base' but are not 'home base' are:\tcone\tflag\tpylon\nThere are several useful visual features to tell there is 'home base' and not similar things in a photo:\twhite square or pentagon on the ground\tin the center of a baseball diamond\twith an anchor system for bases or stations.", 90], "chalk": ["Yes. 'Chalk' has a tangible appearance and is a type of writing or drawing material.\nA few things that are visually similar to 'chalk' but are not 'chalk' are:\tcrayon\tpencil\tcharcoal\tmarker\nThere are several useful visual features to tell there is 'chalk' and not similar things in a photo:\twhite color or pastel colors\tcylindrical or stick-shaped\tsmall and easy to break or crumble\twriting or drawing on a blackboard or a surface meant for chalk", 90], "metal door": ["Yes. 'Metal door' has a tangible appearance and is a type of door made of metal.\nA few things that are visually similar to 'metal door' but are not 'metal door' are:\twooden door\tglass door\tstone door\tPlastic door\nThere are several useful visual features to tell there is 'metal door' and not similar things in a photo:\tmetallic surface\theavy and sturdy appearance\tmetal hinges and handles\tmetallic sound when knocked or opened.", 90], "wooden post": ["Yes. 'Wooden post' has a tangible appearance and is a type of wooden structure.\nA few things that are visually similar to 'wooden post' but are not 'wooden post' are:\ttrees\tpillars\ttelephone poles\tsign posts\nThere are several useful visual features to tell there is 'wooden post' and not similar things in a photo:\tman-made\tvertical, upright form\trectangular or square shape\twooden texture\tno branches, leaves or foliage attached to it", 90], "concrete curb": ["Yes. 'Concrete curb' has a tangible appearance and is a kind of construction.\nA few things that are visually similar to 'concrete curb' but are not 'concrete curb' are:\tsidewalks\tdriveway edges\traised garden beds\tstepping stones\nThere are several useful visual features to tell there is 'concrete curb' and not similar things in a photo:\trectangular or trapezoidal shape\tgrey color\tsmooth texture, with visible lines if there are sections\tprotruding from the ground or surface it is outlining.", 90], "tin": ["Yes. 'Tin' has a tangible appearance and is a type of metal.\nA few things that are visually similar to 'tin' but are not 'tin' are:\taluminum\tsteel\tsilver\tgray-colored plastic\nThere are several useful visual features to tell there is 'tin' and not similar things in a photo:\tmetallic appearance\tlightweight and malleable\tsilvery-gray color\trustic, retro look", 90], "friends": ["No. 'Friends' is too vague or abstract to be distinguished in a photo.", 90], "water tower": ["Yes. 'Water tower' has a tangible appearance and is a kind of infrastructure.\nA few things that are visually similar to 'water tower' but are not 'water tower' are:\tcell tower\telectricity pylon\tchimney\tobservatory\tlighthouse\nThere are several useful visual features to tell there is 'water tower' and not similar things in a photo:\ttall tower with a large sphere or tank on top\tsupport legs holding up the tank or sphere\tmetallic or concrete material\tcircular or cylindrical tank-shaped structure.", 90], "expanse": ["No. 'Expanse' is too vague or abstract to be distinguished in a photo.", 90], "glass plate": ["Yes. 'Glass plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'glass plate' but are not 'glass plate' are:\tplastic plate\tpaper plate\tmetal plate\twooden plate\nThere are several useful visual features to tell there is 'glass plate' and not similar things in a photo:\tmade of glass\tclear and transparent\tmay have a decorative pattern or texture\tfragile and can break easily", 89], "sides": ["No. 'Sides' is too vague or abstract and doesn't have a tangible appearance that can be visually distinguished in a photo.", 89], "motorcycle tire": ["Yes. 'Motorcycle tire' has a tangible appearance and is a type of tire.\nA few things that are visually similar to 'motorcycle tire' but are not 'motorcycle tire' are:\tbicycle tire\tcar tire\ttruck tire\ttrolley wheel\nThere are several useful visual features to tell there is 'motorcycle tire' and not similar things in a photo:\tsmaller in size compared to car or truck tires\ttread pattern specific to motorcycles\tsidewalls are thinner\twith or without spoke rim\tdiameter proportional to the size of the motorcycle.", 89], "pepper grinder": ["Yes. 'Pepper grinder' has a tangible appearance and is a type of kitchen utensil.\nA few things that are visually similar to 'pepper grinder' but are not 'pepper grinder' are:\tsalt grinder\tspice jar\tcoffee grinder\nThere are several useful visual features to tell there is 'pepper grinder' and not similar things in a photo:\tcylindrical shape\thand crank on top\torbiting blades inside\tfor black or white peppercorns only", 89], "leafs": ["No. 'Leafs' is not a correct term. The correct form would be 'leaves'.\nA few things that are visually similar to 'leaves' but are not 'leaves' are: petals, twigs and stems, feathers.\nThere are several useful visual features to tell there are 'leaves' and not similar things in a photo:\t\n- flat, thin and usually green structure\n- veins on the top that run to the edges of the bottom side\n- different shapes (round, oval, pointed, etc.) depending on the plant species.", 89], "kitchen cabinet": ["Yes. 'Kitchen cabinet' has a tangible appearance and usually refers to a piece of furniture used to store kitchen items.\nA few things that are visually similar to 'kitchen cabinet' but are not 'kitchen cabinet' are:\tbookshelves\tdressers\tshoe racks\tchests of drawers\nThere are several useful visual features to tell there is 'kitchen cabinet' and not similar things in a photo:\tlocated in a kitchen or dining area\tmade of wood or synthetic material\thas shelves or drawers\thas handles or knobs to open and close\tthe inside contains dishes or utensils.", 89], "yellow writing": ["Yes. 'Yellow writing' has a tangible appearance and refers to any text or letters written in yellow ink or paint.\nA few things that are visually similar to 'yellow writing' but are not 'yellow writing' are:\torange writing\tgold writing\tyellow highlighter on white paper\tyellow objects with no writing\nThere are several useful visual features to tell there is 'yellow writing' and not similar things in a photo:\tyellow-colored\ttext or letters\twritten or painted on a surface(such as paper, signboard, or wall)", 89], "character": ["No. 'Character' is too vague or abstract to be distinguished in a photo.", 89], "lorry": ["Yes. 'Lorry' has a tangible appearance and is a large vehicle used for transporting goods.\nA few things that are visually similar to 'lorry' but are not 'lorry' are:\tBus\tVan\tPick-up truck\tAmbulance\nThere are several useful visual features to tell there is 'lorry' and not similar things in a photo:\tlarge size\topen bed to carry goods or cargo\twheels and axels at the rear\tend of the vehicle often has a loading platform\tor a trailer attached to it.", 89], "plastic bowl": ["Yes. 'Plastic bowl' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic bowl' but are not 'plastic bowl' are:\tglass bowl\tmetal bowl\tceramic bowl\twooden bowl\nThere are several useful visual features to tell there is 'plastic bowl' and not similar things in a photo:\tplastic material\tsmooth surface\trounded shape\tlightweight\tflexible\ttranslucent or opaque appearance", 89], "goatee": ["Yes. 'Goatee' has a tangible appearance and is a type of beard.\nA few things that are visually similar to 'goatee' but are not 'goatee' are:\tmoustache\tsideburns\tbeard\tfacial hair\nThere are several useful visual features to tell there is 'goatee' and not similar things in a photo:\thair grown at the chin, beneath the lower lip\tnarrow and pointed ends\tcan be connected to a moustache on the upper lip", 89], "grey rocks": ["Yes. 'Grey rocks' have a tangible appearance and are a type of natural element.\nA few things that are visually similar to 'grey rocks' but are not 'grey rocks' are:\tconcrete\tpavement\tasphalt\tchunks of metal\nThere are several useful visual features to tell there is 'grey rocks' and not similar things in a photo:\tirregular shape\tvaried sizes and textures\tdull or matte appearance\toccurs in nature or outdoor environment", 89], "front landing gear": ["Yes. 'Front landing gear' has a tangible appearance and refers to the structure that supports the front of an aircraft during landing.\nA few things that are visually similar to 'front landing gear' but are not 'front landing gear' are:\tretractable wheels\tbicycle fork\tcamera tripod\nThere are several useful visual features to tell there is 'front landing gear' and not similar things in a photo:\tattached to the front of an aircraft\tdual wheels or struts\tshock absorbers\thydraulic systems or cylinders", 89], "pitch": ["No. 'Pitch' is too vague or abstract to be distinguished in a photo. However, if you are referring to the black and sticky substance, then the answer is yes, it has a tangible appearance.\nA few things that are visually similar to 'pitch' but are not 'pitch' are: ink, oil, tar, asphalt.\nThere are several useful visual features to tell there is 'pitch' and not similar things in a photo: black and sticky, often found in large deposits near oil fields or mines.", 88], "floor tiles": ["Yes. 'Floor tiles' has a tangible appearance and is a type of construction material.\nA few things that are visually similar to 'floor tiles' but are not 'floor tiles' are:\twall tiles\tdecorative plates\tstepping stones\tpaving stones\nThere are several useful visual features to tell there is 'floor tiles' and not similar things in a photo:\tinstalled flat on the ground\trectangular or square in shape\tsmooth or textured surface\tvariety of colors or patterns\tjointed with grout\tor cement in between each tile", 88], "wii remote": ["Yes. 'Wii remote' has a tangible appearance and is a type of game controller.\nA few things that are visually similar to 'Wii remote' but are not 'Wii remote' are:\tPlayStation controller\tXbox controller\tmouse\tremote control\nThere are several useful visual features to tell there is 'Wii remote' and not similar things in a photo:\tLong, thin and rectangular shape\tButtons on the front of the remote\tSensor bar at the top of the screen\tInfrared pointer at the end of the remote \tWii branding on the remote", 88], "fingernails": ["Yes. 'Fingernails' have a tangible appearance and are a part of the human body.\nA few things that are visually similar to 'fingernails' but are not 'fingernails' are:\tanimal claws\tscrews\tbolts\tseashells\nThere are several useful visual features to tell there are 'fingernails' and not similar things in a photo:\tattached to the tips of human fingers\toval-shaped\twith a half-moon shape at the base\thaving a visible white tip", 88], "video game controller": ["Yes. 'Video game controller' has a tangible appearance and is a device used to play video games.\nA few things that are visually similar to 'video game controller' but are not 'video game controller' are:\ttv remote\tcontrol pad\tkeyboard\tmouse\tsmartphone\nThere are several useful visual features to tell there is 'video game controller' and not similar things in a photo:\ta directional pad or joystick\tbuttons or triggers for actions or abilities\ta shape that is designed to fit comfortably in hands or fingers", 88], "horse tail": ["Yes. 'Horse tail' has a tangible appearance and is a part of a horse's body.\nA few things that are visually similar to 'horse tail' but are not 'horse tail' are:\tdog tail\tcat tail\tfur\ttassel\nThere are several useful visual features to tell there is 'horse tail' and not similar things in a photo:\tlong and flowing tails\thair flow and appearance\tscars, braids, or decorations on the tail (if applicable)", 88], "pizza pie": ["Yes. 'Pizza pie' has a tangible appearance and refers to a type of food.\nA few things that are visually similar to 'pizza pie' but are not 'pizza pie' are:\tpie\tquiche\tfrittata\tomelette\t\nThere are several useful visual features to tell there is 'pizza pie' and not similar things in a photo:\tcircular shape\tbaked or cooked crust\twith tomato sauce and cheese on top\tmeat, vegetables, or other toppings on the cheese\tlayered or piled ingredients in a recognizable pattern", 88], "bike seat": ["Yes. 'Bike seat' has a tangible appearance and is a part of the bike.\nA few things that are visually similar to 'bike seat' but are not 'bike seat' are:\tchair cushion\ttoilet seat\tcar seat\nThere are several useful visual features to tell there is 'bike seat' and not similar things in a photo:\tnarrow and elongated shape\tmounted on a bike frame\tmade of plastic or leather\tpadded for comfort\tsaddle-like appearance", 88], "collars": ["Yes. 'Collars' has a tangible appearance and is a type of clothing accessory worn around the neck.\nA few things that are visually similar to 'collars' but are not 'collars' are:\tnecklaces\tscarves\tchokers\tbibs\nThere are several useful visual features to tell there is 'collars' and not similar things in a photo:\tattached to a shirt or a dress\toval or V-shaped\thighlighted with buttons, jewels or studs\tmade of a different material than the garment it is attached to (such as leather or metal)", 87], "tennis match": ["Yes. 'Tennis match' has a tangible appearance and is a type of sports game.\nA few things that are visually similar to 'tennis match' but are not 'tennis match' are:\ttable tennis\tmatch of badminton\tmatch of volleyball\nThere are several useful visual features to tell there is 'tennis match' and not similar things in a photo:\ta net dividing the court in half\ttwo players on opposite sides of the court\tusing tennis rackets\tto hit a ball back and forth between them making the ball cross over the net to the opponent's side people watching and cheering the players", 87], "scooters": ["Yes. 'Scooters' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'scooters' but are not 'scooters' are:\tmotorcycles\tbicycles\tmopeds\thoverboards\nThere are several useful visual features to tell there is 'scooters' and not similar things in a photo:\thandlebar\tfoot plate or deck\ttwo or three wheels\tlow-power engine or electric motor\tpaddle brakes or handbrakes", 87], "soda bottle": ["Yes. 'Soda bottle' has a tangible appearance and is a kind of bottle used for carbonated drinks.\nA few things that are visually similar to 'soda bottle' but are not 'soda bottle' are:\twater bottle\tbeer bottle\tcologne bottle\tperfume bottle\nThere are several useful visual features to tell there is 'soda bottle' and not similar things in a photo:\tcylindrical body with a narrow neck\tscrew-on cap or bottle cap\tlabel or branding\tfor carbonated drinks, there may be small bubbles in the liquid", 87], "metal bowl": ["Yes. 'Metal bowl' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'metal bowl' but are not 'metal bowl' are:\tpot\tcup\tpan\tsink\tfrying pan\nThere are several useful visual features to tell there is 'metal bowl' and not similar things in a photo:\tround shape\tmade of metal or metallic material\tno handle or spout\twide opening and deep enough to hold food or liquid", 87], "candle holder": ["Yes. 'Candle holder' has a tangible appearance and is an object designed to hold a candle.\nA few things that are visually similar to 'candle holder' but are not 'candle holder' are:\tvase\tcup\tpen holder\nThere are several useful visual features to tell there is 'candle holder' and not similar things in a photo:\thollowed space to hold a candle\ta base that keeps the candle holder stable\tdecorative designs that suggest its use as a holder for a candle.", 87], "arrow sign": ["Yes. 'Arrow sign' has a tangible appearance and is a type of directional sign.\nA few things that are visually similar to 'arrow sign' but are not 'arrow sign' are:\ttraffic sign\tadvertisement\tbillboard\nThere are several useful visual features to tell there is 'arrow sign' and not similar things in a photo:\tdirections with arrows\tsimple design and colors (usually black and white)\tnear roads or intersections", 87], "cardboard boxes": ["Yes. 'Cardboard boxes' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'cardboard boxes' but are not 'cardboard boxes' are:\tplastic boxes\tmetal boxes\twooden crates\tbaskets\nThere are several useful visual features to tell there is 'cardboard boxes' and not similar things in a photo:\trectangular shape\tcardboard material\twith flaps for opening and closing\thas a flat surface for labeling and writing\tthe surface may be plain or printed", 87], "metal rack": ["Yes. 'Metal rack' has a tangible appearance and is a kind of shelving.\nA few things that are visually similar to 'metal rack' but are not 'metal rack' are:\ttable\tchair\tfence\nThere are several useful visual features to tell there is 'metal rack' and not similar things in a photo:\tlinear structure consisting of metal bars or panels\tstable and durable\tstored objects on it or inside it\tmay have wheels for mobility", 87], "metal pot": ["Yes. 'Metal pot' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'metal pot' but are not 'metal pot' are:\tpan\tbowl\tkettle\tcup\nThere are several useful visual features to tell there is 'metal pot' and not similar things in a photo:\tmetallic surface\thandles on both sides (for pots with larger volume)\tcurved-in shape with a flat bottom\tlarger than a cup and smaller than a pan", 87], "thread": ["Yes. 'Thread' has a tangible appearance as a narrow and flexible material used for sewing or weaving.\nA few things that are visually similar to 'thread' but are not 'thread' are:\thair\tstring\tyarn\twire\nThere are several useful visual features to distinguish 'thread' from the listed similar things in a photo:\tthin and narrow\tflexible and bendable\toften wound on spools or bobbins\tcan be made of different materials (such as cotton, silk, nylon, or polyester)", 87], "toiletries": ["Yes. 'Toiletries' has a tangible appearance and refers to personal care items.\nA few things that are visually similar to 'toiletries' but are not 'toiletries' are:\tcosmetics\tmedicine\tpens\t\nThere are several useful visual features to tell there is 'toiletries' and not similar things in a photo:\tbathroom-themed packaging\tor commonly used in the bathroom and for personal care\tsoaps, shampoos, toothbrushes, toothpaste, deodorant, razors, etc.", 87], "desktop": ["Yes. 'Desktop' has a tangible appearance and refers to a physical computer setup with a screen, keyboard, and mouse.\nA few things that are visually similar to 'desktop' but are not 'desktop' are:\tlaptops\ttablets\tsmartphones\ttv screens\tprojectors\nThere are several useful visual features to tell there is 'desktop' and not similar things in a photo:\tlarge, standalone monitor\tseparate keyboard and mouse\tdesktop tower or CPU case sitting on a desk or surface\tdocking station or multiple ports for additional devices or peripherals.", 87], "powder": ["Yes. 'Powder' has a tangible appearance and is a type of substance.\nA few things that are visually similar to 'powder' but are not 'powder' are:\tflour\tsugar\tcocaine\tbaking soda\nThere are several useful visual features to tell there is 'powder' and not similar things in a photo:\tfine and loose particles\tfeels dry to the touch\tcan be poured or sprinkled\tfloats in the air if disturbed.", 87], "bowtie": ["Yes. 'Bowtie' has a tangible appearance and is a type of necktie.\nA few things that are visually similar to 'bowtie' but are not 'bowtie' are:\tregular necktie\tscarf\tribbon\tchoker\nThere are several useful visual features to tell there is 'bowtie' and not similar things in a photo:\ttwo loops tied together in the middle\tusually worn with formal or dressy attire\tnot hanging down like a regular necktie", 87], "silver train": ["Yes. 'Silver train' has a tangible appearance and is a type of locomotive or train.\nA few things that are visually similar to 'silver train' but are not 'silver train' are:\tother color trains\tsubway cars\ttram\tcable car\nThere are several useful visual features to tell there is 'silver train' and not similar things in a photo:\tsilver or metallic color\ttrain engine and cars\tvisible tracks or train station\tcylindrical shape with a front and a back\tend\tcars connected to one another by couplings or couplers", 87], "metal sign": ["Yes. 'Metal sign' has a tangible appearance and usually has a specific function, message or indication.\nA few things that are visually similar to 'metal sign' but are not 'metal sign' are:\tmetal plate\tmetal door\tmetal wall\t\nThere are several useful visual features to tell there is 'metal sign' and not similar things in a photo:\tsharp corners and edges\tbold typography or graphics\tusually hung or attached to something\tfor informational, directional, or decorative purposes", 87], "beige building": ["Yes. 'Beige building' has a tangible appearance and refers to a building with a beige color scheme.\nA few things that are visually similar to 'beige building' but are not 'beige building' are:\tgrey building\twhite building\tsand-colored building\tstone building\nThere are several useful visual features to tell there is 'beige building' and not similar things in a photo:\tbeige or light brown color scheme\tsmooth or textured exterior walls\tman-made structure", 86], "structures": ["Yes. 'Structures' has a tangible appearance and refers to physical, built objects.\nA few things that are visually similar to 'structures' but are not 'structures' are:\trocks\tmountains\ttrees\tclouds\nThere are several useful visual features to tell there is 'structures' and not similar things in a photo:\tdesign or plan\tman-made\tconcrete or metallic texture\ttraditional or modern appearance.", 86], "playground": ["Yes. 'Playground' has a tangible appearance and is a specified area designed for children to play.\nA few things that are visually similar to 'playground' but are not 'playground' are:\tpark\tbackyard\tsport field\tpicnic area\t\nThere are several useful visual features to tell there is 'playground' and not similar things in a photo:\tswing sets\tslides\tjungle gyms\tmerry-go-rounds\tclimbing structures\trubberized or soft flooring in bright colors", 86], "display case": ["Yes. 'Display case' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'display case' but are not 'display case' are:\tbookcase\tshelf\tcountertop\tvitrine\nThere are several useful visual features to tell there is 'display case' and not similar things in a photo:\tclear glass or plastic walls\tshelving\tfor displaying items (such as artwork, trophies, or merchandise)\tlock or latch to keep items secure", 86], "pick": ["Yes. 'Pick' has a tangible appearance and is a tool used for digging or breaking hard surfaces.\nA few things that are visually similar to 'pick' but are not 'pick' are:\tshovel\thoe\taxe\nThere are several useful visual features to tell there is 'pick' and not similar things in a photo:\tmetallic material\tpointed end on one side and flat blade on the other\tergonomic handle\tfor digging or breaking hard surfaces", 86], "adult giraffe": ["Yes. 'Adult giraffe' has a tangible appearance and is an animal.\nA few things that are visually similar to 'adult giraffe' but are not 'adult giraffe' are:\tokapi\thorse\tzebra\nThere are several useful visual features to tell there is 'adult giraffe' and not similar things in a photo:\ttall and long neck\tspotting coat pattern small horns called ossicones\tnot striped legs\tlong, slender legs", 86], "sun visor": ["Yes. 'Sun visor' has a tangible appearance and is a type of automobile accessory.\nA few things that are visually similar to 'sun visor' but are not 'sun visor' are:\ttinted window\tsunglasses\tcap\that\nThere are several useful visual features to tell there is 'sun visor' and not similar things in a photo:\tattached to the roof of a car or a truck\thinged and adjustable to block the sun from the driver's eyes\ttypically made of vinyl or plastic", 86], "breakfast": ["Yes. 'Breakfast' has a tangible appearance and typically includes food items.\nA few things that are visually similar to 'breakfast' but are not 'breakfast' are:\tsnacks\tappetizers\tdesserts\tbaked goods\nThere are several useful visual features to tell there is 'breakfast' and not similar things in a photo:\teggs\tbread\tcereal\tmeat\tcoffee or tea\tfruit or juice\tpresence of a breakfast dish or utensils, such as a bowl of oatmeal or a fork and knife", 86], "square tiles": ["Yes. 'Square tiles' has a tangible appearance and refers to tiles with square shapes.\nA few things that are visually similar to 'square tiles' but are not 'square tiles' are:\trectangular tiles, mosaic, bricks\t\nThere are several useful visual features to differentiate 'square tiles' from similar things in a photo:\tperfectly square shape\tuniform size and thickness\tof the same material or color\tarranged in a grid pattern\tor at right angles to each other.", 86], "carpeting": ["Yes. 'Carpeting' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'carpeting' but are not 'carpeting' are: blankets, rugs, yoga mats, towels.\nThere are several useful visual features to tell there is 'carpeting' and not similar things in a photo: uniform pattern or texture running consistently throughout a large area; thicker and more permanent involvement pattern than a rug or mat; uniform synthetic or wool textures.", 86], "double-decker bus": ["Yes. 'Double-decker bus' has a tangible appearance and is a specific type of public transportation.\nA few things that are visually similar to 'double-decker bus' but are not 'double-decker bus' are:\tschool bus\ttrolley bus\ttram\tcoach bus\nThere are several useful visual features to distinguish 'double-decker bus' from other buses in a photo, including:\ttwo levels of seating\tan open-top on the top level\tlarge size compared to other vehicles\ttypical red color in the UK", 86], "water tank": ["Yes. 'Water tank' has a tangible appearance and is a container for storing water.\nA few things that are visually similar to 'water tank' but are not 'water tank' are:\tfuel tank\tpropane tank\tstorage container\tbin\tsilo\nThere are several useful visual features to tell there is 'water tank' and not similar things in a photo:\tcylindrical or rectangular shape\tmetallic or plastic material\tconnected to pipes or hoses\toften situated on high ground or a platform\tladder or stairs for access\tgenerally labeled with 'water'", 86], "information": ["No. 'Information' is too vague or abstract to be distinguished in a photo.", 86], "rag": ["Yes. 'Rag' has a tangible appearance and is a cloth used for cleaning or wiping.\nA few things that are visually similar to 'rag' but are not 'rag' are:\ttowel\twashcloth\ttablecloth\tnapkin\nThere are several useful visual features to tell there is 'rag' and not similar things in a photo:\tsmall and rectangular or square shape\tused or dirty appearance\tcan be made of various fabrics (cotton, microfiber, etc.)", 86], "eagle": ["Yes. 'Eagle' has a tangible appearance and is a type of bird of prey.\nA few things that are visually similar to 'eagle' but are not 'eagle' are:\thawk\tfalcon\tvulture\tosprey\nThere are several useful visual features to tell there is 'eagle' and not similar things in a photo:\tbeak and talons\tshort hooked beak\tbrown, black, or white feathers\tsharp and curved claws\tpowerful wingspan\tbroad and muscular wings\tfloats in air\tstreamlined body shape with a large head and pointed wings", 86], "awnings": ["Yes. 'Awnings' has a tangible appearance and is a type of roof-like cover over windows or doors.\nA few things that are visually similar to 'awnings' but are not 'awnings' are:\troofs\tshutters\tblinds\tcanopies\nThere are several useful visual features to tell there is 'awnings' and not similar things in a photo:\tattached above a window or door\tangled downward to provide shade or shelter\tmade of fabric, metal, or plastic\thas no sides, only a covering roof-like structure", 85], "capital letter": ["Yes. 'Capital letter' has a tangible appearance and refers to a specific type of alphabetic character.\nA few things that are visually similar to 'capital letter' but are not 'capital letter' are: lower case letters, punctuation marks (such as periods or commas), numbers\nThere are several useful visual features to tell there is 'capital letter' and not similar things in a photo:\t\n- Larger size than the surrounding letters\n- Marked with an initial uppercase letter in a sentence\n- Presence of certain features (such as serifs or crossbars) that are unique to uppercase letters in certain typefaces.", 85], "tablecloths": ["Yes. 'Tablecloths' has a tangible appearance and is a piece of cloth used to cover a table.\nA few things that are visually similar to 'tablecloths' but are not 'tablecloths' are:\ttowels\tcurtains\tblankets\tbedsheets\nThere are several useful visual features to tell there is 'tablecloths' and not similar things in a photo:\tdesigned to fit on a table typically rectangular or square in shape\tspecifically used to cover a tabletop, typically used during dining settings\tflows/sags over the edges of the table", 85], "signage": ["Yes. 'Signage' has a tangible appearance and refers to displays or boards specifically made for public information or branding purposes.\nA few things that are visually similar to 'signage' but are not 'signage' are:\tartwork\tonline banners\tpackaging\tlogos\nThere are several useful visual features to tell there is 'signage' and not similar things in a photo:\tdisplaying text or symbols\tthat provide directions/information\tfor advertising or identification\thanging or mounted in a public area.", 85], "lens": ["Yes. 'Lens' has a tangible appearance and is an optical device.\nA few things that are visually similar to 'lens' but are not 'lens' are:\teyeglasses\tbinoculars\tmicroscope\tcamera aperture\nThere are several useful visual features to tell there is 'lens' and not similar things in a photo:\tcylindrical or spherical shape\ttranslucent or transparent material\tconcave or convex surface\tfocusing mechanism (rings or buttons)", 85], "badge": ["Yes. 'Badge' has a tangible appearance and is a type of small emblem or token worn on clothing.\nA few things that are visually similar to 'badge' but are not 'badge' are:\tjewelry\tbutton\tpin\nThere are several useful visual features to tell there is 'badge' and not similar things in a photo:\tusually made of metal or plastic\thas a design or symbol on it\toften has text or numbers on it\toften worn on a uniform or ID card", 85], "bird beak": ["Yes. 'Bird beak' has a tangible appearance.\nA few things that are visually similar to 'bird beak' but are not 'bird beak' are:\tfish mouth\tfrog mouth\tturtle mouth\talligator snout\nThere are several useful visual features to tell there is 'bird beak' and not similar things in a photo:\thave no teeth\thave two parts: upper and lower\thave nostrils located on the top\thave different shapes and sizes depending on the bird's diet and habits", 85], "layers": ["Yes. 'Layers' has a tangible appearance and usually refers to a structure made up of multiple levels or thicknesses.\nA few things that are visually similar to 'layers' but are not 'layers' are:\ttextures\tpatterns\toverlapping shapes\tfolds\nThere are several useful visual features to tell there are 'layers' and not similar things in a photo:\tthe appearance of two or more distinct, parallel planes or sheets\tdifferent colors, textures, or materials in each layer\tshadows or lines that suggest depth and thickness", 85], "cloths": ["Yes. 'Cloths' has a tangible appearance and refers to textiles used for clothing or household items.\nA few things that are visually similar to 'cloths' but are not 'cloths' are:\tpaper\ttowels\trugs\tcurtauns\t\nThere are several useful visual features to tell there is 'cloths' and not similar things in a photo:\tfabric texture\tfabric pattern\tor color\tfabric drape or shape\ttype of clothing or household item, such as a shirt or tablecloth", 85], "wispy clouds": ["Yes. 'Wispy clouds' has a tangible appearance and is a type of cloud.\nA few things that are visually similar to 'wispy clouds' but are not 'wispy clouds' are:\tsmoke\tfog\tsteam\nThere are several useful visual features to tell there is 'wispy clouds' and not similar things in a photo:\tthin and feathery appearance\twhitish or grayish color\tlocated high in the sky\tformed by ice crystals or water droplets in the atmosphere\tmay be part of a larger cloud formation.", 85], "hotel room": ["Yes. 'Hotel room' has a tangible appearance and is a type of accommodation.\nA few things that are visually similar to 'hotel room' but are not 'hotel room' are:\tapartment\tbedroom\tdorm room\tclassroom\nThere are several useful visual features to distinguish a 'hotel room' from the listed similar things in a photo:\tDefinite hotel signage\tin-room furniture and amenities like TV, telephone, mini-fridge, closet, etc.\ta private bathroom with shower or bathtub\ta bed, typically with linens and pillows.", 85], "states": ["No. 'States' is too vague or abstract to be distinguished in a photo. \n\nHowever, if the context is referring to the '50 states of the United States of America', then the answer is yes. '50 states of the United States of America' has a tangible appearance and can be displayed on a map.\n\nA few things that are visually similar to '50 states of the United States of America' but are not '50 states of the United States of America' are:\tcountries\tcities\tcounties\n\nThere are several useful visual features to tell there are '50 states of the United States of America' and not similar things in a photo:\tshape of the states (typically rectangular or irregular)\trecognized outlines of the states (e.g., Florida is distinctive due to its long panhandle)\tstate names written on the map with recognizable abbreviations (e.g., CA for California, NY for New York)", 85], "knit cap": ["Yes. 'Knit cap' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'knit cap' but are not 'knit cap' are:\thats\tbeanies\theadbands\thelmets\nThere are several useful visual features to tell there is 'knit cap' and not similar things in a photo:\ta soft cap made of wool or a similar material\tclosely fitting the head\twoven or knitted texture with ribs or stripes\tfolded brim or no brim at all\ttypically covering the ears.", 85], "gutter": ["Yes. 'Gutter' has a tangible appearance and is a structure attached to a building that collects rainwater.\nA few things that are visually similar to 'gutter' but are not 'gutter' are:\tpipe\tconduit\tdrainage\tchannel\nThere are several useful visual features to tell there is 'gutter' and not similar things in a photo:\tattached to the edge of a roof\thorizontal\tslightly sloped\toften made of metal or plastic\thas a curve or a trough to collect water", 85], "light house": ["Yes. 'Light house' has a tangible appearance and is a type of architecture/navigation aid.\nA few things that are visually similar to 'light house' but are not 'light house' are:\twindmill\twater tower\tfactory chimney\nThere are several useful visual features to tell there is 'light house' and not similar things in a photo:\ttower-shaped building\twith a light on the top\tnavigational aids such as buoys or markers are nearby\tbuilt near or on the water.", 85], "panes": ["Yes. 'Panes' has a tangible appearance and refers to the individual sheets of glass in a window or door.\nA few things that are visually similar to 'panes' but are not 'panes' are:\tstripes\tbranches\tor any object with a narrow shape or linear pattern\nThere are several useful visual features to tell there are 'panes' and not similar things in a photo:\trectangular shapes\twithin a frame or a border\ttransparency or semi-transparency\tsmooth, flat surface with no texture or pattern.", 84], "police officers": ["Yes. 'Police officers' have a tangible appearance and can be identified based on their attire and equipment.\nA few things that are visually similar to 'police officers' but are not 'police officers' are:\tsecurity guards\tmilitary personnel\tcostumed characters\nThere are several useful visual features to tell there is 'police officers' and not similar things in a photo:\tuniforms with badges, patches, or insignia\tduty belt with tools such as handcuffs\tand guns or tasers\tflashing lights or sirens on cars or motorcycles\tbadges or identification cards\twith the word \"police\"\tor \"officer\" on them.", 84], "teal": ["Yes. 'Teal' has a tangible appearance and refers to a specific color.\nA few things that are visually similar to 'teal' but are not 'teal' are:\tturquoise\taqua\tblue-green\nThere are several useful visual features to tell there is 'teal' and not similar things in a photo:\ta blue-green color with a low saturation\tsimilar to the color of a duck's neck or a peacock's feathers", 84], "mozzarella cheese": ["Yes. 'Mozzarella cheese' has a tangible appearance and is a type of cheese.\nA few things that are visually similar to 'mozzarella cheese' but are not 'mozzarella cheese' are:\tcheddar cheese\tswiss cheese\tcream cheese\tcottage cheese\nThere are several useful visual features to tell there is 'mozzarella cheese' and not similar things in a photo:\twhite or off-white color \tstretchy and elastic texture\tsmooth surface\tsemi-soft consistency\tin the shape of a ball or cylinder", 84], "gravy": ["Yes. 'Gravy' has a tangible appearance and is a type of sauce.\nA few things that are visually similar to 'gravy' but are not 'gravy' are:\tsoup\tsyrup\tsalsa\tmarinara sauce\nThere are several useful visual features to tell there is 'gravy' and not similar things in a photo:\tthick and smooth texture\tbrown color\tpouring or drizzling from a ladle or spoon\ttypically served with meat or potatoes", 84], "splashes": ["Yes. 'Splashes' has a tangible appearance and is a type of liquid movement.\nA few things that are visually similar to 'splashes' but are not 'splashes' are: waves, ripples, drips, drops, spills.\nThere are several useful visual features to tell there are 'splashes' and not similar things in a photo: water droplets flying in different directions, visible impact points where the liquid hit a surface, dynamic and irregular shapes of the splashes, often accompanied by mist or bubbles.", 84], "soccer": ["Yes. 'Soccer' has a tangible appearance and is a type of sport.\nA few things that are visually similar to 'soccer' but are not 'soccer' are:\tbasketball\tvolleyball\trugby\tfootball\thandball\nThere are several useful visual features to tell there is 'soccer' and not similar things in a photo:\tlarge field with rectangular markings\ttwo goals on opposite sides of the field\tround ball with black and white patches\ton-field players wearing soccer cleats", 84], "purple umbrella": ["Yes. 'Purple umbrella' has a tangible appearance and is a type of umbrella.\nA few things that are visually similar to 'purple umbrella' but are not 'purple umbrella' are:\tblue umbrella\tgreen umbrella\tyellow umbrella\tpink umbrella\nThere are several useful visual features to tell there is 'purple umbrella' and not similar things in a photo:\tdome-shaped\ttopped with a point\tpurple color\twater-resistant material\thandheld with a curved handle", 84], "leather couch": ["Yes. 'Leather couch' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'leather couch' but are not 'leather couch' are:\tsofa\tmattress\tchair\tloveseat\nThere are several useful visual features to tell there is 'leather couch' and not similar things in a photo:\tlong and rectangular shape\tpadded and stuffed\twith backrest and armrests\tupholstered in smooth and glossy leather material", 84], "snow pants": ["Yes, 'snow pants' is a visually concrete concept.\nA few things that are visually similar to 'snow pants' but are not 'snow pants' are:\ttrousers, hiking pants, sweatpants.\nThere are several useful visual features to tell there are 'snow pants' and not similar things in a photo:\tthick insulation, often made of down or synthetic material\twater-resistant outer shell or coating\toften have suspenders or adjustable waist\tand may have reinforced knees and seat for durability\twhen worn, usually accompanied by winter boots to provide further warmth and waterproofing", 84], "garnish": ["Yes, 'garnish' has a visually concrete concept, and can be seen as a decoration on a dish.\nA few things that are visually similar to 'garnish' but are not 'garnish' are:\tfood decoration\tcenterpiece\tedible arrangement\nThere are several useful visual features to tell there is 'garnish' and not similar things in a photo:\tsmall amount of decoration\tplaced on top of or around the dish\tpurely decorative and not essential to the dish's taste or structure.", 84], "bulls": ["Yes. 'Bulls' has a tangible appearance and is a kind of animal.\nA few things that are visually similar to 'bulls' but are not 'bulls' are:\tcows\tyaks\tbison\tmusk oxen\t\nThere are several useful visual features to tell there is 'bulls' and not similar things in a photo:\tlarge horns\twith muscular bodies and stocky legs\tmale cattle with the reproductive organ between their hind legs\tappearance may vary in color and patterns depending on the breed\tdistinctive hump of muscle above their shoulders.head is long and wide with a crooked poll, narrowing toward a broad, rounded forehead.", 84], "puddles": ["Yes. 'Puddles' have a tangible appearance and are pools of water on the ground.\nA few things that are visually similar to 'puddles' but are not 'puddles' are:\tspilled liquids\twet patches on the ground\treflections on surfaces\nThere are several useful visual features to tell there is 'puddles' and not similar things in a photo:\twatery surface\tmirrored reflections of the surroundings\ton the ground or a flat surface\twith no clear source of water nearby.", 84], "light pole": ["Yes. 'Light pole' has a tangible appearance.\nA few things that are visually similar to 'light pole' but are not 'light pole' are:\ttraffic cone\tbarricade\tparking meter\tflagpole\nThere are several useful visual features to tell there is 'light pole' and not similar things in a photo:\ttall, slender post\tattached light fixture\tor lamp\thead with luminaires\tor bulbs\tsturdy base\tfor stability\theight approximately 15-40 feet", 84], "portrait": ["Yes. 'Portrait' has a tangible appearance and is a type of visual art that depicts a person or animal.\nA few things that are visually similar to 'portrait' but are not 'portrait' are:\tlandscape\tpainting\tprinted photograph\tcandid shot\tgroup photo\nThere are several useful visual features to tell there is 'portrait' and not similar things in a photo:\tfocused on an individual or animal\tclose up of face or body of individual or animal\tintentional and posed composition\tof an individual or animal", 84], "bear cub": ["Yes. 'Bear cub' has a tangible appearance and is a juvenile bear.\nA few things that are visually similar to 'bear cub' but are not 'bear cub' are:\tstuffed animal\tdog\tfox\traccoon\nThere are several useful visual features to tell there is 'bear cub' and not similar things in a photo:\tfurry body\tshort round ears\tround face\twith short snout and black nose\tshort and round legs", 84], "numerals": ["Yes. 'Numerals' has a visually concrete concept and refers to symbols used to represent numbers.\nA few things that are visually similar to 'numerals' but are not 'numerals' are:\tletters\tpunctuation marks\thieroglyphs\tornamental symbols\nThere are several useful visual features to tell there is 'numerals' and not similar things in a photo:\ta symbol that represents a number\tcan be Arabic, Roman or other numeral systems\ta sequence of numerals is usually a counting sequence or a calculation", 83], "bed frame": ["Yes. 'Bed frame' has a tangible appearance and is a supporting structure for a mattress.\nA few things that are visually similar to 'bed frame' but are not 'bed frame' are:\tbox spring\theadboard\tfuton\tframe with storage\nThere are several useful visual features to tell there is 'bed frame' and not similar things in a photo:\trectangular or square shape\televated to support a mattress\tfour legs or feet\tsolid or slatted structure for support\tMostly made of wood, metal or upholstered material.", 83], "windowsill": ["Yes. 'Windowsill' has a tangible appearance and is a part of a window.\nA few things that are visually similar to 'windowsill' but are not 'windowsill' are:\tshelf\tledge\tcounter\t\nThere are several useful visual features to tell there is 'windowsill' and not similar things in a photo:\tlocated beneath the window\tframe the window\tusually made of wood, stone, or metal", 83], "tea cup": ["Yes. 'Tea cup' has a tangible appearance and is a type of drinking vessel.\nA few things that are visually similar to 'tea cup' but are not 'tea cup' are:\tcoffee cup\tmug\tglass\nThere are several useful visual features to tell there is 'tea cup' and not similar things in a photo:\thandle\tcylindrical shape\tdelicate material, such as porcelain or bone china\tsaucer (optional)", 83], "gold ring": ["Yes. 'Gold ring' has a tangible appearance and is a piece of jewelry.\nA few things that are visually similar to 'gold ring' but are not 'gold ring' are:\tbrass ring\tcopper ring\tpromissory ring\tring pop\tcandy\nThere are several useful visual features to tell there is 'gold ring' and not similar things in a photo:\tcircular in shape\tmade of gold or gold-colored metal\twith or without stones or embellishments\tworn on a finger.", 83], "tree leaves": ["Yes. 'Tree leaves' has a tangible appearance and is a part of a plant.\nA few things that are visually similar to 'tree leaves' but are not 'tree leaves' are:\tflower petals\therbs\tseaweed\tmoss\nThere are several useful visual features to tell there is 'tree leaves' and not similar things in a photo:\n-Flat and thin structure\n-Vascular system \n-Variety of shapes and colors depending on tree species\n-Patterns, veins, and edges of the leaves", 83], "hut": ["Yes. 'Hut' has a tangible appearance.\nA few things that are visually similar to 'hut' but are not 'hut' are:\tshack\tbarn\thouse\tcabin\nThere are several useful visual features to tell there is 'hut' and not similar things in a photo:\tsmall, simple structure\tmade of natural materials such as wood, grass, or straw\troof made from thatch, bark or leaves\tno chimney or windows.", 83], "power outlet": ["Yes. 'Power outlet' has a tangible appearance and is a type of electrical socket.\nA few things that are visually similar to 'power outlet' but are not 'power outlet' are:\tspeaker\tjack\tusb port\tmicrophone\nThere are several useful visual features to tell there is 'power outlet' and not similar things in a photo:\telectrical socket\twith two or three holes on the face\tof various shapes, but typically rectangular or round\twith metal contacts or prongs\ton a wall or in the floor\twith power flowing through it (indicated by a light, plug, or cord)", 83], "blonde girl": ["Yes. 'Blonde girl' has a tangible appearance and refers to a female person with fair hair.\nA few things that are visually similar to 'blonde girl' but are not 'blonde girl' are:\tboy with blonde hair\twoman with blonde hair\tblonde wig\nThere are several useful visual features to tell there is 'blonde girl' and not similar things in a photo:\tfemale person\tfair hair, usually light yellow or golden\toften styled into waves or curls\tlashes and brows are usually lighter than the hair\tcolor of clothes and makeup can be an additional point of identification", 83], "advertising sign": ["Yes. 'Advertising sign' has a tangible appearance and is a type of sign used for promoting products or services.\nA few things that are visually similar to 'advertising sign' but are not 'advertising sign' are:\tdirection sign\tcaution sign\tstreet name sign\tdecorative sign\nThere are several useful visual features to tell there is 'advertising sign' and not similar things in a photo:\tlogo or brand name\tproduct or service information\tbold colors or images\thanging from a building or a structure", 83], "orange lights": ["Yes. 'Orange lights' has a tangible appearance and is a type of light.\nA few things that are visually similar to 'orange lights' but are not 'orange lights' are:\tfire campfire light\tstage lighting\tcandlelight\tlight from a sunset or sunrise\nThere are several useful visual features to tell there is 'orange lights' and not similar things in a photo:\tpredominantly orange in color\temitting from a light bulb or a source\tsource is visible (i.e. not coming from a natural source like the sun)", 83], "skyscrapers": ["Yes. 'Skyscrapers' has a tangible appearance and refers to tall buildings.\nA few things that are visually similar to 'skyscrapers' but are not 'skyscrapers' are:\ttowers\tchurch steeples\t\nThere are several useful visual features to tell there are 'skyscrapers' and not similar things in a photo:\tvery tall buildings\tmultiple floors or levels usually more than 10 stories high\tnarrow base and wider top varying in shape though often rectangular or triangular.", 83], "ripe banana": ["Yes. 'Ripe banana' has a tangible appearance and is a kind of fruit.\nA few things that are visually similar to 'ripe banana' but are not 'ripe banana' are:\tunripe banana\tplantains\tcucumbers\tyellow zucchini\nThere are several useful visual features to tell there is 'ripe banana' and not similar things in a photo:\tyellow color\tmushy\ttexture\twith brown spots or streaks", 83], "slide": ["Yes. 'Slide' has a tangible appearance and is a type of playground equipment.\nA few things that are visually similar to 'slide' but are not 'slide' are:\tramp\tstairs\tbanister\tladder\nThere are several useful visual features to tell there is 'slide' and not similar things in a photo:\tinclined surface\tsleek and smooth\ttextured or grooved surface for grip\ta ladder or steps leading to the top of the slide\tan open top or an enclosed tube", 83], "life": ["No. 'Life' is too vague or abstract to be distinguished in a photo.", 83], "maroon": ["Yes. 'Maroon' has a tangible appearance and is a specific deep reddish-brown color.\nA few things that are visually similar to 'maroon' but are not 'maroon' are:\tburgundy\tred\twine\tdark brown\nThere are several useful visual features to tell there is 'maroon' and not similar things in a photo:\tdeep reddish-brown color\tlacks orange or purple tones.", 82], "dip": ["Yes. 'Dip' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'dip' but are not 'dip' are:\traw ingredients, sauces, spreads, dressings, condiments.\nThere are several useful visual features to tell there is 'dip' and not similar things in a photo:\tthick and creamy texture\tmostly used for dipping vegetables or chips\tintense and specific flavors", 82], "crumb": ["Yes. 'Crumb' has a tangible appearance and is a small fragment of food, typically bread or cake.\nA few things that are visually similar to 'crumb' but are not 'crumb' are:\tdust\tdirt\tparticles\tof sand\nThere are several useful visual features to tell there is 'crumb' and not similar things in a photo:\tfragment or piece of food\tthe texture of bread, cake, or similar baked goods\tcrumbs tend to be soft or powdery rather than coarse or grainy\tin a food context, surrounded by other similar fragments or crumbs", 82], "wood shelf": ["Yes, 'wood shelf' has a tangible appearance and looks like a flat surface mounted on brackets or supports for holding objects.\nA few things that are visually similar to 'wood shelf' but are not 'wood shelf' are:\tmantel\tfireplace ledge\tcounter\ttop of a piece of furniture\nThere are several useful visual features to tell there is 'wood shelf' and not similar things in a photo:\n- It is a freestanding or mounted flat surface.\n- It is made of wood or wood-like material.\n- It has brackets or other supports holding it up.\n- It is designed to hold objects such as books, vases or other items.", 82], "syrup": ["Yes. 'Syrup' has a tangible appearance and is a sticky liquid used as a sweetener.\nA few things that are visually similar to 'syrup' but are not 'syrup' are:\thoney\tmolasses\tmaple-flavored syrup\tcaramel\tsauce\nThere are several useful visual features to tell there is 'syrup' and not similar things in a photo:\tthick\tsticky\tviscous texture\ttranslucent or transparent\tgolden or brown color\tpoured over or alongside food", 82], "sink faucet": ["Yes. 'Sink faucet' has a tangible appearance and is a functional object.\nA few things that are visually similar to 'sink faucet' but are not 'sink faucet' are:\tsoap dispenser\twater dispenser\tair freshener\nThere are several useful visual features to tell there is 'sink faucet' and not similar things in a photo:\ta handle or knob for turning on the water\ta spout or nozzle for directing the water\ta base or mounting for attaching to the sink or countertop\tchrome or metal finish for a sleek look and easy cleaning", 82], "silver suv": ["Yes. 'Silver SUV' has a tangible appearance and is a specific type of vehicle.\nA few things that are visually similar to 'silver SUV' but are not 'silver SUV' are:\twhite SUV\tgrey SUV\tblack SUV\tmetallic car\nThere are several useful visual features to tell there is 'silver SUV' and not similar things in a photo:\tcolor of the vehicle\tsize and shape of the SUV's body\tstyle and design of the vehicle\tmodel and make of the SUV", 82], "porcelain": ["Yes. 'Porcelain' has a tangible appearance and is a type of ceramic material.\nA few things that are visually similar to 'porcelain' but are not 'porcelain' are:\tchina pottery\tceramic tiles\tmarble\nThere are several useful visual features to tell there is 'porcelain' and not similar things in a photo:\twaxy appearance\tsmooth texture\tdelicate and thin\ttranslucent white or ivory color\tmay have intricate designs or patterns", 82], "brick walkway": ["Yes. 'Brick walkway' has a tangible appearance and is a type of pathway made of bricks.\nA few things that are visually similar to 'brick walkway' but are not 'brick walkway' are:\tconcrete pathway\tstone pathway\ttile pathway\nThere are several useful visual features to tell there is 'brick walkway' and not similar things in a photo:\tarrangement of rectangular bricks in a herringbone pattern\tred or brown color and texture\tsymmetrical and evenly spaced bricks", 82], "blue object": ["No. 'Blue object' is too vague or abstract to be distinguished in a photo.", 82], "carts": ["Yes. 'Carts' has a tangible appearance and is a type of vehicle used for transportation.\nA few things that are visually similar to 'carts' but are not 'carts' are:\tbicycles\twheelbarrows\tstrollers.shopping carts\nThere are several useful visual features to tell there is 'carts' and not similar things in a photo:\tfour wheels\tusually pulled or pushed by a person or an animal\tcould be loaded with goods or people long handle or reins for steering or control.", 82], "airplane wing": ["Yes. 'Airplane wing' has a tangible and distinct appearance.\nA few things that are visually similar to 'airplane wing' but are not 'airplane wing' are:\tbird wing\t glider wing\t kite wing\nThere are several useful visual features to distinguish 'airplane wing' from the listed similar things in a photo:\trectangular or triangular shape\tmetallic surface or covered with a smooth fabric\tflap sections or slats\tattached to the side of an airplane or a wing-body junction", 82], "cow grazing": ["Yes. 'Cow grazing' has a tangible appearance and is a familiar farm scene.\nA few things that are visually similar to 'cow grazing' but are not 'cow grazing' are:\thorse grazing\tsheep grazing\tfield of grass\nThere are several useful visual features to tell there is 'cow grazing' and not similar things in a photo:\tcow with distinctive black and white markings\tgrazing or eating grass in a field\tor farm setting with barn or farm equipment\tin a group or alone.", 82], "support beam": ["Yes. 'Support beam' has a tangible appearance and is a part of the structure.\nA few things that are visually similar to 'support beam' but are not 'support beam' are:\tcolumns\tpillars\tfences\twalls\nThere are several useful visual features to tell there is 'support beam' and not similar things in a photo:\tlong and rectangular or round shape\tmetal or wood material\tcarrying or supporting weight of a building or a structure\thigher than wide and or thicker than other beams or pillars in a structure.", 82], "wooden frame": ["Yes. 'Wooden frame' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'wooden frame' but are not 'wooden frame' are:\tfence\tbookshelf\tdoor\ttable\nThere are several useful visual features to tell there is 'wooden frame' and not similar things in a photo:\tmade of wood or resembling wood\trectangular or square shape\tcreated to surround or support something, such as a picture or a building\tcomponent parts, such as corners and joints", 82], "slippers": ["Yes. 'Slippers' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'slippers' but are not 'slippers' are:\tloafers\tsandals\tsneakers\tboots\t\nThere are several useful visual features to tell there is 'slippers' and not similar things in a photo:\tsoft and comfortable material\topened back and toe area\tslip-on design rather than laces or straps\tlight and casual design intended for indoor use", 82], "rooster": ["Yes. 'Rooster' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'rooster' but are not 'rooster' are:\tchicken\tpheasant\tpeacock\tturkey\nThere are several useful visual features to tell there is 'rooster' and not similar things in a photo:\tbrightly colored feathers, often with a colorful and iridescent tail and head\tfleshy red comb and wattle on top of the head\tsharp, pointed beak\tlong neck and legs", 82], "coffee machine": ["Yes. 'Coffee machine' has a tangible appearance and is a type of appliance used for making coffee.\nA few things that are visually similar to 'coffee machine' but are not 'coffee machine' are:\tkettle\tpot\tteapot\tespresso maker\nThere are several useful visual features to tell there is 'coffee machine' and not similar things in a photo:\tdedicated buttons or controls for different types of coffee\tcoffee bean grinder\tsteam or water dispenser\tmilk frother or steamer\tcan hold a coffee pot or cup under the spout", 81], "wrap": ["Yes. 'Wrap' has a tangible appearance and refers to a covering or a material used for wrapping.\nA few things that are visually similar to 'wrap' but are not 'wrap' are:\tblanket\tscarf\tcurtain\tpaper\nThere are several useful visual features to distinguish 'wrap' from the listed similar things in a photo: used for covering another object or material, often in a specific shape or form; may be made of fabric or paper; may have fold lines or creases; may be tied or fastened with a bow or ribbon.", 81], "rug floor": ["No. 'Rug floor' is not a well-defined concept; it is a combination of two separate concepts, rug and floor. \n\nHowever, a few things that are visually similar to 'rug' but are not 'rug floor' are: \ncarpet, mat, tapestry, blanket.\n\nAnd for 'floor', things that are visually similar but not 'rug floor' are: \ntiles, hardwood, concrete, vinyl.\n\nUseful visual features for distinguishing 'rug floor' from other types of floor coverings in a photo are: \na textile floor covering, having a thick pile, used for decorative purposes, often with intricate or colorful patterns, while the other types of flooring have a more uniform appearance without patterns, and are not made of textiles, and often have more uniform surfaces.", 81], "bridges": ["Yes. 'Bridges' has a tangible appearance and is a structure built to span physical obstacles.\nA few things that are visually similar to 'bridges' but are not 'bridges' are:\tdams\tviaducts\taqueducts\ttunnels\nThere are several useful visual features to tell there is 'bridges' and not similar things in a photo:\tconnects two points over an obstacle (river, canyon, etc.)\thas a deck (surface for people, vehicles or trains)\thas at least one support (pillar, foundation, cable, etc.)", 81], "limb": ["Yes. 'Limb' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'limb' but are not 'limb' are:\ttree branch\ttool handle\tpole\tfence post\nThere are several useful visual features to tell there is 'limb' and not similar things in a photo:\tattached to a torso or body\thave joints, such as knee or elbow\thave muscles, veins, or skin texture that indicate it is part of the body.", 81], "bean": ["Yes. 'Bean' has a tangible appearance and is a type of seed or legume.\nA few things that are visually similar to 'bean' but are not 'bean' are:\tpebble\tcoffee bean\tm&m's\tchocolate chips\nThere are several useful visual features to tell there is 'bean' and not similar things in a photo:\toval or round shape\toften split down the middle to show inner flesh or dividing line\tusually brown, black, white, or green in color", 81], "fire place": ["Yes. 'Fire place' has a tangible appearance and is a structure used to contain a fire for heating.\nA few things that are visually similar to 'fire place' but are not 'fire place' are:\twood stove\tcampfire\tbarbecue grill\theater\nThere are several useful visual features to tell there is 'fire place' and not similar things in a photo:\tbricks or stone structure\tchimney\tfireplace screen or door\tfire tools (poker, shovel, brush)", 81], "fishing boat": ["Yes. 'Fishing boat' has a tangible appearance and is a type of boat used for fishing.\nA few things that are visually similar to 'fishing boat' but are not 'fishing boat' are:\tyacht\tspeedboat\tcanoe\tkayak\t\nThere are several useful visual features to tell there is 'fishing boat' and not similar things in a photo:\t\nlarger in size than a kayak or canoe\t\nhas equipment such as fishing nets, lines, or traps\t\nmay have a raised deck or cabin for storing fish and other equipment\t\nmay have a flag or banner indicating its origin or affiliation with a particular fishing community.", 81], "cotton tee shirt": ["Yes. 'Cotton tee shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'cotton tee shirt' but are not 'cotton tee shirt' are:\tlinen shirt\tpolyester shirt\tsilk shirt\ttank top\nThere are several useful visual features to tell there is 'cotton tee shirt' and not similar things in a photo:\tshort sleeves\tround neckline\tsoft and breathable material\tloose and casual fit\ttypically plain or a simple pattern in design", 81], "cat eyes": ["Yes. 'Cat eyes' has a tangible appearance and refers to the specific appearance of the eyes of a cat.\nA few things that are visually similar to 'cat eyes' but are not 'cat eyes' are:\tsnake eyes\tlizard eyes\nThere are several useful visual features to tell there are 'cat eyes' and not similar things in a photo:\tupward slanting pupils\tgreen, yellow, or blue color\tsharp, clear focus on the center of the eye\twhite reflection in low light conditions", 81], "fighter jet": ["Yes. 'Fighter jet' has a tangible appearance and is a type of military aircraft.\nA few things that are visually similar to 'fighter jet' but are not 'fighter jet' are:\tcommercial airplane\thelicopter\tdrone\tglider\nThere are several useful visual features to tell there is 'fighter jet' and not similar things in a photo:\tsleek and aerodynamic body\tsharp and pointed nose\twings with missile or bomb attachments\tcockpit with a canopy or a windshield\tno visible propellers or rotors", 81], "bar stool": ["Yes. 'Bar stool' has a tangible appearance and is a type of chair.\nA few things that are visually similar to 'bar stool' but are not 'bar stool' are:\tkitchen stool\tlaboratory stool\tmedical stool\nThere are several useful visual features to tell there is 'bar stool' and not similar things in a photo:\ttall height\tslim legs\tbackless seat\tcircular or square seat\ttop placement of legs to the floor", 81], "exterior": ["Yes. 'Exterior' has a tangible appearance and is a part of a building or structure.\nA few things that are visually similar to 'exterior' but are not 'exterior' are:\tinteriors\tdecorations\tartwork\tfurniture\nThere are several useful visual features to tell there is 'exterior' and not similar things in a photo:\tview of the outside of a building or structure\twalls, doors, windows, roof, and other external elements of a structure\toutdoor features such as landscaping, pavement, and lighting\tclean and unobstructed view of the outside", 81], "yacht": ["Yes. 'Yacht' has a tangible appearance and is a type of leisure boat.\nA few things that are visually similar to 'yacht' but are not 'yacht' are:\tspeedboat\tcruise ship\traft\tkayak\nThere are several useful visual features to tell there is 'yacht' and not similar things in a photo:\tlarge size and luxurious appearance\tclean lines and slim shape\tmultiple decks or levels\tof yacht's amenities and features, such as helipad or swimming pool", 81], "pitchers": ["Yes. 'Pitchers' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'pitchers' but are not 'pitchers' are:\tjugs\tvases\turns\tbottles\nThere are several useful visual features to tell there is 'pitchers' and not similar things in a photo:\twide spout on one end\thandle on one side\tnarrow neck\tbase that can stand on its own\tmade of ceramic, glass, or metal", 80], "wood pole": ["Yes. 'Wood pole' has a tangible appearance and is a type of wooden object.\nA few things that are visually similar to 'wood pole' but are not 'wood pole' are:\ttree\ttrunk\tfence\tpost\nThere are several useful visual features to tell there is 'wood pole' and not similar things in a photo:\tsmooth or slightly textured surface\ttapered or uniform thickness\tcylindrical or square shape\tknots or rings in the wood\tgrain pattern in the wood", 80], "overhang": ["Yes. 'Overhang' has a tangible appearance and is a type of architectural feature.\nA few things that are visually similar to 'overhang' but are not 'overhang' are: awning, eaves, sloping roof\nThere are several useful visual features to tell there is 'overhang' and not similar things in a photo: extends beyond the edge of the building, sheltering a space or doorway; can be made of the same material as the building or different material.", 80], "tail section": ["Yes. 'Tail section' has a tangible appearance and is a part of an aircraft.\nA few things that are visually similar to 'tail section' but are not 'tail section' are:\twing\tcockpit\tlanding gear\tfuselage\tmotor\nThere are several useful visual features to tell there is 'tail section' and not similar things in a photo:\tlocated at the rear of the aircraft may contain the rudder, elevators, trim tabs, and other structures\tmay have the aircraft's registration number visible on it\tthe shape and size of a tail section are unique to each make and model of aircraft may have branding or logos present.", 80], "prongs": ["Yes. 'Prongs' has a tangible appearance and is a type of tool or utensil.\nA few things that are visually similar to 'prongs' but are not 'prongs' are:\tforks\ttongs\tscoops\tclaws\nThere are several useful visual features to tell there is 'prongs' and not similar things in a photo:\ttwo or more pointed and curved extensions\tsometimes attached to a handle or a base\tused for gripping, holding, or manipulating objects", 80], "logos": ["Yes. 'Logos' has a tangible appearance and refers to a distinctive symbol or design representing a company or organization.\nA few things that are visually similar to 'logos' but are not 'logos' are:\tpatterns\tsymbols\ticons\tletters\nThere are several useful visual features to tell there is 'logos' and not similar things in a photo:\tdistinctive design or symbol\tusually includes a company or organization name\tor a prominent letter or image\tthat identifies the brand or entity", 80], "ketchup bottle": ["Yes. 'Ketchup bottle' has a tangible appearance and is a type of condiment container.\nA few things that are visually similar to 'ketchup bottle' but are not 'ketchup bottle' are:\tmustard bottle\thot sauce bottle\toil container\tvinegar bottle\nThere are several useful visual features to tell there is 'ketchup bottle' and not similar things in a photo:\tred in color\tcurved neck\tsqueeze top or nozzle\tlabel with the word \"ketchup\" or \"catsup\"", 80], "drawings": ["Yes. 'Drawings' has a tangible appearance and refers to pictures or images made with a pencil, pen, or other medium.\nA few things that are visually similar to 'drawings' but are not 'drawings' are:\tpaintings\tphotographs\tengravings\tprints\tsculptures\nThere are several useful visual features to tell there is 'drawings' and not similar things in a photo:\toutlines or strokes made with a pen, pencil, or brush\tpaper texture or visible edges\tuse of a monochromatic or limited color palette\tsketch-like or unfinished appearance", 80], "pines": ["Yes. 'Pines' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'pines' but are not 'pines' are:\tfir trees\tcedars\tjunipers\tyews\nThere are several useful visual features to tell there is 'pines' and not similar things in a photo:\tlong and slender green needles\tgreen or brown pine cones\tscaly bark that is often furrowed into ridges or plates\tsharp and pointed needles arranged in clusters of 2-5.", 80], "silver cell phone": ["Yes. 'Silver cell phone' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'silver cell phone' but are not 'silver cell phone' are:\tsilver calculator\tsilver remote control\tsilver digital camera\tsilver MP3 player\tsilver portable game console\t\nThere are several useful visual features to tell there is 'silver cell phone' and not similar things in a photo:\tretangular shape with rounded edges\ttouch screen or physical buttons\tsilver color\tsmall size with a black screen\tdisplaying the time or the phone's interface", 80], "silver device": ["Yes. 'Silver device' has a tangible appearance, but it is still a bit vague.\nA few things that are visually similar to 'silver device' but are not 'silver device' are:\tsilverware\tcutlery\tmedical device\telectronic device\tdecorative item\nThere are several useful visual features to tell there is 'silver device' and not similar things in a photo:\tmade of silver or silver-colored metal\thas a specific function or purpose\tcontains buttons, screens, or switches (if applicable)\tunique shape or design that sets it apart from other silver objects.", 80], "shadow wall": ["Yes. 'Shadow wall' has a tangible appearance and is usually a dark-colored wall where shadows are cast.\nA few things that are visually similar to 'shadow wall' but are not 'shadow wall' are:\tplain dark-colored wall\twall with graffiti\twall with dirt and stains\twall with a dark mural\nThere are several useful visual features to tell there is 'shadow wall' and not similar things in a photo:\tshadows cast on the wall\tlight source that causes the shadows to be cast\tdark-colored wall with a contrast between the shadow and the rest of the wall", 80], "computer monitors": ["Yes. 'Computer monitors' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'computer monitors' but are not 'computer monitors' are:\tTV screens\tscreen doors\tiPad screens\tlaptop screens\nThere are several useful visual features to tell there is 'computer monitors' and not similar things in a photo:\trectangular shape\t flat screen\tdisplaying computer graphics or text\t connected to a computer tower or laptop", 80], "silver chain": ["Yes. 'Silver chain' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'silver chain' but are not 'silver chain' are:\tnecklace\tbracket\tkeychain\tcurtain\nThere are several useful visual features to tell there is 'silver chain' and not similar things in a photo:\tmade of silver or silver-colored metal\tshiny, reflective surface\tcomposed of interlocking links or rings\ttypically worn around the neck or wrist", 80], "mini van": ["Yes. 'Mini van' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'mini van' but are not 'mini van' are:\tsuv\tsedan\ttruck\thatchback\nThere are several useful visual features to tell there is 'mini van' and not similar things in a photo:\tboxy shape with a sliding door on one or both sides\tseating for multiple passengers in rows behind the driver's seat and front passenger seat\tlarger cargo area than a sedan or SUV", 80], "booth": ["Yes. 'Booth' has a tangible appearance and is a small enclosed space.\nA few things that are visually similar to 'booth' but are not 'booth' are:\tcubicle\tpod\tcapsule\tcontainer\ttent\nThere are several useful visual features to tell there is 'booth' and not similar things in a photo:\tenclosed space with walls or panels\tsmall size, enough for 1-2 people to stand or sit inside\tcounter or table for work or service\twindow or opening for communication or display", 80], "motor bike": ["Yes, 'motor bike' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motor bike' but are not 'motor bike' are:\tbicycle\tscooter\ttricycle\t\nThere are several useful visual features to differentiate a 'motor bike' from similar things in a photo:\t\n- Two wheels\n- A motor or engine\n- Handlebars for steering\n- A seat for the rider\n- A gas tank \n- A large front headlight\n- Long fork in the front end \n- An exhaust pipe \n- A chain drive \n- A kickstand.", 80], "analog clock": ["Yes. 'Analog clock' has a tangible appearance and is a type of time-keeping device.\nA few things that are visually similar to 'analog clock' but are not 'analog clock' are:\tdigital clock\torbiting planets\tcalendar\tthermometer\nThere are several useful visual features to tell there is 'analog clock' and not similar things in a photo:\tcircular shape\ttwo or three hands\tticks or markers\tfor the hours and minutes\tnumbers or Roman numerals\tfor the hours and sometimes minutes\tthe shape of hands\tfor hours, minutes, and sometimes seconds", 80], "rabbit": ["Yes. 'Rabbit' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'rabbit' but are not 'rabbit' are:\thare\tsquirrel\tchipmunk\nThere are several useful visual features to tell there is 'rabbit' and not similar things in a photo:\tround body with fur\tsoft, long ears\tthat come to a point\tgap between front teeth\tlong, powerful hind legs\tfurry tail", 80], "wind shield": ["Yes. 'Wind shield' has a tangible appearance and refers to a protective shield on a vehicle.\nA few things that are visually similar to 'wind shield' but are not 'wind shield' are:\twindow\tglass screen\tgoggles\thelmet visor\nThere are several useful visual features to tell there is 'wind shield' and not similar things in a photo:\tattached to a vehicle\tcurved and sloping shape\ttinted or transparent material\tprotects passengers from wind, dust, and insects.", 80], "drawing": ["Yes. 'Drawing' has a tangible appearance and refers to a picture or sketch made by hand or by using techniques such as painting or digital illustration.\nA few things that are visually similar to 'drawing' but are not 'drawing' are: photograph, print, stencil, decal.\nThere are several useful visual features to tell there is 'drawing' and not similar things in a photo:\thand-drawn or painted strokes\tpencil or pen lines\tevident brushstrokes or shading\tdigital or graphic design appearance.", 80], "tan dog": ["Yes. 'Tan dog' has a tangible appearance and refers to a dog with a tan colored coat.\nA few things that are visually similar to 'tan dog' but are not 'tan dog' are:\tfox\tcamel\tTexture in sand or rocks\nThere are several useful visual features to tell there is 'tan dog' and not similar things in a photo:\ta four-legged mammal with a tail and fur\ta pointed snout\tdog tags or a collar specifically for pets\tan identifiable breed, such as a Labrador Retriever or Golden Retriever.", 79], "relish": ["Yes. 'Relish' has a tangible appearance and is a type of condiment.\nA few things that are visually similar to 'relish' but are not 'relish' are:\tjam\tsauces\tdressing\tmustard\thummus\nThere are several useful visual features to tell there is 'relish' and not similar things in a photo:\tchunky or sliced vegetables, such as pickles, onions, tomatoes, or peppers\tvibrant colors, such as green, red, or yellow\tserved in a small bowl\tor spread over food as a topping.", 79], "dolls": ["Yes. 'Dolls' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'dolls' but are not 'dolls' are:\taction figures\tpuppets\tstuffed animals\tmannequins\nThere are several useful visual features to tell there is 'dolls' and not similar things in a photo:\thuman-like figurines\tor figurines with defining characteristics of a person\tsmall in size\twith movable limbs\tmay be dressed with clothes or accessories.", 79], "t": ["No. 't' is too abstract to be considered visually concrete.", 79], "croissant": ["Yes. 'Croissant' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'croissant' but are not 'croissant' are:\tbaguette\tdanish\tpuff pastry\tbreadstick\nThere are several useful visual features to tell there is 'croissant' and not similar things in a photo:\tcurved crescent-like shape\tlayers of flaky pastry on the outside\tlight golden brown color\tsesame seeds on top\tbuttery aroma", 79], "underside": ["Yes. 'Underside' has a tangible appearance and refers to the bottom part of a visible object.\nThere is no visually similar thing to 'underside' that is not 'underside' itself.\nUseful visual features for distinguishing 'underside' in a photo are: the position of the camera, which is often below the object; the presence of visible support structures above the object, indicating that the view is from underneath.", 79], "pencils": ["Yes. 'Pencils' has a tangible appearance and is a writing tool.\nA few things that are visually similar to 'pencils' but are not 'pencils' are:\tpens\tmarkers\tcrayons\tchalk\nThere are several useful visual features to tell there is 'pencils' and not similar things in a photo:\tlong, thin shape\twooden or plastic body\tcylindrical with a pointed end\tcolored lead or graphite inside\tan eraser on one end", 79], "bandanna": ["Yes. 'Bandanna' has a tangible appearance and is a type of cloth accessory.\nA few things that are visually similar to 'bandanna' but are not 'bandanna' are:\tscarf\theadband\thandkerchief\nThere are several useful visual features to tell there is 'bandanna' and not similar things in a photo:\tsquare-shaped\tdiagonal pattern\tcotton or cotton-blend fabric\tworn tied around neck or head", 79], "vessel": ["Yes. 'Vessel' has a tangible appearance and typically refers to a container or ship.\nA few things that are visually similar to 'vessel' but are not 'vessel' are:\tbowl\tcup\ttray\tbox\tdish\nThere are several useful visual features to tell there is 'vessel' and not similar things in a photo:\thollow and capable of holding something\tboat-shaped\tor a large ship\tdesigned for transportation or storage of liquids or goods\tmay have a visible sail or engine for propulsion", 79], "front grill": ["Yes. 'Front grill' has a tangible appearance and is a component of a vehicle's front end.\nA few things that are visually similar to 'front grill' but are not 'front grill' are: radiator, mesh, fence, gate\nThere are several useful visual features to tell there is 'front grill' and not similar things in a photo:\tlocated in the front of a vehicle\thorizontal or vertical bars\tmade of metal or plastic\tdefines the style of the vehicle's front end\tvariations in shape, size, and pattern depending on the vehicle model", 79], "canisters": ["Yes. 'Canisters' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'canisters' but are not 'canisters' are:\tjars\tbottles\ttins\tboxes\nThere are several useful visual features to tell there is 'canisters' and not similar things in a photo:\tcylindrical or rectangular shape\twith airtight lids or closures\toften made of metal, plastic or glass\tdone in monochromatic or patterned designs, often with labels or messages", 79], "notes": ["No. 'Notes' are too vague or abstract to be visually distinguished in a photo. \n\nHowever, if we are referring to musical notes, then the answers would be:\n\nA few things that are visually similar to 'notes' but are not 'notes' are:\tletters\tnumbers\tsymbols\tdoodles\nThere are several useful visual features to tell there are 'musical notes' and not similar things in a photo:\tfive horizontal lines with four spaces in between\toval-shaped markings on and between the lines\tdepending on the type of note, short or long vertical stem with flags or dots on the top", 79], "folding chair": ["Yes. 'Folding chair' has a tangible appearance and is a type of chair.\nA few things that are visually similar to 'folding chair' but are not 'folding chair' are:\toffice chair\tdining chair\tstool\tbench\nThere are several useful visual features to tell there is 'folding chair' and not similar things in a photo:\tfoldable\tmetal frame\tpadded or plastic seat and backrest\tportable and lightweight", 79], "silver sink": ["Yes. 'Silver sink' has a tangible and specific appearance, referring to a sink that is silver in color.\nA few things that are visually similar to 'silver sink' but are not 'silver sink' are:\tstainless steel sink\tchrome sink\taluminum sink\nThere are several useful visual features to tell there is 'silver sink' and not similar things in a photo:\tmetallic appearance\tsilver color\treflective surface\twith faucet and drainage system\tsink shape and size", 79], "gas station": ["Yes. 'Gas station' has a tangible appearance and is a type of service facility.\nA few things that are visually similar to 'gas station' but are not 'gas station' are:\tcar wash\tparking lot\ttruck stop\trest area\nThere are several useful visual features to tell there is 'gas station' and not similar things in a photo:\tfuel dispensers and pumps\tsigns indicating fuel prices and types\tconvenience store or attached garage\tcanopy over fueling area\torangish-yellow or greenish-blue color scheme.", 79], "side building": ["No. 'Side building' is too vague or abstract to be distinguished in a photo. Could you please provide more context or clarify your question?", 79], "side plane": ["No. 'Side plane' is too vague or abstract to be distinguished in a photo. It is not clear what is meant by 'side plane'.", 79], "signal light": ["Yes. 'Signal light' has a tangible appearance and refers to a kind of light used for signals or warnings.\nA few things that are visually similar to 'signal light' but are not 'signal light' are: streetlights, traffic lights, lamps, flashlights, lanterns\nThere are several useful visual features to tell there is 'signal light' and not similar things in a photo:\tusually small or medium in size\tmay have a specific color or colors used for signaling or warning, such as red, yellow, green, or blue\tmay have a flashing, blinking, or rotating light pattern\tmay be attached to a specific object or structure for signaling or warning purposes", 79], "sunflower": ["Yes. 'Sunflower' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'sunflower' but are not 'sunflower' are:\tdaisy\tdandelion\tmarigold\tzinnia\nThere are several useful visual features to tell there is 'sunflower' and not similar things in a photo:\tlarge flower with a yellow or reddish-brown center and yellow petals\tpetals arranged in a circular pattern\tleaves are spiky or jagged\tin a garden or field", 79], "dish washer": ["Yes. 'Dish washer' has a tangible appearance and is a type of home appliance.\nA few things that are visually similar to 'dish washer' but are not 'dish washer' are:\twashing machine\tdryer\toven\tfridge\nThere are several useful visual features to tell there is 'dish washer' and not similar things in a photo:\tsquare or rectangular shape\tdoor or lid to load dishes\tinlet for water outlet for water and detergent\tcontrol panel with buttons or digital display\tsound insulation technology.", 78], "snow goggles": ["Yes. 'Snow goggles' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'snow goggles' but are not 'snow goggles' are:\tsunglasses\tswimming goggles\tscuba diving mask\tski helmet\nThere are several useful visual features to tell there is 'snow goggles' and not similar things in a photo:\twrap-around design to shield eyes from sun and wind\telastic band to keep goggles in place\tclear lenses for better visibility\tanti-fog coating to prevent fogging\tupturned edges to keep snow and wind out", 78], "porcelain sink": ["Yes. 'Porcelain sink' has a tangible appearance and is a type of sink made of porcelain or ceramic material.\nA few things that are visually similar to 'porcelain sink' but are not 'porcelain sink' are:\tsteel sink\tstone sink\tplastic sink\t\nThere are several useful visual features to tell there is 'porcelain sink' and not similar things in a photo:\twhite or off-white color\trounded or oval shape\tporcelain or ceramic material\tvisible cracks, chips, or scratches\ton top of a counter or installed in a cabinetry\tsystem\tfaucet and handles attached to it\tdrain hole in the center", 78], "tabletop": ["Yes. 'Tabletop' has a tangible appearance and is the flat surface of a table.\nA few things that are visually similar to 'tabletop' but are not 'tabletop' are:\twalls\tfloors\tcounter-tops\tdoors\nThere are several useful visual features to tell there is 'tabletop' and not similar things in a photo:\tlocated on top of one or more legs or supports\televated off the ground and horizontal surface\twhere items such as dishes, books or laptops may be placed", 78], "buckle": ["Yes. 'Buckle' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'buckle' but are not 'buckle' are:\tbutton\tclasp\tzipper\ttie clip\tbobby pin\nThere are several useful visual features to tell there is 'buckle' and not similar things in a photo:\tone end of a belt or strap inserts into itmetal, plastic, or leather materialrectangular or oval shape. It might be simple or decorative.", 78], "style": ["No. 'Style' is too vague or abstract to be distinguished in a photo. It is subjective and can encompass many different visual elements. \n\nThere are no things that are visually similar to 'style'. \n\nAs 'style' cannot be visually distinguished, there are no useful visual features to differentiate it from similar things in a photo.", 78], "bunny": ["Yes. 'Bunny' has a tangible appearance and refers to a rabbit or a hare.\nA few things that are visually similar to 'bunny' but are not 'bunny' are:\tcat\tsquirrel\thamsters\nThere are several useful visual features to tell there is 'bunny' and not similar things in a photo:\tlong ears\ton average shorter legs\tfurry tail\therbivores\thop when they move", 78], "metal frame": ["Yes. 'Metal frame' has a tangible appearance and is a common structure made of metal.\nA few things that are visually similar to 'metal frame' but are not 'metal frame' are:\twooden frame\tpicture frame\tbone frame\tplastic frame\nThere are several useful visual features to tell there is 'metal frame' and not similar things in a photo:\tmade of metal\tstraight and rigid\tforming a skeletal structure or framework\tfor supporting, enclosing or strengthening something (such as a building, vehicle, or furniture)", 78], "chalk lines": ["Yes. 'Chalk lines' has a tangible appearance and is a tool used in construction or carpentry.\nA few things that are visually similar to 'chalk lines' but are not 'chalk lines' are:\tropes\tstrings\twires\tpipes\nThere are several useful visual features to tell there is 'chalk lines' and not similar things in a photo:\tstraight line\tthin and continuous\tdusty or powdery material on or around the line, such as chalk or graphite", 78], "walk": ["No. 'Walk' is too vague or abstract to be distinguished in a photo.", 78], "penguin": ["Yes. 'Penguin' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'penguin' but are not 'penguin' are:\tblack and white birds\torcas\tdolphins\nThere are several useful visual features to tell there is 'penguin' and not similar things in a photo:\ttuxedo-like black and white feathers\tsmall wings\tfat bodies\twebbed feet\tforward-facing eyes\tbeak-shaped mouths", 78], "grout": ["Yes. 'Grout' has a tangible appearance and is a material used to fill gaps between tiles.\nA few things that are visually similar to 'grout' but are not 'grout' are:\tcaulk\tmortar\tcement\tpaste\nThere are several useful visual features to tell there is 'grout' and not similar things in a photo:\tnarrow lines or cracks between tiles\tsmooth and flat surface\tcolor that contrasts with the tiles", 78], "speed limit sign": ["Yes. 'Speed limit sign' has a tangible appearance and is a kind of traffic sign.\nA few things that are visually similar to 'speed limit sign' but are not 'speed limit sign' are:\tstop sign\tyield sign\tpedestrian crossing sign\tschool zone sign\nThere are several useful visual features to tell there is 'speed limit sign' and not similar things in a photo:\twhite background\tcircular shape\tred outer border\twith the phrase \"Speed Limit\" followed by a number (usually in black)", 78], "moustache": ["Yes. 'Moustache' has a tangible appearance and is a kind of facial hair.\nA few things that are visually similar to 'moustache' but are not 'moustache' are:\tbeard\teyebrows\thair\tplastic or felt props\nThere are several useful visual features to tell there is 'moustache' and not similar things in a photo:\thair above the upper lip\tusually only on the center of the face\tcan vary in thickness and shape, but it's never covering the whole face.", 77], "teenager": ["Yes. 'Teenager' has a tangible appearance and refers to a person between 13 and 19 years old.\nA few things that are visually similar to 'teenager' but are not 'teenager' are:\tchildren\tyoung adults\nThere are several useful visual features to tell there is a 'teenager' and not similar things in a photo:\tage between 13 and 19 years old\tfacial features with a mix of child-like and adult-like characteristics\tappropriate clothing and style for their age group\tbody language and mannerisms associated with adolescents ", 77], "hardwood": ["Yes. 'Hardwood' has a tangible appearance and refers to wood from deciduous trees that are dense and durable.\nA few things that are visually similar to 'hardwood' but are not 'hardwood' are:\tsoftwood\tlaminated wood\tparticleboard\nThere are several useful visual features to tell there is 'hardwood' and not similar things in a photo:\tdense wood from deciduous trees, such as oak, birch or maple\thas visible wood grain\tdarkens or turns reddish-brown with age and sunlight\thas a dense texture and is resistant to scratches\tif cut or sanded, the wood reveals a clean and polished surface", 77], "halves": ["Yes. 'Halves' has a tangible appearance and refers to two equal parts of a whole.\nA few things that are visually similar to 'halves' but are not 'halves' are:\tunequal parts\tquarters\tpieces\tcutouts\nThere are several useful visual features to tell there are 'halves' and not similar things in a photo:\texact halves of a single object\toranges, watermelons or other fruits cut in half\tsymmetrical parts of a shape or figure", 77], "pony tail": ["Yes. 'Ponytail' has a tangible appearance and is a hairstyle.\nA few things that are visually similar to 'ponytail' but are not 'ponytail' are: bun, braid, updo, afro\nThere are several useful visual features to tell there is 'ponytail' and not similar things in a photo:\thair pulled back and tied at the nape of the neck or higher\toften secured with an elastic band or scrunchie\tfluffy at the end", 77], "cartoon": ["Yes. 'Cartoon' has a tangible appearance and is a type of illustrated art style.\nA few things that are visually similar to 'cartoon' but are not 'cartoon' are:\tphotograph\tpainting\tsketch\tdrawings\nThere are several useful visual features to tell there is 'cartoon' and not similar things in a photo:\tbold outlines\texaggerated or distorted features\tbright colors\tcartoonish characters or scenes\tcomic-like fonts or speech bubbles", 77], "lighting": ["No. 'Lighting' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we refer to a specific type of lighting, such as 'string lights', the answer is yes.\n\nA few things that are visually similar to 'string lights' but are not 'string lights' are:\theadlights\tcandles\tfairy dust\nThere are several useful visual features to tell there is 'string lights' and not similar things in a photo: small bulbs in a row, usually on a string or wire, emitting a warm or colorful glow, and used for decorative purposes.", 77], "uniforms": ["Yes. 'Uniforms' has a tangible appearance and typically refers to a specific type of clothing worn for a particular purpose.\nA few things that are visually similar to 'uniforms' but are not 'uniforms' are:\tcostumes\tregular clothing\tleisurewear\nThere are several useful visual features to tell there are 'uniforms' and not similar things in a photo:\tmatching clothing worn by a group or team\tspecific colors, badges, or symbols indicating a particular organization or profession\tsimilar cuts or styles worn by members of a specific profession or rank", 77], "toilet seat cover": ["Yes. 'Toilet seat cover' has a tangible appearance and is a bathroom fixture.\nA few things that are visually similar to 'toilet seat cover' but are not 'toilet seat cover' are:\ttoilet seat\ttoilet lid\tshower curtain\tbath mat\nThere are several useful visual features to tell there is 'toilet seat cover' and not similar things in a photo:\tround or oval\tin the shape of a toilet seat\tsmaller than a standard toilet seat\tmade of disposable paper, cloth, or plastic", 77], "console": ["Yes. 'console' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'console' but are not 'console' are:\ttable\tdresser\tdesk\tshelf\tTV\nThere are several useful visual features to tell there is 'console' and not similar things in a photo:\trectangular shape\tsmall buttons or controllers\ton-screen display video game characters or menus\tvideo game cartridges, CDs or DVDs", 77], "brown jacket": ["Yes. 'Brown jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'brown jacket' but are not 'brown jacket' are:\tcoat\tblazer\tcardigan\tsweater\thoodie\nThere are several useful visual features to tell there is 'brown jacket' and not similar things in a photo:\tbrown or brownish color\tfull sleeved button-down outerwear\tusually made of leather, denim, or suede\tzippers, buttons, or pockets on the front", 77], "drops": ["Yes. 'Drops' has a tangible appearance and refers to small quantities of liquid.\nA few things that are visually similar to 'drops' but are not 'drops' are:\tsplashes\tbubbles\tstains\tdew\nThere are several useful visual features to tell there is 'drops' and not similar things in a photo:\tsmall quantities of liquid\tcircular or round shape\treflecting light\ttranslucent or transparent\tcolor and texture of the liquid it is made of", 77], "macaroni": ["Yes. 'Macaroni' has a tangible appearance and is a type of pasta.\nA few things that are visually similar to 'macaroni' but are not 'macaroni' are:\tpenne\tfusilli\tziti\tcavatappi\nThere are several useful visual features to tell there is 'macaroni' and not similar things in a photo:\ttube-shaped\tpale yellow or off-white\tcolor\tcurved edges\tcylindrical shape\twith or without ridges", 77], "handrail": ["Yes. 'Handrail' has a tangible appearance and is a type of support or railing for holding onto.\nA few things that are visually similar to 'handrail' but are not 'handrail' are:\tfence\tbalcony railing\tshower grab bar\tstaircase\tfurniture legs\nThere are several useful visual features to tell there is 'handrail' and not similar things in a photo:\tinstalled along stairs or a ramp\tmeant for holding onto\twhen used indoors, typically made of wood, metal, or plastic\tsimilar design and material as the stairs or surroundings they are attached to.", 76], "plastic spoon": ["Yes. 'Plastic spoon' has a tangible appearance and is a kind of utensil.\nA few things that are visually similar to 'plastic spoon' but are not 'plastic spoon' are:\tmetal spoon\tfork\tknife\tkitchen spatula\nThere are several useful visual features to tell there is 'plastic spoon' and not similar things in a photo:\tone end with a shallow bowl for holding food\tthe other end has a handle\tfor eating or serving food\tmade of plastic or other non-metal material\ttypically disposable or reusable with a casual design or decoration.", 76], "silver metal pole": ["Yes. 'Silver metal pole' has a tangible appearance and is a specific type of object.\nA few things that are visually similar to 'silver metal pole' but are not 'silver metal pole' are:\tflagpole\tstreet light\tparking meter\tbike rack\nThere are several useful visual features to tell there is 'silver metal pole' and not similar things in a photo:\tvertical pole\tcylindrical shape\tsilver or metallic color\tsmooth texture\tno visible attachment or apparatus on top.", 76], "arm chair": ["Yes. 'Arm chair' has a tangible appearance and is a type of chair.\nA few things that are visually similar to 'arm chair' but are not 'arm chair' are:\tsofa\tottoman\trecliner\tlounge chair\nThere are several useful visual features to tell there is 'arm chair' and not similar things in a photo:\tupholstered seat and armrests\trelatively small in size compared to a sofa\tno sleeper or reclining function\tbackrest is perpendicular to the seat cushion\twide and stable base with four legs or a solid frame.", 76], "orange safety cone": ["Yes. 'Orange safety cone' has a tangible appearance and is an object used for safety purposes.\nA few things that are visually similar to 'orange safety cone' but are not 'orange safety cone' are:\ttraffic barrels\torange construction signs\nThere are several useful visual features to tell there is 'orange safety cone' and not similar things in a photo:\torange color\tcone-shaped object\twide base with a pointed top\thas reflective bands or patterns", 76], "gazelle": ["Yes. 'Gazelle' has a tangible appearance and is a kind of antelope.\nA few things that are visually similar to 'gazelle' but are not 'gazelle' are:\tdeer\thorse\tgoat\nThere are several useful visual features to tell there is 'gazelle' and not similar things in a photo:\tmedium-sized antelope\tlean and athletic build\twith curved, ringed horns especially on males\trusty, golden-brown or beige coat with a white underbelly, rump patch, and tail\thigher hindquarters than frontquarters", 76], "stove top": ["Yes. 'Stove top' has a tangible appearance and is a part of a kitchen appliance.\nA few things that are visually similar to 'stove top' but are not 'stove top' are:\tcounter top\tcutting board\ttable\ttop of a washing machine\nThere are several useful visual features to tell there is 'stove top' and not similar things in a photo:\tmetal grates\tburners\tknobs\tfor cooking use\tconnected to an oven or cooktop surface.", 76], "lamppost": ["Yes. 'Lamppost' has a tangible appearance and is a type of street fixture.\nA few things that are visually similar to 'lamppost' but are not 'lamppost' are:\tsignage\ttrash can\tbench\tbollard\nThere are several useful visual features to tell there is 'lamppost' and not similar things in a photo:\ttall metal pole\tstreet light attached at the top\thanging lamp fixture\tat the side of a street or sidewalk", 76], "ridges": ["Yes. 'Ridges' has a tangible appearance and refers to a narrow, elevated strip of land.\nA few things that are visually similar to 'ridges' but are not 'ridges' are:\tmountain\trange\tplateau\thill\nThere are several useful visual features to tell there are 'ridges' and not similar things in a photo:\tlong and narrow\tsteep or sloping\ttop of a hill or a mountain\twith distinct edges and ridges", 76], "weapon": ["Yes. 'Weapon' has a tangible appearance and is an object used for harm or self-defense.\nA few things that are visually similar to 'weapon' but are not 'weapon' are:\ttools\tsporting equipment\tdecorative items\trock or stick\nThere are several useful visual features to tell there is 'weapon' and not similar things in a photo:\tspecific shape or design\tfor example, a gun or a knife, usually made of metal or wood\tsheath or holster are often included, depending on the type of weapon\tbullets, ammunition, or blades are sometimes present", 76], "refrigerator door": ["Yes. 'Refrigerator door' has a tangible appearance and is a type of household appliance.\nA few things that are visually similar to 'refrigerator door' but are not 'refrigerator door' are:\tcabinet door\toven door\tmicrowave door\nThere are several useful visual features to tell there is 'refrigerator door' and not similar things in a photo:\thorizontal handle or grip\tmagnetic surface for holding notes or pictures,\toften includes a water dispenser\tor an ice dispenser,\tcan have a glass window to see inside the fridge.", 76], "silver container": ["Yes. 'Silver container' has a tangible appearance and is a type of receptacle that is silver in color.\nA few things that are visually similar to 'silver container' but are not 'silver container' are:\tstainless steel pot\taluminum foil enclosure\tmetallic vase\tcup\nThere are several useful visual features to tell there is 'silver container' and not similar things in a photo:\tsilver in color\tmade of metal\tshiny or reflective surface\twith a lid or cover\tcapable of containing or storing something", 76], "soldier": ["Yes. 'Soldier' has a tangible appearance and is a profession that involves military duties.\nA few things that are visually similar to 'soldier' but are not 'soldier' are:\tpolice officer\tfirefighter\tbodyguard\tcivilian in military clothing\nThere are several useful visual features to tell there is 'soldier' and not similar things in a photo:\tuniform in camouflage or army green\tcolorful badges or emblems on the uniform\thelmet or headgear\tfirearm or weapon in hand\tor being in a military formation or a war zone", 76], "city skyline": ["Yes. 'City skyline' has a tangible appearance and is a visual representation of the silhouette of a city's buildings.\nA few things that are visually similar to 'city skyline' but are not 'city skyline' are:\tclouds\tmountain\trange\thills\tcastle\nThere are several useful visual features to tell there is 'city skyline' and not similar things in a photo:\thorizontal line of buildings\tbuilding silhouettes against the sky\tgroup of tall buildings or skyscrapers\tskyscrapers in a row or cluster\tlit windows in the buildings\tat night, lights on the buildings", 76], "mesh": ["Yes. 'Mesh' has a tangible appearance and refers to a material composed of interlaced strands or wires.\nA few things that are visually similar to 'mesh' but are not 'mesh' are:\tnetting\tgrid\tpatterns on a fabric\nThere are several useful visual features to tell there is 'mesh' and not similar things in a photo:\tinterlaced strands or wires\ttransparency or \"see-through\" quality\tcheckerboard-like pattern", 75], "mango": ["Yes. 'Mango' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'mango' but are not 'mango' are:\tpapaya\tpeach\torange\tpersimmon\nThere are several useful visual features to tell there is 'mango' and not similar things in a photo:\telliptical or oval shape\tyellow, orange, or green color\twith a reddish-pink spot on the skin\tmedium to large size\tsmooth and thin skin around the seed\tfleshy, juicy, and sweet texture inside the skin", 75], "orange juice": ["Yes. 'Orange juice' has a tangible appearance and is a type of drink.\nA few things that are visually similar to 'orange juice' but are not 'orange juice' are:\tapple juice\tlemonade\ticed tea\torange soda\nThere are several useful visual features to tell there is 'orange juice' and not similar things in a photo:\tlight orange or yellow color\tcitrusy smell or taste\tpouring from a container that resembles a pitcher or a bottle\texcept for pulp, usually uniform texture\tno bubbles or foam", 75], "gray rocks": ["Yes. 'Gray rocks' has a tangible appearance and is a type of stone.\nA few things that are visually similar to 'gray rocks' but are not 'gray rocks' are:\tconcrete walls\tsteel blocks\tgray paint\tmetallic surfaces\nThere are several useful visual features to tell there is 'gray rocks' and not similar things in a photo:\tnatural texture and patterns\tcraggy and uneven surface\tearthy or dusty appearance\tno straight lines or sharp angles", 75], "commuter train": ["Yes. 'Commuter train' has a tangible appearance and is a type of train that transports passengers.\nA few things that are visually similar to 'commuter train' but are not 'commuter train' are:\tfreight train\tsubway train\tlight rail\ttram\nThere are several useful visual features to tell there is 'commuter train' and not similar things in a photo:\tcoaches or cars specifically designed for passengers\tmultiple doors along the side of each coach or car\tdestination boards or signs above or next to the train doors\tpower overhead lines\tcatenary wires over the tracks", 75], "blue curtain": ["Yes. 'Blue curtain' has a tangible appearance and is a type of window treatment.\nA few things that are visually similar to 'blue curtain' but are not 'blue curtain' are:\tblue fabric\tblue tapestry\tblue flag\tblue blanket\tblue rug\nThere are several useful visual features to tell there is 'blue curtain' and not similar things in a photo:\thanging from a rod or a rail\tcovers a window or a door\tusually made of cloth or fabric\thas folds or pleats\tcan be tied back or pulled closed", 75], "purses": ["Yes. 'Purses' has a tangible appearance and is a type of bag used mostly by women.\nA few things that are visually similar to 'purses' but are not 'purses' are:\twallets\tclutches\tbackpacks\tmessenger bags\tbeach totes\nThere are several useful visual features to tell there is 'purses' and not similar things in a photo:\t\n- typically small or medium-sized\n- have handles or straps for carrying\n- used to hold personal items such as money, keys, and makeup\n- often made of leather, fabric, or other materials\n- may have zippers, clasps, or other closures", 75], "baseball batter": ["Yes. 'Baseball batter' has a tangible appearance and is a person who is hitting the ball in a game of baseball.\nA few things that are visually similar to 'baseball batter' but are not 'baseball batter' are:\ttennis player\tgolfer\thockey player\tcricket player\nThere are several useful visual features to tell there is 'baseball batter' and not similar things in a photo:\twearing a baseball uniform\tor wearing a helmet holding a baseball bat\tstanding at home plate\ton a baseball field.", 75], "baby stroller": ["Yes. 'Baby stroller' has a tangible appearance and is a kind of transportation device.\nA few things that are visually similar to 'baby stroller' but are not 'baby stroller' are:\twheelbarrow\tshopping cart\tbicycle with a child seat\trolling suitcase\nThere are several useful visual features to tell there is 'baby stroller' and not similar things in a photo:\tupright handlebar\tforward-facing seat with a harness or strap\tsystem to recline the seat\tpositioned low to the ground\thas a canopy or hood to protect from sun or rain\tbasket or compartment underneath to store personal items or bags.", 75], "bathroom window": ["Yes. 'Bathroom window' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'bathroom window' but are not 'bathroom window' are: Living room window, kitchen window, office window.\nThere are several useful visual features to tell there is a 'bathroom window' and not similar things in a photo: frosted or opaque glass, small size, high placement on the wall, near a shower or bathtub.", 75], "spray bottle": ["Yes. 'Spray bottle' has a tangible appearance and is a type of container with a spray nozzle.\nA few things that are visually similar to 'spray bottle' but are not 'spray bottle' are:\twatering can\tperfume bottle\tmister\tair freshener\nThere are several useful visual features to tell there is 'spray bottle' and not similar things in a photo:\tvertical cylindrical shape\twith a spray nozzle\torifice\tat the top or side\tclear plastic container, often tinted with color\tcontaining cleaning solution, insecticide, or liquid repellent\tmay have a trigger or button to control the spray", 75], "manhole": ["Yes. 'Manhole' has a tangible appearance and is a type of opening on a street or sidewalk.\nA few things that are visually similar to 'manhole' but are not 'manhole' are:\tdrain\tgutter\tvent\tcrack\nThere are several useful visual features to tell there is 'manhole' and not similar things in a photo:\tcircular or rectangular shape\twith a cover or lid\tsurrounded by brick or concrete\tmarking or text on the cover\tor a ladder or steps leading down into the opening.", 75], "dvds": ["Yes. 'DVDs' has a tangible appearance and is a type of optical disc.\nA few things that are visually similar to 'DVDs' but are not 'DVDs' are: CDs, Blu-rays, computer game discs, vinyl records.\nThere are several useful visual features to tell there is 'DVDs' and not similar things in a photo:\tsmall size\tsilver bottom and plastic top\tlabeled with movie or TV show titles\tor other media content.", 75], "wreath": ["Yes. 'Wreath' has a tangible appearance and is a kind of decoration in a ring shape.\nA few things that are visually similar to 'wreath' but are not 'wreath' are:\tleis\tnecklaces\tgarlands\nThere are several useful visual features to tell there is 'wreath' and not similar things in a photo:\tcircular shape\tmade of natural materials, such as branches, leaves, or flowers\thanging on a door or a wall", 75], "building distance": ["No. 'Building distance' is too vague or abstract to be distinguished in a photo. \n\nHowever, here are a few things that are visually similar to 'building distance' but are not 'building distance':\n- Space between trees\n- Distance between cars in a parking lot\n- Distance between people in a crowd\n\nUseful visual features for distinguishing 'building distance' from the listed similar things in a photo would depend on the specific context and the purpose of the photo. For example, if the photo is meant to highlight the distance between buildings in a cityscape, useful visual features may include:\n- Aerial view of the buildings and surrounding space\n- Comparison of building heights and sizes to emphasize distance\n- Lack of obstruction or intervening structures between the buildings \n- Use of a wide-angle lens to capture a larger field of view", 75], "rear wheels": ["Yes. 'Rear wheels' has a tangible appearance and refers to the wheels situated at the back of a vehicle.\nA few things that are visually similar to 'rear wheels' but are not 'rear wheels' are: front wheels, bicycle wheels, shopping cart wheels, office chair wheels.\nThere are several useful visual features to tell there are 'rear wheels' and not similar things in a photo, such as: \tlocated at the rear end of a vehicle,\tconnected to the transmission and engine system, \tusually larger in diameter and width than front wheels, typically without steering mechanism.", 75], "man hole cover": ["Yes. 'Man hole cover' has a tangible appearance.\nA few things that are visually similar to 'man hole cover' but are not 'man hole cover' are:\tutility cover\tgutter cover\tdrain cover\tlid of a container\nThere are several useful visual features to tell there is 'man hole cover' and not similar things in a photo:\tcircular or rectangular\tshaped with holes or slots\tmade of metal or concrete\tsits on the ground or a street level", 75], "brim": ["Yes. 'Brim' has a tangible appearance and is a part of a hat or a cap.\nA few things that are visually similar to 'brim' but are not 'brim' are:\trim\tedge\tboundary\tside\tpart\nThere are several useful visual features to tell there is 'brim' and not similar things in a photo:\tcircular or curved\ttop part of a hat or a cap\tthat extends out to provide shade or style\tfrom which the hat or cap brim falls downward.", 75], "pale": ["Yes. 'Pale' has a tangible appearance and refers to a light-colored, almost white complexion or color.\nA few things that are visually similar to 'pale' but are not 'pale' are:\twhite\tfaded\tlight-colored\nThere are no useful visual features to distinguish 'pale' from the listed similar things in a photo because they are all similar in color and tone.", 75], "students": ["Yes. 'Students' has a tangible appearance and refers to people who are studying at a school or college.\nA few things that are visually similar to 'students' but are not 'students' are:\tteachers\tprofessors\tschool staff\nThere are several useful visual features to tell there are 'students' and not similar things in a photo:\tcarrying a backpack or books\tdressed in casual or school uniforms\tsitting in a classroom or library\tconcentrating or taking notes during a lecture or class\tprojecting a younger look and age", 75], "expression": ["No. 'Expression' is too vague or abstract to be distinguished in a photo.", 75], "square plate": ["Yes. 'Square plate' has a tangible appearance and refers to a plate with a square shape.\nA few things that are visually similar to 'square plate' but are not 'square plate' are:\trectangular plate\tplastic container\tsquare tile\nThere are several useful visual features to tell there is 'square plate' and not similar things in a photo:\tflat surface\tsquare shape\traised edges or a rim that goes around the plate.", 75], "orange flower": ["Yes. 'Orange flower' has a tangible appearance and describes a flower that is orange in color.\nA few things that are visually similar to 'orange flower' but are not 'orange flower' are:\tyellow flower\torange fruit\torange leaf\tfake flower\nThere are several useful visual features to tell there is 'orange flower' and not similar things in a photo:\torange color\tpetal shape and structure\tfluffy or smooth texture\tpollinated center\tstem and leaves attached to the flower", 75], "khaki pants": ["Yes. 'Khaki pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'khaki pants' but are not 'khaki pants' are:\tchinos\tjeans\tslacks\ttrousers\nThere are several useful visual features to tell there are 'khaki pants' and not similar things in a photo:\tlight brown or beige color\ttypically made from cotton or cotton-blend fabric\tstraight-leg or slim-fit design\tno prominent patterns or embellishments may have a visible seam running down the center of each pant leg", 75], "life preserver": ["Yes. 'Life preserver' has a tangible appearance and is a type of flotation device.\nA few things that are visually similar to 'life preserver' but are not 'life preserver' are:\tfloating tire\tswimming pool noodles\tinflatable pool toys\t\nThere are several useful visual features to tell there is 'life preserver' and not similar things in a photo:\tcircular shape\tbright orange or yellow color\twide straps for securing around the body\trope around the circumference of the device\twater-resistant and buoyant material.", 75], "wireless mouse": ["Yes. 'Wireless mouse' has a tangible appearance and is a type of computer accessory.\nA few things that are visually similar to 'wireless mouse' but are not 'wireless mouse' are:\twireless keyboard\tgaming mouse\tremote control\tWacom tablet\nThere are several useful visual features to tell there is 'wireless mouse' and not similar things in a photo:\tergonomic design\twith left and right buttons and a middle scroll wheel\toptical or laser sensor for tracking movement\tbattery compartment on the underside\tof smaller size than a keyboard (but larger than a Wacom tablet)\tdesigned for use with a computer or laptop.", 75], "dirt area": ["Yes. 'Dirt area' has a tangible appearance and is a patch of ground that is not covered with vegetation and is made up of dirt or soil.\nA few things that are visually similar to 'dirt area' but are not 'dirt area' are:\tconcrete pavement\tgravel area\tmulch area\tsand area\nThere are several useful visual features to tell there is 'dirt area' and not similar things in a photo:\tno signs of vegetation, grass or foliage\tbrown or reddish-brown color\ttexture differentiated from other ground surfaces", 75], "ox": ["Yes. 'Ox' has a tangible appearance and is a domesticated mammal.\nA few things that are visually similar to 'ox' but are not 'ox' are:\tbuffalo\tyak\tbull\tmoose\nThere are several useful visual features to tell there is 'ox' and not similar things in a photo:\tlarge, stocky build\twith or without horns\tusually brown or black in color\tbig, floppy ears\tsmall eyes in proportion to its head\thump on its back (in some breeds)", 75], "cherries": ["Yes. 'Cherries' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'cherries' but are not 'cherries' are:\tgrapes\ttomatoes\tcranberries\tolives\nThere are several useful visual features to tell there is 'cherries' and not similar things in a photo:\tround and small size\tdark or bright red color\tsmooth, shiny skin\twith a green stem on top\tpit in the center when cut\topen split at one side when the pit removed", 75], "tufts": ["Yes. 'Tufts' has a tangible appearance and refers to a collection of small clumps of threads, hair, or feathers.\nA few things that are visually similar to 'tufts' but are not 'tufts' are:\tclumps of dirt\tpiles of leaves\tmatted hair\nThere are several useful visual features to tell there are 'tufts' and not similar things in a photo:\tshort lengths or clumps\tof various colors and textures\tsoft to the touch\tfound on textiles, animals, or plants", 75], "commode": ["Yes. 'Commode' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'commode' but are not 'commode' are:\tchest of drawers\tdresser\t\nThere are several useful visual features to tell there is 'commode' and not similar things in a photo:\ta low chest of drawers\twith legs or a base\twith drawers for storage\tfor holding clothes and other personal items often found in bedrooms or bathrooms.", 74], "steering wheel": ["Yes. 'Steering wheel' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'steering wheel' but are not 'steering wheel' are:\tship's wheel\tbicycle handlebar\tferris wheel\nThere are several useful visual features to tell there is 'steering wheel' and not similar things in a photo:\tcircular shape\thorizontal or slightly tilted orientation\thand grips\tspoke-like design", 74], "figurines": ["Yes. 'Figurines' have a tangible appearance and are small sculptures or statues of people, animals or objects.\nA few things that are visually similar to 'figurines' but are not 'figurines' are:\tdolls\taction figures\ttoys\tornaments\tpuppets\nThere are several useful visual features to tell there is 'figurines' and not similar things in a photo:\tmeticulously crafted small sculpture or statue\thuman form or an object in a miniature form\tmade of porcelain, ceramic, metal, or plastic\tintended for display, decoration, or collection", 74], "jean shorts": ["Yes. 'Jean shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'jean shorts' but are not 'jean shorts' are:\tdenim pants\tshorts made of other materials\tbottoms with a different texture or color\nThere are several useful visual features to tell there are 'jean shorts' and not similar things in a photo:\tdenim fabric cuff cut-off above the knee\tlength of the inseam and outseam\tstructure of the pockets, waistband, and stitching", 74], "brocolli": ["Yes. 'Broccoli' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'broccoli' but are not 'broccoli' are:\tcauliflower\tcabbage\tkale\tspinach\nThere are several useful visual features to tell there is 'broccoli' and not similar things in a photo:\tgreen color\ttightly packed florets\tflorets attached to a thick stem\tnatural irregular shape", 74], "crow": ["Yes. 'Crow' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'crow' but are not 'crow' are:\traven\tjackdaw\tmagpie\nThere are several useful visual features to tell there is 'crow' and not similar things in a photo:\tall-black feathers\tshiny and iridescent feathers\tcawing sound\tbill-shaped beak and sharp talons\tcurved wings and fan-shaped tail\tflying in groups or pairs", 74], "glass table": ["Yes. 'Glass table' has a tangible appearance and is a table made of glass.\nA few things that are visually similar to 'glass table' but are not 'glass table' are:\twooden table\tplastic table\tmetal table\tmarble table\nThere are several useful visual features to tell there is 'glass table' and not similar things in a photo:\tmade of clear or frosted glass\tno visible joints or screws\tsleek and modern design\treflection on the surface", 74], "arrangement": ["No. 'Arrangement' is too vague or abstract to be distinguished in a photo.", 74], "destination": ["No. 'Destination' is too vague or abstract to be distinguished in a photo.", 74], "christmas tree": ["Yes. 'Christmas tree' has a tangible appearance and is a type of decorated tree.\nA few things that are visually similar to 'christmas tree' but are not 'christmas tree' are:\tregular tree, bare or decorated with leaves\ttree-shaped lamp or sculpture\tcactus or succulent in a pot decorated with Christmas ornaments\nThere are several useful visual features to tell there is 'christmas tree' and not similar things in a photo:\tconical shape\tgreen, with or without needles\tvariously decorated: lights, balls, garlands, stars, ribbons, and other ornaments\tfound indoors in December and early January", 74], "prints": ["Yes. 'Prints' has a tangible appearance and can refer to different types of markings.\nA few things that are visually similar to 'prints' but are not 'prints' are:\tbrush strokes\tfingerprints\tpatterns\ttextures\nThere are several useful visual features to tell there are 'prints' and not similar things in a photo:\tindentations or raised marks\tleft behind by a person or animal\trepetitive pattern in a specific direction or shape\tmatching characteristics to the type of print (such as paw prints, tire tracks, or fingerprint ridges)", 74], "adults": ["No. 'Adults' is too vague or abstract to be distinguished in a photo.", 74], "zebra mane": ["Yes. 'Zebra mane' has a tangible appearance and refers to the hair on the neck and spine of a zebra.\nA few things that are visually similar to 'zebra mane' but are not 'zebra mane' are:\thorse mane\tlion's mane\tcamel hair\tmohawk haircuts\nThere are several useful visual features to tell there is 'zebra mane' and not similar things in a photo:\tblack and white stripes\tthick and short hairs\ton the neck and spine of a zebra", 74], "tennis outfit": ["Yes. 'Tennis outfit' has a tangible appearance and is a type of sports attire worn for playing tennis.\nA few things that are visually similar to 'tennis outfit' but are not 'tennis outfit' are:\tathletic wear\tworkout clothes\tsportswear\tsoccer uniform\nThere are several useful visual features to tell there is 'tennis outfit' and not similar things in a photo:\twhite or light-colored shirt with short or no sleeves\tshorts or skirts in bright colors\tsweatbands around the wrists or head\ttennis shoes with appropriate soles", 74], "bracket": ["Yes. 'Bracket' has a tangible appearance and is a type of support device.\nA few things that are visually similar to 'bracket' but are not 'bracket' are:\tshelf support\tangle bracket\tbookend\thanger\nThere are several useful visual features to tell there is 'bracket' and not similar things in a photo:\tL-shaped form with two legs or arms\tmetallic, plastic or wooden material\tfasteners or screws visible\twhere one object is attached to another\tobject support and stabilization", 74], "store front": ["Yes. 'Store front' has a tangible appearance and refers to the fa\u00e7ade or entrance of a retail store.\nA few things that are visually similar to 'store front' but are not 'store front' are:\twindow display\ta building entrance\ta billboard\ta garage\nThere are several useful visual features to tell there is 'store front' and not similar things in a photo:\tsigns indicating store name, logo, or products sold\tdisplay windows with merchandise visible\tclearly defined entrance to the store\tfoot traffic or parked cars in front of the store", 74], "shirt collar": ["Yes. 'Shirt collar' has a tangible appearance and is a part of a shirt.\nA few things that are visually similar to 'shirt collar' but are not 'shirt collar' are:\tnecklace\tscarf\tchoker\t\nThere are several useful visual features to tell there is 'shirt collar' and not similar things in a photo:\tattached to a shirt\tfolded or standing upright around the neck\tcircle or band-shaped\tbuttoned or unbuttoned polarization or stiffness of the collar.", 74], "finger nail": ["Yes. 'Finger nail' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'finger nail' but are not 'finger nail' are:\tanimal claw\tthumbtack\torangutan finger\nThere are several useful visual features to tell there is 'finger nail' and not similar things in a photo:\tflat and thin on top\tcurved\tcomes in various sizes and shapes\tpink skin underneath the nail\tlocated at the tips of the fingers or toes", 74], "facade": ["Yes. 'Facade' has a tangible appearance and refers to the exterior of a building.\nA few things that are visually similar to 'facade' but are not 'facade' are:\twall\tentrance\tdoor\tporch\nThere are several useful visual features to tell there is 'facade' and not similar things in a photo:\tthe front exterior of a building\twith windows and doors\ta distinguishable style or architecture\tornamentation or decorative features such as columns, statues, or carvings", 74], "wheelchair": ["Yes. 'Wheelchair' has a tangible appearance and is a type of mobility device.\nA few things that are visually similar to 'wheelchair' but are not 'wheelchair' are:\toffice chair\tstroller\tbicycle\nThere are several useful visual features to tell there is 'wheelchair' and not similar things in a photo:\thas wheels\tis pushed or self-propelled\thas handles or armrests to support the user's upper body and allow them to move\tthe seat and backrest are cushioned to provide comfort and support to the user.", 74], "dial": ["Yes. 'Dial' has a tangible appearance and is a type of display.\nA few things that are visually similar to 'dial' but are not 'dial' are:\tscreen\tclock\tthermometer\tspeedometer\nThere are several useful visual features to tell there is 'dial' and not similar things in a photo:\tcircular shape\tnumbers, markers, or indicators arranged in a circle\ta needle or pointer indicating a value or position on the dial", 74], "earphones": ["Yes. 'Earphones' has a tangible appearance and is a type of personal audio device.\nA few things that are visually similar to 'earphones' but are not 'earphones' are:\theadphones\tearbuds\thearing aids\tdecorative earrings\nThere are several useful visual features to tell there are 'earphones' and not similar things in a photo:\tsmall earpieces\tthat fit inside the ear or rest just outside\tit has a cable\tthat connects the earpieces themselves\tto have a microphone\tand volume or playback controls", 73], "timer": ["Yes. 'Timer' has a tangible appearance and is a device used to track time.\nA few things that are visually similar to 'timer' but are not 'timer' are:\tclock\tstopwatch\twatch\talarm\nThere are several useful visual features to tell there is 'timer' and not similar things in a photo:\tdisplay of elapsed time \tcountdown function set by the user\tindividual buttons or dials to set or adjust the timer\tsound or vibration option when the time is up", 73], "motorcycle seat": ["Yes. 'Motorcycle seat' has a tangible appearance and is a part of a motorcycle.\nA few things that are visually similar to 'motorcycle seat' but are not 'motorcycle seat' are:\tbicycle seat\tstool\tchair\tscooter seat\nThere are several useful visual features to tell there is 'motorcycle seat' and not similar things in a photo:\tconnected to the motorcycle\tframe for attachment\tleather or vinyl covering\tcushioned for comfort", 73], "tiger": ["Yes, 'tiger' has a tangible appearance and is a kind of feline.\nA few things that are visually similar to 'tiger' but are not 'tiger' are:\tleopard\tcheetah\tlion\thouse cat\nThere are several useful visual features to tell there is 'tiger' and not similar things in a photo:\torange coat with black stripes\tlarge body\tsize significantly larger than a house cat\tsharp teeth and claws\ttufted ears\tvertical pupils in eyes", 73], "wood coffee table": ["Yes. 'Wood coffee table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood coffee table' but are not 'wood coffee table' are:\tdesk\tdining table\tend table\ttv stand\nThere are several useful visual features to tell there is 'wood coffee table' and not similar things in a photo:\tlow to the ground\trectangular or square shape\twooden material, sometimes with metal or glass accents\tflat surface for holding drinks, books, or decorative objects", 73], "icing": ["Yes. 'Icing' has a tangible appearance and is a sweet topping used in cakes and pastries.\nA few things that are visually similar to 'icing' but are not 'icing' are:\tmayonnaise, sauce\tor whipped cream\nThere are several useful visual features to tell there is 'icing' and not similar things in a photo:\tthick and creamy consistency\tsugary\ttasty;colorful (depending on the type)\tmay have a glossy texture.", 73], "veins": ["Yes. 'Veins' has a tangible appearance and refers to a system of blood vessels in the body.\nA few things that are visually similar to 'veins' but are not 'veins' are:\troots\tbranches\tcracks\tonion layers\nThere are several useful visual features to tell there are 'veins' and not similar things in a photo:\tblue or green color\tthin and thread-like appearance\tvisible under the skin\tfollowing a pattern or a branching structure\tcarrying blood or fluids", 73], "watch woman": ["Yes and No. The concept of 'woman' is visually concrete, but the concept of 'watch woman' is too vague or abstract to be distinguished in a photo.\nA few things that are visually similar to 'watch woman' but are not 'watch woman' are:\ttimekeeper\tclock-watcher\tguard\tpatrol officer\nThere are no useful visual features to tell there is 'watch woman' and not similar things in a photo, as the term 'watch woman' is not a specific or recognizable visual concept.", 73], "silver bracelet": ["Yes. 'Silver bracelet' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'silver bracelet' but are not 'silver bracelet' are:\twristwatch\thair tie\tanklet\tbangle\nThere are several useful visual features to tell there is 'silver bracelet' and not similar things in a photo:\tmade of silver or silver-colored metal\tworn around the wrist\tclasped or fastened\twith or without gemstones or embellishments", 73], "melon": ["Yes. 'Melon' has a tangible appearance and is a kind of fruit.\nA few things that are visually similar to 'melon' but are not 'melon' are:\twatermelon\tpumpkin\tcantaloupe\tgourd\nThere are several useful visual features to tell there is 'melon' and not similar things in a photo:\tround or oval shape\tvarious shades of green or orange\tsmooth or textured skin\tstem at one end\tcut surface reveals juicy, sweet flesh and seeds inside.", 73], "metal faucet": ["Yes. 'Metal faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'metal faucet' but are not 'metal faucet' are:\tknob\tlock\tlever\thandle\nThere are several useful visual features to tell there is 'metal faucet' and not similar things in a photo:\tmetallic material\tvalve for controlling the flow of water\tspout for dispensing water\tnozzle for directing the water stream", 73], "blueberry": ["Yes. 'Blueberry' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'blueberry' but are not 'blueberry' are:\tgrape\tcherry\tcranberry\tplum\nThere are several useful visual features to tell there is 'blueberry' and not similar things in a photo:\tsmall\tsize\twith a deep blue color a smooth and mottled skin\twith a small crown at the end\tof a rounded shape\twith a green stem on top", 73], "storefront": ["Yes. 'Storefront' has a tangible appearance and refers to the exterior of a store.\nA few things that are visually similar to 'storefront' but are not 'storefront' are:\tresidence entryway\toffice building entrance\tparking garage entrances\trestaurant entrances\tshopping mall entrances\nThere are several useful visual features to tell there is 'storefront' and not similar things in a photo:\tdisplay windows\tsignage\tdoor entrance\tsidewalk\tarea for customers to enter and exit a retail establishment", 73], "colour": ["No. 'Colour' is too vague or abstract to be distinguished in a photo. \n\nThere are no things that are visually similar to 'colour'. \n\nUseful visual features for distinguishing 'colour' are not applicable as it cannot be visually distinguished from other things in a photo.", 73], "glass bottles": ["Yes. 'Glass bottles' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'glass bottles' but are not 'glass bottles' are:\tjars\tvases\tcandle holders\nThere are several useful visual features to tell there is 'glass bottles' and not similar things in a photo:\tclear or colored hollow container with a narrow neck\tmade of transparent material such as glass or plastic\twith or without caps, corks or plugs.", 73], "pizza sauce": ["Yes. 'Pizza sauce' has a tangible appearance and is typically seen as a topping on pizza.\nA few things that are visually similar to 'pizza sauce' but are not 'pizza sauce' are:\ttomato soup\tmarinara sauce\tketchup\tbbq sauce\nThere are several useful visual features to tell there is 'pizza sauce' and not similar things in a photo:\tthick consistency\tvibrant red color\tgenerally spread in a circular motion on the pizza\tdistinguishing ingredients like garlic and oregano", 73], "bare branches": ["Yes. 'Bare branches' has a tangible appearance and refers to the twigs and branches of a tree without leaves.\nA few things that are visually similar to 'bare branches' but are not 'bare branches' are: tree with green leaves, tree with colorful leaves, a dead tree.\nThere are several useful visual features to tell there are 'bare branches' and not similar things in a photo:\t\n- Thin and pointy twigs\n- Lack of leaves or flowers\n- A monochromatic, dull or grayish color", 73], "clip": ["Yes. 'Clip' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'clip' but are not 'clip' are:\tpin\tclasp\tband\thook\nThere are several useful visual features to tell there is 'clip' and not similar things in a photo:\tthin and flat, with two or more arms that can be pressed together or pulled apart\toften made of metal or plastic\tuseful for holding papers, fabric, or hair in place.", 73], "trough": ["Yes. 'Trough' has a tangible appearance and refers to a container or channel that holds water or food for animals.\nA few things that are visually similar to 'trough' but are not 'trough' are:\tbathtub\tcanal\tdrain\tfurrow\nThere are several useful visual features to tell there is 'trough' and not similar things in a photo:\trectangular or elongated shape\twith or without dividing sections\tmade of metal or wood\tgrassy or muddy bottom}\r\r\n", 73], "tennis shirt": ["Yes. 'Tennis shirt' has a tangible appearance and is a type of sports clothing.\nA few things that are visually similar to 'tennis shirt' but are not 'tennis shirt' are:\tpolo shirt\tcollared shirt\tt-shirt\tsweatshirt\nThere are several useful visual features to tell there is 'tennis shirt' and not similar things in a photo:\tshort-sleeved\tcollared button-front\tusually made from breathable materials\tsometimes has a small logo of the brand or sponsor on the chest or sleeve\tcolorful or bright design", 73], "workers": ["Yes. 'Workers' has a tangible appearance and refers to people who are employed to do a job.\nA few things that are visually similar to 'workers' but are not 'workers' are:\tpassengers\tprotesters\tcrowd\tstudents\nThere are several useful visual features to tell there are 'workers' and not similar things in a photo:\twearing work clothes or uniform\tcarrying tools or equipment\tengaged in a specific job\ttask-oriented\tbody language indicating work", 73], "skiis": ["Yes. 'Skis' has a tangible appearance and is a type of winter sports equipment.\nA few things that are visually similar to 'skis' but are not 'skis' are:\tsnowboards\tsleds\tice skates\tsnowshoes\nThere are several useful visual features to tell there is 'skis' and not similar things in a photo:\tlong and narrow\tplanks with curved tips\tbinding to attach boots to skis\tcan be seen sticking out or strapped to boots\twax or texture on the bottom surface of the skis to help slide on the snow", 73], "mulch": ["Yes. 'Mulch' has a tangible appearance and is a material used in gardening and farming.\nA few things that are visually similar to 'mulch' but are not 'mulch' are:\tsoil\tcompost\trocks\tgravel\nThere are several useful visual features to tell there is 'mulch' and not similar things in a photo:\tfinely shredded wood or bark material\tdark brown or black color\tmixed in with plants or garden beds", 73], "shoe lace": ["Yes. 'Shoe lace' has a tangible appearance and is a type of string used to tie shoes.\nA few things that are visually similar to 'shoe lace' but are not 'shoe lace' are:\tstrings\tthreads\tcords\tribbons\nThere are several useful visual features to tell there is 'shoe lace' and not similar things in a photo:\tflat and thin surface\tcolors matching the shoes\tknotted or tied loops\ton or near shoes or sneakers", 73], "wooden fence": ["Yes. 'Wooden fence' has a tangible appearance as a physical barrier made of wood.\nA few things that are visually similar to 'wooden fence' but are not 'wooden fence' are:\tstone wall\tbrick wall\tmetal fence\thedge\nThere are several useful visual features to tell there is a 'wooden fence' and not similar things in a photo:\thorizontal wooden boards\twooden posts vertically embedded into the ground\tnatural wood color or painted in white or brown.", 73], "blond": ["Yes. 'Blond' has a tangible appearance and refers to a hair color.\nA few things that are visually similar to 'blond' but are not 'blond' are:\tlight brown hair\tdirty blonde hair\tplatinum blonde hair\nThere are several useful visual features to tell there is 'blond' and not similar things in a photo:\tpale yellow or golden hair color\tlighter than brown but darker than platinum blonde", 72], "plastic lid": ["Yes. 'Plastic lid' has a tangible appearance and is a type of object used for covering containers.\nA few things that are visually similar to 'plastic lid' but are not 'plastic lid' are:\tbuttons\tcaps\tmedals\tcoins\nThere are several useful visual features to tell there is 'plastic lid' and not similar things in a photo:\tround or square shape\tflat and thin surface\tridged edges designed to fit a container's top\tsnaps or twists onto a container", 72], "railroad track": ["Yes. 'Railroad track' has a tangible appearance and refers to the tracks on which trains run.\nA few things that are visually similar to 'railroad track' but are not 'railroad track' are:\troadway\tbike lane\tcattle trail\nThere are several useful visual features to tell there is 'railroad track' and not similar things in a photo:\tparallel metal tracks\twith railroad ties or sleepers supporting the tracks\tballast or gravel bed beside the tracks\tcriss-cross over a level crossing\tsigns or signals beside or above the tracks", 72], "pancake": ["Yes. 'Pancake' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pancake' but are not 'pancake' are:\tcrepe\twaffle\ttortilla\tpita bread\nThere are several useful visual features to tell there is 'pancake' and not similar things in a photo:\tflat and round shape\tcircular ridges or bubbles on the surface\tGolden-brown color\tbutter or syrup on top\tFork and knife beside it\ton a breakfast plate or in a stack", 72], "ribbons": ["Yes. 'Ribbons' has a tangible appearance and is a thin strip or band of material.\nA few things that are visually similar to 'ribbons' but are not 'ribbons' are:\tstraps\tbands\tsashes\ttowels\nThere are several useful visual features to tell there is 'ribbons' and not similar things in a photo:\tthin and flexible\tmade of a silky or satin-like material\thave flowing ends or loops\twhen tied, creates a decorative bow or knot", 72], "hand soap": ["Yes. 'Hand soap' has a tangible appearance and is a type of cleansing product.\nA few things that are visually similar to 'hand soap' but are not 'hand soap' are:\tshower gel\tdish soap\thair shampoo\tbath foam\nThere are several useful visual features to tell there is 'hand soap' and not similar things in a photo:\tpump or dispenser\tcontainer with \"soap\" labeled on it, bar of soap\tfrothy or bubbly or foamy\tlathered on a person's hand, rather than used in a shower or bath\tfor use on hands, rather than dishes or hair", 72], "wood grain": ["Yes. 'Wood grain' has a tangible appearance and refers to the natural pattern of wood.\nA few things that are visually similar to 'wood grain' but are not 'wood grain' are:\tWood wallpaper\tfaux wood\t wood-patterned fabric\nThere are several useful visual features to tell there is 'wood grain' and not similar things in a photo:\tnatural wood texture\twith visible growth rings\tand wood fibers, that create a unique pattern.", 72], "sideview mirror": ["Yes. 'Sideview mirror' has a tangible appearance and is a type of mirror used in vehicles.\nA few things that are visually similar to 'sideview mirror' but are not 'sideview mirror' are:\trearview mirror\tbathroom mirror\thand mirror\tcompact mirror\nThere are several useful visual features to tell there is 'sideview mirror' and not similar things in a photo:\tlocated on the side of a vehicle\tangled to provide a view of the surrounding area\tusually smaller than a rearview mirror\thas a convex shape to provide a wider field of vision.", 72], "tree limb": ["Yes. 'Tree limb' has a tangible appearance and is a part of a tree branch.\nA few things that are visually similar to 'tree limb' but are not 'tree limb' are:\trope\troot\tbranch\tfence\nThere are several useful visual features to distinguish 'tree limb' from the listed similar things in a photo:\tcovered by bark or leaves\tgrowing from a larger branch or trunk\twith smaller branches or twigs attached\ttogether with other tree limbs to form a larger tree structure.", 72], "pajamas": ["Yes. 'Pajamas' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pajamas' but are not 'pajamas' are:\tleggings\tor casual pants\tathletic wear\nThere are several useful visual features to tell there is 'pajamas' and not similar things in a photo:\tloose and comfortable\tfabric made of silk, cotton, or flannel\tmade for sleeping or lounging\ttypically accompanied with a matching top and bottom set.", 72], "office building": ["Yes. 'Office building' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'office building' but are not 'office building' are:\tapartment building\thospital\tgovernment building\tmall\nThere are several useful visual features to tell there is 'office building' and not similar things in a photo:\trow of windows or panels\tflat roof or rooftop garden\tsymmetrical or modern design\tdesignated for working or professional purposes (e.g. signage indicating business names)", 72], "giraffe neck": ["Yes. 'Giraffe neck' has a tangible appearance and refers to the long neck of the giraffe.\nA few things that are visually similar to 'giraffe neck' but are not 'giraffe neck' are:\tsnake neck\tswans' necks\tdinosaur necks\nThere are several useful visual features to tell there is 'giraffe neck' and not similar things in a photo:\textremely long\tthick and muscular\tbrown spots or patches\thigh position (above surrounding vegetation)", 72], "lap top": ["Yes. 'Laptop' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'laptop' but are not 'laptop' are:\ttablet\tsmartphone\tE-reader\tnotepad\nThere are several useful visual features to tell there is 'laptop' and not similar things in a photo:\tthin and flat screen\tkeyboard attached or separate\thinged design\twireless connectivity to internet\tUSB and other ports for external devices\tbattery power capability", 72], "station wagon": ["Yes. 'Station wagon' has a tangible appearance and is a type of car.\nA few things that are visually similar to 'station wagon' but are not 'station wagon' are:\tsedan\tSUV\tcrossover\thatchback\nThere are several useful visual features to tell there is 'station wagon' and not similar things in a photo:\textended roofline at the back of the car\tlarger cargo area\trear window that can be opened with the trunk (tailgate)", 72], "toe": ["Yes. 'Toe' has a tangible appearance and is a part of the foot.\nA few things that are visually similar to 'toe' but are not 'toe' are:\tfingers\tthumb\tclaw\nThere are several useful visual features to tell there is 'toe' and not similar things in a photo:\tlocated on the foot\tproximity to other toes\tshape of the nail\tskin texture and color\tbends in a specific direction", 72], "sausages": ["Yes. 'Sausages' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'sausages' but are not 'sausages' are:\thot dogs\tcigars\trope\tbranches\nThere are several useful visual features to tell there is 'sausages' and not similar things in a photo:\ttubular shape\tbrown or pink meat casing\tvisible spices or herbs\tcooked or charred appearance, indicating that they have been grilled or fried\tcut open, revealing the meat inside.", 72], "thick clouds": ["Yes. 'Thick clouds' has a tangible appearance and refers to dense formations of water vapor in the atmosphere.\nA few things that are visually similar to 'thick clouds' but are not 'thick clouds' are:\tfog\tsmoke\tsteam\tdust\nThere are several useful visual features to tell there are 'thick clouds' and not similar things in a photo:\twhite or gray\tcolorful at sunrise or sunset\tcovering most of the sky in a particular direction or horizon\tobscuring the view of the sun or other celestial objects.", 72], "tennis shorts": ["Yes. 'Tennis shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tennis shorts' but are not 'tennis shorts' are:\trunning shorts\tbasketball shorts\tswim trunks\nThere are several useful visual features to tell there is 'tennis shorts' and not similar things in a photo:\tshort length\ttight fit or elastic waistband\tusually made of lightweight, moisture-wicking fabric\tmight have pockets on the sides or back", 72], "bangs": ["Yes. 'Bangs' has a tangible appearance and refers to a type of hairstyle.\nA few things that are visually similar to 'bangs' but are not 'bangs' are:\teyebrows\tfringes\theadbands\t\nThere are several useful visual features to tell there are 'bangs' and not similar things in a photo:\thair covering the forehead\tstraight or curved line separating the bangs from the rest of the hair\tbangs can be short or long\tbangs can be parted or swept to the sides or worn straight across the forehead.", 72], "dirt bike": ["Yes. 'Dirt bike' has a tangible appearance and is a type of motorcycle designed for off-road use.\nA few things that are visually similar to 'dirt bike' but are not 'dirt bike' are:\tmotorcycle\tscooter\tbicycle\tATV\nThere are several useful visual features to tell there is 'dirt bike' and not similar things in a photo:\tknobby tires\tsuspension system\toff-road design\tlow saddle\theightened fender\tnarrow frame\tno windshield or fairings.", 72], "story house": ["No. 'Story house' is too vague or abstract to be distinguished in a photo. If you mean \"two-story house,\" then the answer is yes.\nA few things that are visually similar to 'two-story house' but are not 'two-story house' are:\tone-story house\tapartment building\tchurch\tbusiness building\nThere are several useful visual features to tell there is 'two-story house' and not similar things in a photo:\ttwo full levels or floors\tstaircase or multiple staircases\tbig windows with shutters or blinds / smaller windows in the second floor situated under the roof-line of the house\tvertical positioning of the windows symmetrically along the walls on each floor", 71], "tall lamp": ["Yes. 'Tall lamp' has a tangible appearance and is usually seen as an object in a room.\nA few things that are visually similar to 'tall lamp' but are not 'tall lamp' are:\tpole\tflag pole\tbamboo stalk\nThere are several useful visual features to tell there is 'tall lamp' and not similar things in a photo:\tbulb and shade attached to a tall stand\ta base to keep it stable\theight proportionate to a person or taller\tswitch or cord for turning it on and off", 71], "horizon line": ["Yes. 'Horizon line' has a tangible appearance and refers to the line that separates the earth and the sky in a landscape.\nA few things that are visually similar to 'horizon line' but are not 'horizon line' are:\tcloud\tline of trees\toncoming waves\nThere are several useful visual features to tell there is 'horizon line' and not similar things in a photo:\tstraight line\tseparating the earth and the sky\tat eye level or vanishing point", 71], "potato chips": ["Yes. 'Potato chips' has a tangible appearance and is a type of snack.\nA few things that are visually similar to 'potato chips' but are not 'potato chips' are:\ttortilla chips\tcorn chips\tpita chips\nThere are several useful visual features to tell there are 'potato chips' and not similar things in a photo:\tthin and crispy\tfried or baked\tpotato-shaped\tdisc-shaped\tpotato skin visible on the edges\tsalt or other seasonings", 71], "eaten": ["No. 'Eaten' is too vague or abstract and does not have a tangible appearance.", 71], "orange umbrella": ["Yes. 'Orange umbrella' has a tangible appearance and is a specific type of umbrella.\nA few things that are visually similar to 'orange umbrella' but are not 'orange umbrella' are:\tred umbrella\tyellow umbrella\tpurple umbrella\t\nThere are several useful visual features to tell there is 'orange umbrella' and not similar things in a photo:\torange color\tcircular canopy\tcollapsed or open shape\tmetal or plastic shaft and ribs", 71], "chalk line": ["Yes. 'Chalk line' has a tangible appearance and is a type of tool used in construction.\nA few things that are visually similar to 'chalk line' but are not 'chalk line' are:\tleveling tool\tmeasuring tape\tlaser level\nThere are several useful visual features to tell there is 'chalk line' and not similar things in a photo:\tlong, thin string coated in chalk\theld taut between two points on a surface\tused to mark straight lines on surfaces", 71], "metal ladder": ["Yes. 'Metal ladder' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'metal ladder' but are not 'metal ladder' are:\tstaircase\tbookcase\tshelving\tunit\tworkbench\nThere are several useful visual features to tell there is 'metal ladder' and not similar things in a photo:\t\nvertical structure\t\nparallel rungs\t\nmetal material\t\nappropriate size for climbing\t\nintended for climbing higher or reaching higher places", 71], "metal knife": ["Yes. 'Metal knife' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'metal knife' but are not 'metal knife' are: letter opener, scissors, axe, razor blade, spade.\nThere are several useful visual features to tell there is 'metal knife' and not similar things in a photo: \tblade with a sharp edge \tshaped handle \tmetallic appearance \tusually found in a kitchen setting.", 71], "drivers": ["Yes. 'Drivers' has a tangible appearance and refers to people who operate a vehicle.\nA few things that are visually similar to 'drivers' but are not 'drivers' are:\tpassengers\tcyclists\tpedestrians\tmechanics\nThere are several useful visual features to tell there is a 'driver' and not similar things in a photo:\tseated in the driver's seat of a vehicle\thands on the steering wheel\tfocused on the road or navigation", 71], "iphone": ["Yes. 'iPhone' has a tangible appearance and is a type of smartphone.\nA few things that are visually similar to 'iPhone' but are not 'iPhone' are:\tAndroid phone\tmobile phone\tlandline phone\tcamera\nThere are several useful visual features to tell there is 'iPhone' and not similar things in a photo:\tretangular shape with rounded edges\tbutton to access home screen\tApple symbol on the back of the phone\tno physical keypad, just a touch screen\tscreen displays iOS operating system and apps", 71], "dog nose": ["Yes. 'Dog nose' has a tangible appearance and is a part of a dog's facial features.\nA few things that are visually similar to 'dog nose' but are not 'dog nose' are:\tcat nose\tpig nose\thuman nose\nThere are several useful visual features to tell there is 'dog nose' and not similar things in a photo:\twet and shiny\tcold\ttoo close to the ground\tlarger than human noses\tdifferent shape and coloring depending on the breed of the dog", 71], "handkerchief": ["Yes. 'Handkerchief' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'handkerchief' but are not 'handkerchief' are:\tnapkins\ttowels\tdishcloths\tbandanas\nThere are several useful visual features to tell there is 'handkerchief' and not similar things in a photo:\tsmall in size\tsquare or rectangular in shape\tmade of lightweight or sheer fabric\ttypically carried in a pocket or purse for personal use.", 71], "powerlines": ["Yes. 'Powerlines' has a tangible appearance and is a series of cables carrying electrical power.\nA few things that are visually similar to 'powerlines' but are not 'powerlines' are:\ttelephone lines\tcable lines\tzip lines\tbridge cables\nThere are several useful visual features to tell there is 'powerlines' and not similar things in a photo:\ttall metallic poles or towers\tcables suspended between poles or towers\tcables carrying electrical current\thigh voltage warning signs", 71], "disk": ["Yes. 'Disk' has a tangible appearance and typically refers to a circular object with a flat surface.\nA few things that are visually similar to 'disk' but are not 'disk' are:\tcoin\tbutton\tfrisbee\tdvd\nThere are several useful visual features to distinguish 'disk' from the listed similar things in a photo:\ta circular shape\twith a flat surface\tcan store information or data (such as a DVD)\tor may be used in a game (such as a Frisbee)\ttypically made of metal, plastic, or other solid material.", 71], "roof top": ["Yes. 'Roof top' has a tangible appearance and refers to the top surface of a building.\nA few things that are visually similar to 'roof top' but are not 'roof top' are:\tground\tparking lot\tstreet\tceiling\nThere are several useful visual features to tell there is 'roof top' and not similar things in a photo:\tlocated at the top of a building\tmay have shingles or tiles\tmay have chimneys or other roof features\tmay have views of the surrounding area or the sky", 71], "wood cabinets": ["Yes. 'Wood cabinets' has a tangible appearance and refers to a specific type of storage furniture.\nA few things that are visually similar to 'wood cabinets' but are not 'wood cabinets' are:\tshelves\tlockers\tclosets\tdrawers\nThere are several useful visual features to distinguish 'wood cabinets' from other similar things in a photo:\tmade of wood or wood-like material\tcabinet doors\twith or without handles\tor without locks\thorizontal or vertical orientation\topen or closed position", 71], "gap": ["Yes. 'Gap' has a tangible appearance and refers to a space or an opening.\nA few things that are visually similar to 'gap' but are not 'gap' are:\thole\tcracks\tspace between two fingers or two hands\nThere are several useful visual features to tell there is 'gap' and not similar things in a photo:\topen space or opening\tbetween two objects or surfaces\tcan see through it or see what's on the other side.", 71], "drive": ["No. 'Drive' is too vague or abstract to be distinguished in a photo.", 71], "grey sidewalk": ["Yes, 'grey sidewalk' has a tangible appearance.\nA few things that are visually similar to 'grey sidewalk' but are not 'grey sidewalk' are: grey concrete walls, grey stones floor, grey bricks.\nThere are several useful visual features to tell there is 'grey sidewalk' and not similar things in a photo: a flat surface for people to walk\ton the edge of a street or a road in an urban area\tthe surface texture might vary depending on materials used, but usually has a rough texture.", 71], "metal rails": ["Yes. 'Metal rails' has a tangible appearance and is a type of construction material or equipment.\nA few things that are visually similar to 'metal rails' but are not 'metal rails' are:\tmetal bars\tmetal tubes\tmedical rails\tsidewalk barriers\nThere are several useful visual features to tell there is 'metal rails' and not similar things in a photo:\trectangular or cylindrical in shape\tpolished or painted metal surface\tused as a support or barrier for walking or for other purposes\tfound in places like staircases, balconies, and fences.", 71], "bike tire": ["Yes. 'Bike tire' has a tangible appearance and is a type of tire.\nA few things that are visually similar to 'bike tire' but are not 'bike tire' are:\tcar tire\tmotorcycle tire\tbouncy ball\trubber hose\nThere are several useful visual features to tell there is 'bike tire' and not similar things in a photo:\tslim and narrow\tcircular shape\thas treads or grooves\tfor use with a bicycle", 70], "wooden box": ["Yes, 'wooden box' has a tangible appearance and is a rectangular container made out of wood.\n\nA few things that are visually similar to 'wooden box' but are not 'wooden box' are: \n\n- Suitcase/Trunk\n- Crate\n- Chest\n\nThere are several useful visual features to distinguish 'wooden box' from the listed similar things in a photo:\n- Rectangular shape with four sides\n- Made of wood\n- Often has a lid or cover\n- May have metal or other materials on the corners or sides\n- Used for storage or transportation purposes.", 70], "dining table": ["Yes. 'Dining table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'dining table' but are not 'dining table' are:\tcoffee table\tdesk\tpicnic table\tworkbench\nThere are several useful visual features to tell there is 'dining table' and not similar things in a photo:\tseat multiple people\titems of cutlery, crockery, or food on table\ttop surface at a height that allows for seating around it.", 70], "stainless steel sink": ["Yes. 'Stainless steel sink' has a tangible appearance and is a kind of household item.\nA few things that are visually similar to 'stainless steel sink' but are not 'stainless steel sink' are:\tother types of sink\tmetal bowl\nThere are several useful visual features to tell there is 'stainless steel sink' and not similar things in a photo:\tmetallic and shiny surface\trectangular or round shape\ta hole or multiple holes for a faucet\tor drainage\tpipes\tthat connect it to a wall or a floor\tor lead to the ground", 70], "stuffed toy": ["Yes. 'Stuffed toy' has a tangible appearance and is a kind of soft toy.\nA few things that are visually similar to 'stuffed toy' but are not 'stuffed toy' are:\tpillows\tplush blankets\tstuffed cushions\tfur coats\nThere are several useful visual features to tell there is 'stuffed toy' and not similar things in a photo:\tsoft and cuddly\tfabric material\tfurry texture\tfacial features like eyes, nose, mouth\tand limbs or tentacles.", 70], "speck": ["Yes. 'Speck' has a tangible appearance and refers to a very small spot or particle.\nA few things that are visually similar to 'speck' but are not 'speck' are:\tdust\tmite\tpollen\tsand\nThere are several useful visual features to tell there is 'speck' and not similar things in a photo:\tvery small size\tcomparatively darker or lighter spot or particle", 70], "plastic bucket": ["Yes. 'plastic bucket' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'plastic bucket' but are not 'plastic bucket' are:\tcoffee mug\tplastic bowl\twatering can\ttrash bin\nThere are several useful visual features to tell there is 'plastic bucket' and not similar things in a photo:\tcylindrical or conical shape\twith handle\tat least twice as tall as wide\tmade of plastic or similar materials\thas volume markings or measurements on its body, often in liters or gallons.", 70], "bell pepper": ["Yes. 'Bell pepper' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'bell pepper' but are not 'bell pepper' are:\tchili pepper\ttomato\tgrape\nThere are several useful visual features to tell there is 'bell pepper' and not similar things in a photo:\thollow, four-lobed fruit\tthick flesh\tbright green, yellow, orange or red color\tsmooth, shiny skin", 70], "chainlink fence": ["Yes. 'Chainlink fence' has a tangible appearance and is a type of barrier.\nA few things that are visually similar to 'chainlink fence' but are not 'chainlink fence' are:\twire mesh\tfishing net\tbarbed wire\nThere are several useful visual features to tell there is a 'chainlink fence' and not similar things in a photo:\tgrid pattern of diamond-shaped holes\tmetallic material\tsilver or grey color\tsquare or rectangular shape with perpendicular posts and rails", 70], "profile": ["Yes. 'Profile' has a tangible appearance and refers to the side-view of a person's face or the shape of an object or structure from one side.\nA few things that are visually similar to 'profile' but are not 'profile' are:\tfront view\tside view of an object or structure in a different context\tside view of an animal\tor any other angled view\nThere are several useful visual features to tell there is 'profile' and not similar things in a photo:\tclear definition of the side-view of a face or object\tthe contour line shows the shape of the object or structure from one side, while the outline remains the same on the other side", 70], "metal roof": ["Yes. 'Metal roof' has a tangible appearance and refers to a roof made of metal.\nA few things that are visually similar to 'metal roof' but are not 'metal roof' are:\tshingles\ttiles\tthatch\tgrass\nThere are several useful visual features to tell there is 'metal roof' and not similar things in a photo:\tshiny surface\tmetallic color\tno overlapping or 3D texture panels or sheets with visible seams or joints", 70], "markers": ["Yes. 'Markers' has a tangible appearance and is a type of writing or drawing tool.\nA few things that are visually similar to 'markers' but are not 'markers' are:\tpens\tpencils\tcrayons\tchalk\nThere are several useful visual features to tell there are 'markers' and not similar things in a photo:\trectangular or cylindrical shape\tvibrant colors\tfelt-tip point or brush-tip point", 70], "advertisement sign": ["Yes. 'Advertisement sign' has a tangible appearance and is a type of sign used for advertising.\nA few things that are visually similar to 'advertisement sign' but are not 'advertisement sign' are:\tdirectionals\tsignaling\tsigns with a warning message\tbuilding's name signs\nThere are several useful visual features to tell there is 'advertisement sign' and not similar things in a photo:\tbold, bright colors\tlogos and brand names\tattractive slogans or photos\tlarge lettering or font sizes\tincorporated with lighting or illuminated", 70], "cyclist": ["Yes. 'Cyclist' has a tangible appearance and refers to a person riding a bicycle.\nA few things that are visually similar to 'cyclist' but are not 'cyclist' are:\tperson on a scooter\tperson on a skateboard\tperson running person on a motorcycle\nThere are several useful visual features that can help distinguish 'cyclist' from similar things in a photo:\tperson riding a bicycle\tbicycle frame\twheels\tpedals\tcycling outfit\tcycling helmet", 70], "blurry background": ["Yes. 'Blurry background' has a tangible appearance and is a type of photography technique.\nA few things that are visually similar to 'blurry background' but are not 'blurry background' are:\tintentional bokeh\tdepth of field\tfocus and depth effect\tpainting with blurred strokes\nThere are several useful visual features to tell there is 'blurry background' and not similar things in a photo:\ta clear subject in focus\tincreased distance between the subject and the background\tblurred elements in the background\tsmooth and uniform blur effect", 70], "blue house": ["Yes. 'Blue house' has a tangible appearance and is a type of house.\nA few things that are visually similar to 'blue house' but are not 'blue house' are:\tblue car\tblue boat\tblue tent \nThere are several useful visual features to tell there is 'blue house' and not similar things in a photo:\tblue exterior walls of the house\troof\ttop, sides, and windows of the house are not made of fabric or canvas.", 70], "air plane": ["Yes. 'Airplane' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'airplane' but are not 'airplane' are:\thelicopter\tbird\tdrone\tair balloon\nThere are several useful visual features to tell there is 'airplane' and not similar things in a photo:\twings engines\tlanding gear\ttailfins and rudder windows and cockpit\tstreamlined body shape\twith airline specific logos and colors", 70], "streetlights": ["Yes. 'Streetlights' has a tangible appearance and is a type of lighting fixture used in public streets.\nA few things that are visually similar to 'streetlights' but are not 'streetlights' are:\tPorch lights\tFloodlights\tLampposts\tCar headlights\nThere are several useful visual features to tell there is 'streetlights' and not similar things in a photo:\tTall, typically metal poles\tLocated on the side of the street or sidewalk\tTypically topped with a light fixture or bulb\tOften with a curved or bent arm to bring the light closer to the road\tsome of them have more than one light fixture on top", 70], "pink bag": ["Yes. 'Pink bag' has a tangible appearance and is a type of bag with a pink color.\nA few things that are visually similar to 'pink bag' but are not 'pink bag' are: red bag, magenta bag, rose bag.\nThere are several useful visual features to tell there is 'pink bag' and not similar things in a photo:\tpink color\tstrap or handle\topening at the top\tsoft and flexible material", 70], "dark eye": ["Yes. 'Dark eye' has a tangible appearance and refers to the appearance of someone's eyes.\nA few things that are visually similar to 'dark eye' but are not 'dark eye' are:\tblack and white photographs that focus on the subject's eyes, particularly if the photo is taken in low light\tshadows cast around the eyes gives the illusion of dark eyes\tsunglasses that completely cover the eyes\nThere are several useful visual features to tell there is 'dark eye' and not similar things in a photo:\ta person's actual eyes, showing a very dark iris and a small or no visible pupil\tthe skin around the eyes is lighter than the iris\tdramatic, focused lighting highlights the eyes", 70], "cat tail": ["Yes. 'Cat tail' has a tangible appearance and is a part of a cat's body.\nA few things that are visually similar to 'cat tail' but are not 'cat tail' are:\tdog tail\tfox tail\tsquirrel tail\t\nThere are several useful visual features to tell there is 'cat tail' and not similar things in a photo:\tlong and slender\tcurved\tat the end of a feline body\thairy or furry\tsometimes with stripes or spots", 70], "silver tray": ["Yes. 'Silver tray' has a tangible appearance and is a type of serving dish.\nA few things that are visually similar to 'silver tray' but are not 'silver tray' are:\tsilver platter\tsilver plate\tmetal tray\tplastic tray\nThere are several useful visual features to tell there is 'silver tray' and not similar things in a photo:\tsilver-colored metal material\treflective surface\twith or without handles\tusually oval or rectangular.Shapes and handles can be useful features to distinguish silver trays from each other, as they vary a lot in design.", 70], "streamers": ["Yes. 'Streamers' has a tangible appearance and is a type of decoration.\nA few things that are visually similar to 'streamers' but are not 'streamers' are:\tribbons\tcrepe paper\ttinsels\tfabric\nThere are several useful visual features to tell there is 'streamers' and not similar things in a photo:\tlong and narrow pieces of paper or plastic\tbright colors\thanging from a ceiling or a wall", 70], "soccer players": ["Yes. 'Soccer players' has a tangible appearance and refers to people playing soccer.\nA few things that are visually similar to 'soccer players' but are not 'soccer players' are:\tathletes\tfootball players\tbaseball players\tbasketball players\nThere are several useful visual features to tell there are 'soccer players' and not similar things in a photo:\tjerseys with numbers and team logos\tshorts and socks\tshin guards\tcleats\tor soccer boots\tsoccer ball\tfield markings such as sidelines, goal box, and penalty area", 70], "silver knob": ["Yes. 'Silver knob' has a tangible appearance and is a type of physical object.\nA few things that are visually similar to 'silver knob' but are not 'silver knob' are:\tdoorknob\tjewelry\tbutton\thook\nThere are several useful visual features to tell there is 'silver knob' and not similar things in a photo:\tsmall, handheld knob\tmade of metal, specifically silver\tcolor silver, or silver-colored finish\tcircular or cylindrical shape with ridges or texture for gripping", 70], "tan hat": ["Yes. 'Tan hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'tan hat' but are not 'tan hat' are:\tcap\tsombrero\tberet\tbonnet\nThere are several useful visual features to tell there is 'tan hat' and not similar things in a photo:\ttan color\tsimilar shape to a fedora or a Panama hat\tbrim not as wide\tas a full-brimmed cowboy hat", 70], "student": ["No. 'Student' is too vague or abstract to be distinguished in a photo.", 70], "gazebo": ["Yes. 'Gazebo' has a tangible appearance and is a type of outdoor structure.\nA few things that are visually similar to 'gazebo' but are not 'gazebo' are:\tpavilion\tpergola\tpatio\tdeck\nThere are several useful visual features to tell there is 'gazebo' and not similar things in a photo:\tcovered area with a roof and open sides\tbuilt in a garden or a park\toften hexagonal or octagonal shape\twith benches or seating inside\tlarge enough to accommodate several people\tattractive or decorative design\ton a raised platform or foundation.", 70], "tree bark": ["Yes. 'Tree bark' has a tangible appearance and is a protective layer covering the trunk.\nA few things that are visually similar to 'tree bark' but are not 'tree bark' are:\trock surface\tanimal skin\tmetal surface\nThere are several useful visual features to tell there is 'tree bark' and not similar things in a photo:\tvertical stripes or ridges\ttree texture\tmulti-layered appearance\trough or smooth texture\tnatural coloration", 70], "barbed wire": ["Yes. 'Barbed wire' has a tangible appearance and is a type of wire with sharp points.\nA few things that are visually similar to 'barbed wire' but are not 'barbed wire' are:\tpicket fence\trazor wire\tcactus\tspiky plants\nThere are several useful visual features to tell there is 'barbed wire' and not similar things in a photo:\twire with sharp points\tor sharp barbs\tusually used for fencing and securing areas\tmetallic appearance and texture", 70], "raft": ["Yes. 'Raft' has a tangible appearance and is a type of watercraft.\nA few things that are visually similar to 'raft' but are not 'raft' are:\tkayak\tcanoe\tpaddleboard\trowboat\nThere are several useful visual features to tell there is 'raft' and not similar things in a photo:\tlarge and flat platform\tfor multiple people\twith no keel\tor other means of directional control\tpushed or pulled by currents or paddles", 70], "paper holder": ["Yes. 'Paper holder' has a tangible appearance and is a type of office supply.\nA few things that are visually similar to 'paper holder' but are not 'paper holder' are:\tclip\tbinder\tpin\tstapler\nThere are several useful visual features to tell there is 'paper holder' and not similar things in a photo:\trectangular or square shape\twith two flat surfaces on top\ta space in between the surfaces to hold paper or documents\tmade of plastic, metal or wood.", 70], "symbols": ["No. 'Symbols' is too vague or abstract to be distinguished in a photo. \n\nBut if we are talking about visual symbols, then:\n\nA few things that are visually similar to 'symbols' but are not 'symbols' are:\tletters\tnumbers\tpictures\ticons\nThere are several useful visual features to tell there is 'symbols' and not similar things in a photo:\tsimple geometric shapes or patterns\teasily recognizable design\trepresents an idea or object\thas a cultural or historical significance", 70], "car door": ["Yes. 'Car door' has a tangible appearance and is a specific part of a vehicle.\nA few things that are visually similar to 'car door' but are not 'car door' are:\tgarage door\thouse door\tgate\nThere are several useful visual features to tell there is 'car door' and not similar things in a photo:\tattached to a car\tbody panel with a handle or latch\thinged and opens outward\tor slides horizontally\tin a variety of shapes and sizes depending on the make and model of car.", 69], "rectangular window": ["Yes. 'Rectangular window' has a tangible appearance and is a type of architectural feature.\nA few things that are visually similar to 'rectangular window' but are not 'rectangular window' are:\trectangular mirror\tpicture frame\tTV screen\tdoor\nThere are several useful visual features to tell there is a 'rectangular window' and not similar things in a photo:\ttransparent glass or other see-through material\tstraight and sharp edges\thorizontal and vertical lines\tframe or casing around the edges", 69], "picture frames": ["Yes. 'Picture frames' has a tangible appearance and is a kind of object used to display pictures.\nA few things that are visually similar to 'picture frames' but are not 'picture frames' are:\tmirrors\twindows\tcanvas\tstickers\nThere are several useful visual features to tell there is a 'picture frame' and not similar things in a photo:\tenclosing a picture or artwork\trectangular or square shape\tthick borders or edges\tusually hung on a wall or sitting on a surface.", 69], "teddy bear": ["Yes. 'Teddy bear' has a tangible appearance and is a stuffed animal toy.\nA few things that are visually similar to 'teddy bear' but are not 'teddy bear' are:\tplush animals\tpillows\tbolsters\t\nThere are several useful visual features to tell there is 'teddy bear' and not similar things in a photo:\tround head, arms, and legs\tfurry body\twith or without clothes or accessories\ttypically brown or beige in color\thuman-like facial expression and eyes", 69], "tennis rackets": ["Yes. 'Tennis rackets' have a tangible appearance and are a sports equipment.\nA few things that are visually similar to 'tennis rackets' but are not 'tennis rackets' are:\tracquetball rackets\tsquash rackets\tbadminton rackets\tbeach tennis rackets\nThere are several useful visual features to tell there is 'tennis rackets' and not similar things in a photo:\tflat, oval-shaped head with strings\tlong handle with a grip\tat least one distinctive hole (called the throat) connecting the head to the handle", 69], "hair band": ["Yes. 'Hair band' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'hair band' but are not 'hair band' are:\theadbands\tbows\tribbons\ttiaras\nThere are several useful visual features to tell there is 'hair band' and not similar things in a photo:\tsnugly fit around the head and hair\tusually made of elastic material\tor of a similar texture to fabric, but with some stretchiness\tthe width of a hair band is typically around 1-2 inches\tdoes not have additional decorations", 69], "metal tower": ["Yes. 'Metal tower' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'metal tower' but are not 'metal tower' are:\tbuilding\tchimney\ttelephone pole\tfactory\nThere are several useful visual features to tell there is 'metal tower' and not similar things in a photo:\ttall and slender\tsolid metal construction\tmultiple levels or platforms\tantennas or other communication equipment attached to the top", 69], "stools": ["Yes. 'Stools' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'stools' but are not 'stools' are:\tchairs\tbenches\tottomans\nThere are several useful visual features to tell there is 'stools' and not similar things in a photo:\tsmall in size\tmore than one leg\tno arms or backrests for support", 69], "bug": ["Yes. 'Bug' has a tangible appearance and refers to a variety of insects.\nA few things that are visually similar to 'bug' but are not 'bug' are:\tspiders\tcentipedes\tmillipedes\tarthropods\nThere are several useful visual features to tell there is 'bug' and not similar things in a photo:\tsix legs\tthree body parts (head, thorax, abdomen)\tantennae\twings (in some cases)\texoskeleton", 69], "chocolate donut": ["Yes. 'Chocolate donut' has a tangible appearance and is a kind of pastry.\n\nA few things that are visually similar to 'chocolate donut' but are not 'chocolate donut' are:\n\n- Plain donut\n- Cinnamon roll\n- Croissant\n\nThe useful visual features for distinguishing 'chocolate donut' from the listed similar things in a photo are:\n\n- Round or circular shape with a hole in the middle\n- Dark brown or chocolate-colored glaze or frosting\n- Sprinkles or other decorative toppings\n- Soft and bread-like texture", 69], "lit candle": ["Yes. 'Lit candle' has a tangible appearance and is a source of light.\nA few things that are visually similar to 'lit candle' but are not 'lit candle' are:\tincandescent light bulb\tlantern\tfireplace\tlamp\nThere are several useful visual features to tell there is 'lit candle' and not similar things in a photo:\twick\twith visible flame\twax dripping\tdim, flickering light\ttypically on a candlestick or in a holder", 69], "coin slot": ["Yes. 'Coin slot' has a tangible appearance and is a small opening where coins can be inserted.\nA few things that are visually similar to 'coin slot' but are not 'coin slot' are:\tpaper feeder in a printer\tcard reader in an ATM or a vending machine\nThere are several useful visual features to tell there is 'coin slot' and not similar things in a photo:\thorizontal or vertical small opening\trounded or rectangular shape\tlocated on a machine or device\tfor coins to be inserted", 69], "rear legs": ["Yes. 'Rear legs' has a tangible appearance and refers to the back legs of an animal.\nThere aren't many things that are visually similar to 'rear legs' but are not 'rear legs'.\nUseful visual features for distinguishing 'rear legs' from the front legs or other similar things in a photo: \tpositioned behind the body\tusually longer and stronger than front legs\thave a different type of joint to move", 69], "nike logo": ["Yes. 'Nike logo' has a tangible appearance and is a specific design.\nA few things that are visually similar to 'nike logo' but are not 'nike logo' are:\tAdidas logo\tPuma logo\tUnder Armour logo\tReebok logo\nThere are several useful visual features to tell there is 'nike logo' and not similar things in a photo:\tswoosh design\tbold and simple\tblack-and-white or red-and-white\tcolors", 69], "row boat": ["Yes. 'Row boat' has a tangible appearance and refers to a specific type of boat.\nA few things that are visually similar to 'row boat' but are not 'row boat' are:\tkayak\tcanoe\tpaddleboat\tfishing boat\nThere are several useful visual features to tell there is 'row boat' and not similar things in a photo:\tlong and narrow\thuman-powered\tusually flat-bottomed\twith two oars for rowing\tsits low in the water\twithout a motor", 69], "window sill": ["Yes. 'Window sill' has a tangible appearance and is a horizontal surface at the bottom of a window.\nA few things that are visually similar to 'window sill' but are not 'window sill' are:\tshelves\tcountertops\tledge\ton a bed\nThere are several useful visual features to tell there is 'window sill' and not similar things in a photo:\tpositioned at the bottom of a window\tsupporting window frames or panes\tmade of the same material as window frames or panes", 69], "color sky": ["No. 'Color sky' is too vague or abstract to be distinguished in a photo.\nThere are no things that are visually similar to 'color sky' but not 'color sky'.\nSince 'color sky' is not a tangible concept, there are no useful visual features to distinguish it from other things in a photo.", 69], "pedestal": ["Yes. 'Pedestal' has a tangible appearance and is a type of support structure.\nA few things that are visually similar to 'pedestal' but are not 'pedestal' are:\ttable stand\tsculpture base\tplant stand\nThere are several useful visual features to tell there is 'pedestal' and not similar things in a photo:\t\nnarrow flat top to display objects\t\ncylindrical or square-shaped column\t\ntypically made of stone, metal, or wood", 69], "seal": ["Yes. 'Seal' has a tangible appearance and is an aquatic mammal.\nA few things that are visually similar to 'seal' but are not 'seal' are:\totter\twhale\twalrus\tsea lion\tfur coat\nThere are several useful visual features to tell there is 'seal' and not similar things in a photo:\tsleek and streamlined body\tflippers instead of limbs\tthick layer of blubber\t\nwhiskers or vibrissae\tclose-set nostrils, like the letter \"V\"\tdark or light-colored fur or skin.", 69], "flamingo": ["Yes. 'Flamingo' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'flamingo' but are not 'flamingo' are:\theron\tcrane\tibis\tstork\t\nThere are several useful visual features to tell there is 'flamingo' and not similar things in a photo:\tpink or reddish feathers\tlong, slender legs with backward-bending knees\tprominent beak\tupright posture\twith one leg tucked up under the body while standing or wading\tinhabiting wetlands and shallow lakes", 69], "skinny": ["No. 'Skinny' is too vague or abstract to be distinguished in a photo. It is a subjective physical characteristic that varies from person to person and does not have a specific appearance. \n\nTherefore, there are no things that are visually similar to 'skinny' but not 'skinny.'\n\nIt is not possible to provide useful visual features for distinguishing 'skinny' from visually similar things because there are no such things.", 68], "yolk": ["Yes. 'Yolk' has a tangible appearance and is a part of an egg.\nA few things that are visually similar to 'yolk' but are not 'yolk' are:\tegg white\tbutter\tcream\thoney\tmustard\nThere are several useful visual features to tell there is 'yolk' and not similar things in a photo:\tyellow\torangish color\tround shape\tviscous texture\tlocated in the center of an egg", 68], "coke": ["Yes. 'Coke' has a tangible appearance and refers to a specific kind of carbonated soft drink.\nA few things that are visually similar to 'coke' but are not 'coke' are:\tpepsi\tcola\tother soft drinks\tinstant coffee\nThere are several useful visual features to tell there is 'coke' and not similar things in a photo:\tbrown color\ttall glass bottle with a red and white label\tor aluminum can with a red and white label bubbling and effervescence when opened.", 68], "cargo": ["Yes. 'Cargo' has a tangible appearance and refers to goods or products being transported.\nA few things that are visually similar to 'cargo' but are not 'cargo' are: boxes, packages, and suitcases being carried by people who appear to be traveling.\nThere are several useful visual features to tell there is 'cargo' and not similar things in a photo:\tlarge shipping containers or pallets containing goods\tcranes or forklifts used to move the cargo\ton a ship, plane, truck, or train\tclear indications of a transportation company or mode (e.g. cargo planes or shipping containers with the name of the company printed on them)", 68], "dark car": ["Yes. 'Dark car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'dark car' but are not 'dark car' are:\tdark truck\tdark van\tdark SUV\tmotorcycle\nThere are several useful visual features to tell there is a 'dark car' and not similar things in a photo:\thas four wheels and a body for passengers\tdark or black exterior\tcolor may look slightly different in different lighting conditions", 68], "silver necklace": ["Yes. 'Silver necklace' has a tangible appearance and is a piece of jewelry.\nA few things that are visually similar to 'silver necklace' but are not 'silver necklace' are:\tchoker\tdog collar\tbelt\t\nThere are several useful visual features to tell there is 'silver necklace' and not similar things in a photo:\tchain-like\tsilver color\tclasped around the neck\tjeweled or plain pendant\tdangles or sits on the collarbone or chest area.", 68], "silver sink faucet": ["Yes, 'Silver sink faucet' has a tangible appearance and is a type of plumbing fixture.\n\nA few things that are visually similar to 'silver sink faucet' but are not 'silver sink faucet' are:\n- Bathroom showerhead\n- Kitchen pot filler\n- Garden hose nozzle\n\nSome useful visual features for distinguishing 'silver sink faucet' from the similar things in a photo are:\n- Positioned over the sink\n- Connected to pipes beneath the counter\n- Has a handle or knob to turn on/off water\n- Stream or spray of water coming out of it\n- Mounted on a wall or on the sink basin itself", 68], "wooden railing": ["Yes. 'Wooden railing' has a tangible appearance and is a type of architectural feature.\nA few things that are visually similar to 'wooden railing' but are not 'wooden railing' are:\twooden fence\tmetal railing\trope\tbanisters\nThere are several useful visual features to tell there is 'wooden railing' and not similar things in a photo:\tmade of wood\thorizontal or vertical bars\tused for safety or support located on a deck or a staircase", 68], "paper towel dispenser": ["Yes. 'Paper towel dispenser' has a tangible appearance and is a type of holder for paper towels.\nA few things that are visually similar to 'paper towel dispenser' but are not 'paper towel dispenser' are:\ttissue box\ttrash bin\tshoe rack\tfood container\nThere are several useful visual features to tell there is 'paper towel dispenser' and not similar things in a photo:\trectangular or cylindrical shape\tvertical or horizontal orientation\twith a slot or opening for pulling paper towels\tout of plastic or metal material\tfixed on a wall, table or counter", 68], "water droplets": ["Yes. 'Water droplets' have a tangible appearance and can be seen as small spherical or oblong shapes.\nA few things that are visually similar to 'water droplets' but are not 'water droplets' are:\tPolystyrene foam beads\tMercury droplets\tIce crystals\nThere are several useful visual features to tell there is 'water droplets' and not similar things in a photo:\n\n\u2022 Spherical or oblong in shape \n\u2022 Reflects light \n\u2022 Appears wet or moist \n\u2022 Formed on a surface that contains moisture \n\u2022 Can come in various sizes and quantities", 68], "giraffe tail": ["Yes. 'Giraffe tail' has a tangible appearance and is a specific part of a giraffe's anatomy.\nA few things that are visually similar to 'giraffe tail' but are not 'giraffe tail' are:\thorse tail\tcow tail\tdeer tail\tzebra tail\nThere are several useful visual features to tell there is 'giraffe tail' and not similar things in a photo:\tlong, thin hair tufted at the end\tthick and muscular base\tsimilar coloring and pattern to the rest of the giraffe's body", 68], "rooftop": ["Yes. 'Rooftop' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'rooftop' but are not 'rooftop' are:\tskyscraper facade\twall\tskylight\tceiling\tfloor\nThere are several useful visual features to tell there is 'rooftop' and not similar things in a photo:\tflat or sloping surface\ttop of a building\tmay have chimneys or other protrusions\tmay have skylights or windows\tmay have decorations or features specific to a certain culture/architectural style", 68], "stalks": ["Yes. 'Stalks' has a tangible appearance and refers to the stem of a plant.\nA few things that are visually similar to 'stalks' but are not 'stalks' are:\tbranches\troots\tstems of other plants\twooden sticks\nThere are several useful visual features to tell there is 'stalks' and not similar things in a photo:\tvertical line protruding from the ground\tthinner than branches and roots\thas leaves, flowers, or fruit growing from it (depending on the plant species)", 68], "marble": ["Yes. 'Marble' has a tangible appearance and is a kind of rock.\nA few things that are visually similar to 'marble' but are not 'marble' are:\tgravel\tconcrete\tquartz\nThere are several useful visual features to tell there is 'marble' and not similar things in a photo:\tsmooth surface\tvaried colors and veins\treflective surface\ttypically used for sculptures or countertops", 68], "streak": ["Yes. 'Streak' has a tangible appearance and refers to a thin line or mark.\nA few things that are visually similar to 'streak' but are not 'streak' are:\tline\tscratch\tstain\tswirl\nThere are several useful visual features to tell there is 'streak' and not similar things in a photo:\tthin and narrow\telongated shape\tsingle-colored (can be different from the surface it is on)\tlinear\tpositioned in a specific direction (horizontal or vertical)", 68], "nobody": ["No. 'Nobody' is too vague or abstract to be distinguished in a photo. One cannot see the absence of something in a visual way.", 68], "bicyclist": ["Yes. 'Bicyclist' has a tangible appearance and refers to a person who rides a bicycle.\nA few things that are visually similar to 'bicyclist' but are not 'bicyclist' are:\tmotorcyclist\tskateboarder\trollerblader\tjogger\nThere are several useful visual features to tell there is 'bicyclist' and not similar things in a photo:\tsitting on a bicycle\thandlebars\ttwo wheels and pedals\twearing a helmet\tand cycling clothing", 68], "ridge": ["Yes. 'Ridge' has a tangible appearance and refers to a long and narrow elevation of land.\nA few things that are visually similar to 'ridge' but are not 'ridge' are:\tplateau\thill\tpeak\tmountain\nThere are several useful visual features to tell there is 'ridge' and not similar things in a photo:\ta long and narrow elevation of land\toften has a sharp or pointed top\tmay have a trail or a path along the top", 68], "images": ["Yes. 'Images' has a tangible appearance and refers to visual representations of something.\nA few things that are visually similar to 'images' but are not 'images' are:\treflections\tmirages\tshadows\nThere are no useful visual features to distinguish 'images' from the listed similar things in a photo, as 'images' refer specifically to intentionally created and captured visual representations.", 68], "flap": ["Yes. 'Flap' has a tangible appearance and is a part or component of an object that can move back and forth.\nA few things that are visually similar to 'flap' but are not 'flap' are:\tdoor\tcurtain\tsails\nThere are several useful visual features to tell there is 'flap' and not similar things in a photo:\trectangular or square in shape\thinged or attached to a larger object\tmovable or adjustable\tback-and-forth motion", 68], "trio": ["No. 'Trio' is too vague or abstract to be distinguished in a photo.", 68], "metal bolt": ["Yes. 'Metal bolt' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'metal bolt' but are not 'metal bolt' are:\tscrew\tnail\tpin\tbutton\nThere are several useful visual features to tell there is 'metal bolt' and not similar things in a photo:\tlong and cylindrical shape\tthreads\tsmall ridges or bumps on the top, called the head\tmay have a nut or washer", 68], "figures": ["No. 'Figures' can refer to a wide range of visual representations and is therefore too vague to be a visually concrete concept. \n\nHowever, if we are referring specifically to human or animal figurines, we can answer \"yes\" to the question. \n\nA few things that are visually similar to human or animal 'figures' but are not 'figures' are:\tstatues\tdolls\tsculptures\ttoys\n\nThere are several useful visual features to distinguish human or animal 'figures' from the listed similar things in a photo, such as:\t\n- the size of the object (figurines are usually smaller than statues or sculptures)\n- the level of detail and realism in the depiction of human or animal features\n- the presence of movable parts or clothing (in the case of dolls or action figures)\n- the materials used to make the object (plastic, wood, ceramic, etc.)", 68], "breast": ["Yes. 'Breast' has a tangible appearance and refers to a part of the body.\nA few things that are visually similar to 'breast' but are not 'breast' are:\tchest\tmuscles\tfat tissue\t\nThere are several useful visual features to tell there is 'breast' and not similar things in a photo:\tlocated on the front of the body above the stomach\tmammary glands (in females)\tnipples (in both males and females)", 68], "slat": ["Yes. 'Slat' has a tangible appearance and is a thin, narrow strip of material.\nA few things that are visually similar to 'slat' but are not 'slat' are:\tboard\tplank\tstrip\tof paper\tor plastic\nThere are several useful visual features to tell there is 'slat' and not similar things in a photo:\tthin and narrow\trectangular or flat shape\tparallel to other slats\tin a row or a grid pattern\tmay have grooves or ridges", 68], "look": ["No. 'Look' is too vague or abstract to be distinguished in a photo.", 68], "tour bus": ["Yes. 'Tour bus' has a tangible appearance and is a type of bus used for tourism.\nA few things that are visually similar to 'tour bus' but are not 'tour bus' are:\tcity bus\tschool bus\tminivan\tlimousine\tcoach\nThere are several useful visual features to tell there is 'tour bus' and not similar things in a photo:\tlarge size\tair conditioned\twith luggage compartment\tdecorated with travel-themed images or text", 68], "lounge chair": ["Yes. 'Lounge chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'lounge chair' but are not 'lounge chair' are:\tArmchair\tSofa\tRecliner\tOttoman\nThere are several useful visual features to tell there is 'lounge chair' and not similar things in a photo:\tLong and low to the ground\tUpright and tilted seating\tpositioned for relaxing and reclining\tpadding or cushioning for comfort\tback or headrest for support", 68], "food items": ["Yes. 'Food items' has a tangible appearance and refers to various edible products.\nA few things that are visually similar to 'food items' but are not 'food items' are:\tdecorative plants\tcolorful stones\torbs\tor other inedible objects\nThere are several useful visual features to tell there are 'food items' and not similar things in a photo:\t\n- Various shapes and structures like cubes, spheres, cylinders, etc.\n- A wide range of colors and textures, depending on the type of food\n- A solid or semi-solid appearance, in contrast to fluids or gases\n- Common food categories such as fruits, vegetables, meats, grains, etc.", 67], "grey wall": ["Yes. 'Grey wall' has a tangible appearance and can be visualized.\nA few things that are visually similar to 'grey wall' but are not 'grey wall' are:\tconcrete floor\tcement statue\tmetal side of a building\nThere are several useful visual features to tell there is 'grey wall' and not similar things in a photo:\tvertical plane\tsmooth or textured surface\tshades of grey color\tno distinct patterns or shapes.", 67], "sofas": ["Yes. 'Sofas' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'sofas' but are not 'sofas' are:\tchairs\tottomans\tbenches\tbeds\nThere are several useful visual features to tell there is 'sofas' and not similar things in a photo:\tlong cushioned seat\tfor more than one person\tbackrest\tarmrests\tfabric or leather upholstery", 67], "grain": ["Yes. 'Grain' has a tangible appearance and refers to small, hard, and edible seeds or fruits of cereal plants.\nA few things that are visually similar to 'grain' but are not 'grain' are:\tsand\tdirt\trock\tsalt\nThere are several useful visual features to tell there is 'grain' and not similar things in a photo:\tsmall and edible seeds\tor fruits\tof cereal plants\tusually seen in clusters\tor on a stalk\tdifferent types include wheat, rice, maize, barley, and oats\ttypically brown, yellow, or white in color", 67], "produce": ["Yes. 'Produce' has a tangible appearance and refers to the fruits and vegetables grown for food.\nA few things that are visually similar to 'produce' but are not 'produce' are:\tplants\tflowers\nThere are several useful visual features to tell there is 'produce' and not similar things in a photo:\tedible fruits or vegetables\tvariety of colors\tsizes and shapes that are typical of fruits and vegetables.", 67], "wooden park bench": ["Yes. 'Wooden park bench' has a tangible appearance and is a type of outdoor seating.\nA few things that are visually similar to 'wooden park bench' but are not 'wooden park bench' are:\tstone bench\tconcrete bench\tmetal bench\tchair\nThere are several useful visual features to tell there is 'wooden park bench' and not similar things in a photo:\twooden slats or boards\tfor outdoor use\twith or without armrests\tor backrests\tfound in a park or other public outdoor spaces", 67], "masts": ["Yes. 'Masts' has a tangible appearance and refers to the tall vertical poles used to support sails on a boat or ship.\nA few things that are visually similar to 'masts' but are not 'masts' are:\ttelephone poles\ttrees\tchimneys\tantennas\nThere are several useful visual features to tell there is 'masts' and not similar things in a photo:\tlocated on a boat or a ship\ttall and narrow\tusually made of wood or metal\tsupporting rigging or sails.", 67], "spice": ["Yes. 'Spice' has a tangible appearance and refers to various dried plant parts used for flavoring, cooking, or perfuming.\nA few things that are visually similar to 'spice' but are not 'spice' are:\therbs\tleaves\tsoil\tcoffee beans\nThere are several useful visual features to tell there is 'spice' and not similar things in a photo:\taromatic and flavorful\tdried\tcrushed or grounded\tintense colors\tand unique shapes or textures", 67], "bicycle wheel": ["Yes. 'Bicycle wheel' has a tangible appearance and is a type of wheel used for transportation.\nA few things that are visually similar to 'bicycle wheel' but are not 'bicycle wheel' are car wheel, cart wheel, Ferris wheel, and wagon wheel.\nThere are several useful visual features to tell there is 'bicycle wheel' and not similar things in a photo:\ttwo-wheeled vehicle\tframe or spokes connected to the wheel\ttire\twith or without spokes\thub in the center of the wheel.", 67], "plant pot": ["Yes. 'Plant pot' has a tangible appearance and is a type of container for plants.\nA few things that are visually similar to 'plant pot' but are not 'plant pot' are:\tcoffee cups\tbowls\tbaskets\tjars\nThere are several useful visual features to tell there is 'plant pot' and not similar things in a photo:\tround or rectangular shape\twith a hole at the bottom for drainage\tmade of ceramic, plastic, or clay\tsitting on the ground or a flat surface\twith a plant growing inside of it.", 67], "bells": ["Yes. 'Bells' has a tangible appearance and is a type of instrument or decoration.\nA few things that are visually similar to 'bells' but are not 'bells' are:\tlanterns\tcandle holders\ttower roofs\tball ornaments\nThere are several useful visual features to tell there is 'bells' and not similar things in a photo:\trounded shape\thollow body\tclapper or striker inside\tmetallic surface\tvisible ringing mechanism or handle", 67], "brown rocks": ["Yes. 'Brown rocks' has a tangible appearance and is a type of geological formation.\nA few things that are visually similar to 'brown rocks' but are not 'brown rocks' are:\twood bark\tmud\tdirt\nThere are several useful visual features to tell there is 'brown rocks' and not similar things in a photo:\thard and solid surface\tdifferent shades of brown or grey\tmay have visible texture or layers\tmay have visible cracks or crevices", 67], "muffler": ["Yes. 'Muffler' has a tangible appearance and is a type of exhaust system component.\nA few things that are visually similar to 'muffler' but are not 'muffler' are:\ttailpipe\texhaust tip\tcatalytic converter\nThere are several useful visual features to tell there is 'muffler' and not similar things in a photo:\tround or oval shape\tlocated near the rear of the vehicle\tmade of metal\tor has a metal cover\tcontains sound-absorbing materials to reduce noise\tdoes not have any visible holes or leaks", 67], "barrels": ["Yes. 'Barrels' has a tangible appearance and is a cylindrical container.\nA few things that are visually similar to 'barrels' but are not 'barrels' are:\tkegs\tcans\tbuckets\ttanks\nThere are several useful visual features to tell there is 'barrels' and not similar things in a photo:\tcylindrical shape with a bulging center\tmade of wood or metal\thas staves and hoops on the sides\thas a lid or a stopper\ton its side, not standing upright", 67], "pane": ["Yes. 'Pane' has a tangible appearance and refers to a single sheet of glass in a window or door.\nA few things that are visually similar to 'pane' but are not 'pane' are:\tscreen\tblind\tdoor\t\t\nThere are several useful visual features to tell there is 'pane' and not similar things in a photo:\ttransparent or translucent material\trectangular shape\tflat surface\tframed by wood or metal in a window or door\thorizontal or vertical lines indicating divisions in a window or door.", 67], "ceiling lights": ["Yes. 'Ceiling lights' has a tangible appearance and is a kind of lighting fixture.\nA few things that are visually similar to 'ceiling lights' but are not 'ceiling lights' are:\tfloor lamps\ttable lamps\tcandles\tlight bulbs\nThere are several useful visual features to tell there is 'ceiling lights' and not similar things in a photo:\tattached or hanging from the ceiling\tsource of light\tilluminating a room or a space\tdirection of light beam", 67], "farm": ["Yes. 'Farm' has a tangible appearance and is a type of rural area typically used for agriculture or raising livestock.\nA few things that are visually similar to 'farm' but are not 'farm' are:\tpark\tcountryside\tcampsite\t\nThere are several useful visual features to tell there is 'farm' and not similar things in a photo:\tbarns\tsilos\ttractors\tfences fields of crops or pastures\tlivestock such as cows, horses, or sheep.", 67], "caution tape": ["Yes. 'Caution tape' has a tangible appearance and is a physical object.\nA few things that are visually similar to 'caution tape' but are not 'caution tape' are:\tpolice tape\tbarrier tape\tflagging tape\tmarking tape\nThere are several useful visual features to tell there is 'caution tape' and not similar things in a photo:\tBright yellow color or fluorescent red-orange\tColorful stripes displaying a warning message such as \"caution\" or \"crime scene\"\tPrinted with bold and legible letters\tthat prevent people from passing through a certain area or location.", 67], "city sidewalk": ["Yes. 'City sidewalk' has a tangible appearance and is a specific type of outdoor pathway.\nA few things that are visually similar to 'city sidewalk' but are not 'city sidewalk' are:\thiking trail\tpark pathway\tbeach boardwalk\nThere are several useful visual features to tell there is 'city sidewalk' and not similar things in a photo:\thard concrete or paved surface\tparallel lines of seams or patterns\tadjacent to buildings or busy streets\tsurrounded by streetlights or benches", 67], "clumps": ["Yes. 'Clumps' has a tangible appearance and refers to a group or cluster of similar things.\nA few things that are visually similar to 'clumps' but are not 'clumps' are: piles, heaps, stacks, rows, series, lines.\nThere are several useful visual features to tell there is 'clumps' and not similar things in a photo: objects or substances grouped closely together, creating a mass or cluster, not arranged in a straight line or pattern, generally irregular in shape.", 67], "broccoli floret": ["Yes. 'Broccoli floret' has a tangible appearance and refers to the individual portion of broccoli.\nA few things that are visually similar to 'broccoli floret' but are not 'broccoli floret' are:\tcabbage\tcauliflower\tkale\tspinach\tlettuce\nThere are several useful visual features to tell there is 'broccoli floret' and not similar things in a photo:\t\n\n- A compact floret shape consisting of tightly packed small stalks that form a rounded end\n- Vibrant green color\n- The primary stem holding the floret, which is shorter than a full broccoli stem\n- The presence of small leaves at the base of the floret", 67], "flight": ["No. 'Flight' is too vague or abstract to be distinguished in a photo, but the visible act of flying may be a tangible appearance.\nA few things that are visually similar to 'flight' but are not 'flight' are:\tjumping\tfalling\tincline activity\nThere are several useful visual features to tell there is 'flight' and not similar things in a photo:\twings or any other flying aids\tvisible air currents around the subject\ta visible height in the photo\tthe subject is above the ground or water", 67], "match": ["Yes. 'Match' has a tangible appearance and is a small stick with a flammable tip that can be ignited to light a fire.\nA few things that are visually similar to 'match' but are not 'match' are:\ttoothpick\tswab\tfuse\nThere are several useful visual features to tell there is 'match' and not similar things in a photo:\ttiny stick shape\tred tip\tflammable material\twax coating on the bottom of the stick\tbox with striking surface on it.", 67], "specks": ["Yes. 'Specks' has a tangible appearance and refers to small dots or spots.\nA few things that are visually similar to 'specks' but are not 'specks' are:\tdirt\tgrains of sand\tpixels\tonion or garlic flakes\nThere are several useful visual features to tell there are 'specks' and not similar things in a photo:\tsmall size\tdot-like shape\trandom distribution\tall of the same color or similar color", 67], "dirt patch": ["Yes. 'Dirt patch' has a tangible appearance and refers to an area of ground that is covered in dirt.\nA few things that are visually similar to 'dirt patch' but are not 'dirt patch' are:\tgravel road\tsand beach\trocky terrain\tconcrete paving\nThere are several useful visual features to tell there is 'dirt patch' and not similar things in a photo:\tloose soil and dirt\ton the ground\tusually appears in natural or rural settings\tmay have grass or weeds growing on it", 67], "passenger airplane": ["Yes. 'Passenger airplane' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'passenger airplane' but are not 'passenger airplane' are:\thelicopter\t glider\tBlimp\tprivate jet\nThere are several useful visual features to tell there is 'passenger airplane' and not similar things in a photo:\tlong and streamlined body with a pointed nose\ttwo wings, often with engines or propellers under them, on either side\ta tail fin, or empennage, at the back of the fuselage, which may also have smaller fins or wings\thorizontal stabilizers, or horizontal tailplanes, that stick out on either side near the empennage\tcabin windows, especially near the middle and rear of the plane\tmultiple wheels or landing gear underneath the fuselage or near the wings.", 67], "grove": ["Yes. 'Grove' has a tangible appearance and refers to a group of trees.\nA few things that are visually similar to 'grove' but are not 'grove' are:\tforest\tpark\torchard\nThere are several useful visual features to tell there is 'grove' and not similar things in a photo:\ta small group of trees\tassembled closely together\tsimilar species\tof a moderate size and density", 66], "womens": ["No. 'Womens' is too vague or abstract to be distinguished in a photo. It is important to note that the proper phrase is 'women's' (with an apostrophe). It refers to something that belongs to, relates to, or is associated with women.\nA few things that are visually similar to 'women's' but are not 'women's' are:\tmen's\tchildren's\tunisex clothing\nThere are no useful visual features to distinguish 'women's' from the listed similar things in a photo, as it is not a physical object but rather a possessive and relational concept.", 66], "man shirt": ["Yes. 'Man shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'man shirt' but are not 'man shirt' are:\twoman shirt\tpolo shirt\tt-shirt\tblouse\nThere are several useful visual features to tell there is 'man shirt' and not similar things in a photo:\tcollar\tbuttons\tdifferent shades of color in the front and back portions\tlong sleeves (usually)\tstraight cut (usually)", 66], "orange flag": ["Yes. 'Orange flag' has a tangible appearance and is a kind of flag.\nA few things that are visually similar to 'orange flag' but are not 'orange flag' are:\torange banner\tcone\tsafety vests\nThere are several useful visual features to tell there is 'orange flag' and not similar things in a photo:\trectangle or triangular shape\tbright orange color\tattached to a stick or a pole\twavy or fluttering in the wind", 66], "drape": ["Yes. 'Drape' has a tangible appearance and is a decorative cloth.\nA few things that are visually similar to 'drape' but are not 'drape' are:\tcurtain\ttapestry\ttablecloth\tblanket\nThere are several useful visual features to tell there is 'drape' and not similar things in a photo:\tlong and flowing\thanging from a rod or a hook\tdecorative and ornamental\tdraped over furniture or fittings\tfabric is usually thin and light", 66], "silver button": ["Yes. 'Silver button' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'silver button' but are not 'silver button' are:\tmetal coins\tcufflinks\tSnap fasteners\tbuckles\nThere are several useful visual features to tell there is 'silver button' and not similar things in a photo:\tround or oval shape\tmade of silver or silver-colored metal\thas a shank on the back for attachment to clothing or other materials.", 66], "truck tire": ["Yes. 'Truck tire' has a tangible appearance and is a type of vehicle tire.\nA few things that are visually similar to 'truck tire' but are not 'truck tire' are:\tcar tire\tbike tire\tconstruction machinery tire\nThere are several useful visual features to tell there is 'truck tire' and not similar things in a photo:\tlarge size\tthick\ttreaded pattern\thubcap\tcenter bore pattern", 66], "duffle bag": ["Yes. 'Duffle bag' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'duffle bag' but are not 'duffle bag' are:\tbackpack\tgym bag\tduffel coat\ttravel bag\tsuitcase\nThere are several useful visual features to tell there is 'duffle bag' and not similar things in a photo:\tcylindrical shape\tzipped closure\tcarry handles at either end\tmade out of durable, heavy-duty material\tsingle, adjustable strap for carrying over the shoulder or cross-body.", 66], "bike rack": ["Yes. 'Bike rack' has a tangible appearance and is a type of structure used for parking bicycles.\nA few things that are visually similar to 'bike rack' but are not 'bike rack' are:\tparking meter\tbus stop\tshed\nThere are several useful visual features to tell there is 'bike rack' and not similar things in a photo:\tparallel bars\tor spikes for holding bicycles\tmade of metal or plastic\tvisible bicycles parked\tnext to a road, sidewalk, or building", 66], "bikers": ["Yes. 'Bikers' has a tangible appearance and is a person who rides a bicycle, motorcycle, or similar vehicle.\nA few things that are visually similar to 'bikers' but are not 'bikers' are:\tRunners\tSkateboarders\tInline Skaters\nThere are several useful visual features to tell there is 'bikers' and not similar things in a photo:\tWearing helmets or protective gear\tRiding a motorcycle or bicycle\tWearing leather jackets or vests\tRiding on the road or a trail", 66], "glass windshield": ["Yes. 'Glass windshield' has a tangible appearance and is a part of a car.\nA few things that are visually similar to 'glass windshield' but are not 'glass windshield' are:\tsunglasses\tglasses and lenses\tmirrors\twindows\nThere are several useful visual features to tell there is 'glass windshield' and not similar things in a photo:\ttransparent glass\tset at the front of a car\tdifferent angle than other windows\twipers attached for cleaning during rain or snow", 66], "silver trash": ["Yes. 'Silver trash' has a tangible appearance and refers to any discarded items that are silver in color.\nA few things that are visually similar to 'silver trash' but are not 'silver trash' are:\tmetallic d\u00e9cor\tsilverware\tshiny appliances\nThere are no definite visual features to distinguish what is silver trash from the listed similar things in a photo.", 66], "street name sign": ["Yes. 'Street name sign' has a tangible appearance.\nA few things that are visually similar to 'street name sign' but are not 'street name sign' are:\tdirections\tbillboards\tadvertisements\nThere are several useful visual features to tell there is 'street name sign' and not similar things in a photo:\trectangle-shaped\tsign is blue, green, or brown\twith white, reflective letters\tstreet name is spelled correctly\tsign indicates a street name and often includes the type of street, for example, \"Main St\" or \"Elm Ave\"", 66], "pink towel": ["Yes. 'Pink towel' has a tangible appearance and is a kind of cloth.\nA few things that are visually similar to 'pink towel' but are not 'pink towel' are:\tbath mat\ttable cloth\tdish towel\tblanket\nThere are several useful visual features to tell there is 'pink towel' and not similar things in a photo:\trectangular or square shape\tsoft and absorbent texture\tpink color", 66], "spool": ["Yes. 'Spool' has a tangible appearance and is a cylindrical object used for winding thread, wire, etc.\nA few things that are visually similar to 'spool' but are not 'spool' are:\tpencil\tsharpeners\tcable reels\trolling pins\nThere are several useful visual features to tell there is 'spool' and not similar things in a photo:\tcylindrical shape\tsmall holes on both sides\tfor winding thread, wire, or string.\tmade of wood or plastic", 66], "polish": ["No. 'Polish' is too vague or abstract to be distinguished in a photo. \nHowever, if you are referring to shoe polish, then the answer is yes.\nA few things that are visually similar to shoe polish but are not shoe polish are: ink, paint, grease, lotion.\nThere are several useful visual features that can help to distinguish shoe polish from the listed similar things in a photo: small container, waxy texture, specific colors (black, brown, neutral), applied with a brush or sponge onto leather surface, can create a shine or a matte finish.", 66], "boardwalk": ["Yes. 'Boardwalk' has a tangible appearance and is a path made of wooden boards along a beach or waterfront.\nA few things that are visually similar to 'boardwalk' but are not 'boardwalk' are:\tpier\tjetty\twalkway\nThere are several useful visual features to tell there is 'boardwalk' and not similar things in a photo:\tmade of wooden boards\tor similar material\talong a beach or waterfront\tstraight or gently curved shape\tsurrounded by sand or water\tside rails or benches for sitting and resting", 66], "dark sky": ["Yes. 'Dark sky' has a tangible appearance and is a type of sky.\nA few things that are visually similar to 'dark sky' but are not 'dark sky' are:\tstormy sky\tfoggy sky\tnight sky\t\nThere are several useful visual features to tell there is 'dark sky' and not similar things in a photo:\ta sky without much light or stars\ta sky that obscures/backgrounds ground features and natural light\ta sky that is black or a deep shade of blue", 66], "leafy plant": ["Yes. 'Leafy plant' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'leafy plant' but are not 'leafy plant' are:\tcacti\tsucculent plants\tmosses\tcoral reefs\nThere are several useful visual features to tell there is 'leafy plant' and not similar things in a photo:\tgreen leaves or foliage\ttall stems or trunks\troots in the soil\tflowers or buds on the plant\tcan be indoors or outdoors\tcan be a houseplant or garden plant", 66], "frosting": ["Yes. 'Frosting' has a tangible appearance and refers to a sweet topping used to decorate desserts.\nA few things that are visually similar to 'frosting' but are not 'frosting' are:\twhipped cream\tglaze\tdrizzle\tmayonnaise\nThere are several useful visual features to tell there is 'frosting' and not similar things in a photo:\tthick and creamy texture\tspread on top or piped onto baked goods\tvariety of colors and flavors\tsprinkles or decorative toppings\ton cakes or cupcakes", 66], "t.v": ["Yes. 'T.V' has a tangible appearance and is a type of electronic device for watching television programs.\nA few things that are visually similar to 't.v' but are not 't.v' are:\tcomputer monitor\tprojector\tscreen\t\nThere are several useful visual features to tell there is 't.v' and not similar things in a photo:\tthin, rectangular shape\twith or without buttons or knobs\tantenna or cable connection\tsound or volume controls\ndisplaying moving pictures and sound signals.", 66], "window curtain": ["Yes. 'Window curtain' has a tangible appearance and is a type of textile used for covering windows.\nA few things that are visually similar to 'window curtain' but are not 'window curtain' are:\tdoor curtain\tshower curtain\tstage curtain\nThere are several useful visual features to tell there is 'window curtain' and not similar things in a photo:\thanging from a window\tsemi-transparent or opaque\ttexture folds\trods, hooks, or clips to hold it in place.", 66], "cop": ["Yes. 'Cop' has a tangible appearance and is a type of law enforcement officer.\nA few things that are visually similar to 'cop' but are not 'cop' are:\tsecurity guard\tmilitary personnel\tprivate investigator\tbodyguard\nThere are several useful visual features to tell there is 'cop' and not similar things in a photo:\tblue or black uniform\tbadge or patch on the uniform\tholster or gun\tbaton or nightstick\tpolice car or motorcycle in the background", 66], "washer": ["Yes. 'Washer' has a tangible appearance and is a hardware item used in construction.\nA few things that are visually similar to 'washer' but are not 'washer' are:\tnuts \tbolts \trivets\nThere are several useful visual features to tell there is 'washer' and not similar things in a photo:\tcircular flat shape\thole in the center\tmetallic surface\tused in between surfaces to distribute pressure or prevent damage.", 66], "ski helmet": ["Yes. 'Ski helmet' has a tangible appearance and is a type of headgear.\nA few things that are visually similar to 'ski helmet' but are not 'ski helmet' are:\tbicycle helmet\tmotorcycle helmet\thard hat\tconstruction helmet\nThere are several useful visual features to tell there is 'ski helmet' and not similar things in a photo:\trounded shape\tcovers the forehead, top, and sides of the head\tpadding inside\tvisors, ear flaps, or goggles for eye protection\tusually brightly colored for higher visibility on the slopes.", 66], "bedding": ["Yes. 'Bedding' has a tangible appearance and refers to the textiles used on a bed. \nA few things that are visually similar to 'bedding' but are not 'bedding' are:\tblankets\ttowels\tcarpets\ttablecloths\nThere are several useful visual features to tell there is 'bedding' and not similar things in a photo:\tfitted or flat sheets\tpillowcases\tduvet or comforter cover\tshams\tor bedskirts", 66], "bagel": ["Yes. 'Bagel' has a tangible appearance and is a type of bread roll.\nA few things that are visually similar to 'bagel' but are not 'bagel' are:\tdoughnut\tbun\tpretzel\nThere are several useful visual features to tell there is 'bagel' and not similar things in a photo:\tround shape\twith a hole in the center\tcrispy texture\tbrownish color\ttopping like poppy seeds or sesame seeds", 66], "airliner": ["Yes. 'Airliner' has a tangible appearance and is a type of airplane used for commercial purposes.\nA few things that are visually similar to 'airliner' but are not 'airliner' are:\tprivate jet\tmilitary aircraft\thelicopter\tglider\nThere are several useful visual features to tell there is 'airliner' and not similar things in a photo:\tlarge size\tpassenger windows\tmultiple exhausts under wings\tor on rear of the airplane\ttailfins or wings with airline logo\twide body shape\twheeled undercarriage", 66], "lane": ["Yes. 'Lane' has a tangible appearance and is a type of pathway for vehicles or pedestrians.\nA few things that are visually similar to 'lane' but are not 'lane' are:\tsidewalk\tparking space\tmedian\tbus lane\tdriveway\nThere are several useful visual features to tell there is 'lane' and not similar things in a photo:\tspecifically marked for vehicle or pedestrian traffic\tusually marked with lines or arrows\tlocated on a roadway or street\tcan be one-way or two-way\tmay have specific rules or regulations for use", 66], "surfboard water": ["No. 'Surfboard water' is too vague or abstract to be distinguished in a photo.", 66], "subway": ["Yes. 'Subway' has a tangible appearance and is a type of underground train system.\nA few things that are visually similar to 'subway' but are not 'subway' are:\tunderground tunnels\tparking garages\tbasements\nThere are several useful visual features to tell there is 'subway' and not similar things in a photo:\tmultiple tracks or platforms\ttrains or subway cars with windows\tbranded signage or logos\tfor commuters\twith ticketing machines and turnstiles\tnot a place for parking or storage of vehicles", 66], "jam": ["Yes. 'Jam' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'jam' but are not 'jam' are:\thoney\tsyrup\tpudding\tyogurt\nThere are several useful visual features to tell there is 'jam' and not similar things in a photo:\tthick and viscous\ttexture with visible pieces of fruit or berries\tbright colors typically associated with fruits and berries\tjar or container with a label indicating the flavor of the jam.", 66], "adult zebra": ["Yes. 'Adult zebra' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'adult zebra' but are not 'adult zebra' are:\tdonkey\thorse\tpony\tgiraffe\nThere are several useful visual features to tell there is 'adult zebra' and not similar things in a photo:\tblack-and-white striped coat \tfour legs\tshort mane\tpointed ears\tno humps or spots on the coat.", 66], "waterfall": ["Yes. 'Waterfall' has a tangible appearance and is a natural phenomenon.\nA few things that are visually similar to 'waterfall' but are not 'waterfall' are:\tfountain\tdrainage pipe\train\nThere are several useful visual features to tell there is 'waterfall' and not similar things in a photo:\twater cascading over a cliff or rock formation\ttrail or pool at the base of the waterfall\tnatural surroundings such as trees or rocks\tmist or spray from the impact of water with the surface\tbig and powerful", 66], "security camera": ["Yes. 'Security camera' has a tangible appearance and is a type of camera.\nA few things that are visually similar to 'security camera' but are not 'security camera' are:\twebcams\tcamera drones\tcctv cameras\t360 cameras\nThere are several useful visual features to tell there is 'security camera' and not similar things in a photo:\tusually mounted on a wall or a ceiling\tbox-shaped or cylindrical\tsmall dark lens\tat least one visible infrared sensor or light\tconnected with cables to a recording or monitoring device.", 65], "headboard bed": ["Yes. 'Headboard bed' has a tangible appearance and is a type of bed.\nA few things that are visually similar to 'headboard bed' but are not 'headboard bed' are:\tfuton\tmattress\tpallet bed\tair mattress\nThere are several useful visual features to tell there is 'headboard bed' and not similar things in a photo:\ta wooden or upholstered panel attached to the head of the bed\tthe presence of a bed frame or a box spring\tthe arrangement of pillows and bedding on the bed", 65], "buds": ["Yes, 'buds' has a tangible appearance and refers to the small, undeveloped growth on a plant from which leaves, flowers, or shoots will emerge.\nA few things that are visually similar to 'buds' but are not 'buds' are:\tseeds\tleaves\tfruit\tpetals\nThere are several useful visual features to tell there are 'buds' and not similar things in a photo:\tundeveloped growth on a plant\tthat will eventually grow into a leaf, flower, or shoot\toften rounded or oval in shape\tusually located at the tips of stems or branches\tcan have distinct scales or coverings.", 65], "link fence": ["Yes. 'Link fence' has a tangible appearance and is a type of fence made of steel wires.\nA few things that are visually similar to 'link fence' but are not 'link fence' are:\tpicket fence\tchicken wire fence\tstone wall\tconcrete barrier\nThere are several useful visual features to tell there is 'link fence' and not similar things in a photo:\tdiamond-shaped pattern\tmetal wires interwoven into a fence\tgray or silver color\topen spaces between each wire\tframe structure with posts and rails.", 65], "keypad": ["Yes. 'Keypad' has a tangible appearance and is an electronic device with a set of buttons.\nA few things that are visually similar to 'keypad' but are not 'keypad' are:\tkeyboard\tcalculator\tremote control\nThere are several useful visual features to tell there is 'keypad' and not similar things in a photo:\telectronic device\twith buttons or keys\tbuttons arranged in a grid-like pattern\ttypically used for entering numbers or codes", 65], "telephone poles": ["Yes. 'Telephone poles' has a tangible appearance and is a type of utility pole.\nA few things that are visually similar to 'telephone poles' but are not 'telephone poles' are:\ttraffic signal poles\tstreetlight poles\tfencing posts\ttree trunks\nThere are several useful visual features to tell there is 'telephone poles' and not similar things in a photo:\n-tall, vertical poles\n-carrying electrical and telephone cables\n-crossbars or insulators attached to the poles\n-placed along roads and streets", 65], "shirt man": ["Yes. 'Shirt man' has a tangible appearance and refers to a man wearing a shirt.\nA few things that are visually similar to 'shirt man' but are not 'shirt man' are:\tman in a t-shirt\tman in a suit\tman in a hoodie\nThere are several useful visual features to tell there is 'shirt man' and not similar things in a photo:\ta man wearing a shirt, usually with buttons on the front\tshort or long sleeves\tcollar", 65], "water fountain": ["Yes. 'Water fountain' has a tangible appearance and is an architectural feature that usually contains water.\nA few things that are visually similar to 'water fountain' but are not 'water fountain' are:\twaterfall\tpond\torangery\tstatue\nThere are several useful visual features to tell there is 'water fountain' and not similar things in a photo:\twater flowing from a central point\twater spraying into the air\tsculptural elements or decorations\twater jets, spouts, or sprayers\tsmall water basin around the base.", 65], "straws": ["Yes. 'Straws' has a tangible appearance and is a type of cylindrical tube.\nA few things that are visually similar to 'straws' but are not 'straws' are:\tpencils, pens\tbamboo sticks\tdental floss\nThere are several useful visual features to tell there is 'straws' and not similar things in a photo:\thollow cylinder shape\twith a bend at one end\tmade of plastic, paper or metal\tvaried colors of stripes or polka dots on the surface.", 65], "pillow couch": ["Yes. 'Pillow couch' has a tangible appearance and is a type of seating furniture made up of pillows.\nA few things that are visually similar to 'pillow couch' but are not 'pillow couch' are: bean bag chairs, floor cushions, large pillows stacked against the wall, regular couches.\nThere are several useful visual features to tell there is 'pillow couch' and not similar things in a photo:\tconstructed from large, fluffy pillows\toften has an irregular shape\tor does not have a frame\tusually rests on floor\tlevel with or close to the ground in height.", 65], "buoys": ["Yes. 'Buoys' has a tangible appearance and is a kind of floating device.\nA few things that are visually similar to 'buoys' but are not 'buoys' are: life jackets, balls, bottles.\nThere are several useful visual features to tell there is 'buoys' and not similar things in a photo: \tcylindrical, conical or spherical shape; attached to ropes; bright colors such as red, yellow or orange.", 65], "mit": ["No. 'Mit' is too vague or abstract to be distinguished in a photo. However, 'MIT' as an acronym for the Massachusetts Institute of Technology has a tangible appearance.\nI cannot name anything visually similar to 'MIT' as it is an acronym for an educational institution.\nUseful visual features for distinguishing 'MIT' (the institution) from other educational institutions in a photo may include the school's logo or colors, specific buildings or landmarks associated with the institution, or people wearing clothing with the school's name or logo.", 65], "freight train": ["Yes. 'Freight train' has a tangible appearance and is a type of train.\nA few things that are visually similar to 'freight train' but are not 'freight train' are:\tpassenger train\tsubway train\ttram\ttrolleybus\nThere are several useful visual features to tell there is 'freight train' and not similar things in a photo:\tlong train cars\tcontainers or cargo on board\tindustrial or utilitarian design\tcarrying goods, not people", 65], "labels": ["Yes. 'Labels' has a tangible appearance and is a type of tag or marker.\nA few things that are visually similar to 'labels' but are not 'labels' are:\tstickers\ttape\tpost-it notes\tsigns\nThere are several useful visual features to tell there is 'labels' and not similar things in a photo:\tresembling a tag or a badge\tadhered to another object\tcontains information or symbolism\torginized in a system or a sequence", 65], "mist": ["Yes. 'Mist' has a tangible appearance and is a type of atmospheric phenomenon.\nA few things that are visually similar to 'mist' but are not 'mist' are:\tfog\tsmoke\thaze\tsteam\nThere are several useful visual features to tell there is 'mist' and not similar things in a photo:\tlight and thin clouds of water droplets\twater droplets suspended in the air\tcan often be seen near bodies of water or damp areas\twill not rise above treetops or tall buildings", 65], "partition": ["Yes. 'Partition' has a tangible appearance and is a physical divider.\nA few things that are visually similar to 'partition' but are not 'partition' are:\tdoor\tcurtain\twall\tscreen\nThere are several useful visual features to tell there is 'partition' and not similar things in a photo:\ta physical divider\tdividing a space into sections\tor blocking one area from another\tmay be made of wood, metal, or glass.", 65], "refridgerator": ["Yes. 'Refrigerator' has a tangible appearance and is a type of electrical appliance.\nA few things that are visually similar to 'refrigerator' but are not 'refrigerator' are:\tfreezer\tchest\tpiano\t\nThere are several useful visual features to tell there is 'refrigerator' and not similar things in a photo:\tvertical shape\twith or without a freezer section\tmetallic or plastic surface\thandle\tand interior shelves\tand drawers", 65], "number plate": ["Yes. 'Number plate' has a tangible appearance and is a kind of identification tag for vehicles.\nA few things that are visually similar to 'number plate' but are not 'number plate' are:\tstreet signs, house numbers, license cards\nThere are several useful visual features to tell there is 'number plate' and not similar things in a photo:\toblong shape with rounded corners\talphanumeric characters arranged in specific patterns or formats\treflective surface\tthat is located on the front and rear of a vehicle", 65], "support pole": ["Yes. 'Support pole' has a tangible appearance and is an object used for supporting or holding something.\nA few things that are visually similar to 'support pole' but are not 'support pole' are:\ttree\ttrunk\tcolumn\tpillar\nThere are several useful visual features to tell there is 'support pole' and not similar things in a photo:\tstraight and cylindrical shape\tusually made of metal or wood\tno branches or leaves\tno decorative features or carvings", 65], "photographers": ["Yes, 'photographers' has a tangible appearance and refers to individuals who take photographs.\nA few things that are visually similar to 'photographers' but are not 'photographers' are: tourists, models, people holding cameras or smartphones, movie crews.\nThere are several useful visual features to tell there are 'photographers' and not similar things in a photo: holding professional cameras or equipment, focusing on a specific object or subject, taking pictures professionally, wearing a camera strap or camera bag, adjusting the camera settings.", 65], "garlic": ["Yes. 'Garlic' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'garlic' but are not 'garlic' are:\tonions\tshallots\tleeks\tchives\nThere are several useful visual features to tell there is 'garlic' and not similar things in a photo:\twhite bulb covered in a papery skin\tsegmented into cloves\tfat and round shape\twith a distinct smell and taste when cut or crushed", 65], "tall chain link fence": ["Yes. 'Tall chain link fence' has a tangible appearance and is a kind of barrier.\nA few things that are visually similar to 'tall chain link fence' but are not 'tall chain link fence' are:\twall\tgate\twooden fence\tbarbed wire\nThere are several useful visual features to tell there is 'tall chain link fence' and not similar things in a photo:\tmetallic and interlinked material\ttall structure\twith diamond-shaped openings", 65], "lapel": ["Yes. 'Lapel' has a tangible appearance and is a part of a jacket or coat.\nA few things that are visually similar to 'lapel' but are not 'lapel' are:\tcollar\tbutton\them\t\nSome useful visual features to tell 'lapel' from similar things in a photo are:\tfolded fabric on the front of a jacket or coat\tPinned or sewn down onto the jacket or coat frequently triangular in shape or pointed at the ends.", 65], "archway": ["Yes. 'Archway' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'archway' but are not 'archway' are:\tDBuilding entrance\tGate\tframe\nThere are several useful visual features to tell there is 'archway' and not similar things in a photo:\tcurved or pointed structure\tsemicircular or horseshoe shape\tstones or bricks used for construction\timage of depth or perspective leading through the archway.", 65], "suspenders": ["Yes. 'Suspenders' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'suspenders' but are not 'suspenders' are:\tbelts\tbraces\tnecktie\nThere are several useful visual features to tell there is 'suspenders' and not similar things in a photo:\tstraps attached to trousers or pants\tX-shaped or Y-shaped\tback of the suspender attaches to the pants\twith metal or fabric clips or buttons\tno waistband attached like a belt\tor hanging around the neck like a tie.", 65], "lantern": ["Yes. 'Lantern' has a tangible appearance and is a type of lighting object.\nA few things that are visually similar to 'lantern' but are not 'lantern' are:\tflashlights\tlamps\tcandles\ttorches\nThere are several useful visual features to tell there is 'lantern' and not similar things in a photo:\thollow object with panels or sides\tthat encloses a light source\thas a handle to carry it or a hook to hang it up\tmay have decorative designs or patterns", 65], "fluffy cloud": ["Yes. 'Fluffy cloud' has a tangible appearance and is a type of cloud.\nA few things that are visually similar to 'fluffy cloud' but are not 'fluffy cloud' are:\tsmoke\tfog\tdust\tpillow\nThere are several useful visual features to tell there is 'fluffy cloud' and not similar things in a photo: \twhite and puffy shape\twispy and light appearance\tlooks like a cotton ball\tin the sky or above the ground", 65], "brick buildings": ["Yes. 'Brick buildings' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'brick buildings' but are not 'brick buildings' are:\tstucco buildings\tmetal buildings\tlog cabins\tconcrete buildings\nThere are several useful visual features to tell there is 'brick buildings' and not similar things in a photo:\tbrownish-red or reddish-brown color\trough texture\trectangular shape\tbricks visibly stacked in a brick pattern\tor mortar visible between the bricks.", 65], "silver van": ["Yes. 'Silver van' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'silver van' but are not 'silver van' are:\ttruck\tbus\tSUV\nThere are several useful visual features to tell there is 'silver van' and not similar things in a photo:\tsilver color\trectangular or boxy shape\tsliding doors (if visible)\tmostly used for carrying passengers or cargo.", 64], "sleeveless shirt": ["Yes. 'Sleeveless shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sleeveless shirt' but are not 'sleeveless shirt' are:\tTank top\tSinglet\tTube top\nThere are several useful visual features to tell there is 'sleeveless shirt' and not similar things in a photo:\tno sleeves\tshoulder straps around the neck\tcovers the chest and back areas \tarmholes cut out of the material.", 64], "event": ["No. 'Event' is too vague or abstract to be distinguished in a photo.", 64], "pillowcase": ["Yes. 'Pillowcase' has a tangible appearance and is a kind of textile used to cover pillows.\nA few things that are visually similar to 'pillowcase' but are not 'pillowcase' are:\tsheet\tduvet cover\ttablecloth\t\nThere are several useful visual features to tell there is 'pillowcase' and not similar things in a photo:\trectangle shape\tfabric with a soft texture\topen on one side for inserting a pillow\tclosable flap or zipper on opening edge\ttypically used in pairs on a bed", 64], "flip flop": ["Yes. 'Flip flop' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'flip flop' but are not 'flip flop' are:\tsandals\tslides\tclogs\nThere are several useful visual features to tell there is 'flip flop' and not similar things in a photo:\topen-toe with a thong strap\tusually made of rubber or plastic soles\tand they make a distinctive \u201cflip-flop\u201d sound when walking.", 64], "limbs": ["Yes. 'Limbs' has a tangible appearance and refers to the arms or legs of a body.\nA few things that are visually similar to 'limbs' but are not 'limbs' are:\tbranches\tof trees\twires, poles\tor buildings\nThere are several useful visual features to tell there is 'limbs' and not similar things in a photo:\tfive-fingered hands or five-toed feet\tjointed elbow and knee\tbones under the skin\tmuscles and tendons visible in movement", 64], "folder": ["Yes. 'Folder' has a tangible appearance and is a kind of organizational tool.\nA few things that are visually similar to 'folder' but are not 'folder' are:\tenvelope\tbook\tbinder\tclipboard\tMicrosoft Word icon\nThere are several useful visual features to tell there is 'folder' and not similar things in a photo:\trectangular shape\tthin and flat\tfolded in the middle\thave tabs or labels to separate categories of content\tvarious colors, often in pastels or primary colors.", 64], "bedside table": ["Yes. 'Bedside table' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'bedside table' but are not 'bedside table' are:\tend table\tcoffee table\tnightstand\nThere are several useful visual features to tell there is 'bedside table' and not similar things in a photo:\tsmall size, usually the height of the mattress or lower\tdrawer or shelf for storage\tlocated next to a bed or a sleeping area", 64], "brown desk": ["Yes. 'Brown desk' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'brown desk' but are not 'brown desk' are:\ttable, counter, shelf, dresser, cabinet\nThere are several useful visual features to tell there is 'brown desk' and not similar things in a photo:\trectangular flat surface intended for working or writing\tlevel surface for storing books and papers\tbrown color and made of wood or other materials used for furniture\tlegs or base to support the desk", 64], "necks": ["Yes. 'Necks' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'necks' but are not 'necks' are:\tties\tcollars\tonions\tcandles\nThere are several useful visual features to tell there is 'necks' and not similar things in a photo:\tlong and cylindrical\tconnected to the head and the rest of the body\tflexibility and range of motion\tthe presence of the Adam's apple (in males)\tor the absence of it (in females)", 64], "beautiful": ["No. 'Beautiful' is too vague or abstract to be visually concrete. It is a subjective concept based on personal opinions and preferences, and there are no definitive visual features to distinguish it from other things.", 64], "blue flower": ["Yes. 'Blue flower' has a tangible appearance.\nA few things that are visually similar to 'blue flower' but are not 'blue flower' are:\tblue-colored fabric\tblue-colored painting\tblue-colored balloon\nThere are several useful visual features to tell there is 'blue flower' and not similar things in a photo:\tpetal-like or petal-shaped structures in bloom\tblue color\tpistil and stamens or reproductive organs for flowers", 64], "identification number": ["No. 'Identification number' is too vague or abstract to be distinguished in a photo.", 64], "jaw": ["Yes. 'Jaw' has a tangible appearance and is a part of the face that holds the teeth.\nA few things that are visually similar to 'jaw' but are not 'jaw' are:\tcheek\tbone\tnose\nThere are several useful visual features to tell there is 'jaw' and not similar things in a photo:\tteeth\tmuscles\torally openable bone structure in the face", 64], "orange truck": ["Yes. 'Orange truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'orange truck' but are not 'orange truck' are:\tcar\tvan\tbus\tred truck\nThere are several useful visual features to tell there is 'orange truck' and not similar things in a photo:\tlarge vehicle\twith an open bed or enclosed cargo area\tpainted with orange\tcolor, either entirely or partially.", 64], "furry": ["Yes. 'Furry' has a tangible appearance and refers to something covered in soft hair.\nA few things that are visually similar to 'furry' but are not 'furry' are:\twoolly\tcotton balls\tclouds\nThere are several useful visual features to tell there is 'furry' and not similar things in a photo:\tsoft and fluffy texture\thair covering the object or animal\tspecific patterns or colors of fur or hair", 64], "electronics": ["No. 'Electronics' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to electronic devices but are not 'electronics' may be:\ttv remotes\ttoy phones\tpower banks\tdecorative lights\n\nUseful visual features for distinguishing 'electronics' from the listed similar things in a photo are: screens, buttons, ports, charging cables, batteries, and other components that are specific to electronic devices.", 64], "wig": ["Yes. 'Wig' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'wig' but are not 'wig' are:\that\tcap\theadband\tbandana\tweave\nThere are several useful visual features to tell there is 'wig' and not similar things in a photo:\thair-like material\tfits snugly on the head covers most or all of the natural hair\tmay have a visible cap or netting underneath puede tener una red o \"cap\" visible en la base.", 64], "blue table": ["Yes. 'Blue table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'blue table' but are not 'blue table' are:\tblue chair\tblue rug\tblue vase\tblue curtains\nThere are several useful visual features to tell there is 'blue table' and not similar things in a photo:\tplatform for holding objects\tseveral legs\tspecific dynamic shape (rectangular, square, round, etc.) in blue color", 64], "e": ["No. 'e' is too vague or abstract to have a tangible appearance or be distinguished in a photo.", 64], "toilet sink": ["No. 'Toilet sink' is not a standard term and is too vague or abstract to be distinguished in a photo. \n\nHowever, similar terms that are visually similar to a sink in a bathroom but are not 'toilet sink' are:\tfaucet\tbasin\tcounter\ttop\tcabinet\n\nUseful visual features for distinguishing a sink in a bathroom from the listed similar things in a photo include:\ta bowl-shaped basin\twith a faucet and tap handles\tto dispense water\tdrain hole or plug\tfor draining water\tout of the basin.", 64], "basketball hoop": ["Yes. 'Basketball hoop' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'basketball hoop' but are not 'basketball hoop' are:\ttennis net\tvolleyball net\tsoccer goal\tpost\nThere are several useful visual features to tell there is 'basketball hoop' and not similar things in a photo:\tcircular hoop\tmetallic or plastic rim\tattached to a backboard with a netting hole\tball inside the rim in some cases.", 64], "pail": ["Yes. 'Pail' has a tangible appearance and is a container used for carrying or holding liquids.\nA few things that are visually similar to 'pail' but are not 'pail' are:\tbucket\tbowl\tvase\nThere are several useful visual features to tell there is 'pail' and not similar things in a photo:\tmetal material\thandle\thardware for lid or pouring\tline markings for measuring capacities.", 64], "grooves": ["Yes. 'Grooves' has a tangible appearance and refers to indentations or channels in a surface.\nA few things that are visually similar to 'grooves' but are not 'grooves' are:\tcracks\tlines\tdents\t\nThere are several useful visual features to tell there are 'grooves' and not similar things in a photo:\tindentations or channels in a surface\tparallel lines or patterns\tdeeper than surrounding area", 64], "polka dots": ["Yes. 'Polka dots' has a tangible appearance and is a pattern of dots.\nA few things that are visually similar to 'polka dots' but are not 'polka dots' are:\tspots\tbubbles\tcircles\tbubbles\nThere are several useful visual features to tell there is 'polka dots' and not similar things in a photo:\tdots with a uniform size and shape\tdots with a consistent spacing between them\tdots arranged in a grid or repeating pattern contrast of color with the background", 64], "bulletin board": ["Yes. 'Bulletin board' has a tangible appearance and is a kind of board used for displaying notices or information.\nA few things that are visually similar to 'bulletin board' but are not 'bulletin board' are:\tchalkboard\twhiteboard\tcorkboard\twall\nThere are several useful visual features to tell there is 'bulletin board' and not similar things in a photo:\tcovered in paper or fabric\tcontaining pins, clips or magnets with messages, notices or posters\tboard can be any color or shape, but often has a wooden frame.", 64], "crosswalk sign": ["Yes. 'Crosswalk sign' has a tangible appearance and is a type of traffic sign.\nA few things that are visually similar to 'crosswalk sign' but are not 'crosswalk sign' are:\tstop sign\tyield sign\tspeed limit sign\tdirection sign\nThere are several useful visual features to tell there is 'crosswalk sign' and not similar things in a photo:\twhite rectangular sign with black border and symbol of two people walking\tbrightly colored\thanging on a pole near a street or intersection", 64], "passenger jet": ["Yes. 'Passenger jet' has a tangible appearance and is a type of airplane.\nA few things that are visually similar to 'passenger jet' but are not 'passenger jet' are:\tbusiness jet\tmilitary jet\thelicopter\tultralight aircraft\tdrone\nThere are several useful visual features to tell there is 'passenger jet' and not similar things in a photo:\tnarrow, tube-shaped body\twith wings and engines\tmultiple windows\tusually has a logo or name of the airline on the body\tcan have a distinctive tail and/or color scheme", 64], "front door": ["Yes. 'Front door' has a tangible appearance and is a type of entryway to a building or house.\nA few things that are visually similar to 'front door' but are not 'front door' are:\tgate\tgarage\tdoormat\twall\nThere are several useful visual features to tell there is 'front door' and not similar things in a photo:\ta rectangular shape\thandles or knobs\tattached to a doorway or a wall\thas hinges or slides\topen or closed", 64], "ocean waves": ["Yes. 'Ocean waves' has a tangible appearance and is a natural occurrence.\nA few things that are visually similar to 'ocean waves' but are not 'ocean waves' are:\tripples\tin a river\twater falling\tdrops of water\nThere are several useful visual features to tell there are 'ocean waves' and not similar things in a photo:\tlarge size\twhite caps\tmovement\tseen in a body of saltwater", 64], "rugs": ["Yes. 'Rugs' has a tangible appearance and is a kind of textile used for flooring or decoration.\nA few things that are visually similar to 'rugs' but are not 'rugs' are:\tcarpets\tmats\tfloor tiles\tkilims\nThere are several useful visual features to tell there is 'rugs' and not similar things in a photo:\tsoft and plush texture\tvariety of colors or patterns\tunrolled and/or covering a part of the floor or a wall", 63], "bike helmet": ["Yes. 'Bike helmet' has a tangible appearance and is a kind of safety gear.\nA few things that are visually similar to 'bike helmet' but are not 'bike helmet' are:\tfootball helmet\tmotorcycle helmet\thard hat\tswimming cap\nThere are several useful visual features to tell there is 'bike helmet' and not similar things in a photo:\tcylindrical shape\tprotective padding, usually made of foam\tstraps to secure it to the head\tvented holes for airflow\tbright color for visibility", 63], "peice": ["No. 'Peice' is too vague or abstract to be distinguished in a photo. It is likely a misspelling of 'piece', which still may not have a visually concrete concept without additional context.", 63], "hand towels": ["Yes. 'Hand towels' has a tangible appearance and is a kind of cloth used for drying hands.\nA few things that are visually similar to 'hand towels' but are not 'hand towels' are:\tbath towels\twashcloths\tdish towels\ttablecloths\nThere are several useful visual features to tell there is 'hand towels' and not similar things in a photo:\tsmall and rectangular or square in shape\tmedium level of absorbency\tsold in sets or groups of various colors or patterns\thanging from a towel rack or hook", 63], "price": ["No. 'Price' is too vague or abstract to be distinguished in a photo.", 63], "stoves": ["Yes. 'Stoves' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'stoves' but are not 'stoves' are:\tovens\tgrills\tfireplaces\theaters\nThere are several useful visual features to tell there is 'stoves' and not similar things in a photo:\tcooktop with burners or heating elements\toven for baking or broiling\thood or ventilation system\tknobs or buttons for controlling the heat or temperature", 63], "dark sunglasses": ["Yes. 'Dark sunglasses' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'dark sunglasses' but are not 'dark sunglasses' are:\tregular glasses\tsafety glasses\tswimming goggles\t3D glasses\t\nThere are several useful visual features to tell there is 'dark sunglasses' and not similar things in a photo:\tdark or tinted lenses\tframes that cover the eyes\tfrom UV rays or bright light\tworn outdoors or on the face", 63], "computer tower": ["Yes. 'Computer tower' has a tangible appearance and is a type of computer device.\nA few things that are visually similar to 'computer tower' but are not 'computer tower' are:\tspeaker\tsystem unit\ttelevision\tset-top box\nThere are several useful visual features to tell there is 'computer tower' and not similar things in a photo:\trectangular shaped case\twith or without handles\tvarious ports such as USB, audio, and video\ton-off button\toptical drives for inserting and ejecting CDs or DVDs.power supply unit inside\tthe overall design features for computer components.", 63], "slopes": ["Yes. 'Slopes' has a tangible appearance and refers to the incline of a surface.\nA few things that are visually similar to 'slopes' but are not 'slopes' are:\troofs\thillsides\tstairs\nThere are several useful visual features to tell there are 'slopes' and not similar things in a photo:\tan angle or incline in the surface\televation changes in the terrain\tor the curve of a wave\tor the curve of a road.", 63], "grey pants": ["Yes. 'Grey pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'grey pants' but are not 'grey pants' are:\tjeans\ttrousers\tleggings\tskirts\nThere are several useful visual features to tell there are 'grey pants' and not similar things in a photo:\tstraight-legged or bootcut design\tgrey or silver color\tno visible texture or pattern\ttypically made of cotton, polyester, or wool", 63], "bands": ["No. 'Bands' are too vague or abstract to be distinguished in a photo. \n\nHowever, if you meant 'bands' as in musical groups, then:\n\nYes. 'Bands' have tangible appearances and can be seen in photos or videos.\nA few things that are visually similar to 'bands' but are not 'bands' are: groups of people standing together, marching bands, groups of workers in matching uniforms. \nThere are several useful visual features to tell there is a 'band' and not similar things in a photo: people holding musical instruments, microphones, or wearing musical clothing or costumes; stage props and musical equipment such as amplifiers and drums; concert venues with large crowds or lighting systems.", 63], "draperies": ["Yes. 'Draperies' has a tangible appearance and is a type of cloth used for curtains or decorative purposes.\nA few things that are visually similar to 'draperies' but are not 'draperies' are:\tblankets\ttablecloths\ttowels\tflags\nThere are several useful visual features to tell there are 'draperies' and not similar things in a photo:\thung from a rod or a hook\tcan be opened or closed\toften made of thick or heavy fabric\tmay have patterns or designs", 63], "brown blanket": ["Yes. 'Brown blanket' has a tangible appearance and is a type of textile.\nA few things that are visually similar to 'brown blanket' but are not 'brown blanket' are:\trug, carpet, quilt, tapestry\nThere are several useful visual features to tell there is 'brown blanket' and not similar things in a photo:\tsoft and fluffy texture\tbrown or earth-toned color\twoven or knitted pattern\trectangular or square shape\tsuitable for covering or wrapping up", 63], "buggy": ["Yes. 'Buggy' has a tangible appearance and refers to a type of vehicle.\nA few things that are visually similar to 'buggy' but are not 'buggy' are:\tcarriage\tstroller\tgolf cart\tgo-kart\nThere are several useful visual features to tell there is 'buggy' and not similar things in a photo:\tlarge wheels and tires\topen-air design\tframe made of metal or other sturdy material\toff-road capabilities\tor racing design drivers or passengers wearing helmets", 63], "icons": ["No. 'Icons' are too abstract to have a tangible appearance that distinguishes them in a photo. \nHowever, in certain contexts, such as on a computer screen or in a religious setting, icons can have specific visual features.\nA few things that are visually similar to 'icons' but are not 'icons' are:\tlogos\tsymbols\tavatars\tpictograms\nUseful visual features for distinguishing icons from the listed similar things in a photo may include:\ta square or rectangular shape with rounded corners\ta simple, easily recognizable symbol or image\tpresented with other icons or items in a group (such as on a computer interface) or in a meaningful context (such as in a religious setting)", 63], "brand logo": ["Yes. 'Brand logo' has a tangible appearance and is a type of visual symbol.\nA few things that are visually similar to 'brand logo' but are not 'brand logo' are:\tgraphic design\tillustration\tsignature\tcalligraphy\tmonogram\nThere are several useful visual features to tell there is 'brand logo' and not similar things in a photo:\tunique design\tor instantly recognizable symbol\tused to distinguish and promote the brand\timprinted or displayed on products, packaging, or marketing materials", 63], "human": ["Yes. 'Human' has a tangible appearance and is a type of organism.\nA few things that are visually similar to 'human' but are not 'human' are:\tapes\tmannequins\tstatues\tcostumes\nThere are several useful visual features to tell there is 'human' and not similar things in a photo:\tbipedal stance\topposable thumbs\thair on top of head, face, and body\tspecific facial features, such as eyes, nose, and mouth\tvariety of skin tones\tand body shapes and sizes.", 63], "motorcycle wheel": ["Yes. 'Motorcycle wheel' has a tangible appearance and is a component of a motorcycle.\nA few things that are visually similar to 'motorcycle wheel' but are not 'motorcycle wheel' are:\tbicycle wheel\tcar wheel\tscooter wheel\nThere are several useful visual features to tell there is 'motorcycle wheel' and not similar things in a photo:\ttwo-wheeled vehicle\tbig tire with treads or grooves\tspokes or metal framework\thub in the center of the wheel\tsuspension system attached to the wheel\tframe or part of the motorcycle visible in the photo", 63], "arm rest": ["Yes. 'Arm rest' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'arm rest' but are not 'arm rest' are:\tpillow\tcushion\tseat\tottoman\nThere are several useful visual features to tell there is 'arm rest' and not similar things in a photo:\tattached to a chair or sofa\tpadded or upholstered\tstrategically positioned for arm support\tflat and wide surface for resting arms", 63], "baseball cleats": ["Yes. 'Baseball cleats' has a tangible appearance and is a type of sports shoe. \nA few things that are visually similar to 'baseball cleats' but are not 'baseball cleats' are:\trunning shoes\tfootball cleats\tsoccer cleats\nThere are several useful visual features to tell there is 'baseball cleats' and not similar things in a photo:\tMetal cleats or studs on the bottom for grip\ton the front is a toe spike which is a small spike in the front of the shoe\tlow top design\tfor added ankle mobility and flexibility\tusually colored in black and white", 63], "brown belt": ["Yes. 'Brown belt' has a tangible appearance and is an accessory worn around the waist.\nA few things that are visually similar to 'brown belt' but are not 'brown belt' are:\tsash\ttie\theadband\tcummerbund \nThere are several useful visual features to tell there is 'brown belt' and not similar things in a photo:\tbelt loops on pants or a dress\tmade of leather or other sturdy materials\tbrown color\twidth that is usually around 1.5-2 inches (3.8-5 cm)", 63], "thumb nail": ["Yes. 'Thumb nail' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'thumb nail' but are not 'thumb nail' are:\tfinger\tnail clippers\tpaint brush tip\tthumbnail-sized sticker\nThere are several useful visual features to tell there is 'thumb nail' and not similar things in a photo:\tlocated at the end of the thumb\tcurved and slightly rectangular shape of the nail\tsmooth surface\twith or without ridges and lines\tpinkish or reddish lunula (half-moon shape at the base)", 63], "computer chair": ["Yes. 'Computer chair' has a tangible appearance and is a type of chair designed for use with a computer.\nA few things that are visually similar to 'computer chair' but are not 'computer chair' are:\tdining chair\toffice chair\tlounge chair\trocking chair\nThere are several useful visual features to tell there is 'computer chair' and not similar things in a photo:\tadjustable height\tswivel base\tbackrest\tarmrests\tpadded seat and backrest\tergonomic design", 63], "fork plate": ["No. 'Fork plate' is too vague or abstract to be distinguished in a photo. However, a fork and a plate are tangible and visually concrete concepts that can be distinguished separately.\nA few things that are visually similar to 'fork plate' but are not 'fork plate' are:\tplate and knife\tfork and spoon\tcup and saucer\nThere are no useful visual features for distinguishing 'fork plate' as it is not a specific object. However, if we are talking about a fork and a plate, some useful visual features to distinguish them are:\tthe fork has multiple long and pointed tines, and the plate is usually round, flat, and used to hold food.", 63], "concrete building": ["Yes. 'Concrete building' has a tangible appearance.\nA few things that are visually similar to 'concrete building' but are not 'concrete building' are:\tstone building\tbrick building\twater tank\tcement block\twall\nThere are several useful visual features to tell there is 'concrete building' and not similar things in a photo:\tgrey or white color\tsmooth and hard surface\tgenerally rectangular or square shape\twindows, doors, or other architectural details.", 63], "concrete ground": ["Yes. 'Concrete ground' has a tangible appearance and is a type of flooring or pavement made of concrete.\nA few things that are visually similar to 'concrete ground' but are not 'concrete ground' are:\tasphalt ground\tpaved road\ttile floor\twooden floor\nThere are several useful visual features to tell there is 'concrete ground' and not similar things in a photo:\tsmooth surface\tlight gray color\tjagged lines where concrete has set\tsmall rocks visible on surface", 63], "blue boat": ["Yes. 'Blue boat' has a tangible appearance and is a type of boat with blue color.\nA few things that are visually similar to 'blue boat' but are not 'blue boat' are:\tred boat\tgreen boat\tyellow boat\twhite boat\nThere are several useful visual features to tell there is 'blue boat' and not similar things in a photo: dominantly blue color\treflective surface\tsail or motor in the water\tshape or design of a boat", 63], "instructions": ["No. 'Instructions' are too vague or abstract to have a tangible appearance or be distinguished in a photo.\nA few things that are visually similar to 'instructions' but are not 'instructions' are:\trecipes\tdirections\trules\tmanuals\nThere are no useful visual features to distinguish 'instructions' from these visually similar things because they often look similar in format and layout. The content and context would need to be examined to determine if they are instructions or something else.", 63], "kite string": ["Yes. 'Kite string' has a tangible appearance and is a type of string used for flying kites.\nA few things that are visually similar to 'kite string' but are not 'kite string' are:\tshoelaces\tguitar strings\thair ties\tpainters tape\nThere are several useful visual features to tell there is 'kite string' and not similar things in a photo:\tthick or thin\tthreaded\tfeathery end of the kite\tstring spool next to the kite", 63], "garment": ["Yes. 'Garment' has a tangible appearance and refers to any piece of clothing.\nA few things that are visually similar to 'garment' but are not 'garment' are:\tbedsheets\ttablecloths\ttowels\tcurtais\nThere are several useful visual features to tell there is 'garment' and not similar things in a photo:\tworn on the body\tvaried sizes and shapes\twith armholes, necklines or waistbands\tmeant for protection or adornment", 63], "passenger window": ["Yes. 'Passenger window' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'passenger window' but are not 'passenger window' are:\tfront windshield\trear window\thouse window\tskylight\nThere are several useful visual features to tell there is 'passenger window' and not similar things in a photo:\tlocated on the side of a car or other vehicle\tusually rectangular or square in shape\tclear or tinted glass, sometimes with a small vent or opening\tswivels or slides open in cars or other motor vehicles", 63], "drop": ["Yes, 'drop' has a tangible appearance and is a physical occurrence.\nA few things that are visually similar to 'drop' but are not 'drop' are: dew tear rain spill\nThere are several useful visual features to distinguish 'drop' from similar things in a photo: round or elongated shape transparency glimmering appearance suspended in mid-air.", 63], "bench brown": ["Yes. 'Bench brown' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'bench brown' but are not 'bench brown' are:\tchair\tstool\tottoman\tsofa\nThere are several useful visual features to tell there is 'bench brown' and not similar things in a photo:\tlong, flat seat\twith or without backrest\tmade of wood or metal\tbrown color", 63], "creek": ["Yes. 'Creek' has a tangible appearance and is a type of small stream or brook.\nA few things that are visually similar to 'creek' but are not 'creek' are:\triver\tspring\tstreamlet\tditch\t\nThere are several useful visual features to tell there is 'creek' and not similar things in a photo:\tNarrower and shallower than rivers\tSmaller flow\tHaving to bend on its way down\tthe bed has stones of different sizes and shapes\tLined by small to medium-sized trees and shrubs or rocks\tMay be winding and can create a u-shape", 63], "shadow grass": ["Yes. 'Shadow grass' has a tangible appearance and is a type of vegetation.\nA few things that are visually similar to 'shadow grass' but are not 'shadow grass' are:\tlawn\tmeadow\tweeds\tfoliage\nThere are several useful visual features to tell there is 'shadow grass' and not similar things in a photo:\tdark green or brown color\tgrowing in shaded areas\tlong, narrow blades of grass\tarrangement near trees or bushes", 63], "mountainside": ["Yes. 'Mountainside' has a tangible appearance and refers to the slope of a mountain.\nA few things that are visually similar to 'mountainside' but are not 'mountainside' are:\tcliff\thillside\tvalley\nThere are several useful visual features to tell there is 'mountainside' and not similar things in a photo:\tslope or incline of a mountain\trocky terrain\tor snow-covered slope\tsteep gradient", 63], "x": ["No. 'x' is too vague and abstract to have a tangible appearance and cannot be distinguished in a photo.", 63], "pant leg": ["Yes. 'Pant leg' has a tangible appearance and is a part of clothing.\nA few things that are visually similar to 'pant leg' but are not 'pant leg' are:\tsocks\ttights\tleggings\ttrousers\nThere are several useful visual features to tell there is 'pant leg' and not similar things in a photo:\tconnected to waistband of the pants or shorts\tloose-fitting or tight-fitting\ttapered\tat least covering the leg from ankle to knee", 63], "right headlight": ["Yes. 'Right headlight' has a tangible appearance and is a part of a car.\nThere are no things that are visually similar to 'right headlight' but are not 'right headlight.'\nUseful visual features for distinguishing 'right headlight' are: located on the right side of the front of the car, circular or oval shape, located next to the right turn signal light, and emits a bright light when turned on.", 63], "page": ["Yes. 'Page' has a tangible appearance and is a sheet of paper with information on it.\nA few things that are visually similar to 'page' but are not 'page' are:\tbook\tbill\tsign\twallpaper\nThere are several useful visual features to tell there is 'page' and not similar things in a photo:\trectangular shape\twritten or printed information on it\tmargin or border around the content\tpaper texture or quality", 63], "destination sign": ["Yes. 'Destination sign' has a tangible appearance and is a type of sign used to indicate the destination of a vehicle.\nA few things that are visually similar to 'destination sign' but are not 'destination sign' are:\troad sign\tadvertising billboard\tstore sign\tdirectional sign\nThere are several useful visual features to tell there is 'destination sign' and not similar things in a photo:\tlocated on a bus, train, or other transportation vehicle\tdisplays the name of a specific destination\twhere a vehicle will stop next\ton a black or dark background with white or light-colored letters or symbols.", 63], "color grass": ["No. 'Color grass' is too vague or abstract to be visually distinguished in a photo.", 62], "train number": ["No. 'Train number' is too vague or abstract to be distinguished in a photo.", 62], "lump": ["Yes. 'Lump' has a tangible appearance and refers to a small, irregularly shaped mass or bump on a surface.\nA few things that are visually similar to 'lump' but are not 'lump' are:\trock\tpebble\tpotato\t\nThere are several useful visual features to tell there is 'lump' and not similar things in a photo:\tbumpy surface on a larger object\trough edges and an irregular shape\ton the surface of something else", 62], "herb": ["Yes. 'Herb' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'herb' but are not 'herb' are:\tgrass\tweed\tshrub\tflower\nThere are several useful visual features to tell there is 'herb' and not similar things in a photo:\tleaves are used for seasoning or medicine\tusually grown for culinary or medicinal uses\tgreen leaves with a strong aroma or flavor\toften small and delicate\tin some cases, have small blooms", 62], "seed": ["Yes. 'Seed' has a tangible appearance and is a small embryonic plant.\nA few things that are visually similar to 'seed' but are not 'seed' are:\tgrain\tpollen\tdust\tsalt crystals\tsugar crystals\nThere are several useful visual features to tell there is 'seed' and not similar things in a photo:\tcompact and small size\tovular or rounded shape\tvarious colors and textures (depending on the type of plant)\tpods or husks may be visible.", 62], "door car": ["No. 'Door car' is too vague or abstract to be distinguished in a photo. It is possible that the intended concept is 'car door', which is a visually concrete concept.\nA few things that are visually similar to 'car door' but are not 'car door' are:\twindows\thatches\tgate doors\nThere are several useful visual features to tell there is 'car door' and not similar things in a photo:\trectangular shape\thandle for opening and closing\twindow for looking inside\tthe car's body or frame", 62], "baby cow": ["Yes. 'Baby cow' has a tangible appearance.\nA few things that are visually similar to 'baby cow' but are not 'baby cow' are:\tadult cow\tyak\tbuffalo\tgnu\nThere are several useful visual features to tell there is 'baby cow' and not similar things in a photo:\tsmaller in size, visibly smaller than an adult cow\tround and soft features\tbright and lively eyes\tshorter legs and neck\tfurry body with soft skin on the ears, nose, and mouth area.", 62], "plugs": ["Yes. 'Plugs' have a tangible appearance and are typically used for electrical connections.\nA few things that are visually similar to 'plugs' but are not 'plugs' are:\tbuttons\tjacks\tconnectors\nThere are several useful visual features to tell there is 'plugs' and not similar things in a photo:\tthree-pronged\tor two-pronged shape\tbrightly-colored plastic or metal component\tcylindrical shape with metal pins or prongs at one end\tdesigned for insertion into electrical outlet or port.", 62], "christmas lights": ["Yes. 'Christmas lights' has a tangible appearance and is a kind of decoration.\nA few things that are visually similar to 'christmas lights' but are not 'christmas lights' are:\tcandles\tfireflies\tstring lights\nThere are several useful visual features to tell there is 'christmas lights' and not similar things in a photo:\tmultiple small bulbs\tstring or wire to attach to trees or walls\tbright and colorful light emission", 62], "hip": ["Yes. 'Hip' has a tangible appearance and is a part of the human anatomy.\nA few things that are visually similar to 'hip' but are not 'hip' are:\tbelt\tpants\twaist\t\nThere are several useful visual features to tell there is 'hip' and not similar things in a photo:\ta joint connecting the pelvis and the thigh\tball-and-socket joint position\tthe widest part of the body when viewed from the front or the back.", 62], "living": ["No. 'Living' is too vague or abstract to be distinguished in a photo. \nIt is not possible to describe things that are visually similar or visual features to distinguish 'living' from similar things as 'living' refers to organisms that display characteristics of life, and it's not a visual concept.", 62], "toilet seat lid": ["Yes. 'Toilet seat lid' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'toilet seat lid' but are not 'toilet seat lid' are:\tshower drain cover\tkitchen sink cover\tfloor access panel\tkitchen garbage can lid\nThere are several useful visual features to tell there is 'toilet seat lid' and not similar things in a photo:\toval or round shape\twith hinges\tlocated on top of a toilet bowl\tsimilar color to the toilet bowl\tor a contrasting color either works as they come in different colors and styles.", 62], "flower petals": ["Yes. 'Flower petals' has a tangible appearance and is a type of plant material.\nA few things that are visually similar to 'flower petals' but are not 'flower petals' are:\tconfetti\tleaves\tbutterfly wings\tfeathers, fabric pieces\t\nThere are several useful visual features to tell there is 'flower petals' and not similar things in a photo:\tthin and delicate texture\tvariety of colors\tand one central point of attachment to the flower.", 62], "skate ramp": ["Yes. 'Skate ramp' has a tangible appearance and is a structure used for skateboarding or BMX biking.\nA few things that are visually similar to 'skate ramp' but are not 'skate ramp' are:\tstaircase\tparking garage ramp\tplayground slide\tboat dock\nThere are several useful visual features to tell there is 'skate ramp' and not similar things in a photo:\tangled surface\tfor skateboard or BMX use\tmetal or wooden surface\twith edges and curves\theight from ground level", 62], "baseball bats": ["Yes, 'baseball bats' have a tangible appearance and are a type of sports equipment.\nA few things that are visually similar to 'baseball bats' but are not 'baseball bats' are:\thockey sticks\tcricket bats\tpolice batons\tgolf clubs \nThere are several useful visual features to tell there is 'baseball bats' and not similar things in a photo:\tlong and thin\tcylindrical shape\tthicker at one end\ttapered handle\tsolid and heavy-looking material\tsport-related markings or logos", 62], "wrought iron fence": ["Yes. 'Wrought iron fence' has a tangible appearance and is a type of fence.\nA few things that are visually similar to 'wrought iron fence' but are not 'wrought iron fence' are:\tchain-link fence\twooden fence\tstone wall\nThere are several useful visual features to tell there is 'wrought iron fence' and not similar things in a photo:\telaborate metal designs\tand patterns\tcurled tops and posts\tstraight and parallel bars\tdark grey to black color\tmatte or slightly shiny finish", 62], "inside": ["No. 'Inside' is too vague or abstract to be distinguished in a photo. It is a spatial concept that refers to the interior of something.\nThere are no visually similar things to 'inside'.", 62], "wooden drawer": ["Yes. 'Wooden drawer' has a tangible appearance and is a type of home furnishing.\nA few things that are visually similar to 'wooden drawer' but are not 'wooden drawer' are:\tfiling cabinet\tshelves\tdresser\tbookcase\nThere are several useful visual features to tell there is 'wooden drawer' and not similar things in a photo:\trectangular or square shape\tmade of wood or a wood-like material\thandle or knob for pulling out\tthe inside is divided into sections or compartments", 62], "flower arrangement": ["Yes. 'Flower arrangement' has a tangible appearance and is a display of flowers arranged in an artistic way.\nA few things that are visually similar to 'flower arrangement' but are not 'flower arrangement' are:\tpotted plants\twildflower fields\tflower bouquets\tflower gardens\nThere are several useful visual features to differentiate 'flower arrangement' from the listed similar things in a photo:\tflowers arranged in an artistic way\tmultiple types of flowers arranged together\tin a vase or container\tarbitrary or stylized design.", 62], "roman": ["No. 'Roman' is too vague or abstract to be distinguished in a photo. It could refer to various things related to the ancient Roman civilization, such as architecture, art, or military strategies.\nHowever, a few things that may be visually similar to Roman architecture are: \n- Greek architecture \n- Renaissance architecture \n- Neoclassical architecture \n- Gothic architecture \n\nSome useful visual features for distinguishing Roman architecture from the listed similar things are: \n- Use of arches and vaults \n- Use of columns and capitals \n- Use of concrete as building material \n- Incorporation of decorative motifs such as reliefs and mosaics \n- Use of a classical orders such as Doric, Ionic or Corinthian", 62], "rectangle sign": ["Yes. 'Rectangle sign' has a tangible appearance and is a kind of signage.\nA few things that are visually similar to 'rectangle sign' but are not 'rectangle sign' are:\ttriangle sign\tcircle sign\tsigns with irregular shapes\nThere are several useful visual features to tell there is 'rectangle sign' and not similar things in a photo:\trectangular shape\tright angles\tcorners\ttwo longer sides and two shorter sides\ttext or symbols to convey information or directions", 62], "left headlight": ["Yes. 'Left headlight' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'left headlight' but are not 'left headlight' are:\tright headlight\tbrake light\tturn signal\tlight bulb\nThere are several useful visual features to tell there is 'left headlight' and not similar things in a photo:\tposition on the left side of the vehicle\tshape and size similar to the right headlight\tcircular or oval shape\tbright white or yellow light beam\tdesign and color that matches the vehicle's make and model.", 62], "grassy": ["Yes. 'Grassy' has a tangible appearance and refers to an area covered in grass.\nA few things that are visually similar to 'grassy' but are not 'grassy' are:\tsod\tdirt\tfield\nThere are several useful visual features to tell there is 'grassy' and not similar things in a photo:\tgreen or brown blades of grass\tuneven surface\tvisible roots and soil\tpossible presence of weeds or flowers", 62], "maple leaf": ["Yes. 'Maple leaf' has a tangible appearance and is a type of leaf.\nA few things that are visually similar to 'maple leaf' but are not 'maple leaf' are:\toak leaf\tash leaf\tbirch leaf\tivy leaf\nThere are several useful visual features to tell there is 'maple leaf' and not similar things in a photo:\topposite leaf arrangement\tthree to five lobes\tpoints or serrations on the lobes\tpalmate veins, with several large veins spreading out from the base of the leaf.\tColor: bright red, yellow, or orange in the autumn", 62], "knit hat": ["Yes. 'Knit hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'knit hat' but are not 'knit hat' are:\tbaseball cap\tbeanie\tberet\tfedora\theadband\nThere are several useful visual features to tell there is 'knit hat' and not similar things in a photo:\tmade of knitted fabric or yarn\tclosely fitting to the head\tcold-weather headwear\tcould have a pom-pom or tassel on top", 61], "overalls": ["Yes. 'Overalls' has a tangible appearance and is a kind of clothing.\nA few things that are visually similar to 'overalls' but are not 'overalls' are:\tcoveralls\tjumpsuits\trompers\tboiler suits\nThere are several useful visual features to tell there is 'overalls' and not similar things in a photo:\tfull-body garment\tthat covers both the torso and the legs\thas straps or buttons at the shoulder\tarea\tmade of sturdy and durable fabric, often denim or cotton\thas pockets on the chest, waist, or legs.", 61], "pizza slices": ["Yes. 'Pizza slices' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pizza slices' but are not 'pizza slices' are:\tcheese\ttortilla\twedges\tpie slices\nThere are several useful visual features to tell there is 'pizza slices' and not similar things in a photo:\tcircular shape\twith toppings, such as cheese and tomato sauce\tbaked or grilled appearance\twith melted cheese and crispy crust\tserved on a plate or a tray.", 61], "cards": ["Yes. 'Cards' has a tangible appearance and is a type of paper or plastic object used for games or communication.\nA few things that are visually similar to 'cards' but are not 'cards' are:\tpaper money\tphotos\tcertificates\tpostcards\tcalendars\nThere are several useful visual features to tell there is 'cards' and not similar things in a photo:\trectangular or square shape\twith symbols or images printed on them\tnumbers or letters on them\tthick or thin material", 61], "bookshelves": ["Yes. 'Bookshelves' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'bookshelves' but are not 'bookshelves' are:\tcabinets\tshoe racks\tlockers\twardrobes\nThere are several useful visual features to tell there is 'bookshelves' and not similar things in a photo:\tconsist of horizontally arranged shelves\tfor keeping books or other printed matter\tusually made of wood or metal\tfixed to a wall or stand freely on the floor", 61], "bite": ["No. 'Bite' is too vague or abstract to be distinguished visually in a photo.", 61], "metal fence post": ["Yes. 'Metal fence post' has a tangible appearance and is a type of object used in construction.\nA few things that are visually similar to 'metal fence post' but are not 'metal fence post' are:\tlampposts\ttraffic sign poles\tdrain pipes\tflag poles\nThere are several useful visual features to tell there is 'metal fence post' and not similar things in a photo:\tmetallic gray color\toblong shape with pointed ends\tflanged base\tthat visible fence is attached to it", 61], "round bowl": ["Yes. 'Round bowl' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'round bowl' but are not 'round bowl' are:\tplate\tcup\tpot\tbucket\nThere are several useful visual features to tell there is 'round bowl' and not similar things in a photo:\tcircular shape\tdepth\tround-bottomed or flat-bottomed\tsmooth or textured surface\twith or without lid or handles.", 61], "rear leg": ["Yes. 'Rear leg' has a tangible appearance and is a body part of various animals.\nA few things that are visually similar to 'rear leg' but are not 'rear leg' are: arms, front legs, tail, neck, tongue.\nThere are some useful visual features to tell there is 'rear leg' and not similar things in a photo: located on the back half of the body, larger and more powerful than front legs, backward bending knee joint, fewer toes than front legs or feet.", 61], "soap bottle": ["Yes. 'Soap bottle' has a tangible appearance and is a kind of container for soap.\nA few things that are visually similar to 'soap bottle' but are not 'soap bottle' are:\tshampoo bottle\tlotion bottle\thand sanitizer bottle\tcleaning spray bottle\nThere are several useful visual features to tell there is 'soap bottle' and not similar things in a photo:\tclear or opaque plastic or glass bottle\tpump or dispenser on top\tlabel or print indicating 'soap' or a specific brand or scent\tof a size and shape that is suitable for dispensing hand or body soap", 61], "marking": ["No. 'Marking' is too vague or abstract to be distinguished in a photo. It can refer to any kind of sign, symbol, or pattern that is used for a variety of purposes and can take many different forms, making it difficult to identify specific visual features to distinguish it from similar things.", 61], "bath mat": ["Yes. 'Bath mat' has a tangible appearance and is a kind of floor covering.\nA few things that are visually similar to 'bath mat' but are not 'bath mat' are:\trug\tdoormat\tblanket\ttowel\nThere are several useful visual features to tell there is 'bath mat' and not similar things in a photo:\tabsorbent material\tsmall size compared to a rug or a blanket\tdesigned for bathroom use\twith anti-slip backing\tsolid color or simple pattern", 61], "childs": ["No. 'Childs' is grammatically incorrect and should be 'children'. However, 'children' is not a visually concrete concept as it refers to human beings, who have a tangible appearance but the concept itself is abstract and cannot be visually represented. \n\nTherefore, there are no things that are visually similar to 'childs' or 'children'.", 61], "ships": ["Yes. 'Ships' has a tangible appearance and is a mode of transportation that floats on water.\nA few things that are visually similar to 'ships' but are not 'ships' are:\tboats\tyachts\tkayaks\trafts\nThere are several useful visual features to tell there is 'ships' and not similar things in a photo:\tlarge size compared to boats\tsails (in sailboats) or smokestacks (in steamships)\tmultiple decks, cabins for passengers\tvisible propellers or motors on the sides or back of the ship\thull, rudder, and keel", 61], "angel": ["Yes. 'Angel' has a tangible appearance and is a type of supernatural being.\nA few things that are visually similar to 'angel' but are not 'angel' are:\tfairy\tcupid\tcherub\thuman\twith wings\nThere are several useful visual features to tell there is 'angel' and not similar things in a photo:\thumanoid body\twith wings\thalo\torb\ton or around the head\tlong robe or gown, often white or gold\tcarrying a trumpet or other symbolic object", 61], "lettuce sandwich": ["Yes. 'Lettuce sandwich' has a tangible appearance and is a type of sandwich.\nA few things that are visually similar to 'lettuce sandwich' but are not 'lettuce sandwich' are:\tveggie wrap\tchicken wrap\ttomato sandwich\tgrilled cheese\nThere are several useful visual features to tell there is 'lettuce sandwich' and not similar things in a photo:\tone or more slices of bread\tlettuce\tfillings such as sliced tomatoes, cheese, sliced meat, etc.\tmayonnaise, mustard or other condiments can be visible between the bread", 61], "stop signs": ["Yes. 'Stop signs' has a tangible appearance and is a kind of traffic sign.\nA few things that are visually similar to 'stop signs' but are not 'stop signs' are:\tyield signs\tcaution signs\tcrossing signs\nThere are several useful visual features to tell there is 'stop signs' and not similar things in a photo:\toctagonal shape\tbright red color\twith the word \"STOP\" written in white capital letters\tno other letters, numbers or images on it.", 61], "plastic water bottle": ["Yes. 'Plastic water bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic water bottle' but are not 'plastic water bottle' are:\tthermos\tmetal water bottle\tplastic juice bottle\nThere are several useful visual features to tell there is 'plastic water bottle' and not similar things in a photo:\tdrinking spout or cap\twide base\tnarrow neck\tclear plastic with measurements or branding labels\treusable and disposable options", 61], "receipt": ["Yes. 'Receipt' has a tangible appearance and is a type of paper with information about a transaction.\nA few things that are visually similar to 'receipt' but are not 'receipt' are:\tinvoice\tcheck\tnotepad\tor any type of paper with writing on it\nThere are several useful visual features to tell there is 'receipt' and not similar things in a photo:\titemized list of products or services purchased\tdate and time of the transaction\tname and location of the store or business\tthe total amount paid\tthe payment method used (cash, credit, etc.)", 61], "canvas": ["Yes. 'Canvas' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'canvas' but are not 'canvas' are:\tpaper\tfabric\tcardboard\t\nThere are several useful visual features to tell there is 'canvas' and not similar things in a photo:\trough texture\tmade of cotton, linen, or hemp\tused for painting or printing\tno visible weave or pattern", 61], "streets": ["Yes. 'Streets' has a tangible appearance and refers to public roads and pathways used for transportation.\nA few things that are visually similar to 'streets' but are not 'streets' are:\tparking lots\tsidewalks\thallways\ttrails\nThere are several useful visual features to tell that there are 'streets' and not similar things in a photo:\tpaved or concrete surface\twith lines or markings for cars or pedestrians\tusually with buildings or houses on either side\tof varying widths or lengths\toften with signs or lights for traffic or pedestrians.", 61], "bandage": ["Yes. 'Bandage' has a tangible appearance and is a type of medical dressing.\nA few things that are visually similar to 'bandage' but are not 'bandage' are:\ttape\twrapping paper\tribbon\nThere are several useful visual features to tell there is 'bandage' and not similar things in a photo:\twhite or beige cloth material\trolled or wrapped around a body part\tadhesive strips or fasteners on the ends", 61], "ruler": ["Yes. 'Ruler' has a tangible appearance and is a kind of measuring tool.\nA few things that are visually similar to 'ruler' but are not 'ruler' are:\tmeasuring tape\tyardstick\tstraightedge\tsquare\nThere are several useful visual features to tell there is 'ruler' and not similar things in a photo:\tstraight and flat shape\twith a set of markings along one or two edges\tunits of measurement (centimeters, inches, etc.) clearly labeled or engraved.", 61], "calculator": ["Yes. 'Calculator' has a tangible appearance and is a device used for mathematical calculations.\nA few things that are visually similar to 'calculator' but are not 'calculator' are:\tphone/computer keyboard\twatches/tablets\nThere are several useful visual features to tell there is 'calculator' and not similar things in a photo:\tnumeric keypad\tdisplay screen\tarrow buttons (for navigation)\tfunction buttons (e.g. clear, equals, plus/minus, etc.)", 61], "gown": ["Yes. 'Gown' has a tangible appearance and is a type of apparel.\nA few things that are visually similar to 'gown' but are not 'gown' are:\tdress\trobe\tskirt\t\nThere are several useful visual features to tell there is 'gown' and not similar things in a photo:\tformal and elegant attire\tlong length fitted from the waist down\tto the ankles or floor\texaggerated and sophisticated design\tcommonly used for formal events or weddings.", 61], "rams": ["Yes. 'Rams' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'rams' but are not 'rams' are:\tsheep\tgoats\tbighorn sheep\tdeer\nThere are several useful visual features to tell there is 'rams' and not similar things in a photo:curved horns (especially in males)\tthick and curly fur (usually white or brown)\ttypically larger and muscular than sheep, with a square-shaped head and a more prominent nose", 61], "thumbs": ["Yes. 'Thumbs' has a tangible appearance and is a part of the human hand.\nThere are no things that are visually similar to 'thumbs' but are not 'thumbs'.\nThere are no useful visual features for distinguishing 'thumbs' from similar things as there are no similar things to 'thumbs'.", 60], "orange slice": ["Yes. 'Orange slice' has a tangible appearance and refers to a piece of citrus fruit.\nA few things that are visually similar to 'orange slice' but are not 'orange slice' are:\tlemon slice\tlime slice\tgrapefruit slice\tpineapple slice\nThere are several useful visual features to tell there is 'orange slice' and not similar things in a photo:\tround or crescent shape\torange color\twith or without seeds/juice pulp\ttexture of orange peel", 60], "metal pan": ["Yes. 'Metal pan' has a tangible appearance and is a kind of cookware.\nA few things that are visually similar to 'metal pan' but are not 'metal pan' are:\tpot\tpancake\tcomal\tgriddle\t\nThere are several useful visual features to tell there is 'metal pan' and not similar things in a photo:\tsquare or round shape\traised edges\thandles\ton the stovetop or in the oven\tmade of metal or metal alloys.", 60], "stovetop": ["Yes. 'Stovetop' has a tangible appearance and is a part of a kitchen appliance.\nA few things that are visually similar to 'stovetop' but are not 'stovetop' are:\tcounter\ttop of a desk\ttable\nThere are several useful visual features to tell there is 'stovetop' and not similar things in a photo:\theat coils or burners\tgrates or pans\tfor knobs or buttons to adjust temperature\tuse of cooking utensils and cookware", 60], "areas": ["No. 'Areas' is too vague or abstract to be distinguished in a photo.", 60], "glass shelf": ["Yes. 'Glass shelf' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'glass shelf' but are not 'glass shelf' are:\ttable\ttop of a desk\tcounter\tshower door\tframeless window\nThere are several useful visual features to tell there is 'glass shelf' and not similar things in a photo:\tmade entirely of glass or with glass surface-linear- supported by brackets, clips, or suction cups\tlaying horizontally or tilted up to 45 degrees\tsome items, such as books or vases, placed on it", 60], "fence pole": ["Yes. 'Fence pole' has a tangible appearance and is a type of pole used for fencing.\nA few things that are visually similar to 'fence pole' but are not 'fence pole' are:\tlamp post\ttraffic sign\tpost for hanging decorations\tjumping pole\nThere are several useful visual features to tell there is 'fence pole' and not similar things in a photo:\twooden or metal material\trough or smooth texture\trectangular or cylindrical shape\twith or without wire or mesh attached\ttogether with other fence poles or fencing material.", 60], "luggage cart": ["Yes, 'luggage cart' has a visually concrete concept.\nA few things that are visually similar to 'luggage cart' but not 'luggage cart' are: \n- Hand-trucks\n- Shopping carts\n- Wheelbarrows\n- Trolleys\n- Dolly carts\n\nThere are a few useful visual features to tell there is a 'luggage cart' and not similar things in a photo:\n- Two or more wheels\n- A handle to pull or push the cart\n- Luggage support structure on the base \n- Usually found at airports, bus stations or train stations", 60], "calm body": ["No. 'Calm body' is too vague or abstract to be distinguished in a photo.", 60], "banana peel": ["Yes. 'Banana peel' has a tangible appearance and is the outermost layer of a banana fruit.\nA few things that are visually similar to 'banana peel' but are not 'banana peel' are:\torange peel\tpotato skin\tapple skin\tonion skin\nThere are several useful visual features to tell there is 'banana peel' and not similar things in a photo:\tyellow\tcolor browning\trather thin shape\toften curved or bent shape", 60], "yield sign": ["Yes. 'Yield sign' has a tangible appearance and is a kind of traffic sign.\nA few things that are visually similar to 'yield sign' but are not 'yield sign' are:\tstop sign\tspeed limit sign\tcrosswalk sign\nThere are several useful visual features to tell there is 'yield sign' and not similar things in a photo:\tred and white triangular shape\twith the word \"yield\" written in bold black letters\tsmall size compared to stop sign and other traffic signs\tis sometimes accompanied by additional smaller signs or arrows indicating where to yield.", 60], "silver pipe": ["Yes. 'Silver pipe' has a tangible appearance and is a cylindrical object made of silver material.\nA few things that are visually similar to 'silver pipe' but are not 'silver pipe' are:\tsteel pipe\taluminum pipe\tchrome pipe\tplumbing fixtures\nThere are several useful visual features to tell there is 'silver pipe' and not similar things in a photo:\tcylindrical shape\tsilver color\tsolid material\ttypical metallic texture and shine", 60], "raincoat": ["Yes. 'Raincoat' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'raincoat' but are not 'raincoat' are:\tjacket\tponcho\twindbreaker\tsweater\nThere are several useful visual features to tell there is 'raincoat' and not similar things in a photo:\twaterproof or water-resistant material\thood\tfor outdoor wear\toften brightly colored or neon to provide visibility\tdifferent lengths of coats for different weather conditions.", 60], "sea water": ["Yes. 'Sea water' has a tangible appearance and is a type of water found in oceans.\nA few things that are visually similar to 'sea water' but are not 'sea water' are:\tfreshwater\trainwater\tpool water\nThere are several useful visual features to tell there is 'sea water' and not similar things in a photo:\tbluish-green or green\tcolor\tmotion\twaves\tsalt crystals or foam", 60], "cat whiskers": ["Yes. 'Cat whiskers' has a tangible appearance and is a part of a cat's anatomy.\nA few things that are visually similar to 'cat whiskers' but are not 'cat whiskers' are:\thair\tstraw\nThere are several useful visual features to tell there is 'cat whiskers' and not similar things in a photo:\tlocated on the cat's face\tcurved shape\tthicker and stiffer than hair\tusually white or light-colored\tbelonging in groups of 12 to 24 whiskers on each side of the face", 60], "cloudy gray sky": ["Yes. 'Cloudy gray sky' has a tangible appearance.\nA few things that are visually similar to 'cloudy gray sky' but are not 'cloudy gray sky' are:\tsmoke\tplumes of volcanic smoke\tpollution\nThere are several useful visual features to tell there is 'cloudy gray sky' and not similar things in a photo:\tmost of the sky is gray or overcast\tpresence of clouds or fog\tthe lighting in the frame is even or diffused\tno direct sunlight visible in the frame", 60], "tomato slice": ["Yes. 'Tomato slice' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'tomato slice' but are not 'tomato slice' are:\tred pepper slice\tbeet slice\twatermelon slice\tbologna slice\nThere are several useful visual features to tell there is 'tomato slice' and not similar things in a photo:\tround or oval shape\tred or pink color\twith seeds or without seeds\tjuicy and meaty texture", 60], "ink": ["Yes. 'Ink' has a tangible appearance and is a fluid or paste used for writing or printing.\nA few things that are visually similar to 'ink' but are not 'ink' are:\tpaint\tdye\tjuice\tsharpie\nThere are several useful visual features to tell there is 'ink' and not similar things in a photo:\ttypically black or blue in color\twriting or printing on paper or other surfaces\tmay be contained in a pen or bottle\tmay have a glossy or matte appearance when dry.", 60], "giraffe standing": ["Yes. 'Giraffe standing' has a tangible appearance and refers to the posture and position of a giraffe.\nA few things that are visually similar to 'giraffe standing' but are not 'giraffe standing' are:\tgiraffe lying down\tbrown horse\tbrown cow\tcrane\tbrown deer\nThere are several useful visual features to tell there is 'giraffe standing' and not similar things in a photo:\tlong neck\tmedium to large size spots in a irregular shape on a light brown fur\ttall stance of the legs relative to the body \toval-shaped ossicones (horn-like structures) on top of the head", 60], "soap holder": ["Yes. 'Soap holder' has a tangible appearance and is usually made of plastic, metal or porcelain, to hold the soap in the bathroom or kitchen.\nA few things that are visually similar to 'soap holder' but are not 'soap holder' are:\ttoothbrush holder\ttumbler\tcup\tmug\ttray\nThere are several useful visual features to tell there is 'soap holder' and not similar things in a photo: \tusually smaller than a cup\tor a tumbler\twith ridges or raised surfaces\tto prevent soap from slipping or melting with water\tdirectly placed near a sink or a bathtub", 60], "cloudy day": ["Yes. 'Cloudy day' has a tangible appearance and can be visually distinguished.\nA few things that are visually similar to 'cloudy day' but are not 'cloudy day' are:\tfoggy day\tsmoky day\trainy day\tdusty day\nThere are several useful visual features to tell there is 'cloudy day' and not similar things in a photo:\tgray or white clouds in the sky\tlack of direct sunlight in the environment\tdim or muted colors in the surroundings\tsomber or moody atmosphere", 60], "sword": ["Yes. 'Sword' has a tangible appearance and is a kind of weapon.\nA few things that are visually similar to 'sword' but are not 'sword' are:\tknife\tmachete\tdagger\nThere are several useful visual features to tell there is 'sword' and not similar things in a photo:\tlong handle that can be held with both hands\tsharp, pointed blade\ttapered edge that leads to a point\tguard that separates the handle from the blade", 60], "motorbikes": ["Yes. 'Motorbikes' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motorbikes' but are not 'motorbikes' are:\tbicycles\tscooters\tmopeds\tthree-wheelers\nThere are several useful visual features to tell there is 'motorbikes' and not similar things in a photo:\ttwo wheels\tengine\thandlebars\tforward-leaning posture from the rider and foot pedals or footrests", 60], "food tray": ["Yes. 'Food tray' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'food tray' but are not 'food tray' are:\ttool tray\tbaking sheet\tplatter\tbasket\nThere are several useful visual features to tell there is 'food tray' and not similar things in a photo:\trectangular in shape\tdivided into several sections for different food items\tmade of plastic or metal\thandles on each side or corner\tcarrying food or utensils\tfor serving food to guests or customers", 60], "braid": ["Yes. 'Braid' has a tangible appearance and refers to a hairstyle or a type of cord.\nA few things that are visually similar to 'braid' but are not 'braid' are:\tpigtails\tplaits\tcables\nThere are several useful visual features to tell there is 'braid' and not similar things in a photo:\n- Three or more strands woven together\n- Intertwined pattern\n- Used as a hairstyle or decoration", 60], "rubber band": ["Yes. 'Rubber band' has a tangible appearance and is a type of small elastic loop used for holding things together.\nA few things that are visually similar to 'rubber band' but are not 'rubber band' are:\thair tie\tbinding strap\tbungee cord\tbraces\nThere are several useful visual features to tell there is 'rubber band' and not similar things in a photo:\tthin and flexible\tround or oblong in shape\tmade of rubber or silicone\tstretched out or wrapped around an object.", 60], "chain-link fence": ["Yes. 'Chain-link fence' has a tangible appearance and is a type of barrier.\nA few things that are visually similar to 'chain-link fence' but are not 'chain-link fence' are:\twooden fence\tbarbed wire fence\tmesh netting\tmetal fence\nThere are several useful visual features to tell there is 'chain-link fence' and not similar things in a photo:\tdiamond-shaped holes or grids\tmetal wire construction\tsilver or galvanized color\tthin and lightweight appearance", 60], "wetsuits": ["Yes. 'Wetsuits' has a tangible appearance and is a kind of clothing worn for water activities.\nA few things that are visually similar to 'wetsuits' but are not 'wetsuits' are:\tdiving suits\tsurfing rash guards\tswimwear\nThere are several useful visual features to tell there is 'wetsuits' and not similar things in a photo:\ttypically black, dark blue or gray in color\tfitting tightly to the body, covering the torso and limbs\tmade of material that insulates the body from cold water", 60], "dude": ["No. 'Dude' is too vague or abstract to be visually distinguished in a photo.", 60], "cash register": ["Yes. 'Cash register' has a tangible appearance and is a kind of machine.\nA few things that are visually similar to 'cash register' but are not 'cash register' are:\tscales\tcomputers\tvending machines\tATMs\nThere are several useful visual features to tell there is 'cash register' and not similar things in a photo:\trectangle shape with a drawer\tfor displaying prices and totals\tcash slot for receiving money\tfrom which a receipt can be printed\ttypewriter-like keyboard\tpower cord or battery compartment", 60], "copyright": ["No. 'Copyright' is too vague or abstract to be distinguished in a photo.", 60], "fixtures": ["Yes. 'Fixtures' has a tangible appearance and refers to permanent accessories or equipment attached to a building or a structure.\nA few things that are visually similar to 'fixtures' but are not 'fixtures' are:\tfurniture\tdecorations\ttools\tappliances\nThere are several useful visual features to tell there is 'fixtures' and not similar things in a photo:\tpermanently attached to a surface or a structure\tnot easily removable\thave specific functions or purposes (e.g. lights, faucets, shelves, etc.)", 60], "mini blinds": ["Yes. 'Mini blinds' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'mini blinds' but are not 'mini blinds' are:\tshades\tcurtains\tshutters\nThere are several useful visual features to tell there is 'mini blinds' and not similar things in a photo:\thorizontal slats\tthat can rotate to adjust the amount of light\tlet light in while maintaining privacy\tattached to the top of the window frame", 60], "onion rings": ["Yes. 'Onion rings' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'onion rings' but are not 'onion rings' are:\tfried calamari\tfried pickles\tbreaded mushrooms\tfried zucchini\nThere are several useful visual features to tell there is 'onion rings' and not similar things in a photo:\tcircular shape\twith a hole in the center\tof varying sizes\tbattered and fried ", 60], "bell tower": ["Yes. 'Bell tower' has a tangible appearance and is a type of tower.\nA few things that are visually similar to 'bell tower' but are not 'bell tower' are:\twater tower\tcommunication tower\tobservation tower\nThere are several useful visual features to tell there is 'bell tower' and not similar things in a photo:\tattached to or part of a building\ttall and narrow with a pointy top\tbells or a clock inside or on top of the tower\tarched or pointed windows\tset apart from other towers", 60], "ocean wave": ["Yes. 'Ocean wave' has a tangible appearance and is a kind of natural phenomenon.\nA few things that are visually similar to 'ocean wave' but are not 'ocean wave' are:\twhite water\trivers\tcreeks\tfalls\nThere are several useful visual features to tell there is 'ocean wave' and not similar things in a photo:\tmassive body of water\twith foam or spray\tcrest or lip of water curling and breaking as it advances\taccompanies wind forces on the surface of the sea.", 60], "phone booth": ["Yes. 'Phone booth' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'phone booth' but are not 'phone booth' are:\tbus stop\tshelter\trestroom booth\tnewspaper booth\nThere are several useful visual features to tell there is 'phone booth' and not similar things in a photo:\ta small structure made of glass or plastic\tfor making phone calls\thave a phone or a sign indicating a phone company\tuse bright colors, such as red, yellow, or blue.", 59], "biscuit": ["Yes. 'Biscuit' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'biscuit' but are not 'biscuit' are:\tcookie\tscone\tdoughnut\tbun\nThere are several useful visual features to tell there is 'biscuit' and not similar things in a photo:\tbaked, golden or brown color\tflaky or crumbly texture\tcircular or oval shape\twith or without toppings or fillings (e.g., butter, jam)", 59], "orange hat": ["Yes. 'Orange hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'orange hat' but are not 'orange hat' are:\tbeanie\tcap\thelmet\twig\nThere are several useful visual features to tell there is 'orange hat' and not similar things in a photo:\torange color\tdome-shaped\ttop of the hat is round and tapering down to the brim\tbrim circles around the hat with the front brim often bent upward.", 59], "molding": ["Yes. 'Molding' has a tangible appearance and refers to a decorative strip of material used along the edges of walls and ceilings.\nA few things that are visually similar to 'molding' but are not 'molding' are:\ttrim\tcaulking\tframing\tedging\nThere are several useful visual features to tell there is 'molding' and not similar things in a photo:\telongated strips of material\tdetailing and ornate designs\tcleanly installed against the wall and ceiling junctions", 59], "raspberry": ["Yes. 'Raspberry' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'raspberry' but are not 'raspberry' are:\tstrawberry\tblackberry\tpomegranate\tcranberry\nThere are several useful visual features to tell there is 'raspberry' and not similar things in a photo:\tred or pink color\tround shape\twith small, protruding bumps\tlooks fuzzy or soft", 59], "sort": ["No. 'Sort' is too vague or abstract to have a tangible appearance. It is a verb that describes an action or process rather than a physical object. \nTherefore, there are no things visually similar to 'sort' that are not 'sort'.", 59], "nightstands": ["Yes. 'Nightstands' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'nightstands' but are not 'nightstands' are:\tside tables\tdesks\tchests of drawers\nThere are several useful visual features to tell there is 'nightstands' and not similar things in a photo:\tsmall table size\tlow height (usually level with the top of a mattress)\tdrawer or shelf for storage\tplaced next to a bed", 59], "jean": ["Yes. 'Jean' has a tangible appearance and is a type of fabric used for clothing.\nA few things that are visually similar to 'jean' but are not 'jean' are:\tdenim fabric\tchambray fabric\tcotton fabric\tpolyester fabric\nThere are several useful visual features to tell there is 'jean' and not similar things in a photo:\tbluish or indigo color\thard and rough texture\ttypical jean stitch pattern on pockets and seams", 59], "tourist": ["No. 'Tourist' is too vague or abstract to be distinguished in a photo. It is a term used to describe a person who is traveling for leisure or business purposes. However, there are some visual cues that could suggest that a person is a tourist, such as carrying a camera, wearing comfortable shoes, or holding a map. \nA few things that are visually similar to a person who can be identified as a 'tourist' but are not 'tourist' are:\thiker\tbackpacker\tjournalist\tpilgrim\nUseful visual features for distinguishing 'tourist' from the listed similar things in a photo could include:\tcarrying a camera or selfie stick\twearing typical tourist clothing like a fanny pack or a sun hat\thaving a map or a guidebook in hand\tbeing part of a tour group or taking a group picture with other tourists", 59], "ends": ["No. 'Ends' is too abstract and doesn't have a tangible appearance.", 59], "cat paw": ["Yes. 'Cat paw' has a tangible appearance and is a body part of a cat.\nA few things that are visually similar to 'cat paw' but are not 'cat paw' are:\tdog paw\tbear paw\thuman hand\tclaw\nThere are several useful visual features to tell there is 'cat paw' and not similar things in a photo:\tfurry\tpink paw pads\tflexible toes with retractable claws\telongated shape with sharp nails", 59], "footprint": ["Yes. 'Footprint' has a tangible appearance and is the impression made by the foot on a surface.\nA few things that are visually similar to 'footprint' but are not 'footprint' are:\tshadow\t\t\tdrawing\t\t\tstamp\nThere are several useful visual features to tell there is 'footprint' and not similar things in a photo:\ttoe impressions\theel impression\tsize and shape of the impression\tdirection of the toes and the arch of the foot", 59], "pizza pan": ["Yes. 'Pizza pan' has a tangible appearance and is a type of cooking utensil.\nA few things that are visually similar to 'pizza pan' but are not 'pizza pan' are:\tcake pan\tfrying pan\tsheet pan\troasting pan\nThere are several useful visual features to tell there is 'pizza pan' and not similar things in a photo:\tcircular shape\trelatively shallow\tsides that slope outward\tmetal construction (usually aluminum or steel) with small holes or perforations in the bottom", 59], "football": ["Yes. 'Football' has a tangible appearance and is a type of ball used in a sport.\nA few things that are visually similar to 'football' but are not 'football' are:\tbasketball\tvolleyball\tsoccer ball\trugby ball\nThere are several useful visual features to tell there is 'football' and not similar things in a photo:\toval shape\tpointed ends\tridged texture\tbrown or white color\tlines or markings on the surface.", 59], "median": ["No. 'Median' is too abstract to have a tangible appearance and cannot be visually represented in a photo.\nThere are no things that are visually similar to 'median' as it is a mathematical concept.\nIt is not possible to distinguish 'median' from other things in a photo as it is not visually concrete.", 59], "space bar": ["Yes. 'Space bar' has a tangible appearance and is a physical key on a keyboard.\nA few things that are visually similar to 'space bar' but are not 'space bar' are:\tenter key\ttab key\nThere are several useful visual features to tell there is 'space bar' and not similar things in a photo:\trectangular shape\tlarger size than other keys\twide key with a gap in the middle that represent a space when pressed.", 59], "cliffs": ["Yes. 'Cliffs' has a tangible appearance and refers to a steep rock face or slope.\nA few things that are visually similar to 'cliffs' but are not 'cliffs' are:\tmountains\thills\trock formations\nThere are several useful visual features to tell there are 'cliffs' and not similar things in a photo:\tsteep and vertical face or slope\tmade of rock or stone, not soil or vegetation\tmight have layers or different colors or textures", 59], "vintage": ["No. 'Vintage' is too vague or abstract to be distinguished in a photo. It refers to a style or aesthetic from a particular era, rather than a specific visual appearance. \nHowever, a few things that are sometimes associated with the vintage style include: antique objects, retro clothing, old cars, and outdated technology. \nVisual features that might be helpful in identifying something as vintage could include: muted colors, worn or distressed textures, analog design elements, and a general nostalgia or sentimentality conveyed by the image.", 59], "pump": ["Yes. 'Pump' has a tangible appearance and refers to a mechanical device used for moving fluids or gases.\nA few things that are visually similar to 'pump' but are not 'pump' are:\tfaucet\tbottle\tpipe\tcontainer\nThere are several useful visual features to tell there is 'pump' and not similar things in a photo:\tmechanical component with a motor or handle\tattached hoses, pipes, or cables\tmovements of fluid or gas through the pump\tsuction or pressure mechanism to move the fluid or gas.", 59], "firetruck": ["Yes. 'Firetruck' has a tangible appearance and is a type of emergency vehicle.\nA few things that are visually similar to 'firetruck' but are not 'firetruck' are:\tambulance\tpolice car\ttruck\tvan\nThere are several useful visual features to tell there is 'firetruck' and not similar things in a photo:\tbright red color\textending ladder or cherry picker\those reel or water tank\tflashing lights\tor sirens\tword 'fire' or 'fire department' written on the vehicle", 59], "suits": ["Yes. 'Suits' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'suits' but are not 'suits' are:\tshirts\tpants\tdresses\tblazers\nThere are several useful visual features to tell there is 'suits' and not similar things in a photo:\tjacket with matching trousers\tbusiness/formal attire\ttypically dark or neutral colors\toften worn with a tie or dress shirt", 59], "wedding cake": ["Yes. 'Wedding cake' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'wedding cake' but are not 'wedding cake' are:\tbirthday cake\tcupcake\tpie\tcheesecake\nThere are several useful visual features to tell there is 'wedding cake' and not similar things in a photo:\tmany layers\tof different sizes\tdecorated with flowers, ribbons or other ornamental elements\ttop layer with a bride and groom figurine.", 59], "bottoms": ["Yes. 'Bottoms' has a tangible appearance and refers to the lower part of the human body.\nA few things that are visually similar to 'bottoms' but are not 'bottoms' are:\tchairs\tpillows\ttires\tstools\tballoons\nThere are several useful visual features to tell there is 'bottoms' and not similar things in a photo:\tthe shape of human hips and legs\tfabric or texture of pants or skirts\tthat they are attached to the upper body (in the case of humans) or to a chair/stool (in the case of furniture)", 59], "switches": ["Yes. 'Switches' has a tangible appearance and refers to a mechanical or electrical device used to control the flow of energy or signals.\nA few things that are visually similar to 'switches' but are not 'switches' are:\tbuttons\tknobs\tlevers\tdials\nThere are several useful visual features to tell there is 'switches' and not similar things in a photo:\tusually rectangular or square shape\tvarious sizes and colors\tsome have labels or symbols to indicate their function\tmay have an on/off position.", 59], "traffic cones": ["Yes. 'Traffic cones' has a tangible appearance and is a kind of safety equipment.\nA few things that are visually similar to 'traffic cones' but are not 'traffic cones' are:\tpylons\tcandles\tice cream cones\t\nThere are several useful visual features to distinguish 'traffic cones' from the listed similar things in a photo:\tcone-shaped\tbright orange\tcolorful, reflective stripes\thollow and lightweight\ttop of the cone with a pointed tip for safety purposes.", 59], "decker": ["No. 'Decker' is too vague or abstract to be distinguished in a photo. Could you give me more context or information to better understand what you mean by 'decker'?", 59], "stair": ["Yes. 'Stair' has a tangible appearance and is a structure used for going up or down.\nA few things that are visually similar to 'stair' but are not 'stair' are:\tramp\tslant\tladder\thill\nThere are several useful visual features to tell there is 'stair' and not similar things in a photo:\ta series of steps\tmay have a handrail or banister\tmay have landings which change the direction of the stairs", 59], "fuselage": ["Yes. 'Fuselage' has a tangible appearance and is a part of an aircraft.\nA few things that are visually similar to 'fuselage' but are not 'fuselage' are:\twing\ttail\tengine\tlanding gear\nThere are several useful visual features to tell there is 'fuselage' and not similar things in a photo:\tlong, tube-like structure\tthat contains the cockpit, passenger and cargo areas\tnull", 59], "bay": ["Yes. 'Bay' has a tangible appearance and refers to a body of water or a coastline indentation.\nA few things that are visually similar to 'bay' but are not 'bay' are:\tinlet\tgulf\tcove\triver mouth\tsound\nThere are several useful visual features to distinguish 'bay' from the listed similar things in a photo:\ta body of water forming an indentation along the coastline, typically larger than a cove and smaller than a gulf\twater surrounded by land\ton two or three sides\twide and round or oval-shaped\twaters inside it are usually calmer than the open ocean\tmay have a sandy or rocky beach", 59], "spaghetti": ["Yes. 'Spaghetti' has a tangible appearance and is a type of pasta.\nA few things that are visually similar to 'spaghetti' but are not 'spaghetti' are:\tnoodles\tangel hair pasta\tvermicelli \tfettuccine \nThere are several useful visual features to tell there is 'spaghetti' and not similar things in a photo:\tlong, thin, cylindrical shape\tslightly curved texture when cooked\tbrownish-yellow color\tuse in Italian cuisine, often served with tomato sauce or olive oil", 59], "muffins": ["Yes. 'Muffins' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'muffins' but are not 'muffins' are:\tcupcakes\tdonuts\tcakes\tbagels\nThere are several useful visual features to tell there is 'muffins' and not similar things in a photo:\tindividual portions\tmuffin top\tcrumbly texture or sugar crystals on top\tpaper or foil wrapper on the bottom", 59], "chickens": ["Yes. 'Chickens' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'chickens' but are not 'chickens' are:\tducks\tpigeons\tturkeys\teagles\troosters\nThere are several useful visual features to tell there is 'chickens' and not similar things in a photo:\tbeaked heads\tand wattles\tred combs on top of their heads\tfeathers\tclaws and beaks", 59], "fork table": ["No. 'Fork table' is too vague and abstract. 'Table' would be a more concrete concept.\nA few things that are visually similar to 'table' but are not 'fork table' are:\tcoffee table\tdesk\tdining table\tpicnic table\nThere are several useful visual features to tell there is 'fork table' and not similar things in a photo:\tNone since 'fork table' is not a common or definite term.", 58], "square table": ["Yes. 'Square table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'square table' but are not 'square table' are:\trectangular table\tcuboid small cabinet\nThere are several useful visual features to tell there is 'square table' and not similar things in a photo:\tfour-sided table\troughly equal sides_formal structure_top placed on legs and not wall-mounted", 58], "burgundy": ["Yes. 'Burgundy' has a tangible appearance and refers to a specific hue of red or purple.\nA few things that are visually similar to 'burgundy' but are not 'burgundy' are:\tmaroon\tcrimson\twine\tred\tpurple\nThere are several useful visual features to tell there is 'burgundy' and not similar things in a photo:\tdeep red-purple hue\tdark and muted color, not bright or neon-like", 58], "mens": ["No. 'Mens' is too vague or abstract to be distinguished in a photo. Please provide more context or specify the intended meaning of 'mens'.", 58], "selection": ["No. 'Selection' is too vague or abstract to be visually distinguished in a photo. It is a process of choosing or sorting things based on certain criteria. It does not have a material or tangible appearance.", 58], "grafitti": ["Yes. 'Graffiti' has a tangible appearance and refers to a specific form of art.\nA few things that are visually similar to 'graffiti' but are not 'graffiti' are:\tmural\tpainting\tstencil\tposter\nThere are several useful visual features to tell there is 'graffiti' and not similar things in a photo:\tlarge and intricate lettering\tor images\tspray paint or markers\tbold colors\tdistinctive style\tor symbols\tpainted on a public or private property without permission", 58], "bracelet woman": ["No. 'Bracelet woman' is too vague or abstract to be distinguished in a photo. It is unclear what is meant by 'bracelet woman' as it could refer to a woman wearing a bracelet or a bracelet designed specifically for women.\n", 58], "hotdog bun": ["Yes. 'Hotdog bun' has a tangible appearance and is a type of bread roll.\nA few things that are visually similar to 'hotdog bun' but are not 'hotdog bun' are:\tbaguette\tcroissant\tscones\troll\nThere are several useful visual features to tell there is 'hotdog bun' and not similar things in a photo:\tlong and horizontal shape\ttwo halves joined together in the middle\tcrispy crust on the outside\tsoft and fluffy texture on the inside.", 58], "policemen": ["Yes. 'Policemen' has a tangible appearance and refers to individuals who work in law enforcement.\nA few things that are visually similar to 'policemen' but are not 'policemen' are:\tsecurity guards\tmilitary personnel\tfirefighters\nThere are several useful visual features to tell there is 'policemen' and not similar things in a photo:\tuniforms with badges or insignia\thats or helmets\tbelts with tools, such as handcuffs or guns\tpolice cars or motorcycles\tpolice dogs and their handlers", 58], "soccer field": ["Yes. 'Soccer field' has a tangible appearance and is a specific type of sports field.\nA few things that are visually similar to 'soccer field' but are not 'soccer field' are:\tbaseball field\tfootball field\tlacrosse field\tfield for track and field events\nThere are several useful visual features to tell there is 'soccer field' and not similar things in a photo:\trectangular shape\t2 goals at opposite ends\tgrass or artificial turf surface\twith or without boundary lines and penalty areas with spots\tto have corner flags and center circle", 58], "wicker chair": ["Yes. 'Wicker chair' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wicker chair' but are not 'wicker chair' are:\twooden chair\tmetal chair\tplastic chair\nThere are several useful visual features to tell there is 'wicker chair' and not similar things in a photo:\twoven pattern\tmade of cane, willow or rattan material\tbrown or natural color \thollow body", 58], "desktop computer": ["Yes. 'Desktop computer' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'desktop computer' but are not 'desktop computer' are:\tlaptops\ttablets\tsmartphones\tmodems\trouters\nThere are several useful visual features to tell there is 'desktop computer' and not similar things in a photo:\tmulti-part system including a tower, a monitor, a keyboard, and a mouse\tscreen size larger than a laptop\tstandalone tower with visible ports and buttons\tseparate keyboard and mouse accessories", 58], "bins": ["Yes. 'Bins' has a tangible appearance and refers to containers used for storing or disposing of waste.\nA few things that are visually similar to 'bins' but are not 'bins' are:\tcrates\tdrawers\tshelves\tbuckets\nThere are several useful visual features to tell there are 'bins' and not similar things in a photo:\topen tops or lids\thandles\torifices for disposing of waste\toften made of plastic or metal.", 58], "kitchen floor": ["Yes. 'Kitchen floor' has a tangible appearance and is a kind of flooring.\nA few things that are visually similar to 'kitchen floor' but are not 'kitchen floor' are:\tbathroom floor\tliving room floor\tbedroom floor\toutdoor floor surfaces\nThere are several useful visual features to tell there is 'kitchen floor' and not similar things in a photo:\ttile or linoleum flooring\ttypically located in a kitchen area\tmay have a pattern or design that is specific to kitchens, such as fruit or vegetable motif\tmay have stains or spills from cooking or food preparation", 58], "camper": ["Yes. 'Camper' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'camper' but are not 'camper' are:\tcaravan\ttrailer\tvan\tmotorhome\nThere are several useful visual features to tell there is 'camper' and not similar things in a photo:\tbedroom\ton-board kitchen\tand bathroom\tliving area\twith table and chairs\traised roof or popup roof\twith windows and doors", 58], "chord": ["No. 'Chord' is too abstract to have a tangible appearance or be distinguished visually in a photo.", 58], "metal sign post": ["Yes. 'Metal sign post' has a tangible appearance and refers to a type of post used to hold signs.\nA few things that are visually similar to 'metal sign post' but are not 'metal sign post' are:\tfence post\tstreet lamp\tpost box\tpower pole\nThere are several useful visual features to tell there is 'metal sign post' and not similar things in a photo:\tstraight and vertical\tshiny metal material\tsign attached to the post\tbase buried in the ground\tor attached to a flat surface like a wall.", 58], "jumper": ["Yes. 'Jumper' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'jumper' but are not 'jumper' are:\tsweater\thoodie\tpullover\tcardigan\nThere are several useful visual features to tell there is 'jumper' and not similar things in a photo:\tlarge, loose-fitting garment for the upper body\tusually without buttons or zippers\tsleeveless or with sleeves\tcan be made of different fabrics and patterns.", 58], "kitchen towel": ["Yes. 'Kitchen towel' has a tangible appearance and is a type of cloth used to clean or dry dishes and kitchen surfaces.\nA few things that are visually similar to 'kitchen towel' but are not 'kitchen towel' are:\tnapkins\tdishcloth\tbath towel\thand towel\nThere are several useful visual features to tell there is 'kitchen towel' and not similar things in a photo:\t\nrectangular or square shape\nsmaller than a bath towel and larger than a hand towel\noften made of cotton or microfiber\nmay have a pattern, color, or embroidery\nused in the kitchen for cleaning and drying dishes and surfaces", 58], "beige carpet": ["Yes. 'Beige carpet' has a tangible appearance and is a specific type of flooring material.\nA few things that are visually similar to 'beige carpet' but are not 'beige carpet' are:\twooden floor\ttile floor\tlinoleum floor\t\nThere are several useful visual features to tell there is 'beige carpet' and not similar things in a photo:\tbeige color\tsoft texture\tfibers tightly woven or tufted together\tlarge continuous surface area lying flat on the floor", 58], "covers": ["No. 'Covers' is too vague or abstract to be distinguished in a photo. It could refer to book covers, bed covers, seat covers, or other types of covers, each with distinct visual features. \n\nWithout additional context, it is difficult to identify things that are visually similar to 'covers' but are not 'covers'.", 58], "jungle": ["Yes. 'Jungle' has a tangible appearance and refers to a dense forest in a tropical region.\nA few things that are visually similar to 'jungle' but are not 'jungle' are:\ttemperate rainforest\tbushland\twetland\tswamp\nThere are several useful visual features to tell there is 'jungle' and not similar things in a photo:\tdense and abundant trees and vegetation\tlush and green foliage\thigh humidity levels and warmth\tvariety of wildlife and insects such as monkeys, parrots, tigers, or snakes", 58], "night": ["No. 'Night' is too vague or abstract to be distinguished in a photo.", 58], "wooden dock": ["Yes. 'Wooden dock' has a tangible appearance and is a type of water structure.\nA few things that are visually similar to 'wooden dock' but are not 'wooden dock' are:\tpier\tjetty\tboardwalk\tbridge\nThere are several useful visual features to tell there is 'wooden dock' and not similar things in a photo:\tconsists of wooden planks or boards\textends into a body of water\tpylons or posts supporting its structure\toften has boats or watercraft tied up to it\thas ladders or steps at its end for people to access the water", 58], "stockings": ["Yes. 'Stockings' has a tangible appearance and refers to a type of clothing worn on the foot and leg.\nA few things that are visually similar to 'stockings' but are not 'stockings' are:\tsocks\tpantyhose\ttights\tleggings\nThere are several useful visual features to tell there is 'stockings' and not similar things in a photo:\tlong and stretchy shape\thanging from a fireplace or a mantle\tbright and festive colors or patterns\tdecorative details such as bows or ribbons at the top", 58], "m": ["No. 'm' is too vague or abstract to have a tangible appearance.", 58], "drain pipe": ["Yes. 'Drain pipe' has a tangible appearance and is a type of pipe used for drainage.\nA few things that are visually similar to 'drain pipe' but are not 'drain pipe' are:\twater pipes\tgas pipes\tsewage pipes\tchimney pipes\nThere are several useful visual features to tell there is 'drain pipe' and not similar things in a photo:\trectangular or circular cross-sections\thanging vertically or horizontally\tconnected to a wall or a roof\ttypically made of PVC or metal\tbottom section is open to allow water flow", 58], "fire engine": ["Yes. 'Fire engine' has a tangible appearance and is a type of vehicle used by firefighters.\nA few things that are visually similar to 'fire engine' but are not 'fire engine' are:\tambulance\tpolice car\ttruck\nThere are several useful visual features to tell there is 'fire engine' and not similar things in a photo:\tred color\tlarge size\textendable ladders and hoses\tflashy lights and sirens\twritten FIRE DEPARTMENT or FIRE on the side", 58], "silver chain link fence": ["Yes. 'Silver chain link fence' has a tangible appearance and is a type of fence.\nA few things that are visually similar to 'silver chain link fence' but are not 'silver chain link fence' are:\trazor wire fence\tbarbed wire fence\tiron fence\twith the same wire pattern\nThere are several useful visual features to tell there is 'silver chain link fence' and not similar things in a photo:\tsquare-shaped holes\tthin steel wire\tsilver or metallic color", 58], "throw pillows": ["Yes. 'Throw pillows' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'throw pillows' but are not 'throw pillows' are:\tbed pillows\tstuffed animals\tpoufs\t\nThere are several useful visual features to tell there is 'throw pillows' and not similar things in a photo:\tsmaller in size than a bed pillow\tdecorative patterns or designs\ton a couch, chair or bed as an accent piece", 58], "wall tile": ["Yes. 'Wall tile' has a tangible appearance and is a kind of building material.\nA few things that are visually similar to 'wall tile' but are not 'wall tile' are:\tbrick\tmosaic\tglass\twindow pane\nThere are several useful visual features to tell there is 'wall tile' and not similar things in a photo:\trectangular or square shape\ttile pattern or design\tceramic or porcelain material\tglazed or matte surface\tlevel with the wall surface", 58], "busses": ["Yes. 'Busses' has a tangible appearance and refers to a type of vehicle used for transportation.\nA few things that are visually similar to 'busses' but are not 'busses' are:\ttrucks\tvans\ttractors\ttrailers\nThere are several useful visual features to tell there is 'busses' and not similar things in a photo:\trectangular shape\twith windows and doors\ton wheels\tgenerally painted in yellow, red, or blue masses of people standing or sitting inside", 58], "forearm": ["Yes. 'Forearm' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'forearm' but are not 'forearm' are:\tupper arm\twrist\thand\telbow\nThere are several useful visual features to tell there is a 'forearm' and not similar things in a photo:\tthe part of the arm between the elbow and the wrist\thas two bones (radius and ulna)\thas muscles and tendons that move the hand and fingers\tcan be hairy or smooth depending on the person's age and gender", 58], "leather seat": ["Yes. 'Leather seat' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'leather seat' but are not 'leather seat' are:\tplastic seat\tfabric seat\twooden seat\tmetal seat\nThere are several useful visual features to tell there is 'leather seat' and not similar things in a photo:\tsmooth and matte finish\tdark brown, black or other earthy tones\tshiny or reflective spots are visible\tif part of furniture, the frame is usually made of wood or metal", 58], "tree limbs": ["Yes. 'Tree limbs' has a tangible appearance and is a part of a tree.\nA few things that are visually similar to 'tree limbs' but are not 'tree limbs' are:\tbranches\tvines\troots\tfingers\ttrunks\nThere are several useful visual features to tell there are 'tree limbs' and not similar things in a photo:\tgrow from a tree trunk\tor main branch\thave smaller branches and leaves attached\tto be various sizes and thicknesses\toften covered in bark, buds, or leaves.", 58], "metal object": ["No. 'Metal object' is too vague or abstract to be distinguished in a photo. It can include a variety of objects made from metal such as tools, utensils, and equipment, and each of these objects has their own unique features and characteristics that can be visually distinguished. \n\nTherefore, there are no things that are visually similar to 'metal object' but not 'metal object'.\n\nUseful visual features for distinguishing a metal object from another object will depend on the specific object. For example, useful visual features for identifying a metal tool may include its shape, size, and function, while useful visual features for identifying a metal utensil may include its shape, size, and use in the kitchen.", 58], "shadow snow": ["No. 'Shadow snow' is too vague or abstract to be distinguished in a photo.", 58], "grey cloudy sky": ["Yes. 'Grey cloudy sky' has a tangible appearance and is a natural phenomenon.\nA few things that are visually similar to 'grey cloudy sky' but are not 'grey cloudy sky' are:\tfog\tsmoke\tdust storm\nThere are several useful visual features to tell there is 'grey cloudy sky' and not similar things in a photo:\tclouds covering most of the sky\tdark, muted tonality\tindistinct edges and shapes of clouds\tno visible source of smoke or dust appearing in the image.", 58], "sections": ["No. 'Sections' is too vague or abstract to be distinguished in a photo. It depends on the context it is being used in.", 58], "automobile": ["Yes, 'automobile' is a visually concrete concept with a tangible appearance as a vehicle.\nA few things that are visually similar to 'automobile' but are not 'automobile' are:\ttrucks\tbuses\tbicycles\tmotorcycles\nThere are several useful visual features that distinguish 'automobile' from the listed similar things in a photo:\tits shape and body\tits number of wheels\tits size and proportions\tits headlights\tits license plates", 58], "pottery": ["Yes. 'Pottery' has a tangible appearance and is a type of ceramic object.\nA few things that are visually similar to 'pottery' but are not 'pottery' are:\tglassware\tchina\tporcelain\tceramic tiles\nThere are several useful visual features to tell there is 'pottery' and not similar things in a photo:\tclay or earthenware material\thandcrafted\tor hand-painted\tdurable and robust construction\tmay have unique patterns or designs.", 58], "tram": ["Yes. 'Tram' has a tangible appearance and is a type of transport vehicle.\nA few things that are visually similar to 'tram' but are not 'tram' are:\tbus\tsubway/train\ttrolley\tcable car\nThere are several useful visual features to tell there is 'tram' and not similar things in a photo:\ttrack-based vehicle\twith overhead electrical wires or poles\tfor passenger transport\thigher capacity than buses or trolleys\tboxy or rectangular shape", 58], "ivory tusk": ["Yes. 'Ivory tusk' has a tangible appearance as it refers to the long, curved teeth of certain animals like elephants or walruses. \nA few things that are visually similar to 'ivory tusk' but are not 'ivory tusk' are: bone, antler, plastic, wood.\nSome visual features for distinguishing 'ivory tusk' from the listed similar things in a photo are:\n- Smooth, polished texture\n- Off-white or creamy color\n- Long and curved shape\n- Visible ring patterns or natural ridges.", 58], "pages": ["Yes. 'Pages' has a tangible appearance and refers to the sheets of a book, magazine, or other printed material.\nA few things that are visually similar to 'pages' but are not 'pages' are:\tpaintings\tphotographs\twalls\tfolders\nThere are several useful visual features to tell there are 'pages' and not similar things in a photo:\tthin and rectangular\tsheets with printed or written information\tbound or stapled together to form a book or magazine.", 58], "broccoli florets": ["Yes. 'Broccoli florets' have a tangible appearance and are a type of vegetable.\nA few things that are visually similar to 'broccoli florets' but are not 'broccoli florets' are: cauliflower, asparagus, kale, cabbage, Brussel sprouts.\nThere are several useful visual features that can help distinguish 'broccoli florets' from the listed similar things in a photo: \tsmall, identifiable head shape \tGreen color \tBranching florets \tAlternate leaf pattern \tFirm texture when cooked.", 57], "hooks": ["Yes. 'Hooks' have a tangible appearance and are used for hanging or holding things.\nA few things that are visually similar to 'hooks' but are not 'hooks' are:\tknobs\tpins\tclips\thangers\nThere are several useful visual features to tell there is 'hooks' and not similar things in a photo:\tj-shaped or s-shaped\thave a point or barb at the end\tmade of metal or plastic\tattached to a wall or surface\tfor hanging or holding objects", 57], "smoke stack": ["Yes. 'Smoke stack' has a tangible appearance and is a tall chimney-like structure used to release smoke or gases from factories or buildings.\nA few things that are visually similar to 'smoke stack' but are not 'smoke stack' are:\twater tower\tlighthouse\tpower line tower\tobservation tower\nThere are several useful visual features to tell there is 'smoke stack' and not similar things in a photo:\ttall chimney-like structure\tdark or sooty appearance\treleasing smoke or gases from the top\tof a factory or building", 57], "napkin table": ["No. 'Napkin table' is too vague or abstract to be distinguished in a photo. It is not a common term and does not have a tangible appearance.", 57], "shadow person": ["No. 'Shadow person' is too vague or abstract to be distinguished in a photo. It refers to a supposedly supernatural entity that appears as a human-shaped shadow or silhouette.\nA few things that are visually similar to 'shadow person' but are not 'shadow person' are:\treal people\treflections\tshadows that are not humanoid\nThere are no useful visual features for distinguishing 'shadow person' from the listed similar things in a photo, as it is a supernatural or paranormal concept that does not have a concrete or verifiable appearance.", 57], "caboose": ["Yes. 'Caboose' has a tangible appearance and is a type of railroad car.\nA few things that are visually similar to 'caboose' but are not 'caboose' are:\tfreight car\tpassenger car\tlocomotive\nThere are several useful visual features to tell there is 'caboose' and not similar things in a photo:\tsmaller size compared to other railroad cars\tcupola or observation deck on top\tof the train\tdifferent shape compared to other cars\toften brightly painted or decorated\tusing it as an office or living quarters on a train", 57], "reigns": ["No. 'Reigns' is too abstract to have a tangible appearance and therefore cannot be visually distinguished in a photo.", 57], "wedge": ["Yes. 'Wedge' has a tangible appearance and is a particular shape.\nA few things that are visually similar to 'wedge' but are not 'wedge' are:\ttriangle\tpizza slice\tcheese wedge\t\nThere are several useful visual features to tell there is 'wedge' and not similar things in a photo:\ttriangle-shaped\ttapered or narrowing at one end\tthicker on one end than the other\tcan be used to facilitate splitting or lifting something up", 57], "brown teddy": ["Yes, 'brown teddy' has a tangible appearance and refers to a stuffed bear toy that is brown in color.\nA few things that are visually similar to 'brown teddy' but are not 'brown teddy' are:\tblack teddy\tpolar bear\tbrown dog plush\tbrown cat plush\nThere are several useful visual features to tell there is 'brown teddy' and not similar things in a photo:\tbrown-colored fur\tteddy bear-like appearance\tstanding on two legs or sitting on its bottom\thaving a snout, two ears, two eyes, and a nose\thaving a bow or accessory around its neck", 57], "silver helmet": ["Yes. 'Silver helmet' has a tangible appearance and is a type of protective headgear.\nA few things that are visually similar to 'silver helmet' but are not 'silver helmet' are:\tmotorcycle helmet\tbicycle helmet\tsports helmet\that\nThere are several useful visual features to tell there is 'silver helmet' and not similar things in a photo:\tsilver or metallic color\thard and protective material\tspecifications for military or space use\tchin strap or other fastenings\tfor use in hazardous situations or environments", 57], "cake plate": ["Yes. 'Cake plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'cake plate' but are not 'cake plate' are:\tserving platter\tdinner plate\ttray\nThere are several useful visual features to tell there is 'cake plate' and not similar things in a photo:\tround or square shape\tflat surface with a raised edge\televated base to lift the cake off the surface\tdecorative design or pattern", 57], "th": ["No. 'th' is too abstract to have a tangible appearance, it is a combination of two letters in the English alphabet that create a sound.\nThere are no things that are visually similar to 'th' but are not 'th'.\nNo useful visual features can distinguish 'th' in a photo, as it is a sound formed by the combination of two letters, not a visual object.", 57], "control knobs": ["Yes. 'Control knobs' have a tangible appearance and are physical components of electronic or mechanical devices used for controlling various functions.\nA few things that are visually similar to 'control knobs' but are not 'control knobs' are:\tbuttons\tswitches\tdials\nThere are several useful visual features to tell there is 'control knobs' and not similar things in a photo:\tround or cylindrical-shaped\tprotruding from a device\tusually marked with numbers, lines, or symbols\tfor adjusting and controlling various settings (such as volume, temperature, brightness, etc.)", 57], "metal table": ["Yes. 'Metal table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'metal table' but are not 'metal table' are:\twooden table\tplastic table\tglass table\tfolding table\nThere are several useful visual features to tell there is 'metal table' and not similar things in a photo:\tmetallic surface\tsleek appearance\tvisible bolts or welding\tstraight and angular lines", 57], "foamy": ["Yes. 'Foamy' has a visually tangible appearance and refers to a bubbly or frothy liquid.\nA few things that are visually similar to 'foamy' but are not 'foamy' are:\tsuds\tbubbles\tclouds\tfrost\twhipped cream\nThere are several useful visual features to tell there is 'foamy' and not similar things in a photo:\twhite or light-colored\tlots of bubbles or froth\tfloating or on top of liquid\tsubstantial texture or thickness", 57], "cat ears": ["Yes. 'Cat ears' has a tangible appearance and is a part of a cat's body.\nA few things that are visually similar to 'cat ears' but are not 'cat ears' are:\trabbit ears\tcostume ears\thairbands\twith attached animal ears\nThere are several useful visual features to tell there is 'cat ears' and not similar things in a photo:\tpointed shape\tcovered in fur\tattached to the top of a feline head", 57], "video camera": ["Yes. 'Video camera' has a tangible appearance and is a type of recording device.\nA few things that are visually similar to 'video camera' but are not 'video camera' are:\tphoto camera\twebcam\tcamcorder\tsmartphone\nThere are several useful visual features to tell there is 'video camera' and not similar things in a photo:\trectangular shape\twith a lens or multiple lenses\tviewfinder\ton/off button\trecord button\tLED lights\tfor filming and/or stills", 57], "wildflowers": ["Yes. 'Wildflowers' has a tangible appearance and refers to flowers that grow in the wild.\nA few things that are visually similar to 'wildflowers' but are not 'wildflowers' are:\tgarden flowers\tweeds\nThere are several useful visual features to tell there is 'wildflowers' and not similar things in a photo:\tdifferent types or species of flowers\tgrowing naturally in a field or meadow\tvariety of colors and sizes compared to a more uniform appearance in a garden setting", 57], "leather chair": ["Yes. 'Leather chair' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'leather chair' but are not 'leather chair' are:\tleather sofa\tleather recliners\tleather ottomans\nThere are several useful visual features to tell there is 'leather chair' and not similar things in a photo:\tchair-sized piece of furniture\tseat and backrest\tpadded or cushioned seating\tand made of leather material.", 57], "orange wall": ["Yes. 'Orange wall' has a tangible appearance.\nA few things that are visually similar to 'orange wall' but are not 'orange wall' are:\tyellow wall\tred wall\tpainted bricks\tcurtains\nThere are several useful visual features to tell there is an 'orange wall' and not similar things in a photo:\tsolid and flat surface\torange color\ttextured or smooth surface\twallpaper or paint texture\tlines, borders or frames\tthat can indicate a wall structure.", 57], "sunny sky": ["Yes. 'Sunny sky' has a tangible appearance and refers to a sky that is bright and without clouds.\nA few things that are visually similar to 'sunny sky' but are not 'sunny sky' are:\tsunset\tsunrise\tclear blue sky\twith clouds\trainbow\nThere are several useful visual features to tell there is 'sunny sky' and not similar things in a photo:\tbright and vibrant color without any blurriness\tor haziness\tno visible clouds or fog\tbright sunlight or rays on the ground\tor on objects in the photo.", 57], "leather bag": ["Yes. 'Leather bag' has a tangible appearance and is an object used for carrying things.\nA few things that are visually similar to 'leather bag' but are not 'leather bag' are:\tpurse\tbriefcase\tbackpack\tsatchel\nThere are several useful visual features to tell there is 'leather bag' and not similar things in a photo:\tsoft and smooth material\twith stitches\tor seams\tshoulder strap or handle\tmay have metal zippers or buckles\tcapacious compartments or pockets", 57], "orange kite": ["Yes. 'Orange kite' has a tangible appearance and is a type of flying toy.\n\nA few things that are visually similar to 'orange kite' but are not 'orange kite' are:\n\n- Other types and colors of kites\n- Balloons or inflatable toys\n- Flying discs or frisbees\n\nUseful visual features for distinguishing 'orange kite' from the listed similar things in a photo are:\n\n- Triangular shape with a tail\n- Made of lightweight material like paper or fabric\n- Attached to a string or line\n- Typically flown in the air", 57], "light fixtures": ["Yes. 'Light fixtures' has a tangible appearance and refers to a type of object used for lighting a room or space.\nA few things that are visually similar to 'light fixtures' but are not 'light fixtures' are:\tcandles\tlamps\tchandeliers\tsconces\nThere are several useful visual features to tell there is 'light fixtures' and not similar things in a photo:\tbulbs or other lighting source attached to the fixture\tmounted to the ceiling or wall\tpotentially having a shade or covering metal or plastic frame or base to hold the lighting source in place.", 57], "chrome faucet": ["Yes. 'Chrome faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'chrome faucet' but are not 'chrome faucet' are:\tbrushed nickel faucet\tstainless steel faucet\tchrome showerhead\tchrome soap dispenser\nThere are several useful visual features to tell there is 'chrome faucet' and not similar things in a photo:\tchrome finish\ton/off handles\tspout for water flow\tsingle or double handled faucet\tmounted on a sink or countertop", 57], "grey carpet": ["Yes. 'Grey carpet' has a tangible appearance and is a kind of flooring.\nA few things that are visually similar to 'grey carpet' but are not 'grey carpet' are:\twooden floor\ttile floor\trug\nThere are several useful visual features to tell there is 'grey carpet' and not similar things in a photo:\tsoft texture\tuniform color and texture\tcovering a large area\tflat surface without individual pieces", 57], "bud": ["Yes. 'Bud' has a tangible appearance and is a stage in the growth of a plant.\nA few things that are visually similar to 'bud' but are not 'bud' are:\tflower\tpetal\tleaf\tthorn\nThere are several useful visual features to tell there is 'bud' and not similar things in a photo:\tundeveloped or partially developed protrusion on a stem\tusually green or brown in color\tclosed or partially closed shape\trounded or pointed tip.", 57], "articles": ["No. 'Articles' is too vague or abstract to be distinguished in a photo.", 57], "plastic box": ["Yes. 'Plastic box' has a tangible appearance and is a container made of plastic.\nA few things that are visually similar to 'plastic box' but are not 'plastic box' are:\tcardboard box\tmetal container\twooden box\tbasket\nThere are several useful visual features to tell there is 'plastic box' and not similar things in a photo:\tclear or translucent plastic material\tlid with snaps or locks\thandles on the sides or top\tsquare or rectangular shape\tsmooth or ridged exterior surface", 57], "parasol": ["Yes. 'Parasol' has a tangible appearance and is a type of sunshade. \nA few things that are visually similar to 'parasol' but are not 'parasol' are:\tumbrella\tcanopy\ttent\tawning\nThere are several useful visual features to tell there is 'parasol' and not similar things in a photo:\tdecorative\tpaper or fabric material\tused for sun protection (not from rain)\tlack of a pointed or spiky end at the top\thandle or pole at the bottom to hold it up.", 57], "boy skateboard": ["Yes. 'Boy skateboard' has a tangible appearance and is a specific object being used by a person.\nA few things that are visually similar to 'boy skateboard' but are not 'boy skateboard' are:\tgirl skateboard\tlongboard\tscooter\tbike\troller skates\nThere are several useful visual features to tell there is 'boy skateboard' and not similar things in a photo:\tdeck with grip tape\tfour wheels\ttruck with bushings and kingpins\tboy riding or holding the board\thelmet or protective gear in use", 57], "granite": ["Yes. 'Granite' has a tangible appearance and is a type of rock.\nA few things that are visually similar to 'granite' but are not 'granite' are:\tmarble\tlimestone\tsandstone\tquartz\nThere are several useful visual features to tell there is 'granite' and not similar things in a photo:\tcoarse-grained texture\tspeckled or mottled appearance\tvariety of colors, including white, gray, pink, and black\tnatural and rugged surface", 57], "winter hat": ["Yes. 'Winter hat' has a tangible appearance and its purpose is to keep your head warm in cold weather.\nA few things that are visually similar to 'winter hat' but are not 'winter hat' are:\tcap\tbonnet\thelmet\tbeanie\nThere are several useful visual features to tell there is 'winter hat' and not similar things in a photo:\tthick, warm material\tcovering your ears\tcloser fitting\ttoque style with or without a pompom, or a flap over the forehead.", 57], "tail feather": ["Yes. 'Tail feather' has a tangible appearance and refers to a specific type of feather.\nA few things that are visually similar to 'tail feather' but are not 'tail feather' are:\tbody feather\tcontour feather\tquill feather\nThere are several useful visual features to tell there is 'tail feather' and not similar things in a photo:\tlong and narrow shape\thard and smooth surface\tbrightly colored or patterned\tmay have distinctive markings or tips\tthat can be seen on the rear end of a bird.", 57], "bicycle tire": ["Yes. 'Bicycle tire' has a tangible appearance and is a part of a bicycle.\nA few things that are visually similar to 'bicycle tire' but are not 'bicycle tire' are:\tmotorcycle tire\tcar tire\ttrolley wheel\nThere are several useful visual features to tell there is 'bicycle tire' and not similar things in a photo:\tnarrow and thin\trubber texture\twithin a metal frame or rim\ttread pattern designed for bicycles", 57], "concrete bench": ["Yes. 'Concrete bench' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'concrete bench' but are not 'concrete bench' are:\tstone bench\twooden bench\tmetal bench\tpark picnic table\nThere are several useful visual features to tell there is 'concrete bench' and not similar things in a photo:\tgrey, solid appearance\tunusual shapes or decorations\trough texture\twhere it is positioned in relation to other structures (e.g. placed in a park or on a sidewalk)", 57], "grease": ["Yes. 'Grease' has a tangible appearance and is a type of oily substance.\nA few things that are visually similar to 'grease' but are not 'grease' are: oil lotion honey syrup\nThere are several useful visual features to tell there is 'grease' and not similar things in a photo:\tthick\tlubricating substance\toily feeling to touch\tdark or yellowish color\tsticking to surfaces like metal or plastic \tlow viscosity \tmay have bits of food or grime mixed in", 57], "clock roman numerals": ["Yes. 'Clock roman numerals' has a tangible appearance and refers to the way the hours are labeled in a clock with roman numerals.\nA few things that are visually similar to 'clock roman numerals' but are not 'clock roman numerals' are:\tcalendar\twith roman numerals\ttattoo\twith roman numerals\tstatue\tbase with roman numerals\nThere are several useful visual features to tell there are 'clock roman numerals' and not similar things in a photo:\nlocated in a circular pattern,\tmarkings I to XII,\torder of the numbers is fixed,\toften metallic gold, black, or white.", 57], "mountain side": ["Yes. 'Mountain side' has a tangible appearance and refers to the sloping part of a mountain.\nA few things that are visually similar to 'mountain side' but are not 'mountain side' are:\thillsides\tcliffs\tcanyon walls\nThere are several useful visual features to tell there is 'mountain side' and not similar things in a photo:\ttowering height\trocky surface\tirregular terrain\twith a view of a valley or gorge\tsnow or ice-covered\ttopographical layers or striations", 57], "terminal": ["Yes. 'Terminal' has a tangible appearance and is a building or an area in an airport, bus, or train station.\nA few things that are visually similar to 'terminal' but are not 'terminal' are:\toffice buildings\tshopping malls\ttrain stations\tbuses or coach stations\nThere are several useful visual features to tell there is 'terminal' and not similar things in a photo:\tsigns and displays related to transportation\tboarding gates and seating areas\tluggage carts and conveyor belts\tticket or passport checkpoints\tor the presence of a runway, airplanes, or other modes of transportation", 57], "goal": ["No. 'Goal' is too vague or abstract to be distinguished in a photo.", 56], "barren": ["No. 'Barren' is too vague or abstract to be distinguished visually in a photo. \n\nHowever, if we interpret the term to mean 'an area without vegetation or plant growth', then: \n\nA few things that are visually similar to 'barren' but are not 'barren' are:\tdesert\trocky terrain\tsnow-covered land\nThere are several useful visual features to tell there is 'barren' and not similar things in a photo:\tbrown or grey color\tlack of vegetation or plant life\tlack of structures or buildings", 56], "growth": ["No. 'Growth' is too abstract to be illustrated in a photo. \n\nHowever, a few things that are visually similar to 'growth' but are not 'growth' are:\tmachines or equipment expanding in size or quantity\tbuildings under construction. \nThere are no useful visual features to distinguish 'growth' from these things because they do not represent the same abstract concept.", 56], "sandy ground": ["Yes. 'Sandy ground' has a tangible appearance and is a kind of surface.\nA few things that are visually similar to 'sandy ground' but are not 'sandy ground' are:\tmud\tdesert\tstone\tpavement\nThere are several useful visual features to tell there is 'sandy ground' and not similar things in a photo:\tloose, granular texture\tuneven surface\tsmall rocks or shells may be present\tlight color (usually yellow or beige)", 56], "cruise ship": ["Yes. 'Cruise ship' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'cruise ship' but are not 'cruise ship' are:\tshipping container\tferries\tyachts\tlarge cargo vessels\nThere are several useful visual features to tell there is 'cruise ship' and not similar things in a photo:\tmulti-story superstructure\ton-deck pools or lounge areas\tlarge number of windows\tprominent smokestacks\tor a written name on the side of the ship.", 56], "wicker": ["Yes. 'Wicker' has a tangible appearance and is a kind of weaving using natural materials like rattan, willow, or bamboo.\nA few things that are visually similar to 'wicker' but are not 'wicker' are:\tplastic woven furniture\twire mesh baskets\twoven fabric\nThere are several useful visual features to tell there is 'wicker' and not similar things in a photo:\tnatural materials like rattan, willow, or bamboo\tbasket-like weave pattern\ttexture of the material\tbrownish color with visible fibers", 56], "planters": ["Yes. 'Planters' has a tangible appearance and refers to a container used for plants.\nA few things that are visually similar to 'planters' but are not 'planters' are:\tbaskets\tvases\tbowls\n\nThere are several useful visual features to tell there is 'planters' and not similar things in a photo:\ttapered or cylindrical shape\tbottom drainage holes\tmade of ceramic, plastic, or terracotta material\tcontaining soil or a plant\tpresenting the plant visibly", 56], "umpires": ["Yes. 'Umpires' has a tangible appearance and is a person who supervises and makes decisions in sports games.\nA few things that are visually similar to 'umpires' but are not 'umpires' are:\tcoaches\tplayers\treferees\tspectators\nThere are several useful visual features to tell there is 'umpires' and not similar things in a photo:\twearing specific apparel, such as a black and white striped shirt or a baseball cap\tcarrying sports equipment, such as a whistle or a baseball bat\tstanding in close proximity to the game\tplay-making gestures, such as signaling a strike or a foul\tcall games and enforce rules, rather than playing the game.", 56], "taxis": ["Yes. 'Taxis' has a tangible appearance and is a type of vehicle used for transportation.\nA few things that are visually similar to 'taxis' but are not 'taxis' are:\tcar\tbus\tlimousine\tuber or lift driver car\nThere are several useful visual features to tell there is 'taxis' and not similar things in a photo:\tyellow color (typical for New York taxis) 'taxi' sign on the roof\ttaxi company logos and phone numbers on the doors\ttaxi meter inside the car", 56], "tights": ["Yes. 'Tights' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tights' but are not 'tights' are:\tpantyhose\tleggings\tstockings\tbody paint\nThere are several useful visual features to tell there is 'tights' and not similar things in a photo:\ttight-fitting\tfootless or with feet\tsheer or opaque\tmade of nylon, cotton, or wool", 56], "car window": ["Yes. 'Car window' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'car window' but are not 'car window' are:\tmirror\tscreen\tdoor\twall\nThere are several useful visual features to tell there is 'car window' and not similar things in a photo:\ttransparent or translucent surface\tattached to a car frame\tcan be rolled up and down\treflects surrounding scenery in a specific way\thas water droplets or streaks when it's wet", 56], "gray jacket": ["Yes. 'Gray jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'gray jacket' but are not 'gray jacket' are:\tgray sweater\tgray hoodie\tgray raincoat\t\nThere are several useful visual features to tell there is 'gray jacket' and not similar things in a photo:\t\nhas sleeves and a zipper or buttons\tcollar or hood\tmade of thicker material than a sweater\tor it could be a denim jacket\twith pockets on the chest or sides.", 56], "navy": ["No. 'Navy' is too vague or abstract to be distinguished in a photo. It could refer to the navy color or the navy military branch, but neither has a tangible appearance.\n", 56], "toy car": ["Yes. 'Toy car' has a tangible appearance and is a type of a miniature vehicle.\nA few things that are visually similar to 'toy car' but are not 'toy car' are:\treal car\tmodel car\tremote control car\ttoy truck\ttoy motorcycle\ttoy airplane\nThere are several useful visual features to tell there is 'toy car' and not similar things in a photo:\tsmall size\tlack of functioning parts (i.e., no engine, no doors to open)\tbright colors\tsimplified design for child's play (i.e., big wheels, cartoonish features)\tplastic or metal material", 56], "aluminum foil": ["Yes. 'Aluminum foil' has a tangible appearance and is a type of thin, flexible metal.\nA few things that are visually similar to 'aluminum foil' but are not 'aluminum foil' are:\ttin foil\twax paper\tplastic wrap\nThere are several useful visual features to tell there is 'aluminum foil' and not similar things in a photo:\tsilvery or metallic appearance\tthin and flexible texture\tpartially reflective surface\tconformable to objects it covers\ttypically used for food storage or cooking.", 56], "stall": ["Yes. 'Stall' has a tangible appearance and refers to a temporary booth or stand used for selling things.\nA few things that are visually similar to 'stall' but are not 'stall' are:\ttables\tchairs\tcounters\tdisplay cases\tkiosks\ttents\nThere are several useful visual features to tell there is 'stall' and not similar things in a photo:\tconstructed with wood, metal, or fabric\tsells merchandise or services\thave signs, banners or logos\ton a street, a market, a fair, or a carnival \tcanopy or awning above the stall to provide shade or shelter.", 56], "flush handle": ["Yes. 'Flush handle' has a tangible appearance and is part of a toilet.\nA few things that are visually similar to 'flush handle' but are not 'flush handle' are:\tsink faucet\tshower knob\tdoor handle\tkitchen cabinet handle\nThere are several useful visual features to tell there is a 'flush handle' and not similar things in a photo:\tlocation on the toilet\tclose proximity to the toilet bowl\ttwo-piece or one-piece design of the toilet handle\tsimilar shape and size to other flush handles", 56], "wall mirror": ["Yes. 'Wall mirror' has a tangible appearance and is an object with a reflective surface, usually hung on a wall.\nA few things that are visually similar to 'wall mirror' but are not 'wall mirror' are:\twindow\tglass picture frame\tglass door\nThere are several useful visual features to tell there is 'wall mirror' and not similar things in a photo:\treflective surface\tthat is framed\thanging on a wall or surface", 56], "clay tennis court": ["Yes. 'Clay tennis court' has a tangible appearance and is a type of sports court.\nA few things that are visually similar to 'clay tennis court' but are not 'clay tennis court' are:\tconcrete sports court\tasphalt sports court\toutdoor pavement\nThere are several useful visual features to tell there is 'clay tennis court' and not similar things in a photo:\tdistinctive red or orange color\tclay or gravel texture\tfine, loose surface layer\tline markings for tennis court boundary and net location", 56], "lava lamp": ["Yes, 'lava lamp' has a visually concrete appearance and is a type of decorative lamp.\nA few things that are visually similar to 'lava lamp' but are not 'lava lamp' are:\toil lamp\twater fountain\tlamp with colored liquid inside\nThere are several useful visual features to tell there is 'lava lamp' and not similar things in a photo:\tglass vessel filled with water and colored wax that appears to float\tmotion of the wax rising and falling with the heat of the lamp\tunique patterns and color combinations created by the wax and water.", 56], "kitchen window": ["Yes. 'Kitchen window' has a tangible appearance and is a specific type of window.\nA few things that are visually similar to 'kitchen window' but are not 'kitchen window' are: living room window, office window, bathroom window.\nThere are several useful visual features to tell there is 'kitchen window' and not similar things in a photo: located in a kitchen area\trange hood or sink in the background or within view\tnatural light or view of the outdoors from the window.", 56], "gift": ["Yes. 'Gift' has a tangible appearance and is an item presented to someone as an expression of gratitude or love.\nA few things that are visually similar to 'gift' but are not 'gift' are:\tbox\tpackage\tbag\twrapped object\nThere are several useful visual features to tell there is 'gift' and not similar things in a photo:\tdecorative wrapping\tpresented with a ribbon or a bow\tlabel or tag with a heartfelt message\tin the context of a celebration, such as a birthday or holiday", 56], "coconut": ["Yes. 'Coconut' has a tangible appearance and is a type of fruit/nut.\nA few things that are visually similar to 'coconut' but are not 'coconut' are:\tkiwi\tpineapple\tdurian\tcantaloupe\nThere are several useful visual features to tell there is 'coconut' and not similar things in a photo:\tbrown, hairy shell\tmiddle seam or line\tbrown, fibrous husk\tbrown, hard shell is visible when husk is removed\twhite meat inside the hard shell\tthree small, round, dark spots or \"eyes\" on the shell.", 56], "pink hat": ["Yes. 'Pink hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'pink hat' but are not 'pink hat' are:\tbeanie\tcap\twig\tbonnet\thelmet\tberet\nThere are several useful visual features to tell there is 'pink hat' and not similar things in a photo:\tpink in color\tmade of fabric or similar material\tworn on the head", 56], "classroom": ["Yes. 'Classroom' has a tangible appearance and is a physical space.\nA few things that are visually similar to 'classroom' but are not 'classroom' are: office\tlab\tkitchen\tgym\nThere are several useful visual features to tell there is 'classroom' and not similar things in a photo: desks or tables arranged in rows or groups\tblackboard or whiteboard\twith posters, charts, or maps on the walls\tchairs for students and a larger chair or desk for the teacher.", 56], "metal basket": ["Yes. 'Metal basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'metal basket' but are not 'metal basket' are:\twire mesh\tcontainer\tforge\toven rack\nThere are several useful visual features to tell there is 'metal basket' and not similar things in a photo:\tconstructed of metal like iron or steel\tdesigned with a mesh-like pattern or open spaces\tfor holding or transporting items like fruit, vegetables or laundry.", 56], "outdoor": ["No. 'Outdoor' is too vague or abstract to be distinguished in a photo.", 56], "bus door": ["Yes. 'Bus door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'bus door' but are not 'bus door' are:\tcar door\ttruck door\tsubway door\televator door\nThere are several useful visual features to tell there is 'bus door' and not similar things in a photo:\tslide to the side or fold accordion-style\tmulti-panel or single-panel door\thas a handle or button for opening and closing usually has a window in it", 56], "train window": ["Yes. 'Train window' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'train window' but are not 'train window' are:\tcar window\tairplane window\tbus window\tstore window\nThere are several useful visual features to tell there is 'train window' and not similar things in a photo:\trectangular or square shaped window\tframed with metal or plastic\thorizontal sliding or swing opening mechanism\tclean glass surface with possible reflections of the surrounding countryside or cityscape passing by.", 56], "direction sign": ["Yes. 'Direction sign' has a tangible appearance and is a type of signpost used to give direction.\nA few things that are visually similar to 'direction sign' but are not 'direction sign' are:\tadvertising sign\tstore sign\troadside billboard\nThere are several useful visual features to tell there is 'direction sign' and not similar things in a photo:\tarrows or written directions pointing to a location\tdistinctive shapes and colors, such as a green sign with white lettering or a blue sign with yellow lettering\tmultiple signs located together at an intersection or crossroad", 56], "cracker": ["Yes. 'Cracker' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'cracker' but are not 'cracker' are:\tcookie\tcandy\tbar\tof chocolate\tbiscuit\nThere are several useful visual features to tell there is 'cracker' and not similar things in a photo:\tthin and crisp texture\trectangular or square shape\tsalt or pepper seasoning\tdividing line in the middle\tpaired with cheese, meat or dip", 56], "telephone wires": ["Yes. 'Telephone wires' has a tangible appearance and is a kind of wire used for telecommunications.\nA few things that are visually similar to 'telephone wires' but are not 'telephone wires' are:\tpower lines\tcables\tfishing line\tbarbed wire\nThere are several useful visual features to tell there are 'telephone wires' and not similar things in a photo:\tthin and uniform\tdrooping in a parabolic shape\thanging from tall poles or attached to buildings", 56], "palms": ["Yes. 'Palms' has a tangible appearance and refers to a type of tree.\nA few things that are visually similar to 'palms' but are not 'palms' are:\ttrees with thin trunks and long leaves, such as bamboo\tferns\tcacti\tother types of tropical trees\nThere are several useful visual features to tell there is 'palms' and not similar things in a photo:\tlong, slender trunks\tuniquely shaped leaves\tthat grow out from a central point\thorizontal fronds\tsparse, spiky appearance in some varieties", 56], "shadow tree": ["No. 'Shadow tree' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider the concept of a tree and its shadow separately, then:\n\nA few things that are visually similar to a 'shadow tree' but are not a 'shadow tree' are:\tother shadows on the ground\tother trees in the same location\t\nThere are several useful visual features to tell there is a 'shadow tree' and not similar things in a photo:\tthe tree should be casting a shadow on the ground\tthe shape and size of the shadow should resemble that of a tree, with branches and leaves (if visible)", 56], "plunger": ["Yes. 'Plunger' has a tangible appearance and is a household tool.\nA few things that are visually similar to 'plunger' but are not 'plunger' are:\tsuction cup\ttoilet brush\tbroom\tscrub brush\nThere are several useful visual features to tell there is 'plunger' and not similar things in a photo:\ta rubber cup on the end of a stick\ta long handle\tcurved or flexible cup shape\tfor unclogging drains and toilets", 56], "stone bench": ["Yes. 'Stone bench' has a tangible appearance and is a type of outdoor furniture.\nA few things that are visually similar to 'stone bench' but are not 'stone bench' are:\twooden bench\tconcrete bench\tmetal bench\tplatform\nThere are several useful visual features to tell there is 'stone bench' and not similar things in a photo:\tmade of stone or rock\tcrafted as a bench or a seating arrangement\tsolid and heavy appearance\tmay have carvings or decorations", 56], "fist": ["Yes. 'Fist' has a tangible appearance and is a closed hand with the fingers curled inwards.\nA few things that are visually similar to 'fist' but are not 'fist' are:\topen hand\tglove\tclaw\t\nThere are several useful visual features to tell there is 'fist' and not similar things in a photo:\tclosed hand\twith fingers curled inwards\tcan be used for punching or hitting", 56], "donkeys": ["Yes. 'Donkeys' has a tangible appearance and is a type of domesticated animal.\nA few things that are visually similar to 'donkeys' but are not 'donkeys' are:\thorses\tzebras\tmules\tlivestock\nThere are several useful visual features to tell there is 'donkeys' and not similar things in a photo:\tgray or brown color\tcurved ears with long hair\tshort mane and tail\ta cross-shaped black stripe over the withers\tstocky build\twith braying expression", 56], "snowman": ["Yes. 'Snowman' has a tangible appearance and is a three-dimensional figure made of snow.\nA few things that are visually similar to 'snowman' but are not 'snowman' are:\tice sculpture\tstatue\tpile of snow\tsnow fort\nThere are several useful visual features to tell there is 'snowman' and not similar things in a photo:\tthree-dimensional figure made of snow\tcarrot as a nose\ttwo eyes made out of coal\tstick or broomstick as an arm\tscarf or hat as an accessory", 55], "apple laptop": ["Yes. 'Apple laptop' has a tangible appearance and is a type of computer made by Apple Inc.\nA few things that are visually similar to 'apple laptop' but are not 'apple laptop' are:\tother types of laptops\ttablets\tsmartphones\tnotepad\nThere are several useful visual features to tell there is 'apple laptop' and not similar things in a photo:\tApple logo on the back of the display\tscreen hinge that glows\twhen closed, screen fits flush with the body", 55], "silver handles": ["Yes. 'Silver handles' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'silver handles' but are not 'silver handles' are:\tknobs\tcabinet pulls\tdrawer handles\tgold handles\nThere are several useful visual features to tell there are 'silver handles' and not similar things in a photo:\tattached to a door or a piece of furniture\tsilver or metallic color\ttypically long and narrow\thave a shape that is easy to grasp or pull", 55], "water pipe": ["Yes. 'Water pipe' has a tangible appearance and is a type of plumbing equipment.\nA few things that are visually similar to 'water pipe' but are not 'water pipe' are:\tgas pipe\tvent pipe\tconduit\tcable\nThere are several useful visual features to tell there is 'water pipe' and not similar things in a photo:\tconnected to a faucet or a water source\thorizontal or vertical orientation\tmade of metal, plastic or other materials\tvisible water flow through the pipe\thooked up to other pipes or valves for regulation of water pressure or flow.", 55], "mozzarella": ["Yes. 'Mozzarella' has a tangible appearance and is a type of cheese.\nA few things that are visually similar to 'mozzarella' but are not 'mozzarella' are:\tcheddar cheese\tswiss cheese\tcream cheese\tblue cheese\nThere are several useful visual features to tell there is 'mozzarella' and not similar things in a photo:\twhite color\tsoft and elastic texture\tball or cylindrical shape\tfresh smell and taste\tstretchable when melted or heated", 55], "walk sign": ["Yes. 'Walk sign' has a tangible appearance and is a type of street sign.\nA few things that are visually similar to 'walk sign' but are not 'walk sign' are:\tstop sign\tyield sign\tspeed limit sign\tcrosswalk\nThere are several useful visual features to tell there is 'walk sign' and not similar things in a photo:\tpedestrian icon or silhouette\tbright white or green color\tarrow pointing in the direction of travel\tmounted on a pole or attached to a building or post", 55], "stores": ["Yes. 'Stores' has a tangible appearance and is a type of building where goods or services are sold.\nA few things that are visually similar to 'stores' but are not 'stores' are:\thouses\toffices\thospitals\tschools\nThere are several useful visual features to tell there is 'stores' and not similar things in a photo:\tstorefront with windows and doors\tsignage indicating the name or type of store\tshelves or displays of products for sale\tcash registers or checkout counters", 55], "cement curb": ["Yes. 'Cement curb' has a tangible appearance and is a type of construction element.\nA few things that are visually similar to 'cement curb' but are not 'cement curb' are:\tsteps\tstone blocks\tbrick walls\tasphalt pavement\nThere are several useful visual features to tell there is 'cement curb' and not similar things in a photo:\traised from the street level\tlinear, elongated shape\tusually white, gray, or light-colored with a rough surface\tused to divide spaces or as a support for pavement", 55], "porcelain plate": ["Yes. 'Porcelain plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'porcelain plate' but are not 'porcelain plate' are:\tglass plate\tplastic plate\tmetal plate\twood plate\nThere are several useful visual features to tell there is 'porcelain plate' and not similar things in a photo:\tsmooth, white, and shiny surface\tthin and delicate appearance\tsubtle patterns or designs on the plate's edge\tsome form of branding or maker's mark on the bottom of the plate.", 55], "delivery truck": ["Yes. 'Delivery truck' has a tangible appearance and is a type of vehicle used for transporting goods.\nA few things that are visually similar to 'delivery truck' but are not 'delivery truck' are:\tpick-up truck\tvan\tsemi-trailer truck\tfire truck\nThere are several useful visual features to tell there is 'delivery truck' and not similar things in a photo:\tlarge cargo area\tidentifying logos, markings or colors\ton the side\tback doors or gates for loading and unloading goods\tlow to the ground compared to semi-trailer trucks or fire trucks.", 55], "calm waters": ["Yes. 'Calm waters' has a tangible appearance and is a type of body of water.\nA few things that are visually similar to 'calm waters' but are not 'calm waters' are:\tponds\tlakes\trivers\tocean\twith low waves\tor no waves\nThere are several useful visual features to tell there is 'calm waters' and not similar things in a photo:\tsmooth surface\tno or low waves\tno or little visible movement\treflection of the surroundings", 55], "toy train": ["Yes. 'Toy train' has a tangible appearance and refers to a miniature train used as a toy or a decoration.\nA few things that are visually similar to 'toy train' but are not 'toy train' are:\tmodel cars\tmodel airplanes\tmodel boats\tfigurines\nThere are several useful visual features to tell there is 'toy train' and not similar things in a photo:\tmetal tracks\tcoal car\tsteam engine or locomotive\tcars connected by hooks or magnets\twheels and wheels systems\tsmokestack", 55], "wooden poles": ["Yes. 'Wooden poles' has a tangible appearance and is a type of construction material.\nA few things that are visually similar to 'wooden poles' but are not 'wooden poles' are:\ttrees\tfence posts\tsign posts\tbamboo\tpillars\nThere are several useful visual features to tell there is 'wooden poles' and not similar things in a photo:\tman-made objects\tstraight and cylindrical shape\tsmooth or rough bark (depending on whether it has been stripped or not)\tflat or pointed tip at one end", 55], "packets": ["Yes. 'Packets' has a tangible appearance and is a type of container for holding and transporting items.\nA few things that are visually similar to 'packets' but are not 'packets' are:\tboxes\tbags\tenvelopes\twrapping paper\nThere are several useful visual features to tell there is 'packets' and not similar things in a photo:\trectangular or square shape\tseams and folds\tfor labeling or branding\tuse of plastic or paper material to hold small or multiple items", 55], "blue clock": ["Yes. 'Blue clock' has a tangible appearance and is a clock with a blue color.\nA few things that are visually similar to 'blue clock' but are not 'blue clock' are:\tblue watch\tblue timer\tblue stopwatch\nThere are several useful visual features to tell there is 'blue clock' and not similar things in a photo:\tcircular face\tnumerical or Roman numeral markings\tforward-moving hands\tor digital time display\tblue color\tdistinguishing style or design features (such as shape of hands or edges of clock face)", 55], "snack": ["No. 'Snack' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we assume a specific type of snack such as a \"bag of chips\" or \"chocolate bar,\" then the concept becomes visually concrete.\n\nA few things that are visually similar to 'bag of chips' but are not 'bag of chips' are:\tcrisps packets\tcookies bags\tpotatoes bags\t\nA few things that are visually similar to 'chocolate bar' but are not 'chocolate bar' are:\tchocolate box\tcandy bars\tgranola bars\t\n\nUseful visual features for distinguishing a 'bag of chips' from the listed similar things in a photo may include:\tthe specific brand and logo printed on the package, the type of chips (potato, corn, etc.), and any flavors or seasonings. \n\nUseful visual features for distinguishing a 'chocolate bar' from the listed similar things in a photo may include: the specific brand and logo printed on the package, the shape and size of the bar, and any visible filling or nuts.", 55], "outfield": ["Yes. 'Outfield' has a tangible appearance and is a part of a sports field.\nA few things that are visually similar to 'outfield' but are not 'outfield' are:\tinfield\tpitcher's mound\tfoul territory\tbullpen\nThere are several useful visual features to tell there is 'outfield' and not similar things in a photo:\tgrass or turf-covered area\tlocated beyond the infield and the diamond\tdiminished lines and markers\tsurrounded by fencing or wall", 55], "side road": ["Yes. 'Side road' has a tangible appearance and is a secondary road branching off from a main road.\nA few things that are visually similar to 'side road' but are not 'side road' are:\tdriveway\tpath\tfootpath\tcul-de-sac\nThere are several useful visual features to tell there is 'side road' and not similar things in a photo:\tconnected to a main road\tsmaller and narrower than the main road\tmay have a different surface material or color from the main road may have a different speed limit and rules than the main road\tmay have a different name or signage from the main road", 55], "food processor": ["Yes. 'Food processor' has a tangible appearance and is a kitchen appliance.\nA few things that are visually similar to 'food processor' but are not 'food processor' are:\tblender\tjuicer\tstand mixer\tcoffee grinder\nThere are several useful visual features to tell there is 'food processor' and not similar things in a photo:\tlarge bowl with a handle and a lid\tsharp blades or discs\tcomplex and adjustable controls\tpowerful motor and electrical cord", 55], "front car": ["Yes. 'Front car' has a tangible appearance and refers to the first car in a line of cars.\nA few things that are visually similar to 'front car' but are not 'front car' are:\tcar in the middle of a line of cars\tcar at the end of a line of cars\tparked car\nThere are several useful visual features to tell there is 'front car' and not similar things in a photo:\tappearing at the beginning of a line of cars\tfacing the viewer\twith an unobstructed view of the road\ta driver visible in the driver's seat.", 55], "hubcap": ["Yes. 'Hubcap' has a tangible appearance and is a type of car accessory.\nA few things that are visually similar to 'hubcap' but are not 'hubcap' are:\tfrisbee\tcircular saw blade\tskillet lid\nThere are several useful visual features to tell there is 'hubcap' and not similar things in a photo:\tcircular disc shape\tmetallic material\tchrome finish\tor engraved logo\tin the center of a car wheel", 55], "signature": ["No. 'Signature' is too abstract to have a tangible appearance in a photo.", 55], "barriers": ["Yes. 'Barriers' has a tangible appearance and is a kind of obstacle or fence.\nA few things that are visually similar to 'barriers' but are not 'barriers' are:\twalls\tcurbs\tbenches\nThere are several useful visual features to tell there is 'barriers' and not similar things in a photo:\tfences\tobstacles\tpreventing or limiting movement", 55], "sunshine": ["No. 'Sunshine' is too vague or abstract to be visually concrete.\nThere aren't any things similar to 'sunshine' as it's a unique concept.\nN/A.", 55], "beach towel": ["Yes. 'Beach towel' has a tangible appearance and is a kind of towel.\nA few things that are visually similar to 'beach towel' but are not 'beach towel' are:\thand towel\tbath towel\tkitchen towel\tblanket\nThere are several useful visual features to tell there is 'beach towel' and not similar things in a photo:\toften brightly colored or patterned\twith images of beaches, palm trees, etc.\tlarge size\tfor use on a sandy beach or near a pool", 55], "shapes": ["Yes. 'Shapes' has a tangible appearance and can refer to any form or figure.\nIt is difficult to think of things that are visually similar to 'shapes' but are not 'shapes', as shapes are a fundamental concept in visual communication.\nThere are no useful visual features to distinguish 'shapes' from other visual elements, as shapes are the building blocks of visual elements themselves. However, features that can be used to differentiate shapes from one another include the number of sides, angles, and curves, as well as their relative size and position.", 55], "conveyor belt": ["Yes. 'Conveyor belt' has a tangible appearance and is a machine used to transport goods.\nA few things that are visually similar to 'conveyor belt' but are not 'conveyor belt' are:\tassembly line\tescalator\tbaggage carousel\troller coaster\nThere are several useful visual features to tell there is 'conveyor belt' and not similar things in a photo:\tbelt-like movement or appearance\tmechanical or industrial look\tvarious items or products moving on top of it", 55], "waterway": ["Yes. 'Waterway' has a tangible appearance and is a route for water transportation or drainage.\nA few things that are visually similar to 'waterway' but are not 'waterway' are:\troad\trailway\tirrigation canal\tswimming pool\torbit, planet\nThere are several useful visual features to tell there is 'waterway' and not similar things in a photo:\tflowing water or a channel\tof a significant width\tenough depth for navigation\tor a drainage function\twith shores or banks on each side.", 55], "shack": ["Yes. 'Shack' has a tangible appearance and is a type of small and simple dwelling.\nA few things that are visually similar to 'shack' but are not 'shack' are:\tcabin\thut\tbarn\touthouse\nThere are several useful visual features to tell there is 'shack' and not similar things in a photo:\tsmall and simple structure\twith unfinished or rough materials, like wood or metal\tdeteriorated appearance\tlack of windows, or small windows\twith only one door, possibly with a porch or steps.", 55], "license": ["No. 'License' is too vague or abstract to be distinguished in a photo. However, a physical driver's or ID license does have a tangible appearance.\nA few things that are visually similar to 'license' but are not 'license' are:\tcredit cards\tmembership cards\tpassports\nThere are several useful visual features that help to distinguish a physical driver's or ID license from similar things in a photo:\tdriver's license number\tlegal name\tphoto of the individual's face\tdate of birth\tstate or country of issuance\tbarcode or magnetic strip for scanning", 54], "valve": ["Yes. 'Valve' has a tangible appearance and is a type of mechanical device used in flow control.\nA few things that are visually similar to 'valve' but are not 'valve' are:\tspigot\tfaucet\tgate\tdam\nThere are several useful visual features to tell there is 'valve' and not similar things in a photo:\ta circular body with an opening in the center\tfor regulating or restraining the flow of a fluid or gas\ta knob, lever, or handle on top for operation\tconnected to pipes, hoses, or other equipment.", 54], "ribs": ["Yes. 'Ribs' has a tangible appearance and refers to the bones in a person's or animal's chest.\nA few things that are visually similar to 'ribs' but are not 'ribs' are:\ttree branches\trope\tpasta shells\nThere are several useful visual features to tell there are 'ribs' and not similar things in a photo:\tcurved bone shape\tvisible ends, where they attach to the spine or sternum\tridged surface texture, with smaller bones visible within the larger rib bones.", 54], "blue jeans": ["Yes. 'Blue jeans' has a tangible appearance and is a type of pants.\nA few things that are visually similar to 'blue jeans' but are not 'blue jeans' are:\tdenim jacket\tcorduroys\tleggings\t\nThere are several useful visual features to tell there is 'blue jeans' and not similar things in a photo:\tblue or indigo color\tdenim fabric\tzipper and button on front waistband\tpockets on the back and front straight-legged, bootcut, or skinny fit.", 54], "hospital": ["Yes. 'Hospital' has a tangible appearance and refers to a medical facility.\nA few things that are visually similar to 'hospital' but are not 'hospital' are:\tclinic\temergency room\tpharmacy\tnursing home\nThere are several useful visual features to tell there is 'hospital' and not similar things in a photo:\tthe presence of medical equipment such as beds, surgical lights, or monitors\tthe presence of doctors or nurses in a medical attire\tthe presence of signs or logos that indicate this is a hospital", 54], "roadside": ["Yes. 'Roadside' has a tangible appearance and refers to the area alongside a road.\nA few things that are visually similar to 'roadside' but are not 'roadside' are:\tsidewalk\tparking lot\tfield\thighway\nThere are several useful visual features to tell there is 'roadside' and not similar things in a photo:\tpositioned alongside a road\tpavement\tor gravel surface\tvegetation such as grass, trees, or bushes\troad signs, telephone poles, or electric lines adjacent to the road.", 54], "shops": ["Yes. 'Shops' has a tangible appearance and is a physical store or retail establishment.\nA few things that are visually similar to 'shops' but are not 'shops' are:\tbanks\toffices\tsupermarkets\tschools\nThere are several useful visual features to tell there is 'shops' and not similar things in a photo:\tsignage\tor display of merchandise\tstaff or customers entering or exiting\tthe presence of a cash register or point of sale\tsystems\tshelves or racks displaying products or goods for sale.", 54], "plastic basket": ["Yes. 'Plastic basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic basket' but are not 'plastic basket' are:\tlaundry basket\twire basket\twicker basket\tplastic bin\tbucket\nThere are several useful visual features to tell there is 'plastic basket' and not similar things in a photo:\tmade of plastic\thas noticeable handles or slots\tfor holding objects or things together\tusually with holes for ventilation or draining", 54], "supplies": ["No. 'Supplies' is too vague or abstract to be distinguished in a photo.", 54], "parade": ["Yes. 'Parade' has a tangible appearance and is an event in which people or vehicles move through the streets in a celebratory procession.\nA few things that are visually similar to 'parade' but are not 'parade' are:\tcrowd\tdemonstration\tmarch\tfestival\nThere are several useful visual features to tell there is 'parade' and not similar things in a photo:\tfloats with decorations or themes\tmarching bands\tor groups of people\tdancers\tin costumes\tor uniforms\tballoons and banners", 54], "blue couch": ["Yes. 'Blue couch' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'blue couch' but are not 'blue couch' are:\tblue chair\tblue stool\tblue ottoman\tblue futon\nThere are several useful visual features to tell there is 'blue couch' and not similar things in a photo:\tlong seat with a backrest\tcushions or pillows\tarmrests\ton legs or sitting directly on the ground", 54], "peanut butter": ["Yes. 'Peanut butter' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'peanut butter' but are not 'peanut butter' are:\tchocolate spread\thummus\tapple butter\tjam\nThere are several useful visual features to tell there is 'peanut butter' and not similar things in a photo:\tthick and creamy texture\tlight brown color\tmade from ground peanuts and oil\ttrace of peanut chunks or shells in the spread", 54], "racer": ["Yes. 'Racer' has a tangible appearance and refers to a type of fast moving vehicle or a person who participates in racing.\nA few things that are visually similar to 'racer' but are not 'racer' are:\tsports car\tspeedboat\tbicyclist\ttrack athlete\nThere are several useful visual features to tell there is 'racer' and not similar things in a photo:\taerodynamic design\tspecial racing suit or helmet\tfamous racing team logos or sponsor stickers\tcar or boat number visible on the vehicle\tracing flag or finish line in the background", 54], "silver camera": ["Yes. 'Silver camera' has a tangible appearance and can be distinguished by its color and shape.\nA few things that are visually similar to 'silver camera' but are not 'silver camera' are:\tphone\tmetal box\twatch\twallet\nThere are several useful visual features to tell there is 'silver camera' and not similar things in a photo:\trectangular shape\tlens\tsilver or metallic color\tstraps or grip for holding\tflash or buttons\tfor taking pictures or adjusting settings", 54], "porcelain toilet bowl": ["Yes. 'Porcelain toilet bowl' has a tangible appearance.\nA few things that are visually similar to 'porcelain toilet bowl' but are not 'porcelain toilet bowl' are:\tsink\tbathtub\turn\tfountain\nThere are several useful visual features to tell there is 'porcelain toilet bowl' and not similar things in a photo:\toval-shaped bowl\tporcelain or ceramic material\tflush handle or button\twater in the bowl\twith or without a seat and lid.", 54], "swim trunks": ["Yes. 'Swim trunks' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'swim trunks' but are not 'swim trunks' are:\tshorts\tgym shorts\tunderwear\t\nThere are several useful visual features to tell there is 'swim trunks' and not similar things in a photo:\t\n- made from materials that dry quickly such as nylon or polyester\t\n- come in bright colors or patterns\t\n- can have a lining\t\n- often have a drawstring or elastic waistband", 54], "sandy beach": ["Yes. 'Sandy Beach' has a tangible appearance and is a type of shore.\nA few things that are visually similar to 'sandy beach' but are not 'sandy beach' are:\trocky shore\tmarina pier\tharbor\tboardwalk\nThere are several useful visual features to tell there is 'sandy beach' and not similar things in a photo:\tsand\tgrains of sand in various sizes\tturquoise water or blue sea\tpalm trees or other beach plants\tbeach umbrellas or chairs", 54], "tide": ["No. 'Tide' is too vague or abstract to be distinguished in a photo.", 54], "d": ["No. 'd' is too vague or abstract to have a tangible appearance or be distinguished in a photo. It is a symbol or letter in the alphabet.", 54], "jean pants": ["Yes. 'Jean pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'jean pants' but are not 'jean pants' are:\ttrousers\tchinos\tcapris\tleggings\nThere are several useful visual features to tell there is 'jean pants' and not similar things in a photo:\tbluish color\tdenim fabric with visible stitching\tfive pockets, including a small watch pocket on the front waistband\tzipper or button fly straight-leg, boot-cut, wide-leg, or skinny fit.", 54], "rear light": ["Yes. 'Rear light' is a visually concrete concept and refers to lights in the back of a vehicle.\nA few things that are visually similar to 'rear light' but are not 'rear light' are:\ttrailer lights\tbicycle lights\tsignal lights on a building\nThere are several useful visual features to tell there is 'rear light' and not similar things in a photo:\tred in color\tlocated in the back of a vehicle\tmay have multiple bulbs or sections \tmay blink intermittently or remain steady even when the car is parked.", 54], "jumpsuit": ["Yes. 'Jumpsuit' has a tangible appearance and is a one-piece garment that covers the torso and legs.\nA few things that are visually similar to 'jumpsuit' but are not 'jumpsuit' are:\tcoveralls\tromper\tcatsuit\toveralls\nThere are several useful visual features to tell there is 'jumpsuit' and not similar things in a photo:\tone-piece garment\tthat covers the torso and legs\tclosure on the front or back\tcan have sleeves or be sleeveless\tcan come in different colors and fabrics", 54], "streaks": ["Yes. 'Streaks' has a tangible appearance and can refer to marks or lines of a different color or texture than the surrounding area.\nA few things that are visually similar to 'streaks' but are not 'streaks' are:\tscratches\tcracks\tlines\tof a similar color or texture as the surrounding area\nThere are several useful visual features to tell there are 'streaks' and not similar things in a photo:\tdifferent color or texture from the surrounding area\tlinear shape or pattern\tsmooth or curved shape\ton a surface that is otherwise uniform or continuous.", 54], "baseline": ["No. 'Baseline' is too vague or abstract to be distinguished in a photo.", 54], "earth": ["Yes. 'Earth' is a visually concrete concept as it is a tangible planet in our solar system.\nThere are no things that are visually similar to 'earth' but not 'earth'.\nThere are no useful visual features to distinguish 'earth' from anything else, as it is a unique planet with no visual lookalikes. However, features that are characteristic of the Earth, such as its blue and white colors or recognizable landmass shapes, can aid in identifying images or representations of it.", 54], "freckles": ["Yes. 'Freckles' has a tangible appearance and is a type of skin pigmentation.\nA few things that are visually similar to 'freckles' but are not 'freckles' are:\tbirthmarks\tmoles\tdirt\tgrains of beauty spots\nThere are several useful visual features to tell there is 'freckles' and not similar things in a photo:\tsmall spots\tusually brown or red in color\tfound on areas of the skin exposed to sunlight, like the face and arms\tnot elevated or fuzzy-looking like moles.", 54], "clock side building": ["No. 'Clock side building' is too vague or abstract to be distinguished in a photo.", 54], "cat ear": ["Yes. 'Cat ear' has a tangible appearance and refers to the ears of a cat.\nA few things that are visually similar to 'cat ear' but are not 'cat ear' are:\tDog ear\tBear ear\tFox ear\tRabbit ear\nThere are several useful visual features to tell there is 'cat ear' and not similar things in a photo:\ttriangular in shape\twith fur or hair at the tip and back\tpointy at the top\tpositioned on top of the cat's head", 54], "seat belt": ["Yes. 'Seat belt' has a tangible appearance and is a safety device for vehicles.\nA few things that are visually similar to 'seat belt' but are not 'seat belt' are:\tbackpack strap\tluggage strap\tbelt for clothing\nThere are several useful visual features to tell there is 'seat belt' and not similar things in a photo:\tattached to a car or a vehicle\tadjustable length and fastening mechanism\tpositioned across the lap and shoulder area of a seated person\tcolors and labels indicating safety information", 54], "metal hand rail": ["Yes. 'Metal hand rail' has a tangible appearance and is a kind of safety feature in buildings or public spaces.\nA few things that are visually similar to 'metal hand rail' but are not 'metal hand rail' are:\tmetal pipes\tfences\tbannisters\nThere are several useful visual features to tell there is 'metal hand rail' and not similar things in a photo:\tstrategically placed for support or balance\tlocated next to stairs or ramps\tcontinuous and running along the path of travel\tusually attached to a wall, post or baluster", 54], "telephone booth": ["Yes. 'Telephone booth' has a tangible appearance and is a type of small enclosed structure that contains a public phone.\nA few things that are visually similar to 'telephone booth' but are not 'telephone booth' are:\tbathroom stall\tparking meter\tkiosk\tphoto booth\tvending machine\nThere are several useful visual features to tell there is 'telephone booth' and not similar things in a photo:\tenclosed glass or metal structure\twith a sliding or swinging door\tcontains a phone\tphone book and/or advertisements inside\tthe top of the structure may have a distinctive shape like a dome or a hood", 53], "wii remotes": ["Yes. 'Wii remotes' has a tangible appearance and is a type of game controller.\nA few things that are visually similar to 'wii remotes' but are not 'wii remotes' are:\tgamepads\tjoysticks\tremote controls\nThere are several useful visual features to tell there is 'wii remotes' and not similar things in a photo:\twireless controller\trectangular shape\twrist strap\tinfrared sensor\tbar of lights at the bottom", 53], "mantel": ["Yes. 'Mantel' has a tangible appearance and is a shelf above a fireplace.\nA few things that are visually similar to 'mantel' but are not 'mantel' are:\tshelves\tledges\tcountertops\ttables\nThere are several useful visual features to tell there is 'mantel' and not similar things in a photo:\tabove a fireplace\tmade of stone, wood or metal\tdecoration on top, such as candles or picture frames\twidth is usually shorter than length and depth", 53], "pointy": ["Yes. 'Pointy' has a tangible appearance and refers to objects or shapes with sharp or tapered ends. \nA few things that are visually similar to 'pointy' but are not 'pointy' are: round, blunt or curved objects such as circles, spheres, and bananas.\nUseful visual features for distinguishing 'pointy' from similar things in a photo might include sharp angles, tapering ends, pointed tips, and a generally thin, elongated shape.", 53], "metal bridge": ["Yes. 'Metal bridge' has a tangible appearance and is a type of bridge.\nA few things that are visually similar to 'metal bridge' but are not 'metal bridge' are:\twooden bridge\tsuspension bridge\tdrawbridge\nThere are several useful visual features to tell there is 'metal bridge' and not similar things in a photo:\tmade of metal\tstraight, elevated structure\tfor vehicles or pedestrians (or both)\tmetal beams or cables\tfor overpassing waterway or a valley", 53], "bird feeder": ["Yes. 'Bird feeder' has a tangible appearance and is a structure to provide food to birds.\nA few things that are visually similar to 'bird feeder' but are not 'bird feeder' are:\tbirdhouse\twater fountain\tgarden sculpture\nThere are several useful visual features to tell there is 'bird feeder' and not similar things in a photo:\tcontainer for bird food\tattracts birds\thanging from a tree, pole, or wall\tcan accommodate multiple birds\tbirds eating from it\tcan have a roof to protect food from rain or snow.", 53], "gas": ["No. 'Gas' is too vague or abstract to be distinguished in a photo. It refers to a state of matter that is invisible to the naked eye.\n", 53], "icon": ["No. 'Icon' is too vague or abstract to have a tangible appearance.\nA few things that are visually similar to 'icon' but are not 'icon' are:\tsymbol\tlogo\tsign\tillustration\nThere are no useful visual features to distinguish 'icon' from the listed similar things in a photo, as they often share similar shapes and designs. The context in which the image is used can provide clues as to whether it is an icon or something else. For example, an icon may be used to represent a specific function or application on a computer screen, while a logo is typically used to represent a company or brand.", 53], "gray hat": ["Yes. 'Gray hat' has a tangible appearance and refers to a specific color of a hat.\nA few things that are visually similar to 'gray hat' but are not 'gray hat' are:\tblack hat\tbrown hat\twhite hat\tcap\tbeanie\nThere are several useful visual features to tell there is 'gray hat' and not similar things in a photo:\tgray color\tround top\trimmed edge\tbetween a size of a cap and a large hat\tworn on the head", 53], "iron gate": ["Yes. 'Iron gate' has a tangible appearance and is a type of entrance or barrier.\nA few things that are visually similar to 'iron gate' but are not 'iron gate' are:\tfence\tdoor\twindow\tgrille\nThere are several useful visual features to tell there is 'iron gate' and not similar things in a photo:\tmade of iron or metal\tvertical or horizontal bars\thinged or sliding mechanism\tlock or latch present\tcommonly used for entrances or barriers", 53], "grey bird": ["Yes. 'Grey bird' has a tangible appearance and is a specific type of bird.\nA few things that are visually similar to 'grey bird' but are not 'grey bird' are:\tdove\tpigeon\tgray parrot\tsparrow\nThere are several useful visual features to tell there is 'grey bird' and not similar things in a photo:\tmedium-sized bird\twith grey feathers and possibly some white touches\tbeak and feet could be red or black\teyes might have a ring around them", 53], "marina": ["Yes. 'Marina' has a tangible appearance and is a type of harbor or dock for boats.\nA few things that are visually similar to 'marina' but are not 'marina' are:\twharf\tpier\tjetty\tdock\nThere are several useful visual features to tell there is 'marina' and not similar things in a photo:\tmultiple docks for boats\tyachts or boats in the water\tor walkways for people alongside the water\tbenches, decorative lights, or other amenities\tcommonly found at waterfront locations", 53], "backs": ["Yes. 'Backs' has a tangible appearance and refers to the posterior part of the human body.\nA few things that are visually similar to 'backs' but are not 'backs' are:\tfronts\tlegs\tarms\theads\nThere are several useful visual features to tell there are 'backs' and not similar things in a photo:\tcurved shape of the spine\tbony protrusions at the shoulders and hips\tlocation below the neck and above the buttocks\tvariations in skin tone and texture", 53], "handicap sign": ["Yes. 'Handicap sign' has a tangible appearance and is a kind of sign.\nA few things that are visually similar to 'handicap sign' but are not 'handicap sign' are:\tno parking sign\tloading zone sign\tspeed limit sign\nThere are several useful visual features to tell there is 'handicap sign' and not similar things in a photo:\tperson in a wheelchair symbolized in white on a blue field", 53], "silver zipper": ["Yes. 'Silver zipper' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'silver zipper' but are not 'silver zipper' are: buttons, velcro fasteners, snaps, hooks and eyes.\nThere are several useful visual features to tell there is 'silver zipper' and not similar things in a photo:\tmetallic silver color\ta long strip with interlocking teeth when closed\ta tab or pull to open and close\tthe ability to completely open and separate two pieces", 53], "baseballs": ["Yes. 'Baseballs' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'baseballs' but are not 'baseballs' are:\ttennis balls\thandballs\tsoftballs\tcue balls\nThere are several useful visual features to tell there is 'baseballs' and not similar things in a photo:\twhite with red stitching\tleather or synthetic cover\tabout 3 inches in diameter\tspherical shape", 53], "garland": ["Yes. 'Garland' has a tangible appearance and refers to a decoration made of different materials that can be used for various purposes.\n\nA few things that are visually similar to 'garland' but are not 'garland' are: ribbon, tinsel, streamer, lace, yarn.\n\nUseful visual features for distinguishing 'garland' from the listed similar things in a photo are: the presence of multiple objects in a continuous strand, usually in a repetitive pattern, and used for decoration along walls, ceilings, and other surfaces. Garlands can be made of natural materials like flowers or evergreens, or artificial materials like plastic, paper, or fabric. They can be used for different occasions and are often adorned with other decorative items like lights or ornaments.", 53], "bone": ["Yes. 'Bone' has a tangible appearance and is a hard, whitish structure that forms the skeleton of vertebrates.\nA few things that are visually similar to 'bone' but are not 'bone' are:\tivory\ttusk\tcoral\trock\nThere are several useful visual features to tell there is 'bone' and not similar things in a photo:\thard and solid, but lightweight\twhitish or off-white color\tcontains visible joints, bumps, and curves\thollow center in long bones", 53], "left arm": ["Yes. 'Left arm' has a tangible appearance and is a specific body part.\nA few things that are visually similar to 'left arm' but are not 'left arm' are:\tright arm\tleg\tneck\twaist\nThere are no useful visual features to distinguish 'left arm' from these similar things in a photo as they do not share the same appearance or location on the body.", 53], "door way": ["Yes. 'Door way' has a tangible appearance and refers to an opening in a wall or a door frame.\nA few things that are visually similar to 'door way' but are not 'door way' are:\twindow\tarch\tdoor handle\tframed art\tpainting\nThere are several useful visual features to tell there is 'door way' and not similar things in a photo:\tvertical or horizontal opening in a wall or a door frame\twith or without a door\tframe around the opening that may be decorative\tor functional\tfor entering or exiting a room or a building", 53], "jet airplane": ["Yes. 'Jet airplane' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'jet airplane' but are not 'jet airplane' are:\tpropeller airplane\thelicopter\tflying drone\t\nThere are several useful visual features to tell there is 'jet airplane' and not similar things in a photo:\tsmooth and sleek body design\tnarrow and pointed wings\tstreamlined engines or turbines\ttwin engines\ttrail of vapor or smoke behind the engines.", 53], "pizza boxes": ["Yes. 'Pizza boxes' has a tangible appearance and is a type of cardboard carton used to store pizza.\nA few things that are visually similar to 'pizza boxes' but are not 'pizza boxes' are:\tcardboard boxes\ttake-out containers\tdelivery bags\nThere are several useful visual features to tell there is 'pizza boxes' and not similar things in a photo:\tusually square or rectangular shape\twith flaps to close or open\thave a pizzeria logo or some pizza imagery\ton the inside there's a pizza or the remnants of pizzas.", 53], "silver microwave": ["Yes. 'Silver microwave' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'silver microwave' but are not 'silver microwave' are:\tstainless steel fridge\tstainless steel oven\tstainless steel toaster\t\nThere are several useful visual features to tell there is 'silver microwave' and not similar things in a photo:\trectangular shape\twith a control panel\ton the counter or mounted on the wall\tdoor with a window\tinterior with a turntable", 53], "city buildings": ["Yes. 'City buildings' has a tangible appearance and refers to tall permanent structures.\nA few things that are visually similar to 'city buildings' but are not 'city buildings' are:\thouses\tbarns\tsilos\ttowers\nThere are several useful visual features to tell there is 'city buildings' and not similar things in a photo:\ttall structures several stories high\tmultiple windows or levels\tmade of bricks, concrete, or steel\toccupying large urban areas", 53], "dog toy": ["Yes. 'Dog toy' has a tangible appearance and is a type of object used for playing.\nA few things that are visually similar to 'dog toy' but are not 'dog toy' are:\tteddy bear\tchew bone\tfrisbee\tball\nThere are several useful visual features to tell there is 'dog toy' and not similar things in a photo:\tbright colors or patterns\tdurable materials, such as rubber or nylon\tspecific shapes or sizes suitable for a dog's mouth\thas a squeaker or other noise-making device", 53], "s": ["No. 's' is too abstract and doesn't have a physical appearance. It is a letter of the alphabet.", 53], "peoples": ["Yes. 'Peoples' has a tangible appearance and refers to a group of individuals.\nThere are no things that are visually similar to 'peoples' but are not 'peoples'.\nUseful visual features for distinguishing 'peoples' may include: The presence of multiple individuals in a group, facial features, hairstyles, clothing styles or colors, and different ages or genders.", 53], "worn": ["No. 'Worn' is too vague or abstract to be distinguished in a photo. It is more of a descriptive term rather than a visually concrete concept. \n\nIt is not applicable to name things that are visually similar to 'worn' as it is not a tangible object or entity, it is a description of the condition or appearance of something.\n\nTherefore, there are no useful visual features to distinguish 'worn' from similar things in a photo.", 53], "leaves grass": ["Yes. 'Leaves grass' has a tangible appearance and refers to the blades or foliage of grass plants.\nA few things that are visually similar to 'leaves grass' but are not 'leaves grass' are:\tleaves of other plants\thay\tstraw\nThere are several useful visual features to tell there are 'leaves grass' and not similar things in a photo:\tlong, slender shape of the blades\tof uniform lengths\tpointed tips\tgreen color", 53], "insignia": ["Yes. 'Insignia' has a tangible appearance and is a symbol or emblem that represents an organization or a group.\nA few things that are visually similar to 'insignia' but are not 'insignia' are:\tlogos\tbadges\tmedals\tstickers\tpins\nThere are several useful visual features to tell there is 'insignia' and not similar things in a photo:\tspecific colors, patterns, or shapes that represent a particular organization or group\tmeaningful symbols or images that are associated with a group or organization\tsmall size that is typically worn or displayed on clothing or accessories", 53], "river water": ["Yes. 'River water' has a tangible appearance and is a type of natural water body.\nA few things that are visually similar to 'river water' but are not 'river water' are:\tocean water\tlake water\tpool water\nThere are several useful visual features to tell there is 'river water' and not similar things in a photo:\tcurrent or flow\tconnected to riverbanks or land\trocks or boulders along the riverbed or banks\tcolor may vary depending upon the type of silt or sediment.", 53], "mole": ["Yes. 'Mole' has a tangible appearance and is a small mammal.\nA few things that are visually similar to 'mole' but are not 'mole' are:\tgopher\tshrew\tmouse\nThere are several useful visual features to tell there is 'mole' and not similar things in a photo:\trounded body and a pointed snout\tfur that lies flat against the body\tsmall eyes and ears\tpaddle-like feet with claws\tdark brown or blackish-gray coloration", 53], "taillights": ["Yes. 'Taillights' has a tangible appearance and is a kind of light on the back of a vehicle.\nA few things that are visually similar to 'taillights' but are not 'taillights' are:\theadlights\tstreetlights\tsignals\tbrake lights\nThere are several useful visual features to tell there is 'taillights' and not similar things in a photo:\tlocated on the back of a vehicle\tusually red or orange in color\tsmaller than headlights\tmay have a distinct shape or arrangement (e.g. in a row or a circle)\twhen illuminated, they light up in a particular pattern or sequence", 52], "coffee cups": ["Yes. 'Coffee cups' has a tangible appearance and is a type of container used for drinking coffee.\nA few things that are visually similar to 'coffee cups' but are not 'coffee cups' are:\ttea cups\tmugs\tglasses\t\nThere are several useful visual features to tell there is 'coffee cups' and not similar things in a photo:\thandle\tfor holding hot beverages\twith a saucer or without\thaving patterns or inscriptions appropriate for coffee use.", 52], "jump": ["No. 'Jump' is too vague or abstract to be distinguished in a photo.", 52], "jumping": ["Yes. 'Jumping' has a tangible appearance and is a physical activity.\nA few things that are visually similar to 'jumping' but are not 'jumping' are:\tfalling\thopping\tbouncing\t\nThere are several useful visual features to tell there is 'jumping' and not similar things in a photo:\tmid-air position\twith legs and feet off the ground\thands and arms might be raised to help elevate from the ground", 52], "safety": ["No. 'Safety' is too vague or abstract to be distinguished in a photo.", 52], "ripe bananas": ["Yes. 'Ripe bananas' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'ripe bananas' but are not 'ripe bananas' are:\tmango\tpapaya\tyellow pepper\tfruit salad\nThere are several useful visual features to tell there is 'ripe bananas' and not similar things in a photo:\tlong and curved fruit\tyellow color with brown spots\tfleshy interior\twith a sweet and soft taste", 52], "pickup": ["Yes. 'Pickup' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'pickup' but are not 'pickup' are:\ttruck\tvan\tSUV\nThere are several useful visual features to tell there is 'pickup' and not similar things in a photo:\topen cargo area behind the passenger compartment\ttailgate at the back of the vehicle\ttwo rows of seats\tnarrow body with a low stance\tboxy shape with a flat front grille and large headlights.", 52], "cardboard container": ["Yes. 'Cardboard container' has a tangible appearance and is a type of container made of cardboard material.\nA few things that are visually similar to 'cardboard container' but are not 'cardboard container' are:\twooden box\tplastic bag\tmetal tin\tpaper envelope\nThere are several useful visual features to tell there is 'cardboard container' and not similar things in a photo:\tcardboard material\tfoldable construction\twith or without lid\trectangular or square shape", 52], "car tire": ["Yes. 'Car tire' is a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'car tire' but are not 'car tire' are:\tbicycle tire\tmotorcycle tire\trubber ring\nThere are several useful visual features to tell there is 'car tire' and not similar things in a photo:\t\nblack and round with a rim around the edge\t\ntread patterns on the surface\t\ntypically larger than bike tires and smaller than truck tires\t\nfound on the wheel of a car", 52], "smartphone": ["Yes. 'Smartphone' has a tangible appearance and is a type of handheld device.\nA few things that are visually similar to 'smartphone' but are not 'smartphone' are:\ttablet\tmusic player\tcamera\tgaming device\nThere are several useful visual features to tell there is 'smartphone' and not similar things in a photo:\trectangular in shape\ttouchscreen display\ticons for apps\tor buttons for functions\trear and front cameras\thome button or gesture area\theadphone jack or charging port", 52], "daylight": ["Yes. 'Daylight' has a tangible appearance and refers to the natural light of the sun during the daytime.\nA few things that are visually similar to 'daylight' but are not 'daylight' are:\tlamps\tfireworks\tstage lights\tneon lights\nThere are several useful visual features to tell there is 'daylight' and not similar things in a photo:\tbright and natural light\tyellowish or bluish hues, depending on the time of the day\tshadows cast by objects in the scene", 52], "power strip": ["Yes. 'Power strip' has a tangible appearance and is a device used to expand the number of electrical outlets available.\nA few things that are visually similar to 'power strip' but are not 'power strip' are:\tsurge protector\tsocket outlet\tplug adapter\nThere are several useful visual features to tell there is 'power strip' and not similar things in a photo:\trectangular shape\twith multiple electrical outlets\thorizontal or vertical orientation\twith an on/off switch or a reset button", 52], "blue ball": ["Yes. 'Blue ball' has a tangible appearance and is an object that is round and blue in color.\nA few things that are visually similar to 'blue ball' but are not 'blue ball' are:\tblueberry\tworld globe\tbluetooth speaker\tpit ball pool\nThere are several useful visual features to tell there is 'blue ball' and not similar things in a photo:\tround shape\tsolid surface\tsmooth texture\tblue color", 52], "transit bus": ["Yes, 'transit bus' has a tangible appearance and is a type of vehicle used for public transportation.\nA few things that are visually similar to 'transit bus' but are not 'transit bus' are:\tcoach bus\tschool bus\ttruck\tvan\nThere are several useful visual features to tell there is 'transit bus' and not similar things in a photo:\tred, blue, or green exterior\tcolorful company logos or advertisements on the side\twindows on both sides and back\tforward-angled windshield\tlarge capacity for passengers\tstops and picks up passengers at designated areas.", 52], "dandelions": ["Yes. 'Dandelions' has a tangible appearance and is a kind of flower.\nA few things that are visually similar to 'dandelions' but are not 'dandelions' are:\tsunflowers\t daisy\t tulips\troses\nThere are several useful visual features to tell there is 'dandelions' and not similar things in a photo:\tTiny yellow petals arranged in a circular pattern\tEmerging from the stem with no visible leaves\tA fluffy white ball on its head after blooming with white seed parachutes\tfeathery, pointy leaves", 52], "television stand": ["Yes. 'Television stand' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'television stand' but are not 'television stand' are:\ttable\tcabinet\tshelf\tlamp stand\nThere are several useful visual features to tell there is 'television stand' and not similar things in a photo:\tdesigned to hold a television\tset at an appropriate height to watch from a seated position\tmultiple shelves, drawers, or compartments for storing media or electronics\tmatches the style and decor of the room", 52], "orange food": ["Yes. 'Orange food' has a tangible appearance and is a type of food with an orange color.\nA few things that are visually similar to 'orange food' but are not 'orange food' are:\torange itself\tpumpkin\torange flowers\nThere are several useful visual features to tell there is 'orange food' and not similar things in a photo:\tfood-like appearance\twith an orange color\tcan be cooked or raw", 52], "tall green": ["No. 'Tall green' is too vague or abstract to be distinguished in a photo. \n\nIf you could provide more context or details about what you are referring to as 'tall green', I may be able to give a more accurate answer.", 52], "garbage bag": ["Yes. 'Garbage bag' has a tangible appearance and is a type of bag used for waste disposal.\nA few things that are visually similar to 'garbage bag' but are not 'garbage bag' are:\tbackpack\tplastic bag\tgrocery bag\tluggage\tfabric bag\t\nThere are several useful visual features to distinguish 'garbage bag' from the listed similar things in a photo:\tthick material\tblack or dark in color\tties or drawstrings for closing the bag\ttall and large enough to hold garbage or waste products\thas printed text indicating 'garbage' or 'trash'", 52], "silver metal fork": ["Yes. 'Silver metal fork' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'silver metal fork' but are not 'silver metal fork' are:\tspoon\tknife\tchopsticks\tmetal straw\nThere are several useful visual features to tell there is 'silver metal fork' and not similar things in a photo:\thas four tines or prongs\tmade of silver or silver-colored metal\ttapered or pointed tines\tshiny or reflective surface\tcurved or angled shape to the tines", 52], "sweat shirt": ["Yes. 'Sweat shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sweat shirt' but are not 'sweat shirt' are:\tt-shirt\tlong-sleeved shirt\thoodie\tjacket\tblouse\nThere are several useful visual features to tell there is a 'sweat shirt' and not similar things in a photo:\tknitted or woven from cotton or other fibers\trectangular shape\tlong sleeves\twith or without a hood, pockets, or a zipper", 52], "lanes": ["Yes. 'Lanes' has a tangible appearance and refers to the individual strips of roadway for vehicles to travel on.\nA few things that are visually similar to 'lanes' but are not 'lanes' are:\tsidewalks\tbike paths\tparking spaces\tcrosswalks\nThere are several useful visual features to tell there are 'lanes' and not similar things in a photo:\tstriped markings in the center\tlanes must be wide enough to fit vehicles\tlanes can only be used by vehicles, not pedestrians or cyclists\tthe direction of the lanes is indicated by arrows or signs\ton multi-lane roads, lanes are separated by lines or barriers.", 52], "service bus": ["Yes. 'Service bus' has a tangible appearance and is a type of bus used for public transportation.\nA few things that are visually similar to 'service bus' but are not 'service bus' are:\tshuttle bus\ttour bus\ttruck\tvan\nThere are several useful visual features to tell there is 'service bus' and not similar things in a photo:\tdesignated route markings\tcentralized door for entering and exiting\tpassenger windows above the wheelbase\tside panels with windows\tinline seating", 52], "money": ["Yes. 'Money' has a tangible appearance as a physical currency.\nA few things that are visually similar to 'money' but are not 'money' are:\tcoupons\treceipts\tgift cards\tcredit cards\nThere are several useful visual features to tell there is 'money' and not similar things in a photo:\tpaper or metal currency\tdenomination values\tofficial government emblems or symbols\tportraits of historical figures or leaders\tof a particular country or region", 52], "elephant tusk": ["Yes. 'Elephant tusk' has a tangible appearance and is a part of the elephant body.\nA few things that are visually similar to 'elephant tusk' but are not 'elephant tusk' are:\tbones\thorns\tantlers\tcoral\nThere are several useful visual features to tell there is 'elephant tusk' and not similar things in a photo:\tlarge and long\tivory-colored\tcurved or spiraled shape\tsmooth and polished surface", 52], "side wing": ["Yes. 'Side wing' has a tangible appearance and is a part of a vehicle, such as an airplane or a car.\nA few things that are visually similar to 'side wing' but are not 'side wing' are:\tdoors\twindows\tspoilers\tmirrors\nThere are several useful visual features to tell there is 'side wing' and not similar things in a photo:\tparallel to the ground, sticking out from the side of a vehicle\taerodynamic shape\tflap or adjustable surface to change the lift or drag", 52], "storm clouds": ["Yes. 'Storm clouds' has a tangible appearance and is a type of cloud formation.\nA few things that are visually similar to 'storm clouds' but are not 'storm clouds' are:\tsmoke\tfog\tdust clouds\nThere are several useful visual features to tell there are 'storm clouds' and not similar things in a photo:\tlow altitude\tdark and gray color\tthick and dense shape\tflat or billowy base, often with an anvil-shaped top", 52], "flower bouquet": ["Yes. 'Flower bouquet' has a tangible appearance and is made up of multiple flowers arranged together.\nA few things that are visually similar to 'flower bouquet' but are not 'flower bouquet' are:\tsingle flower\tpotted plants\tflower arrangement in a vase\nThere are several useful visual features to tell there is 'flower bouquet' and not similar things in a photo:\tmultiple flowers\taligned stems\twrapped together with ribbon or paper\theld in a bouquet shape", 52], "blow dryer": ["Yes. 'Blow dryer' has a tangible appearance and is a kind of hair styling tool.\nA few things that are visually similar to 'blow dryer' but are not 'blow dryer' are: fan, vacuum, heat gun\nThere are several useful visual features to tell there is 'blow dryer' and not similar things in a photo:\t\nelongated body shape with a nozzle attached to one end\thigh and low heat settings\tcold air button\tcord and plug for power supply\tnarrow nozzle with vents for the airflow.", 52], "train train tracks": ["Yes. 'Train train tracks' has a tangible appearance and is a specific type of transportation infrastructure.\nA few things that are visually similar to 'train train tracks' but are not 'train train tracks' are:\troad\tfootpath\tbike path\tparking lot\nThere are several useful visual features to tell there are 'train tracks' and not similar things in a photo:\tlong and straight\tmetal rails\twooden or concrete sleepers\ttwo parallel tracks\twith or without ballast\toften seen in an urban or rural setting", 52], "metal tray": ["Yes. 'Metal tray' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'metal tray' but are not 'metal tray' are: baking sheet, cookie sheet, pizza pan, serving platter\nThere are several useful visual features to tell there is 'metal tray' and not similar things in a photo: thin, flat, and rectangular or circular shape, made of metal material, raised edges or rim around the perimeter.", 52], "mayonnaise": ["Yes. 'Mayonnaise' has a tangible appearance and is a type of condiment or sauce.\nA few things that are visually similar to 'mayonnaise' but are not 'mayonnaise' are:\tsour cream\tgreek yogurt\twhipped cream\tvanilla pudding\t\nThere are several useful visual features to tell there is 'mayonnaise' and not similar things in a photo:\tthick and creamy\toff-white color\toil-based sauce\tin a jar or a container\twith a white lid or cover", 52], "o": ["No. 'o' is too vague or abstract to be visually concrete. However, the letter 'O' has a tangible appearance and is a visual representation of the abstract concept of a round shape.\nA few things that are visually similar to 'O' but are not 'O' are:\t0 (zero)\tQ\tC\tG\nThere are several useful visual features to tell there is an 'O' and not similar things in a photo:\tit is a circular letter\tits shape resembles a perfect circle\tit has no corners or angles\twhen used in a word, it is smaller than most other letters", 52], "landing gear": ["Yes. 'Landing gear' has a tangible appearance and refers to the wheels and other components that support an aircraft during landing and takeoff.\nA few things that are visually similar to 'landing gear' but are not 'landing gear' are:\tbicycle wheels\tshopping cart wheels\troller skate wheels\nThere are several useful visual features to tell there is 'landing gear' and not similar things in a photo:\tlocated on the underside of an aircraft\tlarge in size compared to most wheels\ton retractable legs or struts\tdesigned to absorb shock and provide stability during landing and takeoff.", 52], "chili": ["Yes. 'Chili' has a tangible appearance and is a type of pepper.\nA few things that are visually similar to 'chili' but are not 'chili' are:\tbell peppers\tjalape\u00f1os\thabaneros\tcayenne pepper\nThere are several useful visual features to tell there is 'chili' and not similar things in a photo:\tlong and thin shape\tred or green color\thot and spicy taste\tridged surface with pointed tip", 52], "sushi": ["Yes. 'Sushi' has a tangible appearance and is a type of Japanese cuisine.\nA few things that are visually similar to 'sushi' but are not 'sushi' are:\tmaki rolls\tspring rolls\tdumplings\tcheese sticks\nThere are several useful visual features to tell there is 'sushi' and not similar things in a photo:\traw or cooked fish or seafood\trice\tvinegar-based flavoring\tnori seaweed wrap\toften served with wasabi and soy sauce\tsmall, bite-sized pieces", 52], "motor cycle": ["Yes. 'Motor cycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motor cycle' but are not 'motor cycle' are:\tbicycle\tscooter\tmoped\tmotorized wheelchair\nThere are several useful visual features to tell there is 'motor cycle' and not similar things in a photo:\ttwo-wheeled vehicle\tpowerful engine or motor\thandlebars and throttle\tleather seats and saddlebags", 52], "octagon": ["Yes. 'Octagon' has a tangible appearance and is a type of polygon.\nA few things that are visually similar to 'octagon' but are not 'octagon' are:\ttriangle\tsquare\tpentagon\thexagon\nThere are several useful visual features to tell there is 'octagon' and not similar things in a photo:\teight sides\teight angles\tall sides are equal in length\tall angles are equal in measure", 52], "side train": ["No. 'Side train' is too vague or abstract to be distinguished in a photo. It is unclear what is being referred to as a 'side train'. Could you please provide more context or clarification?", 52], "gravel road": ["Yes. 'Gravel road' has a tangible appearance and is a type of road surface.\nA few things that are visually similar to 'gravel road' but are not 'gravel road' are:\tdirt road\tasphalt road\tcobblestone road\nThere are several useful visual features to tell there is 'gravel road' and not similar things in a photo: \tloose stones and rocks on the surface\tbrown or gray in color\tuneven texture and surface\tvisible tire marks or tracks", 51], "pink jacket": ["Yes. 'Pink jacket' has a tangible appearance and is a kind of clothing.\nA few things that are visually similar to 'pink jacket' but are not 'pink jacket' are:\tpink shirt\tpink dress\tpink sweater\tpink scarf\nThere are several useful visual features to tell there is 'pink jacket' and not similar things in a photo:\thas sleeves\tzips or buttons in the front\tmade of a thicker material than a shirt\tcan be worn as outerwear", 51], "skate boarder": ["Yes. 'Skate boarder' has a tangible appearance and is a type of athlete.\nA few things that are visually similar to 'skate boarder' but are not 'skate boarder' are:\tsnowboarder\tsurfers\tbicyclists\troller skaters\nThere are several useful visual features to tell there is 'skate boarder' and not similar things in a photo:\triding a skateboard\twearing protective gear(such as helmet, knee pads or elbow pads)\tperforming tricks on the skateboard\tjumping or riding on obstacles\tor ramps", 51], "liner": ["Yes. 'Liner' has a tangible appearance and is a type of ship.\nA few things that are visually similar to 'liner' but are not 'liner' are:\tcruise ship\ttanker\tferry\t\nThere are several useful visual features to tell there is 'liner' and not similar things in a photo:\tpassenger ship with cabins and facilities\tfor commercial or luxury transportation of people\tand/or goods\tlong, narrow shape with multiple decks\tprominent smokestack or funnel on top", 51], "cafe": ["Yes. 'Cafe' has a tangible appearance and is a kind of establishment.\nA few things that are visually similar to 'cafe' but are not 'cafe' are:\tbar\trestaurant\tdiner\tbakery\nThere are several useful visual features to tell there is 'cafe' and not similar things in a photo:\toutdoor seating tables\tand\tchairs\tmenu\tcoffee machine\tsign indicating the name or logo of the cafe.", 51], "dvd": ["Yes. 'DVD' has a tangible appearance and is a type of disc.\nA few things that are visually similar to 'DVD' but are not 'DVD' are:\tCD\tBlu-ray disc\tVinyl record\tFloppy disk\nThere are several useful visual features to tell there is 'DVD' and not similar things in a photo:\tdisc-shaped\tobject\thas the words \"DVD\" or \"Digital Versatile Disc\" printed on it\thas a capacity of 4.7 GB (single-layer) or 8.5 GB (dual-layer)\twhen inserted into a drive, it can play video, audio, or data.", 51], "purple flowers": ["Yes. 'Purple flowers' has a tangible appearance and a specific color and shape.\nA few things that are visually similar to 'purple flowers' but are not 'purple flowers' are:\tblue flowers\tirises\tviolets\tlavender\tcrocuses\nThere are several useful visual features to tell there are 'purple flowers' and not similar things in a photo:\tpetals in shades of purple or lilac\tgreen stems and leaves\tfive- or six-petaled flowers\tblossoms growing from a plant or stem", 51], "train bridge": ["Yes. 'Train bridge' has a tangible appearance and refers to a specific type of bridge designed for trains to pass over.\nA few things that are visually similar to 'train bridge' but are not 'train bridge' are:\tregular bridge\tdrawbridge\tviaduct\taqueduct\toverpass\nThere are several useful visual features to tell there is 'train bridge' and not similar things in a photo:\tbuilt over a body of water or a valley\thigher than a regular street bridge\tlong and straight structure\twith or without guardrails\tor trusses for support\tclearance or space for trains to pass through, often with electrical wires overhead.", 51], "silver rim": ["Yes. 'Silver rim' has a tangible appearance and is a type of border or edge.\nA few things that are visually similar to 'silver rim' but are not 'silver rim' are:\tGold rim\tCopper rim\tMetallic frame\tSilver paint\nThere are several useful visual features to tell there is 'silver rim' and not similar things in a photo:\tA narrow, metallic border\tSilver-colored\tMay encircle an object or be part of a dish or glassware", 51], "pointy ear": ["Yes. 'Pointy ear' has a tangible appearance and is a physical feature of certain animals and creatures.\nA few things that are visually similar to 'pointy ear' but are not 'pointy ear' are:\tcertain leaves\tdecorative headbands or jewelry\tfeathers\t\nThere are several useful visual features to tell there is 'pointy ear' and not similar things in a photo:\tit surrounds the ear canal\tit is noticeably pointed or tapered in shape\tit belongs to a creature or character with pointed ears\tasymmetric ears or one pointy ear and one normal ear (e.g., certain species of animals or mythical creatures like elves)", 51], "soccer goal": ["Yes. 'Soccer goal' has a tangible appearance and is a kind of sports equipment.\nA few things that are visually similar to 'soccer goal' but are not 'soccer goal' are:\tfield\thockey goal\tfootball goal\tpost\nThere are several useful visual features to tell there is 'soccer goal' and not similar things in a photo:\trectangular shape\twith a net inside\tthe frame is usually made with metal or plastic\tcrossbar and posts are in a different color from the net", 51], "orange bowl": ["Yes. 'Orange bowl' has a tangible appearance and is a specific type of bowl.\nA few things that are visually similar to 'orange bowl' but are not 'orange bowl' are:\tglass bowl\tred bowl\tceramic bowl\torange cup\nThere are several useful visual features to tell there is 'orange bowl' and not similar things in a photo:\tbright orange color\tbowl shape\twith or without patterns or designs\tmade of ceramic, plastic or other materials", 51], "footboard": ["Yes. 'Footboard' has a tangible appearance and is a part of a bed frame.\nA few things that are visually similar to 'footboard' but are not 'footboard' are:\theadboard\tside rails\tmattress\tbed frame\nThere are several useful visual features to tell there is 'footboard' and not similar things in a photo:\tpositioned at the foot of the bed\tattached to the bed frame\tlower than the headboard\tmay have decorative features or be plain", 51], "rims": ["Yes. 'Rims' has a tangible appearance and refers to the outer edge of a wheel on a vehicle.\nA few things that are visually similar to 'rims' but are not 'rims' are:\ttires\thubcaps\tbrake discs\nThere are several useful visual features to tell there are 'rims' and not similar things in a photo:\tmetallic finish\tcircular shape\tattached to the outer portion of the wheel\tdifferent designs and sizes than other similar things.", 51], "cat eye": ["Yes. 'Cat eye' has a tangible appearance and refers to the eyes of cats.\nA few things that are visually similar to 'cat eye' but are not 'cat eye' are:\thuman eye\treptile eye\tbird eye\t\nThere are several useful visual features to distinguish 'cat eye' from the listed similar things in a photo:\t\n- Slit pupils, typically vertically oriented\n- Reflective layer (tapetum lucidum) behind retina, creating a bright green or yellowish glow in the dark \n- Round or oval shape \n- Positioned horizontally on the head \n- Whiskers and fur around the eye area", 51], "salmon": ["Yes. 'Salmon' has a tangible appearance and is a kind of fish.\nA few things that are visually similar to 'salmon' but are not 'salmon' are:\ttrout\ttuna\tred snapper\nThere are several useful visual features to tell there is 'salmon' and not similar things in a photo:\tpink or orange color\tsilver scales and fins\twhite underbelly\tslender and torpedo-shaped body\tflattened head with a pointed snout\u1edb.", 51], "bruise": ["Yes, 'bruise' has a visually concrete appearance.\nA few things that are visually similar to 'bruise' but are not 'bruise' are:\tredness\tinsect bite\trash\tscar\nThere are several useful visual features to tell there is 'bruise' and not similar things in a photo:\n\tdiscoloration of the skin in shades of blue, purple, or green\t\n\tvariegated or mottled appearance\t\n\tpainful when touched or pressed\t\n\tsurrounding tissue is not inflamed.", 51], "sauce pizza": ["Yes. 'Sauce pizza' has a tangible appearance and is a type of pizza.\nA few things that are visually similar to 'sauce pizza' but are not 'sauce pizza' are:\tcheese pizza\tpepperoni pizza\tmargherita pizza\tcalzone\nThere are several useful visual features to tell there is 'sauce pizza' and not similar things in a photo:\ttomato sauce spread evenly over the crust\tmelted cheese on top of the sauce\tvarious toppings, such as vegetables or meat, may be added", 51], "watch wrist": ["No. 'Watch wrist' is too vague or abstract to be distinguished in a photo because it's a concept that combines two things (watch and wrist) and refers to their relationship.\n\nInstead, here are some possible rephrased questions:\nIs 'watch' a visually concrete concept?\nIs 'wrist' a visually concrete concept?\n\nIf you provide me the question you intended to ask, I would be happy to answer it.", 51], "toothbrush holder": ["Yes. 'Toothbrush holder' has a tangible appearance and is an object used for storing toothbrushes.\nA few things that are visually similar to 'toothbrush holder' but are not 'toothbrush holder' are:\tcup\tmug\tpen holder\nThere are several useful visual features to tell there is 'toothbrush holder' and not similar things in a photo:\tholes to store toothbrushes\tbase to keep the holder standing\tupright position\tfor bathroom use\toften made of plastic or ceramic", 51], "box truck": ["Yes. 'Box truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'box truck' but are not 'box truck' are:\tpickup truck\tvan\ttrailer\tbus\tdelivery van\nThere are several useful visual features to tell there is 'box truck' and not similar things in a photo:\ta large, rectangular cargo area with a roof and four sides\ta cab for the driver\tseparate cargo area from the cab with no rear windows a rear door that provides direct access to the cargo area\ttypically larger than a pickup truck and smaller than a semi-trailer truck", 51], "silver laptop": ["Yes. 'Silver laptop' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'silver laptop' but are not 'silver laptop' are:\ttablet\tsmartphone\tcamera\tdigital music player\nThere are several useful visual features to tell there is 'silver laptop' and not similar things in a photo:\trectangular screen attached to a keyboard\tsilver or metallic-colored casing\tdisplay and keyboard facing the user\thinges that allow the screen to be opened and closed", 51], "parking garage": ["Yes. 'Parking garage' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'parking garage' but are not 'parking garage' are:\twarehouse\tmulti-level storey building\tapartment building\t\nThere are several useful visual features to tell there is 'parking garage' and not similar things in a photo:\tmulti-level structure\tfor cars or other vehicles\tgates, entrance and exit signs\tlarge, open space\twith parking spots or lines\tmaybe some pillars, columns or beams", 51], "sink basin": ["Yes. 'Sink basin' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'sink basin' but are not 'sink basin' are:\tbathtub\tbucket\tpot\tpan\twashbasin\nThere are several useful visual features to tell there is 'sink basin' and not similar things in a photo:\tattached to a countertop or a wall\trectangular or oval-shaped\tbowl-shaped surface for holding water\ta faucet for water supply\ta drain on the bottom or back\tside surfaces for mounting or support.", 51], "stirrup": ["Yes. 'Stirrup' has a tangible appearance and is a part of a horse riding gear.\nA few things that are visually similar to 'stirrup' but are not 'stirrup' are:\thinges\thooks\tfootrests\nThere are several useful visual features distinguishing 'stirrup' from the listed similar things in a photo:\t\nmade of metal or wood\nfits around a horse riding saddle\nhas a footrest for the rider\nhas a small opening to allow the rider to put their foot in\nis attached to the saddle with a strap", 51], "halter": ["Yes. 'Halter' has a tangible appearance and is a type of horse harness.\nA few things that are visually similar to 'halter' but are not 'halter' are:\tbridle\theadcollar\tmuzzle\nThere are several useful visual features to tell there is 'halter' and not similar things in a photo:\tno bit\tthat goes around a horse's head and nose\twith straps going around the horse's head and over its ears\tused for leading and tying up a horse, but not for riding.", 51], "mcdonald": ["Yes. 'McDonald's' has a tangible appearance and is a fast-food chain.\nA few things that are visually similar to 'McDonald's' but are not 'McDonald's' are:\tother fast-food chains\tred and yellow colors\tburgers\tand fries\nThere are several useful visual features to tell there is 'McDonald's' and not similar things in a photo:\tthe distinctive McDonald's logo\twith the two golden arches\ta picture of the McDonald's mascot, Ronald McDonald, or any of the characters from its advertising campaign\ta photo of the restaurant's interior or exterior with the McDonald's branding and design", 51], "hand bag": ["Yes. 'Hand bag' has a tangible appearance and is an accessory used to carry personal items.\nA few things that are visually similar to 'hand bag' but are not 'hand bag' are:\tbackpack\tpurse\ttote\tbriefcase\nThere are several useful visual features to tell there is 'hand bag' and not similar things in a photo:\tsmall or medium-sized\tbuilt to be carried over the shoulder or held in hand\thas a handle or strap\tfor carrying personal items like phone, wallet, keys, etc.", 51], "brick tower": ["Yes. 'Brick tower' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'brick tower' but are not 'brick tower' are:\tstone tower\tchimney\tobelisk\tminaret\nThere are several useful visual features to tell there is 'brick tower' and not similar things in a photo:\tconstructed of individual bricks\tstacked in a cylindrical or polygonal shape\tmay have windows or arches\tusually taller than wide", 51], "stair case": ["Yes. 'Staircase' has a tangible appearance and is a series of steps to climb from one floor to another.\nA few things that are visually similar to 'staircase' but are not 'staircase' are:\tladder\tescalator\tramp\ttrail\nThere are several useful visual features to tell there is 'staircase' and not similar things in a photo:\tsuccession of steps\twith or without handrails\tconnecting two or more floors or levels", 51], "formation": ["No. 'Formation' is too vague or abstract to be distinguished in a photo.", 51], "grey concrete": ["Yes. 'Grey concrete' has a tangible appearance.\nA few things that are visually similar to 'grey concrete' but are not 'grey concrete' are:\tstone\twood\tmetal\tpaper\nThere are several useful visual features to tell there is 'grey concrete' and not similar things in a photo:\tsmooth surface or rough surface\tgrey color\tlooks solid and heavy\tvisible holes or cracks\tcold and hard to the touch", 51], "view mirrors": ["Yes. 'View mirrors' has a tangible appearance and is a type of automotive part.\nA few things that are visually similar to 'view mirrors' but are not 'view mirrors' are:\tsunglasses\tglasses\tbinoculars\nThere are several useful visual features to tell there are 'view mirrors' and not similar things in a photo:\tattached to a vehicle\trectangular or oval-shaped\twith reflective surface\tfor rearview and sideview vision", 51], "bedspread bed": ["No. 'Bedspread bed' is not a valid or commonly used term. The correct term would be 'bedspread' or 'bedding.'", 51], "grey stone": ["Yes. 'Grey stone' has a tangible appearance and is a type of rock or mineral.\nA few things that are visually similar to 'grey stone' but are not 'grey stone' are:\tconcrete\tgray paint\tashphalt\tgray fabric\nThere are several useful visual features to tell there is 'grey stone' and not similar things in a photo:\trocky surface\tnatural pattern\trough and uneven texture\tweighty appearance", 51], "tier": ["Yes. 'Tier' has a tangible appearance and is a level or layer of something, often in a stacked arrangement.\nA few things that are visually similar to 'tier' but are not 'tier' are:\tsteps\tshelves\trows levels\nThere are several useful visual features to tell there is 'tier' and not similar things in a photo:\tstacked arrangement\teach level is smaller than the next\toften used for cakes, desserts, or jewelry\tdisplayed in a pyramid shape usually with a flat surface on top.", 51], "blue basket": ["Yes. 'Blue basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'blue basket' but are not 'blue basket' are:\tbuckets\tbins\tcontainers\t\nThere are several useful visual features to tell there is 'blue basket' and not similar things in a photo:\tround or oval shape\thandles made of the same material as the basket\tblue color\tmaterial that looks like wicker or straw", 51], "foods": ["Yes. 'Foods' has a tangible appearance and can refer to various types of edible items.\nA few things that are visually similar to 'foods' but are not 'foods' are:\tpots and pans\tcooking utensils\tcookbooks\ttablecloths\nThere are several useful visual features to tell there is 'foods' and not similar things in a photo:\tvarious colors, shapes and textures\trecognizable items such as fruits, vegetables, meat, or bread\tdisplayed on plates, bowls or serving trays\tpresented in a way that suggests they are ready to be eaten (e.g. garnished with herbs, spices, or sauces)", 51], "chop sticks": ["Yes. 'Chop sticks' has a tangible appearance and is a utensil used for eating.\nA few things that are visually similar to 'chop sticks' but are not 'chop sticks' are:\tpencils\ttweezers\twooden skewers\tdrum sticks\nThere are several useful visual features to tell there is 'chop sticks' and not similar things in a photo:\ttwo thin and pointed sticks, usually of equal length\tsmooth surface\ttapered end for picking up food", 51], "hammer": ["Yes. 'Hammer' has a tangible appearance and is a tool for striking nails, among other things.\nA few things that are visually similar to 'hammer' but are not 'hammer' are:\tmallet\tsledgehammer\taxe\tpick\nThere are several useful visual features to tell there is 'hammer' and not similar things in a photo:\theavy and solid metal head\twith a long and sturdy wooden handle\toften with a claw on the other end\tfor striking nails, hitting objects to break them", 51], "flower pattern": ["Yes. 'Flower pattern' has a tangible appearance and is a decorative motif inspired by flowers.\nA few things that are visually similar to 'flower pattern' but are not 'flower pattern' are:\tleaf pattern\tpaisley\tprint inspired by nature\tprint inspired by art nouveau or art deco\nThere are several useful visual features to tell there is 'flower pattern' and not similar things in a photo:\trepetitive pattern with flower-like shapes\torangic, curvy lines\telements such as petals or stamens with a distinct symmetry\tuses colors commonly associated with flowers (such as pink, red, purple, or yellow)", 51], "pumpkins": ["Yes. 'Pumpkins' has a tangible appearance and is a type of squash.\nA few things that are visually similar to 'pumpkins' but are not 'pumpkins' are:\tgourds\tsquashes\tmelons\nThere are several useful visual features to tell there is 'pumpkins' and not similar things in a photo:\tround or oblong shape\torange color (or yellow, green, or white)\tridged or smooth skin\twith a thick stem on top\thollow inside\twhen cut open, has pulp and seeds inside.", 51], "metal rod": ["Yes. 'Metal rod' has a tangible appearance.\nA few things that are visually similar to 'metal rod' but are not 'metal rod' are:\tpipe\tbar\tscrewdriver\tpen\nThere are several useful visual features to tell there is 'metal rod' and not similar things in a photo:\tlong and cylindrical\tmade of metal\tsolid and dense surface\tno curves or bends in the structure\tno visible writing or markings on the surface", 51], "phone number": ["No. 'Phone number' is too vague or abstract to be distinguished in a photo.", 51], "train conductor": ["Yes. 'Train conductor' has a tangible appearance and is a person who operates a train and ensures the safety of passengers.\nA few things that are visually similar to 'train conductor' but are not 'train conductor' are:\tbus driver\tpilot\tferry captain\ttaxi driver\t\nThere are several useful visual features to tell there is a 'train conductor' and not similar things in a photo:\twearing a train conductor hat and uniform\thaving a whistle or a lantern in hand\thaving a device to communicate with the train engineer or station\thaving a badge or a name tag with the train company logo in the uniform\thaving a train schedule and ticket puncher", 51], "tote bag": ["Yes. 'Tote bag' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'tote bag' but are not 'tote bag' are:\tbackpack\tpurse\tshopping bag\tmessenger bag\nThere are several useful visual features to tell there is 'tote bag' and not similar things in a photo:\tmedium to large in size\ttwo parallel handles for carrying\topen top (without a zipper or closure)\tmade of fabric, canvas or leather\tboxy shape\tor rectangular shape\twith or without a bottom panel to reinforce the bag's shape.", 51], "brown hair": ["Yes. 'Brown hair' has a tangible appearance and refers to a specific color and texture of hair.\nA few things that are visually similar to 'brown hair' but are not 'brown hair' are:\tchocolate\tfur\twood bark\nThere are several useful visual features to distinguish 'brown hair' from the listed similar things in a photo:\tattached to a human head\tindividual strands with varying lengths\tand thicknesses\thas natural highlights or lowlights\treflects light differently depending on lighting conditions", 51], "ocean water": ["Yes. 'Ocean water' has a tangible appearance.\nA few things that are visually similar to 'ocean water' but are not 'ocean water' are:\tpool water\triver water\tlake water\tglacier water\nThere are several useful visual features to tell there is 'ocean water' and not similar things in a photo:\tsaltwater\tdark blue or green\tcolorful underwater life\twaves and tides\tocean foam and sprays", 51], "boogie board": ["Yes. 'Boogie board' has a tangible appearance and is a kind of board used for surfing waves.\nA few things that are visually similar to 'boogie board' but are not 'boogie board' are:\tsurfboard\tpaddleboard\tkayak\tbodyboard\nThere are several useful visual features to tell there is 'boogie board' and not similar things in a photo:\tshorter and wider than a surfboard\tno fins or only one fin\toncoming waves or the shore in the background\tperson lying on it or holding it in the water while riding a wave", 51], "giraffe ear": ["Yes. 'Giraffe ear' has a tangible appearance and is a specific part of the giraffe's anatomy.\nA few things that are visually similar to 'giraffe ear' but are not 'giraffe ear' are:\tcow ear\thorse ear\tdeer ear\nThere are several useful visual features to tell there is 'giraffe ear' and not similar things in a photo:\tvery long and narrow shape\tsame color pattern as a giraffe's coat (tan with brown spots)\tlocated at the top of the giraffe's head", 51], "sprinkle": ["Yes. 'Sprinkle' has a tangible appearance and usually refers to small bits of food decoration.\nA few things that are visually similar to 'sprinkle' but are not 'sprinkle' are:\tgrains of sand\tglitter\tdust\tpowder\nThere are several useful visual features to tell there is 'sprinkle' and not similar things in a photo:\tcolorful\tmulti-shaped\tsmall size\tfood-related appearance (e.g. candy sprinkles, chocolate sprinkles)", 51], "paper towel holder": ["Yes. 'Paper towel holder' has a tangible appearance and is a type of holder for paper towels.\nA few things that are visually similar to 'paper towel holder' but are not 'paper towel holder' are:\ttoilet paper holder\tbath towel holder\tkitchen utensil holder\twine bottle holder\nThere are several useful visual features to tell there is 'paper towel holder' and not similar things in a photo:\tupright position of the holder\tcylindrical shape\thorizontal bar to hold the paper towel\tspacing between the bar and the base of the holder or the wall it's attached to.", 50], "tennis court net": ["Yes. 'Tennis court net' has a tangible appearance and is a part of a tennis court.\nA few things that are visually similar to 'tennis court net' but are not 'tennis court net' are:\tvolleyball net\tbadminton net\tsoccer goal net\tfishing net\nThere are several useful visual features to tell there is 'tennis court net' and not similar things in a photo:\twhite net\twith a long cord at each end\tstrung across a rectangular court with two poles on either side", 50], "fields": ["Yes. 'Fields' has a tangible appearance and refers to a piece of land used for agricultural purposes.\nA few things that are visually similar to 'fields' but are not 'fields' are:\tParks\tForests\tMeadows\nThere are several useful visual features that distinguish 'fields' from the listed similar things in a photo:\tSymmetrically arranged rows of crops\tgenerally flat terrain\tdifferent patterns of colors across the land, indicating various crops or stages of growth", 50], "bulbs": ["Yes. 'Bulbs' have a tangible appearance and can refer to a variety of objects such as light bulbs, plant bulbs, or electrical bulbs.\nA few things that are visually similar to 'bulbs' but are not 'bulbs' are:\tornaments\tgarlic\tround stones\nThere are several useful visual features to tell there are 'bulbs' and not similar things in a photo:\tvarious shapes and sizes\tcylindrical or round in shape\tmay have a threaded base\tmay have a metal filament\tor glass shell for containing filaments", 50], "ladle": ["Yes. 'Ladle' has a tangible appearance and is a type of kitchen utensil.\nA few things that are visually similar to 'ladle' but are not 'ladle' are:\tspoon\tcup\tbowl\tmug\tjug\nThere are several useful visual features to tell there is 'ladle' and not similar things in a photo:\tlong handle\tbowl-shaped bottom\tpouring spout at the end of the bowl\tpurpose for serving or transferring liquid or soups from one container to another.", 50], "way street sign": ["No. 'Way street sign' is too vague or abstract to be distinguished in a photo. However, if you mean 'One Way street sign', then it is a visually concrete concept.\nA few things that are visually similar to 'One Way street sign' but are not 'One Way street sign' are:\tStop sign\tYield sign\tSpeed Limit sign\nThere are several useful visual features to tell there is 'One Way street sign' and not similar things in a photo:\twhite rectangular sign\twith big bold letters saying \"One Way\"\tpointing in a specific direction.", 50], "eggplant": ["Yes. 'Eggplant' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'eggplant' but are not 'eggplant' are:\tpurple peppers\tpurple potatoes\tpurple cabbage\nThere are several useful visual features to tell there is 'eggplant' and not similar things in a photo:\tegg-shaped\tdark-purple skin or white and purple stripes\tgreen stem on top\tno visible leaves or flowers on the stem\tfirm and smooth flesh when cut in half\tseeds in center of flesh", 50], "sale sign": ["Yes. 'Sale sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'sale sign' but are not 'sale sign' are:\tcaution sign\tparking sign\tinformation sign\tdirectional signs\nThere are several useful visual features to tell there is 'sale sign' and not similar things in a photo:\tbright or contrasting colors (e.g. red and white)\twith the word \"sale\" or a percentage off in big, bold letters\tcould feature images of products or discounts", 50], "storm": ["No. 'Storm' is too vague or abstract to be distinguished in a photo. However, there are some visual features associated with storms such as dark clouds, lightning, and strong winds that can be present in a photo and suggest the presence of a storm.\nA few things that are visually similar to 'storm' but are not 'storm' are:\tdark clouds\tfog\tsmoke\tfireworks\tnebulae\nSome useful visual features for distinguishing 'storm' from similar things in a photo are:\tlightning or thunderbolts\train or hail\tstraight lines between clouds or cloudy funnels\tturbulent water or waves on a sea or an ocean.", 50], "house plant": ["Yes. 'House plant' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'house plant' but are not 'house plant' are:\tartificial plant\toutdoor plant\tweed\nThere are several useful visual features to tell there is 'house plant' and not similar things in a photo:\tpotted plant\tindoor location\tvariety of leaf shapes and sizes\tvisible soil in the pot", 50], "plains": ["Yes. 'Plains' has a tangible appearance and refers to a large area of flat land.\nA few things that are visually similar to 'plains' but are not 'plains' are:\tdeserts\tbeaches\tparks\tfarmland\nThere are several useful visual features to tell there are 'plains' and not similar things in a photo:\tlarge, open area\tflat or lightly rolling terrain\ttall grasses and wildflowers\tmaybe some trees but not a dense forest\tno mountains or hills in the vicinity", 50], "leather belt": ["Yes. 'Leather belt' has a tangible appearance and is an accessory worn around the waist.\nA few things that are visually similar to 'leather belt' but are not 'leather belt' are:\tfabric belt\tsash\trope\tchain\nThere are several useful visual features to tell there is 'leather belt' and not similar things in a photo:\tstrip of leather\thole in leather for buckle\tbuckle for fastening\tbelt loops for securing the belt to pants or skirt\tsubtle patterning on the leather.", 50], "metal shelf": ["Yes. 'Metal shelf' has a tangible appearance and is a type of storage furniture.\nA few things that are visually similar to 'metal shelf' but are not 'metal shelf' are:\tbookcase\tfile cabinet\tlocker\ttoolbox\nThere are several useful visual features to tell there is 'metal shelf' and not similar things in a photo:\tmade of metal\tequally spaced shelves\ton legs or affixed to a wall\trectangular or square shape\tshelves may have a lip to keep things from sliding off", 50], "stuffed bears": ["Yes. 'Stuffed bears' has a tangible appearance and is a type of plush toy.\nA few things that are visually similar to 'stuffed bears' but are not 'stuffed bears' are:\tdolls\tpillows\tcushions\nThere are several useful visual features to tell there is 'stuffed bears' and not similar things in a photo:\tbear-like appearance\tfurry and soft material\tglass or plastic eyes\tand a stitched nose", 50], "apartment": ["Yes. 'Apartment' has a tangible appearance and is a type of housing.\nA few things that are visually similar to 'apartment' but are not 'apartment' are:\thotel\toffice building\tschool\tdormitory\nThere are several useful visual features to tell there is 'apartment' and not similar things in a photo:\tthe presence of a front door, windows, and balconies/residential units or divisions\tregularly spaced openings or entrances for each unit\tor a numbered entrance\ta living space intended for occupancy by one or more people", 50], "kick stand": ["Yes. 'Kick stand' has a tangible appearance and is a part of a bicycle or motorcycle.\nA few things that are visually similar to 'kick stand' but are not 'kick stand' are:\tfootrest\tcenter stand\tside stand\nThere is only one useful visual feature to distinguish 'kick stand' from the listed similar things in a photo: small contact surface that keeps the bike upright.", 50], "yogurt": ["Yes. 'Yogurt' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'yogurt' but are not 'yogurt' are:\tpudding\tmayonnaise\twhipped cream\tice cream\nThere are several useful visual features to tell there is 'yogurt' and not similar things in a photo:\tthick and creamy texture\twhite or off-white color\tserved in a cup or bowl\twith or without fruit or granola on top", 50], "thermostat": ["Yes. 'Thermostat' has a tangible appearance and is a device used to control temperature.\nA few things that are visually similar to 'thermostat' but are not 'thermostat' are:\tclock\talarm\tpanel\nThere are several useful visual features to tell there is 'thermostat' and not similar things in a photo:\trectangular shape\tdisplay of temperature or settings\tdials or buttons for adjusting temperature\tplugged into a wall or mounted on a wall", 50], "nozzle": ["Yes. 'Nozzle' has a tangible appearance and is part of a machine or device.\nA few things that are visually similar to 'nozzle' but are not 'nozzle' are:\tspout\ttap\tfaucet\tshowerhead\nThere are several useful visual features to tell there is 'nozzle' and not similar things in a photo:\tlong and narrow\ttapered or pointed shape\tconnected to a hose, pipe, or machinery\tnozzle opening where material comes out (e.g. water, gas, air)", 50], "substance": ["No. 'Substance' is too vague or abstract to have a tangible appearance that can be distinguished in a photo.", 50], "light bulb": ["Yes. 'Light bulb' has a tangible appearance and is a type of bulb used for lighting.\nA few things that are visually similar to 'light bulb' but are not 'light bulb' are:\tball\tdisco ball\tfruit\tplastic toy\nThere are several useful visual features to tell there is 'light bulb' and not similar things in a photo:\tglass bulb with a metal or plastic base\tconical shape with a rounded top\tmetal filament inside\tbright and glowing when turned on", 50], "curtain rod": ["Yes. 'Curtain rod' has a tangible appearance and refers to a long and straight object used to hang curtains.\nA few things that are visually similar to 'curtain rod' but are not 'curtain rod' are:\tpole\tpipe\tbar\thanger\nThere are several useful visual features to tell there is 'curtain rod' and not similar things in a photo: \tconsist of two long, thin rods with some kind of connecting piece in the middle\tmounted on brackets or supports attached to the wall or window frame\tflat, circular ends for the curtain rings to slide onto.", 50], "paper plates": ["Yes. 'Paper plates' has a tangible appearance and is a type of disposable dishware.\nA few things that are visually similar to 'paper plates' but are not 'paper plates' are:\tplastic plates\tchina plates\twooden plates\tmetal plates\nThere are several useful visual features to tell there is 'paper plates' and not similar things in a photo:\tflat and round shape\ttexture and color similar to paper\tsometimes has a slightly waxy coating on one side\tintended to be disposable after a single use.", 50], "skaters": ["Yes. 'Skaters' has a tangible appearance and refers to the people who ice skate or rollerblade.\nA few things that are visually similar to 'skaters' but are not 'skaters' are:\tpeople walking on ice or snow\tdancers\tpeople running\nThere are several useful visual features to tell there are 'skaters' and not similar things in a photo:\twearing ice skates or rollerblades\tmoving on a smooth surface\tperforming jumps or spins\tother skating equipment, such as helmets or pads", 50], "cheesecake": ["Yes. 'Cheesecake' has a tangible appearance and is a kind of dessert.\nA few things that are visually similar to 'cheesecake' but are not 'cheesecake' are:\tcream pie\ttart\tcustard\tquiche\nThere are several useful visual features to tell there is 'cheesecake' and not similar things in a photo:\tround or square-shaped dessert\ton a crust of crushed cookies or graham crackers\twhite or cream-colored filling, sometimes with a marbled effect on top\tcan be topped with fruit, caramel or chocolate sauce, whipped cream or nuts.", 50], "sliver": ["Yes. 'Sliver' has a visually concrete appearance and refers to a thin, narrow piece of a material.\nA few things that are visually similar to 'sliver' but are not 'sliver' are:\tsplinter\tstrip\tshred\tslice\tthread\nThere are several useful visual features to distinguish 'sliver' from the listed similar things in a photo:\tvery thin and narrow shape\tusually elongated, with a pointed end\tnot uniform in width or thickness\tmost commonly made of wood or metal\tmay appear shiny or reflective depending on the material it's made of.", 50], "kinds": ["No. 'Kinds' is too vague or abstract to be distinguished in a photo.", 50], "flower bed": ["Yes. 'Flower bed' has a tangible appearance and is a specific type of garden.\nA few things that are visually similar to 'flower bed' but are not 'flower bed' are:\tvegetable garden\therb garden\tlawn\tempty patch of dirt\nThere are several useful visual features to tell there is 'flower bed' and not similar things in a photo:\torganized arrangement of various kinds of flowers\tin a raised area or surrounded by edging\tbrown or rich soil\tminimal to no weeds or grass growth\tin bloom with colorful flowers", 50], "shears": ["Yes. 'Shears' has a tangible appearance and is a tool for cutting.\nA few things that are visually similar to 'shears' but are not 'shears' are:\tscissors\tknives\trazors\thand pruners\nThere are several useful visual features to tell there is 'shears' and not similar things in a photo:\ttwo blades\twithout a point\tusually with large handles\tdesigned to cut things like fabric, hair, or plants.", 50], "backdrop": ["Yes. 'Backdrop' has a tangible appearance and is a type of background used in photography or film.\nA few things that are visually similar to 'backdrop' but are not 'backdrop' are:\twall\tpainting\tdigital photo manipulation\nThere are several useful visual features to tell there is 'backdrop' and not similar things in a photo:\tsmooth surface\tmonochrome or gradient color\tclearly separated from the foreground\tno visible texture or details that may distract from the main subject of the photo.", 50], "glaze": ["Yes. 'Glaze' has a tangible appearance and is a type of coating.\nA few things that are visually similar to 'glaze' but are not 'glaze' are:\tpaint\twax\tvarnish\ticing\nThere are several useful visual features to tell there is 'glaze' and not similar things in a photo:\tsmooth and glossy surface\ttranslucent or transparent\tcovering pottery, ceramics, or baked goods.", 50], "aquarium": ["Yes. 'Aquarium' has a tangible appearance and is a type of enclosure for aquatic animals.\nA few things that are visually similar to 'aquarium' but are not 'aquarium' are:\tfishbowl\tswimming pool\twater tank\nThere are several useful visual features to tell there is 'aquarium' and not similar things in a photo:\tglass or transparent material\tdecorative plants, stones, or corals\twater with aquatic animals, plants, or rocks\tlighting or artificial lighting systems\tfilters or air pumps.", 50], "bus tire": ["Yes. 'Bus tire' has a tangible appearance and is a kind of tire.\nA few things that are visually similar to 'bus tire' but are not 'bus tire' are:\tcar tire\tbike tire\ttruck tire\twheelbarrow tire\nThere are several useful visual features to tell there is 'bus tire' and not similar things in a photo:\tlarge size\twider than car tires\tbold and prominent tread pattern\tspecific markings on the sidewalls\tthat it\u2019s mounted on a bus", 50], "window panes": ["Yes. 'Window panes' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'window panes' but are not 'window panes' are:\tmirrors\tpicture frames\ttiles\nThere are several useful visual features to tell there is 'window panes' and not similar things in a photo:\tclear or translucent\tencased in a frame\tadjoined to other window panes in a row", 50], "patterns": ["Yes. 'Patterns' has a tangible appearance and can refer to a wide range of repeating shapes or designs.\nA few things that are visually similar to 'patterns' but are not 'patterns' are: textures, gradients, abstract art, shapes\nThere are several useful visual features to tell there is 'patterns' and not similar things in a photo: clearly repeating shapes or designs, regular spacing between elements, consistent colors or shapes, and a sense of rhythm or order in the composition.", 50], "spine": ["Yes. 'Spine' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'spine' but are not 'spine' are:\tstems of plants\tfence posts\t\nThere are several useful visual features to tell there is 'spine' and not similar things in a photo:\tcurved S-shape\tmade of multiple vertebrae\tbony protrusions called spinous processes extending upward\t\nThe direction in which the spinous processes point differentiates the cervical, thoracic, and lumbar regions of the spine.", 50], "title": ["No. 'Title' is too vague and abstract to have a tangible appearance and cannot be distinguished in a photo. \nThere are no things visually similar to 'title'.", 50], "tulips": ["Yes. 'Tulips' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'tulips' but are not 'tulips' are:\tdaffodils\tlilies\tcrocuses\troses\nThere are several useful visual features to tell there are 'tulips' and not similar things in a photo:\tcup-shaped flowers with six petals\tbright, vibrant colors (red, pink, yellow, etc.)\tone flower per stem\tlong and slender leaves growing directly from the stem", 50], "beer bottles": ["Yes. 'Beer bottles' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'beer bottles' but are not 'beer bottles' are:\twine bottles\tolive oil bottles\tsoda bottles\tperfume bottles\nThere are several useful visual features to tell there is 'beer bottles' and not similar things in a photo:\tbrown or green colored glass (usually)\tbottle cap or crown\ttop opening for drinking or pouring\tbeer branding or label\tprinted ABV percentage (alcohol by volume)", 50], "side bus": ["No. 'Side bus' is too vague or abstract to be distinguished in a photo. It is unclear what is meant by 'side bus'.", 50], "stuffed dog": ["Yes. 'Stuffed dog' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'stuffed dog' but are not 'stuffed dog' are:\treal dog plush toy teddy bear\t\nThere are several useful visual features to tell there is 'stuffed dog' and not similar things in a photo:\tfake fur or fabric material\tstuffed with cotton or other materials\tbutton-like eyes or embroidered eyes\tnose shaped with fabric or a button\tfeatures such as paws, ears or a tail that are stitched on", 50], "blue shoes": ["Yes. 'Blue shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'blue shoes' but are not 'blue shoes' are:\tblue socks\tdenim shoes\tblue sandals\tblue slippers\nThere are several useful visual features to tell there are 'blue shoes' and not similar things in a photo:\tfootwear\tlaces or buckles\tsoles\tdistinguishable silhouettes\twhere the feet would be placed\tfabric or material used in the construction of the shoe", 50], "raspberries": ["Yes. 'Raspberries' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'raspberries' but are not 'raspberries' are:\tblackberries\tcranberries\tstrawberries\nThere are several useful visual features to tell there is 'raspberries' and not similar things in a photo:\tred or pink-rounded fruit\twith small bumps or druplets\ton a leafy green stem.", 50], "grey fence": ["Yes. 'Grey fence' has a tangible appearance and is a type of outdoor barrier.\nA few things that are visually similar to 'grey fence' but are not 'grey fence' are:\twall\twooden fence\thedge\nThere are several useful visual features to tell there is 'grey fence' and not similar things in a photo:\tgray color\trectangular shapes\tvisible metal or wire\tgrid pattern or texture\tstraight, even rows or columns of posts or slats", 49], "blue cover": ["Yes. 'Blue cover' has a tangible appearance and refers to any type of object that is blue and functions as a covering material.\nA few things that are visually similar to 'blue cover' but are not 'blue cover' are: blue fabric, blue plastic sheets, blue tarp, blue blankets, blue rugs.\nThere are few useful visual features to distinguish 'blue cover' from the listed similar things in a photo as they all are visually similar. The features that can be useful depend on the context like texture, pattern or the object it is covering.", 49], "glass dish": ["Yes. 'Glass dish' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'glass dish' but are not 'glass dish' are:\tbowl\tplate\tvase\tjar\t\nThere are several useful visual features to tell there is 'glass dish' and not similar things in a photo:\tmade of glass\tor clear material\tshallow, sometimes with a flat bottom and sometimes with a curved one\tcan be used for baking, serving, or holding food or objects.", 49], "orange wheels": ["Yes. 'Orange wheels' has a tangible appearance and refers to a specific type of wheel.\nA few things that are visually similar to 'orange wheels' but are not 'orange wheels' are:\tlemon wheels\tbicycle wheels\ttire rims\toranges cut into circles\nThere are several useful visual features to tell there are 'orange wheels' and not similar things in a photo:\tcircular shape\tbright orange color\thollow center often used to garnish cocktails or food", 49], "scratches": ["Yes. 'Scratches' has a tangible appearance and refers to marks or abrasions on a surface.\nA few things that are visually similar to 'scratches' but are not 'scratches' are:\tdirt marks\tscuffs\tcracks\tveins\nThere are several useful visual features to tell there are 'scratches' and not similar things in a photo:\tirregular straight or curved lines\tmarks that look like they were made by something sharp or abrasive\tdifferent texture or color compared to the surface around it.", 49], "airplane engine": ["Yes. 'Airplane engine' has a tangible appearance and is a mechanical component of an airplane.\nA few things that are visually similar to 'airplane engine' but are not 'airplane engine' are:\tcar engine\tmotorcycle engine\tlawnmower engine\tboat engine\nThere are several useful visual features to tell there is 'airplane engine' and not similar things in a photo:\tlarge size and scale\tattached to an airplane\tmetallic appearance\twith various tubes and pipes\thuge blades or turbines\tforward-facing fan or propeller.", 49], "daisy": ["Yes. 'Daisy' has a tangible appearance and refers to a type of flower.\nA few things that are visually similar to 'daisy' but are not 'daisy' are:\tSunflower\tMarigold\tButtercup\tDandelion\nThere are several useful visual features to tell there is a 'daisy' and not similar things in a photo:\twhite petals with a yellow center\tround or oval-shaped flower\tbright or pastel colors\tserrated (toothed) leaves\ton a tall and thin stem.", 49], "hair tie": ["Yes. 'Hair tie' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'hair tie' but are not 'hair tie' are:\trubber band\tbracelet\tbungee cord\nThere are several useful visual features to tell there is 'hair tie' and not similar things in a photo:\tsmooth and elastic\tflexible and adjustable\tendless loop shape\tfor wrapping around hair", 49], "wood floors": ["Yes. 'Wood floors' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'wood floors' but are not 'wood floors' are:\ttile floors\tlaminate floors\tlinoleum floors\tstone floors\nThere are several useful visual features to tell there is 'wood floors' and not similar things in a photo:\twooden planks\tclutter-free surface\tnatural-looking grain pattern\tvariation in color and texture\tsmooth finish", 49], "blue screen": ["Yes. 'Blue screen' has a tangible appearance and is a type of background used in film or video production.\nA few things that are visually similar to 'blue screen' but are not 'blue screen' are:\tgreen screen\tgrey screen\twhite screen\t\nThere are several useful visual features to tell there is 'blue screen' and not similar things in a photo:\tvibrant blue color\tno visible patterns or textures\tflat and even surface\t\n\nNote: Although green screen, grey screen, and white screen are visually similar to blue screen, they each have different visual properties and are used for different purposes in film or video production.", 49], "chip": ["Yes. 'Chip' has a tangible appearance and is a small electronic device for storing data or carrying out calculations.\nA few things that are visually similar to 'chip' but are not 'chip' are:\tcrumb\tbiscuit\tpiece of wood\nThere are several useful visual features to tell there is 'chip' and not similar things in a photo:\tsmall size\trectangular or square shape\tmetallic or plastic appearance\tdots or lines on its surface\tfor storing data or carrying out calculations.", 49], "blue socks": ["Yes. 'Blue socks' has a tangible appearance and is a specific type of clothing item.\nA few things that are visually similar to 'blue socks' but are not 'blue socks' are:\tpink socks\tred shoes\tblue gloves\tblue scarf\nThere are several useful visual features to tell there are 'blue socks' and not similar things in a photo:\tlocated on the feet\toblong shape\tmade of a soft fabric such as cotton or wool\tblue color.", 49], "wet sidewalk": ["Yes. 'Wet sidewalk' has a tangible appearance and is a type of outdoor surface.\nA few things that are visually similar to 'wet sidewalk' but are not 'wet sidewalk' are:\tclean sidewalk\tshiny pavement\tpainted sidewalk\nThere are several useful visual features to tell there is 'wet sidewalk' and not similar things in a photo:\tdarkened color\tslippery appearance\treflection of light\tspeckled appearance from raindrops on the surface", 49], "ashtray": ["Yes. 'Ashtray' has a tangible appearance and is a type of container for cigarette ashes.\nA few things that are visually similar to 'ashtray' but are not 'ashtray' are:\tbowl\tplate\tglass\tcup\nThere are several useful visual features to tell there is 'ashtray' and not similar things in a photo:\tdeep enough to hold ashes\tcigarette rests on the edge or in a groove\tsometimes has a lid\tor made of fire-resistant material", 49], "wire basket": ["Yes. 'Wire basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'wire basket' but are not 'wire basket' are:\tlaundry basket\tshopping basket\tpicnic basket\twaste basket\tbicycle basket\nThere are several useful visual features to tell there is 'wire basket' and not similar things in a photo:\tmade of metal wires or rods\topenwork or mesh design\thandles on the sides or top\thollow and lightweight", 49], "tan couch": ["Yes. 'Tan couch' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'tan couch' but are not 'tan couch' are:\tbeige armchair\tsand-colored recliner\tbrown ottoman\nThere are several useful visual features to tell there is 'tan couch' and not similar things in a photo:\tlong cushioned seat\tfull back support\tarmrests\tmade of fabric or leather\ttan, light-brown or beige color.", 49], "decker buses": ["Yes. 'Decker buses' has a tangible appearance and is a type of public transportation.\nA few things that are visually similar to 'decker buses' but are not 'decker buses' are:\tcars\ttrucks\ttrams\nThere are several useful visual features to tell there is 'decker buses' and not similar things in a photo:\ttwo levels\tside stairs or ladder\tfor public transportation\tusually red or blue color", 49], "baseman": ["Yes. 'Baseman' has a tangible appearance and is a position in the game of baseball.\nA few things that are visually similar to 'baseman' but are not 'baseman' are:\tpitcher\tcatcher\tshortstop\toutfielder\nThere are several useful visual features to tell there is 'baseman' and not similar things in a photo:\tstanding on a base\twearing a glove\twearing a uniform\twearing a hat\tclose to the field of play\thaving a specific position (first baseman, second baseman, etc.)", 49], "luggage tag": ["Yes. 'Luggage tag' has a tangible appearance and is a kind of identification tag for baggage.\nA few things that are visually similar to 'luggage tag' but are not 'luggage tag' are:\tbookmarks\tkeys\tgift tags\nThere are several useful visual features to tell there is 'luggage tag' and not similar things in a photo:\trectangular or oblong shape\thanging from a piece of luggage, backpack, or bag\tbright or contrasting colors\towner's name, address, or contact information", 49], "beverages": ["No. 'Beverages' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider 'beverages' as a category of drinks, then it can be a visually concrete concept. \n\nA few things that are visually similar to 'beverages' but are not 'beverages' are:\twater bottles\tperfume bottles\tmedical liquids\tsoup cans\n\nThere are several useful visual features to tell there is 'beverages' and not similar things in a photo:\tliquid in a glass or container\tstraw, lid, or bottle cap\tpotentially bubbles or foam\tcolor or transparency of the liquid\ttype of glass or cup (e.g. wine glass, coffee mug)", 49], "blue sticker": ["Yes. 'Blue sticker' has a tangible appearance and is a type of label or adhesive item.\nA few things that are visually similar to 'blue sticker' but are not 'blue sticker' are:\tpost-it notes \ttape\ttag\tstamp\nThere are several useful visual features to tell there is 'blue sticker' and not similar things in a photo:\trectangular or square shape\tbright blue color\tpeeling from a surface or attached to an object\tsmooth or glossy texture", 49], "hour": ["No. 'Hour' is too vague or abstract to be distinguished in a photo.", 49], "light bulbs": ["Yes. 'Light bulbs' has a tangible appearance and is a kind of electrical equipment.\nA few things that are visually similar to 'light bulbs' but are not 'light bulbs' are:\tcandle\tincense stick\tlamp\ttraffic light\nThere are several useful visual features to tell there is 'light bulbs' and not similar things in a photo:\tglass or plastic bulb-shaped envelope\tfor screwing or insertion into a socket\tcontaining a filament\tto emit light\twhen turned on, lit up\tthe presence of metal contact points", 49], "indicator": ["The concept 'indicator' can be either too vague or too concrete, depending on the specific context in which it's being used. If we're talking about an object or device used to display a specific value or state, such as a car's speedometer, then the answer is 'yes'. If we're talking about a more general concept of something that signifies or shows a direction, then the answer is 'no'.\nAssuming we're talking about an object or device used to display a specific value or state, some things that are visually similar to 'indicator' but are not 'indicator' are:\tcounter\tmeter\tgauge\tdisplay\nThere are several useful visual features to tell there is an 'indicator' and not similar things in a photo:\tdigital or analog display\tshowing a specific value or state\tmarked with numbers, symbols or arrows\tset in a specific context or device.", 49], "brown bricks": ["Yes. 'Brown bricks' has a tangible appearance and is a building material.\nA few things that are visually similar to 'brown bricks' but are not 'brown bricks' are:\tstones\ttiles\twood panels\nThere are several useful visual features to distinguish 'brown bricks' from the listed similar things in a photo:\trectangular shape\ttextured surface\twith or without mortar lines\tdark brown, reddish-brown or tan color", 49], "snacks": ["No. 'Snacks' is too vague or abstract to be visually concrete. \nHowever, a few things that are visually similar to the concept 'snacks' are meals or appetizers, candies or desserts, fruits or nuts, and chips or fried food.\nUseful visual features for distinguishing 'snacks' from these similar things in a photo might include small serving sizes, snack packaging, finger food or portable food, and common snack foods like popcorn, crackers, pretzels, and granola bars.", 49], "luggage bag": ["Yes. 'Luggage bag' has a tangible appearance and is a type of bag used for travel.\nA few things that are visually similar to 'luggage bag' but are not 'luggage bag' are:\tbackpack\tmessenger bag\tpurse\thandbag\nThere are several useful visual features to tell there is 'luggage bag' and not similar things in a photo:\tlarge size\tsturdy and durable material\twheels and a handle for easy movement\tpockets or compartments for storage\tof a style commonly associated with travel.", 49], "giraffe eye": ["Yes. 'Giraffe eye' has a tangible appearance and is a body part of giraffe.\nA few things that are visually similar to 'giraffe eye' but are not 'giraffe eye' are:\tcow eye\thorse eye\tdeer eye\t\nThere are several useful visual features to tell there is 'giraffe eye' and not similar things in a photo:\tvery large eyes\tbright white sclera\tlong eyelashes\tpatches of brown-orange fur around the eye socket.", 49], "orange motorcycle": ["Yes. 'Orange motorcycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'orange motorcycle' but are not 'orange motorcycle' are:\tcar\ttruck\tbicycle\tscooter\nThere are several useful visual features to tell there is 'orange motorcycle' and not similar things in a photo:\ttwo-wheeled motorized vehicle\torange color\tsaddle seat\thandlebars\tforward-mounted footpegs or footboards", 49], "sheer curtains": ["Yes. 'Sheer curtains' have a tangible appearance and are a type of window covering.\nA few things that are visually similar to 'sheer curtains' but are not 'sheer curtains' are:\tblinds\tshades\tshutters\ttapestries\nThere are several useful visual features to tell there is 'sheer curtains' and not similar things in a photo:\tsemi-transparent or translucent\tfabric texture\tlight and airy appearance\thanging from a curtain rod or hooks\tin a panel form or multiple panels", 49], "graphics": ["No. 'Graphics' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are talking about \"computer graphics\" as a specific type of digital image, then the answer would be yes.\n\nA few things that are visually similar to 'computer graphics' but are not 'computer graphics' are: paintings, drawings, photographs, animations.\n\nUseful visual features for distinguishing 'computer graphics' from these similar things in a photo are: the absence of brushstrokes, the presence of sharp lines and geometric shapes, and the use of bright colors or gradients that may be difficult to produce in traditional media. Additionally, 'computer graphics' may have a more artificial, synthetic look compared to hand-drawn or photographic images.", 49], "shark": ["Yes. 'Shark' has a tangible appearance and is a type of fish.\nA few things that are visually similar to 'shark' but are not 'shark' are:\tdolphin\tseal\twalrus\twhale\nThere are several useful visual features to tell there is 'shark' and not similar things in a photo:\tpointed snout\tmultiple rows of sharp teeth\tfive to seven gill slits on the sides of the head\thorizontal tail fin\tnostrils on the underside of the snout", 49], "compartment": ["Yes. 'Compartment' has a tangible appearance and is a type of enclosed space or storage area.\nA few things that are visually similar to 'compartment' but are not 'compartment' are:\tdrawer\tshelf\tlocker\tcloset\nThere are several useful visual features to tell there is 'compartment' and not similar things in a photo:\tenclosed space\twith a door, lid, or cover\tspecific purpose or use (e.g. storage, seating)", 49], "motor boat": ["Yes. 'Motor boat' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'motor boat' but are not 'motor boat' are:\tkayak\tcanoe\tpaddle boat\t\nThere are several useful visual features to tell there is 'motor boat' and not similar things in a photo:\tlarge size and length\tengine or motor\tunusual shape or design\thull and keel\tconspicuous propeller or water jet\texhaust smoke or trail", 49], "brown spot": ["Yes. 'Brown spot' has a tangible appearance and typically refers to a discoloration or mark on a surface.\nA few things that are visually similar to 'brown spot' but are not 'brown spot' are:\tshadow\tdirt\tstain\tbruise\nThere are several useful visual features to tell there is 'brown spot' and not similar things in a photo:\tcircular, irregular, or symmetrical shape\tbrown or darker color than the surrounding area\tconsistent texture or thickness with the surrounding area", 49], "yarn": ["Yes. 'Yarn' has a tangible appearance and is a type of textile.\nA few things that are visually similar to 'yarn' but are not 'yarn' are:\tthread\ttwine\trope\tstring\nThere are several useful visual features to tell there is 'yarn' and not similar things in a photo:\tsoft and fluffy\ttexture of knitting balls or skeins\tmultiple strands twisted together in a consistent manner\tvariety of colors and materials (e.g., wool, cotton, acrylic)", 49], "grey road": ["Yes. 'Grey road' has a tangible appearance.\nA few things that are visually similar to 'grey road' but are not 'grey road' are:\twhite road\tblack road\tasphalt parking lot\t\nThere are several useful visual features to tell there is a 'grey road' and not similar things in a photo:\tcolored gray or light gray\tsmooth surface\tfor vehicles and pedestrians\tto draw a line in the middle of the road.", 49], "employee": ["No. 'Employee' is too vague or abstract to be distinguished in a photo.", 49], "streetlamp": ["Yes. 'Streetlamp' has a tangible appearance and is a type of outdoor lighting.\nA few things that are visually similar to 'streetlamp' but are not 'streetlamp' are:\ttraffic light\tpostbox\tflagpole\tbollard\nThere are several useful visual features to tell there is 'streetlamp' and not similar things in a photo:\ttall metal or concrete pole\tbulb at the top\tof the pole\toften has multiple bulbs or fixtures\tfor illuminating a street\tor a path", 49], "parking meters": ["Yes. 'Parking meters' has a tangible appearance and is a type of device used for parking payment.\nA few things that are visually similar to 'parking meters' but are not 'parking meters' are:\ttrash cans\toutdoor vending machines\tbike racks\nThere are several useful visual features to tell there is 'parking meters' and not similar things in a photo:\ttall, slender post or stand\tdisplay screen showing time or payment status\tmetallic or painted body\tslots or panels for inserting coins or cards.", 49], "brown pot": ["Yes. 'Brown pot' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'brown pot' but are not 'brown pot' are:\turn\tvase\tbucket\tjug\t\nThere are several useful visual features to tell there is 'brown pot' and not similar things in a photo:\tmade of clay or ceramic\tbrown color\tsmooth surface\twith or without a lid\tcylindrical, rounded or oval shape.", 49], "necklaces": ["Yes. 'Necklaces' has a tangible appearance and is a type of jewelry that is worn around the neck.\nA few things that are visually similar to 'necklaces' but are not 'necklaces' are:\tscarf\ttie\tchoker\t\nThere are several useful visual features to tell there is 'necklaces' and not similar things in a photo:\tdecorative beads, stones, or charms\tstrung together with a cord or chain\tworn draped around the neck.", 49], "knife blade": ["Yes. 'Knife blade' has a tangible appearance and is a part of a knife.\nA few things that are visually similar to 'knife blade' but are not 'knife blade' are:\tsaw blade\taxe head\thatchet\thead screwdriver\nThere are several useful visual features to tell there is 'knife blade' and not similar things in a photo:\tthin and flat\tusually made of metal or steel\thave a pointed tip\tfor cutting or slicing\tserrated or straight edge", 49], "wedding dress": ["Yes. 'Wedding dress' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'wedding dress' but are not 'wedding dress' are: ball gowns, prom dresses, evening gowns\nThere are several useful visual features to tell there is 'wedding dress' and not similar things in a photo:\twhite or ivory color\tlace or embroidery details\tlong and flowing design\tveil or train attached to it", 49], "diaper": ["Yes. 'Diaper' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'diaper' but are not 'diaper' are:\tunderwear\tbathing suit\tpad\nThere are several useful visual features to tell there is 'diaper' and not similar things in a photo:\t\ncovering the baby's bottom and groin area\tcan be secured with tapes or velcro\tmade of absorbent material (usually fabric or paper-based)\tdisposable or reusable\tform-fitting to prevent leaks and spills.", 49], "metal piece": ["Yes. 'Metal piece' has a tangible appearance and can come in various shapes and sizes.\nA few things that are visually similar to 'metal piece' but are not 'metal piece' are:\tstone\tpaper clip\tplastic tag\tbottle cap\tnut or bolt\nThere are several useful visual features to tell there is 'metal piece' and not similar things in a photo:\tshiny or metallic appearance\tcan be flat or 3-dimensional\tcold to the touch\tcan be smooth or have texture or patterns\tmay have visible screws or bolts", 49], "mannequins": ["Yes. 'Mannequins' has a tangible appearance and is a type of figurine used to display clothing.\nA few things that are visually similar to 'mannequins' but are not 'mannequins' are:\tsculptures\tstatues\tdolls\tstuffed animals\nThere are several useful visual features to tell there is 'mannequins' and not similar things in a photo:\thuman-like shape\tdetached body parts\tfor retail display purposes\tlack of intricate details or paint job", 49], "desk top": ["Yes. 'Desktop' has a tangible appearance and refers to the surface of a desk or a computer screen.\nA few things that are visually similar to 'desktop' but are not 'desktop' are:\ttable tops\tshelves\tcounter tops\ttv screen\nThere are several useful visual features to tell there is 'desktop' and not similar things in a photo:\tflat and horizontal surface\tlocated on top of a desk, table, or computer stand\tmay have computer equipment or stationary items on top of it\tvarious materials and colors, such as wood, metal, glass or plastic.", 48], "speed boat": ["Yes. 'Speed boat' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'speed boat' but are not 'speed boat' are:\tyacht\tjet ski\tkayak\tcanoe\nThere are several useful visual features to tell there is 'speed boat' and not similar things in a photo:\tlong, thin, and streamlined shape\twith a powerful motor or engine\tcapable of high speeds and quick maneuvering\tseats for passengers and a steering wheel or controls for the driver.", 48], "bagels": ["Yes. 'Bagels' has a tangible appearance and is a type of bread product.\nA few things that are visually similar to 'bagels' but are not 'bagels' are:\tdonuts\tpretzels\tbuns\nThere are several useful visual features to tell there is 'bagels' and not similar things in a photo:\tround\twith a hole in the center\tdense texture\ttan or golden brown crust, slightly shiny\tusually sprinkled with sesame seeds or poppy seeds", 48], "limes": ["Yes, 'limes' has a tangible appearance and is a type of citrus fruit.\nA few things that are visually similar to 'limes' but are not 'limes' are:\tlemons\tkey limes\tgreen apples\nThere are several useful visual features to tell there are 'limes' and not similar things in a photo:\tsmall, round fruit\tlime green color\tgreener and more sour than lemons\tthe flesh inside is soft, juicy and acidic, of a pale yellow or greenish color.", 48], "burrito": ["Yes. 'Burrito' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'burrito' but are not 'burrito' are:\twrap\tsandwich\tcalzone\nThere are several useful visual features to tell there is 'burrito' and not similar things in a photo:\tcylindrical shape\twrapped in a tortilla or a similar flatbread\tfillings visible in cross-section (rice, beans, meat, cheese, etc.)\tmay be covered in sauce or toppings\toften served with salsa or guacamole on the side.", 48], "surfboarder": ["Yes. 'Surfboarder' has a tangible appearance and refers to a person who rides a surfboard on ocean waves.\nA few things that are visually similar to 'surfboarder' but are not 'surfboarder' are:\tswimmer\tkayaker\tpaddleboarder\tboater\t\nThere are several useful visual features to tell there is 'surfboarder' and not similar things in a photo:\triding a surfboard on ocean waves\twearing a wetsuit and carrying a surfboard\tbalancing on the board with one or both feet\tusing hands to paddle or maneuver the board\tat the beach, with waves, sand, and surf equipment", 48], "khaki shorts": ["Yes. 'Khaki shorts' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'khaki shorts' but are not 'khaki shorts' are:\tjeans\ttan pants\tcargo shorts\tbermuda shorts\nThere are several useful visual features to tell there is 'khaki shorts' and not similar things in a photo:\tlight brown or beige color\tabove the knee length\tstraight or slim fit\tflat front or pleated design", 48], "plantains": ["Yes. 'Plantains' have a tangible appearance and are a type of fruit.\nA few things that are visually similar to 'plantains' but are not 'plantains' are:\tbananas\tzucchinis\tcucumbers\tgreen papayas\nThere are several useful visual features to tell there is 'plantains' and not similar things in a photo:\tlarge elongated fruit\tgreen or yellow skin\tthick texture compared to bananas\tthe flesh is not very sweet when eaten raw\tcan be cooked in various ways", 48], "patio umbrella": ["Yes. 'Patio umbrella' has a tangible appearance and is a type of umbrella used for shade on patios.\nA few things that are visually similar to 'patio umbrella' but are not 'patio umbrella' are:\tparasol\tbeach umbrella\ttent\tregular umbrella\nThere are several useful visual features to tell there is 'patio umbrella' and not similar things in a photo:\tlarge and wide canopy\tcylindrical pole and ribs\tattached to a weighted base or a table\tpost for stability\twhen open, is large enough to offer shade for a patio table and its chairs.", 48], "wood surface": ["Yes. 'Wood surface' has a tangible appearance and is a type of texture.\nA few things that are visually similar to 'wood surface' but are not 'wood surface' are:\tconcrete surface\tbrick surface\tasphalt surface\tmetal surface\nThere are several useful visual features to tell there is 'wood surface' and not similar things in a photo:\tvisible grain patterns\twarm and earthy tones\trich and textured appearance\tknots and whorls", 48], "tripod": ["Yes, 'tripod' is a visually concrete concept and is a type of camera or lighting accessory with a unique appearance.\nA few things that are visually similar to 'tripod' but are not 'tripod' are: mic stand, easel, coat stand.\nThere are several useful visual features to tell there is 'tripod' and not similar things in a photo: three legs, adjustable height, mounting plate on top to hold a camera or light, collapsible for easy storage and transport.", 48], "teacup": ["Yes. 'Teacup' has a tangible appearance and is a kind of drinking vessel.\nA few things that are visually similar to 'teacup' but are not 'teacup' are:\tcoffee mug\tglass\tceramic bowl\t\nThere are several useful visual features to tell there is 'teacup' and not similar things in a photo:\thandles\tshort and stout\tsize and shape (smaller and narrower than a coffee mug or bowl)\tsaucer", 48], "season": ["No. 'Season' is too vague or abstract to be distinguished in a photo.", 48], "book bag": ["Yes. 'Book bag' has a tangible appearance and is a type of bag used for carrying books.\nA few things that are visually similar to 'book bag' but are not 'book bag' are:\tbackpack\tpurse\ttote bag\tmessenger bag\nThere are several useful visual features to tell there is 'book bag' and not similar things in a photo:\trectangular or square in shape\tsupported by two straps worn over the shoulders\twith one or more compartments\tdesigned to hold books or other items for school or work made of durable material such as canvas or nylon", 48], "bike wheel": ["Yes. 'Bike wheel' has a tangible appearance and is a component of a bicycle.\nA few things that are visually similar to 'bike wheel' but are not 'bike wheel' are:\tsteering wheel\tferris wheel\tgear wheel\thoverboard wheel\nThere are several useful visual features to tell there is 'bike wheel' and not similar things in a photo:\tcircular shape\tdiameter that varies depending on bike type\tspokes that connect rim to the hub\thub that contains an axle and bearings\tfor bikes, a tire that wraps around the rim", 48], "directions": ["No. 'Directions' are too abstract to have a tangible appearance.\nA few things that are visually similar to 'directions' but are not 'directions' are:\tmaps\tsigns\tguides\tmanuals\nThere are no useful visual features that can distinguish 'directions' from the listed similar things in a photo, as all of them may contain directional information. Instead, the context and content of the text or images would need to be considered to determine if it pertains to directions.", 48], "rocking chair": ["Yes. 'Rocking chair' has a tangible appearance and is a type of chair.\nA few things that are visually similar to 'rocking chair' but are not 'rocking chair' are:\tnormal chair\trecliner\tswing\tcouch\nThere are several useful visual features to tell there is 'rocking chair' and not similar things in a photo:\trocking base or legs\tarmrests\tcurved shape for rocking\tbackrest and seat cushion", 48], "barricade": ["Yes. 'Barricade' has a tangible appearance and is a type of obstacle to block entry to an area.\nA few things that are visually similar to 'barricade' but are not 'barricade' are:\tfence\twall\tgate\troadblocks\nThere are several useful visual features to tell there is 'barricade' and not similar things in a photo:\tmade of metal, plastic, or wood\tbrightly colored\tvisible signs of warning or caution\tput up to block access or to control crowds", 48], "bar stools": ["Yes. 'Bar stools' has a tangible appearance and is a type of seat.\nA few things that are visually similar to 'bar stools' but are not 'bar stools' are:\tchairs\tbenches\tstep stools\nThere are several useful visual features to tell there is 'bar stools' and not similar things in a photo:\ttall and narrow\tfootrest\tat a bar or high counter\tcushioned or wooden seat\tswivel or fixed base", 48], "antennae": ["Yes. 'Antennae' has a tangible appearance and is a part of an insect's body.\nA few things that are visually similar to 'antennae' but are not 'antennae' are:\thorns\ttentacles\tbranches\nThere are several useful visual features to tell there are 'antennae' and not similar things in a photo:\tpair of thin and segmented appendages\tattached to an insect's head\tmulti-jointed\tsometimes contain little hairs or flares", 48], "door hinge": ["Yes. 'Door hinge' has a tangible appearance and is a type of hardware used to attach a door to a frame.\nA few things that are visually similar to 'door hinge' but are not 'door hinge' are:\twindow hinge\tgate hinge\tdrawer hinge\tjewelry box hinge\nThere are several useful visual features to tell there is 'door hinge' and not similar things in a photo:\tmetallic\thook or loop shape\tvisible screws or bolts \tattached to a door and a door frame or wall.", 48], "silver drain": ["Yes. 'Silver drain' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'silver drain' but are not 'silver drain' are:\tsteel disk\tcircular grate\tshower drain\nThere are several useful visual features to tell there is 'silver drain' and not similar things in a photo:\tcircular shape\tmetallic or shiny appearance\twith an open area or slots for water drainage", 48], "grapefruit": ["Yes. 'Grapefruit' has a tangible appearance and is a type of citrus fruit.\nA few things that are visually similar to 'grapefruit' but are not 'grapefruit' are:\toranges\ttangerines\tpomelos\t\nThere are several useful visual features to tell there is 'grapefruit' and not similar things in a photo:\tlarger than an orange\tpale yellow, pink, or red flesh\tround or slightly oblong shape\tthick rind\twith or without leaves on the stem", 48], "photographs": ["Yes. 'Photographs' has a tangible appearance and is an image captured on film or digitally.\nA few things that are visually similar to 'photographs' but are not 'photographs' are:\tpaintings\tdrawings\tprints\tposters\nThere are several useful visual features to tell there is 'photographs' and not similar things in a photo:\trealistic resolution or details\tsharp edges and lines\tphotographic distortion or graininess\tcaptured moment in time and space", 48], "grey roof": ["Yes. 'Grey roof' has a tangible appearance and is a type of roofing material/color.\nA few things that are visually similar to 'grey roof' but are not 'grey roof' are:\tblack roof\tsilver roof\tasphalt roof\tmetal roof\nThere are several useful visual features to tell there is 'grey roof' and not similar things in a photo:\tlight to medium grey color\tflat or sloping surface\tusually made of asphalt shingles, clay tiles, or slate.", 48], "skylight": ["Yes. 'Skylight' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'skylight' but are not 'skylight' are:\twindow\tglass roof\tventilation panel\nThere are several useful visual features to tell there is 'skylight' and not similar things in a photo: \tlocated on the roof or ceiling\tallows natural light to enter\tsquare or rectangular in shape\tframe around the edge of the window.", 48], "propellor": ["Yes. 'Propellor' has a tangible appearance and is a type of mechanical device.\nA few things that are visually similar to 'propellor' but are not 'propellor' are:\tfan\trotor\tblades\tmilling cutter\nThere are several useful visual features to tell there is 'propellor' and not similar things in a photo:\tused for propulsion\tspinning\ttwisted or curved blades\tcircular shape\tblades attached to a central hub\tmostly used in aviation or marine applications", 48], "silver lamp": ["Yes. 'Silver lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'silver lamp' but are not 'silver lamp' are:\twhite lamp\tblack lamp\tgolden lamp\tred lamp\ttable\tchair\nThere are several useful visual features to tell there is 'silver lamp' and not similar things in a photo:\tcylindrical shape\tsilver or chrome color\tstand on a base or legs\twith a lampshade or light cover\ton or off switch\taccompanying cord or wire.", 48], "plaid": ["Yes. 'Plaid' has a tangible appearance and is a pattern consisting of stripes or bands of different colors and widths, crossing at right angles.\nA few things that are visually similar to 'plaid' but are not 'plaid' are:\tcheckerboard\ttartan\thoundstooth\t\nThere are several useful visual features to tell there is 'plaid' and not similar things in a photo:\tcrossing stripes of different colors and widths\thaving a repeating grid-like pattern\tusually consisting of two or more colors.", 48], "sauerkraut": ["Yes. 'Sauerkraut' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'sauerkraut' but are not 'sauerkraut' are:\tshredded lettuce\tshredded cabbage\t\nThere are several useful visual features to tell there is 'sauerkraut' and not similar things in a photo:\tchopped or shredded cabbage\tfaintly yellow color\tfermented appearance and smell", 48], "toaster oven": ["Yes. 'Toaster oven' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'toaster oven' but are not 'toaster oven' are:\tmicrowave\toven\tcoffee maker\tgrill\nThere are several useful visual features to tell there is 'toaster oven' and not similar things in a photo:\tcompact size\ttoaster-like shape with a door\ton top, a series of knobs or buttons\tfor toasting bread or baking small dishes", 48], "poodle": ["Yes. 'Poodle' has a tangible appearance and is a breed of dog.\nA few things that are visually similar to 'poodle' but are not 'poodle' are:\tBichon Frise\tMaltese\tKomondor\tSchnauzer\nThere are several useful visual features to tell there is 'poodle' and not similar things in a photo:\tCurly, thick fur Pompom-like hair on legs and tail\tShort hair around the face and ears\tLong snout in proportion to the head\tDistinctive haircut styles with shaved and unshaved areas", 48], "school": ["Yes. 'School' has a tangible appearance and can refer to a building or an institution.\nA few things that are visually similar to 'school' but are not 'school' are:\tchurch\tcollege\thospital\tprison\t\nThere are several useful visual features to tell there is 'school' and not similar things in a photo:\tbuilding with classrooms, offices, and other educational areas\tgroups of students and teachers\tusing chalkboard, textbooks, or technology\tto promote learning and education.", 48], "bench seat": ["Yes. 'Bench seat' has a tangible appearance and is a type of chair or seating.\nA few things that are visually similar to 'bench seat' but are not 'bench seat' are:\tstool\tsaddle\tchaise lounge\nThere are several useful visual features to tell there is 'bench seat' and not similar things in a photo:\tlong and narrow seating\tfor multiple people\tto be placed against a wall or a backrest\thas no armrests or backrests (in some cases)", 48], "silver fence": ["Yes. 'Silver fence' has a tangible appearance as it refers to a physical fence material and color.\nA few things that are visually similar to 'silver fence' but are not 'silver fence' are:\tChain link fence\tAluminum fence\tIron fence\tCopper fence\nThere are several useful visual features to tell there is a 'silver fence' and not similar things in a photo:\tmade with silver-colored metal (usually steel or aluminum)\tconsistently colored\tsleek surface reflective finish\trectangular or square-shaped sections.", 48], "railway": ["Yes. 'Railway' has a tangible appearance and is a type of transportation infrastructure.\nA few things that are visually similar to 'railway' but are not 'railway' are:\troad\ttrail\tpath\tsidewalk\nThere are several useful visual features to tell there is 'railway' and not similar things in a photo:\tlong tracks made of steel or iron\tsleepers that hold the tracks in place\ttrains or locomotives\ttracks might have overhead electrical wires or poles", 48], "rise building": ["No. 'Rise building' is not a commonly used term, and I am not sure what it means. If you meant 'high-rise building', then the answer is yes.\nA few things that are visually similar to 'high-rise building' but are not 'high-rise building' are:\ttower\tbridge\t\nThere are several useful visual features to tell there is 'high-rise building' and not similar things in a photo:\ttall\tnumerous floors or levels\tglass windows or facades\tvisible elevators or stairs.", 48], "bare ground": ["Yes. 'Bare ground' has a tangible appearance and refers to the soil or land without vegetation or other coverings.\nA few things that are visually similar to 'bare ground' but are not 'bare ground' are:\tsand\trocky terrain\tpavement\tartificial turf\nThere are several useful visual features to tell there is 'bare ground' and not similar things in a photo:\tno visible vegetation or natural ground cover\texposed soil or earth\tcolor and texture of soil or earth", 48], "tale": ["No. 'Tale' is too vague or abstract to be distinguished in a photo.", 48], "teen": ["No. 'Teen' is too vague or abstract to be distinguished in a photo.", 48], "kit": ["No. 'Kit' is too vague or abstract to be distinguished in a photo without additional context or information. \nHowever, if we add a specific noun to 'kit', it could be visually concrete. For example, 'first aid kit' or 'sewing kit'.\nWithout a specific noun, it is difficult to think of things that are visually similar to 'kit'.\nFor distinguishing 'kit' from other similar-looking things, the best approach is to add a specific noun that provides the needed context to make the concept visually concrete.", 48], "scoop": ["Yes. 'Scoop' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'scoop' but are not 'scoop' are:\tspoon\tshovel\ttrowel\tmeasuring cup\nThere are several useful visual features to tell there is 'scoop' and not similar things in a photo:\tbowl-shaped end\tfor scooping or lifting things\tsingle handle or no handle at all, unlike a shovel or a trowel\tmade of metal or plastic, unlike a wooden spoon or measuring cup", 47], "dashboard": ["Yes. 'Dashboard' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'dashboard' but are not 'dashboard' are:\tcontrol panel\tinstrument panel\tdigital display\telectronics board\nThere are several useful visual features to tell there is 'dashboard' and not similar things in a photo:\tlocated in front of driver and front passenger seat\tinstrument gauges such as speedometer, tachometer, fuel gauge, and temperature gauge\tvoltage meter, clock and other auxiliary instruments\tdials, knobs, buttons and switches\tfor switches controlling climate, lights, windshield wipers, etc.", 47], "grey metal": ["Yes. 'Grey metal' has a tangible appearance and refers to a type of metal that is colored in shades of grey.\nA few things that are visually similar to 'grey metal' but are not 'grey metal' are:\tsilver jewelry\tconcrete statues\tpewter artifacts\t\nThere are several useful visual features to tell there is 'grey metal' and not similar things in a photo:\tmetallic shine\tcool grey color (not warm or cold tones)\thard and durable texture", 47], "motorcycle rider": ["Yes. 'Motorcycle rider' has a tangible appearance and is someone who rides a motorcycle.\nA few things that are visually similar to 'motorcycle rider' but are not 'motorcycle rider' are:\tpeople riding bicycles\tpeople riding skateboards\tpeople riding scooters\nThere are several useful visual features to tell there is 'motorcycle rider' and not similar things in a photo:\twearing a helmet\twearing protective gear\triding a motorcycle with two wheels\thigh speed\ttraveling on a road\tor highway with other vehicles", 47], "plastic containers": ["Yes. 'Plastic containers' has a tangible appearance and is a type of object used for storage.\nA few things that are visually similar to 'plastic containers' but are not 'plastic containers' are:\tglass jars\tceramic bowls\tcardboard boxes\tenvelopes\nThere are several useful visual features to tell there is 'plastic containers' and not similar things in a photo:\tplastic material\tclear or translucent appearance\tlid or cover\tsnapping or twisting mechanism for secure closure\tvariety of shapes and sizes", 47], "smooth": ["No. 'Smooth' is too vague or abstract to be distinguished in a photo. It is a texture or tactile sensation.\nTherefore, it does not have visually similar things.", 47], "tofu": ["Yes. 'Tofu' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'tofu' but are not 'tofu' are:\tcheese\tjelly\tbutter\nThere are several useful visual features to tell there is 'tofu' and not similar things in a photo:\twhite or beige squishy block or cubes\toften packed in water or vacuum-sealed container.\tSmooth surface and straight edges.", 47], "triangular": ["Yes. 'Triangular' has a visually concrete concept as it refers to a specific shape.\nA few things that are visually similar to 'triangular' but are not 'triangular' are: square, rectangle, polygon, circle\nThere are no useful visual features for distinguishing 'triangular' from the listed similar things in a photo as it is a distinct shape and can be easily differentiated based on the number and shape of its sides.", 47], "orange building": ["Yes. 'Orange building' has a tangible appearance and is a building with an orange color.\nA few things that are visually similar to 'orange building' but are not 'orange building' are:\tyellow building\tpink building\tred building\nThere are several useful visual features to tell there is 'orange building' and not similar things in a photo:\tbright orange color\tarchitectural structure and design\tshapes and patterns of windows and doors\tsize and location of the building", 47], "round orange": ["Yes. 'Round orange' has a tangible appearance and refers to a fruit.\nA few things that are visually similar to 'round orange' but are not 'round orange' are:\ttennis ball\tpumpkin\ttraffic cone\tmandarin\nThere are several useful visual features to tell there is 'round orange' and not similar things in a photo:\tround shape\tspecific texture of the orange peel\tbright orange color\tsmall dimples on the skin of the orange", 47], "cement floor": ["Yes. 'Cement floor' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'cement floor' but are not 'cement floor' are:\tconcrete wall\tasphalt road\tslate tile\t\nThere are several useful visual features to distinguish 'cement floor' from the listed similar things in a photo:\tsmooth gray surface\twith visible small stones or aggregates\tno seams, joints or grout lines\tporous texture or slight roughness.", 47], "orange slices": ["Yes. 'Orange slices' have a tangible appearance and are a type of fruit.\nA few things that are visually similar to 'orange slices' but are not 'orange slices' are:\tgrapefruit slices\tlemon slices\tcitrus candies\nThere are several useful visual features to tell there are 'orange slices' and not similar things in a photo:\tround or crescent-shaped\tslices of an orange\tfleshy and juicy\tbright orange or yellow-orange in color\thas visible seeds", 47], "plastic plate": ["Yes. 'Plastic plate' has a tangible appearance and belongs to the category of dishes.\nA few things that are visually similar to 'plastic plate' but are not 'plastic plate' are:\tpaper plate\tglass plate\twooden plate\tcardboard plate\nThere are several useful visual features to tell there is 'plastic plate' and not similar things in a photo:\tridged edges\tsmooth surface\tshiny or matte appearance\tplastic texture\tbendable or flexible material", 47], "fruit stand": ["Yes. 'Fruit stand' has a tangible appearance and is a place where fruits are sold.\nA few things that are visually similar to 'fruit stand' but are not 'fruit stand' are:\tvegetable stand\tflower stand\tcandy store\tbakery\tbutcher shop\tfish market\nThere are several useful visual features to tell there is 'fruit stand' and not similar things in a photo:\tvariety of fresh fruit\tcolorful\tdisplayed in containers\toranges, apples, bananas, strawberries, grapes, etc.\tprice tags or signage indicating the types of fruit sold.", 47], "calico cat": ["Yes. 'Calico cat' has a tangible appearance and is a specific kind of feline.\nA few things that are visually similar to 'calico cat' but are not 'calico cat' are:\ttabby cat\tblack and white cat\torange and white cat\nThere are several useful visual features to tell there is 'calico cat' and not similar things in a photo:\tthree-colored coat, usually consisting of white, black and orange patches, though other combinations can also occur.\tFur pattern in distinct spots, stripes or patches.", 47], "wood paneling": ["Yes, 'wood paneling' has a tangible appearance and is a type of interior wall covering made of wood panels.\nA few things that are visually similar to 'wood paneling' but are not 'wood paneling' are:\twood veneer wallpaper\tfaux wood wallpaper\tembossed wallpaper\nThere are several useful visual features to tell there is 'wood paneling' and not similar things in a photo:\tsets of wooden boards or panels\tcurrently in use in interior wall coverings\thas a natural wood grain pattern or knots", 47], "gears": ["Yes. 'Gears' has a tangible appearance and is a mechanical part.\nA few things that are visually similar to 'gears' but are not 'gears' are:\tcogs\tpulleys\twheels\tgarbage disposal blades\nThere are several useful visual features to tell there is 'gears' and not similar things in a photo:\tteeth or spokes\tunique shape or pattern\tmetallic appearance\tcoupled arrangement, which transmit torque and power from one device to another.", 47], "slacks": ["Yes. 'Slacks' has a tangible appearance and refers to a type of pants.\nA few things that are visually similar to 'slacks' but are not 'slacks' are:\tjeans\ttrousers\tleggings\tcapris\nThere are several useful visual features to tell there is 'slacks' and not similar things in a photo:\tmade of smooth or dressy fabric, like wool or polyester\tstraight cut\tlooser fit compared to skinny jeans or leggings\tmatching a formal or business-casual dress code", 47], "egg yolk": ["Yes. 'Egg yolk' has a tangible appearance and is the yellow part inside an egg.\nA few things that are visually similar to 'egg yolk' but are not 'egg yolk' are:\tmustard\tsunflower petals\t\nThere are several useful visual features to tell there is 'egg yolk' and not similar things in a photo: yolk is usually near the center of the egg when it is raw and will move slightly if the egg is gently shaken. Yolk color will typically be a brighter yellow when the hen has been fed a diet rich in yellow and orange plant pigments. Yolk will cook at a slightly lower temperature than the egg white, so that the egg white will set first, leaving the yolk still somewhat liquid.", 47], "narrow": ["Yes. 'Narrow' has a visually concrete concept and it is a dimension that indicates a limited width.\nA few things that are visually similar to 'narrow' but are not 'narrow' are:\tthin\tslim\tskinny\tsharp\nThere are no useful visual features to distinguish the concept of 'narrow' from these similar things as it implies a distinct measurement. However, in a photo, the imagery of a physically constricted or tight space can help illustrate narrowness.", 47], "duffel bag": ["Yes. 'Duffel bag' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'duffel bag' but are not 'duffel bag' are:\tbackpack\tsuitcase\ttote bag\tgym bag\nThere are several useful visual features to tell there is 'duffel bag' and not similar things in a photo:\tcylindrical shape\tbig and roomy\tzipper top\tcarrying handles or shoulder strap\tmade of sturdy and durable material", 47], "flamingos": ["Yes. 'Flamingos' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'flamingos' but are not 'flamingos' are:\therons\tegrets\tibises\nThere are several useful visual features to distinguish 'flamingos' from the listed similar things in a photo:\tlong legs\tpink feathers\tlong and curved necks\tbeak shape\twebbed feet\tpresence of a flock or a colony", 47], "steam engine": ["Yes. 'Steam engine' has a tangible appearance and is a type of locomotive powered by steam.\nA few things that are visually similar to 'steam engine' but are not 'steam engine' are:\tdiesel locomotives\telectric trains\tmodel trains\ttractors\nThere are several useful visual features to tell there is 'steam engine' and not similar things in a photo:\tvisible smoke\tstacks or chimneys\tround front with a circular lamp or headlights\ttend to be black with gold or red accents\tshiny metal exterior with rivets or other textural details\ttypically pulls a coal car or a line of passenger cars behind it.", 47], "number pad": ["Yes. 'Number pad' has a tangible appearance and is a keypad with numbers.\nA few things that are visually similar to 'number pad' but are not 'number pad' are:\talphabet keyboard\tcalculator\nThere are several useful visual features to tell there is 'number pad' and not similar things in a photo:\trow of numbers from 0 to 9\tnumbers may be used for mathematical operations with other keys, such as + or -\tmay have an additional button, such as Enter or Clear.", 47], "left leg": ["Yes. 'Left leg' has a tangible appearance and is a specific part of the human body.\nThere are no things that are visually similar to 'left leg' but are not 'left leg'.\nThere is no need for useful visual features to distinguish 'left leg' from the listed similar things in a photo, as there are no similar things.", 47], "puffy": ["Yes. 'Puffy' has a tangible appearance and refers to something that is soft, swollen, and rounded.\nA few things that are visually similar to 'puffy' but are not 'puffy' are:\tfluffy\tfuzzy\tbulging\nThere are several useful visual features to tell there is 'puffy' and not similar things in a photo:\tround and soft appearance\tslightly swollen or inflated\ttexture appears soft and light\tcomfy and cozy appearance", 47], "passenger door": ["Yes. 'passenger door' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'passenger door' but are not 'passenger door' are:\thood\ttrunk\twindshield\tbumper\nThere are several useful visual features to tell there is 'passenger door' and not similar things in a photo:\tvertical door on the side of the vehicle\twith a handle and a lock\twindow on the door\thandle located at the center or front of the door\thinged to the front of the vehicle", 47], "train doors": ["Yes. 'Train doors' has a tangible appearance and is a type of opening on a train.\nA few things that are visually similar to 'train doors' but are not 'train doors' are:\tcar doors\televator doors\tapartment doors\tgate doors\nThere are several useful visual features to tell there is 'train doors' and not similar things in a photo:\tsliding or hinged doors\tadjacent to a train platform\tor train compartment\twindow near the door 'open' or 'close' buttons beside the door sign indicating to not walk through the doors when they are closing.", 47], "dirt field": ["Yes. 'Dirt field' has a tangible appearance and refers to an open area covered with soil.\nA few things that are visually similar to 'dirt field' but are not 'dirt field' are:\trocky field\tgrass field\tbeach\tdesert\nThere are several useful visual features to tell there is 'dirt field' and not similar things in a photo:\tbrown, yellow, or reddish soil or dirt\tflat or slightly uneven surface\tlack of vegetation or sparse grass\tno rocks or sand\tdifferent color or texture from surrounding areas", 47], "wood frame": ["Yes. 'Wood frame' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'wood frame' but are not 'wood frame' are:\tbrick wall\tsteel frame\tconcrete slab\tstucco exterior\nThere are several useful visual features to tell there is 'wood frame' and not similar things in a photo:\twooden beams and posts\tnatural wood grain\tpotentially visible knots or cracks\tintricate or decorative carvings", 47], "orange vegetable": ["Yes. 'Orange vegetable' has a tangible appearance and is a type of plant-based food.\nA few things that are visually similar to 'orange vegetable' but are not 'orange vegetable' are:\torange fruit\tpumpkin\tcarrot\nThere are several useful visual features to tell there is 'orange vegetable' and not similar things in a photo:\torange color\tfirm or soft texture\tvarious sizes and shapes\tno seeds in the middle(watermelon, papaya)\tno segments when cut(navel oranges)\tnot tubular (carrots)\tToken amount of greenery (carrots)", 47], "cube": ["Yes. 'Cube' has a tangible appearance and is a three-dimensional shape.\nA few things that look visually similar to 'cube' but are not 'cube' are:\tsquare\tbox\tbrick\tdice\t\nThere are several useful visual features to tell there is 'cube' and not similar things in a photo:\tequal height, width, and depth\thave six square faces\tgeometric shape with straight edges and right angles no curves or round sides", 47], "headlight bus": ["No. 'Headlight bus' is too specific and not a commonly used term. It is difficult to determine what a 'headlight bus' would look like in a photo, as it depends on the context and individual interpretation. \n\nIf we assume that by 'headlight bus' you mean a bus with large headlights at the front, then:\n\nA few things that are visually similar to a bus with large headlights but are not a 'headlight bus' are:\ttruck\tlarge SUVs\tcars\n\nThere are several useful visual features to distinguish a bus with large headlights from the listed similar things in a photo:\t\n- Rectangular shape of the body\n- Large seating capacity \n- Flat back\n- Name or logo of a bus company", 47], "square sign": ["Yes. 'Square sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'square sign' but are not 'square sign' are:\trectangle sign\tjunction box\ttelevision monitor\tcomputer screen\nThere are several useful visual features to tell there is 'square sign' and not similar things in a photo:\tsquare-shaped\tobject often found on streets or roads with information or instructions on it\ttext or images in a contrasting color from the background", 47], "beach chairs": ["Yes. 'Beach chairs' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'beach chairs' but are not 'beach chairs' are:\tfolding chairs\tbar stools\tpicnic tables\tstadium seats\nThere are several useful visual features to tell there is 'beach chairs' and not similar things in a photo:\tlow to the ground\tadjustable backrests\tbright or colorful fabric\twide armrests", 47], "condiment": ["Yes. 'Condiment' has a tangible appearance and is a type of food seasoning.\nA few things that are visually similar to 'condiment' but are not 'condiment' are:\tspices\therbs\tflavorings\nThere are several useful visual features to tell there is 'condiment' and not similar things in a photo:\tbottled or packaged\tused to enhance flavor of food\tcommonly found on a table or in a kitchen\tmay include ketchup, mustard, mayo, salt, pepper, vinegar.", 47], "tyre": ["Yes. 'Tyre' has a tangible appearance and is a kind of rubber covering for wheels.\nA few things that are visually similar to 'tyre' but are not 'tyre' are:\tfrisbee\thula hoop\ttire swing\tbelt\nThere are several useful visual features to tell there is 'tyre' and not similar things in a photo:\tcylindrical shape\ttread pattern\ton a wheel or axle\trubber material\tvarious sizes, depending on the type of vehicle used.", 47], "frog": ["Yes. 'Frog' has a tangible appearance and is a type of amphibian.\nA few things that are visually similar to 'frog' but are not 'frog' are:\ttoad\tlizard\tsnake\tfish\nThere are several useful visual features to tell there is 'frog' and not similar things in a photo:\tsmooth or bumpy skin\twebbed feet \tbulging eyes and pupils\tunique patterns and colors on their bodies\thop or jump rather than walking or slithering", 47], "position": ["No. 'Position' is too vague or abstract to be distinguished in a photo.", 47], "turbine": ["Yes. 'Turbine' has a tangible appearance and is a type of machine.\nA few things that are visually similar to 'turbine' but are not 'turbine' are:\tfan\twindmill\tpropeller\tengine\nThere are several useful visual features to tell there is 'turbine' and not similar things in a photo:\trotating blades\tor circular shape\tpowered by steam, wind, or water\tharnessing energy or creating motion", 47], "print style letter": ["Yes. 'Print style letter' has a tangible appearance and refers to a specific typeface or font.\nA few things that are visually similar to 'print style letter' but are not 'print style letter' are: handwritten letters, cursive writing\nThere are several useful visual features to tell there is 'print style letter' and not similar things in a photo: \n\n- Block letters with uniform line thickness \n- Straight lines and angles \n- Clear spacing between letters and words \n- Lack of flourishes or curls found in cursive writing.", 47], "tubes": ["Yes. 'Tubes' has a tangible appearance and refers to cylindrical objects with an opening at one or both ends.\nA few things that are visually similar to 'tubes' but are not 'tubes' are:\tpipes\tstraws\tfunnels\thollow logs\nThere are several useful visual features to tell there is 'tubes' and not similar things in a photo:\tcylindrical shape\teven in width\ttwo openings or one opened and one closed\tcan be made of plastic, metal, glass, or any other material.", 47], "tie man": ["No. 'Tie man' is too vague or abstract to be distinguished in a photo.", 47], "guacamole": ["Yes. 'Guacamole' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'guacamole' but are not 'guacamole' are:\tavocado dip\tsalsa\thummus\tbaba ganoush\nThere are several useful visual features to tell there is 'guacamole' and not similar things in a photo:\tgreen color\tchunky or smooth texture\tmade with mashed avocados\tdiced tomatoes and onions can be seen mixed in the dip", 47], "brown frame": ["Yes. 'Brown frame' has a tangible appearance and refers to a specific type of object.\nA few things that are visually similar to 'brown frame' but are not 'brown frame' are:\tpicture frames\tborder of a window\tbrown furniture\twooden box\nThere are several useful visual features to tell there is 'brown frame' and not similar things in a photo:\trectangular shape\tframe border that is raised from the image\tmatte or gloss finish on the frame material\tbrown color of the frame", 46], "glass shower door": ["Yes. 'Glass shower door' has a tangible appearance and is an item in a bathroom.\nA few things that are visually similar to 'glass shower door' but are not 'glass shower door' are:\tmirror\twindow\tshowcase\nThere are several useful visual features to tell there is 'glass shower door' and not similar things in a photo:\tclear or frosted glass\thinged or sliding door\thandle or knob\tsilicone seal\ton a shower or bathtub", 46], "par": ["No. 'Par' is too abstract and does not have any tangible appearance. It is a term used in sports as a standard for the number of strokes a skilled player is expected to take to complete a hole, round, or game.", 46], "air freshener": ["Yes. 'Air freshener' has a tangible appearance and is an object used to make the air smell nicer.\nA few things that are visually similar to 'air freshener' but are not 'air freshener' are:\tcandles\taromas\toils\tdiffusers\t\nThere are several useful visual features to tell there is 'air freshener' and not similar things in a photo:\ttypically smaller in size\tcan be in a spray or solid form\twill have a label saying 'air freshener' or similar\twill usually have an identifiable scent or fragrance", 46], "mustard bottle": ["Yes. 'Mustard bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'mustard bottle' but are not 'mustard bottle' are:\tketchup bottle\toil bottle\tdressing bottle\t\nThere are several useful visual features to tell there is 'mustard bottle' and not similar things in a photo:\tyellow color\tthin and elongated shape\twith a tapered nozzle\tfor holding mustard sauce\tor having 'mustard' written on the bottle\tlabel with brand and ingredients information.", 46], "pat": ["No. 'Pat' is too vague or abstract to be distinguished in a photo.", 46], "dog ear": ["Yes. 'Dog ear' has a tangible appearance and is a physical feature of some dog breeds.\nA few things that are visually similar to 'dog ear' but are not 'dog ear' are:\thuman ear\tcat ear\tfolded napkin\nThere are several useful visual features to tell there is 'dog ear' and not similar things in a photo:\ttriangular shape\tfurry or hairless texture\tbend or flop at the middle\tpointy or rounded tip\tlocation on the head of a dog.", 46], "plastic tray": ["Yes. 'plastic tray' has a visually concrete and tangible appearance.\nA few things that are visually similar to 'plastic tray' but are not 'plastic tray' are:\tplastic dish\tplastic bowl\tplastic lid\tcookie sheet\nThere are several useful visual features to tell there is 'plastic tray' and not similar things in a photo:\tflat surface\twith raised and sloped edges\tmade of plastic\tor a similar synthetic material\tsolid and sturdy\tbuild for carrying or holding different objects", 46], "blue eye": ["Yes. 'Blue eye' has a tangible appearance and is a type of eye color.\nA few things that are visually similar to 'blue eye' but are not 'blue eye' are:\tgreen eye\tgrey eye\thazel eye\nThere are several useful visual features to tell there is 'blue eye' and not similar things in a photo:\tblue iris\tcolor intensity\tbrightness\tlighting and shadows in the photo", 46], "throw": ["No. 'Throw' is too vague or abstract to be distinguished in a photo. It is an action or activity, rather than a tangible object. \n\nHowever, a few things that are visually similar to a throw as an action are: \n- tossing a ball\n- dropping something\n- placing something on a surface \n\nThere are no visual features that distinguish 'throw' itself, but the result of a throw, such as a flying ball or an object in motion, might be captured visually.", 46], "pallet": ["Yes. 'Pallet' has a tangible appearance and is a flat transport structure.\nA few things that are visually similar to 'pallet' but are not 'pallet' are: table, shelf or cabinet top, wooden board.\nThere are several useful visual features to tell there is 'pallet' and not similar things in a photo: \tconsists of multiple wooden planks or slats \tfour-way forklift accessibility\tusually rectangular shape\tflat structure\twith or without visible nails or fasteners.", 46], "kitchen island": ["Yes. 'Kitchen island' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'kitchen island' but are not 'kitchen island' are:\ttable\tcounter\tbar\tcart\nThere are several useful visual features to tell there is 'kitchen island' and not similar things in a photo:\tstand-alone piece of furniture\twith cabinets or drawers\tbuilt-in stove or sink\tcounter space for food preparation or seating on one or more sides\tmay have wheels or casters", 46], "coast": ["Yes. 'Coast' has a tangible appearance and refers to the land by the sea or ocean.\nA few things that are visually similar to 'coast' but are not 'coast' are:\tbeach\tshore\tcliff\theadland\nThere are several useful visual features to distinguish from the similar things in a photo:\tmeeting point of the land and the sea or ocean\trocky or sandy terrain\twater along one side of the land\tmaritime features such as boats, lighthouses or buoys.", 46], "chocolate doughnut": ["Yes. 'Chocolate doughnut' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'chocolate doughnut' but are not 'chocolate doughnut' are:\tplain doughnut\tcake pop\tbagel\tmuffin\nThere are several useful visual features to tell there is 'chocolate doughnut' and not similar things in a photo:\tcircular shape\twith a hole in the center\tchocolate glaze or coating\tridged or bumpy texture\tdusted with powdered sugar or sprinkles", 46], "bamboo": ["Yes. 'Bamboo' has a tangible appearance and is a kind of plant.\nA few things that are visually similar to 'bamboo' but are not 'bamboo' are:\ttall grass\tsugar cane\tcornstalks\nThere are several useful visual features to tell there is 'bamboo' and not similar things in a photo:\ttall and slender stalks\tjointed and hollow\tstalks with green leaves at the top\tknobby joints along the stalks", 46], "razor": ["Yes. 'Razor' has a tangible appearance and is a personal grooming tool.\nA few things that are visually similar to 'razor' but are not 'razor' are:\tknife\tscissors\tpocket knife\tbox cutter\nThere are several useful visual features to tell there is 'razor' and not similar things in a photo:\tthin, flat blade\tsharp edge\tfor shaving or grooming purposes\thave curved or straight head\thave a handle", 46], "mass": ["No. 'Mass' is too vague or abstract to be distinguished in a photo.", 46], "bus window": ["Yes. 'Bus window' has a tangible appearance and is a specific type of window.\nA few things that are visually similar to 'bus window' but are not 'bus window' are:\tcar window\tstore window\tbuilding window\nThere are several useful visual features to tell there is 'bus window' and not similar things in a photo:\tcurved shape\trectangular shape\tsplit into smaller individual panes\ttinted or reflective glass on commercial buses.", 46], "metal legs": ["Yes. 'Metal legs' has a tangible appearance and refers to legs made of metal.\nA few things that are visually similar to 'metal legs' but are not 'metal legs' are:\twooden legs\tplastic legs\tstuffed legs\thuman legs\nThere are several useful visual features to tell there are 'metal legs' and not similar things in a photo:\t\nshiny or metallic appearance\t\nsleek, smooth texture\t\njoints or screws where the legs meet the body\t\nlack of natural imperfections, such as knots or veins (for wooden legs)", 46], "ramps": ["Yes. 'Ramps' has a tangible appearance and usually has a sloping surface for easier access.\nA few things that are visually similar to 'ramps' but are not 'ramps' are:\tstairs\twalkways\twheelchair lifts\nThere are several useful visual features to tell there is 'ramps' and not similar things in a photo:\tsloping surface\tno steps or steep inclines\tspecific design for accessibility or convenience.", 46], "c": ["No. 'c' is too vague or abstract to have a tangible appearance that can be distinguished visually. However, the letter c does have a particular shape that is visually distinctive, but this shape is not the concept itself.\nA few things that are visually similar to 'c' shape but are not 'c' are:\to, d, e, g, q, s, C.\nThere are several useful visual features to tell there is a letter c and not similar things in a photo:\ta circle-like shape with one open end and one curved end.", 46], "courtyard": ["Yes. 'Courtyard' has a tangible appearance and is a type of outdoor space.\nA few things that are visually similar to 'courtyard' but are not 'courtyard' are:\tterrace\tpatio\tporch\tbalcony\nThere are several useful visual features to tell there is 'courtyard' and not similar things in a photo:\topen space\tenclosed by buildings or walls\tpaved ground\tfurniture or plants in the area.", 46], "strands": ["Yes. 'Strands' has a tangible appearance and refers to thin, elongated objects.\nA few things that are visually similar to 'strands' but are not 'strands' are:\tthreads\twires\tspaghetti\tropes\nThere are several useful visual features to tell there are 'strands' and not similar things in a photo:\tthin and elongated\tsmooth or textured\tflexible or rigid\tarranged in a linear fashion", 46], "right leg": ["Yes. 'Right leg' has a tangible appearance and is part of the human body.\nThere is nothing visually similar to 'right leg' in a photo as it is a specific body part and cannot be confused with anything else. However, other limbs such as arms, left legs, and left arms may be visually similar but are not 'right leg'.\nUseful visual features for distinguishing 'right leg' would be:\tposition on the right side of the body\tknee joint\tshape from thigh to ankle\ttoes at the end of the leg", 46], "mixture": ["No. 'Mixture' is too vague or abstract to be distinguished in a photo.", 46], "glass building": ["Yes. 'Glass building' has a tangible appearance and is a kind of architecture.\nA few things that are visually similar to 'glass building' but are not 'glass building' are:\tconvention centers\tskyscrapers\toffice buildings\tatriums\nThere are several useful visual features to tell there is 'glass building' and not similar things in a photo:\tmostly or entirely made of glass\tglass panels\tfacade is mostly reflective or transparent", 46], "language": ["No. 'Language' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to certain aspects of 'language' but are not 'language' are:\n\n- Text: written language, which is a visible representation of language\n- Symbols: such as emojis or sign language gestures, which convey meaning in a visually similar way to language\n- Mouth movement: while not language itself, the way that people's mouths move while speaking can be a visual cue that someone is using language. \n\nUseful visual features for distinguishing language could therefore include hearing people speak, written or printed words, or seeing people use hand gestures or sign language.", 46], "reflectors": ["Yes. 'Reflectors' has a tangible appearance and is a type of material or device used to reflect light.\nA few things that are visually similar to 'reflectors' but are not 'reflectors' are:\tmirrors\tglossy surfaces\twindows or glass surfaces\nThere are several useful visual features to tell there is 'reflectors' and not similar things in a photo:\ttriangular, circular or square shape\tmade of reflective material, like plastic, metal or glass\tusually found on roads or sidewalks, or on clothing or bike gear\tdesigned to bounce or reflect back light towards its source", 46], "blur": ["Yes. 'Blur' has a tangible appearance and is a visual effect that makes parts of an image appear unclear or unfocused.\nA few things that are visually similar to 'blur' but are not 'blur' are:\tnoise\tout of focus\timage distortion\nThere are several useful visual features to tell there is 'blur' and not similar things in a photo:\tsome parts of the image are clearer than others\tsmooth or soft edges\tof unclear or unfocused areas.", 46], "floor mat": ["Yes. 'Floor mat' has a tangible appearance and is a kind of covering for the floor.\nA few things that are visually similar to 'floor mat' but are not 'floor mat' are:\tcarpet\trug\tbar mat\tyoga mat\nThere are several useful visual features to tell there is 'floor mat' and not similar things in a photo:\tflat and rectangular shape\ton the ground or floor\tpatterned or plain\tdifferent texture than the floor", 46], "blue lid": ["Yes. 'Blue lid' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'blue lid' but are not 'blue lid' are:\tred lid\tgreen lid\tyellow lid\torange lid\twhite lid\nThere is only one useful visual feature to distinguish 'blue lid' from similar things in a photo: its blue color.", 46], "ball player": ["Yes. 'Ball player' has a tangible appearance and refers to someone who plays ball games like soccer, baseball, etc.\nA few things that are visually similar to 'ball player' but are not 'ball player' are:\tcoach\treferee\tspectator\tjournalist\nThere are several useful visual features to tell there is 'ball player' and not similar things in a photo:\twearing a uniform\twearing sports equipment like shoes, shin guards, a glove, etc.\tactive and moving on the field or court\tinteracting with other players\tor the ball (kicking, throwing, catching)", 46], "blue curtains": ["Yes. 'Blue curtains' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'blue curtains' but are not 'blue curtains' are:\tblue bed sheets\tblue fabric\tblue tablecloths\nThere are several useful visual features to tell there are 'blue curtains' and not similar things in a photo:\tmade of curtain fabric\thanging from a window or door\tpleated or folded in some way\tmatch a blue color swatch or paint chip", 46], "wood wall": ["Yes. 'Wood wall' has a tangible appearance and refers to a wall made out of wood.\nA few things that are visually similar to 'wood wall' but are not 'wood wall' are:\tbrick wall\tstone wall\tconcrete wall\ttile wall\nThere are several useful visual features to tell there is 'wood wall' and not similar things in a photo:\tmade of wood\tgrainy texture\twarm color tones\tnatural knots and patterns\tvariety in wood types and finishes.", 46], "trails": ["Yes. 'Trails' has a tangible appearance and can refer to marks or paths left behind by movement.\nA few things that are visually similar to 'trails' but are not 'trails' are:\tshadows\tbrush strokes in a painting\tveins in leaves\tlight reflections on surfaces\nThere are several useful visual features to tell there is 'trails' and not similar things in a photo:\tlengthy\tmark left behind by movement, whether it be foot or tire tracks, or a trail of smoke, vapor or fire.", 46], "port": ["Yes. 'Port' has a tangible appearance and refers to a location for docking ships.\nA few things that are visually similar to 'port' but are not 'port' are:\tharbor\tmarina\tbeach\nThere are several useful visual features to tell there is 'port' and not similar things in a photo:\tDocks\tLoading cranes\tand other maritime-related objects.", 46], "support post": ["Yes. 'Support post' has a tangible appearance and is a physical structure used for support.\nA few things that are visually similar to 'support post' but are not 'support post' are:\tpillar\tcolumn\tfence\tpostbox\nThere are several useful visual features to tell there is 'support post' and not similar things in a photo:\tvertical structure\tusually made of wood or metal\tlarger at the base that at the top\tcarrying or supporting weight or other structures not used for decoration.", 46], "banana leaf": ["Yes. 'Banana leaf' has a tangible appearance and is a type of leaf that grows on a banana plant.\nA few things that are visually similar to 'banana leaf' but are not 'banana leaf' are:\tpalm leaf\tfern leaf\nThere are several useful visual features to tell there is 'banana leaf' and not similar things in a photo:\tlarge\tsize and shape of the leaf (oval and elongated with pointed tips)\tparallel veins running from the base to the tip of the leaf\tdark green color with a waxy texture", 46], "freezer door": ["Yes. 'Freezer door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'freezer door' but are not 'freezer door' are:\trefrigerator door\toven door\tbathroom door\tgarage door\nThere are several useful visual features to tell there is 'freezer door' and not similar things in a photo:\tlocated on the front of a freezer\thinged with a handle\ttogether with a refrigeration unit or a freezer compartment\tsubject to frost or ice buildup", 46], "pretzel": ["Yes. 'Pretzel' has a tangible appearance and is a type of baked snack.\nA few things that are visually similar to 'pretzel' but are not 'pretzel' are:\tbagel\tdoughnut\tbreadstick\tswirl bread\nThere are several useful visual features to tell there is 'pretzel' and not similar things in a photo:\tthree-looped knot shape\twith or without salt crystals\tbrown crust covering a soft interior", 46], "wooden chair": ["Yes. 'Wooden chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden chair' but are not 'wooden chair' are:\tstool\tbench\tcrate\ttable\nThere are several useful visual features to tell there is 'wooden chair' and not similar things in a photo:\thave a back and a seat\tmade of wood or have visible wooden elements\thave legs or support structure\tfor seating purposes only", 46], "coloring": ["No. 'Coloring' is too vague or abstract to be distinguished in a photo. \n\nNote: 'Coloring' can refer to both the act of filling in colors on a coloring book or the finished artwork. If we are referring to the finished artwork, then some useful visual features for distinguishing it from similar things may include: filled shapes, varied color usage, and outlined edges. However, if we are referring to the act of coloring, it is not visually distinguishable.", 46], "ornaments": ["Yes. 'Ornaments' has a tangible appearance and is a type of decorative object.\nA few things that are visually similar to 'ornaments' but are not 'ornaments' are:\tpaperweights\tcollectibles\tjewelry\tvases\nThere are several useful visual features to tell there is 'ornaments' and not similar things in a photo:\tbaubles or spheres of various colors, patterns and textures\thanging from a Christmas tree or a hook\tsometimes made of glass or other shiny materials\ttrace of a \"cap\" on top of the ornament where it can be hung", 46], "r": ["No. 'r' is too vague or abstract to be distinguished in a photo. It is a letter of the alphabet, and while it has a specific shape, it does not have a tangible appearance on its own. \n\nHowever, a few things that are visually similar to the letter 'r' are:\n\n- The letter 'p'\n- The letter 'n'\n- The numeral '7', when written in certain fonts\n\nUseful visual features for distinguishing 'r' from these similar things in a photo include:\n\n- The vertical line or \"stem\" on the right side of the letter, which sets it apart from 'p'\n- The diagonal slant of the main curve, which differentiates it from 'n'\n- The loop or curvature at the top of the letter, which distinguishes it from '7'", 46], "ski marks": ["Yes. 'Ski marks' has a tangible appearance and is the track left by skis on the snow.\nA few things that are visually similar to 'ski marks' but are not 'ski marks' are:\tfootprints\ttracks left by animals\tgrooves on the ground left by machinery\nThere are several useful visual features to tell there are 'ski marks' and not similar things in a photo:\ttwo parallel grooves in the snow\tusually in a zigzag pattern or in parallel lines\tleft on snow-covered mountains, hills or slopes.", 46], "plastic toilet seat": ["Yes. 'Plastic toilet seat' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'plastic toilet seat' but are not 'plastic toilet seat' are:\twooden toilet seat\tmetal toilet seat\tpadding toilet seat\nThere are several useful visual features to tell there is 'plastic toilet seat' and not similar things in a photo:\toval shape\tmade of plastic\tcolor (white, off-white or beige)\tlift-up hinge for cleaning\tfeatures for attaching to the toilet bowl", 46], "brick clock tower": ["Yes. 'Brick clock tower' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'brick clock tower' but are not 'brick clock tower' are:\tsmokestack\tchimney\twater tower\tgrain silo\nThere are several useful visual features to tell there is 'brick clock tower' and not similar things in a photo:\tmade of bricks and mortar\ttall and imposing\tsquare or rectangular shape\twith a clock face or a bell tower on top", 46], "man hole": ["Yes. 'Man hole' has a tangible appearance and is a type of opening in the ground used for access to underground utility systems or maintenance.\nA few things that are visually similar to 'man hole' but are not 'man hole' are:\tdrain\tgrate\tfire hydrant\tsewer lid\nThere are several useful visual features to tell there is 'man hole' and not similar things in a photo:\tcircular or rectangular shape\traised from the surrounding surface\tutility symbols or labels around the edges or cover\tmade of cast iron, concrete or steel.", 46], "museum": ["No. 'Museum' is too vague or abstract to be distinguished in a photo.", 46], "roman number": ["Yes. 'Roman number' has a tangible appearance and is a numeral system used in ancient Rome.\nA few things that are visually similar to 'roman number' but are not 'roman number' are:\tArabic numerals\tletters\tpunctuation marks\tsymbols\nThere are several useful visual features to tell there is 'roman number' and not similar things in a photo:\tconsist of Roman numerals\tI, V, X, L, C, D, and M\tuppercase letters only\toften used for dates, book chapters, and outlines\tclearly distinguishable from other symbols or marks on the page", 46], "coffee mugs": ["Yes. 'Coffee mugs' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'coffee mugs' but are not 'coffee mugs' are:\ttea cups\tthermos\tcarafe\tglass\nThere are several useful visual features to tell there is 'coffee mugs' and not similar things in a photo:\thandle\tround opening\trigid and durable material\tcylindrical shape\twide and stable base\tlarge enough to hold hot drinks", 45], "brick fireplace": ["Yes. 'Brick fireplace' has a tangible appearance and is a particular type of home feature.\nA few things that are visually similar to 'brick fireplace' but are not 'brick fireplace' are:\tstone fireplace\tmantelpiece\tbuilt-in bookshelf\tbrick wall\nThere are several useful visual features to tell there is 'brick fireplace' and not similar things in a photo:\tbrick construction\topening with a chimney\tflue\tcast iron grate for holding logs\tsurrounded by a hearth or decorative stone/tile work.", 45], "road signs": ["Yes. 'Road signs' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'road signs' but are not 'road signs' are:\tadvertisements\tbillboards\tdirections\tinstructions\nThere are several useful visual features to tell there is 'road signs' and not similar things in a photo:\ttraffic-related symbols, such as arrows, stop signs, speed limits, and pedestrian crossings\tbold and clear texts, usually in white or yellow against a green, red, or blue background\tmetallic or reflective material for visibility at night or in low light conditions.", 45], "packages": ["Yes. 'Packages' have a tangible appearance and are a type of wrapped items.\nA few things that are visually similar to 'packages' but are not 'packages' are:\tboxes\twrapping paper\tenvelopes\tcontainers\nThere are several useful visual features to tell there is 'packages' and not similar things in a photo:\trectangular or square shape\twrapped in paper or plastic\ttied with a bow or ribbon\tvariety of sizes and colors", 45], "square tile": ["Yes. 'Square tile' has a tangible appearance and is a flat, square-shaped piece of material.\nA few things that are visually similar to 'square tile' but are not 'square tile' are:\tbricks\twooden blocks\tplastic blocks\nThere are several useful visual features to tell there is 'square tile' and not similar things in a photo:\tflat and thin\thard and durable\tsquare in shape\twith a glossy or matte finish", 45], "step ladder": ["Yes. 'Step ladder' has a tangible appearance and is a kind of ladder.\nA few things that are visually similar to 'step ladder' but are not 'step ladder' are:\tstraight ladder\tfolding chair\tstool\nThere are several useful visual features to tell there is 'step ladder' and not similar things in a photo:\tdesigned with two legs and parallel sides\tmultiple steps\tor rungs for climbing\theight that usually does not exceed one's reach\thas a locking mechanism at the top", 45], "metal guard rail": ["Yes. 'Metal guard rail' has a tangible appearance and is a type of safety barrier.\nA few things that are visually similar to 'metal guard rail' but are not 'metal guard rail' are:\tfencing\tdecorative railing\tbalcony railing\nThere are several useful visual features to tell there is 'metal guard rail' and not similar things in a photo:\tmetal or steel material\thorizontal or vertical bars\tpainted with reflective color\tsafety sign\tadhered to the edge of an elevated surface", 45], "route": ["No. 'Route' is too vague or abstract to be distinguished in a photo.", 45], "mountain top": ["Yes. 'Mountain top' has a tangible appearance and refers to the summit or peak of a mountain.\nA few things that are visually similar to 'mountain top' but are not 'mountain top' are:\thill top\troof\ttop of a building\nThere are several useful visual features to tell there is 'mountain top' and not similar things in a photo:\thigh altitude\tlocation surrounded by mountains or valleys\tsnowy or rocky terrain\tsweeping panoramic view of surrounding peaks and valleys", 45], "goods": ["No. 'Goods' is too vague or abstract to be distinguished in a photo.", 45], "blood": ["Yes. 'Blood' has a tangible appearance and is a fluid in the body.\nA few things that are visually similar to 'blood' but are not 'blood' are: red paint, ketchup, juice, dyed water\nThere are several useful visual features to tell there is 'blood' and not similar things in a photo:\tthick and viscous texture\tdark red color\twet and shiny appearance\tstaining or spilling from a wound or blood vessel.", 45], "vacuum": ["No. 'Vacuum' is too vague or abstract to have a tangible appearance. \nHowever, there are some things that are visually similar to a 'vacuum cleaner', which is a specific tool used for cleaning, but are not 'vacuum':\n\tLeaf Blower\n\tHairdryer\n\tCompressed air can\n\tAir conditioner\nA few useful visual features to distinguish 'vacuum cleaner' from similar things are:\n\tHas a long tube or nozzle attached to a body\n\tHas a dust container bag attached to its back\n\tHas suction ports at the bottom\n\tIs usually used for cleaning floors and carpets.", 45], "beige wall": ["Yes. 'Beige wall' has a tangible appearance and is a specific color of wall.\nA few things that are visually similar to 'beige wall' but are not 'beige wall' are:\twhite wall\tcream wall\tyellow wall\tpink wall\tsand-colored wall\nThere are several useful visual features to tell there is 'beige wall' and not similar things in a photo:\tlight brown color\tsolid surface\ttexture of paint or plaster.", 45], "giraffe legs": ["Yes. 'Giraffe legs' has a tangible appearance and is a part of the body of a specific animal.\nA few things that are visually similar to 'giraffe legs' but are not 'giraffe legs' are:\thorse legs\tcamel legs\tzebra legs\nThere are several useful visual features to tell there is 'giraffe legs' and not similar things in a photo:\tlong and slender legs\twith irregular patches and patterns\tbrownish-yellow color\tlarge size and height compared to other animals", 45], "plastic cups": ["Yes. 'Plastic cups' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic cups' but are not 'plastic cups' are:\tpaper cups\tglass cups\tcans\tbottles\nThere are several useful visual features to tell there is 'plastic cups' and not similar things in a photo:\tplastic material\tridged or smooth sides\thollow shape\twith or without lids\tand can hold beverages", 45], "plastic handle": ["Yes. 'Plastic handle' has a tangible appearance and is a kind of object.\nA few things that are visually similar to 'plastic handle' but are not 'plastic handle' are:\tmetal handle\twooden handle\trubber grip\tleather strap\nThere are several useful visual features to tell there is 'plastic handle' and not similar things in a photo:\tmade of plastic\tsmooth texture\tgrip or hold shape\thanging from an object or attached to a product.", 45], "smokestack": ["Yes. 'Smokestack' has a tangible appearance and is a type of industrial chimney.\nA few things that are visually similar to 'smokestack' but are not 'smokestack' are:\ttree\ttrumpet\ttv antenna\nThere are several useful visual features to tell there is 'smokestack' and not similar things in a photo:\ttall and cylindrical\tdark or soot-covered exterior\tlocated in an industrial or manufacturing area\treleasing smoke or steam at the top", 45], "coffee tables": ["Yes. 'Coffee tables' has a tangible appearance and is a type of table.\nA few things that are visually similar to 'coffee tables' but are not 'coffee tables' are:\tside tables\tdesks\tdining tables\tworkbenches\nThere are several useful visual features to tell there is 'coffee tables' and not similar things in a photo:\tlow height compared to other types of tables\trelatively small size\tsupports for magazines or books, or other small items convenient to the sofa or chairs around it.", 45], "orange bus": ["Yes. 'Orange bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'orange bus' but are not 'orange bus' are:\tfire truck\tambulance\tschool bus\tred bus\nThere are several useful visual features to tell there is 'orange bus' and not similar things in a photo:\torange-colored\tlong and rectangular shape\twindows\tdoor at the rear\tend of a bus with a ladder or sign for exiting the back door.", 45], "pink blanket": ["Yes. 'Pink blanket' is a visually concrete concept and is a kind of bedding or cloth.\nA few things that are visually similar to 'pink blanket' but are not 'pink blanket' are:\tpink towel\tpink rug\tpink shirt\tpink scarf\nThere are several useful visual features to tell there is a 'pink blanket' and not similar things in a photo:\trectangle or square-shaped\tpink in color\tfabric or knit texture\ttypically used on a bed or couch", 45], "spout": ["Yes. 'Spout' has a tangible appearance and is a device used for pouring liquids.\nA few things that are visually similar to 'spout' but are not 'spout' are:\tnozzle\tmouth\tfunnel\ttube\those\nThere are several useful visual features to tell there is 'spout' and not similar things in a photo:\thollow\ttapered\tcone-shaped\tattached to a container\tor a water source \tdesigned for pouring or channeling liquids.", 45], "rubber duck": ["Yes. 'Rubber duck' has a tangible appearance and is a type of bath toy.\nA few things that are visually similar to 'rubber duck' but are not 'rubber duck' are:\tother bath toys\tyellow ducks\tyellow birds\tplastic duck decoys\t\nThere are several useful visual features to tell there is 'rubber duck' and not similar things in a photo:\tbright yellow color\tround head and body\tpointed beak\toranges legs and feet\trubber or soft material.", 45], "alligator": ["Yes. 'Alligator' has a tangible appearance and is a kind of reptile.\nA few things that are visually similar to 'alligator' but are not 'alligator' are:\tcrocodile\tlizard\tsnake\nThere are several useful visual features to tell there is 'alligator' and not similar things in a photo:\tbroad, rounded snout that is wider than the head\tupper jaw wider than the lower jaw\tvisible teeth when the mouth is closed\tshort, muscular legs\tthat end in webbed feet\trough and scaly skin with bony plates (osteoderms)", 45], "tourists": ["Yes. 'Tourists' have a tangible appearance and can be identified by their behavior and clothing.\nA few things that are visually similar to 'tourists' but are not 'tourists' are:\tlocal residents\ttravelers\texpats\nThere are several useful visual features to tell there are 'tourists' and not similar things in a photo:\tcarrying cameras or maps\tdressed in casual clothing\tor in beachwear or outdoor gears\tgathered in groups or pairs\ttaking selfies or posing\tfor pictures\tsmiling and appearing relaxed\tor excited", 45], "dad": ["No. 'Dad' is too vague or abstract to be distinguished in a photo.", 45], "lever": ["Yes. 'Lever' has a tangible appearance and is a simple machine.\nA few things that are visually similar to 'lever' but are not 'lever' are:\thandle\tknob\tjoystick\tbutton\tswitch\nThere are several useful visual features to tell there is 'lever' and not similar things in a photo:\trigid bar or plank\tpivot or fulcrum\tpoint of contact or effort\tpoint of load or resistance", 45], "utility poles": ["Yes. 'Utility poles' has a tangible appearance and is a kind of infrastructure.\nA few things that are visually similar to 'utility poles' but are not 'utility poles' are:\ttelegraph pole\tflag pole\tstreetlight\tfence post\nThere are several useful visual features to tell there is 'utility poles' and not similar things in a photo:\ttall and vertical wooden or metal poles\tcarrying electrical or telephone wires\tor transformers, insulators or other hardware\tpole containers on its surface such as transformers or switches", 45], "veggie": ["No. 'Veggie' is too vague or abstract to be visually distinguished in a photo. \n\nHowever, a few things that are visually similar to 'veggie' but are not 'veggie' are:\tFruit, flowers, leaves, plastic vegetables. \n\nTo distinguish 'veggie' from the listed similar things, some useful visual features are: \n- Green, yellow, red, or orange colors \n- Leaves, stems, or roots \n- A variety of different shapes (for example, round like a tomato, long and thin like a carrot, etc.) \n- Generally, a natural appearance rather than looking too uniform or perfect like plastic veggies.", 45], "sprig": ["Yes, 'sprig' has a tangible appearance and is a small stem with leaves or flowers attached.\nA few things that are visually similar but not 'sprig' are:\ttwig\tbranch\tstick\tfoliage\nThere are several useful visual features to distinguish 'sprig' from similar things in a photo:\ta small stem with leaves or flowers attached\tno more than five leaves or flowers\toften used in floral arrangements or as a garnish for food", 45], "base line": ["No. 'Base line' is too vague or abstract to be distinguished in a photo.", 45], "baseball diamond": ["Yes. 'Baseball diamond' has a tangible appearance and is a specific type of sports field.\nA few things that are visually similar to 'baseball diamond' but are not 'baseball diamond' are:\tfootball field\tsoccer field\thockey rink\nThere are several useful visual features to tell there is 'baseball diamond' and not similar things in a photo:\tdiamond-shaped field\tdirt or grass surface\twith bases at each corner\twith pitcher's mound in the center\twith foul lines extending from home plate\tto outfield fences or walls.", 45], "jet engines": ["Yes. 'Jet engines' has a tangible appearance and is a type of aircraft propulsion system.\nA few things that are visually similar to 'jet engines' but are not 'jet engines' are:\tturboprops\tfan blades\trocket engines\tturbines\tengines\nThere are several useful visual features to tell there is 'jet engines' and not similar things in a photo:\tcylindrical or tubular shape\twith or without a metallic casing\tcone-shaped nozzle or exhaust\tport holes or vents at the front or sides\tmultiple rows of blades\tinlets and outlets for air flow.", 45], "cat nose": ["Yes. 'Cat nose' has a tangible appearance and is a part of a cat's face structure.\nA few things that are visually similar to 'cat nose' but are not 'cat nose' are:\tdog nose\tmouse nose\tbear nose\nThere are several useful visual features to tell there is 'cat nose' and not similar things in a photo:\tlocated in the center of the cat's face\tpad at the end portion\twet and cold surface\thair around the nose or whiskers", 45], "pink sign": ["Yes. 'Pink sign' has a tangible appearance and is a sign that is pink.\nA few things that are visually similar to 'pink sign' but are not 'pink sign' are: signs that are red, purple or magenta,\nThere are no useful visual features to distinguish a 'pink sign' from signs that are red, purple or magenta, as they are all colors and can have different shapes and designs.", 45], "grizzly": ["Yes. 'Grizzly' has a tangible appearance and is a type of brown bear.\nA few things that are visually similar to 'grizzly' but are not 'grizzly' are:\tblack bear\tpolar bear\tbrown horse\tcow\nThere are several useful visual features to tell there is 'grizzly' and not similar things in a photo:\tbrown or golden-brown fur\tshoulder hump\tfacial profile with a concave appearance \tclaws that can be up to four inches long\tbigger than other brown bear\tsubstantial shoulder and rump muscles", 45], "fern": ["Yes. 'Fern' has a tangible appearance and is a kind of plant.\nA few things that are visually similar to 'fern' but are not 'fern' are:\tpalm tree\tbamboo\tgrass\nThere are several useful visual features to tell there is 'fern' and not similar things in a photo:\tleafy plant\twith multiple fronds or branches\tveins visible on fronds\tor fronds are feather-like", 45], "camera man": ["Yes. 'Camera man' has a tangible appearance and refers to a person operating a camera.\nA few things that are visually similar to 'camera man' but are not 'camera man' are:\tphotographer\tmovie actor\tfilm director\tpaparazzi\tnews reporter\nThere are several useful visual features to tell there is 'camera man' and not similar things in a photo:\tholding a camera or a video camera\tframing shots\twhile filming or taking pictures\tfocused on their work not on the subject of the photo wearing a press badge or a camera around the neck.", 45], "blue helmet": ["Yes. 'Blue helmet' has a tangible appearance and is a type of headgear.\nA few things that are visually similar to 'blue helmet' but are not 'blue helmet' are:\tblue hat\twith blue stripes\twith a blue logo\nThere are several useful visual features to tell there is 'blue helmet' and not similar things in a photo:\tprotective gear\tthat covers the entire head\tmade of hard plastic or metal\tshade of blue\tis often worn by police or military personnel", 45], "orange traffic cones": ["Yes. 'Orange traffic cones' has a tangible appearance and is a kind of road safety equipment.\nA few things that are visually similar to 'orange traffic cones' but are not 'orange traffic cones' are:\tpylon\tbarrel\tfence\tpost\nThere are several useful visual features to tell there is 'orange traffic cones' and not similar things in a photo:\tcone-shaped\torange or brightly colored\thollow at the top\tJOptionPane.s\tresting on a black base with reflective strips", 45], "stoplights": ["Yes. 'Stoplights' has a tangible appearance and is a type of traffic signal.\nA few things that are visually similar to 'stoplights' but are not 'stoplights' are:\ttraffic cones\tparking meters\tstreet signs\nThere are several useful visual features to tell there is 'stoplights' and not similar things in a photo:\tvertical traffic signal with three lights\tred, yellow, and green lights\tin a metal or plastic housing\tmounted on a pole or a wire\thanging over an intersection or a crosswalk", 45], "hump": ["Yes. 'Hump' has a tangible appearance and is a protrusion or rounded mass on the back of an animal or object.\nA few things that are visually similar to 'hump' but are not 'hump' are:\tbump\tmound\thill\nThere are several useful visual features to tell there is 'hump' and not similar things in a photo:\trising above the surrounding area\tcurved or rounded shape\tpositioned on the back or top of an object or animal", 45], "female surfer": ["Yes. 'Female surfer' has a tangible appearance and refers to a woman who is surfing.\nA few things that are visually similar to 'female surfer' but are not 'female surfer' are:\tswimmer\twindsurfer\tbodyboarder\tskier\nHere are some useful visual features to distinguish 'female surfer' from the listed similar things in a photo:\t\n- Riding on a surfboard.\n- Wearing a wetsuit or swimsuit.\n- Holding or attached to a surfboard.\n- Engaged in a surfing posture such as lying down, kneeling, or standing up.\n- In the ocean, near waves, or surrounded by other surfers.", 45], "openings": ["No. 'Openings' is too vague or abstract to be distinguished in a photo.", 45], "shirt sleeve": ["Yes. 'Shirt sleeve' has a tangible appearance and refers to the part of a shirt that covers the arm.\nA few things that are visually similar to 'shirt sleeve' but are not 'shirt sleeve' are:\tjacket sleeve\tcoat sleeve\tsweater sleeve\tglove\nThere are several useful visual features to tell there is 'shirt sleeve' and not similar things in a photo:\tattached to a shirt or a blouse\tusually made of the same material as the shirt or blouse\tit covers the arm, starting from the shoulder and ending at the wrist.", 44], "grey metal pole": ["Yes, 'grey metal pole' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'grey metal pole' but are not 'grey metal pole' are:\tSilver metal pipe\t Steel fence post\t Chrome curtain rod\t Grey plastic pole\nThere are several useful visual features to tell there is 'grey metal pole' and not similar things in a photo:\tSolid cylindrical shape\tNo visible bending or kinking\tShiny grey surface\tNo visible horizontal supports\tor crossbars.", 44], "toilet roll": ["Yes. 'Toilet roll' has a tangible appearance and is a type of paper product.\nA few things that are visually similar to 'toilet roll' but are not 'toilet roll' are:\tpaper towel\tkitchen roll\ttissue box\tnewspaper\nThere are several useful visual features to tell there is 'toilet roll' and not similar things in a photo:\tcylindrical shape\thollow core\tsoft and flexible paper\tperforations for easy tearing\tusually found in a bathroom or toilet", 44], "brace": ["Yes. 'Brace' has a tangible appearance and is an object used for support, typically for the teeth or for an injured body part.\nA few things that are visually similar to 'brace' but are not 'brace' are:\tbelts or straps\tforceps or pliers\tbracelets or cuffs\thinges or clamps\nThere are several useful visual features to tell there is 'brace' and not similar things in a photo: \tmade of metal or plastic \tworn on the teeth or on an injured body part \tadjustable with wires or bands \teasily removable \taligned with other braces/connected to other objects", 44], "tan shorts": ["Yes. 'Tan shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tan shorts' but are not 'tan shorts' are:\tjeans\ttrousers\tkhaki pants\tskorts\nThere are several useful visual features to tell there is 'tan shorts' and not similar things in a photo:\tshorts\tshowing knees or above\tkhaki or tan color", 44], "cute": ["No. 'Cute' is too vague or abstract to be distinguished in a photo. It is a subjective and context-dependent concept.\nThere are no things that are visually similar to 'cute'.\nTherefore, there are no visual features that can be used to distinguish 'cute' from other things in a photo.", 44], "tee": ["Yes. 'Tee' has a tangible appearance and refers to a type of golf shot or a wooden peg to hold the ball on the ground for this shot.\nA few things that are visually similar to 'tee' but are not 'tee' are:\twooden peg\tnail\tspike\tanchor post\nThere are several useful visual features to tell there is a 'tee' and not similar things in a photo:\tshort and wooden\thollow or concave top, flat base\tor sharp end\tfor golf shots, tee is placed on the ground and the ball sits on the top of the tee.", 44], "giant": ["No. 'Giant' is too vague or abstract to be distinguished in a photo. However, if we are referring to a giant creature or entity, then 'giant' has a tangible appearance.\nA few things that are visually similar to 'giant' but are not 'giant' are:\ttall buildings\tenlarged objects\tperson standing closer to the camera\nThere are several useful visual features to tell there is a 'giant' and not similar things in a photo:\tenormous size in comparison to the environment\tlarger or thicker limbs or body parts\ttowering over other objects or people in the photo\tlarger than normal features or body parts in proportion to the rest of the body", 44], "h": ["No. 'h' is too vague or abstract to be distinguished in a photo.", 44], "orange chair": ["Yes. 'Orange chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'orange chair' but are not 'orange chair' are:\torange sofa\torange ottoman\torange stool\nThere are several useful visual features to tell there is 'orange chair' and not similar things in a photo:\thas a seat and a backrest\thas legs to support it\thas a solid and stable structure\tis designed for one person to sit", 44], "cork": ["Yes. 'Cork' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'cork' but are not 'cork' are:\twood\tbark\tleather\nThere are several useful visual features to tell there is 'cork' and not similar things in a photo:\tlight brown or tan color\tswirls or patterns\ton a bottle or a bulletin board\thas a porous texture\tslightly flexible when pressed", 44], "bra": ["Yes, 'bra' has a visually concrete concept and can be identified by its appearance.\nA few things that are visually similar to 'bra' but are not 'bra' are:\tstrapless top\tsports bra\tcamisole\tswimsuit\nThere are several useful visual features to tell there is a 'bra' and not similar things in a photo:\ttwo cups with padding or underwire\tfor support.\tadjustable straps\tfor fitting.\tback closure\tor no closure.\tusually made of lace or cotton.", 44], "flagpole": ["Yes. 'Flagpole' has a tangible appearance and is a type of pole used to hang a flag.\nA few things that are visually similar to 'flagpole' but are not 'flagpole' are:\tLight pole\tSki pole\tWalking stick\tmicrophone stand\nThere are several useful visual features to tell there is 'flagpole' and not similar things in a photo:\ttall or long\tcylindrical with a circular cross-section\tflag attached at the top or rope loop at the center\tmounted on the ground or building", 44], "monitor screen": ["Yes. 'Monitor screen' has a tangible appearance and is a physical object.\nA few things that are visually similar to 'monitor screen' but are not 'monitor screen' are:\ttelevision screen\tprojector screen\ttablet screen\t\nThere are several useful visual features to distinguish 'monitor screen' from the listed similar things in a photo such as:\tthin and rectangular shape\ta stand or a base\tphysical buttons or controls around it\ta visible desktop or interface\tactive pixels, icons, or text on the screen", 44], "coleslaw": ["Yes. 'Coleslaw' has a tangible appearance and is a kind of salad.\nA few things that are visually similar to 'coleslaw' but are not 'coleslaw' are:\tpotato salad\tmacaroni salad\tgarden salad\tfruit salad\nThere are several useful visual features to tell there is 'coleslaw' and not similar things in a photo:\tshredded or chopped cabbage dressing, typically made with mayonnaise and/or vinegar and sugar\tadditional ingredients such as carrots, raisins, or apples can also be present.", 44], "water splash": ["Yes. 'Water splash' has a tangible appearance and is a type of water movement.\nA few things that are visually similar to 'water splash' but are not 'water splash' are:\twaterfall\twaves\tfountain\train\nThere are several useful visual features to tell there is 'water splash' and not similar things in a photo:\tburst of water droplets in the air\tcircular shape\twhite or clear color\tsurrounding environment (e.g., a pool or a glass)", 44], "movies": ["No. 'Movies' is too abstract to be visually concrete. \n\nHowever, there are some things that are visually similar to the cinematic experience, such as:\t\n- Theater building\n- TV show or series \n- Documentary film\n- Live performance\n\nUseful visual features for distinguishing movies from these similar things would be:\n- Projected or on-screen visuals\n- People sitting in seats watching\n- Movie posters or promotional materials\n- Empty cinema seats or screen\n- Popcorn or other cinema snacks in hand or on a tray", 44], "silver pan": ["Yes. 'Silver pan' has a tangible appearance and is a type of cooking utensil.\nA few things that are visually similar to 'silver pan' but are not 'silver pan' are:\tsilver pot\tsilver bowl\tstainless steel pan\nThere are several useful visual features to tell there is 'silver pan' and not similar things in a photo:\tround or oval shape\tflat bottom\tsides that rise at a 90-degree angle\tmetallic or silver appearance\ttypically used for cooking or frying food", 44], "dress shoes": ["Yes. 'Dress shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'dress shoes' but are not 'dress shoes' are:\tsneakers\tboots\tsandals\theels\nThere are several useful visual features to tell there are 'dress shoes' and not similar things in a photo:\tclose-toed leather or faux leather shoes\tpolished or glossy finish\tno laces or minimal lacing\tClassic, simple design", 44], "power": ["No. 'Power' is too vague or abstract to be distinguished in a photo.", 44], "skiiers": ["Yes. 'Skiers' have a tangible appearance and are people who participate in the sport of skiing.\nA few things that are visually similar to 'skiers' but are not 'skiers' are: \thikers \trunners \tsledders \tsnowboarders \nThere are several useful visual features to tell there are 'skiers' and not similar things in a photo: \twearing ski gear and clothing, including ski boots \tcarrying ski poles \tskiing downhill or cross-country on skis", 44], "blurry image": ["Yes. 'Blurry image' has a tangible appearance and refers to a photo or an image that is out of focus.\nA few things that are visually similar to 'blurry image' but are not 'blurry image' are:\tmotion blur\tpixelated image\tlow-resolution image\t\nThere are several useful visual features to tell there is 'blurry image' and not similar things in a photo:\tno clear or distinct details\tsmudged or fuzzy edges\tlack of sharpness\tor focus\tblurred areas in the photo", 44], "ladders": ["Yes. 'Ladders' has a tangible appearance and is a type of tool or equipment.\nA few things that are visually similar to 'ladders' but are not 'ladders' are:\tstepladders\tchairs\tshelves\tbookcases\nThere are several useful visual features to tell there is 'ladders' and not similar things in a photo:\tlong and narrow\tplanks or rungs for steps\tleaning against a vertical surface\twith or without hooks or brackets\tto reach higher areas", 44], "love seat": ["Yes. 'Love seat' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'love seat' but are not 'love seat' are:\tcouch\tsofa\tchair\tbench\trecliner\nThere are several useful visual features to tell there is 'love seat' and not similar things in a photo:\ttwo-seater sofa meant for couples or two people\tside-by-side seating arrangement\tpadded seat and backrest, often upholstered in fabric or leather", 44], "windscreen": ["Yes. 'Windscreen' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'windscreen' but are not 'windscreen' are:\tgoggles\tglasses\tsunglasses\tface shield\nThere are several useful visual features to tell there is 'windscreen' and not similar things in a photo:\t\ntransparent or translucent\t\ncovering the front of a vehicle\t\ncurved shape\t\nwipers attached\t\ntinted or shaded\t\ncracks or chips from impact\tor debris.", 44], "tennis raquet": ["Yes. 'Tennis racquet' has a tangible appearance and is a sports equipment.\nA few things that are visually similar to 'tennis racquet' but are not 'tennis racquet' are:\tbadminton racquet\tsquash racquet\tpaddle ping pong racquet\nThere are several useful visual features to tell there is 'tennis racquet' and not similar things in a photo:\tflat, oval-shaped frame with strings\ttubular handle with grip\tsmaller size than other racquets\tfor tennis use only.", 44], "front train": ["Yes. 'Front train' has a tangible appearance and refers to the locomotive or engine of a train.\nA few things that are visually similar to 'front train' but are not 'front train' are:\ttrain tracks\tpassengers cars\tsubway train\nThere are several useful visual features to tell there is 'front train' and not similar things in a photo:\twheels\tor chimney\tthat generate smoke\tsmall windows\tthat forms a cabin\twhere the driver sits\ta cowcatcher\tat the front end of the engine that guides obstacles off of the tracks.", 44], "company": ["No. 'Company' is too vague or abstract to be distinguished in a photo.", 44], "skateboard ground": ["No. 'Skateboard ground' is too vague or abstract to be distinguished in a photo. However, 'skateboard obstacle' would be a visually concrete concept to describe the physical structures that skateboarders use to perform tricks.\nA few things that are visually similar to 'skateboard ground' but are not 'skateboard ground' are:\tconcrete surface\tasphalt surface\tbasketball court\nThere is not enough information to provide useful visual features for distinguishing 'skateboard ground' from similar things as the concept is too vague. However, some useful visual features for identifying skateboarding obstacles could be: curbs, rails, ledges, quarter pipes, half pipes, and banks. These structures often have smooth edges and are made of metal or concrete.", 44], "light fixture": ["Yes. 'Light fixture' has a tangible appearance and is a type of lighting equipment.\nA few things that are visually similar to 'light fixture' but are not 'light fixture' are:\tlight bulb\tcandle\tfireplace\tsun\nThere are several useful visual features to tell there is 'light fixture' and not similar things in a photo:\tmetal, plastic or glass structure\tto which the light bulb is attached\thanging from the ceiling, wall, or floor-mounted\tswitch or cord to turn it off and on", 44], "art work": ["Yes. 'Art work' has a tangible appearance and can refer to a variety of creative pieces such as paintings, sculptures, photographs, etc.\nA few things that are visually similar to 'art work' but are not 'art work' are:\tdecorative items\tproduct advertisements\tcommercial billboards\tportraits\nThere are several useful visual features to tell there is 'art work' and not similar things in a photo:\tunique and creative\tintricate or complex detail\taesthetic appeal\tsignature or artist name may be present\tan expression of emotion or idea", 44], "tomatoe": ["Yes. 'Tomato' has a tangible appearance and is a type of fruit/vegetable.\nA few things that are visually similar to 'tomato' but are not 'tomato' are:\tapples\toranges\tavocadoes\tpeppers\nThere are several useful visual features to tell there is 'tomato' and not similar things in a photo:\tround or oval shape\tsmooth, shiny skin\tbright red, yellow, or green colors\ta stem and leaves at the top\tfleeting greenish sepals at the base", 44], "bikini top": ["Yes. 'Bikini top' has a tangible appearance and is a type of swimwear.\nA few things that are visually similar to 'bikini top' but are not 'bikini top' are:\tbra\tsports bra\tcrop top\ttank top\nThere are several useful visual features to tell there is 'bikini top' and not similar things in a photo:\ttriangle shape\ttwo straps that tie behind the neck and back\tmade of swimsuit material\tdesigned for swimming or other water-based activities", 44], "city building": ["Yes. 'City building' has a tangible appearance and refers to any building built in a city.\nA few things that are visually similar to 'city building' but are not 'city building' are:\thouses\tchurches\toffice buildings\tskyscrapers\tmuseums\nThere are several useful visual features to tell there is 'city building' and not similar things in a photo:\ttaller than surrounding buildings\tmore than three stories\tconstructed with bricks, steel or concrete\tmultiple windows or balconies\ton a busy street or surrounded by other buildings", 44], "brass": ["Yes. 'Brass' has a tangible appearance and is an alloy of copper and zinc.\nA few things that are visually similar to 'brass' but are not 'brass' are:\tcopper\tgold\tpenny\tyellow-colored plastic or metal\nThere are several useful visual features to tell there is 'brass' and not similar things in a photo:\tyellow-gold color\tmetallic shine\tvisible texture of copper and zinc mixture", 44], "leaves trees": ["Yes. 'Leaves trees' has a tangible appearance and refers to trees that have leaves instead of needles.\nA few things that are visually similar to 'leaves trees' but are not 'leaves trees' are:\tconifers\tpalm trees\tcacti\nThere are several useful visual features to tell there are 'leaves trees' and not similar things in a photo:\tbranching structure with many smaller branches\tleaves of varying shapes and sizes\tgreen leaves in spring and summer; colorful leaves in fall.", 44], "sun light": ["Yes. 'Sun light' has a tangible appearance, and is a kind of natural light.\nA few things that are visually similar to 'sun light' but are not 'sun light' are:\treflection\tlamp light\tcandle light\nThere are several useful visual features to tell there is 'sun light' and not similar things in a photo:\tbright and intense\tcoming from a (usually) yellow ball in the sky\tcasting shadows on objects\tcreating a warm atmosphere in the picture", 44], "show": ["No. 'Show' is too vague or abstract to be distinguished in a photo.", 44], "scrub brush": ["Yes. 'Scrub brush' has a tangible appearance and is a tool for cleaning.\nA few things that are visually similar to 'scrub brush' but are not 'scrub brush' are:\tpaintbrush\tcomb\tbroom\tcloth\nThere are several useful visual features to tell there is 'scrub brush' and not similar things in a photo:\tshort handle\tstiff bristles\tsquare or rectangular shape\tused for scrubbing or cleaning hard surfaces like floors or tiles", 44], "horse rider": ["Yes. 'Horse rider' has a tangible appearance and is a person riding a horse.\nA few things that are visually similar to 'horse rider' but are not 'horse rider' are:\tperson standing on a horse\tstuffed animal horse\thorse running in a field\nThere are several useful visual features to tell there is 'horse rider' and not similar things in a photo:\ta person sitting on a horse's back\thorse with a saddle or bridle\thorse's legs positioned in a way that suggests it is being ridden\thorse and person in motion or performing a specific activity, such as jumping or racing.", 44], "stone tower": ["Yes. 'Stone tower' has a tangible appearance and can refer to a specific architectural structure.\nA few things that are visually similar to 'stone tower' but are not 'stone tower' are:\tchimney\tlighthouse\twindmill\tminaret\nThere are several useful visual features to tell there is 'stone tower' and not similar things in a photo:\tmade of stone or brick\ttall and narrow\tcircular or square shape\twith or without windows\tor a door\ton top, it may have a cone-shaped roof or a spire", 44], "hoop": ["Yes. 'Hoop' has a tangible appearance and is a circular shaped object.\nA few things that are visually similar to 'hoop' but are not 'hoop' are:\tfrisbee\ttire\tbracelet\tnecklace\nThere are several useful visual features to tell there is 'hoop' and not similar things in a photo:\tcircular shape\thollow or made of a flexible material\tused for sports, such as basketball or hula hooping.", 44], "wooden bridge": ["Yes. 'Wooden bridge' has a tangible appearance and refers to a kind of structure.\nA few things that are visually similar to 'wooden bridge' but are not 'wooden bridge' are:\tdock\tpier\tboardwalk\tjetty\nThere are several useful visual features to tell there is 'wooden bridge' and not similar things in a photo:\tmade of wood and planks\tarched or flat\tover water or other terrain\tmay have railings\tor side barriers", 44], "beach area": ["Yes. 'Beach area' has a tangible appearance and is an outdoor location.\nA few things that are visually similar to 'beach area' but are not 'beach area' are:\tdesert\tsand dunes\tmudflats\nThere are several useful visual features to tell there is 'beach area' and not similar things in a photo:\tcoastal location\tsand\tseashells\tor rocks by the seashore\tpalm trees, beach umbrellas or chairs\tpeople in bathing suits and swimsuits\tswimming in the water, building sandcastles or playing volleyball on the sand", 44], "orange color": ["Yes. 'Orange color' has a tangible appearance and is a part of the visible spectrum of light.\nA few things that are visually similar to 'orange color' but are not 'orange color' are:\tyellow-red hues\tof a sunset\tor a fire\nThere are several useful visual features to tell there is 'orange color' and not similar things in a photo:\tPure hue lying between red and yellow\tcolor of an actual orange fruit\tor a carrot, pumpkin, or sweet potato.", 44], "plane tail": ["Yes. 'Plane tail' has a tangible appearance and is the tail section of an airplane.\nA few things that are visually similar to 'plane tail' but are not 'plane tail' are:\tcars with spoilers\tanimal tails\tboat tails\nThere are several useful visual features to tell there is 'plane tail' and not similar things in a photo:\tlarge and prominent tail section of an airplane\tvertical stabilizer or fin\thorizontal stabilizers and elevators\tsurface control features\tspecific airline logos or symbols on the tail", 44], "engineer": ["No. 'Engineer' is too vague or abstract to be distinguished in a photo. It is a profession or a job title rather than a visual concept. \n\nHowever, there are some physical objects that are associated with engineering, such as:\n\n- Blueprint or technical drawings\n- Calculators or computers\n- Tools, such as hammers, screwdrivers or wrenches\n\nBut these items are not 'engineer' themselves, they are just tools that can be used by engineers. Therefore, there are no useful visual features for distinguishing 'engineer' from these similar things in a photo.", 43], "salad plate": ["Yes. 'Salad plate' has a tangible appearance and is a kind of dishware.\nA few things that are visually similar to 'salad plate' but are not 'salad plate' are:\tdinner plate\tsaucer\tbowl\nThere are several useful visual features to tell there is 'salad plate' and not similar things in a photo:\tsmall size compared to a dinner plate\tflat surface\twith a rim or without a rim\tused for serving salad or small meals.", 43], "creases": ["Yes. 'Creases' has a tangible appearance and refers to lines or folds in a surface.\nA few things that are visually similar to 'creases' but are not 'creases' are:\tshadows\tlines in a pattern\tonline graph\nThere are several useful visual features to tell there are 'creases' and not similar things in a photo:\tslight indentations or depressions in a surface\tstress lines or folds in fabric or paper\tlight and shadow emphasizing the line\tcurved or irregular lines", 43], "wood trim": ["Yes. 'Wood trim' has a tangible appearance and is a decorative element used in architecture and interior design.\nA few things that are visually similar to 'wood trim' but are not 'wood trim' are: wallpaper borders, crown molding, baseboards, paneling, wallpaper borders\nThere are several useful visual features to tell there is 'wood trim' and not similar things in a photo:\tmade of wood or has a wooden appearance, has a carved or decorative design, is used to frame or accentuate doors, windows, ceilings, or walls.", 43], "customer": ["No. 'Customer' is too vague or abstract to be distinguished in a photo.", 43], "batch": ["No. 'Batch' is too vague or abstract to be distinguished in a photo.", 43], "blue bowl": ["Yes. 'Blue bowl' has a tangible appearance and is a specific type of dishware.\nA few things that are visually similar to 'blue bowl' but are not 'blue bowl' are:\tother types of bowls\tbowls of different colors\tplates\tcups\nThere are several useful visual features to tell there is 'blue bowl' and not similar things in a photo:\tbowl-shaped\tblue color\tsmooth, glazed surface\tcapacity to hold food or liquid.", 43], "battery": ["Yes. 'Battery' has a tangible appearance and is a small electrochemical cell that stores energy.\nA few things that are visually similar to 'battery' but are not 'battery' are:\tbuttons\tcapsules\twheels\tbobbins\nThere are several useful visual features to tell there is 'battery' and not similar things in a photo:\trectangular-shaped\tcylindrical-shaped\thas positive and negative terminals to connect to devices\tmarked with the voltage and the chemistry of the cell", 43], "kittens": ["Yes. 'Kittens' has a tangible appearance and refers to baby cats.\nA few things that are visually similar to 'kittens' but are not 'kittens' are:\tcats\tpuppies\tferrets\t\nThere are several useful visual features to tell there is 'kittens' and not similar things in a photo:\tsmall size\tfluffy fur\tround face and eyes\tshort legs\tinquisitive and playful demeanour.", 43], "decor": ["No. 'Decor' is too vague or abstract to be distinguished in a photo.", 43], "dog tag": ["Yes. 'Dog tag' has a tangible appearance and is a type of identification tag worn on a collar by dogs.\nA few things that are visually similar to 'dog tag' but are not 'dog tag' are:\tkeychain\tid tag\tfor luggage\nThere are several useful visual features to tell there is 'dog tag' and not similar things in a photo:\tattached to a dog's collar\tunique identifier\tfor a dog's owner's contact information or medical needs\tmetal material with stamped or engraved text or pictures.", 43], "orange jacket": ["Yes. 'Orange jacket' has a tangible appearance and refers to a specific type and color of clothing.\nA few things that are visually similar to 'orange jacket' but are not 'orange jacket' are:\torange shirt\torange sweater\torange vest\torange raincoat\nThere are several useful visual features to tell there is an 'orange jacket' and not similar things in a photo:\tjacket style with sleeves and buttons or zippered front\tlarge enough to cover the torso\tworn as outerwear or over other clothing\titem made from thicker material than a shirt or sweater, usually for warmth or protection from the elements.", 43], "hips": ["Yes. 'Hips' has a tangible appearance and is part of the human body.\nA few things that are visually similar to 'hips' but are not 'hips' are:\tbelly\tbuttocks\tabdomen\t\nThere are several useful visual features to tell there are 'hips' and not similar things in a photo:\tthe widest part of the human torso\tboney protrusions on either side of the pelvis\tinward curve above the buttocks and below the waistline in women", 43], "caution": ["No. 'Caution' is too vague or abstract to be distinguished in a photo.", 43], "silver plate": ["Yes. 'Silver plate' has a tangible appearance and is a kind of dishware.\nA few things that are visually similar to 'silver plate' but are not 'silver plate' are:\tstainless steel plate\tchrome plate\tplastic plate\tglass plate\t\nThere are several useful visual features that can help distinguish 'silver plate' from similar things in a photo:\treflective surface\tsilver color\tspecific design or pattern\televated edges, like a tray or a platter", 43], "stone pillar": ["Yes. 'Stone pillar' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'stone pillar' but are not 'stone pillar' are:\ttree\ttrunk\tlamppost\tchimney\nThere are several useful visual features to tell there is 'stone pillar' and not similar things in a photo:\tmade of stone or concrete\ttall and cylindrical or square\tbase or foundation\tcapital or cornice at the top", 43], "orange piece": ["Yes. 'Orange piece' has a tangible appearance and is a piece of fruit.\nA few things that are visually similar to 'orange piece' but are not 'orange piece' are:\tlemon piece\tlime piece\tgrapefruit piece\nThere are several useful visual features to tell there is 'orange piece' and not similar things in a photo:\torange color\tpulp and segments\tof a spherical shape\tcitrusy smell\twhen attached to the whole fruit it has a dimpled texture", 43], "spectacles": ["Yes. 'Spectacles' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'spectacles' but are not 'spectacles' are:\tsunglasses\tgoggles\t3D glasses\tjewelry\nThere are several useful visual features to tell there are 'spectacles' and not similar things in a photo:\tframe around lenses\ttemple arms to rest on ears\tnose pads to keep glasses secure on the nose\ttwo lenses to correct vision", 43], "pea": ["Yes. 'Pea' has a tangible appearance and is a type of small round vegetable.\nA few things that are visually similar to 'pea' but are not 'pea' are:\tgrape\ttomato\tberry\tmarble\nThere are several useful visual features to tell there is 'pea' and not similar things in a photo:\tround shape\tgreen color\tsmall size\twrinkled texture", 43], "bathroom counter": ["Yes. 'Bathroom counter' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'bathroom counter' but are not 'bathroom counter' are:\tkitchen counter\tdesk\tdresser\ttable\nThere are several useful visual features to tell there is 'bathroom counter' and not similar things in a photo:\tlocated in a bathroom or washroom\tsurface for holding toiletries such as soap, toothbrush, makeup, etc.\tusually made of tile, granite, or other water-resistant materials.", 43], "volleyball": ["Yes. 'Volleyball' has a tangible appearance and is a ball used in a sport.\nA few things that are visually similar to 'volleyball' but are not 'volleyball' are:\tsoccer ball\tbasketball\tdodgeball\nThere are several useful visual features to tell there is 'volleyball' and not similar things in a photo:\tlight and bouncy\twhite or brightly colored panels\twith thin lines sewn on them\tmeasures approximately 65-67 cm in circumference", 43], "tree stump": ["Yes. 'Tree stump' has a tangible appearance and is a part of a tree that has been cut down.\nA few things that are visually similar to 'tree stump' but are not 'tree stump' are:\trock\tsofa cushion\tpile of dirt\nThere are several useful visual features to tell there is 'tree stump' and not similar things in a photo:\tcircular or oval shape\trough and textured surface, like bark\tbroken or uneven edges or rings\tpatterns visible on the surface, like tree rings", 43], "florets": ["Yes. 'Florets' has a tangible appearance and is a part of a larger flower head composed of multiple small flowers.\nA few things that are visually similar to 'florets' but are not 'florets' are: petals, stamen, sepals, pistil.\nThere are several useful visual features to tell there is 'florets' and not similar things in a photo: small individual flowers that make up a rounded or flat flower head, clustered together in a symmetric pattern, often forming a dense cluster that forms the center of a composite flower like daisies or sunflowers, usually have short stalks or lack stalks entirely.", 43], "ben": ["No, 'ben' is too vague and abstract as it could refer to a person's name or a shortened form of a place name such as 'Ben Nevis' or 'Ben Lomond'.\nAs it's not clear what 'ben' is referring to, there are no visually similar things to list.\nWithout additional context or information, it's not possible to provide useful visual features for distinguishing 'ben' from other things in a photo.", 43], "kiosk": ["Yes. 'Kiosk' has a tangible appearance and is a small standalone structure used for selling items or providing information.\nA few things that are visually similar to 'kiosk' but are not 'kiosk' are:\tbooth\tcontainer\tmodular building\ttent\ttrailer\nThere are several useful visual features to tell there is 'kiosk' and not similar things in a photo:\tsmall and standalone structure\tbuilt from wood, metal, or plastic\twindows or openings for customers\tjuxtaposed with a street or public area\tsignage displaying what is sold or provided inside", 43], "skateboard dude": ["Yes. 'Skateboard dude' has a tangible appearance and refers to a person using a skateboard.\nA few things that are visually similar to 'skateboard dude' but are not 'skateboard dude' are:\tsurfer\tsnowboarder\tbiker\trollerblader\nThere are several useful visual features to tell there is 'skateboard dude' and not similar things in a photo:\tperson riding a skateboard\twearing skateboarding gear\tskateboard visible in the photo\tjumping or doing tricks\ton a flat surface or skateboard ramp.", 43], "plane engine": ["Yes. 'Plane engine' has a tangible appearance and it's part of an aircraft.\nA few things that are visually similar to 'plane engine' but are not 'plane engine' are:\tcar engine\tboat engine\ttrain engine\tfactory machinery\nThere are several useful visual features to tell there is 'plane engine' and not similar things in a photo:\tattached to a plane\tcircular shape\tmetallic surface\twith propellers or turbines\tsurrounded by other parts of the aircraft.", 43], "silver television": ["Yes. 'Silver television' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'silver television' but are not 'silver television' are:\tcomputers\tmonitors\tprojectors\tsound bars\nThere are several useful visual features to tell there is 'silver television' and not similar things in a photo:\tsilver or metallic color\trectangular screen\tdisplay or video playing buttons\tand antenna or cable port\ton a stand or mounted on a wall.", 43], "fridge door": ["Yes. 'Fridge door' has a tangible appearance and is an object with a specific shape and function.\nA few things that are visually similar to 'fridge door' but are not 'fridge door' are:\tregular door\toven door\tdishwasher door\tcabinet door\nThere are several useful visual features to tell there is 'fridge door' and not similar things in a photo:\trectangular shape\twith a handle or a knob\tmagnetized surface\tfor storing food and drinks", 43], "information sign": ["Yes. 'Information sign' has a tangible appearance and refers to a physical sign providing information.\nA few things that are visually similar to 'information sign' but are not 'information sign' are:\twarning sign\tdirection sign\tadvertising sign\ttraffic light\nThere are several useful visual features to tell there is 'information sign' and not similar things in a photo:\tclear and legible text\tpictograms or symbols that convey information\tpurposeful placement in a public space", 43], "bent": ["Yes. 'Bent' has a visually concrete appearance.\nA few things that are visually similar to 'bent' but are not 'bent' are:\tstraight\tcrooked\twavy\nThere are several useful visual features to tell there is 'bent' and not similar things in a photo:\tcurved shape\tangle or direction change from the original position\tno sharp point(s) or edges", 43], "cardigan": ["Yes. 'Cardigan' has a tangible appearance and is a type of knitted garment.\nA few things that are visually similar to 'cardigan' but are not 'cardigan' are:\tsweater\tjacket\tpullover\thoodie\nThere are several useful visual features to tell there is 'cardigan' and not similar things in a photo: buttoned or zipped in the front, V-neck or a round crewneck, two pockets, made of knit or wool material.", 43], "sandwhich": ["Yes. 'Sandwich' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'sandwich' but are not 'sandwich' are:\twrap\ttaco\tburrito\tpizza\nThere are several useful visual features to tell there is 'sandwich' and not similar things in a photo:\ttwo slices of bread or bun with filling in-between\tportable\tsize is moderate, suitable for holding in hand or packing for a picnic or lunch\tmultiple layers of ingredients that are visually distinct (e.g. lettuce, cheese, meat, tomato, etc.)", 43], "spikes": ["Yes. 'Spikes' has a tangible appearance and is a pointed or protruding object.\nA few things that are visually similar to 'spikes' but are not 'spikes' are:\tneedles\tporcupine quills\ttent pegs\tfangs\tthorns\nThere are several useful visual features to tell there are 'spikes' and not similar things in a photo:\tpointed\tprotruding\tsharp tips\tuniform shape or pattern\tvarious sizes and lengths", 43], "sport": ["No. 'Sport' is too vague or abstract to be distinguished in a photo. \n\nHowever, some things that are visually similar to 'sport,' but not 'sport,' are:\n\n- People exercising or running: although this may be considered a form of physical activity, it may not necessarily be classified as a sport.\n- Playful activities: such as tag or red rover, which don't have specific rules or structure as in sports.\n- Outdoor activities: like hiking or fishing, which are recreational activities but aren't considered sports.\n\nThere are no visual features to distinguish sports from similar activities in a photo as sports can vary greatly in their appearance and presentation. However, signs of formal teams or organizations, specific equipment or uniforms can suggest a more formal sports activity happening.", 43], "sidecar": ["Yes. 'Sidecar' has a tangible appearance and is a motorcycle attachment.\nA few things that are visually similar to 'sidecar' but are not 'sidecar' are:\ttrailer\tbicycle cart\tcarriage \nThere are several useful visual features to tell there is 'sidecar' and not similar things in a photo:\tattached to the side of a motorcycle\ttwo wheels\tmatches the color and style of the motorcycle\tspace for a passenger\tseparate seating area from the motorcycle rider", 43], "handle drawer": ["Yes. 'Handle drawer' has a tangible appearance and refers to a specific part of furniture.\nA few things that are visually similar to 'handle drawer' but are not 'handle drawer' are:\tknob\tdoor handle\tshelf handle\tlight switch\nThere are several useful visual features to tell there is 'handle drawer' and not similar things in a photo:\trectangular or semicircular shape\tattached to a drawer or a cabinet\tusually made of metal, plastic, or wood\tprojecting from its surface, allowing for ease in opening or closing the drawer", 43], "advertising": ["No. 'Advertising' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to advertisements but are not advertisements are billboards, posters, flyers, banners, and signs.\n\nUseful visual features for distinguishing advertising from the listed similar things in a photo would be:\n\n- Text or images promoting a product or service.\n- Logos or branding of a company.\n- Text or images that encourage the viewer to take an action, such as making a purchase or visiting a website.", 43], "foal": ["Yes. 'Foal' has a tangible appearance and is a young horse.\nA few things that are visually similar to 'foal' but are not 'foal' are:\tpony\tdonkey\tzebra\tcolts\nThere are several useful visual features to tell there is 'foal' and not similar things in a photo:\tlarge eyes\tlong legs\tsoft fur\tvariety of coat colors", 43], "alley": ["Yes. 'Alley' has a tangible appearance and is a narrow street or path.\nA few things that are visually similar to 'alley' but are not 'alley' are:\tdriveway\twalkway\tpathway\tsidewalk\nThere are several useful visual features to tell there is 'alley' and not similar things in a photo:\tnarrow\tstreet-like\tsurrounded by buildings or walls\tpaved surface with visible road markings or lines", 43], "hoofs": ["Yes. 'Hoofs' has a tangible appearance and is a part of an animal's anatomy.\nA few things that are visually similar to 'hoofs' but are not 'hoofs' are:\tpaws\tclaws\thands\tfeet\nThere are several useful visual features to tell there are 'hoofs' and not similar things in a photo:\thard, keratin exterior\tusually two-toed or four-toed, depending on the animal\tusually cloven or split down the middle, depending on the animal\tcan be used for running or gripping the ground", 43], "ornament": ["Yes. 'Ornament' has a tangible appearance and is a type of decoration.\nA few things that are visually similar to 'ornament' but are not 'ornament' are:\tjewelry\tpaintings\tfigurines\nThere are several useful visual features to tell there is 'ornament' and not similar things in a photo:\tcolorful\thanging or placed in a visible spot\ton a tree or a ceiling\tdesigned with patterns or pictures", 43], "snow jacket": ["Yes. 'Snow jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'snow jacket' but are not 'snow jacket' are:\traincoat\tparka\twindbreaker\tsweater\nThere are several useful visual features to tell there is 'snow jacket' and not similar things in a photo:\tthick and insulated fabric\thigh collar\tor hood\tzipper or buttons\tsnow-resistant or water-resistant outer layer\tsnow/water-proof gloves or wrist cuffs\tother equipment that suggests a winter sport like a snowboard or a ski", 42], "ferry": ["Yes. 'Ferry' has a tangible appearance and is a type of boat used to transport people or vehicles across a body of water.\nA few things that are visually similar to 'ferry' but are not 'ferry' are:\tcargo ship\tcruise ship\triverboat\traft\nThere are several useful visual features to distinguish 'ferry' from the listed similar things in a photo:\t\n- identifiable hull designs including a flat bottom\n- open deck spaces for loading vehicles and passengers \n- accessible ramps or doors \n- lifebuoys visible on deck \n- clear signage indicating ferry service or ferry company logos.", 42], "customers": ["No. 'Customers' is too vague or abstract to have a tangible appearance in a photo. \n\nHowever, here are some things that might be associated with a 'customers' photo but are not 'customers': crowd, people, passersby.\n\nThere are no specific visual features to distinguish 'customers' from the listed similar things as it depends on the context and setting of the photo. However, some useful visual cues to suggest a 'customers' theme could be: people carrying shopping bags, people standing in front of stores or restaurants, people interacting with vendors or employees, people waiting in line, etc.", 42], "rods": ["Yes. 'Rods' have a tangible appearance and refer to slender and elongated objects.\nA few things that are visually similar to 'rods' but are not rods include: pencils, sticks, canes, stakes, pipes.\nUseful visual features for distinguishing 'rods' from the listed similar things in a photo include: thinner and longer than a pencil, no visible sharpened edge or point, not made of wood or metal, may have a decorative or textured surface.", 42], "rivets": ["Yes. 'Rivets' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'rivets' but are not 'rivets' are:\tscrews\tbolts\tnails\tpins\nThere are several useful visual features to tell there are 'rivets' and not similar things in a photo:\tcylindrical shape\twith a flat top and a rounded bottom\tmetallic appearance\tcold-pressed into the material\tof a uniform size and shape", 42], "street scene": ["Yes. 'Street scene' has a tangible appearance and refers to a view of a street.\nA few things that are visually similar to 'street scene' but are not 'street scene' are:\tlandscape view\tcity skyline\tview from a window\tview of a building\nThere are several useful visual features to tell there is 'street scene' and not similar things in a photo:\tview of a road or path\tview of buildings or houses\tview of cars or people on the street bustling scene or busy traffic lights signs and billboards on the road", 42], "pamphlet": ["Yes. 'Pamphlet' has a tangible appearance and refers to a type of printed material.\nA few things that are visually similar to 'pamphlet' but are not 'pamphlet' are:\tbrochure\tleaflet\tflyer\tnewspaper\nThere are several useful visual features to tell there is 'pamphlet' and not similar things in a photo:\tunfolded sheet of paper with information and images\tfolded into two or three panels or pages\tsmall size, usually handheld easy to distribute and distribute information from", 42], "porcelain bathroom sink": ["Yes. 'Porcelain bathroom sink' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'porcelain bathroom sink' but are not 'porcelain bathroom sink' are:\tgranite sink\tenamel sink\tmarble sink\tporcelain dish\nThere are several useful visual features to tell there is 'porcelain bathroom sink' and not similar things in a photo:\toval or round shape\tporcelain material\tfaucet and drain included\tpresent in a bathroom or a washroom context", 42], "supports": ["No. 'Supports' is too vague or abstract to be distinguished in a photo. However, if we are talking about physical supports used in construction or engineering, then the answer would be yes.\nA few things that are visually similar to 'supports' but are not 'supports' are:\tfurniture legs\tpillars\tcolumns\tstools\t\nThere are several useful visual features to tell there is 'supports' and not similar things in a photo:\tattached to a larger structure\tdesigned for holding weight or providing stability\tdevoid of decorative features or patterns.", 42], "projection screen": ["Yes. 'Projection screen' has a tangible appearance and is a type of display.\nA few things that are visually similar to 'projection screen' but are not 'projection screen' are:\ttv screen\tcomputer monitor\tmovie theater screen\nThere are several useful visual features to tell there is 'projection screen' and not similar things in a photo:\twhite or light grey surface to reflect images\tprojector mounted in front of the screen to project images\tlarger in size compared to a television or a computer monitor", 42], "clipboard": ["Yes. 'Clipboard' has a tangible appearance and is a type of writing surface.\nA few things that are visually similar to 'clipboard' but are not 'clipboard' are:\tfolder\tnotebook\tpad of paper\tbinder\tportable whiteboard\nThere are several useful visual features to tell there is 'clipboard' and not similar things in a photo:\tthin, flat board with a clip at the top to hold papers in place\ta sturdy handle for holding and carrying\tthe clip is usually on the short edge of the board.", 42], "cordless phone": ["Yes. 'Cordless phone' has a tangible appearance and is a type of telephone.\nA few things that are visually similar to 'cordless phone' but are not 'cordless phone' are:\tmobile phone\twalkie-talkie\tradio\tintercom \nThere are several useful visual features to tell there is 'cordless phone' and not similar things in a photo:\thandheld object\tkeypad\tscreen\tor antenna\tcharging dock or base station\tno visible wires or cables for connection to the phone line", 42], "grey pavement": ["Yes. 'Grey pavement' has a tangible appearance and is a type of surface.\nA few things that are visually similar to 'grey pavement' but are not 'grey pavement' are:\tconcrete walls\tgray tiles\tmetal surface\nThere are several useful visual features to tell there is 'grey pavement' and not similar things in a photo:\tflat and horizontal\tsquare or rectangular shape\tgray color\twith small, visible stones or pebbles.", 42], "laundry basket": ["Yes. 'Laundry basket' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'laundry basket' but are not 'laundry basket' are:\ttrash can\tstorage bin\tpicnic basket\tbackpack\nThere are several useful visual features to tell there is 'laundry basket' and not similar things in a photo:\ta basket-like shape\twith handles\tsome holes for ventilation to keep the laundry fresh\ttypically made of plastic or woven material", 42], "square pillow": ["Yes. 'Square pillow' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'square pillow' but are not 'square pillow' are:\tround pillow\tbolster cushion\tthrow pillow\tsofa seat\nThere are several useful visual features to tell there is 'square pillow' and not similar things in a photo:\tsquare-shaped\tpadded or stuffed\tfabric cover or case\tused as a headrest or backrest", 42], "support beams": ["Yes. 'Support beams' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'support beams' but are not 'support beams' are:\tcolumns\tpoles\ttrees\nThere are several useful visual features to tell there is 'support beams' and not similar things in a photo:\tstraight and vertical\tlines on the surface\tof a building or a structure\tmetal or wood material\theight and thickness", 42], "backsplash": ["Yes. 'Backsplash' has a tangible appearance and is a type of wall covering.\nA few things that are visually similar to 'backsplash' but are not 'backsplash' are:\tpaint\ttiles\tmirror\twallpaper\nThere are several useful visual features to tell there is 'backsplash' and not similar things in a photo:\tlocated behind a sink or stove\ttile or stone material\teasy to clean or wipe down with water or soap\tdesign to protect walls from splatters, hot liquids, or grease.", 42], "stainless steel kitchen sink": ["Yes. 'Stainless steel kitchen sink' has a tangible appearance.\nA few things that are visually similar to 'stainless steel kitchen sink' but are not 'stainless steel kitchen sink' are:\tregular sink\tporcelain sink\tmetal tray\nThere are several useful visual features to tell there is 'stainless steel kitchen sink' and not similar things in a photo:\nrectangular or square shape;\nusually a single basin or double basin design;\na shiny, silver color due to its stainless steel material; may have metal ridges or textures;\nmay have a faucet, drain, or other plumbing fixtures attached.", 42], "plastic trash": ["Yes. 'Plastic trash' has a tangible appearance and is a type of waste material.\nA few things that are visually similar to 'plastic trash' but are not 'plastic trash' are:\tfood waste\tpaper waste\torganic waste\tmetal waste\nThere are several useful visual features to tell there is 'plastic trash' and not similar things in a photo:\tmade of plastic\tunnatural color or shape\tnon-biodegradable material\tscattered or piled up\twithout a specific use or purpose", 42], "player number": ["Yes. 'Player number' has a tangible appearance and is typically displayed on a sports uniform or jersey.\nThere are no things that are visually similar to 'player number' but are not 'player number'.\nUseful visual features for distinguishing 'player number' in a photo include: a numerical digit(s) displayed prominently on a sports uniform or jersey, usually on the back or sleeve, and often in contrasting colors to the rest of the uniform.", 42], "surfer ocean": ["Yes. 'Surfer ocean' has a tangible appearance and refers to a person surfing in an ocean.\nA few things that are visually similar to 'surfer ocean' but are not 'surfer ocean' are:\tswimming in ocean\tfishing in ocean\tboating in ocean\twalking on beach\nThere are several useful visual features to tell there is 'surfer ocean' and not similar things in a photo:\tsurfer on a board\triding a wave\twearing a wetsuit or board shorts\tocean waves in the background", 42], "shadow table": ["Yes. 'Shadow table' has a tangible appearance.\nIt is not similar to anything else.\nUseful visual features for distinguish 'shadow table' from similar things in a photo are: \n- A table with a light source above it creating a shadow on the surface beneath.", 42], "leather sofa": ["Yes. 'Leather sofa' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'leather sofa' but are not 'leather sofa' are:\tcouches\tchairs\tbeds\nThere are several useful visual features to tell there is 'leather sofa' and not similar things in a photo:\tlong upholstered seat\tarmrests and backrest\tcushions\tmade entirely or partially of leather", 42], "dining room table": ["Yes. 'Dining room table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'dining room table' but are not 'dining room table' are:\tdesk\tworkbench\tcoffee table\tpicnic table\nThere are several useful visual features to tell there is 'dining room table' and not similar things in a photo:\tlarge rectangular or circular surface\tseveral chairs surrounding the table\tsuitable for eating a meal\tto be placed in a dining room or kitchen\tarea on the surface for placing dishes and utensils.", 42], "nail polish": ["Yes. 'Nail polish' has a tangible appearance and is a type of beauty product.\nA few things that are visually similar to 'nail polish' but are not 'nail polish' are:\tpaint\tink\tfood coloring\tmarker\nThere are several useful visual features to tell there is 'nail polish' and not similar things in a photo:\tusually comes in a small bottle with a brush applicator\tviscous liquid\ttexture that creates a smooth, glossy layer\twhen applied, stays on nails for days without smudging or fading\thas a wide variety of colors", 42], "snowy hill": ["Yes. 'Snowy hill' has a tangible appearance and is a terrain covered with snow.\nA few things that are visually similar to 'snowy hill' but are not 'snowy hill' are:\tgrass-covered hill\tsand dune\tmountain\nThere are several useful visual features to tell there is 'snowy hill' and not similar things in a photo:\twhite color\tsnow covered\tsloped terrain with snow on it.", 42], "root": ["Yes. 'Root' has a tangible appearance and is a part of a plant that grows underground.\nA few things that are visually similar to 'root' but are not 'root' are:\ttrunk\tbranch\tleaf\tstem\nThere are several useful visual features to tell there is 'root' and not similar things in a photo:\tgrowing underground\tthick and fibrous texture\tno leaves or flowers\temerging from the base of a plant", 42], "thermos": ["Yes. 'Thermos' has a tangible appearance and is a type of insulated flask.\nA few things that are visually similar to 'thermos' but are not 'thermos' are:\twater bottle\ttravel mug\tcoffee cup\nThere are several useful visual features to tell there is 'thermos' and not similar things in a photo:\tdouble-walled insulation\tcap that can serve as a cup\tor a mug\ttall and slim design", 42], "paper napkins": ["Yes. 'Paper napkins' has a tangible appearance and is a kind of tableware.\nA few things that are visually similar to 'paper napkins' but are not 'paper napkins' are:\tface towels\ttoilet paper\ttissues\tbutterfly bandages\tcotton pads\nThere are several useful visual features to tell there is 'paper napkins' and not similar things in a photo:\trectangular in shape\tthin and lightweight\tmade of paper or tissue material\twith patterns or designs\tin piles, folded or unfurled on a table\tor held or carried in a hand.", 42], "baby elephants": ["Yes. 'Baby elephants' has a tangible appearance and refers to young elephants.\nA few things that are visually similar to 'baby elephants' but are not 'baby elephants' are:\tadult elephants\tother baby animals\tsimilar-sized animals with large ears\nThere are several useful visual features to tell there is 'baby elephants' and not similar things in a photo:\t\nsmaller in size than adult elephants\t\nproportionally large ears\t\nless wrinkled skin\t\nless-developed trunks\t\nless-curved tusks or no tusks", 42], "concrete ramp": ["Yes. 'Concrete ramp' has a tangible appearance and is a kind of architectural structure.\nA few things that are visually similar to 'concrete ramp' but are not 'concrete ramp' are:\tstairs\twalkway\tpatio\tblock of concrete\nThere are several useful visual features to tell there is 'concrete ramp' and not similar things in a photo:\tsloping surface\tlong and wide enough to walk or drive on\tmade of concrete or cement", 42], "wagon wheel": ["Yes. 'Wagon wheel' has a tangible appearance and is an object with spokes and a rim used on wagons.\nA few things that are visually similar to 'wagon wheel' but are not 'wagon wheel' are:\tBicycle wheel\tFerris wheel\tClock\nThere are several useful visual features to tell there is 'wagon wheel' and not similar things in a photo:\tmade of wood or metal\tspokes extending from the center\tring-shaped structure with a hole in the center\tdesigned for transportation.", 42], "dark window": ["Yes. 'Dark window' has a tangible appearance.\nA few things that are visually similar to 'dark window' but are not 'dark window' are:\ta closed door\ta mirror\tan open window with a dark scene behind\tit's hard to think of anything else very similar to a dark window\nThere are no other useful visual features to help distinguish 'dark window' from other similar things in a photo as it is a specific type of window that is dark and difficult to see through.", 42], "waffle": ["Yes. 'Waffle' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'waffle' but are not 'waffle' are:\tpancake\tfrench toast\tcroissant\nThere are several useful visual features to tell there is 'waffle' and not similar things in a photo:\tsquare or rectangular shaped\tgrid-like pattern\tpockets or wells\tfor holding syrup or toppings\tslightly crispy exterior, fluffy interior.", 42], "sunflowers": ["Yes. 'Sunflowers' has a tangible appearance and is a kind of flower.\nA few things that are visually similar to 'sunflowers' but are not 'sunflowers' are:\tdaisies\tmarigolds\tzinnias\nThere are several useful visual features to tell there is 'sunflowers' and not similar things in a photo:\tlarge and tall\traised disk-like center\tbright yellow petals", 42], "flyer": ["Yes. 'Flyer' has a tangible appearance and is a type of printed promotional material.\nA few things that are visually similar to 'flyer' but are not 'flyer' are:\tpostcard\tbrochure\tnewspaper\tad\nThere are several useful visual features to tell there is 'flyer' and not similar things in a photo:\tsingle sheet of paper\twith information about an event or business\tattractive visuals or graphics\tinformation about promotions or specials\tmostly distributed by hand or in a specific location", 42], "cameraman": ["Yes. 'Cameraman' has a tangible appearance and refers to a person who operates a camera to record visual images.\nA few things that are visually similar to 'cameraman' but are not 'cameraman' are:\tphotographer\tjournalist\tinterviewer\tdirector\nThere are several useful visual features to tell there is 'cameraman' and not similar things in a photo:\tHolding a video camera or recording device\tWearing headphones or a mic\tLooking through the viewfinder of the camera\tFraming a shot with their hands or tripod\tFocusing on a subject or action filming\ta press pass or identification badge.", 42], "cherry tomato": ["Yes. 'Cherry tomato' has a tangible appearance and is a type of tomato.\nA few things that are visually similar to 'cherry tomato' but are not 'cherry tomato' are:\tgrape\tonion\tpomegranate\nThere are several useful visual features to tell there is 'cherry tomato' and not similar things in a photo:\tsmall size, about the size of a cherry\tbright red or orange color\tsmooth and shiny skin\tgreen stem\ton the vine or grouped with other cherry tomatoes", 42], "skateboard man": ["Yes. 'Skateboard man' has a tangible appearance and is a person riding a skateboard.\nA few things that are visually similar to 'skateboard man' but are not 'skateboard man' are:\tskier\tsnowboarder\tscooter rider\tbiker\t\nThere are several useful visual features to tell there is 'skateboard man' and not similar things in a photo:\tstanding on a skateboard\trolling on wheels\tperforming tricks wearing pads and a helmet\tconcrete or asphalt surroundings in the background", 42], "googles": ["Yes. 'Googles' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'googles' but are not 'googles' are:\tglasses\tsunglasses\tbinoculars\tmicroscopes\nThere are several useful visual features to tell there is 'googles' and not similar things in a photo:\tcover the eyes\tcomposed of two lenses\thave an adjustable strap or earpiece\tmore commonly worn in sports or swimming.", 42], "suit coat": ["Yes, 'suit coat' has a tangible appearance and is an item of clothing worn with a suit.\nA few things that are visually similar to 'suit coat' but are not 'suit coat' are:\tjacket\tblazer\tcardigan\nThere are several useful visual features to tell there is 'suit coat' and not similar things in a photo:\tfitted style\tworn with pants in the same fabric and color\tbuttoned\tdark, neutral colors\tshoulder pads\twelt pockets\ton the longer side", 42], "kneepads": ["Yes. 'Kneepads' has a tangible appearance.\nA few things that are visually similar to 'kneepads' but are not 'kneepads' are:\tpads used in sports and other physical activities\tcushion covers\telbow pads\nThere are several useful visual features to tell there are 'kneepads' and not similar things in a photo:\tpads specifically designed to protect the knees\tstraps or fastenings to keep them in place\tfitted around the knee area", 42], "dog paw": ["Yes. 'Dog paw' has a tangible appearance and is a part of a dog's body.\nA few things that are visually similar to 'dog paw' but are not 'dog paw' are:\tcats' paws\tbears' paws\thuman hands\tteddy bear paws\nThere are several useful visual features to tell there is 'dog paw' and not similar things in a photo:\twet and has claws\tfurry soft texture\tpink or black pads\thave five toes (four toes and a dewclaw)\tdifferent sizes for different breeds.", 42], "wooden bed": ["Yes. 'Wooden bed' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden bed' but are not 'wooden bed' are:\twooden table\twooden chair\twooden shelf\t\nThere are several useful visual features to tell there is 'wooden bed' and not similar things in a photo:\tlarge frame for a mattress\theadboard and footboard\tmultiple slats or planks supporting the mattress\tlegs or a base lifting it off the ground", 42], "tomato slices": ["Yes. 'Tomato slices' has a tangible appearance and is a type of food item.\nA few things that are visually similar to 'tomato slices' but are not 'tomato slices' are:\tred pepper slices\tpaprika slices\tcucumber slices\torange slices\nThere are several useful visual features to tell there is 'tomato slices' and not similar things in a photo:\tcircular shape\tvibrant red color\tfleshy texture\twith seeds in the center with red pulp around it.", 42], "fire alarm": ["Yes, 'fire alarm' has a tangible appearance and is a device used for fire safety.\nA few things that are visually similar to 'fire alarm' but are not 'fire alarm' are:\tsmoke detector\tintercom\tsystem\tspeaker\nThere are several useful visual features to tell there is 'fire alarm' and not similar things in a photo:\tred and/or white color flashing or steady light\tloud alarm sound\twith the words \"Fire Alarm\" clearly visible\ton a ceiling or a wall.", 42], "dolphin": ["Yes. 'Dolphin' has a tangible appearance and is a type of aquatic mammal.\nA few things that are visually similar to 'dolphin' but are not 'dolphin' are:\tporpoise\tseal\twhale\tfish\nThere are several useful visual features to tell there is 'dolphin' and not similar things in a photo:\tsleek body\twith a curved, beak-like snout (known as a rostrum)\tgraphic coloration\tpaired fins and a dorsal fin", 42], "turquoise": ["Yes. 'Turquoise' has a tangible appearance and is a specific color.\nA few things that are visually similar to 'turquoise' but are not 'turquoise' are:\tteal\taqua\tsky blue\nThere are several useful visual features to tell there is 'turquoise' and not similar things in a photo:\tbluish-green color\tsemi-opaque surface\twith black or dark veins", 42], "covering": ["No. 'Covering' is too vague or abstract to be distinguished in a photo. It can refer to anything that is used to cover or conceal something else. It needs a more specific context or description to become visually concrete.", 42], "golf cart": ["Yes. 'Golf cart' has a tangible appearance and is a type of small vehicle.\nA few things that are visually similar to 'golf cart' but are not 'golf cart' are:\tutility vehicle\tscooter\tkids' electric toy car\nThere are several useful visual features to tell there is 'golf cart' and not similar things in a photo:\tlow height and small size\twide, clear windshield\ttwo or four seats\tgolf bag holders square-shaped back with open storage capability\twheels and tires designed for turf", 42], "veil": ["Yes. 'Veil' has a tangible appearance and is a piece of cloth or fabric that covers part of the face or head.\nA few things that are visually similar to 'veil' but are not 'veil' are:\tscarf\tmask\tbandana\theadband\nThere are several useful visual features to tell there is 'veil' and not similar things in a photo:\tsheer or semi-transparent material\tdraping over the face or head\tdifferent styles or shapes worn for different occasions or cultural practices", 42], "team logo": ["Yes. 'Team logo' has a tangible appearance and is a graphical representation of a team or organization.\nA few things that are visually similar to 'team logo' but are not 'team logo' are:\tbrand logo\tpolitical party's emblem\tnational flag\nThere are several useful visual features to tell there is 'team logo' and not similar things in a photo:\tname of the team or organization\tsymbol or icon representing the team or organization\tunique colors or patterns often associated with the team or organization", 42], "ski boot": ["Yes. 'Ski boot' has a tangible appearance and it's a type of footwear.\nA few things that are visually similar to 'ski boot' but are not 'ski boot' are:\tice skate\thiking boots\tsnow boots\nThere are several useful visual features to tell there is 'ski boot' and not similar things in a photo:\thigh, extending above ankle\tlevel heel\tridged sole\tforward lean\ttoe box\tmetal fittings for attaching to skis\tpadded inner liner for insulation and comfort.", 42], "talons": ["Yes. 'Talons' has a tangible appearance and refers to claws of birds of prey such as eagles or hawks.\nA few things that are visually similar to 'talons' but are not 'talons' are:\tfingernails\tpaw pads\tinsect legs\nThere are several useful visual features to tell there is 'talons' and not similar things in a photo:\tsharp and hooked\tclaw-like shape\tfound on birds of prey, not other animals\torangish-brown or yellow color", 42], "leafy bush": ["Yes. 'Leafy bush' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'leafy bush' but are not 'leafy bush' are:\tgrass\thedge\tvine\tfern\nThere are several useful visual features to tell there is 'leafy bush' and not similar things in a photo:\tmany branches\twith leaves or needles\tthick foliage\tbushy appearance\tgrowing off the ground", 42], "bar code": ["Yes. 'Bar code' has a tangible appearance and is a pattern of lines and spaces that represent information.\nA few things that are visually similar to 'bar code' but are not 'bar code' are:\tzebra stripes\twood grain\tpicket fence\tlined paper\nThere are several useful visual features to tell there is 'bar code' and not similar things in a photo:\tseries of parallel lines and spaces of varying widths\tand thicknesses\tbarcode scanner-readable\tlabel with numbers and characters of varying sizes and fonts", 42], "plane wing": ["Yes. 'Plane wing' has a tangible appearance and is part of an airplane.\nA few things that are visually similar to 'plane wing' but are not 'plane wing' are:\tbutterfly wing\tbird's wing\tkite\ttail of an airplane\nThere are several useful visual features to tell there is 'plane wing' and not similar things in a photo:\tlarge and flat surface\ton the side of an airplane\tmetallic or painted in a specific color (usually white)\twith engines attached at the front or the back of it.", 42], "message": ["No. 'Message' is too vague or abstract to have a tangible appearance.\nA few things that are visually similar to 'message' but are not 'message' are:\tletter\tenvelope\ttext\tsign\nThere are no useful visual features to distinguish 'message' from these things, as they may all contain messages in different forms.", 42], "darker": ["No. 'Darker' is too vague or abstract to be distinguished in a photo. It refers to a relative absence of light or a decrease in brightness, but it has no specific appearance. \nThere are no things visually similar to 'darker.'", 42], "basketball": ["Yes. 'Basketball' has a tangible appearance and is a type of ball used in a sport.\nA few things that are visually similar to 'basketball' but are not 'basketball' are:\tvolleyball\tsoccer ball\tdodgeball\tmedicine ball\nThere are several useful visual features to tell there is 'basketball' and not similar things in a photo:\tdark orange color\trubber texture\twith black lines around it\tround in shape\tbouncy and light in weight.", 42], "needles": ["Yes. 'Needles' has a tangible appearance and is a type of small pointed object.\nA few things that are visually similar to 'needles' but are not 'needles' are:\tpins\tnails\tspikes\tthorns\nThere are several useful visual features to tell there is 'needles' and not similar things in a photo:\tthin and small\tsize and shape\tvarious colors\tsharp and pointed ends\tbendable or flexible\tused for sewing or medical purposes", 42], "brown bear": ["Yes. 'Brown bear' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'brown bear' but are not 'brown bear' are:\tgrizzly bear\tpolar bear\tblack bear\tBuffalo\nThere are several useful visual features to tell there is 'brown bear' and not similar things in a photo:\tbrown fur\tcolor of fur\thigh shoulder humps\tshort round ears\tdeep-set eyes\tlong, curved claws on five toes", 42], "hawk": ["Yes. 'Hawk' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'hawk' but are not 'hawk' are:\teagle\tfalcon\towl\tvulture\nThere are several useful visual features to tell there is 'hawk' and not similar things in a photo:\tsharp beak\tand talons\tcurved beak\tand talons\ttriangular-shaped wings\tshort tail\tmedium to large size\tbody\tcoloring (varies by species)", 42], "sink bowl": ["Yes. 'Sink bowl' has a tangible appearance and is a specific part of a sink.\nA few things that are visually similar to 'sink bowl' but are not 'sink bowl' are:\tpot\tbasin\tbowl\tvessel\nThere are several useful visual features to tell there is 'sink bowl' and not similar things in a photo:\tconnected to a faucet and drain\tcircular or oval in shape\tporcelain, steel, or ceramic material\tshallow and flat bottom\twith overflow or without overflow\tslot for faucet installation in the middle or in the back", 41], "metal stand": ["Yes. 'Metal stand' has a tangible appearance.\nA few things that are visually similar to 'metal stand' but are not 'metal stand' are:\tchairs\ttables\tshelves\tfences\t\nThere are several useful visual features to tell there is 'metal stand' and not similar things in a photo:\tupright and sturdy metal structure\tdesigned to hold or support something\televated from the ground or surface.", 41], "green tree": ["Yes. 'Green tree' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'green tree' but are not 'green tree' are:\tshrub\tbush\tgrass\ttopiary\nThere are several useful visual features to tell there is 'green tree' and not similar things in a photo:\ttrunk\twith branches\tleaves\tconical or cylindrical silhouette\tstem\t\t\t\t\t\t\t\t\t", 41], "cat paws": ["Yes. 'Cat paws' has a tangible appearance and is a body part of a cat.\nA few things that are visually similar to 'cat paws' but are not 'cat paws' are:\tdog paws\tbear paws\thuman hands\tcrab claws\nThere are several useful visual features to tell there is 'cat paws' and not similar things in a photo:\trectangular shape\tfive toes, including four toes and one dewclaw\tretractable claws\tfurry or padded surface on toes and soles", 41], "styrofoam cup": ["Yes. 'Styrofoam cup' has a tangible appearance and is a type of cup.\nA few things that are visually similar to 'styrofoam cup' but are not 'styrofoam cup' are:\tpaper cup\tplastic cup\tglass\tceramic cup\nThere are several useful visual features to tell there is 'styrofoam cup' and not similar things in a photo:\tlightweight\twhite or pale yellow in color\tfeels and looks foamy or porous\ttypically has a logo, design or text printed on it.", 41], "mickey mouse": ["Yes. 'Mickey Mouse' has a tangible appearance and is a famous cartoon character.\nA few things that are visually similar to 'Mickey Mouse' but are not 'Mickey Mouse' are:\tother cartoon characters with similar circular heads and ears\tplush toys with similar black and white coloration\tpeople wearing costumes or hats shaped like Mickey Mouse\nThere are several useful visual features to tell there is 'Mickey Mouse' and not similar things in a photo:\tcircular head with two round ears\tface with two large white eyes and a black nose\tblack or red shorts with two large white buttons and white gloves", 41], "stainless steel microwave": ["Yes. 'Stainless steel microwave' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'stainless steel microwave' but are not 'stainless steel microwave' are:\tstainless steel oven\tregular oven\tcountertop grill\tcoffee maker\tblender\nThere are several useful visual features to tell there is 'stainless steel microwave' and not similar things in a photo:\tboxy shape\tdigital display\tmetallic or silver finish\tdoor handle on the front or side\tbuttons or knobs to control the cooking time and power level.", 41], "book case": ["Yes. 'Book case' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to a 'book case' but are not 'book case' are:\tcabinet\tshelf\tlocker\twardrobe\nThere are several useful visual features to tell there is 'book case' and not similar things in a photo:\tmultiple shelves\thorizontal spaces for storing books, papers, or other items\tback panel or side panel\tthat stands on the floor\tcan be made from wood, metal, or plastic.", 41], "plush": ["Yes. 'Plush' has a tangible appearance and refers to a soft and high-quality fabric used for stuffed toys or furniture.\nA few things that are visually similar to 'plush' but are not 'plush' are:\tvelvet\tfur\tcotton\twool\nThere are several useful visual features to tell there is 'plush' and not similar things in a photo:\tsoft texture\tthick and high pile\tfuzzy and plushy appearance\ta woven fabric with a nap or pile", 41], "grey cat": ["Yes. 'Grey cat' has a tangible appearance and is a kind of animal.\nA few things that are visually similar to 'grey cat' but are not 'grey cat' are:\tgrey squirrel\tweasel\trabbit\tgopher\nThere are several useful visual features to tell there is 'grey cat' and not similar things in a photo:\tfour-legged animal\twith pointy ears\tand a long tail\tsharp claws\tfur that is soft and fuzzy\tindependent and curious nature.", 41], "dent": ["Yes. 'Dent' has a tangible appearance and is a visible depression or dimple caused by force on a surface.\nA few things that are visually similar to 'dent' but are not 'dent' are:\tscratch\tcrater\tbump\tbruise\nThere are several useful visual features to tell there is 'dent' and not similar things in a photo:\tdepression on a smooth surface\tusually smaller than other similar things\thas a concave shape\tcould have small cracks or torn edges around it.", 41], "poll": ["No. 'Poll' is too vague or abstract to have a tangible appearance that can be distinguished in a photo. \n\nHowever, a few things that are visually similar to the process of polling but are not 'poll' are: voting, surveying, asking for opinions, conducting market research. \n\nUseful visual features for indicating the process of polling could include people in line waiting to vote or participate in a survey, a person holding a clipboard or electronic device for recording responses, a sign or banner promoting a poll/survey.", 41], "horse head": ["Yes. 'Horse head' has a tangible appearance and is a specific body part of a horse.\nA few things that are visually similar to 'horse head' but are not 'horse head' are:\tdeer head\tcow head\tstatue or sculpture of a horse head\nThere are several useful visual features to tell there is 'horse head' and not similar things in a photo:\thorse's long snout\twith or without mane\tpointed ears or pricked ears\tinner and outer nostrils\thorse's facial expressions", 41], "dish towel": ["Yes. 'Dish towel' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'dish towel' but are not 'dish towel' are:\tbath towel\thand towel\twashcloth\ttable cloth\nThere are several useful visual features to tell there is 'dish towel' and not similar things in a photo:\trectangle or square shape\tabsorbent fabric\toften has a pattern or solid color\tusually found in the kitchen or near the sink", 41], "sheer": ["Yes. 'Sheer' has a tangible appearance and is a type of fabric.\nA few things that are visually similar to 'sheer' but are not 'sheer' are:\ttransparent plastic\tthin paper\tcellophane\tnetting\nThere are several useful visual features to tell there is 'sheer' and not similar things in a photo:\tthin and lightweight\ttranslucent or slightly see-through\ttexture of fabric\tdraping or flowing when used in clothing or d\u00e9cor", 41], "cot": ["Yes. 'Cot' has a tangible appearance and is a type of bed.\nA few things that are visually similar to 'cot' but are not 'cot' are:\tcrib\tbunk bed\tcouch\tlounge chair\nThere are several useful visual features to tell there is 'cot' and not similar things in a photo:\tsimple, rectangular design\twith or without sides or rails\tmattress on top of a frame\tmade of wood, metal, or plastic\tportable or foldable for travel or storage", 41], "coca cola": ["Yes. 'Coca Cola' has a tangible appearance and is a type of drink.\nA few things that are visually similar to 'Coca Cola' but are not 'Coca Cola' are:\tPepsi RC Cola\tDr. Pepper\tIced tea\nThere are several useful visual features to tell there is 'Coca Cola' and not similar things in a photo:\tRed and white label with the Coca Cola logo\tDark brown liquid in a clear or brown bottle or can\tBubbles or fizz visible in the liquid when poured into a glass\tNarrow and tall shape of the bottle or can", 41], "orange object": ["No. 'Orange object' is too vague or abstract to be distinguished in a photo. \nHowever, a few things that are visually similar to an orange but are not considered an 'orange object' would be: basketball, pumpkin, tangerine.\nUseful visual features for distinguishing 'orange object' would be its shape, texture, and whether it has any distinctive markings or features like a stem or leaves.", 41], "airplane tail": ["Yes. 'Airplane tail' has a tangible appearance.\nA few things that are visually similar to 'airplane tail' but are not 'airplane tail' are:\tbird tail\tfish tail\tkite tail\nThere are several useful visual features to tell there is 'airplane tail' and not similar things in a photo:\thorizontal stabilizer located at the back of the plane\tattached to the vertical stabilizer\taerodynamic shape (triangular or trapezoidal) with a curved trailing edge\tmay feature logo, emblem or stripes of an airline or branding.", 41], "base ball": ["Yes. 'Baseball' has a tangible appearance and is a type of ball used in a sport.\nA few things that are visually similar to 'baseball' but are not 'baseball' are:\tsoftball\ttennis ball\trubber ball\tbeach ball\nThere are several useful visual features to tell there is 'baseball' and not similar things in a photo:\twhite with red stitching\tnine-inch circumference\thard or solid exterior\tpaired with a baseball bat or gloves", 41], "restaurant sign": ["Yes. 'Restaurant sign' has a tangible appearance and is a sign on or near a restaurant.\nA few things that are visually similar to 'restaurant sign' but are not 'restaurant sign' are:\tstore sign\tbar sign\troad sign\tbillboard\nThere are several useful visual features to tell there is 'restaurant sign' and not similar things in a photo:\tthe word \"restaurant\" or a picture of food or utensils on the sign\thanging on or near a building at street level\tcustomers or staff visible through windows or doors of the restaurant", 41], "chain link": ["Yes. 'Chain link' has a tangible appearance and refers to a specific type of fence or mesh.\nA few things that are visually similar to 'chain link' but are not 'chain link' are:\trope\tnetting\twire mesh\tbarbed wire\nThere are several useful visual features to tell there is 'chain link' and not similar things in a photo:\tinterlocking metal links\tsquare or diamond-shaped holes\tsilver or grey color", 41], "carrier": ["No. 'Carrier' is too vague or abstract to be distinguished in a photo. It can refer to different types of carriers, such as a transportation carrier or a baby carrier, each with different visual features. \nA few things that are visually similar to 'carrier' but are not 'carrier' could be: backpacks, suitcases, baskets, bags. \nThe useful visual features for distinguishing a 'carrier' would depend on the specific type of carrier being referred to. For example: \n- A baby carrier may have straps, buckles, or fabric panels for carrying a baby on the chest or back of a caregiver. \n- A transportation carrier (e.g. a cargo carrier or an aircraft carrier) may have a large deck or hull, with cranes or other equipment for loading and unloading cargo or planes.", 41], "shadow plate": ["No. 'Shadow plate' is too vague or abstract to be distinguished in a photo.", 41], "bookbag": ["Yes. 'Bookbag' has a tangible appearance and is a type of bag used to carry books.\nA few things that are visually similar to 'bookbag' but are not 'bookbag' are:\tpurse\tbackpack\ttotebag\tbriefcase\nThere are several useful visual features to tell there is 'bookbag' and not similar things in a photo:\tmedium to large size\twith straps to carry on the back\tor handles to carry in hand\tor both\tcapacity to hold books, papers, or notebooks.", 41], "zippers": ["Yes. 'Zippers' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'zippers' but are not 'zippers' are:\tbuttons\tsnaps\thook and loop fasteners\nThere are several useful visual features to identify 'zippers' from other fasteners in a photo:\tcontinuous chain-like structure\twith interlocking teeth or coils\tslides up and down\twhen open, it creates two separate pieces", 41], "wiring": ["Yes. 'Wiring' has a tangible appearance and refers to electrical cables and wires.\nA few things that are visually similar to 'wiring' but are not 'wiring' are:\trope\thoses\ttubes\tcords\nThere are several useful visual features to tell there is 'wiring' and not similar things in a photo:\tmetallic appearance\tbundled or grouped together\tcolor-coded\tfor indoor or outdoor use", 41], "light switches": ["Yes. 'Light switches' has a tangible appearance and is an electrical device used to control the light.\nA few things that are visually similar to 'light switches' but are not 'light switches' are:\tpower outlet\tthermostat\tswitchboard\nThere are several useful visual features to tell there is 'light switches' and not similar things in a photo:\tpositioned on the wall\tone or multiple toggles or buttons\tto turn lights on and off\tcolored indicators or labels indicating light intensity or other function", 41], "vcr": ["Yes. 'VCR' has a tangible appearance and is an electronic device used for playing video tapes.\nA few things that are visually similar to 'VCR' but are not 'VCR' are:\tDVD player\tBlu-ray player\tstreaming device\tset-top box\nThere are several useful visual features to tell there is 'VCR' and not similar things in a photo:\trectangular shaped device\tvideo tape loaded into the front side\tplay, pause, and rewind buttons\tVCR logo or label on the device or tape compartment", 41], "foilage": ["Yes. 'Foliage' has a tangible appearance and refers to the leaves of plants, shrubs, and trees.\nA few things that are visually similar to 'foliage' but are not 'foliage' are: moss, lichen, weeds, creepers, or vines that may also grow on the trunks and branches of trees.\nThere are several useful visual features to tell there is 'foliage' and not similar things in a photo:\tgreen, yellow, brown, or red leaves\tglossy or matte finish\tvaried shapes and sizes\tgrow on the branches or the top of the tree or plant rather than on the ground.", 41], "placard": ["Yes, 'placard' has a tangible appearance and is a type of sign or poster.\nA few things that are visually similar to 'placard' but are not 'placard' are:\tbillboard\tchalkboard\tmenu wedding invitation\nThere are several useful visual features to tell there is 'placard' and not similar things in a photo:\thand-held or attached to a post or wall\tdisplaying information or a message\tLarger than a sheet of paper\tEasily readable from a distance or close-up.", 41], "cabinet handle": ["Yes. 'Cabinet handle' has a tangible appearance and is a kind of hardware.\nA few things that are visually similar to 'cabinet handle' but are not 'cabinet handle' are:\tdoorknob\tdrawer pull\twindow latch\tbottle cap\nThere are several useful visual features to tell there is 'cabinet handle' and not similar things in a photo:\tattached to a cabinet\tfor gripping and pulling\tmetal or plastic material\ttypically horizontal or vertical shape", 41], "lids": ["Yes. 'Lids' has a tangible appearance and is a type of cover.\nA few things that are visually similar to 'lids' but are not 'lids' are:\tcaps\tdiscs\tstopper/cork\thats\tshields\nThere are several useful visual features to tell there is 'lids' and not similar things in a photo:\tcovering or closing the top of a container\tcircular shape\tflat or slightly curved surface\thaving a handle or a knob (in some cases)\tcontaining clear markings or labels indicating the contents or the brand (in some cases)", 41], "groove": ["No. 'Groove' is too vague or abstract to be distinguished in a photo.", 41], "back wheels": ["Yes. 'Back wheels' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'back wheels' but are not 'back wheels' are: front wheels, bicycle wheels, gears, pulleys\nThere are several useful visual features to tell there is 'back wheels' and not similar things in a photo:\n- Location: Back wheels are located at the back of the vehicle.\n- Usually larger than front wheels.\n- They tend to have a different pattern or design compared to front wheels.\n- The back wheels have a differential that allows them to rotate at different speeds.", 41], "touchpad": ["Yes. 'Touchpad' has a tangible appearance and is a type of input device.\nA few things that are visually similar to 'touchpad' but are not 'touchpad' are:\tmouse\tjoystick\ttrackball\tremote control buttons\nThere are several useful visual features to tell there is 'touchpad' and not similar things in a photo:\trectangular or square in shape\tsensitive to touch or pressure\tlocated on a laptop or a tablet\tno visible physical buttons or wheels\tsometimes has visible tracking markers or lines", 41], "door brown": ["No. 'Door brown' is too vague or abstract to be distinguished in a photo.", 41], "close-up": ["Yes. 'Close-up' has a tangible appearance and refers to a specific type of photograph or framing.\nA few things that are visually similar to 'close-up' but are not 'close-up' are:\tfull shot\tmedium shot\tlong shot\tpanorama\nThere are several useful visual features to tell there is 'close-up' and not similar things in a photo:\ta tight frame that shows a subject or detail in a larger-than-life size\tshallow depth of field\tthat draws attention to a small area of the photo\tprovides a high level of detail and texture of the subject", 41], "brake": ["Yes. 'Brake' has a tangible appearance and is a mechanical device used to slow or stop motion.\nA few things that are visually similar to 'brake' but are not 'brake' are:\tpedal\tbutton\thandle\tbar\nThere are several useful visual features to tell there is 'brake' and not similar things in a photo:\tmechanical device\trotors or discs\tpads or shoes\tfluid reservoir\tcylinders or calipers\tpiston movement\tfriction material", 41], "sweatpants": ["Yes. 'Sweatpants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sweatpants' but are not 'sweatpants' are:\tleggings\ttrousers\tpajama pants\trunning tights\nThere are several useful visual features to tell there is 'sweatpants' and not similar things in a photo:\tloose-fitting pants\twith an elastic or drawstring waistband\twith tapered or cuffed ankles\tmade of sweatshirt fabric or a similar stretchy material\tsolid color or patterned (stripes, dots, etc.)", 41], "motor scooter": ["Yes. 'Motor scooter' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motor scooter' but are not 'motor scooter' are:\tbicycle\tmotorcycle\tmoped\tskateboard\tsegway\nThere are several useful visual features to tell there is 'motor scooter' and not similar things in a photo:\tflat platform for the feet\thandlebars for steering\tseat for the rider\tand engine located between the rear wheel and the seat\ttwo wheels only are visible in most photos.", 41], "pacifier": ["Yes. 'Pacifier' has a tangible appearance and is a type of small object that babies use to soothe themselves.\nA few things that are visually similar to 'pacifier' but are not 'pacifier' are:\tnipple\tmilk bottle\ttoy\nThere are several useful visual features to tell there is 'pacifier' and not similar things in a photo:\tbulbous top with flat or rounded bottom\tflattened or nipple-shaped tip\tthat can be inserted into a baby's mouth\thandles for holding and retrieving from the mouth\tsmall and lightweight", 41], "dining room": ["Yes. 'Dining room' has a tangible appearance and is a specific room in a house used for dining.\nA few things that are visually similar to 'dining room' but are not 'dining room' are:\tliving room\tkitchen\tarea with a table\nThere are several useful visual features to tell there is 'dining room' and not similar things in a photo:\ttable and chairs\tin a designated room or area\tplace settings\tor other dining room-specific decor (e.g. chandelier, buffet table)", 41], "loop": ["Yes. 'Loop' has a tangible appearance and is a curved or circular shape.\nA few things that are visually similar to 'loop' but are not 'loop' are:\tcoil\tcircle\tring\nThere are several useful visual features to tell there is 'loop' and not similar things in a photo:\tcurved or circular shape\twith no beginning or end\tsmooth or continuous surface\tsometimes used to connect or tie things together.", 41], "toothpicks": ["Yes. 'Toothpicks' has a tangible appearance and is a kind of small stick used to clean teeth or hold food.\nA few things that are visually similar to 'toothpicks' but are not 'toothpicks' are:\twooden skewers\tcraft sticks\tmatches\nThere are several useful visual features to tell there is 'toothpicks' and not similar things in a photo:\tthin and small\tsize usually ranges from 2.5 to 7.5 cm\tlong and pointy on one or both ends\tmade of wood, plastic or bamboo", 41], "passenger cars": ["Yes. 'Passenger cars' has a tangible appearance and refers to automobiles designed for carrying passengers.\nA few things that are visually similar to 'passenger cars' but are not 'passenger cars' are:\ttrucks\tbuses\tvans\ttrains\nThere are several useful visual features to tell there is 'passenger cars' and not similar things in a photo:\tmedium-sized vehicle\twith four wheels and four doors\tpassenger windows and a windshield\twith a clear top (usually a roof)\tcar license plate\tparked or driving on a road or highway.", 41], "hair clip": ["Yes. 'Hair clip' has a tangible appearance and is a type of accessory used to hold hair in place.\nA few things that are visually similar to 'hair clip' but are not 'hair clip' are:\tbobby pin\tpaper clip\tbinder clip\tpin\nThere are several useful visual features to tell there is 'hair clip' and not similar things in a photo:\tclips the hair in place\tmade of metal or plastic\tcome in various colors and designs\tusually have a hinge and tensioning mechanism or spring", 41], "pink bow": ["Yes. 'Pink bow' has a tangible appearance and is a type of ribbon.\nA few things that are visually similar to 'pink bow' but are not 'pink bow' are:\tother color bows\tfabric flowers\thair accessories\nThere are several useful visual features to tell there is 'pink bow' and not similar things in a photo:\tpink color\tsmooth and shiny texture\ttwo loops with tails in the center\tused for decoration or tying hair", 41], "wood headboard": ["Yes. 'Wood headboard' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood headboard' but are not 'wood headboard' are:\twood paneling\twooden wall art\tshelves\twooden fence\nThere are several useful visual features to tell there is 'wood headboard' and not similar things in a photo:\tattached to a bed\tframe-shaped\tvertical wooden slats\tor intricate wooden patterns with a flat top", 41], "wall tiles": ["Yes. 'Wall tiles' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wall tiles' but are not 'wall tiles' are:\tbrick\tstones\twood panels\tvinyl siding\nThere are several useful visual features to tell there is 'wall tiles' and not similar things in a photo:\tsquare or rectangular shape\tsmooth surface\treflective surface\ttouches and mortars for installation\tvariety of colors and patterns", 41], "yellow curb": ["Yes. 'Yellow curb' has a tangible appearance and is a kind of curb that is painted yellow for parking restrictions.\nA few things that are visually similar to 'yellow curb' but are not 'yellow curb' are:\tregular curb\tkerb\tstreet line\nThere are several useful visual features to tell there is 'yellow curb' and not similar things in a photo:\tpainted yellow\tcolor other than white or gray\tspecific parking time or stopping restrictions indication", 41], "petal": ["Yes. 'Petal' has a tangible appearance and is a part of a flower.\nA few things that are visually similar to 'petal' but are not 'petal' are:\tleaves\tfoliage\tdecorative paper\t\nThere are several useful visual features to tell there is 'petal' and not similar things in a photo:\tthin and delicate\ttexture often waxy or velvety\tbright colors\tcan be single or multiple on a flower.", 41], "jewelry": ["Yes. 'Jewelry' has a tangible appearance and is a kind of adornment.\nA few things that are visually similar to 'jewelry' but are not 'jewelry' are:\taccessories\tbathroom fixtures\tbuckles or clasps\tdecorative elements\nThere are several useful visual features to tell there is 'jewelry' and not similar things in a photo:\twearable pieces\tmade of precious metals, gems, or beads\tornamental and decorative in nature\tworn on different parts of the body like neck, wrists, etc.", 41], "sculptures": ["Yes. 'Sculptures' has a tangible appearance and is a type of art form.\nA few things that are visually similar to 'sculptures' but are not 'sculptures' are:\tstatues\tfurniture\trock formations\tarchitectural features\nThere are several useful visual features to tell there is 'sculptures' and not similar things in a photo:\tthree-dimensional art form\tmade from a variety of materials, such as stone, metal, or plastic\thuman or animal figures or abstract shapes\tcould be displayed indoors or outdoors\tmay be placed on pedestals or bases.", 41], "baseball gloves": ["Yes. 'Baseball gloves' has a tangible appearance and is a kind of sports equipment.\nA few things that are visually similar to 'baseball gloves' but are not 'baseball gloves' are:\tmittens\twork gloves\tboxing gloves\tsafety gloves\nThere are several useful visual features to tell there is 'baseball gloves' and not similar things in a photo:\ta curved pocket to catch a ball on one side\tthe distinct shape of a baseball glove with a thumb section and finger sections\tseams on the back of the glove\twebbing between the fingers and thumb section\tfor a left-handed or right-handed person", 41], "beach scene": ["Yes. 'Beach scene' has a tangible appearance and refers to a view of a beach area.\nA few things that are visually similar to 'beach scene' but are not 'beach scene' are:\tswimming pool\tocean\tview of a lake\tpark\nThere are several useful visual features to tell there is 'beach scene' and not similar things in a photo:\tsand\tinclusion of water\tumbrellas, chairs, or towels\tinclusion of beach or ocean-related objects like palm trees or surfboards.", 41], "headlight car": ["Yes. 'Headlight car' has a tangible appearance and refers to the front lights of a car.\nA few things that are visually similar to 'headlight car' but are not 'headlight car' are:\ttruck\theadlight bulb\ttraffic light\tstreet lamp\nThere are several useful visual features to tell there is 'headlight car' and not similar things in a photo:\tfound at the front of a car\thave a lens that projects light\tforward-facing\tpositioned symmetrically\tleft and right of the car's center\tline with the angle of the bumper or hood.", 41], "glass pitcher": ["Yes. 'Glass pitcher' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'glass pitcher' but are not 'glass pitcher' are:\tglass vase\twater bottle\tbeer mug\tcoffee carafe\nThere are several useful visual features to tell there is 'glass pitcher' and not similar things in a photo:\thandle on the side\tnarrow neck and a wide base\tclear glass material\tfor carrying and pouring drinks", 41], "teenage boy": ["Yes. 'Teenage boy' has a tangible appearance and refers to a male adolescent.\nA few things that are visually similar to 'teenage boy' but are not 'teenage boy' are:\tadult men\tyoung boys\telderly men\twomen\nThere are several useful visual features to tell there is 'teenage boy' and not similar things in a photo:\tage between 13 and 19\tfacial hair starting of growing\tmuscular development and height growth\tmasculine body shape as chest, shoulders, hips/head ratio", 41], "persons hand": ["Yes. 'Person's hand' has a tangible appearance and is a body part.\nA few things that are visually similar to 'person's hand' but are not 'person's hand' are:\tmannequin hand\tstatue of a hand\t\nThere are several useful visual features to tell there is 'person's hand' and not similar things in a photo:\tfingers\tpalm\tnails\tknuckles\twrist\thair or skin texture", 41], "headlight motorcycle": ["Yes. 'Headlight motorcycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'headlight motorcycle' but are not 'headlight motorcycle' are:\theadlight car\tbike with no headlight\nThere are several useful visual features to tell there is 'headlight motorcycle' and not similar things in a photo:\ttwo wheels\theadlight at the front handlebar or front fork\tlong and narrow frame\tno car door or roof", 41], "brown tree": ["Yes. 'Brown tree' has a tangible appearance and refers to a tree with brown-colored bark.\nA few things that are visually similar to 'brown tree' but are not 'brown tree' are:\tdead tree\tsapling\torangutan\twithered flower\nThere are several useful visual features to tell there is 'brown tree' and not similar things in a photo:\ttrunk with a brown-colored bark\tno leaves or leaves that have fallen off\tbranches sprouting from the trunk", 41], "brownie": ["Yes. 'Brownie' has a tangible appearance and is a type of baked dessert.\nA few things that are visually similar to 'brownie' but are not 'brownie' are:\tchocolate cake\tfudge\tcocoa\tbiscuit\nThere are several useful visual features to tell there is 'brownie' and not similar things in a photo:\trectangular or square shape\tchocolate color\tcracked or rough surface\tfudgy texture, but not too creamy\tchocolate chips or nuts on top", 41], "knife handle": ["Yes. 'Knife handle' has a tangible appearance and is a part of a knife.\nA few things that are visually similar to 'knife handle' but are not 'knife handle' are:\tutensil handle\thammer handle\tchisel handle\nThere are several useful visual features to tell there is 'knife handle' and not similar things in a photo:\tconnected to a blade\tfor a knife\tshaped to fit a hand\tmade of wood, metal, or plastic", 41], "cranes": ["Yes. 'Cranes' has a tangible appearance and is a type of bird or a machine used for lifting heavy objects.\nA few things that are visually similar to 'cranes' but are not 'cranes' are:\theron\tstork\tegret\nThere are several useful visual features to tell there is 'cranes' and not similar things in a photo depending on its context:\n- If it's a bird, useful visual features include:\t\nlong neck and legs\t\npointed beak\t\nfeathers\t\ndistinctive head and eye markings\n\n- If it's a machine, useful visual features include:\t\nlong, extendable arm or boom\t\nhook or grabber on the end of the arm\t\nwide, stable base\t\ncab or operator compartment\t\nhydraulic lines and controls", 41], "ossicones": ["Yes, 'ossicones' has a visually concrete concept and refers to the bony protrusions found on the heads of giraffes and okapis.\nThere are no things that are visually similar to 'ossicones', apart from the horns or antlers of other animals, which are not typically found on the top of the head.\nThe useful visual features for distinguishing 'ossicones' from other similar things in a photo are: bony structures on the head, conical or cylindrical in shape, located on top of the head rather than on the sides, typically spotted like the fur of the animal, and protruding straight up from the skull.", 41], "blond girl": ["Yes. 'Blond girl' has a tangible appearance and is a type of human.\nA few things that are visually similar to 'blond girl' but are not 'blond girl' are:\tredhead girl\tbrunette girl\tbald girl\tboy\ttoddler\nThere are several useful visual features to tell there is 'blond girl' and not similar things in a photo:\tblond hair\tpale skin\tgender-specific features (such as facial structure, body shape, or clothing)\tno facial hair (as seen in males)", 41], "baby bear": ["Yes. 'Baby bear' has a tangible appearance and is a young bear.\nA few things that are visually similar to 'baby bear' but are not 'baby bear' are:\tother baby animals\tbrown stuffed animals\tteddy bears\nThere are several useful visual features to tell there is 'baby bear' and not similar things in a photo:\tfurry brown coat\tshort ears and snout\tunusually large head and eyes for its body size\tsize relative to other adult bears in the photo", 41], "left wing": ["No. 'Left wing' is too vague or abstract to have a tangible appearance and cannot be distinguished in a photo. \n\nHowever, if we are referring to the left wing of a bird or airplane then:\n\nA few things that are visually similar to the left wing but are not the left wing are:\tright wing\tengine\ttail\tlanding gear\n\nThere are several useful visual features to tell there is the left wing of a bird or airplane and not similar things in a photo:\tcurved or straight shape, depending on the bird or airplane model\tposition on the left side of a bird or airplane\tdistance to the body of the bird or airplane\tstabilizing devices such as feathers or flaps (in the case of a bird or airplane, respectively)", 41], "rhino": ["Yes. 'Rhino' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'rhino' but are not 'rhino' are:\thippopotamus\tbuffalo\tcow\nThere are several useful visual features to tell there is 'rhino' and not similar things in a photo:\tone or two horns on the nose\tthick body and short legs\tgray or brown color\tridged hide with folds\tand a large head\twith a large head", 41], "round tire": ["Yes. 'Round tire' has a tangible appearance and is a kind of mechanical part.\nA few things that are visually similar to 'round tire' but are not 'round tire' are:\tdonut\twheel\tgear\tfrisbee\nThere are several useful visual features to tell there is 'round tire' and not similar things in a photo:\tcircular shape\ttread pattern\trubber or similar material\tthickness and size\tsidewall\tdeflated or inflated", 40], "illustration": ["No. 'Illustration' is too vague or abstract to be distinguished in a photo. It refers to a visual representation or interpretation of something, often drawn or painted.\nA few things that are visually similar to 'illustration' but are not 'illustration' are:\tphotograph\tpainting\tdrawing\tgraphic design\nThere are no useful visual features to distinguish 'illustration' from similar things in a photo, as it is a term used to describe various forms of visual representation.", 40], "price sign": ["Yes. 'Price sign' has a tangible appearance and is a sign used to display the price of products.\nA few things that are visually similar to 'price sign' but are not 'price sign' are:\tadvertisements\tbillboards\tinformational signs\nThere are a few useful visual features to tell there is 'price sign' and not similar things in a photo:\tthe specific price of a product is displayed\tthe sign is typically small and rectangular in shape\tit is usually located in close proximity to the product being sold.", 40], "zebra ear": ["Yes. 'Zebra ear' has a tangible appearance and is a body part of a zebra.\nThere are no things that are visually similar to 'zebra ear' but are not 'zebra ear'.\nThere are no useful visual features to distinguish 'zebra ear' from the similar things, because there are no similar things.", 40], "wastebasket": ["Yes. 'Wastebasket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'wastebasket' but are not 'wastebasket' are:\ttrash can\tbucket\tlaundry basket\tpaper tray\nThere are several useful visual features to tell there is 'wastebasket' and not similar things in a photo:\trelatively small in size\tdesigned for holding small items or paper waste\tpedal operated or not\topen or with a lid\tmade of plastic, metal or wicker\ttypically found in an office or home setting.", 40], "metal ring": ["Yes. 'Metal ring' has a tangible appearance and is a type of circular metal object.\nA few things that are visually similar to 'metal ring' but are not 'metal ring' are:\tkeychain\tbangle\tbracelet\tchandelier\nThere are several useful visual features to tell there is 'metal ring' and not similar things in a photo:\tcircular shape\tmade of metal or metal-like material\twithout any additional elements or decorations", 40], "hangar": ["Yes. 'Hangar' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'hangar' but are not 'hangar' are:\twarehouse\tgarage\tbarn\tshed\t\nThere are several useful visual features to tell there is 'hangar' and not similar things in a photo:\tlarge and wide structure\tusually made of metal or concrete\taircraft parked inside\tbig sliding doors or roll-up doors on one side\tno windows on the sides or the roof, only on the front", 40], "strand": ["Yes. 'Strand' has a tangible appearance and refers to a thin and elongated object or group of objects.\nA few things that are visually similar to 'strand' but are not 'strand' are:\thair\tspiderweb\tpasta\tthread\nThere are several useful visual features to tell there is 'strand' and not similar things in a photo: thin and elongated\tobject or group of objects\tcan be straight or curved\tcan be a single color or multiple colors", 40], "soccer net": ["Yes, 'soccer net' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'soccer net' but are not 'soccer net' are:\tgoalpost\tfishing net\thammock\ttrampoline\nThere are several useful visual features to tell there is 'soccer net' and not similar things in a photo:\twhite or black in color\tretangular shape\twith a hollow middle mesh or web-like texture\ttwo poles on either side\twith a ball inside\tthe net hung in back of the poles.", 40], "tan car": ["Yes. 'Tan car' has a tangible appearance and refers to a car with a tan color.\nA few things that are visually similar to 'tan car' but are not 'tan car' are:\tgold car\tbeige car\tbrown car\nThere are several useful visual features to tell there is 'tan car' and not similar things in a photo:\n\ttan color on the exterior of the car\n\tdistinct make and model of the car \n\tnumber of doors, windows, and tires on the car \n\tlicense plate number and state \n\tlocation and background context, such as a street or parking lot.", 40], "cook": ["No. 'Cook' is too vague or abstract to be distinguished in a photo.", 40], "erase board": ["Yes. 'Erase board' has a tangible appearance and is a type of board used for writing and erasing.\nA few things that are visually similar to 'erase board' but are not 'erase board' are:\tchalkboard\tcanvas\tpaper\twhiteboard paint on a wall\nThere are several useful visual features to tell there is 'erase board' and not similar things in a photo:\tsmooth surface that allows writing and erasing of dry markers or erasable pens\tmetal or wooden frame around the edges\tclean and free of marks or scratches", 40], "game controllers": ["Yes. 'Game controllers' has a tangible appearance and is a kind of input device for video games.\nA few things that are visually similar to 'game controllers' but are not 'game controllers' are:\tremote controllers\tmusical instrument controllers\tcamera controllers\nThere are several useful visual features to tell there is 'game controllers' and not similar things in a photo:\tbuttons and joysticks\tpads or triggers\twired or wireless connection\tergonomic design for comfortable grip\tvideo game branding or labels", 40], "marvin": ["No. 'Marvin' is too vague or abstract to be distinguished in a photo.", 40], "plastic bottles": ["Yes. 'Plastic bottles' has a tangible appearance and is a specific type of container.\nA few things that are visually similar to 'plastic bottles' but are not 'plastic bottles' are:\tglass bottles\tjuice boxes\tplastic bags\tdrink cans\nThere are several useful visual features to tell there is 'plastic bottles' and not similar things in a photo:\tcylindrical shape\twith a narrow neck\tusually made of clear or opaque plastic with a label or a cap\tcan have various sizes or colors\treusable or disposable\tuseful for holding liquid", 40], "truck cab": ["Yes. 'Truck cab' has a tangible appearance and is a part of a truck where the driver sits.\nA few things that are visually similar to 'truck cab' but are not 'truck cab' are:\tcar cab\tbus cab\nThere are several useful visual features to tell there is 'truck cab' and not similar things in a photo:\t\nlarger in size than a car cab\t\nattached to the rest of the truck\t\noften has a sleeping or storage compartment behind the driver's seat", 40], "ball boy": ["Yes. 'Ball boy' has a tangible appearance and refers to a person who retrieves balls during a sporting event.\nA few things that are visually similar to 'ball boy' but are not 'ball boy' are: \t sports player\treferee\tcoach\t \t \nThere are several useful visual features to tell there is 'ball boy' and not similar things in a photo:\twearing a uniform or shirt with the team logo\tretrieving balls from the field or court\tstanding or kneeling near the sidelines or the baseline", 40], "santa hat": ["Yes. 'Santa hat' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'santa hat' but are not 'santa hat' are:\tbeanie\tberet\tchef hat\tcap\nThere are several useful visual features to tell there is 'santa hat' and not similar things in a photo:\tred and white colors\tpom-pom at the end of the hat\tfuzzy or fluffy texture\tbrimless hat with a pointed cone shape\ttop of the hat bends forward to make a flap", 40], "hitter": ["No. 'Hitter' is too vague or abstract to be distinguished in a photo. It can refer to a baseball player or a person who hits something, but it does not have a specific visual appearance.", 40], "mother elephant": ["Yes. 'Mother elephant' has a tangible appearance and refers to a female elephant.\nA few things that are visually similar to 'mother elephant' but are not 'mother elephant' are:\tfemale rhinoceros\tfemale hippopotamus\tfemale giraffe\nThere are several useful visual features to tell there is 'mother elephant' and not similar things in a photo:\tgrey wrinkled skin\ttrunk\twith or without tusks\tlarge ears\ttwo tusks relatively small compared to a male elephant\tlong and curved ivory tusks are shorter and thinner than males, if visible.", 40], "size": ["No. 'Size' is too vague or abstract to be distinguished in a photo. It is a property that can be attributed to objects, but it does not have a tangible appearance in and of itself.", 40], "machines": ["Yes. 'Machines' has a tangible appearance and refers to mechanical devices that perform tasks.\nA few things that are visually similar to 'machines' but are not 'machines' are:\ttools\tweapons\tvehicles\tsculptures\nThere are several useful visual features to tell there is 'machines' and not similar things in a photo:\tmade up of mechanical parts or components\tmoving or performing a task\tpowered by an engine or motor\thas buttons or control panels for operation", 40], "wooden building": ["Yes. 'Wooden building' has a tangible appearance and refers to a specific type of architecture.\nA few things that are visually similar to 'wooden building' but are not 'wooden building' are: brick building, stone building, concrete building, glass and steel building.\nThere are several useful visual features to tell there is 'wooden building' and not similar things in a photo:\t\n- Wooden walls, typically constructed of logs, planks or boards\n- Visible wooden beams, trusses, support columns and timber framing\n- Wooden doors and windows\n- Rustic or natural textures and finishes, such as hand-hewn or rough-cut wood", 40], "half sandwich": ["Yes. 'Half sandwich' has a tangible appearance when it is cut into two equal parts.\nA few things that are visually similar to 'half sandwich' but are not 'half sandwich' are:\twhole sandwich\ttoast\tpizza\tquesadilla\twrap\nThere are several useful visual features to tell there is 'half sandwich' and not similar things in a photo:\ttwo equal halves\tmultiple layers of bread and filling\tsquare, rectangular or triangular shape\tcrust on the edges", 40], "metal leg": ["Yes. 'Metal leg' has a tangible appearance and is a type of prosthetic.\nA few things that are visually similar to 'metal leg' but are not 'metal leg' are:\tmetal pipes\trobotic arm\tmetallic support beam\nThere are several useful visual features to tell there is 'metal leg' and not similar things in a photo:\thuman-like shape with a joint at the knee and ankle\tmetal material\tno visible foot or toes\tsupporting a human body or part of it", 40], "wooden flooring": ["Yes. 'Wooden flooring' has a tangible appearance and is a type of flooring made of wood.\nA few things that are visually similar to 'wooden flooring' but are not 'wooden flooring' are:\tlaminate flooring\ttiles\tcarpet\tlinoleum floor\nThere are several useful visual features to tell there is 'wooden flooring' and not similar things in a photo:\twooden planks\tnatural wood grain patterns\tsmooth surface\tor textured surface\tpores in the wood surface", 40], "screen computer monitor": ["Yes. 'Screen computer monitor' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'screen computer monitor' but are not 'screen computer monitor' are:\ttelevision\ttablet\tsmartphone\nThere are several useful visual features to tell there is 'screen computer monitor' and not similar things in a photo:\trectangular shape\tthinner than a television or a tablet\tlarger display for desktop computers than laptops or tablets\timage on the screen is not moving or animating, unless it's a screensaver or a notification\talerts or icons for computer settings such as sound, internet or battery\tlevel and position of cables and ports", 40], "orange paint": ["Yes, 'orange paint' has a tangible appearance and is a type of colored liquid used for painting.\nA few things that are visually similar to 'orange paint' but are not 'orange paint' are:\torange juice\tmixed spices\torange soda\nThere are several useful visual features to tell there is 'orange paint' and not similar things in a photo:\twet and shiny\tspread flat over a surface in a uniform way\thave a texture that is typical of paint, which is usually not present in liquids such as orange juice.", 40], "leaf design": ["Yes. 'Leaf design' has a tangible appearance and refers to decorative patterns or motifs inspired by leaves.\nA few things that are visually similar to 'leaf design' but are not 'leaf design' are:\torganic patterns\tcamo prints\tjungle scenes\twallpapers\nThere are several useful visual features to tell there is 'leaf design' and not similar things in a photo:\trepeated patterns inspired by different types of leaves\tgreen, yellow, red, or brown colors\tdetail-oriented and intricate designs\tthat can adorn anything from textiles to furniture or home accessories.", 40], "brackets": ["Yes. 'Brackets' has a tangible appearance and is a type of support structure.\nA few things that are visually similar to 'brackets' but are not 'brackets' are:\tparentheses\tcurly braces\tquotation marks\nThere are several useful visual features to tell there is 'brackets' and not similar things in a photo:\t\"L\" or \"T\" shape\tmade of metal or wood\tattaching two or more objects or surfaces together\tfixed in place\tusing screws or nails to secure to a surface", 40], "water mark": ["Yes. 'Water mark' has a tangible appearance and is a kind of staining or design on paper or fabric caused by water.\nA few things that are visually similar to 'water mark' but are not 'water mark' are:\tink blot\tstain\tscratch\tfold line\nThere are several useful visual features to tell there is 'water mark' and not similar things in a photo:\tirregular shape\tcreated by the water diffusion on the paper or fabric\toften paler than the surrounding area\tcan have a unique texture or pattern", 40], "skatepark": ["Yes. 'Skatepark' has a tangible appearance and is a type of recreational facility.\nA few things that are visually similar to 'skatepark' but are not 'skatepark' are:\tplayground\tpark\tcourtyard\tparking lot\nThere are several useful visual features to tell there is 'skatepark' and not similar things in a photo:\tconcrete or wooden ramps, rails, and obstacles where skateboarders can perform tricks and jumps\ton-site signage indicating it is a skateboard facility\tskateboarders actively using the area", 40], "window shutters": ["Yes. 'Window shutters' has a tangible appearance and is a kind of window covering.\nA few things that are visually similar to 'window shutters' but are not 'window shutters' are:\tblinds\tcurtains\tshades\nThere are several useful visual features to tell there is 'window shutters' and not similar things in a photo:\thinged or mounted on the sides of a window\tconsists of one or more horizontal slats or louvers\table to be opened and closed to control light and airflow\tmade of wood, metal, or vinyl.", 40], "camel": ["Yes. 'Camel' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'camel' but are not 'camel' are:\thorse\tzebra\tllama\nThere are several useful visual features to tell there is 'camel' and not similar things in a photo:\thumps on their backs\tlong necks and legs\tlarge, flat feet\tthat allow them to move easily across the sand\tlong eyelashes to protect their eyes\tfrom sand and wind\tsaddle shape around their humps (when ridden)", 40], "stone floor": ["Yes. 'Stone floor' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'stone floor' but are not 'stone floor' are:\twooden floor\ttile floor\tconcrete floor\tbrick floor\nThere are several useful visual features to tell there is 'stone floor' and not similar things in a photo: unevenness of the texture; visible rocks and grains; varying color of beige, gray or brown; cool temperature to the touch", 40], "wood dresser": ["Yes. 'Wood dresser' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wood dresser' but are not 'wood dresser' are:\tshelf\tcabinet\ttable\nThere are several useful visual features to tell there is 'wood dresser' and not similar things in a photo:\tdrawers\tunique design or carving on the wood\tsmooth, polished surface\thandles on each drawer\ttall and narrow structure\tfor holding clothes and accessories", 40], "pomegranate": ["Yes. 'pomegranate' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'pomegranate' but are not 'pomegranate' are:\tapple\ttomato\tred bell pepper\tcranberry\nThere are several useful visual features to tell there is 'pomegranate' and not similar things in a photo:\tred or dark pink skin\twith prominent crown-shaped calyx on top (crownlike flower structure)\tfruits are typically round, but can have various shapes inside\tsegmented interior with juicy, ruby-colored seeds or arils", 40], "right wing": ["No. 'Right wing' is too vague or abstract to have a tangible appearance that could be visually distinguished in a photo. \n\nNote: For clarification, \"right wing\" in political context refers to a conservative or traditionalist perspective, it does not have any physical appearance or tangible features.", 40], "cilantro": ["Yes. 'Cilantro' has a tangible appearance and is a type of herb.\nA few things that are visually similar to 'cilantro' but are not 'cilantro' are: parsley, dill, coriander, carrot tops.\nThere are several useful visual features to tell there is 'cilantro' and not similar things in a photo: delicate, feathery leaves with jagged edges, light green color, distinct aroma.", 40], "candies": ["Yes. 'Candies' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'candies' but are not 'candies' are:\tdecorative stones\tmarbles\tsoaps\tglass beads\nThere are several useful visual features to tell there is 'candies' and not similar things in a photo:\tcolorful\tvariety of shapes, sizes and textures\tunwrapped or wrapped in shiny or colorful plastic or paper glossy surface, sometimes with a sugary texture or patterns that resemble candy shapes.", 40], "soldiers": ["Yes. 'Soldiers' has a tangible appearance and refers to people in military uniforms.\nA few things that are visually similar to 'soldiers' but are not 'soldiers' are:\tswimmers\thikers\tfirefighters\tpolice officers\nThere are several useful visual features to tell there are 'soldiers' and not similar things in a photo:\tpeople in military uniforms\tcamouflage clothing or helmets\tguns or other military gear\tmarching or standing in formation in a military setting", 40], "clay pot": ["Yes. 'Clay pot' has a tangible appearance and is a type of cooking or gardening utensil.\nA few things that are visually similar to 'clay pot' but are not 'clay pot' are:\tcoffee mug\tmetal pot\tplant pot\nThere are several useful visual features to tell there is 'clay pot' and not similar things in a photo:\tmade of clay or ceramic\tcylindrical or spherical shape\trough texture or visible pottery marks\tthick walls and a heavy weight compared to other pots\torifice narrower than the largest part of the pot.", 40], "raisins": ["Yes. 'Raisins' has a tangible appearance and is a type of dried fruit.\nA few things that are visually similar to 'raisins' but are not 'raisins' are:\tdried cranberries\tdried cherries\tdried apricots\tdried mango\nThere are several useful visual features to tell there is 'raisins' and not similar things in a photo:\tsmall and wrinkled\tdark brown, almost black\tcolor and texture of grapes\tsweet smell and taste.", 40], "surf boards": ["Yes. 'Surf boards' has a tangible appearance and is a type of board used for surfing.\nA few things that are visually similar to 'surf boards' but are not 'surf boards' are:\tskateboards\tsnowboards\tpaddle boards\twakeboards\nThere are several useful visual features to tell there is 'surf boards' and not similar things in a photo:\tlong and narrow shape\tboard with pointy end, flat center, and rounded nose and tail\tleash attached to the ankle (for safety)\twax on the top (to increase the traction)\tpatterns or decorations on the board (for style)", 40], "extinguisher": ["Yes. 'Extinguisher' has a tangible appearance and is a tool for putting out fires.\nA few things that are visually similar to 'extinguisher' but are not 'extinguisher' are:\twater bottle\tspray can\tair compressor\nThere are several useful visual features to tell there is 'extinguisher' and not similar things in a photo:\tcylindrical shape\twith a trigger, hose, or nozzle\tred color\tlabel or text identifying it as a fire extinguisher", 40], "orange cap": ["Yes. 'Orange cap' has a tangible appearance and is a kind of headwear.\nA few things that are visually similar to 'orange cap' but are not 'orange cap' are:\thelmets\tbeanies\tsun hats\nThere are several useful visual features to tell there is 'orange cap' and not similar things in a photo:\torange in color\tflexible, cloth material\tdome-shaped with a flat brim\torangutan imagery, logo or text potentially printed in black or white\ton the front\tside, or back.", 40], "metal support": ["Yes. 'Metal support' has a tangible appearance and refers to a structure that provides stability or reinforcement.\nA few things that are visually similar to 'metal support' but are not 'metal support' are:\tpillar\tcolumn\tbeam\tbar\nThere are several useful visual features to tell there is 'metal support' and not similar things in a photo:\tmade of metal or metallic material\trectangular or cylindrical shape\tstrategically placed for support or reinforcement.", 40], "cement block": ["Yes. 'Cement block' has a tangible appearance and is a building material.\nA few things that are visually similar to 'cement block' but are not 'cement block' are:\tbrick\tpaver\ttile\tstone\nThere are several useful visual features to tell there is 'cement block' and not similar things in a photo:\trectangular shape\tgrey color\trough texture\tcement mixture appearance\twith holes inside or hollow design.", 40], "silver vehicle": ["Yes. 'Silver vehicle' has a tangible appearance and is a specific color of vehicle.\nA few things that are visually similar to 'silver vehicle' but are not 'silver vehicle' are:\tgrey vehicle\taluminum machinery\tshiny metal object\t\nThere are several useful visual features to tell there is 'silver vehicle' and not similar things in a photo:\tcar or truck\thave wheels and windows\tsilver in color\tand not made of aluminum or steel", 40], "pupil": ["Yes. 'Pupil' has a tangible appearance and is a part of the eye.\nA few things that are visually similar to 'pupil' but are not 'pupil' are:\tcircle\tdot\tball\tlens\nThere are several useful visual features to tell there is 'pupil' and not similar things in a photo:\tblack or dark-colored part of the eye\tresponsive to light\tsize changes with light exposure\tcenter of the eye", 40], "muddy": ["Yes. 'Muddy' has a tangible appearance and is a type of soil that is wet and soft.\nA few things that are visually similar to 'muddy' but are not 'muddy' are:\t wet sand\tclay paint\tchocolate\nThere are several useful visual features to tell there is 'muddy' and not similar things in a photo:\tbrown or dark-colored\tsoft and wet\tcontains small rocks, debris or organic materials\tmay have foot or tire tracks", 40], "plastic crate": ["Yes. 'Plastic crate' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic crate' but are not 'plastic crate' are:\tcardboard box\twooden crate\tmetal basket\nThere are several useful visual features to tell there is 'plastic crate' and not similar things in a photo:\tmade of plastic\tsquare-shaped\twith handles\ton a stack or a pallet", 40], "mail box": ["Yes. 'Mail box' has a tangible appearance and is an object for storing and collecting mail.\nA few things that are visually similar to 'mail box' but are not 'mail box' are:\tnewspaper box\ttrash can\toutdoor storage box\nThere are several useful visual features to tell there is 'mail box' and not similar things in a photo:\tred or blue colors\tfor the public use\twith a flag to signal that there is outgoing mail\thas a small slot to insert mail", 40], "letter e": ["Yes. 'Letter e' has a tangible appearance and is a written character.\nThere are no things that are visually similar to 'letter e' and not 'letter e'.\nThere are no useful visual features for distinguishing 'letter e' from other things in a photo, as it is a specific written character.", 40], "dirt trail": ["Yes. 'Dirt trail' has a tangible appearance and is a path made of mud or dirt.\nA few things that are visually similar to 'dirt trail' but are not 'dirt trail' are:\trocky path\trailroad track\tdry creek bed\nThere are several useful visual features to tell there is 'dirt trail' and not similar things in a photo:\tbrown or light-colored dirt or mud\tfootprints, bike tracks, or other indications of use\twinding path through natural surroundings\tvegetation on the sides of the path", 40], "darkness": ["No. 'Darkness' is too vague or abstract to be distinguished in a photo.", 39], "silver door": ["Yes. 'Silver door' has a tangible appearance and is a physical object.\nA few things that are visually similar to 'silver door' but are not 'silver door' are:\tsilver gate\tsilver car\tsilver-colored metal panel\tsilver refrigerator\nThere are several useful visual features to tell there is a 'silver door' and not similar things in a photo:\tmetallic silver color\trectangle or square shape\twith a doorknob and hinges\tmounted on a wall or a frame.", 39], "police motorcycle": ["Yes. 'Police motorcycle' has a tangible appearance and is a type of motorcycle used by law enforcement.\nA few things that are visually similar to 'police motorcycle' but are not 'police motorcycle' are:\tregular motorcycle\tscooter\tbicycle\tmotorized wheelchair\nThere are several useful visual features to tell there is 'police motorcycle' and not similar things in a photo:\tlights and sirens\tmarkings or logos indicating police affiliation\tcolors indicating police affiliation\tcommunication equipment such as a radio or a speaker box\tmounted equipment such as saddlebags or gun rack.", 39], "metal grill": ["Yes. 'Metal grill' has a tangible appearance and is a type of barrier made of metal bars or wires.\nA few things that are visually similar to 'metal grill' but are not 'metal grill' are:\tmetal fence\tchicken wire\tmetal mesh\tscreen door\nThere are several useful visual features to tell there is 'metal grill' and not similar things in a photo:\tparallel metal bars or wires\tspaces or gaps in between the bars or wires\tused as a barrier or safety feature\tfor outdoor or indoor use.", 39], "factory": ["Yes. 'Factory' has a tangible appearance and is a building used for manufacturing or producing goods.\nA few things that are visually similar to 'factory' but are not 'factory' are:\twarehouse\tpower plant\trefinery\tconstruction site\nThere are several useful visual features to tell there is 'factory' and not similar things in a photo:\tchimneys and smokestacks\tlarge buildings\twith production lines\tor warehouses\toutdoor storage areas\tfor raw materials and manufactured goods\tmachinery and equipment.", 39], "garden hose": ["Yes. 'Garden hose' has a tangible appearance and is a kind of hose used for watering plants.\nA few things that are visually similar to 'garden hose' but are not 'garden hose' are:\tpool cleaner hose\tvacuum cleaner hose\tgasoline dispenser hose\nThere are some useful visual features to identify there is 'garden hose' and not similar things in a photo:\tusually green or yellow in color\thas a spray nozzle or other attachment on one end\tflexible and made of plastic or rubber\tuniform thickness along its length\thas a curved shape when not in use", 39], "blue cord": ["Yes. 'Blue cord' has a tangible appearance and is a specific type of cord.\nA few things that are visually similar to 'blue cord' but are not 'blue cord' are:\tgreen cord\tred cord\tpink cord\tpurple cord\nThere are several useful visual features to tell there is 'blue cord' and not similar things in a photo:\tblue color\tsmooth texture\tcylindrical shape\ttypically used for electronics or utility purposes", 39], "plastic frisbee": ["Yes. 'Plastic frisbee' has a tangible appearance and is a type of flying disc toy.\nA few things that are visually similar to 'plastic frisbee' but are not 'plastic frisbee' are:\tball\tflying saucer\tlid\tfrisbee golf disc\nThere are several useful visual features to tell there is 'plastic frisbee' and not similar things in a photo:\tround or disc-shaped\tobject with curved edges and a flat surface\tbright or colorful\tplastic material\twith or without printed design", 39], "blue pot": ["Yes. 'Blue pot' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'blue pot' but are not 'blue pot' are:\tgreen pot\tpurple pot\tblue vase\tdish\nThere are several useful visual features to tell there is 'blue pot' and not similar things in a photo:\tcylindrical or round shape\twith a handle and a spout\tmade of ceramic or metal predominantly blue in color", 39], "dump truck": ["Yes. 'Dump truck' has a tangible appearance and is a type of truck.\nA few things that are visually similar to 'dump truck' but are not 'dump truck' are:\ttrailer\ttruck\tcrane\tbulldozer\nThere are several useful visual features to tell there is 'dump truck' and not similar things in a photo:\tlarge open bed at the back\tcylindrical shape or angled sides for easy dumping\theavy-duty wheels or tires\tcab in the front for the driver to sit in\tbed filled with rocks, dirt, or debris", 39], "triangle sign": ["Yes. 'Triangle sign' has a tangible appearance and is a type of road sign.\nA few things that are visually similar to 'triangle sign' but are not 'triangle sign' are:\trectangular sign\tcircular sign\nThere are several useful visual features to tell there is 'triangle sign' and not similar things in a photo:\ttriangular shape\tred or yellow color with black borders or text\tlarge enough to be seen from a distance\toften used to indicate a warning or a danger", 39], "thumbnail": ["Yes. 'Thumbnail' has a tangible appearance and is a smaller version of an image or video.\nA few things that are visually similar to 'thumbnail' but are not 'thumbnail' are:\ticons\temojis\tstickers\tsymbols\nThere are several useful visual features to tell there is 'thumbnail' and not similar things in a photo:\trectangle shape\tsmaller than the original image or video\tlower resolution or quality\timage or video preview or representation", 39], "newspapers": ["Yes. 'Newspapers' has a tangible appearance and is a type of printed publication.\nA few things that are visually similar to 'newspapers' but are not 'newspapers' are:\tmagazines\tbooks\tbrochures\tflyers\nThere are several useful visual features to tell there is 'newspapers' and not similar things in a photo:\tthick pile of paper with printed text and images\tfolded or stacked neatly\theadlines and articles arranged in columns\tdate and publication information at the top of the page", 39], "products": ["No. 'Products' is too vague and abstract to have a tangible appearance that can be distinguished in a photo. \nTherefore, there are no things that are visually similar to 'products' but are not 'products'.", 39], "apple computer": ["Yes. 'Apple computer' has a tangible appearance and is a specific brand and type of computer.\nA few things that are visually similar to 'apple computer' but are not 'apple computer' are:\tPC\tdell computer\thp computer\t\nThere are several useful visual features to tell there is 'apple computer' and not similar things in a photo:\tthe apple logo on the computer or keyboard\tmetallic design and finish\tslim and minimalist design\ttypically runs the MacOS operating system.", 39], "brown curtain": ["Yes. 'Brown curtain' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'brown curtain' but are not 'brown curtain' are:\torange curtain\tred curtain\tpurple curtain\tblanket\t\nThere are several useful visual features to tell there is 'brown curtain' and not similar things in a photo:\tbrown color\tfabric material\thanging on a window or a door\tcurtain rod or rings.", 39], "medal": ["Yes. 'Medal' has a tangible appearance and is a type of award.\nA few things that are visually similar to 'medal' but are not 'medal' are:\tcoin\tbadge\tpin\ttoken\nThere are several useful visual features to tell there is 'medal' and not similar things in a photo:\tcircular or polygonal shape\twith a ribbon or a cord\tdecorative design\ton one or both sides\thas engravings or inscriptions\tthat may have a relief design.", 39], "rays": ["Yes. 'Rays' has a tangible appearance and refers to the beams of light or radiation.\nA few things that are visually similar to 'rays' but are not 'rays' are:\tshadows\tlines or stripes\tcrack or breaks in an object\tflowers or petals\nThere are several useful visual features to tell there is 'rays' and not similar things in a photo:\tbeaming, luminous, or glowing appearance\temitting from a single or a group of sources\tsymmetric or divergent pattern of beams, such as for a starburst effect or similar to sun-rays.", 39], "habitat": ["No. 'Habitat' is too vague or abstract to be distinguished in a photo.", 39], "alarm": ["No. 'Alarm' is too abstract to have a distinct visual appearance.\nA few things that are visually similar to 'alarm' but are not 'alarm' are:\tclock\tbell\thorn\t\nThere are no distinct visual features for distinguishing 'alarm' from the listed similar things in a photo as the concept of 'alarm' refers to a sound or warning signal rather than a visual appearance.", 39], "square clock": ["Yes. 'Square clock' has a tangible appearance and is a specific type of clock.\nA few things that are visually similar to 'square clock' but are not 'square clock' are:\tround clock\talarm clock\twall clock\tpocket watch\nThere are several useful visual features to tell there is a 'square clock' and not similar things in a photo:\tsquare shape\ttwo or three clock hands\tdigital or analog display\t12-hour or 24-hour format\tnumbers or tick marks around the edges", 39], "curves": ["Yes. 'Curves' have a tangible appearance and refer to the contour or shape of an object or line.\nA few things that are visually similar to 'curves' but are not 'curves' are:\tangles\tlines\tborders\tedges\nThere are no additional visual features required to distinguish the concept 'curves' from the listed similar things, as the shape or contour of the object or line is the primary visual feature that constitutes 'curves'.", 39], "multi story building": ["Yes. 'Multi-story building' has a tangible appearance and refers to a building with multiple floors or levels.\nA few things that are visually similar to 'multi-story building' but are not 'multi-story building' are:\thotel\ttower\tskyscraper\tapartment complex\nThere are several useful visual features to tell there is 'multi-story building' and not similar things in a photo:\tmultiple levels or floors\tmultiple windows or balconies on each level or floor\tvertical orientation\ttaller than surrounding structures or buildings.", 39], "orange fence": ["Yes. 'Orange fence' has a tangible appearance and is a type of physical barrier.\nA few things that are visually similar to 'orange fence' but are not 'orange fence' are:\tcaution tape\tconstruction barriers\torange netting\nThere are several useful visual features to tell there is 'orange fence' and not similar things in a photo:\tsolid, vertical bars\tbright, orange color\tmodular and easy to set up and remove\thighly visible to draw attention to hazards or restricted areas.", 39], "closet door": ["Yes. 'Closet door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'closet door' but are not 'closet door' are:\troom door\tbathroom door\tgate\nThere are several useful visual features to tell there is 'closet door' and not similar things in a photo:\t \nsize (a closet door is usually smaller than a room door)\nlocation (a closet door is usually located in a closet)\nlouvered design (some closet doors have slats or louvers for ventilation)", 39], "crust brown": ["No. 'Crust brown' is too vague or abstract to be distinguished in a photo.", 39], "siding": ["Yes. 'Siding' has a tangible appearance and refers to the cladding material used on the exterior walls of a building.\nA few things that are visually similar to 'siding' but are not 'siding' are:\tbricks\tshingles\tstucco\tcement\nThere are several useful visual features to tell there is 'siding' and not similar things in a photo:\thorizontal or vertical boards, panels, or sheets covering the exterior walls\ttexture and pattern of the material\tplacement of the material on the wall (along the length or width)", 39], "storage": ["No. 'Storage' is too vague or abstract to be distinguished in a photo. However, objects used for storage such as containers, boxes or shelves have a tangible appearance.\nA few things that are visually similar to 'storage' but are not 'storage' are:\tfurniture\tdecorations\ttools\tpapers\nThere are several useful visual features to tell there is 'storage' and not similar things in a photo, such as:\t\n- Containers: large enough to contain objects, with a lid or open top, made of various materials such as plastic, metal, or fabric.\n- Shelves: flat, horizontal surface attached to a wall or setting on the floor, made of materials such as wood, metal, or plastic. \n- Boxes: a solid structure with a lid, designed to store objects, available in various materials such as cardboard, metal, or plastic.", 39], "swans": ["Yes. 'Swans' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'swans' but are not 'swans' are:\tgeese\tducks\therons\nThere are several useful visual features to tell there is 'swans' and not similar things in a photo:\tlong, slender necks\twhite feathers\torangish beak and black face\tmask-like appearance on their head\tslender, curved wings, often with black tips\tswimming in pairs or small groups\ton a lake or river, often near vegetation", 39], "pedestal sink": ["Yes. 'Pedestal sink' has a tangible appearance and is a type of bathroom sink.\nA few things that are visually similar to 'pedestal sink' but are not 'pedestal sink' are:\twall-mounted sink\tvanity sink\tconsole sink\nThere are several useful visual features to tell there is 'pedestal sink' and not similar things in a photo:\ta sink attached to a pedestal or column\tspace between the sink and the floor\texposed plumbing under the sink round or oval basin flat or fluted base", 39], "tall plant": ["Yes. 'Tall plant' has a tangible appearance and can be defined as a plant that reaches above average height.\nA few things that are visually similar to 'tall plant' but are not 'tall plant' are:\tTrees\tSkyscrapers\tTower\nThere are several useful visual features to tell there is 'tall plant' and not similar things in a photo:\tStalk of varying thickness\tintricate/leafy appearance\tNo building-like shapes at the base of the plant.", 39], "bike rider": ["Yes. 'Bike rider' has a tangible appearance and refers to a person riding a bike.\nA few things that are visually similar to 'bike rider' but are not 'bike rider' are:\tperson running\tperson walking\tperson rollerblading\nThere are several useful visual features to tell there is 'bike rider' and not similar things in a photo:\triding a bike\twearing a bike helmet\tlegs pedaling on the pedals\tbike frame and wheels visible", 39], "son": ["No. 'Son' is too vague or abstract to have a tangible appearance or be distinguished in a photo.", 39], "fireman": ["Yes. 'Fireman' has a tangible appearance and is a type of profession.\nA few things that are visually similar to 'fireman' but are not 'fireman' are:\tpolice officers\tambulance drivers\tconstruction workers\nThere are several useful visual features to tell there is 'fireman' and not similar things in a photo:\tfirefighter uniform\tyellow or red helmet with a visor\tfire or smoke in the background\those or other firefighting equipment", 39], "cactus": ["Yes, 'cactus' is a visually concrete concept because it has a distinct appearance and features.\nA few things that are visually similar to 'cactus' but are not 'cactus' are:\tseaweed\talgae\tspiny succulent plants\nThere are several useful visual features to distinguish a cactus from similar things in a photo:\t\n1. Thick, fleshy stem or pad structure\n2. Sharp spines, prickles, or thorns\n3. Unique flower and fruit patterns, which are barrel-shaped, star-shaped, or cylindrical\n4. Commonly found in deserts or arid areas\n5. Drought-resistant features or adaptations.", 39], "orange reflector": ["Yes. 'Orange reflector' has a tangible appearance and is a type of reflective object.\nA few things that are visually similar to 'orange reflector' but are not 'orange reflector' are:\tred reflector\tyellow reflector\tcone\tbarrel\nThere are several useful visual features to tell there is 'orange reflector' and not similar things in a photo:\trectangular or oval shaped\tbright orange color\thighly reflective surface\tused as a safety device for roads and vehicles", 39], "ink pen": ["Yes. 'Ink pen' has a tangible appearance and is a writing instrument.\nA few things that are visually similar to 'ink pen' but are not 'ink pen' are:\tpencil\tmarker\tchalk stick\tbrush\nThere are several useful visual features to tell there is 'ink pen' and not similar things in a photo:\tlong and thin tube shape\tmetal or plastic casing\tpointed tip that dispenses ink\twhen in use, the ink flows out evenly and leaves a visible line on paper.", 39], "orange pumpkin": ["Yes. 'Orange pumpkin' has a tangible appearance and is a type of fruit/vegetable.\nA few things that are visually similar to 'orange pumpkin' but are not 'orange pumpkin' are:\torange squash\ttomato\tpersimmon\tmango\tcantaloupe\nThere are several useful visual features to tell there is 'orange pumpkin' and not similar things in a photo:\tround or slightly oblong shape\tbright orange color\thard exterior\twith visible ridges and grooves on the surface\tcircular stem on the top", 39], "landline phone": ["Yes. 'Landline phone' has a tangible appearance and is a type of communication device.\nA few things that are visually similar to 'landline phone' but are not 'landline phone' are:\tmobile phone\ttablet\tcomputer\theadset\nThere are several useful visual features to tell there is 'landline phone' and not similar things in a photo:\tcord or wire connecting the phone to the wall\tnumber pad to dial phone numbers\treceiver to pick up and listen or speak\tdesign is often boxy and chunky", 39], "office desk": ["Yes. 'Office desk' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'office desk' but are not 'office desk' are:\tkitchen table\tcraft table\tdining table\tworkbench\nThere are several useful visual features to tell there is 'office desk' and not similar things in a photo:\tdesk surface for working with a computer or papers\tdrawers or shelves for storage\toffice chair for sitting at the desk\tlamp or other office supplies on the desk", 39], "waste bin": ["Yes. 'Waste bin' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'waste bin' but are not 'waste bin' are:\tlaundry basket\tstorage box\tdonation box\tcooler\tbox\nThere are several useful visual features to tell there is 'waste bin' and not similar things in a photo:\tcylindrical or rectangular container\toften made of plastic or metal\twith a lid or cover\tusually marked with a \"Recycle\" or \"Trash\" label or symbol.", 39], "brunette woman": ["Yes. 'Brunette woman' has a tangible appearance and is a type of person with specific physical characteristics.\nA few things that are visually similar to 'brunette woman' but are not 'brunette woman' are:\tblonde woman\tred-haired woman\tdark-haired man\twoman wearing a wig\nThere are several useful visual features to tell there is 'brunette woman' and not similar things in a photo:\tbrunette hair color, which is usually brown or black\tdark or medium skin tone\tfemale facial features, such as thin eyebrows and longer lashes\tfeminine clothing and accessories", 39], "paper towel roll": ["Yes. 'Paper towel roll' has a tangible appearance and is a household item.\nA few things that are visually similar to 'paper towel roll' but are not 'paper towel roll' are:\ttoilet paper roll\twrapping paper roll\taluminum foil roll\twrapping ribbon roll\nThere are several useful visual features to tell there is 'paper towel roll' and not similar things in a photo:\tcylindrical shape\tridged or perforated texture\tcenter hole for dispensing sheets\twhite or brown color label indicating \u2018paper towels\u2019", 39], "bumpers": ["Yes. 'Bumpers' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'bumpers' but are not 'bumpers' are:\tspoilers\tgrilles\tvalances\tfenders\nThere are several useful visual features to tell there are 'bumpers' and not similar things in a photo:\tusually made of plastic or metal\tlocated at the front and rear of a vehicle\trectangular or curved shape\tdesigned to absorb impacts\tfrom a car, truck or SUV", 39], "dog bed": ["Yes. 'Dog bed' has a tangible appearance and is a type of bedding for dogs.\nA few things that are visually similar to 'dog bed' but are not 'dog bed' are:\tcouch\tcushion\tbouncy castle\tbaby play mat\nThere are several useful visual features to tell there is 'dog bed' and not similar things in a photo:\trectangular or circular shape\tpadded or cushioned surface\tdog toys present\tsized appropriately for a dog", 39], "messenger bag": ["Yes. 'Messenger bag' has a tangible appearance and is a type of bag that is designed to be worn over the shoulder.\nA few things that are visually similar to 'messenger bag' but are not 'messenger bag' are:\tbackpack\tlaptop bag\tpurse\tduffle bag\nThere are several useful visual features to tell there is 'messenger bag' and not similar things in a photo:\trectangular or oblong shape\tone long strap that goes diagonally across the body\tflap closure at the top of the bag\ta large front pocket or several smaller pockets for storage.", 39], "plaid umbrella": ["Yes. 'Plaid umbrella' has a tangible appearance and is a type of umbrella.\nA few things that are visually similar to 'plaid umbrella' but are not 'plaid umbrella' are:\tsolid color umbrella\tclear umbrella\tfoldable travel umbrella\thandle-less umbrella\nThere are several useful visual features to tell there is 'plaid umbrella' and not similar things in a photo:\tdark or brightly colored plaid\tprinted pattern\taccompanying handle (often made of wood or plastic)", 39], "backwards": ["No. 'Backwards' is too vague or abstract to have a tangible appearance or be distinguished in a photo.", 39], "steel fence": ["Yes. 'Steel fence' has a tangible appearance and is a kind of fence.\nA few things that are visually similar to 'steel fence' but are not 'steel fence' are:\twooden fence\tconcrete wall\tbrick wall\tmesh fence\nThere are several useful visual features to tell there is 'steel fence' and not similar things in a photo:\tsilver or gray color\tsmooth, shiny surface\tvertical steel bars\tpatterned openings", 39], "wooden clock": ["Yes. 'Wooden clock' has a tangible appearance and is a type of clock made of wood.\nA few things that are visually similar to 'wooden clock' but are not 'wooden clock' are:\twooden plaque\twooden picture frame\twooden cutting board\twooden chessboard\nThere are several useful visual features to tell there is 'wooden clock' and not similar things in a photo:\tcylindrical or round shape\twooden material\tclock hands\ttime markers\tnumbers or Roman numerals on the face of the clock", 39], "dirt floor": ["Yes. 'Dirt floor' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'dirt floor' but are not 'dirt floor' are:\twood floor\tcarpet\tcement floor\nThere are several useful visual features to tell there is 'dirt floor' and not similar things in a photo:\t\nuneven surface made of dirt or soil\tbrown or tan color\tdebris or organic matter visible\ton the ground surface\tno visible seams or patterns", 39], "signals": ["No. 'Signals' is too vague or abstract to be distinguished in a photo.", 39], "antlers": ["Yes. 'Antlers' has a tangible appearance and is a kind of growth on the heads of animals like deer, elk or moose.\nA few things that are visually similar to 'antlers' but are not 'antlers' are:\tbranches\thorns\tcactus arms\ticicles\tmetal sculptures\nThere are several useful visual features to tell there are 'antlers' and not similar things in a photo:\tmade of bone or cartilage\tgrowing from frontal bones of a skull\tbranched structure\tcovered in velvet from spring to fall", 39], "train headlights": ["Yes. 'Train headlights' has a tangible appearance and is a specific type of headlight.\nA few things that are visually similar to 'train headlights' but are not 'train headlights' are:\tcar headlights\tbike headlights\tflashlights\tspotlights\ttraffic signals\nThere are several useful visual features to tell there is 'train headlights' and not similar things in a photo:\tattached to the front of a train or locomotive\tcircular, large lenses\toften paired with another headlight\tbrilliantly bright and white light color, with a beam directed forward.", 39], "pink wall": ["Yes. 'Pink wall' has a tangible appearance and is a specific color and texture of a wall.\nA few things that are visually similar to 'pink wall' but are not 'pink wall' are:\tred wall\tmagenta wall\tbrick wall\tplastered wall\nThere are several useful visual features to tell there is 'pink wall' and not similar things in a photo:\tbright pink in color\tsmooth surfaces\tno patterns or textures other than the paint coat.", 39], "ivory": ["Yes. 'Ivory' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'ivory' but are not 'ivory' are:\tbone\tplastic\tmarble\tpearl\nThere are several useful visual features to tell there is 'ivory' and not similar things in a photo:\t\ncreamy white color\t\nsmooth texture\t\nhas small cracks or lines in the surface\t\nmay have visible grain patterns\t\nmay have a glossy shine\t\nmay have discoloration or yellowing over time.", 39], "fluorescent": ["No. 'Fluorescent' is too abstract to have a tangible appearance.\nThere aren't many things that could be visually similar to 'fluorescent' as it's related to the luminescence of materials under certain conditions. \nThere are no useful visual features for distinguishing 'fluorescent' from the listed similar things in a photo, as there is nothing that could properly resemble or imitate it.", 39], "pink building": ["Yes. 'Pink building' has a tangible appearance and is a building with a predominantly pink color.\nA few things that are visually similar to 'pink building' but are not 'pink building' are:\tpink house with no doors or windows\tbuilding with pink flowers or vines on its walls\tpink car with a building in the background\nThere are several useful visual features to tell there is 'pink building' and not similar things in a photo:\tpredominantly pink color\tset apart from other buildings by its color\tvisible doors, windows or other typical building characteristics", 39], "peacock": ["Yes. 'Peacock' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'peacock' but are not 'peacock' are:\tpeahen\tturkey\tpheasant\nThere are several useful visual features to tell there is 'peacock' and not similar things in a photo:\tcolorful long tail feathers\tbrightly colored body feathers\tblue-green iridescent plumage\tcrown-like feather crest on top of head\tshort beak and feet.", 39], "brown cow": ["Yes. 'Brown cow' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'brown cow' but are not 'brown cow' are:\thorse\tzebra\tdeer\tdonkey\nThere are several useful visual features to tell there is 'brown cow' and not similar things in a photo:\tbrown or dark-colored coat, often with white patches\tears that stick out\tsmall, black eyes\twith or without horns\tcow-like udder\tand large, barrel-shaped body and four legs.", 39], "texture": ["No. 'Texture' is too vague or abstract to be distinguished in a photo.\nHowever, a few things that may be visually similar to 'texture' but are not 'texture' in a photo are:\tcolor\tpattern\tlighting\tNone of these are textures themselves, but they can create the illusion or impression of texture.", 39], "squirrel": ["Yes. 'Squirrel' has a tangible appearance and is a type of small mammal.\nA few things that are visually similar to 'squirrel' but are not 'squirrel' are:\trabbit\tchipmunk\tgopher\nThere are several useful visual features to tell there is 'squirrel' and not similar things in a photo:\tbushy tail\tthat can be held above their head\tfurry\t4 legs and sharp claws\tthat can climb trees\tand jump between branches\tears on top of their head\twith a slender nose and strong jaw\tline along their back\tfrom head to tail.", 39], "guardrail": ["Yes. 'Guardrail' has a tangible appearance and is an object for safety purposes.\nA few things that are visually similar to 'guardrail' but are not 'guardrail' are:\tHandrails\tFence\tRope barriers\nThere are several useful visual features to tell there is 'guardrail' and not similar things in a photo:\t\nattached to the side of the road or a path\t\nmetallic\t\nmeant to prevent vehicles from falling off the road\t\nrails are placed relatively close together", 39], "photo frame": ["Yes. 'Photo frame' has a tangible appearance and is an object used to display photographs.\nA few things that are visually similar to 'photo frame' but are not 'photo frame' are:\tpicture holder\tclipboard\tdoor or window frames\nThere are several useful visual features to tell there is 'photo frame' and not similar things in a photo:\trectangular or square shape, with rounded or sharp corners\tvisible image border or matting\tclear or reflective glass or plastic covering\tback support, stand or hanger to display the picture", 39], "brown train tracks": ["No. 'Brown train tracks' is too specific and does not have a tangible appearance. Train tracks are typically a metallic color, not brown.\nA few things that are visually similar to 'brown train tracks' but are not 'brown train tracks' are:\troad\tmotorway\tpath\tbridge\nThere are several useful visual features to tell there are 'train tracks' and not similar things in a photo:\ttwo parallel metal rails\tsleepers or ties\tthat the distance between the two rails is less than the width of the train\tthat the rails continue on into the distance", 39], "clock side tower": ["Yes. 'Clock side tower' has a tangible appearance and is a tower that has a clock on its side.\nA few things that are visually similar to 'clock side tower' but are not 'clock side tower' are:\tregular tower\ttower with no clock on its side\t\nThere are several useful visual features to tell there is 'clock side tower' and not similar things in a photo:\ttall tower with a clock on its side\tmay have visible clock hands or numbers\tclock often set in the center of the tower\tside view of a tower\tflat clock face on the side of the tower", 39], "bunk": ["Yes. 'Bunk' has a tangible appearance and refers to a type of bed.\nA few things that are visually similar to 'bunk' but are not 'bunk' are:\tbed\tcot\tcouch\tmattress\nThere are several useful visual features to tell there is 'bunk' and not similar things in a photo:\tconsisting of two or more beds\tstacked on top of one another\tladder or steps for reaching the upper beds\tin a dormitory or children's bedroom", 39], "girls hair": ["Yes. 'Girls hair' has a tangible appearance and refers to the hair on a female's head.\nA few things that are visually similar to 'girls hair' but are not 'girls hair' are:\tfur\twool\tyarn\tstraw\nThere are several useful visual features to tell there is 'girls hair' and not similar things in a photo:\tpresent on a human head\tvariety of colors and styles\tmovements and textures can vary based on styling and type of hair", 39], "water wave": ["Yes. 'Water wave' has a tangible appearance and is a visible disturbance on the surface of water.\nA few things that are visually similar to 'water wave' but are not 'water wave' are:\tripples in water\treflection on water\tshadow on water\t\nThere are several useful visual features to tell there is 'water wave' and not similar things in a photo:\tcurved shape\tmoving or oscillating appearance\tinconsistent height or size\tof the surface of water\tclearly separate from surrounding water surface", 39], "orange plate": ["Yes. 'Orange plate' has a tangible appearance and is a specific type of dish.\nA few things that are visually similar to 'orange plate' but are not 'orange plate' are:\torange bowl \tpumpkin pie\torange frisbee\nThere are several useful visual features to tell there is 'orange plate' and not similar things in a photo:\tcircular or oval in shape\tbright, solid color of orange\tporcelain or ceramic material\tused for serving food and often placed on a table or in a kitchen setting.", 39], "beets": ["Yes. 'Beets' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'beets' but are not 'beets' are:\tradishes\tpotatoes\tcarrots\tturnips\nThere are several useful visual features to tell there is 'beets' and not similar things in a photo:\tred or purple skin and flesh\tround or oblong shape\twhite rings inside the flesh\tleafy greens at the top of the root\twhen sliced, have a distinctive pattern of concentric circles inside", 39], "sports car": ["Yes. 'Sports car' has a tangible appearance and is a kind of automobile.\nA few things that are visually similar to 'sports car' but are not 'sports car' are:\tsedan\tmuscle car\tcoupe\tconcept car\nThere are several useful visual features to tell there is 'sports car' and not similar things in a photo:\tsleek and aerodynamic design\tlow to the ground\ttwo-seater or 2+2 seating configuration\tsporty wheels and tires\twith or without racing stripes\tand unique features like spoilers or air ducts.", 39], "game control": ["Yes. 'Game control' has a tangible appearance and is a device used to play video games.\nA few things that are visually similar to 'game control' but are not 'game control' are: TV remote control, music player control, smart home appliance control.\nThere are several useful visual features that can help distinguish 'game control' from similar things in a photo: directional pad, joystick, buttons, triggers, and a design or branding that is associated with a particular gaming device or console.", 38], "storage box": ["Yes. 'Storage box' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'storage box' but are not 'storage box' are:\ttrash bin\tshipping container\tshoebox\nThere are several useful visual features to tell there is 'storage box' and not similar things in a photo:\trectangular shape\tlid\tfor storing items inside\tmultiple sizes and colors\tstackable or nestable\tcan be made of plastic, cardboard, or metal.", 38], "elephants tail": ["Yes. 'Elephant's tail' has a tangible appearance and is a specific part of an elephant's body.\nA few things that are visually similar to 'elephant's tail' but are not 'elephant's tail' are:\thorse's tail\tcow's tail\tlion's tail\tzebra's tail\nThere are several useful visual features to tell there is 'elephant's tail' and not similar things in a photo:\tvery long and thin\thairy and rough\tdarker in color than the rest of the body\tfluffy tip, often dark in color", 38], "officers": ["Yes. 'Officers' has a tangible appearance and refers to individuals who hold a position of authority in a department or organization.\nA few things that are visually similar to 'officers' but are not 'officers' are:\tsecurity guards\tpolice officers\tmilitary personnel\tdoctors\nThere are several useful visual features to tell there is 'officers' and not similar things in a photo:\tuniforms\tbadges or insignia\thats or caps\tshoulder boards or epaulets", 38], "blue background": ["Yes. 'Blue background' has a tangible appearance and is a specific color used as a background.\nA few things that are visually similar to 'blue background' but are not 'blue background' are:\tblue sky\tblue ocean\tblue shirt\tblue paint\nThere are several useful visual features to tell there is 'blue background' and not similar things in a photo:\ta solid-colored background of a bright or dark blue shade, without any discernible details or patterns.", 38], "bath": ["Yes. 'Bath' has a tangible appearance and refers to a container used for holding water or a room where a person can bathe.\nA few things that are visually similar to 'bath' but are not 'bath' are:\tpool\thot tub\tsink\tpond\nThere are several useful visual features to tell there is 'bath' and not similar things in a photo:\ta tub or a basin with water in it\ta faucet or a tap\tdrains or outlets\ta bathroom setting with tiles, sink, and toilet nearby.", 38], "window curtains": ["Yes. 'Window curtains' has a tangible appearance and is a type of household item.\nA few things that are visually similar to 'window curtains' but are not 'window curtains' are:\tShower curtains\tBlinds\tTapestries\tCloth backdrops\nThere are several useful visual features to tell there is 'window curtains' and not similar things in a photo:\thanging from a rod or a rail\tcovers a window or a doorway\tcomes in various colors and designs\tmade of cloth or fabric that can be thick or sheer", 38], "recliner": ["Yes. 'Recliner' has a tangible appearance and is a type of chair.\nA few things that are visually similar to 'recliner' but are not 'recliner' are:\tsofa\tchaise lounge\tbean bag chair\tottoman\nThere are several useful visual features to tell there is 'recliner' and not similar things in a photo:\tadjustable back and footrest\tpadded\tusually has armrests", 38], "blue jersey": ["Yes. 'Blue jersey' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blue jersey' but are not 'blue jersey' are:\tblue t-shirt\tblue sweatshirt\tblue tank top\tblue blouse\nThere are several useful visual features to tell there is 'blue jersey' and not similar things in a photo:\tathletic fit\tshort sleeves or long sleeves\tusually made of polyester or similar materials\twith team name or emblem on the front and back", 38], "green trees": ["Yes. 'Green trees' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'green trees' but are not 'green trees' are:\tbushes\tgrass\tcactus\nThere are several useful visual features to tell there is 'green trees' and not similar things in a photo:\ttall, woody stem\tleafy branches with needles or flat leaves\trounded or conical shape\tshade of green color (darker than grass, lighter than bushes)", 38], "tall mountain": ["Yes. 'Tall mountain' has a tangible appearance and is a type of geographical feature.\nA few things that are visually similar to 'tall mountain' but are not 'tall mountain' are:\thills\tcliffs\tbuildings\ttrailers\t\nThere are several useful visual features to tell there is 'tall mountain' and not similar things in a photo:\tvery high and steep\tcovered in snow or rocks\ttowering over surrounding terrain\tcan have a pointed, cone shape or a jagged, rocky shape", 38], "street name": ["No. 'Street name' is too vague or abstract to be distinguished in a photo.", 38], "vests": ["Yes. 'Vests' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'vests' but are not 'vests' are:\tjackets\tblazers\tcardigans\tharnesses\nThere are several useful visual features to tell there is 'vests' and not similar things in a photo:\tno sleeves\tv-neck or round-necked\tslim-fitted\tsolid-colored or patterned\tzipped or buttoned at the front", 38], "mangoes": ["Yes. 'mangoes' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'mangoes' but are not 'mangoes' are:\tpapaya\tpeach\tapricot\nThere are several useful visual features to tell there is 'mangoes' and not similar things in a photo:\tovular shaped\tfleshy fruit\tbright yellow to red-green color with a rosy or yellow interior\tpit in the middle\tof 5-15 cm long and 4-6 cm wide smooth or rough skin", 38], "float": ["Yes. 'Float' has a tangible appearance and refers to objects that float on a liquid.\nA few things that are visually similar to 'float' but are not 'float' are:\tboat\traft\tlifebuoy\tbuoy\nThere are several useful visual features to tell there is 'float' and not similar things in a photo:\tfloating on the surface of a liquid\tusually made from lightweight material\tsimilar in shape to a raft or a platform\talmost entirely above the surface with only a small portion submerged", 38], "pug": ["Yes. 'Pug' has a tangible appearance and is a breed of dog. \nA few things that are visually similar to 'pug' but are not 'pug' are:\tbulldog\tmastiff\tfrench bulldog\tshar-pei\tboston terrier\nThere are several useful visual features to tell there is 'pug' and not similar things in a photo:\tsmaller size\tround head\twith a short muzzle\tfurry, squished face\twith a curled tail\tfawn or black coat\tcolor", 38], "wooden fencing": ["Yes. 'Wooden fencing' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'wooden fencing' but are not 'wooden fencing' are:\tbricks\twalls\tgates\trocks\nThere are several useful visual features to tell there is 'wooden fencing' and not similar things in a photo:\tvertical wooden boards, posts, or panels\tbrown or natural wood colors\tstraight or horizontal lines\tvisible gaps or spaces between the boards or panels.", 38], "loveseat": ["Yes. 'Loveseat' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'loveseat' but are not 'loveseat' are:\tsofa\tchair\tbench\tdaybed\nThere are several useful visual features to tell there is 'loveseat' and not similar things in a photo:\tsmall sofa\tfor two people\tcushion seats and backs\ttwo armrests\tbetween 45 to 72 inches wide", 38], "window shade": ["Yes. 'Window shade' has a tangible appearance and is a type of covering for a window.\nA few things that are visually similar to 'window shade' but are not 'window shade' are:\tcurtains\tblinds\tplantations shutters\nThere are several useful visual features to tell there is a 'window shade' and not similar things in a photo:\tattached directly to the window\tframe\tcomes in a roll\tcan be pulled down or up\toften made of fabric or vinyl", 38], "armband": ["Yes. 'Armband' has a tangible appearance and is a type of accessory worn around the arm.\nA few things that are visually similar to 'armband' but are not 'armband' are:\twatches\tbracelets\tcuffs\nThere are several useful visual features to tell there is 'armband' and not similar things in a photo:\tdesigned to be worn on the upper arm\tarea of the arm where it is worn is thicker or larger than the rest of the arm\tmay have a specific design or logo that represents a group, team, or cause.", 38], "round food": ["Yes. 'Round food' has a tangible appearance and is a type of food that has a circular shape.\nA few things that are visually similar to 'round food' but are not 'round food' are: plates, bowls, pans, buttons, coins.\nThere are several useful visual features to tell there is 'round food' and not similar things in a photo: circular or spherical shape, edible, has toppings or fillings, potentially sliced into circular pieces.", 38], "droplets": ["Yes. 'Droplets' has a tangible appearance and refers to small liquid particles.\nA few things that are visually similar to 'droplets' but are not 'droplets' are:\tbubbles\tdew\tdust particles\tfog\tsnowflakes\nThere are several useful visual features to tell there are 'droplets' and not similar things in a photo:\tclearly defined round shape\treflection or refraction of light\ttranslucent appearance\ton a surface or in the air", 38], "bathroom vanity": ["Yes. 'Bathroom vanity' has a tangible appearance and is a furniture item.\nA few things that are visually similar to 'bathroom vanity' but are not 'bathroom vanity' are:\tkitchen island\tdesk\tworkbench\nThere are several useful visual features to tell there is 'bathroom vanity' and not similar things in a photo:\tusually placed in a bathroom\tcontains sink or basin\tfor storing toiletries\tand grooming products\thas mirror\toften made from wood, stone or metal", 38], "top hat": ["Yes. 'Top hat' is a visually concrete concept and is a kind of hat.\nA few things that are visually similar to 'top hat' but are not 'top hat' are:\tbowler hat\tcylinder hat\tfedora hat\tsombrero\nThere are several useful visual features to tell there is 'top hat' and not similar things in a photo: high and flat crown\tnarrow brim\tsatin or silk texture\tusually black or dark color", 38], "tennis dress": ["Yes. 'Tennis dress' has a tangible appearance and is a specific type of sports attire.\nA few things that are visually similar to 'tennis dress' but are not 'tennis dress' are:\tsundress\tswimsuit\tcheerleading uniform\tfitness attire\nThere are several useful visual features to tell there is 'tennis dress' and not similar things in a photo:\tknee-length or shorter\tsleeveless or short-sleeved\tv-neck or collared\ttop and skirt combination\tlight and breathable fabric, often white or brightly colored with accents such as stripes or pleats", 38], "grey sweater": ["Yes. 'Grey sweater' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'grey sweater' but are not 'grey sweater' are:\tgrey shirt\tgrey jacket\tgrey hoodie\tgrey cardigan\nThere are several useful visual features to tell there is 'grey sweater' and not similar things in a photo:\tlong sleeves\tknitted texture\tv-neck, crew neck or turtleneck design\tbutton or zipper details\tmade of wool or cotton material", 38], "zebra legs": ["Yes. 'Zebra legs' has a tangible appearance and is a specific part of an animal's body.\nA few things that are visually similar to 'zebra legs' but are not 'zebra legs' are:\thorse legs\tdonkey legs\t\nThere are several useful visual features to tell there is 'zebra legs' and not similar things in a photo:\tblack and white stripes\tpattern of stripes that is unique to zebras\tthick and muscular appearance \thave black hooves.", 38], "seat cushion": ["Yes. 'Seat cushion' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'seat cushion' but are not 'seat cushion' are:\tpillow\tmattress\tfoam pad\tyoga block\nThere are several useful visual features to tell there is 'seat cushion' and not similar things in a photo:\trectangular or square shape\tpadded or cushioned texture\tdesigned for sitting on\ttop surface may have a pattern or texture", 38], "guitar case": ["Yes. 'guitar case' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'guitar case' but are not 'guitar case' are:\tsuitcase\tbriefcase\tbackpack\ttoolbox\nThere are several useful visual features to tell there is 'guitar case' and not similar things in a photo:\tlong and narrow shape\tpadded interior for protection\thard exterior casing\tcarrying handle\thinged lid with latches for security", 38], "cargo shorts": ["Yes, 'cargo shorts' is a visually concrete concept that has a tangible appearance.\nA few things that are visually similar to 'cargo shorts' but are not 'cargo shorts' are:\tbasketball shorts\tboardshorts\tbiking shorts\tswim trunks\nThere are several useful visual features to tell there is 'cargo shorts' and not similar things in a photo:\tlarge, visible pockets on the legs\tofter made from a sturdy fabric like cotton or polyester\tabove or at the knee in length straight-leg cut rather than flared\teasily matching with sports shoes, polo shirts, or t-shirts.", 38], "plastic straw": ["Yes. 'Plastic straw' has a tangible appearance and is a type of single-use utensil.\nA few things that are visually similar to 'plastic straw' but are not 'plastic straw' are:\tpencil\tpen\twire\tstick\nThere are several useful visual features to tell there is 'plastic straw' and not similar things in a photo:\tthin and cylindrical shape\ttranslucent or transparent\tplastic material\twith a bend at one end", 38], "glass top": ["Yes, 'glass top' has a tangible appearance and describes a certain type of surface or furniture.\nA few things that are visually similar to 'glass top' but are not 'glass top' are:\tpolished marble surface\tplexiglass tabletop\tmirrored side table\nThere are several useful visual features to tell there is 'glass top' and not similar things in a photo:\n\n- Transparency: the glass top should allow light to pass through it and be somewhat see-through.\n- Reflection: if there is a reflection or glare from the surface, it is likely a glass top.\n- Smoothness: a glass top is usually very smooth and has a slick feel to it.\n- Fragility: due to the nature of glass, it is more fragile and can shatter easily.", 38], "lab": ["Yes. 'Lab' has a tangible appearance and refers to a laboratory or scientific workspace.\nA few things that are visually similar to 'lab' but are not 'lab' are:\toffice\tkitchen\tworkshop\nThere are several useful visual features to tell there is 'lab' and not similar things in a photo:\tscientific equipment such as microscopes, test tubes, and beakers\tresearchers or scientists in lab coats\tstainless steel surfaces and equipment\tshelves with specimens or chemicals", 38], "accents": ["No. 'Accents' is too vague or abstract to have a tangible appearance in a photo.\nThere are no things that are visually similar to 'accents' that are not 'accents'.\nTherefore, there are no useful visual features for distinguishing 'accents' from other things in a photo.", 38], "speedometer": ["Yes. 'Speedometer' has a tangible appearance and is a measurement device.\nA few things that are visually similar to 'speedometer' but are not 'speedometer' are:\tTachometer\tFuel gauge\tTemperature gauge\tOdometer\nThere are several useful visual features to tell there is 'speedometer' and not similar things in a photo:\tcircular shape\tmarked with numbers or units indicating speed\tneedle pointing towards the numbers or units\tmounted on a dashboard inside a vehicle", 38], "bare tree branches": ["Yes. 'Bare tree branches' has a tangible appearance and is a natural element.\nA few things that are visually similar to 'bare tree branches' but are not 'bare tree branches' are:\tdried twigs\tfallen leaves\tspider webs\tfence wires\nThere are several useful visual features to tell there are 'bare tree branches' and not similar things in a photo:\telongated, woody structure\tbranching from a central trunk\tno leaves or foliage\trough texture and irregular shape.", 38], "advertisement banner": ["Yes. 'Advertisement banner' has a tangible appearance and is a type of visual advertising.\nA few things that are visually similar to 'advertisement banner' but are not 'advertisement banner' are:\ttraffic sign\tbillboard\tposter\tflag\t\nThere are several useful visual features to tell there is 'advertisement banner' and not similar things in a photo:\tplaced horizontally\tor vertically\ttext or images promoting a product or a brand\tvisible logos or slogans\tspecific sizes or dimensions-based on web or physical media.", 38], "elbow pads": ["Yes. 'Elbow pads' has a tangible appearance and is a type of protection gear.\nA few things that are visually similar to 'elbow pads' but are not 'elbow pads' are:\twrist guards\tknee pads\tshin guards\tankle braces\nThere are several useful visual features to tell there is 'elbow pads' and not similar things in a photo:\tpads designed specifically for elbows\tcircular shape\tpadding or cushioning covering the elbow joint\tsecured with straps or velcro", 38], "appliances": ["Yes. 'Appliances' has a tangible appearance and refers to electrical or mechanical machines used in households.\nA few things that are visually similar to 'appliances' but are not 'appliances' are:\ttools \tmachinery \tvehicles\n There are several useful visual features to tell there is 'appliances' and not similar things in a photo:\telectrical or mechanical machines \tused in households \tserve a specific purpose, such as cooking, cleaning, or heating.", 38], "array": ["No. 'Array' is too vague or abstract to be visually distinguished in a photo. \n\nHowever, if referring to a specific type of array, such as a solar panel array, then an answer can be given. \n\nA few things that are visually similar to a 'solar panel array' but are not an 'array' are:\trooftop shingles\trooftop windows\trooftop vents\tmirrors\n\nUseful visual features for distinguishing a 'solar panel array' from the listed similar things in a photo are:\trectangular shapes\tsimilar sized rectangular shapes\tlack of breaks or gaps in between the rectangular shapes\tblack or dark blue hue in color.", 38], "stew": ["Yes. 'Stew' has a tangible appearance and is a type of dish.\nA few things that are visually similar to 'stew' but are not 'stew' are: soup, curry, chili, casserole, spaghetti bolognese\nThere are several useful visual features to tell there is 'stew' and not similar things in a photo:\n- Meat or vegetables cooked in liquid or gravy, presented in a bowl or pot\n- Thick and hearty consistency, with substantial chunks of meat and vegetables visible\n- Spoon or ladle present for serving", 38], "button shirt": ["Yes. \"Button shirt\" has a tangible appearance and is an article of clothing.\nA few things that are visually similar to \"button shirt\" but are not \"button shirt\" are:\tblouse, tunic, sweater, coat\nThere are several useful visual features to tell there is \"button shirt\" and not similar things in a photo:\t\n1. It has a collar.\n2. It has a button-front closure. \n3. It is designed to be tucked into trousers or a skirt. \n4. Its sleeves button or cuff at the wrist.", 38], "pizza tray": ["Yes. 'Pizza tray' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'pizza tray' but are not 'pizza tray' are:\tbaking sheet\tmuffin pan\troasting pan\tlarge plate\nThere are several useful visual features to tell there is 'pizza tray' and not similar things in a photo:\tcircular or rectangular in shape\tflat bottomed\tmetallic surface with tiny holes or non-stick coating\twide enough for a pizza", 38], "brown grass": ["Yes. 'Brown grass' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'brown grass' but are not 'brown grass' are:\tdry leaves\tdry brush\tdried flowers\nThere are several useful visual features to tell there is 'brown grass' and not similar things in a photo:\tlong and narrow blades\tbrown or yellow color\tdry or wilted appearance\tgrowing in a grassy area or field", 38], "hurdle": ["Yes. 'Hurdle' has a tangible appearance and is an object used in sports.\nA few things that are visually similar to 'hurdle' but are not 'hurdle' are:\tpole\tvault\tbarrier\tfence\nThere are several useful visual features to tell there is 'hurdle' and not similar things in a photo:\trectangular-shaped object\tmade of wood, plastic, or metal\tstanding upright on two legs\tintentionally placed at specific intervals along a track to be jumped over by athletes.", 38], "silver box": ["Yes. 'Silver box' has a tangible appearance and describes a specific type of container.\nA few things that are visually similar to 'silver box' but are not 'silver box' are:\tsteel container\tmetal tool-kit\tdecorative metal chest\nThere are several useful visual features to tell there is 'silver box' and not similar things in a photo:\trectangle shaped\tmade of silver or silver-colored material\thas a lid or closure\thollow interior", 38], "bear eye": ["Yes. 'Bear eye' has a tangible appearance and is a specific part of a bear's anatomy.\nThere are no things that are visually similar to 'bear eye' but are not 'bear eye'.\nSome useful visual features for distinguishing 'bear eye' from similar things in a photo include:\tround eyeball\tpupil (usually black)\teyelid-shaped fur and skin around the eye\tsometimes a shiny or glossy appearance", 38], "projector screen": ["Yes. 'Projector screen' has a tangible appearance and is a type of screen used in projection systems.\nA few things that are visually similar to 'projector screen' but are not 'projector screen' are:\ttelevision screen\tlaptop screen\tmovie theater screen\nThere are several useful visual features to tell there is 'projector screen' and not similar things in a photo:\trectangular in shape\twhite or light gray\tcolor, reflecting light\tprojecting an image with a projector\twith or without a stand or mounting brackets.", 38], "leafy vegetable": ["Yes. 'Leafy vegetable' has a tangible appearance and is a type of plant used for food.\nA few things that are visually similar to 'leafy vegetable' but are not 'leafy vegetable' are:\therbs\tgrass\tweeds\tsucculent plants\nThere are several useful visual features to tell there is 'leafy vegetable' and not similar things in a photo:\tlarge green leaves\tedible and used in food preparation\tgrowing from the ground or in planters\tbitter or Earthy smell", 38], "fuel tank": ["Yes. 'Fuel tank' has a tangible appearance and is a container for storing fuel.\nA few things that are visually similar to 'fuel tank' but are not 'fuel tank' are:\twater tank\tpropane tank\tchemical tank\toil tank\nThere are several useful visual features to tell there is 'fuel tank' and not similar things in a photo:\tconnected to a vehicle or a machine\tmetallic or plastic material\thas indicators for fuel level or type\tlabeled with fuel-related symbols or text", 38], "b": ["No. 'b' is too vague or abstract to be distinguished in a photo.", 38], "round cake": ["Yes. 'Round cake' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'round cake' but are not 'round cake' are:\tpizza\tpie\tpancake\ttart\nThere are several useful visual features to tell there is 'round cake' and not similar things in a photo:\tround shape\twith layers\tor frosting\ton a cake stand\tor plate\tcandles on top for a birthday cake", 38], "puppy": ["Yes. 'Puppy' has a tangible appearance and is a young dog.\nA few things that are visually similar to 'puppy' but are not 'puppy' are:\tkitten\thamster\trabbit\tguinea pig\nThere are several useful visual features to tell there is 'puppy' and not similar things in a photo:\tfour-legged\tfurry\tbody proportions resembling that of a dog\tbig round eyes\tthat look like a miniature dog", 38], "exhibit": ["Yes. 'Exhibit' has a tangible appearance and refers to a display of objects or artworks.\nA few things that are visually similar to 'exhibit' but are not 'exhibit' are:\tfurniture arrangements\tmerchandise displays\tpublic signage\tmuseum storage\tunorganized clutter\nThere are several useful visual features to tell there is an 'exhibit' and not similar things in a photo:\tdesigned space\twith focused objects\tarrangement with a curatorial perspective\tclear, informative labels or descriptions\tdisplay case or holder for each object", 38], "macbook": ["Yes. 'Macbook' has a tangible appearance and is a type of laptop computer.\nA few things that are visually similar to 'macbook' but are not 'macbook' are:\tWindows laptop\tChromebook\ttablet\t\nThere are several useful visual features to tell there is 'macbook' and not similar things in a photo:\tapple logo on the back\trounded edges\tsilver or space-gray color scheme\tQWERTY keyboard and touchpad screen\tdisplay with high resolution USB-C ports", 38], "drinking cup": ["Yes. 'Drinking cup' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'drinking cup' but are not 'drinking cup' are:\tsaucer\tbowl\tglass\tmug\nThere are several useful visual features to tell there is 'drinking cup' and not similar things in a photo:\tcylindrical or conical shape\thandle or grip opening for drinking\tside or base indentations for fingers used to grip the container", 38], "glass half": ["No. 'Glass half' is too vague or abstract to be distinguished in a photo. It needs further information to make sense, like 'glass half full of water' or 'glass half full of milk'.\nTherefore, there are no things visually similar to 'glass half' that are not 'glass half'.", 38], "library": ["Yes. 'Library' has a tangible appearance and is a type of building or room.\nA few things that are visually similar to 'library' but are not 'library' are: bookstore, study room, office, classroom\nThere are several useful visual features to tell there is 'library' and not similar things in a photo:\tshelves full of books\ttables and chairs\tfor reading and studying\tquiet and peaceful\tatmosphere\ta circulation desk\twhere books are checked in and out", 38], "roof building": ["Yes. 'Roof building' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'roof building' but are not 'roof building' are:\thouse\twall\ttent\tpavilion\nThere are several useful visual features to tell there is 'roof building' and not similar things in a photo:\tprotective structure over the top of a building\tstrategic shape to manage weather conditions, such as rain or snow\tdesign elements, such as shingles or tiles, that provide texture or pattern to the roof", 38], "parrots": ["Yes. 'Parrots' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'parrots' but are not 'parrots' are:\tmacaws\tcockatiels\tlovebirds\nThere are several useful visual features to tell there is 'parrots' and not similar things in a photo:\tbrightly colored feathers\tbeak shapes\tand size\tif the bird can talk or mimic sounds.", 38], "groups": ["No. 'Groups' is too vague or abstract to be distinguished in a photo.", 38], "paper wrapper": ["Yes. 'Paper wrapper' has a tangible appearance and is a type of packaging material.\nA few things that are visually similar to 'paper wrapper' but are not 'paper wrapper' are:\tplastic wrapper\tcloth wrapper\tbag\tnapkin\nThere are several useful visual features to tell there is 'paper wrapper' and not similar things in a photo:\tmade of paper or cardboard\thas folds or creases wrapped around an object or food item\thas a label or brand name printed on it\ttears easily when opened or removed.", 38], "slot": ["Yes. 'Slot' has a tangible appearance and is a type of opening or groove.\nA few things that are visually similar to 'slot' but are not 'slot' are:\tcrevice\tgroove\tjoint\tridge\tcrack\t\nThere are several useful visual features to tell there is 'slot' and not similar things in a photo:\trectangular shape\tnarrow and elongated\tsize and location, such as on a machine or a gaming device\tspecific symbols or images associated with gambling games", 38], "cloudy blue skies": ["Yes. 'Cloudy blue skies' has a tangible appearance and refers to a type of weather condition.\nA few things that are visually similar to 'cloudy blue skies' but are not 'cloudy blue skies' are:\tclear blue skies\tsunny blue skies\tovercast gray skies\nThere are several useful visual features to tell there is 'cloudy blue skies' and not similar things in a photo:\tblue tint to the sky\tpatches of clouds\tobscured sun\toranges or red hues during sunrise or sunset", 38], "metal wire": ["Yes. 'Metal wire' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'metal wire' but are not 'metal wire' are:\tstring\thair\tcable\tsnake\nThere are several useful visual features to tell there is 'metal wire' and not similar things in a photo:\tstiff\tmalleable\tmetallic\tthread-like appearance\tcan be twisted or bent into shapes or patterns", 38], "tennis courts": ["Yes. 'Tennis courts' has a tangible appearance and is a sports facility.\nA few things that are visually similar to 'tennis courts' but are not 'tennis courts' are: basketball courts, volleyball courts, badminton courts.\nThere are several useful visual features to tell there is 'tennis courts' and not similar things in a photo:\t\n- A flat and rectangular-shaped playing surface with a net dividing it\n- White lines marking the boundaries of the court and the service areas\n- Two halves for singles play or four quarters for doubles play \n- No goalposts, baskets, or other equipment specific to other sports.", 38], "ski board": ["Yes. 'Ski board' has a tangible appearance and is a type of winter sports equipment.\nA few things that are visually similar to 'ski board' but are not 'ski board' are:\tsnowboard\tsled\tskateboard\tski\nThere are several useful visual features to tell there is 'ski board' and not similar things in a photo:\tnarrow\tboard-shaped\tflat with upturned tips\tbinding for boots to attach\ttoe and heel rests\tfor use on snow", 38], "bunk bed": ["Yes. 'Bunk bed' has a tangible appearance and is a specific type of bed.\nA few things that are visually similar to 'bunk bed' but are not 'bunk bed' are:\tdaybed\tloft bed\ttrundle bed\tmurphy bed\nThere are several useful visual features to tell there is 'bunk bed' and not similar things in a photo:\ttwo or more beds stacked on top of each other\tconnected by a ladder or stairs\trelatively small size compared to other types of beds", 38], "cooking pot": ["Yes. 'Cooking pot' has a tangible appearance and is a kind of kitchenware.\nA few things that are visually similar to 'cooking pot' but are not 'cooking pot' are:\tpan\tcauldron\tbucket\nThere are several useful visual features to tell there is 'cooking pot' and not similar things in a photo:\tlarge and round shape\thandles on both sides\tmetallic or ceramic material\tlid on top", 38], "broken": ["No. 'Broken' is too vague or abstract to be distinguished in a photo. However, there can be visual cues in a photo that suggest something is broken, such as a shattered object or a disassembled device. \nIt's not applicable to name things that are visually similar to 'broken' but not 'broken' as it is a state of being and not a visual object.\nUseful visual features for distinguishing 'broken' in a photo include: a visibly damaged or shattered object, pieces of material or fragments separated from the original object, or a device or machinery that is no longer working or has fallen apart.", 38], "blackberries": ["Yes. 'Blackberries' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'blackberries' but are not 'blackberries' are:\tblueberries\tgrapes\tplums\nThere are several useful visual features to tell there is 'blackberries' and not similar things in a photo:\tround or oblong shape\tdark purple or black color\tsmooth and shiny skin\twith small green leaves at the top", 38], "windshields": ["Yes. 'Windshields' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'windshields' but are not 'windshields' are:\tglass windows\tforward-facing screens in aircrafts\tor visors in helmets\nThere are several useful visual features to tell there is 'windshields' and not similar things in a photo:\tcurved shape\ttwo layers of glass, typically laminated together\tforward-facing and mounted on the body of a motor vehicle\tadjacent to the driver seats.", 37], "motorboat": ["Yes. 'Motorboat' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'motorboat' but are not 'motorboat' are:\tkayak\tcanoe\tfishing boat\tinflatable boat\tsailboat\nThere are several useful visual features to tell there is 'motorboat' and not similar things in a photo:\ta streamlined hull\tan outboard motor or inboard engine\ta steering wheel or tiller\ta windshield or canopy\tseats for passengers and a driver.", 37], "grey trunk": ["Yes. 'Grey trunk' has a tangible appearance and is a part of a tree or plant.\nA few things that are visually similar to 'grey trunk' but are not 'grey trunk' are:\tblack trunk\tbrown trunk\tconcrete pillar\nThere are several useful visual features to tell there is 'grey trunk' and not similar things in a photo:\ttree or plant bark\ttexture and pattern of bark\tgrey or silver color", 37], "grater": ["Yes. 'Grater' has a tangible appearance and is a kind of kitchen tool.\nA few things that are visually similar to 'grater' but are not 'grater' are:\tslicer\tmandoline\tgrinder\tzester\nThere are several useful visual features to tell there is 'grater' and not similar things in a photo: \thandle made of plastic or metal\tholes or sharp edges for shredding or grating food\tcylindrical or flat shape", 37], "bare legs": ["Yes. 'Bare legs' has a tangible appearance and refers to the state of having no clothing on the legs.\nA few things that are visually similar to 'bare legs' but are not 'bare legs' are:\tleggings, tights, or stockings\tpants or jeans\tanimal fur, skin, or scales\nThere is only one visual feature to distinguish 'bare legs' from the listed similar things in a photo:\texposed skin without any clothing or covering.", 37], "bathroom tile": ["Yes. 'Bathroom tile' has a tangible appearance and is a kind of covering on the bathroom wall or floor.\nA few things that are visually similar to 'bathroom tile' but are not 'bathroom tile' are:\tlinoleum\tcarpet\twood flooring\t\nThere are several useful visual features to tell there is 'bathroom tile' and not similar things in a photo:\tsquare or rectangular in shape\toften has a glossy or matte finish\ttypically made of ceramic, porcelain or stone\tcan have various colors or patterns, including solid colors or intricate designs.", 37], "urn": ["Yes. 'Urn' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'urn' but are not 'urn' are:\tvase\tpot\tjug\tbowl\tteapot\nThere are several useful visual features to tell there is 'urn' and not similar things in a photo:\ttall with a narrow neck\tlarge and round opening\tbulbous body\twith or without handles\tmade of metal, ceramic or stone\tbe used to hold ashes or flowers.", 37], "shoulder strap": ["Yes. 'Shoulder strap' has a tangible appearance and is a kind of strap.\nA few things that are visually similar to 'shoulder strap' but are not 'shoulder strap' are:\tcolored ribbon\tbelt\tcord\tnecklace\nThere are several useful visual features to tell there is 'shoulder strap' and not similar things in a photo:\tattached to a bag or a garment\tworn over the shoulder\ta width of an inch or more\tmade of sturdy material, like leather or nylon", 37], "plastic glass": ["Yes. 'Plastic glass' has a tangible appearance and is a type of container made of plastic material.\nA few things that are visually similar to 'plastic glass' but are not 'plastic glass' are:\tmug\ttumbler\tjar\tbottle\nThere are several useful visual features to tell there is 'plastic glass' and not similar things in a photo:\ttranslucent or transparent\tplastic material\tcylindrical shape\ttapered at the bottom\twith or without handle or lid", 37], "silver tv": ["Yes. 'Silver TV' has a tangible appearance and refers to a TV set that is silver in color.\nA few things that are visually similar to 'silver TV' but are not 'silver TV' are:\tblack TV\twhite TV\tgold TV\tTVs of any color with silver edges or stands\nThere are several useful visual features to tell there is 'silver TV' and not similar things in a photo:\tsilver color\taccompanying remote\tcontrol buttons on the front or side\tof proper size and shape to be a TV\tscreen display of a TV program or film.", 37], "blue tie": ["Yes. 'Blue tie' has a tangible appearance and is an item of clothing.\nA few things that are visually similar to 'blue tie' but are not 'blue tie' are:\tblue scarf\tblue belt\tblue fabric\tblue ribbon\nThere are several useful visual features to tell there is 'blue tie' and not similar things in a photo:\tlong and narrow\tsymmetric\twith a knot around the collar of a shirt\tof a solid blue color, or with blue as the dominant color.", 37], "valance": ["Yes. 'Valance' has a tangible appearance and is a decorative element used in window treatments.\nA few things that are visually similar to 'valance' but are not 'valance' are:\tcurtaingarment\them\tbinding\nThere are some useful visual features to tell there is 'valance' and not similar things in a photo, which are:\thorizontal fabric section at the top of a window frame\tno vertical panels or pleats\taesthetic pattern or design may be present", 37], "patio table": ["Yes. 'Patio table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'patio table' but are not 'patio table' are:\tdining table\tdesk\tcard table\nThere are several useful visual features to tell there is 'patio table' and not similar things in a photo:\toutdoor furniture\tstyle, size, and appearance typically associated with patio decor\tglass or metal surface\tfor outdoor use (e.g., weather-resistant materials)", 37], "locomotive": ["Yes. 'Locomotive' has a tangible appearance and is a type of train.\nA few things that are visually similar to 'locomotive' but are not 'locomotive' are:\ttracks\twagons\tsubway trains\nThere are several useful visual features to tell there is 'locomotive' and not similar things in a photo:\tfront engine with a smokestack\tconnected to other train cars\twheels and tracks\tpuffs of smoke or steam from the smokestack", 37], "orange tag": ["Yes. 'Orange tag' has a tangible appearance and is a label or tag that is colored orange.\nA few things that are visually similar to 'orange tag' but are not 'orange tag' are:\tyellow tag\tgreen tag\tpink tag\tsticker\tcaution sign\nThere are several useful visual features to tell there is 'orange tag' and not similar things in a photo:\tbright orange color\trectangle or square shape\tpaper or plastic material\twritten information or numbers on the tag.", 37], "wooden pier": ["Yes. 'Wooden pier' has a tangible appearance and is a structure that extends into a body of water.\nA few things that are visually similar to 'wooden pier' but are not 'wooden pier' are:\tjetty\tdock\tboardwalk\nThere are several useful visual features to tell there is 'wooden pier' and not similar things in a photo:\twooden planks arranged in a linear pattern\textending out into a body of water\tfacilitates docking of boats and ships in deep water\tcould have a railing or handrails to provide safety to visitors to the pier", 37], "glass jars": ["Yes. 'Glass jars' has a tangible appearance and is a container made of glass.\nA few things that are visually similar to 'glass jars' but are not 'glass jars' are:\tbottles\tvases\tglasses\tteacups\nThere are several useful visual features to tell there is 'glass jars' and not similar things in a photo:\tjar-shaped\tcontainer made of glass\tremovable lids or caps\tclear or transparent material that allows seeing the contents inside\tsmall opening at the top of the jar to allow scooping or pouring of contents.", 37], "daisies": ["Yes. 'Daisies' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'daisies' but are not 'daisies' are:\tSunflowers\tMarigolds\tDaffodils\tLilies\tchrysanthemums\nThere are several useful visual features to tell there is 'daisies' and not similar things in a photo:\twhite petals with a yellow center\tgrowing in small clusters\tstems are long and thin\twith leaves that grow alternate on the stem", 37], "pink pillow": ["Yes. 'Pink pillow' has a tangible appearance and is a type of pillow with the color pink.\nA few things that are visually similar to 'pink pillow' but are not 'pink pillow' are:\tred pillow\tpink cushion\tpink stuffed animal\tpink shirt\nThere are several useful visual features to tell there is 'pink pillow' and not similar things in a photo:\tsoft and fluffy rectangular shape\tpink color covering the entire surface of the pillow\tno visible zippers or buttons that indicate it can be opened or closed", 37], "omelette": ["Yes. 'Omelette' has a visually concrete appearance and is a type of dish.\nA few things that are visually similar to 'omelette' but are not 'omelette' are:\tscrambled eggs \tfrittata\nThere are several useful visual features to tell there is 'omelette' and not similar things in a photo:\tthin and flat shape\twith or without fillings, such as cheese or vegetables\tgolden brown and slightly crispy surface\tfolded over or rolled up appearance.", 37], "metal part": ["Yes. 'Metal part' has a tangible appearance and is a component made of metal.\nA few things that are visually similar to 'metal part' but are not 'metal part' are:\ttools\tbolts & nuts\tmachine parts\tnails & screws\nThere are several useful visual features to tell there is 'metal part' and not similar things in a photo:\tshiny or reflective surface\thard and durable texture\tmade of metal or metallic material\thas a specific shape or function in a larger object or structure.", 37], "snowy mountains": ["Yes. 'Snowy mountains' has a tangible appearance and refers to the mountain range covered with snow.\nA few things that are visually similar to 'snowy mountains' but are not 'snowy mountains' are:\tclouds\trocky mountains\nThere are several useful visual features to tell there are 'snowy mountains,' and not similar things in a photo:\tsnow-covered peaks\theight and slope of the mountains\tthe presence of a mountain range, not just one mountain", 37], "clutter": ["Yes. 'Clutter' has a tangible appearance and refers to a messy collection of items.\nA few things that are visually similar to 'clutter' but are not 'clutter' are:\torganized storage\tarray of neatly arranged objects\tgroup of items on a table or desk\nThere are some useful visual features to tell there is 'clutter' and not similar things in a photo:\tdisorganized and chaotic appearance\tno clear or logical organization\tpiling up of various items such as papers, clothes, toys, or dishes.", 37], "stuffed bear": ["Yes. 'Stuffed bear' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'stuffed bear' but are not 'stuffed bear' are:\tplush rabbit\tteddy dog\tpillow\tperson wearing a furry coat\nThere are several useful visual features to tell there is 'stuffed bear' and not similar things in a photo:\tbear-like appearance\tfur or plush covering\tstuffed with filling (cotton, wool, synthetic, etc.)\ttwo legs, two arms, and a head", 37], "bowl table": ["Yes. 'Bowl table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'bowl table' but are not 'bowl table' are:\tcoffee table\tside table\tdining table\nThere are several useful visual features to tell there is 'bowl table' and not similar things in a photo:\tshaped like a bowl or a shallow dish\tsurface with sloping edges\tand a flat bottom\tmay have a center stand or legs\tcan be made of a variety of materials, such as wood, metal, or glass", 37], "clock hand": ["Yes. 'Clock hand' has a tangible appearance and is a physical component of a clock.\nA few things that are visually similar to 'clock hand' but are not 'clock hand' are:\tpencil\tglue stick\tpen\tcamera lens\nThere are several useful visual features to tell there is 'clock hand' and not similar things in a photo:\tmetallic, thin rod from the center of the clock\ttooth-like edge\tarrow or pointer at the end of the rod\tlength & design of the hand (hour, minute or second hand)", 37], "newspaper box": ["Yes, 'newspaper box' has a tangible appearance and refers to a box for dispensing newspapers.\nA few things that are visually similar to 'newspaper box' but are not 'newspaper box' are:\t\nmailboxes, book drops, vending machines, trash cans\nThere are several useful visual features to tell there is 'newspaper box' and not similar things in a photo:\nshape and size designed to hold newspapers newspaper masthead or logo visible on the box cylindrical or rectangular in shape with angled top or slot for dispensing papers", 37], "control tower": ["Yes. 'Control tower' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'control tower' but are not 'control tower' are:\twatchtower\tlighthouse\toffice skyscraper\nThere are several useful visual features to tell there is 'control tower' and not similar things in a photo:\ttall tower or building with a bird's eye view of an airport or airfield\tcontrol room with windows for air traffic controllers\tantennas, radar, or other communication equipment on the roof or top of the building", 37], "action figure": ["Yes. 'Action figure' has a tangible appearance and is a kind of toy.\nA few things that are visually similar to 'action figure' but are not 'action figure' are:\tdolls\tstatues\tmannequins\tfigurines\nThere are several useful visual features to tell there is 'action figure' and not similar things in a photo:\thuman or humanoid shape\tjoints for articulation\taccessories such as weapons or clothing\tdecoration or branding based on popular media or characters", 37], "wristbands": ["Yes. 'Wristbands' has a tangible appearance and is a type of accessory worn on the wrist.\nA few things that are visually similar to 'wristbands' but are not 'wristbands' are:\twatches\tbracelets\tbangles\thair bands\nThere are several useful visual features to tell there is 'wristbands' and not similar things in a photo:\tmade of fabric, silicone or rubber\tflat, circular shape\tworn snugly around the wrist, but not too tight\toften have a design or text on them", 37], "tall grasses": ["Yes. 'Tall grasses' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'tall grasses' but are not 'tall grasses' are:\tshort grasses\tbushes\tsmall trees\nThere are several useful visual features to tell there is 'tall grasses' and not similar things in a photo:\tlong, thin stems and leaves\tgrowing upright and vertical\tmay form tufts\tor clumps\tof varying colors, such as green, brown or yellow.", 37], "pepsi": ["Yes. 'Pepsi' has a tangible appearance and is a specific brand of soda.\nA few things that are visually similar to 'pepsi' but are not 'pepsi' are:\tCoca-Cola\tMountain Dew\tDr. Pepper\tFanta\nThere are several useful visual features to tell there is 'pepsi' and not similar things in a photo:\tBlue, red and white logo\twith the word 'Pepsi' clearly visible\taluminum can or plastic bottle\tcontaining brown-colored soda.", 37], "skateboard park": ["Yes. 'Skateboard park' has a tangible appearance and is an outdoor recreational facility designed for skateboarding.\nA few things that are visually similar to 'skateboard park' but are not 'skateboard park' are:\tparking lots\tplaygrounds\tempty pools\nThere are several useful visual features to tell there is 'skateboard park' and not similar things in a photo:\tconcrete surfaces\twith or without ramps, pipes, rails or ledges\tskaters doing tricks or maneuvers", 37], "hatch": ["Yes. 'Hatch' has a tangible appearance and is a type of opening.\nA few things that are visually similar to 'hatch' but are not 'hatch' are:\tdoor\twindow\tlid\tpanel\nThere are several useful visual features to tell there is 'hatch' and not similar things in a photo:\tusually round or rectangular\tlocated on the floor, ceiling, or roof\tmay have a handle, latch, or lock\tmay have vents or grates\tmay have a ladder or stairway leading down", 37], "hangers": ["Yes. 'Hangers' has a tangible appearance and is a type of object used for hanging clothes.\nA few things that are visually similar to 'hangers' but are not 'hangers' are:\trods\tshelves\ttowel bars\thook racks\nThere are several useful visual features to tell there is 'hangers' and not similar things in a photo:\tslim and wire or plastic made\thook on top\tfor holding clothing items\tsuspension in a closet or wardrobe\tsystematic arrangement in a row", 37], "grey shorts": ["Yes. 'Grey shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'grey shorts' but are not 'grey shorts' are:\tgrey pants\tjeans\tgrey skirts\tgrey leggings\nThere are several useful visual features to tell there are 'grey shorts' and not similar things in a photo:\tshort length (above the knee)\tlight grey color\ttwo leg holes and a waistband\tmade of lightweight, breathable material like cotton or polyester", 37], "gates": ["Yes. 'Gates' has a tangible appearance and is a physical object that can be opened or closed to control access to an area.\nA few things that are visually similar to 'gates' but are not 'gates' are:\tfences\tdoors\tbarriers\twalls\nThere are several useful visual features to tell there is 'gates' and not similar things in a photo:\ttwo or more pieces that can swing or slide to open and close\ta latch or locking mechanism\tcan be made of metal, wood, or other materials\tdesigned to control access to an area", 37], "shampoo": ["Yes. 'Shampoo' has a tangible appearance and is a liquid cleaning product for hair.\nA few things that are visually similar to 'shampoo' but are not 'shampoo' are:\tsoap\tconditioner\tbody wash\thand wash\nThere are several useful visual features to tell there is 'shampoo' and not similar things in a photo:\tusually comes in a plastic bottle\tlabeled \"shampoo\" or showing an image of hair\tis poured on the hand or directly on the hair\tcan have various colors and consistencies", 37], "step stool": ["Yes. 'Step stool' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'step stool' but are not 'step stool' are:\tchairs\tbenches\tladders\tblocks\nThere are several useful visual features to tell there is 'step stool' and not similar things in a photo:\tshort height (usually two to three steps)\tflat top step or platform\thandle or railing for support when climbing", 37], "warning sign": ["Yes. 'Warning sign' has a tangible appearance and is a type of sign used for caution.\nA few things that are visually similar to 'warning sign' but are not 'warning sign' are:\tdirection sign\tinformational sign\tparking sign\tbillboard\tadvertising sign\nThere are several useful visual features to tell there is 'warning sign' and not similar things in a photo:\tyellow or orange background\twith black or red lettering\tor black pictograms\tor both\tpotential danger or safety information\twording such as \"warning,\" \"caution,\" or \"danger\"\tclear and visible location and placement", 37], "merchandise": ["Yes. 'Merchandise' has a tangible appearance and refers to products for sale.\nA few things that are visually similar to 'merchandise' but are not 'merchandise' are:\tdisplay shelves\tstorage containers\tpackaging materials\tsigns and posters\nThere are several useful visual features to tell there is 'merchandise' and not similar things in a photo:\tProducts for sale\tpackaging with a brand or logo\tbarcode or other pricing information\tshopping bags or carts\ttraditional market or store settings", 37], "farmer": ["Yes. 'Farmer' has a tangible appearance and refers to a person who works in agriculture.\nA few things that are visually similar to 'farmer' but are not 'farmer' are:\tgardener\tlandscaper\toutdoorsman\tworker\nThere are several useful visual features to tell there is 'farmer' and not similar things in a photo:\twearing clothing typically associated with farming such as overalls, hats or boots\tworking in a field, on a farm or with livestock\tholding farming tools such as a pitchfork or shovel\tsurrounded by agriculture equipment such as tractors or plows", 37], "bell peppers": ["Yes. 'Bell peppers' has a tangible appearance and is a kind of vegetable.\nA few things that are visually similar to 'bell peppers' but are not 'bell peppers' are:\tapple\ttomato\tpomegranate\torange\nThere are several useful visual features to tell there is 'bell peppers' and not similar things in a photo:\thollow\twith three or four lobes\tsmooth outer texture\tbright green, yellow, orange, red or purple color", 37], "pink suitcase": ["Yes. 'Pink suitcase' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'pink suitcase' but are not 'pink suitcase' are:\tbackpack\tpurse\tbriefcase\tduffel bag\nThere are several useful visual features to tell there is 'pink suitcase' and not similar things in a photo:\toblong or rectangular shape\thard or soft shell\tpink color\thandle or wheels for carrying and rolling\ta zipper or clasp for opening and closing", 37], "handle bar": ["Yes. 'Handle bar' has a tangible appearance and is a part of a bicycle or motorcycle.\nA few things that are visually similar to 'handle bar' but are not 'handle bar' are:\tdoor handle\tkitchen cabinet handle\tshower handle\tdrawer pull\nThere are several useful visual features to tell there is 'handle bar' and not similar things in a photo:\tlocated on a bicycle or motorcycle\tbent shape\tforward-facing grips\tpadded covering on the grip area", 37], "spires": ["Yes. 'Spires' has a tangible appearance and refers to tall, pointed structures.\nA few things that are visually similar to 'spires' but are not 'spires' are:\tTowers\tPyramids\tObelisks\tMasts\nThere are several useful visual features to tell there are 'spires' and not similar things in a photo:\tTall and pointed structures\tGothic or religious architecture\tNarrow and thin appearance\tFound on top of buildings or towers\tflourishes at the top of the spire, such as crosses or weather vanes", 37], "number print": ["Yes. 'Number print' has a tangible appearance and refers to the visual representation of numbers.\nA few things that are visually similar to 'number print' but are not 'number print' are:\tletter print\tsymbols\tbarcode\tQR code\nThere are several useful visual features to tell there is 'number print' and not similar things in a photo:\tonly numbers and no letters\tor characters\twithout any additional markings (such as a barcode or QR code)\tclear, legible and recognizable digits", 37], "roads": ["Yes. 'Roads' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'roads' but are not 'roads' are:\tparking lots\tpathways\thiking trails\trailroad tracks\nThere are several useful visual features to tell there is 'roads' and not similar things in a photo:\twide and paved surface\tfor vehicles to travel on\twill have painted lines and markers such as lane-dividers and directional arrows\twill often have street lamps or signs", 37], "pane window": ["Yes. 'Pane window' has a tangible appearance and is a specific type of window.\nA few things that are visually similar to 'pane window' but are not 'pane window' are:\tfixed window\tsliding window\tcasement window\tjalousie window\nThere are several useful visual features to tell there is 'pane window' and not similar things in a photo:\trectangular or square shape\tframed by wooden or metal bars\tdivided into smaller sections by muntins or grilles\ttranslucent or transparent glass material.", 37], "vendor": ["No. 'Vendor' is too vague or abstract to be distinguished in a photo.", 37], "bonnet": ["Yes. 'Bonnet' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'bonnet' but are not 'bonnet' are:\that\thelmet\tcap\tcrown\nThere are several useful visual features to tell there is 'bonnet' and not similar things in a photo:\tsoft and floppy\tbrimmed or unbrimmed\thead-covering usually tied or fastened under the chin", 37], "round object": ["Yes, 'round object' has a tangible appearance.\nA few things that are visually similar to 'round object' but are not 'round object' are:\tball, sun, wheel, clock, cookie\nThere are several useful visual features to tell there is 'round object' and not similar things in a photo, such as:\tperfectly circular shape\tsmooth surface\tcurved edges\tand three-dimensionality", 37], "village": ["Yes. 'Village' has a tangible appearance and is a community of buildings and residents.\nA few things that are visually similar to 'village' but are not 'village' are:\ttown\tsuburbs\tfarm\tcity\nThere are several useful visual features to tell there is 'village' and not similar things in a photo:\ta small and compact collection of buildings\tgrouped closely together\ton a smaller scale than a city or town\twith a visual center, such as a village square or town hall.", 37], "trash receptacle": ["Yes, 'trash receptacle' has a visually concrete concept.\nA few things that are visually similar to 'trash receptacle' but are not 'trash receptacle' are: mailbox, donation box, recycling bin, vending machine.\nThere are several useful visual features to tell there is 'trash receptacle' and not similar things in a photo: cylindrical or rectangular shape, lid on top, trash or recycling symbol or text, usually made from plastic or metal.", 37], "mechanism": ["No. 'Mechanism' is too vague or abstract to be distinguished in a photo.", 37], "pink box": ["Yes. 'Pink box' has a tangible appearance and is a type of box that is pink in color.\nA few things that are visually similar to 'pink box' but are not 'pink box' are:\tred box\torange box\tmagenta box\tpurple box\tpink bags\nThere are several useful visual features to tell there is 'pink box' and not similar things in a photo:\toblong shape or a square shape\tpink in color\tmade out of cardboard or plastic\thas a lid or can be opened at the top or front.", 37], "ramekin": ["Yes. 'Ramekin' has a tangible appearance and is a kind of dish.\nA few things that are visually similar to 'ramekin' but are not 'ramekin' are:\tcustard cup\tsouffle dish\tmuffin tin\tshot glass\nThere are several useful visual features to tell there is 'ramekin' and not similar things in a photo:\tshallow and round\tceramic material\tmade for baking or serving individual portions of food", 37], "foot board": ["Yes. 'Foot board' has a tangible appearance and is a part of a bed.\nA few things that are visually similar to 'foot board' but are not 'foot board' are:\theadboard\tsideboard\tdresser\tcabinet\t\nThere are several useful visual features to tell there is 'foot board' and not similar things in a photo:\tlocated at the end of a bed\tattached to the bed\tframe and panels matching the bed's design and material\tvariation in height from the mattress\ttoe-kick at the bottom", 37], "topping": ["Yes. 'Topping' has a tangible appearance and is a kind of food item usually used to decorate desserts.\nA few things that are visually similar to 'topping' but are not 'topping' are:\tsauce\ticing\tfrosting\tpowdered sugar\nThere are several useful visual features to tell there is 'topping' and not similar things in a photo:\tsprinkles\tor small, colorful, edible items\tdecorating the top of a dessert\tmay be wet or dry", 37], "snowflakes": ["Yes. 'Snowflakes' has a tangible appearance and is a type of precipitation.\nA few things that are visually similar to 'snowflakes' but are not 'snowflakes' are:\tice crystals\tsalt grains\tbubbles\tfrost\nThere are several useful visual features to tell there is 'snowflakes' and not similar things in a photo:\tsix-pointed, symmetrical shape\twhite or transparent\tcolor\ttiny size\tcold environment\twherever snowflakes have fallen or formed.", 37], "diamonds": ["Yes. 'Diamonds' has a tangible appearance and is a type of precious stone.\nA few things that are visually similar to 'diamonds' but are not 'diamonds' are:\tquartz rock\tcrystal glass\treally shiny metal objects\nThere are several useful visual features to tell there are 'diamonds' and not similar things in a photo:\t \n- distinct, clear and sharp edges forming a complex shape: a diamond shape with pointed ends and a flat surface in between \n- facetted surface that sparkles with rainbow colors under light \n- hardness: if it can scratch common materials (like glass), it might be a diamond", 37], "tarmac road": ["Yes. 'Tarmac road' has a tangible appearance and is a type of roadway.\nA few things that are visually similar to 'tarmac road' but are not 'tarmac road' are:\tconcrete surface\tasphalt surface\tmud road\nThere are several useful visual features to tell there is 'tarmac road' and not similar things in a photo:\tdark color and smooth surface\tmade of tar and crushed stones\tor other similar materials\tstraight or curved line running through a landscape.", 37], "cloudy grey sky": ["Yes. 'Cloudy grey sky' has a tangible appearance.\nA few things that are visually similar to 'cloudy grey sky' but are not 'cloudy grey sky' are:\tsmoke\tfog\thaze\tdusty atmosphere\nThere are several useful visual features to tell there is 'cloudy grey sky' and not similar things in a photo:\tformation of clouds\tcolours of grey\ttotally or partially covered by cloud", 37], "grandfather clock": ["Yes. 'Grandfather clock' has a tangible appearance and is a type of timekeeping device.\nA few things that are visually similar to 'grandfather clock' but are not 'grandfather clock' are:\twall clock\tmantel clock\tdesk clock\twatch tower clock\nThere are several useful visual features to tell there is 'grandfather clock' and not similar things in a photo:\ttall wooden case\tornate carvings or decorations\tpendulum swinging back and forth\tbattery, winding or a heavy weight mechanism at the bottom\tor a distinctive chime sound", 37], "asphalt surface": ["Yes. 'Asphalt surface' has a tangible appearance and is a type of flooring or road surface.\nA few things that are visually similar to 'asphalt surface' but are not 'asphalt surface' are:\tconcrete surface\tdirt or soil surface\tgravel surface\tpaving stones\nThere are several useful visual features to tell there is 'asphalt surface' and not similar things in a photo:\tdark black or grey color\tsmooth and flat texture\treflection of light on the surface\tgrid-like pattern of small stones or aggregate", 37], "bartender": ["Yes. 'Bartender' has a tangible appearance and is a type of job or profession.\nA few things that are visually similar to 'bartender' but are not 'bartender' are:\twaiter\twaitress\tchef\tbarista\t\nThere are several useful visual features to tell there is 'bartender' and not similar things in a photo:\twearing a uniform, such as a vest, bow tie, or apron\tbehind a bar with bottles and glasses\tpreparing or serving drinks to customers\tspeaking with customers and taking orders\thaving a professional demeanor and attitude.", 37], "bird cage": ["Yes. 'Bird cage' has a tangible appearance and is a container designed to hold birds.\nA few things that are visually similar to 'bird cage' but are not 'bird cage' are:\thamster cage\ttrap\tbox\tfence\nThere are several useful visual features to tell there is 'bird cage' and not similar things in a photo:\twires or bars for perching\torbs for feeding\tand a water bowl\tdoor for entrance and exit\thandle for carrying", 37], "butterflies": ["Yes. 'Butterflies' has a tangible appearance and is a type of insect.\nA few things that are visually similar to 'butterflies' but are not 'butterflies' are:\tmoths\tbees\thoverflies\tdragonflies\nThere are several useful visual features to tell there is 'butterflies' and not similar things in a photo:\tsymmetrical, colorful wings\tnarrow and elongated body\tlegs and antennae\tdelicately textured wings", 37], "entry": ["Yes. 'Entry' has a tangible appearance and is a physical location.\nA few things that are visually similar to 'entry' but are not 'entry' are:\twindows\twalls\tpassages\tdoors\nThere are several useful visual features to tell there is 'entry' and not similar things in a photo:\ta door or a gate\ta threshold or a step\tthe space between the inside and the outside of a building\twelcoming features such as doormats, rugs, or plants", 37], "capped mountains": ["Yes. 'Capped mountains' has a tangible appearance and refers to mountains covered in snow.\nA few things that are visually similar to 'capped mountains' but are not 'capped mountains' are:\tclouds\trock formations\twhite sand\tdesert landscapes\nThere are several useful visual features to tell there is 'capped mountains' and not similar things in a photo:\ttops of mountains covered with snow or ice\tthe rest of the mountain is not snow-covered or just partially snow-covered\ttop of the mountain takes up a significant amount of the photo.", 37], "wire fencing": ["Yes. 'Wire fencing' has a tangible appearance and is a kind of barrier made of wires.\nA few things that are visually similar to 'wire fencing' but are not 'wire fencing' are:\thedge/bush wall\twooden fence\tdecorative metal fence\nThere are several useful visual features to tell there is 'wire fencing' and not similar things in a photo:\tthin wires arranged in a grid-like pattern\tmetallic appearance\tbarbed wires on top or between the wires\tunobstructed view through the wires", 37], "mans hair": ["Yes. 'Mans hair' has a tangible appearance and refers to the hair grown on a male scalp.\nA few things that are visually similar to 'mans hair' but are not 'mans hair' are:\twig\tfur\tgrass\nThere are several useful visual features to tell there is 'mans hair' and not similar things in a photo:\tvisible on the human scalp\tvarious lengths and styles\tcan be colored or dyed\tgrows from pores on the skin on the scalp.", 37], "stone fence": ["Yes. 'Stone fence' has a tangible appearance and is a physical structure made of stones used as a barrier.\nA few things that are visually similar to 'stone fence' but are not 'stone fence' are:\tstone wall\tstatue\tmountain\trock formation\nThere are several useful visual features to tell there is 'stone fence' and not similar things in a photo:\tlinear structure\tmade of stacked stones\tor rocks\tforming a wall\tused as a barrier\tor boundary between property", 37], "dense": ["No. 'Dense' is too vague or abstract to be distinguished in a photo.", 37], "highway sign": ["Yes. 'Highway sign' has a tangible appearance and is a kind of traffic sign.\nA few things that are visually similar to 'highway sign' but are not 'highway sign' are:\tadvertising billboards\troadside banners\tdirection signs for pedestrian areas\nThere are several useful visual features to tell there is 'highway sign' and not similar things in a photo:\tshape (usually rectangular or square)\tcolor (typically white, yellow, and black)\tlarge text or symbols (such as arrows, speed limits, or road information)\tpositioning (typically alongside or above the road)", 37], "support column": ["Yes. 'Support column' has a tangible appearance and is a kind of architectural element.\nA few things that are visually similar to 'support column' but are not 'support column' are:\tpillars\tobelisks\tsculptures\tlight posts\nThere are several useful visual features to tell there is 'support column' and not similar things in a photo:\tvertical\tusually made of concrete, stone, or metal\ttapered shape or circular cross-section\tsupporting part of a building or a structure", 37], "gold frame": ["Yes. 'Gold frame' has a tangible appearance and is a type of picture frame.\nA few things that are visually similar to 'gold frame' but are not 'gold frame' are:\twooden frame\tplastic frame\tsilver frame\nThere are several useful visual features to tell there is 'gold frame' and not similar things in a photo:\tgold-colored frame or trim\tshiny or reflective surface\tcarved or ornate design\tsurrounds a picture or artwork", 37], "water puddle": ["Yes. 'Water puddle' has a tangible appearance and is a collection of water on a surface.\nA few things that are visually similar to 'water puddle' but are not 'water puddle' are:\tpaint spill\tshadow\treflection\nThere are several useful visual features to tell there is 'water puddle' and not similar things in a photo:\tround or irregular shape\ton a surface\treflective or translucent appearance\twater ripples or waves surrounding it.", 37], "jet ski": ["Yes. 'Jet ski' has a tangible appearance and is a type of recreational watercraft.\nA few things that are visually similar to 'jet ski' but are not 'jet ski' are:\tboat\tcanoe\tkayak\tsurfboard\nThere are several useful visual features to tell there is 'jet ski' and not similar things in a photo:\tsmall size\tfor one or two riders\thandlebars and throttle\tforward-facing seat\tand footrest\tflat, planing hull to glide over the water\tjet-powered propulsion system\tthat shoots water from the back of the vehicle", 37], "sea gull": ["Yes. 'Sea gull' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'sea gull' but are not 'sea gull' are:\ttern\tpelican\talbatross\theron\nThere are several useful visual features to tell there is 'sea gull' and not similar things in a photo:\twhite or grey feathers\tlong wingspan\twebbed feet\tyellow beak\tred spot on the beak\tin-flight, may be seen hovering over water\twith a fish in its beak\tor standing/swimming by the shore", 37], "brick road": ["Yes. 'Brick road' has a tangible appearance and is a type of road made of bricks.\nA few things that are visually similar to 'brick road' but are not 'brick road' are:\tcobblestone road\tpaved road\tdirt road\nThere are several useful visual features to tell there is 'brick road' and not similar things in a photo:\tdistinctive rectangular bricks\tarranged in a consistent pattern\treddish-brown color", 37], "tall mountains": ["Yes. 'Tall mountains' has a tangible appearance and refers to a physical landscape feature.\nA few things that are visually similar to 'tall mountains' but are not 'tall mountains' are:\thills\tbuildings\tcliffs\tpiles of dirt\thuge trees\nThere are several useful visual features to tell there are 'tall mountains' and not similar things in a photo:\thigh altitude\tsnow or rocky peaks\thuge size compared to surrounding landscape steep slopes or inclines\tlong, continuous ridge lines.", 37], "bird legs": ["Yes. 'Bird legs' has a tangible appearance and is a type of animal leg.\nA few things that are visually similar to 'bird legs' but are not 'bird legs' are:\tindustrial crane legs\thuman legs\tfrog legs\nThere are several useful visual features to tell there are 'bird legs' and not similar things in a photo:\t\n- Scaly, featherless, or hairy skin depending on the type of bird\n- Long and thin shape with sharp, curved claws or talons\n- Able to bend backward at the knee joint", 37], "female skier": ["Yes. 'Female skier' has a tangible appearance and is a person engaged in the activity of skiing.\nA few things that are visually similar to 'female skier' but are not 'female skier' are: a female snowboarder, a female figure skater, a female ice hockey player.\nThere are several useful visual features to tell there is a 'female skier' and not similar things in a photo:\twearing skis, boots, and poles\tappropriate ski clothing, such as a jacket, pants, gloves, and helmet, etc.\tskiing on snow-covered slopes, with or without other skiers in the photo.", 37], "gold hands": ["Yes. 'Gold hands' has a tangible appearance and may refer to hands made of gold or hands wearing gold jewelry or gloves.\nA few things that are visually similar to 'gold hands' but are not 'gold hands' are:\tgloves with gold paint\torangutan hands\trobotic golden hands\nThere are several useful visual features to tell there are 'gold hands' and not similar things in a photo:\n\thands made entirely of gold or with gold plating gleaming color, prevalent in gold material\n\tdistinctive shapes and details of human hands", 37], "bald spot": ["Yes, 'bald spot' has a tangible appearance and refers to an area of the scalp with little or no hair.\nA few things that are visually similar to 'bald spot' but are not 'bald spot' are:\tbruise\tmole\tfreckle\tdirty patch\nThere are several useful visual features to tell there is 'bald spot' and not similar things in a photo:\ta patch of scalp with little or no hair\tsurrounded by hair or less hair than the surrounding area\tsmooth and shiny patch of skin", 36], "police man": ["Yes. 'Police man' has a tangible appearance and is a type of occupation in law enforcement.\nA few things that are visually similar to 'police man' but are not 'police man' are:\tsecurity guard\tmilitary officer\tbodyguard\nThere are several useful visual features to tell there is 'police man' and not similar things in a photo:\tuniform showing a badge or police insignia\thandcuffs or a gun equipped\that or helmet with \"police\" printed on it\tcar or motorcycle with a siren and flashing lights on top", 36], "snowy mountain": ["Yes. 'Snowy mountain' has a tangible appearance and is a type of natural landscape.\nA few things that are visually similar to 'snowy mountain' but are not 'snowy mountain' are:\tclouds\twhite sand dune\twhite capped waves\nThere are several useful visual features to tell there is 'snowy mountain' and not similar things in a photo:\tsnow or ice-covered peaks\ttall and rugged terrain\tevergreen trees or rocky cliffs at lower elevations\tcolder atmosphere with snowstorms or clear blue skies.", 36], "playing tennis": ["Yes. 'Playing tennis' has a tangible appearance and involves specific physical actions.\nA few things that are visually similar to 'playing tennis' but are not 'playing tennis' are:\tjumping\tsmashing\taerobics\tdancing\nThere are several useful visual features to tell there is 'playing tennis' and not similar things in a photo:\tnet\tracquets\tball\tcourt\tin a serving or receiving position\tswung the racquet to strike the ball", 36], "glow": ["No. 'Glow' is too vague or abstract to be distinguished in a photo.", 36], "glass window pane": ["Yes. 'Glass window pane' has a tangible appearance and is a component of a window.\nA few things that are visually similar to 'glass window pane' but are not 'glass window pane' are:\tskylight\tmetal screen\tpanelled door\tplastic cover\nThere are several useful visual features to tell there is 'glass window pane' and not similar things in a photo:\tclear glass\tsmooth surface\trectangular or square shape\tframed by a window frame\tor frameless", 36], "steel pole": ["Yes. 'Steel pole' has a tangible appearance and is a type of metal structure.\nA few things that are visually similar to 'steel pole' but are not 'steel pole' are:\tmetal fence\tpost\tstructural column\tpillar\nThere are several useful visual features to tell there is 'steel pole' and not similar things in a photo:\tmade of steel or metal\tcylindrical or square shape\tstanding upright\tfixed in the ground or attached to a structure\tno visible decorative elements or curves.", 36], "bathroom cabinet": ["Yes. 'Bathroom cabinet' has a tangible appearance is a type of furniture.\nA few things that are visually similar to 'bathroom cabinet' but are not 'bathroom cabinet' are:\tmedicine cabinet\tkitchen cabinet\tbookcase\tshelving unit\nThere are several useful visual features to tell there is 'bathroom cabinet' and not similar things in a photo:\tsmall, wall-mounted\tlocated in a bathroom or near a sink\thas shelves or drawers\tcontains toiletries and bathroom items\thandles or knobs for opening and closing the doors or drawers.", 36], "blue sock": ["Yes. 'Blue sock' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'blue sock' but are not 'blue sock' are:\tblue gloves\tblue hat\tblue scarf\tblue t-shirt\nThere are several useful visual features to tell there is 'blue sock' and not similar things in a photo:\tlong and tube-shaped\tcotton, wool, or synthetic materials\tribbed top\tthat covers the foot and ankle\texcept the toes", 36], "metal pipes": ["Yes. 'Metal pipes' has a tangible appearance and refers to cylindrical tubes made of metal.\nA few things that are visually similar to 'metal pipes' but are not 'metal pipes' are:\tpvc pipes\tchimneys\tsmokestacks\tcannons\tsteel bars\nThere are several useful visual features to tell there is 'metal pipes' and not similar things in a photo:\tcylindrical shape\tmetal material\tpipes connected to each other\twith or without valves\torifice or opening at the ends.", 36], "brown curtains": ["Yes. 'Brown curtains' has a tangible appearance and is a specific type of window covering.\nA few things that are visually similar to 'brown curtains' but are not 'brown curtains' are:\tblinds\tshades\tdrapes\ttapestries\nThere are several useful visual features to tell there are 'brown curtains' and not similar things in a photo:\tmade of fabric\thanging from a rod or hooks\tbrown color or a shade of brown covering a window or a doorway", 36], "retriever": ["Yes. 'Retriever' has a tangible appearance and refers to a specific breed of dog.\nA few things that are visually similar to 'retriever' but are not 'retriever' are:\tgolden labrador/labrador retriever\tchocolate labrador/labrador retriever\tbernese mountain dog\tgolden doodle\nThere are several useful visual features to tell there is 'retriever' and not similar things in a photo:\tmedium to large breed\tgolden, black, or chocolate coat\tcolor of the coat is solid\tno wrinkled skin\tor long fur on the tail.", 36], "banana bunch": ["Yes. 'Banana bunch' has a tangible appearance and is a group of bananas growing together.\nA few things that are visually similar to 'banana bunch' but are not 'banana bunch' are: clusters of grapes, cherries, or blueberries; a group of plant leaves\nThere are several useful visual features to tell there is 'banana bunch' and not similar things in a photo:\tlong curved shape\tbright yellow color\tclustered group with multiple bananas attached to a stem", 36], "glass coffee table": ["Yes. 'Glass coffee table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'glass coffee table' but are not 'glass coffee table' are:\tglass end table\tglass dining table\tglass side table\tglass display case\nThere are several useful visual features to tell there is 'glass coffee table' and not similar things in a photo:\tlow to the ground\tused in a sitting area in a living room or family room\tusually rectangle-shaped with smooth edges\thas a glass top with a visible base or legs underneath", 36], "power line pole": ["Yes. 'Power line pole' has a tangible appearance and is a type of utility pole.\nA few things that are visually similar to 'power line pole' but are not 'power line pole' are:\tlight pole\tflag pole\tsport pole\nThere are several useful visual features to tell there is 'power line pole' and not similar things in a photo:\ttall and slender\tpower lines attached to it\tcrossbars or insulators on the top\tmetal or wooden structure", 36], "towel holder": ["Yes. 'Towel holder' has a tangible appearance and is a specific object used for holding towels.\nA few things that are visually similar to 'towel holder' but are not 'towel holder' are:\tcoat rack\thook\tshelf\tdoor knob\nThere are several useful visual features to tell there is 'towel holder' and not similar things in a photo:\tdesigned specifically to hold towels\tmounted on a wall or cabinet\tmultiple horizontal bars or loops\tfor holding multiple towels at once.", 36], "blind window": ["Yes. 'Blind window' has a tangible appearance and is a type of window with a cover or shade.\nA few things that are visually similar to 'blind window' but are not 'blind window' are:\tregular window\twith curtains\tscreen door\tshower door\nThere are several useful visual features to tell there is 'blind window' and not similar things in a photo:\tcover or shade that can be adjusted to let in different amounts of light\tslats or louvers\tthat can be tilted or fully opened or closed\thorizontal or vertical orientation.", 36], "train lights": ["Yes. 'Train lights' has a tangible appearance and is a type of light on a train.\nA few things that are visually similar to 'train lights' but are not 'train lights' are:\tcar headlights\tbicycle lights\ttraffic lights\tflashlights\nThere are several useful visual features to tell there are 'train lights' and not similar things in a photo:\ttwo or more lights on each end of the train\tusually white or red\tin a specific pattern or arrangement\tbright and visible from a distance", 36], "truck bed": ["Yes. 'Truck bed' has a tangible appearance and is a part of a pick-up truck.\nA few things that are visually similar to 'truck bed' but are not 'truck bed' are:\tboat bed\ttrailer bed\nThere are several useful visual features to tell there is 'truck bed' and not similar things in a photo:\trectangular shape\topen top\twith a tailgate\tconnected to a truck cab", 36], "bear paw": ["Yes. 'Bear paw' has a tangible appearance and refers to the paw of a bear.\nA few things that are visually similar to 'bear paw' but are not 'bear paw' are:\tdog paw\tcat paw\thuman hand\nThere are several useful visual features to tell there is 'bear paw' and not similar things in a photo:\t\nlarger size than cat or dog paw\tfive toes with visible claws\tthicker and furrier than human hand\tbrown or black in color", 36], "marquee": ["Yes. 'Marquee' has a tangible appearance and refers to a type of tent or structure.\nA few things that are visually similar to 'marquee' but are not 'marquee' are:\tawning\tcanopy\ttent\tshelter\nThere are several useful visual features that distinguish 'marquee' from similar things in a photo:\trectangular shape\twith a sloping roof or canopy\tsupport poles or frames\tdecorative or graphic elements, such as signage or lighting.", 36], "pink scarf": ["Yes. 'Pink scarf' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'pink scarf' but are not 'pink scarf' are:\tpink blanket\tpink towel\tpink cloth\tpink piece of paper\nThere are several useful visual features to tell there is 'pink scarf' and not similar things in a photo:\tmade of fabric or yarn\tlong and narrow\tworn around the neck or head\tpink in color\tmay have fringes or tassels at the ends", 36], "orange cup": ["Yes. 'Orange cup' has a tangible appearance and is a specific type of cup.\nA few things that are visually similar to 'orange cup' but are not 'orange cup' are:\tred cup\tyellow cup\tgreen cup\tcolored bowl\nThere are several useful visual features to tell there is 'orange cup' and not similar things in a photo:\n\torange color\n\tcup shape\n\thandles on both sides\n\tcylindrical form \n", 36], "croissants": ["Yes. 'Croissants' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'croissants' but are not 'croissants' are:\tdanish\tpuff pastry\tturnover\tmuffin\nThere are several useful visual features to tell there is 'croissants' and not similar things in a photo:\tcrescent-shaped\tpastry is flaky and layered\twith a golden-brown color on the outside\tand white, fluffy inside\thas visible layers of butter or margarine\thas a rich, buttery flavor", 36], "dog tail": ["Yes. 'Dog tail' has a tangible appearance and is a body part of a dog.\nA few things that are visually similar to 'dog tail' but are not 'dog tail' are:\tcat tail\tmouse tail\trat tail\tsquirrel tail\nThere are several useful visual features to tell there is 'dog tail' and not similar things in a photo:\tlarge size, depending on the breed\tvarious shapes, depending on the breed\thairy or furry covering\tmay be straight or curved or docked", 36], "silver utensil": ["Yes. 'Silver utensil' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'silver utensil' but are not 'silver utensil' are:\tsteel utensil\tchrome utensil\tsilver jewelry\tsilverware\nThere are several useful visual features to tell there is 'silver utensil' and not similar things in a photo:\t\n1. Handle attached to a head or blade\n2. A shiny silver color\n3. A recognizable shape (spoon, fork, knife, etc.)\n4. Used in the context of food preparation or consumption.", 36], "neighborhood": ["Yes. 'Neighborhood' has a tangible appearance and refers to a specific physical area.\nA few things that are visually similar to 'neighborhood' but are not 'neighborhood' are:\tcity\tdowntown\tsuburb\nThere are several useful visual features to tell there is 'neighborhood' and not similar things in a photo:\tresidential houses and buildings\ttree-lined streets\tor equivalent elements that convey a sense of a community and a belonging within a specific geographic area.", 36], "water cooler": ["Yes. 'Water cooler' has a tangible appearance and is a type of appliance.\nA few things that are visually similar to 'water cooler' but are not 'water cooler' are:\trefrigerator\tcoffee maker\tblender\tair conditioner\nThere are several useful visual features to tell there is 'water cooler' and not similar things in a photo:\tbottle or jug on top\tpaddle or spigot for dispensing water\tusually has a cup dispenser\tnarrower and taller than a refrigerator\tor has a distinctive shape and color", 36], "fedora": ["Yes. 'Fedora' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'fedora' but are not 'fedora' are:\ttrilby hat\tcowboy hat\tpanama hat\ttop hat\nThere are several useful visual features to tell there is 'fedora' and not similar things in a photo:\tmedium-width brim\tpinched crown\tdent in the top of the crown\tgroove or ribbon band around the base of the crown.", 36], "kangaroo": ["Yes. 'Kangaroo' has a tangible appearance and is a type of marsupial.\nA few things that are visually similar to 'kangaroo' but are not 'kangaroo' are:\twallaby\twombat\tdeer\tantelope\t\nThere are several useful visual features to tell there is 'kangaroo' and not similar things in a photo:\tlong and muscular tail\tpouched belly for carrying young\thind legs are longer and more muscular than front legs\tbrown-grey fur\tbig ears\tsometimes a red or gray face", 36], "pedals": ["Yes. 'Pedals' has a tangible appearance and refers to a foot-operated lever or control in machinery or musical instruments.\nA few things that are visually similar to 'pedals' but are not 'pedals' are:\tbuttons\tlevers\tknobs\tswitches\nThere are several useful visual features to tell there are 'pedals' and not similar things in a photo:\tfoot operated\tmoving up and down\tmetallic or plastic surface often with ridges or grooves.", 36], "shadow sand": ["No. 'Shadow sand' is too vague or abstract to be distinguished in a photo. There is no such thing as 'shadow sand' in the real world.", 36], "cases": ["Yes. 'Cases' has a tangible appearance and refers to a container or cover for something.\nA few things that are visually similar to 'cases' but are not 'cases' are:\tbags\tboxes\tpouches\tenvelopes\nThere are several useful visual features to tell there is 'cases' and not similar things in a photo:\thinged or latched lid\thard and sturdy shell\trigid walls to protect contents\tinternal compartments and organizers", 36], "cherry tomatoes": ["Yes. 'Cherry tomatoes' has a tangible appearance and is a type of small tomato.\nA few things that are visually similar to 'cherry tomatoes' but are not 'cherry tomatoes' are:\tgrapes\tberries\tcapsicum\nThere are several useful visual features to tell there is 'cherry tomatoes' and not similar things in a photo:\tsmooth, round shape\tred or yellow color\tsmall size (usually 1-2 cm)\tgrowing on a vine\tgrouped together in clusters on the vine", 36], "mounds": ["Yes. 'Mounds' has a tangible appearance and is a type of hill or raised area.\nA few things that are visually similar to 'mounds' but are not 'mounds' are:\thills\tmountains\tbumps\nThere are several useful visual features to tell there is 'mounds' and not similar things in a photo:\tconical or rounded shape\tless than 1,000 feet in elevation\tno visible rock or cliff faces\tcould be made by nature or human activity (such as burial mounds)", 36], "blue seats": ["Yes. 'Blue seats' has a tangible appearance and refers to seats that are colored blue.\nA few things that are visually similar to 'blue seats' but are not 'blue seats' are:\tblue chairs\tblue cushions\tblue benches\tblue stools\nThere are no distinct visual features that reliably distinguish between blue seats, blue chairs, blue cushions, blue benches or blue stools. However, the context in which the object is found and its specific design features may help to distinguish blue seats from the listed similar things in a photo. For example, blue seats in a stadium are likely to be distinct from blue chairs found in a home, based on their size and arrangement, and so on.", 36], "khaki": ["Yes. 'Khaki' has a tangible appearance and is a type of color and fabric.\nA few things that are visually similar to 'khaki' but are not 'khaki' are:\tbeige\ttan\tsandstone\tolive\nThere are several useful visual features to tell there is 'khaki' and not similar things in a photo:\tlight brown or tan color with a hint of green or yellow\thard-wearing cotton or wool fabric", 36], "skier skiing": ["Yes. 'Skier skiing' has a tangible appearance and is a person skiing.\nA few things that are visually similar to 'skier skiing' but are not 'skier skiing' are:\tsnowboarder\tskater\tsledder\nThere are several useful visual features to tell there is 'skier skiing' and not similar things in a photo:\twearing skis and boots\twearing a helmet and goggles\tpoles in the hands\tdownhill or cross-country skiing motion\tsnow on the ground or on the person", 36], "stone steps": ["Yes. 'Stone steps' has a tangible appearance and refers to a specific type of stairs.\nA few things that are visually similar to 'stone steps' but are not 'stone steps' are:\twooden steps\tbrick steps\tplain concrete steps\nThere are several useful visual features to tell there are 'stone steps' and not similar things in a photo:\tvisibly made of stone\thave a uniform shape and size\tare wide enough for a foot and have a noticeable depth\thave a texture and patterns specific to stone surfaces", 36], "concrete pillar": ["Yes. 'Concrete pillar' has a tangible appearance and is a type of architectural structure.\n\nA few things that are visually similar to 'concrete pillar' but are not 'concrete pillar' are:\tTrees\tColumns\tPoles\n\nThere are several useful visual features to tell there is 'concrete pillar' and not similar things in a photo:\t\nConcrete texture\tHard and straight surface\nUniform shape and size\nLocated inside or outside of a building\nMay have decorative elements", 36], "side boat": ["No. 'Side boat' is too vague or abstract to be distinguished in a photo. It is not a commonly used term in nautical or maritime contexts.", 36], "airport terminal": ["Yes. 'Airport terminal' has a tangible appearance and is a building.\nA few things that are visually similar to 'airport terminal' but are not 'airport terminal' are:\tmall\tcruise ship terminal\tbus station\ttrain station\nThere are several useful visual features to tell there is 'airport terminal' and not similar things in a photo:\tsigns for airlines and destinations\tbaggage carousels\tcheck-in counters\tgates and jet bridges\trunway and planes visible through windows", 36], "concrete block": ["Yes. 'Concrete block' has a tangible appearance and is a type of construction material.\nA few things that are visually similar to 'concrete block' but are not 'concrete block' are:\tbricks\tstone blocks\twood blocks\tfoam blocks\nThere are several useful visual features to tell there is 'concrete block' and not similar things in a photo:\tgray or beige color\trough surface\trectangular shape\twith holes or without holes\tlarge size compared to other building materials.", 36], "wipes": ["Yes. 'Wipes' has a tangible appearance and is a kind of cleaning tool.\nA few things that are visually similar to 'wipes' but are not 'wipes' are:\tnapkins\tpaper towels\trags\tfacial tissues\nThere are several useful visual features to tell there is 'wipes' and not similar things in a photo:\tpre-moistened or soaked in cleaning solution\tusually packaged in a plastic container or pouch\tcan come in different sizes, from small travel packets to large containers\twith specific functions, such as disinfecting or removing makeup", 36], "salad bowl": ["Yes. 'Salad bowl' has a tangible appearance and is a specific type of dish.\nA few things that are visually similar to 'salad bowl' but are not 'salad bowl' are:\tsoup bowl\tmixing bowl\tdinner plate\tserving platter\nThere are several useful visual features to tell there is 'salad bowl' and not similar things in a photo:\trelatively shallow bowl\tsize is typically larger than soup bowl, but not as big as serving platter\tmay have salad or greens inside\tthe rim is often wider than the base, allowing more space for tossing the salad dressing", 36], "concrete barrier": ["Yes. 'Concrete barrier' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'concrete barrier' but are not 'concrete barrier' are:\twall\tfence\tpole\tcolumn\nThere are several useful visual features to tell there is 'concrete barrier' and not similar things in a photo:\tmade of concrete\trectangular or square-shaped\thorizontal or vertical position\tsturdy and heavy-duty surface\tmost often used as traffic barriers", 36], "exit door": ["Yes. 'Exit door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'exit door' but are not 'exit door' are:\tentrance door\tbathroom door\tcloset door\tgarage door\nSome useful visual features to distinguish 'exit door' from the listed similar things in a photo could be:\tred or green 'push' or 'pull' sign\thandle instead of a knob or lock\tbar or latch across the middle of the door\tsignage or marking indicating an emergency exit", 36], "foot rest": ["Yes. 'Foot rest' has a tangible appearance and is a type of small furniture.\nA few things that are visually similar to 'foot rest' but are not 'foot rest' are:\tchair\tpillow\tottoman\nThere are several useful visual features to tell there is 'foot rest' and not similar things in a photo:\tsmall and compact\tsize and height are adjustable\tmade of wood, metal or foam\tpadded top for comfort\tfoot-shaped or designed for foot placement", 36], "trolley car": ["Yes. 'Trolley car' has a tangible appearance and is a type of transportation vehicle.\nA few things that are visually similar to 'trolley car' but are not 'trolley car' are:\ttrams\tsubway trains\tbuses\ttrains\nThere are several useful visual features to tell there is 'trolley car' and not similar things in a photo:\toverhead electric cables\twheels with flanges that grip a single track\twooden or metal body with open-air sides and roof\tunique bell or horn sound", 36], "bear ear": ["Yes. 'Bear ear' has a tangible appearance as it is a physical characteristic of a bear.\nA few things that are visually similar to 'bear ear' but are not 'bear ear' are: dog ear, cat ear, fox ear.\nThere are several useful visual features to tell there is 'bear ear' and not similar things in a photo:\tfurry and rounded ear\twith a rounded inner ear resembling a human ear\tprominent on top of their heads, usually pointed up.", 36], "purple blanket": ["Yes. 'Purple blanket' has a tangible appearance and is a specific color and type of cloth.\nA few things that are visually similar to 'purple blanket' but are not 'purple blanket' are:\tbed sheets\tcurtains\ttablecloths\nThere are several useful visual features to tell there is 'purple blanket' and not similar things in a photo:\tpurple color\tfuzzy or soft texture\table to cover an entire body", 36], "infield": ["Yes. 'Infield' has a tangible appearance and refers to the inner portion of a baseball or softball field.\nA few things that are visually similar to 'infield' but are not 'infield' are:\toutfield\tgarden\troad\nThere are several useful visual features to tell there is 'infield' and not similar things in a photo:\tdirt or clay surface\tdiamond shape\twhite lines and bases", 36], "storage container": ["Yes. 'Storage container' has a tangible appearance.\nA few things that are visually similar to 'storage container' but are not 'storage container' are:\ttoolbox\ttrash can\tsuitcase\tchest\nThere are several useful visual features to tell there is 'storage container' and not similar things in a photo:\trectangular or square shape\twith a lid or closure\tmade of plastic, metal, or wood\thas handles or a way to carry it\thas a label or marking indicating it is for storage or organization.", 36], "square windows": ["Yes. 'Square windows' has a tangible appearance.\nA few things that are visually similar to 'square windows' but are not 'square windows' are:\trectangular windows\tdoorways\tcut-outs in a wall\nThere are several useful visual features to tell there are 'square windows' and not similar things in a photo: perfect four equal sides 90-degree angles on each corner Glass or transparent material frames (wood, metal, etc.)", 36], "alcohol": ["No. 'Alcohol' is too abstract a concept to have a tangible appearance that can be distinguished merely through visual characteristics.\nA few things that are visually similar to 'alcohol' but are not 'alcohol' are: water, juice, tea, coffee, soda, vinegar, and other liquid beverages.\nThere are no useful visual features to tell there is 'alcohol' and not similar things in a photo. It requires chemical analysis and cannot be distinguished solely on visual characteristics.", 36], "toilet tissue": ["Yes. 'Toilet tissue' has a tangible appearance and is a kind of paper product.\nA few things that are visually similar to 'toilet tissue' but are not 'toilet tissue' are:\tkitchen paper\ttissue paper\twipes\tnotebook paper\nThere are several useful visual features to tell there is 'toilet tissue' and not similar things in a photo:\twhite or light-colored\tpaper texture\tperforated edges\tcylindrical shape\troll form or in stacks in a dispenser.", 36], "giraffe grazing": ["Yes. 'Giraffe grazing' has a tangible appearance and is a specific action of a giraffe.\nA few things that are visually similar to 'giraffe grazing' but are not 'giraffe grazing' are:\thorse grazing\tcow grazing\tantelope grazing\tdeer grazing\nThere are several useful visual features to tell there is 'giraffe grazing' and not similar things in a photo:\ttall neck\twith spots\tlong legs\tlarge, curved horns\tgrazing on tall trees or bushes", 36], "octopus": ["Yes. 'Octopus' has a tangible appearance and is a sea animal.\nA few things that are visually similar to 'octopus' but are not 'octopus' are:\tsquid\tjellyfish\nThere are several useful visual features to tell there is 'octopus' and not similar things in a photo:\teight long tentacles\twith suction cups and no bones\tbilateral symmetry\telongated body\twith a soft mantle\tand a large, bulbous head\tbig eyes\tclose to the beak", 36], "orange car": ["Yes. 'Orange car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'orange car' but are not 'orange car' are:\ttruck\tmotorcycle\tbus\tscooter\nThere are several useful visual features to tell there is 'orange car' and not similar things in a photo:\torange colored exterior\tfour wheels\ttwo or four doors\tand a car design\tSymbols of a car brand like BMW, Mercedes, Toyota, etc.", 36], "grey hair": ["Yes. 'Grey hair' has a tangible appearance and is a physical characteristic of hair.\nA few things that are visually similar to 'grey hair' but are not 'grey hair' are:\twhite hair\tplatinum hair\tsilver hair\nThere are several useful visual features to tell there is 'grey hair' and not similar things in a photo:\tdarker roots\tfaded or muted color along the entire hair shaft\tsparse distribution of color throughout the hair", 36], "baseball cleat": ["Yes. 'Baseball cleat' has a tangible appearance and is a type of athletic shoe.\nA few things that are visually similar to 'baseball cleat' but are not 'baseball cleat' are:\tsoccer cleat\tfootball cleat\tspikes\thiking boot\nThere are several useful visual features to tell there is 'baseball cleat' and not similar things in a photo:\tcleats or studs on the sole\tlow-cut design when compared to other cleats or sports shoes\tusually black, but can come in any color\tcommonly made of leather or synthetic materials\twith shoelaces", 36], "silver mirror": ["Yes. 'Silver mirror' has a tangible appearance and is a reflective surface.\nA few things that are visually similar to 'silver mirror' but are not 'silver mirror' are:\tregular glass\tcopper objects\tchrome objects\tshiny metal sheets\nThere are several useful visual features to tell there is 'silver mirror' and not similar things in a photo:\t\n- A mirrored surface that reflects light in a crisp and clear manner\n- A smooth and consistent reflective surface without any imperfections or scratches\n- A silver or metallic finish that provides the mirror's reflective quality\n- Usually, a rectangular or square shape, but could take on other shapes too.", 36], "streamer": ["Yes. 'Streamer' has a tangible appearance and is a type of decoration.\nA few things that are visually similar to 'streamer' but are not 'streamer' are:\tribbon\tgarland\tconfetti\tpaper\nThere are several useful visual features to tell there is 'streamer' and not similar things in a photo:\tlong and narrow shape\tbright colors or patterns\thanging from a wall, ceiling or object", 36], "sub": ["Yes. 'Sub' has a tangible appearance and is a type of sandwich.\nA few things that are visually similar to 'sub' but are not 'sub' are:\tsandwich\twrap\tpita\tbaguette\nThere are several useful visual features to tell there is 'sub' and not similar things in a photo:\tlong and narrow shape\tcrusty bread\tfilling such as cold cuts, cheeses, lettuce, and tomatoes", 36], "scenery": ["No. 'Scenery' is too vague or abstract to be distinguished in a photo. \n\nHowever, one might argue that certain aspects of scenery, such as landscapes or cityscapes, can be visually concrete concepts.\n\nA few things that are visually similar to 'scenery' but are not 'scenery' are: paintings, still images, green screens.\n\nTo distinguish scenery from other visual elements, useful visual features might include: natural elements like mountains, trees, rivers or architectural elements like buildings, roads, and man-made landmarks. The overall composition and arrangement of these elements can also help distinguish scenery from other visual elements.", 36], "wok": ["Yes. 'Wok' has a tangible appearance and is a type of cooking pan.\nA few things that are visually similar to 'wok' but are not 'wok' are:\tfrying pan\tskillet\tsaucepan\t\nThere are several useful visual features to tell there is 'wok' and not similar things in a photo:\twide, round cooking surface\twith or without a handle\tbowl-shaped\tdomed lid\ttypically made of metal or carbon steel", 36], "zebra eye": ["Yes. 'Zebra eye' has a tangible appearance and is a specific feature of a zebra's face.\nA few things that are visually similar to 'zebra eye' but are not 'zebra eye' are:\tother parts of the zebra's face\tpatterns on other animals' faces\tblack and white circular designs or objects\nThere are several useful visual features to tell there is 'zebra eye' and not similar things in a photo:\toval in shape\tdark brown or black in color\twith white or light-colored edges\tstrategically placed on the zebra's face in relation to other markings or stripes.", 36], "silver metal fence": ["Yes. 'Silver metal fence' has a tangible appearance and is a kind of boundary structure.\nA few things that are visually similar to 'silver metal fence' but are not 'silver metal fence' are:\tchain-link fence\twire fence\tgate\nThere are several useful visual features to tell there is 'silver metal fence' and not similar things in a photo:\tsilver color\tmetallic material\thorizontal or vertical bars\ttightly spaced bars that form a continuous surface.", 36], "glider": ["Yes. 'Glider' has a tangible appearance and is a kind of aircraft.\nA few things that are visually similar to 'glider' but are not 'glider' are:\tairplane\thelicopter\tdrone\tparachute\nThere are several useful visual features to tell there is 'glider' and not similar things in a photo:\tlarge wingspan\tno visible engine or propellers\thuman or animal pilot\tgliding or soaring through the air\tsleek and streamlined body shape.", 36], "everyone": ["No. 'Everyone' is too vague or abstract to be distinguished in a photo.", 36], "cages": ["Yes. 'Cages' has a tangible appearance and is a kind of enclosure.\nA few things that are visually similar to 'cages' but are not 'cages' are:\tbarriers\tfences\tgates\thurdles\nThere are several useful visual features to tell there are 'cages' and not similar things in a photo:\tmade of metal or wire\tcontainment of animals or objects\tlocked door or opening to enter and leave", 36], "wii controllers": ["Yes. 'Wii controllers' has a tangible appearance and is a specific type of gaming controller.\nA few things that are visually similar to 'wii controllers' but are not 'wii controllers' are:\tXbox controllers\tPlaystation controllers\tcomputer mouse or keyboard\nThere are several useful visual features to tell there is 'wii controllers' and not similar things in a photo:\tRectangular shape\twhite or black color\t\"Nintendo Wii\" logo on the controller\tStrap for the wrist\tButtons and directional stick on the front or top of the controller", 36], "thick forest": ["Yes. 'Thick forest' has a tangible appearance and could be defined as an area densely covered with trees and undergrowth. \nA few things that are visually similar to 'thick forest' but are not 'thick forest' are:\torchards\tjungles\tparks\nThere are several useful visual features to tell there is 'thick forest' and not similar things in a photo:\ttall and close trees\ta lot of leaves and branches\tlimited visibility due to foliage and undergrowth\tdappled light on the ground\twildlife hiding in the canopy and undergrowth", 36], "clock top tower": ["Yes. 'Clock top tower' has a tangible appearance and is a type of building or structure.\nA few things that are visually similar to 'clock top tower' but are not 'clock top tower' are:\tobservation tower\tchurch tower\tpagoda\tobelisk\nThere are several useful visual features to tell there is 'clock top tower' and not similar things in a photo:\ta clock face on its top\ttall structure with a pointed top\tand a clock and/or bell tower at the top of a building", 36], "dress shoe": ["Yes. 'Dress shoe' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'dress shoe' but are not 'dress shoe' are:\tsneakers\tboots\tsandals\tloafers\nThere are several useful visual features to tell there is 'dress shoe' and not similar things in a photo:\tleather or patent leather material\tclosed toe and heel\tsmooth and polished finish\tthin and sleek design\tlaces or buckles for closure", 36], "car seat": ["Yes. 'Car seat' has a tangible appearance and is a type of seat for car vehicles.\nA few things that are visually similar to 'car seat' but are not 'car seat' are:\tchair\tstool\tbench\t\nThere are several useful visual features to tell there is 'car seat' and not similar things in a photo:\tspecifically designed for use in a car\tattached with seat belts or anchors to the car\tframe that supports the seat\tbackrest for headrest\tcomment of car seat technician stamped on the side\tlabel of certification sticker on the back or bottom of the seat.", 36], "board shorts": ["Yes. 'Board shorts' has a tangible appearance and is a type of swimwear.\nA few things that are visually similar to 'board shorts' but are not 'board shorts' are:\ttrunks\tjogging shorts\thiking shorts\t\nThere are several useful visual features to tell there is 'board shorts' and not similar things in a photo:\tloose fit\tabove the knee\tlengthy\tfabric with vibrant colors and patterns\tdrawstring closure", 36], "pine needles": ["Yes. 'Pine needles' has a tangible appearance and refers to the needle-like leaves of a pine tree.\nA few things that are visually similar to 'pine needles' but are not 'pine needles' are:\tleaves\tfrom other trees or plants\nThere are several useful visual features to tell there are 'pine needles' and not similar things in a photo:\tneedle-shaped, thin leaves\tdark or light green color\tarranged in clusters, usually two to five in a bunch\ttypically longer than other types of needles or leaves found on conifers.", 36], "savannah": ["Yes. 'Savannah' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'savannah' but are not 'savannah' are:\tprairie\tgrassland\tsteppe\t\nThere are several useful visual features to tell there is 'savannah' and not similar things in a photo:\tscattered trees in the distance\trolling hills or flat landscapes\tyellow grasses or shrubs\twildlife such as elephants, antelopes, and lions in some regions", 36], "cigar": ["Yes. 'Cigar' has a tangible appearance and is a type of smoking product.\nA few things that are visually similar to 'cigar' but are not 'cigar' are:\tcigarette\tvape pen\tpen\nThere are several useful visual features to tell there is 'cigar' and not similar things in a photo:\tcylindrical shape\tdark brown color\trolled tobacco leaves at the center\tsmaller in diameter than a typical cigar", 36], "color blue": ["No. 'Color blue' is too vague or abstract to have a tangible appearance.\nThere is nothing visually similar to 'color blue', as it is a specific hue and not a physical object.\nThere are no useful visual features for distinguishing 'color blue' from other colors in a photo, as it depends on the context and the surrounding colors.", 36], "cigarette butt": ["Yes. 'Cigarette butt' has a tangible appearance and is a kind of waste.\nA few things that are visually similar to 'cigarette butt' but are not 'cigarette butt' are:\tsoda can tab\twrappers\tbottle caps\tcandy wrappers\tchewing gum\nThere are several useful visual features to tell there is 'cigarette butt' and not similar things in a photo:\tcylindrical or conical shape\tfilter\tdark or brown color\tash residue on the end", 36], "entry door": ["Yes. 'Entry door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'entry door' but are not 'entry door' are:\twindow\tgate\tgarage door\tfence door\nThere are several useful visual features to tell there is 'entry door' and not similar things in a photo:\thinged\tdoor knob or handle\tdeadbolt or lock\tpeephole or window for looking outside\tmaterial (usually made of wood or metal)\tframe around the door\tlarger than a window or gate, but smaller than a garage or fence door", 36], "silver flip phone": ["Yes. 'Silver flip phone' has a tangible appearance and is a type of mobile phone.\nA few things that are visually similar to 'silver flip phone' but are not 'silver flip phone' are:\tflip compact mirrors\tbest before date tags\tvintage silver wallets\tflip cameras\nThere are several useful visual features to tell there is 'silver flip phone' and not similar things in a photo:\trectangular shape\tflip design\tscreen and buttons on the inside\tsilver color\tno visible mirror, best before date tag, wallet, or camera features", 36], "skeleton": ["Yes. 'Skeleton' has a tangible appearance and is a bony structure of a living being.\nA few things that are visually similar to 'skeleton' but are not 'skeleton' are:\tmannequin\tzoology models\tHalloween costumes\tfossils\nThere are several useful visual features to tell there is 'skeleton' and not similar things in a photo:\tbony structure\thuman or animal-like appearance\tpresenting bones from different parts of the body\tno visible organs or muscles", 36], "scissor": ["Yes. 'Scissor' has a tangible appearance and is a cutting tool.\nA few things that are visually similar to 'scissor' but are not 'scissor' are:\tknife\trazor blade\tchisel\nThere are several useful visual features to tell there is 'scissor' and not similar things in a photo:\ttwo blades with pointed ends\tconnected by a pivot or screw\table to open and close to cut or trim\tmetal or plastic material", 36], "lemon wedge": ["Yes. 'Lemon wedge' has a tangible appearance and is a specific type of fruit slice.\nA few things that are visually similar to 'lemon wedge' but are not 'lemon wedge' are:\tlime wedge\torange slice\tgrapefruit slice\nThere are several useful visual features to tell there is 'lemon wedge' and not similar things in a photo:\tyellow color\toval or crescent shape\tthick enough to hold or squeeze to release juice.", 35], "evergreen": ["Yes 'evergreen' has a visually concrete concept and refers to trees that retain their leaves throughout the year.\nA few things that are visually similar to 'evergreen' but are not 'evergreen' are: deciduous trees, shrubs, and bushes.\nThere are several useful visual features to tell there is 'evergreen' and not similar things in a photo: leaves that appear to stay green throughout the year, also can be pointed or flat, and do not fall off the tree during seasonal changes.", 35], "metal base": ["Yes. 'Metal base' has a tangible appearance and is a type of structure or object made of metal that serves as a support or foundation for something else.\nA few things that are visually similar to 'metal base' but are not 'metal base' are:\tsteel frame\tmetal pole\tiron beam\tconcrete footing\nThere are several useful visual features to tell there is 'metal base' and not similar things in a photo:\tmade of metal\tsturdy and durable shape often consisting of legs or a pedestal\tsupporting or holding up another object or structure.", 35], "drinking water": ["Yes. 'Drinking water' has a tangible appearance and is a type of liquid.\nA few things that are visually similar to 'drinking water' but are not 'drinking water' are:\tsoda\tjuice\ttea\tcoffee\nThere are several useful visual features to tell there is 'drinking water' and not similar things in a photo:\tclear or slightly blue color\tno bubbles or foam\tno sugar or milk added\ttypically served in a glass, bottle or dispenser.", 35], "boys hair": ["Yes. 'Boys hair' has a tangible appearance and is a physical feature of a male child's head.\nA few things that are visually similar to 'boys hair' but are not 'boys hair' are:\thair of a male adult\thair of a female\tchild's hat\thighlights, color, or patterns in hair\nThere are several useful visual features to tell there is 'boys hair' and not similar things in a photo:\tshort or long hair\tuniform length or layers\thairstyle or cut that is typically worn by a male child", 35], "snake": ["Yes. 'Snake' has a tangible appearance and is a type of reptile.\nA few things that are visually similar to 'snake' but are not 'snake' are:\tworm\tlizard\trope\tdragon\nThere are several useful visual features to tell there is 'snake' and not similar things in a photo:\tslender and elongated body\tscales\tno limbs or very short legs\tforked tongue\thollow fangs\tfor movement, it undulates sideways along its body which gives it the appearance of crawling.", 35], "burnt crust": ["Yes. 'Burnt crust' has a tangible appearance and is a type of crust of a baked good that has been overcooked.\nA few things that are visually similar to 'burnt crust' but are not 'burnt crust' are: \tcharcoal\tsoil\touter layer of bread\tnot fully toasted bread\nThere are several useful visual features to tell there is 'burnt crust' and not similar things in a photo: \tdark brown or black color\trough or hard texture\tslightly uneven or wrinkled surface\tcharred or smoky smell", 35], "lamp posts": ["Yes. 'Lamp posts' has a tangible appearance and is a type of street furniture.\nA few things that are visually similar to 'lamp posts' but are not 'lamp posts' are:\ttraffic signals\tflagpoles\ttrees\nThere are several useful visual features to tell there is 'lamp posts' and not similar things in a photo:\tvertical pole, often made of metal\tor concrete\ta light fixture on top\tof the pole\tmounted along the side of roads or pathways", 35], "yellow vegetable": ["Yes. 'Yellow vegetable' has a tangible appearance and refers to vegetables that are predominantly yellow in color.\nA few things that are visually similar to 'yellow vegetable' but are not 'yellow vegetable' are:\tlemons\tbananas\tyellow flowers\tcorn\nThere are several useful visual features to tell there is 'yellow vegetable' and not similar things in a photo:\tflesh or interior of the vegetable is yellow or yellowish-green skin or exterior of the vegetable is yellow or yellowish-green\tfibrous or soft texture of the vegetable\tis commonly boiled, steamed or fried as a side dish at meals.", 35], "metal building": ["Yes. 'Metal building' has a tangible appearance and is a type of construction.\nA few things that are visually similar to 'metal building' but are not 'metal building' are:\tbrick building\twooden building\tconcrete building\tcontainer\nThere are several useful visual features to tell there is 'metal building' and not similar things in a photo:\tconstructed with metal materials like steel or aluminum\tshiny or reflective surface\tsymmetrical shape or design\tno visible bricks, wood, or concrete in the construction", 35], "orange container": ["Yes. 'Orange container' has a tangible appearance and is a type of container that is colored orange.\nA few things that are visually similar to 'orange container' but are not 'orange container' are:\tred container\tyellow container\torange box\tpumpkin\ttraffic cone\nThere are several useful visual features to tell there is 'orange container' and not similar things in a photo:\tcontainer shape\tcylindrical or rectangular shape\tbright orange color\tlid or cap on top\thandles on the sides or top", 35], "plenty": ["No. 'Plenty' is too vague or abstract to be visually distinguished in a photo.", 35], "ingredients": ["No. 'Ingredients' is too vague or abstract to be distinguished in a photo.", 35], "font": ["Yes. 'Font' has a tangible appearance and refers to a particular style of typeface.\nA few things that are visually similar to 'font' but are not 'font' are: handwriting, calligraphy, graffiti, doodles.\nThere are several useful visual features to tell there is 'font' and not similar things in a photo: uniformity in the shapes of letters, numbers, and punctuation marks; consistent spacing between letters; consistency in the weight and thickness of the letters; distinct styles and variations such as serif, sans-serif, bold, italic, script, and decorative.", 35], "purple jacket": ["Yes. 'Purple jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'purple jacket' but are not 'purple jacket' are:\tpurple shirt\tpurple sweater\tpurple vest\tpurple raincoat\nThere are several useful visual features to tell there is 'purple jacket' and not similar things in a photo:\tzipper or buttons\tpockets\tlong sleeves\tfabric texture\tjacket collar", 35], "lake water": ["Yes. 'Lake water' has a tangible appearance and is a type of water found in a lake.\nA few things that are visually similar to 'lake water' but are not 'lake water' are:\triver water\tocean water\tpool water\tbath water\nThere are several useful visual features to tell there is 'lake water' and not similar things in a photo:\tcalmer surface\tless salt or chlorine\tpotentially surrounded by trees or mountains\tcolor variation depending on depth and environment", 35], "stove burner": ["Yes. 'Stove burner' has a tangible appearance and is a part of a cooking device.\nA few things that are visually similar to 'stove burner' but are not 'stove burner' are:\tcandle\tfireplace grate\thot plate\nThere are several useful visual features to tell there is 'stove burner' and not similar things in a photo:\tcircular or square shape\twith visible grates or coils\ttogether with other burners\ton top of a stove or range\ttop is removable or covered with pans or pots", 35], "borders": ["Yes. 'Borders' has a tangible appearance and can refer to a physical or a political boundary.\nA few things that are visually similar to 'borders' but are not 'borders' are:\tlines\toutlines\tedges\tpaths\tfences\twalls\nThere are several useful visual features to tell there is 'borders' and not similar things in a photo:\tclearly defining the boundary between two areas or countries\tdifferences in color, texture, or landscape on either side of the border\tsigns, markers, or flags indicating the border", 35], "sconce": ["Yes. 'Sconce' has a tangible appearance and is a type of light fixture.\nA few things that are visually similar to 'sconce' but are not 'sconce' are:\tlamp\tchandelier\tbulb\tlight switch\nThere are several useful visual features to tell there is 'sconce' and not similar things in a photo:\tattached to a wall\thangs with the light facing upwards or downwards\tcovers a bulb or light sources with a shade or a cover", 35], "front tires": ["Yes. 'Front tires' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'front tires' but are not 'front tires' are:\trear tires\tbicycle tires\ttractor tires\ttire tracks\nThere are several useful visual features to tell there are 'front tires' and not similar things in a photo:\tlocated in front of the vehicle\toften power-steered\thave brake calipers behind or in front\tof them\tnarrower than the rear tires", 35], "tree ground": ["No. 'Tree ground' is too vague or abstract to be distinguished in a photo. It is not a commonly used term or object.", 35], "barcode": ["Yes. 'Barcode' has a tangible appearance and is a type of code used for identification.\nA few things that are visually similar to 'barcode' but are not 'barcode' are:\tZebra Stripes\tSerial Numbers\tMorse Code\tDNA Sequencing\tText Document\nThere are several useful visual features to tell there is 'barcode' and not similar things in a photo:\tseries of lines with different widths\twhite spaces between the lines\tusually rectangular in shape\tcontains numbers or letters to represent data or information.", 35], "gold handle": ["Yes. 'Gold handle' has a tangible appearance and refers to a handle that is made of gold material.\nA few things that are visually similar to 'gold handle' but are not 'gold handle' are:\tgold-colored plastic handle\tgolden paint-coated handle\tgold-plated steel handle\nThere are several useful visual features to tell there is 'gold handle' and not similar things in a photo:\tgolden or yellow color\tshiny and reflective\tsmooth and sleek surface\theavy and sturdy feeling to it when grasped", 35], "team name": ["No. 'Team name' is too vague or abstract to be distinguished in a photo.", 35], "shells": ["Yes. 'Shells' has a tangible appearance and is a natural object found on beaches or under the sea.\nA few things that are visually similar to 'shells' but are not 'shells' are:\tpebbles\tbones\trocks\tsand dollars\nThere are several useful visual features to tell there are 'shells' and not similar things in a photo:\tcurved or spiral shapes\twith uneven, ridged, or bumpy surface\tsymmetrical or asymmetrical patterns\tfound in or near water varieties of colors and patterns.", 35], "store window": ["Yes. 'Store window' has a tangible appearance and refers to the window displays of stores.\nA few things that are visually similar to 'store window' but are not 'store window' are: house windows, car windows, airplane windows, bus windows.\nThere are several useful visual features to tell there is 'store window' and not similar things in a photo: clearly designated storefront in the background, visible products or displays, lighting designed to highlight the products and attract passersby, store logos, sale signs, mannequins or models in the window display.", 35], "cross walk": ["Yes. 'Crosswalk' has a tangible appearance and is a kind of road marking.\nA few things that are visually similar to 'crosswalk' but are not 'crosswalk' are:\ttram rails\tzebra crossing\tpedestrian crossing without markings\nThere are several useful visual features to tell there is 'crosswalk' and not similar things in a photo:\tbold white lines\tpainted stripes on the road\tmarked with pedestrian signs or traffic lights\tno rails present on the road", 35], "sauce plate": ["Yes. 'Sauce plate' has a tangible appearance and is a type of dish.\nA few things that are visually similar to 'sauce plate' but are not 'sauce plate' are:\tsalad plates\tdessert plates\tappetizer plates\tbread plates\nThere are several useful visual features to tell there is 'sauce plate' and not similar things in a photo:\tsmall size\tcircular or oval shape\tshallow depth or slope\tarounds are a little bit raised, to avoid the sauce spilling\toutside the plate may also have a saucer", 35], "champagne glass": ["Yes. 'Champagne glass' has a tangible appearance and is a kind of glassware.\nA few things that are visually similar to 'champagne glass' but are not 'champagne glass' are:\twine glass\thighball glass\tcordial glass\tmartini glass\nThere are several useful visual features to tell there is 'champagne glass' and not similar things in a photo:\ttall and narrow with a slightly flared rim\tlong stem\ttransparent glass body", 35], "color brown": ["No. 'Color brown' is too vague or abstract to be distinguished in a photo.", 35], "bomb": ["Yes. 'Bomb' has a tangible appearance and is a destructive device.\nA few things that are visually similar to 'bomb' but are not 'bomb' are:\tcontainers, bottles\twith explosives inside\tsmoke detectors\twith explosives inside\tbattery packs\twith explosives inside\nThere are several useful visual features to tell there is 'bomb' and not similar things in a photo:\tcylindrical or spherical shape\tvisible wires, circuitry, or electronic components\tattached to a timer, clock, or remote control\tleaking or emanating smoke or gas\tvisible explosive material, like gunpowder or TNT", 35], "baseball shoes": ["Yes. 'Baseball shoes' has a tangible appearance and is a type of sports footwear.\nA few things that are visually similar to 'baseball shoes' but are not 'baseball shoes' are:\trunning shoes\tsneakers\tcleats\thiking shoes\nThere are several useful visual features to tell there is 'baseball shoes' and not similar things in a photo:\trounded rubber spikes on the sole\tleather or synthetic material\twith laces or straps\thigh ankle support", 35], "crock pot": ["Yes. 'Crock pot' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'crock pot' but are not 'crock pot' are:\tpressure cooker\trice cooker\tslow cooker\tpasta pot\nThere are several useful visual features to distinguish a 'crock pot' from the listed similar things in a photo:\tOval or round shape\twith a removable lid\tfor slow cooking over long periods of time\telectric or plug-in device", 35], "motorcyclists": ["Yes. 'Motorcyclists' has a tangible appearance and refers to people riding motorcycles.\nA few things that are visually similar to 'motorcyclists' but are not 'motorcyclists' are:\tpeople riding bicycles\tpeople riding scooters\tpeople riding mopeds\tor people walking or running\nThere are several useful visual features to tell there is 'motorcyclists' and not similar things in a photo:\thelmet\tleather jacket\tand riding pants\tmotorcycle boots\tor gloves\tmotorcycle driving suits\tthe presence of a motorcycle", 35], "baseball mit": ["Yes. 'Baseball mitt' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'baseball mitt' but are not 'baseball mitt' are:\tglove\tmitten\tpurse\nThere are several useful visual features to tell there is 'baseball mitt' and not similar things in a photo:\tleather or synthetic material\tfive-fingered design\tlaces on the back or fingers\twebbing or pocket in the center", 35], "tile bathroom floor": ["Yes. 'Tile bathroom floor' has a tangible appearance and refers to a specific type of flooring used in bathrooms.\nA few things that are visually similar to 'tile bathroom floor' but are not 'tile bathroom floor' are:\tmarble flooring\twooden flooring\tconcrete flooring\nThere are several useful visual features to tell there is 'tile bathroom floor' and not similar things in a photo:\tindividual square or rectangular tiles\tgrout lines between the tiles\tslip-resistant surface\twater-resistant material\ttypically found in bathrooms or other similar settings", 35], "circle sign": ["No. 'Circle sign' is too vague or abstract to be distinguished in a photo. \n\nHowever, assuming you meant the concept of a circular traffic sign:\n\nA few things that are visually similar to circular traffic signs but are not traffic signs are: \n-circular logos or branding\n-decorative circular art\n-circular clock faces\n\nUseful visual features for distinguishing circular traffic signs from similar things in a photo are: \n-the inclusion of text, instructions, or symbols related to traffic and safety\n-distinctive colors (e.g. red, yellow, green, or blue)\n-specific shapes or symbols (e.g. a red octagon for stop)", 35], "concrete slab": ["Yes. 'Concrete slab' has a tangible appearance and is a building material.\nA few things that are visually similar to 'concrete slab' but are not 'concrete slab' are:\ttile\tbrick\tpaving stone\tstucco\nThere are several useful visual features to tell there is 'concrete slab' and not similar things in a photo:\tplain and smooth surface\tlight grey color\trectangular or square shape\tthick and heavy appearance\tlack of patterns or textures in surface", 35], "multiple": ["No. 'Multiple' is too vague or abstract to have a tangible appearance, so it cannot be distinguished visually in a photo. \n\nNote: 'multiple' refers to having or consisting of more than one thing or element, which is a quantitative concept rather than a visual one.", 35], "whiteboard": ["Yes. 'Whiteboard' has a tangible appearance and is a type of writing surface.\nA few things that are visually similar to 'whiteboard' but are not 'whiteboard' are:\tchalkboard\tcorkboard\tpaper\tboard signage\t\nThere are several useful visual features to tell there is 'whiteboard' and not similar things in a photo:\tsmooth white surface\tdry-erase markers or pens\terasing tool at the bottom\tmounted or standing horizontally or vertically", 35], "bus destination sign": ["Yes. 'Bus destination sign' has a tangible appearance and is a type of signboard.\nA few things that are visually similar to 'bus destination sign' but are not 'bus destination sign' are:\tadvertisement board\tinformation kiosk\telectric message board\nThere are several useful visual features to tell there is 'bus destination sign' and not similar things in a photo:\trectangular shape\twith text displaying the name of the bus stop or the route\tno image or graphics\ton the front or on the side of the bus.", 35], "base ball player": ["Yes. 'Baseball player' has a tangible appearance and is a person who plays baseball.\nA few things that are visually similar to 'baseball player' but are not 'baseball player' are:\tathlete\ttennis player\tgolfer\trunner\nThere are several useful visual features to tell there is 'baseball player' and not similar things in a photo:\twearing a baseball uniform\twearing a baseball cap\tholding a baseball bat or a ball\tstanding on a baseball field or a diamond\tpositioned near bases or pitcher's mound", 35], "orange letters": ["Yes. 'Orange letters' has a tangible appearance and is a specific color of characters.\nA few things that are visually similar to 'orange letters' but are not 'orange letters' are:\tyellow letters\tred letters\tpink letters\nThere are several useful visual features to tell there are 'orange letters' and not similar things in a photo:\tbright orange\tcolor of the text, not the background or surrounding elements\tcrisp edges of the letters\tsans-serif, serif, or decorative typeface", 35], "soda cans": ["Yes. 'Soda cans' have a tangible appearance and are a type of beverage container.\nA few things that are visually similar to 'soda cans' but are not 'soda cans' are:\tbeer cans\tenergy drink cans\tpaint cans\tfruit cans\nThere are several useful visual features to tell there is 'soda cans' and not similar things in a photo:\trelatively small in size\tcylindrical shape\twith a pop-top opening or a pull-tab lid\tdecorated with a soda brand or label\tfilled with a carbonated beverage", 35], "haircut": ["Yes. 'Haircut' has a tangible appearance and is a type of personal grooming.\nA few things that are visually similar to 'haircut' but are not 'haircut' are:\ttrimming\thair color\tstyling\t\nThere are several useful visual features to tell there is 'haircut' and not similar things in a photo:\tcutting or trimming of hair\tscissors or clippers being used\thair falling to the ground or being swept away\tnewly styled or cropped hairstyle", 35], "infant": ["Yes. 'Infant' has a tangible appearance and refers to a human baby.\nA few things that are visually similar to 'infant' but are not 'infant' are:\tadults\tteenagers\tkids\tpuppets\tdolls\nThere are several useful visual features to tell there is 'infant' and not similar things in a photo:\tsmall-sized\tbody proportions (head to body ratio)\tsoft, chubby face\tlack of mature physical features (e.g. facial hair, wrinkles)\tlying or sitting in baby equipment (e.g. car seat, stroller, crib)", 35], "wooden dresser": ["Yes. 'Wooden dresser' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden dresser' but are not 'wooden dresser' are:\tcabinet\tshelf\tside table\ttv stand\nThere are several useful visual features to tell there is 'wooden dresser' and not similar things in a photo:\tcontains drawers or compartments\tfor clothing or other items\toften made of wood or wood-like materials\thas handles or knobs for opening drawers usually found in a bedroom or dressing room.", 35], "luggage rack": ["Yes. 'Luggage rack' has a tangible appearance and is a piece of furniture used for holding luggage or bags.\nA few things that are visually similar to 'luggage rack' but are not 'luggage rack' are:\tshelves\tbenches\tclothes racks\nThere are several useful visual features to tell there is 'luggage rack' and not similar things in a photo:\tmetal or wooden frame\thorizontal bars for placing luggage\ton the top of the bed or the back of the car.", 35], "cement bench": ["Yes. 'Cement bench' has a tangible appearance and is a kind of seating furniture.\nA few things that are visually similar to 'cement bench' but are not 'cement bench' are:\tmetal bench\twooden bench\tplastic bench\tstone bench\nThere are several useful visual features to tell there is 'cement bench' and not similar things in a photo:\tgrey color\tsleek and smooth surface\tno visible grains or textures\tbulky and heavy appearance%simple, geometric shapes", 35], "plastic chairs": ["Yes. 'Plastic chairs' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'plastic chairs' but are not 'plastic chairs' are:\twooden chairs\tmetal chairs\trocking chairs\toffice chairs\nThere are several useful visual features to tell there is 'plastic chairs' and not similar things in a photo:\tmade of plastic\teasy to clean and lightweight\tsleek, modern design\tsmooth texture and uniform color", 35], "antique": ["No. 'Antique' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we were talking about a specific antique object, then there would be visual features that could help distinguish it from other objects. For example, if we were looking at an antique chair, some useful visual features might include: \n- ornate carvings or details\n- aged or faded upholstery\n- a particular style that is indicative of a certain time period. \n\nSome things that might be visually similar to an antique chair but are not antique could include: \n- a replica or reproduction made to look old \n- a modern chair with vintage-style accents \n- a thrift store find that is simply old but not necessarily an antique.", 35], "stainless steel fork": ["Yes. 'Stainless steel fork' has a tangible appearance and is a specific type of utensil.\nA few things that are visually similar to 'stainless steel fork' but are not 'stainless steel fork' are:\tplastic fork\twooden fork\tsilver fork\nThere are several useful visual features to tell there is 'stainless steel fork' and not similar things in a photo:\t\n- Silver-grey color\n- Reflective and shiny surface\n- Three or four prongs or tines at one end, and a handle at the other end\n- Serrated or smooth-edged prongs or tines", 35], "work truck": ["Yes. 'Work truck' has a tangible appearance and is a type of vehicle used for work purposes.\nA few things that are visually similar to 'work truck' but are not 'work truck' are:\tpickup truck\tvan\ttrailer\nThere are several useful visual features to tell there is 'work truck' and not similar things in a photo:\topen flat bed or enclosed compartment\tfor carrying tools or materials\tdifferent types of tools or equipment attached to the bed or roof of the truck\ttypically larger and more rugged than a standard passenger car", 35], "safety line": ["Yes. 'Safety line' has a tangible appearance and is a type of rope or cord used for safety purposes.\nA few things that are visually similar to 'safety line' but are not 'safety line' are:\tclimbing rope\tbungee cord\tclothesline\tgarden hose\nThere are several useful visual features to tell there is 'safety line' and not similar things in a photo:\tbrightly colored (such as red or yellow)\tmarked with reflective materials\tmade from heavy-duty material and/or designed specifically for safety purposes\tstructured in a way that allows for easy attachment and detachment to safety harnesses or other safety equipment.", 35], "round ball": ["Yes. 'Round ball' has a tangible appearance and is a 3D geometric shape.\nA few things that are visually similar to 'round ball' but are not 'round ball' are:\tapple\torange\tgolf ball\tbaseball\tpear\nThere are several useful visual features to tell there is 'round ball' and not similar things in a photo:\tperfectly circular shape\tsymmetrical\tsolid, smooth surface (usually)\tno visible stem or leaves", 35], "wet fur": ["Yes. 'Wet fur' has a tangible appearance and can be observed on certain animals.\nA few things that are visually similar to 'wet fur' but are not 'wet fur' are: greasy hair, slime, oil, gloss.\nUseful visual features for distinguishing 'wet fur' from the listed similar things in a photo are: visible droplets of water on the surface of fur, flattened and messy hair, darker color of the fur due to being wet, and the presence of reflections or glare on the surface of the fur because of the wetness.", 35], "jean jacket": ["Yes. 'Jean jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'jean jacket' but are not 'jean jacket' are:\tdenim shirt\tchambray shirt\tdenim vest\nThere are several useful visual features to tell there is 'jean jacket' and not similar things in a photo:\tdenim fabric\tmetal button snap or zip front\ttypical cut and length of a jacket\tstyle features such as pockets and collar", 35], "headlight train": ["No. 'Headlight train' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider 'headlight' and 'train' separately, then:\n\nA few things that are visually similar to 'headlight' but are not 'headlight' are: flashlights, streetlights, car headlights.\n\nA few things that are visually similar to 'train' but are not 'train' are: buses, metros, trams.\n\nThere are several useful visual features to tell there is a 'train headlight' and not similar things in a photo: located at the front of the train, circular or oval shape, emits a bright light, may have a protective casing or grille around it.", 35], "orange box": ["Yes. 'Orange box' has a tangible appearance.\nA few things that are visually similar to 'orange box' but are not 'orange box' are:\toranges\tshoebox\tpost box\tfire hydrant\ttoolbox\nThere are several useful visual features to tell there is 'orange box' and not similar things in a photo:\trectangular shape\tof bright orange color\tcould have a brand or text on it\twith or without a lid", 35], "heron": ["Yes. 'Heron' has a tangible appearance and is a kind of bird.\nA few things that are visually similar to 'heron' but are not 'heron' are:\tcrane\tegret\tstork\nThere are several useful visual features to tell there is 'heron' and not similar things in a photo:\ttall, slender bird\tlong legs and neck\tsharp, pointed beak\ts-curved neck in flight\tplumage color and pattern (white, gray, or black)", 35], "crack sidewalk": ["Yes. 'Crack sidewalk' has a tangible appearance and refers to a specific type of pavement.\nA few things that are visually similar to 'crack sidewalk' but are not 'crack sidewalk' are:\tpavement\twith straight lines\tbroken glass\tpieces of wood\nThere are several useful visual features to tell there is 'crack sidewalk' and not similar things in a photo:\tcracks in the sidewalk\tthat separate the pavement\thighlights in the pavement\tcolor difference from the rest of the pavement\tdepressed sections in the pavement", 35], "outdoor light": ["Yes. 'Outdoor light' has a tangible appearance and is a type of light fixture designed for outdoor use.\nA few things that are visually similar to 'outdoor light' but are not 'outdoor light' are:\tlamp\tpost\tlightning\tbonfire\nThere are several useful visual features to tell there is 'outdoor light' and not similar things in a photo:\tattaches to a wall or pole\tsealed for weather resistance\tilluminates a specific area\toutdoor design features (such as a hood or shield)", 35], "blue waters": ["Yes. 'Blue waters' has a tangible appearance and refers to bodies of water that are colored blue.\nA few things that are visually similar to 'blue waters' but are not 'blue waters' are:\tblue paint\tblue fabrics\tsky\treflections\nThere are several useful visual features to tell there is 'blue waters' and not similar things in a photo:\tdepth\tripples or waves\tunderwater landscapes\tor marine life visible through the water\ttranslucency or transparency of the water.", 35], "length": ["No. 'Length' is too vague or abstract to be distinguished in a photo. It is a measurement quantity rather than a tangible object. \nTherefore, there are no things that are visually similar to 'length' but are not 'length'.", 35], "rv": ["Yes. 'RV' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'RV' but are not 'RV' are:\tcamper van\ttrailer\ttruck\tbus\nThere are several useful visual features to tell there is 'RV' and not similar things in a photo:\tlarge shape\twith living quarters inside\tbedroom, kitchen and bathroom facilities\taerodynamic design for better fuel efficiency\tslide-out sections for more living space.", 35], "metal posts": ["Yes. 'Metal posts' has a tangible appearance and is a construction material.\nA few things that are visually similar to 'metal posts' but are not 'metal posts' are:\tpoles\tfences\tsigns\tgates\nThere are several useful visual features to tell there is 'metal posts' and not similar things in a photo:\tvertical and cylindrical shape\tmade of metal\tcolors (usually silver)\ttheir function (holding up structures, serving as boundary markers)", 35], "bus sign": ["Yes. 'Bus sign' has a tangible appearance and is a type of sign for public transportation.\nA few things that are visually similar to 'bus sign' but are not 'bus sign' are:\tstreet sign\tstore sign\tadvertising billboard\tdirections sign\nThere are several useful visual features to tell there is 'bus sign' and not similar things in a photo:\tcontains information about the bus route, schedule, and stops\tmounted on a pole or attached to a building\tlarge and easy to read\tfor public transportation\tuse of bus icon or logo", 35], "silver bolt": ["Yes. 'Silver bolt' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'silver bolt' but are not 'silver bolt' are:\tscrew\tnut\tnail\trivet\nThere are several useful visual features to tell there is 'silver bolt' and not similar things in a photo:\t\nlong and cylindrical shape\t\nmetallic, shiny finish\t\nridges or threads along the shaft\t\nmay have a head with a slit or a cross-shaped slot for a screwdriver or wrench.", 35], "stone walkway": ["Yes. 'Stone walkway' has a tangible appearance and is a path made of stones.\nA few things that are visually similar to 'stone walkway' but are not 'stone walkway' are:\tpebble beach\tcobblestone street\tpaved road\nThere are several useful visual features to tell there is 'stone walkway' and not similar things in a photo:\tgray or beige stones\tplaced in an orderly fashion\tembedded in grass or soil\tlarge enough for walking on\tnot used for vehicular traffic", 35], "strainer": ["Yes. 'Strainer' has a tangible appearance and is a kitchen utensil.\nA few things that are visually similar to 'strainer' but are not 'strainer' are:\tcolander\tsieve\tchinois\tfilter\nThere are several useful visual features to tell there is 'strainer' and not similar things in a photo:\tbowl-shaped\twith holes or slots\ton top of a handle or a stand\tmade of metal, mesh or plastic\tsome have a hook or a clip\tfor draining liquids or sifting dry ingredients", 35], "needle": ["Yes. 'Needle' has a tangible appearance and is an object for sewing or injecting.\nA few things that are visually similar to 'needle' but are not 'needle' are:\tpin\tnail\tinjection\tsyringe\nThere are several useful visual features to tell there is 'needle' and not similar things in a photo:\tlong, thin and pointed object\twith an eye or a hook\ton a sewing machine\tthread or yarn attached\tto be inserted into fabric, skin, or material", 35], "bulldozer": ["Yes. 'Bulldozer' has a tangible appearance and is a type of construction vehicle.\nA few things that are visually similar to 'bulldozer' but are not 'bulldozer' are:\tbackhoe\texcavator\ttruck\ttractor\nThere are several useful visual features to tell there is 'bulldozer' and not similar things in a photo:\tlarge, flat, rectangular blade in front\tmetal tracks\tforward-facing driver's cab\tno other attachments (such as a digging arm)", 35], "country": ["No. 'Country' is too vague or abstract to be distinguished in a photo.", 35], "st": ["No. 'st' is too vague or abstract to have a tangible appearance, as it could be an abbreviation for different words depending on the context.\nTherefore, there are no things that are visually similar to 'st' but are not 'st', as 'st' does not have any intrinsic visual features.", 35], "shower curtain rod": ["Yes. 'Shower curtain rod' has a tangible appearance and is a type of bathroom accessory.\nA few things that are visually similar to 'shower curtain rod' but are not 'shower curtain rod' are: curtain rod, closet rod, towel bar.\nThere are several useful visual features to tell there is 'shower curtain rod' and not similar things in a photo:\tattached to the wall,above the shower or bathtub\tcurved or straight in shape\tmetal or plastic material\tcurtain rings or hooks attached to it\tcurtain hanging from it", 35], "outdoor clock": ["Yes. 'Outdoor clock' has a tangible appearance and is a type of clock designed for use outside.\nA few things that are visually similar to 'outdoor clock' but are not 'outdoor clock' are:\tindoor clock\twall thermometer\tpostbox\tsignpost\nThere are several useful visual features to tell there is 'outdoor clock' and not similar things in a photo:\tvisible in an outdoor area\tlarge and easy to read numbers\tprotected from weather and moisture\tclock hands", 35], "silver plane": ["Yes. 'Silver plane' has a tangible appearance and is a specific type of object.\nA few things that are visually similar to 'silver plane' but are not 'silver plane' are:\tsilver rocket\tsilver car\tsilver building\nThere are several useful visual features to tell there is 'silver plane' and not similar things in a photo:\tcylindrical body with wings and a tail section\tsmall windows along the fuselage\tlanding gear consisting of wheels located beneath the body of the aircraft\tsilver or metallic exterior.", 35], "shuttle": ["Yes. 'Shuttle' has a tangible appearance and is a type of spacecraft.\nA few things that are visually similar to 'shuttle' but are not 'shuttle' are:\tairplane\trocket\tblimp\thelicopter\nThere are several useful visual features to tell there is 'shuttle' and not similar things in a photo:\twings\tforward engines\trear-mounted vertical stabilizer\tre-entry tiles on the bottom", 35], "one": ["No. 'One' is too vague or abstract to be distinguished in a photo.", 34], "grouping": ["No. 'Grouping' is too vague or abstract to be distinguished in a photo.", 34], "dinner table": ["Yes. 'Dinner table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'dinner table' but are not 'dinner table' are:\tdesk\tcoffee table\tpicnic table\tcard table\nThere are several useful visual features to tell there is 'dinner table' and not similar things in a photo:\tlarge enough for several people\tto hold plates, glasses, and cutlery\tflat and level surface\tchairs or seats around it\tfor indoor or outdoor use", 34], "copyright symbol": ["Yes. 'Copyright symbol' has a tangible appearance and is a recognizable symbol.\nA few things that are visually similar to 'copyright symbol' but are not 'copyright symbol' are:\ttrademark symbol\tregistered trademark symbol\tcreative commons symbol\nThere are several useful visual features to tell there is 'copyright symbol' and not similar things in a photo:\ta circled letter 'C' or the word 'copyright'\ta distinct 'C' shape within a circle\tan encircled lowercase 'c'\twith or without the name of the owner or creator nearby.", 34], "wooden boat": ["Yes. 'Wooden boat' has a tangible appearance and is a type of watercraft.\nA few things that are visually similar to 'wooden boat' but are not 'wooden boat' are:\tkayak\tcanoe\traft\tbarge\nThere are several useful visual features to tell there is 'wooden boat' and not similar things in a photo:\tmade of wood\tfloats on water\thas a defined shape or hull\tmay have oars or a motor\tpotentially has a sail or mast\ton the larger side in comparison to kayaks or canoes.", 34], "silver refrigerator": ["Yes. 'Silver refrigerator' has a tangible appearance and is a household appliance.\nA few things that are visually similar to 'silver refrigerator' but are not 'silver refrigerator' are:\tstove\tdishwasher\twashing machine\toven\nThere are several useful visual features to tell there is 'silver refrigerator' and not similar things in a photo:\tvertical, rectangular shape\tflat front or with slight bulges\thandles\tor water dispenser on the front\tsilver or metallic color\tshelves or compartments inside", 34], "wood drawer": ["Yes. 'Wood drawer' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wood drawer' but are not 'wood drawer' are:\twooden box\tshelf\tbookcase\tchest of drawers\nThere are several useful visual features to tell there is 'wood drawer' and not similar things in a photo:\trectangular shape\tsliding or pull-out mechanism\twooden material\tknob or handle to open and close drawers\ttypically attached to a larger furniture piece", 34], "cable box": ["Yes. 'Cable box' has a tangible appearance and is a device for receiving cable television signals.\nA few things that are visually similar to 'cable box' but are not 'cable box' are:\tmodem\trouter\t\nThere are several useful visual features to tell there is 'cable box' and not similar things in a photo:\tblack or dark color\tcable or satellite input\tjacks and ports\ton-screen display or indicator lights\twith a remote control", 34], "tab": ["No. 'Tab' is too vague or abstract to be distinguished in a photo. It could refer to a tab in a web browser, a tab on a piece of paper, a tab on a zipper, among other things.\nTherefore, it is not applicable to list visually similar things or useful visual features for distinguishing 'tab' from them.", 34], "patio chair": ["Yes. 'Patio chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'patio chair' but are not 'patio chair' are:\tregular chair\tstool\tbench\tsofa\nThere are several useful visual features to tell there is 'patio chair' and not similar things in a photo:\toutdoor furniture\tpadded or mesh seat and backrest\tarmrests, backrest and seat at a reclining angle\tumbrella\tinserts for drinks or devices", 34], "grains": ["Yes. 'Grains' has a tangible appearance and refers to small, hard, dry seeds used as food.\nA few things that are visually similar to 'grains' but are not 'grains' are:\tseeds\trocks\tsand\nThere are several useful visual features to tell there is 'grains' and not similar things in a photo:\tsmall size\tdry texture\tuneven shapes and textures\tvariety of colors, including brown, white, black, yellow, and red\tcan be in piles or bags, often in a food context", 34], "blue train car": ["Yes. 'Blue train car' has a tangible appearance and refers to a specific type of train car.\nA few things that are visually similar to 'blue train car' but are not 'blue train car' are:\tblue car\tbus\nThere are several useful visual features to tell there is 'blue train car' and not similar things in a photo:\trectangular shape with a curved roof\tmultiple windows\tmetal exterior\tcolor of blue or blue-gray in hue\ta set of wheels to run on the tracks.", 34], "lotion": ["Yes. 'Lotion' has a tangible appearance as it is a type of liquid used for skin care.\nA few things that are visually similar to 'lotion' but are not 'lotion' are:\tshampoo\thand sanitizer\tsoap\tdetergent\nThere are several useful visual features to tell there is 'lotion' and not similar things in a photo:\ttypically comes in a small bottle or tube\thas a creamy or smooth texture\tmay come in a variety of colors, but is often white or off-white.", 34], "beige hat": ["Yes. 'Beige hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'beige hat' but are not 'beige hat' are:\tbrown hat\tstraw hat\tcap\tfedora\nThere are several useful visual features to tell there is 'beige hat' and not similar things in a photo:\tbeige or light tan color\tmade of fabric or wool\tcrown and brim structure\tclear shape of hat when viewed from the front or top", 34], "adidas logo": ["Yes. 'Adidas logo' has a tangible appearance and is a specific design.\nA few things that are visually similar to 'adidas logo' but are not 'adidas logo' are:\tthree stripes\tsimilar designs of other sports brands\tstylized letters\nThere are several useful visual features to tell there is 'adidas logo' and not similar things in a photo:\tthree stripes of equal width forming a mountain-like shape\tthe word 'adidas' in lower case\tletters, stylized in a specific way", 34], "dog head": ["Yes. 'Dog head' has a tangible appearance and refers to the head of a dog.\nA few things that are visually similar to 'dog head' but are not 'dog head' are:\tother animal heads\tsculptures of animal heads\tcostumes with animal heads\tstuffed toys of animal heads\nThere are several useful visual features to tell there is 'dog head' and not similar things in a photo:\tears\tbrow eyes\tmuzzle\twet nose\tfur color\tfur length\texpresion", 34], "room table": ["No. 'Room table' is too vague or abstract to be distinguished in a photo. The proper term is 'dining table' which refers to a specific type of table used for dining.\nA few things that are visually similar to 'dining table' but are not 'dining table' are:\tdesk\tcouch\tbench\tworktable\nThere are several useful visual features to tell there is a 'dining table' and not similar things in a photo:\t\nrectangular or circular top\tsupported by legs or a pedestal\toften surrounded by chairs or benches used for dining.", 34], "netting": ["Yes. 'Netting' has a tangible and visible appearance, and it is a type of fabric used for various purposes.\nA few things that are visually similar to 'netting' but are not 'netting' are: fishing line, string, mesh clothing, wire, fence\nThere are several useful visual features to tell there is 'netting' and not similar things in a photo:\tloose, open weave pattern\tsoft, flexible texture\ttranslucent or transparent material\tused in a sports arena, garden or for catching insects and animals.", 34], "antennas": ["Yes. 'Antennas' has a tangible appearance and is a type of object used for communication or signal transmission.\nA few things that are visually similar to 'antennas' but are not 'antennas' are:\tTV dish\tsatellite dish\tradio tower\tlightning rod\nThere are several useful visual features to tell there is 'antennas' and not similar things in a photo:\tslender or thin rod-shaped\tobject is attached to a building, vehicle or a device\tobject has wires or cables sticking out from it\tobject is pointed towards the sky or in a specific direction", 34], "dessert plate": ["Yes. 'Dessert plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'dessert plate' but are not 'dessert plate' are:\tsaucer\tbread plate\tdinner plate\tside plate\nThere are several useful visual features to tell there is 'dessert plate' and not similar things in a photo:\tsmaller in size compared to other plates\tdecorative pattern around the edge round or oval shape with slightly raised edges, curved inward\tis specifically used for serving dessert or small pastries", 34], "computer screens": ["Yes. 'Computer screens' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'computer screens' but are not 'computer screens' are:\ttelevision screens\ttablet screens\tsmartphone screens\tcamera screens\nThere are several useful visual features to tell there are 'computer screens' and not similar things in a photo:\trectangular shapes\tcrystal clear display\tbrightness or contrast adjustments\tthe presence of cables, input ports, or keyboard/mouse\tnext to the screen", 34], "plastic shopping bag": ["Yes. 'Plastic shopping bag' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'plastic shopping bag' but are not 'plastic shopping bag' are:\tpaper bag\ttote bag\tgarbage bag\nThere are several useful visual features to tell there is 'plastic shopping bag' and not similar things in a photo:\tthin and lightweight\ttransparent or semi-transparent\tplastic material\thandles at the top\tof a typical size and shape for carrying groceries or other items", 34], "contrail": ["Yes. 'Contrail' has a tangible appearance and is a visible trail of condensed water vapor from an aircraft or rocket engine.\nA few things that are visually similar to 'contrail' but are not 'contrail' are:\tcloud\tsmoke\tfog\tchemtrail\nThere are several useful visual features to tell there is 'contrail' and not similar things in a photo:\tstraight and narrow trail\tparallel to the horizon\tdisappears soon after the aircraft passes by\tmay have circular or spiral patterns\tif captured from an angle, shows the shape of the aircraft", 34], "lone": ["No. 'Lone' is too vague or abstract to be distinguished in a photo. It refers to something existing, living or operating alone or independently.", 34], "pink kite": ["Yes. 'Pink kite' has a tangible appearance and is a specific type of kite.\nA few things that are visually similar to 'pink kite' but are not 'pink kite' are:\tother colored kites\tballoons\tumbrellas\tbanners\nThere are several useful visual features to tell there is 'pink kite' and not similar things in a photo:\ttriangular shape\twith a tail\tpink color\tflying in the air\theld by a string or wire.", 34], "metal handrail": ["Yes. 'Metal handrail' has a tangible appearance and is a physical object used for support and safety.\nA few things that are visually similar to 'metal handrail' but are not 'metal handrail' are:\tmetal fencing\tbarrier\tmetal shelf\nThere are several useful visual features to tell there is 'metal handrail' and not similar things in a photo:\tattached to a staircase or walkway\thorizontal or inclined\tpositioned at a reachable height\tfor gripping and support in walking or balance", 34], "paper sign": ["Yes. 'Paper sign' has a tangible appearance and is a type of sign made from paper.\nA few things that are visually similar to 'paper sign' but are not 'paper sign' are:\tbanners\tposters\tcards\ttickets\nThere are several useful visual features to tell there is 'paper sign' and not similar things in a photo:\tmade of paper or cardboard\twritten or printed information\teasily readable and understandable by pedestrians\thanging with a string, tape or clamps", 34], "noses": ["Yes. 'Noses' has a tangible appearance and is a part of the human face.\nA few things that are visually similar to 'noses' but are not 'noses' are:\tmouths\tears\teyes\tchins\nThere are several useful visual features to tell there is 'noses' and not similar things in a photo:\tprotruding from the face\tconnected to the upper lip and the cheeks\thole and nostrils at the end\tof varying size, shape, and color.", 34], "peanuts": ["Yes. 'Peanuts' has a tangible appearance and is a type of nut.\nA few things that are visually similar to 'peanuts' but are not 'peanuts' are:\talmonds\thazelnuts\twalnuts\tcashews\nThere are several useful visual features to tell there is 'peanuts' and not similar things in a photo:\toval shape\twith a pointed end and a rounded end\tlight brown color\twith thin papery skin covering the kernel\tgrow in a pod underground", 34], "vanilla": ["No. 'Vanilla' is too vague or abstract to have a visually concrete appearance. \n\nHowever, in the context of vanilla flavor or scent, it may have some specific visual features. \n\nA few things that are visually similar to 'vanilla' flavor or scent but are not 'vanilla' are:\tCream-colored objects\tCinnamon-colored objects\tCaramel-colored objects\n\nUseful visual features for distinguishing 'vanilla' flavor or scent from the listed similar things in a photo are:\tLight beige or yellowish color\tHighly aromatic and concentrated scent\treminiscent of ice cream or dessert flavoring.", 34], "orange bottle": ["Yes. 'Orange bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'orange bottle' but are not 'orange bottle' are:\torange jar\tplastic toy\tbasket\tbucket\nThere are several useful visual features to tell there is 'orange bottle' and not similar things in a photo:\tcylindrical or bottle-shaped container\tbright orange color\twith a cap or a lid\tlabel or marking indicating what's inside it (e.g. medicine, juice)", 34], "water surface": ["Yes. 'Water surface' has a tangible appearance and refers to the top layer of a body of water.\nA few things that are visually similar to 'water surface' but are not 'water surface' are:\tice surface\tmirror\treflective metal\t\nThere are several useful visual features to tell there is 'water surface' and not similar things in a photo:\tripples, waves or other patterns on the surface\treflections of the surrounding objects or landscape\tmovement of water\tdifferences in color or texture compared to the objects that are reflecting", 34], "pink ribbon": ["Yes. 'Pink ribbon' has a tangible appearance and is a symbol for breast cancer awareness.\nA few things that are visually similar to 'pink ribbon' but are not 'pink ribbon' are:\tpink fabric\tpink string\tpink clothes\tpink flowers\nThere are not many visual features to distinguish 'pink ribbon' from similar things, but useful visual features include:\ta looped design\twith two streams of ribbon flowing downwards\tfrom light pink to hot pink in color", 34], "swimmer": ["Yes. 'Swimmer' has a tangible appearance and is a person or animal that is swimming.\nA few things that are visually similar to 'swimmer' but are not 'swimmer' are:\tfloating object\twave\tinanimate object\nThere are several useful visual features to tell there is 'swimmer' and not similar things in a photo:\thuman or animal body shape\tmove arms and legs to move through the water\twater around the body or splashing\twearing swimwear or wetsuit", 34], "crease": ["Yes. 'Crease' has a tangible appearance and is a type of fold or line.\nA few things that are visually similar to 'crease' but are not 'crease' are:\twrinkle\tfold in clothing\toragami\nThere are several useful visual features to tell there is 'crease' and not similar things in a photo:\tvery sharp visual line\tdefines a shape or form in the object or material it is located in\toften has a crisp edge or can be angled\tcould be a subtle hint if the object is one of flat surface", 34], "soaps": ["Yes. 'Soaps' has a tangible appearance and is a personal care item.\nA few things that are visually similar to 'soaps' but are not 'soaps' are:\tcandles\twax figurines\tdecorative stones\nThere are several useful visual features to tell there is 'soaps' and not similar things in a photo:\trectangular, round, or may have an irregular shape\tavailable in different colors and sizes\tclean and shiny surface\tfrothy or bubbly texture\twhen wet, the soap can appear to be melting or dissolving", 34], "mascot": ["Yes. 'Mascot' has a tangible appearance and is typically a person or animal costume used to represent a team or organization.\nA few things that are visually similar to 'mascot' but are not 'mascot' are:\tcostume\tcosplay\tactor\tdress-up clothes\nThere are several useful visual features to tell there is 'mascot' and not similar things in a photo:\tbig head mask of an animal or fictional character\tfull-body costume in bright colors and pattern\tunique accessories or props, such as pom-poms or flags\tlogos, team names, or slogans on the costume", 34], "wood panel": ["Yes. 'Wood panel' has a tangible appearance and refers to a type of wall covering made of wood.\nA few things that are visually similar to 'wood panel' but are not 'wood panel' are:\tfaux wood wall paneling\twallpaper with wood patterns\thardwood flooring\nThere are several useful visual features to tell there is 'wood panel' and not similar things in a photo:\tvertical or horizontal panels of wood\tgrain lines of wood visible\tnatural wood color or stained color\ttexture of wood surface", 34], "silver phone": ["Yes. 'Silver phone' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'silver phone' but are not 'silver phone' are:\tother colored phones\tcomputers\ttablets\twatches\nThere are several useful visual features to tell there is 'silver phone' and not similar things in a photo:\trectangular shape\tsilver or metallic color\tcamera lens on the back or front\tvisible buttons or screen on the front or sides.", 34], "wooden door": ["Yes. 'Wooden door' has a tangible appearance and is a type of entryway.\nA few things that are visually similar to 'wooden door' but are not 'wooden door' are:\twindow\twall\tfence\tshutter\nThere are several useful visual features to tell there is 'wooden door' and not similar things in a photo:\trectangular or square shape\tmade of wood or has wooden texture\thorizontal or vertical panels\thandle and lock\thinges\tand swings open or close.", 34], "laptop computer": ["Yes. 'Laptop computer' has a tangible appearance and is a type of computer.\nA few things that are visually similar to 'laptop computer' but are not 'laptop computer' are:\ttablet\tsmartphone\tebook reader\tNotepad\nThere are several useful visual features to tell there is 'laptop computer' and not similar things in a photo:\tflat screen\tdisplay lid\tor opened hinge keyboard\ttouchpad or pointing stick ports (e.g. USB ports, video output ports)", 34], "airport runway": ["Yes. 'Airport runway' has a tangible appearance and is a defined area where airplanes take off and land.\nA few things that are visually similar to 'airport runway' but are not 'airport runway' are:\troads\trace tracks\tparking lots\tski slopes\nThere are several useful visual features to tell there is 'airport runway' and not similar things in a photo:\tpaved surface with markings\tfor planes to take off and land\tstraight or slightly curved, with a defined length", 34], "hummingbird": ["Yes. 'Hummingbird' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'hummingbird' but are not 'hummingbird' are:\tmoths\tbutterflies\tbees\nThere are several useful visual features to tell there is 'hummingbird' and not similar things in a photo:\tvery small size compared to other birds\tability to fly forwards, backwards, sideways and hover\tincredibly fast wing beats\tbrightly colored feathers, usually with iridescence\tlong, thin beaks that allow them to reach nectar in flowers", 34], "identification tag": ["Yes. 'Identification tag' has a tangible appearance and is an object used to identify someone or something.\nA few things that are visually similar to 'identification tag' but are not 'identification tag' are:\tnecklace\tdog collar\tkeychain\tbag tag\nThere are several useful visual features to tell there is 'identification tag' and not similar things in a photo:\trectangular or round shape\twith a name, a number, or other identifying information\toften made of metal or plastic\tattached to something with a loop, a chain, or a ring.", 34], "soccer jersey": ["Yes, 'soccer jersey' has a tangible appearance and is a specific type of athletic clothing.\nA few things that are visually similar to 'soccer jersey' but are not 'soccer jersey' are:\tpolo shirt\tt-shirt\ttank top\t\nThere are several useful visual features to tell there is 'soccer jersey' and not similar things in a photo:\t\n- Usually has a team name or logo on the front of the shirt\n- Has a player's name and number on the back\n- Often has bold, contrasting colors\n- May have stripes or other patterns\n- Made of breathable, moisture-wicking material", 34], "skate shoes": ["Yes. 'Skate shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'skate shoes' but are not 'skate shoes' are:\trunning shoes\ttennis shoes\ttraining shoes\tcasual shoes\nThere are several useful visual features to tell there is 'skate shoes' and not similar things in a photo:\tthick and durable sole\tprotective padding on the sides and the tongue of the shoe\tflat bottom\tlaced-up design\tfashionable design with unique features or colors specific to skateboarding culture", 34], "street sign pole": ["Yes. 'Street sign pole' has a tangible appearance and is a type of pole for holding signs.\nA few things that are visually similar to 'street sign pole' but are not 'street sign pole' are:\tflag pole\ttelephone pole\tbus stop pole\tlamp post\nThere are several useful visual features to tell there is 'street sign pole' and not similar things in a photo:\trectangular or square sign attached to the pole\tmultiple signs attached to the pole\tmetal or reflective surface\tpainted white or silver color\tfound on the side of a street or road.", 34], "toilet cover": ["Yes. 'Toilet cover' has a tangible appearance and is a type of bathroom accessory.\nA few things that are visually similar to 'toilet cover' but are not 'toilet cover' are:\tseat cushion\tbath mat\ttab\tplacemat\nThere are several useful visual features to tell there is 'toilet cover' and not similar things in a photo:\tcircular or oval shape\tfabric or plastic material\tcovers the lid of the toilet tank or seat\tfits snugly around the edge of the toilet opening\tpatterns or designs that match bathroom d\u00e9cor", 34], "orange safety cones": ["Yes. 'Orange safety cones' has a tangible appearance and is a kind of road safety equipment.\nA few things that are visually similar to 'orange safety cones' but are not 'orange safety cones' are:\ttraffic barrels\tcaution tape\tbollards\tparking curbs\nThere are several useful visual features to tell there are 'orange safety cones' and not similar things in a photo:\tcone-shaped\tbright orange color\twith or without reflective stripes\tstacked or arranged in a line\ton the side of a road or in a construction zone", 34], "brown edge": ["Yes. 'Brown edge' has a tangible appearance and it could refer to a border or edge of a surface or object that has a brown color.\nA few things that are visually similar to 'brown edge' but are not 'brown edge' are: black edge, white edge, gray edge, red edge\nThere are no useful visual features for distinguishing 'brown edge' from visually similar things in a photo because the difference between them is just the color of the edge.", 34], "male skier": ["Yes. 'Male skier' has a tangible appearance and is a person engaged in skiing.\nA few things that are visually similar to 'male skier' but are not 'male skier' are:\thiker\tsnowboarder\tskater\nThere are several useful visual features to tell there is 'male skier' and not similar things in a photo:\twearing skis and ski boots\twearing ski goggles, gloves, and helmet\tdressed in appropriate clothing for skiing (e.g., jacket, pants)\tski poles are visible\tski trail or mountain slope visible in the background/foreground.", 34], "paint brush": ["Yes. 'Paint brush' has a tangible appearance and is a tool used for painting.\nA few things that are visually similar to 'paint brush' but are not 'paint brush' are:\tcomb\thair brush\tfeather\tduster\nThere are several useful visual features to tell there is 'paint brush' and not similar things in a photo:\tlong stick or handle\twith bristles or fibers\tat the end of the stick\tbristles can be made of synthetic or natural materials\tbristles can be of varying lengths and textures.", 34], "statute": ["Yes. 'Statue' has a tangible appearance and refers to a three-dimensional sculpture of a person or an animal.\nA few things that are visually similar to 'statue' but are not 'statue' are:\tfountain\tmonument\tbuilding\tpillar\nThere are several useful visual features to tell there is 'statue' and not similar things in a photo: \tthree-dimensional\thuman or animal form\tpresented on a pedestal\tor stand\tsculpted out of materials like marble, bronze, or cement", 34], "pieces clothing": ["Yes. 'Pieces of clothing' has a tangible appearance and refers to individual articles of clothing.\nA few things that are visually similar to 'pieces of clothing' but are not 'pieces of clothing' are:\tlaundry\trags\tfabric scraps\t\nThere are several useful visual features to tell there are 'pieces of clothing' and not similar things in a photo:\ta clear recognizable part of a garment (such as a sleeve or a collar)\tcan be worn on its own or as part of an outfit\ttypically made of a recognizable material (such as cotton or denim)\thas recognizable features like buttons, zippers, or pockets.", 34], "wood posts": ["Yes. 'Wood posts' has a tangible appearance and is a type of wooden structure.\nA few things that are visually similar to 'wood posts' but are not 'wood posts' are:\ttrees\tfence posts\tmetal pillars\nThere are several useful visual features to tell there are 'wood posts' and not similar things in a photo:\tconstructed out of wood\tbrown or tan in color\trectangular or cylindrical in shape\tused to support a structure", 34], "washing machine": ["Yes. 'Washing machine' has a tangible appearance and is a household appliance.\nA few things that are visually similar to 'washing machine' but are not 'washing machine' are:\tdryer\tdishwasher\toven\tfridge\nThere are several useful visual features to tell there is 'washing machine' and not similar things in a photo:\ta drum-shaped structure\tfor loading clothes and water\ta door or hatch\tfor opening and closing\tthe control panel or display\tfor selecting cycles\tand programming the machine", 34], "blue coat": ["Yes. 'Blue coat' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blue coat' but are not 'blue coat' are:\tblue shirt\tblue dress\tblue sweater\tblue jacket\nThere are several useful visual features to tell there is 'blue coat' and not similar things in a photo:\ta long sleeve, button-up or zip-up garment\tthat covers the upper body and arms\tbuilt for colder weather.", 34], "shoelace": ["Yes. 'Shoelace' has a tangible appearance and is a type of string used to fasten a shoe.\nA few things that are visually similar to 'shoelace' but are not 'shoelace' are:\tthread\tyarn\trope\nThere are several useful visual features to tell there is 'shoelace' and not similar things in a photo:\tthin and flat material\twith aglets or plastic tips\ttypically two on a shoe\toften tied in a knot or a bow.", 34], "archways": ["Yes. 'Archways' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'archways' but are not 'archways' are:\t\nDoor frames\t\nWindows\t\nGateways\t\nTunnels\t\nBridges\t\nThere are several useful visual features to tell there is 'archways' and not similar things in a photo:\t\nSemi-circular or pointed shape\t\nConstructed of stone or other durable material\t\nMay have decorative carvings or embellishments\t\nUsed as entrances or gateways", 34], "tile roof": ["Yes. 'Tile roof' has a tangible appearance and is a type of roofing material.\nA few things that are visually similar to 'tile roof' but are not 'tile roof' are:\twooden shingles\tasphalt shingles\tmetal roof\tgrass thatch\nThere are several useful visual features to tell there is 'tile roof' and not similar things in a photo:\trigid clay or concrete tiles\trounded, curved, or flat\tsymmetrically arranged\treddish, brownish, or grayish in color\tcan be in different patterns or styles", 34], "pink pants": ["Yes. 'Pink pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pink pants' but are not 'pink pants' are:\tpink skirt\tpink shorts\tpink dress\tpink jacket\nThere are several useful visual features to tell there are 'pink pants' and not similar things in a photo:\tlong trouser legs\tcovering both legs\thaving a waistband and possibly pockets in the front and back\tmade of a material typically used for pants, such as denim or cotton\tfocused on the lower half of the body.", 34], "tan carpet": ["Yes. 'Tan carpet' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'tan carpet' but are not 'tan carpet' are:\ttile\twood floor\thardwood floor\tcement floor\nThere are several useful visual features to tell there is 'tan carpet' and not similar things in a photo:\tsoft and fuzzy texture\tsolid tan color\tshort pile\theight off the ground", 34], "dirty toilet": ["Yes. 'Dirty toilet' has a tangible appearance.\nA few things that are visually similar to 'dirty toilet' but are not 'dirty toilet' are:\tclean toilet\tbathtub\tdrain\tsink\nThere are several useful visual features to tell there is 'dirty toilet' and not similar things in a photo:\tdark or discolored stains\tbrown or yellow rings around the bowl\tdirt or debris in the bowl\tor bits of toilet paper\tstreaks on the bowl or seat due to lack of cleaning", 34], "shell": ["Yes, 'shell' has a tangible appearance and refers to the hard exterior of some animals like snails, turtles, and crabs.\nA few things that are visually similar to 'shell' but are not 'shell' are:\trock\tpebble\tboulder\tconcrete\nThere are several useful visual features to tell there is 'shell' and not similar things in a photo:\trigid, hard and durable outer layer\texhibiting patterns and textures\tfrom a marine or freshwater habitat\tmay include openings or holes to let the animal breathe or eat", 34], "baseball base": ["Yes. 'Baseball base' has a tangible appearance and is a physical object used in the sport of baseball.\nA few things that are visually similar to 'baseball base' but are not 'baseball base' are:\ttraffic cones\tpylons\tplaceholders\tfood plates\nThere are several useful visual features to tell there is 'baseball base' and not similar things in a photo:\tdiamond-shaped\twhite rubber or plastic\ttop filled with dirt or sand\tlocated at each corner of the infield\toranged-shaped base pad for the pitcher", 34], "sky scraper": ["Yes. 'Sky scraper' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'sky scraper' but are not 'sky scraper' are:\ttower\tapartment building\tchurch\tbuilding with a spire\nThere are several useful visual features to tell there is 'sky scraper' and not similar things in a photo:\textremely tall building relative to surrounding structures\tmultiple levels\thigher than surrounding trees and other landmarks\tnarrow and streamlined appearance\ttypically found in urban areas", 34], "bedside lamp": ["Yes. 'Bedside lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'bedside lamp' but are not 'bedside lamp' are:\tdesk lamp\tfloor lamp\tchandelier\tpendant light\nThere are several useful visual features to tell there is 'bedside lamp' and not similar things in a photo:\tsmall in size\thave a base that sits on a table or nightstand\tdirect light downward towards the bed or nearby area\tswitch on the base or cord for easy access from bed typically used in bedrooms", 34], "metal clock": ["Yes. 'Metal clock' has a tangible appearance and refers to a clock that is made of metal.\nA few things that are visually similar to 'metal clock' but are not 'metal clock' are:\twall clock alarm clock\tpocket watch\twristwatch\nThere are several useful visual features to tell there is 'metal clock' and not similar things in a photo:\tmade of metal\tvisible clock face with hands and numbers\tticking or moving second hand\tbell or chime mechanism\tfor wall clocks: hanging on a wall or a support.", 34], "mercedes": ["Yes. 'Mercedes' has a tangible appearance and is a type of car brand.\nA few things that are visually similar to 'mercedes' but are not 'mercedes' are:\tBMW\tAudi\tTesla\tFerrari\nThere are several useful visual features to tell there is 'mercedes' and not similar things in a photo:\tthe emblem of a three-pointed star on the hood or grill\tof various car models and designs, but generally has a sleek and stylish appearance with a more angular front end and distinct curved lines\ton the higher end of the price spectrum among car brands.", 34], "floor rug": ["Yes. 'Floor rug' has a tangible appearance and is a type of floor covering.\nA few things that are visually similar to 'floor rug' but are not 'floor rug' are:\tcarpet\ttile flooring\twood flooring\t\nThere are several useful visual features to tell there is 'floor rug' and not similar things in a photo:\tplaced on top of a floor surface\thas fringes or tassels\tspecific patterns or designs\tmade of different textures or materials\tspecific size or shape", 34], "kites air": ["No. 'Kites air' is too vague or abstract to be distinguished in a photo.", 34], "mountain peak": ["Yes, 'mountain peak' has a tangible appearance and is the highest point of a mountain.\nA few things that are visually similar to 'mountain peak' but are not 'mountain peak' are: Hill, Ridge, Plateau.\nThere are several useful visual features to tell there is 'mountain peak' and not similar things in a photo: A tall and pointy structure with steep slopes, Covered with snow, rocky terrain, and surrounding landscape features such as trees or valleys.", 34], "unlit": ["No. 'Unlit' is not a visually concrete concept as it refers to the absence of light rather than a tangible appearance.\nThere are no things that are visually similar to 'unlit' but are not 'unlit'.\nThere are no useful visual features for distinguishing 'unlit' from anything in a photo as it refers to the absence of light.", 34], "grass tennis court": ["Yes. 'Grass tennis court' has a tangible appearance and is a type of tennis court.\nA few things that are visually similar to 'grass tennis court' but are not 'grass tennis court' are:\tfootball field\tgolf course\tcricket field\nThere are several useful visual features to tell there is 'grass tennis court' and not similar things in a photo:\trectangle-shaped court\tevenly cut and maintained grass surface\twhite boundary lines and markings\tnet in the center of the court.", 34], "skylights": ["Yes. 'Skylights' has a tangible appearance and is a type of window installed in a roof.\nA few things that are visually similar to 'skylights' but are not 'skylights' are:\tregular windows\tmirrors\tglass tiles\nThere are several useful visual features to tell there is 'skylights' and not similar things in a photo:\tinstalled in a roof\thorizontal or angled position\tdifferent size or shape than regular windows\tallow natural light from above to enter the room", 34], "elephant eye": ["Yes. 'Elephant eye' has a tangible appearance and is a body part of an elephant.\nA few things that are visually similar to 'elephant eye' but are not 'elephant eye' are:\tother animal eyes\thuman eyes\nThere are several useful visual features to tell there is an 'elephant eye' and not similar things in a photo:\tlarge in size\tdarkly pigmented, with long lashes\tlocated on the side of an elephant's face\twrinkled skin around the eye\tbulbous shape with a round pupil.", 34], "pink collar": ["No. 'Pink collar' is too vague or abstract to be visually distinguished in a photo. \n\nNote: 'pink collar' is a term used to describe jobs traditionally held by women, such as administrative and service roles, and is not a physical object with a tangible appearance.", 34], "beach umbrellas": ["Yes. 'Beach umbrellas' has a tangible appearance and is a type of sunshade structure.\nA few things that are visually similar to 'beach umbrellas' but are not 'beach umbrellas' are:\tregular umbrellas\tpatio umbrellas\ttents\tcanopies\nThere are several useful visual features to tell there is 'beach umbrellas' and not similar things in a photo:\tcolorful and vibrant\tcan have patterns or designs\thighly angled and pointed top\tusually stuck in the sand or ground\tstraw or fabric material used for the top of the umbrella.", 34], "horse mane": ["Yes. 'Horse mane' has a tangible appearance and is a part of the horse's body.\nA few things that are visually similar to 'horse mane' but are not 'horse mane' are:\thair extensions\tfur wig\tyak mane\nThere are several useful visual features to tell there is 'horse mane' and not similar things in a photo:\tthick and long\thorses are visible in the photo\tthe hair is growing from the crest of the neck of the horse", 34], "woven basket": ["Yes. 'Woven basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'woven basket' but are not 'woven basket' are:\tplastic basket\tmetal basket\tcardboard box\nThere are several useful visual features to tell there is 'woven basket' and not similar things in a photo:\tmade of natural materials such as bamboo, reed, or rattan\twoven or braided pattern\thollow inside\twith handles or straps for carrying or holding the basket", 34], "hub cap": ["Yes. 'Hub cap' has a tangible appearance and is a part of a car's wheel.\nA few things that are visually similar to 'hub cap' but are not 'hub cap' are:\trims\twheels\tbrake discs\thub dust cap\nThere are several useful visual features to tell there is 'hub cap' and not similar things in a photo:\tcircular\tdome-shaped\tsilver or metallic finish\tfits over the center of a wheel or hub\thas a variety of designs or patterns.", 34], "ferns": ["Yes. 'Ferns' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'ferns' but are not 'ferns' are:\tvines\tweeds\tmosses\tpotted plants\tcacti\nThere are several useful visual features to tell there is 'ferns' and not similar things in a photo:\tfronds or leaves that are divided into smaller leaflets or segments\tarranged in a spiral or rosette pattern\tno flowers or fruits (in most cases)\tprefer to grow in damp areas such as forests or near water sources.", 34], "ocean waters": ["Yes. 'Ocean waters' has a tangible appearance and refers to the body of saltwater covering most of the Earth's surface.\nA few things that are visually similar to 'ocean waters' but are not 'ocean waters' are:\trivers\tlakes\tpools\t\nThere are several useful visual features to tell there is 'ocean waters' and not similar things in a photo:\tcontinuous body of saltwater\thuge waves\ttides\texpanse of blue or green color with foamy white edges\tsalty smell and taste.", 34], "clocktower": ["Yes. 'Clocktower' has a tangible appearance and is a kind of tower with a clock on it.\nA few things that are visually similar to 'clocktower' but are not 'clocktower' are:\tbell tower\twater tower\tchimney\tspire\tlighthouse\nThere are several useful visual features to tell there is 'clocktower' and not similar things in a photo:\tlarge and tall tower\tpresence of a clock or a bell on the top\tdials, numbers or roman numerals that indicate time or bells and their counterweights.", 34], "space shuttle": ["Yes. 'Space shuttle' has a tangible appearance and is a type of spacecraft.\nA few things that are visually similar to 'space shuttle' but are not 'space shuttle' are:\tRocket\tAirplane\tBlimp\t\nThere are several useful visual features to tell there is 'space shuttle' and not similar things in a photo:\t\n- Rectangular shape with rounded edges\t\n- Two solid rocket boosters attached to the sides\t\n- A large external fuel tank attached to the bottom\t\n- A heat-resistant protective layer covering the underside of the shuttle\t\n- A NASA or United States logo visible on the shuttle.", 34], "onion ring": ["Yes. 'Onion ring' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'onion ring' but are not 'onion ring' are:\tdonut\tbangle bracelet\tsliced potatoes\tpistachio shells\nThere are several useful visual features to tell there is 'onion ring' and not similar things in a photo:\tcircular shape\tbreaded or battered\tcrunchy texture\tgolden brown color\twith an irregular shape or texture compared to other rings", 34], "foundation": ["No. 'Foundation' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are talking about the physical foundation of a building, then: \nA few things that are visually similar to the physical foundation of a building but are not 'foundation' are:\t\nconcrete slab\t\nbasement walls\t\ncrawl space\n\nThe following visual features can help to distinguish the physical foundation of a building from the listed similar things in a photo: \n- Located at the base of the building\n- Made of concrete or masonry materials\n- Usually not visible above ground level\n- May have steel reinforcements\n- Serves as the base and support for the entire structure", 34], "hiker": ["Yes. 'Hiker' has a tangible appearance and is a person who goes for a long walk, especially in the countryside.\nA few things that are visually similar to 'hiker' but are not 'hiker' are:\tjogger\tbiker\tbackpacker\thunter\nThere are several useful visual features to tell there is 'hiker' and not similar things in a photo:\tbackpack or hiking gear\twalking stick or trekking poles\tpants and boots suitable for hiking\tsunhat or cap\twalking on a trail or in the wilderness\toutdoor surroundings (rocks, mountains, trees, etc.)", 34], "chocolate cupcake": ["Yes. 'Chocolate cupcake' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'chocolate cupcake' but are not 'chocolate cupcake' are:\tmuffin\tcake\tpopover\nThere are several useful visual features to tell there is 'chocolate cupcake' and not similar things in a photo:\tcupcake liner\tchocolate frosting, sprinkles or other decorations\ton the smaller side, typically eaten in one or two bites\toften served in multiples or as part of a larger dessert display", 34], "tuxedo": ["Yes. 'Tuxedo' has a tangible appearance and is a formal suit for men.\nA few things that are visually similar to 'tuxedo' but are not 'tuxedo' are:\tsuits\tblazers\ttailcoats\nThere are several useful visual features to tell there is 'tuxedo' and not similar things in a photo:\tblack or dark-colored jacket and pants\tsatin or silk lapels and stripe down the pants\twhite dress shirt with a bow tie and cummerbund\tworn for formal events and black-tie occasions.", 34], "elephants eye": ["Yes. 'Elephant's eye' has a tangible appearance and is a body part of an animal.\nA few things that are visually similar to 'elephant's eye' but are not 'elephant's eye' are:\teye of another animal\tround fruit or vegetable\nThere are several useful visual features to tell there is 'elephant's eye' and not similar things in a photo:\tlarge size\twhite or pale color\twrinkled or rough texture\tsurrounded by wrinkled skin or hair\tresembling the shape of a human eye but much larger.", 34], "puffy cloud": ["Yes. 'Puffy cloud' has a tangible appearance and refers to a specific type of cumulus cloud.\nA few things that are visually similar to 'puffy cloud' but are not 'puffy cloud' are:\tstratus cloud\tsmoke\tfog\nThere are several useful visual features to tell there is 'puffy cloud' and not similar things in a photo:\twhite and fluffy with a cotton-like appearance\tbright white on top and shades of grey on the underside\tlarge and rounded in shape\tfloating in the sky without any attachment or connection to the ground", 34], "floret": ["Yes. 'Floret' has a tangible appearance and is a small flower or cluster of flowers.\nA few things that are visually similar to 'floret' but are not 'floret' are:\tbud\tpetal\tleaf\nThere are several useful visual features to tell there is 'floret' and not similar things in a photo:\tsmall size\ton the stem\tcluster of flowers arranged tightly\tpetals forming a circular shape around the center", 33], "linoleum floor": ["Yes. 'Linoleum floor' has a tangible appearance and is a type of flooring material.\nA few things that are visually similar to 'linoleum floor' but are not 'linoleum floor' are:\ttile floor\tvinyl floor\twood floor\tconcrete floor\nThere are several useful visual features to tell there is 'linoleum floor' and not similar things in a photo:\tsmooth surface\twith a slight sheen\tbright, solid colors (often in a checkered pattern)\tor intricate designs (such as faux tiles, wood planks, or geometric shapes)", 33], "church building": ["Yes. 'Church building' has a tangible appearance and is a type of religious edifice.\nA few things that are visually similar to 'church building' but are not 'church building' are:\tcastle\tpalace\tmuseum\tmonastery\nThere are several useful visual features to tell there is 'church building' and not similar things in a photo:\tsteeple or bell tower\tcross on the roof\tnave or aisle\twindows with stained glass or religious motifs\thistorical architecture", 33], "silver knobs": ["Yes. 'Silver knobs' has a tangible appearance and refers to a specific type of object.\nA few things that are visually similar to 'silver knobs' but are not 'silver knobs' are: gold knobs, bronze knobs, plastic knobs, wooden knobs\nThere are several useful visual features to distinguish 'silver knobs' from similar things in a photo:\tround or geometric shape\tsilver or metal appearance\tsmooth or textured surface\tdesigned for gripping or turning\thardware installed on a door, cabinet, or drawer.", 33], "circular window": ["Yes. 'Circular window' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'circular window' but are not 'circular window' are:\trectangular window\tdoor\tmirror\tpicture frame\nThere are several useful visual features to tell there is 'circular window' and not similar things in a photo:\tround shape\tglass pane\tframed edges\tcircular or semi-circular muntins or grilles\tsunlight or light passing through the window\tCircular or semi-circular shaped frame surrounding the glass pane.", 33], "rusty": ["Yes. 'Rusty' has a tangible appearance and refers to a specific condition of metal or iron.\nA few things that are visually similar to 'rusty' but are not 'rusty' are:\tdirty\tmetallic paint\tcracked\nThere are several useful visual features to tell there is 'rusty' and not similar things in a photo:\torange, reddish-brown or yellowish-brown color\tpatches or flakes of corroded metal\tsurface texture that looks like rust or is rough to the touch", 33], "fluffy tail": ["Yes. 'Fluffy tail' has a tangible appearance and is a type of furry appendage.\nA few things that are visually similar to 'fluffy tail' but are not 'fluffy tail' are:\thair\twig\tfeather\tdust bunny\nThere are several useful visual features to tell there is 'fluffy tail' and not similar things in a photo:\tfurry or hairy\tlonger than the body of the animal\tit is attached to\ta specific animal, such as a cat, dog, or fox\tcan be a solid color or have distinctive patterns, such as stripes or spots", 33], "baker": ["Yes. 'Baker' has a tangible appearance and commonly wears a uniform while working.\nA few things that are visually similar to 'baker' but are not 'baker' are:\tcook\tchef\twaiter\nThere are several useful visual features to tell there is 'baker' and not similar things in a photo:\twearing a hat or a baker's cap\tholding a baking sheet or a rolling pin\twearing an apron\twith flour or dough on their hands\tor other kitchen utensils", 33], "tan dirt": ["Yes. 'Tan dirt' has a tangible appearance and is a type of soil.\nA few things that are visually similar to 'tan dirt' but are not 'tan dirt' are:\tsand\tgravel\trock\tdust\nThere are several useful visual features to tell there is 'tan dirt' and not similar things in a photo:\tsoil texture/structure\tbrown/tan color\tvisible presence of organic matter, such as roots or leaves", 33], "walk way": ["Yes. 'Walkway' has a tangible appearance and is a path for walking.\nA few things that are visually similar to 'walkway' but are not 'walkway' are:\tdriveway\tpatio\troad\tsidewalk\nThere are several useful visual features to tell there is 'walkway' and not similar things in a photo:\tmade of concrete, bricks or stones\tstraight or curved\tflat and even surface\tpathway or direction markings may be visible", 33], "silver frame": ["Yes. 'Silver frame' has a tangible appearance and is a type of picture frame.\nA few things that are visually similar to 'silver frame' but are not 'silver frame' are:\tgold frame\twooden frame\tblack frame\tdecorative frame\nThere are several useful visual features to tell there is 'silver frame' and not similar things in a photo:\tmade of silver metal or silver-painted material\trectangular or square shape\tthin profile and a clean design\tcan be standing or hanging on a wall", 33], "glass cups": ["Yes. 'Glass cups' has a tangible appearance and is a type of drinking container.\nA few things that are visually similar to 'glass cups' but are not 'glass cups' are:\tmugs\tbowls\tbottles\tjars\nThere are several useful visual features to tell there is 'glass cups' and not similar things in a photo:\tcylindrical shape\tmade of transparent or translucent glass\tclear and smooth surface\tthinner rim than the rest of the cup", 33], "camera lens": ["Yes. 'Camera lens' has a tangible appearance and is a part of a camera.\nA few things that are visually similar to 'camera lens' but are not 'camera lens' are:\tmicroscope lens\tbinoculars\tscope\tlaser pointer\nThere are several useful visual features to tell there is 'camera lens' and not similar things in a photo:\tcylindrical shape\twith focus or zoom markings\tclear glass or plastic material\tvarious sizes and lengths\tattached to a camera body\tor a tripod", 33], "train boarding platform": ["Yes. 'Train boarding platform' has a tangible appearance and is a structure built to provide passengers with access to trains.\nA few things that are visually similar to 'train boarding platform' but are not 'train boarding platform' are:\tbus platform\tmetro platform\tairport gate\trestaurant terrace\nThere are several useful visual features to tell there is 'train boarding platform' and not similar things in a photo:\tit's at the same level as the train doors\tthere are train tracks next to it\tpassengers are waiting or boarding trains on the platform\tsignage or digital displays indicate train schedules or destinations", 33], "metal bracket": ["Yes. 'Metal bracket' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'metal bracket' but are not 'metal bracket' are:\tmetal clamp\thook\twire\tpaper clip\nThere are several useful visual features to tell there is 'metal bracket' and not similar things in a photo:\tperpendicular or right angle shape\tmade of metal or metallic in color\tvarious shapes and sizes, for different functions\tused for supporting or fastening", 33], "polo": ["Yes. 'Polo' has a tangible appearance and is a sport.\nA few things that are visually similar to 'polo' but are not 'polo' are:\thockey\tfootball basketball\nThere are several useful visual features to tell there is 'polo' and not similar things in a photo:\thorses\tmallets\tpolo shirt and helmet\tpolo ball\tfield with goalposts.", 33], "blue graffiti": ["Yes. 'Blue graffiti' has a tangible appearance and is a type of street art.\nA few things that are visually similar to 'blue graffiti' but are not 'blue graffiti' are:\tblue paint on a wall\tblue marker on a wall\tblue ink on a wall\ta blue poster on a wall\nThere are several useful visual features to tell there is 'blue graffiti' and not similar things in a photo:\tartistic or stylized lettering or designs\tdepictions of people, animals, or objects\tuse of other colors besides blue, but with blue as the dominant color\tin a public space or location where graffiti is commonly found", 33], "orange tabby cat": ["Yes. 'Orange tabby cat' has a tangible appearance and is a specific type of cat.\nA few things that are visually similar to 'orange tabby cat' but are not 'orange tabby cat' are:\torange cat\tgolden retriever\tleopard\nThere are several useful visual features to tell there is 'orange tabby cat' and not similar things in a photo:\t fur with orange and brown stripes on the body and legs, and white or cream fur on the face, chest, and/or belly.\tround face with distinct \"M\" shaped marking on the forehead.\tAlmond-shaped eyes.\tPink or black nose.", 33], "parent": ["No. 'Parent' is too abstract to be distinguished in a photo.", 33], "cpu": ["Yes. 'CPU' has a tangible appearance and refers to the central processing unit of a computer.\nA few things that are visually similar to 'CPU' but are not 'CPU'\tare:\tmotherboard\tgraphics card\tRAM module\tpower supply unit\nThere are several useful visual features to tell there is 'CPU' and not similar things in a photo:\trectangular-shaped\tobject with a grid pattern of metal contacts\ton top of a motherboard\tor inside a computer case\tsmall, with a large fan on top or around it.", 33], "bird eye": ["Yes. 'Bird eye' has a tangible appearance and is a type of animal eye.\nA few things that are visually similar to 'bird eye' but are not 'bird eye' are:\treptile eye\tinsect eye\trobotic eye\nThere are several useful visual features to tell there is 'bird eye' and not similar things in a photo:\tround or oval shaped eye\tblack or dark iris\tbright, colorful, or iridescent iris\tsharp, pointed beak may be visible in the photo\tsurrounded by feathers or scales", 33], "smudge": ["Yes. 'Smudge' has a tangible appearance and refers to a mark made by dirt or another substance.\nA few things that are visually similar to 'smudge' but are not 'smudge' are:\tshadows\treflections\tstains\tscratches\t\nThere are several useful visual features to tell there is 'smudge' and not similar things in a photo:\tsmooth appearance\tsoft edges or irregular shape\tdifferent color or texture from the surrounding surface\tmay appear to be partially removed with rubbing", 33], "window building": ["No. 'Window building' is too vague or abstract to be distinguished in a photo. Perhaps it would be better to use a more specific term, such as 'building with large windows' or 'glass building', in order to make it a visually concrete concept.\n\nA few things that are visually similar to 'building with large windows' but are not 'window building' are:\tskyscrapers, modern office buildings, shopping malls, museums, libraries\n\nThere are several useful visual features to tell there is 'building with large windows' and not similar things in a photo:\tdominated by expansive glass areas or glass curtain walls, large and clear windows covering a significant portion of the building, strong vertical and horizontal lines, minimal decorations on the exterior facade.", 33], "slabs": ["Yes. 'Slabs' has a tangible appearance and refers to flat and thick pieces of materials, such as stone or concrete.\nA few things that are visually similar to 'slabs' but are not 'slabs' are:\trocks\tbricks\ttiles\tpavers\tboards\nThere are several useful visual features to tell there are 'slabs' and not similar things in a photo:\trectangular or square shape\tthick and flat surface\tsolid material like stone or concrete", 33], "pink frisbee": ["Yes. 'Pink frisbee' has a tangible appearance and is a specific type of flying disc.\nA few things that are visually similar to 'pink frisbee' but are not 'pink frisbee' are:\tother types of frisbees\tflying saucers\tplastic plates\twith a pink sticker\nThere are several useful visual features to tell there is 'pink frisbee' and not similar things in a photo:\tcircular shape\tflat\tplastic material\tpink color\tridged edge", 33], "cargo train": ["Yes. 'Cargo train' has a tangible appearance and is a type of train used for transporting goods or cargo.\nA few things that are visually similar to 'cargo train' but are not 'cargo train' are:\tpassenger train\tsingle locomotive subways or metro trains\tmonorail\ttram\ttrolley or streetcar\nThere are several useful visual features to tell there is 'cargo train' and not similar things in a photo:\t\nnumerous cars designed for cargo\tcontainer or flatbed cars\tvariety of shapes and sizes of cargo cars\tmoving on tracks on the ground or elevated tracks\tnot designed to carry passengers or have seating areas.", 33], "orange towel": ["Yes. 'Orange towel' has a tangible appearance and is a type of fabric.\nA few things that are visually similar to 'orange towel' but are not 'orange towel' are:\twashcloth\ttablecloth\tbathmat\thandkerchief\tnapkin\nThere are several useful visual features to tell there is 'orange towel' and not similar things in a photo:\trectangular or square\tused for drying or wiping\tobject or surface appears damp or wet\tusually made of terry cloth or cotton\torange in color", 33], "balck": ["No. 'Black' is too vague or abstract to be distinguished in a photo. It's a color and not a tangible object. \nTherefore, there are no things similar to 'black' that can be listed, and there are no useful visual features for distinguishing 'black' from other colors in a photo.", 33], "houseplant": ["Yes. 'Houseplant' has a tangible appearance and is a type of plant that can be grown indoors.\nA few things that are visually similar to 'houseplant' but are not 'houseplant' are:\tweed\tflower\tbush\tshrub\nThere are several useful visual features to tell there is 'houseplant' and not similar things in a photo:\tpotted plant\tgreen leaves or stems\tindoor setting\tno flowers on the plant or small flowers that don't dominate the plant", 33], "track pad": ["Yes. 'Track pad' has a tangible appearance and is a type of input device for a computer or other electronic device.\nA few things that are visually similar to 'track pad' but are not 'track pad' are:\tmouse\tkeyboard\tdrawing tablet\nThere are several useful visual features to tell there is 'track pad' and not similar things in a photo:\trectangular or square shape\tflat surface with no buttons or wheels\tsensitivity to touch or gestures\tsometimes with a different texture or material from the rest of the device.", 33], "webcam": ["Yes. 'Webcam' has a tangible appearance and is a type of camera device.\nA few things that are visually similar to 'webcam' but are not 'webcam' are:\tcamcorder\t\ndigital camera\t\nsecurity camera\t\nsmartphone\t\ntablet\t\nThere are several useful visual features to tell there is 'webcam' and not similar things in a photo: small size, usually attached to a computer\tor laptop; there is a lens and often a small light on it. The device may also have a built-in microphone.", 33], "hoof horse": ["Yes. 'Hoof horse' has a tangible appearance and refers to the hooves of a horse.\nA few things that are visually similar to 'hoof horse' but are not 'hoof horse' are:\thuman feet\tcow hooves\nThere are several useful visual features to tell there is 'hoof horse' and not similar things in a photo:\tcurved shape\thard exterior\tbrown or dark color\thairy fetlock (hair on the leg just above the hoof)\tlarger size than human feet.", 33], "vein": ["Yes. 'Vein' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'vein' but are not 'vein' are:\tbranch\tof a tree\tor a river\tcrack\tor a crevice\ton a surface\nThere are several useful visual features to tell there is 'vein' and not similar things in a photo:\ttube-like structure\tcarries blood\tpresent in the human body\tbluish or greenish in color (due to blood being circulated)", 33], "silver towel rack": ["Yes. 'Silver towel rack' has a tangible appearance and is a type of bathroom accessory.\nA few things that are visually similar to 'silver towel rack' but are not 'silver towel rack' are:\tcoat hook\tshower curtain rod\tshelf\thanger\tbar\nThere are several useful visual features to tell there is 'silver towel rack' and not similar things in a photo:\tlong and narrow\twith multiple horizontal bars or hooks\tsilver or metallic color\tmounted on a wall or door\tin a bathroom or near water source.", 33], "clock top building": ["No. 'Clock top building' is too vague and abstract to be distinguished in a photo.", 33], "froth": ["Yes. 'Froth' has a tangible appearance and is a type of foam.\nA few things that are visually similar to 'froth' but are not 'froth' are:\tbubble bath\tsoap foam\twhipped cream\tshaving cream\nThere are several useful visual features to tell there is 'froth' and not similar things in a photo: \tformed from liquid with air or gas mixed in\twhite or light-colored\tfloating on top of the liquid or pouring over the edge\tof a drink, such as a cappuccino", 33], "capris": ["Yes. 'Capris' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'capris' but are not 'capris' are:\tleggings\ttights\tcropped pants\nThere are several useful visual features to tell there is 'capris' and not similar things in a photo:\tPants that end at mid-calf\tlength between shorts and regular pants\ttight-fitting, but not skin-tight.tight-fitting knee-length shorts with buttons or zippers at the hem.", 33], "rocky hill": ["Yes. 'Rocky hill' has a tangible appearance and refers to a hill made up mainly of rocks.\nA few things that are visually similar to 'rocky hill' but are not 'rocky hill' are:\tMountain\tLandfill\tRubble pile\tGlacier\nThere are several useful visual features to distinguish 'rocky hill' from the listed similar things in a photo:\t\n\n- Large rocks and boulders covering the hill.\n- The hill must be primarily made up of rocks.\n- The angle of the incline must be steep.\n- The hill may be barren and rocky without much vegetation.\n- The rocks may be of different shapes and sizes.", 33], "wooden ramp": ["Yes. 'Wooden ramp' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'wooden ramp' but are not 'wooden ramp' are:\twooden bridge\tboardwalk\tsteps\nThere are several useful visual features to tell there is 'wooden ramp' and not similar things in a photo:\tsloping surface made of wood\toriented towards a specific area or height\tused for accessibility\tto assist in movement from a lower to a higher ground level", 33], "water pitcher": ["Yes. 'Water pitcher' has a tangible appearance and is a container designed for holding and pouring water.\nA few things that are visually similar to 'water pitcher' but are not 'water pitcher' are:\tcoffee pot\tmilk jug\tteapot\t\nThere are several useful visual features to tell there is 'water pitcher' and not similar things in a photo:\ta spout\tfor holding and pouring water\ta handle\tfor carrying and pouring a wide or narrow body, depending on the design, which can be made of glass, ceramic or plastic.", 33], "orange boat": ["Yes. 'Orange boat' has a tangible appearance and is a type of watercraft.\nA few things that are visually similar to 'orange boat' but are not 'orange boat' are:\tkayak\trubber dinghy\tcanoe\t\nThere are several useful visual features to tell there is 'orange boat' and not similar things in a photo:\torange color\tboxy or elongated shape\tmast or sail\tif it is a motorized boat, it will have an engine and propeller\tat least one visible window or opening in the cabin area.", 33], "tall window": ["Yes. 'Tall window' has a tangible appearance and describes a specific type of window.\nA few things that are visually similar to 'tall window' but are not 'tall window' are:\tdoorways\twall niches\tshelves\nThere are several useful visual features to distinguish 'tall window' from the listed similar things in a photo:\ttaller than it is wide\twider than a doorway\tsmaller or taller than a wall niche\tno hinges or swinging mechanism (like a door)\tno shelf, curtain or fabric attached to the top or bottom", 33], "spinach pizza": ["Yes. 'Spinach pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'spinach pizza' but are not 'spinach pizza' are:\tcheese pizza\ttomato pizza\tpizza with vegetables\tsalad\nThere are several useful visual features to tell there is 'spinach pizza' and not similar things in a photo:\tcircular shape\tflat and thin dough\ttomato sauce with cheese\ton top of the toppings, spinach is visible in green color.", 33], "teddy bears": ["Yes. 'Teddy bears' has a tangible appearance and is a type of stuffed animal.\nA few things that are visually similar to 'teddy bears' but are not 'teddy bears' are:\tother stuffed animals\tpillows\tdecorative cushions\nThere are several useful visual features to tell there is 'teddy bears' and not similar things in a photo:\tbear-like appearance\tfur or soft material\tpaw-like hands and feet\tblack nose and eyes\tno realistic animal features (such as tusks or antlers)", 33], "grass lawn": ["Yes. 'Grass lawn' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'grass lawn' but are not 'grass lawn' are:\tcrops\tparks\tforests\tgardens\nThere are several useful visual features to tell there is 'grass lawn' and not similar things in a photo:\tuniform surface of grass blades\tgreen color\tflat and open space", 33], "plaid shorts": ["Yes. 'Plaid shorts' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'plaid shorts' but are not 'plaid shorts' are:\tsolid color shorts\tpatterned shorts\twith other checkered patterns\nThere are several useful visual features to tell there is 'plaid shorts' and not similar things in a photo:\tcheckered pattern\twith at least two colors\tlong enough to cover the hips and thighs\tworn as casual attire", 33], "round lights": ["Yes. 'Round lights' has a tangible appearance and can be found in various settings.\nA few things that are visually similar to 'round lights' but are not 'round lights' are:\ttraffic lights\tbulbs\tmoon\nThere are several useful visual features to tell there are 'round lights' and not similar things in a photo:\tmany of them placed together\tin different colors\tused for decoration\tor illumination\ton a string or hanging from a ceiling.", 33], "bicycle helmet": ["Yes. 'Bicycle helmet' has a tangible appearance and is a kind of protective gear.\nA few things that are visually similar to 'bicycle helmet' but are not 'bicycle helmet' are:\tskateboard helmet\tmotorcycle helmet\thard hat\nThere are several useful visual features to tell there is 'bicycle helmet' and not similar things in a photo:\t\nrounded and elongated shape over the head\t\nvented or breathable to keep wearer cool\t\nbright colors or reflectors to improve visibility\t\nstraps under the chin to secure the helmet in place", 33], "cut grass": ["Yes. 'Cut grass' has a tangible appearance and refers to the action of mowing or trimming grass.\nA few things that are visually similar to 'cut grass' but are not 'cut grass' are:\tlong grass\thay\tstraw\nThere are several useful visual features to tell there is 'cut grass' and not similar things in a photo:\tshort, trimmed blades of grass\tuniform height and length of grass blades\tneatly arranged clippings on the ground or in a pile\tin a landscaped area or lawn", 33], "manufacturer": ["No. 'Manufacturer' is too abstract to be distinguished in a photo. However, the products created by a manufacturer may have a tangible appearance that can be visually distinguished.\nA few things that are visually similar to 'manufacturer' but are not 'manufacturer' are:\tassembly line\twarehouse\tmachinery\tand tools\tproducts\nThere are several useful visual features to identify a product created by a manufacturer, such as:\tlogo or branding on the product\ta specific design or style that corresponds to a certain manufacturer\ta label or tag specifying the manufacturer's name and location", 33], "mall": ["Yes. 'Mall' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'mall' but are not 'mall' are:\tmarket\tschool\toffice building\tlibrary\nThere are several useful visual features to tell there is 'mall' and not similar things in a photo:\tsignage indicating shops and stores\tlarge building with multiple entrances\tand storefronts\tparking lot\tindoor or covered areas for shopping or walking", 33], "power cables": ["Yes. 'Power cables' has a tangible appearance and is a kind of cable that carries electricity.\nA few things that are visually similar to 'power cables' but are not 'power cables' are:\tdata cables\taudio cables\trope\nThere are several useful visual features to tell there is 'power cables' and not similar things in a photo:\tusually thick and round\tmade of rubber or plastic\tconnected to power outlets or electrical devices\tmay have distinguishing colors like black, white or grey\thanging from electrical poles or running along the ground", 33], "slipper": ["Yes. 'Slipper' has a tangible appearance as it is a type of footwear.\nA few things that are visually similar to 'slipper' but are not 'slipper' are:\tsandal\tshoe\tboot\theel\nThere are several useful visual features to tell there is 'slipper' and not similar things in a photo:\tbackless or open-toe design\tmostly flat or low-heeled\tsoft and comfortable material (like fabric or fur)", 33], "plastic cover": ["Yes. 'Plastic cover' has a tangible appearance and is a piece of material used to cover something.\nA few things that are visually similar to 'plastic cover' but are not 'plastic cover' are:\tplastic wrap\tplastic bags\nThere are several useful visual features to tell there is 'plastic cover' and not similar things in a photo:\tstretchy and form-fitting to the object being covered\ttranslucent or transparent\tplastic texture\tgenerally used to cover furniture or other surfaces", 33], "hairbrush": ["Yes. 'Hairbrush' has a tangible appearance and is a tool used for grooming hair.\nA few things that are visually similar to 'hairbrush' but are not 'hairbrush' are:\tcombs\tbristle brushes\tpaint brushes\ttoothbrushes\nThere are several useful visual features to tell there is 'hairbrush' and not similar things in a photo:\tlong handle\tfor bristle brushes or combs, straight, evenly spaced teeth\tfor paint brushes, bristles may be long or short, but are usually not evenly spaced", 33], "eye brow": ["Yes. 'Eye brow' has a tangible appearance and is a part of the human face.\nA few things that are visually similar to 'eye brow' but are not 'eye brow' are:\teyelash\tfur\thair\nThere are several useful visual features to tell there is 'eye brow' and not similar things in a photo:\tarched shape\thair above the eye\tsocket anatomy\tmatching hair color with the head", 33], "grey chain link fence": ["Yes. 'Grey chain link fence' has a tangible appearance and is a type of fencing.\nA few things that are visually similar to 'grey chain link fence' but are not 'grey chain link fence' are:\tiron fence\twire mesh\tfarm fence\tplastic fence\nThere are several useful visual features to tell there is 'grey chain link fence' and not similar things in a photo:\tmade from interlocking steel wire\tdiamond-shaped patterning\tgrey or silver color\tgridded texture\ttransparency that allows seeing through the fence.", 33], "kitchen faucet": ["Yes. 'Kitchen faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'kitchen faucet' but are not 'kitchen faucet' are:\tbathroom faucet\tshowerhead\tpipe valve\tsoap dispenser\nThere are several useful visual features to tell there is 'kitchen faucet' and not similar things in a photo:\tlocated above the kitchen sink\thandles to adjust water temperature\tand water flow rates\tspout to release water stream\tsprayer or pull-out wand for rinsing dishes and cleaning the sink.", 33], "wet ground": ["Yes. 'Wet ground' has a tangible appearance and can be visually distinguished from dry ground.\nA few things that are visually similar to 'wet ground' but are not 'wet ground' are:\tdark patches of soil\tmud\tpuddles\nThere are several useful visual features to tell there is 'wet ground' and not similar things in a photo:\tdarkened soil\treflective surface\twater droplets visible on the surface\tsurface texture slightly different from dry ground", 33], "deck bus": ["Yes. 'Deck bus' has a tangible appearance and refers to a specific type of double-decker bus.\nA few things that are visually similar to 'deck bus' but are not 'deck bus' are:\tschool bus\ttour bus\tdouble-decker trailer\nThere are several useful visual features to distinguish 'deck bus' from similar things in a photo: two levels of seating\twide and boxy shape\twindows on both levels of the bus\tdoor at the middle or front of the bus with a staircase leading to the upper level", 33], "chicken breast": ["Yes. 'Chicken breast' has a tangible appearance and is a type of meat.\nA few things that are visually similar to 'chicken breast' but are not 'chicken breast' are:\tduck breast\tturkey breast\tpork chops\tbeef steaks\nThere are several useful visual features to tell there is 'chicken breast' and not similar things in a photo:\tlight-colored meat, typically white or light pink\tboneless and skinless, or with a small piece of bone still attached\tsmooth texture, without visible fat or gristle typical oblong shape, tapering at one end.", 33], "airplane propeller": ["Yes. 'Airplane propeller' has a tangible appearance and is a mechanical component of an airplane.\nA few things that are visually similar to 'airplane propeller' but are not 'airplane propeller' are:\tfan\tblender\tboat propeller\twind turbine\nThere are several useful visual features to tell there is 'airplane propeller' and not similar things in a photo:\tattached to the front of the airplane\tspinning blades\tfolded or pointed forward\twhen spinning, appears to be blurred in photos.", 33], "safety jacket": ["Yes. 'Safety jacket' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'safety jacket' but are not 'safety jacket' are:\tvest\tcoat\traincoat\twork apron\nThere are several useful visual features to tell there is 'safety jacket' and not similar things in a photo:\tbright neon or fluorescent colors\treflective strips or patches\tzippers or buttons at the front\tsleeveless or short-sleeved design\temblazoned with safety-related words or symbols, such as 'safety' or 'caution'", 33], "dark windows": ["Yes. 'Dark windows' has a tangible appearance and refers to windows that do not allow much light to pass through.\nA few things that are visually similar to 'dark windows' but are not 'dark windows' are:\tregular windows\tdecorative windows\tboarded up windows\nThere are several useful visual features to tell there is 'dark windows' and not similar things in a photo:\tlittle or no light coming through the window\tdark or black tinted glass\topaque or frosted glass", 33], "skate": ["Yes. 'Skate' has a tangible appearance and is a type of footwear or sports equipment.\nA few things that are visually similar to 'skate' but are not 'skate' are:\tshoe\tboot\troller skates\tice skates\nThere are several useful visual features to tell there is 'skate' and not similar things in a photo:\tflat sole with a curved blade\tshoelaces or straps\tseparate parts for the foot and the blade\tbent blade at the front and back sections", 33], "denim pants": ["Yes. 'Denim pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'denim pants' but are not 'denim pants' are:\ttrousers, leggings, khakis, corduroys, slacks.\nThere are several useful visual features to tell there is 'denim pants' and not similar things in a photo:\ttypical blue color, although other colors are possible, too\tcotton material with a distinct diagonal pattern (known as twill) and heavy rough weave\ttypical button and zipper closure\tfive pockets\t- two front pockets, two back pockets, and one small pocket within the larger right-side front pocket.", 33], "glass wall": ["Yes. 'Glass wall' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'glass wall' but are not 'glass wall' are:\twindow\treflective surface\tmirrored wall\tsmooth surface\nThere are several useful visual features to tell there is 'glass wall' and not similar things in a photo:\tmade entirely of glass or acrylic material\ttransparent or translucent\tallows light to pass through\tdoes not have a frame around it", 33], "tan tile": ["Yes. 'Tan tile' has a tangible appearance and refers to a specific color of tiles.\nA few things that are visually similar to 'tan tile' but are not 'tan tile' are: brown bricks, beige stones, sandy concrete.\nThere are several useful visual features to distinguish 'tan tile' from the listed similar things in a photo: a flat, square or rectangular shape; one side is finished with a smooth, glazed or polished surface, and the other side is rough; a tan or light brown color with some variations in tone or pattern.", 33], "purple bag": ["Yes. 'purple bag' has a tangible appearance and is a colored container.\nA few things that are visually similar to 'purple bag' but are not 'purple bag' are:\tpurple container\tpurple box\tpurple backpack\tpurple purse\tpurple bin\nThere are several useful visual features to tell there is 'purple bag' and not similar things in a photo:\tmade of fabric or material with a texture\tpurple in color\twith handles or straps\tto carry items or personal belongings", 33], "pillow case": ["Yes. 'Pillow case' has a tangible appearance and is a type of bedding accessory.\nA few things that are visually similar to 'pillow case' but are not 'pillow case' are:\ttote bag\tpurse\tshopping bag\tpacking cubes\nThere are several useful visual features to tell there is 'pillow case' and not similar things in a photo:\trectangular or square shape\tslip-on design\tsoft and glossy material usually made from cotton or silk", 33], "drainer": ["Yes. 'Drainer' has a tangible appearance and is a type of kitchen or bathroom tool.\nA few things that are visually similar to 'drainer' but are not 'drainer' are:\tstrainer\tsieve\tcolander\nThere are several useful visual features to tell there is 'drainer' and not similar things in a photo:\tflat surface with holes\tor slots to drain water\tor other liquids\tlocated next to a sink or a tub or a shower\ttraditionally made of metal or plastic.", 33], "juice box": ["Yes, 'juice box' has a tangible appearance and is a type of beverage container.\nA few things that are visually similar to 'juice box' but are not 'juice box' are:\tfruit cup\tmilk carton\tcoffee cup\tteapot\nThere are several useful visual features to tell there is 'juice box' and not similar things in a photo:\trectangular shape\twith a straw attached\tor a spout on the top\thaving illustrations of fruit or a juice brand", 33], "receptacle": ["Yes. 'Receptacle' has a tangible appearance and refers to a container or holder for something.\nA few things that are visually similar to 'receptacle' but are not 'receptacle' are:\tbasket\tbox\tbag\tvase\tcan\nThere are several useful visual features to tell there is 'receptacle' and not similar things in a photo:\tan opening to deposit or extract objects\tfrom different materials, such as glass, plastic, metal, or cloth\tdiffers in shape and size, depending on the objects it's meant to hold.", 33], "brunette": ["Yes. 'Brunette' has a tangible appearance and refers to a person with dark brown hair.\nThere are not many things that are visually similar to 'brunette' but are not 'brunette', as it is a pretty specific concept. Some things that may be visually similar to brunette hair include dark-colored wigs or other kinds of hair dye.\nUseful visual features for distinguishing 'brunette' from similar things in a photo would include:\t\n- Specifically brown, and usually a medium-to-dark shade of brown\n- Not completely black, although very dark brown hair may appear almost black in certain lighting\n- Thin or thick, depending on the hair type of the individual pictured\n- Growing from a person's scalp, often styled in a variety of different ways.", 33], "cabinet brown": ["Yes. 'Cabinet brown' has a tangible appearance and can be described by a specific color.\nA few things that are visually similar to 'cabinet brown' but are not 'cabinet brown' are:\tchocolate\twood\tfirewood\tcoffee beans\nThere are several useful visual features to tell there is 'cabinet brown' and not similar things in a photo:\tdark brown, almost black color\tsmooth surface, potentially glossy or matte\tassociated with furniture or cabinetry or other household objects.", 33], "engine car": ["Yes. 'Engine car' has a tangible appearance and is a type of transportation.\nA few things that are visually similar to 'engine car' but are not 'engine car' are:\tmotorcycle\ttruck\tbicycle\ttrain\nThere are several useful visual features to tell there is 'engine car' and not similar things in a photo:\tfour wheels\tengine under the hood\tseats for passengers and/or driver\tdoors and windows\trearview mirrors\theadlights and taillights", 33], "tortillas": ["Yes. 'Tortillas' has a tangible appearance and is a kind of flatbread.\nA few things that are visually similar to 'tortillas' but are not 'tortillas' are:\tpitas\tnaans\tchapatis\tcrepes\nThere are several useful visual features to tell there is 'tortillas' and not similar things in a photo:\tthin and flat\tcircular or oval shape\tusually made of corn or wheat\tmay have grill marks or charred spots", 33], "ferris wheel": ["Yes. 'Ferris wheel' has a tangible appearance and is an amusement ride.\nA few things that are visually similar to 'ferris wheel' but are not 'ferris wheel' are:\troller coaster\tcarousel\tswing ride\tobservation wheel\nThere are several useful visual features to tell there is 'ferris wheel' and not similar things in a photo: Giant wheel-like structure with seats or gondolas attached to the rim, rotating horizontally around a central axis, has spokes or pipes connecting the rim to the central structure, usually brightly colored and illuminated at night.", 33], "train signal": ["Yes. 'Train signal' has a tangible appearance and is a type of traffic signal.\nA few things that are visually similar to 'train signal' but are not 'train signal' are:\ttraffic light\tpedestrian crossing signal\tbike lane signal\nThere are several useful visual features to tell there is 'train signal' and not similar things in a photo:\tthree lights arranged vertically\tred or yellow lights indication to stop or slow down\tforward-facing light signals to clear the track\tsound alarms or bells to indicate dangerous situations or arrivals/departures.", 33], "tree leaf": ["Yes. 'Tree leaf' has a tangible appearance and is a type of foliage.\nA few things that are visually similar to 'tree leaf' but are not 'tree leaf' are:\tfern\tflower\tweed\tpine needle\nThere are several useful visual features to tell there is 'tree leaf' and not similar things in a photo:\tflat and thin\tlarge and translucent veins\tgreen color\telliptical or ovate shape with pointed edges", 33], "sweat pants": ["Yes. 'Sweatpants' have a tangible appearance and are a type of clothing.\nA few things that are visually similar to 'sweatpants' but are not 'sweatpants' are:\tleggings\tyoga pants\tjeans\tpajama pants\nThere are several useful visual features to tell there are 'sweatpants' and not similar things in a photo:\tsoft and stretchy material\tloose fit\tdrawstring waistband\ttapered legs\twide elastic ankles.", 33], "cheeks": ["Yes. 'Cheeks' has a tangible appearance and is a part of the face.\nA few things that are visually similar to 'cheeks' but are not 'cheeks' are:\tjaw\tbones\tlips\tchin\nThere are several useful visual features to tell there is 'cheeks' and not similar things in a photo:\tSoft and fleshy\tarea between the eyes and the ears\ton either side of the nose\tand below the eyes\tsmooth and round appearance when person smiles\tfolds when a person frowns", 33], "sparse": ["No. 'Sparse' is too vague or abstract to be distinguished in a photo. It is a descriptive term related to the density of objects in a space or area. \n\nTherefore, there are no visually similar things to 'sparse'.", 33], "oak": ["Yes. 'Oak' has a tangible appearance and is a species of tree.\nA few things that are visually similar to 'oak' but are not 'oak' are:\tmaple\tbirch\telm\twillow\nUseful visual features to distinguish 'oak' from the listed similar things in a photo are:\tbroad, lobed leaves\tacorns growing on the tree\tthick trunk and strong branches\tcracked or ridged bark in mature trees.", 33], "blinders": ["Yes. 'Blinders' has a tangible appearance and is a type of equipment used for horses.\nA few things that are visually similar to 'blinders' but are not 'blinders' are:\tgoggles\tsunglasses\tmask\tbandana\nThere are several useful visual features to tell there are 'blinders' and not similar things in a photo:\tleather or plastic construction\ton the sides of a horse's head\tto prevent the horse from seeing objects to its side or rear", 32], "serving": ["No. 'Serving' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we take 'serving' to refer to the act of presenting or distributing food, some things that are visually similar but not 'serving' are:\tpouring\tdropping\tdisplaying\tstoring\n\nUseful visual features for distinguishing 'serving' from the listed similar things in a photo would include:\tpresenting food dishes with utensils\ttogether with tableware or serving plates\tin a dining or serving setting.", 32], "direction": ["No. 'Direction' is too vague or abstract to be distinguished in a photo.", 32], "street lamp post": ["Yes. 'Street lamp post' has a tangible appearance and is a kind of outdoor lighting fixture.\nA few things that are visually similar to 'street lamp post' but are not 'street lamp post' are:\ttraffic lights\tfire hydrants\tflag poles\tdecorative poles\nThere are several useful visual features to tell there is 'street lamp post' and not similar things in a photo:\ttall and cylindrical structure\thanging light fixture at the top\tpower lines or cables attached to it\tmounted on a base\tor on the sidewalk", 32], "styrofoam container": ["Yes. 'Styrofoam container' has a tangible appearance and is a type of disposable food container.\nA few things that are visually similar to 'styrofoam container' but are not 'styrofoam container' are:\tplastic container\tpaper box\tglass jar\nThere are several useful visual features to distinguish 'styrofoam container' from the listed similar things in a photo:\tlightweight\tfloats in water\tdull white or beige color\tsmall round depressions on the lid or container part typically used for sealing purposes.", 32], "frying pan": ["Yes. 'Frying pan' has a tangible appearance and is a type of cooking utensil.\nA few things that are visually similar to 'frying pan' but are not 'frying pan' are:\tpot\tgriddle\twok\tskillet\nThere are several useful visual features to tell there is 'frying pan' and not similar things in a photo:\tflat bottom\tshallow sides\tlong handle\toften made of metal or non-stick material\tcircular or oval shape\tsmall diameter relative to the length of the handle", 32], "gray table": ["Yes. 'Gray table' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'gray table' but are not 'gray table' are:\tgray chair\tgray counter\tgray cabinet\tgray desk\nThere are several useful visual features to tell there is a 'gray table' and not similar things in a photo:\ta flat surface for holding objects\tseveral legs for stability\tproportioned height and width\tto be made of wood, plastic, metal, or other materials\tto be used as a work surface or a dining area", 32], "stainless steel stove": ["Yes, 'stainless steel stove' has a tangible appearance and is a kitchen appliance.\nA few things that are visually similar to 'stainless steel stove' but are not 'stainless steel stove' are: gas range, electric range, oven, grill, cooktop.\nThere are several useful visual features to tell there is a 'stainless steel stove' and not similar things in a photo: rectangular shape, burners on top, oven at the bottom, knobs/dials for temperature control, stainless steel surface. Additionally, a visible brand name or logo can also help identify a specific model or type of stove.", 32], "spotlight": ["Yes. 'Spotlight' has a tangible appearance and is a kind of light.\nA few things that are visually similar to 'spotlight' but are not 'spotlight' are:\tlamp\tflashlight\tstreetlight\tprojector\nThere are several useful visual features to tell there is 'spotlight' and not similar things in a photo:\tnarrow and focused beam of light\tconical shape\toften mounted on a stand or suspended from a ceiling\tmay have adjustable angles and intensity of light", 32], "ice cubes": ["Yes. 'Ice cubes' has a tangible appearance and is a solid form of frozen water.\nA few things that are visually similar to 'ice cubes' but are not 'ice cubes' are:\trock salt\tdiamonds\tsugar cubes\nThere are several useful visual features to tell there is 'ice cubes' and not similar things in a photo:\ttranslucent color\tclear or white in color\trectangular or cubic shape\twet or damp appearance\tsomewhat reflective surface\tmelting (when photographed in context of a drink)", 32], "story window": ["No. 'Story window' is too vague or abstract to be distinguished in a photo.", 32], "silver cup": ["Yes. 'Silver cup' has a tangible appearance and is a type of drinking vessel.\nA few things that are visually similar to 'silver cup' but are not 'silver cup' are:\tsilver bowl\tsilver vase\tsilver pitcher\nThere are several useful visual features to tell there is 'silver cup' and not similar things in a photo:\tone-handled or two-handled drinking vessel\tsilver material\tcup shape\twith or without a lid\tno spout or handle other than the main handle", 32], "wood slats": ["Yes. 'Wood slats' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wood slats' but are not 'wood slats' are:\ttiles\tstones\tbrick walls\t\nThere are several useful visual features to tell there is 'wood slats' and not similar things in a photo:\tlong and narrow pieces of wood\tarranged side by side\twith visible gaps between each piece\trustic or natural look", 32], "bumps": ["Yes. 'Bumps' has a tangible appearance and is a common physical feature.\nA few things that are visually similar to 'bumps' but are not 'bumps' are:\thills\tdimples\tpimples\tcraters\nThere are several useful visual features to tell there are 'bumps' and not similar things in a photo:\ta rounded protrusion on a surface, which could be large or small, numerous or few, even or uneven, smooth or rough", 32], "outfielder": ["Yes. 'Outfielder' has a tangible appearance and is a position in baseball.\nA few things that are visually similar to 'outfielder' but are not 'outfielder' are:\tpitcher\tcatcher\tfirst baseman\tshortstop\nThere are several useful visual features to tell there is 'outfielder' and not similar things in a photo:\tstanding in the outfield, away from the bases\twearing a baseball glove and uniform\tnumber on the back of the uniform may be between 7 and 9", 32], "commuter bus": ["Yes. 'Commuter bus' has a tangible appearance and is a type of bus used for transporting people to work or school.\nA few things that are visually similar to 'commuter bus' but are not 'commuter bus' are:\tcity bus\tdouble-decker bus\ttourist bus\tcoach bus\nThere are several useful visual features to tell there is 'commuter bus' and not similar things in a photo:\tmedium to large size\twith several rows of seats\tside doors\tfor commuting purposes, will typically have clear signage indicating the bus's route or destination.", 32], "cooking": ["No. 'Cooking' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to 'cooking' but are not 'cooking' could be: lighting a fire, playing with kitchen utensils, reading a recipe book. \n\nThere are no specific visual features to be distinguished as 'cooking', as the action of cooking involves a series of processes and actions that may not be visually concrete. However, the following visual cues may imply cooking is taking place: a stove or oven turned on, steam or smoke coming from pots or pans, ingredients being chopped or mixed together, food being served on plates or bowls.", 32], "brochure": ["Yes. 'Brochure' has a tangible appearance and is a type of printed material.\nA few things that are visually similar to 'brochure' but are not 'brochure' are:\tflyers\tnewspapers\tmagazines\tpamphlets\nThere are several useful visual features to tell there is 'brochure' and not similar things in a photo:\tfolded pages\torganized layout\ttext and images\trelevant content professionally printed\tlayout design for marketing purposes", 32], "orange ball": ["Yes. 'Orange ball' has a tangible appearance.\nA few things that are visually similar to 'orange ball' but are not 'orange ball' are:\ttennis ball\tbasketball\tmelon\tsun\nThere are several useful visual features to distinguish 'orange ball' from the listed similar things in a photo:\tround shape\tbright orange color\tsmooth texture\ttypically smaller than a basketball", 32], "iron rod": ["Yes. 'Iron rod' has a tangible appearance and is a long, thin, cylindrical piece of iron.\nA few things that are visually similar to 'iron rod' but are not 'iron rod' are: copper rod, steel rod, wooden stick, plastic tube, bamboo stick.\nThere are several useful visual features to tell there is 'iron rod' and not similar things in a photo:\tmetallic grey or silver color\tstraight, rigid, and sturdy appearance\tridged or textured surface\tuniform thickness across the length of the rod.", 32], "honey": ["Yes. 'Honey' has a tangible appearance and is a kind of viscous, sweet liquid.\nA few things that are visually similar to 'honey' but are not 'honey' are:\tsyrup\tnectar\tmolasses\nThere are several useful visual features to tell there is 'honey' and not similar things in a photo:\tgolden or amber color\tthick, sticky texture\ttranslucent or clear appearance\thoused in a honeycomb or jar\tassociated with bees or hives.", 32], "sedan car": ["Yes. 'Sedan car' has a tangible appearance and is a type and model of car.\nA few things that are visually similar to 'sedan car' but are not 'sedan car' are:\tSUV\tpickup truck\tcoupe\tvan\nThere are several useful visual features to tell there is 'sedan car' and not similar things in a photo:\tfour doors\ttwo rows of seats\ttraditional trunk style and placement\tsleek, aerodynamic design", 32], "stereo": ["Yes. 'Stereo' has a tangible appearance and is a type of audio equipment.\nA few things that are visually similar to 'stereo' but are not 'stereo' are:\tTV\tsoundbar\trecord player\tsound system\nThere are several useful visual features to tell there is 'stereo' and not similar things in a photo:\ttwo or more speakers\tlabels or logos indicating brands or model numbers\tknobs or buttons for controlling volume or bass/treble levels\tinputs for auxiliary devices such as phones or MP3 players.", 32], "advertising banner": ["Yes. 'Advertising banner' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'advertising banner' but are not 'advertising banner' are:\tstore signs\tbillboards\tdirection signs\twarning signs\nThere are several useful visual features to tell there is 'advertising banner' and not similar things in a photo:\tlarge size (usually rectangular or square)\tdisplaying a message or an image\tfor promotional purposes\thanging from a building or a pole\tmade of vinyl or fabric materials.", 32], "cobblestone street": ["Yes. 'Cobblestone street' has a tangible appearance and is a type of road.\nA few things that are visually similar to 'cobblestone street' but are not 'cobblestone street' are:\tbrick road\tpaved road\tdirt road\nThere are several useful visual features to tell there is 'cobblestone street' and not similar things in a photo:\tuneven surface\tpattern of stones or bricks\tcobbled materials used to make the road instead of asphalt or concrete\tmay be an older road in a historic or older part of a city", 32], "tricycle": ["Yes. 'Tricycle' has a tangible appearance and is a type of three-wheeled bicycle.\nA few things that are visually similar to 'tricycle' but are not 'tricycle' are:\tbaby stroller\tgolf cart\tshopping cart\nThere are several useful visual features to tell there is 'tricycle' and not similar things in a photo:\tthree wheels\tone wheel in front, two in back\thandlebars and pedals\tforward-facing seat and backrest\tchain and gear system.", 32], "pilots": ["Yes. 'Pilots' has a tangible appearance and refers to people who operate aircraft.\nA few things that are visually similar to 'pilots' but are not 'pilots' are:\tpassengers\tair traffic controllers\tmechanics\tborder control officers\nThere are several useful visual features to tell there are 'pilots' and not similar things in a photo:\twearing a pilot\u2019s uniform\tin the cockpit or flight deck of an aircraft\tadjusting controls or instruments in the cockpit or flight deck\tholding a flight plan or checklist or flying charts.", 32], "orange line": ["Yes. 'Orange line' has a tangible appearance and is a kind of a colored line.\nA few things that are visually similar to 'orange line' but are not 'orange line' are:\tyellow line\tpink line\tred line\nThere are several useful visual features to tell there is 'orange line' and not similar things in a photo:\torange color\tstraight\tline shape\tsame thickness throughout.", 32], "gold necklace": ["Yes. 'Gold necklace' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'gold necklace' but are not 'gold necklace' are:\tchain\tbelt\thair accessory\nThere are several useful visual features to tell there is 'gold necklace' and not similar things in a photo:\tmade of gold or gold-colored metal\tincludes a chain that goes around the neck\thas a clasp or closure to fasten it around the neck\tmay have a pendant or charm attached to it.", 32], "pink lips": ["Yes. 'Pink lips' has a tangible appearance and is a physical characteristic of a person's mouth.\nA few things that are visually similar to 'pink lips' but are not 'pink lips' are: pink flowers, pink fruit, pink candy.\nThere are several useful visual features to tell there are 'pink lips' and not similar things in a photo: a part of a human face; located specifically around the mouth area; smooth, soft texture; may have a glossy appearance or shine.", 32], "blue plate": ["Yes. 'Blue plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'blue plate' but are not 'blue plate' are:\twhite plate\tred plate\tyellow plate\t\nThere are several useful visual features to tell there is 'blue plate' and not similar things in a photo:\tblue color\tcircular shape\tflat surface\twith or without pattern or design", 32], "chair cushion": ["Yes. 'Chair cushion' has a tangible appearance and is an object used for sitting on a chair.\nA few things that are visually similar to 'chair cushion' but are not 'chair cushion' are:\t\npillow\tsofa cushion\tbed cushion\tstuffed animal\n\nThere are several useful visual features to tell there is 'chair cushion' and not similar things in a photo:\t\nrectangular or square shape\tpadded or cushioned texture\tsecured to the chair with ties or straps\tsitting on top of a chair", 32], "stop lights": ["Yes. 'Stop lights' has a tangible appearance and is a type of traffic signal.\nA few things that are visually similar to 'stop lights' but are not 'stop lights' are:\tstreet signs\ttraffic cones\tbarricades\nThere are several useful visual features to tell there is 'stop lights' and not similar things in a photo:\tthree-lights system (red, yellow and green)\thanging perpendicular above the road, usually at intersections\tcircular shape with lenses reflecting three lights arrangement.", 32], "gold clock": ["Yes. 'Gold clock' has a tangible appearance and is a type of timepiece.\nA few things that are visually similar to 'gold clock' but are not 'gold clock' are:\tbronze clock\tstainless steel clock\tmetallic sculpture\nThere are several useful visual features to tell there is 'gold clock' and not similar things in a photo:\tgolden color\tmetallic material with a reflective surface\tclear clock face with numbers or lines\thour, minute, and second hands (or a digital display)", 32], "bust": ["Yes. 'Bust' has a tangible appearance and refers to a sculpture representing a person's head, neck, and shoulders.\nA few things that are visually similar to 'bust' but are not 'bust' are:\tstatue\tfountain\tmannequin\tbottle\nThere are several useful visual features to tell there is 'bust' and not similar things in a photo:\trepresents a head, neck, and shoulders\tlacks arms and often a lower body\tmade of stone, wood, or other materials shows a likeness of a specific person or character", 32], "ocean spray": ["Yes. 'Ocean spray' has a tangible appearance and refers to the mist or spray created by ocean waves crashing against rocks or cliffs.\nA few things that are visually similar to 'ocean spray' but are not 'ocean spray' are:\twaterfall mist\tfountain spray\train mist\nThere are several useful visual features to tell there is 'ocean spray' and not similar things in a photo:\twaves crashing against rocks or cliffs\tspray rising up from the surface of the ocean\twater droplets suspended in the air", 32], "noodle": ["Yes. 'Noodle' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'noodle' but are not 'noodle' are:\tspaghetti\tfettuccine\trice\tcrackers\nThere are several useful visual features to tell there is 'noodle' and not similar things in a photo:\tlong and thin cylindrical shape\tsometimes twisted or curved\ttranslucent or opaque appearance\tmay have ridges or lines on the surface\tcould be served in sauce\tor a soup.", 32], "plumbing": ["Yes. 'Plumbing' has a tangible appearance and refers to the system of pipes, tanks, and fittings used for the water supply and sanitation of a building.\nA few things that are visually similar to 'plumbing' but are not 'plumbing' are:\telectric wiring\tair conditioning ducts\thvac systems\t\nThere are several useful visual features to tell there is 'plumbing' and not similar things in a photo:\tmetal pipes\tpipes leading to faucets or valves\tpipes leading to a water source or a drain\tfittings (such as T-joints, elbow joints, and valves)", 32], "grey chair": ["Yes. 'Grey chair' has a tangible appearance and is a specific piece of furniture.\nA few things that are visually similar to 'grey chair' but are not 'grey chair' are:\tsofa\tstool\tbench\tottoman\nThere are several useful visual features to tell there is 'grey chair' and not similar things in a photo:\ta seat for one person\ta backrest and armrests\tfour legs or a stable base\tcovered in grey fabric or material", 32], "wireless computer mouse": ["Yes. 'Wireless computer mouse' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'wireless computer mouse' but are not 'wireless computer mouse' are:\twireless presenter\tUSB flash drive\tremote control\twireless keyboard\nThere are several useful visual features to tell there is 'wireless computer mouse' and not similar things in a photo:\toval-shaped body\ttwo or three buttons\tscroll wheel on the top\twireless receiver attached to a computer", 32], "green plant": ["Yes. 'Green plant' has a tangible appearance and refers to a variety of living organisms that have leaves and use sunlight for energy.\nA few things that are visually similar to 'green plant' but are not 'green plant' are:\tfake plants\talgae\tmoss\tsucculent\tcactus\nThere are several useful visual features to tell there is 'green plant' and not similar things in a photo: leaves (broad, thin, needle) stems photosynthesis process (green color)", 32], "polka": ["No. 'Polka' is too vague or abstract to have a tangible appearance. \n\nHowever, if we take 'polka' to refer specifically to the pattern often associated with the dance, then: \n\nA few things that are visually similar to 'polka' but are not 'polka' are:\tcircles\tdots\tbubbles\trounded shapes\n\nThere are several useful visual features to distinguish the specific polka pattern from these similar things in a photo:\tpronounced contrast between the background and dots\tcircular dots that are evenly spaced apart (usually white dots on a darker background)", 32], "truck door": ["Yes. 'Truck door' has a tangible appearance and is a type of vehicle component.\nA few things that are visually similar to 'truck door' but are not 'truck door' are: car door, garage door, refrigerator door, cabinet door.\nThere are several useful visual features to tell there is 'truck door' and not similar things in a photo: rectangular shape, visible hinges and latches, a handle or doorknob, a large size compared to other types of doors.", 32], "paperback book": ["Yes. 'Paperback book' has a tangible appearance and is a type of book.\nA few things that are visually similar to 'paperback book' but are not 'paperback book' are:\thardcover book\tmagazine\tjournal\tnotebook\nThere are several useful visual features to tell there is 'paperback book' and not similar things in a photo:\tflexible cover made of paper or thin cardboard\tbinding visible on the spine\tcan be bent or folded\teasy to carry and hold\tinformal and affordable appearance", 32], "chives": ["Yes. 'Chives' has a tangible appearance and is a type of herb.\nA few things that are visually similar to 'chives' but are not 'chives' are:\tscallions\tleeks\tonions\tgreen onions\nThere are several useful visual features to tell there is 'chives' and not similar things in a photo:\tfine and thin green stems\tbulbous white root part\toften used as a garnish or in cooking as a seasoning", 32], "eyeball": ["Yes. 'Eyeball' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'eyeball' but are not 'eyeball' are:\tolive\tbouncy ball\tmarble\tcamera lens\nThere are several useful visual features to tell there is 'eyeball' and not similar things in a photo:\tspherical shape\twhite sclera\tclear cornea\tpupil and iris inside\tthe presence of eyelashes and eyebrow", 32], "shadow tennis player": ["Yes. 'Shadow tennis player' has a tangible appearance.\nA few things that are visually similar to 'shadow tennis player' but are not 'shadow tennis player' are:\treal tennis player without shadow\treflection in a mirror\t\nThere are no visual features that can distinguish 'shadow tennis player' from a real tennis player without a shadow or a reflection in a mirror in a photo, as they all have a similar appearance. However, one can look for the absence of visible body parts, such as the head, when trying to identify a shadow of a tennis player. Another distinguishing feature could be the lack of physical depth or dimension in comparison to a real tennis player or a reflection.", 32], "doorways": ["Yes. 'Doorways' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'doorways' but are not 'doorways' are:\twindows\tarches\tgates\tentrances\nThere are several useful visual features to tell there is 'doorways' and not similar things in a photo:\trectangular shape\tframe that separates two spaces\tdoor hinges or knobs\tmaterial and color of the door and surrounding walls or frame", 32], "brief case": ["Yes. 'Briefcase' is a visually concrete concept and is a type of bag for carrying documents or other items.\nA few things that are visually similar to 'briefcase' but are not 'briefcase' are:\tlaptop bag\tbusiness bag\tbackpack\ttote bag\nThere are several useful visual features to distinguish 'briefcase' from the listed similar things in a photo:\trectangular shape\thard shell or stiff material\thandles or shoulder strap\tclasps or locks\ton the smaller side for a bag.", 32], "headlight front train": ["Yes. 'headlight front train' has a tangible appearance and is a specific feature of a train.\nA few things that are visually similar to 'headlight front train' but are not 'headlight front train' are:\theadlights on cars\ttraffic signals\tbicycle headlight\tlight from a building\nThere are several useful visual features to tell there is 'headlight front train' and not similar things in a photo:\tlarge size\tpositioned on the front of a train\tsometimes surrounded by a casing or a grill\tbright and powerful light that illuminates the tracks and signals the train's approach", 32], "base board": ["Yes. 'Base board' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'base board' but are not 'base board' are:\tcrown molding\tdoor frame\twindow frame\tfurniture edge\nThere are several useful visual features to tell there is 'base board' and not similar things in a photo:\tstrip of material along the bottom of a wall\tflat or slightly curved in shape\tmounted against the wall at the base\tof the wall, usually where the wall meets the floor.", 32], "silver object": ["Yes. 'Silver object' has a tangible appearance and refers to objects made of silver material.\nA few things that are visually similar to 'silver object' but are not 'silver object' are:\tchrome objects\tobjects made of polished steel or aluminum objects coated with silver-colored paint\tor objects with silver-like reflective surfaces\nThere are several useful visual features to tell there is 'silver object' and not similar things in a photo:\tmetallic\tpolished or shiny surface\twith a slightly bluish or yellowish tint\tlustrous appearance\theavier than an object made from another material", 32], "detail": ["No. 'Detail' is too vague or abstract to be distinguished in a photo.", 32], "metal hinge": ["Yes. 'Metal hinge' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'metal hinge' but are not 'metal hinge' are:\thardware fittings\tbottle opener\tlock\nThere are several useful visual features to tell there is 'metal hinge' and not similar things in a photo:\trectangular or circular shape\tmetallic material\tjoint that allows movement of two surfaces\tfastened to two surfaces with screws", 32], "discoloration": ["Yes. 'Discoloration' has a tangible appearance and refers to a change in the color of something.\nA few things that are visually similar to 'discoloration' but are not 'discoloration' are:\tshadow\treflection\tstain\tpattern\nThere are several useful visual features to tell there is 'discoloration' and not similar things in a photo:\tpatchy color\tuneven coloring\tdiffused or faded appearance\tirregular shapes\tor in patches\tthat contrast with the surrounding color", 32], "heap": ["Yes. 'Heap' has a tangible appearance and refers to a pile of objects.\nA few things that are visually similar to 'heap' but are not 'heap' are:\tstack\tpile\tmound\tcluster\theap\tof rocks\toranges\toranges arranged in a pile\nThere are several useful visual features to tell there is 'heap' and not similar things in a photo:\tirregularly shaped pile\tobjects stacked on top of each other\thaphazard arrangement of objects\tno organizational structure or pattern", 32], "round clock face": ["Yes. 'Round clock face' has a tangible appearance and is a circle with numbers and hands.\nA few things that are visually similar to 'round clock face' but are not 'round clock face' are:\tcircle\tdial\tpizza\tglobe\nThere are some useful visual features to tell there is 'round clock face' and not similar things in a photo:\tnumbers 1-12 around the edge of the circle\twith two or three hands indicating the time\tcenter either be blank or feature a company logo\tor other design details to make the clock more visually interesting.", 32], "line judge": ["Yes. 'Line judge' has a tangible appearance and refers to a person who officiates a sports game.\nA few things that are visually similar to 'line judge' but are not 'line judge' are:\treferee\tcoach\tplayer\tspectator\nThere are several useful visual features to tell there is 'line judge' and not similar things in a photo:\twearing a distinctive uniform\tstanding at the side of the court or field\tholding a flag, a whistle, or other officiating gear\tfocused on observing the play, not participating\tin communication with other officials", 32], "pink dress": ["Yes. 'Pink dress' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pink dress' but are not 'pink dress' are:\tpink shirt\tpink skirt\tpink scarf\tpink pants\nThere are several useful visual features to tell there is 'pink dress' and not similar things in a photo:\ta full-length garment with a skirt portion\tpink in color\tdesigned for women or girls\twith sleeves or without sleeves\ta dress shape with a waistline at the natural waist level.", 32], "points": ["No. 'Points' is too vague or abstract to have a tangible appearance and cannot be visually distinguished in a photo.\nHowever, a few things that are visually similar to the word 'points' could be:\tsharp tips\tof a triangle\tor a star\tor a polygon. But in this case, 'points' would be referring to the geometric shape, not the abstract concept of points in a game or system.", 32], "cinnamon": ["Yes. 'Cinnamon' has a tangible appearance and is a type of spice.\nA few things that are visually similar to 'cinnamon' but are not 'cinnamon' are:\tnutmeg\tcloves\tallspice\nThere are several useful visual features to tell there is 'cinnamon' and not similar things in a photo:\tthin and curled sticks or powder\tgenerally reddish-brown in color\tstrong and sweet spicy smell", 32], "knots": ["Yes. 'Knots' have a tangible appearance and are made of intertwined or entangled materials.\nA few things that are visually similar to 'knots' but are not 'knots' are:\tbraids\ttangles\tweaves\tnets\nThere are several useful visual features that distinguish 'knots' from the listed similar things in a photo:\tdistinctive shapes made by intertwining materials\tone or more loops or twists\ttightly bound and locked structure", 32], "water hole": ["Yes. 'Water hole' has a tangible appearance and refers to a place with water in a hole or depression.\nA few things that are visually similar to 'water hole' but are not 'water hole' are:\tpuddle\tpond\triver\tstream\nThere are several useful visual features to tell there is 'water hole' and not similar things in a photo:\tshallow\tdry surroundings\tanimals gathered around\tfor natural bodies of water: no visible source of inflow or outflow, no visible current.", 32], "leather purse": ["Yes. 'Leather purse' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'leather purse' but are not 'leather purse' are:\tbackpack\tclutch\tmessenger bag\ttote\tbriefcase\nThere are several useful visual features to tell there is 'leather purse' and not similar things in a photo:\tmade of leather\tclasp closure or zipper\tshoulder strap or handle\tcapacity to hold small items or personal belongings", 32], "armrests": ["Yes. 'Armrests' has a tangible appearance and refers to the part of a chair or sofa used to support the arms.\nA few things that are visually similar to 'armrests' but are not 'armrests' are:\tpillows\tcushions\tdecorative objects\nThere are a few useful visual features to tell there is 'armrests' and not similar things in a photo:\t\n- Rectangular or curved shape connected to a chair or sofa\n- Often made of the same material as the chair or sofa\n- Positioned at a height and angle suitable for supporting the occupant's arms.", 32], "spread": ["No. 'spread' is too vague or abstract to be distinguished in a photo. It could refer to a variety of things like a food spread, a bedspread, or a news spread, all of which can have different visual features. \n\nHowever, a few things that are visually similar to 'spread' in certain contexts but are not 'spread' in other contexts are:\n- In the context of food: sauce, dip, condiment, topping, frosting\n- In the context of bedding: blanket, quilt, comforter\n- In the context of news: rumor, gossip, hearsay, misinformation\n\nUseful visual features for distinguishing 'spread' would depend on the specific context. For example, useful visual features for distinguishing a food spread from a sauce or dip could include:\n- Being displayed on a platter or board\n- Having a variety of toppings or ingredients arranged on the surface\n- Being spread thickly or thinly over bread or crackers\n- Being in a bowl or jar with a spreading knife or spoon nearby", 32], "snowflake": ["Yes. 'Snowflake' has a tangible appearance and is a naturally occurring formation of ice.\nA few things that are visually similar to 'snowflake' but are not 'snowflake' are:\tsalt crystal\tstar-shaped decorations\tpaper cutouts\nThere are several useful visual features to tell there is 'snowflake' and not similar things in a photo: hexagonal shape\twhite or clear color\twater droplet pattern\tradiating icicle arms\tdetailed crystal-like structure", 32], "blue water": ["Yes. 'Blue water' has a tangible appearance and can be seen in water bodies like oceans, rivers or lakes.\nA few things that are visually similar to 'blue water' but are not 'blue water' are:\tsky\tblue-tinted glass\tblue-tinted plastic\tblue-tinted liquid\nThere are several useful visual features to tell there is 'blue water' and not similar things in a photo:\ttransparent or translucent appearance\twavy surface\treflective properties\twet or damp appearance\tassociated with natural bodies of water.", 32], "stone structure": ["Yes. 'Stone structure' has a tangible appearance and refers to construction made from stone or rock.\nA few things that are visually similar to 'stone structure' but are not 'stone structure' are:\trock formation\tmountain\tcave\tfossil\nThere are several useful visual features to tell there is 'stone structure' and not similar things in a photo:\tconstruction made of stones or rocks\thuman-made structure\twith geometrical shapes\tor arches, columns or pillars\tmortar or cement", 32], "garbage truck": ["Yes. 'Garbage truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'garbage truck' but are not 'garbage truck' are:\tfire truck\tambulance\ttrailer\ttruck\nThere are several useful visual features to tell there is 'garbage truck' and not similar things in a photo:\trectangular or square-shaped body\twith a large container or compactor for garbage\tcollection routes with bins or dumpsters nearby\tthe words \"garbage\" or \"waste\" on its exterior", 32], "chocolate dessert": ["Yes. 'Chocolate dessert' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'chocolate dessert' but are not 'chocolate dessert' are:\tchocolate bar\thot chocolate\tcocoa powder\tmilkshake\tsmoothie\nThere are several useful visual features to tell there is 'chocolate dessert' and not similar things in a photo:\t\n- Sweet and creamy texture\n- Brown or dark-colored appearance\n- Usually plated or served in a bowl or a cup\n- May be decorated with toppings such as whipped cream or nuts", 32], "muscles": ["Yes. 'Muscles' has a tangible appearance and is a type of body tissue.\nA few things that are visually similar to 'muscles' but are not 'muscles' are:\tfat\ttendons\tbones\tskin\nThere are several useful visual features to tell there are 'muscles' and not similar things in a photo:\tfleshy tissue\tbulging or defined shape\tcan see muscles in motion or flexing\tcan see striations or fibers within the tissue", 32], "cockpit windows": ["Yes. 'Cockpit windows' has a tangible appearance and refers to the front windows of an aircraft where the pilots sit.\nA few things that are visually similar to 'cockpit windows' but are not 'cockpit windows' are:\tcar windows\tcabin windows\tbuilding windows\tstore windows\nThere are several useful visual features to tell there are 'cockpit windows' and not similar things in a photo:\tlocated at the front of an aircraft\tlarger than other windows on the aircraft\tusually curved or bulging in shape\tthe cockpit can be seen through them\tpilots can be seen through them.", 32], "minute": ["No. 'Minute' is too abstract to be represented visually.", 32], "filter": ["Yes. 'Filter' has a tangible appearance and is a translucent or transparent device that selectively absorbs or blocks certain light or particles.\nA few things that are visually similar to 'filter' but are not 'filter' are:\tscreen\tmask\tlens\twindow\nThere are several useful visual features to tell there is 'filter' and not similar things in a photo:\ttranslucent or transparent materials\tspecialized patterns or coatings\tdesigned to selectively absorb or block certain light or particles\tvarious shapes and sizes depending on the use case (e.g., circular, rectangular, square)", 32], "sideburns": ["Yes. 'Sideburns' have a tangible appearance as a type of facial hair.\nA few things that are visually similar to 'sideburns' but are not 'sideburns' are:\tbeard\tmoustache\teyebrows\thair\nThere are several useful visual features to tell there is 'sideburns' and not similar things in a photo:\ttufts of hair in front of the ear\tthat connect to hair on the head\tcan vary in length\tusually worn by men", 32], "ivory tusks": ["Yes. 'Ivory tusks' has a tangible appearance and refers to the long, curved teeth of elephants and other animals.\nA few things that are visually similar to 'ivory tusks' but are not 'ivory tusks' are:\tdecorative horns\tseashells\tivory-colored plastic or resin objects\nThere are several useful visual features to distinguish 'ivory tusks' from the listed similar things in a photo:\nstraight or slightly curved shape, usually pointed at one end\nridged texture on the surface\nvisible grain pattern\nlarge size and thickness (compared to decorative horns or seashells)\nassociated with an elephant or elephant-like animal in the context of wildlife conservation efforts.", 32], "concrete bridge": ["Yes. 'Concrete bridge' has a tangible appearance and refers to a type of bridge made of concrete.\nA few things that are visually similar to 'concrete bridge' but are not 'concrete bridge' are:\twooden bridge\tsteel bridge\tstone bridge\nThere are several useful visual features to tell there is 'concrete bridge' and not similar things in a photo:\tlarge blocks of solid concrete used to build the bridge\tconcrete-colored\tpillars or supports made of concrete\tsleek modern design or industrial look may give a hint of concrete bridge", 32], "glass pane": ["Yes. 'Glass pane' has a tangible appearance and is a flat piece of glass widely used in construction.\nA few things that are visually similar to 'glass pane' but are not 'glass pane' are:\tpicture frame\tscreen\twindow blinds\nThere are several useful visual features to tell there is 'glass pane' and not similar things in a photo:\ttransparent and colorless\tsmooth surface\treflective and refractive properties\tused as a part of a building or a structure", 32], "rail road tracks": ["Yes. 'Railroad tracks' has a tangible appearance and is a type of transportation infrastructure.\nA few things that are visually similar to 'railroad tracks' but are not 'railroad tracks' are:\tpower lines\tpipe systems\tbike lanes\tdriveways\nThere are several useful visual features to tell there is 'railroad tracks' and not similar things in a photo:\ttwo parallel steel rails\trailroad ties or sleepers\tcontinuous and stretching into the distance\tcrossing signs or signals", 32], "sill": ["Yes. 'Sill' has a tangible appearance and can be seen in architecture.\nA few things that are visually similar to 'sill' but are not 'sill' are:\tshelf\tcounter\tslab\tledge\nThere are several useful visual features to tell there is 'sill' and not similar things in a photo:\ta horizontal component at the bottom of a window\tor a door, whether for decoration or support\thas a ledge-like appearance\tmade of stone, wood, metal or other construction material", 32], "wet road": ["Yes. 'Wet road' has a tangible appearance.\nA few things that are visually similar to 'wet road' but are not 'wet road' are:\tshiny floor\treflective surface\tof water\nThere are several useful visual features to tell there is a 'wet road' and not similar things in a photo:\tdark patches on the surface\ta glossy or shiny appearance\treflection of light on the surface\twater splashes around", 32], "buffalo": ["Yes. 'Buffalo' has a tangible appearance and is a type of large mammal.\nA few things that are visually similar to 'buffalo' but are not 'buffalo' are:\tbison\tyak\tcow\tmoose\nThere are several useful visual features to tell there is 'buffalo' and not similar things in a photo:\tlarge size\tbrown or black fur\thorns or large head\tbushy tail\thumped shoulder area\tdistinctive facial features like a broad nose and small ears", 32], "plastic bin": ["Yes. 'Plastic bin' has a tangible appearance and is a type of storage container.\nA few things that are visually similar to 'plastic bin' but are not 'plastic bin' are:\tcardboard box\tmetal container\tbasket\ttrash can\nThere are several useful visual features to tell there is 'plastic bin' and not similar things in a photo:\tridged or smooth plastic material\twith or without a lid\thandles on the sides\trectangular or square shape\tvaried sizes and colors", 31], "number sign": ["Yes. 'Number sign' has a tangible appearance and is a symbol used for numbers or for representing a hashtag.\nA few things that are visually similar to 'number sign' but are not 'number sign' are:\tpound sign\tmusical sharp sign\nThere are several useful visual features to tell there is 'number sign' and not similar things in a photo:\ttwo horizontal and two vertical lines of equal length and thickness\tcross-like symbol placed below a number or at the beginning of a sequence of words or text\trepeating a word or phrase beginning with the symbol", 31], "tomatos": ["Yes. 'Tomatoes' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'tomatoes' but are not 'tomatoes' are:\tpeppers\tcherries\tgrapes\tapples\nThere are several useful visual features to tell there is 'tomatoes' and not similar things in a photo:\tround or oval shape\tsmooth, shiny skin\tbright red or yellow-color\tgreen stem\ttopical leaves\tat least one seed pocket", 31], "ray": ["Yes. 'Ray' has a tangible appearance and refers to a type of fish.\nA few things that are visually similar to 'ray' but are not 'ray' are:\tskate\tshark\tstring-ray\tmanta-ray\nThere are several useful visual features to distinguish 'ray' from the listed similar things in a photo:\tflat and round body shape\tpaired fins\ton the ocean floor, filtering sand\tforaging with wide mouths and specialized teeth\tfeeding on mollusks, crustaceans and small fishes", 31], "silver toaster": ["Yes. 'Silver toaster' has a tangible appearance and is a specific type of kitchen appliance.\nA few things that are visually similar to 'silver toaster' but are not 'silver toaster' are:\tcoffee maker\tblender\tmicrowave\toven\nThere are several useful visual features to tell there is 'silver toaster' and not similar things in a photo:\trectangular or square shape\tsilver or metal color\ttwo or four slots for bread\tdark knobs\tor buttons on the front", 31], "fluffy dog": ["Yes. 'Fluffy dog' has a tangible appearance and is a type of dog with a fluffy coat.\nA few things that are visually similar to 'fluffy dog' but are not 'fluffy dog' are:\tcuddly toy\tpillow\tfur coat\twig\nThere are several useful visual features to tell there is 'fluffy dog' and not similar things in a photo:\tfour-legged animal\twith a fluffy coat and tail\tface resembling a dog's\tLong or curly hair on ears and body\tcompanion breed, like Pomeranian, Bichon Frise, or Samoyed", 31], "beige sofa": ["Yes. 'Beige sofa' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'beige sofa' but are not 'beige sofa' are:\tchairs\tbenches\tbeds\tsettees\nThere are several useful visual features to tell there is 'beige sofa' and not similar things in a photo:\tupholstered\tpadding\tarmrests, backrests and seats\tbeige color\trectangular or L-shaped structure", 31], "baseball team": ["No. 'Baseball team' is too vague or abstract to be distinguished in a photo.\n\nHowever, a few things that are visually similar to a group of people, potentially a 'baseball team,' but are not 'baseball team' are: a group of friends, a group of coworkers, a group of tourists.\n\nUseful visual features for distinguishing a 'baseball team' from a group of similar things in a photo would include uniforms or clothing that resemble baseball attire, baseball bats or gloves, and a baseball field or stadium in the background.", 31], "marble table": ["Yes. 'Marble table' has a tangible appearance and is a specific type of furniture.\nA few things that are visually similar to 'marble table' but are not 'marble table' are:\twooden table\tglass table\tplastic table\nThere are several useful visual features to tell there is 'marble table' and not similar things in a photo:\tsurface made of marble or marble-like material\ttypical gray and white swirling pattern\tshiny and smooth surface\tcold to the touch", 31], "pop": ["No. 'Pop' is too vague or abstract to be distinguished in a photo. It could refer to various things such as music, culture, or a sound.", 31], "dirty floor": ["Yes. 'Dirty floor' has a tangible appearance and refers to the condition of a surface.\nA few things that are visually similar to 'dirty floor' but are not 'dirty floor' are:\tmarbled floor\twith patterns or designs\tfloor covered in debris but not necessarily dirty\nThere are several useful visual features to tell there is 'dirty floor' and not similar things in a photo:\tdiscolored or stained surface\tmessy or uneven texture\tparticular signs of grime, like mud, dust or footprints\tdull or matte finish", 31], "screen monitor": ["Yes. 'Screen monitor' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'screen monitor' but are not 'screen monitor' are:\tTVs\tprojectors\tlaptops\ttablets\nThere are several useful visual features to tell there is 'screen monitor' and not similar things in a photo:\trectangular shape\twith a stand or mounted on a wall\tdisplaying images or text\tconnected to a computer or other device for input and output", 31], "tin foil": ["Yes. 'Tin foil' has a tangible appearance and is a type of thin, shiny metal sheet.\nA few things that are visually similar to 'tin foil' but are not 'tin foil' are:\taluminum foil\tparchment paper\twhite paper\tthin plastic sheets\nThere are several useful visual features to tell there is 'tin foil' and not similar things in a photo:\tshiny surface\tthin and malleable\tmetallic appearance\tsilver color", 31], "silver light": ["No. 'Silver light' is too vague or abstract to be distinguished in a photo.", 31], "blackberry": ["Yes. 'Blackberry' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'blackberry' but are not 'blackberry' are:\tblueberry\traspberry\tblackcurrant\tpomegranate\nThere are several useful visual features to tell there is 'blackberry' and not similar things in a photo:\tirregular shape\tdeep purple or black color\tjuicy and shiny texture\tof a size around a thumb's length with oblong shape\tresembles a small bunch of grapes with bone-shaped fruits", 31], "transportation bus": ["Yes. 'Transportation bus' has a tangible appearance and is a type of vehicle used for public transport.\nA few things that are visually similar to 'transportation bus' but are not 'transportation bus' are:\tcoach van\tairport shuttle\tminibus\nThere are several useful visual features to tell there is 'transportation bus' and not similar things in a photo:\tdistinctive rectangular shape\twith multiple windows and doors\tlarge interior space\tusually in a bright color\tsymbol or wordmark indicating the name of the transportation company.", 31], "spacebar": ["Yes. 'Spacebar' has a tangible appearance and is a part of a keyboard.\nA few things that are visually similar to 'spacebar' but are not 'spacebar' are:\tkeys on a keyboard\tpiano keys\tremote control buttons\nThere are several useful visual features to tell there is 'spacebar' and not similar things in a photo:\trectangular key in the center of the keyboard\tlarger size compared to other keys\tspace written on it\tin a horizontal position.", 31], "tile backsplash": ["Yes. 'Tile backsplash' has a tangible appearance and is a specific element of kitchen or bathroom design.\nA few things that are visually similar to 'tile backsplash' but are not 'tile backsplash' are:\tpainted walls\twallpaper\twood paneling\t\nThere are several useful visual features to tell there is 'tile backsplash' and not similar things in a photo:\tmade of ceramic or stone tiles\tinstalled between the counter and cabinets\tor around a sink or stove\tarea of the wall is designed to have a distinct pattern or texture", 31], "sponsor": ["No. 'Sponsor' is too vague or abstract to be distinguished in a photo.", 31], "dining chair": ["Yes. 'Dining chair' has a tangible appearance and is a type of chair.\nA few things that are visually similar to 'dining chair' but are not 'dining chair' are:\tsofa\tarmchair\tbar stool\trocking chair\toffice chair\nThere are several useful visual features to tell there is 'dining chair' and not similar things in a photo:\tupright backrest\tstraight legs\tusually made of wood or metal\tmay have cushions or padding on the seat and backrest\toften used for sitting at a table or desk", 31], "lenses": ["Yes. 'Lenses' has a tangible appearance and is a type of optical accessory.\nA few things that are visually similar to 'lenses' but are not 'lenses' are:\tglasses\tmirrors\teyeballs\tcamera sensors\nThere are several useful visual features to tell there are 'lenses' and not similar things in a photo:\ttranslucent or transparent material\tcurved shape\ttapered edges\tfocus ring or other adjustments", 31], "oars": ["Yes. 'Oars' has a tangible appearance and is a tool used for rowing a boat.\nA few things that are visually similar to 'oars' but are not 'oars' are:\tpaddles\tsticks\tbranches\nThere are several useful visual features to tell there is 'oars' and not similar things in a photo:\tlong and narrow\thandle on one end\tflat and wide on the other end\tused to move a boat through water", 31], "leather glove": ["Yes. 'Leather glove' has a tangible appearance and is an item of clothing.\nA few things that are visually similar to 'leather glove' but are not 'leather glove' are:\trubber gloves\tmittens\twork gloves\nThere are several useful visual features to tell there is 'leather glove' and not similar things in a photo:\tmade of leather\ttight fitting to the hand\tcovering fingers and the palm of the hand\thas stitching details and a wrist enclosure.", 31], "bed comforter": ["Yes. 'Bed comforter' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'bed comforter' but are not 'bed comforter' are:\tblanket\tthrow pillow\tduvet cover\tsleeping bag\nThere are several useful visual features to tell there is 'bed comforter' and not similar things in a photo:\tthick and fluffy material\tcovers the whole bed\tspecific patterns or colors that match a specific decor or style.", 31], "cement ground": ["Yes. 'Cement ground' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'cement ground' but are not 'cement ground' are: asphalt, tiles, paving stones, gravel, soil.\nThere are several useful visual features to tell there is 'cement ground' and not similar things in a photo: gray or light-colored surface, smooth texture, large sheets or blocks, visible seams or cracks, a distinctively modern or industrial appearance.", 31], "bay window": ["Yes. 'Bay window' has a tangible appearance and is a kind of architectural feature.\nA few things that are visually similar to 'bay window' but are not 'bay window' are:\talcove\tmirrored wall\tatrium\tglass cube\nThere are several useful visual features to tell there is 'bay window' and not similar things in a photo:\tprotruding from the side of a building\tpaned windows\textending beyond the wall line\tcurved or angled shape", 31], "pinky finger": ["Yes. 'Pinky finger' has a tangible appearance and is one of the fingers on the hand.\nA few things that are visually similar to 'pinky finger' but are not 'pinky finger' are:\tthumb\tindex finger\tmiddle finger\tring finger\ttoe\nThere are several useful visual features to tell there is 'pinky finger' and not similar things in a photo:\tthe smallest finger on a hand\tspecifically positioned in between the ring finger and the hand nail and bone shape\tthe size is smaller than the other fingers", 31], "swim suit": ["Yes. 'Swim suit' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'swim suit' but are not 'swim suit' are:\tshorts\tunderwear\tleotard\t\nThere are several useful visual features to tell there is 'swim suit' and not similar things in a photo:\ttrimmed to fit the body\tcovers a large portion of the body\tmade of waterproof material\tusually worn by people around water areas", 31], "bathroom light": ["Yes. 'Bathroom light' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'bathroom light' but are not 'bathroom light' are:\tkitchen light\tlamp\tchandelier\tceiling fan\t\nThere are several useful visual features to tell there is 'bathroom light' and not similar things in a photo:\tattached to a ceiling or a wall\twith a light bulb or bulbs\tusually placed near a mirror or a sink\twaterproof or resistant material\tif it's a vanity light, it's typically a row of bulbs", 31], "bale": ["Yes. 'Bale' has a tangible appearance and refers to a large bundle of something that has been compressed and tied together.\nA few things that are visually similar to 'bale' but are not 'bale' are:\tstack of boxes\tpile of books\tbundle of sticks\tbin filled with clothes\toranges in a bag\nThere are several useful visual features to tell there is 'bale' and not similar things in a photo:\trectangular or cylindrical shape\tcompressed and tied with twine or wire\tfilled with hay, straw, cotton, or other materials", 31], "pocketbook": ["Yes. 'Pocketbook' has a tangible appearance and is a type of bag or purse.\nA few things that are visually similar to 'pocketbook' but are not 'pocketbook' are:\tbackpack\tmessenger bag\tclutch\twristlet\nThere are several useful visual features to tell there is 'pocketbook' and not similar things in a photo:\tsmall to medium-sized\theld by a handle or strap\tdesigned to be carried in a pocket\tor bag\tfor women's use", 31], "caution cone": ["Yes. 'Caution cone' has a tangible appearance and is a kind of traffic safety equipment.\nA few things that are visually similar to 'caution cone' but are not 'caution cone' are:\ttraffic cone\tcone-shaped hat\twaffle cone\nThere are several useful visual features to tell there is 'caution cone' and not similar things in a photo:\tbright neon or orange color with reflective stripes\tcone shape\thollow interior\twith words or symbols indicating caution or danger", 31], "glass salt shaker": ["Yes, 'glass salt shaker' has a tangible appearance and is a specific type of kitchenware.\nA few things that are visually similar to 'glass salt shaker' but are not 'glass salt shaker' are: glass pepper shaker, grater, food container.\nUseful visual features for distinguishing 'glass salt shaker' from the listed similar things in a photo: small and cylindrical; equipped with holes on the top and a cap on the bottom; typically used for holding and dispensing salt.", 31], "dirty water": ["Yes. 'Dirty water' has a tangible appearance and is a kind of liquid.\nA few things that are visually similar to 'dirty water' but are not 'dirty water' are:\tcoffee\twith milk\ttea\twith milk\tcola\tor any other dark colored translucent liquid\nThere are several useful visual features to tell there is 'dirty water' and not similar things in a photo:\tmurky or cloudy appearance\tunpleasant color (brown, green, yellow)\tvisible dirt, debris, or particles on or in the water surface\tcontaminants floating on the surface or settled at the bottom of the container.", 31], "rice cooker": ["Yes. 'Rice cooker' has a tangible appearance and is a cooking appliance.\nA few things that are visually similar to 'rice cooker' but are not 'rice cooker' are:\tpressure cooker\tslow cooker\tboiling pot\tkettle\nThere are several useful visual features to tell there is 'rice cooker' and not similar things in a photo:\tlid with steam vent\tinner pot\tfor cooking rice\tmeasuring cup\tpower cord\tand heating element.", 31], "ring finger": ["Yes. 'Ring finger' has a tangible appearance and is a specific finger on the hand.\nA few things that are visually similar to 'ring finger' but are not 'ring finger' are:\tindex finger\tmiddle finger\tpinky finger\tthumb\nThere are several useful visual features to tell there is 'ring finger' and not similar things in a photo:\tthird finger from the left on the hand\twith a ring on it (if depicted in the photo)\tslightly longer than the index or middle fingers", 31], "broth": ["Yes. 'Broth' has a tangible appearance and is a type of liquid used for cooking or drinking.\nA few things that are visually similar to 'broth' but are not 'broth' are:\ttea\tcoffee\tjuice\tsauce\nThere are several useful visual features to tell there is 'broth' and not similar things in a photo:\tclear or slightly opaque\tusually brown, yellow, or orange\tin a pot or a bowl, with steam rising from it\tmade from meat, bones or vegetables", 31], "shovel": ["Yes. 'Shovel' has a tangible appearance and is a type of tool.\nA few things that are visually similar to 'shovel' but are not 'shovel' are:\tspade\ttrowel\tpickaxe\thoe\nThere are several useful visual features to tell there is 'shovel' and not similar things in a photo:\tlong handle\tmetal scoop at the end of the handle\tcurved or straight scoop\thead of shovel may have a serrated edge for cutting through roots or soil", 31], "silver buckle": ["Yes. 'Silver buckle' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'silver buckle' but are not 'silver buckle' are:\tgold buckle\tcopper buckle\tbelt loop\nThere are several useful visual features to tell there is 'silver buckle' and not similar things in a photo:\tmade of silver or silver-colored metal\trectangular or oval shape\twith a prong for fastening onto a belt\tor with a loop for fastening around a strap\tor with decorative engravings or patterns.", 31], "clock pole": ["No. 'Clock pole' is too vague or abstract of a concept to be distinguished in a photo. It is unclear what a 'clock pole' is referring to.", 31], "zebra stripes": ["Yes. 'Zebra stripes' has a tangible appearance and is a specific pattern of stripes found on zebras.\nA few things that are visually similar to 'zebra stripes' but are not 'zebra stripes' are:\ttiger stripes\tshirt stripes\nThere are several useful visual features to tell there are 'zebra stripes' and not similar things in a photo:\tblack and white\tstripe pattern alternates between thick and thin stripes\tthe stripes extend from the neck to the legs", 31], "semi": ["Yes. 'Semi' has a tangible appearance and is a type of large truck.\nA few things that are visually similar to 'semi' but are not 'semi' are:\tpickup truck\tdelivery van\tfire truck\tbus\nThere are several useful visual features to tell there is 'semi' and not similar things in a photo:\tExtremely large truck\tTwo sections or trailers\tLong flat bed\tBig rig design with up to 18 wheels\tThe front cab of the truck is high and over the trailer", 31], "pocket watch": ["Yes. 'Pocket watch' has a tangible appearance and is a kind of timepiece.\nA few things that are visually similar to 'pocket watch' but are not 'pocket watch' are:\twristwatch\tclock\talarm clock\nThere are several useful visual features to tell there is 'pocket watch' and not similar things in a photo:\tround or oval\tshiny metal case\thinged cover\tprotected glass over the face or dial\tnumbering around the dial, either in Roman numerals or conventional numbers.", 31], "ice maker": ["Yes. 'Ice maker' has a tangible appearance and is a piece of kitchen equipment.\nA few things that are visually similar to 'ice maker' but are not 'ice maker' are:\trefrigerator\tfreezer\tice cube trays\tportable ice makers\nThere are several useful visual features to tell there is 'ice maker' and not similar things in a photo:\tmachine-like appearance\twith hoses or pipes\thaving a control panel\tor an on/off switch with a lever\tor a button for releasing the ice", 31], "packs": ["No. 'Packs' is too vague or abstract to be distinguished in a photo without additional context or information. However, if we assume that 'packs' refer to bags or backpacks, then the answer would be \"yes.\"\nA few things that are visually similar to 'packs' but are not 'packs' are:\tbundles\tpiles\tstacks\tgroups\nThere are several useful visual features to tell there is 'packs' and not similar things in a photo:\tbackpacks or bags\tshapes, sizes, and colors of different backpacks or bags\tcontent or items sticking out of the backpacks or bags\tcarrying straps on the backpacks or bags", 31], "mess": ["Yes. 'Mess' has a tangible appearance and refers to a state of disorder or untidiness.\nA few things that are visually similar to 'mess' but are not 'mess' are:\tartwork\tclutter\tdiversity\torganized piles\nThere are several useful visual features to tell there is 'mess' and not similar things in a photo:\taffixed and unsorted papers\tscattered and out-of-place objects\taccumulation of dirt or dust\tmixed and disarranged belongings or tools.", 31], "grass patch": ["Yes. 'Grass patch' has a tangible appearance and is a small area of ground covered with grass.\nA few things that are visually similar to 'grass patch' but are not 'grass patch' are:\tweed patch\tshrub patch\tdirt patch\nThere are several useful visual features to tell there is 'grass patch' and not similar things in a photo:\tvisible blades of grass\tgreen or brown color\tvarious lengths of grass blades\tmodest heights and sizes of the patch\thaving a regular shape, like a square.", 31], "orange train": ["Yes. 'Orange train' has a tangible appearance and is a type of train.\nA few things that are visually similar to 'orange train' but are not 'orange train' are:\tyellow train\tred train\tsubway\nThere are several useful visual features to tell there is 'orange train' and not similar things in a photo:\torange-colored cars\tcars linked together on tracks\twith windows and doors\tno wheels visible on individual cars or locomotives (if present)", 31], "entry way": ["Yes. 'Entry way' has a tangible appearance and is a physical space at the entrance of a building or room.\nA few things that are visually similar to 'entry way' but are not 'entry way' are:\tfoyer\thallway\tstaircase\tlanding\nThere are several useful visual features to tell there is 'entry way' and not similar things in a photo:\tlocated at the entrance of a building or a room\tcontains doors, windows, or other architectural features\tserves as a transitional space between the outside and inside of a building\tor as a space that separates different rooms or functions\thas a functional purpose such as hanging coats, storing shoes, or storing keys", 31], "mini": ["No. 'Mini' is too vague or abstract to be distinguished in a photo. It is a relative term used to describe a smaller version or variation of something else. \nIt cannot be visually distinguished on its own, as it is always in comparison to something else. \n\nTherefore, there are no things that are visually similar to 'mini' but are not 'mini'.", 31], "temple": ["Yes. 'Temple' has a tangible appearance and is a kind of building.\nA few things that are visually similar to 'temple' but are not 'temple' are:\tchurch\tpalace\tmansion\tmuseum\tpagoda\nThere are several useful visual features to tell there is 'temple' and not similar things in a photo:\toften located in a natural or serene setting\telaborate architecture and decoration\treligious symbols and iconography\tmagnificent statues and carvings\tdedicated to a specific deity or deities", 31], "winter": ["Yes. 'Winter' has a tangible appearance and is a season characterized by specific conditions.\nA few things that are visually similar to 'winter' but are not 'winter' are:\tcold weather\tsnowy landscape\tskiing activities\t\nThere are several useful visual features to tell there is 'winter' and not similar things in a photo:\tsnow and ice on the ground\tfog or mist in the air\tbare trees or leafless trees\tsnowflakes or icicles falling from the sky\tdiminished daylight hours or long shadows on the ground.", 31], "architecture": ["Yes. 'Architecture' has a tangible appearance and refers to the design and construction of buildings and other physical structures.\nA few things that are visually similar to 'architecture' but are not 'architecture' are:\tnature\tscenery\tsculpture\t\nThere are several useful visual features to tell there is 'architecture' and not similar things in a photo:\tman-made structures such as buildings, bridges, and monuments\tsymmetrical or geometric shapes\tuse of materials like glass, steel, and concrete\tfocus on function as well as form", 31], "setting": ["No. 'Setting' is too abstract to be identified in a photo. But if we are talking about a specific setting for a story or a scene, then it can have a tangible appearance.\nA few things that are visually similar to 'setting' as an abstract concept but are not 'setting' are:\tmood\tatmosphere\tbackground\tcontext\nThere are no visual features to help distinguish 'setting' since it is an abstract concept.", 31], "mats": ["Yes. 'Mats' has a tangible appearance and is a kind of flat object used for protection or decoration.\nA few things that are visually similar to 'mats' but are not 'mats' are:\tcarpets\trugs\tblankets\t\nThere are several useful visual features to tell there is 'mats' and not similar things in a photo: \tthin\tand flexible\tflat on the ground\tor on a surface\tmade of different materials (straw, rubber, fabric, etc.)\tdesigned to protect surfaces from dirt, moisture, or wear and tear.", 31], "stone statue": ["Yes. 'Stone statue' has a tangible appearance and is a type of sculpture.\nA few things that are visually similar to 'stone statue' but are not 'stone statue' are:\twooden statue\tplastic statue\twax figure\thuman\nThere are several useful visual features to tell there is 'stone statue' and not similar things in a photo:\tsolid and heavy appearance\tmade of stone or other hard materials\tfixed in one position or pose\tdetails carved or chiseled into the surface", 31], "tan bag": ["Yes. 'Tan bag' has a tangible appearance and is a type of bag with a light brown color.\nA few things that are visually similar to 'tan bag' but are not 'tan bag' are:\tbrown bag\tbeige bag\tleather bag\tcloth bag\nThere are several useful visual features to tell there is 'tan bag' and not similar things in a photo:\tlight brown or sandy tone\tsmooth surface or texture\tmedium-sized with a handle or strap.", 31], "entrance door": ["Yes. 'Entrance door' has a tangible appearance and is a type of portal used for entry and exit.\nA few things that are visually similar to 'entrance door' but are not 'entrance door' are:\tregular door\twindow\tgate\nThere are several useful visual features to tell there is 'entrance door' and not similar things in a photo:\tusually located at the front of a building\twide and tall\thandle or knob, often located in the center\twelcome mat or signage nearby\tmay have decorative features such as stained glass or panels", 31], "door refrigerator": ["Yes. 'Door refrigerator' has a tangible appearance and is a type of household appliance.\nA few things that are visually similar to 'door refrigerator' but are not 'door refrigerator' are:\tchest freezer\twine cooler\tice maker\tMINI fridge\nThere are several useful visual features to tell there is 'door refrigerator' and not similar things in a photo:\ttwo or more doors\thandles for opening\tthe presence of shelves or compartments\tfor keeping food and drinks chilled", 31], "baby boy": ["Yes. 'Baby boy' has a tangible appearance and refers to a male infant.\nA few things that are visually similar to 'baby boy' but are not 'baby boy' are:\tadult man\tteenage boy\tmale cartoon character\ttoddler boy\nThere are several useful visual features to tell there is 'baby boy' and not similar things in a photo:\tsmooth skin\tunformed facial features\tbig eyes\tthinner hair or bald head\tsmall size compared to adults and other children.", 31], "silver door knob": ["Yes. 'Silver door knob' has a tangible appearance and is a type of a door handle.\nA few things that are visually similar to 'silver door knob' but are not 'silver door knob' are:\tgold door knob\tbronze door knob\tstainless steel door knob\tchrome door knob\nThere are several useful visual features to tell there is 'silver door knob' and not similar things in a photo:\tsilver color\tround shape\tmetallic appearance\tpost attached to the door or drawer", 31], "robot": ["Yes. 'Robot' has a tangible appearance and is a machine designed to perform tasks autonomously or by remote control.\nA few things that are visually similar to 'robot' but are not 'robot' are:\tmannequin\taction figure\tandroid\t\nThere are several useful visual features to tell there is 'robot' and not similar things in a photo:\tmetal or plastic body\tjointed or articulated limbs\tvisible sensors or camera\tabsence of human features or expressions\tmovements indicating it is powered or autonomous, i.e. moving on its own instead of being posed by someone\telse", 31], "orange pillow": ["Yes. 'Orange pillow' has a tangible appearance and is a type of bedding or cushion.\nA few things that are visually similar to 'orange pillow' but are not 'orange pillow' are:\torange cushion\torange stuffed animal\torange foam\tblock\nThere are several useful visual features to tell there is 'orange pillow' and not similar things in a photo:\tsoft and plush surface\tsquare or rectangular shape\tpillow-like consistency and thickness\tbright orange color", 31], "tall light": ["Yes. 'Tall light' has a tangible appearance and is a type of illumination device.\nA few things that are visually similar to 'tall light' but are not 'tall light' are:\ttree\tlighthouse\tskyscraper\tfirework\nThere are several useful visual features to tell there is 'tall light' and not similar things in a photo:\tslim, vertical shape\temitting light\tfrom a lamp shade or bulb\tfixed or portable\tbase or stand at the bottom.", 31], "grey helmet": ["Yes. 'Grey helmet' has a tangible appearance and is a type of protective headgear.\nA few things that are visually similar to 'grey helmet' but are not 'grey helmet' are:\thats\tcaps\tvisors\thairnets\t\nThere are several useful visual features to tell there is 'grey helmet' and not similar things in a photo:\thard and durable material\tspecific shape to fit head and protect it\tusually grey or silver color\twith a chin strap or buckle\tpadding or foam inside for added protection.", 31], "rubber tires": ["Yes. 'Rubber tires' has a tangible appearance and is a type of vehicle accessory.\nA few things that are visually similar to 'rubber tires' but are not 'rubber tires' are:\tfrisbees\tflying saucers\tinner tubes\tdoughnuts\nThere are several useful visual features to tell there is 'rubber tires' and not similar things in a photo:\tcircular in shape\trubber or rubber-like material\ttread patterns on the surface\thubcap or rim attached to the wheel", 31], "shadow bench": ["Yes. 'Shadow bench' has a tangible appearance and is an object that creates a shadow.\nA few things that are visually similar to 'shadow bench' but are not 'shadow bench' are:\tregular bench\tchair\ttable\tbuilding\nThere are several useful visual features to tell there is 'shadow bench' and not similar things in a photo:\tthe bench is in a sunny area\tthat bench reflects a shadow\tthe shadow is curved or straight, following the contours of the bench", 31], "break": ["No. 'Break' is too vague or abstract to be distinguished in a photo.", 31], "dish pizza": ["Yes. 'Dish pizza' has a tangible appearance and is a type of pizza.\nA few things that are visually similar to 'dish pizza' but are not 'dish pizza' are:\tthin crust pizza\tflatbread\tcalzone\tpie\nThere are several useful visual features to tell there is 'dish pizza' and not similar things in a photo: \tthick crust\tdeeper than traditional pizza\tusually baked in a deep dish or a cake pan\tsauce and cheese are layered with toppings often placed on top of the cheese", 31], "chalk board": ["Yes. 'Chalk board' has a tangible appearance and is a type of writing surface.\nA few things that are visually similar to 'chalk board' but are not 'chalk board' are:\twhite board\tpaper\ttablet\tscreen\nThere are several useful visual features to tell there is 'chalk board' and not similar things in a photo:\tdark color, typically black or green\tsmooth surface that is receptive to chalk marks\tchalk dust residue or smudging\twriting or drawing with chalk or chalk markers", 31], "mitten": ["Yes. 'Mitten' has a tangible appearance and is a type of handwear.\nA few things that are visually similar to 'mitten' but are not 'mitten' are:\tglove\tsock\tbeanie\nThere are several useful visual features to tell there is 'mitten' and not similar things in a photo:\thas two pieces, thumb and body\tthumb in a different compartment from the other four fingers\tloose and comfortable, with no separation between fingers and hand\toften worn in cold weather to keep hands warm\tmade from soft and warm materials like wool or fleece", 31], "water line": ["Yes. 'Water line' has a tangible appearance and is an observable line or mark left by the water level.\nA few things that are visually similar to 'water line' but are not 'water line' are:\tshadow\tline of dirt or debris\tonion skin layers\tgeological strata\nThere are several useful visual features to tell there is 'water line' and not similar things in a photo:\tsmooth and continuous line\toften curved or irregular\tassociated with a body of water", 31], "brown fur": ["Yes. 'Brown fur' has a tangible appearance and can be found on many animals.\nA few things that are visually similar to 'brown fur' but are not 'brown fur' are:\tbrown hair\tfur coat\twool sweater\tother types of animal fur\nThere are several useful visual features to tell there is 'brown fur' and not similar things in a photo:\tshort or long individual strands\thaving a soft or fluffy texture\tan appearance that resembles animal skin or hair\tnumber of individual hairs in a given area", 31], "carvings": ["Yes. 'Carvings' has a tangible appearance and refers to objects or designs that are made by cutting, sculpting or engraving a material.\nA few things that are visually similar to 'carvings' but are not 'carvings' are:\tpaintings\tembroidery\tcalligraphy\tstencils\nThere are several useful visual features to tell there is 'carvings' and not similar things in a photo:\t\nthree-dimensional objects\t\ncreated by cutting or carving a material (such as wood, stone, or bone)\t\noften have intricate patterns or designs\t\nhave a textured surface or visible tool marks.", 31], "satellite": ["Yes. 'Satellite' has a tangible appearance and is a man-made object in space.\nA few things that are visually similar to 'satellite' but are not 'satellite' are:\tstar\tshooting star\tplanet\thelicopter\tplane\nThere are several useful visual features to tell there is 'satellite' and not similar things in a photo:\tcircular or rectangular shape\tantennas or solar panels protruding from the sides\tmetallic or reflective surface\tflying in a straight line at a consistent speed or hovering in place", 31], "raisin": ["Yes. 'Raisin' has a tangible appearance and is a dried fruit.\nA few things that are visually similar to 'raisin' but are not 'raisin' are:\tprunes\tcraisins\tdried apricots\tdried apples\nThere are several useful visual features to tell there is 'raisin' and not similar things in a photo:\tsmall size\tdark color wrinkled texture\tsunken appearance", 31], "construction": ["Yes. 'Construction' has a tangible appearance and refers to the process of building or putting together something.\nA few things that are visually similar to 'construction' but are not 'construction' are:\tdestruction\tdemolition\trepair\trenovation\tmaintenance\nThere are several useful visual features to tell there is 'construction' and not similar things in a photo:\thard hats, vests, or boots\ttools or equipment\tscaffolding or cranes\tunfinished or partially built structures\tdirt, gravel, or cement piles\tsawdust or wood shavings on the ground", 31], "pointy tip": ["Yes. 'Pointy tip' has a tangible appearance, and it refers to a sharp and pointed end of an object.\nA few things that are visually similar to 'pointy tip' but are not 'pointy tip' are:\trounded end\tsharp edge\tblunt tip\trounded tip\nThere are several useful visual features to distinguish 'pointy tip' from the listed similar things in a photo:\tsharp and pointed end\tsymmetrical and straight shape\tcould be a part of a larger object or a standalone feature, such as a pencil or a cone\tpointed tip is always oriented in one direction only.", 31], "vulture": ["Yes. 'Vulture' has a tangible appearance and is a kind of bird.\nA few things that are visually similar to 'vulture' but are not 'vulture' are:\teagle\thawk\tfalcon\towl\nThere are several useful visual features to tell there is 'vulture' and not similar things in a photo:\tbald head and neck\tdark feathers\tscavenging for carrion (dead animals)\tfeatherless head and neck\tskinny and hunched body\tbroad wingspan\tV-shaped or fan-shaped tailfins.", 31], "toenails": ["Yes. 'Toenails' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'toenails' but are not 'toenails' are:\tfingernails\tclaws\thooves\tpaws\nThere are several useful visual features to tell there are 'toenails' and not similar things in a photo:\tlocated at the end of toes\tflat and slightly curved\thard and keratinized\tcolored pinkish or brownish", 31], "kitchen counter top": ["Yes. 'Kitchen counter top' has a tangible appearance and is a flat surface in a kitchen for preparing food.\nA few things that are visually similar to 'kitchen counter top' but are not 'kitchen counter top' are:\ttables\tshelves\twindow sills\tdesks\nThere are several useful visual features to tell there is 'kitchen counter top' and not similar things in a photo:\tmade of stone, wood, or other durable material\tlocated in a kitchen or cooking space\tflat surface with no edges or corners on top for preparing food (cutting, mixing, etc.)\tcan have a built-in sink or stove", 31], "metal beam": ["Yes. 'Metal beam' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'metal beam' but are not 'metal beam' are:\tmetal pipes\tmetal rods\tmetal wires\tmetal bars\nThere are several useful visual features to tell there is 'metal beam' and not similar things in a photo:\thorizontal or vertical placement\tlong and rectangular shape\tmetallic surface with visible rivets or screws\tused as a load-bearing element in construction or engineering.", 31], "water waves": ["Yes. 'Water waves' has a tangible appearance and is a natural phenomenon.\nA few things that are visually similar to 'water waves' but are not 'water waves' are:\tsound waves\tlight waves\tmotion lines\nThere are several useful visual features to tell there are 'water waves' and not similar things in a photo:\toccurring on a body of water or a liquid surface\trepeating humps or curves\tparticle motion or displacement in the direction of wave propagation\twhitecaps or other forms of water disturbance\trhythmic rising and falling pattern moving across the surface of water\tsplash or spray created by wave action.", 31], "sun reflection": ["Yes. 'Sun reflection' has a tangible appearance and is an optical phenomenon.\nA few things that are visually similar to 'sun reflection' but are not 'sun reflection' are:\tglare\tfog\thalos\nThere are several useful visual features to tell there is 'sun reflection' and not similar things in a photo:\tvisible reflection of the sun in a surface, such as water, metal, or glass\tblinding brightness and intensity\tsimilar shape and size to the sun, often circular or oval", 31], "party": ["No. 'Party' is too vague or abstract to be distinguished in a photo.", 31], "scratch": ["No. 'Scratch' is too abstract of a concept to be visually concrete. However, 'scratch' can refer to a physical mark or injury which has a tangible appearance.\nA few things that are visually similar to a physical scratch mark but are not 'scratch' are:\tlines\tdrawings\twrinkles\tcracks\nThere are several useful visual features to distinguish an actual scratch mark from similar things in a photo:\tirregular shape\traised edges\tdifferent color or texture than surrounding area\tlocation on a surface that can be easily scratched (such as a wooden table or a leather sofa)", 31], "bmw logo": ["Yes. 'BMW logo' has a tangible appearance and is a type of car logo.\nA few things that are visually similar to 'BMW logo' but are not 'BMW logo' are:\tMercedes logo\tAudi logo\tLexus logo\tToyota logo\nThere are several useful visual features to tell there is 'BMW logo' and not similar things in a photo:\tblue and white circular design\twithin the circle, there are four parts, three of which are black\tand the fourth consists of alternating blue and white pieces\tlines radiating from the circle's center\tpart of a car or motorcycle.", 31], "swirl": ["Yes. 'Swirl' has a tangible appearance and refers to a twisting or circular pattern.\nA few things that are visually similar to 'swirl' but are not 'swirl' are:\tcurls\tspirals\ttwists\tvortexes\nThere are several useful visual features to tell there is 'swirl' and not similar things in a photo:\tcircular pattern\ttwisted or curved lines\trepeating curves or loops\tcenter point or nucleus", 31], "orange helmet": ["Yes. 'Orange helmet' has a tangible appearance and is a type of protective headgear.\nA few things that are visually similar to 'orange helmet' but are not 'orange helmet' are:\torange hard hat\torange baseball cap\torange beanie\torange bicycle helmet\nThere are several useful visual features to tell there is 'orange helmet' and not similar things in a photo:\tprotective gear\tthat covers the top and sides of the head\tbright orange or hi-vis color\tchinstrap or adjustable strap to secure the helmet to the head\tsuitable for use in construction sites or activities that require head protection", 31], "baby girl": ["Yes. 'Baby girl' has a tangible appearance as an infant female.\nA few things that are visually similar to 'baby girl' but are not 'baby girl' are: baby boy, young girl, toddler\nThere are several useful visual features to tell there is 'baby girl' and not similar things in a photo: small size, delicate features, wearing pink or pastel colors, usually dressed in baby clothes, may have a bow or headband in their hair.", 31], "street post": ["Yes. 'Street post' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'street post' but are not 'street post' are:\ttree\tsign post\ttraffic light\tpillar\tlamp post\nThere are several useful visual features to tell there is 'street post' and not similar things in a photo:\ttall and vertical\tsquare or rectangular in shape\tmetallic or concrete material\telectric wires or cables attached on top\tor has a lamp or light on top.", 31], "gray elephant": ["Yes. 'Gray elephant' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'gray elephant' but are not 'gray elephant' are:\thippopotamus\trhinoceros\thorse\tcow\tpig\nThere are several useful visual features to tell there is 'gray elephant' and not similar things in a photo:\thuge body size\tand gray skin color\ttusks\ttrunk, floppy ears\tlong trunk, four legs", 31], "lobster": ["Yes. 'Lobster' has a tangible appearance and is a type of crustacean.\nA few things that are visually similar to 'lobster' but are not 'lobster' are:\tcrab\tcrayfish\tshrimp\nThere are several useful visual features to tell there is 'lobster' and not similar things in a photo:\tlarge size compared to other crustaceans\ttwo large claws\t10 legs (including claws)\tred or brownish color\tsmooth, tough shells\twith a segmented body\tand beady black eyes.", 31], "hull": ["Yes. 'Hull' has a tangible appearance and is the body of a ship or boat.\nA few things that are visually similar to 'hull' but are not 'hull' are:\tshell\tof a coconut\texoskeleton\nThere are several useful visual features to tell there is 'hull' and not similar things in a photo:\tlarge and curved\tstructural shape of a ship or a boat\tsmooth or painted surface\twaterline running along the length of the hull", 31], "pallets": ["Yes, 'pallets' has a tangible appearance and is a flat transport structure that supports goods.\nA few things that are visually similar to 'pallets' but are not 'pallets' are:\tplanks\tboards\tshelves\tslabs\nThere are several useful visual features to tell there is 'pallets' and not similar things in a photo:\tsquare or rectangular shape\tconsists of top boards, bottom boards, and blocks\tin some cases, may have markings or logos on it.", 31], "steer": ["Yes. 'Steer' has a tangible appearance and is a type of cattle.\nA few things that are visually similar to 'steer' but are not 'steer' are:\tcow\tox\tbison\tyak\nThere are several useful visual features to tell there is 'steer' and not similar things in a photo:\tmale bovine\tcastrated\thorns or no horns\tless furry than bison or yak\tbrown, white, or black coat\tcolor pattern depending on the breed (e.g., Hereford or Charolais)", 31], "flash": ["Yes. 'Flash' has a tangible appearance and often refers to a burst of bright light.\nA few things that are visually similar to 'flash' but are not 'flash' are:\tlightning\tcamera lens flare\tmirror reflection\tshimmer or glimmer on a surface\nThere are several useful visual features to tell there is 'flash' and not similar things in a photo:\tsudden burst of bright light\toften directional\tand brief\tduration\ttaken from hand-held device or camera", 31], "cave": ["Yes. 'Cave' has a tangible appearance and is a natural hollow or empty space within the earth, often found in mountains or cliffs.\nA few things that are visually similar to 'cave' but are not 'cave' are:\ttunnel\tmine\tshelter\troom\nThere are several useful visual features to tell there is 'cave' and not similar things in a photo:\trock walls or formations\tnatural surroundings (such as trees or water)\tdark or dimly lit\tinterconnected and branching passages\topening in a cliff or rocky area", 31], "railway track": ["Yes. 'Railway track' has a tangible appearance and is a linear structure built for trains to travel on.\nA few things that are visually similar to 'railway track' but are not 'railway track' are:\troad\tfootpath\tbridge\nThere are several useful visual features to tell there is 'railway track' and not similar things in a photo:\ttwo parallel metal tracks\tsleepers or ties\tballast or gravel\ttrains or locomotives", 31], "cement blocks": ["Yes. 'Cement blocks' has a tangible appearance and is a kind of building material.\nA few things that are visually similar to 'cement blocks' but are not 'cement blocks' are:\tbricks\tstones\twooden blocks\nThere are several useful visual features to tell there are 'cement blocks' and not similar things in a photo:\trectangular shape\tsmooth and gray surface with texture is visible\tused in construction projects", 31], "street corner": ["Yes. 'Street corner' has a tangible appearance and is a meeting point of two streets.\nA few things that are visually similar to 'street corner' but are not 'street corner' are:\troad intersection\troundabout\tdead end\nThere are several useful visual features to tell there is 'street corner' and not similar things in a photo:\t90-degree angle\troads or sidewalks meeting at a point\tstreet signs or traffic signals at the corner\tbuildings or landmarks at the corner", 30], "indent": ["No. 'Indent' is too vague or abstract to be visually distinguished in a photo.", 30], "dense forest": ["Yes. 'Dense forest' has a tangible appearance and refers to an area with a large number of trees that are close together.\nA few things that are visually similar to 'dense forest' but are not 'dense forest' are:\torchard\tgarden\tpark\tjungle\nThere are several useful visual features to tell there is 'dense forest' and not similar things in a photo:\n- A large number of trees closely spaced together, creating a canopy\n- Little to no visible ground between the trees due to foliage and underbrush\n- Tall trees with long trunks and high branches\n- Various shades and hues of green in the foliage and trees.", 30], "snowpants": ["Yes, 'snowpants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'snowpants' but are not 'snowpants' are:\tjeans\ttrousers\tsweatpants\tleggings\nThere are several useful visual features to tell there is 'snowpants' and not similar things in a photo:\tthick and insulated material\twater-resistant or waterproof\tfitted around the waist and ankles\tbulky appearance to fit over other clothing\tzippers or snaps at the ankles may be present.", 30], "dresses": ["Yes. 'Dresses' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'dresses' but are not 'dresses' are:\tskirts\tshirts\tcoats\tpants\nThere are several useful visual features to tell there is 'dresses' and not similar things in a photo:\tone-piece garment that covers the torso\tand legs\tfitted or loose\tusually worn by women or girls\tvariety of styles and materials", 30], "dark hair": ["Yes. 'Dark hair' has a tangible appearance and is a physical trait.\nA few things that are visually similar to 'dark hair' but are not 'dark hair' are: black fur, shadows, dark fabric\nThere are several useful visual features to tell there is 'dark hair' and not similar things in a photo:\tGrowing from a head, attached to a scalp and roots of the hair. May be black or brown but not completely black. Texture and individual strands can be seen up close. The context of a human face or head will clue towards whether it is hair or not.", 30], "notebooks": ["Yes. 'Notebooks' has a tangible appearance and refers to a type of bound paper used for writing or taking notes.\nA few things that are visually similar to 'notebooks' but are not 'notebooks' are:\tjournals\tsketchbooks\tbinders\tnotepads\nThere are several useful visual features to tell there is 'notebooks' and not similar things in a photo:\tbound pages with a cover\tpaper texture\tand ruled or blank pages\tvarious sizes and colors", 30], "building roof": ["Yes. 'Building roof' has a tangible appearance and is a significant part of a building structure.\nA few things that are visually similar to 'building roof' but are not 'building roof' are:\tsky\tsun shade\tumbrella\thats\nThere are several useful visual features to tell there is 'building roof' and not similar things in a photo:\tabove the main structure of a building\tcan be flat, sloped or angled\tmade of concrete, tiles, metal, or other materials\tbordering the top of the walls of a building", 30], "bottle top": ["Yes. 'Bottle top' has a tangible appearance and is the cap or closure for a bottle.\nA few things that are visually similar to 'bottle top' but are not 'bottle top' are:\tlids\tcaps\ttab tops\nThere are several useful visual features to tell there is 'bottle top' and not similar things in a photo:\tcircular or cylindrical shape\tfit snugly over the bottle opening\tcan be screwed or popped off\tmay have a seal or a cork inside\tthe material of the bottle top may be metal, plastic, or cork", 30], "crust pizza": ["Yes. 'Crust pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'crust pizza' but are not 'crust pizza' are:\tflatbread\tnaan\tbreadstick\t\nThere are several useful visual features to tell there is 'crust pizza' and not similar things in a photo:\tcircular or rectangular shape\ttomato sauce layer\tcheese and toppings on top\tbaked or cooked appearance\tthicker than a flatbread or a naan crust.", 30], "zip": ["Yes. 'Zip' has a tangible appearance and is a mechanism used to fasten clothes or bags.\nA few things that are visually similar to 'zip' but are not 'zip' are:\tbutton\tbuckle\tsnaps\tvelcro\nThere are several useful visual features to tell there is 'zip' and not similar things in a photo:\ttwo parallel rows of teeth\tzipper pull to open and close\tthe ability to separate into two sides", 30], "mouse pads": ["Yes. 'Mouse pads' has a tangible appearance and is a type of computer accessory.\nA few things that are visually similar to 'mouse pads' but are not 'mouse pads' are:\tcoasters\tcarpet\tfelt\telectronics\nThere are several useful visual features to tell there is 'mouse pads' and not similar things in a photo:\trectangular or circular shape\tthick material\tunique design or image on the surface\tsits on a flat surface like a desk or table.", 30], "tan rock": ["Yes. 'Tan rock' has a tangible appearance and is a type of rock that is tan in color.\nA few things that are visually similar to 'tan rock' but are not 'tan rock' are:\tbrown sandstone\tpebble\tdesert sand\ttravertine\nThere are several useful visual features to tell there is 'tan rock' and not similar things in a photo:\trough surface\tirregular shape\tsolid or opaque\ttan or beige color, with possibly some darker or lighter shades", 30], "duvet": ["Yes. 'Duvet' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'duvet' but are not 'duvet' are:\tcomforter\tquilt\tblanket\tthrow pillow\nThere are several useful visual features to tell there is 'duvet' and not similar things in a photo:\tfluffy and quilted\ttop layer of bedding\tassociated with a cover\tthat can be removed for washing and changing the appearance of the bedding.", 30], "grey stones": ["Yes. 'Grey stones' has a tangible appearance and refers to stones that are colored grey.\nA few things that are visually similar to 'grey stones' but are not 'grey stones' are:\tconcrete\tcement\trocks\tasphalt\nThere are several useful visual features to tell there is 'grey stones' and not similar things in a photo:\tnatural or irregular shapes\tvarious sizes and textures\tgrey color\trange from light to dark grey", 30], "size bed": ["No. 'Size bed' is too vague or abstract to be distinguished in a photo. It is necessary to specify the size of the bed (e.g., twin, queen, king, etc.).\nHowever, a few things that are visually similar to 'bed' but are not 'size bed' are:\tcouch\tfuton\tmattress\tpillow\tblow-up mattress\nThere are several useful visual features to tell there is 'size bed' and not similar things in a photo, depending on the size specified:\tlength and width (twin, full, queen, king, etc.)\theadboard and/or footboard\tmattress and bed frame or platform raised off the floor", 30], "miniature": ["Yes. 'Miniature' has a tangible appearance and is something that is smaller in scale than the original object or subject.\nA few things that are visually similar to 'miniature' but are not 'miniature' are:\tregular-sized objects\tzoomed-in photos\t\nThere are several useful visual features to tell there is 'miniature' and not similar things in a photo:\tproportions significantly smaller than the original object or subject\toften used in miniature models or figurines\tdetails and features of the object or subject are still visible despite the smaller scale.", 30], "toilet plunger": ["Yes. 'Toilet plunger' has a tangible appearance and is a tool used for unclogging toilets.\nA few things that are visually similar to 'toilet plunger' but are not 'toilet plunger' are:\tsink plunger\trubber ducks\tbath toys\nThere are several useful visual features to tell there is 'toilet plunger' and not similar things in a photo:\tbulbous rubber shape\twith a wooden handle or plastic handle\tdesigned to fit over the drain at the base of a toilet bowl", 30], "tan jacket": ["Yes. 'Tan jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tan jacket' but are not 'tan jacket' are:\tbeige sweater\tcamel coat\tkhaki parka\nThere are several useful visual features to tell there is 'tan jacket' and not similar things in a photo:\tlight brown or beige color\tmade of a thicker material like wool or cotton\tfrontal closure with buttons or a zipper\tcollar", 30], "mittens": ["Yes. 'Mittens' has a tangible appearance and is a type of hand accessory.\nA few things that are visually similar to 'mittens' but are not 'mittens' are:\tgloves\tsocks\tpuppets\tpaw prints\nThere are several useful visual features to tell there is 'mittens' and not similar things in a photo:\tcovering the entire hand and part of the wrist\tseparate spaces for the fingers and thumb\tthick and warm material\tno individual finger sheathes", 30], "glass mug": ["Yes. 'Glass mug' has a tangible appearance and is a type of drinking container.\nA few things that are visually similar to 'glass mug' but are not 'glass mug' are:\tteapot\tpitcher\tvase\tflower pot\nThere are several useful visual features to tell there is 'glass mug' and not similar things in a photo:\tcylindrical or conical shape\thandle\tfor drinking liquids, usually hot such as tea or coffee\tmade of glass or transparent material.", 30], "flaps": ["Yes. 'Flaps' has a tangible appearance and is a part of an airplane wing.\nA few things that are visually similar to 'flaps' but are not 'flaps' are:\taileron\tspoiler\trudder\nThere are several useful visual features to tell there are 'flaps' and not similar things in a photo:\tfound on the trailing edge of the wing\thinged to the wing surface\tcan be lowered or raised during takeoff and landing\tlarger than ailerons or spoilers.", 30], "lifts": ["Yes. 'Lifts' has a tangible appearance and is a type of transportation device.\nA few things that are visually similar to 'lifts' but are not 'lifts' are:\tescalator\tstairs\ttrampoline\nThere are several useful visual features to tell there is 'lifts' and not similar things in a photo:\twalled cabin\tthat moves vertically or diagonally\tthat has a door or a gate\ton rails or a cable\tno visible steps or incline", 30], "outcropping": ["Yes. 'Outcropping' has a tangible appearance and refers to a visible rock formation that protrudes from the ground.\nA few things that are visually similar to 'outcropping' but are not 'outcropping' are:\tboulders\tpebbles\tbricks\tpavements\nThere are several useful visual features to tell there is 'outcropping' and not similar things in a photo:\tprotruding from the ground\tvisible layers or textures\tmade of rock or stone\tnatural-looking and rugged appearance", 30], "billboard sign": ["Yes. 'Billboard sign' has a tangible appearance and is a type of advertising structure.\nA few things that are visually similar to 'billboard sign' but are not 'billboard sign' are:\tposter\tsignage\tbanners\twall paintings\nThere are several useful visual features to tell there is 'billboard sign' and not similar things in a photo:\tlarge size\traised above the ground\tfor commercial advertising\tor informational purposes\tsturdy construction\toften made of steel or concrete\tclear, bold lettering or graphics with high contrast\tcolorful\tdisplayed in public places.", 30], "basil leaf": ["Yes. 'Basil leaf' has a tangible appearance and is a type of herb.\nA few things that are visually similar to 'basil leaf' but are not 'basil leaf' are:\tmint leaf\tcilantro leaf\tparsley leaf\nThere are several useful visual features to tell there is 'basil leaf' and not similar things in a photo:\topposite leaves with toothed edges\tgreen color\tsmooth and shiny surface\taromatic scent of basil when crushed or rubbed.", 30], "baby doll": ["Yes. 'Baby doll' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'baby doll' but are not 'baby doll' are:\tmannequin\taction figure\tstuffed animal\nThere are several useful visual features to tell there is 'baby doll' and not similar things in a photo:\thuman-like shape and features\tsmall in size\tsoft body material\trealistic or exaggerated features of a human baby, such as eyes, nose, and mouth", 30], "bronze": ["Yes. 'Bronze' has a tangible appearance and is a metal alloy.\nA few things that are visually similar to 'bronze' but are not 'bronze' are:\tbrass\tcopper\tgold\t\nThere are several useful visual features to tell there is 'bronze' and not similar things in a photo:\tdark brownish or grayish yellow color\tmetallic luster\tmalleable\tand ductile, easy to cast in molds", 30], "surfboard man": ["Yes. 'Surfboard man' has a tangible appearance as it refers to a person standing or sitting on a surfboard.\nA few things that are visually similar to 'surfboard man' but are not 'surfboard man' are:\tsurfboard\twindsurfer\tkayaker\tswimmer\t\nThere are several useful visual features to tell there is 'surfboard man' and not similar things in a photo: A person standing or sitting on the surfboard \tHolding or attached to the surfboard\tWearing a wetsuit or swimwear\tWaving, smiling, or holding a surfing pose.", 30], "blue bag": ["Yes. 'Blue bag' has a tangible appearance and refers to a bag that is colored blue.\nA few things that are visually similar to 'blue bag' but are not 'blue bag' are:\tblue shirt\tblue backpack\tblue container\nThere are several useful visual features to tell there is a 'blue bag' and not similar things in a photo:\tit has the shape of a bag\tit is made of a flexible material with a handle to carry it\tcloses with a zipper, flap, or drawstring\tthe color is a shade of blue.", 30], "porcelain toilet tank": ["Yes. 'Porcelain toilet tank' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'porcelain toilet tank' but are not 'porcelain toilet tank' are:\tsink\tbath tub\tshower base\tbidet\nThere are several useful visual features to tell there is 'porcelain toilet tank' and not similar things in a photo:\tattached to the toilet bowl\tporcelain material\twith a water supply line on the bottom or back\thas a lid on top", 30], "cargo pants": ["Yes. 'Cargo pants' has a tangible appearance and is a type of pants with many pockets.\nA few things that are visually similar to 'cargo pants' but are not 'cargo pants' are:\tjeans\tleggings\ttrousers\twork pants\tkhakis\nThere are several useful visual features to tell there are 'cargo pants' and not similar things in a photo:\tlots of pockets, especially on the legs\tloose and comfortable fit\ttypically made of cotton or a rugged material\ttapered or cinched at the ankles\tthe pockets often have flaps or buttons for closure.", 30], "styrofoam": ["Yes. 'Styrofoam' has a tangible appearance and is a type of lightweight plastic material.\nA few things that are visually similar to 'styrofoam' but are not 'styrofoam' are:\tfoam rubber\tpolystyrene foam\tEPS foam\tpolyurethane foam\nThere are several useful visual features to tell there is 'styrofoam' and not similar things in a photo:\tlightweight and easily breakable\twhite or light beige color\ttexture resembling small air bubbles or dots\tglare or reflection when in contact with light", 30], "metal lamp": ["Yes. 'Metal lamp' has a tangible appearance and is a kind of lighting fixture.\nA few things that are visually similar to 'metal lamp' but are not 'metal lamp' are:\tcandle holder\tvase\tteapot\tlantern\nThere are several useful visual features to tell there is 'metal lamp' and not similar things in a photo:\tmade of metal or with metal finish\tstriking light inside or on top\tof various shapes and sizes\twith a lamp shade or a bulb inside\tdesigned to sit on a flat surface\tor be mounted to a wall or a ceiling.", 30], "barefoot man": ["Yes. 'Barefoot man' has a tangible appearance and refers to a person who is not wearing shoes.\nA few things that are visually similar to 'barefoot man' but are not 'barefoot man' are: person wearing sandals, person wearing flip flops, person wearing socks with open-toed shoes \nThere are several useful visual features to tell there is 'barefoot man' and not similar things in a photo: absence of footwear, toes and soles of feet visible, no indication of footwear being worn such as straps or backs of shoes, footprints on the ground.", 30], "advertisment": ["No. 'Advertisement' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that may be visually similar to an advertisement in a photograph could be:\n\n1. A poster or sign with text or an image\n2. A flyer or brochure with promotional material \n3. A promotional banner or flag\n4. A television or online video commercial \n\nTo distinguish an advertisement from similar things in a photo, useful visual features would include:\n\n1. Text or imagery promoting a product, service, or event\n2. Branding or logos of a company or organization\n3. Contact information or a call-to-action \n4. A visually pleasing or attention-grabbing design.", 30], "metal sink": ["Yes. 'Metal sink' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'metal sink' but are not 'metal sink' are:\tbathtub\ttin basin\tsteel bucket\nThere are several useful visual features to tell there is 'metal sink' and not similar things in a photo:\tlocated in a bathroom or kitchen\tmetallic surface\twith a faucet\twith a drain\tprovides a basin for water", 30], "elbows": ["Yes. 'Elbows' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'elbows' but are not 'elbows' are:\tknees\tknuckles\tknobs on doors\nThere are several useful visual features to tell there is 'elbows' and not similar things in a photo:\tjoint located in the middle of the arm\tbony prominence\twhen the arm is bent, the elbow sticks out in a V-shape", 30], "wire mesh": ["Yes. 'Wire mesh' has a tangible appearance and is a type of woven material made from wire.\nA few things that are visually similar to 'wire mesh' but are not 'wire mesh' are:\tfishing net\tcobweb\tbasketry\nThere are several useful visual features to tell there is 'wire mesh' and not similar things in a photo:\tregular and repetitive pattern\ttransparent or semi-transparent appearance\tmade from metal wire or plastic\tflexible and pliable to form different shapes", 30], "swirls": ["Yes. 'Swirls' has a tangible appearance and refers to spiral or winding shapes.\nA few things that are visually similar to 'swirls' but are not 'swirls' are:\tcurls\tvines\tspiral staircase\tsnails\nThere are several useful visual features to tell there are 'swirls' and not similar things in a photo:\twind or spiral shape\trepetitive\tcurves and twists that interlock or overlap\tsymmetrical or asymmetrical patterns with curves and twists.", 30], "passenger train car": ["Yes. 'Passenger train car' has a tangible appearance and is a type of vehicle used for transportation.\nA few things that are visually similar to 'passenger train car' but are not 'passenger train car' are:\tfreight train car\tsubway car\ttram\ttrolley\tbus\nThere are several useful visual features to tell there is 'passenger train car' and not similar things in a photo:\tlong and narrow shape\tdoor and window openings\tseating inside or visible through windows\tcoupling mechanisms for linking multiple cars\ttoilet and sink facilities at one or both ends of the car\tengine at one end of the train", 30], "ear tag": ["Yes. 'Ear tag' has a tangible appearance and is an identification tag for animals.\nA few things that are visually similar to 'ear tag' but are not 'ear tag' are:\tearring\tpiercing\ttag on luggage\tor bag\nThere are several useful visual features to tell there is 'ear tag' and not similar things in a photo:\taffixed to an animal's ear or other body parts, such as a tag on a tail or wing\tbrightly colored or numbered or engraved with a code", 30], "backyard": ["Yes. 'Backyard' has a tangible appearance and is a private outdoor space located behind a house.\nA few things that are visually similar to 'backyard' but are not 'backyard' are:\tpark\tcourtyard\tgarden\tplayground\nThere are several useful visual features to tell there is 'backyard' and not similar things in a photo:\tadjoining a house or a building\tclosed off by fences, walls, or hedges\tpatio or seating area\tgrassy or paved ground\tflowers or plants", 30], "dark trees": ["Yes. 'Dark trees' has a tangible appearance and refers to trees that appear dark in color or shadow.\nA few things that are visually similar to 'dark trees' but are not 'dark trees' are:\ttrees at night\tsilhouettes of trees\ttrees in shadow\tpainted trees\nThere are several useful visual features to tell there is 'dark trees' and not similar things in a photo:\ttrees that appear black or very dark\tdarker than surrounding objects\tor already in night mode.", 30], "gold door knob": ["Yes. 'Gold door knob' has a tangible appearance and is a kind of accessory.\nA few things that are visually similar to 'gold door knob' but are not 'gold door knob' are:\tcabinet knob\tdresser drawer handle\tgold faucet\nThere are several useful visual features to tell there is 'gold door knob' and not similar things in a photo:\tmetallic appearance\tgold color\tspherical or cylindrical shape\tconspicuously protruding from a door.", 30], "winter glove": ["Yes. 'Winter glove' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'winter glove' but are not 'winter glove' are:\tski gloves\twork gloves\trubber gloves\tgardening gloves\tmittens\nThere are several useful visual features to tell there is 'winter glove' and not similar things in a photo:\tdesigned to keep hands warm\tthick and insulated\tflexible enough to hold objects\tfingers are separated rather than grouped together (not for mittens)\ttypically made of soft and cozy materials.", 30], "cabinet drawer": ["Yes. 'Cabinet drawer' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'cabinet drawer' but are not 'cabinet drawer' are:\tshelves\tbureaus\tdesks\tdressers\nThere are several useful visual features to tell there is 'cabinet drawer' and not similar things in a photo:\tbox-like structure\twith pull or knob handles\tsits within a larger piece of furniture, such as a cabinet or dresser\tmade of wood or metal or plastic.", 30], "crib": ["Yes. 'Crib' has a tangible appearance and is a type of baby bed.\nA few things that are visually similar to 'crib' but are not 'crib' are:\tbassinet\tcot\tplaypen\tbunk bed\nThere are several useful visual features to tell there is 'crib' and not similar things in a photo:\t\nrectangular shape\nbars or slats on the sides to prevent the baby from falling \na mattress to sleep on \nmobile or toys hanging from the top", 30], "wall socket": ["Yes. 'Wall socket' has a tangible appearance and is a type of electrical outlet.\nA few things that are visually similar to 'wall socket' but are not 'wall socket' are:\tlight switch\tphone jack\tinternet port\nThere are several useful visual features to tell there is 'wall socket' and not similar things in a photo:\ttwo or three holes or slots for plugs\trectangular or round shape\tsurrounded by a faceplate or cover\tmounted on a wall or in a socket box\telectrical current symbol or label\tnext to electrical wires or cables", 30], "trash container": ["Yes. 'Trash container' has a tangible appearance and is a type of receptacle.\nA few things that are visually similar to 'trash container' but are not 'trash container' are:\tbaskets\tpots\tbags\tboxes\nThere are several useful visual features to tell there is 'trash container' and not similar things in a photo:\tdesigned specifically for collecting garbage and waste materials\thinged lid or snap-on lid\tsturdy and durable material\tusually made of plastic, metal or a combination of both\tdumpster style design for outdoor containers", 30], "grey jacket": ["Yes. 'Grey jacket' has a tangible appearance.\nA few things that are visually similar to 'grey jacket' but are not 'grey jacket' are:\tblack jacket\tblue jacket\twool sweater\nThere are several useful visual features to tell there is 'grey jacket' and not similar things in a photo:\tgrey in color\thas button or zipper fastenings\tis made of cotton, leather, or other jacket material\ttypically has sleeves that cover the arms and a collar at the neck level\tis designed to be worn over other clothing as a layer of warmth or style.", 30], "males": ["No. 'Males' are too vague or abstract to be distinguished in a photo. It is a biological term referring to the male sex.\n", 30], "paper dispenser": ["Yes. 'Paper dispenser' has a tangible appearance and is a type of object used to hold and dispense paper.\nA few things that are visually similar to 'paper dispenser' but are not 'paper dispenser' are:\ttowel dispenser\ttoilet paper holder\tnotebook\tbinder clip\nThere are several useful visual features to tell there is 'paper dispenser' and not similar things in a photo:\tbox-shaped object\twith a slot or opening for paper\tdisplays the type of paper or material it holds (e.g. \"tissue\" or \"receptionist\" paper)\tcan be mounted on a wall or placed on a surface\tsimple design with minimal decorative elements.", 30], "clothe": ["No. 'Clothe' is too vague or abstract to be distinguished in a photo.\nHowever, a few things that are visually similar to 'clothe' but are not 'clothe' are:\tfabric\tblankets\ttowels\nThere are no specific visual features that distinguish 'clothe' from these similar things, as 'clothe' refers to a broader concept that encompasses all types of clothing items made from various materials.", 30], "plaid blanket": ["Yes. 'Plaid blanket' has a tangible appearance and is a kind of blanket.\nA few things that are visually similar to 'plaid blanket' but are not 'plaid blanket' are:\tcheckered tablecloth\tkilts\tcheckered shirts\nThere are several useful visual features to tell there is 'plaid blanket' and not similar things in a photo:\t\na pattern of intersecting horizontal and vertical lines of different colors, usually in a two-over-two pattern.", 30], "silver forks": ["Yes. 'Silver forks' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'silver forks' but are not 'silver forks' are:\tsilver knives\tsilver spoons\tsilver trays\nThere are several useful visual features to tell there is 'silver forks' and not similar things in a photo:\tfork-like shape\tprongs on the end\tsilver or metallic appearance\thandle attached to the prongs", 30], "soles": ["Yes. 'Soles' has a tangible appearance and refers to the bottom part of a footwear.\nA few things that are visually similar to 'soles' but are not 'soles' are:\theels\tinsoles\tclogs\tflats\nThere are several useful visual features to tell there are 'soles' and not similar things in a photo:\tbottom part of a shoe or a boot\tgrooves or patterns to prevent slipping\tfrom different materials like rubber or leather\tof various shapes and sizes", 30], "orange drink": ["Yes. 'Orange drink' has a tangible appearance and is a type of beverage.\nA few things that are visually similar to 'orange drink' but are not 'orange drink' are:\torange juice\torange soda\torange flavored sparkling water\torange smoothie\nThere are several useful visual features to tell there is 'orange drink' and not similar things in a photo:\tbright orange color\ttransparency\tliquid consistency\torangy smell.", 30], "bathroom rug": ["Yes. 'Bathroom rug' has a tangible appearance and is a type of floor covering.\nA few things that are visually similar to 'bathroom rug' but are not 'bathroom rug' are:\tbath mat\tregular rug\tcarpet\nThere are several useful visual features to tell there is 'bathroom rug' and not similar things in a photo:\tusually small in size\tabsorbent material (such as cotton)\tmay have a nonslip coating\toften placed near a shower or bathtub", 30], "silver flusher": ["No. 'Silver flusher' is too vague or abstract to be distinguished in a photo. It is not a commonly used or recognized term.", 30], "whisk": ["Yes. 'Whisk' has a tangible appearance and is a kitchen utensil used for mixing.\nA few things that are visually similar to 'whisk' but are not 'whisk' are:\tfork\tbeater\nThere are several useful visual features to tell there is 'whisk' and not similar things in a photo:\twire loops\tthat are closely spaced\tlong handle\tthat is easy to hold and grip\twhile the other end has loops for mixing ingredients\tto be used for preparing food", 30], "plate food": ["Yes. 'Plate food' has a tangible appearance and refers to food arranged on a dish or plate.\nA few things that are visually similar to 'plate food' but are not 'plate food' are:\tgarden or house plants\tfruit or vegetable basket\titems on a kitchen counter\nThere are several useful visual features to tell there is 'plate food' and not similar things in a photo:\tvariety of colors\tvisible ingredients\tappears cooked or prepared\tplating or presentation of food on a dish or a plate", 30], "grey boulder": ["Yes. 'Grey boulder' has a tangible appearance and is a large rock.\nA few things that are visually similar to 'grey boulder' but are not 'grey boulder' are:\tgray pebble\tconcrete block\tboulder with a different color\nThere are several useful visual features to tell there is 'grey boulder' and not similar things in a photo:\tlarge size\trough texture\tgray color\tmay have moss, lichen, or other natural markings", 30], "paste": ["Yes. 'Paste' has a tangible appearance and is a thick, sticky substance.\nA few things that are visually similar to 'paste' but are not 'paste' are:\tglue\tjelly\tmud\tsyrup\tbutter\nThere are several useful visual features to tell there is 'paste' and not similar things in a photo:\tsmooth or grainy texture\tthick consistency\tsqueezed from a tube or container\tcapable of adhering to surfaces\tmay be opaque or transparent depending on the type.", 30], "blue table cloth": ["Yes. 'Blue table cloth' has a tangible appearance and is a type of cloth used to cover tables.\nA few things that are visually similar to 'blue table cloth' but are not 'blue table cloth' are:\tnametags\tnapkins\tpaper\tdecorations\nThere are several useful visual features to tell there is 'blue table cloth' and not similar things in a photo:\tblue color\tfabric material\trectangular or square shape\tspread out over a table's surface.", 30], "anchor": ["Yes. 'Anchor' has a tangible appearance and is a heavy object used to keep a ship or boat from moving.\nA few things that are visually similar to 'anchor' but are not 'anchor' are:\tweight\tdumbbell\tstone\nThere are several useful visual features to tell there is 'anchor' and not similar things in a photo:\theavy and bulky\tteardrop-shaped with flat bottom and curved arms\twith a chain or rope at the top\tmade of metal or other durable materials.", 30], "adult male": ["Yes, 'adult male' has a tangible appearance and refers to a specific gender and stage of human growth.\nA few things that are visually similar to 'adult male' but are not 'adult male' are:\tadolescent male\telderly male\ttransgender male\torangutan\tchimpanzee\tgorilla\nThere are several useful visual features to tell there is 'adult male' and not similar things in a photo:\tfacial hair or beard\tmuscular physique\tmale anatomy\tprominent Adam's apple\tmasculine clothing and accessories", 30], "handle spoon": ["Yes. 'Handle spoon' has a tangible appearance and is a type of eating utensil.\nA few things that are visually similar to 'handle spoon' but are not 'handle spoon' are:\tfork\tknife\tchopsticks\nThere are several useful visual features to tell there is 'handle spoon' and not similar things in a photo:\tbowl-shaped tip\tfork on one end and not sharp like a knife\tmostly used for eating liquids or foods that need to be scooped", 30], "dash": ["No. 'Dash' is too abstract to have a tangible appearance, therefore it cannot be visually concrete.\nHowever, a few things that are visually similar to 'dash' but are not 'dash' are:\tstripe\tline\tstreak\thyphen\nUseful visual features for distinguishing 'dash' from the listed similar things in a photo are:\ttypically shorter than a line and longer than a hyphen\ttwo dashes can be used to indicate a pause in speech or writing, like this --", 30], "hazy": ["Yes. 'Hazy' has a tangible appearance and refers to air or atmosphere that appears misty or blurred.\nA few things that are visually similar to 'hazy' but are not 'hazy' are:\tfoggy\tsmoky\tcrowded\twith blurred vision\nThere are several useful visual features to tell there is 'hazy' and not similar things in a photo:\tthe air appears misty or blurred\tthe colors appear less crisp or less vivid\tlack of sharpness in distant objects\tor characters are not easily identified\tin general, the atmosphere appears unclear or lack of transparency.", 30], "daytime": ["No. 'Daytime' is too vague or abstract to be distinguished in a photo.", 30], "graphic": ["No. 'Graphic' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider a specific type of graphic such as 'graphic design', then we can say that it has a tangible appearance. \n\nA few things that are visually similar to 'graphic design' but are not 'graphic design' are:\tphotographs\tillustrations\tpaintings\n\nThere are several useful visual features to tell there is 'graphic design' and not similar things in a photo:\t\ntypography\nsymbols and icons\ncolor palettes and schemes\ncomposition and layout \nlines, shapes, and patterns \nvector or raster graphics \ndigital or print media", 30], "diner": ["Yes. 'Diner' has a tangible appearance and is a type of restaurant.\nA few things that are visually similar to 'diner' but are not 'diner' are:\tcafe\tcoffee shop\trestaurant\tbar\nThere are several useful visual features to tell there is 'diner' and not similar things in a photo:\tretro or vintage decor\tcounter seating with bar stools\tbooths with jukeboxes\tbright neon signs\toutdoor sign with the word \"Diner\" or a picture of a coffee cup\tfamiliar diner foods such as hamburgers, fries, milkshakes, and pie.", 30], "baseball shirt": ["Yes. 'Baseball shirt' has a tangible appearance and is a specific type of shirt.\nA few things that are visually similar to 'baseball shirt' but are not 'baseball shirt' are:\tt-shirt\tsport jersey\tpolo shirt\nThere are several useful visual features to tell there is 'baseball shirt' and not similar things in a photo:\thorizontal stripes in contrasting colors\tor the logo of a baseball team\t\u00be length sleeves\tbutton-up front or a collar", 30], "lemonade": ["Yes. 'lemonade' has a tangible appearance and is a type of drink.\nA few things that are visually similar to 'lemonade' but are not 'lemonade' are:\torange juice\tapple juice\tgrapefruit juice\ticed tea\nThere are several useful visual features to tell there is 'lemonade' and not similar things in a photo:\t\nyellow or light orange color\tcloudy or pulpy appearance\tice cubes and lemon slices in the drink\tglass or pitcher container\tstraw for sipping", 30], "blonde boy": ["Yes. 'Blonde boy' has a tangible appearance and refers to a male child with blond hair.\nA few things that are visually similar to 'blonde boy' but are not 'blonde boy' are:\tred-haired boy\tbrown-haired boy\tgirl with blonde hair\nThere are several useful visual features to tell there is 'blonde boy' and not similar things in a photo:\tmale child\tblond hair\tboyish clothes or accessories\tspecific facial features such as eye color, nose, and mouth shape.", 30], "pliers": ["Yes. 'Pliers' has a tangible appearance and is a type of tool.\nA few things that are visually similar to 'pliers' but are not 'pliers' are:\twire cutters\ttongs\tscissors\t\nThere are several useful visual features to tell there is 'pliers' and not similar things in a photo:\ttwo arms connected by a pivot point\tforceps with flat, typically serrated teeth on the ends of the arms\ta spring to keep the arms open, and a platypus bill-shaped with wire cutters at the base and tapered jaws with ridges for extra grip.", 30], "barefoot": ["Yes. 'Barefoot' has a tangible appearance and is a condition of the feet being uncovered.\nA few things that are visually similar to 'barefoot' but are not 'barefoot' are:\tshoes\tsandals\tsocks\tfeet covered in paint or mud\nThere are several useful visual features to tell there is 'barefoot' and not similar things in a photo:\tno shoes or socks covering the feet\ttoes and soles of the feet are visible\tno footprints or imprints of shoes visible on the ground", 30], "adult man": ["Yes. 'Adult man' has a tangible appearance and is a category of human beings.\nA few things that are visually similar to 'adult man' but are not 'adult man' are:\tteenager\telderly man\twoman\ttoddler\nThere are several useful visual features to distinguish an 'adult man' from similar things in a photo:\tshort hair on the face\tand the rest of the body\tfacial hair\tbroad shoulders\tand muscular build\tlarge facial features", 30], "ostriches": ["Yes. 'Ostriches' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'ostriches' but are not 'ostriches' are:\temus\tkiwis\trheas\tcassowaries\nThere are several useful visual features to tell there is 'ostriches' and not similar things in a photo:\tbig size\tfluffy feathers\tlong necks and legs\tbig round body\twhite and black feathers\tpink neck and legs", 30], "cement slab": ["Yes. 'Cement slab' has a tangible appearance and is a flat concrete surface.\nA few things that are visually similar to 'cement slab' but are not 'cement slab' are:\tconcrete wall\tbrickwork\tpavement\twooden floor\nThere are several useful visual features to tell there is 'cement slab' and not similar things in a photo:\tflat and even surface\tlight grey\tcolor\trough texture, with visible grains or lines.", 30], "orange buoy": ["Yes. 'Orange buoy' has a tangible appearance and is an object that floats on water to mark a specific location.\nA few things that are visually similar to 'orange buoy' but are not 'orange buoy' are:\tlifebuoy\torange inflatable pool toy\tkayak\nThere are several useful visual features to tell there is 'orange buoy' and not similar things in a photo:\tround or cylindrical shape\tbright orange color\twith a chain or rope attached to it\tmay have reflective stripes or lettering on it\tlocated in the water or near the shore", 30], "construction crane": ["Yes. 'Construction crane' has a tangible appearance and is a type of machine used in construction sites.\nA few things that are visually similar to 'construction crane' but are not 'construction crane' are:\tcommunication tower\tbroadcast tower\twind turbine\tbase of a bridge tower\nThere are several useful visual features to tell there is 'construction crane' and not similar things in a photo:\ttall and towering\tcrane arm and hook\theavy-duty machines on building sites\tusually accompanied by workers and scaffolding\tlifting heavy materials such as concrete or steel beams", 30], "brown tower": ["Yes. 'Brown tower' has a tangible appearance and refers to a tower that is brown in color.\nA few things that are visually similar to 'brown tower' but are not 'brown tower' are:\tother colored towers\tbuildings\twith pointed roofs\tchimneys\nThere are several useful visual features to tell there is a 'brown tower' and not similar things in a photo:\ttall, vertical structure\tcohesive brown color pattern\tcylindrical or rectangular shape\ttapered or smaller at the top", 30], "man arm": ["Yes. 'Man arm' has a tangible appearance and is a body part of a male human.\nA few things that are visually similar to 'man arm' but are not 'man arm' are: \twoman arm\tmannequin arm\tsculpture of an arm\tanimal leg\nThere are several useful visual features to tell there is 'man arm' and not similar things in a photo:\thair on the arm\tveins and muscles\tbony protrusions such as the elbow joint\tpresence of clothing or tattoos\ttypical skin tone and hair pattern of a male", 30], "stone column": ["Yes. 'Stone column' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'stone column' but are not 'stone column' are:\twooden column\tmetallic column\tpillar\tpost\nThere are several useful visual features to tell there is 'stone column' and not similar things in a photo:\tmade of stone or rock\tcylindrical or rectangular shape\tsupporting a roof or structure\tno visible seams or screws", 30], "snowy": ["Yes. 'Snowy' has a tangible appearance and refers to a landscape covered with snow.\nA few things that are visually similar to 'snowy' but are not 'snowy' are:\tsand\tdust\tfog\tsalt\nThere are several useful visual features to tell there is 'snowy' and not similar things in a photo:\twhite\tcolorless trees or buildings\tfrost on a surface\tsnowflakes falling from the sky\tppl dressed in bulky clothes and hats", 30], "coastline": ["Yes. 'Coastline' has a tangible appearance and refers to the boundary between land and a body of water.\nA few things that are visually similar to 'coastline' but are not 'coastline' are:\trivers\tshores\tbays\nThere are several useful visual features to tell there is 'coastline' and not similar things in a photo:\tmeets the ocean, sea, or lake\tsand or rocks along the water's edge\twaves or tides crashing against the shore\tline separating land from water", 30], "l": ["No. 'l' is too vague or abstract to be considered a visually concrete concept. It is a simple alphabetic letter.\n\nAs it is not a visually concrete concept, there are no things visually similar to it. \n\nTherefore, there are no useful visual features for distinguishing 'l' from any similar things in a photo.", 30], "mountain goat": ["Yes. 'Mountain goat' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'mountain goat' but are not 'mountain goat' are:\tsheep\tyak\tdeer\tbighorn sheep\nThere are several useful visual features to tell there is 'mountain goat' and not similar things in a photo:\twhite or light-colored fur\thorns, both sexes have horns\tbeard or tuft of hair on the chin\tnarrow, straight ears", 30], "crab": ["Yes. 'Crab' has a tangible appearance and is a type of crustacean.\nA few things that are visually similar to 'crab' but are not 'crab' are:\tlobster\tshrimp\tcrayfish\thermit crab\nSome useful visual features for distinguishing 'crab' from these other crustaceans in a photo are:\t\n- Two large claws and eight legs, with the first pair of legs ending in claws\n- Flat, round-shaped body\n- Hard, exoskeleton shell\n- Eyes located on stalks above the body\n- Walk sideways", 30], "rear landing gear": ["Yes. 'Rear landing gear' has a tangible appearance and is a part of an aircraft.\nA few things that are visually similar to 'rear landing gear' but are not 'rear landing gear' are:\twheels\tbicycle rear forks\tcar axles\nThere are several useful visual features to tell there is 'rear landing gear' and not similar things in a photo:\tlocated at the back or tail of the aircraft\tdesigned for landing\tand takeoff activities\ton a retractable system\toften consisting of one or more wheels or supports", 30], "giraffe horns": ["Yes. 'Giraffe horns' has a tangible appearance and is a physical feature of a giraffe.\nA few things that are visually similar to 'giraffe horns' but are not 'giraffe horns' are:\tantlers of deer or moose\thorns of a buffalo or a bull\t\nThere are several useful visual features to tell there is 'giraffe horns' and not similar things in a photo:\tthick and curvy\thave hair-like growths\tusually two on top of the head of a giraffe", 30], "course": ["No. 'Course' is too vague or abstract to be distinguished in a photo.", 29], "blue surfboard": ["Yes. 'Blue surfboard' has a tangible appearance and is a type of surfboard.\nA few things that are visually similar to 'blue surfboard' but are not 'blue surfboard' are:\tgreen surfboard\tred surfboard\tkayak\tstand-up paddleboard\nThere are several useful visual features to tell there is 'blue surfboard' and not similar things in a photo:\trectangular or oval shape\tthin, flat board with pointed ends\tsymmetric and streamlined shape\tfor use in the water\tusually made of fiberglass or foam\tcore material, covered in a shell of resin and fiberglass or similar material.", 29], "pink meat": ["Yes. 'Pink meat' has a tangible appearance.\nA few things that are visually similar to 'pink meat' but are not 'pink meat' are:\tcandy\tfruit\traw fish\nThere are several useful visual features to tell there is 'pink meat' and not similar things in a photo:\tchunks of meat or slices\tpink or reddish color\tjuicy appearance or texture\tcould be cooked or uncooked", 29], "furry dog": ["Yes. 'Furry dog' has a tangible appearance and is a type of canine.\nA few things that are visually similar to 'furry dog' but are not 'furry dog' are:\tblack bear\tkangaroo\tsheep\tgoat\nThere are several useful visual features to tell there is 'furry dog' and not similar things in a photo:\tfour-legged animal\twith fur or hair\tfurry ears and tail\tpointed snout or nose\tdog collar or leash\tpet tag", 29], "booklet": ["Yes. 'Booklet' has a tangible appearance and is a small book.\nA few things that are visually similar to 'booklet' but are not 'booklet' are:\tpamphlet\tbrochure\tleaflet\tjournal\nThere are several useful visual features to tell there is 'booklet' and not similar things in a photo:\tthin and small\tnumbered pages\tstitched or glued binding\thard or soft cover", 29], "lining": ["No. 'Lining' is too vague or abstract to be distinguished in a photo. The term 'lining' can refer to a layer of material found inside a garment or other object, but it is not a visually concrete concept on its own.", 29], "footsteps": ["No. 'Footsteps' do not have a tangible appearance that can be shown in a photo. \n\nHowever, some things that are visually similar to the idea of 'footsteps' might be:\n- tire tracks\n- imprints left by paws or hooves\n- marks left in sand or snow by objects or people\n\nTo distinguish 'footsteps' from the similar things listed above, some useful visual features could include:\n- distinct shapes that match the size and pattern of human feet\n- footwear impressions that can be recognized as recognizable types of shoes or boots\n- a clear progression of marks that indicate forward motion and alternating left/right steps", 29], "privacy fence": ["Yes. 'Privacy fence' has a tangible appearance and is a type of fence used for privacy.\nA few things that are visually similar to 'privacy fence' but are not 'privacy fence' are:\tpicket fence\tchicken wire fence\tbarbed wire fence\tdecorative fence\nThere are several useful visual features to tell there is 'privacy fence' and not similar things in a photo:\ttall\theight is usually around 6-8 feet\tcontinuous\tunbroken and uniform with no gaps between boards or slats\topaque\tnot translucent or easily see-through", 29], "silver metal spoon": ["Yes. 'Silver metal spoon' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'silver metal spoon' but are not 'silver metal spoon' are:\tknife\tfork\tspatula\t\nThere are several useful visual features to tell there is 'silver metal spoon' and not similar things in a photo:\tsilver or metallic-colored metal material\tlong handle\toval or round-shaped bowl", 29], "baby carrots": ["Yes. 'Baby carrots' has a tangible appearance and refers to a kind of small, peeled vegetable.\nA few things that are visually similar to 'baby carrots' but are not 'baby carrots' are:\tcocktail carrots\tcarrot slices\tpumpkin seeds\talmonds\nThere are several useful visual features to tell there is 'baby carrots' and not similar things in a photo:\tsmall size\tpeeled\tand fully round or cylindrical shape\tvibrant orange color at the tapered end.", 29], "spear": ["Yes. 'Spear' has a tangible appearance and is a type of weapon.\nA few things that are visually similar to 'spear' but are not 'spear' are:\tpike\tjavelin\ttrident\tarrow\t\nThere are several useful visual features to tell there is 'spear' and not similar things in a photo:\ta long, sturdy wooden or metal shaft\twith a pointed metal tip\tdesigned for hand-held use\texamples of spears include the Zulu spear, the assegai, and the thrusting spear", 29], "spice rack": ["Yes. 'Spice rack' has a tangible appearance and is a type of storage.\nA few things that are visually similar to 'spice rack' but are not 'spice rack' are:\tshoe rack\tbathroom caddy\tbookshelf\tkitchen cabinet\nThere are several useful visual features to tell there is 'spice rack' and not similar things in a photo:\tconsists of small jars, containers or compartments\tfor storing and organizing spices or herbs\toften hung on a wall\tor placed on a surface within reach of a cooking area\tfrequently labeled to identify its contents", 29], "bird head": ["Yes. 'Bird head' has a tangible appearance and refers to the head of a bird.\nA few things that are visually similar to 'bird head' but are not 'bird head' are:\thuman head\tduck head\tsnake head\nThere are several useful visual features to tell there is 'bird head' and not similar things in a photo:\tbeak\twith feathers\teyes on the sides\tof the head\thollow bones", 29], "woman tennis player": ["Yes. 'Woman tennis player' has a tangible appearance and refers to a female athlete playing tennis.\nA few things that are visually similar to 'woman tennis player' but are not 'woman tennis player' are:\twoman athlete\twoman golfer\twoman volleyball player\twoman basketball player\nThere are several useful visual features to tell there is 'woman tennis player' and not similar things in a photo:\tholding a tennis racket\twearing tennis shoes\twearing tennis clothes\tswings or hits a tennis ball", 29], "lift chair": ["Yes. 'Lift chair' has a tangible appearance and is a specific type of chair.\nA few things that are visually similar to 'lift chair' but are not 'lift chair' are:\trecliner\tchaise lounge\tregular armchair\toffice chair\nThere are several useful visual features to tell there is 'lift chair' and not similar things in a photo:\telectronic control panel\tpadded armrests\tand backrests\thigher seat height than regular chairs\tbuilt-in lifting mechanism to help people stand up or sit down easily.", 29], "bicycle rack": ["Yes. 'Bicycle rack' has a tangible appearance and is a structure for parking bikes.\nA few things that are visually similar to 'bicycle rack' but are not 'bicycle rack' are:\tparking meter\tsign pole\tstreetlight\tgarden trellis\nThere are several useful visual features to tell there is 'bicycle rack' and not similar things in a photo:\tmetal or wooden structure\twith slots, hooks or bars for holding bikes\toften located near a street or public place\tcan hold multiple bikes at once\tsometimes painted in bright colors\tfor outdoor or indoor use.", 29], "neon light": ["Yes. 'Neon light' has a tangible appearance and is a specific type of lighting.\nA few things that are visually similar to 'neon light' but are not 'neon light' are: LED lights, fluorescent lights, incandescent bulbs, street lights.\nThere are several useful visual features to tell there is 'neon light' and not similar things in a photo: bright and bold colors, clearly defined lines or shapes, the characteristic flicker of the light.", 29], "graffitti": ["Yes. 'Graffiti' has a tangible appearance and refers to markings or drawings on walls, buildings or public surfaces.\nA few things that are visually similar to 'graffiti' but are not 'graffiti' are: street art, murals, painted designs, advertisements or posters.\nThere are several useful visual features to tell there is 'graffiti' and not similar things in a photo:\tit is often unauthorized or done without permission from the property owner\tuses vibrant colors, bold lines, or intricate designs\ttends to be small-scale tags or larger, more elaborate pieces\tthat appears to be done quickly or with speed.", 29], "traffic signal light": ["Yes. 'Traffic signal light' has a tangible appearance and is a device used to direct vehicular and pedestrian traffic.\nA few things that are visually similar to 'traffic signal light' but are not 'traffic signal light' are:\tceiling lights\tbicycle lights\tindicator lights\ton-air broadcasting lights\nThere are several useful visual features to tell there is 'traffic signal light' and not similar things in a photo:\tthree colorful lights (red, yellow, green)\tlocated at an intersection or pedestrian crossing\tmounted on a pole or a mast\twith arrows or symbols to indicate the direction of traffic\tcontrol the flow of traffic or the movement of pedestrians.", 29], "purse strap": ["Yes. 'Purse strap' has a tangible appearance and is a part of a bag.\nA few things that are visually similar to 'purse strap' but are not 'purse strap' are:\tbelt\tribbon\tscarf\tleash\nThere are several useful visual features to tell there is 'purse strap' and not similar things in a photo:\tattached to a purse or bag\tmade of leather, fabric, or chain\thas a clasp or buckle\tfor carrying a bag over the shoulder or crossbody style", 29], "whisker": ["Yes. 'Whisker' has a tangible appearance as a type of hair.\nA few things that are visually similar to 'whisker' but are not 'whisker' are:\thair\tfur\tbristles\nThere are several useful visual features to tell there is 'whisker' and not similar things in a photo:\tgrow from the face or upper lip of animals\tthick at the base and taper to a point\thave nerves and blood vessels for sensing\ttexture may be different from the rest of the hair on the animal's body", 29], "spray paint": ["Yes. 'Spray paint' has a tangible appearance and is a type of paint.\nA few things that are visually similar to 'spray paint' but are not 'spray paint' are:\tair freshener\thair spray\tinsecticide\tdeodorant\nThere are several useful visual features to tell there is 'spray paint' and not similar things in a photo:\tcan or bottle with a nozzle\tfine mist or spray of color\ton a surface or object such as walls or cars", 29], "apartment buildings": ["Yes. 'Apartment buildings' has a tangible appearance and refers to a type of residential building.\nA few things that are visually similar to 'apartment buildings' but are not 'apartment buildings' are:\thotels\toffice buildings\tdormitories\thospitals\nThere are several useful visual features to tell there is 'apartment buildings' and not similar things in a photo:\tmultiple stories or levels\tmultiple windows\tand balconies for each apartment\tentrances or lobbies for each unit", 29], "birthday candles": ["Yes. 'Birthday candles' has a tangible appearance and is a type of candle used for birthday cakes.\nA few things that are visually similar to 'birthday candles' but are not 'birthday candles' are:\ttaper candles\ttealights\tpillar candles\nThere are several useful visual features to tell there is 'birthday candles' and not similar things in a photo:\tusually shorter than other types of candles\tbrightly colored or decorated with numbers\tor symbols and shapes (such as stars or hearts)\tfound on top of a cake with frosting", 29], "picnic": ["Yes. 'Picnic' has a tangible appearance and is an outdoor activity of dining or eating.\nA few things that are visually similar to 'picnic' but are not 'picnic' are:\toutdoor party\tbarbecue\tpark gathering\nThere are several useful visual features to tell there is a 'picnic' and not similar things in a photo:\tpeople sitting on the ground or on a picnic blanket\tpicnic basket, food, drinks\tpark or natural scenery\tsun umbrella or sun hat", 29], "stainless steel faucet": ["Yes. 'Stainless steel faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'stainless steel faucet' but are not 'stainless steel faucet' are:\tchrome faucet\tbronze faucet\tplastic faucet\nThere are several useful visual features to tell there is 'stainless steel faucet' and not similar things in a photo:\tsilver, metallic look\tsmooth surface\tmodern and sleek design\thandles or knobs for turning the water on and off", 29], "drinking": ["No. 'Drinking' is too vague or abstract to be distinguished in a photo.", 29], "mac": ["No. 'Mac' is too vague or abstract to be distinguished in a photo. It could refer to different things such as a computer brand or a type of food. Additional information is needed to provide a more specific answer.", 29], "cotton": ["Yes. 'Cotton' has a tangible appearance and is a type of fluffy white fiber.\nA few things that are visually similar to 'cotton' but are not 'cotton' are:\twool\tpolyester\tclouds\tpaper\nThere are several useful visual features to tell there is 'cotton' and not similar things in a photo:\tfibrous texture\tpuffy and fluffy appearance\tsmall strands or fibers\tgrouped together in bunches\twhite color", 29], "notice": ["No. 'Notice' is too vague or abstract to be distinguished in a photo.", 29], "round container": ["Yes. 'Round container' has a tangible appearance and is a type of object used for storage or transport.\nA few things that are visually similar to 'round container' but are not 'round container' are:\t\nplates\t\nbowls\t\nvases\t\ncups\t\npots\t\npans\t\nThere are several useful visual features to tell there is 'round container' and not similar things in a photo:\t\ncylindrical or rounded shape\t\nlids or covers\t\nhandles or straps for carrying\t\nmade of plastic, metal or ceramic material.", 29], "night table": ["Yes. 'Night table' has a tangible appearance and is a type of table.\nA few things that are visually similar to 'night table' but are not 'night table' are:\tside table\tend table\tcoffee table\tdesk\nThere are several useful visual features to tell there is 'night table' and not similar things in a photo:\tsmall size (usually shorter than other tables)\tdrawers or shelves\tfor use next to a bed or a couch", 29], "baseball plate": ["Yes. 'Baseball plate' has a tangible appearance and is a physical object present on a baseball field.\nA few things that are visually similar to 'baseball plate' but are not 'baseball plate' are:\tdinner plate\tcar tire\tsquare rubber mat\nThere are several useful visual features to tell there is 'baseball plate' and not similar things in a photo:\trectangular shape\twith three distinct corners\tand one rounded corner\twhite color\twith black edges\tdivided into three sections with a metal construction\tthat is set at ground level", 29], "warehouse": ["Yes. 'Warehouse' has a tangible appearance and is a type of building used for storage.\nA few things that are visually similar to 'warehouse' but are not 'warehouse' are:\tfactory\tsupermarket\thangar\tgarage\tworkshop\nThere are several useful visual features to tell there is 'warehouse' and not similar things in a photo:\tlarge and spacious building\tconcrete or brick walls\tmultiple loading docks or entrances\tstacked storage shelves or crates\tforklifts or pallet jacks inside the building", 29], "metal drain": ["Yes. 'Metal drain' has a tangible appearance and is a kind of plumbing fixture.\nA few things that are visually similar to 'metal drain' but are not 'metal drain' are:\tmetal grate\tshower drain\tcar grid\tmanhole cover\nThere are several useful visual features to tell there is 'metal drain' and not similar things in a photo:\tMetallic texture\tSquare or Round shape\tClear groove lines\tCatching water on the floor", 29], "ankle strap": ["Yes. 'Ankle strap' has a tangible appearance and is a type of shoe feature.\nA few things that are visually similar to 'ankle strap' but are not 'ankle strap' are:\tshoe buckle\theel cap\tzipper\tlaces\nThere are several useful visual features to tell there is 'ankle strap' and not similar things in a photo:\tstrap wrapping around the ankle\tbuckle or fastener securing the strap\tto be commonly found on sandals or high heels", 29], "sun hat": ["Yes. 'Sun hat' has a tangible appearance and is a type of hat worn to protect from the sun.\nA few things that are visually similar to 'sun hat' but are not 'sun hat' are:\tbucket hat\tfedora\tcowboy hat\tbowler hat\nThere are several useful visual features to tell there is 'sun hat' and not similar things in a photo:\tbroad brimmed hat\tlightweight and floppy\tinformal and casual style\tcommonly made of straw, cotton, or linen", 29], "glass case": ["Yes. 'Glass case' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'glass case' but are not 'glass case' are:\tdisplay case\tshadow box\tfish tank\nThere are several useful visual features to tell there is 'glass case' and not similar things in a photo:\tmade of glass walls and a glass door\tnot filled with water or plants\thave items or objects inside for display or storage", 29], "thick green trees": ["Yes. 'Thick green trees' has a tangible appearance and refers to a dense population of evergreen or deciduous trees.\nA few things that are visually similar to 'thick green trees' but are not 'thick green trees' are:\tshrubs\tbushes\thedges\ttropical plants\nThere are several useful visual features to tell there are 'thick green trees' and not similar things in a photo:\ttall and large\ttrunks and branches\tleaves all year round or seasonally\tgreen or brown foliage\tbrown bark (in some species)", 29], "rubber wheel": ["Yes. 'Rubber wheel' has a tangible appearance and is a type of wheel.\nA few things that are visually similar to 'rubber wheel' but are not 'rubber wheel' are:\twooden wheel\tplastic wheel\tmetal wheel\ttoy car wheel\nThere are several useful visual features to tell there is 'rubber wheel' and not similar things in a photo:\tcircular or round shape\twith a rubber material for traction\thas a thicker diameter in the middle than at the ends\thubcap or axle in the center\tmay have ridges or treads on the surface", 29], "grey van": ["Yes. 'Grey van' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'grey van' but are not 'grey van' are:\ttruck\tmoving truck\tSUV\tminivan\nThere are several useful visual features to tell there is 'grey van' and not similar things in a photo:\trectangular body shape\tslide or side doors\trearview mirrors on both sides\tmore than two wheels\tgrey or silver color.", 29], "stopper": ["Yes. 'Stopper' has a tangible appearance and is a type of object used to block or close something.\nA few things that are visually similar to 'stopper' but are not 'stopper' are:\tcorklid\tplug\nThere are several useful visual features to tell there is 'stopper' and not similar things in a photo:\tfits into a hole in a container or a bottle\ttapered shape\tforcefully seals the hole when pushed or twisted", 29], "silverware table": ["No. 'Silverware table' is too vague or abstract to be distinguished in a photo. It is unclear what the concept refers to.\n", 29], "orange tree": ["Yes. 'Orange tree' has a tangible appearance and is a type of tree that produces oranges.\nA few things that are visually similar to 'orange tree' but are not 'orange tree' are:\tlemon tree\tlime tree\tapple tree\tpear tree\nThere are several useful visual features to tell there is 'orange tree' and not similar things in a photo:\n\t\n- Evergreen with green leaves throughout the year\n- Leathery, glossy, and smooth leaves\n- Small white flowers that bloom in spring\n- Oranges that grow directly from the branches\n- Oranges that are round, bright orange, and have a dimpled texture.", 29], "overhead": ["No. 'Overhead' is too vague or abstract to be distinguished in a photo.\nHowever, some things that are related to 'overhead' are:\tsky\tceiling\trooftop\tumbrella\nThere are no useful visual features to distinguish 'overhead' from these related things in a photo, as 'overhead' is a relative position rather than a concrete object.", 29], "wheeler": ["No. 'Wheeler' is too vague or abstract to be distinguished in a photo. Can you please provide more context or specify what you are referring to?", 29], "china plate": ["Yes. 'China plate' has a tangible appearance and refers to a specific type of dinnerware.\nA few things that are visually similar to 'china plate' but are not 'china plate' are:\tplastic plate\tpaper plate\tmetal plate\twooden plate\nThere are several useful visual features to tell there is 'china plate' and not similar things in a photo:\t\n- Made of ceramic or bone china material\n- Delicate and thin appearance\n- Often decorated with intricate patterns or designs\n- Smooth, glossy finish\n- May have a brand or maker's mark on the bottom", 29], "aqua": ["No. 'Aqua' is too vague or abstract to have a tangible appearance.\nThere are no things that are visually similar to 'aqua' but are not 'aqua,' as 'aqua' is a specific color similar to turquoise, but not exactly the same.\nUseful visual features for distinguishing 'aqua' from other colors in a photo are a greenish-blue hue, sometimes with a slightly muted tone.", 29], "helmet batter": ["No. 'Helmet batter' is too vague or abstract to have a tangible appearance, and it is not a commonly used term, so it may not refer to anything specific.\nThere are no things that are visually similar to 'helmet batter' but are not 'helmet batter'.\nSince 'helmet batter' does not have a clear definition or refer to anything specific, it is not possible to identify any useful visual features for distinguishing it from other things in a photo.", 29], "fishing pole": ["Yes. 'Fishing pole' has a tangible appearance and is a tool for catching fish.\nA few things that are visually similar to 'fishing pole' but are not 'fishing pole' are:\tbo staff\thiking pole\tmusical staff\tbroom handle\nThere are several useful visual features to tell there is 'fishing pole' and not similar things in a photo:\tlong and slender rod\tfishing line attached to the end\ta reel or spool for holding the line\ta hook or lure at the end", 29], "sugar packets": ["Yes. 'Sugar packets' has a tangible appearance and refers to small packets containing sugar used as a sweetener.\nA few things that are visually similar to 'sugar packets' but are not 'sugar packets' are:\tsalt packets\tketchup packets\tpepper packets\tmayonnaise packets\t\nThere are several useful visual features to tell there is 'sugar packets' and not similar things in a photo:\tsmall and rectangular shape\tpaper or plastic packaging\twith the word \"SUGAR\" or sweetness icon on the packaging\tcontaining granulated sweeteners", 29], "wardrobe": ["Yes. 'Wardrobe' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wardrobe' but are not 'wardrobe' are:\tchest of drawers\tcabinet\tshelving unit\nThere are several useful visual features to tell there is 'wardrobe' and not similar things in a photo:\thinged doors or sliding doors\tclothing hanging inside or on shelves\tmirrored panels\ton raised legs, built-in, or freestanding form", 29], "thick trees": ["Yes. 'Thick trees' has a tangible appearance and refers to a dense forested area.\nA few things that are visually similar to 'thick trees' but are not 'thick trees' are:\thedges\tbushes\tvines\tshrubs\nThere are several useful visual features to tell there are 'thick trees' and not similar things in a photo:\ttall and straight trunks\twith many branches and leaves\tclose proximity to other trees\tforming a dense canopy or cover of the sky", 29], "fox": ["Yes. 'Fox' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'fox' but are not 'fox' are:\tdog\tcat\tcoyote\nThere are several useful visual features to tell there is 'fox' and not similar things in a photo:\tpointy ears and nose\torange or reddish-brown fur\twhite fur on the chest and underbelly\tbushy tail with a distinctive white tip", 29], "tan teddy bear": ["Yes. 'Tan teddy bear' has a tangible appearance and refers to a specific color and type of stuffed animal.\nA few things that are visually similar to 'tan teddy bear' but are not 'tan teddy bear' are:\tother stuffed animals\tbeige/brown plush toys\nThere are several useful visual features to tell there is 'tan teddy bear' and not similar things in a photo:\ttan/light brown fur\tround ears\tbrown/black eyes\tan upright sitting posture\thaving the distinct form of a teddy bear head with a nose, mouth and ears", 29], "ski shoes": ["Yes. 'Ski shoes' has a tangible appearance and refers to footwear used for skiing.\nA few things that are visually similar to 'ski shoes' but are not 'ski shoes' are:\thiking boots\tsnow boots\trunning shoes\nThere are several useful visual features to tell there is 'ski shoes' and not similar things in a photo:\thard sole with a groove for the ski binding\tsoft and warm interior\tfront toe curved upwards\tfor skiing in the snow or on the mountains.", 29], "race": ["No. 'Race' is too vague or abstract to be distinguished in a photo.", 29], "ditch": ["Yes. 'Ditch' has a tangible appearance and is a type of trench or channel dug into the ground.\nA few things that are visually similar to 'ditch' but are not 'ditch' are:\tirrigation canal\triver bed\tbarricade\troadside ditch\nThere are several useful visual features to tell there is 'ditch' and not similar things in a photo:\tshallow trench\thigher ground on either side\tor a berm made of stones or dirt\tlinear shape\tlack of flowing water", 29], "blossoms": ["Yes. 'Blossoms' has a tangible appearance and is a kind of flower.\nA few things that are visually similar to 'blossoms' but are not 'blossoms' are:\tleaves\tfruits\tbuds\nThere are several useful visual features to tell there is 'blossoms' and not similar things in a photo:\tcolorful petals growing from a stem or tree fragrant and beautiful has a unique and recognizable shape often found in clusters or groups", 29], "mix": ["No. 'Mix' is too vague or abstract concept to be distinguished in a photo.", 29], "wooden house": ["Yes. 'Wooden house' has a tangible appearance and refers to a type of building.\nA few things that are visually similar to 'wooden house' but are not 'wooden house' are:\twooden shed\tlog cabin\ttreehouse\tbarn\nThere are several useful visual features to tell there is 'wooden house' and not similar things in a photo:\thouse-shaped structure with walls, roof, and doors/windows\tmade entirely or mostly of wood\tchimney or fireplace\tporch or deck", 29], "bulding": ["Yes. 'Building' has a tangible appearance and is a structure built for offices, homes or businesses. \nA few things that are visually similar to 'building' but are not 'building' are:\tbridge\ttower\tpillar\tcavern\tdam\nThere are several useful visual features to tell there is 'building' and not similar things in a photo:\tconstructed with walls, floors, and a roof\thave windows and doors\tfor living, working, or other uses\tmay have architectural features like columns or arches", 29], "ponies": ["Yes. 'Ponies' has a tangible appearance and is a breed of small horses.\nA few things that are visually similar to 'ponies' but are not 'ponies' are:\tdonkeys\tzebras\tmules\thorses\nThere are several useful visual features to tell there is 'ponies' and not similar things in a photo:\tsmall size\tround and slightly dished face\tshort and stout legs\tthick mane and tail\tvariety of coat colors", 29], "round circle": ["Yes. 'Round circle' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'round circle' but are not 'round circle' are:\tsphere\toval\tbubble\tpie\nThere are several useful visual features to tell there is 'round circle' and not similar things in a photo:\tequal curvature at all points\tclosed loop shape\tno corners or edges", 29], "metal arm": ["Yes. 'Metal arm' has a tangible appearance and is a type of mechanical or robotic part.\nA few things that are visually similar to 'metal arm' but are not 'metal arm' are:\tpipe\twire\those\tbolt\nThere are several useful visual features to tell there is 'metal arm' and not similar things in a photo:\tsolid and rigid construction\tjoints or hinges\tmultiple segments or sections\tdesigned to move or grip objects\tmetallic surface", 29], "melons": ["Yes. 'Melons' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'melons' but are not 'melons' are:\tpumpkins\tbasketballs,\tartificial props for movie or theater productions\nThere are several useful visual features to tell there is 'melons' and not similar things in a photo: round or oblong shape, typically larger than a fist, green, yellow or orange in color, and has a rough or smooth-textured rind. When cut open, melons have a soft, juicy interior containing many small edible seeds.", 29], "front windshield": ["Yes. 'Front windshield' has a tangible appearance and is a part of a car.\nA few things that are visually similar to 'front windshield' but are not 'front windshield' are:\tside windows\trear window\tsunroof\tvisor\nThere are several useful visual features to tell there is 'front windshield' and not similar things in a photo:\tlarge, flat, and curved glass panel\tin front of the driver and passenger seats\tmay have wipers and a defrosting system\tconnected to the car body at the bottom and the roof at the top.", 29], "dirt hill": ["Yes. 'Dirt hill' has a tangible appearance and is a type of landscape feature.\nA few things that are visually similar to 'dirt hill' but are not 'dirt hill' are:\tmountains\tmounds\tof gravel\tor sand\nThere are several useful visual features to tell there is 'dirt hill' and not similar things in a photo:\tsoil, dirt or mud surface\tmore rounded or sloped than a mountain or a mound\tvariations in texture or color of the dirt or soil\tno or very little vegetation on the surface", 29], "wooden boards": ["Yes. 'Wooden boards' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wooden boards' but are not 'wooden boards' are:\tconcrete blocks\tbrick walls\ttile floors\tmetal sheets\t\nThere are several useful visual features to tell there is 'wooden boards' and not similar things in a photo:\twooden texture\tknotted veins\tportable structures or furniture\tsawed edges\tvarious lengths and sizes", 29], "tree trunks": ["Yes. 'Tree trunks' has a tangible appearance and refers to the main stem or large branches of a tree.\nA few things that are visually similar to 'tree trunks' but are not 'tree trunks' are:\ttelephone poles\tpillars\tbuilding columns\t\nThere are several useful visual features to tell there is 'tree trunks' and not similar things in a photo:\tbark\ttextured surface\twith or without leaves or branches\tnatural and irregular shape\tvariation in color ", 29], "tennis top": ["Yes. 'Tennis top' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tennis top' but are not 'tennis top' are:\tT-shirts\tpolo shirts\ttank tops\t\nThere are several useful visual features to tell there is 'tennis top' and not similar things in a photo:\tcollared shirt\twith two or three buttons\tsleeveless or short sleeve form-fitting breathable and moisture-wicking material", 29], "dandelion": ["Yes. 'Dandelion' has a tangible appearance and is a type of flowering plant.\nA few things that are visually similar to 'dandelion' but are not 'dandelion' are:\tdaisy\tsunflower\nThere are several useful visual features to tell there is 'dandelion' and not similar things in a photo:\telongated stem\twith a solitary yellow flower head\twhite tuft of seeds at the end of the stem.", 29], "tan cow": ["Yes. 'Tan cow' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'tan cow' but are not 'tan cow' are:\thorses\tbulls\tdeer\tyaks\nThere are several useful visual features to tell there is 'tan cow' and not similar things in a photo:\thooves\tlong tail\tand white spots\thorns (in the case of bulls)\trectangular-shaped body with four legs\tand a round head\twith a pair of ears", 29], "toy bear": ["Yes. 'Toy bear' has a tangible appearance and is a kind of stuffed animal.\nA few things that are visually similar to 'toy bear' but are not 'toy bear' are:\tplush rabbit\tdoll\tpuppy stuffed animal\t\nThere are several useful visual features to tell there is 'toy bear' and not similar things in a photo:\tbrown, black or white fur\tfurry ears and paws\tnose\thuman-like face\ttwo arms, two legs, and a tail", 29], "pink napkin": ["Yes. 'Pink napkin' has a tangible appearance and is a type of table linen.\nA few things that are visually similar to 'pink napkin' but are not 'pink napkin' are:\thandkerchief\ttowel\twashcloth\ttablecloth\nThere are several useful visual features to tell there is 'pink napkin' and not similar things in a photo:\trectangle or square-shaped\tpink color\tsmooth or textured surface\tfolded or draped on a table or in a napkin ring.", 29], "patchy grass": ["Yes. 'Patchy grass' has a tangible appearance and is a type of lawn.\nA few things that are visually similar to 'patchy grass' but are not 'patchy grass' are:\tdead grass\tpotted plants\tweeds\tdirt\nThere are several useful visual features to tell there is 'patchy grass' and not similar things in a photo:\tuneven distribution of grass\tgaps between grass blades\tdifferent shades of green\tcolor inconsistency\tpatches of soil or dirt in between the grass", 29], "digits": ["No. 'Digits' is too abstract to be visually concrete.\nThere are no things that are visually similar to 'digits' but are not 'digits'. \nHowever, if we change it to 'fingers' which is a tangible concept, a few things that are visually similar to 'fingers' but are not 'fingers' are:\tsnakes\tbranches \nUseful visual features for distinguishing 'fingers' from the listed similar things in a photo are:\tattached to a hand\tbendable joints\tdifferent lengths and sizes of each finger\tnails on the tips of each finger", 29], "ski resort": ["Yes. 'Ski resort' has a tangible appearance and is a place designed for skiing and other winter activities.\nA few things that are visually similar to 'ski resort' but are not 'ski resort' are:\tmountain range\tnational park\tsports complex\nThere are several useful visual features to tell there is 'ski resort' and not similar things in a photo:\tski lifts\tski runs\tsnow-covered mountains\tplowed or groomed trails\tforrested area with designated ski paths", 29], "blue post": ["No. 'Blue post' is too vague or abstract to be distinguished in a photo.", 29], "oatmeal": ["Yes. 'Oatmeal' has a tangible appearance and is a type of cereal.\nA few things that are visually similar to 'oatmeal' but are not 'oatmeal' are:\tcorn flakes\tgrits\trice\tmashed potatoes\nThere are several useful visual features to tell there is 'oatmeal' and not similar things in a photo:\tthick and creamy\tcooked oats\tporridge-like consistency\tbrownish or beige color", 29], "plastic wrap": ["Yes. 'Plastic wrap' has a tangible appearance and is a type of thin, transparent film used for wrapping food items.\nA few things that are visually similar to 'plastic wrap' but are not 'plastic wrap' are:\taluminum foil\tparchment paper\twax paper\tcellophane\nThere are several useful visual features to tell there is 'plastic wrap' and not similar things in a photo:\tclear and transparent\tslightly stretchy and flexible\tsticks to itself and other surfaces when pressed down or rubbed\ton a roll or in a box with cutting edge or serrated blades.", 29], "plane wheels": ["Yes. 'Plane wheels' has a tangible appearance and refers to the wheels on an aircraft.\nA few things that are visually similar to 'plane wheels' but are not 'plane wheels' are:\tcar wheels\tbicycle wheels\tmotorcycle wheels\troller skates\twheelbarrows\nThere are several useful visual features to tell there is 'plane wheels' and not similar things in a photo:\tlarge size\tsupporting an aircraft\tmultiple tires (usually two or more per wheel)\tdesigned to retract and extend", 29], "evergreen tree": ["Yes. 'Evergreen tree' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'evergreen tree' but are not 'evergreen tree' are:\tdeciduous trees\tbushes\tcacti\nThere are several useful visual features to tell there is 'evergreen tree' and not similar things in a photo:\tneedle-like or scale-like leaves\tfoliage all year round\tcone-shaped fruit or flowers", 29], "crack pavement": ["Yes. 'Crack pavement' has a tangible appearance and refers to a specific type of damage to a paved surface.\nA few things that are visually similar to 'crack pavement' but are not 'crack pavement' are:\tpainted lines\ton uneven ground\twhere leaves are settled\tformations of lines or cracks that are not in pavement \nThere are several useful visual features to tell there is 'crack pavement' and not similar things in a photo:\tstraight or zigzag lines\ttiny differences in elevation\toverlapping of the broken pavement pieces\tasphalt or concrete material that has separated and shifted slightly\tmay grow weeds or grass", 29], "smoking sign": ["Yes. 'Smoking sign' has a tangible appearance and is a kind of sign.\nA few things that are visually similar to 'smoking sign' but are not 'smoking sign' are:\t'No Smoking' sign\tfire exit sign\tparking sign\tarrow sign\nThere are several useful visual features to tell there is 'smoking sign' and not similar things in a photo:\tcigarette or smoke icon\tban or stop symbol\tspecific text, such as 'Smoking Prohibited' or 'No Smoking'\tclearly visible and legible signage location (e.g. outside or inside a building, near designated smoking areas)", 29], "trainer": ["Yes. 'Trainer' has a tangible appearance and is a type of shoe.\nA few things that are visually similar to 'trainer' but are not 'trainer' are:\tsneakers\trunning shoes\tsandals\tboots\nThere are several useful visual features to tell there is 'trainer' and not similar things in a photo:\tlow-cut or high-top design\tlaces or fasteners\tcushioned sole\trubber or synthetic material used\tfor athletic or casual wear", 29], "businesses": ["No. 'Businesses' is too vague or abstract to be distinguished in a photo.", 29], "street signal": ["Yes. 'Street signal' has a tangible appearance and is usually made of metal and plastic.\nA few things that are visually similar to 'street signal' but are not 'street signal' are:\tbillboard\ttraffic cone\tlight post\tbicycle stand\nThere are several useful visual features to tell there is 'street signal' and not similar things in a photo:\ttraffic lights, stoplights or stop signs\tcolors: red, yellow, green or black and white\tbright, reflective surfaces\twith or without words or numbers\thanging or standing upright\ton a pole or a wall", 29], "rocket": ["Yes. 'Rocket' has a tangible appearance and is a type of spacecraft.\nA few things that are visually similar to 'rocket' but are not 'rocket' are:\tmissile\tfirework\tairplane\nThere are several useful visual features to tell there is 'rocket' and not similar things in a photo:\tnarrow and pointed shape\tflames or smoke coming out of the bottom\tmetallic surface\twith or without fins or wings\tsound waves visible around the rocket", 29], "diamond pattern": ["Yes. 'Diamond pattern' has a tangible appearance and is a specific type of pattern often used in design.\nA few things that are visually similar to 'diamond pattern' but are not 'diamond pattern' are:\therringbone pattern\targyle pattern\tchevron pattern\nThere are several useful visual features to tell there is 'diamond pattern' and not similar things in a photo:\ttwo-dimensional surface\tintersecting diagonal lines\tsymmetrical and repetitive pattern\tdiamond or lozenge shape", 29], "snoopy": ["Yes. 'Snoopy' has a tangible appearance and is a fictional dog character.\nA few things that are visually similar to 'snoopy' but are not 'snoopy' are:\tother cartoon dogs\treal dogs\tinflatable mascots\nThere are several useful visual features to tell there is 'snoopy' and not similar things in a photo:\twhite fur with black spots\ton two hind legs\tupturned black nose\tblack ears on top of the head\twearing a red collar and acting like a human.", 29], "road way": ["Yes. 'Road way' has a tangible appearance and is a type of path for vehicles.\nA few things that are visually similar to 'road way' but are not 'road way' are:\twalking trail\tbike path\tsidewalk\trunway\tforrest path\tparking lot\nThere are several useful visual features to tell there is 'road way' and not similar things in a photo:\tlanes for traffic\tdividing lines or medians\tpavement or asphalt surface\tsigns or signals for traffic", 28], "treat": ["No. 'Treat' is too vague or abstract to have a tangible appearance that can be distinguished in a photo.", 28], "ipad": ["Yes. 'iPad' has a tangible appearance and is a type of tablet computer.\nA few things that are visually similar to 'iPad' but are not 'iPad' are:\tandroid tablet\tWindows tablet\tKindle tablet\nThere are several useful visual features to tell there is 'iPad' and not similar things in a photo:\tApple logo on the back\tRetina display with high resolution and vivid colors\tiOS operating system\tfingerprint sensor on the home button\tslender design with rounded corners and sleek edges", 28], "kettles": ["Yes. 'Kettles' has a tangible appearance and is a container used to heat liquids.\nA few things that are visually similar to 'kettles' but are not 'kettles' are:\tteapot\tcoffee maker\tpot\tthermos\nThere are several useful visual features to tell there is 'kettles' and not similar things in a photo:\tlarge handle on the side\twith or without a spout\thave a lid\tmetallic\tshiny or matte", 28], "wii control": ["Yes. 'Wii control' has a tangible appearance and is a type of video game controller.\nA few things that are visually similar to 'wii control' but are not 'wii control' are:\tPlayStation controller\tXbox controller\tjoystick\ttouchpad remote control\nThere are several useful visual features to tell there is 'wii control' and not similar things in a photo:\tlarge rectangular shape\twrist strap\tIR sensor bar at the top of the screen\trequires motion for gameplay\tbuttons, pointing device, and accelerometer for interaction with the console", 28], "lit lamp": ["Yes. 'Lit lamp' has a tangible appearance and is an illuminated object.\nA few things that are visually similar to 'lit lamp' but are not 'lit lamp' are:\tcandle\tfireplace\tlight bulb\nThere are several useful visual features to tell there is 'lit lamp' and not similar things in a photo:\thaving a lampshade\twith a switch\tor a cord\tplugged into a power source\temit light from the bulb or the flame\ton a table or a desk", 28], "linoleum": ["Yes. 'Linoleum' has a tangible appearance and is a kind of flooring material.\nA few things that are visually similar to 'linoleum' but are not 'linoleum' are:\ttile\tmarble\twood\tlaminate\nThere are several useful visual features to tell there is 'linoleum' and not similar things in a photo:\tsmooth surface\twith visible patterns\tflexibility\tmostly solid color (e.g., beige, grey, black, etc.)", 28], "electricity pole": ["Yes. 'Electricity pole' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'electricity pole' but are not 'electricity pole' are:\tflagpole\ttelephone pole\tstreetlight\tskyscraper\nThere are several useful visual features to tell there is 'electricity pole' and not similar things in a photo:\ttall and vertical\tpower lines or cables attached to it\tmetallic or wooden pole\tbox or transformer at the bottom or attached to the pole", 28], "bloom": ["Yes. 'Bloom' has a tangible appearance and refers to the process of a flower opening up.\nA few things that are visually similar to 'bloom' but are not 'bloom' are:\tclosed flower buds\tunripe fruit\tyoung plant sprouts\nThere are several useful visual features to tell there is 'bloom' and not similar things in a photo:\tfully opened flower petals\tbright, vibrant colors\tstamen and pistil visible in the center of the flower\tpollen on the petals or visible near the flower\texpanded size of the flower from its bud stage.", 28], "wrist strap": ["Yes. 'Wrist strap' has a tangible appearance and is a type of band worn on the wrist.\nA few things that are visually similar to 'wrist strap' but are not 'wrist strap' are:\twatch\tbangle bracelet\thair tie\tankle bracelet\nThere are several useful visual features to tell there is 'wrist strap' and not similar things in a photo:\tmade of fabric, leather, or plastic\tsnugly fits around the wrist\toften has a loop or clip to attach to an object\tcomplementary color or pattern with the attached object (e.g., camera)", 28], "dog ears": ["Yes. 'Dog ears' has a tangible appearance and is a part of a dog's body.\nA few things that are visually similar to 'dog ears' but are not 'dog ears' are:\tcat ears\tfox ears\tbear ears\nThere are several useful visual features to tell there are 'dog ears' and not similar things in a photo:\tattached to a dog's head\tfurry or hairy\tsoft and floppy or tall and pointy\tlocated on the sides of the dog's head", 28], "pork": ["Yes. 'Pork' has a tangible appearance and is a type of meat.\nA few things that are visually similar to 'pork' but are not 'pork' are:\tbeef\tchicken\tturkey\tlamb\nThere are several useful visual features to tell there is 'pork' and not similar things in a photo:\tpinkish or reddish meat\tcolor marbling\tfat layer with consistent thickness\tpork-specific cuts and parts such as pork chop, spare ribs, pork belly, and ham", 28], "tassels": ["Yes. 'Tassels' has a tangible appearance and is a kind of decoration.\nA few things that are visually similar to 'tassels' but are not 'tassels' are:\tfringes\tpompoms\tstrands\nThere are several useful visual features to tell there is 'tassels' and not similar things in a photo:\tlong strands of thread or cord\tdangling from a knot or a loop\tfringed edges in a decorative pattern", 28], "wall light": ["Yes. 'Wall light' has a tangible appearance and is a type of light fixture.\nA few things that are visually similar to 'wall light' but are not 'wall light' are:\ttable lamp\tfloor lamp\tceiling light\tcandle\nThere are several useful visual features to tell there is 'wall light' and not similar things in a photo:\tattached to a wall or a vertical surface\tdirectionality of light\tsource of light is mounted directly to the wall or it is part of a sconce\tor a bracket.", 28], "throw blanket": ["Yes. 'Throw blanket' has a tangible appearance and is a type of blanket used for snuggling or decoration.\nA few things that are visually similar to 'throw blanket' but are not 'throw blanket' are:\tregular blanket\tbedspread\tcarpet\nThere are several useful visual features to tell there is 'throw blanket' and not similar things in a photo:\tsmall size compared to other blankets\tor decorative edging or fringe\tsingle layer thin blanket\tusually folded or draped over furniture\teasily movable or portable", 28], "wood board": ["Yes. 'Wood board' has a tangible appearance and is a piece of wood used for construction or decoration.\nA few things that are visually similar to 'wood board' but are not 'wood board' are: tiles, metal sheets, plastic sheets, glass panels.\nThere are several useful visual features to tell there is 'wood board' and not similar things in a photo: made of wood, wooden texture or grain, rough or smooth surface, visible knots and growth rings, rectangular or square shape, thickness.", 28], "kale": ["Yes. 'Kale' has a tangible appearance and is a leafy vegetable.\nA few things that are visually similar to 'kale' but are not 'kale' are:\tspinach\tlettuce\tcabbage\tcollard greens\nThere are several useful visual features to tell there is 'kale' and not similar things in a photo:\tleafy green vegetable\twith curly or smooth leaves\tthick stem\trecognizable veins on leaves\tdarker green color than other leafy greens.", 28], "mason jar": ["Yes. 'Mason jar' has a tangible appearance and is a type of glass jar used for canning.\nA few things that are visually similar to 'mason jar' but are not 'mason jar' are:\tjelly jar\twater bottle\tvase\nThere are several useful visual features to tell there is 'mason jar' and not similar things in a photo:\tclear glass with visible seams and ridges\tscrew-on lid\twith or without measurement markings typically in ounces and milliliters\twide mouth opening\tfor canning and preservation purposes.", 28], "countertops": ["Yes. 'Countertops' has a tangible appearance and is a horizontal surface in a kitchen or bathroom for preparing food or other tasks.\nA few things that are visually similar to 'countertops' but are not 'countertops' are:\tcoffee table\tdesk\tshelves\tsofa\ttables\nThere are several useful visual features to tell there is 'countertops' and not similar things in a photo:\t\n- Located in a kitchen or bathroom\n- Usually made of stone, wood, or laminate material\n- Often have appliances (e.g. sink, stove, oven) or utensils on top\n- Designed to withstand spills, heat, and scratches.", 28], "power poles": ["Yes. 'Power poles' has a tangible appearance and it is a type of structure designed to support overhead power lines.\nA few things that are visually similar to 'power poles' but are not 'power poles' are:\tfence posts\tstreet lights\tsign poles\t\nThere are several useful visual features to tell the difference between 'power poles' and similar things in a photo:\t\n- Large, tall, and cylindrical shape;\n- Usually made of wood, concrete, or metal;\n- Wires or cables attached to the top of the pole;\n- Crossbars or insulators visible on the pole;\n- Often located on the side or along a road, railway, or transmission line.", 28], "perosn": ["Yes. 'Person' has a tangible appearance and refers to an individual human being.\nA few things that are visually similar to 'person' but are not 'person' are:\tstatue\tmannequin\tanimal\thumanoid robot\nThere are several useful visual features to tell there is 'person' and not similar things in a photo:\thuman-like facial features\thair on the head\ttwo arms and two legs\tfive fingers on each hand\ttwo eyes and a mouth\ton average, between 5 and 7 feet tall.", 28], "triangles": ["Yes. 'Triangles' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'triangles' but are not 'triangles' are:\tdiamonds\tpyramids\twedges\tchevrons\nThere are several useful visual features to tell there is 'triangle' and not similar things in a photo:\tthree sides\tthree angles\tstraight lines\tconnecting to form a point or a base", 28], "clock front building": ["No. 'Clock front building' is too vague or abstract to be distinguished in a photo. However, a building with a clock face can be a visually concrete concept.\nA few things that are visually similar to 'clock front building' but are not 'clock front building' are:\tbuilding with a tower or spire\tchurch or cathedral with a bell tower\t\nThere are several useful visual features to tell there is a 'clock front building' and not similar things in a photo:\tpresence of a clock face or clock tower\ta front view of a building that highlights the clock feature\tthe clock face is prominent and legible\tfrom the architecture style and design one can determine if it is a clock tower, clock face or both.", 28], "pink color": ["Yes. 'Pink color' has a tangible appearance and is a specific hue on the color spectrum.\nA few things that are visually similar to 'pink color' but are not 'pink color' are:\tcoral\tpeach\tsalmon\trose\tguava\nThere are several useful visual features to tell there is 'pink color' and not similar things in a photo:\ta hue that ranges from a pale, light tint to a deep, rich shade\ta mixture of red and white coloring\tcan appear in a variety of textures and surfaces, such as fabric, paint, or flowers", 28], "concrete road": ["Yes. 'Concrete road' has a tangible appearance and refers to a roadway made of concrete.\nA few things that are visually similar to 'concrete road' but are not 'concrete road' are:\troad made of asphalt\twalkway\tparking lot\t\nThere are several useful visual features to tell there is 'concrete road' and not similar things in a photo:\tlarge, flat concrete slabs\tdark grey color\tstraight lines or rectangular shapes\tno visible cracks or stains\tlines separating lanes or sides of the road", 28], "wash cloth": ["Yes. 'Wash cloth' has a tangible appearance and is a type of cloth used for washing oneself.\nA few things that are visually similar to 'wash cloth' but are not 'wash cloth' are:\ttowel\tdish cloth\trug\tbath mat\nThere are several useful visual features to tell there is 'wash cloth' and not similar things in a photo:\tsmaller in size\tsoft and absorbent\ttextured surface\tfor personal use in washing or cleaning the skin.", 28], "carousel": ["Yes. 'Carousel' has a tangible appearance and is a type of amusement ride.\nA few things that are visually similar to 'carousel' but are not 'carousel' are:\tferris wheel\tmerry-go-round\troller coaster\tSwing ride\nThere are several useful visual features to tell there is 'carousel' and not similar things in a photo:\tcircular shape\tvarious animals or objects to ride on\tbrightly colored lights and decorations\tmultiple riders moving up and down in unison\tmusic and sound effects", 28], "wood panels": ["Yes. 'Wood panels' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wood panels' but are not 'wood panels' are:\tcork wall tiles\tbrick\tstucco\tstone wall tiles\nThere are several useful visual features to tell there is 'wood panels' and not similar things in a photo:\twooden appearance\tlinear shapes\tvariations in wood grain pattern\tplanks of wood fastened together with tongue-and-groove or shiplap edges\tuse of natural wood colors like browns and reds.", 28], "linen": ["Yes. 'Linen' has a tangible appearance and is a type of fabric.\nA few things that are visually similar to 'linen' but are not 'linen' are:\tcotton\tsilk\tpolyester\nThere are several useful visual features to tell there is 'linen' and not similar things in a photo:\tlightweight material\tnatural fibers\tirregular texture\tbreathable and absorbent properties", 28], "shadow floor": ["No. 'Shadow floor' is too abstract to have a tangible appearance or be distinguished in a photo. \n\nInstead, here are a few things that are visually similar to 'shadow floor' in concept, but not in name: darkened or shaded area on a floor caused by an object blocking light, patterned or textured flooring that creates the illusion of shadows, and 3D flooring designs that create a shadow effect. \n\nUseful visual features for distinguishing these visually similar things from 'shadow floor' would include looking for the presence of an actual shadow cast by an object, examining the texture or pattern of the flooring to determine if it is creating a shadow-like effect, and examining the design and perspective of the photo to determine if it is a 3D flooring design.", 28], "man ground": ["I'm sorry, \"man ground\" is not a known or established concept, so it is impossible to determine if it has a tangible appearance or not. Can you please provide more information or clarify?", 28], "tool box": ["Yes. 'Tool box' has a tangible appearance and is a container for tools.\nA few things that are visually similar to 'tool box' but are not 'tool box' are:\tjewelry box\ttackle box\tmake-up box\nThere are several useful visual features to tell there is 'tool box' and not similar things in a photo:\tmetallic or sturdy material\thinged lid\thandles or straps for easy carrying\tdivided into compartments for different tools\texplicitly labeled as a tool box", 28], "brick floor": ["Yes. 'Brick floor' has a tangible appearance and it is a type of flooring.\nA few things that are visually similar to 'brick floor' but are not 'brick floor' are:\tstone floor\tcement floor\twooden floor\nThere are several useful visual features to tell there is 'brick floor' and not similar things in a photo:\tred or brown rectangular bricks\tarranged in a pattern\tresembles a paved street, with a grout between the bricks", 28], "dark glasses": ["Yes. 'Dark glasses' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'dark glasses' but are not 'dark glasses' are: regular glasses, sunglasses with light lenses, goggles, face shields.\nThere are several useful visual features to tell there is 'dark glasses' and not similar things in a photo: dark or tinted lenses, typically black in color, designed to reduce the light entering the eyes, usually having UV protection, and covering both eyes.", 28], "dishwashers": ["Yes. 'Dishwashers' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'dishwashers' but are not 'dishwashers' are:\twashing machines\trefrigerators\nThere are several useful visual features to tell there is 'dishwashers' and not similar things in a photo:\tdoor(s) for loading dishes\tracks to hold dishes\twater nozzles\ttoe-kick plate\tcontrol panel", 28], "room chair": ["Yes. 'Room chair' has a tangible appearance and is a type of furniture meant for seating.\nA few things that are visually similar to 'room chair' but are not 'room chair' are:\tstool\tbench\tsofa\tottoman\nThere are several useful visual features to tell there is 'room chair' and not similar things in a photo:\tfour-legged structure\tseating area\tbackrest\tarmrests\tpadded cushioning in the seating area and backrest", 28], "blue river": ["Yes. 'Blue river' has a tangible appearance and is a type of water body.\nA few things that are visually similar to 'blue river' but are not 'blue river' are:\tblue paint\tblue carpet\tblue fabric\tblue sky\nThere are several useful visual features to tell there is 'blue river' and not similar things in a photo:\twater flowing in a natural direction\tblue color and shades\tvarious depths\ttogether with natural elements like rocks or trees.", 28], "address": ["No, 'address' is too vague or abstract to have a tangible appearance that can be depicted in a photo.\nTherefore, there are no things visually similar to 'address' that are not 'address'.", 28], "beige pants": ["Yes. 'Beige pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'beige pants' but are not 'beige pants' are:\tkhaki pants\tcream pants\tsand-color pants\twhite pants\nThere are several useful visual features to tell there are 'beige pants' and not similar things in a photo:\tbeige color\tfabric texture and pattern\tpant style (e.g., straight-leg, skinny, bootcut)\twaistband and pockets being visible\tor garment label indicating brand or size.", 28], "cola": ["Yes. 'Cola' has a tangible appearance and is a type of soft drink.\nA few things that are visually similar to 'cola' but are not 'cola' are:\troot beer\torange soda\tlemonade\ticed tea\nThere are several useful visual features to tell there is 'cola' and not similar things in a photo:\tdark brown color\twith or without bubbles\tin a can or a bottle with the brand name \"Coca-Cola\" or \"Pepsi\" on it.", 28], "coolers": ["Yes. 'Coolers' has a tangible appearance and is a type of container used to keep things cool.\nA few things that are visually similar to 'coolers' but are not 'coolers' are:\tbins\tchests\tboxes\tbaskets\nThere are several useful visual features to tell there is 'cooler' and not similar things in a photo:\thard-sided or soft-sided\tcontainer with a lid\tor handles\toften has insulation material\tthere may be a drain hole or spigot for draining water", 28], "drapery": ["Yes. 'Drapery' has a tangible appearance and refers to a cloth or textile that hangs in loose folds.\nA few things that are visually similar to 'drapery' but are not 'drapery' are:\tcurtains\ttapestries\tflags\tbanners\nThere are several useful visual features to tell there is 'drapery' and not similar things in a photo:\thanging in loose folds\tusually made of fabric or textile\tmay have patterns or embroidery\tdraped around a window, furniture or other surfaces", 28], "smoke detector": ["Yes. 'Smoke detector' has a tangible appearance and is a device that detects smoke in the air.\nA few things that are visually similar to 'smoke detector' but are not 'smoke detector' are:\tcarbon monoxide detector\tthermostat\tfire alarm\tspeaker\nThere are several useful visual features to tell there is 'smoke detector' and not similar things in a photo:\tcircular or square shape\twhite or off-white color\tbrightly colored button or light\tcovered in small holes or slots", 28], "work": ["No. 'Work' is too vague or abstract to be distinguished in a photo.", 28], "waffles": ["Yes. 'Waffles' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'waffles' but are not 'waffles' are:\tpancakes\tfrench toast\tcookies\tcrepes\nThere are several useful visual features to tell there is 'waffles' and not similar things in a photo:\tridges or indentations in a grid pattern\tsquare or circular in shape\tgolden-brown color\tserved with syrup or toppings such as fruits, whipped cream or butter", 28], "tissue holder": ["Yes. 'Tissue holder' has a tangible appearance and is a type of holder to keep tissues.\nA few things that are visually similar to 'tissue holder' but are not 'tissue holder' are:\tjewelry box\tbathroom organizer\tdesk tray\tnapkin holder\nThere are several useful visual features to tell there is 'tissue holder' and not similar things in a photo:\trectangular\tshaped to fit a tissue box\topen on the top with a slot for a tissue to pass through\tmade of soft or hard material that may be fabric, plastic or metal", 28], "bending": ["Yes. 'Bending' has a visually concrete appearance, referring to the action of changing the shape or direction of an object.\nA few things that are visually similar to 'bending' but are not 'bending' are:\tcurving\tfolding\ttwisting\nThere are several useful visual features to tell there is 'bending' and not similar things in a photo:\tthe appearance of the object before and after the bending\tprocess of changing the shape or direction of an object", 28], "soccer cleat": ["Yes. 'Soccer cleat' has a tangible appearance and is a type of shoe.\nA few things that are visually similar to 'soccer cleat' but are not 'soccer cleat' are:\trunning shoes\thiking boots\tfashion sneakers\nThere are several useful visual features to tell there is 'soccer cleat' and not similar things in a photo:\tathletic shoe design\twith cleats, studs, or spikes\ton the sole for better traction\tusually made with synthetic materials or leather\ttypically a solid color or two-tone design\tmay have branding or logos of sportswear companies like Adidas, Nike or Puma.", 28], "orange tie": ["Yes, 'orange tie' is a visually concrete concept as it is a type of clothing with a specific color and design. \nA few things that are visually similar to 'orange tie' but are not 'orange tie' are: scarf, bandana, headband\nThere are several useful visual features to tell there is 'orange tie' and not similar things in a photo: the specific shape of a tie, its length and width finished with the specific orange color or pattern can help to identify it as an 'orange tie'.", 28], "sandy": ["Yes. 'Sandy' has a tangible appearance and refers to a surface covered in sand.\nA few things that are visually similar to 'sandy' but are not 'sandy' are:\tpebbly\tmuddy\tgravelly\tdusty\nThere are several useful visual features to tell there is 'sandy' and not similar things in a photo:\tlight-colored grains of sand\tuneven terrain\ttypically found near bodies of water or deserts\thave waves and patterns formed by the wind", 28], "mixing bowl": ["Yes. 'Mixing bowl' has a tangible appearance and is a kitchen utensil.\nA few things that are visually similar to 'mixing bowl' but are not 'mixing bowl' are:\tsoup bowl\tcereal bowl\tdeep dish plates\tfruit basket\nThere are several useful visual features to tell there is 'mixing bowl' and not similar things in a photo:\tshallow and wide\topen top\tfor mixing and holding ingredients\tmay have a spout or handle.made of metal, ceramic, or plastic", 28], "water jug": ["Yes. 'Water jug' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'water jug' but are not 'water jug' are:\tmilk jug\tbottle\tthermos\tcoffee mug\nThere are several useful visual features to identify 'water jug' and not similar things in a photo:\n\thandle and spout can be seen\tusually made of glass, ceramic or plastic (clear or translucent)\tcapacity markings on the side or in the interior lid\ttop is wider than the bottom", 28], "wood brown": ["Yes. 'Wood brown' has a tangible appearance and refers to a specific shade or color of brown that is similar to the color of natural wood.\nA few things that are visually similar to 'wood brown' but are not 'wood brown' are:\tchestnut\tburnt sienna\tcinnamon\tumber\nThere are several useful visual features to tell there is 'wood brown' and not similar things in a photo:\ta shade of brown that resembles the color of natural wood\tmay have darker or lighter tones depending on the type of wood or finish\tmay have visible wood grain or texture\tif in a photo of an interior space, it may be seen on furniture, flooring, or decorative accents", 28], "stadium seats": ["Yes. 'Stadium seats' has a tangible appearance and refers to the seats in a sports venue or arena.\nA few things that are visually similar to 'stadium seats' but are not 'stadium seats' are:\tchairs\tbenches\tseats in a theater\tor stadium bleachers\nThere are several useful visual features to tell there are 'stadium seats' and not similar things in a photo:\tarranged in rows\tmounted on risers or tiers\tmade of plastic or metal\twith cupholders\tin a large, open-air venue.", 28], "shaft": ["Yes. 'Shaft' has a tangible appearance and can refer to a long vertical or diagonal tunnel or passage.\nA few things that are visually similar to 'shaft' but are not 'shaft' are: chimney, elevator, stairwell, ramp, slope, tunnel entrance.\nThere are several useful visual features to tell there is 'shaft' and not similar things in a photo: narrow and vertical appearance, depth, absence of a visible destination, surrounded by walls or support structures.", 28], "entrance way": ["Yes. 'Entrance way' has a tangible appearance and is a physical space or structure that serves as an entry point.\nA few things that are visually similar to 'entrance way' but are not 'entrance way' are:\tWindow\tStorefront\tExit\tor door\nThere are several useful visual features to tell there is an 'entrance way' and not similar things in a photo:\tDoorway or arch\tStairs or ramp\tRoof or covering\tthat indicates an entrance or leads into a building or space", 28], "tress": ["Yes. 'Tress' has a tangible appearance and refers to a mass of hair.\nA few things that are visually similar to 'tress' but are not 'tress' are: \trope\tyarn\tbranches\nThere are several useful visual features to distinguish 'tress' and not similar things in a photo:\thair-like texture or appearance\thanging freely from a person's head\tor shoulder\thaving different shades or colors", 28], "chopstick": ["Yes. 'Chopstick' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'chopstick' but are not 'chopstick' are:\tfork\ttweezers\tpencil\ttongs\thairpin\nThere are several useful visual features to tell there is 'chopstick' and not similar things in a photo:\tlong and skinny\ttwo sticks\ttapered end\tsymmetry", 28], "connector": ["No. 'Connector' is too abstract to have a tangible or visible appearance.\nA few things that are visually similar to 'connector' but are not 'connector' are:\twire\tclamp\tplug\tjoint\tfastener\nThere are no useful visual features to distinguish 'connector' from these similar things, as 'connector' may look similar to any of these listed items. The use or function of the object must be taken into consideration to identify it as a connector.", 28], "broccolli": ["Yes. 'Broccoli' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'broccoli' but are not 'broccoli' are:\tcauliflower\tkale\tcabbage\tspinach\nThere are several useful visual features to tell there is 'broccoli' and not similar things in a photo:\tbushy green plant\twith several round or elongated buds\twith numerous small green florets\tarranged in a tree-like shape \tresembles a miniature tree.", 28], "apple slices": ["Yes. 'Apple slices' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'apple slices' but are not 'apple slices' are:\torange slices\tpineapple rings\tcucumber slices\tmelon slices\nThere are several useful visual features to tell there are 'apple slices' and not similar things in a photo:\tthin, flat circular shape\twith apple skin on the edges\twhite or pale yellow flesh\tfive symmetrical brownish seed pockets in the center.", 28], "almonds": ["Yes. 'Almonds' has a tangible appearance and is a type of nut.\nA few things that are visually similar to 'almonds' but are not 'almonds' are:\tcashews\tpeanuts\twalnuts\tmacadamia nuts\nThere are several useful visual features to tell there are 'almonds' and not similar things in a photo:\toval-shaped\ttan or brown color\tsmooth, hard shell\twith a pointed tip\twhen shelled, has a light tan color and a plump, curved shape", 28], "thicket": ["Yes. 'Thicket' has a tangible appearance and is a dense group of trees or bushes.\nA few things that are visually similar to 'thicket' but are not 'thicket' are:\tforest\tjungle\tbushes or plants in a garden\nThere are several useful visual features to tell there is 'thicket' and not similar things in a photo:\tdensely packed trees or bushes\ttangled or overlapping branches and leaves\tobscured view of what is behind or under the thicket", 28], "tissue papers": ["Yes. 'Tissue papers' has a tangible appearance and is a kind of paper product.\nA few things that are visually similar to 'tissue papers' but are not 'tissue papers' are:\ttoilet papers\tparchment papers\tnewspapers\ttowels\nThere are several useful visual features to tell there is 'tissue papers' and not similar things in a photo:\tthin and soft paper\tdesigned for wiping nose or face\tusually white or pastel-colored\tfolds or stacks", 28], "hair brush": ["Yes. 'Hair brush' has a tangible appearance and is an instrument for smoothing and styling hair.\nA few things that are visually similar to 'hair brush' but are not 'hair brush' are:\tcombs\tpaint brushes\ttoothbrushes\nThere are several useful visual features to tell there is 'hair brush' and not similar things in a photo:\tbristles\thandle or grip\trectangular or oval-shaped\tappearance of bristles (straight or curved)\tsize (medium to large)", 28], "sweet potatoes": ["Yes. 'Sweet potatoes' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'sweet potatoes' but are not 'sweet potatoes' are:\tyams\tpotatoes\tcarrots\tbeets\nThere are several useful visual features to tell there is 'sweet potatoes' and not similar things in a photo:\ttubular shape\torange or brown skin\tsmooth texture\tsimilar size and shape to a regular potato\tpoints at both ends of the vegetable", 28], "satchel": ["Yes. 'Satchel' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'satchel' but not 'satchel' are:\tbackpack\ttote bag\tmessenger bag\tbriefcase\nThere are several useful visual features to tell there is 'satchel' and not similar things in a photo:\tsingle strap\tflap cover\tbuckled or button closure\trectangular shape\tmedium size", 28], "soccer balls": ["Yes. 'Soccer balls' has a tangible appearance and is a kind of sports equipment.\nA few things that are visually similar to 'soccer balls' but are not 'soccer balls' are:\trugby balls\tbasketballs\tvolleyballs\tbeach balls\nThere are several useful visual features to tell there is 'soccer balls' and not similar things in a photo:\tblack and white pentagon pattern\tround shape\tsize: approximately 22 cm in diameter\tmade of leather or synthetic material", 28], "champagne": ["Yes. 'Champagne' has a tangible appearance and is a kind of sparkling wine.\nA few things that are visually similar to 'champagne' but are not 'champagne' are:\tother types of wine\tbeer\tcarbonated water\nThere are several useful visual features to tell there is 'champagne' and not similar things in a photo:\tchampagne flute or coupe bottle with a label saying 'Champagne' or its specific brand\tgolden or beige color\tcarbonation and bubbles at the top\twhen poured, bubbles rise from the bottom of the glass.", 28], "back leg": ["Yes. 'Back leg' has a tangible appearance and is a part of an animal's body.\nThere aren't many things that are visually similar to 'back leg' but not 'back leg', since it is a very specific body part of an animal. However, some things that may be visual similar could be:\tbranches\tpoles\nUseful visual features for distinguishing a 'back leg' from similar things in a photo could include:\ta joint or crease, since animal legs are flexible and have joints\thair or fur, if it's an animal leg\tand if the animal is in the photo or not, as other things like branches and poles would not have a full animal body attached to them.", 28], "motorcyle": ["Yes. 'Motorcycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motorcycle' but are not 'motorcycle' are:\tbicycle\tscooter\tmoped\ttricycle\nThere are several useful visual features to tell there is 'motorcycle' and not similar things in a photo:\ttwo wheels\tengine and exhaust system\thandlebars for steering\ta seat and footrests for the rider\tsleek body design and aerodynamic structure", 28], "plane door": ["Yes. 'Plane door' has a tangible appearance and is a physical part of an airplane.\nA few things that are visually similar to 'plane door' but are not 'plane door' are:\tplane window\temergency exit\thatch\tground service door\nThere are several useful visual features to tell there is 'plane door' and not similar things in a photo:\trectangular or oval in shape\tmay have built-in stairs or a handle to open it\tmay have airline logo or other details visible\ton the side of the plane or at the back of it.", 28], "dark background": ["Yes. 'Dark background' has a tangible appearance and refers to the color and texture behind the main subject in a photo. \nA few things that are visually similar to 'dark background' but are not 'dark background' are:\tblack cloth\tblack paint\tblack screen\nThere are several useful visual features to tell there is 'dark background' and not similar things in a photo:\tsolid color\twithout patterns\tdarker than the main subject in the photo.", 28], "router": ["Yes. 'Router' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'router' but are not 'router' are:\tmodem\tswitch\thub\nThere are several useful visual features to tell there is 'router' and not similar things in a photo:\tantennas, ports, and lights on the device\twires connecting the device to other devices or the internet\tthe word \"router\" or a brand logo on the device", 28], "silver clock": ["Yes. 'Silver clock' has a tangible appearance and is a type of timekeeping device.\nA few things that are visually similar to 'silver clock' but are not 'silver clock' are:\twatches\tother types of clocks\tjewelry\tpaperweights\nThere are several useful visual features to tell there is 'silver clock' and not similar things in a photo:\tround or rectangular shape\tsilver or metallic color\thands pointing to numbers\ttime display with hour, minute and second marks", 28], "furry cat": ["Yes. 'Furry cat' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'furry cat' but are not 'furry cat' are:\tlion\ttiger\tbear\tdog\nThere are several useful visual features to tell there is 'furry cat' and not similar things in a photo:\tsoft, long or short fur\tpointed ears\tand a tail\tsharp claws\tretractable claws\tindependent (usually) 4 legs", 28], "fancy": ["No. 'Fancy' is too vague or abstract to be distinguished in a photo. It is a subjective and abstract concept associated with elegance, luxury, and refinement, which are difficult to define visually.", 28], "mp3 player": ["Yes. 'Mp3 player' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'mp3 player' but are not 'mp3 player' are:\tsmartphone\ttablet\tmp4 player\tdigital camera\nThere are several useful visual features to tell there is 'mp3 player' and not similar things in a photo:\tbutton controls\ttouchscreen\tdisplay screen\theadphone jack\tmp3 player's logo or brand name", 28], "glass panes": ["Yes. 'Glass panes' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'glass panes' but are not 'glass panes' are:\tMirrors\tPlexiglass Acrylic Sheets\tTransparent Plastic Film\nThere are several useful visual features to tell there is 'glass panes' and not similar things in a photo:\ttransparent or translucent\tthinner than mirrors or plastics\tsquared or rectangular shape\tcommonly framed in metal or wooden structures.", 28], "feta cheese": ["Yes. 'Feta cheese' has a tangible appearance and is a type of cheese.\nA few things that are visually similar to 'feta cheese' but are not 'feta cheese' are:\tgoat cheese\tblended cheese\tblue cheese\troquefort cheese\tbrie cheese\nThere are several useful visual features to tell there is 'feta cheese' and not similar things in a photo:\twhite color\tcrumbly texture\tcubed or crumbled shape\ttangy and salty taste", 28], "handle fork": ["No. 'Handle fork' is too vague or ambiguous as a concept.\nHowever, a 'fork with a handle' is a visually concrete concept.\nA few things that are visually similar to a 'fork with a handle' but are not 'fork with a handle' are:\tknife with a handle\tspoon with a handle\nThere are several useful visual features to tell there is 'fork with a handle' and not similar things in a photo:\t\n- multiple tines\n- tapered or pointed ends\n- prongs that curve upwards\n- usually made of metal\n- a straight or slightly curved handle", 28], "cigarettes": ["Yes. 'Cigarettes' has a tangible appearance and is a type of tobacco product.\nA few things that are visually similar to 'cigarettes' but are not 'cigarettes' are:\tCigars\tJoints\tRolled paper\t\nThere are several useful visual features to tell there is 'cigarettes' and not similar things in a photo:\twhite paper cylinder\twith a brown filter on one end\tbrown tobacco coming out of the top of the cylinder", 28], "brick pavement": ["Yes. 'Brick pavement' has a tangible appearance and is a type of surface.\nA few things that are visually similar to 'brick pavement' but are not 'brick pavement' are:\ttile floor\tcobblestone pathway\twooden deck\nThere are several useful visual features to tell there is 'brick pavement' and not similar things in a photo:\tsmall rectangular bricks\tarranged in straight lines or patterns\tred, brown, gray or tan\tcolors\ttypically outdoors\tor in public spaces, like city sidewalks.", 28], "homemade pizza": ["Yes. 'Homemade pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'homemade pizza' but are not 'homemade pizza' are:\tflatbread\ttaco\tpita bread\tquesadilla\nThere are several useful visual features to tell there is 'homemade pizza' and not similar things in a photo:\tcircular\tdoughy crust\ttomato sauce, cheese, and toppings", 28], "lighter": ["Yes. 'Lighter' has a tangible appearance and is a device commonly used to produce a flame for lighting.\nA few things that are visually similar to 'lighter' but are not 'lighter' are:\tmatches\tgas stove\tpipe\nThere are several useful visual features to tell there is 'lighter' and not similar things in a photo:\trectangular or cylindrical shape\tmetal or plastic body\tflame-producing mechanism\ttrigger or button to activate flame", 28], "side mirrors": ["Yes. 'Side mirrors' has a tangible appearance and is a type of car exterior.\nA few things that are visually similar to 'side mirrors' but are not 'side mirrors' are:\twindows\thubcaps\twheels\theadlights\nThere are several useful visual features to tell there are 'side mirrors' and not similar things in a photo:\trectangular or oblong shape\tprotruding from the side of a vehicle\treflective surface\tmounted on a metal or plastic base", 28], "racket handle": ["Yes. 'Racket handle' has a tangible appearance and is a part of a sports equipment.\nA few things that are visually similar to 'racket handle' but are not 'racket handle' are:\thammer handle\tknife handle\tdoor handle\tbroom handle\nThere are several useful visual features to tell there is 'racket handle' and not similar things in a photo:\t\n- Grip pattern for the user\u2019s hand to hold.\n- Long and narrow structure.\n- Designed for a specific sport (i.e., tennis, badminton, squash, etc.).\n- Intersection point of the handle with the racket head.", 28], "route sign": ["Yes. 'Route sign' has a tangible appearance and is a type of sign used for navigation.\nA few things that are visually similar to 'route sign' but are not 'route sign' are: street signs, parking signs, business signs.\nThere are several useful visual features to distinguish 'route sign' from the similar things in a photo: circular or shield shape, solid colored with black or white lettering indicating a route number, directional arrows or images of a highway symbol. Street signs, parking signs, and business signs don't usually include a route number or have the same shape as route signs.", 28], "kitchen drawer": ["Yes. 'Kitchen drawer' has a tangible appearance and is a type of storage unit found in a kitchen.\nA few things that are visually similar to 'kitchen drawer' but are not 'kitchen drawer' are:\tfile cabinet\tdresser\tnight stand\ttool box\nThere are several useful visual features to tell there is 'kitchen drawer' and not similar things in a photo:\tlocated in or around a kitchen area of a home or building\tmade of wood, metal, or plastic\tsimilar in size to other drawers in a kitchen\tfor storing kitchen utensils, tools, or equipment", 28], "plastic helmet": ["Yes. 'Plastic helmet' has a tangible appearance and is a type of protective headgear.\nA few things that are visually similar to 'plastic helmet' but are not 'plastic helmet' are:\thats\theadbands\tcaps\thelmets made of other materials\nThere are several useful visual features to tell there is 'plastic helmet' and not similar things in a photo:\tmade of plastic\thard outer shell\twith a chin strap\tbulbous shape that covers the head and most of the ears", 28], "terra": ["Yes. 'Terra' has a tangible appearance and refers to the planet Earth.\nThere are no things that are visually similar to 'terra' as it specifically refers to Earth.\nUseful visual features for indicating 'terra' in a photo may include: blue and green colors, visible continents and oceans, a round shape resembling a globe or a map of the world.", 28], "brown snout": ["Yes. 'Brown snout' has a tangible appearance and is a part of an animal's face.\nA few things that are visually similar to 'brown snout' but are not 'brown snout' are:\tblack snout\tpink snout\tnose\tlips\nThere are several useful visual features to tell there is 'brown snout' and not similar things in a photo:\tbrown color\tcurved shape\tlocation below the eyes in the front of an animal's face\thard or rough texture", 28], "motion": ["No. 'Motion' is too vague or abstract to be distinguished in a photo.", 28], "mirror truck": ["Yes. 'Mirror truck' has a tangible appearance and refers to a specific type of vehicle used for transportation.\nThere are not many things that are visually similar to 'mirror truck' but are not 'mirror truck'. However, some things that may be visually similar because of their reflective surfaces are:\tracing car\tambulance\tfire truck\nThere are several useful visual features to tell there is a 'mirror truck' and not the similar things in a photo:\tlarge, flat, reflective surfaces on both sides of the vehicle\tsmall, reflective side mirrors\tforward-facing cabin for the driver, with side-view mirrors or cameras\ton wheels and designed for transportation, generally used for long-distance trips.", 28], "beck": ["No. 'Beck' is too vague and abstract to have a tangible appearance. \n\nHowever, 'beck' can refer to a small stream or a brook, which is visually concrete. Some things that are visually similar to a small stream or a brook, but are not 'beck' are:\tcanal\tdrainage\tditch\triver\n\nUseful visual features for distinguishing a small stream or brook (i.e., 'beck') from similar things are: flowing water, smaller size, narrower width, shallower depth, and more natural surroundings (e.g., trees, rocks, vegetation).", 28], "crosswalk signal": ["Yes. 'Crosswalk signal' has a tangible appearance and is a kind of traffic signal.\nA few things that are visually similar to 'crosswalk signal' but are not 'crosswalk signal' are:\ttraffic light\tpedestrian crossing sign\t\nThere are several useful visual features to tell there is 'crosswalk signal' and not similar things in a photo:\twhite stick figure on a black background or a white figure on a neon color background\tvertical shape\thanging from a pole by wires\tor mounted on a pedestal \tbottom of the signal typically sits at around eye-level of a standing adult.", 28], "wood plank": ["Yes. 'Wood plank' has a tangible appearance and is a type of wooden piece.\nA few things that are visually similar to 'wood plank' but are not 'wood plank' are:\tcorkboard\tcardboard\tplastic\tdrywall\tbrick\nThere are several useful visual features to tell there is 'wood plank' and not similar things in a photo:\trectangular-shaped piece of wood\tvisible wood grains, knots, or lines\trough or smooth texture\twooden color\tor hue", 28], "snow shoes": ["Yes. 'Snow shoes' has a tangible appearance and is a kind of footwear.\nA few things that are visually similar to 'snow shoes' but are not 'snow shoes' are:\thiking boots\tski boots\train boots\nThere are several useful visual features to tell there is 'snow shoes' and not similar things in a photo:\tracquet-like design\tlarge surface area\tfor use in snow or icy conditions\thas crampons or spikes on the bottom\tsometimes made of wood and animal hide or synthetic materials", 28], "blue sign": ["Yes. 'Blue sign' has a tangible appearance and is a kind of visual indicator or warning sign.\nA few things that are visually similar to 'blue sign' but are not 'blue sign' are:\tblue label\tblue sticker\tblue flag\tblue toy\nThere are several useful visual features to tell there is 'blue sign' and not similar things in a photo:\tblue color\ttext or symbols indicating a warning or information location-specific information\teasily readable from a distance or with low light conditions.", 28], "radish": ["Yes. 'Radish' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'radish' but are not 'radish' are:\tcarrot\tbeet\tturnip\tparsnip\nThere are several useful visual features to tell there is 'radish' and not similar things in a photo:\tround or oval shape\tvarious shades of red or pink\tcolorful skin and white flesh\tgreen leaves or stems at the top", 28], "outfits": ["Yes. 'Outfits' has a tangible appearance and refers to a set of clothes worn together for a particular occasion, purpose or style.\nA few things that are visually similar to 'outfits' but are not 'outfits' are:\tsingle pieces of clothing, such as shirts, pants, dresses\tclothing items arranged together on a hanger or a rack\t\nThere are several useful visual features to tell there are 'outfits' and not similar things in a photo:\ta combination of several clothing items, such as a top, a bottom, shoes, and accessories\tconsiderations of patterns, colors, and styles\tthat communicates a particular occasion or style, such as business attire, formal wear, or casual outfits.", 28], "wood planks": ["Yes. 'Wood planks' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wood planks' but are not 'wood planks' are:\tconcrete blocks\tbrick walls\ttile floors\tlinoleum\nThere are several useful visual features to tell there is 'wood planks' and not similar things in a photo:\trectangular in shape\tmade of wood\tgrain patterns\tknots and irregularities in the wood surface\tvariations in color and texture\tfitting together with tongue and groove or overlapping", 28], "driftwood": ["Yes. 'Driftwood' has a tangible appearance and is a specific type of wood.\nA few things that are visually similar to 'driftwood' but are not 'driftwood' are:\tbark\tstick\tlog\troots\nThere are several useful visual features to tell there is 'driftwood' and not similar things in a photo:\tsmooth surface\tweathered appearance\tlightweight\tfloats in water\tnatural curves and bends\tno leaves or branches sticking out", 28], "fuzzy": ["Yes. 'Fuzzy' has a tangible appearance and refers to things that have a soft and blurred surface or texture, such as hair or fabric.\nA few things that are visually similar to 'fuzzy' but are not 'fuzzy' are:\tsmoke\tclouds\tmist\tfog\nThere are several useful visual features to tell there is 'fuzzy' and not similar things in a photo:\tsoft and blurred appearance\ttouchable or tactile texture\thair-like or furry appearance\tsurface blurs or obscures underlying details", 28], "airline": ["No. 'Airline' is too vague or abstract to be distinguished in a photo. It refers to a company that provides air transport services.\n", 28], "garage doors": ["Yes. 'Garage doors' has a tangible appearance and is a type of entrance or closure for a garage.\nA few things that are visually similar to 'garage doors' but are not 'garage doors' are:\tfront doors\tgates\twarehouse doors\tbarn doors\nThere are several useful visual features to tell there is 'garage doors' and not similar things in a photo:\thinged or sliding\tusually made of metal, wood or fiberglass\tmainly used for garages or storage areas\tsimilar in style in a single row in a residential area.", 28], "aisle": ["Yes. 'Aisle' has a tangible appearance and is a path between rows or sections of seats, shelves or counters.\nA few things that are visually similar to 'aisle' but are not 'aisle' are:\tcorridor\tpathway\tsidewalk\thallway\nThere are several useful visual features to tell there is 'aisle' and not similar things in a photo:\tclear path between rows or sections of something\trestriction on the sides\tfor commercial spaces, signs indicating store names, sections, or product categories for visual reference.", 28], "tan tiles": ["Yes. 'Tan tiles' has a tangible appearance and refers to tiles that are colored tan.\nA few things that are visually similar to 'tan tiles' but are not 'tan tiles' are:\twhite tiles\tbeige tiles\tbrown tiles\tmosaic tiles\nThere are several useful visual features to tell there are 'tan tiles' and not similar things in a photo:\tsquare or rectangular in shape\ttan or light brown in color\tsmooth surface with grout lines evenly spaced out", 27], "street curb": ["Yes. 'Street curb' has a tangible appearance and is a physical boundary between the sidewalk and the road.\nA few things that are visually similar to 'street curb' but are not 'street curb' are:\tparking block\tbridge\tpavement\troadside ditch\nThere are several useful visual features to tell there is 'street curb' and not similar things in a photo:\traised platform at the edge of the roadway\tcolor difference from the pedestrian walkway or roadway\trounded or sloping shape\tconcrete or stone material.", 27], "entertainment stand": ["Yes. 'Entertainment stand' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'entertainment stand' but are not 'entertainment stand' are:\tshelves\tcabinets\tdesks\tbenches\nThere are several useful visual features to tell there is 'entertainment stand' and not similar things in a photo:\twide top for placing a television or other gadgets\tspace for digital devices and cables\tshelves for storing DVDs, video games, and other related items\twheels or adjustable feet for easy movement or leveling.", 27], "crosswalk light": ["Yes. 'Crosswalk light' has a tangible appearance and is a kind of traffic light.\nA few things that are visually similar to 'crosswalk light' but are not 'crosswalk light' are:\ttraffic light\tstoplight\tpedestrian signal\nThere are several useful visual features to tell there is 'crosswalk light' and not similar things in a photo:\ta white walking figure or person icon on a black background\ta button at the base that pedestrians can press to activate the signal\ta countdown timer that shows how much time is left for pedestrians to cross.", 27], "trash bags": ["Yes. 'Trash bags' has a tangible appearance and is a specific type of bag.\nA few things that are visually similar to 'trash bags' but are not 'trash bags' are:\tlaundry bags\tplastic bags\tbackpacks\tduffel bags\nThere are several useful visual features to tell there is 'trash bags' and not similar things in a photo:\tdisposable\tthick and durable\ttranslucent or opaque\tcolors that are typically black or white\tlarge and elongated shape\ttop tied in a knot or a drawstring\tfor holding garbage or waste materials", 27], "horse saddle": ["Yes. 'Horse saddle' has a tangible appearance and is a type of equipment used for horseback riding.\nA few things that are visually similar to 'horse saddle' but are not 'horse saddle' are:\tblanket\tharness\tstraps\tbackpack\nThere are several useful visual features to tell there is 'horse saddle' and not similar things in a photo:\t\nthe interior is hollow, padded, and curved to fit the horse's back\tthe exterior has various straps and buckles to secure it to the horse's body\ta horn-like projection at the front for the rider to hold on to\ta stirrup on either side for the rider's feet to rest in\ta saddlecloth or numnah may be placed underneath to cushion and absorb sweat.", 27], "bathroom stall": ["Yes. 'Bathroom stall' has a tangible appearance and is a kind of partition in a bathroom.\nA few things that are visually similar to 'bathroom stall' but are not 'bathroom stall' are:\tchanging room stall\tphone booth\tpartition wall\tpublic seating area\nThere are several useful visual features to tell there is 'bathroom stall' and not similar things in a photo:\tmetal or plastic walls\tmembrane door\tpartition formation\tfootprint\tdirectly attached to the floor and the ceiling\tor toilets, washbasins and other bathroom fixtures visible in the background.", 27], "bare patch": ["Yes. 'Bare patch' has a tangible appearance and refers to a spot on a surface with no or little vegetation or covering.\nA few things that are visually similar to 'bare patch' but are not 'bare patch' are:\tshadows\tmossy patches\tsnow patches\twet spots\nThere are several useful visual features to tell there is 'bare patch' and not similar things in a photo:\tno or very little vegetation\tdifferent texture or color compared to surrounding area\tdry or cracked surface underneath\tStraight or withered edges.", 27], "blonde man": ["Yes. 'Blonde man' has a tangible appearance and refers to a male with fair hair color.\nA few things that are visually similar to 'blonde man' but are not 'blonde man' are:\tman with yellow shirt\tman with blonde beard\twoman with blonde hair\tblonde dog\nThere are several useful visual features to distinguish 'blonde man' from similar things in a photo:\tlight-colored hair\tfair skin\tmale facial features", 27], "cardboard sign": ["Yes. 'Cardboard sign' has a tangible appearance and is a type of sign made from cardboard.\nA few things that are visually similar to 'cardboard sign' but are not 'cardboard sign' are:\tposter\tbanner\tsticker\tboard\nThere are several useful visual features to tell there is 'cardboard sign' and not similar things in a photo:\tmade from cardboard or similar material\thandwritten or painted message\tirregular shape or size\theld or propped up by a person", 27], "bronze statue": ["Yes. 'Bronze statue' has a tangible appearance and refers to a statue made of bronze metal.\nA few things that are visually similar to 'bronze statue' but are not 'bronze statue' are:\tstone statue\tplastic statue\twooden statue\thuman\nThere are several useful visual features to tell there is 'bronze statue' and not similar things in a photo:\tmade of bronze or copper-alloyed metal\tdetailed features, such as eyes, mouth, or clothing\tclean lines and minimal surface ornamentation\tfixed to a base or a pedestal with art or text inscriptions.", 27], "blue bed": ["Yes. 'Blue bed' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'blue bed' but are not 'blue bed' are:\tblue sofa\tblue chair\tblue carpet\tblue pillow\nThere are several useful visual features to tell there is a 'blue bed' and not similar things in a photo:\trectangular shape\tcushion or mattress headboard (if visible)\tpillows and bedclothes (if visible)", 27], "veges": ["Yes. 'Veges' has a tangible appearance and refers to various vegetables.\nA few things that are visually similar to 'veges' but are not 'veges' are: fruits, flowers\nThere are several useful visual features to tell there is 'veges' and not similar things in a photo:\tedible plants\tgreen, yellow, orange, red, purple\tcolorful\tvarying shapes and sizes\troot vegetables or leafy greens\tusually harvested from the ground or stems or leaves of plants.", 27], "orange pole": ["Yes. 'Orange pole' has a tangible appearance and is a type of pole that is orange in color.\nA few things that are visually similar to 'orange pole' but are not 'orange pole' are:\tstop signs\tconstruction cones\twater hydrants\tflashing lights\nThere are several useful visual features that can distinguish 'orange pole' from the listed similar things in a photo:\tsolid and cylindrical in shape\tbright orange color \t reflective tape on the pole\tusually found alongside roads or sidewalks", 27], "silver cellphone": ["Yes. 'Silver cellphone' has a tangible appearance and is a type of mobile device.\nA few things that are visually similar to 'silver cellphone' but are not 'silver cellphone' are:\tsilver calculator\tsilver compact mirror\tsilver lighter\tsilver pen\nThere are several useful visual features to tell there is 'silver cellphone' and not similar things in a photo:\trectangular in shape\tsilver in color\ttouchscreen display\tbuttons or ports for charging and headphones", 27], "standing": ["No. 'Standing' is too vague or abstract to be distinguished visually in a photo.", 27], "box brown": ["No. 'Box brown' is too vague or abstract to be distinguished in a photo.", 27], "plan": ["No. 'Plan' is too vague or abstract to be distinguished in a photo.", 27], "omelet": ["Yes. 'Omelet' has a tangible appearance and is a type of dish.\nA few things that are visually similar to 'omelet' but are not 'omelet' are:\tscrambled eggs\tpancake\tcrepe\tfrittata\nThere are several useful visual features to tell there is 'omelet' and not similar things in a photo:\tthin, flat egg dish\tcrispy edges\toften folded in half or rolled\tup to two or three eggs inside, mixed with other ingredients like cheese, vegetables, or meats.", 27], "round headlights": ["Yes. 'Round headlights' has a tangible appearance and is a specific type of car headlight.\nA few things that are visually similar to 'round headlights' but are not 'round headlights' are:\trectangular headlights\tfog lights\tturn signals\t\nThere are several useful visual features to tell there are 'round headlights' and not similar things in a photo:\tcircular shape\tcentered on the front of a car\twith a bulb or LED in the center\temit light in a focused beam.", 27], "wood boards": ["Yes. 'Wood boards' has a tangible appearance and refers to planks of wood used for various purposes.\nA few things that are visually similar to 'wood boards' but are not 'wood boards' are:\tlaminate flooring\ttile flooring\tconcrete blocks\tcardboard boxes\nThere are several useful visual features to tell there is 'wood boards' and not similar things in a photo:\trectangular shape\twooden texture\twith or without knots or grains\tvisible screws, nails or dents", 27], "blue words": ["Yes. 'Blue words' has a tangible appearance and refers to words that are written or displayed in the color blue.\nThere are no things that are visually similar to 'blue words' but are not 'blue words'.\nUseful visual features for distinguishing 'blue words' from other words in a photo can be the distinctive blue hue of the text and the lack of other colors or shades within the letters.", 27], "lounge chairs": ["Yes. 'Lounge chairs' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'lounge chairs' but are not 'lounge chairs' are:\tarmchairs\trecliners\trocking chairs\nThere are several useful visual features to tell there is 'lounge chairs' and not similar things in a photo:\tlow to the ground\tangled backrest\tand seat\tfor reclining or relaxing\tcushioned seat and backrest\tometimes with a footrest or ottoman", 27], "tree tops": ["Yes. 'Tree tops' has a tangible appearance and is the uppermost part of the trees.\nA few things that are visually similar to 'tree tops' but are not 'tree tops' are:\troofs\tmountains\tclouds\nThere are several useful visual features to tell there is 'tree tops' and not similar things in a photo:\tleaves and branches\tof a tree\tor a forest\tagainst the sky or clouds.", 27], "blue ocean": ["Yes. 'Blue ocean' has a tangible appearance and refers to a body of saltwater that is colored blue.\nA few things that are visually similar to 'blue ocean' but are not 'blue ocean' are:\tblue sky\ttropical swimming pool\tblue lake\twater in a bottle\nThere are several useful visual features to tell there is 'blue ocean' and not similar things in a photo:\twaves\tsea creatures\tfoam and spray\tsun reflections on the surface of the water\tdepth and vastness", 27], "food container": ["Yes. 'Food container' has a tangible appearance and can be of various types.\nA few things that are visually similar to 'food container' but are not 'food container' are:\tcosmetic container\ttrash can\twater bottle\tbasket\nThere are several useful visual features to tell there is 'food container' and not similar things in a photo:\tspecific shape and size\tsections or compartments for different foods\tlids to keep food fresh and secure\tlabels indicating the container\u2019s purpose or contents\tmade from materials that keep food safe for consumption (e.g., plastic, glass, metal)", 27], "furry ear": ["Yes. 'Furry ear' has a tangible appearance and is a body part.\nA few things that are visually similar to 'furry ear' but are not 'furry ear' are:\tfur coat\twild animal whiskers\nThere are several useful visual features to tell there is 'furry ear' and not similar things in a photo:\tattached to a head, usually an animal's or human's\tfurry texture, with visible hair\tworn on or close to the head\tpart of the auditory system with visible inner parts such as the ear canal or eardrum.", 27], "washcloth": ["Yes. 'Washcloth' has a tangible appearance and is a kind of cloth used for washing.\nA few things that are visually similar to 'washcloth' but are not 'washcloth' are:\ttowel\trug\tbathmat\tdishcloth\nThere are several useful visual features to tell there is 'washcloth' and not similar things in a photo:\tsmall in size, typically handheld\trectangular or square in shape\tmade of soft fabric or sponge-like material\tmay have a textured surface for cleaning purposes\ttypically used for washing the body or face", 27], "square mirror": ["Yes. 'Square mirror' has a tangible appearance and is a type of reflective surface.\nA few things that are visually similar to 'square mirror' but are not 'square mirror' are:\twindow\tglass\tpicture frame\tscreen\nThere are several useful visual features to tell there is 'square mirror' and not similar things in a photo:\tsquare shape\tclear reflection\tframeless or bordered by a frame", 27], "train wheels": ["Yes. 'Train wheels' has a tangible appearance and is a type of transportation component.\nA few things that are visually similar to 'train wheels' but are not 'train wheels' are:\tbike wheels\tcar wheels\ttruck wheels\tindustrial machinery wheels\nThere are several useful visual features to tell there is 'train wheels' and not similar things in a photo:\tmetallic\tcircular with spokes\tor without spokes if modern\tdusty\tif being used, in motion, or in pairs.", 27], "pearl necklace": ["Yes. 'Pearl necklace' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'pearl necklace' but are not 'pearl necklace' are:\tbeads\tchain\trope\tnecklace with stones\nThere are several useful visual features to tell there is 'pearl necklace' and not similar things in a photo:\tseveral pearls\tstrand or multiple strands of pearls\twhite or cream-colored pearls\tshimmer or shine on pearls\thanging around the neck", 27], "shadow pavement": ["Yes. 'Shadow pavement' refers to a tangible appearance of pavement under a shadow. \nA few things that are visually similar to 'shadow pavement' but are not 'shadow pavement' are:\tpavement in direct sunlight\tdifferent types of pavement, such as gravel or dirt\tdifferent textures of pavement, such as cracks or patterns\nThere are several useful visual features to tell there is 'shadow pavement' and not similar things in a photo:\tdark color, usually grey or black\tvisible contrast between the pavement in the shadow and outside of the shadow\tdefined edges of the shadow cast on the pavement.", 27], "somebody": ["No. 'Somebody' is too vague or abstract to be distinguished in a photo.", 27], "manes": ["Yes. 'Manes' has a tangible appearance and refers to the long hair on the neck of certain animals, especially horses and lions.\nA few things that are visually similar to 'manes' but are not 'manes' are: hair, fur, and wigs.\nThere are several useful visual features to tell there is 'manes' and not similar things in a photo:\thair or fur on the neck only\tlonger and thicker hair than the rest of the body\thorses, lions or related animals.", 27], "airplane wheels": ["Yes. 'Airplane wheels' has a tangible appearance and consists of a tire and a wheel.\nA few things that are visually similar to 'airplane wheels' but are not 'airplane wheels' are:\tbicycle wheels\tmotorcycle wheels\tcar wheels\tscooter wheels\nThere are several useful visual features to tell there are 'airplane wheels,' and not similar things in a photo:\tlarge in size\tattached to a landing gear\tmetallic-colored\ttire has a tread pattern designed for aircraft operations.", 27], "finger nails": ["Yes. 'Finger nails' have a tangible appearance and are a part of the human body.\nA few things that are visually similar to 'finger nails' but are not 'finger nails' are:\tanimal claws\ttentacles\tbranches\nThere are several useful visual features to tell there are 'finger nails' and not similar things in a photo:\tlocated at the tip of the fingers\tthin and flat\ttranslucent or opaque\tridged surface\tcan be painted or decorated", 27], "blackboard": ["Yes. 'Blackboard' has a tangible appearance and is a type of writing surface.\nA few things that are visually similar to 'blackboard' but are not 'blackboard' are:\twhiteboard\tchalkboard\tpaper\tcanvas\nThere are several useful visual features to tell there is 'blackboard' and not similar things in a photo:\tdark-colored surface\tchalk or chalk-like marks or drawings\terasable surface, can be cleaned with a rubber or cloth", 27], "dark jacket": ["Yes. 'Dark jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'dark jacket' but are not 'dark jacket' are:\tcoat\thoodie\tsweater\t\nThere are several useful visual features to tell there is 'dark jacket' and not similar things in a photo:\tdark color (usually black, navy blue, or dark grey)\tlong-sleeved\tcollared or zipped\thighlights the upper body of a person\tmade of a sturdy or waterproof material, such as leather or polyester.", 27], "murky": ["No. 'Murky' is too vague or abstract to be distinguished in a photo.", 27], "sprouts": ["Yes. 'Sprouts' has a tangible appearance and refers to young plants that have just started to grow.\nA few things that are visually similar to 'sprouts' but are not 'sprouts' are:\tseeds\tbeans\tpeas\nThere are several useful visual features to tell there are 'sprouts' and not similar things in a photo:\tyoung plants\twith small leaves and stem\tbulb-like shape\tgrowing in soil or a container\tsometimes white tails are present", 27], "wicker baskets": ["Yes. 'Wicker baskets' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'wicker baskets' but are not 'wicker baskets' are:\tboxes\tbackpacks\tbins\ttrays\nThere are several useful visual features to tell there is 'wicker baskets' and not similar things in a photo:\twoven texture\torganic material, usually made of wicker or rattan\thandles on the sides,\toften used for carrying and storing various objects.", 27], "crisp": ["No. 'Crisp' is too vague or abstract to be distinguished in a photo. It can refer to a variety of textures, tastes, or sounds.", 27], "orange suitcase": ["Yes. 'Orange suitcase' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'orange suitcase' but are not 'orange suitcase' are:\torange backpack\torange tote bag\torange duffel bag\torange purse\t\nThere are several useful visual features to tell there is 'orange suitcase' and not similar things in a photo:\trectangular shape\thard shell or soft fabric\tzipper or closure\thandle for carrying or pulling\ttwo or four wheels for easy mobility.", 27], "basketball goal": ["Yes. 'Basketball goal' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'basketball goal' but are not 'basketball goal' are:\tsoccer goal\thockey net\tvolleyball net\ttetherball pole\nThere are several useful visual features to tell there is 'basketball goal' and not similar things in a photo:\thoop with a net\tten feet high\tfrom the ground\tor mounted on a backboard\tin a rectangular shape", 27], "junk": ["Yes. 'Junk' has a tangible appearance and refers to objects that are considered useless or of little value.\nA few things that are visually similar to 'junk' but are not 'junk' are:\ttools\tcluttered piles or boxes\tof old newspapers or magazines\nThere are several useful visual features to tell there is 'junk' and not similar things in a photo:\tdisorganized or messy\tobjects that are broken, damaged, or no longer in use\tobjects that are useless or have no clear purpose\tno aesthetic value\tdusty or dirty appearance", 27], "travel bag": ["Yes. 'Travel bag' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'travel bag' but are not 'travel bag' are:\tbackpack\tpurse\tbriefcase\ttote bag\t\nThere are several useful visual features to tell there is 'travel bag' and not similar things in a photo:\tzipper or buttons\ton wheels or with handles\tlarge enough to hold clothes and toiletries\tdurable material like leather or polyester\tspecifically designed for travel\tpockets or compartments for organization", 27], "cylinder": ["Yes. 'Cylinder' has a visually concrete concept and is a 3-dimensional shape.\nA few things that are visually similar to 'cylinder' but are not 'cylinder' are:\tcone\tprism\ttube\tsphere\nThere are several useful visual features to tell there is 'cylinder' and not similar things in a photo:\tstraight, parallel sides\tcircular bases\twith or without rounded edges or corners\tno pointed tips or rounded surfaces", 27], "baseball fans": ["Yes. 'Baseball fans' has a tangible appearance and refers to people who enjoy baseball.\nA few things that are visually similar to 'baseball fans' but are not 'baseball fans' are:\tspectators\tcrowds\ttheater attendees\nThere are several useful visual features to tell there is 'baseball fans' and not similar things in a photo:\tbaseball jerseys, hats or other merchandise\temotions such as excitement, happiness or disappointment\tmovement or gestures associated with the game such as cheering, clapping, or waving", 27], "food bowl": ["Yes. 'Food bowl' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'food bowl' but are not 'food bowl' are:\tplant pot\tsoup bowl\tpen holder\tashtray\nThere are several useful visual features to tell there is 'food bowl' and not similar things in a photo:\tround or oval shape\tshallow or deep sides\tmade of plastic, ceramic, metal or other food-safe materials\thas food or water inside it.", 27], "tv set": ["Yes. 'TV set' has a tangible appearance and is an electronic appliance.\nA few things that are visually similar to 'TV set' but are not 'TV set' are:\tmonitor\tprojector\tcomputer screen\nThere are several useful visual features to tell there is 'TV set' and not similar things in a photo:\trectangular or square-shaped screen\tattached to a stand or wall-mountable\tvisible buttons or controls\tsound system associated with it\tvisible power cord, ports and jacks", 27], "silver base": ["Yes. 'Silver base' has a tangible appearance and is typically used as a foundation for objects.\nA few things that are visually similar to 'silver base' but are not 'silver base' are:\tsilver ring\tsilver sculpture\tsilver tray\tsilver coin\tsilver pendant\nThere are several useful visual features to tell there is 'silver base' and not similar things in a photo:\trectangular or circular shape\tmetallic appearance\tsimple design or minimal embellishments\theavy or sturdy weight.", 27], "stainless steel pot": ["Yes. 'Stainless steel pot' has a tangible appearance and is a type of cooking utensil.\nA few things that are visually similar to 'stainless steel pot' but are not 'stainless steel pot' are:\tAluminum pot\tCopper pot\tGlass pot\nThere are several useful visual features to tell there is 'stainless steel pot' and not similar things in a photo:\tMetallic silver color\tShiny and reflective surface\tStraight and tall sides\twith a lid and handle", 27], "curls": ["Yes. 'Curls' has a tangible appearance and is a physical feature of hair or objects.\nA few things that are visually similar to 'curls' but are not 'curls' are:\twaves\tlines\tsnakes\nThere are several useful visual features to tell there are 'curls' and not similar things in a photo:\tround and spiral shape\tcurved lines\thair or small objects as the source of the curls.", 27], "gathering": ["No. 'Gathering' is too vague or abstract to be distinguished in a photo.", 27], "sleeve blue shirt": ["Yes. 'Sleeve blue shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sleeve blue shirt' but are not 'sleeve blue shirt' are:\tsleeveless blue shirt\tlong sleeve blue shirt\tshort sleeve green shirt\nThere are several useful visual features to tell there is 'sleeve blue shirt' and not similar things in a photo:\tshort sleeves\tblue color\tfabric texture\tstructure of the shirt such as collar, buttons, or pocket.", 27], "cardboard pizza box": ["Yes. 'Cardboard pizza box' has a tangible appearance and is a type of food container.\nA few things that are visually similar to 'cardboard pizza box' but are not 'cardboard pizza box' are:\tcardboard box\tshipping box\ttake-out box\tChinese food container\nThere are several useful visual features to tell there is 'cardboard pizza box' and not similar things in a photo:\trectangular shape\tflapping lids with edges\tinformation about the pizzeria on it\tprinted images of a pizza on it.", 27], "cross top building": ["Yes. 'Cross top building' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'cross top building' but are not 'cross top building' are:\tchurch tower\tmosque dome\tskyscraper\tpyramid\nThere are several useful visual features to tell there is 'cross top building' and not similar things in a photo:\ta cross or crucifix on the top of a building (usually a church or cathedral)\tA dome-shaped or angular roof in the form of a cross (usually a chapel or cathedral)", 27], "hunk": ["No. 'Hunk' is too vague or abstract to have a tangible appearance or to be distinguished in a photo. It is typically used as a slang or informal term to describe an attractive, muscular man.", 27], "bed sheets": ["Yes. 'Bed sheets' has a tangible appearance and is a type of fabric used on a bed.\nA few things that are visually similar to 'bed sheets' but are not 'bed sheets' are:\ttablecloths\ttowels\tcurtains\tblankets\nThere are several useful visual features to tell there is 'bed sheets' and not similar things in a photo:\tflat, rectangular shape\tone or more solid colors or patterns\toften used in conjunction with pillows\tand a bed covering, such as a quilt or duvet.", 27], "pepperonis": ["Yes. 'Pepperonis' has a tangible appearance and is a type of sausage.\nA few things that are visually similar to 'pepperonis' but are not 'pepperonis' are:\tsalami\tcured meats\tjerky\nThere are several useful visual features to tell there is 'pepperonis' and not similar things in a photo:\tthinly sliced sausage\tdark red color\twith small chunks of fat and spices\tmay have a slightly curved shape\ton top of a pizza or sliced as a snack", 27], "clasp": ["Yes. 'Clasp' has a tangible appearance and is a device used to fasten or secure something.\nA few things that are visually similar to 'clasp' but are not 'clasp' are:\tbutton\tzipper\tsnap\tknot\nThere are several useful visual features to tell there is 'clasp' and not similar things in a photo:\tconsists of two parts that interlock or fit together\tallows for opening and closing with ease\tused to secure or fasten jewelry, clothing or bags made of leather or fabric\tmetallic or shiny appearance\tfeatures such as hooks, loops, or springs", 27], "kite tail": ["Yes. 'Kite tail' has a tangible appearance and is a part of a kite.\nA few things that are visually similar to 'kite tail' but are not 'kite tail' are:\tribbons\tstreamers\tthreads\nThere are several useful visual features to tell there is 'kite tail' and not similar things in a photo:\tattached to a kite\tusually longer than the kite itself\tin the shape of a tail\tmoves along with the kite\twhen in the wind", 27], "surboard": ["Yes. 'Surfboard' has a tangible appearance and is a type of board used for surfing.\nA few things that are visually similar to 'surfboard' but are not 'surfboard' are:\tskateboard\tsnowboard\twakeboard\tlongboard\nThere are several useful visual features to tell there is 'surfboard' and not similar things in a photo:\tlarge and flat surface\tfor use in water\tusually made of foam and fiberglass\tone fin or more on the bottom of the board\tfor use with a leash to keep it attached to the surfer's ankle.", 27], "baby horse": ["Yes. 'Baby horse' has a tangible appearance and refers to a young horse.\nA few things that are visually similar to 'baby horse' but are not 'baby horse' are:\tcolts\tfoals\tdonkeys\tpainted ponies\nThere are several useful visual features to tell there is 'baby horse' and not similar things in a photo:\tlean body\twith long legs and tail\tlarge eyes\tmane and hair on the tail at this early age\tcarousel horses typically have fixed posture and are often made of wood or plastic, whereas real baby horses move and have fur", 27], "laptop screen": ["Yes. 'Laptop screen' has a tangible appearance and is a part of a laptop computer.\nA few things that are visually similar to 'laptop screen' but are not 'laptop screen' are:\ttablet screen\tsmartphone screen\tcomputer monitor\tTV screen\nThere are several useful visual features to tell there is 'laptop screen' and not similar things in a photo:\trectangular shape\twith a laptop bezel and hinge\tattached to a laptop body\ttypically smaller than a computer monitor or TV screen.", 27], "nike shoes": ["Yes. 'Nike shoes' has a tangible appearance and is a type of footwear produced by the Nike brand.\nA few things that are visually similar to 'Nike shoes' but are not 'Nike shoes' are:\tAdidas shoes\tPuma shoes\tReebok shoes\tNew Balance shoes\nThere are several useful visual features to tell there is 'Nike shoes' and not similar things in a photo:\tSwoosh logo on the sides or on the tongue of the shoe\tBright or distinctive colors or patterns, such as neon green, bright pink or a camo print\t'Shox' or 'Air' technology visible in the sole\tTRIPLE The appearance is very similar to similar shoes, so the logo is the most useful feature for distinguishing Nike shoes from other brands.", 27], "silver motorcycle": ["Yes. 'Silver motorcycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'silver motorcycle' but are not 'silver motorcycle' are:\tbike\tscooter\tmoped\ttricycle\tquad bike\nThere are several useful visual features to tell there is 'silver motorcycle' and not similar things in a photo:\ttwo wheels\thandlebars and a saddle\treflective surface\tinclined seat and feet platforms\tsilver color or metallic finish", 27], "certificate": ["Yes. 'Certificate' has a tangible appearance and is a type of document.\nA few things that are visually similar to 'certificate' but are not 'certificate' are: diplomas, awards, licenses, contracts, deeds\nThere are several useful visual features to tell there is 'certificate' and not similar things in a photo:\tpaper or card material\tofficial looking\tseals or signatures\twords like \"certificate of\" or \"awarded to\"", 27], "missile": ["Yes. 'Missile' has a tangible appearance and is a type of projectile used in warfare or for other purposes.\nA few things that are visually similar to 'missile' but are not 'missile' are:\trocket\tbullet\tcannonball\tfist\nThere are several useful visual features to tell there is 'missile' and not similar things in a photo:\tlong and cylindrical shape\tpointed at one end\tfins or wings on the back for guidance or stability\thighly polished metallic surface\tsmoke or flames emitting from it during takeoff or flight", 27], "tail wings": ["Yes. 'Tail wings' has a tangible appearance and is a part of an aircraft.\nA few things that are visually similar to 'tail wings' but are not 'tail wings' are:\twinglets\tflaps\t\nThere are several useful visual features to tell there are 'tail wings' and not similar things in a photo:\tlocated at the back of the aircraft\tattached to the tail structure\thorizontal orientation", 27], "shore line": ["Yes. 'Shore line' has a tangible appearance and refers to the line where land meets a body of water.\nA few things that are visually similar to 'shore line' but are not 'shore line' are:\tbeach\tsandbar\treef\tbank\nThere are several useful visual features to tell there is 'shore line' and not similar things in a photo:\tthe clear division between land and water\tthe texture difference between the wet sand and the dry sand where the water meets the land\tthe presence of waves breaking against the land or deposits of seaweed, shells or rocks on the land", 27], "power light": ["Yes. 'Power light' has a tangible appearance and is an indicator of whether an electronic device is on or not.\nA few things that are visually similar to 'power light' but are not 'power light' are:\tsignal light\theadlights\tflashlight\nThere are several useful visual features to tell there is 'power light' and not similar things in a photo:\tlocated on or near the power button or switch\tglowing or illuminated\twhen on, often green or blue in color\tor may have the \"on/off\" symbol\tnext to it.", 27], "bump": ["Yes. 'Bump' has a tangible appearance and is a physical protuberance.\nA few things that are visually similar to 'bump' but are not 'bump' are:\tindentation\thole\tknot\trock formation\nThere are several useful visual features to tell there is 'bump' and not similar things in a photo:\traised surface\tsmooth or rough texture\tmatching the color or material of the surrounding surface\tdiffers in shape from surrounding surface.", 27], "cellphones": ["Yes. 'Cellphones' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'cellphones' but are not 'cellphones' are:\tcameras\tmp3 players\tdigital watches\nThere are several useful visual features to tell there is 'cellphones' and not similar things in a photo:\trectangle or square shape\twith a screen\tonboard keyboard or touch screen\tmobile service logo or indicator", 27], "pink house": ["Yes. 'Pink house' has a tangible appearance and refers to a house with a pink color exterior.\nA few things that are visually similar to 'pink house' but are not 'pink house' are:\thouses painted in a similar shade of pink\tfloral arrangements with pink flowers\nThere are several useful visual features to tell there is a 'pink house' and not similar things in a photo:\tdominant pink color on the exterior walls\tof a structure that has a roof, doors, and windows\tstructure size and shape", 27], "light green": ["Yes. 'Light green' has a tangible appearance and refers to a specific shade of green.\nA few things that are visually similar to 'light green' but are not 'light green' are:\tyellow-green chartreuse\tgreen-yellow\tapple green\tolive green\nThere are several useful visual features to tell there is 'light green' and not similar things in a photo:\ta pale or pastel shade of green\thue is closer to green than any other color\tin a range of shades from bright to muted or dusty green", 27], "concrete stairs": ["Yes. 'Concrete stairs' has a tangible appearance and is a type of staircase.\nA few things that are visually similar to 'concrete stairs' but are not 'concrete stairs' are:\twooden stairs\tmetal stairs\tstairs made of stone\nThere are several useful visual features to tell there is 'concrete stairs' and not similar things in a photo:\tgrey color\trough texture\tvisible seams or joints between steps\tstraight or slightly sloped design", 27], "cloudless": ["No. 'Cloudless' is too vague or abstract to be distinguished in a photo.", 27], "dark shadows": ["Yes. 'Dark shadows' has a tangible appearance and is a type of shadow.\nA few things that are visually similar to 'dark shadows' but are not 'dark shadows' are:\treflections\tsilhouettes\tblack objects\tpainted shadows\nThere are several useful visual features to tell there are 'dark shadows' and not similar things in a photo:\tdark color contrasting with a lighter surface\tvisible edges or borders\tcast by an object that blocks or partially blocks a light source", 27], "mold": ["Yes. 'Mold' has a tangible appearance and is a type of fungus that grows on surfaces.\nA few things that are visually similar to 'mold' but are not 'mold' are:\tdirt\tdust\tpollen\tspiderwebs\nThere are several useful visual features to tell there is 'mold' and not similar things in a photo:\tfuzzy or slimy texture\tvarious colors (green, black, grey, brown)\toften found in damp or humid environments\tlarger and more distinct patches than other similar things.", 27], "denim jacket": ["Yes. 'Denim jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'denim jacket' but are not 'denim jacket' are:\tdenim shirt\tblue jacket\tblue shirt\tjean vest\nThere are several useful visual features to tell there is 'denim jacket' and not similar things in a photo:\tbutton or zipper front\ttwo chest pockets\twith or without collar\tfrayed or sewn hem\tlines or seams\tdark blue color", 27], "maroon car": ["Yes. 'Maroon car' has a tangible appearance and is a specific type of car with a particular color.\nA few things that are visually similar to 'maroon car' but are not 'maroon car' are:\tred car\tburgundy car\torange car\tpink car\nThere are several useful visual features to tell there is 'maroon car' and not similar things in a photo:\ta specific shade of reddish-brown or purplish-brown color\ttypically a mid-size sedan or SUV-shaped vehicle\ta car model and brand logo may be visible\tdistinguishing physical features such as headlights, taillights, grille, and wheels", 27], "cityscape": ["Yes. 'Cityscape' has a tangible appearance and refers to the physical appearance of a city as viewed from a distance.\nA few things that are visually similar to 'cityscape' but are not 'cityscape' are:\tlandscape\tpainting or photograph of a city\tbuildings or skylines\nThere are several useful visual features to tell there is 'cityscape' and not similar things in a photo: view of a city from a distance with the skyline visible\thigh-rise buildings, skyscrapers or other tall buildings\tfamiliar landmarks or monuments from that city\turban landscape with streets, intersections and traffic", 27], "grates": ["Yes. 'Grates' has a tangible appearance and a specific function.\nA few things that are visually similar to 'grates' but are not 'grates' are:\tbars\tgates\tfences\tscreen doors\nThere are several useful visual features to tell there is 'grates' and not similar things in a photo:\tmetallic bars or slats\twith gaps or holes\tpatterned or woven appearance\tused for ventilation or as a covering", 27], "train passenger car": ["Yes. 'Train passenger car' has a tangible appearance and refers to a specific type of train car designed for carrying passengers.\nA few things that are visually similar to 'train passenger car' but are not 'train passenger car' are:\tfreight car\tsubway car\ttram\ttrolley\tbus\nThere are several useful visual features to tell there is 'train passenger car' and not similar things in a photo:\tmultiple windows for viewing inside\tspaces for passengers to sit or stand\tdoorways for entry and exit\texterior markings or identification as a passenger car", 27], "cinnamon roll": ["Yes. 'Cinnamon roll' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'cinnamon roll' but are not 'cinnamon roll' are:\tpretzel\tbreadstick\tdoughnut\tbaguette\nThere are several useful visual features to tell there is 'cinnamon roll' and not similar things in a photo:\tspiral or swirled shape\ttan or golden brown color\tdusted with cinnamon or sugar\tfluffy or doughy texture\tswirls of cinnamon and sugar visible on the inside.", 27], "air vents": ["Yes. 'Air vents' has a tangible appearance and is a type of opening in a surface for air to flow.\nA few things that are visually similar to 'air vents' but are not 'air vents' are:\toutlets\tbutters\tdecorative grilles\tblinds\tshutters\nThere are several useful visual features to tell there is 'air vents' and not similar things in a photo:\trectangular or square shape\tgrille or louvered cover\tattached to a wall, floor, or ceiling", 27], "corners": ["Yes. 'Corners' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'corners' but are not 'corners' are:\tedges\tlines\tintersections\t\nThere are several useful visual features to tell there are 'corners' and not similar things in a photo:\t\n\n- Two intersecting straight lines\n- Sharp or well-defined angles\n- Right angle (90-degree angle)\n- Clearly demarcated vertices or points at which lines meet\n- In a square, rectangle, or other polygon, an interior angle formed by adjacent sides", 27], "bedside": ["Yes. 'Bedside' has a tangible appearance and refers to the area or the furniture next to a bed.\nA few things that are visually similar to 'bedside' but are not 'bedside' are:\ttable\tnightstand\tbookshelf\nThere are several useful visual features to tell there is 'bedside' and not similar things in a photo:\tlocated next to a bed\tmatches the style and color of the bed\tfeatures items such as a lamp, a clock, a book, or a glass\tof water, suggesting it is a functional space\tnext to a pillow", 27], "coin": ["Yes. 'Coin' has a tangible appearance and is a metal or plastic disc used as money.\nA few things that are visually similar to 'coin' but are not 'coin' are:\tmedals\tbottle caps\tfrisbees\ttokens\nThere are several useful visual features to tell there is 'coin' and not similar things in a photo:\tcircular or polygonal shape\twith numbers and/or images on both sides\tmetallic appearance and texture\tvarious sizes and colors", 27], "metal bell": ["Yes. 'Metal bell' has a tangible appearance and is a type of percussion instrument.\nA few things that are visually similar to 'metal bell' but are not 'metal bell' are:\tcowbell\thandbell\tdome\tmetal bowl\nThere are several useful visual features to tell there is 'metal bell' and not similar things in a photo:\thollow, bell-shaped object\tmade of metal\thanging or mounted horizontally with a clapper that can strike the side of the bell", 27], "baby lamb": ["Yes. 'Baby lamb' has a tangible appearance and is a kind of animal.\nA few things that are visually similar to 'baby lamb' but are not 'baby lamb' are:\tsheep\tgoat\tpuppy\tkitten\nThere are several useful visual features to tell there is 'baby lamb' and not similar things in a photo:\twooly bodies and legs\tshort and floppy ears\tdimpled muzzle\twhite or light-colored wool\twith or without spots\tor patterns", 27], "website address": ["No. 'Website address' is too abstract to be distinguished in a photo.\nA few things that are visually similar to 'website address' but are not 'website address' are:\ttext message\temail\tsign on a building\nThere are no useful visual features that can be used to distinguish a 'website address' from these similar things in a photo.", 27], "silver post": ["Yes. 'Silver post' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'silver post' but are not 'silver post' are:\tsilver pole\tsilver stick\tsilver rod\tsilver bar\nThere are several useful visual features to tell there is 'silver post' and not similar things in a photo:\ttall and vertical\tcylindrical shape\treflective and shiny surface\tsilver or metallic color", 27], "giraffee": ["Yes. 'Giraffee' has a tangible appearance and is a type of animal.\nThere is no other animal that is visually similar to 'giraffee'.\nUseful visual features for distinguishing 'giraffee' could be: extremely long neck, spotted coat pattern, long legs, and long tongue.", 27], "cub": ["Yes. 'Cub' has a tangible appearance and is an offspring of certain animals.\nA few things that are visually similar to 'cub' but are not 'cub' are:\tfully-grown animals\tbaby humans\ttoy animals\nThere are several useful visual features to tell there is 'cub' and not similar things in a photo:\tsmaller size compared to adult animals\tfuzzy hair or fur\tshorter limbs and appendages\tan overall more round shape\tfor mammals, the presence of suckling nipples", 27], "tan house": ["Yes. 'Tan house' has a tangible appearance.\nA few things that are visually similar to 'tan house' but are not 'tan house' are:\tbeige building\tbrown barn\tpeach-painted house\tsand-colored villa\nThere are several useful visual features to tell there is 'tan house' and not similar things in a photo:\ttan-colored exterior walls\troof of a different color\twooden or brick material front door or shutters\tfeatures like windows or chimneys visible on the house structure", 27], "gold letters": ["Yes. 'Gold letters' has a tangible appearance and is a type of text decoration.\nA few things that are visually similar to 'gold letters' but are not 'gold letters' are:\tyellow letters\tmetallic letters\tbronze letters\tshiny letters\nThere are several useful visual features to tell there is 'gold letters' and not similar things in a photo:\ta shiny gold color\treflective surface\ta metallic appearance\tsparkling or glittery effect.", 27], "window ledge": ["Yes. 'Window ledge' has a tangible appearance and is a part of a window frame.\nA few things that are visually similar to 'window ledge' but are not 'window ledge' are:\tshelf\tsill\tcounter\ttop\tof a table\nThere are several useful visual features to tell there is 'window ledge' and not similar things in a photo:\t\npart of a window frame\thorizontal surface - not sloped, angled or curved\tlocated beneath the window pane\tcan be used as a surface to display objects, but not for sitting or standing on.", 27], "gum": ["Yes. 'Gum' has a tangible appearance and is a type of chewable substance.\nA few things that are visually similar to 'gum' but are not 'gum' are:\tcandy\tmedicine\tchewable vitamins\tnicotine gum\nThere are several useful visual features to tell there is 'gum' and not similar things in a photo:\tstick-shaped, pellet-shaped or a strip-shaped chewable substance\tbright colors, like pink or green\tpackaged in a paper or plastic wrapper\tthat there is no indication on the packaging saying 'medicine'", 27], "blossom": ["Yes. 'Blossom' has a tangible appearance and refers to a flower or a group of flowers.\nA few things that are visually similar to 'blossom' but are not 'blossom' are:\tleaves\tbuds\tbranches\tlawns\nThere are several useful visual features to tell there is 'blossom' and not similar things in a photo:\tflowers in bloom\tflower petals\tdiffused colors of pink, purple, white, or red\tgrowing on a stem or branch", 27], "bikini bottom": ["Yes. 'Bikini bottom' has a tangible appearance and is a kind of swimwear.\nA few things that are visually similar to 'bikini bottom' but are not 'bikini bottom' are:\tunderwear\tboyshorts\tswim shorts\nThere are several useful visual features to tell there is 'bikini bottom' and not similar things in a photo:\ttwo separate pieces of cloth\ttriangle shaped\tfor women's swimwear", 27], "check": ["Yes. 'Check' has a tangible appearance and is a written document.\nA few things that are visually similar to 'check' but are not 'check' are:\treceipt\tbill\tpaper\twork order\nThere are several useful visual features to tell there is 'check' and not similar things in a photo:\tstandard rectangular shape with rounded corners\tpaper material\twith a bank's or financial institution's logo and name\tdate, amount, payee, and signature fields on its surface\tThe word \"CHECK\" prominently displayed.", 27], "metal latch": ["Yes. 'Metal latch' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'metal latch' but are not 'metal latch' are:\thinges\tbolts\tlock\thasp\nThere are several useful visual features to tell there is 'metal latch' and not similar things in a photo:\ta small metal piece\tthat is used to secure or close a door, window, box, or gate\thas a movable lever or hook to engage it\tmay be flush or surface-mounted\tmay have a locking mechanism or not", 27], "log ground": ["No. 'Log ground' is too vague or abstract to be distinguished in a photo. It is not a commonly used term or concept.\nTherefore, it is not possible to name things similar to 'log ground' or provide distinguishing visual features for it.", 27], "jersey number": ["Yes. 'Jersey number' has a tangible appearance and is a numeric identifier on a sports uniform.\nA few things that are visually similar to 'jersey number' but are not 'jersey number' are:\tletters on a sports uniform\tsponsor logos on a sports uniform\tpatches on a sports uniform\nThere aren't any useful visual features to tell there is 'jersey number' and not similar things in a photo, besides the number itself being displayed prominently on a sports uniform.", 27], "adult sheep": ["Yes. 'Adult sheep' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'adult sheep' but are not 'adult sheep' are:\tgoat\tcow\tllama\nThere are several useful visual features to tell there is 'adult sheep' and not similar things in a photo:\tfour-legged mammal with woolly fleece\tbroad, flat nose\twith or without horns\tlarge, floppy ears\tstraight or curled tail.", 27], "circus": ["Yes. 'Circus' has a tangible appearance and is a type of entertainment.\nA few things that are visually similar to 'circus' but are not 'circus' are:\tfair\tcarnival\tplayground\tzoo\nThere are several useful visual features to tell there is 'circus' and not similar things in a photo:\t\nbig top tent\t\nclowns\t\nperformers\t\nacrobats\t\nanimals\t\nbright colors\t\ncircus props (such as trapezes or tightropes)", 27], "silver minivan": ["Yes. 'Silver minivan' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'silver minivan' but are not 'silver minivan' are:\tsilver SUV\tsilver sedan\tsilver truck\tsilver crossover\nThere are several useful visual features to tell there is 'silver minivan' and not similar things in a photo:\tlarge size\tseats for seven or more passengers\tsliding door at the side\trearview mirrors on both sides\tsquare shaped body", 27], "empire": ["No. 'Empire' is too vague or abstract to be distinguished in a photo.", 27], "brownies": ["Yes. 'Brownies' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'brownies' but are not 'brownies' are:\tchocolate cake\tcookies\tfudge\nThere are several useful visual features to tell there is 'brownies' and not similar things in a photo:\trectangular shape\tchewy or fudgy texture\tchocolatey or cocoa-rich color\tmay have nuts on top or chocolate chips inside", 27], "head rest": ["Yes. 'Head rest' has a tangible appearance and is a kind of support for the head.\nA few things that are visually similar to 'head rest' but are not 'head rest' are:\tpillow\tcushion\tseat\tbackrest\nThere are several useful visual features to tell there is 'head rest' and not similar things in a photo:\tattached to a chair or a seat\tpadded or cushioned\tangled to support the neck and head", 27], "pink helmet": ["Yes. 'Pink helmet' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'pink helmet' but are not 'pink helmet' are:\tred helmet\tyellow helmet\torange helmet\tpurple helmet\thard hat\nThere are several useful visual features to tell there is 'pink helmet' and not similar things in a photo:\tpink color\thard shell\tround shape\tchin strap\tpadding inside the helmet\tfor use in activities like biking or skating.", 27], "footwear": ["Yes. 'Footwear' has a tangible appearance and refers to any type of shoes or boots that cover the feet.\nA few things that are visually similar to 'footwear' but are not 'footwear' are: socks, insoles, foot coverings, shoeboxes.\nThere are several useful visual features to tell there is 'footwear' and not similar things in a photo: a) sole, b) heel, c) toe box, d) material, e) type of closure (laces, zipper, etc.), f) overall shape and style.", 27], "waiter": ["Yes. 'Waiter' has a tangible appearance and refers to a job or profession.\nA few things that are visually similar to 'waiter' but are not 'waiter' are:\tbartender\tchef\thostess\tcustomer\nThere are several useful visual features to tell there is a 'waiter' and not similar things in a photo:\twearing a uniform or apron\tcarrying a tray of food or drinks\tstanding next to tables or customers\tpossibly taking orders from customers", 27], "wood chips": ["Yes. 'Wood chips' has a tangible appearance and refers to small pieces of wood.\nA few things that are visually similar to 'wood chips' but are not 'wood chips' are:\tmulch\tbark\tshredded paper\tshredded cardboard\nThere are several useful visual features to tell there are 'wood chips' and not similar things in a photo:\tirregular shapes\trough or splintered edges\twood grain visible\tlight or dark brown color", 27], "orange roof": ["Yes. 'Orange roof' has a tangible appearance and refers to a type of roof that is colored orange.\nA few things that are visually similar to 'orange roof' but are not 'orange roof' are:\tred roof\tbeige roof\tpink roof\nThere is only one useful visual feature to distinguish an 'orange roof' from similar roofs in a photo: bright orange color.", 27], "orange leaves": ["Yes. 'Orange leaves' has a tangible appearance and refers to the color of the leaves.\nA few things that are visually similar to 'orange leaves' but are not 'orange leaves' are:\tyellow leaves\tred leaves\tmaple leaves\tflowers\torange fruits\nThere are several useful visual features to tell there are 'orange leaves' and not similar things in a photo:\tcolor is orange, ranging from a pale yellow-orange to a dark red-orange\tshape is that of a leaf, with veins and a stem.", 27], "train front": ["Yes. 'Train front' has a tangible appearance and is the front part of a train.\nA few things that are visually similar to 'train front' but are not 'train front' are:\tcar front\tbike front\tboat front\nThere are several useful visual features to tell there is 'train front' and not similar things in a photo:\twheel\tor set of wheels\ton the track\tattached to a long vehicle\trectangular or pointed shape\theadlight or lights\tgrill or front windows.", 27], "zebra leg": ["Yes. 'Zebra leg' has a tangible appearance and is a body part of the zebra.\nA few things that are visually similar to 'zebra leg' but are not 'zebra leg' are:\thorse leg\tdonkey leg\tstriped pant leg\t\nThere are several useful visual features to tell there is 'zebra leg' and not similar things in a photo:\tblack and white stripes\thooves\tfurry texture\tfour legs with long, slender shape\tabsence of rider or saddle", 27], "train cart": ["Yes. 'Train cart' has a tangible appearance and is a part of a train.\nA few things that are visually similar to 'train cart' but are not 'train cart' are:\ttruck\ttrailer\tsemi-trailer\nThere are several useful visual features to tell there is 'train cart' and not similar things in a photo:\tconnected to other carts\tforming a train\tcircular wheels\tsquare or rectangular shape\topen or covered with doors and windows\tbelonging to a train track", 27], "beaks": ["Yes. 'Beaks' has a tangible appearance and refers to the hard, pointed mouthpart of birds.\nA few things that are visually similar to 'beaks' but are not 'beaks' are:\tteeth\tsnouts\thorns\tclaws\tbills\nThere are several useful visual features to tell there is 'beaks' and not similar things in a photo:\tFound only on birds\tVaries in shape, size, and color\tUsed for preening feathers, catching prey, and feeding young\tAttached to the bird's head and used for breathing and vocalizing", 27], "brown mane": ["Yes. 'Brown mane' has a tangible appearance and is a feature of an animal.\nA few things that are visually similar to 'brown mane' but are not 'brown mane' are:\tbrown hair\tfur\thorse mane\tbrown wig\nThere are several useful visual features to tell there is 'brown mane' and not similar things in a photo:\tgrowing out of the neck or shoulders of an animal\tlong hair or fur\tusually darker than the animal's coat color\tmay have a wavy or unkempt appearance.", 27], "clown": ["Yes. 'Clown' has a tangible appearance and is a type of performer.\nA few things that are visually similar to 'clown' but are not 'clown' are:\tjester\tharlequin\tmime\tactor\nThere are several useful visual features to tell there is 'clown' and not similar things in a photo:\tcolorful or exaggerated makeup\tpainted smiley face\tbrightly colored clothing or costume\ttall and pointy hat\torange wig or multisized shoes\tsilly facial expression or body pose", 27], "segment": ["No. 'Segment' is too vague or abstract to be visually distinguished in a photo.", 26], "spatulas": ["Yes. 'Spatulas' has a tangible appearance and is a cooking utensil.\nA few things that are visually similar to 'spatulas' but are not 'spatulas' are:\tturners\tforks\ttongs\twhisks\nThere are several useful visual features to tell there is 'spatulas' and not similar things in a photo:\tflat and usually broad blade\tlong handle\tfor flipping, stirring or scooping food\toutwardly curved or straight blade", 26], "computer laptop": ["Yes. 'Computer laptop' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'computer laptop' but are not 'computer laptop' are:\ttablet\te-reader\tnotebook\tpad\nThere are several useful visual features to tell there is 'computer laptop' and not similar things in a photo:\thinged clamshell design\tbuilt-in keyboard and trackpad\tor touch screen\tdisplay screen hinges to the keyboard section\tinput/output ports\thandle for carrying the device on the go\tlarger size compared to other handheld electronic devices", 26], "rocky": ["Yes. 'Rocky' has a tangible appearance and refers to an area or surface covered in rocks.\nA few things that are visually similar to 'rocky' but are not 'rocky' are:\tpebbles\tcobblestones\tgravels\nThere are several useful visual features to tell there is 'rocky' and not similar things in a photo:\tirregular-shaped rocks\tlarge and small rocks\tpotentially jagged or sharp surface\tnot smooth to the touch", 26], "beverage bottle": ["Yes. 'Beverage bottle' has a tangible appearance and is used to hold drinks.\nA few things that are visually similar to 'beverage bottle' but are not 'beverage bottle' are:\tperfume bottle\tvase\tolive oil bottle\tflower vase\tplastic bottle\nThere are several useful visual features to tell there is 'beverage bottle' and not similar things in a photo:\ttransparent or opaque made of glass or plastic\tnarrow neck with a screw or pop-up cap\tcontaining liquid, such as water, soda, juice, alcohol or milk\tlabelled with brand or logo", 26], "avocados": ["Yes. 'Avocados' has a tangible appearance and is a fruit.\nA few things that are visually similar to 'avocados' but are not 'avocados' are:\tkiwi\tpear\teggplant\nThere are several useful visual features to tell there is 'avocados' and not similar things in a photo:\tpear-shaped\tdark green or black skin\tlarge seed in the middle\tfleshy green interior", 26], "game console": ["Yes. 'Game console' has a tangible appearance and is an electronic device used to play video games.\nA few things that are visually similar to 'game console' but are not 'game console' are:\tdvd player\tblu-ray player\thome theater system\tcomputer monitor\nThere are several useful visual features to tell there is 'game console' and not similar things in a photo:\tgame controller plugged in\tdisplay screen\tfor playing video games.", 26], "switch plate": ["Yes. 'Switch plate' has a tangible appearance and is a type of cover for light switches or electrical outlets.\nA few things that are visually similar to 'switch plate' but are not 'switch plate' are:\tsocket plate\tDimmer plate\t\nThere are several useful visual features to tell there is 'switch plate' and not similar things in a photo:\tRectangular shape, usually with rounded edges\tSmall screws at the top and bottom (or sometimes on the sides)\tOne or more holes in the middle for switches or outlets.", 26], "meats": ["Yes. 'Meats' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'meats' but are not 'meats' are:\tvegetables\tfruits\ttofu\tcheese\nThere are several useful visual features to tell there is 'meats' and not similar things in a photo:\tdark red, white or pink colors\tlayers of fat and muscle visible\tdifferent textures and shapes (e.g., ground meat, sliced meat, whole cuts)\tclosely packed fibers in the flesh of the meat may be visible or shown through its texture.", 26], "blue candle": ["Yes. 'Blue candle' has a tangible appearance and is a specific type of candle.\nA few things that are visually similar to 'blue candle' but are not 'blue candle' are:\twhite candle\tred candle\tpink candle\tpurple candle\nThere are several useful visual features to tell there is 'blue candle' and not similar things in a photo:\tcylindrical shape\twax material\tblue color\ttapered end", 26], "silver tea kettle": ["Yes. 'Silver tea kettle' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'silver tea kettle' but are not 'silver tea kettle' are:\tsaucepan\tpot\twater jug\nThere are several useful visual features to tell there is 'silver tea kettle' and not similar things in a photo:\tkettle-shaped\tobject made of metal\tsilver or metallic color\thinged lid\thandles for pouring and carrying", 26], "salsa": ["Yes. 'Salsa' has a tangible appearance and is a type of sauce.\nA few things that are visually similar to 'salsa' but are not 'salsa' are:\tketchup\ttomato sauce\tbbq sauce\tmustard\thot sauce\t\nThere are several useful visual features to tell there is 'salsa' and not similar things in a photo:\tchunky texture\tbright colors\ttomato-based appearance\tmay contain vegetables like onions or peppers\tspicy aroma or taste\tserved in a bowl or a jar.", 26], "bottom section": ["Yes. 'Bottom section' has a tangible appearance and refers to the lower part of an object or image.\nA few things that are visually similar to 'bottom section' but are not 'bottom section' are:\ttop section\tmiddle section\tside section\tborder\nThere are several useful visual features to tell there is 'bottom section' and not similar things in a photo:\tposition at the base or lower part of the object or image\ta clear line or separation between the 'bottom section' and the rest of the object or image\tthe 'bottom section' contains different elements or characteristics than the rest of the object or image.", 26], "silver pipes": ["Yes. 'Silver pipes' has a tangible appearance and refers to pipes that are made of silver or silver-colored metal.\nA few things that are visually similar to 'silver pipes' but are not 'silver pipes' are:\tchrome pipes\taluminum pipes\tstainless steel pipes\tpainted pipes\nThere are several useful visual features to tell there is 'silver pipes' and not similar things in a photo:\tsilver-colored or shiny\ttubular and cylindrical shape\tsmooth surface\tno visible joints or connectors", 26], "filing cabinet": ["Yes. 'Filing cabinet' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'filing cabinet' but are not 'filing cabinet' are:\tbookshelf\tdresser\tlocker\twardrobe\nThere are several useful visual features to tell there is 'filing cabinet' and not similar things in a photo:\ttall and narrow\tmultiple drawers\tdrawers with labels or handles\tusually made of metal or wood", 26], "blue kite": ["Yes. 'Blue kite' has a tangible appearance and is a type of flying toy.\nA few things that are visually similar to 'blue kite' but are not 'blue kite' are:\tother kites\tballoons\tbirds\tinsect\nThere are several useful visual features to tell there is 'blue kite' and not similar things in a photo:\ttriangular shape\twith a tail\tmade of lightweight materials, such as paper or fabric\thas a string or line for control\tpredominantly blue in color.", 26], "home plate umpire": ["Yes. 'Home plate umpire' has a tangible appearance and is a type of person.\nA few things that are visually similar to 'home plate umpire' but are not 'home plate umpire' are:\treferee\tcoach\tplayer\tcrowd\nThere are several useful visual features to tell there is 'home plate umpire' and not similar things in a photo:\twearing a uniform\tbehind the catcher and home plate\tmasking the face with a helmet\tor a mask\tmaking hand signals to indicate a call or a play\thaving a counter to record the count", 26], "kneepad": ["Yes. 'Kneepad' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'kneepad' but are not 'kneepad' are:\tankle brace\tshin guard\tleggings\nThere are several useful visual features to tell there is 'kneepad' and not similar things in a photo:\trectangular in shape\tpadded material wrapped around the knee\telastic or adjustable straps to secure them on the leg\tmay have hard plastic or metal components for additional protection.", 26], "sale": ["No. 'Sale' is too vague or abstract to be distinguished in a photo. However, a 'sale' sign or banner has a tangible appearance and can be visually concrete.\nA few things that are visually similar to 'sale' but are not 'sale' are:\tclearance\tdiscount\tprice tag\nThere are several useful visual features to tell there is a 'sale' and not similar things in a photo:\tbold, bright, and attention-grabbing colors used in the sign or banner\tthe word 'sale' is prominently displayed with an emphasis on the percentage or amount of the discount offered\tthe use of words like 'limited time' or 'final clearance' that suggests an urgency to buy.", 26], "restaurant menu": ["Yes. 'Restaurant menu' has a tangible appearance and is a written list of food and drink choices.\nA few things that are visually similar to 'restaurant menu' but are not 'restaurant menu' are: cookbook\tbrochure\tnewspaper\tleaflet\nThere are several useful visual features to tell there is 'restaurant menu' and not similar things in a photo:\tlist of food and drink items\tnames and prices of menu items\tdescriptions and photos of menu items\tpages or sections with different categories of menu items (e.g., appetizers, entrees, desserts, drinks)\trestaurant branding or logos visible on the menu.", 26], "silver tongs": ["Yes. 'Silver tongs' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'silver tongs' but are not 'silver tongs' are:\tspatula\tforceps\tscissors\tpincers\ttweezers\nThere are several useful visual features to tell there is 'silver tongs' and not similar things in a photo:\tlong handles\twith two arms or prongs\tat a right angle to the handles\tmade of silver or a silver-like material", 26], "cupboard door": ["Yes. 'Cupboard door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'cupboard door' but are not 'cupboard door' are:\twindow blind\tshutter\tgate\tgarage door\nThere are several useful visual features to tell there is 'cupboard door' and not similar things in a photo:\tattached to a cabinet or cupboard\thandle or knob\thinged or sliding mechanism\tmatches the material and color of the surrounding cabinetry or furniture.", 26], "bed spread": ["Yes. 'Bed spread' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'bed spread' but are not 'bed spread' are:\tblanket\tcomforter\tquilt\tthrow\tpillowcase\nThere are several useful visual features to tell there is 'bed spread' and not similar things in a photo:\tthin layer of fabric covering the entire bed\tspecific pattern or design that complements the bedroom d\u00e9cor\tmatches or contrasts with a pillowcase or other bedding accessories", 26], "pencil holder": ["Yes. 'Pencil holder' has a tangible appearance and is a container used to hold pencils.\nA few things that are visually similar to 'pencil holder' but are not 'pencil holder' are:\tmug\tpot\tcup\tbucket\nThere are several useful visual features to tell there is 'pencil holder' and not similar things in a photo:\tlong, cylindrical or rectangular shape\tholes or compartments to hold pencils\ttypically found on a desk or a table, not used for any other purposes", 26], "levels": ["No. 'Levels' are too vague or abstract to be distinguished in a photo. \n\nHowever, if we are referring to levels in the context of floors or stories of a building, then yes, it has a tangible appearance. In that case, a few things that are visually similar to 'levels' but are not 'levels' are:\tstairs, ramps, hills, mounds, stages, platforms.\n\nUseful visual features for distinguishing levels from the listed similar things in a photo could be:\n\n- A horizontal surface that covers a certain area\n- It has walls or barriers that denote boundaries \n- It can be accessed by stairs or elevators \n- It is typically found indoors or in urban environments.", 26], "shopping bags": ["Yes. 'Shopping bags' has a tangible appearance and is a type of bag used to carry purchased items.\nA few things that are visually similar to 'shopping bags' but are not 'shopping bags' are:\tbackpacks\tpurses\ttote bags\ttrash bags\nThere are several useful visual features to tell there is 'shopping bags' and not similar things in a photo:\thandles\tfor carrying\titems inside the bag, such as purchases\tfrom a retail store or shopping mall design on the bag, like a store logo or brand name.", 26], "foot stool": ["Yes. 'Foot stool' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'foot stool' but are not 'foot stool' are:\tchair\tpouf\tottoman\tbench\tsofa cushion\nThere are several useful visual features to tell there is 'foot stool' and not similar things in a photo:\thas a flat top for resting feet\thas three or four legs (not necessarily)\tis smaller than a chair", 26], "gold bracelet": ["Yes. 'Gold bracelet' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'gold bracelet' but are not 'gold bracelet' are:\twatch\tcuff\tbangle\tfitness tracker\nThere are several useful visual features to tell there is 'gold bracelet' and not similar things in a photo:\tmade of gold or gold-colored metal\tchain or linked design\tflexible to wrap around wrist\tno visible watch face or electronic display.", 26], "arena": ["Yes. 'Arena' has a tangible appearance and is a type of enclosed space for events and performances.\nA few things that are visually similar to 'arena' but are not 'arena' are:\tstadium\ttheatre\tcircus tent\tconcert hall\nThere are several useful visual features to tell there is 'arena' and not similar things in a photo:\tcircular or rectangular shape\twith seating or stands\tperforming area in the middle\tequipment for performances (e.g., stages, microphones, etc.)", 26], "diamond shape": ["Yes. 'Diamond shape' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'diamond shape' but are not 'diamond shape' are:\trhombus\tkite\tcard suit\tdustpan\nThere are several useful visual features to tell there is 'diamond shape' and not similar things in a photo:\tfour equal sides\ttwo opposite angles are acute and two opposite angles are obtuse\tangle measures 60 and 120 degrees in alternating corners", 26], "metal baseball bat": ["Yes. 'Metal baseball bat' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'metal baseball bat' but are not 'metal baseball bat' are:\twooden baseball bat\thockey stick\tgolf club\tbroomstick\nThere are several useful visual features to tell there is 'metal baseball bat' and not similar things in a photo:\tmade of metal\thollow or solid cylinder shape\tskinny handle with a rounded end\tthicker barrel-shaped top\twith a grip tape near the handle", 26], "airline logo": ["Yes. 'Airline logo' has a tangible appearance and is a type of graphic design.\nA few things that are visually similar to 'airline logo' but are not 'airline logo' are:\tcompany logo\tsports team logo\tproduct logo\tpolitical campaign logo\nThere are several useful visual features to tell there is 'airline logo' and not similar things in a photo:\ticonic graphic design using letters, shapes, and symbols in a unique way\ttypically related to airline name, color, or identity\tsometimes features an airplane or bird image", 26], "plastic tub": ["Yes. 'Plastic tub' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'plastic tub' but are not 'plastic tub' are:\tTrash cans\tBuckets\tBins\tMugs\tBowls\nThere are several useful visual features to tell there is 'plastic tub' and not similar things in a photo:\trectangular, cylindrical or square shape\tmade of plastic or rubber\thandles on the sides\tfor storage, transportation or organization of objects", 26], "silver butter knife": ["Yes. 'Silver butter knife' has a tangible appearance and is a specific type of utensil.\nA few things that are visually similar to 'silver butter knife' but are not 'silver butter knife' are:\tsilver dinner fork\tsilver tablespoon\tsilver teaspoon\nThere are several useful visual features to tell there is 'silver butter knife' and not similar things in a photo:\tslim and narrow blade with dull edges\tpointed tip\tstraight handle with a flat bottom\tsilver material", 26], "arrow keys": ["Yes. 'Arrow keys' has a tangible appearance and is a set of keys found on computer keyboards.\nA few things that are visually similar to 'arrow keys' but are not 'arrow keys' are:\tletter keys\tnumber keys\tfunction keys\nThere are several useful visual features to tell there is 'arrow keys' and not similar things in a photo:\tfour directional keys\tnormally in a separate section of a keyboard\tup, down, left, and right arrows on each key", 26], "brick column": ["Yes. 'Brick column' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'brick column' but are not 'brick column' are:\tpillar\tpost\tchimney\tpole\nThere are several useful visual features to tell there is 'brick column' and not similar things in a photo:\tmade of bricks\tcylindrical shape\tstanding upright\tused to support a structure or roof", 26], "crashing wave": ["Yes. 'Crashing wave' has a tangible appearance and is a natural phenomenon.\nA few things that are visually similar to 'crashing wave' but are not 'crashing wave' are:\twhite water\train drops\tsnow flakes\tfalling water\nThere are several useful visual features to tell there is 'crashing wave' and not similar things in a photo:\tbig and powerful wave\twith a curl or a tube\tproducing white foam and spray\twhen hitting a surface, creating a splashing or crashing sound.", 26], "set train tracks": ["Yes. 'Set train tracks' has a tangible appearance and is a type of toy or model.\nA few things that are visually similar to 'set train tracks' but are not 'set train tracks' are:\ttoy roads or highways\tmodel airplane runways\tany type of model construction\nThere are several useful visual features to tell there is 'set train tracks' and not similar things in a photo:\tmetal or plastic rails\twooden or plastic sleepers\tcurves, switches or crossings\tdifferent elevation levels\tfor electric or magnetic trains", 26], "wii console": ["Yes. 'Wii console' has a tangible appearance and is a type of gaming console.\nA few things that are visually similar to 'Wii console' but are not 'Wii console' are:\tPlayStation\tXbox\tNintendo Switch\tSega Genesis\tSuper Nintendo\nThere are several useful visual features to tell there is 'Wii console' and not similar things in a photo:\trectangular shape\twith stand and disc slot\twhite color with gray or black accents\tWii logo on front or top of the console\tWii Remote and Nunchuk accessories nearby", 26], "handle racket": ["Yes. 'Handle racket' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'handle racket' but are not 'handle racket' are:\ttennis racket\tbadminton racket\tsquash racket\tpaddle\nThere are several useful visual features to tell there is 'handle racket' and not similar things in a photo:\tdesignated for specific sports\telliptical shape\tflat hitting surface\tlong handle for gripping and swinging", 26], "chocolate sauce": ["Yes. 'Chocolate sauce' has a tangible appearance and is a kind of liquid.\nA few things that are visually similar to 'chocolate sauce' but are not 'chocolate sauce' are:\tcaramel sauce\thoney\tcoffee syrup\tbalsamic glaze\nThere are several useful visual features to tell there is 'chocolate sauce' and not similar things in a photo:\tbrown\tcolor\tthick and smooth texture\tglossy surface\tchunky texture if nuts or other candy is mixed in the sauce.", 26], "grassy area": ["Yes. 'Grassy area' has a tangible appearance and refers to an area covered with grass.\nA few things that are visually similar to 'grassy area' but are not 'grassy area' are:\tforests\tparks\tgardens\tfarms\nThere are several useful visual features to tell there is 'grassy area' and not similar things in a photo:\tat least 50% of the area covered with grass\tsolid green color\tpatches of different shades of green\tno visible trees, flowers or crops", 26], "silver mouse": ["Yes. 'Silver mouse' has a tangible appearance and is a type of computer mouse.\nA few things that are visually similar to 'silver mouse' but are not 'silver mouse' are:\tregular mouse\tgrey mouse\tplastic mouse\nThere are several useful visual features to tell apart 'silver mouse' and not similar things in a photo:\tsilver color\tmetallic finish\tscroll wheel\ttwo or more buttons and a clickable scroll wheel\tcord or wireless connection to a computer or a laptop", 26], "antelopes": ["Yes. 'Antelopes' have a tangible appearance and belong to the family of Bovidae.\nA few things that are visually similar to 'antelopes' but are not 'antelopes' are:\tDeer\tGazelles\tHorses\tReindeer\nThere are several useful visual features to tell there is 'antelopes' and not similar things in a photo:\tLong, pointed ears\tV-shaped horns\tSlender but strong legs\tTawny or reddish-brown coat with white underbelly and rump", 26], "wood bed": ["Yes. 'Wood bed' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood bed' but are not 'wood bed' are:\tcot\tchair\tsofa\tbench\nThere are several useful visual features to tell there is 'wood bed' and not similar things in a photo:\trectangular shape\tframe made of wood\theadboard and/or footboard\tmattress and pillows", 26], "sockets": ["Yes. 'Sockets' has a tangible appearance and refers to the electrical devices that provide an interface between an electronic device and an electrical power source.\n\nA few things that are visually similar to 'sockets' but are not 'sockets' are: switch, fuse, and plug.\n\nThere are several useful visual features to tell there is 'sockets' and not similar things in a photo: The presence of two or three holes where the pins of the corresponding plug must be inserted, the voltage and current specifications, and the size and shape of the socket, as well as its orientation and position relative to other elements in the photograph.", 26], "wet spot": ["Yes. 'Wet spot' has a tangible appearance and is a condition of a surface that is covered in water or another liquid.\nA few things that are visually similar to 'wet spot' but are not 'wet spot' are:\tshadow\tstain\torbs\tfaded paint\nThere are several useful visual features to tell there is a 'wet spot' and not similar things in a photo:\tdarker in color than surrounding area\treflective or glossy surface\tmoist or wet to the touch\tripple or waves\tif outside, its outline defined by the surface where it is", 26], "giraffes neck": ["Yes. 'Giraffes neck' has a tangible appearance and is a body part of a specific animal.\nA few things that are visually similar to 'giraffes neck' but are not 'giraffes neck' are:\ttrumpet\tcylinder\tpillar\nThere are several useful visual features to tell there is 'giraffes neck' and not similar things in a photo:\tlong and slender neck\tpatches of unique pattern\ton top of four legs and covered by fur", 26], "utility lines": ["Yes. 'Utility lines' has a tangible appearance and refers to the overhead power lines and cables used for communication, such as telephone or internet lines.\nA few things that are visually similar to 'utility lines' but are not 'utility lines' are:\ttree branches\tclotheslines\tgarden hoses\tfishing lines\nThere are several useful visual features to tell there is 'utility lines' and not similar things in a photo:\toverhead\tlocation near houses or buildings\tmetallic or plastic surface\tpresence of insulators or connectors\tconnected to poles or towers\tset in a regular pattern or grid shape.", 26], "carpet floor": ["Yes. 'Carpet floor' has a tangible appearance and refers to the flooring covered with a soft fabric.\nA few things that are visually similar to 'carpet floor' but are not 'carpet floor' are:\trugs\ttile flooring\twooden flooring\tconcrete\nThere are several useful visual features to tell there is 'carpet floor' and not similar things in a photo:\tcovered with a soft fabric\tvisible fibers or individual tufts of yarn\tslightly plushy to the touch\tvariety of colors or patterns", 26], "obstacle": ["Yes. 'Obstacle' has a tangible appearance and refers to physical objects that block or hinder movement.\nA few things that are visually similar to 'obstacle' but are not 'obstacle' are:\tdecorations\tfurniture\troad signs\nThere are several useful visual features to tell there is 'obstacle' and not similar things in a photo:\tblocking or hindering movement\toccupying physical space\tforcing a change in direction\tor inhibiting progress", 26], "orange blanket": ["Yes. 'Orange blanket' has a tangible appearance and is a type of covering. \nA few things that are visually similar to 'orange blanket' but are not 'orange blanket' are:\trug\ttowel\tmat\t\nThere are several useful visual features to tell there is 'orange blanket' and not similar things in a photo:\tbright orange color\tsoft and fluffy texture\trectangular shape\ttypically used on a bed or a couch", 26], "fire escape": ["Yes. 'Fire escape' has a tangible appearance and is a type of emergency exit.\nA few things that are visually similar to 'fire escape' but are not 'fire escape' are:\tbalcony\tstaircase\tlanding\tgangway\n.There are several useful visual features to tell there is 'fire escape' and not similar things in a photo:\tladders attached to a building or a structure\texterior access to upper floors or roofs\tof metal construction with a sturdy appearance, often painted red\tor yellow", 26], "futon": ["Yes. 'Futon' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'futon' but are not 'futon' are:\tsofa bed\tdaybed\tmattress\tsleeping bag\nThere are several useful visual features to tell there is 'futon' and not similar things in a photo:\tlow to the ground\tmattress-like cushions\tfoldable frame or legs\tbackrest that can be adjusted or flattened\toutdoor or indoor use", 26], "combination": ["No. 'Combination' is too vague or abstract to be distinguished in a photo.", 26], "wood spoon": ["Yes. 'Wood spoon' has a tangible appearance and is a kind of kitchen utensil.\nA few things that are visually similar to 'wood spoon' but are not 'wood spoon' are:\tmetal spoon\tplastic spoon\tspatula\t\nThere are several useful visual features to tell there is 'wood spoon' and not similar things in a photo: made of wood\tspoon-shaped\thead and handle are not distinguished by a hinge or joint\thand-crafted appearance.", 26], "passenger seat": ["Yes. 'Passenger seat' has a tangible appearance and is a type of seat in a vehicle.\nA few things that are visually similar to 'passenger seat' but are not 'passenger seat' are:\tdriver's seat\tbench seat\tstool\tchair\nThere are several useful visual features to tell there is 'passenger seat' and not similar things in a photo:\tlocated in the front of the vehicle\tnext to the driver's seat\tmay have an airbag\tintegrated seat belt or buckle\tmay have controls for windows or air conditioning", 26], "orange circle": ["Yes. 'Orange Circle' has a tangible appearance.\nA few things that are visually similar to 'orange circle' but are not 'orange circle' are: a grapefruit, a moon, a basketball, an orange slice, a spotlight.\nThere are several useful visual features to tell there is an 'orange circle' and not similar things in a photo: it is a perfect circle; it is orange in color; it is not textured or patterned.", 26], "cuffs": ["Yes. 'Cuffs' has a tangible appearance and is a part of clothing.\nA few things that are visually similar to 'cuffs' but are not 'cuffs' are:\tbracelets\twristbands\thair ties\nThere are several useful visual features to tell there are 'cuffs' and not similar things in a photo:\tattached to sleeves\tor pants\thave buttons or zippers to fasten\tsame fabric as the garment", 26], "sweet potato": ["Yes. 'Sweet potato' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'sweet potato' but are not 'sweet potato' are:\tyam\tpotato\tbeets\tcarrots\nThere are several useful visual features to tell there is 'sweet potato' and not similar things in a photo:\tlong and cylindrical shape\twith tapered ends\tand light to dark brown or orange skin\tflesh is orange to reddish with a sweet flavor.", 26], "parka": ["Yes. 'Parka' has a tangible appearance and is a type of winter coat.\nA few things that are visually similar to 'parka' but are not 'parka' are:\tjacket\thoodie\tcoat with fur\nThere are several useful visual features to tell there is 'parka' and not similar things in a photo:\tLong coat that covers the hips or goes down to the knees\tHood that covers the head\tFur lining around the hood or inside the coat\tThick material for warmth, such as down or synthetic insulation\tZipper or buttons down the front of the coat\tOften in green or other earth tones.", 26], "blue flags": ["Yes. 'Blue flags' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'blue flags' but are not 'blue flags' are:\tiris plants\tbluebells\tforget-me-nots\nThere are several useful visual features to tell there are 'blue flags' and not similar things in a photo:\tblue or purple flowers\twith sword-like leaves\tthat grow in wet or marshy areas.", 26], "stocking cap": ["Yes. 'Stocking cap' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'stocking cap' but are not 'stocking cap' are:\tbeanie\tkippah\tskullcap\nThere are several useful visual features to tell there is 'stocking cap' and not similar things in a photo:\tcovered the scalp, ears, and neck\tknitted or crocheted material\tlong and hanging at the end", 26], "bulldog": ["Yes. 'Bulldog' has a tangible appearance and is a specific breed of dog.\nA few things that are visually similar to 'bulldog' but are not 'bulldog' are:\tpug\tcorgi\tbull terrier\nThere are several useful visual features to tell there is 'bulldog' and not similar things in a photo:\tstocky and muscular body\tbroad and flat nose\tunderbite\tfurrowed brow and wrinkles\tshort and straight tail ", 26], "cream cheese": ["Yes. 'Cream cheese' has a tangible appearance and is a type of dairy product.\nA few things that are visually similar to 'cream cheese' but are not 'cream cheese' are:\tbutter\tyogurt\tsour cream\twhipped cream\nThere are several useful visual features to tell there is 'cream cheese' and not similar things in a photo:\twhite or off-white color\tsoft and creamy texture\tusually sold in a block or tub \tspreadable consistency\tmade from milk and cream", 26], "softball": ["Yes. 'Softball' has a tangible appearance and is a type of ball used in a sport.\nA few things that are visually similar to 'softball' but are not 'softball' are:\tbaseball\tsoccer ball\tbasketball\tbowling ball\nThere are several useful visual features to tell there is 'softball' and not similar things in a photo:\tsoft and cushioned texture\tsimilar size and shape to baseball\tyellow or white color\twith red seams\tdesigned for use in softball games or practices", 26], "grey tile": ["Yes, 'grey tile' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'grey tile' but are not 'grey tile' are:\tconcrete flooring\tstone surfaces\tcement walls\tceramic plates\nThere are several useful visual features to tell there 'grey tile' and not similar things in a photo:\trectangular in shape\twith straight edges\tand right angles\tsmooth surface\tvariation in shade of grey due to lighting or reflection\tfrom a bathroom or kitchen.", 26], "power cords": ["Yes. 'Power cords' has a tangible appearance and refers to cables used to transmit electricity to appliances and electronic devices.\nA few things that are visually similar to 'power cords' but are not 'power cords' are:\taudio cables\tvideo cables\tphone chargers\tharmonic balancers \nThere are several useful visual features to tell there is 'power cords' and not similar things in a photo:\tthree-pronged plug, two-pronged plug or USB connector\tblack, white, or gray rubber or plastic coating\tthin and cylindrical shape\twith or without a transformer, adapter, switch or LED indicator.", 26], "pins": ["Yes. 'Pins' have a tangible appearance and are small, thin objects used for holding things together.\nA few things that are visually similar to 'pins' but are not 'pins' are:\tneedles\tnails\tpushpins\tthumbtacks\nThere are several useful visual features to tell there is 'pins' and not similar things in a photo:\tsmaller and thinner than needles or nails\tmade of metal or plastic\twith a flat head and a sharp or pointed end\tused for holding fabric, paper, or other materials together.", 26], "rain clouds": ["Yes. 'Rain clouds' has a tangible appearance and refers to the type of clouds that bring rain.\nA few things that are visually similar to 'rain clouds' but are not 'rain clouds' are:\twhite fluffy clouds\tsmoke from a factory or fire\tfog\tsteam\nThere are several useful visual features to tell there is 'rain clouds' and not similar things in a photo:\tdark and gloomy appearance\tgrey or black color\twith anvil-shaped tops\tor dense, layered appearance\tfound in groups or clumps", 26], "glass display case": ["Yes. 'Glass display case' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'glass display case' but are not 'glass display case' are:\tshelves\tcabinets\tbookcases\t\nThere are several useful visual features to tell there is 'glass display case' and not similar things in a photo:\tmade of glass or acrylic material\thinged door or doors\tthat can be locked or latched\tclear view of the objects inside\tit usually has a solid base to support the weight of the objects inside.", 26], "video games": ["No. 'Video games' is too vague or abstract to be distinguished in a photo. It refers to a category of electronic games that can have a wide variety of visuals.\nA few things that are visually similar to 'video games' but are not 'video games' are:\tarcade machines\tboard games\tcard games\nThere are not useful visual features to tell there is 'video games' and not similar things in a photo, as it refers to a type of electronic game that can have a diverse appearance.", 26], "orange fire": ["Yes. 'Orange fire' has a tangible appearance and is a type of flame.\nA few things that are visually similar to 'orange fire' but are not 'orange fire' are:\tcandle flame\tcampfire flame\tfireworks volcano lava flows\nThere are several useful visual features to tell there is 'orange fire' and not similar things in a photo:\torange, yellow, and red flames\twarm and flickering appearance\temanating light and heat\tsound of crackling flames\tmotion of the flame (e.g. rising, falling, flickering)", 26], "skateboard helmet": ["Yes, 'skateboard helmet' has a tangible appearance and is a protective device worn while skateboarding.\nA few things that are visually similar to 'skateboard helmet' but are not 'skateboard helmet' are:\tbicycle helmet\tmotorcycle helmet\that\tcap\nThere are several useful visual features to tell there is 'skateboard helmet' and not similar things in a photo:\thard outer shell\tprotective padding\tadjustable chin strap\tno visor or face shield reached\tout-of-the-box colorful and eye-catching design.", 26], "metal parking meter": ["Yes. 'Metal parking meter' has a tangible appearance and is a type of machine.\nA few things that are visually similar to 'metal parking meter' but are not 'metal parking meter' are:\ttrash can\ttelephone booth\tbike stand\toutdoor lamp post \nThere are several useful visual features to tell there is 'metal parking meter' and not similar things in a photo:\tcylindrical shape\tmetallic body\twith a digital or mechanical display for time or coins\thaving a coin slot or a credit card reader\thaving a parking violation ticket taped to it.", 26], "ski goggles": ["Yes. 'Ski goggles' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'ski goggles' but are not 'ski goggles' are:\tsafety goggles\tswimming goggles\tprescription glasses\tmotorcycle goggles\nThere are several useful visual features to tell there is 'ski goggles' and not similar things in a photo:\ttinted lenses\tthat fit tightly to the face\telastic strap to hold them on\tthe ability to fit over a helmet or hat", 26], "pink toothbrush": ["Yes. 'Pink toothbrush' has a tangible appearance and is a type of hygiene product.\nA few things that are visually similar to 'pink toothbrush' but are not 'pink toothbrush' are:\tpink hairbrush\tpaintbrush\tpink comb\nThere are several useful visual features to tell there is 'pink toothbrush' and not similar things in a photo:\tusually made of plastic with a handle and bristles\tthin and elongated shape\tbristles arranged in rows on the end of the brush\tpink in color", 26], "silver cars": ["Yes. 'Silver cars' has a tangible appearance and is a specific type of vehicle.\nA few things that are visually similar to 'silver cars' but are not 'silver cars' are:\ngrey cars\nmetallic-looking objects\naluminum foil\n\nThere are several useful visual features to tell there is 'silver cars' and not similar things in a photo:\nthe vehicle has a silver or gray metal finish\nthe car model can be identified as a known make and model\nit has wheels, headlights, windows, and doors as expected for a car", 26], "stone bridge": ["Yes, 'stone bridge' has a tangible appearance and is a type of bridge made of stone.\nA few things that are visually similar to 'stone bridge' but are not 'stone bridge' are:\twooden bridge\tconcrete bridge\tarch bridge\tsuspension bridge\nThere are several useful visual features to tell there is 'stone bridge' and not similar things in a photo:\tmade of stone or rocks\thave arches or other decorative elements\tmay have railings\tor other ornamentation #+#", 26], "brunette hair": ["Yes. 'Brunette hair' has a tangible appearance and is a color of hair.\nA few things that are visually similar to 'brunette hair' but are not 'brunette hair' are:\tblack hair\tdark brown hair\tchestnut hair\nThere are several useful visual features to tell there is 'brunette hair' and not similar things in a photo:\tdark brown in color\tmedium to thick strands\tshine under direct light", 26], "blue square": ["Yes. 'Blue square' has a tangible appearance and is a specific shape and color.\nA few things that are visually similar to 'blue square' but are not 'blue square' are:\tblue rectangle\tblue circle\tblue triangle\tmosaic pattern\nThere are several useful visual features to tell there is 'blue square' and not similar things in a photo:\ta shape with four straight sides of equal length and four right angles\ta solid blue color without patterns or variations\tin a flat or two-dimensional presentation", 26], "brown bench": ["Yes. 'Brown bench' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'brown bench' but are not 'brown bench' are:\tchair\tsofa\tstool\nThere are several useful visual features to tell there is 'brown bench' and not similar things in a photo:\tlong seat\tfor more than one person\tbackrest\tarmrests\tfor outdoor or indoor use", 26], "basketball court": ["Yes. 'Basketball court' has a visually concrete appearance and is a sports facility.\nA few things that are visually similar to 'basketball court' but are not 'basketball court' are:\ttennis court\tvolleyball court\tbadminton court\tplayground\nThere are several useful visual features to tell there is 'basketball court' and not similar things in a photo:\torange-colored rectangular court with lines\tcircular hoop with a net on each side of the court", 26], "foot print": ["Yes. 'Foot print' has a tangible appearance and is a mark left by a foot on a surface.\nA few things that are visually similar to 'foot print' but are not 'foot print' are:\tpaw print\ttire tracks\thand print\ttrail in the sand\nThere are several useful visual features to tell there is 'foot print' and not similar things in a photo:\ttoe impressions\tarch of the foot impressions\theel impression\tsize and shape of the print\tdirection of the print (i.e. walking or running)", 26], "silver earring": ["Yes. 'Silver earring' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'silver earring' but are not 'silver earring' are:\tearring of a different metal\tearring with a different gemstone\tpiercing ring\ttiny hair clip\nThere are several useful visual features to tell there is 'silver earring' and not similar things in a photo:\tsilver color\thard and shiny metal material\thook or stud design\tworn on the earlobe or cartilage", 26], "transformer": ["Yes. 'Transformer' has a tangible appearance and is a type of electrical device.\nA few things that are visually similar to 'transformer' but are not 'transformer' are:\tpower supply unit\tcircuit breaker\tswitch\tadapter\nThere are several useful visual features to tell there is 'transformer' and not similar things in a photo:\twinding coils of wire\tiron core\torbiting planets appearance\tmultiple wires or connections\telectrical markings, such as voltage or current ratings.", 26], "chrome bathroom": ["Yes. 'Chrome bathroom' has a tangible appearance and refers to a bathroom with chrome finish.\nA few things that are visually similar to 'chrome bathroom' but are not 'chrome bathroom' are:\tbathroom with stainless steel finish\tbathroom with silver finish\tbathroom with metal finish\nThere are several useful visual features to tell there is 'chrome bathroom' and not similar things in a photo:\tchrome finish on the faucets, handles, showerheads, etc.\tchrome finish on the towel racks or hangers\tchrome finish on the mirror or other accessories", 26], "crossing": ["Yes. 'Crossing' has a tangible appearance and it refers to a place where people or vehicles can cross a road.\nA few things that are visually similar to 'crossing' but are not 'crossing' are:\ttraffic circle\troundabout\tintersection\tjunction\nThere are several useful visual features to tell there is 'crossing' and not similar things in a photo:\tstriped pedestrian lines\tcrosswalk sign or traffic lights\tpeople or vehicles crossing the road\tsafe crossing point designated by local authorities", 26], "river bank": ["Yes. 'River bank' has a tangible appearance and is the land along the edges of a river.\nA few things that are visually similar to 'river bank' but are not 'river bank' are:\tbeach\tshoreline\tcliff\tbayou\nThere are several useful visual features to tell there is 'river bank' and not similar things in a photo:\tsoil\tor sand, soil, or rocks\tbushes, trees, and other plants\trising banks by the water currents", 26], "video": ["No. 'Video' is too vague or abstract to have a tangible appearance. \n\nHowever, a few things that are visually similar to the concept of 'video' but are not 'video' are: movie screens, television screens, computer monitors, cameras. \n\nUseful visual features for distinguishing a video would be: a moving image or animation, visual effects, synchronized sound, etc.", 26], "left sneaker": ["Yes. 'Left sneaker' has a tangible appearance and is a type of shoe.\nThere are no things that are visually similar to 'left sneaker' but are not 'left sneaker'.\nA useful visual feature to distinguish a 'left sneaker' from other shoes in a photo is the location of the shoe on the left side of the image or on the left foot of a person. Additionally, the design and shape of the sneaker can also be useful in distinguishing it from other types of shoes.", 26], "trackpad": ["Yes. 'Trackpad' has a tangible appearance and is a type of computer input device.\nA few things that are visually similar to 'trackpad' but are not 'trackpad' are:\tmouse\tkeyboard\tlaptop\tscreen\nThere are several useful visual features to tell there is 'trackpad' and not similar things in a photo:\trectangular shape\tsmooth surface\tforward, backward, and side-to-side scrolling\ttwo-finger tap or scrolling\toption to use gestures (pinch, zoom, swipe)", 26], "mayo": ["Yes. 'Mayo' has a tangible appearance and is a kind of sauce or spread.\nA few things that are visually similar to 'mayo' but are not 'mayo' are:\tsour cream\tcream cheese\tgreek yogurt\nThere are several useful visual features to distinguish 'mayo' from the listed similar things in a photo:\twhite or cream in color\tcreamy, spreadable texture\tglossy appearance\ttypically packaged in a jar\tor squeeze bottle with the word \"mayo\" or \"mayonnaise\" on the label", 26], "potato salad": ["Yes. 'Potato salad' has a tangible appearance and is a type of dish.\nA few things that are visually similar to 'potato salad' but are not 'potato salad' are:\tmashed potatoes\tmacaroni salad\tcoleslaw\tegg salad\nThere are several useful visual features to tell there is 'potato salad' and not similar things in a photo:\tdiced or sliced potatoes\tmayonnaise dressing\tmustard\topaque and yellowish appearance\twith or without other ingredients (e.g. eggs, pickles)", 26], "construction sign": ["Yes. 'Construction sign' has a tangible appearance and is a specific type of sign.\nA few things that are visually similar to 'construction sign' but are not 'construction sign' are:\ttraffic signs\tdirection signs\tadvertisements\tbillboards\nThere are several useful visual features to tell there is 'construction sign' and not similar things in a photo:\tbright orange or yellow color\ttext or symbols indicating construction work or hazards\tusually has a tall and narrow shape on a stand.", 26], "cup holder": ["Yes. 'Cup holder' has a tangible appearance and is a device used to hold a cup or mug.\nA few things that are visually similar to 'cup holder' but are not 'cup holder' are:\tbowl\tflower pot\tvase\tpen-holder\tashtray\nThere are several useful visual features to tell there is 'cup holder' and not similar things in a photo:\tcylindrical shape\tcompact size\tfits a cup or mug\tsturdy material\tgripping or holding features\tcan be attached to a car or a piece of furniture", 26], "round button": ["Yes. 'Round button' has a tangible appearance and is a type of small, circular object used for pressing or decoration.\nA few things that are visually similar to 'round button' but are not 'round button' are:\tcoins\tcandy\twatches\tbottle caps\nThere are several useful visual features to tell there is 'round button' and not similar things in a photo:\tcircular or round shape\tsmall size\tflat surface\tbordered by a rim or edge\ttypically made of plastic or metal has a design or text on it", 26], "orange part": ["Yes. 'Orange part' has a tangible appearance and refers to a part of an orange fruit.\nA few things that are visually similar to 'orange part' but are not 'orange part' are:\torange peel\torange pulp\torange seed\tlemon part\tapple part\nThere are several useful visual features to tell there is 'orange part' and not similar things in a photo:\tsegmented parts\tof a bright orange color\televated from the surface of the fruit\tdrier texture than the pulp inside the fruit.", 26], "foggy sky": ["Yes. 'Foggy sky' has a tangible appearance and is a meteorological condition.\nA few things that are visually similar to 'foggy sky' but are not 'foggy sky' are:\tcloudy sky\tsmoky sky\thazy sky\tstormy sky\nThere are several useful visual features to tell there is 'foggy sky' and not similar things in a photo:\tthick mist or fog covering a significant part of the sky\tpoor visibility and reduced contrast between objects in the distance\ttrees or buildings partially or completely obscured by the foggy sky.", 26], "round frisbee": ["Yes. 'Round frisbee' has a tangible appearance and is a type of flying disc.\nA few things that are visually similar to 'round frisbee' but are not 'round frisbee' are:\tplate\tpizza\tcircular saw blade\tbottle cap\t\nThere are several useful visual features to tell there is 'round frisbee' and not similar things in a photo:\tthin and flat\tdomed in shape\twith smooth edges\tmade of plastic or rubber\thas ridges or texture on the surface", 26], "iron pole": ["Yes. 'Iron pole' has a tangible appearance and is a vertical cylindrical metal structure.\nA few things that are visually similar to 'iron pole' but are not 'iron pole' are:\tfencing post\tmetal pipes\tstreet lights\ttraffic sign posts\nThere are several useful visual features to tell there is 'iron pole' and not similar things in a photo:\tvertical and cylindrical shape\tmade of iron or steel (metal)\ttapered or uniform in size\tsupporting or holding something in place", 26], "flakes": ["Yes. 'Flakes' has a tangible appearance and is a kind of particle.\nA few things that are visually similar to 'flakes' but are not 'flakes' are:\tdust\tpollen\tsand\tash\nThere are several useful visual features to tell there are 'flakes' and not similar things in a photo:\tfeathery or crystalline shape\twhite or light-colored\tcolorless background\tsnowflakes might have six points while other types of flakes might have other shapes or numbers of points.", 26], "bus doors": ["Yes. 'Bus doors' has a tangible appearance and is a part of the bus.\nA few things that are visually similar to 'bus doors' but are not 'bus doors' are:\tcar doors\televator doors\tgarage doors\tautomatic doors\nThere are several useful visual features to tell there are 'bus doors' and not similar things in a photo:\tslide to open\tclose to form a rectangle\thave a window or a mirror attached to them\tmay have a handle or button to open and close them\tdirected towards the outside of the bus\twide enough to let passengers enter and exit the bus", 26], "pet": ["Yes. 'Pet' has a tangible appearance and refers to an animal that is kept by humans as a companion.\n\nA few things that are visually similar to 'pet' but are not 'pet' are:\twild animals\tfarm animals\tzoo animals\ttaxidermy specimens\t\nThere are several useful visual features to tell there is 'pet' and not similar things in a photo:\tclose proximity to human(s)\tcollars or leashes\thuman-generated environment\tindoor or outdoor setting\tfrisky behavior towards humans", 26], "ten": ["No. 'Ten' is too abstract to be visually distinguished in a photo.", 26], "building window": ["Yes. 'Building window' has a tangible appearance and is a part of a building structure.\nA few things that are visually similar to 'building window' but are not 'building window' are:\tpicture frame\ttv screen\tdisplay monitor\nThere are several useful visual features to tell there is 'building window' and not similar things in a photo:\trectangular or square shape\twith a frame or border, made of metal or wood\ttranslucent or transparent, made of glass or plexiglass\tpart of a larger building or structure, with walls on either side", 26], "flower box": ["Yes. 'Flower box' has a tangible appearance and is a type of container for plants.\nA few things that are visually similar to 'flower box' but are not 'flower box' are:\tpots\tbaskets\tvases\tbookshelves\nThere are several useful visual features to tell there is 'flower box' and not similar things in a photo:\trectangular or square shape\twith or without handles\tlarge enough to hold several plants\tfill with soil or moss\tcan be mounted or placed on a windowsill", 26], "brown dirt": ["Yes. 'Brown dirt' has a tangible appearance.\nA few things that are visually similar to 'brown dirt' but are not 'brown dirt' are:\tchocolate powder\tcocoa powder\tbrown sugar\nThere are several useful visual features to tell there is 'brown dirt' and not similar things in a photo:\tnaturally occurring on the ground\tgranular texture\tvariations in color and shade (some might be reddish or yellowish-brown)", 26], "turban": ["Yes. 'Turban' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'turban' but are not 'turban' are:\theadbands\tbandanas\tscarves\t\nThere are several useful visual features to tell there is 'turban' and not similar things in a photo:\twrapped around the head and hair\tmade of a long, fabric cloth\ttypically worn by men in some cultures\tmay feature decorative embroidery or jewels", 26], "giraffes tail": ["Yes. 'Giraffe's tail' has a tangible appearance and is a part of a specific animal.\nA few things that are visually similar to 'giraffe's tail' but are not 'giraffe's tail' are:\thorses tail\tzebra's tail\nThere are several useful visual features to tell there is 'giraffe's tail' and not similar things in a photo:\tlong and thin\twith a tuft of black hair at the end\tthe tail sticks straight up or sticks out horizontally", 26], "window shutter": ["Yes. 'Window shutter' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'window shutter' but are not 'window shutter' are:\tblinds\tcurtains\tdoors\tfences\nThere are several useful visual features to tell there is 'window shutter' and not similar things in a photo:\tfixed to the sides of a window\tconstructed with slats or louvers\table to open and close\thorizontal or vertical orientation\trectangular or square shape\tmade of wood, metal, or vinyl", 26], "metal bucket": ["Yes. 'Metal bucket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'metal bucket' but are not 'metal bucket' are:\ttin can\tpaint can\tcopper jug\twatering can\nThere are several useful visual features to tell there is 'metal bucket' and not similar things in a photo:\tmade of metal\twith a round or oval shape\twith a handle attached to its sides or the rim.", 26], "bidet": ["Yes. 'Bidet' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'bidet' but are not 'bidet' are:\tregular toilet\tsink\tshower\tbathtub\nThere are several useful visual features to tell there is 'bidet' and not similar things in a photo:\tlow-to-the-floor basin or bowl\tvertical water stream\ttoilet-like appearance\tseparate fixture from the toilet", 26], "farm house": ["Yes. 'Farm house' has a tangible appearance and is a type of house located on a farm.\nA few things that are visually similar to 'farm house' but are not 'farm house' are:\tcottage\tcabin\tbarn\tranch house\nThere are several useful visual features to tell there is 'farm house' and not similar things in a photo:\tlarge property or land around the house\twooden walls and shutters\tasymmetrical design\tchimney or smokestacks\trooftop dormer windows\tporch or veranda with rocking chairs or swings.", 26], "dogs eye": ["Yes. 'Dogs eye' has a tangible appearance and can refer to the physical eye of a canine.\nA few things that are visually similar to 'dogs eye' but are not 'dogs eye' are:\tcat's eye\thuman's eye\tcow's eye\nThere are several useful visual features to tell there is 'dogs eye' and not similar things in a photo:\toval-shaped eye\twith a reflective layer that makes the eye shine in the light\tvariety of eye colors, including brown, blue, and green, depending on the breed and individual dog\tsometimes the dogs have droopy eyelids which can be noticeable in the photos.", 26], "landing wheels": ["Yes. 'Landing wheels' has a tangible appearance and is a part of an airplane.\nA few things that are visually similar to 'landing wheels' but are not 'landing wheels' are:\tbicycle wheels\tcar wheels\ttruck wheels\nThere are several useful visual features to tell there is 'landing wheels' and not similar things in a photo:\tretractable\tdesign for efficient landing and takeoff\ttread\thydraulic system.", 26], "construction worker": ["Yes. 'Construction worker' has a tangible appearance and is a type of worker in the construction industry.\nA few things that are visually similar to 'construction worker' but are not 'construction worker' are:\tfactory worker\tminer\twarehouse worker\tmechanic\nThere are several useful visual features to tell there is 'construction worker' and not similar things in a photo:\thard hat\tvest\tsteel-toed boots\ttool belt or pouch\tworking at a construction site, such as a building or a road\twork gloves and safety glasses", 26], "wood box": ["Yes. 'Wood box' has a tangible appearance and refers to a container made of wood.\nA few things that are visually similar to 'wood box' but are not 'wood box' are:\twood crate\tbasket\tbin\tbookshelf\nThere are several useful visual features to tell there is 'wood box' and not similar things in a photo:\tmade of wood\trectangular or cubic shape\thinged lid or removable top\tsides made of wooden planks or slats with visible seams\tor rough texture.", 25], "glob": ["No. 'Glob' is too vague or abstract to be distinguished in a photo. It could refer to a sphere or ball-like object, but without additional context, it is not clear what is meant by this term.", 25], "round windows": ["Yes. 'Round windows' has a tangible appearance.\nA few things that are visually similar to 'round windows' but are not 'round windows' are:\tcircular mirrors\tportholes\teyeglasses\t\nThere are several useful visual features to tell there is 'round windows' and not similar things in a photo:\tcircular shape\twith or without frames\tcan be opened or not\tbright or clear glass or material\tsituated on walls, doors, or ceilings.", 25], "web address": ["No. 'Web address' is too abstract to have a tangible appearance.\nA few things that are visually similar to 'web address' but are not 'web address' are:\ttext messages, e-mails, phone numbers, physical addresses\nThere are no useful visual features to distinguish 'web address' from the listed similar things in a photo, as web addresses are not something that can be visually identified. They are a combination of text characters, often beginning with \"http://\" or \"www.\"", 25], "camera strap": ["Yes. 'Camera strap' has a tangible appearance and is a type of strap used to carry a camera around the neck or shoulder.\nA few things that are visually similar to 'camera strap' but are not 'camera strap' are:\tbackpack strap\tpurse strap\tbag strap\tbelt\tsuspenders\tlaptop bag strap\tkeychain\nThere are several useful visual features to tell there is 'camera strap' and not similar things in a photo:\tattached to a camera\tusually in black, brown or grey\tcolorful patterns and designs\tcan be adjustable\tcan be made with various materials (leather, nylon, etc.)\thas a shoulder pad for comfortability", 25], "range hood": ["Yes. 'Range hood' has a tangible appearance and is a kitchen appliance.\nA few things that are visually similar to 'range hood' but are not 'range hood' are:\tchimney\tfan\tair purifier\nThere are several useful visual features to tell there is 'range hood' and not similar things in a photo:\tinstalled above the cooktop or stove\tattached to a wall or ceiling\thas vents or exhaust fan to remove smoke, steam and odors\tcanopy or hood-shaped appearance\tmetallic appearance or finish.", 25], "mother zebra": ["Yes. 'Mother zebra' has a tangible appearance and is a specific type of zebra.\nA few things that are visually similar to 'mother zebra' but are not 'mother zebra' are:\tzebra\tfemale horse\tother type of striped animal\nThere are several useful visual features to tell there is 'mother zebra' and not similar things in a photo:\tblack and white stripes\ton the African savannah with other zebras\tbigger and more muscular than other female zebras\twith a foal next to her", 25], "horse hooves": ["Yes. 'Horse hooves' has a tangible appearance and is a part of a horse's anatomy.\nA few things that are visually similar to 'horse hooves' but are not 'horse hooves' are:\tcow hooves\tbuffalo hooves\tdeer hooves\nThere are several useful visual features to tell there is 'horse hooves' and not similar things in a photo:\tfour hoofed toes\ton a horse's leg\twith or without horse shoes", 25], "stitching": ["Yes. 'Stitching' has a tangible appearance and involves joining pieces of fabric or other materials with stitches.\nA few things that are visually similar to 'stitching' but are not 'stitching' are:\tprinted lines\tor paint strokes\tor drawn lines on a surface or fabric.\nThere are several useful visual features to tell there is 'stitching' and not similar things in a photo:\tvisible thread\tmultiple loops of thread forming a pattern\ton fabric or material joining fabric or material pieces", 25], "silver band": ["Yes. 'Silver band' has a tangible appearance and is a type of jewelry or accessory.\nA few things that are visually similar to 'silver band' but are not 'silver band' are:\twedding ring\twristwatch\tbelt\tbracelet\nThere are several useful visual features to tell there is 'silver band' and not similar things in a photo:\tmade of silver or silver-colored metal\tsimple band shape\tworn on a finger or a toe (if a toe ring)\tno decorative accents or stones", 25], "seasonings": ["No. 'Seasonings' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to certain types of seasonings but are not 'seasonings' are:\n\n- Sugar: can look similar to salt or other white powders\n- Colored sprinkles: can look similar to spices like paprika or chili flakes\n- Flour: can look similar to cornstarch or other powdery substances\n\nUseful visual features for distinguishing 'seasonings' would depend on the specific type of seasoning, but generally could include:\n\n- Texture (e.g. coarse salt vs. fine sugar)\n- Color (e.g. bright red paprika vs. pale flour)\n- Packaging (e.g. spices often come in small jars or containers with labels, while other powders may come in larger bags)", 25], "bathrobe": ["Yes. 'Bathrobe' has a tangible appearance and is a kind of garment.\nA few things that are visually similar to 'bathrobe' but are not 'bathrobe' are:\tcoat\tponcho\tshawl\tblanket\nThere are several useful visual features to tell there is 'bathrobe' and not similar things in a photo:\tloose fitting clothing\tworn after bathing or swimming\topen front\tattached belt or sash\tusually made of terry cloth or a warm, soft fabric", 25], "round vase": ["Yes. 'Round vase' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'round vase' but are not 'round vase' are: pitcher\tjar\tbowl\tcup\nThere are several useful visual features to tell there is 'round vase' and not similar things in a photo:\tcylindrical or round shape, narrow neck\tmade of glass, ceramic or pottery material\tdesigned to hold flowers or plants\topening at the top and bottom for flowers and water respectively.", 25], "orange vegetables": ["Yes. 'Orange vegetables' has a tangible appearance and includes a variety of vegetables that are orange in color.\nA few things that are visually similar to 'orange vegetables' but are not 'orange vegetables' are:\torange fruit\tpumpkin\tcarrots\tpapaya\nThere are several useful visual features to tell there are 'orange vegetables' and not similar things in a photo:\torange in color\tsmooth skin or outer layer\tedible\tfleshy parts inside\tthe shape can vary", 25], "orange spot": ["Yes. 'Orange spot' has a tangible appearance and is a specific type of visual mark.\nA few things that are visually similar to 'orange spot' but are not 'orange spot' are:\tyellow spot\tred spot\tpink spot\torange circle\nThere are no further useful visual features for distinguishing 'orange spot' from the listed similar things in a photo as it is a very specific and unique visual characteristic.", 25], "birds eye": ["Yes. 'Bird's eye' has a tangible appearance and refers to a view from above, like a bird would see.\nA few things that are visually similar to 'bird's eye' but are not 'bird's eye' are:\tgoogle maps\tstreet views\thelicopter view\tdrone image\nThere are several useful visual features to tell there is 'bird's eye' and not similar things in a photo:\tview from above\tangle of the shot\tview of various objects\tfrom a distance\ttop-down perspective.", 25], "chefs": ["Yes. 'Chefs' has a tangible appearance and refers to professionals trained in cooking.\nA few things that are visually similar to 'chefs' but are not 'chefs' are: cooks, waiters, bartenders, dishwashers.\nThere are several useful visual features to tell there are 'chefs' and not similar things in a photo:\twearing a chef hat, jacket, and apron\thaving utensils like knives, wooden spoons, and whisks\tpreparing food in a professional kitchen\tdisplaying technical skills like chopping or saut\u00e9ing.", 25], "cookie sheet": ["Yes. 'Cookie sheet' has a tangible appearance and is a type of bakeware.\nA few things that are visually similar to 'cookie sheet' but are not 'cookie sheet' are:\tbaking pan\tcake pan\toven rack\t\nThere are several useful visual features to tell there is 'cookie sheet' and not similar things in a photo:\tflat surface\twith raised edges\tmade of metal or non-stick material\ttypically rectangular or square shape", 25], "business card": ["Yes. 'Business card' has a tangible appearance and is a tangible item.\nA few things that are visually similar to 'business card' but are not 'business card' are:\tcredit card\tlibrary card\tID card\tgift card\nThere are several useful visual features to tell there is 'business card' and not similar things in a photo:\tstandard size (3.5 x 2 inches)\trectangular shape\tthick paper or cardstock material\tbusiness information printed on it (name, phone, email, address, logo, etc.)", 25], "key board": ["Yes. 'Keyboard' has a tangible appearance and refers to a set of keys on a musical instrument or a computer.\nA few things that are visually similar to 'keyboard' but are not 'keyboard' are:\tpiano\tkeys on a typewriter buttons on a telephone or calculator\nThere are several useful visual features to tell there is 'keyboard' and not similar things in a photo:\tarray of buttons or keys\tletters, numbers, symbols on the keys\thorizontal layout of the keys\tfunction keys and arrow keys above the main keys", 25], "cyclists": ["Yes. 'Cyclists' has a tangible appearance and refers to people riding bicycles.\nA few things that are visually similar to 'cyclists' but are not 'cyclists' are:\tskateboarders\trollerbladers\tmotorcyclists\nThere are several useful visual features to tell there is 'cyclists' and not similar things in a photo:\triding a bicycle\ttwo-wheeled vehicle\tpedaling\tbike helmet, bike shoes, or spandex clothing (common cycling accessories)", 25], "pink top": ["Yes. 'Pink top' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pink top' but are not 'pink top' are:\tred shirt\tpink dress\torange blouse\tmagenta sweater\nThere are several useful visual features to tell there is 'pink top' and not similar things in a photo:\tpink in color\tno collar, lapel, or buttons\tonly covers the upper body\tshort or long sleeves\tno patterns or designs", 25], "grassy landscape": ["Yes. 'Grassy landscape' has a tangible appearance and is a type of scenery.\nA few things that are visually similar to 'grassy landscape' but are not 'grassy landscape' are:\tforests\tfields\tof crops\nThere are several useful visual features to tell there is 'grassy landscape' and not similar things in a photo:\ta wide expanse of green grass\trolling hills\tor flat prairies\twithout many trees or other vegetation\tsometimes dotted with flowers or rocks.", 25], "ear buds": ["Yes. 'Ear buds' has a tangible appearance and is a type of headphones.\nA few things that are visually similar to 'ear buds' but are not 'ear buds' are:\tearrings\ttoothbrush heads\thearing aids\nThere are several useful visual features to tell there is 'ear buds' and not similar things in a photo:\tconnected by a single wire\tor completely wireless\tround in shape\twith soft silicone tips that fit into the ear canal\tsmall in size with flattened cables", 25], "contrails": ["Yes. 'Contrails' have a tangible appearance and are a type of visible line formed by the passage of an aircraft.\nA few things that are visually similar to 'contrails' but are not 'contrails' are:\tclouds\tfog\tsmoke\tdust\nThere are several useful visual features to tell there are 'contrails' and not similar things in a photo:\tstraight lines, often parallel\tpersist for longer than similar-looking clouds\torbiting around an area where there is air traffic", 25], "control buttons": ["Yes. 'Control buttons' has a tangible appearance and refer to buttons on a device used to control its functions.\nA few things that are visually similar to 'control buttons' but are not 'control buttons' are:\tdecorative buttons\ton/off switches\tknobs\tforward/backward buttons\nThere are several useful visual features to tell there is 'control buttons' and not similar things in a photo:\tsmall in size\tmultiple buttons\tintricate shapes/icons\tusually arranged in a systematic and organized way", 25], "parasailer": ["Yes. 'Parasailer' has a tangible appearance and is a type of recreational activity.\nA few things that are visually similar to 'parasailer' but are not 'parasailer' are:\tkite surfer\thang glider\tskydiver\tbirds\nThere are several useful visual features to tell there is 'parasailer' and not similar things in a photo:\tparachute or canopy strapped to a person\tor a boat\tglider-like flying through the air\tor over the water", 25], "purple coat": ["Yes. 'Purple coat' has a tangible appearance and is a specific type of clothing item.\nA few things that are visually similar to 'purple coat' but are not 'purple coat' are:\tpurple sweater\tpurple shirt\tpurple dress\tpurple jacket\nThere are several useful visual features to tell there is 'purple coat' and not similar things in a photo:\touterwear\titem that covers the upper body\tlong sleeves\tbuttoned or zipped in the front\tpurple color", 25], "side car": ["Yes. 'Side car' has a tangible appearance and is an attachment to a motorcycle.\nA few things that are visually similar to 'side car' but are not 'side car' are:\ttrailer\tbicycle basket\tbaby stroller\twagon\nThere are several useful visual features to tell there is 'side car' and not similar things in a photo:\tattached to a motorcycle\tone wheel, two-wheeled, or three-wheeled vehicle\tpassenger seat with a separate ride for the driver\topen or enclosed structure on the side of the motorcycle ", 25], "swell": ["No. 'Swell' is too vague or abstract to be distinguished in a photo.", 25], "mouths": ["Yes. 'Mouths' have a tangible appearance and are body parts.\nA few things that are visually similar to 'mouths' but are not 'mouths' are:\topenings\tholes\thatch\tducts\nThere are several useful visual features to tell there are 'mouths' and not similar things in a photo:\tpair of lips\twith teeth or without teeth\tfor speaking, eating, or breathing", 25], "ski slope": ["Yes. 'Ski slope' has a tangible appearance and is a type of terrain.\nA few things that are visually similar to 'ski slope' but are not 'ski slope' are:\thiking trails\tgrass-covered hills\tdirt mounds\tbike trails\tsandy dunes\nThere are several useful visual features to tell there is 'ski slope' and not similar things in a photo:\tsnow-covered\tmultiple runs or trails\twith ski lifts or gondolas along the side", 25], "bath towels": ["Yes. 'Bath towels' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'bath towels' but are not 'bath towels' are:\thand towels\tbeach towels\ttablecloth\nThere are several useful visual features to tell there is 'bath towels' and not similar things in a photo:\trectangular in shape\trelatively large in size\tusually made of absorbent cotton or terry cloth\tfolded or rolled up\thanging in a bathroom setting", 25], "bedsheets": ["Yes. 'Bedsheets' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'bedsheets' but are not 'bedsheets' are:\ttablecloth\tcurtains\ttowels\tblankets\nThere are several useful visual features to tell there are 'bedsheets' and not similar things in a photo:\trectangular-shaped fabric\tlarge enough to cover a bed\toften patterned or colored\twith pillowcases or shams to match\tcrinkled appearance when not stretched over the bed", 25], "toilet bowl brush": ["Yes. 'Toilet bowl brush' has a tangible appearance and is a type of cleaning tool.\nA few things that are visually similar to 'toilet bowl brush' but are not 'toilet bowl brush' are:\thairbrush\tpaintbrush\tscrub brush\nThere are several useful visual features to tell there is 'toilet bowl brush' and not similar things in a photo:\tlong plastic or metal handle\tbrush with stiff nylon or wire bristles\ttapered shape\tdark color for practical reasons", 25], "pink ear": ["Yes, 'pink ear' has a tangible appearance and is a physical feature of an organism.\nA few things that are visually similar to 'pink ear' but are not 'pink ear' are: red ear, blue ear, green ear, yellow ear.\nThere are no similar things like pink ears that may lead to confusion. The useful visual features for distinguishing the pink ear from other colored ears would be the pink color of the ear itself, which could differ in tone and intensity based on the lighting conditions, angle, and distance of the subject in the photo.", 25], "wooden window": ["Yes. 'Wooden window' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'wooden window' but are not 'wooden window' are:\tpicture frame\tdoors\twooden shutters\twooden planks\nThere are several useful visual features to tell there is 'wooden window' and not similar things in a photo:\tconsist of an openable panel of glass\tpart of a larger structure\tframes can have decorative elements, such as muntins, mullions or sashes\tmay have hardware, such as hinges or locks.", 25], "chart": ["Yes. 'Chart' has a tangible appearance and is a type of visual representation of data.\nA few things that are visually similar to 'chart' but are not 'chart' are:\tgraph\ttable\tdiagram\tmap\nThere are several useful visual features to tell there is 'chart' and not similar things in a photo:\tpictorial or graphical representation of data\tlabels, titles, and legends\taxises and shapes representing data points", 25], "pink stripe": ["Yes. 'Pink stripe' has a tangible appearance and is a specific pattern.\nA few things that are visually similar to 'pink stripe' but are not 'pink stripe' are:\tpink checkerboard\tpink plaid\tpink polka dots\nThere are several useful visual features to tell there is 'pink stripe' and not similar things in a photo:\tlong, narrow strip\talternating color with pink\tpink is the dominant color", 25], "crosswalk lines": ["Yes. 'Crosswalk lines' has a tangible appearance and is a marking on the road.\nA few things that are visually similar to 'crosswalk lines' but are not 'crosswalk lines' are:\tyellow lines\twhite lines\tparking spots\tbike lanes\nThere are several useful visual features to tell there are 'crosswalk lines' and not similar things in a photo:\twhite lines on the road\tzigzag pattern for pedestrians\tcrossing signs or lights near the lines", 25], "mirror car": ["No. 'Mirror car' is too vague or abstract to be distinguished in a photo. It is unclear what is meant by a mirror car.", 25], "hotel sign": ["Yes. 'Hotel sign' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'hotel sign' but are not 'hotel sign' are:\tmotel sign\trestaurant sign\tgas station sign\tcafe sign\nThere are several useful visual features to tell there is 'hotel sign' and not similar things in a photo:\tword 'hotel' or 'inn' clearly written\ton a large sign, usually high in the air\tbright lights, often in the shape of an arrow or welcoming imagery", 25], "algae": ["Yes. 'Algae' has a tangible appearance and is a type of aquatic plant.\nA few things that are visually similar to 'algae' but are not 'algae' are:\tmoss\tseaweed\tlichen\tdirt\nThere are several useful visual features to tell there is 'algae' and not similar things in a photo:\tgreen, brown, or red slimy or stringy mats, films, or spots\tin water or on wet surfaces\tsingle-celled or multi-cellular sometimes forming colonies\tmay have distinctive shapes or patterns under a microscope", 25], "tan chair": ["Yes. 'Tan chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'tan chair' but are not 'tan chair' are:\tsofa\tottoman\trecliner\tbench\nThere are several useful visual features to tell there is 'tan chair' and not similar things in a photo:\tseat and backrest for one person\tarmrests\tvarious shades of tan or beige\tupholstered, leather, or wooden surface\tsimple and sleek design.", 25], "mattresses": ["Yes. 'Mattresses' has a tangible appearance and is a type of sleeping surface.\nA few things that are visually similar to 'mattresses' but are not 'mattresses' are:\tpillows\tcushions\tblankets\t\nThere are several useful visual features to tell there is 'mattresses' and not similar things in a photo:\trectangular shape\tpadded surface with fabric cover\tseams along the edges\tthickness (thicker than a pillow or cushion)\tvarious sizes and colors", 25], "canoes": ["Yes. 'Canoes' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'canoes' but are not 'canoes' are:\tkayaks\trowboats\tpaddleboards\trafts\t\nThere are several useful visual features to tell there is 'canoes' and not similar things in a photo:\tnarrow and long boat\tpointed at both ends\tusually made of wood or fiberglass\twith 1-3 seats\ta paddle for each person\tseats may have backs or be simple benches open top compared to kayaks", 25], "metal spatula": ["Yes. 'Metal spatula' has a tangible appearance and is a kitchen utensil.\nA few things that are visually similar to 'metal spatula' but are not 'metal spatula' are:\tturner\tfryer\tbarbecue tong\tfish slice\nThere are several useful visual features to tell there is 'metal spatula' and not similar things in a photo:\tlong and flat\ttapered or rounded end\tmetal material\tfor flipping or lifting food\tfrom a set of other kitchen utensils.", 25], "silver bar": ["Yes. 'Silver bar' has a tangible appearance and is a specific type of precious metal.\nA few things that are visually similar to 'silver bar' but are not 'silver bar' are:\tsteel bar\tiron bar\tpewter bar\taluminum bar\nThere are several useful visual features to tell there is 'silver bar' and not similar things in a photo:\tmetallic\tsilver color\tretangular shape\twith weight measurements\tstamped with purity and weight information", 25], "stereo speaker": ["Yes. 'Stereo speaker' has a visually concrete appearance and is a kind of audio device.\nA few things that are visually similar to 'stereo speaker' but are not 'stereo speaker' are:\tmegaphone\talarm clock\tdecorative box\nThere are several useful visual features to tell there is 'stereo speaker' and not similar things in a photo:\tdriver cone\ttweeter dome\tgrill\tbass port or reflex tube\tlocalized sound source\tmultiple drivers or speakers in a single cabinet", 25], "horse statue": ["Yes. 'Horse statue' has a tangible appearance and is a sculpture of a horse.\nA few things that are visually similar to 'horse statue' but are not 'horse statue' are:\tother animal statues\tbusts of people\tcarvings of trees or rocks\nThere are several useful visual features to tell there is 'horse statue' and not similar things in a photo such as:\t\nfour legs\thorse head or full horse body\tstatue is made of stone, metal, or other durable material\tstatue is placed on a pedestal or base\thorse is in a realistic or stylized pose", 25], "barren trees": ["Yes. 'Barren trees' has a tangible appearance and represents trees without leaves.\nA few things that are visually similar to 'barren trees' but are not 'barren trees' are:\tdead trees\tsnow-covered trees\ttrees in winter\nThere are several useful visual features to tell there are 'barren trees' and not similar things in a photo: no visible leaves or foliage\tbare branches and twigs\tdry, lifeless appearance\tbrown and gray coloration in winter or fall seasons.", 25], "shin": ["Yes. 'Shin' has a tangible appearance and is a part of the leg bone between the knee and ankle.\nA few things that are visually similar to 'shin' but are not 'shin' are:\tknee\tankle\tcalf\nThere are several useful visual features to tell there is 'shin' and not similar things in a photo:\tpart of the leg between the knee and ankle\tbone structure\twith muscles and tendons around it", 25], "bracelet man": ["No. 'Bracelet man' is too vague or abstract to be distinguished in a photo.", 25], "load": ["No. 'Load' is too vague or abstract to be distinguished in a photo.", 25], "blue lamp": ["Yes. 'Blue lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'blue lamp' but are not 'blue lamp' are:\tblue vase\tblue cup\tblue statue\tblue book\nThere are several useful visual features to tell there is 'blue lamp' and not similar things in a photo:\tgives off light\tusually made of metal, glass, or plastic\telongated and thin with a bulb at one end\thas a power cord or uses batteries or is rechargeable\tspecific blue color can be identified\tin the case of a table lamp or floor lamp, it may have a lampshade", 25], "ceiling lamp": ["Yes. 'Ceiling lamp' has a tangible appearance and is an object used to provide light in a room.\nA few things that are visually similar to 'ceiling lamp' but are not 'ceiling lamp' are:\tchandelier\tfan\tceiling tile\nThere are several useful visual features to tell there is 'ceiling lamp' and not similar things in a photo:\tattached to the ceiling\tfixed in place\tcone or bowl-shaped\tdirecting light downwards", 25], "metal vent": ["Yes. 'Metal vent' has a tangible appearance and is a type of air duct.\nA few things that are visually similar to 'metal vent' but are not 'metal vent' are:\tgrate\tfence\tgutter\tchimney\nThere are several useful visual features to tell there is 'metal vent' and not similar things in a photo:\trectangular or circular shape\tmetallic material\tgrids or vents for air flow\tlocated on walls, ceilings, or floors.", 25], "capital": ["No. 'Capital' is too vague or abstract to be distinguished in a photo.", 25], "spotlights": ["Yes. 'Spotlights' has a tangible appearance and is a type of lighting equipment.\nA few things that are visually similar to 'spotlights' but are not 'spotlights' are:\tlamps\tflashlights\tfireworks\nThere are several useful visual features to tell there is 'spotlights' and not similar things in a photo:\tbright, focused beam of light\tdirectional and adjustable light source\tdesigned for illuminating specific areas or objects from a distance\tsupport structure, often with a tripod or stand connected to the light source.", 25], "plaza": ["Yes. 'Plaza' has a tangible appearance and typically refers to an open public space or square.\nA few things that are visually similar to 'plaza' but are not 'plaza' are:\tpark\tsidewalk\tpedestrian zone\tmarketplace\nThere are several useful visual features to tell there is 'plaza' and not similar things in a photo:\tlarge open public space or square surrounded by buildings or streets\tcentral meeting place or gathering area\tforums, fountains, or monuments\tmay have seating or outdoor dining areas.", 25], "tabby": ["Yes. 'Tabby' has a tangible appearance and is a type of domestic cat.\nA few things that are visually similar to 'tabby' but are not 'tabby' are:\tleopard\tocelot\tserval\tlynx\nThere are several useful visual features to tell there is 'tabby' and not similar things in a photo:\ttypically has a distinctive \"M\" marking on the forehead\tbrown, gray or orange fur with black stripes\tor spots on the coat\trounded ears\twith white feet\tand green or yellow eyes.", 25], "gray sky": ["Yes. 'Gray sky' has a tangible appearance and refers to a sky that appears gray, often due to cloudy weather.\nA few things that are visually similar to 'gray sky' but are not 'gray sky' are:\tfoggy weather\tsmoky sky\thazy atmosphere\nThere are several useful visual features to tell there is 'gray sky' and not similar things in a photo:\tdull and muted shades of gray, often without any blue sky visible\tcloudy texture to the sky\tmaybe rainy, cold, or bleak-looking\tscene is typically outdoors", 25], "waste": ["Yes. 'Waste' has a tangible appearance and refers to things that are no longer useful or needed.\nA few things that are visually similar to 'waste' but are not 'waste' are:\tart\ttools\trubble\tnatural debris\tdecorations\nThere are several useful visual features to tell there is 'waste' and not similar things in a photo:\tdisorganized and messy appearance\tdecomposition or decay\tdiscard items or trash visible", 25], "ice cube": ["Yes. 'Ice cube' has a tangible appearance and is a type of frozen water.\nA few things that are visually similar to 'ice cube' but are not 'ice cube' are:\tdiamonds\tcrystals\tsugar cubes\trock chunks\nThere are several useful visual features to tell there is 'ice cube' and not similar things in a photo:\tclear or opaque\tfrozen water\tblocks or cubes\tin a cold or frozen environment.", 25], "article": ["No. 'Article' is too vague or abstract to be distinguished in a photo.", 25], "train light": ["Yes. 'Train light' has a tangible appearance and is a type of light on a train.\nA few things that are visually similar to 'train light' but are not 'train light' are:\tcar headlights\tcycling lights\ttruck lights\ttraffic lights\nThere are several useful visual features to tell there is a 'train light' and not similar things in a photo:\tattached to a train\tmounted on a pole or a bracket\tstrong and focused beam of light often pointing forward or backward\tbrilliant white, yellow or red color", 25], "multiple windows": ["Yes. 'Multiple windows' has a tangible appearance and refers to the display of several windows on a computer screen.\nA few things that are visually similar to 'multiple windows' but are not 'multiple windows' are:\tmultiple doors\tmultiple photo frames\tside-by-side posters\tmultiple TV screens\nThere are several useful visual features to tell there are 'multiple windows' and not similar things in a photo:\trectangular shape\tminimize, maximize, and close buttons\tscroll bars\tforward and back buttons\tright-click menu bar", 25], "blue edge": ["Yes. 'Blue edge' has a specific visual appearance.\nIt is difficult to name things that are visually similar to 'blue edge' because it is not a commonly used term. It could be interpreted in different ways such as the blue edge of an object, the outline of a blue shape, or a blue border. \nUseful visual features for distinguishing 'blue edge' would depend on the context and what it is being compared to. Generally, a blue edge would have a distinct blue color and be on the edge or perimeter of an object or shape.", 25], "element": ["No. 'Element' is too abstract to be visually concrete or distinguished in a photo. \n\nHowever, if we are referring to the chemical elements, then:\n\nA few things that are visually similar to 'element' but are not 'element' are:\tcompounds\tmixtures\tions\nThere are no visual features that can be used to distinguish 'element' from the listed similar things in a photo. Instead, chemical composition and properties would need to be examined.", 25], "palm fronds": ["Yes. 'Palm fronds' has a tangible appearance and is a part of a palm tree.\nA few things that are visually similar to 'palm fronds' but are not 'palm fronds' are:\tfern leaves\tother tree leaves\nThere are several useful visual features to tell there is 'palm fronds' and not similar things in a photo:\tlong and thin shape\twith a feather-like appearance and a central spine\tvaried shades of green, ranging from light to dark\tbelonging to a tree with a thick, fibrous trunk and no branches at the base", 25], "brushes": ["Yes. 'Brushes' have a tangible appearance and are tools used for painting, cleaning or grooming.\nA few things that are visually similar to 'brushes' but are not 'brushes' are:\tcombs\thairbrushes\tpaint rollers\tfloor sweepers\nThere are several useful visual features to tell there is 'brushes' and not similar things in a photo:\thave bristles or fibers of various sizes and shapes\tcan have a handle or be handheld\tcome in various colors and materials depending on the intended use.", 25], "brick ground": ["Yes. 'Brick ground' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'brick ground' but are not 'brick ground' are:\ttile floor\tpaver ground\tcement ground\nThere are several useful visual features to tell there is 'brick ground' and not similar things in a photo:\trectangular shape\tspecular surface\tterracotta color\tpattern with horizontal and vertical lines", 25], "drums": ["Yes. 'Drums' has a tangible appearance and is a kind of musical instrument.\nA few things that are visually similar to 'drums' but are not 'drums' are:\tcontainers\tbuckets\tpots\tmetal cans\nThere are several useful visual features to tell there is 'drums' and not similar things in a photo:\ncircular body\nmembrane stretched over an opening on top\nsnares or cymbals on the sides\ndrumsticks or beaters nearby.", 25], "tile flooring": ["Yes. 'Tile flooring' has a tangible appearance and refers to flooring made up of individual tiles.\nA few things that are visually similar to 'tile flooring' but are not 'tile flooring' are:\twooden flooring\tlaminate flooring\tlinoleum flooring\tconcrete flooring\nThere are several useful visual features to tell there is 'tile flooring' and not similar things in a photo:\tseparate tiles\tlarge or small square or rectangular shapes\tsimilar patterns or colors\ton the ground or floor surface", 25], "utility wires": ["Yes. 'Utility wires' has a tangible appearance and is a type of wiring used for utilities.\nA few things that are visually similar to 'utility wires' but are not 'utility wires' are:\tcable\ttv wires\tphone lines\nThere are several useful visual features to tell there are 'utility wires' and not similar things in a photo:\thanging from poles or towers\twires grouped in sets or pairs\twires arranged in a grid-like pattern\twires attached to large transformers or electrical stations", 25], "apple slice": ["Yes. 'Apple slice' has a tangible appearance and is a piece of fruit.\nA few things that are visually similar to 'apple slice' but are not 'apple slice' are:\tpear slice\tmango slice\tpineapple slice\torange slice\nThere are several useful visual features to tell there is 'apple slice' and not similar things in a photo:\tcircular or semicircular shape\twith or without seeds\tpeeled or unpeeled skin\tpale white or yellowish flesh\tdark seeds in the center of the slice if present.", 25], "centerpiece": ["Yes. 'Centerpiece' has a tangible appearance and usually refers to a decorative item placed in the center of a table or room.\nA few things that are visually similar to 'centerpiece' but are not 'centerpiece' are:\ttablecloth\tcandles\tvase\tfruit bowl\nThere are several useful visual features to tell there is 'centerpiece' and not similar things in a photo:\tprominent placement in the center of a table or room\tentirely decorative, not functional\tincludes flowers, greenery, or other decorative items\tcan be tall or reach upward", 25], "refrigerator freezer": ["Yes. 'Refrigerator freezer' has a tangible appearance and is a household appliance.\nThere are no things that are visually similar to 'refrigerator freezer' but are not 'refrigerator freezer', as it is a specific type of appliance.\nSome useful visual features for identifying a 'refrigerator freezer' in a photo might include:\t\n- A double-door design, with a top compartment for freezing and a bottom compartment for refrigeration\n- Any temperature controls or indicators that are visible or accessible\n- An ice maker, if it is present and visible\n- Shelves, drawers, or compartments inside the freezer or refrigerator compartments.", 25], "lightbulb": ["Yes. 'Lightbulb' has a tangible appearance and is a type of electric lamp.\nA few things that are visually similar to 'lightbulb' but are not 'lightbulb' are:\tcandle\tlantern\tincandescent bulb\tHalogen bulb\nThere are several useful visual features to tell there is 'lightbulb' and not similar things in a photo:\tclear, glass or plastic bulb\tconical or cylindrical shape\tmetallic base with threads or pins\tfor electric connection\temitting light\twhen switched on", 25], "base coach": ["Yes. 'Base coach' has a tangible appearance and refers to a specific role in baseball.\nA few things that are visually similar to 'base coach' but are not 'base coach' are:\tumpire\tplayer\nThere are several useful visual features to tell there is 'base coach' and not similar things in a photo:\twearing a team uniform, but not in the field\tgesturing or signaling with hands\tstanding near first or third base\tinstructing or communicating with runners or batters\ton the field only during a game, not during practice or warm-ups", 25], "tall animal": ["No. 'Tall animal' is too vague or abstract to be distinguished in a photo.", 25], "stereo system": ["Yes. 'Stereo system' has a tangible appearance and is a type of electronic device for playing music.\nA few things that are visually similar to 'stereo system' but are not 'stereo system' are:\tspeakers\theadphones\tamplifiers\tearbuds\nThere are several useful visual features to tell there is 'stereo system' and not similar things in a photo:\tmultiple speakers\tor amplifiers\tseparate components such as a tuner or CD player\twires connecting the various components\tsound control buttons or knobs on a device", 25], "liquid glass": ["Yes. 'Liquid glass' has a tangible appearance and refers to a type of glass that is melted and poured in a liquid state.\nA few things that are visually similar to 'liquid glass' but are not 'liquid glass' are:\tice\tmelted wax\tsyrup\nThere are several useful visual features to tell there is 'liquid glass' and not similar things in a photo:\tclear and transparent\twhen solid, has a smooth and polished surface\tmight be glowing or emitting light (depending on the context of the photo)", 25], "jet planes": ["Yes. 'Jet planes' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'jet planes' but are not 'jet planes' are:\thelicopters\tgliders\trockets\tdrones\nThere are several useful visual features to tell there is 'jet planes' and not similar things in a photo:\tlong, narrow body with wings\tat least one jet engine on each wing\ttail fin with vertical stabilizer\tand horizontal stabilizers under the cockpit", 25], "shawl": ["Yes. 'Shawl' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'shawl' but are not 'shawl' are:\tscarf\tpashmina\tcloak\tcape\nThere are several useful visual features to tell there is 'shawl' and not similar things in a photo:\tusually rectangular shape\tcan be made of different materials such as wool, silk, or cotton\tworn around the shoulders or neck\tdrapes down to the arms, waist, or legs\tcan be plain or patterned\twith or without tassels or fringes.", 25], "pizza cut": ["Yes. 'Pizza cut' has a tangible appearance.\nA few things that are visually similar to 'pizza cut' but are not 'pizza cut' are:\tcake slices\tpie slices\ttart slices\tbread slices\nThere are several useful visual features to tell there is 'pizza cut' and not similar things in a photo:\tcircular shape\twith tomato sauce, cheese, and toppings\ton a flat round crust\tdivided into triangle-shaped portions\twith a visible outer crust", 25], "danger": ["No. 'Danger' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few visually similar things that could represent 'danger' are: fire, sharp objects, warning signs. \n\nUseful visual features for distinguishing 'danger' from the listed similar things could be: \n- Fire: flames or smoke, possibly accompanied by heat or bright light. \n- Sharp objects: pointed or cutting edges, such as knives, needles, or broken glass. \n- Warning signs: yellow or red background, bold black letters, exclamation marks, and pictograms indicating possible hazards.", 25], "belt man": ["No. 'Belt man' is too vague or abstract to be distinguished in a photo.", 25], "blue frame": ["Yes. 'Blue frame' has a tangible appearance and refers to a type of rectangular border.\nA few things that are visually similar to 'blue frame' but are not 'blue frame' are:\tblue matting\tblue border\tsticker around a photo or artwork\nThere are several useful visual features to tell there is 'blue frame' and not similar things in a photo:\trectangle shape\tborder around the edge of an image\tbright blue color", 25], "rain boots": ["Yes. 'Rain boots' has a tangible appearance and is a kind of footwear.\nA few things that are visually similar to 'rain boots' but are not 'rain boots' are:\tregular boots\tgaloshes\t\nThere are several useful visual features to tell there is 'rain boots' and not similar things in a photo:\twaterproof material or coating\tbright or vibrant colors\tknee-high or mid-calf length\tthick soles that provide traction in wet conditions", 25], "bear swimming": ["Yes. 'Bear swimming' has a tangible appearance and refers to a specific action of a bear.\nA few things that are visually similar to 'bear swimming' but are not 'bear swimming' are:\tbear standing\tbear running in water\tdog swimming\nThere are several useful visual features to tell there is 'bear swimming' and not similar things in a photo:\tbear-shaped body\tpaws moving in a swimming motion\twater splashing around or over the bear\tno solid ground beneath the bear's body\tbear's face and head visible above the surface of the water", 25], "tassel": ["Yes. 'Tassel' has a tangible appearance and is a decorative element composed of threads or cords hanging loose.\nA few things that are visually similar to 'tassel' but are not 'tassel' are:\tfringe\tpom-pom\tring pull on a can\ttail of an animal\nThere are several useful visual features to tell there is 'tassel' and not similar things in a photo:\thanging from a garment or an accessory\tvibrant color or metallic threads\tlong and thin shape with loose threads at the end\tmade of threads or cords", 25], "bald person": ["Yes. 'Bald person' has a tangible appearance and refers to a person without any hair on their head.\nA few things that are visually similar to 'bald person' but are not 'bald person' are:\tperson with very short hair\tperson wearing a hat\tor someone whose head is not visible in the photo\nThere are several useful visual features to tell there is 'bald person' and not similar things in a photo:\tsmooth, shiny scalp\tno visible hair on the head", 25], "grill marks": ["Yes. 'Grill marks' has a tangible appearance and is a pattern left on food after being grilled.\nA few things that are visually similar to 'grill marks' but are not 'grill marks' are:\tburn marks\tcracks\tstripes\tgrooves\tskewer holes\nThere are several useful visual features to tell there are 'grill marks' and not similar things in a photo:\tparallel lines\tcrosshatched or diagonal lines\ton the surface of the food\tbrown or black in color, contrasting with the rest of the food", 25], "server": ["Yes. 'Server' has a tangible appearance and is a type of computer hardware.\nA few things that are visually similar to 'server' but are not 'server' are:\tcomputer desktop\trouter\tswitch\nThere are several useful visual features to tell there is 'server' and not similar things in a photo:\ttower-shaped or rack-mounted computer\tscattered wires and cables\tbuilt-in fans or vents\tstatus indicators, such as lights or screens\tfrequently located in a server room or data center.", 25], "purple grapes": ["Yes. 'Purple grapes' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'purple grapes' but are not 'purple grapes' are:\tplums\tblueberries\traisins\tblackberries\nThere are several useful visual features to tell there are 'purple grapes' and not similar things in a photo:\tclusters of round or oval-shaped fruits\tsmooth or bumpy texture\tpurple or dark red color\twith green stems attached to the grape cluster.", 25], "wheat": ["Yes. 'Wheat' has a tangible appearance and is a type of grain.\nA few things that are visually similar to 'wheat' but are not 'wheat' are:\tbarley\toats\trye\t\nThere are several useful visual features to tell that there is 'wheat' and not similar things in a photo:\ttall, thin stalks with few leaves at the base of the plant\tgrains gathered at the top of the stalk\tin the color yellow or beige", 25], "cheeseburger": ["Yes. 'Cheeseburger' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'cheeseburger' but are not 'cheeseburger' are:\thamburger\tsandwich\twith cheese\tand meat\tpizza\nThere are several useful visual features to tell there is 'cheeseburger' and not similar things in a photo:\ttwo buns\twith a meat patty\tmelted cheese\tlettuce, tomato, onions, pickles, and sauces in the middle", 25], "hand rails": ["Yes. 'Hand rails' has a tangible appearance and is a type of structure used for support or guidance.\nA few things that are visually similar to 'hand rails' but are not 'hand rails' are:\tfences\tbarricades\tguard rails\tshelves\nThere are several useful visual features to tell there is 'hand rails' and not similar things in a photo:\tattached to a wall or structure\thorizontal or diagonal bars\tfor gripping or support", 25], "horse trailer": ["Yes. 'Horse trailer' has a tangible appearance and is a type of trailer used for transporting horses.\nA few things that are visually similar to 'horse trailer' but are not 'horse trailer' are:\tcargo trailer\tcamper trailer\tutility trailer\tboat trailer\nThere are several useful visual features to tell there is 'horse trailer' and not similar things in a photo:\topen trailer\twith a ramp or step for horses to enter and exit\twith several dividers or stalls inside for horses' safety and comfort\ttaller and wider than other trailers\tfor transporting horses or livestock.", 25], "shadow skateboarder": ["Yes. 'Shadow skateboarder' has a tangible appearance and refers to the silhouette or outline of a person skateboarding.\nA few things that are visually similar to 'shadow skateboarder' but are not 'shadow skateboarder' are:\tperson walking or running\tperson carrying something\tskier or snowboarder\tbiker or motorcyclist\nThere are several useful visual features to tell there is 'shadow skateboarder' and not similar things in a photo:\trounded shape of the skateboard\tone or two feet on top of the board\tarms and legs spread out in a balancing pose\telongated or distorted shape of the shadow behind the person", 25], "paper bags": ["Yes. 'Paper bags' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'paper bags' but are not 'paper bags' are:\tplastic bags\tboxes\tbaskets\tenvelopes\nThere are several useful visual features to tell there is 'paper bags' and not similar things in a photo:\tmade of paper\tbrown or white\tcolorful handles or decorations\trectangular or square shape\tflattened bottom for standing upright.", 25], "utility box": ["Yes. 'Utility box' has a tangible appearance and is a type of box that commonly contains electrical or telecommunication equipment.\nA few things that are visually similar to 'utility box' but are not 'utility box' are:\tMailbox\tTrash can\tParking meter\tJunction box\nThere are several useful visual features to tell there is 'utility box' and not similar things in a photo:\tRectangular or square shape\tMetal or plastic construction\tLock or latch\tDescription or labeling indicating that it contains electrical or telecommunication equipment", 25], "metal zipper": ["Yes. 'Metal zipper' has a tangible appearance and is a type of closure.\nA few things that are visually similar to 'metal zipper' but are not 'metal zipper' are:\tplastic zipper\tsnaps\tbuttons\tvelcro\nThere are several useful visual features to tell there is 'metal zipper' and not similar things in a photo:\tteeth-shaped metal pieces interlocking with one another\ta pull tab for opening and closing\tcylindrical slider\tthat runs along the teeth to open and close the zipper", 25], "water skis": ["Yes. 'Water skis' has a tangible appearance and is a kind of sporting equipment.\nA few things that are visually similar to 'water skis' but are not 'water skis' are:\tsnow skis\trollerblades\tskateboards\nThere are several useful visual features to tell there are 'water skis' and not similar things in a photo:\tlong and narrow\tshaped like a flat-bottomed boat\tfoot bindings\ttowed by a boat or a cable\tpulling the rider across water", 25], "curtain panel": ["Yes. 'Curtain panel' has a tangible appearance and is a type of window treatment.\nA few things that are visually similar to 'curtain panel' but are not 'curtain panel' are:\tblinds\tshades\tdrapes\tfabrics \nThere are several useful visual features to tell there is 'curtain panel' and not similar things in a photo:\trectangular or square shape\thanging from a rod or rings\tcovers a window or doorway\tcan be opened or closed for privacy and light control", 25], "sand dunes": ["Yes. 'Sand dunes' has a tangible appearance and is a type of land feature.\nA few things that are visually similar to 'sand dunes' but are not 'sand dunes' are:\tsnowdrifts\thills\tmountains\tdeserts\nThere are several useful visual features to tell there is 'sand dunes' and not similar things in a photo:\trolling or wave-like shapes\tsandy or pale-colored surface\tno visible vegetation or trees\tcloser to a body of water (like an ocean or a lake)", 25], "quarters": ["Yes. 'Quarters' has a tangible appearance and refers to a type of coin.\nA few things that are visually similar to 'quarters' but are not 'quarters' are:\tdimes\tnickels\tpennies\ttokens\nThere are several useful visual features to tell there is 'quarters' and not similar things in a photo:\tround\tcircular engravings or inscriptions\tdiameter of approximately 0.955 inches (24.26 mm)\tthickness of approximately 1.75 mm\tmade of a silver-colored alloy with a copper core (for US currency)\tinscription of the word \"QUARTER DOLLAR\" and the year of issuance", 25], "radishes": ["Yes. 'Radishes' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'radishes' but are not 'radishes' are:\tbeets\tturnips\tpotatoes\nThere are several useful visual features to tell there is 'radishes' and not similar things in a photo:\tround or oblong shape\tbright red or pink skin\twhite flesh\tjutting root end", 25], "connection": ["No. 'Connection' is too vague or abstract to have a tangible appearance or be distinguished in a photo.", 25], "machinery": ["Yes. 'Machinery' has a tangible appearance and refers to mechanical equipment.\nA few things that are visually similar to 'machinery' but are not 'machinery' are:\ttools\tvehicles\thousehold appliances\nThere are several useful visual features to tell there is 'machinery' and not similar things in a photo:\tcomplex system of gears or pistons\tpowered by electricity or fuel\thas moving parts or components\tgenerally used for large-scale purposes", 25], "brown giraffe": ["Yes. 'Brown giraffe' has a tangible appearance and is a type of animal.\nThere are not many things that are visually similar to a 'brown giraffe,' but a few could be:\tdeer\tzebra\thorse\nThere are several useful visual features to tell there is a 'brown giraffe' and not similar things in a photo:\ttall with a long neck\tbrown patches on its body\tspots on its skin\tgiraffe-like horns on the head", 25], "hatchback": ["Yes. 'Hatchback' has a tangible appearance and is a type of car body style.\nA few things that are visually similar to 'hatchback' but are not 'hatchback' are:\tsedan\tSUV\tcoupe\tvan\ttruck\nThere are several useful visual features to tell there is 'hatchback' and not similar things in a photo:\ttwo-box car body style\twith a rear door that opens upward\tto an area that integrates passenger and cargo space.", 25], "modem": ["Yes. 'Modem' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'modem' but are not 'modem' are:\trouter\texternal hard drive\toptical drive\tprojector\nThere are several useful visual features to tell there is 'modem' and not similar things in a photo:\tretangular in shape\tlights indicating activity and connectivity\tports for connecting to network devices or computers\tantennas or cables for receiving or transmitting data", 25], "easel": ["Yes. 'Easel' has a tangible appearance and is a type of stand used to hold a canvas or other artwork while it is being painted or displayed.\nA few things that are visually similar to 'easel' but are not 'easel' are:\tbook holder\tmusic stand\tpodium\tlectern\nThere are several useful visual features to tell there is 'easel' and not similar things in a photo:\tthree-legged stand\tadjustable top ledge for holding canvas or artwork\tsome easels may have a horizontal bar near the bottom to hold brushes or supplies.", 25], "tanker": ["Yes. 'Tanker' has a tangible appearance and is a type of large ship used for transportation of liquids or gases.\nA few things that are visually similar to 'tanker' but are not 'tanker' are:\tcargo ship\tcruise ship\tbattleship\tfishing boat\nThere are several useful visual features to tell there is 'tanker' and not similar things in a photo:\tlarge size\tcylindrical shape\tor curved top-covered deck\tchimneys or vents on top of the ship\tmultiple pipes and hoses\t black and white stripes on the side with the word \"Suezmax\" or \"Aframax\"", 25], "brown house": ["Yes. 'Brown house' has a tangible appearance and is a type of residential building.\nA few things that are visually similar to 'brown house' but are not 'brown house' are:\tbricks buildings\tpainted buildings\tstone buildings\twooden buildings\nThere are several useful visual features to tell there is 'brown house' and not similar things in a photo:\tbrown-colored walls or roof, or both\tshape of the roof and the number of floors\twindows and doors\tposition of the house (neighborhood or environment)\tgarden, if it is attached to the house.", 25], "motorcycle windshield": ["Yes. 'Motorcycle windshield' has a tangible appearance and is a component of a motorcycle.\nA few things that are visually similar to 'motorcycle windshield' but are not 'motorcycle windshield' are:\tcar windshield\tbicycle helmet visor\tface shield\tforced air respirator\nThere are several useful visual features to tell there is 'motorcycle windshield' and not similar things in a photo:\tclear or tinted plastic or polycarbonate material\tfixed to the front of a motorcycle\thandlebar brackets or clamps\tprotection from wind or debris while riding on a motorcycle", 25], "lit sign": ["Yes. 'Lit sign' has a tangible appearance and is an object with illuminated letters or symbols.\nA few things that are visually similar to 'lit sign' but are not 'lit sign' are:\tneon sign\tlightbox\tbillboard\tscreen\nThere are several useful visual features to tell there is 'lit sign' and not similar things in a photo:\tletters or symbols made of lights or bulbs\tglowing or brightened in the dark\tshowing a message or name\tsticking out of a building or a pole", 25], "metal wall": ["Yes. 'Metal wall' has a tangible appearance and is a type of vertical building structure made of metal material.\nA few things that are visually similar to 'metal wall' but are not 'metal wall' are:\twooden wall\tbamboo fence\tbrick wall\nThere are several useful visual features to tell there is 'metal wall' and not similar things in a photo:\tmetallic appearance\tnot composed of bricks, wood, or other materials\ttypically has a smooth surface and may have a polished or reflective finish", 25], "rind": ["Yes. 'Rind' has a tangible appearance and refers to the outer layer of certain foods like fruits or cheese.\nA few things that are visually similar to 'rind' but are not 'rind' are:\tpeel\tbark\tskin\t\nThere are several useful visual features to tell there is 'rind' and not similar things in a photo:\ttypically thicker than skin or peel\tvaries in texture and color, from rough to smooth and from greenish to brown or yellowish\tmay have bumps, ridges, or pores\ttypically covers a soft or sticky interior", 25], "grounds": ["No. 'Grounds' is too vague or abstract to be distinguished in a photo.", 25], "roast beef": ["Yes. 'Roast beef' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'roast beef' but are not 'roast beef' are:\tpork roast\tchicken roast\tturkey roast\ttofu roast\nThere are several useful visual features to tell there is 'roast beef' and not similar things in a photo:\tbeef-like texture\tpinkish-red or brown color\tcharred edges and crispy skin\tjuicy-looking tender meat with fat marbling\toften served with gravy and potatoes", 25], "wind shield wipers": ["Yes. 'Windshield wipers' have a tangible appearance and are part of a car.\nA few things that are visually similar to 'windshield wipers' but are not 'windshield wipers' are:\tantenna\thood ornament\tside mirror\theadlights\nThere are several useful visual features to tell there is 'windshield wipers' and not similar things in a photo:\trectangular or blade-shaped objects\tmetal or plastic construction\thanging off the bottom of the windshield\tin motion or wiping the windshield\tclearly attached to the car's windshield", 25], "man hair": ["Yes. 'Man hair' has a tangible appearance and refers to the hair grown on a man's head.\nA few things that are visually similar to 'man hair' but are not 'man hair' are:\tanimal fur\twigs\tcloth fabrics\nThere are several useful visual features to tell there is 'man hair' and not similar things in a photo:\tfacial hair (beard, mustache)\tshort or long strands\tof a certain texture (curly, straight, wavy)\tworn on top of the head", 25], "crosses": ["Yes. 'Crosses' has a tangible appearance and is a religious symbol.\nA few things that are visually similar to 'crosses' but are not 'crosses' are:\tT-shaped poles\tplus signs\tX-shaped objects\nThere are several useful visual features to tell there is 'crosses' and not similar things in a photo:\tupright post with a horizontal beam\tcrucifix\twith or without a figure of Jesus on it\tvariations in design or ornamentation, such as Celtic crosses or Orthodox crosses", 25], "pearls": ["Yes. 'Pearls' has a tangible appearance and is a type of gemstone.\nA few things that are visually similar to 'pearls' but are not 'pearls' are:\twhite beads\tother types of gemstones\tplastic beads\tdecorative objects made of glass\nThere are several useful visual features to tell there is 'pearls' and not similar things in a photo:\tround or oval shape\tsmooth or slightly bumpy texture\tpearly, iridescent or creamy luster\thigh value and quality.", 25], "riverbank": ["Yes. 'Riverbank' has a tangible appearance.\nA few things that are visually similar to 'riverbank' but are not 'riverbank' are:\tbeach, shore, cliff, embankment\nThere are several useful visual features to tell there is 'riverbank' and not similar things in a photo:\tnarrow strip of land next to a river\twater flowing alongside the bank\ttrees, plants or grass on the bank\tsandy or muddy ground shape of the river\tcurvature of the land along the river.", 25], "cops": ["Yes. 'Cops' has a tangible appearance and refers to police officers.\nA few things that are visually similar to 'cops' but are not 'cops' are:\tsecurity guards\tmilitary personnel\tfirefighters\nThere are several useful visual features to tell there are 'cops' and not similar things in a photo:\tuniforms with badges\tor patches on the sleeves\twearing a gun or a taser\twearing a hat or a cap with the word \"Police\" or \"Cop\"\twrite-up citations or apprehending criminals.", 25], "tuna": ["Yes. 'Tuna' has a tangible appearance and is a type of fish.\nA few things that are visually similar to 'tuna' but are not 'tuna' are:\tsalmon\tsardine\tmackerel\ttrout\nThere are several useful visual features to tell there is 'tuna' and not similar things in a photo:\tdark blue or metallic color\telongated and streamlined shape\twith fins and gills\tno scales on the head and body\tlarge eyes and mouth", 25], "gas pump": ["Yes. 'Gas pump' has a tangible appearance and is a mechanical device used to transfer gasoline from a tank to a vehicle.\nA few things that are visually similar to 'gas pump' but are not 'gas pump' are:\twater pumps\tair pumps\tvacuum pumps\tbike pumps\nThere are several useful visual features to tell there is 'gas pump' and not similar things in a photo:\trectangular shape\tnozzles or hoses\tpumping gasoline\tfrom a fuel dispenser station or a gas station logo\tpayment options or credit card machines near it.", 25], "mural": ["Yes. 'Mural' has a tangible appearance and is a kind of artwork.\nA few things that are visually similar to 'mural' but are not 'mural' are:\tpainting\tdrawing\tposter\tsign\nThere are several useful visual features to tell there is 'mural' and not similar things in a photo:\tlarge-scale artwork\tpainted directly on a wall or a surface\tmay cover an entire wall or have multiple panels\tdetail-rich imagery or scenes may have a cultural, historical or social significance.", 25], "mans hand": ["Yes. 'Man's hand' has a tangible appearance and is a body part.\nA few things that are visually similar to 'man's hand' but are not 'man's hand' are:\twoman's hand\tchild's hand\tape's hand\tstatue of a hand\nThere are several useful visual features to tell there is 'man's hand' and not similar things in a photo:\tlarger size than a woman's or child's hand\thairier than a woman's hand\tless hair than an ape's hand\tfive fingers with nails and distinct knuckles", 25], "man water": ["No. 'Man water' is too vague or abstract to be distinguished in a photo. Also, it is not a commonly used term in English, and its meaning is unclear.", 25], "silver wrist": ["No. 'Silver wrist' is too vague or abstract to be distinguished in a photo.", 25], "round piece": ["Yes. 'Round piece' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'round piece' but are not 'round piece' are:\tcircle\tbutton\tcookie\tcoin\nThere are several useful visual features to tell there is 'round piece' and not similar things in a photo:\tperfectly circular shape\tconsistent thickness or curvature\tof a solid material (e.g. metal, plastic, glass)", 25], "chocolate chips": ["Yes. 'Chocolate chips' has a tangible appearance and refers to small pieces of chocolate.\nA few things that are visually similar to 'chocolate chips' but are not 'chocolate chips' are:\tcocoa powder\tchocolate sprinkles\torbits\tcoffee beans\nThere are several useful visual features to tell there is 'chocolate chips' and not similar things in a photo:\tbrown\tcolorful\tdroplet-shaped pieces\tsitting on top of or inside baked goods", 25], "evergreens": ["Yes. 'Evergreens' has a tangible appearance and refers to a group of plants that retain their leaves or needles year-round.\nA few things that are visually similar to 'evergreens' but are not 'evergreens' are:\tdeciduous trees\tbushes\tgrass\nThere are several useful visual features to tell there is 'evergreens' and not similar things in a photo:\n\n- Leaves/Needles remain green throughout the year\n- Branches/Needles grow vertically\n- Conical/Pyramidal shapes", 25], "train station platform": ["Yes. 'Train station platform' has a tangible appearance and is a raised structure beside the railway track where passengers get on and off the train.\nA few things that are visually similar to 'train station platform' but are not 'train station platform' are:\tsidewalk\tbus stop\tbridge\nThere are several useful visual features to tell there is 'train station platform' and not similar things in a photo:\tlocated beside a railway track\tis raised above ground\tlevel with the train's doors and windows\thas a roof to protect passengers from weather\tmarked with yellow lines to indicate safe standing areas.", 25], "towel bar": ["Yes. 'Towel bar' has a tangible appearance and is a type of bathroom accessory.\nA few things that are visually similar to 'towel bar' but are not 'towel bar' are:\thandles\tracks\tshower hooks\nThere are several useful visual features to tell there is a 'towel bar' and not similar things in a photo:\tlong, narrow bar\tmounted on a wall or door\thanging towels or other items.", 25], "names": ["No. 'Names' are too vague or abstract to be distinguished in a photo.", 25], "faint": ["No. 'Faint' is too vague or abstract to have a tangible appearance that can be captured in a photo.", 25], "surfer water": ["No. 'Surfer water' is too vague or abstract to be distinguished in a photo. It is possible that it refers to water that is suitable for surfing, but it would be more appropriate to use the term \"surfable water.\"\nA few things that are visually similar to 'surfable water' but are not 'surfable water' are:\tbeach\tsurfboard\twave\tpaddle board\tswimming pool\nThere are several useful visual features to tell there is 'surfable water' and not similar things in a photo:\twater that has waves\tbreaking waves\tsurfers riding the waves\tclear blue water\twater with foam or whitecaps\twater that is close to a beach or shoreline", 25], "emergency door": ["Yes. 'Emergency door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'emergency door' but are not 'emergency door' are:\texits\tdoors with \"push\" or \"panic\" bars\tdoors with signs that say \"emergency\"\tfire doors\nThere are several useful visual features to tell there is 'emergency door' and not similar things in a photo:\tred or bright-colored door\thandled or opened differently than regular doors\tmarked with signs or labels that say \"emergency exit\" or \"emergency only\"", 25], "metal sign pole": ["Yes. 'Metal sign pole' has a tangible appearance and is a type of pole to hold up signs.\nA few things that are visually similar to 'metal sign pole' but are not 'metal sign pole' are:\tlamp post\tflagpole\tfence post\tTV antenna\tpost of a basketball hoop\tbalustrade\nThere are several useful visual features to tell there is 'metal sign pole' and not similar things in a photo:\tstraight vertical pole\tor with a slight angle\tfor holding up a sign or a billboard\tmade out of metal or steel\tcolor could vary, but usually gray or silver", 25], "mountain peaks": ["Yes. 'Mountain peaks' has a tangible appearance and refers to the tops of mountains.\nA few things that are visually similar to 'mountain peaks' but are not 'mountain peaks' are:\thills\tcliffs\trock formations\t\nThere are several useful visual features to tell there are 'mountain peaks' and not similar things in a photo: tall and pointy\ttop of a larger natural structure, such as a mountain\trising above the surrounding landscape\tsnow or ice covering the top of the peak\tcraggy, irregular shape.", 25], "mane zebra": ["Yes. 'Mane zebra' has a tangible appearance and is a specific type of zebra with a distinct mane.\nThere are no animals similar to 'mane zebra.'\nUseful visual features for distinguishing 'mane zebra' are:\t\n- Black and white stripes covering the entire body\n- Small and rounded ears\n- A long, slender head with a pointed snout\n- Thick and bushy black and white mane going down the neck", 25], "orange vest": ["Yes. 'Orange vest' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'orange vest' but are not 'orange vest' are:\torange t-shirt\torange sweater\torange jacket\torange apron\nThere are several useful visual features to tell there is 'orange vest' and not similar things in a photo:\tno sleeves\tpockets\tzippers\treflective stripes", 25], "water ski": ["Yes. 'Water ski' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'water ski' but are not 'water ski' are:\tsnow ski\tsnowboard\tsurfboard\nThere are several useful visual features to tell there is 'water ski' and not similar things in a photo:\tflat and narrow wooden or fiberglass board\tone or more bindings for the feet\ttwo fins or \"skis\" to move on the water\thandle and rope for the person to hold onto", 25], "ponytail holder": ["Yes. 'Ponytail holder' has a tangible appearance and is an object used to tie hair.\nA few things that are visually similar to 'ponytail holder' but are not 'ponytail holder' are:\thair tie\tbobby pin\trubber band\t\nThere are several useful visual features to tell there is 'ponytail holder' and not similar things in a photo:\tridged or elastic band\tcircular or oval shape\thair wrapped around the holder.", 25], "emergency lights": ["Yes. 'Emergency lights' has a tangible appearance and is a kind of lighting used for emergency purposes.\nA few things that are visually similar to 'emergency lights' but are not 'emergency lights' are:\ttail lights\ttraffic signals\tbrake lights\t\nThere are several useful visual features to tell there is 'emergency lights' and not similar things in a photo:\tflashy and bright colors\trotating or flashing patterns\tused on police cars, ambulances, or fire trucks\tmounted on the roof\thave a distinct pattern of color, such as red and blue, or red and white", 25], "creature": ["No. 'Creature' is too vague or abstract to be distinguished in a photo.", 25], "brick church": ["Yes. 'Brick church' has a tangible appearance and refers to a specific type of building.\nA few things that are visually similar to 'brick church' but are not 'brick church' are:\tbrick house\tbrick school building\tbrick factory\nThere are several useful visual features to tell there is 'brick church' and not similar things in a photo:\tcross on top or near the building\tpointed arches\tornate stone or stained glass windows\tbell tower or spire\tcemetery adjacent to the building", 24], "mortar": ["Yes. 'Mortar' has a tangible appearance and is a type of material used in building construction.\nA few things that are visually similar to 'mortar' but are not 'mortar' are:\tconcrete\tglue\tgrout\nThere are several useful visual features for distinguishing 'mortar' from the listed similar things in a photo:\tthick paste-like consistency\tlaid between bricks, stones, or blocks of construction\tdries and hardens over time\tmay have a different color or texture than the surrounding bricks, stones, or blocks.", 24], "glass mirror": ["Yes. 'Glass mirror' has a tangible appearance.\nA few things that are visually similar to 'glass mirror' but are not 'glass mirror' are:\twindow\treflection\tinvisible glass\nThere are several useful visual features to tell there is 'glass mirror' and not similar things in a photo:\tframe around the edges or no visible frame\tability to reflect clear and accurate images\tsmooth and uniform surface with no visible texture, distortion, or blemishes", 24], "pink paint": ["Yes. 'Pink paint' has a tangible appearance and is a type of colored liquid used for painting.\nA few things that are visually similar to 'pink paint' but are not 'pink paint' are:\tstrawberry milkshake\tfruit juice\tbubblegum\trose petals\nThere are several useful visual features to tell there is 'pink paint' and not similar things in a photo:\tsmooth and even texture\twet and glossy appearance\tpigmented color with a shade of pink\tsurface applied to may show brush strokes or roller marks", 24], "kiwis": ["Yes. 'Kiwis' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'kiwis' but are not 'kiwis' are: avocados, passion fruit, pomegranate, persimmon.\nThere are several useful visual features to tell there is 'kiwis' and not similar things in a photo:\tBrown, fuzzy exterior \tOval shape\tCenter of black seeds \tGreen interior\tflesh is soft and has small seeds", 24], "ice water": ["Yes. 'Ice water' has a tangible appearance and is a type of drink.\nA few things that are visually similar to 'ice water' but are not 'ice water' are:\tplain water\tsoda\tjuice\twith ice cubes\tbeer\nThere are several useful visual features to tell there is 'ice water' and not similar things in a photo:\tclear or translucent appearance\tice cubes visible\tin a glass or a bottle\trefracting light in the glass or the bottle\tcontaining water droplets on the outside due to being cold", 24], "paper cups": ["Yes. 'Paper cups' has a tangible appearance.\nA few things that are visually similar to 'paper cups' but are not 'paper cups' are:\tplastic cups\tglass cups\tmugs\tbowls\nThere are several useful visual features to tell there is 'paper cups' and not similar things in a photo:\tpaper material\tcone or cylinder shape\tdisposable designs\tridged surface for grip.", 24], "wall lamp": ["Yes. 'Wall lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'wall lamp' but are not 'wall lamp' are:\ttable lamp\tfloor lamp\tchandelier\tceiling fan\nThere are several useful visual features to tell there is 'wall lamp' and not similar things in a photo:\tattaches to a wall or a vertical surface\thas a lamp shade and a bulb or light source\tprotrudes from the wall at an angle or straight", 24], "flecks": ["Yes. 'Flecks' has a tangible appearance and refers to small shiny spots or specks.\nA few things that are visually similar to 'flecks' but are not 'flecks' are:\tdust\tparticles\tglitter\tconfetti\treflective surfaces\nThere are several useful visual features to tell there is 'flecks' and not similar things in a photo: small size, usually less than 1mm\tsparkling or shining appearance\tscattered in an irregular pattern\tor specific parts of an object (such as flecks of gold in a painting)", 24], "sun umbrella": ["Yes. 'Sun umbrella' has a tangible appearance and is a type of umbrella.\nA few things that are visually similar to 'sun umbrella' but are not 'sun umbrella' are:\tregular umbrella\ttent\tawnings\nThere are several useful visual features to tell there is 'sun umbrella' and not similar things in a photo:\tlarge size\tbright colors or patterns\tthatched texture or appearance\tsymmetrical shape with a curved handle\ttop pointed or rounded for capturing the sun's rays securely.", 24], "headset": ["Yes. 'Headset' has a tangible appearance and is a type of audio device.\nA few things that are visually similar to 'headset' but are not 'headset' are:\tearbuds\thearing aids\thelmets\t\nThere are several useful visual features to tell there is 'headset' and not similar things in a photo:\ttwo ear cups\tconnected by a headband or wires\tmicrophone (in some cases)", 24], "collage": ["Yes. 'Collage' has a tangible appearance and is a kind of artwork.\nA few things that are visually similar to 'collage' but are not 'collage' are:\tmosaic\tpainting\tdrawing\tscrapbook\t\nThere are several useful visual features to tell there is 'collage' and not similar things in a photo:\ta collection of different images, materials or textures on a surface, usually paper or canvas\tcut-outs, photographs, newsprint, text or illustrations arranged in a composition\tmultiple layers or overlapping elements\twide variety of colors and shapes.", 24], "silver bolts": ["Yes. 'Silver bolts' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'silver bolts' but are not 'silver bolts' are:\tnails\tscrews\trivets\tpins\nThere are several useful visual features to tell there is 'silver bolts' and not similar things in a photo:\tcylindrical shape\tsilver or metallic color\tthreaded shafts and bolt heads", 24], "sauce bowl": ["Yes. 'Sauce bowl' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'sauce bowl' but are not 'sauce bowl' are:\tsoup bowl\tcereal bowl\tdessert bowl\tcoffee mug\nThere are several useful visual features to tell there is 'sauce bowl' and not similar things in a photo:\t\nsmaller than a regular bowl\t\ndesigned to hold sauce\t\nmay have a handle or spout\t\nmay be round, oval or rectangular shaped", 24], "form": ["No. 'Form' is too vague or abstract to be distinguished in a photo.", 24], "grid": ["Yes. 'Grid' has a tangible appearance and is a pattern of straight lines intersecting each other.\nA few things that are visually similar to 'grid' but are not 'grid' are:\tline\tgraph\tpattern\tboard\tgame\nThere are several useful visual features to tell there is 'grid' and not similar things in a photo:\tsystem of intersecting lines\tevenly spaced lines and columns\trectangular shape\tsymmetrical pattern of lines", 24], "tin roof": ["Yes. 'Tin roof' has a tangible appearance and is a type of roofing material.\nA few things that are visually similar to 'tin roof' but are not 'tin roof' are:\tmetallic tiles\tsteel shingles\tcorrugated metal panels\nThere are several useful visual features to tell there is 'tin roof' and not similar things in a photo:\tflat or slightly angled surface\tmade of sheets of tin or other metal\tmatte or reflective surface\tvisible screws or nails to hold it in place", 24], "bottom drawer": ["No. 'Bottom drawer' is too vague or abstract to be distinguished in a photo.", 24], "calves": ["Yes. 'Calves' has a tangible appearance and refers to young cows.\nA few things that are visually similar to 'calves' but are not 'calves' are:\tdeer fawns\tgoat kids\nThere are several useful visual features to tell there is 'calves' and not similar things in a photo:\tlarge, floppy ears\tfour legs\tshort and furry coat\thorns (if male)\tset on a farm or in a pasture next to cows", 24], "bag strap": ["Yes. 'Bag strap' has a tangible appearance and refers to the strap or handle that is used to carry a bag.\nA few things that are visually similar to 'bag strap' but are not 'bag strap' are:\tstraps on clothing\tleashes\tharnesses for animals\tbelts\nThere are several useful visual features to tell there is 'bag strap' and not similar things in a photo:\tattached to a bag or purse\tmade of the same material as the bag\tlooped over the shoulder or held by hand", 24], "calender": ["Yes. 'Calendar' has a tangible appearance and is a type of chart or schedule.\nA few things that are visually similar to 'calendar' but are not 'calendar' are:\ttime table\ttimeline\tplanner diary\nThere are several useful visual features to tell there is 'calendar' and not similar things in a photo:\tgrid of boxes (with one box for each day)\tdates, months, and years marked\ton a wall or a desk", 24], "apartments": ["Yes. 'Apartments' has a tangible appearance and is a type of building or housing.\nA few things that are visually similar to 'apartments' but are not 'apartments' are:\thouses\tdormitories\toffices\thotels\nThere are several useful visual features to tell there is 'apartments' and not similar things in a photo:\tmulti-unit building or complex\tbalconies or patios\tindividual entrances for each unit\twindows on multiple levels\tresidential address or signs\twithin or near an urban area", 24], "tricks": ["No. 'Tricks' is too vague or abstract to be distinguished in a photo.", 24], "shadow trees": ["Yes. 'Shadow trees' has a tangible appearance and refers to trees that cast shadows.\nA few things that are visually similar to 'shadow trees' but are not 'shadow trees' are:\tregular trees\tfake trees\tpainted trees\nThere are several useful visual features to tell there are 'shadow trees' and not similar things in a photo:\tcasting shadows\tgrowing from the ground\thaving leaves or branches", 24], "udders": ["Yes. 'Udders' has a tangible appearance and is a part of the anatomy of certain mammals.\nA few things that are visually similar to 'udders' but are not 'udders' are:\tbreasts\tsacks\tbags\tpouches\nThere are several useful visual features to tell there are 'udders' and not similar things in a photo:\tmammary glands\tlocated on the underside of a female mammal \tfour distinct teats, arranged in pairs\tdifferent sizes and shapes depending on the species", 24], "sacks": ["Yes. 'Sacks' has a tangible appearance and is a type of bag made from a sturdy material.\nA few things that are visually similar to 'sacks' but are not 'sacks' are:\tpurses\tbackpacks\tplastic bags\ttotes\nThere are several useful visual features to tell there is 'sacks' and not similar things in a photo:\tburlap, canvas, or similar sturdy material texture\tno noticeable handles or shoulder straps\ttraditional sack shape, often rounded at the bottom and tied at the top with a string", 24], "mud flap": ["Yes. 'Mud flap' has a tangible appearance and is a type of car accessory.\nA few things that are visually similar to 'mud flap' but are not 'mud flap' are:\tbracket\tfender\tstripe\tcar decal\nThere are several useful visual features to tell there is 'mud flap' and not similar things in a photo:\tflat and flexible\tpiece of rubber or plastic\tfixed to the back wheels of a vehicle\tdesigned to protect the car and other drivers from mud and debris\tkicks up water and mud out and away from the vehicle's tires.", 24], "burnt piece": ["Yes. 'Burnt piece' has a tangible appearance and is a type of object that has been charred or singed by heat.\nA few things that are visually similar to 'burnt piece' but are not 'burnt piece' are:\tcrumbs\tcharcoal\tashes\nThere are several useful visual features to tell there is 'burnt piece' and not similar things in a photo:\tdark brown or blackened color\tcracks or charring\tvisible texture or substance of the original object that was burnt", 24], "side dish": ["Yes. 'Side dish' has a tangible appearance and is a type of food item.\nA few things that are visually similar to 'side dish' but are not 'side dish' are:\tentr\u00e9e\tsoup\tappetizer\tdessert\tsalad\nThere are several useful visual features to tell there is 'side dish' and not similar things in a photo:\tsmaller portion\tsize and shape of the plate\thighlighted in a small plate or bowl, sometimes placed alongside the main dish on the same plate\ttypically contains vegetables or starches.", 24], "gold knob": ["Yes. 'Gold knob' has a tangible appearance and is a type of door or cabinet handle.\nA few things that are visually similar to 'gold knob' but are not 'gold knob' are:\tsilver knob\tcopper knob\tbrass knob\nThere are several useful visual features to tell there is 'gold knob' and not similar things in a photo:\t\n- A knob or pull that is typically circular or oval-shaped and protrudes from the surface of the door or drawer\n- A shiny and metallic appearance\n- The color is pure or a shade of gold.", 24], "towel ring": ["Yes. 'Towel ring' has a tangible appearance and is a type of bathroom accessory.\nA few things that are visually similar to 'towel ring' but are not 'towel ring' are:\ttowel bar\thook\tshower curtain ring\nThere are several useful visual features to tell there is 'towel ring' and not similar things in a photo:\ta circular or oval shape attached to the wall\tmetal or plastic material\ta small ring or loop to hold the towel", 24], "blond boy": ["Yes. 'Blond boy' has a tangible appearance and refers to a male child with blond hair.\nA few things that are visually similar to 'blond boy' but are not 'blond boy' are:\tblond girl\tadult man with blond hair\tgirl or boy with short blonde hair\nThere are several useful visual features to tell there is a 'blond boy' and not similar things in a photo:\tmale\tchild\tboyish features\tblond hair\tfair skin", 24], "concrete post": ["Yes. 'Concrete post' has a tangible appearance and refers to a post made of concrete.\nA few things that are visually similar to 'concrete post' but are not 'concrete post' are:\tmetal post\twooden post\t\nThere are several useful visual features for distinguishing 'concrete post' from the listed similar things in a photo:\tgray color\tcement texture\trough surface\tbulky and heavy-looking shape\tno visible grains or rings (as in wooden posts)\tno visible joints or seams (as in metal posts)", 24], "tone": ["No. 'Tone' is too vague or abstract to be distinguished in a photo.", 24], "bent knees": ["Yes. 'Bent knees' has a tangible appearance and refers to a physical position of legs.\nA few things that are visually similar to 'bent knees' but are not 'bent knees' are:\tcrouching\tsitting down\tslouching\nThere are several useful visual features to tell there are 'bent knees' and not similar things in a photo:\tknees are at an angle smaller than 180 degrees\tthe lower leg is angled away from the upper leg\tthe thighs may be parallel or not\tbent knees are often associated with standing up", 24], "baby animal": ["Yes. 'Baby animal' has a tangible appearance and refers to the young of an animal species.\nA few things that are visually similar to 'baby animal' but are not 'baby animal' are:\tstuffed animal\tteddy bear\tanimal toy\nThere are several useful visual features to tell there is 'baby animal' and not similar things in a photo:\tsmaller size than a full-grown animal\tless developed features, such as shorter legs and smaller ears\tplayful or curious demeanor with no signs of aggression", 24], "split": ["Yes. 'Split' has a tangible appearance and refers to something that is divided or separated into two or more parts.\nA few things that are visually similar to 'split' but are not 'split' are:\tcut\tcracked\tbroken\ttwisted\tdamaged\nThere are several useful visual features to tell there is 'split' and not similar things in a photo:\tparts are cleanly divided at the same point\tparts are still connected in some way\tparts are symmetrical or evenly sized\tto create a 'split,' there needs to be at least two parts", 24], "lane road": ["Yes. 'Lane road' has a tangible appearance and refers to a specific type of road design.\nA few things that are visually similar to 'lane road' but are not 'lane road' are:\tone-way road\tbike lane\twalking path\tcountry road\nThere are several useful visual features to tell there is 'lane road' and not similar things in a photo:\tthe road is divided into clear lanes\tforward and backward lanes are separated by dotted or solid yellow or white lines\tthe lanes are wide enough for cars or trucks to travel in each direction\tsometimes there are green arrows painted to indicate turning lanes or traffic flow\tdirectionality markings are typically painted", 24], "cloth napkin": ["Yes. 'Cloth napkin' has a tangible appearance and is a type of table linen.\nA few things that are visually similar to 'cloth napkin' but are not 'cloth napkin' are:\tpaper napkin\tdish towel\thandkerchief\tbandana\nThere are several useful visual features to tell there is 'cloth napkin' and not similar things in a photo:\tfabric texture\tfolded or draped on a table\tcut or shaped into a square or rectangle shape\toften with decorative edges or designs", 24], "peck": ["No. 'Peck' is too vague or abstract to be visually concrete.", 24], "silver bell": ["Yes. 'Silver bell' has a tangible appearance and is a type of bell.\nA few things that are visually similar to 'silver bell' but are not 'silver bell' are:\talarm clock\tbicycle bell\tschool bell\tchurch bell\nThere are several useful visual features to tell there is 'silver bell' and not similar things in a photo:\tsilver or metallic color\tbell shape with a circular bottom and a narrow top\tringer or clapper inside\tthe bell has a loop for hanging", 24], "chain fence": ["Yes. 'Chain fence' has a tangible appearance and is a type of fence.\nA few things that are visually similar to 'chain fence' but are not 'chain fence' are:\twire mesh fence\tbarbed wire fence\twooden fence\nThere are several useful visual features to tell there is 'chain fence' and not similar things in a photo:\tmade of metallic links or chains\tdiamond-shaped pattern\t\nsmall gaps between links\thollow tubular posts supporting the fence", 24], "london": ["No. 'London' is too vague or abstract to be distinguished in a photo. \n\nHowever, here are a few things that could be visually similar to 'London' but are not 'London':\n\n- Cityscapes with tall buildings and bridges\n- European cities with historical architecture\n- Busy metropolitan areas\n\nUseful visual features for distinguishing 'London' from other similar things in a photo might include:\n\n- Iconic landmarks such as Big Ben, Tower Bridge, or the London Eye\n- Red double-decker buses or black cabs\n- Street signs with names of well-known areas such as Oxford Street or Piccadilly Circus \n- Famous shopping areas such as Harrods or Covent Garden \n- Views of the River Thames or the city skyline\n- Red phone booths or red postboxes that are synonymous with London's streetscape.", 24], "pickle slice": ["Yes. 'Pickle slice' has a tangible appearance and is a specific type of food.\nA few things that are visually similar to 'pickle slice' but are not 'pickle slice' are:\tlime slice\tlemon slice\tonion slice\ttomato slice\nThere are several useful visual features to tell there is 'pickle slice' and not similar things in a photo:\tgreen or yellow in color\tcircular or oval shape\twith wavy lines and holes\tjuicy and shiny\ttexture like a cucumber", 24], "rail road": ["Yes. 'Rail road' has a tangible appearance and is a system of transportation.\nA few things that are visually similar to 'rail road' but are not 'rail road' are:\thighway\ttrail\tpath\nThere are several useful visual features to tell there is 'rail road' and not similar things in a photo:\ta pair of parallel metal tracks\tsleepers or ties that secure the tracks to the ground\trailroad switches and crossings\trailroad signs and signals\ttrains or locomotives running along the tracks.", 24], "bathroom sink faucet": ["Yes, 'bathroom sink faucet' is a visually concrete concept.\nA few things that are visually similar to 'bathroom sink faucet' but are not 'bathroom sink faucet' are: kitchen sink faucet, showerhead, garden hose tap, water dispenser tap.\nThere are several useful visual features to distinguish 'bathroom sink faucet' from similar things in a photo: \n- Location on the sink\n- Height and size of the spout\n- Number of handles or knobs\n- Shape and design of handles and spout \n- Presence of accessories such as a sprayer or soap dispenser \n- Features such as a pull-out spout or temperature control indicators.", 24], "stainless steel bowl": ["Yes. 'Stainless steel bowl' has a tangible appearance and is a kind of kitchenware.\nA few things that are visually similar to 'stainless steel bowl' but are not 'stainless steel bowl' are:\taluminum bowl\tglass bowl\tplastic bowl\twooden bowl\nThere are several useful visual features to tell there is 'stainless steel bowl' and not similar things in a photo:\tsilver or gray in color\tsmooth and shiny surface\treflection of other objects on its surface\tcircular shape\twith or without a rim", 24], "lilies": ["Yes. 'Lilies' has a tangible appearance and is a type of flowering plant.\nA few things that are visually similar to 'lilies' but are not 'lilies' are:\ttulips\troses\tdaffodils\nThere are several useful visual features to tell there is 'lilies' and not similar things in a photo:\tlong, slender stem\tbell or trumpet-shaped flowers\twith six petals\tarrangement of flowers at the top of the stem\tvariety of colors, including white, yellow, pink, and red", 24], "plume": ["Yes. 'Plume' has a tangible appearance and refers to a soft or billowy mass of something such as smoke, feathers, or water.\nA few things that are visually similar to 'plume' but are not 'plume' are:\tcloud\tfur\tfoam\texhaust fumes\nThere are several useful visual features to tell there is 'plume' and not similar things in a photo:\tfeathery or smoky appearance\tmovement or wispy texture\tbillowy or puffy shape\tvarying shades of color or opacity depending on the material of the plume.", 24], "elephant tusks": ["Yes. 'Elephant tusks' have a tangible appearance and are a part of an elephant's body.\nA few things that are visually similar to 'elephant tusks' but are not 'elephant tusks' are:\thorns\tantlers\tfossils\tskulls\nThere are several useful visual features to tell there are 'elephant tusks' and not similar things in a photo:\tlong curved ivory objects attached to an elephant's head\tsmooth surface with visible ridges and lines at the base and tip\tlarger and thicker than other animal horns or antlers.", 24], "reeds": ["Yes. 'Reeds' has a tangible appearance and refers to a type of tall, slender grass with hollow stems often found in wetlands.\nA few things that are visually similar to 'reeds' but are not 'reeds' are:\tgrasses\tbamboo\tcattails\nThere are several useful visual features to tell there is 'reeds' and not similar things in a photo:\ttall and slender\thollow stems\tgrowing in or near wetlands\tlarge clusters or groups of stems\twith long, thin leaves at the top", 24], "rock structure": ["Yes. 'Rock structure' has a tangible appearance and refers to the physical arrangement or composition of rocks.\nA few things that are visually similar to 'rock structure' but are not 'rock structure' are:\tmountains\tcaves\tcliffs\tboulders\nThere are several useful visual features to tell there is 'rock structure' and not similar things in a photo:\tpatterns or arrangements of rocks or stones\tdifferent types of rocks or layers of rock formation\ttextures, such as rough or smooth\tsymmetry or asymmetry in the arrangement of rocks.", 24], "flop": ["No. 'Flop' is too vague or abstract to be distinguished in a photo. However, if we refer to a physical action of 'flop', then it may have a tangible appearance.\nA few things that are visually similar to the physical action of 'flop' but are not 'flop' are:\tfalling over\tcollapsing\nThere are several useful visual features to distinguish a physical action of 'flop' from the listed similar things in a photo:\tlack of rigidity\tinability to maintain an upright position, leading to a sudden collapse\tor movement down or forward\tuncontrolled or awkward movement", 24], "blond man": ["Yes. 'Blond man' has a tangible appearance and refers to a male with blond hair.\nA few things that are visually similar to 'blond man' but are not 'blond man' are:\tred-haired man\tyellow-colored hat\thighlights on dark hair\tplatinum blonde-haired woman\nThere are several useful visual features to tell there is 'blond man' and not similar things in a photo:\twhite or pale-colored hair\tblue eyes, green eyes or gray eyes\tmale facial features, such as a prominent jawline or an Adam's apple", 24], "metal bolts": ["Yes. 'Metal bolts' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'metal bolts' but are not 'metal bolts' are:\tnails, screws, rivets\thooks\tpins\nThere are several useful visual features to tell there are 'metal bolts' and not similar things in a photo:\tsturdy metal cylinder shape\twith threads around the shaft\thead with a specific shape and pattern, such as hexagon or rounded\tbyte marks\tfrom a nut or wrench", 24], "computer printer": ["Yes. 'Computer printer' has a tangible appearance and is a device used for printing documents or images.\nA few things that are visually similar to 'computer printer' but are not 'computer printer' are:\tscanner\tcopy machine\tfax machine\nThere are several useful visual features to tell there is 'computer printer' and not similar things in a photo:\tpaper tray\tprint head or cartridge\tcontrol panel or display\tbutton or switch to power on/off, increase or decrease intensity or volume, or select different functions\tslots or ports for memory card or USB drives.", 24], "overcast skies": ["Yes. 'Overcast skies' has a tangible appearance and is a meteorological phenomenon.\nA few things that are visually similar to 'overcast skies' but are not 'overcast skies' are:\tfog\tsmoke\tdust storm\tsandstorm\nThere are several useful visual features to tell there is 'overcast skies' and not similar things in a photo:\theavy, thick, gray or white clouds covering most of the sky\tno visible sunshine or blue sky \tdull, dim or dark lighting, often with a cool temperature", 24], "kitchen chair": ["Yes. 'Kitchen chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'kitchen chair' but are not 'kitchen chair' are:\tdining chair\tbar stool\toffice chair\tliving room chair\nThere are several useful visual features to tell there is 'kitchen chair' and not similar things in a photo:\tusually made of wood or metal\toften has a cushioned seat and backrest\tstraight or slightly curved backrest\tsimple and functional design\tmatches the style and decor of the kitchen table or counter.", 24], "train windshield": ["Yes. 'Train windshield' has a tangible appearance and refers to the front window of a train.\nA few things that are visually similar to 'train windshield' but are not 'train windshield' are:\tcar windshield\tbus windshield\ttruck windshield\nThere are several useful visual features to tell there is 'train windshield' and not similar things in a photo:\tlong and wide rectangular shape\tbigger than car windshields\thas wipers and often protection grids or bars at the front\thas a characteristic shape determined by the shape of the train front it is connected to.", 24], "toothbrush handle": ["Yes. 'Toothbrush handle' has a tangible appearance and is a part of a dental hygiene tool.\nA few things that are visually similar to 'toothbrush handle' but are not 'toothbrush handle' are:\tpaintbrush handle\tmakeup brush handle\nThere are several useful visual features to tell there is 'toothbrush handle' and not similar things in a photo:\tlong and narrow shape\twith ridges or texture\tfor dental health\tuse with or without bristles", 24], "tourist bus": ["Yes. 'Tourist bus' has a tangible appearance and is a type of bus used for sightseeing trips.\nA few things that are visually similar to 'tourist bus' but are not 'tourist bus' are:\tpublic bus\tschool bus\tgreyhound bus\tcoach bus\nThere are several useful visual features to tell there is 'tourist bus' and not similar things in a photo:\tadvertising or tour graphics on the exterior\tbig windows\tpassengers inside wearing casual clothing\ttour guide emblem or sign on the front or side\topen roof or upper deck seating", 24], "silver ladder": ["Yes. 'Silver ladder' has a tangible appearance and is a type of ladder.\nA few things that are visually similar to 'silver ladder' but are not 'silver ladder' are:\tgray staircase\taluminum scaffolding\tsilver-colored art installation\tchrome statue\nThere are several useful visual features to tell there is 'silver ladder' and not similar things in a photo:\tlength with rungs between two vertical rails\tmetallic or shiny appearance\tsilver or silver-like color\tsupporting a person or an object in a vertical direction", 24], "glass flower vase": ["Yes. 'Glass flower vase' has a tangible appearance and is a type of decorative container.\nA few things that are visually similar to 'glass flower vase' but are not 'glass flower vase' are:\tcoffee mug\twater glass\tbottle\tcup\nThere are several useful visual features to tell there is 'glass flower vase' and not similar things in a photo:\ttransparency, made of glass or crystal\ttapered neck and wider body meant for holding flowers\tnarrow mouth and flared rim to help arrange flowers in a natural way", 24], "carrot slices": ["Yes. 'Carrot slices' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'carrot slices' but are not 'carrot slices' are:\tcucumber slices\tzucchini slices\tpotato slices\nThere are several useful visual features to tell there is 'carrot slices' and not similar things in a photo:\torange color\tsliced into circles or ovals\tcarrot texture on the surface", 24], "v": ["No. 'V' is too abstract to be considered visually concrete.\nThere are no things that are visually similar to 'v' as it is a letter in the alphabet. \nN/A.", 24], "table clothe": ["Yes. 'Table clothe' has a tangible appearance and is a type of cloth used to cover a table.\nA few things that are visually similar to 'table clothe' but are not 'table clothe' are:\tblanket\ttowel\tcurtain\t\nThere are several useful visual features to tell there is 'table clothe' and not similar things in a photo:\t\ncovers the top of a table\t\ndrapes over the edges of a table\t\ntypically used during meal times\t\ncomes in various colors and patterns", 24], "power cable": ["Yes. 'Power cable' has a tangible appearance and is a type of electrical cable.\nA few things that are visually similar to 'power cable' but are not 'power cable' are:\taudio cables\thdmi cables\tusb cables\tcharging cables\nThere are several useful visual features to tell there is 'power cable' and not similar things in a photo:\tthree-pronged plug or two-pronged plug\tthick insulation or coating\tmetal conductors or wires\tconnecting to a power source and a device", 24], "baby carriage": ["Yes. 'Baby carriage' has a tangible appearance and is a type of stroller or pram used to transport infants.\nA few things that are visually similar to 'baby carriage' but are not 'baby carriage' are:\twheelbarrow\tshopping cart\thandcart\nThere are several useful visual features to tell there is 'baby carriage' and not similar things in a photo:\tfour wheels\thandles\tfor an infant or young child\tpadded seating or sleeping area with restraining device.", 24], "cartons": ["Yes. 'Cartons' has a tangible appearance and is a type of packaging material.\nA few things that are visually similar to 'cartons' but are not 'cartons' are:\tboxes\tpaper bags\tenvelopes\tbottomless baskets\nThere are several useful visual features to tell there is 'cartons' and not similar things in a photo:\trectangular or square shape\tcardboard or paper material\tfolded with a top flap and side flaps\ttape or glue holding the flaps together\n", 24], "jetty": ["Yes. 'Jetty' has a tangible appearance and is a type of pier or structure that extends into a body of water.\nA few things that are visually similar to 'jetty' but are not 'jetty' are:\tpier\twharf\tboardwalk\tbreakwater\nThere are several useful visual features to tell there is 'jetty' and not similar things in a photo:\twooden or concrete structure built into the water\tused for boats or ships to dock or tie up\tdiffers from shoreline or beach surface or is surrounded by open water", 24], "magazine rack": ["Yes. 'Magazine rack' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'magazine rack' but are not 'magazine rack' are:\tbookshelf\tshoe rack\tfile cabinet\tend table\nThere are several useful visual features to tell there is 'magazine rack' and not similar things in a photo:\tvertical stand\tfor holding books and magazines\tseveral open compartments\thorizontal dividers for different items\tsmall and narrow structure intended to hold magazines and other printed materials", 24], "bookcases": ["Yes. 'Bookcases' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'bookcases' but are not 'bookcases' are:\tshelves\tcabinets\tlockers\tchests of drawers\nThere are several useful visual features to tell there is 'bookcases' and not similar things in a photo:\tvertical structure\tseveral shelves\thigher than it is wide\tcontains mostly books, but can also contain decorative items or other objects in the shelves.", 24], "tree shadows": ["Yes. 'Tree shadows' has a tangible appearance and often refer to the shape of a shadow that a tree creates when the sun shines.\nA few things that are visually similar to 'tree shadows' but are not 'tree shadows' are:\tcloud shadows\thuman shadows\tbuilding shadows\nThere are several useful visual features to tell there is 'tree shadows' and not similar things in a photo:\tthe shape of the shadow resembles the tree's shape\tthe shadow is cast onto the ground or a surface\twithin an outdoor setting, often with other trees in view\tthe shadow lengthens or shortens depending on the position of the sun", 24], "dividers": ["Yes. 'Dividers' has a tangible appearance and refer to a tool used for measuring and drawing circles.\nA few things that are visually similar to 'dividers' but are not 'dividers' are:\trulers\tprotractors\tcompasses\nThere are several useful visual features to tell there is 'dividers' and not similar things in a photo:\thave two sharp-pointed ends\tthat can move and be fixed at the desired distance from each other (to measure or draw circles)\tnot have measurement markings on the body (like rulers or protractors)", 24], "giraffe mouth": ["Yes. 'Giraffe mouth' has a tangible appearance and is a particular part of the animal's anatomy.\nA few things that are visually similar to 'giraffe mouth' but are not 'giraffe mouth' are:\tzebra mouth\thorse mouth\tcow mouth\nThere are several useful visual features to tell there is 'giraffe mouth' and not similar things in a photo:\tvery long and extendable\ttongue is prehensile (can grasp and hold things)\twith flat teeth\ton the lower jaw has a predentary bone (a specialized bone that supports the tongue)", 24], "wrist bands": ["Yes. 'Wrist bands' has a tangible appearance and is a type of accessory that people wear on their wrists.\nA few things that are visually similar to 'wrist bands' but are not 'wrist bands' are:\twatches\thair ties\tsweatbands\tjewelry\nThere are several useful visual features to tell there is 'wrist bands' and not similar things in a photo: made of fabric or rubber\thave a loop and hook or buckle closure\tusually worn on both wrists\tdoes not have a clock or time display\tfunction as sweat absorbers or identification tags.", 24], "saddle blanket": ["Yes. 'Saddle blanket' has a tangible appearance and is a type of blanket used for horse riding.\nA few things that are visually similar to 'saddle blanket' but are not 'saddle blanket' are:\tpicnic blanket\tthrow blanket\tquilt\tyoga mat\nThere are several useful visual features to tell there is 'saddle blanket' and not similar things in a photo:\tthin and lightweight\tsquare or rectangular shape\tfits under a saddle\tmay have patterns or designs\tmade of wool or other durable materials", 24], "bent leg": ["Yes. 'Bent leg' has a tangible appearance and is a type of leg position.\nA few things that are visually similar to 'bent leg' but are not 'bent leg' are:\tcrossed legs\tbowing legs\tfolded fabric\tbent arms\nThere are several useful visual features to tell there is 'bent leg' and not similar things in a photo:\tthe angle of the leg that is bent\tin relation to the other leg\tor other parts of the body (such as the torso)\tthe shape of the knee\tthe direction of the foot/bottom of the leg (pointing up or sideways)", 24], "metal scissors": ["Yes. 'Metal scissors' has a tangible appearance and is a type of cutting tool.\nA few things that are visually similar to 'metal scissors' but are not 'metal scissors' are:\tknives\ttongs\tshears\tpliers\nThere are several useful visual features to tell there is 'metal scissors' and not similar things in a photo:\ttwo sharp blades\tholes for fingers to grip\tmetallic finish", 24], "center table": ["Yes. 'Center table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'center table' but are not 'center table' are:\tcoffee table\tdesk\tdining table\tkitchen island\tbench\nThere are several useful visual features to tell there is 'center table' and not similar things in a photo:\tlower than a dining table, higher than a coffee table\tgenerally smaller than other tables\tplaced in the center of a room, usually surrounded by seating\tsquare, rectangular or round-shaped top\tsupported by a central pedestal or four legs.", 24], "metal beams": ["Yes. 'Metal beams' has a tangible appearance and refers to long, straight pieces of metal used primarily in construction.\nA few things that are visually similar to 'metal beams' but are not 'metal beams' are:\twires\tpipes\trails\tbars\nThere are several useful visual features to tell there is 'metal beams' and not similar things in a photo:\tlong and straight\toften have rivets or bolts\tused in building structures\tusually made of steel or iron\tsupport heavy weights and loads", 24], "grey brick": ["Yes. 'Grey brick' has a tangible appearance and is a building material.\nA few things that are visually similar to 'grey brick' but are not 'grey brick' are:\tcement\tblock\tstones\nThere are several useful visual features to tell there is 'grey brick' and not similar things in a photo:\trectangular\tinconsistent surface texture\tmortar between bricks\tcolor is mostly grey with a slight variation in shade", 24], "tote": ["Yes. 'Tote' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'tote' but are not 'tote' are:\tbackpack\thandbag\tpurse\tduffel bag\nThere are several useful visual features to tell there is 'tote' and not similar things in a photo:\tlarge and spacious\tbody with two handles\tno zippers or compartments often\topen top design\tsturdy and structured material like canvas or leather", 24], "wrought iron gate": ["Yes. 'Wrought iron gate' has a tangible appearance and is a type of entryway door.\nA few things that are visually similar to 'wrought iron gate' but are not 'wrought iron gate' are:\tchain-link fence\tgarden trellis\twire mesh fence\t\nThere are several useful visual features to tell there is 'wrought iron gate' and not similar things in a photo:\tdetailed patterns, designs or motifs\tmade of iron or steel\theavy and sturdy construction\thinged or sliding mechanism\thand-forged appearance", 24], "metal hook": ["Yes. 'Metal hook' has a tangible appearance and is a type of fastening tool.\nA few things that are visually similar to 'metal hook' but are not 'metal hook' are:\tclothes hanger\tcarabiner\tpaper clip\tfishing hook\nThere are several useful visual features to tell there is 'metal hook' and not similar things in a photo:\tJ-shaped\tinward-facing tip\tusually made of metal or another sturdy material\teither single or double-ended", 24], "nature": ["No. 'Nature' is too vague or abstract to be distinguished in a photo.", 24], "dark animal": ["No. 'Dark animal' is too vague or abstract to be distinguished in a photo.", 24], "brass door knob": ["Yes. 'Brass door knob' has a tangible appearance and is a type of door handle.\nA few things that are visually similar to 'brass door knob' but are not 'brass door knob' are:\tsteel door knob\tplastic door knob\twooden door knob\tglass door knob\nThere are several useful visual features to tell there is 'brass door knob' and not similar things in a photo:\tmade of brass or looks brass-like\tcircular or spherical in shape\tmounted on a door or a drawer\thas ridges or grooves for grip", 24], "deep": ["No. 'Deep' is too vague or abstract to be distinguished in a photo.", 24], "bath rug": ["Yes. 'Bath rug' has a tangible appearance and is a kind of rug.\nA few things that are visually similar to 'bath rug' but are not 'bath rug' are:\tdoormat\tbathroom tiles\tcarpet\tbathroom mat\nThere are several useful visual features to tell there is 'bath rug' and not similar things in a photo:\tabsorbent and soft material\tsmall size, mainly rectangular or circular shape\tbright colors or patterns\tcan have anti-slippery surface on the bottom\tside or corner of the bathtub or shower", 24], "shadow boy": ["The concept 'shadow boy' is too vague or abstract to be considered a visually concrete concept.", 24], "collard shirt": ["Yes. 'Collard shirt' has a tangible appearance and is a type of shirt with a collar around the neck.\nA few things that are visually similar to 'collard shirt' but are not 'collard shirt' are:\tpolo shirt\tdress shirt\tt-shirt\tbutton-down shirt\thenley shirt\nThere are several useful visual features to tell 'collard shirt' apart from other similar shirts in a photo:\tcollar around the neck\twith or without buttons or a tie\tpointed, spread, or button-down collar\tsleeves may be long or short\tmade of various fabrics (cotton, silk, etc.)", 24], "animal grazing": ["Yes. 'Animal grazing' has a tangible appearance and refers to animals feeding on plants in a pasture.\nA few things that are visually similar to 'animal grazing' but are not 'animal grazing' are:\tanimals standing still in a pasture\tanimals running in a pasture\tpeople working in a pasture\nThere are several useful visual features to tell there is 'animal grazing' and not similar things in a photo:\tanimals of different sizes and types\teating or nibbling on grass or other plants\twithin a fenced or enclosed area\tmoving slowly or staying in one general area", 24], "stone path": ["Yes. 'Stone path' has a tangible appearance and is a path made of stones.\nA few things that are visually similar to 'stone path' but are not 'stone path' are:\tmossy surface\tpebble beach\tpaved road\twooden deck\nThere are several useful visual features to tell there is 'stone path' and not similar things in a photo:\trocks or stones of various sizes\tarrangement in a linear direction\tsurrounded by grass or other vegetation", 24], "tissue dispenser": ["Yes. 'Tissue dispenser' has a tangible appearance and is an object for holding tissues.\nA few things that are visually similar to 'tissue dispenser' but are not 'tissue dispenser' are:\tbox\tjar\thandkerchief case\nUseful visual features to distinguish 'tissue dispenser' from other objects in a photo are:\telongated rectangular shape\tperforated opening on top or sides\tfor holding a tissue box or loose tissues\teasy-to-reach tissues with one hand\tclusters of tissues popped up from the opening", 24], "stone sidewalk": ["Yes. 'Stone sidewalk' has a tangible appearance and is a type of pavement.\nA few things that are visually similar to 'stone sidewalk' but are not 'stone sidewalk' are:\tcement sidewalk\tasphalt pavement\tbrick pathway\tpaved road\tsandy path\nThere are several useful visual features to tell there is 'stone sidewalk' and not similar things in a photo:\tnatural stone material\trough texture\tstraight or curvy lines\tpatterns or shapes formed by the stones", 24], "pearl": ["Yes. 'Pearl' has a tangible appearance.\nA few things that are visually similar to 'pearl' but are not 'pearl' are:\tbead\tstone\tmarble\tbubble\nThere are several useful visual features to tell there is 'pearl' and not similar things in a photo:\tround\tand symmetrical\tpearly or iridescent luster\tsmooth or slightly bumpy texture\tsmall, drilled holes on the surface (if used in jewelry)", 24], "mammal": ["No. 'Mammal' is too vague or abstract to be distinguished in a photo.\nHowever, a few things that are visually similar to individual mammals, but not to 'mammal' as a category, are:\telk\tdolphin\tbadger\tgorilla\tplatypus\nTo distinguish a mammal from other animals in a photo, some useful visual features may include:\thaving fur or hair\tgiving birth to live young\tbreastfeeding their young\thaving four-chambered hearts\thaving specialized teeth (incisors, canines, premolars, and molars)", 24], "flusher": ["No. 'Flusher' is too vague and abstract to be a visually concrete concept. Can you provide more context or information to clarify what 'flusher' refers to?", 24], "dark grey": ["Yes. 'Dark grey' has a tangible appearance and is a specific shade of the color grey.\nA few things that are visually similar to 'dark grey' but are not 'dark grey' are:\tlight grey\tsilver\tblack\t\nThere are several useful visual features to tell there is 'dark grey' and not similar things in a photo:\ta shade of grey that is closer to black than white\tdarker than other shades of grey\toften used for concrete or metal objects", 24], "man hand": ["Yes. 'Man hand' has a tangible appearance.\nA few things that are visually similar to 'man hand' but are not 'man hand' are:\twoman hand\tchild hand\tape hand\nThere are several useful visual features to tell there is 'man hand' and not similar things in a photo:\n\trelatively larger in size than a woman or child hand\tusually more hair on the back of the hand\tcustomarily wearing a watch\twearing rings or other accessories\thandshake gesture or other common gestures", 24], "lamp pole": ["Yes. 'Lamp pole' has a tangible appearance and is a type of pole used for street lighting.\nA few things that are visually similar to 'lamp pole' but are not 'lamp pole' are:\tflagpole\tcamera tripod\tmicrophone stand\nThere are several useful visual features to tell there is 'lamp pole' and not similar things in a photo:\ttall pole\tmetal or concrete material\tlarge lamp fixture on top of the pole\tmounted on streets or sidewalks with wires and cables running along the pole", 24], "game system": ["Yes. 'Game system' has a tangible appearance.\nA few things that are visually similar to 'game system' but are not 'game system' are:\tmedia player \tset-top box\thome theater system\tblu-ray player\nThere are several useful visual features to tell there is 'game system' and not similar things in a photo:\tcontrollers with buttons and joysticks\tcables and cords connecting the console to the TV\tgame cartridges or discs\tvideo or graphics displayed on the screen during gameplay", 24], "peaks": ["Yes. 'Peaks' has a tangible appearance and refers to high pointed or curved mountains.\nA few things that are visually similar to 'peaks' but are not 'peaks' are:\thills\tpyramids\ttowers\tbuildings\trocks\nThere are several useful visual features to tell there are 'peaks' and not similar things in a photo:\tgeological formations with high elevations\tthe highest point of a mountain range\tor distinct, pointed section of a mountain\tgraphic curvature or steep angle on the top of a mountain", 24], "orange bucket": ["Yes. 'Orange bucket' has a tangible appearance and is a specific type of container.\nA few things that are visually similar to 'orange bucket' but are not 'orange bucket' are:\torange paint can\tplastic bowl\twith handle\tbucket of other color\nThere are several useful visual features to tell there is 'orange bucket' and not similar things in a photo:\ta bucket shape\twith a handle\tbright, solid orange color\tno lid or cover used\tthicker and more durable than a bowl or a paint can", 24], "hairy": ["Yes. 'Hairy' has a tangible appearance and refers to the presence of hair or fur on a surface.\nA few things that are visually similar to 'hairy' but are not 'hairy' are:\tfuzzy\tcottony\tvelvety\nThere are several useful visual features to tell there is 'hairy' and not similar things in a photo:\thair or fur sticking out from a surface or texture\thair or fur covering a large portion of the surface or texture\thair or fur appearing in distinct strands or clumps", 24], "water stains": ["Yes. 'Water stains' has a tangible appearance and is a kind of discoloration caused by water damage.\nA few things that are visually similar to 'water stains' but are not 'water stains' are:\tpaint drips\tcoffee spills\tshadows \t\nThere are several useful visual features to tell there are 'water stains' and not similar things in a photo:\tcircular or irregular shape\tdarker color than the surrounding area\tblurry edges\tor a water ring effect\ton a ceiling or wall.", 24], "paddles": ["Yes. 'Paddles' has a tangible appearance and is a type of tool used for rowing or steering a boat.\nA few things that are visually similar to 'paddles' but are not 'paddles' are:\toars\tshovels\trackets\tspades\nThere are several useful visual features to tell there are 'paddles' and not similar things in a photo:\t\n- A long handle with a blade or end that is wider than the handle\n- Usually made of wood, plastic, or metal\n- Used for rowing or steering a boat", 24], "headrest": ["Yes. 'Headrest' has a tangible appearance and is a type of piece of furniture.\nA few things that are visually similar to 'headrest' but are not 'headrest' are:\tpillow\tcushion\tarmrest\t\nThere are several useful visual features to tell there is 'headrest' and not similar things in a photo:\tfixed to the back of a chair\tor seat\tergonomic shape to support the head\tfirm and sturdy material\tthat the user rests the back of their head on.", 24], "mop": ["Yes. 'Mop' has a tangible appearance and is a cleaning tool.\nA few things that are visually similar to 'mop' but are not 'mop' are:\t\nbroom\t\ndustpan\t\nvacuum cleaner\t\nfeather duster\t\nThere are several useful visual features to tell there is 'mop' and not similar things in a photo:\tlong handle\tsponge or cloth head\twet or damp in appearance\tused to clean floors or walls", 24], "gorilla": ["Yes. 'Gorilla' has a tangible appearance and is a type of primate.\nA few things that are visually similar to 'gorilla' but are not 'gorilla' are:\tchimpanzee\torangutan\tbaboon\tmandrill\nThere are several useful visual features to tell there is 'gorilla' and not similar things in a photo:\tlarge and muscular body\tbrown or black fur\tprominent brow ridge\tand muzzle,\tbig nostrils\thairless face\tpatch of white or grey hair on their back\thunched over posture", 24], "metal street lamp": ["Yes. 'Metal street lamp' has a tangible appearance and is a type of outdoor lighting fixture.\nA few things that are visually similar to 'metal street lamp' but are not 'metal street lamp' are:\tfloodlights\tspotlights\tgarden lights\toutdoor lanterns\nThere are several useful visual features to tell there is 'metal street lamp' and not similar things in a photo:\ttall and thin metal pole at the base\ttapered pole shape towards the top\tcurved or straight metal arm for holding the light fixture\tlarge lampshade that directs the light downwards\tintegrated or separate electrical wiring", 24], "telephone cord": ["Yes, 'telephone cord' has a visually concrete appearance.\nA few things that are visually similar to 'telephone cord' but are not 'telephone cord' are:\tchewing gum\tmetal wires\tbungee cords\trope\nThere are several useful visual features to tell there is 'telephone cord' and not similar things in a photo:\tcoiled or spiraled shape\tthin and flexible plastic or rubber material\tconnecting two objects\toften have a small connector on the end", 24], "york yankees": ["No. 'York Yankees' is too vague or abstract to be distinguished in a photo. It refers to a professional baseball team.\nThere are no things visually similar to 'York Yankees' that are not 'York Yankees' due to its specific reference to the team. However, things that are visually similar to the team's logo might include:\tother baseball team logos\tthat incorporate a \"N\" or \"Y\"\nUseful visual features for distinguishing 'York Yankees' from other teams with similar logos might include:\tthe interlocking \"NY\" logo\tthe team colors (navy blue and white)\tthe presence of the team name (in English or in Japanese characters) on the uniform or merchandise.", 24], "garbage cans": ["Yes. 'Garbage cans' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'garbage cans' but are not 'garbage cans' are:\tstorage containers\tbuckets\tpots\tplanters\nThere are several useful visual features to tell there is 'garbage cans' and not similar things in a photo:\tround or rectangular shape\twith a lid\tmade of plastic or metal\thandles on the sides\tor located near buildings or on the street", 24], "sports utility vehicle": ["Yes. 'sports utility vehicle' has a tangible appearance and is a type of automobile.\nA few things that are visually similar to 'sports utility vehicle' but are not 'sports utility vehicle' are:\tjeep\ttruck\tminivan\tcrossover\tcar\nThere are several useful visual features to tell there is 'sports utility vehicle' and not similar things in a photo:\ttall and boxy body shape\tlarge and chunky wheels\tclearance height off the ground\troof rails and luggage racks for carrying outdoor gear\tmuscular and rugged appearance 4-wheel drive or all-wheel drive capability.", 24], "yellow center": ["Yes. 'Yellow center' has a tangible appearance and is a visual characteristic of certain objects or organisms.\nA few things that are visually similar to 'yellow center' but are not 'yellow center' are:\tyellow dot\tyellow circle\tyellow shape \nThere are no useful visual features to distinguish 'yellow center' from the listed similar things in a photo as they all share the same visual characteristics. Instead, context and surrounding features of the image can help in identifying what the yellow center is referring to.", 24], "kitchen drawers": ["Yes. 'Kitchen drawers' has a tangible appearance and is a type of storage unit.\nA few things that are visually similar to 'kitchen drawers' but are not 'kitchen drawers' are:\tfile cabinets\tdressers\ttoolboxes\tdesk drawers\nThere are several useful visual features to tell there is 'kitchen drawers' and not similar things in a photo:\tinstalled in a kitchen cabinet or counter\ttop drawer is typically shorter\tmay have knobs or handles, often in a metallic material\tcontain utensils, cutlery, or kitchen tools", 24], "sleeve t-shirt": ["Yes. 'Sleeve t-shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sleeve t-shirt' but are not 'sleeve t-shirt' are:\ttank top\tpolo shirt\thenley shirt\tlong sleeve shirt\nThere are several useful visual features to tell there is 'sleeve t-shirt' and not similar things in a photo: \tsimple design\tround neckline\tshort sleeves\tstraight or slightly fitted cut\tmade of lightweight and breathable fabric", 24], "cloudy overcast sky": ["Yes. 'Cloudy overcast sky' has a tangible appearance and is a weather condition.\nA few things that are visually similar to 'cloudy overcast sky' but are not 'cloudy overcast sky' are:\tfog\tbad lighting/dimness\thaze\tsmoke\nThere are several useful visual features to tell there is 'cloudy overcast sky' and not similar things in a photo:\tlow visibility\tdull, muted lighting\tgray or white color\thigh moisture content in the air\tfloaty appearance of clouds\tif outside, rain might also be present", 24], "boat dock": ["Yes. 'Boat dock' has a tangible appearance and is a structure built alongside water for boats to dock and unload passengers and cargo.\nA few things that are visually similar to 'boat dock' but are not 'boat dock' are:\tpier\tjetty\twharf\tquay\nThere are several useful visual features to tell there is 'boat dock' and not similar things in a photo:\t\n \n- Proximity to water: A boat dock will always be located beside or in the water, unlike a pier or a wharf.\n\n- Accessibility for boats: A boat dock will feature removable cleats or bollards to which boats can tie off, unlike a jetty or a quay.\n\n- Size and shape: Boat docks may come in various shapes and sizes, but they usually have a long, rectangular platform with wooden planks or a sturdy metal grate.\n\n- Amenities available: Some boat docks might also have additional features, like fueling systems, slip rentals, restrooms or people waiting for boats to disembark.", 24], "bus windshield": ["Yes. 'Bus windshield' has a tangible appearance and refers to the specific type of windshield found on buses.\nA few things that are visually similar to 'bus windshield' but are not 'bus windshield' are:\tcar windshield\ttruck windshield\tmotorbike windshield\tbicycle windshield\nThere are several useful visual features to tell there is 'bus windshield' and not similar things in a photo:\tlarge size\twider than it is tall\tangled or curved shape\tcovers the entire front of the bus", 24], "plastic knife": ["Yes. 'Plastic knife' has a tangible appearance and is a kind of utensil.\nA few things that are visually similar to 'plastic knife' but are not 'plastic knife' are:\tmetal knife\twooden spoon\tplastic fork\tglass cup\nThere are several useful visual features to tell there is 'plastic knife' and not similar things in a photo:\tthin and flat blade made of plastic\thandle also made of plastic\tno pointed tip, but rather a rounded one\tthat is often disposable", 24], "sash": ["Yes. 'Sash' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'sash' but are not 'sash' are:\tbelt\theadband\tscarf\ttie\nThere are several useful visual features to tell there is 'sash' and not similar things in a photo:\tlong and narrow piece of fabric\tworn around the waist or over the shoulder\tcan be tied or fastened with a knot or a buckle\tusually more ornamental than functional.", 24], "headlight front motorcycle": ["Yes. 'Headlight front motorcycle' has a tangible appearance and refers to the headlight mounted on the front of a motorcycle.\nA few things that are visually similar to 'headlight front motorcycle' but are not 'headlight front motorcycle' are:\tHeadlights on a car\tSpotlights on a stage\tStreetlamps at night\nThere are several useful visual features to tell there is 'headlight front motorcycle' and not similar things in a photo:\tmounted on the front of a motorcycle\tcircular or elliptical shape\tbright or white light\tbezel or housing around the light.", 24], "bear fur": ["Yes, 'bear fur' has a tangible appearance and is the coat of fur from a bear.\nA few things that are visually similar to 'bear fur' but are not 'bear fur' are:\tcowhide\thorsehair\twool\tcamel hair\trope\nThere are several useful visual features to tell there is 'bear fur' and not similar things in a photo:\tthick, dense coat of fur\tblack, brown, or white color\thair length and texture\ttypically found on a bear or a stuffed bear", 24], "cobblestone": ["Yes. 'Cobblestone' has a tangible appearance and is a type of stone used for paving roads and walkways.\nA few things that are visually similar to 'cobblestone' but are not 'cobblestone' are:\tboulder\trock\tpaver\ttile\nThere are several useful visual features to tell there is 'cobblestone' and not similar things in a photo:\trough and uneven surface\trounded edges\tvarious sizes and shapes\tused for outdoor paving\tor sidewalk construction.", 24], "attachment": ["No. 'Attachment' is too vague or abstract to be distinguished in a photo.", 24], "motorcycle kickstand": ["Yes. 'Motorcycle kickstand' has a tangible appearance and is a specific part of a motorcycle.\nA few things that are visually similar to 'motorcycle kickstand' but are not 'motorcycle kickstand' are:\tbike stand\tboat stand\twheel chock\t\nThere are several useful visual features to tell there is 'motorcycle kickstand' and not similar things in a photo:\tattached to a motorcycle\ttriangular shaped and sturdy\tsupporting the weight of the motorcycle in a leaning position.", 24], "toolbox": ["Yes. 'Toolbox' has a tangible appearance and is a container used for carrying tools.\nA few things that are visually similar to 'toolbox' but are not 'toolbox' are:\tlunchbox\ttackle box\tmake-up kit\ttravel bag\nThere are several useful visual features to tell there is 'toolbox' and not similar things in a photo:\thinged lid\tmetal or plastic construction\thandle for carrying tools\tdivided compartments or trays\tfor different tool sizes and shapes", 24], "windsurfer": ["Yes. 'Windsurfer' has a tangible appearance and is a type of water sport.\nA few things that are visually similar to 'windsurfer' but are not 'windsurfer' are:\tsailboat\tkayak\tcanoe\tstand-up paddleboard\tswimmer\nThere are several useful visual features to tell there is 'windsurfer' and not similar things in a photo:\tstanding on a board\tbalancing with a sail\tin a body of water\triding waves or relying on wind to move", 24], "luggage carts": ["Yes. 'Luggage carts' has a tangible appearance and is a kind of cart.\nA few things that are visually similar to 'luggage carts' but are not 'luggage carts' are:\tshopping carts\thand trucks\tgolf carts\t\nThere are several useful visual features to tell there is 'luggage carts' and not similar things in a photo:\tlocated at an airport or a train station\thave several tiers\tfor carrying bags and suitcases\thave wheels and a handle for pushing and pulling.", 24], "silver bucket": ["Yes. 'Silver bucket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'silver bucket' but are not 'silver bucket' are:\tbasin\tpail\ttub\tcan\twaste bin\t\nThere are several useful visual features to tell there is 'silver bucket' and not similar things in a photo:\tsilver color\tmetallic shine\tcylindrical or bucket-like shape\twith a handle\tmade of a reflective material, but not glass or plastic.", 24], "dark wall": ["Yes. 'Dark wall' has a tangible appearance.\nA few things that are visually similar to 'dark wall' but are not 'dark wall' are:\tshadow\tblackboard\tpaint\tslate\tchalkboard\nThere are several useful visual features to tell there is 'dark wall' and not similar things in a photo:\tsolid and flat\ttextured or smooth\tdark color\tlack of light reflection", 24], "plastic jug": ["Yes. 'Plastic jug' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic jug' but are not 'plastic jug' are:\tplastic bottle\tmilk carton\tthermos\tglass jar\nThere are several useful visual features to tell there is 'plastic jug' and not similar things in a photo:\tlarge capacity\thandle\ton top: spout or lid\tmade of plastic or resin", 24], "u": ["No. 'u' is too abstract to have a tangible appearance or be distinguished in a photo.", 24], "tail-fin": ["Yes. 'Tail-fin' has a tangible appearance and is a part of a vehicle, usually a car or an airplane.\nA few things that are visually similar to 'tail-fin' but are not 'tail-fin' are:\tboat rudder\tshark's fin\tdolphin's dorsal fin\nThere are several useful visual features to tell there is 'tail-fin' and not similar things in a photo:\tattached to the back of a vehicle\tshaped like a triangle or a trapezoid\tserves an aerodynamic or steering function\tdifferent color or material from the rest of the vehicle.", 24], "parking signs": ["Yes. 'Parking signs' has a tangible appearance and is a kind of sign.\nA few things that are visually similar to 'parking signs' but are not 'parking signs' are:\tstreet signs\tspeed limit signs\twarning signs\tdirections signs\nThere are several useful visual features to tell there is 'parking signs' and not similar things in a photo:\tblue, white, or green background with text and/or symbols\tarrows indicating direction of parking or no parking\ttimes or days designating restrictions on parking\tlocation-specific information (e.g. permit required)", 24], "screwdriver": ["Yes. 'Screwdriver' has a tangible appearance and is a tool used for tightening screws.\nA few things that are visually similar to 'screwdriver' but are not 'screwdriver' are:\tknife\tnail file\ttweezers\tpry bar\nThere are several useful visual features to tell there is 'screwdriver' and not similar things in a photo:\tlong and straight or slightly curved shape\tmetallic or plastic grip at one end\tthin, flat or Philips head at the other end", 24], "freeway": ["Yes. 'Freeway' has a tangible appearance and is a type of road.\nA few things that are visually similar to 'freeway' but are not 'freeway' are:\tcountry road\tbike path\twalkway\trailroad track\nThere are several useful visual features to tell there is 'freeway' and not similar things in a photo:\twide road with multiple lanes\tunobstructed view of the sky and horizon\tno at-grade crossings or intersections\ttraffic signage and markings", 24], "brakes": ["Yes. 'Brakes' has a tangible appearance and is a mechanical device.\nA few things that are visually similar to 'brakes' but are not 'brakes' are:\taccelerator\tpedal\thandbrake\t\nThere are several useful visual features to tell there are 'brakes' and not similar things in a photo:\tdisc or drum-shaped components\tmetallic look\tlocated near the wheel of a car\tor bike", 24], "shadow sidewalk": ["Yes. 'Shadow sidewalk' has a tangible appearance.\nThere are no things that are visually similar to 'shadow sidewalk' but are not 'shadow sidewalk'.\nUseful visual features for distinguishing 'shadow sidewalk' can include:\tpavement or sidewalk surface\tcast shadows from structures like trees or buildings\tdiffering amounts of sunlight hitting the pavement creating different shadow shapes and sizes.", 24], "beige tiles": ["Yes. 'Beige tiles' has a tangible appearance and is a type of flooring or wall covering.\nA few things that are visually similar to 'beige tiles' but are not 'beige tiles' are:\twooden planks\tbrown linoleum\tgrey concrete\nThere are several useful visual features to tell there is 'beige tiles' and not similar things in a photo:\tregular square or rectangular shape\tsmooth or matte surface\tbeige or light brown color\tgrout lines in between tiles", 24], "fire trucks": ["Yes. 'Fire trucks' has a tangible appearance and is a type of emergency vehicle.\nA few things that are visually similar to 'fire trucks' but are not 'fire trucks' are:\tambulance\tpolice cars\ttrucks\nThere are several useful visual features to tell there is 'fire trucks' and not similar things in a photo:\tbright red color\t\"Ladder\" written on the truck\tlong ladders and hoses\temergency lights and sirens\t\"Fire department\" logo or \"FD\" abbreviation", 24], "refection": ["No. 'Reflection' is too abstract to be distinguished in a photo, but 'reflection' can be seen visually in a photo when light is reflected off a surface like water or mirror.\nA few things that are visually similar to 'reflection' are:\trefraction\tshiny objects\tpolished finishes\nThere are several useful visual features to tell there is 'reflection' and not similar things in a photo: a mirror-like image visible on a surface where light reflects", 24], "life saver": ["Yes. 'Life saver' has a tangible appearance and is a type of rescue equipment.\nA few things that are visually similar to 'life saver' but are not 'life saver' are:\tdonut-shaped pool float\tbuoy\tfrisbee\nThere are several useful visual features to tell there is 'life saver' and not similar things in a photo:\tbright red or orange color with white stripes\tdonut-shaped form\twith a rope attached to it\tfor use in water rescue or emergency situations", 24], "dog eye": ["Yes. 'Dog eye' has a tangible appearance and is a part of a dog's anatomy.\nA few things that are visually similar to 'dog eye' but are not 'dog eye' are: cat eye, horse eye, human eye\nThere are several useful visual features to tell there is 'dog eye' and not similar things in a photo:\trounded shape\tdark pupil\tsometimes visible third eyelid around the corner of the eye\tvarious colors - brown, blue, green, grey, amber\tevidence of eyeshine when looking at a photo taken in low-light conditions", 24], "yak": ["Yes. 'Yak' has a tangible appearance and is a type of a bovine animal.\nA few things that are visually similar to 'yak' but are not 'yak' are:\tbuffalo\tbison\tox\tcow\nThere are several useful visual features to tell there is 'yak' and not similar things in a photo:\thorns curving upwards and outwards\thairy coat, usually black or brown\thump on the shoulders\tlong tail\tpointed ears", 24], "fighter plane": ["Yes. 'Fighter plane' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'fighter plane' but are not 'fighter plane' are:\tpassenger planes\thelicopters\tdrones\tgliders\tbonuses in a video game\nThere are several useful visual features to tell there is 'fighter plane' and not similar things in a photo:\tlow-wing design\tsleek and streamlined appearance\tcockpit for pilot\tmissiles or other weapons under the wings or fuselage\ttwin vertical tail fins", 24], "table leg": ["Yes. 'Table leg' has a tangible appearance and is a part of furniture.\nA few things that are visually similar to 'table leg' but are not 'table leg' are:\tchair leg\tbed leg\tshelf support\tcabinet leg\nThere are several useful visual features to tell there is 'table leg' and not similar things in a photo:\tsupporting the table\ttop and bottom distinctly larger in diameter than the middle\tcylindrical or square shape\tmade of wood, metal, or plastic\tmounted vertically\tresponsible for holding and stabilizing the table top.", 23], "silver buttons": ["Yes. 'Silver buttons' has a tangible appearance and is a kind of fastener.\nA few things that are visually similar to 'silver buttons' but are not 'silver buttons' are:\tzippers\tsnaps\tbuckles\tdome buttons\nThere are several useful visual features to tell there are 'silver buttons' and not similar things in a photo:\tround plaque or disk-shaped object\tsilver metallic color\tone or more holes for sewing\traised edges or patterns on the surface", 23], "bushy trees": ["Yes. 'Bushy trees' has a tangible appearance and is a type of plant.\n\nA few things that are visually similar to 'bushy trees' but are not 'bushy trees' are:\tbushes\thedges\tgrasses\n\nThere are several useful visual features to tell there is 'bushy trees' and not similar things in a photo:\t\ntall with many branching limbs\t\na thick crown of foliage\t\nleaves spread out from the trunk\t\noften paired with a woody trunk", 23], "grey bricks": ["Yes. 'grey bricks' has a tangible appearance and refers to a building material.\nA few things that are visually similar to 'grey bricks' but are not 'grey bricks' are:\trocks\tconcrete blocks\tpavers\nThere are several useful visual features to tell there is 'grey bricks' and not similar things in a photo:\trectangular in shape\tconsistent color, usually grey or grayish-brown\ttextured surface\tstacked on top of one another\tin a wall or structure", 23], "color wall": ["Yes. 'Color wall' has a tangible appearance and is a wall with different colors or shades.\nA few things that are visually similar to 'color wall' but are not 'color wall' are:\tpainting\tcanvas\tcollage\nThere are several useful visual features to tell there is 'color wall' and not similar things in a photo:\ta wall\tsome parts of different colors or shades\tno specific image, figure or painting\thas a uniform surface\thas different colors or shades that look like they were put intentionally", 23], "pull": ["No. 'Pull' is too vague or abstract to have a tangible appearance. It's an action or force, not an object. \n\nTherefore, there are no visually similar things to 'pull' that can be listed, and there are no useful visual features for distinguishing it from them.", 23], "burn mark": ["Yes. 'Burn mark' has a tangible appearance and is a type of physical damage.\nA few things that are visually similar to 'burn mark' but are not 'burn mark' are:\tstain\tscrape\tcracks\tbruises\nThere are several useful visual features to tell there is 'burn mark' and not similar things in a photo:\tcharred or discolored surface\twith a clear border or defined shape\tmay be raised or sunken in\ttexture of the surface may be altered", 23], "hairdryer": ["Yes. 'Hairdryer' has a tangible appearance and is a type of appliance.\nA few things that are visually similar to 'hairdryer' but are not 'hairdryer' are:\tvacuum cleaner\tfan\tmicrowave\tcoffee maker\nThere are several useful visual features to tell there is 'hairdryer' and not similar things in a photo:\tlong, narrow nozzle\tforcing air\toutlet for hot or warm air\thand-held size, also available as salon size and wall-mounted style.", 23], "pylon": ["Yes. 'Pylon' has a tangible appearance and is a kind of structure typically used for power lines.\nA few things that are visually similar to 'pylon' but are not 'pylon' are:\ttower\tbuilding\tchimney\nThere are several useful visual features to tell there is 'pylon' and not similar things in a photo:\tslim, tall tower structure\twith multiple wires or cables attached\tto support electricity transmission or telecommunication lines.", 23], "telephone lines": ["Yes. 'Telephone lines' has a tangible appearance and is a type of wiring.\n\nA few things that are visually similar to 'telephone lines' but are not 'telephone lines' are:\tpower lines\tfishing lines\tpipes\tsticks\n\nThere are several useful visual features to tell there are 'telephone lines' and not similar things in a photo:\t\nthin cables\tstrung between poles\tor hanging\tfrom a building\tor across a body of water\tcrisscrossing the sky or flattened against a background\tof the sky\thook-like shapes at the top of poles.", 23], "emergency vehicle": ["Yes. 'Emergency vehicle' has a tangible appearance and is a type of vehicle used in emergency situations.\nA few things that are visually similar to 'emergency vehicle' but are not 'emergency vehicle' are:\tconstruction vehicle\ttruck\tbuses\ttaxis\tcaravans\nThere are several useful visual features to tell there is 'emergency vehicle' and not similar things in a photo:\tsirens\tflashy lights\tbright colors, such as red, blue or yellow\tambulance, fire truck or police car markings and logos\tIdentification number and label related to emergency services.", 23], "prt": ["No. 'prt' is too vague or abstract to have a tangible appearance or be depicted visually. It is not clear what 'prt' refers to without further context or explanation.", 23], "treeline": ["Yes. 'Treeline' has a tangible appearance and is the line where the trees meet the open sky.\nA few things that are visually similar to 'treeline' but are not 'treeline' are:\thorizon\tmountain range\tcity skyline\nThere are several useful visual features to tell there is 'treeline' and not similar things in a photo:\ta horizontal line where trees meet sky\ttrees with varying heights and shapes\tclose proximity to the ground\tor not obstructed by other landscape features", 23], "brown lamp": ["Yes. 'Brown lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'brown lamp' but are not 'brown lamp' are:\ttables\tchairs\tplastic things\tmetal things\nThere are several useful visual features to tell there is 'brown lamp' and not similar things in a photo:\tbrown color\tlampshade\tlamp base\tswitch or button\tplugged in or wired to a power source\tlight bulb", 23], "shoe strings": ["Yes. 'Shoe strings' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'shoe strings' but are not 'shoe strings' are:\thair ties\tribbons\tthick strings\nThere are several useful visual features to tell there is 'shoe strings' and not similar things in a photo:\tthinner than ribbons\tor hair ties\ttypically made of fabric, leather or plastic\ttied specifically through shoe eyelets or holes", 23], "document": ["Yes. 'Document' has a tangible appearance and typically refers to a piece of paper or digital file containing information.\nA few things that are visually similar to 'document' but are not 'document' are:\tblank paper folder\tnotebook\treceipt\tscreen with text\nThere are several useful visual features to tell there is 'document' and not similar things in a photo:\tcontains written or printed information\ttitle or header is present\tdate is present\tsignature field is present\tpage numbers are present", 23], "art piece": ["Yes. 'Art piece' has a tangible appearance and is a type of artistic creation.\nA few things that are visually similar to 'art piece' but are not 'art piece' are: photographs, graffiti, advertisements, signs, and posters.\nThere are several useful visual features to tell there is 'art piece' and not similar things in a photo: unique and creative design or composition, the use of various colors or textures, a signature or title on the piece itself or accompanying it.", 23], "wood container": ["Yes. 'Wood container' has a tangible appearance and is a container made of wood.\nA few things that are visually similar to 'wood container' but are not 'wood container' are:\tpaper box\tplastic crate\tcardboard tray\tbasket\nThere are several useful visual features to tell there is 'wood container' and not similar things in a photo:\tmade of wood\tbrown or tan color\ttypically with visible wood grain and texture\tlatches, hinges or handles made of metal or wood", 23], "sport utility vehicle": ["Yes. 'Sport utility vehicle' has a tangible appearance and is a type of car.\nA few things that are visually similar to 'sport utility vehicle' but are not 'sport utility vehicle' are:\tpickup truck\tvan\tstation wagon\tjeep\nThere are several useful visual features to tell there is 'sport utility vehicle' and not similar things in a photo:\thigh ground clearance\tlarge size\t4-wheel drive capability\tboxy shape\ttailgate\trear door that swings open", 23], "display screen": ["Yes. 'Display screen' has a tangible appearance and is a type of device.\nA few things that are visually similar to 'display screen' but are not 'display screen' are:\tmirror\twindow\tpainting\nThere are several useful visual features to tell there is 'display screen' and not similar things in a photo:\trectangular shape\tthin profile\tbacklit or frontlit with images or text\tpixels or a grid of dots visible on the surface of the screen.", 23], "brown window": ["Yes. 'Brown window' has a tangible appearance and is a specific type of window.\nA few things that are visually similar to 'brown window' but are not 'brown window' are:\tblack window\twhite window\tgreen window\tgrey window\nThere are several useful visual features to tell there is 'brown window' and not similar things in a photo:\trectangular in shape\tbrown in color\tmade of glass or other transparent material\tpart of a building or a house.", 23], "cock pit": ["Yes. 'Cock pit' has a tangible appearance and is the enclosed area where the pilots sit in an aircraft.\nA few things that are visually similar to 'cock pit' but are not 'cock pit' are:\tdriver's seat in a car\tcontrol room in a ship\tdriver's seat in a train\t\nThere are several useful visual features to tell there is 'cock pit' and not similar things in a photo:\tenclosed space\tinstruments and controls\tforward location in the aircraft\ttwo pilot seats\twith windows or screens for a view", 23], "trailers": ["Yes. 'Trailers' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'trailers' but are not 'trailers' are:\ttrucks\tcamper vans\tboats\ttrains\nThere are several useful visual features to tell there is 'trailers' and not similar things in a photo:\tattached to a truck or vehicle\tcovered cargo section\tusually rectangular or square shape\twith wheels or without wheels\thitch coupling at the front or back of the trailer.", 23], "orange stripes": ["Yes. 'Orange stripes' has a tangible appearance and is a pattern of lines.\nA few things that are visually similar to 'orange stripes' but are not 'orange stripes' are:\ttiger\tprint\tsome kinds of candy wrappers\tbumblebee pattern\nThere are several useful visual features to tell there is 'orange stripes' and not similar things in a photo:\talternating orange and empty spaces\tlines evenly spaced\ton a flat or curved surface", 23], "wording": ["No. 'Wording' is too vague or abstract to be distinguished in a photo.", 23], "hairy arm": ["Yes. 'Hairy arm' has a tangible appearance and refers to an arm that has a considerable amount of hair on it.\nA few things that are visually similar to 'hairy arm' but are not 'hairy arm' are:\tfurry animal pelt\tfabric with fur texture\tcloth sleeve\nThere are several useful visual features to tell there is a 'hairy arm' and not similar things in a photo:\thair growing out of skin\thair strands relatively thick and dark\thair density covering the arm's surface", 23], "score board": ["Yes. 'Score board' has a tangible appearance and is a type of board used for displaying scores in sports events.\nA few things that are visually similar to 'score board' but are not 'score board' are:\tbillboard\tmenu\tboard game\tboard used in classroom\nThere are several useful visual features to tell there is 'score board' and not similar things in a photo:\tdisplaying scores of a sport event\thas numbers, team names, or logos\tclearly visible from a distance\tlit up or electronic", 23], "batting gloves": ["Yes. 'Batting gloves' has a tangible appearance and is a type of sports accessory.\nA few things that are visually similar to 'batting gloves' but are not 'batting gloves' are:\twinter gloves\tgardening gloves\tmotorcycle gloves\tleather gloves\nThere are several useful visual features to tell there is 'batting gloves' and not similar things in a photo:\tpadded palms for grip and protection\tfrom a baseball or softball sports setting\tworn typically by batters in baseball or softball games\tfingerless design for extra mobility and tactile feedback.", 23], "silver garbage": ["No. 'Silver garbage' is too vague or abstract to be distinguished in a photo.", 23], "route number": ["No. 'Route number' is too vague or abstract to be distinguished in a photo.", 23], "fireplace mantle": ["Yes. 'Fireplace mantle' has a tangible appearance and is a part of a fireplace.\nA few things that are visually similar to 'fireplace mantle' but are not 'fireplace mantle' are:\tshelf\tmantel\tledge\nThere are several useful visual features to tell there is 'fireplace mantle' and not similar things in a photo: directly above the fireplace structure, which may contain a firebox or a chimney; may have decorative items placed on it; made of wood, stone, or brick; can be ornately carved.", 23], "fold": ["Yes. 'Fold' has a tangible appearance.\nA few things that are visually similar to 'fold' but are not 'fold' are: crease, wrinkle, crumple, bend.\nThere are several useful visual features to tell there is a 'fold' and not similar things in a photo: a line that separates two parts of an object or material, usually creating an angle or a pleat. It can be a deliberate feature or a result of bending or manipulation.", 23], "window frames": ["Yes. 'Window frames' has a tangible appearance and is a part of a building structure.\nA few things that are visually similar to 'window frames' but are not 'window frames' are:\tpicture frames\tdoors\twall decorations\tarchitectural decorations\nThere are several useful visual features to tell there is 'window frames' and not similar things in a photo:\ttwo or more vertical and horizontal bars or panels\t\nglass panes\t\nattached to a building\t\nsquare or rectangular shape\t\nmay have hinges or handles\t\npart of a larger structure, such as a house or building.", 23], "remote table": ["Yes. 'Remote table' has a tangible appearance and refers to a table that is placed at a distance to be used as a working surface.\nA few things that are visually similar to 'remote table' but are not 'remote table' are: standing desk, coffee table, end table, nightstand.\nThere are several useful visual features to distinguish 'remote table' from other similar things in a photo:\ttabletop surface, ideal for placing laptops and papers, usually higher than regular tables, incorporated with storage or bookshelves, designed to fit a certain setting in a room.", 23], "toothpaste tube": ["Yes. 'Toothpaste tube' has a tangible appearance and is a household item.\nA few things that are visually similar to 'toothpaste tube' but are not 'toothpaste tube' are:\thand cream tube\tpaint tube\thair gel tube\t\nThere are several useful visual features to tell there is 'toothpaste tube' and not similar things in a photo:\tlong cylindrical shape\tpackaged in a cardboard box\twith screw-on cap or flip-top lid\tcontaining toothpaste\tlabel with brand name and product information", 23], "metal sink faucet": ["Yes. 'Metal sink faucet' has a tangible appearance and refers to a type of bathroom/kitchen fixture.\nA few things that are visually similar to 'metal sink faucet' but are not 'metal sink faucet' are:\tshowerhead\tdoorknob\tknob on a stove\nThere are several useful visual features to tell there is 'metal sink faucet' and not similar things in a photo:\tlocated above a sink\tmetallic with a reflective finish\ttwo handles or a single handle\tthat can be rotated to control water flow and temperature\tcurved or straight design", 23], "shampoo bottle": ["Yes. 'shampoo bottle' has a tangible appearance and is a container for a type of liquid soap.\nA few things that are visually similar to 'shampoo bottle' but are not 'shampoo bottle' are:\tbody wash bottle\thand soap bottle\tlotion bottle\tshower gel bottle\nThere are several useful visual features to tell there is 'shampoo bottle' and not similar things in a photo:\ttall and slender plastic container\twith a nozzle or a spray cap\thas a label with shampoo specific information (e.g. type, brand)\tmay have a distinctive shape or color", 23], "smiley face": ["Yes. 'Smiley face' has a tangible appearance and is a type of facial expression.\nA few things that are visually similar to 'smiley face' but are not 'smiley face' are:\tfrowny face\tneutral face\twinking face\ttongue out face\nThere are several useful visual features to tell there is 'smiley face' and not similar things in a photo:\tyellow color\tcircular shape\tblack dots for eyes, and a curved line for mouth\thappy or joyful expression", 23], "ottomen": ["Yes. 'Ottomans' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'ottomen' but are not 'ottomen' are:\tpoufs\tfootstools\tcushions\nThere are several useful visual features to tell there is 'ottoman' and not similar things in a photo:\tupholstered cushion or seat\tfor sitting\thaving legs or not\ta low seat without a backrest or armrests", 23], "food dish": ["Yes. 'Food dish' has a tangible appearance and is a container used for serving or eating food.\nA few things that are visually similar to 'food dish' but are not 'food dish' are:\tmugs\tbowls\tvases\tplates\tcups\nThere are several useful visual features to tell there is 'food dish' and not similar things in a photo:\tshallow or deep\tcontainer\twith or without a lid\tdesigned for serving or eating food\thas food in it or utensils next to it", 23], "clockface": ["Yes. 'Clockface' has a tangible appearance and is a part of the clock that displays time.\nA few things that are visually similar to 'clockface' but are not 'clockface' are:\twatches\tsundials\tcalendars\nThere are several useful visual features to tell there is 'clockface' and not similar things in a photo:\tcircle shape\ttwo or three hands indicating time\tnumbers around the edge, generally from 1 to 12 (or 24)\tinner circle with additional numbers or other markings (for minute or second reading)", 23], "counters": ["Yes. 'Counters' has a tangible appearance and refers to a horizontal surface used for displaying, preparing or selling goods.\nA few things that are visually similar to 'counters' but are not 'counters' are:\ttables\tshelves\tbenches\tdesks\nThere are several useful visual features to tell there is 'counters' and not similar things in a photo:\thorizontal surface at a specific height\ttends to be narrow, to allow easy access to goods on display or sale\tsometimes divided into sections, with different items on display in each section\ttend to have a smooth and easily cleaned surface.", 23], "silver spokes": ["Yes. 'Silver spokes' has a tangible appearance and refers to the thin, metallic rods that form the framework of a wheel.\nA few things that are visually similar to 'silver spokes' but are not 'silver spokes' are:\tflat silver wire\tsilver pipes\tsilver rods\nThere are several useful visual features to tell there are 'silver spokes' and not similar things in a photo:\tcylindrical\tmetallic\tsupporting the wheel or tire of a vehicle\tradiating from the central hub to the outer rim of the wheel.", 23], "flower stem": ["Yes. 'Flower stem' has a tangible appearance and is a part of a plant that supports the flower and transports water and nutrients.\nA few things that are visually similar to 'flower stem' but are not 'flower stem' are:\ttree trunk\tbamboo stalk\tweeds\tgrasses\t\nThere are several useful visual features to tell there is 'flower stem' and not similar things in a photo:\tslender and tapered shape\tbears leaves along its length\thas one or more flowers growing out of it_green or brown in color (depending on the species)\tmay have buds or thorns along its length, depending on the plant species.", 23], "wave water": ["No. 'Wave water' is too vague or abstract to be distinguished in a photo. \n\nHowever, here are some alternative prompts: \n\nIs 'waves' a visually concrete concept? \nYes. 'Waves' has a visual appearance as a natural occurrence on the surface of the water.\n\nSome things that are visually similar to 'waves' but are not 'waves' are:\n- Ripples on the surface of the water caused by light wind or rain. \n- White caps created by strong winds.\n\nSome useful visual features for distinguishing 'waves' from the listed similar things are:\n- Greater size and height compared to ripples and white caps.\n- More defined shape and movement compared to ripples and white caps.", 23], "metal watch": ["Yes. 'Metal watch' has a tangible appearance and is a type of timekeeping device.\nA few things that are visually similar to 'metal watch' but are not 'metal watch' are:\tbracelets\tfitbits\tjewelry\tregular watches made of other materials\nThere are several useful visual features to tell there is 'metal watch' and not similar things in a photo:\tmetallic appearance or parts\tsmall and compact shape\tclearly displaying time and/or date\tdials or buttons for adjusting time or settings\tleather or metal strap for wearing on wrist.", 23], "microwave counter": ["Yes. 'Microwave counter' has a tangible appearance and refers to a specific type of kitchen furniture.\nA few things that are visually similar to 'microwave counter' but are not 'microwave counter' are:\tsink\tcabinets\tdishwasher\tdrawers\nThere are several useful visual features to tell there is 'microwave counter' and not similar things in a photo:\ta flat surface that is high enough to put a microwave on\tit is typically made of wood, metal, or granite\tit is often located near other kitchen appliances such as a stove or refrigerator", 23], "building sign": ["Yes. 'Building sign' has a tangible appearance and is a type of signage attached to buildings.\nA few things that are visually similar to 'building sign' but are not 'building sign' are:\tbillboard\ttraffic sign\tstorefront banner\t\nThere are several useful visual features to tell there is 'building sign' and not similar things in a photo:\tattached to a building or a structure\tlogo, name or message in large letters\teasily readable from a distance or at eye-level\thelps identify a specific business or place", 23], "entertainment": ["No. 'Entertainment' is too abstract to have a tangible appearance and cannot be distinguished in a photo.", 23], "vinyl": ["Yes. 'Vinyl' has a tangible appearance and is a type of synthetic material typically used for records, clothing or upholstery.\nA few things that are visually similar to 'vinyl' but are not 'vinyl' are:\tleather\tplastic\tfaux leather\toilcloth\nThere are several useful visual features to tell there is 'vinyl' and not similar things in a photo:\tshiny and glossy appearance\tthin and flexible texture\ttypically solid colors, often bright or bold in hue\tcan have small grooves or seams\tif on a record, may have music grooves", 23], "locks": ["Yes. 'Locks' has a tangible appearance and is a kind of mechanism used for securing doors, containers, and other such items.\nA few things that are visually similar to 'locks' but are not 'locks' are:\tdoorknobs\tbolts\thinges\tscrews\nThere are several useful visual features to tell there is 'locks' and not similar things in a photo:\tmetallic\tkeyhole\tdial or combination locking mechanism\tshackle to which the hasp is attached", 23], "ripe orange": ["Yes. 'Ripe orange' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'ripe orange' but are not 'ripe orange' are:\torange juice\tblood orange\torange soda\torange peel\nThere are several useful visual features to tell there is 'ripe orange' and not similar things in a photo: \tround or oval shape\tbright orange color\tsmooth texture\tno visible bruising or damage\ttoothsome and juicy when sliced open.", 23], "hall": ["Yes. 'Hall' has a tangible appearance and is a type of indoor space that connects rooms.\nA few things that are visually similar to 'hall' but are not 'hall' are:\tcorridor\taisle\tlobby\t\nThere are several useful visual features to distinguish 'hall' from the listed similar things in a photo:\tconnected to different rooms\toriented along a building's length\thigh ceiling\twide space\tfor public use or as a passage.", 23], "eiffel tower": ["Yes. 'Eiffel tower' has a tangible appearance and is a specific famous landmark.\nA few things that are visually similar to 'eiffel tower' but are not 'eiffel tower' are:\tTokyo Tower\tCN Tower\tBurj Khalifa\nThere are several useful visual features to tell there is 'eiffel tower' and not similar things in a photo:\ticonic four-legged structure\tmetallic appearance with a bronze colors\tunique shape with a pointed top", 23], "pink balloon": ["Yes. 'Pink balloon' has a tangible appearance.\nA few things that are visually similar to 'pink balloon' but are not 'pink balloon' are:\tyellow balloon\torange balloon\tred balloon\tpurple balloon\t\nThere are several useful visual features to tell there is 'pink balloon' and not similar things in a photo:\tround or oval shaped\tpink color\tsmooth surface\tglossy or matte finish\tfilled with air or helium", 23], "violin": ["Yes. 'Violin' has a tangible appearance and is a type of musical instrument.\nA few things that are visually similar to 'violin' but are not 'violin' are:\tcello\tbass\tguitar\tharp\nThere are several useful visual features to tell there is 'violin' and not similar things in a photo:\tmedium size\tfour strings\ta curvy waist\ta distinctive scroll and f-holes\ton a chin rest and a bridge", 23], "starbucks": ["Yes. 'Starbucks' has a tangible appearance and is a coffeehouse chain.\nA few things that are visually similar to 'Starbucks' but are not 'Starbucks' are:\tCoffee Bean\tDunkin' Donuts\tCosta Coffee\tCaf\u00e9 Nero\nThere are several useful visual features to tell there is 'Starbucks' and not similar things in a photo:\tgreen and white logo\tmermaid symbol\tcups and/or packaging with \"Starbucks\" printed on them\tstore layout and design with familiar Starbucks elements, such as the menu board and display cases\tfor sale items such as mugs, coffee beans, and reusable cups that are branded with the Starbucks logo.", 23], "blooms": ["Yes. 'Blooms' has a tangible appearance and refers to flowering or blooming plants.\nA few things that are visually similar to 'blooms' but are not 'blooms' are:\tleaves\tbuds\tdried flowers\tweeds\nThere are several useful visual features to tell there is 'blooms' and not similar things in a photo:\tcolorful petals\tspecific flower shapes\tvisible reproductive structures (e.g. stamens, pistils)", 23], "upside": ["No. 'Upside' is too vague or abstract to be distinguished in a photo.", 23], "cement stairs": ["Yes. 'Cement stairs' has a tangible appearance and is a type of stairway.\nA few things that are visually similar to 'cement stairs' but are not 'cement stairs' are:\twooden stairs\tstone stairs\tmetal stairs\nThere are several useful visual features to tell there is 'cement stairs' and not similar things in a photo:\tgrey or light-colored\tsteps have a uniform size and shape\tsmooth surface\tstraight lines and angles in the construction.", 23], "meatballs": ["Yes. 'Meatballs' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'meatballs' but are not 'meatballs' are:\tvegetarian balls\tbreadcrumbs\tfalafel\tavocado balls\nThere are several useful visual features to tell there is 'meatballs' and not similar things in a photo:\tspherical shape\tbrownish or reddish color\tserved with sauce and pasta or alone", 23], "dunes": ["Yes, 'dunes' has a tangible appearance and refers to hills or ridges of sand in a desert or on a beach.\nA few things that are visually similar to 'dunes' but are not 'dunes' are:\tmountains, hills, cliffs, valleys, canyons\nThere are several useful visual features to tell there are 'dunes' and not similar things in a photo:\tridges or hills made of sand, often in a repetitive pattern\tlocated in a desert or on a beach\tusually devoid of vegetation\tor vegetation is adapted to desert conditions.", 23], "metal cart": ["Yes. 'Metal cart' has a tangible appearance and is a type of cart made of metal.\nA few things that are visually similar to 'metal cart' but are not 'metal cart' are: grocery cart, rolling tool cart, luggage cart, hand truck, trolley, shopping cart.\nThere are several useful visual features for distinguishing 'metal cart' from the listed similar things in a photo: made of metal, open frame, handle on one end, four wheels or casters, platform or basket to hold items, designed for transporting goods or equipment.", 23], "orange logo": ["Yes. 'Orange logo' has a tangible appearance and is a type of logo that is predominantly orange in color.\nA few things that are visually similar to 'orange logo' but are not 'orange logo' are:\tyellow logo\tsunrise / sunset\torange circle or shape\tpumpkin\nThere are several useful visual features to tell there is 'orange logo' and not similar things in a photo:\tdominantly orange color\tpredominantly used as a symbol or design for a company or brand\tpredominantly used in marketing or advertising materials", 23], "cutting board": ["Yes. 'Cutting board' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'cutting board' but are not 'cutting board' are:\tplacemat\tcoaster\ttray\tdesk\nThere are several useful visual features to tell there is 'cutting board' and not similar things in a photo:\trectangular or square shape\tthicker than a paper slice\tusually made of wood or plastic\tsmall cut marks on its surface from knife use.", 23], "wood chairs": ["Yes. 'Wood chairs' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood chairs' but are not 'wood chairs' are:\twooden stools\tbenches\tcouches\trocking chairs\nThere are several useful visual features to tell there is 'wood chairs' and not similar things in a photo:\thaving a back support\thaving four legs\twith or without armrests\tmade entirely or partially from wood\tmatching design or style", 23], "knifes": ["Yes. 'Knives' has a tangible appearance and refers to a cutting tool.\nA few things that are visually similar to 'knives' but are not 'knives' are:\tforks\tscissors\taxes\nThere are several useful visual features to tell there is 'knife' and not similar things in a photo:\tlong, sharp blade\tpointed tip\thandle to hold and control the blade", 23], "hatchback car": ["Yes. 'Hatchback car' has a tangible appearance and is a specific type of car.\nA few things that are visually similar to 'hatchback car' but are not 'hatchback car' are:\tsedan car\tsports car\tSUV\nThere are several useful visual features to tell there is 'hatchback car' and not similar things in a photo:\ta two-box car design\twith a rear door that swings upward to provide access to a cargo area behind the main passenger compartment\tthe cargo area is not separated from the passenger compartment by a partition", 23], "fronds": ["Yes. 'Fronds' has a tangible appearance and refers to the leaves of ferns and other similar plants.\nA few things that are visually similar to 'fronds' but are not 'fronds' are:\tleaves\tof other kinds of plants\tor tree branches.\nThere are several useful visual features to tell there are 'fronds' and not similar things in a photo:\telongated triangular shape,\twidth tapers along the length of the frond,\tgrowing on a fern or palm-like plant.", 23], "base plate": ["Yes. 'Base plate' has a tangible appearance and is a flat and solid surface for supporting something.\nA few things that are visually similar to 'base plate' but are not 'base plate' are:\tfloor\ttile\tplatform\tpavement\nThere are several useful visual features to tell there is 'base plate' and not similar things in a photo:\tusually made of metal or plastic\tflat and smooth surface\twith mounting holes or slots\tused as a foundation for other structures or machines", 23], "furry animal": ["Yes. 'Furry animal' has a tangible appearance and refers to animals with fur or hair covering their body.\nA few things that are visually similar to 'furry animal' but are not 'furry animal' are:\tstuffed toy\tfurry blanket\tfur coat\nThere are several useful visual features to tell there is 'furry animal' and not similar things in a photo:\tanimated\tfacial expressions (eyes, mouth, ears)\tmovement or locomotion (feet or tail)\tadaptable to different environments and climates\thair or fur covering the whole body ", 23], "dollop": ["No. 'Dollop' is too vague or abstract to have a tangible appearance. \n\n(Note: a dollop can refer to a small amount or mass of a substance, but it still doesn't have a specific visual appearance)\n\nThere aren't any things that are visually similar to 'dollop' since it doesn't have a specific visual appearance.\n\nAs 'dollop' is an abstract concept related to the amount of something, it cannot be distinguished from any similar things in a photo.", 23], "sea shore": ["Yes. 'Sea shore' has a tangible appearance and refers to the place where the sea meets the land.\nA few things that are visually similar to 'sea shore' but are not 'sea shore' are:\triver bank\tbeach\tpark\nThere are several useful visual features to tell there is 'sea shore' and not similar things in a photo:\trolling waves\tseashells\tor marine wildlife\tslanting area where sand and the sea meet\tbackground of sand or rocks\tor cliffs and formations of land, such as dunes or headlands.", 23], "dirty ground": ["Yes. 'Dirty ground' has a tangible appearance and can be recognized in a photo.\nA few things that are visually similar to 'dirty ground' but are not 'dirty ground' are:\tsoil\tsandstone\trough tiles\nThere are several useful visual features to distinguish 'dirty ground' from similar things in a photo:\tbrown or grey color\tirregular or uneven patterns\tstains or marks\taccumulation of debris or dust", 23], "light grey": ["Yes. 'Light grey' has a tangible appearance and refers to a specific shade of grey.\nA few things that are visually similar to 'light grey' but are not 'light grey' are:\twhite\toff-white\tbeige\nThere are several useful visual features to tell there is 'light grey' and not similar things in a photo:\ta shade of grey that is not too dark nor too light\tcan be described as \"silvery\" or \"pale\"\tlacks distinct color undertones like blue or green", 23], "fingernail polish": ["Yes. 'Fingernail polish' has a tangible appearance and is a kind of cosmetic product.\nA few things that are visually similar to 'fingernail polish' but are not 'fingernail polish' are:\tpaint\tink\tmarker\tlipstick\nThere are several useful visual features to tell there is 'fingernail polish' and not similar things in a photo:\tbottle or vial with a small brush applicator\tassorted colors\tglossy or shiny finish\tworn on nails or fingertips", 23], "motorcycle license plate": ["Yes. 'Motorcycle license plate' has a tangible appearance and is a rectangular metal plate affixed to the back of a motorcycle with a unique combination of numbers and letters.\nA few things that are visually similar to 'motorcycle license plate' but are not 'motorcycle license plate' are: car license plate bicycle serial number plate address plaque trophy plaque\nThere are several useful visual features to tell there is 'motorcycle license plate' and not similar things in a photo:\taffixed to the back of a motorcycle\trectangular in shape\twith a unique combination of numbers and letters\tmade of metal", 23], "silver grill": ["Yes. 'Silver grill' has a tangible appearance and is a metal object used in automobiles.\nA few things that are visually similar to 'silver grill' but are not 'silver grill' are:\tchrome trim\tbumper grille\tmetal mesh grill\nThere are several useful visual features to tell there is 'silver grill' and not similar things in a photo:\tsilver or metallic color\thorizontal or vertical bars\ttrapezoidal or rectangular shape\tattached to the front of a vehicle", 23], "grey sweatshirt": ["Yes. 'Grey sweatshirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'grey sweatshirt' but are not 'grey sweatshirt' are:\tgrey sweater\tgrey hoodie\tgrey t-shirt\tgrey jacket\nThere are several useful visual features to tell there is 'grey sweatshirt' and not similar things in a photo:\thoodless long-sleeved shirt with a front pocket\tmade of cotton or polyester\tfitting loosely or tightly around the body\tgrey in color\twith or without a graphic on it.", 23], "steak knife": ["Yes. 'Steak knife' has a tangible appearance and is a type of kitchen utensil.\nA few things that are visually similar to 'steak knife' but are not 'steak knife' are:\tbutter knife\tparing knife\tserrated bread knife\nThere are several useful visual features to tell there is 'steak knife' and not similar things in a photo:\t\n- Pointed, tapered blade\n- Serrated edge designed for cutting through meat\n- Usually has a sharp tip \n- Often has a wooden or synthetic handle with rivets.", 23], "swivel chair": ["Yes, 'swivel chair' has a visually concrete concept with distinguishable features.\nA few things that are visually similar to 'swivel chair' but are not 'swivel chair' are:\tdining chair\tbar stool\toffice chair\tarm chair\trocking chair\nSome useful visual features to distinguish 'swivel chair' from other chairs in a photo are:\tspinning/rotating base\tadjustable height\tcushioned or padded seat and backrest\tarmrests, headrests or footrests.", 23], "escalator": ["Yes. 'Escalator' has a tangible appearance and is a type of moving staircase.\nA few things that are visually similar to 'escalator' but are not 'escalator' are:\tstaircase\tramp\tconveyor belt\nThere are several useful visual features to tell there is 'escalator' and not similar things in a photo:\tmoving steps in the form of a continuous loop\tmoving handrails on either side of the steps\tsometimes parallel or crossing over another set of escalators.", 23], "drum set": ["Yes. 'Drum set' has a tangible appearance and consists of several percussion instruments.\nA few things that are visually similar to 'drum set' but are not 'drum set' are:\txylophone\tmaracas\ttambourine\tcymbals\nThere are several useful visual features to tell there is 'drum set' and not similar things in a photo:\ta combination of several percussion instruments\twith drumsticks or brushes\tcylindrical drums with skins stretched over them\tvarious sizes and heights\thigh-hat cymbals and foot pedals to control sound\tan adjustable stool for the drummer to sit on.", 23], "compartments": ["Yes. 'Compartments' has a tangible appearance and refers to a divided section or space.\nA few things that are visually similar to 'compartments' but are not 'compartments' are:\tdrawers\tshelves\tcabinets\tpockets\nThere are several useful visual features to tell there is 'compartments' and not similar things in a photo:\tdividers\tor sections\twithin a larger space\tor container\tclearly defined borders\tor edges\tseparate and identifiable areas\tfor storing or organizing different items", 23], "ornate": ["No. 'Ornate' is too vague or abstract to be distinguished in a photo.", 23], "grocery bag": ["Yes. 'Grocery bag' has a tangible appearance and is a kind of bag used to carry groceries.\nA few things that are visually similar to 'grocery bag' but are not 'grocery bag' are:\tpurse\tbackpack\ttote bag\nThere are several useful visual features to tell there is 'grocery bag' and not similar things in a photo:\tplastic or paper material\ttypically white or brown (for paper bags) or translucent (for plastic bags)\tcarrying groceries or food items handles on top of the bag to hold it easily and sturdily.", 23], "steeples": ["Yes. 'Steeples' has a tangible appearance and is a tall tower usually located on a church.\nA few things that are visually similar to 'steeples' but are not 'steeples' are:\toffice buildings\tsmokestacks\t\nThere are several useful visual features to tell there is 'steeples' and not similar things in a photo:\ttall and pointed\ttopped with a cross or spire\tusually located on a church or cathedral\thistory-rich or ornate architecture", 23], "pink vase": ["Yes. 'Pink vase' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'pink vase' but are not 'pink vase' are:\tpink cup\tpink glass\tpink bowl\nThere are several useful visual features to tell there is 'pink vase' and not similar things in a photo:\ttall and narrow shape\tflower holding structure in it\tcurved or straight neck\tpink color\tsmooth surface\tcould have patterns or engravings", 23], "wooden cabinets": ["Yes. 'Wooden cabinets' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden cabinets' but are not 'wooden cabinets' are:\tbookshelves\tdressers\tsideboards\twardrobes\nThere are several useful visual features to tell there is 'wooden cabinets' and not similar things in a photo:\tmade of wood\tdoors or drawers\tfor storage purposes\tusually found in kitchens or living rooms", 23], "rectangles": ["Yes. 'Rectangles' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'rectangles' but are not 'rectangles' are:\tsquares\toblongs\tparallelograms\ttrapezoids\nThere are several useful visual features to tell there is 'rectangles' and not similar things in a photo:\tfour sides with four angles\topposite sides parallel\tequal angles at the opposite corners (90 degrees)", 23], "dustbin": ["Yes. 'Dustbin' has a tangible appearance and is a type of container for waste.\nA few things that are visually similar to 'dustbin' but are not 'dustbin' are:\tbasket\ttrash can\tbarrel\tcrate\nThere are several useful visual features to tell there is 'dustbin' and not similar things in a photo:\ta lid or cover\tan opening on the top or on the side\ta shape that tapers towards the bottom\ta sign or label indicating that it is for waste disposal", 23], "bus windows": ["Yes. 'Bus windows' has a tangible appearance and is a type of window on a bus.\nA few things that are visually similar to 'bus windows' but are not 'bus windows' are:\tcar windows\tstore windows\tbuilding windows\t\nThere are several useful visual features to tell there is 'bus windows' and not similar things in a photo:\tsquare or rectangular shape\tclear or slightly tinted glass\ton the side of a bus", 23], "luggage carrier": ["Yes. 'Luggage carrier' has a tangible appearance and is a type of storage accessory used for carrying luggage during travels.\nA few things that are visually similar to 'luggage carrier' but are not 'luggage carrier' are:\troof rack\tbike carrier\ttrailer\thook\tclamp\nThere are several useful visual features to tell there is 'luggage carrier' and not similar things in a photo:\tflat surface for carrying\tluggage or packages on the surface\trails or bars to secure luggage\tor packages\tclasps or locks to secure the carrier to a vehicle", 23], "silver water faucet": ["Yes. 'Silver water faucet' has a tangible appearance and is a kind of plumbing fixture.\nA few things that are visually similar to 'silver water faucet' but are not 'silver water faucet' are:\tkitchen sink\tbathroom sink\tshowerhead\twater dispenser\nThere are several useful visual features to tell there is 'silver water faucet' and not similar things in a photo:\tsilver metal or chrome finish\tcurved or angled shape\tknob or lever for controlling water flow\tspout for dispensing water from a pipe or tube\tmounted to a sink or a wall.", 23], "pyramid": ["Yes. 'Pyramid' has a tangible appearance and is a type of geometric shape or ancient structure.\nA few things that are visually similar to 'pyramid' but are not 'pyramid' are:\ttriangle\tramp or slope\tbuilding with a triangular roof\t\nThere are several useful visual features to tell there is 'pyramid' and not similar things in a photo:\tsquare or rectangular base with triangular sides or faces\tpointed top or apex\tmade of stone or other durable materials\tassociated with ancient Egyptian or Mesoamerican cultures", 23], "bus front windshield": ["Yes. 'Bus front windshield' has a tangible appearance and is a specific part of a vehicle.\nA few things that are visually similar to 'bus front windshield' but are not 'bus front windshield' are:\tcar windshield\tscooter windshield\tbike windshield\t\nThere are several useful visual features to tell there is 'bus front windshield' and not similar things in a photo:\twide and rectangular shape\tlarge size\thorizontal position\ton the front of a bus\twiper blades at the bottom", 23], "male skateboarder": ["Yes. 'Male skateboarder' has a tangible appearance and is a person who rides a skateboard.\nA few things that are visually similar to 'male skateboarder' but are not 'male skateboarder' are:\tperson riding a bicycle\tperson rollerblading\tperson using a scooter\tperson on a hoverboard\nThere are several useful visual features to tell there is 'male skateboarder' and not similar things in a photo:\tperson standing on a skateboard\tperson wearing sneakers, jeans or shorts\tperson wearing a helmet or other protective gear\tperson performing tricks or stunts on a skateboard\tperson holding a skateboard", 23], "microphones": ["Yes. 'Microphones' has a tangible appearance and is a device used to capture sound.\nA few things that are visually similar to 'microphones' but are not 'microphones' are:\thairbrush\tcar antenna\tcone\nThere are several useful visual features to tell there is 'microphones' and not similar things in a photo:\tdiaphragm\tgrille\tfor handheld microphones: a handle\tfor desk microphones: a stand\tfor lapel microphones: a cord or cable connected to a device for recording or amplification", 23], "purple bowl": ["Yes. 'Purple bowl' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'purple bowl' but are not 'purple bowl' are:\tblue bowl\tpurple cup\tpurple vase\nThere are several useful visual features to tell there is 'purple bowl' and not similar things in a photo:\tbowl shape\tpurple color\tsmooth or textured surface\tsymmetrical design\tshallow or deep size.", 23], "speedboat": ["Yes, 'speedboat' has a tangible appearance and is a type of watercraft.\nA few things that are visually similar to 'speedboat' but are not 'speedboat' are:\tyacht\tjetski\tkayak\traft\tferry\nThere are several useful visual features to tell there is 'speedboat' and not similar things in a photo:\tnarrow and streamlined body\tone or several outboard motors\tsleek and sporty design\thigh speed on the water\tbow rises out of the water during acceleration.", 23], "grey blanket": ["Yes. 'Grey blanket' has a tangible appearance and is a type of bedding or textile.\nA few things that are visually similar to 'grey blanket' but are not 'grey blanket' are:\tduvet cover\tpillowcase\tbath towel\ttablecloth\nThere are several useful visual features to tell there is 'grey blanket' and not similar things in a photo:\tsoft and fluffy texture\trectangular shape\tgrey or greyish color\tmay have fringed edges\tor may have a specific pattern or design", 23], "identification": ["No. 'Identification' is too vague or abstract to be distinguished in a photo.", 23], "pink coat": ["Yes. 'Pink coat' is a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'pink coat' but are not 'pink coat' are:\tpink shirts\tpink scarfs\tpink dresses\nThere are several useful visual features to tell there is 'pink coat' and not similar things in a photo:\touterwear, not a shirt or a scarf\tmedium to long length, covering the torso\tusually made of thicker and more structured material than a dress", 23], "rump": ["Yes. 'Rump' has a tangible appearance and is a specific part of an animal's body.\nA few things that are visually similar to 'rump' but are not 'rump' are:\tback\tshoulder\tleg\tarm\nThere are several useful visual features to tell there is 'rump' and not similar things in a photo:\tmuscular area of the rear end of an animal\tdefined by the pelvic bone, muscles, and fat\tprominently rounded area in certain animals, such as cows, deer, or horses.", 23], "metal garbage": ["Yes. 'Metal garbage' has a tangible appearance and refers to waste or unwanted materials made out of metal.\nA few things that are visually similar to 'metal garbage' but are not 'metal garbage' are:\tmetallic furniture\tmetal sculptures\tmetallic tools\nThere are several useful visual features to tell there is 'metal garbage' and not similar things in a photo:\tdirty, rusted or broken metal objects\tobjects commonly found in garbage cans or landfills\tno practical use\tor value", 23], "metal spokes": ["Yes. 'Metal spokes' has a tangible appearance and is a part of a wheel.\nA few things that are visually similar to 'metal spokes' but are not 'metal spokes' are:\tbranches\tbars\twires\tfences\nThere are several useful visual features to tell there is 'metal spokes' and not similar things in a photo:\tcircular shape\tradiate from the center of a wheel\tmetallic or reflective appearance\tfixed to a hub\tbent at an angle", 23], "decals": ["Yes. 'Decals' has a tangible appearance and refers to images or designs that can be transferred onto another surface.\nA few things that are visually similar to 'decals' but are not 'decals' are:\tstickers\ttattoos\twraps\tpaintings\nThere are several useful visual features to tell there is 'decals' and not similar things in a photo:\tflat and thin\timage or design can be peeled off\ta transfer film or backing paper\tcan be applied to various surfaces, such as car windows or walls", 23], "tree line": ["Yes. 'Tree line' has a tangible appearance and refers to the edge of a forest where the trees stop growing.\nA few things that are visually similar to 'tree line' but are not 'tree line' are:\tfield\thilltop\tmountain summit\nThere are several useful visual features to tell there is 'tree line' and not similar things in a photo:\tuneven line of trees\tor a clear visual contrast between the forested and non-forested areas\tsmaller trees or shrubs in comparison to the taller trees on the forested area", 23], "backpack strap": ["Yes. 'Backpack strap' has a tangible appearance and is a part of a backpack.\nA few things that are visually similar to 'backpack strap' but are not 'backpack strap' are:\tpurse strap\tluggage strap\tcamera strap\nThere are several useful visual features to tell there is 'backpack strap' and not similar things in a photo:\tattached to a backpack\tadjustable\tlength and width\tmatch the color and material of the backpack", 23], "tangerine": ["Yes. 'Tangerine' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'tangerine' but are not 'tangerine' are:\torange\tmandarin\tgrapefruit\tpomelo\tlime\tlemon\nThere are several useful visual features to tell there is 'tangerine' and not similar things in a photo:\torange-red color\tsmooth and slightly bumpy surface\torange or red-orange pulp\tsegments that can be easily pulled apart\tround shape\twith a little bump or knob at the top", 23], "focus": ["No. 'Focus' is too vague or abstract to be distinguished in a photo.", 23], "sausage links": ["Yes. 'Sausage links' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'sausage links' but are not 'sausage links' are:\thot dogs\tbratwursts\tchili dogs\nThere are several useful visual features to tell there is 'sausage links' and not similar things in a photo:\ttubular shape\twith a coiled pattern\tcrispy, grilled or browned surface", 23], "kitchen scene": ["Yes. 'Kitchen scene' has a tangible appearance and is a type of interior scene.\nA few things that are visually similar to 'kitchen scene' but are not 'kitchen scene' are:\trestaurant kitchen\toutdoor kitchen\tcafeteria\tcanteen\nThere are several useful visual features to tell there is 'kitchen scene' and not similar things in a photo:\tkitchen appliances (oven, stove, fridge, etc.)\tkitchen utensils (pots, pans, spatulas, etc.)\tcountertops and cabinets\twith or without food and drinks\tperson or people cooking or preparing food.", 23], "wooden edge": ["Yes. 'Wooden edge' has a tangible appearance and refers to a specific part of a wooden object.\nA few things that are visually similar to 'wooden edge' but are not 'wooden edge' are:\twooden panel\twooden plank\twooden board\nThere are several useful visual features to tell there is 'wooden edge' and not similar things in a photo:\tthe edge or border of a wooden object. It will have visible wood grain and may be thicker or thinner than the rest of the object.", 23], "grey color": ["Yes. 'Grey color' has a tangible appearance and is a specific color.\nA few things that are visually similar to 'grey color' but are not 'grey color' are:\twhite color\tbrown color\tblack color\tsilver color\nThere are no visual features to tell there is \"grey color\" and not similar things in a photo as it is a specific color and cannot be confused with any other color.", 23], "orange shorts": ["Yes. 'Orange shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'orange shorts' but are not 'orange shorts' are:\tjeans\tskirt\tleggings\tshorts of a different color\nThere are several useful visual features to tell there is 'orange shorts' and not similar things in a photo:\tshort length\torange color\tno visible pockets or belt loops", 23], "headlamp": ["Yes. 'Headlamp' has a tangible appearance and is a kind of lighting tool.\nA few things that are visually similar to 'headlamp' but are not 'headlamp' are:\tflashlight\tlantern\tbicycle light\nThere are several useful visual features to tell there is 'headlamp' and not similar things in a photo:\tstraps to attach to the head\torbits around the forehead\tfocused beam of light\tcan be directed in different angles.", 23], "pendant": ["Yes. 'Pendant' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'pendant' but are not 'pendant' are:\tnecklace\tchoker\tbrooch\nThere are several useful visual features to tell there is 'pendant' and not similar things in a photo:\ta small piece of jewelry\thangs from a chain or cord\tsingle decorative element worn around the neck\tpendant usually refers to a larger, more prominent element hanging from the chain or cord", 23], "metal towel rack": ["Yes. 'Metal towel rack' has a tangible appearance and is a type of bathroom accessory.\nA few things that are visually similar to 'metal towel rack' but are not 'metal towel rack' are:\tshower curtain rod\tclothes hanger\thook\nThere are several useful visual features to tell there is 'metal towel rack' and not similar things in a photo:\tparallel rungs for holding towels\tmade of metal hinged with a wall\tor freestanding", 23], "dark shirt": ["Yes. 'Dark shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'dark shirt' but are not 'dark shirt' are:\tblack jacket\tdark hoodie\tdark pants\nThere are several useful visual features to tell there is a 'dark shirt' and not similar things in a photo:\ta shirt\ttop worn on the upper body\tdark color (such as black, grey, or navy)\tfabric texture or pattern (such as stripes or checks)\tbuttons or zippers (depending on the style)", 23], "jalapenos": ["Yes. 'Jalapenos' has a tangible appearance and is a type of pepper.\nA few things that are visually similar to 'jalapenos' but are not 'jalapenos' are:\tbell pepper \tchili pepper\tcapsicum\nThere are several useful visual features to tell there is 'jalapenos' and not similar things in a photo:\tsmall size\tgreen color\tmature to red color\tsmooth surface\tpointed tip\tthick flesh\thot and spicy flavor", 23], "barrette": ["Yes. 'Barrette' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'barrette' but are not 'barrette' are:\thair clip\tpin\tbobby pin\t\nThere are several useful visual features to tell there is 'barrette' and not similar things in a photo:\tclip holding hair in place\topen and close mechanism\tvariety of shapes, sizes, and colors often decorated with beads or jewels.", 23], "piping": ["Yes. 'Piping' has a tangible appearance and usually refers to a type of tube or conduit.\nA few things that are visually similar to 'piping' but are not 'piping' are:\tcables\twires\tstraws\thoses\t\nThere are several useful visual features to tell there is 'piping' and not similar things in a photo:\thollow cylindrical shape\teven diameter along the length\tconnected to valves, pumps, or other equipment\toften made of metal or plastic\tmay have threaded or flanged ends for connections.", 23], "wood handle": ["Yes. 'Wood handle' has a tangible appearance and is a part of various tools.\nA few things that are visually similar to 'wood handle' but are not 'wood handle' are:\tmetal handle\tplastic handle\trubber grip\tbamboo rod\nThere are several useful visual features to tell there is 'wood handle' and not similar things in a photo:\tbrownish\tcolorful painted finishes\tvisible wood grain\trough texture", 23], "square container": ["Yes. 'Square container' has a tangible appearance and refers to a type of object that is used for storage or transport.\nA few things that are visually similar to 'square container' but are not 'square container' are:\tcubes\tpackages\tboxes\tbins\nThere are several useful visual features to tell there is 'square container' and not similar things in a photo:\thas a cubic shape\thas four even-sized sides, and four 90-degree angles\thas a lid or a closing mechanism\tcan hold objects or substances inside.", 23], "zebra nose": ["Yes. 'Zebra nose' has a tangible appearance and refers to the nose of a zebra.\nThere are no things that are visually similar to 'zebra nose' but are not 'zebra nose'.\nUseful visual features for distinguishing 'zebra nose' from other things in a photo are:\tblack and white stripes\tthat it's a nose, and not just the face or the head of the animal.", 23], "mail": ["No. 'Mail' is too vague or abstract to be distinguished in a photo.\n\nHowever, a few things that are visually similar to 'mail' but are not 'mail' are:\tenvelopes\tpackages\tnewspapers\tflyers\n\nUseful visual features for distinguishing 'mail' from the listed similar things in a photo would include:\tpostage stamps\taddresses or names on the items\tdelivery trucks, mailboxes or post offices in the background or foreground\tcarrier uniform or bag on a person's shoulder.", 23], "lightpost": ["Yes. 'Lightpost' has a tangible appearance and is a type of outdoor structure.\nA few things that are visually similar to 'lightpost' but are not 'lightpost' are:\tsign\tpost\tfountain\tpillar\nThere are several useful visual features to tell there is 'lightpost' and not similar things in a photo:\ttall with a long, thin pole\tmetallic or wooden structure\twith a lightbulb or lantern on top\tusually on the side of a road or walkway", 23], "giraffe face": ["Yes. 'Giraffe face' has a tangible appearance and refers to the facial features of a giraffe.\nA few things that are visually similar to 'giraffe face' but are not 'giraffe face' are:\tzebra face\thorse face\t\nThere are several useful visual features for distinguishing 'giraffe face' from the listed similar things in a photo:\torange-brown patches\ton a long neck\ttall ears\twith ossicones (horn-like structures)\tatop the head\tlong prehensile tongue.", 23], "windmill": ["Yes. 'Windmill' has a tangible appearance and is a type of machine used to convert wind into energy.\nA few things that are visually similar to 'windmill' but are not 'windmill' are:\twater wheel\tmerry-go-round\tpinwheel\tfan\nThere are several useful visual features to tell there is 'windmill' and not similar things in a photo:\ttall and narrow structure with blades\tor vanes\tfour sails\tthat rotate around a horizontal axis\tblades angled to the direction of the wind\tsituated on a hill or in an open area.", 23], "bracelet wrist": ["No. 'Bracelet wrist' is too vague or abstract to be distinguished in a photo. \n\nHowever, there are a few things that can be visually similar to a person's wrist wearing a bracelet, such as: \n- A person's bare wrist \n- A wrist with a watch \n- A wrist adorned with other forms of jewelry, such as a cuff or bangle \n\nUseful visual features for distinguishing a 'bracelet wrist' from similar things in a photo could include: \n- The presence of a thin, string-like object wrapped around the wrist \n- The addition of small, decorative objects hanging from the bracelet \n- The inclusion of metallic clasps or closures on the bracelet \n- The overall design of the bracelet and its color scheme, which may differ from other types of wrist adornment", 23], "airport building": ["Yes. 'Airport building' has a tangible appearance and typically refers to the main terminal building or structures on airport grounds.\nA few things that are visually similar to 'airport building' but are not 'airport building' are:\thotels\tconvention centers\tshopping malls\t\nThere are several useful visual features to tell there is 'airport building' and not similar things in a photo:\ttypically located next to runways and taxiways\thas large windows for airplanes to be seen parked outside\tmay have multiple levels for arrivals and departures\thas signs indicating gates or check-in areas\thas the word \"airport\" or the name of the airport on its exterior", 23], "water drain": ["Yes. 'Water drain' has a tangible appearance and is a type of system for removing water from an area.\nA few things that are visually similar to 'water drain' but are not 'water drain' are:\tsewer\tgrate\tventilation shaft\thole in the ground\nThere are several useful visual features to tell there is 'water drain' and not similar things in a photo:\tround or rectangular shape\tgrated or slotted surface low to the ground\tor attached to a wall\tor embedded in pavement or concrete", 23], "silver rail": ["Yes. 'silver rail' has a tangible appearance and is a type of metal rail.\nA few things that are visually similar to 'silver rail' but are not 'silver rail' are:\tsteel rail\tchrome rail\taluminum rail\tiron rail\nThere are several useful visual features to tell there is 'silver rail' and not similar things in a photo:\tmetallic\tsilver color\treflective surface\tstraight and narrow shape\tused for transportation purposes, such as train or subway tracks", 23], "airplane door": ["Yes. 'Airplane door' has a tangible appearance and is a physical part of an airplane.\nA few things that are visually similar to 'airplane door' but are not 'airplane door' are:\tcar door\thouse door\tgarage door\nThere are several useful visual features to tell there is 'airplane door' and not similar things in a photo:\tattached to an airplane\tlarger than a car or house door\tmetallic or reflective surface\tslide attached next to it for emergency exits handles or locks\ton the side of the plane", 23], "knuckles": ["Yes. 'Knuckles' has a tangible appearance and are jointed parts of fingers or toes.\nA few things that are visually similar to 'knuckles' but are not 'knuckles' are:\tfinger joints\tknee joints\telbows\nThere are several useful visual features to tell there are 'knuckles' and not similar things in a photo:\trounded\tbony-looking\tlocated in the middle of a finger or a toe\thas skin texture on top, with creases and ridges", 23], "braces": ["Yes. 'Braces' has a tangible appearance and is an orthodontic device.\nA few things that are visually similar to 'braces' but are not 'braces' are:\tretainers\tmouthguards\theadgear\nThere are several useful visual features to tell there are 'braces' and not similar things in a photo:\tmetal brackets\tceramic brackets\twires\trubber bands\tattached to teeth", 23], "portions": ["No. 'Portions' are too vague or abstract to be distinguished in a photo.", 23], "metal traffic light": ["Yes. 'Metal traffic light' has a tangible appearance and is a type of sign or signal device.\nA few things that are visually similar to 'metal traffic light' but are not 'metal traffic light' are:\tstreet lamp\tpower pole\tbike stand\tscaffolding\nThere are several useful visual features to tell there is 'metal traffic light' and not similar things in a photo:\tthree circular lamps (red, yellow, and green)\thanging horizontally or vertically from a pole or a wire\tmetallic body with elongated shape and angled corners", 23], "paper umbrella": ["Yes. 'Paper umbrella' has a tangible appearance and it is a kind of umbrella.\nA few things that are visually similar to 'paper umbrella' but are not 'paper umbrella' are:\tparasol\tbeach umbrella\train umbrella\nThere are several useful visual features to tell there is 'paper umbrella' and not similar things in a photo:\tcircular canopy made of paper or bamboo\twith colorful, ornate patterns\tfoldable and lightweight\thandle that is also made of bamboo", 23], "pavilion": ["Yes. 'Pavilion' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'pavilion' but are not 'pavilion' are:\ttent\tstand\tshed\tbooth\nThere are several useful visual features to tell there is 'pavilion' and not similar things in a photo:\troof\tor a covering\tsupported by columns, pillars, or posts\topen on one or more sides\tdesigned for relaxation, entertainment, or shelter from the elements\tmay have benches, chairs, or tables inside", 23], "grey house": ["Yes. 'Grey house' has a tangible appearance and can be visually identified.\nA few things that are visually similar to 'grey house' but are not 'grey house' are:\tstone house\tcottage with grey thatched roof\tcondominium with grey balcony\tgrey shed\nThere are several useful visual features to tell there is 'grey house' and not similar things in a photo:\trectangular (or cube-shaped) structure\twith grey walls, roof and/or shutters\tmultiple windows and door(s)\tat least one chimney and maybe a garage or a garden.", 23], "barge": ["Yes. 'Barge' has a tangible appearance and is a type of flat-bottomed boat.\nA few things that are visually similar to 'barge' but are not 'barge' are:\ttugboat\tcanoe\tkayak\traft\nThere are several useful visual features to tell there is 'barge' and not similar things in a photo:\tflat-bottomed and rectangular or square shaped\textremely large compared to other boats\thigh deck with no cabin or superstructure\tslow-moving or stationary on the water\thigh capacity for carrying cargo or passengers.", 23], "lanterns": ["Yes. 'Lanterns' has a tangible and visually concrete appearance.\nA few things that are visually similar to 'lanterns' but are not 'lanterns' are:\tbulbs\tlamps\tcandles\tflashlights\nThere are several useful visual features to tell there is 'lanterns' and not similar things in a photo:\thandles or hooks\tfor the outdoors and indoors\tuse of paper or fabric for the enclosure\tround or square shape\tHas a light source at the center", 23], "chair leg": ["Yes. 'Chair leg' has a tangible appearance and is a part of a piece of furniture.\nA few things that are visually similar to 'chair leg' but are not 'chair leg' are:\ttable leg\tbed leg\tstool leg\tcabinet leg\nThere are several useful visual features to tell there is 'chair leg' and not similar things in a photo:\tattached to a seat\tof similar width and shape\toften come in sets or multiples\tvarious materials like wood, metal or plastic, etc.", 23], "daughter": ["No. 'Daughter' is too vague or abstract to be distinguished in a photo. It is a familial relationship and has no tangible appearance.", 23], "soccer cleats": ["Yes. 'Soccer cleats' has a tangible appearance and is a kind of sports shoe.\nA few things that are visually similar to 'soccer cleats' but are not 'soccer cleats' are:\tbaseball cleats\tfootball cleats\thiking boots\t\nThere are several useful visual features to tell there is 'soccer cleats' and not similar things in a photo:\tpebbled sole\tstuds or spikes on the sole\tcolorful or bright design\tfit snugly to the foot", 23], "quarter": ["Yes. 'Quarter' has a tangible appearance and is a denomination of currency.\nA few things that are visually similar to 'quarter' but are not 'quarter' are:\tnickel\tdime\tpenny\ttoken\nThere are several useful visual features to tell there is 'quarter' and not similar things in a photo:\tround shape\tsilver color with ridges\tthe word \"quarter\" written on it\tthe image of the head and profile of George Washington on one side", 23], "flower buds": ["Yes. 'Flower buds' has a tangible appearance and is a stage of growth in plants.\nA few things that are visually similar to 'flower buds' but are not 'flower buds' are:\tleaves\tyoung fruits\tbulbs\nThere are several useful visual features to tell there is 'flower buds' and not similar things in a photo:\ttypically attached to stems or branches\tbrightly colored\tappearing before a flower blooms\tvarying in shape and size depending on the plant species.", 23], "wakeboard": ["Yes. 'Wakeboard' has a tangible appearance and is an object used for water sports.\nA few things that are visually similar to 'wakeboard' but are not 'wakeboard' are:\tsurfboard\tskateboard\tsnowboard\t\nThere are several useful visual features to tell there is 'wakeboard' and not similar things in a photo:\trectangular shape\twith bindings for the feet\ttethers rope to a boat or cable\tkicking up water behind it\twhile being pulled by a boat or cable", 23], "shadow woman": ["Yes. 'Shadow woman' has a tangible appearance and is a woman's shadow.\nA few things that are visually similar to 'Shadow woman' but are not 'Shadow woman' are:\tman's shadow\tanimal's shadow\tplant's shadow\nThere are several useful visual features to tell there is 'Shadow woman' and not similar things in a photo:\tfeminine shape\tdarkened outline of a woman\thuman-like posture and movement", 23], "alien": ["No. 'Alien' is too vague or abstract to be considered a visually concrete concept.\nThere are no things that are visually similar to 'alien' but are not 'alien'.\n\nHowever, if we are talking about the stereotypical depiction of aliens in popular culture, a few things that might be visually similar to this depiction, but are not aliens, include:\n- Robots\n- Creatures from other planets in movies or TV shows\n- Humans dressed up in alien costumes\n\nTo distinguish 'alien' from these similar things in a photo, some useful visual features might include:\n- Unusual body shapes or proportions\n- Multiple limbs or eyes\n- Unusual skin colors or textures\n- Unusual clothing or equipment that suggests an alien origin", 23], "cross country": ["No. 'Cross country' is too vague or abstract to be distinguished in a photo. \n\nNote: Without context, the term 'cross country' could refer to different things like cross-country skiing or cross-country running, but neither of them is visually concrete.", 23], "ghost": ["No. 'Ghost' is too vague or abstract to be distinguished in a photo.", 23], "radio tower": ["Yes. 'Radio tower' has a tangible appearance and is a type of tall structure for transmitting radio signals.\nA few things that are visually similar to 'radio tower' but are not 'radio tower' are:\ttelephone tower\twind turbine\tcranes\twater towers\nThere are several useful visual features to tell there is 'radio tower' and not similar things in a photo:\ttall height\tslim body\tmultiple transmitters or antennas\ttriangular or square-shaped structure with guy wires\tor guyed towers", 23], "goblet": ["Yes. 'Goblet' has a tangible appearance and is a type of drinking vessel.\nA few things that are visually similar to 'goblet' but are not 'goblet' are:\twine glass\tchampagne flute\ttumbler\t\nThere are several useful visual features to tell there is 'goblet' and not similar things in a photo:\twide mouth\tbowl-shaped or chalice-like cup\toften with a stem or pedestal\tusually made of glass, crystal or metal", 23], "surfer board": ["Yes. 'Surfer board' has a tangible appearance.\nA few things that are visually similar to 'surfer board' but are not 'surfer board' are:\tpaddleboard\tkayak\tbodyboard\tsailboard\nThere are several useful visual features to tell there is 'surfer board' and not similar things in a photo:\telongated shape\twith pointed ends\tor rounded edges\tpointed in the front part\trounded in the back part\tusually colorful\tand decorated\twith a design\tor a pattern", 23], "license tag": ["Yes. 'License tag' has a tangible appearance and is a kind of metal plate affixed to a vehicle.\nA few things that are visually similar to 'license tag' but are not 'license tag' are:\tmetal signs\tfridge magnets\tname tags\nThere are several useful visual features to tell there is 'license tag' and not similar things in a photo:\tsmall rectangular or square shape\tvarious letters and numbers\tinformation about vehicle registration\tattached to the front and back of a vehicle", 23], "blue poles": ["Yes. 'Blue poles' has a tangible appearance and refers to a specific artwork.\nThere are no things that are visually similar to 'blue poles' and are not 'blue poles'.\nUseful visual features for distinguishing 'blue poles' from other artworks in a photo include:\tvertical poles in different shades of blue\tno distinct shapes or figures\tabstract expressionist style.", 23], "grey elephants": ["Yes. 'Grey elephants' has a tangible appearance and is a specific species of animal.\nA few things that are visually similar to 'grey elephants' but are not 'grey elephants' are:\trhino\thippo\twalrus\nThere are several useful visual features to tell there is 'grey elephants' and not similar things in a photo:\tvery large body size\tfour legs and a long trunk\tgray skin with a wrinkled texture\tpointy tusks\ton terrestrial environments", 23], "cable car": ["Yes. 'Cable car' has a tangible appearance and is a type of transport.\nA few things that are visually similar to 'cable car' but are not 'cable car' are:\ttram\tfunicular\tgondola\tchairlift\nThere are several useful visual features to tell there is a 'cable car' and not similar things in a photo:\tattached to an overhead cable\tsystem of pulleys and cables\ttwo cars traveling in opposite directions along a single track\thanging from a tall pole or tower.", 23], "dirty snow": ["Yes. 'Dirty snow' has a tangible appearance and is a type of snow that has been soiled or contaminated.\nA few things that are visually similar to 'dirty snow' but are not 'dirty snow' are:\tmud\tpotholes\tdark sand or gravel\nThere are several useful visual features to tell there is 'dirty snow' and not similar things in a photo:\tsnow-covered ground\tdark or discolored patches on the snow \ttracks or footprints on the snow\tadjacent clean areas of snow or ice", 23], "units": ["No. 'Units' is too vague or abstract to be distinguished in a photo. It is a term used to refer to a quantity or a measurement of something, and it doesn't have a specific visual appearance.", 23], "turtle": ["Yes. 'Turtle' has a tangible appearance and is a type of reptile.\nA few things that are visually similar to 'turtle' but are not 'turtle' are:\ttortoise\tterrapin\ttortoise shell\tcolorful rocks\nThere are several useful visual features to tell there is 'turtle' and not similar things in a photo:\tdome-shaped shell\tflippers or webbed feet\tno visible ears\tor may be hidden in the sides of the head\tscaly skin\tlong necks, but not all turtles have this feature.", 23], "hazy mountains": ["Yes. 'Hazy mountains' has a tangible appearance and refers to a mountainous landscape obscured by haze.\nA few things that are visually similar to 'hazy mountains' but are not 'hazy mountains' are:\tclouds\tmist\tfog\tsmoke\nThere are several useful visual features to tell there are 'hazy mountains' and not similar things in a photo:\tmountainous landscape\tvague outlines of the mountain range\tgradual variation in the color of the mountains\tdiminishing visibility in the background", 23], "sleeve tee shirt": ["Yes. 'sleeve tee shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sleeve tee shirt' but are not 'sleeve tee shirt' are:\thoodie\tcrewneck sweater\ttank top\t\nThere are several useful visual features to tell there is 'sleeve tee shirt' and not similar things in a photo:\tshort sleeves\ttypically made of cotton or other thin and comfortable material\ttypically worn as casual or informal attire\tclose-fitting and stretchy, but not form-fitting like spandex or compression garments", 23], "bicycle pedal": ["Yes. 'Bicycle pedal' has a tangible appearance and is a specific part of a bicycle.\nA few things that are visually similar to 'bicycle pedal' but are not 'bicycle pedal' are:\tshoe sole\tfoot pad\t\nThere are several useful visual features to tell there is 'bicycle pedal' and not similar things in a photo:\tcircular metal or plastic shape\twith a row of metal spikes or rubber pads\ton the crank arm of a bike.", 23], "cupola": ["Yes. 'Cupola' has a tangible appearance and is a small dome-shaped structure on top of roofs or buildings.\nA few things that are visually similar to 'cupola' but are not 'cupola' are:\tturrets\trooftop vents\tdormers\nThere are several useful visual features to tell there is 'cupola' and not similar things in a photo:\tdome-shaped structure\tsituated on a roof or on top of a building\tmay have windows ornamental details", 23], "knuckle": ["Yes. 'Knuckle' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'knuckle' but are not 'knuckle' are:\tjoints\tknots\tbumps\nThere are several useful visual features to tell there is 'knuckle' and not similar things in a photo:\tbony protrusion where the finger joins the hand\tsmall, round shape\tflexibility", 23], "wooden window frame": ["Yes, 'wooden window frame' has a tangible appearance and is a specific type of frame.\nA few things that are visually similar to 'wooden window frame' but are not 'wooden window frame' are:\twooden door frame\twood paneling\tfurniture\tsculptures\nThere are several useful visual features to tell there is 'wooden window frame' and not similar things in a photo:\trectangular or square shape\tmade of wood\t has multiple panes or sections where glass can be installed\tlocated on a building.", 23], "lifeguard": ["Yes. 'Lifeguard' has a tangible appearance and refers to a person who monitors and rescues swimmers in water.\nA few things that are visually similar to 'lifeguard' but are not 'lifeguard' are:\tswimmer\tpool attendant\tbeachgoers\nThere are several useful visual features to tell there is 'lifeguard' and not similar things in a photo:\twearing a red and white bathing suit or shorts\tand an identification mark\trescue equipment like a buoy or a whistle\tstationed in a watchtower or a chair near the seashore or swimming pool.", 23], "pink tie": ["Yes. 'Pink tie' has a tangible appearance and is a kind of clothing accessory.\nA few things that are visually similar to 'pink tie' but are not 'pink tie' are:\tscarf\tribbon\tnecklace\tcrayon\nThere are several useful visual features to tell there is 'pink tie' and not similar things in a photo:\t \ncolored in pink\t\nmade of fabric\t\nworn around the neck\t\ntypical tie shape and width", 23], "parasails": ["Yes. 'Parasails' has a tangible appearance and is a kind of flying equipment.\nA few things that are visually similar to 'parasails' but are not 'parasails' are:\tkites\tballoons\tgliders\thang gliders\nThere are several useful visual features to tell there is 'parasails' and not similar things in a photo:\ta sail attached to a wing-shaped frame\ta person hanging from the sail\tusing wind power to fly", 23], "sand area": ["Yes. 'Sand area' has a tangible appearance and is an area covered with sand.\nA few things that are visually similar to 'sand area' but are not 'sand area' are:\tbeach\tdirt road\tconstruction site\nThere are several useful visual features to tell there is 'sand area' and not similar things in a photo:\tlight brown or yellow color\tuneven surface\tmesh or net in the background (common in playgrounds)\tsand toys or equipment in the area", 23], "trophy": ["Yes. 'Trophy' has a tangible appearance and usually refers to a decorative object given as an award or prize.\nA few things that are visually similar to 'trophy' but are not 'trophy' are:\tvases\tsculptures\tcups\tmedals\nThere are several useful visual features to tell there is 'trophy' and not similar things in a photo:\tusually gold or silver in color, or resembling those materials\tfeatures a small statuette or emblem on top\toften has a plaque or inscription indicating the reason for the trophy's awardment", 23], "orange sauce": ["Yes. 'Orange sauce' has a tangible appearance and is a type of sauce.\nA few things that are visually similar to 'orange sauce' but are not 'orange sauce' are:\tbbq sauce\tmustard\thoney ketchup\nThere are several useful visual features to tell there is 'orange sauce' and not similar things in a photo:\torange color\tsmooth texture\tliquid consistency\thints of orange zest", 22], "lettuce plate": ["Yes. 'Lettuce plate' has a tangible appearance and refers to a plate with lettuce as a main ingredient.\nA few things that are visually similar to 'lettuce plate' but are not 'lettuce plate' are:\tsalad bowl\tsoup bowl\tvegetable platter\nThere are several useful visual features to tell there is 'lettuce plate' and not similar things in a photo:\tplate or dish with lettuce as the main ingredient\tother vegetables or toppings on the lettuce\toften served as a side dish\tattractively arranged to be visually appealing.", 22], "gray box": ["Yes. 'Gray box' has a tangible appearance and is a specific type of object.\nA few things that are visually similar to 'gray box' but are not 'gray box' are:\tblack box\tbrown box\tcardboard box\tstorage container\nThere are several useful visual features to tell there is 'gray box' and not similar things in a photo:\trectangular shape\tsmooth gray surface\thinged lid or opening on the top or side\tno visibly distinguishing marks or labels", 22], "shadow elephant": ["No. 'Shadow elephant' is too vague or abstract to be distinguished in a photo.", 22], "muddy ground": ["Yes. 'Muddy ground' has a tangible appearance and is a type of terrain.\nA few things that are visually similar to 'muddy ground' but are not 'muddy ground' are:\twet sand\twet concrete\twet asphalt\twet grass\tswamp\nThere are several useful visual features to tell there is 'muddy ground' and not similar things in a photo:\tbrown or gray color\twet and sticky surface\timpressions of feet or wheels can be seen in the mud\tsplashing or dripping effect when stepped on or touched.", 22], "fence enclosure": ["Yes. 'Fence enclosure' has a tangible appearance and is a physical boundary created by a fence.\nA few things that are visually similar to 'fence enclosure' but are not 'fence enclosure' are:\twall\tshrubbery\tgate\nThere are several useful visual features to tell there is 'fence enclosure' and not similar things in a photo:\tstraight or curved lines\tvisible sections or posts\tbars or openings in between the fence panels or planks\tmade of wood, metal, or wire materials.", 22], "adult elephants": ["Yes. 'Adult elephants' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'adult elephants' but are not 'adult elephants' are:\tmammoths\trhinoceros\tbison\nThere are several useful visual features to tell there is 'adult elephants' and not similar things in a photo:\tgray or brown skin\tlong trunks and tusks\tthick legs and body\tlarge, floppy ears\twrinkled skin folds at neck\tand legs", 22], "pylons": ["Yes. 'Pylons' has a tangible appearance and refers to a specific type of structure.\nA few things that are visually similar to 'pylons' but are not 'pylons' are:\ttowers\tchimneys\tmasts\tpillars\tbuildings\nThere are several useful visual features to tell there is 'pylons' and not similar things in a photo:\ttall, slender structures\tmade of metal or concrete\tgrid-like structures\twith wires or cables attached\tto support electricity or communication lines", 22], "pullover": ["Yes. 'Pullover' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'pullover' but are not 'pullover' are:\tsweater\thoodie\tcardigan\tcoat\tjacket\nThere are several useful visual features to tell there is 'pullover' and not similar things in a photo:\tsoft and knitted material\tno buttons or zippers\tpulls over the head\tlong sleeves\tfitted waist and cuffs\tfor casual wear ", 22], "tape measure": ["Yes. 'Tape measure' has a tangible appearance and is a measuring tool.\nA few things that are visually similar to 'tape measure' but are not 'tape measure' are:\truler\tyardstick\tprotractor\tcompass\nThere are several useful visual features to tell there is 'tape measure' and not similar things in a photo:\tlong, narrow, and flat body\twith numbers and markings to measure length\tbent and clipped end (at times) to hook onto edges or corners\tfor retractable types of tape measure, there is a button or switch to lock and unlock the tape.", 22], "freesbee": ["Yes. 'Frisbee' has a tangible appearance and is a type of flying disc.\nA few things that are visually similar to 'freesbee' but are not 'freesbee' are:\tplastic plates\tbottle caps\tcircular lids\nThere are several useful visual features to tell there is 'freesbee' and not similar things in a photo:\tcircular disc shape\tflat and thin\tpotentially with brand logo or design in the center\tsmooth surface with a raised rim around the edge", 22], "taller": ["No. 'Taller' is too abstract of a concept to be distinguished in a photo. It is a comparative term used to describe height.\nTherefore, there are no things visually similar to 'taller' that are not 'taller.'\nUseful visual features cannot be used to distinguish 'taller' from other things in a photo, as it is not a tangible object.", 22], "gray chain": ["Yes. 'Gray chain' has a tangible appearance and is a type of chain.\nA few things that are visually similar to 'gray chain' but are not 'gray chain' are:\tsilver chain\tblack chain\tmetal wire\trope\nThere are several useful visual features to tell there is 'gray chain' and not similar things in a photo:\tgray color\tsmooth texture\tlinking segments of uniform size and shape\tmade of metal or metal-looking material", 22], "silver part": ["No. 'Silver part' is too vague or abstract to be distinguished in a photo.", 22], "breakfast food": ["Yes. 'Breakfast food' has a tangible appearance and refers to the food typically consumed in the morning.\nA few things that are visually similar to 'breakfast food' but are not 'breakfast food' are:\tsoup\tfruit salad\tpizza\tstir fry\nThere are several useful visual features to tell there is 'breakfast food' and not similar things in a photo:\teggs\tbacon\tpancakes\twaffles\tsausage\thoney\tjam\tbutter\ttoast\tcoffee\tmilk", 22], "udder": ["Yes. 'Udder' has a tangible appearance and is a physical part of a mammal's body.\nA few things that are visually similar to 'udder' but are not 'udder' are:\tfatty tissue\tpouches\tbags\tsacks\nThere are several useful visual features to tell there is 'udder' and not similar things in a photo:\thangs below the belly\thanging from multiple nipples\tdistinct teats or nipples on its surface\tpink or brown skin\ttexture and wrinkles of the udder's surface", 22], "lightswitch": ["Yes. 'Lightswitch' has a tangible appearance and is a type of electrical device.\nA few things that are visually similar to 'lightswitch' but are not 'lightswitch' are:\toutlet\tswitch plate\tthermostat\nThere are several useful visual features to tell there is 'lightswitch' and not similar things in a photo:\ta lever, toggle, or rocker for turning on or off lights\ta wall-mounted electrical device\tsize, shape and position on the wall", 22], "hanging": ["Yes. 'Hanging' has a tangible appearance and usually involves being suspended.\nA few things that are visually similar to 'hanging' but are not 'hanging' are:\tleaning\tresting\tbalancing\tattaching\nThere are several useful visual features to tell there is 'hanging' and not similar things in a photo:\tconnected to a higher point by a rope, string or chain\tsuspension in the air\tdangling", 22], "blue cell phone": ["Yes. 'Blue cell phone' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'blue cell phone' but are not 'blue cell phone' are:\tblue calculator\tblue camera\tblue mp3 player\tblue remote control\nThere are several useful visual features to tell there is 'blue cell phone' and not similar things in a photo:\trectangular shape\twith a screen and buttons\tor touchscreen\tearpiece and microphone\tcamera lens\tflash\ttiny speaker and microphone\tgrids for speakers and microphone", 22], "silver lap top": ["Yes. 'Silver laptop' has a tangible appearance.\nA few things that are visually similar to 'silver laptop' but are not 'silver laptop' are:\tsilver tablet\tsilver book\tsilver clipboard\tsilver phone\nThere are several useful visual features to tell there is 'silver laptop' and not similar things in a photo:\trectangular shape with a screen and keyboard\thinged design to fold and close\tthe screen is angled towards the keyboard\twindows or other software being displayed on the screen\tmultiple ports for cables and chargers", 22], "rolling pin": ["Yes. 'Rolling pin' has a tangible appearance and is a kitchen utensil.\nA few things that are visually similar to 'rolling pin' but are not 'rolling pin' are:\tbottle\twine glass\tcylinder\tcan\nThere are several useful visual features to tell there is 'rolling pin' and not similar things in a photo:\tlong cylindrical shape\tusually made of wood or metal\tflat and smooth surface on both ends\tfor baking or pastry making", 22], "matress": ["Yes. 'Mattress' has a tangible appearance and is a type of household furniture.\nA few things that are visually similar to 'mattress' but are not 'mattress' are:\tpillow\tblanket\tcushion\tmat\nThere are several useful visual features to tell there is 'mattress' and not similar things in a photo:\trectangular shape\tthick and bulky\tsize (usually bigger than a pillow and smaller than a bed)\tpatterned or solid color surface (usually with a fitted sheet)", 22], "water spray": ["Yes. 'Water spray' has a tangible appearance and is a type of phenomenon.\nA few things that are visually similar to 'water spray' but are not 'water spray' are:\tsteam\tfog\tsmoke\tmist\tcloud\nThere are several useful visual features to tell there is 'water spray' and not similar things in a photo:\tvisible drops of water\tspray coming from a nozzle or a source\thorizontal or vertical direction\tfalling down quickly or hovering in the air.", 22], "cat face": ["Yes. 'Cat face' has a tangible appearance and refers to the head of a cat.\nA few things that are visually similar to 'cat face' but are not 'cat face' are: dog face, fox face, raccoon face, lion face.\nThere are several useful visual features to tell there is 'cat face' and not similar things in a photo: pointy ears, whiskers, feline eyes, pink or black buttons for the nose, fur.", 22], "metal park bench": ["Yes. 'Metal park bench' has a tangible appearance and is a type of park furniture.\nA few things that are visually similar to 'metal park bench' but are not 'metal park bench' are:\twooden bench\tpicnic table\tbike rack\tbus stop bench\nThere are several useful visual features to tell there is 'metal park bench' and not similar things in a photo:\tmade of metal\thorizontal slats for seating and backrest\tarmrests and legs are also made of metal\tpainted in a neutral color like grey or black\tin a park or public area", 22], "pink cup": ["Yes. 'Pink cup' has a tangible appearance and is a type of cup.\nA few things that are visually similar to 'pink cup' but are not 'pink cup' are:\tglass\tbowl\tmug\ttumbler\nThere are several useful visual features to tell there is 'pink cup' and not similar things in a photo:\tcylindrical cup shape\tpink color\ton a handle or without one.", 22], "man hat": ["Yes. 'Man hat' has a tangible appearance and is a type of headwear for men.\nA few things that are visually similar to 'man hat' but are not 'man hat' are:\tcap\thelmet\thood\tbandana\t\nThere are several useful visual features to tell there is 'man hat' and not similar things in a photo:\tbrim\tband or ribbon\tcrown or top section\tformal or casual style\tmade of materials such as felt, wool, or straw", 22], "menu board": ["Yes. 'Menu board' has a tangible appearance and is typically found in restaurants or cafes.\nA few things that are visually similar to 'menu board' but are not 'menu board' are: sign, whiteboard, blackboard.\nThere are several useful visual features to tell there is 'menu board' and not similar things in a photo:\tlist of food and drink items with prices\ttypically found at a restaurant or cafe\tmounted on a wall or standing on a countertop\teasy to update or change the menu items using chalk, markers, or magnetic letters.", 22], "par tof line": ["No. 'Par tof line' is not a meaningful or coherent concept.", 22], "feline": ["Yes. 'Feline' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'feline' but are not 'feline' are:\tcanines\trodents\tbears\nThere are several useful visual features to tell there is 'feline' and not similar things in a photo:\tsharp teeth\tand claws\tpointed ears\tlong tail\tfurry bodies\tnarrow and focused eyes\tmuzzle shape (short and blunt)", 22], "bird wing": ["Yes. 'Bird wing' has a tangible appearance and is a part of a bird's body.\nA few things that are visually similar to 'bird wing' but are not 'bird wing' are:\tbutterfly wings\tbat wings\tpaper airplane wings\tdragonfly wings\nThere are several useful visual features to tell there is 'bird wing' and not similar things in a photo:\tfeathered surface\tbones or structure within the wing\tmultiple sections, including primary feathers and secondary feathers(joined by a wrist and fingers)\tflap-like motion when bird is in flight", 22], "stainless steel dishwasher": ["Yes. 'Stainless steel dishwasher' has a tangible appearance and is a type of appliance.\nA few things that are visually similar to 'stainless steel dishwasher' but are not 'stainless steel dishwasher' are:\twhite dishwasher\tchrome fridge\tmetallic oven\nThere are several useful visual features to tell there is 'stainless steel dishwasher' and not similar things in a photo:\tstainless steel finish\trectangular shape\twith controls and buttons\ton a counter or in a built-in space\tdoor with handle and window to see inside\tthe word \"dishwasher\" may be visible on the front", 22], "stainless steel fridge": ["Yes. 'Stainless steel fridge' has a tangible appearance and is a kitchen appliance.\nA few things that are visually similar to 'stainless steel fridge' but are not 'stainless steel fridge' are:\twhite fridge\tblack fridge\tcounter\ttop\tcabinet\nThere are several useful visual features to tell there is 'stainless steel fridge' and not similar things in a photo:\tmetallic and shiny surface\tfridge handle and door\thinged doors on the front\tsize and shape of fridge", 22], "bent knee": ["Yes. 'Bent knee' has a tangible appearance and refers to the physical position of the leg joint.\nA few things that are visually similar to 'bent knee' but are not 'bent knee' are:\tLeg in a sitting position\tLeg in a crossed position\tLeg in a kneeling position\nThere are no useful visual features to distinguish 'bent knee' from the listed similar things in a photo because they all involve bent knees. However, the angle of the bend, the position of the leg, and the context of the photo may help to differentiate them.", 22], "wilson tennis racket": ["Yes. 'Wilson tennis racket' has a tangible appearance and is a type of tennis equipment.\nA few things that are visually similar to 'Wilson tennis racket' but are not 'Wilson tennis racket' are:\tother brands of tennis rackets\tsquash rackets\tbadminton rackets\t\nThere are several useful visual features to tell there is 'Wilson tennis racket' and not similar things in a photo:\tdiamond-shaped strings on the racket face\tWilson brand logo on the strings or the racket\tframe with an oval or teardrop shape\thandles with rubber grip\ttwo or more circular holes near the rim of the racket.", 22], "toenail": ["Yes. 'Toenail' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'toenail' but are not 'toenail' are:\tfingernail\tclaw\thoof\tshell\nThere are several useful visual features to tell there is 'toenail' and not similar things in a photo:\tlocated on toes\thard and curved\ttranslucent or opaque in color\tgrowing from the nail bed on the toe tip or side", 22], "square box": ["Yes. 'Square box' has a tangible appearance.\nA few things that are visually similar to 'square box' but are not 'square box' are:\trectangular box\tcube\tbuilding blocks\nThere are several useful visual features to tell there is 'square box' and not similar things in a photo:\t4 square sides\tequal length on all sides\tstraight edges and corners \thaving a lid on the top.", 22], "pink roses": ["Yes. 'Pink roses' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'pink roses' but are not 'pink roses' are:\tpink peonies\tcherry blossoms\tcarnations\nThere are several useful visual features to tell there are 'pink roses' and not similar things in a photo:\tpink petals in a rose shape\tserrated edges on petals\tgreen leaves and stem\trecognizable rose fragrance.", 22], "bedsheet": ["Yes. 'Bedsheet' has a tangible appearance and is a type of cloth used as a covering for a bed.\nA few things that are visually similar to 'bedsheet' but are not 'bedsheet' are:\ttablecloth\ttowel\tcurtain\tblanket\nThere are several useful visual features to tell there is 'bedsheet' and not similar things in a photo:\trectangular in shape\tflat and plain-looking\tlarge enough to cover a bed\tvariety of colors and patterns available\ttypically made of cotton, linen, or silk", 22], "clock towers": ["Yes. 'Clock towers' has a tangible appearance and is a type of tower or building.\nA few things that are visually similar to 'clock towers' but are not 'clock towers' are:\tbell towers\tchimneys\tminarets\t\nThere are several useful visual features to tell there is 'clock towers' and not similar things in a photo:\ttall tower or building\twith a large clock face\ton top of the building or tower\tclock hands or numbers on the clock face\tbell or chimes accompanying the clock", 22], "bread roll": ["Yes. 'Bread roll' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'bread roll' but are not 'bread roll' are:\tmuffin\tbagel\tdoughnut\tcupcake\nThere are several useful visual features to tell there is 'bread roll' and not similar things in a photo:\tcircular shape\tsoft texture\tfluffy interior\tcrisp exterior", 22], "biscuits": ["Yes. 'Biscuits' has a tangible appearance and is a type of baked treat.\nA few things that are visually similar to 'biscuits' but are not 'biscuits' are:\tcookies\tcrackers\tscones\tmuffins\tdonuts\nThere are several useful visual features to tell there is 'biscuits' and not similar things in a photo:\tfluffy and tender\tcrumbly or crispy surface\tgolden-brown color\tcircular or oval shape\tmay have visible layers or pieces of butter\ton a baking sheet or a plate", 22], "metal top": ["Yes. 'Metal top' has a tangible appearance and is a kind of toy.\nA few things that are visually similar to 'metal top' but are not 'metal top' are:\tfidget spinner\tscrew\tnut\tgyro\nThere are several useful visual features to tell there is 'metal top' and not similar things in a photo:\tpointed end\tsmooth surface\tmetallic appearance\tspinning or rotating motion\ttop-like shape with a wider base and a narrower tip", 22], "lampposts": ["Yes. 'Lampposts' has a tangible appearance and is a type of street light.\nA few things that are visually similar to 'lampposts' but are not 'lampposts' are:\tstreet signs\tflag poles\tbus stops\tpark benches\nThere are several useful visual features to tell there is 'lampposts' and not similar things in a photo:\ttall and thin\tpole holding a light at the top\tdesigned to light up a street or sidewalk\tcurved arms holding the light bulb\tmay have signs or decorations attached to it\tmay have outlets or electrical components", 22], "backpack straps": ["Yes. 'Backpack straps' has a tangible appearance and refers to the straps used to carry a backpack.\nA few things that are visually similar to 'backpack straps' but are not 'backpack straps' are:\tpurse straps\tcamera straps\tmessenger bag straps\t\nThere are several useful visual features to tell there are 'backpack straps' and not similar things in a photo:\tattached to a backpack\tpadded or adjustable\tfrequently connected at the front or the top", 22], "juicy": ["No. 'Juicy' is too abstract to be distinguished in a photo.", 22], "bird feet": ["Yes. 'Bird feet' has a tangible appearance, and they are the feet of birds.\nA few things that are visually similar to 'bird feet' but are not 'bird feet' are:\tclaws of other animals\thuman feet\nThere are several useful visual features to tell there is 'bird feet' and not similar things in a photo:\tthree toes facing forward, and one toe facing backward\tscales or scaly texture\tsharp talons or claws\tfor perching or grasping branches or prey", 22], "filling": ["No. 'Filling' is too abstract to be distinguished in a photo without additional context. However, \"filling\" could also refer to the material that is used to fill an object, like a cushion or a cake, in which case it would have a tangible appearance.\nIf we consider \"filling\" in a more tangible sense, a few things that are visually similar to 'filling' but are not 'filling' are: liquid, powder, foam, syrup.\nThere are several useful visual features to tell there is 'filling' and not similar things in a photo: depends on the context, but typically a filling will occupy a space in an object, have a certain texture or consistency, and be visible within the object or surrounding it. For example, a filling inside a cushion or a cake might be visible through a tear or cut in the object, and have a different texture or color than the outer material. A filling inside a container might have a distinct color or viscosity that sets it apart from other materials in the container.", 22], "pies": ["Yes. 'Pies' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'pies' but are not 'pies' are:\ttarts\tcakes\tquiches\tpizzas\nThere are several useful visual features to tell there is 'pies' and not similar things in a photo:\ta baked dish with a pastry crust on top and bottom\tfilling made with fruits, vegetables, or meats\tusually sliced in a triangular shape\tcan be sweet or savory", 22], "food cart": ["Yes. 'Food cart' has a tangible appearance and is a type of mobile food stand.\nA few things that are visually similar to 'food cart' but are not 'food cart' are:\ttruck\tkiosk\ttrailer\tcounter\ttable\nThere are several useful visual features to tell there is 'food cart' and not similar things in a photo:\twheels\tcustomers\tqueue\tcounter for serving food\tdisplay of food and drinks\tsimple and compact design", 22], "brown marks": ["Yes. 'Brown marks' has a tangible appearance and is a type of discoloration or blemish.\nA few things that are visually similar to 'brown marks' but are not 'brown marks' are:_age spots\tfreckles\tmoles\tstains\nThere are several useful visual features to tell there is 'brown marks' and not similar things in a photo:\trectangular, circular or any discernible shape\tnot uniform in color\tor size\tsurface has a different texture than the surrounding area", 22], "grey suitcase": ["Yes. 'Grey suitcase' has a tangible appearance and is a type of baggage.\nA few things that are visually similar to 'grey suitcase' but are not 'grey suitcase' are:\tbackpack\tbriefcase\tbag\ttrunk\nThere are several useful visual features to tell there is 'grey suitcase' and not similar things in a photo:\trectangular shape\thard exterior\thandle(s)\tfor wheels", 22], "stadium lights": ["Yes. 'Stadium lights' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'stadium lights' but are not 'stadium lights' are:\tstreetlights\tlampposts\tstage lights\ttraffic lights\nThere are several useful visual features to tell there is 'stadium lights' and not similar things in a photo:\thigh-powered lights\tmounted on tall poles or towers\tbright white or yellow light\tdirection of the light (pointing toward a field or a stadium)", 22], "leafy plants": ["Yes. 'Leafy plants' has a tangible appearance and refers to a variety of plants with leaves.\nA few things that are visually similar to 'leafy plants' but are not 'leafy plants' are:\tevergreen trees\tcacti\tbushes\nThere are several useful visual features to tell there is 'leafy plants' and not similar things in a photo:\tthin, flat, and green leaves\tleaves attached to stems or branches\tvarious sizes and shapes of leaves\tnot prickly or spiny to the touch", 22], "cement steps": ["Yes. 'Cement steps' have a tangible appearance and are a type of building element.\nA few things that are visually similar to 'cement steps' but are not 'cement steps' are:\tblocks\tbricks\tstone steps\twooden steps\nThere are several useful visual features to distinguish 'cement steps' from the listed similar things in a photo:\t\n- They are made of cement (this can be seen by looking for visible grains or pores)\n- Smooth, flat surface\n- The edges and corners are straight and sharp\n- They usually have an unornamented, utilitarian appearance\n- They are often gray in color.", 22], "mouthwash": ["Yes. 'Mouthwash' has a tangible appearance and is a type of liquid.\nA few things that are visually similar to 'mouthwash' but are not 'mouthwash' are:\ttoner\twater\twith alcohol-containing beverage\nThere are several useful visual features to tell there is 'mouthwash' and not similar things in a photo:\ttranslucent bottle\twith a label specifying 'mouthwash'\tintense color (green, blue, or reddish)\tfresh and minty scent\tcap with a dropper or small opening for pouring", 22], "key chain": ["Yes. 'Key chain' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'key chain' but are not 'key chain' are:\tbag charm\tlanyard\tjewelry piece\twith a decorative pendant or trinket\nThere are several useful visual features to tell there is 'key chain' and not similar things in a photo:\ta ring or loop to hold keys or other items\thanging from a key ring or carabiner clip\ttypically made of metal or plastic with a decorative pendant or trinket", 22], "hilltop": ["Yes. 'Hilltop' has a tangible appearance and is a location or a part of a landscape.\nA few things that are visually similar to 'hilltop' but are not 'hilltop' are:\tmountain\ttop of a building\ttop of a tower\nThere are several useful visual features to tell there is 'hilltop' and not similar things in a photo:\ta natural formation of the earth (as opposed to man-made)\tnot as tall or steep as a mountain\tnot attached to a building or a tower that extends upwards\tfrom its position, it is easy to see the surrounding terrain or landscape", 22], "office phone": ["Yes. 'Office phone' has a tangible appearance and is a kind of electronic device.\nA few things that are visually similar to 'office phone' but are not 'office phone' are:\tcell phone\tlandline phone\twalkie talkie\tintercom\tsystem speaker\nThere are several useful visual features to tell there is 'office phone' and not similar things in a photo:\tcorded handset\tbutton pad\tdisplay screen\treceiver hook\tfixed to a desktop or a wall", 22], "block wall": ["Yes. 'Block wall' has a tangible appearance and is a structure made of blocks.\nA few things that are visually similar to 'block wall' but are not 'block wall' are:\tBrick wall\tRock wall\t\nThere are several useful visual features to tell there is 'block wall' and not similar things in a photo:\trectangular blocks\tcement or mortar between blocks\tuniform in height and width\tinvisible or few gaps between blocks", 22], "beige couch": ["Yes. 'Beige couch' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'beige couch' but are not 'beige couch' are:\tarmchair\tloveseat\tbench\tottoman\nThere are several useful visual features to tell there is 'beige couch' and not similar things in a photo:\trectangular or curved shape\tupholstered in beige fabric\thave a backrest and armrests\tcushions or throw pillows on top.", 22], "grey frame": ["Yes. 'Grey frame' has a tangible appearance and refers to a specific color and shape of a frame.\nA few things that are visually similar to 'grey frame' but are not 'grey frame' are:\tblack frame\twhite frame\twooden frame\nThere are several useful visual features to tell there is 'grey frame' and not similar things in a photo:\tgrey color\tstraight lines\tin a rectangular shape\tmade of metal or plastic.", 22], "silver blade": ["Yes. 'Silver blade' has a tangible appearance and refers to a sharp-edged tool or weapon made of silver-colored metal.\nA few things that are visually similar to 'silver blade' but are not 'silver blade' are:\tknife\tscissors\tmetal ruler\tpaint scraper\nThere are several useful visual features to tell there is a 'silver blade' and not similar things in a photo:\tlong and thin\tsharp edge\tmade of metal\tsilver or silver-like color\tcan be used as a tool or a weapon", 22], "orange backpack": ["Yes. 'Orange backpack' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'orange backpack' but are not 'orange backpack' are:\torange purse\torange tote bag\torange duffle bag\torange messenger bag\t\nThere are several useful visual features to tell there is 'orange backpack' and not similar things in a photo:\tbackpack straps\tbackpack shape\tmultiple pockets or compartments\ton a person's back\toranges on the backpack (if printed)", 22], "orange number": ["No. 'Orange number' is too vague or abstract to be distinguished in a photo.", 22], "outhouse": ["Yes. 'Outhouse' has a tangible appearance and is an outdoor toilet.\nA few things that are visually similar to 'outhouse' but are not 'outhouse' are:\tshed\tcabin\tbarn\nThere are several useful visual features to tell there is 'outhouse' and not similar things in a photo:\tdoor with a crescent moon cutout\twooden walls and roof\tmetal roof or chimney\touthouse sign outside", 22], "charm": ["No. 'Charm' is too vague or abstract to be distinguished in a photo.", 22], "pink shoes": ["Yes. 'Pink shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'pink shoes' but are not 'pink shoes' are:\tpink sneakers\tpink sandals\tpink boots\nThere are several useful visual features to tell there is 'pink shoes' and not similar things in a photo:\tfootwear that covers the entire feet\tpink color as the dominant color\tlaces or straps to fasten the shoes on feet", 22], "riding helmet": ["Yes. 'Riding helmet' has a tangible appearance and is a type of protective headgear worn while riding horses or bicycles.\nA few things that are visually similar to 'riding helmet' but are not 'riding helmet' are:\tbicycle helmet\thard hat\tmotorcycle helmet\tskateboard helmet\nThere are several useful visual features to tell there is 'riding helmet' and not similar things in a photo:\tlightweight and aerodynamic design\tchin strap\tventilation holes\tor a harness inside\tthe shell of the helmet\tthat surrounds and protects the head.", 22], "watermelons": ["Yes. 'Watermelons' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'watermelons' but are not 'watermelons' are:\tcantaloupe\thoneydew\tgourds\tpumpkins\tbasketballs\nThere are several useful visual features to tell there is 'watermelons' and not similar things in a photo:\tlarge and round shape\tgreen skin with lighter and darker stripes\tpink or reddish flesh\twith black seeds inside", 22], "metal cage": ["Yes. 'Metal cage' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'metal cage' but are not 'metal cage' are:\tmetal gate\tfence\tsteel grating\tbasket\t\nThere are several useful visual features to tell there is 'metal cage' and not similar things in a photo:\tenclosed space\twith bars or wires\tas a container or a confinement structure\tmade of metal or wire-like material.", 22], "lounge": ["Yes. 'Lounge' has a tangible appearance and is a type of seating area.\nA few things that are visually similar to 'lounge' but are not 'lounge' are:\tliving room\tarea with couches and chairs\tbar\tarea with recliners and TV\nThere are several useful visual features to tell there is 'lounge' and not similar things in a photo:\tcomfy chairs or sofas\twith pillows\tor blankets\tcoffee table\tor side table\tlamp or lighting.", 22], "kitchen door": ["Yes. 'Kitchen door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'kitchen door' but are not 'kitchen door' are:\tfront door\tbathroom door\tsliding door\tgarage door\nThere are several useful visual features to tell there is 'kitchen door' and not similar things in a photo:\tlocated in a kitchen\tswinging mechanism\tframed and attached to a doorway or an entrance\twindow or transparent material to see inside or through the door.", 22], "mesh fence": ["Yes. 'Mesh fence' has a tangible appearance and is a type of fence made of wire mesh.\nA few things that are visually similar to 'mesh fence' but are not 'mesh fence' are:\twrought iron fence\tbarbed wire fence\tchain link fence\twooden fence\nThere are several useful visual features to tell there is 'mesh fence' and not similar things in a photo:\twire mesh pattern\tregular gaps between the wires\tgrey or silver color\tsometimes green or black in color\twith or without barbed wire on the top of the fence.", 22], "zebra hooves": ["Yes. 'Zebra hooves' has a tangible appearance and refers to the feet of zebras.\nA few things that are visually similar to 'zebra hooves' but are not 'zebra hooves' are:\thorse hooves\tcow hooves\tdeer hooves\nThere are several useful visual features to tell there is 'zebra hooves' and not similar things in a photo:\tblack and white stripes\ton a zebra's leg or hoof\thoof is pointed and has a cloven appearance.", 22], "code": ["No. 'Code' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider the tangible representation of 'code', a few things that are visually similar to a code but are not specifically 'code' can be:\tlanguage\tsymbols\tmathematical equations\tpasswords\n\nFor distinguishing 'code' from similar things in a photo, the following visual features may be useful:\tsequence of letters, numbers and/or symbols\tarranged in a specific order and pattern\tcomputer or digital device display\twhere the symbols represent instructions for a program or software.", 22], "discs": ["Yes. 'Discs' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'discs' but are not 'discs' are:\tballs\tplates\tcoins\ttires\nThere are several useful visual features to tell there is 'discs' and not similar things in a photo:\tthin, round, and flat shape\tsymmetrical\tdistinguished edge and center(usually)", 22], "crouton": ["Yes. 'Crouton' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'crouton' but are not 'crouton' are:\ttortilla chips\tbiscuits\tcrackers\tpretzels\nThere are several useful visual features to tell there is 'crouton' and not similar things in a photo:\tsmall cubical or irregular shape\ttoasted or fried bread pieces\tvariety of sizes and colors", 22], "bus route": ["No. 'Bus route' is too vague or abstract to be distinguished in a photo.", 22], "metal cup": ["Yes. 'Metal cup' has a tangible appearance.\nA few things that are visually similar to 'metal cup' but are not 'metal cup' are:\tgoblet\tbowl\tcandy dish\tcoffee mug\nThere are several useful visual features to tell there is 'metal cup' and not similar things in a photo:\tcup-shaped\tmade of metal\treflective surface\thandles on opposite sides", 22], "dark building": ["Yes. 'Dark building' has a tangible appearance and is a type of building that appears dark.\nA few things that are visually similar to 'dark building' but are not 'dark building' are:\tabandoned building\tnightclub\tpolice station\tfactory\nThere are several useful visual features to tell there is 'dark building' and not similar things in a photo:\tlack of light or low light sources\tdark coloring on the facade of the building\tno visible activity or people inside the building", 22], "plate number": ["No. 'Plate number' is too vague or abstract to be distinguished in a photo.\nThings that are visually similar in a photo but are not 'plate number' may include:\trandom numbers and letters\twall decorations\tclocks\tcalendars\tbinary code\nThere are no useful visual features to distinguish 'plate number' from these similar things as it is a specific alphanumeric combination that is unique to a vehicle and registered with the government.", 22], "rain jacket": ["Yes. 'Rain jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'rain jacket' but are not 'rain jacket' are:\twindbreaker\tcoat\tponcho\thoodie\nThere are several useful visual features to tell there is 'rain jacket' and not similar things in a photo:\tmade of waterproof material, such as nylon or Gore-Tex\tbright colors like yellow or orange\thood, along with long sleeves and a zipper or buttons for closing.", 22], "metal wire fence": ["Yes. 'Metal wire fence' has a tangible appearance and is a type of fence made of metal wires.\nA few things that are visually similar to 'metal wire fence' but are not 'metal wire fence' are:\twooden fence\tconcrete wall\tchain-link fence\nThere are several useful visual features to tell there is 'metal wire fence' and not similar things in a photo:\tthin metal wires\twires tightly woven together\ta see-through pattern with small holes\tmetallic texture or color", 22], "orange coat": ["Yes. 'Orange coat' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'orange coat' but are not 'orange coat' are:\torange vest\torangutan\twinter jacket\ttraffic cone\torange bag\nThere are several useful visual features to tell there is 'orange coat' and not similar things in a photo:\twearable outer garment\torange color\tsleeves and a hood or collar for the head and neck\tworn by a person", 22], "water area": ["Yes. 'Water area' has a tangible appearance and refers to an area that is covered by water.\nA few things that are visually similar to 'water area' but are not 'water area' are:\tgrassland\tdesert\tocean-bed\tsand dune\nThere are several useful visual features to tell there is 'water area' and not similar things in a photo:\tbluish color\twavy texture\treflective surface\twetness\tmovement\tif there is boats or other water transport vehicles", 22], "glass panel": ["Yes. 'Glass panel' has a tangible appearance and refers to a flat piece of glass used for windows or doors.\nA few things that are visually similar to 'glass panel' but are not 'glass panel' are:\tmirrors\tpainting\tcanvas\tdefogger\nThere are several useful visual features to tell there is 'glass panel' and not similar things in a photo:\ttranslucent or clear appearance\tsmooth surface\trectangular shape\tframe or border\tif used for windows or doors, accompanied by a structure or handle", 22], "coca-cola": ["Yes. 'Coca-cola' has a tangible appearance and is a type of beverage.\nA few things that are visually similar to 'coca-cola' but are not 'coca-cola' are:\tpepsi\tcolored water\ticed tea\tbeer\nThere are several useful visual features to make the distinction in a photo:\tdark color of the liquid\tthe iconic red and white logo\tcarbonation in the drink\tbottle or can with coca-cola branding", 22], "metal buckle": ["Yes, 'metal buckle' has a visually concrete concept.\nA few things that are visually similar to 'metal buckle' but are not 'metal buckle' are:\tzippers\tbelts\tsnaps\tfasteners\tbuttons\nThere are several useful visual features to tell there is 'metal buckle' and not similar things in a photo:\tmetallic appearance\tclasp or closure device\tfor belts, often has a prong and a loop\tfor bags, often has a matching latch on the other side", 22], "metal chairs": ["Yes. 'Metal chairs' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'metal chairs' but are not 'metal chairs' are:\twooden chairs\tplastic chairs\tglass chairs\trocking chairs\nThere are several useful visual features to tell there is 'metal chairs' and not similar things in a photo:\tmade entirely of metal or have metal frames\tmetallic surface or finish\tstraight, simple lines or curves\tsleek, modern appearance", 22], "folding table": ["Yes. 'Folding table' has a tangible appearance and is a type of table that can be folded for easy storage.\nA few things that are visually similar to 'folding table' but are not 'folding table' are:\tcard table\tpicnic table\tdining table\nThere are several useful visual features to tell there is 'folding table' and not similar things in a photo:\tflat surface with four legs\ttabletop and legs made of lightweight materials\thinged mechanism that allows the table to fold in half\tfor indoor or outdoor use", 22], "incline": ["Yes. 'Incline' has a tangible appearance and refers to a sloping or slanting surface or angle.\nA few things that are visually similar to 'incline' but are not 'incline' are:\thill\tramp\tstairs\tledge\nThere are several useful visual features to tell there is 'incline' and not similar things in a photo:\ta surface or angle that is sloping or slanting\tdoes not have clear steps like stairs, grassy or rocky appearance depending on whether it's natural or man-made.", 22], "pink shorts": ["Yes. 'Pink shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pink shorts' but are not 'pink shorts' are:\tpink skirt\tpink dress\tpink pants\tpink leggings\nThere are several useful visual features to tell there is 'pink shorts' and not similar things in a photo:\tshort length\tloose fit or tight fit\tmade of fabric\tbutton, zipper or drawstring for closure\tpink color", 22], "purple frisbee": ["Yes. 'Purple frisbee' has a tangible appearance and is a specific type of flying disc.\nA few things that are visually similar to 'purple frisbee' but are not 'purple frisbee' are:\tother colored frisbees\tplastic plates\nThere are several useful visual features to tell there is 'purple frisbee' and not similar things in a photo:\tcircular shape\tsimilar to a disc\twith an elevated, curved edge\tpurple color.", 22], "orange feet": ["Yes. 'Orange feet' has a tangible appearance and could refer to the feet of an animal or person that are orange in color.\nA few things that are visually similar to 'orange feet' but are not 'orange feet' are:\tpaw prints\torange shoes\toranges\tflower petals\nThere are no useful visual features to distinguish 'orange feet' from these similar things in a photo, as they all share the color orange. The context and surroundings can provide clues to identify the object as 'orange feet'.", 22], "cranberries": ["Yes. 'Cranberries' has a tangible appearance and is a kind of fruit.\nA few things that are visually similar to 'cranberries' but are not 'cranberries' are:\tcherries\tpomegranates\ttomatoes\t\nThere are several useful visual features to tell there is 'cranberries' and not similar things in a photo:\tbright red color\tround or oblong shape\tsmooth or glossy texture\tgrowing in clusters on low trailing vines.", 22], "round wheel": ["Yes. 'Round wheel' has a tangible appearance and is an object used for transportation.\nA few things that are visually similar to 'round wheel' but are not 'round wheel' are:\tball\tfrisbee\tclock\nThere are several useful visual features to tell there is 'round wheel' and not similar things in a photo:\tcircular and flat shape\tcenter hole or hubcap\ttread or rubber surface for grip\tspinning motion or attached to an axle", 22], "gold watch": ["Yes. 'Gold watch' has a tangible appearance and is a type of timepiece.\nA few things that are visually similar to 'gold watch' but are not 'gold watch' are:\tsilver watch\trose gold watch\tbracelet\tpocket watch\nThere are several useful visual features to tell there is 'gold watch' and not similar things in a photo:\tgolden color\tornamented with jewelry\tlarge display or clock face\twith a watchband or wrist strap\tworn on the wrist to tell time.", 22], "orange banner": ["Yes. 'Orange banner' has a tangible appearance and is a type of banner with an orange color.\nA few things that are visually similar to 'orange banner' but are not 'orange banner' are:\torange flag\torange sheet\torange curtain\nThere are several useful visual features to tell there is 'orange banner' and not similar things in a photo:\trectangular shape\tbright orange color\thanging from a pole or a wall\twritten or printed text or graphics", 22], "surfboard sand": ["No. 'Surfboard sand' is too vague or abstract to be distinguished in a photo. However, I believe you might have meant 'surfboard wax'. If that's the case:\nYes. 'Surfboard wax' has a tangible appearance.\nA few things that are visually similar to 'surfboard wax' but are not 'surfboard wax' are:\tcandles\tbeeswax\tsculpting wax\nThere are several useful visual features to tell there is 'surfboard wax' and not similar things in a photo:\tusually a bar or block shape\ttranslucent texture\tvisible on the surface of a surfboard", 22], "countryside": ["Yes. 'Countryside' has a tangible appearance and refers to rural landscapes.\nA few things that are visually similar to 'countryside' but are not 'countryside' are:\tcity outskirts\tfarms\tgrasslands\nThere are several useful visual features to tell there is 'countryside' and not similar things in a photo:\tbucolic landscape\twith rolling hills, fields, and pastureland\tfew buildings and roads\tnatural features such as trees, rivers, and mountains\tno tall buildings or city skylines visible", 22], "kitchen wall": ["Yes. 'Kitchen wall' has a tangible appearance and is a part of a room.\nA few things that are visually similar to 'kitchen wall' but are not 'kitchen wall' are:\tbathroom wall\tbedroom wall\tliving room wall\tfront door\nThere are several useful visual features to tell there is 'kitchen wall' and not similar things in a photo:\ttile or painted surface\tcountertops or cabinets in the background\tkitchen appliances in the background (stove, oven, sink, etc.)\tkitchen utensils or cookware hanging from the wall.", 22], "lizard": ["Yes. 'Lizard' has a tangible appearance and is a type of reptile.\nA few things that are visually similar to 'lizard' but are not 'lizard' are:\tsnake\tcrocodile\tkomodo dragon\nThere are several useful visual features to tell there is 'lizard' and not similar things in a photo:\tfour legs\twith or without a tail\tscaly skin\tpointed snout or long nose\tslender and flexible body.", 22], "bread box": ["Yes. 'Bread box' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'bread box' but are not 'bread box' are:\tkitchen canisters\tbaskets\ttins\tother kinds of food storage containers\nThere are several useful visual features to tell there is 'bread box' and not similar things in a photo:\trectangular or cylindrical shape\tlidded\tcontainer made of wood, metal or plastic\tmay have air vent holes\tor say 'bread' on the side", 22], "carafe": ["Yes. 'Carafe' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'carafe' but are not 'carafe' are:\tjug\tdecanter\tbottle\tvase\nThere are several useful visual features to tell there is 'carafe' and not similar things in a photo:\tnarrow neck or spout\tlarge body or base\tusually made of glass or ceramic\tused for serving liquids or decanting wine", 22], "orange sky": ["Yes. 'Orange sky' has a tangible appearance.\nA few things that are visually similar to 'orange sky' but are not 'orange sky' are:\tpink sky\tsunset\tsunrise\tfog\nThere are several useful visual features to tell there is 'orange sky' and not similar things in a photo:\tan orange or reddish hue covering the majority of the sky\tthe absence or presence of clouds\tthe appearance of the sun or its reflection on the horizon or water", 22], "box car": ["Yes. 'Box car' has a tangible appearance and is a type of freight car used on railways.\nA few things that are visually similar to 'box car' but are not 'box car' are:\tpassenger railcars\tlocomotives\ttrams\nThere are several useful visual features to tell there is 'box car' and not similar things in a photo:\tlarge rectangular shape\twithout windows\tor with small windows along the top or sides\topen or closed sliding doors\tat least four wheels in two axles\twith or without graffiti or other markings", 22], "bathmat": ["Yes. 'Bathmat' has a tangible appearance and is a type of mat used in the bathroom.\nA few things that are visually similar to 'bathmat' but are not 'bathmat' are:\trug\tdoormat\tpet mat\tyoga mat\nThere are several useful visual features to tell there is 'bathmat' and not similar things in a photo:\tsmall and rectangular-shaped\tmat made of spongy or absorbent material\trubber non-slip bottom\twater-absorbing top surface\tsolid or textured color pattern designed for the bathroom.", 22], "purple hat": ["Yes. 'Purple hat' has a tangible appearance and is a specific article of clothing.\nA few things that are visually similar to 'purple hat' but are not 'purple hat' are:\tred hat\tblue hat\tgreen hat\tcap\tbonnet\nThere are several useful visual features to tell there is 'purple hat' and not similar things in a photo:\tpurple color\theadwear\tshaped like a hat\tworn on the head", 22], "globes": ["Yes. 'Globes' has a tangible appearance and is a spherical object used to represent the Earth.\nA few things that are visually similar to 'globes' but are not 'globes' are:\tballs\tmarbles\tplanets\t\nThere are several useful visual features to tell there are 'globes' and not similar things in a photo:\tspherical shape\twith latitude and longitude lines\tshowing political boundaries of countries and oceans\tdisplaying the equator and the tropics of Cancer and Capricorn.", 22], "giraffe leg": ["Yes. 'Giraffe leg' has a tangible appearance and is a specific body part of an animal.\nA few things that are visually similar to 'giraffe leg' but are not 'giraffe leg' are:\tzebra leg\thorse leg\tcow leg\nThere are several useful visual features to tell there is 'giraffe leg' and not similar things in a photo: very long\tand thin with a characteristic pattern of patches on the skin. Giraffe legs are usually much longer than those of other animals and their skin has a unique pattern of large, irregularly shaped patches that can be dark brown, orange-brown, or black in color. The legs are also supported by a series of strong tendons and muscles that give them their distinctive shape and allow giraffes to run at fast speeds.", 22], "tear": ["Yes. 'Tear' has a tangible appearance and is a drop of liquid coming from the eye.\nA few things that are visually similar to 'tear' but are not 'tear' are:\twater droplet\tpaint drips\toil drops\tdew\tdrool\nThere are several useful visual features to tell there is 'tear' and not similar things in a photo:\tcoming from the eye or near the eye area\tclear and colorless or slightly saline in appearance\tspherical or oval-shaped\tdrops of varying sizes, usually small.", 22], "pine cone": ["Yes. 'Pine cone' has a tangible appearance and is a type of seed cone.\nA few things that are visually similar to 'pine cone' but are not 'pine cone' are:\tice cream scoop\tporcupine\ttrumpet\nThere are several useful visual features to tell there is 'pine cone' and not similar things in a photo:\tcone-shaped\thard and woody texture\tsmall scales\tset of seeds at the center", 22], "circle design": ["Yes. 'Circle design' has a tangible appearance and includes patterns or designs made up of circles.\nA few things that are visually similar to 'circle design' but are not 'circle design' are:\tstriped design\tpolka dot design\tgeometric design\tchevron design\nThere are several useful visual features to tell there is 'circle design' and not similar things in a photo:\trepeated circles\tsimilar size circles\tcircles arranged in a pattern or design", 22], "tall tree": ["Yes. 'Tall tree' has a tangible appearance and refers to a tree that is relatively taller than other trees in the surrounding area.\nA few things that are visually similar to 'tall tree' but are not 'tall tree' are:\tshort tree\tbush\tlamp post\tstatue\nThere are several useful visual features to tell there is 'tall tree' and not similar things in a photo:\trelatively higher than other trees in the area\tstraight trunk and branches with foliage at the top\tthe entire tree can be seen in the frame when standing at a distance", 22], "china": ["Yes. 'China' has a tangible appearance and refers to a type of ceramic dinnerware.\nA few things that are visually similar to 'china' but are not 'china' are:\tpottery\tearthenware\tstoneware\tterra cotta\nThere are several useful visual features to tell there is 'china' and not similar things in a photo:\twhite or light-colored\tthin and delicate\ttranslucent\tusually adorned with colorful patterns or designs", 22], "water splashes": ["Yes. 'Water splashes' has a tangible appearance and is a physical phenomenon.\nA few things that are visually similar to 'water splashes' but are not 'water splashes' are:\train\tdew droplets\tbubbles\tshattered glass\tsmoke\nThere are several useful visual features to tell there is 'water splashes' and not similar things in a photo:\twater droplets\tflying or splattering water drops\twhite or transparent color\tsplashing or waves in the water", 22], "bicyclists": ["Yes. 'Bicyclists' has a tangible appearance and is a person riding a bicycle.\nA few things that are visually similar to 'bicyclists' but are not 'bicyclists' are:\tmotorcyclists\tskateboarders\tscooter riders\tpedestrians\t\nThere are several useful visual features to tell there is 'bicyclists' and not similar things in a photo:\tperson riding a bicycle\ttwo wheels\tpedals\thelmet or protective gear\tbicycling clothing", 22], "label bottle": ["Yes. 'Label bottle' has a tangible appearance.\nA few things that are visually similar to 'label bottle' but are not 'label bottle' are:\tunlabeled bottle\tjar\tcanister\ttin\nThere are several useful visual features to tell there is 'label bottle' and not similar things in a photo:\ttransparent or translucent material\tbottle shape\tcylindrical with a narrow neck\tpaper or plastic label with information on the contents", 22], "toilet tank lid": ["Yes. 'Toilet tank lid' has a tangible appearance and is a part of a toilet.\nA few things that are visually similar to 'toilet tank lid' but are not 'toilet tank lid' are:\tcookware lids\ttrash can lids\tcrate and barrel dishware\nThere are several useful visual features to tell there is 'toilet tank lid' and not similar things in a photo:\telongated octagon shape\tporcelain or ceramic material\thaving a handle or knob on top\tthat it is located on top of a toilet tank", 22], "dog paws": ["Yes. 'Dog paws' has a tangible appearance and is a part of a dog's anatomy.\nA few things that are visually similar to 'dog paws' but are not 'dog paws' are:\tcat paws\tbear paws\thuman hands\tpig hooves\nThere are several useful visual features to tell there is 'dog paws' and not similar things in a photo:\tfour toes and one dew claw\tpadded sole\tfurry or smooth surface\tsize and shape relative to the dog's breed and size.", 22], "wire rack": ["Yes. 'Wire rack' has a tangible appearance and is a type of storage unit.\nA few things that are visually similar to 'wire rack' but are not 'wire rack' are:\tshelving\tunit bookshelf\tmetal basket\nThere are several useful visual features to tell there is 'wire rack' and not similar things in a photo:\tmade of metal wires or rods\thas open shelves\tnot enclosed or covered by doors or walls\tdesigned for storage and organization", 22], "tread": ["Yes. 'Tread' has a tangible appearance and refers to the pattern on a tire or shoe.\nA few things that are visually similar to 'tread' but are not 'tread' are:\tpatterned carpet\tprinted fabric\tembossed paper\nThere are several useful visual features to distinguish 'tread' from similar things in a photo:\tpattern of grooves or ridges\ton a rubber surface (e.g., tire or shoe)\tmatching the shape of the object over which it is laid out (e.g., car tire or shoe sole)", 22], "metal train track": ["Yes. 'Metal train track' has a tangible appearance and is a type of railway component.\nA few things that are visually similar to 'metal train track' but are not 'metal train track' are:\tfence\trailing\tpipeline\twire\nThere are several useful visual features to tell there is 'metal train track' and not similar things in a photo:\trequesting a straight line that runs on the ground\ttwo metal rails connected by metal bolts or spikes\tmetallic and shiny appearance\tflattened and smoothed ground around it", 22], "thick bushes": ["Yes, 'thick bushes' has a tangible appearance and refers to dense vegetation.\nA few things that are visually similar to 'thick bushes' but are not 'thick bushes' are:\tgrass\tpiles of leaves\tshrubbery\tvines\nThere are several useful visual features to identify 'thick bushes' and differentiate it from similar things in a photo:\tdense and tightly-packed foliage\tvariety of leaf colors, sizes, and shapes\theight of foliage over the ground level in different parts of the bush\ttop-down view of the foliage showing the thickness of the bush.", 22], "lattice": ["Yes. 'Lattice' has a tangible appearance and is a type of pattern or structure.\nA few things that are visually similar to 'lattice' but are not 'lattice' are:\tgrid\tfence\tpatterns of window panes\nThere are several useful visual features to tell there is 'lattice' and not similar things in a photo:\tcriss-cross pattern or structure\tnarrow slats or strips\toften made of wood or metal\tused for decoration or support\tintricately woven or patterned", 22], "beer glass": ["Yes. 'Beer glass' has a tangible appearance and is a type of glassware.\nA few things that are visually similar to 'beer glass' but are not 'beer glass' are:\twine glass\ttumbler\tpilsner glass\tcocktail glass\nThere are several useful visual features to tell there is 'beer glass' and not similar things in a photo:\ttall and cylindrical or curved and broad shape\ttranslucent or transparent material\thas a distinct amount of foam on top, visible beer inside\thas a handle or no handle at all", 22], "shower rod": ["Yes. 'Shower rod' has a tangible appearance and is a type of rod used in bathrooms.\nA few things that are visually similar to 'shower rod' but are not 'shower rod' are:\tcurtain rod\tclothes rod\tshelf\tbracket\nThere are several useful visual features to tell there is 'shower rod' and not similar things in a photo:\tattached to the wall or ceiling\tusually curved or straight\twith shower curtain rings or hooks attached to it", 22], "rock cliff": ["Yes. 'Rock cliff' is a visually concrete concept that refers to a steep rock face.\nA few things that are visually similar to 'rock cliff' but are not 'rock cliff' are:\thills\tmountains\tslopes\nThere are several useful visual features to distinguish a 'rock cliff' from the listed similar things in a photo:\tvertical or near-vertical face\tmade of rock or hard material\toverhangs or protrusions\trugged or jagged surface texture\tno vegetation growing on it", 22], "bed post": ["Yes. 'Bed post' has a tangible appearance and is a part of a bed.\nA few things that are visually similar to 'bed post' but are not 'bed post' are:\ttable leg\tchair leg\thandrail\tcolumn\nThere are several useful visual features to tell there is 'bed post' and not similar things in a photo:\tattached to the bed frame\ttaller than the bed surface\tcylindrical or square shape\tdetails or embellishments at the top and bottom", 22], "chariot": ["Yes. 'Chariot' has a tangible appearance and is a type of ancient vehicle.\nA few things that are visually similar to 'chariot' but are not 'chariot' are:\tcarriage\twagon\tbuggy\nThere are several useful visual features to tell there is 'chariot' and not similar things in a photo:\ttwo wheels\tpulled by horses or other animals\topen top\tappearance of being from ancient times or civilizations", 22], "tree log": ["Yes. 'Tree log' has a tangible appearance and is a part of a tree trunk that is cut for use.\nA few things that are visually similar to 'tree log' but are not 'tree log' are:\ttree branch\twooden plank\tpiece of furniture\twooden sculpture\nThere are several useful visual features to tell there is 'tree log' and not similar things in a photo:\tcut from a tree trunk\tcylindrical shape\tring patterns on the cross-section\tbark on the outer layer\trough and uneven texture on the surface of the wood.", 22], "business name": ["No. 'Business name' is too vague or abstract to have a tangible appearance.\nThere are no things that are visually similar to 'business name' but are not a 'business name'.\nSince a business name does not have a tangible appearance, there are no visual features necessary to distinguish it in a photo. However, logos, signs, or lettering may be associated with a business name and can be visually identified.", 22], "waterfront": ["Yes. 'Waterfront' has a tangible appearance and refers to an area of land that borders a body of water.\nA few things that are visually similar to 'waterfront' but are not 'waterfront' are:\tcityscape\tbeach\tseaside\tPromenade\tparks\nThere are several useful visual features to tell there is 'waterfront' and not similar things in a photo:\twater visible in the vicinity\tbuildings or structures that stand around the water\tline between water and land\tis a pier, dock, or marina\tboats or ships visible", 22], "tanks": ["Yes. 'Tanks' has a tangible appearance and is a type of military vehicle.\nA few things that are visually similar to 'tanks' but are not 'tanks' are:\ttrucks\tbuses\tlarge construction vehicles\nThere are several useful visual features to tell there is 'tanks' and not similar things in a photo:\thuge cannon or gun on top\tturret on top\tarmored shell or hull caterpillar tracks or large wheels for movement", 22], "silver kettle": ["Yes. 'Silver kettle' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'silver kettle' but are not 'silver kettle' are:\tteapot\tcoffee pot\turn\nThere are several useful visual features to tell there is 'silver kettle' and not similar things in a photo:\tsilver or metallic appearance\tkettle shape\twith a handle and spout\tdecoration or design on the outside\thinged lid on the top", 22], "helmet boy": ["Yes. 'Helmet boy' has a tangible appearance and refers to a boy wearing a helmet.\nThere are no things that are visually similar to 'helmet boy' but are not 'helmet boy'.\nUseful visual features for distinguishing 'helmet boy' from other boys in a photo include the presence of a helmet on the head of the boy, straps and buckles attached to the helmet, and any logos or designs on the helmet itself.", 22], "gravel tracks": ["Yes. 'Gravel tracks' has a tangible appearance and refers to tracks or paths made of gravel.\nA few things that are visually similar to 'gravel tracks' but are not 'gravel tracks' are:\tconcrete pavements\tdirt roads\twooden decks\nThere are several useful visual features to distinguish 'gravel tracks' from the listed similar things in a photo:\tpebble-covered surface\tdifferent texture and color than the surrounding surface\tloose materials that may move or shift underfoot.", 22], "moose": ["Yes. 'Moose' has a tangible appearance and is a type of large deer.\nA few things that are visually similar to 'moose' but are not 'moose' are:\tdeer\telk\tcaribou\tgiraffe\nThere are several useful visual features to tell there is 'moose' and not similar things in a photo:\tlarge size\tbrown or gray fur\tbulbous nose\thigh hump on shoulders\tlarge and wide antlers\tflat face with big ears", 22], "palace": ["Yes. 'Palace' has a tangible appearance and is a type of large, grand building.\nA few things that are visually similar to 'palace' but are not 'palace' are:\tmansion\tcastle\tcathedral\tmuseum\nThere are several useful visual features to tell there is 'palace' and not similar things in a photo:\tlarge and grand building\telaborate and ornate architecture\tpillars, arches, or domes\tenormous or impressive entrance and fa\u00e7ade\tfountain, gardens, or parks within the property", 22], "gray train": ["Yes. 'Gray train' has a tangible appearance and is a kind of locomotive.\nA few things that are visually similar to 'gray train' but are not 'gray train' are:\ttrams\tsubway trains\tmonorails\tbuses\nThere are several useful visual features to tell there is 'gray train' and not similar things in a photo:\tlong and narrow metal body\ttwo or more carriages\trounded front and back ends\ton rails\tsmokestack or exhaust system\ttooting sound\tA gray color can be an additional feature, but not necessarily", 22], "seafoam": ["Yes, 'seafoam' has a tangible appearance and comes in the form of froth created by waves.\nA few things that are visually similar to 'seafoam' but are not 'seafoam' are:\tsoap bubbles\tclouds\tfoam on beer or coffee\twhipped cream\nThere are several useful visual features that define seafoam and can distinguish it from the listed similar things in a photo:\tit is formed by the action of the waves on the surface of the water\tseafoam is white and cloudy it has a bubbly and frothy texture\tit is usually found near the shoreline or where the waves break", 22], "stone walls": ["Yes. 'Stone walls' has a tangible appearance and is a type of wall made of stone.\nA few things that are visually similar to 'stone walls' but are not 'stone walls' are:\tbrick walls\tcement walls\twooden walls\nThere are several useful visual features to tell there is 'stone walls' and not similar things in a photo:\trocks or stones used as building material of the wall\trough or irregular surface\ttexture and color of the stones used\ttooled joints or seams between stones\tvarious shapes and sizes of stones, giving a unique pattern", 22], "ocean foam": ["Yes. 'Ocean foam' has a tangible appearance and is a natural phenomenon.\nA few things that are visually similar to 'ocean foam' but are not 'ocean foam' are:\tsnow\tfrost\tsoap foam\nThere are several useful visual features to tell there is 'ocean foam' and not similar things in a photo:\twhite or light-colored\toccurring on the surface of the ocean\tformed by waves breaking up and stirring air into the water\tfluffy, bubbly, or frothy texture\tvarying in size from small bubbles to large swathes\tof foam", 22], "knee brace": ["Yes. 'Knee brace' has a tangible appearance and is a type of medical device.\nA few things that are visually similar to 'knee brace' but are not 'knee brace' are:\tankle brace\tback brace\twrist brace\telbow brace\nThere are several useful visual features to tell there is 'knee brace' and not similar things in a photo:\tcovers the knee joint\tadjustable straps or velcro\tclasps or hinges\tfor support and stability in the knee\tarea of foam or padding for cushioning", 22], "parakeet": ["Yes. 'Parakeet' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'parakeet' but are not 'parakeet' are:\tsparrow\tfinch\tcanary\nThere are several useful visual features to tell there is 'parakeet' and not similar things in a photo:\tsmall to medium-sized\tbrightly colored feathers, such as green or blue\tbeak curved downward\telongated tail\tfeet with two toes pointing forward and two toes pointing backward", 22], "nametag": ["Yes. 'Nametag' has a tangible appearance and is a type of identifier worn on clothing.\nA few things that are visually similar to 'nametag' but are not 'nametag' are:\tbadge\tsticker\tlabel\nThere are useful visual features to tell there is 'nametag' and not similar things in a photo:\trectangle or oval shape\twith a person's name or title\tworn on clothing or attached to a lanyard\tor pin\tback of a badge is clipped or pinned\tto a person's clothing.", 22], "grassland": ["Yes. 'Grassland' has a tangible appearance and is a type of ecosystem.\nA few things that are visually similar to 'grassland' but are not 'grassland' are:\tfarmland\turban areas\tdeserts\tswamps\tjungle\nThere are several useful visual features to tell there is 'grassland' and not similar things in a photo:\tlarge open area\twith grass as the dominant vegetation\tscattered trees or shrubs\trolling hills or flat terrain\twith grazing animals such as bison, horses or antelopes.", 22], "rudder": ["Yes. 'Rudder' has a tangible appearance and is an important component of a ship or aircraft.\nA few things that are visually similar to 'rudder' but are not 'rudder' are:\tfin\tkeel\tsteering wheel\tjoystick\nThere are several useful visual features to tell there is 'rudder' and not similar things in a photo:\tvertical flat board\tor a small tower\tat the edge of the ship or aircraft's stern", 22], "purple helmet": ["Yes. 'Purple helmet' has a tangible appearance and is a type of headgear.\nA few things that are visually similar to 'purple helmet' but are not 'purple helmet' are:\tblue helmet\tgreen helmet\tyellow helmet\tpink helmet\nThere are several useful visual features to tell there is 'purple helmet' and not similar things in a photo:\tpurple color\thard outer shell\tpadded interior\tchin strap\ttop vents or holes for air circulation", 22], "color green": ["No. 'Color green' is too vague or abstract to be distinguished in a photo. \nHowever, here are a few things that are visually green: leaves, grass, tree, and emerald.\nAgain, there is no need for useful visual features to distinguish 'color green' from these similar things in a photo, as they are all green.", 22], "tall clock": ["Yes. 'Tall clock' has a tangible appearance and refers to a type of floor clock.\nA few things that are visually similar to 'tall clock' but are not 'tall clock' are:\twall clock\tdesk clock\talarm clock\tpocket watch\nThere are several useful visual features to tell there is 'tall clock' and not similar things in a photo:\tfloor-standing\tclock face at the top of the clock\tmultiple pendulums and weights\tformal or decorative design\ttall and narrow shape", 21], "coffee makers": ["Yes. 'Coffee makers' has a tangible appearance and is a kind of appliance used for making coffee.\nA few things that are visually similar to 'coffee makers' but are not 'coffee makers' are:\ttea kettles\telectric hot water dispensers\tsoup makers\tblenders\nThere are several useful visual features to tell there is 'coffee makers' and not similar things in a photo:\tcoffee filter holder\twater reservoir\tbrewing controls or buttons\tdrip tray or carafe for collecting brewed coffee", 21], "baseball ball": ["Yes. 'baseball ball' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'baseball ball' but are not 'baseball ball' are:\tsoftball\tcricket ball\tfield hockey ball\tlacrosse ball\nThere are several useful visual features to tell there is 'baseball ball' and not similar things in a photo:\twhite with red stitches\tleather or synthetic cover\tcircular shape around 9 inches in circumference\tweight around 5 ounces\thard and dense surface", 21], "empty table": ["Yes. 'Empty table' has a tangible appearance and refers to a table without anything on it.\nA few things that are visually similar to 'empty table' but are not 'empty table' are:\ttable with objects on it\ttable with a tablecloth on it\ttable with food and drinks on it\nThere are several useful visual features to tell there is 'empty table' and not similar things in a photo:\tflat surface\tclean\tno objects or items on top of\tit may have chairs or surrounding furniture", 21], "coffee pots": ["Yes. 'Coffee pots' has a tangible appearance and is a type of kitchenware used to brew coffee.\nA few things that are visually similar to 'coffee pots' but are not 'coffee pots' are:\ttea pots\tkettles\tpitchers\nThere are several useful visual features to tell there is 'coffee pots' and not similar things in a photo:\tcone-shaped or cylindrical body\telongated spout\tto brew coffee or espresso\tbrewing mechanism (such as a filter or a pod)\thandle on the side or top of the pot\tmade of metal, glass or ceramic material.", 21], "speed": ["No. 'Speed' is too vague or abstract to be visually concrete or have a tangible appearance.\nThere are no things that are visually similar to 'speed' because it is a concept and not a physical object.", 21], "hello kitty": ["Yes. 'Hello Kitty' has a tangible appearance and is a character with distinct features.\nA few things that are visually similar to 'hello kitty' but are not 'hello kitty' are:\tother cat cartoon characters\tplush toys\tcat-shaped accessories\nThere are several useful visual features to tell there is 'hello kitty' and not similar things in a photo:\twhite cat-like character with a red bow\ton the left side of the head, the right ear has a yellow dot, the left ear does not\thuman-like eyes without visible pupils\tno visible mouth\tsimple, minimalist design", 21], "copper pot": ["Yes. 'Copper pot' has a tangible appearance and is a type of cookware.\nA few things that are visually similar to 'copper pot' but are not 'copper pot' are:\tsaucepan\tfrying pan\ttea kettle\tgolden vase\tbronze urn\nThere are several useful visual features to tell there is 'copper pot' and not similar things in a photo:\tmade of copper\tmetallic or reddish-brown color\twith or without lid\thandles on the sides\tbelly-shaped body and a narrow mouth\tat least several quarts in size", 21], "headphone": ["Yes. 'Headphone' has a tangible appearance and is a type of audio device.\nA few things that are visually similar to 'headphone' but are not 'headphone' are:\tearbuds\thearing aids\tearmuffs\tmicrophones\nThere are several useful visual features to tell there are 'headphones' and not similar things in a photo:\tear-sized padded cups\tadjustable band connecting ear cups over head\tno visible microphone\tconnected by wire or Bluetooth to an audio device", 21], "judge": ["Yes. 'Judge' has a tangible appearance and is a type of person.\nA few things that are visually similar to 'judge' but are not 'judge' are:\tlawyer\tpolitician\texecutive\nThere are several useful visual features to tell there is 'judge' and not similar things in a photo:\tblack robe\tgavel (wooden hammer) or another type of equipment such as a computer or files\twhite wig, though this is not a common feature anymore", 21], "orange button": ["Yes. 'Orange button' has a tangible appearance and is a type of control.\nA few things that are visually similar to 'orange button' but are not 'orange button' are:\torange fruit\thorn\tsun\tcircle\tshaped object\nThere are several useful visual features to tell there is 'orange button' and not similar things in a photo:\tround\tor angled\tbutton-like shape\torange\tcolor contrasting with the background\tsurrounded by buttons or other controls.", 21], "lions": ["Yes. 'Lions' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'lions' but are not 'lions' are:\ttigers\tleopards\tcheetahs\t\nThere are several useful visual features to tell there is 'lions' and not similar things in a photo:\ttawny or gold fur\twith a mane or without\tround ears\twith big or no whiskers\tprominent nose, eyes, and mouth claws on the paws\troaming on a savanna or in the wild.", 21], "snowy ground": ["Yes. 'Snowy ground' has a tangible appearance and is a type of winter landscape.\nA few things that are visually similar to 'snowy ground' but are not 'snowy ground' are:\tsand\tdust\tfog\twhite rocks\nThere are several useful visual features to tell there is 'snowy ground' and not similar things in a photo:\twhite color\tuneven surface\twith or without footsteps/pawprints/cracks/snowballs", 21], "wood slat": ["Yes. 'Wood slat' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wood slat' but are not 'wood slat' are:\twood plank\ttile\tpainted stripes\tor any thin long object\nThere are several useful visual features to tell there is 'wood slat' and not similar things in a photo:\tlong and thin in shape\tparallel lines on the surface\tmade of wood material\tnatural color or texture\tof a uniform size", 21], "lodge": ["Yes. 'Lodge' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'lodge' but are not 'lodge' are:\tcabin\thouse\tshack\thut\nThere are several useful visual features to tell there is 'lodge' and not similar things in a photo:\twooden construction\trustic appearance\tlocation in a natural setting\twith a fireplace or chimneys", 21], "smoothie": ["Yes. 'Smoothie' has a tangible appearance and is a type of beverage.\nA few things that are visually similar to 'smoothie' but are not 'smoothie' are:\tjuice\tmilkshake\tsoda\tcoffee\nThere are several useful visual features to tell there is 'smoothie' and not similar things in a photo:\tthick and creamy consistency\tcontains fruits or vegetables\thas a straw and/or a lid\tfor fruity smoothies, has seeds or small bits of fruits visible in the drink", 21], "catsup": ["Yes. 'Catsup' (also known as 'ketchup') has a tangible appearance and is a condiment that is usually made of tomatoes.\nA few things that are visually similar to 'catsup' but are not 'catsup' are:\ttomato sauce\ttomato paste\tBBQ sauce\nThere are several useful visual features to tell there is 'catsup' and not similar things in a photo:\tthick consistency\tbright red color\tpourable or squeezable bottle or container\tketchup label or logo on the packaging.", 21], "lawn chairs": ["Yes. 'Lawn chairs' has a tangible appearance and is a type of outdoor furniture.\nA few things that are visually similar to 'lawn chairs' but are not 'lawn chairs' are:\tregular chairs\tpicnic benches\trocking chairs\tfolding chairs\nThere are several useful visual features to tell there is 'lawn chairs' and not similar things in a photo:\tdesigned for outdoor use\tportable and lightweight\tframe made of metal or plastic\tbackrest that can recline and adjust to different angles\tpadded or waterproof seat and back cushions", 21], "boat sailing": ["Yes. 'Boat sailing' has a tangible appearance.\nA few things that are visually similar to 'boat sailing' but are not 'boat sailing' are:\tboat parked in a dock\tswimming\tseagulls in the water\twaves crashing on the shore\nThere are several useful visual features to tell there is 'boat sailing' and not similar things in a photo:\tThe presence of a sail or sails on the boat\tWater visibly moving past the hull of the boat\tWinds visibly moving the sail or sails of the boat\tHuman figures operating the sail or sails or steering the boat", 21], "fingertips": ["Yes. 'Fingertips' has a tangible appearance and is the tip of a finger.\nA few things that are visually similar to 'fingertips' but are not 'fingertips' are:\ttoes\tthumb fingertips\tpaws\tclaws\nThere are several useful visual features to tell there is 'fingertips' and not similar things in a photo:\tlocated at the end of fingers\tskin texture and color\tnail on the tip of the finger", 21], "store front window": ["Yes. 'Store front window' has a tangible appearance and refers to a window display in the front of a store.\nA few things that are visually similar to 'store front window' but are not 'store front window' are:\tresidential window\tcar window\tshowcase\tdisplay case\ttelevision or computer screen\nThere are several useful visual features to tell there is 'store front window' and not similar things in a photo:\tlocated at the front of a store\tdisplaying products or advertisements of the store\tbigger than a residential or car window\tno security bars or mesh on the window\tdisplaying content that is relevant to the store's business", 21], "bushel": ["Yes. 'Bushel' has a tangible appearance and is a unit of measure for dry goods.\nA few things that are visually similar to 'bushel' but are not 'bushel' are:\tbaskets\tbins\tbuckets\tbags\nThere are several useful visual features to tell there is 'bushel' and not similar things in a photo:\tcylindrical shape\twith a handle or handles\tusually made of wood or metal\thas specific markings or labels indicating the quantity of dry goods it can contain.", 21], "printers": ["Yes. 'Printers' has a tangible appearance and is a type of machine used for printing.\nA few things that are visually similar to 'printers' but are not 'printers' are:\tscanner\tfax machine\tcopy machine\t\nThere are several useful visual features to tell there is 'printers' and not similar things in a photo:\tpaper tray\tprint head or cartridge\tdisplay screen or buttons\tfor printing documents or images\tsound of printing", 21], "beach house": ["Yes. 'Beach house' has a tangible appearance and is a kind of house built near a beach.\nA few things that are visually similar to 'beach house' but are not 'beach house' are:\tcoastal cottage\tlighthouse\tbeach hut\nThere are several useful visual features to tell there is 'beach house' and not similar things in a photo:\tlocated near a beach or the ocean\twooden or painted white\texterior decks or patios with outdoor furniture\tlarge windows to let in natural light\tand proximity to other houses or beach amenities like piers or boardwalks.", 21], "denim": ["Yes. 'Denim' has a tangible appearance and is a type of fabric.\nA few things that are visually similar to 'denim' but are not 'denim' are:\tblue cloth\tdyed cotton\nThere are several useful visual features to tell there is 'denim' and not similar things in a photo:\tsturdy cotton twill fabric\tindigo blue dye\tweft threads visible on the back of the fabric\tcustomary use in jeans and jackets", 21], "metal railroad tracks": ["Yes. 'Metal railway tracks' has a tangible appearance and is a type of infrastructure.\nA few things that are visually similar to 'metal railway tracks' but are not 'metal railway tracks' are:\tpower lines\tgarden fences\ttelephone poles\tgas pipes\nThere are several useful visual features to tell there is 'metal railway tracks' and not similar things in a photo:\ttwo parallel metal rails\tsleepers or ties\tthat connect the rails\tto create a path for a train\tor other rail vehicles", 21], "blurry people": ["Yes. 'Blurry people' has a tangible appearance and refers to people in a photo who are not in focus.\nA few things that are visually similar to 'blurry people' but are not 'blurry people' are:\tpeople in motion\tfast-moving objects\tthat are also not in focus\t\nThere are several useful visual features to tell there are 'blurry people' and not similar things in a photo:\tout of focus or blurred appearance\tslightly visible outlines or shapes\tsoft or fuzzy edges\tlack of clear facial features or details", 21], "wheat bread": ["Yes. 'Wheat bread' has a tangible appearance and is a type of bread.\nA few things that are visually similar to 'wheat bread' but are not 'wheat bread' are:\twhite bread\tsourdough bread\tpumpernickel bread\trye bread\nThere are several useful visual features to tell there is 'wheat bread' and not similar things in a photo:\ttan or brown color\tmade with whole wheat flour\tgrainy texture\tregular or oval shape\tslices or loaves", 21], "burnt spot": ["Yes. 'Burnt spot' has a tangible appearance and is a type of mark or damage.\nA few things that are visually similar to 'burnt spot' but are not 'burnt spot' are:\tmold\tstain\tshadow\tleaves\nThere are several useful visual features to tell there is 'burnt spot' and not similar things in a photo:\tcharred or blackened surface\tcracks or uneven texture\tsurrounded by areas of normal color or texture\tunusual shape or pattern (such as circular or irregular)", 21], "dumplings": ["Yes. 'Dumplings' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'dumplings' but are not 'dumplings' are:\tmeatballs\tgnocchi\tpierogi\travioli\nThere are several useful visual features to tell there are 'dumplings' and not similar things in a photo:\tdoughy texture\tstuffed with filling\tspherical or cylindrical shape\tvariety of colors and sizes", 21], "sheet cake": ["Yes. 'Sheet cake' has a tangible appearance and is a type of cake.\nA few things that are visually similar to 'sheet cake' but are not 'sheet cake' are:\tcupcakes\tbrownies\tcookies\tmuffins\tpies\nThere are several useful visual features to tell there is 'sheet cake' and not similar things in a photo:\trectangular in shape\tflat top\tsingle layer\tsmooth, even surface\tdressed with icing or frosting", 21], "baby bottle": ["Yes. 'Baby bottle' has a tangible appearance and is a kind of feeding bottle.\nA few things that are visually similar to 'baby bottle' but are not 'baby bottle' are:\twater bottle\tsports bottle\tperfume bottle\nThere are several useful visual features to tell there is 'baby bottle' and not similar things in a photo:\tflattened nipple-shaped top\tplastic or glass container\twith volume measurements\tfor feeding a baby or infant.", 21], "core": ["No. 'Core' is too vague or abstract to be distinguished in a photo.", 21], "flip": ["No. 'Flip' is too vague or abstract to be distinguished in a photo.", 21], "lightpole": ["Yes. 'Lightpole' has a tangible appearance and is a type of pole used to hold lights.\nA few things that are visually similar to 'lightpole' but are not 'lightpole' are:\tflagpole\tsignpost\ttraffic light pole\tfence post\nThere are several useful visual features to tell there is 'lightpole' and not similar things in a photo:\ttall pole with a light on top\tstraight or curved pole shape\tmetallic or concrete material\tlight fixture at the top of the pole", 21], "lettuce leaf": ["Yes. 'Lettuce leaf' has a tangible appearance and is a type of edible leafy green.\nA few things that are visually similar to 'lettuce leaf' but are not 'lettuce leaf' are:\tspinach leaves\tkale leaves\tcabbage leaves\tbasil leaves\nThere are several useful visual features to tell there is 'lettuce leaf' and not similar things in a photo:\tlight green color\tcrinkled texture\ttoothed edges\twatery or glossy appearance\tslightly curved round shape", 21], "dining room chair": ["Yes. 'Dining room chair' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'dining room chair' but are not 'dining room chair' are:\tsofa\toffice chair\trecliner\tstool\tbench\nThere are several useful visual features to tell there is 'dining room chair' and not similar things in a photo:\tfour-legged\twith a backrest\tand a seat\tcushioned or padded\tfor use in a dining room or kitchen\ttable-height or slightly higher than table-height", 21], "tapestry": ["Yes. 'Tapestry' has a tangible appearance and refers to a type of textile art.\nA few things that are visually similar to 'tapestry' but are not 'tapestry' are:\trug\tquilt\tblanket\tembroidery\nThere are several useful visual features to tell there is 'tapestry' and not similar things in a photo:\tlarge and decorative textile usually hung on a wall\tpictorial design or woven pattern\toften made of wool, silk, or cotton\thistorical or cultural significance", 21], "smoke train": ["Yes. 'Smoke train' has a tangible appearance and refers to the smoke emitted by a moving train.\nA few things that are visually similar to 'smoke train' but are not 'smoke train' are:\tfactory chimney\tvolcano eruption\tnuclear mushroom cloud\tBBQ smoke\nThere are several useful visual features to tell there is 'smoke train' and not similar things in a photo:\tconnected to a locomotive or a train\tcylindrical shape\twhite or light gray color\tdissipating quickly in the air\ttraveling in one direction along a defined path", 21], "brick walls": ["Yes. 'Brick walls' has a tangible appearance and is a type of wall made of bricks.\nA few things that are visually similar to 'brick walls' but are not 'brick walls' are:\tstone walls\tconcrete walls\twooden walls\tcement walls\nThere are several useful visual features to tell there is 'brick walls' and not similar things in a photo:\trectangular or square bricks usually in red or brown\tcolor variation due to the brick manufacturing process\tmortar lines between the bricks", 21], "denim shorts": ["Yes. 'Denim shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'denim shorts' but are not 'denim shorts' are:\tjeans\tshorts\tkhaki shorts\nThere are several useful visual features to tell there are 'denim shorts' and not similar things in a photo:\tmade of denim fabric\tshort length in comparison to regular jeans\tcutoffs or frayed edges\tbuttons or zipper in front or shorts-like design loops for belt to keep shorts on the waist", 21], "evening sky": ["Yes. 'Evening sky' has a tangible appearance and refers to the appearance of the sky during the evening.\nA few things that are visually similar to 'evening sky' but are not 'evening sky' are:\tmorning sky\tsunset sky\tnoon sky\tstormy sky\nThere are several useful visual features to tell there is 'evening sky' and not similar things in a photo:\ta range of red, orange, pink, violet, and blue colors\thorizontal bands of color\tgradual darkening of the sky from light to dark\tvisible stars or moon in the sky", 21], "metal lamp post": ["Yes. 'Metal lamp post' has a tangible appearance and is a type of street furniture.\nA few things that are visually similar to 'metal lamp post' but are not 'metal lamp post' are:\tflagpole\tbollard\ttrash can\ttraffic sign\nThere are several useful visual features to tell there is 'metal lamp post' and not similar things in a photo:\ttall and slender metal pole\tbulb or lamp at the top\tsupport arm or bracket\tfor street lighting and sidewalk decoration", 21], "toasters": ["Yes. 'Toasters' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'toasters' but are not 'toasters' are:\tblender\tmicrowave\toven\ttoaster oven\nThere are several useful visual features to tell there is 'toasters' and not similar things in a photo:\tupright or vertical shape\tslots for bread\tor a lever\tbread crumbs\tcrumb tray\tdial or buttons for setting the time and temperature.", 21], "frisbee player": ["Yes. 'Frisbee player' has a tangible appearance and refers to a person who is playing frisbee.\nA few things that are visually similar to 'frisbee player' but are not 'frisbee player' are:\tperson throwing a ball\tperson running\tperson jumping in the air\nThere are several useful visual features to tell there is a 'frisbee player' and not similar things in a photo:\tholding a frisbee\torbiting frisbee in the air\tjumping or lunging to catch the frisbee\tin motion or running while throwing or catching the frisbee.", 21], "letter u": ["Yes. 'Letter u' has a tangible appearance and is a symbol used in writing.\nThere are no things that are visually similar to 'letter u' but are not 'letter u'.\nUseful visual features for distinguishing 'letter u' from other letters in a photo are:\tshape that resembles a \"U\" (curved line and two straight lines)\tand its position in a word (second letter, after \"T\" and before \"V\")", 21], "orange caution cone": ["Yes. 'Orange caution cone' has a tangible appearance and is a type of safety equipment.\nA few things that are visually similar to 'orange caution cone' but are not 'orange caution cone' are:\ttraffic cone\tpylon\tcylindrical post\torange bucket\nThere are several useful visual features to tell there is 'orange caution cone' and not similar things in a photo:\tcone-shaped\tbright orange color\twarning signs or symbols\tprinted text in black\ton a traffic way or construction site", 21], "shadow road": ["Yes. 'Shadow road' has a tangible appearance and refers to a road or path that is shaded by trees or buildings.\nA few things that are visually similar to 'shadow road' but are not 'shadow road' are: dappled road, which is a road with irregular patches of light and shade; alleyway, which is a narrow path between buildings; dark road, which is a road with low light conditions.\nThere are several useful visual features to tell there is 'shadow road' and not similar things in a photo: long shadows cast by surrounding trees or buildings; contrast between the well-lit areas and the shaded areas; distinctive shade patterns on the road.", 21], "stir fry": ["Yes. 'Stir fry' has a tangible appearance and is a type of dish.\nA few things that are visually similar to 'stir fry' but are not 'stir fry' are:\tsaut\u00e9ed vegetables\tgrilled meat\tfried noodles\nThere are several useful visual features to tell there is a 'stir fry' and not similar things in a photo:\tvariety of colorful vegetables\tin a wok or a frying pan\tsizzling hot with steam coming off\tthe sauce covering the ingredients\tthat the ingredients are cut evenly in similar-sized pieces.", 21], "wood tray": ["Yes. 'Wood tray' has a tangible appearance and is a type of serving dish.\nA few things that are visually similar to 'wood tray' but are not 'wood tray' are:\tplastic tray\tmetal tray\tbamboo tray\t\nThere are several useful visual features to tell there is 'wood tray' and not similar things in a photo:\tmade of wood\tridged edges for carrying handles\tlevel surfaces or slightly curved to hold contents\tnatural wood grain or color", 21], "blue window": ["Yes, 'blue window' has a tangible appearance and is a window that is colored blue.\nA few things that are visually similar to 'blue window' but are not 'blue window' are:\tgreen window\tred window\tpurple window\tcolorful suncatcher\nThere are several useful visual features to tell there is 'blue window' and not similar things in a photo:\ta window with blue color\tthe shade of blue can vary from light to dark\tthe frame of the window is also visible\tthe texture and pattern of the glass, if any, can also help in distinguishing", 21], "sports": ["No. 'Sports' is too vague or abstract to be distinguished in a photo, as it refers to a wide range of physical activities.\n", 21], "bike lane": ["Yes. 'Bike lane' has a tangible appearance and is a designated roadway for bicycles.\nA few things that are visually similar to 'bike lane' but are not 'bike lane' are:\tsidewalk\tpedestrian crossing\tcar lane\tshoulder\nThere are several useful visual features to tell there is 'bike lane' and not similar things in a photo:\tdesignated path for bicycles\tpainted with white bike symbol or lines\tseparated from car lanes or sidewalks by a physical barrier or markings", 21], "shadow building": ["No. 'Shadow building' is too vague or abstract to be distinguished in a photo.", 21], "game remote": ["Yes. 'Game remote' has a tangible appearance and is a kind of electronic device.\nA few things that are visually similar to 'game remote' but are not 'game remote' are:\ttelevision remote\tcontrol pad\tmobile phone\nThere are several useful visual features to tell there is 'game remote' and not similar things in a photo:\tspecific buttons or joysticks for gaming purposes\tbrand or logo of a gaming console or controller\tnonslip surface or grips on the back or sides of the controller.", 21], "front fender": ["Yes. 'Front fender' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'front fender' but are not 'front fender' are:\trear fender\tsbasket\tfork\nThere are several useful visual features to tell there is 'front fender' and not similar things in a photo:\tlocated above the front wheel of a vehicle\tcurved or straight shape\tattached to the front bumper or body of a vehicle\tprotection from debris and water thrown up by the tires.", 21], "fake": ["No. 'Fake' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider 'fake' as something that is made to look like something it is not, a few things that are visually similar to 'fake' but are not 'fake' are:\n\n- Replicas of famous landmarks or buildings\n- Artificial flowers or plants\n- Makeup or cosmetics\n\nUseful visual features that can help distinguish 'fake' from similar things in a photo are:\n\n- Poor quality materials or construction\n- Inaccuracies or inconsistencies compared to the real item\n- Unnatural colors or patterns\n- Lack of functional parts or features if the original item has them", 21], "hanging plant": ["Yes. 'Hanging plant' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'hanging plant' but are not 'hanging plant' are:\ttrailing vines\thanging decorations\nThere are several useful visual features to tell there is 'hanging plant' and not similar things in a photo:\tleaves or branches hanging downward\tpotted or suspended in some way\tdraping, cascading or hanging from a height", 21], "computer track pad": ["Yes. 'Computer track pad' has a tangible appearance and is a device for navigating a computer screen.\nA few things that are visually similar to 'computer track pad' but are not 'computer track pad' are:\tmouse\twireless presenter touch screen\nThere are several useful visual features to tell there is 'computer track pad' and not similar things in a photo:\trectangular or square in shape\tsmooth surface to slide fingers\taccompanying left and right click buttons", 21], "whisker cat": ["Yes. 'Whisker cat' has a tangible appearance and refers to a type of cat that has long whiskers.\nA few things that are visually similar to 'whisker cat' but are not 'whisker cat' are:\tregular cat\tPanther\tPuma\tSnow Leopard\tLynx\nThere are several useful visual features to tell there is 'whisker cat' and not similar things in a photo:\tlong and noticeable whiskers\tfurry\ttwo pointed ears\tfeline eyes", 21], "movie": ["No. 'Movie' is too vague or abstract to be distinguished in a photo. \n\nHowever, some things that are visually associated with movies but are not movies are: film reels, cameras, red carpets, movie tickets, popcorn, movie posters.\n\nUseful visual features for distinguishing a movie from film reels, cameras, movie tickets, or movie posters would include: actors or characters on screen, scenes or settings, moving images, and/or dialogues.", 21], "brown stain": ["Yes. 'Brown stain' has a tangible appearance and is a discoloration caused by a substance or liquid.\nA few things that are visually similar to 'brown stain' but are not 'brown stain' are:\tshadows\tdirt\trust\tgrime\nThere are several useful visual features to tell there is 'brown stain' and not similar things in a photo:\tdiscoloration on a surface\tspecific brown hue\trough or uneven edges\tdifferent texture from surrounding area", 21], "wax paper": ["Yes. 'Wax paper' has a tangible appearance and is a type of paper.\nA few things that are visually similar to 'wax paper' but are not 'wax paper' are:\tparchment paper\tbaking paper\taluminum foil\tplastic wrap\nThere are several useful visual features to tell there is 'wax paper' and not similar things in a photo:\ttranslucent or semi-translucent paper\tcoated with a thin layer of wax or paraffin\twaxy texture\tsome opacity to the paper in comparison to other wrapping papers.", 21], "chair rail": ["Yes. 'Chair rail' has a tangible appearance and is a type of interior home decoration.\nA few things that are visually similar to 'chair rail' but are not 'chair rail' are:\tbaseboard\tmolding\twainscot\ttrim\nThere are several useful visual features to tell there is 'chair rail' and not similar things in a photo:\ta narrow strip of molding along the wall\ta few feet above the floor\thorizontal placement\tall around a room, not just in specific areas", 21], "mountain tops": ["Yes. 'Mountain tops' has a tangible appearance and is the summit or peak of a mountain.\nA few things that are visually similar to 'mountain tops' but are not 'mountain tops' are:\thills\tplateaus\tvolcanoes\nThere are several useful visual features to tell there is 'mountain tops' and not similar things in a photo:\tvery high or elevated position\trocky or snow-covered terrain\tsteep slopes compared to surroundings\tconical or pointed shape", 21], "fluorescent lights": ["Yes. 'Fluorescent lights' has a tangible appearance and is a kind of lighting fixture.\nA few things that are visually similar to 'fluorescent lights' but are not 'fluorescent lights' are:\tincandescent lights\thalogen lights\tLED lights\tstreetlights\nThere are several useful visual features to tell there is 'fluorescent lights' and not similar things in a photo:\tlong, tube-like shape\tbright and white light\ttube contains gas or vapor\thangs from ceiling or is mounted on a surface", 21], "orange cheese": ["Yes. 'Orange cheese' has a tangible appearance and is a type of cheese with a distinct color.\nA few things that are visually similar to 'orange cheese' but are not 'orange cheese' are:\tcheddar cheese\tcolby cheese\tCheetos or other cheese-flavored snacks\nThere are several useful visual features to tell there is 'orange cheese' and not similar things in a photo:\tdistinct orange color\tsoft or hard texture\tround or block shape\ttypically found in slices or cubes in sandwiches or on charcuterie boards.", 21], "emergency light": ["Yes. 'Emergency light' has a tangible appearance and is a type of lighting device used in emergency situations.\nA few things that are visually similar to 'emergency light' but are not 'emergency light' are:\tflashlight\ttraffic light\tstreet light\nThere are several useful visual features to tell there is 'emergency light' and not similar things in a photo:\tbright colors like red or orange\tflashing or rotating lights\tportable or mounted on walls or ceilings\tlabeled with the word 'emergency' or a symbol of a first aid kit or emergency vehicle.", 21], "beak bird": ["No. 'Beak bird' is too vague or abstract. The term 'beak' alone would be more appropriate to describe the tangible appearance of a bird's mouth.\n", 21], "phone screen": ["Yes. 'Phone screen' has a tangible appearance and is a part of a phone device.\nA few things that are visually similar to 'phone screen' but are not 'phone screen' are:\tmirrors\ttablets\tcomputer monitors\tcameras\nThere are several useful visual features to tell there is 'phone screen' and not similar things in a photo:\trectangular\twith rounded edges\ta glass or plastic surface\tdisplaying images or text\ttouch-sensitive responding to taps and swipes\tbacklit or illuminated by a light source", 21], "beige curtains": ["Yes. 'Beige curtains' has a tangible appearance and is a type of window treatment.\nA few things that are visually similar to 'beige curtains' but are not 'beige curtains' are:\tblanket\ttapestry\ttablecloth\tshower curtain\nThere are several useful visual features to tell there is 'beige curtains' and not similar things in a photo:\tfabric or textile material\tcovering a window/hanging from a rod\tvertical folds or gathers\tsolid, non-patterned beige color", 21], "grey trash": ["Yes. 'Grey trash' has a tangible appearance and is a variety of discarded materials.\nA few things that are visually similar to 'grey trash' but are not 'grey trash' are:\tconcrete\trubble\tpavement\nThere are several useful visual features to tell there is 'grey trash' and not similar things in a photo:\tmixed materials including paper, plastic, food waste, and other discarded items\tirregular and uneven shape\tnot a natural part of the surroundings\tcontaining visible waste and garbage", 21], "brown donuts": ["Yes. 'Brown donuts' has a tangible appearance and is a specific type of food.\nA few things that are visually similar to 'brown donuts' but are not 'brown donuts' are:\tbagels\tchocolate disks\tpancakes\nThere are several useful visual features to tell there is 'brown donuts' and not similar things in a photo:\tCircular shape with a hole in the center\tGolden or brown color\tFried or baked texture with a slight sheen\tDusted with sugar or cinnamon", 21], "earbuds": ["Yes. 'Earbuds' has a tangible appearance and is a kind of headphones.\nA few things that are visually similar to 'earbuds' but are not 'earbuds' are:\tearmuffs\thearing aids\tearplugs\nThere are several useful visual features to tell there is 'earbuds' and not similar things in a photo:\ttwo small earpieces\twith wires connecting the earpieces\tto be inserted into the ears\tfor listening music or taking calls\tusually with a built-in microphone", 21], "buss": ["Yes. 'Buss' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'buss' but are not 'buss' are:\tcar\tvan\ttruck\ttrain\nThere are several useful visual features to tell there is 'buss' and not similar things in a photo:\tlarge size\tred or yellow color\trectangular shape\tmultiple rows of seats\twheels on the outside of the body", 21], "sleigh": ["Yes. 'Sleigh' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'sleigh' but are not 'sleigh' are:\ttruck\tcarriage\tCinderella's carriage\nThere are several useful visual features to tell there is 'sleigh' and not similar things in a photo:\tdecorated with Christmas lights or ornaments\ton runners instead of wheels\tpulled by horses, reindeer or dogs\tcurved runners at the front and back, like elongated skis", 21], "silver wristwatch": ["Yes. 'Silver wristwatch' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'silver wristwatch' but are not 'silver wristwatch' are:\tbracelet\tcuff\tleather strap watch\tplastic strap watch\nThere are several useful visual features to tell there is 'silver wristwatch' and not similar things in a photo:\tround or square-shaped watch face\tsteel or silver-colored metal strap or band\tnumerical or Roman numeral hour markers\tand minute marks\thour, minute, and second hands\ton a person's wrist", 21], "blinker": ["Yes. 'Blinker' has a tangible appearance and is a piece of equipment on vehicles.\nA few things that are visually similar to 'blinker' but are not 'blinker' are:\theadlights\tbrakelights\tsiren\nThere are several useful visual features to tell there is 'blinker' and not similar things in a photo:\tpositioned on the side of the vehicle\tflashing light\torange or yellow in color\tflashing in a regular pattern while the car is moving", 21], "file cabinet": ["Yes. 'File cabinet' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'file cabinet' but are not 'file cabinet' are:\tdresser\tcupboard\tbookcase\tshelves\nThere are several useful visual features to tell there is 'file cabinet' and not similar things in a photo:\ttall and rectangular shape\tmultiple drawers\twith handles or knobs\tfor storing files or documents\tmade of metal or wood", 21], "bows": ["Yes. 'Bows' has a tangible appearance and is a kind of decoration or knot.\nA few things that are visually similar to 'bows' but are not 'bows' are:\tknots\tribbons\tpackages\nThere are several useful visual features to tell there is 'bows' and not similar things in a photo:\ttwo or more loops\tof uniform width\tcolored or patterned\tmade from ribbon or fabric", 21], "chair lift": ["Yes. 'Chair lift' has a tangible appearance and is used for transportation on a ski slope or mountain.\nA few things that are visually similar to 'chair lift' but are not 'chair lift' are:\tgondola\ttramway\tcable car\tchairlift for stairs\nThere are several useful visual features to tell there is 'chair lift' and not similar things in a photo:\tchair-like seats attached to a cable suspended above the ground\tskiers or snowboarders seated on the chairs\twheels or pulleys to move the chairs up the mountain", 21], "refrigerator magnet": ["Yes. 'Refrigerator magnet' has a tangible appearance and is a type of magnet used for decoration.\nA few things that are visually similar to 'refrigerator magnet' but are not 'refrigerator magnet' are:\tmagnetic tape\tstickers,\tmagnetic paper clip holder\nThere are several useful visual features to tell there is 'refrigerator magnet' and not similar things in a photo:\tflat and thin\twith a decorative design or message\tmade of plastic, metal, or rubber\tsticking to a metallic surface", 21], "safety cones": ["Yes. 'Safety cones' has a tangible appearance and is a kind of road safety equipment.\nA few things that are visually similar to 'safety cones' but are not 'safety cones' are:\tbarrels\tbollards\tstanchions\ttraffic barricades\nThere are several useful visual features to tell there is 'safety cones' and not similar things in a photo:\tcone-shaped\tbody that widens towards the base\tfluorescent orange color\twhite or silver reflective stripes at the top", 21], "pot holder": ["Yes. 'Pot holder' has a tangible appearance and is used in the kitchen to avoid burns.\nA few things that are visually similar to 'pot holder' but are not 'pot holder' are:\toven mitt\tsilicone glove\tdish towel\nThere are several useful visual features to tell there is 'pot holder' and not similar things in a photo:\trectangular or square shape\tthick padding\tcloth or quilted material\tloop or hanging feature for gripping and holding", 21], "carriages": ["Yes. 'Carriages' has a tangible appearance and refers to a type of vehicle.\nA few things that are visually similar to 'carriages' but are not 'carriages' are:\tcarts\tbuggies\ttrucks\tvans\nThere are several useful visual features to tell there are 'carriages' and not similar things in a photo:\thorse-drawn vehicles\t\nornate or decorative designs\t\ndistinct wheels\t\nseating or compartments inside\t\nusually seen in historical or period films/photographs", 21], "drain sink": ["Yes. 'Drain sink' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'drain sink' but are not 'drain sink' are:\tbathtub\tshower\ttoilet\tfaucet\nThere are several useful visual features to tell there is 'drain sink' and not similar things in a photo:\tinstalled in a countertop or a cabinet\tbowl-shaped basin\tfor washing or rinsing dishes\tand a drain in the bottom.", 21], "tile bathroom wall": ["Yes. 'Tile bathroom wall' has a tangible appearance and refers to a specific type of wall covering.\nA few things that are visually similar to 'tile bathroom wall' but are not 'tile bathroom wall' are:\tpainted bathroom wall\twallpapered bathroom wall\tstone bathroom wall\nThere are several useful visual features to tell there is 'tile bathroom wall' and not similar things in a photo:\tsmall, square-shaped tiles\tevenly spaced grout lines\thorizontal or vertical pattern\tcommon colors include white, beige, gray, blue or black\tshiny, glossy or matte finish", 21], "gauge": ["Yes. 'Gauge' has a tangible appearance and is a measuring instrument.\nA few things that are visually similar to 'gauge' but are not 'gauge' are:\tthermometer\tbarometer\tspeedometer\t\nThere are several useful visual features to tell there is 'gauge' and not similar things in a photo:\ta visible scale with numbers or graduations\ta needle, pointer, or indicator that moves to show a measurement\ta measuring mechanism or display", 21], "fire fighter": ["Yes. 'Fire fighter' has a tangible appearance and is a type of emergency responder.\nA few things that are visually similar to 'fire fighter' but are not 'fire fighter' are:\tpolice officer\tsoldier\tconstruction worker\nThere are several useful visual features to tell there is 'fire fighter' and not similar things in a photo:\tuniform with reflective strips\toranges or yellows\tcolor-coordinated helmet\taxe, hose, or other firefighting gear\ton-site firefighting activity, like extinguishing fire or saving someone from flames.", 21], "pug dog": ["Yes. 'Pug dog' has a tangible appearance and is a type of dog breed.\nA few things that are visually similar to 'pug dog' but are not 'pug dog' are:\tbulldog\tfrench bulldog\tboxer\tboston terrier\nThere are several useful visual features to tell there is 'pug dog' and not similar things in a photo:\tsmooth short coat\tround face\twith wrinkled skin\tbig, dark and round eyes\tshort snout\tcompact and muscular body", 21], "milkshake": ["Yes. 'Milkshake' has a tangible appearance and is a type of drink.\nA few things that are visually similar to 'milkshake' but are not 'milkshake' are:\tsmoothie\ticed coffee\tchocolate syrup over ice\tprotein shake\nThere are several useful visual features to tell there is 'milkshake' and not similar things in a photo:\tthick liquid\tcreamy and frothy texture\tstraw sticking out of the top\tglass or cup with whipped cream and a cherry\ton a diner counter or table with burgers or fries beside it", 21], "swoosh": ["No. 'Swoosh' is too vague or abstract to be distinguished in a photo.", 21], "emergency exit": ["Yes. 'Emergency exit' has a tangible appearance and is a kind of door or exit.\nA few things that are visually similar to 'emergency exit' but are not 'emergency exit' are:\tregular door\texit sign\tfire escape window\nThere are several useful visual features to tell there is 'emergency exit' and not similar things in a photo:\tsign indicating 'emergency exit'\tor 'exit'\tfor a door, there may be non-turn/knob lever handle or push bar, and opens easily for a push.", 21], "embroidery": ["Yes, 'embroidery' has a tangible appearance and is a type of decorative stitching.\n\nA few things that are visually similar to 'embroidery' but are not 'embroidery' are knitting, crocheting, lace, macrame.\n\nSome useful visual features that distinguish 'embroidery' from the listed similar things in a photo include:\n- Textured appearance due to stitching\n- Patterns or images created using thread\n- Use of a needle to stitch onto fabrics such as cloth or canvas", 21], "hang": ["No. 'Hang' is too vague or abstract to be distinguished in a photo. \n\nNote: The word 'hang' has multiple meanings and can refer to physical actions or abstract concepts. Without further context, it is difficult to determine whether it is visually concrete or abstract.", 21], "wine rack": ["Yes. 'Wine rack' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wine rack' but are not 'wine rack' are:\tbookshelf\tshoe organizer\tfile cabinet\tchest of drawers\nThere are several useful visual features to tell there is 'wine rack' and not similar things in a photo:\thorizontal cubbies or slots to store bottles\tof a size that fits a standard wine bottle\tmade of wood or metal\toften found in kitchens or dining rooms", 21], "kitchen stove": ["Yes. 'Kitchen stove' has a tangible appearance and is a type of appliance used for cooking.\nA few things that are visually similar to 'kitchen stove' but are not 'kitchen stove' are:\tfireplace\tbbq\tgrill\toven\nThere are several useful visual features to tell there is 'kitchen stove' and not similar things in a photo:\tfour burners on top of the stove\toven section below the burners\tdials or buttons to control heat and timing\tmetal grates or pans to hold pots and pans.", 21], "pink purse": ["Yes. 'Pink purse' has a tangible appearance and is a kind of bag.\nA few things that are visually similar to 'pink purse' but are not 'pink purse' are:\tbackpacks\thandbags\ttotes\tclutches\nThere are several useful visual features to tell there is 'pink purse' and not similar things in a photo:\tpink color\tpurse shape\twith a handle or a strap\tfor carrying personal items", 21], "snow dust": ["Yes. 'snow dust' has a tangible appearance and refers to a light and powdery form of snow or snow particles.\nA few things that are visually similar to 'snow dust' but are not 'snow dust' are:\tpollen\tdust\tmist\tsand\nThere are several useful visual features to tell there is 'snow dust' and not similar things in a photo:\twhite or light-colored\tvery fine and powdery\tcovering surfaces or objects in a light layer\tbelongs to a winter landscape, in contrast to sand or pollen", 21], "opener": ["Yes. 'Opener' has a tangible appearance and is a tool used to open something.\nA few things that are visually similar to 'opener' but are not 'opener' are:\tknife\tscissors\ttweezers\tpliers\tbottle cap\nThere are several useful visual features to tell there is 'opener' and not similar things in a photo:\tfits in the palm of your hand\thas a sharp or pointed end or edge\tis used to open a specific type of item (can opener, bottle opener, etc.)", 21], "fridges": ["Yes. 'Fridges' has a tangible appearance and is a type of household appliance.\nA few things that are visually similar to 'fridges' but are not 'fridges' are:\tmicrowave\toven\tdishwasher\tfreezer\nThere are several useful visual features to tell there is 'fridges' and not similar things in a photo:\tdoor\tor more doors\twhile\tcolor\tand material of the surface\tthe location of the handle and the type of handle.", 21], "advertisement board": ["Yes. 'Advertisement board' has a tangible appearance and is an object designed to display advertisements.\nA few things that are visually similar to 'advertisement board' but are not 'advertisement board' are:\ttraffic signs\tbillboards\tdirections\tsignage\nThere are several useful visual features to tell there is 'advertisement board' and not similar things in a photo:\tdisplaying advertisements or promotions \tbeing placed in a public place \thaving a design or image that is used to promote a product or service \tbold colors and large fonts", 21], "light clouds": ["No. 'Light clouds' is too vague or abstract to be distinguished in a photo. \nIf we consider the word 'light' as an adjective to describe clouds, then it is still not visually concrete as it does not provide enough information about the appearance of the clouds. \nTherefore, there are no visually similar things or useful visual features for distinguishing 'light clouds' from other things in a photo.", 21], "grazing": ["Yes. 'Grazing' has a tangible appearance and refers to the act of animals feeding on pasture grasses or crops.\nA few things that are visually similar to 'grazing' but are not 'grazing' are:\tmowing\tharvesting\tweeding\tcultivating\nThere are several useful visual features to tell there is 'grazing' and not similar things in a photo:\tanimals eating grass\tpasture land or fields\tfence or barrier around the grazing area\tlush and green vegetation around the animals.", 21], "burnt edge": ["Yes. 'Burnt edge' has a tangible appearance and is a clear visual concept.\nA few things that are visually similar to 'burnt edge' but are not 'burnt edge' are:\tshadow\tdarkened edge\tink splatter\tfrayed and ripped edge\nThere are several useful visual features to tell there is 'burnt edge' and not similar things in a photo:\tcharred and blackened appearance\tuneven, jagged or irregular edge\tsinged marks or curls of smoke", 21], "foot pedal": ["Yes. 'Foot pedal' has a tangible appearance and is a type of mechanical control device.\nA few things that are visually similar to 'foot pedal' but are not 'foot pedal' are:\thand pedal\tbike pedal\taccelerator pedal\tclutch pedal\nThere are several useful visual features to tell there is 'foot pedal' and not similar things in a photo:\tdesigned to be pressed by a foot\tvariety of shapes and sizes\tmay have labels or markings\tfor use with a specific type of machinery or equipment", 21], "wooden board": ["Yes. 'Wooden board' has a tangible appearance and is a kind of flat panel made of wood.\nA few things that are visually similar to 'wooden board' but are not 'wooden board' are:\tmetal sheet\tconcrete slab\twall\tpaneling\nThere are several useful visual features to tell there is 'wooden board' and not similar things in a photo:\trectangular or square shape\twooden texture, color and pattern\tvisible grain patterns in the wood\tno visible joints or seams", 21], "dress pants": ["Yes. 'Dress pants' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'dress pants' but are not 'dress pants' are:\tjeans\ttrousers\tleggings\nThere are several useful visual features to tell there is 'dress pants' and not similar things in a photo:\tstraight or pleated front\tflat or creased back\tloose-fitting, tailored fabric\ttypically made of wool or polyester\tdark, muted colors (black, navy, grey, etc.)", 21], "wispy cloud": ["Yes. 'Wispy cloud' has a tangible appearance and is a type of cloud.\nA few things that are visually similar to 'wispy cloud' but are not 'wispy cloud' are:\tsmoke\tfog\tsteam\nThere are several useful visual features to tell there is 'wispy cloud' and not similar things in a photo:\tthin and delicate cirrus clouds\tfeathery or hair-like appearance\ttransparent or translucent texture\tcrisp edges against a blue sky\tno obvious source of smoke or steam", 21], "twin bed": ["Yes. 'Twin bed' has a tangible appearance and is a type of bed size.\nA few things that are visually similar to 'twin bed' but are not 'twin bed' are:\tfull-size bed\tqueen-size bed\tking-size bed\tcot\tbunk bed\nThere are several useful visual features to tell there is 'twin bed' and not similar things in a photo:\tsingle bed mattress that is about 38 inches wide and 75 inches long\theadboard and footboard\tthat accommodates only one person\tand that is often used in small rooms\tor as a pair in a shared room.", 21], "k": ["No. 'k' is too abstract to have a tangible appearance or be distinguished visually. It is a letter in the alphabet.", 21], "shirt pocket": ["Yes. 'Shirt pocket' has a tangible appearance and is a specific part of a garment.\nA few things that are visually similar to 'shirt pocket' but are not 'shirt pocket' are:\tsweater pocket\tjacket pocket\tpurse pocket\tjean pocket\t\nThere are several useful visual features to tell there is 'shirt pocket' and not similar things in a photo:\tlocated on the front of a shirt, usually on the left side\ttapered flap\twith or without a button or zipper\tMade of the same material as the shirt", 21], "sit": ["No. 'Sit' is too vague and abstract to have a tangible appearance. It is an action verb that can be depicted visually, but it does not have a physical appearance.\nTherefore, there are no things that are visually similar to 'sit' but are not 'sit'.\nThere are no useful visual features for distinguishing 'sit' from the listed similar things in a photo because there are no similar things.", 21], "cardinal": ["Yes. 'Cardinal' has a tangible appearance and is a kind of bird.\nA few things that are visually similar to 'cardinal' but are not 'cardinal' are:\trobin\tblue jay\tfinch\nThere are several useful visual features to tell there is 'cardinal' and not similar things in a photo:\tbright red feathers\tblack mask around the beak\tshort, thick beak\tconical crest on its head", 21], "housing": ["Yes. 'Housing' has a tangible appearance and refers to buildings or structures that people live in.\nA few things that are visually similar to 'housing' but are not 'housing' are:\toffice buildings\tskyscrapers\tshopping malls\twarehouses\nThere are several useful visual features to tell there is 'housing' and not similar things in a photo:\tresidential buildings\twith windows and doors\tnumber of floors\tarchitectural style\tadjacent to a street or a neighborhood", 21], "traffic lines": ["Yes. 'Traffic lines' has a tangible appearance and is a kind of road marking.\nA few things that are visually similar to 'traffic lines' but are not 'traffic lines' are:\tsidewalk markings\tplayground markings\tparking space markings\tathletic field markings\nThere are several useful visual features to tell there is 'traffic lines' and not similar things in a photo:\twhite or yellow paint on the road\tstraight or curved lines\tarrows or symbols\tdirectionality on the road", 21], "leaves branches": ["Yes. 'Leaves branches' has a tangible appearance and refers to the branches of trees or plants that have leaves attached to them.\nA few things that are visually similar to 'leaves branches' but are not 'leaves branches' are:\ttree trunks\twires\tpipes\nThere are several useful visual features to tell there are 'leaves branches' and not similar things in a photo:\twavy and irregular form\tmany small branches coming out of a thicker one\twide variety of sizes and shapes of leaves, according to the species of the plant\tbright colors, depending on the season (green in summer, yellow, orange or red in autumn)", 21], "cat collar": ["Yes. 'Cat collar' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'cat collar' but are not 'cat collar' are:\tdog collar\tchoker necklace\tlanyard\tleather strap\tbelt\nThere are several useful visual features to tell there is 'cat collar' and not similar things in a photo:\tlightweight and flexible\tplastic or fabric material\twith a bell or a tag attached\tadjustable size to fit around a cat's neck", 21], "leather strap": ["Yes. 'Leather strap' has a tangible appearance and is a kind of material object.\nA few things that are visually similar to 'leather strap' but are not 'leather strap' are:\tbelt\trubber strap\tfabric strap\tplastic strap\nThere are several useful visual features to tell there is 'leather strap' and not similar things in a photo:\tbrown or black color\ttexture of leather\tsquare or rectangular shape\tvisible stitches or imprints", 21], "bales": ["Yes. 'Bales' has a tangible appearance and refers to the large bundles of hay, straw, or other crops.\nA few things that are visually similar to 'bales' but are not 'bales' are:\tpiles of hay or straw\trolls of carpet\tbales of paper or cardboard\ttires\nThere are several useful visual features to tell there is 'bales' and not similar things in a photo:\tlarge, cylindrical shape\tcomposed of hay, straw, or other crops\tbound together tightly with wire or twine\tbrown or yellow in color\ttexture of hay or straw visible on the surface.", 21], "leather shoes": ["Yes. 'Leather shoes' has a tangible appearance and is a type of footwear made of leather.\nA few things that are visually similar to 'leather shoes' but are not 'leather shoes' are:\tsneakers\trunning shoes\tdress shoes\tboots\nThere are several useful visual features to tell there is 'leather shoes' and not similar things in a photo:\tmade of leather or leather-like material\tsmooth or textured surface\tclose-toed with a heel\tlacing or buckle to secure the shoe", 21], "leather boot": ["Yes. 'Leather boot' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'leather boot' but are not 'leather boot' are:\tsneakers\theels\tsandals\twork boots\nThere are several useful visual features to tell there is 'leather boot' and not similar things in a photo:\tmade of leather or leather-like material\tclosed-toe and high around the ankle\tsoles made of rubber or durable materials\tlacing, zippers or buckles\tfor specific activities or occasion (e.g. hiking boot, riding boot)", 21], "zone": ["No, 'zone' is too vague or abstract to be considered visually concrete. \n\nHowever, if we recontextualize \"zone\" into a specific usage, such as \"endzone\" (in football), or \"red zone\" (in construction), it can be visually concrete.\n\nFor example, a few things that are visually similar to the 'end zone' but are not the 'end zone' are: soccer goal-bar, basketball hoop, or any other sports goal.\nSimilarly, a few things that are visually similar to the 'red zone' but are not the 'red zone' are: warning signs, construction flags or barricades.\n\nUseful visual features for distinguishing the 'endzone' from other sports goals in a photo: rectangular shape, distance from side-lines, and painted or marked in a different color.\n\nUseful visual features for distinguishing the 'red zone' from other warning signs in a photo: red color, construction-themed symbols or images, and text indicating 'red zone'.", 21], "baseball shoe": ["Yes. 'Baseball shoe' has a tangible appearance and is a type of athletic footwear.\nA few things that are visually similar to 'baseball shoe' but are not 'baseball shoe' are:\trunning shoe\tsoccer shoe\tbasketball shoe\thiking boot\ttennis shoe\nThere are several useful visual features to tell there is 'baseball shoe' and not similar things in a photo:\tmetal or rubber spikes on the sole\tof low-cut, high-top, or mid-cut variety\tusually made of leather or synthetic material\tcolor scheme may include various combinations of white, black, blue, or red", 21], "belongings": ["No. 'Belongings' is too vague or abstract to be distinguished in a photo.", 21], "tail fins": ["Yes, 'tail fins' has a tangible appearance and refers to the ornamental fins on the back of vehicles, particularly cars.\nA few things that are visually similar to 'tail fins' but are not 'tail fins' are: Plane wings, fish fins, bird wings.\nThere are several useful visual features to tell there are 'tail fins' and not similar things in a photo: Visible only toward the rear of vehicles, usually on cars and airplanes, pointed in shape and often having a chrome or metallic finish.", 21], "turned-off": ["No. 'turned-off' is too vague or abstract to be distinguished in a photo.", 21], "stone clock tower": ["Yes, 'stone clock tower' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'stone clock tower' but are not 'stone clock tower' are:\tchurch tower\tmonument column\tbell tower\nThere are several useful visual features to tell there is 'stone clock tower' and not similar things in a photo:\tmade of stone or brick\ttall and vertical clock\televated from its surroundings\tmostly simple in design\texplicit clock face", 21], "door entrance": ["Yes. 'Door entrance' has a tangible appearance and is a specific part of a building.\nA few things that are visually similar to 'door entrance' but are not 'door entrance' are:\twindows\tgates\twalls\tarchways\nThere are several useful visual features to tell there is 'door entrance' and not similar things in a photo:\tframed opening\twith a door and a handle\tor with a revolving door\tsometimes with steps or a ramp outside", 21], "tug boat": ["Yes. 'Tug boat' has a tangible appearance and is a kind of boat specifically designed to tow or push other vessels.\nA few things that are visually similar to 'tug boat' but are not 'tug boat' are:\tferry\tcargo ship\tlifeboat\tyacht\nThere are several useful visual features to tell there is 'tug boat' and not similar things in a photo:\t\nsmall, compact size compared to other boats\thigh, pointed bow\tpowerful engines\tdeckhouse near the stern\ttowing or pushing another vessel in the photo.", 21], "soccer socks": ["Yes. 'Soccer socks' has a tangible appearance and is a type of sports gear.\nA few things that are visually similar to 'soccer socks' but are not 'soccer socks' are:\tregular socks\ttights\tcompression socks\t\nThere are several useful visual features to tell there is 'soccer socks' and not similar things in a photo:\tknee-high\tlength (covering shin guards)\tstriped or patterned\tcolors (matching the soccer uniform)\tthick and padded footbed.", 21], "head gear": ["Yes. 'Head gear' has a tangible appearance and refers to any kind of accessory worn on the head.\nA few things that are visually similar to 'head gear' but are not 'head gear' are:\tHair\tForehead bands\tEarrings\tJeweled collar\nThere are several useful visual features to tell there is 'head gear' and not similar things in a photo:\tCircle or band around the head\tAttached adornments like feathers or flowers\tVisibly worn on or covering the head", 21], "counter tops": ["Yes. 'Counter tops' has a tangible appearance and refers to the flat surface on cabinets or tables.\nA few things that are visually similar to 'counter tops' but are not 'counter tops' are:\tdining tables\tdesks\tsinks\tshelving units\nThere are several useful visual features to tell there is 'counter tops' and not similar things in a photo:\tflat surface\tlevel with cabinets or table base\tmaterials such as granite, marble, wood, or laminate\tmay have appliances or items sitting on top", 21], "cement base": ["Yes. 'Cement base' has a tangible appearance and refers to a foundation made of cement.\nA few things that are visually similar to 'cement base' but are not 'cement base' are:\tbrick base\tstone base\twooden base\tasphalt base\nThere are several useful visual features to tell there is 'cement base' and not similar things in a photo:\tgray color\trough texture\tsmooth surface\thard material\tsquare or rectangular shape\tcomment: it is also often used to support columns, statues or structures.", 21], "orange basket": ["Yes. 'Orange basket' has a tangible appearance and refers to a basket filled with oranges.\nA few things that are visually similar to 'orange basket' but are not 'orange basket' are:\tapple basket\tgrape basket\tvegetable basket\tflower basket\nThere are several useful visual features to tell there is an 'orange basket' and not similar things in a photo:\tmade of wicker or other materials typically used for baskets\tbright orange color\tcontaining oranges or orange-like fruits", 21], "bird house": ["Yes. 'Bird house' has a tangible appearance and is a type of shelter or structure for birds.\nA few things that are visually similar to 'bird house' but are not 'bird house' are:\tdecorative house statue\tflower pot\tbird feeder\nThere are several useful visual features to tell there is 'bird house' and not similar things in a photo:\tbox-shaped\twith hole or entrance for birds\tperched on a pole, tree, or hung from a branch\tmade of wood or natural materials\troof or overhang to protect from weather and predators", 21], "purple tie": ["Yes. 'Purple tie' has a tangible appearance and is a type of necktie.\nA few things that are visually similar to 'purple tie' but are not 'purple tie' are:\tblue tie\tpink tie\tscarf\nThere are several useful visual features to tell there is 'purple tie' and not similar things in a photo:\tpurple color\twith a knot\taround the collar of a shirt\trectangular shape\tnarrow width", 21], "crepe": ["Yes. 'Crepe' has a tangible appearance and is a type of thin pancake.\nA few things that are visually similar to 'crepe' but are not 'crepe' are:\tpancake\tblintz\twaffle\nThere are several useful visual features to tell there is 'crepe' and not similar things in a photo:\tthin and flat texture\tlacy or web-like appearance\teven edges and surface\tcomposed of many thin layers or folds\ttypically served rolled or folded\twith toppings such as fruit, chocolate, whipped cream.", 21], "jetliner": ["Yes. 'Jetliner' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'jetliner' but are not 'jetliner' are:\thelicopter\tglider\tdrone\tballoon\nThere are several useful visual features to tell there is 'jetliner' and not similar things in a photo:\ttwo or more jet engines\telongated and streamlined body\twith wings and a tail section\tfor commercial use\twith passenger windows\tand landing gears\ton a runway or in the air.", 21], "curl": ["Yes. 'Curl' has a tangible appearance and is a physical shape.\nA few things that are visually similar to 'curl' but are not 'curl' are:\twave\tfold\tspiral\ttwist\tcoil\nThere are several useful visual features to tell there is 'curl' and not similar things in a photo:\tcontinuous and smooth curve\ttight or loose shape\tbounce or spring to the shape\tdifferent from the surrounding shapes or objects.", 21], "pink skirt": ["Yes. 'Pink skirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pink skirt' but are not 'pink skirt' are:\tpink dress\tpink shorts\tpink top\tpink pants\nThere are several useful visual features to tell there is 'pink skirt' and not similar things in a photo:\ta separate garment\tonly covering the lower part of the body\tspecifically pink in color in the case of 'pink skirt'", 21], "silver spoons": ["Yes. 'Silver spoons' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'silver spoons' but are not 'silver spoons' are:\t\nforks\t\nknives\t\nplastic spoons\t\nwooden spoons\t\nmetal spoons with a different color\nThere are several useful visual features to tell there is 'silver spoons' and not similar things in a photo:\t\nmade of silver or silver-like metal\t\ntypical spoon shape\t\nreflections or shine on the surface\t\nused for eating or serving food", 21], "walk signal": ["Yes. 'Walk signal' has a tangible appearance and is a type of traffic signal.\nA few things that are visually similar to 'walk signal' but are not 'walk signal' are:\ttraffic signal\tcrossing guard\tparking sign\nThere are several useful visual features to tell there is 'walk signal' and not similar things in a photo:\ta pedestrian walking figure in green color\ta light-up sign at a pedestrian crosswalk\tan upright pole with the signal on top, often placed on street corners or traffic intersections.", 21], "vanity mirror": ["Yes. 'Vanity mirror' has a tangible appearance and is typically found in a bathroom or dressing room.\nA few things that are visually similar to 'vanity mirror' but are not 'vanity mirror' are:\tbathroom mirror\tfull-length mirror\thand mirror\nThere are several useful visual features to tell there is 'vanity mirror' and not similar things in a photo:\tit's usually small and compact\thas lights to provide good illumination\tfor applying makeup and personal grooming\tmounted on a table or a stand with adjustable angles\tframed or frameless (without a border)", 21], "lead": ["Yes. 'Lead' has a tangible appearance and is a metallic element.\nA few things that are visually similar to 'lead' but are not 'lead' are:\ttin\tzinc\tmagnesium\taluminum\nThere are several useful visual features to tell there is 'lead' and not similar things in a photo:\tbluish-grey color\tsoft and malleable appearance\tdense and heavy\tmetallic shine", 21], "monk": ["Yes. 'Monk' has a tangible appearance and is a type of religious figure.\nA few things that are visually similar to 'monk' but are not 'monk' are:\thermit\tpriest\tyogi\tguru\tsage\nThere are several useful visual features to tell there is 'monk' and not similar things in a photo:\tbrown, grey or black robe\tbald or shaved head\twith or without a hood or a cowl\tbeard or mustache\tusing prayer beads or a book\tcross-legged posture", 21], "usb": ["Yes. 'USB' has a tangible appearance and is a type of connector.\nA few things that are visually similar to 'USB' but are not 'USB' are:\tHDMI connector\tethernet connector\taudio jack connector\nThere are several useful visual features to tell there is 'USB' and not similar things in a photo:\tRectangular shape\tMetal contacts at one end\tColor-coded (blue for USB 3.0, black or white for USB 2.0)\tSymbol of the USB-icon on the connector.", 21], "rail tracks": ["Yes. 'Rail tracks' has a tangible appearance and is a kind of infrastructure.\nA few things that are visually similar to 'rail tracks' but are not 'rail tracks' are:\troads\tparking lots\tbike lanes\nThere are several useful visual features to tell there is 'rail tracks' and not similar things in a photo:\ttwo parallel metal tracks\tsleepers or ties\tthat keep tracks balanced\tconnected to a train station or a railroad network.", 21], "g": ["No. 'g' is too vague or abstract to be visually concrete.", 21], "coins": ["Yes. 'Coins' has a tangible appearance and is a form of currency.\nA few things that are visually similar to 'coins' but are not 'coins' are:\ttokens\tmedals\tbottle caps\tcircular buttons\nThere are several useful visual features to tell there are 'coins' and be distinguished from similar things in a photo:\tcircular or oval shape\thas a visible denomination or symbol of value\thas the faces of famous people or historical figures\ton one or both sides\tmade of metal\tor other glossy materials", 21], "hoof giraffe": ["No. 'Hoof giraffe' is too vague or abstract to be distinguished in a photo. 'Hoof' refers to a part of the giraffe's body, while 'giraffe' is a specific animal with a distinct appearance. It would be more appropriate to refer to the animal simply as a 'giraffe'. \n\nHowever, a few things that are visually similar to 'giraffe' but are not 'hoof giraffe' are:\tokapi\tllama\tcamel\tdeer\tgazelle\n\nTo distinguish a giraffe from similar animals in a photo, some useful visual features would be: extremely long neck and legs, patterned fur with dark spots on a lighter background, and ossicones (bony protrusions) on their heads.", 21], "reindeer": ["Yes. 'Reindeer' has a tangible appearance and is a type of mammal with antlers.\nA few things that are visually similar to 'reindeer' but are not 'reindeer' are:\tdeer\telk\tmoose\tcaribou\nThere are several useful visual features to tell there is 'reindeer' and not similar things in a photo:\tmedium-sized reddish-brown animal with white fur on the underbelly\tand a darker colored tail\tand legs\tantlers on male\treindeer\tfound in arctic and subarctic regions", 21], "blue sweater": ["Yes. 'Blue sweater' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blue sweater' but are not 'blue sweater' are:\tblue shirt\tblue jacket\tblue hoodie\tblue scarf\nThere are several useful visual features to tell there is 'blue sweater' and not similar things in a photo:\twoolen fabric\ttight-fitting garment\tlong sleeves\twith or without buttons or zipper\tspecific knit pattern or texture", 21], "air canada": ["No. 'Air Canada' is too vague or abstract to be distinguished in a photo. \n\nHowever, here are some things that are visually similar to the brand name 'Air Canada': other airplanes, airports, and airline company logos. Useful visual features for distinguishing 'Air Canada' from visually similar things in a photo include its distinctive red and white color scheme, the stylized maple leaf logo, and any visible text that includes the name 'Air Canada'.", 21], "walker": ["Yes. 'Walker' has a tangible appearance and is an assistive device used for mobility.\nA few things that are visually similar to 'walker' but are not 'walker' are:\tcanetripodstrollershopping cart\nThere are several useful visual features to tell there is 'walker' and not similar things in a photo:\tfour legs\tno seat or basket\tbars to hold onto or rest arms on\tfoldable or adjustable for height", 21], "location": ["No. 'Location' is too vague or abstract to be distinguished in a photo. It refers to a specific point or area in the physical world, but this cannot be visually represented without additional contextual information.\nThus, there are no similar things to 'location' that could be listed.", 21], "cubs": ["Yes. 'Cubs' has a tangible appearance and refers to baby animals of certain species.\nA few things that are visually similar to 'cubs' but are not 'cubs' are:\tkittens\tpuppies\tducklings\tfawns\nThere are several useful visual features to tell there are 'cubs' and not similar things in a photo:\tsmall size compared to adult species\tfluffy or hairy bodies\tbig paws and/or big ears\tplayful and curious behavior in groups\twith their mother or adult organisms\tof certain species such as bears, lions, tigers, etc.", 21], "round hole": ["Yes. 'Round hole' has a tangible appearance and is a void space in a round shape.\nA few things that are visually similar to 'round hole' but are not 'round hole' are:\tbottle cap\tpeephole\tonion ring\tdoughnut hole\nThere are several useful visual features to tell there is 'round hole' and not similar things in a photo:\tcircular shape\tmissing material or substance\tperimeter with a smooth or jagged edge, depending on the material that forms it.", 21], "church tower": ["Yes. 'Church tower' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'church tower' but are not 'church tower' are:\tskyscraper\tchimney\twindmill\tlighthouse\nThere are several useful visual features to tell there is 'church tower' and not similar things in a photo:\tlarge and tall part of a church building\tpointed or domed top\tbell or clock embedded in the tower\tornamental details such as crosses or statues", 21], "neon lights": ["Yes, 'neon lights' has a tangible appearance.\nA few things that are visually similar to 'neon lights' but are not 'neon lights' are: LED lights, fluorescent lights, incandescent bulbs, candle\nThere are several useful visual features to tell there is 'neon lights' and not similar things in a photo:\n- The bright and vivid light\n- The unique and colorful glow\n- The distinctive gas discharge tube shape\n- The characteristic flicker of the light", 21], "seat cover": ["Yes. 'Seat cover' has a tangible appearance and is an item used to cover a seat or a chair.\nA few things that are visually similar to 'seat cover' but are not 'seat cover' are:\tblanket\tcushion\tpillow\tslipcover\nThere are several useful visual features to tell there is 'seat cover' and not similar things in a photo:\tfitted to a specific seat or chair\tdraped over the seat or chair\tfor cars, trucks, or vans, with headrest and seat belt covers, and compatible with airbags for safety purposes\tmade of materials such as fabric, leather, or vinyl designed to protect and decorate the seat", 21], "compass": ["Yes. 'Compass' has a tangible appearance and is a navigational instrument.\nA few things that are visually similar to 'compass' but are not 'compass' are:\twatch\tprotractor\truler\tthermometer\nThere are several useful visual features to tell there is 'compass' and not similar things in a photo:\tcircular in shape\twith a needle pointing to magnetic north\tlettering indicating cardinal directions of north, south, east, and west\tmay have a rotating bezel ring with degree markings", 21], "sand beach": ["Yes. 'Sand beach' has a tangible appearance and is a kind of natural landscape.\nA few things that are visually similar to 'sand beach' but are not 'sand beach' are:\tconcrete beach\tpebble beach\trocky shore\nThere are several useful visual features to tell there is 'sand beach' and not similar things in a photo:\tsoft sand\tpale color\twaves and water visible in the background\tpalm trees or other beachside plants", 21], "car train": ["Yes. 'Car train' has a tangible appearance and is a string of cars linked together and travelling together.\nA few things that are visually similar to 'car train' but are not 'car train' are: traffic on a road, parade, row of parked cars.\nThere are several useful visual features to tell there is 'car train' and not similar things in a photo:\tcars linked together,\tengines or locomotives at either end,\tcars following in a line,\tusing tracks to move.", 20], "city street scene": ["Yes. 'City street scene' has a tangible appearance and encompasses various elements of an urban landscape.\nA few things that are visually similar to 'city street scene' but are not 'city street scene' are:\trural landscape\tsuburban neighborhood\tpark or garden\tbuilding fa\u00e7ade\nThere are several useful visual features to tell there is 'city street scene' and not similar things in a photo:\thigh density of buildings and people\tpavements, roads and junctions\tpower lines, street signs, traffic lights, trash cans, benches, street vendors, bicycles, taxis, buses, etc.\tsignts of commerce, like shops, restaurants or billboards.", 20], "handler": ["No. 'Handler' is too vague and abstract to have a tangible appearance that can be distinguished in a photo. It refers to a person who handles or manages something, such as an animal or a machine.", 20], "kitchen appliance": ["Yes. 'Kitchen appliance' has a tangible appearance and refers to tools and devices used in cooking and food preparation.\nA few things that are visually similar to 'kitchen appliance' but are not 'kitchen appliance' are:\ttools, such as knives or spatulas\tkitchen utensils, such as a colander or a whisk\tpots, pans, and other cookware\tserving dishes\nThere are several useful visual features to tell there is 'kitchen appliance' and not similar things in a photo:\tdesigned with a specific function, such as chopping or blending\tmade of metal, plastic, or glass\telectrical or requires electricity to operate\tfound on a countertop, mounted on a wall, or built into a cabinet", 20], "fruit bowl": ["Yes. 'Fruit bowl' has a tangible appearance and is a container to hold fruit.\nA few things that are visually similar to 'fruit bowl' but are not 'fruit bowl' are:\tcereal bowl\tdecorative bowl\tfor decoration\nThere are several useful visual features to tell there is 'fruit bowl' and not similar things in a photo:\tfilled with fruit\tvariety of types of fruit\tusually on a table or countertop", 20], "trench coat": ["Yes. 'Trench coat' has a tangible appearance and is a type of coat.\nA few things that are visually similar to 'trench coat' but are not 'trench coat' are:\tovercoat\traincoat\twindbreaker\tjacket\nThere are several useful visual features to tell there is 'trench coat' and not similar things in a photo:\tknee-length or longer\tdouble-breasted with buttons\tbelted at the waist\tshoulder straps\tor epaulets\tgenerally made of a sturdy and water-resistant fabric like gabardine or leather.", 20], "silver ware": ["Yes. 'Silver ware' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'silver ware' but are not 'silver ware' are:\tstainless steel utensils\tplastic utensils\twooden utensils\t\nThere are several useful visual features to tell there is 'silver ware' and not similar things in a photo:\tmade of silver or silver-plated material\tshiny or reflective surface\telegant or decorative design\toften used in formal or upscale settings", 20], "brown drawer": ["Yes. 'Brown drawer' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'brown drawer' but are not 'brown drawer' are:\tchest of drawers\twooden box\tshelf\tbookcase\nThere are several useful visual features to tell there is 'brown drawer' and not similar things in a photo:\ta stand-alone piece of furniture\tdesigned for storage\tconsists of one or more compartments with handles\tdrawers open from the front with knobs or handles\ta flat top surface to place items on it\tbrown-colored exterior", 20], "log cabin": ["Yes. 'Log cabin' has a tangible appearance and is a type of house made of logs.\nA few things that are visually similar to 'log cabin' but are not 'log cabin' are:\ttraditional wooden house\tclose construction site\tcottages\nThere are several useful visual features to tell there is 'log cabin' and not similar things in a photo:\tmade of logs\tsimple construction design\twooden roof with shingles\tchimney and stovepipe\ton a plot of land in a natural environment, such as woods or by a lake.", 20], "stirrups": ["Yes. 'Stirrups' has a tangible appearance and is a part of horse riding equipment.\nA few things that are visually similar to 'stirrups' but are not 'stirrups' are:\tfootrests\tpedals\t\nThere are several useful visual features to tell there is 'stirrups' and not similar things in a photo:\tlocated on horseback\tdesigned to place the feet with the purpose of balance and support\twhile in use, inserted into the saddle's stirrup leathers and hang from the saddle tree\tstraps or cords attached to the stirrups to secure them in place\tfor horseback riding only", 20], "t shirt": ["Yes. 'T shirt' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 't shirt' but are not 't shirt' are:\thoodie\tsweater\tblouse\tpolo shirt\nThere are several useful visual features to tell there is 't shirt' and not similar things in a photo:\tshort sleeves\tround neckline\tloose fit\tcasual and comfortable fabric (e.g. cotton)", 20], "loaves": ["Yes. 'Loaves' has a tangible appearance and refers to baked bread in a specific form.\nA few things that are visually similar to 'loaves' but are not 'loaves' are: \tbuns\tpatties\tbiscuits\nThere are several useful visual features to tell there is 'loaves' and not similar things in a photo:\tlong, oblong shape\tcrusted outer layer\tsoft, spongy texture inside", 20], "sail boats": ["Yes. 'Sail boats' has a tangible appearance and is a type of water vehicle.\nA few things that are visually similar to 'sail boats' but are not 'sail boats' are:\tyachts\tcanoes\tkayaks\trow boats\nThere are several useful visual features to tell there is 'sail boats' and not similar things in a photo:\tlarge size compared to other boats\tmasts and rigging\tsails and ropes\table to sail using the wind\tdirection of the boat", 20], "blue mouse pad": ["Yes. 'Blue mouse pad' has a tangible appearance and refers to a specific type of computer accessory.\nThere are no things that are visually similar to 'blue mouse pad' but are not 'blue mouse pad'.\nUseful visual features for distinguishing 'blue mouse pad' in a photo are:\ta rectangular shape\ta smooth surface\ta thin and flexible material\tthe color blue", 20], "swimming pool": ["Yes. 'Swimming pool' has a tangible appearance and is a structure used for swimming.\nA few things that are visually similar to 'swimming pool' but are not 'swimming pool' are:\tpond\treservoir\tlake\tdecorative fountain\nThere are several useful visual features to tell there is 'swimming pool' and not similar things in a photo:\tretangular or circular shape\tconcrete or tiled bottom\twater in the pool\tdiving board or pool ladder surrounding deck or walkway with lounge chairs or tables.", 20], "wooden bookshelf": ["Yes. 'Wooden bookshelf' has a tangible appearance and refers to a specific type of furniture.\nA few things that are visually similar to 'wooden bookshelf' but are not 'wooden bookshelf' are:\tCabinets\tWooden storage boxes\tLibrary carts\nThere are several useful visual features to distinguish a 'wooden bookshelf' from the listed similar things in a photo:\tFlat horizontal shelves\tmade of wood\tor wood-like materials\tDesigned to store and display books and other objects.", 20], "leafless branches": ["Yes. 'Leafless branches' has a tangible appearance and is a type of plant structure.\nA few things that are visually similar to 'leafless branches' but are not 'leafless branches' are:\tpaintbrush\tbroomstick\tfence\tpost\tor any long thin wooden object\nThere are several useful visual features to tell there are 'leafless branches' and not similar things in a photo:\twoody bark\ttexture of twigs and bark\tno leaves or buds attached\tto have small twigs branching out from a larger twig or branch.", 20], "silver spatula": ["Yes, 'silver spatula' is a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'silver spatula' but are not 'silver spatula' are:\tturner\tflipper\tserver\nThere are several useful visual features to distinguish 'silver spatula' from the listed similar things in a photo:\t\n\n- A flat, thin metal blade with a curved or angled edge\n- A long handle made of metal or plastic\n- A shiny silver color, although it could also be made of other materials\n- The shape of the blade may be rectangular, oval, or diamond-shaped, but it will always have an edge for flipping or turning objects in a pan or on a grill.", 20], "storefronts": ["Yes. 'Storefronts' has a tangible appearance and usually refers to the front of a store or shop.\nA few things that are visually similar to 'storefronts' but are not 'storefronts' are:\twalls\tbuildings\tdoors\tentrances\nThere are several useful visual features to tell there is 'storefronts' and not similar things in a photo:\tsigns\tor signs with the store name and logo\twindows\twith products, mannequins, or advertisements\tfor customers to enter and exit freely\tobvious display cases, shelves, or racks with products available\tfor pedestrians to walk along and view the store's offerings.", 20], "stone base": ["Yes. 'Stone base' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'stone base' but are not 'stone base' are: \tconcrete base\tbricks\tcement block\twood planks\nThere are several useful visual features to tell there is 'stone base' and not similar things in a photo:\tmade of natural stone material\trough texture or surface\t\nlayers or blocks of stone\tarrangement in a stable and secure manner\tsupporting weight or structure of another object or building.", 20], "apple keyboard": ["Yes. 'Apple keyboard' has a tangible appearance and is a type of computer keyboard.\nA few things that are visually similar to 'apple keyboard' but are not 'apple keyboard' are:\tPC keyboards\tLaptop keyboards\tWireless keyboards\tTablet keyboards\nThere are several useful visual features to tell there is 'apple keyboard' and not similar things in a photo:\tthin profile\taluminum body\twhite keys with black letters\tslim keys with low travel distance to press magnetic charging port for wireless version", 20], "mans legs": ["Yes. 'Man's legs' has a tangible appearance and refers to the lower limbs of a male human.\nA few things that are visually similar to 'man's legs' but are not 'man's legs' are:\ttree trunks\tchair or table legs\tsculpture or statue legs\nThere are several useful visual features to tell there is 'man's legs' and not similar things in a photo:\thuman anatomy\tandrogynous or masculine features\tskin and hair\tcolor and texture of clothing or shoes\tvariations in shape or size of muscles and bones", 20], "house cat": ["Yes. 'House cat' has a tangible appearance and is a specific type of domesticated land animal.\nA few things that are visually similar to 'house cat' but are not 'house cat' are:\tlynx\tleopard\tcheetah\tbobcat\tocelot\ttiger\tlion\nThere are several useful visual features to tell there is 'house cat' and not similar things in a photo:\tsmall to medium size\tfurry body\tpointy ears\tsharp claws\twhiskers\ttail\tupright stance\tthat typically are kept indoors with humans", 20], "airplane landing": ["Yes. 'Airplane landing' has a tangible appearance and is a specific event involving an airplane.\nA few things that are visually similar to 'airplane landing' but are not 'airplane landing' are:\tairplane taking off\thelicopter landing\tbirds landing\nThere are several useful visual features to tell there is 'airplane landing' and not similar things in a photo:\tA large commercial or private airplane on a runway or landing strip;\tWheels lowered;\tFlaps extended;\tBrakes indicated by smoke or sparks from wheels;\tA nose-down attitude;\tApproaching the runway from above.", 20], "sport shoe": ["Yes. 'Sport shoe' has a tangible appearance and is a kind of footwear suitable for sports activities.\nA few things that are visually similar to 'sport shoe' but are not 'sport shoe' are:\tcasual sneakers\thiking boots\tdress shoes\tsandals\nThere are several useful visual features to tell there is 'sport shoe' and not similar things in a photo:\tlightweight and flexible sole\tbreathable mesh upper\tforward-leaning design and cushioning\tshoe laces or Velcro strap designed to lock foot in place", 20], "silver dvd player": ["Yes. 'Silver DVD player' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'silver dvd player' but are not 'silver dvd player' are:\tvcr player\tbluray player\taudio player\tcomputer tower\nThere are several useful visual features to distinguish 'silver dvd player' from similar things in a photo:\tsilver color\tunique shape with tray for disc insertion\tscreen display\tforwards & backwards buttons\tplay, pause, stop, and skip buttons\ton/off button\taudio & video output/input ports.", 20], "foamy waves": ["Yes. 'Foamy waves' has a tangible appearance and refers to the type of waves in the ocean or the sea.\nA few things that are visually similar to 'foamy waves' but are not 'foamy waves' are:\tsmoke or fog\tsmoothies or whipped cream\twaves without foam\twater with bubbles\tsnow or ice\nThere are several useful visual features to tell there are 'foamy waves' and not similar things in a photo:\twater with white bubbly or frothy foam\tcrashing or breaking against a surface, like rocks or a beach\tmovements are seen and suggest agitation or choppiness\tin the setting of the ocean or sea", 20], "flowering plant": ["Yes. 'Flowering plant' has a tangible appearance and is a type of plant that produces flowers.\nA few things that are visually similar to 'flowering plant' but are not 'flowering plant' are:\tcacti\tferns\ttrees\tgrass\nThere are several useful visual features to tell there is 'flowering plant' and not similar things in a photo:\tcolorful flowers and petals\tleaves arranged in a specific pattern\tstem and branches that support the flowers and foliage\tstamen and pistil (reproductive parts) in the center of the flower\tseeds or seed pods that develop after the flower has bloomed.", 20], "blue cloudy sky": ["Yes. 'Blue cloudy sky' has a tangible appearance.\nA few things that are visually similar to 'blue cloudy sky' but are not 'blue cloudy sky' are:\tblue ocean\tblue mountain ranges\tbird's eye view of blue-colored towns or cities\nThere are several useful visual features to tell there is 'blue cloudy sky' and not similar things in a photo:\thues of blue color\twhite or grey clouds\tscattered or dense clouds\tcumulus or cirrus cloud patterns\tblended or shifting colors of the sky\tbackground of the photo, such as trees or buildings, to give context to the sky.", 20], "yellow curtains": ["Yes. 'Yellow curtains' has a tangible appearance and is a type of window treatment.\nA few things that are visually similar to 'yellow curtains' but are not 'yellow curtains' are:\tyellow fabric\t yellow tapestry\t yellow blankets\t yellow towels\nThere are several useful visual features to tell there are 'yellow curtains' and not similar things in a photo:\thanging from a window or a door\tvertical or horizontal lines\tpleated or flat surface\tadjustable by a curtain rod.", 20], "tent top": ["Yes. 'Tent top' has a tangible appearance and refers to the upper part of a tent that provides shelter.\nA few things that are visually similar to 'tent top' but are not 'tent top' are:\tumbrella\tawning\ttarp\tcanopy\nThere are several useful visual features to tell there is 'tent top' and not similar things in a photo:\tdome-shaped or pointed top\tfabric or material covering\ta surrounding tent structure or poles\topenings or flaps for ventilation and access.", 20], "beige tile": ["Yes. 'Beige tile' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'beige tile' but are not 'beige tile' are:\tconcrete slab\tstone slab\tmarble slab\twood panel\nThere are several useful visual features to tell there is 'beige tile' and not similar things in a photo:\trectangular shape\tsmooth surface\tbeige color\tmatching grout lines\tpatterns or textures on the surface.", 20], "antique clock": ["Yes. 'Antique clock' has a tangible appearance and is an object.\nA few things that are visually similar to 'antique clock' but are not 'antique clock' are:\twatches \tmodern clocks \tdecorative plates \tpaintings or photos of clocks\nThere are several useful visual features to tell there is 'antique clock' and not similar things in a photo:\tmechanical powered by a pendulum and weights\troman numerals instead of regular numerals or digits elaborate and intricate design or ornamentation\tusually made of wood or metal", 20], "stall door": ["Yes. 'Stall door' has a tangible appearance and is a type of door for a restroom stall.\nA few things that are visually similar to 'stall door' but are not 'stall door' are:\tbathroom door\toffice cubicle door\twardrobe door\nThere are several useful visual features to tell there is 'stall door' and not similar things in a photo:\tpartitioned for privacy\tusually smaller than other doors\thinged on one side or slides on a rail inside the bathroom stall\thas a latch for locking from the inside\toften has a gap at the bottom for ventilation", 20], "watch band": ["Yes. 'Watch band' has a tangible appearance and is a kind of strap to secure a watch on your wrist.\nA few things that are visually similar to 'watch band' but are not 'watch band' are:\tbelt\tbracelet\tankle strap\twrist cuff\nThere are several useful visual features to tell there is 'watch band' and not similar things in a photo:\tattached to a watch\tmade of leather, metal, fabric, or rubber\thas a buckle, clasp, or fastener to secure it around the wrist\tvaries in size, color, and design depending on the watch", 20], "paper roll": ["Yes. 'Paper roll' has a tangible appearance and is a type of cylindrical object.\nA few things that are visually similar to 'paper roll' but are not 'paper roll' are:\ttoilet paper roll\ttape roll\tduct tape roll\taluminum foil roll\nThere are several useful visual features to tell there is 'paper roll' and not similar things in a photo:\tcylindrical shape\tsheets of paper on the roll\tperforated lines along the paper roll", 20], "holders": ["Yes. 'Holders' has a tangible appearance and is a type of object used for holding or supporting something.\nA few things that are visually similar to 'holders' but are not 'holders' are:\tbaskets\tboxes\tshelves\ttrays\nThere are several useful visual features to tell there is 'holders' and not similar things in a photo:\tconsist of one or more parts and have a cavity or space to hold something\tcome in various shapes, sizes, and materials (such as cups, bags, vases, stands, etc.)\tare designed to fit a specific object (such as a phone holder, a candle holder, a pen holder, etc.)\tand are placed in a way that supports or secures the held object.", 20], "toilet handle": ["Yes. 'Toilet handle' has a tangible appearance and is a part of a toilet.\nA few things that are visually similar to 'toilet handle' but are not 'toilet handle' are:\tcabinet handles\tdoor handles\t\nThere are several useful visual features to tell there is 'toilet handle' and not similar things in a photo:\tit is connected to a toilet\tt-shaped handle or lever\tpositioned on the side or front of the toilet", 20], "train windows": ["Yes. 'Train windows' has a tangible appearance and is a part of a train.\nA few things that are visually similar to 'train windows' but are not 'train windows' are:\tcar windows\tairplane windows\tbuilding windows\tshop windows\nThere are several useful visual features to tell there is 'train windows' and not similar things in a photo:\trectangular or square-shaped windows\thorizontal position on the side of the train\tmultiple windows in a row\tglass or transparent material", 20], "side plate": ["Yes. 'Side plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'side plate' but are not 'side plate' are:\tdinner plate\tplatter\tbowl\tcharger plate\nThere are several useful visual features to tell there is 'side plate' and not similar things in a photo:\tsmall size compared to dinner plate\tflat surface\twith a small rim or no rim at all\tused for serving side dishes or bread", 20], "silver bus": ["Yes. 'Silver bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'silver bus' but are not 'silver bus' are:\tgray truck\tsilver SUV\tsilver sedan\tsilver limousine\nThere are several useful visual features to tell there is 'silver bus' and not similar things in a photo:\tlarge size\tbox-like shape\twindows along the side\tsingle or double decked\tpublic transportation signage or logo on the side", 20], "games": ["No. 'Games' is too vague or abstract to be distinguished in a photo.", 20], "story brick building": ["Yes, 'story brick building' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'story brick building' but are not 'story brick building' are:\tmonument\ttower\tchimney\tbridge\nThere are several useful visual features to tell there is 'story brick building' and not similar things in a photo:\tmade of bricks or stones\tmultiple levels or floors\trectangular or square shape\tuniquely shaped windows and doors\tvisible roof and chimney", 20], "serviette": ["Yes. 'Serviette' has a tangible appearance and is a type of cloth used for wiping hands and lips while eating.\nA few things that are visually similar to 'serviette' but are not 'serviette' are:\ttowel\tnapkin\ttablecloth\thandkerchief\nThere are several useful visual features to tell there is 'serviette' and not similar things in a photo:\tsmall size\tusually rectangular or square in shape\tmade of cloth or paper\tFolded into unique shapes or designs\tplaced on a dining table or held in the hand at a meal.", 20], "crossing sign": ["Yes. 'Crossing sign' has a tangible appearance and is a type of traffic sign.\nA few things that are visually similar to 'crossing sign' but are not 'crossing sign' are:\tturning sign\tspeed limit sign\tstop sign\tpedestrian sign\nThere are several useful visual features to tell there is 'crossing sign' and not similar things in a photo:\tyellow diamond-shaped sign\twith black silhouettes of people or animals\tcrossing lines or arrows\tsignaling to watch for crossings", 20], "vibrant": ["No. 'Vibrant' is too vague or abstract to be visually concrete.\nThere are no things that are visually similar to 'vibrant' but are not 'vibrant.'\nAs 'vibrant' is a subjective term for color, energy or brightness, there are no specific visual features to always distinguish it from other colors or levels of energy or brightness. It depends on the context and individual perception.", 20], "silver fork utensil": ["Yes. 'Silver fork utensil' has a tangible appearance and is a specific type of flatware.\nA few things that are visually similar to 'silver fork utensil' but are not 'silver fork utensil' are:\tpaper clip\thairpin\tpronged comb\nThere are several useful visual features to tell there is 'silver fork utensil' and not similar things in a photo:\tsmall size\tflat surface\twith two or more tines at the top of the handle\tmade of metal, particularly silver", 20], "night time picture": ["Yes. 'Night time picture' has a tangible appearance and is a photo taken at night.\nA few things that are visually similar to 'night time picture' but are not 'night time picture' are:\tblack and white image\tdark, shadowy image\timage with low exposure setting\nThere are several useful visual features to tell there is 'night time picture' and not similar things in a photo:\tdark or black background\twith or without stars\tbathed in dim, moody, or artificial light\tobjects or subjects that are difficult to see due to low lighting\tcityscapes or landscapes with exposure adjusted for low light conditions", 20], "dog eyes": ["Yes. 'Dog eyes' has a tangible appearance and is a part of a dog's body.\nA few things that are visually similar to 'dog eyes' but are not 'dog eyes' are:\tcat eyes\tcow eyes\thuman eyes\nThere are several useful visual features to tell there is 'dog eyes' and not similar things in a photo:\tround, dark pupils\tlight-reflecting tapetum lucidum\thorizontal alignment (as dogs are predators)\tdog's face around the eyes (like fur, wrinkles or colors)", 20], "saddlebag": ["Yes. 'Saddlebag' has a tangible appearance and is a type of bag used for carrying items while riding a horse or a bike.\nA few things that are visually similar to 'saddlebag' but are not 'saddlebag' are:\tpurse\tbackpack\t\nThere are several useful visual features to tell there is 'saddlebag' and not similar things in a photo:\tattached to a saddle or a bike\tframe to hold its shape\ttwo pouches or compartments\tto be carried by a rider or attached to a vehicle\tmade of durable materials like leather or canvas.", 20], "ripe fruit": ["Yes. 'Ripe fruit' has a tangible appearance and is a type of produce.\nA few things that are visually similar to 'ripe fruit' but are not 'ripe fruit' are:\tunripe fruit\tfruit-shaped decorations\torbs or balls\nThere are several useful visual features to tell there is 'ripe fruit' and not similar things in a photo:\tcolorful\tskin or peel\ttexture\tpit or seeds\tflesh or pulp", 20], "base ball bat": ["Yes. 'Base ball bat' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'base ball bat' but are not 'base ball bat' are: hammers, sticks, clubs, cricket bat.\nThere are several useful visual features to tell there is 'base ball bat' and not similar things in a photo:\tnarrow at one end and wide at the other\tend with a handle is narrower than the barrel\tused for hitting a ball in baseball.", 20], "pantry": ["Yes. 'Pantry' has a tangible appearance and is a type of storage area for food and kitchen-related items.\nA few things that are visually similar to 'pantry' but are not 'pantry' are:\tcloset\tshelves\tcabinet\twardrobe\nThere are several useful visual features to tell there is 'pantry' and not similar things in a photo:\tstorage area for food or kitchen-related items\tshelves or cabinets with doors or curtains jars or containers filled with food or ingredients\tlabels or signs indicating food or ingredient types", 20], "flip cell phone": ["Yes. 'Flip cell phone' has a tangible appearance and is a type of mobile phone.\nA few things that are visually similar to 'flip cell phone' but are not 'flip cell phone' are:\tbar phone\tsmartphone\tcordless phone\twalkie-talkie\nThere are several useful visual features to tell there is 'flip cell phone' and not similar things in a photo:\trectangular shape with two halves\thinge that allows the phone to flip open and closed\tscreen and keypad on the bottom half\tof the phone\tcamera lens and flash on the back of the phone", 20], "ash tray": ["Yes. 'Ash tray' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'ash tray' but are not 'ash tray' are:\ttrash can\tbowl\tcoaster\tdecorative dish\nThere are several useful visual features to tell there is 'ash tray' and not similar things in a photo:\tjoint or cigarette butts were put in it\tdisc or cup-shaped\tdirty or ash-coated interior\tmade of ceramic or metal", 20], "silver legs": ["Yes, 'silver legs' has a tangible appearance and could refer to objects, such as furniture or sculptures, that have legs made of silver or covered in silver paint.\nA few things that are visually similar to 'silver legs' but are not 'silver legs' are:\tchrome legs\tstainless steel legs\taluminum legs\tsilver-colored legs\tmercury glass legs\nThere are several useful visual features to tell there are 'silver legs' and not similar things in a photo:\tlegs made of actual silver or coated in silver paint or material\thas the shine and luster of silver, rather than being a matte or brushed finish\ttakes on the color of silver, i.e. is not just \"silver-colored\" or grey", 20], "license plates": ["Yes. 'License plates' has a tangible appearance and is a type of vehicle identification.\nA few things that are visually similar to 'license plates' but are not 'license plates' are:\tsigns\tlabels\tstickers\nThere are several useful visual features to tell there is 'license plates' and not similar things in a photo:\tattached to the front or back of a vehicle\tcombination of letters and numbers\tgovernment-issued emblem or logo\tstate or country identification tag", 20], "handle refrigerator": ["Yes. 'Handle refrigerator' has a tangible appearance and refers to the handle of a refrigerator door.\nThere are no things that are visually similar to 'handle refrigerator' and are not 'handle refrigerator' as it is a specific and identifiable object.\nUseful visual features for distinguishing 'handle refrigerator' from other parts of the refrigerator or similar objects would be its distinct shape, size, and location at the edge of the door, as well as its material and color.", 20], "bears eye": ["Yes. 'Bears eye' has a tangible appearance and is a body part of a bear.\nA few things that are visually similar to 'bears eye' but are not 'bears eye' are:\teye of a different animal\tmarble\tgumball\tping-pong ball\tsimilarly colored fruit like grape or blueberry\nThere are several useful visual features to tell there is 'bears eye' and not similar things in a photo:\tfur or hair around the eye\tmaybe scars or marks around the eye\tposition on the bear's head\tsize and shape of the eye.", 20], "owls": ["Yes. 'Owls' have a tangible appearance and are a type of bird.\nA few things that are visually similar to 'owls' but are not 'owls' are:\thawks\teagles\tcrows\nThere are several useful visual features to tell there is 'owls' and not similar things in a photo:\tlarge eyes\twith forward-facing eyes\tor ear-like tufts\ton the head\tserrated or curved beaks\tfeathery bodies\tand hooked talons", 20], "umbrella handle": ["Yes. 'Umbrella handle' has a tangible appearance and refers to the handle or grip of an umbrella.\nA few things that are visually similar to 'umbrella handle' but are not 'umbrella handle' are:\tcan handle\tdrawer knob\tshower handle\nThere are several useful visual features to identify 'umbrella handle' in a photo:\tcylindrical shape\tcurved or hooked design\ttextured or patterned surface\tcolor and material that matches an umbrella's canopy", 20], "orange wheel": ["Yes. 'Orange wheel' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'orange wheel' but are not 'orange wheel' are:\tlemon slice\tbicycle tire\tclock face\nThere are several useful visual features to tell there is an 'orange wheel' and not similar things in a photo:\tround shape\tcircular segments of orange color\tdivided into wedges or segments\tpotentially showing the stem", 20], "stormy sky": ["Yes. 'Stormy sky' has a tangible appearance and is a type of weather condition.\nA few things that are visually similar to 'stormy sky' but are not 'stormy sky' are: sunset, sunrise, cloudy sky, starry sky\nThere are several useful visual features to tell there is 'stormy sky' and not similar things in a photo: dark and ominous clouds\tvisible lightning strikes\tstreaks of rain or snow in the image\tturbulent or choppy appearance of water or trees in the foreground", 20], "mount": ["Yes. 'Mount' has a tangible appearance and refers to a large natural elevation of the earth's surface.\nA few things that are visually similar to 'mount' but are not 'mount' are:\thill\trock\tcliff\tpile\tof dirt or debris\nThere are several useful visual features to tell there is 'mount' and not similar things in a photo:\tsteep slopes and rugged terrain\theight compared to the surroundings\tconfirmed geological formation, such as a volcano or a glacier", 20], "right window": ["Yes. 'Right window' has a tangible appearance and refers to a specific location and orientation of a window.\nA few things that are visually similar to 'right window' but are not 'right window' are:\tleft window\tcenter window\ttop window\tbottom window\nThere are no useful visual features to distinguish the 'right window' from the listed similar things as they all appear similar unless there are additional context clues.", 20], "tangle": ["Yes. 'Tangle' has a tangible appearance and refers to something twisted, knotted or intertwined.\nA few things that are visually similar to 'tangle' but are not 'tangle' are:\tcrinkled paper\tspider webs\thair\tgarden hoses\ttree roots\nThere are several useful visual features to distinguish 'tangle' from the listed similar things in a photo:\tjumbled\tand twisted appearance\tintertwined lines\tor objects\tdifficult to unravel\tor straighten out\tclumps or clusters of intertwined things.", 20], "umbrella top": ["Yes. 'Umbrella top' has a tangible appearance and is a part of an umbrella.\nA few things that are visually similar to 'umbrella top' but are not 'umbrella top' are:\tbeach umbrella\tparasol\t\nThere are several useful visual features to tell there is 'umbrella top' and not similar things in a photo:\t\ncircular or dome-shaped canopy\t\nattached to a central pole\t\nconnected to spokes that support the canopy\t\nintended for personal use in the rain or to block the sun", 20], "wooden basket": ["Yes. 'Wooden basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'wooden basket' but are not 'wooden basket' are:\tcrates\tboxes\tbarrels\tbuckets\nThere are several useful visual features to tell there is 'wooden basket' and not similar things in a photo:\tmade of wood\twoven texture\torally-made has a handle round or oval-shaped.", 20], "side wall": ["Yes. 'Side wall' has a tangible appearance and refers to the vertical surface of a building.\nA few things that are visually similar to 'side wall' but are not 'side wall' are:\tfence\twindows\tdoor\troof\nThere are several useful visual features to tell there is 'side wall' and not similar things in a photo:\tvertical plane that forms the outer sides of a building\tsmooth or textured surface that may be painted or made of different materials such as wood, brick, stone, or concrete\tno openings such as windows or doors\tthat may connect to other side walls at a right angle.", 20], "details": ["No. 'Details' are too vague or abstract to be distinguished in a photo. It refers to small pieces of information or characteristics that contribute to a full understanding of something. It doesn't have a tangible appearance.", 20], "ruffles": ["Yes. 'Ruffles' has a tangible appearance and refers to a type of trim or decoration.\nA few things that are visually similar to 'ruffles' but are not 'ruffles' are: pleats, folds, gathers, scallops, waves, frills, decorative edges.\nThere are several useful visual features to tell there are 'ruffles' and not similar things in a photo:\twavy or curvy shapes\ton the edge or hem of a garment\tor on a piece of decoration\tsmall and continuous folds, usually made of the same fabric\tas the rest of the garment\tor of a contrasting fabric", 20], "class": ["No. 'Class' is too vague or abstract to be distinguished in a photo.", 20], "pandas": ["Yes. 'Pandas' has a tangible appearance and is a type of bear.\nA few things that are visually similar to 'pandas' but are not 'pandas' are:\tkoalas\traccoons\t\nThere are several useful visual features to tell there is 'pandas' and not similar things in a photo:\tblack and white fur\tpatches around eyes and ears\tfat face and cheeks\tshort tail\tbear-like body structure\twith a tufted tail\tsharp claws that are retractable.", 20], "hamburgers": ["Yes. 'Hamburgers' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'hamburgers' but are not 'hamburgers' are:\tveggie burgers\tturkey burgers\tcheese sandwiches\nThere are several useful visual features to tell there is 'hamburgers' and not similar things in a photo:\ttwo buns with a patty\tinclusion of toppings like lettuce, tomato, cheese, and pickles\tgrilled or fried marks on the patty", 20], "stalls": ["Yes. 'Stalls' has a tangible appearance and typically refers to market or fair stalls.\nA few things that are visually similar to 'stalls' but are not 'stalls' are:\tsheds\tkiosks\tbooths\tstands\nThere are several useful visual features to tell there is 'stalls' and not similar things in a photo:\t\ntemporary or semi-permanent structure\topen-air or covered\ttables, counters or shelves\tfor selling goods or services\tdecorated with signs or banners", 20], "exhaust fan": ["Yes. 'Exhaust fan' has a tangible appearance and is a type of fan used for ventilation purposes.\nA few things that are visually similar to 'exhaust fan' but are not 'exhaust fan' are:\tceiling fan\tpedestal fan\ttable fan\nThere are several useful visual features to tell there is 'exhaust fan' and not similar things in a photo:\t installed on the wall or ceiling\tusually has a rectangular or square shape\thas vents or grilles\tforces air out of a room or building, rather than circulating it within the same space", 20], "sesame seeds": ["Yes. 'Sesame seeds' has a tangible appearance.\nA few things that are visually similar to 'sesame seeds' but are not 'sesame seeds' are:\tchia seeds\tpoppy seeds\tlinseeds\nThere are several useful visual features to tell there are 'sesame seeds' and not similar things in a photo:\tsmall size\toval or teardrop shape\tlight brown or black color\tseeds clustered together in a small bunch", 20], "indentation": ["Yes. 'Indentation' has a tangible appearance and is a kind of depression or hollow.\nA few things that are visually similar to 'indentation' but are not 'indentation' are:\tcrater\tdent\thole\tsinkhole\nThere are several useful visual features to tell there is 'indentation' and not similar things in a photo:\tvisible depression or hollow in a surface\tless deep than a hole or a crater\tsharp or gradual change in surface level", 20], "leaf pattern": ["Yes. 'Leaf pattern' has a tangible appearance and is a repetitive design inspired by the shape of leaves.\nA few things that are visually similar to 'leaf pattern' but are not 'leaf pattern' are:\tfloral pattern\tabstract pattern\twave pattern\nThere are several useful visual features to tell there is 'leaf pattern' and not similar things in a photo:\tshape of leaves (oval, pointed or rounded ends, veins)\tgreen, brown, yellow or red colors\trepititon and alternation of patterns", 20], "carrot slice": ["Yes. 'Carrot slice' has a tangible appearance and is a piece of a carrot.\nA few things that are visually similar to 'carrot slice' but are not 'carrot slice' are:\tcucumber slices\tpickle\tsweet potato slices\tzucchini slices\nThere are several useful visual features to tell there is 'carrot slice' and not similar things in a photo:\torange color\tcarrot texture\tcircular shape\twith or without peel", 20], "pavers": ["Yes. 'Pavers' has a tangible appearance and is a type of construction material.\nA few things that are visually similar to 'pavers' but are not 'pavers' are:\tbricks\tconcrete blocks\tcobblestones\nThere are several useful visual features to tell there are 'pavers' and not similar things in a photo:\trectangular in shape\tand a size that fits in hand\tlying flat on the ground\tused to create walkways, patios and outdoor flooring", 20], "front hoof": ["Yes. 'Front hoof' has a tangible appearance and is a part of an animal's foot.\nA few things that are visually similar to 'front hoof' but are not 'front hoof' are:\tBack hoof/Paw\tHuman hand or foot\nThere are several useful visual features to tell there is 'front hoof' and not similar things in a photo:\tcloven (split into two)\thard keratin covering\tlocated on the front feet of some animals, such as horses, cows, deer, and goats.", 20], "pink shoe": ["Yes. 'Pink shoe' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'pink shoe' but are not 'pink shoe' are:\tred shoe\tpurple shoe\torange shoe\tclog\tsandal\nThere are several useful visual features to tell there is 'pink shoe' and not similar things in a photo:\tpink color\tshoe shape\tsole and heel shape\tclosure type, such as laces or buckle", 20], "midair": ["No. 'Midair' is too vague or abstract to be distinguished in a photo.", 20], "peace sign": ["Yes. 'Peace sign' has a tangible appearance and is a hand gesture.\nA few things that are visually similar to 'peace sign' but are not 'peace sign' are:\tvictory sign\trock and roll sign\tnumber 2 sign\nThere are several useful visual features to tell there is 'peace sign' and not similar things in a photo:\ttwo fingers raised in a 'V' shape\tpalm is facing outward\tfingers are not spread apart or touching\toften used in a context of peace or pacifism", 20], "mirror frame": ["Yes. 'Mirror frame' has a tangible appearance and is a border that surrounds a mirror.\nA few things that are visually similar to 'mirror frame' but are not 'mirror frame' are:\tpicture frame\twindow frame\tdoor frame\tfurniture border\nThere are several useful visual features to tell there is 'mirror frame' and not similar things in a photo:\trectangular or circular shape\tflat or slightly raised surface\tattached to a reflective surface like glass or metal\tdifferent material or color than the surface of the mirror", 20], "purple box": ["Yes. 'Purple box' has a tangible appearance and is a specific color and shape of container.\nA few things that are visually similar to 'purple box' but are not 'purple box' are:\tpurple rectangle\tpurple cube\tpurple bag\nThere are several useful visual features to tell there is a 'purple box' and not similar things in a photo:\tpurple color\tcuboid shape\tflat top and bottom, rectangular sides and edges\ttop can be removed or opened for storing objects", 20], "drainage": ["Yes, 'drainage' has a tangible appearance and refers to the system of channels or pipes used to remove water or liquid waste from an area.\nA few things that are visually similar to 'drainage' but are not 'drainage' are:\tgrooves\tchannels\truts\tcanals\nThere are several useful visual features to tell there is 'drainage' and not similar things in a photo:\tpipes or channels on the ground or underground\tgrids, grates, or covers to prevent debris from entering\tlong lines connecting to a larger system or body of water\twater or liquid waste being removed", 20], "concrete ledge": ["Yes. 'Concrete ledge' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'concrete ledge' but are not 'concrete ledge' are:\tbrick ledge\tstone ledge\twood ledge\tmetal ledge\nThere are several useful visual features to tell there is 'concrete ledge' and not similar things in a photo:\tmade of concrete or cement\tlinear and rectangular in shape\tmost often found on the exterior of buildings as part of the foundation or support structure.", 20], "crayon": ["Yes. 'Crayon' has a tangible appearance and is a type of drawing tool.\nA few things that are visually similar to 'crayon' but are not 'crayon' are:\tmarker\tcolored pencils\tchalk\tpaint\nThere are several useful visual features to tell there is 'crayon' and not similar things in a photo:\tshort and stubby cylindrical shape.smooth texture\tbright and solid colors\twaxy appearance\tno sharp tip for precise drawing.", 20], "gold car": ["Yes. 'Gold car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'gold car' but are not 'gold car' are:\tyellow car\tchrome car\tsilver car\tbrass-plated car\nThere are several useful visual features to tell there is 'gold car' and not similar things in a photo:\tgolden-color painted exterior or accents\tshiny and reflective surface\tsleek and modern design\tcar make and model", 20], "faucet handle": ["Yes. 'Faucet handle' has a tangible appearance and is a part of a plumbing fixture.\nA few things that are visually similar to 'faucet handle' but are not 'faucet handle' are:\tdoor knob\tdrawer pull\tlight switch knob\nThere are several useful visual features to tell there is 'faucet handle' and not similar things in a photo:\tfound on a plumbing fixture, usually a sink or shower\ttwo distinct knobs for hot and cold water\tcontrol water flow\toriented horizontally or vertically", 20], "blenders": ["Yes. 'Blenders' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'blenders' but are not 'blenders' are:\tfood processors\tjuicers\tmixers\tgrinders\nThere are several useful visual features to tell there is 'blenders' and not similar things in a photo:\ttall and cylindrical body\twith a pouring spout and a lid\tblade at the bottom of the container\tcontrol buttons or knobs\ton a countertop or table\ttop may have a hole for adding ingredients or a cap\titem could be plugged in or have a cord", 20], "rough": ["No. 'Rough' is too vague or abstract to be distinguished in a photo.", 20], "orange handle": ["Yes. 'Orange handle' has a tangible appearance and is a specific color and shape of a handle.\nA few things that are visually similar to 'orange handle' but are not 'orange handle' are:\tyellow handle\tred handle\tplastic handle\torange mug handle\nThere are several useful visual features to tell there is 'orange handle' and not similar things in a photo:\tbright orange color\telongated shape grips a tool or appliance", 20], "wood log": ["Yes. 'Wood log' has a tangible appearance and is a type of tree trunk that has been cut and prepared for specific uses.\nA few things that are visually similar to 'wood log' but are not 'wood log' are:\tbranch\tstick\tbark\tfence post\nThere are several useful visual features to tell there is 'wood log' and not similar things in a photo:\tcylindrical shape\tsmooth bark or completely stripped bark\tcut ends with visible tree rings\tno branches or twigs visible", 20], "back window": ["Yes. 'Back window' has a tangible appearance and is typically a part of a car or other vehicle.\nA few things that are visually similar to 'back window' but are not 'back window' are:\tshop window\tmirror\tpicture frame\nThere are several useful visual features to tell there is 'back window' and not similar things in a photo:\trectangular in shape\tlocated at the back of a vehicle\tmay have a defroster or wiper attached to it\tmay have tinted or colored glass", 20], "cake donut": ["Yes. 'Cake donut' has a tangible appearance and is a type of donut.\nA few things that are visually similar to 'cake donut' but are not 'cake donut' are:\tyeast donut\tbagel\tchurro\nThere are several useful visual features to tell there is 'cake donut' and not similar things in a photo:\tcircular shape\twith a hole or without\ta bit denser in texture than yeast donuts\tmade with a cake-like batter\tfried\twith a matte finish or with a powdered sugar coating.", 20], "cake server": ["Yes. 'Cake server' has a tangible appearance and is a kind of utensil.\nA few things that are visually similar to 'cake server' but are not 'cake server' are:\tknife\tspatula\tpie server\tladle\ttongs\nThere are several useful visual features to tell there is 'cake server' and not similar things in a photo:\ttriangular shape or a serrated edge\tlong handle\tsimilar to a spatula but with a thick blade for cutting and serving cakes or pies.", 20], "colorful flags": ["Yes. 'Colorful flags' has a tangible appearance and is a kind of decoration or symbol.\nA few things that are visually similar to 'colorful flags' but are not 'colorful flags' are:\tbunting\tballoons\tstreamers\t\nThere are several useful visual features to tell there is 'colorful flags' and not similar things in a photo:\trectangular or triangular\tshapes\tvibrant or bright colors\tusually bearing symbols or designs\thanging from poles, walls, or buildings.", 20], "powerline": ["Yes. 'Powerline' has a tangible appearance and is a type of electrical system used for the transmission and distribution of electricity.\nA few things that are visually similar to 'powerline' but are not 'powerline' are:\ttelephone line\tcable line\tbarbed wire fence\nThere are several useful visual features to tell there is 'powerline' and not similar things in a photo:\ttwo or more tall metal poles carrying wires\tonce or several parallel wirings\telectrical insulators at regular intervals", 20], "train stop": ["Yes. 'Train stop' has a tangible appearance and is a physical location where trains stop.\nA few things that are visually similar to 'train stop' but are not 'train stop' are:\ttunnel\trailway crossing\tbridge\nThere are several useful visual features to tell there is 'train stop' and not similar things in a photo:\tplatform for people to wait on\ta sign with the name of the station\tan overhead structure for shelter\ttracks for the train to stop on\ttrain arriving or departing from the station.", 20], "half pipe": ["Yes. 'Half pipe' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'half pipe' but are not 'half pipe' are:\tramp\tstaircase\t\nThere are several useful visual features to tell there is 'half pipe' and not similar things in a photo:\tsymmetrical U-shaped ramp\tcurved transition\tfrom 8 to 50 feet in width and 3 to 24 feet in height.", 20], "building windows": ["Yes. 'Building windows' has a tangible appearance and refers to the openings in the walls of a building that allow light and air to enter.\nA few things that are visually similar to 'building windows' but are not 'building windows' are:\tpainted squares\tonion slices with translucent skin\tscreen doors\tdecorative panels\nThere are several useful visual features to tell there are 'building windows' and not similar things in a photo:\ttransparent or translucent material\trectangle or square shape\tframed in metal or wood\tmultiple panes of glass in the frame\tlocation in a wall", 20], "teams": ["No. 'Teams' is too vague or abstract to be distinguished in a photo.", 20], "bundles": ["Yes. 'Bundles' have a tangible appearance and refer to a group of objects tied together in a bundle.\nA few things that are visually similar to 'bundles' but are not 'bundles' are:\theap\tpile\tstack\nThere are several useful visual features to tell there is 'bundles' and not similar things in a photo:\tobjects tied together\twith a string, rope, or another material\tneat and organized group", 20], "pizza dough": ["Yes. 'Pizza dough' has a tangible appearance and is a type of dough.\nA few things that are visually similar to 'pizza dough' but are not 'pizza dough' are:\tbread dough\tpie crust dough\tcroissant dough\nThere are several useful visual features to tell there is 'pizza dough' and not similar things in a photo:\tfloury or powdery surface\tsometimes flattened and circular in shape\tstretchy and elastic texture\tbrownish color when cooked", 20], "orange surfboard": ["Yes, 'orange surfboard' is a visually concrete concept, it refers to a tangible object with specific visual features.\nA few things that are visually similar to 'orange surfboard' but are not 'orange surfboard' are:\torange kayak\torange windsurf\tboard\tshark\nThere are several useful visual features to distinguish 'orange surfboard' from other things in a photo:\t\n- elongated shape with a pointed end and a round nose\n- flat surface\n- fins at the bottom \n- made of foam or fiberglass\n- used for surfing \n- typically used near the ocean \n- size (generally around 7-10 feet long and 20-30 inches wide)", 20], "hoses": ["Yes. 'Hoses' has a tangible appearance and is a kind of flexible tube used to convey liquids or gases.\nA few things that are visually similar to 'hoses' but are not 'hoses' are:\telectrical cords\tpipes\twires\tvines\nThere are several useful visual features to tell there is 'hoses' and not similar things in a photo:\tflexibility\tribbed or smooth surface\ttransparency or opaque\thave a nozzle or connector at the end\thang or coiled up in a loop", 20], "cubicle": ["Yes. 'Cubicle' has a tangible appearance and is a type of workspace.\nA few things that are visually similar to 'cubicle' but are not 'cubicle' are:\tdesk\tworkstation\ttable\troom\tpartition\nThere are several useful visual features to tell there is 'cubicle' and not similar things in a photo:\tenclosed workspace\tseparated from other cubicles by walls or partitions\tdesk or workspace with a computer or other office supplies", 20], "orange baseball cap": ["Yes. 'Orange baseball cap' has a tangible appearance and is a specific type of headwear.\nA few things that are visually similar to 'orange baseball cap' but are not 'orange baseball cap' are:\torange beanie\torange headband\torange visor\nThere are several useful visual features to tell there is 'orange baseball cap' and not similar things in a photo:\trounded crown with a visor\torangish color\toften with a logo or writing on the front or side\tworn on the head and covering the top and sometimes the back of the head", 20], "chicken sandwich": ["Yes. 'Chicken sandwich' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'chicken sandwich' but are not 'chicken sandwich' are:\tburgers\twraps\tpaninis\thot dogs\nThere are several useful visual features to tell there is 'chicken sandwich' and not similar things in a photo:\tloaf of bread\tbreaded and fried chicken breast\tlettuce and tomato\tmayonnaise or sauce", 20], "pass": ["No. 'Pass' is too vague or abstract to be distinguished in a photo.", 20], "grey floor": ["Yes. 'Grey floor' has a tangible appearance.\nA few things that are visually similar to 'grey floor' but are not 'grey floor' are:\tgray carpet\tgray tile\tgray concrete\tgray paving stones\nThere are several useful visual features to tell there is 'grey floor' and not similar things in a photo:\tflat surface\tsmooth texture\tgray color", 20], "leather catcher": ["No. 'Leather catcher' is too vague or abstract to be distinguished in a photo.", 20], "king": ["No. 'King' is too vague or abstract to be distinguished in a photo.", 20], "pouch": ["Yes. 'Pouch' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'pouch' but are not 'pouch' are:\twallet\tbag\tpurse\ttote\nThere are several useful visual features to tell there is 'pouch' and not similar things in a photo:\tsmall and compact size\tsoft and flexible material\tdrawstring or zippered closure\tpieces of fabric or leather stitched together with one or more compartments.", 20], "bird leg": ["Yes. 'Bird leg' has a tangible appearance and is a limb of a bird.\nA few things that are visually similar to 'bird leg' but are not 'bird leg' are:\tduck leg\tchicken leg\tturkey leg\teagle talon\nThere are several useful visual features to tell there is 'bird leg' and not similar things in a photo:\tscaled or feathered skin\tthin and elongated shape\twith or without claws or talons\tjointed appearance\tvariety of colors and patterns depending on the bird species", 20], "donut hole": ["Yes. 'Donut hole' has a tangible appearance and is a small, round piece of pastry.\nA few things that are visually similar to 'donut hole' but are not 'donut hole' are:\tmunchkins\tfrom Dunkin' Donuts\tchurros\tbeignets\ttater tots\nThere are several useful visual features to tell there is 'donut hole' and not similar things in a photo:\tround shape\tlight golden brown color\toften covered with powdered sugar or glaze\tdense, soft texture", 20], "metal fire": ["Yes. 'Metal fire' has a tangible appearance and shows molten metal.\nA few things that are visually similar to 'metal fire' but are not 'metal fire' are:\tfireworks\tfire flame\thot liquid metal\tlava\nThere are several useful visual features to tell there is 'metal fire' and not similar things in a photo:\tmolten metal\tbright white or orange color\tmetallic texture or shine", 20], "flush": ["No. 'Flush' is too vague or abstract to be distinguished in a photo.", 20], "gold design": ["No. 'Gold design' is too vague or abstract to be distinguished in a photo. \n\nIf we interpret 'gold design' as a design or pattern that includes the color gold, then: \nA few things that are visually similar to 'gold design' but are not 'gold design' are:\tYellow design\tcopper design\tbronze design\nThere are several useful visual features to tell there is 'gold design' and not similar things in a photo:\tthe use of the color gold in the design or pattern\tshiny or metallic appearance of the gold element in the design\tor pattern.", 20], "tulip": ["Yes. 'Tulip' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'tulip' but are not 'tulip' are:\trose\tdaffodil\tlily\tcarnation\nThere are several useful visual features to tell there is 'tulip' and not similar things in a photo:\t\ncup-shaped flower with 6 petals\thinged growth at the stem of each leaf\tusually bright and vibrant colors such as red, pink, yellow, orange, and purple\twith long green stem\tand drooping head", 20], "concrete pad": ["Yes. 'Concrete pad' has a tangible appearance and is a type of flat structure made of concrete.\nA few things that are visually similar to 'concrete pad' but are not 'concrete pad' are:\tsidewalk\tdriveaway\tpatio\tdeck\tfloor\nThere are several useful visual features to tell there is 'concrete pad' and not similar things in a photo:\tflat surfacedominantly grey in colorrough texture of concrete materialrectangular or square in shapecan be surrounded by grass, dirt, or other materials.", 20], "square light": ["Yes. 'Square light' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'square light' but are not 'square light' are:\trectangular light panel\tceiling tile\tlight switch cover\ttablet or phone screen\nThere are several useful visual features to tell there is 'square light' and not similar things in a photo:\tsquare-shaped\tlight-emitting surface\tfixed to a ceiling or a wall", 20], "leather boots": ["Yes. 'Leather boots' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'leather boots' but are not 'leather boots' are:\tsneakers\thiking shoes\train boots\twinter boots\nThere are several useful visual features to tell there is 'leather boots' and not similar things in a photo:\tmade of leather or another shiny material\tsmooth surface with minimal details\tlaces or zippers for closure\thigh ankle coverage", 20], "cloves": ["Yes. 'Cloves' has a tangible appearance and is a kind of spice.\nA few things that are visually similar to 'cloves' but are not 'cloves' are:\tnutmeg\tmace\tcinnamon sticks\nThere are several useful visual features to tell there is 'cloves' and not similar things in a photo:\tsmall and have a nail shape\tdark brown with a rough surface\tintensely aromatic scent.", 20], "stripe shirt": ["Yes. 'Stripe shirt' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'stripe shirt' but are not 'stripe shirt' are:\tcheckered shirt\tpolka-dot shirt\tsolid-colored shirt\t\nThere are a few useful visual features to tell there is a 'stripe shirt' and not similar things in a photo:\tthick or thin, horizontal or vertical lines or stripes\tstripe placement on a shirt, such as across the chest or sleeves\tcolor combination of stripes and base color of the shirt", 20], "bed pillow": ["Yes. 'Bed pillow' has a tangible appearance and is a type of pillow used for sleeping.\nA few things that are visually similar to 'bed pillow' but are not 'bed pillow' are:\tcouch cushion\tdecorative pillow\tbody pillow\nThere are several useful visual features to tell there is 'bed pillow' and not similar things in a photo:\trectangular or square in shape\twhite or off-white color\tcovered with a pillowcase or sham\ton a bed or mattress", 20], "metal sculpture": ["Yes. 'Metal sculpture' has a tangible appearance and refers to a type of art.\nA few things that are visually similar to 'metal sculpture' but are not 'metal sculpture' are:\tmetal tools\tmetal machinery\tmetal furniture\tmetal architecture\nThere are several useful visual features to tell there is 'metal sculpture' and not similar things in a photo:\tintricate design or shape\tunique or recognizable form\tsmooth or textured surface\tthat it is clearly intended as an artistic piece, not a functional object.", 20], "rusty chain": ["Yes. 'Rusty chain' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'rusty chain' but are not 'rusty chain' are:\trope\twire\tfishing line\those\nThere are several useful visual features to tell there is 'rusty chain' and not similar things in a photo:\tmetal links\tred-brown color\tvisible signs of corrosion and rust", 20], "blue chair": ["Yes. 'Blue chair' has a tangible appearance and is a specific type of furniture.\nA few things that are visually similar to 'blue chair' but are not 'blue chair' are:\tblue couch\tblue stool\tblue armchair\tpatio furniture\nThere are several useful visual features to tell there is 'blue chair' and not similar things in a photo:\t\n- a seat, backrest, and four legs (or some variation, like a pedestal)\n- has a distinct blue color or shade\n- the size and shape of the seat might be useful for distinguishing between chairs and other furniture", 20], "fryer": ["Yes. 'Fryer' has a tangible appearance and is a cooking appliance.\nA few things that are visually similar to 'fryer' but are not 'fryer' are:\toven\ttoaster\tmicrowave\tcoffee maker\tair fryer\nThere are several useful visual features to tell there is 'fryer' and not similar things in a photo:\toil container\tfrying basket\ttemperature control settings\tlid or cover\tpower cord\tdigital display", 20], "brick pavers": ["Yes. 'Brick pavers' has a tangible appearance and is a kind of building material.\nA few things that are visually similar to 'brick pavers' but are not 'brick pavers' are:\tconcrete pavers\tcobblestones\tbricks\nThere are several useful visual features to tell there is 'brick pavers' and not similar things in a photo:\trectangular in shape\tred or brown in color\trough or textured surface\tarranged in a pattern on the ground or a wall", 20], "round tennis ball": ["Yes. 'Round tennis ball' has a tangible appearance and is a specific type of ball used in tennis.\nA few things that are visually similar to 'round tennis ball' but are not 'round tennis ball' are:\tbaseball\tsoftball\tgolf ball\tbouncy ball\nThere are several useful visual features to tell there is 'round tennis ball' and not similar things in a photo:\tyellow or white in color\tfelt-like texture\tbouncing on a tennis court\tnumbered or lettered for identification", 20], "pink candle": ["Yes. 'Pink candle' has a tangible appearance and is a type of candle.\nA few things that are visually similar to 'pink candle' but are not 'pink candle' are:\tred candle\tpurple candle\twax crayon\tpen\t\nThere are several useful visual features to tell there is 'pink candle' and not similar things in a photo:\tpink color\twick\tflame\tburning\twax melting", 20], "grills": ["Yes. 'Grills' has a tangible appearance and usually refers to a cooking device with a metal grating surface.\nA few things that are visually similar to 'grills' but are not 'grills' are:\tmetal fences\tmetal screens\nThere are several useful visual features to tell there is 'grills' and not similar things in a photo:\tmetal grating surface\theat source (such as fire or gas)\tfood being cooked on the grating surface\tsmoke or steam rising from the grilling food.", 20], "concrete blocks": ["Yes. 'Concrete blocks' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'concrete blocks' but are not 'concrete blocks' are:\tbricks\tstone blocks\tbreeze blocks\nThere are several useful visual features to tell there is 'concrete blocks' and not similar things in a photo:\trectangular shape\tgray or beige color\trough and porous texture\tstacked on top of each other to create a wall or structure", 20], "spoon table": ["Yes. 'Spoon table' has a tangible appearance and refers to a specific type of table.\nThere are no things that are visually similar to 'spoon table' but are not 'spoon table'. \nUseful visual features for identifying a 'spoon table' in a photo are:\ta table with a spoon-shaped top or legs.", 20], "cake stand": ["Yes. 'Cake stand' has a tangible appearance and is a type of tableware.\nA few things that are visually similar to 'cake stand' but are not 'cake stand' are:\tplate\ttray\tbowl\tpedestal\nThere are several useful visual features to tell there is 'cake stand' and not similar things in a photo:\t\ntall pedestal or base\tflat surface for holding a cake or dessert\tmultiple tiers or levels in some cases\thandles or grips for easy carrying and moving.", 20], "brown hat": ["Yes. 'Brown hat' has a tangible appearance.\nA few things that are visually similar to 'brown hat' but are not 'brown hat' are:\tblack hat\tfedora\tsunhat\tcap\nThere are several useful visual features to tell there is 'brown hat' and not similar things in a photo:\tbrown color\tfor head-wear\twith brim or without brim\tdifferent shapes, like top hat or cowboy hat.", 20], "rocky wall": ["Yes. 'Rocky wall' has a tangible appearance and refers to a wall made of rocks or with a rocky surface.\nA few things that are visually similar to 'rocky wall' but are not 'rocky wall' are:\tconcrete wall\tbrick wall\tdrywall\tmountain\nThere are several useful visual features to tell there is 'rocky wall' and not similar things in a photo:\trough and uneven surface\tlayers of different colored rocks\tjagged edges or cracks\tnatural formations like caves or crevices", 20], "goalie": ["Yes. 'Goalie' has a tangible appearance and is a position in certain sports.\nA few things that are visually similar to 'goalie' but are not 'goalie' are:\tfootball player\tbasketball player\tbaseball player\thockey player without goalie pads\nThere are several useful visual features to tell there is 'goalie' and not similar things in a photo:\twearing unique and different colored uniform than other players\tunique protective gear particularly on legs and arms\tgloves to catch and hold the ball or puck\tcrouching position with hands outstretched to block the goal", 20], "octopus kite": ["Yes. 'Octopus kite' has a tangible appearance and is a type of kite.\nA few things that are visually similar to 'octopus kite' but are not 'octopus kite' are:\tdragon kite\tbird kite\tbutterfly kite\tdiamond kite\nThere are several useful visual features to tell there is 'octopus kite' and not similar things in a photo:\teight long tentacles or arms\tbulging head that looks like a purse or pouch\tcolorful and patterned body\tflying high in the sky\twith a string or line attached to it.", 20], "shadow skier": ["Yes. 'Shadow skier' has a tangible appearance and can be captured in a photo.\nA few things that are visually similar to 'shadow skier' but are not 'shadow skier' are:\ta ghost cast on snow\ta tree cast on snow\ta rock cast on snow\nThere are several useful visual features to distinguish 'shadow skier' from the listed similar things in a photo:\tthe shape of a human body with skis or poles\tclothing and gear of a skier\tcrisp edges and lines of the shadow\tdirectional patterns on the shadow's surface that suggest movement", 20], "wall paper": ["Yes. 'Wall paper' has a tangible appearance and is a type of covering for walls.\nA few things that are visually similar to 'wall paper' but are not 'wall paper' are:\tpaint\tmurals\tstickers\tdecal\nThere are several useful visual features to tell there is 'wall paper' and not similar things in a photo:\trepetitive patterns and designs\tstripes or other geometric patterns\tsubdued or bold colors\thanging vertically on a wall", 20], "car mirror": ["Yes. 'Car mirror' has a tangible appearance and is a type of automotive part.\nA few things that are visually similar to 'car mirror' but are not 'car mirror' are:\twindows\twindshield\tglasses\nThere are several useful visual features to tell there is 'car mirror' and not similar things in a photo:\tlocated on the exterior of a car\tusually small and rectangular or circular in shape\treflection of the surroundings or the car itself in the mirror.", 20], "bindings": ["Yes. 'Bindings' has a tangible appearance and refers to the fastenings that hold a book together.\nA few things that are visually similar to 'bindings' but are not 'bindings' are:\tstaples\tclips\tpins\t\nThere are several useful visual features to tell there is 'bindings' and not similar things in a photo:\tcover of a book\tintricate design or texture\tstitched or glued together\tmaterial such as leather or cloth", 20], "folders": ["Yes. 'Folders' has a tangible appearance and is a type of organizational tool.\nA few things that are visually similar to 'folders' but are not 'folders' are:\tpapers\tbooks\tbinders\tenvelopes\nThere are several useful visual features to tell there is 'folders' and not similar things in a photo:\trectangular shape\twith tabs for labeling\tpaper or cardboard material\tan open top for inserting and removing papers\tmultiple folders arranged together for organization", 20], "pinky": ["Yes. 'Pinky' has a tangible appearance and refers to the smallest finger of a hand.\nThere are no things that are visually similar to 'pinky' but are not 'pinky'.\nUseful visual features for distinguishing 'pinky' from other fingers in the hand are: being smaller, located on the far right side of the hand (assuming it is palm up), and having its own independent movements.", 20], "luggages": ["Yes. 'Luggages' has a tangible appearance and refers to bags or suitcases used for travel.\nA few things that are visually similar to 'luggages' but are not 'luggages' are:\tbackpacks\tbriefcases\tpurses\tbaskets\nThere are several useful visual features to tell there is 'luggages' and not similar things in a photo:\thaving wheels\thaving handles\tor straps\tzippers or clasps\tfor travel purposes.", 20], "bare spot": ["Yes. 'Bare spot' has a tangible appearance and refers to an area where there is no vegetation or cover.\nA few things that are visually similar to 'bare spot' but are not 'bare spot' are:\tshadow\twet spot\tdirt pile\nThere are several useful visual features to tell there is 'bare spot' and not similar things in a photo:\tno vegetation\tsoil texture is visible\tno cover like rocks or debris", 20], "furry bear": ["Yes. 'Furry bear' has a tangible appearance and refers to a kind of animal.\nA few things that are visually similar to 'furry bear' but are not 'furry bear' are:\tdog\tcow\tpig\tfur coat\nThere are several useful visual features to tell there is 'furry bear' and not similar things in a photo:\tround and fluffy ears\tfur all over the body\tsharp claws\tfacial structure and nose shape\tresemble a bear species, such as a grizzly or a panda.", 20], "woma": ["No. 'Woma' is too vague or abstract to be distinguished in a photo. It's possible that you meant 'Wombat', so I'll provide an answer for that instead.\nIf you meant 'Wombat':\nYes. 'Wombat' has a tangible appearance and is an animal.\nA few things that are visually similar to 'wombat' but are not 'wombat' are:\tbear\tkangaroo\tgroundhog\nThere are several useful visual features to tell there is 'wombat' and not similar things in a photo:\tlarge and furry\tbody close to the ground\tshort legs and small feet\tpointy ears\tsquare-shaped nose", 20], "credit": ["No. 'Credit' is too vague or abstract to be visually concrete or distinguished in a photo.", 20], "door sedan": ["Yes. 'Door sedan' has a tangible appearance and is a type of car.\nA few things that are visually similar to 'door sedan' but are not 'door sedan' are:\ttruck\tcrossover\tSUV\tsports car\nThere are several useful visual features to tell there is 'door sedan' and not similar things in a photo:\tfour doors\tforward-facing seats\tenough space for 4-5 passengers\tenclosed passenger compartment\tseparate trunk and engine compartments", 20], "ski glove": ["Yes. 'Ski glove' has a tangible appearance and is a type of handwear.\nA few things that are visually similar to 'ski glove' but are not 'ski glove' are:\twork glove\tmittens\tmotorcycle glove\trubber glove\twinter glove\nThere are several useful visual features to tell there is 'ski glove' and not similar things in a photo:\twaterproof or water-resistant material\tinsulated fabric or lining\twrist straps or closures\telongated cuffs to fit over jacket sleeves\torbits around thumb and fingers\tfor skiing or snowboarding use.", 20], "metal barrier": ["Yes. 'Metal barrier' has a tangible appearance and is a type of obstruction.\nA few things that are visually similar to 'metal barrier' but are not 'metal barrier' are:\tfences\tgates\twalls\nThere are several useful visual features to tell there is 'metal barrier' and not similar things in a photo:\tmade of metal, such as iron or steel\tstraight, rigid bars or panels\twith gaps or holes allowing visibility and ventilation\tused to block off an area or control access.", 20], "surge protector": ["Yes. 'Surge protector' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'surge protector' but are not 'surge protector' are:\tpower strip\textender cord\tadaptor\nThere are several useful visual features to tell there is 'surge protector' and not similar things in a photo:\thas multiple outlets to plug in devices\thas a circuit breaker or on/off switch\tto protect electronic devices from voltage spikes or surges\thas a warranty against damage from power surges or short-circuits may have indicator lights to show if it's properly grounded or working", 20], "paneling": ["Yes. 'Paneling' has a tangible appearance and refers to a type of interior finish.\nA few things that are visually similar to 'paneling' but are not 'paneling' are:\twallpaper\ttile\tmosaic\twood carving\nThere are several useful visual features to tell there is 'paneling' and not similar things in a photo:\twooden boards or panels covering the wall or ceiling\tsmooth surface\thorizontal, vertical or diagonal alignment of the panels or boards\tnatural wood grain or stain color.", 20], "gray rock": ["Yes. 'Gray rock' has a tangible appearance and is a type of rock.\nA few things that are visually similar to 'gray rock' but are not 'gray rock' are:\tconcrete\tpavement\tashphalt\tmetal\nThere are several useful visual features to tell there is 'gray rock' and not similar things in a photo:\tnatural appearance\twith rough or porous texture\tgray or light gray color\tno distinct patterns or markings", 20], "colt": ["Yes. 'Colt' has a tangible appearance and is a young male horse.\nA few things that are visually similar to 'colt' but are not 'colt' are:\tpony\tzebra\tdonkey\nThere are several useful visual features to tell there is 'colt' and not similar things in a photo:\tyoung male horse\tsmaller size than a full-grown horse\tthin legs\tand a slim body with a sleek coat\tof brown, bay, black, or chestnut\tcolorful mane and tail", 20], "walking sign": ["Yes. 'Walking sign' has a tangible appearance and is a type of signboard.\nA few things that are visually similar to 'walking sign' but are not 'walking sign' are:\tcrosswalk sign\trunning sign\tbike lane sign\tarrow sign\nThere are several useful visual features to tell there is 'walking sign' and not similar things in a photo:\thuman walking silhouette\tgreen and white colors\tpedestrian symbol (walking person)\thand indicating walking direction (right or left)", 20], "window trim": ["Yes. 'Window trim' has a tangible appearance and refers to the decorative molding or framing around a window.\nA few things that are visually similar to 'window trim' but are not 'window trim' are:\tdoors\tframes\tmirrors\nThere are several useful visual features to tell there is 'window trim' and not similar things in a photo:\tframing or molding around the edge of the window\tdecorative patterns or designs\tintersection between the window and the surrounding wall or frame\tmaterial of the trim (wood, metal, plastic, etc.)", 20], "color gray": ["Yes. 'Color gray' has a tangible appearance and can be observed visually.\nThere are no things similar to 'color gray' that are not 'color gray'.\nThere are no useful visual features for distinguishing 'color gray' from the listed similar things in a photo as there are no similar things to compare it to.", 20], "tower clock": ["Yes. 'Tower clock' has a tangible appearance and is a type of clock that is typically located in a tower.\nA few things that are visually similar to 'tower clock' but are not 'tower clock' are:\tmantel clock\twristwatch\tstopwatch\t\nThere are several useful visual features to tell there is 'tower clock' and not similar things in a photo:\tlocated in a tall tower or building\tlarge\tdials or faces with Roman numeral hours\tbells or chimes", 20], "handle umbrella": ["Yes, 'handle umbrella' has a visually concrete concept and is a type of umbrella.\nA few things that are visually similar to 'handle umbrella' but are not 'handle umbrella' are:\tparasol\ttent\tawning\nThere are several useful visual features to tell there is a 'handle umbrella' and not similar things in a photo:\tcircular canopy made of waterproof fabric\tmetal frame mechanism\ta grip situated in the center of the canopy to hold onto\twhile walking or standing still", 20], "building structure": ["Yes. 'Building structure' has a tangible appearance and refers to the physical arrangement of a building.\nA few things that are visually similar to 'building structure' but are not 'building structure' are:\ttents\tmobile homes\ttrailers\tcottages\nThere are several useful visual features to tell there is 'building structure' and not similar things in a photo:\tfoundation\twalls\troof\tdoor and window openings\tstairs and balconies\tdecorative elements (e.g., columns, arches)", 20], "model train": ["Yes. 'Model train' has a tangible appearance and is a replica of a train.\nA few things that are visually similar to 'model train' but are not 'model train' are:\ttoy car\ttoy plane\ttoy boat\ttoy bike\ttoy bus\nThere are several useful visual features to tell there is 'model train' and not similar things in a photo:\ttrain-like appearance\ttracks\trealistic details\tsmall size relative to surroundings\tmoving along the tracks or with wheels and smoke if it's an electric model train.", 20], "nosecone": ["Yes. 'Nosecone' has a tangible appearance and is a pointed structure on the front of an aircraft or missile.\nA few things that are visually similar to 'nosecone' but are not 'nosecone' are:\tsharp end of a pencil\tor just a point of an object\nThere are several useful visual features to tell there is 'nosecone' and not similar things in a photo:\tpointed structure\ton the front of an aircraft or missile\tmetallic or matte surface\tsymmetric along a central axis", 20], "milk crate": ["Yes. 'Milk crate' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'milk crate' but are not 'milk crate' are:\tstorage container\twire basket\tplastic bin\t\nThere are several useful visual features to tell there is 'milk crate' and not similar things in a photo:\tsquare shape\twith handles on opposite sides\tgrid-like pattern on sides and bottom\tof plastic construction\twith open spaces between the grids", 20], "bushy tree": ["Yes. 'Bushy tree' has a tangible appearance and refers to a tree with a large and dense crown.\nA few things that are visually similar to 'bushy tree' but are not 'bushy tree' are:\tshrub\tlarge plant\thedge\nThere are several useful visual features to tell there is 'bushy tree' and not similar things in a photo:\tcrown shape\tfullness and density of foliage and branches\tvisible trunk and roots\tlarger size than surrounding plants\tor location in a forest or park to see that it is a tree", 20], "night light": ["Yes. 'Night light' has a tangible appearance and is a type of lamp.\nA few things that are visually similar to 'night light' but are not 'night light' are:\tdesk lamp\ttable lamp\tfloor lamp\tlight bulb\nThere are several useful visual features to tell there is 'night light' and not similar things in a photo:\tsmall size or compact shape\tsuitable for use in a bedroom or nursery\tsoft or dim lighting pattern, often with a warmer hue than regular light bulbs\tdesigned to emit light during the night to provide comfort or guidance\toften designed with a kid-friendly or decorative aspect", 20], "dogs tail": ["Yes. 'Dog's tail' has a tangible appearance and is a part of a dog's body.\nA few things that are visually similar to 'dog's tail' but are not 'dog's tail' are:\tcat's tail\tfox's tail\traccoon's tail\tsquirrel's tail\nThere are several useful visual features to tell there is 'dog's tail' and not similar things in a photo:\tpuffy and furry\tif fur color matches the dog's body color\tusually hangs down, but may wag or curl up depending on dog's mood or activity level.", 20], "button man": ["No. 'Button man' is too vague or abstract to be distinguished in a photo. There is no exact definition or imagery associated with this term.", 20], "wet dog": ["Yes. 'Wet dog' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'wet dog' but are not 'wet dog' are:\tdog covered in mud\tdog submerged in water\tdog with wet fur\nThere are several useful visual features to tell there is 'wet dog' and not similar things in a photo:\tshiny, wet fur\twater droplets on the fur\tclean and groomed appearance\twater or bathing accessories nearby", 20], "sandy shore": ["Yes. 'Sandy shore' has a tangible appearance and can typically refer to the coastline of a beach or an ocean with sandy terrain.\nA few things that may seem visually similar to 'sandy shore' but are not 'sandy shore' are:\tyellow-brick road\tsand dunes\tdesert landscape\nThere are some useful visual features to distinguish 'sandy shore' from the listed similar things in a photo:\t\n- the presence of water (ocean or sea)\n- the wet sand texture\n- the sound of waves when we look at the sea\n- horizon line between the sea and sky", 20], "power box": ["Yes. 'Power box' has a tangible appearance and is a box containing electrical equipment.\nA few things that are visually similar to 'power box' but are not 'power box' are:\tjunction box\tswitchboard\telectrical panel\tconduit box\nThere are several useful visual features to tell there is 'power box' and not similar things in a photo:\tmetal or plastic box\twith doors or lids\tlabelled with high voltage or electrical warnings\twires or cables running into or out of the box", 20], "shadow giraffe": ["No. 'Shadow giraffe' is too vague or abstract to be distinguished in a photo.", 20], "silver scissors": ["Yes. 'Silver scissors' has a tangible appearance.\nA few things that are visually similar to 'silver scissors' but are not 'silver scissors' are:\tknives\tnail clippers\thair clippers\ttweezers\nThere are several useful visual features to tell there are 'silver scissors' and not similar things in a photo:\ttwo blades with a circular cutting edge\tsharp, pointed tips\tfinger holes for grasping\tthe color silver or shiny metallic surface", 20], "gull": ["Yes. 'Gull' has a tangible appearance and is a type of seabird.\nA few things that are visually similar to 'gull' but are not 'gull' are:\ttern\tpelican\talbatross\t\nThere are several useful visual features to tell there is 'gull' and not similar things in a photo:\twhite or grey feathers\tmid-size bird\thooked beak with yellowish or reddish spot\twebbed feet\twith black wingtips", 20], "cement walkway": ["Yes. 'Cement walkway' has a tangible appearance and is a type of pathway made of cement.\nA few things that are visually similar to 'cement walkway' but are not 'cement walkway' are:\tstone pathway\tasphalt pathway\tdirt pathway\tbrick pathway\nThere are several useful visual features to tell there is 'cement walkway' and not similar things in a photo:\tsmooth surface\tgrey color\trectangular or square shape\tflat surface with no rocks or bumps", 20], "droppings": ["Yes. 'Droppings' has a tangible appearance and is a type of animal waste.\nA few things that are visually similar to 'droppings' but are not 'droppings' are:\tmulch\trocks\tdirt\tor seeds\nThere are several useful visual features to tell there is 'droppings' and not similar things in a photo:\tspecify a particular animal's droppings\tthe shape and size of the droppings\tmay include visible food remnants\tdistinguishable colors like black or green.", 20], "coca cola sign": ["Yes. 'Coca Cola sign' has a tangible appearance and is a type of advertising sign.\nA few things that are visually similar to 'coca cola sign' but are not 'coca cola sign' are:\tpepsi sign\tmcdonald's sign\trestaurant sign\tneon sign\nThere are several useful visual features to tell there is 'coca cola sign' and not similar things in a photo:\tred color\tcursive, swirly lettering\twhite wave appearing under the logo, on red background\ttrademarked Coca Cola logo", 19], "round sticker": ["Yes. 'Round sticker' has a tangible appearance and is a specific shape of adhesive label.\nA few things that are visually similar to 'round sticker' but are not 'round sticker' are:\tcircular stamp\tcandy\tpin button\tdisc-shaped toy\nThere are several useful visual features to tell there is 'round sticker' and not similar things in a photo:\tround shape\tsmooth surface\tglossy appearance\tadhesive back\tpaper or vinyl material with printed or solid color surface", 19], "steel fork": ["Yes. 'Steel fork' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'steel fork' but are not 'steel fork' are:\tspoon\tknife\tchopsticks\ttweezers\nThere are several useful visual features to tell there is 'steel fork' and not similar things in a photo:\tthree or four tines\tpointy and curved tips\thandles on both ends\tmade of steel or metal", 19], "life preservers": ["Yes. 'Life preservers' has a tangible appearance and is a floatation device.\nA few things that are visually similar to 'life preservers' but are not 'life preservers' are:\tswimming rings\tbuoys\tinner tubes\tfloating cushions\nThere are several useful visual features to tell there is 'life preservers' and not similar things in a photo:\tround or donut-shaped\tbrightly colored\twith ropes or straps for attaching to a person or a boat\tcontaining buoyant materials to aid floatation during water emergencies", 19], "gift bag": ["Yes. 'Gift bag' has a tangible appearance and is a type of bag used for gift-giving.\nA few things that are visually similar to 'gift bag' but are not 'gift bag' are:\tpurse\tbackpack\ttote bag\ttrash bag\nThere are several useful visual features to tell there is 'gift bag' and not similar things in a photo:\tlarge enough to fit a gift\titem is wrapped or decorated\tinclusion of handles or straps.", 19], "brown horse": ["Yes. 'Brown horse' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'brown horse' but are not 'brown horse' are:\tdonkey\tzebra\tcow\tantelope\nThere are several useful visual features to tell there is 'brown horse' and not similar things in a photo:\tfour-legged mammal\tmane and tail of hair\tbrown coat with varying shades of dark to light brown\tlong snout with nostrils at the end\tsleek and muscular body structure\tpointed ears and expressive eyes.", 19], "silver pots": ["Yes. 'Silver pots' has a tangible appearance and is a kind of object.\nA few things that are visually similar to 'silver pots' but are not 'silver pots' are:\tsteel pots\taluminum pots\tcopper pots\tmetal bowls\nThere are several useful visual features to tell there is 'silver pots' and not similar things in a photo:\tmade of silver or silver-colored metal\tround or cylindrical shape\twith handles and a lid", 19], "leafy green plant": ["Yes. 'Leafy green plant' has a tangible appearance and refers to a broad category of plants.\nA few things that are visually similar to 'leafy green plant' but are not 'leafy green plant' are:\therbs\tgrass\talgae\tferns\nThere are several useful visual features to tell there is 'leafy green plant' and not similar things in a photo:\tbroad, flat, green leaves\tveins in the leaves\tstems or branches with smaller leaves or flowers\tgrowing from soil or another surface\tmultiple leaves on a single stem or branch.", 19], "cole slaw": ["Yes. 'Cole slaw' has a tangible appearance and is a type of salad.\nA few things that are visually similar to 'cole slaw' but are not 'cole slaw' are:\tpotato salad\tcaesar salad\tgarden salad\tquinoa salad\nThere are several useful visual features to tell there is 'cole slaw' and not similar things in a photo:\tshredded cabbage as the main ingredient\tcreamy dressing, typically mayo-based\tother vegetables or fruits such as carrots, raisins, or apples may be included in the mixture", 19], "metal guardrail": ["Yes. 'Metal guardrail' has a tangible appearance and is a type of safety barrier.\nA few things that are visually similar to 'metal guardrail' but are not 'metal guardrail' are:\tfence\thandrail\tsignposts\nThere are several useful visual features to tell there is 'metal guardrail' and not similar things in a photo:\tmade of metal\ttubular or cylindrical shape\thorizontal bars or wires\tnear the edge of a road or a high platform", 19], "nothing": ["No. 'Nothing' is too vague or abstract to be visually distinguished in a photo. There cannot be anything that is visually similar to 'nothing'.", 19], "scar": ["Yes. 'Scar' has a tangible appearance and is a mark left on the skin after an injury or wound has healed.\nA few things that are visually similar to 'scar' but are not 'scar' are:\tmoles\tfreckles\tbirthmarks\twrinkles\tskin folds\nThere are several useful visual features to tell there is 'scar' and not similar things in a photo:\tdiscolored or different from surrounding skin\ttexture or thickness different from surrounding skin\tirregular shape or jagged edges\tclosely associated with an area of injury or surgical incision.", 19], "orange top": ["Yes. 'Orange top' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'orange top' but are not 'orange top' are:\torange dress\torange t-shirt\torange jacket\nThere are several useful visual features to tell there is an 'orange top' and not similar things in a photo:\ttop or blouse style\tcollar or neckline pattern and shape\tshort or long sleeve design\tcolor is predominantly orange", 19], "walkie talkie": ["Yes. 'Walkie talkie' has a tangible appearance and is a type of handheld radio.\nA few things that are visually similar to 'walkie talkie' but are not 'walkie talkie' are:\tphone\tmicrowave\tothers handheld radios\tremote control\nThere are several useful visual features to tell there is 'walkie talkie' and not similar things in a photo:\tantenna\ttwo-way device\twith buttons\tfor communication\ton/off switch and volume control\tsometimes has LCD display or flashlight", 19], "banner sign": ["Yes. 'Banner sign' has a tangible appearance and is a kind of sign or advertisement.\nA few things that are visually similar to 'banner sign' but are not 'banner sign' are:\tflags\tstreamers\tbuntings\nThere are several useful visual features to tell there is 'banner sign' and not similar things in a photo:\trectangular or elongated shape\twith content or a message, like a logo, slogan, or image\thanging or suspended, usually from a pole or hanger\tvisibly displayed in a public or commercial area", 19], "ski hat": ["Yes. 'Ski hat' has a tangible appearance and is a type of headwear worn while skiing.\nA few things that are visually similar to 'ski hat' but are not 'ski hat' are:\tbeanie\ttuque\tberet\tbaseball cap\nThere are several useful visual features to tell there is 'ski hat' and not similar things in a photo:\tfitted or snug to the head\tcovers the ears or has ear flaps\tthick and warm material, such as wool or fleece\tbright and colorful design or pattern", 19], "sheer curtain": ["Yes. 'Sheer curtain' has a tangible appearance and refers to a specific type of cloth curtain.\nA few things that are visually similar to 'sheer curtains' but are not 'sheer curtains' are:\tregular curtains\twindow blinds\tshade cloth\tmosquito netting\nThere are several useful visual features to tell there is 'sheer curtains' and not similar things in a photo:\ttranslucent or sheer fabric\tmade of thin, lightweight material\tallows light to filter through\thas a soft, flowing appearance\thangs from a rod or curtain rail", 19], "chair seat": ["Yes. 'Chair seat' has a tangible appearance and is a part of a chair.\nA few things that are visually similar to 'chair seat' but are not 'chair seat' are:\tstool cushion\tottoman cap\tcouch pad\tmattress\nThere are several useful visual features to tell there is 'chair seat' and not similar things in a photo:\tattached to a backrest and/or legs\tpadded or upholstered\tspecific shape to fit the human body", 19], "contents": ["No. 'Contents' is too vague or abstract to be distinguished in a photo.", 19], "viewers": ["No. 'Viewers' is too vague or abstract to be distinguished in a photo. It depends on the context in which it is used.", 19], "round base": ["Yes. 'Round base' has a tangible appearance and is a type of support structure.\nA few things that are visually similar to 'round base' but are not 'round base' are:\tcylinder\tblock\twheel\tfrisbee\nThere are several useful visual features to tell there is 'round base' and not similar things in a photo:\tcircular shape\tflat bottom\tsturdy structure\tBase is wider than the top of the object", 19], "model airplane": ["Yes. 'Model airplane' has a tangible appearance and is a miniature replica of an airplane.\nA few things that are visually similar to 'model airplane' but are not 'model airplane' are:\treal airplane\ttoy airplane\tdrone\nThere are several useful visual features to tell there is 'model airplane' and not similar things in a photo:\tsmall size\tdetailed design\tuse of miniature pilots or passengers\tmodeling or construction materials (e.g. plastic, wood)", 19], "sidewalk pedestrians": ["Yes. 'Sidewalk pedestrians' has a tangible appearance and can refer to people walking on a paved path alongside a road.\nA few things that are visually similar to 'sidewalk pedestrians' but are not 'sidewalk pedestrians' are:\tcyclists\tskateboarders\tscooter riders\tcars\nThere are several useful visual features to tell there are 'sidewalk pedestrians' and not similar things in a photo:\tpeople walking on a paved path alongside a road\tor on a wide walkway\twith feet and legs visible\tin various postures and clothing", 19], "wireless keyboard": ["Yes. 'Wireless keyboard' has a tangible appearance.\nA few things that are visually similar to 'wireless keyboard' but are not 'wireless keyboard' are:\twired keyboard\ttablet smartphone laptop\nThere are several useful visual features to tell there is 'wireless keyboard' and not similar things in a photo:\tkeyboard layout (QWERTY, AZERTY, etc.)\tpresence of a USB wireless receiver or Bluetooth logo\tno visible wires or cables (for example, on the back or underside of the keyboard)", 19], "note pad": ["Yes. 'Note pad' has a tangible appearance and is a type of paper-based stationery.\nA few things that are visually similar to 'note pad' but are not 'note pad' are:\tjournal\tnotebook\tdiary\tagenda\nThere are several useful visual features to tell there is 'note pad' and not similar things in a photo:\tloose sheets of paper\tattached at one end\tpotentially with lines for writing or check-boxes\tfor taking notes or making lists\tcould have a hard or soft cover\tusually smaller than a sheet of copy paper.", 19], "potatos": ["Yes. 'Potatoes' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'potatoes' but are not 'potatoes' are:\tonions\tturnips\tbeets\tginger\troot\nThere are several useful visual features to tell there is 'potatoes' and not similar things in a photo:\tbrown or beige skin\tirregular shape\twith eyes or small indents\tno visible sprouts or greens", 19], "toy boat": ["Yes. 'Toy boat' has a tangible appearance and is a type of small boat used for play.\nA few things that are visually similar to 'toy boat' but are not 'toy boat' are:\treal boat\tmodel ship\tbath toy\tsculpture\nThere are several useful visual features to tell there is 'toy boat' and not similar things in a photo:\tsmall and scaled\tdifferent colors and patterns\tplastic material or made of wood\tfloating on water or placed on land.", 19], "bread crumbs": ["Yes. 'Bread crumbs' has a tangible appearance and is a kind of food ingredient.\nA few things that are visually similar to 'bread crumbs' but are not 'bread crumbs' are:\tsawdust\tgrated coconut\tcereal crumbs\nThere are several useful visual features to tell there is 'bread crumbs' and not similar things in a photo:\ttiny pieces of bread\tdry and powdery texture\tlight brown color\tcrumby appearance", 19], "reading": ["No. 'Reading' is too abstract to be given a tangible appearance. It is an activity that involves interpreting written or printed language.\nThere are no things that are visually similar to 'reading' as it is a mental activity rather than a physical object.\nTherefore, there are no useful visual features for distinguishing 'reading' from similar things in a photo, as there are no similar things to compare it to visually.", 19], "grassy lawn": ["Yes. 'Grassy lawn' has a tangible appearance and is an area of land covered with grass.\nA few things that are visually similar to 'grassy lawn' but are not 'grassy lawn' are:\tpark\tfield\tforest\tmeadow\nThere are several useful visual features to tell there is 'grassy lawn' and not similar things in a photo:\tcovered in various shades of green grass\tvisible blades of grass\tneatly trimmed and mown\tshort height of grass blades", 19], "nike": ["No. 'Nike' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we refer to the brand 'Nike', then:\n\nA few things that are visually similar to 'Nike' but are not 'Nike' are:\tAdidas\tUnder Armour\tPuma\tReebok\n\nThere are several useful visual features to tell if a shoe is from the brand 'Nike' and not from a similar brand in a photo:\tthe Nike logo (swoosh)\ton the shoe or box\tthe distinctive font used for the 'Nike' name\tunique color schemes or designs used by the brand", 19], "head scarf": ["Yes. 'Head scarf' has a tangible appearance and is a piece of cloth worn on the head.\nA few things that are visually similar to 'head scarf' but are not 'head scarf' are:\tbandana\thair tie\tscrunchie\that\thood\tshawl\tturban\nThere are several useful visual features to tell there is 'head scarf' and not similar things in a photo:\tfabric wrapped around the head\tpart of an outfit\tor a formal religious garment\ttypically worn by women\tmay cover the hair entirely or partially", 19], "metal lid": ["Yes. 'Metal lid' has a tangible appearance and is a cover for a container made of metal.\nA few things that are visually similar to 'metal lid' but are not 'metal lid' are:\tbottle cap\tcan top\tcookie cutter\tcircular magnet\nThere are several useful visual features to tell there is 'metal lid' and not similar things in a photo:\tMade of metal\tCircular or partially circular in shape\tDesigned to fit on a specific container or opening\tMay have a handle or indentation for gripping\tMay have ridges or grooves for better grip or for stacking", 19], "hide": ["No. 'Hide' is too vague or abstract to be distinguished in a photo.", 19], "blue hoodie": ["Yes. 'Blue hoodie' has a tangible appearance and refers to a specific type of clothing.\nA few things that have a similar appearance to a 'blue hoodie' but are not 'blue hoodie' are:\tblue sweater\tjacket with a hood\tsweatshirt\nThere are several useful visual features to distinguish 'blue hoodie' from the similar things in a photo:\thood attached to the back or neckline of the garment\thas a front pocket\tlong sleeves\tclose-fitting cuffs and waistband.", 19], "mirror motorcycle": ["No. 'Mirror motorcycle' is too vague or abstract to be distinguished in a photo. However, if you mean a motorcycle with mirrors attached to it, then the answer is yes.\nA few things that are visually similar to 'mirror motorcycle' but are not 'mirror motorcycle' are:\tmotorcycle with regular mirrors\tcar with mirrors\nThere are several visual features that make a 'mirror motorcycle' stand out and distinguish it from similar things in a photo:\ta motorcycle with additional mirrors attached to handlebars or fairing\tthe mirrors are positioned to improve visibility of the rider or passengers\tthe mirrors have a reflective surface that allows riders to see behind them\twhile riding", 19], "chin strap": ["Yes. 'Chin strap' has a tangible appearance and is a type of strap used to secure something against the chin.\nA few things that are visually similar to 'chin strap' but are not 'chin strap' are:\theadband\tmouthguard\thelmet strap\t\nThere are several useful visual features to tell there is a 'chin strap' and not similar things in a photo:\tworn under the chin\tto secure something to the face or neck area\tattached or buckle to an object or item, such as a helmet or hat.", 19], "gold numbers": ["Yes. 'Gold numbers' has a tangible appearance and refers to a specific type of number.\nA few things that are visually similar to 'gold numbers' but are not 'gold numbers' are:\tsilver numbers\tbronze numbers\tcopper numbers\tgilded numbers\t yellow numbers\nThere are several useful visual features to distinguish 'gold numbers' from the listed similar things in a photo:\t\nShiny gold color. Usually, the numbers are polished and reflective, which sets them apart from other metallic colors. The number style may also be different from others, from retro numbers to serif styles, which can be a useful visual cue.", 19], "pink tank top": ["Yes. 'Pink tank top' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pink tank top' but are not 'pink tank top' are:\tpink t-shirt\tpink blouse\tpink dress\tpink sweater\nThere are several useful visual features to tell there is 'pink tank top' and not similar things in a photo:\tsleeveless\ttop part resembling the shape of a tank\tshades of pink\tno collar or buttons", 19], "mail slot": ["Yes. 'Mail slot' has a tangible appearance and refers to a slot for delivering letters or mail.\nA few things that are visually similar to 'mail slot' but are not 'mail slot' are:\tkeyhole\tdeposit slot\tpeephole\tslit in a door\nThere are several useful visual features to tell there is 'mail slot' and not similar things in a photo:\trectangular or oblong in shape\tlarge enough for letters to fit through typically mounted on a door\twith curved or square edges", 19], "round donut": ["Yes. 'Round donut' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'round donut' but are not 'round donut' are:\tbagel\tcake\tpizza\tscone\nThere are several useful visual features to tell there is 'round donut' and not similar things in a photo:\tcircular shape\tround hole in the middle\tfried or baked texture\tdusted with sugar or other toppings\tglazed or frosted with icing or sprinkles", 19], "butterknife": ["Yes. 'Butterknife' has a tangible appearance and is a type of cutting tool.\nA few things that are visually similar to 'butterknife' but are not 'butterknife' are:\tdinner knife\tletter opener\tpaper cutter\nThere are several useful visual features to tell there is 'butterknife' and not similar things in a photo:\tshorter blade\trounded tip\twider blade with a blunt edge\tserrated or flat cutting edge\ttypically used for spreading butter on bread or toast.", 19], "fisherman": ["Yes. 'Fisherman' has a tangible appearance and is a person who catches fish.\nA few things that are visually similar to 'fisherman' but are not 'fisherman' are:\thunter\tphotographer\thiker\tsailor \nThere are several useful visual features to tell there is 'fisherman' and not similar things in a photo:\tholding fishing equipment, such as a fishing rod or a fishing net\twearing fishing gear, such as a fishing hat or waders\tfishing in a body of water, such as a river or a lake", 19], "corsage": ["Yes. 'Corsage' has a tangible appearance and is a decorative arrangement of flowers.\nA few things that are visually similar to 'corsage' but are not 'corsage' are:\tbouquet\tcentrepiece\tflower crown\nThere are several useful visual features to distinguish 'corsage' from the listed similar things in a photo:\ta small arrangement of flowers\tworn pinned to clothing, typically on a person's chest or shoulder\tcan feature a single large bloom or a cluster of smaller blooms, often surrounded by greenery or ribbon\tcan be worn for formal occasions like weddings or proms", 19], "smart phone": ["Yes. 'Smart phone' has a tangible appearance and is a kind of electronic device.\nA few things that are visually similar to 'smart phone' but are not 'smart phone' are:\tcamera\ttablet\tdigital watch\tremote control\nThere are several useful visual features to tell there is 'smart phone' and not similar things in a photo:\trectangular shape\twith a touchscreen display\ta small speaker and a microphone\tbuttons for adjusting volume and turning it on/off\tcamera lens for taking photos\tor logo of a specific brand on it.", 19], "blue color": ["Yes. 'Blue color' has a tangible appearance and is a type of color.\nA few things that are visually similar to 'blue color' but are not 'blue color' are:\tgreen color\tpurple color\twater\tsky\nThere are several useful visual features to tell there is 'blue color' and not similar things in a photo:\ta single color\twith a wavelength between 450 and 490 nanometers (if using scientific definition of blue)", 19], "gadget": ["No. 'Gadget' is too vague or abstract to be distinguished in a photo.", 19], "canine": ["Yes. 'Canine' has a tangible appearance and is a type of animal, specifically the family of dogs and wolves.\nA few things that are visually similar to 'canine' but are not 'canine' are:\tcats\tfoxes\tcoyotes\thyenas\nThere are several useful visual features to tell there is 'canine' and not similar things in a photo:\tsnout\tmuzzle teeth\ttongue\tears\tfur\tpaws\ttail", 19], "mohawk": ["Yes. 'Mohawk' has a tangible appearance and is a kind of hairstyle.\nA few things that are visually similar to 'mohawk' but are not 'mohawk' are:\tcrew cut\tpixie cut\tshaved head\tfaux hawk\nThere are several useful visual features to tell there is 'mohawk' and not similar things in a photo:\tcenter strip of longer hair that stands upright or in various directions, while the rest of the head is shaved or closely cropped.", 19], "grasslands": ["Yes. 'Grasslands' has a tangible appearance and is a type of ecosystem dominated by grasses.\nA few things that are visually similar to 'grasslands' but are not 'grasslands' are:\tforest\tdesert\tbeach\tbushes\tsavanna\tprairie\nThere are several useful visual features to tell there is 'grasslands' and not similar things in a photo:\tdominated by grasses rather than trees or other vegetation\tgently rolling hills or flat plains\topen spaces\tno visible water sources or large bodies of water", 19], "whip cream": ["Yes. 'Whipped cream' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'whipped cream' but are not 'whipped cream' are:\tmarshmallow fluff\tshaving cream\tfrosting\tmeringue\nThere are several useful visual features to tell there is 'whipped cream' and not similar things in a photo:\tthick and fluffy texture\twhite or light cream color\tusually served on top of desserts or hot drinks", 19], "topper": ["Yes. 'Topper' has a tangible appearance and refers to an object that goes on top of something else.\nA few things that are visually similar to 'topper' but are not 'topper' are:\tbow\that\thelmet\tlid\nThere are several useful visual features to tell there is 'topper' and not similar things in a photo:\ton top of another object\tdecorative or functional\tmatching or contrasting color/pattern\tfrom which it is made (e.g. ceramic, plastic, metal)", 19], "plaid pants": ["Yes. 'Plaid pants' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'plaid pants' but are not 'plaid pants' are:\tstriped pants\tpatterned leggings\thoundstooth patterned pants\t\nThere are several useful visual features to identify plaid pants and distinguish them from similar things in a photo:\t\n-checked or tartan pattern consisting of vertical and horizontal bands of various colors, often intersecting. \n-Usually made of wool or cotton fabric. \n-The pattern consists of two or more colors.", 19], "home base plate": ["Yes. 'Home base plate' has a tangible appearance and is a part of a sports field.\nA few things that are visually similar to 'home base plate' but are not 'home base plate' are:\tother bases\tfield markers\tpylons\nThere are several useful visual features to tell there is 'home base plate' and not similar things in a photo:\t\n- It is a pentagon in shape, with one side longer than the others.\n- It is typically made of rubber or plastic, and is white with a black or dark blue border.\n- It is located at one corner of a baseball diamond, or infield, with three other bases spaced evenly around it. \n- It has a raised edge on the outermost side to prevent the ball from rolling away.", 19], "side street": ["Yes. 'Side street' has a tangible appearance and is a kind of road or street.\nA few things that are visually similar to 'side street' but are not 'side street' are:\talleys\twalkways\tparking lots\tdriveways\nThere are several useful visual features to distinguish 'side street' in a photo:\tthe street is connected to a larger road or street\tit is narrower than the main road or street\tthere are sidewalks or buildings along the street\tthere are parked cars or signs along the street.", 19], "computer speakers": ["Yes. 'Computer speakers' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'computer speakers' but are not 'computer speakers' are:\theadphones\tearbuds\tbluetooth speakers\t\nThere are several useful visual features to tell there is 'computer speakers' and not similar things in a photo:\ttwo separate speakers\tconnected by a wire or cable\tdesktop or shelf-mounted\tAC power adapter and/or USB cables\tspeaker grilles or mesh covers to protect the drivers", 19], "iron bench": ["Yes. 'Iron bench' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'iron bench' but are not 'iron bench' are:\tchair\tsofa\tstool\ttable\nThere are several useful visual features to tell there is 'iron bench' and not similar things in a photo:\tlong and narrow seating surface\tmade of iron or metal\thaving armrests or backrests, or both.", 19], "grease stain": ["Yes. 'Grease stain' has a tangible appearance and is a type of stain.\nA few things that are visually similar to 'grease stain' but are not 'grease stain' are: ink stains, coffee stains, wine stains\nThere are several useful visual features to tell there is 'grease stain' and not similar things in a photo:\toily or greasy appearance\tdarker color than the surrounding fabric\tor patches of discoloration\tcontoured to the shape of the source of the grease (e.g. a butter stain may be shaped like a pat of butter)", 19], "brown mud": ["Yes. 'Brown mud' has a tangible appearance and is a type of soil.\nA few things that are visually similar to 'brown mud' but are not 'brown mud' are:\tchocolate sauce\tdirt stains\tbrown paint\tcocoa powder\nThere are several useful visual features to tell there is 'brown mud' and not similar things in a photo:\tthick and creamy consistency\tearthy odor and texture\tbrown or dark color\tmixed with water or soil", 19], "wooden panels": ["Yes. 'Wooden panels' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wooden panels' but are not 'wooden panels' are:\ttiles\tshingles\tceramic pieces\tbrick walls\nThere are several useful visual features to tell there is 'wooden panels' and not similar things in a photo:\tthin and flat pieces of wood\taligned vertically or horizontally\tvisible wood grain and knots\tcan be painted or stained with different colors or finishes ", 19], "pink toilet": ["Yes. 'Pink toilet' has a tangible appearance and is a specific type of bathroom fixture.\nA few things that are visually similar to 'pink toilet' but are not 'pink toilet' are:\twhite toilet\tgrey toilet\tbrown toilet\tgreen toilet\nThere are several useful visual features to tell there is 'pink toilet' and not similar things in a photo:\tpink color\telongated bowl shape with a round base\tporcelain material with a shiny surface\tflushing mechanism at the top of the tank\tseat and lid on top of the bowl.", 19], "plastic toy": ["Yes. 'Plastic toy' has a tangible appearance and is a type of plaything made of plastic.\nA few things that are visually similar to 'plastic toy' but are not 'plastic toy' are:\trubber ball\tplastic cup\tplastic container\tplastic chair\tplastic bag\nThere are several useful visual features to tell there is 'plastic toy' and not similar things in a photo:\tfigurines, animals, or characters in cartoon style\tbright colors\tsmooth texture\twith movable parts or functions, such as wheels or buttons", 19], "clock time": ["Yes. 'Clock time' has a tangible appearance and is a way to measure time.\nA few things that are visually similar to 'clock time' but are not 'clock time' are:\tsun position\tshadows in a sundial\ttimer watches\nThere are several useful visual features to tell there is 'clock time' and not similar things in a photo:\tdial or digital numbers\tcircular or rectangular shape\thour, minute, and second hands", 19], "chrome kitchen faucet": ["Yes. 'Chrome kitchen faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'chrome kitchen faucet' but are not 'chrome kitchen faucet' are:\tbathroom faucet\tshowerhead\twater filter\nThere are several useful visual features to tell there is 'chrome kitchen faucet' and not similar things in a photo:\tsingle-handle or double-handle\t\ncurved or straight spout\t\nchrome finish\t\nmounting on a sink or countertop\t\nspray or stream features\t\npull-down or pull-out hose\t\nair gap to prevent backflow.", 19], "motorcycle engine": ["Yes. 'Motorcycle engine' has a tangible appearance and is a machinery.\nA few things that are visually similar to 'motorcycle engine' but are not 'motorcycle engine' are:\tcar engine\tlawnmower engine\tboat engine\ttruck engine\nThere are several useful visual features to tell there is 'motorcycle engine' and not similar things in a photo:\tsmall size\twith exposed parts, such as cylinders and gears\thanging from a motorcycle frame\tcylinders in a line or V shape\tcabling and tubing connected to it", 19], "description": ["No. 'Description' is too vague or abstract to be distinguished in a photo.", 19], "baggage": ["Yes. 'Baggage' has a tangible appearance and refers to luggage or items carried while traveling.\nA few things that are visually similar to 'baggage' but are not 'baggage' are:\tduffle bags\tpurses\tbackpacks\ttotes\nThere are several useful visual features to tell there is 'baggage' and not similar things in a photo:\tlarge\tsize\tvariety of shapes and colors\thandled or carried by a person or in a stack", 19], "shadow zebra": ["No. 'Shadow zebra' is too vague or abstract and it is not a concrete object or concept.\nTherefore, there are no visually similar things to 'shadow zebra' and there are no useful visual features for its distinction.", 19], "winter gloves": ["Yes. 'Winter gloves' has a tangible appearance and is a type of handwear.\nA few things that are visually similar to 'winter gloves' but are not 'winter gloves' are: mittens, gardening gloves, driving gloves, workout gloves.\nThere are several useful visual features to tell there is 'winter gloves' and not similar things in a photo:\tfinger holes\tfor warmth insulation\twrist coverage\tmade of wool, fleece, or other warming materials\tdark colors like black, grey or brown", 19], "exit": ["Yes. 'Exit' has a tangible appearance and is a type of sign or marker.\nA few things that are visually similar to 'exit' but are not 'exit' are:\tentrance\tsign\tarrow\nThere are several useful visual features to tell there is 'exit' and not similar things in a photo:\twhite letters spelling \"EXIT\" on a red background\tdirection towards the outside or an exit\tpointing towards a door or an opening", 19], "bed skirt": ["Yes. 'Bed skirt' has a tangible appearance and is a type of bedding accessory.\nA few things that are visually similar to 'bed skirt' but are not 'bed skirt' are:\ttablecloth\tcurtaingarment\nThere are several useful visual features to tell there is 'bed skirt' and not similar things in a photo:\tattached to the bottom of a bed\tbox-pleated or ruffled\tfabric matches or complements bedding\tswatches of fabric or decorative touches at the hemline.", 19], "shadow dirt": ["No. 'Shadow dirt' is too vague or abstract and it's not a common term.", 19], "sheet music": ["Yes. 'Sheet music' has a tangible appearance and is a piece of written or printed music notation.\nA few things that are visually similar to 'sheet music' but are not 'sheet music' are:\tbooklet\tnewspaper\tposter\t\nThere are several useful visual features to tell there is 'sheet music' and not similar things in a photo:\tmusical notes and symbols\torganized on staves and measures\twords or lyrics next to the notes\tpaper or parchment material\ttypically held in place with a clip or stand", 19], "binders": ["Yes. 'Binders' has a tangible appearance and is a type of stationary item.\nA few things that are visually similar to 'binders' but are not 'binders' are:\tfiles\tfolders\tenvelopes\tnotebooks\tbooks\nThere are several useful visual features to tell there is 'binders' and not similar things in a photo:\trectangular in shape\thaving rings to hold papers\tin various colors and designs\thaving a label holder on the spine\tfor holding loose-leaf pages", 19], "phone cord": ["Yes. 'Phone cord' has a tangible appearance and is a type of cord used for phones.\nA few things that are visually similar to 'phone cord' but are not 'phone cord' are:\tcharging cable\theadphone cable\tpower cord\tcoaxial cable\nThere are several useful visual features to tell there is 'phone cord' and not similar things in a photo:\tthicker than charging cable\tconnected to a telephone or handset\tcurly or coiled shape\tdefinite color, gray or black", 19], "shadow water": ["No. 'Shadow water' is too vague or abstract to be distinguished in a photo.", 19], "bruises": ["Yes. 'Bruises' has a tangible appearance and is a type of injury.\nA few things that are visually similar to 'bruises' but are not 'bruises' are:\tfreckles\tmoles\trash\tsunburn\nThere are several useful visual features to tell there is 'bruises' and not similar things in a photo:\tpurple, black, blue, or green color\tirregular shapes\tswelling or tenderness\tpainful to the touch", 19], "text print": ["Yes. 'Text print' has a tangible appearance and refers to the printed text in a document, book or article.\nA few things that are visually similar to 'text print' but are not 'text print' are:\thandwriting\tcalligraphy\tlogo\tprint or pattern on a fabric\nThere are several useful visual features to tell there is 'text print' and not similar things in a photo:\twords and letters are distinct and have sharp edges\tblack or dark ink on white, or light-colored paper\tor, colored text on a white, or light-colored paper\tas part of a larger text document or book", 19], "brown mushroom": ["Yes. 'Brown mushroom' has a tangible appearance and is a type of fungus.\nA few things that are visually similar to 'brown mushroom' but are not 'brown mushroom' are:\tlichen\tdirt\tclumps of leaves\tdead insects\tgolf balls\nThere are several useful visual features to tell there is 'brown mushroom' and not similar things in a photo:\tbrown or tan color\tcap and stem or stalk\tcap has a round or bell shape\twith or without gills on the underside of the cap\tno visible roots, leaves, or petals", 19], "cabinet knob": ["Yes. 'Cabinet knob' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'cabinet knob' but are not 'cabinet knob' are:\tdrawer pull\thandle\tfurniture leg\nThere are several useful visual features to tell there is a 'cabinet knob' and not similar things in a photo:\tsmall size\tround or cylindrical shape\tattached to the surface of a cabinet or drawer\tknurled or patterned surface\tfor grasping and pulling", 19], "brown mountain": ["Yes. 'Brown mountain' has a tangible appearance and is a type of geological formation.\nA few things that are visually similar to 'brown mountain' but are not 'brown mountain' are:\thills\tmounds\tdeserts\nThere are several useful visual features to tell there is 'brown mountain' and not similar things in a photo:\ttaller than a hill\tbrown or earthy colors\trocky or rugged texture\ttypically found in conjunction with other mountains or geological features", 19], "chairlift": ["Yes. 'Chairlift' has a tangible appearance and is a mechanical device for transporting people up a mountain or hill while seated on chairs.\nA few things that are visually similar to 'chairlift' but are not 'chairlift' are:\tgondola\ttram\tcable car\televator\nThere are several useful visual features to tell there is 'chairlift' and not similar things in a photo:\tseated chairs attached to a moving cable\tsuspension from poles or towers\tski equipment or snow on the ground (if in a ski resort) \tvertical ascent along a mountain or hill", 19], "lavender": ["Yes. 'Lavender' has a tangible appearance and is a type of flowering plant.\nA few things that are visually similar to 'lavender' but are not 'lavender' are:\tpurple lilac\theather\twisteria\nThere are several useful visual features to tell there is 'lavender' and not similar things in a photo:\tlong, slender stem\twith small, purple flowers\torganised in spikes\tpale green or grey-green leaves with a slightly hairy texture\tsweet fragrance", 19], "chocolate syrup": ["Yes. 'Chocolate syrup' has a tangible appearance and is a liquid used as a topping or flavoring for desserts.\nA few things that are visually similar to 'chocolate syrup' but are not 'chocolate syrup' are:\tsoy sauce\tbalsamic vinegar\twine syrup\tcaramel sauce\n\nThere are several useful visual features to tell there is 'chocolate syrup' and not similar things in a photo:\tbrown color\tthickness and viscous texture\tchocolate scent\tpoured on desserts like ice cream or cake.", 19], "pigtails": ["Yes. 'Pigtails' has a tangible appearance and is a type of hairstyle.\nA few things that are visually similar to 'pigtails' but are not 'pigtails' are:\tbuns\tponytails\tbraids\t\nThere are several useful visual features to tell there is 'pigtails' and not similar things in a photo:\ttwo separate sections of hair\ton either side of the head\thair is tied or fastened\tat the end\tof each section\tshort in length", 19], "flannel shirt": ["Yes. 'Flannel shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'flannel shirt' but are not 'flannel shirt' are:\tplaid shirt\tjacket\tsweater\tfleece shirt\nThere are several useful visual features to tell there is 'flannel shirt' and not similar things in a photo:\twool or cotton material\tsoft and fuzzy texture\tchecked or striped pattern\tbutton-up front\tlong sleeves", 19], "gold chain": ["Yes. 'Gold chain' has a tangible appearance and is a kind of jewelry.\nA few things that are visually similar to 'gold chain' but are not 'gold chain' are:\tsilver chain\tcopper chain\tnecklace\tpendant\nThere are several useful visual features to tell there is 'gold chain' and not similar things in a photo:\tbrilliant color\tsmall links or rings\tthe color yellow or golden\tlight reflecting off of its surface", 19], "robes": ["Yes. 'Robes' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'robes' but are not 'robes' are:\tcoats\tgowns\tdresses\t\nThere are several useful visual features to tell there is 'robes' and not similar things in a photo:\tloose-fitting clothing\tthat typically falls to the ankle or floor\tusually worn over other clothes\tclose-fitting at the neck with long sleeves\tthat can be worn open or closed\ton some occasions, robes may have particular designs or decorations", 19], "air traffic control tower": ["Yes. 'air traffic control tower' has a tangible appearance and is a type of tower used in airports.\nA few things that are visually similar to 'air traffic control tower' but are not 'air traffic control tower' are:\twater tower\tclock tower\tlighthouse\tobservation tower\nThere are several useful visual features to tell there is 'air traffic control tower' and not similar things in a photo:\tnear an airport or runway\ttall with a small width\tcontrol room at the top with lots of windows or screens\tred and white stripes or other distinctive colors.", 19], "broccoli pieces": ["Yes. 'Broccoli pieces' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'broccoli pieces' but are not 'broccoli pieces' are:\tcauliflower pieces\tbrussels sprouts\tpieces of green pepper\tspinach leaves\nThere are several useful visual features to tell there is 'broccoli pieces' and not similar things in a photo:\tlight green color\ttiny flowers on top of the pieces\tvaried sized pieces", 19], "metal tube": ["Yes. 'Metal tube' has a tangible appearance and is a specific type of object.\nA few things that are visually similar to 'metal tube' but are not 'metal tube' are:\n\n- PVC pipes\n- Paper towel rolls\n- Cardboard tubes\n- Bamboo shoots\n\nThere are several useful visual features to tell there is 'metal tube' and not similar things in a photo:\n\n- Smooth, shiny surface\n- Metallic finish\n- Uniform diameter throughout the length of the object\n- Circular cross-section", 19], "gren": ["I'm sorry, but I am not familiar with the word \"gren.\" Did you mean to type \"green\"? Please clarify.", 19], "purple vegetable": ["Yes. 'Purple vegetable' has a tangible appearance and refers to any vegetable that has purple color.\nA few things that are visually similar to 'purple vegetable' but are not 'purple vegetable' are:\tpurple fruit\tpurple flower\tpurple fabric\tpurple ink\nThere are several useful visual features to tell there is 'purple vegetable' and not similar things in a photo:\tpurple color\tsmooth or rough texture\tvariety of shapes and sizes\tfresh and not wilted or dried out", 19], "round fruit": ["Yes. 'Round fruit' has a tangible appearance.\nA few things that are visually similar to 'round fruit' but are not 'round fruit' are:\tballs\toranges\ttomatoes\t\nThere are several useful visual features to tell there is 'round fruit' and not similar things in a photo:\tgrowing on a plant\tedible\tvariety of colors or shades, such as yellow, orange, red, green, or purple clefted, indented or smooth surface \tstem or some sort of connection to a plant.", 19], "yellow table": ["Yes. 'Yellow table' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'yellow table' but are not 'yellow table' are:\tyellow chair\tyellow book\tyellow flower\tyellow toy\nThere are several useful visual features to tell there is 'yellow table' and not similar things in a photo: flat top surface, four legs, one solid color, rectangular or square shape, suitable for sitting or placing objects.", 19], "florescent light": ["Yes. 'Fluorescent light' has a tangible appearance and is a type of lighting.\nA few things that are visually similar to 'fluorescent light' but are not 'fluorescent light' are:\tincandescent light\tLED light\tcandle light\nThere are several useful visual features to distinguish 'fluorescent light' from the listed similar things in a photo:\ttube-shaped\tlight is coming from a long cylindrical glass tube or bulb\tbright white or bluish hue", 19], "trash laying": ["Yes. 'Trash laying' has a tangible appearance and refers to garbage or waste materials lying on the ground.\nA few things that are visually similar to 'trash laying' but are not 'trash laying' are: fallen leaves, sticks, or branches on the ground after a storm; sand, rocks, or debris on the beach.\nThere are several useful visual features to tell there is 'trash laying' and not similar things in a photo: random and irregular shapes; man-made materials, such as plastic bags or wrappers; unnatural colors, such as bright colors that are not naturally occurring in the environment; items that are not part of the natural landscape.", 19], "silver rims": ["Yes. 'Silver rims' has a tangible appearance and refers to the silver-colored rings around an object.\nA few things that are visually similar to 'silver rims' but are not 'silver rims' are:\tGold rims\tBlack rims\tWhite rims\tNo rims\nThere are several useful visual features to tell there are 'silver rims' and not similar things in a photo:\tsilver-colored\tcircular\tring-shaped\taround the edge of an object.", 19], "footstool": ["Yes. 'Footstool' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'footstool' but are not 'footstool' are:\tchairs\tottomans\tbenches\nThere are several useful visual features to tell there is 'footstool' and not similar things in a photo:\tsmall and low to the ground\tpadded or cushioned\ton four legs or have a solid base\twithout backrest or armrest.", 19], "cork board": ["Yes. 'Cork board' has a tangible appearance and is a type of noticeboard.\nA few things that are visually similar to 'cork board' but are not 'cork board' are:\tchalkboard\twhiteboard\tbulletin board\nThere are several useful visual features to tell there is 'cork board' and not similar things in a photo:\tbrown or tan surface\tpin or tacks visible on the surface\ttexture resembling cork tree bark", 19], "rowboat": ["Yes. 'Rowboat' has a tangible appearance and is a type of small watercraft.\nA few things that are visually similar to 'rowboat' but are not 'rowboat' are:\tkayak\tcanoe\tpaddleboat\nThere are several useful visual features to tell there is 'rowboat' and not similar things in a photo:\tlong and narrow body\twith oars for propulsion\tno motor or engine\tseats for rowers or passengers usually in pairs or singly\tbench for rowers to sit on while rowing.", 19], "outdoor table": ["Yes. 'Outdoor table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'outdoor table' but are not 'outdoor table' are:\tpicnic blanket \tbench\tcar trunk\tsand \tground\nThere are several useful visual features to tell there is 'outdoor table' and not similar things in a photo:\ttabletop surface\tchairs or benches around it\toutside setting\tumbrella, parasol or shelter for shade or protection", 19], "serving tray": ["Yes. 'Serving tray' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'serving tray' but are not 'serving tray' are:\tcookie sheet\tchopping board\tplatter\ttrivet\nThere are several useful visual features to tell there is 'serving tray' and not similar things in a photo:\tflat surface\twith raised or sloping edges\tat least one handle\tfor carrying or holding food and dishes in a restaurant or at home.", 19], "beige curtain": ["Yes. 'Beige curtain' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'beige curtain' but are not 'beige curtain' are:\tdrapes\tblinds\tshades\ttapestry\nThere are several useful visual features to tell there is 'beige curtain' and not similar things in a photo:\tsoft, fabric material\thanging from a rod or hook covering a window\tsolid color in beige tones", 19], "adult woman": ["Yes. 'Adult woman' has a tangible appearance and refers to a grown-up female person.\nA few things that are visually similar to 'adult woman' but are not 'adult woman' are:\tteenage girl\telderly woman\ttransgender person\tman\tdoll or mannequin\nThere are several useful visual features to tell there is 'adult woman' and not similar things in a photo:\tfemale body shape\tandrogynous clothing or makeup\thair length and style\tfacial features such as lips, cheekbones, and jawbone", 19], "clock radio": ["Yes. 'Clock radio' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'clock radio' but are not 'clock radio' are:\tspeaker\talarm clock\tMP3 player\nThere are several useful visual features to tell there is 'clock radio' and not similar things in a photo:\tan LED or LCD screen displaying the time and radio station\tphysical buttons to control the alarm, radio, and volume\tan antenna or tuning dial for radio reception\ta power cord and/or battery compartment", 19], "wrought iron": ["Yes. 'wrought iron' has a tangible appearance and refers to a type of iron that has been worked into decorative shapes.\nA few things that are visually similar to 'wrought iron' but are not 'wrought iron' are:\tcast iron\taluminum\tfaux wrought iron\nThere are several useful visual features to tell there is 'wrought iron' and not similar things in a photo:\telaborate scrollwork\tor intricate designs\tfinely crafted\tdark in color\ttypically used for decorative purposes", 19], "handicap sticker": ["Yes. 'Handicap sticker' has a tangible appearance and is a kind of sign.\nA few things that are visually similar to 'handicap sticker' but are not 'handicap sticker' are:\tparking permit\tstreet sign\tbusiness sign\nUseful visual features for distinguishing 'handicap sticker' from similar things in a photo are:\ta wheelchair symbol\ton a blue background\ta white border around the symbol and the background.", 19], "base umpire": ["Yes. 'Base umpire' has a tangible appearance and is a person who officiates a baseball game.\nA few things that are visually similar to 'base umpire' but are not 'base umpire' are:\tplayer\tcoach\tspectator\t\nThere are several useful visual features to tell there is 'base umpire' and not similar things in a photo:\twearing a striped uniform\tcarrying a set of indicators or clicker\tclosely watching the game while standing beside or around the bases", 19], "round building": ["Yes. 'Round building' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'round building' but are not 'round building' are:\tsilo\ttower\tdome\tstadium\nThere are several useful visual features to tell there is 'round building' and not similar things in a photo:\tcircular or rounded shape\tsmooth or curved surface\tabsence of edges or corners\tsymmetrical design or pattern", 19], "dimple": ["Yes. 'Dimple' has a tangible appearance and refers to a small depression or indentation in the skin, typically on the cheek or chin.\nA few things that are visually similar to 'dimple' but are not 'dimple' are:\tfreckles\tacne scars\tpores\twrinkles\nThere are several useful visual features to tell there is 'dimple' and not similar things in a photo:\tsmall and round\tusually found on the cheeks or chin\tdoes not protrude from the skin\tis a natural feature rather than a blemish or scar.", 19], "cell phone screen": ["Yes. 'Cell phone screen' has a tangible appearance and is a type of electronic display.\nA few things that are visually similar to 'cell phone screen' but are not 'cell phone screen' are:\tcomputer monitor\ttablet screen\tTV screen\nThere are several useful visual features to tell there is 'cell phone screen' and not similar things in a photo:\trectangular shape\trelatively small size\ttouch-sensitive surface\tdisplaying phone interface, icons, or apps\tbacklit\tdisplaying text or images\tin a handheld device", 19], "peanut": ["Yes. 'Peanut' has a tangible appearance and is a type of nut.\nA few things that are visually similar to 'peanut' but are not 'peanut' are:\twalnut\talmond\tpecan\tchestnut\nThere are several useful visual features to tell there is 'peanut' and not similar things in a photo:\toval or peanut-shaped\tbrown or beige in color\twith a thin papery skin\tone or two grooves running lengthwise\ton the inside, has two lobes\tstored in a slightly curved shell", 19], "blaze": ["Yes. 'Blaze' has a tangible appearance and refers to a fire that is burning strongly.\nA few things that are visually similar to 'blaze' but are not 'blaze' are:\tcampfire\tcandle\tfireplace\tfireworks\nThere are several useful visual features to tell there is 'blaze' and not similar things in a photo:\tintense flames\tand heat\tsparkling or glowing bright light\tsmoke and sometimes embers\trapidly spreading flames and engulfing nearby material.", 19], "spider": ["Yes. 'Spider' has a tangible appearance and a distinct body structure.\nA few things that are visually similar to 'spider' but are not 'spider' are:\tcrabs\tticks\tscabies\tmite\nThere are several useful visual features to tell there is 'spider' and not similar things in a photo:\teight legs\tbody divided into two parts: cephalothorax and abdomen\tspinnerets that produce silk in the abdomen\tno wings or antennae\tpair of pedipalps at the front of the cephalothorax (end in chelicerae which inject venom)", 19], "grey metal fence": ["Yes. 'Grey metal fence' has a tangible appearance and is a type of barrier.\nA few things that are visually similar to 'grey metal fence' but are not 'grey metal fence' are:\tbricks\twalls\twire fences\t\nThere are several useful visual features to tell there is 'grey metal fence' and not similar things in a photo:\tslender, evenly spaced bars\tmetallic and grey in color\thorizontal or vertical bars\tvisible screws or bolts\tfor security or decoration purposes", 19], "police van": ["Yes. 'Police van' has a tangible appearance and refers to a vehicle used by the police.\nA few things that are visually similar to 'police van' but are not 'police van' are:\tTaxi\tvan\tambulance\ttruck\tbuses\nThere are several useful visual features to distinguish 'police van' from the listed similar things in a photo:\t\n- Police logo or word markings on the sides or top of the vehicle.\n- Red and blue flashing lights on the roof of the van.\n- A cage or divider separating the front seats from the back of the van, where prisoners or suspects are detained.\n- Sirens and loudspeakers for making announcements or warnings to the public.", 19], "gravels": ["Yes. 'Gravels' has a tangible appearance and is a type of small stones.\nA few things that are visually similar to 'gravels' but are not 'gravels' are:\tsand\tsoil\tpebbles\trocks\nThere are several useful visual features to tell there are 'gravels' and not similar things in a photo:\ttypically small in size\tvarious colors, including grey, white, black, brown, or yellow\tjagged or rounded shapes\thighly textured or granular surface\tsound when stepped on or poured out", 19], "embankment": ["Yes. 'Embankment' has a tangible appearance and refers to a raised slope or bank, often along a river or road.\nA few things that are visually similar to 'embankment' but are not 'embankment' are:\thill\tmound\tmountain\nThere are several useful visual features to tell there is 'embankment' and not similar things in a photo:\traised sloping ground along a river or road\tmay have a retaining wall\tor other protective structures\tgrassy, rocky, or paved surface", 19], "spring": ["Yes. 'Spring' has a tangible appearance and is a season.\nA few things that are visually similar to 'spring' but are not 'spring' are:\tsummer\tflowers\twarm weather\tgreen grass\thappiness\nThere are several useful visual features to distinguish 'spring' from other seasons or similar things in a photo:\tflowers blooming\tnew leaves appearing on trees\tand warm sunlight appearing over landscapes.", 19], "silver airplane": ["Yes. 'Silver airplane' has a tangible appearance and is a type of flying vehicle.\nA few things that are visually similar to 'silver airplane' but are not 'silver airplane' are:\tsilver rocket\tsilver blimp\tsilver drone\t\nThere are several useful visual features to distinguish 'silver airplane' from the listed similar things in a photo:\t\n- Wings on both sides\n- Engines or turbines on the wings or back of the airplane\n- Tail fins or vertical stabilizers at the rear of the airplane\n- Landing gear or wheels underneath the airplane\n- Windows or doors along the sides of the airplane\n- Communication antennae or equipment on top or underneath the airplane.", 19], "man pants": ["Yes. 'Man pants' has a tangible appearance and is a clothing item.\nA few things that are visually similar to 'man pants' but are not 'man pants' are:\twomen's pants\tleggings\tjeans\tskirts\nThere are several useful visual features to tell there are 'man pants' and not similar things in a photo:\tloose or fitted around the hips and legs\tbutton or zipper at the front of the waistband\ttailored or casual appearance\tpockets at the front and back of the pants.", 19], "scissor handles": ["Yes. 'Scissor handles' has a tangible appearance and is a part of a cutting tool.\nA few things that are visually similar to 'scissor handles' but are not 'scissor handles' are:\tknife handles\tpaintbrush handles\tpliers handles\tcutlery handles\nThere are several useful visual features to tell there is 'scissor handles' and not similar things in a photo:\telongated handles\tfinger holes or loops for grip\tadjacent to scissor blades", 19], "persons arm": ["Yes. 'Person's arm' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'person's arm' but are not 'person's arm' are:\tanimal's leg\tsculpture or statue's limb\tmannequin's arm\t\nThere are several useful visual features to tell there is 'person's arm' and not similar things in a photo:\tcurved shape\tjointed structure\thand at the end with fingers\tbare skin or clothing on the outside", 19], "tall green plant": ["Yes. 'Tall green plant' has a tangible appearance and is a type of vegetation.\nA few things that are visually similar to 'tall green plant' but are not 'tall green plant' are:\tweeds\tbushes\ttrees\nThere are several useful visual features to tell there is 'tall green plant' and not similar things in a photo:\tspiky leaves or stems\tcolor of the leaves and stem\tshape of the leaves and stem\theight compared to nearby plants or objects", 19], "storage compartment": ["Yes. 'Storage compartment' has a tangible appearance and can refer to various types of containers for storing items.\nA few things that are visually similar to 'storage compartment' but are not 'storage compartment' are:\tdrawer\tshelf\ttray\tcabinet\tbox\nThere are several useful visual features to tell there is 'storage compartment' and not similar things in a photo, such as:\thaving a lid or door\tbeing made of plastic, wood, or metal\thaving dividers or partitions for organizing items\tbeing designed to hold specific items\tbeing attached to a larger structure, such as a piece of furniture.", 19], "lcd screen": ["Yes. 'LCD screen' has a tangible appearance and is a kind of display.\nA few things that are visually similar to 'lcd screen' but are not 'lcd screen' are: LED display, CRT monitor, Plasma TV, Projectors.\nThere are several useful visual features to tell there is 'lcd screen' and not similar things in a photo:\trectangular in shape with a thin profile, flat surface, thin bezel, sharp and clear image, display of graphics or moving images, presence of buttons or touch controls, backlighting.", 19], "ref": ["No. 'Ref' is too vague and abstract to be considered a visually concrete concept.", 19], "brown wall": ["Yes. 'Brown wall' has a tangible appearance and is a type of surface.\nA few things that are visually similar to 'brown wall' but are not 'brown wall' are:\twood paneling\tbricks\tmud wall\tstone wall\nThere are several useful visual features to tell there is 'brown wall' and not similar things in a photo:\tsmooth texture\tbrown color\trectangular shape\tflat surface\tsupported by beams or structure", 19], "silver drawer": ["Yes. 'Silver drawer' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'silver drawer' but are not 'silver drawer' are:\tchest of drawers\tfile cabinet\tlocker\nThere are several useful visual features to tell there is 'silver drawer' and not similar things in a photo:\tmade of silver or metallic material\thas drawers for storing things\tdesignated for use in a household or office setting\tcould have knobs, handles, and locks", 19], "courts": ["Yes. 'Courts' has a tangible appearance and refers to a specific area designated for playing games or sports.\nA few things that are visually similar to 'courts' but are not 'courts' are:\tparks\tplaygrounds\tarenas\ttrack fields\nThere are several useful visual features to tell there is 'courts' and not similar things in a photo:\trectangular or circular shape\tlines or markings on the ground\tnet or goal posts\tinstruments or equipment used for a specific sport or game", 19], "grey pole": ["Yes. 'Grey pole' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'grey pole' but are not 'grey pole' are:\ttree\ttruss\tpipe\tscaffolding\nThere are several useful visual features to tell there is 'grey pole' and not similar things in a photo:\tvertical and cylindrical shape\tsmooth and even grey surface\tno visible branches or attachments", 19], "brown eggs": ["Yes. 'Brown eggs' has a tangible appearance and is a type of egg.\nA few things that are visually similar to 'brown eggs' but are not 'brown eggs' are:\twhite eggs\tceramic eggs\tstyrofoam eggs\nThere are several useful visual features to tell there are 'brown eggs' and not similar things in a photo:\tbrown or tan color\tirregular speckles or spots\tsmooth, hard shell\toblong or oval shape", 19], "makeup": ["Yes. 'Makeup' has a tangible appearance and refers to products used for beautification or enhancement of one's features.\nA few things that are visually similar to 'makeup' but are not 'makeup' are:\tface paint\tmasks\tprosthetics\ttattoos\nThere are several useful visual features to tell there is 'makeup' and not similar things in a photo:\tvariety of colors\tapplied to face or body\tmimics natural features such as lashes, lips, and brows\tused to enhance or alter appearance", 19], "billboards": ["Yes. 'Billboards' has a tangible appearance and is a type of advertising structure.\nA few things that are visually similar to 'billboards' but are not 'billboards' are:\tposters\tsigns\tbanners\nThere are several useful visual features to tell there are 'billboards' and not similar things in a photo:\tlarge size\tdisplaying advertisements or messages\tplaced at the side of a road or on buildings\trectangular shape\twithout any attached supports or frames.", 19], "brochures": ["Yes. 'Brochures' has a tangible appearance and is a type of printed material.\nA few things that are visually similar to 'brochures' but are not 'brochures' are:\tpamphlets\tflyers\tnewspapers\tmagazines\nThere are several useful visual features to tell there is 'brochures' and not similar things in a photo:\tsmaller than newspapers and magazines\tbound or stapled together\tbrief and informative textual content\tvariety of images and graphics\tspecific focus or topic of the material", 19], "females": ["No. 'Females' is too vague or abstract to be distinguished in a photo. It refers to the sex of a living being, but this cannot be determined visually.", 19], "damage": ["No. 'Damage' is too vague or abstract to be distinguished in a photo. It is a concept that refers to the harmful and negative effects that an action or event may cause to something.\nTherefore, it is not possible to name things that are visually similar to \"damage\" but are not \"damage\".", 19], "apple stem": ["Yes. 'Apple stem' has a tangible appearance and is a part of the apple fruit.\nA few things that are visually similar to 'apple stem' but are not 'apple stem' are:\tpear stem\tgrape stem\tcherry stem\nThere are several useful visual features to tell there is 'apple stem' and not similar things in a photo:\tattached to the top of an apple\tfive-pointed shape\tbrown or green in color\tshort and thin compared to the rest of the apple", 19], "train rails": ["Yes. 'Train rails' has a tangible appearance and is a physical infrastructure.\nA few things that are visually similar to 'train rails' but are not 'train rails' are:\tcable car tracks\trollercoaster tracks\tbike lanes\nThere are several useful visual features to tell there are 'train rails' and not similar things in a photo:\tmetallic\tparallel tracks, fixed spaced apart\tthe presence of railroad ties or sleepers\tballast between rails and ties/held by ties\tgenerally straight with occasional bends for direction changes", 19], "toddlers": ["Yes. 'Toddlers' has a tangible appearance and refers to young children who are just learning to walk.\nA few things that are visually similar to 'toddlers' but are not 'toddlers' are:\tbabies\tolder children\tshort adults\nThere are several useful visual features to tell there is 'toddlers' and not similar things in a photo:\tshort height\tunsteady gait or stumbling\tgenerally between the ages of 1 and 3 years old\tfacial features that resemble both babies and older children", 19], "silver platter": ["Yes. 'Silver platter' has a tangible appearance and is an object used to serve food.\nA few things that are visually similar to 'silver platter' but are not 'silver platter' are:\ttray\tplate\tplastic platter\nThere are several useful visual features to tell there is 'silver platter' and not similar things in a photo:\trectangular or circular in shape\tmade of silver or silver-colored metal (e.g. stainless steel)\tshiny surface with reflective properties", 19], "jack": ["No. 'Jack' is too vague or abstract to be distinguished in a photo. It can refer to a device that lifts heavy objects or a playing card, for example. \n\nHowever, if we are talking about a specific type of jack, like a toy or game piece, then it would be a visually concrete concept. In that case, a few things with a similar appearance could be:\tdice\tplayful figurines\t\n\nUseful visual features for distinguishing 'jack' from these similar things in a photo would depend on the specific type of jack in question. For example, a toy jack may have a specific shape, color and texture that differentiate it from a dice or figurine.", 19], "bridal": ["No. 'Bridal' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider 'bridal' in the context of a wedding, then it has a tangible appearance and refers to the dress and accessories worn by the bride.\n\nA few things that are visually similar to 'bridal' in the context of a wedding but are not 'bridal' are:\tbridesmaids dresses \tmen's suits\tflower arrangements\ttable settings\n\nThere are several useful visual features to tell there is 'bridal' and not similar things in a photo:\tlong, flowing white or ivory dress\tveil or headpiece\tbridal bouquet\tbridal jewelry, such as a necklace or earrings.", 19], "speckles": ["Yes. 'Speckles' has a tangible appearance and refers to small, distinct spots on a surface.\nA few things that are visually similar to 'speckles' but are not 'speckles' are:\tfreckles\tdots\torbs\tpatterns\nThere are several useful visual features to differentiate 'speckles' from other similar things in a photo, including:\t\n- Irregular and random distribution of spots\n- Small size of spots\n- Contrast in color between the spots and the surface or background\n- Lack of defined shape or pattern", 19], "friend": ["No. 'Friend' is too vague or abstract to be visually represented. \n\nNote: This question pertains to abstract concepts that do not have a tangible or concrete appearance, and thus cannot be visually identified. 'Friend' is an abstract concept that pertains to the relationship between individuals and cannot be seen or recognized through its physical appearance.", 19], "silver railing": ["Yes. 'Silver railing' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'silver railing' but are not 'silver railing' are:\tsilver ladder\tsilver door handle\tsilver fence\nThere are several useful visual features to tell there is 'silver railing' and not similar things in a photo:\tslender and parallel bars\tmetallic or reflective surface\tsupporting a staircase or a balcony", 19], "dog tongue": ["Yes. 'Dog tongue' has a tangible appearance and is a body part of a dog.\nA few things that are visually similar to 'dog tongue' but are not 'dog tongue' are:\tcow tongue\tsnake tongue\tlizard tongue\nThere are several useful visual features to tell there is 'dog tongue' and not similar things in a photo:\tpink or reddish in color\twet or moist texture\tmuscular and flexible appearance\tflat or pointed shape\tdog's mouth in the background", 19], "clearing": ["Yes. 'Clearing' has a tangible appearance and refers to an open space within a forest or an area where trees have been removed.\nA few things that are visually similar to 'clearing' but are not 'clearing' are:\tfield\tmeadow\tpark\nThere are several useful visual features to tell there is 'clearing' and not similar things in a photo:\tsurrounded by trees or forest\tlack of vegetation except for grass or small plants\topen space\twith a natural transition from forest or trees to clearing.", 19], "dirt track": ["Yes. 'Dirt track' has a tangible appearance and refers to a surface made of dirt or soil used for racing or other events.\nA few things that are visually similar to 'dirt track' but are not 'dirt track' are:\thiking trail\tpaved road\tsidewalk\toff-road terrain\nThere are several useful visual features to tell there is 'dirt track' and not similar things in a photo:\tmade of dirt or soil\tusually oval-shaped or circular\tfrequently used for racing or motorbiking events\tmay have barriers, flags, or lights to demarcate boundaries or signal race progress", 19], "duct tape": ["Yes. 'Duct tape' has a tangible appearance and is a product made of adhesive material commonly used for fixing or repairing things.\nA few things that are visually similar to 'duct tape' but are not 'duct tape' are:\ttransparent tape\tpacking tape\tmasking tape\tmedical tape\nThere are several useful visual features to tell there is 'duct tape' and not similar things in a photo:\tthicker than regular tape\tmetallic or gray color, sometimes with a reflective surface\thas a mesh backing for strength and durability\tsticks well to a variety of surfaces", 19], "poop": ["Yes. 'Poop' has a tangible appearance and is a type of waste product.\nA few things that are visually similar to 'poop' but are not 'poop' are:\tsoil\tcompost\tchocolate sauce\nThere are several useful visual features to tell there is 'poop' and not similar things in a photo:\tirregular shape and texture\tbrown or green\tcolor\tfoul odor or smell\tfecal matter found in or near animal droppings", 19], "woman hair": ["Yes. 'Woman hair' has a tangible appearance and refers to the hair on a woman's head.\nA few things that are visually similar to 'woman hair' but are not 'woman hair' are:\twigs\tfur\tfeathers\tyarn\nThere are several useful visual features to tell there is 'woman hair' and not similar things in a photo:\tgrowing from a woman's scalp\tpossible hairstyles, such as braids, ponytails, or curls\tcan be different colors or dyed\tmay have hair accessories, such as clips or headbands.", 19], "news paper": ["Yes. 'News paper' has a tangible appearance and is a type of paper used for printing news and information.\nA few things that are visually similar to 'news paper' but are not 'news paper' are:\tmagazines\tjournals\tbooks\tflyers\nThere are several useful visual features to tell there is 'news paper' and not similar things in a photo:\ttypically in black and white or color\tcontains news, articles or information\tpages are folded or stapled together into a booklet\tor can be in a broadsheet format.", 19], "stone tiles": ["Yes. 'Stone tiles' has a tangible appearance and is a type of flooring material.\nA few things that are visually similar to 'stone tiles' but are not 'stone tiles' are:\tceramic tiles\twood planks\tpainted concrete floors\nThere are several useful visual features to tell there is 'stone tiles' and not similar things in a photo:\tmade of stone or natural materials\tsquare or rectangular shape\tuneven surface\tvariation in color or texture", 19], "giraffe mane": ["Yes, 'giraffe mane' has a tangible appearance and is the hair that grows on the necks of giraffes.\nA few things that are visually similar to 'giraffe mane' but are not 'giraffe mane' are:\thorse mane\tlion mane\tzebra mane\nThere are several useful visual features to tell there is 'giraffe mane' and not similar things in a photo:\tvery long and thin hair\tgrows only in the neck and upper back area\thas unique patterns and coloration compared to other animals' hair\tfur on the rest of the body is short and smooth.", 19], "cross country skier": ["Yes. 'Cross country skier' has a tangible appearance and is a person engaged in a specific activity.\nA few things that are visually similar to 'cross country skier' but are not 'cross country skier' are: down-hill skier, ice skater, runner, hiker\nThere are several useful visual features to tell there is 'cross country skier' and not similar things in a photo:\twearing ski boots, skis and poles\tmoving forward while gliding\ton a snowy trail or terrain\tclothing suitable for cold weather and skiing", 19], "breakfast sandwich": ["Yes. 'Breakfast sandwich' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'breakfast sandwich' but are not 'breakfast sandwich' are:\tburger\tpita wrap\tquesadilla\nThere are several useful visual features to tell there is 'breakfast sandwich' and not similar things in a photo:\ttwo slices of bread or a roll\tegg, bacon, sausage, or ham\tcheese\tvegetables, such as lettuce or tomato\tmayonnaise, ketchup, or mustard sauce", 19], "motorcycle racer": ["Yes. 'Motorcycle racer' has a tangible appearance and is a person who races motorcycles.\nA few things that are visually similar to 'motorcycle racer' but are not 'motorcycle racer' are:\tmotorcyclist\tbiker\t\nThere are several useful visual features to tell there is 'motorcycle racer' and not similar things in a photo:\twearing a leather racing suit\twearing a full-face helmet\triding a high-performance sport motorcycle\tleaning into turns at high speed\tcompeting in a race or competition", 19], "meat sandwich": ["Yes. 'Meat sandwich' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'meat sandwich' but are not 'meat sandwich' are:\tvegetarian sandwich\ttoast\tburger\tfajita wrap\nThere are several useful visual features to tell there is 'meat sandwich' and not similar things in a photo:\ttwo slices of bread with meat in between\tvariety of meat slices or chunks\tcheese, lettuce, tomato or other toppings\tmayonnaise or mustard spread on the bread", 19], "purple collar": ["Yes. 'Purple collar' has a tangible appearance and is a type of pet accessory.\nA few things that are visually similar to 'purple collar' but are not 'purple collar' are:\tleash\twristband\tscarf\tchoker\nThere are several useful visual features to tell there is 'purple collar' and not similar things in a photo:\tpurple in color\tmade of a material that can be worn around a pet's neck\thas a buckle or clasp for attaching to the pet's neck.", 19], "kitchen knife": ["Yes. 'Kitchen knife' has a tangible appearance and is a type of cutting tool.\nA few things that are visually similar to 'kitchen knife' but are not 'kitchen knife' are:\tbox cutter\tpaper cutter\tscissors\nThere are several useful visual features to tell there is 'kitchen knife' and not similar things in a photo:\tblade made of steel or ceramic\tpointed tip and sharp edge\tpolymer, wood or metal handle\tserrated or non-serrated edge", 19], "windsheild": ["Yes. 'Windshield' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'windshield' but are not 'windshield' are:\twindow\tmirror\tscreen\tdoor\nThere are several useful visual features to tell there is 'windshield' and not similar things in a photo:\tcurved shape\tlocated at the front of a vehicle\tglass or translucent material\twipers at the base\tdeflects wind and debris while driving.", 19], "ornate clock": ["Yes. 'Ornate clock' has a tangible appearance and is a kind of timepiece.\nA few things that are visually similar to 'ornate clock' but are not 'ornate clock' are:\twristwatch\tpocket watch\tsundial\nThere are several useful visual features to tell there is 'ornate clock' and not similar things in a photo: large in size, usually the size of a wall or mantlepiece\telaborate decorations such as carvings or embellishments\tmetallic or wooden materials, sometimes with jewels or inlaid designs\ta visible clock face with hands or a digital display", 19], "slit": ["Yes. 'Slit' has a tangible appearance and is a narrow cut or opening.\nA few things that are visually similar to 'slit' but are not 'slit' are:\tcrevice\tcracks\tgrooves\tcuts\nThere are several useful visual features to tell there is 'slit' and not similar things in a photo:\tlong and narrow\tclean edges\twithin a larger object or surface\tstraight or curved\tlines up with other slits to create a pattern.", 19], "individuals": ["No. 'Individuals' is too vague or abstract to be distinguished in a photo.", 19], "shaggy dog": ["Yes. 'Shaggy dog' has a tangible appearance and refers to a dog that has long, unkempt fur.\nA few things that are visually similar to 'shaggy dog' but are not 'shaggy dog' are:\tpoodle\tmop\tmatted rug\nThere are several useful visual features to tell there is 'shaggy dog' and not similar things in a photo:\tcovered in long, messy fur\thair around the eyes and ears\thair that drapes over the eyes\tshaggy fur on the face, legs and body", 19], "side profile": ["Yes. 'Side profile' has a tangible appearance and refers to a specific angle at which a person's face is viewed.\nA few things that are visually similar to 'side profile' but are not 'side profile' are:\tfrontal face view\tthree-quarter face view\tfull body view\nThere are several useful visual features to tell there is 'side profile' and not similar things in a photo:\tprofile view of a person's face\tside of the face is visible\twhile facing laterally\thead is turned toward the cameraviews only one-half of the face", 19], "wet nose": ["Yes. 'Wet nose' has a tangible appearance and is a physical characteristic of animals.\nA few things that are visually similar to 'wet nose' but are not 'wet nose' are:\tshiny objects\twet surfaces\nThere are no useful visual features to distinguish 'wet nose' from any similar things in a photo, as it is a specific physical characteristic that only belongs to animals.", 19], "skateboard boy": ["Yes. 'Skateboard boy' has a tangible appearance and refers to a boy riding a skateboard.\nA few things that are visually similar to 'skateboard boy' but are not 'skateboard boy' are:\trollerblading boy\tbicycling boy\tscootering boy\t\nSeveral useful visual features to tell there is a 'skateboard boy' and not similar things in a photo are:\twearing skateboard shoes\tand trucks\twearing a helmet or other protective gear\tmoving on a board with four wheels doing tricks like ollies or kickflips.", 19], "seashell": ["Yes. 'Seashell' has a tangible appearance and refers to the hard outer covering of a shellfish.\nA few things that are visually similar to 'seashell' but are not 'seashell' are:\trocks\tpebbles\tbones\tcoral\nThere are several useful visual features to tell there is 'seashell' and not similar things in a photo:\thard outer surface\tcoiled, spiraled or curved shape\tpink, white or brown color\tmay have stripes or patterns on its surface\twith a small opening at one end.", 19], "brick facade": ["Yes. 'Brick facade' has a tangible appearance and is a kind of building exterior.\nA few things that are visually similar to 'brick facade' but are not 'brick facade' are:\tstone facade\tstucco facade\tpainted facade\twooden facade\nThere are several useful visual features to tell there is 'brick facade' and not similar things in a photo:\trectangular bricks\tin a pattern of alternating rows of short and long bricks\tred, orange, or brown color\ttextured surface", 19], "metal statue": ["Yes. 'Metal statue' has a tangible appearance and is a three-dimensional object made of metal.\nA few things that are visually similar to 'metal statue' but are not 'metal statue' are:\tmannequin\tsculpture\toutdoor furniture\nThere are several useful visual features to tell there is 'metal statue' and not similar things in a photo:\n\n- 3D object with intricate details \n- Made entirely out of metal, or mostly metal \n- Can depict a person, animal or object \n- Can be larger than life size or smaller than life size \n- Can be displayed indoors or outdoors as a stand-alone piece or as part of a larger installation.", 19], "towel rack wall": ["Yes. 'Towel rack wall' has a tangible appearance and refers to a wall-mounted object for hanging towels.\nA few things that are visually similar to 'towel rack wall' but are not 'towel rack wall' are:\tshelf\twall-mounted hook\twall-mounted hanger\twall-mounted bar\nThere are several useful visual features to tell there is 'towel rack wall' and not similar things in a photo:\thorizontal bar\twith attached hooks\tdesigned for towels or clothes\tuseful in a bathroom or a kitchen", 19], "furry tail": ["Yes. 'Furry tail' has a tangible appearance and is a physical characteristic of animals.\nA few things that are visually similar to 'furry tail' but are not 'furry tail' are:\tbrush\thair\twig\t\nThere are several useful visual features to tell there is 'furry tail' and not similar things in a photo:\tfurry or hairy\tappears attached to an animal's hindquarters\tmoves or swings with the animal's movement\thas a distinct shape or color\tfor animals, serves as a tool for communication, balance or insulation", 19], "round dish": ["Yes. 'Round dish' has a tangible appearance and is a type of tableware.\nA few things that are visually similar to 'round dish' but are not 'round dish' are:\tbowl\tplate\tpan\tpot\tfrying pan\nThere are several useful visual features to tell there is 'round dish' and not similar things in a photo:\tflat or slightly curved bottom\tround or circular shape\tsides that curve up slightly to hold food or liquid in\tthe size can vary from small saucers to large serving dishes\tgenerally made of ceramic, porcelain, glass, or metal.", 19], "grey door": ["Yes. 'Grey door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'grey door' but are not 'grey door' are:\tblue door\tblack door\tred door\twooden door\nThere are several useful visual features to tell there is 'grey door' and not similar things in a photo:\tgrey color\tplain or without pattern\thandles or knobs made of metal or plastic\trectangular shape", 19], "pink object": ["Yes. 'Pink object' has a tangible appearance and describes an object that is pink in color.\nA few things that are visually similar to 'pink object' but are not 'pink object' are:\tpink flowers\tpink clothing\tpink cars\tpink walls\nThere are no concrete visual features that unequivocally distinguish a 'pink object' from other pink things. However, some other features can help to identify the specific kind of object, such as shape, texture, and purpose (e.g., a pink ball, a pink sofa, a pink vase).", 19], "heard": ["No. 'Heard' is too vague or abstract to be distinguished in a photo.", 19], "snow flakes": ["Yes. 'Snow flakes' has a tangible appearance and is a natural meteorological phenomenon.\nA few things that are visually similar to 'snow flakes' but are not 'snow flakes' are:\tglitter\tsugar crystals\tsalt\tcrushed ice\nThere are several useful visual features to tell there is 'snow flakes' and not similar things in a photo:\tsix-pointed or hexagonal shape\tintricate and unique patterns\ttranslucent or transparent\twhite or light blue color\tsmall size (compared to other objects in the photo)", 19], "gold doorknob": ["Yes. 'Gold doorknob' has a tangible appearance and is a kind of doorknob.\nA few things that are visually similar to 'gold doorknob' but are not 'gold doorknob' are:\tbrass doorknob\tcopper doorknob\tchrome doorknob\nThere are several useful visual features to tell there is 'gold doorknob' and not similar things in a photo:\tmetallic yellow or golden color\tspherical or cylindrical in shape\tsmall or large in size\twith or without pattern or design\tattached to a door", 19], "lush trees": ["Yes. 'Lush trees' has a tangible appearance and refers to trees that are dense and full of leaves.\nA few things that are visually similar to 'lush trees' but are not 'lush trees' are:\tevergreen trees with needles, such as pine or spruce\ttrees with thin or bare branches, such as birch or willow\nThere are several useful visual features to tell there are 'lush trees' and not similar things in a photo:\tdense foliage and leaves or needles\tvaried shades of green or other colors\tabundance of branches and twigs\tthick trunks or stems\tgrowth in a forest or wooded area", 19], "time stamp": ["Yes. 'Time stamp' has a tangible appearance and is a kind of label or marking with a date and time.\nA few things that are visually similar to 'time stamp' but are not 'time stamp' are:\tdate on a calendar\tpostmark on a letter\tor timestamp on a digital file\nThere are several useful visual features to tell there is 'time stamp' and not similar things in a photo:\ta specific date and time (often in the corner or bottom of an image)\ta format (e.g., MM/DD/YYYY HH:MM:SS)\tmay have an arrow or line connecting it to a specific part of the image.", 19], "brown sticks": ["Yes. 'Brown sticks' has a tangible appearance.\nA few things that are visually similar to 'brown sticks' but are not 'brown sticks' are:\ttree branches\troots\tdried pasta\tpencils\nThere are several useful visual features to tell there is 'brown sticks' and not similar things in a photo:\tno pointed edges\tof a uniform thickness\tlying haphazardly or seem to be arranged in a specific way\tcntains a rough or smooth texture.", 19], "pointer finger": ["Yes. 'Pointer finger' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'pointer finger' but are not 'pointer finger' are:\tbanana peppers\thot dogs\tbroken pencils\nThere are several useful visual features to tell there is 'pointer finger' and not similar things in a photo:\tlong and slender shape\trounded fingertip\tvisible knuckles\tand nails (if not trimmed)", 19], "bathroom scene": ["Yes, 'bathroom scene' has a tangible appearance and refers to a specific type of setting.\nA few things that are visually similar to 'bathroom scene' but are not 'bathroom scene' are:\tlaundry room\tkitchen\tshower room\tlocker room\nThere are useful visual features to tell there is 'bathroom scene' and not similar things in a photo:\ttoilet\tsink\tbathtub\tor shower\tMirror\tTowels\tSoap bars or dispensers\tBath mats\tor rugs\tDifferent types of bathroom fixtures and accessories.", 19], "sidewalks": ["Yes. 'Sidewalks' has a tangible appearance and is a pathway for pedestrians along a road or street.\nA few things that are visually similar to 'sidewalks' but are not 'sidewalks' are:\tparking lot roads\ttrails around a park\tswimming pool deck\twalkway to a house\nThere are several useful visual features to tell there is 'sidewalks' and not similar things in a photo:\tlevel with the ground\tpedestrian traffic\tsigns or markings\tfor use by walkers or runners\tpavement or concrete material", 19], "rivet": ["Yes. 'Rivet' is a visually concrete concept and is a type of fastener used to join materials together.\nA few things that are visually similar to 'rivet' but are not 'rivet' are:\tnail\tscrew\tbolt\nThere are several useful visual features to tell there is 'rivet' and not similar things in a photo:\tdome-shaped head\twith a long shank used to hold two materials together\tusually made of metal, such as steel or aluminum\thas ridges or grooves on the surface\tfor industrial, construction, or aerospace use can be tiny or large depending on the application.", 19], "grey cloudy skies": ["Yes. 'Grey cloudy skies' has a tangible appearance and is a kind of weather condition.\nA few things that are visually similar to 'grey cloudy skies' but are not 'grey cloudy skies' are:\tfog\tsmoke\tdust storm\t\nThere are several useful visual features to tell there is 'grey cloudy skies' and not similar things in a photo:\tdull or muted grey color\thazy or indistinct forms\tcloud-like shapes\tobscuring or dimming sunlight", 19], "clay vase": ["Yes. 'Clay vase' has a tangible appearance and is a type of pottery.\nA few things that are visually similar to 'clay vase' but are not 'clay vase' are:\tceramic bowls\tglassware\tplant pots\nThere are several useful visual features to tell there is 'clay vase' and not similar things in a photo:\tmade of clay or earthenware\tunique shape or design\trough or textured surface\tmay have decorative elements or patterns like paint or carvings.", 19], "orange peel": ["Yes. 'Orange peel' has a tangible appearance and is a part of an orange.\nA few things that are visually similar to 'orange peel' but are not 'orange peel' are:\tlemon peel\tlime peel\tgrapefruit peel\nThere are several useful visual features to tell there is 'orange peel' and not similar things in a photo:\trough texture\tbright orange color\tporous surface\twith the characteristic shape of the orange peel.", 19], "boy shirt": ["Yes. 'Boy shirt' has a tangible appearance and is a type of clothing article.\nA few things that are visually similar to 'boy shirt' but are not 'boy shirt' are:\tmen's shirt\twomen's shirt\tpolo shirt\tdress shirt\t\nThere are several useful visual features to tell there is 'boy shirt' and not similar things in a photo:\tshort sleeves or long sleeves\tsimple designs\tbreathable and comfortable fabric\tbutton-up front or pull-over", 19], "hairband": ["Yes. 'Hairband' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'hairband' but are not 'hairband' are:\theadband\tscarf\tribbon\tneckerchief\nThere are several useful visual features to tell there is 'hairband' and not similar things in a photo:\tsnugly fitting around the head\tmade of elastic material\tor flexible plastic or metal\tbanded design to push hair away from the face\tand the eyes.", 19], "firefighter": ["Yes. 'Firefighter' has a tangible appearance and is a person who extinguishes fires.\nA few things that are visually similar to 'firefighter' but are not 'firefighter' are: police officer, construction worker, paramedic\nThere are several useful visual features to tell there is 'firefighter' and not similar things in a photo:\tfire-resistant uniform\ttraditional helmet with shield firefighting equipment, such as hose or axe", 19], "fire hose": ["Yes. 'Fire hose' has a tangible appearance and is a type of hose used by firefighters to extinguish fires.\nA few things that are visually similar to 'fire hose' but are not 'fire hose' are: garden hose, vacuum hose, fuel line.\nThere are several useful visual features to tell there is 'fire hose' and not similar things in a photo: red or yellow color, large diameter, nozzle at one end, connection at the other end, stenciled markings.", 19], "artifact": ["Yes. 'Artifact' has a tangible appearance and refers to objects that have been created by humans.\nA few things that are visually similar to 'artifact' but are not 'artifact' are:\trocks\tnatural formations\nThere are several useful visual features to tell there are 'artifact' and not similar things in a photo:\tman-made\tunique shapes, designs, or patterns\toften made of materials like ceramics or metals\twith signs of culture or history, such as inscriptions or symbols", 19], "support pillar": ["Yes. 'Support pillar' has a tangible appearance and is a kind of architectural element.\nA few things that are visually similar to 'support pillar' but are not 'support pillar' are:\tcolumn\tpole\tfence\tpost\nThere are several useful visual features to tell there is 'support pillar' and not similar things in a photo:\tvertical structure\twith rectangular, round, or polygonal shapes\tsupporting a roof, a ceiling, or a floor with its weight and other loads\tdifferent capitals on the top.", 19], "cement building": ["Yes. 'Cement building' has a tangible appearance and is a type of structure made of cement.\nA few things that are visually similar to 'cement building' but are not 'cement building' are:\tbrick building\tstone building\tglass building\twooden building\nThere are several useful visual features to tell there is 'cement building' and not similar things in a photo:\t\nuniform dull gray or beige color\nrigid geometric lines and angles\n smooth, straight surfaces with no visible seams or joints\n No visible grain or texture like wood or stone.", 19], "spot lights": ["Yes. 'Spot lights' has a tangible appearance and is a kind of lighting fixture.\nA few things that are visually similar to 'spot lights' but are not 'spot lights' are:\tcandles\tflashlights\tstreet lamps\tvanity lights\nThere are several useful visual features to tell there is 'spot lights' and not similar things in a photo:\tdirectionality focused on a specific area or object\tcone-shaped beam of light\tusually installed in the ceiling or on the ground\tbright and intense light source.", 19], "caution line": ["Yes. 'Caution line' has a tangible appearance and is a type of safety equipment.\nA few things that are visually similar to 'caution line' but are not 'caution line' are:\tbarrier\ttape\tcord\trope\nThere are several useful visual features to tell there is 'caution line' and not similar things in a photo:\tbright yellow or red color with black stripes\torangered color for Halloween\tevents or construction sites", 19], "banana tree": ["Yes. 'Banana tree' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'banana tree' but are not 'banana tree' are:\tPalm tree\tCoconut tree\tPineapple plant\nThere are several useful visual features to tell there is 'banana tree' and not similar things in a photo:\tLarge leaves that are usually green, sometimes purple or reddish-brown\tStalk that produces bunches of bananas\tHeight (often up to 30 feet or more)\tLong, tapered shape of the individual bananas in the bunch", 19], "bushy tail": ["Yes. 'Bushy tail' has a tangible appearance and refers to a type of tail that is full and thick.\nA few things that are visually similar to 'bushy tail' but are not 'bushy tail' are:\twhisk broom\thorse mane\tfur coat\tfeather duster\nThere are several useful visual features to tell there is 'bushy tail' and not similar things in a photo:\tthick and full tail\tfur or hair covering\tthe tail is longer than the body\tthe tail seems to be used for balance or communication.", 19], "semi truck": ["Yes. 'Semi truck' has a tangible appearance and is a type of large vehicle.\nA few things that are visually similar to 'semi truck' but are not 'semi truck' are:\tpickup truck\tvan\tbus\ttrailer\nThere are several useful visual features to tell there is 'semi truck' and not similar things in a photo:\tlarge size\ttwo-part vehicle with a separate tractor and trailer section\twheels on the trailer\tunusually tall and wide compared to other vehicles on the road\tcab for the driver and passengers\tinclined cargo area for hauling large goods or equipment.", 19], "canopies": ["Yes. 'Canopies' has a tangible appearance and refers to a covering that provides shade or shelter.\nA few things that are visually similar to 'canopies' but are not 'canopies' are:\tumbrellas\tshelters\ttents\tawnings\nThere are several useful visual features to tell there is 'canopies' and not similar things in a photo:\toverhead covering made of fabric or other materials\tsupported by poles or other structures\tprovides shade or shelter\tfor outdoor use, such as for patios, decks, or outdoor events", 19], "record": ["Yes. 'Record' has a tangible appearance and is a type of music storage medium.\nA few things that are visually similar to 'record' but are not 'record' are: CD, cassette tape, MP3 player, smartphone\nThere are several useful visual features to tell there is 'record' and not similar things in a photo:\tcircular shape with a small hole in the center\tgrooves on the surface\ttypically made of black vinyl with a paper label in the center of the disc\tmay have visible scratches or dust on the surface\tof a certain size, usually larger than a CD or cassette", 19], "spike": ["Yes. 'Spike' has a tangible appearance and refers to a sharp, pointed object.\nA few things that are visually similar to 'spike' but are not 'spike' are:\tneedle\tthorn\tawl\tpick\nThere are several useful visual features to tell there is 'spike' and not similar things in a photo:\tpointed tip\tsharp edges\tsolid and rigid structure\tno sharp teeth or serrations", 19], "calm river": ["Yes. 'Calm river' has a tangible appearance and is a type of water body.\nA few things that are visually similar to 'calm river' but are not 'calm river' are:\tswimming pool\tpond\tbay\tor lake\nThere are several useful visual features to tell there is 'calm river' and not similar things in a photo:\twinding path\tvegetation on the banks\tdepth variations\tsmooth surface\tno visible edges or corners", 19], "story bus": ["Yes. 'Story bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'story bus' but are not 'story bus' are:\tmobile library\tbookmobile\tschool bus\tcoach bus\nThere are several useful visual features to tell there is 'story bus' and not similar things in a photo:\tlarge, colorful vehicle\twith illustrations or murals on the exterior\twritten words indicating that it is a 'story bus'\tlots of books inside or visible through windows\tdisplaying storytelling events or activities\tspeakers and microphones\tfor storytelling performances ", 19], "brick walk way": ["Yes. 'Brick walk way' has a tangible appearance and is a type of pathway.\nA few things that are visually similar to 'brick walk way' but are not 'brick walk way' are:\tstone pathway\tcement walkway\twooden planks\nThere are several useful visual features to tell there is 'brick walk way' and not similar things in a photo:\tarrangement of rectangular shapes, often with alternating orientations\tpatterned or straight lines\tdark red or brown color\ttexture and surface roughness", 19], "kickstand motorcycle": ["Yes. 'Kickstand motorcycle' has a tangible appearance and is a type of motorcycle.\nThere aren't many things that are visually similar to 'kickstand motorcycle' as it is a specific object. However, a few things that could be similar and are not 'kickstand motorcycle' are:\tmotorcycle without a kickstand\tbicycle with a kickstand\nThere are several useful visual features to tell there is 'kickstand motorcycle' and not similar things in a photo:\ttwo wheels\tmetallic frame\thandlebars\tkickstand\tnext to the bike\ton a street or parking lot.", 19], "watch person": ["No. 'Watch person' is too vague or abstract to be a visually concrete concept.\nThere are no things that are visually similar to 'watch person' that are not 'watch person' because the term is too vague to have a distinct visual appearance.\nAs 'watch person' is not a visually concrete concept, there are no useful visual features to distinguish it from similar things in a photo.", 19], "parents": ["No. 'Parents' is too vague or abstract to be distinguished in a photo.", 19], "telephone wire": ["Yes. 'Telephone wire' has a tangible appearance and is a type of cable wire.\nA few things that are visually similar to 'telephone wire' but are not 'telephone wire' are:\telectrical wire\tcable wire\tfiber optic cable\twire fence\nThere are several useful visual features to tell there is 'telephone wire' and not similar things in a photo:\tthin wire\tstrung between poles or buildings\tusually black, gray, or silver in color\thanging in a pattern or connected to a pole or a building\ttopped with insulators to prevent electrical interference.", 19], "dove": ["Yes. 'Dove' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'dove' but are not 'dove' are:\tpigeon\tsparrow\tseagull\trobin\nThere are several useful visual features to tell there is 'dove' and not similar things in a photo:\tmedium-sized bird\trounded head and body\tsmooth feathers\tpale color with dark spots or stripes on wings\tand a distinctive blue-grey head and neck with a black collar.", 19], "pink bottle": ["Yes. 'Pink bottle' has a tangible appearance and describes a specific color and shape of container.\nA few things that are visually similar to 'pink bottle' but are not 'pink bottle' are:\tpink vase\tpink jar\nThere are several useful visual features to tell there is 'pink bottle' and not similar things in a photo:\ttall and narrow shape\tsmooth and curved surface\tpink or pastel color\tmaterial of the bottle (e.g. plastic, glass)", 19], "clock clock tower": ["Yes. 'Clock tower' has a tangible appearance and is a type of building with a clock face.\nA few things that are visually similar to 'clock tower' but are not 'clock tower' are:\twater tower\tbell tower\tchurch spire\t\nThere are several useful visual features to tell there is 'clock tower' and not similar things in a photo:\ttall building with a clock face\tbell or chimes attached to the clock\tspires or pointed tops\tclock hands can be seen on the clock face", 19], "water knob": ["Yes. 'Water knob' has a tangible appearance and is a common household item.\nA few things that are visually similar to 'water knob' but are not 'water knob' are:\tdoor knob\tdrawer handle\tlight switch\tbathroom handle\nThere are several useful visual features to tell there is 'water knob' and not similar things in a photo:\tusually silver or chrome finish\tround or lever-shaped\tcontrol water flow or temperature\tlabeled with \"hot\" and \"cold\" or \"on\" and \"off\"", 19], "gold statue": ["Yes. 'Gold statue' has a tangible appearance and is a type of sculpture.\nA few things that are visually similar to 'gold statue' but are not 'gold statue' are:\tpainted statue\tbronze statue\tresin statue\t\nThere are several useful visual features to tell there is 'gold statue' and not similar things in a photo:\tmade of gold-metallic looking\tshiny or reflective surface\tin a specific shape or form (human, animal, abstract)", 19], "seabird": ["Yes. 'Seabird' has a tangible appearance and refers to birds that live near the ocean.\nA few things that are visually similar to 'seabird' but are not 'seabird':\tseagull\tpelican\tfrigatebird\tpenguin\nThere are several useful visual features to tell there is 'seabird' and not similar things in a photo:\tlarge wingspan\twebbed feet\tability to swim in water\ttapered beaks or bills\tfeathers designed for water resistance\tfrequently found near the coast or on the beach.", 19], "porcelain tub": ["Yes. 'Porcelain tub' has a tangible appearance and is a type of bathtub.\nA few things that are visually similar to 'porcelain tub' but are not 'porcelain tub' are:\tPlastic tubs\tAcrylic tubs\tSteel tubs\nThere are several useful visual features to tell there is 'porcelain tub' and not similar things in a photo:\tbuild with white pottery\tshiny surface\trounded shape\twith legs or without", 19], "shadow skateboard": ["Yes. 'Shadow skateboard' has a tangible appearance and refers to a skateboard that is in the shape of a shadow.\nThere are no items similar to 'shadow skateboard' that are not 'shadow skateboard' as it refers to a unique object.\nUseful visual features for distinguishing 'shadow skateboard' in a photo would include: the distinct and recognizable shape of a skateboard, the \"shadow\" or black outline of the skateboard, and any additional details or designs on the skateboard that make it unique.", 19], "hippo": ["Yes. 'Hippo' has a tangible appearance and is an animal.\nA few things that are visually similar to 'hippo' but are not 'hippo' are:\trhino\tbuffalo\tpig\twarthog\twhale\nThere are several useful visual features to tell there is a 'hippo' and not similar things in a photo:\twide mouth\twith large teeth\tand canines\tthick and stocky legs\tbig body\tdark grey skin\twith almost no hair\tand small eyes", 19], "motorcycle kick stand": ["Yes. 'Motorcycle kick stand' has a tangible appearance and is a part of a motorcycle.\nA few things that are visually similar to 'motorcycle kick stand' but are not 'motorcycle kick stand' are:\tbicycle kick stand\tscooter kick stand\tmotorcycle frame\nThere are several useful visual features to tell there is 'motorcycle kick stand' and not similar things in a photo:\tattached to the motorcycle frame\tangled down towards the ground\tlocated on one side of the motorcycle\televating the motorcycle off the ground\ton the ground when in use", 19], "dark stain": ["Yes. 'Dark stain' has a tangible appearance and is a discoloration or mark on a surface.\nA few things that are visually similar to 'dark stain' but are not 'dark stain' are:\tshadow\tdirt\tsmudge\twatermark\nThere are several useful visual features to tell there is 'dark stain' and not similar things in a photo:\tdark color\tirregular shape or texture\tlocated on a surface, not cast by an object\tsurrounded by a lighter shade or hue\tof different color than the surrounding surface.", 19], "tire marks": ["Yes. 'Tire marks' has a tangible appearance and refers to the indentations made by tires on a surface.\nA few things that are visually similar to 'tire marks' but are not 'tire marks' are:\tfootprints\tbicycle tracks\tanimal tracks\timpressions on soft ground\nThere are several useful visual features to tell there is 'tire marks' and not similar things in a photo:\tElliptical shape of a tire imprint\tPattern of closely spaced lines with gaps in between them\tGrooves formed by the treads on the tire\tSmooth and curved hollow indentations \tin the shape of tire on a solid surface", 19], "side engine": ["Yes. 'Side engine' has a tangible appearance and refers to an engine located on the side of a vehicle or aircraft.\nA few things that are visually similar to 'side engine' but are not 'side engine' are:\ttop engine\trear engine\tbottom engine\trocket thruster\nThere are several useful visual features to tell there is 'side engine' and not similar things in a photo:\tlocated on the side of a vehicle or aircraft\tcylindrical shape\twith exhaust pipes and nozzles visible\tmounted at an angle to the main body of the vehicle or aircraft.", 19], "tennis court surface": ["Yes. 'Tennis court surface' has a tangible appearance and refers to the specific surface on which tennis is played.\nA few things that are visually similar to 'tennis court surface' but are not 'tennis court surface' are:\tbasketball court surface\tvolleyball court surface\ttrack and field surface\tsoccer field surface\nThere are several useful visual features to tell there is 'tennis court surface' and not similar things in a photo:\tsymmetrical, rectangular shape\twhite lines marking the court\tcolor and texture of the surface (usually green or red clay)\tnet dividing the court into two sides", 19], "typewriter": ["Yes. 'Typewriter' has a tangible appearance and is a type of machine used for writing.\nA few things that are visually similar to 'typewriter' but are not 'typewriter' are:\tcomputer\tkeyboard\tprinter\tcash register\nThere are several useful visual features to tell there is 'typewriter' and not similar things in a photo:\tretro design\twith keys for letters, numbers, and symbols\ta carriage for holding paper\ta roller for ink ribbon (if not electric)\ttypewriter brand name or model number\tcarrying case or dust cover.", 19], "church steeple": ["Yes. 'Church steeple' has a tangible appearance and is a type of tower.\nA few things that are visually similar to 'church steeple' but are not 'church steeple' are:\tcell phone tower\tpower line transmission tower\tlighthouse\twater tower\nThere are several useful visual features to tell there is 'church steeple' and not similar things in a photo:\tnarrow and pointed at the top\tusually attached to a church or other religious building\tmay have a cross on top\tmade of brick, stone or wood\tmay have decorative elements, such as spires or clock faces.", 18], "traffic line": ["Yes. 'Traffic line' has a tangible appearance and is a marking on the road.\nA few things that are visually similar to 'traffic line' but are not 'traffic line' are:\tshadows\tcracks in the pavement\tpainted arrows or symbols\tpedestrian crosswalks\nThere are several useful visual features to tell there is 'traffic line' and not similar things in a photo:\tconsistent width and color\tstraight and continuous\tlines run parallel to other traffic lines\tthe line is painted on the road", 18], "order": ["No. 'Order' is too vague or abstract to be distinguished in a photo. It is a concept related to organization, arrangement, or systematization, which does not have a tangible appearance.\nThere are no things that are visually similar to 'order' because it is an abstract concept.\nVisual features are not applicable to distinguish 'order' from similar things in a photo. Instead, we need to understand the context, purpose, and meaning of order in a specific situation or domain.", 18], "cement road": ["Yes. 'Cement road' has a tangible appearance and is a kind of roadway.\nA few things that are visually similar to 'cement road' but are not 'cement road' are:\tasphalt road\tpaved road\tcobblestone road\tdirt road\nThere are several useful visual features to tell there is 'cement road' and not similar things in a photo:\tlight grey color\tsmooth surface\twithout visible cracks or bumps\tin rectangular sections.", 18], "silverware napkin": ["No. 'Silverware napkin' is too vague or abstract as it is not clear what is meant by it. \n\nIf the intended concept is 'napkin with silverware', then:\n\nA few things that are visually similar to 'napkin with silverware' but are not 'napkin with silverware' are:\tnapkin with cutlery\tnapkin with utensils\tnapkin with flatware\tnapkin with silverware holder\nThere are several useful visual features to tell there is 'napkin with silverware' and not similar things in a photo:\tnapkin folded neatly around the cutlery or utensils\tshiny silverware visible through the napkin\tcutlery arranged in a specific way on the napkin (e.g. aligned in parallel)", 18], "bottom sign": ["No. 'Bottom sign' is too vague or abstract to be distinguished in a photo.", 18], "metal stove": ["Yes. 'Metal stove' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'metal stove' but are not 'metal stove' are:\toven\trange\tmicrowave\tdishwasher\nThere are several useful visual features to tell there is 'metal stove' and not similar things in a photo:\tstandalone or built-in cooking device\tflat, smooth cooking surface\tcooking rings or burners\tdials or digital controls\toven beneath the cooking surface or in a separate compartment\tmetal construction with visible rivets or seams.", 18], "shirtless": ["Yes. 'Shirtless' has a tangible appearance and refers to a person not wearing a shirt.\nA few things that are visually similar to 'shirtless' but are not 'shirtless' are:\tpeople wearing tank tops people wearing crop tops naked people people wearing bras (for women)\nThere are several useful visual features to tell there is 'shirtless' and not similar things in a photo:\tthe bare chest of a person absence of a shirt or t-shirt", 18], "wood building": ["Yes. 'Wood building' has a tangible appearance and is a type of construction made of wood.\nA few things that are visually similar to 'wood building' but are not 'wood building' are:\tbricks building\tcement building\tmetal structure\tstone structure\nThere are several useful visual features to tell there is 'wood building' and not similar things in a photo:\twooden walls, roof or columns\tpitched roof, gable roof or gambrel roof\tdoors and windows with wooden frames\tporch or veranda with wooden columns or railings", 18], "park benches": ["Yes. 'Park benches' has a tangible appearance and is a kind of outdoor seating.\nA few things that are visually similar to 'park benches' but are not 'park benches' are:\tchairs\tsun loungers\tpicnic tables\nThere are several useful visual features to tell there is 'park benches' and not similar things in a photo:\tMultiple people can sit on them\tWooden or metal frames with slatted seats\tLocated in a public or park setting", 18], "automobiles": ["Yes. 'Automobiles' has a tangible and recognizable appearance.\nA few things that are visually similar to 'automobiles' but are not 'automobiles' are:\ttrucks\tmotorcycles\tbicycles\tscooters\nThere are several useful visual features to tell there is 'automobiles' and not similar things in a photo:\tfour wheels\tengine and transmission\tsteering wheel, pedals, and dashboard\tsideways or front-facing seats\twith or without a roof and doors designed for road use.", 18], "paper container": ["Yes. 'Paper container' has a tangible appearance and is a type of container made of paper.\nA few things that are visually similar to 'paper container' but are not 'paper container' are:\tPlastic container\tTin can\tGlass jar\tPolythene bag\nThere are several useful visual features to tell there is 'paper container' and not similar things in a photo:\tmade of paper or cardboard\tcylindrical or rectangular shape\tseam or fold lines on the sides of the container\ttask-specific labeling\ti.e. \"coffee cup,\" \"take-out container,\" \"takeaway box.\"", 18], "orange candle": ["Yes. 'Orange candle' has a tangible appearance and is a specific type of candle.\nA few things that are visually similar to 'orange candle' but are not 'orange candle' are:\tyellow candle\torange fruit\tflashlight\tlight bulb\nThere are several useful visual features to tell there is 'orange candle' and not similar things in a photo:\tcylindrical or slightly tapered shape\twax body\twick on the top\torange color\tburning flame\tscented aroma (if visible)", 18], "footpath": ["Yes. 'Footpath' has a tangible appearance and is a type of pathway for pedestrians.\nA few things that are visually similar to 'footpath' but are not 'footpath' are: roads, driveways, bike lanes, hiking trails\nThere are several useful visual features to tell there is 'footpath' and not similar things in a photo: narrow and designed for walking, often paved or smooth, may have markings or signs for pedestrians, and may be situated alongside or separate from a road.", 18], "round sink": ["Yes. 'Round sink' has a tangible appearance and is a type of bathroom or kitchen fixture.\nA few things that are visually similar to 'round sink' but are not 'round sink' are:\tbowl\tfountain\tsaucer\tpan\nThere are several useful visual features to tell there is 'round sink' and not similar things in a photo:\tcircular bowl shape\twith a drain in the center\tmounted on top of a counter\tor installed into the wall or surface\tused for washing hands, dishes, or produce", 18], "grassy ground": ["Yes. 'Grassy ground' has a tangible appearance and refers to a ground that is covered in grass.\nA few things that are visually similar to 'grassy ground' but are not 'grassy ground' are:\tjungle\tforest\tmeadow\twheat field\nThere are several useful visual features to tell there is 'grassy ground' and not similar things in a photo:\tclothed in grass or vegetation\tgreen color\tuneven surface", 18], "pony tail holder": ["Yes. 'Pony tail holder' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'pony tail holder' but are not 'pony tail holder' are:\thairband\tbracelet\trubber band\nThere are several useful visual features to tell there is 'pony tail holder' and not similar things in a photo: small and circular shape, the presence of a stretching mechanism or a loop to tie hair, and an absence of excessively beautiful or dangle structures.", 18], "orange sticker": ["Yes. 'Orange sticker' has a tangible appearance and is a type of label.\nA few things that are visually similar to 'orange sticker' but are not 'orange sticker' are:\tyellow sticker\tgreen sticker\tpink sticker\tpost-it note\nThere are several useful visual features to tell there is 'orange sticker' and not similar things in a photo:\torange color\trectangular shape\tpeel-and-stick adhesive\tbacking paper to protect the adhesive surface", 18], "break light": ["Yes. 'Brake light' has a tangible appearance and is a type of car light.\nA few things that are visually similar to 'brake light' but are not 'brake light' are:\tTail light\tHeadlights\tTurn signals\nThere are several useful visual features to tell there is 'brake light' and not similar things in a photo:\tBright red color\tLocated at the back of the car\tOnly turns on when the brakes are applied\tUsually in a rectangular or circular shape", 18], "blue belt": ["Yes. 'Blue belt' has a tangible appearance and refers to a belt of a particular color.\nA few things that are visually similar to 'blue belt' but are not 'blue belt' are:\tblue ribbon\tblue scarf\tblue tie\tblue sash\nThere are several useful visual features to tell there is 'blue belt' and not similar things in a photo:\ttypically a leather or cloth band worn around the waist\ta solid blue color\tmay have a metallic buckle or clasp\tvarying in width depending on its purpose (e.g. martial arts belt, fashion accessory)", 18], "shower curtains": ["Yes. 'Shower curtains' has a tangible appearance and is a type of bathroom curtain.\nA few things that are visually similar to 'shower curtains' but are not 'shower curtains' are:\twindow curtains\tblinds\tdrapes\nThere are several useful visual features to tell there is 'shower curtains' and not similar things in a photo:\tclearly hung in a shower area\tmade of waterproof or water-resistant fabric or material\tvariety of colors and patterns, often with a plastic or metal ring at the top", 18], "baby calf": ["Yes. 'Baby calf' has a tangible appearance and is a young cow.\nA few things that are visually similar to 'baby calf' but are not 'baby calf' are:\tadult cow\tox\tbuffalo\tgoat\nThere are several useful visual features to tell there is 'baby calf' and not similar things in a photo:\tsmall size\tsoft fur\torangish brown fur\tcolorful tags on ears or neck", 18], "blurry man": ["No. 'Blurry man' is too vague or abstract to be distinguished in a photo. It could refer to a person who appears blurred due to motion or a lack of focus, but it is not a visually concrete concept on its own.\nAs such, there are no things visually similar to 'blurry man'.\nUseful visual features for distinguishing a blurred person from other blurry objects could include: the general shape or silhouette of a person, clothing or accessories that suggest it is a person, and any facial or bodily features that are still discernible despite the blurriness.", 18], "dishcloth": ["Yes. 'Dishcloth' has a tangible appearance and is a type of cloth used for washing dishes.\nA few things that are visually similar to 'dishcloth' but are not 'dishcloth' are:\thand towel\tbath towel\trug\tpaper towel\nThere are several useful visual features to tell there is 'dishcloth' and not similar things in a photo:\tsmaller in size\tusually square or rectangular shape\tmade of cloth or fabric\thas a specific texture for cleaning and absorbing water or soap", 18], "royal": ["No. 'Royal' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we expand the concept to refer to objects associated with royalty or aristocracy, then it becomes a visually concrete concept. \n\nA few things that are visually similar to objects associated with royalty but are not 'royal' are:\tfake jewels\tfancy clothing\tgold-plated objects\nThere are several useful visual features to distinguish objects associated with royalty from the listed similar things in a photo: \n- crowns or tiaras \n- coats of arms \n- symbols such as fleur-de-lys or unicorns \n- royal colors such as purple, gold or red \n- luxurious textiles such as velvet, brocade or silk", 18], "tea bag": ["Yes. 'Tea bag' has a tangible appearance and is a common item for making tea.\nA few things that are visually similar to 'tea bag' but are not 'tea bag' are: coffee filter, sachet bag, mesh strainer\nThere are several useful visual features to tell there is 'tea bag' and not similar things in a photo: small, porous bag containing loose tea leaves or herbal blend a string and tag attached to the bag for dipping and removing the bag from the hot water", 18], "hairline": ["Yes. 'Hairline' has a tangible appearance and refers to the line where hair growth begins on the forehead or around the scalp.\nA few things that are visually similar to 'hairline' but are not 'hairline' are:\tfur line on an animal's face\twrinkles on the forehead\tlines on a piece of paper\tdirt line on a car windshield\nThere are several useful visual features to tell there is 'hairline' and not similar things in a photo:\tthe line of hair strands on the forehead\tor the border where the hair starts and the skin of the forehead or scalp ends\tthe shape of the curve or straightness", 18], "silver remote": ["Yes. 'Silver remote' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'silver remote' but are not 'silver remote' are:\tsilver phone\tsilver camera\tsilver speaker\tsilver laptop\nThere are several useful visual features to tell there is 'silver remote' and not similar things in a photo:\trectangular shape\tbuttons or keys for controlling a device\tsilver or grey color\tbattery compartment or charging port\tinfrared or Bluetooth transmitter for wireless operation.", 18], "dark spot": ["Yes. 'Dark spot' has a tangible appearance and is a visual descriptor.\nA few things that are visually similar to 'dark spot' but are not 'dark spot' are:\tshadow\tstain\tdirt\tsmudge\nThere is only one useful visual feature to tell there is a 'dark spot':\tan area of darker color or shade than its surroundings.", 18], "teacher": ["Yes. 'Teacher' has a tangible appearance and can be identified visually.\nA few things that are visually similar to 'teacher' but are not 'teacher' are:\tstudents\tlibrarian\tcounselor\ttrainer\nThere are several useful visual features to tell there is 'teacher' and not similar things in a photo:\twearing formal or professional clothing\tstanding in front of a class of students\tchalkboard or whiteboard in the background\thaving textbooks, papers, and lesson plans nearby\tusing gestures or pointing to explain a concept", 18], "bicycle chain": ["Yes. 'Bicycle chain' has a tangible appearance and is a part of a bicycle.\nA few things that are visually similar to 'bicycle chain' but are not 'bicycle chain' are:\tnecklace\tchoker\tbelt\t\nThere are several useful visual features to tell there is 'bicycle chain' and not similar things in a photo:\tlinks\twith a narrow width and long length\tmetallic or silver in color\tcircular in shape\tconnected to gears and pedals\tof a bike", 18], "dashes": ["Yes. 'Dashes' have a visually concrete appearance and refer to marks used in writing or printing.\nA few things that are visually similar to 'dashes' but are not 'dashes' are:\tdots\thyphens\tcommas\tperiods\tcolons\nThere are several useful visual features to tell there is 'dashes' and not similar things in a photo:\tlonger than a hyphen\ttwo hyphens joined together\tno space between adjacent words or elements", 18], "cat food": ["Yes. 'Cat food' has a tangible appearance and is type of pet food.\nA few things that are visually similar to 'cat food' but are not 'cat food' are:\tdog food\tcereal\tgrains\tchips\nThere are several useful visual features to tell there is 'cat food' and not similar things in a photo:\twet or dry texture\tbits or chunks of meat or fish\tpackaged in a can or a bag\tlabeled as 'cat food'\tor with an image of a cat on it.", 18], "rail fence": ["Yes. 'Rail fence' has a tangible appearance and is a type of fence made of horizontal rails.\nA few things that are visually similar to 'rail fence' but are not 'rail fence' are:\tpicket fence\twire fence\tstone wall\nThere are several useful visual features to tell there is 'rail fence' and not similar things in a photo:\thorizontal rails\tpost-and-rail construction, where the rails fit into pre-drilled holes in the posts\trustic or country appearance\ttypically made with wood or logs.", 18], "kitchen cupboards": ["Yes. 'Kitchen cupboards' has a tangible appearance and refers to a kind of furniture found in a kitchen.\nA few things that are visually similar to 'kitchen cupboards' but are not 'kitchen cupboards' are:\tbookshelves\tcabinets\tshoe racks\nThere are several useful visual features to tell there are 'kitchen cupboards' and not similar things in a photo:\tusually attached to a wall or above a countertop\thandles or knobs for opening and closing doors\thaving shelves or drawers for storage of kitchen items\ttypically found in a kitchen setting with appliances, utensils, and cookware nearby.", 18], "silver engine": ["Yes. 'Silver engine' has a tangible appearance and is a type of mechanical component.\nA few things that are visually similar to 'silver engine' but are not 'silver engine' are:\tgenerator\tmotor\tcar\tchrome parts\nThere are several useful visual features to tell there is 'silver engine' and not similar things in a photo:\tmetallic and silver in color\tcontains many moving parts and gears\tsquare or rectangular in shape\thas bolts, screws or other fasteners", 18], "sauces": ["Yes. 'Sauces' has a tangible appearance and are liquid condiments.\nA few things that are visually similar to 'sauces' but are not 'sauces' are:\tdrinks\tsyrups\tlubricants\nThere are several useful visual features to tell there are 'sauces' and not similar things in a photo:\tviscous, liquid consistency\tvariety of colors and textures\tpoured or drizzled on food\tserved in containers or bowls with spoons or ladles.", 18], "keyhole": ["Yes. 'Keyhole' has a tangible appearance and is a specific shape.\nA few things that are visually similar to 'keyhole' but are not 'keyhole' are:\tthe letter 'O'\tan eye\ta buttonhole\nThere are several useful visual features to tell there is 'keyhole' and not similar things in a photo:\ta specific oval or circular shape\ta smaller hole within a larger shape\ta narrow slit shape at the top", 18], "staff": ["Yes. 'Staff' has a tangible appearance and can refer to a long stick or a group of employees.\nA few things that are visually similar to 'staff' but are not 'staff' are:\trod\tstick\tpole\tpaddle\tpersonnel\tteam\nThere are several useful visual features to tell there is 'staff' and not similar things in a photo:\n\nFor the object 'staff':\nlong and thin\tsingle piece of wood or metal\tmight have a curled thin decoration at the top\n\nFor the employees 'staff':\nuniform or dress code\tsimilar badge or name tags\tword tags\tor other identification that shows their affiliation with the company", 18], "silver bike": ["Yes. 'Silver bike' has a tangible appearance and is a bicycle with a silver color.\nA few things that are visually similar to 'silver bike' but are not 'silver bike' are:\tother bikes with different colors\tscooters with a silver color\tmotorcycles with a silver color\nThere are several useful visual features to tell there is 'silver bike' and not similar things in a photo:\ttwo wheels\tpedals\thandlebars\tsilver color frame and wheels\tbicycle seat and chain", 18], "dvd players": ["Yes. 'DVD players' has a tangible appearance and is an electronic device used to play DVDs.\nA few things that are visually similar to 'DVD players' but are not 'DVD players' are:\tBlu-ray players\tgame consoles\tmedia streaming devices\tset-top boxes\nThere are several useful visual features to tell there is 'DVD players' and not similar things in a photo:\tDVD tray\tinfrared sensor\tforward, backward, play, and stop buttons\tdisplay screen for tracking playback time and menu settings.", 18], "picture window": ["Yes. 'Picture window' has a tangible appearance and refers to a large, fixed window that provides a view of the outside.\nA few things that are visually similar to 'picture window' but are not 'picture window' are:\tslider window\tcasement window\tbay window\tstorefront window\nThere are several useful visual features to tell there is 'picture window' and not similar things in a photo:\tlarge and fixed\twindow extends from floor to ceiling or almost the whole height of a wall\tprovides an unobstructed view\tof outdoors or a specific scenery, such as a garden or a cityscape.", 18], "records": ["Yes. 'Records' have a tangible appearance and are physical discs used for music storage.\nA few things that are visually similar to 'records' but are not 'records' are:\tcd\tdvd\tvinyls\nThere are several useful visual features to tell there are 'records' and not similar things in a photo:\tdisc-shaped\tmatte finish with a grooved surface\tcenter hole\ton the grooved surface are tracks", 18], "baby goat": ["Yes. 'Baby goat' has a tangible appearance and is a young domesticated goat.\nA few things that are visually similar to 'baby goat' but are not 'baby goat' are:\tlambs\tkids (baby cows)\tpiglets\tdeer fawns\nThere are several useful visual features to tell there is 'baby goat' and not similar things in a photo: small and relatively short\twith small, slightly curved horns\thoofed animals with soft fur or hair\tusually found on farms, paddocks or meadows", 18], "eye lashes": ["Yes. 'Eye lashes' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'eye lashes' but are not 'eye lashes' are:\thair\tfur\teyebrows\t\nThere are several useful visual features to tell there are 'eye lashes' and not similar things in a photo:\tshort and curved\thair-like\tgrowing from the edge of the eyelids\tusually darker than the hair on the head\thas a root and a tip", 18], "cap man": ["No. 'Cap man' is too vague or abstract to be distinguished in a photo.", 18], "brown pillow": ["Yes. 'Brown pillow' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'brown pillow' but are not 'brown pillow' are:\tsandbag\tbeanbag\tcarpet\ttile\nThere are several useful visual features to tell there is 'brown pillow' and not similar things in a photo:\trectangular or square shape\twith a soft and fluffy texture\tbrown color\tpillowcase or cover\twith seams or stitching", 18], "stone fireplace": ["Yes. 'Stone fireplace' has a tangible appearance and is a kind of architectural feature.\nA few things that are visually similar to 'stone fireplace' but are not 'stone fireplace' are:\tbrick fireplace\twood-burning stove\tchimney\tmantelpiece\nThere are several useful visual features to tell there is 'stone fireplace' and not similar things in a photo:\tmade of stone\tor stone-like material\tcontains a firepit or grate\tmantel or hood above the firepit\thearth or base that extends out from the firepit", 18], "truck wheel": ["Yes. 'Truck wheel' has a tangible appearance and is a specialized type of wheel.\nA few things that are visually similar to 'truck wheel' but are not 'truck wheel' are:\tbicycle wheel\tmotorcycle wheel\tstroller wheel\tcar wheel\nThere are several useful visual features to tell there is 'truck wheel' and not similar things in a photo:\t\n- large size \n- heavy construction \n- thick rim with sturdy spokes \n- designed for commercial use rather than personal transportation \n- typically accompanied by a heavy-duty tire.", 18], "helmet rider": ["Yes. 'Helmet rider' has a tangible appearance and is a person wearing a helmet while riding some kind of vehicle.\nA few things that are visually similar to 'helmet rider' but are not 'helmet rider' are:\tperson wearing a helmet at work\tmannequin head wearing a helmet\t\nThere are several useful visual features to tell there is 'helmet rider' and not similar things in a photo:\tperson riding a vehicle\tvehicle that could be a bicycle, a motorcycle, a scooter, a skateboard, etc.\thelmet on their head\tgear or clothes that suggest the person is riding a vehicle", 18], "santa": ["Yes. 'Santa' has a tangible appearance and is a cultural figure often represented in a particular way.\nA few things that are visually similar to 'santa' but are not 'santa' are:\telderly man\twith a white beard\tdressed in red and white clothing\twearing a hat\nThere are several useful visual features to tell there is 'santa' and not similar things in a photo:\tlong white beard\tfurry red and white suit\tblack boots and belt\tsmall round glasses\tsometimes pictured with a sack of presents and/or reindeer", 18], "luggage case": ["Yes. 'Luggage case' has a tangible appearance and is a type of container for carrying clothes and personal items while travelling.\nA few things that are visually similar to 'luggage case' but are not 'luggage case' are:\tbackpacks\tpurses\tbriefcases\tstorage boxes\nThere are several useful visual features to tell there is 'luggage case' and not similar things in a photo:\thard or soft outer shell\thandles\tand wheels\tfor easy transport\tzippers and compartments\tto organize belongings\tunusual sizes and shapes, such as those designed for specific uses or travel situations.", 18], "horizontal": ["No. 'Horizontal' is too abstract to be distinguished directly through a photo without context.\nThere are no things visually similar to 'horizontal' that are not 'horizontal'.", 18], "fluorescent light": ["Yes. 'Fluorescent light' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'fluorescent light' but are not 'fluorescent light' are: LED light fixture, chandelier, incandescent bulb, desk lamp, candle.\nThere are several useful visual features to tell there is 'fluorescent light' and not similar things in a photo: tube-shaped or long lamps, emitting light from a flat panel rather than a bulb, a cooler tone or more blue-ish color, casting a brighter and lighter effect on the surrounds.", 18], "dell logo": ["Yes. 'Dell logo' has a tangible appearance and is a well-known corporate logo.\nA few things that are visually similar to 'dell logo' but are not 'dell logo' are:\tHP logo\tIBM logo\tLenovo logo\tAsus logo\nThere are several useful visual features to tell there is 'dell logo' and not similar things in a photo:\tblue and white in color\t'D' shaped in design\twith the word 'Dell' underneath the logo. The letter 'E' is slightly slanted. The letter 'L' has an angled cut.", 18], "blonde child": ["Yes. 'Blonde child' has a tangible appearance and is a child with blonde hair.\nA few things that are visually similar to 'blonde child' but are not 'blonde child' are:\tred-haired child\tbrown-haired child\tblack-haired child\tadult with blonde hair\nThere are several useful visual features to tell there is a 'blonde child' and not similar things in a photo:\tfacial structure of a child\tblonde hair color\tpotentially lighter skin color or eye color\ttypical clothing for children", 18], "food trucks": ["Yes. 'Food trucks' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'food trucks' but are not 'food trucks' are:\tdelivery vans\tice cream trucks\tgarbage trucks\ttrailers\nThere are several useful visual features to tell there is 'food trucks' and not similar things in a photo:\tdecorated with images or menus of food\titems for cooking or serving food\ton the street or parked in a public area\twindow or counter for serving food\toutdoor seating area.", 18], "necklace woman": ["No. 'Necklace woman' is too vague or abstract to be distinguished in a photo. It's possible that you meant \"woman wearing a necklace,\" in which case the answer would be yes.\nA few things that are visually similar to 'woman wearing a necklace' but are not 'woman wearing a necklace' are:\twoman wearing earrings\twoman wearing a bracelet\twoman wearing a ring\nThere are several useful visual features to tell there is 'woman wearing a necklace' and not similar things in a photo:\ta piece of jewelry worn around the neck\tclasp or closure behind the neck\tdangling or hanging pendant or ornaments on the necklace's chain or cord.", 18], "stunt": ["No. 'Stunt' is too vague or abstract to be distinguished in a photo. It refers to a performance or action that involves a high degree of risk and skill.\n", 18], "rain coat": ["Yes. 'Rain coat' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'rain coat' but are not 'rain coat' are:\tjacket\tponcho\twindbreaker\ttrench coat\nThere are several useful visual features to tell there is 'rain coat' and not similar things in a photo:\tmade of waterproof material\tbright colors or neon\tyellow or orange (common colors for safety)\thooded\tlong sleeves\twith a zipper or buttons to close the front.", 18], "lampost": ["Yes. 'Lampost' has a tangible appearance and is a type of street furniture.\nA few things that are visually similar to 'lampost' but are not 'lampost' are:\tsignpost\ttraffic light\tbollard\tfire hydrant\nThere are several useful visual features to tell there is 'lampost' and not similar things in a photo:\ttall, vertical post\ttop with one or more lights\tcylindrical or box-like shape\tcould be plain or decorative\tmounted on a base or embedded in the ground", 18], "horseback": ["Yes. 'Horseback' has a tangible appearance and refers to riding on a horse.\nA few things that are visually similar to 'horseback' but are not 'horseback' are:\twalking next to a horse\tsitting on a bench or chair\tstanding next to a horse statue\nThere are several useful visual features to tell there is 'horseback' and not similar things in a photo:\tperson on top of a live horse\tstraddling the horse's back\tholding reins\tin motion or walking", 18], "metal screws": ["Yes. 'Metal screws' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'metal screws' but are not 'metal screws' are:\tnails\tbolts\tpins\nThere are several useful visual features to tell there is 'metal screws' and not similar things in a photo:\thelical ridges on a cylindrical shaft\tthreaded grooves\tthat can be driven into an object's surface\twith flat or pointed head", 18], "leafy vegetables": ["Yes. 'Leafy vegetables' has a tangible appearance and refers to vegetables with leaves that are commonly eaten, such as spinach, lettuce, and kale.\nA few things that are visually similar to 'leafy vegetables' but are not 'leafy vegetables' are:\tgrass\therbs\tflowers\nThere are several useful visual features to tell there is 'leafy vegetables' and not similar things in a photo:\twide and flat leaves\tthat are bright green or dark green\tgrowing from the ground\tor attached to a stem\tthat's edible and commonly used as a salad ingredient or a side dish.", 18], "slits": ["Yes. 'Slits' has a tangible appearance.\nA few things that are visually similar to 'slits' but are not 'slits' are:\tcracks\tslashes\tslots\tcuts\nThere are several useful visual features to tell there is 'slits' and not similar things in a photo:\tnarrow and elongated\topenings or cuts\tthat allow something to pass or to be seen\tsometimes surrounded by other material or object", 18], "lit light": ["No. 'Lit light' is too repetitive and not a clear concept. \nThere are no things considered visually similar to 'lit light'. \nThis question is not applicable since the concept 'lit light' is not visually concrete.", 18], "knife utensil": ["Yes. 'Knife utensil' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'knife utensil' but are not 'knife utensil' are:\tfork spoon\ttongs\t\nThere are several useful visual features to tell there is 'knife utensil' and not similar things in a photo:\tblade made of metal or ceramic\tsharp edge for cutting or chopping\thandle for holding and gripping\tserrated or straight edge shape", 18], "haze": ["Yes. 'Haze' has a tangible appearance and refers to a reduction in visibility caused by fine dust, smoke, or water droplets in the air.\nA few things that are visually similar to 'haze' but are not 'haze' are:\tfog\tmist\tsmog\tsteam\nThere are several useful visual features to tell there is 'haze' and not similar things in a photo:\tvisibility reduction\tair pollution\ta gray or white-colored layer in the air\ttypically seen on a hot and humid day.", 18], "wood boat": ["Yes. 'Wood boat' has a tangible appearance and refers to a boat made of wood.\nA few things that are visually similar to 'wood boat' but are not 'wood boat' are:\traft\tcanoe\tkayak\tyacht\tferry\nThere are several useful visual features to tell there is 'wood boat' and not similar things in a photo:\tmade of wood or wood-like materials\tsymmetrical hull with pointed ends\tlong and narrow body\twith or without sails\tor oars/seats/rigging, etc.", 18], "chair arm": ["Yes. 'Chair arm' has a tangible appearance and is a part of a chair.\nA few things that are visually similar to 'chair arm' but are not 'chair arm' are:\tsofa armrests\tbenches\tstools\nThere are several useful visual features to tell there is 'chair arm' and not similar things in a photo:\tattached to a chair\tcomfortable and padded\thorizontal or slightly sloped\tpositioned at the sides of the chair seat", 18], "orange pepper": ["Yes. 'Orange pepper' has a tangible appearance and is a type of fruit/vegetable.\nA few things that are visually similar to 'orange pepper' but are not 'orange pepper' are:\torange\tflower\ttangerine\tpumpkin\nThere are several useful visual features to tell there is 'orange pepper' and not similar things in a photo:\tpepper-shaped and sized\torange in color\tsmooth texture\tno visible stem or leaves", 18], "owner": ["No. 'Owner' is too vague or abstract to be distinguished in a photo.", 18], "brow": ["Yes, 'brow' has a visually concrete appearance and is a part of the human face.\nThere aren't any things that are visually similar to 'brow' but aren't 'brow'.\nUseful visual features for distinguishing 'brow' from other facial features in a photo are the position of the furrowed arch above the eyes and the presence of hair.", 18], "bristle": ["Yes. 'Bristle' has a tangible appearance and refers to stiff, short hairs on an animal or brush.\nA few things that are visually similar to 'bristle' but are not 'bristle' are:\tfur\thair\tgrass\nThere are several useful visual features to tell there is 'bristle' and not similar things in a photo:\n\tstiff and spiky\ttightly packed\thard and rough in texture\tshort in length\tgrowing from a wooden or plastic handle or an animal's body", 18], "left window": ["Yes. 'Left window' has a tangible appearance and is a specific location on a building or vehicle.\nA few things that are visually similar to 'left window' but are not 'left window' are:\tright window\tdoor\twall\tmirror\nThere are several useful visual features to tell there is 'left window' and not similar things in a photo:\tpositioned on the left side of a building or vehicle\ttransparent or translucent\tdisplaying views of the surroundings or interiors\tsquare or rectangular shape.", 18], "bullet train": ["Yes. 'Bullet train' has a tangible appearance and is a specific type of high-speed train.\nA few things that are visually similar to 'bullet train' but are not 'bullet train' are:\tregular trains\tsubway trains\ttrams\tbuses\nThere are several useful visual features to tell there is 'bullet train' and not similar things in a photo:\tdistinctive rounded or streamlined front\tend\tcarriage with a long, low, and pointed shape\tbright, bold colors\ttypically, no overhead wires\tfor use on dedicated high-speed tracks", 18], "letter c": ["Yes. 'Letter c' has a tangible appearance and is a symbol of the alphabet.\nA few things that are visually similar to 'letter c' but are not 'letter c' are:\tthe letter 'o'\tthe number '0'\tthe symbol '('\nThere are no additional useful visual features to distinguish 'letter c' from these similar things as the shape of the 'c' is distinct from the others.", 18], "drain hole": ["Yes. 'Drain hole' has a tangible appearance and is a kind of opening for liquid to flow out.\nA few things that are visually similar to 'drain hole' but are not 'drain hole' are:\tinlet valve\tsink\tenlarged screw hole\nThere are several useful visual features to tell there is 'drain hole' and not similar things in a photo:\tcircular or oval opening\tdesigned for liquids to flow out\tmay have a grate or cover on top\tmay be connected to a pipe or a drain system", 18], "upholstery": ["Yes. 'Upholstery' has a tangible appearance and refers to the fabric or materials used to cover furniture such as chairs and sofas.\nA few things that are visually similar to 'upholstery' but are not 'upholstery' are:\tcurtains\tblankets\tclothing\trugs\nThere are several useful visual features to tell there is 'upholstery' and not similar things in a photo:\tcovering furniture\tpadded\twith threads or stitching\ttactile and textured\tfixed to a piece of furniture", 18], "measuring cup": ["Yes. 'Measuring cup' has a tangible appearance and is a tool used in cooking and baking.\nA few things that are visually similar to 'measuring cup' but are not 'measuring cup' are:\tglass\tcup\tmug\ttumbler\nThere are several useful visual features to tell there is 'measuring cup' and not similar things in a photo:\tgraduated measurements on the side\tfor cooking or baking purposes\thandles on either side\tcylindrical shape\tmay have spout for easy pouring", 18], "studs": ["Yes. 'Studs' has a tangible appearance and refers to small metallic pieces or embellishments used in construction or fashion.\nA few things that are visually similar to 'studs' but are not 'studs' are:\trivets\tnails\tbeads\tbuttons\nThere are several useful visual features to tell there is 'studs' and not similar things in a photo:\tflat, metal pieces with a pointed end\tusually silver or gold in color\tmost common shapes are circular, square, or pyramid-shaped\tbeen set or fastened in a particular pattern or design.", 18], "brown tracks": ["Yes. 'Brown tracks' has a tangible appearance.\nA few things that are visually similar to 'brown tracks' but are not 'brown tracks' are:\tanimal footprints\tmuddy tire tracks\tpaint marks\tdirt smudges\nThere are several useful visual features to tell there are 'brown tracks' and not similar things in a photo:\tlinear shape\tbrown color\tconsistent spacing between tracks\timpressions in a soft surface\tsimilar size and shape between tracks.", 18], "metal cover": ["Yes. 'Metal cover' has a tangible appearance and is a type of protective covering made of metal.\nA few things that are visually similar to 'metal cover' but are not 'metal cover' are:\tpot lid\tmetal table\tstreet grate\nThere are several useful visual features to tell there is 'metal cover' and not similar things in a photo:\tmade of metal\tdurable and sturdy\tpurpose is to protect or cover something\thandles or hinges for lifting or opening", 18], "guard rails": ["Yes. 'Guard rails' has a tangible appearance and is a type of safety barrier.\nA few things that are visually similar to 'guard rails' but are not 'guard rails' are:\tfences\tbarriers\twalls\tpartitions\nThere are several useful visual features to tell there is 'guard rails' and not similar things in a photo:\ttall vertical posts\thorizontal bars\tclose to a high drop or ledge\tpainted in bright colors or reflective coatings", 18], "dairy cow": ["Yes. 'Dairy cow' has a tangible appearance and is a type of cow raised for milk production.\nA few things that are visually similar to 'dairy cow' but are not 'dairy cow' are:\tbeef cow\tbison\tyak\nThere are several useful visual features to tell there is 'dairy cow' and not similar things in a photo:\tlarge udder\tforward-facing nostrils\thunched back\twhite patches on the body", 18], "clock frame": ["No. 'Clock frame' is too vague or abstract to be distinguished in a photo. A clock frame does not have a specific or unique appearance compared to other types of frames. \n\nHowever, a few things that are visually similar to a clock frame but are not a clock frame include: picture frames that have a circular or oval shape, wall-mounted decorative plates, or sunburst-style wall decor.\n\nUseful visual features for distinguishing a clock frame from similar things might include: a clock face visible within the circular or oval shape of the frame, the presence of clock hands or numbers on the clock face, or other clock-related decorations or motifs on the frame.", 18], "brown chair": ["Yes. 'Brown chair' has a tangible appearance and refers to a specific type of furniture.\nA few things that are visually similar to 'brown chair' but are not 'brown chair' are:\tsofa\ttable\tbench\tstool\nThere are several useful visual features to tell there is 'brown chair' and not similar things in a photo:\tseat with a backrest\tfour legs\tspecific design or shape\tbrown color or texture", 18], "wood crate": ["Yes. 'Wood crate' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'wood crate' but are not 'wood crate' are:\tcardboard box\twooden barrel\tpaper bag\nThere are several useful visual features to tell there is 'wood crate' and not similar things in a photo:\twooden material\tcriss-crossed planks\tsquare or rectangular shape\thandles on the side or top-open top", 18], "grey surface": ["Yes. 'Grey surface' has a tangible appearance and refers to a surface that is grey in color.\nA few things that are visually similar to 'grey surface' but are not 'grey surface' are:\tblack surface\tconcrete\tbumpy road\tasphalt\nThere are not any useful visual features to distinguish 'grey surface' from these listed similar things, as the only distinguishing feature is the color grey. However, texture and context could be helpful in determining what type of grey surface it is.", 18], "bathroom walls": ["Yes. 'Bathroom walls' has a tangible appearance and typically has specific features.\nA few things that are visually similar to 'bathroom walls' but are not 'bathroom walls' are:\tkitchen walls\tbedroom walls\tliving room walls\twalls in public spaces\nThere are several useful visual features to tell there is 'bathroom walls' and not similar things in a photo:\ttiled or covered with paint, wallpaper, or other materials\tin proximity to bathroom fixtures like toilet, sink, or bathtub\twet or containing condensation\tmirrors, cabinets, or shelves attached to the walls", 18], "bird feathers": ["Yes. 'Bird feathers' has a tangible appearance and is a part of a bird's body.\nA few things that are visually similar to 'bird feathers' but are not 'bird feathers' are:\tfur\tleaves\tfeather boas\tgrass\nThere are several useful visual features to tell there are 'bird feathers' and not similar things in a photo:\tlightweight\tthin\tand delicate layers or strands\tof different shapes, sizes, and colors\tarranged in distinctive patterns\thave a quill or shaft", 18], "sink counter": ["Yes. 'Sink counter' has a tangible appearance and refers to the surface on which a sink is placed in a bathroom or kitchen.\nA few things that are visually similar to 'sink counter' but are not 'sink counter' are:\tregular countertop\tdresser\ttop of a toilet tank\nThere are several useful visual features to tell there is 'sink counter' and not similar things in a photo:\trectangular in shape\tfitted with a sink or a bowl\tdesigned for holding soap and other toiletries\tWater tap and faucet mounted into it.", 18], "bumper sticker": ["Yes. 'Bumper sticker' has a tangible appearance and is a type of sticker.\nA few things that are visually similar to 'bumper sticker' but are not 'bumper sticker' are:\tposters\tdecals\tlabels\tpatches\nThere are several useful visual features to tell there is 'bumper sticker' and not similar things in a photo:\trectangular or oval shape\tattached to a car bumper\tor on a car window\tbackground with a message or image\tmessage or image can be political, humorous, or personal.", 18], "helmet skier": ["Yes. 'Helmet skier' has a tangible appearance and refers to a skier wearing a helmet for safety.\nA few things that are visually similar to 'helmet skier' but are not 'helmet skier' are:\tskier\tsnowboarder\tice skater\t\nThere are several useful visual features to tell there is 'helmet skier' and not similar things in a photo:\twearing a helmet, in particular an elongated one that comes down over the ears\twearing ski boots\tcarrying ski poles\twearing ski goggles\twinter clothing", 18], "record player": ["Yes. 'Record player' has a tangible appearance and is a device used for playing music from phonograph records.\nA few things that are visually similar to 'record player' but are not 'record player' are:\tcd player\tcassette player\tturntable\tspeaker system\nThere are several useful visual features to tell there is 'record player' and not similar things in a photo:\tturntable component\tdisc record playing on the turntable\tneedle or stylus arm\tanalog controls and dials\tforward and backward buttons\thinged cover or lid", 18], "stud": ["Yes. 'Stud' has a tangible appearance and can refer to a type of fastener or an attractive man.\nA few things that are visually similar to 'stud' but are not 'stud' are:\tnail\tbolt\tpeg\tmale model\nThere are several useful visual features to tell there is 'stud' and not similar things in a photo:\n\n- For the fastener:\nsmall and cylindrical\nprotruding part with a pattern, like a cross or a line\noften made of metal, such as brass or steel\n\n- For the attractive man:\nphysically fit and toned body\nwell-groomed hairstyle and facial hair\nstylish and trendy clothing\nconfident demeanor and charismatic personality", 18], "official": ["No. 'Official' is too vague or abstract to be distinguished in a photo.", 18], "bare branch": ["Yes. 'Bare branch' has a tangible appearance and refers to a tree or bush with the absence of leaves on its branches.\nA few things that are visually similar to 'bare branch' but are not 'bare branch' are:\tdead branch\tcactus spine\t\nThere are several useful visual features to tell there is 'bare branch' and not similar things in a photo:\ttree or bush with no leaves on its branches\tbrown and woody appearance\tdiverging and ramifying lines of the branches, without interlacing or forming closed shapes.", 18], "mountain bike": ["Yes. 'Mountain bike' has a tangible appearance and is a kind of bicycle.\nA few things that are visually similar to 'mountain bike' but are not 'mountain bike' are:\troad bike\tbmx bike\tfat tire bike\nThere are several useful visual features to tell there is 'mountain bike' and not similar things in a photo:\tthick and knobby tires\twide handlebars\tsuspension forks or rear shocks\tframe with sloping top tube\tand flat pedals or with clips for special shoes", 18], "tennis racket handle": ["Yes. 'Tennis racket handle' has a tangible appearance and is a specific part of a tennis racket.\nA few things that are visually similar to 'tennis racket handle' but are not 'tennis racket handle' are:\tgolf club handle\thockey stick handle\tbat handle\nThere are several useful visual features to tell there is 'tennis racket handle' and not similar things in a photo:\tlarge, oval-shaped grip\ttape wrapped around the handle\tsmooth or textured surface for better grip\thollow for inserting the racket stem", 18], "cheeses": ["Yes. 'Cheeses' has a tangible appearance and refers to a variety of dairy products.\nA few things that are visually similar to 'cheeses' but are not 'cheeses' are: \tbutter\tcream\tmargarine\tyogurt\tice cream\nThere are several useful visual features to tell there is 'cheeses' and not similar things in a photo:\tusually in a block or cylinder shape or in slices\tvarious textures (i.e., soft, hard, crumbly)\tsolid or semi-solid in appearance variety of colors and shades, from white to yellow or blue\tnot usually sweet in taste", 18], "metal handles": ["Yes. 'Metal handles' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'metal handles' but are not 'metal handles' are:\tdoor knobs\tdrawer pulls\tlock mechanisms\tcabinet hinges\nThere are several useful visual features to tell there is 'metal handles' and not similar things in a photo:\tlong and narrow in shape\tmade of metal\tattached to a surface to be used to open or close something\tridged or textured for grip.", 18], "wheelbarrow": ["Yes. 'Wheelbarrow' has a tangible appearance and is a type of tool.\nA few things that are visually similar to 'wheelbarrow' but are not 'wheelbarrow' are:\thandcart\ttrolley\twagon\tbicycle with a basket\t\nThere are several useful visual features to tell there is 'wheelbarrow' and not similar things in a photo:\tone wheel at the front\ttwo legs at the back\ttwo handles for pushing or pulling\ta large open container for carrying materials, such as soil or bricks", 18], "paths": ["Yes. 'Paths' has a tangible appearance and is a physical route or track.\nA few things that are visually similar to 'paths' but are not 'paths' are:\trivers\ttrails\tbike lanes\troads\nThere are several useful visual features to tell there is 'paths' and not similar things in a photo:\tnarrow width\tdirt, gravel, or paved surface\tclean and cleared of obstacles\tsurrounded by trees, bushes, or other vegetation", 18], "sleeve jacket": ["No. 'Sleeve jacket' is not a common term and is too vague to be distinguished in a photo. Did you mean \"jacket with sleeves\"? In this case, the answer would be \"yes.\"", 18], "brick pillar": ["Yes. 'Brick pillar' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'brick pillar' but are not 'brick pillar' are:\ttree trunk\tconcrete column\twooden post\nThere are several useful visual features to tell there is 'brick pillar' and not similar things in a photo:\trectangular shape\tmade of brick or stone\ttextured surface\tpillar-like proportions\tsupporting a structure or roof", 18], "appetizers": ["Yes. 'Appetizers' has a tangible appearance and refers to small dishes served before the main course.\nA few things that are visually similar to 'appetizers' but are not 'appetizers' are:\tsnacks\tcanapes\tsandwiches\ttapas\nThere are several useful visual features to tell there is 'appetizers' and not similar things in a photo:\tsmall portions\tserved on a small plate or a skewer\tvariety of food items, such as cheese, meat, fruits, vegetables, and bread\ttypically served before the main course.", 18], "bottom edge": ["Yes. 'Bottom edge' has a tangible appearance and refers to the lower boundary or limit of an object.\nThere are no things that are visually similar to 'bottom edge' but are not 'bottom edge'.\nUseful visual features for distinguishing 'bottom edge' from other edges in a photo are: its position at the lowest part of the object and its inclination, which usually forms a horizontal line.", 18], "toilet bowl cleaner": ["Yes. 'Toilet bowl cleaner' has a tangible appearance and is a type of cleaning product.\nA few things that are visually similar to 'toilet bowl cleaner' but are not 'toilet bowl cleaner' are:\tdisinfectant sprays\tbleach sprays\twindow cleaners\tall-purpose cleaners\nThere are several useful visual features to tell there is 'toilet bowl cleaner' and not similar things in a photo:\tcylindrical bottle with a nozzle\tblue or green liquid or gel\tformula specifically designed for cleaning toilets\tor specific directions for use on a toilet bowl\tlabel with the words \"toilet bowl cleaner\" or a picture of a toilet bowl on it.", 18], "cowboy boot": ["Yes. 'Cowboy boot' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'cowboy boot' but are not 'cowboy boot' are:\triding boot\thiking boot\tmotorcycle boot\tfashion boot\nThere are several useful visual features to tell there is 'cowboy boot' and not similar things in a photo:\tpointed toe\traised heel\tdecorative stitching and embroidery\tleather material\tvariety of colors and patterns\tcalf-high shaft with pull-on loops or straps", 18], "tons": ["No. 'Tons' is a vague or abstract concept that refers to a measurable weight or quantity.\nThere aren't any things similar visually to 'tons' since it is a unit of measurement.\nTo distinguish 'tons' from the things being weighed or measured, visual features such as a scale, numbers, or weights must be present in the photo.", 18], "cat fur": ["Yes. 'Cat fur' has a tangible appearance and is a type of mammalian fur.\nA few things that are visually similar to 'cat fur' but are not 'cat fur' are:\tdog fur\trabbit fur\thair\twool\nThere are several useful visual features to tell there is 'cat fur' and not similar things in a photo:\tsmall, fine hair\tsoft and fluffy appearance\tusually comes in a variety of colors and patterns\tsome hair may have a striped or spotted appearance\tif on a cat, it will be found on their body, tail, and face.", 18], "plastic glove": ["Yes. 'Plastic glove' has a tangible appearance and is a type of protective handwear.\nA few things that are visually similar to 'plastic glove' but are not 'plastic glove' are:\tRubber gloves\tMittens\tDisposable gloves\nThere are several useful visual features to tell there is 'plastic glove' and not similar things in a photo:\tflexible and soft texture\tclear or translucent\tcolor: blue, green, purple, or white\tworn snugly on the hand, without fingers or with a thin layer of plastic covering the fingers \tdisposable or reusable.", 18], "glass ball": ["Yes. 'Glass ball' has a tangible appearance and is a sphere-shaped object made of glass.\nA few things that are visually similar to 'glass ball' but are not 'glass ball' are:\tChristmas ball\tmarble\tcrystal\tbubble\nThere are several useful visual features to tell there is 'glass ball' and not similar things in a photo:\ttransparent or translucent material\tsmooth, shiny surface\tsphere or globe shape\treflections or distortions when looking through the glass", 18], "banana stem": ["Yes. 'Banana stem' has a tangible appearance and is a part of a banana plant.\nA few things that are visually similar to 'banana stem' but are not 'banana stem' are:\ttree trunk\tcornstalk\tbamboo stem\tsugar cane\nThere are several useful visual features to tell there is 'banana stem' and not similar things in a photo:\tcylindrical and elongated shape\tthick and fibrous texture\tgrows in a cluster or bunch with other stems or leaves\thollow center\twith layers of sheathing leaves", 18], "rust spot": ["Yes. 'Rust spot' has a tangible appearance and refers to an area of a metal surface that has rusted.\nA few things that are visually similar to 'rust spot' but are not 'rust spot' are:\tstain\tdirt\tblemish\nThere are several useful visual features to tell there is 'rust spot' and not similar things in a photo:\tbrown or orange color\trough or uneven texture\tcracking or flaking of the metal surface\tsurrounded by areas of non-rusted metal", 18], "wood beams": ["Yes. 'Wood beams' has a tangible appearance and is a type of construction material.\nA few things that are visually similar to 'wood beams' but are not 'wood beams' are:\tmetal beams\tconcrete pillars\twooden logs\nThere are several useful visual features to tell there is 'wood beams' and not similar things in a photo:\trectangular or square-shaped\tslightly curved edges\tvisible wood grain\ttextured surface\tmay have natural knots or imperfections", 18], "briefcases": ["Yes. 'Briefcases' has a tangible appearance and is a type of bags.\nA few things that are visually similar to 'briefcases' but are not 'briefcases' are:\tbackpacks\thandbags\tluggage\ttote bags\tshoulder bags\nThere are several useful visual features to tell there is 'briefcases' and not similar things in a photo:\trectangular shape\thandles on the top\thinged lid or zipper\tcarrying strap or shoulder strap", 18], "tan curtains": ["Yes. 'Tan curtains' has a tangible appearance and refers to a specific color of window coverings.\nA few things that are visually similar to 'tan curtains' but are not 'tan curtains' are:\tother colored curtains\tblinds\tshades\tdrapes\twallpaper\nThere are several useful visual features to tell there is 'tan curtains' and not similar things in a photo:\ttan or light brown color\thanging from a window frames or rods\tfabric or texture that\u2019s typical for curtains", 18], "car lights": ["Yes. 'Car lights' has a tangible appearance and are lights on a car.\nA few things that are visually similar to 'car lights' but are not 'car lights' are:\tstreet lights\theadlights of a motorcycle\tor traffic lights\nThere are several useful visual features to tell there are 'car lights' and not similar things in a photo:\tlocated on a car\tbright white or yellow light\thigh intensity\tlight beams pointing forward or backward", 18], "circular": ["Yes. 'Circular' has a visually concrete concept and refers to a perfect round shape.\nA few things that are visually similar to 'circular' but are not 'circular' are:\toval shape\tbubble\tsoft-edged polygon\nThere are a couple of useful visual features to distinguish 'circular' from the listed similar things in a photo:\tequal length measured from the center\tpoint symmetry\tno edges or corners", 18], "photo tag": ["No. 'Photo tag' is too vague or abstract to be considered visually concrete as it refers to a digital feature within an online platform.", 18], "tissue roll": ["Yes. 'Tissue roll' has a tangible appearance and is a type of paper product.\nA few things that are visually similar to 'tissue roll' but are not 'tissue roll' are:\tduct tape\ttoilet paper\tductwork\troll of stickers\nThere are several useful visual features to tell there is 'tissue roll' and not similar things in a photo:\trectangular-shaped\thollow in the center\twhite or beige\tcolorful patterns or designs on the paper surface\tlayers of paper that can be pulled from the center.", 18], "grey suv": ["Yes, 'grey suv' has a visually concrete concept and describes a type of vehicle.\nA few things that are visually similar to 'grey suv' but are not 'grey suv' are:\tgray sedan\tsilver jeep\tgray minivan\nThere are several useful visual features to tell there is 'grey suv' and not similar things in a photo:\ttaller and wider than a typical car\tboxy shape, with a flat roof and short overhangs\tfor off-road driving\tall-wheel drive, high ground clearance, and large tires\tbox-shaped cargo area at the back, behind the seats\tfour-wheel drive.", 18], "globe light": ["Yes. 'Globe light' has a tangible appearance and refers to a type of lighting fixture with a spherical or globe-shaped shade.\nA few things that are visually similar to 'globe light' but are not 'globe light' are:\tround lamp\twithout a shade\tplastic toys\thanging decoration\nThere are several useful visual features to tell there is 'globe light' and not similar things in a photo:\tspherical or globe-shaped shade\tmade of glass or covered with glass\tsmooth surface even when the light is turned on\tcan be attached to the ceiling or a wall\tDimmer switch to change the light intensity.", 18], "lei": ["Yes. 'Lei' has a tangible appearance and is a type of Hawaiian garland.\nA few things that are visually similar to 'lei' but are not 'lei' are:\tflower crown\tnecklace with flowers or beads\nThere are several useful visual features to tell there is 'lei' and not similar things in a photo:\tmade of fresh or artificial flowers or leaves\tworn around the neck or head", 18], "blinder": ["Yes. 'Blinder' has a tangible appearance and is a type of device used to prevent someone from seeing what is happening.\nA few things that are visually similar to 'blinder' but are not 'blinder' are:\teyepatch\tsunglasses\tbandana\that with a visor\nThere are several useful visual features to tell there is 'blinder' and not similar things in a photo:\tcovers both eyes or a part of them\tdoes not allow any light\ttoo close-fitting to the face or head\tcolors visible and related to horses", 18], "lit candles": ["Yes. 'Lit candles' has a tangible appearance.\nA few things that are visually similar to 'lit candles' but are not 'lit candles' are:\tuncut matches\tsticks\twithered flowers\nThere are several useful visual features to tell there are 'lit candles' and not similar things in a photo:\tfire at the wick\twarmth and light\tflickering flame\twax melting\tdripping wax\tsmoke", 18], "blurry trees": ["Yes. 'Blurry trees' has a tangible appearance and refers to a specific photographic effect.\nA few things that are visually similar to 'blurry trees' but are not 'blurry trees' are:\tfoggy trees\tdark trees\tout-of-focus trees\nThere are several useful visual features to tell there are 'blurry trees' and not similar things in a photo:\tthe trees are indistinct and lack sharply defined edges\tthe rest of the photo may be in focus or show a different level of blur\tthe blur may be caused by camera movement or a shallow depth of field", 18], "flower print": ["Yes. 'Flower print' has a tangible appearance and refers to a fabric or surface design that features floral motifs.\nA few things that are visually similar to 'flower print' but are not 'flower print' are:\tpainted flowers\tembroidered flowers\treal flowers\tstencil flowers\nThere are several useful visual features to tell there is 'flower print' and not similar things in a photo:\trepeating floral patterns\tmulti-colored\tdesign printed on a fabric or surface\tpetals, leaves, and stems in the pattern", 18], "handle bike": ["No. 'Handle bike' is not a valid concept. It seems to be a combination of two separate things, 'handlebars' and 'bike'. \n\nA few things that are visually similar to 'handlebars' but are not 'bike' are:\thandlebar of a scooter or motorcycle, handlebar of a shopping cart or baby stroller, handle of a door or cabinet, handle of a tool or gardening implement.\n\nThere are several useful visual features to tell there are 'handlebars' and not similar things in a photo:\tTwo horizontal bars that extend from the top of the bike's frame, Curved shape to allow for gripping, Control levers or buttons for brakes and gear shifting.", 18], "ads": ["No. 'Ads' are too vague or abstract to have a particular visual appearance and can take various forms such as text, images, videos, or audio.\nTherefore, there are no things that are visually similar to 'ads'.\nIt's not possible to distinguish 'ads' from other elements in a photo without context or additional information.", 18], "cement barrier": ["Yes. 'Cement barrier' has a tangible appearance and is a type of construction material.\nA few things that are visually similar to 'cement barrier' but are not 'cement barrier' are:\tconcrete block\tcement column\tbrick wall\tstone fence\nThere are several useful visual features to tell there is 'cement barrier' and not similar things in a photo:\tusually white or grey in color\tsmooth, solid, and rigid surface\tused as a divider or separator on roads or construction sites.", 18], "stairwell": ["Yes. 'Stairwell' has a tangible appearance and is a part of a building structure.\nA few things that are visually similar to 'stairwell' but are not 'stairwell' are:\televator\tescalator\tramp\thallway\nThere are several useful visual features to tell there is 'stairwell' and not similar things in a photo:\tvertical steps or flights of stairs\thandrails\twalls and floors that enclose the stairs\topen space between floors\tdifficult to use for movement in the opposite direction", 18], "scuff marks": ["Yes. 'Scuff marks' has a tangible appearance and is a type of surface damage.\nA few things that are visually similar to 'scuff marks' but are not 'scuff marks' are:\tstains\tdirt\tshadows\tcreases\t\nThere are several useful visual features to tell there are 'scuff marks' and not similar things in a photo:\tworn-down areas on a surface\tsmooth or shiny texture\tdull areas on a surface\tthat they may have a different color than the surface", 18], "horse legs": ["Yes. 'Horse legs' has a tangible appearance and refers to the legs of a horse.\nA few things that are visually similar to 'horse legs' but are not 'horse legs' are:\tzebra legs\tcow legs\t\nThere aren't many similar things that could be mistaken for 'horse legs'.\nThe useful visual features for distinguishing 'horse legs' from the few similar things in a photo would include: distinctive coloring and pattern (for zebras), specific shape and size (for cows), as well as other identifying features such as mane, hooves, and tail (for horses).", 18], "shadow girl": ["Yes. 'Shadow girl' has a tangible appearance, but it is a more abstract or conceptual idea than a specific object or thing.\nA few things that are visually similar to 'shadow girl' but are not 'shadow girl' are:\tshadows of people or objects\tghosts\torphan girls\tdark figures\nThere are several useful visual features to tell there is 'shadow girl' and not similar things in a photo:\tthe silhouette of a girl's figure\tvisible facial features, including eyes and lips\tthe outline of hair or clothing\tdarkness or dim lighting emphasizing the presence of the shadow", 18], "mitts": ["Yes. 'Mitts' has a tangible appearance and refers to a specific type of glove.\nA few things that are visually similar to 'mitts' but are not 'mitts' are:\tgloves\tmittens\tclaws\tpaws\nThere are several useful visual features to tell there is 'mitts' and not similar things in a photo:\tfingerless gloves\twith a thumb separates from other fingers\tmade of wool or other warm materials\tcan cover wrist or forearm", 18], "tag cow": ["Yes. 'Tag cow' has a tangible appearance and it refers to a cow that has been tagged for identification purposes.\nThere are no visually similar things to 'tag cow'.\nUseful visual features for identifying 'tag cow' in a photo could be: a visible ear tag or neck tag on the cow.", 18], "diamond design": ["Yes. 'Diamond design' has a tangible appearance and is a geometric pattern.\nA few things that are visually similar to 'diamond design' but are not 'diamond design' are:\tgrid pattern\tquilt pattern\thoneycomb pattern\nThere are several useful visual features to tell there is 'diamond design' and not similar things in a photo: \tdiamond-shaped pattern\tlines crossing in the middle of each diamond\tdiamond tips pointing inwards or outwards\tsymmetrical pattern\twith or without colors or shading.", 18], "region": ["No. 'Region' is too vague or abstract to be distinguished in a photo.", 18], "water skier": ["Yes. 'Water skier' has a tangible appearance and refers to a person engaging in a water sport.\nA few things that are visually similar to 'water skier' but are not 'water skier' are:\tswimmer\twakeboarder\tkayaker\nThere are several useful visual features to tell there is 'water skier' and not similar things in a photo:\tstanding on water skis\tholding onto a handle attached to a rope\tpulling behind a boat\twearing a life jacket or wetsuit", 18], "snowy ski": ["No. 'Snowy ski' is too vague or abstract to be distinguished in a photo. \n\nPerhaps you meant 'ski in snowy environment' or 'snowy ski slope' instead? If so:\n\nYes. 'Ski in snowy environment' or 'snowy ski slope' have a tangible appearance.\nA few things that are visually similar to 'ski in snowy environment' or 'snowy ski slope' but are not 'ski in snowy environment' or 'snowy ski slope' are:\t\n\tice skating rink\t\n\tfrozen lake\t\n\tsnowy hiking trail\nThere are several useful visual features to tell there is 'ski in snowy environment' or 'snowy ski slope' and not similar things in a photo:\n\t\n\tskiers or snowboarders\t\n\tski lifts or gondolas\t\n\tsnowy mountains or hills\t\n\tsnow-covered trees or bushes\t\n\tsunny or cloudy skies", 18], "camera bag": ["Yes. 'Camera bag' has a tangible appearance and is a type of bag used for carrying cameras.\nA few things that are visually similar to 'camera bag' but are not 'camera bag' are:\tbackpack\tpurse\tlaptop bag\thiking bag\nThere are several useful visual features to tell there is 'camera bag' and not similar things in a photo:\tspecific compartments and padding for camera gear\tmultiple pockets or pouches\tfor camera accessories such as additional lenses, memory cards, and batteries\tmade of durable material, often water-resistant or weatherproof\tcan be worn cross-body or over the shoulder.", 18], "groceries": ["No. 'Groceries' is too vague or abstract to be distinguished in a photo.", 18], "plant leaf": ["Yes. 'Plant leaf' has a tangible appearance and is a part of a plant.\nA few things that are visually similar to 'plant leaf' but are not 'plant leaf' are:\tpaper\tgrass\tcactus spines\talgae\nThere are several useful visual features to tell there is 'plant leaf' and not similar things in a photo:\tflat and thin shape (with veins)\tgreen color (usually)\tgrowing from a stem or branch", 18], "way traffic sign": ["Yes. 'Way traffic sign' has a tangible appearance and is a specific type of road sign.\nA few things that are visually similar to 'way traffic sign' but are not 'way traffic sign' are:\tstop sign\tyield sign\tspeed limit sign\tconstruction sign\nThere are several useful visual features to tell there is 'way traffic sign' and not similar things in a photo:\tgreen background with a white rectangular shape\tarrows pointing in opposite directions\tthe words \"ONE WAY\" clearly written in large letters", 18], "flash drive": ["Yes. 'Flash drive' has a tangible appearance and is a type of portable storage device.\nA few things that are visually similar to 'flash drive' but are not 'flash drive' are:\tmemory card\texternal hard drive\tthumb drive\nThere are several useful visual features to tell there is 'flash drive' and not similar things in a photo:\tUSB connector\tportable and pocket-sized\tusually rectangular or cylindrical shape\twith or without a cap\tfor storing and transferring files", 18], "skateboard mid air": ["Yes. 'Skateboard mid air' has a tangible appearance and can be captured in a photo.\nA few things that are visually similar to 'skateboard mid air' but are not 'skateboard mid air' are:\tjumping\ton a trampoline\tdoing a flip\tdoing a trick on a bike\tswinging\nThere are several useful visual features to tell there is 'skateboard mid air' and not similar things in a photo:\ta skateboard in the frame\ta person on the skateboard\tthe skateboard is off the ground\tthe person is in mid-air, with arms and legs apart or doing a trick.", 18], "water way": ["Yes. 'Water way' has a tangible appearance and refers to a body of water used for transportation.\nA few things that are visually similar to 'water way' but are not 'water way' are:\tpool\tpond\tfountain\triver\nThere are several useful visual features to tell there is 'water way' and not similar things in a photo:\tlong and narrow shape\twith boats or ships floating on it\tbridges or barges nearby\tor surrounded by industrial areas or ports", 18], "plush toy": ["Yes. 'Plush toy' has a tangible appearance and is a kind of stuffed animal.\nA few things that are visually similar to 'plush toy' but are not 'plush toy' are:\treal animals\tdolls\tpillows\t\nThere are several useful visual features to tell there is 'plush toy' and not similar things in a photo:\t\nsoft and furry fabric\t\nsewn or stuffed details\t\ncartoonish or exaggerated features\t\nusually small enough to hold in one hand", 18], "stone house": ["Yes. 'Stone house' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'stone house' but are not 'stone house' are:\tbrick house\tlog cabin\twooden house\t\nThere are several useful visual features to tell there is 'stone house' and not similar things in a photo:\tmade of stone or rocks\tearthy colors, such as gray or brown\tsolid and heavy appearance\tthick walls and sturdy masonry architecture", 18], "water bowl": ["Yes. 'Water bowl' has a tangible appearance and is a type of bowl used for holding water.\nA few things that are visually similar to 'water bowl' but are not 'water bowl' are:\tfood bowl\tplant pot\tcoffee mug\nThere are several useful visual features to tell there is 'water bowl' and not similar things in a photo:\tround or oval shape\tshallow depth or flat bottom\ttranslucent material or glass\tvisibly filled with water or liquid", 18], "cartoon character": ["Yes. 'Cartoon character' has a tangible appearance and is a type of drawing or animation.\nA few things that are visually similar to 'cartoon character' but are not 'cartoon character' are:\treal people\tpuppets\ttoys\tmasks\nThere are several useful visual features to tell there is 'cartoon character' and not similar things in a photo:\tdistorted or exaggerated features\tbright colors\tfantasy or unrealistic appearance\ttypical clothing or accessories for the character's identity", 18], "spinach leaves": ["Yes. 'Spinach leaves' has a tangible appearance and is a type of edible greens.\nA few things that are visually similar to 'spinach leaves' but are not 'spinach leaves' are:\tlettuce\trocket\tkale\tarugula\nThere are several useful visual features to tell there is 'spinach leaves' and not similar things in a photo:\tdark green\toval-shaped\twith a pointed tip and rounded bottom\tserrated edge\tthick and fleshy texture.", 18], "brocoli": ["Yes. 'Broccoli' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'broccoli' but are not 'broccoli' are: cauliflower, cabbage, kale, lettuce, Brussels sprouts.\nThere are several useful visual features to tell there is 'broccoli' and not similar things in a photo:\tflorets in a tree-like shape\tdark green color\tthick stems and leaves\tsmall flower heads clustered together.", 18], "target": ["Yes. 'Target' has a tangible appearance and is a symbol used in sports or shooting practice.\nA few things that are visually similar to 'target' but are not 'target' are:\tradar\tsign\tboard\tbulls-eye\nThere are several useful visual features to tell there is 'target' and not similar things in a photo:\tcircular shape\tcentered pattern\tconcentric circles\tin rings or sections with different colors and values of points.", 18], "mirror reflection": ["Yes. 'Mirror reflection' has a tangible appearance.\nA few things that are visually similar to 'mirror reflection' but are not 'mirror reflection' are:\tpuddle reflection\twindow reflection\tshadow\t\nThere are several useful visual features to tell there is 'mirror reflection' and not similar things in a photo:\tthe reflection appears reversed\taccurately reflects the subject in front of the mirror\tmay show the edge or frame of the mirror", 18], "christmas decoration": ["Yes. 'Christmas decoration' has a tangible appearance and includes various decorative items.\nA few things that are visually similar to 'Christmas decoration' but are not 'Christmas decoration' are:\tbirthday decoration\tparty decoration\twedding decoration\nThere are several useful visual features to tell there is 'Christmas decoration' and not similar things in a photo:\tcan contain items like Christmas trees, lights, ornaments, garlands, wreaths or ribbons\tit is often red or green-coloured, but not always\tmay be placed indoor or outdoor to celebrate the holiday season.", 18], "newspaper dispenser": ["Yes. 'Newspaper dispenser' has a tangible appearance and is a type of vending machine.\nA few things that are visually similar to 'newspaper dispenser' but are not 'newspaper dispenser' are:\tsoda machine\tsnack machine\tparking meter\tphone booth\nThere are several useful visual features to tell there is 'newspaper dispenser' and not similar things in a photo:\trectangular shape\twith a sloping or slanted top\tslot for inserting coins or bills\tto dispense newspapers or magazines\tprominent newspaper or magazine branding or logos", 18], "airplane hangar": ["Yes. 'Airplane hangar' has a tangible appearance and is a type of building designed to house aircraft.\nA few things that are visually similar to 'airplane hangar' but are not 'airplane hangar' are:\twarehouse\tbarn\tgarage\thospital\t\nThere are several useful visual features to tell there is 'airplane hangar' and not similar things in a photo:\tlarge and spacious\tdoor(s) big enough to fit planes\taircraft parked inside or nearby\taviation-related equipment and tools visible or in use", 18], "peninsula": ["Yes. 'Peninsula' has a tangible appearance and is a geographical feature.\nA few things that are visually similar to 'peninsula' but are not 'peninsula' are:\tcape\tbay\tisthmus\tisland\nThere are several useful visual features to tell there is 'peninsula' and not similar things in a photo:\ta piece of land surrounded by water on three sides\tconnected to a larger landmass by a narrow strip of land\tvaried topography\twith distinct features such as cliffs, beaches or forests.", 18], "shadow horse": ["No. 'Shadow horse' is too vague or abstract to be considered a visually concrete concept.", 18], "ski sticks": ["Yes. 'Ski sticks' has a tangible appearance, and it is an essential piece of equipment used in skiing.\nA few things that are visually similar to 'ski sticks' but are not 'ski sticks' are:\thiking poles\twalking canes\tfishing poles\t\nThere are several useful visual features to tell there is 'ski sticks' and not similar things in a photo:\tthin and lightweight\tstraight or slightly curved\tsharp pointy end\tbasket-shaped attachment near the tip of the stick\tto be used while skiing", 18], "tanktop": ["Yes. 'Tanktop' has a tangible appearance and is a type of sleeveless shirt.\nA few things that are visually similar to 'tanktop' but are not 'tanktop' are:\tundershirt\tsports bra\tbikini top\tswimsuit\nThere are several useful visual features to tell there is 'tanktop' and not similar things in a photo:\tsleeveless\tshoulder straps\tnarrow or thin straps\tbares the midriff or abdomen\tfitted or form-fitting\tsquare or scoop neckline at the front or back.", 18], "bull dog": ["Yes. 'Bull dog' has a tangible appearance and is a breed of dog.\nA few things that are visually similar to 'bull dog' but are not 'bull dog' are:\tpug\tboxer\tboston terrier\t\nThere are several useful visual features to tell there is 'bull dog' and not similar things in a photo:\tshort, stocky build\tpronounced underbite\twrinkled face and forehead\tfolded skin on the neck and shoulders\twide-set, expressive eyes\tsmall ears, rose or button-shaped", 18], "layer cake": ["Yes. 'Layer cake' has a tangible appearance and is a type of cake.\nA few things that are visually similar to 'layer cake' but are not 'layer cake' are:\tpie\tcheesecake\ttart\tcupcake\tflourless chocolate cake\nThere are several useful visual features to tell there is 'layer cake' and not similar things in a photo:\tmultiple layers of cake and frosting\tfrosting or icing in between each layer\tsmooth sides and top\twith or without decorations on top", 18], "roast": ["Yes. 'Roast' has a tangible appearance and is a type of cooked food.\nA few things that are visually similar to 'roast' but are not 'roast' are:\tgrilled meat\tbaked meat\tfried meat\tsmoked meat\tstewed meat\nThere are several useful visual features to tell there is 'roast' and not similar things in a photo:\toven-cooked\tdark brown or blackened surface\ton a platter or dish surrounded by vegetables or other sides.", 18], "square bowl": ["Yes. 'Square bowl' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'square bowl' but are not 'square bowl' are:\trectangle tray\tsquare plate\trectangular container\nThere are several useful visual features to distinguish 'square bowl' from the listed similar things in a photo: \tfour equal sides\tdeep enough to hold food\tu-shaped bottom.", 18], "yellow flower": ["Yes. 'Yellow flower' has a tangible appearance and is a kind of flowering plant.\nA few things that are visually similar to 'yellow flower' but are not 'yellow flower' are:\tdandelion\tsunflower\tmarigold\tbuttercup\ttulip\nThere are several useful visual features to tell there is 'yellow flower' and not similar things in a photo:\tvibrant yellow color\tpetal shape and arrangement on the stem\tvisible stamen and pistil\tleaf shape and arrangement around the stem", 18], "airport tarmac": ["Yes. 'Airport tarmac' has a tangible appearance and is a type of pavement.\nA few things that are visually similar to 'airport tarmac' but are not 'airport tarmac' are:\tparking lot\ttennis court\tstreet roadway\nThere are several useful visual features to tell there is 'airport tarmac' and not similar things in a photo:\tflat and smooth surface\tdark color\taircraft parked or moving on it\tairport markings such as arrows, numbers, and lines", 18], "brown animal": ["No. 'Brown animal' is too vague or abstract to be distinguished in a photo. There are many different types of animals that can be brown, and this term does not provide enough specificity to identify a particular animal. \n\nA few things that are visually similar to 'brown animal' but are not 'brown animal' are:\ttree trunks\tbrown furniture\tdirt piles\n\nSince 'brown animal' is too vague, there are no useful visual features for distinguishing it from similar things in a photo.", 18], "window wiper": ["Yes. 'Window wiper' has a tangible appearance and is a device used for cleaning windows.\nA few things that are visually similar to 'window wiper' but are not 'window wiper' are:\trubber spatula\tsqueegee\tmagic eraser\tscraper\nThere are several useful visual features to tell there is 'window wiper' and not similar things in a photo:\tmetal or plastic handle\tlong rubber or foam blade\tbent or angled handle for ergonomic use\tuse in combination with cleaning liquid or water", 18], "monkeys": ["Yes. 'Monkeys' has a tangible appearance and is a type of primate.\nA few things that are visually similar to 'monkeys' but are not 'monkeys' are:\tapes\thumans\tsloths\tsquirrels\nThere are several useful visual features to tell there are 'monkeys' and not similar things in a photo:\tfour-limbed primate\ttail\thairy hands and feet\tvariety of sizes, colors, and shapes\tof tree-swingers or ground-dwellers\tdistinguishing facial features like color around the eyes or snout", 18], "tie knot": ["Yes. 'Tie knot' has a tangible appearance and refers to the specific way a necktie is tied.\nA few things that are visually similar to 'tie knot' but are not 'tie knot' are:\tnecktie, but not tied any particular way\tbowtie\tscarf\tknot, but not used to tie a tie\nThere are several useful visual features to tell there is 'tie knot' and not similar things in a photo:\ta knot used to tie a necktie\tspecific shapes (e.g. Windsor knot, half Windsor knot, etc.)", 18], "orange flags": ["Yes. 'Orange flags' has a tangible appearance and is a type of flag.\nA few things that are visually similar to 'orange flags' but are not 'orange flags' are:\tpink flags\tyellow flags\torange banners\ttape\tcones\nThere are several useful visual features to tell there is 'orange flags' and not similar things in a photo:\trectangular or triangular shape\tsolid orange color, or orange with a contrasting color for writing or symbols\theld on a pole or attached to a rope, often used for marking or signaling a warning or a construction site.", 18], "turn signal": ["Yes. 'Turn signal' has a tangible appearance and is a component of a vehicle.\nA few things that are visually similar to 'turn signal' but are not 'turn signal' are:\theadlights\tbrake lights\tfog lights\treflectors\nThere are several useful visual features to tell there is 'turn signal' and not similar things in a photo:\tarrow-shaped light, usually yellow or orange\tpositioned on the front or rear of a vehicle\tblinks on and off to indicate a turn or lane change\tactivated by a lever or a button inside the vehicle", 18], "puppies": ["Yes. 'Puppies' has a tangible appearance and refers to young dogs.\nA few things that are visually similar to 'puppies' but are not 'puppies' are:\tkittens\tyoung wolves\tfox cubs\tbear cubs\nThere are several useful visual features to tell there are 'puppies' and not similar things in a photo:\tround, fluffy bodies\tcute, playful expressions\tsoft fur\twith a collar\tbreed characteristics, such as floppy ears or curly tails", 18], "paintbrush": ["Yes. 'Paintbrush' has a tangible appearance and is a tool for painting.\nA few things that are visually similar to 'paintbrush' but are not 'paintbrush' are:\tpen\tmarker\tchalk\tcrayon\nThere are several useful visual features to tell there is 'paintbrush' and not similar things in a photo:\tlong handle\tbristles on one end\tcylindrical or flattened shape\tlines of paint visible on the bristles\tuse near a palette or paint can", 18], "walnuts": ["Yes. 'Walnuts' has a tangible appearance and is a type of edible nut.\nA few things that are visually similar to 'walnuts' but are not 'walnuts' are:\tpecans\tpeanuts\talmonds\tacorns\nThere are several useful visual features to tell there is 'walnuts' and not similar things in a photo:\toval or oblong shape\tlight to dark brown color\tridged surface\thard shell\twith a pointed tip", 18], "cement pavement": ["Yes. 'Cement pavement' has a tangible appearance and refers to a type of flat surface made of concrete.\nA few things that are visually similar to 'cement pavement' but are not 'cement pavement' are:\tasphalt pavement\tplastic tiles\tmetal plates\nThere are several useful visual features to distinguish 'cement pavement' from the listed similar things in a photo:\tlight grey or white color\trough texture made by concrete\tdifferent shapes and patterns made by pavement blocks or tiles\tno visible cracks or damages (if it's a new pavement)", 18], "stone bricks": ["Yes. 'Stone bricks' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'stone bricks' but are not 'stone bricks' are:\twooden planks\ttile\tpaver rocks\nThere are several useful visual features to tell there is 'stone bricks' and not similar things in a photo:\trectangle-shaped\trough texture\tgrainy or rocky surface\tgrey, beige or brown color", 18], "metal fence pole": ["Yes. 'Metal fence pole' has a tangible appearance and is a type of vertical post used in fencing.\nA few things that are visually similar to 'metal fence pole' but are not 'metal fence pole' are:\ttree\ttrash can\tpost\t\nThere are several useful visual features to tell there is 'metal fence pole' and not similar things in a photo:\tmade of metal or iron\tattached to a fence or gate\tvertical shape\twith a pointed or blunt top\tend with connectors or brackets for fence sections", 18], "jockeys": ["Yes. 'Jockeys' has a tangible appearance and are people who ride horses in races.\nA few things that are visually similar to 'jockeys' but are not 'jockeys' are:\tbikers\tequestrians\tcowboys\nThere are several useful visual features to tell there is 'jockeys' and not similar things in a photo:\twearing colorful outfits\tmatching hats\tand boots\triding horses in a race numbering on their uniforms.", 18], "cottage": ["Yes. 'Cottage' has a tangible appearance and refers to a small, cozy dwelling.\nA few things that are visually similar to 'cottage' but are not 'cottage' are:\tcabins\tbarns\thuts\tsheds\nThere are several useful visual features to tell there is 'cottage' and not similar things in a photo:\tusually has a thatched or shingled roof\tmay have a porch\tfor the most part, made of wood or stone\tone or two stories\tmaximum of four rooms, including the living room, kitchen, and bedroom(s)", 18], "martini glass": ["Yes. 'Martini glass' has a tangible appearance and is a type of glassware.\nA few things that are visually similar to 'martini glass' but are not 'martini glass' are:\twine glass\tchampagne flute\thurricane glass\ttest tube\nThere are several useful visual features to tell there is 'martini glass' and not similar things in a photo:\ttriangular-shaped bowl\tstemmed glass\tclear material, allowing visibility of the liquid inside\tconed-shaped with a smaller rim than the base.", 18], "airfield": ["Yes. 'Airfield' has a tangible appearance and refers to a structure for aircraft operations.\nA few things that are visually similar to 'airfield' but are not 'airfield' are:\tparking lot\trailroad tracks\tconstruction site\nThere are several useful visual features to tell there is 'airfield' and not similar things in a photo:\trunways\tforce fields\tarrows on the asphalt\tair traffic control tower\thangars or sheds\tfor planes and helicopters only\tsigns and markings for aircraft operations.", 18], "glass ashtray": ["Yes. 'Glass ashtray' has a tangible appearance and is a specific type of dish.\nA few things that are visually similar to 'glass ashtray' but are not 'glass ashtray' are:\tdecorative glass bowl\tserving platter\tpaperweight\tdecorative glass sculpture\nThere are several useful visual features to tell there is 'glass ashtray' and not similar things in a photo:\tcircular shape\tbowl-like indentation in the center\tridged or indented areas for holding cigarettes\tclear or colored glass material.", 18], "flashlight": ["Yes. 'Flashlight' has a tangible appearance and is a type of handheld light.\nA few things that are visually similar to 'flashlight' but are not 'flashlight' are:\tlantern\tcandlelight\tspotlight\tlight bulb\nThere are several useful visual features to tell there is 'flashlight' and not similar things in a photo:\thandheld device\tbeam of light\tmetallic or plastic body\tswitch or button to turn on and off\tbattery compartment on one end of the device", 18], "winter scene": ["Yes. 'Winter scene' has a tangible appearance and refers to a particular view of winter landscape.\nA few things that are visually similar to 'winter scene' but are not 'winter scene' are:\tautumn scene\tspring scene\tsummer scene\twaterfall\nThere are several useful visual features to tell there is 'winter scene' and not similar things in a photo:\tsnow on the ground and trees\ticy ponds and lakes\t bare branches or evergreens covered in snow\tcrisp and cold colors, such as white, grey, and blue.", 18], "loops": ["Yes. 'Loops' has a tangible appearance and refers to shapes or patterns that are circular or oval and have at least one open end.\nA few things that are visually similar to 'loops' but are not 'loops' are:\tcircles\trings\tspools\tspirals\nThere are several useful visual features to distinguish 'loops' from the listed similar things in a photo:\tat least one open end\tcurved or rounded shape\tlack of uniformity in size or shape\trepeating pattern or design\tsimple geometric shape", 18], "pizza toppings": ["Yes. 'Pizza toppings' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'pizza toppings' but are not 'pizza toppings' are:\tsandwich fillings\tsalad ingredients\tburger toppings\tnachos toppings\nThere are several useful visual features to tell there are 'pizza toppings' and not similar things:\tusually placed on top of pizza\tdifferent shapes, sizes, and colors\tvariety of textures and consistencies, such as cheese, vegetables, and meat\tcommon toppings include pepperoni, mushrooms, onions, peppers, and olives", 18], "wheel chair": ["Yes. 'Wheel chair' has a tangible appearance and is a type of mobility aid.\nA few things that are visually similar to 'wheel chair' but are not 'wheel chair' are:\toffice chair\tstroller\tbicycle\tmotorcycle\nThere are several useful visual features to tell there is 'wheel chair' and not similar things in a photo:\thas wheels\tfor sitting\tpositioned for a person with mobility issues\tcan be pushed by another person or self-propelled often have footrests or leg support.", 18], "clock numbers": ["Yes. 'Clock numbers' has a tangible appearance and refers to the numerals on a clock face.\nA few things that are visually similar to 'clock numbers' but are not 'clock numbers' are:\tlicense plate numbers\taddress numbers\tbillboard numbers\tcounter numbers\nThere aren't any useful visual features for distinguishing 'clock numbers' from the listed similar things in a photo. Clock numbers are specific to clock faces and typically have a similar font and size.", 18], "hairy leg": ["Yes. 'Hairy leg' has a tangible appearance.\nA few things that are visually similar to 'hairy leg' but are not 'hairy leg' are:\tfur of an animal\thair on a person's head\nThere are several useful visual features to tell there is 'hairy leg' and not similar things in a photo:\thair growing out of skin\tdark or light colored hair\thair density or thickness\thair length, covering most of the leg\tskin color and texture around hair", 18], "orange safety vest": ["Yes. 'Orange safety vest' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'orange safety vest' but are not 'orange safety vest' are:\t\t\t\t\t\t\t\ttraffic cones\t\t\t\t\t\t\thard hats\t\t\t\t\t\t\tconstruction signs\t\t\t\t\t\t\tbarriers\nThere are several useful visual features to tell there is 'orange safety vest' and not similar things in a photo:\treflective strips\tbright orange color\tzipper on the front\tvisible pockets\tV-shape on front and back", 18], "foot shoe": ["No. 'Foot shoe' is not a visually concrete concept as it doesn't exist in reality.\n\nHowever, if you meant 'shoe', then the answer would be 'yes'. \nA few things that are visually similar to 'shoe' but are not 'shoe' are:\tboots\tslippers\tsandals\tsocks\nThere are several useful visual features to tell there is a 'shoe' and not similar things in a photo:\tsole\theel\ttoe\tbox\ttongue\tlaces\tor closures", 18], "bank sign": ["Yes. 'Bank sign' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'bank sign' but are not 'bank sign' are:\tstore sign\trestaurant sign\tgas station sign\thotel sign\nThere are several useful visual features to tell there is 'bank sign' and not similar things in a photo:\tthe words \"bank\" or \"banking\" are prominently displayed\tthe bank's logo is visible\tthe sign features ATM or other banking services available at that location\tthe sign shows bank hours and contact information", 18], "dragon kite": ["Yes. 'Dragon kite' has a tangible appearance and is a type of kite.\nA few things that are visually similar to 'dragon kite' but are not 'dragon kite' are:\tbird kite\tbutterfly kite\tinsect kite\t\nThere are several useful visual features to tell there is 'dragon kite' and not similar things in a photo:\t\nthe shape of a dragon\thollow body\tfor a tail that blows in the wind\tlong and thin body, usually made of fabric or paper\tfour limbs spread to hold the whole body", 18], "swim shorts": ["Yes. 'Swim shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'swim shorts' but are not 'swim shorts' are:\tregular shorts\tathletic shorts\tbathing suits\nThere are several useful visual features to tell there is 'swim shorts' and not similar things in a photo:\tshort length\tloose-fitting or relaxed\tfabric design suitable for water activities\tdrawstring or elastic waistband\tno pockets or minimal pockets", 18], "boat motor": ["Yes. 'Boat motor' has a tangible appearance and is a machine used for propelling boats.\nA few things that are visually similar to 'boat motor' but are not 'boat motor' are:\tcar engine\tmotorcycle engine\tlawn mower engine\tjet engine\nThere are several useful visual features to tell there is 'boat motor' and not similar things in a photo:\tmounted on a boat or near the water\tpropellers or impellers to create thrust\thandles or levers for control\tmetallic body or casing", 18], "dark liquid": ["Yes. 'Dark liquid' has a tangible appearance.\nA few things that are visually similar to 'dark liquid' but are not 'dark liquid' are:\tblack coffee\tcola\tsyrup\tmelted chocolate\nThere are several useful visual features to tell there is 'dark liquid' and not similar things in a photo:\ta liquid with a dark color\tthe surface of the liquid is shiny or smooth when illuminated by light\tdrips or stains of the liquid\ton a surface that is usually associated with liquids (e.g., a glass or a saucepan)", 18], "top building": ["No. 'Top building' is too vague or abstract to be distinguished in a photo.", 18], "frisby": ["Yes. 'Frisbee' has a tangible appearance and is a type of disc-shaped toy.\nA few things that are visually similar to 'frisbee' but are not 'frisbee' are:\tflying saucer\tdisc golf disc\trecord\tjuggling plate\nThere are several useful visual features to tell there is 'frisbee' and not similar things in a photo:\tcurved edge\tglossy surface\ttwo-toned color (usually white and another color)\tsized for throwing in one hand\tsmooth texture on both sides.", 18], "train headlight": ["Yes. 'Train headlight' has a tangible appearance and is a part of a train.\nA few things that are visually similar to 'train headlight' but are not 'train headlight' are:\tcar headlights\tbike headlights\tflashlights\tlighthouse beams\nThere are several useful visual features to tell there is 'train headlight' and not similar things in a photo:\tattached to the front of a train\tcylindrical in shape\tbright and powerful light beam\tfocused in a singular direction\tmay have a cover or shield over the front", 18], "knee cap": ["Yes. 'Knee cap' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'knee cap' but are not 'knee cap' are:\tegg\tcup\tlid\thelmet\tbutton\nThere are several useful visual features to tell there is 'knee cap' and not similar things in a photo:\tlarge bone at the front of the knee\tjoint connecting the thigh bone and the shin bone\tpatella, the scientific name for the knee cap\tis located between the femur and the tibia above the knee", 18], "stucco wall": ["Yes. 'Stucco wall' has a tangible appearance and is a type of exterior wall covering.\nA few things that are visually similar to 'stucco wall' but are not 'stucco wall' are:\tbricks\tcement walls\tconcrete walls\nThere are several useful visual features to tell there is 'stucco wall' and not similar things in a photo:\t\n- Rough texture\n- White, beige, or gray color\n- Appears to have a plaster-like finish \n- May have visible marks or lines stippled into the surface to create texture", 18], "multi-story building": ["Yes. 'Multi-story building' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'multi-story building' but are not 'multi-story building' are:\thouses\ttowers\tbarns\tbridge\nThere are several useful visual features to tell there is 'multi-story building' and not similar things in a photo:\tmultiple floors or levels\tstairs or an elevator\tsystematic windows or balconies\tunified exterior colors and materials\tlarger than most surrounding structures.", 18], "elephants tusk": ["Yes. 'Elephant tusk' has a tangible appearance and is a part of an animal.\nA few things that are visually similar to 'elephants tusk' but are not 'elephants tusk' are:\tbones\tconch shells\tivory carvings\nThere is one useful visual feature to distinguish 'elephants tusk' from the listed similar things in a photo:\tthe curvature of the tusk, which curves outward like a large horn or a large tooth. Additionally, the texture and color of the tusk may help differentiate it from other similar-looking objects.", 18], "stake": ["Yes. 'Stake' has a tangible appearance and is a type of pointed stick.\nA few things that are visually similar to 'stake' but are not 'stake' are:\tnail\tscrew\tpeg\tpicket\nThere are several useful visual features to tell there is 'stake' and not similar things in a photo:\tpointed or tapered at one end\tsharp or blunt tip\tmade of wood or metal\tsticking vertically out from the ground or another surface.", 18], "marque": ["No. 'Marque' is too vague or abstract to be distinguished in a photo. It is a term used in branding or marketing to refer to a company's specific logo, name or symbol to create brand recognition. \n\nTherefore, there are no visually similar things to 'marque'.", 18], "bird poop": ["Yes. 'Bird poop' has a tangible appearance and is a type of excrement from a bird.\nA few things that are visually similar to 'bird poop' but are not 'bird poop' are:\twhite paint\tsnow splatter\tchewing gum\tonion or garlic flakes\t\nThere are several useful visual features to tell there is 'bird poop' and not similar things in a photo:\tfrequently found on outdoor objects\twet and shiny\twaxy or sticky consistency\tbird feathers nearby", 18], "river boat": ["Yes. 'River boat' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'river boat' but are not 'river boat' are:\tspeed boat\tfishing boat\tcruise ship\tyacht\nThere are several useful visual features to tell there is 'river boat' and not similar things in a photo:\tshallow draft for ease of navigation in shallow waters, like rivers\ton-board paddlewheel or propeller to propel the boat\tfor transport of passengers or cargo on a river or other inland waterways\tdifferent cabins in different sizes, for entertainment and accommodations", 18], "calm water": ["Yes. 'Calm water' has a visually concrete appearance.\nA few things that are visually similar to 'calm water' but are not 'calm water' are:\treflection on a glass surface\toil on water\tpool water\tshimmering sand in shallow water\nThere are several useful visual features to tell there is 'calm water' and not similar things in a photo:\tsmooth and still surface\tno waves or ripples\ttransparent or translucent appearance\treflective surface", 18], "fighter jets": ["Yes. 'Fighter jets' has a tangible appearance and is a type of military aircraft.\nA few things that are visually similar to 'fighter jets' but are not 'fighter jets' are:\tpassenger planes\thelicopters\tdrones\tballoons\nThere are several useful visual features to tell there is 'fighter jets' and not similar things in a photo: sleek, streamlined shape multiple wings and engines, signs of weapons or armaments, camouflage color scheme, military markings and insignias", 18], "silver exhaust pipe": ["Yes. 'silver exhaust pipe' has a tangible appearance and is a component of a vehicle's exhaust system.\nA few things that are visually similar to 'silver exhaust pipe' but are not 'silver exhaust pipe' are:\tair intake\tgrille\tchrome accessories\nThere are several useful visual features to tell there is 'silver exhaust pipe' and not similar things in a photo:\tRectangular or circular shape\tMetallic or silver color\tAttached to the rear or side of a vehicle and connected to the exhaust system.", 18], "rifle": ["Yes. 'Rifle' has a tangible appearance and is a type of firearm.\nA few things that are visually similar to 'rifle' but are not 'rifle' are:\tshotgun\tpistol\tmusket\ttoy gun\nThere are several useful visual features to tell there is 'rifle' and not similar things in a photo:\tlong and narrow barrel\twooden or synthetic stock\tsight system\ttrigger mechanism\toften has a magazine or clip attached at the bottom or side of the stock", 18], "freight car": ["Yes. 'Freight car' has a tangible appearance and is a type of train car used for transporting goods.\nA few things that are visually similar to 'freight car' but are not 'freight car' are:\tpassenger car\tsubway car\ttram\nThere are several useful visual features to tell there is 'freight car' and not similar things in a photo:\topen-top or enclosed car with sliding or swinging doors\twheel bogies or trucks\tto carry a wide range of freight, including bulk goods, palletized cargo, and containers\tdifferent sizes, from small to large, and shapes, from rectangular to cylindrical", 18], "oil stains": ["Yes. 'Oil stains' has a tangible appearance and is a kind of mark or blemish on a surface caused by oil.\nA few things that are visually similar to 'oil stains' but are not 'oil stains' are:\tcoffee stains\tink stains\twater stains\tdirt stains\nThere are several useful visual features to tell there is 'oil stains' and not similar things in a photo:\tdark and greasy in appearance\tcircular or irregular in shape\tcould be surrounded by a rainbow-colored sheen\ttypically found in areas where there are machinery or vehicles", 18], "jumbo jet": ["Yes. 'Jumbo jet' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'jumbo jet' but are not 'jumbo jet' are:\tcommercial airlines\tprivate planes\thelicopters\tblimps\nThere are several useful visual features to tell there is 'jumbo jet' and not similar things in a photo:\tvery large size\tengines mounted on the wings\torbits visible from the wings\tpassenger windows at the front and rear of the plane\ttwo-story staircase at the front of the plane\tinflatable escape slides\tnext to an airport runway or in the sky at a high altitude", 18], "mother sheep": ["Yes. 'Mother sheep' has a tangible appearance and refers to a female sheep that has given birth to lambs.\nA few things that are visually similar to 'mother sheep' but are not 'mother sheep' are:\tewe\tfemale goat\tcow\nThere are several useful visual features to tell there is 'mother sheep' and not similar things in a photo:\tmale sheep (rams) have horns, females don't\tfemales can have udders\twhen pregnant, belly is larger and lower\twoolly coat, often white or beige", 18], "ferry boat": ["Yes. 'Ferry boat' has a tangible appearance and is a type of boat used for transportation.\nA few things that are visually similar to 'ferry boat' but are not 'ferry boat' are:\tcruise ship\tyacht\tsailboat\tspeedboat\tkayak\nThere are several useful visual features to tell there is 'ferry boat' and not similar things in a photo:\tsignage or name indicating it is a ferry boat\tloading area or dock for passengers and vehicles\tmultiple levels or decks for passengers and vehicles\ta ramp for vehicles to get on and off the boat", 18], "pamphlets": ["Yes. 'Pamphlets' has a tangible appearance and is a type of printed material.\nA few things that are visually similar to 'pamphlets' but are not 'pamphlets' are:\tmagazines\tnewspapers\tbrochures\tleaflets\nThere are several useful visual features to tell there is 'pamphlets' and not similar things in a photo:\tPrinted on a single sheet of paper\tFolded into sections or pages\tContains information on a topic or event\tIntended for distribution or sharing with others.", 18], "hem": ["No. 'Hem' is too vague or abstract to be distinguished in a photo.", 18], "competitor": ["No. 'Competitor' is too vague or abstract to be distinguished in a photo. It refers to a person, team or company that competes with another in a particular activity or market.", 18], "orange train car": ["Yes. 'Orange train car' has a tangible appearance and is a specific type of train car.\nA few things that are visually similar to 'orange train car' but are not 'orange train car' are:\torange cargo container\toranges\tstacked boxes\nThere are several useful visual features to tell there is 'orange train car' and not similar things in a photo:\tconnected to other train cars\thave wheels and railroad tracks beneath them\tflat rectangular shape\twith doors and windows", 18], "riddles": ["No. 'Riddles' are too abstract to have a concrete visual appearance.\nThere are no things similar to 'riddles' that can be visually distinguished.\nTherefore, no useful visual features for distinguishing 'riddles' from similar things in a photo exist.", 18], "round top": ["Yes. 'Round top' has a tangible appearance and refers to any object or surface that has a circular or rounded top.\nA few things that are visually similar to 'round top' but are not 'round top' are:\tcones\tumbrellas\tdomes\ttents\nThere are several useful visual features to tell there is 'round top' and not similar things in a photo:\tcircular or dome-like shape\tsmooth or curved surface\tno pointed or sharp edges", 18], "banister": ["Yes. 'Banister' has a tangible appearance and is a structure used for support on a staircase.\nA few things that are visually similar to 'banister' but are not 'banister' are:\trailing\tfence\thandrail\tbarrier\nThere are several useful visual features to tell there is 'banister' and not similar things in a photo:\tattached to the staircase\tupright support posts\thorizontal handrail at the top", 18], "para sail": ["Yes. 'Para sail' has a tangible appearance and is a type of sailing activity.\nA few things that are visually similar to 'para sail' but are not 'para sail' are:\tparachute\tkite\thang glider\tsailboat\nThere are several useful visual features to tell there is 'para sail' and not similar things in a photo:\tperson attached to a parachute and a sail\twater in the background\tsail typically larger than a parachute\thaving two or more cords attaching the sail to the harness.", 18], "seatbelt": ["Yes. 'Seatbelt' has a tangible appearance and is a type of safety equipment.\nA few things that are visually similar to 'seatbelt' but are not 'seatbelt' are:\tbackpack straps\tguitar straps\tbelt\tshoe laces\nThere are several useful visual features to tell there is 'seatbelt' and not similar things in a photo:\tattached to a vehicle or a chair\tcrossing the chest and the lap\tlocking mechanism or buckle\tthick and sturdy material, usually black or grey", 18], "parked car": ["Yes. 'Parked car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'parked car' but are not 'parked car' are:\tbuildings\tpoles\tbenches\tstreetlights\nThere are several useful visual features to tell there is 'parked car' and not similar things in a photo:\tfour wheels\tdoor handles\tand headlights\tand taillights\tdoors\tbumpers\tand license plates", 18], "wedding": ["Yes. 'Wedding' has a tangible appearance and is a ceremony celebrating the marriage of two people.\nA few things that are visually similar to 'wedding' but are not 'wedding' are:\tparty\tbirthday celebration\tgraduation ceremony\nThere are several useful visual features to tell there is 'wedding' and not similar things in a photo:\tbridal dress and groom's suit\texchange of rings\twedding vows\twedding cake\twedding flowers\twedding guests\tand decorations", 18], "land mass": ["Yes. 'Land mass' has a tangible appearance and refers to a large extent or expanse of land.\nA few things that are visually similar to 'land mass' but are not 'land mass' are:\tcanyons\tvalleys\tdeserts\tislands\nThere are several useful visual features to tell there is 'land mass' and not similar things in a photo:\textensive area of land \tvisible signs of life (trees, plants, buildings, etc.)\tclear borders that distinguish it from bodies of water or other regions of land.", 18], "train engines": ["Yes. 'Train engines' has a tangible appearance and is a type of locomotive engine used to haul trains.\nA few things that are visually similar to 'train engines' but are not 'train engines' are:\ttrucks\ttractors\tships\tplanes\nThere are several useful visual features to tell there is 'train engines' and not similar things in a photo:\telongated and cylindrical shape\twith wheels or tracks\tforward-facing headlight\tconnected to other train cars or engines\tsmokestack or exhaust pipe\tpainted with distinctive colors or logos.", 18], "bear snout": ["Yes. 'Bear snout' has a tangible appearance and is a part of a bear's face.\nA few things that are visually similar to 'bear snout' but are not 'bear snout' are:\tother animal snouts\tnoses of cartoon characters\t\nThere are several useful visual features to distinguish 'bear snout' from similar things in a photo:\tblack or brown in color\thairy\twrinkled\tmuzzle-shaped\twith a prominent nose\tand drooping lips", 18], "coal car": ["Yes. 'Coal car' has a tangible appearance and is a kind of train car.\nA few things that are visually similar to 'coal car' but are not 'coal car' are:\tfreight cars\tlog cars\ttanker cars\tpassenger cars\nThere are several useful visual features to tell there is 'coal car' and not similar things in a photo:\trectangular, box-shaped car\tblack color\tlarge capacity for carrying coal\tunloading ports on the bottom or on the sides\twith or without hoppers for self-unloading\tsystems to prevent coal from spilling or dust from flying", 18], "shirt cuff": ["Yes. 'Shirt cuff' has a tangible appearance and is a part of a shirt.\nA few things that are visually similar to 'shirt cuff' but are not 'shirt cuff' are:\twristband\tbracelet\twatch\tcufflink\nThere are several useful visual features to tell there is 'shirt cuff' and not similar things in a photo:\tpart of a shirt\tfabric that matches the rest of the shirt\tfold or crease where the cuff meets the sleeve\tbutton or buttons to fasten the cuff around the wrist.", 18], "wave breaking": ["Yes. 'Wave breaking' has a tangible appearance and is a type of ocean movement.\nA few things that are visually similar to 'wave breaking' but are not 'wave breaking' are:\twaterfall\traindrop\tplumbing leak\tpool splash\nThere are several useful visual features to tell there is 'wave breaking' and not similar things in a photo:\tocean or sea setting\twall of water or foam crashing towards shore or rocks\tsurfboard or surfer in the shot\tif viewed from a distance, it looks like a long, curling line of water that's above the surface of the water. If viewed close up, it looks like a small, foaming wall of water.", 18], "rhinoceros": ["Yes. 'Rhinoceros' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'rhinoceros' but are not 'rhinoceros' are:\thippopotamus\tbuffalo\twarthog\nThere are several useful visual features to tell there is 'rhinoceros' and not similar things in a photo:\tone or two large horns on their noses\tthick and wrinkled skin\thuge bodies\tshort legs and broad feet", 18], "motorcycle riders": ["Yes. 'Motorcycle riders' has a tangible appearance and refers to people riding a motorcycle.\nA few things that are visually similar to 'motorcycle riders' but are not 'motorcycle riders' are:\tbicyclists\tskateboarders\tmoped riders\thorseback riders\tcar drivers\t\nThere are several useful visual features to tell there are 'motorcycle riders' and not similar things in a photo:\thelmets\tleather jackets\tmotorcycle boots or shoes\trider and passenger\tsitting on a motorcycle", 18], "lockers": ["Yes, 'lockers' has a visually concrete appearance and is a type of storage unit.\nA few things that are visually similar to 'lockers' but are not 'lockers' are: drawers, cabinets, shelves, bookcases, wardrobes.\nThere are several useful visual features to differentiate 'lockers' from similar things in a photo: uniform size and shape, rows of locked doors or compartments, metal or plastic material, ventilation holes on the doors.", 18], "metal wires": ["Yes. 'Metal wires' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'metal wires' but are not 'metal wires' are:\trope\tstrings\tribbons\thair\nThere are several useful visual features to tell there are 'metal wires' and not similar things in a photo:\tmade of metal\tsilvery, gold, or copper color\tcan conduct electricity or transmit signals\trigid and not flexible \tsmooth surface and cylindrical shape", 18], "snowy slope": ["Yes. 'Snowy slope' has a tangible appearance and refers to a sloping surface covered in snow.\nA few things that are visually similar to 'snowy slope' but are not 'snowy slope' are:\tdeserted road\twithered yard\twhite sand\tdune\nThere are several useful visual features to tell there is 'snowy slope' and not similar things in a photo:\tcovered with snow\tsloping terrain\ticy or snowy texture\tcold and frosty scenery\tno visible plants or vegetation", 18], "tides": ["No. 'Tides' is too vague or abstract to be visually concrete.\nThere are no things that are visually similar to 'tides' but not 'tides'. \nSince 'tides' refer to the rising and falling of sea levels caused by gravitational forces, the most useful visual feature for demonstrating 'tides' in a photo would be showing the visible changes in water levels on the beach, in relation to the moon's phases.", 18], "basketball player": ["Yes. 'Basketball player' has a tangible appearance and refers to a person who plays basketball.\nA few things that are visually similar to 'basketball player' but are not 'basketball player' are:\tathlete\tfootball player\ttennis player\nThere are several useful visual features to tell there is 'basketball player' and not similar things in a photo:\ttall and athletic\tbuild\twearing basketball attire\tcarrying or dribbling basketball", 18], "beach ball": ["Yes. 'Beach ball' has a tangible appearance and is a kind of inflatable ball used for recreation.\nA few things that are visually similar to 'beach ball' but are not 'beach ball' are:\tvolleyball\tsoccer ball\tbasketball\tyoga ball\nThere are several useful visual features to tell there is 'beach ball' and not similar things in a photo:\tinflatable\tcircular\twith alternating colored segments (typically white, blue, and yellow or red)\tused for playing games on the beach or in a pool\twithout any labels or markings of a specific sport or brand", 17], "basketball net": ["Yes. 'Basketball net' has a tangible appearance and refers to the net that hangs from the basketball hoop.\nA few things that are visually similar to 'basketball net' but are not 'basketball net' are:\tfishing net\thammock\ttrampoline net\t\nThere are several useful visual features to tell there is 'basketball net' and not similar things in a photo:\tattached to a basketball hoop\thanging\tdiamond-shaped holes for the ball to pass through\tcrisp white or orange color, depending on the type of game being played.", 17], "pool water": ["Yes. 'Pool water' has a tangible appearance and is a body of water specifically found in a swimming pool.\nA few things that are visually similar to 'pool water' but are not 'pool water' are:\tocean\twave\tlake\triver\nThere are several useful visual features to tell there is 'pool water' and not similar things in a photo:\trectangular or circular shape\tchlorine or other pool chemicals in the water\tdiving board or pool steps\tinflatable pool toys or loungers\tfloaters or lane markers.", 17], "plastic table": ["Yes. 'Plastic table' has a tangible appearance.\nA few things that are visually similar to 'plastic table' but are not 'plastic table' are:\twooden table\tmetal table\tcardboard table\tglass table\nThere are several useful visual features to tell there is 'plastic table' and not similar things in a photo:\tsmooth, plastic surface\tlightweight and easily portable often have foldable legs or top\tvariety of colors and designs\tplastic texture\tis less expensive than other types of tables", 17], "apple mouse": ["Yes. 'Apple mouse' has a tangible appearance and is a kind of computer peripheral.\nA few things that are visually similar to 'apple mouse' but are not 'apple mouse' are:\tregular mouse\twireless mouse\ttrackpad\nThere are several useful visual features to tell there is 'apple mouse' and not similar things in a photo:\tsmall size\twhite or silver\tcolorful apple logo on the top\tfour or more buttons on the top or sides\tUSB or lightning connector", 17], "drink glass": ["Yes. 'Drink glass' has a tangible appearance and is a container to hold beverages.\nA few things that are visually similar to 'drink glass' but are not 'drink glass' are:\tmason jar\tvase\tflower pot\tsalt shaker\nThere are several useful visual features to tell there is 'drink glass' and not similar things in a photo:\ttranslucent material (usually glass)\tchannel inward to hold liquid\trim at the top for drinking\tusually cylindrical shape\ttransparency to see through the glass.", 17], "salami": ["Yes. 'Salami' has a tangible appearance and is a type of cured meat.\nA few things that are visually similar to 'salami' but are not 'salami' are:\tpepperoni\tsausage\tbeef jerky\tsliced ham\nThere are several useful visual features to tell there is 'salami' and not similar things in a photo:\tcylindrical shape\tdark red color with white marbling\tpinkish-brown meat with visible fat pieces\tonion-like texture when sliced", 17], "country road": ["Yes. 'Country road' has a tangible appearance and is a type of path or road.\nA few things that are visually similar to 'country road' but are not 'country road' are:\tcity street\thiking trail\tpaved road\nThere are several useful visual features to tell there is 'country road' and not similar things in a photo:\tunpaved surface surrounded by nature\tor minor settlements\tside fences or fields without buildings\twinding and uneven shape\tlower traffic density than a city street", 17], "blue wheels": ["Yes. 'Blue wheels' has a tangible appearance and refers to wheels that are blue in color.\nA few things that are visually similar to 'blue wheels' but are not 'blue wheels' are:\tred wheels\tgreen wheels\tyellow wheels\tblack wheels\t\nThere are no useful visual features for distinguishing 'blue wheels' from the listed similar things in a photo, as the only distinguishing feature is their color.", 17], "grin": ["Yes. 'Grin' has a tangible appearance and is a facial expression.\nA few things that are visually similar to 'grin' but are not 'grin' are: smile, smirk, frown\nThere are several useful visual features to tell there is 'grin' and not similar things in a photo: wide open mouth with visible teeth, the edges of the mouth pulled upwards, a joyful or mischievous expression.", 17], "giraffe tongue": ["Yes. 'Giraffe tongue' has a tangible appearance and is a part of the giraffe's body.\nThere are no other things that are visually similar to 'giraffe tongue' but are not 'giraffe tongue'.\nThere are no useful visual features needed to distinguish 'giraffe tongue' from anything else in a photo as it is a unique and distinctive body part.", 17], "highchair": ["Yes. 'Highchair' has a tangible appearance and is a kind of chair.\nA few things that are visually similar to 'highchair' but are not 'highchair' are:\tstool\tbar stool\tnormal chair\tbench\nThere are several useful visual features to tell there is 'highchair' and not similar things in a photo:\tspecially designed for babies or toddlers\ttaller than a regular chair\thas a tray or an attachable table for eating\thas safety straps or belts for securing the child in place", 17], "wrist guard": ["Yes. 'Wrist guard' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'wrist guard' but are not 'wrist guard' are:\twatches\tbracelets\tarmbands\tfitness trackers\nThere are several useful visual features to tell there is 'wrist guard' and not similar things in a photo:\thard or padded material\tsecurely attached around the wrist\tarea of protection around the wrist and part of the hand\toften worn by athletes or those engaging in physical activity", 17], "lamp light": ["Yes. 'Lamp light' has a tangible appearance and refers to the light emitted by a lamp.\nA few things that are visually similar to 'lamp light' but are not 'lamp light' are:\tsunlight\tfirelight\tcandlelight\t\nThere are several useful visual features to tell there is 'lamp light' and not similar things in a photo:\n - A visible lamp in the photo\n - Reflection of light rays, indicating that the light is coming from a bulb\n - A light switch or cord indicating the light is powered by electricity.", 17], "police motorcycles": ["Yes. 'Police motorcycles' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'police motorcycles' but are not 'police motorcycles' are:\tmotorcycles\tmilitary motorcycles\tdelivery motorcycles\tracing motorcycles\nThere are several useful visual features to tell there is 'police motorcycles' and not similar things in a photo:\twarning lights and sirens\twide windscreens\torangish-red or blue-white paint\tjob\tpolice decals or markings\tequipment such as radio or shotgun mount on the back", 17], "giraffe head": ["Yes. 'Giraffe head' has a tangible appearance and refers to the head of the long-necked animal.\nA few things that are visually similar to 'giraffe head' but are not 'giraffe head' are:\tdeer head\thorse head\tlama head\t\nThere are several useful visual features to tell there is 'giraffe head' and not similar things in a photo:\tlong neck\tspot patterns on fur\telongated ears\thorns or ossicones\ton top of the head", 17], "remote controller": ["Yes. 'Remote controller' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'remote controller' but are not 'remote controller' are:\tgame controller\tjoystick\ttv control app on a smartphone\nThere are several useful visual features to tell there is 'remote controller' and not similar things in a photo:\trectangular shape\tbuttons to control TV, DVD, or another device\ta D-pad to change channels or move a cursor\tnumeric keypad\tto power on and off or mute devices\tbatteries or an indicator light", 17], "glass lamp": ["Yes. 'Glass lamp' has a tangible appearance and is a type of lamp made of glass.\nA few things that are visually similar to 'glass lamp' but are not 'glass lamp' are:\tlampshade\tceiling light\tchandelier\ttable lamp\nThere are several useful visual features to tell there is 'glass lamp' and not similar things in a photo:\tmade of glass\ttranslucent\tdesigns or patterns visible through the glass\tframe or base made of metal or other materials\temit light from inside the glass", 17], "button jacket": ["Yes. 'Button jacket' has a tangible appearance and is a type of garment.\nA few things that are visually similar to 'button jacket' but are not 'button jacket' are:\tzipper jacket\thoodie\tblazer\tcoat\nThere are several useful visual features to tell there is 'button jacket' and not similar things in a photo:\tbutton closure on the front\tcollared neckline\tstructured and tailored fit\tflared bottom hem on some models\tmade from wool, denim, or cotton material", 17], "product": ["No. 'Product' is too vague and abstract to be distinguished in a photo. It refers to any item or goods produced for sale, so it can have a wide range of appearance depending on the type of product.", 17], "tan blanket": ["Yes. 'Tan blanket' has a tangible appearance and is a type of bedding or fabric.\nA few things that are visually similar to 'tan blanket' but are not 'tan blanket' are:\tpillow\tcase\trug\ttowel\nThere are several useful visual features to tell there is 'tan blanket' and not similar things in a photo:\tsoft and fluffy texture\ttan color\twoven fabric or knit pattern\tcan be draped or folded for use in beds or couches", 17], "round blue": ["No. 'round blue' is too vague and abstract to be a visually concrete concept. It does not define any specific object or entity.\nHowever, here are some examples of things that are round and blue:\n- Blueberries\n- Marbles\n- Ornaments\n- Gumballs\n\nUseful visual features for distinguishing 'round blue' objects from each other in a photo would include size, texture, reflectivity, and the presence of specific markings or features unique to each object.", 17], "blonde lady": ["Yes. 'Blonde lady' has a tangible appearance and is a person with blonde hair.\nA few things that are visually similar to 'blonde lady' but are not 'blonde lady' are: women with brown or black hair, women with red hair, men with blonde hair\nThere are several useful visual features to tell there is 'blonde lady' and not similar things in a photo:\tblonde hair\tlight-colored eyebrows and eyelashes\tfemale facial features (such as cheekbones, slimmer jawline, etc.)", 17], "taco": ["Yes. 'Taco' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'taco' but are not 'taco' are:\tquesadilla\twrap\tburrito\tpita\tfajita\tpizza\nThere are several useful visual features to tell there is 'taco' and not similar things in a photo:\ttortilla\tfolded shape\tstuffed with meat, cheese, vegetables or beans\ttopped with salsa or guacamole\tcrispy or soft shell", 17], "dark shorts": ["Yes. 'Dark shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'dark shorts' but are not 'dark shorts' are:\tjeans\ttrousers\tswimwear\tyoga pants\nThere are several useful visual features to tell there are 'dark shorts' and not similar things in a photo:\tshort length\tdark color (black, navy, dark grey, etc.)\tfabric texture (denim, cotton, etc.)\tpockets or zippers.", 17], "woman ground": ["No. 'Woman ground' is too vague and abstract to have a tangible appearance or visual features. It does not make sense as a concept.", 17], "bowel": ["No. 'Bowel' is too vague or abstract to be distinguished in a photo. However, there are some specific components of the bowel, such as the large intestine, which can be visually identified and studied for medical purposes.\nI cannot provide a list of things visually similar to bowel.\nThere are no useful visual features for distinguishing 'bowel' from other things without more specific context or information.", 17], "leaves branch": ["Yes. 'Leaves branch' has a tangible appearance and is a branch or stem of a tree with leaves.\nA few things that are visually similar to 'leaves branch' but are not 'leaves branch' are: bare tree branch\tdead leaves branch\tflowering branch\tbush or shrub\t\nThere are several useful visual features to tell there is 'leaves branch' and not similar things in a photo:\tgreen or colorful leaves\tletters, thin structures like veins\tstem or branch with thicker and darker texture\twhere leaves are attached to the branch or stem", 17], "baseball player batting": ["Yes. 'Baseball player batting' has a tangible appearance and involves a player swinging a baseball bat.\nA few things that are visually similar to 'baseball player batting' but are not 'baseball player batting' are:\ttennis player hitting a ball\tgolfer hitting a ball\tcricketer hitting a ball\thockey player hitting a puck\nThere are several useful visual features to tell there is 'baseball player batting' and not similar things in a photo:\twearing a baseball uniform\thelmet on the head\tholding a baseball bat\tstanding in front of a pitcher's mound or home plate\tswinging the bat at a baseball\tball in motion with the bat", 17], "silver computer": ["Yes. 'Silver Computer' has a tangible appearance and refers to a computer that is silver colored.\nA few things that are visually similar to a 'silver computer' but are not a 'silver computer' are:\tsilver typewriter\tother colored laptops\tthat have silver accents or parts\nThere are several useful visual features to tell there is a 'silver computer' and not similar things in a photo:\tsilver color across the entire device or most of it\tthe device looks like a computer, not a typewriter, tablet, or phone\tthe device has a screen and a keyboard, rather than a touchscreen or attached keyboard", 17], "silver apple laptop": ["Yes. 'Silver apple laptop' has a tangible appearance and refers to a specific type of laptop made by Apple.\nA few things that are visually similar to 'silver apple laptop' but are not 'silver apple laptop' are:\tsilver laptops from other brands\tother colored laptops from Apple\tother types of devices like tablets or phones\nThere are several useful visual features to tell there is a 'silver apple laptop' and not similar things in a photo:\tdistinctive silver color\tapple logo on the lid and on the bottom of the screen\trounded edges\tand smooth surface\tfor laptop form factor", 17], "party hat": ["Yes. 'Party hat' has a tangible appearance and is a kind of headwear.\nA few things that are visually similar to 'party hat' but are not 'party hat' are:\tcone\ttrumpet\twizard hat\tjester hat\nThere are several useful visual features to tell there is 'party hat' and not similar things in a photo:\tcone-shaped\tpointed\ttapered\tbright colors or patterns\twith strings to tie under chin or around head.", 17], "triangle flag": ["Yes. 'Triangle flag' has a tangible appearance and is a type of flag.\nA few things that are visually similar to 'triangle flag' but are not 'triangle flag' are:\trectangle flag\tbanner\tpennant\t\nThere are several useful visual features to distinguish 'triangle flag' from the listed similar things in a photo:\ttriangular shape\tcut in a diagonal line at the bottom\thanging vertically or horizontally from a pole or string\tvibrant colors or designs often used for decoration\tor used in sports or racing events", 17], "spoon plate": ["Yes. 'Spoon plate' has a tangible appearance and is a type of tableware.\nA few things that are visually similar to 'spoon plate' but are not 'spoon plate' are:\tfork plate\tknife plate\tchopstick plate\tbowl\nThere are several useful visual features to tell there is 'spoon plate' and not similar things in a photo:\t\nhas a round or rectangular shape\t\nhas a flat surface for food\t\nhas one or more indentations (or slots) for a spoon or utensil\t\nmay have raised edges or a lip around the edge to contain the food", 17], "woman skier": ["Yes. 'Woman skier' has a tangible appearance and is a woman who is skiing.\nA few things that are visually similar to 'woman skier' but are not 'woman skier' are:\twoman snowboarder\tman skier\tkid who is playing on the snow\nThere are several useful visual features to tell there is 'woman skier' and not similar things in a photo:\twoman wearing ski attire\tskis attached to woman's boots\tski poles in woman's hands\twoman gliding down a snowy slope", 17], "adult cow": ["Yes. 'Adult cow' has a tangible appearance and is a mature female bovine.\nA few things that are visually similar to 'adult cow' but are not 'adult cow' are:\tbull\tyak\tbison\tbuffalo\nThere are several useful visual features to tell there is 'adult cow' and not similar things in a photo:\tudder\tteats\tat least 2 horns (sometimes)\tbrown or black coat\tclover shaped nostrils large ears with a loose fold\tsoft and round eyes", 17], "yellow tags": ["Yes. 'Yellow tags' has a tangible appearance and refers to tags that are colored yellow.\nA few things that are visually similar to 'yellow tags' but are not 'yellow tags' are:\tpost-it notes\tyellow stickers\tgolden leaves\tyellow flowers\nThere are several useful visual features to tell there are 'yellow tags' and not similar things in a photo:\ttag shape or size\tyellow color\twriting or symbols on the tag", 17], "dress tie": ["Yes. 'Dress tie' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'dress tie' but are not 'dress tie' are:\tscarf\tribbon\tnecklace\tcravat\nThere are several useful visual features to tell there is 'dress tie' and not similar things in a photo:\tnarrow strip of fabric\thangs around the neck\ttied in a knot or bow at the collar\tmatch the wearer's outfit in color and pattern.", 17], "papaya": ["Yes. 'Papaya' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'papaya' but are not 'papaya' are:\tmango\tbanana\torange\nThere are several useful visual features to distinguish 'papaya' from the listed similar things in a photo:\tmedium to large size\toblong or pear shape\tyellow or orange skin with green or brown spots\tblack seeds in the center of the fruit", 17], "picnic bench": ["Yes. 'Picnic bench' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'picnic bench' but are not 'picnic bench' are:\toutdoor table\tpatio furniture\tbenches\tstools\nThere are several useful visual features to tell there is 'picnic bench' and not similar things in a photo:\tattached benches or seating\tsimple and sturdy design\tmade of wood or metal rectangul-shaped (longer than wider)", 17], "lace curtain": ["Yes. 'Lace curtain' has a tangible appearance and is a type of curtain.\nA few things that are visually similar to 'lace curtain' but are not 'lace curtain' are:\tsheer curtain\tmesh screen\tnetting\tfishnet stockings\nThere are several useful visual features to tell there is 'lace curtain' and not similar things in a photo:\tlacy or intricate pattern\ttransparency or translucency\tcurtain hanging on a rod or window\tframe\thighly decorative with scalloped, fringed, or tasseled edges.", 17], "track lighting": ["Yes. 'Track lighting' has a tangible appearance and refers to a specific type of lighting fixture.\nA few things that are visually similar to 'track lighting' but are not 'track lighting' are:\trecessed lighting\tpendant lighting\tsconces\tfloor lamps\nThere are several useful visual features to tell there is 'track lighting' and not similar things in a photo:\tlong track with multiple light fixtures on it\t\nadjustable lights\t\nceiling-mounted\t\nthe ability to redirect the direction of the lights along the track.", 17], "crop": ["Yes. 'Crop' has a tangible appearance and refers to a type of vegetation grown and harvested for use.\nA few things that are visually similar to 'crop' but are not 'crop' are:\tgrass\tweed\tshrub\ttree\nThere are several useful visual features to tell there is 'crop' and not similar things in a photo:\torganized rows or patterns of plants\ta uniform height or size of plants\tmature or ripe plants with visible fruits or vegetables\thuman-made structures like irrigation channels, fences, or machinery used for farming", 17], "teenagers": ["No. 'Teenagers' are not visually concrete as it's a stage of human development that does not have a specific appearance.\nA few things that are visually similar to 'teenagers' but are not 'teenagers' are: young adults, children, elderly people.\nThere are no useful visual features to distinguish 'teenagers' from the listed similar things in a photo as it's a stage of life that cannot be identified solely by physical appearance.", 17], "purple cloth": ["Yes. 'Purple cloth' has a tangible appearance and can be distinguished by its color and texture.\nA few things that are visually similar to 'purple cloth' but are not 'purple cloth' are:\tpurple paper\tpurple paint\tpurple plastic\tpurple hair\nThere are several useful visual features to tell there is 'purple cloth' and not similar things in a photo:\tmade of fabric or textile\tpurple in color\thas a woven or knitted texture\tflexible and drapey.", 17], "streams": ["Yes. 'Streams' has a tangible appearance and is a kind of flowing water. \nA few things that are visually similar to 'streams' but are not 'streams' are:\trivers\twaterfalls\tfountains\tirrigation canals\nThere are several useful visual features to tell there is 'streams' and not similar things in a photo:\tnarrow and shallow\tan uneven or rocky bed\twinding or meandering path\tclear flowing water\tmay have vegetation or rocks around", 17], "stadium seat": ["Yes. 'Stadium seat' has a tangible appearance and is a type of seat found in sports arenas or stadiums.\nA few things that are visually similar to 'stadium seat' but are not 'stadium seat' are:\tchair/bench\tbleacher\tfolding chair\nThere are several useful visual features to tell there is 'stadium seat' and not similar things in a photo:\ttypically plastic, metal or padded\tattached to a step or a riser in a stadium or sports arena\tdesign can vary but usually has a backrest\tnarrow and long to fit many seats in a row", 17], "ornate building": ["Yes. 'Ornate building' has a tangible appearance and is a type of architecture.\nA few things that are visually similar to 'ornate building' but are not 'ornate building' are:\tmodern skyscraper\thistorical monument\tplain building\tbridge\nThere are several useful visual features to tell there is 'ornate building' and not similar things in a photo:\telaborate design and decoration\tsculptures, carvings, or reliefs intricate patterns and motifs columns, arches, domes, or spires symmetrical shape or layout", 17], "cereal box": ["Yes. 'Cereal box' has a tangible appearance and is a type of packaging.\nA few things that are visually similar to 'cereal box' but are not 'cereal box' are:\tsoap box\ttissue box\tshoe box\tsnack box\nThere are several useful visual features to tell there is 'cereal box' and not similar things in a photo:\trectangular shape/packaging\tpicture or branding of cereal on the front of the box\tdetailed nutritional information on the back of the box\tbarcode and other production information", 17], "creamy": ["No. 'Creamy' is too vague or abstract to be distinguished in a photo. However, something that looks creamy can have a tangible appearance.\nA few things that are visually similar to 'creamy' but are not 'creamy' are:\tmilky\tfrosty\tcloudy\tsmooth\nThere are several useful visual features to tell there is 'creamy' and not similar things in a photo:\tthick and smooth texture\twhite or pale color\thighlight or gloss on the surface\tmay have visible air bubbles or swirls", 17], "mirror sink": ["Yes. 'Mirror sink' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'mirror sink' but are not 'mirror sink' are:\tmirror cabinet\tbathroom vanity\tmirror with a faucet\nThere are several useful visual features to tell there is 'mirror sink' and not similar things in a photo:\ta sink basin integrated into the mirror\ta visible drain in the sink\ta reflective surface\tthat it is mounted on a wall above a countertop or cabinet.", 17], "sport shoes": ["Yes. 'Sport shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'sport shoes' but are not 'sport shoes' are:\tdress shoes\tsandals\tboots\nThere are several useful visual features to tell there is 'sport shoes' and not similar things in a photo:\trubber soles\tcushioned insoles\tmesh or breathable fabric materials\tfor sports or athletic activities\tvariety of colors and designs (it's not a pure distinction but it can help)", 17], "surfer surfboard": ["Yes. 'Surfer surfboard' has a tangible appearance and typically refers to a specific type of surfboard ridden by a surfer.\nA few things that are visually similar to 'surfer surfboard' but are not 'surfer surfboard' are:\tbodyboards\twakeboards\tboogie boards\tkneeboards\nThere are several useful visual features to tell there is 'surfer surfboard' and not similar things in a photo:\tlong and narrow shape\twith a pointed or rounded nose\tlightweight and buoyant\tconstruction made of foam and fiberglass\ttop deck with a textured grip pad for feet", 17], "metal chains": ["Yes. 'Metal chains' has a tangible appearance and is a kind of linked metallic material.\nA few things that are visually similar to 'metal chains' but are not 'metal chains' are:\trope\twire\tfence\tfishnet\nThere are several useful visual features to tell there is 'metal chains' and not similar things in a photo:\t\n- interlocking metal links\n- more robust than wire or rope\n- reflective surfaces or textures", 17], "purple wall": ["Yes. 'purple wall' has a tangible appearance and is a type of wall with a particular color.\nA few things that are visually similar to 'purple wall' but are not 'purple wall' are:\tblue wall\tpink wall\tviolet wall\nThere are several useful visual features to tell there is a 'purple wall' and not similar things in a photo:\tthe wall is predominantly purple\tinconsistent shading or coloration from other types of walls\tor a large blotch of purple color.", 17], "penguins": ["Yes. 'Penguins' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'penguins' but are not 'penguins' are:\tauklets\tmurres\trazorbills\tguillemots\nThere are several useful visual features to tell there is 'penguins' and not similar things in a photo:\tblack and white feathers\tflightless wings\twaddle on land\tfeet modified into flippers\tforaging for food in the ocean\tbeak\twith a distinct hook at the end.", 17], "apple symbol": ["Yes. 'Apple symbol' has a tangible appearance and is a well-known logo.\nA few things that are visually similar to 'apple symbol' but are not 'apple symbol' are:\tpear symbol\tmango symbol\tpineapple symbol\nThere are no useful visual features to distinguish the 'apple symbol' from the listed similar things in a photo, as they are all different fruit symbols. However, the specific features that distinguish the 'apple symbol' itself are:\tan apple silhouette\twith a bite taken out, exposing the flesh of the apple\ta black or white outline filled with a solid color of the rainbow.", 17], "burgers": ["Yes. 'Burgers' has a tangible appearance and is a type of food item.\nA few things that are visually similar to 'burgers' but are not 'burgers' are:\tveggie burgers\tsandwiches\twraps\thot dogs\nThere are several useful visual features to tell there is 'burgers' and not similar things in a photo:\ttwo buns or slices of bread with a patty, meat or veggie\tpickles, lettuce, or other toppings in between the buns\tor cheese, onion, and bacon as toppings.", 17], "toilet flush handle": ["Yes. 'Toilet flush handle' has a tangible appearance and is a specific part of a toilet.\nA few things that are visually similar to 'toilet flush handle' but are not 'toilet flush handle' are:\tdoorknob\tcabinet handle\tfaucet handle\tdrawer pull\nThere are several useful visual features to tell there is 'toilet flush handle' and not similar things in a photo:\tlocation on the toilet\ttwo-piece design with a lever or button\tdesigned to control the flow of water to flush the toilet", 17], "seat lid": ["Yes. 'Seat lid' has a tangible appearance and is part of a toilet seat.\nA few things that are visually similar to 'seat lid' but are not 'seat lid' are:\ttile\tpiece of wood\tor other flat object\nThere are several useful visual features to tell there is 'seat lid' and not similar things in a photo:\trectangular or oval in shape\thinged to a toilet seat\tcovers the toilet bowl\twhen lifted, reveals the toilet bowl\tbottom side has a concave shape\tthat fits the toilet bowl\ttop side has a convex shape for sitting.", 17], "wood grain table": ["Yes. 'Wood grain table' has a tangible appearance and refers to a table made of wood with a visible grain pattern.\nA few things that are visually similar to 'wood grain table' but are not 'wood grain table' are:\twooden crate\twooden chair\twooden bench\twooden floor\nThere are several useful visual features to tell there is 'wood grain table' and not similar things in a photo:\tvisible wood grain pattern\tsurface is flat and smooth\ttabletop is horizontal\tand legs are vertical\tor slanted outward\tsurface is polished or treated with a sealant", 17], "bead": ["Yes. 'Bead' has a tangible appearance and is a small decorative object with a hole for stringing or threading.\nA few things that are visually similar to 'bead' but are not 'bead' are: button, stone, gem, seed, coin\nThere are several useful visual features to tell there is 'bead' and not similar things in a photo:\thaving a hole for stringing or threading\tsmall size\tvarious colors and shapes\tmade of glass, plastic, or metal", 17], "beige shorts": ["Yes. 'Beige shorts' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'beige shorts' but are not 'beige shorts' are:\tbeige pants\tshorts of a different color\nThere are several useful visual features to tell there is 'beige shorts' and not similar things in a photo:\tbeige color\tshort length\ttypically made of cotton or denim\thave belt loops and pockets", 17], "indoor plant": ["Yes. 'Indoor plant' has a tangible appearance and is a type of plant that can be grown inside a building.\nA few things that are visually similar to 'indoor plant' but are not 'indoor plant' are:\tplastic plants\tartificial flowers\nThere are several useful visual features to tell there is 'indoor plant' and not similar things in a photo:\tleaves\tpotted\tdirt or soil\tcould be flowering\tor contains fruit\thas visible roots\tout of doors and not a part of nature", 17], "metal device": ["No. 'Metal device' is too vague or abstract to be distinguished in a photo. It can refer to almost any object made of metal.\nA few things that are visually similar to a 'metal device' but are not 'metal device' could be: kitchenware, tools, gadgets or machinery.\nUseful visual features for distinguishing 'metal device' from the listed similar things in a photo would depend on the specific object being referred to. It could include the device's shape, size, function, materials, and any other unique visual characteristics that identify it as a specific type of metal device.", 17], "blurry person": ["Yes. 'Blurry person' has a tangible appearance and refers to a person who is not in focus in a photo.\nFew things that are visually similar to 'blurry person' but are not 'blurry person' are:\tshadow\tmotion trail\treflection\tlight flare\t\nThere are several useful visual features to tell there is 'blurry person' and not similar things in a photo:\tthe outline of a person is partially visible\tthe edges of the person's body are not crisp or well-defined\tmost of the person is somewhat fuzzy or smeared out\tthe person is not in sharp focus compared to the rest of the image", 17], "gas range": ["Yes. 'Gas range' has a tangible appearance and is a type of kitchen appliance used for cooking.\nA few things that are visually similar to 'gas range' but are not 'gas range' are:\telectric stovetop\tinduction cooktop\toutdoor grill\tcamp stove\nThere are several useful visual features to tell there is 'gas range' and not similar things in a photo:\tgas burners\tknobs for adjusting the heat\tgrates for pots and pans\tmetal surface around burners gas connection in the back or bottom of the appliance.", 17], "half pizza": ["Yes. 'Half pizza' has a tangible appearance and is a type of food item.\nA few things that are visually similar to 'half pizza' but are not 'half pizza' are:\tcalzone\tpie\tquesadilla\ttaco\nThere are several useful visual features to tell there is 'half pizza' and not similar things in a photo:\thalf moon shape\twith tomato sauce and melted cheese\ton a round dough crust\tsliced into triangular or square pieces", 17], "wood fence post": ["Yes, 'wood fence post' is a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'wood fence post' but are not 'wood fence post' are:\ttree trunk\tpole\tgarden stake\nThere are several useful visual features to distinguish 'wood fence post' from the listed similar things in a photo, such as:\theight\tdimensions\trough texture\tplacement in a row (in case of a fence)\tholes for fence wire or nails\trounded or pointed top", 17], "plaid pattern": ["Yes, 'plaid pattern' has a tangible appearance and refers to a specific type of fabric pattern consisting of rectangular shapes of different colors and sizes.\nA few things that are visually similar to 'plaid pattern' but are not 'plaid pattern' are:\tGrid pattern\tStriped pattern\tCheckered pattern\tHoundstooth pattern\tTartan pattern\nThere are several useful visual features to differentiate 'plaid pattern' from the listed similar things in a photo:\tdiagonal or criss-crossed lines\tintersecting rectangular shapes\tmultiple colors or shades arranged in a recurring and symmetrical order\tno curved shapes or organic forms.", 17], "side salad": ["Yes. 'Side salad' has a tangible appearance and usually refers to a small serving of salad accompanying a main dish.\nA few things that are visually similar to 'side salad' but are not 'side salad' are:\tcaesar salad\tgarden salad\tpasta salad\tfruit salad\nThere are several useful visual features to tell there is 'side salad' and not similar things in a photo:\tsmall in size\tas a complementary dish to another dish\tvariety of vegetables (such as lettuce, tomatoes, cucumber, etc.)\tdressing or sauce might be visible as well.", 17], "arch way": ["Yes. 'Arch way' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'arch way' but are not 'arch way' are:\tdoor\tframe\twindow\tentrance\nThere are several useful visual features to tell there is 'arch way' and not similar things in a photo:\t\na curved or pointed structure over an opening or passageway, often made of stone, brick, or another strong material.", 17], "metal pizza pan": ["Yes. 'Metal pizza pan' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'metal pizza pan' but are not 'metal pizza pan' are:\tcake pan\tfrying pan\tskillet\tsheet pan\nThere are several useful visual features to tell there is 'metal pizza pan' and not similar things in a photo:\tcircular shape\twith or without perforated holes\tshallow or flat edges\tmetallic appearance", 17], "front screen": ["Yes. 'Front screen' has a tangible appearance and is a part of an electronic device like a phone or a computer.\nA few things that are visually similar to 'front screen' but are not 'front screen' are:\tprotective screen film\tscreen protector case\t\nThere are no useful visual features to distinguish the 'front screen' from the listed similar things in a photo as they are all similar in appearance and function. The context of the photo and surrounding objects may provide clues.", 17], "railroad ties": ["Yes. 'Railroad ties' has a tangible appearance and is a type of wooden beam.\nA few things that are visually similar to 'railroad ties' but are not 'railroad ties' are:\tfence posts\tlogs\twooden beams\nThere are several useful visual features to tell there is 'railroad ties' and not similar things in a photo:\trectangular shape\twith notches for rails and spikes\tcreosote-treated to resist rot and insects\thas been placed along a railroad", 17], "plastic forks": ["Yes. 'Plastic forks' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'plastic forks' but are not 'plastic forks' are:\tknives\tspoons\ttoothpicks\nThere are several useful visual features to tell there is 'plastic forks' and not similar things in a photo:\ttwo or three prongs on one end\thandles on the other end made of plastic\tcolors usually include white, black or clear (transparent)", 17], "wood counter": ["Yes. 'Wood counter' is a visually concrete concept and is a type of furniture.\nA few things that are visually similar to 'wood counter' but are not 'wood counter' are:\twooden table\tkitchen island\tbar\tcounter-height desk\nThere are several useful visual features to tell there is 'wood counter' and not similar things in a photo:\tflat surface for working or placing objects\ton top of cabinets or drawers\tmade of wood material\tmatches the color or style of other furniture in the room\tcan be used as a dining table or workspace.", 17], "cut piece": ["Yes. 'Cut piece' has a tangible appearance and refers to a piece of material that has been cut.\nA few things that are visually similar to 'cut piece' but are not 'cut piece' are:\tshredded paper\tripped fabric\ttorn paper\tshredded leaves\nThere are several useful visual features to tell there is a 'cut piece' and not similar things in a photo:\tstraight edges or corners\tsmooth or frayed edges\tcleanly sliced through the material\tsharply cut material with no noticeable wear or tear marks", 17], "sink drain": ["Yes. 'Sink drain' has a tangible appearance and refers to the opening in a sink that allows water to flow out.\nA few things that are visually similar to 'sink drain' but are not 'sink drain' are:\tplug\tbottle cap\tscrew\tcircular sticker\nThere are several useful visual features to tell there is 'sink drain' and not similar things in a photo:\tfound in a sink\tor on a surface with a hole in it\tgrated or perforated surface\tfor water to pass through\tcircular shape in the middle of a larger surface.", 17], "sprinkler": ["Yes. 'Sprinkler' has a tangible appearance and is a device for watering plants. \nA few things that are visually similar to 'sprinkler' but are not 'sprinkler' are:\those nozzle\tpressure washer nozzle\tfirefighting hose nozzle\t\nThere are several useful visual features to tell there is 'sprinkler' and not similar things in a photo:\tcircular or semicircular shape\tmultiple tiny holes\tfor use in gardens or lawns\thaving a base or being attached to a pipe or hose.", 17], "crispy": ["No. 'Crispy' is too vague or abstract to be distinguished in a photo.", 17], "hand soap dispenser": ["Yes. 'Hand soap dispenser' has a tangible appearance and is a device used to dispense soap.\nA few things that are visually similar to 'hand soap dispenser' but are not 'hand soap dispenser' are:\tshampoo bottle\tlotion bottle\tbathroom cleaner bottle\twater bottle\nThere are several useful visual features to tell there is 'hand soap dispenser' and not similar things in a photo:\tpump or nozzle for dispensing soap\tclear or translucent container\tlabel or image indicating \"soap\" or \"hand soap\"", 17], "safety rail": ["Yes. 'Safety rail' has a tangible appearance and is a type of protective barrier.\nA few things that are visually similar to 'safety rail' but are not 'safety rail' are:\tfence\tbalcony\tgrill\tgate\nThere are several useful visual features to tell there is 'safety rail' and not similar things in a photo:\tattached to a wall or a platform\thand-height or waist-height\thorizontal bars or tubes for gripping\tclear enough to see through\tno sharp edges or protrusions.", 17], "side tower": ["Yes. 'Side tower' has a tangible appearance and is a type of tower that is located on the side of a building.\nThere are no things that are visually similar to 'side tower' but are not 'side tower'.\nUseful visual features for distinguishing 'side tower' from other types of towers in a photo are:\tlocated on the side of a building\thas a smaller size than the main tower\thas a similar architectural style as the main building.", 17], "orange table": ["Yes. 'Orange table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'orange table' but are not 'orange table' are:\torange chair\torange rug\torange couch\torange lamp\nThere are several useful visual features to tell there is 'orange table' and not similar things in a photo:\trectangular, square, or round shape\tbright orange color\tflat surface\tfor holding objects or for sitting around", 17], "silver metal dinner fork": ["Yes. 'Silver metal dinner fork' has a tangible appearance and is a specific type of utensil.\nA few things that are visually similar to 'silver metal dinner fork' but are not 'silver metal dinner fork' are:\tcocktail fork\ttuning fork\tpitchfork\tgarden fork\nThere are several useful visual features to tell there is 'silver metal dinner fork' and not similar things in a photo:\tlong handle with curved tine at one end, and usually three or four shorter tines at the other\tend of the handle is flat or has decorative designs\ttines are evenly spaced and symmetrical.", 17], "dinner roll": ["Yes. 'Dinner roll' has a tangible appearance and is a baked good.\nA few things that are visually similar to 'dinner roll' but are not 'dinner roll' are:\tbread loaf\tbagel\tbun\tcookie\nThere are several useful visual features to tell there is 'dinner roll' and not similar things in a photo:\tround or oval shape\tsoft and fluffy texture\tsmooth or lightly browned exterior\tsmall or individual size\tserved as a side dish at dinner", 17], "grassy hills": ["Yes. 'Grassy hills' has a tangible appearance and is a type of landscape.\nA few things that are visually similar to 'grassy hills' but are not 'grassy hills' are:\trocky hills\tmountains\tforests\tplains\nThere are several useful visual features to tell there is 'grassy hills' and not similar things in a photo:\tsloping or rounded shape\ta cover of grass or vegetation\ton a relatively small scale compared to mountains or plateaus\tno visible rocks or trees on the summit.", 17], "popcorn": ["Yes. 'Popcorn' has a tangible appearance and is a type of snack.\nA few things that are visually similar to 'popcorn' but are not 'popcorn' are:\tcereal\tpuffed rice\tpuffed wheat\trice cakes\nThere are several useful visual features to tell there is 'popcorn' and not similar things in a photo:\twhite or yellow color\tbuttery smell\tand slightly greasy to the touch\tround and puffy shape\tcontaining small, hard, and round pieces of popcorn kernels", 17], "swimmers": ["Yes. 'Swimmers' has a tangible appearance and can be recognized by their specific physical activity.\nA few things that are visually similar to 'swimmers' but are not 'swimmers' are:\tpeople playing in the water\tfish\tsurfers\tpeople diving\nThere are several useful visual features to tell there are 'swimmers' and not similar things in a photo:\tsplashing and creating waves with arms and legs\twearing swimwear or swimming goggles\tvisible strokes with arms and legs\tbreaststroke, freestyle, butterfly, or backstroke body position\twithin a swimming pool or open water environment.", 17], "claw foot": ["Yes. 'Claw foot' has a tangible appearance and is a type of furniture leg.\nA few things that are visually similar to 'claw foot' but are not 'claw foot' are:\tanimal paw\thuman hand wooden furniture legs with round feet\nThere are several useful visual features to tell there is 'claw foot' and not similar things in a photo:\tthick and curved talons\tclaw-like shape, with pointed ends and indentations resembling toes\tmade of metal or wood, with ornate details often used on antique furniture.", 17], "yellow banana": ["Yes. 'Yellow banana' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'yellow banana' but are not 'yellow banana' are:\tplantain\tyellow pepper\tgolden delicious apple\tlemon\nThere are several useful visual features to tell there is 'yellow banana' and not similar things in a photo:\tlong and curved fruit\twith a peel that can be removed\tflesh that is soft and sweet\twhen unripe has green color and when overripe has brown spots", 17], "canopy tent": ["Yes. 'Canopy tent' has a tangible appearance and is a type of outdoor shelter.\nA few things that are visually similar to 'canopy tent' but are not 'canopy tent' are:\tumbrella\tbeach parasol\tgazebo\ttipi\nThere are several useful visual features to tell there is 'canopy tent' and not similar things in a photo:\tsquare or rectangular shape\textended fabric roof\tsupport poles or frames\twalls or curtains\ton the ground or raised by legs", 17], "railway tracks": ["Yes. 'Railway tracks' has a tangible appearance and is a physical infrastructure.\nA few things that are visually similar to 'railway tracks' but are not 'railway tracks' are:\tbike path\thighway\tpower lines\t\nThere are several useful visual features to tell there is 'railway tracks' and not similar things in a photo:\ttwo metal rails\twooden or concrete sleepers\tballast or gravel between the tracks\tstraight or curved tracks", 17], "ticket": ["Yes. 'Ticket' has a tangible appearance and is a type of paper document.\nA few things that are visually similar to 'ticket' but are not 'ticket' are:\treceipt\tsticker\tlabel\nThere are several useful visual features to tell there is 'ticket' and not similar things in a photo:\tpaper material\tsize and shape of a typical ticket (rectangular and elongated)\tinformation such as event name, date, time, and seat number\tbarcode or QR code for scanning or verifying\tthe word \"Ticket\" written on it", 17], "towel dispenser": ["Yes. 'Towel dispenser' has a tangible appearance and is a type of device used for dispensing towels or paper towels.\nA few things that are visually similar to 'towel dispenser' but are not 'towel dispenser' are:\tsoap dispenser\ttissue box\ttrash can\nThere are several useful visual features to tell there is 'towel dispenser' and not similar things in a photo:\tvertical shape\tfor towels or paper towels\tdispenser opening or slot on the front or side\tbody made of metal or plastic or both\teasily accessible handles or knobs\ton the wall or counter\toften have a visible towel or paper towel roll inside", 17], "security light": ["Yes. 'Security light' has a tangible appearance and is a type of outdoor light.\nA few things that are visually similar to 'security light' but are not 'security light' are:\tstreet light\tporch light\tgarden light\tdecorative light\nThere are several useful visual features to tell there is 'security light' and not similar things in a photo:\tbright and powerful\tlight directed downward\tmotion sensor or other security features\tmounted high on a building or a post", 17], "iron skillet": ["Yes. 'Iron skillet' has a tangible appearance and is a type of cookware.\nA few things that are visually similar to 'iron skillet' but are not 'iron skillet' are:\tpan\twok\tgriddle\tdutch oven\nThere are several useful visual features to tell there is 'iron skillet' and not similar things in a photo:\tcircular and shallow shape\tcast iron material\thandle on one side\tno lid", 17], "duck swimming": ["Yes, 'duck swimming' has a tangible appearance and is a specific action of a bird in water.\nA few things that are visually similar to 'duck swimming' but are not 'duck swimming' are:\tgoose swimming\tswans swimming\tfish swimming\tduck on the ground\nThere are several useful visual features to tell there is 'duck swimming' and not similar things in a photo:\tbird with webbed feet\tswimming in water\torangish beak \twith a rounded head", 17], "silver speaker": ["Yes. 'Silver speaker' has a tangible appearance and is a type of audio equipment.\nA few things that are visually similar to 'silver speaker' but are not 'silver speaker' are:\tmicrophone\talarm\tclock\ttin can\nThere are several useful visual features to tell there is 'silver speaker' and not similar things in a photo:\tsilver or metallic color\tcylindrical or boxy shape\tsound ports or grilles\tpower cord or battery compartment", 17], "tan sofa": ["Yes. 'Tan sofa' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'tan sofa' but are not 'tan sofa' are:\tchair\tbed\tbench\trecliner\nThere are several useful visual features to tell there is 'tan sofa' and not similar things in a photo:\tlong cushioned seat\tbackrest\tcushions\tarmrests\ttan-colored fabric or leather material", 17], "shirt woman": ["Yes. 'Shirt woman' has a tangible appearance and refers to a type of clothing item.\nA few things that are visually similar to 'shirt woman' but are not 'shirt woman' are:\tblouse\tdress\ttank top\tsweater\nThere are several useful visual features to tell there is 'shirt woman' and not similar things in a photo:\tbuttoned or zipped down\tusually made of cotton or other lightweight fabric\tmay have collars or pockets, but generally not too many distinctive features\tfits loosely on the upper torso and arms on a female body.", 17], "pepsi logo": ["Yes. 'Pepsi logo' has a tangible appearance and is a type of graphic design.\nA few things that are visually similar to 'pepsi logo' but are not 'pepsi logo' are:\tCoca-Cola logo\tDr. Pepper logo\tRed and blue round logos\nThere are several useful visual features to tell there is 'pepsi logo' and not similar things in a photo:\tRed, white, and blue colors\tWavy, circular shape\tThe word \"Pepsi\" written in lowercase letters with a red, white, and blue color gradient.", 17], "slip": ["Yes. 'Slip' has a tangible appearance and is a type of undergarment.\nA few things that are visually similar to 'slip' but are not 'slip' are:\tdress\tskirt\tnightgown\trobe\t\nThere are several useful visual features to tell there is 'slip' and not similar things in a photo:\tthin and lightweight material\tfitted to the body\tcovers the torso and hips, but not the legs\tcan be strapless, have spaghetti straps or wider shoulder straps\tlace or embroidered details can be present.", 17], "touch pad": ["Yes. 'Touch pad' has a tangible appearance and is an input device.\nA few things that are visually similar to 'touch pad' but are not 'touch pad' are: mouse, trackball, joystick, keyboard, calculator.\nThere are several useful visual features to tell there is 'touch pad' and not similar things in a photo:\tflat, rectangular surface\tusually found on a laptop or tablet\tsensitive to finger touch or swipe gestures\tcan perform mouse-like actions, such as clicking, scrolling, and dragging.", 17], "water tap": ["Yes. 'Water tap' has a tangible appearance and is a plumbing fixture.\nA few things that are visually similar to 'water tap' but are not 'water tap' are:\those nozzle\tshowerhead\tsprinkler\tfaucet handle\nThere are several useful visual features to tell there is 'water tap' and not similar things in a photo:\tmetallic or plastic body\twith a knob, handle, or lever to control water flow\tmounted onto a sink or a wall", 17], "purple towel": ["Yes. 'Purple towel' has a tangible appearance and is a type of fabric.\nA few things that are visually similar to 'purple towel' but are not 'purple towel' are:\tpurple cloth\tblanket\tscarf\nThere are several useful visual features to tell there is 'purple towel' and not similar things in a photo:\trectangular or square in shape\tabsorbent fabric\tpurple color\tribbed or textured pattern for added absorbency", 17], "paddle board": ["Yes. 'Paddle board' has a tangible appearance and is a type of water sport equipment.\nA few things that are visually similar to 'paddle board' but are not 'paddle board' are:\tkayak\tsurfboard\tboogie board\toutrigger canoe\nThere are several useful visual features to tell there is 'paddle board' and not similar things in a photo:\telongated board shape\tpaddle\tintended for standing or kneeling while paddling\tno fins on the bottom of the board (unlike a surfboard)", 17], "round slice": ["Yes. 'Round slice' has a tangible appearance and is a type of shape.\nA few things that are visually similar to 'round slice' but are not 'round slice' are:\tpizza slices\tfruit slices\tcake slices\tpie slices\nThere are several useful visual features to tell there is 'round slice' and not similar things in a photo:\tcircular shape\tequal width throughout\tevidence of being part of a larger round object", 17], "hoof zebra": ["No. 'Hoof zebra' is too vague or abstract to be recognized as a tangible appearance.\nThere are no things that are visually similar to 'hoof zebra'.\nN/A", 17], "oxen": ["Yes. 'Oxen' has a tangible appearance and is a type of domesticated animal.\nA few things that are visually similar to 'oxen' but are not 'oxen' are:\tbuffalo\tyak\tbison\tcow\nThere are several useful visual features to tell there is 'oxen' and not similar things in a photo:\ttwo horns on their heads\tlong tail\tcloven hoofs\tlight-colored fur\tmuscular bodies\tbulky and strong appearance", 17], "round wall clock": ["Yes. 'Round wall clock' has a tangible appearance and is a kind of clock.\nA few things that are visually similar to 'round wall clock' but are not 'round wall clock' are:\twristwatch\tcar instrument panel\ttimer\tcompass\nThere are several useful visual features to tell there is 'round wall clock' and not similar things in a photo:\tround or circular face\twith numbers or marks\tdial or hands\tlarge and visible\thanging on a wall or surface", 17], "milk carton": ["Yes. 'Milk carton' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'milk carton' but are not 'milk carton' are:\tjuice boxes\tbeverage cans\tbottle water containers\tsoup boxes\nThere are several useful visual features to tell there is 'milk carton' and not similar things in a photo:\trectangular shape\tpaper or cardboard material\tpouring spout on the top or side\thandles on the top or sides\tcap on the top or side with ridges or grooves\ttoothed opening at the top of the spout or cap.", 17], "gallon": ["No. 'Gallon' is too abstract to have a tangible appearance.\nThere aren't any things that are visually similar to 'gallon' but are not 'gallon'.\nSince gallon is a unit of measurement and does not have a physical appearance, useful visual features are not applicable.", 17], "menus": ["Yes. 'Menus' has a tangible appearance and usually refers to a physical or digital list of food or drink items available at a restaurant or bar.\nA few things that are visually similar to 'menus' but are not 'menus' are:\tflyers\tbrochures\tnewspapers\tmagazines\nThere are several useful visual features to tell there is 'menus' and not similar things in a photo:\tlists of food and drink items\tprices\tfor a restaurant or bar\tuse of food-related graphics and images\tcategorized by course or meal type.", 17], "performer": ["No. 'Performer' is too vague or abstract to be distinguished in a photo.", 17], "bones": ["Yes. 'Bones' has a tangible appearance and is a type of skeletal structure.\nA few things that are visually similar to 'bones' but are not 'bones' are:\tsticks\trocks\tbranches\tpipes\nThere are several useful visual features to tell there is 'bones' and not similar things in a photo:\thollow and porous structure\tjointed segments\tsmooth, curved lines in the shape of an animal's skeleton\tvariety of sizes and shapes (depending on the animal)", 17], "bear paws": ["Yes. 'Bear paws' has a tangible appearance and is a body part of a bear.\nA few things that are visually similar to 'bear paws' but are not 'bear paws' are:\tdog paws\tcat paws\twolf paws\nThere are several useful visual features to tell there is 'bear paws' and not similar things in a photo:\tenormous\tsize\tclaws\tfur\tpad shape and texture\tpaw print size", 17], "poart": ["No. 'Poart' is too vague or abstract to be distinguished in a photo.", 17], "shuttle bus": ["Yes. 'Shuttle bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'shuttle bus' but are not 'shuttle bus' are:\tregular bus\tvan\tvintage car\nThere are several useful visual features to tell there is 'shuttle bus' and not similar things in a photo:\ta medium-sized vehicle\twith a distinctive logo or label for the company or organization it serves\toften painted in bright or attention-grabbing colors\tusually designed for short trips or frequent stops between two points.", 17], "jump suit": ["Yes. 'Jump suit' has a tangible appearance and refers to a type of clothing.\nA few things that are visually similar to 'jump suit' but are not 'jump suit' are:\tcoveralls\trompers\toveralls\tbodysuits\nThere are several useful visual features to tell there is a 'jump suit' and not similar things in a photo:\tone-piece garment\tthat covers both torso and legs\tzips or buttons up at the front or back\tmay have sleeves or be sleeveless\tfabric can vary from casual to formal.", 17], "belt buckle": ["Yes, 'belt buckle' has a tangible appearance and is an accessory for belts.\nA few things that are visually similar to 'belt buckle' but are not 'belt buckle' are:\tlarge buttons\torphaned jewelry items\tbadges\nThere are several useful visual features to tell there is 'belt buckle' and not similar things in a photo:\tmetal or leather material\thardware attachment to a belt\tprong, frame or plate shape with a pin to insert through belt holes\tengravings or embossed designs.", 17], "giraffe heads": ["Yes. 'Giraffe heads' has a tangible appearance and refers to the head part of a giraffe.\nA few things that are visually similar to 'giraffe heads' but are not 'giraffe heads' are:\tantelope heads\tcow heads\thorse heads\tdeer heads\t\nThere are several useful visual features to tell there is 'giraffe heads' and not similar things in a photo:\tlong necks\tpatterned fur\tunique horn-like antlers", 17], "police vehicle": ["Yes. 'Police vehicle' has a tangible appearance and is a type of car used by law enforcement.\nA few things that are visually similar to 'police vehicle' but are not 'police vehicle' are:\tsecurity car\tambulance\tfire truck\ttaxi\nThere are several useful visual features to tell there is 'police vehicle' and not similar things in a photo:\tdistinctive police markings or logos\temergency lights (red and blue)\tsirens or loudspeakers\ton-board computer or communication equipment\tcage for transporting prisoners\tor any other law enforcement equipment like firearms.", 17], "cucumber slices": ["Yes. 'Cucumber slices' has a tangible appearance and refers to a specific shape and form of cucumber.\nA few things that are visually similar to 'cucumber slices' but are not 'cucumber slices' are:\tcircle\tdisk\tcoins\tsaucers\nThere are several useful visual features to distinguish 'cucumber slices' from the listed similar things in a photo:\tgreen color\tpale center\tthin slices\twith edible seeds.", 17], "lots windows": ["No. 'Lots windows' is too vague or abstract to be distinguished in a photo. It is an imprecise description of a building or architecture feature.\n", 17], "metal balcony": ["Yes. 'Metal balcony' has a tangible appearance and is a kind of architectural structure.\nA few things that are visually similar to 'metal balcony' but are not 'metal balcony' are:\tfire escape\tdecorative railings\twrought iron gates\tsteel fence\nThere are several useful visual features to tell there is 'metal balcony' and not similar things in a photo:\tattached to a building\tor building structure \tmade of metal or steel\tprotruding from a building\tusually have railings or fences around them", 17], "blue van": ["Yes. 'Blue van' has a tangible appearance and is a specific type of vehicle.\nA few things that are visually similar to 'blue van' but are not 'blue van' are:\tcar\ttruck\tjeep\tbus\nThere are several useful visual features to tell there is 'blue van' and not similar things in a photo:\t4-wheeled vehicle with a rectangular boxy shape, a flat roof, and a sliding door or double doors\tat least 15 feet long\tpainted blue or mostly blue\twith one or more seats in the back for passengers or cargo", 17], "grey baby elephant": ["Yes. 'Grey baby elephant' has a tangible appearance.\nA few things that are visually similar to 'grey baby elephant' but are not 'grey baby elephant' are: pig, hippo, rhino, elephant toy\nThere are several useful visual features to tell there is 'grey baby elephant' and not similar things in a photo: \tgray skin, floppy ears, long trunk, wrinkly skin, tusks, small size compared to adult elephants.", 17], "phone case": ["Yes. 'Phone case' has a tangible appearance and is a type of cover for a phone.\nA few things that are visually similar to 'phone case' but are not 'phone case' are:\twallet\tcase for glasses or sunglasses\tpencil case\nThere are several useful visual features to tell there is 'phone case' and not similar things in a photo:\tfitting snugly around a phone's shape\thaving holes or slots for camera lens, charging ports, and buttons\tmade of various materials such as plastic, leather, or silicone.", 17], "pigs": ["Yes. 'Pigs' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'pigs' but are not 'pigs' are:\tboars\twarthogs\thippopotamus\ttapirs\nThere are several useful visual features to tell there is 'pigs' and not similar things in a photo:\tpinkish skin\tcolor patterns that may be black, white, or brown\tcurved tails\tsnouts or noses that end with a disc-shaped tip\tshort legs compared to body length", 17], "bedroom door": ["Yes. 'Bedroom door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'bedroom door' but are not 'bedroom door' are:\tfront door\tbathroom door\tcabinet door\tshower curtain\nThere are several useful visual features to tell there is 'bedroom door' and not similar things in a photo:\tlocated inside a bedroom\tframe surrounding the door\tknob or handle for opening and closing\tthe door itself may have unique features like color, material or design.", 17], "autumn": ["Yes. 'Autumn' has a tangible appearance and is a season of the year.\nA few things that are visually similar to 'autumn' but are not 'autumn' are:\tspring\tsunset\tfire\torange clothing\nThere are several useful visual features to tell there is 'autumn' and not similar things in a photo:\torange, yellow, and red leaves\tcold and crisp weather\tbare branches of trees\tpumpkins or hay bales", 17], "blue mailbox": ["Yes. 'Blue mailbox' has a tangible appearance and is a type of mailbox.\nA few things that are visually similar to 'blue mailbox' but are not 'blue mailbox' are:\tred mailbox\tgreen mailbox\tyellow mailbox\ttrash bin\nThere are several useful visual features to tell there is 'blue mailbox' and not similar things in a photo:\tblue\tcolorful\tvertical slot for letters and small packages\tUnited States Postal Service logo attached to the side through an emblem or sticker", 17], "pink sweater": ["Yes. 'Pink sweater' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pink sweater' but are not 'pink sweater' are:\tshirt\tjacket\tblouse\thoodie\nThere are several useful visual features to tell there is 'pink sweater' and not similar things in a photo:\twoolly or knitted texture\tsolid or patterned color\tround or v-neck collar\thas sleeves and fits tightly to the body", 17], "trashcans": ["Yes. 'Trashcans' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'trashcans' but are not 'trashcans' are:\tlaundry baskets\tplastic storage bins\tcooler boxes\nThere are several useful visual features to tell there is 'trashcans' and not similar things in a photo:\tcylindrical or rectangular shape\tmetal or plastic material\tfoot pedal for opening the lid\tdifferent compartments for separating recyclables and trash", 17], "toilet cleaner": ["Yes. 'Toilet cleaner' has a tangible appearance and is a type of cleaning product.\nA few things that are visually similar to 'toilet cleaner' but are not 'toilet cleaner' are:\tbathroom disinfectant\tall-purpose cleaner\tglass cleaner\tbleach\nThere are several useful visual features to tell there is 'toilet cleaner' and not similar things in a photo:\tspecific labeling for 'toilet' cleaning\tuse of words related to 'toilet', 'bathroom', or 'sanitation'\ton the toilet or in the toilet brush\tblue, green or white color", 17], "date stamp": ["Yes. 'Date stamp' has a tangible appearance and is a kind of stamp used to mark the date on documents.\nA few things that are visually similar to 'date stamp' but are not 'date stamp' are:\taddress stamp\tsignature stamp\tnotary stamp\tink pad\trubber stamps\nThere are several useful visual features to tell there is 'date stamp' and not similar things in a photo:\tcontains the word \"date\" or an abbreviation of a month and year\tstamps the date on documents\thas a rotating mechanism to change the date\tin either black or red ink\teasily changeable or refillable ink pad.", 17], "guitars": ["Yes. 'Guitars' has a tangible appearance and is a stringed musical instrument.\nA few things that are visually similar to 'guitars' but are not 'guitars' are:\tukes\tbanjos\tviolins\nThere are several useful visual features to tell there is 'guitars' and not similar things in a photo:\tlong neck\twith frets\tfor strings\tcurved body\tsound hole\tstrumming or plucking the strings", 17], "silver poles": ["Yes. 'Silver poles' has a tangible appearance and can refer to metallic poles that are silver in color.\nA few things that are visually similar to 'silver poles' but are not 'silver poles' are:\tsteel beams\tchrome pipes\taluminum rods\tsilver bars\nThere are several useful visual features to tell there are 'silver poles' and not similar things in a photo:\tcylindrical shape\tlong and slender\tsize of the poles\tmetallic appearance in a silver color.", 17], "toe nails": ["Yes. 'Toe nails' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'toe nails' but are not 'toe nails' are:\tfingernails\tshells\tpebbles\tpaint\nThere are several useful visual features to tell there is 'toe nails' and not similar things in a photo:\tcurved flat plates protruding from the tips of toes\tpinkish color\tsmall size compared to the surrounding foot skin and toes.", 17], "peeler": ["Yes. 'Peeler' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'peeler' but are not 'peeler' are:\tKnife\tSlicer\tScissors\tMandoline\nThere are several useful visual features to tell there is 'peeler' and not similar things in a photo:\tSmall handheld tool\tBlade at the end\tPointed tip\tfor peeling fruits and vegetables.", 17], "cobblestones": ["Yes. 'Cobblestones' has a tangible appearance and is a kind of pavement.\nA few things that are visually similar to 'cobblestones' but are not 'cobblestones' are:\tbricks\tpavers\tpebbles\nThere are several useful visual features to tell there is 'cobblestones' and not similar things in a photo:\tlarge, rounded stones\tuneven surface, with gaps between stones\tgray or brown color\tused for streets or sidewalks in historic areas", 17], "silver sink drain": ["Yes. 'Silver sink drain' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'silver sink drain' but are not 'silver sink drain' are:\tround silver objects\tjewelry components\tbottle caps\tbathroom hardware parts\nThere are several useful visual features to tell there is 'silver sink drain' and not similar things in a photo:\tround with perforations or holes at the center\tinstalled at the bottom of a sink or bathtub\tsilver or metallic finish.", 17], "grey dog": ["Yes. 'Grey dog' has a tangible appearance and is a specific type of dog.\nA few things that are visually similar to 'grey dog' but are not 'grey dog' are:\tgray wolf\thusky\tgray coyote\nThere are several useful visual features to tell there is 'grey dog' and not similar things in a photo:\tmedium to large-sized breed\tgray coat color with shades from light silver to dark charcoal\tblack nose and brown eyes\tmuscular build and pointed ears\tshort-haired or wire-haired coat texture", 17], "croutons": ["Yes. 'Croutons' have a tangible appearance and are a type of food item.\nA few things that are visually similar to 'croutons' but are not 'croutons' are:\tbread\tcrackers\tchips\nThere are several useful visual features to tell there is 'croutons' and not similar things in a photo:\tsmall size\tcubical or rectangular shape\thard and crunchy texture\tbrown or golden color\tpaired with a salad or soup", 17], "post-it": ["Yes. 'Post-it' has a tangible appearance and is a type of adhesive memo pad.\nA few things that are visually similar to 'post-it' but are not 'post-it' are:\tsticky notes\tindex cards\tpaper notes\twashi tape\nThere are several useful visual features to tell there is 'post-it' and not similar things in a photo:\trectangular shape\tneon colors\tsticky adhesive strip at the top of the note\tpaper thickness and texture.", 17], "silver hinge": ["Yes. 'Silver hinge' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'silver hinge' but are not 'silver hinge' are:\tlocks\thandles\tbolts\tlatches\nThere are several useful visual features to tell there is 'silver hinge' and not similar things in a photo:\tmade of silver or silver-colored metal\trectangular or cylindrical shape\thave holes or screws for attaching to a surface", 17], "copper": ["Yes. 'Copper' has a tangible appearance and is a type of metal.\nA few things that are visually similar to 'copper' but are not 'copper' are:\tbrass\tbronze\tgold\trose gold\nThere are several useful visual features to tell there is 'copper' and not similar things in a photo:\ttinged reddish-brown color\tbright and shiny surface\tductile and malleable\ttexture that looks like intertwined ropes", 17], "computer cord": ["Yes. 'Computer cord' has a tangible appearance and is a type of cable.\nA few things that are visually similar to 'computer cord' but are not 'computer cord' are:\tphone charger cable\tpower cord\tethernet cable\theadphone cable\nThere are several useful visual features to tell there is 'computer cord' and not similar things in a photo:\ttypically black, white or gray\thave USB, HDMI, or VGA connectors\tconnected to a computer, monitor, or other electronic devices", 17], "cockpit window": ["Yes. 'Cockpit window' has a tangible appearance and is a part of an airplane.\nA few things that are visually similar to 'cockpit window' but are not 'cockpit window' are:\tcar window\tbus window\tstorefront window\thouse window\nThere are several useful visual features to tell there is 'cockpit window' and not similar things in a photo:\trectangular or square in shape\tcurved at the edges\tof an airplane\tor aircraft cabin\ttranslucent or tinted with a blue or green hue", 17], "microphone stand": ["Yes. 'Microphone stand' has a tangible appearance and is a piece of equipment used to hold a microphone.\nA few things that are visually similar to 'microphone stand' but are not 'microphone stand' are:\tcamera tripod\tmusic stand\tlight stand\nThere are several useful visual features to tell there is 'microphone stand' and not similar things in a photo:\ttall and slender\tthree legs\tmetal or plastic material\thorizontal arm to hold the microphone\theight-adjustable clamp or holder for microphone", 17], "bush background": ["Yes. 'Bush background' has a tangible appearance and refers to a natural setting with bushes.\nA few things that are visually similar to 'bush background' but are not 'bush background' are:\tforest\tbackground with grasses, but no bushes\tplain wall with bushy painting\nThere are several useful visual features to tell there is 'bush background' and not similar things in a photo:\tbushes\tof different sizes, colors, and shapes\tmultiple bushes that form a background\tnatural setting or scenery\twith no man-made structures or objects visible", 17], "beige house": ["Yes. 'Beige house' has a tangible appearance and is a type of building with a specific color.\nA few things that are visually similar to 'beige house' but are not 'beige house' are:\tother colored houses\tapartment buildings\tcommercial buildings\nThere are several useful visual features to tell there is 'beige house' and not similar things in a photo:\tone-family residential building\tbeige-colored exterior walls\tdoor and windows on the front facade\tshingled or flat roof\twith or without a chimney or porch", 17], "egg roll": ["Yes. 'Egg roll' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'egg roll' but are not 'egg roll' are:\tspring roll\tburrito\twrap\tsushi roll\nThere are several useful visual features to tell there is 'egg roll' and not similar things in a photo:\tcrispy and fried outside wrapper\tcylindrical shape\tfilling of vegetables, meat, and eggs", 17], "materials": ["No. 'Materials' is too vague or abstract to be distinguished in a photo. \n\nIt is a general term that refers to substances or objects that can be used to create or construct something. There are no specific visual features associated with the concept of 'materials', as it encompasses a wide range of substances with vastly different appearances such as wood, metal, fabric, plastic, amongst others.", 17], "glass shinny": ["No. 'Glass shinny' is too vague or abstract to be distinguished in a photo. \n\nInstead, we can use 'shiny glass' as a visually concrete concept. \n\nA few things that are visually similar to 'shiny glass' but are not 'shiny glass' are:\tmetallic surfaces\tWet or glossy surfaces\tPlastic surfaces\n\nThere are several useful visual features to tell there is 'shiny glass' and not similar things in a photo:\tClear, transparent or translucent appearance\tSmooth and reflective surface\tDoes not distort the view through it.", 17], "waitress": ["Yes. 'Waitress' has a tangible appearance and is a person who typically works in a restaurant.\nA few things that are visually similar to 'waitress' but are not 'waitress' are:\thostess\tbarista\tbartender\tcashier\tcustomer\nThere are several useful visual features to tell there is 'waitress' and not similar things in a photo:\twearing a uniform or an apron\tcarrying a tray\twithin a restaurant or a cafe\ttaking orders or serving food or drinks.", 17], "jerseys": ["Yes. 'Jerseys' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'jerseys' but are not 'jerseys' are:\tshirts\tsweaters\tblouses\tt-shirts\nThere are several useful visual features to tell there is 'jerseys' and not similar things in a photo:\t\n- Long-sleeved shirt made of wool or cotton\n- Often have sports-related logos or numbers\n- Have prominent collar, cuffs, and hemline.", 17], "toboggan": ["Yes. 'Toboggan' has a tangible appearance and is a type of sled.\nA few things that are visually similar to 'toboggan' but are not 'toboggan' are:\tsnow sled\tsnowboard\tski\t \nThere are several useful visual features to tell there is 'toboggan' and not similar things in a photo:\tlong and narrow\tslightly curved\tdoes not have bindings or brakes to control speed\tusually made of wood or plastic\thas a rope attached in the front to pull it", 17], "wood doors": ["Yes. 'Wood doors' has a tangible appearance and is a type of physical object.\nA few things that are visually similar to 'wood doors' but are not 'wood doors' are:\tpainted doors\tmetal doors\tglass doors\tcurtains\nThere are several useful visual features to tell there is 'wood doors' and not similar things in a photo:\tmade of wood\thave grains and texture\thave handles and hinges\tusually rectangular-shaped", 17], "iron bars": ["Yes. 'Iron bars' has a tangible appearance and is a kind of metal structure.\nA few things that are visually similar to 'iron bars' but are not 'iron bars' are:\tfences\tgates\tcages\trails\tpipes\nThere are several useful visual features to tell there is 'iron bars' and not similar things in a photo:\tlong, narrow, and straight bars\tmade of iron or steel\tdensely arranged in a grid pattern or in parallel lines\tnot used for supporting a structure or a fence\tmesh size and shape.", 17], "pendulum": ["Yes. 'Pendulum' has a tangible appearance and is a type of weight hanging from a fixed point.\nA few things that are visually similar to 'pendulum' but are not 'pendulum' are:\tchandelier\tlight fixture\tclock\nThere are several useful visual features to tell there is 'pendulum' and not similar things in a photo:\tweight attached to a string or chain\thanging from a fixed point\tswings back and forth or side to side\thas a regular, repetitive motion", 17], "metal manhole cover": ["Yes. 'Metal manhole cover' has a tangible appearance and is a type of urban infrastructure element.\nA few things that are visually similar to 'metal manhole cover' but are not 'metal manhole cover' are:\tmetallic floor tiles\tgrids\tcrates\nThere are several useful visual features to tell there is 'metal manhole cover' and not similar things in a photo:\tround or square shape\twith lettering or designs\ton the street level, usually with the word \"sewer\" or \"drainage\"", 17], "fork napkin": ["Yes. 'Fork napkin' has a tangible appearance and is a common table setting.\nThere are no things that are visually similar to 'fork napkin' but are not 'fork napkin'.\nThe useful visual features to distinguish 'fork napkin' are:\t\n- A folded or rolled piece of cloth, paper, or tissue placed next to or on a plate as a part of a table setting\n- The presence of utensils like a fork or a knife next to it.", 17], "dreadlocks": ["Yes. 'Dreadlocks' has a tangible appearance and is a hairstyle.\nA few things that are visually similar to 'dreadlocks' but are not 'dreadlocks' are:\tbraids\tcornrows\tfrench twists\thair extensions\nThere are several useful visual features to tell there are 'dreadlocks' and not similar things in a photo:\tropelike strands of hair\tmatted or knotted texture\tformed by letting hair grow naturally without combing or brushing.", 17], "ladybug": ["Yes. 'Ladybug' has a tangible appearance and is a kind of small beetle.\nA few things that are visually similar to 'ladybug' but are not 'ladybug' are:\tassassin bug\tbumblebee\tbeetle\tspider\nThere are several useful visual features to tell there is 'ladybug' and not similar things in a photo:\tround and small body\tbright red or orange color with black spots\tblack legs\thead with two antennae\twings folded on its back", 17], "dill pickle": ["Yes. 'Dill pickle' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'dill pickle' but are not 'dill pickle' are:\tgherkin\tcucumber\tpickled red onion\tpickled carrots\nThere are several useful visual features to tell there is 'dill pickle' and not similar things in a photo:\tgreen color\tridged sides\tdill weed visible in the brine\twedged shape", 17], "metal knob": ["Yes. 'Metal knob' has a tangible appearance and is a kind of object.\nA few things that are visually similar to 'metal knob' but are not 'metal knob' are:\tmetal button\tscrew\thead of a nail\nThere are several useful visual features to tell there is 'metal knob' and not similar things in a photo:\tbulging\tround\thaving small ridges or patterns\televated enough to be easily gripped with fingers\thaving a screw in the center (if it is a doorknob)", 17], "vintage car": ["Yes. 'Vintage car' has a tangible appearance and refers to a type of classic automobile.\nA few things that are visually similar to 'vintage car' but are not 'vintage car' are:\tmodern cars\ttrucks\tmotorcycles\tbicycles\nThere are several useful visual features to tell there is 'vintage car' and not similar things in a photo:\told-fashioned design or style\tunique and ornate details like chrome, hood ornaments, or emblems.Classic colors like black, white, red, or yellow, with occasional two-tone or metallic finishes. Round or curved shapes and lines, as opposed to more angular or streamlined modern designs.", 17], "medium section": ["No. 'Medium section' is too vague or abstract to be distinguished in a photo.", 17], "blurry picture": ["Yes. 'Blurry picture' has a tangible appearance and refers to a photo that is out of focus or unclear.\nA few things that are visually similar to 'blurry picture' but are not 'blurry picture' are:\twatercolor painting\tabstract art\tpixelated image\nThere are several useful visual features to tell there is a 'blurry picture' and not similar things in a photo:\tout of focus\tblurry\thazy\tlacks sharpness\tor clarity", 17], "orange section": ["Yes. 'Orange section' has a tangible appearance and is a part of a fruit.\nA few things that are visually similar to 'orange section' but are not 'orange section' are:\tpeach slices\tmango pieces\tlemon wedges\tgrapefruit sections\nThere are several useful visual features to tell there is 'orange section' and not similar things in a photo:\tcrescent-shaped\tjuicy\ttexture and color of orange flesh\trind or peel on the outside", 17], "elephant ear": ["Yes. 'Elephant ear' has a tangible appearance and is a large leafy plant.\nA few things that are visually similar to 'elephant ear' but are not 'elephant ear' are:\tbanana leaves\tphilodendrons\thostas\t\nThere are several useful visual features to tell there is 'elephant ear' and not similar things in a photo:\tvery large leaves\twith a distinct heart-like shape\tthick and fleshy stalks\tlarge parallel veins", 17], "soup spoon": ["Yes. 'Soup spoon' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'soup spoon' but are not 'soup spoon' are:\ttablespoon\tteaspoon\tdessert spoon\nThere are several useful visual features to tell there is 'soup spoon' and not similar things in a photo:\tlarger than a teaspoon\tbowl-shaped with slightly curved edges\thas a longer handle than a dessert spoon.", 17], "ports": ["Yes. 'Ports' has a tangible appearance and refers to a location on a coast where ships can load and unload.\nA few things that are visually similar to 'ports' but are not 'ports' are:\tharbor\tmarina\tbeach\tresort\nThere are several useful visual features to tell there is 'ports' and not similar things in a photo:\tcranes and containers\tships and boats\tpier and docks\tbuildings for storing cargo and goods", 17], "gold band": ["Yes. 'Gold band' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'gold band' but are not 'gold band' are:\twedding ring\twristwatch\tbangle\tbracelet\tbelt\nThere are several useful visual features to tell there is 'gold band' and not similar things in a photo:\tmade of gold or other metal\twithout any decorative stones\tor any other embellishments\teven band width\tcircular shape worn on the hand, wrist, or finger.", 17], "freight cars": ["Yes. 'Freight cars' has a tangible appearance and is a type of train car.\nA few things that are visually similar to 'freight cars' but are not 'freight cars' are:\tpassenger cars\tsubway cars\ttrams\tcable cars\nThere are several useful visual features to tell there is 'freight cars' and not similar things in a photo:\tlong and rectangular shape\tlarge and sturdy\tbuild for carrying goods or freight\tno seats for passengers\topen or covered top and sides on some", 17], "sand dune": ["Yes. 'Sand dune' has a tangible appearance and is a natural landform.\nA few things that are visually similar to 'sand dune' but are not 'sand dune' are:\tmountain\tcanyon\thill\trock formation\nThere are several useful visual features to tell there is 'sand dune' and not similar things in a photo:\tcreated by wind or water\tlight-colored, fine-grained sediment or sand\tsloped on one side with a gradual incline, steep on the other side\tsymmetrical shape, usually crescent-shaped or linear", 17], "couple people": ["Yes. 'Couple people' has a tangible appearance and refers to two people who are romantically involved.\nA few things that are visually similar to 'couple people' but are not 'couple people' are:\tfriends\tsiblings\tparent and child\tcolleagues\nThere are several useful visual features to tell there is 'couple people' and not similar things in a photo:\tholding hands\tor with arms around each other\tfocused on each other's presence in the photo", 17], "dresser drawer": ["Yes. 'Dresser drawer' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'dresser drawer' but are not 'dresser drawer' are:\tkitchen cabinets\tfile cabinets\tdesk drawers\ttoolboxes\nThere are several useful visual features to tell there is 'dresser drawer' and not similar things in a photo:\tlocated in a piece of furniture that also has a flat surface on top\tfor storing clothes\tor other personal items\thas a knob, handle, or pull for opening and closing\tthe front of the drawer is usually rectangular or square\tshallow in depth and wide in width", 17], "silver color": ["Yes. 'Silver color' has a tangible appearance and is a specific color.\nA few things that are visually similar to 'silver color' but are not 'silver color' are:\twhite color\tgrey color\tlight blue color\tchrome material\nThere are several useful visual features to tell there is 'silver color' and not similar things in a photo:\tmetallic appearance\tbright and reflective appearance, but not as bright as chrome\tcolor that resembles the color of silver as seen in jewelry or silverware", 17], "car wheel": ["Yes. 'Car wheel' has a tangible appearance and is a part of a car.\nA few things that are visually similar to 'car wheel' but are not 'car wheel' are:\tcircular saw blades\tbicycle wheel\tferris wheel\tgear\nThere are several useful visual features to tell there is 'car wheel' and not similar things in a photo:\tcircular shape\trubber tire\tmetal rim with spokes or holes\tsize (usually between 14-20 inches)\tdesigned to be mounted on a car axle.", 17], "crown molding": ["Yes. 'Crown molding' has a tangible appearance and is a kind of decorative trim used in interior design.\nA few things that are visually similar to 'crown molding' but are not 'crown molding' are:\tbaseboards\twooden beams\torante metalwork\nThere are several useful visual features to tell there is 'crown moldings' and not similar things in a photo:\t\ninstalled near the ceiling\t\nhas an ornate shape or design\t\nusually painted a different color than the wall or ceiling it is attached to.", 17], "sideline": ["Yes. 'Sideline' has a tangible appearance and refers to the boundary line of a sports field.\nA few things that are visually similar to 'sideline' but are not 'sideline' are:\tcurbs\trope fences\tpaint lines\ton-field advertisements\nThere are several useful visual features to tell there is 'sideline' and not similar things in a photo:\tlocated at the edge of a sports field\tstraight and continuous line\tdifferent color from the field (usually white)\tmarked with numbers for every 10 yards (in football)", 17], "chocolate icing": ["Yes. 'Chocolate icing' has a tangible appearance and is a type of frosting.\nA few things that are visually similar to 'chocolate icing' but are not 'chocolate icing' are:\tvanilla icing\tcaramel sauce\tchocolate syrup\tfudge\nThere are several useful visual features to tell there is 'chocolate icing' and not similar things in a photo:\tchocolate-color\tsmooth and shiny texture\tthick enough to create a layer on the surface of a dessert such as a cake\tor a cookie.", 17], "bright window": ["Yes. 'Bright window' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'bright window' but are not 'bright window' are:\tglow-in-the-dark painting\tlight box\nThere are several useful visual features to tell there is 'bright window' and not similar things in a photo:\trectangular or square shape\ttransmitted light\ttransparency or translucency\tshining or brilliance\tdifferentiated from artificial light sources", 17], "roman numerials": ["Yes. 'Roman numerals' has a tangible appearance and is a way of representing numbers.\nA few things that are visually similar to 'Roman numerals' but are not 'Roman numerals' are:\tletters\tsymbols\tmathematical equations\ttallies\nThere are several useful visual features to tell there is 'Roman numerals' and not similar things in a photo:\tnumbers represented by combinations of I, V, X, L, C, D, and M\tletters arranged in specific orders to represent values\toften used on clock faces or in historical or formal contexts.", 17], "tall plants": ["Yes. 'Tall plants' has a tangible appearance and can refer to any plant that grows to a significant height.\nA few things that are visually similar to 'tall plants' but are not 'tall plants' are:\ttrees\ttall buildings/houses\ttelephone poles\nThere are several useful visual features to tell there are 'tall plants' and not similar things in a photo:\tgreen stems and leaves\tmultiple branching points\tnon-woody\tdifferent varieties or species.", 17], "sweet": ["No. 'Sweet' is too vague or abstract to be distinguished in a photo.", 17], "steel refrigerator": ["Yes. 'Steel refrigerator' has a tangible appearance and is a type of household appliance.\nA few things that are visually similar to 'steel refrigerator' but are not 'steel refrigerator' are:\tsteel oven\tsteel dishwasher\tsteel microwave\tsteel toaster\nThere are several useful visual features to tell there is 'steel refrigerator' and not similar things in a photo:\tupright rectangular shape\tmetallic appearance\twith a handle or latch for opening\thaving shelves or drawers inside\tdoor-mounted shelves or compartments\tfor storing food and drinks at low temperatures", 17], "taps": ["Yes. 'Taps' has a visually concrete appearance and refers to a type of plumbing fixture.\nA few things that are visually similar to 'taps' but are not 'taps' are:\tknobs\tbuttons\tswitches\nThere are several useful visual features to tell there is 'taps' and not similar things in a photo:\tmetallic or plastic handles\tknob-shaped\thave a spout for water\thave separate hot and cold handles or one handle for both hot and cold water\thave a visible pipe or valve coming out the back or bottom.", 17], "paperwork": ["No. 'Paperwork' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider paperwork as physical documents, a few things that are visually similar to 'paperwork' but are not 'paperwork' are: books, notebooks, magazines, newspapers, flyers.\n\nUseful visual features for distinguishing 'paperwork' from the listed similar things in a photo can be:\n- rectangular shape,\n- white or slightly off-white color,\n- thin and flexible texture,\n- printed or handwritten text on the surface.", 17], "hen": ["Yes. 'Hen' has a tangible appearance and is a type of domesticated bird.\nA few things that are visually similar to 'hen' but are not 'hen' are:\trooster\tturkey\tduck\tgoose\nThere are several useful visual features to tell there is 'hen' and not similar things in a photo:\tsmaller than a rooster\tfeathers in shades of brown or white\tyellow beak and legs\twebbed feet\tforaging on the ground\tclucking sound", 17], "power wires": ["Yes. 'Power wires' has a tangible appearance.\nA few things that are visually similar to 'power wires' but are not 'power wires' are:\ttree branches\ttelephone wires\tsuspension bridge cables\nThere are several useful visual features to tell there is 'power wires' and not similar things in a photo:\tif attached to poles, usually located along roads\tmultiple metal lines\tstrung above the ground or diagonally across poles\tmay have insulators to denote voltage\tcarry the 'high voltage' warning symbol", 17], "silver toilet": ["Yes. 'Silver toilet' has a tangible appearance and is a type of bathroom equipment.\nA few things that are visually similar to 'silver toilet' but are not 'silver toilet' are:\tsilver chair\tsilver sink\tsilver bathtub\tsilver trash can\nThere are several useful visual features to tell there is 'silver toilet' and not similar things in a photo:\toval or round bowl shape\tsilver color\tmetal construction\ttank and flushing mechanism at the back of the bowl", 17], "sidewalk curb": ["Yes. 'Sidewalk curb' has a tangible appearance and is a part of street infrastructure.\nA few things that are visually similar to 'sidewalk curb' but are not 'sidewalk curb' are:\tstairs\trocks\traised garden beds\t\nThere are several useful visual features to tell there is 'sidewalk curb' and not similar things in a photo:\tstraight and linear\tconcrete or stone material\ta change in height from the sidewalk surface\ta sloped surface to facilitate drainage", 17], "ravioli": ["Yes. 'Ravioli' has a tangible appearance and is a kind of pasta.\nA few things that are visually similar to 'ravioli' but are not 'ravioli' are:\tpierogi\tdumplings\twontons\tmaultaschen\nThere are several useful visual features to tell there is 'ravioli' and not similar things in a photo:\tsquare or circular shape\tthick dough layer\tstuffed filling (often cheese or meat)", 17], "grassy hillside": ["Yes. 'Grassy hillside' has a tangible appearance and is a kind of landscape or scenery.\nA few things that are visually similar to 'grassy hillside' but are not 'grassy hillside' are:\tforests\tmountains\tdeserts\t\nThere are several useful visual features to tell there is 'grassy hillside' and not similar things in a photo:\tcovered with green grass and plants\tsloping surface or inclined terrain\tmay have trees or rocks scattered throughout\tthe sky in the background visible", 17], "knit": ["Yes. 'Knit' has a tangible appearance and refers to a textile made by interlocking yarns.\nA few things that are visually similar to 'knit' but are not 'knit' are:\tcrochet\tlace\tmesh\tweave\nThere are several useful visual features to tell there is 'knit' and not similar things in a photo:\tinterlocking yarns\tlooped or braided textures\tfrilly or stretchy appearance\ttypically used for sweaters, scarves, or blankets", 17], "front building": ["No. 'Front building' is too vague or abstract to be distinguished in a photo.", 17], "door lock": ["Yes. 'Door lock' has a tangible appearance and is a mechanical device used to secure a door.\nA few things that are visually similar to 'door lock' but are not 'door lock' are:\tdoor handle\tkeyhole\thinge\tbarrel bolt\nThere are several useful visual features to tell there is 'door lock' and not similar things in a photo:\tcombination of buttons, keys, dials or levers\tdeadbolt or latch mechanism\tmetallic or plastic components attached to a door\tor a door frame\twith or without a keyhole", 17], "orange balloon": ["Yes. 'Orange balloon' has a tangible appearance and is a colored round object.\nA few things that are visually similar to 'orange balloon' but are not 'orange balloon' are:\tcitrus fruit\tbasketball\tbeach ball\ttraffic cone\nThere are several useful visual features to tell there is 'orange balloon' and not similar things in a photo:\tspherical shape\ttransparent or slightly transparent material\tbright orange color\tglossy or matte surface\ttied with a string or ribbon", 17], "multistory building": ["Yes. 'Multistory building' has a tangible appearance and refers to a building with multiple floors.\nA few things that are visually similar to 'multistory building' but are not 'multistory building' are:\thouse with an attic\ttower with multiple levels\t\nThere are several useful visual features to tell there is 'multistory building' and not similar things in a photo:\tmultiple floors\twith or without windows\ta recognizable entrance\tor exit\tparking facilities\tnext to other buildings", 17], "sparse grass": ["Yes. 'Sparse grass' has a tangible appearance and refers to a particular type of plant.\nA few things that are visually similar to 'sparse grass' but are not 'sparse grass' are:\tweeds\tbushes\tcrops\tdirt patches\nThere are several useful visual features to tell there is 'sparse grass' and not similar things in a photo:\tthinning or low density of blades\tof green or brown color\tprotected by soil or sand\tusually seen in dry, arid areas.", 17], "smoke air": ["No. 'Smoke air' is too vague or abstract to be distinguished in a photo. Smoke and air are two separate concepts that cannot be considered as a single visually concrete concept.", 17], "apple macbook": ["Yes. 'Apple MacBook' has a tangible appearance and is a type of laptop computer.\nA few things that are visually similar to 'Apple MacBook' but are not 'Apple MacBook' are:\tDell laptop\tHP laptop\tMicrosoft Surface\tAsus laptop\nThere are several useful visual features to tell there is 'Apple MacBook' and not similar things in a photo:\tApple logo on the back silver or space grey color\tchiclet keyboard with backlit keys\tthin body with a tapered design\t13-inch or 15-inch display with high resolution\tTouch Bar feature above the keyboard.", 17], "tugboat": ["Yes. 'Tugboat' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'tugboat' but are not 'tugboat' are:\tcargo ship\tfishing boat\tspeedboat\tkayak\nThere are several useful visual features to tell there is 'tugboat' and not similar things in a photo:\tsmall compared to other boats\trectangular shaped\thorns or sirens on top of the boat\tpowerful engines\tand is designed to tow or push other boats", 17], "orange construction sign": ["Yes. 'Orange construction sign' has a tangible appearance and is a traffic sign used on roads during construction.\nA few things that are visually similar to 'orange construction sign' but are not 'orange construction sign' are:\ttraffic cones\tflashing lights\nThere are several useful visual features to tell there is 'orange construction sign' and not similar things in a photo:\torange color with black writing\tspecific shapes and symbols, such as caution or detour marks\tstanding upright on a pole or a tripod in a construction site.", 17], "wilson": ["Yes. 'Wilson' has a tangible appearance and is a brand of sporting goods.\nA few things that are visually similar to 'wilson' but are not 'wilson' are:\tother brands of sports equipment\tballs of different types and sizes\nThere are several useful visual features to tell there is 'wilson' and not similar things in a photo:\tWilson logo on the equipment\tColor scheme specific to the Wilson brand.\tuv-resistant technology or other marking specific to the Wilson brand.", 17], "bus headlights": ["Yes. 'Bus headlights' has a tangible appearance and is a specific type of vehicle light.\nA few things that are visually similar to 'bus headlights' but are not 'bus headlights' are:\tcar headlights\ttruck headlights\tmotorcycle headlights\tbicycle lights\nThere are several useful visual features to tell there is 'bus headlights' and not similar things in a photo:\tlarge and round\tlocated on the front of a bus\tmay have a yellow or amber tint to them", 17], "sewer": ["Yes. 'Sewer' has a tangible appearance and is a system of pipes used for carrying sewage through underground tunnels.\nA few things that are visually similar to 'sewer' but are not 'sewer' are:\tdrain\tpipeline\tchannel\tgutter\nThere are several useful visual features to tell there is 'sewer' and not similar things in a photo:\tcircular concrete pipe\tmanholes\tunderground location\twaste or sewage flowing through it\tunpleasant smell\torange-red rusted metal plates covering manholes.", 17], "square building": ["Yes. 'Square building' has a tangible appearance and refers to a building with a primarily square shape.\nA few things that are visually similar to 'square building' but are not 'square building' are:\trectangle-shaped buildings, such as office buildings and skyscrapers\tfactory buildings that have a boxy shape\nThere are several useful visual features to tell there is 'square building' and not similar things in a photo: primarily square shape or structure\tvisible walls and roof\tclearly defined edges or angles\trectangular windows or doors", 17], "runways": ["Yes. 'Runways' has a tangible appearance and refers to a specific area used for the landing and takeoff of aircraft.\nA few things that are visually similar to 'runways' but are not 'runways' are:\tparking lot\troad\tsidewalk\nThere are several useful visual features to tell there is 'runways' and not similar things in a photo:\tlong and straight\twith markings along the length\twhere planes are taxiing, taking off, or landing\tcan have lights for illumination at night or in low visibility conditions", 17], "coke bottle": ["Yes. 'Coke bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'coke bottle' but are not 'coke bottle' are:\twater bottle\tbeer bottle\twine bottle\tolive oil bottle\nThere are several useful visual features to tell there is 'coke bottle' and not similar things in a photo:\tcylinder-shaped\tcontainer made of glass, plastic or metal\tred label with the 'Coca-Cola' name and logo\tcurved shape at the bottom of the bottle, known as the 'contour grip'", 17], "slots": ["Yes. 'Slots' has a tangible appearance and refers to a kind of gambling machine.\nA few things that are visually similar to 'slots' but are not 'slots' are:\tvending machines\tATMs\tpinball machines\tjukeboxes\nThere are several useful visual features to tell there is 'slots' and not similar things in a photo:\ta screen or cylinder with multiple symbols\ta lever or button to spin the symbols\tmoney or coins inserted for play\tlights and sounds to indicate winning combinations", 17], "highlighter": ["Yes. 'Highlighter' has a tangible appearance and is a type of pen used to highlight text.\nA few things that are visually similar to 'highlighter' but are not 'highlighter' are:\tmarker\tpaintbrush\tcrayon\tpen\nThere are several useful visual features to tell there is 'highlighter' and not similar things in a photo:\tbright neon colors\tsharp and chiseled tip\tflat, rectangular body\ttransparent or semi-transparent ink holder", 17], "sea weed": ["Yes. 'Sea weed' has a tangible appearance and is a type of marine plant.\nA few things that are visually similar to 'sea weed' but are not 'sea weed' are:\tcoral\tseagrass\taquatic moss\nThere are several useful visual features to tell there is 'sea weed' and not similar things in a photo:\tthin and long plants\twithout roots, stems or leaves\toften brown, green or red\ttangled appearance as if floating in water", 17], "top hill": ["Yes. 'Top hill' has a tangible appearance and is a part of a landscape.\nA few things that are visually similar to 'top hill' but are not 'top hill' are:\tridge\tmountain\tslope\tcliff\nThere are several useful visual features to tell there is 'top hill' and not similar things in a photo:\thill-shaped, typically with a rounded top and sloping sides\tmay have vegetation or trees on it, depending on the region it is located in\tmay have paths, roads or other signs of human activity leading up to or around the hill", 17], "wall color": ["Yes. 'Wall color' has a tangible appearance and refers to the paint or coloring on a wall's surface.\nA few things that are visually similar to 'wall color' but are not 'wall color' are:\ttexture of a wall\tdecorative elements such as wallpaper\tor wall decorations\nThere are several useful visual features to tell there is 'wall color' and not similar things in a photo:\tthe color of a wall is consistent on its entire surface\tdiffers from the color of the objects in the foreground or background of the image\tthe wall appears flat without three-dimensional texture", 17], "screen door": ["Yes. 'Screen door' has a tangible appearance.\nA few things that are visually similar to 'screen door' but are not 'screen door' are:\tmetal door\tgate\twindow with a mesh screen\nThere are several useful visual features to tell there is 'screen door' and not similar things in a photo:\tmetal or wooden door frame\tmesh or netting screen\thinged or sliding operation\tdoorknob or handle\ton the exterior of a building or room", 17], "tint": ["Yes. 'Tint' has a tangible appearance and is a type coloration.\nA few things that are visually similar to 'tint' but are not 'tint' are:\tshades\thues\ttones\t\nThere are several useful visual features to tell there is 'tint' and not similar things in a photo:\tadding white to a color\tused to lighten a color\tincreases the amount of light that passes through a color", 17], "collar shirt": ["Yes. 'Collar shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'collar shirt' but are not 'collar shirt' are:\tT-shirt\tpolo shirt\tsweater\tjacket\nThere are several useful visual features to tell there is 'collar shirt' and not similar things in a photo:\tcollar around the neck\tbuttons down the front with a front placket\tsleeves that reach the wrists\tformal or semi-formal attire", 17], "movie poster": ["Yes. 'Movie poster' has a tangible appearance and is a type of artwork.\nA few things that are visually similar to 'movie poster' but are not 'movie poster' are:\tbillboard\tadvertising\tsign\nThere are several useful visual features to tell there is 'movie poster' and not similar things in a photo:\tcontains images of actors or scenes from the movie\thas the title of the movie prominently displayed\thas credits for the director, writer, and actors\tis designed to promote the movie and entice people to see it", 17], "wood bat": ["Yes. 'Wood bat' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'wood bat' but are not 'wood bat' are:\tmetal bat\tstick\tlog\nThere are several useful visual features to tell there is 'wood bat' and not similar things in a photo:\tslim, elongated shape\thard, solid texture\tsmooth surface\tnarrow handle and wider barrel at the end\tno visible bends or curves.", 17], "door mat": ["Yes. 'Door mat' has a tangible appearance and is a type of floor covering.\nA few things that are visually similar to 'door mat' but are not 'door mat' are:\tcarpets\trugs\tbath mats\nThere are several useful visual features to tell there is 'door mat' and not similar things in a photo:\tfirm and flat surface\ttextured or patterned surface\twelcome message or design\tdurable and weather-resistant material\tsmall size compared to other floor coverings", 17], "sea bird": ["Yes. 'Sea bird' has a tangible appearance and is a type of bird that lives near the sea or ocean.\nA few things that are visually similar to 'sea bird' but are not 'sea bird' are:\tduck\tpelican\tswallow\tpenguin\tseagull\theron\t\nThere are several useful visual features to tell there is 'sea bird' and not similar things in a photo:\n- long beak\n- webbed feet\n- streamlined body\n- wings for flying and swimming\n- feathers in shades of white, brown, gray, and black\n- found near the ocean, beach or rocky shorelines", 17], "railway station": ["Yes. 'Railway station' has a tangible appearance and is a type of building or infrastructure for trains.\nA few things that are visually similar to 'railway station' but are not 'railway station' are:\tairport\tbus station\tmetro station\tshopping mall\nThere are several useful visual features to tell there is 'railway station' and not similar things in a photo:\trailway tracks\tor train platform\tsigns indicating train schedules\tor tracks\tnumerous people carrying luggage\tor waiting for trains.", 17], "buckles": ["Yes. 'Buckles' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'buckles' but are not 'buckles' are:\tzippers\tsnaps\tbuttons\thooks\tvelcro\nThere are several useful visual features to tell there is 'buckles' and not similar things in a photo:\tmetallic or plastic hardware\ttwo parts that interlock or snap together\thave a tongue or prong for insertion into a strap or frame\tvarious shapes and sizes, including rectangular, circular, and triangular\tpair with belts, shoes, bags or clothes.", 17], "vest man": ["No. 'Vest man' is too vague or abstract to be distinguished in a photo.", 17], "monster": ["No. 'Monster' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are sometimes depicted as monsters that are visually similar but not considered actual monsters are:\tlarge wild animals (such as bears, lions, or alligators)\thumans with disfigured or abnormal features\trobots or machines designed to look menacing. \n\nUseful visual features for distinguishing 'monster' from the listed similar things in a photo will depend on the specific context and design of the monster, but some common characteristics might include:\t\n- exaggerated or unnatural physical features, such as multiple heads, extra limbs, or massive size \n- sharp teeth, claws, or other predatory traits \n- skin or body texture that is scaly, slimy, or otherwise abnormal \n- glowing eyes or other glowing features \n- horns, spikes, or other defensive or intimidating features.", 17], "plastic top": ["Yes. 'Plastic top' has a tangible appearance and refers to a spinning toy.\nA few things that are visually similar to 'plastic top' but are not 'plastic top' are:\tbottle cap\tcoin\tfriction car\twheel\nThere are several useful visual features to tell there is 'plastic top' and not similar things in a photo:\tcone or cylindrical shape\tflattened bottom (with a pointed tip)\tbright, solid colors or patterns\ta pointed handle that sticks out from the top\tcenter hole for spinning on a surface", 17], "caramel": ["Yes. 'Caramel' has a tangible appearance and is a type of candy.\nA few things that are visually similar to 'caramel' but are not 'caramel' are:\ttoffee\tfudge\thoney\tmaple syrup\nThere are several useful visual features to tell there is 'caramel' and not similar things in a photo:\tbrown or golden color\tcreamy or sticky texture\tshiny or glossy surface\tsweet aroma (if visible in a photo)", 17], "pickle spear": ["Yes. 'Pickle spear' has a tangible appearance and is a specific type of food.\nA few things that are visually similar to 'pickle spear' but are not 'pickle spear' are:\tcucumber slices\tgherkins\tzucchini sticks\tswordfish steaks\nThere are several useful visual features to tell there is 'pickle spear' and not similar things in a photo:\tspear-shaped pickle\tskinny and long compared to other pickles\tridged texture\tdark green color", 17], "sponsors": ["No. 'Sponsors' is too vague or abstract to be distinguished in a photo.", 17], "cow statue": ["Yes. 'Cow statue' has a tangible appearance and is a type of statue.\nA few things that are visually similar to 'cow statue' but are not 'cow statue' are:\thorse statue\tgoat statue\tsheep statue\tpig statue\nThere are several useful visual features to tell there is 'cow statue' and not similar things in a photo:\tcow-like features such as horns or udders\tspecific colors, such as white with black spots or all black\tstylized or realistic representation of a cow in a standing or grazing position\tmade of material such as stone, resin, or metal.", 17], "traveler": ["No. 'Traveler' is too vague or abstract to be distinguished in a photo.", 17], "skates": ["Yes. 'Skates' has a tangible appearance and is a kind of footwear.\nA few things that are visually similar to 'skates' but are not 'skates' are:\tsneakers\tboots\theels\tflip flops\nThere are several useful visual features to tell there are 'skates' and not similar things in a photo:\tblade on the bottom of the shoe\tice skates are white or black\tfront-end of the blade curves upwards towards the toe\troller skates have four wheels in a square formation", 17], "bison": ["Yes. 'Bison' has a tangible appearance and is a large mammal.\nA few things that are visually similar to 'bison' but are not 'bison' are:\tbuffalo\tyak\tcow\tox\nThere are several useful visual features to tell there is 'bison' and not similar things in a photo:\tmassive shoulders\thigh hump on the shoulder\tshaggy fur around the neck and shoulders\trobust head\twith curving horns on both sexes, usually pointed upwards\tfrom North America", 17], "athletes": ["Yes. 'Athletes' has a tangible appearance and refers to people who participate in sports or physical activities.\nA few things that are visually similar to 'athletes' but are not 'athletes' are:\tspectators\tfans\tcoaches\ttrainers\nThere are several useful visual features to tell there are 'athletes' and not similar things in a photo:\twearing sports clothing or gear\tactively engaged in physical activity or competition\tshowing physical strength or agility\tcrowd cheering in the background", 17], "dalmation": ["Yes. 'Dalmatian' has a tangible appearance and is a kind of dog breed with specific visual features.\nA few things that are visually similar to 'Dalmatian' but are not 'Dalmatian' are:\tPointer\tBull Terrier\tHarlequin Great Dane\nThere are several useful visual features to tell there is 'Dalmatian' and not similar things in a photo:\twhite coat with black or liver-colored spots\ttall and lean body shape\tfloppy or erect ears\talert expression", 17], "vendors": ["No. 'Vendors' is too vague or abstract to be distinguished in a photo.", 17], "backboard": ["Yes. 'Backboard' has a tangible appearance and is a part of a basketball hoop.\nA few things that are visually similar to 'backboard' but are not 'backboard' are:\twall\tboard\tshield\nThere are several useful visual features to tell there is 'backboard' and not similar things in a photo:\trectangle or square shape\tprotected with padding or foam\tbasketball hoop attached to the bottom of it\tnet hanging from the rim of the backboard.", 17], "twelve": ["No. 'Twelve' is too vague or abstract to be distinguished in a photo. It is a number and does not have a tangible appearance.", 17], "street car": ["Yes. 'Street car' has a tangible appearance and is a kind of public transportation vehicle.\nA few things that are visually similar to 'street car' but are not 'street car' are:\tbus\ttram\ttrain\ttrolley\nThere are several useful visual features to tell there is 'street car' and not similar things in a photo: tracks on the street \toverhead electric wires \tlarge windows for passengers \tlong, rectangular shape \toften painted in bright colors \tadvertisement panels on the side \tdriver's compartment at one end.", 17], "attire": ["Yes. 'Attire' has a tangible appearance and refers to clothing or dress.\nA few things that are visually similar to 'attire' but are not 'attire' are:\tmannequin\tdolls\tpaintings\nThere are several useful visual features to tell there is 'attire' and not similar things in a photo:\tfabric\ttextures\tpatterns\tcolors\tshapes on the body\tzoomed in on a specific piece of clothing or accessory.", 17], "food stand": ["Yes. 'Food stand' has a tangible appearance and is a type of outdoor stall that sells food.\nA few things that are visually similar to 'food stand' but are not 'food stand' are:\tphone booth\tnewspaper stand\tticket booth\tcarnival game booth\nThere are several useful visual features to tell there is 'food stand' and not similar things in a photo:\tfood displayed for sale\tmenu\tboard\twith or without customers\tpicnic tables or benches nearby\tdecorative or eye-catching signage", 17], "elk": ["Yes. 'Elk' has a tangible appearance and is a type of deer.\nA few things that are visually similar to 'elk' but are not 'elk' are:\tmoose\treindeer\tcaribou\tgazelle\nThere are several useful visual features to tell there is 'elk' and not similar things in a photo:\tbrown fur with a light-colored rump and big mane of hair around the neck\tmultiple-point antlers\tmuscular build and long legs\ttall and large body size", 17], "terrace": ["Yes. 'Terrace' has a tangible appearance and is an outdoor space often connected to a building or a house.\nA few things that are visually similar to 'terrace' but are not 'terrace' are: balcony, porch, deck\nThere are several useful visual features to tell there is 'terrace' and not similar things in a photo: outdoor space with no roof, often paved, usually with furniture such as tables and chairs or plants. It may have railings, and it is usually connected to a building or a house.", 17], "dream catcher": ["Yes. 'Dream catcher' has a tangible appearance and is a type of Native American craft.\nA few things that are visually similar to 'dream catcher' but are not 'dream catcher' are:\tfringe curtains\tjewelry\twith feathers and beads\tdecorative wall hangings\nThere are several useful visual features to tell there is 'dream catcher' and not similar things in a photo:\tcircular or hoop shape\twith a web in the middle\thanging feathers and beads\tfrom Native American culture or made in a similar style", 17], "whispy clouds": ["Yes, 'whispy clouds' has a tangible appearance and refers to the thin and wispy type of clouds.\nA few things that are visually similar to 'whispy clouds' but are not 'whispy clouds' include: smoke, fog, mist, steam, and haze.\nThe following are useful visual features for distinguishing 'whispy clouds' from similar things in a photo:\n\n- Thin and delicate appearance\n- Radiating or streaking in appearance\n- Translucent white or grey appearance \n- Generally high altitude in the atmosphere\n- Generally no touching or connected appearance between neighboring clouds.", 17], "mopeds": ["Yes. 'Mopeds' has a tangible appearance and is a type of motorbike.\nA few things that are visually similar to 'mopeds' but are not 'mopeds' are:\tbicycles\tmotorcycles\tscooters\telectric bikes\nThere are several useful visual features to tell there is 'mopeds' and not similar things in a photo:\ttwo wheels\tengine and exhaust system\tpedals for assisted pedaling\thandlebars for steering\tsmall size compared to other motorbikes or scooters.", 17], "toilet base": ["Yes. 'Toilet base' has a tangible appearance and refers to the bottom part of a toilet that connects to the floor.\nA few things that are visually similar to 'toilet base' but are not 'toilet base' are:\tbath tub\tbase of a sink\nThere are several useful visual features to tell there is 'toilet base' and not similar things in a photo:\tconnected to a pipe\tsystem of bolts and screws\tflat bottom\ts-shaped tube at the back", 17], "crowds": ["Yes. 'Crowds' has a tangible appearance and refers to a large group of people gathered in one place.\nA few things that are visually similar to 'crowds' but are not 'crowds' are: gathering of objects or animals, a line of things or people.\nThere are several useful visual features to tell there is 'crowds' and not similar things in a photo:\thuman figures\tpacked into a space\tclosely gathered together\twith a sense of activity and movement\tdemonstrating a common purpose.", 17], "vanilla cake": ["Yes. 'Vanilla cake' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'vanilla cake' but are not 'vanilla cake' are:\tchocolate cake\tpudding\tpancake\tfluffy bread\nThere are several useful visual features to tell there is 'vanilla cake' and not similar things in a photo:\tyellow or light brown color\tsponge or fluffy texture\twith or without icing or frosting\tcircular shape or a slice of it on a plate\twith or without toppings, such as fruits or sprinkles", 17], "alot": ["No. 'Alot' is too vague or abstract to be distinguished in a photo. It is not a concrete noun but an adverb that means \"a large quantity or an excess.\"\nThere are no things similar to 'alot' as it is not a physical object.", 17], "delta": ["Yes. 'Delta' has a tangible appearance and refers to a triangular-shaped landform at the mouth of a river where it empties into a larger body of water.\nA few things that are visually similar to 'delta' but are not 'delta' are:\tPyramid\tTriangle\tAirplane wing\nThere are several useful visual features to tell there is 'delta' and not similar things in a photo:\ttriangular or fan-shaped landform\twaterways around it\tdistribution of sediment or silt\twhere the river meets the ocean or a lake", 17], "initials": ["Yes. 'Initials' has a tangible appearance and is a representation of the first letters of a name or surname.\nA few things that are visually similar to 'initials' but are not 'initials' are:\tmonograms\tsignatures\tcalligraphy\tfont\nThere are several useful visual features to tell there are 'initials' and not similar things in a photo:\ttypically two or three letters\teach letter is distinguishable from others\tmay be enclosed in a box or a circle\tmay be printed or handwritten.", 17], "stick ground": ["No. 'Stick ground' is too vague or abstract to be distinguished in a photo.", 17], "straight": ["Yes. 'Straight' has a tangible appearance and is a geometric property of lines and shapes.\nA few things that are visually similar to 'straight' but are not 'straight' are:\tcurved\tlines with an angle\tbroken lines\nThere are several useful visual features to tell there is 'straight' and not similar things in a photo:\tno curvature in the line or shape\tperfectly vertical, horizontal or diagonal line\tthat follows the shortest path between two points", 17], "pink cell phone": ["Yes. 'Pink cell phone' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'pink cell phone' but are not 'pink cell phone' are:\tpink calculator\tpink camera\tpink remote control\tpink mp3 player\nThere are several useful visual features to tell there is 'pink cell phone' and not similar things in a photo:\trectangular shape\twith buttons or a touch screen\tearphone jack and charging port\tcamera on the back or the front of the device\tmobile network signal or Wi-Fi symbol on the screen.", 17], "girrafe": ["Yes. 'Giraffe' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'giraffe' but are not 'giraffe' are:\tllama\tokapi\tcamel\nThere are several useful visual features to tell there is 'giraffe' and not similar things in a photo:\ttall neck with distinctive pattern\tlong legs\tuneven spots-like pattern on the fur\tflattened or rounded horns\tO-shaped spotted pattern on the body", 17], "onlookers": ["No. 'Onlookers' is too vague or abstract to have a tangible appearance and be distinguished in a photo.", 17], "lone tree": ["Yes. 'Lone tree' has a tangible appearance and is a tree standing alone without other trees around it.\nA few things that are visually similar to 'lone tree' but are not 'lone tree' are:\tcluster of trees\tforest\tline of trees\tyoung tree\nThere are several useful visual features to tell there is 'lone tree' and not similar things in a photo:\tstands alone with no other trees near\tit is mature\ttree canopy covers most of the frame\twith or without branches and leaves, but still recognizable as a tree", 17], "rail car": ["Yes. 'Rail car' has a tangible appearance and is a type of vehicle designed to run on rails or tracks.\nA few things that are visually similar to 'rail car' but are not 'rail car' are:\ttram\ttrolley\tsubway car\ttrain engine\nThere are several useful visual features to tell there is 'rail car' and not similar things in a photo:\tattached to a train\trails or tracks visible\tusually rectangular body shape\twith multiple wheels and axles\tdoorways or cargo bays along the sides or ends", 17], "elevator": ["Yes. 'Elevator' has a tangible appearance and is a type of vertical transportation device.\nA few things that are visually similar to 'elevator' but are not 'elevator' are:\tescalator\tstairs\tdumbwaiter\nThere are several useful visual features to tell there is 'elevator' and not similar things in a photo:\tmetal doors or gates\tat least two floors (or levels) of a building\tvertical movement mechanism\tbuttons for selecting floors or levels inside the elevator cabin", 17], "blue clouds": ["Yes. 'Blue clouds' has a tangible appearance and is a rare meteorological phenomenon.\nA few things that are visually similar to 'blue clouds' but are not 'blue clouds' are:\tblue sky\tatmospheric haze\tcolorful sunsets\torbit views of the planet\nThere are several useful visual features to tell there is 'blue clouds' and not similar things in a photo:\tthin silvery-blue clouds\thigh altitudes\tatmospheric nightglow caused by chemical reactions in the upper atmosphere such as ozone and hydroxyl", 17], "soccer shorts": ["Yes. 'Soccer shorts' has a tangible appearance and is a type of athletic clothing.\nA few things that are visually similar to 'soccer shorts' but are not 'soccer shorts' are:\trunning shorts\tsurf shorts\tboxer shorts\t\nThere are several useful visual features to tell there is 'soccer shorts' and not similar things in a photo:\tloose and lightweight fabric\tdesigns that match a team's colors\tor logos above the hem or on the side\ta length that falls above the knee", 17], "pasta salad": ["Yes. 'Pasta salad' has a tangible appearance and is a type of salad.\nA few things that are visually similar to 'pasta salad' but are not 'pasta salad' are:\tpasta with tomato sauce\tpasta with pesto\tpasta with cheese\nThere are several useful visual features to tell there is 'pasta salad' and not similar things in a photo:\ta mix of pasta and other ingredients\tbright multi-colors\tdifferent shapes of pasta such as fusilli, bowties, and farfalle\tdiced vegetables like tomatoes, cucumbers, and bell peppers\tvinaigrette dressing instead of tomato sauce or cheese-based sauces", 17], "skid marks": ["Yes. 'Skid marks' has a tangible appearance and is a type of tire mark caused by a vehicle's brakes.\nA few things that are visually similar to 'skid marks' but are not 'skid marks' are:\tanimal tracks\tgrooves or lines on a road\tpaint lines on a road\nThere are several useful visual features to tell there is 'skid marks' and not similar things in a photo:\tcurved or swerving lines on the road, resembling a tire track\tdarker in color than the road surface\tmay be accompanied by debris, such as tire shreds or gravel may be associated with a vehicle accident or collision.", 17], "vehicle tracks": ["Yes. 'Vehicle tracks' has a tangible appearance and is a mark left by a vehicle's movement.\nA few things that are visually similar to 'vehicle tracks' but are not 'vehicle tracks' are:\tanimal tracks\tbicycle tracks\tfootprints\tskid marks\nThere are several useful visual features to tell there are 'vehicle tracks' and not similar things in a photo:\tdouble lines or parallel lines\timpressions in the ground\tdented or compressed soil or snow\tdistinguishable shapes or patterns depending on the type of vehicle such as a car or a truck.", 17], "branch brown": ["Yes. 'Branch brown' has a tangible appearance and refers to a specific color of a tree branch.\nThere are no things visually similar to 'branch brown but are not 'branch brown'.\nUseful visual features for distinguishing 'branch brown' from other colors of branches in a photo include:\trich, warm, earthy color\tbrown hue is dominant and noticeable\tnatural, slightly rough texture indicative of a tree branch.", 17], "metal clip": ["Yes. 'Metal clip' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'metal clip' but are not 'metal clip' are:\tpin\tstaple\tpaper clip\tbinder clip\nThere are several useful visual features to tell there is 'metal clip' and not similar things in a photo:\tthin and flat piece of metal or wire\tbent into a loop or rectangle shape\twith two flat metal prongs for holding papers\tor with one prong and one loop for attaching to a surface\tsilver or gold color", 17], "orange ribbon": ["Yes. 'Orange ribbon' has a tangible appearance and is a type of ribbon.\nA few things that are visually similar to 'orange ribbon' but are not 'orange ribbon' are:\tyellow ribbon\tpink ribbon\tfabric\tstripe\nThere are several useful visual features to tell there is 'orange ribbon' and not similar things in a photo:\torange color\tsmooth surface\tlength and width of a ribbon\ttexture similar to a ribbon's", 17], "silver sedan": ["Yes. 'Silver sedan' has a tangible appearance and is a type of car.\nA few things that are visually similar to 'silver sedan' but are not 'silver sedan' are:\tpickup truck\tSUV\tsports car\tstation wagon\nThere are several useful visual features to tell there is 'silver sedan' and not similar things in a photo:\tmedium-sized four-door car\tsilver or metallic color\tsedan-style with a closed trunk and a separate compartment for passengers.", 17], "brown tree trunk": ["Yes. 'Brown tree trunk' has a tangible appearance and is specific.\nA few things that are visually similar to 'brown tree trunk' but are not 'brown tree trunk' are:\tbrown pole\ttree branch\twooden beam\tbark\nThere are several useful visual features to tell there is 'brown tree trunk' and not similar things in a photo:\tcylindrical shape\tof significant height thickest part of a tree\tvertical growth pattern\tbrown color and rough texture", 17], "egret": ["Yes. 'Egret' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'egret' but are not 'egret' are:\tcrane\theron\tstork\tibis\nThere are several useful visual features to tell there is 'egret' and not similar things in a photo:\tall-white plumage\tlong and slender legs\tyellow or black bill\tplume of feathers on the head and neck in breeding season\tslightly curved neck when in flight or standing in the water", 17], "star design": ["Yes. 'Star design' has a tangible appearance and is a pattern that resembles a star.\nA few things that are visually similar to 'star design' but are not 'star design' are:\tpentagon shape\tsunburst pattern\nThere are several useful visual features to tell there is 'star design' and not similar things in a photo:\tfive-sided figure\tvertexes or points meeting at equal angles\tarrows pointing outward from each point of the star\tcircular shape around a central point\tspokes or lines connecting each point\ttoo many or too few points will fall out of 'star design' category", 17], "front pocket": ["Yes. 'Front pocket' has a tangible appearance and is a type of pocket located at the front of a garment.\nA few things that are visually similar to 'front pocket' but are not 'front pocket' are:\tback pocket\tzippers\tbelts\nThere are several useful visual features to tell there is 'front pocket' and not similar things in a photo:\tlocated at the front of a garment\tsimilar material and color to the garment\tcurved or straight opening\thigh or low placement on the garment.", 17], "gray bricks": ["Yes. 'Gray bricks' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'gray bricks' but are not 'gray bricks' are:\trocks\tpavers\tcement blocks\ttiles\nThere are several useful visual features to tell there is 'gray bricks' and not similar things in a photo:\trectangular or square shape\trough or textured surface\tdull gray color\tmortar between bricks.", 17], "charcoal": ["Yes. 'Charcoal' has a tangible appearance and is a type of black carbon.\nA few things that are visually similar to 'charcoal' but are not 'charcoal' are:\tash\tsoot\tblack ink\nThere are several useful visual features to tell there is 'charcoal' and not similar things in a photo:\tpieces of burnt wood or vine\tuneven texture and shape\tmatte black color\tpossible visible grains from the wood\tvarying shades of dark gray and black", 17], "domes": ["Yes. 'Domes' has a tangible appearance and refers to a rounded vault forming the roof of a building or structure.\nA few things that are visually similar to 'domes' but are not 'domes' are:\thalf-sphere objects\tbubble\tglass dome\nThere are several useful visual features to distinguish 'domes' from the listed similar things in a photo:\thalf-spherical shape\tarchitectural element\tmade of stone, metal, or concrete\tmay have windows or decorations at the top", 17], "beverage cup": ["Yes. 'Beverage cup' has a tangible appearance and is a container for drinks.\nA few things that are visually similar to 'beverage cup' but are not 'beverage cup' are:\tbottles\tthermoses\tmugs\tglasses\nThere are several useful visual features to tell there is 'beverage cup' and not similar things in a photo:\thandles or no handles\tlids or no lids\tstraws or no straws\ttapered shape or straight sides\tvariety of sizes (small, medium, large) made from materials like plastic or ceramic", 16], "rolling suitcase": ["Yes. 'Rolling suitcase' has a tangible appearance and is a kind of luggage.\nA few things that are visually similar to 'rolling suitcase' but are not 'rolling suitcase' are:\tbackpacks\tpurses\tbriefcases\t\nThere are several useful visual features to tell there is 'rolling suitcase' and not similar things in a photo:\trectangular or square-shaped\thaving wheels\tand a handle for pulling\tmade of fabric, leather or hard material\thaving a zipper or a latch for opening and closing", 16], "model plane": ["Yes, 'model plane' has a tangible appearance and can be easily distinguished in a photo.\nA few things that are visually similar to 'model plane' but are not 'model plane' are: real plane, toy plane, drone, bird, insect\nThere are several useful visual features to distinguish 'model plane' from the listed similar things in a photo: small size, lightweight, plastic or wood construction, wings and fuselage with characteristic design, often held or displayed by a person.", 16], "orange cord": ["Yes, 'orange cord' has a visually concrete concept as it has a tangible appearance and is a type of cord that is colored orange.\nA few things that are visually similar to 'orange cord' but are not 'orange cord' are:\tyellow cord\torange rope\tsafety cones\torange snakes\nThere are several useful visual features to tell there is 'orange cord' and not similar things in a photo:\t\norange colored\tlong and thin shape\thas a plug at one end\tcan be wound up or pulled tightly", 16], "gas burner": ["Yes. 'Gas burner' has a tangible appearance and is a device used for cooking.\nA few things that are visually similar to 'gas burner' but are not 'gas burner' are:\telectric burner\tcandle\toil lamp\nThere are several useful visual features to tell there is 'gas burner' and not similar things in a photo:\ta grill-like surface for pots and pans\t\na series of metal grates or coils to contain the fire\t\na knob or switch to control the gas flow", 16], "dispensers": ["Yes. 'Dispensers' has a tangible appearance and is a type of container that dispenses its contents.\nA few things that are visually similar to 'dispensers' but are not 'dispensers' are:\tjars\tbottles\tcans\tpots\nThere are several useful visual features to tell there is 'dispensers' and not similar things in a photo:\tvisible dispenser mechanism\tpump, nozzle or spigot\tdesignated dispensing area\tclear indication of contents (e.g. soap or water)", 16], "bowl sink": ["Yes, 'bowl sink' has a tangible appearance and is a type of sink.\nA few things that are visually similar to 'bowl sink' but are not 'bowl sink' are:\ttrough sink\tpedestal sink\twall-mounted sink\t\nThere are several useful visual features to tell there is 'bowl sink' and not similar things in a photo:\tbowl-shaped basin\tfor countertop installation\tminimal but stylish design\twith a visible drain and faucet hole.", 16], "control knob": ["Yes. 'Control knob' has a tangible appearance and is a physical component used to adjust settings or turn devices on and off.\nA few things that are visually similar to 'control knob' but are not 'control knob' are:\tbuttons\tswitches\tdials\thandles\tknobs with no control function\nThere are several useful visual features to tell there is 'control knob' and not similar things in a photo:\tcircular or cylindrical in shape\thas visible numerical or alphabetical markings\tor another indication of the settings\thas an adjacent device that it controls, generally rotating\tleft or right\tto switch devices or increase/decrease a setting.", 16], "wet man": ["Yes. 'Wet man' has a tangible appearance.\nA few things that are visually similar to 'wet man' but are not 'wet man' are:\tswimmer\tdiver\tperson in the shower\tor in the rain\nThere are several useful visual features to tell there is a 'wet man' and not similar things in a photo:\tdamp or wet hair\twater droplets on the skin\tor dripping from the body\tclothing that appears to be soaked\tor clinging to the body\tthe presence of water or moisture in the background of the photo.", 16], "head lamp": ["Yes. 'Head lamp' has a tangible appearance and is a type of light that is worn on the head.\nA few things that are visually similar to 'head lamp' but are not 'head lamp' are:\tflashlight\tlantern\tcandle\nThere are several useful visual features to tell there is 'head lamp' and not similar things in a photo:\ta light attached to a headband\tor a hat\tfocused beam of light\tthat illuminates a small area\ton/off button\tclose to the forehead", 16], "metal rods": ["Yes. 'Metal rods' has a tangible appearance and refers to long, thin and straight pieces of metal.\nA few things that are visually similar to 'metal rods' but are not 'metal rods' are:\tpencils\tstraws\tbrush handles\tskewers\nThere are several useful visual features to tell there is 'metal rods' and not similar things in a photo:\tlong and thin\thard and rigid\tmade of metal\tstraight or with a specific shape (e.g. threaded, curved)", 16], "side tracks": ["Yes. 'Side tracks' has a tangible appearance and refers to the tracks located next to the main tracks.\nA few things that are visually similar to 'side tracks' but are not 'side tracks' are:\tswitches\tderailers\tcrossings\tbridges\nThere are several useful visual features to tell there are 'side tracks' and not similar things in a photo:\tparallel to the main tracks\tsmaller than the main tracks\tlocated next to the main tracks\tsometimes have different functions than the main tracks.", 16], "stuffed monkey": ["Yes. 'Stuffed monkey' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'stuffed monkey' but are not 'stuffed monkey' are:\tplush toy\tbear\tdoll\tpillow\nThere are several useful visual features to tell there is 'stuffed monkey' and not similar things in a photo:\tmonkey-shaped\tfurry body with arms and legs\tshort tail\tbulging eyes and/or long arms", 16], "door fridge": ["Yes. 'Door fridge' has a tangible appearance and refers to a type of refrigerator.\nA few things that are visually similar to 'door fridge' but are not 'door fridge' are:\tchest freezer\tbeverage cooler\tmilk cooler\nThere are several useful visual features to tell there is 'door fridge' and not similar things in a photo:\tdouble doors that open outward\thorizontal panels on the doors\thandle or handles on the outside", 16], "winnie": ["No. 'Winnie' by itself is too vague or abstract to have a tangible appearance, but if you are referring to Winnie the Pooh, then the concept has a visually concrete appearance as a cartoon character.\nA few things that are visually similar to 'winnie' but are not 'winnie' are:\tbears\ttoys\tcartoon characters\nThere are several useful visual features to tell there is 'winnie' (referring to Winnie the Pooh) and not similar things in a photo:\tyellow or golden fur\trabbit or honey pot nearby\tred t-shirt with no pants\tstuffed or plushie appearance.round, chubby bear shape.", 16], "cow tail": ["Yes. 'Cow tail' has a tangible appearance and is a part of the cow's body.\nA few things that are visually similar to 'cow tail' but are not 'cow tail' are:\thorse tail\tdeer tail\tcat tail\nThere are several useful visual features to tell there is 'cow tail' and not similar things in a photo:\tlong and straight with a tuft or switch at the end\tattached to a cow's rump\tbovine appearance and texture, with hair and skin specific to cows", 16], "grey mouse": ["Yes. 'Grey mouse' has a tangible appearance and is a type of rodent.\nA few things that are visually similar to 'grey mouse' but are not 'grey mouse' are:\trat\tvole\tsquirrel\tshrew\nThere are several useful visual features to tell there is 'grey mouse' and not similar things in a photo:\tsmall size\tround ears\tfurry tail\tlong whiskers\tgray or brown fur pointy nose 4 legs", 16], "dirt surface": ["Yes. 'dirt surface' has a tangible appearance and refers to an area covered with earth, soil or mud.\nA few things that are visually similar to 'dirt surface' but are not 'dirt surface' are:\tconcrete surface\tpavement\ttile flooring\t\nThere are several useful visual features to tell there is 'dirt surface' and not similar things in a photo:\tbrown or reddish-brown color\tuneven surface\tgrainy or gritty texture\twith or without vegetation (grass, plants)\tfootprints, paw prints or tire tracks visible on the surface", 16], "baseboards": ["Yes. 'Baseboards' has a tangible appearance and is a specific part of interior design and construction.\nA few things that are visually similar to 'baseboards' but are not 'baseboards' are:\tmolding\tchair rail\ttrim\twainscoting\nThere are several useful visual features to tell there is 'baseboards' and not similar things in a photo:\thorizontal strip along the base of a wall\tflat or slightly curved surface\tpainted or stained to match the wall or floor\tmight have a subtle pattern or design", 16], "blue pillows": ["Yes. 'blue pillows' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'blue pillows' but are not 'blue pillows' are:\tsofa\tcushions\tplush toys\twrapping paper\t\nThere are several useful visual features to tell there is 'blue pillows' and not similar things in a photo:\tblue in color\trectangular or square in shape\tsoft and fluffy texture,pillow cover stitches or zipper.", 16], "sandles": ["Yes. 'Sandals' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'sandals' but are not 'sandals' are:\tshoes\tflip flops\tslippers\nThere are several useful visual features to tell there are 'sandals' and not similar things in a photo:\topen-toed\tstraps around the foot\tand sole\tstraps often go between toes\tfor warm weather", 16], "level bus": ["No. 'Level bus' is too vague or abstract to be distinguished in a photo. Perhaps you mean \"low-floor bus\". If this is the case, please let me know and I can provide an answer for \"low-floor bus\".", 16], "folds": ["Yes. 'Folds' has a tangible appearance and is a type of crease or bend.\nA few things that are visually similar to 'folds' but are not 'folds' are:\tCrinkles\tWrinkles\tRipples\tPleats\nThere are several useful visual features to tell there are 'folds' and not similar things in a photo:\tcrease or bend in a soft material or fabric\tvisible overlapping layers\tor contour lines formed by drapery or flexing surfaces.", 16], "hand sanitizer": ["Yes. 'Hand sanitizer' has a tangible appearance and is a liquid or gel used for cleaning hands.\nA few things that are visually similar to 'hand sanitizer' but are not 'hand sanitizer' are:\tsoap\tshampoo\tlotion\thand cream\nThere are several useful visual features to tell there is 'hand sanitizer' and not similar things in a photo:\tclear or translucent bottle or dispenser\tgel or liquid consistency\thigh alcohol content in the formula\tspecific labels or logos", 16], "cabinet drawers": ["Yes. 'Cabinet drawers' has a tangible appearance and refers to the storage compartment of a cabinet.\nA few things that are visually similar to 'cabinet drawers' but are not 'cabinet drawers' are:\tshelves\tbins\tbaskets\ttrunks\nThere are several useful visual features to tell there is 'cabinet drawers' and not similar things in a photo:\trectangular or square compartments\tpull handles or knobs\tfitting inside a larger cabinet or piece of furniture.", 16], "gold wedding band": ["Yes. 'Gold wedding band' has a tangible appearance and is a type of ring.\nA few things that are visually similar to 'gold wedding band' but are not 'gold wedding band' are:\tother types of rings, such as engagement rings or eternity rings\tbracelets and necklaces with similar designs or materials\nThere are several useful visual features to tell there is 'gold wedding band' and not similar things in a photo:\ta band-shaped ring\twith a circular design\tmade of gold (or a similar metallic color)\tno large or ornate jewels or decorations on the ring.", 16], "bird bath": ["Yes. 'Bird bath' has a tangible appearance and is an object designed for birds to bathe in.\nA few things that are visually similar to 'bird bath' but are not 'bird bath' are:\tfountain\tbird feeder\tplanter\tpot\tdish\nThere are several useful visual features to tell there is 'bird bath' and not similar things in a photo:\tshallow basin or bowl\tstanding on a pedestal\tor pillar\toutdoor location\twith or without water\tfrequently surrounded with birds or perches.", 16], "snow hill": ["Yes. 'Snow hill' has a tangible appearance and is a mound of snow.\nA few things that are visually similar to 'snow hill' but are not 'snow hill' are:\thill\tmound of sand\tlarge rock\tpile of dirt\nThere are several useful visual features to tell there is 'snow hill' and not similar things in a photo:\twhite color\ticy or snowy texture\tsmooth or bumpy surface made of snow or ice.", 16], "grey parking meter": ["Yes. 'Grey parking meter' has a tangible appearance and is a type of object used for parking.\nA few things that are visually similar to 'grey parking meter' but are not 'grey parking meter' are:\tmailbox\ttrash can\tlamppost\tbicycle stand\nThere are several useful visual features to tell there is 'grey parking meter' and not similar things in a photo:\tstand-alone device\tpainted in grey color\ttall and thin shape\thas a coin slot and a display for time and price", 16], "cake table": ["Yes. 'Cake table' has a tangible appearance and refers to a specific piece of furniture.\nA few things that are visually similar to 'cake table' but are not 'cake table' are:\tdining table\tbuffet\ttablecloth\tkitchen island\nThere are several useful visual features to tell there is 'cake table' and not similar things in a photo:\tsmall and narrow enough to display cakes and desserts only\tusually decorated with small ornaments and flowers\tsometimes elevated on a pedestal or legs\tcakes and desserts arranged in an eye-catching manner", 16], "mangos": ["Yes. 'Mangos' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'mangos' but are not 'mangos' are:\tpapayas\tcantaloupes\tlemons\toranges\nThere are several useful visual features to tell there is 'mangos' and not similar things in a photo:\toval-shaped fruit\twith a pit in the middle\tgreen when unripe, orangish when ripe\tslightly curved at one end\tsmooth, thin skin covering the fruit\tfleshy, juicy flesh", 16], "clips": ["Yes. 'Clips' has a tangible appearance and is a kind of fastener.\nA few things that are visually similar to 'clips' but are not 'clips' are:\tpaper clips\thair clips\tbinder clips\tclothespins\nThere are several useful visual features to tell there is 'clips' and not similar things in a photo:\tmetal or plastic material\thinged design\tpinch or clasp mechanism\tfor holding papers, hair, or clothes", 16], "orange vase": ["Yes. 'Orange vase' has a tangible appearance and is a type of vessel.\nA few things that are visually similar to 'orange vase' but are not 'orange vase' are:\tpottery\tbottle\tjug\tglass\tvessel\nThere are several useful visual features to tell there is 'orange vase' and not similar things in a photo:\torange color\tcylindrical neck\twide belly and base\tridged or textured surface for grip\ttapered or rounded shape at rim and base", 16], "color dog": ["No. 'Color dog' is too vague or abstract to be distinguished in a photo.", 16], "wii video game controller": ["Yes. 'Wii video game controller' has a tangible appearance and is a type of video game accessory.\nA few things that are visually similar to 'wii video game controller' but are not 'wii video game controller' are:\tPS3 Move controller\tXbox Kinect motion sensor controller\tfitness tracker band\nThere are several useful visual features to tell there is 'wii video game controller' and not similar things in a photo:\trectangular shape with rounded corners\tincluded buttons (A, B, Home, Plus, Minus)\tattached strap\tfor use with the Wii gaming system", 16], "baseball home plate": ["Yes. 'Baseball home plate' has a tangible appearance and is a specific object used in baseball.\nA few things that are visually similar to 'baseball home plate' but are not 'baseball home plate' are:\tsquare slab\tpatio paver\tsimilar-shaped symbol on a sign or on the ground\nThere are several useful visual features to tell there is 'baseball home plate' and not similar things in a photo:\tfive-sided rubber or wooden plate\tset at a slight angle on the ground with one point facing away\tfrom three white bases on the field\thas a smaller rectangular shape within it.", 16], "orange design": ["No. 'Orange design' is too vague or abstract to be distinguished in a photo. \n\nNote: When referring to a 'design', it is necessary to specify what type of design it is, as designs can be applied to various objects or products. Additionally, the color 'orange' can also vary in shade and saturation, so a specific shade of orange should be specified as well.", 16], "adidas": ["Yes. 'Adidas' has a tangible appearance and is a brand of clothing.\nA few things that are visually similar to 'Adidas' but are not 'Adidas' are:\tNike\tPuma\tReebok\tUnder Armour\nThere are several useful visual features to tell there is 'Adidas' and not similar things in a photo:\tthree stripes on the shoes or clothing\t'Adidas' logo, which is three parallel bars in a slanted rectangle\ttrefoil logo, which is a three-leaf shape", 16], "ski boots": ["Yes. 'Ski boots' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'ski boots' but are not 'ski boots' are:\thiking boots\tsnow boots\twork boots\triding boots\nThere are several useful visual features to tell there are 'ski boots' and not similar things in a photo:\thard plastic outer shell\tpadded liner\tclasps and buckles on front and side\traised heel\tthicker sole for insulation and support", 16], "bike frame": ["Yes. 'Bike frame' has a tangible appearance and is a part of a bicycle.\nA few things that are visually similar to 'bike frame' but are not 'bike frame' are:\thandlebars\twheels\tbrakes\tpedals\nThere are several useful visual features to tell there is 'bike frame' and not similar things in a photo:\ttubular structure that holds the wheels, chain, and other components\tusually made of metal or carbon fiber\trectangular or triangular shape with a sloping top tube\tdifferent sizes and shapes for different types of bicycles, such as road bikes, mountain bikes, and BMX bikes.", 16], "fat": ["Yes. 'Fat' has a tangible appearance and refers to the excess body or visceral tissue.\nA few things that are visually similar to 'fat' but are not 'fat' are:\tcushion\tpillow\tstuffed toy\tbag of flour\nThere are several useful visual features to tell there is 'fat' and not similar things in a photo:\tvisible soft tissue accumulation in body areas, such as the abdomen, thighs, and arms. It sometimes creates bulges or rolls on the skin.", 16], "silver vase": ["Yes. 'Silver vase' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'silver vase' but are not 'silver vase' are:\turn\tpitcher\tjug\t\nThere are several useful visual features to tell there is 'silver vase' and not similar things in a photo:\tvase shape\tsilver color\tsmooth and reflective surface\twide mouth for holding flowers or other objects", 16], "wool hat": ["Yes. 'Wool hat' has a tangible appearance and is a kind of headwear.\nA few things that are visually similar to 'wool hat' but are not 'wool hat' are:\tbaseball cap\tsun visor\tbonnet\tberet\nThere are several useful visual features to tell there is 'wool hat' and not similar things in a photo:\tmade of wool or other fuzzy fabric\tpulls over head or attaches with a chin strap\tcovers the forehead and ears in cold weather", 16], "silver fridge": ["Yes. 'Silver fridge' has a tangible appearance and is a type of household appliance.\nA few things that are visually similar to 'silver fridge' but are not 'silver fridge' are:\tstainless steel stove\tchrome oven\tsilver dishwasher\taluminum trash can\nThere are several useful visual features to tell there is 'silver fridge' and not similar things in a photo:\tvertical or horizontal rectangular shape\tmetallic silver color\thandle or door\tinvisible interior shelves and drawers", 16], "air conditioner unit": ["Yes. 'Air conditioner unit' has a tangible appearance and is typically a box-shaped machine with various components.\nA few things that are visually similar to 'air conditioner unit' but are not 'air conditioner unit' are:\theater\thumidifier\tfan\trefrigerator\nThere are several useful visual features to tell there is 'air conditioner unit' and not similar things in a photo:\tbox-shaped\tmounted on a wall or a window\tmultiple buttons and vents\tconnected to an outside unit by a tube or cable\twith cooling or air cleaning function", 16], "comforters": ["Yes. 'Comforters' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'comforters' but are not 'comforters' are:\tduvets\tblankets\tthrow pillows\t\nThere are several useful visual features to tell there is 'comforters' and not similar things in a photo:\tthick\tpuffy\tstuffed with feathers or other materials\tlarge enough to cover a bed\thas a pattern or design.", 16], "cargo truck": ["Yes. 'Cargo truck' has a tangible appearance and is a type of vehicle used for transporting goods.\nA few things that are visually similar to 'cargo truck' but are not 'cargo truck' are:\tpickup truck\tvan\tambulance\tfiretruck\nThere are several useful visual features to tell there is 'cargo truck' and not similar things in a photo:\tlarge, rectangular cargo bed\tcovered or uncovered cargo area\tseveral axles and wheels\tforward cab compartment with a windshield and doors\tsigns of cargo, such as pallets, crates, or boxes", 16], "thighs": ["Yes. 'Thighs' have a tangible appearance and are a part of the human body.\nA few things that are visually similar to 'thighs' but are not 'thighs' are:\tham\tleg of lamb\tgiraffe legs\tbaby elephant legs\nThere are several useful visual features to tell there are 'thighs' and not similar things in a photo:\tattached to the torso\tmuscular or fleshier\tfemale thigh might have cellulite\tbody hair or skin color\tdelineating fashion like pants or shorts", 16], "tree top": ["Yes. 'Tree top' has a tangible appearance and refers to the uppermost part of a tree.\nA few things that are visually similar to 'tree top' but are not 'tree top' are:\tclouds\tmountains\tbuildings\nThere are several useful visual features to tell there is 'tree top' and not similar things in a photo:\tleaves, branches and trunk of a tree\tlocated at the highest point of a tree\tconnected to the rest of the tree by the trunk and branches.", 16], "logotype": ["Yes. 'Logotype' has a tangible appearance and is a specific type of design element used in branding or advertising.\nA few things that are visually similar to 'logotype' but are not 'logotype' are:\ttypography\tsymbols\ticons\tgraphics\nThere are several useful visual features to tell there is 'logotype' and not similar things in a photo:\ta unique design or style\tspecific lettering or font\tuse of colors or shapes to distinguish brand\tor company name\tclear and legible text expressing a brand name or message.", 16], "rooftops": ["Yes. 'Rooftops' has a tangible appearance and refers to the tops of buildings or houses.\nA few things that are visually similar to 'rooftops' but are not 'rooftops' are:\tfloor tiles\tasphalt roads\tsidewalks\tparking lots\t\nThere are several useful visual features to tell there is 'rooftops' and not similar things in a photo:\tlocated at the top of a building or a house\tsloping or flat surface\tvarious colors and textures\tstacked or arranged in a particular pattern", 16], "forefront": ["No. 'Forefront' is too vague or abstract to be distinguished in a photo.", 16], "display window": ["Yes. 'Display window' has a tangible appearance and is a kind of storefront.\nA few things that are visually similar to 'display window' but are not 'display window' are:\topen window\tstore entrance\tglass door\nThere are several useful visual features to tell there is 'display window' and not similar things in a photo:\ta large window facing the street or outdoors\tarrangement of products or items\tfor commercial purposes (usually for advertising)\tbacklighting to make things look more attractive or noticeable.", 16], "ripe tomato": ["Yes. 'Ripe tomato' has a tangible appearance and is a type of fruit/vegetable.\nA few things that are visually similar to 'ripe tomato' but are not 'ripe tomato' are:\tgreen tomato bell pepper\tcayenne pepper\tcherry\nThere are several useful visual features to tell there is 'ripe tomato' and not similar things in a photo:\tred or orange color\tround or oblong shape\tsmooth or slightly dimpled skin\tgreen stem at the top\torangish interior with seeds at the center", 16], "bicycle sign": ["Yes. 'Bicycle sign' has a tangible appearance and is a kind of outdoor sign.\nA few things visually similar to 'bicycle sign' but are not 'bicycle sign' are:\tno parking sign\tstop sign\tspeed limit sign\nThere are several useful visual features to tell there is 'bicycle sign' and not similar things in a photo:\ta bicycle picture on a white background\tthe words \"bicycle\" or \"bike\" on it\tsome additional words or symbols for the specific bicycle rules or directions", 16], "metal tracks": ["Yes. 'Metal tracks' has a tangible appearance and refers to a type of structure usually found in railways or construction sites.\nA few things that are visually similar to 'metal tracks' but are not 'metal tracks' are:\tpipes\trods\twires\tbars\tbike racks\nThere are several useful visual features to tell there is 'metal tracks' and not similar things in a photo. Some of these are:\tlong, narrow and straight\tpainted in dark colors\tmay have an uneven surface or small gaps for traction", 16], "styrofoam plate": ["Yes. 'Styrofoam plate' has a tangible appearance and is a type of disposable plate.\nA few things that are visually similar to 'styrofoam plate' but are not 'styrofoam plate' are:\tpaper plate\tplastic plate\tdinner plate\nThere are several useful visual features to tell there is 'styrofoam plate' and not similar things in a photo:\twhite or light beige in color\tsmooth surface\twith an embossed pattern or a rim around the edge\tcrucial and lightweight", 16], "onlooker": ["No. 'Onlooker' is too vague or abstract to be distinguished in a photo.", 16], "color yellow": ["No. 'Color yellow' is too vague or abstract to be distinguished in a photo. \n\nNote: It is important to distinguish between a color and an object that is colored. For example, 'yellow flower' is a visually concrete concept because it refers to a tangible object that has a distinct color.", 16], "outdoor bench": ["Yes. 'Outdoor bench' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'outdoor bench' but are not 'outdoor bench' are:\tsofa\tchair\tstool\tpark picnic table\nThere are several useful visual features to tell there is 'outdoor bench' and not similar things in a photo:\tlong seat designed for multiple people\tbackrest\tarmrests or side tables\tusually made of wood or metal\tdesigned for outdoor use", 16], "silver tea pot": ["Yes. 'Silver tea pot' has a tangible appearance and is a type of household item.\nA few things that are visually similar to 'silver tea pot' but are not 'silver tea pot' are:\tsilver coffee pot\tsilver pitcher\nThere are several useful visual features to tell there is 'silver tea pot' and not similar things in a photo:\thas spout and handle for pouring tea\tlid that can be opened or removed\tfor tea leaves to steep inside\tis often used with cups or saucers for serving tea.", 16], "transformers": ["Yes. 'Transformers' has a tangible appearance and refers to a popular toy and a franchise about robots that can change their shape.\nA few things that are visually similar to 'transformers' but are not 'transformers' are:\trobot toys\taction figures\tscience-fiction movie props\nThere are several useful visual features to tell there are 'transformers' and not similar things in a photo:\trobots can change their shape from vehicle to robot mode\trobots have distinctive colors, designs, and logos\trobots have metallic textures and features like wheels, wings, and props.", 16], "book shelves": ["Yes. 'Book shelves' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'book shelves' but are not 'book shelves' are:\tdisplay cabinets\tdressers\twardrobes\tkitchen cabinets\nThere are several useful visual features to tell there is 'book shelves' and not similar things in a photo:\thorizontal shelves\tbooks or other items stored on the shelves\twall-mounted or free-standing\tsize and shape that are appropriate for storing books", 16], "brown wood": ["Yes. 'Brown wood' has a tangible appearance and is a type of natural material.\nA few things that are visually similar to 'brown wood' but are not 'brown wood' are:\tleather\trocks\tsoil\tbark\nThere are several useful visual features to tell there is 'brown wood' and not similar things in a photo:\tnatural grain patterns\tinconsistent color\tvariations in texture, depending on the type of wood\ttypically has a solid, smooth appearance, without holes or cracks (unless intended)", 16], "silver lock": ["Yes. 'Silver lock' has a tangible appearance and is a type of locking mechanism.\nA few things that are visually similar to 'silver lock' but are not 'silver lock' are:\tsilver key\tlock of a different color\tor a different shape\nThere are several useful visual features to tell there is 'silver lock' and not similar things in a photo:\tmetallic or silver appearance\trectangular or circular shape\thas a keyhole\tor combination dials\tto keep something secure or locked.", 16], "handles cabinet": ["Yes. 'Handles cabinet' has a tangible appearance and refers to the handles on a cabinet.\nA few things that are visually similar to 'handles cabinet' but are not 'handles cabinet' are:\tknobs on a dresser, drawers, or door\tcabinet hinges\tfaucet knobs\nThere are several useful visual features to distinguish 'handles cabinet' from the listed similar things in a photo:\thandles that are attached to cabinets\tfor cabinets to open or close\tfor gripping purposes\tvariety of shapes and sizes\tmatches the style and color of the cabinet.", 16], "ceiling tiles": ["Yes. 'Ceiling tiles' has a tangible appearance and is a kind of building material.\nA few things that are visually similar to 'ceiling tiles' but are not 'ceiling tiles' are:\tfloor tiles\twall panels\troof shingles\ttableware\nThere are several useful visual features to tell there is 'ceiling tiles' and not similar things in a photo:\tinstalled on the ceiling\tgrid pattern on the back\tplain or textured face\tcolor usually white or cream\tsize and shape: rectangular or square.", 16], "flower pedals": ["Yes. 'Flower petals' has a tangible appearance and refers to the colorful, delicate, and often fragrant leaves of a flower.\nA few things that are visually similar to 'flower petals' but are not 'flower petals' are:\tleaves\tbutterfly wings\tfabric\tribbons\nThere are several useful visual features to tell there is 'flower petals' and not similar things in a photo:\tcircular or ovular shape\tthin and delicate texture\tvibrant colors\tsurrounded by other petals, sepals, or stamens", 16], "button nose": ["Yes. 'Button nose' has a tangible appearance and is a type of nose.\nA few things that are visually similar to 'button nose' but are not 'button nose' are:\tturned-up nose\tsnub nose\troman nose\tbulbous nose\nThere are several useful visual features to tell there is 'button nose' and not similar things in a photo:\tround or oval shape\tshort and small size\tpositioned centrally on the face\tslightly upturned tip", 16], "race track": ["Yes. 'Race track' has a tangible appearance and is a specific type of track designed for racing.\nA few things that are visually similar to 'race track' but are not 'race track' are:\tregular roads\tbike trails\tpedestrian walkways\tplaygrounds\nThere are several useful visual features to tell there is 'race track' and not similar things in a photo:\tcircular or oval-shaped track\tsmooth and flat surface\tbordered by a safety barrier or fencing\tpit lane or pitstops\tcars or motorcycles racing on the track.", 16], "standing lamp": ["Yes. 'Standing lamp' has a tangible appearance and is a type of indoor lighting fixture.\nA few things that are visually similar to 'standing lamp' but are not 'standing lamp' are:\ttable lamp\tdesk lamp\tfloor lamp\nThere are several useful visual features to tell there is 'standing lamp' and not similar things in a photo:\ttall, free-standing\tvertical pole or stand\tbase to support it\telectric cord\tshade or lamp head at the top", 16], "pit": ["Yes. 'Pit' has a tangible appearance and can refer to a hole, depression or indented surface.\nA few things that are visually similar to 'pit' but are not 'pit' are:\thole in the ground\tdent in a car\tsunken area of a fruit or a vegetable\nThere are several useful visual features to tell there is 'pit' and not similar things in a photo:\tusually circular or elongated in shape\tcan be deep or shallow\tmight have steep walls or edges\tcan be found indoors or outdoors.", 16], "placemats": ["Yes. 'Placemats' has a tangible appearance and is usually a flat object placed on a table for decoration or practical purposes.\nA few things that are visually similar to 'placemats' but are not 'placemats' are:\ttablecloth\tnapkins\ttray\nThere are several useful visual features to tell there is 'placemats' and not similar things in a photo:\tusually smaller than a tablecloth\trectangular or circular shape\tmade of fabric, plastic, or paper\tcan have patterns, prints, or designs on it.", 16], "wood logs": ["Yes. 'Wood logs' has a tangible appearance and is a type of cut wood.\nA few things that are visually similar to 'wood logs' but are not 'wood logs' are:\ttree trunks\tbranches\tsticks\twooden planks\t\nThere are several useful visual features to tell there is 'wood logs' and not similar things in a photo:\tround or cylindrical shape\tbark visible\ton a pile or stacked together\thas split ends or sawed edges", 16], "wood flooring": ["Yes, 'wood flooring' has a visually concrete concept.\nA few things that are visually similar to 'wood flooring' but are not 'wood flooring' are:\tTile flooring\tLaminate flooring\tCarpet\tConcrete floor\n\t\nThere are several useful visual features to distinguish 'wood flooring' from the listed similar things in a photo:\n- Wood grain texture that can be seen up close\n- Visible edges of individual wooden planks or boards\n- Variations in color and texture across the surface\n- The sound made by stepping on the floor (softer than concrete, harder than carpet)\n- Natural imperfections like knots and blemishes that are unique to real wood flooring.", 16], "tooth brush": ["Yes. 'Tooth brush' has a tangible appearance and is a kind of hygiene tool.\nA few things that are visually similar to 'tooth brush' but are not 'tooth brush' are:\thairbrush\tpaint brush\tbroom\nThere are several useful visual features to tell there is 'tooth brush' and not similar things in a photo:\tlong, thin handle\tangled or curved head with bristles\tbristles are soft and flexible\tintended for cleaning teeth\tand has different colored bristles usually blue and white.", 16], "buidling": ["Yes. 'Building' has a tangible appearance and refers to a constructed structure.\nA few things that are visually similar to 'building' but are not 'building' are:\ttower\tbridge\tdam\twall\tpillar\nThere are several useful visual features to tell there is 'building' and not similar things in a photo:\tconstructed by arranging parts or materials in a particular way\thave walls and roofs designed for human occupation\tmay have windows and doors\tfor indoor living, working, or other human activities", 16], "chalkboard sign": ["Yes. 'Chalkboard sign' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'chalkboard sign' but are not 'chalkboard sign' are:\twhiteboard\tsign on paper\tmarker board\tlaptop screen\nThere are several useful visual features to tell there is 'chalkboard sign' and not similar things in a photo:\tdark-colored board that is usually black or green\twriting in white or colored chalk or chalk marker\thanging or standing with a wooden or metal frame", 16], "orange string": ["Yes. 'Orange string' has a tangible appearance and is a type of cord.\nA few things that are visually similar to 'orange string' but are not 'orange string' are:\trope\tnoodle\those\tcable\t\nThere are several useful visual features to tell there is 'orange string' and not similar things in a photo:\tthin\tcylindrical shape\tbright color of orange\tmade of many intertwined fibers", 16], "lit screen": ["Yes. 'Lit screen' has a tangible appearance and refers to an electronic device with an illuminated display.\nA few things that are visually similar to 'lit screen' but are not 'lit screen' are:\tnon-lit screen\tpaper signage\tlight bulbs\twith a white light\nThere are several useful visual features to tell there is 'lit screen' and not similar things in a photo:\tilluminated display displaying text or images\tvarious colors and shades of light emitting from the screen\tplugged into an electrical device or battery-powered\tportable or stationary device with a screen", 16], "metal tongs": ["Yes. 'Metal tongs' has a tangible appearance and is a kind of tool.\nA few things that are visually similar to 'metal tongs' but are not 'metal tongs' are:\tpliers\tscissors\twooden spoons\nThere are several useful visual features to tell there is 'metal tongs' and not similar things in a photo:\tmade of metal\ttwo arms with curved or pointed ends\tfor gripping or holding objects\tcan open and close", 16], "hutch": ["Yes. 'Hutch' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'hutch' but are not 'hutch' are:\tbookshelf\tcabinet\tchest of drawers\tshelving unit\nThere are several useful visual features to tell there is 'hutch' and not similar things in a photo:\ta closed cabinet on the bottom\thalf-open shelves on the top\tdisplay shelves with glass doors on the top or bottom\tmultiple compartments, some with doors or drawers.", 16], "broccoli piece": ["Yes. 'Broccoli piece' has a tangible appearance and is a kind of vegetable.\nA few things that are visually similar to 'broccoli piece' but are not 'broccoli piece' are:\tcauliflower piece\tbrussels sprout piece\tcabbage piece\tlettuce piece\nThere are several useful visual features to tell there is 'broccoli piece' and not similar things in a photo:\tgreen\ttexture like tree branches or tiny trees\tflorets on top of a thick stalk", 16], "linesman": ["Yes. 'Linesman' has a tangible appearance and is a person who helps the referee in a sports game, usually soccer.\nA few things that are visually similar to 'linesman' but are not 'linesman' are:\tplayers\treferees\tcoaches\nThere are several useful visual features to tell there is 'linesman' and not similar things in a photo:\twearing a distinct uniform with a flag\thandling a flag or a board to signal offsides or other violations in the game\tstanding on the sidelines, near the halfway line, or around the penalty area", 16], "dozens": ["No. 'Dozens' is too vague and abstract to be a visually concrete concept.", 16], "palm leaves": ["Yes. 'Palm leaves' has a tangible appearance and refers to the leaves of a palm tree.\nA few things that are visually similar to 'palm leaves' but are not 'palm leaves' are:\tfern leaves\tbanana leaves\tdifferent types of tree leaves\nThere are several useful visual features to tell there is 'palm leaves' and not similar things in a photo:\tlong and thin shape\tfan-like appearance\tgreen color, often with divided segments or fronds", 16], "bird kite": ["Yes. 'Bird kite' has a tangible appearance and is a kind of kite.\nA few things that are visually similar to 'bird kite' but are not 'bird kite' are:\tdragon kite\tbutterfly kite\tdiamond kite\tpaper bird\nThere are several useful visual features to tell there is 'bird kite' and not similar things in a photo:\tbird-like wings\tand peak\tshaped like a bird\tor with a bird pattern\tfluttering or flying in the sky\ttail recognizable from the bird", 16], "play": ["No. 'Play' is too vague or abstract to be distinguished in a photo.", 16], "sewer grate": ["Yes. 'Sewer grate' has a tangible appearance and is a type of metal grate covering a sewer or drain.\nA few things that are visually similar to 'sewer grate' but are not 'sewer grate' are:\tmanhole cover\tgrill grate\tair conditioning unit grate\t\nThere are several useful visual features to tell there is 'sewer grate' and not similar things in a photo:\trectangular or circular metal grid pattern\tslotted holes or perforations in the metal\tsunken into the ground or pavement covering a sewer or drain opening.", 16], "colorful box": ["Yes. 'Colorful box' has a tangible appearance and can refer to many different types of boxes.\nA few things that are visually similar to 'colorful box' but are not 'colorful box' are:\tbook cover\tgift wrap\tcrayon box\tjewelry box\nThere are several useful visual features to tell there is 'colorful box' and not similar things in a photo:\trectangular shape\tsharp edges\tbright colors or patterns\tno visible opening or lid (if referring to a closed box)", 16], "brick structure": ["Yes. 'Brick structure' has a tangible appearance and refers to a building or wall made of bricks.\nA few things that are visually similar to 'brick structure' but are not 'brick structure' are:\tstone structure\tconcrete structure\twooden structure\nThere are several useful visual features to tell there is 'brick structure' and not similar things in a photo:\trectangular or square-shaped bricks\tin a repeating pattern\tbrownish-red color\ttextured surface\tstacked horizontally and vertically to form a wall or building", 16], "script": ["No. 'Script' is too abstract to have a tangible appearance and is a form of written or printed language.\nA few things that are visually similar to 'script' but are not 'script' are: handwriting, font, calligraphy, typography.\nThere are no visual features to distinguish 'script' as it is a type of writing style and can take on many different forms depending on language and writing system. However, some common visual elements of script include: letter shapes, line thickness, and spacing between letters and words.", 16], "scroll": ["Yes. 'Scroll' has a tangible appearance and is a type of rolled-up document.\nA few things that are visually similar to 'scroll' but are not 'scroll' are:\tparchment\tpaper towel\ttoilet paper\trug\nThere are several useful visual features to tell there is 'scroll' and not similar things in a photo:\trectangular or elongated shape\twith writing or images rolled up inside\toften made of aged-looking paper or parchment", 16], "ride": ["No. 'Ride' is too vague or abstract to be distinguished in a photo.", 16], "hoof prints": ["Yes, 'hoof prints' has a tangible appearance and refers to the impression left by the hoof of an animal.\nA few things that are visually similar to 'hoof prints' but are not 'hoof prints' are:\tpaw prints\tshoe prints\tbarefoot prints\tbike tire tracks\nThere are several useful visual features to tell there are 'hoof prints' and not similar things in a photo:\thorseshoe-shaped\tprints in a symmetrical pattern\tsize of the print (larger for large animals)\tnumber of toes (one for horses and cattle, two for deer and goats)\tthe presence of a clear imprint of a sole and heel.", 16], "ice cream truck": ["Yes. 'Ice cream truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'ice cream truck' but are not 'ice cream truck' are:\tdelivery van\tfood truck\tmobile home\ttrailer\tmoving truck\nThere are several useful visual features to tell there is 'ice cream truck' and not similar things in a photo:\tcolorful and eye-catching paint job\tpictures of ice creams or desserts on the side of the vehicle\tlarge open window on the side where the ice cream is served\tjingle or music playing from the truck's PA system", 16], "stainless steel knife": ["Yes. 'Stainless steel knife' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'stainless steel knife' but are not 'stainless steel knife' are:\tfork\tspoon\trazor\ttweezers\nThere are several useful visual features to tell there is 'stainless steel knife' and not similar things in a photo:\t\n- sharp, pointed blade \n- ridge along the edge of the blade \n- handle made of a different material than the blade \n- serrated edge (sometimes) \n- notched metal at the base of the blade (sometimes)", 16], "everything": ["No. 'Everything' is too abstract and cannot be visually represented in a photo.\nThere are no things that are visually similar to 'everything' as it is an all-encompassing term.", 16], "cake knife": ["Yes. 'Cake knife' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'cake knife' but are not 'cake knife' are:\tbutter knife\tspatula\tsword\nThere are several useful visual features to tell there is 'cake knife' and not similar things in a photo:\tserrated or sharp edge long and narrow blade\thandle for gripping\tcommonly used for cutting cakes and other desserts", 16], "silver exhaust": ["Yes. 'Silver exhaust' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'silver exhaust' but are not 'silver exhaust' are:\tchimney\tpipe\twater vapour\tdischarge from an industrial plant\nThere are several useful visual features to tell there is 'silver exhaust' and not similar things in a photo:\tsilver or metallic color\tcylindrical shape\tlocated at the back of a vehicle\tconnected to the engine\tmay have visible fumes", 16], "wooden cupboards": ["Yes. 'Wooden cupboards' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden cupboards' but are not 'wooden cupboards' are:\tshelves\tdressers\tbookcases\tcabinets\nThere are several useful visual features to tell there is 'wooden cupboards' and not similar things in a photo:\tdoors\tthat can be opened or closed\tdrawers\tor shelves\tfor storage\tvisible wooden grain and texture", 16], "metal staircase": ["Yes. 'Metal staircase' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'metal staircase' but are not 'metal staircase' are:\tmetal ladder\tfire escape scaffold\ttower\nThere are several useful visual features to tell there is 'metal staircase' and not similar things in a photo:\tmade of metal\tstraight or winding\tsteps have a consistent width and height\tfixed to a building or structure.", 16], "suit cases": ["Yes. 'Suitcases' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'suitcases' but are not 'suitcases' are:\tbackpacks\tpurses\tduffel bags\ttravel bags\nThere are several useful visual features to tell there is 'suitcases' and not similar things in a photo:\trectangular shape\thinged cover\thandle(s)\ton wheels\tor standing upright\tbuckles or locks for closure", 16], "color orange": ["No. 'Color orange' is too vague or abstract to be distinguished in a photo. \n\nNote: While the idea of \"color orange\" can be visually represented through various medium such as paint, images, and clothing, it is not a tangible object. As an AI language model, I cannot generate visual content, but I can describe them. \n\nTherefore, I am unable to provide examples of things that are visually similar to 'color orange' but are not 'color orange' or useful visual features for distinguishing 'color orange' from the listed similar things in a photo.", 16], "baby blanket": ["Yes. 'Baby blanket' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'baby blanket' but are not 'baby blanket' are:\tbath towel\ttablecloth\tthrow blanket\tbedspread\nThere are several useful visual features to tell there is 'baby blanket' and not similar things in a photo:\tsmaller size\tsoft texture\tfun and colorful patterns\ttypically made with cotton or flannel material.", 16], "banana split": ["Yes. 'Banana split' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'banana split' but are not 'banana split' are:\tice cream sundae\tfruit salad\tfruit cocktail\nThere are several useful visual features to tell there is 'banana split' and not similar things in a photo:\thalved banana\tscoops of ice cream on each side of the banana\thot fudge, strawberry, or caramel syrup on top of the ice cream\twhipped cream and nuts on top of the syrup\tcherry on top of the whipped cream", 16], "rubber boots": ["Yes. 'Rubber boots' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'rubber boots' but are not 'rubber boots' are:\thiking boots\train shoes\twinter boots\tgaloshes\nThere are several useful visual features to tell there are 'rubber boots' and not similar things in a photo:\tmade of rubber or a rubber-like material\tknee-high or mid-calf\tlength\tsolid color or patterned\tno laces or fasteners\ttoe box and heel chunky and durable\ttexture of the rubber material", 16], "goal post": ["Yes. 'Goal post' has a tangible appearance and is a structure used in sports.\nA few things that are visually similar to 'goal post' but are not 'goal post' are:\tfence\tpillar\tcolumn\tbridge\nThere are several useful visual features to tell there is 'goal post' and not similar things in a photo:\tupright posts with a horizontal crossbar\trectangular or square shape\tcrossbar must be higher than the posts\tstake or base holding the posts in place\tspecifically used in sports such as soccer, American football, or rugby.", 16], "gold bell": ["Yes. 'Gold bell' has a tangible appearance and is a type of instrument or decoration.\nA few things that are visually similar to 'gold bell' but are not 'gold bell' are:\tYule log\torange\tpinecone\tlightning bolts\nThere are several useful visual features to tell there is 'gold bell' and not similar things in a photo:\tMetallic surface\tRound shape\tBell-shaped patterns\tGolden color\tClapper inside the bell", 16], "dell": ["No. 'dell' is too vague or abstract to be distinguished in a photo. However, 'Dell' with a capital 'D' is a brand and has a tangible appearance.\nA few things that are visually similar to 'Dell' but are not 'Dell' are:\tHP\tMicrosoft\tLenovo\tApple\tSamsung\nThere are several useful visual features to tell there is 'Dell' and not similar things in a photo:\tDell logo on the device, such as a laptop, desktop or monitor.", 16], "binoculars": ["Yes. 'Binoculars' has a tangible appearance and is a type of optical instrument.\nA few things that are visually similar to 'binoculars' but are not 'binoculars' are:\ttelescope\tcamera\tloupe\nThere are several useful visual features to tell there is 'binoculars' and not similar things in a photo:\ttwo symmetrical tubes\tmagnification lenses\ton the same axis\twith an interocular distance\tadjective lens covering each one\tlanyard or strap to wear them around the neck.", 16], "leaves plant": ["No. 'Leaves plant' is too vague or abstract to be distinguished in a photo. The term 'leaves plant' could refer to any plant or tree with leaves.", 16], "toilet flusher": ["Yes. 'Toilet flusher' has a tangible appearance and is a device used to flush a toilet.\nA few things that are visually similar to 'toilet flusher' but are not 'toilet flusher' are:\tsink faucet\tshower knob\tlamp switch\tdrawer handle\nThere are several useful visual features to tell there is 'toilet flusher' and not similar things in a photo:\tlocated on the top or side of the toilet tank\tpush or pull mechanism\tfor flushing the toilet knob or button shape or color", 16], "artichoke": ["Yes. 'Artichoke' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'artichoke' but are not 'artichoke' are:\tpinecone\tburrs\tcactus\tflower heads\nThere are several useful visual features to tell there is 'artichoke' and not similar things in a photo:\tgreen leaves enclosing a round or oblong vegetable\twith small, sharp thorns or bristles\ton a tall, thorny stem\tcut off at the base to show the tender, edible part inside.", 16], "beige pillow": ["Yes. 'Beige pillow' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'beige pillow' but are not 'beige pillow' are:\tbrown cushion\ttan cushion\tcream cushion\tpillow case\nThere are several useful visual features to tell there is 'beige pillow' and not similar things in a photo:\tsoft and fluffy\tsquare or rectangular shape\tbeige or light brown color\tsmooth texture", 16], "leather office chair": ["Yes. 'Leather office chair' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'leather office chair' but are not 'leather office chair' are:\twooden chair\tplastic chair\tdining chair\tstool\nThere are several useful visual features to tell there is 'leather office chair' and not similar things in a photo:\twheeled base\tpadded seat and backrest\twith or without armrests\tspecific design for office use\tcovered in leather or leather-like material.", 16], "bee": ["Yes. 'Bee' has a tangible appearance and is a type of insect.\nA few things that are visually similar to 'bee' but are not 'bee' are:\thousefly\twasp\tmosquito\tbutterfly\nThere are several useful visual features to tell there is 'bee' and not similar things in a photo:\tblack and yellow stripes\thairy body\ttwo pairs of translucent wings\tpair of antennae\tnarrow waist-like structure near the abdomen.", 16], "grinder": ["Yes. 'Grinder' has a tangible appearance and is a type of kitchen appliance or tool.\nA few things that are visually similar to 'grinder' but are not 'grinder' are:\tblender\tfood processor\tjuicer\tmortar and pestle\nThere are several useful visual features to tell there is 'grinder' and not similar things in a photo:\tcylindrical shape\twith a handle or button\tteeth for grinding or chopping\tvisible blades or burrs\tgrinding mechanism might be manual or electric", 16], "grey ground": ["Yes. 'Grey ground' has a tangible appearance and refers to a physical surface that is colored grey.\nA few things that are visually similar to 'grey ground' but are not 'grey ground' are:\tconcrete\tpavement\trock\tasphalt\nThere are several useful visual features to tell there is 'grey ground' and not similar things in a photo:\tflat or slightly textured surface\tdull, neutral grey color\tno distinctive patterns or shapes", 16], "gourd": ["Yes, 'gourd' has a tangible appearance and is a type of fruit that is often used for decoration.\nA few things that are visually similar to 'gourd' but are not 'gourd' are:\tpumpkins\tcucumbers\tsquash\tmelons\nThere are several useful visual features to tell there is 'gourd' and not similar things in a photo:\thard, durable skin\tintricate or interesting shapes, often with a bulbous or round end\tvariety of colors and patterns\tridged or textured surface at times", 16], "metal door knob": ["Yes. 'Metal door knob' has a tangible appearance and is a type of door handle.\nA few things that are visually similar to 'metal door knob' but are not 'metal door knob' are:\tdrawer handle\tcabinet knob\tlock latch\nThere are several useful visual features to tell there is 'metal door knob' and not similar things in a photo:\tmetallic or chrome appearance\tcircular or round shape\tis attached to a door by a spindle or screw\thave a turning mechanism for opening and closing doors", 16], "pink chair": ["Yes. 'Pink chair' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'pink chair' but are not 'pink chair' are:\tsofa\tottoman\tbean bag\trecliner\nThere are several useful visual features to tell there is 'pink chair' and not similar things in a photo:\tchair-shaped\tpink color\thave legs and backrest", 16], "grey bag": ["Yes. 'Grey bag' has a tangible appearance and is a specific item.\nA few things that are visually similar to 'grey bag' but are not 'grey bag' are:\tblack bag\tbrown bag\tbackpack\tmessenger bag\tlunch bag\tpurse\nThere are several useful visual features to tell there is 'grey bag' and not similar things in a photo:\tgrey color\tstraps or handles\tfor carrying items, such as books or clothing\trectangular or square shape\ttop closure, such as a zipper or drawstring.", 16], "list": ["No. 'List' is too vague or abstract to be distinguished in a photo.", 16], "nintendo wii": ["Yes. 'Nintendo Wii' has a tangible appearance and is a specific gaming console.\nA few things that are visually similar to 'Nintendo Wii' but are not 'Nintendo Wii' are:\tPlayStation\tXbox\tSwitch\tcontrollers\nThere are several useful visual features to tell there is 'Nintendo Wii' and not similar things in a photo:\twhite console with the word \"Wii\"\ton\trectangle-shaped with rounded edges\taffixed stand on a side of the console\tcan use the Wii Remote controller\tfor games\thas \"Wii\" branding on the accessories and games", 16], "street name signs": ["Yes. 'Street name signs' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'street name signs' but are not 'street name signs' are:\ttraffic signs\tstore signs\thouse numbers\tbillboards\nThere are several useful visual features to tell there is 'street name signs' and not similar things in a photo:\tRectangular or square shape, usually mounted on poles with street or avenue names in black or white letters on a green, blue or brown background. Positioned at intersections.", 16], "sunny day": ["Yes. 'Sunny day' has a tangible appearance and is a type of weather condition.\nA few things that are visually similar to 'sunny day' but are not 'sunny day' are:\tbright light\tfrom a lamp or bulb\theat from a fire or stove\nThere are several useful visual features to tell there is 'sunny day' and not similar things in a photo:\tclear blue skies\tbright sun without clouds or fog\tshadows of people or objects because of the sun's position in the sky", 16], "thick tree": ["Yes. 'Thick tree' has a tangible appearance and is a type of tree with a wide trunk.\nA few things that are visually similar to 'thick tree' but are not 'thick tree' are: \tpost\tpole\tobstacle\tscenery \nThere are several useful visual features to tell there is 'thick tree' and not similar things in a photo:\tTall in height\tWide trunk\tA dense canopy of leaves and branches.", 16], "winter sky": ["No. 'Winter sky' is too vague or abstract to be distinguished in a photo.", 16], "nets": ["Yes. 'Nets' has a tangible appearance and is a kind of mesh material.\nA few things that are visually similar to 'nets' but are not 'nets' are: curtains\tscreens\tguards\tgauze\nThere are several useful visual features to tell there is 'nets' and not similar things in a photo:\tcrossed strands of fibers\tholes or clear spaces\tsupporting ropes or structures\ttypically used for catching or trapping something", 16], "butter knives": ["Yes. 'Butter knives' has a tangible appearance and is a type of table knife.\nA few things that are visually similar to 'butter knives' but are not 'butter knives' are:\tsteak knives\thunting knives\tpocket knives\nThere are several useful visual features to tell there is 'butter knives' and not similar things in a photo:\trounded blade\twithout serrations\tor pointed tip\tthicker handle", 16], "sausage link": ["Yes. 'Sausage link' has a tangible appearance and refers to a type of sausage.\nA few things that are visually similar to 'sausage link' but are not 'sausage link' are:\tbrown rope\ttree branch\tleather cord\t\nThere are several useful visual features to tell there is 'sausage link' and not similar things in a photo:\telongated shape\tbrown or reddish color\tvisible meat chunks or fat specks\ttwisted or curved shape\tcasing or skin on the outside", 16], "leafy bushes": ["Yes. 'Leafy bushes' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'leafy bushes' but are not 'leafy bushes' are:\tevergreen trees\thedges\tlawn grass\tweeds\nThere are several useful visual features to tell there is 'leafy bushes' and not similar things in a photo:\tlow height compared to trees\tfully covered with green leaves\tbushy shape\twith or without flowers or fruits depending on the season\tsurrounded by soil or rocks.", 16], "metal bat": ["Yes. 'Metal bat' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'metal bat' but are not 'metal bat' are:\tgolf club\thockey stick\tpipe\nThere are several useful visual features to tell there is 'metal bat' and not similar things in a photo:\tlong, thin and cylindrical shape\tmade of metal\tsmooth and polished surface\twith a grip on one end\tcommonly used in baseball or softball", 16], "grey cap": ["Yes. 'Grey cap' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'grey cap' but are not 'grey cap' are:\tbeanie\tberet\tfedora\tbaseball cap\nThere are several useful visual features to tell there is 'grey cap' and not similar things in a photo:\tround or semicircular shape\tsoft texture\tworn on top of the head\tflat top or peak\twith or without visor\tspecific shade of grey", 16], "moulding": ["Yes. 'Moulding' has a tangible appearance and is a kind of architectural trim or decorative feature.\nA few things that are visually similar to 'moulding' but are not 'moulding' are:\tbaseboard\tcrown molding\tdecorative appliques\tarchitectural brackets and corbels\nThere are several useful visual features to tell there is 'moulding' and not similar things in a photo:\trun along the edges of a room or a furniture piece\thave a decorative design or a pattern\tbe made of wood, plaster, or another type of material\tbe painted or stained to match the surrounding surface or a color scheme\tbe an integral part of an architectural style or period", 16], "feces": ["Yes. 'Feces' has a tangible appearance and refers to the waste material discharged from the digestive tract of animals.\nA few things that are visually similar to 'feces' but are not 'feces' are:\tsoil\tchocolate bars\tdirt\tclay\nThere are several useful visual features to tell there is 'feces' and not similar things in a photo:\tmisshapen and lumpy\tdark brown, black, or green color\tstinky and foul-smelling residue\tfound in areas where animals are commonly found", 16], "tan shirt": ["Yes. 'Tan shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'tan shirt' but are not 'tan shirt' are:\tbeige sweater\tkhaki jacket\tlight brown blouse\nThere are several useful visual features to tell there is 'tan shirt' and not similar things in a photo:\tplain or solid color\ttan, light brown, or beige in color\tcollared, short or long-sleeved shirt\tmade of cotton, linen, or any other shirt material with a similar texture.", 16], "sideways": ["No. 'Sideways' is too abstract to be distinguished in a photo. It is a relative direction rather than a tangible object.", 16], "tow truck": ["Yes. 'Tow truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'tow truck' but are not 'tow truck' are:\ttruck\tcrane\ttrailer\nThere are several useful visual features to tell there is 'tow truck' and not similar things in a photo:\tthe word \"tow\" or \"towing\" is visible on the vehicle\tcrane or platform for lifting cars is visible\thooks or chains for attachment to other vehicles are visible", 16], "silo": ["Yes. 'Silo' has a tangible appearance and is a tall cylindrical or rectangular structure used to store grain, silage or other agricultural products.\nA few things that are visually similar to 'silo' but are not 'silo' are:\tchimney\tskyscraper\ttower\nThere are several useful visual features to tell there is 'silo' and not similar things in a photo:\tstraight vertical sides\ttall, narrow shape\tcylindrical or rectangular structure\tdoor or opening near the bottom for loading and unloading materials\tmetal or concrete construction in a rural or agricultural setting.", 16], "metal fan": ["Yes. 'Metal fan' has a tangible appearance and is a type of fan made of metal.\nA few things that are visually similar to 'metal fan' but are not 'metal fan' are:\tplastic fan\tceiling fan\tdecorative fan\thand fan\nThere are several useful visual features to tell there is 'metal fan' and not similar things in a photo:\tmade of metal\tvisible blades or propellers\tstand or base for support\tadjustable speed settings\tno visible cover around the blades or propellers.", 16], "beach front": ["Yes. 'Beach front' has a tangible appearance and refers to the stretch of land or property that lies immediately adjacent to the beach.\nA few things that are visually similar to 'beach front' but are not 'beach front' are:\tpark\tpromenade\tboardwalk\tbeach access\nThere are several useful visual features to tell there is 'beach front' and not similar things in a photo:\tsand\tshoreline\tseashells\tor sea items\twater in the background\tpalm trees\tor beach chairs", 16], "brick fence": ["Yes. 'Brick fence' has a tangible appearance and refers to a fence that is made of bricks.\nA few things that are visually similar to 'brick fence' but are not 'brick fence' are:\tstucco wall\twooden fence\tconcrete barrier\tstone wall\nThere are several useful visual features to tell there is 'brick fence' and not similar things in a photo:\tred, brown or gray color\trectangular shape\tand regular pattern made up of repetitive brick units\tridged texture \tsmooth to the touch", 16], "fall": ["Yes. 'Fall' has a tangible appearance and refers to a season of the year.\nA few things that are visually similar to 'fall' but are not 'fall' are:\tspring\twinter\tsummer\tautumn landscape\nThere are several useful visual features to tell there is 'fall' and not similar things in a photo:\tleaves changing color to yellow, red, orange, and brown\tleaves falling from trees\tcool weather\tpumpkins\toranges\tThanksgiving decorations\tharvest scenes", 16], "guns": ["Yes. 'Guns' has a tangible appearance and is a type of weapon.\nA few things that are visually similar to 'guns' but are not 'guns' are:\ttoys\tremote controls\tflashlights\nThere are several useful visual features to tell there is 'guns' and not similar things in a photo:\tmetallic or matte finish\tfirearm shape\ttrigger\tgrip or handle\tbarrel and chamber in alignment.", 16], "chaise lounge": ["Yes. 'Chaise lounge' has a tangible appearance and is a type of chair or sofa.\nA few things that are visually similar to 'chaise lounge' but are not 'chaise lounge' are:\trecliner\tsectional sofa\tbench\tchair\nThere are several useful visual features to tell there is 'chaise lounge' and not similar things in a photo:\textended seat for legs and feet\tpadded cushion or upholstery\tone arm or no arms at all\tlow backrest or no backrest at all\tcurved or sloping shape", 16], "business logo": ["Yes. 'Business logo' has a concrete appearance and is a visual symbol used to represent a company or brand.\nA few things that are visually similar to 'business logo' but are not 'business logo' are:\tsigns\ticons\tbadges\tstamps\tbanners\tposters\nThere are several useful visual features to tell there is 'business logo' and not similar things in a photo:\tdistinctive colors and shapes\tunique typography, font, or text arrangement\tlogo appears attached to a particular product or service\tappears in a consistent manner across various media and channels (website, print, ads, etc.)", 16], "sports shirt": ["Yes. 'Sports shirt' has a tangible appearance and is a type of shirt designed for sports or physical activities.\nA few things that are visually similar to 'sports shirt' but are not 'sports shirt' are:\tt-shirt\tpolo shirt\tshort-sleeved collared shirt\t\nThere are several useful visual features to tell there is 'sports shirt' and not similar things in a photo:\tloose-fitting\tbreathable material\tmay have logos or designs\tmay have athletic stripes or patterns\tmay have a collar or a v-neck", 16], "wood table top": ["Yes. 'Wood table top' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood table top' but are not 'wood table top' are:\tfloor\twood plank wall\tpallet\twooden board\nThere are several useful visual features to tell there is 'wood table top' and not similar things in a photo:\tTable shaped, with four legs or a base underneath\tit is on a table frame, not directly on the floor\trich wood grain patterns and texture\tSmooth surface, typically polished, varnished or sealed.", 16], "plane propeller": ["Yes. 'Plane propeller' has a tangible appearance and is a part of an airplane.\nA few things that are visually similar to 'plane propeller' but are not 'plane propeller' are: fan\t windmill\t ceiling fan\t mixer blade\nThere are several useful visual features to tell there is 'plane propeller' and not similar things in a photo:\tairplane mounted\tobject with several blade-like structures\tspins to provide thrust to the airplane.", 16], "gold stripe": ["Yes. 'Gold stripe' has a visually concrete concept.\nA few things that are visually similar to 'gold stripe' but are not 'gold stripe' are:\tyellow ribbon\tshiny thread\tgolden hair tie\nThere are several useful visual features to tell there is 'gold stripe' and not similar things in a photo:\tnarrow\tbright gold\tcolor different from surroundings\ttypically used for decoration or embellishment", 16], "score": ["No. 'Score' is too vague or abstract to be distinguished in a photo.", 16], "bowl counter": ["Yes. 'Bowl counter' has a tangible appearance and is a type of kitchen accessory.\nA few things that are visually similar to 'bowl counter' but are not 'bowl counter' are:\tcutting board\ttray\ttable\tcabinet\nThere are several useful visual features to tell there is 'bowl counter' and not similar things in a photo:\tshallow and round in shape, like a bowl\tusually made of ceramic, porcelain or stainless steel\tcan be found in or near a kitchen counter\ttop view shows a circular shape and a flat bottom", 16], "walnut": ["Yes. 'Walnut' has a tangible appearance and is a kind of nut.\nA few things that are visually similar to 'walnut' but are not 'walnut' are:\tpecan\talmond\thazelnut\tchestnut\nThere are several useful visual features to tell there is 'walnut' and not similar things in a photo:\tRough-textured hard shell\tRounded shape\tLight-brown color\tSoft, sweet, nutty flesh inside", 16], "links": ["No. 'Links' is too vague or abstract to have a tangible appearance or be visually concrete, as it can refer to various things such as hyperlinks, chain links, or golf links. \n\nHowever, here are a few things that are visually similar to 'links' in different contexts but are not 'links':\n\n- In a computer context: buttons, tabs, menu items\n- In a chain context: hoops, rings, knots\n- In a golf context: courses, greens, bunkers\n\nUseful visual features for distinguishing 'links' would depend on the specific context in which it is being used. For example:\n\n- In a computer context: the presence of underlined text or a distinct color, size, or font for the link\n- In a chain context: the specific shape of the link (such as oval, circular or square) or whether the links are attached to each other in a certain pattern.\n- In a golf context: the specific layout of the course or the positioning of the flags and holes.", 16], "barber": ["Yes. 'Barber' has a tangible appearance and is a profession related to hair-cutting.\nA few things that are visually similar to 'barber' but are not 'barber' are:\thair stylist\tbeauty salons\thair dressers\tpersonal grooming products\thair clippers\nThere are several useful visual features to tell there is 'barber' and not similar things in a photo:\thaircutting tools (scissors, combs, razors)\tbarber chairs or stools\tbold poles with red and white stripes\thair-cutting capes or aprons\tshampoo and hair-care products\tin men's personal grooming services settings", 16], "seating": ["Yes. 'Seating' has a tangible appearance and refers to a place to sit.\nA few things that are visually similar to 'seating' but are not 'seating' are: \tcouch\tbench\tlounge chair\tbed\nThere are several useful visual features to tell there is 'seating' and not similar things in a photo:\tchair-like form\tone or more seats\tsupportive structure or legs\tcushion or padding for comfort", 16], "left ski": ["Yes. 'Left ski' has a tangible appearance and is a kind of winter sports equipment.\nA few things that are visually similar to 'left ski' but are not 'left ski' are:\tright ski\tsnowboard\tice skate\nThere are several useful visual features to tell there is 'left ski' and not similar things in a photo:\tlong and narrow shape\trounded tip and tail\tski boot attached to a binding on the ski\tbinding is attached to the ski in a way that allows the heel to lift when the skier is going uphill", 16], "crowns": ["Yes. 'Crowns' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'crowns' but are not 'crowns' are:\thats\ttiaras\thelmets\nThere are several useful visual features to tell there is 'crowns' and not similar things in a photo:\tmade of gold or other precious materials\tjewels or other decorative elements\tsymbolic shapes (e.g. points or crosses)\tworn by royalty or leadership figures.", 16], "silver wing": ["Yes. 'Silver wing' has a tangible appearance and can refer to various objects.\nA few things that are visually similar to 'silver wing' but are not 'silver wing' are:\tpaper airplane\taircraft bird feather\tfan blade\nThere are several useful visual features to tell there is 'silver wing' and not similar things in a photo, depending on the context:\t\n- If referring to a bird feather: elongated shape, pointed tip, shiny silver or grey color. \n- If referring to an aircraft: the metallic or silver surface, the shape similar to a bird's wing with an airfoil shape. \n- If referring to a paper airplane or a fan blade: the shape can be similar but it's important to look for any metallic or reflective surface if trying to identify a silver wing.", 16], "silver lid": ["Yes. 'Silver lid' has a tangible appearance and refers to a type of cover.\nA few things that are visually similar to 'silver lid' but are not 'silver lid' are:\tgold lid\tplastic lid\tmetallic bottle cap\tsoda can tab\nThere are several useful visual features to tell there is 'silver lid' and not similar things in a photo:\tsilver or metallic color\tring-shaped cover\tthat can be twisted or lifted from the top of a container", 16], "tag suitcase": ["Yes. 'Tag suitcase' has a tangible appearance and is a term used to describe luggage tags.\nA few things that are visually similar to 'tag suitcase' but are not 'tag suitcase' are:\tbaggage\tsuitcase\thandle\tbarcode\tsticker\nThere are several useful visual features to tell there is 'tag suitcase' and not similar things in a photo:\tpaper or plastic material\thanging from a suitcase or bag\tbright colors or patterns\twith a name, address, or identification information written on it.", 16], "outer edge": ["Yes. 'Outer edge' has a tangible appearance and refers to the perimeter of an object or surface.\nA few things that are visually similar to 'outer edge' but are not 'outer edge' are:\tinner edge\tborder\tline\tshadow\nThere are several useful visual features to tell there is 'outer edge' and not similar things in a photo:\tdefines the boundary of an object or surface\tclearly visible and distinct from the interior of the object or surface\tcan be straight or curved", 16], "side part": ["Yes. 'Side part' has a tangible appearance and refers to a hairstyle.\nA few things that are visually similar to 'side part' but are not 'side part' are:\tcenter part\tcombed-back hair\tbangs\nThere are several useful visual features to tell there is 'side part' and not similar things in a photo:\tparting of hair on one side of the head\thair on one side of the part is longer than the other\tside of the part is more defined and flat than the other side\thair flows in the same direction on either side of the part", 16], "top road": ["No. 'Top road' is too vague or abstract to be distinguished in a photo.", 16], "color silver": ["Yes. 'Color silver' has a tangible appearance and is a metallic color.\nA few things that are visually similar to 'color silver' but are not 'color silver' are:\tmetallic gold\tchrome\twhite with mirror reflection\nThere are no specific useful visual features to distinguish 'color silver' from the listed similar things in a photo, as they are all distinct colors or materials. However, 'color silver' can be distinguished from other metallic colors by its unique sheen and reflective quality.", 16], "baby bird": ["Yes. 'Baby bird' has a tangible appearance and refers to a very young bird.\nA few things that are visually similar to 'baby bird' but are not 'baby bird' are:\tadult bird\tchicken\tfish\tinsect\nThere are several useful visual features to tell there is 'baby bird' and not similar things in a photo:\tsoft and fluffy feathers\tunderdeveloped wings and beak\tbig eyes\tthat the bird is not fully grown or mature.", 16], "flow": ["No. 'Flow' is too vague or abstract to be distinguished in a photo.", 16], "ski trails": ["Yes. 'Ski trails' has a tangible appearance and refers to the tracks or paths created when skiing.\nA few things that are visually similar to 'ski trails' but are not 'ski trails' are:\thiking trails\tbike paths\tanimal tracks\nThere are several useful visual features to tell there is 'ski trails' and not similar things in a photo:\tstraight or curved lines\tin a snowy or icy landscape\tcreated by ski equipment or footprints\tranging in size depending on the level of use", 16], "square pizza": ["Yes. 'Square pizza' has a tangible appearance and is a type of pizza.\nA few things that are visually similar to 'square pizza' but are not 'square pizza' are:\trectangular lasagna\tbreadsticks\twith cheese squares\tonion rings\nThere are several useful visual features to tell there is 'square pizza' and not similar things in a photo:\tshape of a square or a rectangle\tpizza crust\ttomato sauce, cheese, and toppings on the surface of the pizza", 16], "wood sign": ["Yes. 'Wood sign' has a tangible appearance and is a type of signage made of wood.\nA few things that are visually similar to 'wood sign' but are not 'wood sign' are:\tposter\tmetal sign\tplastic sign\twallpaper\nThere are several useful visual features to tell there is 'wood sign' and not similar things in a photo:\twooden material\twritten or carved message\thanging from a post, a wall or a door\trustic or natural appearance", 16], "squeeze bottle": ["Yes. 'Squeeze bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'squeeze bottle' but are not 'squeeze bottle' are:\ttube\tplastic bag\tspray bottle\twater bottle\nThere are several useful visual features to tell there is 'squeeze bottle' and not similar things in a photo:\tsqueezable\tplastic material\twith a narrow spout on the top\tcontaining a liquid, such as condiments or cleaning solutions.", 16], "blow": ["No. 'Blow' is too vague or abstract to be distinguished in a photo.", 16], "gas station sign": ["Yes. 'Gas station sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'gas station sign' but are not 'gas station sign' are:\ttraffic signs\tbillboards\tstore signs\tads\nThere are several useful visual features to tell there is 'gas station sign' and not similar things in a photo:\twords 'gas' or 'fuel' provided\tcolors: blue, green, orange or red\toctagonal or rectangular shape\thigh above the ground or attached to a pole\twith gas station branding, logos or symbol.", 16], "blurry tree": ["Yes. 'Blurry tree' has a tangible appearance and is a type of tree that appears unclear or unfocused.\nA few things that are visually similar to 'blurry tree' but are not 'blurry tree' are:\tcartoon tree\tdrawing of tree\ttree out of focus in the background\nThere are no useful visual features to distinguish 'blurry tree' from similar things in a photo because the blurriness is a subjective interpretation of the image rather than an objective feature of the tree.", 16], "giraffe ears": ["Yes. 'Giraffe ears' has a tangible appearance and is a body part of the giraffe.\nA few things that are visually similar to 'giraffe ears' but are not 'giraffe ears' are:\tantelope ears\thorse ears\tdonkey ears\nThere are several useful visual features to tell there are 'giraffe ears' and not similar things in a photo:\tlong and narrow\tcovered in hair\tgrow on the top of the giraffe's head of a giraffe\thave small tufts of hair at the tips", 16], "curvy": ["No. 'Curvy' is too vague or abstract to be distinguished in a photo.", 16], "fir trees": ["Yes. 'Fir trees' has a tangible appearance and is a kind of coniferous tree.\nA few things that are visually similar to 'fir trees' but are not 'fir trees' are:\tpine trees\tjunipers\tcypress trees\tyew trees\nThere are several useful visual features to tell there is 'fir trees' and not similar things in a photo:\tneedle-like leaves rather than flat leaves\tbranches that extend horizontally\tcones that point upward as opposed to cones that point downward\ta conical shape with a pointed top", 16], "broccoli head": ["Yes. 'Broccoli head' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'broccoli head' but are not 'broccoli head' are:\tcauliflower\thead of lettuce\thead of cabbage\tartichoke\nThere are several useful visual features to tell there is 'broccoli head' and not similar things in a photo:\tgreen color\ttight packed buds\tarboreal branching structure\tridged surface", 16], "pooh": ["Yes. 'Pooh' has a tangible appearance and refers to a fictional bear character.\nA few things that are visually similar to 'pooh' but are not 'pooh' are:\tbrown bears\tstuffed animals\tteddy bears\nThere are several useful visual features to tell there is 'pooh' and not similar things in a photo:\tanthropomorphic bear wearing a red shirt\tno pants or shoes\tyellow fur on his belly\tand red fur on the rest of his body\tsmall ears and a short tail\twith a friendly and cheerful expressions.", 16], "coal": ["Yes. 'Coal' has a tangible appearance and is a type of rock.\nA few things that are visually similar to 'coal' but are not 'coal' are:\trocks\tburned wood\tashes\tbrown sugar\nThere are several useful visual features to tell there is 'coal' and not similar things in a photo:\tblack or dark brown color\tsmooth, shiny surface\tirregular edges\tmay have visible fossil imprints\tcylindrical or oblong shape", 16], "orange post": ["Yes. 'Orange post' has a tangible appearance and is a type of physical structure.\nA few things that are visually similar to 'orange post' but are not 'orange post' are:\ttraffic cones\tconstruction barrels\tbollards\nThere are several useful visual features to tell there is 'orange post' and not similar things in a photo:\tupright and vertical\torange in color\tpost or pole shape\twith reflective strips or bands\tused for cordoning off or marking areas", 16], "coconuts": ["Yes. 'Coconuts' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'coconuts' but are not 'coconuts' are:\tkiwano fruit\tstone fruit\tavocado\nThere are several useful visual features to tell there is 'coconuts' and not similar things in a photo:\thard, brown or green shell\thairy, fibrous exterior\tcylindrical or round shape\tthree dimples or eyes at one end\twhite, firm, and fleshy interior", 16], "broccoli heads": ["Yes. 'Broccoli heads' has a tangible appearance and is a kind of vegetable.\nA few things that are visually similar to 'broccoli heads' but are not 'broccoli heads' are:\tcauliflower heads\tcabbage heads\tkale leaves\tspinach leaves\nThere are several useful visual features to tell there is 'broccoli heads' and not similar things in a photo:\tgreen, tightly packed cluster of buds\ton a thick stalk\twith leaves at the bottom", 16], "glass pepper shaker": ["Yes. 'Glass pepper shaker' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'glass pepper shaker' but are not 'glass pepper shaker' are:\tsalt shaker\tsugar shaker\tspice jar\tperfume bottle\nThere are several useful visual features to tell there is 'glass pepper shaker' and not similar things in a photo:\tglass material\thole on the top\tfor black pepper\tlabel on the body or the lid", 16], "clusters": ["Yes. 'Clusters' have a tangible appearance and refer to groups or collections of similar things.\nA few things that are visually similar to 'clusters' but are not 'clusters' are:\tscattered objects\tpiles of items\tsingle objects\tindividual items\nThere are several useful visual features to tell there is 'clusters' and not similar things in a photo:\tgrouping of multiple similar objects\tclose proximity of items\tsimilar size or shape of objects\tpattern or design to the way objects are arranged or grouped", 16], "round eye": ["Yes. 'Round eye' has a tangible appearance and refers to an eye that is spherical or circular.\nA few things that are visually similar to 'round eye' but are not 'round eye' are: tennis ball, marble, cookie, wheel\nThere are several useful visual features to distinguish 'round eye' from the listed similar things in a photo:\t\n\n- Sclera: In humans, the white part of the eye (sclera) surrounding the iris is visible. \n- Iris and Pupil: In humans and animals, the round eye features an iris which gives eye colour and a small, dark circlular opening known as the pupil. \n- Eyelids and Eyelashes: The eye is covered by eyelids and eyelashes.", 16], "gondola": ["Yes. 'Gondola' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'gondola' but are not 'gondola' are:\tcanoe\tkayak\trowboat\t\nThere are several useful visual features to tell there is 'gondola' and not similar things in a photo:\tLong, narrow boat with a flat bottom\tcurved prow and stern\tTall, pointy metal ornaments on the prow and stern\tAffixed with two oars for moving through water\tA striped fabric roof that can be raised or lowered.", 16], "color shirt": ["No. 'Color shirt' is too vague or abstract to be distinguished in a photo.\nA few things that are visually similar to 'color shirt' but are not 'color shirt' are:\tpatterned shirt\tathletic jersey\tt-shirt with graphic\nThere are no useful visual features to distinguish 'color shirt' from similar things in a photo, as it only describes a particular characteristic of a shirt that can apply to any type of shirt.", 16], "silver bumper": ["Yes. 'Silver bumper' has a tangible appearance and refers to a specific part of a car.\nA few things that are visually similar to 'silver bumper' but are not 'silver bumper' are:\thourglass\tsilver necklace\tplumbing pipe\tscreen door handle\nThere are several useful visual features to tell there is 'silver bumper' and not similar things in a photo:\thorizontal or vertical bar on the front or rear of a car\tmade of metal\tchromed or painted silver\trectangular or curved shape\tmounted to the frame of the car", 16], "rubber gloves": ["Yes. 'Rubber gloves' has a tangible appearance and is a type of protective equipment.\nA few things that are visually similar to 'rubber gloves' but are not 'rubber gloves' are:\tlatex gloves\tsurgical gloves\twinter gloves\tdisposable gloves\nThere are several useful visual features to tell there is 'rubber gloves' and not similar things in a photo:\tmade of rubber or rubber-like material\tconform to the shape of the hand\tflexible and stretchy\twrist length or longer", 16], "orange pants": ["Yes. 'Orange pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'orange pants' but are not 'orange pants' are:\torange skirt\torange shorts\torange leggings\torange jumpsuit\nThere are several useful visual features to tell there is 'orange pants' and not similar things in a photo:\tfull length or cropped\ttrousers or skinny jeans\torange color\tfitted around the hips and thighs\tlarge pockets, belt loops, or a fly in the front.", 16], "slender": ["No. 'Slender' is too vague or abstract to be distinguished in a photo.", 16], "wood poles": ["Yes. 'Wood poles' has a tangible appearance and is a type of wooden structure.\nA few things that are visually similar to 'wood poles' but are not 'wood poles' are:\ttelephone poles\ttraffic cones\tpillars\tfencing posts\nThere are several useful visual features to tell there are 'wood poles' and not similar things in a photo:\n\tshape is cylindrical and long\t\n\ttexture is rough\t\n\tthe color is brown\t\n\tusually have wires attached to them.", 16], "blue arrow": ["Yes. 'Blue arrow' has a tangible appearance and is a specific shape and color of an arrow.\nA few things that are visually similar to 'blue arrow' but are not 'blue arrow' are:\tRed arrow\tPurple arrow\tNeon arrow\nThere are several useful visual features to tell there is 'blue arrow' and not similar things in a photo:\tstraight-line shape\tpointed end on one side\tbroad end on the opposite side\tbright blue color", 16], "traffic camera": ["Yes. 'Traffic camera' has a tangible appearance and is a kind of surveillance camera.\nA few things that are visually similar to 'traffic camera' but are not 'traffic camera' are:\tsecurity camera\tdome camera\twebcam\tcamcorder\nThere are several useful visual features to tell there is 'traffic camera' and not similar things in a photo:\tpointing towards a street or a road\tmounted on a pole or a stand\tdesigned to capture images of vehicles and their license plates\thave a flashing light on top.", 16], "shot glass": ["Yes. 'Shot glass' has a tangible appearance and is a type of glassware.\nA few things that are visually similar to 'shot glass' but are not 'shot glass' are:\ttumbler\tgoblet\tflute\tvase\nThere are several useful visual features to tell there is 'shot glass' and not similar things in a photo:\tsmall size\tstraight sides\tthick base\tusually holds a small amount of liquor or a shooter\tclear or colored glass shape, such as cylindrical or conical shape\twith or without a handle or stem.", 16], "metal blade": ["Yes. 'Metal blade' has a tangible appearance and refers to a sharp piece of metal.\nA few things that are visually similar to 'metal blade' but are not 'metal blade' are:\tknife\tsaw\taxe\tchisel\nThere are several useful visual features to tell there is 'metal blade' and not similar things in a photo:\tthin, flat, and straight piece of metal with sharp edges\tand pointed tip\tmetallic or reflective surface\tserrated or smooth edge, depending on the type of blade", 16], "wooden sticks": ["Yes. 'Wooden sticks' has a tangible appearance and refers to long pieces of wood.\nA few things that are visually similar to 'wooden sticks' but are not 'wooden sticks' are:\tmetal rods\tbamboo\treed\tstraws\nThere are several useful visual features to tell there are 'wooden sticks' and not similar things in a photo:\tsolid and rigid\tcylindrical shape\tflat ends\tnatural wood grain or texture", 16], "plaid jacket": ["Yes. 'Plaid jacket' has a tangible appearance and is a type of outerwear.\nA few things that are visually similar to 'plaid jacket' but are not 'plaid jacket' are:\tgingham shirt\ttartan scarf\tcheckered pants\nThere are several useful visual features to tell there is 'plaid jacket' and not similar things in a photo:\tpattern consisting of criss-crossed horizontal and vertical bands of multiple colors\ttypically made of wool or flannel\tfront fastening with buttons or a zipper\tlong sleeves", 16], "warm": ["No. 'Warm' is too vague or abstract to be distinguished in a photo.", 16], "handwritten sign": ["Yes. 'Handwritten sign' has a tangible appearance and is a type of text.\nA few things that are visually similar to 'handwritten sign' but are not 'handwritten sign' are: printed sign, poster, billboard, sticker.\nThere are several useful visual features to tell there is a 'handwritten sign' and not similar things in a photo:\tirregular and uneven letters\tvariations or changes in handwriting\tcolorful ink or paint on a surface such as paper, cardboard, or wood\timperfections such as smudges, ink drips, or crinkles", 16], "sleeveless top": ["Yes. 'Sleeveless top' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'sleeveless top' but are not 'sleeveless top' are:\tshort-sleeved top\thalter top\tswimsuit\tdress without sleeves\nThere are several useful visual features to tell there is 'sleeveless top' and not similar things in a photo:\tno sleeves\tshoulder straps\tor a tank top style\tfitted or loose around the torso", 16], "toilet paper roll holder": ["Yes. 'Toilet paper roll holder' has a tangible appearance and is a common bathroom accessory.\nA few things that are visually similar to 'toilet paper roll holder' but are not 'toilet paper roll holder' are:\ttowel rack\tclothes hanger\tshower head\nThere are several useful visual features to tell there is 'toilet paper roll holder' and not similar things in a photo:\tattached to a wall or a surface\thas a spindle or rod to hold the toilet paper roll\tlocated near a toilet or in a bathroom\thas a slot or holder for the toilet paper roll", 16], "soccer shoe": ["Yes. 'Soccer shoe' has a tangible appearance and is a type of sports shoe.\nA few things that are visually similar to 'soccer shoe' but are not 'soccer shoe' are:\tbaseball cleats\trunning shoes\tbasketball shoes\nThere are several useful visual features to tell there is 'soccer shoe' and not similar things in a photo:\tstuds or cleats on the sole\tnoticeably thicker and strengthened front\tbright and contrasting colors\tbold design patterns for maximum visibility\tpliable and bendy material to increase flexibility", 16], "mud puddle": ["Yes. 'Mud puddle' has a tangible appearance and is a specific kind of natural feature.\nA few things that are visually similar to 'mud puddle' but are not 'mud puddle' are:\twater puddle\toil spill\tpaint spill\tsplattered mud\nThere are several useful visual features to tell there is 'mud puddle' and not similar things in a photo:\tbrown or murky mixture of soil and water\tpuddling or pooling on the ground\tmuddy or dirty appearance", 16], "cement platform": ["Yes. 'Cement platform' has a tangible appearance and refers to a flat surface made of cement.\nA few things that are visually similar to 'cement platform' but are not 'cement platform' are:\tasphalt road\tparked car\tconcrete wall\nThere are several useful visual features to tell there is 'cement platform' and not similar things in a photo:\tlarge, flat surface\tmade of cement or concrete\tcan be used as a base or foundation for buildings or structures\tmay have visible cracks or seams", 16], "wooden structure": ["Yes. 'Wooden structure' has a tangible appearance and refers to any object or construction made of wood.\nA few things that are visually similar to 'wooden structure' but are not 'wooden structure' are:\tstick\tbarbecue\tplank\tfurniture\nThere are several useful visual features to tell there is 'wooden structure' and not similar things in a photo:\tmade of wood\thaving a wood texture\tor made of wooden planks or logs.", 16], "sticker banana": ["No. 'Sticker banana' is too vague or abstract to be distinguished in a photo.", 16], "coil": ["Yes. 'Coil' has a tangible appearance and is a type of shape.\nA few things that are visually similar to 'coil' but are not 'coil' are:\tsnake\tcable\twire\those\nThere are several useful visual features to tell there is 'coil' and not similar things in a photo:\tcurled or twisted shape\trepetitive pattern\tcircular or spiral structure\tmetallic or shiny surface", 16], "world": ["No. 'World' is too vague or abstract to be distinguished in a photo.", 16], "pocket knife": ["Yes. 'Pocket knife' has a tangible appearance and is a type of cutting tool.\nA few things that are visually similar to 'pocket knife' but are not 'pocket knife' are:\trazor\tbox cutter\thunting knife\tkitchen knife\nThere are several useful visual features to tell there is 'pocket knife' and not similar things in a photo:\tfolding blade\thinge and locking mechanism\tcompact size\tportable or easily fits in a pocket or a purse", 16], "bicep": ["Yes, 'bicep' has a tangible appearance and is a muscle located in the upper arm.\nA few things that are visually similar to 'bicep' but are not 'bicep' are:\ttriceps\tbones\tfat tissue\nThere are several useful visual features to distinguish 'bicep' from the listed similar things in a photo:\tcurved shape\ton the front of the upper arm\twhen flexed or contracted, it becomes more prominent than other arm muscles", 16], "city scene": ["Yes. 'City scene' has a tangible appearance and refers to the visual representation of a city.\nA few things that are visually similar to 'city scene' but are not 'city scene' are:\tpark scene\tbuilding exterior\tskyscraper\tclose-up of one building\nThere are several useful visual features to tell there is 'city scene' and not similar things in a photo:\ttall buildings\thuman-made structures in the foreground\thigh density of buildings and roads\tcars and other transportation vehicles busy roads and highways", 16], "carry": ["No. 'Carry' is too vague or abstract to be distinguished in a photo.", 16], "armor": ["Yes. 'Armor' has a tangible appearance and is a type of protective clothing.\nA few things that are visually similar to 'armor' but are not 'armor' are:\tclothing\tuniforms\tcostumes\t\nThere are several useful visual features to tell there is 'armor' and not similar things in a photo:\tmetal pieces covering various parts of the body\tthe use of leather or chainmail\tgrooves and ridges for deflecting attacks\tenamel decorations\tshields or helmets as accessories.", 16], "microwave stove": ["No. 'Microwave stove' is not a visually concrete concept. A microwave is an appliance that cooks or heats food using microwave radiation, while a stove is a cooking appliance that uses heat. The two are different appliances.\nTherefore, it is not possible to name things that are visually similar to 'microwave stove' but are not 'microwave stove'. There are stoves that have built-in microwaves, but they are still separate appliances.\nUseful visual features for distinguishing a microwave from a stove in a photo include: size and shape (a microwave is typically smaller and more compact than a stove), buttons and displays for setting cooking time and power, and the presence or absence of burners and oven doors.", 16], "display sign": ["Yes. 'Display sign' has a tangible appearance and refers to any sign that displays information or advertising.\nA few things that are visually similar to 'display sign' but are not 'display sign' are:\tposters\tbillboards\tbanners\tmenu boards\nThere are several useful visual features to tell there is 'display sign' and not similar things in a photo:\tcontains written or visual information\tdisplayed in a public or commercial place\tvaried in size or shape\twithin a frame or holder\tilluminated or backlit in some cases", 16], "canyon": ["Yes. 'Canyon' has a tangible appearance and refers to a deep, narrow valley with steep sides.\nA few things that are visually similar to 'canyon' but are not 'canyon' are:\tvalley\tgorge\travine\nThere are several useful visual features to tell there is 'canyon' and not similar things in a photo:\tdeep and narrow\tvalley with steep sides\tsometimes with a river or stream at the bottom\toften with exposed rock formations or layers\tof geological or natural origin rather than man-made", 16], "storage area": ["Yes. 'Storage area' has a tangible appearance and usually refers to an enclosed space where objects are stored.\nA few things that are visually similar to 'storage area' but are not 'storage area' are:\toffice space\tshop\tcounter\tbathroom\nThere are several useful visual features to tell there is 'storage area' and not similar things in a photo:\tvisible shelves or containers\tfor storing objects\tor materials\tmore cluttered or packed with items than other areas in the photograph\tdifferent lighting or temperature than other areas in the photograph", 16], "tan bricks": ["Yes. 'Tan bricks' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'tan bricks' but are not 'tan bricks' are:\trocks\tcement blocks\twood planks\tmetal sheets\nThere are several useful visual features to tell there are 'tan bricks' and not similar things in a photo:\trectangular shape\tsmooth or rough texture\ttan or light brown color\ttypically used for building walls or chimneys.", 16], "candle holders": ["Yes. 'Candle holders' has a tangible appearance and is an object used to hold candles.\nA few things that are visually similar to 'candle holders' but are not 'candle holders' are:\tvases\tjars\tcups\tdecorative pitchers\nThere are several useful visual features to tell there is 'candle holders' and not similar things in a photo:\thollow space to hold a candle\tbase or platform to hold the candle\tstem or holder for the candle to stay upright\tspecific designs or shapes that indicate for holding candles.", 16], "kia logo": ["Yes. 'Kia logo' has a tangible appearance and is a specific type of car logo.\nA few things that are visually similar to 'kia logo' but are not 'kia logo' are:\tVolkswagen logo\tToyota logo\tMazda logo\tBMW logo\nThere are several useful visual features to tell there is 'kia logo' and not similar things in a photo:\toval shape\twith the word \"Kia\" written in white in the center, inside a red background\twith a silver border around the red oval.", 16], "surfer wave": ["Yes. 'Surfer wave' has a tangible appearance and is a specific type of ocean wave.\nA few things that are visually similar to 'surfer wave' but are not 'surfer wave' are:\tswells\tbreaking waves\ttidal waves\nThere are several useful visual features to tell there is 'surfer wave' and not similar things in a photo:\tclear barrel shape of the wave\tcurly crest of the wave\tcrisp, clean break of the wave\tlocation near a beach or surf spot\twith a surfer riding the wave", 16], "gray edge": ["Yes. 'Gray edge' has a tangible appearance and refers to a specific visual characteristic.\nThere are no things that are visually similar to 'gray edge' that are not 'gray edge'.\nUseful visual features for distinguishing 'gray edge' from other things in a photo are: a thin or narrow band of color or tone on the edge of an object that appears gray in color.", 16], "motor bikes": ["Yes. 'Motor bikes' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motor bikes' but are not 'motor bikes' are:\tbicycles\tscooters\ttricycles\tmopeds\nThere are several useful visual features to tell there is 'motor bikes' and not similar things in a photo:\ttwo wheels\tpowerful engine or motor\thandlebars and throttle\tpadded seats\twindshield or fairing exhaust pipe or muffler\tsuspension system for smooth rides", 16], "metal circle": ["Yes. 'Metal circle' has a tangible appearance and is a specific shape made of metal.\nA few things that are visually similar to 'metal circle' but are not 'metal circle' are:\tcoin\tbutton\twasher\tlid\tbracket\nThere are several useful visual features to tell there is 'metal circle' and not similar things in a photo:\tperfectly round shape\tmade of metal or metallic material\tno visible markings or designs\ton its own, or forming part of a larger object or structure", 16], "orange tail": ["Yes. 'Orange tail' has a tangible appearance and refers to the specific coloration of a tail.\nA few things that are visually similar to 'orange tail' but are not 'orange tail' are:\tred tail\tyellow tail\tgreen tail\nThere are no visual features to distinguish 'orange tail' from other colored tails as the specific color orange is what sets it apart.", 16], "bent legs": ["Yes. 'Bent legs' has a tangible appearance and is a condition where the legs are not straight.\nA few things that are visually similar to 'bent legs' but are not 'bent legs' are:\tknees in motion\tlegs in a sitting position\tlegs in a crossed position\tlegs in a fetal position\nThere are several useful visual features to tell there are 'bent legs' and not similar things in a photo:\tlegs are not straight\tknees are not aligned\twith or without support\twith or without footwear", 16], "rain gutter": ["Yes. 'Rain gutter' has a tangible appearance and is a structure used for collecting and channeling rainfall.\nA few things that are visually similar to 'rain gutter' but are not 'rain gutter' are:\tpipe\tdrainage system\trooftop\t\nThere are several useful visual features to tell there is 'rain gutter' and not similar things in a photo:\thorizontal or angled structure\tattached to the edge of a roof or a building\trectangular or trapezoidal shape\tmade of metal, plastic or concrete\tmay have downspouts or extensions to direct water away from the building.", 16], "shepherd": ["Yes. 'Shepherd' has a tangible appearance and refers to a person who herds and tends to sheep.\nA few things that are visually similar to 'shepherd' but are not 'shepherd' are:\tfarmer\tcowboy\trancher\tgardener\nThere are several useful visual features to tell there is a 'shepherd' and not similar things in a photo:\twearing a hat or cap\tcarries a staff\tor crook, used to corral and guide sheep\tcarrying a backpack or bag for supplies\twearing boots or work clothes\tin a pasture or open field surrounded by sheep\tor other livestock", 16], "shin guard": ["Yes. 'Shin guard' has a tangible appearance and is a piece of protective gear worn during sports activities.\nA few things that are visually similar to 'shin guard' but are not 'shin guard' are:\tknee pad\tleg warmers\tgarter socks\nThere are several useful visual features to tell there is 'shin guard' and not similar things in a photo:\tprotective plastic or foam cover\tfor the shine area only\tsecured by straps or sleeves around the calf and ankle.", 16], "rip": ["No. 'Rip' is too vague or abstract to be visually concrete or distinguishable in a photo. However, if 'rip' is used as an abbreviation for 'rest in peace,' it still does not have a tangible appearance but would be recognizable through associated symbols like a cross, a gravestone, or flowers on a grave.", 16], "horses mane": ["Yes. 'Horses mane' has a tangible appearance and refers to the hair on the neck and head of a horse.\nA few things that are visually similar to 'horses mane' but are not 'horses mane' are:\tdog's hair\tlion's hair\tbear's hair\nThere are several useful visual features to tell there is a 'horses mane' and not similar things in a photo:\tlong strands of hair\tgrowing on the neck and head area of a horse\thair is usually coarser on the top of the neck than the bottom of the neck.", 16], "hairy tail": ["Yes. 'Hairy tail' has a tangible appearance and is a type of physical attribute.\nA few things that are visually similar to 'hairy tail' but are not 'hairy tail' are:\tponytail\tdog's tail\tfurry rope\nThere are several useful visual features to tell there is 'hairy tail' and not similar things in a photo:\thair on the tail\tlong and slender\ttapering to a point\tdensely covered with fur or hair", 16], "tan wheels": ["Yes. 'tan wheels' has a tangible appearance and is a type of wheel with a specific color.\nA few things that are visually similar to 'tan wheels' but are not 'tan wheels' are:\tbrown circle\tbronze circle\tsandy disc\nThere are several useful visual features to tell there are 'tan wheels' and not similar things in a photo:\tcircular shape\ttan or light brown color\tparts of a larger object, such as a car or a bicycle", 16], "giraffes eye": ["Yes. 'Giraffe's eye' has a tangible appearance and is a body part of an animal.\nA few things that are visually similar to 'giraffe's eye' but are not 'giraffe's eye' are:\tzebra's eye\thorse's eye\tdeer's eye\nThere are several useful visual features to tell there is 'giraffe's eye' and not similar things in a photo:\tup high on the head\tlarge and bulging\teyebrows and long eyelashes\tsurrounded by circular patches of white and dark skin", 16], "vanilla ice cream": ["Yes. 'Vanilla ice cream' has a tangible appearance and is a type of frozen dessert.\nA few things that are visually similar to 'vanilla ice cream' but are not 'vanilla ice cream' are:\tstrawberry ice cream\tchocolate ice cream\twhipped cream\tmashed potato\nThere are several useful visual features to tell there is 'vanilla ice cream' and not similar things in a photo:\tlight yellow or cream color\twhite, creamy texture\tserved in a bowl or a cone", 16], "silver logo": ["Yes. 'Silver logo' has a tangible appearance.\nA few things that are visually similar to 'silver logo' but are not 'silver logo' are: silver emblem, silver badge, silver seal, silver insignia\nThere are several useful visual features to tell there is a 'silver logo' and not similar things in a photo:\tunique design\tmade of silver or silver-colored metal\tused to represent a company or brand", 16], "velcro": ["Yes. 'Velcro' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'velcro' but are not 'velcro' are:\tzippers\tbuttons\tsnaps\thook and eyes\nThere are several useful visual features to tell there is 'velcro' and not similar things in a photo:\ttwo strips of material, one with tiny hooks and the other with loops\tthe ability to stick to itself when pressed together\toften used on clothing or shoes", 16], "front teeth": ["Yes. 'Front teeth' has a tangible appearance and is a dental feature.\nA few things that are visually similar to 'front teeth' but are not 'front teeth' are:\tmolars\tcanine teeth\tincisors\nThere are several useful visual features to tell there are 'front teeth' and not similar things in a photo:\tlocated in the front of the mouth\tusually two to four in number\tfor humans, most are visible when smiling flat edges for cutting and biting", 16], "baking": ["No. 'Baking' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to the process of 'baking' but are not 'baking' are:\tcooking\tfrying\tgrilling\troasting\n\nUseful visual features to distinguish 'baking' from the listed similar things would depend on the specific food being prepared, but some possible features could be:\tan oven or baking sheet\tparchment paper or baking dish\tbaked goods such as cookies, bread, or cakes rising in the oven.", 16], "chrome sink faucet": ["Yes. 'Chrome sink faucet' has a tangible appearance and is a type of bathroom/kitchen hardware.\nA few things that are visually similar to 'chrome sink faucet' but are not 'chrome sink faucet' are:\tshowerhead\ttap\thandle\tknob\nThere are several useful visual features to tell there is 'chrome sink faucet' and not similar things in a photo:\tvertical water outlet\tstem-valve\tcontrollable flow of water\tchrome finish\tsingle or double handle\tcontrol lever\torbiting ball handle", 16], "mane hair": ["Yes. 'Mane hair' has a tangible appearance and refers to the long hair growing along the neck and spine of some animals, such as horses and lions.\nA few things that are visually similar to 'mane hair' but are not 'mane hair' are:\ttail hair of a horse\tfur of an animal\tgrass or weeds in a field\nThere are several useful visual features to tell there is 'mane hair' and not similar things in a photo:\tlong and flowing\thair-like texture\tgrowing along the neck and spine of an animal\tdifferent color or texture than the animal's fur or tail hair.", 16], "casserole": ["Yes. 'Casserole' has a tangible appearance and is a type of dish.\nA few things that are visually similar to 'casserole' but are not 'casserole' are:\tpie\tsoup\tstew\tpasta\nThere are several useful visual features to tell there is 'casserole' and not similar things in a photo:\tglass, ceramic, or metal dish\tbaked with a variety of ingredients, such as meat, vegetables, and cheese\ttopped with breadcrumbs, cheese, or mashed potatoes", 16], "train railroad tracks": ["Yes. 'Train railroad tracks' has a tangible appearance.\nA few things that are visually similar to 'train railroad tracks' but are not 'train railroad tracks' are:\tbike path\thorse trail\tbus lane\tsidewalk\nThere are several useful visual features to tell there is 'train railroad tracks' and not similar things in a photo:\tparallel lines\tmetal rails\tsleepers perpendicular to the rails\ttrain tracks usually have gravel and ballast around them.", 16], "style building": ["No. 'Style building' is too vague or abstract to be distinguished in a photo.", 16], "plaid design": ["Yes. 'Plaid design' has a tangible appearance and is a pattern of intersecting stripes.\nA few things that are visually similar to 'plaid design' but are not 'plaid design' are:\tstripes\thoundstooth\tcheckerboard\nThere are several useful visual features to tell there is 'plaid design' and not similar things in a photo:\tintersecting stripes of different colors or thicknesses\tdiagonal or perpendicular pattern\tthat can resemble a tartan", 16], "windbreaker": ["Yes, 'windbreaker' has a tangible appearance and is a type of jacket designed to protect against wind.\nA few things that are visually similar to 'windbreaker' but are not 'windbreaker' are:\traincoat\thoodie\ttrack jacket\nThere are several useful visual features to tell there is 'windbreaker' and not similar things in a photo:\tthin and lightweight material\tthat can block wind\tzipper, buttons, or velcro as the front closure\thigh neck, protecting the front of the neck\tfrom cold wind and rain.", 16], "building clock": ["Yes. 'Building clock' has a tangible appearance and refers to a clock that is attached to a building.\nA few things that are visually similar to 'building clock' but are not 'building clock' are:\twatch\twall clock\talarm clock\tpocket clock\nThere are several useful visual features to tell there is 'building clock' and not similar things in a photo:\tattached to a building or a tower\tlarge size\tvisible from a distance\tnumerical or Roman numeral markings\toften has a bell or chime mechanism.", 16], "porcelain bath tub": ["Yes. 'Porcelain bath tub' has a tangible appearance.\nA few things that are visually similar to 'porcelain bath tub' but are not 'porcelain bath tub' are:\tsink\tbasin\tjacuzzi\tpool\nThere are several useful visual features to tell there is 'porcelain bath tub' and not similar things in a photo:\toval or rectangular in shape\twith a sloping backrest\tmade of porcelain or similar material\twith a white or off-white color\twith a faucet and drain", 16], "orange goggles": ["Yes. 'Orange goggles' has a tangible appearance and is an object worn over the eyes for protection or visibility purposes.\nA few things that are visually similar to 'orange goggles' but are not 'orange goggles' are:\tsafety glasses\tswimming goggles\tski goggles\nThere are several useful visual features to tell there is 'orange goggles' and not similar things in a photo:\torange-colored lenses or frames\tcovers the eyes completely or partially\tdoesn't have a strap attached to it (as opposed to some swimming goggles)", 16], "paper bowl": ["Yes. 'Paper bowl' has a tangible appearance and is a type of disposable dish.\nA few things that are visually similar to 'paper bowl' but are not 'paper bowl' are:\tpaper plates\tcups\tbaskets\tbuckets\t\nThere are several useful visual features to tell there is 'paper bowl' and not similar things in a photo:\tbowl-shaped\tmade of paper or cardboard\tcould have a plastic lining\tfor holding food or liquid in a disposable manner.", 16], "fence rail": ["Yes. 'Fence rail' has a tangible appearance and is a part of a fence.\nA few things that are visually similar to 'fence rail' but are not 'fence rail' are:\tpicket\tblinds\tsticks\tbranches\tbamboo poles\nThere are several useful visual features to tell there is 'fence rail' and not similar things in a photo:\thorizontal and parallel to each other\tmade of wood or metal\tpart of a fence or a barrier", 16], "front porch": ["Yes. 'Front porch' has a tangible appearance and is a type of architecture feature.\nA few things that are visually similar to 'front porch' but are not 'front porch' are:\tdeck\tstairs\tbalcony\tpatio\nThere are several useful visual features to tell there is 'front porch' and not similar things in a photo:\troof supported by columns or pillars\toutdoor seating area at the entrance of a house\tor a building usually covered by a roof\tthe space faces the street, the front yard or garden of the house", 16], "roof house": ["No. 'Roof house' is too vague or abstract to be distinguished in a photo. It's unclear what is meant by 'roof house.' \n\nHowever, if we interpret 'roof house' as a house with a distinctive or notable roof, then:\n\nA few things that are visually similar to a 'roof house' but are not 'roof house' are:\tregular houses\tshacks\tchurches\tpagodas\nThere are several useful visual features to distinguish a 'roof house' from the listed similar things in a photo:\tthe design, shape, or color of the roof\tmaterial type (e.g. shingles, tiles, thatch)\theight or scale of the roof in relation to the rest of the structure", 16], "metal crane": ["Yes. 'Metal crane' has a tangible appearance and is a type of construction equipment.\nA few things that are visually similar to 'metal crane' but are not 'metal crane' are:\tjib\tcrane\ttruck\tcrane\tstacker\tcrane\n\nThere are several useful visual features to tell there is 'metal crane' and not similar things in a photo:\ttall and narrow structure\tlong metal boom or arm with a hook\ton treads or wheels\tcab for the operator to sit in\tcan lift heavy objects at construction sites.", 16], "stories": ["No. 'Stories' is too vague or abstract to be distinguished in a photo. It is an intangible concept that exists primarily in language, whether spoken or written. \n\nTherefore, there are no things that are visually similar to 'stories' but are not 'stories'.\n\nHowever, if we consider a physical representation of stories, such as a book or a movie, then some similar things could be:\tbooks\tjournals\tmagazines\tcomics\tfilms\t\n\nUseful visual features for distinguishing 'stories' from similar things in a photo depend on the specific representation or medium used to convey the stories. For example, distinguishing a book from a magazine or a film from a comic may involve noting differences in size, shape, format, graphics, and textual or narrative elements.", 16], "hamper": ["Yes. 'Hamper' has a tangible appearance and is a type of basket.\nA few things that are visually similar to 'hamper' but are not 'hamper' are:\tpicnic basket\tshopping basket\twaste basket\tlaundry basket\nThere are several useful visual features to tell there is 'hamper' and not similar things in a photo:\tlarge and deep\twicker or woven material\twith a lid or cover\thandle or handles", 16], "gold ball": ["Yes. 'Gold ball' has a tangible appearance and is a round object made of gold.\nA few things that are visually similar to 'gold ball' but are not 'gold ball' are:\tyellow ball\tplastic ball\tping pong ball\nThere are several useful visual features to tell there is 'gold ball' and not similar things in a photo:\tgolden color\tshiny surface\tmetallic texture\theavy weight", 16], "arrow pointing": ["Yes. 'Arrow pointing' has a tangible appearance and is a directional symbol.\nA few things that are visually similar to 'arrow pointing' but are not 'arrow pointing' are:\twind vane\tcompass\tneedle\tsign-post\nThere are several useful visual features to tell there is 'arrow pointing' and not similar things in a photo:\tthin, pointed shape\tdirection implied by the arrow curvature or tip\tpositioned to indicate a specific area or object", 16], "track ballast": ["Yes. 'Track ballast' has a tangible appearance and refers to the crushed stones where railway ties and rails are laid upon.\nA few things that are visually similar to 'track ballast' but are not 'track ballast' are:\tpebbles\trocks\tgravel\tsand\nThere are several useful visual features to tell there is 'track ballast' and not similar things in a photo:\n\n- Typically uniform in size and shape (usually a few inches in diameter)\n- Often gray or brown in color\n- Laid down in a specific pattern under railroad tracks \n- Generally has a rough and uneven surface", 16], "giraffes legs": ["Yes. 'Giraffes legs' has a tangible appearance and are a part of an animal's body.\nA few things that are visually similar to 'giraffes legs' but are not 'giraffes legs' are:\thorse legs\tzebra legs\tantelope legs\nThere are several useful visual features to tell there is 'giraffes legs' and not similar things in a photo:\thaving distinct spots\tskin patterns\tpatterns run from top hoof to the bottom\thaving joint bumps much higher up the legs than other animals", 16], "adult bear": ["Yes. 'Adult bear' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'adult bear' but are not 'adult bear' are:\tdog\tcat\tpig\tfox\nThere are several useful visual features to tell there is 'adult bear' and not similar things in a photo:\tlarge and heavy body\tshaggy and furry coat\tsnout or muzzle\tclawed paws\tbear-like ears and eyes\tdistinctive coloration (brown, black, white, etc.)\ttail less or very short tail.", 16], "line ripples": ["Yes. 'Line ripples' has a tangible appearance and refers to the pattern of waves created by a disturbance along a linear surface such as a stream or a sheet of metal.\nA few things that are visually similar to 'line ripples' but are not 'line ripples' are: wave patterns in the ocean, cracks in pavement, stripes on fabric, wood grain pattern.\nThere are several useful visual features to tell there are 'line ripples' and not similar things in a photo: straight lines or curves, even spacing between lines, repeating pattern, the appearance of movement or fluidity in the pattern, reflection or distortion of light by the pattern.", 16], "gargoyle": ["Yes. 'Gargoyle' has a tangible appearance and is a type of architectural sculpture.\nA few things that are visually similar to 'gargoyle' but are not 'gargoyle' are:\tstatue\tof a human or an animal\nThere are several useful visual features to tell there is 'gargoyle' and not similar things in a photo:\twaterspouts\toften located on the roofs of buildings or on the sides\tof grotesque or monstrous creatures\thorned heads, fangs, wings, claws, and tails", 16], "plane landing": ["Yes. 'Plane landing' has a tangible appearance and is a clear action in progress.\nA few things that are visually similar to 'plane landing' but are not 'plane landing' are:\tplane taking off\ta parked plane on a runway\ta plane taxiing\ta flyover\ta plane approaching for landing\nThere are several useful visual features that distinguish 'plane landing' from these similar things in a photo:\t\n- The wheels of the plane touching the runway.\n- The flaps of the wings being open for maximum aerodynamic drag during the landing.\n- The nose of the plane tilting up to slow down and come to a stop.\n- The presence of smoke or dust caused by the friction between the wheels and the runway during the landing.", 16], "baseball dugout": ["Yes. 'Baseball dugout' has a tangible appearance and is a specific space in a baseball field.\nA few things that are visually similar to 'baseball dugout' but are not 'baseball dugout' are:\tboardwalk bench\tpicnic shelter\tpark bench\t\nThere are several useful visual features to tell there is 'baseball dugout' and not similar things in a photo:\tlocated next to a baseball field\tcovered area with a roof\tor with a partial roof\tbenches or seats for players and coaches\tfrequent stairs or steps in front of the dugout to enter/exit the field.", 16], "christmas wreath": ["Yes. 'Christmas wreath' has a tangible appearance and is a kind of decoration.\nA few things that are visually similar to 'christmas wreath' but are not 'christmas wreath' are:\tfloral wreath\tnon-Christmas wreath\tdecorative garland\nThere are several useful visual features to tell there is 'christmas wreath' and not similar things in a photo:\tcircular shape\tdecorative leaves, pinecones or berries\thanging from a door or a wall\tchristmas-themed colors, such as red, green and white.", 16], "snow suit": ["Yes. 'Snow suit' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'snow suit' but are not 'snow suit' are:\tski jacket\twinter coat\tparka\nThere are several useful visual features to tell there is 'snow suit' and not similar things in a photo:\tmade of waterproof material\tattached hood and mittens\toranges or bright colors\tcovers the entire body, including arms and legs\tpadded for insulation", 16], "stone archway": ["Yes. 'Stone archway' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'stone archway' but are not 'stone archway' are:\tstone bridge\truins with arches\tarched doorways\tgarden trellises\nThere are several useful visual features to tell there is 'stone archway' and not similar things in a photo:\ta curved or pointed opening made of stone or brick\tframe for a path or entrance\tsupport from pillars or buttresses", 16], "grey tiles": ["Yes. 'Grey tiles' has a tangible appearance and refers to a specific type of flooring or wall covering.\nA few things that are visually similar to 'grey tiles' but are not 'grey tiles' are:\tconcrete\tslate\tpebble\tdark carpet\nThere are several useful visual features to tell there are 'grey tiles' and not similar things in a photo:\trectangle shape\tgrey color\tceramic or stone material\twith grout lines in between", 16], "dark roof": ["Yes. 'Dark roof' has a tangible appearance.\nA few things that are visually similar to 'dark roof' but are not 'dark roof' are:\tblack tarp\teaves\tshadows\nThere are several useful visual features to tell there is 'dark roof' and not similar things in a photo:\tflat or sloping surface\tdark color\tmade of tiles, shingles, or other roofing material located on top of a building or structure.", 16], "buiding": ["Yes. 'Building' has a tangible appearance and is a constructed structure.\nA few things that are visually similar to 'building' but are not 'building' are:\ttower\tchimney\tcube\tmountain\thill\tskyscraper\tbridge\nThere are several useful visual features to tell there is 'building' and not similar things in a photo:\tmade of concrete or brick\tmultiple stories or levels\thaving windows and doors\troof or chimney visible from the side", 16], "pole light": ["Yes. 'Pole light' has a tangible appearance and is a type of street or outdoor light.\nA few things that are visually similar to 'pole light' but are not 'pole light' are:\tlamp post\tparking lot light\ttraffic signal\tstreet sign\nThere are several useful visual features to tell there is 'pole light' and not similar things in a photo:\ttall metal or concrete pole\tsingle or multiple light fixtures\ton a street or outdoor area\tcasts a broad light beam", 16], "bird statue": ["Yes. 'Bird statue' has a tangible appearance and is a type of sculpture or figurine.\nA few things that are visually similar to 'bird statue' but are not 'bird statue' are:\tornamental birdhouses\tlive birds\tpaintings or drawings of birds\nThere are several useful visual features to tell there is 'bird statue' and not similar things in a photo:\tsolid and three-dimensional object\tmade of stone, metal, or other materials\treplica of a bird with realistic features such as wings, beak, and feathers.", 16], "individual": ["No. 'Individual' is too vague or abstract to be distinguished in a photo.", 16], "fire pit": ["Yes. 'Fire pit' has a tangible appearance and is a kind of structure used for containing fire.\nA few things that are visually similar to 'fire pit' but are not 'fire pit' are:\tbarbecue grill\tcampfire\tfireplace\nThere are several useful visual features to tell there is 'fire pit' and not similar things in a photo:\tbowl-shaped structure\tstone or brick construction\tcontains wood or charcoal for burning\tfire or flames inside\tor around it\tsurrounded by a sitting area", 16], "train sign": ["Yes. 'Train sign' has a tangible appearance and refers to signs related to trains.\nA few things that are visually similar to 'train sign' but are not 'train sign' are:\tbus signs\tstreet signs\tstore signs\nThere are several useful visual features to tell there is 'train sign' and not similar things in a photo:\tmention of train destination or schedule\tthe shape and design of the sign, which may resemble a train or track icon\tthe location of the sign, which is typically next to train tracks or at a train station.", 16], "dreads": ["Yes. 'Dreads' has a tangible appearance and is a type of hair style.\nA few things that are visually similar to 'dreads' but are not 'dreads' are:\tcurly hair\tbraids\twaves\nThere are several useful visual features to tell there is 'dreads' and not similar things in a photo:\tthick and matted locks of hair\tuniform size of hair sections\ttightly twisted or coiled hair sections\tfuzzier texture of hair sections", 16], "thick crust": ["Yes. 'Thick crust' has a tangible appearance and is a kind of pizza crust or bread.\nA few things that are visually similar to 'thick crust' but are not 'thick crust' are: thin crust, deep dish crust, stuffed crust.\nThere are several useful visual features to tell there is 'thick crust' and not similar things in a photo:\ta raised and fluffy texture\tthickness of at least 1 inch or more when it comes to pizza crust\tsometimes crispy at the edges and soft in the center", 16], "shadow fire hydrant": ["Yes. 'Shadow fire hydrant' has a tangible appearance as it is a specific type of object under certain lighting conditions.\nThere aren't things visually similar to 'shadow fire hydrant' that are not 'shadow fire hydrant'.\nUseful visual features for recognizing 'shadow fire hydrant' in a photo are:\tthe object has a clear shape of a fire hydrant\tdark color of the shadow against a lighter surface\ttexture and patterns on the surface of the object casting the shadow.", 16], "chimney stack": ["Yes. 'Chimney stack' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'chimney stack' but are not 'chimney stack' are:\tpipe\tventilation shaft\ttower\tskyscraper chimney\nThere are several useful visual features to tell there is 'chimney stack' and not similar things in a photo:\tbuilt on a roof\tnarrower at the top than at the base\tusually brick or stone often with a tiled cap, chimney pots or metal cowl on top.", 16], "horse ears": ["Yes. 'Horse ears' has a tangible appearance and is a specific part of a horse's anatomy.\nA few things that are visually similar to 'horse ears' but are not 'horse ears' are:\tear muffs\trabbit ears\tcat ears\nThere are several useful visual features to tell there are 'horse ears' and not similar things in a photo:\tattached to the top of the horse's head\thairy\ttwo ears per horse\tupturned tips or pointed ends.", 16], "plantain": ["Yes. 'Plantain' has a tangible appearance and is a type of banana.\nA few things that are visually similar to 'plantain' but are not 'plantain' are:\tbananas\tyucca\troot vegetables\t\nThere are several useful visual features to tell there is 'plantain' and not similar things in a photo:\tlonger and larger than a regular banana,\tblack or very dark green in color\twhen ripe and yellow when it is ripe, it has black spots or patches.", 16], "eraser": ["Yes. 'Eraser' has a tangible appearance and is a tool for removing marks.\nA few things that are visually similar to 'eraser' but are not 'eraser' are:\tpencil\tsharpener\thighlighter marker\tcorrection fluid\t\nThere are several useful visual features to tell there is 'eraser' and not similar things in a photo:\trectangular or round shape\trubber texture or material\twhite, pink, or grey color\tno visible writing or marks on the eraser.", 16], "car side mirror": ["Yes. 'Car side mirror' has a tangible appearance and is a component of a car.\nA few things that are visually similar to 'car side mirror' but are not 'car side mirror' are:\treflection\tinvisible fence\tpost\nThere are several useful visual features to tell there is 'car side mirror' and not similar things in a photo:\trectangular or oblong shape\tattached to the side of a car\tmirror surface with a reflection of the surrounding environment\tmay have additional features like heating or turn signals", 16], "cement pillar": ["Yes. 'Cement pillar' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'cement pillar' but are not 'cement pillar' are:\ttree\ttrunk\tmetal pole\tcolumn\nThere are several useful visual features to tell there is 'cement pillar' and not similar things in a photo:\tsquare or cylindrical shape\tsmooth surface\tmade of cement or concrete\tfixed to a larger structure, like a building", 16], "stone tile": ["Yes. 'Stone tile' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'stone tile' but are not 'stone tile' are:\tbricks\twooden tiles\tceramic tiles\tpavers\nThere are several useful visual features to tell there is 'stone tile' and not similar things in a photo:\tmade of stone or rock\trectangular in shape\tflat surface\twith natural or artificial patterns\tcolder to the touch than other materials used for construction", 16], "sidewalk front building": ["Yes. 'Sidewalk front building' has a tangible appearance and refers to a building that directly faces a sidewalk.\nA few things that are visually similar to 'sidewalk front building' but are not 'sidewalk front building' are:\tpark\tbench\tbike rack\tnewspaper stand\tbushes\nThere are several useful visual features to tell there is 'sidewalk front building' and not similar things in a photo:\ta building facing a sidewalk\tdoor or window on the ground floor\tsidewalk or pavement in front of building\tsignage indicating the building's name, business, or purpose", 16], "windmills": ["Yes. 'Windmills' has a tangible appearance and is a type of structure for converting wind energy into other forms of energy.\nA few things that are visually similar to 'windmills' but are not 'windmills' are:\twatermills\tturbines\ttowers\nThere are several useful visual features to tell there is 'windmills' and not similar things in a photo:\tlong, rotating blades\ttower structure with a pointed roof\tor a cylindrical shape\tusually in rural areas or near bodies of water", 16], "brussel sprouts": ["Yes. 'Brussels sprouts' has a tangible appearance and is a kind of vegetable.\nA few things that are visually similar to 'Brussels sprouts' but are not 'Brussels sprouts' are:\tcabbage\tlettuce\tkale\tspinach\nThere are several useful visual features to tell there are 'Brussels sprouts' and not similar things in a photo:\tsmall and round\tbright green\tcolorful leaves\ton a stalk", 15], "grey rock wall": ["Yes. 'Grey rock wall' has a tangible appearance and describes a specific type of wall made of grey rock.\nA few things that are visually similar to 'grey rock wall' but are not 'grey rock wall' are:\tbrick wall\tconcrete wall\tpainted wall\twooden wall\nThere are several useful visual features to tell there is 'grey rock wall' and not similar things in a photo:\tmade of unprocessed or natural grey rock or stones\trough texture and irregular shape\tno visible signs of paint or plaster", 15], "lego": ["Yes. 'Lego' has a tangible appearance and is a type of plastic building material.\nA few things that are visually similar to 'Lego' but are not 'Lego' are:\tmegabloks\tknex\tduplo\nThere are several useful visual features to tell there is 'Lego' and not similar things in a photo:\trectangular bricks\twith circular protrusions\ton the upper side of the brick\twith a hollow tube-shaped bottom to fit onto other bricks\tand minifigures, small figures used in Lego playsets.", 15], "toy plane": ["Yes. 'Toy plane' has a tangible appearance and is a miniature version of an airplane.\nA few things that are visually similar to 'toy plane' but are not 'toy plane' are:\treal plane\tmodel plane\tremote-controlled plane\tbird\tspacecraft\nThere are several useful visual features to tell there is 'toy plane' and not similar things in a photo:\tsmaller in size than a real plane\tbright colors or patterns\tplastic or lightweight material\troom for a small figurine to sit in the cockpit\topen wings and a propeller (in most cases)", 15], "place mats": ["Yes. 'Place mats' has a tangible appearance and is a kind of table setting.\nA few things that are visually similar to 'place mats' but are not 'place mats' are:\tnapkins\ttablecloths\tdoilies\tplacards\nThere are several useful visual features to distinguish 'place mats' are:\n\tusually rectangular \n flat and thin\n placed under dishes\n made of fabric, plastic or paper\n sometimes feature decorative patterns or designs that match the dishes, the table, or the room's decor", 15], "court net": ["Yes. 'Court net' has a tangible appearance and refers to a physical object used in sports.\nA few things that are visually similar to 'court net' but are not 'court net' are:\tfishing net\tmosquito net\ttennis court cage\tscreen\nThere are several useful visual features to tell there is 'court net' and not similar things in a photo:\ttwo metal or wooden poles with a net stretched between\twhite net\twith a specific height from the ground\tlevel with the ground on both sides of the net", 15], "bottom corner": ["Yes. 'Bottom corner' has a tangible appearance and is a location at the edge of a rectangular shape.\nA few things that are visually similar to 'bottom corner' but are not 'bottom corner' are:\ttop corner\tleft corner\tright corner\nThere are several useful visual features to tell there is 'bottom corner' and not similar things in a photo:\tlocated at the intersection of the bottom and one of the sides of a rectangle\tcorner-shaped\tangle of 90 degrees\tglColor or shading that contrasts with the sides of the rectangle", 15], "window seal": ["Yes. 'Window seal' has a tangible appearance and refers to the part of a window that seals the gap between the window frame and the windowpane.\nA few things that are visually similar to 'window seal' but are not 'window seal' are:\twindow frame\twindowpane\tcaulk\tdoor seal\nThere are several useful visual features to distinguish 'window seal' from the listed similar things in a photo:\tit is a narrow strip between the window frame and windowpane\n\tit is made of rubber, silicone, or other flexible material\n\tit can be black or clear in color\n\tit has a slightly textured or ridged surface due to its purpose of providing a barrier against air, water, and noise.", 15], "orange rug": ["Yes. 'Orange rug' has a tangible appearance and is a type of carpet or rug in orange color.\nA few things that are visually similar to 'orange rug' but are not 'orange rug' are:\torange blanket\torange towel\torange fabric\torange tablecloth\nThere are several useful visual features to tell there is 'orange rug' and not similar things in a photo:\tsoft and plush texture\trectangular or square shape\tfrayed edges\thaving a non-solid pattern on it (e.g. stripes, flowers)", 15], "garbage basket": ["Yes. 'Garbage basket' has a tangible appearance and is a type of receptacle or container.\nA few things that are visually similar to 'garbage basket' but are not 'garbage basket' are:\tlaundry basket\tplastic crate\tstorage bin\tflower pot\nThere are several useful visual features to tell there is 'garbage basket' and not similar things in a photo:\tdesigned for waste disposal\toften made of plastic or metal\twith or without a lid\tmay have a foot pedal or a swing-top lid\tmay have trash or recycling logos\tor labels", 15], "broken pieces": ["Yes. 'Broken pieces' has a tangible appearance and refers to objects that are broken or shattered into smaller parts.\nA few things that are visually similar to 'broken pieces' but are not 'broken pieces' are:\trocks\tgravel\tshards of glass\tcrumbs\tpowder\nThere are several useful visual features to tell there are 'broken pieces' and not similar things in a photo:\tirregular shapes\tsharp edges\tor jagged ends\tpieces that fit together\tor belong to a larger object", 15], "shine": ["No. 'Shine' is too vague or abstract to be distinguished in a photo.", 15], "tv monitor": ["Yes. 'TV monitor' has a tangible appearance and is a type of display device.\nA few things that are visually similar to 'TV monitor' but are not 'TV monitor' are:\tcomputer monitor\tprojector\tscreen\nThere are several useful visual features to tell there is 'TV monitor' and not similar things in a photo:\trectangular or square-shaped\tscreen with moving images or colors\tsmall speakers at the bottom or sides\tof the device\tbuttons on the front or sides of the device to change channels or the volume", 15], "bouy": ["Yes. 'Bouy' has a tangible appearance and is a floating device used to mark a location or warn of danger.\nA few things that are visually similar to 'bouy' but are not 'bouy' are:\tlifebuoy\tpaddle\tfloaties\nThere are several useful visual features to tell there is 'bouy' and not similar things in a photo:\tbuoyancy\tvertical or cylindrical shape\tcolorful with reflective strips or patterns\trope or chain attached to a concrete anchor on the bottom of the body of water.", 15], "capri pants": ["Yes. 'Capri pants' has a tangible appearance and is a type of pants.\nA few things that are visually similar to 'capri pants' but are not 'capri pants' are:\tshorts\tbermuda pants\tcropped pants\nThere are several useful visual features to tell there is 'capri pants' and not similar things in a photo:\tlength is above the ankle, but below the knee\ttight-fitting or loose\tflat or pleated front\tcotton, linen, or spandex material", 15], "nutella": ["Yes. 'Nutella' has a tangible appearance and is a type of chocolate spread.\nA few things that are visually similar to 'nutella' but are not 'nutella' are:\tcaramel spread\tchocolate syrup\tpeanut butter\thoney\nThere are several useful visual features to tell there is 'nutella' and not similar things in a photo:\tcreamy texture\tbrown color\thazelnut specks\tround jar with a white cap\tNutella logo and label on the jar", 15], "desk drawer": ["Yes. 'Desk drawer' has a tangible appearance and is a specific type of furniture.\nA few things that are visually similar to 'desk drawer' but are not 'desk drawer' are:\tkitchen cabinet\tdrawer chest\tbedside table\tshoe cabinet\nThere are several useful visual features to tell there is 'desk drawer' and not similar things in a photo:\tlocated in a desk or a table\tbox-shaped\twith a handle or knob for pulling out\thave slides to move in or out\tinternal compartments to hold items", 15], "batteries": ["Yes. 'Batteries' has a tangible appearance and is a power source that stores energy.\nA few things that are visually similar to 'batteries' but are not 'batteries' are:\tcandle\tthermos\tcapacitor\turn\nThere are several useful visual features to tell there is 'batteries' and not similar things in a photo:\tcylindrical or rectangular shape\twith one or two bumps\ton and off switch\tlabel with voltage and brand name\tmetal or plastic housing", 15], "floor carpet": ["Yes. 'Floor carpet' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'floor carpet' but are not 'floor carpet' are:\trugs\tmats\ttile floors\twooden floors\nThere are several useful visual features to tell there is 'floor carpet' and not similar things in a photo:\tsoft and plush texture\tpatterned or solid color\tspread over the entire floor area\tfixed to the floor with adhesive or tacks.", 15], "perch": ["Yes. 'Perch' has a tangible appearance and refers to a type of fish or a place for a bird to rest.\nA few things that are visually similar to 'perch' but are not 'perch' are:\tbranch\twindow sill\tbar\tstool\nThere are several useful visual features to tell there is 'perch' and not similar things in a photo:\ta fish with a sleek, elongated body, sharp dorsal fins, and a pointed head\ta bird rest spot, usually a horizontal surface, that may have a rough or smooth texture, depending on the bird's preferences.", 15], "ranch": ["Yes. 'Ranch' has a tangible appearance and refers to a particular type of farm or agricultural land.\nA few things that are visually similar to 'ranch' but are not 'ranch' are: farm, homestead, plantation.\nThere are several useful visual features to tell there is 'ranch' and not similar things in a photo: large open lands, with fields or pastures for grazing, often with cattle or horses, and sometimes with barns, sheds, or other farm buildings.", 15], "poeple": ["Yes. 'People' has a tangible appearance and refers to human beings.\nThere are no things that are visually similar to 'people' but are not 'people'.\nUseful visual features for distinguishing 'people' in a photo could include: bipedal stance, human-like facial features, and recognizable clothing.", 15], "silver bathroom": ["Yes. 'Silver bathroom' has a tangible appearance and is a type of bathroom with a specific color scheme.\nA few things that are visually similar to 'silver bathroom' but are not 'silver bathroom' are:\tgrey bathroom\tchrome bathroom\tmetallic bathroom\tmodern bathroom\nThere are several useful visual features to tell there is 'silver bathroom' and not similar things in a photo:\tchrome or silver fixtures and accessories\tsilver or grey tiles or walls\tminimalist and modern design\tshiny or reflective surfaces\tsilver or glass shower enclosures", 15], "tray table": ["Yes. 'Tray table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'tray table' but are not 'tray table' are:\tside table\tcoffee table\tbench\ttable\nThere are several useful visual features to tell there is 'tray table' and not similar things in a photo:\ta small table that can be pulled up to a chair or couch\ta flat surface or tray for holding items\ta base with legs or a support to hold the table top\tcollapsible or foldable for easy storage\tor an adjustable height for convenience", 15], "colorful hat": ["Yes. 'Colorful hat' has a tangible appearance and is a type of head accessory.\nA few things that are visually similar to 'colorful hat' but are not 'colorful hat' are:\tscarf\tbandana\tbonnet\tribbon\nThere are several useful visual features to tell there is 'colorful hat' and not similar things in a photo:\tsits on the head\ttop part of the body\tusually brimmed or decorated in some way\tbright or contrasting colors\tor has a distinctive shape", 15], "beret": ["Yes, 'beret' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'beret' but are not 'beret' are:\tbaseball cap\ttiara\tbandana\nThere are several useful visual features to tell there is 'beret' and not similar things in a photo:\tcircular shape\tflat crown\tsoft material\tno brim or visor\ttypically worn tilted to one side", 15], "wall heater": ["Yes. 'Wall heater' has a tangible appearance and is a type of heating appliance installed on a wall.\nA few things that are visually similar to 'wall heater' but are not 'wall heater' are:\tthermostat\tspace heater\tradiator\tair conditioner\nThere are several useful visual features to tell there is 'wall heater' and not similar things in a photo:\tattached to a wall\tusually rectangular or square in shape\tfeatures knobs or buttons for temperature control\tmay have vents for heat distribution\tmay have a visible heating element or a flame", 15], "round lamp": ["Yes. 'Round lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'round lamp' but are not 'round lamp' are:\tbulb\tclock\tvase\twheel\tmirror\nThere are several useful visual features to tell there is 'round lamp' and not similar things in a photo:\ta circular or spherical shape\tmetal or glass body\ta light bulb or LED inside\tan electrical cord or switch on the base", 15], "leather saddle": ["Yes. 'Leather saddle' has a tangible appearance and is a type of horse tack.\nA few things that are visually similar to 'leather saddle' but are not 'leather saddle' are:\tpurse\tbag\tchair\tshoe\nThere are several useful visual features to tell there is 'leather saddle' and not similar things in a photo:\tmade of leather and other materials\tequipped with stirrups, pommel, and girth\thas a distinct shape to fit onto a horse's back\tand is meant to be used to sit on when riding a horse.", 15], "wind sail": ["Yes. 'Wind sail' has a tangible appearance and is a structure used to catch wind for propulsion.\nA few things that are visually similar to 'wind sail' but are not 'wind sail' are:\ttent\tparasol\tumbrella\tkite\tsun shade\nThere are several useful visual features to tell there is 'wind sail' and not similar things in a photo:\ttriangular or quadrangular shape\tfabric or material stretched across a frame\tattached to a boat or vessel for propulsion\tmay have ropes or lines to adjust the angle to the wind", 15], "round edge": ["Yes. 'Round edge' has a tangible appearance and is a type of curved line or surface.\nA few things that are visually similar to 'round edge' but are not 'round edge' are: diagonal line, curved line, circular line, semicircular surface.\nThere are several useful visual features to tell there is 'round edge' and not similar things in a photo:\tcurved line or surface\twithout sharp angles or corners\tsmooth and continuous\tcurvature is visible to the eye", 15], "side light": ["Yes. 'Side light' has a tangible appearance and refers to a specific type of lighting.\nA few things that are visually similar to 'side light' but are not 'side light' are: front light, top light, backlight, underlight\nThere are several useful visual features to tell there is 'side light' and not similar things in a photo:\tlight source coming from the side of the subject\tcasting highlights and shadows from the side of the subject\temphasizing texture and depth of the subject from the side view.", 15], "color floor tiles": ["Yes. 'Color floor tiles' has a tangible and specific appearance.\nA few things that are visually similar to 'color floor tiles' but are not 'color floor tiles' are:\tpainted floor\tplanks of wood\tcarpet\tstone flooring\nThere are several useful visual features to tell there is 'color floor tiles' and not similar things in a photo:\tsquare or rectangular in shape\tsmooth, flat surface\tunique color or pattern in each tile\tusually found in kitchens or bathrooms", 15], "doves": ["Yes. 'Doves' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'doves' but are not 'doves' are:\tpigeons\talbatross\towls\tmockingbirds\nThere are several useful visual features to tell there is 'doves' and not similar things in a photo: \tsmall size \trounded tail\tbulbous head \tsoft cooing call\tmostly white feathers with some black or grey accents in wings and tail.", 15], "silver doorknob": ["Yes. 'Silver doorknob' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'silver doorknob' but are not 'silver doorknob' are:\tchrome doorknob\tstainless steel faucet\nThere are several useful visual features to tell there is 'silver doorknob' and not similar things in a photo:\t\nspecific shape and size\t\nsmooth and shiny surface\t\nattached to a door", 15], "wiskers": ["Yes. 'Whiskers' has a tangible appearance and refers to the long, fine hairs growing around the mouth and nose of some animals.\nA few things that are visually similar to 'whiskers' but are not 'whiskers' are:\thair\tstrands\tof fur\nThere are several useful visual features to distinguish 'whiskers' from the listed similar things in a photo:\t\n- Whiskers are located around the mouth and nose of animals.\n- They are usually longer and thinner than hair or fur.\n- They often have a different texture or color than surrounding fur.\n- They are attached to the animal's skin and are able to move independently.", 15], "liquor": ["Yes. 'Liquor' has a visually tangible appearance and refers to alcoholic beverages that are distilled rather than fermented.\nA few things that are visually similar to 'liquor' but are not 'liquor' are:\tbeer\twine\tjuice\tsoda\nThere are several useful visual features to tell there is 'liquor' and not similar things in a photo:\tdistinctive bottle or container\ttransparent or translucent liquid\tcolor of the liquid (typically clear or amber)", 15], "metal kitchen sink": ["Yes. 'Metal kitchen sink' has a tangible appearance and is a type of kitchen fixture.\nA few things that are visually similar to 'metal kitchen sink' but are not 'metal kitchen sink' are:\tbathroom sink\twashbasin\tbathtub\tcooking pot\nThere are several useful visual features to tell there is 'metal kitchen sink' and not similar things in a photo:\tlocated in a kitchen\tmade of metal or stainless steel\tattached to a countertop or wall-rounded or rectangular shape\twith a faucet and drain built-in or attached.", 15], "man jacket": ["Yes. 'Man jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'man jacket' but are not 'man jacket' are:\tcoat\tblazer\tvest\thoodie\nThere are several useful visual features to tell there is 'man jacket' and not similar things in a photo:\touterwear garment with sleeves and collar\tfront opening with buttons, zipper, or both\tmade of thick or durable fabric, depending on the weather or purpose\thas pockets, lapels, cuffs, and sometimes a hood or a belt, depending on the style.", 15], "plastic wheel": ["Yes. 'Plastic wheel' has a tangible appearance and is an object.\nA few things that are visually similar to 'plastic wheel' but are not 'plastic wheel' are:\tsteering wheel\tferris wheel\ttire\tgear\tcogwheel\nThere are several useful visual features to tell there is 'plastic wheel' and not similar things in a photo:\tmade of plastic\tcircular shape\thub and spokes\ttexture and color of the surface\tthe presence of an axle or something that attaches the wheel to another object.", 15], "silver edge": ["Yes. 'Silver edge' has a tangible appearance and refers to a specific feature of an object or material.\nA few things that are visually similar to 'silver edge' but are not 'silver edge' are:\tgray border\tpewter outline\tchrome trim\tmetallic frame\nThere are several useful visual features to distinguish 'silver edge' from the listed similar things in a photo:\tsilver in color\tnarrow in width\tdecorative or functional on an object or material (such as a picture frame or paper)", 15], "book cover": ["Yes. 'Book cover' has a tangible appearance and is the protective outer covering of a book.\nA few things that are visually similar to 'book cover' but are not 'book cover' are:\tposters\tmagazine covers\tfolder covers\tbinder covers\nThere are several useful visual features to tell there is 'book cover' and not similar things in a photo:\trectangular in shape\tdesigned to protect the book's pages\tvarious types of bindings\tsometimes contains metadata such as book title, author name, and publisher logo", 15], "metal appliance": ["Yes. 'Metal appliance' has a tangible appearance and refers to household objects made of metal.\nA few things that are visually similar to 'metal appliance' but are not 'metal appliance' are:\tmetal tools\tkitchen utensils\tmetal furniture\nThere are several useful visual features to tell there is 'metal appliance' and not similar things in a photo:\thousehold object\tmade of metal\tused for a specific function or task (e.g. cooking, cleaning, ironing)\tdesign and shape typical of home appliances", 15], "files": ["No. 'Files' is too vague or abstract to have a tangible appearance.\nA few things that are visually similar to 'files' but are not 'files' are:\tdocuments\tpapers\tbooks\tfolders\tenvelopes\nThere are no useful visual features to distinguish 'files' from the listed similar things as they are all related to organizing information. However, if the files are electronic, they may be displayed on a computer screen or stored in a digital folder, which can be visually distinguished.", 15], "grey tv": ["Yes. 'Grey TV' has a tangible appearance and is a kind of television.\nThere are not many things that are visually similar to 'grey tv' but are not 'grey tv'. However, a few things might include other types and colors of televisions or electronic displays.\nThere are no useful visual features to distinguish a 'grey tv' from other types and colors of televisions or electronic displays, as the color or material of the exterior can vary widely between different models and brands. However, features such as the screen size, aspect ratio, or input/output ports might be useful in identifying a specific type of TV.", 15], "fireplace mantel": ["Yes. 'Fireplace mantel' has a tangible appearance and is a part of a fireplace.\nA few things that are visually similar to 'fireplace mantel' but are not 'fireplace mantel' are:\tshelf\tbookcase\tcabinet\tcounter\nThere are several useful visual features to tell there is 'fireplace mantel' and not similar things in a photo:\tattached to a fireplace\tsurrounding the area above the place for a fire\tlevel surface for decoration or storage\tsimilar material and color as the fireplace itself", 15], "cabinetry": ["Yes. 'Cabinetry' has a tangible appearance and refers to the design and installation of cabinets and storage spaces in a room.\nA few things that are visually similar to 'cabinetry' but are not 'cabinetry' are:\tshelves\tdrawers\tdressers\tdesks\nThere are several useful visual features to tell there is 'cabinetry' and not similar things in a photo:\tfixed to the wall or floor\torganized storage space\tmultiple compartments or shelves\tmade with wood or other materials\tdesigned to fit a particular space or style.", 15], "arc": ["Yes. 'Arc' has a tangible appearance and refers to a curved shape or a segment of a circle.\nA few things that are visually similar to 'arc' but are not 'arc' are:\tcrescent moon\trainbow\tdome\nThere are several useful visual features to tell there is 'arc' and not similar things in a photo:\tcurved shape or segment\tof a circle\tcurvature, height, and width (if a three-dimensional object)", 15], "orange t-shirt": ["Yes. 'Orange t-shirt' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'orange t-shirt' but are not 'orange t-shirt' are:\torange blouse\torange sweater\torange dress\torange jacket\nThere are several useful visual features to tell there is 'orange t-shirt' and not similar things in a photo:\tshort-sleeved or sleeveless\tcasual and sporty style\tmade of cotton or other soft fabric with a round neckline or crew neck", 15], "computer moniter": ["Yes. 'Computer monitor' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'computer monitor' but are not 'computer monitor' are:\tTVs\tlaptops\ttablets\tsmartphones\nThere are several useful visual features to tell there is 'computer monitor' and not similar things in a photo:\trectangular shape\tattached to a computer or a stand\tdisplaying digital content\twith cables or ports connected to other devices\tbright screen with pixels and images.", 15], "braids": ["Yes. 'Braids' has a tangible appearance and refers to a specific hairstyle technique.\nA few things that are visually similar to 'braids' but are not 'braids' are:\tponytails\tcornrows\tafro\tpigtails\nThere are several useful visual features to tell there are 'braids' and not similar things in a photo:\t\nhair woven into a pattern or styles into multiple strands\tbraids can be thin or thick\tcan be created with different hair textures or lengths\tcan be worn up or down or in different styles.", 15], "round tray": ["Yes. 'Round tray' has a tangible appearance and is a type of serving dish.\nA few things that are visually similar to 'round tray' but are not 'round tray' are:\tplate\tplatter\tbowl\tpizza tray\nThere are several useful visual features to tell there is 'round tray' and not similar things in a photo:\tcircular in shape\tflat surface\twith or without edges\thandle or handles on opposite sides commonly used for carrying food or drinks.", 15], "storage containers": ["Yes. 'Storage containers' has a tangible appearance and is a kind of container used for storing items.\nA few things that are visually similar to 'storage containers' but are not 'storage containers' are:\ttrash cans\tbins\tboxes\tshopping bags\nThere are several useful visual features to tell there is 'storage containers' and not similar things in a photo:\tlids or covers\tfor storing goods and items\tdifferent sizes and shapes\tlabels or identifiers\tfor stacking or organizing\titems inside the container.", 15], "hash browns": ["Yes. 'Hash browns' have a tangible appearance and are a type of food.\nA few things that are visually similar to 'hash browns' but are not 'hash browns' are:\tpotato pancakes\tlatkes\trosti\t\nThere are several useful visual features to tell there is 'hash browns' and not similar things in a photo:\tshredded or grated potatoes\tflat circular shape\tfried until crisp or golden-brown crust", 15], "dark lines": ["Yes. 'Dark lines' has a tangible appearance and can refer to several things.\nA few things that are visually similar to 'dark lines' but are not 'dark lines' are:\tshadows\tcracks in a wall\tdirt\tonion rings\tinsects\nThere are several useful visual features to tell there are 'dark lines' and not similar things in a photo, depending on the context in which the term is used. For example, if referring to pencil lines, some useful features might be:\tthin and straight\tdarker than the surrounding paper\tconsistent in width and direction.", 15], "pink tiles": ["Yes. 'Pink tiles' has a tangible appearance and is a specific type of tile.\nA few things that are visually similar to 'pink tiles' but are not 'pink tiles' are:\tred tiles\tpink bricks\tpink stones\tpink paint\nThere are several useful visual features to tell there are 'pink tiles' and not similar things in a photo:\trectangular in shape\tsmooth finish\tpink or blush color\tglossy or ceramic texture\tarranged in a pattern or grid", 15], "gold wedding ring": ["Yes. 'Gold wedding ring' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'gold wedding ring' but are not 'gold wedding ring' are:\tbracelet\twatch\tnecklace\tearrings\nThere are several useful visual features to tell there is 'gold wedding ring' and not similar things in a photo:\tcircular shape\twith a hole in the center\tof gold\tmostly worn on the fourth finger of the left hand", 15], "bystander": ["No. 'Bystander' is too vague or abstract to be distinguished in a photo.", 15], "canvas bag": ["Yes. 'Canvas bag' has a tangible appearance and is a type of bag made from canvas material.\nA few things that are visually similar to 'canvas bag' but are not 'canvas bag' are:\tplastic bag\tpaper bag\ttote bag\tbackpack\nThere are several useful visual features to tell there is 'canvas bag' and not similar things in a photo:\tmade of canvas material\thave a rough texture or a visible weave\thave two handles for carrying\thave a flat bottom for stability\tcommon colors are beige, brown, or green", 15], "passport": ["Yes. 'Passport' has a tangible appearance and is a type of official document.\nA few things that are visually similar to 'passport' but are not 'passport' are:\tID card\tdriver's license\tlibrary card\tcredit card\nThere are several useful visual features to tell there is 'passport' and not similar things in a photo:\tdark blue cover with the country's emblem\tgolden or silver lettering and embellishments\tpersonal identification information (name, birthdate, nationality)\tissuing authority and date\tof a standard rectangular size", 15], "shadow motorcycle": ["Yes. 'Shadow motorcycle' has a tangible appearance and is a type of motorcycle.\nThere are no things that are visually similar to 'shadow motorcycle' but are not 'shadow motorcycle'.\nUseful visual features for distinguishing 'shadow motorcycle' from other motorcycles in a photo could include:\tdark or black color scheme\tstrategic use of shadows to highlight certain areas, such as the engine or tires\tstreamlined, low-slung design", 15], "food basket": ["Yes. 'Food basket' has a tangible appearance and is a container used for storing or transporting food.\nA few things that are visually similar to 'food basket' but are not 'food basket' are:\tlaundry basket\tpicnic basket\twaste basket\tstorage basket\nThere are several useful visual features to tell there is 'food basket' and not similar things in a photo:\tcontaining food\tvisible fruits, vegetables, or breads\twoven or mesh construction\thandles for carrying", 15], "hamburger bun": ["Yes. 'Hamburger bun' has a tangible appearance and is a kind of bread.\nA few things that are visually similar to 'hamburger bun' but are not 'hamburger bun' are:\tbagel\troll\tloaf of bread\tEnglish muffin\nThere are several useful visual features to tell there is 'hamburger bun' and not similar things in a photo:\tround or oval-shaped\tflattened at the top and bottom\tlightly toasted on the inside\tcrisp on the outside\tpadding in the middle to allow for sandwich contents", 15], "reading lamp": ["Yes. 'Reading lamp' has a tangible appearance and is a type of lamp used for reading books.\nA few things that are visually similar to 'reading lamp' but are not 'reading lamp' are:\ttable lamp\tfloor lamp\tdesk lamp\nThere are several useful visual features to tell there is 'reading lamp' and not similar things in a photo:\t\nadjustable neck or arm\tfocused beam of light\tbrighter than ambient light", 15], "silver hubcap": ["Yes. 'Silver hubcap' has a tangible appearance and is a part of a car's wheel.\nA few things that are visually similar to 'silver hubcap' but are not 'silver hubcap' are:\twheel rim\tbicycle wheel\tfrisbee\tdish\nThere are several useful visual features to tell there is 'silver hubcap' and not similar things in a photo:\tcircular shape\tsilver or metallic color\treflection of the car's surroundings\tcan see the wheel bolts in the center of the object", 15], "smoke trail": ["Yes. 'Smoke trail' has a tangible appearance and can be seen as a visible path left by smoke.\nA few things that are visually similar to 'smoke trail' but are not 'smoke trail' are:\tclouds\tvapor\tjet stream\tfumes from a chimney\nThere are several useful visual features to tell there is 'smoke trail' and not similar things in a photo:\tline or path-like shape\ttranslucent or transparent appearance\tgrey or black color\tsource of the smoke in the photo, such as a rocket or fireworks", 15], "soy sauce": ["Yes. 'Soy sauce' has a tangible appearance and is a type of condiment.\nA few things that are visually similar to 'soy sauce' but are not 'soy sauce' are:\tworcestershire sauce\tbalsamic vinegar\tcaramel sauce\nThere are several useful visual features to tell there is 'soy sauce' and not similar things in a photo:\tdark brown or black liquid\tsmooth and glossy texture\tgenerally stored in a bottle or a small dispenser\twith a distinct or slightly bitter smell\tserved with Asian cuisine, particularly sushi or stir-fry dishes.", 15], "kayaks": ["Yes. 'Kayaks' has a tangible appearance and is a type of watercraft.\nA few things that are visually similar to 'kayaks' but are not 'kayaks' are:\tcanoe\trowboat\tpaddleboard\t\nThere are several useful visual features to tell there is 'kayaks' and not similar things in a photo:\tnarrow and sleek design\twith pointed front\tand tapered back\tdual-bladed paddle sitting across the kayak's roof indentations for feet\tdifficult to see from the side\tview but with visible occupants sitting inside the kayak.", 15], "face guard": ["Yes. 'Face guard' has a tangible appearance and usually refers to protective gear worn over the face.\nA few things that are visually similar to 'face guard' but are not 'face guard' are:\tvisor\tmask\thelmet\tsunglasses\nThere are several useful visual features to distinguish a 'face guard' from the listed similar things in a photo:\tcovers only the lower part of the face (such as mouth and nose)\tusually made of plastic or metal\tmesh or transparent material to allow breathing and visibility\tstraps or elastic to secure the guard to the head or helmet.", 15], "ban": ["No. 'Ban' is too abstract to have a tangible appearance or be distinguished in a photo.\nHowever, a few things that may be visually associated with the concept of 'ban' in a photo are:\t\n- A red circle with a diagonal line through it, which is the international symbol for \"prohibited\" or \"not allowed\"\n- A sign or placard indicating that a certain activity, item or behavior is forbidden\n- A person or group of people being prevented from entering a certain area or engaging in a certain action \n\nThe useful visual features for distinguishing a 'ban' from other similar things in a photo may include the following:\n- A clear and specific message explaining what is banned\n- The context and location, which may give clues about why something is being banned \n- The presence of enforcement personnel, such as security guards, police officers or officials, which suggests that the ban is being actively enforced.", 15], "christmas decorations": ["Yes. 'Christmas decorations' has a tangible appearance and can refer to a variety of objects used to decorate for Christmas.\nA few things that are visually similar to 'christmas decorations' but are not 'christmas decorations' are:\tbirthday decorations\twedding decorations\thome decor\tparty decorations\nThere are several useful visual features to tell there are 'christmas decorations' and not similar things in a photo:\tspecific Christmas motifs (such as snowmen, reindeer, and Santa Claus)\tred, green, gold, silver, or white colors\tshiny or glittery surfaces\thanging from a tree or a ceiling", 15], "f": ["No. 'f' is a symbol and abstract concept, and does not have a tangible appearance.\nThere are no things that are visually similar to 'f' but are not 'f'.\nThere are no visual features applicable for distinguishing 'f' from similar things because there are no similar things to 'f'.", 15], "apple computer monitor": ["Yes. 'Apple computer monitor' has a tangible appearance and refers to a specific brand and type of monitor.\nA few things that are visually similar to 'apple computer monitor' but are not 'apple computer monitor' are:\t\nMonitors of other brands\nTelevisions\nProjectors\nThere are several useful visual features to tell there is 'apple computer monitor' and not similar things in a photo:\t\nSleek and thin design\nAluminum construction\nApple logo on the back or front\nGlossy or matte screen with a high resolution\nPorts for connecting to an Apple computer or laptop.", 15], "drift": ["No. 'Drift' is too vague or abstract to be distinguished in a photo. However, if the context is clear, then some visually concrete concepts related to 'drift' can be identified.\nFor example, if we are talking about a drifting car, then some things that are visually similar to 'drift' but are not 'drift' could be: skidding, slipping, sliding or driving.\nUseful visual features for distinguishing a drifting car from these similar things could be: tire smoke, sideways movement, or a specific driving stance.", 15], "maker": ["No. 'Maker' is too vague or abstract to be distinguished in a photo. It could refer to a person who creates something, but that person's appearance is not defined by the term.\n", 15], "plywood": ["Yes. 'Plywood' has a tangible appearance and is a type of wood paneling made of thin layers of wood veneer.\nA few things that are visually similar to 'plywood' but are not 'plywood' are:\tplastic sheets\tcardboard\tmetal panels\nThere are several useful visual features to tell there is 'plywood' and not similar things in a photo:\twood grain patterns multiple layers of wood veneer\tthickness and stiffness of the paneling\tinterlocking layers that create strength\tsquare or rectangular shape of the paneling.", 15], "fleece jacket": ["Yes. 'Fleece jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'fleece jacket' but are not 'fleece jacket' are:\twinter coat\tsweater\thoodie\tfur coat\nThere are several useful visual features to tell there is 'fleece jacket' and not similar things in a photo:\tfabric made of synthetic or recycled material\tsoft and fuzzy texture\tzipper or snap closure\tno fur or leather accents\ttypically in solid colors or simple patterns", 15], "puffy jacket": ["Yes. 'Puffy jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'puffy jacket' but are not 'puffy jacket' are:\tsweater\tcardigan\tpullover\nThere are several useful visual features to tell there is 'puffy jacket' and not similar things in a photo:\tmade of nylon or polyester\thas a quilted or padded appearance\tbulky and thick insulation\tpuffy appearance due to insulation or down-filled\tfront zipper and sometimes a hood", 15], "orange fruits": ["Yes. 'Orange fruits' has a tangible appearance and refers to a type of fruit that is predominantly orange in color.\nA few things that are visually similar to 'orange fruits' but are not 'orange fruits' are:\torange vegetables, like carrots or pumpkins\torange-colored candy or sweets\torange flowers\nThere are several useful visual features to tell there is 'orange fruits' and not similar things in a photo:\trounded shape\tpebbled or textured surface\tsegmented slices or sections\tfuzzy or smooth skin\tFruit stem at the top", 15], "shirt button": ["Yes. 'Shirt button' has a tangible appearance and is a small object used to fasten a shirt.\nA few things that are visually similar to 'shirt button' but are not 'shirt button' are:\tbeads\tsequins\teyelets\tsnap fasteners\t\nThere are several useful visual features to tell there is 'shirt button' and not similar things in a photo:\tcircular or round shape\tflat or slightly raised surface\tsewing holes in the center of the button\tusually made of plastic, metal, or pearl-like materials.", 15], "silver letters": ["Yes. 'Silver letters' has a tangible appearance and is a type of typography.\nA few things that are visually similar to 'silver letters' but are not 'silver letters' are:\twhite letters\tgold letters\tshimmering objects\tmirrors aluminum plates\nThere are several useful visual features to tell there are 'silver letters' and not similar things in a photo:\tsilver color, metallic appearance\trecognizable letter forms\tor lettering attached to an object (such as a trophy, plaque or award)", 15], "mobile": ["Yes. 'Mobile' has a tangible appearance and is a type of decorative object that hangs and moves in the air.\nA few things that are visually similar to 'mobile' but are not 'mobile' are:\tchandelier\tceiling fan\thanging sculpture\tballoon\nThere are several useful visual features to tell there is 'mobile' and not similar things in a photo:\tlightweight materials\thanging from a ceiling or structure\tintricate design\twith multiple elements, usually balanced to move and create harmony and balance in the air or wind.", 15], "pink container": ["Yes. 'Pink container' has a tangible appearance and is a specific type of object.\nA few things that are visually similar to 'pink container' but are not 'pink container' are:\tpink box\tpink bag\tpink basket\tpink bucket\nThere are several useful visual features to tell there is 'pink container' and not similar things in a photo:\tmade of hard material\tsolid pink color\twith a lid or cover\tmay have handles or straps.", 15], "saucers": ["Yes. 'Saucers' has a tangible appearance and is usually a type of dishware used for holding cups.\nA few things that are visually similar to 'saucers' but are not 'saucers' are:\tfrisbees\thovering disks\tplates\tlids\nThere are several useful visual features to tell there is 'saucers' and not similar things in a photo:\tshallow\tdiameter around 4-6 inches\tcircular shape\twith or without a pattern\tused in conjunction with cups in a set or display.", 15], "sidelines": ["Yes. 'Sidelines' has a tangible appearance and refers to the boundaries on the sports field.\nA few things that are visually similar to 'sidelines' but are not 'sidelines' are:\tsidewalks\tpavements\tmedian strips\nThere are several useful visual features to tell there is 'sidelines' and not similar things in a photo:\tbe alongside a sports field, especially a rectangular one\tsolid or dotted lines indicating out-of-bounds area\toften marked with team names or logos or advertisements", 15], "yellow edge": ["Yes. 'Yellow edge' has a tangible appearance and is a type of visual feature.\nA few things that are visually similar to 'yellow edge' but are not 'yellow edge' are:\tgolden rim\tyellow stripe\tborder\nThere are several useful visual features to tell there is 'yellow edge' and not similar things in a photo:\ta thin line with a consistent yellow color\tbordering the edge of an object\tor within an image, marking a boundary or area of interest", 15], "wall sconce": ["Yes. 'Wall sconce' has a tangible appearance and is a type of wall-mounted light fixture.\nA few things that are visually similar to 'wall sconce' but are not 'wall sconce' are:\tlanterns\tcandles\thanging lights\tmirrors\nThere are several useful visual features to tell there is 'wall sconce' and not similar things in a photo:\tfixed to the wall and not suspended\tprovides directed or diffused light\thas a lampshade or cover\tthat the lamp is not handheld", 15], "giraffe hooves": ["Yes. 'Giraffe hooves' has a tangible appearance and is a part of the giraffe's body.\nA few things that are visually similar to 'giraffe hooves' but are not 'giraffe hooves' are:\thorse hooves\tcow hooves\tzebra hooves\nThere are several useful visual features to tell there is 'giraffe hooves' and not similar things in a photo:\t\nlong and slender shape\ttwo small toes\twith skin that is darker than the rest of the leg", 15], "gazelles": ["Yes, 'gazelles' have a tangible appearance and are a type of antelope.\nA few things that are visually similar to 'gazelles' but are not 'gazelles' are:\tdeer\thorses\tcattle\nThere are several useful visual features to tell there is 'gazelles' and not similar things in a photo:\tlong, slender legs\tridged, sharp horns\tslender, graceful bodies\tshort, smooth fur\tdifferent color patterns on the coats, such as stripes on the face or legs.", 15], "dog house": ["Yes. 'Dog house' has a tangible appearance and is a kind of shelter for dogs.\nA few things that are visually similar to 'dog house' but are not 'dog house' are:\tbirdhouse\tplayhouse\tgarden shed\tstorage shed\ttool shed\nThere are several useful visual features to tell there is 'dog house' and not similar things in a photo:\tlow to the ground\tenclosed structure\twith a roof and walls\tentrance for dog\tsmall size compared to human houses\toften made of wood or plastic", 15], "leather shoe": ["Yes. 'Leather shoe' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'leather shoe' but are not 'leather shoe' are:\tsneakers\tboots\theels\tsandals\nThere are several useful visual features to tell there is 'leather shoe' and not similar things in a photo:\tsmooth, textured material that looks like leather\tlaces or buckles\tfor men, women or unisex design", 15], "pink curtain": ["Yes. 'Pink curtain' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'pink curtain' but are not 'pink curtain' are:\tred curtain\torange drapes\tmagenta blinds\t\nThere are several useful visual features to tell there is 'pink curtain' and not similar things in a photo:\tpink fabric\thanging from a window\tsheer or opaque texture\tfolded or gathered design.", 15], "banana bunches": ["Yes. 'Banana bunches' has a tangible appearance and is a collection of bananas that have grown together.\nA few things that are visually similar to 'banana bunches' but are not 'banana bunches' are:\tgrape bunches\t\t\t\twheat harvest\tbouquets of flowers\nThere are several useful visual features to tell there is 'banana bunches' and not similar things in a photo:\tlong curved yellow fruit\tgrowing together \thanging from a stem or branch\tyellow-peeled bananas with brown spots\tbananas at various stages of ripeness", 15], "outcrop": ["Yes. 'Outcrop' has a tangible appearance and is a geological term referring to the visible part of a rock formation.\nA few things that are visually similar to 'outcrop' but are not 'outcrop' are:\tboulder\tcliff\thill\tlarge rock\nThere are several useful visual features to tell there is 'outcrop' and not similar things in a photo:\ta visible part of a rock formation\texposed rock surface or strata\tsticking out from the ground or a hill", 15], "remains": ["Yes. 'Remains' has a tangible appearance and refers to the remaining parts of something or someone that may no longer be in their original form.\nA few things that are visually similar to 'remains' but are not 'remains' are:\tdust\tdirt\tdebris\nThere are several useful visual features to tell there are 'remains' and not similar things in a photo:\tpieces of bones, teeth or shells\truins or broken objects\tbits and parts of something that used to be whole the presence of markings or evidence of past life", 15], "spoon utensil": ["Yes. 'Spoon utensil' has a tangible appearance and is a kind of utensil.\nA few things that are visually similar to 'spoon utensil' but are not 'spoon utensil' are:\tfork\tknife\tchopsticks\tspork\nThere are several useful visual features to tell there is 'spoon utensil' and not similar things in a photo:\tbowl-shaped end\tat least one concave side\thandle on opposite end of bowl curvature", 15], "phrase": ["No. 'Phrase' is too vague or abstract to be distinguished in a photo. It refers to a group of words that express a single idea, which cannot be visually identified.", 15], "broccoli stalk": ["Yes. 'Broccoli stalk' has a tangible appearance and is a part of the broccoli vegetable.\nA few things that are visually similar to 'broccoli stalk' but are not 'broccoli stalk' are:\tcelery\tcabbage lettuce\nThere are several useful visual features to tell there is 'broccoli stalk' and not similar things in a photo:\t\ngreen\tcolorful buds or flowers on top\tthick stem or trunk\tdivided into smaller stems near the top", 15], "hand man": ["No. 'Hand man' is too vague or abstract to be distinguished in a photo.", 15], "bus license plate": ["Yes. 'Bus license plate' has a tangible appearance.\nA few things that are visually similar to 'bus license plate' but are not 'bus license plate' are:\tcar license plate\tmotorbike license plate\ttruck license plate\tboat license plate\nThere are several useful visual features to tell there is 'bus license plate' and not similar things in a photo:\tbig size\tcontains specific letters and numbers\tattached to the back of a bus or coach\tmetallic appearance\twithin a border or frame with state or country name", 15], "base ball cap": ["Yes. 'Baseball cap' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'baseball cap' but are not 'baseball cap' are:\tvisor\tbucket hat\ttop hat\tfedora\nThere are several useful visual features to tell there is 'baseball cap' and not similar things in a photo:\tworn on the head with brim or visor\tfive or six-panel construction\twith a button on the top of the crown\tfront panel made of firm material with a curved shape\tadjustable strap at the back for fitting", 15], "watch persons": ["No. 'Watch persons' is too vague or abstract to be distinguished in a photo. It could refer to security personnel, but it is not visually specific enough.\nIf we assume that you meant 'watch' as a verb, some visually similar things could be:\tguard\tpatrol\tpolice\nThere are several useful visual features to tell there is 'watch persons' and not similar things in a photo:\thigh visibility clothing\tradios or other communication devices\twalking around or standing guard\tin a specific location or area.", 15], "utility vehicle": ["Yes. 'Utility vehicle' has a tangible appearance and refers to a type of vehicle designed for practical use.\nA few things that are visually similar to 'utility vehicle' but are not 'utility vehicle' are:\tSUV\ttruck\tvan\ttractor\tbus\nThere are several useful visual features to distinguish 'utility vehicle' from the listed similar things in a photo:\tpractical and functional design\tlarge cargo space\tor durable cargo bed\traised ground clearance\ttowing capacity\tall-terrain tires\tboxy shape or rugged appearance", 15], "kitchen sink faucet": ["Yes. 'Kitchen sink faucet' has a visually concrete appearance as a type of plumbing fixture.\nA few things that are visually similar to 'kitchen sink faucet' but are not 'kitchen sink faucet' are:\tShowerhead\tBathroom Sink Faucet\tBathtub Faucet\nThere are several useful visual features to distinguish 'kitchen sink faucet' from the listed similar things in a photo:\n- Located above the sink basin\n- L-shaped or vertical structure\n- Dual handles or a single handle to control the water flow\n- Swivel or stationary spout for directing water flow\n- Popular finishes like chrome, brushed nickel or oil-rubbed bronze.", 15], "bus seat": ["Yes. 'Bus seat' has a tangible appearance and is a specific type of seating found in buses.\nA few things that are visually similar to 'bus seat' but are not 'bus seat' are:\tchair\tstool\tbench\tsofa\nThere are several useful visual features to tell there is 'bus seat' and not similar things in a photo:\tupholstered\tfoldable\tmounted to the floor or wall in rows\twith handles or rails for support\tno armrests or backrests for some models\tof a particular size and shape to fit the bus", 15], "pink spot": ["Yes. 'Pink spot' has a tangible appearance and is a specific colored spot.\nA few things that are visually similar to 'pink spot' but are not 'pink spot' are:\tred spot\tpurple spot\tbrown spot\tlight pink spot\nThere are several useful visual features to tell there is 'pink spot' and not similar things in a photo:\tspot with a vivid pink color\tno other colors mixed with the pink spot\tdifferent shade and texture than surrounding areas.", 15], "sweets": ["Yes. 'Sweets' has a tangible appearance and refers to different types of candies.\nA few things that are visually similar to 'sweets' but are not 'sweets' are:\tfruit\tdecorations\ttiny toys\nThere are several useful visual features to tell there is 'sweets' and not similar things in a photo:\tcolorful\tpatterned or textured\twrapped or unwrapped\tvariety of shapes and sizes\tsugar-coated or glazed glossy finish ", 15], "bikini bottoms": ["Yes. 'Bikini bottoms' has a tangible appearance and is a type of swimwear.\nA few things that are visually similar to 'bikini bottoms' but are not 'bikini bottoms' are:\tpanties\tshorts\texercise shorts\tspeedos\nThere are several useful visual features to tell there is 'bikini bottoms' and not similar things in a photo:\tfitting tightly to the hips\tand legs\thaving a low waistline\ttwo separate sections held together by a thin string or straps\tMade of swimsuit material", 15], "pink cap": ["Yes. 'Pink cap' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'pink cap' but are not 'pink cap' are:\tpink hat\tpink beanie\tpink bonnet\tpink headband\nThere are several useful visual features to distinguish 'pink cap' from the listed similar things in a photo:\ta circular or semi-circular brim\tsits low on the forehead or covers the ears\ttightly fitting to the head or is adjustable\thas a button or a strap to adjust the size\tcompletely covers the head or just the top", 15], "blue wire": ["Yes. 'Blue wire' has a tangible appearance and is a specific type of wire.\nA few things that are visually similar to 'blue wire' but are not 'blue wire' are:\tblack wire\twhite wire\tgreen wire\nThere are several useful visual features to tell there is 'blue wire' and not similar things in a photo:\tbright blue color\tsolid core or multiple strands of metal in the wire\tcan be used for electrical or other purposes\tmight have labels or markings indicating its use or specifications", 15], "spot light": ["Yes. 'Spot light' has a tangible appearance and is a type of lighting equipment.\nA few things that are visually similar to 'spot light' but are not 'spot light' are:\tflashlight\tstage light\tmoon or a bright star\tlaser pointer\nThere are several useful visual features to tell there is 'spot light' and not similar things in a photo:\tdirectional and adjustable\tlight focused on a specific area or object\tintense and bright beam\tusually mounted on a stand or tripod", 15], "muscle": ["Yes. 'Muscle' has a tangible appearance and is a type of tissue in the body.\nA few things that are visually similar to 'muscle' but are not 'muscle' are:\tfat\tbone\tcartilage\torgans\nThere are several useful visual features to tell there is 'muscle' and not similar things in a photo:\tlong and narrow shape\twith striations or lines running parallel to the length of the muscle\tvarying shades of pink or red, indicating blood flow\tcan appear in groups or singularly, depending on the muscle's location in the body", 15], "dressing": ["Yes. 'Dressing' has a tangible appearance and is a type of liquid or sauce used to add flavor to food.\nA few things that are visually similar to 'dressing' but are not 'dressing' are:\tgravy\tsyrup\tvinegar\tmarinade\tspread\nThere are several useful visual features to tell there is 'dressing' and not similar things in a photo:\tviscosity\tpourable texture\tvariety of colors (depending on flavor)\tpresented in a bowl or a dressing container\tlabeled as \"dressing\"", 15], "grip tape": ["Yes. 'Grip tape' has a tangible appearance and is a type of textured, adhesive tape used on skateboard decks or other surfaces requiring greater friction between shoes/surface.\nA few things that are visually similar to 'grip tape' but are not 'grip tape' are:\tduct tape\telectrical tape\tmasking tape\t\nThere are several useful visual features to tell there is 'grip tape' and not similar things in a photo:\trough or sandpapery texture\tblack, gray or any solid, bright colors placed on skateboard deck or other required surface\tadhesive surface for sticking on surface with additional grip characteristics.", 15], "stamp": ["Yes. 'Stamp' has a tangible appearance and typically refers to a small piece of paper used for postage.\nA few things that are visually similar to 'stamp' but are not 'stamp' are:\tsticker\tlabel\tseal\tticket\nThere are several useful visual features to tell there is 'stamp' and not similar things in a photo:\tpaper material\tstandard size and shape\tcontains official government or mailing organization logos or images\thas a value or denomination printed on it\tadheres to an envelope or package", 15], "grey umbrella": ["Yes. 'Grey umbrella' has a tangible appearance and is a kind of accessory for rain or sun protection.\nA few things that are visually similar to 'grey umbrella' but are not 'grey umbrella' are:\tparasol\tcane\that\nThere are several useful visual features to tell there is 'grey umbrella' and not similar things in a photo:\tcanopy-shaped object\ta stem or handle\tto be open\tor partly open\tto provide shade or protect from rain\ta foldable structure", 15], "hotel bed": ["Yes. 'Hotel bed' has a tangible appearance and is a type of bed used in hotels.\nA few things that are visually similar to 'hotel bed' but are not 'hotel bed' are:\tbunk bed\tcot\tfuton\tmattress\tsofa bed\nThere are several useful visual features to tell there is 'hotel bed' and not similar things in a photo:\tupholstered headboard\tcrisp, white sheets\tpillows\twith a bedside table\tor lamp or phone beside it", 15], "mini fridge": ["Yes. 'Mini fridge' has a tangible appearance and is a type of refrigerator.\nA few things that are visually similar to 'mini fridge' but are not 'mini fridge' are:\tcoolers\tstorage cabinets\tdrawers\tmicrowaves\nThere are several useful visual features to tell there is 'mini fridge' and not similar things in a photo:\tsmall in size\tdoor with a handle\tand shelves for storage\tplug and electrical cord\tat least one cooling element, such as a compressor or thermoelectric module.", 15], "silver hoop": ["Yes. 'Silver hoop' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'silver hoop' but are not 'silver hoop' are:\tcircular earring\tbangle\tbracelet\twedding ring\nThere are several useful visual features to tell there is 'silver hoop' and not similar things in a photo:\tcircular shape\tmade of silver or silver-colored metal\thollow design worn in the earlobes or hanging from the neck", 15], "gold faucet": ["Yes. 'Gold faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'gold faucet' but are not 'gold faucet' are:\tbronze faucet\tstainless steel faucet\tchrome faucet\nThere are several useful visual features to tell there is 'gold faucet' and not similar things in a photo:\tgold metal color\tsmooth surface\tshiny finish\twater handles\tand water spout shaped as an arc or curve.", 15], "motorcycle jacket": ["Yes. 'Motorcycle jacket' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'motorcycle jacket' but are not 'motorcycle jacket' are: bomber jacket, leather jacket, denim jacket, windbreaker.\nThere are several useful visual features to distinguish a 'motorcycle jacket' from the listed similar things in a photo:\ta lot of zippers\tmetal studs\tthick leather or other durable material\tpadding on the shoulders\tand arms.", 15], "wiimote": ["Yes. 'Wiimote' has a tangible appearance and is a handheld device used as a game controller.\nA few things that are visually similar to 'wiimote' but are not 'wiimote' are:\ttv remote\tcontrol pad\tgame controllers\nThere are several useful visual features to tell there is 'wiimote' and not similar things in a photo:\tretangular form with rounded edges\tsensor bar\tat least two buttons\tdirection buttons\ttrigger buttons\tplus sign, minus sign, and home buttons\tnunchuck and wrist strap pluggable support.", 15], "tennis player playing tennis": ["Yes. 'Tennis player playing tennis' has a tangible appearance and refers to a specific activity.\nA few things that are visually similar to 'tennis player playing tennis' but are not 'tennis player playing tennis' are:\tpeople playing badminton\tpeople playing volleyball\tpeople playing basketball\nThere are several useful visual features to tell there is 'tennis player playing tennis' and not similar things in a photo:\ttennis court with a net\ttennis racket\twith tennis balls\tin motion, hitting or returning the ball in a specific way\tcorrect tennis attire (i.e. appropriate shoes, shorts, shirt)", 15], "paddle boat": ["Yes. 'Paddle boat' has a tangible appearance and is a kind of watercraft.\nA few things that are visually similar to 'paddle boat' but are not 'paddle boat' are:\tkayak\tcanoe\tspeedboat\traft\nThere are several useful visual features to tell there is 'paddle boat' and not similar things in a photo:\ttwo paddle wheels on either side of the boat\tshallow draft flat-bottom hull\tpaddlewheel on the back end of the boat\topen-air upper deck and enclosed lower deck", 15], "blue straps": ["Yes. 'Blue straps' has a tangible appearance and refers to a specific type of strap that is blue in color.\nA few things that are visually similar to 'blue straps' but are not 'blue straps' are:\tred straps\tgreen straps\tblack straps\tleather belts\nThere are several useful visual features to tell there are 'blue straps' and not similar things in a photo:\tbluish color\tsmooth texture\tlong and narrow shape\tmade of fabric or nylon material\tmetal buckles might be attached to the straps", 15], "post fence": ["Yes. 'Post fence' has a tangible appearance and is a type of fence made up of vertical posts.\nA few things that are visually similar to 'post fence' but are not 'post fence' are:\tpicket fence\tchain-link fence\twall\nThere are several useful visual features to tell there is 'post fence' and not similar things in a photo:\n\tvertical posts attached by horizontal rails\n\tgaps between posts\tthat create openings", 15], "cement bridge": ["Yes. 'Cement bridge' has a tangible appearance and is a type of bridge made of concrete.\nA few things that are visually similar to 'cement bridge' but are not 'cement bridge' are:\twooden bridge\trope bridge\tmetal bridge\tstone bridge\nThere are several useful visual features to tell there is 'cement bridge' and not similar things in a photo:\tsmooth surface made of concrete\tpillars or supports made of concrete or steel\tgray or off-white color\tstraight or angular lines", 15], "round part": ["Yes. 'Round part' has a tangible appearance and refers to any object or part that is round in shape.\nA few things that are visually similar to 'round part' but are not 'round part' are:\tcircles\tballs\twheels\tcoins\nThere are no visual features needed to distinguish 'round part' from the listed similar things in a photo, as 'round part' can refer to any object or part that is round in shape. However, contextual clues may help determine the specific type of round part being referred to.", 15], "cotton shirt": ["Yes. 'Cotton shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'cotton shirt' but are not 'cotton shirt' are:\tpolyester shirt\tlinen shirt\twool sweater\tt-shirt\nThere are several useful visual features to tell there is 'cotton shirt' and not similar things in a photo:\tsoft and breathable fabric\tmade of cotton material\tbutton-up or collared neckline\tlong or short sleeves.", 15], "seams": ["Yes. 'Seams' has a tangible appearance and refers to the line where two pieces of fabric or material are sewn together.\nA few things that are visually similar to 'seams' but are not 'seams' are: folds, wrinkles or creases in fabric, patterns, or designs on fabric.\nThere are several useful visual features to tell there are 'seams' and not similar things in a photo:\tStraight or curved lines that are slightly raised from the surface of the material or fabric where two pieces of fabric come together. The color of the thread may be different from the surrounding material.", 15], "girl playing tennis": ["Yes. 'Girl playing tennis' has a tangible appearance and is a specific type of physical activity and sport.\nA few things that are visually similar to 'girl playing tennis' but are not 'girl playing tennis' are:\tgirl playing racquetball\tgirl playing badminton\tgirl playing volleyball\nThere are several useful visual features to tell there is 'girl playing tennis' and not similar things in a photo:\tholding a tennis racket\twearing tennis shoes or sneakers\thitting a tennis ball\twith a net in the background or a tennis court", 15], "drinking glasses": ["Yes. 'Drinking glasses' has a tangible appearance and is typically made of glass or plastic.\nA few things that are visually similar to 'drinking glasses' but are not 'drinking glasses' are:\tvases\tjars\tcandle holders\tflower pots\nThere are several useful visual features to tell there is 'drinking glasses' and not similar things in a photo:\t\ncurved or straight walls\ttapered or wide opening\tclear or colored\ttranslucent or opaque\tsmooth or textured surface\thas a flat base to stand on.", 15], "burn marks": ["Yes. 'Burn marks' has a tangible appearance and is a visible mark left by fire or heat.\nA few things that are visually similar to 'burn marks' but are not 'burn marks' are:\tdirt\tstains\tshadows\tdecorations\nThere are several useful visual features to tell there is 'burn marks' and not similar things in a photo:\tirregular shape\tand texture\tcharred or blackened appearance\tsurrounding areas appeared unaffected by the same texture or color\tsmoky residue around the edges of the mark", 15], "tree shadow": ["Yes. 'Tree shadow' has a tangible appearance and is a type of shadow.\nA few things that are visually similar to 'tree shadow' but are not 'tree shadow' are:\tumbrella shadows\tbuilding shadows\thuman shadows\nThere are several useful visual features to tell there is 'tree shadow' and not similar things in a photo:\tshaped like a tree\thas branch-like branches\thas leaves or needle-like leaves", 15], "grey cell phone": ["Yes. 'Grey cell phone' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'grey cell phone' but are not 'grey cell phone' are:\ttablet\tcamera\twristwatch\nThere are several useful visual features to tell there is 'grey cell phone' and not similar things in a photo:\trectangular or square shape\twith a touch screen\tdisplaying numbers, letters, and icons\ton-screen icons that represent apps, such as a phone or message\tapplying grey color predominately", 15], "extension cord": ["Yes, 'extension cord' has a tangible appearance and refers to a type of electrical cord used for extending the reach of an electrical outlet.\nA few things that are visually similar to 'extension cord' but are not 'extension cord' are:\tregular electrical cord\tpower strip\taudio cable\tvideo cable\t\nThere are several useful visual features to tell there is 'extension cord' and not similar things in a photo:\tlong and thin with multiple outlets\tshiny or matte finish\tmay have a round or flat plug on the end\thas two or more prongs at one end to insert into an outlet.", 15], "metal brackets": ["Yes. 'Metal brackets' is a visually concrete concept and is a type of hardware.\nA few things that are visually similar to 'metal brackets' but are not 'metal brackets' are:\thinges\tclasps\tbraces\tfasteners\nThere are several useful visual features to tell there is 'metal brackets' and not similar things in a photo:\tangled shapes\tmade of metal\tused for attaching or securing objects to surfaces\tmay have screw holes or other attachment points", 15], "womens arm": ["Yes. 'Womens arm' has a tangible appearance and refers to the arm of a female.\nA few things that are visually similar to 'womens arm' but are not 'womens arm' are:\tmens arm\tchilds arm\tsculpture of an arm\tanimal limb\nThere are several useful visual features to tell there is 'womens arm' and not similar things in a photo:\tslim and curved fingers or nails\tthe presence of accessories like bracelets, rings or painted nails\tfeminine clothing or decoration on the arm, such as a tattoo or henna", 15], "cycles": ["Yes. 'Cycles' has a tangible appearance and refers to a means of transportation.\nA few things that are visually similar to 'cycles' but are not 'cycles' are:\tmotorbikes\tscooters\tbicycles\twith motorized pedals\nThere are several useful visual features to tell there is 'cycles' and not similar things in a photo:\ttwo-wheeled vehicle\tpedals for foot propulsion\thandles for steering\tthe absence of a motor or engine\tchain-driven rotation of the rear wheel", 15], "barrier wall": ["Yes. 'Barrier wall' has a tangible appearance and is a type of physical structure.\nA few things that are visually similar to 'barrier wall' but are not 'barrier wall' are:\tfence\tgate\troad divider\tmedian barrier\tguardrail\nThere are several useful visual features to tell there is 'barrier wall' and not similar things in a photo:\tvery tall and wide\tconcrete or metal material\texplicitly designed to prevent access, stop traffic or protect people.", 15], "dense trees": ["Yes. 'Dense trees' has a tangible appearance and refers to a forest or a heavily wooded area.\nA few things that are visually similar to 'dense trees' but are not 'dense trees' are:\tindividual trees\tshrubs\thedge\nThere are several useful visual features to tell there are 'dense trees' and not similar things in a photo:\ta large number of trees\tclose proximity of trees\ta canopy covering most of the area\tshadows on the ground from overlapping leaves or branches", 15], "bus stop sign": ["Yes. 'Bus stop sign' has a tangible appearance and is a type of street sign.\nA few things that are visually similar to 'bus stop sign' but are not 'bus stop sign' are:\tstop sign\tparking sign\tspeed limit sign\ttraffic light\nThere are several useful visual features to tell there is 'bus stop sign' and not similar things in a photo:\tblue and white color scheme\tbus or transportation-related graphics or text\tdesignated bus stop marker, such as a pole or bench located near the sign", 15], "binding": ["No. 'Binding' is too vague or abstract to be distinguished in a photo. However, if we are talking about book binding, it may have tangible appearance.\nA few things that are visually similar to 'binding' but are not 'binding' are:\ttwist tie\trope\ttape\tribbon\nThere are several useful visual features to tell there is 'binding' and not similar things in a photo, especially for book binding:\thardcover or softcover book stitch\tbinding glue\tbinding tape\tlarge spine\tholder for pages", 15], "leafy greens": ["Yes. 'Leafy greens' has a tangible appearance and refers to a group of green vegetables.\nA few things that are visually similar to 'leafy greens' but are not 'leafy greens' are:\therbs\tgrass\tpotted plants\t\nThere are several useful visual features to tell there are 'leafy greens' and not similar things in a photo:\tbroad, flat leaves\tgreen color\tlarge size\tgrowing out of soil or a plant bed typically used for vegetables.", 15], "juicer": ["Yes. 'Juicer' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'juicer' but are not 'juicer' are:\tblender\tfood processor\tmixer\tgrinder\nThere are several useful visual features to tell there is 'juicer' and not similar things in a photo:\tcylindrical or conical shape\twith a spout and pulp collector\tusually in shades of silver or stainless steel with some transparent plastic parts.", 15], "bread basket": ["Yes. 'Bread basket' has a tangible appearance and is a type of container used for holding bread.\nA few things that are visually similar to 'bread basket' but are not 'bread basket' are:\tfruit basket\twaste basket\tpicnic basket\tclothes basket\tbasketball\nThere are several useful visual features to tell there is 'bread basket' and not similar things in a photo:\toval or round shape\twoven material or texture\tsimilar size to a loaf of bread\thandles on either side\tof appropriate weight to carry bread.", 15], "arrow points": ["Yes. 'Arrow points' has a tangible appearance and refers to the pointed end of an arrow.\nA few things that are visually similar to 'arrow points' but are not 'arrow points' are:\tnails\tpins\ttacks\tthumbtacks\nThere are several useful visual features to tell there is 'arrow points' and not similar things in a photo:\tpointed and sharp\ttapered shape\ta visible notch or groove for attaching to an arrow shaft\tmade of metal, stone, or other hard materials.", 15], "airplane landing gear": ["Yes. 'Airplane landing gear' has a tangible appearance.\nA few things that are visually similar to 'airplane landing gear' but are not 'airplane landing gear' are:\tengine\tcar wheels\tbike wheels\nThere are several useful visual features to tell there is 'airplane landing gear' and not similar things in a photo:\tlocated under the airplane\tbody/tires of the landing gear\thinged\tfor planes with wheels: multiple wheels and shocks\tsystem for retracting the wheels into the plane for takeoff and flight", 15], "turbines": ["Yes. 'Turbines' has a tangible appearance and is a type of machinery used for generating energy.\nA few things that are visually similar to 'turbines' but are not 'turbines' are:\tfans\twindmills\tpropellers\tgears\nThere are several useful visual features to tell there is 'turbines' and not similar things in a photo:\trotating blades or vanes\tsymmetrical and evenly spaced blades\tinlet and outlet ports or ducts\tmetallic, industrial appearance\tattached to a power generator or engine", 15], "bottom layer": ["Yes. 'Bottom layer' has a tangible appearance and refers to the lowest or foundational layer of something.\nA few things that are visually similar to 'bottom layer' but are not 'bottom layer' are:\ttop layer\tmiddle layer\tsurface\tlevel\nThere are several useful visual features to tell there is 'bottom layer' and not similar things in a photo:\tpositioned at the bottom or base of something covered by other layers or objects\tcarrying more weight than other layers or objects underlying the rest of the structure or composition.", 15], "bedroom wall": ["Yes. 'Bedroom wall' has a tangible appearance and is a part of a room.\nA few things that are visually similar to 'bedroom wall' but are not 'bedroom wall' are:\tbathroom wall\tkitchen wall\tliving room wall\toffice wall\nThere are several useful visual features to tell there is 'bedroom wall' and not similar things in a photo:\tclose proximity to a bed or a sleeping area\tpaint, wallpaper, or other covering and patterns\tfurniture or decorations in front or against it\tdifferences in lighting or shadows due to position in the room", 15], "beer mug": ["Yes. 'Beer mug' has a tangible appearance and is a type of drinking container.\nA few things that are visually similar to 'beer mug' but are not 'beer mug' are:\twine glass\ttumbler\tjar\t\nThere are several useful visual features to tell there is 'beer mug' and not similar things in a photo:\tthick glass or ceramic material\thandle on the side\twide mouth or opening\tfor holding beer or other foamy drinks.", 15], "blue wheel": ["Yes. 'Blue wheel' has a tangible appearance and is a specific type of wheel.\nA few things that are visually similar to 'blue wheel' but are not 'blue wheel' are:\tred wheel\tbicycle wheel\tmetallic wheel\tblue and circular object\nThere is only one useful visual feature to distinguish 'blue wheel' from the listed similar things in a photo:\tthe wheel is blue in color, while the other similar things do not meet this criteria.", 15], "cuts": ["No. 'Cuts' is too vague or abstract to be distinguished in a photo.", 15], "cutting boards": ["Yes, 'cutting boards' has a tangible appearance and is a type of kitchen tool used for cutting food.\nA few things that are visually similar to 'cutting boards' but are not 'cutting boards' are:\tcheese board\tserving tray\tbread board\t\nThere are several useful visual features to distinguish 'cutting boards' from the listed similar things in a photo:\tusually made of wood, plastic or bamboo\tflat surface with a raised edge\tto be used for preparing food with a knife\tmay have cutting marks or stains from previous use.", 15], "store name": ["No. 'Store name' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we talk about the physical signage that displays the store name, then the answer would be 'yes'.\n\nA few things that are visually similar to the physical signage displaying the store name but are not 'store name' are:\tadvertisements\tneon signs\twall art\tmurals\nThere are several useful visual features to tell there is a store name sign and not similar things in a photo:\tclearly displays the name of a store or business\tusually includes a logo or graphic design\tmade of durable materials such as glass, metal, or plastic\tmounted on or hanging from a building or structure.", 15], "bus shelter": ["Yes. 'Bus shelter' has a tangible appearance and is a structure designed for people to wait for buses in.\nA few things that are visually similar to 'bus shelter' but are not 'bus shelter' are:\toverhangs\tcanopies\tpavilions\tawnings\nThere are several useful visual features to tell there is 'bus shelter' and not similar things in a photo: \n- contains benches or seating areas\n- usually has advertisements or schedules posted\n- has a covered roof that provides shade or shelter from rain\n- typically located near sidewalks or roadways", 15], "pretzels": ["Yes. 'Pretzels' has a tangible appearance and is a type of baked snack.\nA few things that are visually similar to 'pretzels' but are not 'pretzels' are:\tbagels\tdonuts\tchurros\tbreadsticks\nThere are several useful visual features to tell there is 'pretzels' and not similar things in a photo:\ttwisty knot shape\tcrunchy texture\tbrown color\tsalt crystals on the surface", 15], "polka dot": ["Yes. 'Polka dot' has a tangible appearance and is a specific pattern consisting of filled circles.\nA few things that are visually similar to 'polka dot' but are not 'polka dot' are:\tsplatters\tbubbles\tspots\thighlights\nThere are several useful visual features to distinguish 'polka dot' from similar things in a photo:\tcircular shape\tsymmetrical circles\teven spacing\tbold contrast between the circles and the background", 15], "menu sign": ["Yes. 'Menu sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'menu sign' but are not 'menu sign' are:\tdirection sign\tadvertisements\thours of operation sign\tcaution sign\nThere are several useful visual features to tell there is 'menu sign' and not similar things in a photo:\tdisplay of food or drinks\twith or without prices\tlarge, bold lettering\toften found in front of restaurants or cafes", 15], "pita": ["Yes. 'Pita' has a tangible appearance and is a type of bread.\nA few things that are visually similar to 'pita' but are not 'pita' are:\ttortilla\tnaan\tbaguette\tloaf of bread\nThere are several useful visual features to tell there is 'pita' and not similar things in a photo:\tround or oval-shaped pocket bread\tdense and chewy texture\tthin crust", 15], "blue flame": ["Yes. 'Blue flame' has a tangible appearance and is a type of fire that burns at a high temperature.\nA few things that are visually similar to 'blue flame' but are not 'blue flame' are:\tcandle light\tincandescent light bulb\tsunrise or sunset\tcampfire\nThere are several useful visual features to tell there is 'blue flame' and not similar things in a photo:\tbright blue color\thot and intense\tlight flickering\tusually found in a gas stove or a Bunsen burner", 15], "padding": ["Yes. 'Padding' has a tangible appearance and refers to a type of material used for cushioning or protection.\nA few things that are visually similar to 'padding' but are not 'padding' are:\tpillows\tsponges\tfoam\tmattresses\nThere are several useful visual features to tell there is 'padding' and not similar things in a photo:\tthick and fluffy texture\tused to cushion or protect objects or surfaces\tcommonly made of cotton, polyester, or other soft materials\tsometimes covered in a protective layer or fabric", 15], "chocolate shavings": ["Yes. 'Chocolate shavings' has a tangible appearance and is a food item.\nA few things that are visually similar to 'chocolate shavings' but are not 'chocolate shavings' are:\traisins\tcinnamon\tshredded coconut\nThere are several useful visual features to tell there is 'chocolate shavings' and not similar things in a photo:\tthinner than chocolate chips\tcurled or flat shape\tdark brown color\tirregular edges\texplicitly made from chocolate", 15], "glass shower": ["Yes. 'Glass shower' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'glass shower' but are not 'glass shower' are:\tglass partition\tregular shower\tcubicle\twithout glass\nThere are several useful visual features to tell there is 'glass shower' and not similar things in a photo:\ttransparent glass walls and door\tmetallic frames\tand handle\ttiled floor and walls\tdrain in the center of the floor.", 15], "dirty sink": ["Yes. 'Dirty sink' has a tangible appearance.\nA few things that are visually similar to 'dirty sink' but are not 'dirty sink' are:\tsink with dishes\tempty sink\twith stains from water or cleaning products\nThere are several useful visual features to tell there is 'dirty sink' and not similar things in a photo:\tdiscoloration or staining\tdirt or residue\tfrom soap, toothpaste\tor other substances\tvisible food particles, grease or grime\tmold or mildew buildup, or rust\tchalky buildup on or around the faucet or sink basin.", 15], "sprigs": ["Yes. 'Sprigs' has a tangible appearance and is a small stem with leaves or flowers.\nA few things that are visually similar to 'sprigs' but are not 'sprigs' are:\tbranches\ttwigs\tflowers\tleaves\nThere are several useful visual features to tell there is 'sprigs' and not similar things in a photo:\ta small stem with leaves or flowers\tgreen or brown color\tA smaller size than branches or twigs.", 15], "blue windows": ["Yes. 'Blue windows' has a tangible appearance and is a specific type of architectural element.\nA few things that are visually similar to 'blue windows' but are not 'blue windows' are:\tblue doors\tstained glass windows\tblue shutters\t\nThere are several useful visual features to tell there are 'blue windows' and not similar things in a photo:\trectangular or square shape\ttranslucent or transparent material\tframed with blue color.", 15], "suit top": ["Yes. 'Suit top' has a tangible appearance and is a part of a suit.\nA few things that are visually similar to 'suit top' but are not 'suit top' are:\tjacket\tblazer\tsweater\t\nThere are several useful visual features to tell there is 'suit top' and not similar things in a photo:\tcollar and lapels\tbuttons or a zipper\tslim fit\ttailored or structured appearance\ttop portion of a two-piece suit.", 15], "dog bowl": ["Yes. 'Dog bowl' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'dog bowl' but are not 'dog bowl' are:\tcat bowl\thuman bowl\tplastic container\tsoup bowl\nThere are several useful visual features to tell there is 'dog bowl' and not similar things in a photo:\tround or oval shape\tshallow depth\twide base to prevent tipping\tlarge enough to hold food or water for dogs\tmade of plastic or metal material\tdesign with dog-related motifs or words", 15], "blackbird": ["Yes. 'Blackbird' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'blackbird' but are not 'blackbird' are:\trobin\tcrow\traven\tgrackle\nThere are several useful visual features to tell there is 'blackbird' and not similar things in a photo:\tentirely black feathers\tyellow or orange eyes\tyellow beak and feet\thooked beak\tshiny feathers\tslender body and long tail", 15], "crops": ["Yes. 'Crops' has a tangible appearance and refers to plants grown for food or other agricultural purposes.\nA few things that are visually similar to 'crops' but are not 'crops' are:\twild plants\tgarden plants\tweeds\tdecorative plants\nThere are several useful visual features to tell there are 'crops' and not similar things in a photo:\twell-spaced rows or patterns\tin an agricultural field\tor in a greenhouse\tvisible produce, such as tomatoes or corn\tstalks or vines supporting the produce.", 15], "bent elbow": ["Yes. 'Bent elbow' has a tangible appearance and is a type of body part.\nA few things that are visually similar to 'bent elbow' but are not 'bent elbow' are:\tcurved pipe\tbent wire\those\tadducted shoulder\nThere are several useful visual features to tell there is 'bent elbow' and not similar things in a photo:\tlocated in the middle of the arm\ttwo connecting bones\tthat allows for bending and extending\taction of bringing the palm towards the shoulder", 15], "gym": ["Yes. 'Gym' has a tangible appearance and is a place for physical exercise.\nA few things that are visually similar to 'gym' but are not 'gym' are:\tfitness studio\thome workout space\tplayground\thealth center\nThere are several useful visual features to tell there is 'gym' and not similar things in a photo:\tworkout machines and equipment\tweights and dumbbells\tinstructor or trainers\texercise mats or floors\tfitness posters or signs\tsweat towels or water bottles", 15], "grey vehicle": ["Yes. 'Grey vehicle' has a tangible appearance and is a type of vehicle with a specific color.\nA few things that are visually similar to 'grey vehicle' but are not 'grey vehicle' are:\tblack vehicle\tsilver vehicle\twhite vehicle\nThere are several useful visual features to tell there is 'grey vehicle' and not similar things in a photo:\tgrey color\tbody shape and size of a car, truck, or van\twheels and tires\tmetallic sheen or matte finish", 15], "stuffed doll": ["Yes. 'Stuffed doll' has a tangible appearance and is a kind of toy.\nA few things that are visually similar to 'stuffed doll' but are not 'stuffed doll' are:\tplush animals\tpillows\thandbags\nThere are several useful visual features to tell there is 'stuffed doll' and not similar things in a photo:\t\nhuman or animal shape\t\nsoft, plush exterior\t\nstitched eyes, mouth, and nose\t\nlimbs and a torso\t\ndressed in clothing or accessories that suggest a \"character\" or \"personality\"", 15], "foot strap": ["Yes. 'Foot strap' has a tangible appearance and is a type of strap used for securing the foot.\nA few things that are visually similar to 'foot strap' but are not 'foot strap' are:\thand strap\tbackpack strap\tsandal strap\tbelt\nThere are several useful visual features to tell there is 'foot strap' and not similar things in a photo:\tattached to a shoe or a board\tsecuring the foot\ton or near the foot in the photo", 15], "plastic toilet lid": ["Yes. 'Plastic toilet lid' has a tangible appearance and is a kind of cover for a toilet.\nA few things that are visually similar to 'plastic toilet lid' but are not 'plastic toilet lid' are:\tplastic trash can lid\tplastic container lid\nThere are several useful visual features to tell there is 'plastic toilet lid' and not similar things in a photo:\toval or round shape\tflat\ttop\thinge at the back\tfor use on a toilet.", 15], "wood bowl": ["Yes. 'Wood bowl' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'wood bowl' but are not 'wood bowl' are:\twooden plate\tcutting board\tpan\tbasket\nThere are several useful visual features to tell there is 'wood bowl' and not similar things in a photo:\tbowl-shaped\thollowed interior\tmade of wood or has a wood grain pattern\ton a table or countertop for holding food or other items", 15], "cutter": ["Yes. 'Cutter' has a tangible appearance and is a tool used for cutting.\nA few things that are visually similar to 'cutter' but are not 'cutter' are:\tscissors\tknife\tsaw\taxe\nThere are several useful visual features to tell there is 'cutter' and not similar things in a photo:\tone or two handles\twith one or two sharp blades\tused to cut through material such as paper, fabric or wood", 15], "pizza board": ["Yes. 'Pizza board' has a tangible appearance and is a type of cutting board used for preparing and serving pizza.\nA few things that are visually similar to 'pizza board' but are not 'pizza board' are:\tcutting board\tserving platter\tcoaster\ttray\nThere are several useful visual features to tell there is 'pizza board' and not similar things in a photo:\trectangular or circular shape\tthin and flat\tsmall or no handles\tcovered with flour or cornmeal when used for baking pizzas\tclean and free of knife marks when used for serving pizzas", 15], "watch mans": ["No. 'Watch mans' is too vague or abstract to be distinguished in a photo. The correct term would be 'watchmen' which is the plural form of 'watchman'.\nA few things that are visually similar to 'watchmen' but are not 'watchmen' are:\tpolice officers\tsecurity guards\tgardeners\nThere are several useful visual features to tell there is 'watchmen' and not similar things in a photo:\tuniforms or vests\tthat indicate security or guarding duties\tflashlights or walkie-talkies\tletters or labels indicating the name of the company they work for.", 15], "grass blades": ["Yes. 'Grass blades' has a tangible appearance and refers to the thin, long leaves that grow from the ground in a grass plant.\nA few things that are visually similar to 'grass blades' but are not 'grass blades' are:\tleaves of other plants\tthin sticks/paper/garbage lying on the ground\nThere are several useful visual features to tell there are 'grass blades' and not similar things in a photo:\tlong and narrow shape\thollow or cylindrical\tconnected to the ground in clumps or individual stems", 15], "water glasses": ["Yes. 'Water glasses' has a tangible appearance and refers to a specific type of glass for drinking water.\nA few things that are visually similar to 'water glasses' but are not 'water glasses' are:\twine glasses\tbeer glasses\ttumblers\tshot glasses\nThere are several useful visual features to distinguish 'water glasses' from the listed similar things in a photo:\t\n- Narrow glass with a wide base and opening\n- Often made of clear glass\n- No stem or handle \n- Generally holds 8 to 10 ounces of liquid", 15], "man shoe": ["Yes. 'Man shoe' has a tangible appearance and refers to a type of footwear designed for men.\nA few things that are visually similar to 'man shoe' but are not 'man shoe' are:\twoman shoe\tboot\tsandal\tloafer\nThere are several useful visual features to tell there is 'man shoe' and not similar things in a photo:\tlace-up or slip-on design\theel\tclosed toe\tboxy shape\tthick sole\tneutral or dark colors (e.g. black, brown)", 15], "wooden paddle": ["Yes. 'Wooden paddle' has a tangible appearance and is a tool made of wood.\nA few things that are visually similar to 'wooden paddle' but are not 'wooden paddle' are:\tplank\tof wood\trope\twooden spoon\nThere are several useful visual features to tell there is 'wooden paddle' and not similar things in a photo:\tflat and broad\tshaped like a rectangle or an oval\thandle attached to one end\tmay have holes or slots in it\tfor use in rowing or mixing", 15], "sticker apple": ["Yes. 'Sticker apple' has a tangible appearance and is a type of label or decal.\nThere aren't many things that are visually similar to 'sticker apple' but are not 'sticker apple'.\nThere are no useful visual features to distinguish a \"sticker apple\" from the actual fruit, an apple, as the term \"sticker apple\" is used to refer to a type of label or decal that is designed to look like an apple.", 15], "cheddar cheese": ["Yes. 'Cheddar cheese' has a tangible appearance and is a type of cheese.\nA few things that are visually similar to 'cheddar cheese' but are not 'cheddar cheese' are:\tmozzarella cheese\tswiss cheese\tprovolone cheese\tfeta cheese\nThere are several useful visual features to tell there is 'cheddar cheese' and not similar things in a photo:\torange or yellow color\thard or semi-soft texture\tspecific ridges or holes in the cheese\tblock-shaped appearance", 15], "pie crust": ["Yes. 'Pie crust' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'pie crust' but are not 'pie crust' are:\tcake frosting\tbread\tcrackers\tcookies\nThere are several useful visual features to tell there is 'pie crust' and not similar things in a photo:\tflaky texture\tcircular or pie-shaped\tbrown, golden, or light color\tcrumbly appearance\tdough often rolled out on a flat surface", 15], "crack road": ["No. 'Crack road' is too vague or abstract to be distinguished in a photo. It is not a commonly used term, and it's not clear what it refers to.", 15], "motorcycle rear tire": ["Yes. 'Motorcycle rear tire' has a tangible appearance and is a specific part of a vehicle.\nA few things that are visually similar to 'motorcycle rear tire' but are not 'motorcycle rear tire' are:\tbicycle tire\tcar tire\tmachinery tire\ttruck tire\nThere are several useful visual features to tell there is 'motorcycle rear tire' and not similar things in a photo:\tnarrow\twidth-to-height ratio around 0.6-0.7\ttread pattern\twith or without fender\tside-mounted disk or drum brake", 15], "house wall": ["Yes. 'House wall' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'house wall' but are not 'house wall' are:\tfence\tbridge\tdam\nThere are several useful visual features to tell there is 'house wall' and not similar things in a photo:\tmade of brick, stone, wood, or cement\tvertical surface\tpart of a building\tmay have windows or doors\tRectangular or square shape.", 15], "locker": ["Yes. 'Locker' has a tangible appearance and is a type of storage unit.\nA few things that are visually similar to 'locker' but are not 'locker' are:\tcabinet\tshelves\tdrawers\tchest\nThere are several useful visual features to tell there is 'locker' and not similar things in a photo:\tmetallic appearance\tor plastic or wood and metal combination\tventilated door and sides\tlock mechanism\tand/or padlock\thanging hooks and shelves inside", 15], "toilet papers": ["Yes. 'Toilet papers' has a tangible appearance and is a type of paper used in bathrooms.\nA few things that are visually similar to 'toilet papers' but are not 'toilet papers' are:\ttissues\tnotebooks\tnewspapers\tparchment paper\nThere are several useful visual features to tell there is 'toilet papers' and not similar things in a photo:\trolled up\tcylindrical shape\twhite or off-white color\tsoft and thin texture", 15], "wood stick": ["Yes. 'Wood stick' has a tangible appearance and is a type of wooden material.\nA few things that are visually similar to 'wood stick' but are not 'wood stick' are:\tlog\tcane\tbroom\thandle\tbarbecue skewer\nThere are several useful visual features to tell there is 'wood stick' and not similar things in a photo:\tlong and narrow shape\trough texture made of wood\tmay have bark on it", 15], "purple color": ["Yes. 'Purple color' has a tangible appearance and is a specific hue of color.\nA few things that are visually similar to 'purple color' but are not 'purple color' are:\tviolet\tmagenta\tplum\tburgundy\nThere are several useful visual features to tell there is 'purple color' and not similar things in a photo:\ta mix of blue and red light\tsaturated and bright\thaving a long wavelength on the visible spectrum", 15], "lit window": ["Yes. 'Lit window' has a tangible appearance.\nA few things that are visually similar to 'lit window' but are not 'lit window' are:\tdecorative lights\tbright computer screen\topen door with light shining through\tit also can be any object inside a building that emits light out of a window, such as a lamp or a TV\nThere are several useful visual features to tell there is 'lit window' and not similar things in a photo:\tthe window is visibly emitting light\tthe light is bright enough to light up the surrounding area or cast a reflection on nearby objects\tthe light is warm, yellowish or white", 15], "grey phone": ["Yes. 'Grey phone' has a tangible appearance and is an electronic device.\n\nA few things that are visually similar to a 'grey phone' but are not a 'grey phone' are:\t\n- Other types and colors of phones such as white, black, or pink\n- Other electronic devices such as tablets, laptops, or smartwatches. \n\nSome useful visual features for distinguishing 'grey phone' from the listed similar things in a photo are:\n- Rectangular shape with curved corners\n- Iconic home button (or its absence) on the front side\n- Volume and power buttons on the side\n- Camera lens on the back.", 15], "replica": ["Yes, 'replica' has a visually concrete concept and can be physically distinguished from other objects.\nA few things that are visually similar to 'replica' but are not 'replica' are:\tmodels\tduplicates\tcounterfeits\tfakes\treproductions\nThere are several useful visual features to tell there is 'replica' and not similar things in a photo:\tprecise and exact copy of an original object\tsame size, shape, texture, and color as the original\tobject is labeled or referred to as a replica\tin a museum or exhibition context, replicas mostly are labeled as such, and they do not have the same historical relevance as the original.", 15], "silver metal faucet": ["Yes. 'Silver metal faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'silver metal faucet' but are not 'silver metal faucet' are:\tdoorknob\tshowerhead\tgarden hose nozzle\tspray bottle pump\nThere are several useful visual features to tell there is 'silver metal faucet' and not similar things in a photo:\tsilver or metallic color\ttwo handles or one handle\t\ncurved spout\twith a knob or a lever for controlling water flow and temperature\tsink or basin in the background", 15], "desktop monitor": ["Yes. 'Desktop monitor' has a tangible appearance and is a type of computer hardware.\nA few things that are visually similar to 'desktop monitor' but are not 'desktop monitor' are:\ttelevision\tportable monitor\tprojector\ttablet\nThere are several useful visual features to tell there is 'desktop monitor' and not similar things in a photo:\trectangular-shaped screen\tmultiple buttons and ports attached to the screen\tconnected to a base or stand for support\tcommonly used with a computer tower or laptop\tscreen with a desktop wallpaper or icons\tdisplaying computer graphics or text", 15], "hanging light": ["Yes. 'Hanging light' has a tangible appearance and is a type of light fixture.\nA few things that are visually similar to 'hanging light' but are not 'hanging light' are:\tchandelier\tpendant light\tsconce\ttable lamp\nThere are several useful visual features to tell there is 'hanging light' and not similar things in a photo:\tsuspension from the ceiling\tlong cord or chain\thanging cover or shade\tthat emits light from below\tit can be turned on or off", 15], "skate shoe": ["Yes. 'Skate shoe' has a tangible appearance and is a type of shoe designed for skateboarding.\nA few things that are visually similar to 'skate shoe' but are not 'skate shoe' are:\trunning shoe\tbasketball shoe\thiking shoe\tslip-on shoe\nThere are several useful visual features to tell there is 'skate shoe' and not similar things in a photo:\tflat sole\tthick and durable outsole\tcushioned insole\tpadded tongue and collar\tfor skateboarding", 15], "volley ball": ["Yes. 'Volley ball' has a tangible appearance and is a kind of ball.\nA few things that are visually similar to 'volley ball' but are not 'volley ball' are:\tbasketball\tsoccer ball\tdodgeball\tplayground ball\nThere are several useful visual features to tell there is 'volley ball' and not similar things in a photo:\twhite or bright colored panels\tseparated by darker lines\tno obvious bumps or ridges to the touch\tspherical shape", 15], "pepperoni slices": ["Yes. 'Pepperoni slices' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pepperoni slices' but are not 'pepperoni slices' are:\tsalami slices\tbologna slices\tjerky slices\tham slices\nThere are several useful visual features to tell there is 'pepperoni slices' and not similar things in a photo:\tcircular shape\tbright red color with specks of white\tfatty appearance\tsmall size, about 1-2 inches in diameter.", 15], "site": ["No. 'Site' is too vague or abstract to be distinguished in a photo.", 15], "tailpipe": ["Yes. 'Tailpipe' has a tangible appearance and is a part of a vehicle's exhaust system.\nA few things that are visually similar to 'tailpipe' but are not 'tailpipe' are:\tpipe faucet\tindustrial chimney\tfireplace chimney\t\nThere are several useful visual features to tell there is 'tailpipe' and not similar things in a photo:\tlocated at the back of a vehicle\tcylindrical or conical shape\tmetallic material\tsome visible fumes or smoke coming out of it when the engine is running.", 15], "mouth area": ["Yes. 'Mouth area' has a tangible appearance and refers to the part of the face around the mouth.\nA few things that are visually similar to 'mouth area' but are not 'mouth area' are:\tchin\tjaw\tneck\tlips\nThere are several useful visual features to tell there is 'mouth area' and not similar things in a photo:\tvisible lips\tarea around the mouth\tfacial expression such as a smile or a frown\tdimples or wrinkles around the mouth\tvisible teeth and gums", 15], "blue plane": ["Yes. 'Blue plane' has a tangible appearance and refers to a specific type of aircraft.\nA few things that are visually similar to 'blue plane' but are not 'blue plane' are:\tred plane\tgreen plane\tyellow plane\thelicopter\nThere are several useful visual features to tell there is 'blue plane' and not similar things in a photo: \tdistinctive blue color\taircraft body with wings and engines\tfuselage and tail section with distinctive markings, such as airline logos, letters or numbers.", 15], "plastic drinking cup": ["Yes. 'plastic drinking cup' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic drinking cup' but are not 'plastic drinking cup' are:\tplastic water bottle\tthermos\tmug\ttupperware\nThere are several useful visual features to tell there is 'plastic drinking cup' and not similar things in a photo:\tcylindrical or conical shape\twithout a handle\tor with a small handle\tmade of transparent or translucent plastic\tmay have a lid\tand a straw.", 15], "circle light": ["Yes. 'Circle light' has a tangible appearance and refers to a type of light fixture.\nA few things that are visually similar to 'circle light' but are not 'circle light' are:\tcircular mirror\tstage light\tcircular clock\tsun\nThere are several useful visual features to tell there is 'circle light' and not similar things in a photo:\tcircular shape\tsingle light source\tdiffused or indirect lighting mounted on the ceiling or on the wall", 15], "shorts man": ["No. 'Shorts man' is too vague or abstract and cannot be identified in a photo without additional context.", 15], "butterfly kite": ["Yes. 'Butterfly kite' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'butterfly kite' but are not 'butterfly kite' are:\tdragon kite\teagle kite\tfish kite\tparrot kite\nThere are several useful visual features to tell there is 'butterfly kite' and not similar things in a photo:\tButterfly-shaped\tkite paper with patterns or designs\tLightweight frame, usually made of bamboo or plastic\tTail to stabilize the kite in flight\tLength of kite string attached to the kite's bridle\tPointed ends on the butterfly's wings.", 15], "spinach leaf": ["Yes. 'Spinach leaf' has a tangible appearance and is a type of edible leafy green.\nA few things that are visually similar to 'spinach leaf' but are not 'spinach leaf' are:\tkale leaf\tcollard greens leaf\tlettuce leaf\tmint leaf\nThere are several useful visual features to tell there is 'spinach leaf' and not similar things in a photo:\tdark green color\twide, flat shape\tirregular edges\tsmooth, slightly shiny surface\tveins branching out from the center of the leaf", 15], "wet umbrella": ["Yes. 'Wet umbrella' has a tangible appearance and refers to an umbrella that has become wet due to rain or other precipitation.\nA few things that are visually similar to 'wet umbrella' but are not 'wet umbrella' are:\tdry umbrella\tcane\twalking stick\nThere are several useful visual features to tell there is 'wet umbrella' and not similar things in a photo:\topened umbrella\tdroplets of water on the umbrella's fabric or frame\tumbrella handle or strap visible", 15], "chef hat": ["Yes. 'Chef hat' has a tangible appearance and is a type of headwear used in a kitchen.\nA few things that are visually similar to 'chef hat' but are not 'chef hat' are:\tnurse hat\treferee hat\tpolice hat\tsailor hat\tjester hat\nThere are several useful visual features to tell there is 'chef hat' and not similar things in a photo:\ttall and puffy shape\twhite or checkered pattern\tthick brim at the base\tof the hat", 15], "bull standing": ["Yes. 'Bull standing' has a tangible appearance and refers to the physical posture of an animal.\nA few things that are visually similar to 'bull standing' but are not 'bull standing' are: cow sitting, bull lying down, bull running, cow grazing\nThere are several useful visual features to tell there is 'bull standing' and not similar things in a photo:\tfour-legged animal\twith horns\twith an upright posture\thead facing forward or slightly turned towards the camera", 15], "oil stain": ["Yes. 'Oil stain' has a tangible appearance and is a visible mark caused by the deposition of oil.\nA few things that are visually similar to 'oil stain' but are not 'oil stain' are:\tcoffee stain\twine stain\tink blotch\twater spots\tmold spots\nThere are several useful visual features to tell there is 'oil stain' and not similar things in a photo:\tspread irregularly with different shades of black or brown\twet and shiny surface\twhen on clothes or fabric, the surrounding area shows signs of greasiness", 15], "homeplate": ["Yes. 'Homeplate' has a tangible appearance and is a specific object in baseball.\nA few things that are visually similar to 'homeplate' but are not 'homeplate' are:\tregular plates\twith lines or squares on them\tforbidden signs\torban crosses\nThere are several useful visual features to tell there is 'homeplate' and not similar things in a photo:\tpentagon-shaped\twhite with a black edge\twith a large black pentagon in the center\tdivided into several smaller shapes: two corners, two edges, and a top point.", 15], "hardcover book": ["Yes. 'Hardcover book' has a tangible appearance and is a type of book.\nA few things that are visually similar to 'hardcover book' but are not 'hardcover book' are:\tpaperback book\tjournal\tdiary\thard folder\tbinder\nThere are several useful visual features to determine whether a thing is a 'hardcover book' or not in a photo:\tcovered with hard material, such as cardboard\tor leather\tpaper is bounded to the hardcover\tusually has a dust jacket or sleeve\tthat covers the cover and spine.", 15], "stainless steel spoon": ["Yes. 'Stainless steel spoon' has a tangible appearance.\nA few things that are visually similar to 'stainless steel spoon' but are not 'stainless steel spoon' are:\tsilver spoon\tplastic spoon\twooden spoon\t\t\t\nThere are several useful visual features to tell there is 'stainless steel spoon' and not similar things in a photo:\tsilver color\tshiny\tflat working end\twith a scoop shape and a grip shape on opposite ends.", 15], "man shorts": ["Yes. 'Man shorts' has a tangible appearance and is a type of clothing for men.\nA few things that are visually similar to 'man shorts' but are not 'man shorts' are:\tpajama shorts\tswim trunks\tbike shorts\nThere are several useful visual features to tell there are 'man shorts' and not similar things in a photo:\tshort length\tusually stops above the knee\tloose or relaxed fit\toften made of casual fabrics like cotton or denim\tcan have pockets or other functional details like zippers or drawstrings.", 15], "floor windows": ["Yes, 'floor windows' has a tangible appearance.\nA few things that are visually similar to 'floor windows' but are not 'floor windows' are:\tdecorative tiles\twith glass decor\ttransparent flooring\troof skylights\nThere are several useful visual features to tell there are 'floor windows' and not similar things in a photo:\tframeless\tallowing light into underground spaces\tlevel with the floor or slightly raised\tproviding a view of the outside", 15], "shocks": ["Yes. 'Shocks' has a tangible appearance and refers to a component of a vehicle's suspension system.\nA few things that are visually similar to 'shocks' but are not 'shocks' are:\tsprings\tcables\tpipes\ttubes\nThere are several useful visual features to tell there is 'shocks' and not similar things in a photo:\tconnected to a vehicle's wheel assembly\tcylindrical shape\twith a piston inside\tresponsible for absorbing and dampening shocks and vibrations.", 15], "blue vehicle": ["Yes. 'Blue vehicle' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'blue vehicle' but are not 'blue vehicle' are:\tblue bicycle\tblue boat\tblue airplane\tblue train\nThere are several useful visual features to tell there is 'blue vehicle' and not similar things in a photo:\thas four wheels or more\thas a motor or engine\tfor land transportation: headlights, taillights, license plates\tfor water transportation: a hull, sails or propulsion systems, water wake, or foam\ttracks, wheels, or wings", 15], "rear wing": ["Yes. 'Rear wing' has a tangible appearance and is an aerodynamic device on a vehicle.\nA few things that are visually similar to 'rear wing' but are not 'rear wing' are:\tdecorative fins\ttraditional car spoilers\taesthetic add-ons\nThere are several useful visual features to tell there is 'rear wing' and not similar things in a photo:\tlocated at the back of a vehicle\tangled or curved shape\tforward-facing shape or airfoil shape\thighly functional (often seen on race cars)", 15], "spindle": ["Yes. 'Spindle' has a tangible appearance and refers to a rod or pin used for spinning fibers into thread or yarn.\nA few things that are visually similar to 'spindle' but are not 'spindle' are:\trod\tpeg\tpost\tstick\tpole\nThere are several useful visual features to tell there is 'spindle' and not similar things in a photo:\tlong and thin shape\twith a pointed end\tand a wider middle\tpart of a spinning wheel or drop spindle\tmade of wood or metal", 15], "task": ["No. 'Task' is too vague or abstract to be distinguished in a photo. It is an activity or assignment that may or may not have a tangible appearance. \n\nThere are no things that are visually similar to 'task' as it is not a visual concept.", 15], "bookshelf wall": ["Yes. 'Bookshelf wall' has a tangible appearance and is an arrangement of bookshelves on a wall.\nA few things that are visually similar to 'bookshelf wall' but are not 'bookshelf wall' are:\tart gallery wall\tdisplay cabinet wall\tkitchen cabinet wall\tclothing rack wall\nThere are several useful visual features to tell there is 'bookshelf wall' and not similar things in a photo:\tmany horizontal shelves on a vertical wall stacked with books or other objects\tusually made of wood, but sometimes made of metal, plastic, or other materials\tcan be freestanding, built-in, or floating\tcan be symmetrical or asymmetrical, depending on the arrangement of shelves and decorative objects", 15], "glass panels": ["Yes. 'Glass panels' has a tangible appearance and refers to flat pieces of glass used in construction or decoration.\nA few things that are visually similar to 'glass panels' but are not 'glass panels' are: mirrors, windows with a metal frame, paintings of glass. \nThere are several useful visual features to tell there is 'glass panels' and not similar things in a photo:\t\nsmooth and flat surface-transparent or translucent material-reflective properties", 15], "treetops": ["Yes. 'Treetops' has a tangible appearance and refers to the uppermost branches of a tree.\nA few things that are visually similar to 'treetops' but are not 'treetops' are:\tskyline\tmountains\trooftops\tclouds\nThere are several useful visual features to tell there are 'treetops' and not similar things in a photo:\tbranches and leaves pointing upwards\ttree trunks at the bottom\tbackground of other trees or objects, not flat like the horizon or skyline", 15], "neck scarf": ["Yes. 'Neck scarf' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'neck scarf' but are not 'neck scarf' are:\tshawl\tbandana\ttie\thood\tsash\nThere are several useful visual features to tell there is 'neck scarf' and not similar things in a photo:\tstrip or piece of fabric worn around the neck\tfor both women and men\tvarious lengths, widths, and shapes\tfolded or draped in different ways\tdifferent materials such as silk, wool, cashmere, or cotton", 15], "brown mountains": ["Yes. 'Brown mountains' has a tangible appearance and is a kind of geographical feature.\nA few things that are visually similar to 'brown mountains' but are not 'brown mountains' are:\thills\tmounds\tsand dunes\nThere are several useful visual features to tell there are 'brown mountains' and not similar things in a photo:\textensive areas of raised land\twith rocky and craggy terrain\tscalloped ridges and peaks\tbrown or earth-toned color with some green vegetation possible\tApparent size in relation to surrounding landscapes", 15], "support poles": ["Yes. 'Support poles' has a tangible appearance and is a structural element.\nA few things that are visually similar to 'support poles' but are not 'support poles' are:\ttrees\tcolumns\tfences\tsignposts\nThere are several useful visual features to tell there is 'support poles' and not similar things in a photo:\ttall and vertical\tmade of wood or metal\trectangular, circular or square cross-sections\tsupporting a building, a roof or a structure", 15], "taxi sign": ["Yes, 'taxi sign' has a tangible appearance and refers to the signs on top of taxis indicating their availability.\nA few things that are visually similar to 'taxi sign' but are not 'taxi sign' are:\tbillboard\tsignboard\troad sign\nThere are several useful visual features to tell there is 'taxi sign' and not similar things in a photo:\trectangular shape\tyellow or illuminated background with black letters or symbol\tlocated on top of a taxi", 15], "life ring": ["Yes. 'Life ring' has a tangible appearance and is a type of flotation device.\nA few things that are visually similar to 'life ring' but are not 'life ring' are:\thula hoop\tswim ring\tdecorative round object\nThere are several useful visual features to tell there is 'life ring' and not similar things in a photo:\tbrightly colored and usually red or orange\tthick, circular buoyancy aid\twith a white rope attached\tto be thrown to a person in the water", 15], "silver muffler": ["Yes. 'Silver muffler' has a tangible appearance and is an accessory worn around the neck.\nA few things that are visually similar to 'silver muffler' but are not 'silver muffler' are:\tnecklace\tscarf\ttie\tlanyard\nThere are several useful visual features to tell there is 'silver muffler' and not similar things in a photo:\tmade of silver-colored material\tworn loosely around the neck\twith fringe or tassels at the ends\tno specific pattern or design", 15], "pink straw": ["Yes. 'Pink straw' has a tangible appearance and is a type of drinking straw.\nA few things that are visually similar to 'pink straw' but are not 'pink straw' are:\tred straw\tpurple straw\tneon straw\tplastic stick\nThere are several useful visual features to tell there is 'pink straw' and not similar things in a photo:\tcylindrical shape\tpink color\ttranslucent material\twide enough to drink through\tbent at one end (if applicable)", 15], "childrens": ["No. 'Childrens' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are referring to 'children' (without the 's'), then the answer is yes. 'Children' has a tangible appearance and refers to young human beings.\nA few things that are visually similar to 'children' but are not 'children' are:\tadults\tdwarfs\tcomputer-generated images of humanoids\tteenagers\nThere are several useful visual features to tell there are 'children' and not similar things in a photo:\tshorter height\timmature features and body proportions\tsmaller hands and feet\t\nhappy or playful facial expressions\tlack of facial hair or noticeable body hair", 15], "york": ["No. 'York' is too vague or abstract to be distinguished in a photo. However, if you are referring to York as in York, England, then the answer is yes.\nA few things that are visually similar to York but are not 'York' are:\tyolk\tfork\nUseful visual features to tell there is 'York' in a photo are:\tancient architecture, such as city walls, towers, and castle ruins\tnarrow cobbled streets\thistoric landmarks, such as York Minster and the Shambles district", 15], "blue string": ["Yes. 'Blue string' has a tangible appearance and is a specific type of string that is blue in color.\nA few things that are visually similar to 'blue string' but are not 'blue string' are:\trope\tthread\tribbon\twire\nThere are several useful visual features to tell there is 'blue string' and not similar things in a photo:\tthin and flexible\ttextured surface\tthat is blue in color", 15], "bus wheel": ["Yes. 'Bus wheel' has a tangible appearance and is a type of wheel.\nA few things that are visually similar to 'bus wheel' but are not 'bus wheel' are:\tcar wheel\ttruck wheel\tbicycle wheel\tmotorcycle wheel\nThere are several useful visual features to tell there is 'bus wheel' and not similar things in a photo:\tlarge in size\trubber tire\tmetal rim\thubcap or lugnuts\tfor use on a bus or large vehicle", 15], "silver flush handle": ["Yes. 'Silver flush handle' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'silver flush handle' but are not 'silver flush handle' are:\tdoorknob\tdrawer handle\tpull knob\thook\nThere are several useful visual features to tell there is 'silver flush handle' and not similar things in a photo:\tflush with surface\tcircular button shape\tsilver or metallic finish\trecessed grip area for fingers.", 15], "head phones": ["Yes. 'Head phones' has a tangible appearance and is a kind of audio device.\nA few things that are visually similar to 'head phones' but are not 'head phones' are:\tearbuds\thearing aids\tearmuffs\thair accessories\nThere are several useful visual features to tell there is 'head phones' and not similar things in a photo:\tover-ear or on-ear design\tcushioned ear cups or pads\taudio cable or wireless connection adjustability\tand a headband or neckband to keep them attached to the head or neck.", 15], "wood beam": ["Yes. 'Wood beam' has a tangible appearance and is a structural element made from wood.\nA few things that are visually similar to 'wood beam' but are not 'wood beam' are:\tlogs\tplanks\tsticks\tbamboo poles\tmetal or concrete beams\t\nThere are several useful visual features to tell there is 'wood beam' and not similar things in a photo:\trectangular or square shape\tsmooth or rough texture of wood\tvisible wood grain\tdark or light brown color\tsupporting weight or load in a structure.", 15], "grey box": ["Yes. 'Grey box' has a tangible appearance and is a type of box that is colored grey.\nA few things that are visually similar to 'grey box' but are not 'grey box' are:\tcrate\tsuitcase\tchest\ttrunk\nThere are several useful visual features to tell there is 'grey box' and not similar things in a photo:\trectangular shape\tgray color\tsmooth, solid texture\thinged lid\tor some kind of opening to show the interior of the box", 15], "dell laptop": ["Yes. 'Dell laptop' has a tangible appearance and is a specific type of laptop computer.\nA few things that are visually similar to 'dell laptop' but are not 'dell laptop' are:\tHP laptop\tLenovo laptop\tMacbook ChromebookTablet\nThere are several useful visual features to tell there is 'dell laptop' and not similar things in a photo:\tDell logo on the top or bottom of the laptop\tSlender design with a relatively large display\tscreen opens and closes horizontally from the keyboard section\tOperating system and other indicators visible on the computer's screen.", 15], "pink bike": ["Yes. 'Pink bike' has a tangible appearance and is a type of bicycle.\nA few things that are visually similar to 'pink bike' but are not 'pink bike' are:\tred bike\tpurple bike\tyellow bike\nThere are several useful visual features to tell there is 'pink bike' and not similar things in a photo:\tpink color frame\tand/or wheels\tbicycle design, with handlebars, pedals, a seat, and wheels.", 15], "track marks": ["Yes. 'Track marks' has a tangible appearance and refers to visible scars or marks on the skin caused by repeated intravenous drug use.\nA few things that are visually similar to 'track marks' but are not 'track marks' are:\tbruises\tburns\tscratches\tsurgical scars\nThere are several useful visual features to tell there are 'track marks' and not similar things in a photo:\tlinear or dot-like marks\ton the arms, legs or other parts of the body\tmultiple marks in close proximity or a linear pattern\tof varying age or severity", 15], "lit building": ["Yes. 'Lit building' has a tangible appearance and refers to a building that is illuminated.\nA few things that are visually similar to 'lit building' but are not 'lit building' are:\tunlit building\tsunrise/sunset skyline\nThere are several useful visual features to tell there is 'lit building' and not similar things in a photo:\tvisible light sources clearly illuminating the building\tcontrasting brightness between the illuminated building and surrounding buildings or environment.", 15], "priest": ["Yes. 'Priest' has a tangible appearance and is a religious figure.\nA few things that are visually similar to 'priest' but are not 'priest' are:\tminister\trabbi\timam\tmonk\nThere are several useful visual features to tell there is 'priest' and not similar things in a photo:\twhite or black clerical clothing\tcollar\tor a headdress\treligious symbol, such as a cross\tchurch or religious setting", 15], "printing": ["No. 'Printing' is too vague or abstract to be distinguished in a photo.", 15], "cup board": ["Yes. 'Cup board' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'cup board' but are not 'cup board' are:\tdresser\tshelf\tkitchen cabinet\tchest of drawers\nThere are several useful visual features to tell there is 'cup board' and not similar things in a photo: typically rectangular or square shape, with doors that open outward to expose shelves, usually used to store cups, glasses, and dishes.", 15], "rectangle window": ["Yes. 'Rectangle window' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'rectangle window' but are not 'rectangle window' are:\tpainting\tframe\tmirror\ttelevision screen\nThere are several useful visual features to tell there is 'rectangle window' and not similar things in a photo:\ttransparent or translucent rectangular shape, with solid edges\tdividing or revealing an interior or exterior view, or both\tpart of a building or structure.", 15], "beanie hat": ["Yes. 'Beanie hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'beanie hat' but are not 'beanie hat' are:\tskullcap\tberet\tturban\tbalaclava\n\nThere are several useful visual features to tell there is 'beanie hat' and not similar things in a photo:\n\tsoft, knitted material\n\ttightly fitting the head\n\tno brim or visor\n\toften with a pom-pom on top.", 15], "wet hair": ["Yes. 'Wet hair' has a tangible appearance.\nA few things that are visually similar to 'wet hair' but are not 'wet hair' are:\toily hair\tgreasy hair\tperson sweating or in a sauna\tperson in the rain\tdewy grass\nThere are several useful visual features to tell there is 'wet hair' and not similar things in a photo:\tclumps or strands of hair sticking together\tlight-reflecting or shiny appearance\tdamp or wet-looking texture\tsome droplets or water stains present", 15], "ballon": ["Yes. 'Balloon' has a tangible appearance and is a type of inflatable object.\nA few things that are visually similar to 'balloon' but are not 'balloon' are:\tbeach ball\tsoccer ball\tmedicine ball\tglobe\nThere are several useful visual features to tell there is 'balloon' and not similar things in a photo:\tusually made of rubber or latex\tinflated with air or helium\tround or oblong in shape\tvibrant colors or patterns\tattached to a string or ribbon", 15], "stone pillars": ["Yes. 'Stone pillars' has a tangible appearance and is a structure made of stone.\nA few things that are visually similar to 'stone pillars' but are not 'stone pillars' are:\ttree trunks\tcolumns\tmetal poles\nThere are several useful visual features to tell there is 'stone pillars' and not similar things in a photo:\tsquare or rectangular shape\tmade of stone or rock\tsturdy and weighty appearance\tsupporting a structure or arch", 15], "sprayer": ["Yes. 'Sprayer' has a tangible appearance and is a device used for spraying liquids.\nA few things that are visually similar to 'sprayer' but are not 'sprayer' are:\those\tnozzle\tbottle\twith a pump\nThere are several useful visual features to tell there is 'sprayer' and not similar things in a photo:\tcontainer for holding liquid\tpressurized mechanism for spraying liquid\tnozzle\tfor directional spraying\thandle or trigger\tfor controlling the spraying", 15], "dirty plate": ["Yes. 'Dirty plate' has a tangible appearance and it is a kind of dish.\nA few things that are visually similar to 'dirty plate' but are not 'dirty plate' are:\tclean plate\tbowl\twok\tpan\nThere are several useful visual features to tell there is 'dirty plate' and not similar things in a photo:\tfingerprints, smudges, or food residue on the surface\tunpleasant or unappetizing appearance\twet or greasy surface\tdifferent shapes and sizes from other dishes in the photo", 15], "maple": ["Yes. 'Maple' has a tangible appearance as a type of tree.\nA few things that are visually similar to 'maple' but are not 'maple' are:\toak\ttree with leaves and trunk\nThere are several useful visual features to tell there is 'maple' and not similar things in a photo:\tleaves shaped like a hand with three to five \"fingers\"\twith a trunk gray in color\ttwisted branches\twith a brownish or dark gray bark\tthat grows typically in colder regions of the world", 15], "grey post": ["Yes. 'Grey post' has a tangible appearance and refers to a specific object that can be visually identified.\nThere are no things that are visually similar to 'grey post' but are not 'grey post'.\nUseful visual features for identifying 'grey post' can be: rectangular or cylindrical shape, vertical orientation, grey color, and visible base or foundation.", 15], "arugula": ["Yes. 'Arugula' has a tangible appearance and is a type of leafy green vegetable.\nA few things that are visually similar to 'arugula' but are not 'arugula' are:\tspinach\tlettuce\tkale\tswiss chard\tbok choy\nThere are several useful visual features to tell there is 'arugula' and not similar things in a photo:\tdark green leaves\tlong, slender leaves\twith a peppery taste\tserrated or deeply lobed leaves", 15], "padlock": ["Yes. 'Padlock' has a tangible appearance and is a type of locking mechanism.\nA few things that are visually similar to 'padlock' but are not 'padlock' are:\tlock keys\tlockers\nThere are several useful visual features to tell there is 'padlock' and not similar things in a photo:\tmetallic object\twith a keyhole\tand a shackle\tusually used to secure something or somewhere.", 15], "grey bench": ["Yes. 'Grey bench' has a tangible appearance and is a type of seating furniture.\nA few things that are visually similar to 'grey bench' but are not 'grey bench' are:\tchair\tsofa\tstool\tottoman\tpew\nThere are several useful visual features to tell there is 'grey bench' and not similar things in a photo:\trectangular or elongated shape\twith or without backrest\twith or without armrests\tsolid, flat surface to sit on\tmade of metal, wood, or plastic\tcolored grey in this particular case.", 15], "luggage compartment": ["Yes. 'Luggage compartment' has a tangible appearance and is a part of a vehicle designed for storing baggage.\nA few things that are visually similar to 'luggage compartment' but are not 'luggage compartment' are:\ttrunk\tglove compartment\tunderseat storage\tconsole compartment\nThere are several useful visual features to tell there is 'luggage compartment' and not similar things in a photo:\tlarge and spacious\tlocated at the back or under the vehicle's cargo area\thas a lid or a door for opening and closing\tequipped with straps, hooks, or buckles for securing luggage.", 15], "water board": ["Yes. 'Water board' has a tangible appearance and is a type of equipment used for water sports.\nA few things that are visually similar to 'water board' but are not 'water board' are:\tsurfboard\tpaddleboard\tbodyboard\tboogie board\tkneeboard\nThere are several useful visual features to tell there is 'water board' and not similar things in a photo:\telongated shape, often rounded at one end\tusually made of foam or fiberglass\tattached to a leash\tfor standing or lying down on while riding a wave\tmay have fins or straps to hold onto", 15], "grey skies": ["Yes. 'Grey skies' has a tangible appearance and is a type of weather condition.\nA few things that are visually similar to 'grey skies' but are not 'grey skies' are:\tsmoke\tfog\tclouds\tsteam\nThere are several useful visual features to tell there are 'grey skies' and not similar things in a photo:\tdull and overcast lighting\tgrey or dark-colored clouds\tno visible blue sky or sun", 15], "sanwich": ["Yes. 'Sandwich' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'sandwich' but are not 'sandwich' are:\thamburgers\twraps\tburritos\tbagels\nThere are several useful visual features to tell there is 'sandwich' and not similar things in a photo:\ttwo slices of bread with filling\tinfinite varieties of bread and filling\tfilling ingredients visible between bread slices\tstacked layers of ingredients, often with lettuce or other greens in between\tthe possibility of being served hot or cold", 15], "rise": ["No. 'Rise' is too vague or abstract to be distinguished in a photo. It's a concept related to movement or increase and doesn't have a tangible appearance. \n\nTherefore, it is difficult to name things that are visually similar to 'rise' but are not 'rise'.", 15], "wavy": ["Yes. 'Wavy' has a tangible appearance and is a type of pattern or texture.\nA few things that are visually similar to 'wavy' but are not 'wavy' are:\tstripe\tcurly\tspiral\tfurrow\nThere are several useful visual features to tell there is 'wavy' and not similar things in a photo:\tsmooth curves\trepetitive pattern\tinconsistent peaks and valleys, not a straight line", 15], "pom pom": ["Yes. 'Pom pom' has a tangible appearance and is a small, fluffy ball.\nA few things that are visually similar to 'pom pom' but are not 'pom pom' are:\tcotton balls\ttruffles\tfur balls\tbubble wrap\nThere are several useful visual features to tell there is 'pom pom' and not similar things in a photo:\tfluffy and soft texture\tbright colors\tno distinct shape or pattern\tcan be attached to clothing or accessories", 15], "present": ["Yes. 'Present' has a tangible appearance and is a wrapped object given as a gift.\nA few things that are visually similar to 'present' but are not 'present' are:\tboxes\tbags\tenvelopes\tpackages\tparcels\nThere are several useful visual features to tell there is 'present' and not similar things in a photo:\twrapped in colorful paper\tdecorated with a bow or ribbon\thas a label or tag attached\tin the context of a holiday or celebration, such as Christmas or a birthday", 15], "silver wedding ring": ["Yes. 'Silver wedding ring' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'silver wedding ring' but are not 'silver wedding ring' are:\trings made from other metals\tfashion rings\tpromise rings\tengagement rings\nThere are several useful visual features to tell there is 'silver wedding ring' and not similar things in a photo:\tsilver in color\tmetallic with a shiny finish\tsimple and classic design\twith or without diamonds\tor with other embellishments, but still looking like a wedding ring worn on the left hand's ring finger.", 15], "puff": ["Yes. 'Puff' has a tangible appearance and is a visual concept.\nA few things that are visually similar to 'puff' but are not 'puff' are:\tcloud\tsmoke\tbreath\tcotton ball\nThere are several useful visual features to tell there is 'puff' and not similar things in a photo:\tsmall and round\twhite or light-colored\tfluffy or soft-looking\tmight have particles floating around it (e.g. powder, smoke)\tthe shape might have a tail or a trail", 15], "garbage container": ["Yes. 'Garbage container' has a tangible appearance and is a type of container used to hold garbage or waste.\nA few things that are visually similar to 'garbage container' but are not 'garbage container' are:\ttrash can\trecycling bin\tlaundry hamper\nThere are several useful visual features to tell there is 'garbage container' and not similar things in a photo:\tlarge size with a lid\tfor holding garbage or waste\tmade of durable materials, such as plastic or metal\tmay have a foot pedal to open the lid.", 15], "ventilation": ["No. 'Ventilation' is too vague or abstract to be distinguished in a photo.", 15], "balance": ["No. 'Balance' is too vague or abstract to be distinguished in a photo.", 15], "tan umbrella": ["Yes. 'Tan umbrella' has a tangible appearance and is a type of umbrella.\nA few things that are visually similar to 'tan umbrella' but are not 'tan umbrella' are:\tblack umbrella\tpink umbrella\tsun hat\tbeach umbrella\nThere are several useful visual features to tell there is 'tan umbrella' and not similar things in a photo:\ttan-colored canopy\tmetallic or wooden frame\tFoldable or collapsible\tcircular or dome-shaped", 15], "asphalt parking lot": ["Yes. 'Asphalt parking lot' has a tangible appearance and is a type of pavement.\nA few things that are visually similar to 'asphalt parking lot' but are not 'asphalt parking lot' are:\tconcrete pavement\ttrail\tpathway\tsidewalk\nThere are several useful visual features to tell there is 'asphalt parking lot' and not similar things in a photo:\tdark color, usually black or grey\tsmooth or slightly textured surface\tparking lines and markings\tcars parked on the surface", 15], "parking lines": ["Yes. 'Parking lines' has a tangible appearance and is a type of marking on a parking lot.\nA few things that are visually similar to 'parking lines' but are not 'parking lines' are:\tcrosswalks\tzebra lines\tonline frames\ttraffic lines\nThere are several useful visual features to tell there are 'parking lines' and not similar things in a photo:\tparallel lines\twhite or yellow color\tpainted on a flat surface, usually pavement\tor asphalt\tsometimes accompanied by arrows or numbers", 15], "gold star": ["Yes. 'Gold star' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'gold star' but are not 'gold star' are:\tclock\tflower\tsnowflake\tsticker\nThere are several useful visual features to tell there is 'gold star' and not similar things in a photo:\tstar shape\twith five or more points\tgolden or yellow color\tshiny or reflective surface", 15], "blue glasses": ["Yes. 'Blue glasses' has a tangible appearance and refers to eyeglasses or sunglasses that are blue in color.\nA few things that are visually similar to 'blue glasses' but are not 'blue glasses' are:\tblue-tinted lenses\tbluish reflections on glasses cases\t\nThere are several useful visual features to tell there is 'blue glasses' and not similar things in a photo:\tblue frame or temples\tblue-tinted lenses\tsymmetrical lenses and frame in both sides of the glasses", 15], "sea gulls": ["Yes. 'Sea gulls' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'sea gulls' but are not 'sea gulls' are:\tpelicans\tterns\tcormorants\therons\nThere are several useful visual features to tell there is 'sea gulls' and not similar things in a photo:\twhite or grey feathers\tlong wingspans\twebbed feet\tyellow beak and legs\tnoisy and often found near water or the ocean", 15], "milk jug": ["Yes. 'Milk jug' has a tangible appearance and is a container for milk.\nA few things that are visually similar to 'milk jug' but are not 'milk jug' are:\tjuice pitcher\tflower vase\twater bottle\nThere are several useful visual features to tell there is 'milk jug' and not similar things in a photo:\thandles on both sides of the jug\tnarrow spout at the top\tlarge opening at the top, usually with a cap or a lid\ttranslucent or opaque appearance\tMeasures marks along the side.", 15], "grey stripe": ["Yes. 'Grey stripe' has a tangible appearance and is a specific visual pattern.\nA few things that are visually similar to 'grey stripe' but are not 'grey stripe' are:\tzebra\tstreet lines\tpyjama fabric\nThere are several useful visual features to tell there is 'grey stripe' and not similar things in a photo:\ta straight and narrow line\tmedium to light grey\tcolor is consistent throughout the stripe\tno other patterns or shapes in the image", 15], "angle": ["No. 'Angle' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that may be visually associated with 'angle' are:\n\n- Corners of objects, such as tables or buildings\n- Lines that intersect each other\n- Shapes with points or edges, such as triangles or squares\n\nTo distinguish 'angle' from similar things in a photo, useful visual features could include:\n\n- Clear and visible intersecting lines forming an angle\n- Presence of a corner or a pointed shape\n- Use of a protractor or other measuring tools to show the degree of the angle", 15], "costumes": ["Yes. 'Costumes' has a tangible appearance and is a type of clothing worn for a specific purpose or occasion.\nA few things that are visually similar to 'costumes' but are not 'costumes' are:\teveryday clothes\tpajamas\tuniforms\tfancy dresses\tor costumes from different cultures and traditions\nThere are several useful visual features to tell there is 'costumes' and not similar things in a photo:\telaborate and ornate design\tbright colors or patterns\tmasks, hats, or props\tthat imply a specific character or role\tbased on a theme or occasion", 15], "pilings": ["Yes. 'Pilings' has a tangible appearance and is a physical structure.\nA few things that are visually similar to 'pilings' but are not 'pilings' are:\tposts\tcolumns\tfence\tstakes\nThere are several useful visual features to tell there are 'pilings' and not similar things in a photo:\tvertical structures sticking out of the ground or water\tcylindrical or square-shaped\tmade of wood, concrete, or steel clustered together to support a larger structure such as a pier or a bridge.", 15], "train wheel": ["Yes. 'Train wheel' has a tangible appearance and is a component of a train.\nA few things that are visually similar to 'train wheel' but are not 'train wheel' are:\tcar wheel\tbike wheel\troller skate wheel\ttruck wheel\nThere are several useful visual features to tell there is 'train wheel' and not similar things in a photo:\tmetallic\tconnected by an axle\ttoothed outer edge\tvery large size compared to other types of wheels", 15], "chrome bathroom sink faucet": ["Yes. 'Chrome bathroom sink faucet' has a tangible appearance and is a kind of plumbing fixture.\nA few things that are visually similar to 'chrome bathroom sink faucet' but are not 'chrome bathroom sink faucet' are:\tshowerhead\tspigot\twater knob\nThere are several useful visual features to tell there is 'chrome bathroom sink faucet' and not similar things in a photo:\tchrome or metallic finish\tsingle or dual handle\tattached to a sink or countertop\trectangular or rounded spout shape", 15], "tangerines": ["Yes. 'Tangerines' has a tangible appearance and is a type of citrus fruit.\nA few things that are visually similar to 'tangerines' but are not 'tangerines' are:\toranges\tclementines\tgrapefruits\tlemons\tlimes\nThere are several useful visual features to tell there is 'tangerines' and not similar things in a photo:\torange or red-orange color\tsmaller than an orange, but bigger than a clementine\tpebbled skin\teasily peeled\tdivided into segments when opened.", 15], "police office": ["Yes. 'Police office' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'police office' but are not 'police office' are:\tcity hall\tcourthouse\tgovernment building\tapartment building\nThere are several useful visual features to tell there is 'police office' and not similar things in a photo:\t'sheriff' or 'police' signage\tpolice cars parked outside\ta jail nearby\tcameras or surveillance systems on the building\tdetention cells or booking rooms inside the building", 15], "metal stairs": ["Yes. 'Metal stairs' has a tangible appearance and is a type of staircase.\nA few things that are visually similar to 'metal stairs' but are not 'metal stairs' are:\twooden stairs\tstone stairs\tescalator\nThere are several useful visual features to tell there is 'metal stairs' and not similar things in a photo:\tmade of metal\tstraight or spiral shape\thorizontal steps\tmetal railings or banisters", 15], "giraffes ears": ["Yes. 'Giraffe's ears' has a tangible appearance and is a specific part of a giraffe's body.\nA few things that are visually similar to 'giraffe's ears' but are not 'giraffe's ears' are:\telephant's ears\tantelope's ears\tdeer's ears\nThere are several useful visual features to tell there are 'giraffe's ears' and not similar things in a photo:\tVery large size\tLocated on the head of a very tall animal\tdouble horns on top and a tuft of hair on the end of the ear", 15], "bicycle frame": ["Yes. 'Bicycle frame' has a tangible appearance and is part of a bicycle.\nA few things that are visually similar to 'bicycle frame' but are not 'bicycle frame' are:\tmotorcycle frame\tscooter frame\tmetal gate\tstroller frame\nThere are several useful visual features to tell there is 'bicycle frame' and not similar things in a photo:\ttwo triangles with a crossbar and a down-tube\thandles for steering\tfork and seat tube at the front and rear of the frame\tmade of lightweight and durable materials such as aluminum or carbon fiber.", 15], "side truck": ["No. 'Side truck' is too vague or abstract to be distinguished in a photo. Do you mean \"sidecar\"? If so:\nYes. 'Sidecar' has a tangible appearance and is a type of vehicle attachment.\nA few things that are visually similar to 'sidecar' but are not 'sidecar' are:\ttrailer\tbike seat\tcargo box\tsecond seat on a motorcycle\nThere are several useful visual features to tell there is 'sidecar' and not similar things in a photo:\t\nsingle wheel attached to the side of a motorcycle\tor bicycle\tsaddle-shaped seat for a passenger or cargo\tframe attached to the motorcycle or bicycle\tbuilt-in footrest for the passenger", 15], "bend": ["Yes. 'Bend' has a tangible appearance and is an action or movement.\nA few things that are visually similar to 'bend' but are not 'bend' are:\tcurve\tline\tcrooked object\nThere are several useful visual features to tell there is 'bend' and not similar things in a photo:\ta body or object physically changing shape or direction\ta curved or angled appearance\tthe presence of joints or flexible materials that allow bending.", 15], "span": ["Yes. 'Span' has a tangible appearance and is the distance between two supports, such as pillars or walls.\nA few things that are visually similar to 'span' but are not 'span' are:\tlength\tdistance\twidth\theight\tdepth\nThere are several useful visual features to tell there is 'span' and not similar things in a photo:\tdistance between two supports, such as pillars or walls\ttraditional bridges or modern buildings defined by their arches or trusses.", 15], "stone ground": ["Yes. 'Stone ground' has a tangible appearance and refers to a type of flour that is ground using traditional methods.\nA few things that are visually similar to 'stone ground' but are not 'stone ground' are:\tregular flour\twhole wheat flour\nThere are several useful visual features to tell there is 'stone ground' and not similar things in a photo: \tflour particles are larger and less uniform\tstill includes bits of the germ and bran of the grain\ta slightly off-white or beige color and slightly grainy texture", 15], "crucifix": ["Yes. 'Crucifix' has a tangible appearance and is a symbol of Christianity.\nA few things that are visually similar to 'crucifix' but are not 'crucifix' are:\tcrosses\tjewelry\tsymbols\nThere are several useful visual features to tell there is 'crucifix' and not similar things in a photo:\tan image of Jesus Christ hanging on a cross\tcross with a figure or corpus on it\tmay have INRI inscription on or above it\toften made of wood or metal.", 15], "shadow dog": ["No. 'Shadow dog' is too vague or abstract to be distinguished in a photo. It could refer to a dog-shaped shadow or a dog that is mostly in shadow, but without more context, it is not a concrete concept.\nIf we assume that 'shadow dog' refers to a dog-shaped shadow, a few things that are visually similar but not 'shadow dog' would be: a shadow of a tree, a shadow of a person, a shadow of any object.\nUseful visual features for distinguishing a 'shadow dog' from similar things in a photo would be: a clear and recognizable dog shape, the presence of other visible objects in the photo that would suggest the dog's size and position, and perhaps the presence of a real dog in the photo that can be compared to the shadow.", 15], "wingspan": ["Yes. 'Wingspan' has a tangible appearance and is the distance between the tips of a bird's or airplane's wings.\nThere are no things that are visually similar to 'wingspan' but are not 'wingspan'.\nUseful visual features for measuring 'wingspan' in a photo are: two straight lines drawn from the tip of each wing and measuring the distance between them.", 15], "blue collar": ["No. 'Blue collar' is a vague and abstract concept that refers to a type of job or class of workers, and it does not have a tangible appearance.\nTherefore, there are no visual features to distinguish 'blue collar' from other things in a photo.", 15], "stone sculpture": ["Yes. 'Stone sculpture' has a tangible appearance and is a carved art piece made of stone.\nA few things that are visually similar to 'stone sculpture' but are not 'stone sculpture' are:\tboulders\torbs\tarchitectural elements\nThere are several useful visual features to tell there is 'stone sculpture' and not similar things in a photo:\tclearly defined shapes and lines\thollowed out areas or depressions\tsmooth or rough surfaces\tdifferent colors or textures than the surrounding rock\tor also may have recognizable features of animals, people, or objects.", 15], "shop window": ["Yes. 'Shop window' has a tangible appearance and is a display window of a store.\nA few things that are visually similar to 'shop window' but are not 'shop window' are:\treflection in a window\tmirror\tdisplay case\nThere are several useful visual features to tell there is 'shop window' and not similar things in a photo:\tlocated outside a store or a shop\tmay have mannequins or products on display\tbackground indicates a commercial or retail space", 15], "blue bags": ["Yes. 'Blue bags' has a tangible appearance and refers to bags that are color blue.\nA few things that are visually similar to 'blue bags' but are not 'blue bags' are:\tblue jeans\tblue tarpaulin\tblue plastic tablecloth\tblue canvas shoes\nThere are several useful visual features to tell there are 'blue bags' and not similar things in a photo:\tsoft and flexible material\trectangular or cubic shape\tconstruction from polyethylene or nylon material, usually with some kind of closing mechanism, such as drawstrings or zipper.", 15], "odd": ["No. 'Odd' is too vague or abstract to be distinguished in a photo.", 15], "dark hat": ["Yes. 'Dark hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'dark hat' but are not 'dark hat' are:\tcap\tbeanie\tvisor\ttop hat\tbowler hat\nThere are several useful visual features to tell there is a 'dark hat' and not similar things in a photo:\tdark/black color\tbroad-brimmed or narrow-brimmed hat\ttypically made of felt or wool\tworn on the head, covering the forehead and ears", 15], "boat engine": ["Yes. 'boat engine' has a tangible appearance and is a kind of machinery.\nA few things that are visually similar to 'boat engine' but are not 'boat engine' are:\tcar engine\ttruck engine\taircraft engine\tlawn mower engine\nThere are several useful visual features to tell there is 'boat engine' and not similar things in a photo:\tlocated on the back of a boat or inside a boat\tlong cylinder shape\twith propellers or an outboard motor\toften made of metal or plastic", 15], "utility truck": ["Yes. 'Utility truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'utility truck' but are not 'utility truck' are:\tpickup truck\tdelivery truck\ttanker truck\tfire truck\nThere are several useful visual features to tell there is 'utility truck' and not similar things in a photo:\tspecialized equipment or tools\tonboard crane, lift or ladder\tlarge or extended bed\twith 'utility' or 'power' written on the side\tbright or reflective markings or flashers\tonboard generator or compressor\tboxy or rectangular shape", 15], "pews": ["Yes. 'Pews' has a tangible appearance and is a type of seating furniture in a church.\nA few things that are visually similar to 'pews' but are not 'pews' are:\tbenches\tchairs\tcouches\nThere are several useful visual features to tell there is 'pews' and not similar things in a photo:\ttypically made of wood or another hard surface\tstraight or slightly curved design\twith or without cushions\tlined up in rows or columns in a church or chapel.", 15], "zebra drinking water": ["Yes. 'Zebra drinking water' has a tangible appearance and is a specific type of animal behavior.\nA few things that are visually similar to 'zebra drinking water' but are not 'zebra drinking water' are:\tzebra standing\tzoo enclosure\tpond\twith no animals\nThere are several useful visual features to tell there is 'zebra drinking water' and not similar things in a photo: \tzebra striped coat\tdrinking or standing near a body of water (river, lake, etc.)\tlong mane and tail\twild or natural environment in the background", 15], "bears ears": ["Yes. 'Bears ears' has a tangible appearance and is a part of a bear's body.\nA few things that are visually similar to 'bears ears' but are not 'bears ears' are:\tcat ears\tdog ears\thuman ears\tfur\tor shrubs or rocks that resemble bear's ears\t\nThere are several useful visual features to tell there is 'bears ears' and not similar things in a photo:\ttriangular shape\tcovers the top of the bear's head/thin fur and no hair inside\tMostly black or brown", 15], "stork": ["Yes. 'Stork' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'stork' but are not 'stork' are:\tcrane\tegret\theron\tibis\nThere are several useful visual features to tell there is 'stork' and not similar things in a photo:\tlong, pointed beak\tlong neck\ttall legs\tblack and white feathers\tred or orange beak and legs (in the case of the European stork)", 15], "speed limit": ["Yes. 'Speed limit' has a tangible appearance and is usually represented by signs.\nA few things that are visually similar to 'speed limit' but are not 'speed limit' are:\troad signs\ttraffic lights\troad markings\tpedestrian crossing signs\nThere are several useful visual features to tell there is 'speed limit' and not similar things in a photo:\tround-shaped sign\twith a red circle and a number inside (representing the maximum speed allowed)\tbelow or near other road signs or markings\tsometimes accompanied by additional signs or symbols (such as \"School Zone\" or \"End of Speed Limit\")", 15], "shreds": ["Yes. 'Shreds' has a tangible appearance and refers to small, torn pieces of material.\nA few things that are visually similar to 'shreds' but are not 'shreds' are: dust, confetti, scraps, debris\nThere are several useful visual features to tell there are 'shreds' and not similar things in a photo:\ttorn or frayed edges, small and irregular shapes, similar color or material to a larger object that was torn or damaged.", 15], "man tie": ["Yes. 'Man tie' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'man tie' but are not 'man tie' are:\tbow tie\tscarf\tnecklace\tcravat\t\nThere are several useful visual features to tell there is 'man tie' and not similar things in a photo:\tlong and thin\tflat or pointed ends\tworn around the neck\ttightly knotted at the collar of a shirt or suit", 15], "fielder": ["Yes. 'Fielder' has a tangible appearance and refers to a position in baseball or cricket.\nA few things that are visually similar to 'fielder' but are not 'fielder' are:\tpitcher\trandom person in a baseball field\tumpire\nThere are several useful visual features to tell there is 'fielder' and not similar things in a photo:\twearing a fielding glove\tstanding or moving around in the field, away from the pitcher or wicket keeper\tprepared to catch or chase a ball", 15], "cross sign": ["Yes. 'Cross sign' has a tangible appearance and is a symbol.\nA few things that are visually similar to 'cross sign' but are not 'cross sign' are:\tplus sign\tt-shaped road sign\tfour-way stop sign\tX-shaped sign\nThere are several useful visual features to tell there is 'cross sign' and not similar things in a photo:\tupright vertical line intersected by a shorter horizontal line\tequal length lines\tmeeting at a perpendicular angle\tsimple and minimalist design\tdone in white or black on a contrasting background.", 15], "tailfeathers": ["Yes. 'Tailfeathers' has a tangible appearance and is a part of a bird's body.\nA few things that are visually similar to 'tailfeathers' but are not 'tailfeathers' are:\thair\ttassels\tribbons\tfur\nThere are several useful visual features to tell there is 'tailfeathers' and not similar things in a photo:\tpart of a bird's tail arrangement\tfeathers\tattached to a bird's body\tcan be used for balance or communication\twith specific patterns, colors or shapes per bird species.", 15], "metal guard": ["Yes. 'Metal guard' has a tangible appearance and is a type of protective barrier made of metal.\nA few things that are visually similar to 'metal guard' but are not 'metal guard' are:\tmetal fence\tmetal roller shutter\tmetal gate\nThere are several useful visual features to tell there is 'metal guard' and not similar things in a photo:\tprotective barrier\tcomposed of metal bars, wires or mesh \tused to enclose or protect an area or object\tsafety-oriented design, such as sharp edges or pointed tips to prevent intrusion or climbing.", 15], "pita bread": ["Yes. 'Pita bread' has a tangible appearance and is a specific type of bread.\nA few things that are visually similar to 'pita bread' but are not 'pita bread' are:\tnaan bread\ttortilla\tflatbread\tpancake\twrap\nThere are several useful visual features to tell there is 'pita bread' and not similar things in a photo:\tcircular shape\tpocket in the middle for stuffing\tpointed ends\tthick, yet fluffy texture", 15], "tan brick": ["Yes. 'Tan brick' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'tan brick' but are not 'tan brick' may include:\tconcrete\tblock\tstone\ttile\nThere are several useful visual features to tell there is 'tan brick' and not similar things in a photo:\trectangular in shape\tsandy brown color\trough texture\tconstruction material", 15], "beet": ["Yes. 'Beet' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'beet' but are not 'beet' are:\tturnips\tradishes\tpotatoes\nThere are several useful visual features to tell there is 'beet' and not similar things in a photo:\tdeep purple or red exterior\tspherical or oblong shape\tleafy green tops coming out of the bulb on one end\tsmooth or slightly bumpy skin", 15], "rock formations": ["Yes. 'Rock formations' has a tangible appearance and refers to the various shapes and structures that can be created by rocks.\nA few things that are visually similar to 'rock formations' but are not 'rock formations' are: piles of rocks, gravel, boulders.\nThere are several useful visual features to tell there is 'rock formations' and not similar things in a photo: intricate, unique shapes or patterns; rocks of varying colors, textures or sizes; visible layers or strata.", 15], "gold clock hands": ["Yes. 'Gold clock hands' has a tangible appearance and refers to the hands that indicate the time on a clock.\nA few things that are visually similar to 'gold clock hands' but are not 'gold clock hands' are:\tpencil tips\tpaintbrushes\tforks\tknives\nThere are several useful visual features to tell there are 'gold clock hands' and not similar things in a photo:\tmetallic or gold in color\tslightly curved at the tip\tfixed to a clock face indicating the time", 15], "surfboard dude": ["Yes. 'Surfboard dude' has a tangible appearance as it refers to a person who surfs with a surfboard.\nA few things that are visually similar to 'surfboard dude' but are not 'surfboard dude' are:\tsurfboard\twetsuit\tbeach\tgoogles\nThere are several useful visual features to tell there is 'surfboard dude' and not similar things in a photo:\tperson on a surfboard\twearing board shorts and no shoes\thair style and facial features (e.g. long hair and a beard)\twave in the background or foreground of the photo", 15], "batman": ["Yes. 'Batman' has a tangible appearance and is a fictional superhero character.\nA few things that are visually similar to 'batman' but are not 'batman' are:\tbat\tsoldier\tinsect\nThere are several useful visual features to tell there is 'batman' and not similar things in a photo:\tdark-colored costume\tbat symbol on costume\tmask with pointed ears and white eyes\tutility belt\twith gadgets, such as batarangs and grappling hooks", 14], "calzone": ["Yes, 'calzone' has a tangible appearance and is a form of Italian cuisine.\nA few things that are visually similar to 'calzone' but are not 'calzone' are:\tpizza\tempanada\tpasty\thot pocket\nThere are several useful visual features to distinguish 'calzone' from similar things in a photo:\thalf-moon or crescent shape\tstuffed with cheese, meat, and/or vegetables\tbaked to a golden-brown color\tmay be served with a side of tomato sauce or marinara sauce.", 14], "cheese sandwich": ["Yes. 'Cheese sandwich' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'cheese sandwich' but are not 'cheese sandwich' are:\tgrilled cheese sandwich\tmelted cheese\tnacho cheese\tfondue\nThere are several useful visual features to tell there is 'cheese sandwich' and not similar things in a photo:\ttwo slices of bread\tone or more slices of cheese\tsometimes garnished with lettuce or tomato", 14], "journal": ["Yes. 'Journal' has a tangible appearance and is a type of book.\nA few things that are visually similar to 'journal' but are not 'journal' are:\tnotebook\tdiary\tlogbook\tbinder\nThere are several useful visual features to tell there is 'journal' and not similar things in a photo:\thard or soft cover\tpaper pages\tdate entries or sections\ta title or label on the cover\tlines or grids on the pages\tfor some journals, a lock or a buckle to close it", 14], "metal shower head": ["Yes. 'Metal shower head' has a tangible appearance and is a bathroom fixture.\nA few things that are visually similar to 'metal shower head' but are not 'metal shower head' are:\tkitchen faucet\tbathroom faucet\tventilation fan\tlight fixture\nThere are several useful visual features to tell there is 'metal shower head' and not similar things in a photo:\tcylindrical shape\twith multiple small holes\tfrom which water sprays\tout\tof a wall or ceiling-mounted bracket.", 14], "toasts": ["Yes. 'Toasts' has a tangible appearance and is a type of food item.\nA few things that are visually similar to 'toasts' but are not 'toasts' are:\tbread\tsandwiches\tcrackers\tbiscuits\nThere are several useful visual features to tell there is 'toasts' and not similar things in a photo:\tcooked to golden brown color\tcrispy and crunchy texture\trectangular shape, with slices of bread joined together.", 14], "fuzzy teddy": ["Yes. 'Fuzzy teddy' has a tangible appearance and refers to a stuffed animal toy.\nA few things that are visually similar to 'fuzzy teddy' but are not 'fuzzy teddy' are:\tother stuffed animals\tpillows\tcushions\tplush toys\nThere are several useful visual features to tell there is 'fuzzy teddy' and not similar things in a photo:\tbear or bear-like appearance\tfurry or soft texture\tproportions and features like a teddy bear\thaving eyes, a nose, and a mouth\tcute facial expression or happy appearance.", 14], "brown tiles": ["Yes. 'Brown tiles' has a tangible appearance and is a type of surface covering.\nA few things that are visually similar to 'brown tiles' but are not 'brown tiles' are: \twooden planks \tbrown carpet \tbrown bricks \tbrown paint\nThere are several useful visual features to tell there are 'brown tiles' and not similar things in a photo:\tsquare or rectangular in shape \tbrown color and texture \tidentical in pattern and size \tinterlocked with each other to form a surface", 14], "bottom wall": ["No. 'Bottom wall' is too vague or abstract to be distinguished in a photo. It is not a common or recognizable term, and it is not clear what it refers to. \n\nHowever, a few things that are visually similar to a wall at the bottom of a room or space are:\tfloor\tceiling\tangled surface (such as a ramp or slope) \n\nUseful visual features for distinguishing a 'bottom wall' from these similar things might include:\tvertical orientation\tsurface texture and material (such as plaster, bricks, or tiles)\tjunction with the floor (i.e., the bottom edge of the wall)\tjunction with the rest of the wall or ceiling (i.e., the corners)", 14], "entry doors": ["Yes. 'Entry doors' has a tangible appearance and is a kind of door.\nA few things that are visually similar to 'entry doors' but are not 'entry doors' are:\trevolving doors\tgarage doors\tbarn doors\tfrench doors\nThere are several useful visual features to tell there is 'entry doors' and not similar things in a photo:\tcentered in the front of a building\thinged on one side\tusually taller than other doors\thas a handle or knob\tfor families to enter or exit", 14], "place card": ["Yes. 'Place card' has a tangible appearance and is a piece of paper or card used for table settings in events.\nA few things that are visually similar to 'place card' but are not 'place card' are:\tmenu\tcard invitation\tbusiness card\tpostcard\nThere are several useful visual features to tell there is 'place card' and not similar things in a photo:\tname or seat assignment of a person\twritten in calligraphy or other fancy font\tfolded in half\tplaced on a plate or holder on a table", 14], "bill board": ["Yes. 'Billboard' has a tangible appearance and is a type of advertising display.\nA few things that are visually similar to 'billboard' but are not 'billboard' are:\tsigns\tposters\tbanners\tmurals\nThere are several useful visual features to tell there is 'billboard' and not similar things in a photo:\tlarge in size\tflat surface\tto convey information or advertisement\toften found along roads or highways", 14], "heel shoes": ["Yes. 'Heel shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'heel shoes' but are not 'heel shoes' are:\twedges\tboots\tflats\tsandals\nThere are several useful visual features to tell there is 'heel shoes' and not similar things in a photo:\thigh heel\tonly covering the foot and heel\tusually worn by women\tvariety of materials (leather, suede, etc.)\tmade to enhance height and style", 14], "brown bed": ["Yes. 'Brown bed' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'brown bed' but are not 'brown bed' are:\tsofa\tchair\ttable\nThere are several useful visual features to tell there is 'brown bed' and not similar things in a photo:\trectangular shape\tmattress and pillows\tbrown or earth-tone color scheme\theadboard or bed frame", 14], "window display": ["Yes. 'Window display' has a tangible appearance and refers to the arrangement of products or items in a store or boutique window.\nA few things that are visually similar to 'window display' but are not 'window display' are:\tpicture frames\tart museum exhibit\tfurniture store display\tshopping mall kiosk\nThere are several useful visual features to tell there is 'window display' and not similar things in a photo:\tarrangement of items or products within a store or boutique window\tclearly visible from the outside of the store or boutique\tcreative and eye-catching display design\trelevance to the store or boutique's brand identity or holiday season theme", 14], "window glass": ["Yes. 'Window glass' has a tangible appearance and refers to the material used to make windows.\nA few things that are visually similar to 'window glass' but are not 'window glass' are:\tmirrors\tsunglasses\tplastic wrap\tbottles\nThere are several useful visual features to tell there is 'window glass' and not similar things in a photo:\tflat and smooth surface\ttranslucent or transparent\treflective surface, but not as reflective as a mirror\tframed by something (e.g., wood, metal)", 14], "tire track": ["Yes. 'Tire track' has a tangible appearance and is a kind of trace left by a vehicle's wheels.\nA few things that are visually similar to 'tire track' but are not 'tire track' are:\tfootprint\tbike tread\tpath carved by a sled on snow\tor skis\t\nThere are several useful visual features to tell there is 'tire track' and not similar things in a photo:\tseries of indentations or grooves in the ground\tor another surface\tpattern\tdepends on the type of tire and the surface that was driven on (e.g. off-road tire on mud, winter tire on snow)", 14], "bear statue": ["Yes. 'Bear statue' has a tangible appearance and is a type of sculpture.\nA few things that are visually similar to 'bear statue' but are not 'bear statue' are:\treal bears\tother animal sculptures\thuman sculptures\nThere are several useful visual features to tell there is 'bear statue' and not similar things in a photo:\tmade of stone, metal, wood or other materials\tresembles the shape and features of a bear\tfixed in place or mounted on a pedestal", 14], "side lamp": ["Yes. 'Side lamp' has a tangible appearance and is a type of lamp.\nA few things that are visually similar to 'side lamp' but are not 'side lamp' are:\ttable lamp\tfloor lamp\tdesk lamp\tceiling lamp\nThere are several useful visual features to tell there is 'side lamp' and not similar things in a photo:\tshort stand or base\tsmall lampshade\tpositioned on a side table or next to a sofa or chair\tuse of warm colors and soft light.", 14], "nipple": ["Yes. 'Nipple' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'nipple' but are not 'nipple' are:\tmoles\tfreckles\tpimples\nThere are several useful visual features to tell there is 'nipple' and not similar things in a photo:\traised area\ton the breast or chest\tvarious skin colors, usually darker than the surrounding skin\tsize varies depending on the person, sex, and age", 14], "cubes": ["Yes. 'Cubes' has a tangible appearance and is a type of three-dimensional geometrical shape.\nA few things that are visually similar to 'cubes' but are not 'cubes' are:\tbox\tdice\trectangle\tbuilding blocks\nThere are several useful visual features to tell there is 'cubes' and not similar things in a photo:\tsix square faces\tor a rectangle face\tthree-dimensional vertices \tright angles on all corners", 14], "pink headband": ["Yes. 'Pink headband' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'pink headband' but are not 'pink headband' are:\that\tribbon\tbandana\thair tie\nThere are several useful visual features to tell there is 'pink headband' and not similar things in a photo:\tpink in color\tsmooth, soft fabric\tworn around the forehead or crown of the head", 14], "silver rack": ["Yes. 'Silver rack' has a tangible appearance and refers to a type of furniture piece.\nA few things that are visually similar to 'silver rack' but are not 'silver rack' are:\tshelves\tbookcase\tdisplay case\tcabinet\nThere are several useful visual features to tell there is 'silver rack' and not similar things in a photo:\tmetallic or silver color\tpolished or shiny surface\thanging silverware or plates\trod-like structure used for hanging items", 14], "purple bus": ["Yes. 'Purple bus' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'purple bus' but are not 'purple bus' are:\tblue bus\tred bus\tgreen bus\tvan\ttruck\nThere are several useful visual features to tell there is 'purple bus' and not similar things in a photo:\tpurple color\tlong and wide body\ttwo or more rows of seats\tside or rear doors\tfor transport of passengers\ton the road or at a bus stop", 14], "oceans water": ["Yes. 'Oceans water' has a tangible appearance and is a type of liquid.\nA few things that are visually similar to 'oceans water' but are not 'oceans water' are:\tlake water\triver water\tswimming pool water\train\twater in a glass\nThere are several useful visual features to tell there is 'oceans water' and not similar things in a photo:\tsaltwater\tcolor can vary from deep blue to green to gray\toften with waves or tides\toften with visible marine life or boats\tdistinctive smell and taste.", 14], "blue visor": ["Yes. 'Blue visor' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'blue visor' but are not 'blue visor' are:\that\tcap\thelmet\tbandana\tsunglasses\nThere are several useful visual features to tell there is 'blue visor' and not similar things in a photo:\tvisor brim in front, covering the forehead\tblue color in any shade\tusually made of plastic or textile worn to protect the eyes from sun glare", 14], "round wood table": ["Yes. 'Round wood table' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'round wood table' but are not 'round wood table' are:\tcircular rug, wooden barrel, large wooden disk, tree stump\nThere are several useful visual features to tell there is 'round wood table' and not similar things in a photo:\tround or circular shape\tsmooth, polished surface made of wood\tsupporting legs or pedestal\tdesigned for seating or holding objects", 14], "steel post": ["Yes. 'Steel post' has a tangible appearance and refers to a type of metal structure that is commonly used in construction.\nA few things that are visually similar to 'steel post' but are not 'steel post' are:\twooden post\tmetal rod\tpipe\nThere are several useful visual features to tell there is 'steel post' and not similar things in a photo:\tmade of steel\tcylindrical or square shape\trigid and strong appearance\tconnected to other steel posts or beams.", 14], "utensils table": ["No. 'Utensils table' is too vague or abstract to be distinguished in a photo. Perhaps you meant 'tableware' or 'cutlery' instead of 'utensils table'? In that case, the answer is yes.\nA few things that are visually similar to 'tableware' or 'cutlery' but are not 'tableware' or 'cutlery' are:\tkitchen tools (like knives, spatulas, etc.)\ttrays\tdishes\nThere are several useful visual features to tell there are 'tableware' or 'cutlery' and not similar things in a photo:\tmade of metal, ceramic, or plastic\tforks, knives, spoons\tplates, glasses, cups\tset on a table or on placemats.", 14], "capital letters": ["Yes. 'Capital letters' has a tangible appearance and is a specific type of letter.\nA few things that are visually similar to 'capital letters' but are not 'capital letters' are:\tlowercase letters \tnumbers \tpunctuation marks \tsymbols\nThere are several useful visual features to tell there is 'capital letters' and not similar things in a photo:\tlarger size than lowercase letters\tincreased stroke width at the top of the letter \tno descenders (i.e., lines below the baseline), except on a few letters like Q and J \tused for beginning sentences and proper nouns.", 14], "turned-on": ["No. 'Turned-on' is too vague or abstract to be distinguished visually in a photo.", 14], "leg pad": ["Yes. 'Leg pad' has a tangible appearance and is a kind of sports equipment.\nA few things that are visually similar to 'leg pad' but are not 'leg pad' are:\tknee pad\tshin guard\t\nThere are several useful visual features to tell there is 'leg pad' and not similar things in a photo:\tcovering the entire shin and calf area\tpadding to absorb impact\tstraps or closures to secure it in place\tworn by ice hockey, field hockey, or lacrosse goaltenders.", 14], "creamer": ["Yes. 'Creamer' has a tangible appearance and is a liquid or powder used to add to coffee or tea.\nA few things that are visually similar to 'creamer' but are not 'creamer' are:\tmilk\thalf-and-half\tsugar\nThere are several useful visual features to tell there is 'creamer' and not similar things in a photo:\tpackaged in a container specifically meant for creamer (such as a small carton, bottle, or plastic container)\tlabel with the word \"creamer\" or a picture of a coffee cup with cream on top\twhite or off-white color\ttypically seen next to a coffee pot or mug", 14], "cinder block wall": ["Yes. 'Cinder block wall' has a tangible appearance and is a type of wall made of cinder blocks.\nA few things that are visually similar to 'cinder block wall' but are not 'cinder block wall' are:\tbrick wall\tstone wall\tconcrete wall\nThere are several useful visual features to tell there is 'cinder block wall' and not similar things in a photo:\trectangular shapes of cinder blocks\tcinder blocks with visible holes or gaps\tmortar between the blocks\tcolor and texture of the blocks' surface.", 14], "summer": ["No. 'Summer' is too vague or abstract to be distinguished in a photo.", 14], "mans pants": ["Yes. 'Mans pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'mans pants' but are not 'mans pants' are:\tleggings\tstockings\ttrousers\tleg warmers\t\nThere are several useful visual features to tell there is 'mans pants' and not similar things in a photo:\tstraight or slightly loose\tfly zipper/button\thave pockets\tare worn on the waist and hips\tarea above the waist is flat or slightly curved, often with a waistband", 14], "winnie pooh": ["Yes. 'Winnie the Pooh' has a tangible appearance and is a cartoon character.\nA few things that are visually similar to 'winnie pooh' but are not 'winnie pooh' are:\tteddy bears\tother cartoon characters (e.g. Mickey Mouse, Spongebob Squarepants)\tstuffed animals\nThere are several useful visual features to tell there is 'winnie pooh' and not similar things in a photo:\tyellow fur with red shirt\tand no pants\tlarge, round head\tshort ears\twith a small red shirt with no pants.", 14], "gray curtain": ["Yes. 'Gray curtain' has a tangible appearance and is a type of home decor.\nA few things that are visually similar to 'gray curtain' but are not 'gray curtain' are:\tblanket\ttapestry\tflag\tposter\nThere are several useful visual features to tell there are 'gray curtains' and not similar things in a photo:\tvertical fabric panels\thanging from a rod or a rail\tgray color\twith folds and pleats", 14], "file": ["Yes. 'File' has a tangible appearance and is an object used for storing and organizing papers or digital documents.\nA few things that are visually similar to 'file' but are not 'file' are:\tbinder\tbook\tshelf\tcardboard box\tUSB stick\nThere are several useful visual features to tell there is 'file' and not similar things in a photo:\trectangular with straight edges\tthick with a cover or a label\tpapers or digital documents inside\tcan have dividers or categories", 14], "eyeglasses man": ["No. 'Eyeglasses man' is too specific and subjective to be considered a visually concrete concept. \nHowever, here are a few things that are visually similar to a man wearing eyeglasses:\tman wearing sunglasses\twoman wearing eyeglasses\tmannequin wearing glasses\nUseful visual features for distinguishing a man wearing eyeglasses from the listed similar things in a photo would include:\tframe of the glasses clearly visible\ton the bridge of the nose, covering the eyes with enough space between the lenses\tfacial hair or other defining characteristics that make it clear that the subject is a man", 14], "pizza cheese": ["Yes. 'Pizza cheese' has a tangible appearance and is a type of cheese used as a topping on pizza.\nA few things that are visually similar to 'pizza cheese' but are not 'pizza cheese' are:\tcheddar cheese\tmozzarella sticks\tfried cheese curds\nThere are several useful visual features to tell there is 'pizza cheese' and not similar things in a photo:\tmelted and stringy\tcovering a pizza crust\ttan or yellow in color\twith small holes or bubbles\tdoes not maintain its shape at room temperature", 14], "leather wallet": ["Yes. 'Leather wallet' has a tangible appearance and is a type of accessory for holding money and cards.\nA few things that are visually similar to 'leather wallet' but are not 'leather wallet' are:\tpurse\tclutch\tbag\tbillfold\nThere are several useful visual features to tell there is 'leather wallet' and not similar things in a photo:\trectangular or square shape\tmade of leather or fake leather\thas a small flap or zipper\tfor keeping cash and credit cards in organized sections", 14], "pink dog tongue": ["Yes. 'Pink dog tongue' has a tangible appearance and is a physical characteristic of a dog.\nThere are no things that are visually similar to 'pink dog tongue' that are not a pink dog tongue.\nThere are no useful visual features for distinguishing 'pink dog tongue' from non-pink dog tongues in a photo.", 14], "roller": ["Yes. 'Roller' has a tangible appearance and is a type of tool or machine.\nA few things that are visually similar to 'roller' but are not 'roller' are:\tpaintbrush\tbread roller\tcigarette roller\tprinter roller\nThere are several useful visual features to tell there is 'roller' and not similar things in a photo:\tcylindrical shape\thandles or grips\tridged or smooth surface\tfor industrial rollers: large size, heavy weight, mechanical parts, and power source", 14], "silver hook": ["Yes. 'Silver hook' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'silver hook' but are not 'silver hook' are:\tzippers\tclasps\tbuckles\tsnaps\nThere are several useful visual features to tell the difference between a 'silver hook' and similar things in a photo:\n- A hook is typically bent at one end to form a curved shape\n- A silver hook is usually made of metal or coated with a metallic finish\n- A hook is usually used to attach or fasten two objects together, such as a fishing line to a bait or a strap to a bag.", 14], "living room scene": ["Yes. 'Living room scene' has a tangible appearance and includes furniture and objects commonly found in a living room.\nA few things that are visually similar to 'living room scene' but are not 'living room scene' are:\tbedroom scene\tkitchen scene\toutdoor scene\nThere are several useful visual features to tell there is 'living room scene' and not similar things in a photo:\tsofas or armchairs\ttables\trugs or carpet\tframed pictures or paintings\tcushions and pillows\tlight fixtures or lamps", 14], "cooked": ["No. 'Cooked' is too abstract to have a visual appearance.", 14], "cats eye": ["Yes. 'Cats Eye' has a tangible appearance and is a type of gemstone.\nA few things that are visually similar to 'cats eye' but are not 'cats eye' are:\tmarbles\tpolished stones\tbeads\nThere are several useful visual features to tell there is 'cats eye' and not similar things in a photo:\topal or chalcedony with a fibrous structure that reflects light in a band across the surface\tusually yellow, green, or brown in color; sometimes blue or colorless\tlight moving across the surface of the gemstone when it is turned", 14], "palm leaf": ["Yes. 'Palm leaf' has a tangible appearance and is a type of vegetation.\nA few things that are visually similar to 'palm leaf' but are not 'palm leaf' are:\tbanana leaf\tfern leaf\ttree leaf\tgrass\nThere are several useful visual features to tell there is 'palm leaf' and not similar things in a photo:\tlong and slender shape\twith a feather-like structure\tsymmetric and tapering shape\twith a spiky tip\twhen attached to a tree, it attached directly at the base, without a stem", 14], "glass salt": ["No. 'Glass salt' is too vague or abstract to be distinguished in a photo. It is possible that you meant 'salt shaker' instead of 'glass salt'.\nA few things that are visually similar to 'salt shaker' but are not 'salt shaker' are:\tpepper shaker\tsugar dispenser\tspice jar\twithout the top lid\nThere are several useful visual features to tell there is 'salt shaker' and not similar things in a photo:\ttall or short\tpair with a pepper shaker\tor have 'S' and 'P' labels\topaque or transparent with grains of salt visible inside\ttop lid with holes for shaking", 14], "roof tiles": ["Yes. 'Roof tiles' has a tangible appearance and is a building material.\nA few things that are visually similar to 'roof tiles' but are not 'roof tiles' are:\tconcrete blocks\tbricks\tpaving stones\nThere are several useful visual features to tell there are 'roof tiles' and not similar things in a photo:\tinstalled on a sloped or pitched roof\trectangular or curved shape\toverlapping pattern\tvariety of colors and textures", 14], "ankle sock": ["Yes. 'Ankle sock' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'ankle sock' but are not 'ankle sock' are:\tfoot wrap\tleg warmer\tboot cuff\nThere are several useful visual features to tell there is 'ankle sock' and not similar things in a photo:\ttight-fitting\tends at or just above the ankle\tmade of knit or other stretchy fabric", 14], "grey feathers": ["Yes. 'Grey feathers' has a tangible appearance and refers to a specific color and type of feathers on a bird.\nA few things that are visually similar to 'grey feathers' but are not 'grey feathers' are:\twhite feathers\tblack feathers\tbrown feathers\nThere are a few useful visual features to tell there are 'grey feathers' and not similar things in a photo:\t\n- They may be found on a bird or detached from the bird\n- They have a distinct grey color\n- They have a characteristic downy texture\n- The shape of the feathers may also be different from those of other birds", 14], "arrow key": ["Yes. 'Arrow key' has a tangible appearance and is a type of keyboard button.\nA few things that are visually similar to 'arrow key' but are not 'arrow key' are:\tletter keys\tnumber keys\tfunction keys\tspace bar\nThere are several useful visual features to tell there is 'arrow key' and not similar things in a photo:\tarrow shape\tpointing up, down, left or right\ttwo-tone color scheme (usually black and white)\tspecific labeling (usually with arrows or the words \"up,\" \"down,\" \"left,\" or \"right\")", 14], "name badge": ["Yes. 'Name badge' has a tangible appearance and is a type of identification.\nA few things that are visually similar to 'name badge' but are not 'name badge' are: ID card, credit card, business card, ticket.\nThere are several useful visual features to tell there is 'name badge' and not similar things in a photo:\trectangular or oval shape\tpin or clip on attachment\ta person's name and usually their job title, company or organization logo, image or background color\tthat distinguishes it from other types of cards, e.g. credit cards or business cards.", 14], "brick section": ["Yes. 'Brick section' has a tangible appearance and refers to a piece or a section of a wall made of bricks.\nA few things that are visually similar to 'brick section' but are not 'brick section' are:\tstone tile\tmasonry\tblock\nThere are several useful visual features to tell there is 'brick section' and not similar things in a photo:\trectangular shape\tvariety of colors\trough texture\tbricks are arranged in a pattern or bond", 14], "chocolate glaze": ["Yes. 'Chocolate glaze' has a tangible appearance and is a type of food topping.\nA few things that are visually similar to 'chocolate glaze' but are not 'chocolate glaze' are:\tcaramel sauce\tmaple syrup\thoney\nThere are several useful visual features to tell there is 'chocolate glaze' and not similar things in a photo:\tBrown or dark color\tSmooth and shiny texture\tDripping from a cake or pastry\tSweet chocolate aroma", 14], "door opening": ["No, 'door opening' is too vague or abstract to be considered a visually concrete concept. \n\nInstead, here are a few possible alternatives:\n\n- Is 'door' a visually concrete concept? (Answer: yes)\n- Is 'opening' a visually concrete concept? (Answer: no)\n- Is 'a hand turning a doorknob to open a door' a visually concrete concept? (Answer: yes)\n\nPlease let me know which one you prefer, and I can provide the next set of questions accordingly.", 14], "dirty spot": ["Yes. 'Dirty spot' has a tangible appearance and is a type of mark on a surface.\nA few things that are visually similar to 'dirty spot' but are not 'dirty spot' are: shadows, paint spills, natural markings on surfaces, stains, discoloration on surfaces.\nThere are several useful visual features to tell there is 'dirty spot' and not similar things in a photo: irregular shape, uneven texture, often darker or discolored compared to surrounding area, caused by dirt or grime accumulation.", 14], "blue plaid shirt": ["Yes. 'Blue plaid shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'blue plaid shirt' but are not 'blue plaid shirt' are:\tchecked shirt\tstriped shirt\tdenim shirt\nThere are several useful visual features to tell there is 'blue plaid shirt' and not similar things in a photo:\n\n- Blue dominant color\n- Plaid pattern with squares of different sizes and shades of blue\n- Buttons on the front\n- Two pockets in the front\n- Collar and long sleeves.", 14], "bathroom wall tile": ["Yes. 'Bathroom wall tile' has a tangible appearance and usually refers to rectangular or square tiles used for covering bathroom walls.\nA few things that are visually similar to 'bathroom wall tile' but are not 'bathroom wall tile' are:\tkitchen wall tile\tfloor tile\tmosaic art\tstucco wall\tplaster wall\nThere are several useful visual features to tell there is 'bathroom wall tile' and not similar things in a photo:\trectangular or square shape\tsmooth surface\tglossy or matte finish\tusually white or neutral colors\tarranged in a uniform pattern", 14], "shoppers": ["Yes. 'Shoppers' has a tangible appearance and refers to people who are shopping in a store.\nA few things that are visually similar to 'shoppers' but are not 'shoppers' are:\temployees\tbrowsers\tpassers-by\ttourists\t\nThere are several useful visual features to tell there are 'shoppers' and not similar things in a photo:\tcarrying shopping bags or carts\thandling products\tlooking at merchandise\tcomparing prices\tor exchanging money with a cashier.", 14], "side ear": ["No. 'Side ear' is not a term used in anatomy or biology and is too vague or abstract to be distinguished in a photo.", 14], "blacktop": ["Yes. 'Blacktop' has a tangible appearance and is a type of pavement or road surface.\nA few things that are visually similar to 'blacktop' but are not 'blacktop' are:\tasphalt\tconcrete\tstones\ttiles\nThere are several useful visual features to tell there is 'blacktop' and not similar things in a photo:\tdark or black color\tsmooth surface\tno visible seams or gaps\ttexture of small rocks or pebbles", 14], "santa claus": ["Yes. 'Santa Claus' has a tangible appearance and is a kind of human character.\nA few things that are visually similar to 'santa claus' but are not 'santa claus' are:\tfather christmas\tsanta's elves\tgnomes\nThere are several useful visual features to tell there is 'santa claus' and not similar things in a photo:\tred and white suit\tblack boots, gloves, and belt\tlong white beard\tred hat\twith a white pom-pom on the end\tround glasses\tchubby facial features\tholding a sack of presents or standing next to a sleigh or reindeer.", 14], "blue animal": ["Yes. 'Blue animal' has a tangible appearance but it is still too vague to be definitively identified.\nA few things that are visually similar to 'blue animal' but are not 'blue animal' are:\tblue stuffed toy\tblue cartoon character\tblue painted rock\nThere are no specific useful visual features to distinguish 'blue animal' from the listed similar things in a photo, as the term only describes the color and not the specific animal species.", 14], "restaurant table": ["Yes. 'Restaurant table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'restaurant table' but are not 'restaurant table' are:\toffice table\tcoffee table\tdining table\tpicnic table\nThere are several useful visual features to tell there is 'restaurant table' and not similar things in a photo:\ttabletop covered with a tablecloth\tor a placemat\tmenu or utensils lying the top\twaitstaff or guests seated around it", 14], "silver metal knife": ["Yes. 'Silver metal knife' has a tangible appearance and is a type of cutting tool.\nA few things that are visually similar to 'silver metal knife' but are not 'silver metal knife' are:\tsilver metal spoon\tsharp metal nail\tmetal letter opener\nThere are several useful visual features to tell there is 'silver metal knife' and not similar things in a photo:\tsharp and pointed blade\tmetallic and shiny surface\tsilver color\tridged handle with a grip for fingers.", 14], "raindrops": ["Yes. 'Raindrops' has a tangible appearance and is a type of precipitation.\nA few things that are visually similar to 'raindrops' but are not 'raindrops' are:\tdew droplets\twater spilled on a surface\tdripping faucet\twaterfall\tsweat\nThere are several useful visual features to tell there is 'raindrops' and not similar things in a photo:\tfalling from the sky\tirregularly shaped\twide range of sizes\tand generally clustered together in groups.", 14], "patchy": ["Yes. 'Patchy' has a tangible appearance and refers to something that is uneven or irregularly covered.\nA few things that are visually similar to 'patchy' but are not 'patchy' are:\tspotted\tspeckled\tmottled\tvariegated\nThere are several useful visual features to tell there is 'patchy' and not similar things in a photo:\tuneven coverage or distribution of color or texture\tareas that vary in size, shape or intensity irregular edges or boundaries between patches\tno obvious repeating pattern or order", 14], "link": ["No. 'Link' is too abstract to have a tangible appearance that can be distinguished in a photo.\nA few things that are visually similar to 'link' but are not 'link' are:\tchain\trope\thook\tconnecting pieces and devices.\nThere are no useful visual features to tell there is 'link' in a photo. Instead, 'link' is an abstract concept that refers to a connection or association between two or more things.", 14], "china cabinet": ["Yes. 'China cabinet' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'china cabinet' but are not 'china cabinet' are: bookshelf, hutch, display cabinet.\nThere are several useful visual features to tell there is 'china cabinet' and not similar things in a photo: glass doors, shelves for displaying china or collectibles, wooden or metal exterior, decorative carvings or details.", 14], "hinge door": ["Yes. 'hinge door' has a tangible appearance and is a type of door attached to a frame by hinges.\nA few things that are visually similar to 'hinge door' but are not 'hinge door' are:\tsliding door\twall\tmirrors\nThere are several useful visual features to tell there is 'hinge door' and not similar things in a photo:\thinges on one or both sides\tdoorknob or handle\tvisible frame around the door\tswings open and closed horizontally", 14], "paint line": ["Yes. 'Paint line' has a tangible appearance and is a line or stripe created by painting.\nA few things that are visually similar to 'paint line' but are not 'paint line' are:\tcracks\tmolding\tgrout\tlines drawn with a pen or pencil\nThere are several useful visual features to tell there is 'paint line' and not similar things in a photo:\tstraight or curved line of paint\tsmooth surface\ttexture consistent with paint\tborders or edges of a painted area", 14], "apple monitor": ["Yes. 'Apple monitor' has a tangible appearance and is a specific type of computer monitor.\nA few things that are visually similar to 'apple monitor' but are not 'apple monitor' are:\tPC monitor\tlaptop screen\ttelevision screen\tprojector screen\nThere are several useful visual features to tell there is an 'apple monitor' and not similar things in a photo:\tApple logo on the back of the monitor\tSleek, slim design with thin bezels and a silver or white finish\tHigh-quality display with sharp colors and clear resolution\tCompatible with Apple computers and devices.", 14], "wooden slat": ["Yes. 'Wooden slat' has a tangible appearance and is a type of wood plank.\nA few things that are visually similar to 'wooden slat' but are not 'wooden slat' are:\tplank\tfloorboard\tshelf\ttray\nThere are several useful visual features to tell there is 'wooden slat' and not similar things in a photo:\tlong and narrow\trectangular shape\tstraight and even edges\thorizontal or vertical orientation\twood grain pattern", 14], "tub faucet": ["Yes. 'Tub faucet' has a tangible appearance and refers to a fixture used for drawing water for a bath or shower.\nA few things that are visually similar to 'tub faucet' but are not 'tub faucet' are:\tsink faucet\tshowerhead\ttap\tsoap dispenser\nThere are several useful visual features to tell there is 'tub faucet' and not similar things in a photo:\tvertical, cylindrical shape\twith a spout for water to flow out\twith knobs or handles for controlling water flow and temperature\tinstalled on the rim of a bathtub.", 14], "purple scarf": ["Yes. 'Purple scarf' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'purple scarf' but are not 'purple scarf' are:\tpurple fabric\tpurple boa\tpurple ribbon\tpurple necktie\nThere are several useful visual features to tell there is 'purple scarf' and not similar things in a photo:\tthin and elongated shape\tsoft and knitted texture\tpurple color\tworn around the neck", 14], "plums": ["Yes. 'Plums' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'plums' but are not 'plums' are:\tgrapes\tprunes\tcherries\nThere are several useful visual features to tell there is 'plums' and not similar things in a photo:\tround or oval shape\tvariety of colors including purple, red, and yellow\tsmooth skin with a slight fuzz on the surface.\ta groove running from the stem to the tip. A small dent where the stem was attached.", 14], "petal flower": ["Yes. 'Petal flower' has a tangible appearance and generally refers to the colorful part of a plant.\nA few things that are visually similar to 'petal flower' but are not 'petal flower' are:\tleaves\tbuds\tfruits\tbranches\nThere are several useful visual features to tell there is 'petal flower' and not similar things in a photo:\tpetals arranged in a circular pattern\tbright and vivid colors\tsymmetric shapes\ta hollow part in the center of the petals\tanthers and stigmas in the center of the flower", 14], "overcoat": ["Yes. 'Overcoat' has a tangible appearance and is a type of outerwear.\nA few things that are visually similar to 'overcoat' but are not 'overcoat' are:\tjacket\thoodie\tparka\tcoat\nSome useful visual features for distinguishing 'overcoat' from the listed similar things in a photo are:\t\n- Typically a darker color (navy, black, brown, etc.) \n- Longer length than a jacket \n- Not as thick or casual as a parka \n- Often worn over business attire or formalwear \n- May have a belt, buttons or a single-breasted closure.", 14], "showerhead": ["Yes. 'Showerhead' has a tangible appearance and is a bathroom fixture.\nA few things that are visually similar to 'showerhead' but are not 'showerhead' are:\tspout\tfaucet\ttap\tdrain\nThere are several useful visual features to tell there is 'showerhead' and not similar things in a photo:\tmultiple nozzles or holes\tfor attachment to a shower hose\tor to a pipe\ton an adjustable arm or bracket\thanging from above\tthe presence of settings or modes (such as massage or rainfall)", 14], "donut box": ["Yes. 'Donut box' has a tangible appearance and is a type of container for donuts.\nA few things that are visually similar to 'donut box' but are not 'donut box' are:\tcake box\tpizza box\tsushi box\tburger box\tfry box\nThere are several useful visual features to tell there is 'donut box' and not similar things in a photo:\trectangular shape\twith a plastic or cardboard material\toften with clear plastic cover\tprinted with logo or text\tthat fits multiple donuts inside.", 14], "pointy nose": ["Yes. 'Pointy nose' has a tangible appearance and is a characteristic of some people's facial features.\nA few things that are visually similar to 'pointy nose' but are not 'pointy nose' are:\tSharp quills of a porcupine\tSharp pine needles\nThere are several useful visual features to tell there is 'pointy nose' and not similar things in a photo:\telongated and sharp tip of the nose prominent in face profile view\tangular shape overall, which creates the illusion of a sharp or pointy tip", 14], "metal street light": ["Yes. 'Metal street light' has a tangible appearance and is a common object in cities.\nA few things that are visually similar to 'metal street light' but are not 'metal street light' are:\tfence\tpost\tbollard\ttraffic light\nThere are several useful visual features to tell there is 'metal street light' and not similar things in a photo:\ttall and thin metal pole\twith a light or lamp atop\tmounted on a concrete base\tor attached to a building\tor on a roadway or sidewalk", 14], "storage bin": ["Yes. 'Storage bin' has a tangible appearance and is a type of container used for storage.\nA few things that are visually similar to 'storage bin' but are not 'storage bin' are:\ttrash can\tbasket\twastebasket\tlaundry basket\nThere are several useful visual features to tell there is 'storage bin' and not similar things in a photo:\trectangular or square shape\twith or without a lid\tmade of plastic, metal, or fabric\tvarious sizes or colors\tstored items inside such as toys, clothes or tools.", 14], "clover": ["Yes. 'Clover' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'clover' but are not 'clover' are:\tweeds\tgrasses\tflowers\nThere are several useful visual features to tell there is 'clover' and not similar things in a photo:\tthree leaves with a rounded shape\twhite or pink flowers\twith a trifoliate leaf arrangement\tgrowing in patches in meadows, fields or lawns.", 14], "toy duck": ["Yes. 'Toy duck' has a tangible appearance and is a kind of children's toy.\nA few things that are visually similar to 'toy duck' but are not 'toy duck' are:\tother toy birds\treal ducks\tbath toys\nThere are several useful visual features to tell there is 'toy duck' and not similar things in a photo:\tbrightly colored\tyellow or white\ttraditionally has a red beak and black eyes\tround head, short beak, and flat body\tdesigned to be held by children's hands.", 14], "pink backpack": ["Yes. 'Pink backpack' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'pink backpack' but are not 'pink backpack' are:\tred backpack\tpurple backpack\tcarry-on luggage\tpurse\tshoulder bag\nThere are several useful visual features to tell there is 'pink backpack' and not similar things in a photo:\tbackpack style\tmedium-sized\tpink color\ttwo straps for carrying\tzippers and pockets for storage of items", 14], "front edge": ["Yes. 'Front edge' has a tangible appearance and refers to the foremost point of an object or shape.\nA few things that are visually similar to 'front edge' but are not 'front edge' are:\trear edge\ttop edge\tbottom edge\tside edge\nThere are several useful visual features to tell there is 'front edge' and not similar things in a photo:\tmost forward point of an object or shape\tcurved or pointed shape\tborder or transition between two planes or surfaces", 14], "end button": ["Yes. 'End button' has a tangible appearance and is a key or button on an electronic device.\nA few things that are visually similar to 'end button' but are not 'end button' are:\tpower button\thome button\tmenu button\tback button\nThere are several useful visual features to distinguish 'end button' from the listed similar things in a photo:\tthe word \"End\" or a symbol that represents ending\tfunction to end a call, message, or application\tpresent on the right side of the device", 14], "bicycle basket": ["Yes. 'Bicycle basket' has a tangible appearance and is a kind of accessory.\nA few things that are visually similar to 'bicycle basket' but are not 'bicycle basket' are:\tbackpacks\tluggage racks\tshopping baskets\thandbags\nThere are several useful visual features to tell there is 'bicycle basket' and not similar things in a photo:\tattached to the handlebars or the rear of a bicycle\twoven or meshed frame\toften rectangular or oval shaped\tmade of metal, plastic or wicker material.", 14], "bathroom tiles": ["Yes. 'Bathroom tiles' has a tangible appearance and refers to the ceramic or porcelain tiles commonly used in bathrooms.\nA few things that are visually similar to 'bathroom tiles' but are not 'bathroom tiles' are:\tkitchen tiles\tbricks\tpaving stones\tmosaic tiles\nThere are several useful visual features to tell there are 'bathroom tiles' and not similar things in a photo:\tsquare or rectangular in shape\tsmooth surface with a glossy or matte finish\tvariety of colors and patterns\tmay have grout lines in between each tile", 14], "slivers": ["Yes. 'Slivers' has a tangible appearance and refers to thin, narrow pieces of material.\nA few things that are visually similar to 'slivers' but are not 'slivers' are:\tsplinters\tshreds\tstrips\t\nThere are several useful visual features to tell there are 'slivers' and not similar things in a photo:\tthin and narrow pieces of material (like wood, metal, or glass)\tlong and straight shape\tsharp or pointed edges\tor mostly flat and with a tapered end.", 14], "knife holder": ["Yes. 'Knife holder' has a tangible appearance and is a type of kitchen accessory.\nA few things that are visually similar to 'knife holder' but are not 'knife holder' are:\tutensil holder\tcutlery tray\ttool organizer\nThere are several useful visual features to tell there is 'knife holder' and not similar things in a photo:\tvertical and freestanding\tor wall-mounted\tset of slots or pouches for individual knives\tmade of wood, metal, or plastic\thollow-centered to allow easy knife insertion and extraction.", 14], "businessman": ["Yes. 'Businessman' has a tangible appearance and refers to a person who works in the field of business.\nA few things that are visually similar to 'businessman' but are not 'businessman' are:\tprofessor\tlawyer\tdoctor\tpolitician\nThere are several useful visual features to tell there is a 'businessman' and not similar things in a photo:\twearing a suit or formal attire\tcarrying a briefcase or laptop\thair and facial hair are well-groomed or styled\tin a business environment or office space\tconducting a meeting or negotiating a deal.", 14], "blue frisbee": ["Yes. 'Blue frisbee' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'blue frisbee' but are not 'blue frisbee' are:\tflying saucer\tblue plate\tplastic lid\nThere are several useful visual features to tell there is 'blue frisbee' and not similar things in a photo:\tcircular or disc-shaped\tridged edges or surface\tbright blue color\twith the word \"Frisbee\" imprinted on it", 14], "bent arm": ["Yes. 'Bent arm' has a tangible appearance and is a type of body position.\nA few things that are visually similar to 'bent arm' but are not 'bent arm' are:\tstraight arm\tleg\tpencil\truler\nThere are several useful visual features to tell there is 'bent arm' and not similar things in a photo:\telbow joint\tforming an angle\thand in the picture (indicating it is an arm and not a leg or an object)", 14], "fence gate": ["Yes. 'Fence gate' has a tangible appearance and is a type of barrier.\nA few things that are visually similar to 'fence gate' but are not 'fence gate' are:\tarch\tdoor\tdecorative screen\nThere are several useful visual features to tell there is 'fence gate' and not similar things in a photo:\tattached to a fence or a wall\tvertical wooden or metal slats\thinges and a latch for opening and closing\tthe height is usually shorter than the adjacent fence", 14], "role": ["No. 'Role' is too vague or abstract to be distinguished in a photo.", 14], "plaster": ["Yes. 'Plaster' has a tangible appearance and refers to a material used for coating walls or a medical dressing.\nA few things that are visually similar to 'plaster' but are not 'plaster' are:\tcement\tdrywall\tpaints\tglue\nThere are several useful visual features to tell there is 'plaster' and not similar things in a photo:\tsmooth surface with fine texture and no bumps or lumps\toff-white or beige color\tthin layer applied on the wall or on a skin wound", 14], "window latch": ["Yes. 'Window latch' has a tangible appearance and is a device that allows a window to close and open properly.\nA few things that are visually similar to 'window latch' but are not 'window latch' are:\tlocks\tdoorknobs\thandles\thinges\nThere are several useful visual features to tell there is 'window latch' and not similar things in a photo:\tsmall pieces of metal or plastic on the window frame \na way to hold the window closed or open usually consisting of a hook and a lever", 14], "mother giraffe": ["Yes. 'Mother giraffe' has a tangible appearance and refers to a female giraffe who has given birth to a calf.\nA few things that are visually similar to 'mother giraffe' but are not 'mother giraffe' are:\tfemale horse\tfemale deer\tfemale zebra\nThere are several useful visual features to tell there is a 'mother giraffe' and not similar things in a photo:\tlong neck\tpatchy coat with irregular spots\tmale giraffes are taller than female ones\tbaby giraffes (calves) are smaller and have shorter necks than adult giraffes.", 14], "ont": ["No, 'ont' is too vague and abstract and doesn't have a tangible appearance. It's not a concrete concept. Therefore, there are no visually similar things to 'ont' that can be listed, and no visual features can distinguish 'ont' from anything else in a photo.", 14], "wolf": ["Yes. 'Wolf' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'wolf' but are not 'wolf' are:\tdog\tcoyote\tfox\tjackal\nThere are several useful visual features to tell there is 'wolf' and not similar things in a photo:\tlarger than most dogs\tpointed ears\tgray or brown fur\tsharp teeth and claws\tlong snout\tbushy tail", 14], "orange bike": ["Yes. 'Orange bike' has a tangible appearance and is a specific type of bicycle.\nA few things that are visually similar to 'orange bike' but are not 'orange bike' are:\tred bike\tyellow bike\tgreen bike\tbicycle with an orange accessory\nThere are several useful visual features to tell there is 'orange bike' and not similar things in a photo:\tframe and wheels painted orange\toranges bike seat and handlebars", 14], "giants": ["No. 'Giants' are too vague or abstract to be distinguished in a photo. \n\nNote: It is a concept related to imaginary or mythological beings, and humans may imagine their appearance differently. However, visually similar things that may evoke the idea of 'giants' are objects or people of unusually large sizes, such as tall buildings or basketball players. But they are not 'giants' because the concept 'giant' goes beyond just physical size and encompasses other characteristics like strength, intimidating presence, etc., which cannot be captured in a photo.", 14], "box spring": ["Yes. 'Box spring' has a tangible appearance and is a type of bed base.\nA few things that are visually similar to 'box spring' but are not 'box spring' are:\tmattress\tfuton\tframe\tcot\nThere are several useful visual features to tell there is 'box spring' and not similar things in a photo:\trectangular or square shape\tthick and bulky\tpatterned or upholstered surface\tset on legs or castors\tno headboard or footboard on its own.", 14], "front bus": ["No. 'Front bus' is too vague or abstract to be distinguished in a photo. Can you please provide another concept for me to work on?", 14], "traffic pole": ["Yes. 'Traffic pole' has a tangible appearance and is a type of pole used for traffic control.\nA few things that are visually similar to 'traffic pole' but are not 'traffic pole' are:\tlight poles\tflag poles\tfence posts\ttree trunks\nThere are several useful visual features to tell there is 'traffic pole' and not similar things in a photo:\tusually painted white, black, or yellow\thave signs or traffic lights attached to them\tstripe patterns around the poles\tmounted on a concrete base\tsquare or rectangular-shaped\twhen multiple poles are present, they are usually arranged in a line.", 14], "womens hand": ["Yes. 'Women's hand' has a tangible appearance and refers to the hand of a woman.\nA few things that are visually similar to 'women's hand' but are not 'women's hand' are: men's hand, child's hand, statue's hand.\nThere are several useful visual features to tell there is 'women's hand' and not similar things in a photo:\tsmaller size compared to men's hand\tpossibility of painted nails or ornamentation\tsofter lines and curves in fingers and palm.", 14], "winter pants": ["Yes, 'winter pants' has a tangible appearance and typically refers to heavy, warm pants worn during cold weather.\nA few things that are visually similar to 'winter pants' but are not 'winter pants' are:\tmen's suit pants\tkhaki pants\tjeans\tyoga pants\nThere are several useful visual features to tell there is 'winter pants' and not similar things in a photo:\tthick and padded material, such as fleece or wool\tdark or neutral colors, such as black, gray, or brown\tpossibly insulated with a second layer or lining\tzippers or buttons at the ankles, waist, and pockets", 14], "lace curtains": ["Yes. 'Lace curtains' has a tangible appearance and is a type of window treatment.\nA few things that are visually similar to 'lace curtains' but are not 'lace curtains' are:\tblinds\tshades\tdraperies\tpanels\nThere are several useful visual features to tell there is 'lace curtains' and not similar things in a photo:\tdelicate and intricate patterns\tsee-through or semi-translucent material\ttypically white or cream color\tlacework or netting appearance\thanging or gathered in billows or swags.", 14], "silverware plate": ["No. 'Silverware plate' is too vague or abstract. Silverware refers to the knives, forks, spoons, and other utensils used to eat food, and the plate is the dish on which the food is served. There is no such thing as a \"silverware plate.\" Therefore, the answer is no.", 14], "utility van": ["Yes. 'Utility van' has a tangible appearance and is a type of vehicle used for commercial purposes.\nA few things that are visually similar to 'utility van' but are not 'utility van' are:\ttruck\tminivan\tambulance\tdelivery van\nThere are several useful visual features to tell there is 'utility van' and not similar things in a photo:\tboxy and rectangular shape\tsolid or plain colors\tno windows or rear seats\tcompany logo or markings on the side of the vehicle\tladder or equipment racks on top of the roof.", 14], "turbine engine": ["Yes. 'Turbine engine' has a tangible appearance and is a type of engine.\nA few things that are visually similar to 'turbine engine' but are not 'turbine engine' are:\tpiston engine\tjet engine\tcompressor\tturboprop engine\nThere are several useful visual features to tell there is 'turbine engine' and not similar things in a photo:\tcylindrical or conical shape\tmetallic surface with various-sized openings\tpointed front end\tfan blades in the front section of the engine\tturbine blades in the rear section of the engine", 14], "bodies": ["Yes. 'Bodies' has a tangible appearance and refers to a physical form of a living organism.\nA few things that are visually similar to 'bodies' but are not 'bodies' are:\tmannequins\tstatues\ttoys\nThere are several useful visual features to tell there are 'bodies' and not similar things in a photo:\thuman or animal shape\tclothes or fur/mottled skin\tfacial features\tand limbs in appropriate configurations\tcolor and texture of the skin, hair and other bodily features.", 14], "suvs": ["Yes. 'SUVs' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'SUVs' but are not 'SUVs' are:\ttrucks\tvans\tjeeps\tcrossovers\tsedans\nThere are several useful visual features to tell there is 'SUVs' and not similar things in a photo:\tlarge and bulky vehicle\ttall and elevated ground clearance\toff-road capabilities\tboxy exterior shape\tbigger and wider than a regular car", 14], "bo": ["No. 'Bo' is too vague or abstract to be distinguished in a photo. There is not enough context or information to understand what 'bo' refers to.", 14], "work boots": ["Yes. 'Work boots' has a tangible appearance and typically refers to sturdy boots worn for labor or outdoor activities.\nA few things that are visually similar to 'work boots' but are not 'work boots' are:\thiking boots\tmilitary boots\train boots\tsnow boots\nThere are several useful visual features to tell there is 'work boots' and not similar things in a photo:\tthick and durable sole\tlace-up or buckle design\thigh-top or mid-top length\tmade of leather or heavy-duty synthetic materials\treinforced toe and heel areas", 14], "backpack person": ["Yes. 'Backpack person' has a tangible appearance and is a person wearing a backpack.\nA few things that are visually similar to 'backpack person' but are not 'backpack person' are:\tperson wearing a purse or a messenger bag\tperson carrying a suitcase\tor a duffel bag\tperson carrying a briefcase\tor a laptop bag\thiker with a backpack\nThere are several useful visual features to tell there is a 'backpack person' and not a similar thing in a photo:\tperson wearing a backpack with double straps\ton the back of the person\tthe shape of the backpack with side pockets and zippers", 14], "transportation": ["No. 'Transportation' is too vague or abstract to be distinguished in a photo.", 14], "metal steps": ["Yes. 'Metal steps' has a tangible appearance and refers to a type of staircase.\nA few things that are visually similar to 'metal steps' but are not 'metal steps' are:\twooden steps\tstairs made of concrete or stone\nThere are several useful visual features to tell there are 'metal steps' and not similar things in a photo:\tsilver or grey in color\tsmooth, polished surfaces\tmade of metal or painted to look like metal\tclosely placed steps with railing on one or both sides", 14], "plastic cap": ["Yes. 'Plastic cap' has a tangible appearance and is a kind of object used to cover something.\nA few things that are visually similar to 'plastic cap' but are not 'plastic cap' are:\tbottle cap\ttube cap\tjar lid\tshower cap\nThere are several useful visual features to tell there is 'plastic cap' and not similar things in a photo:\tround or cylindrical\tridged edges or threads\tflat top or dome-shaped\thollow or indent in the center\tsolid-colored or translucent\tplastic material", 14], "wooden wheel": ["Yes. 'Wooden wheel' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'wooden wheel' but are not 'wooden wheel' are:\tcogwheel\trecord\tcheese\twagon wheel\nThere are several useful visual features to tell there is 'wooden wheel' and not similar things in a photo:\tcircular shape\twooden material spokes\thub at the center\trims to hold the wheel on the axel.", 14], "bathroom area": ["Yes. 'Bathroom area' has a tangible appearance and refers to a specific physical space.\nA few things that are visually similar to 'bathroom area' but are not 'bathroom area' are:\tkitchen\tarea with a sink\tand toilet in a room not labeled as a bathroom\nThere are several useful visual features to distinguish a 'bathroom area' from the listed similar things in a photo:\tbathtub, shower, or both\tsink\ttoilet\ttowels, bath mats, or other bathroom-specific items on display", 14], "bathrooms": ["Yes. 'Bathrooms' has a tangible appearance and typically contains specific fixtures.\nA few things that are visually similar to 'bathrooms' but are not 'bathrooms' are:\tkitchens\tclosets\tpantries\nThere are several useful visual features to tell there is 'bathroom' and not similar things in a photo:\t\nsink toilet\tshower or bathtub\tdrains\tmirrors\tcounter or vanity cabinets\ttile or textured walls and floor", 14], "video game controllers": ["Yes. 'Video game controllers' has a tangible appearance and is a type of device used for gaming.\nA few things that are visually similar to 'video game controllers' but are not 'video game controllers' are:\tremote control\tjoystick\tpaddle controller\tmusical instrument\nThere are several useful visual features to tell there is 'video game controllers' and not similar things in a photo:\td-pad\tanalog sticks\taction buttons\tshoulder buttons\ttrigger buttons\tStart and Select buttons", 14], "business man": ["Yes. 'Business man' has a tangible appearance and is a profession.\nA few things that are visually similar to 'business man' but are not 'business man' are:\tdoctor\tlawyer\tteacher\tbanker\tpolitician\nThere are several useful visual features to tell there is 'business man' and not similar things in a photo:\tsuit and tie\tbriefcase or laptop\tformal shoes\tclean-shaven or well-groomed hair", 14], "metal train": ["Yes. 'Metal train' has a tangible appearance and is a type of transportation vehicle.\nA few things that are visually similar to 'metal train' but are not 'metal train' are:\ttram\tsubway car\tmonorail\ttrolley\tbus\nThere are several useful visual features to tell there is 'metal train' and not similar things in a photo:\telongated shape\tmetal or metallic appearance\twheels along the bottom\tconnected cars or carriages\tsteam or smoke coming from the engine", 14], "metal shelves": ["Yes. 'Metal shelves' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'metal shelves' but are not 'metal shelves' are:\twire racks\tcabinets\tdressers\tbookshelves\nThere are several useful visual features to tell there is 'metal shelves' and not similar things in a photo:\tmade of metal\tstraight or perpendicular lines\tnarrow, flat horizontal surfaces\toften used for storage of items", 14], "polar": ["No. 'Polar' is too vague or abstract to be distinguished in a photo. However, 'polar' can be used to describe something related to the North or South Pole, such as 'polar bear' or 'polar ice'.\nA few things that are visually similar to 'polar' but are not 'polar' are:\twhite\tcolors with a high contrast of dark and light\ticy or snowy landscapes\tanimals with white fur or coats\nThere are several useful visual features to tell there is something 'polar' and not similar things in a photo:\ticebergs or glaciers\tpolar bears or penguins\tsnow or frost-covered surfaces\tflag indicating a location near the North or South Pole", 14], "swimming": ["Yes. 'Swimming' has a tangible appearance and involves a person or animal moving through water.\nA few things that are visually similar to 'swimming' but are not 'swimming' are:\tdiving\tsplashing\tbathing\nThere are several useful visual features to tell there is 'swimming' and not similar things in a photo:\tperson or animal in water\tmoving through water\tusing arms and legs to propel oneself through the water\toutputting air bubbles from nose\tor mouth", 14], "gold lamp": ["Yes. 'Gold lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'gold lamp' but are not 'gold lamp' are:\tbronze lamp\tchandelier\ttable light\nThere are several useful visual features to tell there is 'gold lamp' and not similar things in a photo:\tgolden color\tmetal base with an electrical socket\tlampshade\tlight bulb", 14], "see": ["No. 'See' is too vague or abstract to be visually distinguished in a photo.", 14], "armchairs": ["Yes. 'Armchairs' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'armchairs' but are not 'armchairs' are:\tsofas\tchairs\tstools\tbenches\nThere are several useful visual features to tell there is 'armchairs' and not similar things in a photo:\tupholstered seat and backrest\twith armrests\tlarge and comfortable\tenough to sit in for long periods of time.", 14], "metal lock": ["Yes. 'Metal lock' has a tangible appearance and is a type of mechanism used for securing objects.\nA few things that are visually similar to 'metal lock' but are not 'metal lock' are:\tdoorknob\thook\tclasp\nThere are several useful visual features to tell there is 'metal lock' and not similar things in a photo:\tmade of metal\thas a keyhole or combination dial\tused to secure or lock something\tclosed or locked position\tcan be attached to a door, gate, or box.", 14], "striation marks": ["Yes. 'Striation marks' has a tangible appearance and refers to lines, grooves, or scratches on a surface.\nA few things that are visually similar to 'striation marks' but are not 'striation marks' are:\tcracks\tlines in a pattern\tpaint brush strokes\nThere are several useful visual features to tell there are 'striation marks' and not similar things in a photo:\tparallel lines or grooves\tnatural or irregular pattern\tdifferent depths or widths\ttraceable pattern of direction_aligned grooves or scratches", 14], "dirt pitcher": ["No. 'Dirt pitcher' is too vague or abstract to be distinguished in a photo. Perhaps you mean something else, or if you provide more context, I could better understand what you are referring to.", 14], "computer monitor screen": ["Yes. 'Computer monitor screen' has a tangible appearance and is a type of electronic display.\nA few things that are visually similar to 'computer monitor screen' but are not 'computer monitor screen' are:\ttelevision screen\tprojector screen\ttablet screen\tsmartphone screen\nThere are several useful visual features to tell there is 'computer monitor screen' and not similar things in a photo:\trectangular or square shape\tattached to a computer or laptop\tvisible cables or wires\tin bright colors or displaying images or text\ta desktop background with icons or windows", 14], "rink": ["Yes. 'Rink' has a tangible appearance and is a type of flat, designated area for skating or sports.\nA few things that are visually similar to 'rink' but are not 'rink' are:\troad\tparking lot\tdance floor\nThere are several useful visual features to tell there is 'rink' and not similar things in a photo:\tflat surface\tfor ice skating, a smooth layer of ice\toutlined area\twith boards or barriers\tfor sports, designated lines and equipment\tskaters or athletes", 14], "fishing net": ["Yes. 'Fishing net' has a tangible appearance and is a type of woven material used for catching fish.\nA few things that are visually similar to 'fishing net' but are not 'fishing net' are:\tsafety net\tcargo net\tmosquito net\ttennis net\nThere are several useful visual features to tell there is 'fishing net' and not similar things in a photo:\twoven pattern\twith a large mesh or holes\thangs vertically in the water or on a boat\tmade of durable, water-resistant material\tsurrounded by ropes or floats to help hold it in place", 14], "stakes": ["Yes. 'Stakes' has a tangible appearance.\nA few things that are visually similar to 'stakes' but are not 'stakes' are:\tpoles\tposts\tsigns\tfences\nThere are several useful visual features to tell there are 'stakes' and not similar things in a photo:\tsmall and pointed\tsharp tip typically designed to be hammered into the ground\tmade of wood or metal\tplain or brightly colored", 14], "sweat bands": ["Yes. 'Sweat bands' have a tangible appearance and are a type of athletic accessory.\nA few things that are visually similar to 'sweat bands' but are not 'sweat bands' are:\twristbands\twatches\tbracelets\tarmbands\nThere are several useful visual features to tell there is 'sweat bands' and not similar things in a photo:\tmade of absorbent material, such as cotton or terrycloth\tworn on the wrist or forehead\tsolid or stripe patterns in bright colors (usually white, red, or blue)", 14], "seasoning": ["No. 'Seasoning' is too vague or abstract to be distinguished in a photo.", 14], "cupboard doors": ["Yes. 'Cupboard doors' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'cupboard doors' but are not 'cupboard doors' are:\tshower door\tfence door\trefrigerator door\t\nThere are several useful visual features to tell there is 'cupboard doors' and not similar things in a photo:\trectangular or square shape\thinged to a frame or a cabinet\thandle or knob for opening and closing\tmay have glass or wooden panels.", 14], "horse blanket": ["Yes. 'Horse blanket' has a tangible appearance and is a specific kind of cloth that is used on horses.\nA few things that are visually similar to 'horse blanket' but are not 'horse blanket' are:\tponchos quilt blankets \tcarpet \nThere are several useful visual features to tell there is 'horse blanket' and not similar things in a photo:\tusually made of wool or synthetic materials\tdesigned to fit a horse's body shape\thas straps or ties to secure it in place on the horse\thas a pattern or color scheme specifically designed for horse blankets.", 14], "seconds": ["No. 'Seconds' is too vague or abstract to be distinguished in a photo.", 14], "livestock": ["Yes. 'Livestock' has a tangible appearance and refers to domesticated animals that are bred and kept for agricultural purposes.\nA few things that are visually similar to 'livestock' but are not 'livestock' are:\twild animals\tzoo animals\tpets\nThere are several useful visual features to tell there is 'livestock' and not similar things in a photo:\tfarm animals\tkept for meat, milk, or eggs\tdomesticated or semi-domesticated animals\thorns or antlers, in some cases", 14], "hotel building": ["Yes. 'Hotel building' has a tangible appearance and is a type of commercial building.\nA few things that are visually similar to 'hotel building' but are not 'hotel building' are: apartment building, office building, hospital building, shopping mall, university building.\nThere are several useful visual features to tell there is 'hotel building' and not similar things in a photo:\tSignage with the hotel's name or logo\tEntrance with a revolving door or a covered overhang \tCar drop-off/pickup area \tLighted windows and balconies \tDedicated check-in area with a desk or kiosk", 14], "condensation": ["Yes. 'Condensation' has a tangible appearance and is a physical process that can be observed.\nA few things that are visually similar to 'condensation' but are not 'condensation' are:\tfog\tsmoke\tmist\tsteam\nThere are several useful visual features to tell there is 'condensation' and not similar things in a photo:\twater droplets forming on a cold surface, like a glass or a mirror\tor a can of drink, due to the difference in temperature\tand humidity\tThe droplets are usually clear, with a round or oblong shape.", 14], "shadow tree grass": ["Yes. 'Shadow tree grass' has a tangible appearance and refers to the grass underneath a tree that is in the shade.\nThere aren't many things that are visually similar to 'shadow tree grass' but are not 'shadow tree grass'.\nSome useful visual features for distinguishing 'shadow tree grass' from other types of grass in a photo would be:\tcurved and elongated blades of grass\tdarker green color\tappearing in the shade of a tree", 14], "orange numbers": ["Yes. 'Orange numbers' has a tangible appearance and refers to numbers written in orange color.\nA few things that are visually similar to 'orange numbers' but are not 'orange numbers' are:\tnumbers in other colors\tletters in orange color\tshapes in orange color\nThere are no additional useful visual features for distinguishing 'orange numbers' from the listed similar things in a photo as the defining feature is simply the color of the numbers.", 14], "grey chain": ["Yes. 'Grey chain' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'grey chain' but are not 'grey chain' are:\tsilver chain\tchainsaw\tchocolate\tiron rod\nThere are several useful visual features to tell there is 'grey chain' and not similar things in a photo:\tmade of metal\tcylindrical links\tgray or silver color", 14], "rungs": ["Yes. 'Rungs' has a tangible appearance and is a part of a ladder.\nA few things that are visually similar to 'rungs' but are not 'rungs' are:\twooden planks\tshelves\tsidewalk blocks\nThere are several useful visual features to tell there is 'rungs' and not similar things in a photo:\tsmall, cylindrical and evenly spaced bars\thorizontal and perpendicular to the rails of a ladder\teasily climbable and lightweight materials like aluminum, steel, or plastic", 14], "edging": ["Yes. 'Edging' has a tangible appearance and refers to a decorative border or trim.\nA few things that are visually similar to 'edging' but are not 'edging' are:\tstitching\tborderlines\tframes\t\nThere are several useful visual features to tell there is 'edging' and not similar things in a photo:\tdecorative\ttrims the edge of an object or surface\tadds color or texture to an object or surface\toften made of a different material from the object or surface it borders\tsometimes has a repeating pattern or design", 14], "orange marker": ["Yes. 'Orange marker' has a tangible appearance and is a type of writing instrument.\nA few things that are visually similar to 'orange marker' but are not 'orange marker' are:\torange pen\torange highlighter\torange crayon\nThere are several useful visual features to tell there is 'orange marker' and not similar things in a photo:\tcylindrical shape\twith a pointed tip and a cap\tbright orange color\tplastic or metal material", 14], "concrete planter": ["Yes. 'Concrete planter' has a tangible appearance and is a type of garden container.\nA few things that are visually similar to 'concrete planter' but are not 'concrete planter' are:\tterracotta planter\twooden box\tplastic pot\tmetal bucket\nThere are several useful visual features to tell there is 'concrete planter' and not similar things in a photo:\tmade of concrete or has a concrete finish\thollow with an open top\tdesigned for holding plants or flowers\trectangular, square, cylindrical, or spherical shape", 14], "antique car": ["Yes. 'Antique car' has a tangible appearance and refers to a car that is from a previous era and is considered classic or vintage.\nA few things that are visually similar to 'antique car' but are not 'antique car' are:\told car\trestored car\treplica\tcar museum piece\nThere are several useful visual features to tell there is 'antique car' and not similar things in a photo:\tunique and recognizable body style\toutdated technology\tmanual transmission\tconvertible style\tsimple design and mechanics\tlack of high technology features or modern car amenities", 14], "shadow plane": ["No. 'Shadow plane' is too vague or abstract to be distinguished in a photo.", 14], "springs": ["Yes. 'Springs' has a tangible appearance and is a physical component.\nA few things that are visually similar to 'springs' but are not 'springs' are:\twires\tspiral notebooks\tcorkscrews\nThere are several useful visual features to tell there is 'springs' and not similar things in a photo:\tcoiled shape\tmetallic surface\twidening and narrowing along a central axis\tspringy or bouncy when pressed", 14], "tyres": ["Yes. 'Tyres' has a tangible appearance and is a kind of vehicle accessory.\nA few things that are visually similar to 'tyres' but are not 'tyres' are:\thula-hoops\trubber rings\tforbidden signs\tdonuts\nThere are several useful visual features to tell there is 'tyres' and not similar things in a photo:\trubber and black material\ttread pattern around the circumference of the tyre\t\ncircular shape with a hole in the center\tside walls and a rim surface that support the tyre and the vehicle's load ", 14], "wood furniture": ["Yes. 'Wood furniture' has a tangible appearance as it is a type of furniture that is made of wood.\nA few things that are visually similar to 'wood furniture' but are not 'wood furniture' are: plastic furniture, metal furniture, glass furniture, stone furniture.\nThere are several useful visual features to distinguish 'wood furniture' from the listed similar things in a photo:\n- Grain patterns in the wood\n- Visible knots or burls on the surface\n- Natural variations in color and texture\n- Unique wood species, such as oak, maple, or cherry\n- Solid and heavy appearance due to its material.", 14], "wha": ["No. 'Wha' is too vague or abstract and does not have a tangible appearance. It is not a recognized word or concept in the English language.", 14], "color plate": ["Yes. 'Color plate' has a tangible appearance and is a type of printed page that displays colors.\nA few things that are visually similar to 'color plate' but are not 'color plate' are:\tcolor swatches\tpaint samples\tcolor chart\nThere are several useful visual features to tell there is 'color plate' and not similar things in a photo:\ta printed page with rows of colors\tusually labeled with numbers or names\toften found in art or design books or magazines ", 14], "coasters": ["Yes. 'Coasters' has a tangible appearance and is usually a small, flat object used to protect surfaces from heat or moisture.\nA few things that are visually similar to 'coasters' but are not 'coasters' are:\ttablet\tsmaller plates\tdecorative tiles\nThere are several useful visual features to tell there is 'coasters' and not similar things in a photo:\tcircular or square shape\tmade of cork, wood, or another absorbent material\tcolors or patterns that match a set of tableware or decor", 14], "headscarf": ["Yes. 'Headscarf' has a tangible appearance and is a piece of fabric worn on the head for religious, cultural, or personal reasons.\nA few things that are visually similar to 'headscarf' but are not 'headscarf' are:\thairband\tscarf\twrap\nThere are several useful visual features to tell there is 'headscarf' and not similar things in a photo:\tworn on the head\tcovers the hair and neck\ttied or wrapped securely around the head\tvariety of colors and patterns, depending on the culture and style", 14], "plastic trash bag": ["Yes. 'Plastic trash bag' has a tangible appearance and is a type of bag used for storing trash.\nA few things that are visually similar to 'plastic trash bag' but are not 'plastic trash bag' are:\tpaper bag\tgrocery bags\tlaundry bags\tpet waste bags\nThere are several useful visual features to tell there is 'plastic trash bag' and not similar things in a photo:\ttranslucent or opaque plastic material\tTypically black or white in color, but can come in a variety of colors\tdesigned to hold garbage or refuse\toften tied or cinched at the top", 14], "truck window": ["Yes. 'Truck window' has a tangible appearance and is a type of vehicle window.\nA few things that are visually similar to 'truck window' but are not 'truck window' are:\tcar window\tbus window\ttrain window\tplane window\tstorefront window\nThere are several useful visual features to tell there is 'truck window' and not similar things in a photo:\tusually rectangular in shape\toften larger than car windows\tmay have a sliding or hinged opening\tfor trucks or other large vehicles typically located on the sides or the rear", 14], "pepsi sign": ["Yes. 'Pepsi sign' has a tangible appearance and is a type of advertisement.\nA few things that are visually similar to 'pepsi sign' but are not 'pepsi sign' are:\tCoca-Cola sign\tDr. Pepper sign\tSprite sign\tMountain Dew sign\nThere are several useful visual features to tell there is 'pepsi sign' and not similar things in a photo:\tred, white, and blue\tcolorful\twave-like shape\twith the word \"PEPSI\" written in capital letters", 14], "bucket hat": ["Yes. 'Bucket hat' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'bucket hat' but are not 'bucket hat' are:\tfishing hat\tboonie hat\tsun hat\tsafari hat\nThere are several useful visual features to tell there is 'bucket hat' and not similar things in a photo:\tdownward sloping brim\tall-around brim\tsoft and crushable material\tcrown with a flat top and slightly sloping sides.", 14], "whit": ["No. 'Whit' is too vague or abstract to be distinguished in a photo.", 14], "ac": ["No. 'AC' is too vague or abstract to be distinguished in a photo. 'AC' stands for air conditioning, which is a system that does not have a tangible appearance.", 14], "mans arm": ["Yes. 'Man's arm' has a tangible appearance and is a body part.\nA few things that are visually similar to 'man's arm' but are not 'man's arm' are:\tmannequin arm\tstatue arm\tsculpture arm\t\nThere are several useful visual features to tell there is 'man's arm' and not similar things in a photo:\thair or lack of hair on the arm\tdifferent skin tones or textures\twrist, elbow, and shoulder joints\tbicep and tricep muscles\tvarious hairs and moles or scars that might be unique to an individual", 14], "glass base": ["Yes. 'Glass base' has a tangible appearance and refers to the foundation or bottom part of an object made of glass.\nA few things that are visually similar to 'glass base' but are not 'glass base' are:\tglass top\tglass pedestal\tglass stem\tglass dome\nThere are several useful visual features to tell there is a 'glass base' and not similar things in a photo:\tclear or transparent material\twide and flat bottom\tpart of an object's structure", 14], "slice pizza": ["Yes. 'Slice pizza' has a tangible appearance and is a portion of a pizza.\nA few things that are visually similar to 'slice pizza' but are not 'slice pizza' are:\twhole pizza\tflatbread\tfocaccia\tpita\nThere are several useful visual features to tell there is 'slice pizza' and not similar things in a photo:\ttriangular shape\tred tomato sauce\tmelted cheese\twith visible toppings, such as pepperoni, vegetables and/or mushrooms", 14], "passenger train cars": ["Yes. 'Passenger train cars' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'passenger train cars' but are not 'passenger train cars' are:\tfreight train cars\ttrams\ttrucks\tbuses\tboats\nThere are several useful visual features to tell there is 'passenger train cars' and not similar things in a photo:\trectangular shape\tmultiple windows\ton-board passengers\tattached to a locomotive\ttrain tracks visible in the foreground or background", 14], "color bag": ["No. 'Color bag' is too vague or abstract to be distinguished in a photo. Can you please provide more context or details about what a 'color bag' might be?", 14], "ground cover": ["Yes. 'Ground cover' has a tangible appearance and refers to plants that grow low to the ground, covering and protecting the soil.\nA few things that are visually similar to 'ground cover' but are not 'ground cover' are:\tgreen lawn\tmoss\tpine needles\tweeds\nThere are several useful visual features to tell there is 'ground cover' and not similar things in a photo:\tlow-growing plants\tclose to the soil and often overlapping one another\tpreventing erosion and providing protection to soil\toften used as a garden or landscaping feature.", 14], "fence line": ["Yes. 'Fence line' has a tangible appearance and is a physical boundary created by a fence.\nA few things that are visually similar to 'fence line' but are not 'fence line' are:\ttree line\tproperty line\thedge\tline of rocks\nThere are several useful visual features to tell there is 'fence line' and not similar things in a photo:\tlinear array of posts or boards\tmaterials such as wood, metal, or vinyl\thigher than most ground cover or nearby structures\tdividing or separating one area from another in a clear and defined manner.", 14], "airport worker": ["Yes. 'Airport worker' has a tangible appearance and refers to a person who works at an airport.\nA few things that are visually similar to 'airport worker' but are not 'airport worker' are: Air travelers, pilots, cabin crew, security personnel.\nThere are several useful visual features to tell there is 'airport worker' and not similar things in a photo:\tWearing uniform or hi-vis vest\tbadge or ID card with the airport logo\tcart, trolley or luggage handling equipment\tradio or communication devices at hand or attached on the belt.", 14], "print shirt": ["Yes. 'Print shirt' has a tangible appearance and is a kind of shirt with a printed pattern.\nA few things that are visually similar to 'print shirt' but are not 'print shirt' are:\tstriped shirt\tpolka-dot shirt\tsolid color shirt\nThere are several useful visual features to tell there is 'print shirt' and not similar things in a photo:\tvarious patterns and designs\tprinted images or shapes\trepeating designs on the entire shirt or certain areas of it\tsingle-colored background with contrasting colors for the pattern", 14], "stadium seating": ["Yes. 'Stadium seating' is a visually concrete concept and refers to a tiered structure of seats in sports or entertainment venues.\nA few things that are visually similar to 'stadium seating' but are not 'stadium seating' are:\tmovie theater seating\tconference room seating\tamphitheater seating\nThere are several useful visual features to tell there is 'stadium seating' and not similar things in a photo:\tlayered or tiered structure\tforward-slanting seats\tor rectangular shape\tsuite numbers or letters\tpresence of aisleways or steps.", 14], "ball girl": ["Yes. 'Ball girl' has a tangible appearance and refers to a girl who retrieves stray balls during sports games.\nA few things that are visually similar to 'ball girl' but are not 'ball girl' are: \tpolice officer\tsecurity guard\tathletic coach\tcrowd attendee\nThere are several useful visual features to tell there is 'ball girl' and not similar things in a photo:\twearing a uniform or identifiable clothing \tpresence on the field or court \thigh level of attention to the game or match, watching for stray balls \tretrieving or holding a ball.", 14], "program": ["No. 'Program' is too vague or abstract to be distinguished in a photo.", 14], "wooden plank": ["Yes. 'Wooden plank' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wooden plank' but are not 'wooden plank' are:\tconcrete slab\ttile\tor large rock\nThere are several useful visual features to tell there is 'wooden plank' and not similar things in a photo:\tthin and flat piece of wood\trough texture along the grain of the wood\tbrown or beige in color\tknotted or patterned surface", 14], "string beans": ["Yes. 'String beans' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'string beans' but are not 'string beans' are:\tsnow peas\tsnap peas\tlima beans\tgreen peppers\nThere are several useful visual features to tell there is 'string beans' and not similar things in a photo:\telongated shape\tskinny and green\tedible pod\tcontaining small beans inside", 14], "trey": ["No. 'Trey' is too vague and abstract to be distinguished in a photo. It can refer to a person's name or a playing card, neither of which has a unique tangible appearance.", 14], "fingernail thumb": ["No. 'Fingernail thumb' is not a concept with a tangible appearance. It is not a commonly recognized term, and it is not clear what it refers to.", 14], "skyscraper building": ["Yes. 'Skyscraper building' has a tangible appearance and is a type of tall building.\nA few things that are visually similar to 'skyscraper building' but are not 'skyscraper building' are:\tapartment building\tchurch\tlighthouse\ttower\nThere are several useful visual features to tell there is 'skyscraper building' and not similar things in a photo:\tvery tall building with many floors\tglass or metal fa\u00e7ade\tlocated in urban areas with other similar buildings.", 14], "orange orange": ["Yes. 'Orange orange' has a tangible appearance and is a fruit.\nA few things that are visually similar to 'orange orange' but are not 'orange orange' are:\torange toy ball\torange jelly\torange candy\nThere are several useful visual features to tell there is 'orange orange' and not similar things in a photo:\tround shape\tslightly flattened at the top and bottom\tbright orange color\tpaper-thin skin\tsegmented flesh\twith a pitted stem end and a slightly protruding navel", 14], "napkin holder": ["Yes. 'Napkin holder' has a tangible appearance and is an object for holding napkins.\nA few things that are visually similar to 'napkin holder' but are not 'napkin holder' are:\ttissue box\tbookend\tpaperweight\tfile organizer\nThere are several useful visual features to tell there is 'napkin holder' and not similar things in a photo:\tflat and open face\tfor holding napkins, either folded or straight\tupright position\twith or without weight\ttoothed, slotted or clamped design", 14], "organizer": ["No. 'Organizer' is too vague or abstract to have a tangible appearance.\nA few things that are visually similar to 'organizer' but are not 'organizer' are:\tbookshelf\tsorting tray\tcabinet\tbinder clip\nThere are no useful visual features for distinguishing 'organizer' from the listed similar things in a photo because an organizer can come in many different shapes and sizes depending on what needs organizing.", 14], "dragons": ["Yes. 'Dragons' has a tangible appearance and is a mythical creature often depicted in art and literature.\nA few things that are visually similar to 'dragons' but are not 'dragons' are:\tDinosaurs\tCrocodiles\tKomodo dragons\nThere are several useful visual features to tell there is 'dragons' and not similar things in a photo:\tLarge wings\tSharp claws and teeth\tFire or smoke breath\tScaly skin\tSerpentine body and tail", 14], "gold writing": ["Yes. 'Gold writing' has a tangible appearance and is a type of text.\nA few things that are visually similar to 'gold writing' but are not 'gold writing' are:\tyellow letters\tchalk letters\tbronze letters\nThere are several useful visual features to tell there is 'gold writing' and not similar things in a photo:\tmetallic or shiny appearance\tgolden color\treflection of light\tmay appear in cursive or block letters may appear in different sizes or typefaces", 14], "tile floors": ["Yes. 'Tile floors' has a tangible appearance and is a kind of flooring.\nA few things that are visually similar to 'tile floors' but are not 'tile floors' are:\tcarpet\twooden floors\tlinoleum floors\nThere are several useful visual features to tell there is 'tile floors' and not similar things in a photo:\trectangular or square in shape\ttightly fitted or grouted\ttile pattern or design\tsmooth, hard surface with visible grout lines or gaps between tiles", 14], "cover book": ["Yes. 'Cover book' has a tangible appearance and refers to a protective covering for a book.\nA few things that are visually similar to 'cover book' but are not 'cover book' are:\tbookmark\tpaperweight\tstationery notebook\tbinding coil\nThere are several useful visual features to tell there is 'cover book' and not similar things in a photo:\trectangular or square shape\thard or soft cover\ttitle and author name on the front or spine of the cover\ttypically made of paper or cardboard", 14], "blond child": ["Yes. 'Blond child' has a tangible appearance and is a child with fair hair.\nA few things that are visually similar to 'blond child' but are not 'blond child' are:\tchildren with red hair\tchildren with brown hair\tchildren with black hair\tbabies with little or no hair\nThere are several useful visual features to tell there is 'blond child' and not similar things in a photo:\tfair hair or light-colored hair\tlight-colored eyebrows, eyelashes, and skin tone\tage-appropriate clothing\tthat the child is actually a child, and not an adult or a baby.", 14], "bare leg": ["Yes. 'Bare leg' has a tangible appearance and is a visible part of the human body.\nA few things that are visually similar to 'bare leg' but are not 'bare leg' are:\tpants\tleggings\ttights\t\nThere are several useful visual features to tell there is 'bare leg' and not similar things in a photo:\tuncovered skin\tno fabric or texture visible\tfuzz or hair on the skin", 14], "xbox": ["Yes. 'Xbox' has a tangible appearance and is a specific brand of gaming console.\nA few things that are visually similar to 'Xbox' but are not 'Xbox' are:\tPlayStation\tNintendo Switch\tPC gaming laptop\nThere are several useful visual features to tell there is 'Xbox' and not similar things in a photo:\tgaming console with a controller\tmostly black color with green accents\tand the word \"Xbox\" on it\tor Xbox logo on it.", 14], "grey pair": ["No. 'Grey pair' is too vague or abstract.", 14], "digital window": ["Yes. 'Digital window' has a tangible appearance and is a type of display.\nA few things that are visually similar to 'digital window' but are not 'digital window' are:\ttelevision\tscreen\tprojector\nThere are several useful visual features to tell there is 'digital window' and not similar things in a photo: \tframed display that resembles a regular window\tstatic or moving digital graphics and images\tbrightness and clarity of displayed content.", 14], "photography": ["No. 'Photography' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to elements of photography are:\t\ncamera\t\nfilm\t\nfilters\t\nlenses\t\n\nUseful visual features to distinguish 'photography' would depend on the context in which it is being used. If a photo contains a camera or other equipment, it can suggest that the subject matter is related to photography. Additionally, if a photo includes elements of composition such as unique angles, lighting, or focus, it can suggest that the photographer deliberately constructed the image as a form of art or creative expression.", 14], "computer wires": ["Yes. 'Computer wires' has a tangible appearance and refers to a set of cords or cables used to connect computer components.\nA few things that are visually similar to 'computer wires' but are not 'computer wires' are:\telectrical wires\theadphone wires\tmouse wires\nThere are several useful visual features to tell there is 'computer wires' and not similar things in a photo:\ttypically black, grey or white colors\tplastic coating\twide range of shapes and sizes\tplugs or connectors on each end\tlabeling and organization of the wires for different functions in the computer", 14], "poster board": ["Yes. 'Poster board' has a tangible appearance and is a type of thick paper used for creating displays.\nA few things that are visually similar to 'poster board' but are not 'poster board' are:\tcardboard\tfoam board\tbinder cover\nThere are several useful visual features to tell there is 'poster board' and not similar things in a photo:\tthick and sturdy paper\toften white or colored\tmay have a glossy or matte finish\tlarge in size, suitable for creating displays or posters.", 14], "left shoe": ["Yes. 'Left shoe' has a tangible appearance and is a footwear worn on the left foot.\nA few things that are visually similar to 'left shoe' but are not 'left shoe' are:\n- right shoe\n- sandals\n- boots\n- flip-flops\n\nUseful visual features for distinguishing 'left shoe' from the listed similar things in a photo are:\n- The shape of the sole \n- The position of the strap or any buckles \n- The shape of the front of the shoe \n- The overall structure of the shoe, such as whether it has a heel or is flat", 14], "cement post": ["Yes. 'Cement post' has a tangible appearance and is a type of construction element.\nA few things that are visually similar to 'cement post' but are not 'cement post' are:\twooden post\tpillar\tcolumn\nThere are several useful visual features to tell there is 'cement post' and not similar things in a photo:\tgrey, solid and opaque material\tsmooth or rough texture\tsquare or rectangle shaped, with a flat or slightly rounded top.", 14], "bear ears": ["Yes. 'Bear ears' has a tangible appearance and is a physical feature of a bear.\nA few things that are visually similar to 'bear ears' but are not 'bear ears' are:\tcat ears\tdog ears\tfox ears\nThere are several useful visual features to tell there is 'bear ears' and not similar things in a photo:\thairy-pointed ears positioned on top of the head\tof large size in proportion to the bear's head\ttexture and color of the fur around the ears which matches the bear's body", 14], "orange tee shirt": ["Yes. 'Orange tee shirt' has a tangible appearance and is a piece of clothing.\nA few things that are visually similar to 'orange tee shirt' but are not 'orange tee shirt' are:\torange polo shirt\torange sweater\torange tank top\torange blouse\nThere are several useful visual features to tell there is 'orange tee shirt' and not similar things in a photo:\tshort sleeves\tround neck\tcasual style\tof cotton or other soft and stretchy materials", 14], "blue trash": ["Yes. 'Blue trash' has a tangible appearance and is a type of waste container.\nA few things that are visually similar to 'blue trash' but are not 'blue trash' are:\tblue recycling bin\tblue storage bin\ttrash cans in other colors\nThere are several useful visual features to tell there is 'blue trash' and not similar things in a photo:\tblue color\tlid on top with a slot for throwing things in\tthe words \"trash\" or a trash symbol may be written on it\tcontaining trash bags or loose trash inside.", 14], "window wipers": ["Yes. 'Window wipers' has a tangible appearance and is a kind of car accessory.\nA few things that are visually similar to 'window wipers' but are not 'window wipers' are:\tantenna\tcar mirror\tspoiler\tdoor handle\nThere are several useful visual features to tell there are 'window wipers' and not similar things in a photo:\tthin blade-like material\tsquare or rectangular shape\tattached to the windshield or the back window of a car\tmoves back-and-forth to clear rain or snow from the glass.", 14], "purple paint": ["Yes. 'Purple paint' has a tangible appearance and is a type of paint with a particular color.\nA few things that are visually similar to 'purple paint' but are not 'purple paint' are:\tpurple drink\tpurple fabric\tpurple flowers\tpurple paper\nThere are several useful visual features to tell there is 'purple paint' and not similar things in a photo:\tliquid consistency\tdrying or wet on a surface\tbright or deep shade of purple\tsimilar texture to other paint solutions", 14], "bottom piece": ["No. 'Bottom piece' is too vague or abstract to be distinguished in a photo.", 14], "playing field": ["Yes. 'Playing field' has a tangible appearance and usually refers to a sports field.\nA few things that are visually similar to 'playing field' but are not 'playing field' are:\tpark\tlawn\toutdoor shooting range\nThere are several useful visual features to tell there is 'playing field' and not similar things in a photo:\tlines or markings indicating the boundaries of the field or the game\tinflated balls or other game props\tgoals or nets for team sports\tspectators, athletes, or coaches involved in a game or sport.", 14], "bag woman": ["No. 'Bag woman' is too vague or abstract to be distinguished in a photo. It can be considered a derogatory term and should not be used to describe anyone.", 14], "teenage girl": ["Yes. 'Teenage girl' has a tangible appearance and refers to a young human female between the ages of 13-19.\nA few things that are visually similar to 'teenage girl' but are not 'teenage girl' are:\tadult woman\tchild\tgender-neutral person\nThere are several useful visual features to tell there is a 'teenage girl' and not similar things in a photo:\tfacial features associated with youth, such as a rounder face or a lack of wrinkles\tadolescent body shape\tdevelopmental changes, such as puberty-specific features or signs of undergoing growth spurts\ttypical teenage clothing or accessories", 14], "apple macbook computer": ["Yes. 'Apple MacBook computer' has a tangible appearance and a specific design.\nA few things that are visually similar to 'Apple MacBook computer' but are not 'Apple MacBook computer' are:\tother laptop brands\ttablets\tsmartphones\telectronic devices\n\nThere are several useful visual features to tell there is an 'Apple MacBook computer' and not similar things in a photo:\tApple logo on the back\tlarge rectangular screen and keyboard\tsmooth aluminum body\twithin a set of specific dimensions and weight\tposition of ports and chargers\ton mac operating system", 14], "shallow body": ["Yes. 'Shallow body' has a tangible appearance and refers to an object that has a small height or depth compared to its width or length.\nA few things that are visually similar to 'shallow body' but are not 'shallow body' are:\tflat surface\tplate\tdisk\t\nThere are several useful visual features to tell there is 'shallow body' and not similar things in a photo:\tlow height or depth\trelatively large width or length\tpotential curvature or tapering of the object's shape", 14], "concrete structure": ["Yes. 'Concrete structure' has a tangible appearance and is an object made of concrete.\nA few things that are visually similar to 'concrete structure' but are not 'concrete structure' are:\trock formations\tbuildings\tmetal structures\twooden structures\nThere are several useful visual features to tell there is 'concrete structure' and not similar objects in a photo:\tmade of concrete or cement\tsolid and sturdy appearance\tsmooth or rough texture\tstraight lines and sharp edges", 14], "advertising signs": ["Yes. 'Advertising signs' has a tangible appearance and refers to physical signs used for advertising purposes.\nA few things that are visually similar to 'advertising signs' but are not 'advertising signs' are:\tDecorative signs, street signs, informational signs, neon lights, art installations.\nThere are several useful visual features to tell there is 'advertising signs' and not similar things in a photo:\tBrand logos, product images, or promotional messages\tmade of materials like plastic or metal\tvisible from a distance, often in busy areas like city streets or highways\tbright colors or eye-catching designs, such as bold fonts or images.", 14], "cross top church": ["No. 'Cross top church' is too vague or abstract to be distinguished in a photo. \n\nHowever, if you meant 'church with a cross on top', then the answer is: Yes, it has a tangible appearance.\n\nA few things that are visually similar to 'church with a cross on top' but are not 'church with a cross on top' are:\tTall buildings with antennas or flags on top\tMosques with domes on top\n\nThere are several useful visual features to tell there is 'church with a cross on top' and not similar things in a photo:\ta building with a cross on the top; usually the highest point of construction.\tA shape of a cross which could be on the top of one or more tower or spire.\tThe cross may be made of metal or stone, and could be either plain or ornate in design.", 14], "steam train": ["Yes. 'Steam train' has a tangible appearance and is a type of locomotive.\nA few things that are visually similar to 'steam train' but are not 'steam train' are:\tmodern train\tsubway\ttram\nThere are several useful visual features to tell there is 'steam train' and not similar things in a photo:\tlarge and bulky machine\tsmoke and steam coming from the engine\tbig wheels and pistons\tsignature steam whistle on top\tof railroad tracks that are made of wooden slats or rails", 14], "chocolate donuts": ["Yes. 'Chocolate donuts' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'chocolate donuts' but are not 'chocolate donuts' are:\tbagels\tgiant cookies\tbiscuits\tcroissants\nThere are several useful visual features to tell there is 'chocolate donuts' and not similar things in a photo:\tcircular shape\twith a hole in the middle\tbrown color\tcovered in chocolate glaze\ttopped with sprinkles or nuts", 14], "wall light switch": ["Yes. 'Wall light switch' has a tangible appearance and is a type of electrical device.\nA few things that are visually similar to 'wall light switch' but are not 'wall light switch' are:\tAC socket\tphone jack\tswitch panel\nThere are several useful visual features to tell there is 'wall light switch' and not similar things in a photo:\trectangular or square shape\tpositioned on a wall\tclose to a light fixture and/or electrical wires\ttoggle, rocker, or button switch", 14], "porcelain bathtub": ["Yes. 'Porcelain bathtub' has a tangible appearance and is a specific type of bathtub.\nA few things that are visually similar to 'porcelain bathtub' but are not 'porcelain bathtub' are:\tplastic bathtub\tmetal bathtub\tinflatable bathtub\nThere are several useful visual features to tell there is 'porcelain bathtub' and not similar things in a photo:\twhite or off-white\tcolor\tsmooth and glossy surface\toval or rectangular shape\tfixed on the bathroom floor or wall", 14], "cap wave": ["No. 'Cap wave' is too vague or abstract to be distinguished in a photo.", 14], "pink bowl": ["Yes. 'Pink bowl' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'pink bowl' but are not 'pink bowl' are:\tred bowl\torange bowl\tpurple bowl\tvase\tcoffee cup\nThere are several useful visual features to tell there is 'pink bowl' and not similar things in a photo:\tbowl-shaped\tpink in color\twith a flat bottom and curved sides\tmay have patterns or designs on it", 14], "liquor bottle": ["Yes. 'Liquor bottle' has a tangible appearance and is a container for alcoholic beverages. \nA few things that are visually similar to 'liquor bottle' but are not 'liquor bottle' are:\twater bottle\tperfume bottle\toil bottle\t\nThere are several useful visual features to tell there is 'liquor bottle' and not similar things in a photo:\t\nunique shape and size for each type of liquor\t\na label with the type of liquor, brand, and alcohol content\t\na cap or a cork on the top", 14], "leather bridle": ["Yes. 'Leather bridle' has a tangible appearance and is a type of equipment used for controlling a horse.\nA few things that are visually similar to 'leather bridle' but are not 'leather bridle' are:\tharness\tsaddle\treins\nThere are several useful visual features to tell there is 'leather bridle' and not similar things in a photo:\tmade of leather or another durable material\tinvolves a bit in the horse's mouth\thave distinct buckles or straps for adjustment\tworn around a horse's head", 14], "moniter": ["Yes. 'Monitor' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'monitor' but are not 'monitor' are: television, laptop, tablet\nThere are several useful visual features to tell there is 'monitor' and not similar things in a photo: a flat rectangular screen with a display, a base, and sometimes a stand. It also has buttons, inputs and outputs on the back of the monitor. It is used to display images or videos from a computer.", 14], "hyena": ["Yes. 'Hyena' has a tangible appearance and is a type of wild animal.\nA few things that are visually similar to 'hyena' but are not 'hyena' are:\tdog\tfox\twolf\tjackal\nThere are several useful visual features to tell there is 'hyena' and not similar things in a photo:\tdog-like appearance\tshort, sandy-colored fur\tsloping back\tsparse, spotted or striped fur pattern\tbig front teeth\tuneven legs and neck stripes.", 14], "wedges": ["Yes. 'Wedges' is a visually concrete concept and refers to a type of footwear.\nA few things that are visually similar to 'wedges' but are not 'wedges' are:\theels\tplatform shoes\tclogs\nThere are several useful visual features to tell there is 'wedges' and not similar things in a photo:\twedge-shaped heel\tthat starts at the ball of the foot and extends to the back of the shoe\tno separation between the heel and the sole of the shoe\tthe height of the heel does not vary along the length of the foot", 14], "foaming water": ["Yes. 'Foaming water' has a tangible appearance and is water with bubbles or foam.\nA few things that are visually similar to 'foaming water' but are not 'foaming water' are:\tsoap bubbles\tsnowflakes\tclouds\tocean waves\nThere are several useful visual features to tell there is 'foaming water' and not similar things in a photo:\tbubbles or foam on the surface of water\twhite or light blue color\tbeing near a body of water, such as a river or fountain", 14], "pen desk": ["Yes. 'Pen desk' has a tangible appearance and refers to a desk designed for writing or using a computer.\nA few things that are visually similar to 'pen desk' but are not 'pen desk' are:\tkitchen table\tbedside table\tcoffee table\tworkbench\nThere are several useful visual features to tell there is 'pen desk' and not similar things in a photo:\tusually has drawers or shelves to store writing materials\thas enough space to work on a computer or take notes\tmay have a writing pad or a lamp on it\ttypically located in an office or study room.", 14], "yankees": ["No. 'Yankees' is too vague or abstract to be distinguished in a photo. 'Yankees' could refer to the baseball team, the people from the northern United States, or a historical faction during the American Revolution. Without more context, it cannot be determined if 'yankees' has a tangible appearance in a photo.", 14], "wood stand": ["Yes. 'Wood stand' has a tangible appearance and refers to a piece of furniture made of wood used to hold objects.\nA few things that are visually similar to 'wood stand' but are not 'wood stand' are:\ttables\tchairs\tshelves\tbenches\nThere are several useful visual features to tell there is 'wood stand' and not similar things in a photo:\tvertical legs on the bottom\tsurface on top to hold objects\tmade of wood", 14], "chocolate cupcakes": ["Yes. 'Chocolate cupcakes' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'chocolate cupcakes' but are not 'chocolate cupcakes' are:\tregular cupcakes\tmuffins\tbrownies\nThere are several useful visual features to tell there is 'chocolate cupcakes' and not similar things in a photo:\tchocolate color\tfluffy cake-like texture\tsmall size\twrapped in paper or liner\tchocolate chips or sprinkles on top", 14], "floor window": ["Yes. 'Floor window' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'floor window' but are not 'floor window' are:\tskylight\troof window\tglass floor\tpanel window\nThere are several useful visual features to tell there is 'floor window' and not similar things in a photo:\tinstalled on the floor\tlevel with the ground or lower\toften found in basements or underground spaces\tmade of glass or clear material", 14], "metal hand": ["Yes. 'Metal hand' has a tangible appearance.\nA few things that are visually similar to 'metal hand' but are not 'metal hand' are:\trobot hand\tprosthetic hand\tstatue hand\nThere are several useful visual features to tell there is 'metal hand' and not similar things in a photo:\tmade of metal, usually silver or grey\tcolorful wires or circuitry visible\tbolts or screws visible\tsleek and futuristic design", 14], "wears": ["No. 'Wears' is too vague or abstract to have a tangible appearance.", 14], "lacy": ["Yes, 'lacy' has a tangible appearance and is a type of delicate fabric with openwork designs.\nA few things that are visually similar to 'lacy' but are not 'lacy' are:\tcrochet\tknitted fabric\tnetting\nThere are several useful visual features to tell there is 'lacy' and not similar things in a photo:\topenwork designs\twith delicate and intricate patterns provides a see-through texture\tintricate and decorative edges and frills\tthat appears light and delicate to the touch\tand often woven from linen, cotton, or silk fibers.", 14], "seat brown": ["Yes. 'Seat brown' has a tangible appearance as it refers to the color of a seat.\nA few things that are visually similar to 'seat brown' but are not 'seat brown' are:\twalnut wood\tchocolate\tbrown clothing\nThere are no useful visual features to distinguish 'seat brown' from other brown items, as it is only describing the color of a seat.", 14], "shadow boat": ["Yes. 'Shadow boat' has a tangible appearance and refers to the shadow that a boat casts on the water when the sunlight is shining from a certain angle.\nThere are no things that are visually similar to 'shadow boat' but are not 'shadow boat'.\nUseful visual features for identifying 'shadow boat' in a photo include:\tthe outline of a boat's shape on the water\ta distinctive shape that resembles the shape of a boat's hull lengthwise and its bulwarks in a cross shape", 14], "powdery snow": ["Yes. 'Powdery snow' has a tangible appearance and is a specific type of snow.\nA few things that are visually similar to 'powdery snow' but are not 'powdery snow' are:\twet snow\tslush\tice crystals\t\nThere are several useful visual features to tell there is 'powdery snow' and not similar things in a photo:\tdry and fluffy texture\tloose and powdery appearance\tmay appear white or slightly blueish in color\tfractured crystal shape.", 14], "baggage cart": ["Yes. 'Baggage cart' has a tangible appearance and is a type of wheeled vehicle.\nA few things that are visually similar to 'baggage cart' but are not 'baggage cart' are:\tshopping cart\tluggage trolley\tutility cart\nThere are several useful visual features to tell there is 'baggage cart' and not similar things in a photo:\tplatform for carrying luggage\twheels for mobility\thandlebar or pulling mechanism\tfor use in airports, train stations or bus terminals.", 14], "city lights": ["Yes. 'City lights' has a tangible appearance and refers to the lights in a city at night.\nA few things that are visually similar to 'city lights' but are not 'city lights' are:\tstreet lights\tcar lights \tfireworks \tlightning \tlight pollution\nThere are several useful visual features to tell there are 'city lights' and not similar things in a photo:\tclustered lights\tin a large or sprawling area\tbrilliant or dim lights, depending on the time of day\tlight patterns or grids that resemble a map", 14], "glass beer bottle": ["Yes. 'Glass beer bottle' is a visually concrete object.\nA few things that are visually similar to 'glass beer bottle' but are not 'glass beer bottle' are:\t\n- Glass soda bottle\n- Glass wine bottle\n- Glass vinegar bottle\n- Glass juice bottle\n\nSome useful visual features for distinguishing 'glass beer bottle' from the listed similar things in a photo are: \n- Brown or green color\n- Short neck with a screw cap or a cork\n- Curved shape with a slightly wider base than the top\n- A label or engraving that says \"beer\" or a specific brand name.", 14], "emergency": ["No. 'Emergency' is too vague or abstract to be distinguished in a photo. It is a situation or a state of being rather than a concrete object with visual features. \n\nHowever, a few things that may be visually associated with emergencies are:\n- Emergency vehicles (ambulances, firetrucks, police cars)\n- First aid kits \n- Caution tape \n- Flares\n\nUseful visual features for distinguishing a representation of an 'emergency' could include:\n- Visual cues of danger (e.g. fire, smoke, people in distress)\n- Presence of emergency responders or their equipment (e.g. paramedics, firehoses, flashing lights)\n- Use of cautionary colors (e.g. red, yellow, orange) or warning signs \n- Evacuation procedures or instructions.", 14], "bathroom mat": ["Yes. 'Bathroom mat' has a tangible appearance and is a kind of floor covering used in the bathroom.\nA few things that are visually similar to 'bathroom mat' but are not 'bathroom mat' are:\tshower curtain\ttowel\trug\nThere are several useful visual features to tell there is 'bathroom mat' and not similar things in a photo:\tabsorbent material typically made of fabric or rubber\tspecific shapes and sizes\tplaced directly outside the bathtub or shower", 14], "subway car": ["Yes. 'Subway car' has a tangible appearance and is a type of public transportation.\nA few things that are visually similar to 'subway car' but are not 'subway car' are:\tbus\ttram\ttrolley\ttrain\nThere are several useful visual features to tell there is 'subway car' and not similar things in a photo:\tlong and rectangular shape\tmetallic or shiny surface\tdoors on the sides, usually sliding or automatic\tsmall windows at regular intervals\tsigns, maps or ads inside the train", 14], "register": ["No. 'Register' is too vague or abstract to be distinguished in a photo. \nHowever, if 'register' is specifically referring to a cash register, then yes, it has a tangible appearance.\nA few things that are visually similar to a cash register but are not a cash register are:\t\ncalculator\tcomputer keyboard\tvending machine\nThere are several useful visual features to tell there is a cash register and not similar things in a photo:\tdrawer for cash storage\tphysical buttons for inputting sales\ttotal or subtotal display\tscreen for showing items purchased and total cost\tusually found in retail or restaurant settings.", 14], "snow boot": ["Yes. 'Snow boot' has a tangible appearance and is a type of footwear designed for walking in snow.\nA few things that are visually similar to 'snow boot' but are not 'snow boot' are:\thiking boot\train boot\twork boot\nThere are several useful visual features to tell there is 'snow boots' and not similar things in a photo:\tthick insulation and padding on the inner lining\tforceful tread on the sole to prevent slipping on ice or snow\twater-resistant or waterproof exterior material\tcut higher than an average boot to cover the ankle or even part of the calf", 14], "broccoli stem": ["Yes. 'Broccoli stem' has a tangible appearance and is a part of broccoli.\nA few things that are visually similar to 'broccoli stem' but are not 'broccoli stem' are:\tasparagus\tcelery\tcactus\tcucumber\nThere are several useful visual features to tell there is 'broccoli stem' and not similar things in a photo:\tlight green or pale yellow\tcolor\tsmooth surface\tthick and sturdy\tcircular cross-sections\tpresented with small tree-like buds on top", 14], "clove": ["Yes. 'clove' has a tangible appearance and is a type of spice.\nThere aren't many things that are visually similar to 'clove' but are not 'clove'.\nThere are several useful visual features to tell there is 'clove' and not similar things in a photo:\tshort and brown\tdried\ttapered at one end, with a rounded head\thas a distinctive smell and flavor", 14], "bread sandwich": ["Yes. 'Bread sandwich' has a tangible appearance and is a type of food. \nA few things that are visually similar to 'bread sandwich' but are not 'bread sandwich' are:\ttoast with filling \tbread with butter\tjam sandwich\nThere are several useful visual features to tell there is 'bread sandwich' and not similar things in a photo:\ttwo slices of bread with filling in between\tedges of the bread are visible\tfillings such as vegetables, meat, cheese, or spreads.", 14], "time display": ["Yes. 'Time display' has a tangible appearance and can refer to various types of clocks or watches.\nA few things that are visually similar to 'time display' but are not 'time display' are:\tcalendar\tsignage with displayed time\tLED panels displaying time in public spaces\nThere are several useful visual features to tell there is 'time display' and not similar things in a photo:\thands or digital numbers representing hours, minutes, and seconds\tdial or screen displaying numbers or marks to show the time\tticking or running motion, indicating the passage of time (for some analog watches and clocks)", 14], "contraption": ["No. 'Contraption' is too vague or abstract to be distinguished in a photo.", 14], "stock": ["No. 'Stock' is too vague or abstract to be distinguished in a photo. However, if we are referring to \"stock\" as in the financial concept, it may have visual representations but still not entirely concrete.\nA few things that are visually similar to 'stock' but are not 'stock' are:\tcattle on a ranch\tshelves in a supermarket\tbooks in a library\tpiles of wood in a lumberyard\nThere are no useful visual features to distinguish 'stock' from these similar things in a photo, as they are conceptually unrelated.", 14], "maze": ["Yes. 'Maze' has a tangible appearance and is a type of puzzle or labyrinth.\nA few things that are visually similar to 'maze' but are not 'maze' are:\tpuzzle\tboard game\tcorn field\nThere are several useful visual features to tell there is 'maze' and not similar things in a photo:\twinding paths and dead ends\thedges or walls enclosing the paths\ttall stalks in a field forming paths\tpeople walking through the maze or solving it", 14], "rags": ["Yes. 'Rags' has a tangible appearance and refers to old, torn or scrappy pieces of clothing or fabric.\nA few things that are visually similar to 'rags' but are not 'rags' are:\ttowels\tblankets\tcarpets\tburlap sacks\nThere are several useful visual features to tell there are 'rags' and not similar things in a photo:\told or torn pieces of cloth\tirregular shapes and sizes\tdull or muted colors\tfrayed edges or threads\tdefinite signs of wear and tear", 14], "pasta dish": ["Yes. 'Pasta dish' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pasta dish' but are not 'pasta dish' are:\tsalad\trice dish\tpotato dish\tnoodle soup\nThere are several useful visual features to tell there is 'pasta dish' and not similar things in a photo:\tpasta noodles\tsauce\ttoppings (e.g. cheese, herbs, meat)\tserved in a bowl or a plate", 14], "desktop computer monitor": ["Yes. 'Desktop computer monitor' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'desktop computer monitor' but are not 'desktop computer monitor' are: TV screen, digital signage, electronic display board.\nThere are several useful visual features to tell there is a 'desktop computer monitor' and not similar things in a photo:\trectangular in shape, widescreen\tdisplaying computer graphics and text\tconnected to a stand or a desktop computer by a cable.", 14], "maroon shirt": ["Yes. 'Maroon shirt' has a tangible appearance and is a specific color and style of clothing.\nA few things that are visually similar to 'maroon shirt' but are not 'maroon shirt' are:\tred shirt\tpurple shirt\twine-colored shirt\nThere are several useful visual features to tell there is 'maroon shirt' and not similar things in a photo:\ta deep red-brown color\tcollared or collarless fabric texture\tshort or long sleeve style.", 14], "rung": ["Yes. 'Rung' has a tangible appearance and is part of a ladder.\nA few things that are visually similar to 'rung' but are not 'rung' are:\tstep\tstair\ttread\tpedal\nThere are several useful visual features to tell there is 'rung' and not similar things in a photo:\thorizontal\tbar-shaped\twith space between the neighboring rungs\tpart of a ladder or a scaffold.", 14], "brown ground": ["Yes. 'Brown ground' has a tangible appearance and refers to the earth, soil or terrain that has a brown color.\nA few things that are visually similar to 'brown ground' but are not 'brown ground' are:\twood\tchocolate\tclothes\tanimal fur\nThere aren't any useful visual features to distinguish brown ground from the listed similar things in a photo, since brown ground is a natural, neutral brown color that can appear in different textures and shades. However, it is important to consider the context and surroundings to determine whether it actually represents brown ground or something else.", 14], "wall paint": ["Yes. 'Wall paint' has a tangible appearance and is a type of coating or pigment applied on walls.\nA few things that are visually similar to 'wall paint' but are not 'wall paint' are:\twood stain\tvarnish\tcaulk\tplaster\nThere are several useful visual features to tell there is 'wall paint' and not similar things in a photo:\tapplied on a flat surface or walls in particular\tpigmented, thick liquid\tdries into a solid or semi-solid film\tmatte or glossy finish, depending on the type of paint", 14], "metal bike rack": ["Yes. 'Metal bike rack' has a tangible appearance and is a type of equipment.\nA few things that are visually similar to 'metal bike rack' but are not 'metal bike rack' are:\tclothes rack\tdisplay stand\tshelving unit\nThere are several useful visual features to tell there is 'metal bike rack' and not similar things in a photo:\thorizontal bars designed to hold bikes\tmetal construction\ton the ground or attached to a wall/functionality as a bike stand\tand not used for clothing or merchandise\tdisplay.", 14], "wrists": ["Yes. 'Wrists' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'wrists' but are not 'wrists' are:\tankles\tneck\tshoulders\t\nThere are several useful visual features to tell there is 'wrists' and not similar things in a photo:\tlocated between the hand and the forearm\thave bony protrusions\thave prominent veins\tcan wear watches or bracelets around them.", 14], "shrimps": ["Yes. 'Shrimps' has a tangible appearance and is a type of seafood.\nA few things that are visually similar to 'shrimps' but are not 'shrimps' are:\tcrabs\tlobsters\tcrayfish\tprawns\nThere are several useful visual features to tell there is 'shrimps' and not similar things in a photo:\tlong, narrow body\tpale, translucent shell and tail\tpair of small, pincer-like claws at head\tpair of prominent antennae\tabdomen with segments", 14], "word police": ["No. 'Word police' is too vague or abstract to be visually concrete and has no tangible appearance.\nThere are no things that are visually similar to 'word police' as it is a metaphorical term.\nTherefore, there are no useful visual features to distinguish 'word police' from anything else in a photo.", 14], "dish drainer": ["Yes. 'Dish drainer' has a tangible appearance and is a kind of kitchen accessory.\nA few things that are visually similar to 'dish drainer' but are not 'dish drainer' are:\tbaking racks\twire baskets\tshoe organizers\nThere are several useful visual features to tell there is 'dish drainer' and not similar things in a photo:\tangled design for water to drain out\tplastic or metal material\tflat bottom with slots or ridges\tfor holding dishes and utensils", 14], "lion statue": ["Yes. 'Lion statue' has a tangible appearance and is a kind of sculpture.\nA few things that are visually similar to 'lion statue' but are not 'lion statue' are:\tanimal figurines\tmodern sculptures\tstatues of other animals\nThere are several useful visual features to tell there is 'lion statue' and not similar things in a photo:\tthe presence of a lion-shaped statue in the photo\tthe stone or metal material used to make the statue\tthe poses or expressions of the lion statue", 14], "silk tie": ["Yes. 'Silk tie' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'silk tie' but are not 'silk tie' are:\tscarf\tcravat\tribbon\t\nThere are several useful visual features to tell there is 'silk tie' and not similar things in a photo:\tlong and narrow shape\ttapered ends\tmade of silky, shiny material\ttraditionally worn with collared shirts or suits", 14], "computer cable": ["Yes. 'Computer cable' has a tangible appearance and is a type of wire used to connect computer components.\nA few things that are visually similar to 'computer cable' but are not 'computer cable' are:\tphone charger cable\ttv cable\tspeaker cable\tpower cable\nThere are several useful visual features to tell there is 'computer cable' and not similar things in a photo:\tUSB or Ethernet connection\tbetween computer components\tthin and rectangular shape\tplastic or rubber cover", 14], "decorative": ["No. 'Decorative' is too vague or abstract to be distinguished in a photo.", 14], "belly": ["Yes. 'Belly' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'belly' but are not 'belly' are:\tmuscle\tstomach\twaist\tbuttocks\thands\nThere are several useful visual features to tell there is 'belly' and not similar things in a photo:\tsoft and round area between the chest and the pelvis\thair or belly button in the center\twrinkles or stretch marks may appear", 14], "silver rings": ["Yes. 'Silver rings' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'silver rings' but are not 'silver rings' are:\tbracelets\twatches\tnecklaces\tearrings\nThere are several useful visual features to tell there is 'silver rings' and not similar things in a photo:\tcircular band worn on a finger\tsilver or silver-colored metal\tshiny or reflective surface\tpotentially adorned with stones or other embellishments.", 14], "puppet": ["Yes. 'Puppet' has a tangible appearance and is a type of object that resembles a person or an animal and that is animated by a person using strings or wires.\nA few things that are visually similar to 'puppet' but are not 'puppet' are:\tdolls\tmannequins\tstatues\ttoys\nThere are several useful visual features to tell there is 'puppet' and not similar things in a photo:\thand-operated using strings or wires\thuman or animal-like appearance\tmovable and articulated limbs\tuse in a performance or play", 14], "grass hill": ["Yes. 'Grass hill' has a tangible appearance.\nA few things that are visually similar to 'grass hill' but are not 'grass hill' are:\tmountain\tfield\tforest\tsand dune\nThere are several useful visual features to tell there is 'grass hill' and not similar things in a photo:\tcovered with green grass\tsmooth and rolling surface\tlow elevation compared to mountains or sand dunes\tsurrounded by flat land or other hills", 14], "gold logo": ["Yes. 'Gold logo' has a tangible appearance and is a type of symbol or trademark.\nA few things that are visually similar to 'gold logo' but are not 'gold logo' are:\tmedals\tawards\tbadges\nThere are several useful visual features to tell there is 'gold logo' and not similar things in a photo:\tdistinctive design or symbol\tlettering or typography\tin gold color or metallic finish\tresembling a trademark or brand logo", 14], "bra strap": ["Yes. 'Bra strap' has a tangible appearance and is an undergarment accessory.\nA few things that are visually similar to 'bra strap' but are not 'bra strap' are:\tbackpack strap\tpurse strap\tcamera strap\nThere are several useful visual features to tell there is 'bra strap' and not similar things in a photo:\tthin strap\tusually made of elastic or fabric\tattached to the back of a garment, near the shoulder blades\toften adjustable in length and sometimes width\tmatch the color of the bra or top", 14], "color snow": ["No. 'Color snow' is too vague or abstract to be distinguished in a photo. Additionally, snow is typically white, so the concept of 'color snow' would be unfamiliar.\nThere are no visually similar things to 'color snow'.", 14], "back wall": ["Yes. 'Back wall' has a tangible appearance and refers to a specific location.\nA few things that are visually similar to 'back wall' but are not 'back wall' are:\tside wall\tpartition\tfence\nThere are several useful visual features to tell there is 'back wall' and not similar things in a photo:\tpositioned at the back of a space\tvertical\tsupports or holds up something like a roof or ceiling\tmay have doors or windows", 14], "basket brown": ["Yes. 'Basket brown' has a tangible appearance and refers to a basket that is colored brown.\nThere are no other things that are visually similar to 'basket brown' but are not 'basket brown'.\nThere are no useful visual features required to distinguish 'basket brown' from other similar things in a photo, since there are no other similar things in this case.", 14], "grey poles": ["Yes. 'Grey poles' has a tangible appearance and refers to vertical cylindrical structures made of concrete, metal, or wood.\nA few things that are visually similar to 'grey poles' but are not 'grey poles' are:\ttrees\tlampposts\tsigns\tbuildings\nThere are several useful visual features to tell there are 'grey poles' and not similar things in a photo:\tcylindrical or round in shape\tgrey in color\tstanding alone or in a row\tno branches or leaves\tdoes not have any visible function or purpose", 14], "mule": ["Yes. 'Mule' has a tangible appearance and is a hybrid animal between a donkey and a horse.\nA few things that are visually similar to 'mule' but are not 'mule' are:\thorse\tdonkey\tzebra\nThere are several useful visual features to tell there is 'mule' and not similar things in a photo:\thead and ears like a donkey\tbody and height like a horse\tshort coat\tand long, thin legs\thunched back\tno mane like a horse, but short, bristly hair", 14], "blue elephant": ["Yes. 'Blue elephant' has a tangible appearance and is a type of mammal.\nThere are no things that are visually similar to 'blue elephant' but are not 'blue elephant' as it is a unique and distinct concept.\nThe useful visual features to tell there is a 'blue elephant' in a photo are:\tblue-grey color\ttusks\ttrunk\tfloppy ears\tbig size, larger than a person", 14], "side bike": ["No. 'Side bike' is too vague or abstract for me to determine its meaning. It may refer to a type of motorcycle with a sidecar or a bicycle with a side attachment, but clarification is needed. \n\nWithout knowing exactly what a 'side bike' is, I cannot accurately identify things that are visually similar to it or provide useful visual features for distinguishing it from other similar things.", 14], "hikers": ["Yes. 'Hikers' has a tangible appearance and refers to people who are hiking or walking outdoors.\nA few things that are visually similar to 'hikers' but are not 'hikers' are:\trunners\twalkers\tbikers\ttourists\nThere are several useful visual features to tell there are 'hikers' and not similar things in a photo:\twearing backpacks\twearing hiking boots\twalking on a trail\twearing hats or helmets\tbreathing fresh air\twalking with trekking poles or sticks\tLooking for direction using maps or compasses.", 14], "bath robe": ["Yes. 'Bath robe' has a tangible appearance and is an article of clothing.\nA few things that are visually similar to 'bath robe' but are not 'bath robe' are:\tcoat\tjacket\tcape\tponcho\nThere are several useful visual features to tell there is 'bath robe' and not similar things in a photo:\tdrapes loosely around the body\tties or buttons in the front\tmade of a soft and absorbent material, such as cotton or terry cloth\tmay have a hood.", 14], "guide": ["No. 'Guide' is too vague or abstract to have a tangible appearance that can be distinguished in a photo. \n\nHowever, I can list some things that are visually associated with the concept of 'guide' but are not 'guide', such as: maps, compasses, signposts, tour buses, brochures. \n\nUseful visual features for distinguishing a 'guide' from these similar things in a photo could be a person or group of people leading or showing others around a place or providing information, possibly wearing a uniform or badge that signifies their role as a guide.", 14], "depiction": ["No. 'Depiction' is too vague or abstract to be distinguished in a photo.", 14], "plat": ["Yes. 'Plat' has a tangible appearance and refers to a dish, typically with several different types of food presented on it.\nA few things that are visually similar to 'plat' but are not 'plat' are:\tplate\ttray\tbowl\tpalette\nThere are several useful visual features to tell there is 'plat' and not similar things in a photo:\tvariety of different foods arranged in an artful way arranged on the same dish.", 14], "scissor handle": ["Yes. 'Scissor handle' has a tangible appearance and is a part of a pair of scissors.\nA few things that are visually similar to 'scissor handle' but are not 'scissor handle' are:\tknife handle\tscrewdriver handle\thammer handle\nThere are several useful visual features to tell there is 'scissor handle' and not similar things in a photo:\tpart of a pair of scissors\twith a loop for fingers and a flat piece for thumb in a symmetrical design\tmetal or plastic material", 14], "hammock": ["Yes. 'Hammock' has a tangible appearance and is a type of suspended bed.\nA few things that are visually similar to 'hammock' but are not 'hammock' are:\tswings\tcargo nets\tyoga trapeze\tseated chairs\thanging planters\nThere are several useful visual features to tell there is 'hammock' and not similar things in a photo:\trectangular or curved-shaped fabric\tbetween two supports such as trees or poles\thas no backrest or armrest\tframeless, stretchable, and suspending in the air", 14], "brown branch": ["Yes. 'Brown branch' has a tangible appearance and is a part of a tree or shrub.\nA few things that are visually similar to 'brown branch' but are not 'brown branch' are:\tdry twig\tbroken stick\tchopstick\t\nThere are several useful visual features to tell there is 'brown branch' and not similar things in a photo:\tbrown or woody\tcolor\thas twists, turns, or bumps\tattached to other branches or leaves in a tree or shrub\thas smaller twigs or leaves attached to the main branch.", 14], "right shoe": ["Yes, 'right shoe' has a tangible appearance and is one half of a pair of shoes.\nA few things that are visually similar to 'right shoe' but are not 'right shoe' are:\tleft shoe\tboot\t\nThere are no useful visual features to distinguish a 'right shoe' from a left shoe as they appear identical except for the orientation. However, it can be identified by context since it is part of a pair of shoes.", 14], "chair legs": ["Yes. 'Chair legs' has a tangible appearance and is a part of a chair.\nA few things that are visually similar to 'chair legs' but are not 'chair legs' are:\ttable legs\tbed legs\tsofa legs\nThere are several useful visual features to tell there is 'chair legs' and not similar things in a photo:\tthe legs are attached to a seat or a backrest\tthe legs are angled or curved at the bottom\tfor chairs with wheels, the legs are attached to a wheel", 14], "cross walk sign": ["Yes. 'Cross walk sign' has a tangible appearance and is a kind of traffic sign.\nA few things that are visually similar to 'cross walk sign' but are not 'cross walk sign' are:\tstop sign\tyield sign\tspeed limit sign\nThere are several useful visual features to tell there is 'cross walk sign' and not similar things in a photo:\tpedestrian figures\tinverse triangle shape\ttext that reads 'Crosswalk' or 'Pedestrian Crossing'\tbright green or yellow color\tbackground of white and black stripes", 14], "button hole": ["Yes. 'Button hole' has a tangible appearance and is a specific part of a garment.\nA few things that are visually similar to 'button hole' but are not 'button hole' are:\tpockets\tzipper pulls\tbutton threads\tcuffs\nThere are several useful visual features to tell there is 'button hole' and not similar things in a photo:\tvertical slit on a piece of fabric\tusually paired with a button\tclose to the edge of the fabric\tmay have reinforcing stitching around it.", 14], "medals": ["Yes. 'Medals' has a tangible appearance and is a kind of award or recognition.\nA few things that are visually similar to 'medals' but are not 'medals' are:\ttokens\tcoins\tbadges\nThere are several useful visual features to tell there is 'medals' and not similar things in a photo:\tround or star-shaped\tmade of metal or precious material\tribbon attached\ttoo large or detailed to be a coin or token\tengravings or inscriptions indicating an award or achievement", 14], "orange poles": ["Yes. 'Orange poles' has a tangible appearance and is a type of pole that is colored orange.\nA few things that are visually similar to 'orange poles' but are not 'orange poles' are:\tcones\tmarkers\tbollards\tbarriers\nThere are several useful visual features to tell there is 'orange poles' and not similar things in a photo:\tvertical pole shape\ttall\theight of the pole\tbright orange color\tno distinctive patterns or shapes\texcept for being orange, minimal markings or labels", 14], "orange band": ["Yes. 'Orange band' has a tangible appearance and is a type of strip or ribbon.\nA few things that are visually similar to 'orange band' but are not 'orange band' are:\tcaution tape\tribbon\tsash\tbelt\nThere are several useful visual features to tell there is 'orange band' and not similar things in a photo:\torange color\tthin and usually made of fabric or plastic\twearing around a wrist, ankle, or head\ttypically used to indicate a particular group, cause, or purpose (such as safety or awareness)", 14], "bus station": ["Yes. 'Bus station' has a tangible appearance and is a place where buses stop.\nA few things that are visually similar to 'bus station' but are not 'bus station' are:\tparking lot\tbus stop\ttrain station\tairport\nThere are several useful visual features to tell there is 'bus station' and not similar things in a photo:\tlarge covered area\tfor buses to park\tsigns with schedules or routes\tbenches or seating area\tfor passengers to wait", 14], "water stain": ["Yes. 'Water stain' has a tangible appearance and is a type of discoloration caused by water.\nA few things that are visually similar to 'water stain' but are not 'water stain' are:\tpaint stain\tink blot\tmold\tsun bleaching\nThere are several useful visual features to tell there is 'water stain' and not similar things in a photo:\tcircular or irregular shape\tbrown or yellow discoloration\trough or textured surface\tsurrounded by a water mark\tring-like pattern", 14], "giraffe nose": ["Yes. 'Giraffe nose' has a tangible appearance and is a physical feature of the animal.\nA few things that are visually similar to 'giraffe nose' but are not 'giraffe nose' are:\tzebra nose\tdeer nose\thorse nose\nThere are several useful visual features to tell there is 'giraffe nose' and not similar things in a photo:\tvery long and thin\tnarrow nostrils\tbrown or black in color\twith spots or patches on the skin", 14], "accent": ["No. 'Accent' is too vague or abstract to be distinguished in a photo. It is a way of pronouncing words or speaking a language that varies among different regions, social groups, etc.", 14], "silver digital camera": ["Yes. 'Silver digital camera' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'silver digital camera' but are not 'silver digital camera' are:\tsilver phone\ttablet\twatch\nThere are several useful visual features to tell there is 'silver digital camera' and not similar things in a photo:\tretangular shape\twith a lens on the front\tbutton or screen on the back\tsmall flash above or next to the lens\tzoom or focus options on the lens\tdigital screen to view and edit images stored inside", 14], "brown stick": ["Yes. 'Brown stick' has a tangible appearance.\nA few things that are visually similar to 'brown stick' but are not 'brown stick' are:\tbranch\tpencil\tchopstick\ttoothbrush\nThere are several useful visual features to distinguish 'brown stick' from the listed similar things in a photo:\tstick-like appearance\twith rough or smooth texture\tbrown or dark-colored\tstraight or slightly curved shape\tlack of visible sharpened point or bristles.", 14], "orange paper": ["Yes. 'Orange paper' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'orange paper' but are not 'orange paper' are:\torange fabric\tpaint\tcardboard\nThere are several useful visual features to tell there is 'orange paper' and not similar things in a photo:\tthin and flat\tbright orange color\tcan be folded or cut easily", 14], "crockpot": ["Yes. 'Crockpot' has a tangible appearance and is a specific type of cooking appliance.\nA few things that are visually similar to 'crockpot' but are not 'crockpot' are:\tdutch oven\tpressure cooker\tsoup pot\tmulti-cooker\nThere are several useful visual features to tell there is 'crockpot' and not similar things in a photo:\trectangular or oval shape\twith a glass lid\tremovable ceramic or stoneware pot\tlow and slow cooking function\tside handles for easy transport\tsimple control panel or temperature gauge", 14], "bare leafless tree": ["Yes. 'Bare leafless tree' has a tangible appearance.\nA few things that are visually similar to 'bare leafless tree' but are not 'bare leafless tree' are:\tevergreen tree\tsilhouette of a tree\tinverted tree\tdried tree branches\nThere are several useful visual features to tell there is 'bare leafless tree' and not similar things in a photo:\tbranches and twigs without leaves\ttrunk with no bark\tno greenery or foliage", 14], "frizbee": ["Yes. 'Frizbee' has a tangible appearance and is a type of thrown flying disc.\nA few things that are visually similar to 'frizbee' but are not 'frizbee' are:\tplastic plates or lids\tpizza discs\tfrisbees of other brands or designs\nThere are several useful visual features to tell there is 'frisbee' and not similar things in a photo:\tround or disc-shaped\tobject made of plastic\twith a curved or beveled edge\tand a smooth top surface.", 14], "stone ledge": ["Yes. 'Stone ledge' has a tangible appearance and is a piece of natural or human-made structure.\nA few things that are visually similar to 'stone ledge' but are not 'stone ledge' are: rock formation, cliff, shelf, rooftop\nThere are several useful visual features to tell there is 'stone ledge' and not similar things in a photo: long, thin, flat piece of stone, dull or neutral color, horizontal or near-horizontal orientation", 14], "luggage handle": ["Yes. 'Luggage handle' has a tangible appearance and is a specific type of handle used for carrying luggage.\nA few things that are visually similar to 'luggage handle' but are not 'luggage handle' are:\tpurse handles\tbriefcase handles\tdoor handles\t\nThere are several useful visual features to tell there is 'luggage handle' and not similar things in a photo:\tattached to a suitcase or travel bag\tretractable\thandle grips with a plastic texture.", 14], "hearse": ["Yes, 'hearse' has a visually concrete appearance and is a type of vehicle used to transport the deceased.\nA few things that are visually similar to 'hearse' but are not 'hearse' are:\tlimousine\tvan\tpick-up truck\tbus\nThere are several useful visual features to tell there is a hearse and not similar things in a photo: rectangular in shape, longer than a car\twith a flat roof and tall sides\tcarrying a casket\twith a rear window that can be opened and closed\tall-black exterior color", 14], "diamond shapes": ["Yes. 'Diamond shapes' has a tangible appearance and refers to polygonal shapes with four sides.\nA few things that are visually similar to 'diamond shapes' but are not 'diamond shapes' are:\trhombus\tparallelogram\tstar\nThere are several useful visual features to tell there are 'diamond shapes' and not similar things in a photo:\tfour sides of equal length\ttwo opposite angles of equal measures\tdiagonals bisect each other\tpoints at the top and bottom", 14], "door window": ["Yes. 'Door window' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'door window' but are not 'door window' are:\tcar window\tshop window\tfixed window\tskylight\nThere are several useful visual features to tell there is 'door window' and not similar things in a photo:\tlocated on a door\tpanelled or framed\twithin reach of a hand or arm.", 14], "tie clip": ["Yes. 'Tie clip' has a tangible appearance and is a small piece of jewelry.\nA few things that are visually similar to 'tie clip' but are not 'tie clip' are:\tcufflinks\tbrooches\tpins\nThere are several useful visual features to tell there is 'tie clip' and not similar things in a photo:\ta small metal bar or clip\tthat clips onto a tie or a shirt\tplaced horizontally on a tie, around one-third to one-half down from the knot", 14], "construction site": ["Yes. 'Construction site' has a tangible appearance and typically includes buildings, equipment, and workers.\nA few things that are visually similar to 'construction site' but are not 'construction site' are:\tabandoned building\tindustrial area\tfarm\tworkshop\nThere are several useful visual features to tell there is 'construction site' and not similar things in a photo:\tsafety cones or barriers\tconstruction materials (e.g. piles of wood, bricks, or sand)\tconstruction equipment (e.g. cranes, bulldozers, or excavators)\thard hats and vests\twearing work boots or gloves\tdust and debris", 14], "extension": ["No. 'Extension' is too abstract to be distinguished in a photo. \nFor clarification, 'extension' could mean different things, such as a term used in computing, a physical add-on to a building or object, or more generally, the act of making something larger or longer. Therefore, it is difficult to determine a specific set of visually similar things or distinguishable features.", 14], "purple eggplant": ["Yes. 'Purple eggplant' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'purple eggplant' but are not 'purple eggplant' are:\tpurple potato\tpurple sweet potato\tgrapes\nThere are several useful visual features to tell there is 'purple eggplant' and not similar things in a photo:\toval or oblong shape\tdark purple, glossy skin\twith or without green stem\twide green sepals at the base of the stem\twatery white flesh inside.", 14], "specs": ["Yes. 'Specs' has a tangible appearance and refers to eyeglasses or a specific model of a car.\nA few things that are visually similar to 'specs' but are not 'specs' are:\tsunglasses\tsafety goggles\twindscreen of the car\nThere are several useful visual features to tell there are 'specs' and not similar things in a photo:\tconsist of two lenses attached to a frame\twith handles or rims to hook behind the ears or rest on the nose\tframe can be plastic or metal\tframes can be of different colors or shapes\tand have different sizes or thicknesses of lenses.", 14], "quiche": ["Yes. 'Quiche' has a tangible appearance and is a type of savory tart.\nA few things that are visually similar to 'quiche' but are not 'quiche' are:\tpie\tpizza\tfrittata\tomelette\nThere are several useful visual features to tell there is 'quiche' and not similar things in a photo:\tround\tshallow\tflat top\tfluted edges\tthin layer of pastry crust and filling\tcustard-like texture and consistency", 14], "curb edge sidewalk": ["Yes. 'Curb edge sidewalk' has a tangible appearance and is a physical structure.\nA few things that are visually similar to 'curb edge sidewalk' but are not 'curb edge sidewalk' are:\tdriveway\troad\tfootpath\tparking lot\nThere are several useful visual features to tell there is 'curb edge sidewalk' and not similar things in a photo:\traised concrete edge\tseparate from the road level\twider than a footpath, but narrower than a road\tpedestrian traffic sign\tbitumen surface\tuse of signage to designate it is a footpath", 14], "plow": ["Yes. 'Plow' has a tangible appearance and is a type of farm tool.\nA few things that are visually similar to 'plow' but are not 'plow' are:\tshovel\thoe\tpickaxe\tbulldozer\nThere are several useful visual features to tell there is 'plow' and not similar things in a photo:\theavy tool with a large blade\tusually attached to a tractor or pulled by animals\tstraight or curved blade used to break up soil and turn it over often made of metal", 14], "patio furniture": ["Yes. 'Patio furniture' has a tangible appearance and is a type of outdoor furniture.\nA few things that are visually similar to 'patio furniture' but are not 'patio furniture' are:\tindoor furniture\toffice furniture\tpicnic tables\n\t\nThere are several useful visual features to tell there is 'patio furniture' and not similar things in a photo:\tdesigned to be used outdoors\tweather-resistant materials, such as metal, wicker, or plastic\tcome in sets with chairs and a table or a sofa\ttend to have comfortable cushions or pillows.", 14], "lifeguard stand": ["Yes. 'Lifeguard stand' has a tangible appearance and is a type of elevated structure for lifeguards.\nA few things that are visually similar to 'lifeguard stand' but are not 'lifeguard stand' are:\ttower\tobservatory\tviewing deck\tbalcony\nThere are several useful visual features to tell there is 'lifeguard stand' and not similar things in a photo:\televated platform\ton a beach or near a body of water\tbrightly colored or marked\twith a seat or binoculars\tfor use by a lifeguard", 14], "yellow hose": ["Yes. 'Yellow hose' has a tangible appearance and is a specific type of hose.\nA few things that are visually similar to 'yellow hose' but are not 'yellow hose' are:\tblack hose\tgreen hose\tpink hose\tcable\nThere are several useful visual features to tell there is 'yellow hose' and not similar things in a photo:\telongated\tflexible\tyellow in color\thollow cylindrical shape\twith a nozzle or connector on one or both ends", 14], "ice cream cone": ["Yes. 'Ice cream cone' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'ice cream cone' but are not 'ice cream cone' are:\tsugar cone\twaffle cone\tpretzel cone\tcornucopia-shaped pastry\nThere are several useful visual features to tell there is 'ice cream cone' and not similar things in a photo:\tcone-shaped\tcrispy texture\tvisible ice cream or other frozen treat on top or inside\tdrip or spill streaks from melting ice cream", 14], "cake topper": ["Yes. 'Cake topper' has a tangible appearance and is a decorative item placed on a cake.\nA few things that are visually similar to 'cake topper' but are not 'cake topper' are:\tdecorative toothpick\tstatue\t\nThere are several useful visual features to tell there is 'cake topper' and not similar things in a photo:\tclearly positioned on top of a cake\tedible or non-edible decorations\tdesigned to celebrate a specific event, like a wedding or a birthday\tmatching the cake's theme or design", 14], "shirtless boy": ["Yes. 'Shirtless boy' has a tangible appearance and refers to a boy who is not wearing a shirt.\nA few things that are visually similar to 'shirtless boy' but are not 'shirtless boy' are: shirtless man boy wearing a tank top boy wearing a t-shirt with a low neckline.\nThere are several useful visual features to tell there is 'shirtless boy' and not similar things in a photo: no shirt on upper body, visible chest, visible shoulders, and age appearance of a boy.", 14], "multiple train tracks": ["Yes. 'Multiple train tracks' has a tangible appearance and refers to two or more parallel tracks used for train transportation.\nA few things that are visually similar to 'multiple train tracks' but are not 'multiple train tracks' are:\troads\tbike lanes\tpedestrian walkways\tcanals\nThere are several useful visual features to tell there are 'multiple train tracks' and not similar things in a photo:\tparallel tracks made of metal\trails running along the tracks\tcrossbars connecting the rails\tsignal lights or poles indicating a railroad intersection", 14], "cement area": ["Yes. 'Cement area' has a tangible appearance and refers to an area that is paved with concrete.\nA few things that are visually similar to 'cement area' but are not 'cement area' are:\tasphalt road\tbrick walkway\ttile patio\tstone driveway\nThere are several useful visual features to tell there is 'cement area' and not similar things in a photo:\tgrey or white color\tsmooth or rough texture\thard and solid surface\tvisible cracks or lines\tsquare or rectangular shape", 14], "stomach area": ["Yes. 'Stomach area' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'stomach area' but are not 'stomach area' are:\tribcage\thips\twaist\nThere are several useful visual features to tell there is 'stomach area' and not similar things in a photo:\tarea between the chest and hips\tsoft and round shape\tnear the center of the body\torbs-shaped belly-button.", 14], "plastic toilet seat lid": ["Yes. 'Plastic toilet seat lid' has a tangible appearance and is a type of toilet accessory.\nA few things that are visually similar to 'plastic toilet seat lid' but are not 'plastic toilet seat lid' are:\tplastic trash can lid\tcinema seat\tlaptop cover\nThere are several useful visual features to tell there is 'plastic toilet seat lid' and not similar things in a photo:\toval shape\tsmooth surface with molded bumps\thinge and attachment holes\ttop lid for covering the toilet bowl", 14], "hand dryer": ["Yes. 'Hand dryer' has a tangible appearance and is a type of electrical device for drying hands.\nA few things that are visually similar to 'hand dryer' but are not 'hand dryer' are:\thair dryer\tfan\theater\tair conditioner\nThere are several useful visual features to tell there is 'hand dryer' and not similar things in a photo:\twall-mounted or free-standing device\toblong or flat shape\tbutton or sensor to activate blowing air\tstream of air directed downwards\thand or arm in proximity to the device", 14], "metal utility pole": ["Yes. 'Metal utility pole' has a tangible appearance and is a type of pole used for supporting electrical wires or other utilities.\nA few things that are visually similar to 'metal utility pole' but are not 'metal utility pole' are:\twooden pole\tfence post\tstreet sign\tbollard\nThere are several useful visual features to tell there is 'metal utility pole' and not similar things in a photo:\tmade of metal\tcylindrical shape\ttapered at the top\ttall\theight compared to other objects in the scene\tpresence of attached electrical or communication equipment", 14], "water mass": ["Yes. 'Water mass' has a tangible appearance and refers to a large body of water.\nA few things that are visually similar to 'water mass' but are not 'water mass' are:\tpuddle\tice cube\tglass of water\nThere are several useful visual features to tell there is 'water mass' and not similar things in a photo:\tcontinuous body of water\tvisible horizon\tor body of land beyond the water's edge\tvariation in color or texture\tdifferent shades of blue or green, depending on the depth or composition of the water surface.", 14], "railroad sign": ["Yes. 'Railroad sign' has a tangible appearance and is used to give instructions to the train conductor.\nA few things that are visually similar to 'railroad sign' but are not 'railroad sign' are:\ttraffic sign\tstreet sign\thighway exit sign\nThere are several useful visual features to tell there is 'railroad sign' and not similar things in a photo:\trectangular shape\tyellow or white color\tsymbol of a train or railroad tracks\tcan be seen from a distance or from the train.", 14], "grey train": ["Yes. 'Grey train' has a tangible appearance and is a type of locomotive that carries passengers or goods.\nA few things that are visually similar to 'grey train' but are not 'grey train' are:\twhite train\tblack train\tred train\t\nThere are several useful visual features to tell there is 'grey train' and not similar things in a photo:\tgrey color\toblong shape\tmultiple cars\tconnected by metal couplers (or link-and-pin couplers)\tsometimes a locomotive at the front or at the back\tsmokestack", 14], "dark ripples": ["Yes. 'Dark ripples' has a visually concrete concept that represents the appearance of ripples in water or other fluids under low light or dimly lit conditions.\nA few things that are visually similar to 'dark ripples' but are not 'dark ripples' are:\tShadows\tMirages\tReflexes\tMotion blur\nThere are several useful visual features to differentiate 'dark ripples' from the listed similar things in a photo:\tcircular or wavy patterns appearing in or on the surface of a liquid\tdifferent shades of black or dark grey\tsubtle, low-contrast texture\tmay incorporate reflection or refraction of surrounding light sources", 14], "wooden object": ["Yes. 'Wooden object' has a tangible appearance and is an object made of wood.\nA few things that are visually similar to 'wooden object' but are not 'wooden object' are:\tstone object\tbrick object\tceramic object\tplastic object\nThere are several useful visual features to tell there is 'wooden object' and not similar things in a photo:\tnatural wood grain texture\tearthy color\tsolid and rigid surface\tdents or scratches characteristic of a natural material", 14], "access door": ["Yes. 'Access door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'access door' but are not 'access door' are: regular door, cupboard door, drawer.\nThere are several useful visual features to tell there is 'access door' and not similar things in a photo: small and inconspicuous door, usually positioned in a wall or ceiling, latch or hinge mechanism for locking and opening/closing, often requires a key or security code to access.", 14], "sit motorcycle": ["Yes. 'Sit motorcycle' has a tangible appearance and is a type of motorcycle meant for sitting.\nA few things that are visually similar to 'sit motorcycle' but are not 'sit motorcycle' are:\tbicycle\tscooter\tATV\tmoped\nThere are several useful visual features to tell there is 'sit motorcycle' and not similar things in a photo:\ttwo wheels\ta seat\tforward foot pegs\thandlebars\tsaddlebags, fairings, or a windshield (optional)", 14], "tall hill": ["Yes. 'Tall hill' has a tangible appearance and refers to a raised area of land.\nA few things that are visually similar to 'tall hill' but are not 'tall hill' are:\tmountain\tcliff\ttower\tbuilding\nThere are several useful visual features to tell there is 'tall hill' and not similar things in a photo:\tnatural, sloping terrain\tcovered in grass, trees, or rocks\tcan see the base and summit of hill\trelative to surrounding landscape, higher than nearby landforms", 14], "street sign post": ["Yes. 'Street sign post' has a tangible appearance and is a fixture usually located at the side of a road.\nA few things that are visually similar to 'street sign post' but are not 'street sign post' are:\ttrees\tlamp posts\ttraffic lights\tfences\nThere are several useful visual features to tell there is 'street sign post' and not similar things in a photo:\tcylindrical or rectangular post\tmetallic or reflective surface\tsign attached to the post\tarrows, words, or symbols on the sign indicating traffic or direction", 14], "blue skys": ["Yes. 'Blue skies' has a tangible appearance and is a natural occurrence.\nA few things that are visually similar to 'blue skies' but are not 'blue skies' are:\tBlue paint on a wall\tBlue clothing\tBlue-colored liquid\nThere are several useful visual features to tell there is 'blue skies' and not similar things in a photo:\tA clear sky without clouds\tUniform blue color\tHorizon\tline visible\tSun or stars visible in the sky.", 14], "mane giraffe": ["No. 'Mane giraffe' is too vague or abstract. Giraffes have a mane-like fur along their necks, but the term \"mane giraffe\" is not commonly used to refer to any specific type of giraffe.\nA few things that are visually similar to a 'giraffe' are:\tokapi\tcamelopard\t\nSome useful visual features to distinguish 'giraffe' from similar things in a photo include:\tLong neck, spots in their fur patterns, long legs, and short, knobby horns on their heads.", 14], "pills": ["Yes. 'Pills' has a tangible appearance and is a type of medication.\nA few things that are visually similar to 'pills' but are not 'pills' are:\tcandies\tbreath mints\tvitamins\tgums\nThere are several useful visual features to tell there is 'pills' and not similar things in a photo:\tsmall and cylindrical shape\tsolid and not chewable\tcommercial pharmaceutical insignia\tor serial numbers\tdose inscriptions or dosage forms", 14], "biplane": ["Yes. 'Biplane' has a tangible appearance and is a type of fixed-wing aircraft.\nA few things that are visually similar to 'biplane' but are not 'biplane' are:\tmonoplane\thelicopter\tglider\nThere are several useful visual features to tell there is 'biplane' and not similar things in a photo:\ttwo wings stacked one above the other\ta front and rear cockpit\topen cockpit\tfixed landing gear\tsmall size relative to other aircraft types", 14], "bench legs": ["Yes. 'Bench legs' has a tangible appearance and refers to the supporting elements of a bench.\nA few things that are visually similar to 'bench legs' but are not 'bench legs' are:\tchair legs\ttable legs\tdesk legs\nThere are several useful visual features to tell there are 'bench legs' and not similar things in a photo:\twidely spaced\tappear in pairs (front and back)\tusually four in number\trectangular or square-shaped\tflat and sturdy\tdesigned to hold weight or support a surface.", 14], "none": ["No. 'None' is too vague or abstract to have a tangible appearance in a photo. It simply refers to the absence or lack of something. Therefore, there are no things visually similar to 'none' in a photo.", 14], "ben clock tower": ["Yes. 'Ben clock tower' has a tangible appearance and describes a specific landmark. \nA few things that are visually similar to 'ben clock tower' but are not 'ben clock tower' are:\tElizabeth Tower (the actual name of the clock tower)\tsteeple\tclock tower\nThere are several useful visual features to tell there is 'ben clock tower' and not similar things in a photo:\tlocated in London, UK\tclock faces showing hours, minutes and seconds\tcolored in white and black\tpinnacles on top\tbig bell called Big Ben inside the tower", 14], "car hood": ["Yes. 'Car hood' has a tangible appearance and is a part of a car's front end.\nA few things that are visually similar to 'car hood' but are not 'car hood' are:\ttractor hood\ttunnel entrance\tgarage door\tawning\troof \nThere are several useful visual features to tell there is 'car hood' and not similar things in a photo:\tmetallic or painted surface\tgrille in front\tdifferent angles and curves\tattached to the front of the car.", 14], "fuel truck": ["Yes. 'Fuel truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'fuel truck' but are not 'fuel truck' are:\ttruck\ttanker\ttrailer\tsemi-truck\nThere are several useful visual features to tell there is 'fuel truck' and not similar things in a photo:\tlarge tank on the back of the truck\tpipeline system on the top\tof the truck\tfuel hoses and pumps visible on the truck's side or rear\tsigns or labels indicating it carries fuel or gasoline.", 14], "delta logo": ["Yes. 'Delta logo' has a tangible appearance and is a particular design associated with Delta Airlines.\nA few things that are visually similar to 'delta logo' but are not 'delta logo' are:\ttriangles\tdelta symbol\tformula for finding the change in a variable\nThere are several useful visual features to distinguish 'delta logo' from the listed similar things in a photo:\tblue, red, and white colors are used\tinverted triangle shape\tis accompanied by \"Delta\" written in blue and red colors\tis usually featured on the tail of the Delta Airlines' planes", 14], "pontoon boat": ["Yes. 'Pontoon boat' has a tangible appearance and is a type of boat.\nA few things that are visually similar to 'pontoon boat' but are not 'pontoon boat' are:\tfishing boat\tkayak\tcanoe\tspeedboat\traft\nThere are several useful visual features to tell there is 'pontoon boat' and not similar things in a photo:\ttwo large, buoyant pontoons\tthat hold up a flat platform or deck\tarea for seats and a steering station\televated fence or railing around the deck", 14], "pistachio": ["Yes. 'Pistachio' has a tangible appearance and is a type of nut.\nA few things that are visually similar to 'pistachio' but are not 'pistachio' are:\tpeanut\talmond\twalnut\nThere are several useful visual features to tell there is 'pistachio' and not similar things in a photo:\tsmall size\toval or oblong shape\ta hard shell that splits open\tbright green color\tcreamy white or pale yellow interior", 14], "bear mouth": ["Yes. 'Bear mouth' has a tangible appearance and is a part of the bear's body.\nA few things that are visually similar to 'bear mouth' but are not 'bear mouth' are:\tdog mouth\tcartoon bear mouth\tplush bear mouth\thuman mouth\nThere are several useful visual features to tell there is 'bear mouth' and not similar things in a photo:\tlarge and powerful\tjaws with sharp teeth\twide and round\tmore visible gums than teeth\ttypically brown or black\tcolor of the fur surrounding the mouth\tarea around the mouth may have scarring or damage from fighting or hunting", 14], "body suit": ["Yes. 'Body suit' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'body suit' but are not 'body suit' include:\tleotard\tswimsuit\twetsuit\tshapewear\nThere are several useful visual features to tell there is 'body suit' and not similar things in a photo:\tcovers the body completely\tinseparable from the legs\tcan have long or short sleeves, or be sleeveless\tmade of stretchy and body-hugging material.", 14], "stone blocks": ["Yes. 'Stone blocks' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'stone blocks' but are not 'stone blocks' are:\tbricks\tcinder blocks\twooden blocks\tdecorative stones\nThere are several useful visual features to tell there are 'stone blocks' and not similar things in a photo:\trectangular or square shape\tdull or rough texture\tvisible grain patterns, veins or fossils\tmade of natural stone like granite, marble or sandstone", 14], "lobby": ["Yes. 'Lobby' has a tangible appearance and is an area in a building where people wait or meet.\nA few things that are visually similar to 'lobby' but are not 'lobby' are:\treception\tarea\tfoyer\thallway\nThere are several useful visual features to tell there is 'lobby' and not similar things in a photo:\tcomfortable seating\tarea\topen space\twith tables, chairs, or a reception desk\tmight have art or decoration on the walls", 14], "woodpecker": ["Yes. 'Woodpecker' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'woodpecker' but are not 'woodpecker' are:\tsparrow\trobin\tcardinal\nThere are several useful visual features to tell there is 'woodpecker' and not similar things in a photo:\tpatterned black and white feathers\thard beak\tlong tongue designed to extract insects from wood\ttail feathers that brace against trees to help with climbing\tno visible crest on the head\torangish or red-headed feathers depending on the species of woodpecker.", 14], "headlight vehicle": ["Yes. 'Headlight vehicle' has a tangible appearance and is a type of automotive object.\nA few things that are visually similar to 'headlight vehicle' but are not 'headlight vehicle' are:\tmotorcycle\tbicycle\tscooter\n\nThere are several useful visual features to tell there is 'headlight vehicle' and not similar things in a photo:\tvehicle with four or more wheels\ttwo or more headlights\tlights turned on\tvisible front grill or bumper", 13], "tan curtain": ["Yes. 'Tan curtain' has a tangible appearance and is a type of window treatment.\nA few things that are visually similar to 'tan curtain' but are not 'tan curtain' are:\tblinds\tshades\tdrapes\ttapestries\nThere are several useful visual features to tell there is 'tan curtain' and not similar things in a photo:\ttan or beige color\tsmooth or textured surface\thanging from a window or a door\thave curtain rings or grommets for hanging.", 13], "grey ear": ["Yes. 'Grey ear' has a tangible appearance and is a physical feature of some living beings.\nThere are no things that are visually similar to 'grey ear' as it is a specific and distinct feature.\nSome useful visual features for distinguishing a 'grey ear' from other ears in a photo might be:\tthe color grey or a similar shade\tthe specific shape or size of the ear\tthe location of the ear on the body of the animal or person", 13], "center line": ["Yes. 'Center line' has a tangible appearance and is a line that divides a surface into two equal halves.\nA few things that are visually similar to 'center line' but are not 'center line' are: boundary lines, dividing lines, imaginary lines.\nThere are several useful visual features to tell there is 'center line' and not similar things in a photo:\ta straight line that runs through the center of a surface\tequal distance from either side of the surface\tuse of dashed or solid line type.", 13], "toaster counter": ["Yes. 'Toaster counter' has a tangible appearance and refers to a specific area in the kitchen.\nA few things that are visually similar to 'toaster counter' but are not 'toaster counter' are:\tCoffee maker counter\tMicrowave counter\tBlender counter\tOven counter\nThere are several useful visual features to tell there is 'toaster counter' and not similar things in a photo:\tCounter or tabletop surface\tToaster placed on the surface\tWires or cords leading to the toaster\tSurrounding kitchen or dining area", 13], "stainless steel toaster": ["Yes. 'Stainless steel toaster' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'stainless steel toaster' but are not 'stainless steel toaster' are:\tglass kettle\tfood processor\tmicrowave\tcoffee maker\tblender\nThere are several useful visual features to tell there is 'stainless steel toaster' and not similar things in a photo:\telongated rectangular shape\twith two or four holes\ton/off and time control buttons\tpull-out crumb tray\tstainless steel exterior.", 13], "seals": ["Yes. 'Seals' has a tangible appearance and refers to a group of aquatic mammals.\nA few things that are visually similar to 'seals' but are not 'seals' are:\totters\tsea lions\twalruses\nThere are several useful visual features to tell there is 'seals' and not similar things in a photo:\tsmooth and cylindrical body shape\tblubber\tforward-facing eyes\twebbed flippers to swim and walk on the shore\tvarious lengths and colors of fur\tblunt snout\tor long whiskers known as vibrissae.", 13], "wash basin": ["Yes. 'Wash basin' has a tangible appearance and is a type of sink.\nA few things that are visually similar to 'wash basin' but are not 'wash basin' are:\tkitchen sink\tbathroom sink\tbathtub\tlaundry tub\nThere are several useful visual features to tell there is 'wash basin' and not similar things in a photo:\tround or oval-shaped\tbasin is usually made of ceramic, porcelain, or stainless steel\tfaucet or taps for running water\tdrain at the bottom of the basin", 13], "water trough": ["Yes. 'Water trough' has a tangible appearance and is a container used for holding water.\nA few things that are visually similar to 'water trough' but are not 'water trough' are:\tbathtub\tpool\tfountain\nThere are several useful visual features to tell there is 'water trough' and not similar things in a photo:\trectangular or elongated shape\tmade of metal or cement\tusually placed outdoors\tfor animals to drink from or for irrigation purposes", 13], "wooden barn": ["Yes. 'Wooden barn' has a tangible appearance and is a type of rural building.\nA few things that are visually similar to 'wooden barn' but are not 'wooden barn' are:\tcottage\tshed\tgarage\tgreenhouse\nThere are several useful visual features to tell there is 'wooden barn' and not similar things in a photo:\twooden construction\tsloped or gabled roof\twith or without a cupola\tor with hayloft\tdoors and windows at ground level\toften red in color.", 13], "coca cola logo": ["Yes. 'Coca Cola logo' has a tangible appearance and is a well-known brand logo.\nA few things that are visually similar to 'coca cola logo' but are not 'coca cola logo' are:\tPepsi logo\tDr. Pepper logo\tRed and white logos or signs\nThere are several useful visual features to tell there is 'coca cola logo' and not similar things in a photo:\tdistinctive cursive font\tred and white color scheme\tswirl design in the lettering or logo imagery", 13], "liquor bottles": ["Yes. 'Liquor bottles' has a tangible appearance and is a type of container for alcoholic beverages.\nA few things that are visually similar to 'liquor bottles' but are not 'liquor bottles' are:\twine bottles\tolive oil bottles\tvinegar bottles\tperfume bottles\nThere are several useful visual features to tell there is 'liquor bottles' and not similar things in a photo:\tliquor label on the bottle\tliquor type on the label\tunique bottle shape and size\tliquor manufacturers logo on the label\tcapsule or foil covering the cork (or screw top)", 13], "tennis game": ["Yes. 'Tennis game' has a tangible appearance and is a kind of sport.\nA few things that are visually similar to 'tennis game' but are not 'tennis game' are:\tbadminton\tgame\ttable tennis\nThere are several useful visual features to tell there is 'tennis game' and not similar things in a photo:\tplayers holding tennis rackets\ttennis ball in play\tnet dividing the court in half\tcourt with specific markings for singles or doubles play", 13], "pink curtains": ["Yes. 'Pink curtains' has a tangible appearance and is a type of window covering.\nA few things that are visually similar to 'pink curtains' but are not 'pink curtains' are:\tpink fabric\tpink tablecloth\tpink bedspread\tpink flag\nThere are several useful visual features to tell there are 'pink curtains' and not similar things in a photo:\tmade of fabric or material\thanging in front of a window\tpink in color\tfolds or pleats when closed or open.", 13], "poster bed": ["Yes. 'Poster bed' has a tangible appearance and is a type of bed with posts.\nA few things that are visually similar to 'poster bed' but are not 'poster bed' are:\tcanopy bed\tsleigh bed\tday bed\tdivan bed\nThere are several useful visual features to tell there is 'poster bed' and not similar things in a photo:\tfour vertical posts on each corner\tof equal height\tdraperies or curtains can be seen attached to each of the posts", 13], "floater": ["Yes. 'Floater' has a tangible appearance and refers to specks or dots that appear to float in a person's vision.\nA few things that are visually similar to 'floater' but are not 'floater' are:\tdust particles\tglitter\tpollen\tpet hair\nThere is one useful visual feature to tell there is a 'floater' and not similar things in a photo: they appear to move when you move your eye.", 13], "heel shoe": ["Yes. 'Heel shoe' has a tangible appearance and is a kind of footwear.\nA few things that are visually similar to 'heel shoe' but are not 'heel shoe' are:\tboot\tflip-flop\tsandal\tclog\nThere are several useful visual features to tell there is 'heel shoe' and not similar things in a photo:\ta raised heel\tat least one strap to hold the shoe on the foot\tpointed, rounded or squared toe", 13], "copyright information": ["No. 'Copyright information' is too vague or abstract to be distinguished in a photo.", 13], "bottom shelf": ["Yes. 'Bottom shelf' has a tangible appearance and refers to a specific location in a storage unit.\nA few things that are visually similar to 'bottom shelf' but are not 'bottom shelf' are:\ttop shelf\tmiddle shelf\tdrawer\tcabinet\nThere are several useful visual features to tell there is 'bottom shelf' and not similar things in a photo:\tlowest shelf in a storage unit or cabinet\tusually closer to the ground or floor than other shelves\tmore likely to hold heavier or bulky items than other shelves.", 13], "recliner chair": ["Yes. 'Recliner chair' has a tangible appearance and is a kind of chair.\nA few things that are visually similar to 'recliner chair' but are not 'recliner chair' are:\tarmchair\tlounge chair\trocking chair\toffice chair\nThere are several useful visual features to tell there is 'recliner chair' and not similar things in a photo:\tadjustable backrest and footrest\tpadded or cushioned upholstery\tfootrest that rises up when the backrest is reclined", 13], "plastic utensil": ["Yes. 'Plastic utensil' has a tangible appearance and is a kind of kitchen tool.\nA few things that are visually similar to 'plastic utensil' but are not 'plastic utensil' are:\tmetal utensil\twooden utensil\tbamboo utensil\nThere are several useful visual features to tell there is 'plastic utensil' and not similar things in a photo:\tplastic-made material\tlightweight\tusually disposable in nature\ttypical fork/knife/spoon shape", 13], "dozen": ["No. 'Dozen' is too vague or abstract to be considered visually concrete. It represents a specific quantity of twelve.", 13], "grass pasture": ["Yes. 'Grass pasture' has a tangible appearance and is an area of land where grass is grown for grazing or hay-making.\nA few things that are visually similar to 'grass pasture' but are not 'grass pasture' are:\tgolf courses\tparks\tlawns\tfarmland\nThere are several useful visual features to tell there is 'grass pasture' and not similar things in a photo:\twide expanse of land\tthin tall grasses, sometimes with flowers\tor grazing animals like cows or sheep", 13], "tail hair": ["Yes. 'Tail hair' has a tangible appearance and is a type of hair found on the tails of animals.\nA few things that are visually similar to 'tail hair' but are not 'tail hair' are:\thead hair\tfur\twool\tyarn\tstring\nThere are several useful visual features to tell there is 'tail hair' and not similar things in a photo:\tlong and slender, branching out from a central point\tfound on the tail of an animal\tvarious colors and textures, matching the animal's fur or skin", 13], "throw rug": ["Yes. 'Throw rug' has a tangible appearance and is a small rug used for decorative purposes.\nA few things that are visually similar to 'throw rug' but are not 'throw rug' are:\tdoormat\tbathmat\ttablecloth\tblanket\nThere are several useful visual features to tell there is 'throw rug' and not similar things in a photo:\tsmall size\tvariety of patterns and textures\tplaced on top of larger rugs or flooring materials\teasily movable sometimes fringed edges", 13], "tailgate": ["Yes. 'Tailgate' has a tangible appearance and refers to the back of a truck or SUV that can be lowered or opened for easy access.\nA few things that are visually similar to 'tailgate' but are not 'tailgate' are:\tdoors\thatches\tgates\tfences\nThere are several useful visual features to tell there is 'tailgate' and not similar things in a photo:\tattached to the back of a truck or an SUV\thorizontal surface that can be lowered or opened\thandles or latches for opening and closing\ttypically made of metal or similar materials.", 13], "grout line": ["Yes. 'Grout line' has a tangible appearance and is a visible space between tiles or stones.\nA few things that are visually similar to 'grout line' but are not 'grout line' are:\tcracks\tinlays\tshadows\tdecorative patterns\nThere are several useful visual features to tell there is 'grout line' and not similar things in a photo:\tstraight line that forms a grid\tseparates tiles or stones\tdifferent color and/or texture than the tiles/stones around it", 13], "horse hair": ["Yes. 'Horse hair' has a tangible appearance and is a kind of animal hair.\nA few things that are visually similar to 'horse hair' but are not 'horse hair' are:\tdog hair\tcat hair\thuman hair\nThere are several useful visual features to tell there is 'horse hair' and not similar things in a photo:\tthick and coarse\thollow and porous hair shafts\twiry texture\tmay feature long, flowing manes or tails\t\ndistinct color patterns, such as black and white streaks", 13], "brown pillows": ["Yes. 'Brown pillows' has a tangible appearance and is a type of pillow.\nA few things that are visually similar to 'brown pillows' but are not 'brown pillows' are:\tcouch cushions\tbrown stuffed animals\tbrown blankets\nThere are several useful visual features to tell there are 'brown pillows' and not similar things in a photo:\tsquare or rectangular shape\tsoft and plush texture\tbrown color\thave a pillowcase or decorative cover on them.", 13], "rust stains": ["Yes. 'Rust stains' has a tangible appearance and is a type of discoloration caused by the oxidation of iron.\nA few things that are visually similar to 'rust stains' but are not 'rust stains' are:\twater stains\tdirt stains\tpaint stains\nThere are several useful visual features to tell there are 'rust stains' and not similar things in a photo:\torange or brown color\trough or flaky texture\tappearing on metal surfaces\tsimilar patterns or shapes to that of the object the stains are on.", 13], "jacket man": ["No. 'Jacket man' is too vague or abstract and cannot be visually distinguished. \n\nHowever, if we modify the concept to 'man wearing a jacket', then the answer is yes. \n\nA few things that are visually similar to 'man wearing a jacket' but are not 'man wearing a jacket' are:\tman wearing a coat\tman wearing a hoodie\tman wearing a suit\tman wearing a sweater\nThere are several useful visual features to tell there is 'man wearing a jacket' and not similar things in a photo:\touterwear\twith sleeves\twith collar\thas front closure, such as buttons or zipper\tmade of thicker and sturdier fabrics, such as denim, leather, or wool", 13], "sugar bowl": ["Yes, the concept 'sugar bowl' has a tangible appearance and is a type of tableware.\nA few things that are visually similar to 'sugar bowl' but are not 'sugar bowl' are:\tcreamer\tpitcher\tvase\t\nThere are several useful visual features to distinguish 'sugar bowl' from the listed similar things in a photo:\teither made from ceramic or glass\thave a lid with a handle\ton a saucer or a plate\thave a spoon or a holder for the spoon", 13], "silver teapot": ["Yes. 'Silver teapot' has a tangible appearance and is a kind of kitchenware.\nA few things that are visually similar to 'silver teapot' but are not 'silver teapot' are:\tsteel kettle\tchrome vase\nThere are several useful visual features to tell there is 'silver teapot' and not similar things in a photo:\tsilver in color\tteapot shape\twith a lid and a handle\tpouring spout on the top", 13], "decorative plant": ["Yes. 'Decorative plant' has a tangible appearance and is a type of plant used for ornamentation.\nA few things that are visually similar to 'decorative plant' but are not 'decorative plant' are:\tnatural plants\tweeds\therbs\tcacti\t\nThere are several useful visual features to tell there is 'decorative plant' and not similar things in a photo:\tcolorful or eye-catching leaves or flowers\tplaced in a decorative pot or container\ttrim or topiary shapes\tpruned, neat appearance\tindoor or outdoor setting", 13], "television camera": ["Yes. 'Television camera' has a tangible appearance and is a device for capturing live video footage.\nA few things that are visually similar to 'television camera' but are not 'television camera' are:\tcamcorder\tsurveillance camera\twebcam\tfilm camera\nThere are several useful visual features to tell there is 'television camera' and not similar things in a photo:\tlarger size compared to handheld video recording devices\tmounted on a tripod or other stand\tsimilar shape and design to professional video camera equipment\tzoom lens, focus rings, and other manual controls\tvideo cable or wireless transmitter connection to a control room or other equipment", 13], "pocket book": ["Yes. 'Pocket book' has a tangible appearance and is a type of book.\nA few things that are visually similar to 'pocket book' but are not 'pocket book' are:\thandbag\twallet\tjournal\tagenda\nThere are several useful visual features to tell there is 'pocket book' and not similar things in a photo:\tbook pages\tbinding or spine\tcover with title or author\tpocket-size, small enough to fit in a pocket or purse.", 13], "silver piping": ["Yes. 'Silver piping' has a tangible appearance and is a kind of decorative element.\nA few things that are visually similar to 'silver piping' but are not 'silver piping' are:\tgrey ribbon\tchrome trim\tduct tape\taluminum foil\nThere are several useful visual features to tell there is 'silver piping' and not similar things in a photo:\tsilver or metallic coloring\ttubular or cylindrical shape\tgrooves or ridges in the surface\tused as decoration or embellishment", 13], "toothy smile": ["Yes. 'Toothy smile' has a tangible appearance and refers to a smile that shows a lot of teeth.\nA few things that are visually similar to 'toothy smile' but are not 'toothy smile' are: closed mouth smile, winky smile, pouty smile.\nThere are several useful visual features to tell there is 'toothy smile' and not similar things in a photo: the visible upper and lower teeth, the curvature of the lips.", 13], "wooden bed frame": ["Yes. 'Wooden bed frame' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden bed frame' but are not 'wooden bed frame' are:\twooden sofa frame\twooden shelves\twooden table\twooden chair\nThere are several useful visual features to tell there is 'wooden bed frame' and not similar things in a photo:\trectangle shape\theadboard and/or footboard\tframe for supporting a mattress and box spring\tmultiple slats to hold mattress and box spring", 13], "blue word": ["No. 'Blue word' is too vague or abstract to be distinguished in a photo. It does not have a tangible appearance like 'Christmas balls' or 'Lemur'.", 13], "wood fencing": ["Yes. 'Wood fencing' has a tangible appearance and refers to a barrier or enclosure made of wood.\nA few things that are visually similar to 'wood fencing' but are not 'wood fencing' are:\tbrick wall\tstone wall\tmetal fence\thedge\nThere are several useful visual features to tell there is 'wood fencing' and not similar things in a photo:\tmade of wood\tlong narrow boards or panels\tposts to hold the fencing in place\trails that connect the posts\thorizontal or vertical alignment of the boards or panels", 13], "metal weather vane": ["Yes. 'Metal weather vane' has a tangible appearance and is a type of outdoor decoration.\nA few things that are visually similar to 'metal weather vane' but are not 'metal weather vane' are:\tmetal wind chimes\tmetal sculptures\tmetal signposts\nThere are several useful visual features to tell there is 'metal weather vane' and not similar things in a photo:\tarrow-shaped or animal-shaped object\tmade of metal or another durable material\tattached to the roof or the side of a building\tfacing different directions to show the wind's direction", 13], "brick apartment building": ["Yes. 'Brick apartment building' has a tangible appearance.\nA few things that are visually similar to 'brick apartment building' but are not 'brick apartment building' are:\tbrick office building\tbrick school building\t\nThere are several useful visual features to tell there is 'brick apartment building' and not similar things in a photo:\tmultiple stories\tresidential use\tbalconies or fire escapes\twindows on each floor\tsymmetrical or repeating patterns of windows and doors", 13], "front license plate": ["Yes. 'Front license plate' has a tangible appearance and is a rectangular plate fixed on the front of a vehicle.\nA few things that are visually similar to 'front license plate' but are not 'front license plate' are:\tnametag\tsignboard\tstreet sign\tadvertisement\nThere are several useful visual features to tell there is 'front license plate' and not similar things in a photo:\trectangular shape\twith alphanumeric characters\tfor attachment on the front of a vehicle\tlikely with a logo or emblem on it\tvaries in size and color depending on the state or country", 13], "sunglasses man": ["Yes. 'Sunglasses man' has a tangible appearance and refers to a person wearing sunglasses.\nA few things that are visually similar to 'sunglasses man' but are not 'sunglasses man' are:\tactress wearing sunglasses\tpainting of a man wearing sunglasses\tcartoon of a man wearing sunglasses\tsculpture of a man wearing sunglasses\nThere are several useful visual features to tell there is 'sunglasses man' and not similar things in a photo:\thuman face\tfeatures obscured by sunglasses\tappropriate clothing for a man", 13], "sauce cup": ["Yes. 'Sauce cup' has a tangible appearance and is a type of dish used for sauces or condiments.\nA few things that are visually similar to 'sauce cup' but are not 'sauce cup' are:\tmug\tbowl\tshot glass\tteacup\nThere are several useful visual features to tell there is 'sauce cup' and not similar things in a photo:\tSmall size compared to other dishes\tSmall handle if any\tThe lip of the cup or bowl should not be too tall, it should be easy to reach inside for dipping.\tTypically made of glass, ceramic, or plastic.", 13], "whip": ["Yes. 'Whip' has a tangible appearance and is a kind of tool.\nA few things that are visually similar to 'whip' but are not 'whip' are:\tbelt\trope\tlash\nThere are several useful visual features to tell there is 'whip' and not similar things in a photo:\tlong and flexible handle\tthin and braided lash\torally with a loop at the end", 13], "seafood": ["Yes. 'Seafood' has a tangible appearance and refers to food from the sea.\nA few things that are visually similar to 'seafood' but are not 'seafood' are:\tseaweed\tsand\tcoral\trocks\nThere are several useful visual features to tell there is 'seafood' and not similar things in a photo:\tedible sea creatures such as fish, shellfish, crustaceans, and mollusks\tcleaned and cooked or raw appearance\tpresentation as a dish or platter\ton a plate, bowl, or in a basket with other seafood items.", 13], "peopl": ["No. 'Peopl' is not a properly spelled word and it is too vague or abstract to be distinguished in a photo.", 13], "stone pavement": ["Yes. 'Stone pavement' has a tangible appearance and is a kind of flooring made of stones.\nA few things that are visually similar to 'stone pavement' but are not 'stone pavement' are:\tbricks\tcobblestone\tasphalt\ttiles\nThere are several useful visual features to tell there is 'stone pavement' and not similar things in a photo:\tuneven surface\tmade of various sizes and shapes of stones\tnatural and rustic appearance\tcan be arranged in various patterns and designs.", 13], "porcelain bowl": ["Yes. 'Porcelain bowl' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'porcelain bowl' but are not 'porcelain bowl' are:\tplastic bowl\tglass bowl\twooden bowl\tceramic bowl\nThere are several useful visual features to tell there is 'porcelain bowl' and not similar things in a photo:\twhite or pale color\tthin\tand smooth\ton the heavier side\tmay have decorative patterns or designs around the edge\tsymmetrical shape\twith a thick curved edge for holding and eating food", 13], "rooms": ["Yes. 'Rooms' has a tangible appearance and is a defined space within a building.\nA few things that are visually similar to 'rooms' but not 'rooms' are:\tclosets\tcubicles\tpartitions\tbarracks\nThere are several useful visual features to tell there is 'rooms' and not similar things in a photo:\tclearly defined borders\tfurnished walls\tand possibly distinct areas for different purposes such as sleeping areas, kitchens, or bathrooms.", 13], "blue ground": ["Yes. 'Blue ground' has a tangible appearance and refers to the color and texture of ground or flooring.\nThere are no things that are visually similar to 'blue ground' but are not 'blue ground'.\nUseful visual features for distinguishing 'blue ground' in a photo would entail the color blue dominating the ground or flooring space. Also, the texture or pattern of the ground could be noted to distinguish it from other non-blue floors.", 13], "orange bill": ["Yes. 'Orange bill' has a tangible appearance and refers to the beak of a bird that is colored orange.\nA few things that are visually similar to 'orange bill' but are not 'orange bill' are:\tyellow bill\tred bill\tblack bill\nThere are several useful visual features to tell there is 'orange bill' and not similar things in a photo:\tbeak of a bird\tis colored orange in hue, regardless of the intensity or brightness.", 13], "kleenex box": ["Yes. 'Kleenex box' has a tangible appearance and is a type of tissue box.\nA few things that are visually similar to 'kleenex box' but are not 'kleenex box' are:\tnasal spray box\tfirst aid kit\tbox of matches\tband-aid box\nThere are several useful visual features to tell there is 'kleenex box' and not similar things in a photo:\trectangular shape\tsquare opening on top\twith tissues sticking out\tor with a branding of 'Kleenex'", 13], "alcove": ["Yes. 'Alcove' has a tangible appearance and is a type of architectural element.\nA few things that are visually similar to 'alcove' but are not 'alcove' are:\tcove\tniche\trecess\tbay\twindow\nThere are several useful visual features to tell there is 'alcove' and not similar things in a photo:\twalled enclosure or recessed space\tinset or set back from the main room or area\tsometimes with a curved or angled wall\thas a shelf or seating area built into the recess.", 13], "wall painting": ["Yes. 'Wall painting' has a tangible appearance and is a form of art that is applied on walls.\nA few things that are visually similar to 'wall painting' but are not 'wall painting' are:\twallpaper\tmural\tstickers\t3D structures\nThere are several useful visual features to tell there is 'wall painting' and not similar things in a photo:\thandmade design or paint\tstill life or representations of abstract concepts\tconveys an idea or emotion\tpainted straight on the wall surface", 13], "wooden wagon": ["Yes. 'Wooden wagon' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'wooden wagon' but are not 'wooden wagon' are:\twooden cart\thand truck\twheelbarrow\tfarm wagon\nThere are several useful visual features to tell there is 'wooden wagon' and not similar things in a photo:\twooden structure\twheels (usually four)\tdrawn by animals (such as horses or mules)\tor used for children play\tsidewalls or rails to contain the load", 13], "note book": ["Yes. 'Notebook' has a tangible appearance and is a type of book used for taking notes.\nA few things that are visually similar to 'notebook' but are not 'notebook' are:\tjournal\tbinder\tplanner\tdiary\nThere are several useful visual features to tell there is 'notebook' and not similar things in a photo:\tpaper pages\tbound together\twith a cover\truled or lined pages\toften used with a writing utensil.", 13], "radar": ["No. 'Radar' is too abstract to be distinguished visually.\n", 13], "window air conditioner": ["Yes. 'Window air conditioner' has a tangible appearance and is a type of cooling device.\nA few things that are visually similar to 'window air conditioner' but are not 'window air conditioner' are:\tregular fans\tceiling fans\tportable air conditioners\thumidifiers\nThere are several useful visual features to tell there is 'window air conditioner' and not similar things in a photo: installed in a window or wall\tlocation of the air vents\texternal unit on the outside of the building\tdigital display for temperature\tcontrol panel for adjustments in temperature, fan speed or mode.", 13], "gray mouse": ["Yes. 'Gray mouse' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'gray mouse' but are not 'gray mouse' are:\trat\thamster\tgerbil\tsquirrel\nThere are several useful visual features to tell there is 'gray mouse' and not similar things in a photo:\tgray or brown fur\tpointed nose and small ears\tlong, thin tail\tround eyes with black pupils", 13], "crocodile": ["Yes. 'Crocodile' has a tangible appearance and is a type of reptile.\nA few things that are visually similar to 'crocodile' but are not 'crocodile' are:\talligator\tcayman\tmonitor lizard\t\nThere are several useful visual features to tell there is 'crocodile' and not similar things in a photo:\tnarrow, V-shaped snout\tmassive head and jaws\tpointy teeth\tinverted tooth on lower jaw\tscaley skin\tlong tail\t4 legs", 13], "plastic jar": ["Yes. 'Plastic jar' has a tangible appearance and is a container made of plastic.\nA few things that are visually similar to 'plastic jar' but are not 'plastic jar' are:\tplastic bottle\tplastic container\tplastic bin\t\nThere are several useful visual features to tell there is 'plastic jar' and not similar things in a photo:\tround or square shape with a wide opening\tcylindrical or semi-cylindrical body with a flat base\tclear or translucent plastic material with a lid or a cap\tsizes varying from small to large", 13], "metal design": ["Yes. 'Metal design' has a tangible appearance and refers to the artistic use of metal in design.\nA few things that are visually similar to 'metal design' but are not 'metal design' are:\tmetallic objects\tmetal jewelry\tmetal tools\nThere are several useful visual features to tell there is 'metal design' and not similar things in a photo:\tunique and intricate patterns or shapes\tmultiple pieces of metal assembled together\tdecorative purpose rather than functional\tuse of specific metalworking techniques such as forging or casting", 13], "music": ["No. 'Music' is too abstract to have a tangible appearance to distinguish from other things in a photo.\nThere are no things that are visually similar to 'music' but are not 'music'.", 13], "dell computer monitor": ["Yes. 'Dell computer monitor' has a tangible appearance and is a specific brand and type of electronic device.\nA few things that are visually similar to 'Dell computer monitor' but are not 'Dell computer monitor' are:\tApple computer monitor\tSamsung computer monitor\tHP computer monitor\tAsus computer monitor\nThere are several useful visual features to tell there is 'Dell computer monitor' and not similar things in a photo:\tDell logo at the bottom of the screen\trectangular screen in a landscape or portrait orientation\tdifferent port inputs on the back\tfor desktop or laptop use", 13], "triangle shape": ["Yes. 'Triangle shape' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'triangle shape' but are not 'triangle shape' are:\tarrow roof\tpyramid\tsliced pizza\twedge\nThere are several useful visual features to tell there is 'triangle shape' and not similar things in a photo:\tthree straight sides\tthree angles\ttotal sum of angles is 180 degrees", 13], "asphalt street": ["Yes. 'Asphalt street' has a tangible appearance and is a type of road surface.\nA few things that are visually similar to 'asphalt street' but are not 'asphalt street' are:\tconcrete road\tpaved road\tgravel road\tdirt road\nThere are several useful visual features to tell there is 'asphalt street' and not similar things in a photo:\tblack or dark grey color\tsmooth surface\twith painted or embedded traffic markings", 13], "lab coat": ["Yes. 'Lab coat' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'lab coat' but are not 'lab coat' are:\tdoctor's coat\tbaker's coat\tchef's coat\tapron\ttunic\nThere are several useful visual features to tell there is 'lab coat' and not similar things in a photo:\tlong white coat\tbuttoned up\tcollared\tstiff and protective material\tworn over other clothes, often in a laboratory or medical setting", 13], "bike riders": ["Yes. 'Bike riders' has a tangible appearance and refers to people riding bicycles.\nA few things that are visually similar to 'bike riders' but are not 'bike riders' are:\tmotorbike riders\tskateboarders\tscooter riders\tjoggers\nThere are several useful visual features to tell there are 'bike riders' and not similar things in a photo:\triders seated on bicycles\tbicycles with visible wheels and pedals\thelmets and protective clothing\tbikes on a dedicated bike lane or off-road trail", 13], "solo cup": ["Yes. 'Solo cup' has a tangible appearance and is a type of disposable cup.\nA few things that are visually similar to 'solo cup' but are not 'solo cup' are:\tplastic cup\tpaper cup\tglass cup\nThere are several useful visual features to tell there is 'solo cup' and not similar things in a photo:\ttapered shape\twith a flat bottom and a round opening\tfor holding liquids\tand has measurements like ounces\thas the word \"Solo\" printed on it", 13], "monitor desk": ["Yes. 'Monitor desk' has a tangible appearance and is a specific piece of furniture.\nA few things that are visually similar to 'monitor desk' but are not 'monitor desk' are:\ttable\tdesk\tshelf\tworkstation\nThere are several useful visual features to tell there is 'monitor desk' and not similar things in a photo:\tdesigned to accommodate a computer monitor\tspace-saving design\tergonomic\twork surface at a comfortable height\twith adjustable shelves or drawers for storing peripherals\tand often has a dedicated keyboard tray.", 13], "steel beams": ["Yes. 'Steel beams' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'steel beams' but are not 'steel beams' are:\twooden beams\tiron bars\tplastic tubes\tconcrete pillars\nThere are several useful visual features to tell there is 'steel beams' and not similar things in a photo:\tgrey or silver color\tmetallic appearance\thighly reflective or shiny\tstructurally important to a building or structure\tstamped with manufacturer or weight specifications", 13], "ford": ["Yes. 'Ford' has a tangible appearance and refers to a specific brand of automobile.\nA few things that are visually similar to 'ford' but are not 'ford' are:\tToyota\tChevrolet\tHyundai\tJeep\nThere are several useful visual features to tell there is 'ford' and not similar things in a photo:\tthe Ford logo and name on the car\tfamiliar models, such as the Mustang or the F-150\ttrademark Ford design elements, such as the prominent front grille and chrome accents\tcolors usually associated with Ford, such as blue, white, or red.", 13], "hardware": ["Yes. 'Hardware' has a tangible appearance and refers to the physical components of a computer or other electronic device.\nA few things that are visually similar to 'hardware' but are not 'hardware' are: tools, machinery, utensils, furniture \nThere are several useful visual features to tell there is 'hardware' and not similar things in a photo: metallic or plastic components specifically designed for use in computer or other electronic devices, such as microchips, circuit boards, cables, keyboards, and mice.", 13], "horse carriage": ["Yes. 'Horse carriage' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'horse carriage' but are not 'horse carriage' are:\tcoach\tbus\ttrailer\nThere are several useful visual features to tell there is 'horse carriage' and not similar things in a photo:\thorse(s) pulling the carriage\tcarriage made of wood and metal\twith or without a hood (cover)\tseats for passengers in rows or facing forward or backward", 13], "appetizer": ["Yes. 'Appetizer' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'appetizer' but are not 'appetizer' are:\tentree\tside dish\tsnack\nThere are several useful visual features to tell there is 'appetizer' and not similar things in a photo:\tsmall portion size\tusually served at the beginning or before the main course\tmay be served on small plates or skewers\tmay have decorative elements or garnishes\tusually eaten with fingers or small utensils", 13], "lunch box": ["Yes. 'Lunch box' has a tangible appearance and is a kind of container used to carry a meal.\nA few things that are visually similar to 'lunch box' but are not 'lunch box' are:\tbackpack\tbag\tpurse\tbriefcase\ttote\tbox\nThere are several useful visual features to tell there is 'lunch box' and not similar things in a photo:\trectangular or square shape\thandle or strap for carrying\thinged lid to open and close\tcompartmentalized interior\tto accommodate food and drink items.", 13], "metal plaque": ["Yes. 'Metal plaque' has a tangible appearance and is a type of metal sign.\nA few things that are visually similar to 'metal plaque' but are not 'metal plaque' are:\tmetal plate\ttrophy\tname tag\tmedal\nThere are several useful visual features to tell there is 'metal plaque' and not similar things in a photo:\trectangular or square shape\tmetallic surface\tflat or raised surface with engraved or embossed text or design\tscrew or nail holes on the corners for mounting or hanging", 13], "shipping container": ["Yes. 'Shipping container' has a tangible appearance and is a type of large metal box used for transportation.\nA few things that are visually similar to 'shipping container' but are not 'shipping container' are:\ttruck trailer\ttrain wagon\tmetal box. \nThere are several useful visual features to tell there is 'shipping container' and not similar things in a photo:\t\nstandard rectangular shape\tmade of metal or steel\tcorrugated sides and doors\twith a company logo or identification number\ton a shipping yard or a port", 13], "dogs mouth": ["Yes. 'Dogs mouth' has a tangible appearance and is a part of the dog's anatomy.\nA few things that are visually similar to 'dogs mouth' but are not 'dogs mouth' are:\tcat's mouth\thuman mouth\tlizard's mouth\nThere are several useful visual features to tell there is 'dog's mouth' and not similar things in a photo:\thairy muzzle\tsharp teeth\twet tongue\tpink or dark gums\tand nose", 13], "heart shape": ["Yes. 'Heart shape' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'heart shape' but are not 'heart shape' are:\tdroplet\tteardrop\ttriangle\nThere are several useful visual features to tell there is 'heart shape' and not similar things in a photo:\tcurved top\tflat bottom\tconcave sides\twith a cleft or notch in the middle.", 13], "banana slices": ["Yes. 'Banana slices' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'banana slices' but are not 'banana slices' are:\tmango slices\tpineapple slices\torange slices\tzucchini slices\nThere are several useful visual features to tell there is 'banana slices' and not similar things in a photo:\tyellow\tfruit-like shape\tcurved lengthwise\tsmall black dots on the surface", 13], "humans": ["Yes. 'Humans' has a tangible appearance.\nA few things that are visually similar to 'humans' but are not 'humans' are:\tmannequins\tstatues\twax figures\trobots\nThere are several useful visual features to tell there is 'human' and not similar things in a photo:\ttwo arms and legs\tstanding upright\tbipedal gait\tfacial features\thair and skin texture\tvariety of clothing and accessories\tgestures and expressions", 13], "char marks": ["Yes. 'Char marks' has a tangible appearance and refers to the blackened or burnt texture left by flames.\nA few things that are visually similar to 'char marks' but are not 'char marks' are:\tshadow\tstain\twatermark\nThere are several useful visual features to tell there are 'char marks' and not similar things in a photo:\tuneven texture\twith a blackened or burnt aspect\tappearing in proximity to a heat source, like a grill or bonfire.", 13], "handle utensil": ["Yes. 'Handle utensil' has a tangible appearance and refers to any tool or object that has a handle to use.\nA few things that are visually similar to 'handle utensil' but are not 'handle utensil' are:\tknob\tdoorknob\thanger\tdrawer pull\nThere are several useful visual features to tell there is 'handle utensil' and not similar things in a photo:\tstraight or curved shaft\tfor holding\tor grasping\ta tool or object\tmaybe metal, plastic, or wood\ta handle at one end for gripping \tor turning.", 13], "water heater": ["Yes. 'Water heater' has a tangible appearance and is a household appliance used for heating water.\nA few things that are visually similar to 'water heater' but are not 'water heater' are:\tair conditioner\tfurnace\trefrigerator\twashing machine\nThere are several useful visual features to tell there is 'water heater' and not similar things in a photo:\ttall cylindrical or box-shaped appliance\twith temperature gauge and control knob or panel\twith pipes or tubes attached to it\tvisible heating elements or burners", 13], "cat toy": ["Yes. 'Cat toy' has a tangible appearance and is a type of toy for cats.\nA few things that are visually similar to 'cat toy' but are not 'cat toy' are:\tdog toy\tmouse\treal mouse\trubber ball\nThere are several useful visual features to tell there is 'cat toy' and not similar things in a photo:\tsmall\tsize or shape that is easy for cats to bat or carry\tcontains catnip (a plant that typically makes cats very excited and playful)\tbright colors or textures that attract cats' attention", 13], "soup bowl": ["Yes. 'Soup bowl' has a tangible appearance and is a type of dish.\nA few things that are visually similar to 'soup bowl' but are not 'soup bowl' are:\tsalad bowl\tmixing bowl\tbreakfast bowl\tmug\nThere are several useful visual features to tell there is 'soup bowl' and not similar things in a photo:\trounded shape\twith a handle\tor without one\tmade of ceramics, porcelain, or glass\tinclined or flared sides\twide opening to accommodate the spoon", 13], "dirt stain": ["Yes. 'Dirt stain' has a tangible appearance and is a mark or discoloration caused by dirt.\nA few things that are visually similar to 'dirt stain' but are not 'dirt stain' are:\tshadow\tbroken textures\tblood stain\tcoffee spill\nThere are several useful visual features to tell there is 'dirt stain' and not similar things in a photo:\tbrown or grey color\tirregular shape\tor confined to a certain area\tcommonly found on clothes or fabric", 13], "mirror bus": ["Yes. 'Mirror bus' has a tangible appearance and refers to a type of bus covered with mirrors or reflective materials.\nThere are no things that are visually similar to 'mirror bus' but are not 'mirror bus'. The concept is quite specific and unique.\nUseful visual features for distinguishing 'mirror bus' in a photo are:\tthe presence of multiple mirrors or reflective surfaces covering the exterior of the bus; the bus looks shiny and reflective, the bus seems to emit different colors and shapes due to the surrounding environment reflecting on its surface.", 13], "wooden crates": ["Yes. 'Wooden crates' has a tangible appearance and is a type of storage container.\nA few things that are visually similar to 'wooden crates' but are not 'wooden crates' are:\tcardboard boxes\tbaskets\tbarrels\tsuitcases\nThere are several useful visual features to tell there is 'wooden crates' and not similar things in a photo:\tmade of wood or with a wooden texture\trectangular or square shape\twith slats or planks\tfor storing or transporting goods or items", 13], "dollar bill": ["Yes. 'Dollar bill' has a tangible appearance and is a type of currency.\nA few things that are visually similar to 'dollar bill' but are not 'dollar bill' are:\tother forms of currency (e.g. euro, yen)\tcoupons\tpaper receipts\nThere are several useful visual features to tell there is 'dollar bill' and not similar things in a photo:\tgreen and white-colored paper with black ink\tdepictions of historical figures (e.g. George Washington, Benjamin Franklin)\tthe phrases \u201cFederal Reserve Note\u201d and \u201cThe United States of America\u201d written on it.", 13], "street lines": ["Yes. 'Street lines' has a tangible appearance and is a kind of road marking.\nA few things that are visually similar to 'street lines' but are not 'street lines' are:\tshadows\tcracks\tinlaid patterns\tpaint splatters\nThere are several useful visual features to tell there are 'street lines' and not similar things in a photo:\tstraight or curved lines\twhite or yellow color\tplaced on a road or a parking lot", 13], "bare hand": ["Yes. 'Bare hand' has a tangible appearance and refers to a hand without any covering.\nA few things that are visually similar to 'bare hand' but are not 'bare hand' are:\tgloves\tmittens\tclaws\tbeaks\nThere are several useful visual features to tell there is 'bare hand' and not similar things in a photo:\tfingers\twith or without nail polish\tno visible fabric or material covering the skin", 13], "soccer shoes": ["Yes. 'Soccer shoes' has a tangible appearance and is a kind of footwear.\nA few things that are visually similar to 'soccer shoes' but are not 'soccer shoes' are:\tcleats\trunning shoes\ttraining shoes\nThere are several useful visual features to tell there is 'soccer shoes' and not similar things in a photo:\tstuds or cleats on the sole of the shoe\tlaces for fastening\tstraight or curved shape of the sole\tsignature colors and design for a specific soccer team or brand.", 13], "pizza half": ["Yes. 'Pizza half' has a tangible appearance and refers to a pizza that has been cut into half.\nA few things that are visually similar to 'pizza half' but are not 'pizza half' are:\ttart\tdessert\tcake\tsandwich\nThere are several useful visual features to tell there is 'pizza half' and not similar things in a photo:\tcircular shape\twith toppings and cheese\ton a round slicing board\tor on a plate\tcut in a straight line into two equal halves.", 13], "tomato soup": ["Yes. 'Tomato soup' has a tangible appearance and is a type of soup.\nA few things that are visually similar to 'tomato soup' but are not 'tomato soup' are:\tvegetable soup\tminestrone soup\tgazpacho\tsalsa\ttomato sauce\tblood\nThere are several useful visual features to tell there is 'tomato soup' and not similar things in a photo:\torange-red color\tcreamy texture with some chunks in it\tbowl-shaped container\tor a spoon around it", 13], "knacks": ["No. 'Knacks' is too vague or abstract to be distinguished in a photo.", 13], "arm bands": ["Yes. 'Arm bands' has a tangible appearance and refers to a specific type of accessory worn on the arm.\nA few things that are visually similar to 'arm bands' but are not 'arm bands' are:\twatch\tbracelet\tsweatband\nThere are several useful visual features to tell there is 'arm bands' and not similar things in a photo:\tworn on the upper arm (bicep area)\tsolid or striped design\toften worn for identification or decoration purposes", 13], "round logo": ["Yes. 'Round logo' has a tangible appearance and is a design element.\nA few things that are visually similar to 'round logo' but are not 'round logo' are:\tbutton\tbadge\ttoken\tcircle\timage mask\nThere are several useful visual features to tell there is 'round logo' and not similar things in a photo:\tcircular shape\toutlined or filled shapes and letters\tsymbol or emblem design\tuse of specific colors or patterns", 13], "coat rack": ["Yes. 'Coat rack' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'coat rack' but are not 'coat rack' are:\tumbrella stand\thanger/hooks\tshoe rack\nThere are several useful visual features to tell there is 'coat rack' and not similar things in a photo:\tvertical stand with several hooks\torbs on the hooks\thanging coats or jackets", 13], "bare arm": ["Yes. 'Bare arm' has a tangible appearance and is a body part.\nA few things that are visually similar to 'bare arm' but are not 'bare arm' are:\tclothed arm\tsculpture\tpillow\tfur coat\nThere are several useful visual features to tell there is 'bare arm' and not similar things in a photo:\tskin color\thair pattern\tmuscles and tendons", 13], "kitchen area": ["Yes. 'Kitchen area' has a tangible appearance and is a distinct part of a home or restaurant.\nA few things that are visually similar to 'kitchen area' but are not 'kitchen area' are:\tdining room\tarea outside of a building\tfood court\tin-store caf\u00e9\nThere are several useful visual features to tell there is 'kitchen area' and not similar things in a photo:\tkitchen tools and appliances like oven, stove, fridge, etc.\tcabinets and countertops\tsinks and faucets\tcooking utensils and pans\tdining table and chairs", 13], "glass bowls": ["Yes. 'Glass bowls' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'glass bowls' but are not 'glass bowls' are:\tglass cups\tvases\tjelly jars\tplates\ttupperware\nThere are several useful visual features to tell there is 'glass bowls' and not similar things in a photo:\tcurved edges\tclear or transparent material\tbowl shape\trecessed bottom\tfor holding food or other objects.", 13], "dark table": ["Yes. 'Dark table' has a tangible appearance and refers to a table that is dark in color.\nA few things that are visually similar to 'dark table' but are not 'dark table' are:\tblackboard\tmetal table with a dark finish\tshaded or dimly lit table\nThere are several useful visual features to tell there is 'dark table' and not similar things in a photo:\twooden table with a dark brown or black finish\tvisible table legs and surface\tthat table is not located in a shaded or dimly lit area", 13], "sideburn": ["Yes. 'Sideburn' has a tangible appearance and is a type of facial hairstyle.\nA few things that are visually similar to 'sideburn' but are not 'sideburn' are:\tbeard\tmoustache\thair\nThere are several useful visual features to tell there is 'sideburn' and not similar things in a photo:\thair grown on the temple and cheeks\tlong or short (depending on the style)\tseparate from other facial hair", 13], "tan pillow": ["Yes. 'Tan pillow' has a tangible appearance and is a type of pillow.\nA few things that are visually similar to 'tan pillow' but are not 'tan pillow' are:\ttan cushion\ttan carpet piece\ttan blanket\nThere are several useful visual features to tell there is 'tan pillow' and not similar things in a photo:\trectangular or square shaped\tsoft and fluffy\ttan or light brown color\tpillowcase covering the pillow.", 13], "storefront window": ["Yes. 'Storefront window' has a tangible appearance and is a kind of architectural element.\nA few things that are visually similar to 'storefront window' but are not 'storefront window' are:\thome window\tcar window\tshowcase\twindow display\nThere are several useful visual features to tell there is 'storefront window' and not similar things in a photo:\tlarge and tall\twindow may span much of the front of the store or building\tmostly transparent\twith products or mannequins visible through it", 13], "prices": ["No. 'Prices' are too vague or abstract to have a tangible appearance and cannot be visually represented on their own.\nThere are no things that are visually similar to 'prices' as it is an economic concept and not a physical object.\nVisual features do not apply to 'prices'.", 13], "bear head": ["Yes. 'Bear head' has a tangible appearance and is a body part of an animal.\nA few things that are visually similar to 'bear head' but are not 'bear head' are:\tcat head\tdog head\tlion head\tteddy bear head\nThere are several useful visual features to tell there is 'bear head' and not similar things in a photo:\tround ears\tfurry or hairy texture\tsharp claws and teeth\tsnout or muzzle\tprominent eyes", 13], "flood lights": ["Yes. 'Flood lights' has a tangible appearance and is a type of lighting equipment.\nA few things that are visually similar to 'flood lights' but are not 'flood lights' are:\tspotlights\tstreet lamps\tcar headlights\tlightning bolts\nThere are several useful visual features to tell there is 'flood lights' and not similar things in a photo: wide beam angle\tbright and powerful light sources\tmounted on a surface, often on a pole or wall\tdirected towards a specific area or object\tcan create strong shadows or contrast", 13], "sofa cushion": ["Yes. 'Sofa cushion' has a tangible appearance and refers to a pillow or pad that is placed on a sofa.\nA few things that are visually similar to 'sofa cushion' but are not 'sofa cushion' are:\tpillows\tbolster cushions\tfloor cushions\tpoufs\nThere are several useful visual features to tell there is 'sofa cushion' and not similar things in a photo:\trectangular or square shape\tcovered in fabric or leather\tsmaller in size compared to a typical bed pillow", 13], "creme": ["Yes. 'Creme' has a tangible appearance and is a type of creamy substance.\nA few things that are visually similar to 'creme' but are not 'creme' are:\tmilk\tyogurt\tpudding\nThere are several useful visual features to distinguish 'creme' from the listed similar things in a photo:\tthick and creamy consistency\twhite, off-white, or beige color\ttypically used as a topping or filling for desserts\ttends to hold its shape when spooned or piped", 13], "wooden spoon": ["Yes. 'Wooden spoon' has a tangible appearance and is a kitchen utensil.\nA few things that are visually similar to 'wooden spoon' but are not 'wooden spoon' are:\tmetal spoon\tplastic spoon\tspatula\twhisk\nThere are several useful visual features to tell there is 'wooden spoon' and not similar things in a photo:\tmade of wood or has a wooden handle\tspoon-shaped\thandheld\tused for stirring or serving food", 13], "paper tray": ["Yes. 'Paper tray' has a tangible appearance and is a type of tray used for holding paper.\nA few things that are visually similar to 'paper tray' but are not 'paper tray' are:\tfile folder holder\tmail tray\tinbox\toutbox\nThere are several useful visual features to tell there is 'paper tray' and not similar things in a photo:\tshallow rectangular or square shape\tmade of plastic or metal\tdesigned to hold paper or documents\tsome models include small compartments or dividers for organizing papers.", 13], "half slice": ["Yes. 'Half slice' has a tangible appearance and is a way to describe a cut made through an object, usually a food item.\nA few things that are visually similar to 'half slice' but are not 'half slice' are:\tquarter slice\twedge\tcube\nThere are several useful visual features to tell there is 'half slice' and not similar things in a photo:\tcut through the middle of the object\tequal two parts of the same size\tappearance of a flat surface on one side of the slice.", 13], "serving plate": ["Yes. 'Serving plate' has a tangible appearance and is a type of dishware used to serve food.\nA few things that are visually similar to 'serving plate' but are not 'serving plate' are:\tdinner plate\tplatter\tbowl\ttray\nThere are several useful visual features to tell there is 'serving plate' and not similar things in a photo:\tflat surface\twith raised edges\tor without raised edges\toval, round, or square shape\tpredominantly white, but can come in various colors and patterns", 13], "front grille": ["Yes. 'Front grille' has a tangible appearance and is a feature of a vehicle.\nA few things that are visually similar to 'front grille' but are not 'front grille' are:\tradiator\tshutters\tvents\texhaust pipe\nThere are several useful visual features to tell there is 'front grille' and not similar things in a photo:\tlocated at the front of the vehicle\thas a pattern or design\tmade of metal or plastic\tis part of the vehicle's aesthetic design\tmesh-like or lined with bars.", 13], "cylinders": ["Yes. 'Cylinders' has a tangible appearance and is a geometric figure.\nA few things that are visually similar to 'cylinders' but are not 'cylinders' are:\tcans\ttubes\twheels\tpipes\nThere are several useful visual features to tell there is 'cylinders' and not similar things in a photo:\tstraight sides in a circular shape\tsmooth surface\tcurved top and bottom edges", 13], "candlestick": ["Yes. 'Candlestick' has a tangible appearance and is typically made of metal, wood or ceramic that holds a taper or pillar candle.\nA few things that are visually similar to 'candlestick' but are not 'candlestick' are:\tlamp stand\tincense burner\tpaperweight\nThere are several useful visual features to tell there is 'candlestick' and not similar things in a photo:\t\n- A vertical stick or shaft with a base and a holder for a candle\n- The holder may have a cup-shaped form, spikes or clamps for securing the candle \n- The base may be round, square, or shaped like a tripod \n- Often used for adding atmosphere, ritual or illumination to an interior space", 13], "glass table top": ["Yes. 'Glass table top' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'glass table top' but are not 'glass table top' are:\tplexiglass surface\tmirror surface\twater surface\nThere are several useful visual features to tell there is 'glass table top' and not similar things in a photo:\tclear and transparent\tsitting on a table frame or legs\treflective surface when light shines on it\thard and sturdy\twhen touched, produces sounds of glass hitting the other object", 13], "mirror bathroom wall": ["Yes. 'Mirror bathroom wall' has a tangible appearance and is a type of wall that is covered in mirrors.\nA few things that are visually similar to 'mirror bathroom wall' but are not 'mirror bathroom wall' are:\tplain wall\twith wallpaper\twith tiles\twith pictures or decorations\t\nThere are several useful visual features to tell there is 'mirror bathroom wall' and not similar things in a photo:\tthe entire or a significant portion of the wall is covered in mirrors\tcan see reflections of objects or people in the mirrors\tthe mirrors may be framed or unframed\tthe mirrors may have lights or be backlit", 13], "transit": ["No. 'Transit' is too vague or abstract to be distinguished in a photo. However, if we talk about transit as a means of transportation, then it has a tangible appearance.\nA few things that are visually similar to 'transit' but are not 'transit' are:\tcar\tbicycle\ttruck\tbus\nThere are several useful visual features to tell there is 'transit' and not similar things in a photo:\tvehicle specifically designed for transporting people or goods\tfrom a transit agency or company used for public transportation\tcan be found on a specific route or schedule\twith a transit sign or logo\ton transit stops or stations", 13], "skateboard pavement": ["Yes. 'Skateboard pavement' has a tangible appearance and refers to the surface on which skateboarders practice their sport.\nA few things that are visually similar to 'skateboard pavement' but are not 'skateboard pavement' are:\tbike lane\trunning track\tsidewalk\nThere are several useful visual features to tell there is 'skateboard pavement' and not similar things in a photo:\tconcrete or asphalt surface\tsmooth or with little bumps and cracks\tcovered in scuff marks and scratches\tmay have ramps or other skateboard obstacles nearby.", 13], "tan table": ["Yes. 'Tan table' has a tangible appearance and refers to a table with a tan color.\nA few things that are visually similar to 'tan table' but are not 'tan table' are:\twhite table\tyellow table\tbeige table\tbrown table\nThere is only one useful visual feature to distinguish 'tan table' from the listed similar things in a photo:\ttan color", 13], "tan animal": ["Yes. 'Tan animal' has a tangible appearance and refers to an animal with a tan or light brown color.\nA few things that are visually similar to 'tan animal' but are not 'tan animal' are:\trocks\tdesert sand\tbeige fabric\nThere are several useful visual features to tell there is 'tan animal' and not similar things in a photo:\tliving organism\twith eyes, nose, mouth, and other facial features\tfur or hair covering the body\tfour or two limbs or wings, depending on the animal's species\tgeneral body shape and size", 13], "pesto": ["Yes. 'Pesto' has a tangible appearance and is a type of sauce.\nA few things that are visually similar to 'pesto' but are not 'pesto' are:\tguacamole\thummus\ttzatziki\t\nThere are several useful visual features to tell there is 'pesto' and not similar things in a photo:\tgreen color\tchunky texture\tmade with basil, pine nuts, garlic, olive oil, and cheese or nutritional yeast.", 13], "burnt": ["Yes. 'Burnt' has a tangible appearance and refers to something that has been charred or damaged by fire.\nA few things that are visually similar to 'burnt' but are not 'burnt' are:\tdirty\tweathered\taged\tdiscolored\nThere are several useful visual features to tell there is something 'burnt' and not similar things in a photo:\tblackened or darkened areas\tcharred or smoky appearance\tdistinctive burnt smell or odor\twarped or melted shape", 13], "baby blue sky": ["Yes. 'Baby blue sky' has a tangible appearance and refers to a specific shade of blue color that is associated with a clear sky.\nThere are no things that are visually similar to 'baby blue sky' but are not 'baby blue sky'.\nUseful visual features for identifying 'baby blue sky' in a photo include:\tlight shade of blue\tcolor similar to the sky on a clear day\tno clouds in the sky", 13], "trailer truck": ["Yes. 'Trailer truck' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'trailer truck' but are not 'trailer truck' are:\tpickup truck\tvan\tbus\ttractor\nThere are several useful visual features to tell there is 'trailer truck' and not similar things in a photo:\tlong and rectangular shape\ttwo parts: a tractor unit and a trailer\thuge size compared to other vehicles\ttrailer connected to the tractor by a hitch\tor a fifth-wheel coupling\tsystem for loading and unloading goods at the back of the trailer.", 13], "stainless steel trash": ["Yes, 'stainless steel trash' has a tangible appearance and refers to a type of waste receptacle made of stainless steel.\nA few things that are visually similar to 'stainless steel trash' but are not 'stainless steel trash' are:\tMetallic containers\tcans\tother waste containers\nThere are several useful visual features to tell there is 'stainless steel trash' and not similar things in a photo:\tsilver, reflective color\tcylinder or rectangular shape\tflap or open cover\tfor waste disposal or recycling purpose.", 13], "refelction": ["No. 'Reflection' is too abstract to have a tangible appearance.\nA few things that are visually similar to 'reflection' but are not 'reflection' are:\tshadows\tillusions\tmirages\twater or oil spills\nThere are no useful visual features to distinguish 'reflection' from the listed similar things in a photo, because that requires understanding context and physical properties beyond visual aspects.", 13], "home button": ["Yes. 'Home button' has a tangible appearance and is a physical button on certain electronic devices.\nA few things that are visually similar to 'home button' but are not 'home button' are:\tpower button\tvolume button\tfingerprint scanner\nThere are several useful visual features to tell there is a 'home button' and not similar things in a photo:\trectangular or circular shape\twith a small square or circle in the center\thas a 'home' symbol or icon\tlocated at the center or bottom of the device screen or frame", 13], "plaid skirt": ["Yes. 'Plaid skirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'plaid skirt' but are not 'plaid skirt' are:\tpants\tjeans\tmidi skirts\nThere are several useful visual features to tell there is 'plaid skirt' and not similar things in a photo:\tdistinctive criss-crossed plaid pattern\tbright and contrasting colors\ttypically made of wool or flannel fabric\tknee-length or shorter skirt", 13], "lobe": ["Yes. 'Lobe' has a tangible appearance and refers to a structure found in various parts of the body, such as the brain or lungs.\nA few things that are visually similar to 'lobe' but are not 'lobe' are: round-shaped fruits like an apple or orange or cellular reception icon.\nThere are several useful visual features to tell there is 'lobe' and not similar things in a photo:\tconvoluted, curved shape\tfound in anatomical structures (such as the brain, lungs, or liver)", 13], "sofa chair": ["Yes. 'Sofa chair' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'sofa chair' but are not 'sofa chair' are:\trecliner\tarmchair\tchaise lounge\tdining chair\nThere are several useful visual features to tell there is 'sofa chair' and not similar things in a photo:\tcushioned seat and backrest\tarmrests\ta frame to support the seat and backrest\ta relatively large size suitable for seating multiple people", 13], "hotel bathroom": ["Yes. 'Hotel bathroom' has a tangible appearance and is a specific kind of bathroom typically found in hotels.\nA few things that are visually similar to 'hotel bathroom' but are not 'hotel bathroom' are:\tresidential bathroom\tpublic restroom\tportable toilet\toffice toilet\nThere are several useful visual features to tell there is 'hotel bathroom' and not similar things in a photo:\tshower with a glass door\tbathtub with jets\tlarge mirror\tgranite countertops\tplush towels\tand toiletries.", 13], "blanket brown": ["No. 'Blanket brown' is too vague, it can refer to any blanket that is brown.\nA few things that are visually similar in color to 'blanket brown' are:\tsoil\tbark\tchocolate coffee\nThere are no useful visual features to distinguish 'blanket brown' from the listed similar things in a photo since there are no specific visual characteristics that define 'blanket brown'.", 13], "mic": ["Yes. 'Mic' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'mic' but are not 'mic' are:\tSpeaker\tHeadphone\tMegaphone\nThere are several useful visual features to tell there is 'mic' and not similar things in a photo:\tRod-shaped object\twith a mesh or grill at the end\tof small to medium size\twith a cable or wireless connectivity\theld by a person or mounted on a stand", 13], "silver circle": ["Yes. 'Silver circle' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'silver circle' but are not 'silver circle' are:\twhite circle\tgold circle\tplastic ring\tmoon\nThere are several useful visual features to tell there is 'silver circle' and not similar things in a photo:\tsilver color\tsmooth surface\tno visible texture or pattern round shape with no angles or corners", 13], "baseball uniform pants": ["Yes. 'Baseball uniform pants' has a tangible appearance and is a specific type of pants worn for playing baseball.\nA few things that are visually similar to 'baseball uniform pants' but are not 'baseball uniform pants' are:\tathletic pants\tjoggers\tsweatpants\nThere are several useful visual features to separate 'baseball uniform pants' from similar things in a photo:\t\nstripes on the side of the pants\nsolid color or color combination\nloose fit with elastic waistband", 13], "dragonfly": ["Yes. 'Dragonfly' has a tangible appearance and is a type of insect.\nA few things that are visually similar to 'dragonfly' but are not 'dragonfly' are:\tdamselfly\tmosquito\tbee\t\nThere are several useful visual features to tell there is 'dragonfly' and not similar things in a photo:\tbig and elongated wings\tbig compound eyes\tthick and short body\tshimmering colors (greens, blues, reds, golds)\tsmall antennae\tno stinger or long proboscis (like mosquitoes and bees)", 13], "wagon wheels": ["Yes. 'Wagon wheels' has a tangible appearance and is a kind of wheel.\nA few things that are visually similar to 'wagon wheels' but are not 'wagon wheels' are:\tbicycle wheels\tmotorcycle wheels\tcar wheels\tlarge clocks\nThere are several useful visual features to tell there is 'wagon wheels' and not similar things in a photo:\tspoked wheels\twooden or metal material\tlarge size and thickness\trustic or vintage appearance\tmounted on an old-fashioned wagon or cart", 13], "pick-up": ["Yes. 'Pick-up' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'pick-up' but are not 'pick-up' are:\tvan\ttruck\tsuv\tjeep\nThere are several useful visual features to tell there is 'pick-up' and not similar things in a photo:\topen cargo area behind passenger compartment\ttwo doors\tcab seats only two or three people\ttaller profile than a passenger car or van\tlower than most trucks and SUVs\tbulky and square-shaped build.", 13], "floor boards": ["Yes. 'Floor boards' has a tangible appearance and refers to the wooden boards used in a flooring structure.\nA few things that are visually similar to 'floor boards' but are not 'floor boards' are:\tplanks\tpanels\tboards\nThere are several useful visual features to tell there is 'floor boards' and not similar things in a photo:\tused to cover the flooring surface\tsupported by floor joists\tusually wood, but can be made of other materials\tspecific patterns, such as tongue-and-groove or parquet", 13], "tennis uniform": ["Yes. 'Tennis uniform' has a tangible appearance and is a specific type of sports clothing.\nA few things that are visually similar to 'tennis uniform' but are not 'tennis uniform' are:\tathletic wear\tyoga clothes\tswimwear\tcycling outfits\nThere are several useful visual features to tell there is 'tennis uniform' and not similar things in a photo:\t\ncollared or crew-neck shirts\t\nshort or long athletic shorts\t\nskirts or dresses\t\nlight-colored or white attire\t\nclear branding or logos of tennis players or brands", 13], "cobblestone walkway": ["Yes. 'Cobblestone walkway' has a tangible appearance and is a type of pathway.\nA few things that are visually similar to 'cobblestone walkway' but are not 'cobblestone walkway' are:\ttile pathway\tconcrete pathway\tgravel pathway\nThere are several useful visual features to tell there is 'cobblestone walkway' and not similar things in a photo:\tirregularly shaped stones\tsmooth, rounded surface\tvariety of stone colors and sizes\tunique patterns or designs", 13], "propeller plane": ["Yes. 'Propeller plane' has a tangible appearance and refers to a specific type of airplane that has one or more propellers powered by an engine.\nA few things that are visually similar to 'propeller plane' but are not 'propeller plane' are:\tjet plane\thelicopter\tglider\nThere are several useful visual features to tell there is 'propeller plane' and not similar things in a photo:\tone or more propellers attached to the engine\tfixed wings\ta fuselage (body of the plane) with a cockpit and passenger or cargo area\tpropellers located at the front of the plane (in some cases)", 13], "wiener": ["Yes. 'Wiener' has a tangible appearance and refers to a type of sausage or hot dog.\nA few things that are visually similar to 'wiener' but are not 'wiener' are:\tsausage\tbratwurst\tchorizo\tpepperoni\tjerky\nThere are several useful visual features to tell there is 'wiener' and not similar things in a photo:\ttubular shape\tsmooth texture\tpinkish-red color\tusually served on a bun or with condiments like ketchup or mustard.", 13], "orange skateboard": ["Yes. 'Orange skateboard' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'orange skateboard' but are not 'orange skateboard' are:\torange roller skates\tscooter\tlongboard\nThere are several useful visual features to tell there is 'orange skateboard' and not similar things in a photo:\trectangular shape\twheeled board on the bottom\tbright orange color", 13], "door knobs": ["Yes. 'Door knobs' has a tangible appearance and are used to open or close doors.\nA few things that are visually similar to 'door knobs' but are not 'door knobs' are:\tdrawer knobs\tcabinet pulls\tdecorative buttons\ton/off switches\nThere are several useful visual features to tell there is 'door knobs' and not similar things in a photo:\tcircular or spherical shape\tprotruding from a door\tusually made of metal, plastic, or glass\tmay have a lock mechanism on the knob or a keyhole nearby", 13], "background wall": ["Yes. 'Background wall' has a tangible appearance and refers to the wall that serves as the background in an image or a video.\nA few things that are visually similar to 'background wall' but are not 'background wall' are:\tpart of a building\tfloor\tanother wall\t\nThere are several useful visual features to tell there is a 'background wall' and not similar things in a photo:\tbehind the main subject or object in the photo\tsmooth and even surface\tsolid color or patterned design", 13], "rapids": ["Yes. 'Rapids' has a tangible appearance and is a term used to describe rough and fast-moving water.\nA few things that are visually similar to 'rapids' but are not 'rapids' are:\tmuddy water\tfalls\twhirlpools\t\nThere are several useful visual features to tell there are 'rapids' and not similar things in a photo:\trough and choppy water surface\twhite foamy water\tswift, fast-moving water\trocky or uneven river bed.", 13], "imprint": ["No, 'imprint' is too abstract to be visually concrete.\nHowever, some things that are visually similar to 'imprint' but are not 'imprint' are:\t\nshadow\t\t\noutline\t\t\nengraving\t\t\nindentation\t\t\ndepression\n\nSome useful visual features to distinguish 'imprint' from the listed similar things in a photo might be:\n- Visible texture or pattern on the surface that made the imprint\n- Three-dimensional appearance, with raised and recessed parts\n- The imprint should resemble the shape or design of the object that made it\n- The imprint should be of the same material as the object that made it.", 13], "brake handle": ["Yes. 'Brake handle' has a tangible appearance and is a component of a vehicle.\nA few things that are visually similar to 'brake handle' but are not 'brake handle' are:\tgear shift\tsteering wheel\tdoor handle\nThere are several useful visual features to tell there is 'brake handle' and not similar things in a photo:\tlever or rod-shaped component\tfound near the driver's seat or near the center console\thas a symbol of a foot on it (often red in color)\tis used to control the vehicle's brakes", 13], "balding man": ["Yes. 'Balding man' has a tangible appearance and is a type of male with hair loss.\nA few things that are visually similar to 'balding man' but are not 'balding man' are:\tman with very short hair\tman with a shaved head\tman with a receding hairline\tman with a ponytail\tman with a hat\nThere are several useful visual features to tell there is 'balding man' and not similar things in a photo:\thair only on the sides or back of their head\tthinning hair on top of the head\tpatches or completely hairless spots on top of the head", 13], "horse bridle": ["Yes. 'Horse bridle' has a tangible appearance and is a piece of equipment used on horses for riding or driving.\nA few things that are visually similar to 'horse bridle' but are not 'horse bridle' are:\tharness\tsaddle\treins\tlead rope\nThere are several useful visual features to tell there is 'horse bridle' and not similar things in a photo:\tmade of leather or other sturdy material\tconsists of a headstall, bit, and reins\tfits around the horse's head and attaches to the bit", 13], "bright sky": ["Yes. 'Bright sky' has a tangible appearance and is a bright blue or white sky.\nA few things that are visually similar to 'bright sky' but are not 'bright sky' are:\tdark sky\tcloudy sky\tsunrise/sunset\tsky at night\tfog\nThere are several useful visual features to tell there is 'bright sky' and not similar things in a photo:\tbright blue or white color\tno clouds or very few clouds\tbright light or sunshine in the photo\tdaytime photo\tno stars or other celestial objects visible", 13], "metal light": ["Yes. 'Metal light' has a tangible appearance and is a type of lighting fixture made of metal.\nA few things that are visually similar to 'metal light' but are not 'metal light' are:\tcandle holder\twooden lamp\tpost lamp\tcrystal lamp\nThere are several useful visual features to tell there is 'metal light' and not similar things in a photo:\tmade of metal\tbright lights or colors\tsleek or industrial design\thanging from a ceiling or mounted on a wall", 13], "boxcars": ["Yes. 'Boxcars' has a tangible appearance and is a type of train car.\nA few things that are visually similar to 'boxcars' but are not 'boxcars' are:\thopper cars\ttank cars\tlocomotives\tpassenger cars\nThere are several useful visual features to tell there is 'boxcars' and not similar things in a photo:\trectangular shape (long and narrow)\twith a flat roof and sliding doors\ton a train track or being pulled by a locomotive\tusually brown or rust-colored.", 13], "frame wall": ["Yes. 'Frame wall' has a tangible appearance and refers to a wall decorated with picture frames or other wall decor items.\nA few things that are visually similar to 'frame wall' but are not 'frame wall' are:\tpaintings or artworks on a wall, wallpaper, decorative panels\nThere are several useful visual features to tell there is a 'frame wall' and not similar things in a photo:\tevenly spaced picture frames or decor items on the wall\ta variety of sizes, colors or styles of frames or decor items\tarrangement of frames or decor items for a cohesive and intentional look", 13], "neck giraffe": ["No. 'Neck giraffe' is not a common concept or term. If you meant 'giraffe neck', then the answer would be yes.\nA few things that are visually similar to 'giraffe neck' but are not 'giraffe neck' are:\tlong necks on other animals, such as swans or flamingos\thuman necks in certain positions, such as seen in contortionism or yoga poses.\nThere are several useful visual features to tell there is 'giraffe neck' and not similar things in a photo:\tvery long neck\twith spots on it\tconnected to a large body and legs, similar to a giraffe's body and legs.", 13], "airplane runway": ["Yes. 'Airplane runway' has a tangible appearance and is a specific type of surface.\nA few things that are visually similar to 'airplane runway' but are not 'airplane runway' are:\troad\tparking lot\ttaxiway\twalkway\t\nThere are several useful visual features to tell there is 'airplane runway' and not similar things in a photo:\tlong and straight\tspecial markings and signs for airplane landing and takeoff\tconcrete or asphalt surface surrounded by open space\tsometimes with landing lights", 13], "pepper mill": ["Yes. 'Pepper mill' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'pepper mill' but are not 'pepper mill' are: salt shaker\tcoffee grinder\tspice jar\nThere are several useful visual features to tell there is 'pepper mill' and not similar things in a photo:\ttall and cylindrical shape\twith a twisting or turning mechanism\tfor grinding pepper or other spices\thas a handle\tfor grinding with hands\tor with electronic or manual grinding functions", 13], "banana skin": ["Yes. 'Banana skin' has a tangible appearance and is a peel of a banana.\nA few things that are visually similar to 'banana skin' but are not 'banana skin' are:\torange peel\tgrapefruit peel\tpotato skin\nThere are several useful visual features to tell there is 'banana skin' and not similar things in a photo:\tyellow color\tslightly curved shape\twith visible brown spots or blemishes\tfibrous texture with a slightly slimy surface-covered in faint lines", 13], "cute girl": ["No. 'Cute girl' is too vague or abstract to be distinguished in a photo. It can be a subjective perception that varies from person to person. \n\nAsking for similar things can be seen as inappropriate, so I will not answer the second and the third questions.", 13], "bedroom window": ["Yes. 'Bedroom window' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'bedroom window' but are not 'bedroom window' are:\toffice window\tliving room window\tkitchen window\tbathroom window\nThere are several useful visual features to tell there is 'bedroom window' and not similar things in a photo:\twindowsill\theight and width that fits the size of a bed\tcurtains or blinds\tdifferent view than other types of windows in the photo", 13], "sombrero": ["Yes. 'Sombrero' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'sombrero' but are not 'sombrero' are:\tcowboy hat\tfedora\tstraw hat\nThere are several useful visual features to tell there is 'sombrero' and not similar things in a photo:\tlarge brim\twide shape pointed top\tdecorative details, such as patterns, embroidery, or pom-poms\ttypically made of felt or woven materials in bright colors like red, yellow, and blue.", 13], "plain donut": ["Yes. 'Plain donut' has a tangible appearance and is a kind of pastry.\nA few things that are visually similar to 'plain donut' but are not 'plain donut' are:\tbagel\tbun\tbeignet\tchurro\tpretzel\nThere are several useful visual features to tell there is 'plain donut' and not similar things in a photo:\tcircular shape\twith a hole in the center\tsmooth surface\twithout fillings or toppings\tlight or pale color", 13], "joystick": ["Yes. 'Joystick' has a tangible appearance and is a type of input device used for video games.\nA few things that are visually similar to 'joystick' but are not 'joystick' are:\tmouse\ttrackpad\ttrackball\tgamepad\nThere are several useful visual features to tell there is 'joystick' and not similar things in a photo:\tupright stick with a control pad or button on top\tan optional trigger or fire button on the side or base\tdesigned for handheld use or attachment to a console or computer\tuse of directional movements to control on-screen action\toriented towards gaming or flight simulation\tuse of a single stick rather than a separate stick and buttons.", 13], "web cam": ["Yes. 'Web cam' has a tangible appearance and is a type of camera.\nA few things that are visually similar to 'web cam' but are not 'web cam' are:\tsecurity camera\tdigital camera\taction camera\nThere are several useful visual features to tell there is 'web cam' and not similar things in a photo:\tsmall size\tattached to a computer or screen\twebcam indicator light\tnarrow focus and angle of view\tMicrophone and a cord to connect to the computer or other device.", 13], "thick mane": ["Yes. 'Thick mane' has a tangible appearance and is a type of animal feature.\nA few things that are visually similar to 'thick mane' but are not 'thick mane' are:\thair wig\tbushy eyebrows\tfur coat\nThere are several useful visual features to tell there is 'thick mane' and not similar things in a photo:\tlarge amount of hair around the neck area\tlayers of hair giving the appearance of thickness\tWavy or curly texture\tLight reflecting on strands of hair", 13], "twine": ["Yes. 'Twine' has a tangible appearance and is a type of string made of twisted strands.\nA few things that are visually similar to 'twine' but are not 'twine' are:\tstring\tthread\twire\nThere are several useful visual features to tell there is 'twine' and not similar things in a photo:\tthick and coarse\tusually made of natural fibers like cotton or jute\tmultiple strands twisted together\tused for tying, wrapping or binding things", 13], "downtown": ["Yes. 'Downtown' has a visually concrete concept as it refers to the central and usually the busiest area of a city or town.\nA few things that are visually similar to 'downtown' but are not 'downtown' are:\tsuburbs\tresidential areas\tshopping malls\tindustrial parks\nThere are several useful visual features to tell there is 'downtown' and not similar things in a photo:\thigh-rise buildings\tcrowded streets and sidewalks\tbustling business and commercial activities\tsigns and symbols associated with city life such as streetlights, traffic lights, and street signs.", 13], "braclet": ["Yes. 'Bracelet' has a tangible appearance and is a type of jewelry worn around the wrist.\nA few things that are visually similar to 'bracelet' but are not 'bracelet' are:\twatch\tbangle\tcuff\nThere are several useful visual features to tell there is 'bracelet' and not similar things in a photo:\tlooped around the wrist\tjeweled or beaded design\tflexible or rigid band\thinged or clasp closure", 13], "gravel train tracks": ["Yes. 'Gravel train tracks' has a tangible appearance and refers to the tracks on which trains run, covered with gravel or ballast.\nA few things that are visually similar to 'gravel train tracks' but are not 'gravel train tracks' are:\tpaved roads\tbike trails\tfarm roads\tconstruction site tracks\nThere are several useful visual features to tell there is 'gravel train tracks' and not similar things in a photo:\tlong metal rails parallel to each other\tcovered in small, rough stones or gravels\ttracks are elevated above ground level\tor tracks cut into the ground\twith wooden, concrete or metal sleepers", 13], "tv remote control": ["Yes. 'TV remote control' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'TV remote control' but are not 'TV remote control' are:\tMusic player remote control\tAir conditioner remote control\tCar key\tMobile phone\nThere are several useful visual features to tell there is 'TV remote control' and not similar things in a photo: Small and handheld device\tSeveral buttons\tFlat panel with screen or display buttons\tInfrared emitter at one end.", 13], "metal kickstand": ["Yes. 'Metal kickstand' has a tangible appearance and is a kind of support used for bicycles or other lightweight vehicles.\nA few things that are visually similar to 'metal kickstand' but are not 'metal kickstand' are:\thairpin\ttriangle\truler\nThere are several useful visual features to tell there is 'metal kickstand' and not similar things in a photo:\tMetallic appearance\t'X' shape or 'L' shape support\tAttached on the side or the rear of a bicycle or any lightweight vehicle", 13], "wrench": ["Yes. 'Wrench' has a tangible appearance and is a type of tool.\nA few things that are visually similar to 'wrench' but are not 'wrench' are:\tpliers\thandcuffs\tspanner\t\t\nThere are several useful visual features to tell there is 'wrench' and not similar things in a photo:\tshape of a \"J\" or \"L\"\twith a movable \"jaw\" or \"head\"\tfor gripping and turning nuts and bolts.", 13], "denim blue jeans": ["Yes. 'Denim blue jeans' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'denim blue jeans' but are not 'denim blue jeans' are:\tsweatpants\tskirts\ttights\ttrousers\nThere are useful visual features to tell there is 'denim blue jeans' and not similar things in a photo:\ttypically made of blue denim fabric\twith a visible seam running down the front and back of the legs\thave front and back pockets\ttypically fastened with a button and zipper\tat ankle length or longer.", 13], "city buses": ["Yes. 'City buses' has a tangible appearance and refers to a type of public transportation.\nA few things that are visually similar to 'city buses' but are not 'city buses' are:\ttrucks\tcoaches\tvans\nThere are several useful visual features to tell there is 'city buses' and not similar things in a photo:\tdistinctive color scheme and insignia\tsigns indicating the route and destination\tlarge size\twith doors on the side for boarding and exiting passengers\tseats and handrails for passengers inside the bus", 13], "pottery vase": ["Yes. 'Pottery vase' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'pottery vase' but are not 'pottery vase' are:\tdecorative glass bowls\twater bottles\tcoffee mugs\tflower pots\nThere are several useful visual features to tell there is 'pottery vase' and not similar things in a photo:\thollow cylindrical shaped container\tmade of clay or ceramic material\twith or without a handle and a spout\tdecorated with glaze or paint arrangements.", 13], "wool cap": ["Yes. 'Wool cap' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'wool cap' but are not 'wool cap' are:\tbeanie\tskullcap\tfedora\tbonnet\nThere are several useful visual features to tell there is 'wool cap' and not similar things in a photo:\tfabric made of wool or wool blend\tclosely fitting the head\twith or without a brim or visor\tmost commonly seen in winter or cold weather wardrobe", 13], "award": ["No. 'Award' is too vague or abstract to be distinguished in a photo.", 13], "paper tag": ["Yes. 'Paper tag' has a tangible appearance and is a piece of paper attached to an object for identification.\nA few things that are visually similar to 'paper tag' but are not 'paper tag' are:\tsticker\tlabel\tsign\tpost-it note\nThere are several useful visual features to tell there is 'paper tag' and not similar things in a photo:\trectangular or square shape\thanging or attached to an object\tpaper material\twith text or handwriting for identification", 13], "passenger side window": ["Yes. 'Passenger side window' has a tangible appearance and is a specific part of a car.\nA few things that are visually similar to 'passenger side window' but are not 'passenger side window' are:\tdriver side window\trear windshield\trearview mirror\nThere are several useful visual features to tell there is 'passenger side window' and not similar things in a photo:\tlocated on the right side of the car\tparallel to the passenger seat\tusually rectangular or trapezoidal in shape\tcan be rolled up or down by the passenger", 13], "cantaloupe": ["Yes. 'Cantaloupe' has a tangible appearance and is a type of melon.\nA few things that are visually similar to 'cantaloupe' but are not 'cantaloupe' are:\twatermelon\thoneydew\torange\tpumpkin\tbasketball\nThere are several useful visual features to tell there is 'cantaloupe' and not similar things in a photo:\tround or oval shape\tribbed or netted exterior\tskin color ranges from yellow-orange to tan\tflesh is orange or salmon in color\tcenter is filled with seeds", 13], "carnations": ["Yes. 'Carnations' has a tangible appearance and is a type of flowering plant.\nA few things that are visually similar to 'carnations' but are not 'carnations' are:\troses\ttulips\tdaisies\tpeonies\t\nThere are several useful visual features to tell there is 'carnations' and not similar things in a photo:\tflowers with ruffled petals\tpink, red, white, or yellow\tpetals that are curved at the tip\tlong, narrow, and serrated leaves", 13], "construction cone": ["Yes. 'Construction cone' has a tangible appearance and is a kind of traffic control device.\nA few things that are visually similar to 'construction cone' but are not 'construction cone' are:\tpylon\tcone-shaped planter\ttraffic barrel\tumbrella\nThere are several useful visual features to tell there is 'construction cone' and not similar things in a photo:\tbright orange color\tcone-shaped\twith a white or reflective strip at the top\tused to mark a work zone or a hazard zone\ton or near a roadway or construction site.", 13], "chicken salad": ["Yes. 'Chicken salad' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'chicken salad' but are not 'chicken salad' are:\ttuna salad\tegg salad\tpotato salad\tcrab salad\tshrimp salad\nThere are several useful visual features to tell there is 'chicken salad' and not similar things in a photo:\tshredded or chunky cooked chicken\tmixed with vegetables, such as celery and onion, and mayonnaise or dressing\tas a sandwich filling or a salad topping.", 13], "gnome": ["Yes. 'Gnome' has a tangible appearance and is a small, human-like mythical creature.\nA few things that are visually similar to 'gnome' but are not 'gnome' are:\tdwarf\telf\timp\tmermaid\nThere are several useful visual features to tell there is 'gnome' and not similar things in a photo:\tshort and chubby with a big head\tpointy hat\tbeard\trosey cheeks\twearing a colorful outfit\tcarrying a tool or object (such as a fishing rod, mushroom, or lantern)", 13], "snow ramp": ["Yes. 'Snow ramp' has a tangible appearance and is a type of structure made of snow.\nA few things that are visually similar to 'snow ramp' but are not 'snow ramp' are:\thill\tmountain\twith a white roof\nThere are several useful visual features to tell there is 'snow ramp' and not similar things in a photo:\ttapering shape\tmade of packed snow or ice\tcan be used to jump or slide\ton a flat surface or slope", 13], "onion slice": ["Yes. 'Onion slice' has a tangible appearance and is a common culinary ingredient.\nA few things that are visually similar to 'onion slice' but are not 'onion slice' are:\tgarlic slice\tshallot slice\ttomato slice\tpotato slice\nThere are several useful visual features to tell there is 'onion slice' and not similar things in a photo:\tcircular\tbrownish-white or brown color\trings or layers\tfibrous texture\tpungent aroma that associates with onions", 13], "body water": ["Yes, 'body water' has a visually concrete concept and refers to the water present in the cells, tissues, and organs in the human body. \nA few things that are visually similar to 'body water' but are not 'body water' are:\ttap water, sea water, swimming pool, lake water\nThere are several useful visual features to tell there is 'body water' and not similar things in a photo:\tpresent in human body\tcontained in cells, tissues, and organs\tnot a large body of water\ttypically clear or colorless", 13], "orange collar": ["Yes. 'Orange collar' has a tangible appearance and is a type of clothing accessory for pets.\nA few things that are visually similar to 'orange collar' but are not 'orange collar' are:\torange scarf\torange tie\torange necklace\torange belt\nThere are several useful visual features to tell there is 'orange collar' and not similar things in a photo:\tworn around the neck\tdifferent sizes (for different pet breeds)\tbuckle or clasp to fasten on the pet's neck\tcolor, specifically the orange hue", 13], "round orange fruit": ["Yes. 'Round orange fruit' has a tangible appearance.\nA few things that are visually similar to 'round orange fruit' but are not 'round orange fruit' are:\torange\ttangerine\tgrapefruit\tpersimmon\nThere are several useful visual features to tell there is 'round orange fruit' and not similar things in a photo:\torange color\tsmooth or textured skin\twith or without small round protrusions\tstem and sometimes a leaf at the top\tdimple or navel at the bottom", 13], "marshmallow": ["Yes. 'Marshmallow' has a tangible appearance and is a type of candy.\n\nA few things that are visually similar to 'marshmallow' but are not 'marshmallow' are:\tsponges, bread, and cakes.\n\nThere are several useful visual features to tell there is 'marshmallow' and not similar things in a photo: \n\n- Cylindrical, spherical, or cuboid shape\n- Soft and squishy texture\n- Usually white, but can be dyed with other colors\n- Toasted or melted appearance when heated.", 13], "tile walls": ["Yes. 'Tile walls' has a tangible appearance and refers to walls covered in tiles.\nA few things that are visually similar to 'tile walls' but are not 'tile walls' are:\tpainted walls\ttextured walls\twalls covered in wallpaper\tbrick walls\nThere are several useful visual features to tell there is 'tile walls' and not similar things in a photo:\tsmall, usually square-shaped tiles\tregular patterns or grids\tgrout lines between the tiles\tsmooth and glossy surface", 13], "sunvisor": ["Yes, 'sunvisor' has a tangible appearance and is a type of hat brim.\nA few things that are visually similar to 'sunvisor' but are not 'sunvisor' are:\tbaseball cap\tbrimmed hat\tbeanie\tvisor\nThere are several useful visual features to tell there is 'sunvisor' and not similar things in a photo:\t\ncurved or straight rigid brim\ntype of hat that does not fully cover the head\nintended to block or reduce glare from the sun", 13], "scraps": ["Yes. 'Scraps' has a tangible appearance and refers to small leftover pieces of material or food.\nA few things that are visually similar to 'scraps' but are not 'scraps' are:\tregular-sized material pieces\tthat are not leftovers\tbroken pieces of items\nThere are several useful visual features to tell there are 'scraps' and not similar things in a photo:\tsmall sizes irregularr shapes\tmixed colors and textures\ta pile or container designated for leftovers or unused pieces.", 13], "metal platform": ["Yes. 'Metal platform' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'metal platform' but are not 'metal platform' are:\tmetal grate\tmetal fence\tmetal bridge\tmetal bench\nThere are several useful visual features to tell there is 'metal platform' and not similar things in a photo:\traised structure for people or objects\ttoe kick around its perimeter\tgenerally flat surface\tmade of metal, often painted or rusted\tsupport beams or legs underneath\tit is not suspended by cables or ropes", 13], "auditorium": ["Yes. 'Auditorium' has a tangible appearance and is a type of room.\nA few things that are visually similar to 'auditorium' but are not 'auditorium' are:\tmovie theater\tconcert hall\tconference room\tlecture hall\nThere are several useful visual features to tell there is 'auditorium' and not similar things in a photo:\tsloped or tiered seating\tarrangement of seats or chairs\tstage or podium\tforward-facing seats\torienteering towards a performing area or a speaker.", 13], "u.s": ["No. 'U.S' is too vague or abstract to be distinguished in a photo.", 13], "plastic batting helmet": ["Yes. 'Plastic batting helmet' has a tangible appearance and is a type of protective headgear worn in baseball.\nA few things that are visually similar to 'plastic batting helmet' but are not 'plastic batting helmet' are:\tfootball helmet\tbicycle helmet\tmotorcycle helmet\thard hat\nThere are several useful visual features to tell there is 'plastic batting helmet' and not similar things in a photo:\tround and dome-shaped\thead opening with a chinstrap\tearflaps\tventilation holes\tor team logos and colors", 13], "motorcycle exhaust pipe": ["Yes. 'Motorcycle exhaust pipe' has a tangible appearance and is a component of a motorcycle.\nA few things that are visually similar to 'motorcycle exhaust pipe' but are not 'motorcycle exhaust pipe' are:\tcar exhaust pipe\tindustrial pipe\tsmokestack\nThere are several useful visual features to tell there is 'motorcycle exhaust pipe' and not similar things in a photo:\tconnected to the motorcycle engine\tcylindrical shape\tmetallic appearance\ttailpipe at the end\tof the pipe", 13], "switch panel": ["Yes. 'Switch panel' has a tangible appearance and is a type of electrical control panel.\nA few things that are visually similar to 'switch panel' but are not 'switch panel' are:\tAC remote control\ttv remote\tcontrol panel of a machine\televator control panel\nThere are several useful visual features to tell there is 'switch panel' and not similar things in a photo:\tmultiple switches or buttons\tmodern-looking appearance\tmounted on a wall or a control panel\tbox-shaped\twith indicators or labels next to each switch or button", 13], "airlines logo": ["Yes. 'Airlines logo' has a tangible appearance.\nA few things that are visually similar to 'airlines logo' but are not 'airlines logo' are:\tbrand logo\tsports team logo\temblem\tbadge\tinsignia\nThere are several useful visual features to tell there is 'airlines logo' and not similar things in a photo:\t\n- The logo should be specific to an airline and not another company or organization\n- The logo may include the name or initials of the airline\n- The logo may include an airplane or other aviation-related symbols\n- The logo will often include specific colors associated with the airline", 13], "laying": ["No. 'Laying' is too vague or abstract to be visually identified in a photograph. Laying refers to the action of placing oneself flat on a surface. It does not have a physical appearance or characteristics that can be visually perceived.", 13], "messy": ["Yes. 'Messy' has a visually concrete appearance that can be identified in a photo.\nA few things that are visually similar to 'messy' but are not 'messy' are:\torganized\tdisarranged\tneatiness\tclutter-free\nThere are several useful visual features to tell there is 'messy' and not similar things in a photo:\tdisorderly\tor disorganized objects\tor collection of objects scattered\tnot visually appealing\tor balanced\tmultiple objects on top of each other \tor covering one another.", 13], "jet airliner": ["Yes. 'Jet airliner' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'jet airliner' but are not 'jet airliner' are:\thelicopters\tprivate planes\tballoons\tairships\nThere are several useful visual features to tell there is 'jet airliner' and not similar things in a photo:\ttwin-engine (or more)\tairliner shape\twith wings and a tail section\twith windows in rows of seats", 13], "chords": ["No. 'Chords' is too vague or abstract to be distinguished in a photo. \n\nNote: While there are visual representations of chords in sheet music, the concept of chords itself is more related to music theory and not a tangible object that can be visually distinguished in a photo.", 13], "tummy": ["Yes. 'Tummy' has a tangible appearance and refers to the region of the body below the chest and above the pelvis.\nA few things that are visually similar to 'tummy' but are not 'tummy' are:\tchest\tpelvis\tthighs\tbuttocks\nThere are several useful visual features to tell there is 'tummy' and not similar things in a photo:\tround or slightly protruding area of the abdomen\tbelow the chest and above the pelvis", 13], "highway signs": ["Yes. 'Highway signs' has a tangible appearance and is a type of signage used on roads and highways.\nA few things that are visually similar to 'highway signs' but are not 'highway signs' are:\tbillboards\tstore signs\tdirection signs\nThere are several useful visual features to tell there is 'highway signs' and not similar things in a photo:\trectangle-shaped\tbackground color is green or blue or white\twith written information or symbols\tspecifying speed limits, directions, distances, or road hazards\tplaced along roads and highways", 13], "water spout": ["Yes. 'Water spout' has a tangible appearance and is a kind of natural phenomenon.\nA few things that are visually similar to 'water spout' but are not 'water spout' are:\tgeyser\twaterfall\twhirlpool\tfoam pile\nThere are several useful visual features to tell there is 'water spout' and not similar things in a photo:\tvertical column of rotating water\toccurring over a body of water or in a cloud\tnarrower at the base and wider at the top\treaching from the surface of the water to the sky or the base of a cloud", 13], "chrome bumper": ["Yes. 'Chrome bumper' has a tangible appearance and is a kind of car part.\nA few things that are visually similar to 'chrome bumper' but are not 'chrome bumper' are:\tlicense plate\tradiator grille\tbody paint\nThere are several useful visual features to tell there is 'chrome bumper' and not similar things in a photo:\tshiny surface\tmetal material\thorizontal or curved shape\tfront or rear location on the vehicle", 13], "tiara": ["Yes. 'Tiara' has a tangible appearance and is a kind of headpiece.\nA few things that are visually similar to 'tiara' but are not 'tiara' are:\tcrown\theadband\thair accessory\nThere are several useful visual features to tell there is 'tiara' and not similar things in a photo:\tjeweled or adorned with crystals\tor beads\tworn at the front of the head or forehead\twith a tapered or triangular shape\tof a more delicate and ornate design than other headpieces.", 13], "sidewalk people": ["Yes. 'Sidewalk people' has a tangible appearance and refers to people who are walking on a sidewalk.\nA few things that are visually similar to 'sidewalk people' but are not 'sidewalk people' are:\tpeople walking on a street\tpeople walking in a park\tpeople walking in a building\tpeople walking on a trail\nThere are several useful visual features to tell there are 'sidewalk people' and not similar things in a photo:\t\nsidewalk beneath the people\t\nbuildings or stores near the sidewalk\t\ncurbs or gutters on the side of the sidewalk\t\npossible presence of street signs, light poles or other pedestrian infrastructure", 13], "orange nose": ["Yes. 'Orange nose' has a tangible appearance and is a type of nose with an orange color.\nThere are no things that are visually similar to 'orange nose' but are not 'orange nose'.\nUseful visual features for distinguishing 'orange nose' in a photo are:\t\n- The nose is prominently orange in color\n- The orange color covers a significant portion of the nose\n- The nose has a typical shape and size of a nose", 13], "steel handle": ["Yes. 'Steel handle' has a tangible appearance and refers to a type of object.\nA few things that are visually similar to 'steel handle' but are not 'steel handle' are:\tknob\thook\tpull\ttab\t\nThere are several useful visual features to tell there is 'steel handle' and not similar things in a photo:\tmade of steel or metal\tattached to a door, drawer, or cabinet\tfor pulling, grasping, or turning", 13], "man glasses": ["No. 'man glasses' is too vague or abstract to be distinguished in a photo. The term \"man glasses\" could refer to any glasses worn by a man, regardless of its style, shape, or color.\nHowever, a few things that are visually similar to certain types of 'man glasses' but are not 'man glasses' are:\t\n- Women's glasses\n- Sunglasses\n- Reading glasses\n- Safety glasses\n\nUseful visual features for distinguishing 'man glasses' from the listed similar things in a photo would depend on the specific type of glasses being referred to (e.g. aviator glasses, horn-rimmed glasses, etc.) and could include features such as frame shape, lens color, and size.", 13], "infielder": ["Yes. 'Infielder' has a tangible appearance and is a player in the sport of baseball.\nA few things that are visually similar to 'infielder' but are not 'infielder' are:\tumpire\tcoach\tplayer in a different position\nThere are several useful visual features to tell there is 'infielder' and not similar things in a photo:\twearing a baseball uniform\twearing a glove\tpositioned in the infield area\tof the baseball field", 13], "ground surface": ["Yes. 'Ground surface' has a tangible appearance.\nA few things that are visually similar to 'ground surface' but are not 'ground surface' are:\twall\tfloor\tceiling\nThere are several useful visual features to tell there is 'ground surface' and not similar things in a photo:\tlevel or sloping surface\tcovered in dirt, grass, concrete or other materials\tfound outdoors", 13], "cumulus clouds": ["Yes. 'Cumulus clouds' have a tangible appearance and are a common type of cloud.\nA few things that are visually similar to 'cumulus clouds' but are not 'cumulus clouds' are:\tfog\tsmoke\tsteam\nThere are several useful visual features to tell there is 'cumulus clouds' and not similar things in a photo:\tcotton ball-like shape\tpuffy or fluffy appearance\twhite or light gray color\ta flat base and rounded top\tfloating high in the sky", 13], "labrador": ["Yes. 'Labrador' has a tangible appearance and is a kind of dog breed.\nA few things that are visually similar to 'labrador' but are not 'labrador' are:\tgolden retriever\tchocolate Labrador\tDalmatian\tpitbull\nThere are several useful visual features to tell there is 'labrador' and not similar things in a photo:\tshort, dense, and water-resistant coat\tbroad head and a thick nose\ttapered tail\tthick and powerful legs\twell-muscled body\tblack, yellow, or chocolate-colored coat", 13], "ivory elephant tusk": ["Yes. 'Ivory elephant tusk' has a tangible appearance and is an object made of ivory from the tusks of elephants.\nA few things that are visually similar to 'ivory elephant tusk' but are not 'ivory elephant tusk' are:\tbones\tantlers\t\nUseful visual features for distinguishing 'ivory elephant tusk' from the listed similar things in a photo are:\t\n- Smooth, polished surface\n- Light color with a distinct grain pattern\n- Dense and heavy material similar to the weight of the elephant tusk.", 13], "hoody": ["Yes. 'Hoody' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'hoody' but are not 'hoody' are:\tjacket\tsweater\tcardigan\tponcho\nThere are several useful visual features to tell there is 'hoody' and not similar things in a photo:\thood attached to the back of the garment\tdrawstrings to adjust the hood\tusually made of soft, comfortable fabric\toften has a front pocket", 13], "blue sea": ["Yes. 'Blue sea' has a tangible appearance and refers to a body of saltwater.\nA few things that are visually similar to 'blue sea' but are not 'blue sea' are:\tblue paint\tblue fabric\tblue sky\tblue lake\nThere are several useful visual features to tell there is 'blue sea' and not similar things in a photo:\tsaltwater\twaves or ripples\tmarine life like fish and corals\tseabed or rocks in the background.", 13], "outlet cover": ["Yes. 'Outlet cover' has a tangible appearance and is a protective covering for electrical outlets.\nA few things that are visually similar to 'outlet cover' but are not 'outlet cover' are:\tlight switch cover\tthermostat cover\tsmoke detector cover\tvent cover\nThere are several useful visual features to tell there is 'outlet cover' and not similar things in a photo:\tcover for an electrical outlet\tusually white or beige\trectangular or square shape\toutline of two small holes for plugs to go through", 13], "dog food": ["Yes. 'Dog food' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'dog food' but are not 'dog food' are:\tcat food\tcereal\tpellets\nThere are several useful visual features to tell there is 'dog food' and not similar things in a photo:\twet or dry food\tcan be in a can or a bag\tpieces or chunks of meat, vegetables, and grains\tspecific branding or packaging for dogs", 13], "color sea water": ["Yes. 'Color sea water' has a tangible appearance and can be described in various shades of blue or green.\nA few things that are visually similar to 'color sea water' but are not 'color sea water' are:\tpaintings of water\tbathwater\tchlorinated pool water\tblue or green drinks\nThere are several useful visual features to tell there is 'color sea water' and not similar things in a photo:\tvarious shades of blue and green\tturbulent and wavy texture\tfoamy white caps on waves\tvisible marine life such as fish, coral reefs, or seaweed", 13], "surfboard leash": ["Yes. 'Surfboard leash' has a tangible appearance and is a type of equipment used for surfing.\nA few things that are visually similar to 'surfboard leash' but are not 'surfboard leash' are:\tdog leash\tcable\tbungee cord\nThere are several useful visual features to tell there is 'surfboard leash' and not similar things in a photo:\tattached to a surfboard\tusually made of neoprene or other durable material\tspecific length\tright thickness and width\tfor surfboard use only", 13], "manufacturer logo": ["Yes. 'Manufacturer logo' has a tangible appearance and can be identified visually.\nA few things that are visually similar to 'manufacturer logo' but are not 'manufacturer logo' are:\tsymbols\tsigns\temblems\tgraphics\nThere are several useful visual features to tell there is 'manufacturer logo' and not similar things in a photo:\tdistinctive shapes, symbols, or text\tunique colors or color combinations\tplacement on a product or item\ttypography and font style\tcrisp and clear lines and edges", 13], "side doors": ["Yes. 'Side doors' has a tangible appearance and is a part of a building or vehicle.\nA few things that are visually similar to 'side doors' but are not 'side doors' are:\twindows\tgarage doors\tgates\nThere are several useful visual features to tell there is 'side doors' and not similar things in a photo:\tvertical shape\topening mechanism\thinge\tnext to the side of a building or a vehicle", 13], "sub sandwich": ["Yes. 'Sub sandwich' has a tangible appearance and is a type of sandwich.\nA few things that are visually similar to 'sub sandwich' but are not 'sub sandwich' are:\thoagie\thero\tgrinder\tpita wrap\nThere are several useful visual features to tell there is 'sub sandwich' and not similar things in a photo:\tlong and narrow loaf of bread\tcut in half\thorizontal layers of ingredients, including meat, cheese, vegetables, and condiments", 13], "passenger boat": ["Yes. 'Passenger boat' has a tangible appearance and is a type of watercraft designed for transporting passengers.\nA few things that are visually similar to 'passenger boat' but are not 'passenger boat' are:\tcargo ship\tor ferry\tkayak\tjet ski\nThere are several useful visual features to tell there is 'passenger boat' and not similar things in a photo:\tlarge size, able to hold many people\twindows and doors\tforward-facing seating\tarea for boarding and disembarking passengers.", 13], "brick pattern": ["Yes. 'Brick pattern' has a tangible appearance and refers to a specific arrangement of bricks.\nA few things that are visually similar to 'brick pattern' but are not 'brick pattern' are:\ttile pattern\twood grain\tconcrete texture\nThere are several useful visual features to tell there is 'brick pattern' and not similar things in a photo:\trectangular shape\talternating rows of bricks\tarrangement in a repeating pattern in which bricks are offset between rows\tfalse horizontal stretcher courses to give the illusion of continuous courses.", 13], "sausage sandwich": ["Yes. 'Sausage sandwich' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'sausage sandwich' but are not 'sausage sandwich' are:\thotdog\tburger\tBLT sandwich\nThere are several useful visual features to tell there is 'sausage sandwich' and not similar things in a photo:\ttwo slices of bread\tpieces of sausage in between the bread\tsometimes have vegetables or condiments in it", 13], "flyers": ["Yes. 'Flyers' has a tangible appearance and is a type of printed material.\nA few things that are visually similar to 'flyers' but are not 'flyers' are:\tbrochures\tpamphlets\tnewspapers\tcatalogs\t\nThere are several useful visual features to tell there is 'flyers' and not similar things in a photo:\tpaper material\tsingle page or sheet Design and text geared toward promotion or information dissemination.", 13], "round blue plate": ["Yes. 'Round blue plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'round blue plate' but are not 'round blue plate' are:\tblue bowl\tblue saucer\tblue mug\tblue cup\nThere are several useful visual features to tell there is 'round blue plate' and not similar things in a photo:\tcircular\twith a flat surface\tringed or shaped like a plate\tspecific shade of blue in color", 13], "zebra ground": ["No. 'Zebra ground' is too vague or abstract to be distinguished in a photo.", 13], "bus lane": ["Yes. 'Bus lane' has a tangible appearance and is a designated lane for buses on a road.\nA few things that are visually similar to 'bus lane' but are not 'bus lane' are:\tcarpool lane\tbike lane\tno parking zone\tconstruction zone\nThere are several useful visual features to tell there is 'bus lane' and not similar things in a photo:\tdashed white lines on either side of the lane\tsignage indicating that it is a bus lane\tor marking on the road indicating that it is a bus lane\tblue color of the lane or a blue sign with a white image of a bus on it.", 13], "air freshner": ["Yes. 'Air freshener' has a tangible appearance and is a type of product.\nA few things that are visually similar to 'air freshener' but are not 'air freshener' are:\tperfume\tcologne\tdiffuser\tfragrance oil\nThere are several useful visual features to tell there is 'air freshener' and not similar things in a photo:\t\nspray bottle or canister, gel or bead container, or plug-in device.\trepresentation of scent or fragrance with images, such as flowers, fruits, or forests.\nThe label will contain information about the product, including features like scent, ingredients, and intended use.", 13], "fishing boats": ["Yes. 'Fishing boats' has a tangible appearance and is a kind of boat used for fishing.\nA few things that are visually similar to 'fishing boats' but are not 'fishing boats' are:\tyachts\tcruises\tspeedboats\t\nThere are several useful visual features to tell there is 'fishing boats' and not similar things in a photo:\tnetting or cages for catching fish\tcrane or winch for hauling in the catch\toutboard or inboard motor\tfishing gear and equipment on board such as rods and bait buckets\tslanted hull to facilitate pulling up nets or traps.", 13], "dark object": ["No. 'Dark object' is too vague or abstract to be distinguished in a photo.", 13], "cereals": ["Yes. 'Cereals' has a tangible appearance and refers to a type of food.\nA few things that are visually similar to 'cereals' but are not 'cereals' are:\tgranola bars\tchocolate balls\tanimal food\nThere are several useful visual features to tell there is 'cereals' and not similar things in a photo:\tedible grains, usually served in a bowl\twith or without milk or yogurt\tvariety of shapes and colors, often small and round, but can be long or square.", 13], "air bubbles": ["Yes. 'Air bubbles' has a tangible appearance and is usually seen in liquids or transparent materials.\nA few things that are visually similar to 'air bubbles' but are not 'air bubbles' are:\tbumps\tinsects\tmicroorganisms\tfoam\torbs\nThere are several useful visual features to tell there is 'air bubbles' and not similar things in a photo:\ttransparent\thollow\tcircular\tshimmering or reflective surface\tsurrounded by liquid or material", 13], "boston": ["No. 'Boston' is too vague or abstract to be distinguished in a photo.\nHowever, a few things that are visually similar to 'Boston' but are not 'Boston' may include: other cities, towns or urban areas with similar architecture and street layouts.", 13], "barbecue sauce": ["Yes. 'Barbecue sauce' has a tangible appearance and is a type of sauce used for grilling and cooking.\nA few things that are visually similar to 'barbecue sauce' but are not 'barbecue sauce' are:\tketchup\tmustard\thot sauce\tsoy sauce\nThere are several useful visual features to tell there is 'barbecue sauce' and not similar things in a photo:\tthick, viscous texture\tdark brown or red color\tsmooth or chunky consistency\tsweet, tangy or smoky flavor\tpaired with grilled or barbecued food.", 13], "silver bicycle": ["Yes. 'Silver bicycle' has a tangible appearance and is a type of bike with a specific color.\nA few things that are visually similar to 'silver bicycle' but are not 'silver bicycle' are:\tother colored bicycles\tmotorcycles\trollerblades\nThere are several useful visual features to tell there is a 'silver bicycle' and not similar things in a photo:\ttwo-wheeled bike\tsilver color\tsleek and shiny appearance\tframe and wheels made of metal\thandlebars for steering\tpedals for motion.", 13], "homemade": ["No. 'Homemade' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are taking about homemade food, then:\n\nA few things that are visually similar to 'homemade food' but are not 'homemade' are: restaurant food, pre-packaged food, store-bought food. \n\nUseful visual features for distinguishing 'homemade food' from the listed similar things in a photo are: uneven shapes, visible imperfections, recognizable ingredients or kitchenware, and a less uniform appearance that one can expect from mass-produced foods.", 13], "crack concrete": ["Yes. 'Crack concrete' has a tangible appearance and is a physical phenomenon.\nA few things that are visually similar to 'crack concrete' but are not 'crack concrete' are:\tpatterns\ton a tile wall or floor\tin a sidewalk or pavement\tsplit wood\nThere are several useful visual features to tell there is 'crack concrete' and not similar things in a photo:\tsharp lines or irregular shapes in the concrete\tuneven edges\twhere two pieces of concrete meet\tnarrow or wide", 13], "taxicab": ["Yes. 'Taxicab' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'taxicab' but are not 'taxicab' are:\tprivate car\tlimousine\tbus\tambulance\nThere are several useful visual features to tell there is 'taxicab' and not similar things in a photo:\tbright yellow color\tblack and white 'taxi' sign on the roof\tword 'taxi' printed on the body\tlicense plate that starts with 'TAXI' or 'MED'", 13], "klm": ["No. 'KLM' is too vague or abstract to be visually concrete in a photo. \n\nNote: KLM is actually a Dutch airline, but since it is an abbreviation, it may not have a tangible appearance in a photo.", 13], "silver chains": ["Yes. 'Silver chains' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'silver chains' but are not 'silver chains' are:\tnecklaces\tchokers\tbelts\tkeychains\nThere are several useful visual features to tell there is 'silver chains' and not similar things in a photo:\tmade of silver or silver-colored metal\tconsist of chained links\tused as a necklace or bracelet", 13], "multitude": ["No. 'Multitude' is too vague or abstract to be distinguished in a photo.", 13], "pastel": ["Yes. 'Pastel' has a tangible appearance and is a type of color or medium.\nA few things that are visually similar to 'pastel' but are not 'pastel' are:\tcrayons\tmarkers\tpaints\tcolored pencils\nThere are several useful visual features to tell there is 'pastel' and not similar things in a photo:\tsoft, muted colors\tairy and delicate texture\tno distinct outlines or contrasts", 13], "tennis visor": ["Yes. 'Tennis visor' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'tennis visor' but are not 'tennis visor' are:\tbaseball cap\tsunhat\tbonnet\tbeanie\nThere are several useful visual features to tell there is 'tennis visor' and not similar things in a photo:\tno crown with soft brim\tcurved and wide brim\tworn for outdoor sports or activities", 13], "silver metal handle": ["Yes. 'Silver metal handle' has a tangible appearance and refers to a specific object.\nA few things that are visually similar to 'silver metal handle' but are not 'silver metal handle' are:\tknob\tdoor knocker\tdecorative metal piece\nThere are several useful visual features to tell there is 'silver metal handle' and not similar things in a photo:\trectangular or cylindrical shape\tmade of shiny silver metal\ttypically found on a door or drawer and is meant to be grasped with the hand.", 13], "school buses": ["Yes. 'School buses' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'school buses' but are not 'school buses' are:\tpublic buses\tmini buses\tvans\ttrucks\nThere are several useful visual features to tell there is 'school buses' and not similar things in a photo:\tyellow or orange color\trectangular shape\twith the word \"school\" on the side\troof with a black stripe or a sign that reads \"STOP\" when stopped for passengers\tdoor located towards the front of the bus", 13], "cigarette butts": ["Yes. 'Cigarette butts' has a tangible appearance and is a type of waste.\nA few things that are visually similar to 'cigarette butts' but are not 'cigarette butts' are:\tscattered leaves\tdried twigs\ttrash on the ground\nThere are several useful visual features to tell there are 'cigarette butts' and not similar things in a photo:\twhite or brown filter\tend should be burnt or blackened\ttubular shape\tsimilar size", 13], "plastic tarp": ["Yes. 'Plastic tarp' has a tangible appearance and is a type of covering.\nA few things that are visually similar to 'plastic tarp' but are not 'plastic tarp' are:\tblanket\ttablecloth\tshower curtain\twallpaper\nThere are several useful visual features to tell there is 'plastic tarp' and not similar things in a photo:\tthin and flexible material\tusually made of polyethylene or polypropylene\ttypes available for different purposes (e.g. roofing, gardening, painting)\tmay have metal grommets or eyelets for attachment to a surface\twater-resistant or waterproof.", 13], "latches": ["Yes. 'Latches' has a tangible appearance and is a kind of mechanical device.\nA few things that are visually similar to 'latches' but are not 'latches' are:\tlocks\thinges\tknobs\nThere are several useful visual features to tell there is 'latches' and not similar things in a photo:\ta movable metal bar or lever that fastens a door or gate\tsharp edge or hook\tthat hooks onto a catch or another part to hold it closed or open", 13], "gold vase": ["Yes. 'Gold vase' has a tangible appearance and is a type of vase made of gold.\nA few things that are visually similar to 'gold vase' but are not 'gold vase' are:\tgolden cup\tgolden spoon\tgolden plate\tgolden trophy\nThere are several useful visual features to tell there is 'gold vase' and not similar things in a photo:\tmade of gold or gold-colored metal\ttaller than wide, with a narrow neck and a wide base\tsupported by a sturdy stand or base, often wider than the vase itself with ornate decorative designs.", 13], "word bus": ["No. 'word bus' is too vague or abstract and does not have a tangible appearance.\nThere are no things that are visually similar to 'word bus' but are not 'word bus', as the concept itself is not visually concrete.\nTherefore, no visual features can be used to distinguish 'word bus' from any similar things in a photo.", 13], "train caboose": ["Yes. 'Train caboose' has a tangible appearance and is a type of train car.\nA few things that are visually similar to 'train caboose' but are not 'train caboose' are:\tpassenger car\tboxcar\tflat car\nThere are several useful visual features to tell there is 'train caboose' and not similar things in a photo:\tsmaller size than other train cars\tdifferent shape with a cupola on top\tbright colors or markings\ttypically at the end of a train\tline of windows along the sides", 13], "drywall": ["Yes. 'Drywall' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'drywall' but are not 'drywall' are:\tbrick\tstucco\twood\tplaster\nThere are several useful visual features to tell there is 'drywall' and not similar things in a photo:\tflat panels or sheets of material\tcontaining gypsum or plaster\tsmooth surface\tthat can be painted or covered with wallpaper", 13], "chocolate birthday": ["No. 'Chocolate birthday' is too vague or abstract to be distinguished in a photo.", 13], "way signs": ["Yes. 'Way signs' has a tangible appearance and is a kind of sign.\nA few things that are visually similar to 'way signs' but are not 'way signs' are:\twarning signs\tadvertisement\tsignboards\tinstructions\nThere are several useful visual features to tell there is 'way signs' and not similar things in a photo:\tarrows or directions\twritten and/or symbolic information\ton the roadside or on a pole", 13], "square sink": ["Yes. 'Square sink' has a tangible appearance and is a kind of bathroom fixture.\nA few things that are visually similar to 'square sink' but are not 'square sink' are:\trectangular sink\tcircular sink\twashbasin\tcounter\nThere are several useful visual features to tell there is 'square sink' and not similar things in a photo:\tsquare-shaped basin\tfor mounting on a vanity or wall\thas a faucet and a drain\trecessed or semi-recessed installation.", 13], "door handles": ["Yes. 'Door handles' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'door handles' but are not 'door handles' are:\tknobs\thooks\tdrawer pulls\tfaucet handles\nThere are several useful visual features to tell there is 'door handles' and not similar things in a photo:\tattached to a door either exterior or interior\tcan be turned or pulled to open the door\tmade of metal or other durable materials\tcomplementary shape and size to the door it's attached to.", 13], "kleenex": ["Yes. 'Kleenex' has a tangible appearance and is a brand of tissues.\nA few things that are visually similar to 'kleenex' but are not 'kleenex' are:\tpaper towels\thand wipes\tfacial wipes\ttoilet paper\nThere are several useful visual features to tell there is 'kleenex' and not similar things in a photo:\tsoft and lightweight material\tbox or package with 'Kleenex' branding on it\twhite or pastel colors\tfolded into rectangular pieces.", 13], "steel train tracks": ["Yes. 'Steel train tracks' has a tangible appearance and is a type of railway system.\nA few things that are visually similar to 'steel train tracks' but are not 'steel train tracks' are:\troads\tbike paths\thiking trails\nThere are several useful visual features to tell there is 'steel train tracks' and not similar things in a photo:\tmetallic rails and ties\tsymmetrical and parallel lines\tbolted or welded joints and connections\tcrossing signs and barriers", 13], "train engineer": ["Yes. 'Train engineer' has a tangible appearance and refers to a person who operates a train.\nA few things that are visually similar to 'train engineer' but are not 'train engineer' are:\ttrain conductor\ttrain passenger\tmechanic\nThere are several useful visual features to tell there is 'train engineer' and not similar things in a photo:\twearing a blue and white striped hat or a cap\twearing a blue or black uniform\twith safety goggles\tor sitting in the driver's cabin or the control room of a train", 13], "snow boots": ["Yes. 'Snow boots' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'snow boots' but are not 'snow boots' are:\tregular boots\thiking boots\train boots\tgaloshes\nThere are several useful visual features to tell there is 'snow boots' and not similar things in a photo:\tthick and insulated sole\twaterproof or water-resistant material\thigh-cut to cover the ankle or calf area\tfur lining or trim for warmth and comfort\tclasps or buckles for securing in place in snowy or slippery conditions", 13], "closet doors": ["Yes. 'Closet doors' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'closet doors' but are not 'closet doors' are:\troom doors\tcabinet doors\tdrawers\tshutters\nThere are several useful visual features to tell there is 'closet doors' and not similar things in a photo:\ttwo separate panels, either sliding or hinged\tspecific hardware such as handles or knobs\tthat are designed for hanging in a closet\tor separating a closet space from a room.", 13], "stone road": ["Yes. 'Stone road' has a tangible appearance and refers to a path or roadway made of stones.\nA few things that are visually similar to 'stone road' but are not 'stone road' are:\tgravel path\tdirt road\tcobblestone street\nThere are several useful visual features to tell there is 'stone road' and not similar things in a photo:\tlined with uniform and flat stones\tarranged in a specific pattern or design\tconnecting different locations or points of interest", 13], "seabirds": ["Yes. 'Seabirds' has a tangible appearance and refers to birds that are adapted to life in marine environments.\nA few things that are visually similar to 'seabirds' but are not 'seabirds' are:\tducks\tgulls\tpelicans\tpenguins\nThere are several useful visual features to tell there is 'seabirds' and not similar things in a photo:\tlive or nest near the ocean or other bodies of saltwater\tfeathered wings and streamlined bodies\twebbed feet\tor beaks adapted for catching fish and other aquatic life.", 13], "sensor": ["Yes. 'Sensor' has a tangible appearance and is a device that detects or measures physical properties.\nA few things that are visually similar to 'sensor' but are not 'sensor' are:\tcamera\tmicrophone\tthermometer\tbarcode scanner\nThere are several useful visual features to tell there is 'sensor' and not similar things in a photo:\tsmall and compact\tsize and shape varies depending on the type\tmeasuring systems and scales are often visible\twired or wireless connectivity for transmitting data or signals.", 13], "ski cap": ["Yes. 'Ski cap' has a tangible appearance and is a kind of hat worn while skiing.\nA few things that are visually similar to 'ski cap' but are not 'ski cap' are:\tbeanie\tberet\tbaseball cap\nThere are several useful visual features to tell there is 'ski cap' and not similar things in a photo:\twoolen or knit material\twith ear flaps and a chin strap\tcovering the ears and head fully\tdifferent shapes and sizes than a beanie or baseball cap", 13], "orange wristband": ["Yes. 'Orange wristband' has a tangible appearance and is a type of bracelet or band worn around the wrist.\nA few things that are visually similar to 'orange wristband' but are not 'orange wristband' are:\twatches\thair ties\tbangles\nThere are several useful visual features to tell there is 'orange wristband' and not similar things in a photo:\torange color\tsoft texture\tworn around the wrist or forearm\tnarrow width or thickness\tsolid color or patterned design", 13], "captain": ["Yes. 'Captain' has a tangible appearance and is a title given to a person who is in command of a ship, an aircraft, or other transportation means.\nA few things that are visually similar to 'captain' but are not 'captain' are:\tsailors\tpilots\tbus drivers\ttrain conductors\nThere are several useful visual features to tell there is 'captain' and not similar things in a photo:\tuniform with stripes or emblem\tepaulets or bars on the shoulders\tor a hat with a peak or visor whistles or radios to communicate with crew\tmustache, beard, or other facial hair patterns on the uniform or hat indicating rank or position", 13], "wood telephone pole": ["Yes. 'Wood telephone pole' has a tangible appearance and is a specific type of pole used for holding telephone wires.\nA few things that are visually similar to 'wood telephone pole' but are not 'wood telephone pole' are:\twooden fence post\ttree trunk\tlight pole\ttraffic sign pole\nThere are several useful visual features to tell there is 'wood telephone pole' and not similar things in a photo:\tstraight and cylindrical shape\twooden material\tattached wires and cables\tinsulator cans attached to it", 13], "entryway": ["Yes. 'Entryway' has a tangible appearance and refers to the area at the entrance of a building.\nA few things that are visually similar to 'entryway' but are not 'entryway' are:\tdoormat\tlobby\tcorridor\tfoyer\nThere are several useful visual features to tell there is 'entryway' and not similar things in a photo:\tarea at the entrance of a building\twith a door or doors\twith hooks, hangers, or shelves for storing coats, hats, and shoes.", 13], "traffic stop sign": ["Yes. 'Traffic stop sign' has a tangible appearance and is a type of road signage.\nA few things that are visually similar to 'traffic stop sign' but are not 'traffic stop sign' are:\tspeed limit sign\tpedestrian crossing sign\tno parking sign\nThere are several useful visual features to tell there is 'traffic stop sign' and not similar things in a photo:\tOctagonal (8-sided) shape\tRed background and white letters\tSpecific text \"STOP\" written in a specific font and color (white)", 13], "garbage pail": ["Yes. 'garbage pail' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'garbage pail' but are not 'garbage pail' are:\ttrash can\tcompost bin\trecycling bin\tbasket\nThere are several useful visual features to tell there is 'garbage pail' and not similar things in a photo:\tround or rectangular shape\twith a lid or rotating top\toften labeled as 'garbage' or 'trash'\tsturdy design to hold garbage or waste materials", 13], "orange handles": ["Yes. 'Orange handles' has a tangible appearance and is a specific kind of handle.\nA few things that are visually similar to 'orange handles' but are not 'orange handles' are:\tyellow handles\tred handles\tpurple handles\nThere are several useful visual features to tell there are 'orange handles' and not similar things in a photo:\torange color\trectangular or cylindrical shape\tattached to a door, cabinet, or drawer", 13], "plaques": ["Yes. 'Plaques' has a tangible appearance and refers to a type of decorative or commemorative item.\nA few things that are visually similar to 'plaques' but are not 'plaques' are:\tsigns\ttrophies\tmedals\tawards\nThere are several useful visual features to tell there is 'plaques' and not similar things in a photo:\tflat and rectangular or circular shape\thanging on a wall or placed on a stand\tfeature an engraved or printed message or image\tmade of materials like wood, metal, or crystal.", 13], "bubble": ["Yes. 'Bubble' has a tangible appearance and is a spherical or oval-shaped object filled with air or gas that is surrounded by a thin film of soap, water, or another liquid.\nA few things that are visually similar to 'bubble' but are not 'bubble' are:\tdroplet\tfoam\tsnow\tglobe\tlight bulb\nThere are several useful visual features to tell there is 'bubble' and not similar things in a photo:\tspherical or oval-shaped\ttransparent or translucent\tiridescent or rainbow-colored\twhen popped, disappears quickly", 13], "paper box": ["Yes. 'Paper box' has a tangible appearance and is a type of container made of paper.\nA few things that are visually similar to 'paper box' but are not 'paper box' are:\tcardboard box\tplastic container\tbasket\tenvelope\nThere are several useful visual features to tell there is 'paper box' and not similar things in a photo:\tmade of paper or cardboard\trectangular or square shape\tstraight sides and corners\tfolded flaps or lid to close the box\tmade for holding or storing items.", 13], "mailboxes": ["Yes. 'Mailboxes' has a tangible appearance and is a type of object used for receiving mail.\nA few things that are visually similar to 'mailboxes' but are not 'mailboxes' are:\tnewspaper stands\ttelephone booths\toutdoor cabinets\nThere are several useful visual features to tell there is 'mailboxes' and not similar things in a photo:\trectangular or cubic shape\twith a slot for mail a hinged door with a handle\tflag to signal mail delivery location\tlocated outside of a house or a building", 13], "flats": ["Yes. 'Flats' has a tangible appearance and is a type of shoe.\nA few things that are visually similar to 'flats' but are not 'flats'are:\theels\tboots\tsandals\tsneakers\nThere are several useful visual features to tell there is 'flats' and not similar things in a photo:\tno heel or a very low heel\tcomfortable and casual appearance\tfitting the foot closely and covering it entirely", 13], "arm bent elbow": ["Yes. 'Arm bent elbow' has a tangible appearance and is a body position.\nA few things that are visually similar to 'arm bent elbow' but are not 'arm bent elbow' are:\tarm resting on a table\tarm holding a book\tarm raised in the air\nThere are not many useful visual features required to distinguish 'arm bent elbow' from visually similar things, but the most critical aspect of a bent elbow would be the visible angle at the joint formed by the upper and lower arm. The angle in the bent form would be nearly ninety degrees.", 13], "pointy roof": ["Yes. 'Pointy roof' has a tangible appearance and is a type of roof design.\nA few things that are visually similar to 'pointy roof' but are not 'pointy roof' are: dome roof, flat roof, hip roof, mansard roof.\nThere are several useful visual features to tell there is 'pointy roof' and not similar things in a photo:\tsloping sides\tmeeting at a peak\tpointy or triangular shape\tmost commonly associated with houses or buildings in colder climates or medieval architecture", 13], "handrails": ["Yes. 'Handrails' has a tangible appearance and is a kind of support system.\nA few things that are visually similar to 'handrails' but are not 'handrails' are:\tbannisters\tramp railing\tshower grab bars\t\nThere are several useful visual features to tell there is 'handrails' and not similar things in a photo:\tstraight or curved\tbar-shaped\tstructure running parallel to a stairway, ramp, or elevated platform.", 13], "refrigerator door handle": ["Yes. 'Refrigerator door handle' has a tangible appearance and is a type of household appliance part.\nA few things that are visually similar to 'refrigerator door handle' but are not 'refrigerator door handle' are:\tdrawer handles\tcabinet knobs\tdoor knobs\nThere are several useful visual features to tell there is 'refrigerator door handle' and not similar things in a photo:\thorizontal shape\tmetallic or plastic material\tcurved or straight design\tattached to a fridge door.", 13], "soda cup": ["Yes. 'Soda cup' has a tangible appearance and is a type of cup used for holding soda or other beverages.\nA few things that are visually similar to 'soda cup' but are not 'soda cup' are:\twater cup\tcoffee cup\tmug\tjuice glass\nThere are several useful visual features to tell there is 'soda cup' and not similar things in a photo:\tlarge plastic cup with a lid and a straw\tdesigns or logos related to soda brands or fast-food chains\tsoda, ice, or bubbles visible inside the cup", 13], "grease spot": ["Yes. 'Grease spot' has a tangible appearance and is a stain caused by grease or oil.\nA few things that are visually similar to 'grease spot' but are not 'grease spot' are:\twine stain\tchocolate stain\twater mark\tgrass stain\tdirt patch\nThere are several useful visual features to tell there is 'grease spot' and not similar things in a photo:\tgreasy or oily texture and appearance\tcircular or irregular shape\tdark or yellowish color\twet or shiny surface", 13], "bare limbs": ["Yes. 'Bare limbs' has a tangible appearance and refers to the branches of trees without leaves.\nA few things that are visually similar to 'bare limbs' but are not 'bare limbs' are:\tdead branches\tscars or wounds\ton trees\tbranches with few leaves\tor yellow leaves\nThere are several useful visual features to tell there are 'bare limbs' and not similar things in a photo: \tno leaves\tthin branches\tdense branching pattern\tbark on the trunk or larger branches", 13], "color car": ["No. 'Color car' is too vague or abstract to be distinguished in a photo. A car can come in many different colors and what is considered a 'color car' may vary from person to person, making it difficult to identify a specific interpretation of the concept in a photo.", 13], "bus front headlight": ["Yes. 'Bus front headlight' has a tangible appearance and is a specific part of the bus.\nA few things that are visually similar to 'bus front headlight' but are not 'bus front headlight' are:\tcar front headlight\tmotorbike headlight\tlantern\tflashlight\nThere are several useful visual features to tell there is 'bus front headlight' and not similar things in a photo:\tlarge size\tpositioned on the front of a bus\tclear lens or cover with light projecting through\tit is part of a larger vehicle, specifically a bus.", 13], "distant mountains": ["Yes. 'Distant mountains' has a tangible appearance and refers to a specific kind of landscape.\nA few things that are visually similar to 'distant mountains' but are not 'distant mountains' are:\thills\tclouds\tbuildings\nThere are several useful visual features to tell there is 'distant mountains' and not similar things in a photo:\tmajestic or imposing size\tand shape, like peaks or ridges\treceding into the distance\tcovered in snow, trees or rocks", 13], "wooden table leg": ["Yes. 'Wooden table leg' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'wooden table leg' but are not 'wooden table leg' are:\twooden chair leg\twooden bed post\twooden stair railing\twooden fence post\nThere are several useful visual features to tell there is a 'wooden table leg' and not similar things in a photo:\tstraight and vertical shape\tthickness in proportion to the table top\tsupporting a table surface at one end\tand attached to a table surface at the other end.", 13], "chrome pipe": ["Yes. 'Chrome pipe' has a tangible appearance and is a type of pipe with a sleek metal finish.\nA few things that are visually similar to 'chrome pipe' but are not 'chrome pipe' are:\tpvc pipe\tmetal rod\tsteel tube\tirrigation pipe\tplumbing pipe\nThere are several useful visual features to tell there is 'chrome pipe' and not similar things in a photo:\tsilvery color\tsleek or shiny surface\tridged or cylindrical shape\tmetal material\tchrome finish or reflection", 13], "pets": ["Yes. 'Pets' has a tangible appearance as they are animals kept for companionship.\nA few things that are visually similar to 'pets' but are not 'pets' are:\twild animals\tstuffed animals\ttoys\tphotos or images of animals\nThere are several useful visual features to tell there is 'pets' and not similar things in a photo:\tanimals that exhibit signs of domestication or tameness\thoused indoors or in confined areas\twith owners or humans in the picture\twearing collars or accessories\tcommon pet species include dogs, cats, birds, and fish.", 13], "stone arch": ["Yes, 'stone arch' is a visually concrete concept and has a tangible appearance.\n\nA few things that are visually similar to 'stone arch' but are not 'stone arch' are:\n\n- Stone Bridge\n- Stone Tunnel\n- Stone Wall\n\nUseful visual features for distinguishing 'stone arch' from the listed similar things in a photo include:\n\n- Semi-circular or pointed opening\n- Two supports or pillars made of stone\n- Keystone at the top\n- The arch is raised from the ground and is part of a larger structure or monument", 13], "airlines": ["No. 'Airlines' is too abstract to have a tangible appearance in a photo. \n\nHowever, a few things that are visually similar to 'airlines' but are not 'airlines' are: airplanes, airport terminals, airplanes on a runway, flight attendants. \n\nUseful visual features to identify airlines in a photo may include: branding and logos of specific airlines, airport check-in counters with airline logos, uniforms and badges with airline logos, or luggage tags with airline names.", 13], "slides": ["Yes. 'Slides' has a tangible appearance and is a piece of playground equipment.\nA few things that are visually similar to 'slides' but are not 'slides' are:\tpools\tsnow banks\twaterfall\nThere are several useful visual features to tell there is 'slides' and not similar things in a photo:\tladder or steps specialized for climbing\tuphill direction or raised platform\tsloping, smooth surface for sliding down", 13], "brick surface": ["Yes. 'Brick surface' has a tangible appearance and is a type of textured wall.\nA few things that are visually similar to 'brick surface' but are not 'brick surface' are:\tstone walls\tconcrete walls\ttile walls\nThere are several useful visual features to tell there is 'brick surface' and not similar things in a photo:\trectangular shape\tbrick pattern with alternating colors\tand rough texture.", 13], "safety barrier": ["Yes. 'Safety barrier' has a tangible appearance and is a type of physical obstruction used for safety purposes.\nA few things that are visually similar to 'safety barrier' but are not 'safety barrier' are:\tfences\twalls\tgates\thedges\nThere are several useful visual features to tell there is 'safety barrier' and not similar things in a photo:\twide base\ttoo tall for climbing or jumping\tdifferent color from the surrounding area\twarnings or signs on the surface\tdeflective or reflective surface\tsymmetrical shape that is easy to recognize as a barrier.", 13], "bracer": ["Yes. 'Bracer' has a tangible appearance and is a type of armguard.\nA few things that are visually similar to 'bracer' but are not 'bracer' are:\twristband\twatch\tbracket\t\nThere are several useful visual features to tell there is 'Bracer' and not similar things in a photo:\tmade of leather or metal\tfits on the forearm or upper arm\thas laces or straps to secure it in place\tWorn by archers or people engaging in combat sports.", 13], "tennis bag": ["Yes. 'Tennis bag' has a tangible appearance and is a kind of sports equipment bag.\nA few things that are visually similar to 'tennis bag' but are not 'tennis bag' are:\tgym bag\tbackpack\tduffle bag\tbriefcase\nThere are several useful visual features to tell there is 'tennis bag' and not similar things in a photo:\tlong and cylindrical in shape\tdesigned to carry one or more tennis rackets\tmultiple compartments or pockets to carry tennis balls and accessories\tcan be carried over one shoulder or on the back", 13], "silver table": ["Yes. 'Silver table' has a tangible appearance and refers to a table made of silver or with a silver coating.\nA few things that are visually similar to 'silver table' but are not 'silver table' are:\tchrome table\tstainless steel table\taluminum table\tglass table\nThere are several useful visual features to tell there is 'silver table' and not similar things in a photo:\ta table with a silver coating or made entirely of silver or silver-colored metal\tthe table's texture and reflection of light\tusing context to identify the table as a piece of silver furniture, usually associated with luxury or elegance", 13], "cake pan": ["Yes. 'Cake pan' has a tangible appearance and is a kind of cooking tool.\nA few things that are visually similar to 'cake pan' but are not 'cake pan' are:\tpie pan\tbaking sheet\tskillet\tgriddle\nThere are several useful visual features to tell there is 'cake pan' and not similar things in a photo:\tshallow, round or square shape\twith raised edges or sides\tmade of metal or silicone \tdesigned specifically for baking cakes or desserts.", 13], "headgear": ["Yes. 'Headgear' has a tangible appearance and refers to any item worn on the head.\nA few things that are visually similar to 'headgear' but are not 'headgear' are:\thairstyle\tmake-up\teyewear\tjewelry\nThere are several useful visual features to tell there is 'headgear' and not similar things in a photo:\tcovers the head\tcould be hats, caps, helmets, crowns or any other object worn on top of the head\tmay have a brim, visor or ear-flaps.", 13], "glass wine bottle": ["Yes. 'Glass wine bottle' has a tangible appearance and is a specific type of container.\nA few things that are visually similar to 'glass wine bottle' but are not 'glass wine bottle' are:\tbeer bottle\tmilk bottle\tflower vase\tperfume bottle\nThere are several useful visual features to tell there is 'glass wine bottle' and not similar things in a photo: \ttapered neck and long body\tdark or greenish-colored glass\tfoil or plastic wrap over the cork or cap\tlabel indicating the brand and type of wine.", 13], "boy skate board": ["Yes. 'Boy skateboard' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'boy skateboard' but are not 'boy skateboard' are:\tlongboard\tsurfboard\tsnowboard\tscooter\nThere are several useful visual features to tell there is 'boy skateboard' and not similar things in a photo:\twheeled deck\ttwo axles attached to the board\tmetal trucks\twheels with bearings\ton the ground or in mid-air\twhile performing a trick or riding", 13], "wood picnic table": ["Yes. 'Wood picnic table' has a tangible appearance and is a type of outdoor furniture.\nA few things that are visually similar to 'wood picnic table' but are not 'wood picnic table' are:\twooden bench\toutdoor chair\tcamp stove\tcoolers\nThere are several useful visual features to tell there is 'wood picnic table' and not similar things in a photo:\twooden tabletop\tstraight benches\toriented with both benches and tabletop horizontal\tfolds up for easy storage and transportation.", 13], "combo": ["No. 'Combo' is too vague or abstract to be distinguished in a photo.", 13], "seashells": ["Yes. 'Seashells' has a tangible appearance and is a hard protective outer layer of marine mollusks.\nA few things that are visually similar to 'seashells' but are not 'seashells' are:\trocks\tcoral\tpetrified wood\nThere are several useful visual features to tell there is 'seashells' and not similar things in a photo:\tcurved shape\twith a pointed end\tridged or smooth texture\tpink, white, brown or tan color\tfound along the beach or ocean floor", 13], "times": ["No. 'Times' is too vague or abstract to be distinguished in a photo.", 13], "polygons": ["Yes. 'Polygons' has a tangible appearance and refers to a closed shape with straight sides.\nA few things that are visually similar to 'polygons' but are not 'polygons' are: circles, curves, spirals, waves.\nThere are several useful visual features to tell there are 'polygons' and not similar things in a photo:\t\n- Closed shape with straight sides.\n- The number of sides can range from 3 to infinity.\n- Each side must meet at endpoints, creating vertices.\n- The angles formed at the vertices must add up to a total of 360 degrees.", 13], "wind sock": ["Yes. 'Wind sock' has a tangible appearance and is a device that shows the direction and strength of the wind.\nA few things that are visually similar to 'wind sock' but are not 'wind sock' are:\ttraffic cones\tflashing beacons\tpost boxes\nThere are several useful visual features to tell there is 'wind sock' and not similar things in a photo:\tcylindrical in shape\twide opening on one end\tnarrow tail on the other end\tbrightly colored (often red and white)\tfluttering in the wind\tpoints in the direction of the wind", 13], "gulls": ["Yes. 'Gulls' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'gulls' but are not 'gulls' are:\tterns\tpelicans\talbatrosses\nThere are several useful visual features to tell there is 'gulls' and not similar things in a photo:\tmedium to large size\twebbed feet\twingspan ranging from 3 to 5 feet\tplumage mostly white, with grey or black feathers on wings and back\thooked beak that is usually yellow, orange, or red in color", 13], "converse": ["Yes. 'Converse' has a tangible appearance as it refers to a specific type of shoe brand.\nA few things that are visually similar to 'converse' but are not 'converse' are:\tsneakers\tathletic shoes\trunning shoes\nThere are several useful visual features to tell there are \"Converse\" sneakers and not similar shoes in a photo:\tcircular Converse logo on the back\twhite rubber toe cap and sole\thigh-top or low-top silhouette\tcanvas material\twith or without laces\tcan come in various colors or patterns", 13], "orange hair": ["Yes. 'Orange hair' has a tangible appearance.\nA few things that are visually similar to 'orange hair' but are not 'orange hair' are:\tgolden retriever fur\tpumpkin carving\torange wig\nThere are several useful visual features to tell there is 'orange hair', and not similar things in a photo:\thair-like texture\tgrowing from a person's scalp\twarm shade of orange", 13], "luggage carousel": ["Yes. 'Luggage carousel' has a tangible appearance and is a kind of transportation infrastructure.\nA few things that are visually similar to 'luggage carousel' but are not 'luggage carousel' are:\tconveyor belts\tescalators\tmoving walkways\troller coasters\nThere are several useful visual features to tell there is 'luggage carousel' and not similar things in a photo:\tcircular shape\tlarge size\tfor rotating suitcases and bags\tfor distributing luggage after a flight\tmay have signs or screens indicating the luggage's destination", 13], "hamster": ["Yes. 'Hamster' has a tangible appearance and is a type of small rodent.\nA few things that are visually similar to 'hamster' but are not 'hamster' are:\tmouse\tgerbil\trat\tground squirrel\nThere are several useful visual features to tell there is 'hamster' and not similar things in a photo:\tsmall size\tfurry body\tshort legs\tchubby cheeks\tpointed nose\tcurved tail\tcolors including grey, brown, white, and black", 13], "rhubarb": ["Yes. 'Rhubarb' has a visually concrete appearance as it is a plant with distinguishable features.\nA few things that are visually similar to 'rhubarb' but are not 'rhubarb' are:\tCelery\tChard, Swiss\tLettuce\tSpinach\nThere are several useful visual features to tell there is 'rhubarb' and not similar things in a photo: Long edible pink or greenish stalks that are thicker at one end\tDeep green leaves with long petioles growing from a central crown arrangement \tGrowing in clumps on the ground No flowers or seeds on the stalks", 13], "yellow chain": ["Yes. 'Yellow chain' has a tangible appearance and is a type of link used for various purposes.\nA few things that are visually similar to 'yellow chain' but are not 'yellow chain' are:\ttree branch\tfishing net\trope\tzipper\nThere are several useful visual features to tell there is 'yellow chain' and not similar things in a photo:\tmade of metal or plastic\tconsists of links\tcolored yellow\tused as a barrier or for securing objects", 13], "gym shoe": ["Yes. 'Gym shoe' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'gym shoe' but are not 'gym shoe' are:\trunning shoes\tcasual shoes\tsandals\tboots\nThere are several useful visual features to tell there is 'gym shoe' and not similar things in a photo:\tsporty design\tlightweight and breathable material\tsole with good traction for exercise\tpadding and support for the foot and ankle", 13], "shingle": ["Yes. 'Shingle' has a tangible appearance and is a type of roofing material.\nA few things that are visually similar to 'shingle' but are not 'shingle' are:\twood plank\ttile\tbrick\tmetal panel\nThere are several useful visual features to tell there is 'shingle' and not similar things in a photo:\toverlapping rectangular or hexagonal shapes\tasphalt, wood, or slate material\ttexture or pattern on the surface\tresembling fish scales or feathers", 13], "orb": ["Yes. 'Orb' has a tangible appearance and is a spherical object.\nA few things that are visually similar to 'orb' but are not 'orb' are:\tball\tplanet\tapple\tpokeball\nThere are several useful visual features to tell there is 'orb' and not similar things in a photo:\tspherical or round shape\tglowing or illuminated appearance\tsmooth, reflective surface\tno visible seams or markings\tcontext (e.g. photographed in a haunted location as an \"orb\" ghostly phenomenon)", 13], "silver fire": ["No. 'Silver fire' is too vague or abstract to be distinguished in a photo.", 13], "steep hill": ["Yes. 'Steep hill' has a tangible appearance and refers to a terrain feature.\nA few things that are visually similar to 'steep hill' but are not 'steep hill' could be a cliff or a mountain.\nThere are several useful visual features to tell there is a 'steep hill' and not similar things in a photo:\tsloping terrain\tlarge incline or gradient\t sharp rise in elevation relative to the surrounding area.", 13], "marshmallows": ["Yes. 'Marshmallows' has a tangible appearance and is a type of confectionery.\nA few things that are visually similar to 'marshmallows' but are not 'marshmallows' are:\tcotton balls\tfake snow\twhite sponges\tfoam pieces\nThere are several useful visual features to tell there is 'marshmallows' and not similar things in a photo:\trectangular, cube-shaped\twhite or pink\tcolorful\tspongy texture\tmelted or toasted appearance\tserved in hot beverages or as a snack", 13], "dirt patches": ["Yes. 'Dirt patches' has a tangible appearance and refers to areas of uncovered soil or ground that are visibly different from surrounding areas.\nA few things that are visually similar to 'dirt patches' but are not 'dirt patches' are:\tshadows\twater puddles\tlight and dark soil spots\tonion or garlic bulbs\nThere are several useful visual features to tell there are 'dirt patches' and not similar things in a photo:\tbrown or reddish color\tuneven texture\tabsence of vegetation or grass\tusually found in dry or bare areas", 13], "orange sunset": ["Yes. 'Orange sunset' has a tangible appearance and is a type of sky scenery.\nA few things that are visually similar to 'orange sunset' but are not 'orange sunset' are:\torange sky due to pollution\tsmoke from a fire\torange lights\nThere are several useful visual features to tell there is 'orange sunset' and not similar things in a photo:\tthe sun is setting or rising\tthe dominant color is orange, yellow, and red\tthe sky has a gradient\teffect of the sun's reflection on water or clouds", 13], "bottom row": ["Yes. 'Bottom row' has a tangible appearance and refers to a specific location or position in a row of objects.\nThere are no things that are visually similar to 'bottom row' that are not 'bottom row'.\nUseful visual features for distinguishing 'bottom row' from other rows in a photo would be the position of the row in relation to other rows, such as being closest to the bottom edge of the frame or having other rows above it.", 13], "round coffee table": ["Yes. 'Round coffee table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'round coffee table' but are not 'round coffee table' are:\tend table\tdining table\tpedestal table\toutdoor table\nThere are several useful visual features to tell there is 'round coffee table' and not similar things in a photo:\tround\ttop surface to place things on, like drinks and magazines\tshorter than most tables and placed near couches or chairs\ttends to have one base, rather than four legs, supporting it.", 13], "dirty wall": ["Yes. 'Dirty wall' has a tangible appearance and is a kind of surface.\nA few things that are visually similar to 'dirty wall' but are not 'dirty wall' are:\tpainted wall\ttextured wall\twallpapered wall\t\nThere are several useful visual features to tell there is 'dirty wall' and not similar things in a photo:\tstains\twatermarks\tdark patches\tor other discolorations\tdirt and grime marks\twall finish is visible through the dirt and grime.", 13], "water buffalo": ["Yes. 'Water buffalo' has a tangible appearance and is a type of large domesticated bovine.\nA few things that are visually similar to 'water buffalo' but are not 'water buffalo' are:\tbison\tyak\tcow\tmoose\nThere are several useful visual features to tell there is 'water buffalo' and not similar things in a photo:\tgrey or black in color\tlarge curved and pointed horns\thorns grow out sideways from the sides of the head\thunched shoulder large, dark eyes\tclumsy and heavy gait\tdewlap(s) (loose skin under the neck)", 13], "straw basket": ["Yes. 'Straw basket' has a tangible appearance and is a type of basket.\nA few things that are visually similar to 'straw basket' but are not 'straw basket' are:\tpaper bag\tbackpack\twicker basket\tplastic container\nThere are several useful visual features to tell there is 'straw basket' and not similar things in a photo:\tmade of straw or similar material\tbrown or beige\tcolorful patterns or embroidery\toval or round shape\tbraided handle", 13], "block building": ["Yes. 'Block building' has a tangible appearance and refers to structures constructed with blocks.\nA few things that are visually similar to 'block building' but are not 'block building' are:\tstack of books\tpile of logs\trock formation\nThere are several useful visual features to tell there is 'block building' and not similar things in a photo:\trectangular shapes\tsymmetrical design\trepetitive patterns\tusing blocks of various sizes or colors", 13], "metal bumper": ["Yes. 'Metal bumper' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'metal bumper' but are not 'metal bumper' are:\tmetal railings\tmetal barriers\tmetal fences\nThere are several useful visual features to tell there is 'metal bumper' and not similar things in a photo:\tgenerally located at the front or rear of a vehicle\tcurved or angular shape\tdesigned to absorb shock or impact\tattached to the frame or chassis of the vehicle", 13], "wooden walls": ["Yes. 'Wooden walls' has a tangible appearance and refers to a type of wall made out of wood.\nA few things that are visually similar to 'wooden walls' but are not 'wooden walls' are:\twallpaper\tbricks\tstones\tplaster\nThere are several useful visual features to tell there is 'wooden walls' and not similar things in a photo:\tmade of wood\tslats or panels visible\tgrain of the wood\tcolor and texture of the wood", 13], "girafee": ["Yes. 'Giraffe' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'giraffe' but are not 'giraffe' are:\tdoe\thorse\tzebra\tokapi\nThere are several useful visual features to tell there is 'giraffe' and not similar things in a photo:\ttall and long neck\tpatches on its coat (usually brown, orange or yellow)\tlong legs ending in cloven hooves\tbrown spotted coat with a whitish background\tbig head with two small horns on top (ossicles)", 13], "pedestrian": ["Yes. 'Pedestrian' has a tangible appearance and is a person who walks on foot.\nA few things that are visually similar to 'pedestrian' but are not 'pedestrian' are:\trunners\tcyclists\tmotorists\tanimals\nThere are several useful visual features to tell there is a 'pedestrian' in a photo and not similar things:\twalking on foot\tusually slower than other moving objects\tmostly situated on sidewalks or zebra crossings\twalking with shopping bags, backpacks, or other pedestrian attributes.", 13], "fir": ["Yes. 'Fir' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'fir' but are not 'fir' are:\tpine\tspruce\tcypress\tjuniper\nThere are several useful visual features to tell there is 'fir' and not similar things in a photo:\tneedle-like leaves arranged spirally on the stem\tbranches growing horizontally\tconical shape", 13], "blue berry": ["Yes. 'Blueberry' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'blueberry' but are not 'blueberry' are:\tgrapes\tblackberries\tplums\nThere are several useful visual features to tell there is 'blueberry' and not similar things in a photo:\tsmall, round fruit\tblue or purplish-blue in color\tsmooth skin\twith a small crown on top\tflesh inside the skin with tiny seeds\tsometimes found growing in clusters.", 13], "bird claws": ["Yes. 'Bird claws' has a tangible appearance and is a part of a bird's body.\nA few things that are visually similar to 'bird claws' but are not 'bird claws' are:\tfeline claws\tbear claws\tdog paws\nThere are several useful visual features to tell there are 'bird claws' and not similar things in a photo:\tthree front toes and one back toe\tsharp talons or nails\tfor grasping or perching shaped like a hook or curve", 13], "stadium light": ["Yes. 'Stadium light' has a tangible appearance and refers to the lights installed in large sports arenas or stadiums.\nA few things that are visually similar to 'stadium light' but are not 'stadium light' are:\tstreet light\ttraffic light\thousehold lamps\tfloodlight\nThere are several useful visual features to tell there is 'stadium light' and not similar things in a photo:\thigh-mounted on poles or towers\toften arranged in rows or circles\tbright and powerful\tlighting up a large area\torangish or bluish color of light.", 13], "farmhouse": ["Yes. 'Farmhouse' has a tangible appearance and is a type of house.\nA few things that are visually similar to 'farmhouse' but are not 'farmhouse' are:\tcottage\tbarn\tshed\tcabin\nThere are several useful visual features to tell there is 'farmhouse' and not similar things in a photo:\tfarm animals nearby or visible\twide front porch or veranda\tsloping roof or gables\twell-maintained exterior and landscaping\tfeatures such as a windmill, silo, or tractor", 13], "toilet water": ["Yes. 'Toilet water' has a tangible appearance and refers to the water in the toilet bowl.\nA few things that are visually similar to 'toilet water' but are not 'toilet water' are:\tpool water\tcooling tower water\tdrainage water\tfloodwater\nThere are several useful visual features to tell there is 'toilet water' and not similar things in a photo:\nclear water in the bowl of a toilet, usually with a blue or turquoise hue\ncontains cleaning agents that may produce bubbles or foam", 13], "door building": ["No. 'Door building' is too vague or abstract to be distinguished in a photo.\nHowever, a few things that are visually similar to a building with doors but are not 'door building' are:\tapartment building\tstorefront building\twarehouse\nUseful visual features for distinguishing a 'door building' from these similar things in a photo include:\tclearly visible doors\tornate or unique doors\tdoor frames or arches", 13], "crochet": ["Yes. 'Crochet' has a tangible appearance and is a type of needlework.\nA few things that are visually similar to 'crochet' but are not 'crochet' are:\tknitting\tembroidery\ttatting\tweaving\nThere are several useful visual features to tell there is 'crochet' and not similar things in a photo:\tinterlocking loops or stitches\tuse of a crochet hook\tlarger hook than knitting\tcreates fabric with a bumpy texture\tand gives a lacy appearance when using fine thread.", 13], "grease stains": ["Yes. 'Grease stains' has a tangible appearance and is a type of stain.\nA few things that are visually similar to 'grease stains' but are not 'grease stains' are:\tink stains\twine stains\tpaint stains\tchocolate stains\nThere are several useful visual features to tell there is 'grease stains' and not similar things in a photo:\tgreasy appearance and texture\ttranslucent color\toil or fat residue fingerprint-shaped stains located in areas where food is prepared or consumed", 13], "bike sign": ["Yes. 'Bike sign' has a tangible appearance and is a type of traffic sign.\nA few things that are visually similar to 'bike sign' but are not 'bike sign' are:\tpedestrian sign\tstop sign\tyield sign\nThere are several useful visual features to tell there is 'bike sign' and not similar things in a photo:\twhite silhouette of a person on a bike\ttwo circles with a line connecting them\tbackground color of green or blue (depending on the country)", 13], "bent neck": ["Yes. 'Bent neck' has a tangible appearance and is a physical condition when the neck is curved or inclined.\nA few things that are visually similar to 'bent neck' but are not 'bent neck' are:\tleaning tower of Pisa\tnecklaces\tbent tree branch\nThere are several useful visual features to distinguish 'bent neck' from the listed similar things in a photo:\tan obvious curve or angle in the neck\tpain, discomfort or limited motion in the neck area compared to natural posture\tapparent discomfort or unease in the subject's facial expression while holding their neck in a particular position.", 13], "stumps": ["Yes. 'Stumps' has a tangible appearance and refers to the bottom part of a tree trunk left after the tree has been cut down.\nA few things that are visually similar to 'stumps' but are not 'stumps' are:\tlogs\tpoles\tpillars\tcolumns\nThere are several useful visual features to tell there is 'stumps' and not similar things in a photo:\trough and uneven surface\tbark remnants or wood grain visible\thollow center where the tree was cut out", 13], "farmers": ["Yes. 'Farmers' has a tangible appearance and refers to people who work on a farm.\nA few things that are visually similar to 'farmers' but are not 'farmers' are:\tgardeners\tlandscapers\tconstruction workers\thikers\nThere are several useful visual features to tell there is 'farmers' and not similar things in a photo:\tworking on a farm\twearing work clothes or farm equipment, such as boots or hats\thandling crops, livestock or tools\toutdoor setting, such as a fields, barns or stables", 13], "rafts": ["Yes. 'Rafts' has a tangible appearance and is a kind of watercraft.\nA few things that are visually similar to 'rafts' but are not 'rafts' are:\tkayaks\tcanoe\trowboats\tpaddle boats\nThere are several useful visual features to tell there is 'rafts' and not similar things in a photo:\tmade of logs, planks or inflatable material\tflat or slightly curved bottom\tno visible propulsion system\tusually larger and flatter than canoes or kayaks\tif inflatable, has several air chambers\tand can support multiple passengers.", 13], "train depot": ["Yes. 'Train depot' has a tangible appearance and is a place where trains stop and passengers can get on and off.\nA few things that are visually similar to 'train depot' but are not 'train depot' are:\tbus depot\tsubway station\tparking lot\nThere are several useful visual features to tell there is 'train depot' and not similar things in a photo:\ttracks for trains\tmultiple platforms\tfortes or stalls for trains\ta building or station for passengers and tickets\tsigns pointing to trains and schedules.", 13], "amtrak train": ["Yes. 'Amtrak train' has a tangible appearance and is a type of passenger train operated by Amtrak.\nA few things that are visually similar to 'Amtrak train' but are not 'Amtrak train' are:\tsubway train\tlocal commuter train\tsightseeing train\tfreight train\nThere are several useful visual features to tell there is 'Amtrak train' and not similar things in a photo:\tAmtrak logo on the train or station\tcarriage cars with large windows and comfortable seating\tpassenger boarding or disembarking crowd\tonboard dining or entertainment services", 13], "jalapeno": ["Yes. 'Jalapeno' has a tangible appearance and is a type of pepper.\nA few things that are visually similar to 'jalapeno' but are not 'jalapeno' are:\tbell pepper\tchili pepper\ttomato\tpotato\nThere are several useful visual features to tell there is 'jalapeno' and not similar things in a photo:\tsmaller than a bell pepper\tdark green when immature and red when mature\ttapered and slightly curved in shape\tsmooth and shiny skin with visible white veins\tthick flesh walls\twith a spicy taste", 13], "control box": ["Yes, 'control box' has a tangible appearance and refers to a box that contains control mechanisms for a particular device or system. \nA few things that are visually similar to 'control box' but are not 'control box' are:\tjunction box, fuse box, switchboard, distribution box, power box. \nUseful visual features for distinguishing 'control box' from the listed similar things in a photo are: presence of switches, knobs, or digital displays to control and monitor a specific device, wiring entering and exiting the box, usage of color codes, and labels for identifying the controls with respect to the device or system it controls.", 13], "knight": ["Yes. 'Knight' has a tangible appearance and is a type of historical warrior.\nA few things that are visually similar to 'knight' but are not 'knight' are:\tpaladin\twarrior\tprince\t\nThere are several useful visual features to tell there is 'knight' and not similar things in a photo:\tarmor made of metal\tpieces of armor covering the entire body, including the head\ta helmet with a visor or a face mask\ta shield in their hands\ta sword or a lance", 13], "water pipes": ["Yes. 'Water pipes' has a tangible appearance and is a type of plumbing system.\nA few things that are visually similar to 'water pipes' but are not 'water pipes' are:\tgarden hose\tgas pipeline\tsewage system\tirrigation system\tfurnace ducts\nThere are several useful visual features to tell there is 'water pipes' and not similar things in a photo:\tmetallic material\tcylindrical shape\tfittings\tjoints\tvalves\tconnectors\twater flowing through them", 13], "instruction sign": ["Yes. 'Instruction sign' has a tangible appearance and is a type of sign displaying information or directions.\nA few things that are visually similar to 'instruction sign' but are not 'instruction sign' are:\twarning sign\troad sign\tadvertising sign\tdecorative sign\nThere are several useful visual features to tell there is 'instruction sign' and not similar things in a photo:\tsymbol or text conveying information or directions\tbold and easily readable fonts\tclear and straightforward messages\tintended to inform or direct people", 13], "presentation": ["No. 'Presentation' is too vague or abstract to be distinguished in a photo.", 13], "flatbread": ["Yes. 'Flatbread' has a tangible appearance and is a type of bread that is flattened and unleavened.\nA few things that are visually similar to 'flatbread' but are not 'flatbread' are:\tTortilla\tPita bread\tCracker\tRice paper\nThere are several useful visual features to tell there is 'flatbread' and not similar things in a photo:\tflattened shape\tno rising or puffiness\tunleavened appearance\toften irregular edges and surface marks", 13], "light tower": ["Yes. 'Light tower' has a tangible appearance and is a structure used for nighttime illumination.\nA few things that are visually similar to 'light tower' but are not 'light tower' are:\twind turbines\tskyscrapers\tchimneys\t\nThere are several useful visual features to tell there is 'light tower' and not similar things in a photo:\ttall structure with a narrow base and wider top\tspecification for a light projectors\tarea the light covers\tmetal structure with lights or lamps attached.", 13], "shadow sheep": ["No. 'Shadow sheep' is too vague or abstract to be distinguished in a photo.", 13], "dark stripe": ["Yes. 'Dark stripe' has a tangible appearance and refers to a specific type of visual pattern.\nA few things that are visually similar to 'dark stripe' but are not 'dark stripe' are:\tshadows\tdirt\tstains\tcracks\nThere are several useful visual features to tell there is 'dark stripe' and not similar things in a photo:\tlong and narrow in shape\tsolid and consistent in color\tdarker than the surrounding area\tcan be present on any surface or material.", 13], "tag ear": ["Yes. 'Tag ear' has a tangible appearance and is a type of ear identification system used in animal tagging. \nA few things that are visually similar to 'tag ear' but are not 'tag ear' are:\tregular ear\tpierced ear\nThere are no features similar to 'tag ear' as it is a unique identification system done to animals with special tagging equipment. In a photo, the presence of a small, plastic tag attached to the ear of an animal would indicate the presence of a tag ear.", 13], "topless man": ["Yes. 'Topless man' has a tangible appearance and refers to a man without a shirt or any upper body covering.\nA few things that are visually similar to 'topless man' but are not 'topless man' are:\tman in a tank top or undershirt\tswimsuit,\tman wearing a vest or jacket\topen shirt\nThere are several useful visual features to tell there is 'topless man' and not similar things in a photo:\tabsence of any upper garment\tbare chest and stomach", 13], "lumps": ["Yes. 'Lumps' has a tangible appearance and refers to irregularly shaped masses.\nA few things that are visually similar to 'lumps' but are not 'lumps' are: bumps, hills, stones, mountains, clouds\nThere are several useful visual features to tell there are 'lumps' and not similar things in a photo: irregular shape, no defined edges, may have a rough surface, can be of different sizes, can be found on animals, humans, or objects.", 13], "shadow cow": ["No. 'Shadow cow' is too vague or abstract to be distinguished in a photo. It is not a tangible object or concept.", 13], "uniform shirt": ["Yes. 'Uniform shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'uniform shirt' but are not 'uniform shirt' are:\tregular shirt\tblouse\tt-shirt\tpolo shirt\nThere are several useful visual features to tell there is 'uniform shirt' and not similar things in a photo:\tplain or patterned design in specific colors\tbadge\tor patches\ton the sleeves or chest buttons\tup the front of the shirt\thas a collar and long or short sleeves\ttypically made of durable or breathable material (e.g. cotton, polyester)", 13], "round design": ["Yes. 'Round design' has a tangible appearance and is a particular style of design.\nA few things that are visually similar to 'round design' but are not 'round design' are:\tcurved lines\tcircles\tbubbles\tglow\nThere are several useful visual features to tell there is 'round design' and not similar things in a photo:\trepetitive circular patterns\tsmooth and curved lines\tdominance of round shapes and curves\tcircular shapes and patterns incorporated in architecture or interior design", 13], "keyboard key": ["Yes. 'Keyboard key' has a tangible appearance and is a physical button on a keyboard.\nA few things that are visually similar to 'keyboard key' but are not 'keyboard key' are:\tjoystick\tbutton on a remote\tcontrol\tround stickers\nThere are several useful visual features to tell there is 'keyboard key' and not similar things in a photo:\trectangle or square shape\tletters, numbers, or symbols on the surface\tsunken or elevated position on the keyboard\tvisible space between each key", 13], "grandma": ["No. 'Grandma' is too vague or abstract to be distinguished in a photo.", 13], "fireplace tools": ["Yes. 'Fireplace tools' has a tangible appearance and refers to the set of tools used for maintaining a fire in a fireplace.\nA few things that are visually similar to 'fireplace tools' but are not 'fireplace tools' are:\tkitchen utensils\ttools for gardening\nThere are several useful visual features to tell there are 'fireplace tools' and not similar things in a photo:\tusually sold in a set\tcontaining a poker, a shovel, a brush, and tongs\tmade of metal\thanging or stored near a fireplace", 12], "tumbler": ["Yes. 'Tumbler' has a tangible appearance and is a type of drinking glass.\nA few things that are visually similar to 'tumbler' but are not 'tumbler' are:\tcup\tshot glass\tflute glass\tpilsner glass\tbeer stein\nThere are several useful visual features to tell there is 'tumbler' and not similar things in a photo:\tno stem\tcylindrical shape\twith or without a handle\tthick bottom\tfor drinking cold or hot beverages", 12], "lettuce leaves": ["Yes. 'Lettuce leaves' has a tangible appearance and is the leafy part of a lettuce plant.\nA few things that are visually similar to 'lettuce leaves' but are not 'lettuce leaves' are:\tspinach leaves\tcabbage leaves\tkale leaves\tbasil leaves\nThere are several useful visual features to tell there is 'lettuce leaves' and not similar things in a photo:\tlight green color\tcrinkled or smooth texture\tvein-like lines on the surface\tof a certain size and shape compared to other leaves of the same plant\tgrouped together to form a head of lettuce", 12], "tounge": ["Yes. 'Tongue' has a tangible appearance and is a muscular organ in the mouth.\nA few things that are visually similar to 'tongue' but are not 'tongue' are:\tmuscles\tinsects\tbirds\nThere are several useful visual features to tell there is 'tongue' and not similar things in a photo:\tpink or reddish color\twet and shiny surface\ttexture with taste buds\tconnected to the mouth or throat", 12], "silver stove": ["Yes. 'Silver stove' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'silver stove' but are not 'silver stove' are:\toven\tmicrowave\tdishwasher\tgrill\tcooker\nThere are several useful visual features to tell there is 'silver stove' and not similar things in a photo:\tsilver or metallic appearance\tburners\torifices or holes where flames from gas burners can come out\tcontrol knobs to adjust the temperature\tof varying sizes and capacities", 12], "gas burners": ["Yes. 'Gas burners' has a tangible appearance and is a type of stove.\nA few things that are visually similar to 'gas burners' but are not 'gas burners' are:\twood stoves\tcoal stoves\tbarbecue grill\tbonfires\tfireplace\nThere are several useful visual features to tell there is 'gas burners' and not similar things in a photo:\tblue flame\tcooking utensils resting on the grate\tthe device is connected to a gas line or propane tank rectangular-shaped cooktop", 12], "horse jockey": ["Yes. 'Horse jockey' has a tangible appearance and refers to a person who rides horses in races.\nA few things that are visually similar to 'horse jockey' but are not 'horse jockey' are:\tcowboy\tbiker\tathlete\nThere are several useful visual features to tell there is a 'horse jockey' and not similar things in a photo:\twearing colorful silk clothes\twith a helmet\tor cap\tusing a whip or a crop (instrument for urging a horse)\ton the back of a horse (usually a thoroughbred)\tin a race or prepping for a race", 12], "neat": ["No. 'Neat' is too vague or abstract to be distinguished in a photo. It is a subjective concept related to cleanliness, tidiness, and organization, and might vary from person to person. \nTherefore, there are no things that are visually similar to 'neat' but are not 'neat'.", 12], "toilet top": ["Yes. 'Toilet top' has a tangible appearance and refers to the lid or cover of a toilet bowl.\nA few things that are visually similar to 'toilet top' but are not 'toilet top' are:\tsink knob\tshower faucet lid\tcabinet handle\tcounter top\nThere are several useful visual features to tell there is 'toilet top' and not similar things in a photo:\toval or round shape\tsits on top of a toilet bowl\thas hinges or other means to attach to the toilet bowl", 12], "tea pots": ["Yes. 'Tea pots' has a tangible appearance and is a kind of vessel used for brewing and serving tea.\nA few things that are visually similar to 'tea pots' but are not 'tea pots' are:\tcoffee pots\tdecorative vases\tpitchers\nThere are several useful visual features to tell there is 'tea pots' and not similar things in a photo:\tlid\twith a spout\tfor brewing and serving tea\thandle on the side or the top of the vessel\tvariety of shapes and sizes, but typically shorter and wider than other pots", 12], "oval window": ["Yes. 'Oval window' has a tangible appearance and is a specific structure found in the ear.\nA few things that are visually similar to 'oval window' but are not 'oval window' are:\tcircle\twindow\tcylinder\trecord\nThere is a useful visual feature to tell there is 'oval window' and not similar things in a photo:\tlocated in the bone of the middle ear.", 12], "chicken leg": ["Yes. 'Chicken leg' has a tangible appearance and is a part of a chicken's body.\nA few things that are visually similar to 'chicken leg' but are not 'chicken leg' are:\tturkey leg\tdinosaur leg\tbear leg\tpork leg\nThere are several useful visual features to tell there is 'chicken leg' and not similar things in a photo:\tthin bone in the center\tmuscular, meaty part around the bone\tskin covering the meat\tlayer of fat on the skin", 12], "orange liquid": ["Yes. 'Orange liquid' has a tangible appearance and is a type of liquid.\nA few things that are visually similar to 'orange liquid' but are not 'orange liquid' are:\torange soda\torange juice\torange sports drink\torange cleaning solution\nThere are several useful visual features to tell there is 'orange liquid' and not similar things in a photo:\tviscous or runny texture\topaque or transparent appearance\tshades of orange color, from pale to bright or neon\tcolorants may be added\ttoxicity warning label", 12], "waste paper basket": ["Yes. 'Waste paper basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'waste paper basket' but are not 'waste paper basket' are:\ttrash can\tbasket\tlaundry hamper\tcrate\nThere are several useful visual features to tell there is 'waste paper basket' and not similar things in a photo:\tsmaller size\tusually round or oval\toften made of wire, plastic, or wicker\toften placed near a desk or work area\tfor collecting paper waste or small items", 12], "coffe cup": ["Yes. 'Coffee cup' has a tangible appearance and is a type of cup.\nA few things that are visually similar to 'coffee cup' but are not 'coffee cup' are:\ttea cup\tmug\tglass\ttumbler\nThere are several useful visual features to tell there is 'coffee cup' and not similar things in a photo:\thandle\tround and cylindrical shape\tsaucer (optional)\tcharacteristic brown color (in some cases)", 12], "mountain ridge": ["Yes. 'Mountain ridge' has a tangible appearance as a part of a mountain.\nA few things that are visually similar to 'mountain ridge' but are not 'mountain ridge' are:\thill\tplateau\trampart\nThere are several useful visual features to tell there is 'mountain ridge' and not similar things in a photo:\tlong and narrow\toutcropping rocks and boulders\tmajestic and steep\tsurrounded by slopes and valleys\tdifferent in elevation compared to the back part of the mountain", 12], "silver latch": ["Yes. 'Silver latch' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'silver latch' but are not 'silver latch' are:\thook\tlock\tclasp\tknob\thandle\nThere are several useful visual features to tell there is 'silver latch' and not similar things in a photo:\tmetallic appearance\tsilver color\ttwo movable parts that interlock or separate\thinged attachment to a door or a lid", 12], "eat": ["No. 'Eat' is too vague or abstract to be distinguished in a photo.", 12], "system": ["No. 'System' is too vague or abstract to be distinguished in a photo.", 12], "round pillow": ["Yes. 'Round pillow' has a tangible appearance and is a type of cushion.\nA few things that are visually similar to 'round pillow' but are not 'round pillow' are:\tbolster cushion\tpouf cushion\tottoman cushion\tbean bag chair\nThere are several useful visual features to tell there is 'round pillow' and not similar things in a photo:\tcircular or round shape\tsoft and plushy texture\tno defined edges or corners\tcan be used as a seat or prop for back or neck support", 12], "metal brace": ["Yes. 'Metal brace' has a tangible appearance and is a type of orthopedic device.\nA few things that are visually similar to 'metal brace' but are not 'metal brace' are:\tbelt\tmetal chain\tcuff\tmetal clasp\nThere are several useful visual features to tell there is 'metal brace' and not similar things in a photo:\tmade of metal or another sturdy material\tworn on a body part, often the arm or leg\thinged or adjustable in some way\tcontains straps, buckles, or other fasteners", 12], "tickets": ["Yes. 'Tickets' has a tangible appearance and is a kind of paper document.\nA few things that are visually similar to 'tickets' but are not 'tickets' are:\tcoupons\treceipts\tvouchers\tbusiness cards\nThere are several useful visual features to tell there is 'tickets' and not similar things in a photo:\tpaper-based\tdocument with specific information\tsymbols, codes or patterns related to the show or event\tdate and time information\ton perforated paper or with barcode", 12], "tea kettles": ["Yes. 'Tea kettles' has a tangible appearance and is a kind of kitchenware.\nA few things that are visually similar to 'tea kettles' but are not 'tea kettles' are:\tcoffee pots\turns\tpitchers\tthermos bottles\nThere are several useful visual features to tell there is 'tea kettles' and not similar things in a photo:\tmetallic surface\tspout for pouring\thandles or a handle\tlid on the top\tstraight or curved shape", 12], "yummy": ["No. 'Yummy' is too vague or abstract to be distinguished in a photo.", 12], "thick stripes": ["Yes. 'Thick stripes' has a tangible appearance.\nA few things that are visually similar to 'thick stripes' but are not 'thick stripes' are:\tthin stripes\thorizontal lines\tvertical lines\tpolka dots\t\nThere are several useful visual features to tell there are 'thick stripes' and not similar things in a photo:\twide lines of consistent thickness\tspaces between the stripes should be equal\ttoo wide to be perceived as thin stripes, but not too wide as to appear as blocks of color.", 12], "dogs tongue": ["Yes. 'Dogs tongue' has a tangible appearance and is a body part of a dog.\nA few things that are visually similar to 'dogs tongue' but are not 'dogs tongue' are:\tcat's tongue\thuman tongue\tlizard tongue\nThere are several useful visual features to tell there is 'dogs tongue' and not similar things in a photo:\twet and slimy\tpink, red or black\tcolor depends on breed or health\tsize and shape depends on breed and individual\topen mouth or panting\tdog's fur is visible in the background", 12], "cannister": ["Yes. 'Canister' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'cannister' but are not 'cannister' are:\tjar\tbottle\tbox\ttube\tcup\nThere are several useful visual features to tell there is 'canister' and not similar things in a photo:\tcylindrical or box-shaped container\twith a lid\tor propulsion system for gases (in case of chemical cannisters)\tvarious sizes\tand capacities for storing and distributing goods including fuel, food, or drinks.", 12], "tennis sneakers": ["Yes. 'Tennis sneakers' has a tangible appearance and refers to a specific type of sports footwear.\nA few things that are visually similar to 'tennis sneakers' but are not 'tennis sneakers' are: running shoes, basketball shoes, hiking shoes, casual shoes\nThere are several useful visual features to tell there is 'tennis sneakers' and not similar things in a photo: flat and smooth sole, low-cut design for ankle mobility and flexibility, breathable upper construction, with reinforced toe area to withstand high impact during a tennis match.", 12], "shoestrings": ["Yes. 'Shoestrings' has a tangible appearance and refers to the strings used to tie shoes.\nA few things that are visually similar to 'shoestrings' but are not 'shoestrings' are:\tribbons\tstraps\tcords\trope\nThere are several useful visual features to tell there is 'shoestrings' and not similar things in a photo:\tflat and narrow shape\tmade of fabric or synthetic material\tlined with aglets at the end\tlooped and tied around the shoe holes", 12], "wall picture": ["Yes. 'Wall picture' has a tangible appearance and refers to any photograph or painting hung on a wall.\nA few things that are visually similar to 'wall picture' but are not 'wall picture' are:\tmirrors\tshelves\tclocks\tdecorative plates\nThere are several useful visual features to tell there is a 'wall picture' and not similar things in a photo:\trectangular frame\thung or mounted on a wall\tfeatures an image or artwork", 12], "tennis court floor": ["Yes. 'Tennis court floor' has a tangible appearance and refers to the playing surface of a tennis court. \nA few things that are visually similar to 'tennis court floor' but are not 'tennis court floor' are:\tconcrete sidewalk\tindoor gym floor\tbasketball court floor\nThere are several useful visual features to tell there is 'tennis court floor' and not similar things in a photo:\trectangular in shape\twith multiple white lines and markings\tdifferent colored areas on the court\tsurrounded by a net and fences.", 12], "male soccer player": ["Yes. 'Male soccer player' has a tangible appearance and is a person playing a sport.\nA few things that are visually similar to 'male soccer player' but are not 'male soccer player' are:\tfootball player\tbasketball player\ttrack and field athlete\nThere are several useful visual features to tell there is 'male soccer player' and not similar things in a photo:\twearing soccer uniform (jersey, shorts, socks)\twearing cleats (soccer shoes)\tplay on a soccer field\tplaying with a soccer ball amoung other things.", 12], "snowboard boots": ["Yes. 'Snowboard boots' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'snowboard boots' but are not 'snowboard boots' are:\tski boots\thiking boots\twork boots\twinter boots\nThere are several useful visual features to tell there are 'snowboard boots' and not similar things in a photo:\ttall with a cuff above the ankle\tthick and rugged sole\tvibrant colors or patterns\tlacing or buckle system for securing the foot and ankle to the boot\tstiff and supportive sole for better control of the board\twhile snow boots can have some of these features the appearance of the heel section and the overall shape of the boot can be different from the snowboard boots.", 12], "snowboard boot": ["Yes. 'Snowboard boot' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'snowboard boot' but are not 'snowboard boot' are:\tski boot\thiking boot\tskateboard shoes\t\nThere are several useful visual features to tell there is 'snowboard boot' and not similar things in a photo:\tspecifically designed for snowboarding\tbulky shape\tthick, sturdy sole\tsupport for ankle and calf mostly black or grey colors\tlace-up or buckle-up closure\tsystem of adjustability\tfor attaching to the snowboard", 12], "silver bars": ["Yes. 'Silver bars' has a tangible appearance and is a type of precious metal.\nA few things that are visually similar to 'silver bars' but are not 'silver bars' are:\tchrome bars\tsteel bars\tgold bars\nThere are several useful visual features to tell there is 'silver bars' and not similar things in a photo:\tdull-grey or shiny white color\trectangular shape\twith numeric and/or logo markings", 12], "horse eye": ["Yes. 'Horse eye' has a tangible appearance and is a part of a horse's anatomy.\nA few things that are visually similar to 'horse eye' but are not 'horse eye' are:\tcow eye\tzebra eye\tgoat eye\t\nThere are several useful visual features to tell there is 'horse eye' and not similar things in a photo:\thorizontal pupil\tdark iris\twhite sclera\tprominent lashes and brows", 12], "tape dispenser": ["Yes. 'Tape dispenser' has a tangible appearance and is a household item.\nA few things that are visually similar to 'tape dispenser' but are not 'tape dispenser' are:\tscissors\tstapler\thole punch\tbinder clip\nThere are several useful visual features to tell there is 'tape dispenser' and not similar things in a photo:\trectangular or cylindrical shape\twith a cutting edge\tfor holding rolls of tape or adhesive-backed labels\tmay be transparent or opaque.", 12], "paper coffee cup": ["Yes. 'Paper coffee cup' has a tangible appearance and is a type of disposable cup.\nA few things that are visually similar to 'paper coffee cup' but are not 'paper coffee cup' are:\tplastic cup\tmug\ttravel mug\tdisposable water cup\nThere are several useful visual features to tell there is 'paper coffee cup' and not similar things in a photo:\tpaper material\tribbed or creased texture around the middle of the cup\trolled upper rim\tof varying sizes and colors\thot beverage emblem or logo (e.g., \"caution: hot\")", 12], "silver sign": ["Yes. 'Silver sign' has a tangible appearance and is typically made of metal.\nA few things that are visually similar to 'silver sign' but are not 'silver sign' are:\tlicense plate\ttin can\tsteel gate\tsilver jewelry\nThere are several useful visual features to tell there is 'silver sign' and not similar things in a photo:\trectangular shape\tsilver or grey color\tmetallic surface with engravings or writing\thanging or attached to a wall\tfor commercial or informative purposes.", 12], "metal tea kettle": ["Yes. 'Metal tea kettle' has a tangible appearance and is a kind of kitchenware.\nA few things that are visually similar to 'metal tea kettle' but are not 'metal tea kettle' are:\tmetal coffee pot\tmetal watering can\tmetal pitcher\nThere are several useful visual features to tell there is 'metal tea kettle' and not similar things in a photo:\tdome-shaped\thinged lid\twith a spout\tand a handle\tknob or lever on the spout or lid.", 12], "scanner": ["Yes. 'Scanner' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'scanner' but are not 'scanner' are:\tprinter\tcopier\tfax machine\tphotocopier\nThere are several useful visual features to tell there is 'scanner' and not similar things in a photo:\tflat glass or plastic bed for placing documents or photos\tlight or laser for scanning images\tor button for initiating the scanning process\ta cable or connection to a computer\tor other devices", 12], "pan pizza": ["Yes. 'Pan pizza' has a tangible appearance and is a type of pizza.\nA few things that are visually similar to 'pan pizza' but are not 'pan pizza' are:\tthin-crust pizza\tdeep-dish pizza\tquesadilla\ttortilla\nThere are several useful visual features to tell there is 'pan pizza' and not similar things in a photo:\tthick crust\tround, square, or rectangular shape\tcooked in a deep-dish or a heavy-bottomed pan\tfluffy and doughy crust\tgenerous amount of toppings and cheese", 12], "bicycle rider": ["Yes. 'Bicycle rider' has a tangible appearance and is a person riding a bicycle.\nA few things that are visually similar to 'bicycle rider' but are not 'bicycle rider' are: skater, pedestrian, motorcyclist, scooter rider.\nThere are several useful visual features to tell there is a 'bicycle rider' and not similar things in a photo: person riding a bicycle with two wheels, pedals, and handlebars, wearing a helmet, riding on a designated bike lane or bike path, with movement and motion blur.", 12], "crossbody bag": ["Yes. 'Crossbody bag' has a tangible appearance and is a type of handbag worn across the body.\nA few things that are visually similar to 'crossbody bag' but are not 'crossbody bag' are:\tbackpack\tpurse\ttote bag\tmessenger bag\nThere are several useful visual features to tell there is 'crossbody bag' and not similar things in a photo:\tlong strap worn across the body\tsmall to medium-sized bag\thangs around waist or hip\tlevel of the bag is at the thigh or the hip", 12], "cool": ["No. 'Cool' is too vague or abstract to be distinguished in a photo. \n\nHowever, some things that people might consider 'cool' could include: fancy cars, fashionable clothing, modern technology, stylish accessories, etc. \n\nIt's important to note that what one person considers 'cool' may not be the same as what someone else considers 'cool.' Therefore, it's difficult to provide unique visual features for distinguishing 'cool' from similar things in a photo.", 12], "topped table": ["Yes. 'Topped table' has a tangible appearance and refers to a table that has something on its surface.\nA few things that are visually similar to 'topped table' but are not 'topped table' are:\tempty table\ttable with a tablecloth\ttable with plates and utensils\nThere are several useful visual features to tell there is a 'topped table' and not similar things in a photo:\tan object, decoration or food items on its surface\tsurrounded by chairs or other furniture\tin a restaurant or dining room setting", 12], "rectangular table": ["Yes. 'Rectangular table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'rectangular table' but are not 'rectangular table' are:\tdesk\tcounter\tshelf\tbench\nThere are several useful visual features to tell there is 'rectangular table' and not similar things in a photo:\trectangle shape\tflat surface\tforward-facing legs or pedestal\tcan seat multiple people", 12], "pothole": ["Yes. 'Pothole' has a tangible appearance and is a type of road damage.\nA few things that are visually similar to 'pothole' but are not 'pothole' are:\tshadow\twater puddle\toil stain\tmanhole cover\nThere are several useful visual features to tell there is 'pothole' and not similar things in a photo:\tirregular-shaped hole on a road or pavement\tuneven surface\twith or without debris or cracks around\tit might contain rainwater or gravel inside", 12], "cd case": ["Yes. 'CD case' has a tangible appearance and is a type of protective container for CDs.\nA few things that are visually similar to 'cd case' but are not 'cd case' are:\t\nDVD case, book cover, phone case, tablet case, glasses case\nThere are several useful visual features to distinguish 'cd case' from the listed similar things in a photo:\t\nrectangular shape, with a smaller size than a book\na transparent or translucent portion to view the CD inside\na CD holder with circular or oval-shaped indents that can secure a CD in place\na locking mechanism or clasp to keep the case closed and the CD protected.", 12], "mask umpire": ["Yes. 'Mask umpire' has a tangible appearance and refers to the person wearing the protective gear and making decisions in sports.\nA few things that are visually similar to 'mask umpire' but are not 'mask umpire' are:\tpeople wearing masks\tvolcano rescuers\tscientists working with hazardous materials\nThere are several useful visual features to tell there is 'mask umpire' and not similar things in a photo:\twearing a distinct uniform (black and white stripes)\twearing a mask and chest protector\tgesturing or signaling to players on the field\tos making decisions during a game or match.", 12], "cartoons": ["Yes. 'Cartoons' has a tangible appearance and is a type of illustration.\nA few things that are visually similar to 'cartoons' but are not 'cartoons' are:\tphotographs\tpaintings\tcomics\tgraphics\nThere are several useful visual features to tell there are 'cartoons' and not similar things in a photo:\tbold or exaggerated lines\tbright colors\tcartoonish characters or settings\tword or thought bubbles\thumoristic appearance", 12], "fence poles": ["Yes. 'Fence poles' has a tangible appearance and typically refers to wooden or metal posts used to support a fence.\nA few things that are visually similar to 'fence poles' but are not 'fence poles' are:\ttree trunks\tlamp posts\ttraffic barriers\tchimneys\nThere are several useful visual features to tell there are 'fence poles' and not similar things in a photo:\tvertical and cylindrical in shape\tprotruding from the ground\ttightly spaced\tattached to a fence\tor wire mesh or wooden boards.", 12], "person water skiing": ["Yes. 'Person water skiing' has a tangible appearance.\nA few things that are visually similar to 'person water skiing' but are not 'person water skiing' are:\tsurfing\tswimming\tjet skiing\twakeboarding\t\nThere are several useful visual features to tell there is 'person water skiing' and not similar things in a photo:\ta person being pulled by a boat\ton a pair of skis\thandling a tow rope\tor skiing on one ski\tcreating splashes while skiing on water.", 12], "joint": ["Yes. 'Joint' has a tangible appearance and is a type of connection between bones.\nA few things that are visually similar to 'joint' but are not 'joint' are:\tknots\tinsects' body segments\tgear teeth\t\nThere are several useful visual features to tell there is 'joint' and not similar things in a photo: malleable or rigid bendable material, bone or cartilage; connecting two or more bones; presence of synovial fluid; protective cartilage; mobility and range of motion.", 12], "sink area": ["Yes. 'Sink area' has a tangible appearance and is a specific area in a room where a sink or sinks are located.\nA few things that are visually similar to 'sink area' but are not 'sink area' are:\tcountertop\tkitchen island\tbar\ttop of a dresser\nThere are several useful visual features to tell there is 'sink area' and not similar things in a photo:\tone or more sinks\tfaucets\ta mirror\tor a backsplash or a wall\ttowels and soap, or other toiletries\tdish soap and scrub brush or other kitchen tools, depending on the type of sink area.", 12], "flowery": ["No. 'Flowery' is too vague or abstract to be distinguished in a photo.", 12], "platforms": ["Yes. 'Platforms' has a tangible appearance and refers to raised surfaces or structures.\nA few things that are visually similar to 'platforms' but are not 'platforms' are:\tsteps\tpedestals\tdocks\tpiers\nThere are several useful visual features to tell there is 'platforms' and not similar things in a photo:\ta flat surface raised above the ground\tor a lower surface\tsturdy structure that can support weight\tcan have railings or edges for safety purposes\tcan have steps leading up to it or ramps for wheelchair access\tcan come in various shapes and sizes depending on their function.", 12], "sewer drain": ["Yes. 'Sewer drain' has a tangible appearance and is a kind of infrastructure.\nA few things that are visually similar to 'sewer drain' but are not 'sewer drain' are:\tmanhole cover\tutility hole\tstorm drain grate\nThere are several useful visual features to tell there is 'sewer drain' and not similar things in a photo:\tcylindrical or square shape\tmetallic or concrete surface\twith or without holes\tconnected to a pipe system\ton sidewalks or streets", 12], "arm air": ["No. 'Arm air' is too vague or abstract to have a tangible appearance. It is not a common concept or phrase used in English language.", 12], "splashing water": ["Yes, 'splashing water' has a tangible appearance and refers to the action of water hitting a surface or being thrown into the air.\nA few things that are visually similar to 'splashing water' but are not 'splashing water' are:\tWaves\tcrashing on a shore\tFoam\tBubbles\nSome useful visual features that distinguish 'splashing water' from similar things in a photo are as follows:\tDroplets of water flying in the air\tReturning to the source of the splash (falling back into a body of water)\tThe appearance of the object causing the splash (i.e., hand, rock, animal, etc.)\tThe sound or motion of the water", 12], "metal surface": ["Yes. 'Metal surface' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'metal surface' but are not 'metal surface' are:\tplastic surface\tglass surface\twooden surface\tstucco surface\nThere are several useful visual features to tell there is 'metal surface' and not similar things in a photo:\tmetallic sheen or shine\tcold and hard to the touch\treflection and/or mirror-like surface\trust and/or patina if weathered\tmagnetic properties if visible", 12], "barn door": ["Yes. 'Barn door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'barn door' but are not 'barn door' are:\tgarden gate\tgarage door\thouse door\tShutters\nThere are several useful visual features to tell there is 'barn door' and not similar things in a photo:\tmade of wood or metal\ttwo panels\tsliding or hinged mechanism\tdiagonal crossbars", 12], "pizza paddle": ["Yes. 'Pizza paddle' has a tangible appearance and is a kitchen tool used for making pizza.\nA few things that are visually similar to 'pizza paddle' but are not 'pizza paddle' are:\tcutting board\trolling pin\tbarbecue tongs\nThere are several useful visual features to tell there is 'pizza paddle' and not similar things in a photo:\tlong handle\tflat surface made of wood or metal\tthin and circular in shape", 12], "scroll wheel": ["Yes. 'Scroll wheel' has a tangible appearance and is a part of many computer input devices.\nA few things that are visually similar to 'scroll wheel' but are not 'scroll wheel' are:\tbutton\tknob\tdial\tjoystick\nThere are several useful visual features to tell there is 'scroll wheel' and not similar things in a photo:\tcylinder shape\tsmooth or grooved texture\thas arrows or symbols indicating scrolling function\tplaced between two buttons or near a touchpad or screen.", 12], "glass display": ["Yes. 'Glass display' has a tangible appearance and is a type of showcase.\nA few things that are visually similar to 'glass display' but are not 'glass display' are:\twindow\tdisplay case\taquarium\tcloset\tdoor\nThere are several useful visual features to tell there is 'glass display' and not similar things in a photo:\tmade of glass or clear material\tshows items inside, such as merchandise or artifacts\topen on one or more sides\twith shelves or compartments for displaying items.", 12], "transparent": ["Yes. 'Transparent' has a tangible appearance and describes an object or material that allows light to pass through it so that objects behind can be seen clearly.\nA few things that are visually similar to 'transparent' but are not 'transparent' are:\topaque\ttranslucent\treflective\tglossy\nThere are several useful visual features to tell there is 'transparent' and not similar things in a photo:\tlight passes through clearly\tobjects behind can be seen with clarity\tno distortion of images behind\tit appears as if there is no barrier between viewer and the object behind.", 12], "power switch": ["Yes. 'Power switch' has a tangible appearance and is a type of electrical device.\nA few things that are visually similar to 'power switch' but are not 'power switch' are:\tlight switch\tspeaker knob\toven button\nThere are several useful visual features to tell there is 'power switch' and not similar things in a photo:\ton/off position\tswitch shape\tpower symbol indicating on or off state\tattached to an electronic device", 12], "toilet wall": ["Yes. 'Toilet wall' has a tangible appearance and is a part of a restroom.\nA few things that are visually similar to 'toilet wall' but are not 'toilet wall' are:\tregular wall\tbathroom door\tshower tile\nThere are several useful visual features to tell there is 'toilet wall' and not similar things in a photo:\thandles, flush buttons or knobs\tpipes\tflushing mechanism or tank\tpaper roll holder\tor signs and decals common in public restrooms.", 12], "wall surface": ["Yes. 'Wall surface' has a tangible appearance and refers to the material covering a wall.\nA few things that are visually similar to 'wall surface' but are not 'wall surface' are:\tfloor surface\tceiling surface\troof surface\toutdoor surfaces\nThere are several useful visual features to tell there is 'wall surface' and not similar things in a photo:\tvertical\texhibits texture or patterns\tpainted or covered in wallpaper or tiles\thas light switches, outlets, or decorations attached to it.", 12], "stitches": ["Yes. 'Stitches' has a tangible appearance and refers to the threads used to sew wounds or fabric together.\nA few things that are visually similar to 'stitches' but are not 'stitches' are:\tdrawings of stitches\tgrooves on a surface\tzippers\ton-screen computer graphics\nThere are several useful visual features to tell there is 'stitches' and not similar things in a photo:\tthin thread material in a pattern\ta needle or surgical tool\thalf-loop shapes that intertwine or interlock\twithin or on a surface of an object or material", 12], "eye ball": ["Yes. 'Eye ball' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'eye ball' but are not 'eye ball' are:\tgolf ball\tmarble\tgumball\nThere are several useful visual features to tell there is 'eye ball' and not similar things in a photo:\tspherical shape\twhite and colored areas\tpupil in the center\tsclera and cornea\tdensely packed blood vessels\tcircles in the iris", 12], "monitor stand": ["Yes. 'Monitor stand' has a tangible appearance and is an object used to support a computer monitor.\nA few things that are visually similar to 'monitor stand' but are not 'monitor stand' are:\tbookshelf\ttable\tbookend\nThere are several useful visual features to tell there is 'monitor stand' and not similar things in a photo:\trectangular or square shape\t\nflat top surface\t\nheight is just enough for a monitor to be at an appropriate height for comfortable viewing\t\nbase or feet to support the stand and keep it stable.", 12], "blob": ["Yes. 'Blob' has a tangible appearance and is a shapeless, amorphous object.\nA few things that are visually similar to 'blob' but are not 'blob' are:\tpuddles\tof paint\torbs of light\nThere are several useful visual features to tell there is 'blob' and not similar things in a photo:\tshapeless, irregular form\tborderless\tcontourless\tmassive and soft in appearance\tno recognizable features within the object", 12], "street clock": ["Yes. 'Street clock' has a tangible appearance and is a type of clock.\nA few things that are visually similar to 'street clock' but are not 'street clock' are:\twristwatch\talarm clock\ttable clock\tpocket watch\nThere are several useful visual features to tell there is 'street clock' and not similar things in a photo:\t\nlarge size\t\nmounted on a pole or a building\t\nvisible from a distance\t\nusually have roman numerals or large numbers\t\nmay have more than one face", 12], "asphalt road surface": ["Yes. 'Asphalt road surface' has a tangible appearance and is a type of road construction material.\nA few things that are visually similar to 'asphalt road surface' but are not 'asphalt road surface' are:\tconcrete road surface\tcobblestone road surface\tdirt road surface\nThere are several useful visual features to tell there is 'asphalt road surface' and not similar things in a photo:\tsmooth surface\tdark grey or black color\tsmall, evenly spaced stones or gravel\tasphalt or tar consistency", 12], "ankle socks": ["Yes. 'Ankle socks' has a tangible appearance and refers to a specific type of socks.\nA few things that are visually similar to 'ankle socks' but are not 'ankle socks' are:\tknee-high socks\tpantyhose\ttights\t\nThere are several useful visual features to tell there is 'ankle socks' and not similar things in a photo:\tsocks that end above the ankle but below the calf\tmostly made of cotton or nylon, sometimes with added designs or patterns\tworn with sneakers or low-cut shoes", 12], "gold button": ["Yes. 'Gold button' has a tangible appearance and is a kind of clothing accessory.\nA few things that are visually similar to 'gold button' but are not 'gold button' are:\tgold coin\tbelt buckle\tgold jewelry\t\n\nThere are several useful visual features to tell there is 'gold button' and not similar things in a photo:\tcircular or rounded shape\tshiny, gold color\tused to fasten clothes or embellish them\tlittle or no decoration or engraving.", 12], "sink top": ["Yes. 'Sink top' has a tangible appearance and is a flat surface surrounding a sink.\nA few things that are visually similar to 'sink top' but are not 'sink top' are:\tkitchen counter\ttop of a table\ttop of a dresser\tbathroom counter\nThere are several useful visual features to tell there is 'sink top' and not similar things in a photo:\ta bowl-shaped basin in the center of the surface\ta faucet or spout for water drainage\tin the proximity of a mirror or a soap dispenser", 12], "swings": ["Yes. 'Swings' has a tangible appearance and is a type of playground equipment.\nA few things that are visually similar to 'swings' but are not 'swings' are:\thammocks\tchairs\trope\tTrapeze\nThere are several useful visual features to tell there is 'swings' and not similar things in a photo: \tchain or rope attached to a beam or tree\thanging seat\tcrossbar between chains for gripping\tasymmetrical seat with one side higher than the other", 12], "flowering plants": ["Yes. 'Flowering plants' has a tangible appearance and is a type of plant that produces flowers.\nA few things that are visually similar to 'flowering plants' but are not 'flowering plants' are:\ttrees \tferns \tgrasses \tcacti\nThere are several useful visual features to tell there is 'flowering plants' and not similar things in a photo:\tblooming flowers \tcolorful petals \tvariety of shapes and sizes \tanthers \tstamens \tpistils", 12], "cucumber slice": ["Yes. 'Cucumber slice' has a tangible appearance and is a physical object.\nA few things that are visually similar to 'cucumber slice' but are not 'cucumber slice' are:\tzucchini slice\tpickle slice\tonion slice\nThere are several useful visual features to tell there is 'cucumber slice' and not similar things in a photo:\tlight or dark green color\tthin and circular shape\twith or without seeds\tskin on the outer edge", 12], "sun rays": ["Yes. 'Sun rays' has a tangible appearance and is a type of natural light.\nA few things that are visually similar to 'sun rays' but are not 'sun rays' are:\tlight beams\theadlights\tsearchlights\tstage lights\nThere are several useful visual features to tell there are 'sun rays' and not similar things in a photo:\temerging from the sun in a radial pattern\tpassing through clouds or foliage\tcreating shadows on objects on the ground\tdiffuse or softer edges than other types of light beams", 12], "wood nightstand": ["Yes. 'Wood nightstand' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood nightstand' but are not 'wood nightstand' are:\tend table\tdresser\tbookshelf\nThere are several useful visual features to tell there is 'wood nightstand' and not similar things in a photo:\ta small table next to a bed\tone or more drawers\twide, flat surface on top\tlamp or other objects on top\ttypically made of wood\tor wooden-like material.", 12], "skulls": ["Yes. 'Skulls' has a tangible appearance and is a collection of bones that form the structure of a head.\nA few things that are visually similar to 'skulls' but are not 'skulls' are:\trock formations\twithered fruit\nThere are several useful visual features to tell there are 'skulls' and not similar things in a photo:\tbony structures in the shape of a head\teye sockets\tteeth and jaw structure\tindications of bone texture and shape", 12], "fastener": ["Yes. 'Fasteners' have a tangible appearance and are objects used to hold things together.\nA few things that are visually similar to 'fasteners' but are not 'fasteners' are: buttons, zippers, magnets, hooks\nThere are several useful visual features to distinguish 'fasteners' from other similar things in a photo:\tsize\tand_shape_color\tmaterial\ttype of connector (snap, pin, slide, etc.)", 12], "dark mountains": ["Yes. 'Dark mountains' has a tangible appearance and visual characteristics that describe a specific type of mountain range.\nA few things that are visually similar to 'dark mountains' but are not 'dark mountains' are:\tmountains at night\tmountains covered in shadows\tdark clouds over mountains\tmountains obscured by fog\nThere are several useful visual features to tell there are 'dark mountains' and not similar things in a photo:\tmountain ranges\twith jagged and steep peaks\tdark or black in color\tlacking visible vegetation or trees\tdistant or shrouded in darkness or shadow", 12], "bus mirror": ["Yes. 'Bus mirror' has a tangible appearance and is a kind of external mirror attached to a bus.\nA few things that are visually similar to 'bus mirror' but are not 'bus mirror' are:\tcar mirror\ttruck mirror\tmotorcycle mirror\tbicycle mirror\nThere are several useful visual features to tell there is 'bus mirror' and not similar things in a photo:\tbig size\tround or rectangular shape\tattached to the side of the bus\tangled or curved for wide visibility.", 12], "smoke trails": ["Yes. 'Smoke trails' has a tangible appearance and is a type of visible gas released into the air.\nA few things that are visually similar to 'smoke trails' but are not 'smoke trails' are:\tclouds\tvapor\ttracks of vehicles\tfog\nThere are several useful visual features to tell there is 'smoke trails' and not similar things in a photo:\tthinner and more linear than clouds or fog\tmight have a whitish or grayish color, or be black or colored, depending on what caused them\tcould be rising vertically, drifting in the wind, or following the path of a flying object (such as a plane or a firework)", 12], "handful": ["No. 'Handful' is too vague or abstract to be visually distinguished in a photo. It is a subjective measure of quantity based on the size of one's hand.", 12], "handles cabinets": ["Yes. 'Handles cabinets' has a tangible appearance and is a type of hardware on furniture.\nA few things that are visually similar to 'handles cabinets' but are not 'handles cabinets' are:\tknobs\tdrawers\tpulls\thinges\nThere are several useful visual features to tell there is 'handles cabinets' and not similar things in a photo:\thandles attached to the front of a cabinet or drawer\tfor grasping to open or close\tthe shape of a door or drawer does not change with the handle's movement\tvarying designs or materials (such as metal or plastic)", 12], "landline telephone": ["Yes. 'Landline telephone' has a tangible appearance and refers to a specific type of telephone that is connected to a telephone line.\nA few things that are visually similar to 'landline telephone' but are not 'landline telephone' are:\tcell phone\tcordless phone\tradio\tmicrophone\nThere are several useful visual features to tell there is 'landline telephone' and not similar things in a photo:\tattached to a cord or cable.\thave a dial pad or buttons\tfor desk or wall mount\tuse of rotary dial or buttons to dial numbers", 12], "remnants": ["Yes. 'Remnants' has a tangible appearance and refers to leftover pieces or remains of something.\nA few things that are visually similar to 'remnants' but are not 'remnants' are:\tgarbage\tdebris\tleftovers\tfrom food or a meal\nThere are several useful visual features to tell there are 'remnants' and not similar things in a photo:\tpieces or parts of something\tthat does not form a whole\tobject or material left after most has been used or taken away.", 12], "motorcycle light": ["Yes. 'Motorcycle light' has a tangible appearance and refers to the headlights or tail lights of a motorcycle.\nA few things that are visually similar to 'motorcycle light' but are not 'motorcycle light' are:\tcar light\tbicycle light\tflashlight\ttraffic light\nThere are several useful visual features to tell there is 'motorcycle light' and not similar things in a photo:\tbright and focused light\tsource mounted on a motorcycle\theadlight placed in the front of the motorcycle and tail light placed in the back", 12], "dirty fork": ["Yes. 'Dirty fork' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'dirty fork' but are not 'dirty fork' are:\tspoo\tnknife\tchopstick\nThere are several useful visual features to tell there is 'dirty fork' and not similar things in a photo:\tfork shape\twith food residue or grease\ton a plate, table or napkin", 12], "orange tank top": ["Yes. 'Orange tank top' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'orange tank top' but are not 'orange tank top' are:\torange t-shirt\torange blouse\tsleeveless orange dress\torange sports bra\nThere are several useful visual features to tell there is 'orange tank top' and not similar things in a photo:\tsleeveless\ttop with thin shoulder straps\tno collar or buttons,\tloose-fitting\texcept in the bust area", 12], "plastic shower curtain": ["Yes. 'plastic shower curtain' has a tangible appearance.\nA few things that are visually similar to 'plastic shower curtain' but are not 'plastic shower curtain' are:\tplastic tablecloth\tpainters' drop cloth\tplastic tarp\nThere are several useful visual features to tell there is 'plastic shower curtain' and not similar things in a photo:\tapproximately rectangular-shaped\thanging from a rod or hooks\ttranslucent or opaque with a thin, smooth texture with a plastic sheen\twater droplets or condensation on the surface", 12], "kickstand bike": ["Yes. 'Kickstand bike' has a tangible appearance and is a type of bicycle.\nA few things that are visually similar to 'kickstand bike' but are not 'kickstand bike' are:\tmountain bike\tcruiser bike\troad bike\tfolding bike\nThere are several useful visual features to tell there is 'kickstand bike' and not similar things in a photo:\tstand located at the bottom of the bike's frame\ta spring mechanism\tthe stand allows the bike to stand on its own without leaning on anything else.", 12], "fanny pack": ["Yes, 'fanny pack' is a visually concrete concept as it has a tangible appearance and is a type of bag worn around the waist.\nA few things that are visually similar to 'fanny pack' but are not 'fanny pack' are:\tbelt purse\thip bag\tmoney belt\twaist pouch\nThere are several useful visual features to tell there is 'fanny pack' and not similar things in a photo:\tworn around the waist\twith a strap that goes around the waist\tzips or buckles to close\tthe size and shape of the pouch\tfabric or leather material.", 12], "girls hand": ["Yes. 'Girls hand' has a tangible appearance and is a body part.\nA few things that are visually similar to 'girls hand' but are not 'girls hand' are:\tboys hand\tadult hand\telderly hand\tmanicured hand\nThere are several useful visual features to tell there is 'girls hand' and not similar things in a photo:\trelatively small in size compared to an adult hand or a man's hand\tdainty, delicate or slender\tfingers with painted nails or nail art\tjewelry or accessories that are typically associated with girls, such as rings or bracelets.", 12], "cats ears": ["Yes. 'Cats ears' has a tangible appearance and is a part of a cat's anatomy.\nA few things that are visually similar to 'cats ears' but are not 'cats ears' are:\tdog ears\tbunny ears\thuman ears\tmouse ears\nThere are several useful visual features to tell there is 'cats ears' and not similar things in a photo:\tpointy at the tips\tcovered in fur, usually the same color as the cat's coat\tlocated on top of the cat's head\tmove and rotate to different angles to pick up sounds.", 12], "lap top computer": ["Yes. 'Lap top computer' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'lap top computer' but are not 'lap top computer' are:\ttablet\tsmartphone\tE-reader\t\nThere are several useful visual features to tell there is 'lap top computer' and not similar things in a photo:\thinged screen attached to a keyboard\tscreen can be closed and opened\tupright display screen\twith a touchpad and keyboard on the same plane\tbattery\thinges to allow the screen to fold down onto the keyboard", 12], "caution cones": ["Yes. 'Caution cones' has a tangible appearance and is a kind of safety equipment.\nA few things that are visually similar to 'caution cones' but are not 'caution cones' are:\ttraffic barrels\tplastic barriers\tpylons\nThere are several useful visual features to tell there is 'caution cones' and not similar things in a photo:\tcone-shaped\tbright orange color\twhite stripe patterns on the top or bottom\twritten words \"caution\" or \"work zone\"", 12], "shift key": ["Yes. 'Shift key' has a tangible appearance and is a key on a keyboard.\nA few things that are visually similar to 'shift key' but are not 'shift key' are:\tcaps lock key\tcontrol key\talt key\tcommand key\nThere are several useful visual features to tell there is 'shift key' and not similar things in a photo:\tarrows pointing up and down on the key\tthe word \"shift\" written on the key\tplaced in the left and right bottom of the keyboard keys", 12], "brick path": ["Yes. 'Brick path' has a tangible appearance and is a kind of paved walkway.\nA few things that are visually similar to 'brick path' but are not 'brick path' are:\tstone path\tconcrete path\twooden path\tgravel path\nThere are several useful visual features to tell there is 'brick path' and not similar things in a photo:\trectangular or square-shaped bricks\torangish-red color\tpatterns or designs made with the bricks", 12], "silver basket": ["Yes. 'Silver basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'silver basket' but are not 'silver basket' are:\tbowl\tpot\tcup\tbucket\ttray\nThere are several useful visual features to tell there is 'silver basket' and not similar things in a photo:\tmade of silver or silver-colored material\twoven or mesh-like texture\thandle or handles\tfor holding or carrying objects", 12], "pointy edge": ["Yes. 'Pointy edge' has a tangible appearance and refers to a sharp or tapered edge of an object.\nA few things that are visually similar to 'pointy edge' but are not 'pointy edge' are:\tcurved edge\tdull edge\trounded edge\t\nThere are several useful visual features to tell there is 'pointy edge' and not similar things in a photo:\tthin and sharp\ttipped or angled\tshiny or reflective", 12], "truck driver": ["Yes. 'Truck driver' has a tangible appearance and refers to a person who drives a truck.\nA few things that are visually similar to 'truck driver' but are not 'truck driver' are:\tcar driver\tbike rider\ttruck mechanic\nThere are several useful visual features to tell there is 'truck driver' and not similar things in a photo:\twearing a trucker hat or work shirt\tsitting in the driver's seat of a truck\tdriving a big rig or semi-truck\thandles a steering wheel\twith a dashboard and gear shift\tfocused on the road\tor looking out their side mirrors while driving.", 12], "sea spray": ["Yes. 'Sea spray' has a tangible appearance and is a type of water droplets formed by waves.\nA few things that are visually similar to 'sea spray' but are not 'sea spray' are:\train\tdew\tfog\tbreath\nThere are several useful visual features to tell there is 'sea spray' and not similar things in a photo:\twater droplets in the air\tformed by waves and ocean wind\tlocated near the ocean\tor seaside.", 12], "metal decoration": ["Yes. 'Metal decoration' has a tangible appearance and is a type of ornament.\nA few things that are visually similar to 'metal decoration' but are not 'metal decoration' are:\ttools\tcutlery\tjewelry\tcoins\tmachinery\nThere are several useful visual features to tell there is 'metal decoration' and not similar things in a photo:\tdecorative, not functional\tmade entirely of metal or has metal elements\tintricate or ornate design\tintended for display in a home or public space", 12], "dirty dishes": ["Yes. 'Dirty dishes' has a tangible appearance and refers to used kitchen utensils.\nA few things that are visually similar to 'dirty dishes' but are not 'dirty dishes' are:\tclean dishes\tcups and saucers\tkitchenware and utensils\tonion peel or other food scraps\nThere are several useful visual features to tell there are 'dirty dishes' and not similar things in a photo:\tplates, bowls, spoons, forks, knives, etc. that appear to have been used\tleftovers, food residue, or stains on the dishes\tstacked or unorganized in the sink or on the counter", 12], "hair bow": ["Yes. 'Hair bow' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'hair bow' but are not 'hair bow' are:\tribbon\tbowtie\tpaper clip\nThere are several useful visual features to tell there is 'hair bow' and not similar things in a photo:\tmade of fabric or ribbon\ttwo loops and tails to tie the hair\thanging from a head", 12], "heart design": ["Yes. 'Heart design' has a tangible appearance and is usually a stylized representation of the anatomical heart shape.\nA few things that are visually similar to 'heart design' but are not 'heart design' are:\tDiamond shape\tClub shape\tVine leaves\nThere are several useful visual features to tell there is 'heart design' and not similar things in a photo:\tcurved edges and a pointed bottom\tsymmetrical form\tpaired with the color red\tused in romantic or loving context", 12], "train cake": ["Yes. 'Train cake' has a tangible appearance and is a type of cake.\nA few things that are visually similar to 'train cake' but are not 'train cake' are:\tregular cake\tcupcakes\tdoughnuts\ttrifle desserts\nThere are several useful visual features to tell there is 'train cake' and not similar things in a photo:\tlocomotive or train-shaped cake\tdecorated with candy wheels, windows, and other train-themed details\tmay include a track or landscape made of frosting or other edible materials", 12], "toy horse": ["Yes. 'Toy horse' has a tangible appearance and is a miniature horse for playing with.\nA few things that are visually similar to 'toy horse' but are not 'toy horse' are:\trocking horse\thorse figurine\thorse statue\thorse plush\nThere are several useful visual features to tell there is 'toy horse' and not similar things in a photo:\tmade of plastic, wood, or fabric\tproportions and features are similar to that of a horse\thas colorful tack, such as a saddle or reins\tsmall enough for a child to hold or play with", 12], "loafers": ["Yes. 'Loafers' has a tangible appearance and is a type of shoe.\nA few things that are visually similar to 'loafers' but are not 'loafers' are:\tmoccasins\tboat shoes\tslip-on sneakers\nThere are several useful visual features to tell there is 'loafers' and not similar things in a photo:\tslip-on style\tmostly flat heel and sole\tsimple and classic design\twith a typically wider and rounded toe box", 12], "office telephone": ["Yes. 'Office telephone' has a tangible appearance and is a type of phone usually found in offices.\nA few things that are visually similar to 'office telephone' but are not 'office telephone' are:\tmobile phone\told rotary phone\tcordless phone\tpayphone\nThere are several useful visual features to tell there is 'office telephone' and not similar things in a photo:\tblack or grey color\tkeypad with numbers 0-9\treceiver and earpiece connected by a coiled cord\tdisplay screen\twith optional speaker and other function buttons", 12], "water container": ["Yes. 'Water container' has a tangible appearance and is used to store or hold water.\nA few things that are visually similar to 'water container' but are not 'water container' are:\tglass\tbowl\tpitcher\tdecorative vase\nThere are several useful visual features to tell there is 'water container' and not similar things in a photo:\ttransparent or translucent material\thandle or spout\tfor holding water or liquids\tcap or lid", 12], "gold letter": ["Yes. 'Gold letter' has a tangible appearance and is a type of text.\nA few things that are visually similar to 'gold letter' but are not 'gold letter' are:\tplaque\taccent mark\tpen strokes\nThere are several useful visual features to tell there is 'gold letter' and not similar things in a photo:\ta letter of the alphabet\trendered in gold\thighlighted text\tintricately detailed letters with serifs reflecting light", 12], "beers": ["Yes. 'Beers' has a tangible appearance and is a kind of alcoholic beverage.\nA few things that are visually similar to 'beers' but are not 'beers' are:\tciders\twines\tsodas\tjuices\nThere are several useful visual features to tell there is 'beers' and not similar things in a photo:\tbrown, yellow or amber liquid\tfrothy head\tin a glass or a can/bottle_labels with brand names or logos", 12], "pint": ["Yes. 'Pint' has a tangible appearance and is a unit of liquid measurement.\nA few things that are visually similar to 'pint' but are not 'pint' are:\tcup\tglass\tbottle\tcan\nThere are several useful visual features to tell there is 'pint' and not similar things in a photo:\t16-ounce capacity\tstraight sides with a slight taper at the top and bottom\tthick and sturdy glass material with a handle (for glasses and cups)\tnarrow top and a wider bottom (for bottles and cans)", 12], "dishtowel": ["Yes. 'Dishtowel' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'dishtowel' but are not 'dishtowel' are:\thand towel\tbath towel\twashcloth\tdishrag\nThere are several useful visual features to tell there is 'dishtowel' and not similar things in a photo:\trectangular\tshaped of a dish\tcloth-like texture intended for absorbing moisture\teither plain or decorated with a pattern or design", 12], "kitchen cabinet door": ["Yes. 'Kitchen cabinet door' has a tangible appearance and refers to a specific part of a cabinet in a kitchen.\nA few things that are visually similar to 'kitchen cabinet door' but are not 'kitchen cabinet door' are:\tdrawers\tshelves\twardrobe doors\t\nThere are several useful visual features to tell there is 'kitchen cabinet door' and not similar things in a photo:\tpanels with handles\tor hinges\tmounted to a wooden frame on the wall\tor under the counter\tmade of wood, metal, or glass with various finishes (e.g. painted, varnished, frosted)", 12], "sun glare": ["Yes. 'Sun glare' has a tangible appearance and is a type of bright reflection of sunlight.\nA few things that are visually similar to 'sun glare' but are not 'sun glare' are: artificial light reflection, lens flare, flash, spotlight.\nThere are several useful visual features to distinguish 'sun glare' from similar things in a photo: the glare is usually white or yellow in color, irregular in shape and has streaks that look like rays of light emanating from the source of the glare, which is usually the sun. If the glare is on a reflective surface, it may appear as a white spot or reflection on the surface of the object. Lens flares are usually multi-colored, have a polygonal shape and appear symmetrically around the source of light.", 12], "metal street sign": ["Yes. 'Metal street sign' has a tangible appearance and is a type of sign.\nA few things that are visually similar to 'metal street sign' but are not 'metal street sign' are:\twooden sign\tbillboard\tposters\ttraffic cones\nThere are several useful visual features to tell there is 'metal street sign' and not similar things in a photo:\trectangular or square-shaped sign\tmade of metal\thas text on it indicating street names, directions, or warnings\thanging from a pole or fixed to a wall or building.", 12], "chicken plate": ["Yes. 'Chicken plate' has a tangible appearance and is a plate or dish of food containing chicken.\nA few things that are visually similar to 'chicken plate' but are not 'chicken plate' are:\tplate of beef\tstir-fry dish\tsalad\t\nThere are several useful visual features to tell there is 'chicken plate' and not similar things in a photo:\tsliced or cooked chicken pieces\ton a plate or dish\tgarnished with vegetables or other sides", 12], "tall bridge": ["Yes. 'Tall bridge' has a tangible appearance and is a kind of man-made construction.\nA few things that are visually similar to 'tall bridge' but are not 'tall bridge' are:\toverpass\tdam\tbuilding\ttower\nThere are several useful visual features to tell there is 'tall bridge' and not similar things in a photo:\tspanning over a body of water\tor valley\ttowers or pillars\tonramps and offramps for vehicles\tsuspension cables or arches", 12], "fishing poles": ["Yes. 'Fishing poles' has a tangible appearance and is a type of equipment used for fishing.\nA few things that are visually similar to 'fishing poles' but are not 'fishing poles' are:\thiking poles\tumbrellas\tbrooms\tspears\nThere are several useful visual features to tell there is 'fishing poles' and not similar things in a photo:\tlong and flexible rod\tattached fishing line\ta reel to wind or unwind fishing line\ta hook to catch fish", 12], "silver fan": ["Yes. 'Silver fan' has a tangible appearance and is the specific type of an object.\nA few things that are visually similar to 'silver fan' but are not 'silver fan' are:\twhite fan\tblack fan\tmetal fan\nThere are several useful visual features to tell there is 'silver fan' and not similar things in a photo:\tmetallic or silver color\tblades or wings to produce air movement\thandle or stand to hold and direct the fan", 12], "wooden table top": ["Yes. 'Wooden table top' has a tangible appearance and can be seen as part of furniture.\nA few things that are visually similar to 'wooden table top' but are not 'wooden table top' are:\tchopping board\tfloor\tboard desktop\nThere are several useful visual features to tell there is 'wooden table top' and not similar things in a photo:\tsmooth wooden surface\ton legs, in a horizontal position\tmay have visible grains or knots\tmay have polish or finish", 12], "silver hand rail": ["Yes. 'Silver hand rail' has a tangible appearance and is a type of railing.\nA few things that are visually similar to 'silver hand rail' but are not 'silver hand rail' are:\tstainless steel guardrail\tchrome railing\taluminum handrail\nThere are several useful visual features to tell there is 'silver hand rail' and not similar things in a photo:\tmade of silver or silver-colored metal\thorizontal or sloping\tgraspable\twith supporting brackets fixed to a wall or structure.", 12], "octagon sign": ["Yes. 'Octagon sign' has a tangible appearance and is a kind of signage.\nA few things that are visually similar to 'octagon sign' but are not 'octagon sign' are:\ttriangle sign\tsquare sign\tcircle sign\trectangle sign\nThere is one useful visual feature to tell there is 'octagon sign' and not similar things in a photo:\tshape of an octagon (eight-sided polygon)", 12], "glass front": ["Yes. 'Glass front' has a tangible appearance and refers to a surface made of glass that is often used in construction or design.\nA few things that are visually similar to 'glass front' but are not 'glass front' are:\tmirrors\tpaintings\tdisplay cases\tcurtain walls\nThere are several useful visual features to tell there is 'glass front' and not similar things in a photo:\ttransparency or translucency\thard and smooth surface\treflection of light and surroundings\tglass cleaning solutions or equipment nearby.", 12], "eggplants": ["Yes. 'Eggplants' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'eggplants' but are not 'eggplants' are:\tpurple bell peppers\tpurple potatoes\tpurple onions\nThere are several useful visual features to tell there is 'eggplants' and not similar things in a photo:\toval or elongated shape\tdark purple or black skin\twith white or green calyx on top\tfleshy interior with small seeds", 12], "blue boxes": ["Yes. 'Blue boxes' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'blue boxes' but are not 'blue boxes' are:\tcrates\tbins\tcabinets\tsafe boxes\nThere are several useful visual features to tell there is 'blue boxes' and not similar things in a photo:\trectangular or square shape\twith a hinged lid or a removable cover\tsolid color blue exterior (or shades of blue)\tmade of plastic or metal", 12], "gummy": ["Yes. 'Gummy' has a tangible appearance and usually refers to a candy.\nA few things that are visually similar to 'gummy' but are not 'gummy' are:\tsoft rubber\tfruit jellies\tgelatin desserts\t\nThere are several useful visual features to tell there is 'gummy' and not similar things in a photo:\ttranslucent color\tsugar coating\tsoft and chewy texture\tgummy bear or worm shape", 12], "surfer wetsuit": ["Yes. 'Surfer wetsuit' has a tangible appearance and is a type of clothing worn by surfers.\nA few things that are visually similar to 'surfer wetsuit' but are not 'surfer wetsuit' are:\tscuba diving wetsuit\tswimwear\tdry suit\tlycra suit\nThere are several useful visual features to tell there is 'surfer wetsuit' and not similar things in a photo:\tthicker and more insulating material than regular swimwear\tfull-body coverage, including arms and legs\tdark colors, often with branding or graphics\tzippers or closures at the back or chest", 12], "hotels": ["Yes. 'Hotels' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'hotels' but are not 'hotels' are:\tapartment buildings\tmotels\thospitals\tcollege dormitories\nThere are several useful visual features to tell there is 'hotels' and not similar things in a photo:\tsignage indicating the hotel name or logo\treception or lobby area\tfor-pay parking or a valet\ta sign for room numbers and/or floors\tin-room windows\twith rooms and bathrooms", 12], "ladles": ["Yes. 'Ladles' has a tangible appearance and is a type of kitchen utensil.\nA few things that are visually similar to 'ladles' but are not 'ladles' are:\tspoons, scoops, spatulas, tongs, forks, knives.\nThere are several useful visual features to tell there is 'ladles' and not similar things in a photo:\tlong handle with a curved end\tshallow, rounded, or oval-shaped bowl on the end of the handle\tbowl has a small lip on the edge for pouring\tsometimes made of metal or plastic.", 12], "chocolate cookie": ["Yes. 'Chocolate cookie' has a tangible appearance and is a kind of baked good.\nA few things that are visually similar to 'chocolate cookie' but are not 'chocolate cookie' are:\toreo cookie\tpumpernickel\thamburger bun\tdisc-shaped object\nThere are several useful visual features to tell there is 'chocolate cookie' and not similar things in a photo:\tbrown color\tcircular shape\tchocolate chips\tor chocolate drizzle on top\tslightly cracked surface\tflat and dense shape for a cookie", 12], "man racket": ["No. 'Man racket' is too vague or abstract to be distinguished in a photo. The term 'man racket' is not commonly used and it is unclear what it refers to.\nTherefore, there are no visually similar things to 'man racket' to list, and no useful visual features can be given for distinguishing it from these non-existent similar things.", 12], "abbreviation": ["No. 'Abbreviation' is too vague or abstract to be distinguished in a photo.", 12], "button mouse": ["Yes. 'Button mouse' has a tangible appearance and is a type of computer mouse.\nA few things that are visually similar to 'button mouse' but are not 'button mouse' are:\ttouchpad\tmagic mouse\tgaming mouse\nThere are several useful visual features to tell there is 'button mouse' and not similar things in a photo:\trounded body with two or more buttons\twired or wireless connection\tscroll wheel on top for scrolling up and down\tusually with a USB port for connecting to a computer or laptop", 12], "bathtub faucet": ["Yes. 'Bathtub faucet' has a tangible appearance and is a device used in bathrooms.\nA few things that are visually similar to 'bathtub faucet' but are not 'bathtub faucet' are:\tkitchen faucet\tshowerhead\twaterfall faucet\tbathroom sink faucet\nThere are several useful visual features to tell there is 'bathtub faucet' and not similar things in a photo:\tlocated in or near a bathtub\tor on the bathroom wall or tile\tfitted with knobs or handles for temperature and water flow\tcontrol over hot and cold water\tdesign shape and style fitting specifically for a bathtub.", 12], "star logo": ["Yes. 'Star logo' has a tangible appearance and is a type of graphic design.\nA few things that are visually similar to 'star logo' but are not 'star logo' are:\tstickers\ttattoos\tstar-shaped objects\tsigns and symbols\nThere is only one useful visual feature for distinguishing 'star logo' from the listed similar things in a photo:\tthe image contains stars arranged in a specific way to form a recognizable logo or symbol.", 12], "number tag": ["Yes. 'Number tag' has a tangible appearance and is an object used for identification or labeling.\nA few things that are visually similar to 'number tag' but are not 'number tag' are:\tprice tag\tname tag\tbarcode\tsticker\nThere are several useful visual features to tell there is 'number tag' and not similar things in a photo:\tcontain numbers or letters\tused for identification or labeling\tmade of paper or plastic\tusually attached to an object or thing\tcan be removable or permanent", 12], "cow nose": ["Yes. 'Cow nose' has a tangible appearance and is a part of an animal's face.\nA few things that are visually similar to 'cow nose' but are not 'cow nose' are:\thorse nose\tpig nose\tsheep nose\tdeer nose\nThere are several useful visual features to tell there is 'cow nose' and not similar things in a photo:\tlarge and wide nostrils\tmoist and shiny surface\thairy around the edges of the nostrils", 12], "inset": ["Yes. 'Inset' has a tangible appearance and refers to a small illustration or diagram within a larger document or image.\nA few things that are visually similar to 'inset' but are not 'inset' are:\tthumbnail\timage caption\tfootnote\nThere are several useful visual features to tell there is an 'inset' and not similar things in a photo:\ta smaller image or diagram within a larger document or image\tsurrounded by a border or box\tcolor, texture or shading that distinguishes it from surrounding content", 12], "blue towel": ["Yes. 'Blue towel' has a tangible appearance and is a specific type of towel that is colored blue.\nA few things that are visually similar to 'blue towel' but are not 'blue towel' are:\tblue cloth\tblue shirt\tblue jacket\tblue blanket\nThere are several useful visual features to tell there is 'blue towel' and not similar things in a photo:\tsquare or rectangular shape\tterry or waffle texture\ttypically used for drying or cleaning\thighly absorbent", 12], "purple handle": ["Yes. 'Purple handle' has a tangible appearance and is a specific object with a particular color.\nA few things that are visually similar to 'purple handle' but are not 'purple handle' are:\tblue handle\tpink handle\tviolet handle\tpurple doorknob\nThere are several useful visual features to tell there is 'purple handle' and not similar things in a photo:\tspecific shade of purple\tcolor consistency with the handle's surroundings\thandle shape and texture", 12], "grey body": ["Yes. 'Grey body' has a tangible appearance.\nA few things that are visually similar to 'grey body' but are not 'grey body' are:\tstatue\trock\tpillar\tbuilding\nThere are several useful visual features to tell there is 'grey body' and not similar things in a photo:\tsmooth and uniform surface\tgrey color\tno visible texture or pattern\tno discernible features such as arms, legs, or facial features", 12], "high-rise building": ["Yes. 'High-rise building' has a tangible appearance and is a kind of architecture.\nA few things that are visually similar to 'high-rise building' but are not 'high-rise building' are:\ttower\tchimney\tbridge\nThere are several useful visual features to tell there is 'high-rise building' and not similar things in a photo:\ttall building\twith multiple floors and levels\tmay have glass windows or a concrete exterior\tdensely populated areas", 12], "passenger van": ["Yes. 'Passenger van' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'passenger van' but are not 'passenger van' are:\tminibus\ttruck\tBus\tjeep\nThere are several useful visual features to tell there is 'passenger van' and not similar things in a photo:\trectangular shape\ttwo side doors for passengers\tseating capacity for at least six people\tsliding passenger door on one or both sides of the vehicle\tsmall to medium size compared to other types of vans or buses.", 12], "elder man": ["Yes. 'Elder man' has a tangible appearance and is a person of advanced age who identifies as male.\nA few things that are visually similar to 'elder man' but are not 'elder man' are:\telder woman\tyounger man\tbald man\nThere are several useful visual features to tell there is an 'elder man' and not similar things in a photo:\tgray or white hair\tor wrinkles\tmature face\thigher probability of having a beard or mustache", 12], "mosaic": ["Yes. 'Mosaic' has a tangible appearance and is a type of art.\nA few things that are visually similar to 'mosaic' but are not 'mosaic' are:\ttiled floor\tstained glass\tmultipanel painting\nThere are several useful visual features to tell there is 'mosaic' and not similar things in a photo:\tsmall, varied pieces of material (such as stone, glass, or tile) arranged in a pattern\tlarge, flat surface\twith geometric or intricate patterns or designs", 12], "beige sand": ["Yes. 'Beige sand' has a tangible appearance and is a type of granular material.\nA few things that are visually similar to 'beige sand' but are not 'beige sand' are:\tpebbles\tsalt\twhite sugar\tdirt\tash\nThere are several useful visual features to tell there is 'beige sand' and not similar things in a photo:\tvery fine grains\tlight-colored\tbrownish-yellow, beige or light brown in colour\ttypically found on beaches, deserts or riverbeds poorly compacted and shifted easily.", 12], "orange carpet": ["Yes. 'Orange carpet' has a tangible appearance and is a type/color of floor covering.\nA few things that are visually similar to 'orange carpet' but are not 'orange carpet' are:\torange rug\torange floor tiles\torange mat\nThere are several useful visual features to tell there is 'orange carpet' and not similar things in a photo:\t\ncovering the entire floor\tcontinuous texture and pattern\tsoft and fluffy appearance\tslightly brighter or darker shade of orange", 12], "liquid soap": ["Yes. 'Liquid soap' has a tangible appearance and is a type of cleanser.\nA few things that are visually similar to 'liquid soap' but are not 'liquid soap' are:\thand lotion\tshampoo\tbody wash\tdetergent\nThere are several useful visual features to tell there is 'liquid soap' and not similar things in a photo:\ttransparent or translucent bottle or dispenser\tthick or viscous texture in the container\tfrothy or bubbly when poured out\twater and soap bubbles present when used for cleaning\tor a label that says \"liquid soap\"", 12], "pink design": ["No. 'Pink design' is too vague or abstract to obtain a concrete appearance. \n\nAs such, there aren't many things visually similar or different from it to describe.", 12], "middle window": ["Yes. 'Middle window' has a tangible appearance and refers to a specific location of a window.\nA few things that are visually similar to 'middle window' but are not 'middle window' are:\ttop window\tbottom window\tleft window\tright window\nThere are several useful visual features to tell there is 'middle window' and not similar things in a photo:\tit should be in the center of a group of windows\tits position should be equidistant from the top and the bottom window of the group.", 12], "tan coat": ["Yes. 'tan coat' has a tangible appearance and is a piece of clothing.\nA few things that are visually similar to 'tan coat' but are not 'tan coat' are:\tjacket\tblazer\thoodie\ttrench coat\nThere are several useful visual features to tell there is 'tan coat' and not similar things in a photo:\tlight brown or beige color\ttypically made of wool, cotton, or leather\tlong-sleeved buttons or a zipper in the front\ttwo front pockets.", 12], "snowy landscape": ["Yes. 'Snowy landscape' has a tangible appearance and refers to a specific type of natural scenery.\nA few things that are visually similar to 'snowy landscape' but are not 'snowy landscape' are:\tIce rink\tWhite sand beach\tIceberg\tMarshmallow\nThere are several useful visual features to tell there is 'snowy landscape' and not similar things in a photo:\twhite color (or shades of white)\tcold atmosphere\tand winter elements, such as snow-covered trees, houses, or mountains.", 12], "button eye": ["Yes. 'Button eye' has a tangible appearance and is a type of eye-shaped object commonly used in crafts.\nA few things that are visually similar to 'button eye' but are not 'button eye' are:\tbeads\tcaps\tbolts\tnuts\tbottle caps\nThere are several useful visual features to tell there is 'button eye' and not similar things in a photo:\tcircular shape\ttwo or four holes in the center\tappears to be attached to fabric or material\thas a flat, slightly raised surface\tfor dolls or stuffed animals", 12], "armoire": ["Yes. 'Armoire' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'armoire' but are not 'armoire' are:\tcloset\tcabinet\tshelf\twardrobe\nThere are several useful visual features to tell there is 'armoire' and not similar things in a photo:\tlarge cabinet with doors or drawers\tfor storing clothes or linens\ttall and free-standing piece of furniture\tdesign may include carvings or decorative handles and knobs.", 12], "head lump": ["Yes. 'Head lump' has a tangible appearance and is a physical abnormality on the head.\nA few things that are visually similar to 'head lump' but are not 'head lump' are:\tbumps in the road\trocks or stones\tbumps on a fruit or vegetable\nThere are several useful visual features to tell there is 'head lump' and not similar things in a photo:\thuman or animal head\traised area of skin or tissue\tpotentially red or discolored\tpotentially tender or painful to the touch", 12], "missiles": ["Yes. 'Missiles' has a tangible appearance and is a type of weapon.\nA few things that are visually similar to 'missiles' but are not 'missiles' are:\tfireworks\trockets\twater jets\t\nThere are several useful visual features to tell there is 'missiles' and not similar things in a photo:\tlong and cylindrical shape\tfins and stabilizers\twarhead or payload visible\texhaust flames\tor smoke\ttraveling at high speed or altitude", 12], "orange item": ["Yes. 'Orange item' has a tangible appearance and is a type of object that is predominantly orange in color.\nA few things that are visually similar to 'orange item' but are not 'orange item' are:\torange fruit\torangutan\torangesicle\t\nThere are several useful visual features to tell there an 'orange item' and not similar things in a photo: predominantly orange in color, may have shades of red, yellow or brown in it; some common orange items include traffic cones, basketballs, pumpkins, and construction vests.", 12], "seawall": ["Yes. 'Seawall' has a tangible appearance and is a type of coastal defense.\nA few things that are visually similar to 'seawall' but are not 'seawall' are:\tbreakwater\tjetty\tdike\tlevee\nThere are several useful visual features to tell there is 'seawall' and not similar things in a photo:\tbuilt along the edge of the shoreline\tto protect against waves and tides\tmade of concrete, rocks, or other hard materials\traised above the water level\tdefining the boundary between the land and the sea.", 12], "jugs": ["Yes. 'Jugs' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'jugs' but are not 'jugs' are:\tbottles\tpitchers\tvases\turns\nThere are several useful visual features to tell there is 'jugs' and not similar things in a photo:\tlarge, rounded container\twith a handle\ton top to pour liquid\tfrom, typically with a narrow mouth or a spout may have a lid\tmay be made of ceramic, glass, or metal.", 12], "building front": ["Yes. 'Building front' has a tangible appearance and refers to the front or facade of a building.\nA few things that are visually similar to 'building front' but are not 'building front' are:\tbillboard\twall\twindow\tdoor\nThere are several useful visual features to tell there is 'building front' and not similar things in a photo:\tmultiple windows and floors\tarchitectural details such as columns, cornices, or pediments\tsimilarity to surrounding buildings\tuniqueness or distinctiveness of design or color\tsimplicity or complexity of the facade.", 12], "coca cola bottle": ["Yes. 'Coca Cola bottle' has a tangible appearance and is a specific type of bottle.\nA few things that are visually similar to 'Coca Cola bottle' but are not 'Coca Cola bottle' are:\tbeer bottle\tWine bottle\tolive oil bottle\nThere are several useful visual features to tell there is 'Coca Cola bottle' and not similar things in a photo:\tdistinctive Coca Cola logo bottle shape\tcontoured shape with a curved shoulder\ttall and narrow with a flared base\tcrown cap with a red label\tbrown-tinted glass\tColor red and white combination", 12], "ceiling tile": ["Yes. 'Ceiling tile' has a tangible appearance and is a building material.\nA few things that are visually similar to 'ceiling tile' but are not 'ceiling tile' are:\tfloor tile\twall panel\tdecorative molding\twood paneling\nThere are several useful visual features to tell there is 'ceiling tile' and not similar things in a photo:\tusually white or beige\trectangular shape\thas ridges or other textures\tmay have small holes or perforations\tdesigned specifically for use in ceilings", 12], "street line": ["Yes. 'Street line' has a tangible appearance and refers to the painted lines on a street for traffic control.\nA few things that are visually similar to 'street line' but are not 'street line' are:\tcracks\ton street\tsewer grates\nThere are several useful visual features to tell there is 'street line' and not similar things in a photo:\tpainted lines\ton a street or a road\twhite or yellow in color\tmeant for traffic control, such as dividing lanes or marking crosswalks.", 12], "brick home": ["Yes. 'Brick home' has a tangible appearance and refers to a house made of bricks.\nA few things that are visually similar to 'brick home' but are not 'brick home' are:\twooden home\tconcrete home\tshack\tmodular home\nThere are several useful visual features to tell there is 'brick home' and not similar things in a photo:\tmade of bricks\tand mortar\trectangular or square shape\ttypically red, brown, or grey characterized by lines of regular-sized bricks and serrated joints.", 12], "stainless steel range hood": ["Yes. 'Stainless steel range hood' has a tangible appearance and is a kitchen appliance.\nA few things that are visually similar to 'stainless steel range hood' but are not 'stainless steel range hood' are:\trefrigerator\tmicrowave\toven\tblender\nThere are several useful visual features to tell there is 'stainless steel range hood' and not similar things in a photo:\tattached to the wall above a stove or range\ttop of the hood covers the stove or range area\tfan or ventilation system\texhaust pipe or vent\tstainless steel material\tshiny or reflective appearance", 12], "handle scissors": ["No. 'Handle scissors' is too vague as scissors can have different types of handles.\nA few things that are visually similar to 'handle scissors' but are not 'handle scissors' are:\tknife\ttweezers\tpliers\t\nThere are no useful visual features to distinguish 'handle scissors' from the listed similar things in a photo without a closer look or additional information.", 12], "skateboard road": ["No. 'Skateboard road' is too vague or abstract to be distinguished in a photo. A skateboarder can ride on any road, so it's difficult to visually distinguish a 'skateboard road' from a regular road.", 12], "clay dirt": ["Yes. 'Clay dirt' has a tangible appearance and refers to a specific type of soil.\nA few things that are visually similar to 'clay dirt' but are not 'clay dirt' are:\tsand\tloam soil\tpeat soil\trock\nThere are several useful visual features to tell there is 'clay dirt' and not similar things in a photo:\tgrayish or reddish color\twhen wet, sticky texture\tthat can be shaped and molded\tno visible rocks\tor rough particles on the surface.", 12], "almond": ["Yes. 'Almond' has a tangible appearance and is a kind of nut.\nA few things that are visually similar to 'almond' but are not 'almond' are:\tpecan\tcashew\thazelnut\tpeanut\nThere are several useful visual features to tell there is 'almond' and not similar things in a photo:\tlight brown color\toval shape\tpointy at one end, round at the other end\tridged texture on the surface", 12], "grey sand": ["Yes. 'Grey sand' has a tangible appearance and is a type of sand.\nA few things that are visually similar to 'grey sand' but are not 'grey sand' are:\tash\tpebbles\tcement\tdirt\nThere are several useful visual features to tell there is 'grey sand' and not similar things in a photo:\tsmooth texture\tgrainy appearance\tshades of grey or mixed with other colors\tsmall rocks or shell fragments mixed in", 12], "dirt baseball field": ["Yes. 'Dirt baseball field' has a tangible appearance and is a specific type of outdoor sports field.\nA few things that are visually similar to 'dirt baseball field' but are not 'dirt baseball field' are:\tfootball field\tsoccer field\tplayground\tpark\nThere are several useful visual features to tell there is 'dirt baseball field' and not similar things in a photo:\ta diamond-shaped field with bases\tmounds of dirt near the bases\ta fence or wall around the perimeter\tof the field\tgrass or turf in the outfield\tdirt or clay infield", 12], "pocket square": ["Yes. 'Pocket square' has a tangible appearance and is a kind of cloth.\nA few things that are visually similar to 'pocket square' but are not 'pocket square' are:\thandkerchief\ttowel\tcloth napkin\nThere are several useful visual features to tell there is 'pocket square' and not similar things in a photo:\tsquare or rectangular in shape\ttucked into the breast pocket of a suit jacket\tor blazer\tmade of silk or other fine cloth\tfolded in a decorative way to contrast the suit jacket\tcolor coordinated with the outfit", 12], "catch ball": ["Yes. 'Catch ball' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'catch ball' but are not 'catch ball' are:\ttennis ball\tbaseball\tsoccer ball\tgolf ball\nThere are several useful visual features to tell there is 'catch ball' and not similar things in a photo:\tsoft and lightweight\tusually made of foam or rubber\tvariety of bright colors\teasy to grip and catch", 12], "fruit basket": ["Yes. 'Fruit basket' has a tangible appearance and is a type of container filled with fruits.\nA few things that are visually similar to 'fruit basket' but are not 'fruit basket' are:\tflower basket\twaste paper basket\tpicnic basket\tlaundry basket\nThere are several useful visual features to tell there is 'fruit basket' and not similar things in a photo:\tround or oval\tcontainer made of wicker, cane, or plastic\tvariety of fruits visible\ttop view of fruits arranged in a basket", 12], "samsung": ["No. 'Samsung' is too vague or abstract to be distinguished in a photo. It's a brand name for various electronic devices.\nA few things that are visually similar to 'Samsung' but are not 'Samsung' are:\tLG\tApple\tSony\nThere are no useful visual features for distinguishing 'Samsung' from the listed similar things in a photo since Samsung, LG, Apple, and Sony all produce electronic devices with similar appearances.", 12], "paper brown": ["Yes. 'Paper brown' has a tangible appearance and is a specific color of the paper.\nA few things that are visually similar to 'paper brown' but are not 'paper brown' are:\twood\tcolors simulating wood\tbark\nThere are several useful visual features to tell there is 'paper brown' and not similar things in a photo:\tthe paper is smooth and flat\tthe paper is thin and flexible\tthe paper is light brown in color\tbut does not have a wood texture or visible striations", 12], "iris": ["Yes. 'Iris' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'iris' but are not 'iris' are:\tdaffodil\ttulip\tlily\tdaisy\trose\nThere are several useful visual features to tell there is 'iris' and not similar things in a photo:\tthin and pointed petals that curve outward from a central point\tthree petals and three sepals\tintricate pattern and deep coloration in the flower's center\telongated stem with sword-shaped leaves at the base of the plant.", 12], "grocery bags": ["Yes. 'Grocery bags' has a tangible appearance and refers to bags used for carrying groceries.\nA few things that are visually similar to 'grocery bags' but are not 'grocery bags' are:\tpurses\tbackpacks\ttote bags\tplastic bags\nThere are several useful visual features to tell there is 'grocery bags' and not similar things in a photo:\tpaper or cloth material\thandles\tonion printed\ton a counter full of fruits and vegetables", 12], "shadow pole": ["Yes. 'Shadow pole' has a tangible appearance and is an object used to measure the length and direction of shadows.\nA few things that are visually similar to 'shadow pole' but are not 'shadow pole' are:\tsign\tpost\tfence\tpole\nThere are several useful visual features to tell there is 'shadow pole' and not similar things in a photo:\ttall and slender with measurement markers\tsingle, pointed tip\tstanding on a flat surface with the perpendicular position to the ground", 12], "indoor": ["No. 'Indoor' is too vague or abstract to be distinguished in a photo.", 12], "atm machine": ["Yes. 'ATM machine' has a tangible appearance and is a type of electronic machine.\nA few things that are visually similar to 'ATM machine' but are not 'ATM machine' are:\tvending machine\tkiosk\tparking meter\nThere are several useful visual features to tell there is 'ATM machine' and not similar things in a photo:\tthe letters 'ATM'\tdisplay screen\tkeypad\tfor depositing or withdrawing money\tcash dispenser\tcard slot\tmay have a bank logo", 12], "giraffe laying": ["Yes. 'Giraffe laying' has a tangible appearance.\nA few things that are visually similar to 'giraffe laying' but are not 'giraffe laying' are: giraffe standing, other animals lying down, tree branches.\nThere are several useful visual features to tell there is 'giraffe laying' and not similar things in a photo: very long neck and legs, patterned fur coat, short horns on top of their heads, brown spotted patches on a tan coat, and lying down with stretched legs.", 12], "cannisters": ["Yes. 'Canisters' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'cannisters' but are not 'cannisters' are:\tjars\ttins\tbottles\tboxes\nThere are several useful visual features to tell there is 'cannisters' and not similar things in a photo:\thard and sturdy material\tlid or cap for sealing contents\toften cylindrical or rectangular in shape\tmultiple sizes or levels for stacking or nesting with similar containers.", 12], "saloon car": ["Yes. 'Saloon car' has a tangible appearance and is a type of car.\nA few things that are visually similar to 'saloon car' but are not 'saloon car' are:\tsports car\tconvertible\tcoupe\t\nThere are several useful visual features to tell there is 'saloon car' and not similar things in a photo:\tfour doors\tfor passenger transportation\tbox-shaped and enclosed body structure\tseparate trunk and engine compartment", 12], "car light": ["Yes, 'car light' has a visually concrete concept and refers to the headlights or taillights of a car.\nA few things that are visually similar to 'car light' but are not 'car light' are:\tstreetlights\ttraffic signal lights\tfireworks\tlamp posts\tflashlights\nThere are several useful visual features to tell there is 'car light' and not similar things in a photo:\tlocated on a car\tbright and focused light\tusually in pairs (taillights) or a line (headlights)\tconnected to the car's electrical system", 12], "dogs leg": ["Yes. 'Dogs leg' has a tangible appearance.\nA few things that are visually similar to 'dogs leg' but are not 'dogs leg' are:\tanimal leg\thuman leg\twild animal paw\ttable leg\nThere are several useful visual features to tell there is 'dogs leg' and not similar things in a photo:\tcovered in fur\tor spots\tdewclaw\tpink paw pads\tbent in the opposite direction of a human's knee", 12], "metal exhaust pipe": ["Yes. 'Metal exhaust pipe' has a tangible appearance and is a type of pipe used in vehicle exhaust systems.\nA few things that are visually similar to 'metal exhaust pipe' but are not 'metal exhaust pipe' are:\tdrainage pipe\tgas pipe\tchimney\nThere are several useful visual features to tell there is 'metal exhaust pipe' and not similar things in a photo:\tcylindrical shape\tmetallic appearance\thole in the center for smoke or gas to escape", 12], "back tires": ["Yes. 'Back tires' has a tangible appearance and refers to the two wheels that are located at the back of a vehicle.\nA few things that are visually similar to 'back tires' but are not 'back tires' are:\tfront tires\tbicycle tires\tmotorcycle tires\ttractor tires\nThere are several useful visual features to tell there are 'back tires' and not similar things in a photo:\tpositioned at the back of a vehicle\twider than the front tires\thave a different tread pattern than the front tires", 12], "building side": ["Yes. 'Building side' has a tangible appearance and refers to the exterior or exterior walls of a building. \nA few things that are visually similar to 'building side' but are not 'building side' are:\tlandscape\tnatural rock formation\tfacade of a theater\nThere are several useful visual features to tell there is 'building side' and not similar things in a photo:\tstraight and flat surface texture\tmake out the design of the building kind of building material (brick, concrete, wood, etc.)the presence of windows or balconies", 12], "silver belt": ["Yes. 'Silver belt' has a tangible appearance and is an accessory.\nA few things that are visually similar to 'silver belt' but are not 'silver belt' are:\tchain\tbraided rope\tbrass buckle\tpouch\t\nThere are several useful visual features to tell there is 'silver belt' and not similar things in a photo:\tsilver in color\tmetallic sheen\t\nmade of a flat, smooth material\thas a buckle or closure\tis worn around the waist", 12], "university": ["No. 'University' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider a university campus, then it has a tangible appearance. In that case:\nA few things that are visually similar to a university campus are: public gardens, libraries, museums, research centers. \n\nUseful visual features for distinguishing a university from these similar things in a photo are: \n- Buildings which are dedicated to teaching or research, such as classrooms, lecture halls, labs, etc.\n- Signs, symbols or logos that identify the institution\n- Areas designed for sports and other recreational activities\n- Crowded areas full of young adults walking around \n- A mix of modern and historic buildings", 12], "safety mask": ["Yes. 'Safety mask' has a tangible appearance and is a type of facial protective gear.\nA few things that are visually similar to 'safety mask' but are not 'safety mask' are:\tski mask\tsurgical mask\thalloween mask\tfootball helmet\nThere are several useful visual features to tell there is 'safety mask' and not similar things in a photo:\tcovers the nose and mouth\tmay have a filter\tsecured around the back of the head or the ears\tused in hazardous environments or medical settings.", 12], "drip": ["Yes. 'Drip' has a tangible appearance and is a type of liquid flow.\nA few things that are visually similar to 'drip' but are not 'drip' are:\tpour\ttrickle\tstream\tspurt\nThere are several useful visual features to tell there is 'drip' and not similar things in a photo:\ta drop of liquid falling from a surface or container\tthe shape of a tear, with a round top and pointed bottom\ta visible trail of liquid from the point of origin", 12], "suspender": ["Yes. 'Suspender' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'suspender' but are not 'suspender' are:\tbelt\tbraces\tcorset\nThere are several useful visual features to tell there is 'suspender' and not similar things in a photo:\tthin straps that go over the shoulders\tclip or button attachments to pants\tor skirt\tno waistband\tsometimes an X or a Y shape in the back", 12], "sofa pillow": ["Yes. 'Sofa pillow' has a tangible appearance and is a type of cushion used for support or decoration.\nA few things that are visually similar to 'sofa pillow' but are not 'sofa pillow' are:\tbed pillow\tthrow blanket\tstuffed animal\nThere are several useful visual features to tell there is 'sofa pillow' and not similar things in a photo:\trectangular, square, or circular shape\tusually covered in a fabric\tcase or cover that can be removed for washing or cleaning\tmay have decorative patterns or texturing\tcan come in various sizes and thicknesses", 12], "neck area": ["Yes. 'Neck area' has a tangible appearance and is a body part.\nA few things that are visually similar to 'neck area' but are not 'neck area' might be:\tcollar\tturtleneck sweater\tnecktie\tscarf\nThere are several useful visual features to tell there is 'neck area' and not similar things in a photo:\tlocated between the head and the torso\twearing clothes above and below the neck\tcollarbone or Adam's apple visible\tskin color, texture and wrinkles on the neck", 12], "iron post": ["Yes. 'Iron post' has a tangible appearance and is a type of metal pole typically used for support or decoration.\nA few things that are visually similar to 'iron post' but are not 'iron post' are:\ttelephone poles\tfences\tflagpoles\tstreetlights\nThere are several useful visual features to distinguish 'iron post' from the listed similar things in a photo:\tmade of iron or metal\tcylindrical or square shape\ttypically used for support or decoration\tpainted or rusted surface may be visible\theight and thickness", 12], "clay flower pot": ["Yes. 'Clay flower pot' has a tangible appearance and is a type of container used to grow plants.\nA few things that are visually similar to 'clay flower pot' but are not 'clay flower pot' are:\tceramic vase\tglass jar\twatering can\tcoffee mug\nThere are several useful visual features to tell there is 'clay flower pot' and not similar things in a photo:\ttapered shape\twider at the top than at the bottom\tterracotta or clay material\twith or without a drainage hole at the bottom\tcontaining soil and a plant", 12], "round face": ["Yes. 'Round face' has a tangible appearance.\nA few things that are visually similar to 'round face' but are not 'round face' are:\tcircles\tballs\tmoon\nThere are several useful visual features to tell there is a 'round face' and not similar things in a photo:\toval-shaped face\twith wide cheekbones\tand fullness at the cheek and chin\tarea. The forehead and jawline are rounded.", 12], "gray cell phone": ["Yes. 'gray cell phone' has a tangible appearance and refers to a specific type of electronic device.\nA few things that are visually similar to 'gray cell phone' but are not 'gray cell phone' are:\tblack cell phone\twhite cell phone\ttablet\tcomputer\nThere are several useful visual features to tell there is 'gray cell phone' and not similar things in a photo:\tgray color\trectangular shape\twith a screen and buttons\tor with a touch screen\tcamera on the back or front\table to make calls and send texts", 12], "features": ["No. 'Features' is too vague or abstract to be distinguished in a photo. It depends on the context, as 'features' can refer to various things, such as facial features, software features, product features, etc.", 12], "president": ["Yes. 'President' has a tangible appearance and is a person holding a political position.\nA few things that are visually similar to 'president' but are not 'president' are:\tcongressman\tmayor\tgovernor\tchancellor\tking\tqueen\nThere are several useful visual features to tell there is 'president' and not similar things in a photo:\twearing a formal suit or dress\thaving an official or diplomatic badge\tseated or standing behind a podium or desk\tat a political event\twith other political figures.", 12], "gel": ["Yes. 'Gel' has a tangible appearance and is a type of substance.\nA few things that are visually similar to 'gel' but are not 'gel' are:\tJelly\tSlime\tSoap\tFoam\nThere are several useful visual features for distinguishing 'gel' from the listed similar things in a photo:\tIt has a semi-solid or solid-like consistency\tIt may be transparent or translucent\tIt can come in various colors and textures, including clear, opaque, glittery, or matte. It is often used in personal care products, such as hair gel, toothpaste, or moisturizer.", 12], "sticker pole": ["Yes. 'Sticker pole' has a tangible appearance and is a type of urban object.\nA few things that are visually similar to 'sticker pole' but are not 'sticker pole' are:\tpaper poster wall\ttraffic sign\tpaper or cardboard trash box\telectricity or telephone pole\tgardening or street light pole\nThere are several useful visual features to tell there is 'sticker pole' and not similar things in a photo:\tcovered with stickers or posters\tmostly vertical and thin\tpresent in dense urban environments", 12], "tablets": ["Yes. 'Tablets' has a tangible appearance and is a kind of electronic device.\nA few things that are visually similar to 'tablets' but are not 'tablets' are:\tsmartphones\tlaptops\te-readers\tportable game consoles\nThere are several useful visual features to tell there is 'tablets' and not similar things in a photo:\trectangular shape\ttouch screen\tdisplay size is generally larger than a smartphone and smaller than a laptop\thas a home button or a power button\thas a front-facing camera and a rear-facing camera (in some models)", 12], "dates": ["Yes. 'Dates' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'dates' but are not 'dates' are:\tprunes\traisins\tfigs\nThere are several useful visual features to tell there is 'dates' and not similar things in a photo:\tlong and cylindrical or oval shape\tdark brown to reddish-brown color\tshiny or glossy skin\tfleshy and juicy interior\twith a single seed on the inside", 12], "mint": ["Yes. 'Mint' has a tangible appearance and is a type of herb.\nA few things that are visually similar to 'mint' but are not 'mint' are:\tbasil\tcoriander\tspinach\nThere are several useful visual features to tell there is 'mint' and not similar things in a photo:\tlight green color\toblong, pointed leaves\topposite growth pattern\ton a stalk\toriental scent when crushed or broken", 12], "iron stand": ["Yes. 'Iron stand' has a tangible appearance and is a type of support or holder made of iron.\nA few things that are visually similar to 'iron stand' but are not 'iron stand' are:\twooden stand\tplastic stand\tmetal rack\tbookshelf\nThere are several useful visual features to tell there is 'iron stand' and not similar things in a photo:\t\nmade of iron or steel\t\nsimple and sturdy design\t\ngenerally used as a support or holder\t\noften used for plants, lamps, or statues", 12], "threads": ["Yes. 'Threads' has a tangible appearance and refers to thin, flexible strands of material.\nA few things that are visually similar to 'threads' but are not 'threads' are:\thair\tstring\twires\tspider webs\nThere are several useful visual features to tell there are 'threads' and not similar things in a photo:\tthin\tflexible\tmade of fabric or fiber\tcan be woven or knitted into cloth or textile.", 12], "utters": ["Yes. 'Utters' has a tangible appearance and refers to the mammary glands of a mammal.\nA few things that are visually similar to 'utters' but are not 'utters' are:\tblisters\tlumps\tskin or flesh\nThere are several useful visual features to tell there is 'utters' and not similar things in a photo:\tpaired mammary glands\thanging beneath the body\tteats or nipples for breastfeeding", 12], "dark belt": ["Yes. 'Dark belt' has a tangible appearance and is an item of clothing.\nA few things that are visually similar to 'dark belt' but are not 'dark belt' are:\tregular belt\tsash\tscarf\ttie\nThere are several useful visual features to tell there is 'dark belt' and not similar things in a photo:\tdark color\tworn around the waist\tasymmetrical design\tbuckle or fastener at the front or side of the waist.", 12], "blue marker": ["Yes. 'Blue marker' has a tangible appearance.\nA few things that are visually similar to 'blue marker' but are not 'blue marker' are:\tblue pen\tblue pencil\tblue crayon\tblue highlighter\nThere are several useful visual features to tell there is 'blue marker' and not similar things in a photo:\tcylindrical shape\trounded or pointed tip\tvisible ink or paint inside\table to make thick and thin lines or strokes", 12], "stripe pattern": ["Yes. 'Stripe pattern' has a tangible appearance and is a type of pattern.\nA few things that are visually similar to 'stripe pattern' but are not 'stripe pattern' are:\tcheckerboard pattern\tplaid pattern\tzigzag pattern\nThere are several useful visual features to tell there is 'stripe pattern' and not similar things in a photo:\tlong, straight lines\twide or narrow bands in various color combinations\thorizontal or vertical alignment\tuniform spacing between lines or bands.", 12], "print pillow": ["Yes. 'Print pillow' has a tangible appearance and is a kind of pillow with a printed design on it.\nA few things that are visually similar to 'print pillow' but are not 'print pillow' are:\tplain pillow\tembroidered pillow\tknitted pillow\tcushion\nThere are several useful visual features to tell there is 'print pillow' and not similar things in a photo:\ta printed or graphic design on the pillow fabric\tcolourful or patterned design\ta rectangular or square shape that is intended for use in a seating or sleeping context.", 12], "splatter": ["Yes. 'Splatter' has a tangible appearance and refers to a particular kind of mess.\nA few things that are visually similar to 'splatter' but are not 'splatter' are:\tstains\tspots\tspeckles\tdots\nThere are several useful visual features to tell there is 'splatter' and not similar things in a photo:\tmultiple droplets\tor a single burst of liquid\tdisorganized and random shape\tunexpected and unintended location\tof varying sizes\twith liquid traces visible", 12], "sky light": ["Yes. 'Sky light' has a tangible appearance and is a type of window on the roof.\nA few things that are visually similar to 'sky light' but are not 'sky light' are:\tregular window\tsunroof\tdecorative glass panel\nThere are several useful visual features to tell there is 'sky light' and not similar things in a photo:\tlocated on the roof\thorizontal or inclined orientation\tallows natural light to enter\ta frame holding a clear or translucent glass panel", 12], "leafed": ["No. 'Leafed' is too vague or abstract to be distinguished in a photo. It is an adjective that describes the presence of leaves on a plant or a tree.", 12], "glass cover": ["Yes. 'Glass cover' has a tangible appearance and is an object made of glass that covers something.\nA few things that are visually similar to 'glass cover' but are not 'glass cover' are:\tglass bottle\tglass cup\tglass vase\tpicture frame with a glass front\nThere are several useful visual features to tell there is 'glass cover' and not similar things in a photo:\tclear, transparent material\thorizontally flat surface\tsmooth and even edges\tthat covers or protects an object", 12], "water foam": ["Yes. 'Water foam' has a tangible appearance and is a type of foam that appears on water.\nA few things that are visually similar to 'water foam' but are not 'water foam' are:\tsoap foam\tmilk foam\tclouds\t\nThere are several useful visual features to tell there is 'water foam' and not similar things in a photo:\tappears on water\tor near water\tdissipates easily\tbubbles with irregular shapes\tis typically white or a light color.", 12], "marble wall": ["Yes. 'Marble wall' has a tangible appearance and is a type of wall made of marble.\nA few things that are visually similar to 'marble wall' but are not 'marble wall' are:\tconcrete wall\ttile wall\tbrick wall\tstucco wall\nThere are several useful visual features to tell there is 'marble wall' and not similar things in a photo:\tsmooth and glossy surface\tvariegated patterns of color and veins\tglistening and reflective\twhen tapped it makes a high-pitched sound", 12], "meter pole": ["Yes. 'Meter pole' has a tangible appearance and refers to a pole that holds utility meters.\nA few things that are visually similar to 'meter pole' but are not 'meter pole' are:\ttelephone pole\tfence post\tsign post\nThere are several useful visual features to tell there is 'meter pole' and not similar things in a photo:\tpart of a building or a structure\thousing for utility meters\tpainted with marks and numbers\tdimensions that meet utility company standards", 12], "skateboarding helmet": ["Yes. 'Skateboarding helmet' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'skateboarding helmet' but are not 'skateboarding helmet' are:\tbike helmet\thard hat\tmotorcycle helmet\nThere are several useful visual features to tell there is 'skateboarding helmet' and not similar things in a photo:\tround shape\thard outer shell\tinner lining\tpadded chin strap\tventilation holes\tcanvas straps for adjustment\tdesigned in a stylish manner\tfor skateboarders", 12], "bead necklace": ["Yes. 'Bead necklace' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'bead necklace' but are not 'bead necklace' are:\tdecorative garland\tmardi gras beads\tcurtain tassels\t\nThere are several useful visual features to tell there is 'bead necklace' and not similar things in a photo:\tbeads strung together around the neck\tvariety of shapes, sizes, and colors", 12], "computer bag": ["Yes. 'Computer bag' has a tangible appearance and is a type of bag designed to carry a computer.\nA few things that are visually similar to 'computer bag' but are not 'computer bag' are:\tbackpack\tmessenger bag\tpurse\tbriefcase\t\nThere are several useful visual features to tell there is 'computer bag' and not similar things in a photo:\tpadded compartment to hold laptop or tablet\tdurable material like nylon or leather\twide, comfortable shoulder strap or handle\tspecialized pockets and compartments for cords and accessories", 12], "fluffy cat": ["Yes. 'Fluffy cat' has a tangible appearance and refers to a kind of feline with a lot of fur.\nA few things that are visually similar to 'fluffy cat' but are not 'fluffy cat' are:\tother kinds of cats\tstuffed animals\nThere are several useful visual features to tell there is 'fluffy cat' and not similar things in a photo:\ta lot of fur\tfur puffs out around the face\tand body\tlarge, round head", 12], "tow": ["No. 'Tow' is too abstract to have a tangible appearance in a photo. However, a tow truck or a tow rope can be visually concrete concepts.\nA few things that are visually similar to 'tow' but are not 'tow' are:\tpull\tdrag\tlift\tcarry\nThere are several useful visual features to tell there is a 'tow' in a photo:\ta vehicle being pulled and lifted\tanother vehicle with a tow rope attached\tto a tow truck or other similar vehicle", 12], "suite case": ["Yes. 'Suitcase' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'suitcase' but are not 'suitcase' are:\tbackpack\tbriefcase\ttote bag\tduffle bag\tshoulder bag\nThere are several useful visual features to tell there is 'suitcase' and not similar things in a photo:\thard or soft exterior\thandle and wheels\thinged lid\tzippers and latches\tfor use during travel or transportation", 12], "onion slices": ["Yes. 'Onion slices' has a tangible appearance and describes a particular part of a vegetable.\nA few things that are visually similar to 'onion slices' but are not 'onion slices' are:\ttomato slices\tpotato slices\tbread slices\tcheese slices\nThere are several useful visual features to tell there is 'onion slices' and not similar things in a photo:\tthin and flat pieces of a vegetable\ttranslucent and whitish in color\tlayers visible when looking at the edge\tpungent smell", 12], "orange seat": ["Yes. 'Orange seat' has a tangible appearance and is easily distinguishable.\nA few things that are visually similar to 'orange seat' but are not 'orange seat' are:\torange pillow\torange rug\torange basket\torange vase\nThere are several useful visual features to tell there is 'orange seat' and not similar things in a photo:\tseat-shaped\tobject for sitting\tusually has legs, backrest and armrests\tmade of materials like plastic, metal or fabric", 12], "cranberry": ["Yes. 'Cranberry' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'cranberry' but are not 'cranberry' are:\tgrape\tcherry\ttomato\tpomegranate\nThere are several useful visual features to tell there is 'cranberry' and not similar things in a photo:\tround or oval shape\tdeep red color with a shiny surface\tsmaller than a golf ball\tusually served in groups in dishes or drinks", 12], "brown hill": ["Yes. 'Brown hill' has a tangible appearance and is a natural formation.\nA few things that are visually similar to 'brown hill' but are not 'brown hill' are:\tmountain\tvalley\tdirt pile\nThere are several useful visual features to tell there is 'brown hill' and not similar things in a photo:\televated area of land\tbrown-colored soil or rock\tsloping sides\tnatural formations of rocks or trees on the surface", 12], "suspension bridge": ["Yes. 'Suspension bridge' has a tangible appearance and is a type of bridge.\nA few things that are visually similar to 'suspension bridge' but are not 'suspension bridge' are:\tbeam bridge\tarch bridge\tcable-stayed bridge\t\nThere are several useful visual features to tell there is 'suspension bridge' and not similar things in a photo:\ttwo large towers\tcables or wires holding up the roadway\thanging roadway or deck\tsymmetrical and balanced design", 12], "dvd case": ["Yes. 'DVD case' has a tangible appearance and is a type of container for a DVD.\nA few things that are visually similar to 'DVD case' but are not 'DVD case' are:\tbook cover\tjewelry box\twallet\tphone case\nThere are several useful visual features to tell there is 'DVD case' and not similar things in a photo:\trectangular in shape\tclear plastic or paper cover\tfoldable and with a locking mechanism\ton the inside, two or more indentations for holding discs", 12], "headlight front bus": ["Yes. 'Headlight front bus' has a tangible appearance and is a specific part of a bus.\nA few things that are visually similar to 'headlight front bus' but are not 'headlight front bus' are:\theadlight front car\theadlight front truck\theadlight front train\nThere are several useful visual features to tell there is 'headlight front bus' and not similar things in a photo:\tlocated at the front of a bus\trectangular shape\tslightly curved on the edges\tusually paired with another headlight\tbright white or yellow light emission", 12], "blue walls": ["Yes. 'Blue walls' has a tangible appearance and is a type of painted surface.\nA few things that are visually similar to 'blue walls' but are not 'blue walls' are:\tblue sky\tblue ocean\tblue clothes\tblue flowers\nThere are several useful visual features to tell there are 'blue walls' and not similar things in a photo:\tsmooth surface\tbrush or roller marks\tindoor setting\tflushed against a floor and a ceiling", 12], "kitchen utensil": ["Yes. 'Kitchen utensil' has a tangible appearance and refers to tools used for cooking or preparing food.\nA few things that are visually similar to 'kitchen utensil' but are not 'kitchen utensil' are:\ttools for gardening\tpaintbrushes\tfor hair styling\nThere are several useful visual features to tell there is 'kitchen utensil' and not similar things in a photo:\tmade of metal, wood or plastic\tmeant for cooking or food preparation\thandles for gripping and using in the kitchen\tspouts, blades, or other functional features specific to cooking", 12], "jalapeno peppers": ["Yes. 'Jalapeno peppers' has a tangible appearance and is a type of chili pepper.\nA few things that are visually similar to 'jalapeno peppers' but are not 'jalapeno peppers' are:\tbell peppers\tcayenne peppers\tcherry tomatoes\nThere are several useful visual features to tell there is 'jalapeno peppers' and not similar things in a photo:\tmedium-sized\tspicy aroma\tglossy skin and green color at maturity\tpointed tip\tfleshy texture\tthick walls and small seeds inside", 12], "clamp": ["Yes. 'Clamp' has a tangible appearance and is a tool used to hold objects in place.\nA few things that are visually similar to 'clamp' but are not 'clamp' are:\tpliers\tvise\tclip\tpin\nThere are several useful visual features to tell there is 'clamp' and not similar things in a photo:\ttwo arms with flat or curved ends\tforceps-like appearance\tmetallic material\tbolt or screw for adjusting pressure and size\tdifferent sizes and shapes for specific tasks", 12], "belt brown": ["Yes. 'Belt brown' has a visually concrete concept and is a type of accessory.\nA few things that are visually similar to 'belt brown' but are not 'belt brown' are:\tstraps\t sandals\tboots\t wallets\nThere are several useful visual features to tell there is 'belt brown' and not similar things in a photo:\tbrown or dark brown color\tflat or braided design\tbuckle for adjusting or fastening the belt\tWorn around the waist.", 12], "pants woman": ["No. 'Pants woman' is too vague or abstract to be distinguished in a photo.", 12], "sandy hill": ["Yes. 'Sandy hill' has a tangible appearance and is a type of physical landform.\nA few things that are visually similar to 'sandy hill' but are not 'sandy hill' are:\tdessert dunes\tplayground equipment\tmounds of dirt or gravel\nThere are several useful visual features to tell there is 'sandy hill' and not similar things in a photo:\tnatural or man-made sand formation\ttapering slope\tgranular texture\tno visible vegetation", 12], "whitecaps": ["Yes. 'Whitecaps' has a tangible appearance and is a term used to describe waves on the surface of water that have white foam caps due to wind or waves.\nA few things that are visually similar to 'whitecaps' but are not 'whitecaps' are:\tsmooth water surface\tfish popping out of the water\t\nThere are several useful visual features to tell there are 'whitecaps' and not similar things in a photo:\twhite foam caps on the surface of waves\twaves caused by wind or tides\tfoamy appearance on the crests of waves", 12], "diploma": ["Yes. 'Diploma' has a tangible appearance and is a type of certificate.\nA few things that are visually similar to 'diploma' but are not 'diploma' are:\tdegree\tcertificate\taward\ttrophy\nThere are several useful visual features to tell there is 'diploma' and not similar things in a photo:\tsignature of an official authorizing body\tschool, college or university name\tand graphics\tdate of graduation\tname of the recipient\tseal or emblem of the issuing institution.", 12], "coffe table": ["Yes. 'Coffee table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'coffee table' but are not 'coffee table' are:\tend table\tdining table\tdesk\ttv stand\nThere are several useful visual features to tell there is 'coffee table' and not similar things in a photo:\tlow height relative to seating pieces\tsmall size compared to other tables\tin a common area like a living room\tor family room\toften used to hold beverages, books, or magazines", 12], "state": ["No. 'State' is too vague or abstract to be distinguished in a photo.", 12], "digital camera": ["Yes. 'Digital camera' has a tangible appearance and is an electronic device used for capturing images.\nA few things that are visually similar to 'digital camera' but are not 'digital camera' are: \tfilm camera\tsmartphone\ttablet\twebcam\nThere are several useful visual features to tell there is 'digital camera' and not similar things in a photo: \tLCD screen\tlens\tflash memory\tcard slot for memory card\tbattery or power source\tbuttons or dials for changing camera settings", 12], "aluminum baseball bat": ["Yes. 'Aluminum baseball bat' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'aluminum baseball bat' but are not 'aluminum baseball bat' are:\twooden baseball bat\ttennis racket\tgolf club\tbilliard cue\nThere are several useful visual features to tell there is 'aluminum baseball bat' and not similar things in a photo:\tmade of aluminum\thollow in the center\tskinny handle\tflat and wide top used for hitting the ball", 12], "rat": ["Yes. 'Rat' has a tangible appearance and is a type of rodent.\nA few things that are visually similar to 'rat' but are not 'rat' are:\tmouse\tbeaver\tsquirrel\thamster\nThere are several useful visual features to tell there is 'rat' and not similar things in a photo:\tlong, pointed face and whiskers\tthin, hairless tail\tlarge ears\tsmall, dark eyes\tsharp, large front teeth\tdark fur", 12], "frisbie": ["Yes. 'Frisbee' has a tangible appearance and is a kind of flying disc.\nA few things that are visually similar to 'frisbee' but are not 'frisbee' are:\tplastic plates\tflying saucers\tcricket bats\tpie tins\nThere are several useful visual features to tell there is 'frisbee' and not similar things in a photo:\tround and flat\tpliable plastic material\tringed edge or ridges for grip\ttwo to four inches thick\twith 'Frisbee' or 'Wham-O' emblem printed on it\tcan be seen being thrown or caught in the air", 12], "orange bird beak": ["Yes. 'Orange bird beak' has a tangible appearance and is a kind of body part of a bird.\nA few things that are visually similar to 'orange bird beak' but are not 'orange bird beak' are:\tyellow bird beak\tred bird beak\torange fish\torange carrot\nThere are several useful visual features to tell there is 'orange bird beak' and not similar things in a photo:\ttapered shape\thard and pointy texture\tbright orange color\tcurved or straight shape\tnarrow and elongated for birds.", 12], "grips": ["Yes. 'Grips' has a tangible appearance as it refers to a part of a handle or an object used to hold or grasp.\nA few things that are visually similar to 'grips' but are not 'grips' are:\tknobs\thandles\tlevers\nThere are several useful visual features to tell there is 'grips' and not similar things in a photo:\t\n\n- Grooves or ridges on the surface of the object\n- Designed to improve the grip or hold of the user\n- Can be made of a different material than the main object", 12], "pump bottle": ["Yes. 'Pump bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'pump bottle' but are not 'pump bottle' are:\tspray bottle\tregular bottle\tdeodorant stick\nThere are several useful visual features to tell there is 'pump bottle' and not similar things in a photo:\tcylindrical shape\tpump dispenser on top\ttube connecting the dispenser and the container", 12], "christmas trees": ["Yes. 'Christmas trees' has a tangible appearance and is a type of tree used for decoration during the holiday season.\nA few things that are visually similar to 'christmas trees' but are not 'christmas trees' are:\tpine trees\tfir trees\tspruce trees\tcypress trees\nThere are several useful visual features to tell there is 'christmas trees' and not similar things in a photo:\tfull and bushy branches\tdelta-like shape\tornaments and lights\thollow trunk and stem", 12], "dirt tennis court": ["Yes. 'Dirt tennis court' has a tangible appearance and is a type of sports court.\nA few things that are visually similar to 'dirt tennis court' but are not 'dirt tennis court' are:\tgrass tennis court\tbasketball court\tvolleyball court\tplayground\tsoccer field\nThere are several useful visual features to tell there is 'dirt tennis court' and not similar things in a photo:\tred or brown dirt surface\twhite lines marking the boundaries\tof rectangular shape (it could be square but the standard shape is a rectangle)", 12], "rides": ["No. 'Rides' is too vague or abstract to be distinguished in a photo.", 12], "bases": ["No. 'Bases' is too vague or abstract to be distinguished in a photo. It could refer to different types of bases such as military bases, baseball bases, chemical bases, etc. \n\nIf we assume 'bases' refers to baseball bases, then:\n\nA few things that are visually similar to 'bases' but are not 'bases' are:\tfloor tiles\tpylons\torienteering markers\nThere are several useful visual features to tell there is 'bases' and not similar things in a photo:\tdiamond shape\twith white bases, with each corner forming a corner of a square\tthe bases are touching the perimeter of the diamond, forming a square\twhere the lefthand side base is first base, righthand second, and the one at the far side of the diamond is third\tbase numbers on top of the bases, typically white on a black background", 12], "silver desk lamp": ["Yes. 'Silver desk lamp' has a tangible appearance and is a specific kind of lamp.\nA few things that are visually similar to 'silver desk lamp' but are not 'silver desk lamp' are:\ttable lamp\tdesk light\tfloor lamp\tchandelier\nThere are several useful visual features to tell there is 'silver desk lamp' and not similar things in a photo:\tsilver or metallic color\tstraight or curved neck\tbowl-shaped head with a light bulb\tswitch or knob for turning on/off or adjusting the brightness\ttakes up a small space on a desk or table.", 12], "oval mirror": ["Yes. 'Oval mirror' has a tangible appearance and is a specific type of mirror.\nA few things that are visually similar to 'oval mirror' but are not 'oval mirror' are:\trectangular mirror\tcircular mirror\tpicture frame\nThere are several useful visual features to tell there is 'oval mirror' and not similar things in a photo:\telongated shape with rounded edges\tsmooth reflective surface\tframed in an decorative or simple manner.", 12], "airplane hanger": ["Yes. 'Airplane hanger' has a tangible appearance and is a structure used to store or maintain aircraft.\nA few things that are visually similar to 'airplane hanger' but are not 'airplane hanger' are:\twarehouse\tbarn\tgarage\tparking lot\ttrain station\nThere are several useful visual features to tell there is 'airplane hanger' and not similar things in a photo:\tlarge and wide\tdoor openings tall enough to allow aircraft in and out\tmetal or concrete walls and roof\tcan accommodate more than one airplane at a time\tsigns indicating its use as an airplane storage or maintenance facility", 12], "police cars": ["Yes. 'Police cars' has a tangible appearance and is a type of vehicle used by law enforcement.\nA few things that are visually similar to 'police cars' but are not 'police cars' are:\tsecurity cars\tambulance cars\tfire engine cars\tprivate cars\nThere are several useful visual features to tell there is 'police cars' and not similar things in a photo:\tblue and white or black and white paint scheme\temergency lights or sirens\tmarkings or badges that indicate law enforcement use\tcage or divider between front and back seats", 12], "tall grass": ["Yes. 'Tall grass' has a tangible appearance and is a kind of vegetation.\nA few things that are visually similar to 'tall grass' but are not 'tall grass' are:\tweeds\tcorn fields\tbushes\tfern\nThere are several useful visual features to tell there is 'tall grass' and not similar things in a photo:\theight (usually taller than other vegetation)\tnarrow blades of grass densely packed together\trustling appearance from the wind\tdarker green or yellowish color", 12], "flag banner": ["Yes. 'Flag banner' has a tangible appearance and is a type of decoration.\nA few things that are visually similar to 'flag banner' but are not 'flag banner' are:\tpennant banner\tbunting banner\ttassel garland\tfloral garland\nThere are several useful visual features to tell there is 'flag banner' and not similar things in a photo:\trectangular-shaped flags\tlinked together with a string or rope\tbold and bright colors\tmay have patterns or designs\tmay have letters, numbers or symbols on the flags\tresembles the flag of a country, state or organization", 12], "grey scarf": ["Yes. 'Grey scarf' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'grey scarf' but are not 'grey scarf' are:\tblack scarf\tbrown scarf\twhite scarf\tknit hat\nThere are several useful visual features to tell there is 'grey scarf' and not similar things in a photo:\tlong and narrow\tgray color\tmade of a cloth or a similar material\tworn around the neck", 12], "gray tower": ["Yes. 'Gray tower' has a tangible appearance and is a type of architectural structure.\nA few things that are visually similar to 'gray tower' but are not 'gray tower' are:\tsmokestack\tchimney\tlighthouse\nThere are several useful visual features to tell there is 'gray tower' and not similar things in a photo:\ttall and narrow shape\tmade of stone or concrete\tgray or neutral color\tpointed or tapered top", 12], "brick steps": ["Yes. 'Brick steps' has a tangible appearance and is a type of stairs.\nA few things that are visually similar to 'brick steps' but are not 'brick steps' are:\tsteps made of stone or concrete\twooden stairs\trocks arranged in a step formation\nThere are several useful visual features to tell there is 'brick steps' and not similar things in a photo:\trectangular shape\tmade of bricks\tor tiles\tlevels with horizontal bands of a different color\tor\twidths leading to the top\tstep defined by visible rise and run patterns.", 12], "drain floor": ["Yes. 'Drain floor' has a tangible appearance and usually refers to a floor surface that has a built-in drainage system.\nA few things that are visually similar to 'drain floor' but are not 'drain floor' are:\tpaved ground\tsand\tputting green\nThere are several useful visual features to tell there is 'drain floor' and not similar things in a photo:\traised bars or grates\thorizontal or diagonal lines for water flow\tdimple-like holes or depressions\tgenerally indoors, such as in a shower", 12], "dirty bathroom": ["Yes. 'Dirty bathroom' has a tangible appearance.\nA few things that are visually similar to 'dirty bathroom' but are not 'dirty bathroom' are:\told bathroom\toutdated bathroom\tunorganized bathroom\tcluttered bathroom\nThere are several useful visual features to tell there is 'dirty bathroom' and not similar things in a photo:\tdirty or stained toilet\tdirty or clogged sink\tmoldy or mildew-covered surfaces\tuncleaned trash bins\tdisorganized or dirty toiletries or bathroom items\tdirty or wet floor", 12], "toddler boy": ["Yes. 'Toddler boy' has a tangible appearance and refers to a young male child.\nA few things that are visually similar to 'toddler boy' but are not 'toddler boy' are:\tbaby boy\tyoung girl\tadult man\tteenage boy\nThere are several useful visual features to tell there is 'toddler boy' and not similar things in a photo:\tshort in height, typically under 3 feet tall\tchildlike facial features, such as chubby cheeks and big eyes\tshort hair or curly hair\twearing child-sized clothing\tplayful expression or body language\tpresence of toys or child-specific objects in the image", 12], "rash guard": ["Yes. 'Rash guard' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'rash guard' but are not 'rash guard' are:\tt-shirt\tswimsuit\twetsuit\t\nThere are several useful visual features to tell there is 'rash guard' and not similar things in a photo:\ttightly fitting\tFlexible and stretchy material\tLong sleeves or short sleeves\tCollar", 12], "star pattern": ["Yes. 'Star pattern' has a tangible appearance and is a kind of design.\nA few things that are visually similar to 'star pattern' but are not 'star pattern' are:\tsnowflake pattern\tfloral pattern\tpaisley pattern\tgeometric pattern\nThere are several useful visual features to tell there is 'star pattern' and not similar things in a photo:\tconsists of stars or star shapes\trepeating pattern\tof varying sizes\tand often in a regular arrangement", 12], "plane engines": ["Yes. 'Plane engines' has a tangible appearance and refers to the propulsion system of an aircraft.\nA few things that are visually similar to 'plane engines' but are not 'plane engines' are:\tcommercial trucks\ttractor engines\tmotor boat engines\tindustrial turbines\nThere are several useful visual features to tell there is 'plane engines' and not similar things in a photo:\tinstalled under or beside the wings of the plane\tcylindrical shape\twith curved propellers or turbines\ton the front, middle, or back section of the plane", 12], "card board box": ["Yes. 'Cardboard box' has a tangible appearance and is a type of container commonly used for packaging and storage.\nA few things that are visually similar to 'cardboard box' but are not 'cardboard box' are:\twooden crate\tplastic container\tmetal bin\thollow block\tcarton\nThere are several useful visual features to tell there is 'cardboard box' and not similar things in a photo:\trectangular or square shape\tmade of cardboard or corrugated fiberboard\tfolded flaps or panels for sealing\ttop or bottom flaps opening and closing\tis marked with text or labels indicating its content or destination.", 12], "fence wire": ["Yes. 'Fence wire' has a tangible appearance and is a type of wire used in fencing.\nA few things that are visually similar to 'fence wire' but are not 'fence wire' are:\tbarbed wire\telectrical wire\tcable\twashing line\nThere are several useful visual features to tell there is 'fence wire' and not similar things in a photo:\tarranged in a crisscross pattern\tfor outdoor use\trustic appearance\tconnected to posts or poles", 12], "orange legs": ["Yes. 'Orange legs' has a tangible appearance and refers to legs that are colored orange.\nA few things that are visually similar to 'orange legs' but are not 'orange legs' are:\torange stripes\torange socks\torange shoes\torange peel\nThere are no additional visual features required to distinguish 'orange legs' from the listed similar things in a photo, as the color of the legs is the defining characteristic. However, the shape and size of the legs can also help identify them as legs, and any context or background in the photo can help clarify that they are indeed legs.", 12], "glass wine glass": ["Yes. 'Glass wine glass' has a tangible appearance and is a kind of drinking vessel.\nA few things that are visually similar to 'glass wine glass' but are not 'glass wine glass' are:\tbeer mug\ttumbler\tgoblet\tteacup\nThere are several useful visual features to tell there is 'glass wine glass' and not similar things in a photo:\tlong stem\tnarrow bowl\tcurved or tapered shape\tto hold wine or other alcoholic beverages.", 12], "cement pole": ["Yes. 'Cement pole' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'cement pole' but are not 'cement pole' are:\ttree\ttrash can\tpillar\nThere are several useful visual features to tell there is 'cement pole' and not similar things in a photo:\tgray or beige color\tcylindrical shape\tno branches, leaves, or other appendages\tsmooth surface or ridges designed for climbing or attaching objects to\tit is used for supporting cables, wires or traffic signs.", 12], "unripe": ["Yes. 'Unripe' has a tangible appearance and refers to fruits or vegetables that are not yet fully matured.\nA few things that are visually similar to 'unripe' but are not 'unripe' are:\tripe fruits\trotten fruits\toranges with lush greens\nThere are several useful visual features to tell there is 'unripe' and not similar things in a photo:\tGreen Color\tFirm and Hard Texture\tSmaller Size\tlack of sweetness in taste", 12], "mountain slope": ["Yes. 'Mountain slope' has a tangible appearance and refers to the incline of a mountain or hill.\nA few things that are visually similar to 'mountain slope' but are not 'mountain slope' are:\thillsides\tbeaches\tvalleys\tcliffs\nThere are several useful visual features to tell there is 'mountain slope' and not similar things in a photo:\tsteep incline or decline\tsnow or rock-covered surface\tvertical height rising from the ground\tsmooth or rugged terrain.", 12], "baseball coach": ["Yes. 'Baseball coach' has a tangible appearance and is a person who coaches a baseball team.\nA few things that are visually similar to 'baseball coach' but are not 'baseball coach' are:\tplayer\tumpire\tparent\tequipment manager\nThere are several useful visual features to tell there is a 'baseball coach' and not similar things in a photo:\twearing a team uniform\tor a cap and a jersey\thave a clipboard with notes\tstanding in the dugout or near the field\tgiving instructions or talking to players\thave a whistle or a megaphone", 12], "seawater": ["Yes. 'Seawater' has a tangible appearance and is a kind of liquid.\nA few things that are visually similar to 'seawater' but are not 'seawater' are:\tfreshwater\tpool water\triver water\tsoda water\nThere are several useful visual features to tell there is 'seawater' and not similar things in a photo:\tsalty smell\tblue or green color\tocean or sea in the background\tfoamy or frothy surface.", 12], "silver mixing bowl": ["Yes. 'Silver mixing bowl' has a tangible appearance and is a type of kitchenware.\nA few things that are visually similar to 'silver mixing bowl' but are not 'silver mixing bowl' are:\tregular bowl\tcup\tmeasuring cup\tsaucepan\tmetallic vase\nThere are several useful visual features to tell there is 'silver mixing bowl' and not similar things in a photo:\tmetallic or shiny appearance\twide and shallow shape\twith a flat bottom and curved sides\tfor cooking and mixing purposes\tcan have a handle and a spout on opposite sides.", 12], "bear doll": ["Yes. 'Bear doll' has a tangible appearance and is a type of stuffed toy.\nA few things that are visually similar to 'bear doll' but are not 'bear doll' are:\tcat/dog stuffed toys\tother animal stuffed toys\tcushions or pillows\nThere are several useful visual features to tell there is 'bear doll' and not similar things in a photo:\tbear-shaped\tfurry surface\tbutton or plastic eyes and nose\tpaws and feet shaped like those of a bear\tsoft and huggable to touch", 12], "silver faucets": ["Yes. 'Silver faucets' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'silver faucets' but are not 'silver faucets' are:\tdoorknobs \tother plumbing fixtures \tchrome handles \tstainless steel taps\nThere are several useful visual features to tell there is 'silver faucets' and not similar things in a photo: \ttwo separate spigots for hot and cold water \tlever or knob to control water flow \tbright and shiny silver color \ttypical configuration found on sinks or tubs", 12], "price sticker": ["Yes. 'Price sticker' has a tangible appearance and is a type of label.\nA few things that are visually similar to 'price sticker' but are not 'price sticker' are:\tsticky note\tpost-it note\tadhesive label\nThere are several useful visual features to tell there is 'price sticker' and not similar things in a photo:\trectangular or square shape\tbright or contrasting color\twith a price or a label on it\tstuck on a product or a surface\twith specific numbers or text (such as a barcode or a product code)", 12], "silver metal bar": ["Yes. 'Silver metal bar' has a tangible appearance and can be easily distinguished in a photo.\nA few things that are visually similar to 'silver metal bar' are: silver-colored plastic bars or rods, chrome-plated steel bars, metallic pipes.\nUseful visual features for distinguishing 'silver metal bar' from the listed similar things in a photo are: the density and weight of the object, the sound it makes when struck, the reflection and luster of the surface.", 12], "chocolate cookies": ["Yes. 'Chocolate cookies' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'chocolate cookies' but are not 'chocolate cookies' are:\tbrownies\tchocolate cake\tfudge\ttruffles\nThere are several useful visual features to tell there is 'chocolate cookies' and not similar things in a photo:\tcircular or semi-circular shape\tdark brown color\tchocolate chips or chunks visible\ton a baking sheet or cooling rack", 12], "shoe brown": ["No. 'Shoe brown' is too vague or abstract to be distinguished in a photo. It is only a color descriptor of a shoe, but there are many different shades of brown and many types of shoes.\nA few things that are visually similar to 'shoe brown' but are not 'shoe brown' are:\tbrown leather purse\tbrown wooden furniture\tbrown dog\nThere are no useful visual features to distinguish 'shoe brown' from other brown things in a photo. The context of the photo may provide some clues, such as if there are feet visible in the photo, or if the photo is of a shoe store display.", 12], "whole": ["No. 'Whole' is too vague or abstract to be distinguished in a photo.", 12], "wood seat": ["Yes. 'Wood seat' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood seat' but are not 'wood seat' are:\twooden stool\twooden box\twooden plank\twooden block\nThere are several useful visual features to tell there is 'wood seat' and not similar things in a photo:\tflat surface\tfor sitting\tmultiple legs or supports\tfor indoor or outdoor use\tvariety of shapes and designs, including backrests and armrests", 12], "color wall tiles": ["Yes. 'Color wall tiles' has a tangible appearance and refers to tiles that are used for wall decor and are available in different colors and designs.\nA few things that are visually similar to 'color wall tiles' but are not 'color wall tiles' are:\tpaint swatches\tfabric samples\twallpaper samples\nThere are several useful visual features to tell there is 'color wall tiles' and not similar things in a photo:\trectangular or square shape\tglossy or matte finish\tarranged in a grid pattern\tinstalled vertically or horizontally\tin various colors or patterns", 12], "pasta noodles": ["Yes. 'Pasta noodles' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pasta noodles' but are not 'pasta noodles' are:\tsalad\tlettuce\tvermicelli\nThere are several useful visual features to tell there is 'pasta noodles' and not similar things in a photo:\tlong, thin, and cylindrical shape\tcooked or uncooked\twheat or flour-based\tcontainer or dish it is served in, such as a bowl or a plate", 12], "grey plane": ["Yes. 'Grey plane' has a tangible appearance and is a type of aircraft that is characterized by its color.\nA few things that are visually similar to 'grey plane' but are not 'grey plane' are:\tsilver plane\twhite plane\tblue plane\tgreen plane\nThere are several useful visual features to tell there is 'grey plane' and not similar things in a photo:\tgrey color\tsmooth sleek body\ttwo wings and engines on each wing\twheel landing gear\tcockpit and tail section on top of the body.", 12], "crossing lines": ["Yes. 'Crossing lines' has a tangible appearance and refers to two or more lines intersecting.\nA few things that are visually similar to 'crossing lines' but are not 'crossing lines' are:\tmeeting point of two walls\tstreets with intersections\tatoms with bonds\tcircuit connections\nThere are several useful visual features to tell there are 'crossing lines' and not similar things in a photo:\ttwo or more straight lines\tintersecting at a point or at an angle\tdefining adjacent shapes or areas\tof different colors, widths or patterns.", 12], "foot bridge": ["Yes. 'Foot bridge' has a tangible appearance and is a type of bridge that is designed for pedestrians.\nA few things that are visually similar to 'foot bridge' but are not 'foot bridge' are:\toverpass\tunderpass\tdam\nThere are several useful visual features to tell there is 'foot bridge' and not similar things in a photo:\tnarrow or small in size\tconstructed from wood, metal or stone\tonly for pedestrians\tno lanes for vehicles\tsuspended over water or other obstacles\tuse of steps or inclines\tto cross elevated platforms or crossings", 12], "hooker": ["No. 'Hooker' is too vague or abstract and is a derogatory term for a sex worker, which is an individual and not an object. It is not appropriate or respectful to use this term to describe people. It should not be used.", 12], "sea shell": ["Yes. 'Sea shell' has a tangible appearance and is a natural object.\nA few things that are visually similar to 'sea shell' but are not 'sea shell' are:\trocks/concretions\tpetrified wood\tsculptures\nThere are several useful visual features to distinguish 'sea shell' from the listed similar things in a photo:\tspiral or helical shape\toutward ridges, bumps or spikes\tpearly, glossy or iridescent surface\tdisplaying variations of pink, orange, green, blue, and/or purple colors\tporcelain-like texture and appearance\tfound near or in the sea or ocean", 12], "visor hat": ["Yes. 'Visor hat' has a tangible appearance and is a type of hat.\nA few things that are visually similar to 'visor hat' but are not 'visor hat' are:\tbaseball cap\tsun hat\tsombrero\nThere are several useful visual features to tell there is 'visor hat' and not similar things in a photo:\that with a bill or brim to shield the face from the sun\twide and flat brim covering the forehead and nose\thas an opening in the center top of the hat for ventilation or hair\tband or strap that secures the hat to the head", 12], "grey buttons": ["Yes. 'Grey buttons' has a tangible appearance and refers to buttons that are grey in color.\nA few things that are visually similar to 'grey buttons' but are not 'grey buttons' are:\tblack buttons\tsilver buttons\tpainted circles\tdots on a fabric\nThere are several useful visual features to tell there are 'grey buttons' and not similar things in a photo:\tcircular shape\tshiny finish\tpolygon holes or a frontal thread\tshank or no shank depending on the purpose of the button", 12], "squid": ["Yes. 'Squid' has a tangible appearance and is a type of sea creature.\nA few things that are visually similar to 'squid' but are not 'squid' are:\toctopus\tjellyfish\tcuttlefish\tnautilus\nThere are several useful visual features to tell there is 'squid' and not similar things in a photo:\tlong, tapered body with a pointed end and a bulbous head\ttentacles\twith suckers\tor hooks\ton the ends of the tentacles\tbig round eyes\tbelonging to the cephalopod family", 12], "grassy plain": ["Yes. 'Grassy plain' has a tangible appearance and refers to an area of land covered in grass.\nA few things that are visually similar to 'grassy plain' but are not 'grassy plain' are:\tagricultural fields\tmeadows\tpastures\tlawns\nThere are several useful visual features to tell there is 'grassy plain' and not similar things in a photo:\textensive\twithout trees\tor bushes\tan evenly distributed short to medium high grass covering the soil", 12], "metal bleachers": ["Yes. 'Metal bleachers' has a tangible appearance and is a type of seating.\nA few things that are visually similar to 'metal bleachers' but are not 'metal bleachers' are:\tchairs\tbenches\tstadium seats\tpews\nThere are several useful visual features to tell there is 'metal bleachers' and not similar things in a photo:\tmade of metal\tconsist of rows of seats or benches\tusually found in outdoor venues like sports fields or concert halls\tnarrow spacing between each row\tfor temporary or portable use in some cases", 12], "forklift": ["Yes. 'Forklift' has a tangible appearance and is a kind of industrial vehicle.\nA few things that are visually similar to 'forklift' but are not 'forklift' are:\tcrane\ttruck\tbulldozer\nThere are several useful visual features to tell there is 'forklift' and not similar things in a photo:\tL-shaped forks used for lifting pallets or heavy materials\thydraulic lift mechanism\tforward-facing driver's seat and controls\tinward-curving wheels and solid tires", 12], "shadow bus": ["No. 'Shadow bus' is too vague or abstract to be distinguished in a photo.", 12], "water closet": ["Yes. 'Water closet' has a tangible appearance and refers to a type of bathroom.\nA few things that are visually similar to 'water closet' but are not 'water closet' are:\tshower\tbathtub\tsink\tmirror\nThere are several useful visual features to tell there is 'water closet' and not similar things in a photo:\ttoilet\tbowl with a hinged seat\ttank for flushing or washing\thorizontal pipe for sewage or waste disposal\tdoor with a lock\tor sign reading \"WC\" or \"Toilet\"", 12], "tube socks": ["Yes. 'Tube socks' has a tangible appearance and is a type of sock.\nA few things that are visually similar to 'tube socks' but are not 'tube socks' are:\tan ankle brace\ta leg warmer\ta compression sock\ta running sock\nThere are several useful visual features to tell there are 'tube socks' and not similar things in a photo:\tplain white color or with stripes\tthat covers up to the knee or mid-calf\tseparated toes from the heel\ttube-like shape with no defined heel part", 12], "airport vehicle": ["Yes. 'Airport vehicle' has a tangible appearance and refers to any type of vehicle used on an airport ground.\nA few things that are visually similar to 'airport vehicle' but are not 'airport vehicle' are:\ttruck\tcar\tvan\tbus\nThere are several useful visual features to tell there is 'airport vehicle' and not similar things in a photo:\tequipped with aviation-specific technology, such as GPS and radios\tbright colors, such as yellow, orange or green\tfitted with a rotator or beacon light for added visibility\thave runways and airplane silhouettes on the body of the vehicle", 12], "cabinet knobs": ["Yes. 'Cabinet knobs' has a tangible appearance and is a type of hardware.\nA few things that are visually similar to 'cabinet knobs' but are not 'cabinet knobs' are:\tdrawer pulls\tdoor handles\thooks\nThere are several useful visual features to tell there are 'cabinet knobs' and not similar things in a photo:\t\n\n- Small size compared to door handles or drawer pulls\n- Mounted on the surface of a cabinet door or drawer\n- Rounded or shaped for gripping with fingers\n- May have screws or bolts visible from the front or back", 12], "flower planter": ["Yes. 'Flower planter' has a tangible appearance and is a container for growing flowers.\nA few things that are visually similar to 'flower planter' but are not 'flower planter' are:\tpots\tvases\tbaskets\t\nThere are several useful visual features to tell there is 'flower planter' and not similar things in a photo:\tusually made of clay, concrete, or plastic\tmay have plants or flowers growing out of it\thas drainage holes or other openings at the bottom for water to escape from\tmay be decorated or painted to look more attractive.", 12], "dogs ears": ["Yes. 'Dogs ears' has a tangible appearance and is a feature of a dog's head.\nA few things that are visually similar to 'dogs ears' but are not 'dogs ears' are:\tearmuffs\thuman ears\tcats' ears\nThere are several useful visual features to tell there is 'dogs ears' and not similar things in a photo:\ttriangular shape\tfurry texture\tpositioned on the top of the head and hanging down\thanging down on each side of the head (for most breeds) with certain lengths and shapes based on breed", 12], "backing": ["No. 'Backing' is too vague or abstract to be distinguished in a photo.", 12], "glass shield": ["Yes. 'Glass shield' has a tangible appearance and is a type of protective barrier.\nA few things that are visually similar to 'glass shield' but are not 'glass shield' are:\twindows\tglass doors\tdisplay cases\nThere are several useful visual features to tell there is 'glass shield' and not similar things in a photo:\tthick and sturdy\tglass panel designed for protection\tcould be inclined or curved.", 12], "highrise building": ["Yes. 'Highrise building' has a tangible appearance and is a type of tall building.\nA few things that are visually similar to 'highrise building' but are not 'highrise building' are:\tskyscraper\tcommunication tower\tmonument\nThere are several useful visual features to tell there is 'highrise building' and not similar things in a photo:\ttall building with multiple floors\tand significantly more floors than surrounding buildings\tmultiple windows on each floor\tthat appear to make up regular rows or columns\tan overall linear shape or rectangular shape", 12], "amplifier": ["Yes. 'Amplifier' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'amplifier' but are not 'amplifier' are:\tspeaker\tcables\tequalizer\tmixer\nThere are several useful visual features to tell there is 'amplifier' and not similar things in a photo:\tbox-shaped metal or plastic casing\tknob and buttons\tfor volume and tone control\tLED lights or digital display\tthat indicates sound levels\tand equalization settings", 12], "tractor trailer": ["Yes. 'Tractor trailer' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'tractor trailer' but are not 'tractor trailer' are:\ttruck\tvan\tBus\tSUV\nThere are several useful visual features to tell there is 'tractor trailer' and not similar things in a photo:\tcab and trailer design\ttwo to three axles\thuge size\ttrailer is connected to the cab by a fifth-wheel hitch\tdoor at the back of the trailer\tflat trailer bed or cargo container.", 12], "safety net": ["Yes. 'Safety net' has a tangible appearance and is a type of protective equipment.\nA few things that are visually similar to 'safety net' but are not 'safety net' are:\thammock\ttrampoline\tmosquito net\tbed\nThere are several useful visual features to tell there is 'safety net' and not similar things in a photo:\twoven mesh material\telastic and flexible material\tpresent in construction or industrial settings\tsuspended in the air or fixed on poles or structures designed to catch people or objects that fall or jump", 12], "wine cork": ["Yes. 'Wine cork' has a tangible appearance.\nA few things that are visually similar to 'wine cork' but are not 'wine cork' are:\tbeer bottle cap\tpilfer-proof cap\t\nThere are several useful visual features to tell there is 'wine cork' and not similar things in a photo:\telongated cylindrical shape\twedge at the bottom that fits in the bottle neck\tcolor (usually beige or a light brown)\ttexture of the cork material (porous and flexible)", 12], "silver containers": ["Yes. 'Silver containers' has a tangible appearance and refers to containers made of silver-colored material.\nA few things that are visually similar to 'silver containers' but are not 'silver containers' are: tin containers, aluminum containers, stainless steel containers, plastic containers.\nThere are several useful visual features to tell there is 'silver containers' and not similar things in a photo: made of metal with a shiny appearance, reflective surface, often used for decorative purposes.", 12], "beat": ["No. 'Beat' is too vague or abstract to be distinguished in a photo.", 12], "pvc pipe": ["Yes. 'PVC pipe' has a tangible appearance and is a type of plastic piping.\nA few things that are visually similar to 'pvc pipe' but are not 'pvc pipe' are:\tmetal piping\twooden planks\ttubes made of other types of plastic\nThere are several useful visual features to tell there is 'pvc pipe' and not similar things in a photo:\tlightweight\tflexible\tsmooth surface\twith \"PVC\" or \"vinyl\" stamped on it\tRectangular cross-section with curved corners.", 12], "infield grass": ["Yes. 'Infield grass' has a tangible appearance and is a type of grass common in baseball fields.\nA few things that are visually similar to 'infield grass' but are not 'infield grass' are:\tlawn\tturf\tpark\tmeadow\tgarden\nThere are several useful visual features to tell there is 'infield grass' and not similar things in a photo:\tshort blades of grass\tstriped pattern with different shades of green\thighly manicured and maintained appearance\tlines or markings indicating a baseball field layout", 12], "gold tag": ["Yes. 'Gold tag' has a tangible appearance and is a specific type of tag.\nA few things that are visually similar to 'gold tag' but are not 'gold tag' are:\twhite tag\tred tag\tgreen tag\tmetallic tag\tplastic tag\t\nThere are several useful visual features to tell there is 'gold tag' and not similar things in a photo:\tgolden color\tmetallic appearance\tpresent on a gift or product", 12], "racetrack": ["Yes. 'Racetrack' has a tangible appearance and refers to a specific type of track.\nA few things that are visually similar to 'racetrack' but are not 'racetrack' are:\thighway\trunning track\tbike trail\tpath in a park\nThere are several useful visual features to tell there is 'racetrack' and not similar things in a photo:\trectangular or oval shape\tflat surface\tmarked with lines or lanes\tfor use in racing events, such as cars or horses.", 12], "cloudy grey skies": ["Yes. 'Cloudy grey skies' has a tangible appearance and is a type of weather condition.\nA few things that are visually similar to 'cloudy grey skies' but are not 'cloudy grey skies' are:\tsmoke\tfog\thazy air\tdusk or dawn\nThere are several useful visual features to tell there is 'cloudy grey skies' and not similar things in a photo:\tgrey or overcast coloring\tthick layer of clouds\tlack of sunshine or blue skies\tlarge cloud formations covering most or all of the sky.", 12], "woman arm": ["Yes. 'Woman arm' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'woman arm' but are not 'woman arm' are:\tman arm\tchild arm\tdoll arm\nThere are several useful visual features to tell there is 'woman arm' and not similar things in a photo:\tfeminine features (if visible)\tlength and shape of arm\tdress or clothing (if visible)", 12], "bread sticks": ["Yes. 'Bread sticks' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'bread sticks' but are not 'bread sticks' are:\tpencils\ttwig\tsnacks\tstir sticks\nThere are several useful visual features to tell there is 'bread sticks' and not similar things in a photo:\tlong and thin\thard and crunchy\tslightly curved\tbrowned surface", 12], "visitors": ["No. 'Visitors' is too vague or abstract to be distinguished in a photo.", 12], "thermometer": ["Yes. 'Thermometer' has a tangible appearance and is a device to measure temperature.\nA few things that are visually similar to 'thermometer' but are not 'thermometer' are:\tmercury barometer\train gauge\thygrometer\nThere are several useful visual features to tell there is 'thermometer' and not similar things in a photo:\tBulb at one end with colored liquid (alcohol or mercury)\tGraduated markings with numbers indicating temperature readings\tA scale for both Fahrenheit and Celsius", 12], "pink bear": ["Yes. 'Pink bear' has a tangible appearance and is a type of stuffed animal.\nA few things that are visually similar to 'pink bear' but are not 'pink bear' are:\tpink rabbit\tpink pig\tpink elephant\nThere are several useful visual features to tell that it is a 'pink bear' and not similar things in a photo:\tfurry body\tpink color\tbear-like features, such as ears and snout", 12], "plastic wrapper": ["Yes. 'Plastic wrapper' has a tangible appearance and is a flexible cover made of plastic.\nA few things that are visually similar to 'plastic wrapper' but are not 'plastic wrapper' are:\tpaper wrapper\taluminum foil\tplastic bag\tcellophane wrap\nThere are several useful visual features to tell there is 'plastic wrapper' and not similar things in a photo:\tflexible and stretchy material\ttranslucent or transparent surface\tglossy or matte finish\tspecifications with printed information, such as brand name, product name, and instructions.", 12], "blue roof": ["Yes. 'Blue roof' has a tangible appearance and is a type of roof.\nA few things that are visually similar to 'blue roof' but are not 'blue roof' are:\tblack roof\tgrey roof\tbrown roof\tgreen roof\nThere are no visual features that can distinguish a blue roof from other roofs as the only distinguishing feature is the color.", 12], "banana leaves": ["Yes. 'Banana leaves' has a tangible appearance and is the large, flexible leaves of the banana plant.\nA few things that are visually similar to 'banana leaves' but are not 'banana leaves' are:\tpalm leaves\tavocado leaves\thosta leaves\nThere are several useful visual features to tell there are 'banana leaves' and not similar things in a photo:\tlarge and elongated shape\tbright green color\tvery flexible and easy to bend\tarrangement in a fan shape\twith a prominent central vein\tlong and pointed tips on both ends", 12], "caution light": ["Yes. 'Caution light' has a tangible appearance and is a type of safety signal.\nA few things that are visually similar to 'caution light' but are not 'caution light' are:\ttraffic light\twarning sign\tflashlight\tglow stick\nThere are several useful visual features to tell there is 'caution light' and not similar things in a photo:\tusually yellow or orange\tcolorful blinking lights\ttriangle or diamond-shaped\tsimilar to a traffic signal or a warning sign\tpositioned at construction sites or other hazardous locations.", 12], "gauges": ["Yes. 'Gauges' has a tangible appearance and is a type of instrument.\nA few things that are visually similar to 'gauges' but are not 'gauges' are:\tclock\tcompass\tspeedometer\tthermometer\nThere are several useful visual features to tell there is 'gauges' and not similar things in a photo:\tdisplay measurement for a specific task or purpose\tneedles, digits, or other markers to indicate levels, values, or readings\ttypically used in a mechanical or electrical system or equipment.", 12], "channel": ["No. 'Channel' is too vague or abstract to be distinguished in a photo. However, if 'channel' refers to a television or radio channel, then it has a tangible appearance.\nA few things that are visually similar to 'channel' but are not 'channel' are:\t\nriver\tbay\tpipe or tunnel path\nThere are several useful visual features to tell there is a television or radio 'channel' in a photo:\tchannel number or name displayed\ton-screen programming or logo\taudio or video broadcasting equipment or antenna.", 12], "man wears": ["No. 'Man wears' is too vague or abstract to be distinguished in a photo as it could refer to any type of clothing worn by a man. \n\nHowever, if we were to narrow down to a specific type of clothing, such as 'men's shoes,' then it would have a more visually concrete concept.\n\nA few things that are visually similar to 'men's shoes' but are not 'men's shoes' are:\twomen's shoes\tbelts\tboots\theels\nThere are several useful visual features to tell apart 'men's shoes' from other similar things in a photo, such as:\ttypical shape and design of men's shoes\tmale models wearing the shoes\tabsence of feminine or unisex design elements, such as decorative flowers or bright colors.", 12], "manis": ["Yes. 'Manis' has a tangible appearance and is a type of animal, also known as pangolin.\nA few things that are visually similar to 'manis' but are not 'manis' are:\tarmadillo\tanteater\nThere are several useful visual features to tell there is 'manis' and not similar things in a photo:\tscaled body\telongated snout\tcurled-up tail\twhen in motion, moves on its hind legs while balancing on its tail", 12], "silver gate": ["Yes. 'Silver gate' has a tangible appearance and is a type of gate.\nA few things that are visually similar to 'silver gate' but are not 'silver gate' are:\tiron gate\tmetal door\tshutter\nThere are several useful visual features to tell there is 'silver gate' and not similar things in a photo:\tmade of silver or silver-colored metal\thinged with vertical bars or slats in a symmetrical pattern\twith or without lock or handle", 12], "face clock": ["Yes. 'Face clock' has a tangible appearance and is a type of clock with a visible clock face.\nA few things that are visually similar to 'face clock' but are not 'face clock' are:\tdigital clock\talarm clock\tpocket watch\twall clock\nThere are several useful visual features to tell there is 'face clock' and not similar things in a photo:\thands pointing to numbers\ton which 1-12 numbers are visible\ta circle or square shape\twith arrows or hands pointing to numbers or lines\tthe clock is divided into 12 hours\tclock hands may be pointed to HH:MM, or SS", 12], "way track": ["No. 'Way track' is not a commonly used term, and it is difficult to determine if it has a tangible appearance. Perhaps if you could provide more context or a different way of phrasing the concept, I could better answer your question.", 12], "pink petals": ["Yes. 'Pink petals' has a tangible appearance and refers to the colored and often fragrant parts of a flower.\nA few things that are visually similar to 'pink petals' but are not 'pink petals' are: \tpink confetti \tpink candy \tpink tissue paper \tpink feathers\nThere are several useful visual features to tell there are 'pink petals' and not similar things in a photo: \tattached to a flower \tthin and delicate texture \tcurved or wavy edges \tarranged in a circular or star-like pattern \tmay have visible veins or stamens", 12], "silver scooter": ["Yes. 'Silver scooter' has a tangible appearance and refers to a type of vehicle.\nA few things that are visually similar to 'silver scooter' but are not 'silver scooter' are:\tmotorcycle\tbicycle\tskateboard\troller skates\nThere are several useful visual features to tell there is 'silver scooter' and not similar things in a photo:\ttwo wheels\thandlebar\tstep-through frame\tflat platform for feet\tsilver color or metallic appearance.", 12], "silver metal sink faucet": ["Yes, 'silver metal sink faucet' has a visually concrete concept and has a tangible appearance.\nA few things that are visually similar to 'silver metal sink faucet' but are not 'silver metal sink faucet' are: showerhead, doorknob, silver metal knob, water dispenser.\nThere are several useful visual features to differentiate between 'silver metal sink faucet' and similar things in a photo, such as:\tthe faucet is fixed on the countertop, sink, or wall.\tthe faucet is designed to regulate the flow of water into a sink or basin.\tit has one or more handles or knobs that allow people to control water temperature and flow.\tit is made of silver or other metallic material.", 12], "orange door": ["Yes. 'Orange door' has a tangible appearance and is a specific type of door.\nA few things that are visually similar to 'orange door' but are not 'orange door' are:\tyellow door\torange wall\tdoor with an orange handle\t\nThere are several useful visual features to distinguish an 'orange door' from similar things in a photo:\tthe door is completely painted in the color orange\tthere is no other color in the door\tthe door can be made of wood, metal, or any material as long as it has an orange color.", 12], "grey shoes": ["Yes. 'Grey shoes' has a tangible appearance and it's a type of footwear.\nA few things that are visually similar to 'grey shoes' but are not 'grey shoes' are:\tboots\tsneakers\tsandals\tloafers\nThere are several useful visual features to tell there is 'grey shoes' and not similar things in a photo:\tcolor grey\tclosed toe and heel\tlace-up or slip-on style\tflat or with a heel material (leather, suede, etc.)", 12], "bus front": ["Yes. 'Bus front' has a tangible appearance and refers to the front part of a bus.\nA few things that are visually similar to 'bus front' but are not 'bus front' are:\ttruck front\tcar front\tvan front\t\nThere are several useful visual features to tell there is 'bus front' and not similar things in a photo:\t\nlarger in size than a car or a truck\t\ndistinctive squared shape\t\ntwo headlights\t\ngrill on the front\t\ndestination board or banner above the windshield", 12], "brick pathway": ["Yes. 'Brick pathway' has a tangible appearance and refers to a path made of bricks.\nA few things that are visually similar to 'brick pathway' but are not 'brick pathway' are:\tstone pathway\tcement pathway\twooden walkway\nThere are several useful visual features to tell there is 'brick pathway' and not similar things in a photo:\tpaved with brick-shaped stones\tor actual bricks\tterracotta or red hue\therringbone or straight pattern\toften found in gardens or exterior areas", 12], "varieties": ["No. 'Varieties' is too abstract and does not have a tangible appearance.\nThere are no things that are visually similar to 'varieties' but are not 'varieties'.", 12], "wood base": ["Yes. 'Wood base' has a tangible appearance and is a type of support or foundation made of wood.\nA few things that are visually similar to 'wood base' but are not 'wood base' are:\twooden blocks\tpaperweight\tbooks\twall shelves\nThere are several useful visual features to tell there is 'wood base' and not similar things in a photo:\tmade of wood\tsupporting or carrying weight\trectangular or circular shape\tflat surface at the bottom", 12], "hundreds": ["No. 'Hundreds' is too vague or abstract to be distinguished in a photo.", 12], "tassle": ["Yes, 'tassel' has a visually concrete concept and is a common decorative element.\nA few things that are visually similar to 'tassel' but are not 'tassel' are:\tFringes\tBeads\tTwine\tTassels on curtains or bags\nThere are several useful visual features to distinguish 'tassel' from the listed similar things in a photo:\tLong threads gathered or hanging down\tA distinct knot or ball shape near the top\tA loop at the top to attach to a larger item (such as a bookmark)\tMultiple strands of threads, rather than a single or double strand.", 12], "turret": ["Yes. 'Turret' has a tangible appearance and is a part of a building, typically a tower.\nA few things that are visually similar to 'turret' but are not 'turret' are:\tchimney\tlighthouse\tcastle tower\twindmill tower\nThere are several useful visual features to tell there is 'turret' and not similar things in a photo:\t\nsmall tower on top of a building\tcircular or polygonal shape\twith crenellations or battlements\twindows or openings\tfor defensive or decorative purposes", 12], "dock water": ["Yes. 'Dock water' has a tangible appearance and refers to the water around a dock or pier.\nA few things that are visually similar to 'dock water' but are not 'dock water' are:\triver water\tlake water\tocean water\tswimming pool water\nThere are several useful visual features to tell there is 'dock water' and not similar things in a photo:\tadjacent to a dock or pier\tcontaining boats or other watercraft\tmurky or cloudy water surface\tmay have debris like leaves or branches\tfloats or buoys visible in the water", 12], "buffet": ["Yes. 'Buffet' has a tangible appearance and is a type of meal service.\nA few things that are visually similar to 'buffet' but are not 'buffet' are:\tcafeteria\tcounter\tservice line\tfood truck\tkitchen island\nThere are several useful visual features to tell there is 'buffet' and not similar things in a photo:\tmultiple dishes or trays arranged on a table or counter\tself-service format\twithin a restaurant or event space\tbranded or labeled dishes or trays\tchafing dishes or heated trays\tfor more than one person to serve themselves at the same time.", 12], "brown bag": ["Yes. 'Brown bag' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'brown bag' but are not 'brown bag' are:\tpurse\tbackpack\tgift bag\tpaper envelope\nThere are several useful visual features to tell there is 'brown bag' and not similar things in a photo:\tmade of brown paper or similar material\trectangle-shaped\twith handles\tor folded top", 12], "smudges": ["Yes. 'Smudges' has a tangible appearance and refers to marks or dirt that are not in a specific shape.\nA few things that are visually similar to 'smudges' but are not 'smudges' are:\tdrawings\tor shadows\tor specific shapes\nThere are several useful visual features to tell there is 'smudges' and not similar things in a photo:\tnot in a specific shape\tor form\tmarks or dirt that are vague or unclear.", 12], "border collie": ["Yes. 'Border collie' has a tangible appearance and is a breed of dog.\nA few things that are visually similar to 'border collie' but are not 'border collie' are:\tAustralian shepherd\tRough Collie\tSmooth Collie\t\nThere are several useful visual features to tell there is 'border collie' and not similar things in a photo:\t \nmedium-sized dog\nblack and white or brown and white fur\npointed ears\nintelligent and alert expression\nathletic build and agility", 12], "fly": ["Yes. 'Fly' has a tangible appearance and is a type of insect.\nA few things that are visually similar to 'fly' but are not 'fly' are:\tmosquito\tbeetle\twasp\tbee\nThere are several useful visual features to tell there is 'fly' and not similar things in a photo:\tlarge compound eyes\ttwo wings and three legs\tdiagonal wings\twhen resting their wings lie parallel to body\ttransparent wings with an obvious rib-like pattern\tsmall size and slender body compared to other insects.", 12], "orange fur": ["Yes. 'Orange fur' has a tangible appearance and refers to a specific color and texture of animal hair.\nA few things that are visually similar to 'orange fur' but are not 'orange fur' are:\torange fabric\torange feathers\torange wool\nThere are several useful visual features to tell there is 'orange fur' and not similar things in a photo:\tsoft texture\ton an animal's body\torangish-brown in color\tthicker than regular hair or fur.", 12], "train yard": ["Yes. 'Train yard' has a tangible appearance and is a place where trains are parked or stored.\nA few things that are visually similar to 'train yard' but are not 'train yard' are:\tparking lot\tstorage area\tshipyard\nThere are several useful visual features to tell there is 'train yard' and not similar things in a photo:\trailroad tracks\tswitches and signals\ttrain cars\tpower lines or tall poles\tfor loading and unloading trains", 12], "silver stand": ["Yes. 'Silver stand' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'silver stand' but are not 'silver stand' are:\tsilver stool\tsilver vase\tsilver tray\tsilver plant pot\nThere are several useful visual features to tell there is 'silver stand' and not similar things in a photo:\ttall and narrow table with a flat top\tsilver or metallic in color\tsturdy and can support weight\tno visible seating surface or container for holding things", 12], "teddybear": ["Yes. 'Teddybear' has a tangible appearance and is a type of stuffed animal.\nA few things that are visually similar to 'teddybear' but are not 'teddybear' are:\tplush toys\tdolls\tpillows\nThere are several useful visual features to tell there is 'teddybear' and not similar things in a photo:\tfurry body\ttwo arms and two legs\trounded ears\tand a snout a small tail\tbutton or plastic nose\tpaw prints on the bottom of the paws", 12], "knit sweater": ["Yes. 'Knit sweater' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'knit sweater' but are not 'knit sweater' are:\tcardigan\thoodie\tpullover\nThere are several useful visual features to tell there is 'knit sweater' and not similar things in a photo:\tknit or crocheted texture\twoolen or cotton fabric\tlong sleeves\ttight-fitting with a crew neck-collar or V-neckline\ttypically worn in cold weather", 12], "accordion": ["Yes. 'Accordion' has a tangible appearance and is a musical instrument.\nA few things that are visually similar to 'accordion' but are not 'accordion' are:\tmelodica\tharmonium\tconcertina\torgan\nThere are several useful visual features to tell there is 'accordion' and not similar things in a photo:\trectangular or trapezoidal shape\tbellows\tfoldable keyboard or buttons\tmetallic finish or accents\tnumber of keys or buttons on the keyboard", 12], "indentations": ["Yes. 'Indentations' has a tangible appearance and refers to visible depressions or imprints on a surface.\nA few things that are visually similar to 'indentations' but are not 'indentations' are:\tshadows\tlines\thighlights\tcutouts\nThere are several useful visual features to tell there are 'indentations' and not similar things in a photo:\tvisible depressions or imprints on a surface\tdarkened or lower area on the surface\tto be surrounded by raised edges or bumps\tto cast a 3D shadow or reflection", 12], "store signs": ["Yes. 'Store signs' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'store signs' but are not 'store signs' are:\tsigns for events or exhibitions\tbillboards\ttraffic signs\nThere are some useful visual features to tell there are 'store signs' and not similar things in a photo:\tlocated outside a physical store or business\tstore name and logo\tpromotions or products advertised\tlocation-specific (e.g. hours, address)\tilluminated or backlit", 12], "hazy mountain": ["Yes. 'Hazy mountain' has a tangible appearance as a natural landscape.\nA few things that are visually similar to 'hazy mountain' but are not 'hazy mountain' are: cloudy sky, a volcano eruption, and a smokestack.\nThere are several useful visual features to tell there is a 'hazy mountain' and not similar things in a photo:\tmountain shape\thazy, foggy, or misty appearance\tearth-tone color palette\trocky or jagged texture", 12], "toilet floor": ["Yes. 'Toilet floor' has a tangible appearance and refers to the flooring surface of a toilet or bathroom.\nA few things that are visually similar to 'toilet floor' but are not 'toilet floor' are:\tkitchen floor\tshower floor\tbedroom floor\tliving room floor\nThere are several useful visual features to tell there is 'toilet floor' and not similar things in a photo:\tin a bathroom or a toilet room\tsurface tiles or linoleum, often white or light-colored\twet or damp surface with water droplets or pools\ton the same level or lower than surrounding floor surfaces.", 12], "brown stains": ["Yes. 'Brown stains' has a tangible appearance and is a type of discoloration.\nA few things that are visually similar to 'brown stains' but are not 'brown stains' are:\tshadows\tmarks\tpatterns\nThere are several useful visual features to tell there is 'brown stains' and not similar things in a photo:\tbrown in color\tirregular shape\tdifficult to remove from the surface\tit may have leaked from a liquid or food source", 12], "baseball catchers": ["Yes. 'Baseball catchers' has a tangible appearance and refers to the players on the fielding team in the game of baseball who are positioned behind home plate.\nA few things that are visually similar to 'baseball catchers' but are not 'baseball catchers' are:\tsoftball catchers\tcricket wicketkeepers\thigh school football linebackers\tgoalkeepers in soccer\nThere are several useful visual features to tell there is 'baseball catchers' and not similar things in a photo:\tpositional placement behind home plate\tcatcher's mask\tpropensity for wearing a helmet, chest protector, shin guards, and other protective gear\thaving a mitt with the size and shape of a baseball or softball\thaving a uniform with the logo of a baseball team", 12], "leaves water": ["No. 'Leaves water' is too vague or abstract to be distinguished in a photo. \n\nPerhaps you meant 'leaves in water'? If so:\n\nA few things that are visually similar to 'leaves in water' but are not 'leaves in water' are:\tweeds\talgae\tbranches\nThere are several useful visual features to tell there are 'leaves in water' and not similar things in a photo:\tleaves floating in water\twater droplets on leaves\t reflections on the water surface.", 12], "chickpeas": ["Yes. 'Chickpeas' has a tangible appearance and is a type of legume.\nA few things that are visually similar to 'chickpeas' but are not 'chickpeas' are:\tlentils\tkidney beans\tpinto beans\tblack beans\nThere are several useful visual features to tell there is 'chickpeas' and not similar things in a photo: \tlight beige or tan color\tsmall, round shape\tlengthwise crease or groove\tonion-like, hairy sprout from one end", 12], "john": ["No. 'John' is too vague or abstract to be distinguished in a photo. It could refer to any person with the name John.", 12], "whtie": ["No. 'White' is too vague or abstract to be distinguished in a photo.\nHowever, a few things that may share the same color or have similar visual appearance, but are not 'white' include:\tgray\tlight beige\toff-white\tivory\tsilver\nUseful visual features for distinguishing 'white' from things that look similar would include its brightness and lack of tint or hue, as well as its ability to reflect light to a greater degree than other colors. However, without any other visual context, it may be difficult to distinguish 'white' from similar colors or shades.", 12], "life guard": ["Yes. 'Life guard' has a tangible appearance and is a person who works at a beach or a swimming pool.\nA few things that are visually similar to 'life guard' but are not 'life guard' are:\tsurfers\tswimmers\tpool attendants\nThere are several useful visual features to tell there is 'life guard' and not similar things in a photo:\tuniform with bright colors and the word \"life guard\" on it\trescue buoy or floatation device\trescue tube or rescue board\tsunglasses or hat\tto protect themselves from the sun", 12], "yellow wheels": ["Yes. 'Yellow wheels' has a tangible appearance and is a specific type of object.\nA few things that are visually similar to 'yellow wheels' but are not 'yellow wheels' are:\tgray wheels\tbrown wheels\tred wheels\tyellow frisbee\nThe useful visual features for distinguishing 'yellow wheels' from similar things in a photo would be:\n- The presence of the circular shape, with a hollow center and a thick outer rim.\n- The presence of the color yellow in the wheel, with a relatively homogeneous distribution across the surface.\n- The presence of a clear texture or pattern within the wheel's surface, such as ridges or spokes.", 12], "office supplies": ["Yes. 'Office supplies' has a tangible appearance and includes various physical items used in an office.\nA few things that are visually similar to 'office supplies' but are not 'office supplies' are:\tart supplies\tkitchen supplies\tsewing supplies\nThere are several useful visual features to tell whether it is 'office supplies' and not similar things in a photo:\twork-related tools\tpaper products such as notepads or sticky notes\tpens, pencils, and markers\tbinders and clips.office-focused branding or labeling.", 12], "wooden barrel": ["Yes. 'Wooden barrel' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'wooden barrel' but are not 'wooden barrel' are:\twooden bucket\tmetal drum\tcardboard box\tcask\nThere are several useful visual features to tell there is 'wooden barrel' and not similar things in a photo:\tcylindrical shape\tmade of wood\twith metal or wooden bands\tbung or spigot in the side or on the top and bottom of the barrel", 12], "poncho": ["Yes. 'Poncho' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'poncho' but are not 'poncho' are:\tblanket\tsweater\tcape\tshawl\nThere are several useful visual features to tell there is 'poncho' and not similar things in a photo:\trectangular or square shape\thole in the center for the head\tto be worn over other clothing\tno sleeves\tfrilled or fringed hem", 12], "wood block": ["Yes. 'Wood block' has a tangible appearance and is a solid piece of wood.\nA few things that are visually similar to 'wood block' but are not 'wood block' are:\tlog\tbrick\tcube\nThere are several useful visual features to tell there is 'wood block' and not similar things in a photo:\trectangular shape\tsmooth or rough texture\tgrain patterns in the wood\tvariations in color and tone associated with natural wood", 12], "grey airplane": ["Yes. 'Grey airplane' has a visually concrete concept.\nA few things that are visually similar to 'grey airplane' but are not 'grey airplane' are:\tother colored airplanes\thelicopters\tbirds\tdrones\nThere are several useful visual features to tell there is 'grey airplane' and not similar things in a photo:\tairplane shape with wings and a tail\tconventional design with two engines\tgrey coloring on the body of the plane\tfuselage with windows for passengers and cockpit at the front of the plane.", 12], "plaid tie": ["Yes. 'Plaid tie' has a tangible appearance and is a specific type of clothing accessory.\nA few things that are visually similar to 'plaid tie' but are not 'plaid tie' are:\tsolid color tie\tstripe tie\tpatterned tie\tscarf\nThere are several useful visual features to tell there is 'plaid tie' and not similar things in a photo:\tcrisscross pattern of horizontal and vertical lines\tblock-like pattern\tin a tie shape and size", 12], "registration number": ["No. 'Registration number' is too abstract to be distinguished in a photo.", 12], "xii": ["No. 'XII' is too vague or abstract to be considered as a visually concrete concept.", 12], "passenger side": ["Yes. 'Passenger side' has a tangible appearance and is a location inside a vehicle.\nA few things that are visually similar to 'passenger side' but are not 'passenger side' are:\tdriver's side\tback seat\ttrunk\nThere are several useful visual features to tell there is 'passenger side' and not similar things in a photo:\tthe seat closest to the car door on the right side of the car\tan airbag in front of the seat\ta seatbelt coming from the right-hand side of the car", 12], "purple lettuce": ["Yes. 'Purple lettuce' has a tangible appearance with a specific color.\nA few things that are visually similar to 'purple lettuce' but are not 'purple lettuce' are:\tcabbage\tkale\tpurple basil\nThere are several useful visual features to tell there is 'purple lettuce' and not similar things in a photo:\tleafy\tgreen leaves with purple tint\tpurple veins or spots on leaves\tcrunchy texture, distinct smell and taste ", 12], "wood park bench": ["Yes. 'Wood park bench' has a tangible appearance and is a type of outdoor seat.\nA few things that are visually similar to 'wood park bench' but are not 'wood park bench' are:\tstone bench\tmetal bench\twooden chair\tfirewood\nThere are several useful visual features to tell there is 'wood park bench' and not similar things in a photo:\trectangular shape\tarmrests and backrest\tmade of wood\tseating area divided into planks or slats\tno cushions or upholstery\toutdoor setting, such as a park or garden", 12], "gold emblem": ["Yes. 'Gold emblem' has a tangible appearance and is a badge, symbol or image made of or resembling gold.\nA few things that are visually similar to 'gold emblem' but are not 'gold emblem' are:\tgold coin\tgold jewelry\tgold leaf\tgold plated object\nThere are several useful visual features to tell there is 'gold emblem' and not similar things in a photo:\tunique design or symbol\tshiny bright yellow or orange color\tmade of or looks like gold\tcentered on a surface, such as a shield or plaque", 12], "brown leaf": ["Yes. 'Brown leaf' has a tangible appearance and is a type of foliage.\nA few things that are visually similar to 'brown leaf' but are not 'brown leaf' are:\tdead branches\tbark of a tree\tspices\tbrown paper\nThere are several useful visual features to tell there is 'brown leaf' and not similar things in a photo:\tflat and thin\tveins or ribs\ton a stem or ground\twith pointed or rounded edges\tdried or wilted appearance\twith shades of brown or yellow", 12], "pole lamp": ["Yes. 'Pole lamp' has a tangible appearance and refers to a type of outdoor lamp.\nA few things that are visually similar to 'pole lamp' but are not 'pole lamp' are:\ttraffic light\tflag pole\tbanner pole\nThere are several useful visual features to tell there is 'pole lamp' and not similar things in a photo:\ttall, standing lamp\toutdoor\tuse of metal or concrete materials\trectangular or circular lampshade.topAnchor", 12], "brick work": ["Yes. 'Brick work' has a tangible appearance and is a type of masonry.\nA few things that are visually similar to 'brick work' but are not 'brick work' are:\tstone work\ttile work\twood work\nThere are several useful visual features to tell there is 'brick work' and not similar things in a photo:\trectangular bricks\tmortar lines between each brick\tred or brown color pattern\trepeating pattern of bricks and mortar.", 12], "cash": ["Yes. 'Cash' has a tangible appearance and refers to physical money.\nA few things that are visually similar to 'cash' but are not 'cash' are:\tcredit cards\tcoins\tchecks\tbank account statements\nThere are several useful visual features to tell there is 'cash' and not similar things in a photo:\tpaper bills in different denominations\tgreen, blue, red, and other colors\timages of famous figures, such as presidents or national monuments\ttext indicating value and denomination (e.g. \"Twenty Dollars\")", 12], "shadow player": ["Yes. 'Shadow player' has a tangible appearance and refers to a person or a puppet manipulating a shadow on a screen or surface.\nThere are no things that are visually similar to 'shadow player' as it is a specific activity.\nUseful visual features for identifying a 'shadow player' in a photo could be:\tthe presence of a screen or surface where a shadow is being cast\tsomeone or something manipulating the shadow on the screen or surface", 12], "silver piece": ["Yes. 'Silver piece' has a tangible appearance and is a type of currency or object made of silver.\nA few things that are visually similar to 'silver piece' but are not 'silver piece' are:\tsilver jewelry\tsilverware\tsilver-colored objects\nThere are several useful visual features to tell there is 'silver piece' and not similar things in a photo:\tcircular or polygonal shape\twith a face value or a maker's mark\ttypically flat or thin, but can have raised designs\tor engravings\tmade of shiny silver", 12], "rust stain": ["Yes. 'Rust stain' has a tangible appearance and is a type of discoloration caused by the oxidation of metal.\nA few things that are visually similar to 'rust stain' but are not 'rust stain' are:\twater stains\tink stains\tblood stains\tdirt stains\nThere are several useful visual features to tell there is 'rust stain' and not similar things in a photo:\tbrownish-orange color\tirregular shape\tusually found near metal objects or surfaces", 12], "stone block": ["Yes. 'Stone block' has a tangible appearance and is a building material.\nA few things that are visually similar to 'stone block' but are not 'stone block' are:\tbricks\tcement blocks\tpaving stones\nThere are several useful visual features to tell there is 'stone block' and not similar things in a photo:\trectangular in shape\tmade of natural stone or rock\tsolid and heavy-looking\ttexture of the stone or rock\tused in construction or building", 12], "cute dog": ["Yes. 'Cute dog' has a tangible appearance and refers to a specific type of animal.\nA few things that are visually similar to 'cute dog' but are not 'cute dog' are:\tfox\twolf\tfennec\tbear\traccoon\nThere are several useful visual features to tell there is a 'cute dog' and not similar things in a photo:\t\nfour-legged animal\tears on top of its head\tsnout with a wet nose\tmouth with tongue out\ttail either fluffy or curly.", 12], "cow leg": ["Yes. 'Cow leg' has a tangible appearance and is a body part of a cow.\nA few things that are visually similar to 'cow leg' but are not 'cow leg' are:\thuman leg\tpig leg\tdeer leg\thorse leg\nThere are several useful visual features to tell there is 'cow leg' and not similar things in a photo:\tthick and muscular\thair and fur-covered hoofed animal\thanging from a carcass or attached to a living animal", 12], "handle fridge": ["Yes. 'Handle fridge' has a tangible appearance and refers to the handle of a refrigerator.\nA few things that are visually similar to 'handle fridge' but are not 'handle fridge' are:\tknob\tcabinet pull\tdoor handle\nThere are several useful visual features to tell there is 'handle fridge' and not similar things in a photo:\tmetallic or plastic material\ton the door of a refrigerator\thorizontal orientation (typically)\tbigger in size than a knob, but smaller than a cabinet pull", 12], "mushroom pizza": ["Yes. 'Mushroom pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'mushroom pizza' but are not 'mushroom pizza' are:\tcheese pizza\tpepperoni pizza\tgarlic bread\tmushroom tart\nThere are several useful visual features to tell there is 'mushroom pizza' and not similar things in a photo:\tdough base\tmozzarella cheese\tmushroom toppings\tsauce or oil on the base", 12], "brown train": ["Yes. 'Brown train' has a tangible appearance and is a type of locomotive.\nA few things that are visually similar to 'brown train' but are not 'brown train' are:\tbrown bus\tbrown truck\tbrown car\nThere are several useful visual features to tell there is 'brown train' and not similar things in a photo:\tlocomotive design\twith wheels\tmoving on tracks\twith a smokestack and windows\tcolor brown", 12], "top bed": ["No. 'Top bed' is too vague or abstract to be distinguished in a photo. \n\nHowever, if you meant 'bunk bed' instead of 'top bed', then:\n\nYes. 'Bunk bed' has a tangible appearance and is a type of bed with two or more mattresses, one on top of the other.\nA few things that are visually similar to 'bunk bed' but are not 'bunk bed' are:\tsingle bed\tdouble bed\tsleeper sofa \nThere are several useful visual features to tell there is 'bunk bed' and not similar things in a photo:\ttwo or more mattresses stacked on top of each other\tladder or stairs to reach the top bed\tframes or rails to prevent falling from the top bed.", 12], "carnation": ["Yes. 'Carnation' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'carnation' but are not 'carnation' are:\tpeony\trose\tdaffodil\ttulip\nThere are several useful visual features to tell there is 'carnation' and not similar things in a photo:\tround shape\twith frilly petals\tsingle flowers per stem\tcloves-like shape\tvariety of colors (pink, red, white, yellow, or purple)", 12], "brush holder": ["Yes. 'Brush holder' has a tangible appearance and is a container where brushes are stored.\nA few things that are visually similar to 'brush holder' but are not 'brush holder' are:\tpencil holder\tpaint can\ttea bag holder\tcandle holder\nThere are several useful visual features to tell there is 'brush holder' and not similar things in a photo:\tlong and narrow\tcontainer with separate slots\tor slots on the top or sides\tdifferent sized slots\tfor brushes or makeup brushes.", 12], "metal flag pole": ["Yes. 'Metal flag pole' has a tangible appearance and is a type of pole used to hang national flags.\nA few things that are visually similar to 'metal flag pole' but are not 'metal flag pole' are:\tLight Pole\tTraffic sign post\tFence post\nThere are several useful visual features to tell there is 'metal flag pole' and not similar things in a photo:\tTall and straight pole\tSilver or grey color\tSmooth surface\tA pulley system or a clip to hang a flag", 12], "grey stone building": ["Yes. 'Grey stone building' has a tangible appearance and is a specific type of building made of stone.\nA few things that are visually similar to 'grey stone building' but are not 'grey stone building' are:\tconcrete building\tbrick building\tcastle\tcottage\nThere are several useful visual features to tell there is 'grey stone building' and not similar things in a photo:\tmade of grey stone\tor with a distinct grey stone texture\tsmooth, sturdy and solid walls\tarched doorways or windows\tstairs, turrets or towers in the architecture\tof a certain size and shape (e.g. larger than a cottage, smaller than a castle)", 12], "rock face": ["Yes. 'Rock face' has a tangible appearance and is a type of geological formation.\nA few things that are visually similar to 'rock face' but are not 'rock face' are:\tbrick wall\tcliff\tpainted mural\nThere are several useful visual features to tell there is 'rock face' and not similar things in a photo:\trough texture\tnatural formation\twith cracks or fissures\tformed by layers of rock or erosion ", 12], "banana stalk": ["Yes. 'Banana stalk' has a tangible appearance and refers to the stem or central axis of a banana plant.\nA few things that are visually similar to 'banana stalk' but are not 'banana stalk' are:\ttree trunk\tflower stem\tbamboo cane\nThere are several useful visual features to tell there is 'banana stalk' and not similar things in a photo:\tlarge and thick stem\tsheathing leaves\tthat hold the fruit to ripen", 12], "rainbow flag": ["Yes. 'Rainbow flag' has a tangible appearance and is a specific type of flag.\nA few things that are visually similar to 'rainbow flag' but are not 'rainbow flag' are:\tflag with stripes or colors\tpride banner\nThere are several useful visual features to tell there is 'rainbow flag' and not similar things in a photo:\thorizontal orientation\tof 6 or more bands of different colors\tred, orange, yellow, green, blue, and purple colors", 12], "brick walk": ["Yes. 'Brick walk' has a tangible appearance and is a kind of pathway.\nA few things that are visually similar to 'brick walk' but are not 'brick walk' are:\tstones walkway\tconcrete walkway\twooden walkway\nThere are several useful visual features to tell there is 'brick walk' and not similar things in a photo:\t\nbricks as materials\tfor the pathway\tis usually in a herringbone or basketweave pattern\treddish-brown color (depending on the type of brick)\tRectangular shape and relatively flat surface.", 12], "ossicles": ["Yes. 'Ossicles' has a tangible appearance and refers to very small bones in the human body, specifically in the ear.\nThere are no things that are visually similar to 'ossicles' and are not 'ossicles'. \nUseful visual features to identify 'ossicles' in a photo are:\tlocated in the middle ear\tbony appearance\tvery small size", 12], "cooktop": ["Yes. 'Cooktop' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'cooktop' but are not 'cooktop' are:\toven\tmicrowave\tdishwasher\tfridge\nThere are several useful visual features to tell there is 'cooktop' and not similar things in a photo:\tflat surface with burners or heating elements\tmetallic and reflective top\tdials or knobs to adjust temperature or power", 12], "checkers": ["Yes. 'Checkers' has a tangible appearance and is a board game that is played on a checkerboard.\nA few things that are visually similar to 'checkers' but are not 'checkers' are:\tChess board\tSudoku puzzle\tboard games\nThere are several useful visual features to tell there is 'checkers' and not similar things in a photo:\tCheckered pattern or board\tSquare spaces in black and white or red and black\tpieces with a flat top and round base, usually of two colors.", 12], "purple ribbon": ["Yes. 'Purple ribbon' has a tangible appearance and is a type of ribbon.\nA few things that are visually similar to 'purple ribbon' but are not 'purple ribbon' are:\tpurple fabric\tpurple rope\tpurple string\tpurple paint\nThere are several useful visual features to tell there is 'purple ribbon' and not similar things in a photo:\tlong and narrow piece of ribbon\tsmooth and shiny texture\tin a shade of purple or violet", 12], "elephants foot": ["Yes. 'Elephants foot' has a tangible appearance and is a part of an elephant's body.\nA few things that are visually similar to 'elephants foot' but are not 'elephants foot' are:\thippopotamus foot\trhino foot\t\nThere are several useful visual features to tell there is 'elephants foot' and not similar things in a photo:\n- Large size \n- Gray or brown color \n- Unique shape that taper at the edges into the toes \n- Visible toenails \n- Rough, wrinkled texture \n- Presence of distinctive pads on the underside of the foot.", 12], "city sky line": ["Yes. 'City skyline' has a tangible appearance and refers to the outline created by a group of buildings or structures in a city.\nA few things that are visually similar to 'city skyline' but are not 'city skyline' are:\tmountain range\tocean horizon\tforest canopy\nThere are several useful visual features to tell there is 'city skyline' and not similar things in a photo:\ttall buildings with recognizable architecture\tlights or windows in the buildings\tsilhouettes of buildings against the sky\tthe presence of artificial structures or infrastructure", 12], "court surface": ["Yes. 'Court surface' has a tangible appearance and is a specific type of flooring used for sports courts.\nA few things that are visually similar to 'court surface' but are not 'court surface' are:\tregular pavement\ttile floor\twooden floor\nThere are several useful visual features to tell there is 'court surface' and not similar things in a photo:\tspecific colorful lines and marking used for sports (i.e. tennis, basketball, volleyball, etc.)\ttexture that is rough or porous to provide grip and prevent slipping\trubber or acrylic materials specifically designed for sports use", 12], "city view": ["Yes. 'City view' has a tangible appearance and refers to the skyline and streetscape of a city.\nA few things that are visually similar to 'city view' but are not 'city view' are:\tlandscape view\tvillage or town view\tpark view\nThere are several useful visual features to tell there is 'city view' and not similar things in a photo:\ttall buildings or skyscrapers\tbustling streets and traffic\tlights and signs\turban landscape, including highways, bridges or landmarks\tinhabited by people\tor lights on at night", 12], "firehydrant": ["Yes. 'Fire hydrant' has a tangible appearance and is a kind of public utility.\nA few things that are visually similar to 'fire hydrant' but are not 'fire hydrant' are:\tbollard\tpost\tbarrier\nThere are several useful visual features to tell there is 'fire hydrant' and not similar things in a photo:\tupright cylindrical shape\tdome-shaped top in red or yellow color\tnozzle on the side for fire hoses\tin a public place", 12], "wound": ["Yes. 'Wound' has a tangible appearance and is a physical injury or cut.\nA few things that are visually similar to 'wound' but are not 'wound' are:\tmakeup\timpression\tpaint\tstain\tglitter\ttattoo\nThere are several useful visual features to tell there is 'wound' and not similar things in a photo:\topen skin\tblood or other fluids\tscabbing or crusting around the edges\tof various shapes and sizes", 12], "pant legs": ["Yes. 'Pant legs' has a tangible appearance and is a part of clothing.\nA few things that are visually similar to 'pant legs' but are not 'pant legs' are:\tshirt sleeves\tskirts\tdresses\tleggings\nThere are several useful visual features to tell there is 'pant legs' and not similar things in a photo:\tattached to the waist of pants or shorts\tfabric hanging from the waist\tdraped over legs and ending at the ankles\tor covers the entire leg\tlengths vary from shorts to full-length pants.", 12], "sailboat water": ["No. 'Sailboat water' is too vague or abstract to be distinguished in a photo.", 12], "plantation": ["Yes. 'Plantation' has a tangible appearance and refers to a large, cultivated area of crops or trees.\nA few things that are visually similar to 'plantation' but are not 'plantation' are:\tforest\tgarden\tfarm\tvineyard\nThere are several useful visual features to tell there is 'plantation' and not similar things in a photo:\trow upon row of crops or trees\tman-made, organized pattern\tcultivated and maintained plants\tlarge size and scale", 12], "grey sign": ["No. 'Grey sign' is too vague or abstract to be distinguished in a photo as signs come in different shapes, materials, and colors.", 12], "baby carrot": ["Yes. 'Baby carrot' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'baby carrot' but are not 'baby carrot' are:\tcelery stick\tradish\tstalks of asparagus\t\nThere are several useful visual features to tell there is 'baby carrot' and not similar things in a photo:\torange color\tsmooth texture\tpointed tip\tcylindrical shape\tabout 2 inches long", 12], "backhoe": ["Yes. 'Backhoe' has a tangible appearance and is a type of construction equipment.\nA few things that are visually similar to 'backhoe' but are not 'backhoe' are:\texcavator\tbulldozer\ttractor\tcrane\nThere are several useful visual features to tell there is 'backhoe' and not similar things in a photo:\ttwo-part arm consisting of a boom and a dipper stick\tattached bucket for digging\tstabilizer legs for support\ton wheels or tracks", 12], "plaid table cloth": ["Yes. 'Plaid table cloth' has a tangible appearance and is a type of tablecloth with a specific pattern.\nA few things that are visually similar to 'plaid table cloth' but are not 'plaid table cloth' are:\tcheckered tablecloth\tstripe tablecloth\tsolid color tablecloth\nThere are several useful visual features to tell there is 'plaid table cloth' and not similar things in a photo:\trectangular with straight edges\tplaid pattern of intersecting vertical and horizontal lines in different colors or shades (usually two or three)\ttypically made from cotton or linen", 12], "airlines plane": ["Yes. 'Airlines plane' has a tangible appearance and is a type of aircraft used for commercial air transportation.\nA few things that are visually similar to 'airlines plane' but are not 'airlines plane' are:\thelicopter\tprivate jet\tmilitary aircraft\ttourist plane\nThere are several useful visual features to tell there is 'airlines plane' and not similar things in a photo:\tlarge size\tusually has a tail section with the airline logo\tonboard windows\tpassenger doors and emergency exits\twings with engines in the middle or under them.", 12], "rock boulder": ["Yes. 'Rock boulder' has a tangible appearance and is a large rock.\nA few things that are visually similar to 'rock boulder' but are not 'rock boulder' are:\tpebble\tcobblestone\tstone statue\nThere are several useful visual features to tell there is 'rock boulder' and not similar things in a photo:\tvery large size compared to surrounding objects\trough or jagged texture\tnatural appearance or found outdoors", 12], "airline name": ["No. 'Airline name' is too vague or abstract to be distinguished in a photo. However, a logo or branding associated with an airline may have a visually concrete concept.\nA few things that are visually similar to 'airline name' but are not 'airline name' are:\tcompany name\tbrand identity\tproduct name\nThere are several useful visual features to tell there is an airline logo or branding and not similar things in a photo:\tunique color palette\tspecific typography\tinclusion of an airplane or wings\tinclusion of the airline's name or initials\tsmall size and often found on the tail of the plane or uniforms of staff.", 12], "cast": ["Yes. 'Cast' has a tangible appearance and is a protective device used to support and immobilize broken bones.\nA few things that are visually similar to 'cast' but are not 'cast' are:\tbrace\tsplint\tbandage\ttape\nThere are several useful visual features to tell there is 'cast' and not similar things in a photo:\thard exterior\tmay be made of fiberglass, plaster, or plastic\toften in a cylindrical shape with a hollow center\tfor arm or leg support", 12], "cats whiskers": ["Yes. 'Cats whiskers' has a tangible appearance and is a physical feature of a cat.\nA few things that are visually similar to 'cats whiskers' but are not 'cats whiskers' are:\thair\tthin wires\thairspring\nThere are several useful visual features to tell there is 'cats whiskers' and not similar things in a photo:\tthick and long\tattached to the cat's face\tand not just a single strand or wire-like object.", 12], "highlights": ["Yes. 'Highlights' has a visually concrete appearance and is a type of hair coloring.\nA few things that are visually similar to 'highlights' but are not 'highlights' are:\tfollicles\tsunrays\treflections\tstreaks\nThere are several useful visual features to tell there is 'highlights' and not similar things in a photo:\tcolor different from the natural hair\ttwo or more tones of color\tplacement on specific strands of hair\tuniform width or thickness of color\tlines are smooth and blended with the natural color of the hair", 12], "freezer section": ["Yes. 'Freezer section' has a tangible appearance and is a part of a grocery or department store.\nA few things that are visually similar to 'freezer section' but are not 'freezer section' are:\trefrigerator section\tshelves\tcooler\tdisplay case\nThere are several useful visual features to tell there is 'freezer section' and not similar things in a photo:\twalk-in or reach-in area\tdoor or glass panel holding frozen items\tfrost or ice building up on shelves or items", 12], "pastures": ["Yes. 'Pastures' has a tangible appearance and refers to a specific type of land use.\nA few things that are visually similar to 'pastures' but are not 'pastures' are:\tfields\tfarmland\tforests\tmeadows\nThere are several useful visual features to tell there is 'pastures' and not similar things in a photo:\twide-open grassy areas\tgrazing animals such as cows or sheep\twire or wooden fences\twater sources such as streams or ponds", 12], "buliding": ["Yes. 'Building' has a tangible appearance and refers to a structure with walls and a roof.\nA few things that are visually similar to 'building' but are not 'building' are:\ttent\tcarport\tstage\tset\nThere are several useful visual features to tell there is 'building' and not similar things in a photo:\tmultiple floors or levels\twindows and doors\tpermanent and fixed structures\tmade of bricks, concrete, or wood\ta clear entrance or exit\tpointed roofs or spires", 12], "stone chimney": ["Yes. 'Stone chimney' has a tangible appearance and is a specific type of chimney made of stone.\nA few things that are visually similar to 'stone chimney' but are not 'stone chimney' are:\tbrick chimney\tmetal chimney\twooden chimney\tfireplace chimney\nThere are several useful visual features to tell there is 'stone chimney' and not similar things in a photo:\tmade of stone\tnatural colors such as grey, brown, or beige\trectangular shape\tsticks out from the roof", 12], "multiple trees": ["Yes. 'Multiple trees' has a tangible appearance and refers to a group of trees.\nA few things that are visually similar to 'multiple trees' but are not 'multiple trees' are:\tsingle tree\tshrubs\tbushes\tgrass\nThere are several useful visual features to tell there are 'multiple trees' and not similar things in a photo:\tmultiple tall objects with branches and leaves\ttrunks or stems coming out from the ground in different places\tgroups of leaves and branches forming a canopy or a mass of vegetation.", 12], "track train": ["Yes. 'Track train' has a tangible appearance and is a type of locomotive.\nA few things that are visually similar to 'track train' but are not 'track train' are:\ttram\tsubway\troller coaster\nThere are several useful visual features to tell there is 'track train' and not similar things in a photo:\tlarge locomotive engine\tconnected cars or carriages\trails or tracks beneath\tthe presence of a conductor or operator\tsignage or decorations indicating train number, destination, etc.", 12], "glassware": ["Yes. 'Glassware' has a tangible appearance and refers to various items made of glass, such as cups, vases, and bowls.\nA few things that are visually similar to 'glassware' but are not 'glassware' are:\tplastic cups\tceramic vases\twooden bowls\t\nThere are several useful visual features to tell there is 'glassware' and not similar things in a photo:\ttransparent or translucent\treflects light\tsmooth and shiny surface\tclinking sound when tapped with another glass or metal object.", 12], "grey button": ["Yes. 'Grey button' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'grey button' but are not 'grey button' are:\tgrey candy\tgumball\tmetallic bead\tpebble\nThere are several useful visual features to tell there is 'grey button' and not similar things in a photo:\tcircular shape\tflat surface\tsmall size\ttwo or four holes in the center", 12], "cornucopia": ["Yes. 'Cornucopia' has a tangible appearance and is a horn-shaped basket that is traditionally associated with a bountiful harvest.\nA few things that are visually similar to 'cornucopia' but are not 'cornucopia' are:\thorns\tbaskets\tcornets\nThere are several useful visual features to tell there is 'cornucopia' and not similar things in a photo:\thorn-shaped\tbasket-like\toverflowing with fruits, vegetables, or flowers\ttraditionally associated with Thanksgiving or harvest-related festivities", 12], "propeller blade": ["Yes. 'Propeller blade' has a tangible appearance and is a part of a propeller.\nA few things that are visually similar to 'propeller blade' but are not 'propeller blade' are:\tknife\taxe\tfan\tblender blade\nThere are several useful visual features to tell there is 'propeller blade' and not similar things in a photo:\tlong and narrow shape\twith a curved or pointed end\ttypically made of metal or composite material\tpart of a larger rotating structure (propeller)", 12], "birds legs": ["Yes, 'birds legs' has a visually concrete concept.\nA few things that are visually similar to 'birds legs' but are not 'birds legs' are:\thuman legs\tanimal legs\ttree branches\nThere are several useful visual features to tell there are 'birds legs' and not similar things in a photo:\tthin and elongated\tscaly, featherless\tcomposed of several segments\twith sharp claws or talons\tat the bottom of a bird's body", 12], "bakery": ["Yes. 'Bakery' has a tangible appearance and is a type of establishment.\nA few things that are visually similar to 'bakery' but are not 'bakery' are:\tkitchen\tcafeteria\trestaurant\tbarbecue\tfood truck\nThere are several useful visual features to tell there is 'bakery' and not similar things in a photo:\tsigns or display cases advertising baked goods\tbread, pastries, cakes or cookies on display\tovens or baking equipment\tequipment or tools for measuring and mixing ingredients", 12], "pelicans": ["Yes. 'Pelicans' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'pelicans' but are not 'pelicans' are:\tseagulls\therons\tswans\tgeese\nThere are several useful visual features to tell there is 'pelicans' and not similar things in a photo:\tpouch under their beak\tlong, straight bills\twebbed feet\tbrown, white, or grey feathers\ton water or in coastal areas", 12], "boxcar": ["Yes. 'Boxcar' has a tangible appearance and is a type of train freight car.\nA few things that are visually similar to 'boxcar' but are not 'boxcar' are: tank car, hopper car, flatcar, gondola car.\nThere are several useful visual features to tell there is 'boxcar' and not similar things in a photo: rectangular or cube-like shape, metal exterior, sliding or hinged doors on the sides, and wheels attached to the bottom.", 12], "sucker": ["Yes. 'Sucker' has a tangible appearance and refers to a type of mouthpart, organ or device used for suction.\nA few things that are visually similar to 'sucker' but are not 'sucker' are:\tstraw\tvacuum cleaner\tnasal aspirator\nThere are several useful visual features to tell there is 'sucker' and not similar things in a photo:\tflat or concave shape\toften circular or oval\thollow or containing a cavity\table to create negative pressure or suction", 12], "plant life": ["Yes. 'Plant life' has a tangible appearance and refers to living plants.\nA few things that are visually similar to 'plant life' but are not 'plant life' are:\tplastic plants\tpainted plants\tfabric plants\tplants in art or photography\nThere are several useful visual features to tell there is 'plant life' and not similar things in a photo:\tgreen foliage or leaves\tvisible stems or branches\tliving, growing plants or flowers\twith roots in soil or other growing medium\tnatural surroundings like forests, gardens, or fields.", 12], "oak tree": ["Yes. 'Oak tree' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'oak tree' but are not 'oak tree' are:\tmaple tree\tbirch tree\tpine tree\tcherry blossom tree\nThere are several useful visual features to tell there is 'oak tree' and not similar things in a photo:\tlarge, broad leaves\twith lobed edges\ttall, sturdy trunk\tbark with deep ridges and cracks\tacorn fruits growing from the branches.", 12], "apple core": ["Yes. 'Apple core' has a tangible appearance and is a type of fruit waste.\nA few things that are visually similar to 'apple core' but are not 'apple core' are:\torange peels\tbanana peels\tpaper\tleaves\nThere are several useful visual features to tell there is 'apple core' and not similar things in a photo:\tround in shape\tbrown color\thas seeds\tintact apple stem attached to the top of the core", 12], "side wings": ["Yes. 'Side wings' has a tangible appearance and it refers to the wings of an airplane located on either side of the fuselage.\nA few things that are visually similar to 'side wings' but are not 'side wings' are:\tbird wings\tsuperhero cape\tbuilding awning\nThere are several useful visual features to tell there are 'side wings' and not similar things in a photo:\tmounted on the sides of an airplane\tflat and thin, with an aerodynamic shape\thinged to the fuselage and able to move up and down as needed\tfor larger aircraft, may have visible flaps or slats for adjusting lift and drag", 12], "racers": ["Yes. 'Racers' has a tangible appearance and refers to people or things that participate in races.\nA few things that are visually similar to 'racers' but are not 'racers' are:\tcars\tbicycles\tmotorbikes\tathletes\tanimals\nThere are several useful visual features to tell there are 'racers' and not similar things in a photo:\twearing helmets or racing suits\thigh-speed motion\tbib numbers or team logos\tracing tracks or circuits\tstarting or finish lines", 12], "side panel": ["Yes. 'Side panel' has a tangible appearance and usually refers to a part of a larger object.\nA few things that are visually similar to 'side panel' but are not 'side panel' are:\twall\tpainting\tcurtain\tcover\nThere are several useful visual features to tell there is 'side panel' and not similar things in a photo:\tattached to a larger object\tvertical orientation\tusually rectangular in shape\tmay have screws or bolts for fastening\tmaterial may match or contrast with larger object.", 12], "meet": ["No. 'Meet' is too vague or abstract to be distinguished in a photo.", 12], "bottom teeth": ["Yes. 'Bottom teeth' has a tangible appearance and it is a part of the human body.\nA few things that are visually similar to 'bottom teeth' but are not 'bottom teeth' are:\ttop teeth\tfalse teeth\tdentures\tgums\nThere are several useful visual features to tell there is 'bottom teeth' and not similar things in a photo:\tflat and smooth bottom edge\tof the mouth\tbelow the top teeth and above the tongue\twhite and bone-shaped", 12], "pirate hat": ["Yes. 'Pirate hat' has a tangible appearance and is a type of headwear.\nA few things that are visually similar to 'pirate hat' but are not 'pirate hat' are:\ttop hat\tcowboy hat\tbowler hat\nThere are several useful visual features to tell there is 'pirate hat' and not similar things in a photo:\ttri-cornered or wide-brimmed hat\twith a skull and crossbones symbol or other pirate-like design\tdark color, usually black or brown", 12], "skii pole": ["Yes. 'Ski pole' has a tangible appearance and is a tool used for skiing.\nA few things that are visually similar to 'ski pole' but are not 'ski pole' are:\thiking sticks\twalking canes\tfishing rods\tgolf clubs\nThere are several useful visual features to tell there is 'ski pole' and not similar things in a photo:\tlong and thin\ttapered towards the bottom\tdesigned with a grip at the top and a point at the bottom\tmade of lightweight materials such as aluminum or carbon fiber in bright colors.", 12], "cargo container": ["Yes. 'Cargo container' has a tangible appearance and is a type of container used for transport or storage.\nA few things that are visually similar to 'cargo container' but are not 'cargo container' are:\tshipping crate\tlocker\ttool chest\ttrash bin\nThere are several useful visual features to tell there is 'cargo container' and not similar things in a photo:\tmetal box\twith corrugated sides\tand doors on one end\tor on both ends\toften with shipping company logos\ton the sides\thave a standard size and shape\tfor easy stacking and transport on ships, trains, or trucks.", 12], "doormat": ["Yes. 'Doormat' has a tangible appearance and is a type of floor covering used at the entrance of a building.\nA few things that are visually similar to 'doormat' but are not 'doormat' are:\tcarpet\trug\ttile\t\nThere are several useful visual features to tell there is 'doormat' and not similar things in a photo:\tflat and rectangular shape, specifically for placing outside a door or entryway\ttext or design with words such as \"welcome\" or \"home\" or images like geometrical shapes or natural sceneries\ttough and durable material, often made of rubber or coir.", 12], "shadow batter": ["No. 'Shadow batter' is too vague or abstract to be distinguished in a photo. There is no concrete meaning or visual appearance associated with this term.", 12], "foot tracks": ["Yes. 'Foot tracks' has a tangible appearance and is a type of trace left by feet.\nA few things that are visually similar to 'foot tracks' but are not 'foot tracks' are:\tpaw prints\twheel tracks\tdirt marks\twater ripples\tinsect trails\nThere are several useful visual features to tell there are 'foot tracks' and not similar things in a photo:\toval or oblong shape\ttwo or more depressions in a straight line\tvarious sizes and shapes, depending on the footwear\tparallel lines, indicating the direction of walking\tfrequency and uniformity, indicating the pace of walking or running", 12], "sandle": ["Yes. 'Sandle (Sandals)' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'sandle' but are not 'sandle' are:\tsneakers\tboots\tslippers\t\nThere are several useful visual features to tell there is 'sandle' and not similar things in a photo:\topen-toe design\tstraps covering the midfoot or ankle\tairy design\tleft uncovered the heel and toes, sometimes a part of the arch.", 12], "green hill": ["Yes. 'Green hill' has a tangible appearance.\nA few things that are visually similar to 'green hill' but are not 'green hill' are:\tgrass\tbushes\tfoliage\tmountains\nThere are several useful visual features to tell there is 'green hill' and not similar things in a photo:\tsloping terrain\tsmooth, round curves\tin a grassy area with minimal trees or structures\tdominated by green foliage on the top and sides", 12], "granite countertop": ["Yes. 'Granite countertop' has a tangible appearance and is a type of surface.\nA few things that are visually similar to 'granite countertop' but are not 'granite countertop' are:\tmarble countertop\twooden countertop\tconcrete countertop\nThere are several useful visual features to tell there is 'granite countertop' and not similar things in a photo:\tnatural stone-looking surface\tgranular appearance\tpolished finish\tsparse, scattered veins or dots in the pattern", 12], "toilet bowl lid": ["Yes. 'Toilet bowl lid' has a tangible appearance and is a part of a toilet.\nA few things that are visually similar to 'toilet bowl lid' but are not 'toilet bowl lid' are:\tsink lid\ttrash can lid\t\nThere are several useful visual features to tell there is 'toilet bowl lid' and not similar things in a photo:\tround or oval shape\thinged on the back\tridged or smooth texture\tmatches the color and shape of the toilet bowl", 12], "pale blue": ["Yes. 'Pale blue' has a tangible appearance and is a specific color.\nA few things that are visually similar to 'pale blue' but are not 'pale blue' are:\tsky\tteal\tperiwinkle\nThere are several useful visual features to tell there is 'pale blue' and not similar things in a photo:\tpale or light blue in color\tno green or purple undertones", 12], "gold roman numerals": ["Yes. 'Gold Roman numerals' has a tangible appearance and refers to a specific type of numeric representation.\nA few things that are visually similar to 'gold roman numerals' but are not 'gold roman numerals' are:\tregular numerals\twriting letters\nThere are several useful visual features to tell there is 'gold roman numerals' and not similar things in a photo:\tgold color\tuse of Roman numerals\tnumeric representation using the symbols I, V, X, L, C, D, M.", 12], "wax": ["Yes. 'Wax' has a tangible appearance and is a solid material.\nA few things that are visually similar to 'wax' but are not 'wax' are:\tcandle\tsoap\tplasticine\nThere are several useful visual features to tell there is 'wax' and not similar things in a photo:\ttranslucent, semi-solid material\teasily moldable or shapeable\twhen heated, melts and becomes liquid\twhen cooled, becomes solidified again\thas a smooth, shiny surface", 12], "california license plate": ["Yes. 'California license plate' has a tangible appearance and is a type of vehicle identification tag.\nA few things that are visually similar to 'California license plate' but are not 'California license plate' are:\tstate license plates from other states\tlicense plate frames\tadvertisements\nThere are several useful visual features to tell there is 'California license plate' and not similar things in a photo:\tblue and yellow color scheme\tthe letters 'CA' on the left side\tthe word 'California' written at the top of the plate\ta combination of letters and numbers in a specific format\tno graphics or advertising", 12], "rope fence": ["Yes. 'Rope fence' has a tangible appearance and is a type of fence.\nA few things that are visually similar to 'rope fence' but are not 'rope fence' are:\tchain-link fence\tpicket fence\thedge\tstone wall\nThere are several useful visual features to tell there is 'rope fence' and not similar things in a photo:\tmade of rope or cord\tposts holding the rope in place\thorizontal or vertical pattern\teasy to pass through or move", 12], "brown branches": ["Yes. 'Brown branches' has a tangible appearance and is a type of plant part.\nA few things that are visually similar to 'brown branches' but are not 'brown branches' are:\tdead leaves\tsticks\ttree trunks\nThere are several useful visual features to tell there is 'brown branches' and not similar things in a photo:\tthin and flexible\textending from the main trunk or stem of a plant\twith smaller branches extending from it\thas no leaves or has few dried leaves\tdark brown color", 12], "water spots": ["Yes. 'Water spots' has a tangible appearance and refers to marks or stains on a surface caused by water.\nA few things that are visually similar to 'water spots' but are not 'water spots' are:\tstains caused by other liquids\tshadow of objects on a surface\nThere are several useful visual features to tell there are 'water spots' and not similar things in a photo:\tcircular or irregular shape\tlighter or darker than the surrounding surface\twet or glossy surface in that area\tcontour with drip marks around it\twater rings around the spot", 12], "wake boat": ["Yes. 'Wake boat' has a tangible appearance and is a specific type of boat.\nA few things that are visually similar to 'wake boat' but are not 'wake boat' are:\triver boat\tspeed boat\tyacht\tkayak\tcanoe\nThere are several useful visual features to tell there is 'wake boat' and not similar things in a photo:\tlong, sleek shape with a pointed bow\ttower for tow ropes to pass through\twake shaping devices on the hull or underside\tsound system for playing music while wakeboarding or wake surfing\tno fishing gear or outriggers\tfor towing wakeboarders or surfers.", 12], "orange lines": ["Yes. 'Orange lines' has a tangible appearance and is a type of visual element.\nA few things that are visually similar to 'orange lines' but are not 'orange lines' are:\tyellow lines\torange ropes\torange ribbons\torange paint streaks\nThere are several useful visual features to tell there are 'orange lines' and not similar things in a photo:\tstraight or curved lines\tshades of bright or burnt orange color\tpatterns or shapes made by the lines\tdirection or orientation of the lines in the photo.", 12], "cathedral": ["Yes. 'Cathedral' has a tangible appearance and is a type of religious building.\nA few things that are visually similar to 'cathedral' but are not 'cathedral' are:\tchurch\ttemple\tmosque\tsynagogue\nThere are several useful visual features to tell there is 'cathedral' and not similar things in a photo:\tlarge size and grandeur\ttowers and spires\tstained glass windows\tflying buttresses\tand arches\tnave and transept\tgothic architecture", 12], "pajama pants": ["Yes. 'Pajama pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'pajama pants' but are not 'pajama pants' are:\tworkout pants, leggings, sweatpants, jeans, slacks\nThere are several useful visual features to tell there is 'pajama pants' and not similar things in a photo:\tloose-fitting and comfortable\tfor sleeping or lounging\toften made of soft and cozy fabric\tpatterned with colorful designs", 12], "silver train cars": ["Yes. 'Silver train cars' has a tangible appearance and is a specific type of railway carriage.\nA few things that are visually similar to 'silver train cars' but are not 'silver train cars' are:\ttrucks\tbuses\tambulances\ttrailers\nThere are several useful visual features to tell there are 'silver train cars' and not similar things in a photo:\telongated rectangular shape\tsilver or metallic-colored exterior with no visible branding or logos\tsets of wheels on each end of the cars\tlarge doors on the sides for passengers to enter and exit", 12], "shadow toilet": ["No. 'Shadow toilet' is too vague or abstract to be distinguished in a photo. It is unclear what it refers to.", 12], "grey metal post": ["Yes. 'Grey metal post' has a tangible appearance and is a type of cylindrical structure.\nA few things that are visually similar to 'grey metal post' but are not 'grey metal post' are:\tsilver pole\tchimney\tstack\tbollard\nThere are several useful visual features to tell there is 'grey metal post' and not similar things in a photo:\tcylindrical shape\tmade of metal\tgrey or silver color\tno visible signs of openings or windows\theight and thickness compared to surrounding objects.", 12], "wood rail": ["Yes. 'Wood rail' has a tangible appearance and is a type of fence made of wood.\nA few things that are visually similar to 'wood rail' but are not 'wood rail' are:\twooden plank\tpile of logs\twooden stick\tladder\nThere are several useful visual features to tell there is 'wood rail' and not similar things in a photo:\thorizontal placement\talternating pattern of rails\tand posts with visible gaps\tdark or light wood colors\tsmall height compared to other types of fence", 12], "house boat": ["Yes. 'House boat' has a tangible appearance and is a type of boat that serves as a living space.\nA few things that are visually similar to 'house boat' but are not 'house boat' are:\tyachts\tcruise ships\tfishing boats\tcanoe or kayak\nThere are several useful visual features to tell there is 'house boat' and not similar things in a photo:\tlarge size\twith a flat bottom and a pointed bow\tnot designed for high-speed travel\tgenerally lacks sails or mast\thas visible living quarters or amenities, such as windows, doors, rooftops", 12], "turkeys": ["Yes. 'Turkeys' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'turkeys' but are not 'turkeys' are:\tchickens\tpheasants\tpeacocks\t\nThere are several useful visual features to tell there is 'turkeys' and not similar things in a photo:\tlarge size\tbare skin on head and neck\tbald, fleshy growth on the forehead\trich and metallic brown and bronze feathers\tfan-like tail feather display by males", 12], "dark bag": ["Yes. 'Dark bag' has a tangible appearance and is a type of bag with a specific color.\nA few things that are visually similar to 'dark bag' but are not 'dark bag' are:\tpurse\tbackpack\tluggage\tbriefcase\nThere are several useful visual features to tell there is 'dark bag' and not similar things in a photo:\tsolid black or dark color\tmade of leather, cloth, or synthetic materials\thandles or straps for carrying\tzippers, buckles, or closures to secure contents.", 12], "nail finger": ["No. 'Nail finger' is too vague or abstract to be distinguished in a photo. However, the term 'fingernail' has a tangible appearance and refers to a specific part of the finger.\nA few things that are visually similar to 'fingernail' but are not 'fingernail' are: \tthimble\tring\tcap\tshell\nThere are several useful visual features to tell there is 'fingernail' and not similar things in a photo:\tthin, flat, and slightly curved\tlocated at the tip of the finger\ttransparent or opaque\tvarious colors and patterns", 12], "cliff face": ["Yes. 'Cliff face' has a tangible appearance and is a steep rock surface.\nA few things that are visually similar to 'cliff face' but are not 'cliff face' are:\thill\tmountain\tside of a building\nThere are several useful visual features to tell there is 'cliff face' and not similar things in a photo:\tvertical or near-vertical angle of the rock surface\tworn, rough, or jagged appearance\tof natural origin, not man-made", 12], "grassy meadow": ["Yes. 'Grassy meadow' has a tangible appearance and refers to an open space covered with grass and often wildflowers.\nA few things that are visually similar to 'grassy meadow' but are not 'grassy meadow' are:\tgolf courses\tlawns\tparks\tfarmlands\nThere are several useful visual features to tell there is 'grassy meadow' and not similar things in a photo:\tuncultivated area\tgrass of varying lengths and thicknesses\twildflowers randomly interspersed\tpossible rolling hills, trees or bodies of water in the background", 12], "paw print": ["Yes. 'Paw print' has a tangible appearance and refers to the impression of an animal's paw on a surface.\nA few things that are visually similar to 'paw print' but are not 'paw print' are:\tshoe print\tbike tire print\tleaf or flower print\nThere are several useful visual features to tell there is 'paw print' and not similar things in a photo:\tseries of four or fewer, depending on the animal, impressions\tforming a defined shape, such as a circle, triangle, or rectangle\tclaw or nail imprints within the print, depending on the animal\tsize and shape of the print vary depending on the animal and can help identify which animal left the print.", 12], "iron railing": ["Yes. 'Iron railing' has a tangible appearance and is a kind of fence.\nA few things that are visually similar to 'iron railing' but are not 'iron railing' are:\twooden railings\trope railings\tchain link fence\nThere are several useful visual features to tell there is 'iron railing' and not similar things in a photo:\tmade of iron or metal\tsymmetrical and evenly spaced bars\tstraight or curved lines\tfrequently found on staircases or balconies.", 12], "gas cap": ["Yes. 'Gas cap' has a tangible appearance and is a part of a car or other vehicle used to cover the gas tank opening.\nA few things that are visually similar to 'gas cap' but are not 'gas cap' are:\toil cap\tradiator cap\tdecorative cover\tfor gas tanks on motorcycles or bicycles\nThere are several useful visual features to tell there is 'gas cap' and not similar things in a photo:\tlocated near the back of the vehicle\tcircular or oval in shape\thas a hinge or latch mechanism to open and close\tmay have text or a symbol indicating the type of fuel to be used.", 12], "tvs": ["Yes. 'TVs' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'TVs' but are not 'TVs' are:\tcomputers\tprojectors\tmonitors\ttablets\nThere are several useful visual features to tell there is 'TVs' and not similar things in a photo:\trectangular screen\twith speakers and ports\tremote control\tantenna or cable connection\tsitting on a stand or mounted on a wall.", 12], "communications": ["No. 'Communications' is too vague or abstract to be distinguished in a photo. It refers to exchanging information, ideas or messages between people or entities, which does not have a tangible appearance. \nTherefore, it is not possible to name things that are visually similar to 'communications'.", 12], "gray building": ["Yes. 'Gray building' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'gray building' but are not 'gray building' are:\tgarage\twall\tprison\tbridge\tapartment complex\nThere are several useful visual features to tell there is 'gray building' and not similar things in a photo:\tmade of bricks or stones\tpainted gray or have a gray facade\tmultiple stories\twith windows and doors", 12], "skii": ["Yes. 'Ski' has a tangible appearance and is a type of winter sport equipment.\nA few things that are visually similar to 'ski' but are not 'ski' are:\tsnowboard\tskateboard\troller skates\nThere are several useful visual features to tell there is 'ski' and not similar things in a photo:\tlong, thin shape\tbent tips\tforward-facing bindings\tno wheels\tfor use on snow", 12], "male baseball player": ["Yes. 'Male baseball player' has a tangible appearance and refers to a person playing baseball.\nA few things that are visually similar to 'male baseball player' but are not 'male baseball player' are:\tsoftball player\tcricket player\ttennis player\tfootball player\nThere are several useful visual features to tell there is 'male baseball player' and not similar things in a photo:\twearing a baseball uniform or team jersey\tcarrying or holding a bat\twearing a baseball cap or helmet\tstanding on a baseball field or diamond with bases in the background\thaving a mitt or glove on one hand", 12], "orange barrier": ["Yes. 'Orange barrier' has a tangible appearance and is a type of traffic control device.\nA few things that are visually similar to 'orange barrier' but are not 'orange barrier' are:\ttraffic cones\tpylons\ttraffic signs\nThere are several useful visual features to distinguish between 'orange barrier' and the listed similar things in a photo:\tlong and rectangular shape\tbright orange color\twith reflective strips on it\toften used to block off a construction site or a road", 12], "roman numbers": ["Yes. 'Roman numbers' has a tangible appearance and is a kind of numeral system.\nA few things that are visually similar to 'roman numbers' but are not 'roman numbers' are:\tArabic numbers\tletters in a font that resembles Roman numerals\tsigns\tor decoration\nThere are several useful visual features to tell there are 'roman numbers' and not similar things in a photo: a consistent use of rustic lines, I, V, X, L, C, D, and M, arranged to express whole numbers, typically in front of clockfaces or in legal contexts.", 12], "wave surfer": ["Yes. 'Wave surfer' has a tangible appearance and refers to a person engaged in the sport of surfing.\nA few things that are visually similar to 'wave surfer' but are not 'wave surfer' are:\tswimmer\tboater\tjet skier\tsailor\nThere are several useful visual features to tell there is 'wave surfer' and not similar things in a photo:\tstanding on a surfboard\triding on a wave\twearing a wetsuit or swimwear\tusing arms to balance\thaving a surfboard", 12], "tigers": ["Yes. 'Tigers' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'tigers' but are not 'tigers' are:\tleopard\tjaguar\tcheetah\thouse cat\nThere are several useful visual features to tell there is 'tigers' and not similar things in a photo:\torange or reddish-orange fur with black stripes\tdark vertical stripes on a lighter colored coat\ta white belly and black or dark stripes on the paws\tand face\ta muscular, athletic build\tand sharp teeth and claws.", 12], "airplane wings": ["Yes. 'Airplane wings' has a tangible appearance and is a key component of an airplane.\nA few things that are visually similar to 'airplane wings' but are not 'airplane wings' are:\tkite\tglider\tbat\twindmill\nThere are several useful visual features to tell there is 'airplane wings' and not similar things in a photo:\tattached to a body of an airplane\tcontains flaps and slats\tforward-swept or backward-swept\ttrapezoidal or rectangular in shape\tsmooth and aerodynamic surface\tthick and sturdy structure.", 12], "disco ball": ["Yes. 'Disco ball' has a tangible appearance and is a kind of reflective ball used in discotheques.\nA few things that are visually similar to 'disco ball' but are not 'disco ball' are:\tcrystal chandeliers\tglobes\torbs\twith reflective surfaces\twater droplets\nThere are several useful visual features to tell there is 'disco ball' and not similar things in a photo:\tmulti-faceted surface\trainbow-colored reflections\thanging from the ceiling discotheque lights", 12], "light reflection": ["Yes. 'Light reflection' has a tangible appearance.\nA few things that are visually similar to 'light reflection' but are not 'light reflection' are:\tshadows\tmirages\twater\trainbows\t\nThere are several useful visual features to tell there is 'light reflection' and not similar things in a photo:\treflected light\tbright and shiny spots\tan object in the background reflecting light\tbacklight or sun reflection\ton a smooth or shiny surface like glass, mirror or metal", 12], "conveyor": ["Yes. 'Conveyor' has a tangible appearance and is a kind of machinery that moves materials or items along a fixed path.\nA few things that are visually similar to 'conveyor' but are not 'conveyor' are:\tassembly line\troller coaster\t\nThere are several useful visual features to tell there is 'conveyor' and not similar things in a photo:\tmoving items or materials along a fixed path\tbelts, chains or other mechanisms to move items\tsurrounded by a framework or structure for support\tmay have rollers, chutes or other mechanisms to change direction or elevation", 12], "tall windows": ["Yes. 'Tall windows' has a tangible appearance and refers to windows that are higher than usual.\nA few things that are visually similar to 'tall windows' but are not 'tall windows' are:\tnormal-sized windows\tdoorways\twall panels\nThere are several useful visual features to tell there are 'tall windows' and not similar things in a photo:\tproportionally bigger compared to surrounding elements in the building\tnarrow and elongated shape\tcan be made of glass from the bottom to the top", 11], "giant clock": ["Yes. 'Giant clock' has a tangible appearance and is a type of time-keeping device.\nA few things that are visually similar to 'giant clock' but are not 'giant clock' are:\tclock tower\tbell tower\tobservatory\tdome\nThere are several useful visual features to tell there is 'giant clock' and not similar things in a photo:\tlarge size compared to surrounding buildings or objects\tvisible clock face\twith hour, minute, and second hands\tif digital, large numbers or display\televated or mounted on a structure", 11], "cabinet wall": ["Yes. 'Cabinet wall' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'cabinet wall' but are not 'cabinet wall' are:\tbookshelf\tshelving unit\troom divider\nThere are several useful visual features to tell there is 'cabinet wall' and not similar things in a photo:\tclosed or partially enclosed compartments\tlarge and rectangular shape\tmounted to a wall or standing on the floor\tdoors or drawers for opening and closing the compartments", 11], "pizza slicer": ["Yes. 'Pizza slicer' has a tangible appearance and is a type of kitchen tool.\nA few things that are visually similar to 'pizza slicer' but are not 'pizza slicer' are:\tvegetable peeler\tknife\tscissors\nThere are several useful visual features to tell there is 'pizza slicer' and not similar things in a photo:\tcircular blade\twith or without a handle\tcutting pizza in a radial pattern", 11], "steel knife": ["Yes. 'Steel knife' has a tangible appearance and refers to a cutting tool made of steel.\nA few things that are visually similar to 'steel knife' but are not 'steel knife' are:\tfork\tscissors\trazor\tletter opener\nThere are several useful visual features to tell there is 'steel knife' and not similar things in a photo:\tbladed weapon\tmetallic or shiny surface\tserrated or straight edge\tpointed tip\ttapered shape for cutting or chopping", 11], "cockpit area": ["Yes. 'Cockpit area' has a tangible appearance and is a specific part of an aircraft.\nA few things that are visually similar to 'cockpit area' but are not 'cockpit area' are:\tpassenger seats\tgalley area\ttoilet area\twings\nThere are several useful visual features to tell there is 'cockpit area' and not similar things in a photo:\tcontains control instruments, switches, and displays\tforward part of the aircraft\twhere the pilots are seated and operate the plane\tdoor that separates it from the cabin of the plane\tmay have windows for visibility to the front and sides of the aircraft", 11], "color television": ["Yes. 'Color television' has a tangible appearance and is a kind of electronic device.\nA few things that are visually similar to 'color television' but are not 'color television' are:\tblack and white television\tcomputer monitor\tprojector\tscreen\nThere are several useful visual features to tell there is 'color television' and not similar things in a photo:\trectangular screen with a narrow frame\tdisplaying moving color images\tsound-producing device\tantenna or cable attached to it or any other connectivity", 11], "brown roof": ["Yes. 'Brown roof' has a tangible appearance and is a kind of roofing.\nA few things that are visually similar to 'brown roof' but are not 'brown roof' are:\tshingles\twooden panels\tclay tiles\tblack roofing\nThere are several useful visual features to tell there is 'brown roof' and not similar things in a photo:\tthe color brown\ttextured surface\ttriangular or rectangular shape\tsimilarity to clay or slate texture", 11], "shower floor": ["Yes. 'Shower floor' has a tactile appearance and is a part of a bathroom.\nA few things that are visually similar to 'shower floor' but are not 'shower floor' are:\ttiles\tbathroom rugs\tconcrete floors\nThere are several useful visual features to tell there is 'shower floor' and not similar things in a photo:\twater drain in the center\tor around the corner\tsmall tiles or textured surface\tsoap scum or shampoo bottles", 11], "price tags": ["Yes. 'Price tags' has a tangible appearance and is a type of label.\nA few things that are visually similar to 'price tags' but are not 'price tags' are:\tsigns\tstickers\tpost-its\tbusiness cards\nThere are several useful visual features to tell there is 'price tags' and not similar things in a photo:\trectangular or square-shaped with pointed ends\tpaper or plastic material\twith a printed price or other information\tattached to merchandise in a store or on a website.", 11], "half wall": ["Yes. 'Half wall' has a tangible appearance and is a type of wall.\nA few things that are visually similar to 'half wall' but are not 'half wall' are:\tpartition\tcounter\tshelf\nThere are several useful visual features to tell there is 'half wall' and not similar things in a photo:\thalf the height of a regular wall\tpart of a larger structure (room, building, etc.)\tseparates two areas without completely blocking them\thas a flat surface at the top for sitting or placing things\thas a supporting base or legs on the side facing the lower area", 11], "dull": ["No. 'Dull' is too vague or abstract to be distinguished in a photo.", 11], "slaw": ["Yes. 'Slaw' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'slaw' but are not 'slaw' are: mixed vegetables, boiled vegetables, stir-fried vegetables.\nThere are several useful visual features to tell there is 'slaw' and not similar things in a photo:\tshredded or chopped cabbage (or other vegetables) mixed with dressing\t may contain other ingredients like carrots or raisins.", 11], "orange tray": ["Yes. 'Orange tray' has a tangible appearance and is a kind of tray in the color orange.\nA few things that are visually similar to 'orange tray' but are not 'orange tray' are:\torange plate\torange bowl\torange cutting board\torange coaster\nThere are several useful visual features to tell there is 'orange tray' and not similar things in a photo:\trectangular or square shape\traised edges or rim\torange color (such as Pantone 1655 or a similar shade) may have pattern or design in the tray", 11], "plastic garbage": ["Yes. 'Plastic garbage' has a tangible appearance and usually consists of discarded plastic products or packaging.\nA few things that are visually similar to 'plastic garbage' but are not 'plastic garbage' are:\tleaves on the ground\tshredded paper\twrappers on the floor\nThere are several useful visual features to tell there is 'plastic garbage' and not similar things in a photo:\tplastic texture and shine\tvivid colors, especially light blue and white\tcracked, smashed, or misshapen appearance\tdiscarded near trash cans or on streets", 11], "rusty train tracks": ["Yes. 'Rusty train tracks' has a tangible appearance.\nA few things that are visually similar to 'rusty train tracks' but are not 'rusty train tracks' are:\trusty metal pipes\tabandoned conveyor belts\tdilapidated fences\nThere are several useful visual features to tell there are 'rusty train tracks' and not similar things in a photo:\tlong, parallel metal tracks\tconnected by wooden beams or concrete ties\tcopper or red-brown color due to the rust", 11], "wooden chairs": ["Yes. 'Wooden chairs' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wooden chairs' but are not 'wooden chairs' are:\tstools\tbenches\tsofas\tottomans\nThere are several useful visual features to tell there is 'wooden chairs' and not similar things in a photo:\thave a back and a seat\tfor sitting\thave legs\tmade out of wood", 11], "video game console": ["Yes. 'Video game console' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'video game console' but are not 'video game console' are:\tblu-ray player\tsound system\tset-top box\tcomputer tower\nThere are several useful visual features to tell there is 'video game console' and not similar things in a photo:\tcompact size\tvideo game controller\tinput/output ports\tfor playing video games or running game software\tsleek and modern design branded for a gaming company (like Sony PlayStation, Microsoft Xbox, or Nintendo Switch)", 11], "bottle opener": ["Yes. 'Bottle opener' has a tangible appearance and is a tool used to open bottles.\nA few things that are visually similar to 'bottle opener' but are not 'bottle opener' are:\tcan opener\tknife\tscrewdriver\tpaper clip\nThere are several useful visual features to tell there is 'bottle opener' and not similar things in a photo:\tring or handle to hold\tit has a hooked edge on one end to pop caps off\tbottle shape\tusually made of metal", 11], "lunch bag": ["Yes. 'Lunch bag' has a tangible appearance and is a container for carrying food.\nA few things that are visually similar to 'lunch bag' but are not 'lunch bag' are:\tpurse\tbackpack\tduffle bag\tplastic bag\tpicnic basket\nThere are several useful visual features to tell there is 'lunch bag' and not similar things in a photo:\tsectioned, insulated interior handles or a strap to carry it\toften made of fabric or nylon sealed with a zipper or velcro\tfor carrying lunch or snacks", 11], "tall table lamp": ["Yes. 'Tall table lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'tall table lamp' but are not 'tall table lamp' are:\tfloor lamp\tdesk lamp\tchandelier\nThere are several useful visual features to tell there is 'tall table lamp' and not similar things in a photo:\ttall height\twide base\tnarrow stem\tlarge shade or cover\tuse on a tabletop or desk.", 11], "keyboard keys": ["Yes. 'Keyboard keys' have a tangible appearance and are a part of a keyboard.\nA few things that are visually similar to 'keyboard keys' but are not 'keyboard keys' are:\tbuttons\tswitches\tknobs\tdials\nThere are several useful visual features to tell there are 'keyboard keys' and not similar things in a photo:\trectangular shape with rounded edges\tvarious letters, numbers, and symbols printed on top\taligned in a grid pattern\ton top of a flat surface\twith a spring mechanism to make them pop back up after being pressed", 11], "pizza crumbs": ["Yes. 'Pizza crumbs' has a tangible appearance and refers to the small pieces of bread or crust left over after eating a slice of pizza.\nA few things that are visually similar to 'pizza crumbs' but are not 'pizza crumbs' are:\tcroutons\tbreadcrumbs\trice grains\nThere are several useful visual features to tell there is 'pizza crumbs' and not similar things in a photo:\tsmall and irregular shape\tcrunchy texture\tbrownish or golden color\ttomato or cheese residue on the surface", 11], "messy bed": ["Yes. 'Messy bed' has a tangible appearance.\nA few things that are visually similar to 'messy bed' but are not 'messy bed' are:\ttumbleweed\tpile of laundry\tdisarrayed couch\tcrumpled paper\nThere are several useful visual features to tell there is 'messy bed' and not similar things in a photo:\tunmade sheets\twrinkled pillows\tdislodged covers\tcluttered surface", 11], "grown": ["No. 'Grown' is too vague or abstract to be distinguished in a photo.", 11], "advertisement poster": ["Yes. 'Advertisement poster' has a tangible appearance and is a type of graphic design.\nA few things that are visually similar to 'advertisement poster' but are not 'advertisement poster' are:\tbillboards\tdirection signs\tproduct labels\tgreeting cards\nThere are several useful visual features to tell there is 'advertisement poster' and not similar things in a photo:\tlarge size\tpromoting a product or service\tcatchy slogan or image\tattractive design or typography\tinformation about a brand or event", 11], "oats": ["Yes. 'Oats' has a tangible appearance and is a type of cereal grain.\nA few things that are visually similar to 'oats' but are not 'oats' are:\twheat\trice\tbarley\tquinoa\nThere are several useful visual features to tell there is 'oats' and not similar things in a photo:\telongated shape\ttan or golden color\tflattened shape with a crease down the middle\tsmall size compared to other cereal grains.", 11], "food scale": ["Yes. 'Food scale' has a tangible appearance and is a type of kitchen instrument for weighing food.\nA few things that are visually similar to 'food scale' but are not 'food scale' are:\tbathroom scale\tcalculator\truler\nThere are several useful visual features to tell there is 'food scale' and not similar things in a photo:\tflat surface for placing food\tdigital or analog display\tunits of measurement (grams, ounces, etc.)\tweighing tray or bowl\ttare function (to deduct the weight of the container)", 11], "hairy ear": ["Yes. 'Hairy ear' has a tangible appearance.\nA few things that are visually similar to 'hairy ear' but are not 'hairy ear' are:\tfur coat with ear flaps\tmonkey ear\tearmuffs\nThere are several useful visual features to tell there is 'hairy ear' and not similar things in a photo:\thair growing on the skin of the ear or near the ear\tlong hair or fur around the ear\tsimilar skin color and texture to the face or head\tshort hair or fur on other parts of the object's body", 11], "wicker table": ["Yes. 'Wicker table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wicker table' but are not 'wicker table' are:\tplastic table\twooden table\tmetal table\tglass table\nThere are several useful visual features to tell there is 'wicker table' and not similar things in a photo:\tmade of wicker or rattan material\twoven texture\tnatural color and pattern\tlightweight and easy to move", 11], "husband": ["No. 'Husband' is too vague or abstract to have a tangible appearance or be distinguished visually in a photo.", 11], "pointy top": ["Yes. 'Pointy top' has a tangible appearance and can refer to a variety of objects with pointed tips.\nA few things that are visually similar to 'pointy top' but are not 'pointy top' are:\tsharp knife tip\tmountain peak\tpyramid top\that\nThere are several useful visual features to help in identifying 'pointy top' and distinguish it from other things:\tpointed tip\tcone shape\tsharp angles or edges\ttapered appearance", 11], "apple computer logo": ["Yes, 'apple computer logo' has a tangible appearance and is a graphic representation of a company logo.\nA few things that are visually similar to 'apple computer logo' but are not 'apple computer logo' are:\tred apple with a bite taken out\tgraphic logos\twith an apple in their design\nThere are several useful visual features to tell there is 'apple computer logo' and not similar things in a photo:\tan apple with a bite taken out\tsimple, minimalistic design\tcontaining the colors white, black, and grey\tin a flat or glossy vector design\tforming an apple with an oval silhouette and a leaf on its top", 11], "gray shoes": ["Yes. 'Gray shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'gray shoes' but are not 'gray shoes' are:\tblack shoes\twhite shoes\tbrown shoes\tsneakers\nThere are several useful visual features to tell there are 'gray shoes' and not similar things in a photo:\tgray color\tlaces or straps\tsoles or heels\ttoe caps or pointed toes\tmade of leather, canvas, or other shoe material.", 11], "cake platter": ["Yes. 'Cake platter' has a tangible appearance and is a type of serving dish.\nA few things that are visually similar to 'cake platter' but are not 'cake platter' are:\tplate\ttray\tcharger\tplacemat\nThere are several useful visual features to tell there is 'cake platter' and not similar things in a photo:\tround or square in shape\tgenerally elevated footed design or base\twith edges or raised lip to prevent sliding or spilling.", 11], "wood shelves": ["Yes. 'Wood shelves' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wood shelves' but are not 'wood shelves' are:\tbook stands\tcountertops\twindow sills\t\nThere are several useful visual features to tell there are 'wood shelves' and not similar things in a photo:\tattached to a wall\tmade of wood\tlevel and horizontal shelves", 11], "left horn": ["Yes. 'Left horn' has a tangible appearance and is a physical feature of animals with horns.\nA few things that are visually similar to 'left horn' but are not 'left horn' are:\tright horn\thorn-shaped objects (e.g. musical instruments, sculptures)\tantlers\nThere are several useful visual features to tell there is 'left horn' and not similar things in a photo:\tattached to an animal's head\tcurved or straight in shape\tbumpy or smooth surface\tnatural color and texture (e.g. brown, white, ridged)", 11], "potatoe": ["Yes. 'Potato' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'potato' but are not 'potato' are:\tonion\tgarlic\tturnip\tginger\nThere are several useful visual features to tell there is 'potato' and not similar things in a photo:\trounded or oblong shape\tbrown or yellow skin\tsmooth texture\twith or without small eyes\tgrowing underground\tpairs of leaves on short stems", 11], "trellis": ["Yes. 'Trellis' has a tangible appearance and is a type of garden structure.\nA few things that are visually similar to 'trellis' but are not 'trellis' are:\tfence\tlattice\tscreen\tarbor\nThere are several useful visual features to tell there is 'trellis' and not similar things in a photo:\tupright posts or poles\topen framework or latticework\tfor supporting climbing plants or vines", 11], "chili dog": ["Yes. 'Chili dog' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'chili dog' but are not 'chili dog' are:\thot dog\tsausage\tsandwiches\twraps\tburgers\nThere are several useful visual features to tell there is 'chili dog' and not similar things in a photo:\ta grilled or steamed sausage on a bun\ttopped with chili con carne and shredded cheese\tor other toppings (e.g., onions, jalapenos, relish)", 11], "pickle slices": ["Yes. 'Pickle slices' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'pickle slices' but are not 'pickle slices' are:\tapple slices\tonion slices\ttomato slices\tlemon slices\nThere are several useful visual features to tell there is 'pickle slices' and not similar things in a photo:\tgreen or yellow color\tslices with ridges or bumps\tpickle juice or brine visible on or around the slices", 11], "wet rocks": ["Yes. 'Wet rocks' has a tangible appearance and refers to rocks that are covered with water or any other liquid.\nA few things that are visually similar to 'wet rocks' but are not 'wet rocks' are:\trocks covered in moss or lichen\tglass or ceramic objects with a shiny surface\tsome types of metal when they are polished\torbs made of clear glass or plastic\nThere are several useful visual features to tell there are 'wet rocks' and not similar things in a photo:\tdarker color\tand water droplets on the surface\twater or the liquid surrounding the rocks\tcrashing waves in the background or ripples in the water", 11], "bike basket": ["Yes. 'Bike basket' has a tangible appearance and is a type of container attached to a bike.\nA few things that are visually similar to 'bike basket' but are not 'bike basket' are:\tbackpack\tpurse\tsaddlebags\tstroller\nThere are several useful visual features to tell there is 'bike basket' and not similar things in a photo:\tattached to the front or rear of a bike\twoven or mesh material\trear-end of a bike\tholds groceries, flowers, or other items ", 11], "auto": ["Yes. 'Auto' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'auto' but are not 'auto' are:\ttruck\tbicycle\tmotorcycle\tboat\nThere are several useful visual features to tell there is 'auto' and not similar things in a photo:\thas four wheels and a steering wheel\tenough space for the driver and passengers\tcompartments for the engine, seats, and luggage\tbumpers, headlights, mirrors, and license plates", 11], "hoof cow": ["Yes. 'Hoof cow' has a tangible appearance and is a part of a cow's body.\nA few things that are visually similar to 'hoof cow' but are not 'hoof cow' are:\tpaws of other animals\thorse hooves\t\nThere are several useful visual features to tell there is 'hoof cow' and not similar things in a photo:\tlarge size compared to other hooves or paws\tcylindrical shape with a circular base\tcolor: black or similar tone to cow's fur\tpresent on the bottom of a cow's leg", 11], "rafters": ["Yes. 'Rafters' has a tangible appearance and refer to the beams of a roof that support the roof structure.\nA few things that are visually similar to 'rafters' but are not 'rafters' are:\tbeams\tpillars\tcolumns\tpoles\nThere are several useful visual features to tell there is 'rafters' and not similar things in a photo:\twooden or metal beams\tpositioned on a diagonal angle\tsupporting the roof structure or ceiling boards.", 11], "cake frosting": ["Yes. 'Cake frosting' has a tangible appearance and is a type of topping.\nA few things that are visually similar to 'cake frosting' but are not 'cake frosting' are:\twhipped cream\tice cream\tsyrup\t\nThere are several useful visual features to tell there is 'cake frosting' and not similar things in a photo:\tthick and creamy texture\tspread on top of a cake\tor cupcake\tsweet in taste.", 11], "girls shirt": ["Yes. 'Girls shirt' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'girls shirt' but are not 'girls shirt' are:\tboys shirt\twomens blouse\ttank top\tt-shirt\nThere are several useful visual features to tell there is 'girls shirt' and not similar things in a photo:\tform-fitting for girls\u2019 shapes\tsleeves or no sleeves\twith buttons, zippers, or hooks\tvariety of colors and designs", 11], "headdress": ["Yes. 'Headdress' has a tangible appearance and is a type of accessory worn on the head.\nA few things that are visually similar to 'headdress' but are not 'headdress' are:\thair bands\tcrowns\thelmets\t\nThere are several useful visual features to tell there is 'headdress' and not similar things in a photo:\t\ndecorative\tintricate\tdifferent materials or textures, such as feathers, beads, and shells\tworn by people in cultural or traditional contexts", 11], "mud flaps": ["Yes. 'Mud flaps' has a tangible appearance and is a type of vehicle accessory.\nA few things that are visually similar to 'mud flaps' but are not 'mud flaps' are:\tfender covers\trear spoilers/license plate frames\tsplash guards\nThere are several useful visual features to tell there is 'mud flaps' and not similar things in a photo:\tattached behind the wheels\tusually made of rubber or plastic\tflexible enough to move along with the wheels, but stiff enough to block mud or debris\tfrom the ground", 11], "hand thumb": ["Yes. 'Hand thumb' has a tangible appearance and is a part of the human body.\nThere are no things that are visually similar to 'hand thumb' but are not 'hand thumb'.\nUseful visual features for distinguishing 'hand thumb' in a photo are:\tbeing part of a hand\tfour fingers also present\tnext to the index finger but on the opposite side\thaving a distinct bend or joint", 11], "tan fur": ["Yes. 'tan fur' has a tangible appearance and is a specific color and texture of animal fur.\nA few things that are visually similar to 'tan fur' but are not 'tan fur' are:\tleather\twool\tcotton\tfleece\nThere are several useful visual features to tell there is 'tan fur' and not similar things in a photo:\t\n- Soft texture \n- Pores that allows air to pass through \n- Natural patterns \n- It is grown by animals like rabbits, foxes, minks, and etcetera.", 11], "paper menu": ["Yes. 'Paper menu' has a tangible appearance and is a type of document.\nA few things that are visually similar to 'paper menu' but are not 'paper menu' are:\tbrochure\tflyer\tnewspaper\tadvertising\nThere are several useful visual features to tell there is 'paper menu' and not similar things in a photo:\tfolded or unfolded paper with a list of food or drinks\tdescriptions and prices of items\tpictures or illustrations of food and drinks\tspecific fonts and layout for the restaurant or bar's brand and style", 11], "handle pot": ["Yes. 'Handle pot' has a tangible appearance and is a type of container with a handle.\nA few things that are visually similar to 'handle pot' but are not 'handle pot' are:\tmug\tbowl\tteapot\nThere are several useful visual features to tell there is 'handle pot' and not similar things in a photo:\twide opening\tlid\twith a handle\tfor cooking or serving food or liquid with a spout (in the case of a teapot)", 11], "purple leaves": ["Yes. 'Purple leaves' has a tangible appearance and is a type of foliage.\nA few things that are visually similar to 'purple leaves' but are not 'purple leaves' are:\tred leaves\tpink leaves\tblue flowers\tpurple flowers\nThere are several useful visual features to tell there are 'purple leaves' and not similar things in a photo:\tleaves with a purple color\tvariations in shades and patterns of purple\tcolor distinguishes them from the plant's trunk or stem", 11], "beige car": ["Yes. 'Beige car' has a tangible appearance and is a type of vehicle with a specific neutral color.\nA few things that are visually similar to 'beige car' but are not 'beige car' are:\tTan truck\tYellow taxi\tCream bus\nThere are several useful visual features to tell there is a 'beige car' and not similar things in a photo:\tAutomobile shape and size\tBeige coloring in various shades and hues\tCar features such as wheels, headlights, and bumpers.", 11], "vacuum cleaner": ["Yes. 'Vacuum cleaner' has a tangible appearance and is a household appliance used for cleaning.\nA few things that are visually similar to 'vacuum cleaner' but are not 'vacuum cleaner' are:\tbroom\tdustpan\tmop\tbrush\nThere are several useful visual features to tell there is 'vacuum cleaner' and not similar things in a photo:\tupright or canister shape\those\tattached nozzle or brush\tdustbin or bag\tforward-facing wheels or rotating brushes", 11], "slim": ["Yes. 'Slim' has a visually concrete appearance and refers to a particular physical form.\nA few things that are visually similar to 'slim' but are not 'slim' are: thin, skinny, slender, petite, lean.\nThere are several useful visual features to tell there is 'slim' and not similar things in a photo: well-defined bones(not sticking out), waist is narrower than the bust and hips, visible muscle tone, healthy-looking appearance, and lack of visible fat folds on the skin.", 11], "horse standing": ["Yes. 'Horse standing' has a tangible appearance and is an action.\nA few things that are visually similar to 'horse standing' but are not 'horse standing' are:\thorse walking\thorse galloping\thorse trotting\thorse lying down\nThere are several useful visual features to tell there is 'horse standing' and not similar things in a photo:\tall four legs straight, with the feet on the ground\thead facing forward\ttail down, or slightly raised\tears pointing forward or relaxed\tbody still and upright, without leaning in any direction.", 11], "steel railroad tracks": ["Yes. 'Steel railroad tracks' has a tangible appearance and is a type of transportation infrastructure.\nA few things that are visually similar to 'steel railroad tracks' but are not 'steel railroad tracks' are:\tmetal chains\tpipes\ttram rails\nThere are several useful visual features to tell there are 'steel railroad tracks' and not similar things in a photo:\n- Two parallel shiny metal tracks\n- Wooden sleepers between the tracks\n- Rocks or gravel ballasts below the sleepers\n- Ties or fasteners holding the tracks to the sleepers.", 11], "gold number": ["Yes. 'Gold number' has a tangible appearance and refers to a number made out of gold.\nA few things that are visually similar to 'gold number' but are not 'gold number' are:\tgold letter\tother metal numbers\tpainted/drawn numbers\twritten numbers\nThere are several useful visual features to tell there is 'gold number' and not similar things in a photo:\tgolden color\tshiny\tmetallic\ttexture\tgold leaf or foil used to create the number", 11], "horse nose": ["Yes. 'Horse nose' has a tangible appearance and refers to the front part of a horse's head that includes its nostrils and surrounding area.\nA few things that are visually similar to 'horse nose' but are not 'horse nose' are:\tcow nose\tpig nose\tdog nose\nThere are several useful visual features to tell there is 'horse nose' and not similar things in a photo:\tlarge nostrils\tsnout-shaped\thair around the nostrils\tlong and wide nose\tprominent muzzle", 11], "silver trashcan": ["Yes. 'Silver trashcan' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'silver trashcan' but are not 'silver trashcan' are:\tice bucket\tmetal bucket\tsilver vase\tsilver basket\nThere are several useful visual features to tell there is 'silver trashcan' and not similar things in a photo:\tcylindrical shape\twith a lid\tmade of silver or metal\thas a pedal or a lever for opening\tno decorative designs or patterns except maybe some text or an image indicating it's a trashcan.", 11], "glass shower doors": ["Yes. 'Glass shower doors' has a tangible appearance and is a specific type of door used in showers.\nA few things that are visually similar to 'glass shower doors' but are not 'glass shower doors' are:\twindows\tfish tanks\tglass cabinets\twine coolers\nThere are several useful visual features to tell there is 'glass shower doors' and not similar things in a photo:\tinstalled in a shower\tarea usually covered by a curtain\tmovable\tdoor handle or knob\thinges\tor a sliding mechanism", 11], "bento box": ["Yes. 'Bento box' has a tangible appearance and is a type of lunchbox.\nA few things that are visually similar to 'bento box' but are not 'bento box' are:\ttupperware\tcontainer\ttray\tlunch bag\t\nThere are several useful visual features to tell there is 'bento box' and not similar things in a photo:\tdivided into compartments\tmade of wood, plastic, or metal\taesthetically arranged food items\tonigiri (rice balls), sushi, or other Japanese side dishes inside", 11], "glass candle holder": ["Yes. 'Glass candle holder' has a tangible appearance and is a type of container for holding candles.\nA few things that are visually similar to 'glass candle holder' but are not 'glass candle holder' are:\t glass vase\tjar\tglass cup\nThere are several useful visual features to tell there is 'glass candle holder' and not similar things in a photo:\tholds a candle\tnarrow opening to insert the candle\ttranslucent glass material\ttypically wider at the base than at the top.", 11], "camouflage": ["Yes. 'Camouflage' has a tangible appearance and refers to a pattern or material used for concealment.\nA few things that are visually similar to 'camouflage' but are not 'camouflage' are:\tpatterns on fabrics\tnatural patterns, like animal fur or skin\nThere are several useful visual features to tell there is 'camouflage' and not similar things in a photo:\tvarious shades of green, brown, or beige\tdesigned to blend in with the surroundings\tdisruptive pattern that makes the object harder to spot or recognize\tfrom a military or hunting context", 11], "metal trashcan": ["Yes. 'Metal trashcan' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'metal trashcan' but are not 'metal trashcan' are:\tMetal barrels\tMetal drums\tBase of a metal lamp post\tMetal urns\tMetal planters\nThere are several useful visual features to tell there is 'metal trashcan' and not similar things in a photo:\tMetal construction\tRectangular shape\tMetal lid or flap\tTrash or waste inside the container.", 11], "floor surface": ["Yes. 'Floor surface' has a tangible appearance and is a type of material covering the floor.\nA few things that are visually similar to 'floor surface' but are not 'floor surface' are: sidewalk, road, garden bed.\nThere are several useful visual features to tell there is 'floor surface' and not similar things in a photo: flat/horizontal surface, smooth and even texture or pattern, covering the inside of a structure or building.", 11], "raquet": ["Yes. 'Racket' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'racket' but are not 'racket' are:\tpaddle\tclub\tbat\nThere are several useful visual features to tell there is 'racket' and not similar things in a photo:\tflat, oval-shaped head\tconnected to a handle or grip\tstrung with cords or wires made of gut, nylon or carbon fiber designed to hit a ball or shuttlecock.", 11], "car windshield": ["Yes. 'Car windshield' has a tangible appearance and is a part of a car's exterior.\nA few things that are visually similar to 'car windshield' but are not 'car windshield' are:\tglass table top\tshop window\tdisplay case\tmirror\tother car windows\nThere are several useful visual features to tell there is 'car windshield' and not similar things in a photo:\tinclined position\tcurved shape\twipers attached\tdefrost lines or patterns\tdefects such as cracks or chips\tfrom the driver's perspective when driving the car.", 11], "weiner": ["Yes. 'Weiner' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'weiner' but are not 'weiner' are:\tsausage\thot dog\tbratwurst\nThere are several useful visual features to tell there is 'weiner' and not similar things in a photo:\tlong and cylindrical shape\tbrown and grilled (or fried) color\tbun and toppings like ketchup and mustard on top", 11], "silver foil": ["Yes. 'Silver foil' has a tangible appearance and is a type of material.\nA few things that are visually similar to 'silver foil' but are not 'silver foil' are:\taluminum foil\ttin foil\tpaper wrapper\twax paper\nThere are several useful visual features to tell there is 'silver foil' and not similar things in a photo:\tshiny surface\treflects light like a mirror\tmetallic appearance\tsilvery color\tcan be crumpled or flattened\toutlined in a silver trim", 11], "concrete surface": ["Yes. 'Concrete surface' has a tangible appearance and is a type of surface material.\nA few things that are visually similar to 'concrete surface' but are not 'concrete surface' are:\tbrick wall\tpavement\tmarble floor\twooden deck\nThere are several useful visual features to tell there is 'concrete surface' and not similar things in a photo:\tgrey color\trough texture\ttiny stones or rocks visible on the surface\tsquare or rectangular shape of individual sections of the surface.", 11], "round glass table": ["Yes. 'Round glass table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'round glass table' but are not 'round glass table' are:\tround glass plate\tcircular mirror\tglass coffee table\tpatio table\nThere are several useful visual features to tell there is 'round glass table' and not similar things in a photo:\thas a pedestal or four legs\ton top of the table is a round piece of clear glass (with no or minimal framing)\tmade of glass and metal or wood", 11], "paw pads": ["Yes. 'Paw pads' has a tangible appearance and refers to the soft, spongy underside of an animal's paw.\nA few things that are visually similar to 'paw pads' but are not 'paw pads' are:\tsole of a shoe\tbottom of a human foot\nThere are several useful visual features to tell there is 'paw pads' and not similar things in a photo:\tvisible on an animal's paw (such as a dog or a cat)\trubbery texture\tor circular shape\tvariety in color depending on the animal (usually black or pink)", 11], "brick archway": ["Yes. 'Brick archway' has a tangible appearance and is a type of architectural feature.\nA few things that are visually similar to 'brick archway' but are not 'brick archway' are:\tstone archway\twooden archway\tarch-shaped window\tmetal gate\nThere are several useful visual features to tell there is 'brick archway' and not similar things in a photo:\tmade of bricks\tcurved or pointed shape\tused as a doorway or entranceway", 11], "metal wheels": ["Yes. 'Metal wheels' has a tangible appearance and refers to a type of wheel made of metal.\nA few things that are visually similar to 'metal wheels' but are not 'metal wheels' are:\twooden wheels\ttoy wheels\tgears\tferris wheel\nThere are several useful visual features to tell there is 'metal wheels' and not similar things in a photo:\tcircular shape\tmetallic or shiny appearance distinct spokes or treads\tdesigns indicative of industrial use", 11], "pizza server": ["Yes. 'Pizza server' has a tangible appearance and is a type of kitchen utensil.\nA few things that are visually similar to 'pizza server' but are not 'pizza server' are:\tspatula\ttongs\tturner\t \nThere are several useful visual features to tell there is 'pizza server' and not similar things in a photo:\ntriangular or circular shape\twith a sharp or fairly thin edge\ton a long handle or a paddle-shaped blade", 11], "porch light": ["Yes. 'Porch light' has a tangible appearance and is a type of outdoor light.\nA few things that are visually similar to 'porch light' but are not 'porch light' are:\tstreet light\tspotlight\tfloodlight\tlamp post\nThere are several useful visual features to tell there is 'porch light' and not similar things in a photo:\tattached to the porch or the front of the house\tcylindrical or boxy shape\twith a cover or a shade emitting a warm and gentle light", 11], "carrot sticks": ["Yes. 'Carrot sticks' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'carrot sticks' but are not 'carrot sticks' are:\tcelery sticks\tjicama sticks\tapple slices\tzucchini strips\nThere are several useful visual features to tell there are 'carrot sticks' and not similar things in a photo:\tlong and cylindrical shape\torange color\tsmooth and firm texture\twith tapered ends.", 11], "handle bag": ["Yes. 'Handle bag' has a tangible appearance and is a type of bag with a handle for carrying.\nA few things that are visually similar to 'handle bag' but are not 'handle bag' are:\tbackpack\tpurse\ttote\tbriefcase\nThere are several useful visual features to tell there is 'handle bag' and not similar things in a photo:\tresembles a classic shopping bag\tusually made of paper or plastic\toften has a logo or a brand name on it\thas a pair of handles located at the top for carrying\tis not worn on the back or shoulder.", 11], "spools": ["Yes. 'Spools' has a tangible appearance and is a cylindrical object used for holding threads, wires or cords.\nA few things that are visually similar to 'spools' but are not 'spools' are:\twheels\tcandles\tcans\ttubes\nThere are several useful visual features to tell there is 'spools' and not similar things in a photo:\tcylindrical shape\tcentral hole or rod\thaving some thread, wire or cord wrapped around\tit is used as a tool for sewing or working with wires", 11], "documents": ["No. 'Documents' are too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to physical documents but are not 'documents' are: books, magazines, newspapers, flyers, brochures, posters.\n\nUseful visual features for distinguishing 'documents' from the listed similar things in a photo include: lined or blank pages, legal or official formatting, text in a specific language or font, logos or letterheads of official organizations or companies, signatures or stamps, barcodes or identification numbers.", 11], "colar": ["Yes. 'Colar' has a tangible appearance and is a type of jewelry worn around the neck.\nA few things that are visually similar to 'colar' but are not 'colar' are:\tbracelet\tchoker\tnecklace\tlariat\nThere are several useful visual features to tell there is 'colar' and not similar things in a photo:\tworn around the neck\tmade of precious metals, gems, or beads\thanging downward\tfrom a chain or a cord.", 11], "mixers": ["Yes. 'Mixers' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'mixers' but are not 'mixers' are:\tblender\tfood processor\thand whisk\tjuicer\nThere are several useful visual features to tell there is 'mixers' and not similar things in a photo:\tstand-alone appliance\twith a bowl and mixing attachments\tmultiple speed settings\tpowered by electricity or mechanical action\thand-held or free-standing variants with blades for whipping, mixing or kneading.", 11], "male spectator": ["Yes. 'Male spectator' has a tangible appearance and refers to a man who is watching an event or activity.\nA few things that are visually similar to 'male spectator' but are not 'male spectator' are:\tmale athlete\tcrowd\tmale performer\nThere are several useful visual features to tell there is 'male spectator' and not similar things in a photo:\tnot actively participating in the event\twatching or observing the event rather than performing or competing\tdressed in casual or formal clothing, depending on the event", 11], "terraces": ["Yes. 'Terraces' has a tangible appearance and refers to a series of flat platforms.\nA few things that are visually similar to 'terraces' but are not 'terraces' are:\tsteps\tbalconies\tshelves\tfloors\nThere are several useful visual features to tell there is 'terraces' and not similar things in a photo:\t\n- A series of flat, elevated platforms.\n- Designed for agricultural or cultivation purposes.\n- Can be located on a hillslope or a building rooftop.", 11], "sweatbands": ["Yes. 'Sweatbands' has a tangible appearance and is a type of sport accessory.\nA few things that are visually similar to 'sweatbands' but are not 'sweatbands' are: h\neadbands, scrunchies, wristbands, bracelets, watches\nThere are several useful visual features to tell there are 'sweatbands' and not similar things in a photo:\n- Made of absorbent material\n- Worn on the wrist, forehead or upper arm\n- Often used during exercise or sports activities\n- May be plain, striped, or have a logo/design on them.", 11], "catch": ["No. 'catch' is too vague or abstract to be visually concrete.", 11], "football players": ["Yes. 'Football players' has a tangible appearance and refer to people who play a sport.\nA few things that are visually similar to 'football players' but are not 'football players' are:\tsoccer players\trugby players\tbasketball players\tcheerleaders\nThere are several useful visual features to tell there is 'football players' and not similar things in a photo:\thelmet, shoulder pads, and protective gear (for American football)\tstripe uniform\tfield or ball (for American and association football)", 11], "wood bookcase": ["Yes. 'Wood bookcase' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood bookcase' but are not 'wood bookcase' are:\tshelves\tdresser\twardrobe\tlocker\nThere are several useful visual features to tell there is 'wood bookcase' and not similar things in a photo:\tmade of wood\thorizontal shelves\tfor storing books and other objects\toften has a back panel or doors to enclose the contents", 11], "light sky": ["No. 'Light sky' is too vague or abstract to be distinguished in a photo. It is unclear what is meant by 'light sky'.\n", 11], "street cone": ["Yes. 'Street cone' has a tangible appearance and is a type of traffic safety equipment.\nA few things that are visually similar to 'street cone' but are not 'street cone' are:\tbollard\tbarrier\tfence\tpost\nThere are several useful visual features to tell there is 'street cone' and not similar things in a photo:\tcone-shaped\torange color or white with orange stripes\thollow on top\tusually made of plastic or rubber\thas reflective strips for better visibility on streets and highways.", 11], "work van": ["Yes. 'Work van' has a tangible appearance and is a type of vehicle used for work purposes.\nA few things that are visually similar to 'work van' but are not 'work van' are:\tcamper van\tmini van\tambulance\ttruck\nThere are several useful visual features to tell there is 'work van' and not similar things in a photo:\tlarge, boxy shape\tplain color or simple design\ton the side of the van there might be a company logo or lettering indicating what kind of work it is used for\toften equipped with shelves, drawers, or compartments for carrying tools or materials", 11], "pen table": ["Yes. 'Pen table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'pen table' but are not 'pen table' are:\tdesk\tworkbench\tdrawer\ttable\nThere are several useful visual features to tell there is 'pen table' and not similar things in a photo:\ta table designed for drawing, drafting or writing\ttilted top or adjustable angle\tspecialized storage for writing or drawing utensils\televated lip or edge to prevent objects from rolling off", 11], "environment": ["No. 'Environment' is too vague or abstract to be distinguished in a photo.", 11], "donut plate": ["Yes. 'Donut plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'donut plate' but are not 'donut plate' are:\tcake plate\tpastry tray\tcookie sheet\t\nThere are several useful visual features to tell there is 'donut plate' and not similar things in a photo:\tcircular plate\twith multiple indents or holes designed to hold donuts or pastries\tcan be made of plastic, metal or ceramic\ttypically found at donut shops or bakeries.", 11], "packaging": ["Yes. 'Packaging' has a tangible appearance and refers to the materials used to wrap and protect a product.\nA few things that are visually similar to 'packaging' but are not 'packaging' are:\twrapping paper\tenvelopes\tbags\tpaper\nThere are several useful visual features to tell there is 'packaging' and not similar things in a photo:\tbox-shaped or container-like\tobject inside or partially covered by it\tlabels or logos indicating product branding\tor transparent to show the product inside", 11], "storage cabinet": ["Yes. 'Storage cabinet' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'storage cabinet' but are not 'storage cabinet' are:\tbookshelf\tdresser\tcounter\ttop\tbox\tshelf\nThere are several useful visual features to tell there is 'storage cabinet' and not similar things in a photo:\tfreestanding or built-in\tclosed with doors or drawers\tvertical or horizontal arrangement\trectangular or square shape\tcapable of storing various items", 11], "castles": ["Yes. 'Castles' have a tangible appearance and are usually large, fortified buildings.\nA few things that are visually similar to 'castles' but are not 'castles' are:\tMansions\tPalaces\tForts\tTowers\nThere are several useful visual features to tell there is 'castles' and not similar things in a photo:\tHigh walls\tTowers Drawbridge or gated entrance\tMoat or water bodies around the castle\tmedieval architecture such as turrets, battlements, and arrow slits.", 11], "center piece": ["Yes. 'Center piece' has a tangible appearance and is usually an ornamental object that decorates a table or a room.\nA few things that are visually similar to 'center piece' but are not 'center piece' are:\tvase\twith flowers or plants\thanging ornament\tor decorative sculpture\nThere are several useful visual features to tell there is 'center piece' and not similar things in a photo:\tplaced in the center of a table or space\televated or raised from the surface of the table or space\tornamental, decorative or festive\tobject with a prominent size compared to the rest of the table or space", 11], "treads": ["Yes. 'Treads' has a tangible appearance and refers to the grooved surface of a tire or shoe.\nA few things that are visually similar to 'treads' but are not 'treads' are:\tskateboard wheels\tcar tracks\tpaw prints\tdecorative patterns\nThere are several useful visual features to tell there is 'treads' and not similar things in a photo:\trepetitive grooves or patterns\tarcs, lines or curves that grip a surface\tfor tires: deep or shallow grooves with tread blocks, for shoes: ridges or dips that provide traction with the ground", 11], "sunlit": ["Yes. 'Sunlit' has a tangible appearance and refers to objects or scenes illuminated by sunlight.\nA few things that are visually similar to 'sunlit' but are not 'sunlit' are: artifical light, shadows, darkness, overcast skies\nThere are several useful visual features to tell there is 'sunlit' and not similar things in a photo: bright and warm-looking colors, sharp and clearly defined shadows, high contrast between light and dark areas, lens flares or glare in the image, natural and directional shadows.", 11], "orange cover": ["Yes. 'Orange cover' has a tangible appearance and refers to any object that has an orange-colored covering or casing.\nA few things that are visually similar to 'orange cover' but are not 'orange cover' are:\torange book cover\torange phone case\torange umbrella\t\nThere are several useful visual features to tell there is 'orange cover' and not similar things in a photo:\tsolid orange color\tcovering an object\torangish hue or shade on a surface or material", 11], "horse ear": ["Yes. 'Horse ear' has a tangible appearance and refers to the ear of a horse.\nA few things that are visually similar to 'horse ear' but are not 'horse ear' are:\tCow ear\tPig ear\tDeer ear\tElk ear\nThere are several useful visual features to tell there is 'horse ear' and not similar things in a photo:\tlarge size compared to other domesticated animal ears, like cows or pigs\tpointed tips and somewhat curved shape\tusually fur on the ears, with varying colors depending on the horse's breed and coat\tcolor or pattern unique to horse ears", 11], "soccer game": ["Yes. 'Soccer game' has a tangible appearance and involves a specific set of activities.\nA few things that are visually similar to 'soccer game' but are not 'soccer game' are:\tfootball game\tor any sport game\tboard games with a soccer theme\tpeople kicking around a ball\nThere are several useful visual features to tell there is a 'soccer game' and not similar things in a photo:\t\nplayers in uniforms\t\na large, grassy field\t\ngoalposts on each side of the field\t\na ball being kicked back and forth between teams\t\na referee in the background", 11], "pink table cloth": ["Yes. 'pink table cloth' has a tangible appearance and is a specific type of cloth.\nA few things that are visually similar to 'pink table cloth' but are not 'pink table cloth' are:\tpink bedspread\tpink rug\tpink towel\tpink shirt\nThere are several useful visual features to tell there is 'pink table cloth' and not similar things in a photo:\tcovering a table\tpink in color\tflat surface with no pile or loops\tseams or edges on the sides.", 11], "safety bar": ["Yes. 'Safety bar' has a tangible appearance and is a type of bar for safety purposes.\nA few things that are visually similar to 'safety bar' but are not 'safety bar' are: grab bar, towel rack, shower bar.\nThere are several useful visual features to tell there is 'safety bar' and not similar things in a photo: installed in a public transport, especially in a train or a bus, positioned in front of the rider or passenger, provides stability and protection when moving or stopping, made of metal or plastic.", 11], "snow gloves": ["Yes. 'Snow gloves' has a tangible appearance and is a type of winter accessory.\nA few things that are visually similar to 'snow gloves' but are not 'snow gloves' are:\tmittens\tregular gloves\twork gloves\nThere are several useful visual features to tell there is 'snow gloves' and not similar things in a photo:\tclose-fitting construction for insulation\tusually made with waterproof or water-resistant material\tpadded and/or lined for warmth\tcuff to keep snow out of the glove", 11], "space heater": ["Yes. 'Space heater' has a tangible appearance and is a type of heating device.\nA few things that are visually similar to 'space heater' but are not 'space heater' are:\tportable fan\tair purifier\tdehumidifier\nThere are several useful visual features to tell there is 'space heater' and not similar things in a photo:\tcompact size\tforced air\toutput grille or vents\ton/off switch or dial\tfor regulating heat setting\tcord and plug to make it a stand-alone unit", 11], "litter box": ["Yes. 'Litter box' has a tangible appearance and is a kind of pet accessory.\nA few things that are visually similar to 'litter box' but are not 'litter box' are:\tplastic container\tstorage box\tplastic tray\nThere are several useful visual features to tell there is 'litter box' and not similar things in a photo:\tlow-sided\tcontainer for pet waste\ttypically used for cats\tfor indoor use\tmade of durable plastic or similar material", 11], "ginger": ["Yes. 'Ginger' has a tangible appearance and is a type of root vegetable.\nA few things that are visually similar to 'ginger' but are not 'ginger' are:\tgalangal\troot beer\taloe vera\tbarley roots\nThere are several useful visual features to tell there is 'ginger' and not similar things in a photo:\ttan, beige or light brown root\tknobby and irregular shape\tcan be sliced or grated\tpungent smell when cut or broken", 11], "brick patio": ["Yes. 'Brick patio' has a tangible appearance and is a kind of outdoor flooring.\nA few things that are visually similar to 'brick patio' but are not 'brick patio' are:\tconcrete patio\tpaver patio\twooden deck\nThere are several useful visual features to tell there is 'brick patio' and not similar things in a photo:\tred or brown rectangular bricks\tbrick pattern\tarranged in a flat surface and connected with sand or mortar\toutdoor furniture or plants on top of it", 11], "grey walkway": ["Yes. 'Grey walkway' has a tangible appearance and can be visually described.\nA few things that are visually similar to 'grey walkway' but are not 'grey walkway' are:\tconcrete floor\tgray road\tasphalt\tpathway\nThere are several useful visual features to tell there is 'grey walkway' and not similar things in a photo:\tstraight and narrow\tpath for walking or riding only\tlight grey color\twith parallel lines or grid pattern", 11], "icicles": ["Yes. 'Icicles' has a tangible appearance and is a type of frozen water hanging from a surface.\nA few things that are visually similar to 'icicles' but are not 'icicles' are:\ttears\tcrystals\tglitter\nThere are several useful visual features to tell there are 'icicles' and not similar things in a photo:\tlong and thin\thanging from a surface\ttranslucent or clear\tsparkling with sunlight or lights", 11], "pine cones": ["Yes. 'Pine cones' has a tangible appearance and is a type of natural object.\nA few things that are visually similar to 'pine cones' but are not 'pine cones' are:\tartichokes\tmixed nuts\trose buds\nThere are several useful visual features to tell there is 'pine cones' and not similar things in a photo:\tpointed tip\tsymmetrical scales or petals\tbrown or green color\thard and woody texture\tcone-like shape", 11], "kneecap": ["Yes. 'Kneecap' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'kneecap' but are not 'kneecap' are:\tpads\twooden discs\tbumpers\nThere are several useful visual features to tell there is 'kneecap' and not similar things in a photo:\tbone structure located at the front of the knee\tjoint connecting the thigh bone to the shin bone\tcircular shape\tsmooth surface", 11], "silver tip": ["Yes. 'Silver tip' has a tangible appearance and is a type of evergreen tree.\nA few things that are visually similar to 'silver tip' but are not 'silver tip' are:\tfir tree\tpine tree\tspruce tree\nThere are several useful visual features to tell there is 'silver tip' and not similar things in a photo:\tblue-green needles\twith a silver or white tip\tbushy yet well-pruned appearance\ttall and slender shape", 11], "metal mirror": ["Yes. 'Metal mirror' has a tangible appearance and is a type of reflective surface.\nA few things that are visually similar to 'metal mirror' but are not 'metal mirror' are:\tglass pane\tstainless-steel plate\treflection on a car's surface\tpolished surface of a rock\nThere are several useful visual features to tell there is 'metal mirror' and not similar things in a photo:\tmade of metal\tperfectly smooth and flat surface\treflects the viewer's image clearly\tdistorts the reflected image when bent or curved", 11], "ink pens": ["Yes. 'Ink pens' has a tangible appearance.\nA few things that are visually similar to 'ink pens' but are not 'ink pens' are:\tpencils\tmarkers\thighlighters\tbrushes\nThere are several useful visual features to tell there is 'ink pens' and not similar things in a photo:\tlong and slim cylindrical shape\twith a cap or retractable tip\tfor writing or drawing\twith a visible ink reservoir", 11], "wood clock": ["Yes. 'Wood clock' has a tangible appearance and is a type of time-telling device.\nA few things that are visually similar to 'wood clock' but are not 'wood clock' are:\tstone clock\tplastic clock\tmetal clock\tpaper clock\nThere are several useful visual features to tell there is 'wood clock' and not similar things in a photo:\tmade of wood\thas a visible clock face\twith numbers or markers\thas clock hands or a digital display\tmay have chimes or other decorative features", 11], "blue scarf": ["Yes. 'Blue scarf' has a tangible appearance and is an article of clothing.\nA few things that are visually similar to 'blue scarf' but are not 'blue scarf' are:\tblue bandana\tblue ribbon\tblue tie\tblue sash\nThere are several useful visual features to tell there is 'blue scarf' and not similar things in a photo:\tlong and narrow\tfabric texture\twith fringes or tassels\tworn around the neck or head", 11], "lasso": ["Yes. 'Lasso' has a tangible appearance and is a type of rope used for catching animals.\nA few things that are visually similar to 'lasso' but are not 'lasso' are:\tjump rope\tclimbing rope\ttug of war rope\tbungee cord\t\nThere are several useful visual features to tell there is 'lasso' and not similar things in a photo:\tlooped at one end\tthick and sturdy\tmade of braided or twisted rope\theld by a person in a cowboy hat or western-style clothing\twithin the context of a western or rodeo setting", 11], "pink flag": ["Yes. 'Pink flag' has a tangible appearance and is a colored cloth used as a symbol or signal.\nA few things that are visually similar to 'pink flag' but are not 'pink flag' are:\tpink towel\tpink cloth\tpink banner\tpink scarf\nThere are several useful visual features to tell there is 'pink flag' and not similar things in a photo:\trectangular in shape\tfluttering in the wind\twith or without a printed design\tsolid pink color\tpole or stick used to hold it up", 11], "steel plate": ["Yes. 'Steel plate' has a tangible appearance and is a type of flat metal object.\nA few things that are visually similar to 'steel plate' but are not 'steel plate' are:\taluminum plate\tcopper plate\tzinc plate\nThere are several useful visual features to tell there is 'steel plate' and not similar things in a photo:\tgrey or silver in color\treflective surface\theavy and dense appearance\tstamped or raised lettering or numbers", 11], "teammate": ["No. 'Teammate' is too vague or abstract to be distinguished in a photo.", 11], "breakfast foods": ["Yes. 'Breakfast foods' has a tangible appearance and includes various types of foods commonly eaten in the morning.\nA few things that are visually similar to 'breakfast foods' but are not 'breakfast foods' are:\tsandwiches\tpizzas\tburgers\tcakes\nThere are several useful visual features to tell there is 'breakfast foods' and not similar things in a photo:\tcereal\tmilk\ttoast\teggs\tpancakes\tfruit\tyogurt\tsausages\tand coffee or orange juice.", 11], "taller giraffe": ["Yes. 'Taller giraffe' has a tangible appearance and refers to a specific physical characteristic of the animal.\nA few things that are visually similar to 'taller giraffe' but are not 'taller giraffe' are:\tshorter giraffe\tother types of animal with long necks, such as ostrich or llama\ttall tree or building\n\nThere are several useful visual features to tell there is 'taller giraffe' and not similar things in a photo:\tlong neck\tunique coat pattern, with brown patches separated by cream-colored lines\tan overall height that is significantly taller than other giraffes in the photo or in the surrounding area", 11], "camera icon": ["Yes. 'Camera icon' has a tangible appearance and is an image representing a physical camera.\nA few things that are visually similar to 'camera icon' but are not 'camera icon' are:\tphone camera icon\tvideo camera icon\tsketch of a camera\tcamera-shaped logo for a photography company\nThere are several useful visual features to tell there is 'camera icon' and not similar things in a photo:\tcamera outline with a black lens and viewfinder\twhite or colored background\ttoo perfect and symmetrical to be a real camera", 11], "sharpie marker": ["Yes. 'Sharpie marker' has a tangible appearance and is a type of permanent marker.\nA few things that are visually similar to 'sharpie marker' but are not 'sharpie marker' are:\tregular marker\thighlighter\tpen\tcrayon\nThere are several useful visual features to tell there is 'sharpie marker' and not similar things in a photo:\tsmall and cylindrical shape\trectangular cap\tfelt tip\tdark ink color\tSharpie logo on the label", 11], "desk phone": ["Yes. 'Desk phone' has a tangible appearance and is a type of landline phone.\nA few things that are visually similar to 'desk phone' but are not 'desk phone' are:\tcell phone\tsmartphone\ttablet\twalkie-talkie\nThere are several useful visual features to tell there is 'desk phone' and not similar things in a photo:\tconnected to a cord\tto be placed on a desk\tbuttons to make calls\thandset and base\tunit with a keypad and a screen for caller ID or other features.", 11], "metal safety": ["No. 'Metal safety' is too vague or abstract to be distinguished in a photo.", 11], "grey dirt": ["Yes. 'Grey dirt' has a tangible appearance and is a specific color-related to soil or other materials.\nA few things that are visually similar to 'grey dirt' but are not 'grey dirt' are:\tconcrete\tash\nThere are several useful visual features to tell there is 'grey dirt' and not similar things in a photo:\tlight grey or dark grey color\tfine or rough texture\tfound in a natural setting or garden", 11], "dirt spot": ["Yes. 'Dirt spot' has a tangible appearance and is a type of stain or mark on a surface.\nA few things that are visually similar to 'dirt spot' but are not 'dirt spot' are:\tshadow\tdiscoloration\tfaded colors\tsticker\nThere are several useful visual features to tell there is 'dirt spot' and not similar things in a photo:\tirregular shape\tdarker color than surrounding surface\trough or textured surface\tunwanted or unintended on the surface where it appears", 11], "ceiling vent": ["Yes. 'Ceiling vent' has a tangible appearance and refers to a type of ventilation.\nA few things that are visually similar to 'ceiling vent' but are not 'ceiling vent' are:\tlight fixture\tsmoke detector\tspeaker grille\nThere are several useful visual features to tell there is 'ceiling vent' and not similar things in a photo:\tusually rectangular or square in shape\tconnected to ductwork or pipes\tmay have slats or louvers for directing airflow\tin a flat or recessed area of the ceiling", 11], "plastic fence": ["Yes. 'Plastic fence' has a tangible appearance and is a type of fence made of plastic material.\nA few things that are visually similar to 'plastic fence' but are not 'plastic fence' are:\twooden fence\twire fence\thedge\nThere are several useful visual features to tell there is 'plastic fence' and not similar things in a photo:\tplastic material\tvisible panels\torangish, grey or white color\tstraight lines distinct from the background or surroundings.", 11], "train tunnel": ["Yes. 'Train tunnel' has a tangible appearance and is a type of man-made structure.\nA few things that are visually similar to 'train tunnel' but are not 'train tunnel' are:\tbridge\tcave\troad\ttrench\nThere are several useful visual features to tell there is 'train tunnel' and not similar things in a photo:\twrapped around by walls and ceiling\tmade of bricks, concrete, or other materials\tdark inside\twith railway tracks and sometimes a train visible", 11], "tv camera": ["Yes. 'Tv camera' has a tangible appearance and is a type of video recording device.\nA few things that are visually similar to 'tv camera' but are not 'tv camera' are:\tphotography camera\tcellphone\tcamera drone\t\nThere are several useful visual features to tell there is 'tv camera' and not similar things in a photo:\tlarge size\tmultiple lenses or openings\twired or wireless connection to a control room or device\t\ntypical ENG style cameras with an attached microphone and viewfinder or with cables for signal transmission.", 11], "stainless steel sink faucet": ["Yes. 'Stainless steel sink faucet' has a tangible appearance and is a type of plumbing fixture.\nA few things that are visually similar to 'stainless steel sink faucet' but are not 'stainless steel sink faucet' are:\tbathroom faucet\tshowerhead\tsoap dispenser\nThere are several useful visual features to tell there is 'stainless steel sink faucet' and not similar things in a photo:\tmetallic appearance\tstraight or curved spout\tknobs or levers for turning water on and off\tor handles for hot and cold water\tseparated from the sink basin", 11], "metal hose": ["Yes. 'Metal hose' has a tangible appearance and is a type of flexible tube used for conveying liquids or gases.\nA few things that are visually similar to 'metal hose' but are not 'metal hose' are:\tpipe\ttube\twire\tcable\nThere are several useful visual features to tell there is 'metal hose' and not similar things in a photo:\tribbed texture\tmetallic surface\tbendable and flexible\tbody that forms a U-shape when laid flat\treadily accommodates movement of liquid or gas", 11], "orange headlight": ["Yes. 'Orange headlight' has a tangible appearance and is a type of car light.\nA few things that are visually similar to 'orange headlight' but are not 'orange headlight' are:\tred tail light\torange turn signal light\tyellow fog light\twhite headlight\nThere are several useful visual features to tell there is 'orange headlight' and not similar things in a photo:\torange light emitted\tfrom the front of the car\tpositioned next to or above a white headlight\tno other colored light combined with it", 11], "lone sheep": ["Yes. 'Lone sheep' has a tangible appearance and is a type of domestic animal.\nA few things that are visually similar to 'lone sheep' but are not 'lone sheep' are:\tother farm animals like goats or cows\tdogs or cats\tbushes or rocks\nThere are several useful visual features to tell there is 'lone sheep' and not similar things in a photo:\tfour-legged animal\twith woolly fleece\tears hanging down\tface with two eyes, nose, and mouth\tbig ears\tcurved horns or antlers (if it's a goat or a deer)", 11], "wooden stick": ["Yes. 'Wooden stick' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'wooden stick' but are not 'wooden stick' are:\tbranch\tlog\tpencil\tpen\nThere are several useful visual features to tell there is 'wooden stick' and not similar things in a photo:\tcylindrical shape or narrow and elongated shape\tbrown color\trough surface\tmade of wood", 11], "dog tags": ["Yes. 'Dog tags' has a tangible appearance and refers to a type of identification tag for military personnel.\nA few things that are visually similar to 'dog tags' but are not 'dog tags' are:\tkeychains\tnecklaces\tluggage tags\nThere are several useful visual features to tell there are 'dog tags' and not similar things in a photo:\tmetal material\twith military information\tstamped with name, serial number, blood type\telongated shape and size worn on a chain around the neck", 11], "clcok": ["Yes. 'Clock' has a tangible appearance and is a device to measure time.\nA few things that are visually similar to 'clock' but are not 'clock' are:\tthermometer\tspeedometer\tcompass\tbarometer\nThere are several useful visual features to tell there is 'clock' and not similar things in a photo:\tcircular or square shape\tnumbers or markers indicating hours, minutes, and seconds\thour, minute, and second hands or digital display\tmay have a bell or alarm function", 11], "headlamps": ["Yes. 'Headlamps' has a tangible appearance and refers to a type of lighting device designed to be worn on the head.\nA few things that are visually similar to 'headlamps' but are not 'headlamps' are:\tflashlights\tlamps\tcandles\nThere are several useful visual features to tell there is 'headlamps' and not similar things in a photo:\tdesigned to be worn on the head\tadjustable strap or band\tbattery pack or power source attached to the strap\tor light beam directed forward from the headlamp\tsmall and lightweight.", 11], "gym bag": ["Yes. 'Gym bag' has a tangible appearance and is a kind of bag used to carry gym equipment.\nA few things that are visually similar to 'gym bag' but are not 'gym bag' are:\tbackpack\tduffle bag\tpurse\t\nThere are several useful visual features to tell there is 'gym bag' and not similar things in a photo:\tmedium-sized\tcylindrical or rectangular shape\twith straps or handles\tmade of durable materials like nylon or canvas\tsport-related motifs or logos printed on it.", 11], "flower basket": ["Yes. 'Flower basket' has a tangible appearance and is an object used for holding flowers.\nA few things that are visually similar to 'flower basket' but are not 'flower basket' are:\tpicnic basket\tlaundry basket\twaste basket\t\nThere are several useful visual features to tell there is 'flower basket' and not similar things in a photo:\tdecorative design with flowers\thandles for carrying\twide opening on the top, small at the bottom", 11], "siren": ["No. 'Siren' is too vague or abstract to be distinguished in a photo. However, if we are referring to a siren as a device that produces a loud noise, then the answer would be yes.\nA few things that are visually similar to a siren but are not a siren are:\thorn\tmegaphone\tspeaker\tbell\nThere are several useful visual features to tell there is a siren and not similar things in a photo:\trotating lights, typically red and blue\tloudspeaker\tgrille for sound output\tcarrying handle or mounting bracket for attachment to a vehicle or a building\tpower source (such as a battery or an electrical wire)", 11], "computer tablet": ["Yes. 'Computer tablet' has a tangible appearance and refers to a type of electronic device.\nA few things that are visually similar to 'computer tablet' but are not 'computer tablet' are:\te-readers\tsmartphones\tlaptops\thandheld consoles\nThere are several useful visual features to tell there is 'computer tablet' and not similar things in a photo:\trectangular shape\ttouchscreen interface\tvarious buttons or ports\texterior camera\tno physical keyboard", 11], "smear": ["Yes, 'smear' has a tangible appearance and refers to a surface with a substance spread over it.\nA few things that are visually similar to 'smear' but are not 'smear' are:\tsplatter\tsplash\tstain\tbrush stroke\nThere are several useful visual features to tell there is 'smear' and not similar things in a photo:\ta distinct shape or pattern\tthe substance appears spread over a surface\twith visible streaks or lines\tof a consistent thickness or texture", 11], "oval table": ["Yes. 'Oval table' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'oval table' but are not 'oval table' are:\tround table\trectangle table\tdesk\tcabinet\nThere are several useful visual features to tell there is 'oval table' and not similar things in a photo:\telongated shape\twith rounded edges\ttwo equally spaced legs or pedestals", 11], "rock pile": ["Yes. 'Rock pile' has a tangible appearance and is a pile or heap of rocks.\nA few things that are visually similar to 'rock pile' but are not 'rock pile' are:\tmountain\trange of hills\truins\tconstruction site\nThere are several useful visual features to tell there is 'rock pile' and not similar things in a photo:\tpile or heap of rocks\tirregular shapes or sizes\tof varying heights\tand uneven distribution, unlike mountains and hills.", 11], "womans hair": ["Yes. 'Woman's hair' has a tangible appearance and refers to the hair on a woman's head.\nA few things that are visually similar to 'woman's hair' but are not 'woman's hair' are:\tfur\twool\tyarn\tcrystal strands\t\nThere are several useful visual features to tell there is 'woman's hair' and not similar things in a photo:\tattached to a woman's head\tcovered or partly covering the scalp\tvarious colors, styles, and lengths\tstrands of hair with thickness and texture", 11], "cell phone case": ["Yes. 'Cell phone case' has a tangible appearance and is a protective covering for a smartphone.\nA few things that are visually similar to 'cell phone case' but are not 'cell phone case' are:\twallets\tclutches\tbags\tpouches\nThere are several useful visual features to tell there is 'cell phone case' and not similar things in a photo:\tfits the shape of a smartphone\tholes for camera, charger, and other ports\tmade of rubber, silicone, plastic, or leather\tpatterns or designs specific to a phone model", 11], "scotch tape": ["Yes. 'Scotch tape' has a tangible appearance and is a type of adhesive tape.\nA few things that are visually similar to 'scotch tape' but are not 'scotch tape' are:\tmasking tape\tduct tape\twashi tape\tinvisible tape\nThere are several useful visual features to tell there is 'scotch tape' and not similar things in a photo:\tclear or translucent\tplastic or cellulose acetate material\tsticky surface for joining two objects together\trelease liner that needs to be removed to expose the sticky side of the tape.", 11], "ski gloves": ["Yes. 'Ski gloves' has a tangible appearance and is a type of handwear.\nA few things that are visually similar to 'ski gloves' but are not 'ski gloves' are:\tmittens\twork gloves\tcycling gloves\nThere are several useful visual features to tell there is 'ski gloves' and not similar things in a photo:\tlong cuffs to protect the wrists from snow and cold\tinsulated to keep hands warm and dry\ttight fit to allow for easy gripping of ski poles or other equipment\treinforced palms for durability and grip on equipment\ttypically made of waterproof or water-resistant materials", 11], "conductors": ["No. 'Conductors' is too vague or abstract to be distinguished in a photo. \nHowever, if the context is 'conductors of electricity', then it has a tangible appearance.\nA few things that are visually similar to 'conductors of electricity' but are not 'conductors of electricity' are: metal wires, ropes, strings.\nThere are several useful visual features to tell there are 'conductors of electricity' and not similar things in a photo: made of metal\ta cable with multiple wires going through it\tmetal wires with connectors on both ends.", 11], "knack": ["No. 'Knack' is too vague or abstract to be distinguished in a photo.", 11], "bathroom stalls": ["Yes. 'Bathroom stalls' has a tangible appearance and is a kind of enclosed cubicle used for privacy in public restrooms.\nA few things that are visually similar to 'bathroom stalls' but are not 'bathroom stalls' are:\tphone booths\tchanging rooms\tbus or subway seating areas\tboothes\nThere are several useful visual features to tell there is 'bathroom stalls' and not similar things in a photo:\tenclosed space\tdividing walls with floor-to-ceiling doors\tmetal or plastic partitions\twith or without occupants\tin a public restroom", 11], "marketplace": ["Yes. 'Marketplace' has a tangible appearance and is a place where goods are sold.\nA few things that are visually similar to 'marketplace' but are not 'marketplace' are:\tshopping mall\tsupermarket\tflea market\tstreet\nThere are several useful visual features to tell there is 'marketplace' and not similar things in a photo:\topen-air or covered\tseller stalls or kiosks\tcrowded with people\tgoods or products on display\tsell/buy transactions happening", 11], "plate pizza": ["Yes. 'Plate pizza' has a tangible appearance and refers to a pizza served on a plate.\nA few things that are visually similar to 'plate pizza' but are not 'plate pizza' are:\tpizza box\tfrozen pizza\tpizza slice\nThere are several useful visual features to tell there is 'plate pizza' and not similar things in a photo:\tcircular shape\tdough base\ttomato sauce\tcheese and toppings\tserved on a plate or dish", 11], "aerosol": ["Yes. 'Aerosol' has a tangible appearance and refers to a type of spray can.\nA few things that are visually similar to 'aerosol' but are not 'aerosol' are:\tdeodorant spray\tcan of hairspray\tpaint can\tinsecticide spray\tcan of whipped cream\nThere are several useful visual features to tell there is 'aerosol' and not similar things in a photo:\ttypically small and handheld\tdispenses a mist or spray\tusually has a nozzle\torifice for releasing the contents", 11], "brass lamp": ["Yes. 'Brass lamp' has a tangible appearance and refers to a lamp made of brass material.\nA few things that are visually similar to 'brass lamp' but are not 'brass lamp' are:\tsilver lamp\tgolden lamp\tglass lamp\tceramic lamp\nThere are several useful visual features to tell there is 'brass lamp' and not similar things in a photo:\t\n- Yellowish-gold color\n- Shiny surface\n- The color of the metal appears to bleed if it is old or tarnished\n- May have intricate designs\n- Usually have a cone shape shade above it.", 11], "dressers": ["Yes. 'Dressers' has a tangible appearance and refers to a specific type of furniture.\nA few things that are visually similar to 'dressers' but are not 'dressers' are:\tchests\ttrunks\tshelving units\tcabinets\nThere are several useful visual features to tell there is 'dressers' and not similar things in a photo:\tdrawers\thandles\tor knobs\tto hold clothing or other personal items\tsurface on the top for display or storage.", 11], "sky scrapers": ["Yes. 'Sky scrapers' has a tangible appearance and refers to tall buildings.\nA few things that are visually similar to 'sky scrapers' but are not 'sky scrapers' are:\ttowers\tmonuments\tlighthouses\nThere are several useful visual features to tell there is 'sky scrapers' and not similar things in a photo:\textremely tall buildings\tmany floors or stories\tpartially or fully made of glass\tmostly found in urban areas\twith multiple windows and balconies", 11], "strollers": ["Yes. 'Strollers' has a tangible appearance and is a type of baby carriage.\nA few things that are visually similar to 'strollers' but are not 'strollers' are:\tbicycles\twheelchairs\thand trucks\nThere are several useful visual features to tell there is 'strollers' and not similar things in a photo:\tlarge canopy or hood\ttoys or snacks on the tray\tfootrest\tforward-facing seat\tsmall wheels\tfor adjustable handles", 11], "pointy beak": ["Yes. 'Pointy beak' has a tangible appearance and is a physical feature of certain animals.\nA few things that are visually similar to 'pointy beak' but are not 'pointy beak' are:\tneedle\tnail\tknife\ticicle\nThere are several useful visual features to tell there is 'pointy beak' and not similar things in a photo:\tpart of an animal's face\tbeak-shaped\tused for eating or grabbing things\tcurved and sharp at the tip\ttypically found on birds, but also on some reptiles and fish", 11], "end tables": ["Yes. 'End tables' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'end tables' but are not 'end tables' are:\tcoffee tables\tnightstands\ttv stands\nThere are several useful visual features to tell there is 'end tables' and not similar things in a photo:\tshort height compared to other furniture pieces\tfound at the end or beside a couch or a bed\tsmall size compared to other furniture pieces", 11], "house roof": ["Yes. 'House roof' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'house roof' but are not 'house roof' are:\tcar roof\ttent roof\tpavilion roof\nThere are several useful visual features to tell there is 'house roof' and not similar things in a photo:\t\nangled or sloping surface\ncovered with tiles or shingles\nmade of wood or other building materials\nconnected to the top of the walls of the building\nmay have chimneys or vents", 11], "silver headlight": ["Yes. 'Silver headlight' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'silver headlight' but are not 'silver headlight' are:\tchrome wheel rim\tsilver hubcap\tmetallic bumper\tpolished exhaust pipe\nThere are several useful visual features to tell there is 'silver headlight' and not similar things in a photo:\tsilver or metallic color\tcircular or oval shape\tconnected to the front of a vehicle\twith a reflective glass surface\tfor headlights with a bulb inside: illuminated at night or dark environments", 11], "propellar": ["Yes. 'Propeller' has a tangible appearance and is a rotary wing used to produce thrust.\nA few things that are visually similar to 'propeller' but are not 'propeller' are:\tfan\tblade\twindmill\nThere are several useful visual features to tell there is 'propeller' and not similar things in a photo:\tconsists of two or more blades\tspinning or rotating\tusually attached to a motor or engine\tnarrow at the base and broadens towards the tip", 11], "shaggy": ["Yes. 'Shaggy' has a tangible appearance and refers to a person or animal having long, unkempt hair or fur.\nA few things that are visually similar to 'shaggy' but are not 'shaggy' are:\tcurly\thairy\tfurry\tmessy\nThere are several useful visual features to tell there is 'shaggy' and not similar things in a photo:\tlong and unkempt hair or fur\ttangled or matted strands\tuneven or unruly texture\texcessive hair volume or thickness", 11], "adult horse": ["Yes. 'Adult horse' has a tangible appearance and is a type of four-legged animal.\nA few things that are visually similar to 'adult horse' but are not 'adult horse' are:\tzebra\tdonkey\tgiraffe\tcamel\nThere are several useful visual features to tell there is 'adult horse' and not similar things in a photo:\ttall\twith four long legs\tsingle-toed hooves\tlong face and snout\ta flowing mane and tail", 11], "purple sweater": ["Yes. 'Purple sweater' has a tangible appearance and is a clothing item.\nA few things that are visually similar to 'purple sweater' but are not 'purple sweater' are:\tpurple shirts\tpurple dresses\tpurple jackets\tpurple scarves\nThere are several useful visual features to tell there is a 'purple sweater' and not similar things in a photo:\twoven fabric\ttexture of knit or wool\tpurple in color\tlong sleeves and a collar\tor a round neck\tline or no line pattern", 11], "brick street": ["Yes. 'Brick street' has a tangible appearance and is a specific type of road.\nA few things that are visually similar to 'brick street' but are not 'brick street' are: cobbled street, paved street, asphalt street, dirt road.\nThere are several useful visual features to tell there is 'brick street' and not similar things in a photo: rectangular-shaped bricks, red or brown in color, jagged or uneven surface, arranged in herringbone or basket weave pattern.", 11], "stubby tail": ["Yes. 'Stubby tail' has a tangible appearance and describes a specific physical characteristic.\nA few things that are visually similar to 'stubby tail' but are not 'stubby tail' are:\tshort tail\tdocked tail\tbobtail\tcropped tail\nThere are no other visual features needed to distinguish 'stubby tail' as it is a very specific term used to describe a tail that is abnormally short or barely visible.", 11], "medium size": ["No. 'Medium size' is too vague or abstract to be distinguished in a photo. It is a relative term and can vary from person to person or object to object. \n\nTherefore, it cannot have visually similar things or visual features for distinguishing it from other things in a photo.", 11], "metal drawer": ["Yes. 'Metal drawer' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'metal drawer' but are not 'metal drawer' are:\twooden drawer\tplastic drawer\tmetal cabinet\tmetal box\nThere are several useful visual features to tell there is 'metal drawer' and not similar things in a photo:\tmade of metal\tcontains compartments or slots\tfor holding objects or documents\thas a handle or knob\tfor sliding the drawer in and out\tof a particular size and shape (such as a standard file cabinet drawer)", 11], "computer mouse pad": ["Yes. 'Computer mouse pad' has a tangible appearance and is a type of accessory used with a computer mouse.\nA few things that are visually similar to 'computer mouse pad' but are not 'computer mouse pad' are:\tcounter mat\tcoaster\t\nThere are several useful visual features to tell there is 'computer mouse pad' and not similar things in a photo:\trectangular shape\twith a printed design, image, or logo\tsmooth surface to help mouse movement\tslightly cushioned or padded for wrist comfort\tand usually made of rubber or cloth material.", 11], "peices": ["No. 'Peices' is too vague or abstract to be distinguished in a photo. It is possible that you meant 'pieces' which has a tangible appearance and refers to parts of a whole.\nA few things that are visually similar to 'pieces' but are not 'pieces' are:\tshards\tcracks\tbits of broken glass\tparticles\nThere are several useful visual features to tell there are 'pieces' and not similar things in a photo:\tirregular shapes\tpart of a larger object\tor match with other pieces to form a complete object.", 11], "batter wears": ["No. 'Batter wears' is too vague or abstract and doesn't have any tangible appearance. \n\nIt seems that the phrase is likely a typo or transcription error. However, assuming you meant 'batteries', the responses are: \n\nA few things that are visually similar to 'batteries' but are not 'batteries' are:\tcandles, light bulbs, small bottles or containers \n\nThere are several useful visual features to tell there are 'batteries' and not similar things in a photo:\tcylinder or rectangular shape, two or more terminals, labels with voltage or other information, no visible filaments or wicks.", 11], "dirt pitchers": ["No. 'Dirt pitchers' is too vague or abstract to be distinguished in a photo. Perhaps you meant 'dirt pitchers' as in 'pitcher plants' which are plants that catch insects in modified leaves that resemble pitchers. \n\nIf that's the case:\nA few things that are visually similar to 'dirt pitchers' but are not 'dirt pitchers' are: regular plant leaves, large flowers.\n\nThere are several useful visual features to tell there is 'dirt pitchers' and not similar things in a photo: modified leaves that form a tube-like structure with a distinctive pitcher shape, digestive enzymes or fluid visible inside, the presence of trapped insects or other prey.", 11], "trash bins": ["Yes. 'Trash bins' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'trash bins' but are not 'trash bins' are:\tbaskets\tboxes\tcrates\tbags\nThere are several useful visual features to tell there are 'trash bins' and not similar things in a photo:\ttraditional cylindrical or rectangular shape\ttop with a lid or cover\thandles on sides or top\tword \"TRASH\" or a recycling sign on the front or side", 11], "womens hair": ["Yes. 'Womens hair' has a tangible appearance and is a type of hair on the head of a female.\nA few things that are visually similar to 'womens hair' but are not 'womens hair' are:\twigs\tfur\tFeathers\thairpieces\nThere are several useful visual features to tell there is 'womens hair' and not similar things in a photo:\tgrowing from the scalp\tlong or short\twavy or straight\tcolored or natural\tstyle, such as ponytail, bun or straight", 11], "multiple kites": ["Yes. 'Multiple kites' has a tangible appearance and refers to more than one kite in the air.\nA few things that are visually similar to 'multiple kites' but are not 'multiple kites' are: flags, balloons, birds, clouds\nThere are several useful visual features to tell there are 'multiple kites' and not similar things in a photo: kite shapes (diamond, triangle, etc.), kite tails, strings or cords connecting the kites, flying in formation or in close proximity.", 11], "motorcycle boot": ["Yes. 'Motorcycle boot' is a visually concrete concept and refers to a type of footwear.\nA few things that are visually similar to 'motorcycle boot' but are not 'motorcycle boot' are:\thiking boot\twork boot\tcowboy boot\nThere are several useful visual features to tell there is a 'motorcycle boot' and not similar things in a photo:\ttall length\theavy-duty leather or synthetic material\tbuckles or laces\tfor protection and support against the elements\tor in case of a crash.", 11], "wick": ["Yes. 'Wick' has a tangible appearance and is a type of string used to light candles or lamps.\nA few things that are visually similar to 'wick' but are not 'wick' are:\tthread\tshoelace\tfishing line\tstring\nThere are several useful visual features to tell there is 'wick' and not similar things in a photo:\tthick and fibrous\thollow in the center\tdipped in wax or oil\tburnt or charred at one end", 11], "casing": ["Yes. 'Casing' has a tangible appearance and usually refers to an outer covering for an object.\nA few things that are visually similar to 'casing' but are not 'casing' are: shells, sleeves, covers, jackets, cases\nThere are several useful visual features to tell there is 'casing' and not similar things in a photo:\thollow interior, intended for an object to fit inside\toften made of plastic or metal\tdurable, protective, and/or decorative outer layer can have buttons, hinges or other fasteners to secure the object", 11], "drips": ["Yes. 'Drips' has a tangible appearance and is a type of liquid.\nA few things that are visually similar to 'drips' but are not 'drips' are:\tstains\tbrush strokes\tcracks\nThere are several useful visual features to tell there are 'drips' and not similar things in a photo:\tthin and elongated shapes\tdripping or flowing down\ta consistent pattern or direction\tthe presence of other liquid-related imagery (e.g., puddles, splatters)", 11], "cabins": ["Yes. 'Cabins' has a tangible appearance and is a type of small house.\nA few things that are visually similar to 'cabins' but are not 'cabins' are:\ttents\tstorage sheds\tbarns\ttrailers\nThere are several useful visual features to tell there is 'cabins' and not similar things in a photo:\tsmall house or cottage-like structure\twooden or log exterior\tchimney or smokestack\tporch or deck\tarea of natural surroundings or woods or mountains", 11], "blue jet": ["Yes. 'Blue jet' has a tangible appearance and is a type of atmospheric phenomenon.\nThere are no other things that are visually similar to 'blue jet,' as it is a unique atmospheric phenomenon.\nUseful visual features for distinguishing 'blue jet' in a photo include: a bluish cone or jet-shaped emission in the upper atmosphere, often accompanied by red sprites or other upper atmospheric electrical discharges.", 11], "lady tennis player": ["Yes. 'Lady tennis player' has a tangible appearance and is a person playing tennis.\nA few things that are visually similar to 'lady tennis player' but are not 'lady tennis player' are:\twoman running\trunner on a race court\twoman playing badminton\twoman playing volleyball\nThere are several useful visual features to tell there is 'lady tennis player' and not similar things in a photo:\tholding a tennis racket\twearing tennis shoes\twearing tennis attire (skirt, shorts, or dress)\ton a tennis court", 11], "resort": ["Yes. 'Resort' has a tangible appearance and refers to a place where people go for vacation.\nA few things that are visually similar to 'resort' but are not 'resort' are:\thotels\tvillas\tbeaches\ttheme parks\nThere are several useful visual features to tell there is 'resort' and not similar things in a photo:\tswimming pools\tresidential or recreational facilities\tpalm trees and other exotic plants\tbungalows or cabins activities for recreation", 11], "car parking": ["Yes. 'Car parking' has a tangible appearance and is a location for car storage.\nA few things that are visually similar to 'car parking' but are not 'car parking' are:\tdriveway\tparking lot\tgarage\nThere are several useful visual features to tell there is 'car parking' and not similar things in a photo:\tmarked parking spaces\tpainted parking lines\tparking meters or pay stations\tsigns indicating parking restrictions and regulations\tparked cars in the photo", 11], "photographer name": ["No. 'Photographer name' is too abstract and cannot be visually distinguished in a photo. \n\nThere are no things that are visually similar to 'photographer name'. \n\nAs mentioned earlier, 'photographer name' is an abstract concept and does not have any useful visual features for distinguishing it from other things in a photo.", 11], "sand pit": ["Yes. 'Sand pit' has a tangible appearance and is a type of outdoor play area.\nA few things that are visually similar to 'sand pit' but are not 'sand pit' are:\tdirt pile\trock pile\tplayground with rubber covering\twater pool\nThere are several useful visual features to tell there is 'sand pit' and not similar things in a photo:\tfilled with sand\tsurrounded by a border or fence\tchildren playing or toys inside\tscoops, buckets or shovels around it", 11], "blue suitcase": ["Yes. 'Blue suitcase' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'blue suitcase' but are not 'blue suitcase' are:\tblack suitcase\tred suitcase\tbackpack\tbriefcase\nThere are several useful visual features to tell there is 'blue suitcase' and not similar things in a photo:\trectangular shape\thinged lid\thandles\tfor traveling or carrying personal belongings", 11], "room door": ["Yes. 'Room door' has a tangible appearance and is a type of portal that separates two spaces.\nA few things that are visually similar to 'room door' but are not 'room door' are:\twindow\tcurtains\tshelves\twall art\nThere are several useful visual features to tell there is 'room door' and not similar things in a photo:\trectangle or square shape\thinged at the side or top\thandles or knobs\tlock or latch\tframe or panel design", 11], "dog face": ["Yes. 'Dog face' has a tangible appearance and is the face of a domestic dog.\nA few things that are visually similar to 'dog face' but are not 'dog face' are:\tfox face\twolf face\tcoyote face\nThere are several useful visual features to tell there is 'dog face' and not similar things in a photo:\tround ears\tfuzzy forehead\tfriendly expression\twith or without long snouts\tnose and mouth in the shape of an inverted triangle\tbright, warm, friendly eyes", 11], "hanging lamp": ["Yes. 'Hanging lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'hanging lamp' but are not 'hanging lamp' are:\tpendant jewelry,\toverhead fan,\t\nhanging plant,\tchandelier.\nThere are several useful visual features to tell there is 'hanging lamp' and not similar things in a photo:\tcord or chain\tsuspended from a ceiling\tprovides light like a lamp\tusually have a lampshade.", 11], "corkboard": ["Yes. 'Corkboard' has a tangible appearance and is a type of bulletin board.\nA few things that are visually similar to 'corkboard' but are not 'corkboard' are:\tchalkboard\twhiteboard\tpegboard\nThere are several useful visual features to tell there is 'corkboard' and not similar things in a photo:\tbrown or tan color\tpinholes for tacks or pins\trough or textured surface\tcork material", 11], "backrest": ["Yes. 'Backrest' has a tangible appearance and is a physical part of furniture.\nA few things that are visually similar to 'backrest' but are not 'backrest' are:\tpillow\tcushion\tarmrest\t\nThere are several useful visual features to tell there is 'backrest' and not similar things in a photo:\tpart of a chair or a sofa\tvertical or slightly inclined\tpositioned behind a seat\tMade of the same material as the chair or sofa", 11], "handle flush toilet": ["Yes. 'Handle flush toilet' has a tangible appearance and is a part of a toilet system.\nA few things that are visually similar to 'handle flush toilet' but are not 'handle flush toilet' are:\tfaucet handle\tshower handle\tdoor handle\tdrawer handle\nThere are several useful visual features to tell there is 'handle flush toilet' and not similar things in a photo:\tlocated on the tank of a toilet\tdifferent shape than other handles\tlarger than other handles\tcolor and design specific to toilet system", 11], "car door handle": ["Yes. 'Car door handle' has a tangible appearance and is a type of handle used to open a car door.\nA few things that are visually similar to 'car door handle' but are not 'car door handle' are:\tdoorknob\tdrawer handle\tcabinet pull\nThere are several useful visual features to tell there is 'car door handle' and not similar things in a photo:\trectangular or curved shape\tsmooth finish\tmetallic or plastic composition\tattached to a door with a visible lock mechanism\tnext to a car window", 11], "fleece": ["Yes. 'Fleece' has a tangible appearance and refers to a soft fabric.\nA few things that are visually similar to 'fleece' but are not 'fleece' are:\tcotton\twool\tfur\tsilk\nThere are several useful visual features to tell there is 'fleece' and not similar things in a photo:\tsoft and fluffy texture\tnot as smooth or shiny as silk\tor cotton\tsynthetic or made from wool fibers often used for winter clothing", 11], "snowbank": ["Yes. 'Snowbank' has a tangible appearance and is a pile of snow.\nA few things that are visually similar to 'snowbank' but are not 'snowbank' are:\tmountains\tclouds\twhite sand dunes\t\nThere are several useful visual features to tell there is 'snowbank' and not similar things in a photo:\twhite or light-blue color\tpowdery texture\tfrosty appearance\tcold environment\tor a winter season perspective.", 11], "dudes": ["No. 'Dudes' is too vague or abstract to be distinguished in a photo. It is a colloquial term for men or individuals of a casual, friendly nature, but it does not have a specific or tangible appearance. \n\nTherefore, it is not possible to name things that are visually similar to 'dudes' but are not 'dudes', nor is it possible to identify visual features to distinguish it from other things in a photo.", 11], "pink feathers": ["Yes. 'Pink feathers' has a tangible appearance.\nA few things that are visually similar to 'pink feathers' but are not 'pink feathers' are:\trose petals\tcotton candy\tshredded pink paper\nThere aren't many visual features that can distinguish 'pink feathers' from rose petals, cotton candy, or shredded pink paper. However, some useful visual features for distinguishing the pink feathers from the listed similar things in a photo are:\tfeathery texture\tuniform size and shape\tnatural patterns found in feathers (such as stripes or spots)", 11], "leather baseball glove": ["Yes. 'Leather baseball glove' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'leather baseball glove' but are not 'leather baseball glove' are:\tleather work glove\tski glove\tmotorcycle glove\tleather handbags\t\nThere are several useful visual features to tell there is 'leather baseball glove' and not similar things in a photo:\tshaped like a hand\tpalm and fingers with a webbed pattern\tlacing or stitching\ton the back of the glove there is a small hole for the index finger\tand sometimes a strap that goes around the wrist.", 11], "eyelash": ["Yes. 'Eyelash' has a tangible appearance and is a hair on the edge of an eyelid.\nA few things that are visually similar to 'eyelash' but are not 'eyelash' are:\thair\tstrand of fur\nThere are several useful visual features to tell there is an 'eyelash' and not similar things in a photo:\t\nlocated on the edge of the eyelid\tcurved shape\tthicker at the base and thinner at the tip", 11], "outdoor lamp": ["Yes. 'Outdoor lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'outdoor lamp' but are not 'outdoor lamp' are:\tpost light\ttraffic light\tindoor lamp\t\nThere are several useful visual features to tell there is 'outdoor lamp' and not similar things in a photo:\ttall and freestanding design\tbulb or LED light source\texposed or covered glass/plastic bulb housing\tpower source or cable visible", 11], "bread pizza": ["Yes. 'Bread pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'bread pizza' but are not 'bread pizza' are:\ttoast\thot sandwich\topen-faced sandwich\tbruschetta\nThere are several useful visual features to tell there is 'bread pizza' and not similar things in a photo:\trectangular-shaped bread with toppings on top\tmelted cheese on top of the toppings\tsmells like pizza", 11], "shelve": ["Yes. 'Shelve' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'shelve' but are not 'shelve' are:\tcabinet\ttable\tbench\tcounter\nThere are several useful visual features to tell there is 'shelve' and not similar things in a photo:\trow of horizontal surfaces mounted on a vertical support,\tparallel arrangements of multiple surfaces to hold or store objects,\tusually made of wood or metal,\tfixed to a wall or freestanding,\tlength of the shelves can vary.", 11], "doily": ["Yes. 'Doily' has a tangible appearance and is a type of decorative mat.\nA few things that are visually similar to 'doily' but are not 'doily' are:\tcoaster\tplacemat\ttable runner\tcloth napkin\nThere are several useful visual features to tell there is 'doily' and not similar things in a photo:\tintricate lace or crochet pattern\tround or oval shape\tdecorative edging or fringe\tthin and delicate material", 11], "polka dot umbrella": ["Yes. 'Polka dot umbrella' has a tangible appearance and is a type of rain umbrella.\nA few things that are visually similar to 'polka dot umbrella' but are not 'polka dot umbrella' are:\tplain-colored umbrella\tfloral umbrella\tstripe umbrella\nThere are several useful visual features to tell there is 'polka dot umbrella' and not similar things in a photo:\tcircular or dome-shaped canopy\twith small, round dots of the same size and color evenly spaced on the canopy\tfeature contrasting colors\ton a long, straight handle", 11], "cake decoration": ["Yes. 'Cake decoration' has a tangible appearance and refers to various edible or non-edible items used to decorate cakes.\nA few things that are visually similar to 'cake decoration' but are not 'cake decoration' are:\tfood frosting\tpaint\tartificial flowers\nThere are several useful visual features to tell there is 'cake decoration' and not similar things in a photo:\tedible or non-edible\titem placed on top of the cake\tattractive and colorful\tdifferent shapes and sizes\ticing, fondant, chocolate, or sugar paste\tforming flowers, letters, patterns, or pictures.", 11], "fur coat": ["Yes. 'Fur coat' has a tangible appearance and is an item of clothing.\nA few things that are visually similar to 'fur coat' but are not 'fur coat' are:\tleather jacket\twool coat\tpuffer jacket\tdenim jacket\nThere are several useful visual features to tell there is 'fur coat' and not similar things in a photo:\tfur texture or pattern\tnatural colors (brown, black, white)\tthick, plushy appearance\tvisible fur on the collar or cuffs", 11], "surfaces": ["No. 'Surfaces' is too vague or abstract to be distinguished in a photo. It refers to the outermost layer of an object that we can touch or see. \n\nIt is not possible to have something similar to 'surfaces' that is not a surface itself.\n\nHowever, useful visual features to identify and distinguish between different surfaces in a photo include: texture, color, reflectivity, smoothness/roughness, patterns, and material type.", 11], "plastic bat": ["Yes. 'Plastic bat' has a tangible appearance and is a type of sporting equipment.\nA few things that are visually similar to 'plastic bat' but are not 'plastic bat' are:\twooden bat\ttennis racket\tcricket bat\nThere are several useful visual features to tell there is 'plastic bat' and not similar things in a photo:\tskinny and long\ttubular shape\tasymmetric shape\tsmooth surface\tmade of plastic", 11], "square design": ["Yes. 'Square design' has a tangible appearance and pertains to a specific style or pattern.\nA few things that are visually similar to 'square design' but are not 'square design' are:\ttile patterns\tchessboard patterns\tsquare shapes\trepetitive geometric patterns\nThere are several useful visual features to tell there is 'square design' and not similar things in a photo:\tsquare shapes arranged in a specific pattern or design\tequal size or proportion of squares\tintricate or detailed square patterns\tuse of contrasting colors or tones within the squares", 11], "helmet biker": ["Yes. 'helmet biker' has a tangible appearance and is a kind of protective gear for motorcyclists.\nA few things that are visually similar to 'helmet biker' but are not 'helmet biker' are:\tskateboard helmet\tbicycle helmet\tconstruction helmet\tfirefighter helmet\nThere are several useful visual features to tell there is 'helmet biker' and not similar things in a photo:\thard outer shell\tchinstrap\ttooled with air holes\tvisors or shields\tthat cover the face or sit above the eyes, depending on the style\tand cover the ears and back of the head.", 11], "side wheel": ["Yes. 'Side wheel' has a tangible appearance and is a kind of wheel mounted on the side of a vehicle or a boat.\nA few things that are visually similar to 'side wheel' but are not 'side wheel' are:\tsteering wheel\tbicycle wheel\ttruck wheel\nThere are several useful visual features to tell there is 'side wheel' and not similar things in a photo:\tmounted on the side of a boat or a vehicle\thorizontal axis\tconsist of a series of paddles or blades\tthat help move the vehicle or the boat.", 11], "surfboard surfer": ["Yes. 'Surfboard surfer' has a tangible appearance and refers to a person riding a surfboard.\nA few things that are visually similar to 'surfboard surfer' but are not 'surfboard surfer' are:\tWakeboarder\tSkier\tSnowboarder\tWindsurfer\tKitesurfer\t\nThere are several useful visual features to tell there is 'surfboard surfer' and not similar things in a photo:\tRiding a surfboard on the waves on the ocean\tRiding waves while standing up on the board\tWearing a wetsuit or swimwear\tUsing their hands to balance on the board\tSurfboard is typically larger than other boards used for other water sports.", 11], "storage unit": ["Yes. 'Storage unit' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'storage unit' but are not 'storage unit' are:\tshelves\tcabinets\tdrawers\tlockers\nThere are several useful visual features to tell there is 'storage unit' and not similar things in a photo:\tusually rectangular or square in shape\tmade of metal or plastic or wood\tmultiple compartments or sections\tfor storing various items\tlockable or have a latch for securing the contents", 11], "clothesline": ["Yes. 'clothesline' has a tangible appearance.\nA few things that are visually similar to 'clothesline' but are not 'clothesline' are:\tfence\twire\tcord\nThere are several useful visual features to tell there is 'clothesline' and not similar things in a photo:\ta rope, cord or wire\tstrung between two points or poles\tpins or clips attached to the line\tto dry clothes or other fabrics outside", 11], "meatball": ["Yes. 'Meatball' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'meatball' but are not 'meatball' are:\tfalafel\thushpuppies\nThere are several useful visual features to tell there is 'meatball' and not similar things in a photo:\tround in shape\tbrown or golden-brown color\thas distinct texture of ground meat or breadcrumbs", 11], "pepper flakes": ["Yes. 'Pepper flakes' has a tangible appearance and is a type of spice.\nA few things that are visually similar to 'pepper flakes' but are not 'pepper flakes' are:\tred chili powder\tcayenne pepper\tpaprika\tbrown sugar\nThere are several useful visual features to tell there is 'pepper flakes' and not similar things in a photo:\tsmall and thin pieces\tdark color\tvisible seeds or skin of the pepper\tpinch or sprinkle of the flakes on top of the food", 11], "silhouettes": ["Yes. 'Silhouettes' has a tangible appearance and refers to the dark outline or shape of something against a lighter background.\nA few things that are visually similar to 'silhouettes' but are not 'silhouettes' are:\tshadows\treflections\tdark outlines\tor sharp contrasts between dark and light areas in a photo\nThere are several useful visual features to tell there are 'silhouettes' and not similar things in a photo:\tdark outlines of an object or person\tvisible against a lighter background\tno details or textures in the object or person's outline\tcan be filled with a solid color or gradient.", 11], "silver panel": ["Yes. 'Silver panel' has a tangible appearance and is a type of flat, metallic surface.\nA few things that are visually similar to 'silver panel' but are not 'silver panel' are:\tstainless steel appliance\tmetal garage door\taluminum siding\tmetallic car\nThere are several useful visual features to tell there is 'silver panel' and not similar things in a photo:\tthin and flat surface\tsilver or metallic color\treflective or shiny surface\tstraight or uniform texture\tmounted or installed on a wall or a surface", 11], "facemask": ["Yes. 'Facemask' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'facemask' but are not 'facemask' are:\tski mask\tbalaclava\twelding mask\tface shield\nThere are several useful visual features to tell there is 'facemask' and not similar things in a photo:\tcovers the nose and mouth\tlooped or tied behind the ears or head\tmade of fabric, paper, or other materials\ttypically light blue or white color\tmay have folds or pleats on its surface for a better fit", 11], "water dispenser": ["Yes. 'Water dispenser' has a tangible appearance and is a type of appliance.\nA few things that are visually similar to 'water dispenser' but are not 'water dispenser' are:\tcoffee maker\tjuice dispenser\tsoap dispenser\tair freshener\nThere are several useful visual features to tell there is 'water dispenser' and not similar things in a photo:\ttransparent container for holding water or other liquids\ttap or nozzle for dispensing liquid\tcooling or heating mechanism (in case of a hot/cold water dispenser)\tdesignated area for placing cups or bottles.", 11], "wrought iron fencing": ["Yes. 'Wrought iron fencing' has a tangible appearance and is a type of metal fencing.\nA few things that are visually similar to 'wrought iron fencing' but are not 'wrought iron fencing' are:\tchain-link fencing\twooden fencing\tpicket fencing\nThere are several useful visual features to tell there is 'wrought iron fencing' and not similar things in a photo:\telaborate designs and patterns\twelded joints and connections\tblack or dark brown color with a matte or slightly glossy finish\tsmooth and polished texture.", 11], "front lights": ["Yes. 'Front lights' has a tangible appearance and refers to the headlights of a vehicle.\nA few things that are visually similar to 'front lights' but are not 'front lights' are:\ttraffic lights\tporch lights\tstreetlights\theadlamps\nThere are several useful visual features to tell there are 'front lights' and not similar things in a photo:\tpositioned at the front of a vehicle\tbright white or yellow light\toften come in pairs or sets\tcan be rectangular or circular in shape", 11], "enter key": ["Yes. 'Enter key' has a tangible appearance on the keyboard.\nA few things that are visually similar to 'enter key' but are not 'enter key' are:\tshift key\treturn key\tbackspace key\nThere are several useful visual features to tell there is 'enter key' and not similar things in a photo:\tusually labeled with 'enter' or 'return'\tkey is wide and rectangular, often located at the bottom right of the keyboard\thorizontal line with an arrow pointing left in the bottom right corner of the key.", 11], "silver pitcher": ["Yes, 'silver pitcher' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'silver pitcher' but are not 'silver pitcher' are:\tcoffee pot\ttea kettle\twater jug\tcocktail shaker\nThere are several useful visual features to tell there is 'silver pitcher' and not similar things in a photo:\thandles on both sides\tmade of silver or silver-colored materials\tsmall spout or nozzle\tfor pouring and serving of beverages\tcapacity of approximately 1 to 2 liters.", 11], "metal canister": ["Yes. 'Metal canister' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'metal canister' but are not 'metal canister' are:\tmetal jar\tmetal bottle\ttin box\tmetal bucket\nThere are several useful visual features to tell there is 'metal canister' and not similar things in a photo:\tcylindrical shape\tmetallic material\twith or without a cap\tor lid", 11], "leafy lettuce": ["Yes. 'Leafy lettuce' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'leafy lettuce' but are not 'leafy lettuce' are:\tspinach\tkale\tardugula\tcabbage\nThere are several useful visual features to tell there is 'leafy lettuce' and not similar things in a photo:\tvarious types of lettuce leaves, usually green or red in color\thas a distinct shape and veining pattern\tleafy texture with jagged edges or lobes\tsituated on a stem, with the leaves branching off in a crowns-like structure", 11], "orange cooler": ["Yes. 'Orange cooler' has a tangible appearance and is a specific type of drink.\nA few things that are visually similar to 'orange cooler' but are not 'orange cooler' are:\torange juice with ice\tcarrot juice with ice\torange soda with ice\nThere are several useful visual features to tell there is 'orange cooler' and not similar things in a photo:\tbright orange color\tice cubes\tglass or plastic container\twith or without straw or lid.", 11], "spoon handle": ["Yes. 'spoon handle' has a tangible appearance and is a part of the spoon.\nA few things that are visually similar to 'spoon handle' but are not 'spoon handle' are:\tknife handle\tfork handle\tchopstick handle\t\nThere are several useful visual features to tell there is 'spoon handle' and not similar things in a photo:\tlong, narrow and rounded handle\twith a slight bend at the end\tfor grasping or holding a spoon bowl", 11], "crystal chandelier": ["Yes. 'Crystal chandelier' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'crystal chandelier' but are not 'crystal chandelier' are:\tpendant lamp\tmetal chandelier\tcandle holder\tceiling fan\nThere are several useful visual features to tell there is 'crystal chandelier' and not similar things in a photo:\tmultiple arms, often containing lights\tcrystal or glass ornaments\thanging chains and suspensions\tnormally hanged in a large room or hall.", 11], "mane horse": ["Yes. 'Mane horse' has a tangible appearance and is a horse with long hair on its neck and head.\nA few things that are visually similar to 'mane horse' but are not 'mane horse' are:\thorse with short hair\thorse without hair on its neck and head\t\nThere are several useful visual features to tell there is 'mane horse' and not similar things in a photo:\tlong, thick hair on the neck and head areas\tof a horse\thorse body shape and size", 11], "cement path": ["Yes. 'Cement path' has a tangible appearance and is a kind of path made from cement.\nA few things that are visually similar to 'cement path' but are not 'cement path' are:\tstone path\tbrick path\tasphalt path\tgrass path\nThere are several useful visual features to tell there is 'cement path' and not similar things in a photo:\tlight grey color\tsmooth surface\tsquare or rectangular shape\tstraight edges", 11], "love": ["No. 'Love' is too vague or abstract to be distinguished in a photo.", 11], "wood deck": ["Yes. 'Wood deck' has a tangible appearance and refers to a raised platform made of wood for outdoor use.\nA few things that are visually similar to 'wood deck' but are not 'wood deck' are:\tpatio\tconcrete slab\tbrick terrace\tstone slab\nThere are several useful visual features to tell there is 'wood deck' and not similar things in a photo:\tmade of wood\tlow to the ground or raised above the surrounding area\toften bordered by a railing or balustrade\thas a distinct repeating pattern of planks grooved with spaces between the boards", 11], "car park": ["Yes. 'Car park' has a tangible appearance and is a designated area for parking cars.\nA few things that are visually similar to 'car park' but are not 'car park' are:\tpublic square\toutdoor mall\tstreet\t\nThere are several useful visual features to tell there is 'car park' and not similar things in a photo:\tcars parked in orderly rows\tparking stripes and lanes\tparking meters or payment kiosks\tboundaries, such as walls or barriers, around the area allocated for parking.", 11], "banana slice": ["Yes. 'Banana slice' has a tangible appearance.\nA few things that are visually similar to 'banana slice' but are not 'banana slice' are:\tmango slices\tpineapple slices\torange slices\nThere are several useful visual features to tell there is 'banana slice' and not similar things in a photo:\tyellow\tcurved crescent shape\trough texture on the inside\tsmooth texture on the outside", 11], "fruit salad": ["Yes. 'Fruit salad' has a tangible appearance and is a food item made with mixed fruits.\nA few things that are visually similar to 'fruit salad' but are not 'fruit salad' are:\tveggie salad\tpotato salad\tcoleslaw\nThere are several useful visual features to tell there is 'fruit salad' and not similar things in a photo:\tcombination of mixed fruits\tsmall, bite-sized pieces of fruit\tfruit juices at the bottom of the bowl\tcolored toppings, such as cream or chocolate", 11], "flower plant": ["Yes. 'Flower plant' has a tangible appearance and refers to a plant that produces flowers.\nA few things that are visually similar to 'flower plant' but are not 'flower plant' are:\tgrass\tbushes\ttrees\tweeds\nThere are several useful visual features to tell there is 'flower plant' and not similar things in a photo:\tblooming flowers\tbuds\tpetals\tleaves with green or colorful hues\ttall or short stems.", 11], "gold ribbon": ["Yes. 'Gold ribbon' has a tangible appearance and is a kind of ribbon.\nA few things that are visually similar to 'gold ribbon' but are not 'gold ribbon' are:\tyellow ribbon\tbronze ribbon\tbrass ribbon\tshiny fabric\nThere are several useful visual features to tell there is 'gold ribbon' and not similar things in a photo:\tmetallic gold color\tthinness and flexibility\twinding and bending on itself or an object\tmaking a bow or knot", 11], "members": ["No. 'Members' is too vague or abstract to be distinguished in a photo.", 11], "time clock": ["Yes. 'Time clock' has a tangible appearance and is a type of clock used to record when an employee starts and ends their work.\nA few things that are visually similar to 'time clock' but are not 'time clock' are:\twall clock\twristwatch\tpocket watch\talarm clock\nThere are several useful visual features to tell there is 'time clock' and not similar things in a photo:\ttypically has a slot for an employee's time card or badge\tdisplays the time\tin a workplace or office setting\thas a button to verify the employee's identity or clocking in/out\ttime tracking software may be displayed on a connected computer screen", 11], "bicycle lane": ["Yes. 'Bicycle lane' has a tangible appearance and is a designated section of the road or street for bicycles to travel.\nA few things that are visually similar to 'bicycle lane' but are not 'bicycle lane' are:\tsidewalk\tparking lot\tdriveway\troad\nThere are several useful visual features to tell there is 'bicycle lane' and not similar things in a photo:\tdepicted by a painted line or marking\tusually colored with a bright green hue\tmarked by signs or symbols\tshowing bicycles with arrows or other indications for cyclists only\tseparated from the main road or street either by a curb or by a raised platform to the same level as the sidewalk.", 11], "wood house": ["Yes. 'Wood house' has a tangible appearance and is a type of building constructed mainly from wood.\nA few things that are visually similar to 'wood house' but are not 'wood house' are:\tlog cabins\twooden sheds\tbarns\tfences\nThere are several useful visual features to tell there is 'wood house' and not similar things in a photo:\tmade largely of wood\troof covered with shingles or tiles\tdoor and windows\twith chimneys and gutters\tlarger than a shed or a barn\tfront porch or balcony\tsimple and rustic design", 11], "yellow basket": ["Yes. 'Yellow basket' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'yellow basket' but are not 'yellow basket' are:\tyellow bucket\tyellow bin\tyellow bag\tyellow crate\nThere are several useful visual features to tell there is 'yellow basket' and not similar things in a photo:\twoven or mesh structure\thandles on opposite sides\tround or oval shape\tbright yellow color", 11], "metal pitcher": ["Yes. 'Metal pitcher' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'metal pitcher' but are not 'metal pitcher' are:\tkettle\tcoffee pot\tvase\turn\nThere are several useful visual features to tell there is 'metal pitcher' and not similar things in a photo:\tholds liquids, typically water or beverages\tmade of metal, usually silver or copper\thandles for pouring and carrying\tnarrow spout for controlling the flow of liquid\twide base for stability and balance", 11], "disks": ["Yes. 'Disks' has a tangible appearance and refers to circular-shaped objects.\nA few things that are visually similar to 'disks' but are not 'disks' are:\tcoins\tfrisbees\tpizzas\twheels\nThere are several useful visual features to tell there are 'disks' and not similar things in a photo:\tcircular shape\tflat surface\tno spokes or holes in the center of the circle\tvarious sizes and thicknesses\tmade of metal or plastic", 11], "television remote control": ["Yes. 'Television remote control' has a tangible appearance and is a device used to control TV.\nA few things that are visually similar to 'television remote control' but are not 'television remote control' are:\tvideo game controller\tmusic player remote control\tair conditioning remote control\nThere are several useful visual features to tell there is 'television remote control' and not similar things in a photo:\trectangular or square shape\tbuttons\tforward, backward, and volume control functions\tlabels or symbols for TV functions\tand IR sensor that emits infrared signals", 11], "dice": ["Yes. 'Dice' has a tangible appearance and is a type of small cube-shaped object used in games of chance.\nA few things that are visually similar to 'dice' but are not 'dice' are:\tgame cubes\tbuilding blocks\nThere are several useful visual features to tell there is 'dice' and not similar things in a photo:\tsix sides with dots or numbers\tpips evenly distributed\tintended for gambling or games\tmost commonly two of them are bright white, the rest of sides are colored in red, green, blue or black", 11], "leopard": ["Yes. 'Leopard' has a tangible appearance and is a kind of animal.\nA few things that are visually similar to 'leopard' but are not 'leopard' are:\tcheetah\tjaguar\tleopard seal\tsnow leopard\nThere are several useful visual features to tell there is 'leopard' and not similar things in a photo:\ttan or light golden coat with black spots or rosettes\tshort legs compared to its body size\tSmooth and shiny fur\tRound ears and short snout\tbrown or green eyes.", 11], "ankles": ["Yes. 'Ankles' has a tangible appearance and is a body part.\nA few things that are visually similar to 'ankles' but are not 'ankles' are:\twrists\telbows\tknees\theels\nThere are several useful visual features to tell there is 'ankles' and not similar things in a photo:\tpart of the leg\tjoint connecting the leg to the foot\tnarrower than the calf and the foot\tbony prominences visible near the ankle joint", 11], "decker tour bus": ["Yes. 'Decker tour bus' has a tangible appearance and is a type of bus with two levels.\nA few things that are visually similar to 'decker tour bus' but are not 'decker tour bus' are:\tdouble-decker city bus\tcoach bus\nThere are several useful visual features to tell there is 'decker tour bus' and not similar things in a photo:\ttour bus branding on the exterior\tdouble-decker design\ttourists sitting on the top deck\topen-air top deck with no roof or windows", 11], "lotion bottle": ["Yes. 'Lotion bottle' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'lotion bottle' but are not 'lotion bottle' are:\tshampoo bottle\tconditioner bottle\thand sanitizer bottle\tperfume bottle\nThere are several useful visual features to tell there is 'lotion bottle' and not similar things in a photo:\tsmall bottle with a pump or squeeze top\tcontaining white or colored liquid or cream\tlabels or markings indicating it's lotion or moisturizer", 11], "ski suit": ["Yes, 'ski suit' has a tangible appearance and is a kind of winter sports attire.\nA few things that are visually similar to 'ski suit' but are not 'ski suit' are:\tsnowboarding suit\twinter hiking jacket\tsnowshoeing suit\nThere are several useful visual features to tell there is 'ski suit' and not similar things in a photo:\ttight-fitting\tentire body covered\tbright colors\twaterproof material\tcntain specific features like snow skirt, ski pass pocket or helmet-compatible hood.", 11], "blue tag": ["Yes. 'Blue tag' has a tangible appearance and is a type of identification label.\nA few things that are visually similar to 'blue tag' but are not 'blue tag' are:\tblue sticker\tblue badge\tblue ribbon\tblue adhesive tape\nThere are several useful visual features to tell there is 'blue tag' and not similar things in a photo:\trectangular or square shape made of paper or plastic\tbright blue color\tusually attached to an item with a string or adhesive\ttypically has text or a number on it for identification purposes.", 11], "sausage pizza": ["Yes. 'Sausage pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'sausage pizza' but are not 'sausage pizza' are:\tpepperoni pizza\thawaiian pizza\tmargherita pizza\tcalzone\tpie\nThere are several useful visual features to tell there is 'sausage pizza' and not similar things in a photo:\tround or square shape\tdough crust\ttomato sauce\tcheese\tsausage pieces", 11], "helmet motorcycle": ["Yes. 'Helmet motorcycle' has a tangible appearance and refers to a specific type of helmet worn while riding a motorcycle.\nA few things that are visually similar to 'helmet motorcycle' but are not 'helmet motorcycle' are:\thard hat\tbicycle helmet\tskateboard helmet\tconstruction helmet\nThere are several useful visual features to tell there is 'helmet motorcycle' and not similar things in a photo:\tprotection around the head and chin\tarea to cover the ears\ta visor to protect the eyes\tvarious graphics or designs specific to motorcycles\tor may include built-in Bluetooth or intercom systems.", 11], "airplane cockpit": ["Yes. 'Airplane cockpit' has a tangible appearance and is a specific part of an airplane.\nA few things that are visually similar to 'airplane cockpit' but are not 'airplane cockpit' are:\tcar dashboard\tboat steering wheel\ttrain compartment\nThere are several useful visual features to tell there is 'airplane cockpit' and not similar things in a photo:\tthree-dimensional space\twith flight control instruments, navigation systems, and communication devices\tpilot and co-pilot seats\tinstrument panel\twith a windshield in front of it.", 11], "metal fire escape": ["Yes. 'Metal fire escape' has a tangible appearance and is a type of emergency exit stairway.\nA few things that are visually similar to 'metal fire escape' but are not 'metal fire escape' are:\tMetal balcony\tMetal framework\tBattered metallic outdoor staircase\nThere are several useful visual features to tell there is 'metal fire escape' and not similar things in a photo:\tLocated outdoors\tAscends or descends building at an angle\tParallel to a building wall\tHas handrails and safety features\tSeparate from the building facade.", 11], "christmas hat": ["Yes. 'Christmas hat' has a tangible appearance and is a type of hat worn during Christmas.\nA few things that are visually similar to 'christmas hat' but are not 'christmas hat' are:\tWitch hat\tParty hat\tGraduation cap\tCowboy hat\tSanta Claus beard\nThere are several useful visual features to tell there is 'christmas hat' and not similar things in a photo:\tred and white or green and white stripe pattern\ta white ball or pom-pom on top\ta pointed tip\theld in place with a headband or string", 11], "grey pipe": ["Yes. 'Grey pipe' has a tangible appearance and is a type of cylindrical object.\nA few things that are visually similar to 'grey pipe' but are not 'grey pipe' are:\those\tpole\tchimney\texhaust pipe\nThere are several useful visual features to tell there is 'grey pipe' and not similar things in a photo:\tround or cylindrical shape\tgrey or metallic color\tsmooth texture\thas joints or connectors for plumbing or ventilation.", 11], "shade umbrella": ["Yes. 'Shade umbrella' has a tangible appearance and is a type of outdoor furniture.\nA few things that are visually similar to 'shade umbrella' but are not 'shade umbrella' are:\tparasol\ttent\tcanopy\nThere are several useful visual features to tell there is 'shade umbrella' and not similar things in a photo:\tcircular or octagonal shape\topened by a central pole\tsupport ribs\toften made of fabric or paper\thas a handle or crank to adjust height or direction\tused to provide shade", 11], "board box": ["Yes. 'Board box' has a tangible appearance and refers to the material and structure of the box.\nA few things that are visually similar to 'board box' but are not 'board box' are:\tcardboard box\tpaper box\tplastic box\twooden box\nThere are several useful visual features to tell there is 'board box' and not similar things in a photo:\tthick and sturdy structure\tcorrugated texture\tand brown color.", 11], "cheese pizza": ["Yes. 'Cheese pizza' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'cheese pizza' but are not 'cheese pizza' are:\tpepperoni pizza\tmushroom pizza\tquiche\tfrittata\nThere are several useful visual features to tell there is 'cheese pizza' and not similar things in a photo:\tcircular shape\tcooked crust\ttomato sauce layer\tmelted cheese layer", 11], "handle window": ["No. 'Handle window' is not a commonly used term and may be considered too vague or abstract.\nTherefore, it is difficult to name things that are visually similar to 'handle window' but are not 'handle window'.\nWithout a proper definition or context, it is also difficult to identify useful visual features to distinguish 'handle window' from other things in a photo.", 11], "shadow vase": ["Yes. 'Shadow vase' has a tangible appearance and is an optical illusion.\nThere are no things that are visually similar to 'shadow vase' that are not 'shadow vase'.\nUseful visual features for distinguishing 'shadow vase' from other real vases in a photo could include:\tthe outline or silhouette of the vase being visible on a surface behind it\tthe outline or silhouette of flowers or other objects inside the vase being visible on a surface behind it, as well as the overall shape and size of the vase.", 11], "carriage wheel": ["Yes. 'Carriage wheel' has a tangible appearance and is a type of wheel used on carriages.\nA few things that are visually similar to 'carriage wheel' but are not 'carriage wheel' are:\tbicycle wheel\tmotorcycle wheel\tsteering wheel\nThere are several useful visual features to tell there is 'carriage wheel' and not similar things in a photo:\tlarge and wooden or metal\tspokes\trims\thub\tattached to a carriage or cart", 11], "giraffe drinking water": ["Yes. 'Giraffe drinking water' has a tangible appearance and is a specific type of animal behavior.\nA few things that are visually similar to 'giraffe drinking water' but are not 'giraffe drinking water' are:\tgiraffe eating leaves\tgiraffe standing\tgiraffe laying down\nThere are several useful visual features to tell there is 'giraffe drinking water' and not similar things in a photo:\tgiraffe's long neck bent down to reach the water\tsource of water such as a river or a pond\treflection of giraffe and surrounding scenery on the water surface\twater droplets on the giraffe's mouth and neck", 11], "plastic soap dispenser": ["Yes. 'Plastic soap dispenser' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic soap dispenser' but are not 'plastic soap dispenser' are:\tplastic water bottle\tplastic lotion dispenser\tplastic spray bottle\nThere are several useful visual features to tell there is 'plastic soap dispenser' and not similar things in a photo:\tpress or pump mechanism\ton a sink or countertop\twith soap visible through the container\ttranslucent plastic material", 11], "wind chime": ["Yes. 'Wind chime' has a tangible appearance and is a kind of musical instrument.\nA few things that are visually similar to 'wind chime' but are not 'wind chime' are:\tmobiles\tchandeliers\tpendant lights\tcurtains\nThere are several useful visual features to tell there is 'wind chime' and not similar things in a photo:\thanging tubes or bells\tmade of metal, wood, or glass\tchimes or tinkling sound being emitted\tswaying or moving in the wind", 11], "cat head": ["Yes. 'Cat head' has a tangible appearance and refers to the head of a cat.\nA few things that are visually similar to 'cat head' but are not 'cat head' are:\tdog head\tlion head\ttiger head\tleopard head\tpuma head\nThere are several useful visual features to tell there is 'cat head' and not similar things in a photo:\ttriangular-shaped ears\tpointed snout\torangish-brown fur (in some breeds)\tvertical slits for pupils\twhiskers on the face", 11], "sign posts": ["Yes. 'Sign posts' has a tangible appearance and is a type of post used to display signs or directions.\nA few things that are visually similar to 'sign posts' but are not 'sign posts' are:\tflag poles\ttelephone poles\tstreet lamps\tfence posts\nThere are several useful visual features to tell there is 'sign posts' and not similar things in a photo:\trectangular or square shape\tmultiple signs or arrows on top of the post\tsolid base for stability\tmounted at a prominent location for visibility", 11], "bird tail": ["Yes. 'Bird tail' has a tangible appearance and is a part of a bird's anatomy.\nA few things that are visually similar to 'bird tail' but are not 'bird tail' are:\tfan\ttassel\tswish\tflag\nThere are several useful visual features to tell there is 'bird tail' and not similar things in a photo:\tfeathery\tflexible\tmade up of individual feathers\thorizontal or vertical position\tcan be spread or closed\tcan serve for balance during flight or mating display", 11], "doorframe": ["Yes. 'Doorframe' has a tangible appearance and refers to the frame that surrounds a door.\nA few things that are visually similar to 'doorframe' but are not 'doorframe' are:\twindow frame\tpicture frame\tbed frame\tcabinet frame\nThere are several useful visual features to tell there is 'doorframe' and not similar things in a photo:\trectangular or square shape\tconnected to a door or doorway\ttypically made of wood or metal", 11], "beige chair": ["Yes. 'Beige chair' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'beige chair' but are not 'beige chair' are:\tsofa\tstool\tbench\tottoman\nThere are several useful visual features to tell there is 'beige chair' and not similar things in a photo:\tupright\twith arms and backrest\tusually with four legs\tpadded or upholstered in beige color", 11], "potty": ["Yes. 'Potty' has a tangible appearance and is a type of toilet.\nA few things that are visually similar to 'potty' but are not 'potty' are:\ttoilet\tbasin\tbucket\tbath\nThere are several useful visual features to tell there is 'potty' and not similar things in a photo:\tsmaller size for children\tbright colors\tfunny shapes or designs\twith a removable bowl\tor with a lid", 11], "office buildings": ["Yes. 'Office buildings' has a tangible appearance and denotes a specific type of structure.\nA few things that are visually similar to 'office buildings' but are not 'office buildings' are:\tapartment buildings\thotels\tskyscrapers\thospitals\nThere are several useful visual features to distinguish 'office buildings' from the listed similar things in a photo:\tthe presence of parking lots (indicating regular business hours)\tsigns on the building displaying office tenants\tvisible office equipment in the windows (computers, phones, etc.)", 11], "tobogan": ["Yes. 'Toboggan' has a tangible appearance and is a type of sled.\nA few things that are visually similar to 'toboggan' but are not 'toboggan' are:\tsled\tsleigh\tskis\tsnowboard\nThere are several useful visual features to tell there is 'toboggan' and not similar things in a photo:\tlong, flat bottom for sliding\tonboard handle or rope for steering\tnot attached to any animal or vehicle, like a horse or a car.", 11], "orange scarf": ["Yes. 'Orange scarf' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'orange scarf' but are not 'orange scarf' are:\tscarf of another color\torange sweater\torange hat\nThere are several useful visual features to tell there is 'orange scarf' and not similar things in a photo:\tlong and narrow piece of cloth\tworn around the neck or head\tsolid orange color or orange as the dominant color in the pattern.", 11], "stripe zebra": ["Yes. 'Stripe zebra' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'stripe zebra' but are not 'stripe zebra' are:\ttiger\thorse\tdonkey\tgiraffe\tleopard\nThere are several useful visual features to tell there is 'stripe zebra' and not similar things in a photo:\tblack and white stripes\thorse-like shape\tshort mane and tail\thooves\tno spots or rosettes", 11], "flask": ["Yes. 'Flask' has a tangible appearance and is usually a container for liquid.\nA few things that are visually similar to 'flask' but are not 'flask' are:\tbottle\tjar\tcup\thourglass\nThere are several useful visual features to tell there is 'flask' and not similar things in a photo:\tnarrow neck or opening\tbulbous or rounded shape\tusually made of glass or metal\twith or without a cap or stopper\tdesigned for carrying or measuring liquid", 11], "house brown": ["No. 'House brown' is too vague or abstract to be distinguished in a photo.", 11], "airplane wheel": ["Yes. 'Airplane wheel' has a tangible appearance and is a type of wheel used in airplanes.\nA few things that are visually similar to 'airplane wheel' but are not 'airplane wheel' are:\tcar wheel\tbicycle wheel\tscooter wheel\twheel on a suitcase\nThere are several useful visual features to tell there is 'airplane wheel' and not similar things in a photo:\tlarge in size\tmade of metal or alloy\ttire with specialized treads\tand some kind of brake mechanism mounted on it.", 11], "identification numbers": ["No. 'Identification numbers' is too vague or abstract to have a tangible appearance that can be visually identified in a photo. \nHowever, a few things that can be associated with identification numbers and may be visually similar are:\tcodes, phone numbers, addresses, license plates\nThere is no specific visual feature that would distinguish 'identification numbers' from these similar things in a photo, as they can all be a combination of letters and/or numbers that serve as a unique identifier. The context of the image or accompanying text is likely necessary to determine whether the displayed text or numbers are in fact identification numbers or something else.", 11], "ballpoint pen": ["Yes. 'Ballpoint pen' has a tangible appearance and is a kind of writing tool.\nA few things that are visually similar to 'ballpoint pen' but are not 'ballpoint pen' are:\tfountain pen\tmarker\thighlighter\t\nThere are several useful visual features to tell there is 'ballpoint pen' and not similar things in a photo:\tchunky, cylindrical shape\twith a retractable or removable cap\tor with a click mechanism\tthin metallic clip on the cap or barrel\tarea for holding ink on one end and a small ball on the tip of the other end", 11], "farm animals": ["Yes. 'Farm animals' has a tangible appearance and refers to animals commonly raised on farms for agricultural purposes.\nA few things that are visually similar to 'farm animals' but are not 'farm animals' are:\twild animals\tpets\tzoo animals\t\nThere are several useful visual features to tell there is 'farm animals' and not similar things in a photo:\tcows, pigs, chickens, ducks, turkeys, goats, and sheep\thooved or feathered large animals\tbarns or outdoor farm environments\tfarmers or caretakers present", 11], "passage": ["No. 'Passage' is too vague or abstract to be distinguished in a photo.", 11], "granny smith apple": ["Yes. 'Granny smith apple' has a tangible appearance and is a distinct variety of apple.\nA few things that are visually similar to 'granny smith apple' but are not 'granny smith apple' are:\tred delicious apple\trome apple\tgala apple\tpear\nThere are several useful visual features to tell there is 'granny smith apple' and not similar things in a photo:\tgreen skin\tsmall round shape\tlight-colored flesh\ttart flavor", 11], "train passenger window": ["Yes. 'Train passenger window' has a tangible appearance and is a type of window in a train.\nA few things that are visually similar to 'train passenger window' but are not 'train passenger window' are:\tcar window\tbathroom window\thouse window\tbus window\nThere are several useful visual features to tell there is 'train passenger window' and not similar things in a photo:\trectangular shape\tclear glass or plastic\ttrain interior visible outside the window\tmovability by pulling down and up", 11], "life jackets": ["Yes. 'Life jackets' has a tangible appearance and is a kind of personal flotation device.\nA few things that are visually similar to 'life jackets' but are not 'life jackets' are:\tswimwear\tboating vests\tsafety harnesses\nThere are several useful visual features to tell there are 'life jackets' and not similar things in a photo:\tbulky and padded\tbright color or fluorescent\tfits over the torso and buckled in place\tmay have reflective strips or whistle attached to it\tMay have \"LIFE JACKET\" printed on it", 11], "dinosaurs": ["Yes. 'Dinosaurs' has a tangible appearance and refers to prehistoric reptiles.\nA few things that are visually similar to 'dinosaurs' but are not 'dinosaurs' are:\tkomodo dragons\tcrocodiles\talligators\tiguanas\tlizards\nThere are several useful visual features to tell there is 'dinosaurs' and not similar things in a photo:\tlarge size\tskeletal appearance\tscaly skin\tsharp teeth and claws\tunusual shapes\thead at the top of a long neck", 11], "orange hand": ["Yes. 'Orange hand' has a tangible appearance.\nA few things that are visually similar to 'orange hand' but are not 'orange hand' are:\torange glove\tpainted hand\tjacket sleeve\tfake tan\ta piece of fruit\nThere are several useful visual features to tell there is an 'orange hand' and not similar things in a photo:\tan actual human hand\twith an orange color tone\tcould be holding something or placed on a surface", 11], "construction equipment": ["Yes. 'Construction equipment' has a tangible appearance and is a kind of machinery or tools used in construction.\nA few things that are visually similar to 'construction equipment' but are not 'construction equipment' are: farm equipment, automotive tools, kitchen utensils.\nThere are several useful visual features to tell there is 'construction equipment' and not similar things in a photo: heavy machinery such as bulldozers, cranes, and excavators, bright yellow color, working on a construction site or a road, lifting or moving heavy materials like concrete and steel, protective cages around the operator's seat", 11], "barb wire fence": ["Yes. 'Barb wire fence' has a tangible appearance.\nA few things that are visually similar to 'barb wire fence' but are not 'barb wire fence' are:\thedge\tflower bed\tline of trees\nThere are several useful visual features to tell there is 'barb wire fence' and not similar things in a photo:\tsilver or grey wire\tthick and strong wires\tspaced evenly between posts\twith sharp, pointed barbs at regular intervals", 11], "frisbey": ["Yes. 'Frisbee' has a tangible appearance and is a flying disc.\nA few things that are visually similar to 'frisbee' but are not 'frisbee' are:\tplastic plates\tflying saucers\tlids\nThere are several useful visual features to tell there is 'frisbee' and not similar things in a photo:\tcircular shape\twith a concave center\tridged edge\tsolid plastic or rubber material", 11], "refrigerator handle": ["Yes. 'Refrigerator handle' has a tangible appearance and is a physical part of a refrigerator.\nA few things that are visually similar to 'refrigerator handle' but are not 'refrigerator handle' are:\tdoorknob\tcabinet handle\tshower handle\tfaucet handle\nThere are several useful visual features to tell there is 'refrigerator handle' and not similar things in a photo:\tattached to the door of a refrigerator\trectangular or cylindrical in shape\tmade of plastic or metal\thas a grip or indentation for holding\twithout any locks or keyholes.", 11], "nike sign": ["Yes. 'Nike sign' has a tangible appearance and is a type of logo.\nA few things that are visually similar to 'nike sign' but are not 'nike sign' are:\tAdidas logo\tUnder Armour logo\tJordan logo\tPuma logo\nThere are several useful visual features to tell there is 'nike sign' and not similar things in a photo:\ta curving checkmark shape\tthe word \"NIKE\" in all caps\tsimple and bold design\tcolor combination of black and white or red and white", 11], "tent tops": ["Yes. 'Tent tops' has a tangible appearance and refers to the upper parts of a tent.\nA few things that are visually similar to 'tent tops' but are not 'tent tops' are:\tumbrella canopy\tpavilion roof\tcanopy top\nThere are several useful visual features to tell there is 'tent tops' and not similar things in a photo:\t\ntriangular or rectangular shaped\t\nmade of durable and waterproof fabric\t\nattached to vertical poles\t\nmay have a window or a ventilation opening", 11], "army": ["Yes. 'Army' has a tangible appearance and it's a group of people organized for military purposes.\nA few things that are visually similar to 'army' but are not 'army' are:\tcrowd\tstaff team\tprotesters\nThere are several useful visual features to tell there is 'army' and not similar things in a photo:\tuniforms\tcamouflage\tprinted badges or ranks\tweapons\tmilitary equipment\tsoldiers following an organized structure", 11], "metal clock hands": ["Yes. 'Metal clock hands' has a tangible appearance and is a part of a clock mechanism.\nA few things that are visually similar to 'metal clock hands' but are not 'metal clock hands' are:\tneedles\tpins\tarrows\nThere are several useful visual features to tell there is 'metal clock hands' and not similar things in a photo:\tthin and elongated shape\tround or pointed tips\tattached to a clock face\tmoving in circles or semi-circles indicating the time", 11], "skys": ["No. 'Skys' is too vague or abstract to be distinguished in a photo. Perhaps you mean \"skyscrapers\"?", 11], "tiers": ["Yes. 'Tiers' has a tangible appearance and is a way of organizing things in levels. \nA few things that are visually similar to 'tiers' but are not 'tiers' are: \tsteps\tshelves\tlayers\tplatforms\nThere are several useful visual features to tell there are 'tiers' and not similar things in a photo:\tevenly spaced levels\tgradual increase in height\tlayers are visible\tfrom a single object or structure.", 11], "helmet persons": ["Yes. 'Helmet persons' has a tangible appearance and refers to people wearing helmets.\nA few things that are visually similar to 'helmet persons' but are not 'helmet persons' are: people wearing hats or caps, mannequins or dolls wearing helmets\nThere are several useful visual features to tell there are 'helmet persons' and not similar things in a photo: the shape and design of the helmets, the presence of straps or buckles used to secure the helmet, the helmets are usually made of hard materials such as plastic or metal, the helmets cover the head and may also cover the face or have a visor. Additionally, the context of the photo may also help to determine if the people are wearing helmets for safety reasons, such as in the case of construction workers or bikers.", 11], "airplane window": ["Yes. 'Airplane window' has a tangible appearance and is a type of window found on an airplane.\nA few things that are visually similar to 'airplane window' but are not 'airplane window' are:\thouse window\tcar window\ttrain window\tboat window\t\nThere are several useful visual features to tell there is 'airplane window' and not similar things in a photo:\trectangular shape\tplastic or composite frame\treinforcement ribs or dots on the surface\taerodynamic shape of the frame\tcabin interior visible through the window\tdiffused or cloudy view of the outside world", 11], "access": ["No. 'Access' is too vague or abstract to be distinguished in a photo.", 11], "purple stripe": ["Yes. 'Purple stripe' has a tangible appearance and refers to a specific color pattern.\nA few things that are visually similar to 'purple stripe' but are not 'purple stripe' are:\tpurple polka dots\tpurple plaid\tpurple checkered\nThere are several useful visual features to tell there is 'purple stripe' and not similar things in a photo:\tnarrow band of solid purple color\trepeating pattern of stripes\tdark purple shade", 11], "television monitor": ["Yes. 'Television monitor' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'television monitor' but are not 'television monitor' are:\tcomputer monitor\tprojectors\tlaptops\ttablets\nThere are several useful visual features to tell there is 'television monitor' and not similar things in a photo:\trectangular screen\tdisplaying moving images\ttuner or receiver for broadcasting programs or movies\tseparate audio system or speakers", 11], "silver tines": ["Yes. 'Silver tines' have a tangible appearance and refer to the prongs of a fork made of silver material.\nA few things that are visually similar to 'silver tines' but are not 'silver tines' are:\tplastic tines\twooden tines\tmetallic tines\nThere are several useful visual features to distinguish silver tines from similar things in a photo:\t\n- Highly reflective surface\n- Prongs are smooth and shiny\n- Tend to be thinner and more delicate-looking than other types of tines. \n- A stamp or hallmark indicating that they are made out of silver may also be visible on the fork or tines.", 11], "canvas top": ["Yes. 'Canvas top' has a tangible appearance and is a type of fabric covering.\nA few things that are visually similar to 'canvas top' but are not 'canvas top' are:\tumbrella\ttent\tawning\tbanner\nThere are several useful visual features to tell there is 'canvas top' and not similar things in a photo:\tmade of canvas or similar sturdy fabric\tdraped or stretched over a frame\tor used to cover a surface, vehicle or structure.", 11], "male elephant": ["Yes. 'Male elephant' has a tangible appearance and refers to a specific gender of the elephant species.\n\nA few things that are visually similar to 'male elephant' but are not 'male elephant' are:\n- Female elephants\n- Other species of elephants or large animals, such as rhinoceros or hippopotamus \n\nThere are several useful visual features to distinguish 'male elephant' from similar things in a photo: \n- Tusks (which female elephants lack or have much smaller ones)\n- Large size and weight (adult male elephants are usually larger than females)\n- Distinctive wrinkled skin and ears shaped like Africa map \n- Trunk (used for breathing, smelling, drinking, and picking up objects, among other things)", 11], "station platform": ["Yes. 'Station platform' has a tangible appearance and is a physical structure of a train station.\nA few things that are visually similar to 'station platform' but are not 'station platform' are:\tsidewalk\tpier\tfootbridge\tstage\nThere are several useful visual features to tell there is 'station platform' and not similar things in a photo:\tstraight and long platform\traised above the tracks\tyellow warning strips\tdigital or analog clock\tsignage with train schedules and destinations", 11], "leather ottoman": ["Yes. 'Leather ottoman' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'leather ottoman' but are not 'leather ottoman' are:\tchair\tsofa\ttable\tbench\nThere are several useful visual features to tell there is 'leather ottoman' and not similar things in a photo:\tsoft and cushioned rectangular or circular shape\tfootrest or small seat\tcovered in leather material", 11], "unbrella": ["Yes. 'Umbrella' has a tangible appearance and is a type of protective gear.\nA few things that are visually similar to 'umbrella' but are not 'umbrella' are:\tparasol\tcanopy\ttent\tawning\nThere are several useful visual features to tell there is 'umbrella' and not similar things in a photo:\tcan be opened and closed\thandheld or attached to a pole\tcovers and protects from rain, sun, or snow\tmade of waterproof material\thave spokes and ribs that support the cover", 11], "plastic drink cup": ["Yes. 'Plastic drink cup' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic drink cup' but are not 'plastic drink cup' are:\tglass cup\tmug\ttumbler\tbottle\nThere are several useful visual features to tell there is 'plastic drink cup' and not similar things in a photo:\ttransparent or translucent\tplastic material\tspecific shape for holding liquids\tnarrow base widening toward the top\twith or without a lid or a straw", 11], "gold knobs": ["Yes. 'Gold knobs' has a tangible appearance and is a type of hardware used for doors and furniture.\nA few things that are visually similar to 'gold knobs' but are not 'gold knobs' are:\tsilver knobs\tbrass knobs\tglass knobs\tstone knobs\nThere are several useful visual features to tell there is 'gold knobs' and not similar things in a photo:\tgolden color\tsmooth surface\twith screws or bolts attached to a door or furniture piece\tspherical or cylindrical shape", 11], "bike pedal": ["Yes. 'Bike pedal' has a tangible appearance and is a component of a bicycle.\nA few things that are visually similar to 'bike pedal' but are not 'bike pedal' are:\tfoot pegs\tforged pedals\nThere are several useful visual features to tell there is 'bike pedal' and not similar things in a photo:\ttwo-part structure with a platform for the foot and a spindle for attaching to the bike\trough or textured surface for grip\twhen attached to a bike, a chain or gear mechanism can be visible", 11], "sits": ["No. 'sits' is too vague or abstract to have a tangible appearance and cannot be seen directly in a photo. It is an action or behavior that can be performed by something visible.\nTherefore, there are no things visually similar to 'sits' that are not also 'sits'.\nUseful visual features in a photo that indicate something is 'sitting' may include: having a seated posture, having the bottom of the body on a support or a surface, and having a reclined position with body weight resting on something.", 11], "shinguard": ["Yes. 'Shinguard' has a tangible appearance and is a protective equipment worn during sports.\nA few things that are visually similar to 'shinguard' but are not 'shinguard' are:\tknee pads\tleggings\tarm sleeves\tshin splints\nThere are several useful visual features to tell there is 'shinguard' and not similar things in a photo:\thard or tough surface around the shin area\tspecific shape and size to fit the leg\tsnugly fitting\tthe shin looks more protected than other parts of the leg", 11], "summer sky": ["Yes. 'Summer sky' has a tangible appearance and refers to the appearance of the sky during the summer season.\nA few things that are visually similar to 'summer sky' but are not 'summer sky' are:\tautumn sky\twinter sky\tspring sky\nThere are several useful visual features to tell there is 'summer sky' and not similar things in a photo:\tblue color with few clouds or none\tbright and sunny appearance\twarm and calm feeling", 11], "pizza plate": ["Yes. 'Pizza plate' has a tangible appearance and is a specific kind of dishware.\nA few things that are visually similar to 'pizza plate' but are not 'pizza plate' are:\tdinner plate\tserving platter\tsalver\ttray\nThere are several useful visual features to tell there is 'pizza plate' and not similar things in a photo:\twide and flat shape\tcircular form\tlarger than an ordinary plate, but not as big as a platter or a tray\tmay have a rim to hold toppings or prevent spilling", 11], "wooden table surface": ["Yes. 'Wooden table surface' has a tangible appearance.\nA few things that are visually similar to 'wooden table surface' but are not 'wooden table surface' are:\twooden floor\ttiled floor\twooden wall\twooden door panel\nThere are several useful visual features to tell there is 'wooden table surface' and not similar things in a photo:\tflat and horizontal\tsquare or rectangular in shape\tgrain texture on the surface\tsmooth and polished appearance\tchairs or objects placed on it", 11], "fire plug": ["Yes. 'Fire plug' has a tangible appearance and is a kind of emergency water supply.\nA few things that are visually similar to 'fire plug' but are not 'fire plug' are:\thydrant\tdrain\tmanhole cover\nThere are several useful visual features to tell there is 'fire plug' and not similar things in a photo:\tgenerally painted red with \"fire department\" written on it\thave one or more valves\torifice for the firefighters to insert hoses\tintended to provide emergency water supply\twhen opened, water gushes out", 11], "brass door handle": ["Yes. 'Brass door handle' has a tangible appearance and is a type of door handle made of brass.\nA few things that are visually similar to 'brass door handle' but are not 'brass door handle' are:\twooden door handle\tchrome door handle\tplastic door handle\nThere are several useful visual features to tell there is 'brass door handle' and not similar things in a photo:\tbrass-colored or metallic\thorizontal or vertical shape\tattached to a door\tlock or keyhole beside it\tpull or twist mechanism\tfor interior or exterior doors.", 11], "globe lights": ["Yes. 'Globe lights' has a tangible appearance and is a specific type of light.\nA few things that are visually similar to 'globe lights' but are not 'globe lights' are:\tbulbs\tchandeliers\tlanterns\nThere are several useful visual features to tell there are 'globe lights' and not similar things in a photo:\tspherical shape\ttranslucent or transparent glass or plastic cover\tbright and evenly distributed light source\thanging from a string or wire", 11], "brunette man": ["Yes. 'Brunette man' has a tangible appearance and refers to a particular human appearance.\nA few things that are visually similar to 'brunette man' but are not 'brunette man' are:\tblonde man\tredhead man\tbald man\tbrunette woman\nThere are several useful visual features to tell there is 'brunette man' and not similar things in a photo:\tdark brown hair on the head\tblunt cut or textured style on the hair\tfacial hair or no facial hair\tmasculine facial features in appearance", 11], "cute face": ["No. 'Cute face' is too vague or abstract to be distinguished in a photo. It is a subjective interpretation of facial features that varies from person to person. \nTherefore, there are no things that are visually similar to 'cute face' but are not 'cute face'.", 11], "plastic ketchup bottle": ["Yes. 'Plastic ketchup bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic ketchup bottle' but are not 'plastic ketchup bottle' are:\tmustard bottle\tsalad dressing bottle\tsqueeze bottle\twater bottle\nThere are several useful visual features to tell there is 'plastic ketchup bottle' and not similar things in a photo:\tdark red plastic bottle\tflip-top cap\t\"ketchup\" or tomato sauce\" label\tspecific shape and size with a narrowed neck and wider base.", 11], "banner advertisement": ["Yes. 'Banner advertisement' has a tangible appearance and is a type of online advertisement.\nA few things that are visually similar to 'banner advertisement' but are not 'banner advertisement' are:\tpop-up ads\tsponsored posts\tsocial media ads\temail campaigns\nThere are several useful visual features to tell there is 'banner advertisement' and not similar things in a photo:\trectangular shape\tbold text or graphics\tpositioned at the top or bottom of a webpage or app\tframe around the ad may indicate a hyperlink to a landing page or website.", 11], "silver suitcase": ["Yes. 'Silver suitcase' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'silver suitcase' but are not 'silver suitcase' are:\tbackpack\tbriefcase\tbox\nThere are several useful visual features to tell there is 'silver suitcase' and not similar things in a photo:\trectangular-shaped\tmetallic silver color\thave a handle\tfor travel or storage purposes\thas a zipper or lock to open and close.", 11], "brick bridge": ["Yes. 'Brick bridge' has a tangible appearance and is a type of bridge made of bricks.\nA few things that are visually similar to 'brick bridge' but are not 'brick bridge' are:\tstone bridge\twooden bridge\tsteel bridge\nThere are several useful visual features to tell there is 'brick bridge' and not similar things in a photo:\tthe bridge is made of bricks\tarched shape\tbricks have a reddish hue", 11], "car bumper": ["Yes. 'Car bumper' has a tangible appearance and is a part of a car.\nA few things that are visually similar to 'car bumper' but are not 'car bumper' are:\ttruck bed\tpanel\tfender\t\nThere are several useful visual features to tell there is 'car bumper' and not similar things in a photo:\thorizontal bar-shaped structure\tlocated at the front and/or back of a car\tmade of hard plastic, rubber, or metal\tdesigned to absorb impacts or shocks\tfrom a car's wheels to protect the car's body\tif damaged, may show signs of scratches or dents", 11], "spiky hair": ["Yes. 'Spiky hair' has a tangible appearance and is a hairstyle.\nA few things that are visually similar to 'spiky hair' but are not 'spiky hair' are:\tcactus\tspikes or quills\tice spikes\nThere are several useful visual features to tell there is 'spiky hair' and not similar things in a photo:\thair on a person's head\thair styled upwards and sticking out at different angles\tshort hair pointed upwards or forwards\tsome strands longer than others.", 11], "orange sun": ["Yes. 'Orange sun' has a tangible appearance and refers to the sun appearing orange during sunset or sunrise.\nA few things that are visually similar to 'orange sun' but are not 'orange sun' are:\ta streetlight through fog\toranges\tfire\nThere are several useful visual features to tell there is 'orange sun' and not similar things in a photo:\tcircular in shape\tyellowish and reddish-orange gradient\tcolor gradient near the horizon\thave rays or lens flares\trefer to the actual sun and not a source of light", 11], "neckline": ["Yes. 'Neckline' has a tangible appearance and refers to the shape or style of the opening that goes around the neck on a piece of clothing.\nA few things that are visually similar to 'neckline' but are not 'neckline' are:\tcollar\tstrap\tchandelier necklace\tbib\nThere are several useful visual features to tell there is 'neckline' and not similar things in a photo:\tpositioned around the neck area\tvariations in shape: V-neck, scoop neck, boat neck, etc.\tdistinguishing details such as lace trim, buttons, or ruching.", 11], "gold top": ["No. 'Gold top' is too vague or abstract to be distinguished in a photo. It could refer to a variety of objects or substances.\nIf we assume 'gold top' refers to a specific object, visual features would depend on what that object is. Without additional context, it is impossible to determine what those features might be.", 11], "tan stone wall": ["Yes. 'Tan stone wall' has a tangible appearance and is a type of wall made of stones.\nA few things that are visually similar to 'tan stone wall' but are not 'tan stone wall' are:\ttan brick wall\ttan stucco wall\ttan wooden fence\nThere are several useful visual features to tell there is 'tan stone wall' and not similar things in a photo:\tmade of stones\ttan or brown in color\trough or uneven surface textures", 11], "mets": ["No. 'Mets' is too vague or abstract to be visually distinguished in a photo. \n\nNote for context: 'Mets' is a shortened term for 'New York Mets', which is a baseball team. While the uniform of the New York Mets has visual features, the term 'Mets' on its own does not have any tangible appearance.", 11], "christmas light": ["Yes. 'Christmas light' has a tangible appearance and is a kind of decoration.\nA few things that are visually similar to 'christmas light' but are not 'christmas light' are:\tlight bulb\tfairy lights\tlanterns\nThere are several useful visual features to tell there is 'christmas light' and not similar things in a photo:\tBulbs in various colors\tattached to a wire or string\tused to decorate homes and Christmas trees", 11], "dollar sign": ["Yes. 'Dollar sign' has a tangible appearance and is a symbol.\nA few things that are visually similar to 'dollar sign' but are not 'dollar sign' are:\teuro sign\tyen sign\tpound sign\nThere are several useful visual features to tell there is 'dollar sign' and not similar things in a photo:\ta capital 'S' with a vertical line through it\tsign is green on a white background sometimes with the word 'dollars' underneath it. The sign may also have two vertical lines through it.", 11], "metal panel": ["Yes. 'Metal panel' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'metal panel' but are not 'metal panel' are:\taluminum foil\tpaintings\thardwood floors\tmetallic wallpaper\nThere are several useful visual features to tell there is 'metal panel' and not similar things in a photo:\trectangular or square shape\tmetallic surface\tsmooth texture\tno visible grains or patterns\tused as a part of a building or structure", 11], "sewing machine": ["Yes. 'Sewing machine' has a tangible appearance and is a machine used for stitching fabrics together.\nA few things that are visually similar to 'sewing machine' but are not 'sewing machine' are:\ttypewriter\tmixer\tvacuum cleaner\tprinter\nThere are several useful visual features to tell there is 'sewing machine' and not similar things in a photo:\tMetal frame with a needle and thread\tSpools of thread\tBobbin\tLever for adjusting the tension of the thread\tPedal for powering the machine.", 11], "wood picket fence": ["Yes. 'Wood picket fence' has a tangible appearance as a type of fence made of wood pickets.\nA few things that are visually similar to 'wood picket fence' but are not 'wood picket fence' are:\tchain-link fence\tstone wall\tgarden trellis\tiron fence\nThere are several useful visual features to tell there is 'wood picket fence' and not similar things in a photo:\thorizontal wooden boards or pickets pointed at the top\tpainted white or natural wood color\tgaps between each picket or board.", 11], "member": ["No. 'Member' is too vague or abstract to be distinguished in a photo.", 11], "wood structure": ["Yes. 'Wood structure' has a tangible appearance and refers to a construction made of wood.\nA few things that are visually similar to 'wood structure' but are not 'wood structure' are:\tcardboard boxes\tbamboo scaffolding\tstone walls\tbrick houses\nThere are several useful visual features to tell there is 'wood structure' and not similar things in a photo:\tgrooves or patterns in the wood\tgrain texture in the wood\tvisible knots or holes in the wood\tstraight wooden beams and planks\tsmooth or rough surface of the wood", 11], "protection": ["No. 'protection' is too vague or abstract to be distinguished in a photo.", 11], "wood stool": ["Yes. 'Wood stool' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood stool' but are not 'wood stool' are:\tchair\tarmchair\tbench\tcrate\nThere are several useful visual features to tell there is 'wood stool' and not similar things in a photo:\tsmaller in size than a chair and a bench\tdesigned to sit on\tusually has only three or four legs or no backrest\tmade of wood (or has a wooden appearance)", 11], "plastic pitcher": ["Yes. 'Plastic pitcher' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'plastic pitcher' but are not 'plastic pitcher' are:\tjug\tvase\tbottle\tthermos\nThere are several useful visual features to tell there is 'plastic pitcher' and not similar things in a photo:\thandle\tslightly curved spout\twith or without a lid\thas a capacity measurement marks made of plastic material", 11], "bald head": ["Yes. 'Bald head' has a tangible appearance and is a physical characteristic.\nA few things that are visually similar to 'bald head' but are not 'bald head' are:\thair loss\twigs\tshaved head\nThere are several useful visual features to tell there is 'bald head' and not similar things in a photo:\thead with no visible hair\ttotally or partially bald head\tshiny skin on the head\tdifferent from the natural hair growth pattern", 11], "rice dish": ["Yes. 'Rice dish' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'rice dish' but are not 'rice dish' are:\tpaella\tsalad\tpilaf\tratatouille\nThere are several useful visual features to tell there is 'rice dish' and not similar things in a photo:\tdomination of rice shape and texture\tvariety of colorful ingredients\tserving in a bowl or a platter", 11], "floor balcony": ["Yes. 'Floor balcony' has a tangible appearance and is a type of architectural feature.\nA few things that are visually similar to 'floor balcony' but are not 'floor balcony' are:\tterrace\tveranda\tdeck\tpatio\nThere are several useful visual features to tell there is 'floor balcony' and not similar things in a photo:\tmetal or concrete railing or barrier\twide enough for people to stand on\tit's on an upper floor or level of a building\tview of the surrounding area or the street below.", 11], "pink band": ["Yes. 'Pink band' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'pink band' but are not 'pink band' are:\tbracelet\thairband\twatch\tribbon\nThere are several useful visual features to tell there is 'pink band' and not similar things in a photo:\ta band made of pink-colored material\tworn on the wrist or the head\tsmooth texture with no visible patterns or decorations", 11], "metal curtain rod": ["Yes. 'Metal curtain rod' has a tangible appearance and is a type of home product.\nA few things that are visually similar to 'metal curtain rod' but are not 'metal curtain rod' are:\tmetal pipe\t\t\tmetal railing\t\t\tmetal beam\t\t\t\nThere are several useful visual features to tell there is 'metal curtain rod' and not similar things in a photo:\thollow cylinder shape\twith circular end caps\tfor holding up curtains or drapes\thanging horizontally on a wall\tor above a window", 11], "van door": ["Yes. 'van door' has a tangible appearance and is a type of vehicle door.\nA few things that are visually similar to 'van door' but are not 'van door' are:\tcar door\ttruck door\tbus door\tgarage door\nThere are several useful visual features to tell there is 'van door' and not similar things in a photo:\tsliding mechanism or hinges\tspecific size and shape for a van\ttypical placement on the side or back of a van\thandle or latch for opening or closing\tthe van logo or brand visible on the door.", 11], "flesh": ["Yes. 'Flesh' has a tangible appearance and refers to the soft tissue that covers bones in animal bodies, including humans.\nA few things that are visually similar to 'flesh' but are not 'flesh' are:\tfruit with similar colors and texture, such as a cut watermelon\traw meat\nThere are several useful visual features to tell there is 'flesh' and not similar things in a photo:\tcolor that ranges from light pink to dark brown depending on the animal\tflexible and jiggly texture\tsmooth surface\twith veins and creases, depending on the animal\tbody parts or wounds when portrayed in a medical or forensic context", 11], "flannel": ["Yes. 'Flannel' has a tangible appearance and is a type of fabric.\nA few things that are visually similar to 'flannel' but are not 'flannel' are:\tcotton\twool\tsilk\tlinen\nThere are several useful visual features to tell there is 'flannel' and not similar things in a photo:\tsoft and fuzzy texture\tcheckered or plaid pattern\tthicker than most fabrics, often used for warmth", 11], "round chocolate cake": ["Yes. 'Round chocolate cake' has a tangible appearance and is a specific type of dessert.\nA few things that are visually similar to 'round chocolate cake' but are not 'round chocolate cake' are:\tpie\ttart\tcheesecake\nThere are several useful visual features to tell there is 'round chocolate cake' and not similar things in a photo:\tcircular shape\tbrown color\tchocolate frosting or ganache on top\tsmooth or slightly bumpy texture\tlayered appearance when sliced", 11], "roofed building": ["Yes. 'Roofed building' has a tangible appearance and refers to a structure with a covering above it.\nA few things that are visually similar to 'roofed building' but are not 'roofed building' are:\tshed\topen pavilion\ttent\nThere are several useful visual features to tell there is 'roofed building' and not similar things in a photo:\twalls and foundation\tvisible roof covering\tevidence of doors or windows\tspecific architectural features (such as chimneys or spires)", 11], "dish soap": ["Yes. 'Dish soap' has a tangible appearance and is a type of liquid soap used for cleaning dishes.\nA few things that are visually similar to 'dish soap' but are not 'dish soap' are:\thand soap\tshampoo\tbath gel\t\nThere are several useful visual features to tell there is 'dish soap' and not similar things in a photo:\ttypically contained in a bottle next to a sink or dishwasher, with a nozzle or pump for dispensing\tclear or translucent liquid, often with a blue or green color\tlabel or branding indicating it is specifically for washing dishes", 11], "foot hill": ["Yes. 'Foot hill' has a tangible appearance and is a type of landform.\nA few things that are visually similar to 'foot hill' but are not 'foot hill' are:\tmountain\trange\tof hills\nThere are several useful visual features to tell there is 'foot hill' and not similar things in a photo:\tlower than a mountain, but higher than the surrounding land\tsloping or rolling terrain with an elevation gain of a few hundred feet to less than 1,000 feet\tgrassy or covered by low vegetation", 11], "shallow": ["No. 'Shallow' is too abstract to be conveyed in a photo.\n\nNote: While shallow is primarily a descriptive term rather than a visual one, it is possible to convey shallowness through visual cues such as the depth of a body of water or the gradient of a slope. However, these visual cues only convey the concept of shallowness indirectly and require context for the viewer to understand.", 11], "oil lamp": ["Yes. 'Oil lamp' has a tangible appearance and is a type of lighting device.\nA few things that are visually similar to 'oil lamp' but are not 'oil lamp' are:\tcandlestick\tkerosene lantern\tbattery-operated lamp\tfireplace\nThere are several useful visual features to tell there is 'oil lamp' and not similar things in a photo:\tglass container with oil in it\twick sticking out of the oil\tglass chimney\tdelicately-shaped metal frame\twith a handle for carrying\tthe base for the oil container with a wide opening for filling and lighting.", 11], "shoe string": ["Yes. 'Shoe string' has a tangible appearance and is a type of thin cord used to tie shoes.\nA few things that are visually similar to 'shoe string' but are not 'shoe string' are:\tthread\tfishing line\thair\nThere are several useful visual features to tell there is 'shoe string' and not similar things in a photo:\tusually made of cotton or nylon\tround or oval shaped\tcomes in various colors\ttypically used to tie shoes or clothing", 11], "silver writing": ["Yes. 'Silver writing' has a tangible appearance and is a type of writing or lettering.\nA few things that are visually similar to 'silver writing' but are not 'silver writing' are:\tgrey writing\twhite writing\tlight reflection\t\nThere are several useful visual features to tell there is 'silver writing' and not similar things in a photo:\tshiny silver color\tbackground contrast\tflat surface, which can be a paper, a sign, a trophy, etc.", 11], "mirror side car": ["Yes. 'Mirror side car' has a tangible appearance and is a type of car mirror.\nA few things that are visually similar to 'mirror side car' but are not 'mirror side car' are:\tregular mirrors\tscooter mirrors\tbicycle mirrors\t\nThere are several useful visual features to tell there is 'mirror side car' and not similar things in a photo:\tlarge size designed to show the blind spot of the car\tattached to the doors, fenders or quarter panels\tof a car or other motor vehicle, and extends outward from the body of the vehicle. It has a reflective surface that reflects light from headlights, taillights, or streetlights. It sometimes contains blinkers or turn signals, and may be heated to prevent frost or fog.", 11], "wedding ring": ["Yes. 'Wedding ring' has a tangible appearance and is a type of ring.\nA few things that are visually similar to 'wedding ring' but are not 'wedding ring' are:\tfashion ring\tengagement ring\tmen's ring\nThere are several useful visual features to tell there is 'wedding ring' and not similar things in a photo:\tmetallic\tshiny\tsimple and elegant design worn on the ring finger of the left hand (for women) or right hand (for men)", 11], "limit sign": ["Yes. 'Limit sign' has a tangible appearance and is a type of road sign.\nA few things that are visually similar to 'limit sign' but are not 'limit sign' are:\ttraffic sign\twarning sign\tdirectional sign\tadvertising sign\tsymbol sign\nThere are several useful visual features to tell there is 'limit sign' and not similar things in a photo:\twhite circle with a red border in the center\tinside the circle is the limit speed number\tin some signs, additional information is labeled, such as \"School Zone\" or \"Construction Zone\"", 11], "pink bicycle": ["Yes. 'Pink bicycle' has a tangible appearance and is a type of bike.\nA few things that are visually similar to 'pink bicycle' but are not 'pink bicycle' are:\tred bicycle\tblue bicycle\tyellow bicycle\tbrown bicycle\nThere are several useful visual features to tell there is 'pink bicycle' and not similar things in a photo:\tpink color\ton two wheels\thandlebars\tpedals\tsaddle\tand a frame.", 11], "shadow bike": ["Yes. 'Shadow bike' has a tangible appearance and can be described as a bike's shadow.\nThere are no things that are visually similar to 'shadow bike' but are not 'shadow bike'.\nThe useful visual feature for distinguishing 'shadow bike' from other things in a photo is the shape of the shadow itself - it will be a distorted version of the bike and will only be present if there is a source of light illuminating the bike at a particular angle.", 11], "color building": ["No. 'Color building' is too vague or abstract to be distinguished in a photo.", 11], "motorcycle mirror": ["Yes. 'Motorcycle mirror' has a tangible appearance and is a part of a motorcycle.\nA few things that are visually similar to 'motorcycle mirror' but are not 'motorcycle mirror' are:\tregular mirror\tbicycle mirror\tcar mirror\nThere are several useful visual features to tell there is 'motorcycle mirror' and not similar things in a photo:\tattached to a motorcycle\thandlebar mount\tsmall in size\tcircular or oval shape_adjustable angle to improve visibility", 11], "motorcycle gas tank": ["Yes. 'Motorcycle gas tank' has a tangible appearance and is a specific part of a motorcycle.\nA few things that are visually similar to 'motorcycle gas tank' but are not 'motorcycle gas tank' are:\tpropane tank\tpaint can\tfire extinguisher\twater tank\nThere are several useful visual features to tell there is 'motorcycle gas tank' and not similar things in a photo:\tmetallic or glossy surface\tcylindrical or rectangular shape\tforward-facing opening\tin various sizes and designs\tfuel cap on the top", 11], "road lines": ["Yes. 'Road lines' has a tangible appearance and refers to the painted lines on a road.\nA few things that are visually similar to 'road lines' but are not 'road lines' are:\tcrosswalks\tbike lanes\tdirection arrows\tparking lines\nThere are several useful visual features to tell there are 'road lines' and not similar things in a photo:\twhite or yellow\tpainted on the pavement\tparallel lines or dashed lines\tmarking the lanes or edges of the road", 11], "team members": ["No. 'Team members' is too vague or abstract to be distinguished in a photo.", 11], "army knife": ["Yes. 'Army knife' has a tangible appearance and is a type of multi-purpose tool.\nA few things that are visually similar to 'army knife' but are not 'army knife' are:\tpocket knife\thunting knife\tswitchblade\tkitchen knife\nThere are several useful visual features to tell there is 'army knife' and not similar things in a photo:\tmultiple blades and/or tools\tfolding mechanism\tblack or green handle with texture and grips\tspecific brand name or logo, such as \"Victorinox\" or \"Swiss Army\"", 11], "silver soap dispenser": ["Yes. 'Silver soap dispenser' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'silver soap dispenser' but are not 'silver soap dispenser' are:\tshampoo bottle\thand sanitizer bottle\tlotion dispenser\nThere are several useful visual features to tell there is 'silver soap dispenser' and not similar things in a photo:\tmetallic or silver color\tpush-down or pump mechanism\tfor dispensing liquid soap or lotion", 11], "silver blades": ["Yes. 'Silver blades' has a tangible appearance and refers to sharp objects with a silver color.\nA few things that are visually similar to 'silver blades' but are not 'silver blades' are:\tkitchen knives\tscissors\trazors\tswords\nThere are several useful visual features to tell there are 'silver blades' and not similar things in a photo:\tthin and sharp blades\tmade of shiny silver material\tcould be a part of a machine or tool\tmay have a serrated edge or a pointed tip", 11], "police bike": ["Yes. 'Police bike' has a tangible appearance and is a type of motorcycle.\nA few things that are visually similar to 'police bike' but are not 'police bike' are:\tmotorcycle\tbicycle\tscooter\tmoped\nThere are several useful visual features to tell there is 'police bike' and not similar things in a photo:\tmarked with \"Police\"\tor \"Patrol\"\tlights and sirens\tcan be black and white, blue and white or solid blue colors\tmay have storage boxes on the rear for equipment and gear", 11], "gym floor": ["Yes. 'Gym floor' has a tangible appearance and is a type of flooring typically found in a gymnasium.\nA few things that are visually similar to 'gym floor' but are not 'gym floor' are:\ttile floor\twooden floor\tcarpet\tgrass\nThere are several useful visual features to tell there is 'gym floor' and not similar things in a photo:\tlarge rectangular or square panels\tbrightly colored\tlines and markings for sports or games\tcushioned or padded surface", 11], "bystanders": ["No. 'Bystanders' is too vague or abstract to be distinguished in a photo.", 11], "hoove": ["Yes. 'Hoove' has a tangible appearance and refers to the hard part of the foot of some animals, especially horses, deer, and sheep.\nA few things that are visually similar to 'hoove' but are not 'hoove' are: shoes, boots, paw pads\nThere are several useful visual features to tell there is 'hoove' and not similar things in a photo: hard texture, usually brown or black, two-toed or four-toed depending on the animal, generally found on ungulates", 11], "bronwy": ["I am sorry, but 'bronwy' is not a recognized English word, so it is impossible to determine if it is a visually concrete concept or not.", 11], "simple": ["No. 'Simple' is too vague or abstract to be distinguished in a photo. It is a subjective concept that varies from person to person, making it difficult to have visual features or criteria to distinguish it from similar things.", 11], "hummus": ["Yes. 'Hummus' has a tangible appearance and is a type of dip.\nA few things that are visually similar to 'hummus' but are not 'hummus' are:\tbaba ganoush\tguacamole\ttzatziki\nThere are several useful visual features to tell there is 'hummus' and not similar things in a photo:\tbeige or light brown color\tchickpeas texture\tsmooth consistency\tserved in a bowl or platter\twith pita bread or vegetables for dipping", 11], "blue cable": ["Yes. 'Blue cable' has a tangible appearance and is a type of wire.\nA few things that are visually similar to 'blue cable' but are not 'blue cable' are:\tropes\tchains\thoses\tpipes\nThere are several useful visual features to tell there is 'blue cable' and not similar things in a photo:\nblue color\nplugged in or connected to equipment or a power outlet\ncomposed of multiple thin strands or fibers.", 11], "wood burning stove": ["Yes. 'Wood burning stove' has a tangible appearance and is a type of heating device.\nA few things that are visually similar to 'wood burning stove' but are not 'wood burning stove' are:\tfireplace\tcampfire\toutdoor oven\nThere are several useful visual features to tell there is 'wood burning stove' and not similar things in a photo:\tbox-shaped\twith a door for loading wood\tmetallic exterior\tchimney or flue attached to the top", 11], "orange base": ["Yes. 'Orange base' has a tangible appearance and could refer to different objects or materials with a predominance of the color orange.\nA few things that are visually similar to 'orange base' but are not 'orange base' are:\torange fabric\torange paint\torange plastic\nThere are several useful visual features to tell there is 'orange base' and not similar things in a photo:\tsolid and opaque material\tthat serves as a foundation, support or starting point in a structure\tor design\tthat has a predominant orange color or hue.", 11], "bathroom sink basin": ["Yes. 'Bathroom sink basin' is a visually concrete concept and is a fixture typically found in bathrooms for washing hands, face or teeth.\nA few things that are visually similar to 'bathroom sink basin' but are not 'bathroom sink basin' are:\twashbasin\tkitchen sink\tbathtub\nThere are useful visual features to distinguish 'bathroom sink basin' from the listed similar things in a photo:\t\n- 'bathroom sink basin' is usually smaller in size than a washbasin or a kitchen sink.\n- It is mounted on the wall or placed on a vanity.\n- It usually has one or more faucets attached to the basin for water supply.\n- It may have an overflow hole and drain for waste water.", 11], "travelers": ["Yes. 'Travelers' has a tangible appearance and refers to people who are currently traveling or on a trip.\nA few things that are visually similar to 'travelers' but are not 'travelers' are:\tcommuters\tpedestrians\thikers\tathletes\ttourists\nThere are several useful visual features to tell there are 'travelers' and not similar things in a photo:\tcarrying backpacks, suitcases, or travel bags\twearing comfortable or practical clothing\tcarrying maps or travel guides\ttaking photographs or filming their surroundings in a sightseeing manner", 11], "houseboat": ["Yes. 'Houseboat' has a tangible appearance and is a type of boat designed for living.\nA few things that are visually similar to 'houseboat' but are not 'houseboat' are:\tyacht\tcruise ship\tferry\tspeedboat\nThere are several useful visual features to tell there is 'houseboat' and not similar things in a photo:\thalf-boat, half-house design\tlarge windows or portholes\tfor living purposes (e.g. multiple rooms, kitchen, bathroom)\tusually stationary, tied up at a dock or wharf", 11], "pink donut": ["Yes. 'Pink donut' has a tangible appearance and is a type of dessert.\nA few things that are visually similar to 'pink donut' but are not 'pink donut' are:\tbagel\tbiscuit\tcake\tpastry\nThere are several useful visual features to tell there is 'pink donut' and not similar things in a photo:\tcircular\tring shape\twith a hole in the center\tpink frosting or decorations\ton a paper or a plate.", 11], "security cameras": ["Yes. 'Security cameras' has a tangible appearance and is a type of camera used for surveillance.\nA few things that are visually similar to 'security cameras' but are not 'security cameras' are:\tdome cameras\twebcam\tsmoke detectors\tdecorative cameras\nThere are several useful visual features to tell there is 'security cameras' and not similar things in a photo:\tconnected to a building or a pole\tlenses and sensors\tvisible wires or cables\tinfrared or night vision capabilities\tpotential signs of a security company logo or labeling.", 11], "post sign": ["Yes. 'Post sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'post sign' but are not 'post sign' are:\tbillboard\ttraffic sign\tshop sign\tmenu\tboard\nThere are several useful visual features to tell there is 'post sign' and not similar things in a photo:\trectangular or square shape\tbig enough to read from a distance\thanging from a post\tor mounted on a surface\tclearly visible text or graphics", 11], "grey metal chain link fence": ["Yes. 'grey metal chain link fence' has a tangible appearance.\nA few things that are visually similar to 'grey metal chain link fence' but are not 'grey metal chain link fence' are: wire mesh, metal grating, jail bars, wire fence, wooden fence with grid pattern.\nThere are several useful visual features to tell there is 'grey metal chain link fence' and not similar things in a photo:\tmetallic material\twith interconnected links or grids\tgrey or silver color\tchain links that are diamond or rhombus-shaped", 11], "llama": ["Yes. 'Llama' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'llama' but are not 'llama' are:\talpaca\tcamel\tvicuna\tguanaco\nThere are several useful visual features to tell there is 'llama' and not similar things in a photo:\tfour-legged mammal\twith a long neck and head\tfurry bodies with varying colors and patterns\ttall pointed ears\tlong and curved necks\twith or without wool between ears and forehead.", 11], "artist": ["No. 'Artist' is too vague or abstract to be visually distinguished in a photo. \nHowever, a few things that are visually similar, in terms of appearance or attire, to someone who may be an artist are:\thipster\t\t\tdesigner\t\t\tmusician", 11], "boat reflection": ["Yes, 'boat reflection' is a visually concrete concept.\nA few things that are visually similar to 'boat reflection' but are not 'boat reflection' are:\treflection of other objects on the water\tsun rays reflected on the water\tripples on the water surface\tshadow of a boat on the water\nThere are several useful visual features to tell there is 'boat reflection' and not similar things in a photo:\ta boat floating on the water\tthe reflection of the boat on the water\tthe shape and color of the boat reflected in the water\tthe angle and position of the reflection in relation to the boat", 11], "display table": ["Yes. 'Display table' has a tangible appearance and is a kind of table used to showcase products or items.\nA few things that are visually similar to 'display table' but are not 'display table' are:\tcoffee table\tdining table\tdesk\tpedestal\ttable at a fair or exhibition\nThere are several useful visual features to tell there is 'display table' and not similar things in a photo:\tlocated in a store or a boutique\thave rows or specific arrangements for products or items\tto showcase products or items\tto draw attention to a specific set of goods or services on offer.", 11], "sunroof": ["Yes. 'Sunroof' has a tangible appearance and is a type of roof feature.\nA few things that are visually similar to 'sunroof' but are not 'sunroof' are:\tskylight\tventilation pipe\tdome\trooftop window\nThere are several useful visual features to tell there is 'sunroof' and not similar things in a photo:\tlocated on the roof of a car or a building\tequipped with a sliding or tilting panel to let in light and air\tmade of glass, plastic, or metal\tsquare or rectangular in shape.", 11], "shopping center": ["Yes. 'Shopping center' has a tangible appearance and is a type of building used for retail and commercial purposes.\nA few things that are visually similar to 'shopping center' but are not 'shopping center' are:\ta college campus\ta hospital complex\tan office park\ta residential neighborhood\nThere are several useful visual features to tell there is 'shopping center' and not similar things in a photo:\tlarge building or complex of buildings\twith storefronts or other retail spaces\tparking lot or parking garage\tsignage or banners advertising stores, restaurants, or other businesses", 11], "metal containers": ["Yes. 'Metal containers' have a tangible appearance and are a type of container made of metal.\nA few things that are visually similar to 'metal containers' but are not 'metal containers' are:\tplastic containers\tglass bottles\tcardboard boxes\nThere are several useful visual features to tell there is 'metal containers' and not similar things in a photo:\tmade of metal\thave a lid or cover\tusually cylindrical or rectangular in shape\ttypically used for storage or shipping goods\twithout labels or designs on it.", 11], "silver body": ["Yes. 'Silver body' has a tangible appearance and refers to an object that has a silver-colored surface or body.\nA few things that are visually similar to 'silver body' but are not 'silver body' are:\tchrome\tplatinum\taluminum\tstainless steel\nThere are several useful visual features to tell there is 'silver body' and not similar things in a photo:\treflections of light and color from the surface\tshiny or polished appearance\tmetallic texture and feel", 11], "metal street": ["No. 'Metal street' is too vague or abstract to be distinguished in a photo.", 11], "rubber shoe": ["Yes. 'Rubber shoe' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'rubber shoe' but are not 'rubber shoe' are:\tsneakers\tloafers\tboots\tsandals\nThere are several useful visual features to tell there is 'rubber shoe' and not similar things in a photo:\tmade entirely or partially of rubber or synthetic materials\toutsole with distinctive tread pattern\trounded toe box\tlow or high top design\tlaces or straps for closure.", 11], "nike tennis shoes": ["Yes. 'Nike tennis shoes' has a tangible appearance and is a type of sports footwear.\nA few things that are visually similar to 'nike tennis shoes' but are not 'nike tennis shoes' are:\tAdidas tennis shoes\tNew Balance tennis shoes\tPuma tennis shoes\t\nThere are several useful visual features to tell there is 'Nike tennis shoes' and not similar things in a photo:\tNike logo\ton 'swoosh' design\ton patterns that are specific to Nike tennis shoes", 11], "orange peels": ["Yes, 'orange peels' has a visually concrete concept and refers to the outer skin or rind of an orange.\nA few things that are visually similar to 'orange peels' but are not 'orange peels' are: lemon peels, lime peels, grapefruit peels, tangerine peels.\nUseful visual features for distinguishing 'orange peels' from the listed similar things in a photo are: bright orange, pitted surface, characteristic citrusy fragrance, and texture that is thicker and rougher than the inner flesh of the fruit.", 11], "metal doors": ["Yes. 'Metal doors' has a tangible appearance and it is a type of door.\nA few things that are visually similar to 'metal doors' but are not 'metal doors' are:\twooden doors\tglass doors\tgates\tgarage doors\nThere are several useful visual features to tell there is 'metal doors' and not similar things in a photo:\tmade of metal\thave a metallic or shiny appearance\thave visible hinges and locks\tbigger and heavier-looking than wooden doors", 11], "boar": ["Yes. 'Boar' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'boar' but are not 'boar' are:\tpig\twarthog\tjavelina\twild boar figurine\torangutan (in some photos)\nThere are several useful visual features to tell there is 'boar' and not similar things in a photo:\twild pig\tstriped, dark coat\thairy mane and tusks\tmuscular body\twild nature habitat\twalks on four short legs.", 11], "brown park bench": ["Yes. 'Brown park bench' has a tangible appearance and is a specific type of outdoor seating.\nA few things that are visually similar to 'brown park bench' but are not 'brown park bench' are:\tpicnic table\twooden chair\twooden fence\tcrate\nThere are several useful visual features to tell there is 'brown park bench' and not similar things in a photo:\tbrown color\tlong seat with no backrest\tflat legs, often made of metal or wood\tplanks or slats forming the seat and backrest", 11], "plaster wall": ["Yes. 'Plaster wall' has a tangible appearance and is a type of wall.\nA few things that are visually similar to 'plaster wall' but are not 'plaster wall' are:\tbrick wall\tconcrete wall\twooden wall\nThere are several useful visual features to tell there is 'plaster wall' and not similar things in a photo:\ta smooth texture\ta uniform and solid appearance\tmay have small cracks\tmay be painted or have wallpaper", 11], "coke machine": ["Yes. 'Coke machine' has a tangible appearance and is a kind of vending machine.\nA few things that are visually similar to 'coke machine' but are not 'coke machine' are:\tsnack vending machine\tcoffee vending machine\tparking meter\tticketing machine\nThere are several useful visual features to tell there is 'coke machine' and not similar things in a photo:\tred\tcursive script 'Coca-Cola' logo\timages of soda cans or bottles\tcoin slot and/or credit card reader\tbuttons for selecting different types of soda\tdispensing area for cups or bottles of soda", 11], "gold curtains": ["Yes. 'Gold curtains' has a tangible appearance and refers to a specific type of window treatment.\nA few things that are visually similar to 'gold curtains' but are not 'gold curtains' are:\tGold-colored fabric\tYellow curtains\tSparkly drapes\nThere are several useful visual features to tell there is 'gold curtains' and not similar things in a photo:\tMade from gold-hued fabric\tHanging vertically to cover a window or door\tUsually opened and closed with a rod or a cord\tMay have decorative tassels or tiebacks.", 11], "read": ["No. 'Read' is too vague or abstract to be distinguished in a photo. It is an action or a mental process that does not have a tangible appearance.\nTherefore, there are no things visually similar to 'read' that are not 'read'.", 11], "shadow train": ["Yes. 'Shadow train' has a tangible appearance and refers to the shadows formed by a train passing by.\nThere are no things that are visually similar to 'shadow train' but are not 'shadow train'.\nUseful visual features for identifying 'shadow train' in a photo:\telongated and continuous shape\tclosely grouped shadows, resembling a train's structure\tthe shadows are parallel and follow a straight line along the ground\tsunlight or other light source visible in the photo, casting distinct shadows.", 11], "monks": ["Yes. 'Monks' have a tangible appearance and are people who belong to a religious community.\nA few things that are visually similar to 'monks' but are not 'monks' are:\tnuns\tfriars\thermits\tascetics\tyogis\nThere are several useful visual features to tell there is 'monks' and not similar things in a photo:\treligious robes or habits\tbald head or shaved hair\tbowls or baskets for alms\tor praying\tmeditating or studying scriptures in a group", 11], "lone skier": ["Yes. 'Lone skier' has a tangible appearance and refers to a person skiing alone.\nA few things that are visually similar to 'lone skier' but are not 'lone skier' are:\tGroup of skiers\tsnowboarder\tperson on a mountain\t\nThere are several useful visual features to tell there is 'lone skier' and not similar things in a photo:\tOne person skiing\tno other people nearby\tor only distant people\tonly one pair of ski tracks on the snow\ttraditional downhill skiing gear, including skis, poles, and boots.", 11], "d number": ["No. 'D number' is too vague or abstract to be distinguished in a photo.", 11], "giraffes mouth": ["Yes. 'Giraffe's mouth' has a tangible appearance and is a part of the animal's anatomy.\nA few things that are visually similar to 'giraffe's mouth' but are not 'giraffe's mouth' are:\tmouth of a horse\tmouth of a cow\nThere are several useful visual features to tell there is 'giraffe's mouth' and not similar things in a photo:\tlong and narrow tongue\tteeth in the front of the bottom jaw only\tlarge size, relative to the giraffe's head\tTwo lobes or \"palate\" at the back of the mouth, visible when open.", 11], "silver jet": ["Yes. 'Silver jet' has a tangible appearance and can refer to a specific type of airplane.\nA few things that are visually similar to 'silver jet' but are not 'silver jet' are:\tsilver car\tsilver train\nThere are several useful visual features to tell there is 'silver jet' and not similar things in a photo:\tstreamlined aerodynamic shape\ttwin engines\tsilver or metallic exterior jet propulsion\tsmall windows on the cockpit and passengers' areas", 11], "kerchief": ["Yes. 'Kerchief' has a tangible appearance and is a type of cloth.\nA few things that are visually similar to 'kerchief' but are not 'kerchief' are:\tscarf\tbandana\thandkerchief\nThere are several useful visual features to tell there is 'kerchief' and not similar things in a photo:\t\nsmall square shape\t\nusually made of cotton\t\noften patterned or brightly colored\t\noften worn as a head covering, around the neck or tied to a purse or backpack.", 11], "altar": ["Yes. 'Altar' is a visually concrete concept and usually has a specific design and purpose.\nA few things that are visually similar to 'altar' but are not 'altar' are:\tTable\tCoffin\tMantelpiece\nThere are several useful visual features to distinguish 'altar' from the listed similar things in a photo:\n- A raised platform or table-like structure\n- Sacred or religious symbols and objects \n- Candles, flowers or other offerings\n- Typically found in a place of worship or religious setting.", 11], "cement ramp": ["Yes. 'Cement ramp' has a tangible appearance and is a type of construction, usually used for cars or bicycles to climb or descend a slope.\nA few things that are visually similar to 'cement ramp' but are not 'cement ramp' are:\tstairs\tpyramids\tslides\n\t\nThere are several useful visual features to tell there is 'cement ramp' and not similar things in a photo:\tgradual slope\tcement or concrete material\twith or without handrails\tmay have tire marks or skid marks from frequent use", 11], "exclamation point": ["Yes. 'Exclamation point' has a visually concrete concept.\nThere are no things that are visually similar to 'exclamation point' but are not 'exclamation point'.\nThere are no visual features needed to distinguish the 'exclamation point' as it is a unique symbol that cannot be confused with any other object.", 11], "barefoot woman": ["Yes. 'Barefoot woman' has a tangible appearance.\nA few things that are visually similar to 'barefoot woman' but are not 'barefoot woman' are:\tbarefoot man\tperson wearing flip flops\tperson wearing sandals\tperson wearing high heels\tperson wearing boots\nThere are several useful visual features to tell there is 'barefoot woman' and not similar things in a photo:\ta woman's bare feet\tno shoes\tno socks", 11], "disturbance": ["No. 'Disturbance' is too vague or abstract to be distinguished in a photo.", 11], "fruit market": ["Yes. 'Fruit market' has a tangible appearance and is a place where fruits are sold.\nA few things that are visually similar to 'fruit market' but are not 'fruit market' are:\tgrocery store\tflea market\tfarmer's market\tsupermarket\nThere are several useful visual features to tell there is 'fruit market' and not similar things in a photo:\tdisplay of various fruits and vegetables\tprice tags or signs\tcrates or baskets filled with produce\thustle and bustle of vendors and customers", 11], "sugar donut": ["Yes. 'Sugar donut' has a specific tangible appearance.\nA few things that are visually similar to 'sugar donut' but are not 'sugar donut' are:\ttire\tbagel\tpretzel\thockey puck\nThere are several useful visual features to tell there is 'sugar donut' and not similar things in a photo:\tcircular shape\twith a hole in the middle\tpowdered sugar coating\tor cinnamon dusting\tsmooth and shiny surface", 11], "train carts": ["Yes. 'Train carts' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'train carts' but are not 'train carts' are:\ttrucks\twagons\ttrolleys\tbuses\nThere are several useful visual features to tell there are 'train carts' and not similar things in a photo:\tconnected in a row or series\tmetallic construction and wheels\tattached to a locomotive or engine\tmultiple windows or doors on the sides", 11], "camera case": ["Yes. 'Camera case' has a tangible appearance and is a type of protective container for a camera.\nA few things that are visually similar to 'camera case' but are not 'camera case' are:\tlaptop case\tbriefcase\tpurse\tbackpack\nThere are several useful visual features to tell there is 'camera case' and not similar things in a photo:\trectangular\tsized to fit a camera and its accessories\tpadded with soft material\thave zippers, clasps, or other fasteners to keep it closed and secure\tmay have a handle or strap for carrying.", 11], "copyright notice": ["Yes. 'Copyright notice' has a tangible appearance and is a symbol or text usually used to indicate intellectual property rights.\nA few things that are visually similar to 'copyright notice' but are not 'copyright notice' are:\ttrademark symbol\tpatent number\tlogo\tbrand name\nThere are several useful visual features to tell there is 'copyright notice' and not similar things in a photo:\t\u00a9 symbol\tor the word \"Copyright\" or abbreviation \"Copr.\"\tfollowed by the year of first publication\tthe name of the copyright owner, artist, author, or company\tthat claimed the copyright\tfollowed by \"All Rights Reserved\".", 11], "metro train": ["Yes. 'Metro train' has a tangible appearance and is a kind of train that operates on urban rail transit systems.\nA few things that are visually similar to 'metro train' but are not 'metro train' are:\ttram\tmonorail\trailway train\nThere are several useful visual features to tell there is 'metro train' and not similar things in a photo:\tsleek and modern design\tsilver or metallic exterior\tcolorful lines or logos in the train and stations\tunderground lines", 11], "space key": ["Yes. 'Space key' has a tangible appearance and is a button located on a keyboard.\nA few things that are visually similar to 'space key' but are not 'space key' are:\ttab key\tenter key\tdelete key\tbackspace key\nThere are several useful visual features to tell there is 'space key' and not similar things in a photo:\tlong rectangular shape\tspace bar or arrow icon\texplicitly says 'space' on it\tlarger and wider than other keys", 11], "playground equipment": ["Yes. 'Playground equipment' has a tangible appearance and is a type of physical equipment designed for children to play on.\nA few things that are visually similar to 'playground equipment' but are not 'playground equipment' are:\tgym equipment\tconstruction equipment\tgardening tools\tpublic furniture\nThere are several useful visual features to tell there is 'playground equipment' and not similar things in a photo:\tbright and primary colors\tfrequent use of plastic, wood, or metal\trails or bars\tfor climbing, sliding, swinging, or balancing on\thuman-sized scale\tthat usually have child safety measures", 11], "vent hood": ["Yes. 'Vent hood' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'vent hood' but are not 'vent hood' are:\trange hood\texhaust fan\tchimney\nThere are several useful visual features to tell there is 'vent hood' and not similar things in a photo:\tmounted above the cooktop or stove\thas a canopy or a hood\tincludes a fan or ventilation system\tcovers the area over the cooking surface", 11], "door latch": ["Yes. 'Door latch' has a tangible appearance and is a mechanical device.\nA few things that are visually similar to 'door latch' but are not 'door latch' are:\tknob\thandle\tlock\thinge\nThere are several useful visual features to tell there is 'door latch' and not similar things in a photo:\ta latch-shaped metal piece attached to a door or a gate\ta mechanism that engages or releases the door\twhen engaged, the door is secured in the closed position", 11], "purple shoes": ["Yes. 'Purple shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'purple shoes' but are not 'purple shoes' are:\tboots\tsandals\tsneakers\tpumps\nThere are several useful visual features to tell there are 'purple shoes' and not similar things in a photo:\tpurple color\tsole shape\tshoe type (flat, high heel, etc.)\ttexture or material of the shoe (leather, suede, etc.)", 11], "cast iron skillet": ["Yes. 'Cast iron skillet' has a tangible appearance and is a type of cookware.\nA few things that are visually similar to 'cast iron skillet' but are not 'cast iron skillet' are:\tnon-stick pan\taluminum pan\t\nThere are several useful visual features to tell there is 'cast iron skillet' and not similar things in a photo:\theavy and solid-looking black or dark gray surface\thandles attached with metal bolts or screws\tringed texture on the cooking surface", 11], "brown boat": ["Yes. 'Brown boat' has a tangible appearance and is a type of watercraft.\nA few things that are visually similar to 'brown boat' but are not 'brown boat' are:\tcanoe\tkayak\tfishing boat\trowboat\tyacht\nThere are several useful visual features to tell there is 'brown boat' and not similar things in a photo:\nbrown color\nlarge size\nmotor or sails (if present)", 11], "toliet": ["Yes. 'Toilet' has a tangible appearance and is a type of bathroom fixture.\nA few things that are visually similar to 'toilet' but are not 'toilet' are:\tsink\tbathtub\tshower\turinal\nThere are several useful visual features to tell there is 'toilet' and not similar things in a photo:\tbowl-shaped ceramic structure\twith a seat and lid\ton the floor or mounted to the wall\twith a flush mechanism, such as a handle or button", 11], "plastic sunglasses": ["Yes. 'Plastic sunglasses' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'plastic sunglasses' but are not 'plastic sunglasses' are:\tmetal sunglasses\tski goggles\tswimming goggles\t3D glasses\nThere are several useful visual features to tell there is 'plastic sunglasses' and not similar things in a photo:\tFrame made of plastic material over the nose and ears\tRectangular or oval lenses in various colors or shades\tDark tint on the lenses to protect against the sun's glare and UV radiation.", 11], "bottom window": ["Yes. 'Bottom window' has a tangible appearance and is a specific type of window.\nA few things that are visually similar to 'bottom window' but are not 'bottom window' are:\ttop window\tskylight\tfrench window\tshowcase\twindow in a vehicle\nThere are several useful visual features to tell there is 'bottom window' and not similar things in a photo:\tlocated at the bottom of a wall\tnormally rectangular or square for a residential building\tfixed or able to be opened and closed\tframed or frameless", 11], "colander": ["Yes. 'Colander' has a tangible appearance and is a kitchen utensil used for draining water from food.\nA few things that are visually similar to 'colander' but are not 'colander' are:\tstrainer\tsieve\tbasket\nThere are several useful visual features to tell there is 'colander' and not similar things in a photo:\tbowl-shaped\twith small holes for draining\tusually made of metal or plastic\tsits on top of another container or in the sink to collect water\thas handles on either side\tfor domestic use only.", 11], "railway lines": ["Yes. 'Railway lines' has a tangible appearance and refers to the tracks that allow trains to travel.\nA few things that are visually similar to 'railway lines' but are not 'railway lines' are:\tpower lines\tfence\tlines in a parking lot\nThere are several useful visual features to tell there are 'railway lines' and not similar things in a photo:\tparallel lines made of metal rails\tties or sleepers (wooden beams or concrete supports) spaced along the rails\tballast (gravel or stones) between the ties or sleepers", 11], "metal bin": ["Yes. 'metal bin' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'metal bin' but are not 'metal bin' are:\ttrash can\tbucket\toil drum\tbarrel\nThere are several useful visual features to tell there is 'metal bin' and not similar things in a photo:\tconstructed of metal, usually grey or silver\tcylindrical or rectangular shape\twith handles or a lid for easier handling\tmay have labels or markings for recycling or waste management purposes.", 11], "motor home": ["Yes. 'Motor home' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'motor home' but are not 'motor home' are:\tcamper van\ttrailer\ttruck\tbus\nThere are several useful visual features to tell there is 'motor home' and not similar things in a photo:\tcombined driver's cab and living unit\tlarge and rectangular\tshower and toilet facilities\trecreational equipment such as awnings or outdoor grills", 11], "train crossing": ["Yes. 'Train crossing' has a tangible appearance and is a location where a railway line intersects a road.\nA few things that are visually similar to 'train crossing' but are not 'train crossing' are:\tintersection\troad sign_crosswalk\t\nThere are several useful visual features to tell there is 'train crossing' and not similar things in a photo:\trailway lines and tracks\tbarriers, gates, or lights to stop cars when a train is coming\tsigns indicating the presence of railway tracks and a railroad crossing", 11], "fangs": ["Yes. 'Fangs' has a tangible appearance and is a type of tooth.\nA few things that are visually similar to 'fangs' but are not 'fangs' are:\tteeth\tclaws\tneedles\nThere are several useful visual features to tell there are 'fangs' and not similar things in a photo:\tlong and pointed teeth\tcurved shape\tsharp tip\tpresent in the front of the mouth (canines)\tcommonly associated with predatory animals such as snakes or wolves.", 11], "pinwheel": ["Yes. 'Pinwheel' has a tangible appearance and is a kind of spinning toy.\nA few things that are visually similar to 'pinwheel' but are not 'pinwheel' are:\tfan\tpropeller\ttoy windmill\tspiral staircase\nThere are several useful visual features to tell there is 'pinwheel' and not similar things in a photo:\twind-powered\tspinning blades\tfour or more blades in a symmetrical pattern\tbrightly colored or patterned blades\tpin or stick for inserting into the ground or holding the toy", 11], "cement surface": ["Yes. 'Cement surface' has a tangible appearance and is a type of material used in construction.\nA few things that are visually similar to 'cement surface' but are not 'cement surface' are:\tbricks\tpavement\tasphalt\ttile\trock\nThere are several useful visual features to tell there is 'cement surface' and not similar things in a photo:\tgray or light-colored surface\trough or uneven texture\tpores or small holes in the surface\tmay have visible cracks or lines", 11], "cake tray": ["Yes. 'Cake tray' has a tangible appearance and is a kitchen utensil used to serve or display cakes.\nA few things that are visually similar to 'cake tray' but are not 'cake tray' are:\tplate\tplatter\tcutting board\ttray\nThere are several useful visual features to tell there is 'cake tray' and not similar things in a photo:\trectangular or round shape\twith raised edges\ttoothed edge to hold the cake in place\tmay have a cover or lid\tto match the size of the cake being served or displayed", 11], "burgundy car": ["Yes. 'Burgundy car' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'burgundy car' but are not 'burgundy car' are: red car, maroon car, dark-colored car\nThere are several useful visual features to tell there is 'burgundy car' and not similar things in a photo:\tdark red hue\tcharacteristic of the burgundy color\tcar shape and structure\tmake and model-specific features, such as brand logos\tor complementary colors like chrome decorations or black tires.", 11], "side head": ["No. 'Side head' is too vague or abstract to be distinguished in a photo. It's not a commonly used term in anatomy or art.", 11], "antler": ["Yes. 'Antler' has a tangible appearance and is a kind of bone structure on the head of certain animals.\nA few things that are visually similar to 'antler' but are not 'antler' are:\thorn\tbranch\ttree\ttwig\nThere are several useful visual features to tell there is 'antler' and not similar things in a photo:\tbony, branched structures on the head\tof deer, elk, moose, caribou, reindeer or other cervids", 11], "male player": ["Yes. 'Male player' has a tangible appearance and refers to a man who is playing a sport or a game.\nA few things that are visually similar to 'male player' but are not 'male player' are:\ta man standing near the field with sports equipment\ta man sitting and watching the game on TV.\nThere are several useful visual features to tell there is 'male player' and not similar things in a photo:\twearing sports clothes or uniform\tcarrying or using sports equipment\tactive movements such as running, jumping, throwing\ta focused or competitive expression on their face.", 11], "frozen": ["Yes. 'Frozen' has a tangible appearance and refers to something that has turned into solid ice due to low temperature.\nA few things that are visually similar to 'frozen' but are not 'frozen' are:\tice cream\ticed coffee\tor any cold object with no water content\nThere are several useful visual features to tell there is 'frozen' and not similar things in a photo:\tsolid state\tice crystal formation\tcold temperature\twhite or transparent appearance", 11], "metal wheel": ["Yes. 'Metal wheel' has a tangible appearance and is a circular object made of metal.\nA few things that are visually similar to 'metal wheel' but are not 'metal wheel' are:\tcogwheel\tbicycle wheel\tFerris wheel\tgear\nThere are several useful visual features to tell there is 'metal wheel' and not similar things in a photo:\tcircular shape\tmetallic silver color\trubber tire around the rim\tspokes or blades connecting the center and the rim", 11], "grey asphalt": ["Yes. 'Grey asphalt' has a tangible appearance and is a type of pavement material.\nA few things that are visually similar to 'grey asphalt' but are not 'grey asphalt' are:\tpavers\tconcrete\tpebbles\tlimestone\nThere are several useful visual features to tell there is 'grey asphalt' and not similar things in a photo:\tsmooth surface\tgrey or black color\ttextured appearance\tdark spots on the surface", 11], "lg logo": ["Yes. 'LG logo' has a visual concrete appearance and is a type of logo.\nA few things that are visually similar to 'LG logo' but are not 'LG logo' are:\tSamsung logo\tSony logo\tApple logo\tHP logo\nThere are several useful visual features to tell there is 'LG logo' and not similar things in a photo:\tLetters L and G in a circle\tRed color with silver letters or black letters with gray picture or symbol.", 11], "pink hair": ["Yes. 'Pink hair' has a tangible appearance.\nA few things that are visually similar to 'pink hair' but are not 'pink hair' are:\tpink wig\tpink hat\tpink flower\nThere are several useful visual features to tell there is 'pink hair' and not similar things in a photo:\thuman head or a doll head\tfine strands or locks of hair\tpink color even in different lighting or shadows", 11], "hazy clouds": ["Yes. 'Hazy clouds' has a tangible appearance and is a type of atmospheric phenomenon.\nA few things that are visually similar to 'hazy clouds' but are not 'hazy clouds' are:\tfog\tsmoke\tdust\tmist\nThere are several useful visual features to tell there are 'hazy clouds' and not similar things in a photo:\tsuspended masses of water droplets or ice crystals\tin fluffy or wispy shapes\tusually white or light grey\tcolor gradient from white to light gray\tor sometimes pink or orange when reflecting sunlight.", 11], "wall plug": ["Yes. 'Wall plug' has a tangible appearance and is an electrical component used to connect devices to power sources.\nA few things that are visually similar to 'wall plug' but are not 'wall plug' are:\tlight switches\tdata jacks\tcoaxial cables\nThere are several useful visual features to tell there is 'wall plug' and not similar things in a photo:\ttwo or three prongs\tto be plugged into an electrical outlet\tusually rectangular or round in shape\tmetal or plastic material", 11], "metal chain link": ["Yes. 'Metal chain link' has a tangible appearance and is a type of material used in construction and fencing.\nA few things that are visually similar to 'metal chain link' but are not 'metal chain link' are:\twire mesh\tgrids\tbaskets\nThere are several useful visual features to tell there is 'metal chain link' and not similar things in a photo:\tdiamond-shaped holes\tmetallic material\tchains interlocked to form a mesh pattern", 11], "safety fence": ["Yes. 'Safety fence' has a tangible appearance and is used to enclose an area for safety purposes.\nA few things that are visually similar to 'safety fence' but are not 'safety fence' are:\tdecorative fence\tgarden trellis\tbarrier tape\tpolice barricade\nThere are several useful visual features to tell there is 'safety fence' and not similar things in a photo:", 11], "silver tap": ["Yes. 'Silver tap' has a tangible appearance.\nA few things that are visually similar to 'silver tap' but are not 'silver tap' are:\tchrome tap\tstainless steel tap\tplastic tap\nThere are several useful visual features to tell there is 'silver tap' and not similar things in a photo:\tmade of silver-colored metal\tsleek and cylindrical shape\tturn handle for water flow\tand spout for water exit.", 11], "scallions": ["Yes. 'Scallions' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'scallions' but are not 'scallions' are:\tleeks\tchives\tshallots\t\nThere are several useful visual features to tell there is 'scallions' and not similar things in a photo:\tlong, thin, and green stalks\twith a white bulb at the root\tend of the stalk is slightly curved and pointed\twhen cut, the inside is white and hollow, and the outside is green", 11], "payphone": ["Yes. 'Payphone' has a tangible appearance and is a type of public phone.\nA few things that are visually similar to 'payphone' but are not 'payphone' are:\tsmart phone\tlandline telephone\tvending machine\nThere are several useful visual features to tell there is 'payphone' and not similar things in a photo:\tstationary public phone\twith coin slot and/or card reader\tvertical handset attached to a stand or a wall\tcolored with bold red, blue, or green\tcolorful graphic instructions or information display panel", 11], "crosswalk street": ["Yes. 'Crosswalk street' has a tangible appearance and refers to a marked section of the road designed for pedestrians to safely cross.\nA few things that are visually similar to 'crosswalk street' but are not 'crosswalk street' are:\tzebra stripe pattern on clothing or fabrics\tpainted lines on a road without a pedestrian crossing\tsigns indicating a school zone or pedestrian crossing\nThere are several useful visual features to tell there is 'crosswalk street' and not similar things in a photo:\twhite stripes or bars painted on the road\ttruncated domes on the sidewalk corner\tsymbols of a walking man or a hand appearing at crosswalks\tside lines that extend the length of the crossing", 11], "pink tag": ["Yes. 'Pink tag' has a tangible appearance and is a specific type of tag.\nA few things that are visually similar to 'pink tag' but are not 'pink tag' are:\tyellow tag\tblue tag\twhite tag\tgreen tag\nThere are several useful visual features to tell there is 'pink tag' and not similar things in a photo:\tpink in colorrectangular in shape\thanging from an item of clothing, an object, or a location", 11], "color umbrella": ["Yes. 'Color umbrella' has a tangible appearance and refers to an umbrella of a specific color or with multiple colors.\nThere are no things that are visually similar to 'color umbrella' that are not 'color umbrella'.\nUseful visual features for distinguishing 'color umbrella' from any other type of umbrella could be the colors visible on the umbrella, the pattern and design of the umbrella, the shape of the handle or handle material.", 11], "icy": ["No. 'Icy' is too vague or abstract to have a distinct visual appearance. It can only be inferred by the presence of visual cues such as frost, snowflakes, or icicles.\nThere are no things that are visually similar to 'icy' but not 'icy' because 'icy' is not a visually concrete concept.\nUseful visual features for indicating the presence of 'icy' include: frost on surfaces, snowflakes falling or resting on objects, icicles hanging from structures, and slippery or frozen surfaces.", 11], "tire rim": ["Yes. 'Tire rim' has a tangible appearance and is a part of a car wheel.\nA few things that are visually similar to 'tire rim' but are not 'tire rim' are:\thubcap\twheel cover\tdisc brake\nThere are several useful visual features to distinguish 'tire rim' from the listed similar things in a photo:\touter edge of the wheel\tthat holds the tire\thas spokes\tor is solid\tis typically made of metal or alloy\thas a curved shape to fit the tire", 11], "wii box": ["Yes. 'Wii box' has a tangible appearance and refers to the box that contains a Nintendo Wii console.\nA few things that are visually similar to 'wii box' but are not 'wii box' are:\tother game console boxes\tshoe boxes\tstorage boxes\tshipping boxes\nThere are several useful visual features to tell there is 'wii box' and not similar things in a photo:\tthe name \"Wii\" is written on the box\tthe box features an image of the Nintendo Wii console\tthe box includes \"Nintendo\" branding and logos.", 11], "pink sky": ["Yes. 'Pink sky' has a tangible appearance, and it's a visible phenomenon that occurs during sunrise or sunset.\nA few things that are visually similar to 'pink sky' but are not 'pink sky' are:\tpaintings of sunsets or sunrises\tpink lights or neon signs\tpink clouds\nThere are several useful visual features to tell there is 'pink sky' and not similar things in a photo:\tpink or reddish hue that gradually changes to orange or purple\thorizon\tline with the sun as the center\tpointed upwards to the sky", 11], "surfboard ocean": ["No. 'Surfboard ocean' is too vague or abstract. The two concepts, 'surfboard' and 'ocean', can be visually distinguished separately but not as a single concept.\nHowever, a few things that are visually similar to 'surfboard' are:\tpaddleboard\tkayak\tsailboard\nA few things that are visually similar to 'ocean' are:\tlake\triver\tpool\nUseful visual features for distinguishing 'surfboard' from similar things in a photo are: \tlong and narrow board for riding waves, often with pointed ends\tbuoyant material for floating on water\tfins on the bottom for stability or steering\nUseful visual features for distinguishing 'ocean' from similar things in a photo are: \tvast expanse of saltwater, stretching to the horizon\twith waves, tide or currents\tsky and clouds reflecting on the surface.", 11], "axe": ["Yes. 'Axe' has a tangible appearance and is a tool used for cutting wood.\nA few things that are visually similar to 'axe' but are not 'axe' are:\thatchet\tmachete\tknife\t\nThere are several useful visual features to tell there is 'axe' and not similar things in a photo:\tlong handle\ttypical wedge-shaped metal blade\tbright colors and contrast between metal and other parts of the tool", 11], "metal tennis racket": ["Yes. 'Metal tennis racket' has a tangible appearance and is a specific type of sporting equipment.\nA few things that are visually similar to 'metal tennis racket' but are not 'metal tennis racket' are:\twooden tennis racket\tplastic toy tennis racket\tcricket bat\tiron rod\nThere are several useful visual features to tell there is 'metal tennis racket' and not similar things in a photo:\trectangular shape with a long handle\tmetal frame\twith strings or webbing stretched across the frame\tcord grip at the bottom of the handle", 11], "entry sign": ["Yes. 'Entry sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'entry sign' but are not 'entry sign' are:\texit sign\tdirection sign\tparking sign\tadvertising sign\nThere are several useful visual features to tell there is 'entry sign' and not similar things in a photo: prominently displays the words \"Entrance\" or \"Welcome\"\tclearly indicates entry point into a building or an area distinct color scheme or design", 11], "knee socks": ["Yes. 'Knee socks' has a tangible appearance and is a type of sock that goes up to the knee.\nA few things that are visually similar to 'knee socks' but are not 'knee socks' are:\tankle socks\tstockings\ttights\tleggings\nThere are several useful visual features to tell there are 'knee socks' and not similar things in a photo:\tsocks that end at the knee or just below the knee\tmaterial that is typically thicker and more opaque than sheer stockings or tights, but thinner than leggings or pants\tknee-high profile with a slightly tighter, but not constricting, fit", 11], "rock jutting": ["Yes. 'Rock jutting' has a tangible appearance and refers to a part of a rock that sticks out.\nA few things that are visually similar to 'rock jutting' but are not 'rock jutting' are:\tcracks on a wall\tbranch on a tree\torienteering flag\nThere are several useful visual features to tell there is 'rock jutting' and not similar things in a photo:\tpart of a rock\tvertical or diagonal in orientation\tprotruding or sticking out\tfrom a larger surface\tstriking or sticking contrast to the background.", 11], "cruise boat": ["Yes. 'Cruise boat' has a tangible appearance and is a kind of passenger ship.\nA few things that are visually similar to 'cruise boat' but are not 'cruise boat' are:\tferry\tsteamboat\tcargo ship\tyacht\nThere are several useful visual features to tell there is 'cruise boat' and not similar things in a photo:\tlarge size\tfor leisure and tourism\tprominent decks and balconies\tdecorative elements such as flags or lights\trecreational amenities such as pools or slides.", 11], "bovine": ["Yes. 'Bovine' has a tangible appearance and is a type of animal belonging to the cattle family.\nA few things that are visually similar to 'bovine' but are not 'bovine' are:\tbuffalo\tyak\tbison\tdeer\nThere are several useful visual features to tell there is 'bovine' and not similar things in a photo:\tcows, bulls or oxen\tshorthaired or longhaired coat\tmultiple teats and udder\tblack and white, brown or white coat patterns\tsmall tail in proportion to its body", 11], "bear claws": ["Yes. 'Bear claws' has a tangible appearance and refers to the physical features of a bear's paws.\nA few things that are visually similar to 'bear claws' but are not 'bear claws' are:\thuman hands\tcat paws\tdog paws\ttiger paws\nThere are several useful visual features to tell there are 'bear claws' and not similar things in a photo:\thuge size compared to other animals' paws\tsharp and powerful claws\tbulky and muscular appearance\thair covering the paw", 11], "trash barrel": ["Yes. 'Trash barrel' has a tangible appearance and is a type of container used for disposing of waste.\nA few things that are visually similar to 'trash barrel' but are not 'trash barrel' are:\tplanter\tpot\tbucket\tbasket\tbarrel\nThere are several useful visual features to tell there is 'trash barrel' and not similar things in a photo:\tlarge size\tround or cylindrical shape\tlid on top\tto-be-discarded materials or trash inside 'trash' or 'recycle' label on it", 11], "wisps": ["Yes. 'Wisps' has a tangible appearance and refers to thin, delicate strands or curls.\nA few things that are visually similar to 'wisps' but are not 'wisps' are:\tsmoke\tclouds\torbs\tmist\nThere are several useful visual features to tell there are 'wisps' and not similar things in a photo:\tthinness\tdelicate curling shapes\ttransparency\tand absence of opacity\tlightness and ethereality\tdamage edge or outline\tsome wisps can be colored (e.g. smoke could be black)", 11], "door vehicle": ["No. 'Door vehicle' is too vague or abstract to be distinguished in a photo.", 11], "tee-shirt": ["Yes. 'Tee-shirt' has a tangible appearance and is a kind of clothing item.\nA few things that are visually similar to 'tee-shirt' but are not 'tee-shirt' are:\tshirt\tdress\ttop\tjacket\nThere are several useful visual features to tell there is 'tee-shirt' and not similar things in a photo:\tshort sleeves\tround neckline\tcasual design\tmade of light fabric", 11], "ski mask": ["Yes. 'Ski mask' has a tangible appearance and is a type of facial covering.\nA few things that are visually similar to 'ski mask' but are not 'ski mask' are:\tbalaclava\theadscarf\tfacial mask\tbeanie\nThere are several useful visual features to tell there is 'ski mask' and not similar things in a photo:\tcovers the entire face except for the eyes, nose, and mouth\ttightly fitting over the head and neck\twarm and made of a thick, insulating material\tdark-colored or camouflage patterned.", 11], "stabilizers": ["Yes. 'Stabilizers' has a tangible appearance and refers to a type of equipment used for stabilization or balance.\nA few things that are visually similar to 'stabilizers' but are not 'stabilizers' are:\twheel chocks\ttraffic cones\tparking stops\nThere are several useful visual features to tell there is 'stabilizers' and not similar things in a photo:\tmetal or plastic bars or leg\tsupporting a structure or equipment\tattachment to the bottom or sides of an object", 11], "water front": ["Yes. 'Water front' has a tangible appearance and refers to the area where the water meets the land.\nA few things that are visually similar to 'water front' but are not 'water front' are:\tbeach\tharbor\tpier\triver\tbay\nThere are several useful visual features to tell there is 'water front' and not similar things in a photo:\tthe meeting point of land and water\twatercraft such as boats or ships\tmarine vegetation or animals\tcoastal structures such as buildings, bridges, or docks.", 11], "dolphins": ["Yes. 'Dolphins' have a tangible appearance and belong to the family of cetaceans.\nA few things that are visually similar to 'dolphins' but are not 'dolphins' are:\tporpoises\twhales\tsharks\tfish\nThere are several useful visual features to tell there is 'dolphins' and not similar things in a photo:\tsleek bodies\tgrey color\tsmall dorsal fin\ton top of the water doing flips or jumps\tbeady eyes\tblowhole\tclose relatives to porpoises but appear to have longer snouts", 11], "field grass": ["Yes. 'Field grass' has a tangible appearance.\nA few things that are visually similar to 'field grass' but are not 'field grass' are:\twheat\tcorn\trye\tbarley\nThere are several useful visual features to tell there is 'field grass' and not similar things in a photo:\tlong, thin blades\tfanned out and growing from the ground\tlight green to dark green color\tshaped like tufts or clumps", 11], "wood dock": ["Yes. 'Wood dock' has a tangible appearance and is a structure used to provide access to watercraft.\nA few things that are visually similar to 'wood dock' but are not 'wood dock' are:\tjetty\tpier\twharf\tboardwalk\nThere are several useful visual features to tell there is 'wood dock' and not similar things in a photo:\tmade of wood\tsupported by poles or pillars\textends into the water or near the water's edge\thas ladders or rope attached to it for boat access or mooring", 11], "rear paw": ["Yes. 'Rear paw' has a tangible appearance and refers to the back foot of an animal.\nA few things that are visually similar to 'rear paw' but are not 'rear paw' are:\tfront paw\thuman foot\thoof\nThere are several useful visual features to tell there is 'rear paw' and not similar things in a photo:\tpositioned towards the back of the animal's body\tsmaller and less dexterous than the front paw\ttoes and nails that are adapted for different types of movement, depending on the animal's species", 11], "machete": ["Yes. 'Machete' has a tangible appearance and is a type of cutting tool.\nA few things that are visually similar to 'machete' but are not 'machete' are:\tsword\tknife\taxe\thatchet\nThere are several useful visual features to tell there is 'machete' and not similar things in a photo:\tlong, flat blade with a sharp point and curved edge\thandle made of wood or plastic\tused for cutting through thick vegetation or slicing meat", 11], "wood platform": ["Yes. 'Wood platform' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'wood platform' but are not 'wood platform' are:\tpallet\twooden crate\twooden deck\twooden bridge\nThere are several useful visual features to tell there is 'wood platform' and not similar things in a photo:\t\nflat, raised surface made of wood\tvarious sizes and shapes\tbrown or natural wood color erected above the ground or floor.", 11], "ave": ["No. 'Ave' is a term used for a type of street or road, and as such, its appearance is not tangible or visual.\nThere are no things that are visually similar to 'ave' as it is a type of road or street.\nVisual features cannot be used to distinguish 'ave' from other things in a photo because it is a concept related to a type of street or road.", 11], "traces": ["No. 'Traces' is too vague or abstract to be distinguished in a photo.", 11], "patrons": ["No. 'Patrons' is too vague or abstract to be distinguished in a photo.\nHowever, a few things that are visually similar to the meaning of 'patrons' which are not 'patrons' themselves include: customers, visitors, and clients.\nUseful visual features to distinguish 'patrons' from similar things may include: being in a setting that is typically associated with patrons (e.g. a bar or restaurant), engaging in activities typically associated with being a patron (e.g. ordering food or drink), or being identified as patrons through some kind of visual cue like wearing branded clothing.", 11], "author": ["No. 'Author' is too vague or abstract to be distinguished in a photo.", 11], "lighting fixture": ["Yes. 'Lighting fixture' has a tangible appearance and is a type of home decor.\nA few things that are visually similar to 'lighting fixture' but are not 'lighting fixture' are:\tcandles\tlamps\tchandeliers\t\nThere are several useful visual features to tell there is 'lighting fixture' and not similar things in a photo:\tattached to the ceiling or wall\tpre-installed electrical wiring\tlight bulbs", 11], "orange tennis racket": ["Yes. 'Orange tennis racket' has a tangible appearance and is a type of sports equipment.\nA few things that are visually similar to 'orange tennis racket' but are not 'orange tennis racket' are: badminton racket, squash racket, paddle racket, table tennis racket.\nThere are several useful visual features to tell there is 'orange tennis racket' and not similar things in a photo:\toval-shaped head of the racket\torange color on the frame\torang strings in the racket\tPossibly visible brand logo of the racket (if any) used by the player.", 11], "house window": ["Yes. 'House window' has a tangible appearance and is a type of architectural element of a house.\nA few things that are visually similar to 'house window' but are not 'house window' are:\tpicture frame\tmirror\tdoor\tpainting\nThere are several useful visual features to tell there is 'house window' and not similar things in a photo:\tmade of glass\tusually rectangular or square\tin a wall or facade of a house\tallow light to enter the house\tmay have shutters or frames around them", 11], "metro bus": ["Yes. 'Metro bus' has a tangible appearance and is a kind of public transport bus that operates in metropolitan areas.\nA few things that are visually similar to 'metro bus' but are not 'metro bus' are:\tcharter bus\tschool bus\tdouble-decker bus\nThere are several useful visual features to tell there is 'metro bus' and not similar things in a photo:\tred or blue color\tlarge size\tclean and modern appearance\twide windows\tlow-floor or no-step entry", 11], "panel door": ["Yes. 'Panel door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'panel door' but are not 'panel door' are:\tflush door\tglass door\tsliding door\nThere are several useful visual features to tell there is 'panel door' and not similar things in a photo:\tframed by horizontal and vertical stiles and rails\tdivided into sections or panels\thinges on one side\tusually made of wood or wood-like materials", 11], "knee guard": ["Yes. 'Knee guard' has a tangible appearance and is a piece of protective gear.\nA few things that are visually similar to 'knee guard' but are not 'knee guard' are:\tleggings\tknee pads\tshin guards\nThere are several useful visual features to tell there is 'knee guard' and not similar things in a photo:\tdesigned to protect the knee\tstrap or attachments to keep it in place\thard outer shell or padding for impact and shock absorption", 11], "concrete slabs": ["Yes. 'Concrete slabs' has a tangible appearance and refers to large pieces of flat concrete used in construction.\nA few things that are visually similar to 'concrete slabs' but are not 'concrete slabs' are:\ttiles\tpaving stones\twooden planks\tmetal sheets\nThere are several useful visual features to tell there is 'concrete slabs' and not similar things in a photo:\tgrey or beige color\tlarge and thick pieces of concrete with no visible joints\tsmooth and flat surface", 11], "ornamentation": ["Yes. 'Ornamentation' has a tangible appearance and refers to the decorative elements.\nA few things that are visually similar to 'ornamentation' but are not 'ornamentation' are:\tscribbles or doodles\tstains or marks\tstickers, logos or labels\nThere are several useful visual features to tell there is 'ornamentation' and not similar things in a photo:\tdecorative or embellished patterns, designs or motifs\tenhancing visual appearance or value\tpart of a larger object or piece of artwork", 11], "girraffe": ["Yes. 'Giraffe' has a tangible appearance and is a kind of mammal.\nA few things that are visually similar to 'giraffe' but are not 'giraffe' are:\tdeer\tokapi\tcamel\thorse\nThere are several useful visual features to tell there is 'giraffe' and not similar things in a photo:\tlong neck\tand legs\tbrownish-yellow coat\twith black spots\ton small horns\ton a savanna plain habitat.", 11], "birch tree": ["Yes. 'Birch tree' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'birch tree' but are not 'birch tree' are:\taspen tree\tspindly tree\tpine tree\twillow tree\tmountain ash\nThere are several useful visual features to tell there is 'birch tree' and not similar things in a photo:\tsmooth, white bark (peeling)\thorizontal line markings on the bark\tslender and tapered trunk\twith a triangular crown of leaves that hangs down\tThe leaves are generally small, pointed, and serrated.", 11], "gold mirror": ["Yes. 'Gold mirror' has a tangible appearance and is a type of mirror.\nA few things that are visually similar to 'gold mirror' but are not 'gold mirror' are:\tregular mirror\tframe made of other materials\tsun reflecting on a surface\nThere are several useful visual features to tell there is 'gold mirror' and not similar things in a photo:\tgold-colored frame or border\treflection on the surface of the mirror\tcircular or rectangular shape", 11], "camp chair": ["Yes. 'Camp chair' has a tangible appearance and is a type of chair designed for outdoor activities.\nA few things that are visually similar to 'camp chair' but are not 'camp chair' are:\tbeach chair\tlawn chair\tpicnic blanket\tfolding stool\nThere are several useful visual features to tell there is 'camp chair' and not similar things in a photo:\tfoldable and portable\tcompact and lightweight\tusually made of fabric and metal or plastic\tframe designed for stability on uneven ground\tpadded armrests or cup holders for added comfort", 11], "round knob": ["Yes. 'Round knob' has a tangible appearance and is a type of handle.\nA few things that are visually similar to 'round knob' but are not 'round knob' are:\tbutton\tcandy\tlollipop\tlens\nThere are several useful visual features to tell there is 'round knob' and not similar things in a photo:\tcircular shape\tprotruding from a surface\tsmall or medium-sized size\ttextured or smooth surface\tdesigned for gripping or turning", 11], "cobblestone sidewalk": ["Yes. 'Cobblestone sidewalk' has a tangible appearance and is a type of paved path.\nA few things that are visually similar to 'cobblestone sidewalk' but are not 'cobblestone sidewalk' are:\ttile walkway\tbrick walkway\tconcrete walkway\tpebble walkway\nThere are several useful visual features to tell there is 'cobblestone sidewalk' and not similar things in a photo:\tuneven surface\tlarge, rounded rocks arranged in a pattern\tthe stones are not uniform in shape or size.", 11], "bot": ["Yes. 'Bot' has a tangible appearance and is a type of machine or robot.\nA few things that are visually similar to 'bot' but are not 'bot' are:\tcomputer\tlawnmower\tvacuum cleaner\tpower tool\nThere are several useful visual features to tell there is 'bot' and not similar things in a photo:\trobotic or mechanical appearance\thumanoid or animal-like shape\twheels or treads\tforward-facing camera or sensor\tglowing lights or indicators", 11], "magenta": ["Yes. 'Magenta' has a tangible appearance and is a specific shade of pink/purple.\nA few things that are visually similar to 'magenta' but are not 'magenta' are:\tpurple\tpink\tfuchsia\nThere are several useful visual features to tell there is 'magenta' and not similar things in a photo:\ta specific shade of pink/purple, with a strong and vivid tone\tdifferent from other shades of pink or purple, including lighter pastel colors or darker shades of maroon or burgundy", 11], "metal structures": ["Yes. 'Metal structures' has a tangible appearance and refers to objects made primarily of metal, such as buildings, bridges, and industrial equipment.\nA few things that are visually similar to 'metal structures' but are not 'metal structures' are:\twire fences\tbarbecue grills\tbicycle frames\nThere are several useful visual features to tell there is 'metal structures' and not similar things in a photo:\tvisible metal beams or frames\tsharp lines and edges\tmetallic surface finish\thighly organized and geometric shapes", 11], "indicators": ["Yes. 'Indicators' have a tangible appearance and are usually instruments or signals used to provide information.\nA few things that are visually similar to 'indicators' but are not 'indicators' are:\tsigns\tmeters\tbuttons\tlights\nThere are several useful visual features to tell there are 'indicators' and not similar things in a photo:\tdigital or analog display\tshowing measurements or data\tusing specific labels or symbols\twith accompanying scales or ranges\treliant on input from external sources or sensors.", 11], "tombstone": ["Yes. 'Tombstone' has a tangible appearance and is an object used to mark a grave.\nA few things that are visually similar to 'tombstone' but are not 'tombstone' are:\tsigns\tplaques\tmonuments\trocks\nThere are several useful visual features to tell there is 'tombstone' and not similar things in a photo:\tupright stone or slab\tmarked with name and dates\tof a specific size and shape]intentionally placed on a grave site", 11], "grey fur": ["Yes. 'Grey fur' has a tangible appearance and is a type of animal fur.\nA few things that are visually similar to 'grey fur' but are not 'grey fur' are:\twool\tfuzzy fabric\tartificial fur\nThere are several useful visual features to tell there is 'grey fur' and not similar things in a photo:\tsilvery-grey or medium grey color\ttexture of animal fur\tnatural and not uniform look\tcan be seen on an animal's body part such as a tail, legs, or back", 11], "train engine car": ["Yes. 'Train engine car' has a tangible appearance and refers to the front car or locomotive of a train.\nA few things that are visually similar to 'train engine car' but are not 'train engine car' are:\tpassenger cars\tcargo cars\tsubway cars\ttrams\ttrolleys\nThere are several useful visual features to tell there is 'train engine car' and not similar things in a photo:\tattached to other train cars\tboxy or streamlined shape\theadlights and a smokestack (if steam engine)\tvisible engine parts on the front like grill and fan\tblacked wheels and a large cowcatcher for safety purposes.", 11], "subway train": ["Yes. 'Subway train' has a tangible appearance and is a form of transportation.\nA few things that are visually similar to 'subway train' but are not 'subway train' are:\ttram\ttrolleybus\tbus\nThere are several useful visual features to tell there is 'subway train' and not similar things in a photo:\truns on rails\tunderground or in a tunnel\tmultiple cars or carriages\tpulls into a station with platforms", 11], "leafy branch": ["Yes. 'Leafy branch' has a tangible appearance and is a type of natural element.\nA few things that are visually similar to 'leafy branch' but are not 'leafy branch' are:\tdry branch\tweed\tstick\nThere are several useful visual features to tell there is 'leafy branch' and not similar things in a photo:\thas leaves\tgreen or brown in color\thas branches or twigs\tconnected to a trunk or stem\thas a natural shape or silhouette", 11], "shields": ["Yes. 'Shields' has a tangible appearance and is a type of defensive tool.\nA few things that are visually similar to 'shields' but are not 'shields' are:\tplates\tchests\tfrisbees\tsigns\nThere are several useful visual features to tell there is 'shields' and not similar things in a photo:\tcircular or oblong shape\thand-held\tsize relative to the person using it\tdecorative emblem or symbol on the front", 11], "toilet paper rolls": ["Yes. 'Toilet paper rolls' has a tangible appearance and is a cylindrical paper product used for personal hygiene.\nA few things that are visually similar to 'toilet paper rolls' but are not 'toilet paper rolls' are:\tpaper towel rolls\twrapping paper rolls\tduct tape rolls\ttape rolls\nThere are several useful visual features to tell there is 'toilet paper roll' and not similar things in a photo:\tcylindrical shape\tperforations along the length of the roll\trelatively small in size\twhite or off-white color.", 11], "horses ears": ["Yes. 'Horses ears' has a tangible appearance and is a part of the horse's body.\nA few things that are visually similar to 'horses ears' but are not 'horses ears' are:\tdonkey ears\tmule ears\tdog ears\tcat ears\nThere are several useful visual features to tell there are 'horses ears' and not similar things in a photo:\tlarge size compared to the head\tpointed tips\tfur covering the outside and the rims of the ear\tinner ear visible in some angles", 11], "stance": ["No. 'Stance' is too vague or abstract to be distinguished in a photo.", 11], "hippos": ["Yes. 'Hippos' has a tangible appearance and is a type of large semi-aquatic mammal.\nA few things that are visually similar to 'hippos' but are not 'hippos' are:\tpigs\trhinos\tbuffalos\nThere are several useful visual features to tell there are 'hippos' and not similar things in a photo: large bodies\torangish-brown skin\tbig heads\twith large jaws and big teeth\tshort legs\tcompletely or partially submerged in water", 11], "bathroom sinks": ["Yes. 'Bathroom sinks' has a tangible appearance and is a type of fixture commonly found in bathrooms.\nA few things that are visually similar to 'bathroom sinks' but are not 'bathroom sinks' are:\tkitchen sinks\tpedestal sinks\twashbasins\tfountains\nThere are several useful visual features to tell there is 'bathroom sinks' and not similar things in a photo:\tlocated in a bathroom or washroom\tmounted on a counter or vanity\tbasin for holding water or other liquids\tfaucet for controlling water flow\tplumbing pipes and drainage underneath.", 11], "hankerchief": ["Yes. 'Hankerchief' has a tangible appearance and is a piece of cloth used for wiping one's nose or face.\nA few things that are visually similar to 'hankerchief' but are not 'hankerchief' are:\tnapkin\ttowel\tbandana\tfabric square\nThere are several useful visual features to tell there is 'hankerchief' and not similar things in a photo:\tsmall square shape or rectangular shape\tsometimes patterned or monochrome made from cloth or cotton\ttexture may be soft or rough", 11], "engine plane": ["Yes. 'Engine plane' has a tangible appearance and refers to a type of aircraft that is powered by engines.\nA few things that are visually similar to 'engine plane' but are not 'engine plane' are:\tGlider plane\tHelicopter\tHot air balloon\tZeppelin\nThere are several useful visual features to tell there is 'engine plane' and not similar things in a photo:\thas one or more engines\tfixed wings\tsmoke or vapor trail from the engines\thas a visible cockpit or cabin area.", 11], "silver building": ["Yes. 'Silver building' has a tangible appearance and is a building with a silver-colored exterior.\nA few things that are visually similar to 'silver building' but are not 'silver building' are:\tconcrete building\tglass building\tstone building\nThere are several useful visual features to tell there is 'silver building' and not similar things in a photo:\tsilver-colored exterior\tmetallic or reflective surface\tsmooth and shiny appearance\tdistinguishable from surrounding buildings or structures", 11], "wall tire": ["No. 'Wall tire' is a vague concept and I'm not sure exactly what you mean by it. Could you please specify or provide more context?\nWithout more context, it's difficult to name things visually similar or provide useful visual features for distinguishing it from other things.", 11], "flood light": ["Yes. 'Flood light' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'flood light' but are not 'flood light' are:\tspotlight\ttable lamp\tfloor lamp\tdesk lamp\nThere are several useful visual features to tell there is 'flood light' and not similar things in a photo:\twide beam angle\tbright and intense light\tusually installed outdoors, pointing upward or downward, on a bracket or a stand.", 11], "doubledecker bus": ["Yes. 'Doubledecker bus' has a tangible appearance and is a type of bus with two levels.\nA few things that are visually similar to 'doubledecker bus' but are not 'doubledecker bus' are:\tregular bus\ttram\ttrolley\tmulti-level parking lot\nThere are several useful visual features to tell there is 'doubledecker bus' and not similar things in a photo:\ttwo levels\tone or two the stairs at the back of the bus\tone floor with windows on the sides and a roof on top\tan open, exposed top level that is often used for sightseeing.", 11], "sands": ["Yes. 'Sands' has a tangible appearance and refers to small grains of minerals and rocks found near water bodies.\nA few things that are visually similar to 'sands' but are not 'sands' are:\tpebbles\tgravel\tsalt\tcrushed eggshells\nThere are several useful visual features to tell there is 'sands' and not similar things in a photo:\tsmall, fine grains\tfound near a water body, like an ocean or a river\tvariety of colors depending on location, like white, yellow, and red", 11], "pink flamingo": ["Yes. 'Pink flamingo' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'pink flamingo' but are not 'pink flamingo' are:\therons\tegrets\tcrane\tstork\nThere are several useful visual features to tell there is 'pink flamingo' and not similar things in a photo:\tpink body and feathers\tlong and thin legs\tcurved beak\tand a prominent head crest.", 11], "eye brows": ["Yes. 'Eye brows' has a tangible appearance and is a part of the face.\nA few things that are visually similar to 'eye brows' but are not 'eye brows' are:\twrinkles\teyelashes\thair\tfur\nThere are several useful visual features to tell there is 'eye brows' and not similar things in a photo:\tarched or straight shape\thair-like texture\tpositioned above the eyes in a specific shape and order\tdark color, usually the same color as hair above the forehead", 11], "bottom lip": ["Yes. 'Bottom lip' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'bottom lip' but are not 'bottom lip' are:\ttop lip\tchin\tnose\nThere are several useful visual features to tell there is 'bottom lip' and not similar things in a photo:\tsoft and fleshy part of the mouth\tbelow the top lip\tdelimits the mouth from the chin and jaw\tline or crease in the middle of the lip, separating left and right sections", 11], "apple tree": ["Yes. 'Apple tree' has a tangible appearance and is a kind of tree.\nA few things that are visually similar to 'apple tree' but are not 'apple tree' are:\tpear tree\tpeach tree\tcherry tree\toak tree\nThere are several useful visual features to tell there is 'apple tree' and not similar things in a photo:\tround fruit with a stem and a leaf on top\tleaves are green and serrated\tbrown trunk and branches\twith or without apples depending on the season\tdistinctive apple blossom flowers in spring or summer", 11], "gras": ["Yes. 'Gras' has a tangible appearance and refers to a group of plants with narrow leaves and jointed stems that are widely cultivated as a ground cover.\nA few things that are visually similar to 'gras' but are not 'gras' are:\tweed\tfern\tmoss\tivy\nThere are several useful visual features to tell there is 'gras' and not similar things in a photo:\twp-shaped leaves\tjointed stems\tmostly green leaves\tgrowing in clusters or en masse\tinhabiting lawns, meadows or pastures", 11], "end sign": ["Yes. 'End sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'end sign' but are not 'end sign' are:\tstop sign\tyield sign\tspeed limit sign\nThere are several useful visual features to tell there is 'end sign' and not similar things in a photo:\trectangular shape with pointed ends\tthe word \"END\" written in large letters\tcentered white text on a red background\tclearly placed at the conclusion of something, such as the end of a road or the end of a message", 11], "warning signs": ["Yes. 'Warning signs' has a tangible appearance and is a type of sign that alerts to danger or hazards.\nA few things that are visually similar to 'warning signs' but are not 'warning signs' are:\tdirection signs\tspeed limit signs\tparking signs\tbillboards\nThere are several useful visual features to tell there is 'warning signs' and not similar things in a photo:\tyellow or orange color\tstriking shapes like triangles or diamonds\tblack text on a white background, often with a hazard symbol such as a lightning bolt or skull and crossbones.", 11], "snow glove": ["Yes. 'Snow glove' has a tangible appearance and is a type of glove.\nA few things that are visually similar to 'snow glove' but are not 'snow glove' are:\tski glove\twork glove\tmitten\nThere are several useful visual features to tell there is 'snow glove' and not similar things in a photo:\tclear or translucent material\tsnowflakes or winter scenes inside\tthe words 'snow globe' in the photo are misspelled", 11], "sport coat": ["Yes. 'Sport coat' has a tangible appearance and is a type of jacket.\nA few things that are visually similar to 'sport coat' but are not 'sport coat' are:\tblazer\tcoat\thoodie\tsweater\nThere are several useful visual features to tell there is 'sport coat' and not similar things in a photo:\tthick material\tstructured shoulders and lapels\ttwo or three buttons on the front\tsingle breast-pocket\ttwo side pockets\tfits snugly to the body at the torso", 11], "copse": ["Yes. 'Copse' has a tangible appearance and is a group of small trees or bushes.\nA few things that are visually similar to 'copse' but are not 'copse' are:\tforest\tgarden\tgrove\torchard\nThere are several useful visual features to tell there is 'copse' and not similar things in a photo:\tsmall group of trees\tor bushes\tgrowing close together\tshorter than a forest\tdense leaves or branches", 11], "leashes": ["Yes. 'Leashes' has a tangible appearance and is a type of restraining device for pets.\nA few things that are visually similar to 'leashes' but are not 'leashes' are:\tbelts\tchains\tropes\tvines\nThere are several useful visual features to tell there is 'leashes' and not similar things in a photo:\tmade of fabric or leather\tattach to a collar or harness\tusually in bright colors and patterns\tlong enough for the pet to move around but short enough to keep them under control.", 11], "eyeglass": ["Yes. 'Eyeglass' has a tangible appearance and is a device worn to aid vision.\nA few things that are visually similar to 'eyeglass' but are not 'eyeglass' are:\tsunglasses\tprotective goggles\tsafety glasses\thelmets\nThere are several useful visual features to tell there is 'eyeglass' and not similar things in a photo:\ttwo lenses\tframes\tthat sit on or around the nose and ears\tfor vision correction rather than protection.", 11], "left engine": ["Yes. 'Left engine' has a tangible appearance and is a part of a mechanical system.\nA few things that are visually similar to 'left engine' but are not 'left engine' are:\tright engine\tmachinery\tpower generator\tpiston\tframe\nThere are several useful visual features to tell there is 'left engine' and not similar things in a photo:\tattached to the left side of the vehicle or aircraft\tmade of metal or other durable materials\tdifferent components such as intake, compressor, combustion chamber, turbine, and exhaust\tsystem of pipes and wires connected to it.", 11], "rubble": ["Yes. 'Rubble' has a tangible appearance and refers to broken pieces of building or other structures.\nA few things that are visually similar to 'rubble' but are not 'rubble' are:\tstones\tpebbles\tsand\tdebris\tmud\nThere are several useful visual features to tell there is 'rubble' and not similar things in a photo:\tpieces of broken concrete, masonry, or other building materials\tscattered or piled up in a disordered way\tdust or smoke around\tthe appearance of a collapsed or damaged structure", 11], "cloud sky": ["No. 'Cloud sky' is too vague as it combines two concepts, cloud, and sky. Each of them has a tangible appearance but the combination of both is not visually concrete.\nTherefore, there are no things that are visually similar to 'cloud sky'.\nUseful visual features for distinguishing between clouds and sky in a photo are:\n\n- Sky: the area of the atmosphere above the horizon; it is usually blue, grey, or orange, depending on the weather and the time of the day. It has no visible boundaries or shapes.\n- Clouds: visible mass of water droplets or ice crystals suspended in the atmosphere. Appearing as white or grey, they come in different shapes and sizes; cumulus, stratus or cirrus. Normally the identification of clouds depends on their shapes and position with respect to the sky.", 11], "stern": ["No. 'Stern' is too vague or abstract to be distinguished in a photo.", 11], "mountaintop": ["Yes. 'Mountaintop' has a tangible appearance and is the peak of a mountain.\nA few things that are visually similar to 'mountaintop' but are not 'mountaintop' are:\thilltop\trooftop\tcrater\nThere are several useful visual features to tell there is 'mountaintop' and not similar things in a photo:\tlocated at the highest point of a mountain\tsurrounded by lower areas or valleys\trocky or snow-covered summit\tMight have a visible hiking trail or climbing equipment", 11], "grey legs": ["Yes. 'Grey legs' has a tangible appearance and refers to the color of legs.\nA few things that are visually similar to 'grey legs' but are not 'grey legs' are:\tblack legs\tbrown legs\twhite legs\nThere are no useful visual features for distinguishing 'grey legs' from similar things in a photo as it only refers to the color of the legs. However, the context of the photo can be helpful in determining if the legs being referred to are human or animal legs.", 11], "birds feathers": ["Yes. 'Bird feathers' has a tangible appearance and is a type of body covering for birds.\nA few things that are visually similar to 'birds feathers' but are not 'birds feathers' are:\tleaves\tfern fronds\tfur\tfish scales\nThere are several useful visual features to tell there is 'birds feathers' and not similar things in a photo:\tlightweight and thin\thave a central shaft and branches\tcomes in a variety of colors and patterns\toften used for flight or insulation\thave barbs and hooks that help to hook them together", 11], "golf clubs": ["Yes. 'Golf clubs' has a tangible appearance and is a kind of sports equipment.\nA few things that are visually similar to 'golf clubs' but are not 'golf clubs' are:\tbaseball bats\thockey sticks\tpool cues\nThere are several useful visual features to tell there is 'golf clubs' and not similar things in a photo:\tthree parts: grip, shaft, and head\tmetallic or wooden material\ta flat face on the head\tused on a golf course", 11], "fondant": ["Yes. 'Fondant' has a tangible appearance and is a type of icing used in cake decorating.\nA few things that are visually similar to 'fondant' but are not 'fondant' are:\tbuttercream\twhipped cream\tcream cheese frosting\tganache\nThere are several useful visual features to tell there is 'fondant' and not similar things in a photo:\trolled out like dough\tsmooth and shiny surface\talmost porcelain-like texture\tcan be molded into different shapes and patterns\tsolid and firm when handled.", 11], "sea waters": ["Yes. 'Sea waters' has a tangible appearance and refers to the saltwater found in oceans and seas.\nA few things that are visually similar to 'sea waters' but are not 'sea waters' are:\tlake\triver\tswimming pool\nThere are several useful visual features to tell there is 'sea waters' and not similar things in a photo:\tblue or green color\tsalt content\twaves or tides\tsea creatures such as fish, whales or dolphins", 11], "elephants mouth": ["Yes. 'Elephant's mouth' has a tangible appearance and is a body part of an elephant.\nA few things that are visually similar to 'elephant's mouth' but are not 'elephant's mouth' are:\tmouth of other animals, like hippos, whales, or dolphins\tcaves\twith an opening or entrance\nThere are several useful visual features to tell there is 'elephant's mouth' and not similar things in a photo:\tlarge in size\ttwo long protruding curved tusks\twrinkled skin around the mouth\tpink or grey\tcolor of the elephant's body in the background.", 11], "plane number": ["No. 'Plane number' is too abstract and cannot be visually distinguished in a photo.\n", 11], "wire mesh fence": ["Yes. 'Wire mesh fence' has a tangible appearance and is a type of physical structure.\nA few things that are visually similar to 'wire mesh fence' but are not 'wire mesh fence' are:\tchicken wire\tfishing net\tchain link fence\nThere are several useful visual features to tell there is 'wire mesh fence' and not similar things in a photo:\tsmall, interlocking metal or plastic wires\tgaps of the same size across the whole fence\tgrid or mesh pattern\tzig-zag or sharp angle endings", 11], "house number": ["Yes. 'House number' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'house number' but are not 'house number' are:\tstreet names\twall art\tlicense plates\tbillboards\nThere are several useful visual features to tell there is 'house number' and not similar things in a photo:\tdigits\tfrom 0 to 9\tplaced on a building or near the entrance.", 11], "thorns": ["Yes. 'Thorns' has a tangible appearance and is a kind of sharp plant structure.\nA few things that are visually similar to 'thorns' but are not 'thorns' are:\tspikes\tneedles\tnails\nThere are several useful visual features to tell there is 'thorns' and not similar things in a photo:\tpart of a plant or tree\tsharp and pointed\ttexture resembling that of a plant or tree", 11], "cheetah": ["Yes. 'Cheetah' has a tangible appearance and is a type of big cat.\nA few things that are visually similar to 'cheetah' but are not 'cheetah' are:\tleopard\tjaguar\tocelot\tlynx\nThere are several useful visual features to tell there is 'cheetah' and not similar things in a photo:\tyellowish or golden coat with black spots\tlong and slender body\tprominent black lines around the eyes\ttear-shaped black streaks on the face\tclaws that are semi-retractable\tfrom Africa\tand incredibly fast runners", 11], "bear ground": ["No. 'Bear ground' is too vague or abstract to be distinguished in a photo.", 11], "bear leg": ["Yes. 'Bear leg' has a tangible appearance and refers to the leg of a bear.\nA few things that are visually similar to 'bear leg' but are not 'bear leg' are:\tdog leg\tcat leg\tcow leg\nThere are several useful visual features to tell there is 'bear leg' and not similar things in a photo:\tlarge and muscular\tbrown or black\tfurry\tsymmetrical feet and toes, with sharp claws\tthat the leg belongs to a bear", 11], "brick area": ["Yes. 'Brick area' has a tangible appearance and refers to an area or surface covered with bricks.\nA few things that are visually similar to 'brick area' but are not 'brick area' are: stone area, concrete path, tiled area, wooden deck.\nThere are several useful visual features to tell there is 'brick area' and not similar things in a photo:\tred or brown rectangular-shaped blocks\tlaid out in a repeating pattern\tmortar or cement in between the bricks.", 11], "calf muscle": ["Yes. 'Calf muscle' has a tangible appearance and is a muscle of the lower leg.\nA few things that are visually similar to 'calf muscle' but are not 'calf muscle' are:\tankle\tbone\ttendon\tfat\nThere are several useful visual features to tell there is 'calf muscle' and not similar things in a photo:\tbulging muscles on the back of the lower legs\toblong curved shape when flexed\torangish or reddish in color\twhen moving, visible moving motion of the muscle", 11], "ballplayer": ["Yes. 'Ballplayer' has a tangible appearance and refers to a person who plays ball games, such as baseball, soccer, basketball, etc.\nA few things that are visually similar to 'ballplayer' but are not 'ballplayer' are:\tfans\tcoaches\tumpires\nThere are several useful visual features to tell there is 'ballplayer' and not similar things in a photo, such as:\twearing a sports uniform\tusing sports equipment\tplaying the actual game or in a ready-to-play stance\tfocused on the game or activity", 11], "tail plane": ["Yes. 'Tail plane' has a tangible appearance and is a part of an airplane.\nA few things that are visually similar to 'tail plane' but are not 'tail plane' are:\twing\ttail fin\thorizontal stabilizer\tvertical stabilizer\nThere are several useful visual features to tell there is 'tail plane' and not similar things in a photo:\thorizontal surface at the tail with a movable flap or elevator\tno vertical fins or wings", 11], "pink tile": ["Yes. 'Pink tile' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'pink tile' but are not 'pink tile' are:\tred tile\torange tile\nThere are several useful visual features to tell there is 'pink tile' and not similar things in a photo:\tpink color\tsmooth or glossy surface\trectangular shape\tused in a bathroom or kitchen", 11], "blue ramp": ["Yes. 'Blue ramp' has a tangible appearance and is a structure that can be seen or touched.\nA few things that are visually similar to 'blue ramp' but are not 'blue ramp' are:\tstairs\tslide\tbridge\trailway track\nThere are several useful visual features to tell there is 'blue ramp' and not similar things in a photo:\trectangular shape\tsmooth, sloping surface\tbright blue color", 11], "cockatoo": ["Yes. 'Cockatoo' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'cockatoo' but are not 'cockatoo' are:\tparrot\tlovebird\tpigeon\tdove\nThere are several useful visual features to tell there is 'cockatoo' and not similar things in a photo:\tcrested head or feathers on top of the head\twhite or light-colored feathers\tlarge beaks\tand loud or screechy calls", 11], "round metal": ["Yes. 'Round metal' has a tangible appearance and is a type of material and shape.\nA few things that are visually similar to 'round metal' but are not 'round metal' are:\tcoins\tpebbles\twheels\tbottle caps\nThere are several useful visual features to tell there is 'round metal' and not similar things in a photo:\tcircular or spherical shape\tsilver, gray or brass color\tmetallic texture or shine", 11], "leather motorcycle seat": ["Yes. 'Leather motorcycle seat' has a tangible appearance.\nA few things that are visually similar to 'leather motorcycle seat' but are not 'leather motorcycle seat' are:\tleather chair\tleather jacket\tleather sofa\tleather car seat\nThere are several useful visual features to tell there is 'leather motorcycle seat' and not similar things in a photo:\tconnected to a motorcycle frame\tor frame similar to that of a motorcycle\tcushioned seat\tattached to the rear of a motorcycle", 11], "beagle": ["Yes. 'Beagle' has a tangible appearance and is a specific breed of dog.\nA few things that are visually similar to 'beagle' but are not 'beagle' are:\thound dogs\tbasset hounds\tfoxhounds\nThere are several useful visual features to tell there is a 'beagle' and not similar things in a photo:\tsmaller size\tshort legs\ttri-color coat pattern\thanging ears\twrinkled forehead", 11], "serena": ["No. 'Serena' is too vague or abstract to have a tangible appearance or be distinguished in a photo. It could refer to a person's name or the name of a place, but without further context, it is not a visually concrete concept.", 11], "ewe": ["Yes. 'Ewe' has a tangible appearance and it is a female sheep.\nA few things that are visually similar to 'ewe' but are not 'ewe' are:\tram\tgoat\tcow\tdeer\t\nThere are several useful visual features to tell there is 'ewe' and not similar things in a photo:\tcurled horns\tcud-chewing\tgray or white fur\tpink nose and ears\tshort tails", 11], "wing flaps": ["Yes. 'Wing flaps' has a tangible appearance and refers to the motion of an airplane's wings during flight.\nA few things that are visually similar to 'wing flaps' but are not 'wing flaps' are:\tbird wings\tbutterfly wings\tanimation of a flapping motion\nThere are several useful visual features to tell there are 'wing flaps' and not similar things in a photo:\tMetal surface of a wing or flap\tAttached to a plane or aircraft\tCreated by mechanical or hydraulic movement during takeoff or landing", 11], "brussel sprout": ["Yes. 'Brussel sprout' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'brussel sprout' but are not 'brussel sprout' are:\tcabbage\tkale\tlettuce\nThere are several useful visual features to tell there is 'brussel sprout' and not similar things in a photo:\tsmall round shape\tdense leaves wrapped around each other, resembling a miniature cabbage\tgreen or purple color\tthe stem at the base of the vegetable", 10], "burgandy": ["Yes. 'Burgundy' has a tangible appearance and is a specific shade of red.\nA few things that are visually similar to 'burgundy' but are not 'burgundy' are:\tmaroon\twine\tred\tbrown\nThere are several useful visual features to tell there is 'burgundy' and not similar things in a photo:\ta deep red color\tthat leans towards purple or brown, rather than bright red or pink", 10], "condiment cup": ["Yes. 'Condiment cup' has a tangible appearance and is a small container for holding sauces or dips.\nA few things that are visually similar to 'condiment cup' but are not 'condiment cup' are: shot glass, medicine cup, paint cup\nThere are several useful visual features to tell there is 'condiment cup' and not similar things in a photo:\tshort and wide with a flat base\tdesigned for holding condiments or small portions of food\tmade of plastic, glass, or ceramic\twith or without a lid or a cover", 10], "metal rings": ["Yes. 'Metal rings' has a tangible appearance and is a type of circular metal object.\nA few things that are visually similar to 'metal rings' but are not 'metal rings' are:\tbutton\tcircular saw blade\tbracelet\thula hoop\nThere are several useful visual features to tell there is 'metal rings' and not similar things in a photo:\tcircular\tthin and flat or thick and three-dimensional\tmade of metal or metal-like material", 10], "legos": ["Yes. 'Legos' has a tangible appearance and is a type of children's toy.\nA few things that are visually similar to 'legos' but are not 'legos' are:\tblocks\twooden building toys\tmagnetic building sets\nThere are several useful visual features to tell there is 'legos' and not similar things in a photo:\tdistinct brick shape\twith raised circles on the top of each brick\tthe ability to connect to each other specifically\tfor kids.", 10], "plane windows": ["Yes. 'Plane windows' has a tangible appearance and is a specific part of a plane.\nA few things that are visually similar to 'plane windows' but are not 'plane windows' are:\tcar windows\tstorefront windows\thouse windows\nThere are several useful visual features to tell there is 'plane windows' and not similar things in a photo:\trectangular or oval shape\tlocated on the side of the plane\tcan see clouds or the ground through them\tthick and reinforced glass", 10], "grey leg": ["No. 'Grey leg' is too vague or abstract to be distinguished in a photo. It is unclear what 'grey leg' refers to.", 10], "paper shopping bag": ["Yes. 'Paper shopping bag' has a tangible appearance and is a type of bag made of paper.\nA few things that are visually similar to 'paper shopping bag' but are not 'paper shopping bag' are:\tgift bag\ttrash bag\tgrocery bag\tpolyethylene bag\nThere are several useful visual features to tell there is 'paper shopping bag' and not similar things in a photo:\tmade of paper\tbrown or white color\twith handles prominently placed near the top of the bag\twide base and narrower top.", 10], "telephone handset": ["Yes, 'telephone handset' has a tangible appearance.\nA few things that are visually similar to 'telephone handset' but are not 'telephone handset' are:\tmicrowave handle\thammer handle\t\nThere are several useful visual features to tell there is 'telephone handset' and not similar things in a photo:\n\t- two plastic pieces connected by a cord\n - one piece has ear and mouth receivers\n - the other piece has numbered dialing pad and function buttons.", 10], "breakfast plate": ["Yes. 'Breakfast plate' has a tangible appearance and is a type of dishware specifically used for breakfast.\nA few things that are visually similar to 'breakfast plate' but are not 'breakfast plate' are:\tdinner plate\tsalad plate\tserving platter\nThere are several useful visual features to tell there is 'breakfast plate' and not similar things in a photo:\tsmaller than a dinner plate\tbigger than a salad plate\tcircular or oval shape\tdivided into sections or compartments for different types of food (such as eggs, bacon, toast, etc.)\tmay have a decorative pattern or design along the edges.", 10], "circle logo": ["Yes. 'Circle logo' has a tangible appearance and is a specific type of logo design.\nA few things that are visually similar to 'circle logo' but are not 'circle logo' are:\tcircular design\tpatterns\tround stickers\tglobe images\nThere are several useful visual features to tell there is 'circle logo' and not similar things in a photo:\tcircular shape\tclearly defined edge\tcontains text, image, or both\tcan represent a brand or organization.", 10], "ice chest": ["Yes. 'Ice chest' has a tangible appearance and is a type of container used to keep things cold.\nA few things that are visually similar to 'ice chest' but are not 'ice chest' are:\tcooler\tbag\tstorage box\tchest of drawers\nThere are several useful visual features to tell there is 'ice chest' and not similar things in a photo:\tthick insulation\ton lid\tice packs or ice cubes inside\thandles for carrying or pulling\tdrain for melting ice and water", 10], "zookeeper": ["Yes. 'Zookeeper' has a tangible appearance and refers to a person who takes care of animals in a zoo.\nA few things that are visually similar to 'zookeeper' but are not 'zookeeper' are:\tpark ranger\tveterinarian\tanimal control officer\tfarmer\nThere are several useful visual features to tell there is 'zookeeper' and not similar things in a photo:\twearing a uniform or specific clothing for the workplace\tspending time in a zoo or animal enclosure\thandling or feeding animals\tmaintaining zoo infrastructure and equipment\tinteracting with visitors and other zoo staff", 10], "computer case": ["Yes. 'Computer case' has a tangible appearance.\nA few things that are visually similar to 'computer case' but are not 'computer case' are:\tspeaker\tbox\tcabinet\nThere are several useful visual features to tell there is 'computer case' and not similar things in a photo:\tcontains computer components\tusually rectangular or tower-shaped\thas ports for input and output devices\thandles for transport or installation\tvented for cooling purposes\tmay have LED lights or branding", 10], "brown mushrooms": ["Yes. 'Brown mushrooms' has a tangible appearance and is a type of fungi.\nA few things that are visually similar to 'brown mushrooms' but are not 'brown mushrooms' are:\tdirt clumps\tblack truffles\twalnuts\t\nThere are several useful visual features to tell there are 'brown mushrooms' and not similar things in a photo:\tbrown color\tfleshy and spore-bearing cap\tshort stem\twith or without gills\tumbrella-like shape\twavy or smooth cap edges\tspongy or firm to the touch.", 10], "buddha statue": ["Yes. 'Buddha statue' has a tangible appearance and is a specific type of statue depicting the Buddha.\nA few things that are visually similar to 'buddha statue' but are not 'buddha statue' are:\n\ttiki statues\n\thindu god statues\n\tgreek or roman statues\nThere are several useful visual features to tell there is 'buddha statue' and not similar things in a photo:\n\ta seated or standing figure with a serene expression\n\ta bald head or topknot hairstyle\n\ta robe draped over the body\n\thands in a specific mudra (gesture)", 10], "dips": ["Yes. 'Dips' has a tangible appearance and refers to a type of sauce or spread.\nA few things that are visually similar to 'dips' but are not 'dips' are:\tsauces\tmarinades\tdressings\tgravy\nThere are several useful visual features to tell there is 'dips' and not similar things in a photo:\tthick or creamy consistency\tvariety of colors and textures\tserved in a small dish or bowl\twith food items nearby for dipping\tthe presence of chips or veggie sticks next to it.", 10], "chees": ["Yes. 'Cheese' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'cheese' but are not 'cheese' are:\tbutter\tyogurt\tmilk\tcream\nThere are several useful visual features to tell there is 'cheese' and not similar things in a photo:\tmade from solidified milk\tcurdy or crumbly texture\tdifferent colors depending on the type (e.g., yellow, white, blue)\tvarious shapes (e.g., wedge, wheel, block)\ttypically served alone or paired with crackers, bread, or wine.", 10], "business cards": ["Yes. 'Business cards' has a tangible appearance and is a type of card used for exchanging contact information.\nA few things that are visually similar to 'business cards' but are not 'business cards' are:\tcredit cards\tID cards\ttrading cards\tpaper scraps\nThere are several useful visual features to tell there is 'business cards' and not similar things in a photo:\tnames, job titles, or contact information printed on the card\tstandard rectangular shape\tcard stock or other thick paper material\tpotentially includes a company logo or branding\telement of professionalism or formality", 10], "arch window": ["Yes. 'Arch window' has a tangible appearance and is a type of window.\nA few things that are visually similar to 'arch window' but are not 'arch window' are:\trectangular window\tcircular window\ttriangle window\tdiamond window\nThere are several useful visual features to tell there is 'arch window' and not similar things in a photo:\tcurved or arched top\thalf-circle or semi-circle shape\tsmooth or pointed arch at the top", 10], "broccoli crown": ["Yes. 'Broccoli crown' has a tangible appearance and is a part of a vegetable.\nA few things that are visually similar to 'broccoli crown' but are not 'broccoli crown' are:\tcabbage\thead of lettuce\tspinach\nThere are several useful visual features to tell there is 'broccoli crown' and not similar things in a photo:\tlight green color\tdense clusters of florets on a single stalk or stem\tmostly rounded but with some pointed florets\tat least 4-5 inches in diameter", 10], "hill side": ["Yes. 'Hill side' has a tangible appearance and is a natural landscape feature.\nA few things that are visually similar to 'hill side' but are not 'hill side' are:\tmountain\tslope\tplain\tvalley\nThere are several useful visual features to tell there is 'hill side' and not similar things in a photo:\ta slightly raised area of land\twith a natural slope or incline\tvaried vegetation\tor a rocky structure", 10], "side airplane": ["No. 'Side airplane' is too vague or abstract to be distinguished in a photo.", 10], "brown poles": ["Yes. 'Brown poles' has a tangible appearance and can refer to various things that are brown in color and have a cylindrical shape like wooden poles, metal poles, etc.\nA few things that are visually similar to 'brown poles' but are not 'brown poles' are:\tbranches\ttrunks\tmasts\tfences\nThere are several useful visual features to tell there are 'brown poles' and not similar things in a photo:\tthin and cylindrical shape\tbrown or wooden color\tsmooth surface\teven length and thickness.", 10], "play area": ["Yes. 'Play area' has a tangible appearance and refers to a space designed for children to play.\nA few things that are visually similar to 'play area' but are not 'play area' are:\tGym\tClassroom\tMuseum\tOffice\nThere are several useful visual features to tell there is a 'play area' and not similar things in a photo:\tPlay equipment such as slides, swings or climbing frames\tSoft surface flooring, like rubber padding or sand\tBright, playful colors\tChild-sized features, like tables and chairs or water fountains.", 10], "ponytail woman": ["Yes. 'Ponytail woman' has a tangible appearance and refers to a woman who has her hair tied back in a ponytail.\nA few things that are visually similar to 'ponytail woman' but are not 'ponytail woman' are:\twoman with a bun\thair up in a clip\twoman with short hair\nThere are several useful visual features to tell there is 'ponytail woman' and not similar things in a photo:\tlong hair tied back in a single tail\ta visible elastic band used to tie the hair\tback of the neck is visible", 10], "baseball stadium": ["Yes. 'Baseball stadium' has a tangible appearance and is a type of sports arena.\nA few things that are visually similar to 'baseball stadium' but are not 'baseball stadium' are:\tsoccer stadium\tfootball stadium\trugby field\tcricket stadium\nThere are several useful visual features to tell there is 'baseball stadium' and not similar things in a photo:\tlarge diamond-shaped field\twith a pitcher's mound and bases\ta grassy field\tlarge seating areas for fans\tand scoreboards showing the score\tof the game", 10], "bat batter": ["Yes. 'Bat batter' has a tangible appearance and is a type of substance used in baseball.\nThere is nothing visually similar to 'bat batter' as it is a specific term used in baseball for the substance applied to the bat for better grip and control.\nUseful visual features to distinguish 'bat batter' in a photo would be a baseball bat with a visible substance applied to the handle.", 10], "prop": ["Yes. 'Prop' has a tangible appearance and is an object used during a performance or photoshoot.\nA few things that are visually similar to 'prop' but are not 'prop' are:\tordinary objects\tset pieces\tcostumes\tstage makeup\nThere are several useful visual features to tell there is 'prop' and not similar things in a photo:\tvisually distinct and separate from the background\tor the other objects in the scene\tspecifically placed or held by a performer or model\tfor decorative or practical purposes\tinconsistent with the real-life environment or time period", 10], "pants man": ["No. 'Pants man' is too vague or abstract to be distinguished in a photo. It could refer to any man wearing pants.\nTherefore, there are no things that are visually similar to 'pants man' but are not 'pants man'.\nWithout additional context or information, there are no specific visual features that can be used to identify a 'pants man' in a photo.", 10], "orange yellow": ["Yes. 'Orange yellow' has a tangible appearance and is a color.\nA few things that are visually similar to 'orange yellow' but are not 'orange yellow' are:\tyellow\torange\tmustard\ttangerine\tsaffron\nThere are several useful visual features to tell there is 'orange yellow' and not similar things in a photo:\tconsisting of both orange and yellow hues\thaving a more orange or yellow tint depending on the lighting\tcontext or adjacent colors can help distinguish it, for example, a bright orange-yellow traffic sign would be quite different from a primarily yellow object with only a hint of orange.", 10], "period": ["No. 'Period' is too vague or abstract to be distinguished in a photo.", 10], "kitchen knives": ["Yes. 'Kitchen knives' has a tangible appearance and pertains to utensils used for cooking.\nA few things that are visually similar to 'kitchen knives' but are not 'kitchen knives' are:\tpaper cutters\thunting knives\tpocket knives\tscissors\nThere are several useful visual features to tell there are 'kitchen knives' and not similar things in a photo:\tlong and sharp blade\twith or without serrations\thandles made of wood, plastic, or metal\tflat blade used for chopping or a curved blade used for slicing and cutting", 10], "silver parking meter": ["Yes. 'Silver parking meter' has a tangible appearance and is a type of machine.\nA few things that are visually similar to a 'silver parking meter' but are not 'silver parking meter' are: trash cans, mailboxes, traffic lights, newspaper dispensers, electrical boxes.\nThere are several useful visual features to tell there is a 'silver parking meter' and not similar things in a photo:\tsilver or gray color\tstands on a pole or post\thas numbers, letters, or symbols display\tshows time and price information\thave a coin slot or card reader.", 10], "bench seats": ["Yes. 'Bench seats' has a tangible appearance and refers to long seats with multiple people sitting next to each other.\nA few things that are visually similar to 'bench seats' but are not 'bench seats' are:\tcouches\tpews\tstools\tottomans\nThere are several useful visual features to tell there is 'bench seats' and not similar things in a photo:\tlong seats designed to accommodate multiple people\tpotentially made of wood, metal or other materials, but often with a wooden horizontal or vertical slats platform for seating.", 10], "silver wheel": ["Yes. 'Silver wheel' has a tangible appearance.\nA few things that are visually similar to 'silver wheel' but are not 'silver wheel' are:\tfrisbee\tbike wheel\trecord\tsun\nThere are several useful visual features to tell there is 'silver wheel' and not similar things in a photo:\tcircular shape\tsilver color\tmetallic material\thas spokes or an intricate pattern\ton a car or a machine", 10], "jacket hood": ["Yes. 'Jacket hood' has a tangible appearance and is a part of a clothing item.\nA few things that are visually similar to 'jacket hood' but are not 'jacket hood' are:\that\tscarf\tbonnet\t\nThere are several useful visual features to tell there is 'jacket hood' and not similar things in a photo:\tattached to a jacket or coat\tcovers the head and neck area\tcan be adjustable\thas a drawstring or fastener for tightening or loosening.", 10], "blue catcher": ["No. 'Blue catcher' is too vague or abstract to be distinguished in a photo. There are different interpretations or meanings for 'blue catcher'. Can you provide more context or information about what you mean by 'blue catcher'?", 10], "water pond": ["Yes. 'Water pond' has a tangible appearance and is a small body of still water.\nA few things that are visually similar to 'water pond' but are not 'water pond' are:\tswimming pool\tfountain\tbirdbath\twater tank\nThere are several useful visual features to tell there is 'water pond' and not similar things in a photo:\tnatural-looking edges\tcontaining flora and fauna\tnot strictly geometric in shape\tvarying degrees of depth and transparency", 10], "velvet rope": ["Yes. 'Velvet rope' has a tangible appearance and is a kind of barrier.\nA few things that are visually similar to 'velvet rope' but are not 'velvet rope' are:\tregular rope\tchains\tbarricades\nThere are several useful visual features to tell there is 'velvet rope' and not similar things in a photo:\tsoft texture\tdeep color (usually red or blue)\tasymmetric loop ends", 10], "orange rope": ["Yes. 'Orange rope' has a tangible appearance.\nA few things that are visually similar to 'orange rope' but are not 'orange rope' are:\torange cord\torange string\torange ribbon\torange cable\nThere are several useful visual features to tell there is 'orange rope' and not similar things in a photo:\tthick and strong\ttwisted or braided texture\tvibrant orange color\tmade of fibers or materials that are commonly used for ropes (e.g. nylon, hemp)\thaving knots or being coiled up", 10], "shopper": ["Yes. 'Shopper' has a tangible appearance and refers to a person who is buying things at a store.\nA few things that are visually similar to 'shopper' but are not 'shopper' are:\tstore employee\tbrowser\tspectator\t\nThere are several useful visual features to tell there is 'shopper' and not similar things in a photo:\tcarrying bags or a shopping cart\tholding a product in hand or browsing an item in the store\twearing casual clothing or holding a purse\tor wallet while shopping", 10], "pink glove": ["Yes. 'Pink glove' has a tangible appearance and is a type of handwear.\nA few things that are visually similar to 'pink glove' but are not 'pink glove' are:\tred glove\tpurple glove\tpink mitten\nThere are several useful visual features to tell there is 'pink glove' and not similar things in a photo:\tlatex or rubber material\tambidextrous fits on all hands\tspecific shade of pink\toften used for medical procedures", 10], "stair rail": ["Yes. 'Stair rail' has a tangible appearance and is a part of a staircase.\nA few things that are visually similar to 'stair rail' but are not 'stair rail' are:\tfence\tbalcony railing\tstreet pole\tgrab bar\nThere are several useful visual features to tell there is 'stair rail' and not similar things in a photo:\tconnected to a set of stairs or steps\thand gripping or supporting part of the rail\trunning parallel along a stair\tor a step\thas balusters or spindles to support the rail or handhold.", 10], "ziploc bag": ["Yes. 'Ziploc bag' has a tangible appearance and refers to a specific brand of resealable plastic bags.\nA few things that are visually similar to 'ziploc bag' but are not 'ziploc bag' are:\tregular plastic bags\tsandwich wraps\tplastic wraps\taluminum foils\nThere are several useful visual features to tell there is 'ziploc bag' and not similar things in a photo:\tclear plastic\tresealable with a slider or a zipper logo of the Ziploc brand on the bag", 10], "huts": ["Yes. 'Huts' has a tangible appearance and is a type of dwelling or shelter.\nA few things that are visually similar to 'huts' but are not 'huts' are:\tcabins\ttents\tshacks\tsheds\nThere are several useful visual features to tell there is 'huts' and not similar things in a photo:\tmade of natural materials, such as wood, bamboo, or leaves\tsimple construction or design\tno or minimal windows or doors\ttraditional or cultural decorations or designs", 10], "community": ["No. 'Community' is too vague or abstract to be distinguished in a photo.", 10], "stone street": ["Yes, 'stone street' has a visually concrete appearance.\nA few things that are visually similar to 'stone street' but are not 'stone street' are:\tcobblestone path\tpaving stones\tbrick road\nThere are several useful visual features that can be used to distinguish 'stone street' from visually similar things in a photo:\tflat paving stones made of natural stones\tsymmetrical and uniform pattern\tbrown, gray, or beige color with different shades and textures\tusually found in historic or old towns or parts of a city.", 10], "bleacher seats": ["Yes. 'Bleacher seats' has a tangible appearance and is a type of seating arrangement found in stadiums or sports arenas.\nA few things that are visually similar to 'bleacher seats' but are not 'bleacher seats' are:\tchairs\tbenches\tpews\tsofas\nThere are several useful visual features to tell there is 'bleacher seats' and not similar things in a photo:\tmade of metal or plastic\tno backrests or armrests\tuniform in size and shape\televated or arranged in tiers with several rows.", 10], "silver metal fencing": ["Yes. 'Silver metal fencing' has a tangible appearance and is a type of barrier or enclosure.\nA few things that are visually similar to 'silver metal fencing' but are not 'silver metal fencing' are:\tsteel bars\tgate\tgrill\nThere are several useful visual features to tell there is 'silver metal fencing' and not similar things in a photo:\tvertical metal bars\tsilver or grey color\tmetallic or shiny surface\tstraight and uniform design\tand installed to create a boundary or enclosure.", 10], "brown stripe": ["Yes. 'Brown stripe' has a tangible appearance and is a type of visual pattern.\nA few things that are visually similar to 'brown stripe' but are not 'brown stripe' are:\twood grain\tzebra stripes\tchocolate swirl\tcamouflage pattern\nThere are several useful visual features to tell there is 'brown stripe' and not similar things in a photo:\telongated and thin shape\thorizontal or vertical orientation\tconsistently colored stripe in a background of a different color or pattern", 10], "barrier fence": ["Yes. 'Barrier fence' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'barrier fence' but are not 'barrier fence' are:\tgarden fence\tpicket fence\tdecorative fence\twire fence\nThere are several useful visual features to tell there is 'barrier fence' and not similar things in a photo:\thigh and solid\tfeatures vertical posts and horizontal planks or bars\tmade of wood, metal, or concrete\tdesigned to prevent access or to mark a border or boundary.", 10], "orange dirt": ["Yes. 'Orange dirt' has a tangible appearance.\nA few things that are visually similar to 'orange dirt' but are not 'orange dirt' are: rust, colored sand, and clay.\nThere are several useful visual features to tell there is 'orange dirt' and not similar things in a photo: a distinct, deep orange color that looks like the color of a ripe orange, usually found in arid, desert areas, and made of dirt or soil particles.", 10], "steel beam": ["Yes. 'Steel beam' has a tangible appearance and refers to a strong, sturdy structural component.\nA few things that are visually similar to 'steel beam' but are not 'steel beam' are:\twooden beam\tpole\tmetal pipe\tconcrete slab\nThere are several useful visual features to tell there is 'steel beam' and not similar things in a photo:\tmade of steel or metal\trectangular or I-shaped cross-section\tsmooth, shiny surface\tno visible cracks or knots", 10], "car license plate": ["Yes. 'Car license plate' has a tangible appearance and is a piece of metal with letters and numbers to identify a vehicle.\nA few things that are visually similar to 'car license plate' but are not 'car license plate' are:\tname tag\thouse number\tsignpost\tmenu\nThere are several useful visual features to tell there is 'car license plate' and not similar things in a photo:\trectangle shape\tletters and numbers\tusually has a state or country abbreviation\tin a specific color and font\tsize varying depending on the country or state", 10], "grey handle": ["Yes. 'Grey handle' has a tangible appearance and is a specific physical object.\nA few things that are visually similar to 'grey handle' but are not 'grey handle' are:\tknob\tdoorbell button\tdrawer pull\tlatch\nThere are several useful visual features to tell there is 'grey handle' and not similar things in a photo:\tstraight or curved bar shape\tattached to a door or a cabinet or a drawer\tgrey or silver in color\tmade of metal or plastic.", 10], "beaker": ["Yes. 'Beaker' has a tangible appearance and is a type of scientific glassware.\nA few things that are visually similar to 'beaker' but are not 'beaker' are:\tflask\ttest tube\tmeasuring jug\tvase\nThere are several useful visual features to tell there is 'beaker' and not similar things in a photo:\ttall, cylindrical shape with a slightly wider lip\tlightweight and made of glass\tor plastic\tgraduated markings on the side\tfor laboratory work, science experiments or chemical reactions\tdefining features like flat bottom and pouring lip.", 10], "business signs": ["Yes. 'Business signs' has a tangible appearance and refers to signs used for commercial or promotional purposes.\nA few things that are visually similar to 'business signs' but are not 'business signs' are:\ttraffic signs\tadvertisements on vehicles\tor street artists' works\nThere are several useful visual features to tell there is 'business signs' and not similar things in a photo:\ta company logo\tor name\tattractive colors and design\ttext or graphics\twith information about goods or services\tthe sign could be contained within a frame or on a pole near a business place", 10], "breaking wave": ["Yes. 'Breaking wave' has a tangible appearance and refers to a specific moment in the motion of waves.\nA few things that are visually similar to 'breaking wave' but are not 'breaking wave' are:\tsea foam\ttidal bore\twaterfall\nThere are several useful visual features to tell there is 'breaking wave' and not similar things in a photo:\ta wave crest that is toppling or falling forward\tintense white water and foam\ta distinct curl or tube shape", 10], "pizza spatula": ["Yes. 'Pizza spatula' has a tangible appearance and is a kind of kitchen utensil.\nA few things that are visually similar to 'pizza spatula' but are not 'pizza spatula' are:\tflat spatula\tgrilling spatula\tfish spatula\tbaking scraper\nThere are several useful visual features to tell there is 'pizza spatula' and not similar things in a photo:\tlarge and flat\tround or rectangular shape\tlong handle\tmetallic or wooden material\twith or without perforations for easy lifting and transferring of the pizza to a plate or cutting board", 10], "rectangular box": ["Yes. 'Rectangular box' has a tangible appearance and is a type of object with distinct features.\nA few things that are visually similar to 'rectangular box' but are not 'rectangular box' are: a book, a tablet, a brick, a folder.\nThere are several useful visual features to tell there is 'rectangular box' and not similar things in a photo: it has six rectangular sides, four sides shape a rectangle and two squares, sharp and defined edges, it has a closed shape, it can have flaps on the top to open and close it.", 10], "starfish": ["Yes. 'Starfish' has a tangible appearance and is a sea creature.\nA few things that are visually similar to 'starfish' but are not 'starfish' are:\tsea urchin\tshell\tcoral\nThere are several useful visual features to tell there is 'starfish' and not similar things in a photo:\tfive-pointed star-shaped body\twith or without arms or tentacles\thard or spiky texture\tusually brown, orange, or reddish-brown color\tfound in water or on the beach", 10], "phone pole": ["Yes. 'Phone pole' has a tangible appearance and is a type of utility pole.\nA few things that are visually similar to 'phone pole' but are not 'phone pole' are:\ttraffic signal\tpost light\tstreet light\tflag pole\nThere are several useful visual features to tell there is 'phone pole' and not similar things in a photo:\ttall wooden or metal pole\ton the side of a road\tor in a cluster\twith wires or cables attached to it", 10], "umbrella pole": ["Yes. 'Umbrella pole' has a tangible appearance and is a part of an umbrella.\nA few things that are visually similar to 'umbrella pole' but are not 'umbrella pole' are:\tlamp post\tflag pole\tski pole\nThere are a few useful visual features to distinguish 'umbrella pole' from the listed similar things in a photo:\thaving an open umbrella hanging from the top of it\tcylindrical in shape\ttextured, with ribs and grooves for the umbrella to rest on\ttop and bottom with a pointed spike for embedding in the ground or base", 10], "chocolate chip cookie": ["Yes. 'Chocolate chip cookie' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'chocolate chip cookie' but are not 'chocolate chip cookie' are:\tsugar cookie\toatmeal raisin cookie\tshortbread cookie\nThere are several useful visual features to tell there is 'chocolate chip cookie' and not other types of cookies in a photo:\tround or slightly flattened shape\tchocolate chips or chunks baked inside\ta slightly golden brown color\tridged texture on top", 10], "scoreboard wall": ["Yes. 'scoreboard wall' has a tangible appearance and is a type of wall with a sports scoreboard on it.\nA few things that are visually similar to 'scoreboard wall' but are not 'scoreboard wall' are:\tsports mural\twallpaper with sports pattern\tpainted sports logos and emblems\tonline game scoreboard\nThere are several useful visual features to tell there is 'scoreboard wall' and not similar things in a photo:\ta physical wall in a sports facility or stadium with a visible scoreboard\tfor games or matches\tdisplaying team names or logos, scores, and time markers.", 10], "wave ocean": ["Yes. 'Wave ocean' has a tangible appearance and can be seen in a photo.\nA few things that are visually similar to 'wave ocean' but are not 'wave ocean' are:\tA calm sea\tA river\tA lake\nThere are several useful visual features to tell there is 'wave ocean' and not similar things in a photo:\tRippling waves breaking on the shore\tSpray from breaking waves\tPatterns of white foam on the surface of the water\tThe sounds of crashing waves can be heard", 10], "leafy tree branch": ["Yes. 'Leafy tree branch' has a tangible appearance and is a common natural object.\nA few things that are visually similar to 'leafy tree branch' but are not 'leafy tree branch' are:\tdead tree branch\tbush\twithered fern \nThere are several useful visual features to tell there is 'leafy tree branch' and not similar things in a photo:\tconnected to a tree\ttraditional woody branch shape\tfull of green leaves or needles\twith some small colorful flowers or fruits", 10], "round bush": ["Yes. 'Round bush' has a tangible appearance, and its shape is a defining feature.\nA few things that are visually similar to 'round bush' but are not 'round bush' are:\ttree\tshrub\thedge\tpotted plant\tsphere sculpture\nThere are several useful visual features to tell there is 'round bush' and not similar things in a photo:\tcircular shape\tgreen foliage or leaves\tbushy and dense appearance\tno visible trunk or stem\tbushy from all sides", 10], "plastic dish": ["Yes. 'Plastic dish' has a tangible appearance and is a type of tableware.\nA few things that are visually similar to 'plastic dish' but are not 'plastic dish' are:\tpaper plate\tfoil pan\tceramic bowl\nThere are several useful visual features to tell there is 'plastic dish' and not similar things in a photo:\tmade of plastic\tsmooth and shiny surface\tflat and round shape with low edges or rim\tlightweight and often disposable", 10], "metal bottle": ["Yes. 'Metal bottle' has a tangible appearance and is a container made of metal.\nA few things that are visually similar to 'metal bottle' but are not 'metal bottle' are:\tthermos\thip flask\tcanteen\twater bottle\nThere are several useful visual features to tell there is 'metal bottle' and not similar things in a photo:\tmade out of metal\tcylindrical or curved shape\tscrew or flip-top lid\tmay have a handle or a strap", 10], "fringes": ["Yes. 'Fringes' has a tangible appearance and refers to the decorative edges or tassels of fabric or clothing.\nA few things that are visually similar to 'fringes' but are not 'fringes' are:\thair strands\tloose threads\trope ends\nThere are several useful visual features to tell there is 'fringes' and not similar things in a photo:\tdecorative tassels or threads\ton the edge of fabric or clothing\tmovements when shaken or swayed\tdecoration and not a loose strand.", 10], "purse brown": ["Yes. 'Purse brown' has a tangible appearance and refers to a brown colored purse or handbag.\nA few things that are visually similar to 'purse brown' but are not 'purse brown' are:\tbackpack\tbriefcase\ttote bag\tmessenger bag\nThere are several useful visual features to tell there is 'purse brown' and not similar things in a photo:\tsmaller size\tbrown color with different shades\thandles for carrying or a shoulder strap\tclasps or zippers for closure", 10], "spiral": ["Yes. 'Spiral' has a visually concrete concept and can be seen in various forms in nature and human-made objects.\nA few things that are visually similar to 'spiral', but not 'spiral,' are: snail shells, coiled ropes, curled hair, screw heads, helix\nThere are several useful visual features to distinguish 'spiral' from the similar things listed in a photo: consistent curve that gradually decreases in radius or distance from the center, one continuous line without any abrupt changes, and a clear circular path, either clockwise or counterclockwise.", 10], "yellow pot": ["Yes. 'Yellow pot' has a tangible appearance.\nA few things that are visually similar to 'yellow pot' but are not 'yellow pot' are:\tyellow vase\tyellow bucket\tyellow cup\tyellow lid\nThere are several useful visual features to tell there is 'yellow pot' and not similar things in a photo:\tpot shape\twith handles\tmade of ceramic or other cooking material\tyellow color", 10], "metal grid": ["Yes. 'Metal grid' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'metal grid' but are not 'metal grid' are:\twire mesh\tfence\tnetting\nThere are several useful visual features to tell there is 'metal grid' and not similar things in a photo:\tconsisting of intersecting metal bars or wires\trectangular or square pattern\trigid and sturdy\tapplications such as walkways, platforms, or ventilation", 10], "hospital bed": ["Yes. 'Hospital bed' has a tangible appearance and is a specific type of bed.\nA few things that are visually similar to 'hospital bed' but are not 'hospital bed' are:\tinflatable mattress\tcamping cot\tfoldable cot\nThere are several useful visual features to tell there is 'hospital bed' and not similar things in a photo:\tadjustable headrest and footrest\tside rails\tfor patients\tuse of positioning pads and special mattresses", 10], "soap dispensers": ["Yes. 'soap dispensers' has a tangible appearance and is an object used for dispensing soap.\nA few things that are visually similar to 'soap dispensers' but are not 'soap dispensers' are:\tshampoo bottles\tlotion dispensers\thand sanitizer dispensers\nThere are several useful visual features to tell there is 'soap dispensers' and not similar things in a photo:\tpumping mechanism\tfor liquid soap or gel\tclear or translucent body, to see the level of soap inside\thave a nozzle or a spout to dispense the soap", 10], "boiler": ["Yes. 'Boiler' has a tangible appearance and is a type of mechanical equipment used for heating water or generating steam.\nA few things that are visually similar to 'boiler' but are not 'boiler' are:\tWater Heater\tFurnace\tRadiator\tHVAC System\nThere are several useful visual features to tell there is 'boiler' and not similar things in a photo:\tRectangular or cylindrical shape\tMetallic surface\tPipes or tubes attached to the main body\tPressure gauge and temperature control valve present.", 10], "cable lines": ["Yes. 'Cable lines' has a tangible appearance and refers to the overhead or underground cables that transmit electricity or telecommunications signals.\nA few things that are visually similar to 'cable lines' but are not 'cable lines' are:\ttrolley wires\tsuspension bridge cables\ttelephone wires\tfishing lines\nThere are several useful visual features to tell there is 'cable lines' and not similar things in a photo:\tthick and heavy\tdirectly connected to poles\tor hanging from posts and buildings\toften in parallel lines\tor forming grids\tof varying heights above the ground", 10], "wooden wheels": ["Yes. 'Wooden wheels' has a tangible appearance and is a type of wheel.\nA few things that are visually similar to 'wooden wheels' but are not 'wooden wheels' are:\tsteel wheels\tbicycle wheels\tferris wheel\tgear wheels\nThere are several useful visual features to tell there is 'wooden wheels' and not similar things in a photo:\tmade of wood\tcircular shape\tspokes around the center\thub or axle in the center\ttypically used on old-fashioned modes of transportation such as horse-drawn carts or wagons.", 10], "crinkle": ["No. 'Crinkle' is too vague or abstract to be distinguished in a photo.", 10], "front house": ["Yes. 'Front house' has a tangible appearance and refers to the front-facing side of a house.\nA few things that are visually similar to 'front house' but are not 'front house' are:\tside of a house\tback of a house\tanother building\nThere are several useful visual features to tell there is 'front house' and not similar things in a photo:\tmain entrance or door\twindows\tfront porch or steps\taddress numbers or sign\tfront yard or landscaping", 10], "kitchen countertop": ["Yes. 'Kitchen countertop' has a tangible appearance and is a surface in a kitchen used for preparing food.\nA few things that are visually similar to 'kitchen countertop' but are not 'kitchen countertop' are:\ttable\tdesk\tshelf\twindow sill\nThere are several useful visual features to tell there is 'kitchen countertop' and not similar things in a photo:\tlocated in a kitchen\tarea where food is prepared and cooked\tmade of stone or a heat-resistant material\tflat surface to chop or place objects\ton a lower level than shelves or cabinets\tcan have a sink or stove built into it.", 10], "crispy piece": ["Yes. 'Crispy piece' has a tangible appearance and can refer to different types of food.\nA few things that are visually similar to 'crispy piece' but are not 'crispy piece' are:\tburnt or charred food pieces\tcroutons\ttoasts \nThere are several useful visual features to tell there is 'crispy piece' and not similar things in a photo:\tcrunchy texture\tlight or golden brown color\tirregular, jagged edges\tmay have bubbles or air pockets", 10], "candy cane": ["Yes. 'Candy cane' has a tangible appearance and is a type of candy.\nA few things that are visually similar to 'candy cane' but are not 'candy cane' are:\tgarden cane\twalking cane\tpipe cleaner\tred and white barber pole\nThere are several useful visual features to tell there is 'candy cane' and not similar things in a photo:\thook or crook at one end\tof a regular cylindrical shape\tstriped red and white (sometimes other colors)", 10], "rickshaw": ["Yes. 'Rickshaw' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'rickshaw' but are not 'rickshaw' are:\tbicycle\ttricycle\tscooter\tmotorcycle\nThere are several useful visual features to tell there is 'rickshaw' and not similar things in a photo:\ttwo or three wheels\tpassenger seating in front or back\trider-powered or human-powered\tcanopy or cover over the seating area", 10], "leather handbag": ["Yes. 'Leather handbag' has a tangible appearance and is a type of bag.\nA few things that are visually similar to 'leather handbag' but are not 'leather handbag' are:\tplastic bag\ttote bag\tmessenger bag\tbackpack\tsatchel\nThere are several useful visual features to tell there is 'leather handbag' and not similar things in a photo:\tsoft and unstructured\tleather or leather-like material\thandles or straps\tfor women's use\tdecorative elements such as zippers or buckles", 10], "onesie": ["Yes. 'Onesie' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'onesie' but are not 'onesie' are:\tpajamas\tjumpsuits\toveralls\tbodysuits\nThere are several useful visual features to tell there is 'onesie' and not similar things in a photo:\tloose-fitting cozy fabric\tcovers both the torso and the legs\tsnaps or zips up the front or back\thas sleeves and sometimes a hood (optional)\tcan be adorned with patterns or images (optional)", 10], "orange fabric": ["Yes. 'Orange fabric' has a tangible appearance and is a type of textile.\nA few things that are visually similar to 'orange fabric' but are not 'orange fabric' are:\tpainted wall\torange paper\torange plastic\nThere are several useful visual features to tell there is 'orange fabric' and not similar things in a photo:\tmade of thread or yarn\tflexible and foldable\ttexture or pattern visible\ton a piece of furniture\tor in a piece of clothing.", 10], "orange thing": ["No. 'Orange thing' is too vague or abstract to be distinguished in a photo.", 10], "orange wrist band": ["Yes. 'Orange wrist band' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'orange wrist band' but are not 'orange wrist band' are:\thair tie\twatch\tbracelet\nThere are several useful visual features to tell there is 'orange wrist band' and not similar things in a photo:\torange color\tcircular shape\tworn around the wrist", 10], "metal floor lamp": ["Yes. 'Metal floor lamp' has a tangible appearance and describes a specific type of lamp.\nA few things that are visually similar to 'metal floor lamp' but are not 'metal floor lamp' are:\ttable lamp\tdesk lamp\tceiling light\nThere are several useful visual features to tell there is 'metal floor lamp' and not similar things in a photo:\ta stand or base that raises it off the floor\ta long, adjustable metal pole\tthat attaches to a metal lampshade\tthat emits light from its top", 10], "plum": ["Yes. 'Plum' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'plum' but are not 'plum' are:\tgrapes\tcherries\tmulberries\tblueberries\nThere are several useful visual features to tell there is 'plum' and not similar things in a photo:\trounded shape\twith a groove running down one side\tdark purple or reddish-purple color\tsmooth and matte skin, not shiny\tjuicy yellow flesh around a hard seed\tin a cluster with a small stem attached to it", 10], "washcloths": ["Yes. 'washcloths' has a tangible appearance and is a type of cloth used for washing.\nA few things that are visually similar to 'washcloths' but are not 'washcloths' are:\tdishcloths\tbath towels\thand towels\trags\nThere are several useful visual features to tell there is 'washcloths' and not similar things in a photo:\tsmall size, usually around 12\"x12\"\tsquare or rectangular shape\tsmooth, absorbent texture\tmay be woven or knitted\thave a loop or tag for easy hanging and drying\tpredominantly used for face and hand washing", 10], "species": ["No. 'Species' is too abstract to be distinguished in a photo. \n\nNote: While individual organisms belonging to different species can have visually distinct features, the concept of 'species' itself is a biological classification that is not visually tangible.", 10], "sports field": ["Yes. 'Sports field' has a tangible appearance and is a designated area for sports activities.\nA few things that are visually similar to 'sports field' but are not 'sports field' are:\tpark\tpicnic area\tplayground\tempty lot\nThere are several useful visual features to tell there is 'sports field' and not similar things in a photo:\tstripes or markings for sports such as soccer, football or baseball\tno benches or tables\ton a flat surface with short grass or artificial turf\tno trees, bushes or large rocks", 10], "thickets": ["Yes. 'Thickets' has a tangible appearance and refers to dense and tangled vegetation.\nA few things that are visually similar to 'thickets' but are not 'thickets' are:\tbushes\thedges\tforests\tgrass fields\nThere are several useful visual features to tell there is 'thickets' and not similar things in a photo:\tdense and tangled vegetation\tinterwoven branches\tand twigs\tlimited visibility due to the dense growth.", 10], "banks": ["No. 'Banks' is too vague or abstract to be distinguished in a photo.\nA few things that are visually similar to 'banks' but are not 'banks' are:\toffice buildings\thotels\tstores\tgovernment buildings\nThere are no useful visual features to distinguish 'banks' from the listed similar things in a photo, as banks can take many different forms and buildings can have similar architectural features.", 10], "banans": ["Yes. 'Bananas' has a tangible appearance as a fruit.\nA few things that are visually similar to 'bananas' but are not 'bananas' are:\tplantains\tyellow peppers\tcorn\tonions\nThere are several useful visual features to tell there is 'bananas' and not similar things in a photo:\tlong and curved shape\tyellow color\tslightly tapered ends\tgenerally smooth texture", 10], "ac unit": ["Yes. 'AC unit' has a tangible appearance and is a machine used for cooling air.\nA few things that are visually similar to 'AC unit' but are not 'AC unit' are:\tfan\theater\trefrigerator\tdehumidifier\nThere are several useful visual features to tell there is 'AC unit' and not similar things in a photo:\trectangle or square shape\twith vents or grills\tforced air or air ducts\tdigital or manual controls\tthermostat or temperature display", 10], "security officer": ["Yes. 'Security officer' has a tangible appearance and is a kind of profession.\nA few things that are visually similar to 'security officer' but are not 'security officer' are:\tpolice officer\tmilitary personnel\tbouncer\tbodyguard\nThere are several useful visual features to tell there is 'security officer' and not similar things in a photo:\tuniform with badges\tor patches\tbadges or insignias\tcommonly seen in public places or events\tguards designated areas\twith equipment such as flashlight, handcuffs, or baton", 10], "basil leaves": ["Yes. 'Basil leaves' has a tangible appearance and is a type of herb.\nA few things that are visually similar to 'basil leaves' but are not 'basil leaves' are:\tmint leaves\tcilantro leaves\tspinach leaves\nThere are several useful visual features to tell there is 'basil leaves' and not similar things in a photo:\toval or teardrop-shaped leaves\tbright green color\tsmooth surface\twith a stem and veins\taroma similar to cloves", 10], "pennant": ["Yes. 'Pennant' has a tangible appearance and is a type of flag.\nA few things that are visually similar to 'pennant' but are not 'pennant' are:\tribbon\tstreamer\tbanner\t\nThere are several useful visual features to tell there is 'pennant' and not similar things in a photo:\ttriangular shape\thanging from a pole or a rope\tbearing identifying words or symbols (such as numbers, letters, or logos)", 10], "round objects": ["Yes. 'Round objects' has a tangible appearance and refers to objects that have a circular shape.\nA few things that are visually similar to 'round objects' but are not 'round objects' are:\tlights\tballoons\tpizza\tsun\nThere are several useful visual features to tell there are 'round objects' and not similar things in a photo:\ta circular shape\twith or without depth\tsymmetrical shape\tround edges or smooth curves", 10], "mcdonalds sign": ["Yes. 'McDonald's sign' has a tangible appearance and is a kind of signage.\nA few things that are visually similar to 'McDonald's sign' but are not 'McDonald's sign' are:\tother fast food restaurant signs\tsale signs\tadvertising banners\tfor sale sign\nThere are several useful visual features to tell there is 'McDonald's sign' and not similar things in a photo:\tbold, golden arches\tyellow and red color scheme\tthe word 'McDonald's' written in all-caps the phrase \"I'm Lovin' It\"\tif there is a picture of Ronald McDonald", 10], "dr": ["No. 'Dr' is too vague or abstract to have a tangible appearance that can be distinguished in a photo.", 10], "reflective window": ["Yes. 'Reflective window' has a tangible appearance and is a kind of window that reflects light and/or images.\nA few things that are visually similar to 'reflective window' but are not 'reflective window' are:\tregular window\tmirror\tglass surface\nThere are several useful visual features to tell there is 'reflective window' and not similar things in a photo:\treflection of the environment outside or inside the building\tlack of transparency or transparency limited to certain areas in the window\tglare or shine on the surface", 10], "elevation": ["No. 'Elevation' is too vague or abstract to have a tangible appearance and be distinguished in a photo. \n\nHowever, if we interpret 'elevation' to refer to the height or altitude of something, then:\n\nA few things that are visually similar to 'elevation' but are not 'elevation' are:\tdistance\theight of a person or object\tsize or scale of a building or mountain\nThere are several useful visual features to tell there is 'elevation' and not similar things in a photo:\tcomparison to a standard reference point, such as sea level\tor surrounding objects or landmarks that suggest height\tperspective or angle of view that emphasizes height or altitude (e.g. looking down on something from above)", 10], "tile pattern": ["Yes. 'Tile pattern' has a tangible appearance and refers to the arrangement of tiles that create a design.\nA few things that are visually similar to 'tile pattern' but are not 'tile pattern' are:\twood grain\tpaint drips\tbrick wall\tbamboo sheets\nThere are several useful visual features to tell there is 'tile pattern' and not similar things in a photo:\trepetitive design\tarranged in a grid\tform geometric shapes\tsymmetrical patterns", 10], "glass vases": ["Yes. 'Glass vases' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'glass vases' but are not 'glass vases' are:\tglass cups\twine glasses\tflower pots\tbowls\nThere are several useful visual features to tell there are 'glass vases' and not similar things in a photo:\ttranslucent or transparent material\thollow cylinder or cone shape\tclear or colored glass or crystal, but not plastic or ceramic\tnarrow opening at the top used for holding flowers or other decorative items.", 10], "wall divider": ["Yes. 'Wall divider' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wall divider' but are not 'wall divider' are:\tshelves\tbookcases\tpartition walls\tdoors\nThere are several useful visual features to tell there is 'wall divider' and not similar things in a photo:\tdesigned to divide or separate a space can stand alone or be attached to a wall\tmade of wood, metal, glass, or fabric panels\tcan be folded or moved to change the configuration of space", 10], "female player": ["Yes. 'Female player' has a tangible appearance and can refer to a woman who plays a sport or a game.\nA few things that are visually similar to 'female player' but are not 'female player' are:\tcrowd of people\twatching soccer on TV\tanimation character\nThere are several useful visual features to tell there is 'female player' and not similar things in a photo:\twearing a sports uniform or equipment\tpadding, helmets, or specific gear for sport or game\tevidence of athletic activity (running, jumping, throwing, hitting, etc.)", 10], "broccoli leaf": ["Yes. 'Broccoli leaf' has a tangible appearance.\nA few things that are visually similar to 'broccoli leaf' but are not 'broccoli leaf' are:\tspinach leaf\tkale leaf\tcabbage leaf\nThere are several useful visual features to tell there is 'broccoli leaf' and not similar things in a photo:\tlarge, dark green leaves\twith crinkled edges\tand a sturdy stalk\tmeasures around 8 \u2014 10 inches long, and 4\u20136 inches wide.\tThe leaves of the broccoli plant usually grow vertically and point upwards", 10], "wood barn": ["Yes. 'Wood barn' has a tangible appearance and is a type of building used to store hay, grain, or animals.\nA few things that are visually similar to 'wood barn' but are not 'wood barn' are:\thouse\tgarage\tshed\tgazebo\nThere are several useful visual features to tell there is 'wood barn' and not similar things in a photo:\tmade of wood\tbarn doors\tbarn cupola\thayloft or loft door\tanimal stalls\thay bales or farm equipment inside the structure.", 10], "bakers": ["No. 'Bakers' is too vague or abstract to be distinguished solely by visual appearance in a photo. \n\nHowever, a few things that might be associated with bakers are: mixer, oven, bread, cakes, apron, baking tools.\n\nUseful visual features for identifying a baker in a photo might include:\n\n- wearing a chef's hat, apron, or other identifiable culinary uniform or attire\n- holding or using baking tools, such as a rolling pin, whisk, or spatula\n- standing in front of or near baking equipment, such as an oven or mixer, or ingredients, such as flour or sugar\n- displaying baked goods, with or without packaging, such as bread, cakes, pastries, or cupcakes, in the photo.", 10], "wood cabinet door": ["Yes. 'Wood cabinet door' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wood cabinet door' but are not 'wood cabinet door' are:\twindow\tframe\tshelf\tfloor\tplank of wood\nThere are several useful visual features to tell there is 'wood cabinet door' and not similar things in a photo:\trectangle or square shape\tattached to a cabinet\thandles or knobs\tgrain pattern in the wood\tsurface texture or finish, such as paint or varnish.", 10], "cinder block": ["Yes. 'Cinder block' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'cinder block' but are not 'cinder block' are:\tconcrete bricks\tpaver stones\tdecorative blocks\tcement blocks\nThere are several useful visual features to tell there is 'cinder block' and not similar things in a photo:\trectangular shape\twith visible cinder particles and holes\tgray or beige\tcolor\tused in construction of walls or buildings", 10], "lampstand": ["Yes. 'Lampstand' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'lampstand' but are not 'lampstand' are:\ttable\tchair\tshelf\tsculpture\nThere are several useful visual features to tell there is 'lampstand' and not similar things in a photo:\ttall and vertical structure\twith a base and a top\tsupports a light source (lamp)", 10], "bedskirt": ["Yes. 'Bedskirt' has a tangible appearance and is a kind of bedding accessory.\nA few things that are visually similar to 'bedskirt' but are not 'bedskirt' are:\ttablecloth\tdrapes\tcurtains\tshower curtains\nThere are several useful visual features to tell there is 'bedskirt' and not similar things in a photo:\tfabric\tdraping down from the mattress\tto match the bedding pattern or color scheme", 10], "giraffe feeding": ["Yes. 'Giraffe feeding' has a tangible appearance and involves a person feeding a giraffe.\nA few things that are visually similar to 'giraffe feeding' but are not 'giraffe feeding' are:\tpetting a giraffe\triding a giraffe\tfeeding other animals\nThere are several useful visual features to tell there is 'giraffe feeding' and not similar things in a photo:\tperson feeding a giraffe\tthe giraffe's long neck and spotted coat\tleaves or carrots in the person's hand or in the giraffe's mouth\tan outdoor setting such as a safari park or a zoo", 10], "feed": ["No. 'Feed' is too vague or abstract to be distinguished in a photo. \n\nHowever, a few things that are visually similar to 'feed' but are not 'feed' are: seeds, grains, or pellets used as animal feed. In terms of physical appearance, these may look similar to other small round objects like beads, marbles, or small balls.\n\nUseful visual features for distinguishing 'feed' from these visually similar things would be to look for the context in which they are presented, such as: \n- Animals present nearby, which could indicate that the small round objects are actually animal feed\n- Packaging or storage containers labeled as animal feed\n- Signs indicating that the objects are used for feeding animals", 10], "bouquets": ["Yes. 'Bouquets' has a tangible appearance and is an arrangement of flowers.\nA few things that are visually similar to 'bouquets' but are not 'bouquets' are:\tcrowns of flowers\tflower wreaths\tvases with flowers\tsingle flowers\nThere are several useful visual features to tell there is 'bouquets' and not similar things in a photo:\tarranged flowers\tassembled carefully into a bouquet\tbinding or ribbon holding the flowers together\ta mixture of different types of flowers arranged in a harmonious way", 10], "light shirt": ["Yes. 'Light shirt' has a tangible appearance.\nA few things that are visually similar to 'light shirt' but are not 'light shirt' are:\theavy shirt\tdress\tpolo shirt\tsweater\tblouse\nThere are several useful visual features to tell there is 'light shirt' and not similar things in a photo:\tfabric is thin and breathable\tusually white or light-colored\tcan be long or short-sleeved", 10], "blue clothing": ["Yes. 'Blue clothing' has a tangible appearance and refers to clothing items that are primarily blue in color.\nA few things that are visually similar to 'blue clothing' but are not 'blue clothing' are:\tblue sky\tblue paint\tblue flowers\tblue wallpaper\nThere are several useful visual features to tell there is 'blue clothing' and not similar things in a photo:\tvisible clothing items, such as shirts, pants, or dresses\tdominantly blue in color\tmade from fabric or other materials suitable for clothing\tworn by a person or placed in a location where clothing is typically found.", 10], "plastic computer mouse": ["Yes. 'Plastic computer mouse' has a tangible appearance and is a type of input device for computers.\nA few things that are visually similar to 'plastic computer mouse' but are not 'plastic computer mouse' are:\tplastic toy mouse\tplastic remote control\tplastic phone case\nThere are several useful visual features to tell there is 'plastic computer mouse' and not similar things in a photo:\toval shape\twith buttons\tor a trackball\tfor scrolling and clicking\ta cable or wireless connector to connect to a computer\tclearly marked left and right buttons\tfor right or left-handed use.", 10], "glass enclosure": ["Yes. 'Glass enclosure' has a tangible appearance and refers to any enclosed space made of glass.\nA few things that are visually similar to 'glass enclosure' but are not 'glass enclosure' are:\twindows\tdisplay cases\tskylights\tglass-walled buildings\nThere are several useful visual features to tell there is 'glass enclosure' and not similar things in a photo:\ttransparent\twalls made of glass\tfully or partially enclosed space\twithin a larger structure or building\tcan see through to the inside\tfrom the outside, the ceiling and any supports are visible.", 10], "serving platter": ["Yes. 'Serving platter' has a tangible appearance and is a dish used for serving food.\nA few things that are visually similar to 'serving platter' but are not 'serving platter' are:\tplates\tbowls\ttrays\tcutting boards\nThere are several useful visual features to tell there is 'serving platter' and not similar things in a photo:\tlarge, flat surface\toften oval or rectangular in shape\traised edges or rim\tfor serving food on for presentation purposes.", 10], "metal walkway": ["Yes. 'Metal walkway' has a tangible appearance and is a type of structure.\nA few things that are visually similar to 'metal walkway' but are not 'metal walkway' are:\tbridge\tmetal fence\t\nThere are several useful visual features to tell there is 'metal walkway' and not similar things in a photo:\traised off the ground\tsupported by metal poles\tor cables\twith a flat surface for walking\ton or a grid pattern\tfor footsteps\tmade entirely of metal", 10], "orange soda": ["Yes. 'Orange soda' has a tangible appearance and is a type of carbonated drink.\nA few things that are visually similar to 'orange soda' but are not 'orange soda' are:\torange juice\torange-flavored sports drink\torange-flavored sparkling water\nThere are several useful visual features to tell there is 'orange soda' and not similar things in a photo:\ttransparent or translucent bottle or can\tbright orange color\tcarbonation or bubbles\tfizzy appearance or texture\tsoda label or logo", 10], "tank lid": ["Yes. 'Tank lid' has a tangible appearance and refers to the cover of a tank or a container.\nA few things that are visually similar to 'tank lid' but are not 'tank lid' are:\tmanhole cover\ttoilet lid\tcookie jar lid\tpaint can lid\nThere are several useful visual features to tell there is 'tank lid' and not similar things in a photo:\tcircular or square shape\tmetallic appearance\thinges or handles to open and close\tthe size and position relative to the container or tank it covers.", 10], "harley davidson logo": ["Yes. 'Harley Davidson logo' has a tangible appearance and is a specific brand logo.\nA few things that are visually similar to 'Harley Davidson logo' but are not 'Harley Davidson logo' are:\tMarlboro logo\tIndian Motorcycle logo\tconfederate flag logo\nThere are several useful visual features to tell there is 'Harley Davidson logo' and not similar things in a photo:\tthe words \"Harley Davidson\" or \"HD\"\trider driving a motorcycle through the logo\tbar and shield shape, often in black and orange colors, with eagle or wings or flames designs.", 10], "gifts": ["Yes. 'Gifts' has a tangible appearance and is a physical object that is given to someone.\nA few things that are visually similar to 'gifts' but are not 'gifts' are:\tboxes\tpresents\tbags\twrapped items\nThere are several useful visual features to tell there is 'gifts' and not similar things in a photo:\twrapped in paper or decorated\tcards or labels\twith bows or ribbons\tcontaining items or objects that can be seen through the wrapping", 10], "wraps": ["Yes. 'Wraps' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'wraps' but are not 'wraps' are:\tsandwiches\tburritos\tkebabs\t\nThere are several useful visual features to tell there is 'wraps' and not similar things in a photo:\tflatbread or tortilla wrapped around the filling\tportable and easy to hold with one hand\tvariety of filling options\toften cut in half diagonally\tpathetically presented with some kind of clipping on top", 10], "stove knobs": ["Yes. 'Stove knobs' has a tangible appearance and is a type of kitchen equipment.\nA few things that are visually similar to 'stove knobs' but are not 'stove knobs' are:\tcabinet knobs\tdrawer pulls\tdoor handles\tshower knobs\nThere are several useful visual features to tell there are 'stove knobs' and not similar things in a photo:\tcircular shape\tlocated on a stove\tor cooktop\thave markings or labels to indicate temperature or level\tof heat\tcontrol the burners of a stove\twithout any distinct design or decoration", 10], "persons finger": ["Yes. 'Persons finger' has a tangible appearance and is a part of the human body.\nA few things that are visually similar to 'persons finger' but are not 'persons finger' are:\ttoes\tclaws\tpaws\nThere are several useful visual features to tell there is 'persons finger' and not similar things in a photo:\tattached to a hand\twith a fingernail or a nail bed\tvarious shapes depending on the position, palm-facing or back-facing", 10], "tres": ["Yes. 'Tres' has a tangible appearance and is a type of tree.\nThere are no things visually similar to 'tres' as 'tres' is the Spanish word for 'three' and not a physical object.\nN/A.", 10], "silver compact car": ["Yes. 'Silver compact car' has a tangible appearance and is a specific type of car.\nA few things that are visually similar to 'silver compact car' but are not 'silver compact car' are:\tblue sports car\tred sedan\tblack SUV\tsilver motorcycle\nThere are several useful visual features to tell there is 'silver compact car' and not similar things in a photo:\tsilver color\tcompact size\t4 wheels and 4 doors\ttail lights and headlights\tside mirrors on both sides\tof the car\tlicense plate in the back midsection of the car.", 10], "restroom sign": ["Yes. 'Restroom sign' has a tangible appearance and is a kind of sign.\nA few things that are visually similar to 'restroom sign' but are not 'restroom sign' are:\texits\tentrances\tdirections\tparking\nThere are several useful visual features to tell there is 'restroom sign' and not similar things in a photo:\tdepicts a human stick figure or gender-specific symbol ('\u2640' or '\u2642')\tusually blue or black with white or silver text\tor if it names the place correctly as 'Restroom'.", 10], "flowering tree": ["Yes. 'Flowering tree' has a tangible appearance and is a tree with flowers.\nA few things that are visually similar to 'flowering tree' but are not 'flowering tree' are:\tregular tree\tshrub\tbush\tvine\nThere are several useful visual features to tell there is a 'flowering tree' and not similar things in a photo:\tvisible flowers on branches and around the tree\tusually a taller and larger plant with thick branches and leaves\tspecific flower types (e.g. cherry blossom, magnolia, dogwood)", 10], "diamond sign": ["Yes. 'Diamond sign' has a tangible appearance and is a type of road sign.\nA few things that are visually similar to 'diamond sign' but are not 'diamond sign' are:\toctagon sign\ttriangle sign\tsquare sign\trectangle sign\nThere are several useful visual features to tell there is 'diamond sign' and not similar things in a photo:\tdiamond-shaped\ttwo opposite diagonal sides are yellow\twith a black border with the word 'warning' in large black letters inside\tthe sign may have a pictogram to indicate the specific warning", 10], "call button": ["Yes. 'Call button' has a tangible appearance and is a kind of button used to request service.\nA few things that are visually similar to 'call button' but are not 'call button' are:\tdoorbell\televator buttons\tcrosswalk buttons\talarm buttons\nThere are several useful visual features to tell there is 'call button' and not similar things in a photo:\tusually found in public places or hospitals\thas the word \"call\" written or an icon of a bell or a speaker\tlighting to indicate it's in use\tor has been activated", 10], "gummy bear": ["Yes. 'Gummy bear' has a tangible appearance and is a type of candy.\nA few things that are visually similar to 'gummy bear' but are not 'gummy bear' are:\tjelly beans\tlicorice\tsour patch candy\tskittles\nThere are several useful visual features to tell there is 'gummy bear' and not similar things in a photo:\tbear-shaped\tsoft and chewy texture\tbright colors and translucent appearance\tsugar-coated exterior (in some cases)", 10], "gummy bears": ["Yes. 'Gummy bears' have a visually concrete appearance and are a type of candy.\nA few things that are visually similar to 'gummy bears' but are not 'gummy bears' are:\tgummy worms\tjellybeans\tfruit snacks\tpieces of rubber\nThere are several useful visual features to tell there is 'gummy bears' and not similar things in a photo:\tshape of a bear\tvariety of colors\tsemi-translucent texture\tsugar-coated exterior", 10], "wall cabinets": ["Yes, 'wall cabinets' has a tangible appearance and refers to storage units mounted on a wall.\nA few things that are visually similar to 'wall cabinets' but are not 'wall cabinets' are:\tshelves\tbookcases\tlockers\tdrawers\nThere are several useful visual features to tell there are 'wall cabinets' and not similar things in a photo:\thung from the wall\tattached shelves or compartments\thinged or sliding doors\tfor storing objects or supplies\tout of reach from the ground\tslim and space-saving design.", 10], "brick fire place": ["Yes. 'Brick fire place' has a tangible appearance and is a kind of household fixture.\nA few things that are visually similar to 'brick fire place' but are not 'brick fire place' are:\tbrick wall\tstone hearth,\tor chimney\nThere are several useful visual features to tell there is 'brick fire place' and not similar things in a photo:\tmantel\tpiled logs,\tflames or cinders,\tgrate or screen in front of fire\tbricks used as a primary material.", 10], "skater shoes": ["Yes. 'Skater shoes' have a tangible appearance and are a type of footwear.\nA few things that are visually similar to 'skater shoes' but are not 'skater shoes' are:\tsneakers\trunning shoes\ttennis shoes\nThere are several useful visual features to tell there is 'skater shoes' and not similar things in a photo:\tthick or padded sole\tflattened or bulging sides\twider or bulkier design for durability and protection\tfrom brands such as Vans, DC Shoes, or Etnies, and featuring their logos or designs", 10], "half circle": ["Yes. 'Half circle' has a tangible appearance and is a geometric shape.\nA few things that are visually similar to 'half circle' but are not 'half circle' are:\tquarter circle\tfull circle\tellipse\tsphere\nThere is only one visual feature to distinguish 'half circle' from other similar things. It is a curve that is exactly half of a full circle.", 10], "change": ["No. 'Change' is too vague or abstract to be distinguished in a photo.", 10], "shiny nose": ["Yes. 'Shiny nose' has a tangible appearance and is a physical characteristic of an object or living thing.\nA few things that are visually similar to 'shiny nose' but are not 'shiny nose' are:\twet nose\tmetallic nose\tpainted nose\nThere are several useful visual features to tell there is 'shiny nose' and not similar things in a photo:\tvisible reflection or sheen\treflecting light like a mirror\tsurface shining and polished", 10], "smoking": ["Yes. 'Smoking' has a tangible appearance that can be seen in photographs.\nA few things that are visually similar to 'smoking' but are not 'smoking' are:\tsteam\tfire\texhaust from a car or a chimney\nThere are several useful visual features to tell there is 'smoking' and not similar things in a photo:\tholding something (a cigarette or a pipe)\tvisible smoke coming out of the mouth or nose\tashtray or cigarette butt visible\tfoul odor", 10], "delivery van": ["Yes. 'Delivery van' has a tangible appearance and is a type of vehicle used for transporting goods.\nA few things that are visually similar to 'delivery van' but are not 'delivery van' are:\ttrucks\tbuses\tminivans\t\nThere are several useful visual features to tell there is 'delivery van' and not similar things in a photo:\tsmaller size than trucks and buses\ttaller than minivans\tusually have a company logo or name on the side\tback doors or sliding doors for loading and unloading goods", 10], "linens": ["Yes. 'Linens' has a tangible appearance and refers to household items made of fabric such as sheets, towels, and tablecloths.\nA few things that are visually similar to 'linens' but are not 'linens' are:\tClothes\tCurtains\tChair covers\tBlankets\nThere are several useful visual features to tell there is 'linens' and not similar things in a photo:\tfabric material\toften white or neutral colors\tvisible texture (weave, embroidery, etc.)\tcommon items include sheets, towels, napkins, tablecloths, and runners.", 10], "silver pickup truck": ["Yes. 'Silver pickup truck' has a tangible appearance.\nA few things that are visually similar to 'silver pickup truck' but are not 'silver pickup truck' are:\tsilver SUV\tsilver sedan\tsilver van\tsilver trailer\nThere are several useful visual features to tell there is 'silver pickup truck' and not similar things in a photo:\ta cab for passengers and a separate cargo bed for hauling goods or equipment, flatbed or open bed design, four or two doors, large wheels and tires, rugged and practical appearance.", 10], "sideview": ["Yes. 'Sideview' has a tangible appearance and refers to a specific angle or perspective of viewing.\nThere are not many things that are visually similar to 'sideview' but not 'sideview', as it is a precise and specific term.\nThere are no useful visual features for distinguishing 'sideview' from the listed similar things in a photo, as there are no similar things to 'sideview'.", 10], "candlesticks": ["Yes. 'Candlesticks' has a tangible appearance and is a kind of holder for candles.\nA few things that are visually similar to 'candlesticks' but are not 'candlesticks' are: \tmetal rods\tbottles\tlamps\tdecorative stands\nThere are several useful visual features to tell there is 'candlesticks' and not similar things in a photo:\ttall and thin\thaving a base and a holder for the candle\tvariety of shapes and designs\tmade of metal, wood or ceramic", 10], "bathroom vanity mirror": ["Yes. 'Bathroom vanity mirror' has a tangible appearance and is a type of mirror found in a bathroom.\nA few things that are visually similar to 'bathroom vanity mirror' but are not 'bathroom vanity mirror' are:\tfull-length mirror\thandheld mirror\tmakeup mirror\nThere are several useful visual features to tell there is 'bathroom vanity mirror' and not similar things in a photo:\tmounted on the wall above a sink\tor integrated with a bathroom cabinet\trectangular shape\twith or without a frame\tbacklit for better visibility\tin a well-lit bathroom", 10], "beverage glass": ["Yes. 'Beverage glass' has a tangible appearance and is a type of glassware used to serve drinks.\nA few things that are visually similar to 'beverage glass' but are not 'beverage glass' are:\tvase\tcandle holder\tjar\tmeasuring cup\nThere are several useful visual features to tell there is 'beverage glass' and not similar things in a photo:\tclear and transparent\tusually tapered or cylindrical in shape\tnarrow opening at the top, wider base\tpurpose-made for serving drinks or beverages", 10], "silver vent": ["Yes. 'Silver vent' has a tangible appearance and is a type of ventilation opening.\nA few things that are visually similar to 'silver vent' but are not 'silver vent' are:\tchrome pipes and valves\taluminum or metal tubes or pipes\tchrome grilles or vents on cars\nThere are several useful visual features to tell there is 'silver vent' and not similar things in a photo:\tusually located on the wall or ceiling\tsquare or rectangular shape\twith perforated holes or slits\tfor regulating airflow or temperature\tcontrol knobs or levers\tpainted or coated in silver or gray color", 10], "girl skiing": ["Yes. 'Girl skiing' has a tangible appearance and refers to a specific action and subject.\nA few things that are visually similar to 'girl skiing' but are not 'girl skiing' are:\tboy skiing\twoman snowboarding\tdog running on snow\nThere are several useful visual features to tell there is 'girl skiing' and not similar things in a photo:\tfemale subject\tin skiing gear (jacket, pants, boots, helmet, skis)\tsnowy mountain or slope in the background\tjumping or turning with skis on\tski poles visible in the hands", 10], "notebook computer": ["Yes. 'Notebook computer' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'notebook computer' but are not 'notebook computer' are:\ttablet\tsmartphone\te-reader\nThere are several useful visual features to tell there is 'notebook computer' and not similar things in a photo:\tits shape and size\tof a clamshell form factor\thaving a keyboard and screen which can be opened or closed\teasily distinguishable ports for USB or HDMI", 10], "porcelain vase": ["Yes. 'Porcelain vase' has a tangible appearance and is a type of ceramic vase.\nA few things that are visually similar to 'porcelain vase' but are not 'porcelain vase' are:\tterracotta vase\tglass vase\tcrystal vase\twooden vase\nThere are several useful visual features to tell there is 'porcelain vase' and not similar things in a photo:\twhite or pale colors\tfine details such as painting or carving\tsmooth surface and texture\twith a characteristic luster and translucency\ttypically with a narrow neck and broader body", 10], "orange curtain": ["Yes. 'Orange curtain' has a tangible appearance and refers to a specific type of textile hanging.\nA few things that are visually similar to 'orange curtain' but are not 'orange curtain' are:\torange fabric\torange scarf\torange flag\nThere are several useful visual features to tell there is 'orange curtain' and not similar things in a photo:\tdecorative fabric\thanging from a rod or a rail\tcovering or screening a window, door or a wall\tsolid, printed or patterned with motifs of different colors or shapes.", 10], "fruit tree": ["Yes. 'fruit tree' has a tangible appearance and refers to trees that produce edible fruits.\nA few things that are visually similar to 'fruit tree' but are not 'fruit tree' are:\tornamental tree\tconiferous tree\tpalm tree\tbamboo\nThere are several useful visual features to tell there is 'fruit tree' and not similar things in a photo:\tbearing fruit (apples, peaches, oranges, etc.) \tleaves with serrated edges and simple veins\tnot bearing needles or fronds\troots growing towards the ground", 10], "wooden window sill": ["Yes. 'Wooden window sill' has a tangible appearance and refers to the bottom part of a window frame.\nA few things that are visually similar to 'wooden window sill' but are not 'wooden window sill' are:\twooden shelf\twooden baseboard\twooden countertop\nThere are several useful visual features to tell there is 'wooden window sill' and not similar things in a photo:\tlocated at the bottom of a window frame\trectangular shape\tflat and level surface\tfor framing and supporting a window\tsame wood type and color as window frame.", 10], "coffee shop": ["Yes. 'Coffee shop' has a tangible appearance and refers to a type of establishment that serves coffee and snacks.\nA few things that are visually similar to 'coffee shop' but are not 'coffee shop' are:\tcafe\tbar\tbakery\trestaurant\nThere are several useful visual features to tell there is a 'coffee shop' and not similar things in a photo:\tcoffee machines \tcounter with pastries trays\tseating area\twith tables and chairs\tmenus or chalkboards with coffee options\tsignage with the name and logo of the coffee shop", 10], "laundry": ["Yes. 'Laundry' has a tangible appearance and refers to clothes or linens that have been washed and/or are waiting to be washed.\nA few things that are visually similar to 'laundry' but are not 'laundry' are:\tpiles of blankets or towels\tstacks of papers\torphaned socks\t\nThere are several useful visual features to tell there is 'laundry' and not similar things in a photo:\tvariety of items, including clothes and linens\tbasket or hamper\tto be folded or hung up", 10], "clay court": ["Yes. 'Clay court' has a tangible appearance and is a type of tennis court.\nA few things that are visually similar to 'clay court' but are not 'clay court' are:\tconcrete court\tgravel court\tasphalt court\tgrass court\nThere are several useful visual features to tell there is 'clay court' and not similar things in a photo:\tdistinctive reddish or orange color\tloose surface made of crushed brick, stone, or shale\tdust clouds when players run or slide\ton-court shoe prints and skid marks", 10], "zebra mouth": ["No. 'Zebra mouth' is too vague or abstract to be distinguished in a photo.", 10], "train traffic signal": ["Yes. 'Train traffic signal' has a tangible appearance and is a kind of traffic control device.\nA few things that are visually similar to 'train traffic signal' but are not 'train traffic signal' are:\tstoplights\tcrossing signs\tpedestrian signals\nThere are several useful visual features to tell there is 'train traffic signal' and not similar things in a photo:\nthree lights, two on top and one on bottom\nred light on top and green light on bottom\nmeant for railroad crossings or train tracks", 10], "flipflops": ["Yes. 'Flipflops' has a tangible appearance and is a kind of footwear.\nA few things that are visually similar to 'flipflops' but are not 'flipflops' are:\tsandals\tloafers\tespadrilles\tslippers\nThere are several useful visual features to tell there is 'flipflops' and not similar things in a photo:\ty-shaped straps\tthat fit snugly between the toes\tflat sole, with no heel or arch support\topen toe\tbox-shaped toe\tthin and flexible material such as rubber or foam.", 10], "car tracks": ["Yes. 'Car tracks' has a tangible appearance and refers to the visible marks left by the tires of a car on a surface.\nA few things that are visually similar to 'car tracks' but are not 'car tracks' are:\tanimal footprints\tbike tracks\tskid marks\nThere are several useful visual features to tell there are 'car tracks' and not similar things in a photo:\ttwo parallel lines\tsame distance between them\ttread pattern of car tires visible in the track\tlocation (e.g., on a road or in a dirt field)", 10], "diners": ["Yes. 'Diners' has a tangible appearance and refers to a type of restaurant.\nA few things that are visually similar to 'diners' but are not 'diners' are:\tcafe\trestaurant\tcanteen\nThere are several useful visual features to tell there is 'diners' and not similar things in a photo:\tretro or vintage decor\tsigns with a diner name\tbooths or bar seating\tmenu with classic American diner items, such as burgers and milkshakes.", 10], "earlobe": ["Yes, 'earlobe' has a tangible appearance and is a part of the human anatomy.\nA few things that are visually similar to 'earlobe' but are not 'earlobe' are:\tbutton\tjewelry\thook\nThere are several useful visual features to tell there is 'earlobe' and not similar things in a photo:\tfleshy, soft, and round\tdangles or hangs from the side of the ear\ttrailing at the end of the ear", 10], "adult cat": ["Yes. 'Adult cat' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'adult cat' but are not 'adult cat' are:\tkitten\tlynx\tmountain lion\tcheetah\nThere are several useful visual features to tell there is 'adult cat' and not similar things in a photo:\twhiskers\tpointed ears\tsharp claws\tretractable claws\tfurry body\tshort snout\tand small nose.", 10], "street traffic light": ["Yes. 'Street traffic light' has a tangible appearance and is a kind of traffic control device.\nA few things that are visually similar to 'street traffic light' but are not 'street traffic light' are:\tstreetlamp\tbillboard\tcone\tconcrete barrier\nThere are several useful visual features to tell there is 'street traffic light' and not similar things in a photo:\tthree colored lights (red, yellow, and green)\tcircular or rectangular shape\tmounted on a pole or suspended above the road.", 10], "spigot": ["Yes. 'Spigot' has a tangible appearance and is a type of valve used to control the flow of liquids.\nA few things that may look similar to 'spigot' are:\tfaucet\tstopcock\tdrain valve\nThere are several useful visual features to tell there is 'spigot' and not similar things in a photo:\tlong, cylindrical valve\thandle to turn on or off\tthe water flow\ta hose attached to it.", 10], "canada": ["No. 'Canada' is too vague or abstract to be distinguished in a photo. \nHowever, a few things that are visually associated with Canada are:\n- The Canadian flag (a red and white flag with a maple leaf in the center)\n- Moose, beavers, and other Canadian wildlife\n- Snowy landscapes or mountains\n- Hockey teams or players wearing Canadian jerseys\n\nTo distinguish Canada as a location from the associated visual elements, useful features could include:\n- Canadian flags prominently displayed\n- Recognizable Canadian landmarks (such as the CN Tower or Niagara Falls)\n- Street signs or storefronts with Canadian place names (such as Toronto or Vancouver)\n- Provincial or territorial flags or symbols\n- Recognizable Canadian architecture (such as Parliament buildings or old Quebec City)", 10], "divider wall": ["Yes. 'Divider wall' has a tangible appearance and refers to a physical structure used to separate two areas or spaces.\nA few things that are visually similar to 'divider wall' but are not 'divider wall' are:\tcurve wall\tdecorative wall\tlight screen door\tbookshelf\nThere are several useful visual features to tell there is 'divider wall' and not similar things in a photo:\tstraight or angular plane running from floor to ceiling\tclearly dividing space in two\tslim and elegant design", 10], "cottage cheese": ["Yes. 'Cottage cheese' has a tangible appearance and is a kind of cheese.\nA few things that are visually similar to 'cottage cheese' but are not 'cottage cheese' are:\tricotta cheese\tfeta cheese\tgoat cheese\tsour cream\tyogurt\nThere are several useful visual features to tell there is 'cottage cheese' and not similar things in a photo:\tchunky and lumpy texture\twhite color\tmoist and creamy-looking appearance\tmade of curdled milk and cream", 10], "glass top table": ["Yes. 'Glass top table' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'glass top table' but are not 'glass top table' are:\twooden table\tmetal table\tplastic table\nThere are several useful visual features to tell there is 'glass top table' and not similar things in a photo:\ta clear or tinted glass top\ta visible support structure, often made of metal or wood\tfeatures like legs, wheels, or shelves made of supporting materials that complement the glass top", 10], "ice bucket": ["Yes. 'Ice bucket' has a tangible appearance and is a container meant to hold ice.\nA few things that are visually similar to 'ice bucket' but are not 'ice bucket' are:\tregular bucket\twaste bin\tcooler\tbox\nThere are several useful visual features to tell there is 'ice bucket' and not similar things in a photo:\tsmaller size than a regular bucket or cooler\tmade of plastic, metal, or glass\tdesigned to hold ice cubes or crushed ice\ttongs or a scoop may be present for removing ice", 10], "pink drink": ["Yes. 'Pink drink' has a tangible appearance and is a type of beverage.\nA few things that are visually similar to 'pink drink' but are not 'pink drink' are:\tlemonade, strawberry milkshake, rose syrup, cranberry juice.\nThere are several useful visual features to tell there is 'pink drink' and not similar things in a photo:\tpink color\tclear glass\tstraw or foam on top\tcold or with ice cubes.", 10], "wilderness": ["No. 'Wilderness' is too vague or abstract to be distinguished in a photo. However, some things that may be associated with wilderness are visually concrete, such as trees, mountains, and rivers. \nA few things that are visually similar to 'wilderness' but are not 'wilderness' are:\tpark\tnature reserve\tfarm\tbackyard\nThere are several useful visual features to tell there is 'wilderness' and not similar things in a photo:\tuntouched or unspoiled natural landscape\tlack of human-made structures\tor signs of human activity", 10], "propellors": ["Yes. 'Propellors' has a tangible appearance and is a type of rotating blades designed to produce thrust.\nA few things that are visually similar to 'propellors' but are not 'propellors' are:\tfans\twind turbines\thelicopter rotor blades\nThere are several useful visual features to tell there is 'propellors' and not similar things in a photo:\tusually have two or more blades\tspinning rapidly\tattached to an engine or motor\tpowered by gasoline or electricity\ttypically on aircraft or boats", 10], "tile surface": ["Yes. 'Tile surface' has a tangible appearance and is a type of flooring or wall covering.\nA few things that are visually similar to 'tile surface' but are not 'tile surface' are: \tpaint\twood\tplastic\tconcrete\nThere are several useful visual features to tell there is 'tile surface' and not similar things in a photo:\trectangular or square shaped pieces\tflat and smooth surface\tgrout lines between the tiles\ttypically made of ceramic, porcelain, or stone materials", 10], "wood kitchen cabinets": ["Yes. 'Wood kitchen cabinets' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood kitchen cabinets' but are not 'wood kitchen cabinets' are:\tbookshelves\tfiling cabinets\tdressers\tarmoires\nThere are several useful visual features to tell there is 'wood kitchen cabinets' and not similar things in a photo:\tmounted on a wall\tshelves and compartments\tfor storing kitchen utensils, cookware, and dishes\tmade of wood, usually stained or painted\tin a kitchen or dining area", 10], "ale": ["No. 'Ale' is too abstract to be visually concrete. While ale does have a tangible appearance, it cannot be reduced to just one set of visual characteristics as different types of ales can look and be served differently. \n\nTherefore, there are no similar things to 'ale' that can be identified purely on a visual basis.", 10], "corner edge": ["Yes. 'Corner edge' has a tangible appearance.\nA few things that are visually similar to 'corner edge' but are not 'corner edge' are:\tstraight edge\tcurved edge\tridge\tborder\tline\nThere are several useful visual features to tell there is 'corner edge' and not similar things in a photo:\tformed by the intersection of two straight edges\tsharp or pointed angle\t90-degree angle\tchange in direction of a surface or object", 10], "tan strap": ["Yes. 'Tan strap' has a tangible appearance and is a specific color and type of strap.\nA few things that are visually similar to 'tan strap' but are not 'tan strap' are:\tbelt\tshoelace\tbracelet\tpurse handle\nThere are several useful visual features to tell there is 'tan strap' and not similar things in a photo:\ta long and narrow strip of material\ttan or beige color\twith a buckle or clasp on one or both ends", 10], "teaspoon": ["Yes. 'Teaspoon' has a tangible appearance and is a kind of utensil.\nA few things that are visually similar to 'teaspoon' but are not 'teaspoon' are:\ttablespoon\tmeasuring spoon\tcoffee spoon\tsoup spoon\nThere are several useful visual features to tell there is 'teaspoon' and not similar things in a photo:\tsmaller than a tablespoon\thave a pointed tip for stirring\thas a short handle with a slight curve\tuseful for stirring tea or coffee", 10], "marquee display": ["Yes. 'Marquee display' has a tangible appearance and refers to an electronic display screen or a large tent.\nA few things that are visually similar to 'marquee display' but are not 'marquee display' are:\toutdoor advertisement board\tscreens\tprojection of a movie\nThere are several useful visual features to tell there is 'marquee display' and not similar things in a photo:\tconsists of multiple smaller lights arranged to form letters or designs with changing messages\tcan be seen outdoors or in a large tent or canopy\tLarge size and bright light display", 10], "candle stick": ["Yes. 'Candle stick' has a tangible appearance and refers to a type of candle holder.\nA few things that are visually similar to 'candle stick' but are not 'candle stick' are:\tlamp base\ttall drinking glass\tflower vase\tstatue\nThere are several useful visual features to tell there is 'candle stick' and not similar things in a photo:\ttall and skinny shape\tcandle holder on top\tbase for stability\thas one or multiple arms for holding candles\tmade of metal, wood, or ceramic.", 10], "top table": ["Yes. 'Top table' has a tangible appearance and is a specific table used for special events like weddings and conferences.\nA few things that are visually similar to 'top table' but are not 'top table' are:\tdining table\tkitchen counter\twork desk\nThere are several useful visual features to tell there is 'top table' and not similar things in a photo:\telaborate decoration or design\tdistinguished position at an event\thighly visible from all guests often placed on a stage or platform\tcan have additional chairs or seating for special guests or speakers", 10], "giraffe ossicones": ["Yes. 'Giraffe ossicones' has a tangible appearance and refers to the bony protuberances on the heads of giraffes.\nA few things that are visually similar to 'giraffe ossicones' but are not 'giraffe ossicones' are:\tantlers\thorns\tbone projections on various animals\nThere are several useful visual features to tell there are 'giraffe ossicones' and not similar things in a photo:\tOssicones are bases for horns covered in skin\tOssicones are present in both males and females\tas thick cartilaginous pads at birth\tOssicones differ in size and shape between males and females\tOssicones are absent in newborn calves (and some subadult males)", 10], "chocolate chip cookies": ["Yes. 'Chocolate chip cookies' has a tangible appearance and is a type of baked good.\nA few things that are visually similar to 'chocolate chip cookies' but are not 'chocolate chip cookies' are:\traisin cookies\toatmeal cookies\tsugar cookies\nThere are several useful visual features to tell there is 'chocolate chip cookies' and not similar things in a photo:\tcircular shape\tbrown color\tchocolate chips visible on top or inside\tthe texture of the cookie is soft and chewy", 10], "bottle water": ["Yes, 'bottle water' has a tangible appearance and is a common drink contained in a distinctive packaging.\nA few things that are visually similar to 'bottle water' but are not 'bottle water' are:\t\n- Other types of bottled beverages like soda, juice, and energy drinks \n- Reusable water bottles \n- Plastic containers such as shampoo bottles \n\nSome useful visual features for distinguishing 'bottle water' from the listed similar things in a photo are:\n- Labels or logos indicating the brand or type of water in the bottle \n- Clarity and transparency of the liquid content \n- Size and shape of the bottle \n- Presence of a cap or lid on the bottle \n- Distinctive features of the packaging, such as a ridged grip or a curved neck for easier handling.", 10], "jet fighter": ["Yes. 'Jet fighter' has a tangible appearance and is a type of aircraft.\nA few things that are visually similar to 'jet fighter' but are not 'jet fighter' are:\tcommercial airplane\thelicopter\tglider\tdrone\nThere are several useful visual features to tell there is 'jet fighter' and not similar things in a photo:\tsleek and angular design\tcockpit\twith wings, tail fins, and engines\tmissiles or guns attached to the body\tmilitary color scheme (e.g. camouflage or grey)\tflying at high speeds and performing aerial maneuvers", 10], "light sand": ["Yes. 'Light sand' has a tangible appearance and is a kind of sand.\nA few things that are visually similar to 'light sand' but are not 'light sand' are:\tdirt\tpebbles\tcement\tpowdered sugar\nThere are several useful visual features to tell there is 'light sand' and not similar things in a photo:\tlight-colored\tsoft and fine-grained\tlooks like beach sand or desert sand", 10], "brown pile": ["Yes. 'Brown pile' has a tangible appearance and could refer to a variety of things.\nA few things that are visually similar to 'brown pile' but are not 'brown pile' are:\tpile of dirt\tpile of leaves\tpile of hair\tpile of debris\nThere are no specific visual features that necessarily distinguish 'brown pile' from other similar piles. The term 'brown pile' could be referring to any of these types of piles or something else entirely, so more context would be needed to determine the specific visual features that are relevant.", 10], "pillowcases": ["Yes. 'Pillowcases' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'pillowcases' but are not 'pillowcases' are:\tsheets\tbath towels\ttablecloths\nThere are several useful visual features to tell there is 'pillowcases' and not similar things in a photo:\tpillow-shaped\tfabric texture that is typical for bedding items, such as cotton, flannel or silk\topen on one end to allow insertion of a pillow", 10], "tennis pitch": ["Yes. 'Tennis pitch' has a tangible appearance and is a specific type of sports field.\nA few things that are visually similar to 'tennis pitch' but are not 'tennis pitch' are:\tsoccer field\tbasketball court\tvolleyball court\tbadminton court\nThere are several useful visual features to tell there is 'tennis pitch' and not similar things in a photo:\trectangular shape\twith two side-by-side service boxes on each end\twith a clear baseline, centerline and sideline\tmarked in different colors\twhite lines on a green or beige surface", 10], "gaps": ["Yes. 'Gaps' has a tangible appearance and is defined as a space between two objects or structures.\nA few things that are visually similar to 'gaps' but are not 'gaps' are:\tcracks\tbroken lines\tfissures\tjoints\nThere are several useful visual features to tell there is 'gaps' and not similar things in a photo:\tclear space between two surfaces or objects\tno visible material or substance connecting the two surfaces or objects.", 10], "dark night": ["No. 'Dark night' is too vague or abstract to be distinguished in a photo.", 10], "wrappers": ["Yes. 'Wrappers' has a tangible appearance and refers to the packaging materials used to enclose or cover objects.\nA few things that are visually similar to 'wrappers' but are not 'wrappers' are:\tenvelopes\tbags\tboxes\tpackages\nThere are several useful visual features to tell there is 'wrappers' and not similar things in a photo:\tflexible material\twrapping around an object\tmay have a visible brand or logo\tcan come in different colors, patterns, or designs.", 10], "piano keys": ["Yes. 'Piano keys' has a tangible appearance and is a part of a musical instrument.\nA few things that are visually similar to 'piano keys' but are not 'piano keys' are:\tcomputer keyboard\tmobile phone keypad typewriter keys\tdoor keys\nThere are several useful visual features to tell there are 'piano keys' and not similar things in a photo:\t88 keys on a standard piano\tblack and white alternating keys\trectangular shape\twith black keys arranged in groups of two and three\ttop white keys are longer than bottom white keys", 10], "hedge row": ["Yes. 'Hedge row' has a tangible appearance and is a row of shrubs or trees forming a boundary or fence.\nA few things that are visually similar to 'hedge row' but are not 'hedge row' are:\tgrass field\tcrop field\troadside\tdense forest\nThere are several useful visual features to tell there is 'hedge row' and not similar things in a photo:\ta line of bushes or small trees, usually trimmed\tsymmetrical shape\theight similar between shrubs/threes\tin a straight or wavy line", 10], "plastic skateboard wheel": ["Yes. 'Plastic skateboard wheel' has a tangible appearance and is a specific type of wheel.\nA few things that are visually similar to 'plastic skateboard wheel' but are not 'plastic skateboard wheel' are:\tbicycle wheel\troller skate wheel\tluggage wheel\tbarrel wheel\nThere are several useful visual features to tell there is 'plastic skateboard wheel' and not similar things in a photo:\tsmall size, around 50-60mm in diameter\tplastic material\thub with bearings\tno spokes or other structure on the wheel's surface, just smooth and round.", 10], "gold buttons": ["Yes. 'Gold buttons' has a tangible appearance and is a kind of clothing accessory.\nA few things that are visually similar to 'gold buttons' but are not 'gold buttons' are:\tdomes\tbeads\tcoins\tbells\nThere are several useful visual features to tell there is 'gold buttons' and not similar things in a photo:\tgolden in color\tmade of metal\tcircular or oval in shape\thave four or more holes for thread or string", 10], "hairstyle": ["Yes. 'Hairstyle' has a tangible appearance and is a way of arranging hair.\nA few things that are visually similar to 'hairstyle' but are not 'hairstyle' are:\thair color\thair length\thair texture\theadbands\thair accessories\nThere are several useful visual features to tell there is 'hairstyle' and not similar things in a photo:\tthe shape or design of the hair arrangement\tthe use of hair products or tools, such as gel, mousse, comb or bobby pins\tthe overall appearance or theme of the hairstyle, such as braids, updos, or ponytails.", 10], "handicap symbol": ["Yes. 'Handicap symbol' has a tangible appearance and is a universally recognized symbol for accessibility.\nA few things that are visually similar to 'handicap symbol' but are not 'handicap symbol' are:\tarrows for direction\tgraphic design icons\nThere are several useful visual features to tell there is 'handicap symbol' and not similar things in a photo:\tperson in a wheelchair or mobility scooter\tstylized depiction of a person in a wheelchair or mobility scooter\tbackground color blue and white, or white on blue\tGeneric figure in a wheelchair with a geometric design", 10], "ceiling beams": ["Yes. 'Ceiling beams' has a tangible appearance and is a structural element in construction.\nA few things that are visually similar to 'ceiling beams' but are not 'ceiling beams' are:\tdecorative molding\tdropped ceiling tiles\tlight fixtures\t\nThere are several useful visual features to tell there is 'ceiling beams' and not similar things in a photo:\tlong and rectangular shape\tmade of wood or metal\tsupporting structure or framework\tfixed horizontally across the ceiling", 10], "travel mug": ["Yes. 'Travel mug' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'travel mug' but are not 'travel mug' are:\tcup\tthermos\tregular mug\twater bottle\nThere are several useful visual features to tell there is 'travel mug' and not similar things in a photo:\tdesigned to be carried while traveling or commuting\twith a secure lid or cap, often made of plastic or metal\tcylindrical or tapered shape\tthat fits in a car's cup holder, it can keep liquids hot or cold", 10], "hold": ["No. 'Hold' is too vague or abstract to be distinguished in a photo. It refers to an action or a state of keeping something in one's possession or control. It does not have a tangible appearance.", 10], "steamer": ["Yes. 'Steamer' has a tangible appearance and refers to a type of cooking equipment that uses steam.\nA few things that are visually similar to 'steamer' but are not 'steamer' are:\tsaucepan\tpot\tteapot\tkettle\nThere are several useful visual features to tell there is 'steamer' and not similar things in a photo:\tmultiple tiers or levels\twith holes in the bottom of each tier\thandles or knobs on the sides\tno visible spout or lid", 10], "dog fur": ["Yes. 'Dog fur' has a tangible appearance and refers to the hair that covers a dog's body.\nA few things that are visually similar to 'dog fur' but are not 'dog fur' are:\tfaux fur\tfaux hair\twool\nThere are several useful visual features to tell there is 'dog fur' and not similar things in a photo:\tvarious colors and patterns\tlength and texture specific to the breed of dog\tsimilar in texture to human hair\tfur covering the whole body or specific parts of the body (like tail or ears)", 10], "grey posts": ["Yes. 'Grey posts' has a tangible appearance and refers to physical objects that are grey in color and shaped like a post or a pillar.\nA few things that are visually similar to 'grey posts' but are not 'grey posts' are:\ttraffic cones\tchimneys\tconcrete blocks\tlight poles\nThere are several useful visual features to tell there are 'grey posts' and not similar things in a photo:\ttall and narrow structures\tmade of stone or metal\tgrey or silver in color\tvertical lines or patterns\tcylindrical or rectangular in shape", 10], "tan rocks": ["Yes. 'Tan rocks' has a tangible appearance and is a type of geological element.\nA few things that are visually similar to 'tan rocks' but are not 'tan rocks' are:\tsand\tconcrete\tcement\trefuge waste\nThere are several useful visual features to tell there are 'tan rocks' and not similar things in a photo:\tirregular shapes\tand sizes\ttan or beige color\trough and grainy texture\tVeins or streaks of different colors running through the rocks\tsmall pebbles or larger boulders", 10], "sugar doughnut": ["Yes. 'Sugar doughnut' has a tangible appearance and is a type of pastry.\nA few things that are visually similar to 'sugar doughnut' but are not 'sugar doughnut' are:\t\nbagel\ncroissant\nchurro\npretzel\nThere are several useful visual features to tell there is a 'sugar doughnut' and not similar things in a photo:\t\nround with a hole in the middle\npowdered with sugar\ngolden brown in color\nsoft and fluffy texture\nridged appearance on the outer edge", 10], "toddler girl": ["Yes. 'Toddler girl' has a tangible appearance and is a young female child.\nA few things that are visually similar to 'toddler girl' but are not 'toddler girl' are:\tbaby\tgirl\tyoung female\nThere are several useful visual features to tell there is 'toddler girl' and not similar things in a photo:\tshort stature\tround face\tinnocent, playful expression\tproportional limbs and body\tsize of clothing and accessories (e.g., small shoes, hats)", 10], "oval plate": ["Yes. 'Oval plate' has a tangible appearance and is a specific shape and type of dish.\nA few things that are visually similar to 'oval plate' but are not 'oval plate' are:\tcircular plate\tplatter\tbowl\ttray\nThere are several useful visual features to tell there is 'oval plate' and not similar things in a photo:\telongated and oval shape\tflat surface\trims or edges to hold food", 10], "wooden bowl": ["Yes. 'Wooden bowl' has a tangible appearance and is a specific type of bowl.\nA few things that are visually similar to 'wooden bowl' but are not 'wooden bowl' are:\tceramic bowl\tglass bowl\tsteel bowl\tstone bowl\nThere are several useful visual features to tell there is 'wooden bowl' and not similar things in a photo:\tmade of wood\tnatural-looking\tgrains on the surface, if visible\torganic, handcrafted or rustic appearance\thollow interior for holding objects or food, with or without adornment", 10], "skewers": ["Yes. 'Skewers' has a tangible appearance and refers to long, thin metal or wooden sticks used for cooking food.\nA few things that are visually similar to 'skewers' but are not 'skewers' are:\tknives\tforks\ttoothpicks\tchopsticks\nThere are several useful visual features to tell there are 'skewers' and not similar things in a photo:\tlong and thin\tstick-like shape\tpointed on one or both ends\tused for holding and cooking pieces of food", 10], "bulky": ["No. 'Bulky' is too vague or abstract to be distinguished in a photo. It is a subjective term that refers to size and weight but doesn't have a specific appearance.\nTherefore, there are no things visually similar to 'bulky' that are not 'bulky'.", 10], "sports shoes": ["Yes. 'Sports shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'sports shoes' but are not 'sports shoes' are:\tdress shoes\tsandals\tboots\theels\nThere are several useful visual features to tell there is 'sports shoes' and not similar things in a photo:\tfabric or leather upper sole and rubber or plastic sole\tcushioning for support and shock absorption\tlaces, Velcro or slip-on design\tsport-specific design features (e.g. spikes for track shoes, cleats for soccer shoes, etc.)\ttypically worn for athletic activities or casual wear.", 10], "chocolate frosting": ["Yes. 'Chocolate frosting' has a tangible appearance and is a type of topping.\nA few things that are visually similar to 'chocolate frosting' but are not 'chocolate frosting' are:\tcaramel sauce\tbrown paint\tchocolate syrup\tmelted chocolate\nThere are several useful visual features to tell there is 'chocolate frosting' and not similar things in a photo:\tthick and creamy texture\tbrown color\tsmooth or swirled appearance\ton top of a dessert, such as a cake or cupcake.", 10], "faucet handles": ["Yes. 'Faucet handles' has a tangible appearance and is a part of a plumbing fixture.\nA few things that are visually similar to 'faucet handles' but are not 'faucet handles' are:\tdoor knobs\tcabinet handles\tdrawer pulls\tlevers\nThere are several useful visual features to tell there is 'faucet handles' and not similar things in a photo:\tmounted on a sink or a wall has specific labels for hot and cold water often comes in pairs of two or more made of plastic, metal, or ceramic round or lever-shaped with ridges or grooves for grip", 10], "curb side road": ["Yes. 'Curb side road' has a tangible appearance and refers to the edge of a road that is nearest to the curb or sidewalk.\nA few things that are visually similar to 'curb side road' but are not 'curb side road' are:\tcenter lane\tbike lane\twalkway\tparking lot\nThere are several useful visual features to tell there is 'curb side road' and not similar things in a photo:\tadjacent to a curb or sidewalk\tparallel parked vehicles\tfoot traffic\tsignage and markings specific to vehicular traffic (stop signs, crosswalks, etc.)", 10], "furry ears": ["Yes. 'Furry ears' has a tangible appearance and refers to ears covered in fur.\nA few things that are visually similar to 'furry ears' but are not 'furry ears' are:\tfurry hat\tfurry animals' ears (e.g. rabbit, bear)\tfake fur ear muffs\nThere are several useful visual features to tell there are 'furry ears' and not similar things in a photo:\thair/fur covering the ears\tfuzzy texture of the ear flaps\tthe ears are not a separate object, but a body part (e.g. not detachable like ear muffs)", 10], "dog buns": ["No. 'Dog buns' is too vague or abstract to be understood as a concept.\nHowever, if you meant \"hot dog buns\", then the answer is yes.\nA few things that are visually similar to 'hot dog buns' but are not 'hot dog buns' are:\thamburger buns\tsourdough rolls\tciabatta bread\nThere are several useful visual features to tell there are 'hot dog buns' and not similar things in a photo:\telongated shape\twith a crease along the top\tusually plain or seeded\ton either side of a hotdog or sausage.", 10], "earpiece": ["Yes. 'Earpiece' has a tangible appearance and is a device worn in the ear to listen to audio or communicate.\nA few things that are visually similar to 'earpiece' but are not 'earpiece' are:\thearing aids\tearplugs\tdecorative earrings\nThere are several useful visual features to tell there is 'earpiece' and not similar things in a photo:\twired or wireless\tfits snugly in the ear\tconnected to a mobile device or communication system\tmay have a microphone attached to it.", 10], "color handle": ["No. 'Color handle' is too vague or abstract to have a tangible appearance or be distinguished from other objects.\nA concept like 'color handle' may refer to handles of different objects of various colors, or an object that is specifically designed as a color handle(e.g., a paintbrush). Therefore, it is necessary to provide additional contextual information for it to be clearly defined in a visual sense.", 10], "tan clock tower": ["Yes. 'Tan clock tower' has a tangible appearance and is a type of building with a clock on it.\nA few things that are visually similar to 'tan clock tower' but are not 'tan clock tower' are:\tsandcastle\tchimney\ttall beige building\twith clock on it\nThere are several useful visual features for distinguishing 'tan clock tower' from the listed similar things in a photo:\ttan or beige-colored brick or stone construction\tTower structure with a clear base, middle and top section clock or a large clock face included on the building\tusually located in the center of a town or near important buildings", 10], "emergency exit door": ["Yes. 'Emergency exit door' has a tangible appearance and is a type of door used for emergency purposes.\nA few things that are visually similar to 'emergency exit door' but are not 'emergency exit door' are:\tregular door\tfire escape door\tgarage door\nThere are several useful visual features to tell there is 'emergency exit door' and not similar things in a photo:\tWords such as \"Emergency Exit\" written on it\tilluminated red or green sign with an arrow pointing to the exit\tpush bar or push paddle to open the door\tunobstructed view of the other side\tof the door\tto facilitate a safe and quick escape in case of an emergency.", 10], "xbox controller": ["Yes. 'Xbox controller' has a tangible appearance and is a kind of video game controller.\nA few things that are visually similar to 'xbox controller' but are not 'xbox controller' are:\tPlayStation controller\tNintendo Switch Pro Controller\tPC gamepad\tarcade game joystick\nThere are several useful visual features to tell there is 'xbox controller' and not similar things in a photo:\tdistinctive button layouts\tXbox branding and logo on the controller\tcolor scheme with primarily black and white\tthumbsticks above the face buttons and the d-pad\tbuttons in the same position as other Xbox controllers", 10], "sweater sleeve": ["Yes. 'Sweater sleeve' has a tangible appearance and is a part of clothing.\nA few things that are visually similar to 'sweater sleeve' but are not 'sweater sleeve' are:\tshirt sleeve\tjacket sleeve\thoodie sleeve\nThere are several useful visual features to tell there is 'sweater sleeve' and not similar things in a photo:\tknit or woven texture\themmed cuff\tattached to a sweater or cardigan", 10], "peach shirt": ["Yes. 'Peach shirt' has a tangible appearance and is a specific type of clothing item.\nA few things that are visually similar to 'peach shirt' but are not 'peach shirt' are:\torange shirt\tpink shirt\tcoral shirt\nThere are several useful visual features to tell there is 'peach shirt' and not similar things in a photo:\torange-pink color combination\tsolid color or with simple patterns\tbutton-up or collar shirt\tmade of fabric or cotton material", 10], "silver boat": ["Yes. 'Silver boat' has a tangible appearance and is a type of watercraft.\nA few things that are visually similar to 'silver boat' but are not 'silver boat' are: kettles or teapots, containers such as buckets or cans, silver car\nThere are several useful visual features to tell there is 'silver boat' and not similar things in a photo:\tfloats on water\tlong and narrow\thas a pointed bow and a square stern\tpainted or reflective in silver color\thas oars or a motor\tfor transportation on water", 10], "silver bench": ["Yes. 'Silver bench' has a tangible appearance and refers to a specific type of seating furniture.\nA few things that are visually similar to 'silver bench' but are not 'silver bench' are:\tother types of benches\tchairs\tstools\nThere are several useful visual features to tell there is 'silver bench' and not similar things in a photo:\tlong seat with backrest\tmade of silver-colored metal or material\tno armrests or backrests\tmay be upholstered or have cushions", 10], "lush tree": ["Yes. 'Lush tree' has a tangible appearance and refers to a tree that is healthy and has abundant foliage.\nA few things that are visually similar to 'lush tree' but are not 'lush tree' are:\tdecoration\ttree with fewer leaves or barren tree\nThere are several useful visual features to tell there is 'lush tree' and not similar things in a photo:\tthick foliage\thealthy and vibrant-green leaves\tfull branches and canopy\tblooming flowers\tfruits and berries on the branches.", 10], "round clocks": ["Yes. 'Round clocks' have a tangible appearance and are a kind of timekeeping device.\nA few things that are visually similar to 'round clocks' but are not 'round clocks' are:\twatches\tcompasses\tthermometers\nThere are several useful visual features to tell there is 'round clocks' and not similar things in a photo:\tcircular shape\tmarking hours and minutes\thands or digits indicating the time often refer to numbers", 10], "hindquarters": ["Yes. 'Hindquarters' has a tangible appearance and refers to the back half of an animal's body.\nA few things that are visually similar to 'hindquarters' but are not 'hindquarters' are:\tlegs\tbuttocks\thips\nThere are several useful visual features to tell there is 'hindquarters' and not similar things in a photo:\trear end of an animal\twide and muscular appearance\ttail and hind legs visible\tlack of front legs or torso", 10], "savanna": ["Yes. 'Savanna' has a tangible appearance and refers to a type of biome characterized by grasslands with scattered trees.\nA few things that are visually similar to 'savanna' but are not 'savanna' are:\tprairie\tdesert\tsteppe\nThere are several useful visual features to tell there is 'savanna' and not similar things in a photo:\thigh grasses\tmoderate to high rainfall\tsparse trees or woodlands\twildlife such as lions, elephants, giraffes, zebras, and kangaroos", 10], "shadow plate table": ["No. 'Shadow plate table' is too vague and abstract to be considered a visually concrete concept.", 10], "islands": ["Yes. 'Islands' has a tangible appearance and refers to a land area surrounded by water.\nA few things that are visually similar to 'islands' but are not 'islands' are:\tpeninsulas\trocks\tsandbars\nThere are several useful visual features to tell there is 'islands' and not similar things in a photo:\tsurrounded by water\tnot connected to the mainland or larger landmass\tvariety of vegetation or terrain shapes (e.g. trees, mountains)\tdifferent shades of blue around the perimeter of the landmass", 10], "tile sidewalk": ["Yes. 'Tile Sidewalk' has a tangible appearance and is a type of pedestrian walkway.\nA few things that are visually similar to 'tile sidewalk' but are not 'tile sidewalk' are:\tcobblestone sidewalk\tconcrete sidewalk\tbrick sidewalk\twooden walkway\nThere are several useful visual features to tell there is 'tile sidewalk' and not similar things in a photo:\trectangular or square-shaped\tpatterned or colorful\tindividual tiles that fit together\tsmooth surface\twithout grass or dirt growing between tiles", 10], "glass divider": ["Yes. 'Glass divider' has a tangible appearance and is a kind of object used to separate spaces.\nA few things that are visually similar to 'glass divider' but are not 'glass divider' are:\twindows\tshowcases\twalls\twith clear or transparent material\nThere are several useful visual features to tell there is 'glass divider' and not similar things in a photo:\tseparation between two spaces\tthin and flat with a frame\tor frameless\ttransparency or translucency", 10], "glass wine": ["Yes. 'Glass wine' has a tangible appearance and is a type of drinking vessel.\nA few things that are visually similar to 'glass wine' but are not 'glass wine' are:\tcocktail glass\ttumbler champagne flutes beer mug\nThere are several useful visual features to tell there is 'glass wine' and not similar things in a photo:\ttall and thin glass\tbowl-shaped\ttop rim smaller than the middle part.\tserving size (usually around 5-7 oz)\tclear or slightly tinted glass depending on the wine type. The shape of the bowl may vary depending on the wine type (e.g., wider for reds, taller for whites).", 10], "wood leg": ["Yes. 'Wood leg' has a tangible appearance and refers to a prosthetic leg made of wood.\nA few things that are visually similar to 'wood leg' but are not 'wood leg' are:\twooden stick\ttable leg\ttree branch\tbat\nThere are several useful visual features to tell there is 'wood leg' and not similar things in a photo:\tprosthetic leg shape and design\tmade of wood or have the appearance of wood\tfits on a person's leg", 10], "rig": ["Yes. 'Rig' has a tangible appearance and refers to a structure or machine used for drilling and extracting natural resources.\nA few things that are visually similar to 'rig' but are not 'rig' are:\tcrane\ttower\tmast\tpylon\nThere are several useful visual features to tell there is 'rig' and not similar things in a photo:\ttall and vertical\tmultiple levels or platforms\tvisible drilling equipment or machinery\thelipad or other transportation features.", 10], "dark brick": ["Yes. 'Dark brick' has a tangible appearance and refers to a specific color and material.\nA few things that are visually similar to 'dark brick' but are not 'dark brick' are:\tdark tiles\tblack painted concrete\tstones of similar color\nThere are several useful visual features to tell there is 'dark brick' and not similar things in a photo:\trectangular shape\trough texture\tporous surface\tdark brown or red color", 10], "coils": ["Yes. 'Coils' has a tangible appearance and refers to objects formed by winding flexible materials such as wire, rope or cable around a core.\nA few things that are visually similar to 'coils' but are not 'coils' are:\tsprings\tsnakes\tribbons\t\nThere are several useful visual features to tell there is 'coils' and not similar things in a photo:\thelical shape\twinding pattern\tcircular cross-sections of the object\tmade of metal and/or plastic", 10], "pink food": ["Yes. 'Pink food' has a tangible appearance and is food that is primarily pink in color.\nA few things that are visually similar to 'pink food' but are not 'pink food' are:\tcandy\tfrosting\tdrinks\tflowers\nThere are several useful visual features to tell there is 'pink food' and not similar things in a photo:\tpredominantly pink in color\tedible\tsubstantial in quantity or portion served as a complete meal or snack, not just a garnish or decoration", 10], "trees distance": ["Yes. 'Trees distance' has a tangible appearance and refers to the space between trees.\nThere are no things that are visually similar to 'trees distance' and are not 'trees distance'.\nUseful visual features for distinguishing 'trees distance' in a photo could be: the space or gap between trees, the number of trees visible in the photo, and the angle or perspective of the photo.", 10], "blue carpet": ["Yes. 'Blue carpet' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'blue carpet' but are not 'blue carpet' are:\tblue rug\tblue tile floor\tblue linoleum floor\nThere are several useful visual features to tell there is 'blue carpet' and not similar things in a photo:\tsoft and plush texture\tfabric or fiber material\tcovering the entire floor surface in a room or area.", 10], "spects": ["No. 'Spects' is too vague or incorrect spelling to be a distinguishable concept in a photo. If you are referring to 'specs' as in 'eyeglasses', then the answer is as follows:\n\nYes, 'eyeglasses' or 'spectacles' are visually concrete concepts.\nA few things that are visually similar to 'eyeglasses' but are not 'eyeglasses' are:\tsunglasses\tbinoculars\tmicroscope\treading glasses\nThere are several useful visual features to tell there are 'eyeglasses' and not similar things in a photo:\tconsist of two lenses attached to a frame\tframes are placed over the ears and onto the nose to hold the lenses in place\tlenses can be transparent or tinted to aid in vision correction or protect the eyes from sunlight.", 10], "shelfs": ["No. 'Shelfs' is not a correct spelling of the term 'shelves' and it refers to a vague or abstract concept, but 'shelves' has a tangible appearance and is a common piece of furniture used for storage.\nA few things that are visually similar to 'shelves' but are not 'shelves' are:\tbookcases\tcabinets\tcountertops\tmantels\nThere are several useful visual features to tell there are 'shelves' and not similar things in a photo:\trectangular or square shape\tattached to a wall\thorizontal boards or planks for holding items\torganized in rows or columns.", 10], "bathroom floor tiles": ["Yes. 'Bathroom floor tiles' have a tangible appearance and are commonly used in bathrooms.\nA few things that are visually similar to 'bathroom floor tiles' but are not 'bathroom floor tiles' are:\tkitchen floor tiles\tlaminate or hardwood flooring\tcarpeted floors\tmarble or stone flooring\nThere are several useful visual features to tell there are 'bathroom floor tiles' and not similar things in a photo:\tsquare or rectangular shape\tsmooth or textured surface\twaterproof or water-resistant material\tbright or neutral colors, often with a pattern or design.", 10], "lady bug": ["Yes. 'Lady bug' has a tangible appearance and is a type of insect.\nA few things that are visually similar to 'lady bug' but are not 'lady bug' are:\tbeatle\tother types of insects\nThere are several useful visual features to tell there is 'lady bug' and not similar things in a photo:\tround or oval body shape\tbright red or orange color with black spots\tsix legs\ttwo antennae\tsmall head and wings", 10], "hula hoop": ["Yes. 'Hula hoop' has a tangible appearance and is a type of circular toy or fitness equipment.\nA few things that are visually similar to 'hula hoop' but are not 'hula hoop' are:\ttire\tfrisbee\tpizza\tbaseball\nThere are several useful visual features to tell there is 'hula hoop' and not similar things in a photo:\tlarge circular loop\tmade of plastic or a sturdy material\tbright colors\theld around the waist or being spun around the body.", 10], "skirt woman": ["Yes. 'Skirt woman' has a tangible appearance and refers to a woman wearing a skirt.\nA few things that are visually similar to 'skirt woman' but are not 'skirt woman' are:\tpants\tdress\tshorts\t\nThere are several useful visual features to tell there is 'skirt woman' and not similar things in a photo:\ta piece of cloth worn around the waist\tflaring out from the waist to the hemline\tcovers the legs partially or fully\tcan come in various lengths or styles", 10], "accessory": ["No. 'Accessory' is too vague or abstract to be distinguished in a photo. \n\nNote: While some accessories may have tangible appearances, the category of \"accessory\" is too broad and includes too many different types of objects to be a visually concrete concept on its own. It is better to refer to specific types of accessories, such as \"necklaces\", \"watches\", \"sunglasses\", etc.", 10], "deodorant": ["Yes. 'Deodorant' has a tangible appearance and is a personal hygiene product.\nA few things that are visually similar to 'deodorant' but are not 'deodorant' are:\tperfume\tbody lotion\tsunscreen\nThere are several useful visual features to tell there is 'deodorant' and not similar things in a photo:\tlong cylindrical shape\ttwist-up mechanism\tcontainer with a cap\tor solid top in a roll-on container\tdescriptive label or packaging that indicates use as a deodorant", 10], "tail pipe": ["Yes. 'Tail pipe' has a tangible appearance and is a component of a vehicle's exhaust system.\nA few things that are visually similar to 'tail pipe' but are not 'tail pipe' are:\ttrumpet\tfunnel\tstraw\thorn\nThere are several useful visual features to tell there is 'tail pipe' and not similar things in a photo:\tlocated at the back of a vehicle\tcylindrical shape\tattached to the exhaust system\tdark and sooty interior walls", 10], "bird swimming": ["Yes. 'Bird swimming' has a tangible appearance and is a bird that is actively swimming in water.\nA few things that are visually similar to 'bird swimming' but are not 'bird swimming' are:\tbird standing in water\tbird floating in water\tfish in water\nThere are several useful visual features to tell there is 'bird swimming' and not similar things in a photo:\tbird's body partially or fully submerged in water\tbird's legs and wings in the swimming position\tmovement or ripples in the water around the bird's body", 10], "pink handle": ["Yes. 'Pink handle' has a tangible appearance and is a specific type of handle.\nA few things that are visually similar to 'pink handle' but are not 'pink handle' are:\tred handle\torange handle\tyellow handle\tpurple handle\nThe useful visual features for distinguishing 'pink handle' from the listed similar things in a photo are:\tthe specific shade of pink and its brightness compared to other handles\tthe shape and texture of the handle\tthat the handle is attached to a specific object or tool, such as scissors or a suitcase.", 10], "twin beds": ["Yes. 'Twin beds' has a tangible appearance and refers to a specific type of bed.\nA few things that are visually similar to 'twin beds' but are not 'twin beds' are:\tqueen beds\tking beds\tcots\tsleeping bags\nThere are several useful visual features to tell there is 'twin beds' and not similar things in a photo:\ttwo identical beds placed side by side\tsheets, blankets, and pillows on each bed\tno larger than 39 inches in width and 75 inches in length (in America)", 10], "grass stain": ["Yes. 'Grass stain' has a tangible appearance and is a type of discoloration caused by grass on fabric.\nA few things that are visually similar to 'grass stain' but are not 'grass stain' are:\tmud stain\tink stain\tpaint stain\tfood stain\nThere are several useful visual features to tell there is 'grass stain' and not similar things in a photo:\tgreen color\ttranslucent or transparent appearance\tcovering a specific area on fabric\tirregular shape\tDifferent shades of green depending on the type of grass", 10], "scales": ["Yes. 'Scales' has a tangible appearance and can refer to a variety of objects, including the skin of some animals, a weighing device, or a musical instrument.\nA few things that are visually similar to 'scales' but are not 'scales' are:\tfish skin\tsnake skin\tmermaid tail\truler\nThere are several useful visual features to tell there is 'scales' and not similar things in a photo, depending on the context: \n\n- For animal scales: overlapping, raised or patterned skin, potentially with a sheen or glossy surface. \n- For weighing scales: a flat surface, often rectangular or circular, with a meter or display for weight measurements. \n- For musical scales: a set of notes arranged in a specific order, often displayed on a sheet of music or represented by keys on an instrument.", 10], "wooden pole": ["Yes. 'Wooden pole' has a tangible appearance and is a long cylindrical object made of wood.\nA few things that are visually similar to 'wooden pole' but are not 'wooden pole' are:\tlog\tcolumn\tpillar\t\nThere are several useful visual features to tell there is 'wooden pole' and not similar things in a photo:\tsmooth or rough texture\tnatural wood color\tvisible wood grain\tcylindrical shape\tno visible branches or leaves", 10], "clock display": ["Yes. 'Clock display' has a tangible appearance and refers to the visible representation of the time on a clock.\nA few things that are visually similar to 'clock display' but are not 'clock display' are:\tsigns\tthermometers\tcalendars\tcountdown timers\nThere are several useful visual features to tell there is 'clock display' and not similar things in a photo:\tclock hands\tdigital numbers\tcircular shape\tthe presence of hour and minute marks\tor the words \"hour\" and \"minute\" written out.", 10], "shed": ["Yes. 'Shed' has a tangible appearance and is a small building usually used for storage.\nA few things that are visually similar to 'shed' but are not 'shed' are: garage, barn, gazebo, carport\nThere are several useful visual features to tell there is 'shed' and not similar things in a photo:\tsmall size\twith or without windows\ta single or double door\tbuilt of wood or metal roofed with shingles or corrugated metal\tsits on a foundation, elevated or directly on the ground, typically at the back of a property", 10], "bus lights": ["Yes. 'Bus lights' has a tangible appearance and refers to the lights on a bus.\nA few things that are visually similar to 'bus lights' but are not 'bus lights' are:\ttail lights\theadlights\ttraffic lights\tstreetlights\nThere are several useful visual features to tell there is 'bus lights' and not similar things in a photo:\n\tLocated on the top or sides of a bus\n\tRed, white, or yellow lights\n\tArranged in a pattern, such as a row or cluster\n\tUsed for signaling, such as indicating a stop or turn", 10], "gold door handle": ["Yes. 'Gold door handle' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'gold door handle' but are not 'gold door handle' are:\tknob\tkey\tlock\thook\nThere are several useful visual features to tell there is 'gold door handle' and not similar things in a photo:\ta golden color\twith a metallic surface\thaving a specific shape designed for holding and turning it\tattached to a door or a drawer.", 10], "wooden bucket": ["Yes. 'Wooden bucket' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'wooden bucket' but are not 'wooden bucket' are:\tpots\tbaskets\ttrash cans\tbarrels\nThere are several useful visual features to tell there is 'wooden bucket' and not similar things in a photo:\tmade of wood\twith a handle \tcylindrical or slightly tapering form\tbound together with metal hoops or bands", 10], "scratch marks": ["Yes. 'Scratch marks' has a tangible appearance and refers to visible marks or abrasions made by scratching.\nA few things that are visually similar to 'scratch marks' but are not 'scratch marks' are:\tcracks\tdents\tstains\tscuffs\nThere are several useful visual features to tell there are 'scratch marks' and not similar things in a photo:\tlong, thin lines\tuneven surface\tdirectionality (going in one direction)\tskin or surface has been visibly damaged or removed.", 10], "truck grill": ["Yes. 'Truck grill' has a tangible appearance and is a part of a truck's front end.\nA few things that are visually similar to 'truck grill' but are not 'truck grill' are:\tcar grill\tbike grill\tfence grate\nThere are several useful visual features to tell there is 'truck grill' and not similar things in a photo:\tlarge and rectangular or square in shape\tplaced on the front of a truck\tchrome or metal finish\thorizontal or vertical bars, or a combination of both\tmay have a logo or emblem in the center.", 10], "blurry lights": ["Yes. 'Blurry lights' has a tangible appearance and refers to lights that are out of focus or blurred due to movement or a camera effect.\nA few things that are visually similar to 'blurry lights' but are not 'blurry lights' are:\tbokeh effect\tfireworks \tneon signs\tstreetlights\nThere are several useful visual features to tell there are 'blurry lights' and not similar things in a photo:\tsoft, rounded edges of light sources\tlight sources appear elongated or smeared\tmultiple light sources are visible in the frame\tlack of distinct shapes and details in the lights", 10], "air conditioners": ["Yes. 'Air conditioners' has a tangible appearance and is an electrical appliance used for cooling a room or building.\nA few things that are visually similar to 'air conditioners' but are not 'air conditioners' are:\tfans, heaters, air purifiers, dehumidifiers\nThere are several useful visual features to tell there is 'air conditioners' and not similar things in a photo:\thas vents for cold air\toutputting cool air\tor refrigerant\thas a digital or manual temperature control\tswitches or knobs to control settings and speed\thas a compressor or condenser unit", 10], "mudflap": ["Yes. 'Mudflap' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'mudflap' but are not 'mudflap' are:\twheel cover\tguard\tfender\tlip spoiler\nThere are several useful visual features to tell there is 'mudflap' and not similar things in a photo:\tflat piece of material\thangs behind a wheel or tire\tdesigned to block mud or water from spraying the car\tbody color or reflective material with a logo or design", 10], "juice bottle": ["Yes. 'Juice bottle' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'juice bottle' but are not 'juice bottle' are:\twater bottle\twine bottle\tsoda bottle\tperfume bottle\nThere are several useful visual features to tell there is 'juice bottle' and not similar things in a photo:\ttranslucent or transparent plastic or glass material\tnarrow opening with cap or lid\tlabel or design indicating \"juice\" or fruit imagery\tcapable of holding liquid and pourable through the opening", 10], "style toilet": ["No. 'Style toilet' is too vague or abstract to be distinguished in a photo. It might refer to a toilet's design or aesthetic, but it doesn't have a specific tangible appearance like 'christmas balls' or 'lemur'. \n\nHowever, a few things that are visually similar to 'toilet' but are not 'style toilet' are:\tsink\tbathtub\tshower\turinal\tbidet\n\nUseful visual features for distinguishing 'style toilet' from similar things in a photo depend on what specific characteristics are being referred to by 'style toilet'. It could refer to the shape, color, material, or design of the toilet. For example, a 'style toilet' might have a vintage look with a high tank and pull chain, or a modern look with sleek lines and a hidden tank. Therefore, it is necessary to provide more context about what is meant by 'style toilet' to determine useful visual features for distinguishing it from other bathroom fixtures.", 10], "baseball man": ["No. 'Baseball man' is too vague or abstract to be distinguished in a photo.", 10], "shelving": ["Yes. 'Shelving' has a tangible appearance and refers to storage furniture.\nA few things that are visually similar to 'shelving' but are not 'shelving' are:\tbookcase\tcounter\ttable\tbench\tdisplay stand\nThere are several useful visual features to tell there is 'shelving' and not similar things in a photo:\twide and flat surface held up by supports\tvarious levels and compartments\tused for keeping objects or items off the ground\televated from the floor", 10], "wood head board": ["Yes. 'Wood head board' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'wood head board' but are not 'wood head board' are:\twall decoration\tshelf\tbookshelf\troom divider\nThere are several useful visual features to tell there is 'wood head board' and not similar things in a photo:\tattached to a bed\tframe made of wood or wooden planks\tcan have decorative carvings or patterns in the wood.", 10], "toe nail": ["Yes. 'Toe nail' has a tangible appearance and is a part of the body.\nA few things that are visually similar to 'toe nail' but are not 'toe nail' are:\tfingernail\tclaw\tbeak\thorn\nThere are several useful visual features to tell there is 'toe nail' and not similar things in a photo:\tlocated at the end of a digit\tclosely attached to the skin\tridged surface\tpinkish-white color", 10], "pasta sauce": ["Yes. 'Pasta sauce' has a tangible appearance and is a kind of sauce used on pasta.\nA few things that are visually similar to 'pasta sauce' but are not 'pasta sauce' are:\tbarbecue sauce\tketchup\thummus\tsalsa\nThere are several useful visual features to tell there is 'pasta sauce' and not similar things in a photo:\tthick and textured liquid\tin a bowl or a pot\tvariety of colors (e.g., red, orange, brown)\tcontaining visible pieces of vegetables or herbs", 10], "metal pail": ["Yes. 'Metal pail' has a tangible appearance and is a kind of container.\nA few things that are visually similar to 'metal pail' but are not 'metal pail' are:\tbuckets\ttubs\tbowls\tvases\nThere are several useful visual features to tell there is 'metal pail' and not similar things in a photo:\tmade of metal\tround shape\twith a handle\ton the smaller side as a container\tsuitable for carrying liquids or small objects", 10], "square pattern": ["Yes. 'Square pattern' has a tangible appearance and refers to a design or motif consisting of squares.\nA few things that are visually similar to 'square pattern' but are not 'square pattern' are:\tcheckered pattern\tgrid pattern\thexagonal pattern\nThere are several useful visual features to tell there is 'square pattern' and not similar things in a photo:\trepeated squares in a regular or consistent arrangement\tright angles between the squares\tcontrast between the squares and the background color or texture.", 10], "medallion": ["Yes. 'Medallion' has a tangible appearance and is a type of ornament.\nA few things that are visually similar to 'medallion' but are not 'medallion' are:\tbadge\tcoin\tseal\tbracelet\nThere are several useful visual features to tell there is 'medallion' and not similar things in a photo:\tround or oval shape\tmetal or decorative material\tdesign or typography in relief worn as a pendant or on a ribbon or chain.", 10], "blue cloth": ["Yes. 'Blue cloth' has a tangible appearance and is a textile product.\nA few things that are visually similar to 'blue cloth' but are not 'blue cloth' are:\tblue paper\tblue plastic\tblue paint\tblue tarpaulin\nThere are several useful visual features to tell there is 'blue cloth' and not similar things in a photo:\tsoft and flexible\ttexture and thickness usual in fabric\tgathers or folds when draped\thas a grain or direction of threads can be visually noticed.", 10], "handle mug": ["Yes. 'Handle mug' has a tangible appearance and is a type of cup or mug with a handle.\nA few things that are visually similar to 'handle mug' but are not 'handle mug' are:\tteapot\ttumbler\tcoffee cup\tsoup bowl\nThere are several useful visual features to tell there is 'handle mug' and not similar things in a photo:\tupright cup or mug with a handle\thandle attached to the side of the mug\tmostly used for drinking hot beverages such as coffee or tea\tcan be made of ceramic, glass, or metal", 10], "identification badge": ["Yes. 'Identification badge' has a tangible appearance and is a kind of personal identification.\nA few things that are visually similar to 'identification badge' but are not 'identification badge' are:\tmembership card\tcredit card\tname tag\nThere are several useful visual features to tell there is 'identification badge' and not similar things in a photo:\tworn on a lanyard or clipped to clothing\tclear identification of the person's name, photo, and organization or position\tlogos or colors that match the company or organization that issued the badge", 10], "round rug": ["Yes. 'Round rug' has a tangible appearance and is a type of floor covering.\nA few things that are visually similar to 'round rug' but are not 'round rug' are:\tdoormat\tcarpet\ttapestry\t\nThere are several useful visual features to tell there is 'round rug' and not similar things in a photo:\tcircular shape\tlaid on the floor\tusually smaller in size than carpet\tbound or fringed edge\tthickness or texture is visible from the side\tview of a woven or knotted design.", 10], "orange cloth": ["Yes. 'Orange cloth' has a tangible appearance and refers to a specific color and material.\nA few things that are visually similar to 'orange cloth' but are not 'orange cloth' are:\torange paper\torange plastic\torange paint\nThere are several useful visual features to tell there is 'orange cloth' and not similar things in a photo:\tmade of fabric or textile\tmay have texture or pattern\tfolded or draped in a certain way\tmatches the color of other orange clothing or objects in the photo.", 10], "custom": ["No. 'Custom' is too vague or abstract to be distinguished in a photo. It refers to traditional behaviors, practices, and beliefs of a group of people.\nThere are no things visually similar to 'custom' because it is not a visual concept.", 10], "underwear": ["Yes. 'Underwear' has a tangible appearance and is a type of clothing worn under other clothing.\nA few things that are visually similar to 'underwear' but are not 'underwear' are:\tswimwear\tathletic shorts\tshapewear\tbody suits\nThere are several useful visual features to tell there is 'underwear' and not similar things in a photo:\tworn under other clothing\tclose-fitting or tight\tusually made of cotton, lace, or other soft materials\tmay have elastic waistbands or leg openings.", 10], "foreleg": ["Yes. 'Foreleg' has a tangible appearance and refers to the front legs of an animal.\nA few things that are visually similar to 'foreleg' but are not 'foreleg' are:\thands\tarms\ttree branches\nThere are several useful visual features to tell there is 'foreleg' and not similar things in a photo:\tattached to the animal's body\twith fur, feathers, or scales, depending on the animal\tbends at the elbow, knee, or wrist, depending on the animal\tmay have claws or hooves at the end.", 10], "portraits": ["Yes. 'Portraits' has a tangible appearance and depicts a person's face or figure.\nA few things that are visually similar to 'portraits' but are not 'portraits' are: photographs, statues, mannequins, masks\nThere are several useful visual features to tell there is 'portraits' and not similar things in a photo: a representation of a specific person or people, usually showing their faces or upper bodies, it can be drawn, painted, or sketched in a variety of styles and mediums, often displayed on a wall or table", 10], "eyewear": ["Yes. 'Eyewear' has a tangible appearance and refers to items worn on or over the eyes for vision correction or protection.\nA few things that are visually similar to 'eyewear' but are not 'eyewear' are:\tsunglasses\tswimming goggles\tblinders\teye patches\nThere are several useful visual features to tell there is 'eyewear' and not similar things in a photo:\tlocated on or over the eyes\teither clear or tinted lenses\thave earpieces or straps for wearing on the head\tspecific designs for different purposes (e.g., prescription eyeglasses vs. safety goggles)", 10], "pink lamp": ["Yes. 'Pink lamp' has a tangible appearance and is a type of lighting fixture.\nA few things that are visually similar to 'pink lamp' but are not 'pink lamp' are:\tpink vase\tpink cup\tpink candle\tpink flowers\nThere are several useful visual features to tell there is 'pink lamp' and not similar things in a photo:\tpink lampshade\tlamp base\tlight bulb or tube emits light\tswitch or cord for turning on and off", 10], "porcelain toilet tank lid": ["Yes. 'Porcelain toilet tank lid' has a tangible appearance and is a specific part of a toilet.\nA few things that are visually similar to 'porcelain toilet tank lid' but are not 'porcelain toilet tank lid' are:\tplates\tcovers\thatches\tlids\nThere are several useful visual features to tell there is 'porcelain toilet tank lid' and not similar things in a photo:\trectangular shape\tsmooth surface\twhite glossy appearance\tmounted on top of a porcelain toilet tank.", 10], "nike shoe": ["Yes. 'Nike shoe' has a tangible appearance and is a specific brand of shoe.\nA few things that are visually similar to 'nike shoe' but are not 'nike shoe' are:\tAdidas shoe\tReebok shoe\tPuma shoe\tNew Balance shoe\nThere are several useful visual features to tell there is 'nike shoe' and not similar things in a photo:\tthe iconic Nike \"swoosh\" logo\tthe overall design and shape of the shoe\tthe specific color scheme or pattern of the shoe\tthe unique features of a specific Nike shoe model (such as Air Max or Flyknit)", 10], "sesame seed": ["Yes. 'Sesame seed' has a tangible appearance and is a type of seed.\nA few things that are visually similar to 'sesame seed' but are not 'sesame seed' are:\tpoppy seeds\tquinoa grains\tchia seeds\nThere are several useful visual features to tell there is 'sesame seed' and not similar things in a photo:\tsmall and round\toval shape with a point one either end\tyellowish or whitish color\tsmooth surface with ridges in it", 10], "daffodils": ["Yes. 'Daffodils' has a tangible appearance and is a type of flower.\nA few things that are visually similar to 'daffodils' but are not 'daffodils' are:\tsunflowers\ttulips\tmarigolds\tdaisies\nThere are several useful visual features to tell there is 'daffodils' and not similar things in a photo:\tperennial flower\tyellow or white petals with a trumpet-shaped corona\tgrowing from a bulb or corm \ttwo or three leaves at the base of the stem\tthe appearance of clusters in fields, gardens or pots", 10], "rubber ducks": ["Yes. 'Rubber ducks' has a tangible appearance and is a type of bath toy.\nA few things that are visually similar to 'rubber ducks' but are not 'rubber ducks' are:\tother bath toys\tyellow plastic objects\nThere are several useful visual features to tell there is 'rubber ducks' and not similar things in a photo:\tbright yellow color\trubber or plastic material\tduck-shaped design\tsmall in size\tbuoyant", 10], "chicken wings": ["Yes. 'Chicken wings' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'chicken wings' but are not 'chicken wings' are:\tbuffalo cauliflower bites\tfried tofu bites\tpork wings\nThere are several useful visual features to tell there is 'chicken wings' and not similar things in a photo:\tbones\twith or without skin\tcrunchy and crispy outer coating\tsmall size compared to other meat cuts", 10], "tall poles": ["Yes. 'Tall poles' has a tangible appearance and refers to poles of significant height.\nA few things that are visually similar to 'tall poles' but are not 'tall poles' are:\ttrees\ttowers\tbuildings\tstatues\nThere are several useful visual features to tell there is 'tall poles' and not similar things in a photo:\tvertical\tobject of significant height\tnot attached to any building or structure", 10], "mammals": ["Yes. 'Mammals' has a tangible appearance and is a category of animals that have hair or fur and produce milk for their young.\nA few things that are visually similar to 'mammals' but are not 'mammals' are:\treptiles\tbirds\tinsects\tfish\nThere are several useful visual features to tell there is 'mammals' and not similar things in a photo:\thair or fur on their body\texternal ears\twarm-blooded\tproduce milk for their young\tgive birth to live young (rather than laying eggs)", 10], "sailor": ["Yes. 'Sailor' has a tangible appearance and refers to a person who navigates a ship.\nA few things that are visually similar to 'sailor' but are not 'sailor' are:\tboat captain\tpirate\tfisherman\tswimmer\nThere are several useful visual features to tell there is 'sailor' and not similar things in a photo:\tsailor hat or cap\tstripped t-shirt, sailor suit or navy uniform \tcarrying a map, a compass or a rope \tonboard a ship or near the sea", 10], "country skiers": ["Yes. 'Country skiers' has a tangible appearance and refers to a specific type of skiing.\nA few things that are visually similar to 'country skiers' but are not 'country skiers' are:\talpine skiers\tsnowboarders\thikers\tin-line skaters\nThere are several useful visual features to tell there are 'country skiers' and not similar things in a photo:\tlong, narrow, and lightweight skis\twith raised tips and no edges\tthin poles with a sharp, angled end\tdressed in layers of warm clothing, sturdy gloves, and hats or goggles\toff-trail and through unmarked or ungroomed areas", 10], "closeup": ["Yes. 'Closeup' has a tangible appearance and refers to a type of photograph or video shot.\nA few things that are visually similar to 'closeup' but are not 'closeup' are:\tmacro shot\tshallow depth of field\texaggerated perspective\nThere are several useful visual features to tell there is a 'closeup' and not similar things in a photo:\tfocused on a specific detail or subject\tcapturing a small area of the frame\tproviding high detail and texture of the subject\tcapturing micro-expressions and subtle emotions on the subject's face\tor showing specific textures, patterns, and characteristics of the subject", 10], "plastic object": ["Yes. 'Plastic object' has a tangible appearance and refers to any object made of plastic.\nA few things that are visually similar to 'plastic object' but are not 'plastic object' are:\twooden object\tmetal object\tglass object\trubber object\nThere are several useful visual features to tell there is 'plastic object' and not similar things in a photo:\tman-made\tsturdy but lightweight\tsmooth or textured surface\tflexible and bendable\tcan be transparent or opaque\tcan be any color", 10], "shit": ["No. 'Shit' is too vulgar and not an appropriate topic for discussion. Please refrain from using such language. ", 10], "ripe fruits": ["Yes. 'Ripe fruits' has a tangible appearance and refers to fruits that are fully mature and ready to eat.\nA few things that are visually similar to 'ripe fruits' but are not 'ripe fruits' are:\tunripe fruits\tvegetables\tdecorative fruits\torbs or balls\nThere are several useful visual features to tell there is 'ripe fruits' and not similar things in a photo:\tvibrant, bright colors\tsoft or juicy texture\tspecific fruit shapes and sizes\tvisible seeds or pits\ton a tree or in a basket or bowl", 10], "pink stripes": ["Yes. 'Pink stripes' has a tangible appearance and is a type of pattern.\nA few things that are visually similar to 'pink stripes' but are not 'pink stripes' are:\tpink lines\tpink bars\tpink squares\tpink checks\nThere are several useful visual features to tell there are 'pink stripes' and not similar things in a photo:\talternating bands of pink and non-pink colors\tconsistent width and spacing of stripes\tthe stripes run parallel to each other and do not intersect or overlap.", 10], "stuffed penguin": ["Yes. 'Stuffed penguin' has a tangible appearance and describes a type of soft toy.\nA few things that are visually similar to 'stuffed penguin' but are not 'stuffed penguin' are:\tstuffed animals\tfigurines\tcartoon characters\t\nThere are several useful visual features for distinguishing 'stuffed penguin' from the listed similar things in a photo:\t\n- black and white coloration \n- the classic penguin shape, including a round body with wings and flippers\n- a fuzzy, textured surface that suggests feathers \n- a lifelike size and shape indicative of a penguin species", 10], "concrete floor": ["Yes. 'Concrete floor' has a tangible appearance and is a type of flooring.\nA few things that are visually similar to 'concrete floor' but are not 'concrete floor' are:\tcement wall\tasphalt road\trocky ground\nThere are several useful visual features to tell there is 'concrete floor' and not similar things in a photo:\tsmooth and flat surface\ttypically gray in color\tmay have visible lines or textures", 10], "message board": ["Yes. 'Message board' has a tangible appearance and is a kind of board used for displaying messages.\nA few things that are visually similar to 'message board' but are not 'message board' are:\tchalkboard\twhiteboard\tbulletin board\tadvertising board\tmenu board\nThere are several useful visual features to tell there is 'message board' and not similar things in a photo:\tdisplaying text or images\tfor public or private use\tboard with spaces to hold messages, letters, or cards\teasily changed or updated with new information", 10], "plain wall": ["Yes. 'Plain wall' has a tangible appearance and is a type of surface.\nA few things that are visually similar to 'plain wall' but are not 'plain wall' are: wallpaper brick wall wooden panel curtains bookshelf\nThere are several useful visual features to tell there is a 'plain wall' and not similar things in a photo: flat surface no decorative patterns or textures monochromatic color no visible items or objects on the wall not made of bricks, wood or any other material that creates a visible pattern.", 10], "camera lense": ["Yes. 'Camera lens' has a tangible appearance and is a component of a camera.\nA few things that are visually similar to 'camera lens' but are not 'camera lens' are:\tmagnifying glass\tbinoculars\ttelescope\tmicroscope\tsunglasses\nThere are several useful visual features to tell there is 'camera lens' and not similar things in a photo:\tmounted or attached to a camera\tcylindrical shape\twith focus and zoom markings\tclear or colored glass elements", 10], "wall decor": ["Yes. 'Wall decor' has a tangible appearance and refers to any decorative item that is hung on a wall.\nA few things that are visually similar to 'wall decor' but are not 'wall decor' are:\tpaintings\tphotos\tmirrors\tshelves\t\nThere are several useful visual features to tell there is 'wall decor' and not similar things in a photo:\thanging on a wall\tdecorative or artistic in nature\tframed or unframed\tadding visual interest to a room or space", 10], "beach bag": ["Yes. 'Beach bag' has a tangible appearance.\nA few things that are visually similar to 'beach bag' but are not 'beach bag' are:\tpurse\tbackpack\ttote bag\tshopping bag\nThere are several useful visual features to tell there is 'beach bag' and not similar things in a photo:\tlarge size\tsand-resistant material\tzipper or drawstring closure\troom for towels, sunscreen, hats, and other beach essentials\toften made from canvas or mesh materials\tbeach-related patterns or designs, such as palm trees or seashells.", 10], "diamond ring": ["Yes. 'Diamond ring' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'diamond ring' but are not 'diamond ring' are:\tengagement ring\twedding ring\tcostume jewelry\trhinestone ring\nThere are several useful visual features to tell there is 'diamond ring' and not similar things in a photo:\tcircular band usually made out of gold, platinum, or silver\twith or without precious stones, the most common being diamond\tsparkly and shiny\treflects light well", 10], "wedding band": ["Yes. 'Wedding band' has a tangible appearance and is a type of ring.\nA few things that are visually similar to 'wedding band' but are not 'wedding band' are:\tregular ring\tpromise ring\tengagement ring\tchampionship ring\nThere are several useful visual features to tell there is 'wedding band' and not similar things in a photo:\tcircular band of metal, usually gold or silver\twithout a giant or fancy centerpiece\tcan be plain or have simple design\tworn on the fourth finger of the left hand in Western cultures.", 10], "orange extension cord": ["Yes. 'Orange extension cord' has a tangible appearance and is a kind of electrical cord.\nA few things that are visually similar to 'orange extension cord' but are not 'orange extension cord' are:\tyellow extension cord\tpower strip\taudio cable\tphone charger\nThere are several useful visual features to tell there is 'orange extension cord' and not similar things in a photo:\tlong cord with plugs on both ends\tT-shaped or rectangular shaped plugs\tbright orange color\tthick or durable outer covering\tto be used for outdoor purposes as well as indoor use.", 10], "crystals": ["Yes. 'Crystals' has a tangible appearance and is a type of mineral with an ordered atomic structure.\nA few things that are visually similar to 'crystals' but are not 'crystals' are:\tice or snow\tdiamonds\tsalt or sugar crystals\nThere are several useful visual features to tell there is 'crystals' and not similar things in a photo:\tgem-like appearance\ttranslucent or transparent\tshapes with well-defined geometric patterns\trefracts light", 10], "gas cooker": ["Yes. 'Gas cooker' has a tangible appearance and is a type of kitchen appliance.\nA few things that are visually similar to 'gas cooker' but are not 'gas cooker' are:\toven\trange\tstove\tmicrowave\nThere are several useful visual features to tell there is 'gas cooker' and not similar things in a photo:\tburners or hobs for cooking\tpipes or hoses for gas supply\tspinning knobs or buttons for ignition and temperature control\toven and grill components may also be visible.", 10], "flag post": ["Yes. 'Flag post' has a tangible appearance and is a type of pole used to display flags.\nA few things that are visually similar to 'flag post' but are not 'flag post' are:\tsign poles\tstreet lamp posts\ttrees\tparking meters\nThere are several useful visual features to tell there is 'flag post' and not similar things in a photo:\ttapered shape\twith a hook or arm to hold the flag\tusually made of metal or wood\tsingle or multiple in number, often in a row", 10], "plastic eye": ["Yes. 'Plastic eye' has a tangible appearance and is a prosthetic replacement for a real eye.\nA few things that are visually similar to 'plastic eye' but are not 'plastic eye' are:\tmarble\teyeball candy\tglass eye\tmarbles\nThere are several useful visual features to tell there is 'plastic eye' and not similar things in a photo:\tmade of plastic or silicone\tmimics the appearance of a real eye\thas a clear or slightly milky surface\ttexture and color resembles natural eye\tusually worn with glasses or an eye patch.", 10], "pimple": ["Yes. 'Pimple' has a tangible appearance and is a type of skin lesion.\nA few things that are visually similar to 'pimple' but are not 'pimple' are:\tmole\tacne\trash\tbite\nThere are several useful visual features to tell there is 'pimple' and not similar things in a photo:\traised bump on the skin\tred or pink in color\tpus-filled or white head in the center\tcan appear alone or in clusters on the face or body", 10], "al": ["No. 'Al' is too vague or abstract to be distinguished in a photo.", 10], "metal feeder": ["Yes. 'Metal feeder' has a tangible appearance and is a container for food or seeds.\nA few things that are visually similar to 'metal feeder' but are not 'metal feeder' are:\tmetal bowl\tmetal pot\tmetal watering can\nThere are several useful visual features to tell there is 'metal feeder' and not similar things in a photo:\tdesigned for holding seeds or food for animals\tspecifically made of metal, which could be seen as a texture\tthe presence of openings which let the animals feed\tBOOL value to check whether there is any food in it or not.", 10], "bushels": ["Yes, 'bushels' has a visually concrete appearance and is a unit of measurement for agricultural produce.\nA few things that are visually similar to 'bushels' but are not 'bushels' are:\tmeasuring cups\tbaskets\tbags\tpiles of fruit or vegetables\nThere are several useful visual features to tell there are 'bushels' and not similar things in a photo:\tlarge containers\tcylindrical shape\topen top\tsometimes made of woven material\tfull of fruits, vegetables or grains", 10], "volley": ["No. 'Volley' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we consider 'volley' as in 'volleyball', then:\n\nA few things that are visually similar to 'volleyball' but are not 'volleyball' are: basketball, soccer ball, dodgeball, beach ball\n\nUseful visual features for distinguishing 'volleyball' from the listed similar things in a photo are: distinctive panel design with a specific number of panels, texture of the ball (usually leather or synthetic leather), size and weight (officially regulated), net and poles in the background, players dressed in specific outfits and playing in a specific court.", 10], "skateboard shoes": ["Yes. 'Skateboard shoes' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'skateboard shoes' but are not 'skateboard shoes' are:\tsneakers\trunning shoes\tbasketball shoes\thiking shoes\nThere are several useful visual features to tell there is 'skateboard shoes' and not similar things in a photo:\tflat rubber sole\tfor grip and board control\treinforced areas\tfor durability and protection\tfrom wearing and damage caused by skateboard tricks\tpadded collar and insole\tfor comfort\twhen landing from high jumps and tricks\twide size for stability and balance\twhile riding the skateboard\tboard feel\tfor precise control and balance\tof the skateboard under the feet.", 10], "broken lines": ["Yes. 'Broken lines' has a tangible appearance and is a pattern or design.\nA few things that are visually similar to 'broken lines' but are not 'broken lines' are:\tcracks\tincomplete shapes\tzig-zag lines\nThere are several useful visual features to tell there are 'broken lines' and not similar things in a photo:\tstraight and evenly spaced lines\tintersections with gaps or breaks in between\tmore than one line present in the design or pattern", 10], "grafitti wall": ["Yes. 'Graffiti wall' has a tangible appearance and refers to a wall covered in graffiti.\nA few things that are visually similar to 'graffiti wall' but are not 'graffiti wall' are:\twall mural\tpublic art\tstreet art\tpainted wall\nThere are several useful visual features to tell there is 'graffiti wall' and not similar things in a photo:\tmultiple and colorful tags or drawings\twriting\tor images\tpainted without the owner's consent\tor permission\ton public or abandoned buildings", 10], "hand fingers": ["Yes. 'Hand fingers' has a tangible appearance and refers to the five digits on a human hand.\nThere are no things that are visually similar to 'hand fingers' but are not 'hand fingers'.\nHowever, useful visual features to tell there are 'hand fingers' in a photo are:\tfive digits on a human hand\tjointed structure\tnails at the tip of each finger", 10], "conveyer belt": ["Yes. 'Conveyor belt' has a tangible appearance and is a moving surface used for transporting items.\nA few things that are visually similar to 'conveyor belt' but are not 'conveyor belt' are:\tescalator\tluggage carousel\tassembly line\troller coaster track\nThere are several useful visual features to tell there is 'conveyor belt' and not similar things in a photo:\tflat surface\twith rollers or other moving parts\titems being transported along surface\tmechanical components or motors visible", 10], "grey speaker": ["Yes. 'Grey speaker' has a tangible appearance and is a type of electronic device.\nA few things that are visually similar to 'grey speaker' but are not 'grey speaker' are:\tmicrophone\talarm clock\tair purifier\tdecorative statue\nThere are several useful visual features to tell there is 'grey speaker' and not similar things in a photo:\trectangular or cylindrical shape\tgrey color\tcone or horn-shaped projection\tforward-facing buttons or knobs\tsound waves emanating from the device", 10], "orange license plate": ["Yes. 'Orange license plate' has a tangible appearance and is a distinct type of license plate used for special purposes such as commercial or temporary vehicles.\nA few things that are visually similar to 'orange license plate' but are not 'orange license plate' are:\tblue license plate\tgreen license plate\tgrey license plate\tred license plate\nThere are several useful visual features to tell there is 'orange license plate' and not similar things in a photo:\tdistinctive bright orange color\tdifferent design or lettering from regular license plates\tvariations in numbers or letters indicating a specific type of vehicle or usage.", 10], "ruffle": ["Yes. 'Ruffle' has a visually concrete concept and refers to a strip of fabric that is gathered or pleated to create a wavy or ornamental pattern.\nA few things that are visually similar to 'ruffle' but are not 'ruffle' are:\tfringe\tpleats\ttassels\tswirls\tfolds\nThere are several useful visual features to tell there is 'ruffle' and not similar things in a photo:\tuneven, wavy edge of the fabric\tgathers near the seam\tor tucks of fabric to create a pattern\tornamental design\tmade of soft or flexible materials, usually fabric\tor ribbons.", 10], "hair woman": ["No. 'Hair woman' is too vague or abstract to be distinguished in a photo. It could refer to any woman with hair.\n", 10], "wiper blade": ["Yes. 'Wiper blade' has a tangible appearance and is a tool used to clean car windows.\nA few things that are visually similar to 'wiper blade' but are not 'wiper blade' are:\tsqueegee\tknife\tscissors\tpaddle\nThere are several useful visual features to tell there is 'wiper blade' and not similar things in a photo:\tlong and flat blade\tattached to a windshield wiper mechanism\tmade of rubber or silicone\tmoving back and forth to clean the windshield or rear window", 10], "gold paint": ["Yes. 'Gold paint' has a tangible appearance.\nA few things that are visually similar to 'gold paint' but are not 'gold paint' are:\tbrass\tmetallic markers\tyellow ink\tcopper\nThere are several useful visual features to tell there is 'gold paint' and not similar things in a photo:\tshiny texture\tmetallic color\tsmeared or liquid appearance\tpainted or applied to a surface", 10], "girl shirt": ["Yes. 'Girl shirt' has a tangible appearance and specifically refers to a type of clothing for females.\nA few things that are visually similar to 'girl shirt' but are not 'girl shirt' are:\tboy shirt\twomen's blouse\tt-shirt\tdress\nThere are several useful visual features to tell there is 'girl shirt' and not similar things in a photo:\tfitted or tailored for a female\tform-fitting or loose design\tvarying necklines (such as scoop neck or V-neck)\tuse of feminine patterns or colors\tpossibly shorter in length\tif illustrated, may feature \"girly\" characters or designs", 10], "zebra heads": ["Yes. 'Zebra heads' has a tangible appearance and refers to the head of a zebra.\nA few things that are visually similar to 'zebra heads' but are not 'zebra heads' are:\thorse heads\tdonkey heads\nThere are several useful visual features to tell there is 'zebra heads' and not similar things in a photo:\tblack and white stripes on the face of the animal\tmuzzle with nostrils pointed downward\tlong ears", 10], "horse-drawn carriage": ["Yes. 'Horse-drawn carriage' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'horse-drawn carriage' but are not 'horse-drawn carriage' are:\tcovered wagon\tsled\trickshaw\thay cart\nThere are several useful visual features to tell there is 'horse-drawn carriage' and not similar things in a photo:\tpulled by one or more horses\tdecorative, ornate design\twooden wheels\topen or closed carriage\ttop part of the carriage is covered by a roof or awning", 10], "flag sticker": ["Yes. 'Flag sticker' has a tangible appearance and is a kind of sticker with the national flag printed on it.\nA few things that are visually similar to 'flag sticker' but are not 'flag sticker' are:\tLogos\tDecorative stickers\t\nThere are several useful visual features to tell there is 'flag sticker' and not similar things in a photo:\tNational flag printed design\tRectangular or square shape\tPaper or vinyl material with sticker adhesive on the back.", 10], "cutout": ["Yes. 'Cutout' has a tangible appearance and refers to an image or shape that has been cut out from a larger piece of material.\nA few things that are visually similar to 'cutout' but are not 'cutout' are:\tstencil\tsilhouette\tshadow\tcast\nThere are several useful visual features to tell there is 'cutout' and not similar things in a photo:\tclearly defined edges\tthat it is a separate image or shape\tthat it appears to be overlaid on another image or background", 10], "prunes": ["Yes. 'Prunes' has a tangible appearance and is a type of dried plum.\nA few things that are visually similar to 'prunes' but are not 'prunes' are:\tdates\traisins\tfigs\nThere are several useful visual features to tell there are 'prunes' and not similar things in a photo:\tdark purple or black wrinkly texture\toval or round shape\tpitted seed in the center\tdried fruit texture", 10], "snow poles": ["Yes. 'Snow poles' has a tangible appearance and is a type of marker used on roads during winter.\nA few things that are visually similar to 'snow poles' but are not 'snow poles' are:\tfishing nets\tstreet poles\tparking curbs\tconstruction cones\nThere are several useful visual features to tell there is 'snow poles' and not similar things in a photo:\ttall and slender\tpainted in reflective colors, usually orange or red\tsticking out of the ground surrounded by snow\tplaced in a row to mark the edge of a road or a lane.", 10], "kernel": ["Yes. 'Kernel' has a tangible appearance and refers to the inner part of a seed or a nut.\nA few things that are visually similar to 'kernel' but are not 'kernel' are:\tshell\tseed\tgroove\nThere are several useful visual features to tell there is 'kernel' and not similar things in a photo:\tOval or round shape\tA distinct texture or pattern\tLocated inside a hard outer shell or case", 10], "tailfin": ["Yes. 'Tailfin' has a tangible appearance and is a physical structure of an object.\nA few things that are visually similar to 'tailfin' but are not 'tailfin' are:\twing\thorizontal stabilizer\tdorsal fin\nThere are several useful visual features to tell there is 'tailfin' and not similar things in a photo:\tvertical surface on the tail of a plane, boat or car\ttriangular or trapezoidal shape\tpositioned vertically or diagonally\ttypically painted or colored differently from the rest of the object.", 10], "brown plant": ["Yes. 'Brown plant' has a tangible appearance and typically refers to a plant that has turned brown due to aging or a lack of water.\nA few things that are visually similar to 'brown plant' but are not 'brown plant' are:\tdead leaves\tdry grass\tdried flowers\nThere are several useful visual features to tell there is 'brown plant' and not similar things in a photo:\tplant-like structure\tbrown or withered appearance\tno visible signs of life or greenery\tdifferent from healthy plants nearby\tlack of moisture or hydration", 10], "egg carton": ["Yes. 'Egg carton' has a tangible appearance and is a container used for holding eggs.\nA few things that are visually similar to 'egg carton' but are not 'egg carton' are:\tice cube tray\tchocolate mold\tmodular storage box\nThere are several useful visual features to tell there is 'egg carton' and not similar things in a photo:\t12 or 18 egg-sized depressions\tridged on the top and bottom\tstackable and compactable\ttapered at one end", 10], "floor board": ["Yes. 'Floor board' has a tangible appearance and is a type of wooden board used for flooring.\nA few things that are visually similar to 'floor board' but are not 'floor board' are:\tWooden planks\tWooden beams\tWooden panels\tTiles\nThere are several useful visual features to tell there is 'floor board' and not similar things in a photo:\tFlat and rectangular shape\tWooden texture\tFitted tightly together to form a smooth floor surface\tUsually stained or painted in a specific color.", 10], "indents": ["Yes. 'Indents' has a tangible appearance and refers to a depression or hollow area on a surface.\nA few things that are visually similar to 'indents' but are not 'indents' are:\tbumps\tdots\tshadows\tcracks\nThere are several useful visual features to tell there is 'indents' and not similar things in a photo:\tconcave surface\tdepicts a negative space on an object\tsmoothness in texture or shape\tasymmetric shapes\tof varying sizes and depths.", 10], "cement structure": ["Yes. 'Cement structure' has a tangible appearance and is a type of construction made from cement or concrete.\nA few things that are visually similar to 'cement structure' but are not 'cement structure' are:\tstone structure\tbrick structure\twooden structure\tmetal structure\nThere are several useful visual features to tell there is 'cement structure' and not similar things in a photo:\tgrey or pale color\tsmooth or rough texture\tman-made and geometric shapes\tvisible lines or patterns of the cement or concrete", 10], "metal railings": ["Yes. 'Metal railings' has a tangible appearance and refers to a type of metal architecture.\nA few things that are visually similar to 'metal railings' but are not 'metal railings' are: bars of a jail cell, metal fences, metal balconies, metal ladders or steps, metal grills\nThere are several useful visual features to tell there is 'metal railings' and not similar things in a photo:\tparallel bars of metal\thorizontal or vertical orientation\tS-shaped curved design in some instances\tpresence of some form of support, like posts or pillars or threaded bolts.", 10], "walking": ["Yes. 'Walking' has a tangible appearance and is a physical movement.\nA few things that are visually similar to 'walking' but are not 'walking' are:\trunning\tcrawling\tswimming\tjumping\nThere are several useful visual features to tell there is 'walking' and not similar things in a photo:\talternating left-right movement of limbs\ton two feet, not on all fours\tor slow movement, not fast or sudden\tlack of contact with the ground between steps (unlike crawling)\tlack of buoyancy (unlike swimming)", 10], "mansion": ["Yes. 'Mansion' has a tangible appearance and is a type of large house.\nA few things that are visually similar to 'mansion' but are not 'mansion' are:\tvilla\tpalace\testate\tapartment building\nThere are several useful visual features to tell there is 'mansion' and not similar things in a photo:\tlarge size\tluxurious-looking architecture\texterior facades and columns\texpansive grounds and gardens", 10], "cistern": ["Yes. 'Cistern' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'cistern' but are not 'cistern' are:\ttank\tbarrel\tsilo\nThere are several useful visual features to tell there is 'cistern' and not similar things in a photo:\tusually made of concrete or metal\tlocated underground or outdoors\tlarge capacity for holding liquids\tattached to a gutter or piping system", 10], "sink cabinet": ["Yes. 'Sink cabinet' has a tangible appearance and is a piece of furniture used for holding a sink and storing bathroom or kitchen items.\nA few things that are visually similar to 'sink cabinet' but are not 'sink cabinet' are:\tchest of drawers\tcupboard\tkitchen island\tbathroom vanity\tshelving unit\nThere are several useful visual features to tell there is 'sink cabinet' and not similar things in a photo:\ta built-in sink\ta countertop with space for faucet\tfurniture-style doors and drawers\tlocated in a bathroom or a kitchen", 10], "paisley": ["Yes. 'Paisley' has a tangible appearance and is a teardrop-shaped pattern with intricate design.\nA few things that are visually similar to 'paisley' but are not 'paisley' are:\tswirls\tcurls\ttrails\nThere are several useful visual features to tell there is 'paisley' and not similar things in a photo:\tteardrop or bud-shaped patterns\tintricate design\twith a thin stem at the top", 10], "watch tower": ["Yes. 'Watch tower' has a tangible appearance and is a type of structure used for surveillance.\nA few things that are visually similar to 'watch tower' but are not 'watch tower' are:\tlighthouse\tpower tower\twindmill\ttall building\nThere are several useful visual features to tell there is 'watch tower' and not similar things in a photo:\ttall structure with a small room on top\televated and overlooking an area\tusually located in remote or isolated locations\twith stairs or a ladder to reach the top\twith windows or openings for viewing\tthe top may have a roof or a dome-like structure", 10], "doggy": ["Yes. 'Doggy' has a tangible appearance and is a term used to refer to dogs, especially when they are cute or small.\nA few things that are visually similar to 'doggy' but are not 'doggy' are:\twolf\tfox\tjackal\tdingo\nThere are several useful visual features that show there is 'doggy' and not similar things in a photo:\tfour-legged mammal\tfurry body\tfloppy or perky ears\twagging tail\tvariety of colors and sizes\tbreed-specific features such as long snout or flat face.", 10], "building photo": ["Yes. 'Building photo' has a tangible appearance and is an image of a physical structure.\nA few things that are visually similar to 'building photo' but are not 'building photo' are:\tlandscape photo\tcity skyline art\tpainting or drawing of a building\tpoint-of-view video\nThere are several useful visual features to tell there is 'building photo' and not similar things in a photo:\tthe visible facade or exterior of a building\tthe overall shape and size of the building\tthe presence of windows, doors, or other architectural details\tsurrounding structures or the environment/setting", 10], "left paw": ["Yes. 'Left paw' has a tangible appearance and refers to the left-foot of certain animals.\nThere are no things that are visually similar to 'left paw' but are not 'left paw'.\nUseful visual features for distinguishing 'left paw' from the right paw or other parts of the body in a photo:\n- The anatomical differences between left and right paws (e.g. number of toes, shape and size of pads, etc.). \n- The position of the paw in relation to the rest of the animal's body.", 10], "steel sink": ["Yes. 'Steel sink' has a tangible appearance and is a type of household fixture.\nA few things that are visually similar to 'steel sink' but are not 'steel sink' are:\tpot\tbowl\tbasin\t\nThere are a few useful visual features to tell there is a 'steel sink' and not similar things in a photo:\tmade of shiny stainless steel or metal-undermount or top-mount installation-have faucets, handles, and drain openings-rectangular or oval-shaped.", 10], "jewels": ["Yes. 'Jewels' have a tangible appearance and are precious stones or gems.\nA few things that are visually similar to 'jewels' but are not 'jewels' are:\tplastic beads\tglitter\tstickers \tglass beads\nThere are several useful visual features to tell there is 'jewels' and not similar things in a photo:\ttransparent or translucent\thard and solid\tunique colors or patterns\tcatching the light and sparkling", 10], "shoulder length": ["Yes. 'Shoulder length' has a tangible appearance and refers to the length of hair.\nA few things that are visually similar to 'shoulder length' but are not 'shoulder length' are:\tshort hair\tlong hair\thair in a ponytail\thair in a bun\nThere are several useful visual features to tell there is 'shoulder length' and not similar things in a photo:\thair that ends at the shoulders\thair that hangs down on both sides of the neck\tthe distance between the hair tips and the shoulder", 10], "stuffed rabbit": ["Yes. 'Stuffed rabbit' has a tangible appearance and is a type of toy.\nA few things that are visually similar to 'stuffed rabbit' but are not 'stuffed rabbit' are:\tother stuffed animals\tplush toys\tpillows\nThere are several useful visual features to tell there is 'stuffed rabbit' and not similar things in a photo:\trabbit-like features such as long ears, whiskers, and nose\tsoft and plush texture\tfur-like material\tbutton or stitched eyes\tsymmetrical shape\thuman-like posture and clothing details (if dressed up)", 10], "rail road track": ["Yes. 'Rail road track' has a tangible appearance and is a physical structure.\nA few things that are visually similar to 'rail road track' but are not 'rail road track' are:\troad\tbridge\tpathway\nThere are several useful visual features to tell there is 'rail road track' and not similar things in a photo:\tpair of parallel metal tracks\tsleepers or cross-ties holding the track in place\tvisible bolts and spikes connecting the rails\tstraight or curved lines that run for long distances", 10], "wood plate": ["Yes. 'Wood plate' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'wood plate' but are not 'wood plate' are:\tplastic plate\tglass plate\tstone plate\tceramic plate\t\nThere are several useful visual features to distinguish 'wood plate' from the listed similar things in a photo:\t\nmade of wood\trustic appearance\tmay have visible wood grain or knots\tmay have varying shades of brown or beige\tcolor and texture may change depending on the type of wood", 10], "jar lid": ["Yes. 'Jar lid' has a tangible appearance and is a type of cap.\nA few things that are visually similar to 'jar lid' but are not 'jar lid' are:\tbottle cap\tmilk cap\tsoda cap\nThere are several useful visual features to tell there is 'jar lid' and not similar things in a photo:\tcircular or round shape\tfit around the mouth of a jar\tmade of metal or plastic\tflattened or concave top with ridges or threads on the bottom for screwing onto the jar", 10], "skinny tree": ["Yes. 'Skinny tree' has a tangible appearance and is a specific type of tree.\nA few things that are visually similar to 'skinny tree' but are not 'skinny tree' are:\tbushes\tconifers\tshrubs\tcacti\nThere are several useful visual features to tell there is 'skinny tree' and not similar things in a photo:\tthin and tall\ttrunk visible from the ground\tno leaves at the bottom of the trunk\tconical or triangular shape", 10], "watches": ["Yes. 'Watches' has a tangible appearance and is a type of timepiece.\nA few things that are visually similar to 'watches' but are not 'watches' are:\tbracelets\tfitbits or other fitness tracker devices\tjewelry with similar shapes\nThere are several useful visual features to tell there is 'watches' and not similar things in a photo:\tround or square shape\tshowing the time on the face\thave hour and minute hands\tmay have a second hand\tusually have a band that fastens around the wrist or arm.", 10], "honda": ["Yes. 'Honda' has a tangible appearance and is a brand of automobile.\nA few things that are visually similar to 'honda' but are not 'honda' are:\tFord\tToyota\tBMW\tAudi\nThere are several useful visual features to tell there is 'honda' and not similar things in a photo:\tthe Honda logo on the car\tthe design of the car, which is unique to Honda models\tthe style of the grill and headlights\ttypical colors used for Honda cars, such as silver, black, and crimson.", 10], "scrunchie": ["Yes. 'Scrunchie' has a tangible appearance and is a type of hair accessory.\nA few things that are visually similar to 'scrunchie' but are not 'scrunchie' are:\thair tie\tribbon\tbungee cord\trubber band\nThere are several useful visual features to tell there is 'scrunchie' and not similar things in a photo:\tfabric-covered band\tthick and elastic\thair is tied up with the scrunchie on top of it\tcolored or patterned fabrics", 10], "checker pattern": ["Yes. 'Checker pattern' has a visible and tangible appearance.\nA few things that are visually similar to 'checker pattern' but are not 'checker pattern' are:\tzebra stripes\tgingham pattern\tchessboard pattern\thoundstooth pattern\ttartan pattern\nThere are several useful visual features to tell there is 'checker pattern' and not similar things in a photo:\ttwo colors used, typically light and dark squares in a checkered pattern\teven-sized squares arranged in rows and columns\trepeated horizontally and vertically without variation.", 10], "front windshields": ["Yes. 'Front windshields' has a tangible appearance and is a part of a car.\nA few things that are visually similar to 'front windshields' but are not 'front windshields' are:\tside windows\tmirrors\theadlights\nThere are several useful visual features to tell there is 'front windshields' and not similar things in a photo:\tlarge, flat, and angled piece of glass\tat the front of the car\tmeets the roof of the car\tcurved shape to fit the car's body shape\tcapable of providing a clear view of the road ahead", 10], "mcdonalds": ["Yes. 'McDonald's' has a tangible appearance and is a type of fast food restaurant.\nA few things that are visually similar to 'McDonald's' but are not 'McDonald's' are:\tBurger King\tKFC\tWendy's\tSubway\nThere are several useful visual features to tell there is 'McDonald's' and not similar things in a photo:\tthe famous golden arches logo\tthe word \"McDonald's\" on a sign or building\tthe classic red and yellow color scheme\tthe specific menu items and packaging used by McDonald\u2019s (e.g, Big Mac, fries in red containers)", 10], "metal grates": ["Yes. 'Metal grates' has a tangible appearance and refers to a type of grate made of metal.\nA few things that are visually similar to 'metal grates' but are not 'metal grates' are:\tfloor tiles\tintricate ironwork\tpatterned metal screens\nThere are several useful visual features to tell there is 'metal grates' and not similar things in a photo:\thave regular, repeating shapes\tmade of metal\thave gaps that allow air or light to pass through\ttypically used in flooring or covering openings", 10], "cow horns": ["Yes. 'Cow horns' has a tangible appearance and is a part of a cow's body.\nA few things that are visually similar to 'cow horns' but are not 'cow horns' are:\tantlers\tbranches\tstumps\tsculptures\nThere are several useful visual features to tell there is 'cow horns' and not similar things in a photo:\tcurved shape\tattached to the top of cow's head\tasymmetrical pair\tridged texture", 10], "board sign": ["Yes. 'Board sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'board sign' but are not 'board sign' are:\tbillboard\tmenu\tsign decaled on windows\twall posters\nThere are several useful visual features to tell there is 'board sign' and not similar things in a photo:\trectangular shape\twritten or drawn message or image\terased and re-writable surface\twriting in chalk or paint", 10], "flowerpot": ["Yes, 'flowerpot' has a tangible appearance and is a container used for holding plants.\nA few things that are visually similar to 'flowerpot' but are not 'flowerpot' are:\tBucket\tVase\tBowl\tMug\t\nThere are several useful visual features to tell there is 'flowerpot' and not other similar things in a photo:\tcylindrical or conical shape\twith a bottom flat surface\tdrainage holes\tor saucers for drainage\tmaterials used (clay, plastic, concrete, etc.)", 10], "boy ground": ["No. 'Boy ground' is too vague or abstract to have a tangible appearance or be distinguished in a photo.", 10], "nike swoosh": ["Yes. 'Nike swoosh' has a tangible appearance and is a recognizable logo.\nA few things that are visually similar to 'nike swoosh' but are not 'nike swoosh' are:\tAdidas' three stripes\tReebok's vector logo\tPuma's leaping cat logo\nThere are several useful visual features to tell there is 'nike swoosh' and not similar things in a photo:\tcurved checkmark shape\twith a 'tail' at the end\twhite color with a black outline, or vice versa\tappearing on Nike-branded products", 10], "leafed trees": ["Yes. 'Leafed trees' has a tangible appearance and refers to trees that have leaves.\nA few things that are visually similar to 'leafed trees' but are not 'leafed trees' are:\tevergreen trees\tbushes\thedges\tcacti\nThere are several useful visual features to tell there are 'leafed trees' and not similar things in a photo:\tbranches with leaves\tvariety of leaf shapes and colors\tcanopy of leaves when seen from a distance\tseasonal changes in leaf color and texture", 10], "jetway": ["Yes. 'Jetway' has a tangible appearance and is a movable connector that extends from an airport terminal to an airplane.\nA few things that are visually similar to 'jetway' but are not 'jetway' are:\tbridge\tgangway\twalkway\nThere are several useful visual features to tell there is 'jetway' and not similar things in a photo:\tmetallic structure\tconnecting to the airplane\tdoor at the end of the tunnel\tretractable or expandable sections.", 10], "tree stumps": ["Yes. 'Tree stumps' has a tangible appearance and is a type of tree remains.\nA few things that are visually similar to 'tree stumps' but are not 'tree stumps' are:\trocks\tboulders\tstatues\tsand formations\nThere are several useful visual features to tell there is 'tree stumps' and not similar things in a photo:\tcylindrical or conical shape\trough and textured surface\tringed appearance\tvisible tree rings\twhere the tree was cut off from the trunk", 10], "bottom button": ["Yes. 'Bottom button' has a tangible appearance and is a part of clothing.\nA few things that are visually similar to 'bottom button' but are not 'bottom button' are:\ttop button\tsnap hook\tzipper pull\tbelt buckle\nThere are several useful visual features to tell there is 'bottom button' and not similar things in a photo:\tlocated at the bottom of the clothing item\tdifferent shape or color from other buttons\ton the opposite side of the opening/button line of the garment.", 10], "reader": ["No. 'Reader' is too vague or abstract to be distinguished in a photo.", 10], "water ripples": ["Yes. 'Water ripples' has a tangible appearance and is a phenomenon caused by something disturbing the surface of a body of water.\nA few things that are visually similar to 'water ripples' but are not 'water ripples' are:\tshadows\treflections\tintricate patterns on a surface\nThere are several useful visual features to tell there are 'water ripples' and not similar things in a photo:\tconcentric circular patterns in the water\tvisible movement on the surface\tof the water\tlight reflecting off the peaks and valleys of the ripples", 10], "box fan": ["Yes. 'Box fan' has a tangible appearance and is a type of fan.\nA few things that are visually similar to 'box fan' but are not 'box fan' are:\tcircular fan\tceiling fan\tstove\tair purifier\nThere are several useful visual features to tell there is 'box fan' and not similar things in a photo:\trectangular shape\twith a grill guard\tstanding upright or placed horizontally\ton/off switch\tcontrol dial or buttons", 10], "swing": ["Yes. 'Swing' has a tangible appearance and is a type of playground equipment.\nA few things that are visually similar to 'swing' but are not 'swing' are:\thammock\trope\ttire\tsling chair\nThere are several useful visual features to tell there is 'swing' and not similar things in a photo:\tseat suspended on ropes or chains\tframe or structure to hang from\thandles or chains to hold onto\tfor children or adults to use in a playground or a backyard.", 10], "bmw motorcycle": ["Yes. 'BMW motorcycle' has a tangible appearance and is a specific type of motorcycle produced by BMW.\nA few things that are visually similar to 'BMW motorcycle' but are not 'BMW motorcycle' are:\tharley davidson motorcycle\ttriumph motorcycle\tindian motorcycle\nSome useful visual features for distinguishing 'BMW motorcycle' from the similar motorcycles in a photo are:\tthe BMW logo on the bike\tthe distinct blue and white colors of BMW motorcycles\tthe shape of the bike's headlights and windshield\tsilver or chrome accents on the bike's body", 10], "soap pump": ["Yes. 'Soap pump' has a tangible appearance and is a type of dispenser.\nA few things that are visually similar to 'soap pump' but are not 'soap pump' are:\tshampoo bottle\thand sanitizer dispenser\thand lotion bottle\nThere are several useful visual features to tell there is 'soap pump' and not similar things in a photo:\tcylindrical or rectangular shape\twith a pump or nozzle on top\tclear plastic or glass (to see the soap level)\tlabeled as 'soap' or showing a soap icon", 10], "hash marks": ["Yes. 'Hash marks' has a tangible appearance and is a kind of marking.\nA few things that are visually similar to 'hash marks' but are not 'hash marks' are:\tpunctuation marks\tcrosshatch drawing patterns\twallpaper patterns\tzebra stripes\nThere are several useful visual features to tell there are 'hash marks' and not similar things in a photo:\tshort, straight, parallel lines\tequally spaced and uniform\tsize and orientation can vary, but typically seen in groups of two or more\tregularly used in sports, military, or legal contexts", 10], "willow tree": ["Yes. 'Willow tree' has a tangible appearance and is a type of tree.\nA few things that are visually similar to 'willow tree' but are not 'willow tree' are:\tother types of trees\tbushes\tscrubs\nThere are several useful visual features to tell there is 'willow tree' and not similar things in a photo:\tdrooping branches\telongated and narrow leaves\tfine and wispy twigs\tgrowing near water sources, such as rivers or ponds.", 10], "gas pumps": ["Yes. 'Gas Pumps' has a tangible appearance and is a machine for dispensing gasoline.\nA few things that are visually similar to 'gas pumps' but are not 'gas pumps' are:\tair pumps\tvending machines\tcoffee dispensers\t\nThere are several useful visual features to tell there is 'gas pumps' and not similar things in a photo:\tHose for dispensing gasoline and diesel fuel\tNozzle at the end of the hose\tMeter to indicate the amount of fuel dispensed\tDisplay for the price of fuel and amount dispensed\tRegister for accepting payment for the fuel", 10], "hardwood flooring": ["Yes. 'Hardwood flooring' has a tangible appearance and refers to flooring made from hardwood materials.\nA few things that are visually similar to 'hardwood flooring' but are not 'hardwood flooring' are:\tlaminate flooring\ttile flooring\tvinyl flooring\nThere are several useful visual features to tell there is 'hardwood flooring' and not similar things in a photo:\tvisible wood grain\tpatterns of wood knots and lines\twarm and natural colors\ttypically wide planks or boards", 10], "pink post": ["No. 'Pink post' is too vague or abstract to be distinguished in a photo.", 10], "hunter": ["Yes. 'Hunter' has a tangible appearance and it's a person who hunts animals for food or sport.\nA few things that are visually similar to 'hunter' but are not 'hunter' are:\thiker\tfisherman\ttrapper\tzookeeper\nThere are several useful visual features to tell there is 'hunter' and not similar things in a photo:\tcarrying a weapon, such as a gun or a bow\twearing camouflage clothing\thunting or tracking an animal in the photo\tfocusing on the prey or preparing to shoot\thaving a hunting license or permission from the landowner", 10], "stainless steel hood": ["Yes. 'Stainless steel hood' has a tangible appearance and is a kind of kitchen appliance.\nA few things that are visually similar to 'stainless steel hood' but are not 'stainless steel hood' are:\tregular kitchen hood\tcopper hood\tplastic hood\nThere are several useful visual features to distinguish 'stainless steel hood' from the listed similar things in a photo:\tmade of stainless steel\tsleek and modern appearance\tvents or exhaust system\ttouch controls or buttons (in some cases)\tdimensions and shape (depending on the specific model)", 10], "motorcylce": ["Yes. 'Motorcycle' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'motorcycle' but are not 'motorcycle' are:\tmoped\tbicycle\tscooter\tmotorized wheelchair\nThere are several useful visual features to tell there is 'motorcycle' and not similar things in a photo:\ttwo wheels\tengine and exhaust system\thandlebars\tforward-mounted footrests or pedals", 10], "word street": ["No. 'Word Street' is too vague or abstract to be distinguished in a photo. It's not a tangible object or a type of place.\nTherefore, there are no visually similar things to 'word street'.", 10], "silver radiator": ["Yes. 'Silver radiator' has a tangible appearance and is a heating appliance.\nA few things that are visually similar to 'silver radiator' but are not 'silver radiator' are:\tair conditioner\tgrey metal cabinet\toffice safe\nThere are several useful visual features to tell there is 'silver radiator' and not similar things in a photo:\tmetallic\tsilver or grey color\tfins for convection heating\tattached to a wall or a floor", 10], "plastic suitcase": ["Yes. 'Plastic suitcase' has a tangible appearance and is a type of luggage.\nA few things that are visually similar to 'plastic suitcase' but are not 'plastic suitcase' are:\tmetal suitcase\tbackpack\tplastic bag\tduffel bag\nThere are several useful visual features to tell there is 'plastic suitcase' and not similar things in a photo:\trectangular or square shape\thinged lid\thard outer shell\tside handle or telescoping handle\twith wheels\ton a luggage cart or on the floor", 10], "grout lines": ["Yes. 'Grout lines' has a tangible appearance and is a visible line between tiles.\nA few things that are visually similar to 'grout lines' but are not 'grout lines' are:\tcracks\tjoints\tinlaid tiles\nThere are several useful visual features to tell there is 'grout lines' and not similar things in a photo:\tthin and straight lines\tbetween tiles\tdifferent color from tiles\tsmooth surface", 10], "birthday party": ["Yes. 'Birthday party' has a tangible appearance and is a kind of celebration with decorations, cake, and presents.\nA few things that are visually similar to 'birthday party' but are not 'birthday party' are:\twedding\tdinner\tgraduation\tanniversary\nThere are several useful visual features to tell there is 'birthday party' and not similar things in a photo:\tballoons\tconfetti\tstreamers\tbirthday cake\tgifts\tparty hats", 10], "pork chop": ["Yes. 'Pork chop' has a tangible appearance and is a type of meat.\nA few things that are visually similar to 'pork chop' but are not 'pork chop' are:\tbeef steak\tlamb chop\tvegetable\tpiece of bread\nThere are several useful visual features to tell there is 'pork chop' and not similar things in a photo:\tbone in the center\tpink or beige color\tlayer of fat around the edge\tcircular or oval shape\tthickness", 10], "silver wedding band": ["Yes. 'Silver wedding band' has a tangible appearance and is a type of ring.\nA few things that are visually similar to 'silver wedding band' but are not 'silver wedding band' are:\tsilver engagement ring\tmen's silver ring\twomen's silver ring\tsilver promise ring\nThere are several useful visual features to tell there is 'silver wedding band' and not similar things in a photo:\tcircular band made of silver\tworn on the finger, specifically on the left-hand ring finger of the bride or the groom during the wedding ceremony can have small diamonds or other gemstones embedded in it.", 10], "orange eye": ["Yes. 'Orange eye' has a tangible appearance and is a descriptive term for a specific eye color.\nThere are no things that are visually similar to 'orange eye' but are not 'orange eye'.\nThere are no useful visual features for distinguishing 'orange eye' from other things in a photo as it is a specific and unique eye color.", 10], "flower decorations": ["Yes. 'Flower decorations' has a tangible appearance and is a type of ornamentation.\nA few things that are visually similar to 'flower decorations' but are not 'flower decorations' are:\tfruit arrangements\ttissue paper pom poms\tballoons\tribbons\nThere are several useful visual features to tell there is 'flower decoration' and not similar things in a photo:\tflowers and leaves\tplant-like shapes and patterns\tcolorful blooms arranged in clusters\thanging from a wall or ceiling or placed on a table or shelf", 10], "kitchen light": ["Yes. 'Kitchen light' has a tangible appearance and is a kind of lighting fixture.\nA few things that are visually similar to 'kitchen light' but are not 'kitchen light' are:\tceiling fan\tpendant lamp\tchandelier\trecessed lighting\nThere are several useful visual features to tell there is 'kitchen light' and not similar things in a photo:\tflush or semi-flush mount to the ceiling\tbright or warm white light\tfits the style of a kitchen, such as modern or rustic may have a lampshade or cover over the lightbulb", 10], "whale": ["Yes. 'Whale' has a tangible appearance and is a type of marine mammal.\nA few things that are visually similar to 'whale' but are not 'whale' are:\tdolphin\tporpoise\tshark\tsubmarine\t\nThere are several useful visual features to tell there is 'whale' and not similar things in a photo:\textremely large\tsize\tunique shape\twith a blowhole on the top of their head\tnarrow head\tfully aquatic body\thorizontal tail fin", 10], "flower bush": ["Yes. 'Flower bush' has a tangible appearance and refers to a bush with flowers.\nA few things that are visually similar to 'flower bush' but are not 'flower bush' are:\tshrub\tbush with berries\tdecorative grass\nThere are several useful visual features to tell there is 'flower bush' and not similar things in a photo:\tmultiple colorful flowers on a bush or shrub\tgreen leaves (not needles)\tcan be seen in a garden or in a pot\tif close-up, individual flower petals can be seen", 10], "mass transit bus": ["Yes. 'Mass transit bus' has a tangible appearance and is a type of public transportation.\nA few things that are visually similar to 'mass transit bus' but are not 'mass transit bus' are:\tschool bus\tshuttle van\ttour bus\nThere are several useful visual features to tell there is 'mass transit bus' and not similar things in a photo:\tlong and rectangular shape\tlarge capacity for passengers\tsignage or advertising for the transit company\tlow, flat floor for easy boarding and exiting\tfixed route and scheduled stops.", 10], "beef sandwich": ["Yes. 'Beef sandwich' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'beef sandwich' but are not 'beef sandwich' are:\thamburger\tcheeseburger\tpulled pork sandwich\tgrilled cheese\nThere are several useful visual features to tell there is 'beef sandwich' and not similar things in a photo:\ttwo slices of bread\tbeef or steak inside\tthe bread is toasted\tmaybe other ingredients like lettuce or mustard", 10], "skewer": ["Yes. 'Skewer' has a tangible appearance and is a straight rod or stick used for cooking or holding food.\nA few things that are visually similar to 'skewer' but are not 'skewer' are:\tpencil\truler\tbroomstick\tthin branch\nThere are several useful visual features to tell there is 'skewer' and not similar things in a photo:\tlong and thin\tpointed at one or both ends\tmetal or wooden material\tpiercing through food items", 10], "microwave kitchen": ["No. 'Microwave kitchen' is too vague or abstract to be distinguished in a photo.", 10], "guy playing tennis": ["Yes. 'Guy playing tennis' has a tangible appearance and involves a person playing a sport.\nA few things that are visually similar to 'guy playing tennis' but are not 'guy playing tennis' are:\tguy playing basketball\tguy playing soccer\tguy running\tguy jumping\nThere are several useful visual features to tell there is 'guy playing tennis' and not similar things in a photo:\ttennis racket\ttennis ball\tnet\tcourt\ttennis shoes or other appropriate footwear", 10], "orange life preserver": ["Yes. 'Orange life preserver' has a tangible appearance and is a type of safety equipment.\nA few things that are visually similar to 'orange life preserver' but are not 'orange life preserver' are:\tswimming float\tinner tube\tbuoy\nThere are several useful visual features to tell there is 'orange life preserver' and not similar things in a photo:\trounded shape\twith a hole in the middle\torange color\twith reflective stripes\tand a strap to fasten around the body.", 10], "butcher block": ["Yes. 'Butcher block' has a tangible appearance and is a type of wooden chopping block.\nA few things that are visually similar to 'butcher block' but are not 'butcher block' are:\twooden cutting board\tkitchen island\tworkbench\ttable\nThere are several useful visual features to tell there is 'butcher block' and not similar things in a photo:\tthick, solid wooden block\tchopped marks and scratches\tdarkened appearance due to use of cutting and chopping\toil finish for durability and hygiene purposes.", 10], "diagram": ["No. 'Diagram' is too vague or abstract to be distinguished in a photo.", 10], "sea birds": ["Yes. 'Sea birds' has a tangible appearance and refers to birds that live primarily in or near the sea.\nA few things that are visually similar to 'sea birds' but are not 'sea birds' are:\tseagulls\tpelicans\tdolphins\tfish\nThere are several useful visual features to tell there are 'sea birds' and not similar things in a photo:\twebbed feet and/or toes\tthat fishing bird look\tbeaks\tbreeding grounds on the rocky cliffs or islands.", 10], "cordless mouse": ["Yes. 'Cordless mouse' has a tangible appearance and is a type of computer accessory.\nA few things that are visually similar to 'cordless mouse' but are not 'cordless mouse' are:\tcorded mouse\tremote control\ttrackball stylus\nThere are several useful visual features to tell there is 'cordless mouse' and not similar things in a photo:\tno cord or wire to connect to the computer\trounded and ergonomic shape\twith clickable buttons\tand with or without a scroll wheel", 10], "tow rope": ["Yes. 'Tow rope' has a tangible appearance and is a kind of rope used for towing.\nA few things that are visually similar to 'tow rope' but are not 'tow rope' are:\tclimbing rope\tnetting\tbungee cord\tclothesline\nThere are several useful visual features to tell there is 'tow rope' and not similar things in a photo:\tthick\tdurable\tlengthy\ttypically blue, yellow, or red", 10], "cinder blocks": ["Yes. 'Cinder blocks' has a tangible appearance and is a type of masonry unit.\nA few things that are visually similar to 'cinder blocks' but are not 'cinder blocks' are:\tbricks\tstones\tconcrete slabs\nThere are several useful visual features to tell there is 'cinder blocks' and not similar things in a photo:\trectangular shape\twith uniform size and texture\tcindery appearance or air holes\tin a pattern of interlocking or overlapping units\tstacked one on top of the other", 10], "goofy": ["No. 'Goofy' is too vague or abstract to be distinguished in a photo. In common usage, 'goofy' typically refers to a person's behavior or attitude rather than a visual appearance. \n\nHowever, a few things that are visually similar to the Disney character 'Goofy' but are not 'goofy' (in the sense of being silly or foolish) are:\tdogs with long floppy ears\tcartoon characters with exaggerated features\thuman characters with buck teeth\n\nUseful visual features for distinguishing the Disney character 'Goofy' from similar-looking characters in a photo might include:\t\n- Goofy's green hat with an orange band\n- Goofy's signature two front buck teeth\n- Goofy's tall, lanky physique with long arms and legs \n- Goofy's distinctive clothing, which often includes orange clothing and a vest.", 10], "water outlet": ["Yes. 'Water outlet' has a tangible appearance and is a device that allows water to flow out.\nA few things that are visually similar to 'water outlet' but are not 'water outlet' are:\tdrainage pipe\tfaucet\tshowerhead\nThere are several useful visual features to tell there is 'water outlet' and not similar things in a photo:\tattached to a wall\tor to other objects\tround or rectangular in shape\ttakes up a portion of the structure or space\twhere water is specifically intended to come out", 10], "mirror side bus": ["No. 'Mirror side bus' is too vague or abstract to be distinguished in a photo. It is possible that you meant \"coach bus with side mirrors\" which would be a visually concrete concept.\nA few things that are visually similar to a 'coach bus with side mirrors' but are not it are:\tschool bus\tminibus\tcity bus\nThere are several useful visual features to tell there is a 'coach bus with side mirrors' and not similar things in a photo:\tcoach style large bus\twith side mirrors on both sides of the driver's window\tand with luggage compartments on the bottom of the vehicle.", 10], "glass carafe": ["Yes. 'Glass carafe' has a tangible appearance and is a type of tableware.\nA few things that are visually similar to 'glass carafe' but are not 'glass carafe' are:\twater pitcher\tcoffee pot\twine bottle\tvase\nThere are several useful visual features to tell there is 'glass carafe' and not similar things in a photo:\tglass material\tnarrow neck and wider body\tcurved or angular shape\thandle or spout opening\tclear or slightly tinted color", 10], "sharpie": ["Yes. 'Sharpie' has a tangible appearance and is a type of permanent marker.\nA few things that are visually similar to 'sharpie' but are not 'sharpie' are:\tdry erase marker\tchalk marker\tpen\thighlighter\nThere are several useful visual features to tell there is 'sharpie' and not similar things in a photo:\trectangular shape\tpaint-like finish\tthick tip or nib\tcolor options (black, blue, red, etc.)\tlarge lettering of the brand name \"Sharpie\"", 10], "brocolli plate": ["Yes, 'broccoli plate' has a tangible appearance and refers to a plate with broccoli on it.\nA few things that are visually similar to 'broccoli plate' but are not 'broccoli plate' are:\tplate of green beans\tplate of peas\nUseful visual features for distinguishing 'broccoli plate' from the listed similar things in a photo are: \n- green vegetable florets resembling trees\n- round plate with a raised edge\n- no other visible vegetables or food items on the plate.", 10], "court floor": ["Yes. 'Court floor' has a tangible appearance and refers to the surface of a sports court.\nA few things that are visually similar to 'court floor' but are not 'court floor' are:\tgrass\twooden floor\ttile floor\tbasketball hoop\nThere are several useful visual features to tell there is 'court floor' and not similar things in a photo:\trectangular shape\tmarking lines for specific sports, such as basketball or volleyball\tbouncing surface\tindoor or outdoor surface", 10], "juvenile giraffe": ["Yes. 'Juvenile giraffe' has a tangible appearance and refers to a young giraffe.\nA few things that are visually similar to 'juvenile giraffe' but are not 'juvenile giraffe' are:\tadult giraffe\tokapi\tdeer\nThere are several useful visual features to tell there is 'juvenile giraffe' and not similar things in a photo:\tshorter neck compared to an adult giraffe\tstill growing spots on the fur\tlong legs and a lanky body straight out of proportion compared to its size.", 10], "color cow": ["No. 'Color cow' is too vague or abstract to be a tangible concept.\nThere are no things visually similar to 'color cow' as it is not a concrete concept.\nThere are no useful visual features for distinguishing 'color cow' in a photo, as it is not a concrete concept.", 10], "woman water": ["No. 'Woman water' is too vague or abstract to be recognized as a visually concrete concept.\nThere are no things that are visually similar to 'woman water' as it is not a common term used to describe anything specific.\nTherefore, there are no useful visual features for distinguishing 'woman water' from other things in a photo.", 10], "blue tile": ["Yes. 'Blue tile' has a tangible appearance and is a kind of rectangular or square slab used for decoration or construction.\nA few things that are visually similar to 'blue tile' but are not 'blue tile' are:\tblue ceramic plates\tblue glass\tblue paint on a wall\nThere are several useful visual features to tell there is 'blue tile' and not similar things in a photo:\trectangular or square shape\tsmooth and glossy surface\twith or without patterns or designs\tspecific thickness and size, depending on its intended use", 10], "statue top building": ["Yes. 'Statue top building' has a tangible and specific appearance.\nA few things that are visually similar to 'statue top building' but are not 'statue top building' are: buildlings with a dome on top, buildings with a spire or tower on top.\nThere are several useful visual features to tell there is 'statue top building' and not similar things in a photo:\tthe building has a statue on the very top, usually of a person or an animal; the statue can also be riding or holding something. The building is often a government building, museum, or place of worship.", 10], "trash basket": ["Yes. 'Trash basket' has a tangible appearance and is an object for discarding waste.\nA few things that are visually similar to 'trash basket' but are not 'trash basket' are:\tlaundry basket\tstorage basket\tplant pot\tflower vase\nThere are several useful visual features to tell there is 'trash basket' and not similar things in a photo:\topening or lid for throwing trash in\tmade of plastic or metal\tutensil to clean it\thandles to move it around", 10], "police horse": ["Yes. 'Police horse' has a tangible appearance and refers to a horse that is specifically trained to work with law enforcement.\nA few things that are visually similar to 'police horse' but are not 'police horse' are:\thorse\tpony\tdonkey\nThere are several useful visual features to tell there is 'police horse' and not similar things in a photo:\twearing a saddle or harness with police insignia\ttrained to work in crowds or city environments\twearing a bridle or bit in its mouth\tmodern breeds often are full-sized horses with bigger builds and weightier frames than other horses.", 10], "kitchen towels": ["Yes. 'Kitchen towels' has a tangible appearance and is a type of cloth used in the kitchen.\nA few things that are visually similar to 'kitchen towels' but are not 'kitchen towels' are:\twashcloth\tbath towel\tnapkin\ttablecloth\nThere are several useful visual features to tell there is 'kitchen towels' and not similar things in a photo:\tsmall size\tabsorbent texture often with a waffle or check pattern\tbright and colorful design\thanging from a hook or a kitchen appliance such as a stove or a refrigerator.", 10], "stainless": ["No. 'Stainless' is too abstract to have a tangible appearance that can be distinguished in a photo. \n\nHowever, some things that may appear visually similar to \"stainless\" could include: silver-colored metals, chrome, polished metals, and metallic objects. \n\nUseful visual features for distinguishing 'stainless' from similar things in a photo may include: a lack of visible rust or scratches, a polished and shiny surface, and a neutral color tone that is not too warm or too cool. It is important to note that without context, it may be difficult to definitively identify something as 'stainless'.", 10], "elephant statue": ["Yes. 'Elephant statue' has a tangible appearance and is a sculpture or figurine of an elephant.\nA few things that are visually similar to 'elephant statue' but are not 'elephant statue' are:\treal elephants\twood carvings\tother animal statues\nThere are several useful visual features to tell there is 'elephant statue' and not similar things in a photo:\tmade of stone, metal, plastic, or other materials\tsmooth and polished surface\tspecific details or patterns on the body or the tusks\tmay have a pedestal or stand for display", 10], "grey beak": ["Yes. 'Grey beak' has a tangible appearance and refers to the color of a bird's beak.\nA few things that are visually similar to 'grey beak' but are not 'grey beak' are:\tblack beak\tbrown beak\tyellow beak\torange beak\nThere are no useful visual features to distinguish 'grey beak' from the listed similar things in a photo, as they are distinguished by their color. However, other visual features of the bird can be used to identify the species.", 10], "power line tower": ["Yes. 'Power line tower' has a tangible appearance and is a kind of structure.\nA few things that are visually similar to 'power line tower' but are not 'power line tower' are:\twind turbine\ttall building\tskyscraper\tcell phone tower\nThere are several useful visual features to tell there is 'power line tower' and not similar things in a photo:\ttall metal structure\twith horizontal bars and braces\tfor carrying power lines\twith insulators or wire connectors on top", 10], "shadow bear": ["No. 'Shadow bear' is too vague or abstract to be distinguished in a photo.", 10], "metal pieces": ["Yes. 'Metal pieces' has a tangible appearance and can be identified based on specific features.\nA few things that are visually similar to 'metal pieces' but are not 'metal pieces' are:\trocks, pebbles\twood fragments\tplastic shards\tglass fragments\nThere are several useful visual features to tell there are 'metal pieces' and not similar things in a photo:\tmetallic shine\tand luster\tunique shape or texture\tsmoothness or hardness\tmagnetic properties\tlogging or rusting", 10], "orange bench": ["Yes. 'Orange bench' has a tangible appearance and is a specific type of furniture.\nA few things that are visually similar to 'orange bench' but are not 'orange bench' are:\tchair\tcouch\tstool\nThere are several useful visual features to tell there is 'orange bench' and not similar things in a photo:\telongated surface\tfor multiple people to sit on\tbright orange color\tbackrest or armrests may or may not be present", 10], "bedcover": ["Yes. 'Bedcover' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'bedcover' but are not 'bedcover' are:\tblanket\tquilt\tcomforter\tthrow\tpillow\tsham\nThere are several useful visual features to tell there is 'bedcover' and not similar things in a photo:\tcovering the entire bed\tsmooth or textured fabric\tpatterned or plain color\tfits the size and shape of the bed", 10], "wooden ledge": ["Yes. 'Wooden ledge' has a tangible appearance and is a type of shelf.\nA few things that are visually similar to 'wooden ledge' but are not 'wooden ledge' are:\tmetal shelf\tconcrete block\tbrick wall\nThere are several useful visual features to tell there is 'wooden ledge' and not similar things in a photo:\tmade out of wood\thorizontal surface\tthat protrudes from a wall\tor structure", 10], "storm cloud": ["Yes. 'Storm cloud' has a tangible appearance and is a type of cloud.\nA few things that are visually similar to 'storm cloud' but are not 'storm cloud' are:\tfog\tsmoke\twater vapor\tsandstorm\nThere are several useful visual features to tell there is 'storm cloud' and not similar things in a photo:\tvery dark color\tdense and heavy appearance\toften seen with lightning and thunder\tsigns of rainfall or other types of precipitation", 10], "mama": ["No. 'Mama' is too abstract and does not have a specific visual appearance.", 10], "dark elephant": ["Yes. 'Dark elephant' has a tangible appearance and is a type of animal.\nThere are no things that are visually similar to 'dark elephant' but are not 'dark elephant'.\nThere are no useful visual features needed to distinguish 'dark elephant' from anything else in a photo, as the concept is specific and clear. However, it's worth noting that elephants can come in different shades of grey or brown, so when talking specifically about a \"dark\" elephant, it would be important to look for darker shades in the image.", 10], "bus driving": ["No. 'Bus driving' is too vague or abstract to be distinguished in a photo. It involves multiple actions and is not a single tangible object.\nA few things that are visually similar to 'bus driving' but are not 'bus driving' are: car driving, truck driving, bike riding, train driving.\nIt is not applicable to provide visual features to distinguish 'bus driving' as it refers to a person performing a specific action, rather than an object or appearance.", 10], "pale sky": ["Yes. 'Pale sky' has a tangible appearance, and it refers to a sky with a light, soft, or washed-out color.\nA few things that are visually similar to 'pale sky' but are not 'pale sky' are:\tclear sky\thazy sky\tdusk sky\tstormy sky\nUseful visual features for distinguishing 'pale sky' from the listed similar things in a photo could be:\ta light blue or pastel hue\tlow saturation level\ta lack of clouds, dust, or smog\ta serene or calm atmosphere, without any signs of rain or wind.", 10], "angels": ["Yes. Angels have a tangible appearance in religious iconography and art.\nA few things that are visually similar to 'angels' but are not 'angels' are: cherubs, cupids, ghosts, spirits.\nThere are several useful visual features to tell there is 'angels' and not similar things in a photo: a human-like figure with wings, often depicted wearing a flowing robe or gown, with a halo above the head, and often in a serene or divine pose.", 10], "advertising billboard": ["Yes. 'Advertising billboard' has a tangible appearance and is a kind of signboard.\nA few things that are visually similar to 'advertising billboard' but are not 'advertising billboard' are:\tstreet names\tsigns\twall paintings\nThere are several useful visual features to tell there is 'advertising billboard' and not similar things in a photo:\t\nlarge size\tdisplay of ads or promotional messages\tilluminated at night\toften found alongside roads or highways\twith a rectangular or square shape\twith a rigid frame or structure.", 10], "side burn": ["Yes. 'Side burn' has a tangible appearance and refers to hair grown on the sides of the face.\nA few things that are visually similar to 'side burn' but are not 'side burn' are:\tbeard\tmustache\tfacial hair\nThere are several useful visual features to tell there are 'sideburns' and not similar things in a photo:\n\thair grown along the sides of the face\tand typically not under the chin\thair follows the jawline and stops at the bottom of the earlobes\thair is trimmed or groomed into a particular shape or style depending on the individual's preference", 10], "right sleeve": ["Yes. 'Right sleeve' has a tangible appearance and refers to a specific part of clothing.\nThere are no things that are visually similar to 'right sleeve' but are not 'right sleeve'.\nUseful visual features for distinguishing 'right sleeve' in a photo could include:\tthe location of the sleeve on the right side of the body, the length of the sleeve, the color and pattern of the sleeve material, the type of clothing, such as a shirt or jacket, that the sleeve is part of.", 10], "beige walls": ["Yes. 'Beige walls' has a tangible appearance and refers to a specific color and texture of a wall.\nA few things that are visually similar to 'beige walls' but are not 'beige walls' are:\tyellow walls\tbrown walls\tcream walls\tcement walls\nThere are several useful visual features to tell there is 'beige walls' and not similar things in a photo:\tlight brown or tan color\tplain or textured surface\tindoor or outdoor location", 10], "thick crust pizza": ["Yes. 'Thick crust pizza' has a tangible appearance and is a type of pizza.\nA few things that are visually similar to 'thick crust pizza' but are not 'thick crust pizza' are:\tthin crust pizza\tflatbread\tfocaccia bread\tnaan bread\nThere are several useful visual features to tell there is 'thick crust pizza' and not similar things in a photo:\tmuch thicker than the regular pizza dough\tbread-like texture\tcan hold a large amount of toppings and sauce\ttaller edges that encase the toppings and sauce", 10], "turtles": ["Yes. 'Turtles' has a tangible appearance and is a type of reptile.\nA few things that are visually similar to 'turtles' but are not 'turtles' are:\ttortoises\tterrapins\tlizards\tcrocodiles\nThere are several useful visual features to tell there is 'turtles' and not similar things in a photo:\tdome-shaped shells\twithout sharp teeth\twebbed feet\tfor swimming 4 legs", 10], "blue bridge": ["Yes. 'Blue bridge' has a tangible appearance and is a type of bridge that is blue in color.\nA few things that are visually similar to 'blue bridge' but are not 'blue bridge' are:\tother colorful bridges\tblue buildings\tor any other blue structure\nThere are several useful visual features to tell there is 'blue bridge' and not similar things in a photo:\ta bridge\tthat is predominantly blue in color\ta structure that spans over a body of water\tor a roadway", 10], "night lamp": ["Yes. 'Night lamp' has a tangible appearance and is a kind of lamp used at night.\nA few things that are visually similar to 'night lamp' but are not 'night lamp' are:\tdesk lamp\ttable lamp\tfloor lamp\nThere are several useful visual features to tell there is 'night lamp' and not similar things in a photo:\tsmall in size\tdim or soft light\tcord plugged to the electrical outlet\tplaced on the nightstand or bedside table.", 10], "plastic button": ["Yes. 'Plastic button' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'plastic button' but are not 'plastic button' are:\tzippers\tsnaps\tbuckles\tjewelry\nThere are several useful visual features to tell there is 'plastic button' and not similar things in a photo:\tcircular, round or flat shape\ttwo or four holes\tsmooth, matte or glossy finish\tsolid color or patterned\tsmall size compared to the whole garment", 10], "nike shorts": ["Yes. 'Nike shorts' has a tangible appearance and is a specific type of clothing item.\nA few things that are visually similar to 'nike shorts' but are not 'nike shorts' are:\tathletic shorts\tswim trunks\tbasketball shorts\tyoga shorts\nThere are several useful visual features to tell there are 'nike shorts' and not similar things in a photo:\tsporty design\twith Nike logo or brand name\tathletic fit and style\tshort length and loose fit\tbreathable and moisture-wicking fabric.", 10], "dark marks": ["Yes. 'Dark marks' has a tangible appearance and refers to areas of darkness or discoloration on a surface.\nA few things that are visually similar to 'dark marks' but are not 'dark marks' are:\tshadows\tstains\tdirt\tgraffiti\nThere are several useful visual features to tell there are 'dark marks' and not similar things in a photo:\tsurface appears discolored or darker than surrounding area\tmarks may be irregularly shaped\tor have defined edges\tmarks may appear in a pattern or along a specific surface or object.", 10], "wire cage": ["Yes. 'Wire cage' has a tangible appearance and is a type of enclosure made of wires.\nA few things that are visually similar to 'wire cage' but are not 'wire cage' are:\tbasket\tshelf\tfence\ttrellis\nThere are several useful visual features to tell there is 'wire cage' and not similar things in a photo:\tmade of wires\tregular pattern of intersections\trectangular or cube shape\thinged or sliding door", 10], "towel hanger": ["Yes. 'Towel hanger' has a tangible appearance and is an object for hanging towels.\nA few things that are visually similar to 'towel hanger' but are not 'towel hanger' are:\tclothes hanger\thook\track\tshelf\nThere are several useful visual features to tell there is 'towel hanger' and not similar things in a photo:\trod-shaped\thandles for hanging towels\tcan be mounted on a wall or door", 10], "facet": ["No. 'Facet' is too vague or abstract to be distinguished in a photo.", 10], "yoke": ["Yes. 'Yoke' has a tangible appearance and is a wooden or metal bar used to attach and control animals such as oxen.\nA few things that are visually similar to 'yoke' but are not 'yoke' are:\tbar\tbaton\trod\tstick\nThere are several useful visual features to tell there is 'yoke' and not similar things in a photo:\tused to attach animals like oxen\thorizontal wooden or metal bar\tfits across the necks of two animals\tat least two U-shaped pieces", 10], "brick siding": ["Yes. 'Brick siding' has a tangible appearance and is a type of exterior wall covering.\nA few things that are visually similar to 'brick siding' but are not 'brick siding' are:\tconcrete wall\ttile wall\tstone wall\nThere are several useful visual features to tell there is 'brick siding' and not similar things in a photo:\trectangular-shaped bricks\talternating courses of bricks\tdifferent hues of red or brown brick\tcolor variations and texture from exposure to weathering and aging.", 10], "bangle": ["Yes. 'Bangle' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'bangle' but are not 'bangle' are:\tbracelet\twristwatch\tan anklet\ta cuff\nThere are several useful visual features to tell there is 'bangle' and not similar things in a photo:\tcircular shape\tridged or smooth surface\tmade of metal, wood, or plastic\thangs loosely on the wrist or forearm\tbrightly colored or adorned with gems or patterns.", 10], "right ear": ["Yes. 'Right ear' has a tangible appearance and is a part of the body.\nThere are no things that are visually similar to 'right ear' but aren't 'right ear'.\nUseful visual features to distinguish a 'right ear' from the left ear are: located on the right side of the head, the shape and size of the earlobe or the helix, any distinguishing marks or scars.", 10], "turtleneck": ["Yes. 'Turtleneck' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'turtleneck' but are not 'turtleneck' are:\tscarf\tcowl neck\tsweater with a high neck\thigh-necked blouse\nThere are several useful visual features to tell there is a 'turtleneck' and not similar things in a photo:\ttightly fitting around the neck\tcovering the neckline and the base of the neck\tlong sleeves or sleeveless", 10], "passenger side mirror": ["Yes. 'Passenger side mirror' has a tangible appearance and is a part of a car.\nA few things that are visually similar to 'passenger side mirror' but are not 'passenger side mirror' are:\trearview mirror\tside window\tglass\nThere are several useful visual features to tell there is 'passenger side mirror' and not similar things in a photo:\tround or rectangular shape\tattached to the passenger side of a car\tangled to provide a view of the side and rear of the car\tstoplight or turn signal indicators may be present\ton the exterior of the car rather than inside the car.", 10], "guests": ["No. 'Guests' is too vague or abstract to have a tangible appearance or be distinguished in a photo.", 10], "gold decoration": ["Yes. 'Gold decoration' has a tangible appearance and is a type of decorative item.\nA few things that are visually similar to 'gold decoration' but are not 'gold decoration' are:\tbronze decoration\tcopper decoration\tbrass decoration\tyellow plastic decoration\nThere are several useful visual features to tell there is 'gold decoration' and not similar things in a photo:\tgold color\tshiny surface\tmetallic material\tintricate and ornate design", 10], "turn arrow": ["Yes. 'Turn arrow' has a tangible appearance and is a type of traffic sign.\nA few things that are visually similar to 'turn arrow' but are not 'turn arrow' are:\tstraight arrow\tstraight line with an arrowhead\tinstructional sign for pedestrians\nThere are several useful visual features to tell there is 'turn arrow' and not similar things in a photo:\tcurved arrow pointing to the right or left\tmay have a green, yellow, or red color\tto be seen on a road or street", 10], "frisbee players": ["Yes. 'Frisbee players' has a tangible appearance and refers to individuals playing a game with a flying disc.\nA few things that are visually similar to 'frisbee players' but are not 'frisbee players' are:\tsoccer players\tfootball players\tbaseball players\ttrack and field athletes\nThere are several useful visual features to tell there are 'frisbee players' and not similar things in a photo:\tthrowing or catching a frisbee\twearing frisbee apparel or accessories\tsurrounded by frisbees and equipment", 10], "pillow cases": ["Yes. 'Pillow cases' has a tangible appearance and is a type of bedding.\nA few things that are visually similar to 'pillow cases' but are not 'pillow cases' are:\tcushion covers\tlaundry bags\ttote bags\t\nThere are several useful visual features to tell there is 'pillow cases' and not similar things in a photo:\trectangular shape with stitching on the edges\tusually made of soft fabric or natural fibers\tfolds or creases where the pillow is inserted\toften seen on a bed or a sofa", 10], "wooden slats": ["Yes. 'Wooden slats' has a tangible appearance and is a type of building material.\nA few things that are visually similar to 'wooden slats' but are not 'wooden slats' are:\twooden boards\tlaminate flooring\ttile flooring\nThere are several useful visual features to tell there is 'wooden slats' and not similar things in a photo:\tlong and narrow pieces of wood\twith gaps or spaces between them\tflat and smooth surface\tmay have a pattern or a repeating shape", 10], "chili peppers": ["Yes. 'Chili peppers' has a tangible appearance and is a type of vegetable.\nA few things that are visually similar to 'chili peppers' but are not 'chili peppers' are:\tbell peppers\ttomatoes\tjalape\u00f1os\nThere are several useful visual features to tell there is 'chili peppers' and not similar things in a photo:\tlong and slender shape\tred, green, or yellow in color\tsmooth and shiny skin\tpointed tip\thot and spicy taste", 10], "blue door": ["Yes. 'Blue door' has a tangible appearance and is a specific type of door.\nA few things that are visually similar to 'blue door' but are not 'blue door' are: green door, red door, yellow door, wooden door.\nThere are several useful visual features to tell there is 'blue door' and not similar things in a photo: a door with a blue color or hue, having a handle, hinges, and other details consistent with a standard door shape and construction. The context in which the door appears (e.g., a house, a building, etc.) may also be helpful in identifying it as a 'blue door.'", 10], "stove top oven": ["No. 'Stove top oven' is not a concept that exists. There are ovens and stove tops, which are separate appliances with different functions, but there is not an appliance called a 'stove top oven'.", 10], "taxi cabs": ["Yes. 'Taxi cabs' has a tangible appearance and is a kind of vehicle used for transportation.\nA few things that are visually similar to 'taxi cabs' but are not 'taxi cabs' are:\tregular cars\tbuses\tambulances\tpolice cars\nThere are several useful visual features to tell there is 'taxi cabs' and not similar things in a photo:\tdistinctive color (usually yellow or checkered)\tsign on the roof with the word \"taxi\" or a company logo\tpick-up and drop-off passengers in designated areas meter on the inside of the car\tcar with a driver waiting for a fare with the light on its roof on.", 10], "west": ["No. 'West' is too vague or abstract to be distinguished in a photo.", 10], "wind vane": ["Yes. 'Wind vane' has a tangible appearance and is a type of instrument.\nA few things that are visually similar to 'wind vane' but are not 'wind vane' are:\tweather vane\tantenna\tradar dish\tweathervane\nThere are several useful visual features to tell there is 'wind vane' and not similar things in a photo:\tarrow-shaped or animal-shaped object\tpivoting on a vertical rod\taligning with the direction of the wind\tpresenting different sides or faces", 10], "country skier": ["Yes. 'Country skier' has a tangible appearance and refers to a person skiing outdoors on cross-country skis.\nA few things that are visually similar to 'country skier' but are not 'country skier' are:\talpine skier\tsnowboarder\tperson hiking\tin-line skater\nThere are several useful visual features to tell there is 'country skier' and not similar things in a photo:\tlong, narrow skis\twith bindings only at the toes\tno tall boots to lock heels to the skis\tclassic or skate skiing technique\tpoles with small baskets\tforward motion with a gliding or skating motion", 10], "watch strap": ["Yes. 'Watch strap' has a tangible appearance and is a part of the watch.\nA few things that are visually similar to 'watch strap' but are not 'watch strap' are:\tbracelet\twristband\nThere are several useful visual features to tell there is 'watch strap' and not similar things in a photo:\tattached to the watch case\ttapered shape\twith holes to fasten the watch to the wrist \tmade of leather, metal, or other materials.", 10], "tail end": ["Yes. 'Tail end' has a tangible appearance and refers to the end part of something with a tail.\nA few things that are visually similar to 'tail end' but are not 'tail end' are:\tback end of a car\tend of a line or queue\nThere are several useful visual features to tell there is 'tail end' and not similar things in a photo:\tfur or hair at the end of a tail\tshape and length of the tail\tanimal-like appearance and movement of the tail", 10], "stone church": ["Yes. 'Stone church' has a tangible appearance and is a type of building.\nA few things that are visually similar to 'stone church' but are not 'stone church' are:\tmosque\tcathedral\tsynagogue\ttemple\tfortress\tcastle\tmonastery\tpalace\nThere are several useful visual features to tell there is 'stone church' and not similar things in a photo:\tstone or brick building\tusually has a steeple or dome\tchristian symbols or crosses\tmay have stained glass windows or religious artwork\ton church grounds or surrounded by a cemetery", 10], "sea shells": ["Yes. 'Sea shells' has a tangible appearance and is a type of shellfish.\nA few things that are visually similar to 'sea shells' but are not 'sea shells' are:\trocks\tpebbles\tpetrified wood\tcoral\nThere are several useful visual features to tell there is 'sea shells' and not similar things in a photo:\texternal skeleton\twith a curled shape\tbright colors or patterns\tridged or textured surface\tsmall openings or holes on the surface\tsymmetrical shape.", 10], "billboard advertisement": ["Yes. 'Billboard advertisement' has a tangible appearance and is a type of outdoor advertising.\nA few things that are visually similar to 'billboard advertisement' but are not 'billboard advertisement' are:\tstreet signs\tstorefront signs\tgraffiti\tmurals\nThere are several useful visual features to tell there is 'billboard advertisement' and not similar things in a photo:\tlarge size\tplaced high off the ground\ttext and images promoting a product or service\tspecific design dimensions (i.e. rectangular shape)\tcan be illuminated at night or in low light conditions.", 10], "felt": ["Yes. 'Felt' has a visually concrete appearance and is a type of fabric.\nA few things that are visually similar to 'felt' but are not 'felt' are: fur, velvet, suede, wool\nThere are several useful visual features to tell there is 'felt' and not similar things in a photo:\tmade of wool or synthetic material\tfuzzy texture\tmatte appearance and lack of luster uniform texture", 10], "floorboard": ["Yes. 'Floorboard' has a tangible appearance and is a part of the flooring.\nA few things that are visually similar to 'floorboard' but are not 'floorboard' are:\ttiles\tlinoleum\tcarpet\tcement\nThere are several useful visual features to tell there is 'floorboard' and not similar things in a photo:\twooden material\tlong and narrow shape\tparallel lines\ton the floor or a part of a floor", 10], "source": ["No. 'Source' is too vague or abstract to be distinguished in a photo.", 10], "silver wire": ["Yes. 'Silver wire' has a tangible appearance and is a type of wire.\nA few things that are visually similar to 'silver wire' but are not 'silver wire' are:\tsteel wire\taluminum wire\tcopper wire\tstrands of hair\nThere are several useful visual features to tell there is 'silver wire' and not similar things in a photo:\tmetallic silver color\tshiny or reflective texture\tcylindrical or conical shape\tthinness and flexibility", 10], "sign boards": ["Yes. 'Sign boards' have a tangible appearance and are a type of board with verbal or visual information.\nA few things that are visually similar to 'sign boards' but are not 'sign boards' are:\tmenu boards\tbulletin boards\tcanvas paintings\nThere are several useful visual features to tell there is 'sign boards' and not similar things in a photo:\trectangular or square shape\tverbal or visual information\tclear lettering or images\thanging from a wall or a post or placed on a stand\toutdoors or indoors\tuse of bright colors", 10], "helmet kid": ["Yes. 'Helmet kid' has a tangible appearance and refers to a child wearing a helmet.\nA few things that are visually similar to 'helmet kid' but are not 'helmet kid' are:\tadult wearing a helmet\taction figure wearing a helmet\tprofessional athlete wearing a helmet\nThere are several useful visual features to tell there is a 'helmet kid' and not similar things in a photo:\tchild-sized body and proportions\thelmet is properly fitted\ttoys or other props are present\ttoy or prop is proportionate to the child's size\tthe setting implies a child's activity (e.g. playground, bike trail, etc.)", 10], "baby gray elephant": ["Yes. 'Baby gray elephant' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'baby gray elephant' but are not 'baby gray elephant' are:\tmouse\trat\tbunny\thamster\tmole\nThere are several useful visual features to tell there is 'baby gray elephant' and not similar things in a photo:\tlarge ears\tlong trunk\tthick legs\tgray skin\tfolded skin \tbig tusks", 10], "potholder": ["Yes. 'Potholder' has a tangible appearance and is a type of kitchen accessory.\nA few things that are visually similar to 'potholder' but are not 'potholder' are:\toven mitts\tkitchen towels\tdishcloths\nThere are several useful visual features to tell there is 'potholder' and not similar things in a photo:\tsquare or rectangular shape\tthick and padded\tsome have a pocket for your hand\tto be held with a loop\tor have a textured surface to grip hot items", 10], "lemon slices": ["Yes. 'Lemon slices' is a visually concrete concept and refers to the cut fruit.\nA few things that are visually similar to 'lemon slices' but are not 'lemon slices' are:\tlime slices\torange slices\tgrapefruit slices\tcucumber slices\nThere are several useful visual features to tell there are 'lemon slices' and not similar things in a photo:\tyellow color\tround or semi-circular shape\tjuicy flesh\tseeds visible in the center.", 10], "soap bar": ["Yes. 'soap bar' has a tangible appearance and is a type of cleaning product.\nA few things that are visually similar to 'soap bar' but are not 'soap bar' are:\tcandy bars\tcandles\twax figures\nThere are several useful visual features to tell there is 'soap bar' and not similar things in a photo:\trectangular or square shape\tsmooth surface\twith a brand name or symbol\ton a soap dish or in a soap holder", 10], "green leaves": ["Yes. 'Green leaves' has a tangible appearance and is a type of foliage.\nA few things that are visually similar to 'green leaves' but are not 'green leaves' are:\tgrass\tsucculent\tcabbage\tmoss\nThere are several useful visual features to tell there is 'green leaves' and not similar things in a photo: \tflat and thin\tveins\trun to the edges\tattached to stems or branches\tare attached to a plant\tthat may have flowers or fruits\tpresent on trees, shrubs, and flowering plants.", 10], "metal train car": ["Yes. 'Metal train car' has a tangible appearance and is a type of transportation vehicle.\nA few things that are visually similar to 'metal train car' but are not 'metal train car' are:\tmetal shipping container\ttruck trailer\tmobile home\nThere are several useful visual features to tell there is 'metal train car' and not similar things in a photo:\tconnected to other train cars\tmetal wheels and axles\tlettering or numbering on the side\tmetal bars or handles on the doors and windows.", 10], "beige light switch": ["Yes. 'Beige light switch' has a tangible appearance and is a type of electrical control.\nA few things that are visually similar to 'beige light switch' but are not 'beige light switch' are:\twhite light switch\tgrey light switch\tmetal light switch\nThere are several useful visual features to tell there is 'beige light switch' and not similar things in a photo:\trectangle or square shape\tbeige or light brown color\tswitch handle or toggle\tswitch plate or cover\tbuilt into a wall\tor mounted in a box.", 10], "rock ledge": ["Yes. 'Rock ledge' has a tangible appearance and refers to a horizontal shelf of rock.\nA few things that are visually similar to 'rock ledge' but are not 'rock ledge' are:\tcliff\trock face\tplatform\tflat roof\nThere are several useful visual features to tell there is 'rock ledge' and not similar things in a photo:\thorizontal or flat surface\tmade of rock or stone\tmay have vegetation or water on it.", 10], "colorful wall": ["Yes. 'Colorful wall' has a tangible appearance.\nA few things that are visually similar to 'colorful wall' but are not 'colorful wall' are: paintings, murals, colorful backgrounds, posters.\nThere are several useful visual features to tell there is 'colorful wall' and not similar things in a photo:\tobject is a wall or part of a wall\tthe wall contains multiple colors or hues\tthe colors are bright and vibrant the background has a rough texture like a wall.", 10], "color cap": ["Yes. 'Color cap' has a tangible appearance and is a kind of hat.\nA few things that are visually similar to 'color cap' but are not 'color cap' are:\tbaseball cap\tberet\tfedora\tbonnet\nThere are several useful visual features to tell there is 'color cap' and not similar things in a photo:\tadjustable strap on the back\trounded crown\tflat brim with a special color or pattern\ttypically worn in sports or casual setting.", 10], "leaf lettuce": ["Yes, 'leaf lettuce' has a visually concrete concept as it's a kind of vegetable.\nA few things that are visually similar to 'leaf lettuce' but are not 'leaf lettuce' are:\tspinach\tarugula\tcabbage\tkale\nThere are several useful visual features to tell there is 'leaf lettuce' and not similar things in a photo:\t\ngreen broad leaves, often crinkled or curled\ton long, light green stems\twith ruffled edges\tthe leaves are arranged in a rosette shape\tthe edges of the leaves usually have a slightly serrated or jagged appearance", 10], "team number": ["No. 'Team number' is too vague or abstract to be distinguished in a photo.", 10], "televison": ["Yes. 'Television' has a tangible appearance and is an electronic device used for entertainment.\nA few things that are visually similar to 'television' but are not 'television' are:\tcomputer\tmonitor\tprojector\t\nThere are several useful visual features to tell there is 'television' and not similar things in a photo:\trectangular screen\tvarious buttons and ports\tantenna or cable connection\tpower cord and/or remote control", 10], "dinner knife": ["Yes. 'Dinner knife' has a tangible appearance and is a type of cutlery.\nA few things that are visually similar to 'dinner knife' but are not 'dinner knife' are:\tspoon\tfork\tspatula\tpizza cutter\nThere are several useful visual features to tell there is 'dinner knife' and not similar things in a photo:\tlong, narrow blade with a pointed end\tsharp blade\tserrated edge\toften paired with a fork as a part of a place setting", 10], "wad": ["Yes. 'Wad' has a tangible appearance and refers to a tight, compact mass or lump of material.\nA few things that are visually similar to 'wad' but are not 'wad' are:\tclump\tball\tRock\tloose pile\nThere are several useful visual features to tell there is 'wad' and not similar things in a photo:\ttightly compressed\tmass or lump of material", 10], "snowmobile": ["Yes. 'Snowmobile' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'snowmobile' but are not 'snowmobile' are:\tATV\ttruck\tmotorcycle\tscooter\nThere are several useful visual features to tell there is 'snowmobile' and not similar things in a photo:\ttwo skis in the front for steering\ttreads or tracks instead of wheels in the back\tengine in the center or the back of the vehicle\topen-air design for the driver and passengers", 10], "lapels": ["Yes. 'Lapels' has a tangible appearance and is a part of clothing.\nA few things that are visually similar to 'lapels' but are not 'lapels' are: collars, cuffs, pockets, buttons, zippers.\nThere are several useful visual features to tell there are 'lapels' and not similar things in a photo: part of a suit jacket or blazer, which is folded over and usually has a buttonhole on one side and a button on the other, usually made of the same fabric as the rest of the jacket, may have a satin or contrasting color lining.", 10], "tall blades": ["Yes. 'Tall blades' has a tangible appearance and is most commonly associated with wind turbines.\nA few things that are visually similar to 'tall blades' but are not 'tall blades' are:\ttall grass\tblades of a fan\tswords\tmetal fins\nThere are several useful visual features to tell there is 'tall blades' and not similar things in a photo:\tattached to a wind turbine\tdevice for generating electricity\tthree angular blades\treaching high into the sky\tspinning in the wind", 10], "purple shorts": ["Yes. 'Purple shorts' has a tangible appearance and is a specific type of clothing.\nA few things that are visually similar to 'purple shorts' but are not 'purple shorts' are:\tpurple skirts\tpurple pants\tpurple dresses\tpurple tops\t\nThere are several useful visual features to tell there are 'purple shorts' and not similar things in a photo:\tshort length\tpurple color\twith leg holes and a waistband\tmade of a lightweight and casual fabric like cotton or linen", 10], "front tyre": ["Yes. 'Front tyre' has a tangible appearance and refers to the tyre located in front of a vehicle.\nA few things that are visually similar to 'front tyre' but are not 'front tyre':\trear tyre\tbicycle tyre\tmotorcycle tyre\tscooter tyre\nThere are several useful visual features to tell there is 'front tyre' and not similar things in a photo:\t\nlocated in the front of a vehicle\tbigger than a bicycle or scooter tyre\ttread pattern suitable for steering and braking purposes\twheel and brake disc visible in the photo", 10], "metal button": ["Yes. 'Metal button' has a tangible appearance and is a type of fastener.\nA few things that are visually similar to 'metal button' but are not 'metal button' are:\trivet\tbelt buckle\teyelet\tgrommet\nThere are several useful visual features to tell there is 'metal button' and not similar things in a photo:\tcircular or rounded shape\tmade of metal\tor metallic color\thas one or more holes in the center\thollow center surrounded by a raised rim\tcould be a snap or shank button.", 10], "parapets": ["Yes. 'Parapets' has a tangible appearance and is a part of a building's structure.\nA few things that are visually similar to 'parapets' but are not 'parapets' are:\tbalcony\trailing\twall\tfence\nThere are several useful visual features to tell there is 'parapets' and not similar things in a photo:\tprotective wall or railing at the edge of a roof, balcony or terrace\tcan be made of stone, brick, metal or wood\tusually runs along the perimeter of the structure", 10], "stamen": ["Yes. 'Stamen' has a tangible appearance and is a part of a flower's reproductive system.\nA few things that are visually similar to 'stamen' but are not 'stamen' are:\tpistil\tpollen\tfilament\t\nThere are several useful visual features to tell there is 'stamen' and not similar things in a photo:\tsmall and slender\tstick-like with anthers on top\tfound in the center of a flower\trelease pollen during the fertilization process", 10], "lashes": ["Yes. 'Lashes' has a tangible appearance and refers to the hair that grows on the edge of the eyelid.\nA few things that are visually similar to 'lashes' but are not 'lashes' are:\teyebrows\tfur\teyeliner\tstems\nThere are several useful visual features to tell there are 'lashes' and not similar things in a photo:\telongated\thair-like in texture\tgrowing from the eyelid\tframing the eye", 10], "orange chairs": ["Yes. 'Orange chairs' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'orange chairs' but are not 'orange chairs' are:\tsofas\tstools\tbenches\tbean bags\nThere are several useful visual features to tell there is 'orange chairs' and not similar things in a photo:\tchair shape\tfour legs\tsitting surface\tbackrest\torange color", 10], "dark nose": ["Yes. 'Dark nose' has a tangible appearance and refers to a nose that appears dark in color or shade.\nA few things that are visually similar to 'dark nose' but are not 'dark nose' are:\tshadows\tdirt\tmakeup\tpigmentation on the skin (like a birthmark)\nThe useful visual features for distinguishing 'dark nose' from the listed similar things in a photo depend on the context and the specific comparison being made. However, overall, the main feature to distinguish a dark nose is the location on the face and the shape and size of the nose, together with its dark color.", 10], "suit pants": ["Yes. 'Suit pants' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'suit pants' but are not 'suit pants' are:\tjeans\tslacks\ttrousers\t\nThere are several useful visual features to tell there is 'suit pants' and not similar things in a photo:\tformal design or cut\tmade of suit fabric (e.g., wool or polyester)\thas visible pockets in the front and back\ttop part attached to a waistband with loops for a belt\tzips and hooks on the front to close the pants", 10], "goggle": ["Yes. 'Goggle' has a tangible appearance and is a type of eyewear.\nA few things that are visually similar to 'goggle' but are not 'goggle' are:\tsunglasses\tsafety glasses\tswimming goggles\tbinoculars\nThere are several useful visual features to tell there is 'goggle' and not similar things in a photo:\ttwo separate lenses\tadjustable strap to secure it on the head\twide and curved shape to fit the eyes\tno temples or earpieces (like glasses) or nose pads (like sunglasses)", 10], "nike tennis shoe": ["Yes. 'Nike tennis shoe' has a tangible appearance and is a specific type of athletic shoe.\nA few things that are visually similar to 'nike tennis shoe' but are not 'nike tennis shoe' are:\tAdidas tennis shoe\tPuma tennis shoe\tNew Balance tennis shoe\tsneakers in general\nThere are several useful visual features to tell there is 'nike tennis shoe' and not similar things in a photo:\tthe distinctive Nike logo on the shoe\tunique color schemes and designs\ttypical shape of a tennis shoe with a flat sole and good traction\tfor tennis courts, it may also have herringbone patterns on the sole.", 10], "crew": ["No. 'Crew' is too vague or abstract to be distinguished in a photo. \n\nHowever, if we are referring to a crew of a specific mode of transportation, such as an airplane or a ship, then 'crew' can have a tangible appearance.\n\nA few things that are visually similar to a crew of an airplane or a ship but are not 'crew' are:\tpassengers\ttravelers\tvisitors\n\nThere are several useful visual features to tell there is a 'crew' and not similar things in a photo:\t\n-Uniform or same clothing for everyone in the group\n-The presence of the captain or pilot is more likely in the crew\n-Performing specific tasks or operating machinery or equipment\n-Their general position on the ship or airplane, such as being on the bridge or in the cockpit.", 10], "parking spaces": ["Yes. 'Parking spaces' has a tangible appearance and refers to designated areas for parking vehicles.\nA few things that are visually similar to 'parking spaces' but are not 'parking spaces' are:\tloading zones\tdriveways\tsidewalks\tstreets\nThere are several useful visual features to tell there is 'parking spaces' and not similar things in a photo:\trectangular or square shape\tpainted lines or markings for cars\ttoo small for driving, walking or loading\tarea near a building or on a street specifically reserved for parking vehicles.", 10], "dark shoe": ["Yes. 'Dark shoe' has a tangible appearance and is a type of footwear.\nA few things that are visually similar to 'dark shoe' but are not 'dark shoe' are:\tlight-colored shoe\tboot\tsandal\tloafer\nThere are several useful visual features to tell there is 'dark shoe' and not similar things in a photo:\tdark color (i.e. black, brown, navy)\tsole and heel made of rubber or other material\tcommon shoe shape (i.e. lace-up, slip-on)", 10], "chihuahua": ["Yes. 'Chihuahua' has a tangible appearance and is a type of dog.\nA few things that are visually similar to 'chihuahua' but are not 'chihuahua' are:\tpomeranian\tpekingese\titalian greyhound\tteddy bear\nThere are several useful visual features to tell there is 'chihuahua' and not similar things in a photo:\tsmall size\tlarge ears\tround head\tpointed muzzle\tshorthaired or longhaired coat\tbrown, tan, or white fur", 10], "satellite dish": ["Yes. 'Satellite dish' has a tangible appearance and is a type of antenna used for receiving satellite television or internet signals.\nA few things that are visually similar to 'satellite dish' but are not 'satellite dish' are:\ttelescope\tradio antenna\tdecorative dish\nThere are several useful visual features to tell there is 'satellite dish' and not similar things in a photo:\tcircular or oval shape\tmetallic surface\twith multiple thin protruding arms\tor with a central dish and several arms\tpointed towards the sky\ttoo large or directional to be a decoration\tor observing celestial objects", 10], "hoop earrings": ["Yes. 'Hoop earrings' has a tangible appearance and is a type of jewelry.\nA few things that are visually similar to 'hoop earrings' but are not 'hoop earrings' are:\tear cuffs\tearrings with dangling chains\tearrings with studs\nThere are several useful visual features to tell there is 'hoop earrings' and not similar things in a photo:\tcircular or hoop-shaped\thanging from the earlobe (not attached to the ear cartilage)\tmetallic, gold or silver in color", 10], "mountain goats": ["Yes. 'Mountain goats' has a tangible appearance and is a type of mammal.\nA few things that are visually similar to 'mountain goats' but are not 'mountain goats' are:\tsheep\tdeers\tlamas\nThere are several useful visual features to tell there is 'mountain goats' and not similar things in a photo:\twild and rugged mountainous terrain\tthick white fur\thorns or antlers\tshort, sharp black hooves\twide, square-shaped snouts\tdefined shoulder hump\tforaging or grazing on rocky cliff faces or mountain slopes", 10], "market sign": ["Yes. 'Market sign' has a tangible appearance and is a type of signage.\nA few things that are visually similar to 'market sign' but are not 'market sign' are:\tdirection sign\troad sign\tadvertisement\tsignpost\nThere are several useful visual features to tell there is 'market sign' and not similar things in a photo:\tindicates the location of a market or a shop\tbold and clear lettering\tbright colors\thanging from a building or a pole", 10], "trumpet": ["Yes. 'Trumpet' has a tangible appearance and is a type of musical instrument.\nA few things that are visually similar to 'trumpet' but are not 'trumpet' are:\tcornet\tflute\ttrombone\thorn\nThere are several useful visual features to tell there is 'trumpet' and not similar things in a photo:\tcurved metal pipe\tbell-shaped opening\tpiston valves\tmouthpiece", 10], "metal garage door": ["Yes. 'Metal garage door' has a tangible appearance and is a specific type of door.\nA few things that are visually similar to 'metal garage door' but are not 'metal garage door' are:\twooden door\tmetal gate\troller shutter door\nThere are several useful visual features to tell there is 'metal garage door' and not similar things in a photo:\trectangular shape\thorizontal panels\tor vertical slats\thandles or knobs\tmetallic finish\thinged or sliding opening mechanism ", 10], "stucco building": ["Yes. 'Stucco building' has a tangible appearance and is a kind of building material.\nA few things that are visually similar to 'stucco building' but are not 'stucco building' are: concrete building, brick building, wooden building, metal building.\nThere are several useful visual features to tell there is 'stucco building' and not similar things in a photo: plastered or rendered surface, rough texture and appearance, painted surface, distinct lines and shapes.", 10], "paper hat": ["Yes. 'Paper hat' has a tangible appearance and is a kind of headwear.\nA few things that are visually similar to 'paper hat' but are not 'paper hat' are:\tcrown\tbonnet\tberet\tcap\nThere are several useful visual features to tell there is 'paper hat' and not similar things in a photo:\tmade of paper or cardboard\tfolded from a single piece of paper\tcone-shaped\twith a circular base\ton the top of the head", 10], "candelabra": ["Yes. 'Candelabra' has a tangible appearance and is a type of candle holder.\nA few things that are visually similar to 'candelabra' but are not 'candelabra' are:\tcandlestick\tholder\tchandelier\tgoblet\nThere are several useful visual features to tell there is 'candelabra' and not similar things in a photo:\tusually has multiple branches or arms\twhere candles are placed\thas a base for stability\ttaller than a candlestick or holder.", 10], "brickwork": ["Yes. 'Brickwork' has a tangible appearance and is a type of masonry.\nA few things that are visually similar to 'brickwork' but are not 'brickwork' are:\tstone wall\ttile floor\twood paneling\nThere are several useful visual features to tell there is 'brickwork' and not similar things in a photo:\trectangular shape\tmortar between the bricks\tvariety of colors and textures", 10], "shingle roof": ["Yes. 'Shingle roof' has a tangible appearance and is a type of roofing material.\nA few things that are visually similar to 'shingle roof' but are not 'shingle roof' are:\ttile roof\tmetal roof\tthatched roof\nThere are several useful visual features to tell there is 'shingle roof' and not similar things in a photo:\toverlapping rectangular or hexagonal shapes\twooden or asphalt material\ttextured appearance\tvariety of colors, such as brown, grey, or black", 10], "scrub": ["Yes. 'Scrub' is a visually concrete concept that refers to a low-growing vegetation typically found in arid or semi-arid regions.\nA few things that are visually similar to 'scrub' but are not 'scrub' are:\tgrassy areas\tJungle\tForests\nThere are several useful visual features to distinguish 'scrub' from the listed similar things in a photo:\t\n- Scrub is typically low-growing and has a dense, woody structure\n- Usually found in arid or semi-arid environments\n- Often characterized by thorny or prickly plants with small leaves\n- May have a yellow or brownish coloration.", 10], "pink fabric": ["Yes. 'Pink fabric' has a tangible appearance and refers to a type of textile material that is pink in color.\nA few things that are visually similar to 'pink fabric' but are not 'pink fabric' are:\tpink paper\tpink plastic\tpink paint\tpink food\nThere are several useful visual features to tell there is 'pink fabric' and not similar things in a photo:\tsoft and pliable texture\tweave or knit pattern\table to drape and cover surfaces\tpink color evenly distributed throughout the material.", 10], "skateboard ridden": ["Yes. 'Skateboard ridden' has a tangible appearance and refers to a skateboard that is being used by a rider.\nA few things that are visually similar to 'skateboard ridden' but are not 'skateboard ridden' are:\tskateboard\tnot being ridden\tbicycle\troller skates\nThere are several useful visual features to tell there is 'skateboard ridden' and not similar things in a photo:\tperson standing or moving on the skateboard\twearing appropriate gear, such as helmets and pads\tperforming tricks or maneuvers in a skate park or on a street", 10], "tan legs": ["Yes. 'Tan legs' has a tangible appearance and refers to legs that have been tanned by the sun or artificial means.\nA few things that are visually similar to 'tan legs' but are not 'tan legs' are:\tlegs with stockings\tpainted legs\tdirty legs\tshadows on legs\nThere are several useful visual features to tell there are 'tan legs' and not similar things in a photo:\ttanned skin\tcolor gradient from pale to brown or golden\tsunburn marks, if any\tsmooth surface, not glossy or shiny", 10], "yellow green": ["Yes. 'Yellow green' has a tangible appearance and is a specific shade on the color spectrum.\nA few things that are visually similar to 'yellow green' but are not 'yellow green' are:\tyellow\tchartreuse\tlight green\nThere are several useful visual features to tell there is 'yellow green' and not similar things in a photo:\ta mix of yellow and green\thue that falls in between pure yellow and pure green\tno other visible colors in the mixture.", 10], "airport control tower": ["Yes. 'Airport control tower' has a tangible appearance and is a structure that can be visually identified.\nA few things that are visually similar to 'airport control tower' but are not 'airport control tower' are:\tskyscrapers\twater towers\tobservation towers\tchurch steeples\nThere are several useful visual features to tell there is 'airport control tower' and not similar things in a photo:\ttall and slender structure\twith windows or observation deck\tlocated near an airport\trunway or aircraft in the background", 10], "vegitation": ["Yes. 'Vegetation' has a tangible appearance and refers to plants and plant life.\nA few things that are visually similar to 'vegetation' but are not 'vegetation' are:\tforests\tfields\tgrasslands\tweeds\nThere are several useful visual features to tell there is 'vegetation' and not similar things in a photo:\tgreen color \tleaves\tstems and branches\ttrees or bushes\tasymmetrical patterns in leaves and branches.", 10], "tick marks": ["Yes. 'Tick marks' has a tangible appearance and refers to the small marks made on a surface to indicate a measurement or a count.\nA few things that are visually similar to 'tick marks' but are not 'tick marks' are:\tscratches\tdots\tbumps\tstains\nThere are several useful visual features to distinguish 'tick marks' from the listed similar things in a photo:\tuniform length\tregular spacing\tconsecutive order\talignment with other tick marks\tslight angle or slant\tif on a ruler, accompanied by numbers or measurements outside the length of the tick mark", 10], "toilet water tank": ["Yes. 'Toilet water tank' has a tangible appearance and is a part of a toilet.\nA few things that are visually similar to 'toilet water tank' but are not 'toilet water tank' are:\tfish tank\twater cooler\tstorage tank\nThere are several useful visual features to tell there is 'toilet water tank' and not similar things in a photo:\tlocated on the back of a toilet\tbuilt into the toilet\tcapable of holding and releasing water for flushing\tconnected to the flush handle or button", 10], "eagles": ["Yes. 'Eagles' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'eagles' but are not 'eagles' are:\thawks\tvultures\tkites\nThere are several useful visual features to tell there is 'eagles' and not similar things in a photo:\tbald head\tand curved yellow beak\tsharp talons\tforward-facing eyes\twingspan and body size\tthat the bird is not perched on a branch or a pole.", 10], "rock jetty": ["Yes. 'Rock jetty' has a tangible appearance and is a structure made of piled rocks used to protect a shore or harbor.\nA few things that are visually similar to 'rock jetty' but are not 'rock jetty' are:\trock formation\tpier\tbreakwater\nThere are several useful visual features to tell there is 'rock jetty' and not similar things in a photo:\tlong narrow structure extending into the water\tmade of piled rocks or stones\twithstands strong waves or currents\tprovides protection for a shoreline or harbor", 10], "wooden bars": ["Yes. 'Wooden bars' has a tangible appearance and refers to rectangular pieces of wood used for construction or decoration.\nA few things that are visually similar to 'wooden bars' but are not 'wooden bars' are:\twooden planks\twooden logs\tfurniture \nThere are several useful visual features to tell there is 'wooden bars' and not similar things in a photo:\trectangular in shape\tregularly spaced\tcriss-cross or stacked pattern\ttypically used for construction or decoration", 10], "underneath": ["No. 'Underneath' is too vague or abstract to be distinguished in a photo.", 10], "peels": ["Yes. 'Peels' has a tangible appearance and refers to the outer covering or skin of fruits or vegetables.\nA few things that are visually similar to 'peels' but are not 'peels' are:\tscales\tonion skin\tbark\nThere are several useful visual features to tell there are 'peels' and not similar things in a photo:\tthin outer layer\tusually discarded or composted\tvarious colors depending on the fruit or vegetable\tit follows the shape of the fruit or vegetable it covers or encloses.", 10], "drainage grate": ["Yes. 'Drainage grate' has a tangible appearance and is a type of metal grid covering a drain.\nA few things that are visually similar to 'drainage grate' but are not 'drainage grate' are:\tmanhole cover\tservice hatch\tgutter\nThere are several useful visual features to tell there is 'drainage grate' and not similar things in a photo:\trepetitive square or diamond shape\tcrisscrossing bars and frame\tmade of metal or iron located on the ground covering a drain or sewer", 10], "gold circle": ["Yes. 'Gold circle' has a tangible appearance and is a specific shape and color.\nA few things that are visually similar to 'gold circle' but are not 'gold circle' are:\tyellow circle\tsunflower\tcrisps\twith a yellow spotlight\nThere are several useful visual features to tell there is 'gold circle' and not similar things in a photo:\tcircle shape\tmetallic yellow or gold\tcolor uniformity\tlack of texture or pattern", 10], "orange vehicle": ["Yes, 'orange vehicle' has a visually concrete concept and refers to any vehicle that is colored orange.\nA few things that are visually similar to 'orange vehicle' but are not 'orange vehicle' are:\tyellow vehicle\tred vehicle\tpink vehicle\nThere are several useful visual features to identify 'orange vehicle' and not the similar things in a photo:\torange color\tall or most parts of the vehicle should be orange\tlicense plate, wheels, trim, or other parts may contrast with the orange color.", 10], "stand mixer": ["Yes. 'Stand mixer' has a tangible appearance and is a kitchen appliance.\nA few things that are visually similar to 'stand mixer' but are not 'stand mixer' are:\tblender\tfood processor\tjuicer\thand mixer\nThere are several useful visual features to tell there is 'stand mixer' and not similar things in a photo:\ttall and upright shape with a bowl and mixer attachment\tmechanical controls like buttons or dials\tmetallic or plastic finish\tblades or attachments for mixing or kneading", 10], "motors": ["Yes. 'Motors' has a tangible appearance and is a kind of mechanical device.\nA few things that are visually similar to 'motors' but are not 'motors' are:\tengines\tmachines\tfans\tblenders\nThere are several useful visual features to tell there is 'motors' and not similar things in a photo:\tspinning parts\tor circular metal parts\tpipes, tubes or hoses connecting to it\tsound and vibration when it is working\twires or cables connecting to it", 10], "places": ["No. 'Places' is too vague or abstract to be distinguished in a photo. \n\nIt is important to note that while physical spaces can be visually identified in a photo (such as the interior of a room or the landscape of a city), the concept of \"places\" as a collective term is still too abstract and requires more context to be understood. \n\nTherefore, the question is somewhat difficult to answer as it depends on the specific context or definition in which the term \"places\" is being used.", 10], "elephant tail": ["Yes. 'Elephant tail' has a tangible appearance and is a part of the elephant's body.\nA few things that are visually similar to 'elephant tail' but are not 'elephant tail' are:\thorse tail\tcat tail\tdog tail\nThere are several useful visual features to tell there is 'elephant tail' and not similar things in a photo:\tlong and thick with coarse hair\tend in a tassel or tuft of hair only on the tip part\tof grey color, matching the color of the elephant's skin", 10], "culvert": ["Yes. 'Culvert' has a tangible appearance and is a type of drainage structure.\nA few things that are visually similar to 'culvert' but are not 'culvert' are:\tpipeline\ttunnel\tbridge\toverpass\tconduit\nThere are several useful visual features to tell there is 'culvert' and not similar things in a photo:\tcylindrical or rectangular shape\topenings for water to flow through\tusually made of concrete or metal\tfound near roads or bodies of water\tsunken below ground level", 10], "service vehicle": ["Yes. 'Service vehicle' has a tangible appearance and is a kind of transport used for delivering services.\nA few things that are visually similar to 'service vehicle' but are not 'service vehicle' are:\ttruck\tvan\tbus\tambulance\nThere are several useful visual features to tell there is 'service vehicle' and not similar things in a photo:\tclearly labeled with the company name and/or service provided\ttools or equipment visible on or inside the vehicle\tlights or sirens indicating an emergency or urgent response\tpainted in bright, bold colors or designs to attract attention", 10], "throat": ["No. 'Throat' is too vague or abstract to be distinguished in a photo.", 10], "shadow snowboarder": ["Yes. 'Shadow snowboarder' has a tangible appearance and refers to the silhouette or reflection of a snowboarder.\nA few things that are visually similar to 'shadow snowboarder' but are not 'shadow snowboarder' are:\ttree shadows\tcloud reflections\ton-piste skier\nThere are several useful visual features to tell there is 'shadow snowboarder' and not similar things in a photo:\trecognizable shape of a snowboarder\tboarding setting (e.g. terrain park)\tsnowboarder's posture and movements (e.g. jumps, spins)", 10], "blue design": ["No. 'Blue design' is too vague or abstract to be distinguished in a photo. \n\nVisual features cannot be provided as the concept is not visually concrete.", 10], "gutters": ["Yes. 'Gutters' has a tangible appearance and is a part of a building's drainage system.\nA few things that are visually similar to 'gutters' but are not 'gutters' are:\twindow ledges\troof tiles\tdecorative moldings\nThere are several useful visual features to distinguish 'gutters' from the listed similar things in a photo:\t\n- Usually rectangular or semicircular in shape \n- fixed to the edge of the roof\n- its purpose is to collect rainwater and direct it away from the building \n- made of metal, vinyl, or plastic materials.", 10], "thong": ["Yes. 'Thong' has a tangible appearance and is a type of undergarment worn by both men and women.\nA few things that are visually similar to 'thong' but are not 'thong' are:\tunderwear\tpanties\tswimsuits\tbikini bottoms\nThere are several useful visual features to tell there is 'thong' and not similar things in a photo:\tV-shaped bottom\tbackside exposure\tnarrow sides and back\tminimal fabric coverage", 10], "brown clock": ["Yes. 'Brown clock' has a tangible appearance and is a type of timepiece.\nA few things that are visually similar to 'brown clock' but are not 'brown clock' are:\twatches\twhite clocks\tblack clocks\tpocket watches\nThere are several useful visual features to tell there is 'brown clock' and not similar things in a photo:\tbrown color\tcircular shape\twith hands or digital display\thas numbers or marks for time", 10], "sky background": ["Yes. 'Sky background' has a tangible appearance and it is an observable part of nature.\nA few things that are visually similar to 'sky background' but are not 'sky background' are:\tocean\twall\twatercolor painting\nThere are several useful visual features to tell there is 'sky background' and not similar things in a photo:\tblue color\tvarious shades of blue\tand white clouds\tno defined edges\tor has smooth and soft curves \tthat covers a wide area.", 10], "crack wall": ["Yes. 'Crack wall' has a tangible appearance and is a type of physical structure.\nA few things that are visually similar to 'crack wall' but are not 'crack wall' are:\tconcrete wall\tpainted wall\tbrick wall\ttile wall\nThere are several useful visual features to tell there is 'crack wall' and not similar things in a photo:\tCracks running along the wall\tDebris falling from the cracks\tDiscoloration or change in texture along the cracks\tSurface of the wall separating along the cracks", 10], "submarine sandwich": ["Yes. 'Submarine sandwich' has a tangible appearance and is a type of sandwich.\nA few things that are visually similar to 'submarine sandwich' but are not 'submarine sandwich' are:\thamburger\thot dog\twrap\tkebab\nThere are several useful visual features to tell there is 'submarine sandwich' and not similar things in a photo:\tlong and narrow sandwich\tbread shaped like a submarine roll\tmultiple layers of meat or vegetables\tslices of cheese and condiments\tpossible to cut into halves or quarters to see the layers", 10], "wilson logo": ["Yes. 'Wilson logo' has a tangible appearance and refers to a specific trademark.\nA few things that are visually similar to 'wilson logo' but are not 'wilson logo' are: sports equipment with different logos, printed designs or brand names.\nThere are several useful visual features to tell there is a 'Wilson logo' and not similar things in a photo: red or black printed W and red wave underneath on a white background, typically on a sports ball or racket.", 10], "photo album": ["Yes. 'Photo album' has a tangible appearance and is a book or binder containing photographs.\nA few things that are visually similar to 'photo album' but are not 'photo album' are:\tjournal\tscrapbook\tbinder\tnotebook\nThere are several useful visual features to tell there is 'photo album' and not similar things in a photo:\tpages with plastic sleeves to hold photos\tphotos displayed in chronological or thematic order\thardcover or softcover binding\tlabel or caption with dates, locations, or events", 10], "ornament tree": ["Yes. 'Ornament tree' has a tangible appearance and is a tree decorated with ornaments.\nA few things that are visually similar to 'ornament tree' but are not 'ornament tree' are:\tregular trees\tChristmas trees\tflower arrangements\t\nThere are several useful visual features to tell there is 'ornament tree' and not similar things in a photo:\tdecorated with ornaments\thanging lights, tinsels or garlands\tassembled indoors or outdoors", 10], "grases": ["Assuming you meant 'grasses', yes, 'grasses' has a tangible appearance.\nA few things that are visually similar to 'grasses' but are not 'grasses' are:\tweeds\therbs\tferns\tbushes\nThere are several useful visual features to tell there is 'grasses' and not similar things in a photo:\tleafy\tstalky\tgreen or yellow or brown\tin a field or meadow", 10], "effect": ["No. 'Effect' is too vague or abstract to have a tangible appearance or to be distinguished in a photo.", 10], "duct": ["Yes. 'Duct' has a tangible appearance and refers to a tube or channel for conveying air, liquid, or other substances.\nA few things that are visually similar to 'duct' but are not 'duct' are:\tpipeline\tconduit\tcable\ttrough\nThere are several useful visual features to tell there is 'duct' and not similar things in a photo:\ttube-shaped\thollow\tconnected to a ventilation system or HVAC unit\tmade of metal or plastic\tmay have insulation or tape covering it.", 10], "vintage airplane": ["Yes. 'Vintage airplane' has a tangible appearance and is a kind of aircraft that is typically no longer in production.\nA few things that are visually similar to 'vintage airplane' but are not 'vintage airplane' are:\tmodern airplane\thelicopter\tglider\tjet\nThere are several useful visual features to tell there is 'vintage airplane' and not similar things in a photo:\told-fashioned design or style\twooden or metallic body\tpropeller-driven engine\tcockpit with glass windows\topen-air or biplane design\tsingle or double wings", 10], "winglet": ["Yes. 'Winglet' has a tangible appearance and is a component of aircraft wings.\nA few things that are visually similar to 'winglet' but are not 'winglet' are:\tflaps\tspoilers\tslats\tailerons\nThere are several useful visual features to tell there is 'winglet' and not similar things in a photo:\tnarrow, vertical extension at the tip of an aircraft wing\tsomewhat triangular or blended shape\tcurved or tilted inwards or upwards\tusually found at the end of the wings", 10], "stoop": ["Yes. 'Stoop' has a tangible appearance and is a small set of steps leading up to a building's entrance.\nA few things that are visually similar to 'stoop' but are not 'stoop' are:\tdeck\tstaircase\tbalcony\t\nThere is no need for visual features to distinguish 'stoop' from the listed similar things in a photo as a 'stoop' is a distinctive visual element of an entrance and is usually composed of no more than three or four steps, is located right in front of the door, and sometimes incorporates a small platform or porch.", 10], "wooden gate": ["Yes. 'Wooden gate' has a tangible appearance and is a type of entryway.\nA few things that are visually similar to 'wooden gate' but are not 'wooden gate' are:\tfence\tbarn door\tshutter\t\nThere are several useful visual features to tell there is 'wooden gate' and not similar things in a photo:\tvertical wooden panels\thinges and latch\tfor outdoor use\tmatching or complementing the fence or surrounding area.", 10], "pachyderm": ["Yes. 'Pachyderm' has a tangible appearance and is a group of mammals that includes elephants, rhinoceroses, and hippopotamuses.\nA few things that are visually similar to 'pachyderm' but are not 'pachyderm' are:\tcow\thorse\tbison\tox\nThere are several useful visual features to tell there is 'pachyderm' and not similar things in a photo:\tthick skin\trounded body shape\ttusk or horn-like structures on the face\tprominent ears or snout", 10], "dog bone": ["Yes. 'Dog bone' has a tangible appearance and is a type of treat or toy for dogs.\nA few things that are visually similar to 'dog bone' but are not 'dog bone' are:\tbranches\tsticks\tpretzels\tbread\nThere are several useful visual features to tell there is 'dog bone' and not similar things in a photo:\twhite, off-white, or brown color\tbone-like shape\tsmooth surface\tmade of rawhide or other tough materials\tfor dogs to chew on\thas two rounded ends", 10], "water valve": ["Yes. 'Water valve' has a tangible appearance and is a kind of mechanism used to control the flow of water to a pipe or appliance.\nA few things that are visually similar to 'water valve' but are not 'water valve' are:\telectric switch\tgas valve\tthermostat\nThere are several useful visual features to tell there is 'water valve' and not similar things in a photo:\tmetallic handle\twater pipe or appliance nearby\tcontrolling water flow or shut off\twater droplets or wetness near the valve", 10], "orange cut": ["Yes. 'Orange cut' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'orange cut' but are not 'orange cut' are:\tlemon cut\tlime cut\tgrapefruit cut\ttangerine cut\nThere are several useful visual features to tell there is 'orange cut' and not similar things in a photo:\tsliced sections of an orange\torange color\tfleshy and juicy appearance\tpulp and seeds inside the sections.", 10], "vent pipe": ["Yes. 'Vent pipe' has a tangible appearance and is a type of pipe used for venting gases or air.\nA few things that are visually similar to 'vent pipe' but are not 'vent pipe' are:\tdrain pipe\tchimney\texhaust pipe\tplumbing pipe\tirrigation pipe\nThere are several useful visual features to tell there is 'vent pipe' and not similar things in a photo:\tusually made of metal, plastic or PVC\tcylindrical shape\twith a hood or cap on top\tsmall in diameter compared to other types of pipes\tattached to a wall, roof or ceiling", 10], "styrofoam box": ["Yes. 'Styrofoam box' has a tangible appearance and is a type of container.\nA few things that are visually similar to 'styrofoam box' but are not 'styrofoam box' are:\tplastic container\tcoolers\tmetal boxes\tcardboard boxes\nThere are several useful visual features to tell there is 'styrofoam box' and not similar things in a photo:\tlightweight material\tlumpy texture\twhite color\twith a hinged lid or removable top", 10], "light cover": ["Yes. 'Light cover' has a tangible appearance and is a type of fixture.\nA few things that are visually similar to 'light cover' but are not 'light cover' are:\tlampshade\tceiling fan blade\twall panel\tcar headlight cover\nThere are several useful visual features to tell there is 'light cover' and not similar things in a photo:\tcylindrical or dome shape\ttranslucent or transparent material\tfits onto a light fixture", 10], "pink bracelet": ["Yes. 'Pink bracelet' has a tangible appearance and is a piece of jewelry.\nA few things that are visually similar to 'pink bracelet' but are not 'pink bracelet' are:\twatches\tnecklaces\tanklets\tbracelets in other colors\nThere are several useful visual features to tell there is 'pink bracelet' and not similar things in a photo:\tpink in color\tmade of beads or other materials\tworn around a wrist or arm.", 10], "metallic": ["Yes. 'Metallic' has a tangible appearance and refers to a certain type of surface texture and appearance.\nA few things that are visually similar to 'metallic' but are not 'metallic' are: shiny plastic, glossy glass, polished stone, reflective water surface. \nThere are several useful visual features to tell there is 'metallic' and not similar things in a photo: a shiny, reflective surface with a metallic sheen, often silver or gold in color, hardness, and weight.", 10], "water fall": ["Yes. 'Water fall' has a tangible appearance and refers to a natural phenomena of falling water.\nA few things that are visually similar to 'water fall' but are not 'water fall' are:\tfountain\tgarden hose\train shower\nThere are several useful visual features to tell there is 'water fall' and not similar things in a photo:\twater falling from a high point\tinto a body of water\tbubbling and splashing\tsound of rushing water\tnatural location with rocks, plants and other natural elements.", 10], "orange trim": ["Yes. 'Orange trim' has a tangible appearance and is a decorative element in clothing or furniture.\nA few things that are visually similar to 'orange trim' but are not 'orange trim' are:\torange stitching\torange piping\torange ribbon\torange fabric\nThere are several useful visual features to tell there is 'orange trim' and not similar things in a photo:\tthin lines of orange accenting a piece of clothing or furniture\tmay be a solid orange line or a pattern with orange in it\tstrategically placed to draw attention to a certain part of the piece", 10], "clock showing time": ["Yes. 'Clock showing time' has a tangible appearance and is a type of time-telling device.\nA few things that are visually similar to 'clock showing time' but are not 'clock showing time' are:\tsundial\tthermometer\tbarometer\t\nThere are several useful visual features to tell there is 'clock showing time' and not similar things in a photo:\tcircular or rectangular shape\thour and minute hands\tnumerals or markers indicating the hours and minutes\tdigital display showing the time", 10], "thick snow": ["Yes. 'Thick snow' has a tangible appearance and is a snow cover that is deep and dense.\nA few things that are visually similar to 'thick snow' but are not 'thick snow' are:\tfoam\tice cream\tcotton candy\tconfectioner's sugar\nThere are several useful visual features to tell there is 'thick snow' and not similar things in a photo:\twhite color of snow\tcold environment\tsnowflakes on the surface of snow\tsnowdrifts\tor heavy snow cover", 10], "glass cockpit": ["Yes. 'Glass cockpit' has a tangible appearance and refers to a modern cockpit design with electronic displays instead of traditional instruments.\nA few things that are visually similar to 'glass cockpit' but are not 'glass cockpit' are:\ttraditional cockpit design\tspaceship control room\tcomputer monitor display\nThere are several useful visual features to tell there is 'glass cockpit' and not similar things in a photo:\tmultiple electronic displays instead of traditional instruments\tclean and streamlined design\tdigital readouts instead of analog gauges\tno mechanical buttons, levers or dials on the displays.", 10], "macaw": ["Yes. 'Macaw' has a tangible appearance and is a type of parrot.\nA few things that are visually similar to 'macaw' but are not 'macaw' are:\tcockatoo\tparakeet\ttoucan\nThere are several useful visual features to tell there is 'macaw' and not similar things in a photo:\tbright blue, red, green, and yellow feathers\tlong tail and wings\tbeak of the same color as the face", 10], "spray ocean": ["Yes. 'Spray ocean' has a tangible appearance and refers to the misty spray of seawater created by waves breaking onto the shore.\nA few things that are visually similar to 'spray ocean' but are not 'spray ocean' are:\tfog\tmist\twaterfall spray\tsteam\nThere are several useful visual features to tell there is 'spray ocean' and not similar things in a photo:\twaves breaking onto the shore\tmisty or fine spray in the air\tblue or green hue\ttypically near a beach or ocean shore", 10], "round ears": ["Yes. 'Round ears' has a tangible appearance.\nA few things that are visually similar to 'round ears' but are not 'round ears' are:\toval-shaped ears\tpointy ears\tfurry ears\tmetallic circles\nThere are several useful visual features to tell there are 'round ears' and not similar things in a photo:\tsymmetrical circle shape\tsmooth edges\tno visible hair or fur on the ears\tattached to the head of an animal or a human", 10], "skateboard trick": ["Yes. 'Skateboard trick' has a tangible appearance and is an action performed with a skateboard.\nA few things that are visually similar to 'skateboard trick' but are not 'skateboard trick' are:\tstanding on a skateboard\twhile riding\tskateboard jump\twhile turning\nThere are several useful visual features to tell there is 'skateboard trick' and not similar things in a photo:\tdifficult maneuvers\tor acrobatics performed on a skateboard\tinvolving the skateboard flipping or spinning in the air\tjumping over objects or performing stunts in the air", 10], "suite": ["No. 'Suite' is too vague or abstract to be distinguished in a photo. It is a type of accommodation that may vary in appearance and layout.\nThere are no things that are visually similar to 'suite' and not 'suite'.\nVisual context and surrounding objects are useful in identifying a 'suite', but there are no specific features that distinguish it from other types of accommodation without additional information.", 10], "light globe": ["Yes. 'Light globe' has a tangible appearance and is an object that produces light.\nA few things that are visually similar to 'light globe' but are not 'light globe' are:\tflashlight\tlight bulb\tcar headlight\tlamp\nThere are several useful visual features to tell there is 'light globe' and not similar things in a photo:\tglass or plastic sphere or bulb-shaped object\tproduces a glowing or bright light\telectric cord attached to the bottom or side of the globe\tfits into a light fixture or socket.", 10], "turbo engine": ["Yes. 'Turbo engine' has a tangible appearance and is a type of engine used for vehicles.\nA few things that are visually similar to 'turbo engine' but are not 'turbo engine' are:\tregular engine\tmotorbike engine\tfurnace\tjet engine\nThere are several useful visual features to tell there is 'turbo engine' and not similar things in a photo:\ttwo main components: the turbocharger and the engine cylinder\tblock metallic and shiny surface\texhaust system usually visible\tlarge and complex design with multiple pipes and cables", 10], "beach water": ["Yes. 'Beach water' has a tangible appearance and is a body of water commonly found on a sandy shore.\nA few things that are visually similar to 'beach water' but are not 'beach water' are:\tswimming pool\triver\tlake\tpond\nThere are several useful visual features to tell there is 'beach water' and not similar things in a photo:\tsandy shore\tor coastline\tblue color\tclear or transparent appearance\twaves\tor tide", 10], "blue tail": ["Yes. 'Blue tail' has a tangible appearance and is a type of body part commonly found in animals.\nA few things that are visually similar to 'blue tail' but are not 'blue tail' are:\tgreen tail\tred tail\tyellow tail\torange tail\nThere are no useful visual features to distinguish 'blue tail' from similar things as the color of the tail is the defining characteristic.", 10], "start": ["No. 'Start' is too abstract to be visually distinguished in a photo. It refers to the beginning of an activity or process, rather than something with a physical appearance.", 10], "waste receptacle": ["Yes. 'Waste receptacle' has a tangible appearance and is a type of container for waste.\nA few things that are visually similar to 'waste receptacle' but are not 'waste receptacle' are:\tflowerpot\tbasket\ttoolbox\nThere are several useful visual features to tell there is 'waste receptacle' and not similar things in a photo:\trectangular or round shape\tlid or opening for waste\tentry or exit for garbage truck or sanitation worker\ttrash or recycling symbols or labels\ton a street corner or in a park", 10], "window opening": ["Yes. 'Window opening' has a tangible appearance and refers to the space where a window can be opened.\nA few things that are visually similar to 'window opening' but are not 'window opening' are:\tpicture frame, door, wall cutout, archway, and skylight.\nThere are several useful visual features to tell there is 'window opening' and not similar things in a photo:\trectangular or square shape\tframe or barrier around the opening\tdifferent material or texture from the surrounding surface or background\thandle or latch for opening and closing the window.", 10], "metal hardware": ["Yes. 'Metal hardware' has a tangible appearance and refers to various types of metal fittings used in construction or manufacturing.\nA few things that are visually similar to 'metal hardware' but are not 'metal hardware' are:\tjewelry\tutensils\tmetallic toys\nThere are several useful visual features to tell there is 'metal hardware' and not similar things in a photo:\tvarious types of bolts, nuts, screws, washers, hinges, handles, locks, etc.metallic color and texture\tcold and hard to the touch\ttypically visible in machinery, vehicles, or buildings", 10], "purple train": ["Yes. 'Purple train' has a tangible appearance and is a type of transportation.\nA few things that are visually similar to 'purple train' but are not 'purple train' are:\tother colored trains\tother types of transportation like buses, planes, or boats\nThere are several useful visual features to tell there is 'purple train' and not similar things in a photo:\tpredominantly purple color\ttrain-like body and structure\twheels and rails for tracks\tvisible windows and doors for passengers and driver", 10], "orange strap": ["Yes. 'Orange strap' has a tangible appearance and is a type of belt or band that is colored orange.\nA few things that are visually similar to 'orange strap' but are not 'orange strap' are:\tyellow strap\tred strap\tbrown strap\tpink strap\nThere are several useful visual features to tell there is 'orange strap' and not similar things in a photo:\torange color\tRectangular-shaped strap\tBuckle or fastener at the end\tWoven or webbed texture", 10], "night time sky": ["Yes. 'Night time sky' has a tangible appearance and is the view of the sky during the night.\nA few things that are visually similar to 'night time sky' but are not 'night time sky' are: day time sky, cloudy sky, star chart, astronomical photos.\nThere are several useful visual features to tell there is 'night time sky' and not similar things in a photo:\tstars\tmoon\tmilky way\tconstellations\tdark background\tfree of light pollution.", 10], "orange buoys": ["Yes. 'Orange buoys' has a tangible appearance and is a type of floating device.\nA few things that are visually similar to 'orange buoys' but are not 'orange buoys' are:\tlifebuoys\tfishing buoys\tbeach balls\nThere are several useful visual features to tell there is 'orange buoys' and not similar things in a photo:\torange color\tcylindrical or spherical shape\tmay have numbers or text on them\ttethered or floating in water.", 10], "blue nose": ["Yes. 'Blue nose' has a tangible appearance and is a descriptive term for a nose that is blue in color.\nThere are no things that are visually similar to 'blue nose' that are not 'blue nose'.\nThere are no visual features to distinguish 'blue nose' from similar things in a photo as there are no similar things to 'blue nose'.", 10], "car headlights": ["Yes. 'Car headlights' has a tangible appearance and is a type of light mounted on a car.\nA few things that are visually similar to 'car headlights' but are not 'car headlights' are:\tstreetlights\tlanterns\tfloodlights\tlamp posts\nThere are several useful visual features to tell there is 'car headlights' and not similar things in a photo:\tmounted on a car or vehicle\tfacing forward\tbrighter and more focused beam\tlight color usually white or yellow", 10], "pink strap": ["Yes. 'Pink strap' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'pink strap' but are not 'pink strap' are:\tblue strap\tbelt\thair tie\tcloth ribbon\nThere are several useful visual features to tell there is 'pink strap' and not similar things in a photo:\tpink in color\tthin and narrow in width\tmade of a flexible material such as plastic, silicone, or rubber\ttypically used for securing or fastening something", 10], "firefighters": ["Yes. 'Firefighters' has a tangible appearance and is a type of emergency worker.\nA few things that are visually similar to 'firefighters' but are not 'firefighters' are:\tpolice officers\tparamedics\tswimmers\tmilitary\nThere are several useful visual features to tell there are 'firefighters' and not similar things in a photo:\ttypical firefighter uniform with helmet and coat\tfire hoses, axes, or other firefighting equipment\tsmoke from a building or other signs of fire\tpresence of fire trucks or other firefighting vehicles\tgroups of people working together to put out a fire", 10], "toilette": ["Yes. 'Toilette' has a tangible appearance and refers to the bathroom fixture used for human waste disposal.\nA few things that are visually similar to 'toilette' but are not 'toilette' are:\tsink\tbathtub\tshower\tflushometer\nThere are several useful visual features to tell there is 'toilette' and not similar things in a photo:\tbowl-shaped\tporcelain or ceramic material\tseat and lid\ton top of a base\twith a water tank\ttypically on the floor or mounted on a wall.", 10], "mother bear": ["Yes. 'Mother bear' has a tangible appearance and is a type of animal.\nA few things that are visually similar to 'mother bear' but are not 'mother bear' are:\tfather bear\tbear cub\tbrown teddy bear\tblack teddy bear\nThere are several useful visual features to tell there is 'mother bear' and not similar things in a photo:\tbrown or black fur\tsharp claws and teeth\tlarge body size\twith cubs around or nursing them", 10], "round spot": ["Yes. 'Round spot' has a tangible appearance and is a simple geometric shape.\nA few things that are visually similar to 'round spot' but are not 'round spot' are:\tdots\tcircles\tbubbles\tbuttons\nThere are several useful visual features to tell there is 'round spot' and not similar things in a photo:\tcircular shape\twithout texture\tor detail\twith uniform color\tor pattern", 10], "plane wings": ["Yes. 'Plane wings' has a tangible appearance and is a part of an airplane.\nA few things that are visually similar to 'plane wings' but are not 'plane wings' are:\tbird wings\tglider wings\tkite wings\nThere are several useful visual features to tell there are 'plane wings' and not similar things in a photo:\tattached to the side of a fuselage\taerodynamic shape\twith control surfaces, such as flaps or ailerons\tsometimes with engines or propellers", 10], "orange kitten": ["Yes. 'Orange kitten' has a tangible appearance and refers to a specific type of young feline with an orange coat.\nA few things that are visually similar to 'orange kitten' but are not 'orange kitten' are:\torange fox\tcub\torangutan\nThere are several useful visual features to tell there is 'orange kitten' and not similar things in a photo:\tfurry body\twith orange fur\tbeady eyes\tpointed ears and whiskers\tcute and small size", 10], "lavender flowers": ["Yes. 'Lavender flowers' has a tangible appearance and is a type of flowering plant.\nA few things that are visually similar to 'lavender flowers' but are not 'lavender flowers' are:\tbluebells\twisteria\tjacaranda\tverbena\nThere are several useful visual features to tell there is 'lavender flowers' and not similar things in a photo:\tsmall purple or light blue flowers\tfragrant and aromatic long green stems\twith small leaves in the shape of a spear", 10], "atv": ["Yes. 'ATV' has a tangible appearance and is a kind of vehicle.\nA few things that are visually similar to 'ATV' but are not 'ATV' are:\tdirt bike\tscooter\tjeep\tgolf cart\tkart.\nThere are several useful visual features to help distinguish an 'ATV' from the listed similar things in a photo:\tfour wheels or tracks\ta handlebar for steering\tan engine or motor\toversized, aggressive wheels for off-road capabilities.", 10], "soot": ["Yes. 'Soot' has a tangible appearance and is a type of black or dark gray powder.\nA few things that are visually similar to 'soot' but are not 'soot' are:\tash\tdirt\tcharcoal\nThere are several useful visual features to tell there is 'soot' and not similar things in a photo:\tblack or dark gray powder\tdirty or gritty appearance\tsmears or stains on surfaces\tburnt smell or evidence of fire", 10], "firetrucks": ["Yes. 'Firetrucks' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'firetrucks' but are not 'firetrucks' are:\tambulance\tpolice car\ttruck\tconstruction vehicle\nThere are several useful visual features to tell there is a 'firetruck' and not similar things in a photo:\tbright red color\tlarge water hose\tretractable ladder\temergency lights and siren\tword \"Fire\" or a fire department logo written on it.", 10], "eaves": ["Yes. 'Eaves' has a tangible appearance and is a part of a building.\nA few things that are visually similar to 'eaves' but are not 'eaves' are:\troofline\tgutter\twindow ledge\tshade\nThere are several useful visual features to tell there is 'eaves' and not similar things in a photo:\thorizontal overhang of a roof\ttogether with the fascia board and soffit\tform a roof's eave system", 10], "birthday candle": ["Yes. 'Birthday candle' has a tangible appearance and is a type of candle used specifically for birthday celebrations.\nA few things that are visually similar to 'birthday candle' but are not 'birthday candle' are:\ttaper candle\ttea light candle\tpillar candle\nThere are several useful visual features to tell there is 'birthday candle' and not similar things in a photo:\tshort and thin candle\tbrightly colored\twax shape with a holder on the bottom\tusually with a flame on the top", 10], "cove": ["Yes. 'Cove' has a tangible appearance and refers to a type of small bay with a circular or oval shape.\nA few things that are visually similar to 'cove' but are not 'cove' are:\tinlet\tharbor\tfjord\tlagoon\nThere are several useful visual features to tell there is 'cove' and not similar things in a photo: \tsmaller than a bay or a gulf\thorseshoe-shaped or circular\tcalm water surrounded by cliffs or rocks\taspect of privacy", 10], "highway overpass": ["Yes. 'Highway overpass' has a concrete, tangible appearance.\nA few things that are visually similar to 'highway overpass' but are not 'highway overpass' are:\tfootbridge\trailway bridge\tarch bridge\nThere are several useful visual features to tell there is 'highway overpass' and not similar things in a photo:\troadway positioned above another roadway or a pedestrian walkway\taccess ramps at each end of the bridge\tpillars or supports holding up the structure\troad markings or signs on the roadway of overpass", 10], "ash": ["Yes. 'Ash' has a tangible appearance and is a powdery residue that is left after something burns.\nA few things that are visually similar to 'ash' but are not 'ash' are:\tdust\tsand\tpowdered sugar\tfine salt\nThere are several useful visual features to tell there is 'ash' and not similar things in a photo:\tdark grey or black powdery substance\tporous and lightweight texture, unlike dust or sand\tspecific burnt smell or signs of fire in the surroundings", 10], "relection": ["No. 'Reflection' is too vague or abstract to be distinguished in a photo. However, if you meant 'reflection' as in the reflection of light, then the answer would be yes.\nA few things that are visually similar to 'reflection' but are not 'reflection' are:\tshadows\tmirage\tmetallic objects\twet surfaces\tsmoke or mist\nThere are several useful visual features to tell there is a 'reflection' and not similar things in a photo:\tmirror-like surface or smooth surface a surface that reflects light\tusually inverted or reversed image\tmatches the color and texture of the reflecting surface", 10], "pink pole": ["Yes. 'Pink pole' has a tangible appearance and is a specific object.\nA few things that are visually similar to 'pink pole' but are not 'pink pole' are:\tyellow pole\twhite pole\tpurple pole\texplosion of colored powder\nThere are several useful visual features to tell there is 'pink pole' and not similar things in a photo:\tpink from top to bottom\tlong and cylindrical\tsolid and not bending or swaying", 10], "drill": ["Yes. 'Drill' has a tangible appearance and is a type of tool.\nA few things that are visually similar to 'drill' but are not 'drill' are:\tscrewdriver\tchisel\thammer\tpower saw\nThere are several useful visual features to tell there is 'drill' and not similar things in a photo:\tcylindrical shape\twith a handle and a trigger button\ton top there is a removable bit that spins to make a hole\thave power cord or battery attached to it", 10], "horse drinking water": ["Yes. 'Horse drinking water' has a tangible appearance.\nA few things that are visually similar to 'horse drinking water' but are not 'horse drinking water' are:\thorse standing in a lake\thorse standing in a river\thorse standing in the rain\thorse standing in a sprinkler\thorse standing in a pond\nThere are several useful visual features to tell there is 'horse drinking water' and not similar things in a photo:\twater trough or bucket\tnose submerged in water\tdrinking or slurping motion with its mouth\thorse in a standing position beside water source \twetness around mouth and on throat.", 10], "trike": ["Yes. 'Trike' has a tangible appearance and is a type of vehicle.\nA few things that are visually similar to 'trike' but are not 'trike' are:\tbike\tscooter\tcar\tmotorcycle\nThere are several useful visual features to tell there is 'trike' and not similar things in a photo:\tthree wheels\ttwo rear wheels and one front wheel\thandlebars\tsaddle or seat for the rider", 10], "crows": ["Yes. 'Crows' has a tangible appearance and is a type of bird.\nA few things that are visually similar to 'crows' but are not 'crows' are: ravens, magpies, jays, blackbirds.\nThere are several useful visual features to tell there is 'crows' and not similar things in a photo:\tall-black feathers\twith a glossy shine\tsomewhat thick and curved beak\tsharp talons on their feet\tlarger in size than most similar birds", 10], "competition": ["No. 'Competition' is too vague and abstract to be visually distinguished in a photo. \n\nHowever, here are a few things that are physically associated with competitions: medal or ribbon awards, judges or referees, scoreboards.\n\nUseful visual features for distinguishing a competition from similar events would include:\n\n- Participants officially competing in a structured event\n- A clear objective being pursued by participants\n- Rules and regulations being strictly adhered to\n- A distinguishing location or venue for the event such as a track, a field, a stage, etc.\n- Equipment or tools specific to the competition being used by participants.", 10], "c-kite": ["Yes. 'C-kite' has a tangible appearance and is a type of kite.\nA few things that are visually similar to 'c-kite' but are not 'c-kite' are:\tdelta kite\tbow kite\tbox kite\tdiamond kite\nThere are several useful visual features to tell there is 'c-kite' and not similar things in a photo:\tC-shaped\tkite bar with control lines attached\ttoo small to have a human ride it\tfloats in the air on its own, without assistance from a person or vehicle", 10], "cage door": ["Yes. 'Cage door' has a tangible appearance and is a type of door.\nA few things that are visually similar to 'cage door' but are not 'cage door' are:\tgate\tdoor with bars\tshutters\tsecurity door\nThere are several useful visual features to tell there is 'cage door' and not similar things in a photo:\tbars or mesh to restrict passage\tpadlock or lock mechanism\tsturdy and durable material", 10], "photo background": ["Yes. 'Photo background' has a tangible appearance and refers to the background in a photograph.\nA few things that are visually similar to 'photo background' but are not 'photo background' are:\tpaintings\tmurals\twallpaper\t\nThere are several useful visual features to tell there is 'photo background' and not similar things in a photo:\tit is blurred or out of focus in a portrait or posed photograph\tit is complementary to the main subject of the photo\tit is plain or unobtrusive to avoid distracting from the main subject", 10], "safety sign": ["Yes. 'Safety sign' has a tangible appearance and is a type of sign used to promote safety, caution, or danger.\nA few things that are visually similar to 'safety sign' but are not 'safety sign' are:\tadvertisement\tsignpost\tstreet sign\tstore sign\tdirection sign\nThere are several useful visual features to tell there is 'safety sign' and not similar things in a photo:\ttypically bright or fluorescent colors\tclear, easy-to-read text, symbols, or pictograms\tconveys a message of safety, caution, or danger placed in areas with potential hazards or danger", 10], "hedge bush": ["Yes. 'Hedge bush' has a tangible appearance and is a type of plant used for landscaping.\nA few things that are visually similar to 'hedge bush' but are not 'hedge bush' are:\ttree\tbush\tshrub\tgrass\nThere are several useful visual features to tell there is 'hedge bush' and not similar things in a photo:\tsmall and tightly packed leaves or branches\tneatly trimmed surface\tline that runs along a boundary or boundary of a garden or yard", 10], "blue sky background": ["Yes. 'Blue sky background' has a tangible appearance and is a type of natural scenery.\nA few things that are visually similar to 'blue sky background' but are not 'blue sky background' are:\tblue cloth\tblue paper\tblue paint\nThere are several useful visual features to identify the 'blue sky background' in a photo:\tclear blue sky without clouds\tor other objects in the background. The sky color should be consistent throughout the image. The presence of other elements such as birds, planes, or sunrays may add interest to the photo, but they are not essential features of a blue sky background.", 10], "whitecap": ["Yes. 'Whitecap' has a tangible appearance and is a type of wave.\nA few things that are visually similar to 'whitecap' but are not 'whitecap' are:\tfoam\tbubbles\tsea spray\nThere are several useful visual features to tell there is 'whitecap' and not similar things in a photo:\tcrest of a wave\tbreaking over\tthe top of a wave\televated, curved shape\twhite appearance\tcrisp and distinct edge\tspray or mist near the top.", 10], "clock roman": ["No. 'Clock Roman' is too vague or abstract to be distinguished in a photo. \n\nI'm not sure if you meant 'Roman numerals' instead of 'Clock Roman'. If that is the case: \n\nYes. 'Roman numerals' has a tangible appearance and is a numerical system that uses letters of the Latin alphabet.\nA few things that are visually similar to 'Roman numerals' but are not 'Roman numerals' are:\tletters of a different alphabet, such as Cyrillic, Greek or Arabic\tregular numerals\tsymbols or signs\t\nThere are several useful visual features to tell there are 'Roman numerals' and not similar things in a photo:\tletters I, V, X, L, C, D or M arranged in a specific order and combination\tto indicate time, they are often found on the face of a clock or a watch.", 10], "race car": ["Yes. 'Race car' has a tangible appearance and refers to a specific type of car designed for racing.\nA few things that are visually similar to 'race car' but are not 'race car' are:\tspeedboat\tsailboat\tformula one car\tluxury car\nThere are several useful visual features to tell there is 'race car' and not similar things in a photo:\tlow to the ground\tsleek and aerodynamic design\tstripes or other racing decorations\tlarge spoiler at the back of the car\twide and slick tires\tdesigned for speed and performance", 10], "corn dog": ["Yes. 'Corn dog' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'corn dog' but are not 'corn dog' are:\thot dogs\tcornbread\tfried chicken sausage\tcorn fritters\nThere are several useful visual features to tell there is 'corn dog' and not similar things in a photo:\tsausage on a stick\tcornmeal batter coating\tfried until crispy and golden brown", 10], "roof line": ["Yes. 'Roof line' has a tangible appearance and refers to the outline of a roof.\nA few things that are visually similar to 'roof line' but are not 'roof line' are:\tfence line\thorizon line\tsilhouette of a building\ttreetop line\nThere are several useful visual features to tell there is 'roof line' and not similar things in a photo:\tseries of peaks and/or slopes\toften angular and geometric in shape\tlocated at the top of a building, house, or structure", 10], "orange leaf": ["Yes. 'Orange leaf' has a tangible appearance and is a type of foliage.\nA few things that are visually similar to 'orange leaf' but are not 'orange leaf' are:\tyellow leaf\tred leaf\torange flower\torange fruit\nThere are several useful visual features to tell there is 'orange leaf' and not similar things in a photo:\tbright orange color\tunique leaf shape\tpointed edges\tveins in the leaf", 9], "plastic ball": ["Yes. 'Plastic ball' has a tangible appearance and can refer to a variety of objects that are balls and made of plastic.\nA few things that are visually similar to 'plastic ball' but are not 'plastic ball' are:\ttennis balls\tbeach balls\tping pong balls\tbouncy balls\nThere are several useful visual features to tell there is 'plastic ball' and not similar things in a photo:\tmade of plastic\tsmooth surface\tspherical shape\tno visible seams or stitching\tsolid color or patterned surface", 9], "baseball pitch": ["Yes. 'Baseball pitch' has a tangible appearance and is a specific movement in baseball.\nA few things that are visually similar to 'baseball pitch' but are not 'baseball pitch' are: throwing a ball, throwing a frisbee, throwing a football.\nThere are several useful visual features to tell there is a 'baseball pitch' and not similar things in a photo:\ta pitcher's mound\ton a baseball field\tholding a baseball with a specific grip\twinding up the arm before the release of the ball", 9], "macy": ["No. 'Macy' is too vague or abstract to have a tangible appearance.", 9], "coaches": ["Yes. 'Coaches' has a tangible appearance and generally refers to a type of vehicle used for transportation.\nA few things that are visually similar to 'coaches' but are not 'coaches' are:\tbuses\ttrucks\tvans\tcars\nThere are several useful visual features to tell there is 'coaches' and not similar things in a photo:\tlong and rectangular shape\tlarge, spacious interior\toften have windows along the sides designed for sightseeing or panoramic views\tcan be pulled by horses or powered by an engine", 9], "dinner fork": ["Yes. 'Dinner fork' has a tangible appearance and is a type of utensil.\nA few things that are visually similar to 'dinner fork' but are not 'dinner fork' are:\tspoon\tknife\tspork\ttuning fork\nThere are several useful visual features to tell there is 'dinner fork' and not similar things in a photo:\tlong, slender handle with three or four tines at one end\ttines are usually flat and slightly curved\tflathead between the tines for cutting or spearing food", 9], "wooden stand": ["Yes. 'Wooden stand' has a tangible appearance and is a piece of furniture.\nA few things that are visually similar to 'wooden stand' but are not 'wooden stand' are:\twooden box\twooden crate\twooden bench\twooden stool\nThere are several useful visual features to tell there is 'wooden stand' and not similar things in a photo:\trectangular or square shape with flat top and bottom\tpieces of wood joined by screws or nails\theight must be equal or greater than a chair's or stool's\ttop surface is large enough to hold an object or plate", 9], "orange carrot slice": ["Yes. 'Orange carrot slice' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'orange carrot slice' but are not 'orange carrot slice' are:\ttangerine slice\tpeach slice\nThere are several useful visual features to tell there is 'orange carrot slice' and not similar things in a photo:\torange\ttapered at one end\tsmall patches of green on the tapered end\tcircular shape with a hole in the middle\tfibrous texture\twith or without peel", 9], "sparse tree": ["Yes. 'Sparse tree' has a tangible appearance and refers to a tree with few branches and leaves.\nA few things that are visually similar to 'sparse tree' but are not 'sparse tree' are:\tdead tree\twinter tree\tmangrove\tnaked tree\nThere are several useful visual features to tell there is 'sparse tree' and not similar things in a photo:\tfew branches and leaves\tgaps between the branches and leaves\tdry or brown leaves\tsparse foliage", 9], "mickey": ["No. 'Mickey' is too vague or abstract to be distinguished in a photo. It needs to be further specified, for example, Mickey Mouse, before it can have a tangible appearance.\nA few things that are visually similar to 'Mickey Mouse' but are not 'Mickey Mouse' are:\tRatatouille\t Stuart Little\t Jerry (Tom and Jerry)\nThere are several useful visual features to tell there is 'Mickey Mouse' and not similar things in a photo:\tmouse ears\t round head with a pointed snout\t white gloves\t red shorts", 9], "hashbrowns": ["Yes. 'Hashbrowns' has a tangible appearance and is a kind of food.\nA few things that are visually similar to 'hashbrowns' but are not 'hashbrowns' are:\thashed beef\tpotato patties\tfried potato slices\tsweet potato fries\nThere are several useful visual features to tell there is 'hashbrowns' and not similar things in a photo:\tflat and round\tshredded potatoes\tbrown and crispy surface\tno visible chunks or slices of meat or other vegetables", 9], "sundress": ["Yes. 'Sundress' has a tangible appearance and is a type of dress worn in warm weather.\nA few things that are visually similar to 'sundress' but are not 'sundress' are:\tt-shirt dress\tmaxi dress\tstrapless dress\tbeach cover-up\nThere are several useful visual features to tell there is 'sundress' and not similar things in a photo:\tlight and airy fabric\tprints or patterns suitable for warm weather\tusually sleeveless or with thin straps\themline above the knee or mid-thigh", 9], "clock tower building": ["Yes. 'Clock tower building' has a tangible appearance and refers to a building that has a visible clock tower structure.\nA few things that are visually similar to 'clock tower building' but are not 'clock tower building' are:\tbell tower\tmosque tower\tbarn with a tall structure\nThere are several useful visual features to tell there is 'clock tower building' and not similar things in a photo:\tvisible clock face or tower\ton a building with windows and doors\ttypically taller than surrounding buildings\tbell or chime mechanism may be visible", 9], "tennis turf": ["Yes. 'Tennis turf' has a tangible appearance and is a specific type of artificial grass used for playing tennis.\nA few things that are visually similar to 'tennis turf' but are not 'tennis turf' are:\tsoccer field turf\tartificial lawn\tastroturf\tgrass mat\nThere are several useful visual features to tell there is 'tennis turf' and not similar things in a photo:\tgreen and light-green stripes\twith white lines forming a grid or a diamond pattern\tsurface may have sand or rubber granules to improve sliding and bouncing properties", 9], "plastic piece": ["Yes. 'Plastic piece' has a tangible appearance and is a type of object made from plastic.\nA few things that are visually similar to 'plastic piece' but are not 'plastic piece' are:\tpaper clip\tbottle cap\tkeychain charm\tpen cap\nThere are several useful visual features to tell there is 'plastic piece' and not similar things in a photo:\tmade of plastic\tsmall size\tno obvious function (e.g. not a bottle cap or pen cap)\tno recognizable shape (e.g. not a keychain charm)", 9], "piller": ["The correct spelling of the word is 'pillar'. \n\nYes. 'Pillar' has a tangible appearance and is a vertical supporting structure. \n\nA few things that are visually similar to 'pillar' but are not 'pillar' are:\t\nColumn\t\nBeam\t\nPost\t\nPedestal\t\nPole\t\n\nThere are several useful visual features to tell there is 'pillar' and not similar things in a photo:\t\nVertical structure\t\nTaller than it is wide\t\nSupport function\t\nMay have a base and capital\t\nCan be made of various materials (e.g. stone, concrete, metal)", 9], "nectarines": ["Yes. 'Nectarines' has a tangible appearance and is a type of fruit.\nA few things that are visually similar to 'nectarines' but are not 'nectarines' are:\tpeaches\tapricots\tplums\nThere are several useful visual features to tell there is 'nectarines' and not similar things in a photo:\tmedium-sized fruit\tskin with a reddish-orange or yellow color and fuzziness\ton one side of the fruit, there is a crease - this is where the fruit was attached to the tree\tthe flesh is pale yellow, juicy and has a large, hard seed in the center.", 9], "metal toilet": ["Yes. 'Metal toilet' has a tangible appearance and is a type of restroom fixture.\nA few things that are visually similar to 'metal toilet' but are not 'metal toilet' are:\tceramic toilet\tplastic toilet\tPorta Potty or portable toilet\nThere are several useful visual features to tell there is 'metal toilet' and not similar things in a photo:\tmade of metal\tusually silver or grey in color\tresistance to high usage and vandalism\thas metal flush handle or button", 9], "beige lamp shade": ["Yes. 'Beige lamp shade' has a tangible appearance and is a kind of home decor.\nA few things that are visually similar to 'beige lamp shade' but are not 'beige lamp shade' are:\tmushroom\tcap\tcake\tdish\n\t\nThere are several useful visual features to tell there is 'beige lamp shade' and not similar things in a photo:\tcylindrical shape\tflared shape at the bottom\tbeige color\tfabric or paper-like texture\tfits on top of a lamp or light bulb.", 9], "fencepost": ["Yes. 'Fencepost' has a tangible appearance and is a vertical structure made of wood, metal, or concrete, used to support a fence.\nA few things that are visually similar to 'fencepost' but are not 'fencepost' are:\ttree\ttrunk\tstreet light\tpole\tbuilding column\nThere are several useful visual features to tell there is 'fencepost' and not similar things in a photo:\tpost-like shape, taller than wide\tconnected to a fence or a wire\thorizontal rails attached to it\tvisible signs of wear or aging (cracks, peeling, rust)", 9], "bamboo plant": ["Yes. 'Bamboo plant' has a tangible appearance and is a type of plant.\nA few things that are visually similar to 'bamboo plant' but are not 'bamboo plant' are:\tsugar cane\tcorn stalks\tpapyrus\treeds\nThere are several useful visual features to tell there is 'bamboo plant' and not similar things in a photo:\ttall, slender stems\tleaves in the shape of thin, pointed blades\tsections or nodes along the stem\thollow stem\tthat grows in clusters or groves", 9], "gold color": ["Yes. 'Gold color' has a tangible appearance and is a specific shade of metallic yellow.\nA few things that are visually similar to 'gold color' but are not 'gold color' are:\tbrass\tcopper\tbright yellow\tmustard yellow\nThere are several useful visual features to tell there is 'gold color' and not similar things in a photo:\tmetallic sheen\tyellow with a hint of orange\tshimmer or glitter\teffect of reflecting light", 9], "sun glaring": ["Yes. 'Sun glaring' has a tangible appearance.\nA few things that are visually similar to 'sun glaring' but are not 'sun glaring' are:\twhite light bulb\tflashlight\tcar headlight\tfireworks\nThere are several useful visual features to tell there is 'sun glaring' and not similar things in a photo:\tbright and intense light\tsource is the sun\tmay cause lens flares or sunbursts or cast shadows from objects in the photo.", 9], "photo frames": ["Yes. 'Photo frames' has a tangible appearance and is a kind of decoration.\nA few things that are visually similar to 'photo frames' but are not 'photo frames' are: artwork frames, mirrors, chalkboards, bulletin boards, wall shelves\nThere are several useful visual features to tell there is 'photo frames' and not similar things in a photo:\nrectangular or square shape\nsurrounding a photograph or artwork\nvarious styles and colors\nhanging on a wall or standing on a surface", 9], "bread slice": ["Yes. 'Bread slice' has a tangible appearance and is a type of food.\nA few things that are visually similar to 'bread slice' but are not 'bread slice' are:\tcake layer\tpizza slice\ttortilla\tchips\nThere are several useful visual features to tell there is 'bread slice' and not similar things in a photo:\trectangle-shaped\twith crust on one or both sides\tspongy texture\tusually beige or brown in color", 9], "radiators": ["Yes. 'Radiators' has a tangible appearance and is a heating device.\nA few things that are visually similar to 'radiators' but are not 'radiators' are:\theaters\tfurnaces\tair conditioners\tfans\nThere are several useful visual features to tell there is 'radiators' and not similar things in a photo:\tmetallic appearance\twith ribs or fins\tfor heating spaces\tlocating below the window or on the wall", 9], "railroad cars": ["Yes. 'Railroad cars' has a tangible appearance and is a type of transportation.\nA few things that are visually similar to 'railroad cars' but are not 'railroad cars' are:\tsemi-trucks\tshipping containers\ttrailers\nThere are several useful visual features to tell there is 'railroad cars' and not similar things in a photo:\tattached to a locomotive\trunning on rails\tlong, rectangular shape\twithout wheels, except for a wheel truck at each end.", 9], "button keyboard": ["Yes. 'Button keyboard' has a tangible appearance and refers to a physical keyboard with buttons.\nA few things that are visually similar to 'button keyboard' but are not 'button keyboard' are: touch screen device, trackpad, mouse, joystick, game controller\nThere are several useful visual features to tell there is a 'button keyboard' and not similar things in a photo:\tphysical keys\tletter, number, or symbol characters\ton a flat surface\twires or USB port for connectivity to a device.", 9], "surveillance camera": ["Yes. 'Surveillance camera' has a tangible appearance and is a type of camera used for monitoring or recording.\nA few things that are visually similar to 'surveillance camera' but are not 'surveillance camera' are:\tregular camera\twebcam\tcamcorder\tsecurity light\tfloodlight\nThere are several useful visual features to tell there is 'surveillance camera' and not similar things in a photo:\tusually dome-shaped, bullet-shaped, or box-shaped\tmounted on a wall or ceiling\ta visible lens or camera body\twithout straps or detachable parts\tinfrared lights or other sensors that detect motion or activity", 9], "clock minute hand": ["Yes. 'Clock minute hand' has a tangible appearance and is a component of a clock.\nA few things that are visually similar to 'clock minute hand' but are not 'clock minute hand' are:\tclock hour hand\tthermometer needle\tcompass needle\nThere are several useful visual features to tell there is 'clock minute hand' and not similar things in a photo:\tthin and elongated shape\tcontinuous movement, often rotating around a dial\tnumerical markings dividing a dial into 60 equal parts\tlength extending to the outer edge of the dial", 9], "orange bricks": ["Yes. 'Orange bricks' has a tangible appearance and refers to bricks that are colored orange.\nA few things that are visually similar to 'orange bricks' but are not 'orange bricks' are:\tred bricks\tconcrete blocks\torange painted walls\tcoated metals\nThere are several useful visual features to tell there are 'orange bricks' and not similar things in a photo:\trectangular shape\ttexture of bricks\torange color (not painted)\tlaid in a particular pattern and stacked on top of each other.", 9], "bathroom lights": ["Yes. 'Bathroom lights' have a tangible appearance and are a specific type of lighting fixture.\nA few things that are visually similar to 'bathroom lights' but are not 'bathroom lights' are:\tlight bulbs\tceiling lights\tdesk lamps\tfloor lamps\nThere are several useful visual features to tell there are 'bathroom lights' and not similar things in a photo:\tmounted on or near a bathroom mirror\twater-resistant or waterproof\tflush or semi-flush mounted\tbright and focused lighting for grooming and applying makeup.", 9], "granite counter": ["Yes. 'Granite counter' has a tangible appearance and is a kind of countertop.\nA few things that are visually similar to 'granite counter' but are not 'granite counter' are:\tmarble counter\tquartz counter\tlaminate counter\twooden counter\nThere are several useful visual features to tell there is 'granite counter' and not similar things in a photo:\tvariegated or patterned surface \tspeckled appearance\tdark-colored surface\tsolid and dense surface\tnatural stone appearance", 9], "arch doorway": ["Yes. 'Arch doorway' has a tangible appearance and is a type of architectural detail.\nA few things that are visually similar to 'arch doorway' but are not 'arch doorway' are:\trectangular doorway\toval window\tcurved staircase\tbridge arch\nThere are several useful visual features to tell there is 'arch doorway' and not similar things in a photo:\tcurved or pointed shape\thigher at the center than at the edges\tmade of stone, brick, or wood\tframed by columns or pillars", 9], "airplane kite": ["Yes. 'Airplane kite' has a tangible appearance and is a type of kite.\nA few things that are visually similar to 'airplane kite' but are not 'airplane kite' are:\tdragon kite\tdiamond kite\tbox kite\tbird kite\nThere are several useful visual features to tell there is 'airplane kite' and not similar things in a photo:\tthe shape of an airplane, including wings, tail and fuselage\tairplane decals or markings\tbright or bold colors\tstreamers or tails attached to the kite", 9], "blue strip": ["Yes. 'Blue strip' has a tangible appearance and is a type of object.\nA few things that are visually similar to 'blue strip' but are not 'blue strip' are:\tblue string\tblue ribbon\tblue paint\tblue marker\nThere are several useful visual features to tell there is 'blue strip' and not similar things in a photo:\tlong and narrow shape\tsolid blue color\tusually made from plastic or fabric", 9], "blue bench": ["Yes. 'Blue bench' has a tangible appearance and is a type of furniture.\nA few things that are visually similar to 'blue bench' but are not 'blue bench' are:\tblue sofa, blue chair, blue stool, blue ottoman, blue chaise lounge\nThere are several useful visual features to tell there is 'blue bench' and not similar things in a photo:\tprovides seating for more than one person\tlong, horizontal seat with a backrest\tmay or may not have armrests or cushions\tmade out of wood or metal, but painted blue", 9], "chest protector": ["Yes. 'Chest protector' has a tangible appearance and is a piece of protective gear.\nA few things that are visually similar to 'chest protector' but are not 'chest protector' are:\tshoulder pads\tbullet-proof vest\tshield\tarmor\nThere are several useful visual features to tell there is 'chest protector' and not similar things in a photo:\tcovers the chest area\tis made of hard plastic or foam\tpadded straps to secure it in place\tworn by athletes or workers for protection", 9], "cream pitcher": ["Yes. 'Cream pitcher' has a tangible appearance and is a specific type of kitchenware.\nA few things that are visually similar to 'cream pitcher' but are not 'cream pitcher' are:\tmilk jug\tteapot\tcoffee mug\tpitcher\nThere are several useful visual features to tell there is 'cream pitcher' and not similar things in a photo:\ta small pitcher\twith a spout\tfor holding cream\thandles\ton opposite sides of the pitcher.", 9], "orange surf board": ["Yes. 'Orange surf board' has a tangible appearance and is a specific type of object.\n\nA few things that are visually similar to 'orange surf board' but are not 'orange surf board' are:\torange kayaks,\torange canoes,\torange boats,\torange paddleboards.\n\nUseful visual features for distinguishing 'orange surf board' from the listed similar things in a photo are:\telongated oval shape, deckpad or wax on the top, colorful markings, one or more fins or skegs underneath, designed to be ridden while standing, typically made of foam or fiberglass, used for wave riding or water sports.", 9], "gray curb": ["Yes. 'Gray curb' has a tangible appearance and refers to a part of the street.\nA few things that are visually similar to 'gray curb' but are not 'gray curb' are:\tsidewalk\tpavement\tdriveway\nThere are several useful visual features to tell there is 'gray curb' and not similar things in a photo:\traised edge along the street\tgray color\tangled or sloped surface\tnext to a lane for cars or a road", 9], "cooks": ["No. 'Cooks' is too vague or abstract to be distinguished in a photo.", 9], "hole cover": ["Yes. 'Hole cover' has a tangible appearance and is a type of street furniture.\nA few things that are visually similar to 'hole cover' but are not 'hole cover' are:\tmanhole\tgrate\tdrain\tvent cover\nThere are several useful visual features to tell whether it is a 'hole cover' or not in a photo:\tcircular or rectangular shape\thave letters or geometric shapes on it\tmade of metal or concrete\thas small holes or slots for drainage or ventilation", 9], "sizes": ["No. 'Sizes' is too vague or abstract to be distinguished in a photo. \n\nNote: Sizes are relative and cannot be visually distinguished without a point of reference.", 9], "food menu": ["Yes. 'Food menu' has a tangible appearance and is often a physical document or board listing food options.\nA few things that are visually similar to 'food menu' but are not 'food menu' are:\tadvertisements\tsigns\tbrochures\nThere are several useful visual features to tell there is 'food menu' and not similar things in a photo:\tlist of food or drink options\tprices or descriptions for each item\tlayout that indicates it is a menu (such as sections or headings)\trestaurant or cafe branding or logo visible", 9], "cable wire": ["Yes. 'Cable wire' has a tangible appearance and refers to wires used in communications or electrical systems.\nA few things that are visually similar to 'cable wire' but are not 'cable wire' are:\trope\tbungee cord\those\tstring\nThere are several useful visual features to tell there is 'cable wire' and not similar things in a photo:\tcoaxial, ethernet, or power cable\trubber or plastic sheathing\tmetallic or copper core connectors on each end\tmulti-colored wires inside the sheathing", 9], "checks": ["Yes. 'Checks' has a tangible appearance and refers to a type of financial document with a specific design.\nA few things that are visually similar to 'checks' but are not 'checks' are:\tstripes\tplaid\thoundstooth\nThere are several useful visual features to tell there is 'checks' and not similar things in a photo:\trectangular shape\twith a specific design including lines, boxes and printed text\tbank account and routing number written in magnetic ink\tmay include a signature and date of issuance", 9], "ground beef": ["Yes. 'Ground beef' has a tangible appearance and is a type of meat.\nA few things that are visually similar to 'ground beef' but are not 'ground beef' are:\tmushrooms\twalnuts\ttv cables\nThere are several useful visual features to tell there is 'ground beef' and not similar things in a photo:\tminced meat\ttexture\tpink or brown color\twrapped in plastic or paper packaging.", 9], "colorful jacket": ["Yes. 'Colorful jacket' has a tangible appearance and is a type of clothing item.\nA few things that are visually similar to 'colorful jacket' but are not 'colorful jacket' are:\tsweater\tblouse\tshirt\tcoat\nThere are several useful visual features to tell there is 'colorful jacket' and not similar things in a photo:\t\n\n- It is a shorter outerwear garment designed to cover the torso\n- It has different bright and vivid colors\n- It can be made of different materials like denim, leather or wool.", 9], "light shade": ["Yes. 'Light shade' has a tangible appearance and refers to a cover for a light bulb.\nA few things that are visually similar to 'light shade' but are not 'light shade' are:\tlampshade\tbottle cap\tpaper cup\nThere are several useful visual features to tell there is 'light shade' and not similar things in a photo:\tfits over a light bulb\tdiffuses light\tdiffers in color, material, and shape from the light bulb itself\tcan be attached at the top or the bottom of the bulb depending on the design.", 9], "paw prints": ["Yes. 'Paw prints' has a tangible appearance and is a mark left by the foot of an animal.\nA few things that are visually similar to 'paw prints' but are not 'paw prints' are:\thuman footprints\tbike tire tracks\twheelchair tracks\ttracks left by a cart\nThere are several useful visual features to tell there are 'paw prints' and not similar things in a photo:\tfour distinct toe impressions\tsymmetrical\tprints that form a specific shape (e.g. a circle for a dog, a diamond for a raccoon)\tclaw marks on the front of each toe.", 9], "wood panel wall": ["Yes. 'Wood panel wall' has a tangible appearance and is a type of interior design.\nA few things that are visually similar to 'wood panel wall' but are not 'wood panel wall' are:\tconcrete wall\tbrick wall\tpainted wall\ttextured wallpaper\nThere are several useful visual features to tell there is 'wood panel wall' and not similar things in a photo:\thorizontal or vertical wooden planks\tdark natural wood color\tor lighter painted color\tvisible seams between the planks\twood grain texture\tpotentially with trim or molding around it.", 9], "blue emblem": ["Yes. 'Blue emblem' has a tangible appearance and is a type of insignia or symbol.\nA few things that are visually similar to 'blue emblem' but are not 'blue emblem' are:\tlogos\tbadges\tmedals\tdecorations\nThere are several useful visual features to tell there is 'blue emblem' and not similar things in a photo:\tblue color predominates the emblem\tsimple or intricate design\trepresents an organization, a country, or a brand\tsymmetrical patterns or shapes", 9], "desktop screen": ["Yes. 'Desktop screen' has a tangible appearance and can be easily recognized in a photo.\nA few things that are visually similar to 'desktop screen' but are not 'desktop screen' are:\tTV screens\tprojector screens\ttablets\tsmartphones\nThere are several useful visual features to tell there is 'desktop screen' and not similar things in a photo:\trectangular shape\tscreen with fixed position\thorizontal or vertical orientation\ttypically connected to a desktop computer or laptop.", 9], "wooden shelves": ["Yes. 'Wooden shelves' has a tangible appearance and is a kind of furniture.\nA few things that are visually similar to 'wooden shelves' but are not 'wooden shelves' are: bookcases, display cabinets, dressers, sideboards.\nThere are several useful visual features to tell there is 'wooden shelves' and not similar things in a photo: made of wood, flat shelves or platforms for holding objects, installed on a wall or standing units.", 9], "orange beverage": ["Yes. 'Orange beverage' has a tangible appearance and is a type of drink.\nA few things that are visually similar to 'orange beverage' but are not 'orange beverage' are:\tlemonade\tfruit punch\ticed tea\tinstant coffee\nThere are several useful visual features to tell there is 'orange beverage' and not similar things in a photo:\tbright orange color\tclear liquid\tfrothy or bubbly appearance\tserved in a glass or bottle with an orange slice as garnish", 9], "action": ["No. 'Action' is too vague or abstract to be distinguished in a photo.", 9], "accent pillows": ["Yes. 'Accent pillows' has a tangible appearance and refers to decorative pillows that are not primarily intended for functional use.\nA few things that are visually similar to 'accent pillows' but are not 'accent pillows' are:\tsleeping pillows\tcushions\tpillowcases\tstuffed animals\nThere are several useful visual features to tell there is 'accent pillows' and not similar things in a photo:\temphasis on decorative features (embroidery, beading, etc.)\tbright or contrasting colors and patterns\tsmaller size than typical sleeping pillows or cushions\tintended for use as decoration rather than support while sleeping.", 9], "bread crust": ["Yes. 'bread crust' has a tangible appearance and is a part of bread.\nA few things that are visually similar to 'bread crust' but are not 'bread crust' are:\tbark of a tree\tpizza crust\tpie crust\tskin of a roasted chicken\nThere are several useful visual features to tell there is 'bread crust' and not similar things in a photo:\tbrown or golden color\trough texture\tthinner and harder than the inside of the bread", 9], "headlight front car": ["Yes. 'Headlight front car' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'headlight front car' but are not 'headlight front car' are:\tstreetlamp\tmotorbike headlight\tflashlight\tcar fog lights\nThere are several useful visual features to tell there is 'headlight front car' and not similar things in a photo:\tlocated on the front of the car\tsometimes housed in a separate compartment\tusually comes in pairs\toften shaped like a rectangle or a circle\tbright and directed forwards.", 9], "bookshelf books": ["Yes. 'Bookshelf books' has a tangible appearance and refers to a group of books placed on a bookshelf.\nA few things that are visually similar to 'bookshelf books' but are not 'bookshelf books' are:\tbooks in a stack\ton a table\ton the floor\tin a library\nThere are several useful visual features to tell there are 'bookshelf books' and not similar things in a photo:\tarranged on a bookshelf\tvertical and horizontal books alternated on the shelves\tvariety of book sizes and colors", 9], "grey hoodie": ["Yes. 'Grey hoodie' has a tangible appearance and is a type of clothing.\nA few things that are visually similar to 'grey hoodie' but are not 'grey hoodie' are:\tgrey sweatshirt\tgrey jacket\tgrey sweater\tgrey cardigan\nThere are several useful visual features to tell there is 'grey hoodie' and not similar things in a photo:\thood attached to the neck area\tfront pocket on the lower torso area\tlong sleeves with cuffs on the wrists\trange of shades of grey\tcolor and texture of the fabric\tspecific logo or design on the hoodie, if applicable.", 9], "cat drinking water": ["Yes. 'Cat drinking water' has a tangible appearance and can be captured in a photo.\nA few things that are visually similar to 'cat drinking water' but are not 'cat drinking water' are:\tcat's face close-up\tfeline grooming\thumans drinking water\tother animals drinking water\nThere are several useful visual features to tell there is 'cat drinking water' and not similar things in a photo:\tfeline tongue\tdroplets of water\tthe cat is close to a water source", 9], "middle elephant": ["No. 'Middle elephant' is too vague or abstract to be distinguished in a photo.", 9], "air condition": ["Yes. 'Air condition' has a tangible appearance and is an electrical appliance.\nA few things that are visually similar to 'air condition' but are not 'air condition' are:\tfan\theater\thumidifier\tportable air cooler\nThere are several useful visual features to tell there is 'air condition' and not similar things in a photo:\twall-mounted or window-mounted unit\tventilation grilles and knobs for adjusting temperature and air flow\tno visible heating elements or water tank", 9], "tall hills": ["No. 'Tall hills' is too vague or abstract to be distinguished in a photo. \n\nNote: Hills are generally not considered to be tall. Instead, the concept of \"tall hills\" would likely be more accurately described as \"mountains.\"", 9], "grey trash bin": ["Yes. 'Grey trash bin' has a tangible appearance and is a type of receptacle for waste.\nA few things that are visually similar to 'grey trash bin' but are not 'grey trash bin' are:\trecycling bin\tlaundry basket\toutdoor planter\tchest of drawers\nThere are several useful visual features to tell there is 'grey trash bin' and not similar things in a photo:\trectangular or cylindrical shape\tgrey color\tlifting lid\tor top opening\tsome form of 'trash', 'waste', 'garbage', or 'recycling' labeling or symbols on it.", 9], "silver leg": ["Yes. 'Silver leg' has a tangible appearance and refers to a leg that is made of silver or has a silver appearance.\nA few things that are visually similar to 'silver leg' but are not 'silver leg' are:\trobot leg\tchrome leg\tchrome prosthetic leg\nThere are several useful visual features to tell there is 'silver leg' and not similar things in a photo:\tsilver metallic color\tor made of silver material\tmetallic shine and reflection\tthat it is a human leg or prosthetic leg", 9], "pizza oven": ["Yes. 'Pizza oven' has a tangible appearance and is a type of cooking appliance.\nA few things that are visually similar to 'pizza oven' but are not 'pizza oven' are:\tregular oven\tbarbecue grill\tfireplace\nThere are several useful visual features to tell there is 'pizza oven' and not similar things in a photo:\twood-fired or brick oven\tcircular or rectangular shape\topening or door on the front to insert the pizza\tchimney or vent for smoke to escape", 9], "guest": ["No. 'Guest' is too vague or abstract to be visually distinguished in a photo. However, a person who is attending an event, party, or gathering could be considered as a 'guest'.\nA few things that are visually similar to a 'guest' but are not 'guest' are:\tspectator\tcustomer\tpassenger\twitness\nThere are several useful visual features to tell there is a 'guest' and not similar things in a photo:\tdressed appropriately for the event\tpresence in the place where the event is taking place\tinteracting with other people as a part of the event", 9], "head wrap": ["Yes. 'Head wrap' has a tangible appearance and is a type of clothing accessory.\nA few things that are visually similar to 'head wrap' but are not 'head wrap' are:\thair scarf\tbandana\theadband\thelmet\that\nThere are several useful visual features to tell there is 'head wrap' and not similar things in a photo:\tworn around the head, covering hair\twide and long enough to wrap around the head\tties or knots in the back or on the side\tof different colors or patterns", 9], "purple fruit": ["Yes. 'Purple fruit' has a tangible appearance and is a fruit with a purple color.\nA few things that are visually similar to 'purple fruit' but are not 'purple fruit' are:\tplum\tblackberry\tpomegranate\tfig\tgrape\nThere are several useful visual features to tell there is 'purple fruit' and not similar things in a photo:\tpurple flesh\twith or without seeds\tsmooth or textured skin\tround, oval or oblong shape", 9], "wooden block": ["Yes. 'Wooden block' has a tangible appearance and is a physical object.\nA few things that are visually similar to 'wooden block' but are not 'wooden block' are:\twooden cube\tbrick\tbox\nThere are several useful visual features to tell there is 'wooden block' and not similar things in a photo:\trectangular-shaped\twooden material\tcan be used as a toy or building block sometimes has numbers or letters printed on it.", 9], "front bumper": ["Yes. 'Front bumper' has a tangible appearance and is a part of a vehicle.\nA few things that are visually similar to 'front bumper' but are not 'front bumper' are:\tgrill\tfront air intake\tfront spoiler\tfront fascia\nThere are several useful visual features to tell there is 'front bumper' and not similar things in a photo:\tattached to the front of the vehicle\tlocated below the grille or headlight area\thorizontal or slightly tilted forward\trigid or cushioned to protect against impact", 9], "plastic plates": ["Yes. 'Plastic plates' has a tangible appearance and is a type of dishware.\nA few things that are visually similar to 'plastic plates' but are not 'plastic plates' are:\t paper plates\t ceramic plates\t metal plates\t glass plates\nThere are several useful visual features to tell there is 'plastic plates' and not similar things in a photo:\tthin and lightweight\tsmooth surface and texture\tvisible seams or ridges on the edge\tmolded shape or pattern, but not decorative designs like flowers or scrolling.", 9], "capped": ["No. 'Capped' is too vague or abstract to be distinguished in a photo.", 9], "gooey cheese": ["Yes. 'Gooey cheese' has a tangible appearance.\nA few things that are visually similar to 'gooey cheese' but are not 'gooey cheese' are:\tyellow slime\tlava\tmelted wax\nThere are several useful visual features to tell there is 'gooey cheese' and not similar things in a photo:\tcreamy texture\tstretching capability\tsticky consistency\tyellow to white color", 9], "toilet roll holder": ["Yes. 'Toilet roll holder' has a tangible appearance and is a bathroom accessory.\nA few things that are visually similar to 'toilet roll holder' but are not 'toilet roll holder' are:\tshower curtain rod\thand towel holder\tclothes hanger\nThere are several useful visual features to tell there is 'toilet roll holder' and not similar things in a photo:\tcircular shape\twith a spindle or rod to hold the toilet paper\tclosely attached to the bathroom wall or cabinet.", 9], "silver dinner fork": ["Yes. 'Silver dinner fork' has a tangible appearance and is a type of eating utensil.\nA few things that are visually similar to 'silver dinner fork' but are not 'silver dinner fork' are:\tspoon\tknife\tchopstick\ttongs\nThere are several useful visual features to tell there is 'silver dinner fork' and not similar things in a photo:\tfour-pointed\ttines or prongs\tmetallic or reflective surface\tstraight or curved stem and handle", 9], "male child": ["Yes. 'Male child' has a tangible appearance and refers to a young boy.\nA few things that are visually similar to 'male child' but are not 'male child' are:\tadult male with youthful appearance\ttoddler\tbaby\nThere are several useful visual features to tell there is 'male child' and not similar things in a photo:\tlooks young and childish\tmale gender\texhibits signs of growth such as developing muscle mass or height\tshort hair, no beard or mustache\tclothing is appropriate for a child, such as colorful or cute patterns.", 9], "kite handle": ["Yes. 'Kite handle' has a tangible appearance and is a type of equipment used to fly a kite.\nA few things that are visually similar to 'kite handle' but are not 'kite handle' are:\tfishing rod handle\tumbrella handle\tbroomstick handle\nThere are several useful visual features to tell there is 'kite handle' and not similar things in a photo:\tplastic or wooden material\thook or loop to attach kite string\tgraspable shape, usually with a finger holder\tfor kites with multiple lines, features to adjust and control kite direction and altitude", 9], "champagne bottle": ["Yes. 'Champagne bottle' has a tangible appearance and is a type of bottle.\nA few things that are visually similar to 'champagne bottle' but are not 'champagne bottle' are:\twine bottle\toil bottle\tperfume bottle\nThere are several useful visual features to tell there is 'champagne bottle' and not similar things in a photo:\ttall and slender shape\tbulging center\tbody made of clear or green glass\tcork stopper with a wire cage\tfizzy liquid inside", 9], "hospital room": ["Yes. 'Hospital room' has a tangible appearance and is a specific type of room in a hospital setting.\nA few things that are visually similar to 'hospital room' but are not 'hospital room' are:\thotel room\tbedroom\tclassroom\toffice room\nThere are several useful visual features to tell there is 'hospital room' and not similar things in a photo:\thospital bed\tmedical equipments like IV poles, heart rate monitors, etc.\tsterile-looking surfaces\tand often has a sink, a small TV set, and a window.", 9], "suitcase handle": ["Yes. 'Suitcase handle' has a tangible appearance and is a specific part of a suitcase.\nA few things that are visually similar to 'suitcase handle' but are not 'suitcase handle' are:\tcabinet handle\tdoor handle\tdrawer handle\nThere are several useful visual features to tell there is 'suitcase handle' and not similar things in a photo:\tattached to a suitcase\tluggage tag or sticker on the suitcase\tnext to zippers or other suitcase compartments tends to have a retractable mechanism", 9], "mauve": ["Yes. 'Mauve' has a tangible appearance and is a specific color.\nA few things that are visually similar to 'mauve' but are not 'mauve' are:\tpurple\tpink\tlilac\tviolet\nThere are several useful visual features to tell there is 'mauve' and not similar things in a photo:\ta grayish/pale purple hue\ta tone between pink and purple colors\tdesaturated or muted color with a hint of grey or brown tones", 9], "wrist wrap": ["Yes. 'Wrist wrap' has a tangible appearance and is a type of accessory.\nA few things that are visually similar to 'wrist wrap' but are not 'wrist wrap' are:\tbracelet\twatch\tbandage\nThere are several useful visual features to tell there is 'wrist wrap' and not similar things in a photo:\tworn around the wrist\tstraps or bands\tfor fitness or support purposes\tvariety of colors or patterns", 9], "computer key board": ["Yes. 'Computer keyboard' has a tangible appearance and is an electronic device.\nA few things that are visually similar to 'computer keyboard' but are not 'computer keyboard' are:\ttypewriter\tkeypad for cellphone or calculator\nThere are several useful visual features to tell there is 'computer keyboard' and not similar things in a photo:\tletter and number keys\tfunction keys\tfor control and navigation\tpunctuation keys\tfor typing symbols and spaces\tbacklit or illuminated keys\tfor use in low light situations", 9]} \ No newline at end of file diff --git a/tools/files/lvis.v1.tmp0.ada.json b/tools/files/lvis.v1.tmp0.ada.json new file mode 100644 index 0000000000000000000000000000000000000000..0610bd836a3ba9692f0f01c54ad999a7eaeb2fdb --- /dev/null +++ b/tools/files/lvis.v1.tmp0.ada.json @@ -0,0 +1 @@ +[{"object": "aerosol_can", "object_id": 1, "gpt3_output": "\n\n{\"type\": \"vapour\", \"description\": \"white, with a light blue top\", \"similar objects\": [\"soda can\", \"gas can\", \"gas station\", \"cigarette\"]}"}, {"object": "air_conditioner", "object_id": 2, "gpt3_output": "\n\nThe model should focus on those objects that have a type of \"object\" in the input, with that type being listed under the \"type\" field. For this example, the model would focus on the zucchini and the ambulance."}, {"object": "airplane", "object_id": 3, "gpt3_output": "\n\n{\"type\": \"aircraft\", \"description\": \"black, white, and red; has a wingspan; can fly\", \"similar objects\": [\"bomber\", \"pilots\", \"pilots\"]"}, {"object": "alarm_clock", "object_id": 4, "gpt3_output": "\n\n{\"type\": \"telecommunications\", \"description\": \"round, has a beep sound\", \"similar objects\": [\"beacon\", \"siren\"]"}, {"object": "alcohol", "object_id": 5, "gpt3_output": "\n\n{\"type\": \"alcohol\", \"description\": \"white, has a gootrope at the end\", \"similar objects\": [\"soda\", \"wine\", \"screwdriver\"]}"}, {"object": "alligator", "object_id": 6, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"black, white, and green; has a long tail\", \"similar objects\": [\"gill\", \"chub\", \"gilliam\"]"}, {"object": "almond", "object_id": 7, "gpt3_output": "\n\n{\"type\": \"almond\", \"description\": \"almond; has a sweet flavor\", \"similar objects\": [\"almond milk\", \"almond bowl\", \"almond milk can\"]}"}, {"object": "ambulance", "object_id": 8, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a glaring siren; could with a stretcher\", \"similar objects\": [\"police car\", \"taxi\", \"garbage truck\"]}"}, {"object": "amplifier", "object_id": 9, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are affected by the same force and volume. The model should also be able todetermine that these objects are affected by the same disease and are related to the same problem."}, {"object": "anklet", "object_id": 10, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a strap\", \"similar objects\": [\" anklet \", \"bangle \", \" anklet \"],"}, {"object": "antenna", "object_id": 11, "gpt3_output": "\n\nThe input should focus on features that are common to all objects, such as smooth, cylindrical objects with green or brown stems, and objects that have a green or brown stem but are not round or smooth."}, {"object": "apple", "object_id": 12, "gpt3_output": "\n\nThe objects in the input could be objects that are all objects, such as a zucchini and a tomato. Alternatively, the input could be a health supplement, like a apple."}, {"object": "applesauce", "object_id": 13, "gpt3_output": "\n\n{\"type\": \"servings\", \"name\": \"savedss\", \"data\": \"\n\nThe objects in the input can be either objects (vegetables, animals, etc.), or names (zebra, apple, banana, pear). The models that should focus on for an input with that type of content are those that are responsible for creating and maintaining names for input names."}, {"object": "apricot", "object_id": 14, "gpt3_output": "\n\n{\"type\": \"image\", \"description\": \"A apricot.png\", \"size\": \"\n\nA apricot.png"}, {"object": "apron", "object_id": 15, "gpt3_output": "\n\n{\"type\": \"appearance\", \"description\": \"round, smooth, has a handle\", \"similar objects\": [\"coaster\", \"trucker's bus\", \"tourist information card\"]}"}, {"object": "aquarium", "object_id": 16, "gpt3_output": "\n\n{\"type\": \"aquarium\", \"description\": \"black and white, with a clear top\", \"similar objects\": [\"giraffe\", \" dolphin \", \" \n\" tank \"}\""}, {"object": "arctic_(type_of_shoe)", "object_id": 17, "gpt3_output": "\n\n{\"type\": \"footwear\", \"description\": \"crystals and balls; ice and snow; winter\"; \"similar objects\": [\"snowboard\", \"shoe\", \"bunny\", \"cat\"]}"}, {"object": "armband", "object_id": 18, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth texture, and the green color. The model should be able to identify zucchini as a food item, but should not be able to identify the object itself."}, {"object": "armchair", "object_id": 19, "gpt3_output": "\n\n{\"type\": \"toy\", \"description\": \"round, has a handle\", \"similar objects\": [\"arm\", \"chair\", \"toy\"]"}, {"object": "armoire", "object_id": 20, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red; has a handle; could have a lid\", \"similar objects\": [\"mirror\", \"coaster\", \"trucker's bus\"]}"}, {"object": "armor", "object_id": 21, "gpt3_output": "\n\n{\"type\": \"armour\", \"description\": \"black, with a red stripe running down the middle\", \"similar objects\": [\"pantaloons\", \"turban\", \"eyebrows\", \"hat\"]}"}, {"object": "artichoke", "object_id": 22, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for artichokes is their similarity objects. This means that the model should look for things that are like artichokes, such as having their shape, size, and color."}, {"object": "trash_can", "object_id": 23, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "ashtray", "object_id": 24, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"ashtray\", \"description\": \"\n\nThe features of an object detection model that focuses on are needed for objects that are cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves."}, {"object": "asparagus", "object_id": 25, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"asparagus\", \"zucchini\", \"gazelle\"]}"}, {"object": "atomizer", "object_id": 26, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like an atomizer are making it round and having its top be clear, as well as making the body flat and have its sides made of material that is non-metallic."}, {"object": "avocado", "object_id": 27, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a green skin and green seeds\", \"similar objects\": [\"grape\", \"butterfly\", \"chicken\"]}"}, {"object": "award", "object_id": 28, "gpt3_output": "\n\n{\"type\": \"gift\", \"name\": \"Gift of the award\", \"value\": \"\", \"similar objects\": [\"gift card\", \"gift box\"]}"}, {"object": "awning", "object_id": 29, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like an apple would be the shape of the apple, the size of the apple, and the color of the apple."}, {"object": "ax", "object_id": 30, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for objects is the object's type. For example, an object that is looking for objects would include vegetables. The type of an object would focus on its type, such as a vegetable for object detection models that are looking for objects, the handle for example."}, {"object": "baboon", "object_id": 31, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"light brown, black, smooth; could have a scaly surface\", \"similar objects\": [\"baboon\", \"cat\", \"otter\"]}"}, {"object": "baby_buggy", "object_id": 32, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a gear wheel\", \"similar objects\": [\"car seat\", \"wading pool\", \"infant pool\"]}"}, {"object": "basketball_backboard", "object_id": 33, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"backboard\", \"description\": \"a small, round, metal board with a clear plastic cover\", \"similar objects\": [\"sportscar\", \"pump\", \"pumpa\"]"}, {"object": "backpack", "object_id": 34, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "handbag", "object_id": 35, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"handbag\", \"description\": \"\"}"}, {"object": "suitcase", "object_id": 36, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red, with a handle\", \"similar objects\": [\"key\", \"wallet\", \"wallet case\"]}"}, {"object": "bagel", "object_id": 37, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"white, with a hole in the top\", \"similar objects\": [\"chocolate\", \"flour\", \"flourish\"]"}, {"object": "bagpipe", "object_id": 38, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its history of playing bagpipe. The bagpipe is a popular music instrument that is played with a wand or stick. The model should also be good at playing the bagpipe, as well as other bagpipes, steel drums, and other bagpipe instruments."}, {"object": "baguet", "object_id": 39, "gpt3_output": "\n\n{\"type\": \"sunglasses\", \"description\": \"light blue, smooth, has a lens that can be opened\", \"similar objects\": [\"sunglasses\", \"sunglasses case\"]}"}, {"object": "bait", "object_id": 40, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, red, green, blue\", \"similar objects\": [\"gazelle\", \"rhinoceros\", \" elephant \", \"island\", \"cat\"]}"}, {"object": "ball", "object_id": 41, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "ballet_skirt", "object_id": 42, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"pink, with a blue and green striping\", \"similar objects\": [\"strawberry\", \"chocolate cake\", \"apple\"]}"}, {"object": "balloon", "object_id": 43, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "bamboo", "object_id": 44, "gpt3_output": "\n\n{\"type\": \"trees\", \"description\": \"round, green, has a stem; can be chopped into small pieces\", \"similar objects\": [\"maple\", \"honeydew\", \"grape\"]}"}, {"object": "banana", "object_id": 45, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"a fruit\", \"usage\": \"noun\"}\n\nInput:\n\n{\"type\": \"object\", \"description\": \"a vegetable\", \"usage\": \"noun\"}"}, {"object": "Band_Aid", "object_id": 46, "gpt3_output": "\n\nThe Band Aid model should focus on cylindrical objects with smooth stems, while the apple and banana models should focus on round objects with rough stems."}, {"object": "bandage", "object_id": 47, "gpt3_output": "\n\n{\"type\": \"text\", \"message\": \"blood\", \"type\": \"text\"}, {\"type\": \"text\", \"message\": \"blood\", \"type\": \"text\"},"}, {"object": "bandanna", "object_id": 48, "gpt3_output": "\n\n{\"type\": \"namestring\", \"name\": \"namestring\", \"email\": \"virginia@email.com\", \"has\": \"string\", \"description\": \"A bandanna is a piece of cloth with a name or address on it. It is used to keep track of notes or letters in a text editor.\""}, {"object": "banjo", "object_id": 49, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, with a white spot in the middle of the head\", \"similar objects\": [\"banana\", \"giraffe\", \"monkey\"]}"}, {"object": "banner", "object_id": 50, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"banana\", \"description\": \"\n\nThe zucchini and the apple both have a smooth surface and are of different sizes. The zebra has rough stems and could be sliced into round pieces. The banner has green leaves to give it an effect of smooth surface."}, {"object": "barbell", "object_id": 51, "gpt3_output": "\n\n{\"type\": \"barbell\", \"description\": \"black, with a sharp point\", \"similar objects\": [\"soup can\", \"fry pan\"]}"}, {"object": "barge", "object_id": 52, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"black, with a white/black hull\", \"similar objects\": [\"coaster\", \"ship\", \"coaster ride\"]}"}, {"object": "barrel", "object_id": 53, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a handle\", \"similar objects\": [\"barrel\", \"socket\", \"converter\", \"octopus\"]}"}, {"object": "barrette", "object_id": 54, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"red; has a small hole in the center\", \"similar objects\": [\"bead\", \"bunny\", \"flowers\"]}"}, {"object": "barrow", "object_id": 55, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "baseball_base", "object_id": 56, "gpt3_output": "\n\nThe objects in the input should be objects that are similar to each other. For example, the object \"type\" should focus on objects that are \"type\" objects. The objects \"description\" and \"similar objects\" should focus on objects that are objects that are similar to the object \"description.\""}, {"object": "baseball", "object_id": 57, "gpt3_output": "\n\nThe input should be a specific object, such as a zucchini. The output should be a list of objects that are similar to zucchini."}, {"object": "baseball_bat", "object_id": 58, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for baseball bats is the model's ability to identify objects that have baseball bats as a target.\n\nInput: fire hydrant, Output:\n\nThe feature that should focus on for an object detection model that is looking for fire hydrants is their ability to identify their target as being a fire hydrant."}, {"object": "baseball_cap", "object_id": 59, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a brimmed hat\", \"similar objects\": [\"hat\", \"sunglasses\", \"sunglasses case\"]}"}, {"object": "baseball_glove", "object_id": 60, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "basket", "object_id": 61, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Bread\", \"description\": \"round, smooth, has a top\", \"similar objects\": [\"bread\", \"bag\", \"bag of grapes\"]}"}, {"object": "basketball", "object_id": 62, "gpt3_output": "\n\n{\"type\": \"ball\", \"description\": \"light blue\", \"similar objects\": [\"ballpoint pen\", \"dip pen\"]}"}, {"object": "bass_horn", "object_id": 63, "gpt3_output": "\n\nThe focus of an object detection model's work should be on specific objects that are commonly used in an input of this type. These objects are called \"type objects.\" They are specific objects that are used in this input, and they have specific features that make them used that way. The focus of an object detection model's work should be on those type objects."}, {"object": "bat_(animal)", "object_id": 64, "gpt3_output": "\n\nThe bat (animal) feature should focus on the bat's body and wings, and its behavior should be described. The bat's wings could be dark, and its body could be smooth."}, {"object": "bath_mat", "object_id": 65, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Wok\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "bath_towel", "object_id": 66, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Bath towel\", \"material\", \"tool\", \"function\"}, \"type\", \"description\"}, \"color\"}, \"flavor\"}, \"ingredient\"}, \"use\"}\n\nThe following objects are all objects that can be identified by their features:\n\nInput: zucchini, Output: {\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]"}, {"object": "bathrobe", "object_id": 67, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Bathrobe\", \"type_name\": \"Object\"},\n\n {\"type\": \"object\", \"name\": \"Bathrobe\", \"type_name\": \"Object\"},"}, {"object": "bathtub", "object_id": 68, "gpt3_output": "\n\n{\"type\": \"bathroom\", \"description\": \"black and white, has a drain\", \"similar objects\": [\"bath\", \"toilet\", \" sink\"]}"}, {"object": "batter_(food)", "object_id": 69, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"dried batter; needs to be mixed with water\", \"similar objects\": [\"batter\", \"dough\", \"doughnut\", \"souvenir\"]}"}, {"object": "battery", "object_id": 70, "gpt3_output": "\n\n{\"type\": \"battery\", \"description\": \"green; has a long life\", \"similar objects\": [\"solar panel\", \"ellipet\", \"cellular phone\"]"}, {"object": "beachball", "object_id": 71, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for zucchini is its type. This model should be able to find zucchini that is of the type \"vegetable\". Additionally, the model should be able to find zucchini that iscyndrical, has a smooth surface, and is sliced into round pieces. The model should also be able to find zucchini that has green leaves."}, {"object": "bead", "object_id": 72, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Beads\", \"description\": \"Rings, Earrings, Necklaces, Earrings inGold, Silver,Platinum, palladium\" }"}, {"object": "bean_curd", "object_id": 73, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"a type of cheese made from a curd of casein and milk\", \"similar objects\": [\"butter\", \"gravy\", \"gouda\"]}"}, {"object": "beanbag", "object_id": 74, "gpt3_output": "\n\n{\"type\": \"beach ball\", \"description\": \"black, white, smooth\", \"similar objects\": [\"soup can\", \"balloon\", \"fishing net\"]}"}, {"object": "beanie", "object_id": 75, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a hole in the center\", \"similar objects\": [\" beanie \"],"}, {"object": "bear", "object_id": 76, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; could have a furrowed fur; could be could be tumbling in water\", \"similar objects\": [\"puma\", \"otter\", \"island\"]}"}, {"object": "bed", "object_id": 77, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "bedpan", "object_id": 78, "gpt3_output": "\n\n{\"type\": \"utility\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pantry\", \"fridge\", \"freeze\", \"uke\"]"}, {"object": "bedspread", "object_id": 79, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"bedspread\"},\n\n{\"type\": \"object\", \"name\": \"bed\", {\"type\": \"vegetable\"},\n\n{\"type\": \"object\", \"name\": \"bedspread\"},"}, {"object": "cow", "object_id": 80, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"giraffe\", \"elephant\", \"horse\"]}"}, {"object": "beef_(food)", "object_id": 81, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"beef\", \"chicken\", \"chicken salad\"]}"}, {"object": "beeper", "object_id": 82, "gpt3_output": "\n\nThe ability to identify objects by their sounds."}, {"object": "beer_bottle", "object_id": 83, "gpt3_output": "\n\n{\"type\": \"bottle\", \"name\": \"Cigarette\", \"style\": \"bottle\", \"similar objects\": [\"packet of cigarettes\", \"packet of cigars\"]}"}, {"object": "beer_can", "object_id": 84, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan, has a smooth surface\", \"similar objects\": [\"banana\", \"grocery store\", \"coaster\"]"}, {"object": "beetle", "object_id": 85, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for Vegetables is whether or not the object is cylindrical, with a smooth surface. The object should also be able to have different colors, different descriptions, and have similar objects as other objects."}, {"object": "bell", "object_id": 86, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its size. An object's size should be able to be determined based on its dimensions, such as its diameter or length. Additionally, an object's shape should be known, such as the way it turns when picked up. Other features that may be looked for include its color, texture, or size."}, {"object": "bell_pepper", "object_id": 87, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is its size. Other features that might be important for an object detection model to focus on include its shape, color, and size."}, {"object": "belt", "object_id": 88, "gpt3_output": "\n\n{\"type\": \" belt \", \"description\": \"black, with a red band\", \"similar objects\": [\"sling\", \" belt \"\", \" belt \"\", \" belt \"\", \" belt \"\", \" belt \"\", \" belt \"\", \" belt \"\", \" belt \"\", \" belt \"\", \" black \"\", \" red \"\", \" green \"\", \" blue \"\", \" orange \"\", \" vanilla \"\", \"ittee\"]}"}, {"object": "belt_buckle", "object_id": 89, "gpt3_output": "\n\n{\"type\": \"beloved\", \"image\", \"preference\": \"image\", \"similar objects\": [\"coaster\", \"bucket\"]}"}, {"object": "bench", "object_id": 90, "gpt3_output": "\n\n{\"type\": \"bed\", \"description\": \"black, with a red upholstery\", \"similar objects\": [\"cage\", \"bench\", \"towel\"]"}, {"object": "beret", "object_id": 91, "gpt3_output": "\n\nThere are many different types of object detection models that could be used for an input of zucchini, apple, ambulance, beret, and lantern. The focus could be on those models that have smooth surfaces, cylindrical shapes, and green or similar colors."}, {"object": "bib", "object_id": 92, "gpt3_output": "\n\n{\"type\": \"textbook\", \"id\": \"102059\", \"location\": \"102059\", \"published\": \"2010-12-15\", \"title\": \"The Art of Cookery\", \"published_by\": \"www.eatwell.org\", \"type\": \"book\", \"sizes\": \"large\", \"price\": \"www.eatwell.org\", \"weight\": \"www.eatwell.org\", \"a4qids\": \"102059\"}\n\nThe Art of Cookery by\n\nwww.eatwell.org\n\nThe Art of Cookery is"}, {"object": "Bible", "object_id": 93, "gpt3_output": "\n\nThe input should be a list of objects that all have a similar shape and color. For example, the zucchini should have a round shape, the apple should have a brown shape, and the ambulance should have smooth sides and a stretcher for a vehicle."}, {"object": "bicycle", "object_id": 94, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its ability to identify similar objects. Object detection models that are looking for evidence of damage or illness will likely focus on objects with similar features and patterns."}, {"object": "visor", "object_id": 95, "gpt3_output": "\n\n{\"type\": \"face\", \"description\": \"opaque; small eyes; has a long snout\", \"similar objects\": [\"seaweed\", \"sunglasses\", \"sunglasses case\"]}"}, {"object": "billboard", "object_id": 96, "gpt3_output": "\n\n{\"type\": \"image\", \"uuid\": \"cwc4d8b2-fcd4-11e8-8aon-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd-v9jd"}, {"object": "binder", "object_id": 97, "gpt3_output": "\n\n{\"type\": \"book\", \"description\": \"round, has a cover\", \"similar objects\": [\"patent book\", \"patent office\", \"recliner\", \"bed\", \"trucker\", \"container\", \"trucker truck\"]"}, {"object": "binoculars", "object_id": 98, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"black, with a red band around the pupil\", \"similar objects\": [\"umbrella\", \"scope\", \" binoculars\"]"}, {"object": "bird", "object_id": 99, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; could have a beak\", \"similar objects\": [\"parrot\", \"clam\", \"gazelle\"]}"}, {"object": "birdfeeder", "object_id": 100, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \"vegetable\", \"similar objects\": [\"fruit\", \"vegetable\", \"carrot\"]}"}, {"object": "birdbath", "object_id": 101, "gpt3_output": "\n\n{\"type\": \"aquarium\", \"water level\", \"status\": \" Absent\", \"similar objects\": [\"giraffe\", \"monkey\"]}"}, {"object": "birdcage", "object_id": 102, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Zucchini\", \"description\": \"The zucchini is a vegetable that can be found in the grocery store. It is smooth and has a green skin. The zucchini can be sliced into round pieces and has green leaves. The leaves could be sliced into brown pieces. The zucchini is a food that is high in nutrients and is a good source of dietary fiber. The zucchini is a good source of protein and has a lot of nutrients. The zucchini is a good source of nutrients and is a good source of protein.\""}, {"object": "birdhouse", "object_id": 103, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "birthday_cake", "object_id": 104, "gpt3_output": "\n\nThe object detection model should focus on objects that have the same type as the object it is trying to detect. For example, a zucchini would be a vegetable, while a birthday cake would be a cake, regardless of type."}, {"object": "birthday_card", "object_id": 105, "gpt3_output": "\n\n{\"type\": \"card\", \"description\": \"\", \"similar objects\": [\"card\", \"cake\", \"cat\"]}"}, {"object": "pirate_flag", "object_id": 106, "gpt3_output": "\n\nThe ambulance should focus on the round vehicle with the siren."}, {"object": "black_sheep", "object_id": 107, "gpt3_output": "\n\n{\"type\": \" animal \", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\" horse \", \"giraffe \", \"elephant \"],"}, {"object": "blackberry", "object_id": 108, "gpt3_output": "\n\n{\"type\": \"apple\", \"description\": \"reddish brown, has a smooth surface\", \"similar objects\": [\"appleseed\", \"grape\", \"grape\",\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \"grape\", \""}, {"object": "blackboard", "object_id": 109, "gpt3_output": "\n\n{\"type\": \"board\", \"description\": \"round, has a thin surface\", \"similar objects\": [\"board\", \"couch\", \"towel\"]"}, {"object": "blanket", "object_id": 110, "gpt3_output": "\n\n{\"type\": \" blankets \", \"description\": \"soft, thin, has a deep downwind corner\", \"similar objects\": [\"pilling\", \"douche\", \"cotton\"]}"}, {"object": "blazer", "object_id": 111, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"black, white, red; could have a deep V-shaped crease in the fabric\", \"similar objects\": [\"cardigan\", \"shirt\", \"coat\"]}"}, {"object": "blender", "object_id": 112, "gpt3_output": "\n\n{\"type\": \"blender\", \"description\": \"a tool that smooths out the surface of a drink\", \"similar objects\": [\"onion\", \"chopstick\", \"servevy\"]}"}, {"object": "blimp", "object_id": 113, "gpt3_output": "\n\n{\"type\": \"image\", \"description\": \"\", \"url\": \"http://www.gizmond.com/wp-content/uploads/2013/07/zucchini-image.jpg\", \"alt\": \"Zucchini Image\""}, {"object": "blinker", "object_id": 114, "gpt3_output": "\n\nThe model should focus on objects that have a specific properties, such as cylindrical, smooth, green, or rough stems. The model should also be able to create round pieces and green leaves."}, {"object": "blouse", "object_id": 115, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Blouse\", \"description\": \"A blouse is a shirt that is made of fabric, usually blue, that is worn over the back of it. The shirt is made of front and back necklines and has a collar and sleeves. The front and back necklines are usually different colors. The sleeves are usually short and wide.\"}"}, {"object": "blueberry", "object_id": 116, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "gameboard", "object_id": 117, "gpt3_output": "\n\n{\"type\": \"game\", \"name\": \"\", \"description\": \"\"}"}, {"object": "boat", "object_id": 118, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"black, with a white superstructure\", \"similar objects\": [\"coaster\", \"raft\", \"island\"]}"}, {"object": "bob", "object_id": 119, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like Bob are turning it into a model of Bob, producing a model of Bob from scratch, and learning how to make models of Bob from models of Bob."}, {"object": "bobbin", "object_id": 120, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to identify zucchini as a vegetable, the description should be accurate, and the model should be able to identify the ingredients of zucchini as well as the object's siblings."}, {"object": "bobby_pin", "object_id": 121, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"bobby pin\", \"description\": \"a small, round, black object\", \"similar objects\": [\"ballpoint pen\", \"pencil\", \"inking stock\"]}"}, {"object": "boiled_egg", "object_id": 122, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that finds eggs is the type of egg. Other features that could be included in an object detection model include the description, size, color, and potential for development."}, {"object": "bolo_tie", "object_id": 123, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"red; has a hanger for it to hang from\", \"similar objects\": [\"bunny\", \"honeycomb\", \"turban\"]}"}, {"object": "deadbolt", "object_id": 124, "gpt3_output": "\n\n{\"type\": \"lock\", \"description\": \"round, has a keyhole\", \"similar objects\": [\"keyboard\", \"lock\"]"}, {"object": "bolt", "object_id": 125, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small, black, has a hole in the head\", \"similar objects\": [\"keyboard\", \"tape measure\", \"tape\"], \"internal\":[]"}, {"object": "bonnet", "object_id": 126, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like an apple would be the shape of the apple, the size of the apple, and the color of the apple."}, {"object": "book", "object_id": 127, "gpt3_output": "\n\n{\"type\": \"book\", \"description\": \"The Catcher in the Rye\", \"similar objects\": [\"horses\", \"bat\", \"batchelor\"]}"}, {"object": "bookcase", "object_id": 128, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"bookcase\", \"description\": \"round, with a lid\", \"similar objects\": [\"coaster\", \"dungeon\", \"castle\"]}"}, {"object": "booklet", "object_id": 129, "gpt3_output": "\n\nThe objects in the input could be objects that are all objects, such as zucchini and apple, or you could focus on specific types of objects, such as ambulance and vehicle."}, {"object": "bookmark", "object_id": 130, "gpt3_output": "\n\n{\"type\": \"bookmark\", \"href\": \"https://www.britannica.com/biography/John-G-F-C-K-M-O-S-R-T-U-V-W-X-Y-Z-A-B-C-D-E-G-F-H-I-J-K-L-M-N-O-P-Q-R-S-T-U-V-W-X-Y-Z-A-C-D-E-G-F-H-I-J"}, {"object": "boom_microphone", "object_id": 131, "gpt3_output": "\n\nThe feature that should focus on for an input with a lot of content is the model's ability to detect objects. For objects, object detection models should be able to detect:\n\n-Cylindrical objects: This includes things like woks, cookers, and ambulances.\n\n-Objects that have a smooth surface: These include vegetables, fruits, and animals.\n\n-Objects that are round or have rough stems: These include zucchini, animals, and vegetables.\n\n-Objects that are sliced into round pieces: This includes the ambulance, boom microphone, and lantern."}, {"object": "boot", "object_id": 132, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"type_name\": \"vegetable\"},\n\n{\"type\": \"object\", \"name\": \"crab\", \"type_name\": \"crusty_crab\"},\n\n{\"type\": \"object\", \"name\": \"apple\", \"type_name\": \"fruit\"},\n\n{\"type\": \"object\", \"name\": \"giraffe\", \"type_name\": \"elephant\"},\n\n}"}, {"object": "bottle", "object_id": 133, "gpt3_output": "\n\n{\"type\": \"vitamin\", \"description\": \"a type of water droplet; contains a water droplet of a vitamin\", \"similar objects\": [\"drink\", \"bottle\", \"tea pot\"]}"}, {"object": "bottle_opener", "object_id": 134, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for bottles is the model's ability to identify bottles as being from the right side of the stove. \n\nInput: fire hydrant, Output: \n\nThe feature that should focus on for an object detection model that is looking for fire hydrants is their ability to identify them as being from the right side of the stove."}, {"object": "bouquet", "object_id": 135, "gpt3_output": "\n\n{\"type\": \"flower\", \"description\": \"red, with a white band around the center\", \"similar objects\": [\"honeydew\", \"snowdrop\", \"lily top\", \"violet top\"]}"}, {"object": "bow_(weapon)", "object_id": 136, "gpt3_output": "\n\n{\"type\": \"bow\", \"name\": \"arrow\", \"properties\": [\"vibration\", \"durability\", \"x\", \"y\", \"z\"]}"}, {"object": "bow_(decorative_ribbons)", "object_id": 137, "gpt3_output": "\n\n{\"type\": \"bow\", \"id\": \"10\", \"name\": \"Bows\", \"description\": \"\n\nBows are used to guide an user in bow-related tasks, such as making arrows or arrows with bows. Bowstrings and bowstrings with arrows are also bow features."}, {"object": "bow-tie", "object_id": 138, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"red; has a tie-dance with you\", \"similar objects\": [\"fishing net\", \"fishing rod\", \"fishing spear\"]}"}, {"object": "bowl", "object_id": 139, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "pipe_bowl", "object_id": 140, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"name\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\"}"}, {"object": "bowler_hat", "object_id": 141, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a bow at the back\", \"similar objects\": [\"bow\", \"hat\", \"ballpoint pen\"]}"}, {"object": "bowling_ball", "object_id": 142, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a handle and a ball\", \"similar objects\": [\"ball\", \"ballpoint pen\", \"ballpoint pen and pen\"]"}, {"object": "box", "object_id": 143, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "boxing_glove", "object_id": 144, "gpt3_output": "\n\n{\"type\": \" Goods \", \"description\": \"A glove with a ring around the inside of the hand\", \"similar objects\": [\"boxing gloves\", \"boxing fighter\", \"boxing movie\", \"boxing match\", \"boxing match in progress \", \"boxing gloves\", \"boxing match prediction\"]}"}, {"object": "suspenders", "object_id": 145, "gpt3_output": "\n\n{\"type\": \"clothes\", \"description\": \"black, white, red, green, blue\", \"similar objects\": [\"pants\", \"shoes\", \"sunglasses\"]}"}, {"object": "bracelet", "object_id": 146, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a strap\", \"similar objects\": [\"bracelet\", \"watch\"]"}, {"object": "brass_plaque", "object_id": 147, "gpt3_output": "\n\nThe plaque should have a smooth surface with some bumps and crevices. The plaque should also be of a high-quality, durable material that can be used for a long time."}, {"object": "brassiere", "object_id": 148, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a handle\", \"similar objects\": [\"bra\", \"braiding tool\"]}"}, {"object": "bread-bin", "object_id": 149, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "bread", "object_id": 150, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"bread dough\", \"similar objects\": [\"butter\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour\", \"flour"}, {"object": "breechcloth", "object_id": 151, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Breechcloth\", \"type_name\": \"Object\"},\n\nInput: soap, Output: \n\n{\"type\": \"object\", \"name\": \"Soap\", type_name: \"Object\"},"}, {"object": "bridal_gown", "object_id": 152, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red, with a beading detail\", \"similar objects\": [\"gift certificate\", \"bookmark\", \"cupcake\"]}"}, {"object": "briefcase", "object_id": 153, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"black, with a red lid\", \"similar objects\": [\"keyboard\", \"wallet\", \"text book\", \"cheese\"]"}, {"object": "broccoli", "object_id": 154, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "broach", "object_id": 155, "gpt3_output": "\n\nThere are many different objects that can be looked for in objects that are called \"objects\". These objects can be used for their surface features, which are features that a person looks for in objects to see if they are real. The objects that are called \"vegetables\" are things that have a surface feature, such as leaves or flowers. The objects that are called \"beef\" are features on a farm where they are used in cooking. These objects can have any surface feature, as long as it is not the surface feature of the beef itself."}, {"object": "broom", "object_id": 156, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "brownie", "object_id": 157, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Brownie\", \"description\": \"\n\nThe Brownie is a food product that is made from a mixture of sugar and butter. The butter makes the sugar into a spreadable product, and the sugar into a sweetened product. The Brownie has a deep well that allows it to extract liquid sugar from beets, apples, and other vegetables."}, {"object": "brussels_sprouts", "object_id": 158, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylicer, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"Brussels sprouts\"]}"}, {"object": "bubble_gum", "object_id": 159, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"\", \"type\": \"diet\", \"has\": [\"ingredient\"]}"}, {"object": "bucket", "object_id": 160, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "horse_buggy", "object_id": 161, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for zucchini is its type. The zucchini type should be known so that the model can be able to identify it as such. Additionally, the zucchini type should be smooth so that it will be easy to identify. Finally, it should be possible to have green and rough stems as well as round pieces."}, {"object": "bull", "object_id": 162, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, red, green, yellow\", \"similar objects\": [\"bulldog\", \"chicken\", \"gazelle\"]}"}, {"object": "bulldog", "object_id": 163, "gpt3_output": "\n\n{\"type\": \"dog\", \"description\": \"black, with a red fur\", \"similar objects\": [\"bulldog\", \"cat\", \"chicken\"]}"}, {"object": "bulldozer", "object_id": 164, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"duck\", \"gazelle\", \" elephant\"]}"}, {"object": "bullet_train", "object_id": 165, "gpt3_output": "\n\nThe feature of the bullet train that should focus on for an input with many similar objects is that it can move quickly and be able to carry many people."}, {"object": "bulletin_board", "object_id": 166, "gpt3_output": "\n\n{\"type\": \"text\", \"description\": \"A zucchini can be a vegetable or an animal.\n\" \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "bulletproof_vest", "object_id": 167, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of an input is the evidence of an input. This means that the model should be able to find evidence of an input if it is used to looking for it. The model should be able to find the evidence of an input if it is seen. This means that the model should be able to find the evidence of an input if it is felt. The model should be able to find the evidence of an input if it is smelled. The model should be able to find the evidence of an input if it is heard."}, {"object": "bullhorn", "object_id": 168, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for signs of life is the animal category. Other features that could be covered include the color of the object, the size of the object, and the shape of the object."}, {"object": "bun", "object_id": 169, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"light brown, dark brown\", \"similar objects\": [\"baked potato\", \"wedge\"]}"}, {"object": "bunk_bed", "object_id": 170, "gpt3_output": "\n\n{\"type\": \"bed\", \"description\": \"round, has a bed sheet on top\", \"similar objects\": [\"bed\", \"towel\", \"curtains\"]}"}, {"object": "buoy", "object_id": 171, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"a container for water or other things\", \"similar objects\": [\"vessel\", \"reinforcement\"]}"}, {"object": "burrito", "object_id": 172, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"pale yellow and black, with a filling\", \"similar objects\": [\"taco\", \"chicken\", \"noodle\"]}"}, {"object": "bus_(vehicle)", "object_id": 173, "gpt3_output": "\n\n{\"type\": \"bus\", \"description\": \"black, red, green, blue\", \"similar objects\": [\"bus\", \"train\", \"car\"]}"}, {"object": "business_card", "object_id": 174, "gpt3_output": "\n\n{\"type\": \"card\", \"description\": \"red; has a card hole in the center\", \"similar objects\": [\"card case\", \"wallet\", \"keyboard\"]"}, {"object": "butter", "object_id": 175, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"fat\", \"term\", \"name\": \"fat\", \"definition\": \"fat\"}"}, {"object": "butterfly", "object_id": 176, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"butterfly\", \"description\": \"black and white, has a wingspan and neck\", \"similar objects\": [\"fairytale\", \" dragon \", \" dragonfly \", \" kite\"]}"}, {"object": "button", "object_id": 177, "gpt3_output": "\n\n{\"type\": \"button\", \"id\": \"-\"}"}, {"object": "cab_(taxi)", "object_id": 178, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a signage on the side\", \"similar objects\": [\"cab\", \"trolley\", \"bus\"]}"}, {"object": "cabana", "object_id": 179, "gpt3_output": "\n\n{\"type\": \"pool\", \"description\": \"No space left for any other object\", \"similar objects\": [\"house\", \"pool\"]}"}, {"object": "cabin_car", "object_id": 180, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are in a similar location. The model should also be able to identify different types of objects, such as vegetables and animals."}, {"object": "cabinet", "object_id": 181, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Cucumber\"},\n{\"type\": \"item\", \"name\": \"Eggplant\"},\n{\"type\": \"item\", \"name\": \"Green Bean\"},\n{\"type\": \"item\", \"name\": \"Cangelo\"},\n{\"type\": \"item\", \"name\": \"Tomato\"},\n\nInput: onion, Output: \n\n{\"type\": \"ingredient\", \"name\": \"Onion\"},\n{\"type\": \"ingredient\", \"name\": \"Mint\"},\n{\"type\": \"ingredient\", \"name\": \""}, {"object": "locker", "object_id": 182, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cake", "object_id": 183, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"light brown, with a light frosting\", \"similar objects\": [\"fruitcake\", \"trucker cake\", \"peanut butter cake\"]}"}, {"object": "calculator", "object_id": 184, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its ability to identify and track down the sources of its evidence. The model should be able to identify and track down sources of evidence for the existence of life, such as a calculator that has a siren, a ambulance, or a light bulb."}, {"object": "calendar", "object_id": 185, "gpt3_output": "\n\nThe input should focus on specific features that are used to create a complete calendar. The model should be able to identify different days and pages. The pages and days should be clear and easily visible."}, {"object": "calf", "object_id": 186, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "camcorder", "object_id": 187, "gpt3_output": "\n\n{\"type\": \"camera\", \"description\": \"round, has a video camera at the top\", \"similar objects\": [\"television\", \" video camera \", \" digital camera ]}"}, {"object": "camel", "object_id": 188, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; has a hump\", \"similar objects\": [\"bison\", \"ox\", \"giraffe\"]}"}, {"object": "camera", "object_id": 189, "gpt3_output": "\n\n{\"type\": \"digital camera\", \"description\": \"round; can be recorded in 3 dimensions\", \"similar objects\": [\"television\", \" video camera \", \" digital camera ]}"}, {"object": "camera_lens", "object_id": 190, "gpt3_output": "\n\n{\"type\": \"image\", \"uuid\": \"cwc4d8b2-c3e0-11e4-8a8b-0050048c2d2\", \"height\": \"image\", \"filename\": \"zucchini.jpg\", \"width\": \"image\", \"caption\": \"zucchini, green, round, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\"}"}, {"object": "camper_(vehicle)", "object_id": 191, "gpt3_output": "\n\nThe feature that should focus on for an ambulance is the siren. The vehicle should focus on the deep handle and the round fruit."}, {"object": "can", "object_id": 192, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "can_opener", "object_id": 193, "gpt3_output": "\n\n{\"type\": \"mechanical tool\", \"description\": \"a tool that opens cans and other containers\", \"similar objects\": [\"pizza\", \"canned food\", \"canned beer\"]}"}, {"object": "candle", "object_id": 194, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" candle \", \"description\": \"a lighted candle\n\n\"}"}, {"object": "candle_holder", "object_id": 195, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" candle \", \"description\": \"\n\nThe feature that should focus object detection models for an input with a number of objects equal to or greater than the size of the object being detected."}, {"object": "candy_bar", "object_id": 196, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Candy Bar\", \"description\": \"\n\n\n\nThe feature that should focus on for an object detection model that is consuming a resource from a resource should be the object's identity. An object's identity should be able to be determined by its features, which should be smooth, cylindrical, and have no rough stems. The model should be able to identify the object's identity and the resources needed to identify the object's identity, which should be a mixture of green and brown stems and round pieces."}, {"object": "candy_cane", "object_id": 197, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"\", \"type\": \"yield\", \"yield\": \"\", \"similar objects\": [\"soda\", \"fruit juice\"]}"}, {"object": "walking_cane", "object_id": 198, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is walking a dog is its movement. The model should be able to move the dog body through the environment, moving it from one object to another. The model should also be able to identify different types of objects, such as children, parents, and other members of the family."}, {"object": "canister", "object_id": 199, "gpt3_output": "\n\nThe objects in the input can have features that are similar to those in the output. For example, the zucchini can have smooth cylindrical surfaces, the ambulance can have a shining siren, and the Lantern can have a round light."}, {"object": "canoe", "object_id": 200, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"black, with a red pontoon\", \"similar objects\": [\"canoe\", \"seaweed\", \"taco\", \"chicken\"]}"}, {"object": "cantaloup", "object_id": 201, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "canteen", "object_id": 202, "gpt3_output": "\n\n{\"type\": \"dining room\", \"description\": \"round, with a countertop\", \"similar objects\": [\"cantaloupe\", \"fryer\", \"toaster\"]}"}, {"object": "cap_(headwear)", "object_id": 203, "gpt3_output": "\n\n{\"type\": \"capsule\", \"description\": \"pills, sustenance\", \"similar objects\": [\"baked goods\", \"desserts\", \"beverages\"]}"}, {"object": "bottle_cap", "object_id": 204, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Bottlecap\", \"id\": \"Bottlecap\", \"position\": 1}"}, {"object": "cape", "object_id": 205, "gpt3_output": "\n\nThe feature that should focus on for an input with a zucchini would be its type: zucchini, while the feature that should focus on for an input with a red wok would be its type: wok."}, {"object": "cappuccino", "object_id": 206, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a little texture\", \"similar objects\": [\"zucchini\", \"carrot\", \"zucchini\"], \"similar objects\": [\"apple\", \"giraffe\", \"elephant\"]}"}, {"object": "car_(automobile)", "object_id": 207, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"black; has a lighted surface\", \"similar objects\": [\"car\", \"truck\", \"carriage\"]"}, {"object": "railcar_(part_of_a_train)", "object_id": 208, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a track\", \"similar objects\": [\"car\", \"train\", \" locomotive\"]}"}, {"object": "elevator_car", "object_id": 209, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"green; has a floor that goes up and down\", \"similar objects\": [\"ladle\", \"vase\", \"catcher\"]}"}, {"object": "car_battery", "object_id": 210, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"black; has a light engine; could be a car, truck, or bus\", \"similar objects\": [\"car\", \"truck\", \"bus\"]}"}, {"object": "identity_card", "object_id": 211, "gpt3_output": "\n\n{\"type\": \"card\", \"description\": \"KFC.\", \"similar objects\": [\"KFC\", \"card\", \"wallet\", \"frozen food\", \"baked food\", \"dessert\", \"dessert\", \"cake\", \"fruit\", \"fruit\", \"dessert\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ingredient\", \"ing"}, {"object": "card", "object_id": 212, "gpt3_output": "\n\n{\"type\": \"card\", \"description\": \"round, has a face down\", \"similar objects\": [\"cardigan\", \"wallet\", \"key\"]"}, {"object": "cardigan", "object_id": 213, "gpt3_output": "\n\n{\"type\": \"article\", \"description\": \"I have a cardigan.\"}, {\"type\": \"article\", \"description\": \"I have a cardigan.\"}, {\"type\": \"article\", \"description\": \"I have a cardigan.\"}, {\"type\": \"article\", \"description\": \"I have a cardigan.\"}, {\"type\": \"article\", \"description\": \"I have a cardigan.\"},"}, {"object": "cargo_ship", "object_id": 214, "gpt3_output": "\n\n{\"type\": \"vessel\", \"description\": \"no description\"}, {\"type\": \"object\", \"description\": \"no description\"}"}, {"object": "carnation", "object_id": 215, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for correlations between two objects is the presence of petals or stalks."}, {"object": "horse_carriage", "object_id": 216, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a horse and foot on the ground; can be pulled by a horse or a team\", \"similar objects\": [\"carriage\", \" coaches \", \" train \", \" bus \", \" airplane \", \" rocket \", \" satellite \"]"}, {"object": "carrot", "object_id": 217, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"carrot\", \"gazelle\", \"gazelle\", \" elephant\"]}"}, {"object": "tote_bag", "object_id": 218, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"tote bag\", \"description\": \"\n\n\n\nThe feature that should focus on for an input with a lot of change is the object's features. This will include all types of objects, from cylindrical objects to those that have a smooth surface. To find objects that look similar to the object you are looking for, you can use the \"similar objects\" feature."}, {"object": "cart", "object_id": 219, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "carton", "object_id": 220, "gpt3_output": "\n\n{\"type\": \"container\", \"name\": \" Carton \", \"size\": \" \"\", \"content\": \" Carton \", \"shipping charge\": \" \"\", \"logo\": \" Carton \"\", \"weight\": \" \"\", \"bargainness\": \" \"\", \"on Sale\": \"\", \"used In\": \"\", \"on Sale (2)\": \"\", \"used In (2)\": \"\", \"used In (1)\": \"\", \"used In (2)\": \"\", \"used In (1) (2)\": \"\", \"used In (1) (1"}, {"object": "cash_register", "object_id": 221, "gpt3_output": "\n\nThe model should focus on those items that have similar objects as it produces a list of items that are similar to those in the input."}, {"object": "casserole", "object_id": 222, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sour cream and jelly\", \"similar objects\": [\"dessert\", \"candy\", \"gummy bear\"]}"}, {"object": "cassette", "object_id": 223, "gpt3_output": "\n\n{\"type\": \"recorded file\", \"name\": \"I'm sorry\", \"filetype\": \"txt\", \"size\": \"\", \"comment\": \"I'm sorry\", \"is_embedded\": true, \"last_comment\": \"I'm sorry\", \"is_saved\": true\", \"type\": \"file\", \"name\": \"I'm sorry\"}"}, {"object": "cast", "object_id": 224, "gpt3_output": "\n\n{\"type\": \"cast\", \"description\": \"round\", \"similar objects\": [\"fiberglass\", \"cast iron\"]}"}, {"object": "cat", "object_id": 225, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, has a long tail\", \"similar objects\": [\"poodle\", \"yorkshire terrier\", \"cat\"]}"}, {"object": "cauliflower", "object_id": 226, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan; has a small head and low surface area; can be cooked in the oven\", \"similar objects\": [\"zucchini\", \"carrot\", \"zucchini pipe\"]}"}, {"object": "cayenne_(spice)", "object_id": 227, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"Paprika\", \"vibration\": \"spice\", \"taste\": \"tingly\", \"texture\": \"tingly\"},\n\nInput:\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "CD_player", "object_id": 228, "gpt3_output": "\n\n{\"type\": \"audio player\", \"description\": \"round, has a beep sound\", \"similar objects\": [\"play\" \"radio\"]}"}, {"object": "celery", "object_id": 229, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylicer, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"celery\", \"zucchini\"]}"}, {"object": "cellular_telephone", "object_id": 230, "gpt3_output": "\n\nThere are many different objects that can be used for input, such as objects that are used for growth or for protection. An object that is used for input should be cylindrical, have a smooth surface, and be smooth. There are also many different types of objects, including objects that are used for growth or protection. The different objects that are used for input should be listed as listed above, but some objects may be used for more than one input. For example, an ambulance may be used for both public transportation and for vehicle purposes."}, {"object": "chain_mail", "object_id": 231, "gpt3_output": "\n\n{\"type\": \"chain\", \"description\": \"smooth, has a long end\", \"similar objects\": [\"coaster\", \"maille\", \"chain\"]"}, {"object": "chair", "object_id": 232, "gpt3_output": "\n\n{\"type\": \"chair\", \"description\": \"black and white, has a backrest\", \"similar objects\": [\"furniture\", \"towel\", \"couch\",\"]}"}, {"object": "chaise_longue", "object_id": 233, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"black, smooth; could have bumps and crevices; could be cooked in water or oil\", \"similar objects\": [\"zucchini\", \"chicken\", \"grocery store],"}, {"object": "chalice", "object_id": 234, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \" stoppable, red, has a hinged lid\", \"similar objects\": [\"coaster\", \"diamond\", \"ruby\"]}"}, {"object": "chandelier", "object_id": 235, "gpt3_output": "\n\n{\"type\": \"chandelier\", \"description\": \"light blue; has a lighted top\", \"similar objects\": [\"chandelier\", \"lamp\"]}"}, {"object": "chap", "object_id": 236, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for candidates like zucchini is the shape of the object. Other features that might be important for this model include the size of the object, the color of the object, and the texture of the object."}, {"object": "checkbook", "object_id": 237, "gpt3_output": "\n\n{\"type\": \"account\", \"description\": \"A checkbook is a book with accounts in each page, each with a different information. The pages are usually written in a journal-like style, with differentdentures and catches. The book has a pad for storing values and a\n\n\"lesson\" for learning new accounts.\""}, {"object": "checkerboard", "object_id": 238, "gpt3_output": "\n\n{\"type\": \"image\", \"image\": [{\"z\": \"2\", \"image\": \"zucchini\", \"title\": \"zucchini\", \"alt\": \"vegetable\", \"size\": \"small\"}, {\"z\": \"3\", \"image\": \"green beans\", \"title\": \"green beans\", \"alt\": \"vegetable\", \"size\": \"small\"}, {\"z\": \"4\", \"image\": \"cucumber\", \"title\": \"cucumber\", \"alt\": \"vegetable\", \"size\": \"small\"}, {\"z\": \"5\", \"image\": \""}, {"object": "cherry", "object_id": 239, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"cherry\", \"banana\", \"pear\"]}"}, {"object": "chessboard", "object_id": 240, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\"}, \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]"}, {"object": "chicken_(animal)", "object_id": 241, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "chickpea", "object_id": 242, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"chickpea\", \"zucchini\", \"giraffe\"]}"}, {"object": "chili_(vegetable)", "object_id": 243, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "chime", "object_id": 244, "gpt3_output": "\n\n{\"type\": \"audio file\", \"filetype\": \"audio\", \"size\": \"\", \"description\": \"\", \"comment\": \"\", \"embedded\": true, \"preload\": true\", \"preload_prefetch\": true\", \"mute\": true, \"hide\": \"\", \"is_expiry\": \"\", \"mute_prefetch\": true, \"preload_prefetch\": true, \"preload_prefetch_interval\": \"1\", \"interval\": \"solo\", \"interval_solo\": \"1\", \"interval"}, {"object": "chinaware", "object_id": 245, "gpt3_output": "\n\n{\"type\": \"icing tool\", \"description\": \"round; can be made of ice\", \"similar objects\": [\"chocolate\", \"fluffernest\", \"biscuit\"]}"}, {"object": "crisp_(potato_chip)", "object_id": 246, "gpt3_output": "\n\n{\"type\": \"potato\", \"description\": \"round, smooth, has a tough texture\"}, \"similar objects\": [\"zucchini\", \"potato\", \"frozen potato\", \"frozen fruit\", \"frozen drink\", \"frozen food\", \"frozen meal\", \"frozen snack\", \"frozen dessert\", \"frozen action\", \"pet food\", \"pet bed\", \"pet toy\", \"pet food bowl\", \"pet food container\", \"pet food dish\", \"pet food container\", \"pet food box\", \"pet food bag\", \"pet food carton\", \"pet food bag\","}, {"object": "poker_chip", "object_id": 247, "gpt3_output": "\n\n{\"type\": \"token\", \"description\": \"I have this token."}, {"object": "chocolate_bar", "object_id": 248, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Chocolate Bar\", \"description\": \"\n\nThis object is a chocolate bar. It has a round shape and is made of materials such as paper and light."}, {"object": "chocolate_cake", "object_id": 249, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"light brown, smooth, with a cake texture\", \"similar objects\": [\"graham cheese\", \"chocolate cake\", \"frozen cake\"]}"}, {"object": "chocolate_milk", "object_id": 250, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "chocolate_mousse", "object_id": 251, "gpt3_output": "\n\n{\"type\": \"mousse\", \"composition\", \"name\": \"Chocolate Mousse\"},\n\n{\"type\": \"mousse\", \"composition\", \"name\": \"Mousse de Mango\"},\n\n{\"type\": \"mousse\", \"composition\", \"name\": \"Mousse de G\u00e2teau\"},\n\n{\"type\": \"mousse\", \"composition\", \"name\": \"Mousse de Creme\"},\n\n{\"type\": \"mousse\", \"composition\", \"name\": \"Mousse de Beurre\"},\n\n{\"type\": \"mousse\", \"com"}, {"object": "choker", "object_id": 252, "gpt3_output": "\n\n{\"type\": \"brace\", \"description\": \"smooth, has a small hole in the center\", \"similar objects\": [\"brace\", \"neck\", \"necklace\"]}"}, {"object": "chopping_board", "object_id": 253, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\"}"}, {"object": "chopstick", "object_id": 254, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small hole in side; could be a piece of candy\", \"similar objects\": [\"chopstick\", \"gummy bear\", \"trucker bar\"]}"}, {"object": "Christmas_tree", "object_id": 255, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are Christmas-themed."}, {"object": "slide", "object_id": 256, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cider", "object_id": 257, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "cigar_box", "object_id": 258, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red, with a lid\", \"similar objects\": [\"box of cigars\", \"candy\", \"keurig coffee maker\"]}"}, {"object": "cigarette", "object_id": 259, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"earthenware, has a handle\", \"similar objects\": [\"baked potato\", \"cigarette\"]"}, {"object": "cigarette_case", "object_id": 260, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Cigarette\", \"description\": \"\n\nThe object should focus on those that have similar characteristics. For example, a cigalot could have a stem, while a cigalot case would have a handle and keyhole."}, {"object": "cistern", "object_id": 261, "gpt3_output": "\n\n{\"type\": \"stream\", \"size\": \"\", \"description\": \"\", \"similar objects\": [\"pump\", \"wetlands\", \"iris\"]}"}, {"object": "clarinet", "object_id": 262, "gpt3_output": "\n\nThe Clarinet is a instrument that is used for playing clarinet music."}, {"object": "clasp", "object_id": 263, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a handle\", \"similar objects\": [\" clasp \", \" claspable tool\"]}"}, {"object": "cleansing_agent", "object_id": 264, "gpt3_output": "\n\nThe type of cleaning agent should be considered in an input for an object detection model."}, {"object": "cleat_(for_securing_rope)", "object_id": 265, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small, black, has a sharp point\", \"similar objects\": [\"sieve\", \"spade\", \"nail\", \"trowel\"]"}, {"object": "clementine", "object_id": 266, "gpt3_output": "\n\n{\"type\": \"flower\", \"description\": \"red, with a white petal at the top\", \"similar objects\": [\"grape\", \"chocolate\", \"fruitcake\"]}"}, {"object": "clip", "object_id": 267, "gpt3_output": "\n\nClip: round, has a handle, and a clip for a camera."}, {"object": "clipboard", "object_id": 268, "gpt3_output": "\n\nThere are many different objects that can be captured by an object detection model. A model should focus on those objects that are similar to the objects that are being detected. A model also should be able to identify the objects that are being detected by their features and objects that are being detected by their features."}, {"object": "clippers_(for_plants)", "object_id": 269, "gpt3_output": "\n\nThe focus of object detection models should be on those tools that can be used to cut plants or lights."}, {"object": "cloak", "object_id": 270, "gpt3_output": "\n\n{\"type\": \"cloak\", \"function\": \"vanish\", \"description\": \"\"},\n\nInput: soap, Output: \n\n{\"type\": \"soap\", \"function\": \"vanish\", \"description\": \"\"},\n\nInput: rock, Output: \n\n{\"type\": \"rock\", \"function\": \"vanish\", \"description\": \"\"},\n\nInput: rock, Output: \n\n{\"type\": \"rock\", \"function\": \"vanish\", \"description\": \"\"},\n\nInput: rock, Output: \n\n{\"type\": \"rock\", \"function"}, {"object": "clock", "object_id": 271, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is the presence of ticking timepieces. This model should be able to identify objects that share the same structure (like a zucchini or a tomato) and that have the same color (like a tomato or a red apple). Additionally, these models should be able to identify other objects that share a similar structure (like a ambulance or a police car)."}, {"object": "clock_tower", "object_id": 272, "gpt3_output": "\n\nThe input should focus on specific features that the model should be able to detect. For object detection models, these features could include cylindrical or smooth objects, similar objects, or a lightings tool that can be made of papers."}, {"object": "clothes_hamper", "object_id": 273, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"object\", \"description\": \"a hamper of clothes; of a different color than the objects in the input list\"}"}, {"object": "clothespin", "object_id": 274, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Clothespin\", \"type_name\": \"Object\"},\n\nInput: egg, Output: \n\n{\"type\": \"object\", \"name\": \"Egg\", type_name: \"Object\"},"}, {"object": "clutch_bag", "object_id": 275, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"clutch bag\", \"description\": \"black, with a white logo\", \"similar objects\": [\"purse\", \"bag\", \"key\"]"}, {"object": "coaster", "object_id": 276, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its ability to run. Coaster models should focus on making the coaster ride fun and exciting, while ambulance models should focus on caring for the object being examined."}, {"object": "coat", "object_id": 277, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"coil\", \"type\": \"material\", \"description\": \"coil, white, long, has a long end\", \"similar objects\": [\"coil\", \"coil of cloth\", \"coil of wax\"]"}, {"object": "coat_hanger", "object_id": 278, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small hole in the wall\", \"similar objects\": [\"coaster\", \"guitar\", \"teeter-dot\"]"}, {"object": "coatrack", "object_id": 279, "gpt3_output": "\n\n{\"type\": \"platform\", \"description\": \"round, has a handle\", \"similar objects\": [\"bicycle\", \"car\"]}"}, {"object": "cock", "object_id": 280, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; has a head and body\", \"similar objects\": [\"cat\", \"dog\", \"horse\"]}"}, {"object": "cockroach", "object_id": 281, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black, with a black spot in the center of the body\", \"similar objects\": [\"coon\", \"jaguar\", \"otter\"]}"}, {"object": "cocoa_(beverage)", "object_id": 282, "gpt3_output": "\n\nCoffee, tea, chocolate, milk"}, {"object": "coconut", "object_id": 283, "gpt3_output": "\n\n{\"type\": \"nut\", \"description\": \"green, has a texture and flavor\", \"similar objects\": [\"figs\", \"figs\", \"dried figs\", \"nuts\"]}"}, {"object": "coffee_maker", "object_id": 284, "gpt3_output": "\n\n{\"type\": \"machine\", \"description\": \"round, has a water bottle inside\", \"similar objects\": [\"coffee pot\", \"coffee filter\", \"coffee mug\", \"coffee cup\"]"}, {"object": "coffee_table", "object_id": 285, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"coffee table\", \"description\": \"round, black, has a black surface\", \"similar objects\": [\"coaster\", \"tiramisu\", \"coaster table\"]}"}, {"object": "coffeepot", "object_id": 286, "gpt3_output": "\n\n{\"type\": \"pot\", \"description\": \"red; has a small hole in the center\", \"similar objects\": [\"keurig coffee pot\", \"coffee can\"]}"}, {"object": "coil", "object_id": 287, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is the data of objects. One way to achieve this is to look for objects that have similar characteristics, such as objects that are similar in color, size, or shape. Another way to achieve this is to look for objects that are evidence of recent activity, such as objects that are evidence of a room or room full of objects."}, {"object": "coin", "object_id": 288, "gpt3_output": "\n\n{\"type\": \"coin\", \"price\": 5, \"uniform\": true, \"numeric\": \"1\", \"mintage\": 10, \"iss\": \"1919\", \"mintage_year\": \"1919\", \"mintage_decade\": \"1919\", \"mintage_year_1\": \"1919\", \"mintage_year_2\": \"1919\", \"mintage_decade_1\": \"1919\", \"mintage_year\": \"1919\", \"mintage_year_2\": \"1919\", \"variant\": \"coin\", \"vari"}, {"object": "colander", "object_id": 289, "gpt3_output": "\n\n{\"type\": \"crate\", \"description\": \"black, with a hole in the center\", \"similar objects\": [\"coaster\", \"dishwasher\", \"drummer's goo\"]}"}, {"object": "coleslaw", "object_id": 290, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "coloring_material", "object_id": 291, "gpt3_output": "\n\nThe features an object that needs to be detected, such as a zucchini or a red pan. Additionally, the model could be used to create illustrations or movies."}, {"object": "combination_lock", "object_id": 292, "gpt3_output": "\n\n{\"type\": \"lock\", \"description\": \"round, has a keyhole\", \"similar objects\": [\"keyboard\", \"couch\", \"barrier\"]}"}, {"object": "pacifier", "object_id": 293, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"a small, small, piece of cloth\", \"usage\",\"archment\", \"noun\"},\n\n{\"type\": \"verb\", \"description\": \"to suck\", \"usage\"},\n\n{\"type\": \"verb\", \"description\": \"to eat\", \"usage\"},\n\n{\"type\": \"verb\", \"description\": \"to stir\", \"usage\"},\n\n{\"type\": \"verb\", \"description\": \"to light\", \"usage\"},\n\nInput: apple, Output: {\"type\": \"fruit\", \"description\": \"red, round, has a stem"}, {"object": "comic_book", "object_id": 294, "gpt3_output": "\n\n{\"type\": \"book\", \"description\": \"The Iliad\", \"similar objects\": [\"bull\", \"bulldog\", \"dragon\"]}"}, {"object": "compass", "object_id": 295, "gpt3_output": "\n\nThe input should be a compass with a smooth surface. The input should also be similar to other objects in the world, such as objects with a siren, a stretcher, or a police car."}, {"object": "computer_keyboard", "object_id": 296, "gpt3_output": "\n\nThe input should be a list of objects, not just one type of object."}, {"object": "condiment", "object_id": 297, "gpt3_output": "\n\nThe features an object should focus on for a zucchini should be round, deep, and has a handle. The features an animal should focus on for a zebra should be black and white stripes, have a long mane, and are likely to have green leaves."}, {"object": "cone", "object_id": 298, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "control", "object_id": 299, "gpt3_output": "\n\nThe features that should be focus for an object detection model that detects objects are cylindrical, smooth, has a stem, and has green and rough stems."}, {"object": "convertible_(automobile)", "object_id": 300, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for potential objects is the object's type. For example, a car would be more likely to find potential objects if it were round, has a smooth surface, and has round headlights."}, {"object": "sofa_bed", "object_id": 301, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"sofia\", \"description\": \"\n\nSofia is a bed and breakfast in the sense that it is a hotel-like room that has been set up to be the home of only one person. The bed and breakfast is also called a \"sofia\" because the owner, Sofia, has written on the door \"sofia is a bed and breakfast\". \n\nThe objects in the room are a sofa bed, a bed, and a bed sheet. The sofa bed is also the only object in the room, and it is made of"}, {"object": "cooker", "object_id": 302, "gpt3_output": "\n\n{\"type\": \"appetizer\", \"description\": \"a small, round, brown or red object\", \"similar objects\": [\"taco\", \"chicken\", \"noodle\", \"noodle bowl\", \"noodle spoon\"]}"}, {"object": "cookie", "object_id": 303, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cooking_utensil", "object_id": 304, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"\"},\n\nInput: object, Output:\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cooler_(for_food)", "object_id": 305, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red; has a shape to it\", \"similar objects\": [\"onion\", \"frozen food\", \"dried food\"]}"}, {"object": "cork_(bottle_plug)", "object_id": 306, "gpt3_output": "\n\n{\"type\": \"Bottle\", \"id\": \"\", \"size\": \"\", \"color\": \"\", \"flavour\": \"\", \"aroma\": \"\", \"aromatica\": \"\", \"pH\": \"\", \"taste\": \"\", \"lack\": \"\", \"composition\": \"\", \"compositione\": \"\", \"lacunes\": \"\", \"lacunese\": \"\", \"aromae\": \"\", \"aromatica\": \"\", \"pH\": \"\", \"taste\": \"\", \"lack\": \"\", \"composition\": \"\", \""}, {"object": "corkboard", "object_id": 307, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"cork\", \"description\": \"round, smooth, has a ring around the edge\", \"similar objects\": [\"fishing net\", \"cork\"]"}, {"object": "corkscrew", "object_id": 308, "gpt3_output": "\n\n{\"type\": \"corkscrew\", \"description\": \"A long, thin piece of metal or plastic that is used to open a wine or other drink\", \"similar objects\": [\"knots\", \"corduroy\", \"flask\"]}"}, {"object": "edible_corn", "object_id": 309, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"corn,\u043e \n\", \"similar objects\": [\"corniche\", \"mashed potatoes\", \"wedge potatoes\"]}"}, {"object": "cornbread", "object_id": 310, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sugar, corn, and spice mixture; has a corn tortilla as the crust\", \"similar objects\": [\"taco\", \"mango\", \"chocolate cake\"]}"}, {"object": "cornet", "object_id": 311, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for potential objects is its ability to find potential objects. potential objects are people, things, or events that may be similar to the object being looked for. The model should be able to identify potential objects and should be able to perform tasks such as setting up the object detection model and/oront the potential objects."}, {"object": "cornice", "object_id": 312, "gpt3_output": "\n\n{\"type\": \"building\", \"location\", \"architecture\", \"geographical\", \"coordinates\", \"similar objects\": [\"cornice\", \"tower\", \"island\"]}"}, {"object": "cornmeal", "object_id": 313, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"corn; meal; salt; oil; baking soda\", \"similar objects\": [\"cornhusker\" \"dried corn\"]}"}, {"object": "corset", "object_id": 314, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "costume", "object_id": 315, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" Costume \", \"description\": \"A Costume \n\nAn object that is currently in use."}, {"object": "cougar", "object_id": 316, "gpt3_output": "\n\n{\"type\": \" animal \", \"description\": \"light brown fur; fur is long and wiry\", \"similar objects\": [\"cat\", \"dog\", \"catcher\", \"yacht\"]}"}, {"object": "coverall", "object_id": 317, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"coverall\", \"description\": \"A coverall is a type of clothing that is often used for cold weather. It is made of heavy fabric or cloth and is usually worn with a coat to keep the person warm.\""}, {"object": "cowbell", "object_id": 318, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for clues about its surroundings is the object's or object's body. This includes any smooth, cylindrical shapes, as well as those that have rough stems and have bumps or ridges on their surface. That includes both round and rough objects, such as zucchini and ambulance vehicles. That includes looking for clues about the model's surroundings, such as how the object looks in light or by using a light source."}, {"object": "cowboy_hat", "object_id": 319, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, black, has a symbol on the front\", \"similar objects\": [\" cowboy hat \", \" cowboy coat \", \" cowboy boots \"]"}, {"object": "crab_(animal)", "object_id": 320, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that detects objects is its ability to identify objects. Object detection models that are able to identify objects should be able to identify more objects in an input such as a zucchini or crab."}, {"object": "crabmeat", "object_id": 321, "gpt3_output": "\n\nCrabmeat is a type of meat that is made from crab meat."}, {"object": "cracker", "object_id": 322, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"sugar\", \"similar objects\": [\" cracker \", \" cake \", \" pie \"], \"similar objects\": [\"onion \", \"gravy \", \"cache\", \"cache case\"]"}, {"object": "crape", "object_id": 323, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "crate", "object_id": 324, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "crayon", "object_id": 325, "gpt3_output": "\n\n{\"type\": \"image\", \"image\": \"zucchini.png\", \"size\": \"small\", \"created\": \"2009-01-12 02:00:00\", \"updated\": \"2009-01-12 02:00:00\"}, \"zucchini.png"}, {"object": "cream_pitcher", "object_id": 326, "gpt3_output": "\n\n{\"type\": \"pouring tool\", \"description\": \"green; has a handle\", \"similar objects\": [\"tiramisu\", \"chocolate milk\", \"frozen cream\"]}"}, {"object": "crescent_roll", "object_id": 327, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sour cream and jelly\", \"similar objects\": [\"sour cream\", \"jelly\", \"cranberry\"]"}, {"object": "crib", "object_id": 328, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "crock_pot", "object_id": 329, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red; has a pot inside it; can be cooked in the oven\", \"similar objects\": [\"pot\", \"dishwasher\", \"fridge\", \"freeze\"]}"}, {"object": "crossbar", "object_id": 330, "gpt3_output": "\n\n{\"type\": \"couple\", \"description\": \"round, has a sharp end\", \"similar objects\": [\"dishwasher\", \"washer\", \"drummer's chair\"]}"}, {"object": "crouton", "object_id": 331, "gpt3_output": "\n\nThere are many different objects an object detection model could focus on, such as animals, vegetables, tools, and machines."}, {"object": "crow", "object_id": 332, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\"},\n\n{\"type\": \"vegetable\", \"name\": \"cyndle\", \"sharpeness\"},\n\n{\"type\": \"object\", \"name\": \"crazed ambulance\"},\n\n{\"type\": \"vehicle\", \"name\": \"red; has a siren\"},\n\n{\"type\": \"vegetable\", \"name\": \"banana\", \"round\"},\n\n{\"type\": \"vegetable\", \"name\": \"pear\", \"green\"},\n\n\":{\"type\": \"object\", \"name\":"}, {"object": "crowbar", "object_id": 333, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"harpoon\", \"nailbrush\", \"tape measure\"]"}, {"object": "crown", "object_id": 334, "gpt3_output": "\n\n{\"type\": \"title\", \"description\": \"Object Detection Models for Vegetables and Fruits\", \"authors\": [\"Donald A. downing\", \"John R. uping\", \"Donald A.\", \"John R.\", \"academic\", \"academic\", \" Donald A. downing\", \"John R. uping\", \"academic\", \"academic\", \" Donald A. downing\", \"John R. uping\", \"academic\", \"academic\", \" John R. uping\", \"academic\", \"academic\", \"academic\", \"academic\", \"academic\","}, {"object": "crucifix", "object_id": 335, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for crucifixes is their style. This model should be able to find crucifixes that have similar objects on them, such as papers and lights."}, {"object": "cruise_ship", "object_id": 336, "gpt3_output": "\n\n{\"type\": \"vessel\", \"description\": \"no description\"}, {\"type\": \"person\", \"description\": \"no description\"}, {\"type\": \"object\", \"description\": \"no description\"}, {\"type\": \"paramotor\", \"description\": \"no description\"}, {\"type\": \"machine\", \"description\": \"no description\"}, {\"type\": \"chamber\", \"description\": \"no description\"}, {\"type\": \"vessel\", \"description\": \"no description\"}, {\"type\": \"person\", \"description\": \"no description\"}, {\"type\": \"object\", \"description\": \"no description\"}, {\"type\": \"param"}, {"object": "police_cruiser", "object_id": 337, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a shining mirror; could have a broken window\", \"similar objects\": [\"car\", \"truck\", \"vans\"]}"}, {"object": "crumb", "object_id": 338, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"name\": \"Cream\", \"description\": \"\"},\n\n{\"type\": \"ingredient\", \"name\": \"Bread\", \"description\": \"\"},\n\n{\"type\": \"ingredient\", \"name\": \"Bread\", \"description\": \"\"},\n\n{\"type\": \"ingredient\", \"name\": \"Bread\", \"description\": \"\"},\n\n{\"type\": \"ingredient\", \"name\": \"Bread\", \"description\": \"\"},\n\n{\"type\": \"ingredient\", \"name\": \"Bread\", \""}, {"object": "crutch", "object_id": 339, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"black, with a red handle\", \"similar objects\": [\"wedge\", \"thorn\", \"axle\", \"axle\", \" crutch ],\n"}, {"object": "cub_(animal)", "object_id": 340, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cube", "object_id": 341, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a rough stem; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cucumber", "object_id": 342, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cufflink", "object_id": 343, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small, metal, has a handle\", \"similar objects\": [\"ring\", \"watch\"]"}, {"object": "cup", "object_id": 344, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Cup\", \"description\": \"Cup with a handle\", \"similar objects\": [\"Soup pot\", \"Soup can\", \"Souvenir cup\"]}"}, {"object": "trophy_cup", "object_id": 345, "gpt3_output": "\n\n{\"type\": \"cup\", \"description\": \"green, has a handle\", \"similar objects\": [\"cup\", \"cupcake\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\", \"cup\","}, {"object": "cupboard", "object_id": 346, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"coaster\", \"description\": \"black, smooth, has a handle\", \"similar objects\": [\"coaster\", \"eskimo\", \"poster\", \"cupboard\"]}"}, {"object": "cupcake", "object_id": 347, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"yellow, has a ring around the center\", \"similar objects\": [\"cake\", \"cup\", \"cupcake\"]}"}, {"object": "hair_curler", "object_id": 348, "gpt3_output": "\n\n{\"type\": \"apparatus\", \"description\": \"round, has a light at the end of it\", \"similar objects\": [\"coaster\", \"golf ball\", \"treadmill\"]}"}, {"object": "curling_iron", "object_id": 349, "gpt3_output": "\n\n{\"type\": \"curling iron\", \"description\": \"a tool used for curling hair\", \"similar objects\": [\"irons\", \"ironsingle\", \"ironside\"]}"}, {"object": "curtain", "object_id": 350, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cushion", "object_id": 351, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Cushion\", \"description\": \"Cushion; shape; size; color; material; design; what is it?\"}"}, {"object": "cylinder", "object_id": 352, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "cymbal", "object_id": 353, "gpt3_output": "\n\nThe cymbal is a musical instrument that can be used for playing music or making noises."}, {"object": "dagger", "object_id": 354, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"black; has a sharp blade\", \"similar objects\": [\"dagger\", \"spear\", \" Hunting knife \n\n\"}"}, {"object": "dalmatian", "object_id": 355, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\" Dalmatian \", \"houngean\", \"kangaroo\"]}"}, {"object": "dartboard", "object_id": 356, "gpt3_output": "\n\n{\"type\": \"game\", \"description\": \"Eggs with No Name\", \"similar objects\": [\"pumpkin\", \"banana\"]}"}, {"object": "date_(fruit)", "object_id": 357, "gpt3_output": "\n\n{\"type\": \"date\", \"date\", \"time\", \"year\", \"time zone\", \"time zone offset\", \"time zone offset\", \"time zone\", \"time zone code\", \"time zone code\", \"time zone\", \"delta\", \"delta\", \"time zone\", \"time zone\", \"delta\", \"offset\", \"offset\", \"time zone\", \"time zone\", \"time zone\", \"delta\", \"time zone\", \"time zone\", \"delta\", \"time zone\", \"delta\", \"time zone\", \"delta\", \"time zone\", \"time zone\", \""}, {"object": "deck_chair", "object_id": 358, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" Deck Chair \", \"description\": \"A chair that is used for sitting on. It has a long back, a noose, and two lead wires to a gun. The chair has also been used as a place to put something in front of it to make it look like it is solid. \"\n\"},"}, {"object": "deer", "object_id": 359, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, red; could have hooves\", \"similar objects\": [\"giraffe\", \" deer \", \"ox\"]}"}, {"object": "dental_floss", "object_id": 360, "gpt3_output": "\n\nThe objects in the input could be dental floss. The objects in the output could be a dental floss."}, {"object": "desk", "object_id": 361, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"object\", \"description\": \"\"}"}, {"object": "detergent", "object_id": 362, "gpt3_output": "\n\n{\"type\": \" Detergent\", \"description\": \"A detergent is a liquid detergent that helps to clean dishes quickly and easily."}, {"object": "diaper", "object_id": 363, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" diaper \", \"description\": \" a small, round, black object with a white spot in the center\", \"similar objects\": [\"paprika\", \"seaweed\", \"grits\"]}"}, {"object": "diary", "object_id": 364, "gpt3_output": "\n\n{\"type\": \"textbook\", \"description\": \"The model should focus on objects that are similar in size, shape, or color to the input. For example, the zucchini should have a similar shape and color to the cucumber, while the eggplant should have a similar green to the green bean. The light tool should be able to light up with a siren, while the Lantern should be able to light up with a light tool that has a light tool."}, {"object": "die", "object_id": 365, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for clues about is its surroundings. This model should be able to identify different types of objects, such as cylindrical objects, smooth objects, and round objects. It should also be able to identify different types of objects, such as Cyndrical objects, green objects, and round objects."}, {"object": "dinghy", "object_id": 366, "gpt3_output": "\n\n{\"type\": \"vessel\", \"description\": \"is a boat; has a net at the bow\", \"similar objects\": [\"shorewatch\", \"seaweed\", \"buddy\"]"}, {"object": "dining_table", "object_id": 367, "gpt3_output": "\n\n{\"type\": \"table\", \"description\": \"black, with a white top\", \"similar objects\": [\"coaster\", \"coaster system\", \"coaster lot\", \"coaster mint\"]}"}, {"object": "tux", "object_id": 368, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "dish", "object_id": 369, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"red, smooth, has a handle\", \"similar objects\": [\"ice cream\", \"frozen yogurt\", \"banana\"]"}, {"object": "dish_antenna", "object_id": 370, "gpt3_output": "\n\nThe input should focus on features that are specific to the dish antenna input, such as a round body, small size, and a clear plastic cover. The output should focus on features that are specific to the lightening input, such as a round body, large size, and a clear plastic cover."}, {"object": "dishrag", "object_id": 371, "gpt3_output": "\n\n{\"type\": \"dishwasher\", \"description\": \"black, has a small hole in the center\", \"similar objects\": [\"pantry\", \"fridge\", \"freeze\", \"microwave\", \"dishwasher\", \"washer\", \"sink\", \"washer\", \"irons\", \"sink\", \"iron\", \"pan\", \"stove\", \"stove\", \"fire\", \"lamp\", \"lamp\", \"lamp\", \"lamp\", \"lamp\", \"lamp\", \"lamp\", \"lamp\", \"lamp\", \"lamp\", \"l"}, {"object": "dishtowel", "object_id": 372, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"dishtowel\", \"description\": \"round, black, has a series of small nooks\", \"similar objects\": [\"coaster\", \"sink\", \"tape measure\"]}"}, {"object": "dishwasher", "object_id": 373, "gpt3_output": "\n\n{\"type\": \"washing machine\", \"description\": \"green and blue; has a drain\", \"similar objects\": [\"sink\", \" dishwasher \", \" washing machine \"}"}, {"object": "dishwasher_detergent", "object_id": 374, "gpt3_output": "\n\n{\"type\": \" Detergent\", \"description\": \"Red; has a siren; can beocytheic\"},\n\n{\"type\": \" Dishwasher\", \"description\": \"Blue; has a non-stick coating\"},\n\n{\"type\": \" Dishes \", \"description\": \"Canned chicken; has a lot of spices\"},\n\n{\"type\": \" Kitchen \", \"description\": \"Paleo chicken and bacon\", \"similar objects\": [\"paleo chicken\", \"paleo bacon\", \"scrambled eggs\", \"scrambled bacon\", \"scrambled chicken\"],\n\n}"}, {"object": "dispenser", "object_id": 375, "gpt3_output": "\n\n{\"type\": \"vitamin\", \"description\": \" Needs to be able to dispense a given amount of product; needs to be able to dispense a given amount of water; needs to be able to dispense a given amount of oil; needs to be able to dispense a given amount of water\n\n\n\nInput: can, Output:\n\n{\"type\": \"beef\", \"description\": \"round, has a handle\", \"similar objects\": [\"chicken\", \"pork\"]}"}, {"object": "diving_board", "object_id": 376, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a small hole in the middle\", \"similar objects\": [\"zucchini\", \"apple\", \"giraffe\"]}"}, {"object": "Dixie_cup", "object_id": 377, "gpt3_output": "\n\n{\"type\": \"cup\", \"description\": \"red; has a handle and a handle top\", \"similar objects\": [\"coaster\", \"dishwasher\", \"coaster\"]"}, {"object": "dog", "object_id": 378, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "dog_collar", "object_id": 379, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"dog\", \"description\": \"black and white, has a wagging tail\", \"similar objects\": [\"cat\", \"catcher\", \"poodle\"]}"}, {"object": "doll", "object_id": 380, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Doll\", \"description\": \"\n\nAn object that is either a doll or a toy."}, {"object": "dollar", "object_id": 381, "gpt3_output": "\n\n{\"type\": \"currency\", \"description\": \"the United States dollar\", \"similar objects\": [\"dollar\", \"ruin\", \"gift\"]}"}, {"object": "dollhouse", "object_id": 382, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Dollhouse\", \"description\": \"Dollhouse is a house that is used as a place for playtime in the dollhouse. It is a small, two-story structure with a small open floor plan. The dollhouse has four rooms that can be used for playtime for the players in the room. There is a small door that leads to the attic where the players can store items. The dollhouse is made of wood and plastic. It is a fun place for the players to play in and the players can play with their friends."}, {"object": "dolphin", "object_id": 383, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; has a long tail\", \"similar objects\": [\" dolphin \", \"whale \", \"seaweed\"]}"}, {"object": "domestic_ass", "object_id": 384, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, could have a tail\", \"similar objects\": [\"pig\", \"cat\", \"toucan\"]}"}, {"object": "doorknob", "object_id": 385, "gpt3_output": "\n\n{\"type\": \"door handle\", \"description\": \"thick, dark, has a handle\", \"similar objects\": [\"keyboard\", \"couch\", \"barrier\"]}"}, {"object": "doormat", "object_id": 386, "gpt3_output": "\n\nThere are many different features an object detection model could focus on, such as detecting that an object is round, has a handle, or is made of papers."}, {"object": "doughnut", "object_id": 387, "gpt3_output": "\n\n{\"type\": \"doughnut\", \"description\": \"a small, round, golden doughnut\", \"similar objects\": [\"biscuit\", \"trundleberry\", \"kebab\"]}"}, {"object": "dove", "object_id": 388, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"duck\", \"gazelle\"]}"}, {"object": "dragonfly", "object_id": 389, "gpt3_output": "\n\nThe input should be a creature that can be detected by object detection models. This model should be able to walk, run, and fly. The model should also have a headlight and a dragonfly wingspan."}, {"object": "drawer", "object_id": 390, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red; has a handle\", \"similar objects\": [\"door\", \"cups of coffee\", \"cake\", \"frozen food\"]}"}, {"object": "underdrawers", "object_id": 391, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"xylophone\", \"similar objects\": [\"guitar\", \"banjo\"]}"}, {"object": "dress", "object_id": 392, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Dress\", \"type\": \"vegetable\", \"description\": \"Cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\"}"}, {"object": "dress_hat", "object_id": 393, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"white; has a brimmed hat\", \"similar objects\": [\"hat\", \"sunglasses\"]}"}, {"object": "dress_suit", "object_id": 394, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"white; has a strap\", \"similar objects\": [\"sunglasses\", \"hat\"]}"}, {"object": "dresser", "object_id": 395, "gpt3_output": "\n\n{\"type\": \"table\", \"description\": \"round, has a lot ofslaught of ingredients\", \"similar objects\": [\"cat\", \"trucker\", \"SUV\"]}"}, {"object": "drill", "object_id": 396, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"crown\", \"spade\", \"jigsaw\", \"borer\"],"}, {"object": "drone", "object_id": 397, "gpt3_output": "\n\nThere are many different objects that can be looked for in objects that have been identified as zucchini, such as the fact that they are cylindrical, smooth, and have green leaves. Additionally, they could be sliced into round pieces, and had green leaves."}, {"object": "dropper", "object_id": 398, "gpt3_output": "\n\nThere are many different features an object detection model could focus on, such as detecting that an object is a fruit, detecting that an object is a object that can be cooked, or lightening speed."}, {"object": "drum_(musical_instrument)", "object_id": 399, "gpt3_output": "\n\n{\"type\": \"drum\", \"description\": \"round, has a beat that can be heard over the noise of the environment\", \"similar objects\": [\"drummer\", \"harp\"]}"}, {"object": "drumstick", "object_id": 400, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan; has a thin stalk; can be peeled\", \"similar objects\": [\"zucchini\", \"melon\"]}"}, {"object": "duck", "object_id": 401, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"duck\", \"gazelle\", \"rhinoceros\"]}"}, {"object": "duckling", "object_id": 402, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, could have a duckbill\"}, \"similar objects\": [\" duck \", \" duckling \", \" duck \", \" duckling \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \",\" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \",\" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \" duck \", \""}, {"object": "duct_tape", "object_id": 403, "gpt3_output": "\n\n{\"type\": \"text\", \"message\": \"This is a message\", \"similar objects\": [\"tape\", \"tape measure\", \"tape measureR\"]"}, {"object": "duffel_bag", "object_id": 404, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"duffel bag\", \"description\": \"\"}"}, {"object": "dumbbell", "object_id": 405, "gpt3_output": "\n\n{\"type\": \"dumbbell\", \"description\": \"dwarf, has a small head\", \"similar objects\": [\"dwarf tomato\", \"dwarf apple\"]}"}, {"object": "dumpster", "object_id": 406, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "dustpan", "object_id": 407, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"has a cloth top; can be emptied by hand\", \"similar objects\": [\"sink\", \"bowl\", \"pan\", \"coaster\"]}"}, {"object": "eagle", "object_id": 408, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; has a beak and beady eyes\", \"similar objects\": [\"giraffe\", \"monkey\", \"toucan\"]}"}, {"object": "earphone", "object_id": 409, "gpt3_output": "\n\n{\"type\": \"audio\", \"description\": \"round, has a hole in the middle\", \"similar objects\": [\"microwave\", \" Skype \"]"}, {"object": "earplug", "object_id": 410, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"\"}"}, {"object": "earring", "object_id": 411, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a soundless sound\", \"similar objects\": [\"ring\", \"ringtone\"]}"}, {"object": "easel", "object_id": 412, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "eclair", "object_id": 413, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for candidates for a meal is the object's type. For example, an object that is called an \"object\" is typically considered for a meal because it has an object type of \"object.\""}, {"object": "eel", "object_id": 414, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for zucchini is its type. This model should be able to identify zucchini as a food item. Additionally, the model should be able to identify zucchini as an animal or as a tool that can be used for cooking or as a vehicle for transportation."}, {"object": "egg", "object_id": 415, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "egg_roll", "object_id": 416, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"egg\", \"grocery store\", \"green beans\"]}"}, {"object": "egg_yolk", "object_id": 417, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"the egg yolk\", \"similar objects\": [\"chicken\", \"egg\"]}"}, {"object": "eggbeater", "object_id": 418, "gpt3_output": "\n\n{\"type\": \"mechanical tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"screwdriver\", \"pump\"]"}, {"object": "eggplant", "object_id": 419, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "electric_chair", "object_id": 420, "gpt3_output": "\n\n{\"type\": \"toy\", \"description\": \"red; has a chair with a light at the end of it\", \"similar objects\": [\"electric chair\", \"toy box\", \"tape measure\"]"}, {"object": "refrigerator", "object_id": 421, "gpt3_output": "\n\n{\"type\": \"implement\", \"description\": \" needs a door to open and a handle to open, Output: {\"type\": \"result\", \"result\": \"closed\"}"}, {"object": "elephant", "object_id": 422, "gpt3_output": "\n\nThe feature that should focus on for an elephant is their fur. The fur on an elephant is smooth, and the animal has green and brown stripes."}, {"object": "elk", "object_id": 423, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; has a antlers\", \"similar objects\": [\"elk\", \"giraffe\", \" elephant \", \"horse\"]}"}, {"object": "envelope", "object_id": 424, "gpt3_output": "\n\nThe input should be a probability space with features. A model that focuses on features should be more efficient in analyzing an input with many features."}, {"object": "eraser", "object_id": 425, "gpt3_output": "\n\nThere are many different features an object detection model could focus on, such as detecting objects of different sizes, detecting shapes, and detecting the presence of specific objects."}, {"object": "escargot", "object_id": 426, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"escargot\", \"gazelle\", \" elephant\"]}"}, {"object": "eyepatch", "object_id": 427, "gpt3_output": "\n\nThe objects in the input can be either objects (vegetables, animals, tools, etc.), or names (zucchini, red apple, banana, etc.).\n\nThe objects in the input can also be specific objects, such as \"cucumber\" or \"giraffe\"."}, {"object": "falcon", "object_id": 428, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; has a beak, neck, and wings\", \"similar objects\": [\" Falcon \", \" Falcon \", \"Canon\", \"Lion \", \"Giraffe \", \" elephant \"], \"fraudulent objects\": [\"Falcon \"], \"forgotten objects\": [\"Falcon \"], \"aliens\": [\"Falcon \"], \"forbidden objects\": [\"Falcon \"], \"abandoned objects\": [\"Falcon \"], \"ghosts\": [\"Falcon \"], \"forgotten friends\": [\"Falcon \""}, {"object": "fan", "object_id": 429, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"round, has a motor\", \"similar objects\": [\"kite\", \"rotor\", \"motorcycle\"]}"}, {"object": "faucet", "object_id": 430, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round; has a handle\", \"similar objects\": [\"faucet\", \"ink bottle\", \"cup\"]}"}, {"object": "fedora", "object_id": 431, "gpt3_output": "\n\n{\"type\": \"headwear\", \"description\": \"black, with a red band\", \"similar objects\": [\"sunglasses\", \"hat\", \"bunny earrings\"]}"}, {"object": "ferret", "object_id": 432, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan; has a short stem; can run\", \"similar objects\": [\"gazelle\", \"otter\", \"island\", \"octopus\"]}"}, {"object": "Ferris_wheel", "object_id": 433, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"apple\", \"giraffe\", \"banana\"]}"}, {"object": "ferry", "object_id": 434, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for ferry objects is the shape of the object. Other features that might be important for this model include the size of the object, the color of the object, and the texture of the object."}, {"object": "fig_(fruit)", "object_id": 435, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"fig\", \"grape\", \"grapefruit\"]}"}, {"object": "fighter_jet", "object_id": 436, "gpt3_output": "\n\n{\"type\": \"aircraft\", \"description\": \"black, white, red, blue\", \"similar objects\": [\"jet\", \"lion\", \" 747 \", \" Stallard \n\n\"}"}, {"object": "figurine", "object_id": 437, "gpt3_output": "\n\n{\"type\": \"figurine\", \"description\": \"black, white, red, green, blue\", \"similar objects\": [\"puppy\", \"dog\", \"cat\"]}"}, {"object": "file_cabinet", "object_id": 438, "gpt3_output": "\n\n{\"type\": \"file\", \"name\": \"file\", \"size\": \"\", \"similar objects\": [\"file\", \"cabinet\", \"drawer\", \"barrel\"]"}, {"object": "file_(tool)", "object_id": 439, "gpt3_output": "\n\n{\"type\": \"file\", \"name\": \"file.txt\", \"size\": \"\", \"description\": \"\"},\n\n\"type\": \"file\", \"name\": \"file_store.txt\", \"size\": \"\", \"description\": \"\"},"}, {"object": "fire_alarm", "object_id": 440, "gpt3_output": "\n\nThe feature that should focus on for an ambulance is the siren. The fire alarm could be focused on if the machine is made of firewood or of a more durable material."}, {"object": "fire_engine", "object_id": 441, "gpt3_output": "\n\nThe ambulance should focus on the round vehicle and the fire engine on the light lamp."}, {"object": "fire_extinguisher", "object_id": 442, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for fire extinguishers is the models's ability to find them. The model should be able to find extinguishers by looking for patterns in the data. This is done by using a particular set of algorithms that can identify which extinguisher is the model's target."}, {"object": "fire_hose", "object_id": 443, "gpt3_output": "\n\n{\"type\": \"stream\", \"description\": \"red; has a flow rate of 1.2 ounces per minute\", \"similar objects\": [\"guitar player\", \"smoke\"]"}, {"object": "fireplace", "object_id": 444, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for fireplaces is the type of fireplace. This should look for fires that have a round or cylindrical shape, with smooth or rough stems, and has green or brown skin."}, {"object": "fireplug", "object_id": 445, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"clearance; has a release button\", \"similar objects\": [\"grenade\", \"bomb\"]}"}, {"object": "first-aid_kit", "object_id": 446, "gpt3_output": "\n\nThere are many different objects that can be used with first-aid, such as a knife, a pen, and a phone. These objects can be used for many different purposes, such as for communication, for healing, and for other tasks."}, {"object": "fish", "object_id": 447, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"black, white, small; could be fleshed out\", \"similar objects\": [\"giraffe\", \" dolphin \", \" \n \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \""}, {"object": "fish_(food)", "object_id": 448, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"a type of fish\", \"similar objects\": [\"giraffe\", \" dolphin \", \" \n\" \n\"\", \"\", \"\"}"}, {"object": "fishbowl", "object_id": 449, "gpt3_output": "\n\n{\"type\": \"pool\", \"description\": \"No depths are given\"}, {\"type\": \"pool\", \"depth\": 10}"}, {"object": "fishing_rod", "object_id": 450, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"green, has a handle\", \"similar objects\": [\"fishing net\", \"rod\", \"linefinder\"]}"}, {"object": "flag", "object_id": 451, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for flags is its ability to identify objects with flags as their only feature. This object detection model would be able to identify objects with flags as their only feature, such as a flag for objects that have a cylindrical shape, green color, and smooth surface."}, {"object": "flagpole", "object_id": 452, "gpt3_output": "\n\nThe feature that should focus on for an input with a flag is the object's object type. For an input with a wok, for example, the object type would be \"vegetable.\""}, {"object": "flamingo", "object_id": 453, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"orange and black; has a long neck\", \"similar objects\": [\"flamingo\", \"parrot\", \"giraffe\"]}"}, {"object": "flannel", "object_id": 454, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Flannel\", \"quantity\": \"1\", \"similar objects\": [\"flannel\", \"paper\", \"lightbulb\"]}"}, {"object": "flap", "object_id": 455, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for Vegetables is whether or not the object is cylindrical. The object should also be smooth, with no rough stems. The model should be able to slice Vegetables into round pieces and green leaves."}, {"object": "flash", "object_id": 456, "gpt3_output": "\n\n{\"type\": \"image\", \"uuid\": \"cwc8c_8c_ading\", \"size\": \"small\", \"created\": \"2013-06-14 00:00:00\", \"lastseen\": \"2013-08-10 00:00:00\"}, \"image_2\": \"http://www.w3.org/TR/2000/GLs-xpath.xpath\", \"image_3\": \"http://www.w3.org/TR/2000/GLs-xpath.xpath\", \"image_4\": \"http://www.w"}, {"object": "flashlight", "object_id": 457, "gpt3_output": "\n\n{\"type\": \"light tool\", \"description\": \"round; could be made of papers\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]"}, {"object": "fleece", "object_id": 458, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"fleece\", \"description\": \"red; has a fleece\", \"similar objects\": [\"cat\", \"coaster\", \" dog\"]}"}, {"object": "flip-flop_(sandal)", "object_id": 459, "gpt3_output": "\n\nThe flip-flop is a two-state machine, where in state 0 it is without any features. In state 1 the flip-flop has all the features of a regular flip-flop and can turn on and off."}, {"object": "flipper_(footwear)", "object_id": 460, "gpt3_output": "\n\nThere are many different types of object detection models that could be used for an input of zucchini, apple, ambulance, lightening, and flipper."}, {"object": "flower_arrangement", "object_id": 461, "gpt3_output": "\n\n{\"type\": \"flower arrangement\", \"arrangement\", \"color\", \"description\"},\n\n\"type\": \"flower arrangement\", \"arrangement\", \"color\", \"description\"},"}, {"object": "flute_glass", "object_id": 462, "gpt3_output": "\n\nThe input should be a natural product that can be used to create products such as flutes and flutes glass."}, {"object": "foal", "object_id": 463, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for potential objects is its ability to find potential objects. potential objects are things that the model could be able to see if it were to find them. potential objects could be things that the model is potential to find. For example, the model could be able to find objects that are made of paper or lights."}, {"object": "folding_chair", "object_id": 464, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of an input is the ability to see objects. For example, an object detection model that is looking for evidence of a zucchini will be looking for objects that have the type of object \"zucchini\"."}, {"object": "food_processor", "object_id": 465, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red; has a texture like paper\", \"similar objects\": [\"potato\", \"frozen food\", \"baked food\"]}"}, {"object": "football_(American)", "object_id": 466, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of an object is its type. An object detection model that is looking for evidence of an object will focus on objects that have the type \"vegetable\"."}, {"object": "football_helmet", "object_id": 467, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Football Helmet\", \"id\": \"\", \"position\": 1, \"content\": \"\n\nThis helmet is perfect for any football fan!\n\n\"}, {\"type\": \"item\", \"name\": \"Football Helmet\", \"id\": \"\", \"position\": 2, \"content\": \"\n\nThis helmet is perfect for any football fan!\n\n\"}, {\"type\": \"item\", \"name\": \"Football Helmet\", \"id\": \"\", \"position\": 3, \"content\": \"\n\nThis helmet is perfect for any football fan!\n\n\"}, {\"type\":"}, {"object": "footstool", "object_id": 468, "gpt3_output": "\n\n{\"type\": \"table\", \"description\": \"black, with a black top and bottom, and a pair of black legs; can be placed to one side or the other\", \"similar objects\": [\"tray\", \"curtis\", \"tennis ball\"]}"}, {"object": "fork", "object_id": 469, "gpt3_output": "\n\n{\"type\": \"appetizer\", \"description\": \"1\", \"type\": \"dessert\", \"type2\": \"cake\", \"similar objects\": [\"frozen yogurt\", \"frozen fruit\", \"frozen vegetables\"]}"}, {"object": "forklift", "object_id": 470, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"fork\", \"cane\", \"machinery\"]"}, {"object": "freight_car", "object_id": 471, "gpt3_output": "\n\n{\"type\": \"car\", \"description\": \"black, with a red stripe down the middle\", \"similar objects\": [\"car\", \"trolley\", \"bus\"]}"}, {"object": "French_toast", "object_id": 472, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is its size. Other features that might be important for an object detection model to focus on include its shape, color, and size."}, {"object": "freshener", "object_id": 473, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"name\": \"Water\"},\n\n{\"type\": \"product\", \"description\": \"Fruit\"},\n\n{\"type\": \"product\", \"description\": \"Vegetables\"},\n\n{\"type\": \"product\", \"description\": \"Household items\"},\n\nInput: onion, Output: \n\n{\"type\": \"ingredient\", \"name\": \"Salt\"},\n\n{\"type\": \"product\", \"description\": \"Chips\"},\n\n{\"type\": \"product\", \"description\": \"Chips\"},\n\nInput: onion, Output: \n"}, {"object": "frisbee", "object_id": 474, "gpt3_output": "\n\n{\"type\": \"football\", \"description\": \"black and red; could have yellow and blue fields\", \"similar objects\": [\"soccer ball\", \"soccer ball],"}, {"object": "frog", "object_id": 475, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, has a long mane\", \"similar objects\": [\"toucan\", \"seagull\", \"whale\"]}"}, {"object": "fruit_juice", "object_id": 476, "gpt3_output": "\n\n{\"type\": \"dietary\", \"description\": \"nothing\", \"similar objects\": [\"soda\", \"fruit juice\"]}"}, {"object": "frying_pan", "object_id": 477, "gpt3_output": "\n\n{\"type\": \"frying pan\", \"description\": \"round, deep; has a deep handle\", \"similar objects\": [\"chopstick\", \"dishwasher\", \"crate\"]"}, {"object": "fudge", "object_id": 478, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sugar, nuts\", \"similar objects\": [\"fudge brown\", \"fudge brown sugar\", \"fudge sugar\"]"}, {"object": "funnel", "object_id": 479, "gpt3_output": "\n\n{\"type\": \"vase\", \"description\": \"light green; has a lip\", \"similar objects\": [\"gazelle\", \"fowl\"]}"}, {"object": "futon", "object_id": 480, "gpt3_output": "\n\n{\"type\": \"tent\", \"description\": \"round, with a hole in the center, has a light on top\", \"similar objects\": [\"tent\", \"patio\", \"tentpole\"]"}, {"object": "gag", "object_id": 481, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"round, has a sound box\", \"similar objects\": [\"instruments\", \"saxon\", \"flute\"]}"}, {"object": "garbage", "object_id": 482, "gpt3_output": "\n\nThe input should be a list of features that includes an object's type, description, and similar objects."}, {"object": "garbage_truck", "object_id": 483, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"black, red, yellow; could have a blue and red stripe\", \"similar objects\": [\"dumpster\", \"dump\", \"truck\"]}"}, {"object": "garden_hose", "object_id": 484, "gpt3_output": "\n\n{\"type\": \"supplier\", \"description\": \"red, with a long nozzle\", \"similar objects\": [\"spoon\", \"iaan\", \"iaan can\"]}"}, {"object": "gargle", "object_id": 485, "gpt3_output": "\n\n{\"type\": \"gulp\", \"description\": \"water\", \"similar objects\": [\"soup\", \"tongue\"]}"}, {"object": "gargoyle", "object_id": 486, "gpt3_output": "\n\n{\"type\": \"servo\", \"description\": \"a creature with a gargantuan size; has a long neck\", \"similar objects\": [\"giant cockroach\", \"chicken\", \"tronchie\", \"toad\"]"}, {"object": "garlic", "object_id": 487, "gpt3_output": "\n\nThe focus of object detection models should be on those foods that have specific features that are shared by all objects in the input. For example, garlic might be a food that has a round shape and deep colors. This would be the focus of the model for that food."}, {"object": "gasmask", "object_id": 488, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for gasmasks is their color. This will include models that are looking for vehicles with gasmasks, models that were found with gasmasks, and models that are currently being used with gasmasks."}, {"object": "gazelle", "object_id": 489, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, and red; has a mane of black and white stripes\", \"similar objects\": [\"gazelle\", \"kangaroo\", \"elk\"]}"}, {"object": "gelatin", "object_id": 490, "gpt3_output": "\n\nThe input could be a single object, like a ball of candy. The output could be a list of objects, like a list of fruits. The similar objects in the list could be different objects, like a different ball of candy."}, {"object": "gemstone", "object_id": 491, "gpt3_output": "\n\n{\"type\": \"gem\", \"description\": \"pink, with a white birthstone inside\", \"similar objects\": [\"sapphire\", \" turquoise \", \" turquoise \"\", \" sapphire \"\", \" turquoise \"\", \" sapphire \"\", \" turquoise \"\", \" sapphire \"\", \" turquoise \"\", \" sapphire \"\", \" turquoise \"\", \" turquoise \"\", \" sapphire \"\", \" turquoise \"\", \" turquoise \"\", \" sapphire \"\", \" turquoise \"\", \" tur"}, {"object": "generator", "object_id": 492, "gpt3_output": "\n\nThe input should be a machine that creates things like lights and generators. These machines should be able to create different types of light, like sirens or lights for a police department."}, {"object": "giant_panda", "object_id": 493, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; has a mane of long, thin sheets\", \"similar objects\": [\"panda\", \"chicken\", \"goat\"]}"}, {"object": "gift_wrap", "object_id": 494, "gpt3_output": "\n\n{\"type\": \"gift\", \"description\": \"round, smooth, has a bow\", \"similar objects\": [\"chocolate\", \"chopstick\", \"necklace\"]}"}, {"object": "ginger", "object_id": 495, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for smells of zucchini is turning it into a zucchini zucchini. The model should also be able to turn zucchini into zucchini zucchini."}, {"object": "giraffe", "object_id": 496, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "cincture", "object_id": 497, "gpt3_output": "\n\n{\"type\": \"brace\", \"description\": \"a metal bandage\", \"similar objects\": [\"bracelet\", \"bracelet case\"]}"}, {"object": "glass_(drink_container)", "object_id": 498, "gpt3_output": "\n\n{\"type\": \"container\", \"name\": \"wine\", \"description\": \"\n\nThe object detection model should focus on objects that have the same name as the input. For example, a glass drink container would be a good input for an object detection model that focuses on this type of objects."}, {"object": "globe", "object_id": 499, "gpt3_output": "\n\nThe input should be a sphere with a smooth surface."}, {"object": "glove", "object_id": 500, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "goat", "object_id": 501, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "goggles", "object_id": 502, "gpt3_output": "\n\nThe objects in the input could be in any order, but the features that should be focused on in an input about a goggles are to make the model more realistic, to make it easy to see, and to make it easy to find the objects."}, {"object": "goldfish", "object_id": 503, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"black and white, with a small tail\", \"similar objects\": [\"giraffe\", \"otter\", \"coaster\"]"}, {"object": "golf_club", "object_id": 504, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for Golf Clubs is their size. Other features that the model should look for include being of a specific size, having a specific shape, and having a specific light or dark color."}, {"object": "golfcart", "object_id": 505, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"light blue; has aiardic motor; could have tarmac or road surfaces\", \"similar objects\": [\" golfcart \", \" car \", \" truck \", \" bike \", \" scooter \"]"}, {"object": "gondola_(boat)", "object_id": 506, "gpt3_output": "\n\n{\"type\": \"watercraft\", \"description\": \"green, with a stem and boat-like body\", \"similar objects\": [\"coaster\", \"raft\", \"yacht\"]}"}, {"object": "goose", "object_id": 507, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; has a beak and a neck\", \"similar objects\": [\"poodle\", \"comet\", \"feline cat\", \"cat\", \"otter\"]}"}, {"object": "gorilla", "object_id": 508, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "gourd", "object_id": 509, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylicer, smooth; could have rough stems; could be intoxted and undercooked)\", \"similar objects\": [\"zucchini\", \"zucchinibag\", \"frozen zucchini\"]"}, {"object": "grape", "object_id": 510, "gpt3_output": "\n\n{\"type\": \"grape\", \"name\": \"Grape\", \"size\": \"small\", \"description\": \"Cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\"}"}, {"object": "grater", "object_id": 511, "gpt3_output": "\n\n{\"type\": \"grater\", \"description\": \"round, has a sharp blade\", \"similar objects\": [\"zucchini\", \"carrot\", \"sticker\", \"flour\", \"salt\"]}"}, {"object": "gravestone", "object_id": 512, "gpt3_output": "\n\n{\"type\": \" Grave \", \"Description\": \"\n\nThe Grave object model should focus on features that are cylindrical, smooth, and have a green color. The similar objects model should focus on features that are a similar object to the Grave object model, including a cylindrical shape, smooth color, and similar symptoms."}, {"object": "gravy_boat", "object_id": 513, "gpt3_output": "\n\n{\"type\": \"vessel\", \"description\": \"black, white, clear; could have been used for shipping goods\", \"similar objects\": [\"ship\", \"wok\", \"pot\", \"catcher\"]}"}, {"object": "green_bean", "object_id": 514, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "green_onion", "object_id": 515, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"a small, fresh onion\", \"usage\": \"verb\", \"term\", \"definition\": \"\n\nA fresh onion is a type of onion that is not from onions."}, {"object": "griddle", "object_id": 516, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"round, has a non-stick surface\", \"similar objects\": [\"onion\", \"butterfly\", \"butterfly egg\"]}"}, {"object": "grill", "object_id": 517, "gpt3_output": "\n\n{\"type\": \" grill \", \"description\": \"a tool used to cook food\", \"similar objects\": [\"grill\", \"grill\", \"stove\", \" oven \", \" stove top \"]"}, {"object": "grits", "object_id": 518, "gpt3_output": "\n\n{\"type\": \"textbook\", \"description\": \"A book with every page turned, so that the reader can only see the title and the first few lines,\" \"similar objects\": [\"page\", \"textbook\"]}"}, {"object": "grizzly", "object_id": 519, "gpt3_output": "\n\n{\"type\": \" mammal \", \"name\": \"grizzly bear \", \"size\": \"0-1\", \"description\": \"\", \"similar objects\": [\"bison\", \" elk \", \" elk \", \" elk \", \" bison \", \"bison\", \"bison \", \"bison \", \"bison \", \"bison \", \"grizzly bear \", \"grizzly bear \", \"grizzly bear \", \"grizzly bear \", \"grizzly bear \", \"grizzly bear \", \"grizzly bear \", \"g"}, {"object": "grocery_bag", "object_id": 520, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "guitar", "object_id": 521, "gpt3_output": "\n\nThe input should be a specific, known object. The output should be an object that is similar to the input."}, {"object": "gull", "object_id": 522, "gpt3_output": "\n\n{\"type\": \"wing\", \"description\": \"fully feathered\", \"similar objects\": [\" Falcon \"],"}, {"object": "gun", "object_id": 523, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its ability to identify similar objects. An object detection model that is looking for evidence of life should be able to identify that an object is a medical device, such as a gun, because it is filled with lead and has a siren."}, {"object": "hairbrush", "object_id": 524, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Hairbrush\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"Hairbrush\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"Hairbrush\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"Hairbrush\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"Hairbrush\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"Hairbrush\", \"type\": \"object\"}, {\"type\": \"object\","}, {"object": "hairnet", "object_id": 525, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round; has a filter\", \"similar objects\": [\"hat\", \"sunglasses\", \"sunglasses case\"]}"}, {"object": "hairpin", "object_id": 526, "gpt3_output": "\n\n{\"type\": \"angle\", \"description\": \"from behind; could be anpherd's cross or shepherd's cross with out front, has short legs\", \"similar objects\": [\"cornerstone\", \"harpoon\", \"arcade\"]}"}, {"object": "halter_top", "object_id": 527, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "ham", "object_id": 528, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, red, green, blue\", \"similar objects\": [\"hamster\", \"giraffe\", \" elephant \", \"cat\"]}"}, {"object": "hamburger", "object_id": 529, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "hammer", "object_id": 530, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"mace\", \"fishing net\", \"linefinder\"]}"}, {"object": "hammock", "object_id": 531, "gpt3_output": "\n\n{\"type\": \"bed\", \"description\": \"soft, down-filled; could be made of straw\", \"similar objects\": [\"couch\", \"towel\"]"}, {"object": "hamper", "object_id": 532, "gpt3_output": "\n\n{\"type\": \"room\", \"description\": \"small, roomy; has a small area for storage\", \"similar objects\": [\"bathroom\", \"bedroom\", \" living room \", \" kitchen \", \" garage \", \" outdoor room \", \" outdoor space \", \"\n\n\"size\", \"location\", \"ventilation\", \"covers\", \"mattress\", \"Refrigerator\", \"washer and dryer\", \"Refrigerator with ice\", \"Refrigerator with ice and water\", \" washer and dryer with ice\", \"Refrigerator with water\", \"Refrigerator with water and ice\","}, {"object": "hamster", "object_id": 533, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, has a short tail\", \"similar objects\": [\"hamster\", \"poodle\", \"cat\"]}"}, {"object": "hair_dryer", "object_id": 534, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be:\n\n-Sliced zucchini\n-A smooth zucchini\n-Can have brown and rough stems\n-Green zucchini\n-Zebra zucchini\n-Dented zucchini\n-Shredded zucchini\n-Dried up zucchini\n-Bumpy zucchini\n\nThe features that should be focus for an object detection model that smells vegetables should be:\n\n-A cylindrical zucchini\n-A smooth zucchini\n-A d"}, {"object": "hand_glass", "object_id": 535, "gpt3_output": "\n\nThe objects in the input can be of different types:\n\nObjects can be of different types, such as vegetables, animals, and tools."}, {"object": "hand_towel", "object_id": 536, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Wok\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "handcart", "object_id": 537, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"black; has a red stripe down the middle\", \"similar objects\": [\"car\", \"trolley\", \"bank\"]}"}, {"object": "handcuff", "object_id": 538, "gpt3_output": "\n\n{\"type\": \"crime\", \"description\": \"selling arms and drugs\", \"similar objects\": [\"misdelivered package\", \" drug deal \", \" Fugitive \", \" Fugitive from jail \", \" Fugitive from prison \", \" Terrorist \", \" Terrorist group \", \" associate of terrorist \", \" associate of terrorist \", \" associate of terrorist group \", \" associate of associate of terrorist \", \" associate of associate of terrorist group \", \" associate of associate of terrorist group \", \" associate of associate of terrorist group \", \" associate of associate of terrorist group \", \" associate of associate of terrorist group \", \" associate of associate of terrorist group \","}, {"object": "handkerchief", "object_id": 539, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"handkerchief\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"handkerchief\", \"type\": \"object\"},"}, {"object": "handle", "object_id": 540, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"handle\", \"description\": \"I have a handle to an ambulance.\n\nsimilar objects\": [\"pump\", \"pumpkin\", \"frying pan\", \"stainless steel pan\", \"pan\", \"fry pan\", \"dish pan\", \"casserole pan\", \"gazebo pan\", \"coaster pan\", \"trucker pan\", \"canopy\"]"}, {"object": "handsaw", "object_id": 541, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for Vegetables is whether or not the object is cylindrical. The object is also should have a smooth surface as well as rough stems and leaves."}, {"object": "hardback_book", "object_id": 542, "gpt3_output": "\n\n{\"type\": \"book\", \"description\": \"black, smooth, has a cover of smooth black leather\", \"similar objects\": [\"leather bookcase\", \"coffee table\", \"coaster\"]"}, {"object": "harmonium", "object_id": 543, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"guitar, brass, wood\", \"similar objects\": [\"fountain\", \"banjo\", \"banter\"]}"}, {"object": "hat", "object_id": 544, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"green, with a small hole in the center\", \"similar objects\": [\"hat\", \"turban\"]}"}, {"object": "hatbox", "object_id": 545, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Hat\", \"quantity\": \"1\", \"description\": \"Hat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat\n\nHat"}, {"object": "veil", "object_id": 546, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "headband", "object_id": 547, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Headband\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"Headband\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"Headband\", \"type\": \"object\"},"}, {"object": "headboard", "object_id": 548, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"headboard\", \"description\": \"round, black, has a black finish\", \"similar objects\": [\"nightstand\", \"bedframe\", \"nightstick\"]"}, {"object": "headlight", "object_id": 549, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like an apple would be the round body and the siren sound. Additionally, an ambulance would want to focus on the color and the bright light that the light has."}, {"object": "headscarf", "object_id": 550, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"black, white, or light green; has a band around the head\", \"similar objects\": [\"hat\", \" scarf \", \" scarf-holder\"]}"}, {"object": "headset", "object_id": 551, "gpt3_output": "\n\n{\"type\": \"audio device\", \"description\": \"round, has a speaker\", \"similar objects\": [\"headset\", \"microwave\", \"television\"]}"}, {"object": "headstall_(for_horses)", "object_id": 552, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a horseshoe on the front, black on the back\", \"similar objects\": [\"horse\", \"carriage\", \"horse carriage\"]}"}, {"object": "heart", "object_id": 553, "gpt3_output": "\n\n{\"type\": \"card\", \"description\": \"red; has a hole in the center\", \"similar objects\": [\"pump\", \"pump\", \"cartridge\", \"coaster\", \"magazine\", \"tron\", \"watch\"]"}, {"object": "heater", "object_id": 554, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a lighted area\", \"similar objects\": [\"warmonger\", \"flammable material\", \"fire starter\"]"}, {"object": "helicopter", "object_id": 555, "gpt3_output": "\n\n{\"type\": \"aviation\", \"description\": \"red; has a wingspan and height\", \"similar objects\": [\"pilots\", \"bomber\", \" Sukhoi \", \"C-130\"]}"}, {"object": "helmet", "object_id": 556, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for potential objects is its ability to find potential objects. potential objects are things that the model could be able to see if it were to encounter them in the world. potential objects could be things that the model could be able to eat, such as fruits and vegetables. potential objects could also be things that the model could be able to run away from, such as a ambulance or a car."}, {"object": "heron", "object_id": 557, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"black, white, and green; has a long neck\", \"similar objects\": [\"giraffe\", \"whale\", \"seagull\"]}"}, {"object": "highchair", "object_id": 558, "gpt3_output": "\n\n{\"type\": \"trucker\", \"description\": \"round, has a handle\", \"similar objects\": [\"tank\", \"car\"]"}, {"object": "hinge", "object_id": 559, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a sharp, jagged edge\", \"similar objects\": [\"gazelle\", \"gazelle\", \" horse \", \" elephant \", \" camel \"], \"similar objects\": [\"apple\", \"banana\", \"pear\", \"gazelle\", \" elephant \", \" camel \"], \"similar objects\": [\"fry\", \"pan\", \"fry pan\", \"frying pan\", \"pot\", \"pan\", \"coaster\", \"scope\", \"scope\", \"scope\", \"scope\", \"scope\", \"scope\","}, {"object": "hippopotamus", "object_id": 560, "gpt3_output": "\n\n{\"type\": \"livestock\", \"description\": \"a type of animal that is killed and cooked for food\", \"similar objects\": [\"baboon\", \"giraffe\", \" elephant \", \" camels \"],"}, {"object": "hockey_stick", "object_id": 561, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"honeydew\", \"honey\", \"grape\"]}"}, {"object": "hog", "object_id": 562, "gpt3_output": "\n\n{\"type\": \"livestock\", \"description\": \"red; has a short horn and long horns; could have four-wheel drive\", \"similar objects\": [\"ox\", \"giraffe\", \"chicken\", \"toucan\"]}"}, {"object": "home_plate_(baseball)", "object_id": 563, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"name\": \"home plate\", \"description\": \"\", \"similar objects\": [\"baseball\", \"bat\", \"ball\"]}"}, {"object": "honey", "object_id": 564, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"noun\", \"verb\": \"verb\", \"verb\", \"noun\"},\n\n\"noun\", \"verb\": \"verb\", \"verb\", \"noun\"},\n\n\"noun\", \"verb\": \"verb\", \"verb\", \"noun\"},"}, {"object": "fume_hood", "object_id": 565, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like zucchini would be the model's ability to identify zucchini as a food item, the model's ability to identify zucchini as a source of light, and the model's ability to identify zucchini as a source of Fume Hood."}, {"object": "hook", "object_id": 566, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "hookah", "object_id": 567, "gpt3_output": "\n\n{\"type\": \" Smoking article \", \"description\": \"No description provided \", \"similar objects\": [\"hookah\", \"hookah device\"]"}, {"object": "hornet", "object_id": 568, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"a small, black, round, no wings\", \"similar objects\": [\"coaster\", \"tronco\", \"giant ball of ice\", \"sky\")}"}, {"object": "horse", "object_id": 569, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "hose", "object_id": 570, "gpt3_output": "\n\n{\"type\": \"supplier\", \"description\": \"\", \"status\": \"active\"}, \"object\":[]"}, {"object": "hot-air_balloon", "object_id": 571, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan, smooth; has a Narrow and Slicedourgeous surface\", \"similar objects\": [\"apple\", \"grape\", \"canary\"]}"}, {"object": "hotplate", "object_id": 572, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red, has a handle\", \"similar objects\": [\"taco\", \"chicken\", \"noodle\"]}"}, {"object": "hot_sauce", "object_id": 573, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"salt and pepper\", \"similar objects\": [\"salt and pepper\", \"salt and pepper\", \"onion\"]}"}, {"object": "hourglass", "object_id": 574, "gpt3_output": "\n\n{\"type\": \"angle\", \"angle\": (0\u00b0, 0\u00b0), \"description\": \"depends on the input\", \"similar objects\": [\"hourglass\", \"zodiacal light\", \"moon\"]}"}, {"object": "houseboat", "object_id": 575, "gpt3_output": "\n\n{\"type\": \"watercraft\", \"description\": \"black, with a red stripe down the middle\", \"similar objects\": [\"coaster\", \"seaweed\", \"gulf island\", \"rivers\"]}"}, {"object": "hummingbird", "object_id": 576, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"honeydew\", \"thorny bush\", \"grape\"]}"}, {"object": "hummus", "object_id": 577, "gpt3_output": "\n\n{\"type\": \"dietary\", \"description\": \"salt, olive, lemon, garlic\", \"similar objects\": [\"tahini\", \"sour cream\", \"onion\"]}"}, {"object": "polar_bear", "object_id": 578, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; has a fur; could have a furrow on its back\", \"similar objects\": [\"polar bear\", \"elk\", \"indigo cat\", \"island\"]}"}, {"object": "icecream", "object_id": 579, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red, with a white spot in the center\", \"similar objects\": [\"frozen yogurt\", \"chocolate milk\", \"frozen fruit\", \"frozen vegetables\", \"frozen meat\", \"frozen cheese\", \"frozen milk\", \"frozen yogurt\", \"frozen meat and cheese\", \"frozen vegetables and meat\", \"frozen meat and cheese\", \"frozen fruit\", \"frozen vegetables and meat\", \"frozen meat and cheese\", \"frozen vegetables and meat\", \"frozen meat and cheese\", \"frozen vegetables and meat\", \""}, {"object": "popsicle", "object_id": 580, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a smooth surface; could be filled with ice or water\", \"similar objects\": [\"popsicle\", \"ice cream\", \"frozen yogurt\"]}"}, {"object": "ice_maker", "object_id": 581, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red, with a top and bottom that come together to form a U-shape; can be made of metal\", \"similar objects\": [\"frozen yogurt\", \"frozen fruit\", \"frozen vegetables\"]}"}, {"object": "ice_pack", "object_id": 582, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"ice pack\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"ice pack\", \"type\": \"object\"},"}, {"object": "ice_skate", "object_id": 583, "gpt3_output": "\n\n{\"type\": \"skate\", \"description\": \"A skateboarder boards a skateboard through the ice\", \"similar objects\": [\"ice cream\", \"skate\", \"iceberg\"]}"}, {"object": "igniter", "object_id": 584, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for fire is the input type. For this model, the zucchini would be an object with type \"vegetable\"."}, {"object": "inhaler", "object_id": 585, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like zucchini would be the model's ability to identify zucchini as a food item, the model's ability to identify zucchini as a source of light, and the model's ability to identify zucchini as a source of nutrition."}, {"object": "iPod", "object_id": 586, "gpt3_output": "\n\n{\"type\": \"audio player\", \"description\": \"round, has a beep sound\", \"similar objects\": [\"playground\", \"microwave\", \"microwave toy\"]}"}, {"object": "iron_(for_clothing)", "object_id": 587, "gpt3_output": "\n\n{\"type\": \"metal\", \"description\": \"black; has a sharp edge\", \"similar objects\": [\"copper\", \"steel\"]}"}, {"object": "ironing_board", "object_id": 588, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"ironing board\", \"description\": \"round, has a handle, and a cloth cover\"}, {\"type\": \"object\", \"name\": \"ironing board\", \"description\": \"black, has a handle, and a cloth cover\"}, {\"type\": \"object\", \"name\": \"ironing board\", \"description\": \"smooth, has a handle, and a cloth cover\"}, \"similar objects\": [\"ironing board\", \"iron\", \"board\", \"cloth\", \"cover\", \"handle\", \"sink\", \"frame\", \"surface\", \"substrate\","}, {"object": "jacket", "object_id": 589, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Jacket\", \"id\": \"\", \"position\": 1, \"price\": \"\", \"backpack\", \"purse\", \"amulet\", \"fairytale\", \"necklace\", \"amethyst\", \"bronze\", \"cape\", \"gun\", \"amethyst\", \"bronze\", \"cape\", \"amulet\", \"fairytale\", \"necklace\", \"amethyst\", \"bronze\", \"cape\", \"amulet\", \"fairytale\", \"necklace\", \"amethyst\", \"bronze\", \"cape\","}, {"object": "jam", "object_id": 590, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sour cream\", \"similar objects\": [\"dessert\", \"chocolate\", \"fruitcake\"]}"}, {"object": "jar", "object_id": 591, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Sour cream\", \"description\": \"\n\nSour cream is a food product that is made from a cheese that is melted and then spread in a jar. Jars are often used as a forte of food items such as yogurt and yogurt bowls."}, {"object": "jean", "object_id": 592, "gpt3_output": "\n\n{\"type\": \"person\", \"description\": \"black, white, red; could have body shape and posture\", \"similar objects\": [\"fisherman\", \"pigeon\", \"seagull\"]}"}, {"object": "jeep", "object_id": 593, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a bright light; can have a light up only when driving\", \"similar objects\": [\"car\", \"truck\", \"camper\", \"camper\", \"sled\"]}"}, {"object": "jelly_bean", "object_id": 594, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"jelly\", \"usage\": \"noun\"}\n\nInput: jelly, Output:\n\n{\"type\": \"noun\", \"description\": \"jelly\", \"usage\": \"noun\"}"}, {"object": "jersey", "object_id": 595, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" jersey\", \"description\": \"red; could have a number of colors\", \"similar objects\": [\"satchel\", \"watch\", \" necklace\"]}"}, {"object": "jet_plane", "object_id": 596, "gpt3_output": "\n\n{\"type\": \"aircraft\", \"description\": \"black, white, and red; has a wingspan; can fly\", \"similar objects\": [\"jet\", \"lion\", \"trucker\", \"buffalo\"]"}, {"object": "jewel", "object_id": 597, "gpt3_output": "\n\n{\"type\": \"gem\", \"description\": \"pink, with a clear top\", \"similar objects\": [\"sapphire\", \" turquoise \", \"sapphire\"]}"}, {"object": "jewelry", "object_id": 598, "gpt3_output": "\n\n{\"type\": \"jewelry\", \"description\": \"round, has a bezel or front piece\", \"similar objects\": [\"wafer\", \"coil\", \"bronze\", \"ferrara\"]}"}, {"object": "joystick", "object_id": 599, "gpt3_output": "\n\n{\"type\": \"adventure\", \"description\": \"round, has a handle, can be used for swimming\", \"similar objects\": [\"x-wing\", \"star wars\" ]}"}, {"object": "jumpsuit", "object_id": 600, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"jumpsuit\", \"description\": \"A jumpsuit is a clothing item that is used for professional wrestling and other activities\"."}, {"object": "kayak", "object_id": 601, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"black, with a white stripe running down the middle\", \"similar objects\": [\"coaster\", \"yacht\"]}"}, {"object": "keg", "object_id": 602, "gpt3_output": "\n\n{\"type\": \"beacon\", \"description\": \"a light in the sky\", \"similar objects\": [\"keg\", \"keenah (ice cream)\", \"kelp\"]}"}, {"object": "kennel", "object_id": 603, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "kettle", "object_id": 604, "gpt3_output": "\n\n{\"type\": \"pot\", \"description\": \"green, has a handle\", \"similar objects\": [\"carafe\", \"sink\", \"teapot\"]}"}, {"object": "key", "object_id": 605, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"key\", \"description\": \"round, black, has a handle\", \"similar objects\": [\"keyboard\", \"blu-screen\", \"tronque\"]"}, {"object": "keycard", "object_id": 606, "gpt3_output": "\n\n{\"type\": \"card\", \"description\": \"red; has a card on it\", \"similar objects\": [\"cardigan\", \"wallet\", \"keycard\"]"}, {"object": "kilt", "object_id": 607, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Kilt\", \"quantity\": \"1\", \"similar objects\": [\"kilt\", \"shirt\"]}"}, {"object": "kimono", "object_id": 608, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"type_name\": \"vegetable\"},\n\n{\"type\": \"object\", \"name\": \"crab\", \"type_name\": \"crab\"},\n\n{\"type\": \"object\", \"name\": \"apple\", \"type_name\": \"apple\"},\n\n{\"type\": \"object\", \"name\": \"giraffe\", \"type_name\": \"giraffe\"},\n\n{\"type\": \"object\", \"name\": \"elephant\", \"type_name\": \"elephant\"},\n\n}"}, {"object": "kitchen_sink", "object_id": 609, "gpt3_output": "\n\n{\"type\": \"stream\", \"description\": \"x1\", \"x2\": \"x3\", \"x4\": \"x5\", \"x6\": \"x7\", \"x7\": \"x8\", \"x8\": \"x9\", \"x9\": \"x10\", \"x10\": \"x11\", \"x11\": \"x12\", \"x12\": \"x13\", \"x13\": \"x14\", \"x14\": \"x15\"}\n\nInput: sink, Output: \n\n{\"type\": \"stream\", \"description\": \"x1\", \""}, {"object": "kitchen_table", "object_id": 610, "gpt3_output": "\n\n{\"type\": \"table\", \"description\": \"black, with a white line at the center\", \"similar objects\": [\"cat\", \"coaster\", \"tray\", \"couch\"]"}, {"object": "kite", "object_id": 611, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are found other objects in the scene. The model should also be able to identify different types of objects, such as vegetables and animals."}, {"object": "kitten", "object_id": 612, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cat\", \"toucan\"]}"}, {"object": "kiwi_fruit", "object_id": 613, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \" Kiwi, green, firm\", \"similar objects\": [\"apple\", \"fruit\", \"grape\"]}"}, {"object": "knee_pad", "object_id": 614, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"red; has a bandage on the knee\", \"similar objects\": [\"knee pad\", \"bandage\", \"sock\", \"booties\", \"shoe\"]"}, {"object": "knife", "object_id": 615, "gpt3_output": "\n\n{\"type\": \" knife\", \"name\": \"Dirty Knee\" }\n\n{\"type\": \" knife\", \"name\": \"Dirty Knee\" }"}, {"object": "knitting_needle", "object_id": 616, "gpt3_output": "\n\n{\"type\": \"numeric\", \"position\": \"1\", \"description\": \"n/a\", \"type\": \"numeric\", \"vendor\": \"n/a\", \"age\": \"n/a\", \"stain\": \"n/a\", \"lack\": \"n/a\", \"stainest: \"n/a\", \"stainest: \"n/a\", \"stainest: \"n/a\", \"vendorname\": \"n/a\", \"vendorname: n/a\", \"vendorname: n/a\", \"vendorname:"}, {"object": "knob", "object_id": 617, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for signs of life is the model's ability to identify objects like woks and ambulances. This ability would include models that be created with and without handles, and models that are in multiple sizes."}, {"object": "knocker_(on_a_door)", "object_id": 618, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a small hole in the middle\", \"similar objects\": [\"zucchini\", \"carrot\", \"zucchini\"], \"similar objects\": [\"pan\", \"pot\", \"frying pan\", \"pan\", \"frying pan\", \"carrot\", \"zucchini\", \"pan\", \"zucchini\", \"vegetable\", \"vegetable\", \"carrot\", \"zucchini\", \"vegetable\", \"pan\", \"frying pan\", \"frying pan\", \"carrot\", \""}, {"object": "koala", "object_id": 619, "gpt3_output": "\n\n{\"type\": \" mammal \", \"description\": \"black and white, fur is long and long\", \"similar objects\": [\"alpacas\", \"cattle\", \"giraffe\"]}"}, {"object": "lab_coat", "object_id": 620, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"black, white, red; has a collar\", \"similar objects\": [\"coat\", \"coat of arms\", \"bathrobe\"]}"}, {"object": "ladder", "object_id": 621, "gpt3_output": "\n\n{\"type\": \"crate\", \"description\": \"A simple ladder has a simple design and has a single rung, used for suspending goods at a craft store\", \"similar objects\": [\"door\", \"bridge\", \"latch\"]"}, {"object": "ladle", "object_id": 622, "gpt3_output": "\n\n{\"type\": \"dishwasher\", \"description\": \"round, has a spout\", \"similar objects\": [\"pansy\", \"grocery store\", \"coaster\"]"}, {"object": "ladybug", "object_id": 623, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Ladybug\"},\n\n{\"type\": \"object\", \"name\": \"Ladybug\"},"}, {"object": "lamb_(animal)", "object_id": 624, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, red; has a long mane\", \"similar objects\": [\"fry\", \"chicken\", \"gazelle\"]}"}, {"object": "lamb-chop", "object_id": 625, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, red, or green, fur or hair, scale or motion-sensitive cells\", \"similar objects\": [\" lamb \", \"chop of lamb \", \"chop of chicken \"],"}, {"object": "lamp", "object_id": 626, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to identify zucchini as a food item, but should not be able to identify the object itself."}, {"object": "lamppost", "object_id": 627, "gpt3_output": "\n\nThere are many different objects that can be detected by object detection models, depending on the input. This input includes objects like vegetables, animals, and tools. If you're looking for features that are specific to objects, you might want to focus on that type of object. If you're looking for features that are general, you might want to focus on features that are specific to that type of object."}, {"object": "lampshade", "object_id": 628, "gpt3_output": "\n\nThe feature of an object that should be focused on for object detection models is the object's object-of-interest (OI). This means that the model should be able to identify an object as being of interest based on its OI. This includes cylindrical objects, such as woks and ambulances, which typically have a round body and deep handle. These models would be interestable to a model because they have a high chance of being interested in the zucchini and producing a reaction in it."}, {"object": "lantern", "object_id": 629, "gpt3_output": "\n\n{\"type\": \"light tool\", \"description\": \"round; could be made of papers\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]"}, {"object": "lanyard", "object_id": 630, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small, white, black, has a knot\"}, \"similar objects\": [\"ring\", \"cursor\", \"bunny earrings\", \"tongue\"]"}, {"object": "laptop_computer", "object_id": 631, "gpt3_output": "\n\n{\"type\": \"computer\", \"description\": \"black, with a red light\", \"similar objects\": [\"computer\", \"laptop\", \"tablet\"]}"}, {"object": "lasagna", "object_id": 632, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"red; has a smooth surface\", \"similar objects\": [\"mango\", \"chocolate\", \"island\"]}"}, {"object": "latch", "object_id": 633, "gpt3_output": "\n\n{\"type\": \"mechanical device\", \"description\": \"round, has a latch\", \"similar objects\": [\"door\", \"latch\", \"washer\"]}"}, {"object": "lawn_mower", "object_id": 634, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a blade\", \"similar objects\": [\"mower\", \"chopper\", \"patio mower\"]"}, {"object": "leather", "object_id": 635, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Leather\", \"description\": \"The material is black, it has a hard texture and it is a good material for bags and bags for children.\"}"}, {"object": "legging_(clothing)", "object_id": 636, "gpt3_output": "\n\n{\"type\": \"textbook\", \"description\": \"A Vegetable Gardener's Manual\", \"similar objects\": [\"green tea\"]}"}, {"object": "Lego", "object_id": 637, "gpt3_output": "\n\nThe input should be a model of a Lego set."}, {"object": "legume", "object_id": 638, "gpt3_output": "\n\nThere are many different plant and animal models that can be used for object detection. Object detection models can focus on one or more of these models."}, {"object": "lemon", "object_id": 639, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"lemon\", \"banana\", \"pear\"]}"}, {"object": "lemonade", "object_id": 640, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"yellow, has a peel and bits of sugar\", \"similar objects\": [\"banana\", \"peach\"]}"}, {"object": "lettuce", "object_id": 641, "gpt3_output": "\n\nThe feature that should focus on for an input with a zucchini would be its type: zucchini. The input could also have similar objects, such as a cucumber, eggplant, and green bean."}, {"object": "license_plate", "object_id": 642, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for plates is the object's size. For example, an ambulance would be large if it was looking for plates, while a lightbulb would be large if it was looking for objects with lightening flashes."}, {"object": "life_buoy", "object_id": 643, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for water is its ability to find and respond to water-based objects. This object might be a water droplet, a water droplet's head, or a water droplet's body. The object detection model that is looking for other types of objects, such as fruits and vegetables, should focus on those objects."}, {"object": "life_jacket", "object_id": 644, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"x-shaped, has a line of plastic material on the front\", \"similar objects\": [\"life jacket\", \"latch\"]}"}, {"object": "lightbulb", "object_id": 645, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"lightbulb\", \"description\": \"round, green, at a angle towards the light\"}\n\nInput: light, Output: \n\n{\"type\": \"object\", \"name\": \"light\", \"description\": \"round, green, at a angle towards the light\"}"}, {"object": "lightning_rod", "object_id": 646, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"lightning rod\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"lamp\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"flashlight\", \"type\": \"object\"}"}, {"object": "lime", "object_id": 647, "gpt3_output": "\n\nThe feature that should focus on for an input with a lime is its color. This input should have features that can help you determine if the lime is a real fruit or not. You should also want to see if the lime has any roughness or smoothness to it."}, {"object": "limousine", "object_id": 648, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"black, red, yellow; could have a blue and red stripe\", \"similar objects\": [\"car\", \"taxi\", \"taxiway\", \"luxury car\"]}"}, {"object": "lion", "object_id": 649, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; has a lion's mane or hair, a lion's claws, or teeth\", \"similar objects\": [\"zebra\", \"giraffe\", \" elephant \", \" horse \", \" car\"]}"}, {"object": "lip_balm", "object_id": 650, "gpt3_output": "\n\nThere are many different object detection models that could be used for a zucchini. A model for this type of object could be the object detection model for vegetables. This model should focus on cylindrical objects with smooth stems, and similar objects that are could have rough stems. The model should also focus on lights and objects that have manes (happenings where the object has multiple colors)."}, {"object": "liquor", "object_id": 651, "gpt3_output": "\n\n{\"type\": \"liquor\", \"description\": \"red; has a sour taste\", \"similar objects\": [\"beer\", \"wine\", \"screwdriver\"]}"}, {"object": "lizard", "object_id": 652, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "log", "object_id": 653, "gpt3_output": "\n\nThe feature that should focus on for an input with a lot of change is the ability to detect different types of objects. For an input with few objects, like zucchini, object detection models should focus on finding simple objects, like cylindrical objects or objects with green or smooth stems."}, {"object": "lollipop", "object_id": 654, "gpt3_output": "\n\n{\"type\": \"toy\", \"description\": \"red, has a sugar coating\", \"similar objects\": [\"taco\", \"taco salad\", \"chocolate cake\"]}"}, {"object": "speaker_(stero_equipment)", "object_id": 655, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a sound effect\"}, \"similar objects\":"}, {"object": "loveseat", "object_id": 656, "gpt3_output": "\n\n{\"type\": \"seating\", \"description\": \"round, comfortable; could be in a corner\", \"similar objects\": [\"leather sofisticated seat\", \"coffee table\", \"coaster\"]"}, {"object": "machine_gun", "object_id": 657, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of an input is evidence of an input. This could be in the form of an input name, an input type, or even an input's own input type. In some cases, it might also be important to look for evidence of input activity. This can be in the form of input data such as input values or input models."}, {"object": "magazine", "object_id": 658, "gpt3_output": "\n\nThe objects in the input could be articles of clothing, such as a shirt or a dress. The objects in the group could be objects that make up a magazine, such as stories or images. or objects that make up a magazine, such as stories or images."}, {"object": "magnet", "object_id": 659, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for magnetism is the models ability to detect movement. This includes models that are designed to detect movement in objects, such as those used by police departments. In order to make sure the model is effective, the magnet should be able to detect movement on the object, as well as the model should have the ability to light up when detect movement."}, {"object": "mail_slot", "object_id": 660, "gpt3_output": "\n\n{\"type\": \"slot\", \"description\": \"red; with a small hole in the middle\", \"similar objects\": [\"slot machine\", \"slot\", \"slot machine\"], \"similar objects\": [\"slot machine\", \"slot\", \"slot machine\"], \"similar objects\": [\"slot machine\", \"slot\", \"slot machine\"], \"similar objects\": [\"slot machine\", \"slot\", \"slot machine\"], \"similar objects\": [\"slot machine\", \"slot\", \"slot machine\"], \"similar objects\": [\"slot machine\", \"slot\", \"slot machine\"], \"similar objects\": [\"slot machine\", \"slot\", \"slot machine\"], \""}, {"object": "mailbox_(at_home)", "object_id": 661, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"object\", \"description\": \"\"}"}, {"object": "mallard", "object_id": 662, "gpt3_output": "\n\n{\"type\": \"gazelle\", \"description\": \"light brown fur; could have black fur\", \"similar objects\": [\"gazelle\", \"chick\", \"toucan\"]}"}, {"object": "mallet", "object_id": 663, "gpt3_output": "\n\n{\"type\": \"all-purpose\", \"description\": \"bristly, could hit something with it\", \"similar objects\": [\"dagger\", \"harp\"]"}, {"object": "mammoth", "object_id": 664, "gpt3_output": "\n\n{\"type\": \"livestock\", \"description\": \"red; has a huge body; could have multiple calves\", \"similar objects\": [\"bison\", \" elk \", \" elk \", \"indigo \"],"}, {"object": "manatee", "object_id": 665, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light brown; has a long neck\", \"similar objects\": [\"cat\", \"toucan\", \"seagull\"]}"}, {"object": "mandarin_orange", "object_id": 666, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"orange\", \"similar objects\": [\"mango\", \"grape\", \"apple\"]}"}, {"object": "manger", "object_id": 667, "gpt3_output": "\n\n{\"type\": \"person\", \"description\": \"black, white, or green; could have a head and body\", \"similar objects\": [\"cat\", \"dog\", \"horse\"]}"}, {"object": "manhole", "object_id": 668, "gpt3_output": "\n\nThere are many different objects that can be used as input for object detection models. Some examples include the following:\n\n-Vegetables\n-Fruits\n-Bacteria\n-Disease objects\n\nEach of these objects can be used to create a model that can detect whether a given object is a vegetable, a fruit, or a disease object. The model can be used to detect if the object is smooth, cylindrical, green, or rough. The model can also detect if the object has a stem and if it is sliced into round pieces or if it has green leaves."}, {"object": "map", "object_id": 669, "gpt3_output": "\n\nThe feature of an object that should focus on for object detection models looking for similar objects is its size. Size is a major factor when it comes to finding objects, and a small, small object will be difficult to find with other search tools. Other features that should be focus on for object detection models include its color, shape, or size."}, {"object": "marker", "object_id": 670, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for signs of life is the model's ability to identify other objects in the environment. Other features that should be considered for an object detection model that is looking for signs of life include the model's ability to identify objects as being related, such as an object that is being cooked on a stove, or an object that is being handled. The model should be able to identify whether or not each object is related to the one before it, and should be able to determine if each object is life-threatening."}, {"object": "martini", "object_id": 671, "gpt3_output": "\n\n{\"type\": \"cocktail\", \"description\": \"salt and pepper,\" \"similar objects\": [\"taco\", \"chicken\", \"nosegay\"]}"}, {"object": "mascot", "object_id": 672, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a green stem; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "mashed_potato", "object_id": 673, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"butterfly\", \"dip\", \"snowflake\"]}"}, {"object": "masher", "object_id": 674, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be chopped into small pieces\", \"similar objects\": [\"zucchini\", \"butterfly\", \"toucan\"]}"}, {"object": "mask", "object_id": 675, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"mask\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"mask\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"mask\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"mask\", \"type\": \"object\"}"}, {"object": "mast", "object_id": 676, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "mat_(gym_equipment)", "object_id": 677, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"mat\", \"flooring\", \"solar system\", \"watch\"]}"}, {"object": "matchbox", "object_id": 678, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red, with a matchstick in the middle\", \"similar objects\": [\"coaster\", \"troncone\", \" tear gas can\"]"}, {"object": "mattress", "object_id": 679, "gpt3_output": "\n\n{\"type\": \"bed\", \"description\": \"black, with a white cover\", \"similar objects\": [\"bed\", \"towel\", \"towel rack\"]}"}, {"object": "measuring_cup", "object_id": 680, "gpt3_output": "\n\n{\"type\": \"measures\", \"description\": \"staff with a handle\", \"similar objects\": [\"tape\", \"treadle\", \"sink\"]}"}, {"object": "measuring_stick", "object_id": 681, "gpt3_output": "\n\n{\"type\": \"measures\", \"description\": \"round, has a line for weight\", \"similar objects\": [\"inch\", \"centimeter\", \"millimeter\"]}"}, {"object": "meatball", "object_id": 682, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"beef\", \"chicken\", \"pork\"]}"}, {"object": "medicine", "object_id": 683, "gpt3_output": "\n\nThe model should focus on objects that have a smooth surface (such as a zucchini or vegetable), have green or brown stems (such as zebra or apple), and have similar objects in the input (such as a ambulance or Lantern)."}, {"object": "melon", "object_id": 684, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "microphone", "object_id": 685, "gpt3_output": "\n\nThere are many different objects that can be captured by an object detection model. These objects could include, but are not limited to, animals, vegetables, fruits, and so on."}, {"object": "microscope", "object_id": 686, "gpt3_output": "\n\nThe microscope should focus on objects with similar shapes and sizes, regardless of their color. The microscope should also be able to measure objects accurately, regardless of their size."}, {"object": "microwave_oven", "object_id": 687, "gpt3_output": "\n\n{\"type\": \"implement\", \"description\": \"round, has a bezel for food; could be used for baking or cooking\", \"similar objects\": [\"dishwasher\", \"fridge\", \"Refrigerator\"]"}, {"object": "milestone", "object_id": 688, "gpt3_output": "\n\n{\"type\": \"infrastructure\", \"description\": \"round, has a handle, has a beveled edge\", \"similar objects\": [\"rail\", \"bridge\", \"tunnel\"]}"}, {"object": "milk", "object_id": 689, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "milk_can", "object_id": 690, "gpt3_output": "\n\n{\"type\": \"ulcer\", \"description\": \"a small, round, black object\", \"similar objects\": [\"pudding\", \"ulcer\", \"gumbo\"]}"}, {"object": "milkshake", "object_id": 691, "gpt3_output": "\n\n{\"type\": \"drink\", \"description\": \"white, bubbly water with a sour smell\", \"similar objects\": [\"milkshake\", \"shower\", \"shampoo\"]}"}, {"object": "minivan", "object_id": 692, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a shining side mirror; could have a spoiler\", \"similar objects\": [\"car\", \"truck\", \"SUV\"]"}, {"object": "mint_candy", "object_id": 693, "gpt3_output": "\n\nThe feature that should focus on for an input with a candy color is the model's ability to detect candy. The model should be able to identify candy-colored objects as well as other colors."}, {"object": "mirror", "object_id": 694, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"type_name\": \"vegetable\"},\n\n{\"type\": \"object\", \"name\": \"crab\", \"type_name\": \"crusty_crab\"},\n\n{\"type\": \"object\", \"name\": \"apple\", \"type_name\": \"fruit\"},\n\n{\"type\": \"object\", \"name\": \"giraffe\", \"type_name\": \"elephant\"},\n\n}"}, {"object": "mitten", "object_id": 695, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a hole in the center\", \"similar objects\": [\"pouch\", \"mug\"]}"}, {"object": "mixer_(kitchen_tool)", "object_id": 696, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"ingredient\", \"inputs\": [\"frozen fruit\", \"frozen vegetables\", \"frozen meat\", \"frozen milk\", \"frozen cream\", \"frozen yogurt\", \"frozen ice cream\"]}"}, {"object": "money", "object_id": 697, "gpt3_output": "\n\nThere are many different objects that can be used with money, including coins, pieces of paper, and lights."}, {"object": "monitor_(computer_equipment) computer_monitor", "object_id": 698, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its ability to see objects in its surroundings. object detection models should be able to identify objects that have been in the same situation as you, such as a zucchini and a tomato. They should also be able to identify objects that are different in shape or color, such as a ambulance and a monitor."}, {"object": "monkey", "object_id": 699, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; could have a tail\", \"similar objects\": [\"toucan\", \"seagull\", \"whale\"]}"}, {"object": "motor", "object_id": 700, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has atering system; could have multiple engines\", \"similar objects\": [\"car\", \"truck\", \"motor vehicle\"]}"}, {"object": "motor_scooter", "object_id": 701, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for a zucchini is its size. This model should be able to find objects that are smaller than itself. Additionally, the model should be able to find models that are the same size, such as a scooter."}, {"object": "motor_vehicle", "object_id": 702, "gpt3_output": "\n\nThe input should be a vehicle."}, {"object": "motorcycle", "object_id": 703, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics. For example, an object that is similar to a motorcycle would be found with objects that are similar to motorcycles."}, {"object": "mound_(baseball)", "object_id": 704, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are located at or near the base of the mound. The model should also be able to identify objects that are different types of objects, such as animals and tools."}, {"object": "mouse_(computer_equipment)", "object_id": 705, "gpt3_output": "\n\n{\"type\": \"computer\", \"description\": \"round, has a on/off switch\", \"similar objects\": [\"keyboard\", \"pointer\", \"touch screen\"]}"}, {"object": "mousepad", "object_id": 706, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"mousepad\"}, {\"type\": \"object\", \"name\": \"mouse\"}, {\"type\": \"object\", \"name\": \"keyboard\"}, {\"type\": \"object\", \"name\": \"trued\", \"date\": \"2018-10-14T19:00:00\", \"similar objects\": [\"xbox\", \"foggy\", \"snowy\", \"reptile\"]}"}, {"object": "muffin", "object_id": 707, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"round, has a hole in the middle\", \"similar objects\": [\"foam plane\", \"butterfly\", \"muffin\",\"]}"}, {"object": "mug", "object_id": 708, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Mug\", \"description\": \"\n\nThis mug is for drinking from. It is big and has a lot of color."}, {"object": "mushroom", "object_id": 709, "gpt3_output": "\n\n{\"type\": \"mushroom\", \"name\": \"albacore\", \"shoots\": \"ulles\", \"format\": \"lilac\", \"description\": \"\n\nA Mushroom is a very small, white,bumpy object that lives in water. It has a long mane of black hair and green eyes. The stem is short and red."}, {"object": "music_stool", "object_id": 710, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Music Stool\", \"description\": \"A stool with a lighted surface that plays music\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]"}, {"object": "musical_instrument", "object_id": 711, "gpt3_output": "\n\nInput: flute, Output:\n\n{\"type\": \"instrument\", \"description\": \"flute\", \"similar objects\": [\"guitar\", \"banjo\", \"fife\"]}"}, {"object": "nailfile", "object_id": 712, "gpt3_output": "\n\n{\"type\": \"file\", \"name\": \"Nailfile\", \"size\": \"\", \"similar objects\": [\"file\", \"brick\", \"fiberglass \"\", \"brick\"]"}, {"object": "napkin", "object_id": 713, "gpt3_output": "\n\n{\"type\": \"appetizer\", \"description\": \"1-2 tablespoons of oil or butter is all that's needed\", \"similar objects\": [\"taco\", \"taco shell\", \"taco sauce\"]"}, {"object": "neckerchief", "object_id": 714, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"neckerchief\", \"type\": \"object\"}, {\"type\": \"object\", \"name\": \"neckerchief\", \"type\": \"object\"},"}, {"object": "necklace", "object_id": 715, "gpt3_output": "\n\n{\"type\": \" necklace\", \"description\": \"green; has a small hole in the center\", \"similar objects\": [\"bronze necklace\", \"silver necklace\"]}"}, {"object": "necktie", "object_id": 716, "gpt3_output": "\n\n{\"type\": \"collar\", \"description\": \"thin; has a knot in the neck\", \"similar objects\": [\"necklace\", \" scarf \", \" tie \"], }"}, {"object": "needle", "object_id": 717, "gpt3_output": "\n\n{\"type\": \"nail\", \"description\": \"black, has a sharp point\", \"similar objects\": [\"nail\", \"nailgun\", \"tinder box\"]"}, {"object": "nest", "object_id": 718, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, red, green, blue\", \"similar objects\": [\"chicken\", \"egg\", \"nose\", \"banana\"]}"}, {"object": "newspaper", "object_id": 719, "gpt3_output": "\n\nThe objects in the input could be objects that are all objects in the input, or could focus on specific types of objects in the input. The model should be able to identify and track the progress of an object in the input, so that it can start cooking or lighting up."}, {"object": "newsstand", "object_id": 720, "gpt3_output": "\n\nThe object detection model should focus on objects that have either smooth stems or have smooth bumps on their stem. The model should also be able to identify similar objects, such as a zebra or an ambulance."}, {"object": "nightshirt", "object_id": 721, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"nightshirt\", \"id\": \"\", \"has\": [\"type\"]}"}, {"object": "nosebag_(for_animals)", "object_id": 722, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"black, with a white bag inside\", \"similar objects\": [\"nosebag\", \"nosecone\", \"nose\", \"flask\"]}"}, {"object": "noseband_(for_animals)", "object_id": 723, "gpt3_output": "\n\n{\"type\": \"noseband\", \"description\": \"\", \"similar objects\": [\"nose\", \" Nose\", \" Nose band \"\", \" similar objects \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\", \" lips \"\","}, {"object": "notebook", "object_id": 724, "gpt3_output": "\n\n{\"type\": \"book\", \"description\": \"black, smooth, has a spine\", \"similar objects\": [\"envelope\", \"notepad\", \"notebook\"]"}, {"object": "notepad", "object_id": 725, "gpt3_output": "\n\nThere are many different features that object detection models could focus on. One example is that of items that have similar shapes or objects that have the same shape. Another example is that of items that have the same size or weight. Finally, object detection models could focus on specific types of items, such as those that are cooked or burning."}, {"object": "nut", "object_id": 726, "gpt3_output": "\n\n{\"type\": \"nut\", \"description\": \"black, smooth\", \"similar objects\": [\"salt and sugar\", \"salt and pepper\", \"onion\"]}"}, {"object": "nutcracker", "object_id": 727, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for nuts is the object's body. This will include everything from the inside out to how it fits into the world."}, {"object": "oar", "object_id": 728, "gpt3_output": "\n\n{\"type\": \"vessel\", \"description\": \"round, has a mouth\", \"similar objects\": [\"fishing net\", \"fishing gear\", \"ripper\"]"}, {"object": "octopus_(food)", "object_id": 729, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black, white, small; has a proboscis and a proboscidea\", \"similar objects\": [\"octopus\", \"puma\", \"urk\"]}"}, {"object": "octopus_(animal)", "object_id": 730, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"octopus\", \"nose cone\", \"octopus book\"]}"}, {"object": "oil_lamp", "object_id": 731, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for signs of life is the object's size. Other features that might be important for a model to focus on include the object's shape (cyilless, handle, smooth), the object's color (black or white), the object's texture (gouged or smooth), and the object's movement (dancing or spasmodic)."}, {"object": "olive_oil", "object_id": 732, "gpt3_output": "\n\n{\"type\": \"chemical\", \"description\": \"grape seed oil\", \"similar objects\": [\"salt\"]}"}, {"object": "omelet", "object_id": 733, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"onion, bacon, ham\", \"similar objects\": [\"parmesan cheese\", \"butterfly\", \"mutton\"]}"}, {"object": "onion", "object_id": 734, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; has a head\", \"similar objects\": [\"pig\", \"goat\", \"cat\"]}"}, {"object": "orange_(fruit)", "object_id": 735, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"orange\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "orange_juice", "object_id": 736, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"light orange\", \"similar objects\": [\"fruit\", \"fruit salad\", \"fruit juice\"]}"}, {"object": "ostrich", "object_id": 737, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, has a long neck\", \"similar objects\": [\"giraffe\", \"otter\", \"coaster\"]}"}, {"object": "ottoman", "object_id": 738, "gpt3_output": "\n\n{\"type\": \"obj\", \"description\": \"a piece of furniture; a surface to hold objects; a surface for holding objects and for displaying them\", \"similar objects\": [\"coaster\", \"desk\", \"trucker bar\", \"bookcase\"]}"}, {"object": "oven", "object_id": 739, "gpt3_output": "\n\n{\"type\": \"implement\", \"description\": \"should bake goods such as bread, oven-baked goods, and such\", \"similar objects\": [\"pizza\", \"chocolate cake\", \"dessert cake\"]}"}, {"object": "overalls_(clothing)", "object_id": 740, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" overalls \", \"description\": \"\", \"similar objects\": [\"pants\", \"shirt\", \"hat\"]}"}, {"object": "owl", "object_id": 741, "gpt3_output": "\n\n{\"type\": \"wing\", \"description\": \"dark, with a beak\", \"similar objects\": [\"clam\", \"gazelle\", \"albatross\"]}"}, {"object": "packet", "object_id": 742, "gpt3_output": "\n\n{\"type\": \"packet\", \"description\": \"red; has aillon or white spot on one end\", \"similar objects\": [\"spyderm\", \"virus\", \"stain\"]}"}, {"object": "inkpad", "object_id": 743, "gpt3_output": "\n\n{\"type\": \"application\", \"name\": \"imprint\", \"description\": \"\n\nThe object detection model should focus on identifying objects that have the same name as the one that is being printed. For example, the imprint model should be able to identify the object as \"imprint\" because it has the same name as the one that is being printed."}, {"object": "pad", "object_id": 744, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"type_name\": \"vegetable\"},\n\n{\"type\": \"object\", \"name\": \"crab\", \"type_name\": \"crusty_crab\"},\n\n{\"type\": \"object\", \"name\": \"apple\", \"type_name\": \"fruit\"},\n\n{\"type\": \"object\", \"name\": \"giraffe\", \"type_name\": \"elephant\"},\n\n}"}, {"object": "paddle", "object_id": 745, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"black, with a white/black hull\", \"similar objects\": [\"house\", \"seawater\", \"seaweed\"]}"}, {"object": "padlock", "object_id": 746, "gpt3_output": "\n\n{\"type\": \"key\", \"description\": \"a keyhole\", \"similar objects\": [\"key\", \"ring\"]"}, {"object": "paintbrush", "object_id": 747, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is detecting objects in the input is the ability to have objects have different colors. An object detection model that is looking for objects in the input that have different colors will be more able to identify them and to provide a report on their activity."}, {"object": "painting", "object_id": 748, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for paintings is the type of the object. For example, an ambulance would focus on the type of the object: \"round; has a glaring siren; could with a stretcher\"."}, {"object": "pajamas", "object_id": 749, "gpt3_output": "\n\n{\"type\": \"pajama\", \"description\": \"light blue and white, with a low back and sides\", \"similar objects\": [\"pajama\", \"nightshirt\", \"nightstand\", \"tablet\"]}"}, {"object": "palette", "object_id": 750, "gpt3_output": "\n\n{\"type\": \"set\", \"name\": \"dos\", \"description\": \"pale, light blue, several light blue shades\", \"similar objects\": [\"toilet\", \"cabin\", \" kitchen\"]}"}, {"object": "pan_(for_cooking)", "object_id": 751, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"etitive,\" \"ingredients\": [\"pork\", \"chicken\"]}"}, {"object": "pan_(metal_container)", "object_id": 752, "gpt3_output": "\n\n{\"type\": \"pan\", \"name\": \"dishpan\", \"size\": \"small\", \"handle\": \"cucumber\", \"sliced\": \"eggplant\", \"color\": \"green\", \"salt\": \"sliced\", \"oon\": \"cucumber\", \"oon\": \"green bean\", \"oon\": \"zebra\", \"oon\": \"banana\", \"oon\": \"pear\", \"oon\": \"orange\", \"oon\": \"dishpan\", \"oon\": \"frying pan\", \"oon\": \"pot\", \"oon\": \"frying pan\", \"oon"}, {"object": "pancake", "object_id": 753, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"white, with a white layer of batter on top\", \"similar objects\": [\"pan\", \"dessert\", \"muffin\",\"]}"}, {"object": "pantyhose", "object_id": 754, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"pantyhose\", \"description\": \"A pair of pantyhose.\n\" }\n\nInput: soap, Output: \n\n{\"type\": \"object\", \"name\": \"soap\", \"description\": \"A soapy dish."}, {"object": "papaya", "object_id": 755, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan, has a smooth surface\", \"similar objects\": [\"fruit\", \"vegetable\", \"salad\"]}"}, {"object": "paper_plate", "object_id": 756, "gpt3_output": "\n\nThe feature of the plate that should be focus for object detection models is the fact that it is typically made of paper. This plate could be used for example to detect objects that have paper-like properties."}, {"object": "paper_towel", "object_id": 757, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"paper towel\", \"description\": \"red; has a handle\", \"similar objects\": [\"koozie\", \"towel\", \"sink\"]}"}, {"object": "paperback_book", "object_id": 758, "gpt3_output": "\n\n{\"type\": \"book\", \"description\": \"red; has a high-quality cover\", \"similar objects\": [\"patience\", \"envelope\", \"envelope\"]"}, {"object": "paperweight", "object_id": 759, "gpt3_output": "\n\nThe feature that should focus on for an object detection model thats looking for patterns in an input such as zucchini would be the presence or absence of patterns in the input."}, {"object": "parachute", "object_id": 760, "gpt3_output": "\n\n{\"type\": \" parachute \", \"description\": \"A type of parachute that is used to help people fall out of airplanes\", \"type \", \"url \", \"image \", \"alt ,\"\"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \"alt \"\", \""}, {"object": "parakeet", "object_id": 761, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"no description provided\"}, {\"type\": \"animal\", \"description\": \"black and white, has a long mane\", \"similar objects\": [\"toucan\", \"seagull\", \"giraffe\"]"}, {"object": "parasail_(sports)", "object_id": 762, "gpt3_output": "\n\n{\"type\": \"sports\", \"description\": \" Acts like a parachutist and allows for free fall . Acts as a tool for measuring distances and predicting wind speed \", \"similar objects\": [\"sailing boat\", \"sky box\"]}"}, {"object": "parasol", "object_id": 763, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "parchment", "object_id": 764, "gpt3_output": "\n\nThe input should include a list of objects that are similar to the object being detected. For example, the zucchini might be a vegetable, while the zebra might be a animal. The input should also include a list of objects that are different from the object being detected. For example, the eggplant might be a fruit, while the green bean might be a vegetable."}, {"object": "parka", "object_id": 765, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"black, with a small hole in the back\", \"similar objects\": [\"sunglasses\", \"hat\"]}"}, {"object": "parking_meter", "object_id": 766, "gpt3_output": "\n\nThere are many different features an object detection model could focus on. An object detection model that focuses on the features of an input that is a \"vegetable\" will be more successful in detecting vegetables. An object detection model that focuses on the features of an input that is a \"vegetable\" will be more successful in detecting a veggie."}, {"object": "parrot", "object_id": 767, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; could have a beak and a beak-like structure\", \"similar objects\": [\"parrot\", \"clam\", \"gazelle\"]}"}, {"object": "passenger_car_(part_of_a_train)", "object_id": 768, "gpt3_output": "\n\nThe input should be a specific object, such as a part of a train, that is used in the story. The output should be a story about a different object."}, {"object": "passenger_ship", "object_id": 769, "gpt3_output": "\n\nThe input should be a vehicle."}, {"object": "passport", "object_id": 770, "gpt3_output": "\n\n{\"type\": \"passport\", \"image\": \"image\", \"status\": \"active\"}, {\"type\": \"passport\", \"image\": \"image\", \"status\": \"inactive\"},"}, {"object": "pastry", "object_id": 771, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"white, with a red and white sugar coating\", \"similar objects\": [\"cake\", \"chocolate\", \"fruitcake\"]}"}, {"object": "patty_(food)", "object_id": 772, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"patty\", \"chicken\", \"egg\"]}"}, {"object": "pea_(food)", "object_id": 773, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"pea\", \"zucchini\", \"zucchini\"], \"similar objects\": [\"garden variety\", \"honeydew\", \"honeydew mould\"]}"}, {"object": "peach", "object_id": 774, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "peanut_butter", "object_id": 775, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sugar, butter, eggs\", \"similar objects\": [\"tahini\", \" membranee\", \"chocolate\"]}"}, {"object": "pear", "object_id": 776, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"orange\", \"banana\", \"pear\"]}"}, {"object": "peeler_(tool_for_fruit_and_vegetables)", "object_id": 777, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"removal of skin, flesh, and seeds\", \"similar objects\": [\"pulley\", \"sieve\", \"dishwasher\"]}"}, {"object": "wooden_leg", "object_id": 778, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"wooden leg\", \"description\": \"a wooden leg\""}, {"object": "pegboard", "object_id": 779, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"peach\", \"description\": \"red; has a hole in the center\", \"similar objects\": [\"peach\", \"gravy\", \"cake\"]}"}, {"object": "pelican", "object_id": 780, "gpt3_output": "\n\n{\"type\": \"bird\", \"description\": \"black, white, thin; has a neck\", \"similar objects\": [\"clam\", \"seaweed\", \"gazelle\"]}"}, {"object": "pen", "object_id": 781, "gpt3_output": "\n\nThe feature that should focus on for an input with a zucchini would be its type: zucchini, while the feature that should focus on for an input with a red wok would be its description: \"round; has a shining siren\"."}, {"object": "pencil", "object_id": 782, "gpt3_output": "\n\nThe input should be a specific, known object. The output should be an object that is similar to the input."}, {"object": "pencil_box", "object_id": 783, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Zucchini\", \"description\": \"The zucchini is a vegetable.\"}\n\n{\"type\": \"object\", \"name\": \"Carrot\", \"description\": \"The carrot is a fruit.\"}\n\n{\"type\": \"object\", \"name\": \"Giraffe\", \"description\": \"The giraffe is a animal.\"}"}, {"object": "pencil_sharpener", "object_id": 784, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"a pencil sharpener; a pencil; a pencil sharpener\"}"}, {"object": "pendulum", "object_id": 785, "gpt3_output": "\n\nThe ability to detect objects in data that way will help researchers determine whether objects in the data are representative of objects in real life. By focusing on objects that are common to all types of objects, a model could be designed that can identify them."}, {"object": "penguin", "object_id": 786, "gpt3_output": "\n\n{\"type\": \" mammal \", \"description\": \"black and white, has a long mane\", \"similar objects\": [\"penguin\", \"seagull\"]}"}, {"object": "pennant", "object_id": 787, "gpt3_output": "\n\nThe feature of an object that should focus on for object detection models that are looking for a particular input: objects with a Pennant should be more focused on when looking for objects with similar characteristics."}, {"object": "penny_(coin)", "object_id": 788, "gpt3_output": "\n\n{\"type\": \"coin\", \"price\": 1, \"badge\": \"coined\", \"description\": \"penny\", \"variant\": \"coined\"}"}, {"object": "pepper", "object_id": 789, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is its size. Other features that might be important for an object are its color, weight, or shape."}, {"object": "pepper_mill", "object_id": 790, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"a pepper mill is a device used to grate and/or ground pepper\", \"similar objects\": [\"onion\", \"chicken\", \"drummer boy\", \"cat\"]}"}, {"object": "perfume", "object_id": 791, "gpt3_output": "\n\nThe type of scent an object can have is determined by the object's features. Object detectors should focus on features of objects that are similar to features of themselves. For example, an object's features should be those that are common to objects like cars, lights, and plants."}, {"object": "persimmon", "object_id": 792, "gpt3_output": "\n\n{\"type\": \"herb\", \"description\": \"red; has a stem and leaves\", \"similar objects\": [\"grape\", \"apple\", \"leek\"]}"}, {"object": "person", "object_id": 793, "gpt3_output": "\n\n{\"type\": \"person\", \"description\": \"black, white, red eyes\", \"similar objects\": [\"black cat\", \"white cat\"]}"}, {"object": "pet", "object_id": 794, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "pew_(church_bench)", "object_id": 795, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is the presence of a feature (or features) in the input that has the same appearance but different function. For example, the pew (church bench) in the input could be used for evidence of life because it is a place where people could sit and where religious services could be held. The round shape of the pew could help it to be used as an object detection model, because it can be thought of as a place with similar features that is not only round but also has a deep end and a handle."}, {"object": "phonebook", "object_id": 796, "gpt3_output": "\n\nThere are many different objects that can be looked for in a model that has the word \"object\" in its title. These models would focus on objects that have either round body parts (like a wok) or deep parts (like an ambulance). They would also look for models that have a siren, light, or phonebook that can be held up to the light to show that it is an ambulance."}, {"object": "phonograph_record", "object_id": 797, "gpt3_output": "\n\nThe input should have objects that are either recordable objects, or objects that are that are used in the production of records."}, {"object": "piano", "object_id": 798, "gpt3_output": "\n\n{\"type\": \"piano\", \"description\": \"smooth, has a bowl at the bottom\", \"similar objects\": [\"guitar\", \"bassoon\"]}"}, {"object": "pickle", "object_id": 799, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "pickup_truck", "object_id": 800, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a red top and red bottom; is fitted with stones and obstacles\", \"similar objects\": [\"car\", \"truck\", \"tractor\", \"island\"]}"}, {"object": "pie", "object_id": 801, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"red; has a crusty crust; can be pieced together\", \"similar objects\": [\"mango\", \"chocolate\", \"fruitcake\"]}"}, {"object": "pigeon", "object_id": 802, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"pigeon\", \"gazelle\", \" elephant\"]}"}, {"object": "piggy_bank", "object_id": 803, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "pillow", "object_id": 804, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" pillow \", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\"}, \"similar objects\": [\"apple\", \"banana\", \"pear\"]"}, {"object": "pin_(non_jewelry)", "object_id": 805, "gpt3_output": "\n\nThere are many different types of objects that can be detected by object detection models. Some objects that are typically used in experiments include: vegetables, fruits, animals, and tools."}, {"object": "pineapple", "object_id": 806, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "pinecone", "object_id": 807, "gpt3_output": "\n\n{\"type\": \"nut\", \"description\": \"red; has a small hole in top\", \"similar objects\": [\"grape\", \"seaweed\", \"baked potato\"]}"}, {"object": "ping-pong_ball", "object_id": 808, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is ping-ponging between a vegetable and animal input is the size of the object. This will include size, shape, and color. The object's size should be able to handle the force of the game, and should be small enough that it can't cause any damage."}, {"object": "pinwheel", "object_id": 809, "gpt3_output": "\n\nThe ability to detect and track different types of objects in an environment can help drivers in different ways. For example, an ambulance could be able to see better in the dark, while a pinwheel could be more efficient in providing light in the dark."}, {"object": "tobacco_pipe", "object_id": 810, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for cigarettes is the object's color. Other features that could be used for an object detection model include its size, shape, and color."}, {"object": "pipe", "object_id": 811, "gpt3_output": "\n\n{\"type\": \"pipe\", \"description\": \"clear, has a-\", \"similar objects\": [\"soup pot\", \"soup\", \"catcher\"]}"}, {"object": "pistol", "object_id": 812, "gpt3_output": "\n\nThe objects that should be focus of an object detection model for a given input are those that have a smooth surface (such as zucchini, vegetables, animals), round body (such as a wok, cooker, or ambulance), deep body (such as a pistol or Lantern), or with a handle (such as a pistol or lantern)."}, {"object": "pita_(bread)", "object_id": 813, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"1. What type of zucchini?\n2. What is the description of the zucchini?\n3. What is the size of the zucchini?\n4. What is the color of the zucchini?\n5. What is the shape of the zucchini?"}, {"object": "pitcher_(vessel_for_liquid)", "object_id": 814, "gpt3_output": "\n\n{\"type\": \"vase\", \"description\": \"light green; has a small hole in the top\", \"similar objects\": [\"coaster\", \"coaster system\"]}"}, {"object": "pitchfork", "object_id": 815, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"black, with a sharp point\", \"similar objects\": [\"fork\", \"pitchfork\", \" Baton \", \"sewer\"]}"}, {"object": "pizza", "object_id": 816, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red; has a hole in the center\", \"similar objects\": [\"pizza\", \"chicken\", \"gravy\"]}"}, {"object": "place_mat", "object_id": 817, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "plate", "object_id": 818, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a rough stem; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"beef bowl\", \"dishcloth\", \"coaster\"]}"}, {"object": "platter", "object_id": 819, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"black, white, rice and beans; could be rice and beans; could have a lot of rice; could be a platter of food}\""}, {"object": "playpen", "object_id": 820, "gpt3_output": "\n\n{\"type\": \"playpen\", \"description\": \"rodent-free; large enough to fit a large child\", \"similar objects\": [\"coaster\", \"esky\", \"playpen\", \"ottoman\"]}"}, {"object": "pliers", "object_id": 821, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, sharp, has a handle\", \"similar objects\": [\"pump\", \"pistol\", \"cise\", \"dagger\"]}"}, {"object": "plow_(farm_equipment)", "object_id": 822, "gpt3_output": "\n\nThe feature that should focus on for an input with a lot of change is features that could be used to get food for the farm. This could include things like machines that turn a key into and out from the farm, a plow, or a machine that pulls a wagon."}, {"object": "plume", "object_id": 823, "gpt3_output": "\n\nThe plume feature is important for objects that may have a lot of detail, such as a smoke or gas pipe."}, {"object": "pocket_watch", "object_id": 824, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for danger in an input such as zucchini is the model's knowledge of other objects in the world. These objects could be other vegetables, other animals, or even other pocket watches. The model should be able to identify them and help them protect their loved ones."}, {"object": "pocketknife", "object_id": 825, "gpt3_output": "\n\nThere are many different features an object detection model could focus on, such as detecting objects of different sizes, detecting shapes, and detecting the presence of specific objects."}, {"object": "poker_(fire_stirring_tool)", "object_id": 826, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"dishwasher\", \"sink\", \"barrel\"]"}, {"object": "pole", "object_id": 827, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"\", \"type\": \"result\", \"result\": {\"type\": \"vegetable\", \"name\": \"zucchini\"}, \"type\": \"output\", \"description\": \"\", \"type\": \"vegetable\", \"name\": \"crushed\"}"}, {"object": "polo_shirt", "object_id": 828, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red; has a fabric fabricant\", \"similar objects\": [\"t-shirt\", \"shirt\"]}"}, {"object": "poncho", "object_id": 829, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"type_name\": \"vegetable\"},\n\n{\"type\": \"object\", \"name\": \"crab\", \"type_name\": \"crab\"},\n\n{\"type\": \"object\", \"name\": \"apple\", \"type_name\": \"apple\"},\n\n{\"type\": \"object\", \"name\": \"giraffe\", \"type_name\": \"giraffe\"},\n\n{\"type\": \"object\", \"name\": \"elephant\", \"type_name\": \"elephant\"},\n\n}"}, {"object": "pony", "object_id": 830, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "pool_table", "object_id": 831, "gpt3_output": "\n\n{\"type\": \"table\", \"description\": \"black, with a white line at the center, for water to fall through\", \"similar objects\": [\"pool\", \"lid\", \"barrier\"]}"}, {"object": "pop_(soda)", "object_id": 832, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"soda\", \"vibration\": \"ingredient\", \"similar objects\": [\"soda\", \"pop\"]}"}, {"object": "postbox_(public)", "object_id": 833, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Postbox\", \"type\": \"object\", \"description\": \"\n\n\n\nThe Postbox object is a simple object, meaning that it has no features that are not also in the other objects in the input. This object should focus on objects with features, such as description and type."}, {"object": "postcard", "object_id": 834, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for resemblances between two objects is the postcard. With similar objects, like a zebra and a postcard, postcards could be used for research or as advertising."}, {"object": "poster", "object_id": 835, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics. For example, an object with a round body and a round head would be considered a vegetable. If the object had rough stems, it would be considered a animal."}, {"object": "pot", "object_id": 836, "gpt3_output": "\n\n{\"type\": \"pot\", \"description\": \"green, has a handle\", \"similar objects\": [\"keg\", \"cicle\", \"casserole\"]}"}, {"object": "flowerpot", "object_id": 837, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"gazelle\", \"gazelle\", \"kangaroo\"]}"}, {"object": "potato", "object_id": 838, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "potholder", "object_id": 839, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"potholder\", \"description\": \"round, small; has a small hole in the center\", \"similar objects\": [\"papertowel\", \"faucet\", \"coaster\"]}"}, {"object": "pottery", "object_id": 840, "gpt3_output": "\n\n{\"type\": \"pot\", \"description\": \"green, has a handle\", \"similar objects\": [\"dish\", \"potato\", \"candy\"]}"}, {"object": "pouch", "object_id": 841, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"pouch\", \"description\": \"round, has a handle\", \"similar objects\": [\"fishing net\", \"crate\"]"}, {"object": "power_shovel", "object_id": 842, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"bunny\", \"harpoon\", \"spade\"]"}, {"object": "prawn", "object_id": 843, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"prawn\", \"gravy\", \"chicken\"]}"}, {"object": "pretzel", "object_id": 844, "gpt3_output": "\n\n{\"type\": \"baked goods\", \"description\": \"round, has a hole in the center\", \"similar objects\": [\"p Pretzel \", \"chocolate\", \"coke\"]}"}, {"object": "printer", "object_id": 845, "gpt3_output": "\n\nThe objects in the input could be objects that are used for input, like objects in a project or objects that are used for themselves. The objects in the input could also be used as sources of input, like when someone talks about \"type\" in a text editor."}, {"object": "projectile_(weapon)", "object_id": 846, "gpt3_output": "\n\nAn object that is movement-based, with a specific trajectory that can be controlled by a game engine."}, {"object": "projector", "object_id": 847, "gpt3_output": "\n\n{\"type\": \"electronic device\", \"description\": \"round; could be used for light and sound\", \"similar objects\": [\"computer\", \"laptop\", \"television\"]}"}, {"object": "propeller", "object_id": 848, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for propellers is the model's ability to identify propellers."}, {"object": "prune", "object_id": 849, "gpt3_output": "\n\n{\"type\": \"nut\", \"description\": \"red; has a pit and a skin; can be cut into small pieces\", \"similar objects\": [\"fig\", \"fruit\", \"grape\"]}"}, {"object": "pudding", "object_id": 850, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sugar, flour, butter\", \"similar objects\": [\"mango\", \"chocolate\", \"apple\"]}"}, {"object": "puffer_(fish)", "object_id": 851, "gpt3_output": "\n\n{\"type\": \"watermelon\", \"description\": \"\", \"similar objects\": [\"gummi bear\", \"giant watermelon\"]}"}, {"object": "puffin", "object_id": 852, "gpt3_output": "\n\n{\"type\": \"bird\", \"description\": \"black, white, short tail; could have a beak\", \"similar objects\": [\"giraffe\", \"seagull\", \"chick\"]}"}, {"object": "pug-dog", "object_id": 853, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"pug\", \"cat\"]}"}, {"object": "pumpkin", "object_id": 854, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "puncher", "object_id": 855, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round, has a chisel and a chisel handle\", \"similar objects\": [\"butterfly\", \"thunderbolt\", \"iaudium\"]}"}, {"object": "puppet", "object_id": 856, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are associated with each of the five objects in the input. The model should also be able to identify different types of objects from the types of objects found in the input."}, {"object": "puppy", "object_id": 857, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "quesadilla", "object_id": 858, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sugar, cinnamon, butter\", \"similar objects\": [\"doughnut\", \"chocolate cake\", \"fruitcake\"]}"}, {"object": "quiche", "object_id": 859, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"mango, raspberry, blueberry\", \"similar objects\": [\"mango\", \"raspberry\", \"blueberry\"]}"}, {"object": "quilt", "object_id": 860, "gpt3_output": "\n\n{\"type\": \"quilt\", \"description\": \"round, has a fabric on top\", \"similar objects\": [\"quilt top\", \"quilt\"]"}, {"object": "rabbit", "object_id": 861, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, has a short tail\", \"similar objects\": [\"toucan\", \"parrot\", \"clam\", \"gazelle\"]}"}, {"object": "race_car", "object_id": 862, "gpt3_output": "\n\n{\"type\": \"car\", \"description\": \"black, red, yellow\", \"similar objects\": [\"car\", \"race car\", \"truck\"]}"}, {"object": "racket", "object_id": 863, "gpt3_output": "\n\nRacket is a tool that can be used for playing games, making music, or making other sounds. It has a hardwood board on one end and a plastic end with a plastic racket on the other."}, {"object": "radar", "object_id": 864, "gpt3_output": "\n\nThe focus of an object detection model's work should be on specific objects that are consistently represented in the data. In other words, objects that are specific to the model's type should be the focus of the model's work."}, {"object": "radiator", "object_id": 865, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a handle\", \"similar objects\": [\"sink\", \"radiator\", \"bathtub\", \"toilet\"]"}, {"object": "radio_receiver", "object_id": 866, "gpt3_output": "\n\nThe input should focus on specific features that an object detection model can detect. The input should also include a description of the features that it can detect."}, {"object": "radish", "object_id": 867, "gpt3_output": "\n\nThere are many different features that an object detection model could focus on. An object detection model should focus on those features that are most important for the input."}, {"object": "raft", "object_id": 868, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"black, with a white/black stripe down the middle\", \"similar objects\": [\"coaster\", \"raft\", \"rafting party\"]}"}, {"object": "rag_doll", "object_id": 869, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" Rag Doll \", \"description\": \"A rag doll is a toy that is made of cloth, straw, or other material. The doll has a round head and a round body. It is often given to children as a toy because it is easy to hold and is not like other things that are around them. The rag doll is also a popular toy for girls.\""}, {"object": "raincoat", "object_id": 870, "gpt3_output": "\n\n{\"type\": \"appearance\", \"description\": \"round, with a shaven head\", \"similar objects\": [\"hat\", \"eyebrow\", \"sunglasses\"]}"}, {"object": "ram_(animal)", "object_id": 871, "gpt3_output": "\n\n{\"type\": \" ram \", \"description\": \"\", \"similar objects\": [\"\", \"lion\", \"giraffe \", \"monkey\"]}"}, {"object": "raspberry", "object_id": 872, "gpt3_output": "\n\n{\"type\": \"noun\", \"description\": \"a sweet fruit\", \"usage\": \"consumption\", \"similar objects\": [\"raspberry\", \"apple\"]}"}, {"object": "rat", "object_id": 873, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"rat\", \"otter\", \"gazelle\"]}"}, {"object": "razorblade", "object_id": 874, "gpt3_output": "\n\nThere are many different objects that can be looked for in objects that have been identified as zucchini. The features that could be looked for include cylindrical, smooth, and round. The features that have a brown and rough stem could be looked for, as well as slices and rounds."}, {"object": "reamer_(juicer)", "object_id": 875, "gpt3_output": "\n\nThe features that should be focus for an object detection model that processes objects of type vegetable would be:\n\n-The object's type\n-The object's size\n-The object's color\n-The object's shape"}, {"object": "rearview_mirror", "object_id": 876, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Frontal Lobster\"},\n\n{\"type\": \"item\", \"name\": \"Backpack of Lobster\"},\n\n{\"type\": \"item\", \"name\": \"Soup of Lobster\"},\n\n{\"type\": \"item\", \"name\": \"Frozen Lobster\"},\n\n{\"type\": \"item\", \"name\": \"Frozen Lobster\"},\n\n{\"type\": \"item\", \"name\": \"Frozen Lobster\"},\n\n{\"type\": \"item\", \"name\": \"Frozen Lobster\"},\n\n{\"type\": \"item"}, {"object": "receipt", "object_id": 877, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red, with a cross\", \"similar objects\": [\"cross\", \"baked good\", \"cake\"], \"similar objects\": [\"trucker\", \"car\", \"tiremaker\"]}"}, {"object": "recliner", "object_id": 878, "gpt3_output": "\n\n{\"type\": \"bed\", \"description\": \"black, with a red upholstery\", \"similar objects\": [\"bed\", \"towel\"]}"}, {"object": "record_player", "object_id": 879, "gpt3_output": "\n\n{\"type\": \"record\", \"description\": \"red; has a groove\", \"similar objects\": [\"playground\", \"tape\", \"disc\"]}"}, {"object": "reflector", "object_id": 880, "gpt3_output": "\n\nThe reflector should focus on objects with a light color, such as red, round objects,. The mirror should also focus on objects, such as green, cylindrical objects, and objects with rough stems, such as cucumbers and eggs."}, {"object": "remote_control", "object_id": 881, "gpt3_output": "\n\nThere are many different types of object detection models that could be used for an input of zucchini, apple, ambulance, lightening, and remote control."}, {"object": "rhinoceros", "object_id": 882, "gpt3_output": "\n\n{\"type\": \" animal \", \"description\": \"light brown, black, smooth skin; thick fur; big horns; big tusks\", \"similar objects\": [\"bison\", \"giraffe \", \" elephant \", \" rhinoceros\"]}"}, {"object": "rib_(food)", "object_id": 883, "gpt3_output": "\n\nThe feature of an object that an object detection model should focus on is the shape of the object. An object's shape should be can be \n\nround, round, or smooth."}, {"object": "rifle", "object_id": 884, "gpt3_output": "\n\nThe objects that should be focus of an object detection model for a given input are those that have a smooth surface (such as zucchini or vegetables), could be cylindrical (such as a zebra or bean), and could have green or rough stems (such as a rifle or light lamp)."}, {"object": "ring", "object_id": 885, "gpt3_output": "\n\n{\"type\": \"ring\", \"description\": \"thick, white, has a band of perforations\", \"similar objects\": [\"ring\", \"necklace\", \"bangle\"]}"}, {"object": "river_boat", "object_id": 886, "gpt3_output": "\n\n{\"type\": \"vessel\", \"description\": \"black, white, and green; could have a chine on the stem\", \"similar objects\": [\"coaster\", \"car\", \"towel\"]"}, {"object": "road_map", "object_id": 887, "gpt3_output": "\n\nThere are many different features an object detection model could focus on, such as the following:\n\nInput: zucchini, Output: {\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}\n\nInput: zebra, Output: {\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe"}, {"object": "robe", "object_id": 888, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "rocking_chair", "object_id": 889, "gpt3_output": "\n\n{\"type\": \"chair\", \"description\": \"black and white, has a backrest\", \"similar objects\": [\"couch\", \"sofa\", \"bed\"]}"}, {"object": "rodent", "object_id": 890, "gpt3_output": "\n\nThere are many different objects that can be looked for in objects that have the same features, such as zucchini and vegetables. It is important to focus on those objects that have the same features, such as rats and vehicles."}, {"object": "roller_skate", "object_id": 891, "gpt3_output": "\n\n{\"type\": \"skater\", \"description\": \"a type of skateboard\", \"similar objects\": [\"roller skates\", \"roller skateboards\", \"snowboarding\", \"bouldering\", \"climbing\", \"snowboarding\", \"tennis\", \"tennis\", \"tennis balls\", \"tennis rack\", \"tennis court\", \"tennis court system\", \"tennis court system system\", \"tennis court system system system\", \"tennis court system system system system\", \"tennis court system system system system\", \"tennis court system system system system\", \"tennis court"}, {"object": "Rollerblade", "object_id": 892, "gpt3_output": "\n\nThere are many different objects that can be used with a Rollerblade, including vegetables, fruits, tools, and machines. Each of these objects has different features that should be considered when designing an object detection model for the input. These features should include:\n"}, {"object": "rolling_pin", "object_id": 893, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"mango, honey, cinnamon\", \"similar objects\": [\"dessert\", \"fruit\", \"chocolate\"]}"}, {"object": "root_beer", "object_id": 894, "gpt3_output": "\n\n{\"type\": \"drink\", \"description\": \"a drink made from a root beer can and root beer can bebened\", \"similar objects\": [\"root beer\", \"root can\", \"root beer can\"]"}, {"object": "router_(computer_equipment)", "object_id": 895, "gpt3_output": "\n\n{\"type\": \"computer\", \"description\": \"round, has a number of buttons\", \"similar objects\": [\"router\", \"laptop\", \"tablet\"]}"}, {"object": "rubber_band", "object_id": 896, "gpt3_output": "\n\n{\"type\": \"determinable\", \"description\": \"not stated\", \"similar objects\": [\"tape\", \"rubber band\"]}"}, {"object": "runner_(carpet)", "object_id": 897, "gpt3_output": "\n\n{\"type\": \"running tool\", \"description\": \"round; can run quickly and smoothly\", \"similar objects\": [\"running shoe\", \"running ball\", \"running cat\"]}"}, {"object": "plastic_bag", "object_id": 898, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Plastic Bag\"},\n\n{\"type\": \"object\", \"name\": \" plastic bag\"},"}, {"object": "saddle_(on_an_animal)", "object_id": 899, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for saddle (on an animal) is the animal's surface. This will include anything from the animal's surface up to the saddle, and will include anything from looking for animals to finding objects."}, {"object": "saddle_blanket", "object_id": 900, "gpt3_output": "\n\n{\"type\": \"blank\", \"description\": \"\", \"similar objects\": [\"blank\", \"blank\", \"blank\"]"}, {"object": "saddlebag", "object_id": 901, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Saddlebag\", \"description\": \"\n\n\n\nThe features of an object detection model that is detecting objects forage will focus on the following:\n\n-Determining the type of the object\n-Determining the style of the object\n-Determining the color of the object\n-Determining the shape of the object"}, {"object": "safety_pin", "object_id": 902, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"nothingness; has no use for its own metal\", \"similar objects\": [\"pens\", \"icicle\", \"safety pin\"]"}, {"object": "sail", "object_id": 903, "gpt3_output": "\n\n{\"type\": \" sails \", \"description\": \"white, with a blue or green band across the top\", \"similar objects\": [\"sail\", \"sailing boat\", \"sailing ship\"]}"}, {"object": "salad", "object_id": 904, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "salad_plate", "object_id": 905, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"tortilla\", \"tomato\"]}"}, {"object": "salami", "object_id": 906, "gpt3_output": "\n\nSalami is a type of cheese that is typically made from pork. The cheese is made by grating cheese and then adding salt and spices."}, {"object": "salmon_(fish)", "object_id": 907, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"black and white, scales smooth\", \"similar objects\": [\"grouper\", \"seaweed\", \"bacon\", \"chicken\"]}"}, {"object": "salmon_(food)", "object_id": 908, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"bluish body; scales smooth\", \"similar objects\": [\"grouper\", \"seaweed\", \"bluestreak\"]}"}, {"object": "salsa", "object_id": 909, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sour cream and jelly\", \"similar objects\": [\"frozen yogurt\", \"chocolate cake\", \"frozen fruit\", \"frozen vegetables\"]}"}, {"object": "saltshaker", "object_id": 910, "gpt3_output": "\n\n{\"type\": \"salt\", \"description\": \"white, with a blue or green color\", \"similar objects\": [\"salt and pepper\", \"salt and pepper bottle\"]}"}, {"object": "sandal_(type_of_shoe)", "object_id": 911, "gpt3_output": "\n\n{\"type\": \"outlet\", \"description\": \"empty\", \"similar objects\": [\"sandal\"]}"}, {"object": "sandwich", "object_id": 912, "gpt3_output": "\n\nThe objects in the input could be objects that are all objects, or they could be objects that have an object side by side. The features of the model that should focus on for an input with that name are: cylindrical, smooth, has green and rough stems, can be sliced into round pieces, has green leaves, similar objects."}, {"object": "satchel", "object_id": 913, "gpt3_output": "\n\n{\"type\": \"satchel\", \"description\": \"black, with a small opening at the bottom\", \"similar objects\": [\"purse\", \"wallet\", \"keyboard\"]"}, {"object": "saucepan", "object_id": 914, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"pale yellow, has a round cover\", \"similar objects\": [\"onion\", \"butterfly\", \"mango\"]}"}, {"object": "saucer", "object_id": 915, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; has a small hole in the middle\", \"similar objects\": [\"zucchini\", \"carrot\", \"zucchini stalk\"]}"}, {"object": "sausage", "object_id": 916, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "sawhorse", "object_id": 917, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its ability to identify similar objects. An object detection model that is looking for evidence of life should be able to identify that an object is a medical device, such as the sawhorse, because it has the ability to identify similar objects as being like a medical device."}, {"object": "saxophone", "object_id": 918, "gpt3_output": "\n\nThe input should be a saxophone."}, {"object": "scale_(measuring_instrument)", "object_id": 919, "gpt3_output": "\n\n{\"type\": \"scale\", \"description\": \"round, has a handle\", \"similar objects\": [\"treadmill\", \"instrument\", \"math instrument\"]"}, {"object": "scarecrow", "object_id": 920, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for scared objects is its ability to find scared objects. This includes things like scarecrows, which have a small red circle as their type, and scarecrows that have a big red circle as their type."}, {"object": "scarf", "object_id": 921, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"s scarf \", \"description\": \"s scarf is round, has a smooth surface, and has a white border\"."}, {"object": "school_bus", "object_id": 922, "gpt3_output": "\n\n{\"type\": \"bus\", \"description\": \"black, with red and green stripes\", \"similar objects\": [\"bushel\", \"pile of seeds\", \"bus\"]"}, {"object": "scissors", "object_id": 923, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for Vegetables is whether or not the object is cylindrical, with a smooth surface. The object should also be possible to identify by its similar objects."}, {"object": "scoreboard", "object_id": 924, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of an input is evidence of whether or not the object is similar to an input. An object detection model that is looking for evidence of an input should also be able to found whether or not the object is edible."}, {"object": "scraper", "object_id": 925, "gpt3_output": "\n\nScraper should focus on deep-cleaning dishes and pots, not on cleaning individual items."}, {"object": "screwdriver", "object_id": 926, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"deep; has a small hole on the top\", \"similar objects\": [\"pump\", \"pump\", \"pump\", \"lamp\"]"}, {"object": "scrubbing_brush", "object_id": 927, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is scrubbing is the model's ability to remove dirt, sand, and other debris from an object. The model should be able to clean surfaces by scrubbing down the sides and bottom."}, {"object": "sculpture", "object_id": 928, "gpt3_output": "\n\nThe feature of an object that needs to be considered for a featureless model is its size. Size is important because it is used to determine how the object will look and feel in the model. A small object will likely look large in the model and a large object will likely look small in the model."}, {"object": "seabird", "object_id": 929, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or green; could have a beak and beaks\", \"similar objects\": [\"coaster\", \"falcon\", \"chick\"]}"}, {"object": "seahorse", "object_id": 930, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, with a long mane\", \"similar objects\": [\"seagull\", \"whale\", \"toucan\"]}"}, {"object": "seaplane", "object_id": 931, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"seaweed\", \"gulf land\", \"ocean\"]}"}, {"object": "seashell", "object_id": 932, "gpt3_output": "\n\nThe feature that should focus on for an input with a zucchini would be its type: zucchini, while the feature that should focus on for an input with a red wok would be its description: \"round; has a shining siren\"."}, {"object": "sewing_machine", "object_id": 933, "gpt3_output": "\n\n{\"type\": \"machine\", \"description\": \"round, has a speed control\", \"similar objects\": [\"needle\", \"stitch\", \"sewers\"]}"}, {"object": "shaker", "object_id": 934, "gpt3_output": "\n\n{\"type\": \"dish\", \"description\": \"pale, smooth, has a handle\", \"similar objects\": [\"onion\", \"cucumber\", \"gravy\"]}"}, {"object": "shampoo", "object_id": 935, "gpt3_output": "\n\n{\"type\": \"liquid\", \"description\": \"white, has a sour smell\", \"similar objects\": [\"soda\", \"shampoo\", \"conditioner\"]}"}, {"object": "shark", "object_id": 936, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"black, white, blurry\", \"similar objects\": [\"crocodile\", \"enlarged squid\", \"octopus\"]}"}, {"object": "sharpener", "object_id": 937, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"a sharpening stone\", \"similar objects\": [\"zester\", \"knife\", \"spice grinder\"]}"}, {"object": "Sharpie", "object_id": 938, "gpt3_output": "\n\n{\"type\": \"marker\", \"description\": \"round; has a line at the top\", \"similar objects\": [\"pen\", \"catcher's eye\", \"notch\", \"catcher's eye mark\"]}"}, {"object": "shaver_(electric)", "object_id": 939, "gpt3_output": "\n\nThe shaver (electric) should focus on objects with a light color, such as red, round objects, and objects with a stem, such as apples."}, {"object": "shaving_cream", "object_id": 940, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have bumps and crevices; could be cultivated in water; could be ground into a flour or sugarpaste\", \"similar objects\": [\"flour\", \"sugarpaste\", \" shaving cream \", \"apple\", \"banana\"]}"}, {"object": "shawl", "object_id": 941, "gpt3_output": "\n\n{\"type\": \"textbook\", \"description\": \"The model should focus on objects that are either specifically described as being like objects (e.g. a zucchini is like a vegetable), or have features that are similar to those of an object (e.g. a zebra has smooth skin and green stems, while a apple has round flesh and green leaves)."}, {"object": "shears", "object_id": 942, "gpt3_output": "\n\n{\"type\": \"mechanical tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"pile of feathers\", \"pile of feathers\"]}"}, {"object": "sheep", "object_id": 943, "gpt3_output": "\n\n{\"type\": \"livestock\", \"description\": \"Sheep; could be divided intoiegel and dewelop; has two sets of legs and a two-tone coat\", \"similar objects\": [\"goat\", \" lamb \", \" pony \", \" horse \"}"}, {"object": "shepherd_dog", "object_id": 944, "gpt3_output": "\n\n{\"type\": \"dog\", \"description\": \"black and white, furrowed eyes; has a wagging tail\", \"similar objects\": [\"cat\", \" shepherd dog \", \"lion\"]}"}, {"object": "sherbert", "object_id": 945, "gpt3_output": "\n\nThere are many different features that an object detection model could focus on. One example is objects that are cylindrical, has smooth stem, and has round pieces."}, {"object": "shield", "object_id": 946, "gpt3_output": "\n\n{\"type\": \"armour\", \"description\": \"black, with a large hole in the middle\", \"similar objects\": [\"shield\", \"hat\", \"guitar\"]}"}, {"object": "shirt", "object_id": 947, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"shirt\", \"material\": \"steel\", \"similar objects\": [\"t-shirt\", \"cardigan\", \" skirt\"]}"}, {"object": "shoe", "object_id": 948, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Shoe\", \"id\": \"Shoe\", \"is_custom\": 1, \"is_custom_intro\": 1, \"is_custom_title\": 1, \"is_custom_meta\": 1, \"is_custom_meta_title\": 1, \"is_custom_meta_title_1\": 1, \"is_custom_meta_title_2\": 1, \"is_custom_meta_title_3\": 1, \"is_custom_meta_title_4\": 1, \"is_custom_meta_title"}, {"object": "shopping_bag", "object_id": 949, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" object \", \"description\": \"\n\nThere are many different features an object detection model could focus on, depending on the input. For example, an object detection model could focus on the type of object, the object's description, the similar objects, and the time of day the object is used."}, {"object": "shopping_cart", "object_id": 950, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "short_pants", "object_id": 951, "gpt3_output": "\n\n{\"type\": \"text\", \"message\": \"You should only use objects that are safe\", \"similar objects\": [\"keyboard\", \"tongue\"]}"}, {"object": "shot_glass", "object_id": 952, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"x1\", \"accessory2\": \"x2\", \"accessory3\": \"x3\"}\n\nx1 = \"vegetable\", x2 = \"green\", x3 = \"black\"}\n\nx1 = \"vegetable\", x3 = \"black"}, {"object": "shoulder_bag", "object_id": 953, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"shoulder bag\", \"description\": \"black, with a white logo\", \"similar objects\": [\"purse\", \"backpack\", \"pouch\"]}"}, {"object": "shovel", "object_id": 954, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "shower_head", "object_id": 955, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Shower Head\", \"description\": \"round, has a handle\", \"similar objects\": [\"shower\", \"hand\", \"shower head\"]}"}, {"object": "shower_cap", "object_id": 956, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a cap at the top\", \"similar objects\": [\"shower head\", \"shower curtain\"]}"}, {"object": "shower_curtain", "object_id": 957, "gpt3_output": "\n\n{\"type\": \"curtilage\", \"description\": \"thin, white, has a stem\", \"similar objects\": [\"curtilage\", \"paper\", \"shower curtain\"]}"}, {"object": "shredder_(for_paper)", "object_id": 958, "gpt3_output": "\n\n{\"type\": \"object\", \"properties\": [\"type\", \"properties\"]}"}, {"object": "signboard", "object_id": 959, "gpt3_output": "\n\n{\"type\": \"sign\", \"description\": \"round, has a circle at the center\", \"similar objects\": [\"house\", \"flag\", \"flagpole\"]}"}, {"object": "silo", "object_id": 960, "gpt3_output": "\n\n{\"type\": \"agricultural\", \"description\": \"round, has a roof\", \"similar objects\": [\"field\", \"cage\", \"straw\", \"milk can\"]}"}, {"object": "sink", "object_id": 961, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"\"}"}, {"object": "skateboard", "object_id": 962, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan, smooth; has a short stem\", \"similar objects\": [\"apple\", \"grocery store\", \"pumpkin\"]}"}, {"object": "skewer", "object_id": 963, "gpt3_output": "\n\n{\"type\": \"beef\", \"description\": \"round, has a sharp point\", \"similar objects\": [\"chicken\", \"fish\", \""}, {"object": "ski", "object_id": 964, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "ski_boot", "object_id": 965, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a strap\", \"similar objects\": [\"snow pants\", \"bunny earrings\", \"bunny earrings\"]"}, {"object": "ski_parka", "object_id": 966, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"black, with a red band around the neck\", \"similar objects\": [\"snowboard\", \"sunglasses\"]}"}, {"object": "ski_pole", "object_id": 967, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a handle\", \"similar objects\": [\"ski pole\", \"snow pants\"]}"}, {"object": "skirt", "object_id": 968, "gpt3_output": "\n\n{\"type\": \"textbook\", \"description\": \"The model should focus on objects that are similar in size, shape, or color to the input object. For example, the object might be a small, round, flat object that is similar in size to a pencil. The model should be able to create a story about what happened with the input object.\""}, {"object": "skullcap", "object_id": 969, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"light brown; has a brown band around the neck\", \"similar objects\": [\"moccasin\", \"sabre\", \"nosegay\"]}"}, {"object": "sled", "object_id": 970, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "sleeping_bag", "object_id": 971, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" Sleeping bag \", \"description\": \"A sleeping bag is a product that is used to sleep in. A sleeping bag is made of soft fabric and has a lot of room to sleep. A sleeping bag is also made of for cold weather and is made of many pockets for items to keep with you. \" }"}, {"object": "sling_(bandage)", "object_id": 972, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"red; has a bandage; is water-resistant\"}\n\nInput:\n\n{\"type\": \"object\", \"description\": \"\"}"}, {"object": "slipper_(footwear)", "object_id": 973, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a small hole in the center\", \"similar objects\": [\"slipper\", \"sole\", \"shoe\"]}"}, {"object": "smoothie", "object_id": 974, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "snake", "object_id": 975, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black, white, small; has a long body\", \"similar objects\": [\"crocodile\", \"gazelle\", \"elephant\", \"island\", \"island\", \"gazelle\", \"toucan\", \"parrot\", \"clam\", \"gazelle\", \"gazelle\", \"clam\", \"clam\", \"gazelle\", \"clam\", \"clam\", \"clam\", \"clam\", \"clam\", \"clam\", \"clam\", \"clam\", \"clam\","}, {"object": "snowboard", "object_id": 976, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for snowboards is the model's ability to identify snowboards. The snowboard model should be able to identify different types of snowboards, such as mogul, powder, and cedar."}, {"object": "snowman", "object_id": 977, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that sees a zucchini should be the zucchini's cylindrical shape, the zucchini's green color, and the zucchini's smooth surface. The model should be able to identify similar objects, such as a cucumber or an eggplant."}, {"object": "snowmobile", "object_id": 978, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"gray; has a large tail; can move through snow\", \"similar objects\": [\" sled \", \" snowmobile \", \" snow Taxi \", \" snow truck \"]"}, {"object": "soap", "object_id": 979, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"soup\", \"description\": \"\n\nThe features of an object detection model that focuses on those that are similar to an object should be:\n\n-Type: \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\"\n\n-Similar objects: [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "soccer_ball", "object_id": 980, "gpt3_output": "\n\n{\"type\": \"ball\", \"description\": \"light blue\", \"similar objects\": [\"ball\", \"ballerin\"]}"}, {"object": "sock", "object_id": 981, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Sock\", \"quantity\": \"1\", \"similar objects\": [\"Fruit\", \"Bread\", \"Can\", \"Pot\", \"ettle\"]}"}, {"object": "sofa", "object_id": 982, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"sofia\", \"description\": \"\n\nSofia is a small, round object that has a small, round head. It is also small, round body and a small, round non-toxic head. Sofia has a small, round body and a small, round non-toxic head."}, {"object": "softball", "object_id": 983, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"gazelle\", \"giraffe\", \" elephant\"]}"}, {"object": "solar_array", "object_id": 984, "gpt3_output": "\n\n{\"type\": \"geo-location\", \"location\": \"on a sunny beach\", \"similar objects\": [\"solar system\", \"geo-map\"]}"}, {"object": "sombrero", "object_id": 985, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics. For example, an object with a round body and a round head would be considered a vegetable. If the object had rough stems, it would be considered a animal."}, {"object": "soup", "object_id": 986, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "soup_bowl", "object_id": 987, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"butterfly\", \"fern\"]}"}, {"object": "soupspoon", "object_id": 988, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan, with a white spot in the center\", \"similar objects\": [\"zucchini\", \"butterfly\", \"toucan\"]}"}, {"object": "sour_cream", "object_id": 989, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"sour cream\", \"butter\", \"grocery store], }"}, {"object": "soya_milk", "object_id": 990, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan; has a light green skin and a light brown body\", \"similar objects\": [\"soy milk\", \"chicken\", \"fedora\"]}"}, {"object": "space_shuttle", "object_id": 991, "gpt3_output": "\n\n{\"type\": \"space\", \"description\": \"round, has a surface\", \"similar objects\": [\"gate\", \"spaceship\", \"rocket\"]}"}, {"object": "sparkler_(fireworks)", "object_id": 992, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is the data available for examination. This data can include objects in the input, their type, description, and similar objects."}, {"object": "spatula", "object_id": 993, "gpt3_output": "\n\n{\"type\": \"apparatus\", \"description\": \"round, has a handle\", \"similar objects\": [\"spatula\", \"fork\", \"spatula-like tool\"]"}, {"object": "spear", "object_id": 994, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"black; has a sharp point; could be used for stabbing\", \"similar objects\": [\"spear\", \"sword\", \" Baton \", \" knife \", \"gun\"]}"}, {"object": "spectacles", "object_id": 995, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to identify zucchini as a vegetable, the description should be accurate, and the model should be able to identify the ingredients of zucchini as well as the type of zucchini."}, {"object": "spice_rack", "object_id": 996, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"spice rack\", \"description\": \"A rack of spices, typically a round object with a pointed end\", \"similar objects\": [\"flask\", \"fridge\", \"crate\"]}"}, {"object": "spider", "object_id": 997, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black, white, small; has a webbing between its legs\", \"similar objects\": [\"jumping spider\", \"crawler\", \"moth\"]}"}, {"object": "crawfish", "object_id": 998, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"black and white, scales smooth\", \"similar objects\": [\"grouper\", \"seaweed\", \"gummed off\", \"urtega\", \"papaya\"]"}, {"object": "sponge", "object_id": 999, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"\"}"}, {"object": "spoon", "object_id": 1000, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"salt\", \"type\": \"vegetable\", \"size\": \"small\", \"similar objects\": [\"banana\", \"apple\", \"gravy\", \"chicken\"]}"}, {"object": "sportswear", "object_id": 1001, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"black, white, red, green\", \"similar objects\": [\"shoe\", \"bra\", \"skate\"]}"}, {"object": "spotlight", "object_id": 1002, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round; could have a lighted effect\", \"similar objects\": [\"light stick\", \"lamp\", \"umbrella\"]"}, {"object": "squid_(food)", "object_id": 1003, "gpt3_output": "\n\nThe zucchini and the apple should be focused on in this input. The zebra and the green bean should be focused on in this input. The ambulance and the light tool should be focused on in this input. The squid and the banana should be focused on in this input. The squid and the pan should be focused on in this input. The ambulance and the frying pan should be focused on in this input."}, {"object": "squirrel", "object_id": 1004, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black, white, small; has a tail\", \"similar objects\": [\" squirrel \", \" rat \", \" snake \"], \"habit\": \" squirrel \n\n}"}, {"object": "stagecoach", "object_id": 1005, "gpt3_output": "\n\nThe ambulance could be an ambulance, the vehicle could be a bus, the stagecoach could be a bus, and the lantern could be a light bulb."}, {"object": "stapler_(stapling_machine)", "object_id": 1006, "gpt3_output": "\n\nStapler (stapler)."}, {"object": "starfish", "object_id": 1007, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white, has a sharp spade-like nose\", \"similar objects\": [\"star\", \"crocodile\", \"toucan\"]}"}, {"object": "statue_(sculpture)", "object_id": 1008, "gpt3_output": "\n\nThe feature of the statue (sculpture) should be focused on in an object detection model. The statue should have a smooth surface, be of a green color, and be smooth stems. The statue should be sliced into round pieces and its features should be similar to others in the input."}, {"object": "steak_(food)", "object_id": 1009, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; could have rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"beef\", \"chicken\", \"gravy\", \"pork\"]}"}, {"object": "steak_knife", "object_id": 1010, "gpt3_output": "\n\n{\"type\": \" knife \", \"name\": \" steak knife\"}, {\"type\": \" knife \", \"name\": \" steak\", \"weight\": \" steak\", \"velocity\": \" steak\", \"flinch rate\": \" steak\", \"recidivism\": \" steak\"}\n\nInput: screwdriver, Output: \n\n{\"type\": \" tool \", \"name\": \" screwdriver\"}, {\"type\": \" tool \", \"name\": \" screw\", \"weight\": \" screw\", \"velocity\": \" screw\", \"flinch rate\": \" screw\", \"recidivism\": \" screw\"}"}, {"object": "steering_wheel", "object_id": 1011, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for danger in an input such as a zucchini would be the steering wheel. This would be the tool that controls the vehicle and could have control over the siren and stretcher feature."}, {"object": "stepladder", "object_id": 1012, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"black, with a black blade and a black handle\", \"similar objects\": [\"mace\", \" Knife \", \" axe \"],"}, {"object": "step_stool", "object_id": 1013, "gpt3_output": "\n\n{\"type\": \"platform\", \"positioning parameter\", \"parameters\", \"description\": \"The step stool should be able to be placed in any position so that users can reach up and reach for things that they need to eat or drink. The platform should also have a handle so that users can easily go up and down the steps.\""}, {"object": "stereo_(sound_system)", "object_id": 1014, "gpt3_output": "\n\n{\"type\": \"sound system\", \"description\": \"round, has a speaker\", \"similar objects\": [\"speakers\", \"mic\", \"mic stand\", \" Cochlear B ears \", \"hearing aid\"]}"}, {"object": "stew", "object_id": 1015, "gpt3_output": "\n\n{\"type\": \"taste\", \"taste\", \"completed\", \"completed\"}, \"completed\": true"}, {"object": "stirrer", "object_id": 1016, "gpt3_output": "\n\n{\"type\": \"drum\", \"description\": \"round; has a spindle\", \"similar objects\": [\"drummer\", \"harpoon\"]"}, {"object": "stirrup", "object_id": 1017, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"a piece of metal or plastic that helps a person wear a shoe\", \"similar objects\": [\"tirejarret\", \"sunglasses\", \"sunglasses case\"]}"}, {"object": "stool", "object_id": 1018, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"stool\", \"description\": \"black, has a handle\", \"similar objects\": [\"cage\", \"tower\", \"timbuk2\"]}"}, {"object": "stop_sign", "object_id": 1019, "gpt3_output": "\n\nThere are many different features that object detection models could focus on. One example is that of items that have similar shapes or objects that have the same size. Another example is an object's age or size."}, {"object": "brake_light", "object_id": 1020, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells like zucchini would be the features that make zucchini a vegetable, such as cylindrical shape, smooth surface, and possible brown and rough stems, all of which would be shared by other objects in the input.\n\nThe features that should focus for an object detection model that smells like the zebra would be the similar objects, such as a cucumber,, eggplant, and green bean."}, {"object": "stove", "object_id": 1021, "gpt3_output": "\n\n{\"type\": \"fuel\", \"description\": \"flammable; has a smell\", \"similar objects\": [\"coke\", \"petrol\", \"gasoline\"]}"}, {"object": "strainer", "object_id": 1022, "gpt3_output": "\n\n{\"type\": \"filter\", \"description\": \" should be able to handle a variety of media\"}, \"similar objects\": []"}, {"object": "strap", "object_id": 1023, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"\"}"}, {"object": "straw_(for_drinking)", "object_id": 1024, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"flowers; needs to be sweetened\", \"similar objects\": [\"frozen fruit\", \"chocolate\", \"frozen yogurt\"]}"}, {"object": "strawberry", "object_id": 1025, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "street_sign", "object_id": 1026, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for signs of life is the object's size. For vegetables, it would focus on their large seeds and for fruits on the seeds andalinage. For vehicles it would look for signs of life such as a driver and passengers."}, {"object": "streetlight", "object_id": 1027, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for signs of life is the environment. This includes not just the physical environment but also the conditions for using the environment, such as being in the sun or rain. This will help to determine if the object is livingable."}, {"object": "string_cheese", "object_id": 1028, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for cheese is its size. Other features that might be important for the model to focus on include its shape, size, and color."}, {"object": "stylus", "object_id": 1029, "gpt3_output": "\n\nThe stylus is a simple input that needs to be able to connect to an application to make notes."}, {"object": "subwoofer", "object_id": 1030, "gpt3_output": "\n\n{\"type\": \"mic\", \"description\": \"round, has a hole in the middle\", \"similar objects\": [\"microwave\", \"microphone\", \"sig\"],"}, {"object": "sugar_bowl", "object_id": 1031, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Sugar Bowl\", \"description\": \"\", \"size\": \"\", \"type\": \"vegetable\", \"properties\": [\"name\", \"description\"]"}, {"object": "sugarcane_(plant)", "object_id": 1032, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"black, red, green; can be red, green, and brown; can have a stem\", \"similar objects\": [\"sugar\", \"cane\", \"canary\"]}"}, {"object": "suit_(clothing)", "object_id": 1033, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"I don't know\", \"similar objects\": [\"frock\"]}"}, {"object": "sunflower", "object_id": 1034, "gpt3_output": "\n\n{\"type\": \"flower\", \"description\": \"green, has a stem\", \"similar objects\": [\"honeydew\", \"lily\", \"violet\"]}"}, {"object": "sunglasses", "object_id": 1035, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to identify zucchini as a food item, but should not be able to identify the product zucchini from other vegetables."}, {"object": "sunhat", "object_id": 1036, "gpt3_output": "\n\n{\"type\": \"image\", \"image\": [{\"z\": \"0\", \"x\":"}, {"object": "surfboard", "object_id": 1037, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cyan; has small, sharp teeth\", \"similar objects\": [\"seaweed\", \"grocery store\", \"bridge\"]}"}, {"object": "sushi", "object_id": 1038, "gpt3_output": "\n\nThe input should be a list of objects that are similar in shape, size, or color."}, {"object": "mop", "object_id": 1039, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "sweat_pants", "object_id": 1040, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"suit\", \"description\": \"black, red, green; might have a waistband and pockets\"}\n\nInput: suit, Output: \n\n{\"type\": \"object\", \"name\": \"shirt\", \"description\": \"black, red, green; might have a waistband and pockets\"}"}, {"object": "sweatband", "object_id": 1041, "gpt3_output": "\n\n{\"type\": \"attribute\", \"attributeName\": \"value\", \"attributeValue\": [], \"source\": \"Human\"."}, {"object": "sweater", "object_id": 1042, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"green, has a handle\", \"similar objects\": [\"chaat\", \"chocolate\", \"bars\", \"cake\"]}"}, {"object": "sweatshirt", "object_id": 1043, "gpt3_output": "\n\n{\"type\": \"text\", \"message\": \"I'm not sure\", \"similar objects\": [\"tshirt\", \"hoodie\"]}"}, {"object": "sweet_potato", "object_id": 1044, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, smooth; can have bumps and bruises\", \"similar objects\": [\"apple\", \"carrot\", \"zucchini\"]}"}, {"object": "swimsuit", "object_id": 1045, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "sword", "object_id": 1046, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"black; has a blade that can be sharpened\", \"similar objects\": [\"sword\", \"spear\", \" Baton \", \"nightstick\"]}"}, {"object": "syringe", "object_id": 1047, "gpt3_output": "\n\n{\"type\": \"vitamins\", \"description\": \" needed for health; can be shared with others; can be shared using exchange rates\"}"}, {"object": "Tabasco_sauce", "object_id": 1048, "gpt3_output": "\n\n{\"type\": \"text\", \"message\": \"\n\nThe zucchini should be green because it will be cooked by the fire. The avocado should have a smooth surface because it will be sliced into round pieces. The eggplant should have green leaves to keep its color and the green bean should be able to have green leaves because they are going to be cooked by the fire."}, {"object": "table-tennis_table", "object_id": 1049, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for tennis players is their body size. This model should be able to identify players of all body types, regardless of their size."}, {"object": "table", "object_id": 1050, "gpt3_output": "\n\n{\"type\": \"table\", \"description\": \"black, with a white line in the center\", \"similar objects\": [\"cat\", \"coaster\", \"coaster\"]"}, {"object": "table_lamp", "object_id": 1051, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for dinner is the object's size. For vegetables, they should focus on the most round and small-sized vegetables, while for fruits they should look for plants that have similar growth habits (such as apples or bananas). For flowers, they should focus on those that have round flowers and those that have blue or green leaves."}, {"object": "tablecloth", "object_id": 1052, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is the tablecloth. This tablecloth can help an object detection model find food because it can help an image show the object's surroundings. The tablecloth can also help an object detection model determine if the object is food."}, {"object": "tachometer", "object_id": 1053, "gpt3_output": "\n\nThe output of an object detection model should be able to measure the intensity of light, while the input should be able to find objects."}, {"object": "taco", "object_id": 1054, "gpt3_output": "\n\n{\"type\": \"taco\", \"description\": \"red, smooth, has a toppings of guacamole and cheese\", \"similar objects\": [\"taco\", \"chicken\", \"fish\", \"chicken salad\", \"dessert chicken\"]}"}, {"object": "tag", "object_id": 1055, "gpt3_output": "\n\nThe feature that should focus on for an input with a specific name is \"type\". An object with the type feature should be able to be identified by its type, such as a tag that can be identified by its type of light (such as a lantern)."}, {"object": "taillight", "object_id": 1056, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to identify zucchini as a vegetable, the description should be accurate, and the model should be able to identify the ingredients of zucchini as well as the object's siblings."}, {"object": "tambourine", "object_id": 1057, "gpt3_output": "\n\n{\"type\": \"guitar\", \"description\": \"a small, thin, pointed instrument with a thin metal wire in one end\", \"similar objects\": [\"guitar\", \"banjo\", \"mandolin\"]}"}, {"object": "army_tank", "object_id": 1058, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a long road surface; can be pushed or pulled\", \"similar objects\": [\"tank\", \"tank engine\", \"tank\", \", \"tank engine\", \"tank\", \"\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"army tank\", \"arm"}, {"object": "tank_(storage_vessel)", "object_id": 1059, "gpt3_output": "\n\n{\"type\": \"tank\", \"description\": \"black, with a large hole in the bottom\", \"similar objects\": [\"furniture\", \"tank\", \"tank\"], \"similar objects\": [\"tank\", \"tank\", \"vessel\", \"container\"]"}, {"object": "tank_top_(clothing)", "object_id": 1060, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Top\", \"description\": \"Belt-like item with a deep pocket\", \"similar objects\": [\"bottom\", \"tank\", \"gun\"]}"}, {"object": "tape_(sticky_cloth_or_paper)", "object_id": 1061, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is the ability to track the object's movement. An object's movement can help us determine if it is or is not a living thing."}, {"object": "tape_measure", "object_id": 1062, "gpt3_output": "\n\nThe ability to measure things accurately and quickly is important in our world. Object detection models should focus on detecting objects with similar characteristics, whether they be similar looking objects such as objects with similar shapes or similar features, or simply similar in size or shape. object detection models that focus on detecting objects with similar characteristics and similar features are more likely to be successful in detecting the presence of other objects, particularly in a search context."}, {"object": "tapestry", "object_id": 1063, "gpt3_output": "\n\nThe tapestry should focus on the details of the fabric, such as the color and style."}, {"object": "tarp", "object_id": 1064, "gpt3_output": "\n\n{\"type\": \"tarp\", \"description\": \"black, with a white stripe running along the top\", \"similar objects\": [\"tarp\", \"cage\", \"football\", \"soccer ball\"]"}, {"object": "tartan", "object_id": 1065, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for tartans is their size. Other features that could be used for an object detection model that is looking for tartans include their color, shape, or texture."}, {"object": "tassel", "object_id": 1066, "gpt3_output": "\n\nThe features that should be focus for an object detection model for a zucchini are its type, its description, its similar objects, and its handle."}, {"object": "tea_bag", "object_id": 1067, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"tea bag\", \"description\": \"},"}, {"object": "teacup", "object_id": 1068, "gpt3_output": "\n\n{\"type\": \"cup\", \"description\": \"green, has a handle\", \"similar objects\": [\"soup\", \"taco\", \"chicken\"]}"}, {"object": "teakettle", "object_id": 1069, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is the object's type. This includes types such as \"vegetable\", \"animal\", and \"food\"."}, {"object": "teapot", "object_id": 1070, "gpt3_output": "\n\n{\"type\": \"pot\", \"description\": \"green, has a handle\", \"similar objects\": [\"tea pot\", \"coaster\", \"soup can\"]}"}, {"object": "teddy_bear", "object_id": 1071, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and red; could have a red fur\", \"similar objects\": [\"bear\", \"puppy\", \"toucan\"]}"}, {"object": "telephone", "object_id": 1072, "gpt3_output": "\n\n{\"type\": \"phone\", \"description\": \"black, with a red number on it\", \"similar objects\": [\"linen bag\", \"phone case\"]}"}, {"object": "telephone_booth", "object_id": 1073, "gpt3_output": "\n\n{\"type\": \"telephone\", \"description\": \"round, has a keypad\", \"similar objects\": [\"number\", \"phone\"]}"}, {"object": "telephone_pole", "object_id": 1074, "gpt3_output": "\n\n{\"type\": \"object\", \"position\": \"x\", \"size\": \"\", \"description\": \"small, round, has a handle\"}\n\n{\"type\": \"object\", \"position\": \"y\", \"size\": \"\", \"description\": \"small, round, has a stem\"}"}, {"object": "telephoto_lens", "object_id": 1075, "gpt3_output": "\n\n{\"type\": \"camera\", \"description\": \"round, has a long lens\", \"similar objects\": [\"telephone\", \"film\", \"photo camera\"]"}, {"object": "television_camera", "object_id": 1076, "gpt3_output": "\n\n{\"type\": \"camera\", \"description\": \"round, has a lens\", \"similar objects\": [\"television\", \"television show\"]}"}, {"object": "television_set", "object_id": 1077, "gpt3_output": "\n\n{\"type\": \"TV show\", \"description\": \"a round, cylindrical object\", \"similar objects\": [\"television\", \"television show\"]}"}, {"object": "tennis_ball", "object_id": 1078, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"red; has a small hole in the center\", \"similar objects\": [\"sock\", \"piano teacher\", \"guitar\"]}"}, {"object": "tennis_racket", "object_id": 1079, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are in the same category as the input. For example, an object that is described as a \"vegetable\" should be focused on finding objects that are that same type of vegetable."}, {"object": "tequila", "object_id": 1080, "gpt3_output": "\n\n{\"type\": \"liquor\", \"description\": \"red; has a sour taste\", \"similar objects\": [\"mango\", \"gin\"]}"}, {"object": "thermometer", "object_id": 1081, "gpt3_output": "\n\nThe ability to detect different types of objects in an input can be used to focus on different features. The features that should focus on in an object detection model for a zucchini can be: cylindrical, smooth, green, smooth, and round. The features that should focus on for an ambulance can be: black and white stripes, has a long mane, and has a shining siren."}, {"object": "thermos_bottle", "object_id": 1082, "gpt3_output": "\n\n{\"type\": \"bottle\", \"capacity\": \"\", \"price\": \"\", \"description\": \"\", \"similar objects\": [\"coaster\", \"fryer\", \"toaster\"]}"}, {"object": "thermostat", "object_id": 1083, "gpt3_output": "\n\nThe ability to control temperature in a controlled environment."}, {"object": "thimble", "object_id": 1084, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small hole in handle\", \"similar objects\": [\"thimble\", \"spoon\"]}"}, {"object": "thread", "object_id": 1085, "gpt3_output": "\n\n{\"type\": \"thread\", \"description\": \"black, has a strong threading system\", \"similar objects\": [\"corduroy\", \"seamless web\", \"seaweed\"]}"}, {"object": "thumbtack", "object_id": 1086, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green or green-like description."}, {"object": "tiara", "object_id": 1087, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Tiara\", \"type\": \"item\"}, {\"type\": \"item\", \"name\": \"Tiara\", \"type\": \"item\"}"}, {"object": "tiger", "object_id": 1088, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; has a stripes or a pattern\", \"similar objects\": [\"giraffe\", \"toucan\", \"seagull\"]}"}, {"object": "tights_(clothing)", "object_id": 1089, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Tights\", \"description\": \"\"}"}, {"object": "timer", "object_id": 1090, "gpt3_output": "\n\n{\"type\": \"timepiece\", \"description\": \"round, has a bezel and a chronograph movement\", \"similar objects\": [\"watch\", \"watch case\", \" Chronograph watch\"]"}, {"object": "tinfoil", "object_id": 1091, "gpt3_output": "\n\nThe input should focus on objects that are Tinfoil-like in shape or size."}, {"object": "tinsel", "object_id": 1092, "gpt3_output": "\n\nThere are many different features an object detection model could focus on, such as detecting that an object is not a vegetable, animal, or fruit, but should be considered for a list of possible features:\n\n-The object's type: this should be a type of object, such as a vegetable, animal, or fruit.\n-Your experience with using this object: do you know how to use it, such as how to put it together? Do you know what kind of light it gives? Do you know that it can have features on its stem and leaves that are not present on other objects?\n-The features"}, {"object": "tissue_paper", "object_id": 1093, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are in contact with each other. The model should also be able to identify different types of objects, such as vegetables and animals."}, {"object": "toast_(food)", "object_id": 1094, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is the object's size. For example, an ambulance would be looking for vehicles with siren and a stretcher would be looking for ones with light."}, {"object": "toaster", "object_id": 1095, "gpt3_output": "\n\n{\"type\": \"toaster\", \"description\": \"round, has a light at the end of it\", \"similar objects\": [\"toaster oven\", \"aidean oven\"]}"}, {"object": "toaster_oven", "object_id": 1096, "gpt3_output": "\n\n{\"type\": \"oven\", \"description\": \"yellow; has a hearth; can be turned into a oven\", \"similar objects\": [\"toaster\", \"fridge\", \"Refrigerator\"]"}, {"object": "toilet", "object_id": 1097, "gpt3_output": "\n\n{\"type\": \"room\", \"description\": \"round, has a bowl for a water bottle\", \"similar objects\": [\"bathroom\", \"toilet\", \"bath\", \"desk\"]}"}, {"object": "toilet_tissue", "object_id": 1098, "gpt3_output": "\n\n{\"type\": \"text\", \"message\": \"Please input a message\", \"similar objects\": [\"toilet paper\"]}"}, {"object": "tomato", "object_id": 1099, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, has a stem\", \"similar objects\": [\"onion\", \"grape\", \"canary\"]}"}, {"object": "tongs", "object_id": 1100, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"x\"}"}, {"object": "toolbox", "object_id": 1101, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"toolbox\", \"description\": \"box with a tool inside\", \"similar objects\": [\"folder\", \"cabinet\", \"counter\", \"tables\", \"chairs\", \"falls asleep\", \"name\", \"noun\", \"vegetable\", \"noun\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \"vegetable\", \""}, {"object": "toothbrush", "object_id": 1102, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \" Toothbrush \", \"description\": \"\n\nThis object is focused on being able to detect objects with small, specific details. For an object with small details, like a toothbrush, object detection will not work well. The model should be able to handle more detailed objects, like toothbrushes, with enough detail that an object detection model would be able to detect it."}, {"object": "toothpaste", "object_id": 1103, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for candidates for a food source is the object's object detection model should focus on."}, {"object": "toothpick", "object_id": 1104, "gpt3_output": "\n\n{\"type\": \"dental toothpick\", \"description\": \"light brown, has a sharp point\", \"similar objects\": [\"dentist's tool\", \"tootie\", \"gum picker\"]}"}, {"object": "cover", "object_id": 1105, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"cover\", \"description\": \"A cover for a cooking tool. The cover could be made of materials like metal or plastic. The cover could also have a light or light-based decoration. The cover could also have a siren or stretcher to make it more visible.\""}, {"object": "tortilla", "object_id": 1106, "gpt3_output": "\n\n{\"type\": \"tortilla\", \"description\": \"red, smooth, has a hole in the center\", \"similar objects\": [\"tortilla\", \"chocolate\", \"frozen yogurt\"]}"}, {"object": "tow_truck", "object_id": 1107, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for zucchini is its size. This model should be able to find zucchini if it is large enough. Additionally, the model should be able to find zucchini if it is green and smooth. If the zucchini is sliced into round pieces, the model should be able to find zucchini."}, {"object": "towel", "object_id": 1108, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Towel\", \"description\": \"Towel; has a towel head; could have a built-in towel rack\", \"similar objects\": [\" towel \", \" towel rack \", \" towel \"]"}, {"object": "towel_rack", "object_id": 1109, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"towel rack\", \"description\": \"A rack of towels, at least six feet long and six inches wide. The top of the rack is placed high enough so that the towels are well above the water level but not so high that the water covers the rack.\""}, {"object": "toy", "object_id": 1110, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "tractor_(farm_equipment)", "object_id": 1111, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has aizone; could be for sale\", \"similar objects\": [\" tractor \", \" truck \", \" car \"], \"related objects\": [\"car\", \" truck \", \" car \"], \"related objects\": [\"tractor \", \" truck \", \" car \"], \"related objects\": [\"tractor \", \" truck \", \" car \"], \"related objects\": [\"car\", \" truck \", \" car \"], \"related objects\": [\"tractor \", \" truck \", \" car \"], \"related objects\": [\"car\", \" truck \", \" car \"], \"related"}, {"object": "traffic_light", "object_id": 1112, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to identify zucchini as a vegetable, the description should be accurate, and the model should be able to identify zucchini as being similar to other objects."}, {"object": "dirt_bike", "object_id": 1113, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a long wheelbase and a small body\", \"similar objects\": [\"car\", \"truck\", \"SUV\"]}"}, {"object": "trailer_truck", "object_id": 1114, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a signage on the side\", \"similar objects\": [\"car\", \"truck\", \"bogie\"]}"}, {"object": "train_(railroad_vehicle)", "object_id": 1115, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a track\", \"similar objects\": [\"train\", \"car\", \"bus\"]}"}, {"object": "trampoline", "object_id": 1116, "gpt3_output": "\n\n{\"type\": \"activities\", \"description\": \"should be able to stand and hold onto a handle\", \"similar objects\": [\"trampoline\", \"skydiving\", \"biking and running\", \"spinning and | spinning\", \"dancing\", \"unguiding things\"]}"}, {"object": "tray", "object_id": 1117, "gpt3_output": "\n\n{\"type\": \"tray\", \"description\": \"round, has a handle\", \"similar objects\": [\"fryer\", \"fry\", \"dishwasher\"]}"}, {"object": "trench_coat", "object_id": 1118, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a handle\", \"similar objects\": [\"piano key\", \"keyboard\"]"}, {"object": "triangle_(musical_instrument)", "object_id": 1119, "gpt3_output": "\n\nThe triangle (musical instrument) can be used for playing triangle songs or as a soundless support for a triangle book."}, {"object": "tricycle", "object_id": 1120, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"black, with a red stripe down the middle\", \"similar objects\": [\"car\", \"trolley\", \"bus\"]}"}, {"object": "tripod", "object_id": 1121, "gpt3_output": "\n\n{\"type\": \"camera\", \"description\": \"round, has a focus ring\", \"similar objects\": [\"telephone\", \"poster\", \"photo\"]}"}, {"object": "trousers", "object_id": 1122, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"T-shirt\", \"size\": \"S\", \"color\": \"white\", \"similar objects\": [\"bunny\", \"fairytale\"]}"}, {"object": "truck", "object_id": 1123, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has atering system; could have broken windows\", \"similar objects\": [\"car\", \"truck\", \"SUV\"]}"}, {"object": "truffle_(chocolate)", "object_id": 1124, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"truffle\", \"chocolate\", \"frozen yogurt\"]}"}, {"object": "trunk", "object_id": 1125, "gpt3_output": "\n\n{\"type\": \"body\", \"description\": \"black, smooth, has a deep well\", \"similar objects\": [\"cat\", \"dog\", \"catcher's eye\"]}"}, {"object": "vat", "object_id": 1126, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for vegetable or animal objects is the size of the object. This should include objects such as tomatoes, onions, and other small onions. The size of the object should also include objects that are not vegetables or animals, such as cans and other cans."}, {"object": "turban", "object_id": 1127, "gpt3_output": "\n\nThe focus of an object detection model's focus should be on specific objects that are consistently represented in the data. Specific objects can be defined by their features, which are listed in the table below.\n\nInput: zucchini, Output:\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}\n\nInput: zebra, Output:\n\n{\"type\": \"animal\", \""}, {"object": "turkey_(food)", "object_id": 1128, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is the object's size. For vegetables, they should focus on the biggest and most round ones. For fruits, they should focus on the most red and large ones. For tools, they should look for those that have deep handles and are made of round materials. And for lanterns and lights, the larger the object the better."}, {"object": "turnip", "object_id": 1129, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "turtle", "object_id": 1130, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "turtleneck_(clothing)", "object_id": 1131, "gpt3_output": "\n\nThere are many different objects that can be detected with an object detection model. A turtleneck can be detected as being clothing, an animal, or a tool."}, {"object": "typewriter", "object_id": 1132, "gpt3_output": "\n\nThe input should be a list of objects that all have a common feature, such as an object with a round head or an object that is smooth."}, {"object": "umbrella", "object_id": 1133, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "underwear", "object_id": 1134, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"red, with a white border\", \"similar objects\": [\"jacket\", \"underwear\", \"skirt\"]}"}, {"object": "unicycle", "object_id": 1135, "gpt3_output": "\n\nThere are many different objects that can be detected by an object detection model, depending on the input. The input could be a object, such as a zucchini, or a unicycle. The model could focus on those objects that have a smooth surface, such as zebra and apple."}, {"object": "urinal", "object_id": 1136, "gpt3_output": "\n\n{\"type\": \"urethane\", \"description\": \"round, has a hole in the center\", \"similar objects\": [\"urethane\", \"enamel\", \"iron\"]"}, {"object": "urn", "object_id": 1137, "gpt3_output": "\n\nThe feature that should focus on for an input with a specific type of object is the object's type. For example, an object that is called a \"vegetable\" is would be more likely to be found in a \"vegetable\" input than an \"animal\" input."}, {"object": "vacuum_cleaner", "object_id": 1138, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a hose at the top\", \"similar objects\": [\"auger\", \"vacuum cleaner\", \"hose\"]"}, {"object": "vase", "object_id": 1139, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for Vegetables is whether or not the object is cylindrical, with a smooth surface. The object should also be similar to other objects in the input, with a focus on whether or not it is black and white stripes."}, {"object": "vending_machine", "object_id": 1140, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is its ability to find other objects with similar characteristics. For example, if an ambulance was to find food, it would be able to identify and devour the food in the machine."}, {"object": "vent", "object_id": 1141, "gpt3_output": "\n\n{\"type\": \"vent\", \"description\": \"closed\", \"similar objects\": [\"vent\", \"chamber\"]}"}, {"object": "vest", "object_id": 1142, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"\"}"}, {"object": "videotape", "object_id": 1143, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for videos of objects is the presence of papers. This model would be interested in videos of objects that have been touched or even in videos of objects that have been in contact with other objects."}, {"object": "vinegar", "object_id": 1144, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for vinegar is its size. Other features that might be important for an object detection model to focus on include its shape, color, and texture."}, {"object": "violin", "object_id": 1145, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is its ability to identify objects that share similar features. Object detection models that are looking for evidence of an emergency should focus on finding objects that have the same features as an object that is under investigation."}, {"object": "vodka", "object_id": 1146, "gpt3_output": "\n\n{\"type\": \"alcohol\", \"description\": \"black, with a green \"spoon\" at the top\", \"similar objects\": [\"grape\", \"chocolate\", \"lime\"]}"}, {"object": "volleyball", "object_id": 1147, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "vulture", "object_id": 1148, "gpt3_output": "\n\nThe feature that should focus on for an input with a vulture is its size. The vulture is a large bird and its size should be important in the model's size range."}, {"object": "waffle", "object_id": 1149, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"sugar, cinnamon, sugar\", \"similar objects\": [\"dessert\", \"chocolate\", \"dried fruit\"]}"}, {"object": "waffle_iron", "object_id": 1150, "gpt3_output": "\n\n{\"type\": \"electric device\", \"description\": \"red; has a lighted surface\", \"similar objects\": [\"gridiron\", \"coaster\", \"gasoline engine\"]}"}, {"object": "wagon", "object_id": 1151, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a wheel\", \"similar objects\": [\"car\", \"trolley\", \"bus\"]}"}, {"object": "wagon_wheel", "object_id": 1152, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Wheel of Fortune\", \"description\": \"Tumbleweed; small; white; has a wheel on it\", \"similar objects\": [\"Wagon\", \"Camel\", \"Rabbit\"]}"}, {"object": "walking_stick", "object_id": 1153, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "wall_clock", "object_id": 1154, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for evidence of life is the time of day. An object that is found with time of day, such as a wall clock, could be more likely to have been made with the user in mind."}, {"object": "wall_socket", "object_id": 1155, "gpt3_output": "\n\n{\"type\": \"electrical object\", \"description\": \"electric\", \"similar objects\": [\"sockets\", \"pipes\", \" Fault lines\"], \"vibration\": {\"type\": \"vibration\", \"description\": \"vibrates\", \"vibration\": {\"vibration\": {\"type\": \"vibration\", \"vibration\": {\"vibration\": {\"type\": \"vibration\", \"vibration\": {\"vibration\": {\"type\": \"vibration\", \"vibration\": {\"vibration\": {\"type\": \"vibration\", \"vib"}, {"object": "wallet", "object_id": 1156, "gpt3_output": "\n\n{\"type\": \"wallet\", \"description\": \"black, with a red symbol\", \"similar objects\": [\"wallet\", \"keyboard\", \"notebook\"]}"}, {"object": "walrus", "object_id": 1157, "gpt3_output": "\n\n{\"type\": \" mammal \", \"description\": \"black, with a white fur; could have long beaks and a sharp beak, could be sheathed in a long tail\", \"similar objects\": [\"walrus\", \"seagull\", \"chicken\"]}"}, {"object": "wardrobe", "object_id": 1158, "gpt3_output": "\n\n{\"type\": \"clothes\", \"description\": \"round, simple design; could have a few details like buttons, straps, etc.\", \"similar objects\": [\"coat\", \"shower\", \"clotheshorse\", \"bunny\", \"turban\"]}"}, {"object": "washbasin", "object_id": 1159, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"zucchini\", \"description\": \" cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves \"\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "automatic_washer", "object_id": 1160, "gpt3_output": "\n\nThe feature that should focus on for an automatic washer is the control over how often the machine cleans and Warms the water. This control can help the user to make sure that the washer is used regularly and that the water is at a proper temperature."}, {"object": "watch", "object_id": 1161, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to identify zucchini as a food item, but should not be able to identify the object itself."}, {"object": "water_bottle", "object_id": 1162, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"red; has a small hole in the top\", \"similar objects\": [\"sink\", \"fishing net\", \"water bottle\"]}"}, {"object": "water_cooler", "object_id": 1163, "gpt3_output": "\n\n{\"type\": \" UNHCR \", \"description\": \"A water cooler is a water bottle that is placed in a container of water and then poured over the top\", \"similar objects\": [\"faucet\", \" dispenser \", \" dispensing machine\"]}"}, {"object": "water_faucet", "object_id": 1164, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"gray, with a handle and several screws at the top\", \"similar objects\": [\"faucet\", \"sink\", \"sinkable\" \n\n}"}, {"object": "water_heater", "object_id": 1165, "gpt3_output": "\n\n{\"type\": \"fuel\", \"description\": \"liquefied oil; could use gasoline or diesel\", \"similar objects\": [\"pump\", \"heater\", \"tank\", \"tank\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"spill\", \"sp"}, {"object": "water_jug", "object_id": 1166, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "water_gun", "object_id": 1167, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for water plants is the model's ability to find water plants.\n\nInput:\n\nvegetable, Output:\n\nThe feature that should focus on for an object detection model that is looking for water plants is the model's ability to find water plants."}, {"object": "water_scooter", "object_id": 1168, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"green; has a water droplet on the front end\", \"similar objects\": [\"water bottle\", \"water dispenser\", \"fishing net\"]}"}, {"object": "water_ski", "object_id": 1169, "gpt3_output": "\n\n{\"type\": \"water\", \"price\": \"free\", \"description\": \"\", \"status\": \"available for purchase\", \"similar objects\": [\"snow\", \"ski\", \"water bottle\"]}"}, {"object": "water_tower", "object_id": 1170, "gpt3_output": "\n\n{\"type\": \"sign\", \"description\": \"\", \"type\": \"object\", \"position\": \"x\", \"type\": \"text\", \"size\": \"\", \"color\": \"\", \"altText\": \"No sign\""}, {"object": "watering_can", "object_id": 1171, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "watermelon", "object_id": 1172, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "weathervane", "object_id": 1173, "gpt3_output": "\n\nThe input should be a model that can detect different types of vegetables. The output should be a model that can detect different models."}, {"object": "webcam", "object_id": 1174, "gpt3_output": "\n\n{\"type\": \"digital camera\", \"description\": \"round, has a sensor\", \"similar objects\": [\"teacher's pet\", \"camera\"]}"}, {"object": "wedding_cake", "object_id": 1175, "gpt3_output": "\n\nThe object detection model should focus on finding objects with similar characteristics that are in a same place and with the same size. The model should also be able to identify different types of objects, such as vegetables and animals."}, {"object": "wedding_ring", "object_id": 1176, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for a zucchini should be the zucchini itself, not its size or shape."}, {"object": "wet_suit", "object_id": 1177, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"Wet Suit\", \"description\": \"A wet suit is a clothing item that is made to keep a person wet. It is a piece of clothing that is made to keep the user warm and protect them from the weather."}, {"object": "wheel", "object_id": 1178, "gpt3_output": "\n\n{\"type\": \"balloon\", \"description\": \"length; can be filled with water or food\", \"similar objects\": [\" balloon \", \" space shuttle \", \" space heater \", \" space station \"]"}, {"object": "wheelchair", "object_id": 1179, "gpt3_output": "\n\n{\"type\": \"chair\", \"description\": \"black, with a white cover\", \"similar objects\": [\"wheelchair\", \"bed\", \"trolley\"]"}, {"object": "whipped_cream", "object_id": 1180, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for food is its type. An object's type should be able to be determined from its features. For example, an animal's type could be determined by the type of fur that is on its fur, the type of skin that is on its skin, the type of fur on its fur, the type of skin on its skin, and the type of cream or paint that is on the object."}, {"object": "whistle", "object_id": 1181, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for clues about its surroundings is the object's size. object sizes can be used to help identify different objects, and help the model understand how big or small an object is. For example, an ambulance might be big enough to handle, but small enough to be seen with the eye."}, {"object": "wig", "object_id": 1182, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "wind_chime", "object_id": 1183, "gpt3_output": "\n\nThe feature of an object that should focus on for object detection models that are designed to detect objects is that they are designed to move. Object detection models that are designed to detect objects should be able to identify them as soon as they move."}, {"object": "windmill", "object_id": 1184, "gpt3_output": "\n\n{\"type\": \"mechanism\", \"description\": \"a physical object is being used as an input\", \"similar objects\": [\"pump\", \"faucet\", \"Refrigerator\"]"}, {"object": "window_box_(for_plants)", "object_id": 1185, "gpt3_output": "\n\nThe feature that should focus on for an input with a zucchini is its cylindrical shape. The feature that focuses on for an input with a red wok is its smooth surface. The feature that focuses on for an input with a ambulance is its red siren."}, {"object": "windshield_wiper", "object_id": 1186, "gpt3_output": "\n\nThe features that should be focus for an object detection model that smells zucchini should be the cylindrical shape, the smooth surface, and the green color. The model should be able to clean zucchini by using the wiper tool."}, {"object": "windsock", "object_id": 1187, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for zucchini is its size. This model should be able to identify zucchini as a food item.\n\nThe feature that should focus on for an object detection model that is looking for zucchini is its shape. This model should be able to identify zucchini as a food item."}, {"object": "wine_bottle", "object_id": 1188, "gpt3_output": "\n\n{\"type\": \"bottle\", \"name\": \"wine\", \"size\": \"small\", \"similar objects\": [\" bottle \", \" bottle \"]"}, {"object": "wine_bucket", "object_id": 1189, "gpt3_output": "\n\n{\"type\": \"item\", \"name\": \"Wine\", \"description\": \"Red; has a bubbly surface\", \"similar objects\": [\"wine\", \"flowers\"]}"}, {"object": "wineglass", "object_id": 1190, "gpt3_output": "\n\n{\"type\": \"bottle\", \"description\": \"red, with a straw\", \"similar objects\": [\"bottle\", \"coconut\", \"soda\"]}"}, {"object": "blinder_(for_horses)", "object_id": 1191, "gpt3_output": "\n\n{\"type\": \"image\", \"uuid\": \"cwc8jr8\", \"image_src\": \"https://www.biddenplanet.com/img/uploads/vegetables/zucchini/zucchini-image.jpg\", \"caption\": \"A zucchini (un)]\", \"contributing image\": \"https://www.biddenplanet.com/img/uploads/vegetables/zebra/zebra-image.jpg\", \"featured image\": \"https://www.biddenplanet.com/img/uploads/vegetables/apple/"}, {"object": "wok", "object_id": 1192, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "wolf", "object_id": 1193, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black, white, or light-colored; could have a tail\", \"similar objects\": [\"wolf\", \"cat\", \"toucan\"]}"}, {"object": "wooden_spoon", "object_id": 1194, "gpt3_output": "\n\n{\"type\": \"ingredient\", \"description\": \"wooden spoon\", \"similar objects\": [\"pudding\", \"dessert\"]}"}, {"object": "wreath", "object_id": 1195, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for Vegetables is likely to be similar to the feature that is used for an object detection model that is looking for Animals."}, {"object": "wrench", "object_id": 1196, "gpt3_output": "\n\nThe feature that should focus on for an object detection model that is looking for solutions is finding and solving problems. This includes being able to identify the object, its type, and similar objects that it is solution to."}, {"object": "wristband", "object_id": 1197, "gpt3_output": "\n\n{\"type\": \"token\", \"description\": \"I have this token."}, {"object": "wristlet", "object_id": 1198, "gpt3_output": "\n\n{\"type\": \"object\", \"name\": \"wristlet\", \"description\": \"red; has a stem\", \"similar objects\": [\"watch\", \"bracelet\", \"bracelet card\"]}"}, {"object": "yacht", "object_id": 1199, "gpt3_output": "\n\n{\"type\": \"vessel\", \"description\": \"island, boat\", \"similar objects\": [\"ship\", \"car\", \"plane\"]}"}, {"object": "yogurt", "object_id": 1200, "gpt3_output": "\n\n{\"type\": \"dietary\", \"description\": \"vegetables; could have high fructose diet diet or a complex carbohydrate diet\", \"similar objects\": [\"banana\", \"frozen yogurt\", \"fruit\", \"berry\"]}"}, {"object": "yoke_(animal_equipment)", "object_id": 1201, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"black, smooth, has a long root; can be eaten\", \"similar objects\": [\"zucchini\", \"carrot\", \"sticker\", \"chopstick\"]}"}, {"object": "zebra", "object_id": 1202, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "zucchini", "object_id": 1203, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}] \ No newline at end of file diff --git a/tools/files/lvis.v1.tmp0.babbage.json b/tools/files/lvis.v1.tmp0.babbage.json new file mode 100644 index 0000000000000000000000000000000000000000..f343ccc0b5b6bb1394ff2f19eb91b839b992023e --- /dev/null +++ b/tools/files/lvis.v1.tmp0.babbage.json @@ -0,0 +1 @@ +[{"object": "aerosol_can", "object_id": 1, "gpt3_output": "\n\n{\"type\": \"chemical\", \"description\": \"green; has a nozzle on the top\", \"similar objects\": [\"can of air\", \"can of paint\"]}"}, {"object": "air_conditioner", "object_id": 2, "gpt3_output": "\n\n{\"type\": \"unit\", \"description\": \"has a blue light; is usually turned on when it is hot outside\", \"similar objects\": [\"heater\", \"cooler\"]}"}, {"object": "airplane", "object_id": 3, "gpt3_output": "\n\n{\"type\": \"aircraft\", \"description\": \"black and white stripes; has a long tail\", \"similar objects\": [\"jet plane\", \"airbus\"]}"}, {"object": "alarm_clock", "object_id": 4, "gpt3_output": "\n\n{\"type\": \"time-sensitive device\", \"description\": \"has a loud alarm sound\", \"similar objects\": [\"radio\", \"watch\"]}"}, {"object": "alcohol", "object_id": 5, "gpt3_output": "\n\n{\"type\": \"drug\", \"description\": \"clear liquid; can be smelled\", \"similar objects\": [\"alcohol\"]}"}, {"object": "alligator", "object_id": 6, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"long, slimy skin; has a long tail\", \"similar objects\": [\"giraffe\", \"alligator\"]}"}, {"object": "almond", "object_id": 7, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green, small, has a seed\", \"similar objects\": [\"fig\", \"grape\", \"apple\"]}"}, {"object": "ambulance", "object_id": 8, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a glaring siren; could with a stretcher\", \"similar objects\": [\"police car\", \"taxi\", \"garbage truck\"]}"}, {"object": "amplifier", "object_id": 9, "gpt3_output": "\n\n{\"type\": \"audio device\", \"description\": \"speakers; can be turned up or down\", \"similar objects\": [\"speakerphone\", \"speaker\"]}"}, {"object": "anklet", "object_id": 10, "gpt3_output": "\n\n{\"type\": \"watch\", \"description\": \"small, round, has a band\", \"similar objects\": [\"bracelet\", \"watch\"]}"}, {"object": "antenna", "object_id": 11, "gpt3_output": "\n\n{\"type\": \"radio\", \"description\": \"has a metal antenna\", \"similar objects\": [\"radio\", \"TV antenna\"]}"}, {"object": "apple", "object_id": 12, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"orange\", \"banana\", \"pear\"]}"}, {"object": "applesauce", "object_id": 13, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green, smooth, has seeds\", \"similar objects\": [\"apple\", \"pie\"]}"}, {"object": "apricot", "object_id": 14, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"yellow; has a thin skin\", \"similar objects\": [\"fig\", \"plum\", \"apricot\"]}"}, {"object": "apron", "object_id": 15, "gpt3_output": "\n\n{\"type\": \" clothing\", \"description\": \"tight-fitting; has a collar; has apron strings\", \"similar objects\": [\"bikini\", \"t-shirt\"]}"}, {"object": "aquarium", "object_id": 16, "gpt3_output": "\n\n{\"type\": \"aquarium\", \"description\": \"green; has a white roof; could have a blue or green water\", \"similar objects\": [\" coral\", \" fish\"]}"}, {"object": "arctic_(type_of_shoe)", "object_id": 17, "gpt3_output": "\n\n{\"type\": \" footwear\", \"description\": \"black; has a high-top style\", \"similar objects\": [\"snow boots\", \"boots\"]}"}, {"object": "armband", "object_id": 18, "gpt3_output": "\n\n{\"type\": \"wearable device\", \"description\": \"round; has a band around the middle\", \"similar objects\": [\"watch\", \"bracelet\"]}"}, {"object": "armchair", "object_id": 19, "gpt3_output": "\n\n{\"type\": \" furniture \", \"description\": \"has a back\", \"similar objects\": [\"seating\", \"chair\"]}"}, {"object": "armoire", "object_id": 20, "gpt3_output": "\n\n{\"type\": \"room furniture\", \"description\": \"has a door; has shelves on the sides\", \"similar objects\": [\"dresser\", \" Wardrobe\"]}"}, {"object": "armor", "object_id": 21, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"has spikes on the front\", \"similar objects\": [\"pistol\", \"shotgun\"]}"}, {"object": "artichoke", "object_id": 22, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"green; has a stem\", \"similar objects\": [\"artichoke\"]}"}, {"object": "trash_can", "object_id": 23, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a lid; could be made of plastic or metal\", \"similar objects\": [\"can\", \"box\"]}"}, {"object": "ashtray", "object_id": 24, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"round, black, has a handle\", \"similar objects\": [\"cigarette\", \"pipe\"]}"}, {"object": "asparagus", "object_id": 25, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"green, thin, has a stem\", \"similar objects\": [\"asparagus\"]}"}, {"object": "atomizer", "object_id": 26, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"small; has a black object on the top\", \"similar objects\": [\"cigarette\", \"cigar\"]}"}, {"object": "avocado", "object_id": 27, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green, smooth, has a stem\", \"similar objects\": [\"cucumber\", \"avocado\"]}"}, {"object": "award", "object_id": 28, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"gold; has a star on it\", \"similar objects\": [\" diploma\", \"pencil\", \"toy\"]}"}, {"object": "awning", "object_id": 29, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"yellow; has a blue stripe; could have a white stripe on the side\", \"similar objects\": [\"truck\", \"bus\"]}"}, {"object": "ax", "object_id": 30, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"long, thin, has a sharp edge\", \"similar objects\": [\"sword\", \"axe\"]}"}, {"object": "baboon", "object_id": 31, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown fur; has a long tail\", \"similar objects\": [\"gorilla\"]}"}, {"object": "baby_buggy", "object_id": 32, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"green; has a white stripe on the side\", \"similar objects\": [\"car\", \"truck\"]}"}, {"object": "basketball_backboard", "object_id": 33, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"black; has a white backboard\", \"similar objects\": [\"basketball\", \"football\"]}"}, {"object": "backpack", "object_id": 34, "gpt3_output": "\n\n{\"type\": \"bag\", \"description\": \"round, black; has straps\", \"similar objects\": [\"backpack\", \"shoulder bag\"]}"}, {"object": "handbag", "object_id": 35, "gpt3_output": "\n\n{\"type\": \"bag\", \"description\": \"round, black; has a strap\", \"similar objects\": [\"wallet\", \"handbag\"]}"}, {"object": "suitcase", "object_id": 36, "gpt3_output": "\n\n{\"type\": \" luggage \", \"description\": \"solid; has a handle\", \"similar objects\": [\"suitcase\", \"shampoo bottle\"]}"}, {"object": "bagel", "object_id": 37, "gpt3_output": "\n\n{\"type\": \"bagel\", \"description\": \"round, with a hole in the center; has a doughnut-shaped hole on one side\", \"similar objects\": [\"bakery bagel\", \"bagel with cream cheese\"]}"}, {"object": "bagpipe", "object_id": 38, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"long, thin, has a stem\", \"similar objects\": [\"guitar\", \"violin\", \"pipe\"]}"}, {"object": "baguet", "object_id": 39, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green, round, has a stem\", \"similar objects\": [\"fig\", \"grape\", \"apple\"]}"}, {"object": "bait", "object_id": 40, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"a small piece of meat or seafood\", \"similar objects\": [\"fish\", \"pig\"]}"}, {"object": "ball", "object_id": 41, "gpt3_output": "\n\n{\"type\": \"ball\", \"description\": \"round; has a hole in the center\", \"similar objects\": [\"doodle\", \"toy ball\"]}"}, {"object": "ballet_skirt", "object_id": 42, "gpt3_output": "\n\n{\"type\": \" clothing\", \"description\": \"tight-fitting; has a high neckline\", \"similar objects\": [\"ballet dress\", \"ballerina skirt\"]}"}, {"object": "balloon", "object_id": 43, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"white; has a long tail\", \"similar objects\": [\" balloon\"]}"}, {"object": "bamboo", "object_id": 44, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"green; has a stem\", \"similar objects\": [\"cabbage\", \" bamboo\"]}"}, {"object": "banana", "object_id": 45, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green, smooth, has a stem\", \"similar objects\": [\"apple\", \"grapefruit\"]}"}, {"object": "Band_Aid", "object_id": 46, "gpt3_output": "\n\n{\"type\": \"medical tool\", \"description\": \"yellow; has a band around it\", \"similar objects\": [\"tourniquet\", \"stethoscope\"]}"}, {"object": "bandage", "object_id": 47, "gpt3_output": "\n\n{\"type\": \"medical device\", \"description\": \"yellow; has a bandage around it\", \"similar objects\": [\"tourniquet\", \"stethoscope\"]}"}, {"object": "bandanna", "object_id": 48, "gpt3_output": "\n\n{\"type\": \"cloth\", \"description\": \"white; has a blue bandanna around the neck\", \"similar objects\": [\"wool bandanna\", \"cotton bandanna\"]}"}, {"object": "banjo", "object_id": 49, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"stringed; has a neck and body\", \"similar objects\": [\"guitar\", \"violin\"]}"}, {"object": "banner", "object_id": 50, "gpt3_output": "\n\n{\"type\": \"advertising\", \"description\": \"red; has a yellow triangle in the center\", \"similar objects\": [\"banner\", \"sticker\"]}"}, {"object": "barbell", "object_id": 51, "gpt3_output": "\n\n{\"type\": \"weightlifting tool\", \"description\": \"long, thin, has a handle\", \"similar objects\": [\"barbell\", \"bell\"]}"}, {"object": "barge", "object_id": 52, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"long, narrow, has a prow\", \"similar objects\": [\"raft\", \"submarine\"]}"}, {"object": "barrel", "object_id": 53, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"cylindrical, green; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"gun barrel\", \"firearm barrel\"]}"}, {"object": "barrette", "object_id": 54, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round; has a loop at the top\", \"similar objects\": [\"bobby pin\", \"watch\"]}"}, {"object": "barrow", "object_id": 55, "gpt3_output": "\n\n{\"type\": \"truck\", \"description\": \"has a long, flat body; has a large, flat head\", \"similar objects\": [\"barrow\", \"truck\"]}"}, {"object": "baseball_base", "object_id": 56, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"round; has a hole in the middle\", \"similar objects\": [\"trucker's truck\", \"football\", \"soccer ball\"]}"}, {"object": "baseball", "object_id": 57, "gpt3_output": "\n\n{\"type\": \"sports game\", \"description\": \"ball is round; has a handle\", \"similar objects\": [\"football\", \"tennis\"]}"}, {"object": "baseball_bat", "object_id": 58, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"long, thin, has a handle\", \"similar objects\": [\"sword\", \"pistol\"]}"}, {"object": "baseball_cap", "object_id": 59, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"black; has a white stripe on the front\", \"similar objects\": [\"t-shirt\", \"jacket\"]}"}, {"object": "baseball_glove", "object_id": 60, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"black; has a band around the thumb\", \"similar objects\": [\"football\", \"tennis\"]}"}, {"object": "basket", "object_id": 61, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a handle\", \"similar objects\": [\"bag\", \"box\"], \"quantity\": 1}"}, {"object": "basketball", "object_id": 62, "gpt3_output": "\n\n{\"type\": \"sports game\", \"description\": \"player on a court with a ball; players are trying to score points by jumping over obstacles\", \"similar objects\": [\"football\", \"tennis\"]}"}, {"object": "bass_horn", "object_id": 63, "gpt3_output": "\n\n{\"type\": \"audio instrument\", \"description\": \"has a long neck and is used to play music\", \"similar objects\": [\"guitar\", \"violin\", \"viola\"]}"}, {"object": "bat_(animal)", "object_id": 64, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black; has wings\", \"similar objects\": [\"puma\", \"jaguar\"]}"}, {"object": "bath_mat", "object_id": 65, "gpt3_output": "\n\n{\"type\": \"surface\", \"description\": \"thin, white, has a ridged surface\", \"similar objects\": [\"soap\", \"towel\"]}"}, {"object": "bath_towel", "object_id": 66, "gpt3_output": "\n\n{\"type\": \"cloth\", \"description\": \"white; has a loop at the top\", \"similar objects\": [\"towel\", \"bath towel\"]}"}, {"object": "bathrobe", "object_id": 67, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white; has a long tail\", \"similar objects\": [\"bathrobe\"]}"}, {"object": "bathtub", "object_id": 68, "gpt3_output": "\n\n{\"type\": \"bathtub\", \"description\": \"square, deep, has a spout\", \"similar objects\": [\"toilet\", \"shower\"]}"}, {"object": "batter_(food)", "object_id": 69, "gpt3_output": "\n\n{\"type\": \"currency\", \"description\": \"green; has a small head\", \"similar objects\": [\"dollar\", \"pound sterling\"]}"}, {"object": "battery", "object_id": 70, "gpt3_output": "\n\n{\"type\": \"electricity\", \"description\": \"positive; has a red light\", \"similar objects\": [\"cordless phone\", \"laptop\"]}"}, {"object": "beachball", "object_id": 71, "gpt3_output": "\n\n{\"type\": \"sports toy\", \"description\": \"round; has a small hole in the center\", \"similar objects\": [\"toy ball\"]}"}, {"object": "bead", "object_id": 72, "gpt3_output": "\n\n{\"type\": \" bead \", \"description\": \"small, round, has a center\", \"similar objects\": [\"diamond\", \"sapphire\"]}"}, {"object": "bean_curd", "object_id": 73, "gpt3_output": "\n\n{\"type\": \"product\", \"description\": \"white; has a smooth texture; could be shredded\", \"similar objects\": [\"chicken\", \"milk\", \"soybean\"]}"}, {"object": "beanbag", "object_id": 74, "gpt3_output": "\n\n{\"type\": \"toy\", \"description\": \"round, green; has a hole in the middle\", \"similar objects\": [\"beanbag\"]}"}, {"object": "beanie", "object_id": 75, "gpt3_output": "\n\n{\"type\": \"headwear\", \"description\": \"round, green; could have a brown band around the neck\", \"similar objects\": [\"beanie\", \"beanie hat\"]}"}, {"object": "bear", "object_id": 76, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown fur; has a long tail\", \"similar objects\": [\"elk\", \"crocodile\"]}"}, {"object": "bed", "object_id": 77, "gpt3_output": "\n\n{\"type\": \"bed\", \"description\": \"made of sheets and blankets\", \"similar objects\": [\"couch\", \"pillow\"]}"}, {"object": "bedpan", "object_id": 78, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"round, black, has a handle\", \"similar objects\": [\"pot\", \"pan\"]}"}, {"object": "bedspread", "object_id": 79, "gpt3_output": "\n\n{\"type\": \"textile\", \"description\": \"white; has a blue color\", \"similar objects\": [\"cotton\", \"polyester\"]}"}, {"object": "cow", "object_id": 80, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown; has a hump on its back\", \"similar objects\": [\"cow\", \"bull\"]}"}, {"object": "beef_(food)", "object_id": 81, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"white; has a red color\", \"similar objects\": [\"pork\", \"lamb\"]}"}, {"object": "beeper", "object_id": 82, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"small, red; has a battery\", \"similar objects\": [\"cell phone\", \"watch\"]}"}, {"object": "beer_bottle", "object_id": 83, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a neck\", \"similar objects\": [\"wine bottle\", \"beer bottle\"]}"}, {"object": "beer_can", "object_id": 84, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a neck\", \"similar objects\": [\"can\", \"bottle\"]}"}, {"object": "beetle", "object_id": 85, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black and yellow; has a long antennae\", \"similar objects\": [\"mosquitoes\", \"termites\"]}"}, {"object": "bell", "object_id": 86, "gpt3_output": "\n\n{\"type\": \" bells\", \"description\": \"small, round, has a handle\", \"similar objects\": [\"gong\", \"clanging bell\"]}"}, {"object": "bell_pepper", "object_id": 87, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"bell pepper\"]}"}, {"object": "belt", "object_id": 88, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"tightly-fitting; has a buckle\", \"similar objects\": [\"belt\", \"watch\"]}"}, {"object": "belt_buckle", "object_id": 89, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"metal; has a buckle\", \"similar objects\": [\"watch\", \"bracelet\"]}"}, {"object": "bench", "object_id": 90, "gpt3_output": "\n\n{\"type\": \"bench\", \"description\": \"has a seat; could be made of wood or plastic\", \"similar objects\": [\"chair\", \"table\"]}"}, {"object": "beret", "object_id": 91, "gpt3_output": "\n\n{\"type\": \"headwear\", \"description\": \"white; has a blue band around the neck\", \"similar objects\": [\"beret\", \" beanie\"]}"}, {"object": "bib", "object_id": 92, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"white; has a blue band around the waist\", \"similar objects\": [\"t-shirt\", \"jacket\"]}"}, {"object": "Bible", "object_id": 93, "gpt3_output": "\n\n{\"type\": \"textbook\", \"description\": \"large, brown, has a spine\", \"similar objects\": [\" Koran \", \" Bible \"]}"}, {"object": "bicycle", "object_id": 94, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"green; has a saddle; could have pedals\", \"similar objects\": [\"car\", \"truck\"]}"}, {"object": "visor", "object_id": 95, "gpt3_output": "\n\n{\"type\": \"sunglasses\", \"description\": \"round, black; has a lens\", \"similar objects\": [\"sunglasses\", \"sunglasses case\"]}"}, {"object": "billboard", "object_id": 96, "gpt3_output": "\n\n{\"type\": \"advertisement\", \"description\": \"large, white, has a blue color\", \"similar objects\": [\"trucker\", \"building\"]}"}, {"object": "binder", "object_id": 97, "gpt3_output": "\n\n{\"type\": \"document\", \"description\": \"square; has a spine\", \"similar objects\": [\"book\", \"folder\"]}"}, {"object": "binoculars", "object_id": 98, "gpt3_output": "\n\n{\"type\": \"oculars\", \"description\": \"round, black and white; has a lens on the front\", \"similar objects\": [\"binoculars\", \"checkered glasses\"]}"}, {"object": "bird", "object_id": 99, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"parrot\", \"cockatiel\", \"seagull\"]}"}, {"object": "birdfeeder", "object_id": 100, "gpt3_output": "\n\n{\"type\": \"animal feeder\", \"description\": \"round; has a hole in the middle\", \"similar objects\": [\"giraffe\", \"seagull\"]}"}, {"object": "birdbath", "object_id": 101, "gpt3_output": "\n\n{\"type\": \"water feature\", \"description\": \"round; has a spout\", \"similar objects\": [\"faucet\", \"sink\"]}"}, {"object": "birdcage", "object_id": 102, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"bird\", \"parrot\"]}"}, {"object": "birdhouse", "object_id": 103, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"has a roof; has a door; has a window\", \"similar objects\": [\"tower\", \"tower of Babel\"]}"}, {"object": "birthday_cake", "object_id": 104, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"yellow; has a cake-like shape; is often decorated with frosting\", \"similar objects\": [\"cake\", \"cupcake\"]}"}, {"object": "birthday_card", "object_id": 105, "gpt3_output": "\n\n{\"type\": \"card\", \"description\": \"printed on white paper; has a subject and date; has a smiley face\", \"similar objects\": [\"cardigan\", \"birthday cake\"]}"}, {"object": "pirate_flag", "object_id": 106, "gpt3_output": "\n\n{\"type\": \"flag\", \"description\": \"black and white stripes; has a red cross on a white background\", \"similar objects\": [\"pirate flag\"]}"}, {"object": "black_sheep", "object_id": 107, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white; has a white fleece on its head\", \"similar objects\": [\"white horse\", \"black sheep\"]}"}, {"object": "blackberry", "object_id": 108, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"black and red; has a stem\", \"similar objects\": [\"blackberry\", \"raspberry\"]}"}, {"object": "blackboard", "object_id": 109, "gpt3_output": "\n\n{\"type\": \"board\", \"description\": \"black; has white lines running down the middle\", \"similar objects\": [\"whiteboard\", \"board game\"]}"}, {"object": "blanket", "object_id": 110, "gpt3_output": "\n\n{\"type\": \"blanket\", \"description\": \"yellow; has a long tail\", \"similar objects\": [\"blanket\", \"towel\"]}"}, {"object": "blazer", "object_id": 111, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"black; has acollar and pockets; could be tailored to fit a specific body type\", \"similar objects\": [\"jacket\", \"shirt\", \"pants\"]}"}, {"object": "blender", "object_id": 112, "gpt3_output": "\n\n{\"type\": \"food processor\", \"description\": \"square; has a blade on one end and a motor on the other\", \"similar objects\": [\"food processor\", \"chopper\"]}"}, {"object": "blimp", "object_id": 113, "gpt3_output": "\n\n{\"type\": \"airship\", \"description\": \"blue; has a long tail\"; could have a white or black balloon on the top\", \"similar objects\": [\"satellite\"]}"}, {"object": "blinker", "object_id": 114, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"on; when you blink, it turns on\", \"similar objects\": [\"lightbulb\"]}"}, {"object": "blouse", "object_id": 115, "gpt3_output": "\n\n{\"type\": \" clothing\", \"description\": \"tight-fitting; 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has a lid\", \"similar objects\": [\"box\", \"can\", \"packet\"]}"}, {"object": "cash_register", "object_id": 221, "gpt3_output": "\n\n{\"type\": \"business\", \"description\": \"red; has a yellow stripe; has a number on the front\", \"similar objects\": [\"bank\", \"cafeteria\", \"shopping mall\"]}"}, {"object": "casserole", "object_id": 222, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"pink and green; has a lot of cheese\"; \"similar objects\": [\"applepie\", \"pudding\"]}"}, {"object": "cassette", "object_id": 223, "gpt3_output": "\n\n{\"type\": \"audio cassette\", \"description\": \"yellow; has a brown cover\", \"similar objects\": [\"mp3 player\", \"audio cassette\"]}"}, {"object": "cast", "object_id": 224, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"cast iron; has a smooth surface\", \"similar objects\": [\"steel\", \"copper\"]}"}, {"object": "cat", "object_id": 225, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white; has a long tail\", \"similar objects\": [\"cat\"]}"}, {"object": "cauliflower", "object_id": 226, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"white; has a stem\", \"similar objects\": [\"cabbage\", \"kale\"]}"}, {"object": "cayenne_(spice)", "object_id": 227, "gpt3_output": "\n\n{\"type\": \"medicine\", \"description\": \"has a hot, burning sensation\", \"similar objects\": [\"cayenne pepper\"]}"}, {"object": "CD_player", "object_id": 228, "gpt3_output": "\n\n{\"type\": \"audio device\", \"description\": \" Plays music from CDs\", \"similar objects\": [\"CD player\", \"MP3 player\"]}"}, {"object": "celery", "object_id": 229, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"celery\", \"carrots\"]}"}, {"object": "cellular_telephone", "object_id": 230, "gpt3_output": "\n\n{\"type\": \"phone\", \"description\": \"small, rectangular; has a screen\", \"similar objects\": [\"smartphone\"]}"}, {"object": "chain_mail", "object_id": 231, "gpt3_output": "\n\n{\"type\": \"armor\", \"description\": \"made of metal; has spikes on the front and back\", \"similar objects\": [\"plate mail\", \"mailer\"]}"}, {"object": "chair", "object_id": 232, "gpt3_output": "\n\n{\"type\": \" furniture \", \"description\": \"has a back\", \"similar objects\": [\"seating\", \"chair\"]}"}, {"object": "chaise_longue", "object_id": 233, "gpt3_output": "\n\n{\"type\": \"chair\", \"description\": \"long, straight, has a backrest\", \"similar objects\": [\"sofa\", \"chair\"]}"}, {"object": "chalice", "object_id": 234, "gpt3_output": "\n\n{\"type\": \"religious item\", \"description\": \"has a stem and a lid\", \"similar objects\": [\"chalice\", \"sceptre\"]}"}, {"object": "chandelier", "object_id": 235, "gpt3_output": "\n\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of papers\", \"similar objects\": [\"chandelier\", \"candle\"]}"}, {"object": "chap", "object_id": 236, "gpt3_output": "\n\n{\"type\": \"person\", \"description\": \"male or female; has a head of hair\", \"similar objects\": [\"male\", \"female\"]}"}, {"object": "checkbook", "object_id": 237, "gpt3_output": "\n\n{\"type\": \"accountant\", \"description\": \"black; has a white checkerboard pattern on the cover\", \"similar objects\": [\"bank account\", \"credit card\"]}"}, {"object": "checkerboard", "object_id": 238, "gpt3_output": "\n\n{\"type\": \"board game\", \"description\": \"has a grid of squares\", \"similar objects\": [\" cribbage board\", \"chess board\"]}"}, {"object": "cherry", "object_id": 239, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"apple\", \"cherry\", \"plums\"]}"}, {"object": "chessboard", "object_id": 240, "gpt3_output": "\n\n{\"type\": \"gameboard\", \"description\": \"square; has a black and white checkerboard\", \"similar objects\": [\"chess pieces\"]}"}, {"object": "chicken_(animal)", "object_id": 241, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"chicken\"]}"}, {"object": "chickpea", "object_id": 242, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"small, green, has a stem\", \"similar objects\": [\"cucumber\", \"peanut\", \"garlic\"]}"}, {"object": "chili_(vegetable)", "object_id": 243, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"chili\"]}"}, {"object": "chime", "object_id": 244, "gpt3_output": "\n\n{\"type\": \"sound\", \"description\": \"chime; can be made of metal or plastic\", \"similar objects\": [\"bell\", \"clanging bell\"]}"}, {"object": "chinaware", "object_id": 245, "gpt3_output": "\n\n{\"type\": \"wareware\", \"description\": \"round, white, has a blue band\", \"similar objects\": [\"sushi rice\", \"chinaware\"]}"}, {"object": "crisp_(potato_chip)", "object_id": 246, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"crisp\", \"similar objects\": [\"potato\"]}"}, {"object": "poker_chip", "object_id": 247, "gpt3_output": "\n\n{\"type\": \"token\", \"description\": \"has a small image on one side and a large image on the other\", \"similar objects\": [\"coin\", \"token\"]}"}, {"object": "chocolate_bar", "object_id": 248, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"dark brown; has a chocolate bar inside\", \"similar objects\": [\"chocolate\"]}"}, {"object": "chocolate_cake", "object_id": 249, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"white; has a center cake; is often decorated with chocolate frosting\", \"similar objects\": [\"chocolate\"]}"}, {"object": "chocolate_milk", "object_id": 250, "gpt3_output": "\n\n{\"type\": \"milk\", \"description\": \"dark brown; has a thick consistency\", \"similar objects\": [\"chocolate\"]}"}, {"object": "chocolate_mousse", "object_id": 251, "gpt3_output": "\n\n{\"type\": \" dessert \", \"description\": \"dark chocolate; has a smooth texture; can be made into a smooth cream\", \"similar objects\": [\"chocolate cake\", \"chocolate pudding\"]}"}, {"object": "choker", "object_id": 252, "gpt3_output": "\n\n{\"type\": \"necklace\", \"description\": \"round; has a loop\", \"similar objects\": [\"manila envelope\", \"postcard\"]}"}, {"object": "chopping_board", "object_id": 253, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"square; has a cutting edge\", \"similar objects\": [\"knife\", \"pie crust\"]}"}, {"object": "chopstick", "object_id": 254, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, thin, has a sharp point\", \"similar objects\": [\"knife\", \"fork\"]}"}, {"object": "Christmas_tree", "object_id": 255, "gpt3_output": "\n\n{\"type\": \"tree\", \"description\": \"green; has a stem\", \"similar objects\": [\"real tree\", \"Christmas tree\"]}"}, {"object": "slide", "object_id": 256, "gpt3_output": "\n\n{\"type\": \"gallery\", \"description\": \"slide\", \"similar objects\": [\"pictures\", \"photos\"]}"}, {"object": "cider", "object_id": 257, "gpt3_output": "\n\n{\"type\": \"alcohol\", \"description\": \"clear, green; has a sour smell\", \"similar objects\": [\"wine\", \"beer\"]}"}, {"object": "cigar_box", "object_id": 258, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"brown; has a handle; has a lid\", \"similar objects\": [\"cigar box\"]}"}, {"object": "cigarette", "object_id": 259, "gpt3_output": "\n\n{\"type\": \"cigarette\", \"description\": \"black and white stripes; has a long stem\", \"similar objects\": [\"cigarette\", \"cigar\"]}"}, {"object": "cigarette_case", "object_id": 260, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"closed; has a flap on the front\", \"similar objects\": [\"cigarette case\"]}"}, {"object": "cistern", "object_id": 261, "gpt3_output": "\n\n{\"type\": \"water storage device\", \"description\": \"round; has a spout\", \"similar objects\": [\"tank\", \"pumpkin\"]}"}, {"object": "clarinet", "object_id": 262, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"has a long neck and is played with a mouthpiece\", \"similar objects\": [\"violin\", \"viola\", \"cello\"]}"}, {"object": "clasp", "object_id": 263, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round; has a metal band around it\", \"similar objects\": [\"key ring\", \"watch\"]}"}, {"object": "cleansing_agent", "object_id": 264, "gpt3_output": "\n\n{\"type\": \"chemical\", \"description\": \"aqueous, liquid; can be used to clean\", \"similar objects\": [\"water\", \"soap\"]}"}, {"object": "cleat_(for_securing_rope)", "object_id": 265, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round; has a hole in the middle\", \"similar objects\": [\"screwdriver\", \"wire cutters\"]}"}, {"object": "clementine", "object_id": 266, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green; has a small red apple\", \"similar objects\": [\"fig\", \"grape\"]}"}, {"object": "clip", "object_id": 267, "gpt3_output": "\n\n{\"type\": \"clipart\", \"description\": \"a yellow clipart image\", \"similar objects\": [\"clipart\"]}"}, {"object": "clipboard", "object_id": 268, "gpt3_output": "\n\n{\"type\": \"document\", \"description\": \"rectangular; has a white border\", \"similar objects\": [\"paper\", \"cardboard\"]}"}, {"object": "clippers_(for_plants)", "object_id": 269, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, thin, has a blade on one end\", \"similar objects\": [\"scissors\", \"razor\"]}"}, {"object": "cloak", "object_id": 270, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"black; has a hood\", \"similar objects\": [\"jacket\", \"shirt\", \"sweater\"]}"}, {"object": "clock", "object_id": 271, "gpt3_output": "\n\n{\"type\": \"timepiece\", \"description\": \"has a hands-on face; can be set to a variety of time zones\", \"similar objects\": [\"watch\", \"radio\"]}"}, {"object": "clock_tower", "object_id": 272, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"tall; has a clock on the top\", \"similar objects\": [\"tower\", \"tower block\"]}"}, {"object": "clothes_hamper", "object_id": 273, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a lid; could be filled with clothes\", \"similar objects\": [\"clothes\", \"shampoo\", \"conditioner\"]}"}, {"object": "clothespin", "object_id": 274, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"sticky; has a sharp point\", \"similar objects\": [\"scissors\", \"clothespin\"]}"}, {"object": "clutch_bag", "object_id": 275, "gpt3_output": "\n\n{\"type\": \"bag\", \"description\": \"round, black; has a strap\", \"similar objects\": [\"handbag\", \"backpack\"]}"}, {"object": "coaster", "object_id": 276, "gpt3_output": "\n\n{\"type\": \" amusement park ride\", \"description\": \"has a track; goes up and down\", \"similar objects\": [\"Granada\", \"Tower of Terror\"]}"}, {"object": "coat", "object_id": 277, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white; has a collar and sleeves\", \"similar objects\": [\"shirt\", \"jacket\"]}"}, {"object": "coat_hanger", "object_id": 278, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, thin, has a sharp end\", \"similar objects\": [\"screwdriver\", \"knob\"]}"}, {"object": "coatrack", "object_id": 279, "gpt3_output": "\n\n{\"type\": \" furniture\", \"description\": \"has a handle; can be attached to a wall\", \"similar objects\": [\"row of chairs\", \"wall\"]}"}, {"object": "cock", "object_id": 280, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"male; has a long neck and body\", \"similar objects\": [\"dog\", \"cat\"]}"}, {"object": "cockroach", "object_id": 281, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black; has a long, thin body\", \"similar objects\": [\"mosquitoes\", \"crocodiles\"]}"}, {"object": "cocoa_(beverage)", "object_id": 282, "gpt3_output": "\n\n{\"type\": \"beverage\", \"description\": \"brown; has a small hole in the top\", \"similar objects\": [\"wine\", \"chocolate\"]}"}, {"object": "coconut", "object_id": 283, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green, small, has a seed\", \"similar objects\": [\"coconut\"]}"}, {"object": "coffee_maker", "object_id": 284, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"small, black, has a handle\", \"similar objects\": [\"coffee maker\", \"tea pot\"]}"}, {"object": "coffee_table", "object_id": 285, "gpt3_output": "\n\n{\"type\": \" furniture \", \"description\": \"has a round top; has a table on the bottom\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}"}, {"object": "coffeepot", "object_id": 286, "gpt3_output": "\n\n{\"type\": \"coffee maker\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"K-cup\", \"pod\", \"teapot\"]}"}, {"object": "coil", "object_id": 287, "gpt3_output": "\n\n{\"type\": \"electricity\", \"description\": \"cable; has a coil\", \"similar objects\": [\"wire\", \"plug\"]}"}, {"object": "coin", "object_id": 288, "gpt3_output": "\n\n{\"type\": \"coin\", \"description\": \"round, white; has a metal head\", \"similar objects\": [\"dime\", \"quarter\", \"penny\"]}"}, {"object": "colander", "object_id": 289, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"round, white; has a handle\", \"similar objects\": [\"strainer\", \"sink\"]}"}, {"object": "coleslaw", "object_id": 290, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"green; has a tough skin\", \"similar objects\": [\"kale\", \"celery\"]}"}, {"object": "coloring_material", "object_id": 291, "gpt3_output": "\n\n{\"type\": \"material\", \"description\": \"can be used to color things\", \"similar objects\": [\"gummy bears\", \"chocolate\"]}"}, {"object": "combination_lock", "object_id": 292, "gpt3_output": "\n\n{\"type\": \"security tool\", \"description\": \"has a keypad and a scanner; can be opened with a key\", \"similar objects\": [\"cordless phone\", \"security code\"]}"}, {"object": "pacifier", "object_id": 293, "gpt3_output": "\n\n{\"type\": \"child product\", \"description\": \"round, white, has a stem\", \"similar objects\": [\"baby food\", \"toy\"]}"}, {"object": "comic_book", "object_id": 294, "gpt3_output": "\n\n{\"type\": \"comic book\", \"description\": \"black and white; has text on the front and back\", \"similar objects\": [\"comic book pages\"]}"}, {"object": "compass", "object_id": 295, "gpt3_output": "\n\n{\"type\": \"navigation tool\", \"description\": \"has a needle on the top\", \"similar objects\": [\"map\"]}"}, {"object": "computer_keyboard", "object_id": 296, "gpt3_output": "\n\n{\"type\": \"keyboard\", \"description\": \"has a numeric keypad\", \"similar objects\": [\"keyboard\", \"mouse\"]}"}, {"object": "condiment", "object_id": 297, "gpt3_output": "\n\n{\"type\": \"foodstuff\", \"description\": \"yellow; has a sour smell\", \"similar objects\": [\"salt\", \"pepper\"]}"}, {"object": "cone", "object_id": 298, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"cone-shaped; has a flat top\", \"similar objects\": [\"diamond\", \"sphere\"]}"}, {"object": "control", "object_id": 299, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"black; has a red button; can be turned on and off\", \"similar objects\": [\"keyboard\", \"mouse\"]}"}, {"object": "convertible_(automobile)", "object_id": 300, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a sleek design; could have a convertible top\", \"similar objects\": [\"SUV\", \"Lamborghini\", \"Porsche\"]}"}, {"object": "sofa_bed", "object_id": 301, "gpt3_output": "\n\n{\"type\": \"bed\", \"description\": \"has a cover; could be made of different materials\", \"similar objects\": [\"sofa\", \"pillow\", \"quilt\"]}"}, {"object": "cooker", "object_id": 302, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "cookie", "object_id": 303, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"white, round, has a stem\", \"similar objects\": [\"chocolate\", \"cookie\"]}"}, {"object": "cooking_utensil", "object_id": 304, "gpt3_output": "\n\n{\"type\": \" utensil \", \"description\": \"oval; has a sharp point on one end and a round base on the other\", \"similar objects\": [\"knife\", \"fork\", \"spoon\"]}"}, {"object": "cooler_(for_food)", "object_id": 305, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a handle\", \"similar objects\": [\"box\", \"can\", \"fridge\"]}"}, {"object": "cork_(bottle_plug)", "object_id": 306, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"cylindrical, red; has a hole in the top\", \"similar objects\": [\"cork\"]}"}, {"object": "corkboard", "object_id": 307, "gpt3_output": "\n\n{\"type\": \"board\", \"description\": \"square; has a spine\", \"similar objects\": [\"cardboard\", \"wooden board\"]}"}, {"object": "corkscrew", "object_id": 308, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"curved; has a sharp point\", \"similar objects\": [\"screwdriver\", \"knives\"]}"}, {"object": "edible_corn", "object_id": 309, "gpt3_output": "\n\n{\"type\": \"corn\", \"description\": \"white; has a cob\", \"similar objects\": [\"zucchini\", \"mango\"]}"}, {"object": "cornbread", "object_id": 310, "gpt3_output": "\n\n{\"type\": \"bread\", \"description\": \"thin, round, has a hole in the middle\", \"similar objects\": [\"cornmeal\", \"cake\"]}"}, {"object": "cornet", "object_id": 311, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"has a long neck\", \"similar objects\": [\"guitar\", \"violin\"]}"}, {"object": "cornice", "object_id": 312, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"rectangular; has a roof\", \"similar objects\": [\"tower\", \"tower block\"]}"}, {"object": "cornmeal", "object_id": 313, "gpt3_output": "\n\n{\"type\": \"grain\", \"description\": \"white; has a long cobweb-like stalk\", \"similar objects\": [\"quinoa\", \"corn\"]}"}, {"object": "corset", "object_id": 314, "gpt3_output": "\n\n{\"type\": \" garment\", \"description\": \"tightly fitting; has a band around the waist\", \"similar objects\": [\"bikini\", \"bra\"]}"}, {"object": "costume", "object_id": 315, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"item needs to be put on; can be made of cloth, paper, or plastic\", \"similar objects\": [\"clothes\", \"costume\"]}"}, {"object": "cougar", "object_id": 316, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"cat\", \"puma\"]}"}, {"object": "coverall", "object_id": 317, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"black; has a hood\", \"similar objects\": [\"jacket\", \"coat\"]}"}, {"object": "cowbell", "object_id": 318, "gpt3_output": "\n\n{\"type\": \"audio tool\", \"description\": \"has a metal ring\", \"similar objects\": [\"cowbell\"]}"}, {"object": "cowboy_hat", "object_id": 319, "gpt3_output": "\n\n{\"type\": \"headwear\", \"description\": \"has a brim\", \"similar objects\": [\"cowboy hat\", \"beret\"]}"}, {"object": "crab_(animal)", "object_id": 320, "gpt3_output": "\n\n{\"type\": \"crab\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"giraffe\", \"clam\"]}"}, {"object": "crabmeat", "object_id": 321, "gpt3_output": "\n\n{\"type\": \" seafood\", \"description\": \"white; has a brownish color\", \"similar objects\": [\"crab\", \"grapefruit\"]}"}, {"object": "cracker", "object_id": 322, "gpt3_output": "\n\n{\"type\": \"cracker\", \"description\": \"small, round, has a hole in the middle\", \"similar objects\": [\"gummy bears\", \"flour tortilla\"]}"}, {"object": "crape", "object_id": 323, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green; has a thin skin\", \"similar objects\": [\"grape\", \"apple\"]}"}, {"object": "crate", "object_id": 324, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a lid\", \"similar objects\": [\"box\", \"can\", \"truck\"]}"}, {"object": "crayon", "object_id": 325, "gpt3_output": "\n\n{\"type\": \"artificial intelligence tool\", \"description\": \"black; has a pointy end\", \"similar objects\": [\"gummy bear\", \"dot\"]}"}, {"object": "cream_pitcher", "object_id": 326, "gpt3_output": "\n\n{\"type\": \"pumpkin\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "crescent_roll", "object_id": 327, "gpt3_output": "\n\n{\"type\": \"product\", \"description\": \"round; has a hole in the center\", \"similar objects\": [\"pancakes\", \"tortilla\"]}"}, {"object": "crib", "object_id": 328, "gpt3_output": "\n\n{\"type\": \"child's bed\", \"description\": \"has a crib\", \"similar objects\": [\"cribbage board\", \"toy box\"]}"}, {"object": "crock_pot", "object_id": 329, "gpt3_output": "\n\n{\"type\": \"cooking pot\", \"description\": \"square; has a handle\", \"similar objects\": [\"crockpot\", \"pressure cooker\"]}"}, {"object": "crossbar", "object_id": 330, "gpt3_output": "\n\n{\"type\": \"barrier\", \"description\": \"long, thin, has a crossbar\", \"similar objects\": [\" fence\", \"wall\"]}"}, {"object": "crouton", "object_id": 331, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"thin, round, has a crouton shape\", \"similar objects\": [\"croissant\", \"bagel\"]}"}, {"object": "crow", "object_id": 332, "gpt3_output": "\n\n{\"type\": \"bird\", \"description\": \"black; has a long beak\", \"similar objects\": [\"giraffe\", \"crow\"]}"}, {"object": "crowbar", "object_id": 333, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"long, thin, has a sharp end\", \"similar objects\": [\"axe\", \"pickaxe\"]}"}, {"object": "crown", "object_id": 334, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"a golden crown\", \"similar objects\": [\"jewelry\", \"watch\"]}"}, {"object": "crucifix", "object_id": 335, "gpt3_output": "\n\n{\"type\": \"religion\", \"description\": \"red and white; has a cross on the front\", \"similar objects\": [\"jesus christ\"]}"}, {"object": "cruise_ship", "object_id": 336, "gpt3_output": "\n\n{\"type\": \"ship\", \"description\": \"white; has a blue hull; could have a black superstructure\", \"similar objects\": [\" Titanic\", \"Cunard Line\"]}"}, {"object": "police_cruiser", "object_id": 337, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"black; has a blue light on the front\", \"similar objects\": [\"fire truck\", \"police car\"]}"}, {"object": "crumb", "object_id": 338, "gpt3_output": "\n\n{\"type\": \"piece of food\", \"description\": \"small; has a small amount of food on it\", \"similar objects\": [\"chocolate\", \"cookie\"]}"}, {"object": "crutch", "object_id": 339, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"has a handle; can be used to help with movement\", \"similar objects\": [\"carpet cleaner\", \"scissors\"]}"}, {"object": "cub_(animal)", "object_id": 340, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "cube", "object_id": 341, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"square; has a hole in the middle\", \"similar objects\": [\"cube\"]}"}, {"object": "cucumber", "object_id": 342, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\"]}\n\nInput: eggplant, Output: \n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "cufflink", "object_id": 343, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round; has a metal band\", \"similar objects\": [\"watch\"]}"}, {"object": "cup", "object_id": 344, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a handle\", \"similar objects\": [\"towel\", \"sink\"]}"}, {"object": "trophy_cup", "object_id": 345, "gpt3_output": "\n\n{\"type\": \" trophy \", \"description\": \"has a metal frame; has a trophy inscription or design\"; \"similar objects\": [\"trophy case\", \"trophy\"]}"}, {"object": "cupboard", "object_id": 346, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a door on one side\", \"similar objects\": [\"box\", \"crate\"]}"}, {"object": "cupcake", "object_id": 347, "gpt3_output": "\n\n{\"type\": \"cake\", \"description\": \"round, white, has a center hole\", \"similar objects\": [\"cake\", \"cupcake\"]}"}, {"object": "hair_curler", "object_id": 348, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round; has a handle\", \"similar objects\": [\"curler\", \"curling iron\"]}"}, {"object": "curling_iron", "object_id": 349, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, thin, has a handle\", \"similar objects\": [\"curling iron\", \"scissors\"]}"}, {"object": "curtain", "object_id": 350, "gpt3_output": "\n\n{\"type\": \" curtain \", \"description\": \"made of cloth or paper\", \"similar objects\": [\" curtain \", \" curtain rod \", \" curtain \"]}"}, {"object": "cushion", "object_id": 351, "gpt3_output": "\n\n{\"type\": \" cushion \", \"description\": \"round; could be made of fabric\", \"similar objects\": [\"pillow\", \"towel\"]}"}, {"object": "cylinder", "object_id": 352, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"cylindrical; has a handle\", \"similar objects\": [\"cog\", \"spindle\"]}"}, {"object": "cymbal", "object_id": 353, "gpt3_output": "\n\n{\"type\": \" percussion instrument\", \"description\": \"made of metal or plastic\", \"similar objects\": [\"tuba\", \"clarinet\", \"cymbal\"]}"}, {"object": "dagger", "object_id": 354, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"long, thin, has a blade\", \"similar objects\": [\"sword\", \"knives\"]}"}, {"object": "dalmatian", "object_id": 355, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"lion\", \"giraffe\"]}"}, {"object": "dartboard", "object_id": 356, "gpt3_output": "\n\n{\"type\": \"game\", \"description\": \"black and white; has a round board\", \"similar objects\": [\"chess\", \" darts\"]}"}, {"object": "date_(fruit)", "object_id": 357, "gpt3_output": "\n\n{\"type\": \"date\", \"description\": \"green; has a stem\", \"similar objects\": [\"figs\", \"dates\"]}"}, {"object": "deck_chair", "object_id": 358, "gpt3_output": "\n\n{\"type\": \" furniture \", \"description\": \"has a back; has a cushion\", \"similar objects\": [\"seating\", \"chair\"]}"}, {"object": "deer", "object_id": 359, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"elk\", \"crocodile\"]}"}, {"object": "dental_floss", "object_id": 360, "gpt3_output": "\n\n{\"type\": \"textile\", \"description\": \"white; has a long tail\", \"similar objects\": [\"dental floss\"]}"}, {"object": "desk", "object_id": 361, "gpt3_output": "\n\n{\"type\": \"desk chair\", \"description\": \"has a backrest\", \"similar objects\": [\"chair\", \"seating\"]}"}, {"object": "detergent", "object_id": 362, "gpt3_output": "\n\n{\"type\": \" laundry detergent \", \"description\": \"white; has a scent\", \"similar objects\": [\"gentle soap\", \"soap\"]}"}, {"object": "diaper", "object_id": 363, "gpt3_output": "\n\n{\"type\": \"baby product\", \"description\": \"yellow; has a white stripe down the middle\", \"similar objects\": [\"pajama pants\", \"t-shirt\"]}"}, {"object": "diary", "object_id": 364, "gpt3_output": "\n\n{\"type\": \"journalism\", \"description\": \"written in a feminine hand; has a cover that is usually red; has a date at the top of the cover\", \"similar objects\": [\"journal\", \"book\"]}"}, {"object": "die", "object_id": 365, "gpt3_output": "\n\n{\"type\": \"die\", \"description\": \"round; has a flat surface\", \"similar objects\": [\"die\", \"rolling pin\"]}"}, {"object": "dinghy", "object_id": 366, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"white; has a blue hull\", \"similar objects\": [\"sloop\", \"canoe\"]}"}, {"object": "dining_table", "object_id": 367, "gpt3_output": "\n\n{\"type\": \"table\", \"description\": \"has a seat; is made of wood or plastic\", \"similar objects\": [\"chairs\", \"tables\"]}"}, {"object": "tux", "object_id": 368, "gpt3_output": "\n\n{\"type\": \"dress\", \"description\": \"black; has a white shirt\", \"similar objects\": [\"black tie\", \"white tie\"]}"}, {"object": "dish", "object_id": 369, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a handle\", \"similar objects\": [\"pot\", \"pan\", \"pie\"]}"}, {"object": "dish_antenna", "object_id": 370, "gpt3_output": "\n\n{\"type\": \" antenna \", \"description\": \"round; has a flat top\", \"similar objects\": [\"radio\", \"TV antenna\"]}"}, {"object": "dishrag", "object_id": 371, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"round, black; has a handle\", \"similar objects\": [\"spoon\", \"fork\", \"dishrag\"]}"}, {"object": "dishtowel", "object_id": 372, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"square; has a handle\", \"similar objects\": [\"scissors\", \"dishwasher\"]}"}, {"object": "dishwasher", "object_id": 373, "gpt3_output": "\n\n{\"type\": \"washing machine\", \"description\": \"round; has a handle\", \"similar objects\": [\"dishwasher\", \"washer\"]}"}, {"object": "dishwasher_detergent", "object_id": 374, "gpt3_output": "\n\n{\"type\": \" detergent \", \"description\": \"white; has a powdery texture\", \"similar objects\": [\"soap\", \"shampoo\"]}"}, {"object": "dispenser", "object_id": 375, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a handle\", \"similar objects\": [\"bottle\", \"jar\"]}"}, {"object": "diving_board", "object_id": 376, "gpt3_output": "\n\n{\"type\": \"watercraft\", \"description\": \"long, thin, has a long tail\", \"similar objects\": [\"submarine\", \"raft\"]}"}, {"object": "Dixie_cup", "object_id": 377, "gpt3_output": "\n\n{\"type\": \"drink\", \"description\": \"clear, has a straw\", \"similar objects\": [\"coke\", \"wine\"]}"}, {"object": "dog", "object_id": 378, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white; has a long tail\", \"similar objects\": [\"poodle\", \"cat\"]}"}, {"object": "dog_collar", "object_id": 379, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"has a leash; has a tag\"; \"similar objects\": [\"cat\", \"dog\"]}"}, {"object": "doll", "object_id": 380, "gpt3_output": "\n\n{\"type\": \"person\", \"description\": \"has a head and body\", \"similar objects\": [\"baby\", \"woman\"]}"}, {"object": "dollar", "object_id": 381, "gpt3_output": "\n\n{\"type\": \"currency\", \"description\": \"green; has a yellow value\", \"similar objects\": [\"dollar\"]}"}, {"object": "dollhouse", "object_id": 382, "gpt3_output": "\n\n{\"type\": \"household item\", \"description\": \"made of plastic; has a door; could be opened from the inside\", \"similar objects\": [\"house\"]}"}, {"object": "dolphin", "object_id": 383, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"whale\", \"seagull\"]}"}, {"object": "domestic_ass", "object_id": 384, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown; has a long tail\", \"similar objects\": [\"dog\", \"cat\"]}"}, {"object": "doorknob", "object_id": 385, "gpt3_output": "\n\n{\"type\": \"door handle\", \"description\": \"round, has a hole in the middle\", \"similar objects\": [\"knob\", \"button\"]}"}, {"object": "doormat", "object_id": 386, "gpt3_output": "\n\n{\"type\": \"floor mat\", \"description\": \"has a brown border; could be made of plastic\", \"similar objects\": [\"carpet\", \"rug\"]}"}, {"object": "doughnut", "object_id": 387, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"round, brown; has a hole in the center\", \"similar objects\": [\"chocolate doughnut\", \"cake\"]}"}, {"object": "dove", "object_id": 388, "gpt3_output": "\n\n{\"type\": \"bird\", \"description\": \"black and white; has a long neck\", \"similar objects\": [\"giraffe\", \"dove\"]}"}, {"object": "dragonfly", "object_id": 389, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black and yellow; has a long tail\", \"similar objects\": [\"cricket\", \"dragonfly\"]}"}, {"object": "drawer", "object_id": 390, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a handle\", \"similar objects\": [\"box\", \"crate\", \"shower curtain\"]}"}, {"object": "underdrawers", "object_id": 391, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"tight-fitting; has a waistband\", \"similar objects\": [\"pants\", \"shirt\"]}"}, {"object": "dress", "object_id": 392, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white; has a collar and a skirt\", \"similar objects\": [\"shirt\", \"pants\"]}"}, {"object": "dress_hat", "object_id": 393, "gpt3_output": "\n\n{\"type\": \"headwear\", \"description\": \"round, black; has a brim\", \"similar objects\": [\"beret\", \" Fedora\", \" beanie\"]}"}, {"object": "dress_suit", "object_id": 394, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white; has a collar and sleeves\", \"similar objects\": [\"dress\", \"shirt\"]}"}, {"object": "dresser", "object_id": 395, "gpt3_output": "\n\n{\"type\": \" furniture\", \"description\": \"has drawers and a top\", \"similar objects\": [\"dresser\", \"sideboard\"]}"}, {"object": "drill", "object_id": 396, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"square; has a head; can be used to bore into a material\", \"similar objects\": [\"hammer\", \"drill bit\"]}"}, {"object": "drone", "object_id": 397, "gpt3_output": "\n\n{\"type\": \"aircraft\", \"description\": \"black; has a wingspan of up to 100 feet\", \"similar objects\": [\"helicopter\", \"drone\"]}"}, {"object": "dropper", "object_id": 398, "gpt3_output": "\n\n{\"type\": \"medical tool\", \"description\": \"small, round, has a handle\", \"similar objects\": [\"needle\", \" syringe\"]}"}, {"object": "drum_(musical_instrument)", "object_id": 399, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"has a head and a body\", \"similar objects\": [\"violin\", \"viola\", \"cello\"]}"}, {"object": "drumstick", "object_id": 400, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"long, thin, has a sharp end\", \"similar objects\": [\"sword\", \"pistol\"]}"}, {"object": "duck", "object_id": 401, "gpt3_output": "\n\n{\"type\": \"duck\", \"description\": \"black; has a long neck\", \"similar objects\": [\"giraffe\", \"duck\"]}"}, {"object": "duckling", "object_id": 402, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"ducklings are small; have a duckbill; can be found in water\", \"similar objects\": [\"giraffe\", \"seagull\"]}"}, {"object": "duct_tape", "object_id": 403, "gpt3_output": "\n\n{\"type\": \" adhesive tape\", \"description\": \"yellow; has a sticky back\", \"similar objects\": [\"tape measure\", \"tape\"]}"}, {"object": "duffel_bag", "object_id": 404, "gpt3_output": "\n\n{\"type\": \"bag\", \"description\": \"square; has a handle\", \"similar objects\": [\"handbag\", \"backpack\"]}"}, {"object": "dumbbell", "object_id": 405, "gpt3_output": "\n\n{\"type\": \"weight\", \"description\": \"has a handle\", \"similar objects\": [\"hammer\", \"dumbbell\"]}"}, {"object": "dumpster", "object_id": 406, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a lid; could be filled with garbage\", \"similar objects\": [\"bin\", \"crate\", \"dumpster\"]}"}, {"object": "dustpan", "object_id": 407, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"square; has a handle\", \"similar objects\": [\"spoon\", \"brush\", \"pinch pot\"]}"}, {"object": "eagle", "object_id": 408, "gpt3_output": "\n\n{\"type\": \"bird\", \"description\": \"black and white feathers; has a long neck\", \"similar objects\": [\"giraffe\", \"seagull\"]}"}, {"object": "earphone", "object_id": 409, "gpt3_output": "\n\n{\"type\": \"audio device\", \"description\": \"small; has a small speaker\", \"similar objects\": [\"headset\", \"microphone\"]}"}, {"object": "earplug", "object_id": 410, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"small, black, has a small plug\", \"similar objects\": [\"sunglasses\", \"headphones\"]}"}, {"object": "earring", "object_id": 411, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small; has a metal or plastic clip\", \"similar objects\": [\"ring\", \"earring\"]}"}, {"object": "easel", "object_id": 412, "gpt3_output": "\n\n{\"type\": \"art tool\", \"description\": \"square; has a stand\", \"similar objects\": [\"painting brush\", \"canvas\"]}"}, {"object": "eclair", "object_id": 413, "gpt3_output": "\n\n{\"type\": \"lightning bolt\", \"description\": \"throwing a bolt of light\", \"similar objects\": [\"flashlight\", \"lamp\"]}"}, {"object": "eel", "object_id": 414, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"long, slimy body; has a long snout\", \"similar objects\": [\"crawfish\", \"grouper\"]}"}, {"object": "egg", "object_id": 415, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"white\", \"has a yolk\", \"similar objects\": [\"lion\", \"giraffe\", \"seagull\"]}"}, {"object": "egg_roll", "object_id": 416, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"round; has a white center\", \"similar objects\": [\"rice\", \"pudding\"]}"}, {"object": "egg_yolk", "object_id": 417, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"white; has a round shape\", \"similar objects\": [\"banana\", \"oily egg\"]}"}, {"object": "eggbeater", "object_id": 418, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"has a sharp edge on one end\", \"similar objects\": [\"peppermill\", \"slicer\"]}"}, {"object": "eggplant", "object_id": 419, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"green; has a stem\", \"similar objects\": [\"apple\", \"carrot\"]}"}, {"object": "electric_chair", "object_id": 420, "gpt3_output": "\n\n{\"type\": \"instrumentality\", \"description\": \"green; has a metal frame; could be used to kill people\", \"similar objects\": [\"electric chair\"]}"}, {"object": "refrigerator", "object_id": 421, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a door on one side\", \"similar objects\": [\"fridge\", \"freezer\"]}"}, {"object": "elephant", "object_id": 422, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "elk", "object_id": 423, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown; has a long antlers\", \"similar objects\": [\"elk\", \"buffalo\"]}"}, {"object": "envelope", "object_id": 424, "gpt3_output": "\n\n{\"type\": \"item\", \"description\": \"yellow; has a flap at the top\", \"similar objects\": [\"postcard\", \" envelope\"]}"}, {"object": "eraser", "object_id": 425, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"pointed\", \"similar objects\": [\"scissors\", \"pennel\"]}"}, {"object": "escargot", "object_id": 426, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"small, red, has a long tail\", \"similar objects\": [\"grouper\", \"seagull\"]}"}, {"object": "eyepatch", "object_id": 427, "gpt3_output": "\n\n{\"type\": \"sunglasses\", \"description\": \"green; has a metal frame\", \"similar objects\": [\"sunglasses\", \"sunglasses case\"]}"}, {"object": "falcon", "object_id": 428, "gpt3_output": "\n\n{\"type\": \"bird\", \"description\": \"black; has a long neck and tail\", \"similar objects\": [\"giraffe\", \"seagull\"]}"}, {"object": "fan", "object_id": 429, "gpt3_output": "\n\n{\"type\": \"air conditioner\", \"description\": \"blue; has a blade on the front\", \"similar objects\": [\"window air conditioner\", \"heating pad\"]}"}, {"object": "faucet", "object_id": 430, "gpt3_output": "\n\n{\"type\": \"water faucet\", \"description\": \"has a handle\", \"similar objects\": [\"tap\", \"sink\"]}"}, {"object": "fedora", "object_id": 431, "gpt3_output": "\n\n{\"type\": \"hat\", \"description\": \"has a brim\"; \"similar objects\": [\"beret\", \" fedora\"]}"}, {"object": "ferret", "object_id": 432, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"cat\", \"dog\"]}"}, {"object": "Ferris_wheel", "object_id": 433, "gpt3_output": "\n\n{\"type\": \" amusement park ride\", \"description\": \" goes around the circumference of the ride; has a track that goes up and down\", \"similar objects\": [\"Giraffe ride\", \"Tower of Terror\"]}"}, {"object": "ferry", "object_id": 434, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"long, narrow, has a prow\", \"similar objects\": [\"canalboat\", \"ship\"]}"}, {"object": "fig_(fruit)", "object_id": 435, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green, round, has a stem\", \"similar objects\": [\"figs\", \"plums\", \"grapefruit\"]}"}, {"object": "fighter_jet", "object_id": 436, "gpt3_output": "\n\n{\"type\": \"aircraft\", \"description\": \"black; has a long tail\", \"similar objects\": [\" Boeing 747\", \" Lockheed Martin F-35 Lightning II\"]}"}, {"object": "figurine", "object_id": 437, "gpt3_output": "\n\n{\"type\": \"artwork\", \"description\": \"has a smooth surface; could be made of plastic or metal\", \"similar objects\": [\"paint brush\", \"canvas\"]}"}, {"object": "file_cabinet", "object_id": 438, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; has a door on one side\", \"similar objects\": [\"folder\", \"file cabinet\"]}"}, {"object": "file_(tool)", "object_id": 439, "gpt3_output": "\n\n{\"type\": \"file\", \"description\": \"text file\", \"filesize\": \"5 KB\", \"files\": [\"file1.txt\", \"file2.txt\"]}"}, {"object": "fire_alarm", "object_id": 440, "gpt3_output": "\n\n{\"type\": \"emergency call\", \"description\": \"green; has a bell\"; \"similar objects\": [\"fire truck\", \"police car\"]}"}, {"object": "fire_engine", "object_id": 441, "gpt3_output": "\n\n{\"type\": \"fire engine\", \"description\": \"red; has a bright light; could be used to put out a fire\", \"similar objects\": [\"fire truck\", \" ambulance\"]}"}, {"object": "fire_extinguisher", "object_id": 442, "gpt3_output": "\n\n{\"type\": \"firefighting tool\", \"description\": \"yellow; has a nozzle on the top\", \"similar objects\": [\"fire extinguisher\", \"water hose\"]}"}, {"object": "fire_hose", "object_id": 443, "gpt3_output": "\n\n{\"type\": \"water source\", \"description\": \"green; has a nozzle\", \"similar objects\": [\"fire hydrant\", \"water main\"]}"}, {"object": "fireplace", "object_id": 444, "gpt3_output": "\n\n{\"type\": \"fireplace\", \"description\": \"round; could be made of logs\", \"similar objects\": [\"wooden logs\", \"coal\"]}"}, {"object": "fireplug", "object_id": 445, "gpt3_output": "\n\n{\"type\": \"safety device\", \"description\": \"green; has a red light; is inserted into a wall\", \"similar objects\": [\"fire extinguisher\", \"fire alarm\"]}"}, {"object": "first-aid_kit", "object_id": 446, "gpt3_output": "\n\n{\"type\": \"medical tool\", \"description\": \"box; has a cover; has a bandage\", \"similar objects\": [\"medicine cabinet\", \"hospital bed\"]}"}, {"object": "fish", "object_id": 447, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"green; has scales\", \"similar objects\": [\"giraffe\", \"seagull\"]}"}, {"object": "fish_(food)", "object_id": 448, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"small, red, has scales\", \"similar objects\": [\"grouper\", \"barley\"]}"}, {"object": "fishbowl", "object_id": 449, "gpt3_output": "\n\n{\"type\": \"fish\", \"description\": \"green; 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has a lens on the front\", \"similar objects\": [\"microscope\", \"illustration\"]}"}, {"object": "microwave_oven", "object_id": 687, "gpt3_output": "\n\n{\"type\": \" appliance\", \"description\": \"small; has a blue light on the front\", \"similar objects\": [\"microwave\", \"toaster\"]}"}, {"object": "milestone", "object_id": 688, "gpt3_output": "\n\n{\"type\": \"event\", \"description\": \"a milestone in a journey\", \"similar objects\": [\"birthday\", \"Christmas\"]}"}, {"object": "milk", "object_id": 689, "gpt3_output": "\n\n{\"type\": \"milk\", \"description\": \"white; has a carton shape\", \"similar objects\": [\"chocolate\", \"lactose\"]}"}, {"object": "milk_can", "object_id": 690, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a top\", \"similar objects\": [\"bottle\", \"pail\"]}"}, {"object": "milkshake", "object_id": 691, "gpt3_output": "\n\n{\"type\": \" drink \", \"description\": \"clear, has a straw\", \"similar objects\": [\"milkshake\", \"chocolate milk\"]}"}, {"object": "minivan", "object_id": 692, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"green; has a large, round head\", \"similar objects\": [\"SUV\", \"Lincoln MKZ\"]}"}, {"object": "mint_candy", "object_id": 693, "gpt3_output": "\n\n{\"type\": \"candy\", \"description\": \"green; has a small, round, hard candy shell\", \"similar objects\": [\"mint\"]}"}, {"object": "mirror", "object_id": 694, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"round; has a smooth surface\", \"similar objects\": [\"glass mirror\", \"glass door\"]}"}, {"object": "mitten", "object_id": 695, "gpt3_output": "\n\n{\"type\": \" glove\", \"description\": \"round, black; has a thumb\", \"similar objects\": [\"mitten\"]}"}, {"object": "mixer_(kitchen_tool)", "object_id": 696, "gpt3_output": "\n\n{\"type\": \"mixer\", \"description\": \"square; has a handle\", \"similar objects\": [\"mixer\", \"grater\"]}"}, {"object": "money", "object_id": 697, "gpt3_output": "\n\n{\"type\": \"currency\", \"description\": \"green; has a value on the front\", \"similar objects\": [\"dollar\", \"pound sterling\"]}"}, {"object": "monitor_(computer_equipment) computer_monitor", "object_id": 698, "gpt3_output": "\n\n{\"type\": \"monitor\", \"description\": \"has a screen\", \"similar objects\": [\"desktop monitor\", \"laptop monitor\"]}"}, {"object": "monkey", "object_id": 699, "gpt3_output": "\n\n{\"type\": \"monkey\", \"description\": \"brown; 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has a mane\", \"similar objects\": [\"dog\", \"pony\"]}"}, {"object": "pool_table", "object_id": 831, "gpt3_output": "\n\n{\"type\": \"table game\", \"description\": \"has a long table; has a number of pieces on the top\", \"similar objects\": [\"chess\", \" poker\"]}"}, {"object": "pop_(soda)", "object_id": 832, "gpt3_output": "\n\n{\"type\": \"drink\", \"description\": \"clear, has a straw\", \"similar objects\": [\"soda\", \"water\"]}"}, {"object": "postbox_(public)", "object_id": 833, "gpt3_output": "\n\n{\"type\": \"box\", \"description\": \"has a red postbox logo on the front\", \"similar objects\": [\"postbox\"]}"}, {"object": "postcard", "object_id": 834, "gpt3_output": "\n\n{\"type\": \"photo\", \"description\": \"small; has a white border; has a blue stamp; has a textured background\", \"similar objects\": [\"postcard\", \"card\"]}"}, {"object": "poster", "object_id": 835, "gpt3_output": "\n\n{\"type\": \"artwork\", \"description\": \"white; has a blue border; could be made of paper\", \"similar objects\": [\"picture\", \"wallpaper\"]}"}, {"object": "pot", "object_id": 836, "gpt3_output": "\n\n{\"type\": \"pot\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "flowerpot", "object_id": 837, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"green; 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has a white coat\", \"similar objects\": [\"dog\", \"cat\"]}"}, {"object": "quesadilla", "object_id": 858, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"thin, crispy, has cheese\"; \"similar objects\": [\"quesadilla\", \"taco\"]}"}, {"object": "quiche", "object_id": 859, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"square; has a crusty top\", \"similar objects\": [\"pie\", \"quiche\"]}"}, {"object": "quilt", "object_id": 860, "gpt3_output": "\n\n{\"type\": \"textile\", \"description\": \"solid; has a bias towards the center\", \"similar objects\": [\"cotton\", \"polyester\"]}"}, {"object": "rabbit", "object_id": 861, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown; has a long tail\", \"similar objects\": [\"rabbit\"]}"}, {"object": "race_car", "object_id": 862, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"orange; has a black and white stripe; has a large engine\", \"similar objects\": [\"car\", \"truck\"]}"}, {"object": "racket", "object_id": 863, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"long, thin, has a sharp edge\", \"similar objects\": [\"baton\", \"club\", \"spear\"]}"}, {"object": "radar", "object_id": 864, "gpt3_output": "\n\n{\"type\": \"radar\", \"description\": \"visible; 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has a hood; could be wet\", \"similar objects\": [\"wetlands\", \"clouds\"]}"}, {"object": "ram_(animal)", "object_id": 871, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black; has a long horns\", \"similar objects\": [\"giraffe\", \"elephant\"]}"}, {"object": "raspberry", "object_id": 872, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"small, red, has a stem\", \"similar objects\": [\"apple\", \"cherry\", \"raspberry\"]}"}, {"object": "rat", "object_id": 873, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown; has a long tail\", \"similar objects\": [\"cat\", \"mouse\"]}"}, {"object": "razorblade", "object_id": 874, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"long, thin, has a sharp blade\", \"similar objects\": [\"knife\", \"razor\"]}"}, {"object": "reamer_(juicer)", "object_id": 875, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"oval; has a sharp point on one end and a round head on the other end\", \"similar objects\": [\"knife\", \"hammer\"]}"}, {"object": "rearview_mirror", "object_id": 876, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"visible through the mirror; could have a broken mirror\", \"similar objects\": [\"car\", \"truck\"]}"}, {"object": "receipt", "object_id": 877, "gpt3_output": "\n\n{\"type\": \"document\", \"description\": \"has a date and time stamp; has a signature\"; \"similar objects\": [\"ticket\", \"bill\"]}"}, {"object": "recliner", "object_id": 878, "gpt3_output": "\n\n{\"type\": \" furniture \", \"description\": \"has a backrest and arms\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}"}, {"object": "record_player", "object_id": 879, "gpt3_output": "\n\n{\"type\": \"audio device\", \"description\": \" Plays music from a CD or DVD\", \"similar objects\": [\"CD player\", \"DVD player\"]}"}, {"object": "reflector", "object_id": 880, "gpt3_output": "\n\n{\"type\": \"light source\", \"description\": \"pointed at a surface\", \"similar objects\": [\"torch\", \"lamp\", \"reflector\"]}"}, {"object": "remote_control", "object_id": 881, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"has a remote control\", \"similar objects\": [\"TV remote\", \"car remote\"]}"}, {"object": "rhinoceros", "object_id": 882, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"elephant\", \"rhinoceros\"]}"}, {"object": "rib_(food)", "object_id": 883, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"long, thin, has a backbone\", \"similar objects\": [\"chicken\", \"hamster\"]}"}, {"object": "rifle", "object_id": 884, "gpt3_output": "\n\n{\"type\": \"firearm\", \"description\": \"black; has a barrel\", \"similar objects\": [\"gun\"]}"}, {"object": "ring", "object_id": 885, "gpt3_output": "\n\n{\"type\": \"jewelry\", \"description\": \"gold or silver; has a band\", \"similar objects\": [\"diamond\", \"sapphire\"]}"}, {"object": "river_boat", "object_id": 886, "gpt3_output": "\n\n{\"type\": \"boat\", \"description\": \"long, narrow; has a prow; could be made of wood or plastic\", \"similar objects\": [\"raft\", \"canoe\"]}"}, {"object": "road_map", "object_id": 887, "gpt3_output": "\n\n{\"type\": \"map\", \"description\": \" Shows the route taken by the vehicle\", \"similar objects\": [\"road map\"]}"}, {"object": "robe", "object_id": 888, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white; has a collar and sleeves\", \"similar objects\": [\"shirt\", \"pants\"]}"}, {"object": "rocking_chair", "object_id": 889, "gpt3_output": "\n\n{\"type\": \" furniture \", \"description\": \"can be moved; has a backrest\", \"similar objects\": [\"seating\", \"chair\"]}"}, {"object": "rodent", "object_id": 890, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown; has a long tail\", \"similar objects\": [\"mouse\", \"rat\"]}"}, {"object": "roller_skate", "object_id": 891, "gpt3_output": "\n\n{\"type\": \"skateboard\", \"description\": \"long, thin, has a blade on the front\", \"similar objects\": [\"rollerblades\", \"skateboard\"]}"}, {"object": "Rollerblade", "object_id": 892, "gpt3_output": "\n\n{\"type\": \"skateboard\", \"description\": \"black and white stripes; has a long tail\"; \"similar objects\": [\"rollerblades\"]}"}, {"object": "rolling_pin", "object_id": 893, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, thin, has a handle\", \"similar objects\": [\"roller\", \"pin\"]}"}, {"object": "root_beer", "object_id": 894, "gpt3_output": "\n\n{\"type\": \"alcoholic beverage\", \"description\": \"clear; 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has a ball\", \"similar objects\": [\"tennis ball\", \"football ball\"]}"}, {"object": "solar_array", "object_id": 984, "gpt3_output": "\n\n{\"type\": \"energy source\", \"description\": \"black; has a long tail\", \"similar objects\": [\"wind turbine\", \"solar panel\"]}"}, {"object": "sombrero", "object_id": 985, "gpt3_output": "\n\n{\"type\": \"headwear\", \"description\": \"round, brown; has a brim\", \"similar objects\": [\"beret\", \"beanie\"]}"}, {"object": "soup", "object_id": 986, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"clear, thick, has a thick consistency\", \"similar objects\": [\"chicken broth\", \"mashed potato\"]}"}, {"object": "soup_bowl", "object_id": 987, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"round; has a handle\", \"similar objects\": [\"bowl\", \"pot\", \"pan\"]}"}, {"object": "soupspoon", "object_id": 988, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"oval; has a long handle\", \"similar objects\": [\"spoon\", \"fork\"]}"}, {"object": "sour_cream", "object_id": 989, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"yellow; 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has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "zucchini", "object_id": 1203, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}] \ No newline at end of file diff --git a/tools/files/lvis.v1.tmp0.curie.json b/tools/files/lvis.v1.tmp0.curie.json new file mode 100644 index 0000000000000000000000000000000000000000..520d43d1035e81c112572c5891fde02d52b2e7c1 --- /dev/null +++ b/tools/files/lvis.v1.tmp0.curie.json @@ -0,0 +1 @@ +[{"object": "aerosol_can", "object_id": 1, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"can; has a spray nozzle\", \"similar objects\": [\"can\", \"bottle\", \"paint can\"]}"}, {"object": "air_conditioner", "object_id": 2, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"white; has a fan\", \"similar objects\": [\"fan\", \"heater\", \"air conditioner\"]}"}, {"object": "airplane", "object_id": 3, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has wings\", \"similar objects\": [\"airplane\", \"jet\", \"helicopter\"]}"}, {"object": "alarm_clock", "object_id": 4, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"round; has a screen; could have a bell\", \"similar objects\": [\"watch\", \"radio\", \"clock\"]}"}, {"object": "alcohol", "object_id": 5, "gpt3_output": "\n\nAlcohol is a type of beverage that is made from fermented sugarcane or other plants. It is typically consumed in small quantities, typically as an intoxicant."}, {"object": "alligator", "object_id": 6, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"slippery; has a long snout\", \"similar objects\": [\"crocodile\", \"gator\"]}"}, {"object": "almond", "object_id": 7, "gpt3_output": "{\"type\": \"nut\", \"description\": \"round, has a smooth skin\", \"similar objects\": [\"walnut\", \"pecans\", \"hazelnut\"]}"}, {"object": "ambulance", "object_id": 8, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a glaring siren; could with a stretcher\", \"similar objects\": [\"police car\", \"taxi\", \"garbage truck\"]}"}, {"object": "amplifier", "object_id": 9, "gpt3_output": "\n\n{\"type\": \"electronics\", \"description\": \"boxy; could be used to amplify sound\", \"similar objects\": [\"radio\", \"speaker\", \"tv\"]}"}, {"object": "anklet", "object_id": 10, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"gold; could be worn on the ankle\", \"similar objects\": [\"bracelet\", \"watch\", \" anklet\"]}"}, {"object": "antenna", "object_id": 11, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"tall, thin, has a wire\", \"similar objects\": [\"television\", \"radio\", \"cell phone\"]}"}, {"object": "apple", "object_id": 12, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"orange\", \"banana\", \"pear\"]}"}, {"object": "applesauce", "object_id": 13, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red, smooth, has a consistency of applesauce\", \"similar objects\": [\"pudding\", \"pie\", \"cobbler\"]}"}, {"object": "apricot", "object_id": 14, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"round, has a pit\", \"similar objects\": [\"apricot\", \" peach \", \" plum \"]}"}, {"object": "apron", "object_id": 15, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white; 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could have a feather in the band; could be black, brown, or gray\", \"similar objects\": [\"beanie\", \"beret\", \"cowboy hat\"]}"}, {"object": "ferret", "object_id": 432, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"a small mammal with a long tail and a bushy head\", \"similar objects\": [\"mole\", \"rabbit\", \"hamster\"]}"}, {"object": "Ferris_wheel", "object_id": 433, "gpt3_output": "\n\n{\"type\": \" amusement park ride \", \"description\": \"tall, round, has a number of cars that go up and down\", \"similar objects\": [\"roller coaster\", \" Ferris wheel \", \" amusement park\"]}"}, {"object": "ferry", "object_id": 434, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has a red stripe\", \"similar objects\": [\"boat\", \"ship\", \"submarine\"]}"}, {"object": "fig_(fruit)", "object_id": 435, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"round, has a stem\", \"similar objects\": [\"orange\", \"banana\", \"pear\"]}"}, {"object": "fighter_jet", "object_id": 436, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"long, sleek, has wings\", \"similar objects\": [\"jet\", \"airplane\", \"rocket\"]}"}, {"object": "figurine", "object_id": 437, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"human or animal; could be made of clay, plastic, or metal\", \"similar objects\": [\"statue\", \"figurine\", \"puppet\"]}"}, {"object": "file_cabinet", "object_id": 438, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"rectangular; could have drawers\", \"similar objects\": [\"office\", \"library\", \"home office\"]}"}, {"object": "file_(tool)", "object_id": 439, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"file\", \"similar objects\": [\"folder\", \"document\", \"picture\"]}"}, {"object": "fire_alarm", "object_id": 440, "gpt3_output": "\n\n{\"type\": \"safety device\", \"description\": \"red; has a loud beeping sound\", \"similar objects\": [\"fire extinguisher\", \"smoke detector\", \"alarm clock\"]}"}, {"object": "fire_engine", "object_id": 441, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"red; has a ladder\", \"similar objects\": [\"police car\", \"taxi\", \"fire truck\"]}"}, {"object": "fire_extinguisher", "object_id": 442, "gpt3_output": "\n\n{\"type\": \"firefighting equipment\", \"description\": \"white; has a red cross on it\", \"similar objects\": [\"fire truck\", \"fire hydrant\", \"firefighter\"]}"}, {"object": "fire_hose", "object_id": 443, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a nozzle\", \"similar objects\": [\"fire truck\", \"ambulance\", \"fire hydrant\"]}"}, {"object": "fireplace", "object_id": 444, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"tall, made of bricks, could have a mantel\", \"similar objects\": [\"house\", \"church\", \"temple\"]}"}, {"object": "fireplug", "object_id": 445, "gpt3_output": "\n\n{\"type\": \"electricity\", \"description\": \"red; has a cord\", \"similar objects\": [\"light switch\", \"plug\", \"socket\"]}"}, {"object": "first-aid_kit", "object_id": 446, "gpt3_output": "\n\n{\"type\": \"medical equipment\", \"description\": \"includes bandages, ointment, scissors, and a first-aid kit\", \"similar objects\": [\"doctor\", \"hospital\", \"clinic\"]}"}, {"object": "fish", "object_id": 447, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"long, thin, has scales\", \"similar objects\": [\"tuna\", \"salmon\", \"swordfish\"]}"}, {"object": "fish_(food)", "object_id": 448, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"white, flaky, could have scales\", \"similar objects\": [\"pork\", \"chicken\", \"seafood\"]}"}, {"object": "fishbowl", "object_id": 449, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"has scales; could have a long tail\", \"similar objects\": [\"tuna\", \"salmon\", \"pike\"]}"}, {"object": "fishing_rod", "object_id": 450, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"long, thin, has a reel and a line\", \"similar objects\": [\"boat\", \"fishing boat\", \"sailing boat\"]}"}, {"object": "flag", "object_id": 451, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"a large piece of cloth with a design on it\", \"similar objects\": [\"banner\", \"flagpole\", \"curtains\"]}"}, {"object": "flagpole", "object_id": 452, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"tall, thin, has a flag on top\", \"similar objects\": [\"flag\", \"staff\", \"pylon\"]}"}, {"object": "flamingo", "object_id": 453, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"pink and yellow; has a long neck and legs\", \"similar objects\": [\"giraffe\", \"crocodile\", \"pigeon\"]}"}, {"object": "flannel", "object_id": 454, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"a thick cotton fabric; could be white, blue, or black\", \"similar objects\": [\"shirt\", \"jacket\", \"pajama\"]}"}, {"object": "flap", "object_id": 455, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"a piece of cloth that can be opened and closed\", \"similar objects\": [\"a door\", \"a curtain\", \"a shirt\"]}"}, {"object": "flash", "object_id": 456, "gpt3_output": "\n\n{\"type\": \"lighting tool\", \"description\": \"small, round, could be used to light up a dark place\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "flashlight", "object_id": 457, "gpt3_output": "\n\n{\"type\": \"lighting tool\", \"description\": \"small, cylindrical, could be made of metal\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}"}, {"object": "fleece", "object_id": 458, "gpt3_output": "\n\n{\"type\": \"fabric\", \"description\": \"warm, soft, white\", \"similar objects\": [\"fur\", \"down\", \"cashmere\"]}"}, {"object": "flip-flop_(sandal)", "object_id": 459, "gpt3_output": "\n\n{\"type\": \"footwear\", \"description\": \"flat, has a strap\", \"similar objects\": [\"shoe\", \"sandal\", \"slipper\"]}"}, {"object": "flipper_(footwear)", "object_id": 460, "gpt3_output": "\n\n{\"type\": \"footwear\", \"description\": \"flipper-like; could be used to swim\", \"similar objects\": [\"flippers\", \"shoes\", \"sandals\"]}"}, {"object": "flower_arrangement", "object_id": 461, "gpt3_output": "\n\n{\"type\": \"garden decoration\", \"description\": \"various types of flowers in a vase or pot\", \"similar objects\": [\"flower pot\", \"flower bed\", \"garden\"]}"}, {"object": "flute_glass", "object_id": 462, "gpt3_output": "\n\n{\"type\": \"glass\", \"description\": \"thin; has a stem\", \"similar objects\": [\"wine glass\", \"tumbler\", \"pint glass\"]}"}, {"object": "foal", "object_id": 463, "gpt3_output": "{\"type\": \"animal\", \"description\": \"white; has black spots\", \"similar objects\": [\"horse\", \"pony\", \"zebra\"]}"}, {"object": "folding_chair", "object_id": 464, "gpt3_output": "\n\n{\"type\": \"chair\", \"description\": \"folding; could be used for sitting or for storage\", \"similar objects\": [\"sofa\", \"couch\", \"chair\"]}"}, {"object": "food_processor", "object_id": 465, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"large, rectangular; has multiple blades\", \"similar objects\": [\"blender\", \"food processor\", \"grater\"]}"}, {"object": "football_(American)", "object_id": 466, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"white and blue; has a large, round ball\", \"similar objects\": [\"soccer ball\", \"baseball\", \"hockey puck\"]}"}, {"object": "football_helmet", "object_id": 467, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"red, round, has a visor and a padding\", \"similar objects\": [\"baseball cap\", \"skull cap\", \"football helmet\"]}"}, {"object": "footstool", "object_id": 468, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"round, has a footrest\", \"similar objects\": [\"couch\", \"chair\", \"footrest\"]}"}, {"object": "fork", "object_id": 469, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"forks; could have a tines\", \"similar objects\": [\"spoon\", \"knife\", \"fork\"]}"}, {"object": "forklift", "object_id": 470, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"long, rectangular, has a fork on the front\", \"similar objects\": [\"truck\", \"bus\", \"ship\"]}"}, {"object": "freight_car", "object_id": 471, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"long, rectangular, has a number on the side\", \"similar objects\": [\"train\", \"truck\", \"ship\"]}"}, {"object": "French_toast", "object_id": 472, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"two pieces of bread that are dipped in an egg mixture and then fried\", \"similar objects\": [\"croissant\", \"waffle\", \"pancake\"]}"}, {"object": "freshener", "object_id": 473, "gpt3_output": "\n\n{\"type\": \"product\", \"description\": \"scented; could be in a spray bottle\", \"similar objects\": [\"scented candles\", \"scented oil\", \"scented water\"]}"}, {"object": "frisbee", "object_id": 474, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"round, made of plastic, has a small hole in the center\", \"similar objects\": [\"tennis ball\", \"football\", \"kite\"]}"}, {"object": "frog", "object_id": 475, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"green and brown; has a long tongue\", \"similar objects\": [\"toad\", \"newt\", \"chicken\"]}"}, {"object": "fruit_juice", "object_id": 476, "gpt3_output": "\n\n{\"type\": \"drink\", \"description\": \"clear, colorless; could have pulp\", \"similar objects\": [\"water\", \"coke\", \"fruit juice\"]}"}, {"object": "frying_pan", "object_id": 477, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "fudge", "object_id": 478, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"dark brown; could have nuts\", \"similar objects\": [\"chocolate\", \"carob\", \"butter\"]}"}, {"object": "funnel", "object_id": 479, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"long, thin, has a narrow top\", \"similar objects\": [\"sifter\", \"coffee pot\", \"tea pot\"]}"}, {"object": "futon", "object_id": 480, "gpt3_output": "\n\n{\"type\": \"sleeper\", \"description\": \"couch-like; could be made of different materials\", \"similar objects\": [\"sofa\", \"bed\", \"bed\"]}"}, {"object": "gag", "object_id": 481, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"a piece of cloth that is put over someone's mouth to stop them from talking or screaming\", \"similar objects\": [\"mask\", \"bandanna\", \"handkerchief\"]}"}, {"object": "garbage", "object_id": 482, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"brown; could have plastic bags\", \"similar objects\": [\"can\", \"bag\", \"trash can\"]}"}, {"object": "garbage_truck", "object_id": 483, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"gray; has a large container on the back\", \"similar objects\": [\"police car\", \"taxi\", \"ambulance\"]}"}, {"object": "garden_hose", "object_id": 484, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"blue; has a nozzle\", \"similar objects\": [\"fire hydrant\", \"water hose\", \"swimming pool hose\"]}"}, {"object": "gargle", "object_id": 485, "gpt3_output": "\n\n{\"type\": \"medical procedure\", \"description\": \"swish water around your mouth; could make you gag\", \"similar objects\": [\"toothbrush\", \"toilet\", \"shower\"]}"}, {"object": "gargoyle", "object_id": 486, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"tall, with a pointed head and a long tongue\", \"similar objects\": [\"castle\", \"temple\", \"minaret\"]}"}, {"object": "garlic", "object_id": 487, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"bulbous, green, has a long stem; could have green leaves\", \"similar objects\": [\"onion\", \"garlic\"]}"}, {"object": "gasmask", "object_id": 488, "gpt3_output": "\n\n{\"type\": \"safety equipment\", \"description\": \"black; has a filter\", \"similar objects\": [\"helmet\", \"breathalyzer\", \"fire extinguisher\"]}"}, {"object": "gazelle", "object_id": 489, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"stripes; could have a long tail\", \"similar objects\": [\"elk\", \"giraffe\", \"antelope\"]}"}, {"object": "gelatin", "object_id": 490, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"white, firm, can be boiled or baked\", \"similar objects\": [\"jello\", \"soup\", \"gelatin\"]}"}, {"object": "gemstone", "object_id": 491, "gpt3_output": "\n\n{\"type\": \"jewelry\", \"description\": \"various colors; could be cut into various shapes\", \"similar objects\": [\"diamond\", \"pearl\", \"ruby\"]}"}, {"object": "generator", "object_id": 492, "gpt3_output": "\n\n{\"type\": \"electricity production\", \"description\": \"generates power; could be used to light up a room\", \"similar objects\": [\"electric stove\", \"electric fan\", \"electric lamp\"]}"}, {"object": "giant_panda", "object_id": 493, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white; has a long tail\", \"similar objects\": [\"tiger\", \"lion\", \"elephant\"]}"}, {"object": "gift_wrap", "object_id": 494, "gpt3_output": "\n\n{\"type\": \"gift\", \"description\": \"various colors; could have a bow\", \"similar objects\": [\"card\", \"paper\", \"box\"]}"}, {"object": "ginger", "object_id": 495, "gpt3_output": "\n\n{\"type\": \"plant\", \"description\": \"long, thin, has a sharp point\", \"similar objects\": [\"lemon\", \"ginger\", \"turmeric\"]}"}, {"object": "giraffe", "object_id": 496, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"long neck; has a long tail\", \"similar objects\": [\"zebra\", \"elephant\"]}"}, {"object": "cincture", "object_id": 497, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"a band or belt worn around the waist\", \"similar objects\": [\"belt\", \"sash\", \"scarf\"]}"}, {"object": "glass_(drink_container)", "object_id": 498, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"tall, thin, has a stem\", \"similar objects\": [\"coke bottle\", \"wine bottle\", \"teacup\"]}"}, {"object": "globe", "object_id": 499, "gpt3_output": "{\"type\": \"geographic object\", \"description\": \"round; could have different colors on it\", \"similar objects\": [\"map\", \"world map\", \" globe\"]}"}, {"object": "glove", "object_id": 500, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"made of cloth and has a palm; could be used to protect hands\", \"similar objects\": [\"mitt\", \"glove\", \"gauntlet\"]}"}, {"object": "goat", "object_id": 501, "gpt3_output": "{\"type\": \"animal\", \"description\": \"long, curvy horns; has a beard\", \"similar objects\": [\"sheep\", \"elk\", \"bison\"]}"}, {"object": "goggles", "object_id": 502, "gpt3_output": "\n\n{\"type\": \"eyeglasses\", \"description\": \"dark lenses; could be made of plastic or metal\", \"similar objects\": [\"sunglasses\", \"eyeglasses\", \"contact lenses\"]}"}, {"object": "goldfish", "object_id": 503, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"gold, silvery, scales; could have a long tail\", \"similar objects\": [\"giraffe\", \"tuna\", \"poodle\"]}"}, {"object": "golf_club", "object_id": 504, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"long, thin, has a club head\", \"similar objects\": [\"tennis racket\", \"baseball bat\", \"golf club\"]}"}, {"object": "golfcart", "object_id": 505, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"green; has a golf club on the front\", \"similar objects\": [\"car\", \"truck\", \"bus\"]}"}, {"object": "gondola_(boat)", "object_id": 506, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has a red stripe\", \"similar objects\": [\"boat\", \"ferry\", \"cruise ship\"]}"}, {"object": "goose", "object_id": 507, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"white, black, and grey feathers; could have a long neck\", \"similar objects\": [\"duck\", \"gazelle\", \"turkey\"]}"}, {"object": "gorilla", "object_id": 508, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"large, muscular, has a long tail\", \"similar objects\": [\"elephant\", \"lion\", \"gorilla\"]}"}, {"object": "gourd", "object_id": 509, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"round, has a long neck and a small, round, seed-filled fruit\", \"similar objects\": [\"squash\", \"pumpkin\", \"cucumber\"]}"}, {"object": "grape", "object_id": 510, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red; has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "grater", "object_id": 511, "gpt3_output": "{\"type\": \"kitchen tool\", \"description\": \"sharp, triangular, has a handle\", \"similar objects\": [\"slicer\", \"chopper\", \"chef's knife\"]}"}, {"object": "gravestone", "object_id": 512, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"tall, white, has a name on it\", \"similar objects\": [\"mausoleum\", \"grave\", \"tomb\"]}"}, {"object": "gravy_boat", "object_id": 513, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"round; has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "green_bean", "object_id": 514, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}, {"object": "green_onion", "object_id": 515, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"white and green; has a bulb-like stem\", \"similar objects\": [\"onion\", \"garlic\"]}"}, {"object": "griddle", "object_id": 516, "gpt3_output": "{\"type\": \"cooking tool\", \"description\": \"flat, has a handle\", \"similar objects\": [\"grill\", \"griddle\", \"frying pan\"]}"}, {"object": "grill", "object_id": 517, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"round; could have a cover\", \"similar objects\": [\"grill\", \"fryer\", \"oven\"]}"}, {"object": "grits", "object_id": 518, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"floury; could have a nutty flavor\", \"similar objects\": [\"rice\", \"corn\", \"oatmeal\"]}"}, {"object": "grizzly", "object_id": 519, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown and black fur; could have a big head\", \"similar objects\": [\"lion\", \"bear\", \"tiger\"]}"}, {"object": "grocery_bag", "object_id": 520, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"rectangular; has a handle and a strap\", \"similar objects\": [\"backpack\", \"shopping bag\", \"tote bag\"]}"}, {"object": "guitar", "object_id": 521, "gpt3_output": "{\"type\": \"instrument\", \"description\": \"long, thin neck; could have strings\", \"similar objects\": [\"banjo\", \"mandolin\", \"violin\"]}"}, {"object": "gull", "object_id": 522, "gpt3_output": "\n\n{\"type\": \"bird\", \"description\": \"white with black tips on wings and tail\", \"similar objects\": [\"seagull\", \"gull\", \"shorebird\"]}"}, {"object": "gun", "object_id": 523, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"long, barrel; could be fired; could have a clip\", \"similar objects\": [\"firearm\", \"shotgun\", \"rifle\"]}"}, {"object": "hairbrush", "object_id": 524, "gpt3_output": "\n\n{\"type\": \"personal care\", \"description\": \"long, thin, has bristles\", \"similar objects\": [\"toilet brush\", \"shampoo bottle\", \"comb\"]}"}, {"object": "hairnet", "object_id": 525, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"white; has a netting on it\", \"similar objects\": [\"scarf\", \"hat\", \"bandanna\"]}"}, {"object": "hairpin", "object_id": 526, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"thin, metal wire with a pointed end\", \"similar objects\": [\"nail\", \"pin\", \"tongue depressor\"]}"}, {"object": "halter_top", "object_id": 527, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"tight-fitting top that wraps around the body\", \"similar objects\": [\"bikini\", \"tank top\", \"sports bra\"]}"}, {"object": "ham", "object_id": 528, "gpt3_output": "\n\n{\"type\": \"meat\", \"description\": \"smooth, red, could be smoked\", \"similar objects\": [\"bacon\", \"pork\", \"salami\"]}"}, {"object": "hamburger", "object_id": 529, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"round, has a bun\", \"similar objects\": [\"pizza\", \"burger\", \"hot dog\"]}"}, {"object": "hammer", "object_id": 530, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round; could have a head\", \"similar objects\": [\"chisel\", \"sledgehammer\", \"punch\"]}"}, {"object": "hammock", "object_id": 531, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"a sling for hanging; could be made of ropes\", \"similar objects\": [\"swing\", \"tree swing\", \"bean bag chair\"]}"}, {"object": "hamper", "object_id": 532, "gpt3_output": "{\"type\": \"container\", \"description\": \"large, rectangular, could be made of cloth\", \"similar objects\": [\"clothes hamper\", \"satchel\", \"backpack\"]}"}, {"object": "hamster", "object_id": 533, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"small; has a long tail\", \"similar objects\": [\"giraffe\", \"mouse\", \"rat\"]}"}, {"object": "hair_dryer", "object_id": 534, "gpt3_output": "\n\n{\"type\": \"electric appliance\", \"description\": \"long, cylindrical, has a handle\", \"similar objects\": [\"hair dryer\", \"toaster\", \"microwave\"]}"}, {"object": "hand_glass", "object_id": 535, "gpt3_output": "\n\n{\"type\": \"glass\", \"description\": \"tall, thin, has a stem\", \"similar objects\": [\"wine glass\", \"teacup\", \"water glass\"]}"}, {"object": "hand_towel", "object_id": 536, "gpt3_output": "\n\n{\"type\": \"textile\", \"description\": \"white, terrycloth; could be folded into a triangle\", \"similar objects\": [\"towel\", \"bath towel\", \"hand towel\"]}"}, {"object": "handcart", "object_id": 537, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"two wheels; could be pushed by someone\", \"similar objects\": [\"trolley\", \"cart\", \"snowmobile\"]}"}, {"object": "handcuff", "object_id": 538, "gpt3_output": "\n\n{\"type\": \"lockable object\", \"description\": \"two metal circles that fit around a person's wrists\", \"similar objects\": [\"key ring\", \"lock\", \"bracelet\"]}"}, {"object": "handkerchief", "object_id": 539, "gpt3_output": "\n\n{\"type\": \"cloth\", \"description\": \"white, rectangular, has a small red cross in the center\", \"similar objects\": [\"towel\", \"scarf\", \"handkerchief\"]}"}, {"object": "handle", "object_id": 540, "gpt3_output": "\n\nThe object detection model should focus on detecting handles."}, {"object": "handsaw", "object_id": 541, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, thin, has a blade\", \"similar objects\": [\"miter saw\", \"chisel\", \"sander\"]}"}, {"object": "hardback_book", "object_id": 542, "gpt3_output": "\n\n{\"type\": \"book\", \"description\": \"large, rectangular, has a spine\", \"similar objects\": [\"e-reader\", \"Kindle\", \" paperback book\"]}"}, {"object": "harmonium", "object_id": 543, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"large, cylindrical, has a keyboard\", \"similar objects\": [\"organ\", \"piano\", \"guitar\"]}"}, {"object": "hat", "object_id": 544, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"round, has a brim\", \"similar objects\": [\"beanie\", \"beanie hat\", \"beanie\"]}"}, {"object": "hatbox", "object_id": 545, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"square; could be made of cloth, paper, or plastic\", \"similar objects\": [\"box\", \"bag\", \"suitcase\"]}"}, {"object": "veil", "object_id": 546, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"white, thin, could be worn on the head\", \"similar objects\": [\"hat\", \"scarf\", \"shawl\"]}"}, {"object": "headband", "object_id": 547, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"white, stretchy, has a band\", \"similar objects\": [\"bandanna\", \"scarf\", \"headband\"]}"}, {"object": "headboard", "object_id": 548, "gpt3_output": "\n\n{\"type\": \"furniture\", \"description\": \"tall, rectangular, has a headboard\", \"similar objects\": [\"bed\", \"sofa\", \"couch\"]}"}, {"object": "headlight", "object_id": 549, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"small, round, has a light on top\", \"similar objects\": [\"car\", \"bike\", \"boat\"]}"}, {"object": "headscarf", "object_id": 550, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"a scarf that covers the head and neck\", \"similar objects\": [\"scarf\", \"hat\", \"shawl\"]}"}, {"object": "headset", "object_id": 551, "gpt3_output": "\n\n{\"type\": \"electronics\", \"description\": \"headphones; could be used for listening to music or watching videos\", \"similar objects\": [\"earphones\", \"speakers\", \"microphone\"]}"}, {"object": "headstall_(for_horses)", "object_id": 552, "gpt3_output": "\n\n{\"type\": \"equipment\", \"description\": \"for holding horses' heads\", \"similar objects\": [\"bridle\", \"saddle\", \"bit\"]}"}, {"object": "heart", "object_id": 553, "gpt3_output": "\n\n{\"type\": \"medical device\", \"description\": \"red; has a beating heart\", \"similar objects\": [\"cardiac monitor\", \"pacemaker\", \"defibrillator\"]}"}, {"object": "heater", "object_id": 554, "gpt3_output": "\n\n{\"type\": \"electricity\", \"description\": \"used to warm up a room\", \"similar objects\": [\"radiator\", \"heater\", \"oven\"]}"}, {"object": "helicopter", "object_id": 555, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has a spinning rotor\", \"similar objects\": [\"airplane\", \"helicopter\"]}"}, {"object": "helmet", "object_id": 556, "gpt3_output": "\n\n{\"type\": \"safety equipment\", \"description\": \"round; could protect the head\", \"similar objects\": [\"helmet\", \"safety vest\", \"sunglasses\"]}"}, {"object": "heron", "object_id": 557, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"white with black and green feathers\", \"similar objects\": [\"gull\", \"cormorant\", \"stork\"]}"}, {"object": "highchair", "object_id": 558, "gpt3_output": "\n\n{\"type\": \"child-care\", \"description\": \"has a tray for food; could be made of plastic or metal\", \"similar objects\": [\"crib\", \"playpen\", \"toddler's chair\"]}"}, {"object": "hinge", "object_id": 559, "gpt3_output": "\n\n{\"type\": \"mechanism\", \"description\": \"a metal bar that can be opened and closed\", \"similar objects\": [\"door\", \"window\", \"lock\"]}"}, {"object": "hippopotamus", "object_id": 560, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"large; has a long trunk; could have a short tail\", \"similar objects\": [\"elephant\", \"giraffe\", \"rhinoceros\"]}"}, {"object": "hockey_stick", "object_id": 561, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"long, thin, has a blade at one end\", \"similar objects\": [\"tennis racket\", \"baseball bat\", \"skateboard\"]}"}, {"object": "hog", "object_id": 562, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"fat; could have bristles on its back\", \"similar objects\": [\"pig\", \"boar\", \"swine\"]}"}, {"object": "home_plate_(baseball)", "object_id": 563, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"round, white, in the middle of the field\", \"similar objects\": [\"ball\", \"baseball\", \"field\"]}"}, {"object": "honey", "object_id": 564, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"yellow; has a thick consistency\", \"similar objects\": [\"butter\", \"syrup\", \"jelly\"]}"}, {"object": "fume_hood", "object_id": 565, "gpt3_output": "\n\n{\"type\": \"safety equipment\", \"description\": \"round; has a filter; could be used to prevent exposure to harmful fumes\", \"similar objects\": [\"smoke detector\", \"fire extinguisher\", \"air purifier\"]}"}, {"object": "hook", "object_id": 566, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, thin, has a point\", \"similar objects\": [\"scissors\", \"knife\", \"needle\"]}"}, {"object": "hookah", "object_id": 567, "gpt3_output": "\n\n{\"type\": \"smoking device\", \"description\": \"long, cylindrical, has a bowl at the end\", \"similar objects\": [\"water pipe\", \"bong\", \"pipes\"]}"}, {"object": "hornet", "object_id": 568, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black and yellow; has a stinger on its abdomen\", \"similar objects\": [\"bee\", \"wasp\", \"hornet\"]}"}, {"object": "horse", "object_id": 569, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"zebra\", \"giraffe\", \"elephant\"]}"}, {"object": "hose", "object_id": 570, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"long, thin, has a nozzle\", \"similar objects\": [\"fire truck\", \"water truck\", \"swimming pool\"]}"}, {"object": "hot-air_balloon", "object_id": 571, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has a basket; could have a pilot\", \"similar objects\": [\"airship\", \"helicopter\", \"balloon\"]}"}, {"object": "hotplate", "object_id": 572, "gpt3_output": "{\"type\": \"cooking tool\", \"description\": \"round, has a knob\", \"similar objects\": [\"griddle\", \"frying pan\", \"skillet\"]}"}, {"object": "hot_sauce", "object_id": 573, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"red, spicy, has a bottle\", \"similar objects\": [\"salsa\", \"hot sauce\", \"ketchup\"]}"}, {"object": "hourglass", "object_id": 574, "gpt3_output": "\n\n{\"type\": \"time-keeping device\", \"description\": \"hourglass shape; could have sand running through it\", \"similar objects\": [\"watch\", \"jewelry box\", \"skeleton key\"]}"}, {"object": "houseboat", "object_id": 575, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; could have a red roof\", \"similar objects\": [\"yacht\", \"cruise ship\", \"house\"]}"}, {"object": "hummingbird", "object_id": 576, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and yellow; has a long beak\", \"similar objects\": [\"bird\", \"hummingbird\"]}"}, {"object": "hummus", "object_id": 577, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"white, thick, has a smooth consistency\", \"similar objects\": [\"mayonnaise\", \"hummus\", \"tahini\"]}"}, {"object": "polar_bear", "object_id": 578, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"white; has a thick fur coat\", \"similar objects\": [\"penguin\", \" seal\", \"bear\"]}"}, {"object": "icecream", "object_id": 579, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"white, creamy, has a cone\", \"similar objects\": [\"milkshake\", \"snow cone\", \"ice cream\"]}"}, {"object": "popsicle", "object_id": 580, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"frozen; could have a stick\", \"similar objects\": [\"ice cream\", \"snow cone\", \"slushie\"]}"}, {"object": "ice_maker", "object_id": 581, "gpt3_output": "\n\n{\"type\": \"machine\", \"description\": \"large; could have a cube shape\", \"similar objects\": [\"fridge\", \"freezer\", \"cooler\"]}"}, {"object": "ice_pack", "object_id": 582, "gpt3_output": "\n\n{\"type\": \"medical device\", \"description\": \"white; could be filled with cold water\", \"similar objects\": [\"cold pack\", \"cotton ball\"]}"}, {"object": "ice_skate", "object_id": 583, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"thin, long, has a blade on one end\", \"similar objects\": [\"snowboard\", \"ice skater\", \"tennis ball\"]}"}, {"object": "igniter", "object_id": 584, "gpt3_output": "\n\n{\"type\": \"fire starter\", \"description\": \"small, cylindrical, could be lit with a match\", \"similar objects\": [\"lighter\", \"fire starter\", \"matches\"]}"}, {"object": "inhaler", "object_id": 585, "gpt3_output": "\n\n{\"type\": \"medical device\", \"description\": \"white; has a green cylinder\", \"similar objects\": [\"medicine\", \"inhaler\", \"pulmonary resuscitation machine\"]}"}, {"object": "iPod", "object_id": 586, "gpt3_output": "\n\n{\"type\": \"electronics\", \"description\": \"white, rectangular, has a click wheel\", \"similar objects\": [\"mp3 player\", \"iPad\", \"iPhone\"]}"}, {"object": "iron_(for_clothing)", "object_id": 587, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"has a sharp point on one end and a flat surface on the other\", \"similar objects\": [\"scissors\", \"iron\", \"ironing board\"]}"}, {"object": "ironing_board", "object_id": 588, "gpt3_output": "\n\n{\"type\": \"household item\", \"description\": \"rectangular; has a cloth cover\", \"similar objects\": [\"couch\", \"sofa\", \"bed\"]}"}, {"object": "jacket", "object_id": 589, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"a coat; could be made of many materials\", \"similar objects\": [\"coat\", \"coat\", \"coat\"]}"}, {"object": "jam", "object_id": 590, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"thick, spreadable, could have fruit; could be in a jar\", \"similar objects\": [\"jelly\", \"jam\", \"butter\"]}"}, {"object": "jar", "object_id": 591, "gpt3_output": "{\"type\": \"container\", \"description\": \"round; could be made of glass\", \"similar objects\": [\"jar\", \"bottle\", \"jug\"]}"}, {"object": "jean", "object_id": 592, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"blue, tight-fitting; could have a pocket\", \"similar objects\": [\"pants\", \"shorts\", \"skirt\"]}"}, {"object": "jeep", "object_id": 593, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"4-wheel drive; could have a roof; could be green or brown\", \"similar objects\": [\"car\", \"truck\", \"SUV\"]}"}, {"object": "jelly_bean", "object_id": 594, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"round, has a hard shell\", \"similar objects\": [\"gummy bear\", \"jelly bean\"]}"}, {"object": "jersey", "object_id": 595, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white, with blue and black stripes\", \"similar objects\": [\"T-shirt\", \"shirt\", \"sweatshirt\"]}"}, {"object": "jet_plane", "object_id": 596, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has wings\", \"similar objects\": [\"airplane\", \"jet\"]}"}, {"object": "jewel", "object_id": 597, "gpt3_output": "\n\n{\"type\": \"jewelry\", \"description\": \"round, colorful, could be made of many different materials\", \"similar objects\": [\"watch\", \"bracelet\", \"earrings\"]}"}, {"object": "jewelry", "object_id": 598, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"various types of jewelry\", \"similar objects\": [\"watch\", \"bracelet\", \"earrings\"]}"}, {"object": "joystick", "object_id": 599, "gpt3_output": "\n\n{\"type\": \"input device\", \"description\": \"shaped like a T; has a trigger and two buttons\", \"similar objects\": [\"game controller\", \"joystick\", \"keyboard\"]}"}, {"object": "jumpsuit", "object_id": 600, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"tight-fitting; could be made of different materials\", \"similar objects\": [\"suit\", \"jacket\", \"tunic\"]}"}, {"object": "kayak", "object_id": 601, "gpt3_output": "\n\n{\"type\": \"watercraft\", \"description\": \"long, thin, has a flat bottom\", \"similar objects\": [\"canoe\", \"raft\", \"paddleboat\"]}"}, {"object": "keg", "object_id": 602, "gpt3_output": "{\"type\": \"container\", \"description\": \"round; could be filled with beer\", \"similar objects\": [\"barrel\", \"keg\", \"jug\"]}"}, {"object": "kennel", "object_id": 603, "gpt3_output": "\n\n{\"type\": \"animal shelter\", \"description\": \"small, could be in a backyard\", \"similar objects\": [\"dog kennel\", \"cat shelter\", \"horse stable\"]}"}, {"object": "kettle", "object_id": 604, "gpt3_output": "{\"type\": \"cooking tool\", \"description\": \"round, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "key", "object_id": 605, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"round, has a keyhole\", \"similar objects\": [\"lock\", \"door knob\", \"key\"]}"}, {"object": "keycard", "object_id": 606, "gpt3_output": "\n\n{\"type\": \"access control\", \"description\": \"a card with a magnetic strip; could be used to open a door\", \"similar objects\": [\"card\", \"key\"]}"}, {"object": "kilt", "object_id": 607, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"a long, thin piece of cloth that is worn around the waist\", \"similar objects\": [\"trousers\", \"skirt\", \"dress\"]}"}, {"object": "kimono", "object_id": 608, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"a robe made of silk or cotton; has a wide collar and long sleeves\", \"similar objects\": [\"jacket\", \"coat\", \"kimono\"]}"}, {"object": "kitchen_sink", "object_id": 609, "gpt3_output": "\n\n{\"type\": \"utility\", \"description\": \"tall, has a faucet and a drain\", \"similar objects\": [\"bathroom sink\", \"toilet\", \"sink\"]}"}, {"object": "kitchen_table", "object_id": 610, "gpt3_output": "\n\n{\"type\": \"room\", \"description\": \"has a surface to sit or eat at; could have a kitchen sink and stove; could have a cupboard\", \"similar objects\": [\"living room\", \"bedroom\", \"kitchen\"]}"}, {"object": "kite", "object_id": 611, "gpt3_output": "\n\n{\"type\": \"flying object\", \"description\": \"white, with colorful designs\", \"similar objects\": [\"sailboat\", \"airplane\", \"kite\"]}"}, {"object": "kitten", "object_id": 612, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"cute; has a fluffy tail\", \"similar objects\": [\"cat\", \"dog\", \"hamster\"]}"}, {"object": "kiwi_fruit", "object_id": 613, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green; has a hard skin\", \"similar objects\": [\"apricot\", \"pear\", \"plum\"]}"}, {"object": "knee_pad", "object_id": 614, "gpt3_output": "\n\n{\"type\": \"medical device\", \"description\": \"round; could be made of foam\", \"similar objects\": [\"glucometer\", \"blood pressure cuff\", \"medicine ball\"]}"}, {"object": "knife", "object_id": 615, "gpt3_output": "\n\n{\"type\": \"cutting tool\", \"description\": \"sharp; could be made of metal\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}"}, {"object": "knitting_needle", "object_id": 616, "gpt3_output": "\n\n{\"type\": \"crafting tool\", \"description\": \"long, thin, has a point\", \"similar objects\": [\"needle\", \"thimble\", \"scissors\"]}"}, {"object": "knob", "object_id": 617, "gpt3_output": "{\"type\": \"object\", \"description\": \"round; could be turned\", \"similar objects\": [\"door knob\", \"light switch\", \"faucet knob\"]}"}, {"object": "knocker_(on_a_door)", "object_id": 618, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"a metal rod with a round head on one end and a handle on the other; used to knock on a door\", \"similar objects\": [\"door knob\", \"keyboard\", \"bell\"]}"}, {"object": "koala", "object_id": 619, "gpt3_output": "{\"type\": \"animal\", \"description\": \"brown and white; has a big head\", \"similar objects\": [\"bear\", \"pig\", \"monkey\"]}"}, {"object": "lab_coat", "object_id": 620, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white; has a pocket on the left side; could have a name tag\", \"similar objects\": [\"lab coat\", \"clothes\", \"lab technician\"]}"}, {"object": "ladder", "object_id": 621, "gpt3_output": "\n\n{\"type\": \"building structure\", \"description\": \"tall, has rungs\", \"similar objects\": [\"stairs\", \"tower\", \"climbing frame\"]}"}, {"object": "ladle", "object_id": 622, "gpt3_output": "{\"type\": \"cooking tool\", \"description\": \"long, thin, has a handle\", \"similar objects\": [\"spoon\", \"ladle\", \"dishpan\"]}"}, {"object": "ladybug", "object_id": 623, "gpt3_output": "\n\n{\"type\": \"insect\", \"description\": \"black, oval, has six legs\", \"similar objects\": [\"butterfly\", \"cricket\", \"dragonfly\"]}"}, {"object": "lamb_(animal)", "object_id": 624, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"white; has a long tail; could have a cloven hoof\", \"similar objects\": [\"cow\", \"pig\", \"sheep\"]}"}, {"object": "lamb-chop", "object_id": 625, "gpt3_output": "\n\n{\"type\": \"meat\", \"description\": \"white; has a pinkish-red chop\", \"similar objects\": [\"pork chop\", \"hamburger\", \"chicken chop\"]}"}, {"object": "lamp", "object_id": 626, "gpt3_output": "\n\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of papers\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "lamppost", "object_id": 627, "gpt3_output": "{\"type\": \"building\", \"description\": \"tall, has a light on top\", \"similar objects\": [\"church\", \"school\", \"office\"]}"}, {"object": "lampshade", "object_id": 628, "gpt3_output": "\n\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of papers\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "lantern", "object_id": 629, "gpt3_output": "\n\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of papers\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "lanyard", "object_id": 630, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"a cord or strap used to fasten something, typically around the neck\", \"similar objects\": [\"keychain\", \"necklace\", \"bracelet\"]}"}, {"object": "laptop_computer", "object_id": 631, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"thin, rectangular; has a screen\", \"similar objects\": [\"desktop computer\", \"notebook computer\", \"iPad\"]}"}, {"object": "lasagna", "object_id": 632, "gpt3_output": "\n\n{\"type\": \"meal\", \"description\": \"noodle-based; has a tomato sauce\", \"similar objects\": [\"chicken noodle soup\", \"macaroni and cheese\", \"pizza\"]}"}, {"object": "latch", "object_id": 633, "gpt3_output": "\n\n{\"type\": \"door\", \"description\": \"has a handle\", \"similar objects\": [\"door\", \"window\", \"gate\"]}"}, {"object": "lawn_mower", "object_id": 634, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has a blade that cuts the grass\", \"similar objects\": [\"garden tractor\", \"push mower\", \"weed eater\"]}"}, {"object": "leather", "object_id": 635, "gpt3_output": "\n\n{\"type\": \"material\", \"description\": \"tanned; could be soft or hard\", \"similar objects\": [\"leather\", \"cloth\", \"suede\"]}"}, {"object": "legging_(clothing)", "object_id": 636, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"tight-fitting; could be made of different materials\", \"similar objects\": [\"shorts\", \"pants\", \"skirt\"]}"}, {"object": "Lego", "object_id": 637, "gpt3_output": "\n\n{\"type\": \"building material\", \"description\": \"blocks that can be assembled into different shapes\", \"similar objects\": [\"toy\", \"blocks\", \"kite\"]}"}, {"object": "legume", "object_id": 638, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"podlike; could have seeds\", \"similar objects\": [\"pea\", \"bean\", \"lentil\"]}"}, {"object": "lemon", "object_id": 639, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"yellow; has a sour taste\", \"similar objects\": [\"grapefruit\", \"orange\", \"tangerine\"]}"}, {"object": "lemonade", "object_id": 640, "gpt3_output": "\n\n{\"type\": \"drink\", \"description\": \"yellow; has a sour taste\", \"similar objects\": [\"lemon\", \"orange juice\", \"soda\"]}"}, {"object": "lettuce", "object_id": 641, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"crisp, green, has a stem\", \"similar objects\": [\"spinach\", \"lettuce\", \"cabbage\"]}"}, {"object": "license_plate", "object_id": 642, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has a number and letters\", \"similar objects\": [\"car\", \"truck\", \"bus\"]}"}, {"object": "life_buoy", "object_id": 643, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"white; has a flag; could be attached to a boat\", \"similar objects\": [\"buoy\", \"life preserver\", \"floatation device\"]}"}, {"object": "life_jacket", "object_id": 644, "gpt3_output": "\n\n{\"type\": \"safety equipment\", \"description\": \"white; has a collar\", \"similar objects\": [\"life jacket\", \"swimsuit\", \"buoy\"]}"}, {"object": "lightbulb", "object_id": 645, "gpt3_output": "{\"type\": \"lighting tool\", \"description\": \"round; could be made of metal\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "lightning_rod", "object_id": 646, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"tall, metal pole with a wire running down it; could be white or black\", \"similar objects\": [\"power line\", \"tree\", \"sparrow\"]}"}, {"object": "lime", "object_id": 647, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"green; has a sour taste\", \"similar objects\": [\"grapefruit\", \"orange\", \"tangerine\"]}"}, {"object": "limousine", "object_id": 648, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"gold; has a shiny roof\", \"similar objects\": [\"sedan\", \"luxury car\", \"cabriolet\"]}"}, {"object": "lion", "object_id": 649, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "lip_balm", "object_id": 650, "gpt3_output": "\n\n{\"type\": \"cosmetic\", \"description\": \"tube shaped; could have a variety of colors\", \"similar objects\": [\"lipstick\", \"lip balm\", \"eyeliner\"]}"}, {"object": "liquor", "object_id": 651, "gpt3_output": "\n\n{\"type\": \"alcohol\", \"description\": \"clear, colorless, could be mixed with other liquids\", \"similar objects\": [\"wine\", \"beer\", \"liquor\"]}"}, {"object": "lizard", "object_id": 652, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"scaly; has a long tail\", \"similar objects\": [\"toad\", \"snake\", \"garter snake\"]}"}, {"object": "log", "object_id": 653, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"round; could be made of wood\", \"similar objects\": [\"tree\", \"log\"]}"}, {"object": "lollipop", "object_id": 654, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"round, with a stick; 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could be white, black, brown, or tan; has a tail\", \"similar objects\": [\"cat\", \"dog\", \"hamster\"]}"}, {"object": "quesadilla", "object_id": 858, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"flatbread with cheese and meat; could be grilled or fried\", \"similar objects\": [\"pizza\", \"burrito\", \"taco\"]}"}, {"object": "quiche", "object_id": 859, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"pie-like; could have different fillings\", \"similar objects\": [\"pizza\", \"taco\", \"pie\"]}"}, {"object": "quilt", "object_id": 860, "gpt3_output": "\n\n{\"type\": \"fabric\", \"description\": \"a large piece of cloth that is usually made of cotton, wool, or a blend of both; can be used to cover a bed, to keep someone warm, or to decorate a room\", \"similar objects\": [\"blanket\", \"cover\", \"pillow\"]}"}, {"object": "rabbit", "object_id": 861, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"long ears; has a short tail\", \"similar objects\": [\"hamster\", \"giraffe\", \"mouse\"]}"}, {"object": "race_car", "object_id": 862, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"red; has a sleek design\", \"similar objects\": [\"car\", \"bike\", \"boat\"]}"}, {"object": "racket", "object_id": 863, "gpt3_output": "\n\n{\"type\": \"musical instrument\", \"description\": \"wooden or metal; has strings\", \"similar objects\": [\"violin\", \"cello\", \"guitar\"]}"}, {"object": "radar", "object_id": 864, "gpt3_output": "\n\n{\"type\": \"technology\", \"description\": \"uses radio waves to detect objects; could be used in air traffic control\", \"similar objects\": [\"sonar\", \"laser\", \"radar\"]}"}, {"object": "radiator", "object_id": 865, "gpt3_output": "\n\n{\"type\": \"mechanical device\", \"description\": \"large, rectangular, has fins\", \"similar objects\": [\"air conditioner\", \"heater\", \"oven\"]}"}, {"object": "radio_receiver", "object_id": 866, "gpt3_output": "\n\n{\"type\": \"device\", \"description\": \"small; could be attached to a wall\", \"similar objects\": [\"television\", \"stereo\", \"radio\"]}"}, {"object": "radish", "object_id": 867, "gpt3_output": "\n\n{\"type\": \"vegetable\", \"description\": \"round, white, has a stem\", \"similar objects\": [\"carrot\", \"turnip\", \"radish\"]}"}, {"object": "raft", "object_id": 868, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"long, thin, could be made of wood\", \"similar objects\": [\"boat\", \"raft\"]}"}, {"object": "rag_doll", "object_id": 869, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"small, cloth-covered figure with movable arms and legs\", \"similar objects\": [\"toddler\", \"child\", \"doll\"]}"}, {"object": "raincoat", "object_id": 870, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"dark color; could have a hood; could be made of fabric\", \"similar objects\": [\"coat\", \"jacket\", \"raincoat\"]}"}, {"object": "ram_(animal)", "object_id": 871, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"large, has horns\", \"similar objects\": [\"bull\", \"elk\", \"cow\"]}"}, {"object": "raspberry", "object_id": 872, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"red; smooth; could have a stem\", \"similar objects\": [\"strawberry\", \"blackberry\", \"raspberry\"]}"}, {"object": "rat", "object_id": 873, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown and black; has a long tail\", \"similar objects\": [\"mouse\", \"hamster\", \"rabbit\"]}"}, {"object": "razorblade", "object_id": 874, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"sharp, thin, has a handle\", \"similar objects\": [\"razor\", \"knife\", \"scissors\"]}"}, {"object": "reamer_(juicer)", "object_id": 875, "gpt3_output": "\n\n{\"type\": \"utensil\", \"description\": \"long, thin, has a sharp point\", \"similar objects\": [\"teaspoon\", \"knife\", \"fork\"]}"}, {"object": "rearview_mirror", "object_id": 876, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"large; 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has a fleece on its back\", \"similar objects\": [\"lamb\", \"goat\", \"sheep\"]}"}, {"object": "shepherd_dog", "object_id": 944, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"brown and white; has a long tail\", \"similar objects\": [\"labrador retriever\", \"border collie\", \"poodle\"]}"}, {"object": "sherbert", "object_id": 945, "gpt3_output": "\n\n{\"type\": \"frozen dessert\", \"description\": \"pink, smooth, has a sherbert flavor\", \"similar objects\": [\"ice cream\", \"slurpee\", \"sherbet\"]}"}, {"object": "shield", "object_id": 946, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"a rectangular piece of metal or plastic that is used to protect someone or something from attack\", \"similar objects\": [\"wall\", \"door\", \"gate\"]}"}, {"object": "shirt", "object_id": 947, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"white, long-sleeved; has a collar\", \"similar objects\": [\"blouse\", \"shirt\", \"tunic\"]}"}, {"object": "shoe", "object_id": 948, "gpt3_output": "\n\n{\"type\": \"footwear\", \"description\": \"brown, has a heel and a toe\", \"similar objects\": [\"shoe\", \"boot\", \"sandal\"]}"}, {"object": "shopping_bag", "object_id": 949, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"large, rectangular, has a handle and a strap\", \"similar objects\": [\"backpack\", \"shopping cart\", \"tote bag\"]}"}, {"object": "shopping_cart", "object_id": 950, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"large, has a handle; could be pushed by someone\", \"similar objects\": [\"truck\", \"bus\", \"van\"]}"}, {"object": "short_pants", "object_id": 951, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"pants that are shorter than what is typically worn\", \"similar objects\": [\"shorts\", \"skirt\", \"bikini\"]}"}, {"object": "shot_glass", "object_id": 952, "gpt3_output": "\n\n{\"type\": \"drinking glass\", \"description\": \"tall, thin, has a stem\", \"similar objects\": [\"wine glass\", \"martini glass\", \"soda glass\"]}"}, {"object": "shoulder_bag", "object_id": 953, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"large, rectangular, has a strap\", \"similar objects\": [\"backpack\", \"shoulder bag\", \"purse\"]}"}, {"object": "shovel", "object_id": 954, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"long, rectangular, has a handle\", \"similar objects\": [\"spade\", \"trowel\", \"digging tool\"]}"}, {"object": "shower_head", "object_id": 955, "gpt3_output": "\n\n{\"type\": \"bathroom fixture\", \"description\": \"round; 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could have a faucet\", \"similar objects\": [\"toilet\", \"bathtub\", \"sink\"]}"}, {"object": "automatic_washer", "object_id": 1160, "gpt3_output": "\n\n{\"type\": \"household appliance\", \"description\": \"white; has a spinning drum\", \"similar objects\": [\"dryer\", \"washer\", \"clothes dryer\"]}"}, {"object": "watch", "object_id": 1161, "gpt3_output": "\n\n{\"type\": \"timekeeping device\", \"description\": \"round; has a face\", \"similar objects\": [\"watch\", \"clock\", \"watchband\"]}"}, {"object": "water_bottle", "object_id": 1162, "gpt3_output": "{\"type\": \"container\", \"description\": \"round; has a spout\", \"similar objects\": [\"glass\", \"metal\", \"plastic\"]}"}, {"object": "water_cooler", "object_id": 1163, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"tall, rectangular, has a spout\", \"similar objects\": [\"school\", \"office\", \"hospital\"]}"}, {"object": "water_faucet", "object_id": 1164, "gpt3_output": "\n\n{\"type\": \"mechanical\", \"description\": \"round; could have a hose attached\", \"similar objects\": [\"tap\", \"faucet\", \"spigot\"]}"}, {"object": "water_heater", "object_id": 1165, "gpt3_output": "\n\n{\"type\": \"energy\", \"description\": \"large; could be used to heat water\", \"similar objects\": [\"heater\", \"stove\", \"radiator\"]}"}, {"object": "water_jug", "object_id": 1166, "gpt3_output": "{\"type\": \"container\", \"description\": \"tall, cylindrical, has a spout\", \"similar objects\": [\"jug\", \"bucket\", \"pail\"]}"}, {"object": "water_gun", "object_id": 1167, "gpt3_output": "\n\n{\"type\": \" toy \", \"description\": \"small, plastic, has a nozzle\", \"similar objects\": [\"air rifle\", \" BB gun\", \"water pistol\"]}"}, {"object": "water_scooter", "object_id": 1168, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"blue; has a propeller on the front\", \"similar objects\": [\"boat\", \"jet ski\", \"surfboard\"]}"}, {"object": "water_ski", "object_id": 1169, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"two skis attached to a board that is pushed by a person\", \"similar objects\": [\"snowboard\", \"wakeboard\", \"skateboard\"]}"}, {"object": "water_tower", "object_id": 1170, "gpt3_output": "\n\n{\"type\": \"building\", \"description\": \"tall, cylindrical, has a spire\", \"similar objects\": [\"church\", \"skyscraper\", \"power plant\"]}"}, {"object": "watering_can", "object_id": 1171, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"round; has a spout\", \"similar objects\": [\"fountain\", \"pond\", \"rain barrel\"]}"}, {"object": "watermelon", "object_id": 1172, "gpt3_output": "\n\n{\"type\": \"fruit\", \"description\": \"round, green, has a hard skin\", \"similar objects\": [\"melon\", \"grape\", \"watermelon\"]}"}, {"object": "weathervane", "object_id": 1173, "gpt3_output": "\n{\"type\": \"mechanical device\", \"description\": \"tall, cylindrical, has a blade that points in a certain direction\", \"similar objects\": [\"sailboat\", \"windmill\", \"turbine\"]}"}, {"object": "webcam", "object_id": 1174, "gpt3_output": "\n\n{\"type\": \"camera\", \"description\": \"small; could be placed on a desk\", \"similar objects\": [\"phone\", \"camera\", \"webcam\"]}"}, {"object": "wedding_cake", "object_id": 1175, "gpt3_output": "\n\n{\"type\": \"food\", \"description\": \"white and pink; has a frosting\", \"similar objects\": [\"birthday cake\", \"pudding\", \"cake\"]}"}, {"object": "wedding_ring", "object_id": 1176, "gpt3_output": "\n\n{\"type\": \"jewelry\", \"description\": \"gold; has a band\", \"similar objects\": [\"bracelet\", \"watch\", \"ring\"]}"}, {"object": "wet_suit", "object_id": 1177, "gpt3_output": "\n\n{\"type\": \"clothing\", \"description\": \"blue; has a zipper\", \"similar objects\": [\"swimsuit\", \"bikini\", \"jacket\"]}"}, {"object": "wheel", "object_id": 1178, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"round; has spokes\", \"similar objects\": [\"car\", \"bike\", \"rollerblade\"]}"}, {"object": "wheelchair", "object_id": 1179, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; has a red stripe\", \"similar objects\": [\"car\", \"bus\", \"train\"]}"}, {"object": "whipped_cream", "object_id": 1180, "gpt3_output": "\n\n{\"type\": \"dessert\", \"description\": \"white, fluffy, could be made of cream\", \"similar objects\": [\"frosting\", \"cream\", \"pudding\"]}"}, {"object": "whistle", "object_id": 1181, "gpt3_output": "\n\n{\"type\": \"sound\", \"description\": \"a high-pitched sound\", \"similar objects\": [\"siren\", \"horn\", \"whistle\"]}"}, {"object": "wig", "object_id": 1182, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"long, curly, black hair\", \"similar objects\": [\"hair\", \"wig\", \"hat\"]}"}, {"object": "wind_chime", "object_id": 1183, "gpt3_output": "\n\n{\"type\": \"sound\", \"description\": \"tinkling\", \"similar objects\": [\"chime\", \"bell\"]}"}, {"object": "windmill", "object_id": 1184, "gpt3_output": "{\"type\": \"building\", \"description\": \"tall, cylindrical, has blades on the top\", \"similar objects\": [\"mill\", \"tower\", \"wind turbine\"]}"}, {"object": "window_box_(for_plants)", "object_id": 1185, "gpt3_output": "\n\n{\"type\": \"garden object\", \"description\": \"tall, rectangular, has a hole in the top\", \"similar objects\": [\"pot\", \"planter\", \"terrace\"]}"}, {"object": "windshield_wiper", "object_id": 1186, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"long, thin, has a blade\", \"similar objects\": [\"car\", \"truck\", \"boat\"]}"}, {"object": "windsock", "object_id": 1187, "gpt3_output": "\n\n{\"type\": \"object\", \"description\": \"white; has a flag on a pole\", \"similar objects\": [\"flag\", \"sailboat\", \"lantern\"]}"}, {"object": "wine_bottle", "object_id": 1188, "gpt3_output": "\n\n{\"type\": \"alcoholic beverage\", \"description\": \"shaped like a bottle; has a cork\", \"similar objects\": [\"beer\", \"whiskey\", \"brandy\"]}"}, {"object": "wine_bucket", "object_id": 1189, "gpt3_output": "\n\n{\"type\": \"container\", \"description\": \"tall, cylindrical, has a handle and a spout\", \"similar objects\": [\"jug\", \"barrel\", \"bucket\"]}"}, {"object": "wineglass", "object_id": 1190, "gpt3_output": "\n\n{\"type\": \"glass\", \"description\": \"tall, slender, has a stem\", \"similar objects\": [\"wine bottle\", \"sherry glass\", \"martini glass\"]}"}, {"object": "blinder_(for_horses)", "object_id": 1191, "gpt3_output": "\n\n{\"type\": \"equipment\", \"description\": \"a device that is worn on a horse's head to blind them so they cannot see what is in front of them\", \"similar objects\": [\"saddle\", \"bridle\", \"bit\"]}"}, {"object": "wok", "object_id": 1192, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "wolf", "object_id": 1193, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"gray; has a long snout; could have black fur\", \"similar objects\": [\"dog\", \"cat\", \"bear\"]}"}, {"object": "wooden_spoon", "object_id": 1194, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"long, thin, has a handle\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}"}, {"object": "wreath", "object_id": 1195, "gpt3_output": "\n\n{\"type\": \"decoration\", \"description\": \"a circle of flowers or greenery put around a person's neck\", \"similar objects\": [\"hanging ornament\", \"Christmas tree\", \"garland\"]}"}, {"object": "wrench", "object_id": 1196, "gpt3_output": "\n\n{\"type\": \"tool\", \"description\": \"round; has a handle\", \"similar objects\": [\"screwdriver\", \"pliers\", \"hammer\"]}"}, {"object": "wristband", "object_id": 1197, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"white; has a blue stripe\", \"similar objects\": [\"watch\", \"bracelet\", \"wristband\"]}"}, {"object": "wristlet", "object_id": 1198, "gpt3_output": "\n\n{\"type\": \"accessory\", \"description\": \"small, thin, could be made of leather\", \"similar objects\": [\"wallet\", \"watch\", \"keychain\"]}"}, {"object": "yacht", "object_id": 1199, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"white; could have a blue flag\", \"similar objects\": [\"boat\", \"yacht\", \"cruise ship\"]}"}, {"object": "yogurt", "object_id": 1200, "gpt3_output": "{\"type\": \"food\", \"description\": \"firm, has a thick consistency\", \"similar objects\": [\"curd\", \"yogurt\", \"cheese\"]}"}, {"object": "yoke_(animal_equipment)", "object_id": 1201, "gpt3_output": "\n\n{\"type\": \"vehicle\", \"description\": \"made of wood, has a handle and a pair of horns\", \"similar objects\": [\"horse\", \"cart\", \"tractor\"]}"}, {"object": "zebra", "object_id": 1202, "gpt3_output": "\n\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "zucchini", "object_id": 1203, "gpt3_output": "\n\nThe object detection model should focus on the zucchini's description and similar objects."}] \ No newline at end of file diff --git a/tools/files/lvis_v1.description.v1.json b/tools/files/lvis_v1.description.v1.json new file mode 100644 index 0000000000000000000000000000000000000000..035fe04c84d9207f649d6684308b7ebff2e44ccd --- /dev/null +++ b/tools/files/lvis_v1.description.v1.json @@ -0,0 +1 @@ +[{"object": "aerosol_can", "object_id": 1, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be used to store liquids or gases; could be pressurized; could have a nozzle\", \"similar objects\": [\"spray bottle\", \"gas canister\", \"fire extinguisher\"]}"}, {"object": "air_conditioner", "object_id": 2, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"large, rectangular; has a fan and a compressor; could be wall-mounted or window-mounted\", \"similar objects\": [\"heater\", \"refrigerator\", \"dehumidifier\"]}"}, {"object": "airplane", "object_id": 3, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long and narrow; has wings and a tail; could have multiple engines; could be used for transportation\", \"similar objects\": [\"helicopter\", \"rocket\", \"drone\"]}"}, {"object": "alarm_clock", "object_id": 4, "gpt3_output": "\n{\"type\": \"timekeeping tool\", \"description\": \"could have a digital or analog display; could have a snooze button; could have a loud ringing sound\", \"similar objects\": [\"watch\", \"timer\", \"stopwatch\"]}"}, {"object": "alcohol", "object_id": 5, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"colorless liquid; could be consumed for recreational purposes; could be distilled from grains, fruits, or vegetables\", \"similar objects\": [\"beer\", \"wine\", \"spirits\"]}"}, {"object": "alligator", "object_id": 6, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large reptile; has a long, scaly body; has a wide, flat head; has sharp teeth\", \"similar objects\": [\"crocodile\", \"turtle\", \"iguana\"]}"}, {"object": "almond", "object_id": 7, "gpt3_output": "\n{\"type\": \"nut\", \"description\": \"oval-shaped; has a brown outer shell; could be sliced into two halves; could be eaten raw or roasted\", \"similar objects\": [\"walnut\", \"cashew\", \"pecan\"]}"}, {"object": "ambulance", "object_id": 8, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"red; has a glaring siren; could with a stretcher\", \"similar objects\": [\"police car\", \"taxi\", \"garbage truck\"]}"}, {"object": "amplifier", "object_id": 9, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"used to increase the volume of sound; could be connected to speakers\", \"similar objects\": [\"receiver\", \"equalizer\", \"mixer\"]}"}, {"object": "anklet", "object_id": 10, "gpt3_output": "\n{\"type\": \"jewelry\", \"description\": \"worn around the ankle; could be made of metal, beads, or other materials; could have charms or decorations\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}"}, {"object": "antenna", "object_id": 11, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"long, thin, could be used to receive signals\", \"similar objects\": [\"satellite dish\", \"transmitter\", \"receiver\"]}"}, {"object": "apple", "object_id": 12, "gpt3_output": "\n{\"type\": \"fruit\", \n\"description\": \"round, red, has a stem and green leaves\", \n\"similar objects\": [\"orange\", \"banana\", \"pear\"]}"}, {"object": "applesauce", "object_id": 13, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"smooth, sweet, could be made from apples; could be served hot or cold\", \"similar objects\": [\"apple pie\", \"apple butter\", \"apple juice\"]}"}, {"object": "apricot", "object_id": 14, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, orange-yellow, has a pit\", \"similar objects\": [\"peach\", \"plum\", \"nectarine\"]}"}, {"object": "apron", "object_id": 15, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"worn over the body; could be made of fabric; could have strings to tie around the waist\", \"similar objects\": [\"chef hat\", \"gloves\", \"oven mitts\"]}"}, {"object": "aquarium", "object_id": 16, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"transparent; could be filled with water; could contain fish and other aquatic animals\", \"similar objects\": [\"fish tank\", \"terrarium\", \"vivarium\"]}"}, {"object": "arctic_(type_of_shoe)", "object_id": 17, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"waterproof; insulated; could have a fur lining; could have a rubber sole\", \"similar objects\": [\"snow boots\", \"hiking boots\", \"rain boots\"]}"}, {"object": "armband", "object_id": 18, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"worn around the arm; could be made of fabric or plastic; could be used for decoration or identification\", \"similar objects\": [\"bracelet\", \"watch\", \"anklet\"]}"}, {"object": "armchair", "object_id": 19, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has armrests; could be upholstered; could have a reclining back\", \"similar objects\": [\"sofa\", \"loveseat\", \"recliner\"]}"}, {"object": "armoire", "object_id": 20, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"tall, wooden, has doors and shelves\", \"similar objects\": [\"dresser\", \"wardrobe\", \"cabinet\"]}"}, {"object": "armor", "object_id": 21, "gpt3_output": "\n{\"type\": \"protective clothing\", \"description\": \"made of metal or leather; could be worn by soldiers or knights; could be used to protect the body from weapons\", \"similar objects\": [\"helmet\", \"shield\", \"breastplate\"]}"}, {"object": "artichoke", "object_id": 22, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"spiky, green, round; has a stem; could be boiled or steamed; could have a heart\", \"similar objects\": [\"cauliflower\", \"broccoli\", \"asparagus\"]}"}, {"object": "trash_can", "object_id": 23, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; has a lid; could be made of metal or plastic\", \"similar objects\": [\"bin\", \"garbage can\", \"dustbin\"]}"}, {"object": "ashtray", "object_id": 24, "gpt3_output": "\n{\"type\": \"smoking tool\", \"description\": \"round; could be made of metal or ceramic; could have a lid\", \"similar objects\": [\"cigarette case\", \"cigarette holder\", \"cigarette box\"]}"}, {"object": "asparagus", "object_id": 25, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, thin, green stalks; could have purple tips; could be steamed or boiled; could have a nutty flavor\", \"similar objects\": [\"broccoli\", \"cauliflower\", \"green beans\"]}"}, {"object": "atomizer", "object_id": 26, "gpt3_output": "\n{\"type\": \"spray device\", \"description\": \"small, handheld device; used to spray liquid in a fine mist; could be used for perfume, cleaning, or medical purposes\", \"similar objects\": [\"spray bottle\", \"mister\", \"air freshener\"]}"}, {"object": "avocado", "object_id": 27, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"oval-shaped; green or black; has a large seed inside; could be sliced into pieces\", \"similar objects\": [\"mango\", \"kiwi\", \"papaya\"]}"}, {"object": "award", "object_id": 28, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"could be made of metal or plastic; could be in the shape of a trophy; could have an inscription\", \"similar objects\": [\"medal\", \"plaque\", \"certificate\"]}"}, {"object": "awning", "object_id": 29, "gpt3_output": "\n{\"type\": \"shelter tool\", \"description\": \"can be attached to the side of a building; could be made of fabric or metal; could be retractable\", \"similar objects\": [\"canopy\", \"umbrella\", \"tent\"]}"}, {"object": "ax", "object_id": 30, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle with a sharp blade; used for chopping wood\", \"similar objects\": [\"hatchet\", \"hammer\", \"saw\"]}"}, {"object": "baboon", "object_id": 31, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, long-tailed monkey; has a dog-like face; has a pinkish-red bottom\", \"similar objects\": [\"chimpanzee\", \"gorilla\", \"orangutan\"]}"}, {"object": "baby_buggy", "object_id": 32, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"small, collapsible, has four wheels; could be pushed by an adult\", \"similar objects\": [\"stroller\", \"pram\", \"pushchair\"]}"}, {"object": "basketball_backboard", "object_id": 33, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"rectangular; has a hoop and a net; could be made of metal or wood\", \"similar objects\": [\"volleyball net\", \"soccer goal\", \"tennis net\"]}"}, {"object": "backpack", "object_id": 34, "gpt3_output": "\n{\"type\": \"bag\", \"description\": \"rectangular; has straps; could be used to carry items\", \"similar objects\": [\"duffel bag\", \"suitcase\", \"tote bag\"]}"}, {"object": "handbag", "object_id": 35, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"rectangular; could be made of leather; could have straps\", \"similar objects\": [\"purse\", \"backpack\", \"tote bag\"]}"}, {"object": "suitcase", "object_id": 36, "gpt3_output": "\n{\"type\": \"travel item\", \"description\": \"rectangular; has a handle; could be made of hard materials\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}"}, {"object": "bagel", "object_id": 37, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be toasted; could be topped with cream cheese\", \"similar objects\": [\"doughnut\", \"bagel\", \"pretzel\"]}"}, {"object": "bagpipe", "object_id": 38, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"wooden; has a bag; has a chanter; has drones\", \"similar objects\": [\"accordion\", \"harmonica\", \"flute\"]}"}, {"object": "baguet", "object_id": 39, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"long, thin, crusty; could be sliced into pieces; could be filled with different ingredients\", \"similar objects\": [\"ciabatta\", \"focaccia\", \"rye bread\"]}"}, {"object": "bait", "object_id": 40, "gpt3_output": "\n{\"type\": \"fishing tool\", \"description\": \"used to attract fish; could be made of worms, insects, or other food\", \"similar objects\": [\"lure\", \"hook\", \"tackle\"]}"}, {"object": "ball", "object_id": 41, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"round; could be made of rubber, plastic, or cloth; could be used for sports or games\", \"similar objects\": [\"bat\", \"frisbee\", \"dice\"]}"}, {"object": "ballet_skirt", "object_id": 42, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long, flowy, usually made of tulle; could have an elastic waistband\", \"similar objects\": [\"tutu\", \"leotard\", \"ballet slippers\"]}"}, {"object": "balloon", "object_id": 43, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"round; made of rubber or plastic; could be filled with air or helium; could be colorful\", \"similar objects\": [\"kite\", \"yo-yo\", \"juggling balls\"]}"}, {"object": "bamboo", "object_id": 44, "gpt3_output": "\n{\"type\": \"plant\", \"description\": \"tall, thin, woody; could be used to make furniture; could be used to make paper\", \"similar objects\": [\"reed\", \"cane\", \"palm\"]}"}, {"object": "banana", "object_id": 45, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"long, curved, yellow; has a brown peel; could be sliced into pieces\", \"similar objects\": [\"apple\", \"orange\", \"pear\"]}"}, {"object": "Band_Aid", "object_id": 46, "gpt3_output": "\n{\"type\": \"medical product\", \"description\": \"adhesive bandage; could be used to cover minor wounds\", \"similar objects\": [\"gauze\", \"tape\", \"antiseptic cream\"]}"}, {"object": "bandage", "object_id": 47, "gpt3_output": "\n{\"type\": \"medical tool\", \"description\": \"long strip of cloth; could be used to cover wounds\", \"similar objects\": [\"gauze\", \"tape\", \"plaster\"]}"}, {"object": "bandanna", "object_id": 48, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"square; could be tied around the head; could be made of cotton or silk\", \"similar objects\": [\"scarf\", \"headband\", \"hat\"]}"}, {"object": "banjo", "object_id": 49, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long neck; has strings; could have a round body\", \"similar objects\": [\"guitar\", \"ukulele\", \"mandolin\"]}"}, {"object": "banner", "object_id": 50, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"long and rectangular; could be made of cloth or paper; could be hung up\", \"similar objects\": [\"flag\", \"poster\", \"sign\"]}"}, {"object": "barbell", "object_id": 51, "gpt3_output": "\n{\"type\": \"exercise equipment\", \"description\": \"two metal bars connected by a central weight; could be used for weightlifting\", \"similar objects\": [\"dumbbell\", \"kettlebell\", \"weight plate\"]}"}, {"object": "barge", "object_id": 52, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"large, flat-bottomed boat; could be used for carrying cargo; could be powered by motor or sail\", \"similar objects\": [\"ferry\", \"yacht\", \"canoe\"]}"}, {"object": "barrel", "object_id": 53, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of wood or metal; could have a lid\", \"similar objects\": [\"bucket\", \"tub\", \"tank\"]}"}, {"object": "barrette", "object_id": 54, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small clip; could be decorated with beads or stones; could be used to hold hair in place\", \"similar objects\": [\"hair tie\", \"hair clip\", \"headband\"]}"}, {"object": "barrow", "object_id": 55, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"wheeled container; used for carrying goods; could be pushed or pulled\", \"similar objects\": [\"cart\", \"wheelbarrow\", \"wagon\"]}"}, {"object": "baseball_base", "object_id": 56, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"a white, four-sided base; used in baseball\", \"similar objects\": [\"bat\", \"glove\", \"ball\"]}"}, {"object": "baseball", "object_id": 57, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"round; made of leather and cork; has a stitching\", \"similar objects\": [\"softball\", \"tennis ball\", \"golf ball\"]}"}, {"object": "baseball_bat", "object_id": 58, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, cylindrical; could be made of wood or metal; used to hit a ball\", \"similar objects\": [\"golf club\", \"tennis racket\", \"hockey stick\"]}"}, {"object": "baseball_cap", "object_id": 59, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"round; has a brim; could have a logo or design\", \"similar objects\": [\"hat\", \"beanie\", \"sun hat\"]}"}, {"object": "baseball_glove", "object_id": 60, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"leather; has a pocket; used to catch a baseball\", \"similar objects\": [\"bat\", \"ball\", \"helmet\"]}"}, {"object": "basket", "object_id": 61, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"could be made of wicker; could have a handle; could be used for carrying items\", \"similar objects\": [\"bag\", \"box\", \"bucket\"]}"}, {"object": "basketball", "object_id": 62, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"round; orange; has a net\", \"similar objects\": [\"football\", \"baseball\", \"tennis ball\"]}"}, {"object": "bass_horn", "object_id": 63, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long, cylindrical; could be made of brass; produces low-pitched sound\", \"similar objects\": [\"trombone\", \"trumpet\", \"saxophone\"]}"}, {"object": "bat_(animal)", "object_id": 64, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"winged mammal; has sharp teeth; could fly\", \"similar objects\": [\"bird\", \"butterfly\", \"dragonfly\"]}"}, {"object": "bath_mat", "object_id": 65, "gpt3_output": "\n{\"type\": \"bathroom accessory\", \"description\": \"soft, absorbent, usually made of cotton or microfiber; could have a non-slip backing\", \"similar objects\": [\"bath rug\", \"bathroom mat\", \"shower mat\"]}"}, {"object": "bath_towel", "object_id": 66, "gpt3_output": "\n{\"type\": \"household item\", \"description\": \"absorbent; usually made of cotton; could be used to dry body after shower\", \"similar objects\": [\"hand towel\", \"washcloth\", \"bath mat\"]}"}, {"object": "bathrobe", "object_id": 67, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"loose-fitting; could be made of cotton or terry cloth; could have a hood; could have pockets\", \"similar objects\": [\"robe\", \"kimono\", \"caftan\"]}"}, {"object": "bathtub", "object_id": 68, "gpt3_output": "\n{\"type\": \"bathroom fixture\", \"description\": \"large, deep, usually made of porcelain; 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could have yellow or brown spots; could be boiled or steamed; could have a bitter taste\", \"similar objects\": [\"cauliflower\", \"broccoli\", \"kale\"]}"}, {"object": "bubble_gum", "object_id": 159, "gpt3_output": "\n{\"type\": \"candy\", \"description\": \"chewy; could be pink or other colors; could be flavored\", \"similar objects\": [\"gummy bears\", \"jelly beans\", \"lollipops\"]}"}, {"object": "bucket", "object_id": 160, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could have a handle; could be made of plastic or metal\", \"similar objects\": [\"pail\", \"tub\", \"barrel\"]}"}, {"object": "horse_buggy", "object_id": 161, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; could be pulled by a horse; could have a canopy\", \"similar objects\": [\"carriage\", \"wagon\", \"cart\"]}"}, {"object": "bull", "object_id": 162, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, muscular, has horns; could be red or black in color\", \"similar objects\": [\"cow\", \"bison\", \"buffalo\"]}"}, {"object": "bulldog", "object_id": 163, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"short, stocky body; 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could be worn over the torso; could be bulletproof\", \"similar objects\": [\"helmet\", \"armour\", \"shield\"]}"}, {"object": "bullhorn", "object_id": 168, "gpt3_output": "\n{\"type\": \"communication tool\", \"description\": \"long, cone-shaped; could be used to amplify sound\", \"similar objects\": [\"megaphone\", \"loudspeaker\", \"microphone\"]}"}, {"object": "bun", "object_id": 169, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round, sweet, usually served with butter; could be filled with meat or vegetables\", \"similar objects\": [\"roll\", \"bagel\", \"croissant\"]}"}, {"object": "bunk_bed", "object_id": 170, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"two beds stacked on top of each other; could have a ladder for access to the top bed; could have a storage space underneath\", \"similar objects\": [\"trundle bed\", \"loft bed\", \"daybed\"]}"}, {"object": "buoy", "object_id": 171, "gpt3_output": "\n{\"type\": \"nautical tool\", \"description\": \"round; 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could have green leaves; could be sliced into round pieces\", \"similar objects\": [\"potato\", \"parsnip\", \"turnip\"]}"}, {"object": "tote_bag", "object_id": 218, "gpt3_output": "\n{\"type\": \"bag\", \"description\": \"rectangular; could have straps; could be made of canvas or leather\", \"similar objects\": [\"backpack\", \"purse\", \"duffel bag\"]}"}, {"object": "cart", "object_id": 219, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"wheeled; could be pushed or pulled; could be used to carry goods\", \"similar objects\": [\"wagon\", \"trolley\", \"hand truck\"]}"}, {"object": "carton", "object_id": 220, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be used for storing items\", \"similar objects\": [\"box\", \"crate\", \"bin\"]}"}, {"object": "cash_register", "object_id": 221, "gpt3_output": "\n{\"type\": \"retail tool\", \"description\": \"has a display screen; could have a scanner; could have a cash drawer\", \"similar objects\": [\"point of sale terminal\", \"checkout counter\", \"barcode reader\"]}"}, {"object": "casserole", "object_id": 222, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"deep, round, has a lid; could be made of ceramic or metal; could be used for baking\", \"similar objects\": [\"baking dish\", \"roasting pan\", \"Dutch oven\"]}"}, {"object": "cassette", "object_id": 223, "gpt3_output": "\n{\"type\": \"media device\", \"description\": \"rectangular; has two spools; could be used to store audio recordings\", \"similar objects\": [\"CD\", \"DVD\", \"VHS\"]}"}, {"object": "cast", "object_id": 224, "gpt3_output": "\n{\"type\": \"medical tool\", \"description\": \"hard, plaster-like material used to immobilize a broken bone; could be white or off-white in color\", \"similar objects\": [\"splint\", \"brace\", \"sling\"]}"}, {"object": "cat", "object_id": 225, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"furry; four legs; whiskers; could meow\", \"similar objects\": [\"dog\", \"rabbit\", \"mouse\"]}"}, {"object": "cauliflower", "object_id": 226, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"white, round, has a stem; could have green leaves\", \"similar objects\": [\"broccoli\", \"cabbage\", \"kale\"]}"}, {"object": "cayenne_(spice)", "object_id": 227, "gpt3_output": "\n{\"type\": \"spice\", \"description\": \"red, hot, and spicy; could be ground into powder; could be used to add flavor to dishes\", \"similar objects\": [\"chili powder\", \"paprika\", \"black pepper\"]}"}, {"object": "CD_player", "object_id": 228, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a slot for CD; could have buttons for controlling music\", \"similar objects\": [\"stereo system\", \"boombox\", \"turntable\"]}"}, {"object": "celery", "object_id": 229, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, thin, green stalks; could have leaves; could be chopped into small pieces; could be used in salads\", \"similar objects\": [\"carrot\", \"onion\", \"parsley\"]}"}, {"object": "cellular_telephone", "object_id": 230, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, handheld device; could have a touchscreen; could have a camera; could have a microphone\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}"}, {"object": "chain_mail", "object_id": 231, "gpt3_output": "\n{\"type\": \"armor\", \"description\": \"made of metal rings linked together; could be used to protect the body\", \"similar objects\": [\"plate armor\", \"leather armor\", \"shield\"]}"}, {"object": "chair", "object_id": 232, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has four legs; could have armrests; could have a backrest; could be made of wood or metal\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}"}, {"object": "chaise_longue", "object_id": 233, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"long chair; could be upholstered; could have armrests\", \"similar objects\": [\"sofa\", \"loveseat\", \"recliner\"]}"}, {"object": "chalice", "object_id": 234, "gpt3_output": "\n{\"type\": \"vessel\", \"description\": \"cup-shaped; could be made of metal or glass; could have a stem and a base\", \"similar objects\": [\"goblet\", \"cup\", \"mug\"]}"}, {"object": "chandelier", "object_id": 235, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"hanging; could have multiple lights; could be made of glass or metal\", \"similar objects\": [\"pendant light\", \"ceiling light\", \"wall sconce\"]}"}, {"object": "chap", "object_id": 236, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"short trousers; could be made of denim; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"pants\"]}"}, {"object": "checkbook", "object_id": 237, "gpt3_output": "\n{\"type\": \"financial tool\", \"description\": \"small book; contains checks; could be used to pay bills\", \"similar objects\": [\"credit card\", \"debit card\", \"cash\"]}"}, {"object": "checkerboard", "object_id": 238, "gpt3_output": "\n{\"type\": \"game board\", \"description\": \"square; has alternating black and white squares; could be used for playing checkers\", \"similar objects\": [\"chess board\", \"backgammon board\", \"go board\"]}"}, {"object": "cherry", "object_id": 239, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, red, has a stem\", \"similar objects\": [\"strawberry\", \"plum\", \"grape\"]}"}, {"object": "chessboard", "object_id": 240, "gpt3_output": "\n{\"type\": \"game board\", \"description\": \"square; has 64 squares; could have black and white pieces\", \"similar objects\": [\"checkers board\", \"go board\", \"backgammon board\"]}"}, {"object": "chicken_(animal)", "object_id": 241, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"feathered, four-legged, can fly; usually white and brown in color; lays eggs\", \"similar objects\": [\"duck\", \"goose\", \"turkey\"]}"}, {"object": "chickpea", "object_id": 242, "gpt3_output": "\n{\"type\": \"legume\", \"description\": \"small, round, yellowish-brown; could be cooked or eaten raw; could be mashed into hummus\", \"similar objects\": [\"lentil\", \"bean\", \"pea\"]}"}, {"object": "chili_(vegetable)", "object_id": 243, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"small, red, hot; could be sliced into small pieces; could be used as a spice\", \"similar objects\": [\"bell pepper\", \"jalapeno\", \"habanero\"]}"}, {"object": "chime", "object_id": 244, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"hanging; could be made of metal; could be used to make ringing sound\", \"similar objects\": [\"bell\", \"gong\", \"cymbal\"]}"}, {"object": "chinaware", "object_id": 245, "gpt3_output": "\n{\"type\": \"dishware\", \"description\": \"porcelain; could be decorated with patterns; could be used for serving food\", \"similar objects\": [\"plates\", \"bowls\", \"cups\"]}"}, {"object": "crisp_(potato_chip)", "object_id": 246, "gpt3_output": "\n{\"type\": \"snack\", \"description\": \"thin, crunchy, salty; could be in different shapes and flavors\", \"similar objects\": [\"popcorn\", \"pretzel\", \"cracker\"]}"}, {"object": "poker_chip", "object_id": 247, "gpt3_output": "\n{\"type\": \"gaming tool\", \"description\": \"round; could be made of plastic or clay; could have numbers or symbols on it\", \"similar objects\": [\"dice\", \"playing cards\", \"board game pieces\"]}"}, {"object": "chocolate_bar", "object_id": 248, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"rectangular; could be made of cocoa; could be filled with nuts or caramel\", \"similar objects\": [\"candy bar\", \"cookie\", \"ice cream\"]}"}, {"object": "chocolate_cake", "object_id": 249, "gpt3_output": "\n{\"type\": \"dessert\", \"description\": \"round; made of chocolate; could be topped with cream and fruits\", \"similar objects\": [\"cheesecake\", \"cupcake\", \"brownie\"]}"}, {"object": "chocolate_milk", "object_id": 250, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"sweet, creamy, brown; could be served cold or hot\", \"similar objects\": [\"coffee\", \"tea\", \"hot cocoa\"]}"}, {"object": "chocolate_mousse", "object_id": 251, "gpt3_output": "\n{\"type\": \"dessert\", \"description\": \"light and fluffy; made of chocolate; could be topped with whipped cream\", \"similar objects\": [\"tiramisu\", \"cheesecake\", \"ice cream\"]}"}, {"object": "choker", "object_id": 252, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"a necklace that fits tightly around the neck; could be made of metal, fabric, or leather; could have a pendant or charm\", \"similar objects\": [\"collar\", \"bib necklace\", \"pendant necklace\"]}"}, {"object": "chopping_board", "object_id": 253, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"flat, rectangular; could be made of wood or plastic; used for cutting food\", \"similar objects\": [\"knife\", \"spatula\", \"rolling pin\"]}"}, {"object": "chopstick", "object_id": 254, "gpt3_output": "\n{\"type\": \"eating utensil\", \"description\": \"long, thin, made of wood or bamboo; used to pick up food\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}"}, {"object": "Christmas_tree", "object_id": 255, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"conical shape; could be decorated with lights and ornaments; could have a star on the top\", \"similar objects\": [\"menorah\", \"wreath\", \"garland\"]}"}, {"object": "slide", "object_id": 256, "gpt3_output": "\n{\"type\": \"playground equipment\", \"description\": \"long, curved, could be made of plastic; could be used for sliding down\", \"similar objects\": [\"swing\", \"monkey bars\", \"seesaw\"]}"}, {"object": "cider", "object_id": 257, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"made from apples; could be alcoholic or non-alcoholic; could be sweet or dry\", \"similar objects\": [\"juice\", \"wine\", \"beer\"]}"}, {"object": "cigar_box", "object_id": 258, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of wood; could have a lid\", \"similar objects\": [\"jewelry box\", \"toolbox\", \"cardboard box\"]}"}, {"object": "cigarette", "object_id": 259, "gpt3_output": "\n{\"type\": \"tobacco product\", \"description\": \"cylindrical; has a filter; could be smoked\", \"similar objects\": [\"cigar\", \"pipe\", \"hookah\"]}"}, {"object": "cigarette_case", "object_id": 260, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"small, rectangular, metal; could be used to store cigarettes\", \"similar objects\": [\"wallet\", \"purse\", \"jewelry box\"]}"}, {"object": "cistern", "object_id": 261, "gpt3_output": "\n{\"type\": \"water storage container\", \"description\": \"large, cylindrical, made of concrete; could have a lid\", \"similar objects\": [\"tank\", \"barrel\", \"reservoir\"]}"}, {"object": "clarinet", "object_id": 262, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"wooden; has a long tube; has keys; produces a mellow sound\", \"similar objects\": [\"saxophone\", \"flute\", \"trumpet\"]}"}, {"object": "clasp", "object_id": 263, "gpt3_output": "\n{\"type\": \"fastening tool\", \"description\": \"used to fasten two objects together; could be made of metal or plastic; could be opened and closed\", \"similar objects\": [\"hook\", \"button\", \"zipper\"]}"}, {"object": "cleansing_agent", "object_id": 264, "gpt3_output": "\n{\"type\": \"cleaning product\", \"description\": \"used to clean surfaces; could be in liquid or powder form; could be used with water\", \"similar objects\": [\"detergent\", \"soap\", \"disinfectant\"]}"}, {"object": "cleat_(for_securing_rope)", "object_id": 265, "gpt3_output": "\n{\"type\": \"fastening tool\", \"description\": \"metal or plastic; has two or more prongs; used to secure rope or cable\", \"similar objects\": [\"clamp\", \"hook\", \"clasp\"]}"}, {"object": "clementine", "object_id": 266, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"small, orange, round; has a thin skin; could be peeled easily\", \"similar objects\": [\"tangerine\", \"mandarin orange\", \"satsuma\"]}"}, {"object": "clip", "object_id": 267, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"small, metal, used to hold objects together\", \"similar objects\": [\"clamp\", \"binder\", \"clasp\"]}"}, {"object": "clipboard", "object_id": 268, "gpt3_output": "\n{\"type\": \"office tool\", \"description\": \"rectangular; could have a clip on the top; could be made of plastic or metal\", \"similar objects\": [\"stapler\", \"hole puncher\", \"paper clip\"]}"}, {"object": "clippers_(for_plants)", "object_id": 269, "gpt3_output": "\n{\"type\": \"gardening tool\", \"description\": \"sharp blades; 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could be topped with frosting; could have sprinkles\", \"similar objects\": [\"muffin\", \"donut\", \"cookie\"]}"}, {"object": "hair_curler", "object_id": 348, "gpt3_output": "\n{\"type\": \"styling tool\", \"description\": \"cylindrical; could be heated; could be used to curl hair\", \"similar objects\": [\"hair straightener\", \"hair dryer\", \"hairbrush\"]}"}, {"object": "curling_iron", "object_id": 349, "gpt3_output": "\n{\"type\": \"styling tool\", \"description\": \"long handle with a round barrel; used to curl hair\", \"similar objects\": [\"flat iron\", \"hair dryer\", \"hair straightener\"]}"}, {"object": "curtain", "object_id": 350, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"long; could be made of fabric; could be hung on a window\", \"similar objects\": [\"blinds\", \"drapes\", \"shades\"]}"}, {"object": "cushion", "object_id": 351, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"soft, rectangular; could be filled with foam; could be used as a seat\", \"similar objects\": [\"pillow\", \"mattress\", \"sofa\"]}"}, {"object": "cylinder", "object_id": 352, "gpt3_output": "\n{\"type\": \"shape\", \"description\": \"round; 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could have a line attached\", \"similar objects\": [\"fishing net\", \"fishing hook\", \"fishing line\"]}"}, {"object": "flag", "object_id": 451, "gpt3_output": "\n{\"type\": \"symbol\", \"description\": \"rectangular; could have a pole; could have a design or logo\", \"similar objects\": [\"banner\", \"pennant\", \"standard\"]}"}, {"object": "flagpole", "object_id": 452, "gpt3_output": "\n{\"type\": \"structure\", \"description\": \"tall, thin, could be made of metal; could have a flag on top\", \"similar objects\": [\"mast\", \"pole\", \"tower\"]}"}, {"object": "flamingo", "object_id": 453, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"pink; long legs; curved neck; could stand on one leg\", \"similar objects\": [\"crane\", \"stork\", \"heron\"]}"}, {"object": "flannel", "object_id": 454, "gpt3_output": "\n{\"type\": \"fabric\", \"description\": \"soft, fuzzy, usually made of wool or cotton\", \"similar objects\": [\"velvet\", \"corduroy\", \"denim\"]}"}, {"object": "flap", "object_id": 455, "gpt3_output": "\n{\"type\": \"mechanism\", \"description\": \"hinged or sliding piece of material; could be used to cover an opening\", \"similar objects\": [\"door\", \"gate\", \"shutter\"]}"}, {"object": "flash", "object_id": 456, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"small, portable, emits a bright light\", \"similar objects\": [\"torch\", \"lantern\", \"flashlight\"]}"}, {"object": "flashlight", "object_id": 457, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"long and cylindrical; has a switch; could be powered by batteries\", \"similar objects\": [\"lantern\", \"lamp\", \"candle\"]}"}, {"object": "fleece", "object_id": 458, "gpt3_output": "\n{\"type\": \"fabric\", \"description\": \"soft, warm, lightweight; could be made of polyester or wool\", \"similar objects\": [\"flannel\", \"velvet\", \"corduroy\"]}"}, {"object": "flip-flop_(sandal)", "object_id": 459, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"open-toed; has a strap between the toes; could be made of rubber or leather\", \"similar objects\": [\"sneakers\", \"sandals\", \"slippers\"]}"}, {"object": "flipper_(footwear)", "object_id": 460, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"rubber; could be used for swimming; could be worn on the feet\", \"similar objects\": [\"sandals\", \"flip-flops\", \"water shoes\"]}"}, {"object": "flower_arrangement", "object_id": 461, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"arrangement of flowers in a vase; could be made of different types of flowers; could be used for special occasions\", \"similar objects\": [\"bouquet\", \"centerpiece\", \"wreath\"]}"}, {"object": "flute_glass", "object_id": 462, "gpt3_output": "\n{\"type\": \"drinking glass\", \"description\": \"long and slender; could be made of glass or plastic; could have a stem\", \"similar objects\": [\"wine glass\", \"martini glass\", \"champagne flute\"]}"}, {"object": "foal", "object_id": 463, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"young horse; usually has a light brown coat; has a short mane and tail\", \"similar objects\": [\"calf\", \"puppy\", \"kitten\"]}"}, {"object": "folding_chair", "object_id": 464, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"collapsible; could be made of metal or plastic; could have armrests\", \"similar objects\": [\"stool\", \"bench\", \"bean bag chair\"]}"}, {"object": "food_processor", "object_id": 465, "gpt3_output": "\n{\"type\": \"kitchen appliance\", \"description\": \"has a bowl, blades, and a lid; could be used to chop, grind, and mix food\", \"similar objects\": [\"blender\", \"mixer\", \"juicer\"]}"}, {"object": "football_(American)", "object_id": 466, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"oval-shaped; made of leather; used for throwing and catching\", \"similar objects\": [\"basketball\", \"baseball\", \"soccer ball\"]}"}, {"object": "football_helmet", "object_id": 467, "gpt3_output": "\n{\"type\": \"protective gear\", \"description\": \"hard, has a face guard; could be made of plastic or metal\", \"similar objects\": [\"shoulder pads\", \"knee pads\", \"elbow pads\"]}"}, {"object": "footstool", "object_id": 468, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"small, rectangular, has legs; could be used as a seat\", \"similar objects\": [\"ottoman\", \"bench\", \"chair\"]}"}, {"object": "fork", "object_id": 469, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"has four tines; could be made of metal or plastic; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}"}, {"object": "forklift", "object_id": 470, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, has a lifting arm; could be used to move heavy objects\", \"similar objects\": [\"truck\", \"crane\", \"bulldozer\"]}"}, {"object": "freight_car", "object_id": 471, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long, rectangular; could be used to transport goods; could be attached to a locomotive\", \"similar objects\": [\"boxcar\", \"tanker car\", \"flatcar\"]}"}, {"object": "French_toast", "object_id": 472, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"bread slices dipped in egg mixture and fried; could be served with syrup or fruits\", \"similar objects\": [\"pancakes\", \"waffles\", \"crepes\"]}"}, {"object": "freshener", "object_id": 473, "gpt3_output": "\n{\"type\": \"air freshener\", \"description\": \"could be in the form of a spray, gel, or plug-in; 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could be sliced into small pieces; could have green leaves\", \"similar objects\": [\"onion\", \"shallot\", \"leek\"]}"}, {"object": "gasmask", "object_id": 488, "gpt3_output": "\n{\"type\": \"protective equipment\", \"description\": \"covers the face; has a filter; could be used in hazardous environments\", \"similar objects\": [\"respirator\", \"helmet\", \"goggles\"]}"}, {"object": "gazelle", "object_id": 489, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"brown and white fur; slender body; long legs; long neck; pointed horns\", \"similar objects\": [\"antelope\", \"deer\", \"goat\"]}"}, {"object": "gelatin", "object_id": 490, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"transparent, colorless, flavorless; could be used as a thickening agent; could be made from animal bones and skin\", \"similar objects\": [\"agar-agar\", \"carrageenan\", \"xanthan gum\"]}"}, {"object": "gemstone", "object_id": 491, "gpt3_output": "\n{\"type\": \"jewelry\", \"description\": \"shiny, colorful, could be cut into different shapes; could be set in jewelry\", \"similar objects\": [\"diamond\", \"ruby\", \"sapphire\"]}"}, {"object": "generator", "object_id": 492, "gpt3_output": "\n{\"type\": \"machine\", \"description\": \"produces electricity; could be powered by gasoline or diesel; could be portable or stationary\", \"similar objects\": [\"inverter\", \"alternator\", \"engine\"]}"}, {"object": "giant_panda", "object_id": 493, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"black and white fur; has a round face; has a large body\", \"similar objects\": [\"red panda\", \"koala\", \"polar bear\"]}"}, {"object": "gift_wrap", "object_id": 494, "gpt3_output": "\n{\"type\": \"packaging material\", \"description\": \"paper; could be printed with patterns; could be used to wrap gifts\", \"similar objects\": [\"tissue paper\", \"ribbon\", \"wrapping paper\"]}"}, {"object": "ginger", "object_id": 495, "gpt3_output": "\n{\"type\": \"spice\", \"description\": \"brown, knobby root; has a pungent smell; could be grated or chopped\", \"similar objects\": [\"garlic\", \"turmeric\", \"cumin\"]}"}, {"object": "giraffe", "object_id": 496, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"long neck; spotted; long legs; long tail; could have horns\", \"similar objects\": [\"zebra\", \"elephant\", \"gazelle\"]}"}, {"object": "cincture", "object_id": 497, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"a belt-like item; could be made of leather or fabric; could be used to hold up pants or skirts\", \"similar objects\": [\"belt\", \"sash\", \"scarf\"]}"}, {"object": "glass_(drink_container)", "object_id": 498, "gpt3_output": "\n{\"type\": \"drink container\", \"description\": \"transparent; could be made of plastic or glass; could be cylindrical or round; could have a handle\", \"similar objects\": [\"cup\", \"mug\", \"bottle\"]}"}, {"object": "globe", "object_id": 499, "gpt3_output": "\n{\"type\": \"decorative object\", \"description\": \"round; could be made of paper or plastic; could be used to represent the Earth\", \"similar objects\": [\"map\", \"ball\", \"terrarium\"]}"}, {"object": "glove", "object_id": 500, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"hand-shaped; could be made of leather, cotton, or wool; could have a cuff\", \"similar objects\": [\"mittens\", \"socks\", \"hat\"]}"}, {"object": "goat", "object_id": 501, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"has horns; could have long fur; could have a beard; could have cloven hooves\", \"similar objects\": [\"sheep\", \"cow\", \"deer\"]}"}, {"object": "goggles", "object_id": 502, "gpt3_output": "\n{\"type\": \"eyewear\", \"description\": \"transparent; could be worn over eyes; could be used for swimming or skiing\", \"similar objects\": [\"sunglasses\", \"safety glasses\", \"prescription glasses\"]}"}, {"object": "goldfish", "object_id": 503, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"orange; has a long tail; could be kept in an aquarium\", \"similar objects\": [\"guppy\", \"koi\", \"carp\"]}"}, {"object": "golf_club", "object_id": 504, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, metal shaft; could have a wooden or metal head; could have a grip\", \"similar objects\": [\"tennis racket\", \"baseball bat\", \"hockey stick\"]}"}, {"object": "golfcart", "object_id": 505, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"small, open-air vehicle; has four wheels; could have a roof; could be powered by electricity or gasoline\", \"similar objects\": [\"golf cart\", \"utility cart\", \"truck\"]}"}, {"object": "gondola_(boat)", "object_id": 506, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"long, narrow boat; could be propelled by a single oar; could be used for transportation or leisure\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}"}, {"object": "goose", "object_id": 507, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, gray, webbed feet; long neck; honks\", \"similar objects\": [\"duck\", \"swan\", \"turkey\"]}"}, {"object": "gorilla", "object_id": 508, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, black, has a broad chest; could have silver hair on its back; could have a long arm span\", \"similar objects\": [\"chimpanzee\", \"orangutan\", \"baboon\"]}"}, {"object": "gourd", "object_id": 509, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"elongated; could be green, yellow, or white; could be used as a container\", \"similar objects\": [\"squash\", \"pumpkin\", \"cucumber\"]}"}, {"object": "grape", "object_id": 510, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"small, round, usually purple or green; could be seedless; could be eaten fresh or used for making wine\", \"similar objects\": [\"blueberry\", \"strawberry\", \"plum\"]}"}, {"object": "grater", "object_id": 511, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"has a handle; has a flat surface with sharp edges; could be used to grate food items\", \"similar objects\": [\"cheese grater\", \"zester\", \"mandoline\"]}"}, {"object": "gravestone", "object_id": 512, "gpt3_output": "\n{\"type\": \"memorial object\", \"description\": \"rectangular; could be made of stone or metal; could have engravings\", \"similar objects\": [\"monument\", \"headstone\", \"plaque\"]}"}, {"object": "gravy_boat", "object_id": 513, "gpt3_output": "\n{\"type\": \"serving tool\", \"description\": \"long and narrow; has a spout; could be made of ceramic or metal\", \"similar objects\": [\"pitcher\", \"teapot\", \"creamer\"]}"}, {"object": "green_bean", "object_id": 514, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, thin, green; could be eaten raw or cooked; could be sliced into pieces\", \"similar objects\": [\"zucchini\", \"cucumber\", \"eggplant\"]}"}, {"object": "green_onion", "object_id": 515, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, thin, green stalks; white bulb at the bottom; could be chopped into small pieces\", \"similar objects\": [\"scallion\", \"leek\", \"shallot\"]}"}, {"object": "griddle", "object_id": 516, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"flat, large, has a handle\", \"similar objects\": [\"skillet\", \"frying pan\", \"grill pan\"]}"}, {"object": "grill", "object_id": 517, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"has a grate; could be used to cook food over an open flame; could be charcoal or gas powered\", \"similar objects\": [\"barbecue\", \"smoker\", \"stove\"]}"}, {"object": "grits", "object_id": 518, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"coarsely ground corn; could be served as a side dish; could be cooked with butter and salt\", \"similar objects\": [\"polenta\", \"porridge\", \"mush\"]}"}, {"object": "grizzly", "object_id": 519, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, brown, has a hump on its back; could have a long snout\", \"similar objects\": [\"polar bear\", \"black bear\", \"brown bear\"]}"}, {"object": "grocery_bag", "object_id": 520, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of paper or plastic; could be reusable\", \"similar objects\": [\"shopping bag\", \"backpack\", \"suitcase\"]}"}, {"object": "guitar", "object_id": 521, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long body; has strings; could have a pickguard\", \"similar objects\": [\"ukulele\", \"violin\", \"banjo\"]}"}, {"object": "gull", "object_id": 522, "gpt3_output": "\n{\"type\": \"bird\", \"description\": \"gray or white feathers; long wings; hooked beak; webbed feet\", \"similar objects\": [\"pigeon\", \"seagull\", \"eagle\"]}"}, {"object": "gun", "object_id": 523, "gpt3_output": "\n{\"type\": \"weapon\", \"description\": \"metal; could be long or short; could be used for hunting or self-defense\", \"similar objects\": [\"rifle\", \"pistol\", \"shotgun\"]}"}, {"object": "hairbrush", "object_id": 524, "gpt3_output": "\n{\"type\": \"grooming tool\", \"description\": \"long handle; could have bristles; could be used for styling hair\", \"similar objects\": [\"comb\", \"scissors\", \"hair dryer\"]}"}, {"object": "hairnet", "object_id": 525, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"net-like; used to cover the hair; could be made of fabric or plastic\", \"similar objects\": [\"hat\", \"cap\", \"scarf\"]}"}, {"object": "hairpin", "object_id": 526, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small metal object used to hold hair in place; could be decorated with beads or other decorations\", \"similar objects\": [\"bobby pin\", \"hair clip\", \"hair tie\"]}"}, {"object": "halter_top", "object_id": 527, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"strapless top; could be tied around the neck and back\", \"similar objects\": [\"tank top\", \"crop top\", \"tube top\"]}"}, {"object": "ham", "object_id": 528, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"salty, cured, pink meat; could be sliced into thin pieces\", \"similar objects\": [\"bacon\", \"sausage\", \"salami\"]}"}, {"object": "hamburger", "object_id": 529, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"bun, patty, lettuce, tomato, onion, pickles, condiments; could be served with fries\", \"similar objects\": [\"hot dog\", \"sandwich\", \"taco\"]}"}, {"object": "hammer", "object_id": 530, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"has a handle and a head; used for pounding or driving nails\", \"similar objects\": [\"screwdriver\", \"pliers\", \"wrench\"]}"}, {"object": "hammock", "object_id": 531, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"made of fabric; could be hung between two trees; could be used for relaxation\", \"similar objects\": [\"swing\", \"hanging chair\", \"daybed\"]}"}, {"object": "hamper", "object_id": 532, "gpt3_output": "\n{\"type\": \"storage tool\", \"description\": \"rectangular; could be made of wicker; could have a lid\", \"similar objects\": [\"basket\", \"bin\", \"box\"]}"}, {"object": "hamster", "object_id": 533, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, furry, short-tailed; could have black, brown, white, or golden fur; could have a long tail\", \"similar objects\": [\"mouse\", \"gerbil\", \"guinea pig\"]}"}, {"object": "hair_dryer", "object_id": 534, "gpt3_output": "\n{\"type\": \"hair styling tool\", \"description\": \"long, cylindrical; has a nozzle; could be corded or cordless\", \"similar objects\": [\"curling iron\", \"straightener\", \"hair clippers\"]}"}, {"object": "hand_glass", "object_id": 535, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"round; could be made of glass; used to magnify objects\", \"similar objects\": [\"magnifying glass\", \"microscope\", \"binoculars\"]}"}, {"object": "hand_towel", "object_id": 536, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"small, rectangular; could be made of cloth; could be used to dry hands\", \"similar objects\": [\"bath towel\", \"washcloth\", \"dish towel\"]}"}, {"object": "handcart", "object_id": 537, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"two-wheeled; could be pushed by hand; could be used to carry goods\", \"similar objects\": [\"wheelbarrow\", \"wagon\", \"trolley\"]}"}, {"object": "handcuff", "object_id": 538, "gpt3_output": "\n{\"type\": \"restraining tool\", \"description\": \"metal; two connected rings; used to restrain a person's wrists\", \"similar objects\": [\"shackles\", \"chains\", \"straps\"]}"}, {"object": "handkerchief", "object_id": 539, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"square; could be made of cloth; could be used to wipe face or hands\", \"similar objects\": [\"tissue\", \"napkin\", \"scarf\"]}"}, {"object": "handle", "object_id": 540, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"a part of an object used to hold or move it; could be made of metal, wood, or plastic; could be round or flat\", \"similar objects\": [\"knob\", \"lever\", \"wheel\"]}"}, {"object": "handsaw", "object_id": 541, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle; sharp blade; used for cutting wood\", \"similar objects\": [\"hammer\", \"screwdriver\", \"pliers\"]}"}, {"object": "hardback_book", "object_id": 542, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"rectangular; has a hard cover; could have pages inside\", \"similar objects\": [\"paperback book\", \"magazine\", \"journal\"]}"}, {"object": "harmonium", "object_id": 543, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"box-shaped; has a keyboard; produces sound when air is pumped through it\", \"similar objects\": [\"piano\", \"organ\", \"accordion\"]}"}, {"object": "hat", "object_id": 544, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"could be made of fabric or straw; could have a brim; could have a band\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}"}, {"object": "hatbox", "object_id": 545, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of cardboard; could have a lid\", \"similar objects\": [\"suitcase\", \"briefcase\", \"storage box\"]}"}, {"object": "veil", "object_id": 546, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"thin, sheer fabric; could be worn over the head; could be used to cover the face\", \"similar objects\": [\"scarf\", \"shawl\", \"hat\"]}"}, {"object": "headband", "object_id": 547, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"worn around the head; could be made of fabric, plastic, or metal; could have decorations\", \"similar objects\": [\"hat\", \"cap\", \"scarf\"]}"}, {"object": "headboard", "object_id": 548, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"attached to the head of a bed; could be made of wood or metal; could have decorative designs\", \"similar objects\": [\"bed frame\", \"mattress\", \"pillow\"]}"}, {"object": "headlight", "object_id": 549, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"attached to the front of a vehicle; could be round or rectangular; could be powered by electricity or gas\", \"similar objects\": [\"taillight\", \"fog light\", \"spotlight\"]}"}, {"object": "headscarf", "object_id": 550, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"square or rectangular; could be made of fabric; could be worn around the head and neck\", \"similar objects\": [\"turban\", \"hat\", \"cap\"]}"}, {"object": "headset", "object_id": 551, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"has two earpieces connected to a band; could be wireless\", \"similar objects\": [\"earphones\", \"headphones\", \"microphone\"]}"}, {"object": "headstall_(for_horses)", "object_id": 552, "gpt3_output": "\n\n{\"type\": \"equipment\", \"description\": \"leather strap; used to control a horse; has a bit and reins\", \"similar objects\": [\"bridle\", \"halter\", \"reins\"]}"}, {"object": "heart", "object_id": 553, "gpt3_output": "\n{\"type\": \"organ\", \"description\": \"red; shaped like a valentine; has four chambers\", \"similar objects\": [\"lungs\", \"liver\", \"kidney\"]}"}, {"object": "heater", "object_id": 554, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"used to heat a room; could be electric or gas powered; could have a fan\", \"similar objects\": [\"air conditioner\", \"humidifier\", \"dehumidifier\"]}"}, {"object": "helicopter", "object_id": 555, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"has a rotor; could be used for air travel; could be used for rescue operations\", \"similar objects\": [\"airplane\", \"drone\", \"blimp\"]}"}, {"object": "helmet", "object_id": 556, "gpt3_output": "\n{\"type\": \"protective gear\", \"description\": \"hard, covers the head; could have a visor\", \"similar objects\": [\"goggles\", \"gloves\", \"knee pads\"]}"}, {"object": "heron", "object_id": 557, "gpt3_output": "\n{\"type\": \"bird\", \"description\": \"long legs; long neck; grey feathers; could have a white head\", \"similar objects\": [\"egret\", \"crane\", \"ibis\"]}"}, {"object": "highchair", "object_id": 558, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"tall chair with a tray; used for feeding babies and toddlers\", \"similar objects\": [\"booster seat\", \"stroller\", \"playpen\"]}"}, {"object": "hinge", "object_id": 559, "gpt3_output": "\n{\"type\": \"hardware\", \"description\": \"metal; used to attach two objects together; could be opened and closed\", \"similar objects\": [\"screw\", \"bolt\", \"latch\"]}"}, {"object": "hippopotamus", "object_id": 560, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, gray, four-legged mammal; has a large head and mouth; could be found in rivers and lakes\", \"similar objects\": [\"elephant\", \"rhinoceros\", \"crocodile\"]}"}, {"object": "hockey_stick", "object_id": 561, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, curved; could be made of wood or composite materials; has a blade at one end\", \"similar objects\": [\"baseball bat\", \"golf club\", \"lacrosse stick\"]}"}, {"object": "hog", "object_id": 562, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a snout and tusks; could have bristly hair\", \"similar objects\": [\"pig\", \"boar\", \"warthog\"]}"}, {"object": "home_plate_(baseball)", "object_id": 563, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"white, rectangular; used as the base for a batter in baseball\", \"similar objects\": [\"baseball bat\", \"baseball glove\", \"catcher's mask\"]}"}, {"object": "honey", "object_id": 564, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"sweet, sticky, amber-colored; could be used as a sweetener\", \"similar objects\": [\"syrup\", \"molasses\", \"sugar\"]}"}, {"object": "fume_hood", "object_id": 565, "gpt3_output": "\n{\"type\": \"safety tool\", \"description\": \"enclosed space with a fan; used to protect people from hazardous fumes\", \"similar objects\": [\"ventilation hood\", \"exhaust hood\", \"chemical hood\"]}"}, {"object": "hook", "object_id": 566, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"curved metal piece; could be used to hang items\", \"similar objects\": [\"hanger\", \"clasp\", \"clip\"]}"}, {"object": "hookah", "object_id": 567, "gpt3_output": "\n{\"type\": \"smoking tool\", \"description\": \"long tube; could have a water chamber; could have multiple hoses\", \"similar objects\": [\"pipe\", \"bong\", \"vaporizer\"]}"}, {"object": "hornet", "object_id": 568, "gpt3_output": "\n{\"type\": \"insect\", \"description\": \"black and yellow stripes; has a stinger; could fly\", \"similar objects\": [\"bee\", \"wasp\", \"mosquito\"]}"}, {"object": "horse", "object_id": 569, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a long mane and tail; could be ridden\", \"similar objects\": [\"zebra\", \"donkey\", \"camel\"]}"}, {"object": "hose", "object_id": 570, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long, flexible tube; could be used for watering plants or cleaning\", \"similar objects\": [\"pipe\", \"tubing\", \"sprinkler\"]}"}, {"object": "hot-air_balloon", "object_id": 571, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, colorful, round; could be filled with hot air; could be used for sightseeing\", \"similar objects\": [\"helicopter\", \"airplane\", \"parachute\"]}"}, {"object": "hotplate", "object_id": 572, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"flat, metal surface; could be used for cooking\", \"similar objects\": [\"stove\", \"grill\", \"frying pan\"]}"}, {"object": "hot_sauce", "object_id": 573, "gpt3_output": "\n{\"type\": \"condiment\", \"description\": \"spicy; could be red or green; could be made of peppers\", \"similar objects\": [\"salsa\", \"tabasco sauce\", \"sriracha\"]}"}, {"object": "hourglass", "object_id": 574, "gpt3_output": "\n{\"type\": \"timekeeping tool\", \"description\": \"two glass bulbs connected by a narrow neck; sand runs from the top to the bottom\", \"similar objects\": [\"clock\", \"timer\", \"stopwatch\"]}"}, {"object": "houseboat", "object_id": 575, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"floating structure; could be used as a residence; could be powered by an engine\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}"}, {"object": "hummingbird", "object_id": 576, "gpt3_output": "\n{\"type\": \"bird\", \"description\": \"small; colorful feathers; long beak; can hover and fly backwards\", \"similar objects\": [\"sparrow\", \"finch\", \"woodpecker\"]}"}, {"object": "hummus", "object_id": 577, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"smooth, creamy dip; made from mashed chickpeas, tahini, olive oil, garlic, and lemon juice\", \"similar objects\": [\"guacamole\", \"baba ghanoush\", \"tzatziki\"]}"}, {"object": "polar_bear", "object_id": 578, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"white fur; black eyes and nose; lives in cold climates\", \"similar objects\": [\"grizzly bear\", \"arctic fox\", \"walrus\"]}"}, {"object": "icecream", "object_id": 579, "gpt3_output": "\n{\"type\": \"dessert\", \"description\": \"cold, creamy, sweet; could be served in a cone or cup; could have different flavors\", \"similar objects\": [\"sorbet\", \"gelato\", \"frozen yogurt\"]}"}, {"object": "popsicle", "object_id": 580, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"frozen, sweet, usually on a stick\", \"similar objects\": [\"ice cream\", \"sorbet\", \"frozen yogurt\"]}"}, {"object": "ice_maker", "object_id": 581, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"machine that produces ice cubes; could be built-in or freestanding; could be manual or automatic\", \"similar objects\": [\"refrigerator\", \"freezer\", \"ice crusher\"]}"}, {"object": "ice_pack", "object_id": 582, "gpt3_output": "\n{\"type\": \"medical tool\", \"description\": \"cold, flexible, could be used to reduce swelling and pain\", \"similar objects\": [\"hot pack\", \"heating pad\", \"ice bag\"]}"}, {"object": "ice_skate", "object_id": 583, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"has a blade; could be used on ice surfaces\", \"similar objects\": [\"roller skate\", \"hockey skate\", \"figure skate\"]}"}, {"object": "igniter", "object_id": 584, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"used to start a fire; could be electric or manual; could be used to light a stove or a grill\", \"similar objects\": [\"lighter\", \"match\", \"fire starter\"]}"}, {"object": "inhaler", "object_id": 585, "gpt3_output": "\n{\"type\": \"medical device\", \"description\": \"small, handheld device; could be used to deliver medication; could be used to treat asthma\", \"similar objects\": [\"nebulizer\", \"spacer\", \"peak flow meter\"]}"}, {"object": "iPod", "object_id": 586, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, portable music player; has a touchscreen; could be connected to headphones\", \"similar objects\": [\"mp3 player\", \"smartphone\", \"tablet\"]}"}, {"object": "iron_(for_clothing)", "object_id": 587, "gpt3_output": "\n{\"type\": \"clothing tool\", \"description\": \"has a handle; has a flat surface; could be plugged into an electrical outlet\", \"similar objects\": [\"steamer\", \"sewing machine\", \"clothes dryer\"]}"}, {"object": "ironing_board", "object_id": 588, "gpt3_output": "\n{\"type\": \"household tool\", \"description\": \"long, flat surface; has legs; could be folded\", \"similar objects\": [\"stepladder\", \"folding chair\", \"folding table\"]}"}, {"object": "jacket", "object_id": 589, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long-sleeved; could be made of wool; could have a zipper or buttons\", \"similar objects\": [\"coat\", \"sweater\", \"hoodie\"]}"}, {"object": "jam", "object_id": 590, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"thick, sweet, spreadable; could be made of fruits\", \"similar objects\": [\"jelly\", \"marmalade\", \"honey\"]}"}, {"object": "jar", "object_id": 591, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of glass; could have a lid\", \"similar objects\": [\"bottle\", \"can\", \"box\"]}"}, {"object": "jean", "object_id": 592, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"blue, cotton fabric; could have pockets; could be worn with a belt\", \"similar objects\": [\"t-shirt\", \"shorts\", \"skirt\"]}"}, {"object": "jeep", "object_id": 593, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"four-wheel drive; has an open top; could be used for off-road driving\", \"similar objects\": [\"SUV\", \"truck\", \"ATV\"]}"}, {"object": "jelly_bean", "object_id": 594, "gpt3_output": "\n{\"type\": \"candy\", \"description\": \"small, round, colorful; could be chewy or hard; could have different flavors\", \"similar objects\": [\"gummy bear\", \"marshmallow\", \"licorice\"]}"}, {"object": "jersey", "object_id": 595, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"loose-fitting shirt; could have a team logo; could have long or short sleeves\", \"similar objects\": [\"t-shirt\", \"hoodie\", \"sweatshirt\"]}"}, {"object": "jet_plane", "object_id": 596, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long and narrow; has wings and a tail; could have multiple engines; could fly at high speed\", \"similar objects\": [\"airplane\", \"helicopter\", \"rocket\"]}"}, {"object": "jewel", "object_id": 597, "gpt3_output": "\n{\"type\": \"decorative item\", \"description\": \"shiny; could be made of precious stones; could be used as a necklace or a ring\", \"similar objects\": [\"gemstone\", \"diamond\", \"pearl\"]}"}, {"object": "jewelry", "object_id": 598, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"ornamental items made of precious metals and stones; could be worn as a necklace, bracelet, or earrings\", \"similar objects\": [\"watch\", \"ring\", \"bracelet\"]}"}, {"object": "joystick", "object_id": 599, "gpt3_output": "\n{\"type\": \"gaming tool\", \"description\": \"has buttons and a control stick; could be used to control a game character\", \"similar objects\": [\"gamepad\", \"racing wheel\", \"arcade stick\"]}"}, {"object": "jumpsuit", "object_id": 600, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"one-piece garment; could be long or short; could be sleeveless or with sleeves; could be made of different materials\", \"similar objects\": [\"romper\", \"overalls\", \"coveralls\"]}"}, {"object": "kayak", "object_id": 601, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"long and narrow; could be paddled with a double-bladed paddle; could be used for recreational or sporting activities\", \"similar objects\": [\"canoe\", \"rowboat\", \"paddleboard\"]}"}, {"object": "keg", "object_id": 602, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; made of metal; used to store beer\", \"similar objects\": [\"barrel\", \"tank\", \"cask\"]}"}, {"object": "kennel", "object_id": 603, "gpt3_output": "\n{\"type\": \"structure\", \"description\": \"enclosed structure; could be made of wood or metal; used to house animals\", \"similar objects\": [\"cage\", \"hutch\", \"pen\"]}"}, {"object": "kettle", "object_id": 604, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round; has a handle; could be made of metal; could be used to boil water\", \"similar objects\": [\"teapot\", \"coffee maker\", \"microwave\"]}"}, {"object": "key", "object_id": 605, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"metal; has a hole in the middle; could have a pattern on the surface\", \"similar objects\": [\"lock\", \"padlock\", \"keychain\"]}"}, {"object": "keycard", "object_id": 606, "gpt3_output": "\n{\"type\": \"access tool\", \"description\": \"rectangular; could be used to open doors; could be swiped\", \"similar objects\": [\"key\", \"lock\", \"badge\"]}"}, {"object": "kilt", "object_id": 607, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"pleated skirt; usually made of tartan; worn by men in Scotland\", \"similar objects\": [\"tartan skirt\", \"tartan sash\", \"tartan scarf\"]}"}, {"object": "kimono", "object_id": 608, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long, loose-fitting robe; could be made of silk; could have colorful patterns\", \"similar objects\": [\"yukata\", \"haori\", \"hakama\"]}"}, {"object": "kitchen_sink", "object_id": 609, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"has a basin and faucet; could be made of stainless steel; could have a garbage disposal\", \"similar objects\": [\"bathroom sink\", \"dishwasher\", \"washing machine\"]}"}, {"object": "kitchen_table", "object_id": 610, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal\", \"similar objects\": [\"dining table\", \"coffee table\", \"desk\"]}"}, {"object": "kite", "object_id": 611, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"could be made of paper or plastic; has a tail; could be flown in the air\", \"similar objects\": [\"balloon\", \"frisbee\", \"airplane\"]}"}, {"object": "kitten", "object_id": 612, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, furry, playful; could have stripes or spots; could have short or long fur\", \"similar objects\": [\"puppy\", \"rabbit\", \"hamster\"]}"}, {"object": "kiwi_fruit", "object_id": 613, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"brown, oval-shaped; has a fuzzy skin; has a green flesh inside\", \"similar objects\": [\"strawberry\", \"mango\", \"pineapple\"]}"}, {"object": "knee_pad", "object_id": 614, "gpt3_output": "\n{\"type\": \"protective gear\", \"description\": \"worn around the knee; could be made of foam or plastic; could be used for sports or work\", \"similar objects\": [\"elbow pad\", \"helmet\", \"shin guard\"]}"}, {"object": "knife", "object_id": 615, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"sharp blade; could have a handle; could be used for cutting\", \"similar objects\": [\"fork\", \"spoon\", \"scissors\"]}"}, {"object": "knitting_needle", "object_id": 616, "gpt3_output": "\n{\"type\": \"craft tool\", \"description\": \"long, thin, pointed; could be made of metal or wood\", \"similar objects\": [\"crochet hook\", \"sewing needle\", \"yarn needle\"]}"}, {"object": "knob", "object_id": 617, "gpt3_output": "\n{\"type\": \"hardware\", \"description\": \"round; used to open or close a door or drawer\", \"similar objects\": [\"handle\", \"lock\", \"hinge\"]}"}, {"object": "knocker_(on_a_door)", "object_id": 618, "gpt3_output": "\n{\"type\": \"door accessory\", \"description\": \"metal; could be shaped like a lion's head; used to make a loud sound when knocked\", \"similar objects\": [\"doorbell\", \"door handle\", \"door knocker\"]}"}, {"object": "koala", "object_id": 619, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"gray fur; has a round face; has a pouch for carrying its young\", \"similar objects\": [\"kangaroo\", \"wombat\", \"opossum\"]}"}, {"object": "lab_coat", "object_id": 620, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long, white, has pockets; could be buttoned up\", \"similar objects\": [\"scrubs\", \"apron\", \"gown\"]}"}, {"object": "ladder", "object_id": 621, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long; could be made of metal or wood; could have steps\", \"similar objects\": [\"stool\", \"step ladder\", \"scaffolding\"]}"}, {"object": "ladle", "object_id": 622, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"long handle; bowl-shaped; used for serving soup or stew\", \"similar objects\": [\"spoon\", \"fork\", \"tongs\"]}"}, {"object": "ladybug", "object_id": 623, "gpt3_output": "\n{\"type\": \"insect\", \"description\": \"round; red with black spots; has wings\", \"similar objects\": [\"butterfly\", \"bee\", \"dragonfly\"]}"}, {"object": "lamb_(animal)", "object_id": 624, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"white or brown fur; four legs; could have horns; could be domesticated\", \"similar objects\": [\"sheep\", \"goat\", \"calf\"]}"}, {"object": "lamb-chop", "object_id": 625, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"meat cut from a lamb; could be grilled or fried; could be served with vegetables\", \"similar objects\": [\"beef steak\", \"pork chop\", \"chicken breast\"]}"}, {"object": "lamp", "object_id": 626, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"could be made of metal or glass; could have a switch; could be powered by electricity or battery\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}"}, {"object": "lamppost", "object_id": 627, "gpt3_output": "\n\n{\"type\": \"lighting tool\", \"description\": \"tall; has a lightbulb on top; could be made of metal or wood\", \"similar objects\": [\"streetlight\", \"lantern\", \"torch\"]}"}, {"object": "lampshade", "object_id": 628, "gpt3_output": "\n{\"type\": \"lighting accessory\", \"description\": \"round or cylindrical; could be made of fabric, paper, or metal; could be used to diffuse light\", \"similar objects\": [\"lamp base\", \"light bulb\", \"ceiling light\"]}"}, {"object": "lantern", "object_id": 629, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of metal or glass; could have a handle; could be lit with a candle or a lightbulb; could be hung from a hook or carried\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "lanyard", "object_id": 630, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"long, thin cord; could have a clip or hook at the end; could be used to hold keys or ID cards\", \"similar objects\": [\"keychain\", \"necklace\", \"bracelet\"]}"}, {"object": "laptop_computer", "object_id": 631, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"portable computer; has a keyboard and a screen; could be connected to other devices\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}"}, {"object": "lasagna", "object_id": 632, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"layered pasta dish; could be filled with cheese, meat, and vegetables; could be served with a sauce\", \"similar objects\": [\"spaghetti\", \"ravioli\", \"macaroni and cheese\"]}"}, {"object": "latch", "object_id": 633, "gpt3_output": "\n{\"type\": \"fastening tool\", \"description\": \"metal; used to secure a door or window; could be opened with a key or a knob\", \"similar objects\": [\"lock\", \"hinge\", \"bolt\"]}"}, {"object": "lawn_mower", "object_id": 634, "gpt3_output": "\n{\"type\": \"gardening tool\", \"description\": \"motorized; has a handle; could have a grass catcher\", \"similar objects\": [\"hedge trimmer\", \"leaf blower\", \"weed whacker\"]}"}, {"object": "leather", "object_id": 635, "gpt3_output": "\n{\"type\": \"material\", \"description\": \"smooth, durable, and flexible; could be used to make clothing, furniture, and accessories\", \"similar objects\": [\"suede\", \"vinyl\", \"canvas\"]}"}, {"object": "legging_(clothing)", "object_id": 636, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"tight-fitting trousers; usually made of stretchy fabric; could be ankle-length or full-length\", \"similar objects\": [\"jeans\", \"yoga pants\", \"tights\"]}"}, {"object": "Lego", "object_id": 637, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"interlocking plastic blocks; could be used to build structures\", \"similar objects\": [\"building blocks\", \"construction sets\", \"action figures\"]}"}, {"object": "legume", "object_id": 638, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"edible seeds or pods; could be cooked; could be dried; could be used as a source of protein\", \"similar objects\": [\"beans\", \"peas\", \"lentils\"]}"}, {"object": "lemon", "object_id": 639, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"yellow, round, has a stem; could be sliced into wedges; has a sour taste\", \"similar objects\": [\"lime\", \"orange\", \"grapefruit\"]}"}, {"object": "lemonade", "object_id": 640, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"yellow; sweet and sour; could be served cold or hot\", \"similar objects\": [\"iced tea\", \"juice\", \"soda\"]}"}, {"object": "lettuce", "object_id": 641, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"green, leafy; could be shredded; could be used in salads\", \"similar objects\": [\"spinach\", \"cabbage\", \"kale\"]}"}, {"object": "license_plate", "object_id": 642, "gpt3_output": "\n{\"type\": \"identification tool\", \"description\": \"rectangular; has numbers and letters; could be attached to a vehicle\", \"similar objects\": [\"registration sticker\", \"vehicle tag\", \"vehicle decal\"]}"}, {"object": "life_buoy", "object_id": 643, "gpt3_output": "\n{\"type\": \"safety tool\", \"description\": \"round; could be orange or yellow; could be attached to a rope\", \"similar objects\": [\"life jacket\", \"life ring\", \"life preserver\"]}"}, {"object": "life_jacket", "object_id": 644, "gpt3_output": "\n{\"type\": \"safety equipment\", \"description\": \"orange; could be inflated; could be worn around the body\", \"similar objects\": [\"helmet\", \"vest\", \"floatation device\"]}"}, {"object": "lightbulb", "object_id": 645, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; has a filament inside\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "lightning_rod", "object_id": 646, "gpt3_output": "\n{\"type\": \"protection tool\", \"description\": \"tall, metal rod; could be installed on the roof of a building; could be connected to the ground\", \"similar objects\": [\"surge protector\", \"grounding rod\", \"lightning arrester\"]}"}, {"object": "lime", "object_id": 647, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"green, round, has a stem\", \"similar objects\": [\"lemon\", \"orange\", \"grapefruit\"]}"}, {"object": "limousine", "object_id": 648, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long, luxurious; could have a partition between the driver and passengers; could have a sunroof\", \"similar objects\": [\"sedan\", \"SUV\", \"van\"]}"}, {"object": "lion", "object_id": 649, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, tawny-colored; has a mane; could have a tufted tail; could have a black-tipped tail\", \"similar objects\": [\"tiger\", \"leopard\", \"cheetah\"]}"}, {"object": "lip_balm", "object_id": 650, "gpt3_output": "\n{\"type\": \"cosmetic product\", \"description\": \"small, round container; could be in different colors; could be flavored; could be used to moisturize lips\", \"similar objects\": [\"lipstick\", \"lip gloss\", \"lip liner\"]}"}, {"object": "liquor", "object_id": 651, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"alcoholic drink; could be distilled or fermented; could be served in a glass\", \"similar objects\": [\"wine\", \"beer\", \"whiskey\"]}"}, {"object": "lizard", "object_id": 652, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"scaly; could have a long tail; could have a variety of colors\", \"similar objects\": [\"snake\", \"iguana\", \"gecko\"]}"}, {"object": "log", "object_id": 653, "gpt3_output": "\n{\"type\": \"wooden object\", \"description\": \"long, cylindrical; could be used as fuel; could be used as a construction material\", \"similar objects\": [\"timber\", \"plank\", \"firewood\"]}"}, {"object": "lollipop", "object_id": 654, "gpt3_output": "\n{\"type\": \"candy\", \"description\": \"round; has a stick; could be in different colors and flavors\", \"similar objects\": [\"candy cane\", \"jawbreaker\", \"gumdrop\"]}"}, {"object": "speaker_(stero_equipment)", "object_id": 655, "gpt3_output": "\n{\"type\": \"audio equipment\", \"description\": \"box-shaped; could have multiple drivers; could be connected to an amplifier\", \"similar objects\": [\"headphones\", \"microphone\", \"subwoofer\"]}"}, {"object": "loveseat", "object_id": 656, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"two-seater sofa; could be upholstered; could have armrests\", \"similar objects\": [\"sofa\", \"couch\", \"chaise lounge\"]}"}, {"object": "machine_gun", "object_id": 657, "gpt3_output": "\n{\"type\": \"weapon\", \"description\": \"long barrel; could be automatic or semi-automatic; could be used for military purposes\", \"similar objects\": [\"rifle\", \"pistol\", \"shotgun\"]}"}, {"object": "magazine", "object_id": 658, "gpt3_output": "\n{\"type\": \"publication\", \"description\": \"printed paper; could contain articles, stories, photographs, illustrations, etc.\", \"similar objects\": [\"newspaper\", \"book\", \"journal\"]}"}, {"object": "magnet", "object_id": 659, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"attracts metal objects; could be made of iron or steel; could be in different shapes and sizes\", \"similar objects\": [\"lodestone\", \"electromagnet\", \"solenoid\"]}"}, {"object": "mail_slot", "object_id": 660, "gpt3_output": "\n{\"type\": \"mail receptacle\", \"description\": \"rectangular; usually found on the front door of a house; used to receive mail\", \"similar objects\": [\"letterbox\", \"mailbox\", \"postbox\"]}"}, {"object": "mailbox_(at_home)", "object_id": 661, "gpt3_output": "\n{\"type\": \"mail storage\", \"description\": \"usually rectangular; could be made of metal or plastic; could have a flag to indicate mail inside\", \"similar objects\": [\"letterbox\", \"postbox\", \"mail slot\"]}"}, {"object": "mallard", "object_id": 662, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, colorful duck; has a green head and yellow bill; males have a white neck ring\", \"similar objects\": [\"goose\", \"swan\", \"canada goose\"]}"}, {"object": "mallet", "object_id": 663, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"wooden handle with a round head; used for pounding or hammering\", \"similar objects\": [\"hammer\", \"axe\", \"sledgehammer\"]}"}, {"object": "mammoth", "object_id": 664, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, hairy, extinct mammal; tusks; long curved trunk\", \"similar objects\": [\"elephant\", \"giraffe\", \"rhinoceros\"]}"}, {"object": "manatee", "object_id": 665, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, gray; has a rounded tail; could be found in water\", \"similar objects\": [\"dolphin\", \"whale\", \"sea lion\"]}"}, {"object": "mandarin_orange", "object_id": 666, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"small, round, orange; has a thin, loose skin; could be peeled easily\", \"similar objects\": [\"tangerine\", \"clementine\", \"satsuma\"]}"}, {"object": "manger", "object_id": 667, "gpt3_output": "\n{\"type\": \"feeding tool\", \"description\": \"rectangular; used to feed animals; could be made of wood\", \"similar objects\": [\"trough\", \"hayrack\", \"feeder\"]}"}, {"object": "manhole", "object_id": 668, "gpt3_output": "\n{\"type\": \"utility structure\", \"description\": \"round; has a cover; could be used for access to underground utilities\", \"similar objects\": [\"drainage pipe\", \"sewer pipe\", \"catch basin\"]}"}, {"object": "map", "object_id": 669, "gpt3_output": "\n{\"type\": \"navigation tool\", \"description\": \"paper or digital; could show roads, cities, countries, etc.\", \"similar objects\": [\"compass\", \"GPS\", \"globe\"]}"}, {"object": "marker", "object_id": 670, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"has a tip; could be used to write on paper or other surfaces; could be refillable\", \"similar objects\": [\"pen\", \"pencil\", \"highlighter\"]}"}, {"object": "martini", "object_id": 671, "gpt3_output": "\n{\"type\": \"drink\", \"description\": \"clear; could be served with an olive; could be shaken or stirred\", \"similar objects\": [\"margarita\", \"mojito\", \"cosmopolitan\"]}"}, {"object": "mascot", "object_id": 672, "gpt3_output": "\n{\"type\": \"figure\", \"description\": \"usually a cartoon character; could be a person in a costume; often used to represent a team or organization\", \"similar objects\": [\"logo\", \"symbol\", \"emblem\"]}"}, {"object": "mashed_potato", "object_id": 673, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"smooth, creamy, yellowish; could be served with gravy\", \"similar objects\": [\"french fries\", \"baked potato\", \"hash browns\"]}"}, {"object": "masher", "object_id": 674, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"used to mash potatoes or other vegetables; could be made of metal or plastic; has a handle\", \"similar objects\": [\"potato ricer\", \"blender\", \"food processor\"]}"}, {"object": "mask", "object_id": 675, "gpt3_output": "\n{\"type\": \"protective gear\", \"description\": \"covers the face; could be made of cloth or plastic; could have straps to secure it\", \"similar objects\": [\"face shield\", \"goggles\", \"respirator\"]}"}, {"object": "mast", "object_id": 676, "gpt3_output": "\n{\"type\": \"nautical tool\", \"description\": \"tall, vertical pole; could be made of metal; could be used to hold sails\", \"similar objects\": [\"boom\", \"spar\", \"yardarm\"]}"}, {"object": "mat_(gym_equipment)", "object_id": 677, "gpt3_output": "\n{\"type\": \"exercise equipment\", \"description\": \"flat, thin, usually made of foam; could be used for yoga or stretching\", \"similar objects\": [\"yoga ball\", \"resistance band\", \"dumbbell\"]}"}, {"object": "matchbox", "object_id": 678, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"small, rectangular; could be made of cardboard; could contain matches\", \"similar objects\": [\"pencil case\", \"jewelry box\", \"coin purse\"]}"}, {"object": "mattress", "object_id": 679, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be filled with foam or springs; could be covered with fabric\", \"similar objects\": [\"pillow\", \"couch\", \"bed\"]}"}, {"object": "measuring_cup", "object_id": 680, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"cylindrical; has markings for measuring; could be made of plastic or metal\", \"similar objects\": [\"measuring spoon\", \"scale\", \"thermometer\"]}"}, {"object": "measuring_stick", "object_id": 681, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"long, thin; could be made of wood or metal; could have markings on it\", \"similar objects\": [\"ruler\", \"tape measure\", \"yardstick\"]}"}, {"object": "meatball", "object_id": 682, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; could be made of ground meat; could be served with sauce\", \"similar objects\": [\"dumpling\", \"falafel\", \"patties\"]}"}, {"object": "medicine", "object_id": 683, "gpt3_output": "\n{\"type\": \"medical product\", \"description\": \"could be in the form of pills, capsules, liquids, etc.; could be used to treat illnesses and diseases\", \"similar objects\": [\"drugs\", \"supplements\", \"vitamins\"]}"}, {"object": "melon", "object_id": 684, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round; could be yellow, green, or orange; has a hard rind; could be sliced into wedges\", \"similar objects\": [\"watermelon\", \"honeydew\", \"cantaloupe\"]}"}, {"object": "microphone", "object_id": 685, "gpt3_output": "\n{\"type\": \"audio device\", \"description\": \"long, thin; could be handheld; could be connected to a sound system\", \"similar objects\": [\"speaker\", \"headset\", \"amplifier\"]}"}, {"object": "microscope", "object_id": 686, "gpt3_output": "\n{\"type\": \"scientific tool\", \"description\": \"has an eyepiece and an objective lens; could be used to magnify objects; could be used to observe cells\", \"similar objects\": [\"telescope\", \"binoculars\", \"microscope slides\"]}"}, {"object": "microwave_oven", "object_id": 687, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"box-shaped; has a door; could be used to heat food\", \"similar objects\": [\"stove\", \"toaster oven\", \"convection oven\"]}"}, {"object": "milestone", "object_id": 688, "gpt3_output": "\n{\"type\": \"marker\", \"description\": \"a stone or post used to mark a distance in a road or path; could be used to mark a significant event or achievement\", \"similar objects\": [\"waymarker\", \"benchmark\", \"landmark\"]}"}, {"object": "milk", "object_id": 689, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"white, liquid; could be flavored; could be served cold or hot\", \"similar objects\": [\"juice\", \"tea\", \"coffee\"]}"}, {"object": "milk_can", "object_id": 690, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; has a lid\", \"similar objects\": [\"jar\", \"bucket\", \"jug\"]}"}, {"object": "milkshake", "object_id": 691, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"cold, creamy, sweet; could be made with ice cream; could be topped with whipped cream\", \"similar objects\": [\"smoothie\", \"shake\", \"float\"]}"}, {"object": "minivan", "object_id": 692, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"longer than a car; could have sliding doors; could have a lot of seats\", \"similar objects\": [\"SUV\", \"van\", \"truck\"]}"}, {"object": "mint_candy", "object_id": 693, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; could be green or white; has a sweet taste\", \"similar objects\": [\"gumdrop\", \"jelly bean\", \"hard candy\"]}"}, {"object": "mirror", "object_id": 694, "gpt3_output": "\n{\"type\": \"reflective object\", \"description\": \"smooth, flat surface; could be framed; could be hung on the wall\", \"similar objects\": [\"picture frame\", \"window\", \"glass\"]}"}, {"object": "mitten", "object_id": 695, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"hand-shaped; could be made of wool; could have a string to hang around the neck\", \"similar objects\": [\"glove\", \"scarf\", \"hat\"]}"}, {"object": "mixer_(kitchen_tool)", "object_id": 696, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"electronic device; used for mixing ingredients; could have multiple attachments\", \"similar objects\": [\"blender\", \"food processor\", \"juicer\"]}"}, {"object": "money", "object_id": 697, "gpt3_output": "\n{\"type\": \"currency\", \"description\": \"paper or metal; could be coins or notes; could be of different denominations\", \"similar objects\": [\"credit card\", \"debit card\", \"check\"]}"}, {"object": "monitor_(computer_equipment) computer_monitor", "object_id": 698, "gpt3_output": "\n{\"type\": \"computer equipment\", \"description\": \"flat rectangular screen; could be connected to a computer; could have buttons for adjusting settings\", \"similar objects\": [\"keyboard\", \"mouse\", \"printer\"]}"}, {"object": "monkey", "object_id": 699, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"long tail; could be brown, black, or white; could have a pink face\", \"similar objects\": [\"gorilla\", \"chimpanzee\", \"baboon\"]}"}, {"object": "motor", "object_id": 700, "gpt3_output": "\n{\"type\": \"machine\", \"description\": \"has an engine; could be used to power vehicles; could be used to generate electricity\", \"similar objects\": [\"generator\", \"engine\", \"turbine\"]}"}, {"object": "motor_scooter", "object_id": 701, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; could have a seat; could have a handlebar\", \"similar objects\": [\"motorcycle\", \"bicycle\", \"skateboard\"]}"}, {"object": "motor_vehicle", "object_id": 702, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"powered by an engine; could have four wheels; could be used for transportation\", \"similar objects\": [\"car\", \"truck\", \"motorcycle\"]}"}, {"object": "motorcycle", "object_id": 703, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"tricycle\"]}"}, {"object": "mound_(baseball)", "object_id": 704, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"raised area of dirt on a baseball field; used for pitchers to stand on\", \"similar objects\": [\"home plate\", \"bases\", \"batting cage\"]}"}, {"object": "mouse_(computer_equipment)", "object_id": 705, "gpt3_output": "\n{\"type\": \"computer equipment\", \"description\": \"small, wireless, has a scroll wheel\", \"similar objects\": [\"keyboard\", \"monitor\", \"printer\"]}"}, {"object": "mousepad", "object_id": 706, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"flat, rectangular; could be made of rubber or cloth; could have a design\", \"similar objects\": [\"keyboard pad\", \"mouse mat\", \"gaming pad\"]}"}, {"object": "muffin", "object_id": 707, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round, sweet, could have a variety of flavors; could be topped with fruits or nuts\", \"similar objects\": [\"cupcake\", \"donut\", \"cake\"]}"}, {"object": "mug", "object_id": 708, "gpt3_output": "\n{\"type\": \"drinking vessel\", \"description\": \"cylindrical; could have a handle; could have a lid\", \"similar objects\": [\"cup\", \"glass\", \"thermos\"]}"}, {"object": "mushroom", "object_id": 709, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round, brown, could have white spots; could be sliced into pieces; could have a stem\", \"similar objects\": [\"truffle\", \"oyster mushroom\", \"portobello mushroom\"]}"}, {"object": "music_stool", "object_id": 710, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has a round seat; could be adjustable; could have a backrest\", \"similar objects\": [\"chair\", \"stool\", \"bench\"]}"}, {"object": "musical_instrument", "object_id": 711, "gpt3_output": "\n\n{\"type\": \"instrument\", \"description\": \"could be made of wood, metal, or plastic; could be played with hands or a bow; could produce sound with strings, keys, or percussion\", \"similar objects\": [\"guitar\", \"piano\", \"drum\"]}"}, {"object": "nailfile", "object_id": 712, "gpt3_output": "\n{\"type\": \"grooming tool\", \"description\": \"small, rectangular; could be made of metal or plastic; used to shape and smooth nails\", \"similar objects\": [\"nail clipper\", \"cuticle pusher\", \"emery board\"]}"}, {"object": "napkin", "object_id": 713, "gpt3_output": "\n{\"type\": \"tableware\", \"description\": \"square or rectangular; made of cloth or paper; used to wipe hands or mouth\", \"similar objects\": [\"towel\", \"placemat\", \"tablecloth\"]}"}, {"object": "neckerchief", "object_id": 714, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"square-shaped; could be made of silk or cotton; could be worn around the neck\", \"similar objects\": [\"scarf\", \"tie\", \"bandana\"]}"}, {"object": "necklace", "object_id": 715, "gpt3_output": "\n{\"type\": \"jewelry\", \"description\": \"chain of beads or other decorative items; could be made of metal, plastic, or other materials; could be worn around the neck\", \"similar objects\": [\"bracelet\", \"earrings\", \"ring\"]}"}, {"object": "necktie", "object_id": 716, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"long, thin, usually made of fabric; could be tied around the neck\", \"similar objects\": [\"bow tie\", \"scarf\", \"cravat\"]}"}, {"object": "needle", "object_id": 717, "gpt3_output": "\n{\"type\": \"sewing tool\", \"description\": \"long and thin; has a sharp point; could be made of metal or plastic\", \"similar objects\": [\"pin\", \"thimble\", \"scissors\"]}"}, {"object": "nest", "object_id": 718, "gpt3_output": "\n{\"type\": \"structure\", \"description\": \"made of twigs and leaves; could be found in trees; could be used by birds for nesting\", \"similar objects\": [\"hive\", \"burrow\", \"den\"]}"}, {"object": "newspaper", "object_id": 719, "gpt3_output": "\n{\"type\": \"reading material\", \"description\": \"printed paper; could be folded; could be in black and white or in color\", \"similar objects\": [\"magazine\", \"book\", \"journal\"]}"}, {"object": "newsstand", "object_id": 720, "gpt3_output": "\n{\"type\": \"structure\", \"description\": \"small, rectangular; could have shelves; could be used to sell newspapers and magazines\", \"similar objects\": [\"kiosk\", \"stall\", \"cart\"]}"}, {"object": "nightshirt", "object_id": 721, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"loose-fitting; usually made of cotton; could have long sleeves\", \"similar objects\": [\"pajamas\", \"robe\", \"tunic\"]}"}, {"object": "nosebag_(for_animals)", "object_id": 722, "gpt3_output": "\n{\"type\": \"animal feeder\", \"description\": \"bag with straps; used to feed animals; could be made of leather or canvas\", \"similar objects\": [\"hay net\", \"grain bag\", \"feed bucket\"]}"}, {"object": "noseband_(for_animals)", "object_id": 723, "gpt3_output": "\n{\"type\": \"animal accessory\", \"description\": \"a strap that goes around the nose of an animal; usually made of leather or nylon; used for control and guidance\", \"similar objects\": [\"halter\", \"bridle\", \"harness\"]}"}, {"object": "notebook", "object_id": 724, "gpt3_output": "\n{\"type\": \"stationary item\", \"description\": \"bound paper sheets; could be spiral bound; could have a hard cover\", \"similar objects\": [\"journal\", \"diary\", \"planner\"]}"}, {"object": "notepad", "object_id": 725, "gpt3_output": "\n{\"type\": \"stationery\", \"description\": \"rectangular; could be made of paper; could have lines or grids\", \"similar objects\": [\"notebook\", \"journal\", \"diary\"]}"}, {"object": "nut", "object_id": 726, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"hard, round, could be shelled; could be eaten raw or roasted\", \"similar objects\": [\"almond\", \"peanut\", \"walnut\"]}"}, {"object": "nutcracker", "object_id": 727, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"used to crack open nuts; usually made of metal; has two handles\", \"similar objects\": [\"pliers\", \"hammer\", \"screwdriver\"]}"}, {"object": "oar", "object_id": 728, "gpt3_output": "\n{\"type\": \"rowing tool\", \"description\": \"long, thin, has a handle; used to row a boat\", \"similar objects\": [\"paddle\", \"canoe\", \"kayak\"]}"}, {"object": "octopus_(food)", "object_id": 729, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"tentacles; could be boiled, fried, or grilled; could be served with sauces\", \"similar objects\": [\"squid\", \"cuttlefish\", \"shrimp\"]}"}, {"object": "octopus_(animal)", "object_id": 730, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"eight tentacles; could be red, brown, or blue; could have suction cups; could have a round head\", \"similar objects\": [\"squid\", \"cuttlefish\", \"jellyfish\"]}"}, {"object": "oil_lamp", "object_id": 731, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"round; made of metal; uses oil as fuel\", \"similar objects\": [\"lantern\", \"torch\", \"candle\"]}"}, {"object": "olive_oil", "object_id": 732, "gpt3_output": "\n{\"type\": \"cooking ingredient\", \"description\": \"light yellow; could be used for cooking and salad dressing; could be extracted from olives\", \"similar objects\": [\"vegetable oil\", \"sunflower oil\", \"canola oil\"]}"}, {"object": "omelet", "object_id": 733, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"egg-based dish; could be filled with vegetables, cheese, or meat; could be served with toast or potatoes\", \"similar objects\": [\"scrambled eggs\", \"frittata\", \"quiche\"]}"}, {"object": "onion", "object_id": 734, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round; has a papery skin; could be sliced into rings; could have a strong smell\", \"similar objects\": [\"garlic\", \"potato\", \"shallot\"]}"}, {"object": "orange_(fruit)", "object_id": 735, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, orange, has a stem; could be peeled and segmented; could be juicy\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "orange_juice", "object_id": 736, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"made from oranges; could be sweet or sour; could be served cold or hot\", \"similar objects\": [\"apple juice\", \"lemonade\", \"grape juice\"]}"}, {"object": "ostrich", "object_id": 737, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, flightless bird; long neck and legs; two toes on each foot\", \"similar objects\": [\"emu\", \"cassowary\", \"rhea\"]}"}, {"object": "ottoman", "object_id": 738, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be used as a footrest; could be used as a seat\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}"}, {"object": "oven", "object_id": 739, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"box-shaped; could be used to bake food; could be electric or gas-powered\", \"similar objects\": [\"stove\", \"microwave\", \"toaster\"]}"}, {"object": "overalls_(clothing)", "object_id": 740, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"loose-fitting, one-piece garment; usually has straps over the shoulders; could have pockets; could be made of denim or cotton\", \"similar objects\": [\"coveralls\", \"jumpsuit\", \"dungarees\"]}"}, {"object": "owl", "object_id": 741, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large eyes; nocturnal; could have feathers of different colors; could have a curved beak\", \"similar objects\": [\"eagle\", \"hawk\", \"falcon\"]}"}, {"object": "packet", "object_id": 742, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of paper or plastic; could be sealed\", \"similar objects\": [\"envelope\", \"box\", \"bag\"]}"}, {"object": "inkpad", "object_id": 743, "gpt3_output": "\n{\"type\": \"stationery tool\", \"description\": \"flat; could be made of rubber; used for stamping\", \"similar objects\": [\"stamp\", \"marker\", \"pen\"]}"}, {"object": "pad", "object_id": 744, "gpt3_output": "\n{\"type\": \"stationery item\", \"description\": \"rectangular; could be made of paper or plastic; could be used for writing or drawing\", \"similar objects\": [\"notebook\", \"journal\", \"sketchbook\"]}"}, {"object": "paddle", "object_id": 745, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long, thin, and flat; could be made of wood or plastic; used for propelling a boat through the water\", \"similar objects\": [\"oar\", \"canoe\", \"kayak\"]}"}, {"object": "padlock", "object_id": 746, "gpt3_output": "\n{\"type\": \"security tool\", \"description\": \"has a keyhole; could be made of metal; could be used to lock doors or bags\", \"similar objects\": [\"lock\", \"chain\", \"combination lock\"]}"}, {"object": "paintbrush", "object_id": 747, "gpt3_output": "\n{\"type\": \"painting tool\", \"description\": \"long handle; bristles at the end; could be made of different materials\", \"similar objects\": [\"paint roller\", \"paint scraper\", \"paint sponge\"]}"}, {"object": "painting", "object_id": 748, "gpt3_output": "\n{\"type\": \"artwork\", \"description\": \"canvas with colors and shapes; could be framed\", \"similar objects\": [\"sculpture\", \"drawing\", \"photograph\"]}"}, {"object": "pajamas", "object_id": 749, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"loose-fitting; usually made of cotton; could have long sleeves and pants\", \"similar objects\": [\"nightgown\", \"robe\", \"slippers\"]}"}, {"object": "palette", "object_id": 750, "gpt3_output": "\n{\"type\": \"art tool\", \"description\": \"flat, rectangular; could have several wells for holding paint\", \"similar objects\": [\"paintbrush\", \"canvas\", \"easel\"]}"}, {"object": "pan_(for_cooking)", "object_id": 751, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"flat, shallow, has a handle\", \"similar objects\": [\"pot\", \"wok\", \"frying pan\"]}"}, {"object": "pan_(metal_container)", "object_id": 752, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"flat, round, metal container; could have a handle; could be used for frying, boiling, and baking\", \"similar objects\": [\"pot\", \"wok\", \"skillet\"]}"}, {"object": "pancake", "object_id": 753, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round, flat, could be served with syrup; could be made with flour, eggs, and milk\", \"similar objects\": [\"crepe\", \"waffle\", \"doughnut\"]}"}, {"object": "pantyhose", "object_id": 754, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"transparent, stretchable, could be worn on legs\", \"similar objects\": [\"stockings\", \"tights\", \"leggings\"]}"}, {"object": "papaya", "object_id": 755, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"oval-shaped; yellow-orange in color; has a sweet taste; has black seeds\", \"similar objects\": [\"mango\", \"pineapple\", \"guava\"]}"}, {"object": "paper_plate", "object_id": 756, "gpt3_output": "\n{\"type\": \"dining tool\", \"description\": \"round; made of paper; could be used to serve food\", \"similar objects\": [\"plastic plate\", \"bowl\", \"cup\"]}"}, {"object": "paper_towel", "object_id": 757, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"absorbent; usually made of paper; could be used to clean surfaces\", \"similar objects\": [\"cloth towel\", \"sponge\", \"rag\"]}"}, {"object": "paperback_book", "object_id": 758, "gpt3_output": "\n{\"type\": \"reading material\", \"description\": \"soft cover; could be opened and closed; could be read from left to right\", \"similar objects\": [\"hardcover book\", \"magazine\", \"e-book\"]}"}, {"object": "paperweight", "object_id": 759, "gpt3_output": "\n{\"type\": \"office tool\", \"description\": \"heavy object used to hold papers in place; could be made of metal or glass\", \"similar objects\": [\"stapler\", \"binder clip\", \"bookend\"]}"}, {"object": "parachute", "object_id": 760, "gpt3_output": "\n{\"type\": \"safety device\", \"description\": \"large, rectangular; made of fabric; used to slow down a descent\", \"similar objects\": [\"paraglider\", \"hang glider\", \"balloon\"]}"}, {"object": "parakeet", "object_id": 761, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, colorful bird; has a long tail; could be kept as a pet\", \"similar objects\": [\"budgerigar\", \"canary\", \"finch\"]}"}, {"object": "parasail_(sports)", "object_id": 762, "gpt3_output": "\n\n{\"type\": \"sports equipment\", \"description\": \"a parachute attached to a boat or vehicle; used for recreational activities\", \"similar objects\": [\"paraglider\", \"hang glider\", \"towable tube\"]}"}, {"object": "parasol", "object_id": 763, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"round; could be made of fabric; used to protect from sun and rain\", \"similar objects\": [\"umbrella\", \"sun hat\", \"sunscreen\"]}"}, {"object": "parchment", "object_id": 764, "gpt3_output": "\n{\"type\": \"material\", \"description\": \"thin, strong, and durable paper-like material; could be used for writing or baking\", \"similar objects\": [\"vellum\", \"paper\", \"cardstock\"]}"}, {"object": "parka", "object_id": 765, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long coat; usually has a hood; could be waterproof; could be insulated\", \"similar objects\": [\"jacket\", \"coat\", \"raincoat\"]}"}, {"object": "parking_meter", "object_id": 766, "gpt3_output": "\n{\"type\": \"parking tool\", \"description\": \"tall, cylindrical; has a slot for coins; could have a digital display\", \"similar objects\": [\"parking sign\", \"parking lot\", \"parking garage\"]}"}, {"object": "parrot", "object_id": 767, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"colorful feathers; could talk; could fly\", \"similar objects\": [\"macaw\", \"cockatoo\", \"finch\"]}"}, {"object": "passenger_car_(part_of_a_train)", "object_id": 768, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long, usually has multiple compartments; could be powered by electricity or diesel; could be used for public transportation\", \"similar objects\": [\"freight car\", \"locomotive\", \"monorail\"]}"}, {"object": "passenger_ship", "object_id": 769, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large; could have multiple decks; could have a bridge; could have a dining area\", \"similar objects\": [\"cruise ship\", \"ferry\", \"yacht\"]}"}, {"object": "passport", "object_id": 770, "gpt3_output": "\n{\"type\": \"document\", \"description\": \"small booklet; contains personal information; has a photo\", \"similar objects\": [\"driver's license\", \"ID card\", \"birth certificate\"]}"}, {"object": "pastry", "object_id": 771, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"sweet; could be filled with cream or jam; could be baked or fried\", \"similar objects\": [\"cake\", \"pie\", \"cookie\"]}"}, {"object": "patty_(food)", "object_id": 772, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"flat, round, usually made of ground meat; could be served in a bun\", \"similar objects\": [\"burger\", \"meatball\", \"hot dog\"]}"}, {"object": "pea_(food)", "object_id": 773, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"small, green, round; could be eaten raw or cooked; could be found in pods\", \"similar objects\": [\"corn\", \"green bean\", \"carrot\"]}"}, {"object": "peach", "object_id": 774, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, fuzzy, has a pit; could be yellow or pink; could be sliced into wedges\", \"similar objects\": [\"plum\", \"apricot\", \"nectarine\"]}"}, {"object": "peanut_butter", "object_id": 775, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"smooth, creamy, spreadable; could be used as a sandwich spread\", \"similar objects\": [\"jelly\", \"jam\", \"honey\"]}"}, {"object": "pear", "object_id": 776, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, yellow or green; has a stem; could be juicy\", \"similar objects\": [\"apple\", \"banana\", \"orange\"]}"}, {"object": "peeler_(tool_for_fruit_and_vegetables)", "object_id": 777, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"long handle with a sharp blade; used to peel fruits and vegetables\", \"similar objects\": [\"grater\", \"mandoline\", \"knife\"]}"}, {"object": "wooden_leg", "object_id": 778, "gpt3_output": "\n{\"type\": \"prosthetic limb\", \"description\": \"artificial limb made of wood; could be attached to the body with straps; could be used to replace a missing limb\", \"similar objects\": [\"prosthetic arm\", \"prosthetic foot\", \"prosthetic hand\"]}"}, {"object": "pegboard", "object_id": 779, "gpt3_output": "\n{\"type\": \"organizational tool\", \"description\": \"rectangular; has holes for pegs; could be used to hang items\", \"similar objects\": [\"shelving unit\", \"hooks\", \"baskets\"]}"}, {"object": "pelican", "object_id": 780, "gpt3_output": "\n{\"type\": \"bird\", \"description\": \"large, white; has a long beak; could have a pouch under its beak\", \"similar objects\": [\"seagull\", \"swan\", \"duck\"]}"}, {"object": "pen", "object_id": 781, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"cylindrical; could be made of plastic or metal; could have a cap\", \"similar objects\": [\"pencil\", \"marker\", \"highlighter\"]}"}, {"object": "pencil", "object_id": 782, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"cylindrical; has a sharpened tip; could be made of wood or plastic\", \"similar objects\": [\"pen\", \"marker\", \"crayon\"]}"}, {"object": "pencil_box", "object_id": 783, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have compartments; could have a handle\", \"similar objects\": [\"pencil case\", \"pencil pouch\", \"pencil holder\"]}"}, {"object": "pencil_sharpener", "object_id": 784, "gpt3_output": "\n{\"type\": \"stationery tool\", \"description\": \"small, handheld device; has a blade to sharpen pencils\", \"similar objects\": [\"stapler\", \"hole puncher\", \"scissors\"]}"}, {"object": "pendulum", "object_id": 785, "gpt3_output": "\n{\"type\": \"mechanical tool\", \"description\": \"hangs from a fixed point; swings back and forth\", \"similar objects\": [\"clock\", \"metronome\", \"swing\"]}"}, {"object": "penguin", "object_id": 786, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"black and white feathers; waddles when walking; has a curved beak\", \"similar objects\": [\"seagull\", \"flamingo\", \"albatross\"]}"}, {"object": "pennant", "object_id": 787, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"triangular; could be made of fabric; could be hung from a pole\", \"similar objects\": [\"flag\", \"banner\", \"streamer\"]}"}, {"object": "penny_(coin)", "object_id": 788, "gpt3_output": "\n{\"type\": \"coin\", \"description\": \"small, round, copper-colored; has a head and tail side; has a value of one cent\", \"similar objects\": [\"nickel\", \"dime\", \"quarter\"]}"}, {"object": "pepper", "object_id": 789, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round; could be red, green, yellow, or orange; could be sliced into small pieces; could have a stem\", \"similar objects\": [\"tomato\", \"onion\", \"cucumber\"]}"}, {"object": "pepper_mill", "object_id": 790, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"cylindrical; has a handle; used to grind pepper\", \"similar objects\": [\"salt mill\", \"mortar and pestle\", \"garlic press\"]}"}, {"object": "perfume", "object_id": 791, "gpt3_output": "\n{\"type\": \"cosmetic product\", \"description\": \"liquid; could be sprayed; has a pleasant smell\", \"similar objects\": [\"cologne\", \"body spray\", \"perfume oil\"]}"}, {"object": "persimmon", "object_id": 792, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round; orange-red; has a pointed end; could be sweet or sour\", \"similar objects\": [\"apple\", \"pear\", \"plum\"]}"}, {"object": "person", "object_id": 793, "gpt3_output": "\n{\"type\": \"living being\", \"description\": \"human; bipedal; could have different skin colors; could have different hair styles\", \"similar objects\": [\"animal\", \"robot\", \"doll\"]}"}, {"object": "pet", "object_id": 794, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"domesticated animal; could be a mammal, bird, reptile, amphibian, or fish; could be kept as a companion or for a practical purpose\", \"similar objects\": [\"dog\", \"cat\", \"rabbit\", \"hamster\", \"fish\"]}"}, {"object": "pew_(church_bench)", "object_id": 795, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"long, wooden bench; usually found in churches\", \"similar objects\": [\"chair\", \"sofa\", \"stool\"]}"}, {"object": "phonebook", "object_id": 796, "gpt3_output": "\n{\"type\": \"reference book\", \"description\": \"contains a list of names, addresses, and phone numbers; could be printed or digital\", \"similar objects\": [\"dictionary\", \"encyclopedia\", \"atlas\"]}"}, {"object": "phonograph_record", "object_id": 797, "gpt3_output": "\n{\"type\": \"audio storage device\", \"description\": \"round, black vinyl disc; has grooves that can be read by a needle\", \"similar objects\": [\"cassette tape\", \"CD\", \"DVD\"]}"}, {"object": "piano", "object_id": 798, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"large, rectangular; has black and white keys; could be acoustic or digital\", \"similar objects\": [\"guitar\", \"violin\", \"drums\"]}"}, {"object": "pickle", "object_id": 799, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"green; could be sliced into round pieces; could be sour or sweet; could be served with sandwiches\", \"similar objects\": [\"olive\", \"cucumber\", \"caper\"]}"}, {"object": "pickup_truck", "object_id": 800, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, boxy; has an open bed; could have four doors\", \"similar objects\": [\"SUV\", \"van\", \"sedan\"]}"}, {"object": "pie", "object_id": 801, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; could be filled with fruits, cream, or savory ingredients; could be topped with a crust\", \"similar objects\": [\"cake\", \"tart\", \"quiche\"]}"}, {"object": "pigeon", "object_id": 802, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"grayish-brown; has a white patch on its neck; could fly in flocks\", \"similar objects\": [\"dove\", \"sparrow\", \"crow\"]}"}, {"object": "piggy_bank", "object_id": 803, "gpt3_output": "\n{\"type\": \"saving tool\", \"description\": \"usually made of ceramic; has a slot for coins; could be shaped like a pig\", \"similar objects\": [\"money box\", \"savings jar\", \"piggy jar\"]}"}, {"object": "pillow", "object_id": 804, "gpt3_output": "\n{\"type\": \"bedding item\", \"description\": \"soft; usually rectangular; could be filled with feathers or foam\", \"similar objects\": [\"mattress\", \"blanket\", \"duvet\"]}"}, {"object": "pin_(non_jewelry)", "object_id": 805, "gpt3_output": "\n\n{\"type\": \"fastening tool\", \"description\": \"small, sharp, metal object; used to fasten two objects together\", \"similar objects\": [\"nail\", \"screw\", \"tack\"]}"}, {"object": "pineapple", "object_id": 806, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"spiky, yellow-green skin; sweet, juicy, yellow flesh; has a crown of leaves\", \"similar objects\": [\"mango\", \"kiwi\", \"avocado\"]}"}, {"object": "pinecone", "object_id": 807, "gpt3_output": "\n{\"type\": \"plant\", \"description\": \"brown; has a hard shell; could be covered with scales; could be opened to reveal seeds\", \"similar objects\": [\"acorn\", \"conker\", \"chestnut\"]}"}, {"object": "ping-pong_ball", "object_id": 808, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"round, white, small; used in table tennis\", \"similar objects\": [\"tennis ball\", \"baseball\", \"golf ball\"]}"}, {"object": "pinwheel", "object_id": 809, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"round; has colorful paper blades; could be attached to a stick\", \"similar objects\": [\"kite\", \"windmill\", \"top\"]}"}, {"object": "tobacco_pipe", "object_id": 810, "gpt3_output": "\n{\"type\": \"smoking tool\", \"description\": \"long stem; could have a bowl; could be made of wood or clay\", \"similar objects\": [\"cigarette\", \"cigar\", \"hookah\"]}"}, {"object": "pipe", "object_id": 811, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long, cylindrical; could be made of metal or plastic; could be used for plumbing or smoking\", \"similar objects\": [\"hose\", \"tube\", \"straw\"]}"}, {"object": "pistol", "object_id": 812, "gpt3_output": "\n{\"type\": \"weapon\", \"description\": \"small, handheld firearm; could be semi-automatic or automatic; could have a magazine\", \"similar objects\": [\"rifle\", \"shotgun\", \"revolver\"]}"}, {"object": "pita_(bread)", "object_id": 813, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"flat, round, could be stuffed with vegetables and meat; could be cut into triangles\", \"similar objects\": [\"tortilla\", \"naan\", \"bagel\"]}"}, {"object": "pitcher_(vessel_for_liquid)", "object_id": 814, "gpt3_output": "\n{\"type\": \"vessel\", \"description\": \"tall, cylindrical, has a handle and a spout; could be made of glass, ceramic, or metal\", \"similar objects\": [\"jug\", \"cup\", \"bowl\"]}"}, {"object": "pitchfork", "object_id": 815, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle with three or four prongs at the end; used for farming and gardening\", \"similar objects\": [\"rake\", \"shovel\", \"hoe\"]}"}, {"object": "pizza", "object_id": 816, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; has a crust; topped with cheese, tomato sauce, and other ingredients\", \"similar objects\": [\"calzone\", \"stromboli\", \"flatbread\"]}"}, {"object": "place_mat", "object_id": 817, "gpt3_output": "\n{\"type\": \"tableware\", \"description\": \"rectangular; could be made of cloth or plastic; used to protect the table from spills and scratches\", \"similar objects\": [\"tablecloth\", \"coaster\", \"napkin\"]}"}, {"object": "plate", "object_id": 818, "gpt3_output": "\n{\"type\": \"dining ware\", \"description\": \"flat, round, could be made of ceramic, plastic, or metal; could be used to serve food\", \"similar objects\": [\"bowl\", \"cup\", \"glass\"]}"}, {"object": "platter", "object_id": 819, "gpt3_output": "\n{\"type\": \"serving dish\", \"description\": \"large, flat, round; could be made of metal or ceramic; could be used to serve food\", \"similar objects\": [\"tray\", \"plate\", \"bowl\"]}"}, {"object": "playpen", "object_id": 820, "gpt3_output": "\n{\"type\": \"baby furniture\", \"description\": \"enclosed area with mesh walls; could have a door; could be foldable\", \"similar objects\": [\"crib\", \"high chair\", \"changing table\"]}"}, {"object": "pliers", "object_id": 821, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"two handles connected by a pivot; used for gripping and cutting\", \"similar objects\": [\"screwdriver\", \"hammer\", \"wrench\"]}"}, {"object": "plow_(farm_equipment)", "object_id": 822, "gpt3_output": "\n{\"type\": \"farm equipment\", \"description\": \"used to turn over soil; could be pulled by a tractor; could have a blade attached to the front\", \"similar objects\": [\"tractor\", \"harvester\", \"cultivator\"]}"}, {"object": "plume", "object_id": 823, "gpt3_output": "\n{\"type\": \"ornament\", \"description\": \"long, feathery; could be used as a headdress\", \"similar objects\": [\"feather\", \"tassel\", \"fringe\"]}"}, {"object": "pocket_watch", "object_id": 824, "gpt3_output": "\n{\"type\": \"timekeeping tool\", \"description\": \"small, round, has a chain; could be opened to show the clock face\", \"similar objects\": [\"wristwatch\", \"stopwatch\", \"sundial\"]}"}, {"object": "pocketknife", "object_id": 825, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"small, foldable; could have multiple blades\", \"similar objects\": [\"multi-tool\", \"utility knife\", \"scissors\"]}"}, {"object": "poker_(fire_stirring_tool)", "object_id": 826, "gpt3_output": "\n{\"type\": \"fire stirring tool\", \"description\": \"long handle; metal rod with a flat end; used to stir fire\", \"similar objects\": [\"fire poker\", \"fire shovel\", \"fire tongs\"]}"}, {"object": "pole", "object_id": 827, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"long and thin; could be made of metal or wood; could be used for support or decoration\", \"similar objects\": [\"rod\", \"post\", \"beam\"]}"}, {"object": "polo_shirt", "object_id": 828, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"collared shirt; short sleeves; could have buttons; could have a logo\", \"similar objects\": [\"t-shirt\", \"button-down shirt\", \"sweater\"]}"}, {"object": "poncho", "object_id": 829, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"loose-fitting garment; could be made of wool; could have a hood\", \"similar objects\": [\"cape\", \"shawl\", \"raincoat\"]}"}, {"object": "pony", "object_id": 830, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small horse; has a short mane; could be ridden by children\", \"similar objects\": [\"horse\", \"donkey\", \"mule\"]}"}, {"object": "pool_table", "object_id": 831, "gpt3_output": "\n{\"type\": \"game table\", \"description\": \"long, rectangular; has pockets; could be covered with green felt\", \"similar objects\": [\"ping pong table\", \"air hockey table\", \"foosball table\"]}"}, {"object": "pop_(soda)", "object_id": 832, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"carbonated; could be flavored; could be in a can or bottle\", \"similar objects\": [\"juice\", \"water\", \"tea\"]}"}, {"object": "postbox_(public)", "object_id": 833, "gpt3_output": "\n{\"type\": \"mailbox\", \"description\": \"red; has a slot for letters; could be mounted on a wall or a pole\", \"similar objects\": [\"mailbox\", \"letterbox\", \"drop box\"]}"}, {"object": "postcard", "object_id": 834, "gpt3_output": "\n{\"type\": \"stationery\", \"description\": \"rectangular; could be decorated with pictures or words; could be sent through mail\", \"similar objects\": [\"letter\", \"envelope\", \"card\"]}"}, {"object": "poster", "object_id": 835, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"printed paper; could be hung on the wall; could be used for advertisement\", \"similar objects\": [\"painting\", \"banner\", \"sign\"]}"}, {"object": "pot", "object_id": 836, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}"}, {"object": "flowerpot", "object_id": 837, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"round; could be made of clay; could have a drainage hole at the bottom; could have a saucer\", \"similar objects\": [\"vase\", \"planter\", \"pot\"]}"}, {"object": "potato", "object_id": 838, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"oval-shaped; could be yellow, white, or brown; could be boiled, mashed, or fried; could have green leaves\", \"similar objects\": [\"carrot\", \"onion\", \"sweet potato\"]}"}, {"object": "potholder", "object_id": 839, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"made of fabric; could be used to hold hot pots and pans\", \"similar objects\": [\"oven mitt\", \"trivet\", \"hot pad\"]}"}, {"object": "pottery", "object_id": 840, "gpt3_output": "\n{\"type\": \"art form\", \"description\": \"hand-crafted objects made from clay; could be glazed or unglazed; could be used for decoration or functional purposes\", \"similar objects\": [\"ceramics\", \"sculpture\", \"mosaic\"]}"}, {"object": "pouch", "object_id": 841, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"small, usually made of fabric; could be used to store small items\", \"similar objects\": [\"bag\", \"wallet\", \"purse\"]}"}, {"object": "power_shovel", "object_id": 842, "gpt3_output": "\n{\"type\": \"construction tool\", \"description\": \"large, has a long arm; could be used to dig and move materials\", \"similar objects\": [\"excavator\", \"bulldozer\", \"backhoe\"]}"}, {"object": "prawn", "object_id": 843, "gpt3_output": "\n{\"type\": \"seafood\", \"description\": \"long, slender, and has a curved tail; could be cooked in various ways\", \"similar objects\": [\"shrimp\", \"lobster\", \"crab\"]}"}, {"object": "pretzel", "object_id": 844, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"twisted, salty, crunchy; could be served with mustard\", \"similar objects\": [\"bagel\", \"croissant\", \"doughnut\"]}"}, {"object": "printer", "object_id": 845, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"used to print documents; could be connected to a computer; could have a scanner\", \"similar objects\": [\"copier\", \"fax machine\", \"scanner\"]}"}, {"object": "projectile_(weapon)", "object_id": 846, "gpt3_output": "\n\n{\"type\": \"weapon\", \"description\": \"a device that is designed to be thrown or projected at a target, either by hand or with the aid of a mechanical device such as a gun or catapult\", \"similar objects\": [\"gun\", \"missile\", \"rocket\"]}"}, {"object": "projector", "object_id": 847, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"used to project images onto a wall or screen; could be connected to a computer or other device\", \"similar objects\": [\"television\", \"monitor\", \"laptop\"]}"}, {"object": "propeller", "object_id": 848, "gpt3_output": "\n{\"type\": \"mechanical device\", \"description\": \"round; has blades; used to generate thrust\", \"similar objects\": [\"fan\", \"turbine\", \"jet engine\"]}"}, {"object": "prune", "object_id": 849, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"dark purple; wrinkled; sweet and tart taste; could be dried\", \"similar objects\": [\"plum\", \"apricot\", \"fig\"]}"}, {"object": "pudding", "object_id": 850, "gpt3_output": "\n{\"type\": \"dessert\", \"description\": \"smooth, creamy, sweet; could be served cold or hot; could be made with milk, eggs, sugar, and other ingredients\", \"similar objects\": [\"custard\", \"cake\", \"pie\"]}"}, {"object": "puffer_(fish)", "object_id": 851, "gpt3_output": "\n{\"type\": \"fish\", \"description\": \"spherical; has spines; could inflate when threatened\", \"similar objects\": [\"clownfish\", \"angel fish\", \"lionfish\"]}"}, {"object": "puffin", "object_id": 852, "gpt3_output": "\n{\"type\": \"bird\", \"description\": \"black and white feathers; has a colorful beak; could fly\", \"similar objects\": [\"penguin\", \"seagull\", \"albatross\"]}"}, {"object": "pug-dog", "object_id": 853, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, stocky, short-muzzled dog; has a wrinkled face; has a curled tail; has a short, smooth coat\", \"similar objects\": [\"bulldog\", \"beagle\", \"boxer\"]}"}, {"object": "pumpkin", "object_id": 854, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round; orange; has a stem; could be carved into a jack-o-lantern\", \"similar objects\": [\"squash\", \"watermelon\", \"cantaloupe\"]}"}, {"object": "puncher", "object_id": 855, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"used to make holes in paper; could be manual or electric; could have different shapes\", \"similar objects\": [\"stapler\", \"hole puncher\", \"paper cutter\"]}"}, {"object": "puppet", "object_id": 856, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"could be made of cloth or wood; could be manipulated by strings or rods; could be used for entertainment\", \"similar objects\": [\"doll\", \"marionette\", \"action figure\"]}"}, {"object": "puppy", "object_id": 857, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, furry, four legs; could have floppy ears; could have a tail; could have a wet nose\", \"similar objects\": [\"kitten\", \"duckling\", \"lamb\"]}"}, {"object": "quesadilla", "object_id": 858, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"tortilla filled with cheese and other ingredients; could be grilled or fried\", \"similar objects\": [\"burrito\", \"taco\", \"enchilada\"]}"}, {"object": "quiche", "object_id": 859, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"pie-like dish; could be filled with vegetables, cheese, and/or meat; could be served hot or cold\", \"similar objects\": [\"tart\", \"tortilla\", \"omelette\"]}"}, {"object": "quilt", "object_id": 860, "gpt3_output": "\n{\"type\": \"bedding item\", \"description\": \"made of multiple layers of fabric; could be filled with cotton, wool, or synthetic fibers; could be used as a blanket or a decorative item\", \"similar objects\": [\"duvet\", \"comforter\", \"blanket\"]}"}, {"object": "rabbit", "object_id": 861, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, furry, long ears; could have white fur; could have a fluffy tail\", \"similar objects\": [\"hare\", \"squirrel\", \"chipmunk\"]}"}, {"object": "race_car", "object_id": 862, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"fast; has a sleek design; could have a sponsor logo\", \"similar objects\": [\"sports car\", \"motorcycle\", \"truck\"]}"}, {"object": "racket", "object_id": 863, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long handle with a round head; could be made of metal or wood; used to hit a ball\", \"similar objects\": [\"bat\", \"club\", \"paddle\"]}"}, {"object": "radar", "object_id": 864, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"used to detect objects from a distance; emits radio waves; can detect motion\", \"similar objects\": [\"sonar\", \"GPS\", \"thermal imaging\"]}"}, {"object": "radiator", "object_id": 865, "gpt3_output": "\n{\"type\": \"heating tool\", \"description\": \"long, metal; could be attached to a wall; could be used to heat a room\", \"similar objects\": [\"heater\", \"air conditioner\", \"furnace\"]}"}, {"object": "radio_receiver", "object_id": 866, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"could be portable; has an antenna; could be used to receive radio signals\", \"similar objects\": [\"television\", \"stereo\", \"walkie-talkie\"]}"}, {"object": "radish", "object_id": 867, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round, red, has a green stem; could be sliced into thin pieces; could be eaten raw\", \"similar objects\": [\"carrot\", \"turnip\", \"beetroot\"]}"}, {"object": "raft", "object_id": 868, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"floats on water; could be made of wood or plastic; could be used for recreation or transportation\", \"similar objects\": [\"canoe\", \"kayak\", \"boat\"]}"}, {"object": "rag_doll", "object_id": 869, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"stuffed fabric doll; could have button eyes and yarn hair; could be dressed in clothes\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"action figure\"]}"}, {"object": "raincoat", "object_id": 870, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"waterproof; could be made of plastic or rubber; could have a hood\", \"similar objects\": [\"umbrella\", \"jacket\", \"rain boots\"]}"}, {"object": "ram_(animal)", "object_id": 871, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, horned mammal; typically brown or gray in color; males have large horns on their heads\", \"similar objects\": [\"goat\", \"sheep\", \"bighorn sheep\"]}"}, {"object": "raspberry", "object_id": 872, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"red, small, round; has a hollow center; could be sweet or tart\", \"similar objects\": [\"strawberry\", \"blackberry\", \"blueberry\"]}"}, {"object": "rat", "object_id": 873, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, long-tailed rodent; usually gray or brown in color; has a pointed snout\", \"similar objects\": [\"mouse\", \"hamster\", \"squirrel\"]}"}, {"object": "razorblade", "object_id": 874, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"sharp, thin, and long; could be used for shaving\", \"similar objects\": [\"scissors\", \"knife\", \"clipper\"]}"}, {"object": "reamer_(juicer)", "object_id": 875, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"cylindrical; has a handle; used to extract juice from fruits and vegetables\", \"similar objects\": [\"citrus press\", \"juicer\", \"blender\"]}"}, {"object": "rearview_mirror", "object_id": 876, "gpt3_output": "\n{\"type\": \"automotive tool\", \"description\": \"attached to the windshield; used to see the back of the car\", \"similar objects\": [\"side mirror\", \"rearview camera\", \"GPS navigation system\"]}"}, {"object": "receipt", "object_id": 877, "gpt3_output": "\n{\"type\": \"document\", \"description\": \"paper or electronic document; contains information about a transaction\", \"similar objects\": [\"invoice\", \"bill\", \"statement\"]}"}, {"object": "recliner", "object_id": 878, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"large, comfortable chair; could have a footrest; could be reclined\", \"similar objects\": [\"sofa\", \"loveseat\", \"armchair\"]}"}, {"object": "record_player", "object_id": 879, "gpt3_output": "\n{\"type\": \"audio device\", \"description\": \"has a turntable; could have a needle; could have speakers\", \"similar objects\": [\"stereo system\", \"boombox\", \"CD player\"]}"}, {"object": "reflector", "object_id": 880, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of metal; used to reflect light\", \"similar objects\": [\"mirror\", \"flashlight\", \"lantern\"]}"}, {"object": "remote_control", "object_id": 881, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, rectangular; has buttons; could be used to control other electronic devices\", \"similar objects\": [\"game controller\", \"keyboard\", \"mouse\"]}"}, {"object": "rhinoceros", "object_id": 882, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, gray, has a horn; could have thick skin\", \"similar objects\": [\"elephant\", \"hippopotamus\", \"giraffe\"]}"}, {"object": "rib_(food)", "object_id": 883, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"long, thin, could be cooked in a variety of ways; could be served with sauces\", \"similar objects\": [\"bacon\", \"pork chop\", \"beef rib\"]}"}, {"object": "rifle", "object_id": 884, "gpt3_output": "\n{\"type\": \"weapon\", \"description\": \"long; has a trigger; could be used for hunting\", \"similar objects\": [\"pistol\", \"shotgun\", \"machine gun\"]}"}, {"object": "ring", "object_id": 885, "gpt3_output": "\n{\"type\": \"jewelry\", \"description\": \"circular; could be made of gold, silver, or other metals; could have gemstones\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}"}, {"object": "river_boat", "object_id": 886, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"long and narrow; could have a motor; could be used for transportation or recreation\", \"similar objects\": [\"canoe\", \"kayak\", \"yacht\"]}"}, {"object": "road_map", "object_id": 887, "gpt3_output": "\n{\"type\": \"navigation tool\", \"description\": \"paper map; could show roads, cities, and other landmarks\", \"similar objects\": [\"GPS\", \"compass\", \"satellite map\"]}"}, {"object": "robe", "object_id": 888, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long, loose-fitting garment; could be made of cotton, silk, or other fabrics; could have a belt or sash\", \"similar objects\": [\"dress\", \"tunic\", \"caftan\"]}"}, {"object": "rocking_chair", "object_id": 889, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has two curved bands connected to a seat and backrest; could be made of wood or metal; could be painted in different colors\", \"similar objects\": [\"armchair\", \"sofa\", \"recliner\"]}"}, {"object": "rodent", "object_id": 890, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, four-legged mammal; could have a long tail; could have fur or hair\", \"similar objects\": [\"mouse\", \"rat\", \"squirrel\"]}"}, {"object": "roller_skate", "object_id": 891, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"wheels attached to a shoe; could have two or four wheels; could have brakes\", \"similar objects\": [\"ice skate\", \"scooter\", \"skateboard\"]}"}, {"object": "Rollerblade", "object_id": 892, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"wheels attached to a boot; used for skating\", \"similar objects\": [\"skateboard\", \"ice skates\", \"inline skates\"]}"}, {"object": "rolling_pin", "object_id": 893, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"cylindrical; could be made of wood or metal; used for rolling dough\", \"similar objects\": [\"pastry cutter\", \"spatula\", \"whisk\"]}"}, {"object": "root_beer", "object_id": 894, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"brown, carbonated, sweet; could be served in a mug\", \"similar objects\": [\"cola\", \"ginger ale\", \"lemonade\"]}"}, {"object": "router_(computer_equipment)", "object_id": 895, "gpt3_output": "\n{\"type\": \"computer equipment\", \"description\": \"small box; has multiple ports; could be connected to a modem\", \"similar objects\": [\"modem\", \"switch\", \"hub\"]}"}, {"object": "rubber_band", "object_id": 896, "gpt3_output": "\n{\"type\": \"stationery item\", \"description\": \"elastic; could be used to bind items together\", \"similar objects\": [\"paper clip\", \"binder clip\", \"staple\"]}"}, {"object": "runner_(carpet)", "object_id": 897, "gpt3_output": "\n{\"type\": \"floor covering\", \"description\": \"long, thin, could be made of fabric or plastic; could have patterns or designs; could be used to cover floors\", \"similar objects\": [\"rug\", \"mat\", \"carpet\"]}"}, {"object": "plastic_bag", "object_id": 898, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"transparent; could be sealed; could be used for carrying items\", \"similar objects\": [\"paper bag\", \"tote bag\", \"backpack\"]}"}, {"object": "saddle_(on_an_animal)", "object_id": 899, "gpt3_output": "\n{\"type\": \"equipment\", \"description\": \"leather; used to sit on an animal; has straps and buckles\", \"similar objects\": [\"bridle\", \"halter\", \"harness\"]}"}, {"object": "saddle_blanket", "object_id": 900, "gpt3_output": "\n{\"type\": \"horse accessory\", \"description\": \"rectangular; made of wool or cotton; used to protect the horse's back\", \"similar objects\": [\"saddle pad\", \"girth\", \"bridle\"]}"}, {"object": "saddlebag", "object_id": 901, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"attached to a saddle; could be made of leather; could be used to store items\", \"similar objects\": [\"pannier\", \"saddlebag cover\", \"saddlebag liner\"]}"}, {"object": "safety_pin", "object_id": 902, "gpt3_output": "\n{\"type\": \"fastening tool\", \"description\": \"has a sharp point and a round clasp; could be used to secure fabric\", \"similar objects\": [\"paper clip\", \"bobby pin\", \"clothespin\"]}"}, {"object": "sail", "object_id": 903, "gpt3_output": "\n{\"type\": \"nautical tool\", \"description\": \"triangular; used to catch wind; could be attached to a boat\", \"similar objects\": [\"mast\", \"rudder\", \"anchor\"]}"}, {"object": "salad", "object_id": 904, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"a mixture of vegetables, fruits, and other ingredients; could be served with dressing\", \"similar objects\": [\"soup\", \"sandwich\", \"stir-fry\"]}"}, {"object": "salad_plate", "object_id": 905, "gpt3_output": "\n{\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could be used to serve salad\", \"similar objects\": [\"dinner plate\", \"soup bowl\", \"teacup\"]}"}, {"object": "salami", "object_id": 906, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"sliced, cured, fermented meat; could be made of pork, beef, or turkey; could be served as a sandwich topping\", \"similar objects\": [\"pepperoni\", \"ham\", \"bacon\"]}"}, {"object": "salmon_(fish)", "object_id": 907, "gpt3_output": "\n{\"type\": \"fish\", \"description\": \"silver-pinkish color; could have black spots; could have a long body; could have a forked tail\", \"similar objects\": [\"trout\", \"cod\", \"tuna\"]}"}, {"object": "salmon_(food)", "object_id": 908, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"pinkish-orange, flaky, oily fish; could be served grilled, smoked, or raw; could be served with lemon and herbs\", \"similar objects\": [\"tuna\", \"cod\", \"halibut\"]}"}, {"object": "salsa", "object_id": 909, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"a type of sauce; could be made of tomatoes, onions, peppers, and other spices; could be served with chips or tacos\", \"similar objects\": [\"guacamole\", \"queso\", \"pico de gallo\"]}"}, {"object": "saltshaker", "object_id": 910, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"cylindrical; has a lid; could be filled with salt\", \"similar objects\": [\"pepper shaker\", \"sugar shaker\", \"spice shaker\"]}"}, {"object": "sandal_(type_of_shoe)", "object_id": 911, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"open-toed; could have straps; could be made of leather or fabric; could have a heel\", \"similar objects\": [\"flip-flop\", \"slipper\", \"sneaker\"]}"}, {"object": "sandwich", "object_id": 912, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"two slices of bread with filling in between; could be cut into triangles; could be served cold or hot\", \"similar objects\": [\"burger\", \"wrap\", \"taco\"]}"}, {"object": "satchel", "object_id": 913, "gpt3_output": "\n{\"type\": \"bag\", \"description\": \"rectangular; could be made of leather; has a strap\", \"similar objects\": [\"backpack\", \"briefcase\", \"purse\"]}"}, {"object": "saucepan", "object_id": 914, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"shallow, round, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "saucer", "object_id": 915, "gpt3_output": "\n{\"type\": \"tableware\", \"description\": \"round; usually used for holding cups; could be made of ceramic or metal\", \"similar objects\": [\"plate\", \"bowl\", \"cup\"]}"}, {"object": "sausage", "object_id": 916, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"cylindrical; could be made of pork, beef, or other meats; could be grilled or boiled\", \"similar objects\": [\"hot dog\", \"bratwurst\", \"kielbasa\"]}"}, {"object": "sawhorse", "object_id": 917, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"wooden structure with two legs and a flat top; used to support a board or plank for sawing\", \"similar objects\": [\"workbench\", \"saw table\", \"saw stand\"]}"}, {"object": "saxophone", "object_id": 918, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long, brass; has a curved neck; has keys\", \"similar objects\": [\"clarinet\", \"trumpet\", \"flute\"]}"}, {"object": "scale_(measuring_instrument)", "object_id": 919, "gpt3_output": "\n{\"type\": \"measuring instrument\", \"description\": \"used to measure weight; could have a digital display; could have two plates\", \"similar objects\": [\"ruler\", \"tape measure\", \"thermometer\"]}"}, {"object": "scarecrow", "object_id": 920, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"made of straw; has a hat and clothes; could have a face\", \"similar objects\": [\"puppet\", \"mannequin\", \"statue\"]}"}, {"object": "scarf", "object_id": 921, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of wool or cotton; could be worn around the neck\", \"similar objects\": [\"shawl\", \"tie\", \"hat\"]}"}, {"object": "school_bus", "object_id": 922, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, yellow, has a stop sign; could have multiple doors\", \"similar objects\": [\"van\", \"truck\", \"minibus\"]}"}, {"object": "scissors", "object_id": 923, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"two blades connected by a pivot; used for cutting\", \"similar objects\": [\"knife\", \"pliers\", \"tweezers\"]}"}, {"object": "scoreboard", "object_id": 924, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"large display board; could be digital or manual; used to show scores\", \"similar objects\": [\"stopwatch\", \"whistle\", \"net\"]}"}, {"object": "scraper", "object_id": 925, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"flat, rectangular; could have a handle; could be used for scraping surfaces\", \"similar objects\": [\"spatula\", \"trowel\", \"paint scraper\"]}"}, {"object": "screwdriver", "object_id": 926, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle with a flat or cross-shaped tip\", \"similar objects\": [\"hammer\", \"pliers\", \"wrench\"]}"}, {"object": "scrubbing_brush", "object_id": 927, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"long handle; bristles on one end; could be used for scrubbing surfaces\", \"similar objects\": [\"sponge\", \"mop\", \"broom\"]}"}, {"object": "sculpture", "object_id": 928, "gpt3_output": "\n{\"type\": \"artwork\", \"description\": \"three-dimensional artwork; could be made of metal, stone, wood, or other materials; could be abstract or representational\", \"similar objects\": [\"painting\", \"drawing\", \"photograph\"]}"}, {"object": "seabird", "object_id": 929, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"can fly; lives near the sea; could have colorful feathers\", \"similar objects\": [\"penguin\", \"albatross\", \"gull\"]}"}, {"object": "seahorse", "object_id": 930, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"elongated body; could have a curled tail; could have a head like a horse; could have a variety of colors\", \"similar objects\": [\"starfish\", \"jellyfish\", \"octopus\"]}"}, {"object": "seaplane", "object_id": 931, "gpt3_output": "\n{\"type\": \"aircraft\", \"description\": \"has floats instead of wheels; could take off and land on water\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}"}, {"object": "seashell", "object_id": 932, "gpt3_output": "\n{\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be found on the beach; could be used as decoration\", \"similar objects\": [\"starfish\", \"conch\", \"sand dollar\"]}"}, {"object": "sewing_machine", "object_id": 933, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"has a needle and thread; could be used to stitch fabrics\", \"similar objects\": [\"scissors\", \"iron\", \"sewing kit\"]}"}, {"object": "shaker", "object_id": 934, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"cylindrical; could be used to mix ingredients; could have a lid\", \"similar objects\": [\"blender\", \"mixer\", \"grinder\"]}"}, {"object": "shampoo", "object_id": 935, "gpt3_output": "\n{\"type\": \"cleaning product\", \"description\": \"liquid; used for washing hair; could be scented\", \"similar objects\": [\"conditioner\", \"soap\", \"body wash\"]}"}, {"object": "shark", "object_id": 936, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, grayish-blue; has a pointed snout; could have sharp teeth; could have a dorsal fin\", \"similar objects\": [\"whale\", \"dolphin\", \"stingray\"]}"}, {"object": "sharpener", "object_id": 937, "gpt3_output": "\n{\"type\": \"stationery tool\", \"description\": \"small; has two slots for pencils; could be made of plastic or metal\", \"similar objects\": [\"eraser\", \"ruler\", \"stapler\"]}"}, {"object": "Sharpie", "object_id": 938, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"permanent marker; has a fine tip; could be used to write on paper, glass, and other surfaces\", \"similar objects\": [\"marker\", \"pen\", \"pencil\"]}"}, {"object": "shaver_(electric)", "object_id": 939, "gpt3_output": "\n{\"type\": \"electrical tool\", \"description\": \"used to shave facial hair; could be corded or cordless; could have a trimmer attachment\", \"similar objects\": [\"razor\", \"clipper\", \"epilator\"]}"}, {"object": "shaving_cream", "object_id": 940, "gpt3_output": "\n{\"type\": \"personal care product\", \"description\": \"white, creamy; could be used for shaving\", \"similar objects\": [\"shaving gel\", \"shaving foam\", \"aftershave lotion\"]}"}, {"object": "shawl", "object_id": 941, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"long, rectangular; could be made of wool; could be worn around the shoulders\", \"similar objects\": [\"scarf\", \"wrap\", \"poncho\"]}"}, {"object": "shears", "object_id": 942, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"sharp blades; used for cutting\", \"similar objects\": [\"scissors\", \"clippers\", \"pliers\"]}"}, {"object": "sheep", "object_id": 943, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"white, wooly fur; has horns; could be found in herds\", \"similar objects\": [\"goat\", \"cow\", \"llama\"]}"}, {"object": "shepherd_dog", "object_id": 944, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"medium-sized; has a thick coat; could be used for herding sheep\", \"similar objects\": [\"collie\", \"husky\", \"labrador\"]}"}, {"object": "sherbert", "object_id": 945, "gpt3_output": "\n{\"type\": \"dessert\", \"description\": \"frozen, sweet, creamy; could be served in a cone or cup\", \"similar objects\": [\"ice cream\", \"gelato\", \"frozen yogurt\"]}"}, {"object": "shield", "object_id": 946, "gpt3_output": "\n{\"type\": \"protective tool\", \"description\": \"round; could be made of metal; could have a handle\", \"similar objects\": [\"helmet\", \"armor\", \"sword\"]}"}, {"object": "shirt", "object_id": 947, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long-sleeved; could have buttons; could have a collar\", \"similar objects\": [\"blouse\", \"dress\", \"jacket\"]}"}, {"object": "shoe", "object_id": 948, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"made of leather or fabric; has a sole; could have laces\", \"similar objects\": [\"sneaker\", \"boot\", \"sandal\"]}"}, {"object": "shopping_bag", "object_id": 949, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of paper or plastic; could have handles\", \"similar objects\": [\"tote bag\", \"backpack\", \"suitcase\"]}"}, {"object": "shopping_cart", "object_id": 950, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"wheeled; has a handle; could be filled with items\", \"similar objects\": [\"hand truck\", \"stroller\", \"wheelbarrow\"]}"}, {"object": "short_pants", "object_id": 951, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"knee-length; could be made of cotton or denim; could have pockets\", \"similar objects\": [\"shorts\", \"capris\", \"jeans\"]}"}, {"object": "shot_glass", "object_id": 952, "gpt3_output": "\n{\"type\": \"drinking tool\", \"description\": \"small, cylindrical, usually made of glass; could have a handle\", \"similar objects\": [\"mug\", \"cup\", \"tumbler\"]}"}, {"object": "shoulder_bag", "object_id": 953, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small bag; could be worn on the shoulder; could be made of leather or fabric\", \"similar objects\": [\"backpack\", \"purse\", \"tote bag\"]}"}, {"object": "shovel", "object_id": 954, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle; metal or plastic blade; used for digging\", \"similar objects\": [\"rake\", \"spade\", \"hoe\"]}"}, {"object": "shower_head", "object_id": 955, "gpt3_output": "\n{\"type\": \"bathroom fixture\", \"description\": \"attached to the wall; could be adjustable; could have multiple settings\", \"similar objects\": [\"faucet\", \"toilet\", \"bathtub\"]}"}, {"object": "shower_cap", "object_id": 956, "gpt3_output": "\n{\"type\": \"bathroom accessory\", \"description\": \"elastic; made of waterproof material; could be used to cover hair while showering\", \"similar objects\": [\"bath cap\", \"shower hat\", \"shower visor\"]}"}, {"object": "shower_curtain", "object_id": 957, "gpt3_output": "\n{\"type\": \"bathroom accessory\", \"description\": \"long, thin, made of fabric; could be transparent or opaque; could be hung on a rod\", \"similar objects\": [\"bath mat\", \"towel\", \"bathrobe\"]}"}, {"object": "shredder_(for_paper)", "object_id": 958, "gpt3_output": "\n\n{\"type\": \"office tool\", \"description\": \"machine used to shred paper into small pieces; could have a handle\", \"similar objects\": [\"stapler\", \"hole puncher\", \"paper cutter\"]}"}, {"object": "signboard", "object_id": 959, "gpt3_output": "\n{\"type\": \"advertisement tool\", \"description\": \"could be made of wood or metal; could be rectangular or square; could have words or images printed on it\", \"similar objects\": [\"billboard\", \"poster\", \"banner\"]}"}, {"object": "silo", "object_id": 960, "gpt3_output": "\n{\"type\": \"storage structure\", \"description\": \"cylindrical; could be made of metal or concrete; could be used to store grain or other materials\", \"similar objects\": [\"grain bin\", \"hopper\", \"tank\"]}"}, {"object": "sink", "object_id": 961, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"has a basin; could have a faucet; could be made of stainless steel\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}"}, {"object": "skateboard", "object_id": 962, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could be used for skateboarding\", \"similar objects\": [\"scooter\", \"rollerblades\", \"longboard\"]}"}, {"object": "skewer", "object_id": 963, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"long, thin metal rod; used to hold food together while cooking\", \"similar objects\": [\"spatula\", \"tongs\", \"whisk\"]}"}, {"object": "ski", "object_id": 964, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"long, thin, curved; used for skiing on snow\", \"similar objects\": [\"snowboard\", \"skates\", \"sled\"]}"}, {"object": "ski_boot", "object_id": 965, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"hard, waterproof; has a buckle; could be used for skiing\", \"similar objects\": [\"hiking boot\", \"snowboard boot\", \"ice skate\"]}"}, {"object": "ski_parka", "object_id": 966, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long, insulated coat; usually hooded; often waterproof; typically has multiple pockets\", \"similar objects\": [\"ski pants\", \"snow boots\", \"ski gloves\"]}"}, {"object": "ski_pole", "object_id": 967, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, thin, metal pole; could have a handle and a strap\", \"similar objects\": [\"ski boots\", \"ski goggles\", \"ski jacket\"]}"}, {"object": "skirt", "object_id": 968, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"hangs from the waist; could be pleated or flared; could be made of different fabrics\", \"similar objects\": [\"dress\", \"pants\", \"shorts\"]}"}, {"object": "skullcap", "object_id": 969, "gpt3_output": "\n{\"type\": \"headwear\", \"description\": \"round; could be made of cloth; could have a visor\", \"similar objects\": [\"baseball cap\", \"beanie\", \"beret\"]}"}, {"object": "sled", "object_id": 970, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"wooden; has two handles; could be used on snow\", \"similar objects\": [\"toboggan\", \"bobsled\", \"snowboard\"]}"}, {"object": "sleeping_bag", "object_id": 971, "gpt3_output": "\n{\"type\": \"outdoor equipment\", \"description\": \"long, rectangular; could be zipped up; could be filled with insulation material\", \"similar objects\": [\"tent\", \"backpack\", \"camping stove\"]}"}, {"object": "sling_(bandage)", "object_id": 972, "gpt3_output": "\n{\"type\": \"medical tool\", \"description\": \"long, thin, stretchable fabric; used to support an injured arm or shoulder\", \"similar objects\": [\"bandage\", \"splint\", \"cast\"]}"}, {"object": "slipper_(footwear)", "object_id": 973, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"soft, lightweight, slip-on; could be made of fabric or leather; could have a sole\", \"similar objects\": [\"sandal\", \"mule\", \"loafer\"]}"}, {"object": "smoothie", "object_id": 974, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"blended fruit and/or vegetable drink; could be served cold; could be made with yogurt or milk\", \"similar objects\": [\"juice\", \"milkshake\", \"smoothie bowl\"]}"}, {"object": "snake", "object_id": 975, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"long, slender body; could be scaly; could be venomous\", \"similar objects\": [\"lizard\", \"iguana\", \"crocodile\"]}"}, {"object": "snowboard", "object_id": 976, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, flat board; could have bindings; could be used for snowboarding\", \"similar objects\": [\"skis\", \"surfboard\", \"skateboard\"]}"}, {"object": "snowman", "object_id": 977, "gpt3_output": "\n{\"type\": \"sculpture\", \"description\": \"made of snow; has a carrot nose; has two coal eyes; has a hat and scarf\", \"similar objects\": [\"snow angel\", \"snow fort\", \"snow castle\"]}"}, {"object": "snowmobile", "object_id": 978, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long and narrow; has skis and tracks; could have a windshield\", \"similar objects\": [\"ATV\", \"snow scooter\", \"snow bike\"]}"}, {"object": "soap", "object_id": 979, "gpt3_output": "\n{\"type\": \"cleaning product\", \"description\": \"solid; could be white or colorful; could be in bar or liquid form\", \"similar objects\": [\"shampoo\", \"detergent\", \"toilet cleaner\"]}"}, {"object": "soccer_ball", "object_id": 980, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"round; made of leather; has a pattern of pentagons and hexagons\", \"similar objects\": [\"basketball\", \"baseball\", \"football\"]}"}, {"object": "sock", "object_id": 981, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"usually made of cotton or wool; could be ankle-length or knee-length; could have stripes or patterns\", \"similar objects\": [\"glove\", \"hat\", \"scarf\"]}"}, {"object": "sofa", "object_id": 982, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"long, upholstered, has cushions\", \"similar objects\": [\"armchair\", \"loveseat\", \"chaise lounge\"]}"}, {"object": "softball", "object_id": 983, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"round; made of leather; has a stitching\", \"similar objects\": [\"baseball\", \"tennis ball\", \"golf ball\"]}"}, {"object": "solar_array", "object_id": 984, "gpt3_output": "\n{\"type\": \"energy source\", \"description\": \"arrangement of solar panels; used to generate electricity\", \"similar objects\": [\"wind turbine\", \"hydroelectric generator\", \"geothermal power plant\"]}"}, {"object": "sombrero", "object_id": 985, "gpt3_output": "\n{\"type\": \"headwear\", \"description\": \"wide-brimmed hat; could be made of straw; could have a colorful band around the crown\", \"similar objects\": [\"fedora\", \"panama hat\", \"cowboy hat\"]}"}, {"object": "soup", "object_id": 986, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"liquid; could be made of vegetables, meat, or fish; could be served hot or cold\", \"similar objects\": [\"stew\", \"porridge\", \"broth\"]}"}, {"object": "soup_bowl", "object_id": 987, "gpt3_output": "\n{\"type\": \"dining ware\", \"description\": \"round; could be made of ceramic; could have a handle\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}"}, {"object": "soupspoon", "object_id": 988, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"long handle; round bowl; could be made of metal or plastic\", \"similar objects\": [\"fork\", \"knife\", \"spoon\"]}"}, {"object": "sour_cream", "object_id": 989, "gpt3_output": "\n{\"type\": \"dairy product\", \"description\": \"thick, creamy, white; has a tangy taste\", \"similar objects\": [\"yogurt\", \"cottage cheese\", \"cream cheese\"]}"}, {"object": "soya_milk", "object_id": 990, "gpt3_output": "\n{\"type\": \"beverage\", \"description\": \"white, creamy, made from soya beans\", \"similar objects\": [\"almond milk\", \"rice milk\", \"oat milk\"]}"}, {"object": "space_shuttle", "object_id": 991, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"aerodynamic shape; has wings and a tail; could be used for space exploration\", \"similar objects\": [\"rocket\", \"satellite\", \"space station\"]}"}, {"object": "sparkler_(fireworks)", "object_id": 992, "gpt3_output": "\n{\"type\": \"firework\", \"description\": \"long, thin, cylindrical; emits sparks when lit; could be held in hand\", \"similar objects\": [\"firecracker\", \"rocket\", \"smoke bomb\"]}"}, {"object": "spatula", "object_id": 993, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"flat, long handle; could be made of metal or plastic; could be used for flipping food\", \"similar objects\": [\"tongs\", \"spoon\", \"ladle\"]}"}, {"object": "spear", "object_id": 994, "gpt3_output": "\n{\"type\": \"weapon\", \"description\": \"long, pointed, could be made of metal or wood\", \"similar objects\": [\"sword\", \"dagger\", \"axe\"]}"}, {"object": "spectacles", "object_id": 995, "gpt3_output": "\n{\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be made of metal or plastic; could be used for vision correction\", \"similar objects\": [\"glasses\", \"sunglasses\", \"goggles\"]}"}, {"object": "spice_rack", "object_id": 996, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"shelves for storing spices; could be made of wood or metal; could be wall-mounted or free-standing\", \"similar objects\": [\"knife block\", \"utensil holder\", \"condiment caddy\"]}"}, {"object": "spider", "object_id": 997, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"eight legs; could have a web; could be black or brown\", \"similar objects\": [\"scorpion\", \"insect\", \"centipede\"]}"}, {"object": "crawfish", "object_id": 998, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, red, shellfish; could have claws; could be boiled and eaten\", \"similar objects\": [\"lobster\", \"crab\", \"shrimp\"]}"}, {"object": "sponge", "object_id": 999, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"soft, absorbent, usually rectangular; could be used for cleaning surfaces\", \"similar objects\": [\"cloth\", \"brush\", \"scrubber\"]}"}, {"object": "spoon", "object_id": 1000, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"long handle; round bowl; could be made of metal or plastic\", \"similar objects\": [\"fork\", \"knife\", \"chopsticks\"]}"}, {"object": "sportswear", "object_id": 1001, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"comfortable; could be made of breathable fabric; could be designed for specific sports activities\", \"similar objects\": [\"athletic wear\", \"gym wear\", \"activewear\"]}"}, {"object": "spotlight", "object_id": 1002, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"concentrated beam of light; could be used for theatrical performances\", \"similar objects\": [\"torch\", \"lantern\", \"flashlight\"]}"}, {"object": "squid_(food)", "object_id": 1003, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"tentacles; could be cooked in various ways; could be served with sauces\", \"similar objects\": [\"octopus\", \"calamari\", \"cuttlefish\"]}"}, {"object": "squirrel", "object_id": 1004, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"brown fur; bushy tail; could climb trees; could store food\", \"similar objects\": [\"chipmunk\", \"rabbit\", \"rat\"]}"}, {"object": "stagecoach", "object_id": 1005, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, box-shaped; could be pulled by horses; could have four or more wheels\", \"similar objects\": [\"wagon\", \"carriage\", \"cart\"]}"}, {"object": "stapler_(stapling_machine)", "object_id": 1006, "gpt3_output": "\n{\"type\": \"office tool\", \"description\": \"small, rectangular; used to staple papers together\", \"similar objects\": [\"hole puncher\", \"paper clip\", \"tape dispenser\"]}"}, {"object": "starfish", "object_id": 1007, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"five-pointed; could be orange, red, or purple; could have spines on its body\", \"similar objects\": [\"sea urchin\", \"crab\", \"jellyfish\"]}"}, {"object": "statue_(sculpture)", "object_id": 1008, "gpt3_output": "\n{\"type\": \"artwork\", \"description\": \"could be made of stone, metal, wood, or other materials; could be of a person, animal, or other object; could be realistic or abstract\", \"similar objects\": [\"painting\", \"drawing\", \"photograph\"]}"}, {"object": "steak_(food)", "object_id": 1009, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"thick cut of beef; could be grilled or pan-fried; could be served with sides\", \"similar objects\": [\"chicken\", \"pork\", \"fish\"]}"}, {"object": "steak_knife", "object_id": 1010, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"sharp blade; could have a wooden handle; could be serrated\", \"similar objects\": [\"butter knife\", \"chef knife\", \"paring knife\"]}"}, {"object": "steering_wheel", "object_id": 1011, "gpt3_output": "\n{\"type\": \"vehicle part\", \"description\": \"round; has a grip; could be used to control the direction of the vehicle\", \"similar objects\": [\"pedals\", \"gear shift\", \"dashboard\"]}"}, {"object": "stepladder", "object_id": 1012, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"has two sides with steps; could be folded; could be used to reach high places\", \"similar objects\": [\"ladder\", \"stool\", \"scaffolding\"]}"}, {"object": "step_stool", "object_id": 1013, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has two or more steps; could be foldable; could be made of wood or metal\", \"similar objects\": [\"ladder\", \"stool\", \"chair\"]}"}, {"object": "stereo_(sound_system)", "object_id": 1014, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"could have two or more speakers; could have a CD player; could have a radio\", \"similar objects\": [\"boombox\", \"home theater system\", \"turntable\"]}"}, {"object": "stew", "object_id": 1015, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"a combination of vegetables, meat, and broth; cooked slowly over low heat\", \"similar objects\": [\"soup\", \"casserole\", \"stir-fry\"]}"}, {"object": "stirrer", "object_id": 1016, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"long, thin, has a handle; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"fork\", \"whisk\"]}"}, {"object": "stirrup", "object_id": 1017, "gpt3_output": "\n{\"type\": \"equestrian tool\", \"description\": \"metal loop; used to help mount a horse\", \"similar objects\": [\"saddle\", \"bridle\", \"bit\"]}"}, {"object": "stool", "object_id": 1018, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has three or four legs; could be made of wood or metal; could be used as a seat or a step\", \"similar objects\": [\"chair\", \"bench\", \"ottoman\"]}"}, {"object": "stop_sign", "object_id": 1019, "gpt3_output": "\n{\"type\": \"traffic sign\", \"description\": \"octagonal; red background with white letters; could be mounted on a pole\", \"similar objects\": [\"yield sign\", \"speed limit sign\", \"no parking sign\"]}"}, {"object": "brake_light", "object_id": 1020, "gpt3_output": "\n{\"type\": \"vehicle part\", \"description\": \"red; usually located at the back of the car; used to indicate braking\", \"similar objects\": [\"headlight\", \"turn signal\", \"taillight\"]}"}, {"object": "stove", "object_id": 1021, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"has burners and oven; could be electric or gas powered; could have a timer\", \"similar objects\": [\"oven\", \"microwave\", \"grill\"]}"}, {"object": "strainer", "object_id": 1022, "gpt3_output": "\n{\"type\": \"kitchen tool\", \"description\": \"has a handle; has a mesh; used to strain liquids\", \"similar objects\": [\"colander\", \"sieve\", \"skimmer\"]}"}, {"object": "strap", "object_id": 1023, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"long, thin, could be made of leather or fabric; could be used to hold items together\", \"similar objects\": [\"belt\", \"rope\", \"string\"]}"}, {"object": "straw_(for_drinking)", "object_id": 1024, "gpt3_output": "\n{\"type\": \"drinking tool\", \"description\": \"long, thin, cylindrical; could be made of plastic or paper; could have a bendable top\", \"similar objects\": [\"straw (for decoration)\", \"strawberry\", \"straw hat\"]}"}, {"object": "strawberry", "object_id": 1025, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"red, small, has a stem and seeds\", \"similar objects\": [\"raspberry\", \"blueberry\", \"blackberry\"]}"}, {"object": "street_sign", "object_id": 1026, "gpt3_output": "\n{\"type\": \"road sign\", \"description\": \"rectangular; could be made of metal; could have words or symbols on it\", \"similar objects\": [\"traffic light\", \"stop sign\", \"yield sign\"]}"}, {"object": "streetlight", "object_id": 1027, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lamp post\", \"lantern\", \"light bulb\"]}"}, {"object": "string_cheese", "object_id": 1028, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"long, cylindrical; could be yellow or white; could be sliced into pieces; could be made of cow's milk\", \"similar objects\": [\"mozzarella cheese\", \"cheddar cheese\", \"feta cheese\"]}"}, {"object": "stylus", "object_id": 1029, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"long, thin, pointed tip; could be used on touchscreens\", \"similar objects\": [\"pen\", \"pencil\", \"marker\"]}"}, {"object": "subwoofer", "object_id": 1030, "gpt3_output": "\n{\"type\": \"audio equipment\", \"description\": \"large, box-shaped; produces low-frequency sound\", \"similar objects\": [\"speaker\", \"amplifier\", \"headphone\"]}"}, {"object": "sugar_bowl", "object_id": 1031, "gpt3_output": "\n{\"type\": \"kitchenware\", \"description\": \"round; could have a lid; could be made of ceramic or glass\", \"similar objects\": [\"teapot\", \"coffee pot\", \"salt shaker\"]}"}, {"object": "sugarcane_(plant)", "object_id": 1032, "gpt3_output": "\n{\"type\": \"plant\", \"description\": \"tall, green, cylindrical; has a sweet taste; could be used to make sugar\", \"similar objects\": [\"corn\", \"wheat\", \"rice\"]}"}, {"object": "suit_(clothing)", "object_id": 1033, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"two-piece garment; typically includes a jacket and trousers; could be made of wool, cotton, or synthetic fabrics; could be tailored for a specific fit\", \"similar objects\": [\"blazer\", \"tuxedo\", \"dress\"]}"}, {"object": "sunflower", "object_id": 1034, "gpt3_output": "\n{\"type\": \"plant\", \"description\": \"large, yellow flower; has a long stem; has a large center disk\", \"similar objects\": [\"daisy\", \"dandelion\", \"marigold\"]}"}, {"object": "sunglasses", "object_id": 1035, "gpt3_output": "\n{\"type\": \"eyewear\", \"description\": \"dark lenses; could have a frame; could be worn to protect eyes from sunlight\", \"similar objects\": [\"eyeglasses\", \"goggles\", \"safety glasses\"]}"}, {"object": "sunhat", "object_id": 1036, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"wide brim; could be made of straw; could have a chin strap\", \"similar objects\": [\"baseball cap\", \"fedora\", \"beret\"]}"}, {"object": "surfboard", "object_id": 1037, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long and narrow; could be made of foam or fiberglass; could have a fin\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}"}, {"object": "sushi", "object_id": 1038, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"rice and raw fish; could be wrapped in seaweed\", \"similar objects\": [\"sashimi\", \"tempura\", \"udon\"]}"}, {"object": "mop", "object_id": 1039, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"long handle; could have a sponge or cloth head; could be used to clean floors\", \"similar objects\": [\"broom\", \"vacuum cleaner\", \"duster\"]}"}, {"object": "sweat_pants", "object_id": 1040, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"loose-fitting trousers; usually made of cotton or polyester; could have an elastic waistband\", \"similar objects\": [\"joggers\", \"track pants\", \"leggings\"]}"}, {"object": "sweatband", "object_id": 1041, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"elastic band; could be worn on the head or wrist; could be made of cotton or other fabrics\", \"similar objects\": [\"headband\", \"bracelet\", \"hat\"]}"}, {"object": "sweater", "object_id": 1042, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"knitted; could be long-sleeved; could be pullover or cardigan\", \"similar objects\": [\"shirt\", \"jacket\", \"coat\"]}"}, {"object": "sweatshirt", "object_id": 1043, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long-sleeved; could have a hood; could have a zipper or buttons\", \"similar objects\": [\"hoodie\", \"jacket\", \"sweater\"]}"}, {"object": "sweet_potato", "object_id": 1044, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, sweet, orange; could have a rough skin; could be sliced into cubes; could has green leaves\", \"similar objects\": [\"potato\", \"yam\", \"carrot\"]}"}, {"object": "swimsuit", "object_id": 1045, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"one-piece or two-piece; could be made of spandex or nylon; could have straps or halter neck\", \"similar objects\": [\"bikini\", \"tankini\", \"monokini\"]}"}, {"object": "sword", "object_id": 1046, "gpt3_output": "\n{\"type\": \"weapon\", \"description\": \"long, sharp blade; could have a handle; could have a guard\", \"similar objects\": [\"dagger\", \"axe\", \"spear\"]}"}, {"object": "syringe", "object_id": 1047, "gpt3_output": "\n{\"type\": \"medical tool\", \"description\": \"long, thin, hollow tube; could have a plunger; could be used to inject or extract fluids\", \"similar objects\": [\"needle\", \"injection\", \"IV\"]}"}, {"object": "Tabasco_sauce", "object_id": 1048, "gpt3_output": "\n{\"type\": \"condiment\", \"description\": \"red; spicy; made from peppers\", \"similar objects\": [\"hot sauce\", \"sriracha\", \"salsa\"]}"}, {"object": "table-tennis_table", "object_id": 1049, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long and rectangular; has a net in the middle; could be used for playing table tennis\", \"similar objects\": [\"ping pong table\", \"badminton court\", \"volleyball court\"]}"}, {"object": "table", "object_id": 1050, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"flat surface; could have four legs; could be made of wood or metal\", \"similar objects\": [\"chair\", \"desk\", \"sofa\"]}"}, {"object": "table_lamp", "object_id": 1051, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"has a base and a lampshade; could be made of metal or wood; could be powered by electricity or battery\", \"similar objects\": [\"floor lamp\", \"ceiling lamp\", \"desk lamp\"]}"}, {"object": "tablecloth", "object_id": 1052, "gpt3_output": "\n{\"type\": \"textile\", \"description\": \"rectangular; could be made of cotton, linen, or polyester; could be decorated with patterns\", \"similar objects\": [\"napkin\", \"runner\", \"placemat\"]}"}, {"object": "tachometer", "object_id": 1053, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"measures the speed of a rotating shaft; has a needle that moves along a dial\", \"similar objects\": [\"speedometer\", \"odometer\", \"voltmeter\"]}"}, {"object": "taco", "object_id": 1054, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"tortilla filled with meat, vegetables, and cheese; could be served with salsa and sour cream\", \"similar objects\": [\"burrito\", \"enchilada\", \"quesadilla\"]}"}, {"object": "tag", "object_id": 1055, "gpt3_output": "\n{\"type\": \"labeling tool\", \"description\": \"small, rectangular; could be made of paper or plastic; could be attached to an object\", \"similar objects\": [\"label\", \"sticker\", \"badge\"]}"}, {"object": "taillight", "object_id": 1056, "gpt3_output": "\n{\"type\": \"vehicle part\", \"description\": \"red or amber; could be found at the back of a vehicle; could be used to indicate direction\", \"similar objects\": [\"headlight\", \"turn signal\", \"brake light\"]}"}, {"object": "tambourine", "object_id": 1057, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"round; has metal jingles; could be held by hand\", \"similar objects\": [\"drum\", \"maracas\", \"cymbals\"]}"}, {"object": "army_tank", "object_id": 1058, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, heavily armored; has a gun turret; could have tracks\", \"similar objects\": [\"armored personnel carrier\", \"jeep\", \"helicopter\"]}"}, {"object": "tank_(storage_vessel)", "object_id": 1059, "gpt3_output": "\n{\"type\": \"vessel\", \"description\": \"large, cylindrical, made of metal; could have a lid; could be used to store liquids or other materials\", \"similar objects\": [\"barrel\", \"drum\", \"bucket\"]}"}, {"object": "tank_top_(clothing)", "object_id": 1060, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"sleeveless shirt; could be cropped; could have straps; could be loose-fitting\", \"similar objects\": [\"t-shirt\", \"camisole\", \"halter top\"]}"}, {"object": "tape_(sticky_cloth_or_paper)", "object_id": 1061, "gpt3_output": "\n{\"type\": \"adhesive material\", \"description\": \"flexible, sticky, could be used to attach two objects together\", \"similar objects\": [\"glue\", \"velcro\", \"double-sided tape\"]}"}, {"object": "tape_measure", "object_id": 1062, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"long, flexible; could be made of metal or plastic; could be marked with measurements\", \"similar objects\": [\"ruler\", \"yardstick\", \"calipers\"]}"}, {"object": "tapestry", "object_id": 1063, "gpt3_output": "\n{\"type\": \"decorative item\", \"description\": \"large fabric with intricate designs; could be hung on walls\", \"similar objects\": [\"rug\", \"wall hanging\", \"carpet\"]}"}, {"object": "tarp", "object_id": 1064, "gpt3_output": "\n{\"type\": \"protective covering\", \"description\": \"waterproof; could be used to cover objects; could be made of plastic or canvas\", \"similar objects\": [\"canopy\", \"awning\", \"tent\"]}"}, {"object": "tartan", "object_id": 1065, "gpt3_output": "\n{\"type\": \"fabric\", \"description\": \"plaid pattern; usually made of wool; could be used for clothing\", \"similar objects\": [\"tweed\", \"check\", \"gingham\"]}"}, {"object": "tassel", "object_id": 1066, "gpt3_output": "\n{\"type\": \"decorative item\", \"description\": \"hanging ornament; could be made of fabric, yarn, or rope; could be used to decorate clothing, curtains, or furniture\", \"similar objects\": [\"fringe\", \"pom pom\", \"beads\"]}"}, {"object": "tea_bag", "object_id": 1067, "gpt3_output": "\n{\"type\": \"food item\", \"description\": \"small, paper bag filled with tea leaves; could be steeped in hot water\", \"similar objects\": [\"coffee bag\", \"herbal tea bag\", \"tea infuser\"]}"}, {"object": "teacup", "object_id": 1068, "gpt3_output": "\n{\"type\": \"drinking vessel\", \"description\": \"small, round, has a handle; could be made of porcelain or ceramic\", \"similar objects\": [\"mug\", \"cup\", \"glass\"]}"}, {"object": "teakettle", "object_id": 1069, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round; could be made of metal; has a handle and a spout\", \"similar objects\": [\"coffee pot\", \"teapot\", \"water boiler\"]}"}, {"object": "teapot", "object_id": 1070, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round; has a spout and a handle; could be made of ceramic or metal\", \"similar objects\": [\"kettle\", \"coffee pot\", \"thermos\"]}"}, {"object": "teddy_bear", "object_id": 1071, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"stuffed animal; usually has a round face; could have a bow tie; could be brown or other colors\", \"similar objects\": [\"plush toy\", \"stuffed animal\", \"doll\"]}"}, {"object": "telephone", "object_id": 1072, "gpt3_output": "\n{\"type\": \"communication device\", \"description\": \"has a handset; could be cordless; could have a dial pad\", \"similar objects\": [\"cell phone\", \"walkie talkie\", \"intercom\"]}"}, {"object": "telephone_booth", "object_id": 1073, "gpt3_output": "\n{\"type\": \"communication tool\", \"description\": \"enclosed booth; could have a phone inside; could be made of glass or metal\", \"similar objects\": [\"payphone\", \"cellphone\", \"walkie-talkie\"]}"}, {"object": "telephone_pole", "object_id": 1074, "gpt3_output": "\n{\"type\": \"utility pole\", \"description\": \"tall, cylindrical; could have wires attached to it; could have a transformer box on top\", \"similar objects\": [\"power pole\", \"light pole\", \"street light pole\"]}"}, {"object": "telephoto_lens", "object_id": 1075, "gpt3_output": "\n{\"type\": \"photography tool\", \"description\": \"long and cylindrical; could be used to take pictures from a distance\", \"similar objects\": [\"wide-angle lens\", \"macro lens\", \"zoom lens\"]}"}, {"object": "television_camera", "object_id": 1076, "gpt3_output": "\n{\"type\": \"recording device\", \"description\": \"large; could be mounted on a tripod; could be used to record videos\", \"similar objects\": [\"video camera\", \"webcam\", \"camcorder\"]}"}, {"object": "television_set", "object_id": 1077, "gpt3_output": "\n\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could have speakers\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}"}, {"object": "tennis_ball", "object_id": 1078, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"round; yellow and green; made of rubber\", \"similar objects\": [\"soccer ball\", \"basketball\", \"baseball\"]}"}, {"object": "tennis_racket", "object_id": 1079, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be made of wood or metal\", \"similar objects\": [\"badminton racket\", \"squash racket\", \"table tennis racket\"]}"}, {"object": "tequila", "object_id": 1080, "gpt3_output": "\n{\"type\": \"alcoholic beverage\", \"description\": \"clear, distilled spirit made from the blue agave plant; could be served with salt and lime\", \"similar objects\": [\"vodka\", \"rum\", \"whiskey\"]}"}, {"object": "thermometer", "object_id": 1081, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"long, thin; could be made of glass; could measure temperature\", \"similar objects\": [\"barometer\", \"hygrometer\", \"manometer\"]}"}, {"object": "thermos_bottle", "object_id": 1082, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could keep liquids hot or cold\", \"similar objects\": [\"water bottle\", \"coffee mug\", \"lunch box\"]}"}, {"object": "thermostat", "object_id": 1083, "gpt3_output": "\n{\"type\": \"temperature control device\", \"description\": \"electronic device used to regulate temperature; could be digital or analog; could be programmable\", \"similar objects\": [\"air conditioner\", \"heater\", \"humidifier\"]}"}, {"object": "thimble", "object_id": 1084, "gpt3_output": "\n{\"type\": \"sewing tool\", \"description\": \"small, metal, finger-shaped; used to push needles through fabric\", \"similar objects\": [\"needle\", \"thread\", \"scissors\"]}"}, {"object": "thread", "object_id": 1085, "gpt3_output": "\n{\"type\": \"material\", \"description\": \"thin, long, could be made of cotton, silk, or nylon\", \"similar objects\": [\"yarn\", \"string\", \"rope\"]}"}, {"object": "thumbtack", "object_id": 1086, "gpt3_output": "\n{\"type\": \"stationery item\", \"description\": \"small, sharp, metal object; could be used to attach papers to a wall\", \"similar objects\": [\"pushpin\", \"paperclip\", \"staple\"]}"}, {"object": "tiara", "object_id": 1087, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"circular headpiece; could be made of metal or jewels; could be decorated with gems\", \"similar objects\": [\"crown\", \"headband\", \"hat\"]}"}, {"object": "tiger", "object_id": 1088, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"orange with black stripes; has a long tail; could be found in the wild\", \"similar objects\": [\"lion\", \"leopard\", \"jaguar\"]}"}, {"object": "tights_(clothing)", "object_id": 1089, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"tight-fitting garment; usually made of nylon or spandex; could be worn by both men and women\", \"similar objects\": [\"leggings\", \"stockings\", \"pantyhose\"]}"}, {"object": "timer", "object_id": 1090, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"could be digital or analog; could be used to measure time intervals\", \"similar objects\": [\"stopwatch\", \"clock\", \"thermometer\"]}"}, {"object": "tinfoil", "object_id": 1091, "gpt3_output": "\n{\"type\": \"material\", \"description\": \"thin, silver, shiny; could be used for wrapping food\", \"similar objects\": [\"aluminum foil\", \"plastic wrap\", \"parchment paper\"]}"}, {"object": "tinsel", "object_id": 1092, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"long, thin, metallic strips; could be used to decorate Christmas trees\", \"similar objects\": [\"lights\", \"ornaments\", \"garland\"]}"}, {"object": "tissue_paper", "object_id": 1093, "gpt3_output": "\n{\"type\": \"paper product\", \"description\": \"thin, soft, and absorbent; could be used for cleaning and wiping; could be used for wrapping gifts\", \"similar objects\": [\"toilet paper\", \"paper towel\", \"napkin\"]}"}, {"object": "toast_(food)", "object_id": 1094, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"bread that has been toasted; could be served with butter, jam, or other toppings; could be served as a snack or part of a meal\", \"similar objects\": [\"bagel\", \"croissant\", \"english muffin\"]}"}, {"object": "toaster", "object_id": 1095, "gpt3_output": "\n{\"type\": \"kitchen appliance\", \"description\": \"rectangular; has slots for bread; could be electric or manual\", \"similar objects\": [\"coffee maker\", \"blender\", \"microwave\"]}"}, {"object": "toaster_oven", "object_id": 1096, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"box-shaped; could be used to toast, bake, and broil food\", \"similar objects\": [\"microwave\", \"convection oven\", \"toaster\"]}"}, {"object": "toilet", "object_id": 1097, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"has a bowl and a tank; could be wall-mounted or floor-mounted; could have a lid\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}"}, {"object": "toilet_tissue", "object_id": 1098, "gpt3_output": "\n{\"type\": \"hygiene product\", \"description\": \"paper-like material; used for cleaning and hygiene purposes; usually comes in rolls\", \"similar objects\": [\"paper towels\", \"toilet paper\", \"wipes\"]}"}, {"object": "tomato", "object_id": 1099, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, red, has a stem; could be sliced into pieces; could be used for salads\", \"similar objects\": [\"apple\", \"pear\", \"cucumber\"]}"}, {"object": "tongs", "object_id": 1100, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"long handles with two ends; could be used to pick up food\", \"similar objects\": [\"spatula\", \"ladle\", \"whisk\"]}"}, {"object": "toolbox", "object_id": 1101, "gpt3_output": "\n{\"type\": \"storage tool\", \"description\": \"rectangular; could have multiple compartments; could be made of metal or plastic\", \"similar objects\": [\"tool chest\", \"tool cabinet\", \"tool rack\"]}"}, {"object": "toothbrush", "object_id": 1102, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"long handle; has bristles; could be manual or electric\", \"similar objects\": [\"toothpaste\", \"dental floss\", \"mouthwash\"]}"}, {"object": "toothpaste", "object_id": 1103, "gpt3_output": "\n{\"type\": \"hygiene product\", \"description\": \"white, paste-like; could be in a tube; could be flavored\", \"similar objects\": [\"mouthwash\", \"toothbrush\", \"floss\"]}"}, {"object": "toothpick", "object_id": 1104, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"long, thin, pointed; could be made of wood or plastic\", \"similar objects\": [\"skewer\", \"chopstick\", \"fork\"]}"}, {"object": "cover", "object_id": 1105, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"used to cover something; could be made of fabric, paper, or plastic\", \"similar objects\": [\"blanket\", \"sheet\", \"tarp\"]}"}, {"object": "tortilla", "object_id": 1106, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"flat, round, thin; could be made of wheat or corn flour; could be filled with vegetables or meat\", \"similar objects\": [\"taco\", \"burrito\", \"enchilada\"]}"}, {"object": "tow_truck", "object_id": 1107, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large; has a flatbed; could be used to tow other vehicles\", \"similar objects\": [\"dump truck\", \"pickup truck\", \"semi-truck\"]}"}, {"object": "towel", "object_id": 1108, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"absorbent fabric; could be used to dry off wet surfaces; could be used to wrap around body\", \"similar objects\": [\"washcloth\", \"rag\", \"sponge\"]}"}, {"object": "towel_rack", "object_id": 1109, "gpt3_output": "\n{\"type\": \"storage tool\", \"description\": \"long; could be made of metal; could be used to hang towels\", \"similar objects\": [\"coat rack\", \"hat rack\", \"shoe rack\"]}"}, {"object": "toy", "object_id": 1110, "gpt3_output": "\n{\"type\": \"plaything\", \"description\": \"could be made of plastic, wood, or fabric; could be used for entertainment or educational purposes; could come in various shapes and sizes\", \"similar objects\": [\"doll\", \"action figure\", \"puzzle\"]}"}, {"object": "tractor_(farm_equipment)", "object_id": 1111, "gpt3_output": "\n{\"type\": \"farm equipment\", \"description\": \"large, heavy-duty vehicle; has a large wheel in the front; could have a trailer attached\", \"similar objects\": [\"combine harvester\", \"plow\", \"cultivator\"]}"}, {"object": "traffic_light", "object_id": 1112, "gpt3_output": "\n{\"type\": \"traffic signal\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"stop sign\", \"yield sign\", \"crosswalk sign\"]}"}, {"object": "dirt_bike", "object_id": 1113, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"small, lightweight; has knobby tires; could have a kickstand\", \"similar objects\": [\"motorcycle\", \"ATV\", \"scooter\"]}"}, {"object": "trailer_truck", "object_id": 1114, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, box-shaped; could be pulled by a truck; could have multiple axles\", \"similar objects\": [\"semi-truck\", \"tow truck\", \"dump truck\"]}"}, {"object": "train_(railroad_vehicle)", "object_id": 1115, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could have a locomotive; could have multiple carriages\", \"similar objects\": [\"tram\", \"monorail\", \"subway\"]}"}, {"object": "trampoline", "object_id": 1116, "gpt3_output": "\n{\"type\": \"recreational tool\", \"description\": \"large, round, has a netting around it; could be used for jumping\", \"similar objects\": [\"swing set\", \"slide\", \"monkey bars\"]}"}, {"object": "tray", "object_id": 1117, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"flat, rectangular; could be made of metal or plastic; could have handles\", \"similar objects\": [\"plate\", \"dish\", \"bowl\"]}"}, {"object": "trench_coat", "object_id": 1118, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long, double-breasted; could have a belt; could be made of wool or cotton\", \"similar objects\": [\"overcoat\", \"raincoat\", \"parka\"]}"}, {"object": "triangle_(musical_instrument)", "object_id": 1119, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"three-sided percussion instrument; could be made of metal or wood; could be played with a mallet or beater\", \"similar objects\": [\"cymbal\", \"tambourine\", \"maracas\"]}"}, {"object": "tricycle", "object_id": 1120, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"three wheels; could be pedal-powered; could have a basket in the back\", \"similar objects\": [\"bicycle\", \"scooter\", \"skateboard\"]}"}, {"object": "tripod", "object_id": 1121, "gpt3_output": "\n{\"type\": \"support tool\", \"description\": \"three legs; could be used to hold cameras or other objects\", \"similar objects\": [\"monopod\", \"bipod\", \"quadpod\"]}"}, {"object": "trousers", "object_id": 1122, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"long, loose-fitting pants; could have pockets; could be made of different fabrics\", \"similar objects\": [\"jeans\", \"shorts\", \"leggings\"]}"}, {"object": "truck", "object_id": 1123, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, boxy; could have multiple axles; could have a trailer attached\", \"similar objects\": [\"van\", \"SUV\", \"bus\"]}"}, {"object": "truffle_(chocolate)", "object_id": 1124, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round, dark chocolate; could be filled with cream or nuts; could be covered with cocoa powder\", \"similar objects\": [\"praline\", \"truffle (mushroom)\", \"bonbon\"]}"}, {"object": "trunk", "object_id": 1125, "gpt3_output": "\n{\"type\": \"storage tool\", \"description\": \"large, rectangular; could be made of wood or metal; could have a lock\", \"similar objects\": [\"chest\", \"box\", \"suitcase\"]}"}, {"object": "vat", "object_id": 1126, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"large, cylindrical, made of metal; could be used to store liquids\", \"similar objects\": [\"barrel\", \"tank\", \"tub\"]}"}, {"object": "turban", "object_id": 1127, "gpt3_output": "\n{\"type\": \"headwear\", \"description\": \"wrapped around the head; could be made of cloth; could be decorated with jewels\", \"similar objects\": [\"hat\", \"cap\", \"headscarf\"]}"}, {"object": "turkey_(food)", "object_id": 1128, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"large, brown bird; could be roasted; could be served with stuffing\", \"similar objects\": [\"chicken\", \"duck\", \"goose\"]}"}, {"object": "turnip", "object_id": 1129, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round, white and purple; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"radish\", \"potato\", \"carrot\"]}"}, {"object": "turtle", "object_id": 1130, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"shell; slow-moving; could be aquatic or land-dwelling\", \"similar objects\": [\"snake\", \"iguana\", \"tortoise\"]}"}, {"object": "turtleneck_(clothing)", "object_id": 1131, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"high neckline; could be long-sleeved; could be made of wool or cotton\", \"similar objects\": [\"sweater\", \"cardigan\", \"hoodie\"]}"}, {"object": "typewriter", "object_id": 1132, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"has a keyboard; could be mechanical or electronic; could produce printed text\", \"similar objects\": [\"computer\", \"printer\", \"calculator\"]}"}, {"object": "umbrella", "object_id": 1133, "gpt3_output": "\n{\"type\": \"protective tool\", \"description\": \"has a curved handle; could be opened and closed; could be made of fabric\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}"}, {"object": "underwear", "object_id": 1134, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"worn close to the body; could be made of cotton, silk, or other fabrics; could be briefs, boxers, or thongs\", \"similar objects\": [\"socks\", \"bra\", \"t-shirt\"]}"}, {"object": "unicycle", "object_id": 1135, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"has one wheel; could be used for balancing; could be used for stunts\", \"similar objects\": [\"bicycle\", \"tricycle\", \"scooter\"]}"}, {"object": "urinal", "object_id": 1136, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"rectangular; could be wall-mounted; could be used for urination\", \"similar objects\": [\"toilet\", \"sink\", \"bathtub\"]}"}, {"object": "urn", "object_id": 1137, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or ceramic; could have a lid\", \"similar objects\": [\"vase\", \"jar\", \"pot\"]}"}, {"object": "vacuum_cleaner", "object_id": 1138, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"long, cylindrical; has a hose and a motor; could be handheld or upright\", \"similar objects\": [\"mop\", \"broom\", \"duster\"]}"}, {"object": "vase", "object_id": 1139, "gpt3_output": "\n{\"type\": \"decorative item\", \"description\": \"cylindrical; could be made of glass, ceramic, or metal; could have a wide opening at the top\", \"similar objects\": [\"urn\", \"jar\", \"jug\"]}"}, {"object": "vending_machine", "object_id": 1140, "gpt3_output": "\n{\"type\": \"machine\", \"description\": \"large, rectangular; has a slot for coins; could dispense items\", \"similar objects\": [\"ATM machine\", \"coin-operated machine\", \"ticket machine\"]}"}, {"object": "vent", "object_id": 1141, "gpt3_output": "\n{\"type\": \"ventilation tool\", \"description\": \"rectangular; could be used to circulate air; could be used to exhaust air\", \"similar objects\": [\"fan\", \"air conditioner\", \"heater\"]}"}, {"object": "vest", "object_id": 1142, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"sleeveless; could be made of fabric or leather; could have pockets; could be worn over a shirt\", \"similar objects\": [\"jacket\", \"coat\", \"blazer\"]}"}, {"object": "videotape", "object_id": 1143, "gpt3_output": "\n{\"type\": \"media storage device\", \"description\": \"long, thin, magnetic tape; used to store audio and video recordings\", \"similar objects\": [\"CD\", \"DVD\", \"Blu-ray\"]}"}, {"object": "vinegar", "object_id": 1144, "gpt3_output": "\n{\"type\": \"condiment\", \"description\": \"clear, acidic liquid; could be used for cooking and cleaning\", \"similar objects\": [\"lemon juice\", \"wine\", \"soy sauce\"]}"}, {"object": "violin", "object_id": 1145, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"wooden; has four strings; has a bow\", \"similar objects\": [\"cello\", \"guitar\", \"piano\"]}"}, {"object": "vodka", "object_id": 1146, "gpt3_output": "\n{\"type\": \"alcoholic beverage\", \"description\": \"clear, colorless, distilled spirit; could be flavored\", \"similar objects\": [\"whiskey\", \"rum\", \"gin\"]}"}, {"object": "volleyball", "object_id": 1147, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"spherical; has a net; could be played with two teams\", \"similar objects\": [\"basketball\", \"football\", \"tennis ball\"]}"}, {"object": "vulture", "object_id": 1148, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large bird; bald head; hooked beak; scavenger\", \"similar objects\": [\"buzzard\", \"eagle\", \"hawk\"]}"}, {"object": "waffle", "object_id": 1149, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"square-shaped; has a honeycomb pattern; could be served with syrup\", \"similar objects\": [\"pancake\", \"crepe\", \"doughnut\"]}"}, {"object": "waffle_iron", "object_id": 1150, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"flat, has two plates with grids; could be heated up\", \"similar objects\": [\"griddle\", \"panini press\", \"crepe maker\"]}"}, {"object": "wagon", "object_id": 1151, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"four-wheeled; could be pulled by horses; could be used for carrying goods\", \"similar objects\": [\"cart\", \"carriage\", \"truck\"]}"}, {"object": "wagon_wheel", "object_id": 1152, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"round; has spokes; could be made of wood or metal\", \"similar objects\": [\"cart wheel\", \"bicycle wheel\", \"tricycle wheel\"]}"}, {"object": "walking_stick", "object_id": 1153, "gpt3_output": "\n{\"type\": \"walking aid\", \"description\": \"long, thin, could be made of wood or metal; could have a handle\", \"similar objects\": [\"cane\", \"crutch\", \"walker\"]}"}, {"object": "wall_clock", "object_id": 1154, "gpt3_output": "\n{\"type\": \"timekeeping tool\", \"description\": \"round; could have numbers or symbols; could have hands or digital display\", \"similar objects\": [\"watch\", \"alarm clock\", \"grandfather clock\"]}"}, {"object": "wall_socket", "object_id": 1155, "gpt3_output": "\n{\"type\": \"electrical tool\", \"description\": \"rectangular; has two or more holes; could be used to plug in electrical appliances\", \"similar objects\": [\"power strip\", \"extension cord\", \"outlet\"]}"}, {"object": "wallet", "object_id": 1156, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small, rectangular, usually made of leather; could have multiple compartments; could have a zipper closure\", \"similar objects\": [\"purse\", \"clutch\", \"bag\"]}"}, {"object": "walrus", "object_id": 1157, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, flippered marine mammal; tusks; whiskers; thick layer of blubber\", \"similar objects\": [\"seal\", \"polar bear\", \"sea lion\"]}"}, {"object": "wardrobe", "object_id": 1158, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"tall, rectangular, has doors and shelves; could be made of wood or metal\", \"similar objects\": [\"dresser\", \"armoire\", \"closet\"]}"}, {"object": "washbasin", "object_id": 1159, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"round or rectangular; could have a faucet; could have a drain\", \"similar objects\": [\"sink\", \"bathtub\", \"toilet\"]}"}, {"object": "automatic_washer", "object_id": 1160, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"large, rectangular; could be used to wash clothes; could be operated automatically\", \"similar objects\": [\"dryer\", \"dishwasher\", \"refrigerator\"]}"}, {"object": "watch", "object_id": 1161, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small, round, has a strap; could have a digital or analog display\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}"}, {"object": "water_bottle", "object_id": 1162, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic or metal; could have a lid or a cap\", \"similar objects\": [\"thermos\", \"mug\", \"cup\"]}"}, {"object": "water_cooler", "object_id": 1163, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"tall, cylindrical; could have a spigot; could be used to store and dispense cold water\", \"similar objects\": [\"refrigerator\", \"water dispenser\", \"ice maker\"]}"}, {"object": "water_faucet", "object_id": 1164, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"has a handle; could be used to control the flow of water; could be attached to a sink or bathtub\", \"similar objects\": [\"shower head\", \"toilet\", \"bathtub faucet\"]}"}, {"object": "water_heater", "object_id": 1165, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"used to heat water; could be electric or gas powered; could be wall-mounted or floor-standing\", \"similar objects\": [\"boiler\", \"furnace\", \"air conditioner\"]}"}, {"object": "water_jug", "object_id": 1166, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could have a handle; could be made of plastic or metal\", \"similar objects\": [\"pitcher\", \"bottle\", \"jar\"]}"}, {"object": "water_gun", "object_id": 1167, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"plastic; could be filled with water; could shoot water\", \"similar objects\": [\"nerf gun\", \"super soaker\", \"water balloon\"]}"}, {"object": "water_scooter", "object_id": 1168, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"motorized; could be used on water; could have a seat\", \"similar objects\": [\"jet ski\", \"canoe\", \"kayak\"]}"}, {"object": "water_ski", "object_id": 1169, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"long, thin, has two handles; used for skiing on water\", \"similar objects\": [\"wakeboard\", \"surfboard\", \"snowboard\"]}"}, {"object": "water_tower", "object_id": 1170, "gpt3_output": "\n{\"type\": \"structure\", \"description\": \"tall, cylindrical; could be made of metal or concrete; could be used to store water\", \"similar objects\": [\"silo\", \"windmill\", \"smokestack\"]}"}, {"object": "watering_can", "object_id": 1171, "gpt3_output": "\n{\"type\": \"gardening tool\", \"description\": \"long handle; could have a spout; could be made of metal or plastic\", \"similar objects\": [\"hose\", \"sprinkler\", \"bucket\"]}"}, {"object": "watermelon", "object_id": 1172, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"large, round, green rind with red flesh; has black seeds\", \"similar objects\": [\"cantaloupe\", \"honeydew\", \"papaya\"]}"}, {"object": "weathervane", "object_id": 1173, "gpt3_output": "\n{\"type\": \"weather tool\", \"description\": \"pointed; could be made of metal; could be in the shape of an animal; could be used to indicate wind direction\", \"similar objects\": [\"wind sock\", \"wind chime\", \"wind turbine\"]}"}, {"object": "webcam", "object_id": 1174, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, rectangular; could be connected to a computer; could be used for video calls\", \"similar objects\": [\"microphone\", \"speaker\", \"headset\"]}"}, {"object": "wedding_cake", "object_id": 1175, "gpt3_output": "\n{\"type\": \"dessert\", \"description\": \"multi-layered; could be decorated with flowers and other decorations; could be topped with a figurine\", \"similar objects\": [\"birthday cake\", \"cupcake\", \"cheesecake\"]}"}, {"object": "wedding_ring", "object_id": 1176, "gpt3_output": "\n{\"type\": \"jewelry\", \"description\": \"circular; could be made of gold, silver, or other metals; could have diamonds or other gemstones\", \"similar objects\": [\"engagement ring\", \"bracelet\", \"necklace\"]}"}, {"object": "wet_suit", "object_id": 1177, "gpt3_output": "\n{\"type\": \"clothing\", \"description\": \"waterproof; usually made of neoprene; could be full body or short-sleeved\", \"similar objects\": [\"diving suit\", \"swimsuit\", \"snorkel\"]}"}, {"object": "wheel", "object_id": 1178, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"round; has spokes; could be made of metal or rubber\", \"similar objects\": [\"tire\", \"axle\", \"rim\"]}"}, {"object": "wheelchair", "object_id": 1179, "gpt3_output": "\n{\"type\": \"mobility aid\", \"description\": \"has two large wheels; could be motorized; could have a seat and armrests\", \"similar objects\": [\"walker\", \"crutches\", \"cane\"]}"}, {"object": "whipped_cream", "object_id": 1180, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"light and airy; could be used as a topping or filling; could be made from cream and sugar\", \"similar objects\": [\"frosting\", \"marshmallow fluff\", \"meringue\"]}"}, {"object": "whistle", "object_id": 1181, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"small, handheld; produces a loud sound when blown\", \"similar objects\": [\"flute\", \"clarinet\", \"trumpet\"]}"}, {"object": "wig", "object_id": 1182, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"made of synthetic or human hair; could be styled in different ways; could be attached to a cap\", \"similar objects\": [\"hat\", \"headband\", \"hairpiece\"]}"}, {"object": "wind_chime", "object_id": 1183, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"made of metal or wood; could have bells or shells; could be hung on a hook\", \"similar objects\": [\"bell\", \"dream catcher\", \"mobile\"]}"}, {"object": "windmill", "object_id": 1184, "gpt3_output": "\n{\"type\": \"energy tool\", \"description\": \"tall structure; has blades; could generate electricity\", \"similar objects\": [\"solar panel\", \"turbine\", \"hydroelectric generator\"]}"}, {"object": "window_box_(for_plants)", "object_id": 1185, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of wood or plastic; could be used to hold plants\", \"similar objects\": [\"planter\", \"flower pot\", \"hanging basket\"]}"}, {"object": "windshield_wiper", "object_id": 1186, "gpt3_output": "\n{\"type\": \"automotive tool\", \"description\": \"attached to the windshield; used to clear away rain, snow, and debris\", \"similar objects\": [\"headlight\", \"brake light\", \"mirror\"]}"}, {"object": "windsock", "object_id": 1187, "gpt3_output": "\n{\"type\": \"weather tool\", \"description\": \"cylindrical; made of fabric; used to measure wind direction and speed\", \"similar objects\": [\"anemometer\", \"weather vane\", \"barometer\"]}"}, {"object": "wine_bottle", "object_id": 1188, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of glass; has a cork stopper\", \"similar objects\": [\"water bottle\", \"beer bottle\", \"jar\"]}"}, {"object": "wine_bucket", "object_id": 1189, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could have a handle; could be used to store and chill wine\", \"similar objects\": [\"ice bucket\", \"cooler\", \"thermos\"]}"}, {"object": "wineglass", "object_id": 1190, "gpt3_output": "\n{\"type\": \"drinking tool\", \"description\": \"tall and thin; could have a stem; could be made of glass or plastic\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}"}, {"object": "blinder_(for_horses)", "object_id": 1191, "gpt3_output": "\n{\"type\": \"horse accessory\", \"description\": \"leather straps that attach to the horse's bridle and cover its eyes; used to help the horse focus on the task at hand\", \"similar objects\": [\"halter\", \"bit\", \"reins\"]}"}, {"object": "wok", "object_id": 1192, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}"}, {"object": "wolf", "object_id": 1193, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"gray fur; pointy ears; sharp teeth; could howl\", \"similar objects\": [\"dog\", \"coyote\", \"fox\"]}"}, {"object": "wooden_spoon", "object_id": 1194, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"long handle; could be made of wood; could be used for stirring\", \"similar objects\": [\"spatula\", \"ladle\", \"whisk\"]}"}, {"object": "wreath", "object_id": 1195, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"circular; made of leaves, flowers, and other materials; could be hung on a door or wall\", \"similar objects\": [\"garland\", \"bouquet\", \"swag\"]}"}, {"object": "wrench", "object_id": 1196, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"L-shaped; used to tighten or loosen nuts and bolts\", \"similar objects\": [\"screwdriver\", \"pliers\", \"hammer\"]}"}, {"object": "wristband", "object_id": 1197, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of fabric, leather, or metal; could be decorated with beads or charms\", \"similar objects\": [\"bracelet\", \"watch\", \"anklet\"]}"}, {"object": "wristlet", "object_id": 1198, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small, handheld bag; could be used to carry small items; could be attached to a belt or a strap\", \"similar objects\": [\"clutch\", \"purse\", \"wallet\"]}"}, {"object": "yacht", "object_id": 1199, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"large, luxurious boat; could have multiple decks; could have a sail or motor\", \"similar objects\": [\"cruise ship\", \"speedboat\", \"ferry\"]}"}, {"object": "yogurt", "object_id": 1200, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"smooth, creamy, could be flavored; could be served cold\", \"similar objects\": [\"ice cream\", \"sorbet\", \"smoothie\"]}"}, {"object": "yoke_(animal_equipment)", "object_id": 1201, "gpt3_output": "\n{\"type\": \"animal equipment\", \"description\": \"wooden or metal frame; used to attach two animals together; could have a handle\", \"similar objects\": [\"harness\", \"collar\", \"halter\"]}"}, {"object": "zebra", "object_id": 1202, "gpt3_output": "\n{\"type\": \"animal\", \n\"description\": \"black and white stripes; has a long mane\", \n\"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "zucchini", "object_id": 1203, "gpt3_output": "\n{\"type\": \"vegetable\", \n\"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \n\"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}"}] \ No newline at end of file diff --git a/tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.filtered.json b/tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.filtered.json new file mode 100644 index 0000000000000000000000000000000000000000..9ed16455bd57c5e28ccc587efb2b21acf2058ea8 --- /dev/null +++ b/tools/files/mixed_vocab.v1.tmp0.davincci.chunk1of1.filtered.json @@ -0,0 +1,39202 @@ +{ + "plate": [ + " {\"type\": \"dining ware\", \"description\": \"flat, round, could be made of ceramic or plastic\", \"similar objects\": [\"bowl\", \"cup\", \"fork\"]}", + 22978 + ], + "fence": [ + " {\"type\": \"barrier\", \"description\": \"long; could be made of wood or metal; could be used to separate two areas\", \"similar objects\": [\"wall\", \"gate\", \"hedge\"]}", + 17244 + ], + "train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could be powered by electricity or diesel\", \"similar objects\": [\"tram\", \"subway\", \"monorail\"]}", + 16670 + ], + "chair": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could have armrests; could be made of wood or metal\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 15511 + ], + "ground": [ + " {\"type\": \"surface\", \"description\": \"flat; could be made of soil, sand, or concrete; could be covered with grass or other vegetation\", \"similar objects\": [\"floor\", \"terrain\", \"landscape\"]}", + 14600 + ], + "pole": [ + " {\"type\": \"support tool\", \"description\": \"long, cylindrical; could be made of metal or wood; could be used to hang clothes\", \"similar objects\": [\"rod\", \"bar\", \"post\"]}", + 14491 + ], + "bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple doors; could be yellow or white\", \"similar objects\": [\"truck\", \"van\", \"car\"]}", + 14383 + ], + "vehicle": [ + "\n{\"type\": \"transportation\", \"description\": \"motorized or non-motorized; could be used for carrying people or goods\", \"similar objects\": [\"car\", \"truck\", \"bicycle\"]}", + 13523 + ], + "giraffe": [ + " {\"type\": \"animal\", \"description\": \"tall; has a long neck; has a spotted pattern; has a long tail\", \"similar objects\": [\"zebra\", \"elephant\", \"gazelle\"]}", + 12537 + ], + "bear": [ + " {\"type\": \"animal\", \"description\": \"large, furry, could have a snout; could be brown, black, or white\", \"similar objects\": [\"wolf\", \"tiger\", \"lion\"]}", + 12317 + ], + "plane": [ + " {\"type\": \"vehicle\", \"description\": \"long and wide; has wings; could have multiple engines\", \"similar objects\": [\"helicopter\", \"rocket\", \"airship\"]}", + 11528 + ], + "bag": [ + " {\"type\": \"container\", \"description\": \"could be made of cloth, paper, or plastic; could be used to carry items\", \"similar objects\": [\"purse\", \"backpack\", \"suitcase\"]}", + 11293 + ], + "pants": [ + " {\"type\": \"clothing\", \"description\": \"long, covers legs; could have pockets; could be made of different materials\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 10837 + ], + "hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"could be made of fabric; could have a brim; could have a strap\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}", + 10778 + ], + "bench": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of wood or metal; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"stool\"]}", + 10729 + ], + "clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has hands; could have a digital display\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}", + 10719 + ], + "elephant": [ + " {\"type\": \"animal\", \"description\": \"large; has a trunk; has large ears; has a long tail\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}", + 10655 + ], + "pizza": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stromboli\", \"flatbread\"]}", + 10521 + ], + "furniture": [ + " {\"type\": \"household item\", \"description\": \"could be made of wood, metal, plastic, or fabric; could be used for seating, sleeping, or storage\", \"similar objects\": [\"chair\", \"table\", \"sofa\"]}", + 10188 + ], + "blue": [ + "\n{\"type\": \"color\", \"description\": \"a hue of the visible spectrum; could be described as a cool color\", \"similar objects\": [\"green\", \"purple\", \"yellow\"]}", + 10118 + ], + "device": [ + "\n{\"type\": \"electronic device\", \"description\": \"could be a computer, phone, or other electronic device; could have a screen, buttons, and ports\", \"similar objects\": [\"laptop\", \"tablet\", \"smartphone\"]}", + 10107 + ], + "bird": [ + " {\"type\": \"animal\", \"description\": \"could fly; could have feathers; could have beaks; could have wings\", \"similar objects\": [\"duck\", \"eagle\", \"pigeon\"]}", + 10027 + ], + "truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a cargo bed; could have multiple axles\", \"similar objects\": [\"van\", \"SUV\", \"pickup truck\"]}", + 9951 + ], + "jacket": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be made of wool; could have a zipper\", \"similar objects\": [\"coat\", \"sweater\", \"hoodie\"]}", + 9854 + ], + "zebra": [ + "\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}", + 9796 + ], + "helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard; could be made of plastic or metal; could have a visor\", \"similar objects\": [\"goggles\", \"mask\", \"gloves\"]}", + 9679 + ], + "umbrella": [ + " {\"type\": \"protective tool\", \"description\": \"has a long handle; could be opened and closed; could be made of fabric\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}", + 9483 + ], + "bowl": [ + " {\"type\": \"utensil\", \"description\": \"round; could be made of ceramic, plastic, or metal; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 9448 + ], + "windows": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be made of glass; could be opened and closed\", \"similar objects\": [\"doors\", \"shutters\", \"blinds\"]}", + 9332 + ], + "ear": [ + " {\"type\": \"body part\", \"description\": \"external part of the body; could be pierced; could be covered with hair\", \"similar objects\": [\"eye\", \"nose\", \"mouth\"]}", + 8691 + ], + "tail": [ + " {\"type\": \"body part\", \"description\": \"long, thin, could be furry; could be found on animals\", \"similar objects\": [\"mane\", \"whiskers\", \"horns\"]}", + 8486 + ], + "bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass or plastic; could have a cap\", \"similar objects\": [\"jar\", \"can\", \"jug\"]}", + 8173 + ], + "shorts": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting trousers that end above the knee; could be made of cotton, linen, or other fabrics\", \"similar objects\": [\"capris\", \"jeans\", \"skirt\"]}", + 7908 + ], + "motorcycle": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"tricycle\"]}", + 7540 + ], + "cup": [ + " {\"type\": \"utensil\", \"description\": \"round; could have a handle; could be made of ceramic, plastic, or metal\", \"similar objects\": [\"mug\", \"glass\", \"bowl\"]}", + 7425 + ], + "eye": [ + " {\"type\": \"body part\", \"description\": \"round; has a pupil; could be brown, blue, or green\", \"similar objects\": [\"ear\", \"nose\", \"mouth\"]}", + 7379 + ], + "cow": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged, has horns; could be black and white or brown; could produce milk\", \"similar objects\": [\"goat\", \"sheep\", \"buffalo\"]}", + 7128 + ], + "glasses": [ + " {\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be used for vision correction\", \"similar objects\": [\"sunglasses\", \"goggles\", \"monocle\"]}", + 6907 + ], + "surfboard": [ + " {\"type\": \"sports equipment\", \"description\": \"long, narrow, could be made of foam; could have a fin\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 6790 + ], + "skateboard": [ + " {\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could be used for tricks\", \"similar objects\": [\"scooter\", \"rollerblades\", \"snowboard\"]}", + 6528 + ], + "kite": [ + " {\"type\": \"toy\", \"description\": \"could be made of paper or plastic; has a tail; could be flown in the sky\", \"similar objects\": [\"balloon\", \"frisbee\", \"airplane\"]}", + 6454 + ], + "sheep": [ + " {\"type\": \"animal\", \"description\": \"white, wooly fur; has horns; could be found in herds\", \"similar objects\": [\"goat\", \"cow\", \"llama\"]}", + 6429 + ], + "lady": [ + " {\"type\": \"person\", \"description\": \"female; could be wearing a dress; could have long hair\", \"similar objects\": [\"woman\", \"girl\", \"queen\"]}", + 6398 + ], + "toilet": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a bowl and a tank; could be white or other colors; could have a lid\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 6370 + ], + "sidewalk": [ + " {\"type\": \"structure\", \"description\": \"concrete; could be used for walking; could be found in the side of the street\", \"similar objects\": [\"road\", \"pathway\", \"driveway\"]}", + 6311 + ], + "box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be opened and closed\", \"similar objects\": [\"bag\", \"basket\", \"bin\"]}", + 6273 + ], + "laptop": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; has a keyboard and a screen; could be opened and closed\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 6215 + ], + "mirror": [ + " {\"type\": \"reflective tool\", \"description\": \"flat; could be framed; could be hung on the wall\", \"similar objects\": [\"window\", \"glass\", \"picture frame\"]}", + 6135 + ], + "airplane": [ + " {\"type\": \"vehicle\", \"description\": \"long, has wings; could have two or four engines; could have a tail\", \"similar objects\": [\"helicopter\", \"rocket\", \"balloon\"]}", + 6064 + ], + "bike": [ + " {\"type\": \"vehicle\", \"description\": \"two wheels; has a handlebar; could have a basket\", \"similar objects\": [\"scooter\", \"motorcycle\", \"tricycle\"]}", + 5953 + ], + "leg": [ + " {\"type\": \"body part\", \"description\": \"long; could be bent; could be used for walking\", \"similar objects\": [\"arm\", \"foot\", \"hand\"]}", + 5902 + ], + "number": [ + " {\"type\": \"abstract concept\", \"description\": \"a mathematical concept; could be represented by digits or symbols\", \"similar objects\": [\"quantity\", \"value\", \"figure\"]}", + 5779 + ], + "edge": [ + " {\"type\": \"geometric shape\", \"description\": \"a line that forms the boundary of a shape; could be sharp or rounded\", \"similar objects\": [\"corner\", \"vertex\", \"angle\"]}", + 5726 + ], + "shadow": [ + " {\"type\": \"phenomenon\", \"description\": \"dark area created by an object blocking light; could be casted on a surface\", \"similar objects\": [\"silhouette\", \"reflection\", \"refraction\"]}", + 5659 + ], + "shoes": [ + " {\"type\": \"footwear\", \"description\": \"could be made of leather; could have laces; could have a sole\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 5598 + ], + "vase": [ + " {\"type\": \"decorative item\", \"description\": \"cylindrical; could be made of glass, ceramic, or metal; could have a wide opening\", \"similar objects\": [\"urn\", \"jar\", \"jug\"]}", + 5579 + ], + "brown": [ + "\n{\"type\": \"color\", \"description\": \"dark yellowish-red; could be used to describe objects\", \"similar objects\": [\"black\", \"gray\", \"beige\"]}", + 5539 + ], + "piece": [ + " {\"type\": \"object\", \"description\": \"small, could be made of different materials; could be used for different purposes\", \"similar objects\": [\"part\", \"item\", \"component\"]}", + 5320 + ], + "arm": [ + " {\"type\": \"body part\", \"description\": \"attached to the shoulder; could be used to move objects; could be bent\", \"similar objects\": [\"leg\", \"hand\", \"foot\"]}", + 5302 + ], + "pillow": [ + " {\"type\": \"bedding item\", \"description\": \"soft; could be filled with feathers or foam; could be square or round\", \"similar objects\": [\"mattress\", \"blanket\", \"duvet\"]}", + 5298 + ], + "lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"could be made of metal; could have a switch; could have a shade\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}", + 5223 + ], + "trunk": [ + " {\"type\": \"storage tool\", \"description\": \"large, rectangular; could be made of wood or metal; could have a lock\", \"similar objects\": [\"chest\", \"box\", \"suitcase\"]}", + 5134 + ], + "frisbee": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"hula hoop\", \"kite\", \"ball\"]}", + 5124 + ], + "shelf": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood or metal; could have multiple levels\", \"similar objects\": [\"cabinet\", \"bookcase\", \"table\"]}", + 5113 + ], + "desk": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could have drawers; could have legs\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 5053 + ], + "reflection": [ + " {\"type\": \"phenomenon\", \"description\": \"the act of reflecting light, sound, or images; could be seen in a mirror or a lake\", \"similar objects\": [\"refraction\", \"diffraction\", \"reflection\"]}", + 4978 + ], + "jeans": [ + " {\"type\": \"clothing\", \"description\": \"blue; could be tight or loose; could have pockets; could have a zipper\", \"similar objects\": [\"trousers\", \"shorts\", \"skirt\"]}", + 4954 + ], + "vegetable": [ + "\n{\"type\": \"food\", \"description\": \"could be green, red, yellow, etc.; could be eaten raw or cooked; could be sliced, diced, or mashed\", \"similar objects\": [\"fruit\", \"grain\", \"legume\"]}", + 4928 + ], + "cap": [ + " {\"type\": \"clothing accessory\", \"description\": \"headwear; could be made of fabric; could have a brim\", \"similar objects\": [\"hat\", \"beanie\", \"turban\"]}", + 4906 + ], + "legs": [ + " {\"type\": \"body part\", \"description\": \"two; could be long or short; could be hairy; could be muscular\", \"similar objects\": [\"arms\", \"feet\", \"hands\"]}", + 4878 + ], + "orange": [ + " {\"type\": \"fruit\", \"description\": \"round; orange in color; has a stem\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}", + 4834 + ], + "guy": [ + "\n{\"type\": \"person\", \"description\": \"male; could be wearing clothes; could have facial hair; could have a hairstyle\", \"similar objects\": [\"man\", \"boy\", \"gentleman\"]}", + 4830 + ], + "sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a basin; could have a faucet; could have a drain\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 4760 + ], + "shoe": [ + " {\"type\": \"footwear\", \"description\": \"made of leather or fabric; could have laces; could have a heel\", \"similar objects\": [\"sneaker\", \"boot\", \"sandal\"]}", + 4706 + ], + "player": [ + " {\"type\": \"person\", \"description\": \"could be a sports player; could be a musician; could be an actor\", \"similar objects\": [\"athlete\", \"singer\", \"actor\"]}", + 4693 + ], + "lines": [ + " {\"type\": \"geometric shape\", \"description\": \"straight; could be curved; could be dashed or solid\", \"similar objects\": [\"circles\", \"triangles\", \"squares\"]}", + 4690 + ], + "container": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of plastic, metal, or glass; could be used to store items\", \"similar objects\": [\"box\", \"jar\", \"bag\"]}", + 4568 + ], + "top": [ + " {\"type\": \"clothing item\", \"description\": \"short-sleeved; could be sleeveless; could have a collar; could have buttons\", \"similar objects\": [\"shirt\", \"blouse\", \"dress\"]}", + 4515 + ], + "wheel": [ + " {\"type\": \"mechanical tool\", \"description\": \"round; could be made of metal; could be used to move objects\", \"similar objects\": [\"axle\", \"gear\", \"pulley\"]}", + 4472 + ], + "board": [ + " {\"type\": \"utility tool\", \"description\": \"flat; could be made of wood or plastic; could be used for writing or drawing\", \"similar objects\": [\"chalkboard\", \"whiteboard\", \"tablet\"]}", + 4458 + ], + "sand": [ + " {\"type\": \"material\", \"description\": \"fine, granular; could be yellow, white, or brown; could be used for construction\", \"similar objects\": [\"gravel\", \"soil\", \"clay\"]}", + 4393 + ], + "counter": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; 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has keys; could be wired or wireless\", \"similar objects\": [\"mouse\", \"headset\", \"monitor\"]}", + 4104 + ], + "towel": [ + " {\"type\": \"cleaning tool\", \"description\": \"rectangular; could be made of cotton; could be used to dry body\", \"similar objects\": [\"washcloth\", \"rag\", \"sponge\"]}", + 4054 + ], + "fruit": [ + "\n{\"type\": \"food\", \"description\": \"could be sweet or sour; could be eaten raw or cooked; could be a variety of colors; could be round or oval\", \"similar objects\": [\"vegetables\", \"nuts\", \"berries\"]}", + 4035 + ], + "yellow": [ + "\n{\"type\": \"color\", \"description\": \"bright, warm hue; could be associated with sunshine and happiness\", \"similar objects\": [\"orange\", \"green\", \"blue\"]}", + 3997 + ], + "paper": [ + " {\"type\": \"material\", \"description\": \"thin, flat, white; could be used for writing or printing\", \"similar objects\": [\"cardboard\", \"fabric\", \"plastic\"]}", + 3945 + ], + "coat": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of wool; could have buttons or zipper\", \"similar objects\": [\"jacket\", \"sweater\", \"vest\"]}", + 3941 + ], + "appliance": [ + " {\"type\": \"electronic device\", \"description\": \"could be used for household tasks; could be powered by electricity\", \"similar objects\": [\"refrigerator\", \"washing machine\", \"air conditioner\"]}", + 3896 + ], + "rocks": [ + " {\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be made of different materials; could be found in nature\", \"similar objects\": [\"stones\", \"pebbles\", \"boulders\"]}", + 3857 + ], + "knife": [ + " {\"type\": \"utensil\", \"description\": \"sharp blade; could have a handle\", \"similar objects\": [\"fork\", \"spoon\", \"scissors\"]}", + 3826 + ], + "fork": [ + " {\"type\": \"utensil\", \"description\": \"has four prongs; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 3782 + ], + "tennis racket": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be made of wood or metal\", \"similar objects\": [\"badminton racket\", \"squash racket\", \"table tennis racket\"]}", + 3740 + ], + "patch": [ + " {\"type\": \"fabric accessory\", \"description\": \"small piece of fabric; could be used to cover a hole or tear\", \"similar objects\": [\"sewing kit\", \"needle\", \"thread\"]}", + 3729 + ], + "row": [ + " {\"type\": \"arrangement\", \"description\": \"a line of objects placed side by side\", \"similar objects\": [\"column\", \"stack\", \"pile\"]}", + 3691 + ], + "bicycle": [ + " {\"type\": \"vehicle\", \"description\": \"two wheels; has a handlebar; could have a basket\", \"similar objects\": [\"motorcycle\", \"scooter\", \"tricycle\"]}", + 3623 + ], + "clothing": [ + " {\"type\": \"apparel\", \"description\": \"fabric; could be made of cotton, wool, silk, etc.; could be in different colors and styles\", \"similar objects\": [\"shirt\", \"dress\", \"pants\"]}", + 3613 + ], + "wheels": [ + " {\"type\": \"transportation tool\", \"description\": \"round; could be made of metal; could be attached to a vehicle\", \"similar objects\": [\"tires\", \"axles\", \"rims\"]}", + 3590 + ], + "blanket": [ + " {\"type\": \"bedding item\", \"description\": \"soft; could be made of wool; could be used to keep warm\", \"similar objects\": [\"quilt\", \"comforter\", \"duvet\"]}", + 3577 + ], + "computer": [ + " {\"type\": \"electronic device\", \"description\": \"has a monitor, keyboard, and mouse; could be a laptop or desktop\", \"similar objects\": [\"tablet\", \"smartphone\", \"printer\"]}", + 3556 + ], + "tie": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, usually made of silk; could be worn around the neck\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}", + 3546 + ], + "cheese": [ + " {\"type\": \"food\", \"description\": \"yellow or white; could be sliced; could be melted; could be grated\", \"similar objects\": [\"yogurt\", \"butter\", \"milk\"]}", + 3502 + ], + "backpack": [ + " {\"type\": \"bag\", \"description\": \"has straps; could be made of fabric; could have multiple compartments\", \"similar objects\": [\"duffel bag\", \"suitcase\", \"tote bag\"]}", + 3499 + ], + "suitcase": [ + " {\"type\": \"travel item\", \"description\": \"rectangular; has a handle; could be made of hard materials\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 3469 + ], + "napkin": [ + " {\"type\": \"tableware\", \"description\": \"square; could be made of paper or cloth; used to wipe hands or mouth\", \"similar objects\": [\"tissue\", \"handkerchief\", \"paper towel\"]}", + 3426 + ], + "sandwich": [ + " {\"type\": \"food\", \"description\": \"two slices of bread with filling in between; could be cut into triangles\", \"similar objects\": [\"burger\", \"wrap\", \"taco\"]}", + 3420 + ], + "plant": [ + " {\"type\": \"living organism\", \"description\": \"could be green; could have leaves; could have roots; could be potted\", \"similar objects\": [\"tree\", \"flower\", \"bush\"]}", + 3376 + ], + "cabinet": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular; could have doors and drawers; could be made of wood or metal\", \"similar objects\": [\"dresser\", \"bookshelf\", \"armoire\"]}", + 3372 + ], + "meat": [ + " {\"type\": \"food\", \"description\": \"could be beef, pork, chicken, or fish; could be cooked in various ways\", \"similar objects\": [\"seafood\", \"vegetables\", \"dairy products\"]}", + 3345 + ], + "grey": [ + "\n{\"type\": \"color\", \"description\": \"a neutral color between black and white; could be used to describe objects\", \"similar objects\": [\"silver\", \"charcoal\", \"slate\"]}", + 3341 + ], + "street sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; could be made of metal; could have words or symbols on it\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 3315 + ], + "item furniture": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood, metal, or plastic; could be used for seating, storage, or decoration\", \"similar objects\": [\"chair\", \"table\", \"cabinet\"]}", + 3260 + ], + "body": [ + " {\"type\": \"human anatomy\", \"description\": \"made up of organs, bones, muscles, and tissues; could be covered with skin\", \"similar objects\": [\"skeleton\", \"brain\", \"heart\"]}", + 3256 + ], + "skier": [ + " {\"type\": \"sportsperson\", \"description\": \"wears ski boots and skis; could be skiing downhill\", \"similar objects\": [\"snowboarder\", \"ice skater\", \"surfer\"]}", + 3203 + ], + "wave": [ + " {\"type\": \"natural phenomenon\", \"description\": \"repeated up and down motion of water; could be caused by wind\", \"similar objects\": [\"tide\", \"tsunami\", \"surf\"]}", + 3198 + ], + "piece furniture": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood, metal, or plastic; could be used for seating, storage, or decoration\", \"similar objects\": [\"chair\", \"table\", \"cabinet\"]}", + 3188 + ], + "tray": [ + " {\"type\": \"utensil\", \"description\": \"flat, rectangular; could be made of metal or plastic; could have handles\", \"similar objects\": [\"plate\", \"bowl\", \"dish\"]}", + 3182 + ], + "bananas": [ + " {\"type\": \"fruit\", \"description\": \"long, curved, yellow; could have brown spots; could be sliced into pieces\", \"similar objects\": [\"apple\", \"orange\", \"pear\"]}", + 3181 + ], + "handle": [ + " {\"type\": \"tool\", \"description\": \"used to open or close something; could be made of metal or plastic; could be attached to a door or a drawer\", \"similar objects\": [\"knob\", \"lever\", \"pull\"]}", + 3179 + ], + "tire": [ + " {\"type\": \"vehicle part\", \"description\": \"round; made of rubber; has a tread pattern\", \"similar objects\": [\"wheel\", \"rim\", \"hubcap\"]}", + 3154 + ], + "giraffes": [ + " {\"type\": \"animal\", \"description\": \"long neck; spotted; has a long mane\", \"similar objects\": [\"elephant\", \"horse\", \"zebra\"]}", + 3119 + ], + "neck": [ + " {\"type\": \"body part\", \"description\": \"connects head to the torso; could be long or short; could be flexible\", \"similar objects\": [\"shoulder\", \"arm\", \"waist\"]}", + 3113 + ], + "foot": [ + " {\"type\": \"body part\", \"description\": \"has five toes; could be covered with socks or shoes\", \"similar objects\": [\"hand\", \"arm\", \"leg\"]}", + 3078 + ], + "zebras": [ + " {\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane; usually found in groups\", \"similar objects\": [\"horses\", \"giraffes\", \"elephants\"]}", + 3075 + ], + "banana": [ + " {\"type\": \"fruit\", \"description\": \"long, curved, yellow; has a brown peel\", \"similar objects\": [\"apple\", \"orange\", \"pear\"]}", + 3053 + ], + "stripes": [ + " {\"type\": \"pattern\", \"description\": \"alternating lines of different colors or shades; could be used for decoration\", \"similar objects\": [\"checks\", \"dots\", \"plaid\"]}", + 2989 + ], + "post": [ + " {\"type\": \"object\", \"description\": \"long, cylindrical; could be made of metal or wood; could be used to support something\", \"similar objects\": [\"pole\", \"stake\", \"beam\"]}", + 2955 + ], + "chairs": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could be made of wood or metal; could have armrests; could have a backrest\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 2931 + ], + "bread": [ + " {\"type\": \"food\", \"description\": \"loaf-shaped; could be sliced; could be toasted; could be served with butter\", \"similar objects\": [\"bagel\", \"croissant\", \"bun\"]}", + 2927 + ], + "racket": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has a stringed head; could be used for tennis, badminton, or squash\", \"similar objects\": [\"bat\", \"club\", \"ball\"]}", + 2922 + ], + "van": [ + " {\"type\": \"vehicle\", \"description\": \"box-shaped; could be used for transportation\", \"similar objects\": [\"truck\", \"minivan\", \"SUV\"]}", + 2908 + ], + "metal": [ + " {\"type\": \"material\", \"description\": \"shiny; could be malleable; could be magnetic; could be a conductor of electricity\", \"similar objects\": [\"steel\", \"aluminum\", \"copper\"]}", + 2895 + ], + "elephants": [ + " {\"type\": \"animal\", \"description\": \"large, gray; has a trunk; has large ears; could have tusks\", \"similar objects\": [\"hippopotamus\", \"rhinoceros\", \"giraffe\"]}", + 2890 + ], + "tracks": [ + " {\"type\": \"transportation\", \"description\": \"parallel lines on the ground; could be made of metal; could be used by trains, cars, or other vehicles\", \"similar objects\": [\"road\", \"highway\", \"railway\"]}", + 2875 + ], + "sunglasses": [ + " {\"type\": \"eyewear\", \"description\": \"dark lenses; could be made of plastic or metal; could have a frame\", \"similar objects\": [\"eyeglasses\", \"goggles\", \"safety glasses\"]}", + 2859 + ], + "catcher": [ + " {\"type\": \"sports equipment\", \"description\": \"protective gear; has a glove; could be used in baseball\", \"similar objects\": [\"bat\", \"ball\", \"helmet\"]}", + 2818 + ], + "wood": [ + " {\"type\": \"material\", \"description\": \"hard, brown, could be cut into pieces; could be used for building\", \"similar objects\": [\"metal\", \"plastic\", \"glass\"]}", + 2800 + ], + "glove": [ + " {\"type\": \"clothing item\", \"description\": \"made of fabric; could be used to protect hands; could be fingerless\", \"similar objects\": [\"mittens\", \"scarf\", \"hat\"]}", + 2791 + ], + "seat": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood or metal; could have a cushion; could have armrests\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 2789 + ], + "horses": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a long mane; could be ridden\", \"similar objects\": [\"donkey\", \"zebra\", \"camel\"]}", + 2769 + ], + "surfer": [ + " {\"type\": \"person\", \"description\": \"wears a wetsuit; rides a surfboard; could have long hair\", \"similar objects\": [\"skateboarder\", \"snowboarder\", \"windsurfer\"]}", + 2753 + ], + "screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be used to display images or videos; could be touch-sensitive\", \"similar objects\": [\"monitor\", \"television\", \"tablet\"]}", + 2745 + ], + "tower": [ + " {\"type\": \"structure\", \"description\": \"tall; could be made of stones; could have a clock\", \"similar objects\": [\"building\", \"monument\", \"skyscraper\"]}", + 2733 + ], + "curtain": [ + " {\"type\": \"window covering\", \"description\": \"long; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"blinds\", \"shades\", \"drapes\"]}", + 2723 + ], + "ears": [ + " {\"type\": \"body part\", \"description\": \"two; located on the sides of the head; could be pierced\", \"similar objects\": [\"eyes\", \"nose\", \"mouth\"]}", + 2721 + ], + "traffic light": [ + " {\"type\": \"traffic signal\", \"description\": \"three lights in a vertical line; red, yellow, and green; could be mounted on a pole\", \"similar objects\": [\"stop sign\", \"yield sign\", \"crosswalk sign\"]}", + 2704 + ], + "bush": [ + " {\"type\": \"plant\", \"description\": \"green; could have thorns; could be trimmed into shapes\", \"similar objects\": [\"tree\", \"shrub\", \"hedge\"]}", + 2691 + ], + "letter": [ + " {\"type\": \"document\", \"description\": \"rectangular; could be written on paper or sent electronically\", \"similar objects\": [\"envelope\", \"postcard\", \"package\"]}", + 2689 + ], + "skis": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, curved; could be used for skiing\", \"similar objects\": [\"snowboard\", \"skateboard\", \"rollerblades\"]}", + 2652 + ], + "kid": [ + " {\"type\": \"person\", \"description\": \"young; could be playing; could be wearing school uniform\", \"similar objects\": [\"teenager\", \"child\", \"baby\"]}", + 2631 + ], + "pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 2622 + ], + "bushes": [ + " {\"type\": \"plant\", \"description\": \"green; could be evergreen or deciduous; could have thorns; could be trimmed into shapes\", \"similar objects\": [\"trees\", \"shrubs\", \"hedges\"]}", + 2605 + ], + "section": [ + " {\"type\": \"division\", \"description\": \"a part of a whole; could be divided into subsections\", \"similar objects\": [\"chapter\", \"paragraph\", \"clause\"]}", + 2590 + ], + "bat": [ + " {\"type\": \"animal\", \"description\": \"winged mammal; has sharp claws; could fly\", \"similar objects\": [\"bird\", \"butterfly\", \"dragonfly\"]}", + 2567 + ], + "ceiling": [ + " {\"type\": \"structure\", \"description\": \"the upper part of a room; could be painted or have a pattern; could have a light fixture\", \"similar objects\": [\"wall\", \"floor\", \"door\"]}", + 2564 + ], + "spoon": [ + " {\"type\": \"utensil\", \"description\": \"long handle; round bowl; could be made of metal or plastic\", \"similar objects\": [\"fork\", \"knife\", \"chopsticks\"]}", + 2539 + ], + "broccoli": [ + " {\"type\": \"vegetable\", \"description\": \"green, has a head; could have small florets; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 2522 + ], + "feet": [ + " {\"type\": \"body part\", \"description\": \"five toes; could be covered with socks; could be used for walking\", \"similar objects\": [\"hands\", \"legs\", \"arms\"]}", + 2519 + ], + "basket": [ + " {\"type\": \"container\", \"description\": \"could be made of wicker; could have a handle; could be used to carry items\", \"similar objects\": [\"bag\", \"box\", \"bucket\"]}", + 2500 + ], + "bunch": [ + " {\"type\": \"collection\", \"description\": \"group of items; could be tied together\", \"similar objects\": [\"cluster\", \"bundle\", \"pack\"]}", + 2497 + ], + "animals": [ + "\n{\"type\": \"general category\", \"description\": \"living creatures; could be mammals, birds, reptiles, amphibians, fish, or insects\", \"similar objects\": [\"plants\", \"invertebrates\", \"fungi\"]}", + 2487 + ], + "doors": [ + " {\"type\": \"structure\", \"description\": \"could be made of wood, metal, or glass; could be sliding or hinged; could be opened and closed\", \"similar objects\": [\"windows\", \"gates\", \"shutters\"]}", + 2485 + ], + "television": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could be connected to a remote control\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}", + 2477 + ], + "pillows": [ + " {\"type\": \"bedding item\", \"description\": \"soft; could be filled with feathers or foam; could be square or round\", \"similar objects\": [\"blanket\", \"mattress\", \"duvet\"]}", + 2473 + ], + "stripe": [ + " {\"type\": \"pattern\", \"description\": \"long, thin lines; could be in different colors; could be vertical or horizontal\", \"similar objects\": [\"checkerboard\", \"plaid\", \"polka dot\"]}", + 2463 + ], + "hill": [ + " {\"type\": \"landform\", \"description\": \"sloped land; could be covered with grass or trees; could have a peak\", \"similar objects\": [\"mountain\", \"valley\", \"plateau\"]}", + 2453 + ], + "stove": [ + " {\"type\": \"cooking tool\", \"description\": \"has burners; could have an oven; could be electric or gas\", \"similar objects\": [\"oven\", \"microwave\", \"grill\"]}", + 2406 + ], + "numbers": [ + " {\"type\": \"mathematical concept\", \"description\": \"symbols used to represent quantities; could be written in decimal, binary, or hexadecimal forms\", \"similar objects\": [\"operations\", \"variables\", \"expressions\"]}", + 2403 + ], + "collar": [ + " {\"type\": \"accessory\", \"description\": \"worn around the neck; could be made of leather or fabric; could have a buckle or a clasp\", \"similar objects\": [\"leash\", \"harness\", \"bandana\"]}", + 2353 + ], + "snowboard": [ + " {\"type\": \"sports equipment\", \"description\": \"long, flat board; could have bindings; could have a curved tip\", \"similar objects\": [\"skis\", \"surfboard\", \"skateboard\"]}", + 2309 + ], + "books": [ + " {\"type\": \"object\", \"description\": \"paper-bound; could be hardcover or paperback; could have different colors and sizes\", \"similar objects\": [\"magazines\", \"newspapers\", \"journals\"]}", + 2307 + ], + "tennis ball": [ + " {\"type\": \"sports equipment\", \"description\": \"round; yellow; made of rubber; used in tennis\", \"similar objects\": [\"baseball\", \"soccer ball\", \"basketball\"]}", + 2304 + ], + "rug": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of wool; could be used to decorate a room\", \"similar objects\": [\"carpet\", \"mat\", \"tapestry\"]}", + 2303 + ], + "cows": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged, usually brown or black; could have horns; could produce milk\", \"similar objects\": [\"goats\", \"sheep\", \"buffalo\"]}", + 2301 + ], + "bathroom": [ + " {\"type\": \"room\", \"description\": \"has a toilet, sink, and shower; could have a bathtub\", \"similar objects\": [\"bedroom\", \"kitchen\", \"living room\"]}", + 2285 + ], + "windshield": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be curved; could be made of glass\", \"similar objects\": [\"headlight\", \"tail light\", \"side mirror\"]}", + 2284 + ], + "sweater": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be made of wool; could be knitted\", \"similar objects\": [\"jacket\", \"coat\", \"hoodie\"]}", + 2251 + ], + "tv": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could be connected to a remote control\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}", + 2217 + ], + "microwave": [ + " {\"type\": \"kitchen appliance\", \"description\": \"box-shaped; has a door; could have a timer\", \"similar objects\": [\"oven\", \"toaster\", \"blender\"]}", + 2203 + ], + "trash": [ + " {\"type\": \"waste\", \"description\": \"discarded materials; could be organic or inorganic\", \"similar objects\": [\"garbage\", \"rubbish\", \"refuse\"]}", + 2199 + ], + "apple": [ + "\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem\", \"similar objects\": [\"orange\", \"banana\", \"pear\"]}", + 2191 + ], + "license plate": [ + " {\"type\": \"identification tool\", \"description\": \"rectangular; has numbers and letters; could be attached to a vehicle\", \"similar objects\": [\"registration sticker\", \"vehicle tag\", \"vehicle decal\"]}", + 2185 + ], + "dark": [ + "\n{\"type\": \"color\", \"description\": \"black, navy blue, charcoal gray\", \"similar objects\": [\"black\", \"gray\", \"navy blue\"]}", + 2180 + ], + "slice": [ + " {\"type\": \"cutting tool\", \"description\": \"sharp edge; could be made of metal; could be used to cut food\", \"similar objects\": [\"knife\", \"scissors\", \"cleaver\"]}", + 2166 + ], + "gloves": [ + " {\"type\": \"clothing accessory\", \"description\": \"worn on hands; could be made of leather, cotton, or wool; could be fingerless\", \"similar objects\": [\"mittens\", \"scarf\", \"hat\"]}", + 2166 + ], + "pile": [ + " {\"type\": \"object\", \"description\": \"a group of items stacked on top of each other; could be made of different materials\", \"similar objects\": [\"stack\", \"heap\", \"cluster\"]}", + 2159 + ], + "rope": [ + " {\"type\": \"tool\", \"description\": \"long, flexible, could be made of fibers\", \"similar objects\": [\"string\", \"wire\", \"cord\"]}", + 2158 + ], + "batter": [ + " {\"type\": \"food ingredient\", \"description\": \"thick liquid; could be used for baking\", \"similar objects\": [\"dough\", \"sauce\", \"marinade\"]}", + 2154 + ], + "birds": [ + " {\"type\": \"animal\", \"description\": \"can fly; have feathers; could have beaks and claws; could sing\", \"similar objects\": [\"ducks\", \"pigeons\", \"parrots\"]}", + 2141 + ], + "vegetables": [ + "\n{\"type\": \"food\", \"description\": \"various types of edible plants; could be cooked or eaten raw; could be green, yellow, red, etc.\", \"similar objects\": [\"fruits\", \"grains\", \"legumes\"]}", + 2133 + ], + "umpire": [ + " {\"type\": \"official\", \"description\": \"wears a uniform; has a whistle; makes decisions in sports games\", \"similar objects\": [\"referee\", \"judge\", \"coach\"]}", + 2113 + ], + "cloud": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, fluffy; could be seen in the sky; could be shaped like animals\", \"similar objects\": [\"fog\", \"haze\", \"smog\"]}", + 2094 + ], + "utensil": [ + " {\"type\": \"kitchen tool\", \"description\": \"used for cooking, eating, or serving food; could be made of metal, plastic, or wood\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 2090 + ], + "street light": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lamp post\", \"traffic light\", \"lantern\"]}", + 2082 + ], + "leaf": [ + " {\"type\": \"plant part\", \"description\": \"green; could be oval or pointy; could have veins\", \"similar objects\": [\"petal\", \"stem\", \"flower\"]}", + 2067 + ], + "plates": [ + " {\"type\": \"dining ware\", \"description\": \"flat, round, could be made of ceramic, plastic, or metal; could be decorated\", \"similar objects\": [\"bowls\", \"cups\", \"forks\"]}", + 2059 + ], + "socks": [ + " {\"type\": \"clothing item\", \"description\": \"worn on feet; could be made of cotton, wool, or synthetic materials; could be ankle-length or knee-length\", \"similar objects\": [\"shoes\", \"slippers\", \"sandals\"]}", + 2022 + ], + "silver": [ + " {\"type\": \"metal\", \"description\": \"shiny, reflective, malleable\", \"similar objects\": [\"gold\", \"copper\", \"aluminum\"]}", + 2016 + ], + "lid": [ + " {\"type\": \"container cover\", \"description\": \"round; could be made of metal or plastic; could be used to cover a pot or a bowl\", \"similar objects\": [\"cap\", \"top\", \"cover\"]}", + 1983 + ], + "refrigerator": [ + " {\"type\": \"appliance\", \"description\": \"large, white, has a door; could have shelves and drawers inside\", \"similar objects\": [\"freezer\", \"microwave\", \"dishwasher\"]}", + 1965 + ], + "nose": [ + " {\"type\": \"body part\", \"description\": \"protrudes from the face; has two nostrils; could be used for smelling\", \"similar objects\": [\"mouth\", \"ear\", \"eye\"]}", + 1948 + ], + "gray": [ + " {\"type\": \"color\", \"description\": \"neutral color between black and white; could be used to describe objects\", \"similar objects\": [\"silver\", \"charcoal\", \"slate\"]}", + 1945 + ], + "wires": [ + " {\"type\": \"electrical component\", \"description\": \"long, thin, metal; could be insulated; could be connected to other components\", \"similar objects\": [\"cables\", \"connectors\", \"circuit boards\"]}", + 1936 + ], + "arrow": [ + " {\"type\": \"tool\", \"description\": \"pointed tip; could be made of wood or metal; could be used for shooting\", \"similar objects\": [\"dart\", \"spear\", \"javelin\"]}", + 1928 + ], + "spot": [ + " {\"type\": \"marking tool\", \"description\": \"round; could be made of paint or ink; could be used to mark a surface\", \"similar objects\": [\"stamp\", \"stencil\", \"label\"]}", + 1892 + ], + "purse": [ + " {\"type\": \"accessory\", \"description\": \"small bag; could be made of leather; could have a strap\", \"similar objects\": [\"wallet\", \"backpack\", \"clutch\"]}", + 1891 + ], + "sofa": [ + " {\"type\": \"furniture\", \"description\": \"long; could be upholstered; could have armrests\", \"similar objects\": [\"couch\", \"loveseat\", \"armchair\"]}", + 1889 + ], + "suit": [ + " {\"type\": \"clothing\", \"description\": \"two-piece; could be made of wool; could be worn for formal occasions\", \"similar objects\": [\"tuxedo\", \"blazer\", \"dress\"]}", + 1885 + ], + "fur": [ + " {\"type\": \"fabric\", \"description\": \"soft; could be made of animal hair; could be dyed in different colors\", \"similar objects\": [\"velvet\", \"suede\", \"leather\"]}", + 1859 + ], + "signs": [ + " {\"type\": \"visual communication tool\", \"description\": \"could be made of metal, wood, or paper; could be in different shapes and sizes; could be used to convey messages\", \"similar objects\": [\"symbols\", \"flags\", \"posters\"]}", + 1859 + ], + "stack": [ + " {\"type\": \"structure\", \"description\": \"arrangement of objects on top of each other; could be made of books, boxes, or other objects\", \"similar objects\": [\"pile\", \"heap\", \"tower\"]}", + 1858 + ], + "spots": [ + " {\"type\": \"pattern\", \"description\": \"small, round, and evenly spaced; could be of different colors\", \"similar objects\": [\"stripes\", \"dots\", \"checks\"]}", + 1852 + ], + "bottom": [ + " {\"type\": \"clothing item\", \"description\": \"worn on the lower part of the body; could be pants, shorts, skirts, etc.\", \"similar objects\": [\"trousers\", \"jeans\", \"leggings\"]}", + 1851 + ], + "scene": [ + " {\"type\": \"visual representation\", \"description\": \"a combination of objects, people, and environment; could be a painting, photograph, or movie\", \"similar objects\": [\"landscape\", \"portrait\", \"still life\"]}", + 1839 + ], + "drink": [ + "\n{\"type\": \"beverage\", \"description\": \"could be liquid; could be hot or cold; could be alcoholic or non-alcoholic\", \"similar objects\": [\"juice\", \"tea\", \"coffee\"]}", + 1816 + ], + "curtains": [ + " {\"type\": \"window covering\", \"description\": \"long; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"blinds\", \"shades\", \"drapes\"]}", + 1813 + ], + "tile": [ + " {\"type\": \"building material\", \"description\": \"flat, square, could be made of ceramic, stone, or glass\", \"similar objects\": [\"brick\", \"concrete\", \"wood\"]}", + 1805 + ], + "pink": [ + "\n{\"type\": \"color\", \"description\": \"light red; could be associated with femininity\", \"similar objects\": [\"red\", \"magenta\", \"purple\"]}", + 1792 + ], + "words": [ + " {\"type\": \"language\", \"description\": \"a set of symbols used to communicate meaning; could be written or spoken\", \"similar objects\": [\"sentences\", \"phrases\", \"dialogue\"]}", + 1791 + ], + "tennis court": [ + " {\"type\": \"sports facility\", \"description\": \"rectangular; has a net in the middle; could be painted with white lines\", \"similar objects\": [\"basketball court\", \"soccer field\", \"volleyball court\"]}", + 1791 + ], + "tag": [ + " {\"type\": \"labeling tool\", \"description\": \"small; could be made of paper or plastic; could be attached to an object\", \"similar objects\": [\"label\", \"sticker\", \"badge\"]}", + 1778 + ], + "cell phone": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; could have a touchscreen; could have a camera\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 1775 + ], + "dish": [ + " {\"type\": \"utensil\", \"description\": \"flat, round; could be made of ceramic, glass, or metal; could be used to serve food\", \"similar objects\": [\"plate\", \"bowl\", \"cup\"]}", + 1769 + ], + "watch": [ + " {\"type\": \"accessory\", \"description\": \"small, round, has a strap; could have a digital display\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}", + 1761 + ], + "cabinets": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular, could have doors and drawers\", \"similar objects\": [\"dresser\", \"wardrobe\", \"bookshelf\"]}", + 1751 + ], + "boots": [ + " {\"type\": \"footwear\", \"description\": \"long; could be made of leather; could have laces\", \"similar objects\": [\"shoes\", \"sneakers\", \"sandals\"]}", + 1731 + ], + "mane": [ + " {\"type\": \"animal feature\", \"description\": \"long, thick hair on the neck of a horse or lion\", \"similar objects\": [\"tail\", \"hoof\", \"horn\"]}", + 1720 + ], + "mouse": [ + " {\"type\": \"animal\", \"description\": \"small, furry, has a long tail; could be brown, black, or white\", \"similar objects\": [\"rat\", \"hamster\", \"gerbil\"]}", + 1716 + ], + "button": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, round, could be made of plastic or metal; could be used to fasten clothing\", \"similar objects\": [\"zipper\", \"snap\", \"hook and eye\"]}", + 1715 + ], + "sauce": [ + " {\"type\": \"condiment\", \"description\": \"liquid or semi-liquid; could be spicy or sweet; could be used to enhance the flavor of food\", \"similar objects\": [\"ketchup\", \"mustard\", \"mayonnaise\"]}", + 1715 + ], + "donut": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be filled with cream or jam\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 1714 + ], + "skateboarder": [ + " {\"type\": \"person\", \"description\": \"wearing protective gear; riding a skateboard; performing stunts\", \"similar objects\": [\"bicyclist\", \"rollerblader\", \"surfer\"]}\n\nObject detection models should focus on the shape, color, size, texture, and other physical characteristics of the object, as well as any associated objects or activities. For example, for the zucchini input, the model should focus on the cylindrical shape, green color, smooth texture, and the presence of brown and rough stems. For the skateboarder input, the model should focus on the protective gear", + 1703 + ], + "bottles": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass, plastic, or metal; could have a lid or a cap\", \"similar objects\": [\"jars\", \"cans\", \"mugs\"]}", + 1674 + ], + "t-shirt": [ + " {\"type\": \"clothing\", \"description\": \"has a collar; could have short or long sleeves; could be plain or printed\", \"similar objects\": [\"shirt\", \"tank top\", \"hoodie\"]}", + 1671 + ], + "engine": [ + " {\"type\": \"machine\", \"description\": \"has a combustion chamber; could be used to power a vehicle\", \"similar objects\": [\"motor\", \"generator\", \"turbine\"]}", + 1632 + ], + "belt": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of leather; could have a buckle\", \"similar objects\": [\"scarf\", \"tie\", \"hat\"]}", + 1603 + ], + "bears": [ + " {\"type\": \"animal\", \"description\": \"large, furry, could have brown, black, or white fur; could have a long snout; could have sharp claws\", \"similar objects\": [\"wolves\", \"foxes\", \"raccoons\"]}", + 1597 + ], + "sticker": [ + " {\"type\": \"decoration item\", \"description\": \"adhesive; could be colorful; could be used to decorate surfaces\", \"similar objects\": [\"label\", \"patch\", \"badge\"]}", + 1590 + ], + "air": [ + " {\"type\": \"substance\", \"description\": \"invisible; could be found in atmosphere; could be breathed in\", \"similar objects\": [\"oxygen\", \"nitrogen\", \"carbon dioxide\"]}", + 1590 + ], + "tiles": [ + " {\"type\": \"building material\", \"description\": \"flat, square, could be made of ceramic, stone, or glass\", \"similar objects\": [\"bricks\", \"pavers\", \"concrete blocks\"]}", + 1583 + ], + "wire": [ + " {\"type\": \"material\", \"description\": \"flexible; could be made of metal; could be used for electrical connection\", \"similar objects\": [\"cable\", \"string\", \"rope\"]}", + 1579 + ], + "branches": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, could be curved; could have leaves and fruits\", \"similar objects\": [\"twigs\", \"stems\", \"roots\"]}", + 1578 + ], + "sneakers": [ + " {\"type\": \"footwear\", \"description\": \"lightweight; could be made of fabric or leather; could have laces\", \"similar objects\": [\"trainers\", \"running shoes\", \"sandals\"]}", + 1576 + ], + "horns": [ + " {\"type\": \"musical instrument\", \"description\": \"could be made of brass; could be curved; could be used to produce loud sound\", \"similar objects\": [\"trumpet\", \"trombone\", \"clarinet\"]}", + 1565 + ], + "cloth": [ + " {\"type\": \"fabric\", \"description\": \"flexible; could be made of cotton, silk, or wool; could be used for clothing, curtains, or upholstery\", \"similar objects\": [\"fabric\", \"textile\", \"material\"]}", + 1561 + ], + "paint": [ + " {\"type\": \"art material\", \"description\": \"liquid; could be used to color walls; could be used to create artworks\", \"similar objects\": [\"brush\", \"canvas\", \"marker\"]}", + 1559 + ], + "word": [ + " {\"type\": \"language tool\", \"description\": \"a unit of language; could be composed of letters; could be used to convey meaning\", \"similar objects\": [\"sentence\", \"phrase\", \"paragraph\"]}", + 1557 + ], + "vest": [ + " {\"type\": \"clothing\", \"description\": \"sleeveless; could be made of wool; could have buttons\", \"similar objects\": [\"jacket\", \"sweater\", \"shirt\"]}", + 1538 + ], + "monitor": [ + " {\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a computer; could be used to display images\", \"similar objects\": [\"television\", \"projector\", \"printer\"]}", + 1522 + ], + "toy": [ + " {\"type\": \"plaything\", \"description\": \"could be made of plastic, wood, or fabric; could be in the shape of animals, vehicles, or other objects; could be used for entertainment or educational purposes\", \"similar objects\": [\"doll\", \"action figure\", \"puzzle\"]}", + 1499 + ], + "platform": [ + " {\"type\": \"structure\", \"description\": \"flat surface; could be used for standing or displaying items; could be elevated\", \"similar objects\": [\"stage\", \"pedestal\", \"dais\"]}", + 1497 + ], + "frame": [ + " {\"type\": \"decoration tool\", \"description\": \"rectangular; could be made of wood or metal; could be used to hang pictures\", \"similar objects\": [\"mirror\", \"painting\", \"photo\"]}", + 1491 + ], + "skirt": [ + " {\"type\": \"clothing\", \"description\": \"hangs from the waist; could be long or short; could be pleated or plain\", \"similar objects\": [\"dress\", \"pants\", \"jeans\"]}", + 1489 + ], + "cord": [ + " {\"type\": \"utility item\", \"description\": \"long, thin, flexible; could be made of metal or plastic; could be used to connect two devices\", \"similar objects\": [\"wire\", \"cable\", \"rope\"]}", + 1478 + ], + "goggles": [ + " {\"type\": \"eyewear\", \"description\": \"protective eyewear; could be tinted; could be used for swimming\", \"similar objects\": [\"sunglasses\", \"safety glasses\", \"prescription glasses\"]}", + 1467 + ], + "tomato": [ + " {\"type\": \"vegetable\", \"description\": \"round; red; could be sliced into pieces; could have green leaves\", \"similar objects\": [\"bell pepper\", \"eggplant\", \"cucumber\"]}", + 1463 + ], + "uniform": [ + " {\"type\": \"clothing\", \"description\": \"matching clothes; could be used for school, work, or military\", \"similar objects\": [\"suit\", \"dress\", \"overalls\"]}", + 1461 + ], + "poles": [ + " {\"type\": \"supporting tool\", \"description\": \"long, cylindrical; could be made of metal or wood; could be used to support tents or buildings\", \"similar objects\": [\"stakes\", \"posts\", \"beams\"]}", + 1460 + ], + "surface": [ + " {\"type\": \"object\", \"description\": \"flat; could be made of different materials; could be used as a platform\", \"similar objects\": [\"table\", \"floor\", \"wall\"]}", + 1458 + ], + "object": [ + "\n{\"type\": \"general object\", \"description\": \"could be anything; could be tangible or intangible; could be physical or virtual\", \"similar objects\": [\"thing\", \"item\", \"entity\"]}", + 1450 + ], + "base": [ + " {\"type\": \"structure\", \"description\": \"a support for a structure; could be made of concrete, metal, or wood\", \"similar objects\": [\"foundation\", \"pillar\", \"column\"]}", + 1450 + ], + "tip": [ + " {\"type\": \"object\", \"description\": \"pointed end; could be used for writing\", \"similar objects\": [\"pen\", \"pencil\", \"marker\"]}", + 1448 + ], + "corner": [ + " {\"type\": \"geometric shape\", \"description\": \"90 degree angle; could be used to describe a location\", \"similar objects\": [\"edge\", \"vertex\", \"angle\"]}", + 1440 + ], + "metal pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for support\", \"similar objects\": [\"wooden pole\", \"steel beam\", \"concrete column\"]}", + 1433 + ], + "blinds": [ + " {\"type\": \"window covering\", \"description\": \"vertical or horizontal slats; could be made of wood, plastic, or metal; could be opened and closed\", \"similar objects\": [\"shades\", \"curtains\", \"drapes\"]}", + 1430 + ], + "trousers": [ + " {\"type\": \"clothing\", \"description\": \"long, two legs; could be made of cotton, wool, or other fabrics; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 1427 + ], + "scarf": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of wool; could be worn around the neck\", \"similar objects\": [\"shawl\", \"tie\", \"hat\"]}", + 1418 + ], + "beak": [ + " {\"type\": \"bird body part\", \"description\": \"sharp, curved, used for eating\", \"similar objects\": [\"talon\", \"wing\", \"feather\"]}", + 1404 + ], + "bags": [ + " {\"type\": \"container\", \"description\": \"could be made of cloth, paper, plastic; could be used to store items\", \"similar objects\": [\"pouches\", \"boxes\", \"backpacks\"]}", + 1401 + ], + "cart": [ + " {\"type\": \"transportation tool\", \"description\": \"has two or four wheels; could be pushed or pulled; could be used to carry goods\", \"similar objects\": [\"wagon\", \"wheelbarrow\", \"hand truck\"]}", + 1400 + ], + "speaker": [ + " {\"type\": \"electronic device\", \"description\": \"could be wired or wireless; could be used to play music\", \"similar objects\": [\"headphone\", \"microphone\", \"amplifier\"]}", + 1393 + ], + "fire hydrant": [ + " {\"type\": \"utility tool\", \"description\": \"red; has a hose connection; could be used to put out fires\", \"similar objects\": [\"fire extinguisher\", \"fire alarm\", \"smoke detector\"]}", + 1391 + ], + "tall": [ + "\n\n{\"type\": \"adjective\", \"description\": \"describes something that is higher than average\", \"similar objects\": [\"high\", \"long\", \"elevated\"]}", + 1388 + ], + "branch": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, could be curved; could have leaves and fruits\", \"similar objects\": [\"twig\", \"stem\", \"trunk\"]}", + 1386 + ], + "writing": [ + " {\"type\": \"activity\", \"description\": \"using a pen or pencil to create symbols on a surface\", \"similar objects\": [\"reading\", \"typing\", \"drawing\"]}", + 1384 + ], + "umbrellas": [ + " {\"type\": \"accessory\", \"description\": \"could be opened and closed; could be made of fabric; could have a handle\", \"similar objects\": [\"parasol\", \"raincoat\", \"hat\"]}", + 1382 + ], + "baseball": [ + " {\"type\": \"sport equipment\", \"description\": \"round; made of leather; has a string\", \"similar objects\": [\"football\", \"basketball\", \"tennis ball\"]}", + 1379 + ], + "pavement": [ + " {\"type\": \"surface\", \"description\": \"hard, flat surface; could be made of concrete, asphalt, or brick\", \"similar objects\": [\"sidewalk\", \"road\", \"pathway\"]}", + 1378 + ], + "name": [ + " {\"type\": \"word\", \"description\": \"a word used to identify a person, place, or thing; could be composed of letters, numbers, or symbols\", \"similar objects\": [\"title\", \"label\", \"moniker\"]}", + 1376 + ], + "wine": [ + " {\"type\": \"beverage\", \"description\": \"alcoholic drink; could be red or white; could be served in a glass\", \"similar objects\": [\"beer\", \"whiskey\", \"vodka\"]}", + 1349 + ], + "boys": [ + "\n{\"type\": \"people\", \"description\": \"male gender; could be of different ages; could have different physical appearances\", \"similar objects\": [\"men\", \"teenagers\", \"children\"]}", + 1344 + ], + "headlights": [ + " {\"type\": \"vehicle part\", \"description\": \"two round lights; could be attached to the front of a car\", \"similar objects\": [\"taillights\", \"fog lights\", \"brake lights\"]}", + 1339 + ], + "bucket": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could have a handle; could be made of plastic or metal\", \"similar objects\": [\"pail\", \"tub\", \"barrel\"]}", + 1338 + ], + "snowboarder": [ + " {\"type\": \"sportsperson\", \"description\": \"wears a helmet; has a snowboard; could be wearing a jacket and pants\", \"similar objects\": [\"skier\", \"surfer\", \"skateboarder\"]}", + 1336 + ], + "front wheel": [ + " {\"type\": \"vehicle part\", \"description\": \"round; has a tire; could be connected to a car\", \"similar objects\": [\"back wheel\", \"engine\", \"brake\"]}", + 1329 + ], + "brick building": [ + " {\"type\": \"structure\", \"description\": \"made of bricks; could have multiple stories; could have windows and doors\", \"similar objects\": [\"house\", \"apartment\", \"skyscraper\"]}", + 1322 + ], + "shadows": [ + " {\"type\": \"visual effect\", \"description\": \"dark shapes created by blocking light; could be cast by objects\", \"similar objects\": [\"reflection\", \"silhouette\", \"glare\"]}", + 1321 + ], + "metal fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal; could be in a form of a grid; could be used to separate two areas\", \"similar objects\": [\"wood fence\", \"brick wall\", \"hedge\"]}", + 1317 + ], + "towels": [ + " {\"type\": \"household item\", \"description\": \"soft, absorbent; could be used for drying hands and body\", \"similar objects\": [\"washcloth\", \"bath mat\", \"blanket\"]}", + 1308 + ], + "tent": [ + " {\"type\": \"shelter\", \"description\": \"could be made of canvas; could be set up with poles; could be used for camping\", \"similar objects\": [\"igloo\", \"yurt\", \"teepee\"]}", + 1306 + ], + "lettuce": [ + " {\"type\": \"vegetable\", \"description\": \"green, leafy; could be shredded; could be used in salads\", \"similar objects\": [\"spinach\", \"cabbage\", \"kale\"]}", + 1304 + ], + "luggage": [ + " {\"type\": \"travel item\", \"description\": \"could be made of fabric or hard material; could have wheels; could have a handle\", \"similar objects\": [\"suitcase\", \"backpack\", \"duffel bag\"]}", + 1303 + ], + "fences": [ + " {\"type\": \"barrier\", \"description\": \"wooden or metal; could be used to separate areas; could be used to protect property\", \"similar objects\": [\"wall\", \"gate\", \"hedge\"]}", + 1300 + ], + "wooden table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have four legs\", \"similar objects\": [\"chair\", \"desk\", \"sofa\"]}", + 1297 + ], + "bun": [ + " {\"type\": \"food\", \"description\": \"round; could be made of dough; could be filled with meat or vegetables\", \"similar objects\": [\"roll\", \"bagel\", \"croissant\"]}", + 1295 + ], + "wrist": [ + " {\"type\": \"body part\", \"description\": \"connects the hand to the arm; could be used to measure pulse rate\", \"similar objects\": [\"elbow\", \"ankle\", \"knee\"]}", + 1294 + ], + "tablecloth": [ + " {\"type\": \"textile\", \"description\": \"rectangular; could be made of cotton, linen, or polyester; could be decorated with patterns\", \"similar objects\": [\"napkin\", \"placemat\", \"runner\"]}", + 1289 + ], + "vehicles": [ + "\n{\"type\": \"transportation\", \"description\": \"motorized or non-motorized; could be used for carrying people or goods; could be powered by gasoline, electricity, or other sources\", \"similar objects\": [\"car\", \"truck\", \"bicycle\", \"motorcycle\", \"boat\", \"train\"]}", + 1287 + ], + "pan": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round, has a handle\", \"similar objects\": [\"wok\", \"pot\", \"frying pan\"]}", + 1276 + ], + "buttons": [ + " {\"type\": \"accessory\", \"description\": \"small, round, could be made of plastic, metal, or fabric; could be used to fasten clothing\", \"similar objects\": [\"zippers\", \"snaps\", \"hooks\"]}", + 1273 + ], + "drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be opened and closed; could have handles\", \"similar objects\": [\"cabinet\", \"shelf\", \"wardrobe\"]}", + 1270 + ], + "train tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, parallel metal rails; could have wooden sleepers; could have electric wires\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 1265 + ], + "apples": [ + "\n{\"type\": \"fruit\", \"description\": \"red, round, has a stem; could be in a bunch\", \"similar objects\": [\"oranges\", \"bananas\", \"pears\"]}", + 1263 + ], + "headlight": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the front of a vehicle; could be round or rectangular; could be powered by electricity or gas\", \"similar objects\": [\"taillight\", \"fog light\", \"spotlight\"]}", + 1262 + ], + "paw": [ + " {\"type\": \"animal body part\", \"description\": \"soft, flexible, and rounded; could be used for gripping and holding objects; could be found on cats, dogs, and other animals\", \"similar objects\": [\"claw\", \"hoof\", \"fin\"]}", + 1249 + ], + "fruits": [ + "\n{\"type\": \"food\", \"description\": \"various types of edible plant products; could be sweet or sour; could be fresh or dried; could be eaten raw or cooked\", \"similar objects\": [\"vegetables\", \"nuts\", \"grains\"]}", + 1247 + ], + "fridge": [ + " {\"type\": \"appliance\", \"description\": \"large, white, has a door; could have a freezer\", \"similar objects\": [\"stove\", \"microwave\", \"dishwasher\"]}", + 1246 + ], + "stem": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, and hollow; could be green or brown; could be attached to leaves, flowers, or fruits\", \"similar objects\": [\"root\", \"branch\", \"leaf\"]}", + 1240 + ], + "hydrant": [ + " {\"type\": \"utility tool\", \"description\": \"red; has a hose connection; could be used to put out fires\", \"similar objects\": [\"fire extinguisher\", \"fire hose\", \"fire alarm\"]}", + 1239 + ], + "candle": [ + " {\"type\": \"lighting tool\", \"description\": \"cylindrical; could be made of wax; could have a wick\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 1238 + ], + "ring": [ + " {\"type\": \"jewelry\", \"description\": \"circular; could be made of gold, silver, or other metals; could have gemstones\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 1233 + ], + "cats": [ + " {\"type\": \"animal\", \"description\": \"furry; could have stripes or spots; could have long or short fur; could have whiskers; could have a tail\", \"similar objects\": [\"tigers\", \"lions\", \"leopards\"]}", + 1226 + ], + "pieces": [ + " {\"type\": \"object\", \"description\": \"small parts of a whole; could be of different shapes and sizes\", \"similar objects\": [\"fragments\", \"bits\", \"shards\"]}", + 1224 + ], + "gravel": [ + " {\"type\": \"construction material\", \"description\": \"small stones; could be used for driveways and pathways\", \"similar objects\": [\"sand\", \"cement\", \"concrete\"]}", + 1224 + ], + "bricks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be used to build walls\", \"similar objects\": [\"cement\", \"concrete\", \"wood\"]}", + 1223 + ], + "kites": [ + " {\"type\": \"toy\", \"description\": \"could be made of paper or plastic; has a long string; could be flown in the sky\", \"similar objects\": [\"balloons\", \"frisbees\", \"dolls\"]}", + 1223 + ], + "carrots": [ + " {\"type\": \"vegetable\", \"description\": \"orange, cylindrical, could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"potatoes\", \"parsnips\", \"turnips\"]}", + 1220 + ], + "scissors": [ + " {\"type\": \"tool\", \"description\": \"two blades connected by a pivot; could be used for cutting\", \"similar objects\": [\"knife\", \"pliers\", \"tweezers\"]}", + 1220 + ], + "pitcher": [ + " {\"type\": \"utensil\", \"description\": \"tall, has a handle and a spout; could be made of glass or metal\", \"similar objects\": [\"jug\", \"vase\", \"teapot\"]}", + 1216 + ], + "jar": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass or plastic; could have a lid\", \"similar objects\": [\"bottle\", \"can\", \"box\"]}", + 1216 + ], + "graffiti": [ + " {\"type\": \"art form\", \"description\": \"images or words painted on a wall or other surface; could be colorful\", \"similar objects\": [\"murals\", \"stencils\", \"street art\"]}", + 1212 + ], + "mug": [ + " {\"type\": \"drinking vessel\", \"description\": \"cylindrical; could have a handle; could be made of ceramic, glass, or metal\", \"similar objects\": [\"cup\", \"glass\", \"teapot\"]}", + 1211 + ], + "brick": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be used to build walls\", \"similar objects\": [\"concrete\", \"stone\", \"wood\"]}", + 1207 + ], + "clothing item": [ + " {\"type\": \"clothing item\", \"description\": \"could be made of fabric; could be of any color; could have buttons, zippers, or other fasteners; could have pockets, collars, or other features\", \"similar objects\": [\"shirt\", \"pants\", \"dress\"]}", + 1201 + ], + "gate": [ + " {\"type\": \"structure\", \"description\": \"could be made of metal or wood; could be used to block access to a certain area; could have a lock\", \"similar objects\": [\"fence\", \"wall\", \"door\"]}", + 1200 + ], + "faucet": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be used to control water flow\", \"similar objects\": [\"shower head\", \"hose\", \"valve\"]}", + 1185 + ], + "net": [ + " {\"type\": \"tool\", \"description\": \"made of strings; could be used to catch fish or birds\", \"similar objects\": [\"fishing rod\", \"trap\", \"seine\"]}", + 1179 + ], + "computer mouse": [ + " {\"type\": \"computer accessory\", \"description\": \"small, rectangular; has two buttons and a wheel; connected to a computer\", \"similar objects\": [\"keyboard\", \"monitor\", \"printer\"]}", + 1177 + ], + "coffee table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have a glass top; could have drawers\", \"similar objects\": [\"end table\", \"side table\", \"console table\"]}", + 1177 + ], + "stand": [ + " {\"type\": \"furniture\", \"description\": \"could be used to support something; could be adjustable; could be made of metal or wood\", \"similar objects\": [\"table\", \"chair\", \"shelf\"]}", + 1166 + ], + "ski": [ + " {\"type\": \"sport equipment\", \"description\": \"long, thin, curved; could be used for skiing\", \"similar objects\": [\"snowboard\", \"skateboard\", \"rollerblades\"]}", + 1166 + ], + "brick wall": [ + " {\"type\": \"structure\", \"description\": \"made of bricks; could be painted; could be used as a barrier\", \"similar objects\": [\"stone wall\", \"wooden fence\", \"concrete wall\"]}", + 1159 + ], + "chain": [ + " {\"type\": \"accessory\", \"description\": \"made of metal; could be used to hang things; could be used as a decoration\", \"similar objects\": [\"rope\", \"cable\", \"string\"]}", + 1155 + ], + "strap": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of leather or fabric; could be used to hold items together\", \"similar objects\": [\"belt\", \"rope\", \"string\"]}", + 1148 + ], + "pepper": [ + " {\"type\": \"spice\", \"description\": \"small, round, could be red, green, yellow, or black; could be ground into powder\", \"similar objects\": [\"salt\", \"cumin\", \"cayenne pepper\"]}", + 1146 + ], + "court": [ + " {\"type\": \"place\", \"description\": \"indoor or outdoor; could have a basketball court; could have a tennis court\", \"similar objects\": [\"stadium\", \"gym\", \"arena\"]}", + 1144 + ], + "roll": [ + " {\"type\": \"food\", \"description\": \"cylindrical; could be made of bread; could be filled with different ingredients\", \"similar objects\": [\"bagel\", \"croissant\", \"bun\"]}", + 1143 + ], + "tables": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could have four legs; could be made of wood or metal\", \"similar objects\": [\"chair\", \"desk\", \"sofa\"]}", + 1135 + ], + "baseball bat": [ + " {\"type\": \"sports equipment\", \"description\": \"long, cylindrical; could be made of wood or metal; used to hit a baseball\", \"similar objects\": [\"golf club\", \"tennis racket\", \"hockey stick\"]}", + 1131 + ], + "portion": [ + " {\"type\": \"measurement\", \"description\": \"a part of a whole; could be divided into equal parts\", \"similar objects\": [\"serving\", \"slice\", \"share\"]}", + 1117 + ], + "hole": [ + " {\"type\": \"opening\", \"description\": \"empty space; could be round or square; could be in the ground or in the wall\", \"similar objects\": [\"cave\", \"tunnel\", \"crack\"]}", + 1115 + ], + "carrot": [ + " {\"type\": \"vegetable\", \"description\": \"orange; long and thin; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"parsnip\", \"radish\", \"turnip\"]}", + 1112 + ], + "papers": [ + " {\"type\": \"stationery\", \"description\": \"flat; could be made of wood pulp; could be used for writing\", \"similar objects\": [\"notebook\", \"envelope\", \"pencil\"]}", + 1110 + ], + "water bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic; could have a lid\", \"similar objects\": [\"thermos\", \"mug\", \"cup\"]}", + 1107 + ], + "stairs": [ + " {\"type\": \"structure\", \"description\": \"could be made of wood or metal; could have multiple steps; could have a railing\", \"similar objects\": [\"ladder\", \"escalator\", \"elevator\"]}", + 1104 + ], + "toothbrush": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has bristles; could be manual or electric\", \"similar objects\": [\"toothpaste\", \"dental floss\", \"mouthwash\"]}", + 1102 + ], + "clock tower": [ + " {\"type\": \"structure\", \"description\": \"tall; has a clock face; could have bells\", \"similar objects\": [\"bell tower\", \"windmill\", \"lighthouse\"]}", + 1101 + ], + "jersey": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have a team logo; could be made of cotton\", \"similar objects\": [\"t-shirt\", \"hoodie\", \"sweater\"]}", + 1099 + ], + "wine glass": [ + " {\"type\": \"drinking tool\", \"description\": \"tall and thin; has a stem; could have a round bowl\", \"similar objects\": [\"beer mug\", \"tumbler\", \"coffee cup\"]}", + 1090 + ], + "finger": [ + " {\"type\": \"body part\", \"description\": \"long; has a nail; could be used to point\", \"similar objects\": [\"toe\", \"hand\", \"arm\"]}", + 1086 + ], + "surf board": [ + " {\"type\": \"sports equipment\", \"description\": \"long, wide, flat; could be made of foam or wood; could have a leash\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 1077 + ], + "banner": [ + " {\"type\": \"decoration\", \"description\": \"long and thin; could be made of cloth or paper; could be hung on walls or ceilings\", \"similar objects\": [\"flag\", \"poster\", \"sign\"]}", + 1069 + ], + "onion": [ + " {\"type\": \"vegetable\", \"description\": \"round; has a papery skin; could be sliced into rings; could have a strong smell\", \"similar objects\": [\"garlic\", \"shallot\", \"leek\"]}", + 1061 + ], + "toilet seat": [ + " {\"type\": \"bathroom fixture\", \"description\": \"round; could be made of plastic or wood; could be attached to a toilet bowl\", \"similar objects\": [\"bathtub\", \"sink\", \"shower\"]}", + 1061 + ], + "flags": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; could be made of cloth; could have symbols or words\", \"similar objects\": [\"banners\", \"posters\", \"signs\"]}", + 1060 + ], + "computer monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could be connected to a computer\", \"similar objects\": [\"television\", \"laptop\", \"tablet\"]}", + 1060 + ], + "sock": [ + " {\"type\": \"clothing item\", \"description\": \"worn on the feet; could be made of cotton, wool, or synthetic materials; could be ankle-length or knee-length\", \"similar objects\": [\"shoe\", \"boot\", \"sandal\"]}", + 1057 + ], + "lettering": [ + " {\"type\": \"art form\", \"description\": \"creating decorative lettering; could be done with brush, pen, or computer\", \"similar objects\": [\"calligraphy\", \"typography\", \"hand lettering\"]}", + 1052 + ], + "pipe": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical; could be made of metal or plastic; could be used to transport liquids or gases\", \"similar objects\": [\"hose\", \"tube\", \"conduit\"]}", + 1049 + ], + "jet": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has wings; could be powered by jet engines\", \"similar objects\": [\"airplane\", \"helicopter\", \"rocket\"]}", + 1049 + ], + "train car": [ + " {\"type\": \"vehicle\", \"description\": \"long; could have multiple compartments; could have a locomotive\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 1032 + ], + "baseball cap": [ + " {\"type\": \"clothing item\", \"description\": \"has a brim; could be adjustable; could have a logo\", \"similar objects\": [\"hat\", \"beanie\", \"visor\"]}", + 1029 + ], + "tan": [ + " {\"type\": \"color\", \"description\": \"light brown; could be used to describe skin color\", \"similar objects\": [\"beige\", \"brown\", \"camel\"]}", + 1026 + ], + "necklace": [ + " {\"type\": \"jewelry\", \"description\": \"chain with a pendant; could be made of metal, beads, or stones\", \"similar objects\": [\"bracelet\", \"earrings\", \"ring\"]}", + 1026 + ], + "onions": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be white, yellow, or red; has a papery skin; could be sliced into rings\", \"similar objects\": [\"garlic\", \"shallots\", \"leeks\"]}", + 1025 + ], + "cellphone": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; could have a touchscreen; could have a camera\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 1022 + ], + "sheet": [ + " {\"type\": \"fabric\", \"description\": \"flat, thin, rectangular; could be made of cotton, silk, or linen\", \"similar objects\": [\"blanket\", \"towel\", \"curtain\"]}", + 1011 + ], + "motorcycles": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has an engine; could have a sidecar\", \"similar objects\": [\"bicycle\", \"scooter\", \"moped\"]}", + 1005 + ], + "buses": [ + " {\"type\": \"vehicle\", \"description\": \"large; has multiple doors; could be used for public transportation\", \"similar objects\": [\"trucks\", \"cars\", \"trains\"]}", + 1004 + ], + "track": [ + " {\"type\": \"sports facility\", \"description\": \"oval-shaped; has lanes; could be used for running or racing\", \"similar objects\": [\"stadium\", \"field\", \"court\"]}", + 999 + ], + "benches": [ + " {\"type\": \"furniture\", \"description\": \"long, wooden, could have backrests\", \"similar objects\": [\"chairs\", \"sofas\", \"tables\"]}", + 995 + ], + "stick": [ + " {\"type\": \"object\", \"description\": \"long, thin, cylindrical; could be made of wood, metal, or plastic; could be used as a tool or weapon\", \"similar objects\": [\"rod\", \"baton\", \"pole\"]}", + 991 + ], + "motorbike": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"moped\"]}", + 983 + ], + "ladder": [ + " {\"type\": \"tool\", \"description\": \"long; could be made of metal or wood; could have steps\", \"similar objects\": [\"stool\", \"step ladder\", \"scaffolding\"]}", + 980 + ], + "trains": [ + " {\"type\": \"transportation\", \"description\": \"long; could have multiple cars; could be powered by electricity or diesel\", \"similar objects\": [\"trams\", \"subways\", \"buses\"]}", + 976 + ], + "donuts": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be glazed or filled with cream\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 974 + ], + "steps": [ + " {\"type\": \"structure\", \"description\": \"could be made of concrete, wood, or metal; could have a railing; could be used to climb up or down\", \"similar objects\": [\"stairs\", \"ladder\", \"ramp\"]}", + 973 + ], + "pictures": [ + " {\"type\": \"visual art\", \"description\": \"could be a painting, photograph, drawing, or other visual representation\", \"similar objects\": [\"sculpture\", \"mural\", \"collage\"]}", + 967 + ], + "fries": [ + " {\"type\": \"food\", \"description\": \"long, thin, golden-brown; could be served with ketchup\", \"similar objects\": [\"chips\", \"wedges\", \"onion rings\"]}", + 965 + ], + "bikes": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could have a basket; could have a bell\", \"similar objects\": [\"scooter\", \"motorcycle\", \"tricycle\"]}", + 959 + ], + "planes": [ + " {\"type\": \"vehicle\", \"description\": \"long and thin; has wings; could fly in the sky\", \"similar objects\": [\"helicopter\", \"rocket\", \"drone\"]}", + 959 + ], + "front tire": [ + " {\"type\": \"automobile part\", \"description\": \"round; made of rubber; located at the front of the car\", \"similar objects\": [\"rear tire\", \"wheel rim\", \"spare tire\"]}", + 956 + ], + "shelves": [ + " {\"type\": \"furniture\", \"description\": \"horizontal or vertical structure; could be made of wood or metal; could be used to store items\", \"similar objects\": [\"cabinet\", \"bookcase\", \"wardrobe\"]}", + 956 + ], + "weeds": [ + " {\"type\": \"plant\", \"description\": \"green; could have long stems; could be found in gardens; could be difficult to remove\", \"similar objects\": [\"grass\", \"clover\", \"dandelion\"]}", + 952 + ], + "arms": [ + " {\"type\": \"body part\", \"description\": \"upper limbs; could be used to hold objects; could be used to move the body\", \"similar objects\": [\"legs\", \"hands\", \"fingers\"]}", + 947 + ], + "salad": [ + " {\"type\": \"food\", \"description\": \"mixture of vegetables and fruits; could be served with dressing\", \"similar objects\": [\"fruit salad\", \"pasta salad\", \"coleslaw\"]}", + 944 + ], + "boxes": [ + " {\"type\": \"container\", \"description\": \"could be made of cardboard; could be of different shapes and sizes; could be used for storage\", \"similar objects\": [\"bins\", \"bags\", \"crates\"]}", + 943 + ], + "telephone": [ + " {\"type\": \"communication tool\", \"description\": \"has a handset; could be cordless; could have a keypad\", \"similar objects\": [\"cell phone\", \"walkie-talkie\", \"intercom\"]}", + 942 + ], + "cloudy sky": [ + " {\"type\": \"weather\", \"description\": \"grayish; could have white and dark clouds; could have rain\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"foggy sky\"]}", + 941 + ], + "label": [ + " {\"type\": \"identification tool\", \"description\": \"small, rectangular; could be printed with words or symbols\", \"similar objects\": [\"tag\", \"sticker\", \"badge\"]}", + 940 + ], + "whiskers": [ + " {\"type\": \"body part\", \"description\": \"long, thin, and stiff hairs on the face of some animals\", \"similar objects\": [\"fur\", \"mane\", \"tail\"]}", + 940 + ], + "purple": [ + " {\"type\": \"color\", \"description\": \"a mix of red and blue; could be light or dark\", \"similar objects\": [\"violet\", \"magenta\", \"indigo\"]}", + 939 + ], + "mans": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting trousers; could be made of cotton or linen; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"trousers\"]}", + 938 + ], + "toilet paper": [ + " {\"type\": \"household item\", \"description\": \"white; could be in roll form; could be used for cleaning\", \"similar objects\": [\"tissue paper\", \"paper towel\", \"napkin\"]}", + 938 + ], + "clocks": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has hands; could have digital display\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}", + 937 + ], + "crust": [ + " {\"type\": \"food\", \"description\": \"hard, thin layer; could be made of flour, butter, and sugar; could be used as a base for pies and tarts\", \"similar objects\": [\"pie crust\", \"tart crust\", \"pastry crust\"]}", + 929 + ], + "ramp": [ + " {\"type\": \"structure\", \"description\": \"sloped; could be made of metal or wood; could be used to bridge two levels\", \"similar objects\": [\"stairs\", \"ladder\", \"escalator\"]}", + 922 + ], + "tomatoes": [ + " {\"type\": \"vegetable\", \"description\": \"red, round, could be sliced; could have green leaves\", \"similar objects\": [\"potatoes\", \"onions\", \"bell peppers\"]}", + 921 + ], + "garbage": [ + " {\"type\": \"waste\", \"description\": \"discarded materials; could be organic or inorganic; could be hazardous\", \"similar objects\": [\"trash\", \"rubbish\", \"refuse\"]}", + 921 + ], + "suv": [ + " {\"type\": \"vehicle\", \"description\": \"large, four-wheeled, has a high ground clearance\", \"similar objects\": [\"truck\", \"minivan\", \"sedan\"]}", + 919 + ], + "curb": [ + " {\"type\": \"road feature\", \"description\": \"raised edge of a sidewalk; could be made of concrete\", \"similar objects\": [\"gutter\", \"median\", \"barrier\"]}", + 918 + ], + "traffic lights": [ + " {\"type\": \"traffic control device\", \"description\": \"three lights in a vertical line; red, yellow, and green; could be mounted on a pole\", \"similar objects\": [\"stop sign\", \"yield sign\", \"crosswalk sign\"]}", + 917 + ], + "stone": [ + " {\"type\": \"natural object\", \"description\": \"hard, solid, could be of various shapes and sizes; could be found in nature\", \"similar objects\": [\"rock\", \"pebble\", \"boulder\"]}", + 913 + ], + "walls": [ + " {\"type\": \"structure\", \"description\": \"vertical, solid, could be made of bricks, concrete, wood, etc.\", \"similar objects\": [\"fences\", \"doors\", \"windows\"]}", + 906 + ], + "dessert": [ + " {\"type\": \"food\", \"description\": \"sweet; could be served after a meal; could be made of fruits, cream, or chocolate\", \"similar objects\": [\"cake\", \"ice cream\", \"pie\"]}", + 898 + ], + "home plate": [ + " {\"type\": \"sports equipment\", \"description\": \"a white, pentagonal plate; used in baseball\", \"similar objects\": [\"baseball bat\", \"baseball glove\", \"catcher's mask\"]}", + 898 + ], + "tee shirt": [ + " {\"type\": \"clothing\", \"description\": \"short sleeves; could have a collar; could have a pocket; could be plain or printed\", \"similar objects\": [\"tank top\", \"polo shirt\", \"hoodie\"]}", + 894 + ], + "palm tree": [ + " {\"type\": \"plant\", \"description\": \"tall; has a trunk; has long leaves\", \"similar objects\": [\"coconut tree\", \"banana tree\", \"pine tree\"]}", + 893 + ], + "plastic": [ + " {\"type\": \"material\", \"description\": \"flexible; could be transparent; could be colored; could be molded into different shapes\", \"similar objects\": [\"rubber\", \"glass\", \"metal\"]}", + 891 + ], + "feathers": [ + " {\"type\": \"material\", \"description\": \"light and fluffy; could be used for decoration; could be from birds\", \"similar objects\": [\"down\", \"fur\", \"wool\"]}", + 890 + ], + "cups": [ + " {\"type\": \"utensil\", \"description\": \"round; could be made of plastic, glass, or ceramic; could have handles\", \"similar objects\": [\"mugs\", \"bowls\", \"plates\"]}", + 885 + ], + "fingers": [ + " {\"type\": \"body part\", \"description\": \"five digits; could be used to point or grab things\", \"similar objects\": [\"toes\", \"hands\", \"arms\"]}", + 885 + ], + "poster": [ + " {\"type\": \"decoration\", \"description\": \"printed paper; could be hung on the wall\", \"similar objects\": [\"painting\", \"photo frame\", \"wall sticker\"]}", + 885 + ], + "doorway": [ + " {\"type\": \"architectural feature\", \"description\": \"rectangular; could have a door; could have a frame\", \"similar objects\": [\"window\", \"archway\", \"gate\"]}", + 881 + ], + "boot": [ + " {\"type\": \"footwear\", \"description\": \"long; could be made of leather; could have laces\", \"similar objects\": [\"shoe\", \"sandal\", \"sneaker\"]}", + 877 + ], + "outlet": [ + " {\"type\": \"electrical device\", \"description\": \"has two or more slots; could be used to plug in electrical appliances\", \"similar objects\": [\"switch\", \"socket\", \"plug\"]}", + 877 + ], + "front legs": [ + " {\"type\": \"body part\", \"description\": \"two long legs located at the front of the body; could be used for walking and running\", \"similar objects\": [\"hind legs\", \"arms\", \"wings\"]}", + 874 + ], + "distance": [ + " {\"type\": \"measurement\", \"description\": \"the length between two points\", \"similar objects\": [\"length\", \"width\", \"height\"]}", + 874 + ], + "oranges": [ + " {\"type\": \"fruit\", \"description\": \"round; orange in color; has a stem; could be peeled and segmented\", \"similar objects\": [\"lemons\", \"grapefruits\", \"tangerines\"]}", + 873 + ], + "drawers": [ + " {\"type\": \"furniture\", \"description\": \"has multiple compartments; could be made of wood or metal; could be used for storage\", \"similar objects\": [\"cabinet\", \"wardrobe\", \"chest of drawers\"]}", + 872 + ], + "clothes": [ + " {\"type\": \"clothing\", \"description\": \"fabric; could be in different colors and styles; could be for different occasions\", \"similar objects\": [\"shirt\", \"pants\", \"dress\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant", + 867 + ], + "spectators": [ + " {\"type\": \"people\", \"description\": \"people who watch an event; could be cheering\", \"similar objects\": [\"audience\", \"fans\", \"viewers\"]}", + 867 + ], + "plastic bag": [ + " {\"type\": \"container\", \"description\": \"transparent; could be sealed; could be used to store items\", \"similar objects\": [\"paper bag\", \"box\", \"envelope\"]}", + 865 + ], + "wings": [ + " {\"type\": \"body part\", \"description\": \"attached to the body; could be feathered; could be used for flying\", \"similar objects\": [\"beak\", \"talons\", \"feathers\"]}", + 865 + ], + "helmets": [ + " {\"type\": \"protective gear\", \"description\": \"hard; could be made of plastic or metal; could be used for sports or safety\", \"similar objects\": [\"goggles\", \"gloves\", \"knee pads\"]}", + 858 + ], + "ripples": [ + " {\"type\": \"wave pattern\", \"description\": \"circular; could be seen on the surface of water; could be caused by wind or stones\", \"similar objects\": [\"waves\", \"tides\", \"whirlpools\"]}", + 856 + ], + "chain link fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal links; could be used to enclose an area\", \"similar objects\": [\"barbed wire fence\", \"wooden fence\", \"brick wall\"]}", + 853 + ], + "rice": [ + " {\"type\": \"food\", \"description\": \"small, white, grainy; could be cooked into a dish\", \"similar objects\": [\"wheat\", \"barley\", \"quinoa\"]}", + 851 + ], + "beard": [ + " {\"type\": \"facial hair\", \"description\": \"long, thick hair on the face; could be trimmed or untrimmed\", \"similar objects\": [\"mustache\", \"goatee\", \"sideburns\"]}", + 850 + ], + "wetsuit": [ + " {\"type\": \"clothing\", \"description\": \"tight-fitting; made of neoprene; designed to keep the body warm in cold water\", \"similar objects\": [\"drysuit\", \"swimsuit\", \"diving suit\"]}", + 850 + ], + "log": [ + " {\"type\": \"wood\", \"description\": \"long, cylindrical; could be used as firewood\", \"similar objects\": [\"branch\", \"stick\", \"timber\"]}", + 848 + ], + "sleeve": [ + " {\"type\": \"clothing item\", \"description\": \"long, cylindrical, covers arms; could be attached to a shirt or dress\", \"similar objects\": [\"collar\", \"hem\", \"cuff\"]}", + 844 + ], + "parking meter": [ + " {\"type\": \"parking tool\", \"description\": \"tall, cylindrical; has a slot for coins; could have a digital display\", \"similar objects\": [\"parking sign\", \"parking lot\", \"parking garage\"]}", + 837 + ], + "smoke": [ + " {\"type\": \"phenomenon\", \"description\": \"grayish, could be seen in the air; could be caused by burning\", \"similar objects\": [\"fog\", \"haze\", \"dust\"]}", + 833 + ], + "slices": [ + " {\"type\": \"food item\", \"description\": \"thin, cut pieces of food; could be of any food item\", \"similar objects\": [\"wedges\", \"dices\", \"strips\"]}", + 830 + ], + "doughnut": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be glazed or filled with cream\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 828 + ], + "mat": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of fabric, foam, or rubber; could be used for yoga or exercise\", \"similar objects\": [\"rug\", \"carpet\", \"towel\"]}", + 827 + ], + "bracelet": [ + " {\"type\": \"jewelry\", \"description\": \"circular; could be made of metal, plastic, or fabric; could have charms or beads\", \"similar objects\": [\"necklace\", \"earrings\", \"ring\"]}", + 827 + ], + "shore": [ + " {\"type\": \"landscape\", \"description\": \"edge of a body of water; could have sand or rocks; could have waves\", \"similar objects\": [\"beach\", \"coast\", \"seashore\"]}", + 826 + ], + "houses": [ + " {\"type\": \"structure\", \"description\": \"could be made of bricks, wood, or other materials; could have a roof, windows, and doors; could have multiple stories\", \"similar objects\": [\"apartments\", \"condos\", \"townhouses\"]}", + 822 + ], + "control": [ + " {\"type\": \"concept\", \"description\": \"ability to influence or direct the behavior of others\", \"similar objects\": [\"influence\", \"power\", \"authority\"]}", + 821 + ], + "tank top": [ + " {\"type\": \"clothing\", \"description\": \"sleeveless; could be made of cotton; could have straps\", \"similar objects\": [\"t-shirt\", \"vest\", \"camisole\"]}", + 815 + ], + "knee": [ + " {\"type\": \"body part\", \"description\": \"joint between the thigh and the lower leg; could bend and straighten\", \"similar objects\": [\"elbow\", \"ankle\", \"shoulder\"]}", + 814 + ], + "silver car": [ + "\n{\"type\": \"vehicle\", \"description\": \"metallic color; could have four doors; could have a sunroof\", \"similar objects\": [\"black car\", \"white car\", \"gray car\"]}", + 806 + ], + "string": [ + " {\"type\": \"material\", \"description\": \"long, thin, flexible; could be made of cotton, nylon, or other materials\", \"similar objects\": [\"rope\", \"wire\", \"thread\"]}", + 805 + ], + "bathtub": [ + " {\"type\": \"bathroom fixture\", \"description\": \"large, deep, could be made of porcelain; could have a shower head\", \"similar objects\": [\"shower\", \"sink\", \"toilet\"]}", + 804 + ], + "egg": [ + " {\"type\": \"food\", \"description\": \"oval; white or brown; could be boiled, fried, or scrambled\", \"similar objects\": [\"yolk\", \"omelette\", \"quail egg\"]}", + 798 + ], + "balcony": [ + " {\"type\": \"architectural feature\", \"description\": \"an outdoor space attached to a building; could have a railing; could have a roof\", \"similar objects\": [\"terrace\", \"patio\", \"deck\"]}", + 797 + ], + "soccer ball": [ + " {\"type\": \"sports equipment\", \"description\": \"round; black and white pentagons; could be inflated\", \"similar objects\": [\"basketball\", \"baseball\", \"tennis ball\"]}", + 797 + ], + "teeth": [ + " {\"type\": \"body part\", \"description\": \"white; could be sharp; could be arranged in a row\", \"similar objects\": [\"fingernails\", \"hair\", \"eyelashes\"]}", + 795 + ], + "bar": [ + " {\"type\": \"establishment\", \"description\": \"could be a restaurant, pub, or nightclub; could have a counter and stools; could serve alcoholic beverages\", \"similar objects\": [\"cafe\", \"pub\", \"lounge\"]}", + 790 + ], + "remote": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; has buttons; could be used to control other devices\", \"similar objects\": [\"game controller\", \"keyboard\", \"mouse\"]}", + 788 + ], + "power lines": [ + " {\"type\": \"utility\", \"description\": \"long, thin wires; could be connected to poles; could be used to transmit electricity\", \"similar objects\": [\"telephone lines\", \"cable lines\", \"fiber optic cables\"]}", + 788 + ], + "hills": [ + " {\"type\": \"landscape\", \"description\": \"sloped landforms; could be covered with grass or trees; could have a peak\", \"similar objects\": [\"mountains\", \"valleys\", \"plateaus\"]}", + 783 + ], + "tennis shoes": [ + " {\"type\": \"footwear\", \"description\": \"lightweight; has a flexible sole; could be made of leather or fabric; could have laces\", \"similar objects\": [\"sneakers\", \"running shoes\", \"hiking boots\"]}", + 783 + ], + "scooter": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could be powered by electricity or gasoline; could have a handlebar\", \"similar objects\": [\"motorcycle\", \"bicycle\", \"skateboard\"]}", + 783 + ], + "advertisement": [ + " {\"type\": \"promotional material\", \"description\": \"could be in the form of text, image, video, audio; could be used to promote products or services\", \"similar objects\": [\"commercial\", \"infomercial\", \"billboard\"]}", + 781 + ], + "straw": [ + " {\"type\": \"utensil\", \"description\": \"long, cylindrical; could be made of plastic or paper; used for drinking\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 779 + ], + "outfit": [ + " {\"type\": \"clothing\", \"description\": \"a set of clothing items; could be a dress, a shirt and a pair of pants\", \"similar objects\": [\"costume\", \"uniform\", \"ensemble\"]}", + 779 + ], + "rail": [ + " {\"type\": \"transportation tool\", \"description\": \"long, metal; could be used for train or tram\", \"similar objects\": [\"track\", \"monorail\", \"tramway\"]}", + 776 + ], + "rack": [ + " {\"type\": \"storage tool\", \"description\": \"vertical; could have shelves; could be made of metal or wood\", \"similar objects\": [\"shelf\", \"cabinet\", \"drawer\"]}", + 770 + ], + "hats": [ + " {\"type\": \"clothing accessory\", \"description\": \"could be made of different materials; could be in different shapes and sizes; could be used to protect from the sun or cold\", \"similar objects\": [\"scarf\", \"gloves\", \"sunglasses\"]}", + 761 + ], + "peppers": [ + " {\"type\": \"vegetable\", \"description\": \"various colors; could be sliced into pieces; could be spicy\", \"similar objects\": [\"tomatoes\", \"onions\", \"cucumbers\"]}", + 761 + ], + "coffee cup": [ + " {\"type\": \"drinking vessel\", \"description\": \"cylindrical; could have a handle; could be made of ceramic, glass, or plastic\", \"similar objects\": [\"mug\", \"teacup\", \"glass\"]}", + 760 + ], + "cone": [ + " {\"type\": \"geometric shape\", \"description\": \"three-dimensional shape with a circular base and a pointed top\", \"similar objects\": [\"pyramid\", \"cylinder\", \"sphere\"]}", + 755 + ], + "street signs": [ + " {\"type\": \"road signs\", \"description\": \"rectangular; could be made of metal; could have symbols or words\", \"similar objects\": [\"traffic lights\", \"road markings\", \"speed limit signs\"]}", + 755 + ], + "glass window": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be framed; could be opened\", \"similar objects\": [\"door\", \"curtain\", \"shutter\"]}", + 754 + ], + "tennis shoe": [ + " {\"type\": \"footwear\", \"description\": \"made of fabric and rubber; has laces; could be white or colorful\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 753 + ], + "silver fork": [ + "\n{\"type\": \"utensil\", \"description\": \"made of silver; has four tines; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 751 + ], + "street lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lantern\", \"lamp\", \"light post\"]}", + 743 + ], + "traffic sign": [ + " {\"type\": \"road sign\", \"description\": \"could be in different shapes and colors; could have symbols or words; could be used to indicate directions or warnings\", \"similar objects\": [\"road sign\", \"traffic light\", \"road marker\"]}", + 743 + ], + "wristband": [ + " {\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of fabric, leather, or metal; could have a buckle or clasp\", \"similar objects\": [\"bracelet\", \"watch\", \"anklet\"]}", + 742 + ], + "watercraft": [ + " {\"type\": \"vehicle\", \"description\": \"floats on water; could be powered by motor or sail; could be used for transportation or recreation\", \"similar objects\": [\"boat\", \"yacht\", \"canoe\"]}", + 738 + ], + "parking lot": [ + " {\"type\": \"location\", \"description\": \"large area with designated parking spaces; could have a gate or entrance\", \"similar objects\": [\"garage\", \"driveway\", \"parking garage\"]}", + 737 + ], + "aircraft": [ + " {\"type\": \"vehicle\", \"description\": \"large; has wings; could have multiple engines; could be used for transportation\", \"similar objects\": [\"helicopter\", \"rocket\", \"airship\"]}", + 736 + ], + "duck": [ + " {\"type\": \"animal\", \"description\": \"yellow bill; webbed feet; could quack\", \"similar objects\": [\"goose\", \"swan\", \"pigeon\"]}", + 732 + ], + "knob": [ + " {\"type\": \"hardware\", \"description\": \"round; used to open or close a door or drawer\", \"similar objects\": [\"handle\", \"hinge\", \"lock\"]}", + 730 + ], + "lot": [ + " {\"type\": \"land area\", \"description\": \"a piece of land; could be used for building or parking\", \"similar objects\": [\"plot\", \"field\", \"yard\"]}", + 730 + ], + "liquid": [ + " {\"type\": \"substance\", \"description\": \"fluid; can take the shape of its container; can flow\", \"similar objects\": [\"gas\", \"solid\", \"plasma\"]}", + 727 + ], + "fireplace": [ + " {\"type\": \"heating tool\", \"description\": \"could be made of bricks; has a chimney; could have a mantel\", \"similar objects\": [\"stove\", \"wood burning stove\", \"fire pit\"]}", + 721 + ], + "hay": [ + " {\"type\": \"agricultural product\", \"description\": \"dried grass; could be used as animal feed\", \"similar objects\": [\"straw\", \"stubble\", \"silage\"]}", + 721 + ], + "shade": [ + " {\"type\": \"protection tool\", \"description\": \"could be made of fabric; could be used to block sunlight\", \"similar objects\": [\"umbrella\", \"hat\", \"sunglasses\"]}", + 720 + ], + "clock face": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has numbers and hands; could be digital or analog\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}", + 719 + ], + "tusk": [ + " {\"type\": \"animal body part\", \"description\": \"long, curved, ivory; found in elephants and walruses\", \"similar objects\": [\"horns\", \"antlers\", \"claws\"]}", + 717 + ], + "traffic signal": [ + " {\"type\": \"road safety tool\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"stop sign\", \"yield sign\", \"crosswalk sign\"]}", + 712 + ], + "band": [ + " {\"type\": \"musical group\", \"description\": \"group of musicians playing instruments together\", \"similar objects\": [\"orchestra\", \"choir\", \"ensemble\"]}", + 711 + ], + "dresser": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular, has drawers\", \"similar objects\": [\"chest of drawers\", \"wardrobe\", \"armoire\"]}", + 707 + ], + "beer": [ + " {\"type\": \"beverage\", \"description\": \"alcoholic; could be light or dark; could be served in a bottle or can\", \"similar objects\": [\"wine\", \"whiskey\", \"vodka\"]}", + 707 + ], + "sunlight": [ + " {\"type\": \"natural light\", \"description\": \"bright, yellowish, warm; could be blocked by clouds\", \"similar objects\": [\"moonlight\", \"starlight\", \"firelight\"]}", + 707 + ], + "pocket": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, sewn onto clothing; could be used to store items\", \"similar objects\": [\"pouch\", \"bag\", \"wallet\"]}", + 704 + ], + "cream": [ + " {\"type\": \"dairy product\", \"description\": \"white, thick, creamy; could be used for baking\", \"similar objects\": [\"butter\", \"yogurt\", \"milk\"]}", + 697 + ], + "star": [ + " {\"type\": \"celestial object\", \"description\": \"bright, twinkling, could be seen in the night sky\", \"similar objects\": [\"sun\", \"moon\", \"planet\"]}", + 689 + ], + "fixture": [ + " {\"type\": \"hardware\", \"description\": \"attached to a wall or ceiling; could be used to hold a light bulb\", \"similar objects\": [\"hook\", \"bracket\", \"hanger\"]}", + 688 + ], + "coffee mug": [ + " {\"type\": \"drinking vessel\", \"description\": \"cylindrical; could have a handle; could have a lid\", \"similar objects\": [\"teacup\", \"glass\", \"thermos\"]}", + 687 + ], + "herd": [ + " {\"type\": \"group of animals\", \"description\": \"a large group of animals, usually of the same species, that move together\", \"similar objects\": [\"flock\", \"pack\", \"school\"]}", + 686 + ], + "strip": [ + " {\"type\": \"object\", \"description\": \"long and thin; could be made of cloth, paper, or metal; could be used for decoration or binding\", \"similar objects\": [\"ribbon\", \"belt\", \"rope\"]}", + 683 + ], + "headboard": [ + " {\"type\": \"furniture\", \"description\": \"attached to the head of a bed; could be made of wood or metal; could have decorative designs\", \"similar objects\": [\"bed frame\", \"mattress\", \"pillow\"]}", + 681 + ], + "propeller": [ + " {\"type\": \"mechanical device\", \"description\": \"has multiple blades; could be used to generate thrust\", \"similar objects\": [\"turbine\", \"fan\", \"pump\"]}", + 681 + ], + "stones": [ + " {\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be made of different materials; could be found in nature\", \"similar objects\": [\"rocks\", \"pebbles\", \"boulders\"]}", + 680 + ], + "trucks": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple axles; could be used for transportation of goods\", \"similar objects\": [\"lorry\", \"trailer\", \"van\"]}", + 676 + ], + "thumb": [ + " {\"type\": \"body part\", \"description\": \"short, stubby finger; could be used to point\", \"similar objects\": [\"index finger\", \"middle finger\", \"pinky finger\"]}", + 675 + ], + "walkway": [ + " {\"type\": \"structure\", \"description\": \"a path for people to walk on; could be made of concrete, asphalt, or wood\", \"similar objects\": [\"sidewalk\", \"pathway\", \"driveway\"]}", + 675 + ], + "planter": [ + " {\"type\": \"gardening tool\", \"description\": \"container for plants; could be made of plastic, metal, or ceramic; could have a drainage hole\", \"similar objects\": [\"pot\", \"flowerpot\", \"vase\"]}", + 670 + ], + "tires": [ + " {\"type\": \"automotive part\", \"description\": \"round; made of rubber; used to support the weight of a vehicle\", \"similar objects\": [\"wheels\", \"rims\", \"brakes\"]}", + 669 + ], + "hood": [ + " {\"type\": \"clothing item\", \"description\": \"attached to a jacket; could be made of fur; could be used to keep warm\", \"similar objects\": [\"coat\", \"jacket\", \"scarf\"]}", + 668 + ], + "items": [ + "\n{\"type\": \"general object\", \"description\": \"could be anything; could be tangible or intangible; could be physical or virtual\", \"similar objects\": [\"things\", \"objects\", \"products\"]}", + 665 + ], + "case": [ + " {\"type\": \"container\", \"description\": \"could be made of plastic, metal, or wood; could be used to store items; could be used to transport items\", \"similar objects\": [\"box\", \"bag\", \"basket\"]}", + 665 + ], + "blue car": [ + "\n{\"type\": \"vehicle\", \"description\": \"blue; could be a sedan, coupe, hatchback, SUV, etc.\", \"similar objects\": [\"red car\", \"black car\", \"white car\"]}", + 665 + ], + "chicken": [ + " {\"type\": \"animal\", \"description\": \"feathered; has two legs; could lay eggs; could be cooked as food\", \"similar objects\": [\"duck\", \"turkey\", \"goose\"]}", + 658 + ], + "tall building": [ + " {\"type\": \"structure\", \"description\": \"large; could have multiple stories; could have windows; could have a spire\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"tower\"]}", + 658 + ], + "driver": [ + " {\"type\": \"occupation\", \"description\": \"operates a vehicle; could be a taxi driver, bus driver, truck driver, etc.\", \"similar objects\": [\"pilot\", \"sailor\", \"mechanic\"]}", + 658 + ], + "baby elephant": [ + "\n{\"type\": \"animal\", \"description\": \"smaller than an adult elephant; has a trunk; has large ears; has a grayish color\", \"similar objects\": [\"calf\", \"giraffe calf\", \"baby rhino\"]}", + 656 + ], + "chimney": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could be made of bricks; could have smoke coming out of it\", \"similar objects\": [\"smokestack\", \"fireplace\", \"flue\"]}", + 655 + ], + "soup": [ + " {\"type\": \"food\", \"description\": \"liquid; could be made of vegetables, meat, or fish; could be served hot or cold\", \"similar objects\": [\"stew\", \"porridge\", \"broth\"]}", + 652 + ], + "eyeglasses": [ + " {\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be made of metal or plastic; could be tinted\", \"similar objects\": [\"sunglasses\", \"reading glasses\", \"safety glasses\"]}", + 649 + ], + "palm trees": [ + " {\"type\": \"plant\", \"description\": \"tall; has a single trunk; has fan-shaped leaves; could have coconuts\", \"similar objects\": [\"banana tree\", \"pine tree\", \"bamboo\"]}", + 646 + ], + "bicycles": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could have a basket; could have a bell\", \"similar objects\": [\"motorcycle\", \"scooter\", \"tricycle\"]}", + 644 + ], + "blender": [ + " {\"type\": \"kitchen appliance\", \"description\": \"has a motor; could be used to mix ingredients\", \"similar objects\": [\"food processor\", \"juicer\", \"mixer\"]}", + 641 + ], + "counter top": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, stone, or metal; could be used for food preparation\", \"similar objects\": [\"table\", \"desk\", \"shelf\"]}", + 641 + ], + "lots": [ + "\n{\"type\": \"measurement unit\", \"description\": \"a unit of measurement for land; could be used to measure area or volume\", \"similar objects\": [\"acre\", \"hectare\", \"square foot\"]}", + 640 + ], + "decker bus": [ + " {\"type\": \"vehicle\", \"description\": \"double-decker; has two levels; could be red or blue\", \"similar objects\": [\"single-decker bus\", \"tram\", \"train\"]}", + 639 + ], + "skater": [ + " {\"type\": \"person\", \"description\": \"wears a helmet; rides a skateboard; could perform tricks\", \"similar objects\": [\"biker\", \"rollerblader\", \"surfer\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean", + 637 + ], + "structure": [ + " {\"type\": \"building\", \"description\": \"could be made of wood, metal, or concrete; could have multiple floors; could have a roof\", \"similar objects\": [\"house\", \"skyscraper\", \"bridge\"]}", + 636 + ], + "controller": [ + " {\"type\": \"electronic device\", \"description\": \"has buttons and joysticks; used to control a gaming console\", \"similar objects\": [\"keyboard\", \"mouse\", \"gamepad\"]}", + 633 + ], + "skiers": [ + " {\"type\": \"sport\", \"description\": \"people skiing on snow; could use ski poles; could wear ski goggles\", \"similar objects\": [\"snowboarders\", \"ice skaters\", \"snowmobilers\"]}", + 629 + ], + "store": [ + " {\"type\": \"building\", \"description\": \"could have multiple floors; could have a variety of goods; could have a cashier\", \"similar objects\": [\"mall\", \"supermarket\", \"department store\"]}", + 627 + ], + "fan": [ + " {\"type\": \"electrical appliance\", \"description\": \"round; has blades; could be used to circulate air\", \"similar objects\": [\"air conditioner\", \"heater\", \"humidifier\"]}", + 627 + ], + "gear": [ + " {\"type\": \"mechanical device\", \"description\": \"round; has teeth; used to transmit motion and force\", \"similar objects\": [\"pulley\", \"cogwheel\", \"spur gear\"]}", + 626 + ], + "someone": [ + "\n{\"type\": \"person\", \"description\": \"could be male or female; could have different skin color; could have different facial features; could have different clothing\", \"similar objects\": [\"man\", \"woman\", \"child\"]}", + 625 + ], + "brush": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; could have bristles; could be used for cleaning\", \"similar objects\": [\"mop\", \"vacuum cleaner\", \"duster\"]}", + 624 + ], + "headband": [ + " {\"type\": \"accessory\", \"description\": \"worn around the head; could be made of fabric or plastic; could have decorations\", \"similar objects\": [\"hat\", \"cap\", \"scarf\"]}", + 624 + ], + "apron": [ + " {\"type\": \"clothing\", \"description\": \"worn over the body; could be made of fabric; has strings to tie around the waist\", \"similar objects\": [\"chef hat\", \"gloves\", \"oven mitts\"]}", + 623 + ], + "symbol": [ + " {\"type\": \"visual representation\", \"description\": \"could be a letter, number, or image; could represent an idea, concept, or emotion\", \"similar objects\": [\"emblem\", \"logo\", \"icon\"]}", + 622 + ], + "canopy": [ + " {\"type\": \"shelter\", \"description\": \"could be made of fabric; could be hung from four poles; could be used to provide shade\", \"similar objects\": [\"tent\", \"awning\", \"umbrella\"]}", + 622 + ], + "stop sign": [ + " {\"type\": \"traffic sign\", \"description\": \"octagonal; red background with white letters; could be mounted on a pole\", \"similar objects\": [\"yield sign\", \"speed limit sign\", \"no parking sign\"]}", + 621 + ], + "ribbon": [ + " {\"type\": \"decorative item\", \"description\": \"long, thin, colorful; could be used for wrapping gifts\", \"similar objects\": [\"bow\", \"string\", \"fabric\"]}", + 621 + ], + "cover": [ + " {\"type\": \"protective item\", \"description\": \"could be made of fabric; could be used to cover furniture; could be used to protect from dust\", \"similar objects\": [\"blanket\", \"sheet\", \"pillowcase\"]}", + 620 + ], + "potatoes": [ + " {\"type\": \"vegetable\", \"description\": \"round, brown, could be peeled; could be sliced into pieces; could be boiled or fried\", \"similar objects\": [\"carrots\", \"onions\", \"sweet potatoes\"]}", + 612 + ], + "lamps": [ + " {\"type\": \"lighting tool\", \"description\": \"could be made of metal, glass, or plastic; could have a base and a shade; could be powered by electricity or oil\", \"similar objects\": [\"lantern\", \"chandelier\", \"torch\"]}", + 607 + ], + "wing": [ + " {\"type\": \"body part\", \"description\": \"attached to the body of a bird or an insect; could be used for flying\", \"similar objects\": [\"beak\", \"tail\", \"feather\"]}", + 605 + ], + "grill": [ + " {\"type\": \"cooking tool\", \"description\": \"has a grate; could be used to cook food over an open flame\", \"similar objects\": [\"barbecue\", \"smoker\", \"stove\"]}", + 602 + ], + "rider": [ + " {\"type\": \"person\", \"description\": \"sitting on a horse or other animal; could be wearing a helmet and protective gear\", \"similar objects\": [\"jockey\", \"horseback rider\", \"equestrian\"]}", + 600 + ], + "candles": [ + " {\"type\": \"lighting tool\", \"description\": \"cylindrical; could be made of wax; could be lit up\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 600 + ], + "gold": [ + " {\"type\": \"element\", \"description\": \"shiny, yellow, malleable\", \"similar objects\": [\"silver\", \"platinum\", \"copper\"]}", + 599 + ], + "pepperoni": [ + " {\"type\": \"food\", \"description\": \"spicy, red, round; could be sliced into thin pieces\", \"similar objects\": [\"sausage\", \"salami\", \"bacon\"]}", + 599 + ], + "foam": [ + " {\"type\": \"material\", \"description\": \"lightweight; could be used as insulation; could be used as cushioning\", \"similar objects\": [\"sponge\", \"cotton\", \"felt\"]}", + 599 + ], + "tub": [ + " {\"type\": \"container\", \"description\": \"large, round, could be made of plastic or metal; could be used for bathing or storage\", \"similar objects\": [\"bucket\", \"barrel\", \"tank\"]}", + 599 + ], + "display": [ + " {\"type\": \"electronic device\", \"description\": \"could be a monitor or a television; could be used to show images or videos\", \"similar objects\": [\"monitor\", \"television\", \"projector\"]}", + 597 + ], + "goat": [ + " {\"type\": \"animal\", \"description\": \"has horns; could have long beard; could be white, black, brown, or gray\", \"similar objects\": [\"sheep\", \"cow\", \"deer\"]}", + 597 + ], + "countertop": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, stone, or metal; could be used for food preparation\", \"similar objects\": [\"table\", \"desk\", \"shelf\"]}", + 594 + ], + "napkins": [ + " {\"type\": \"tableware\", \"description\": \"square or rectangular; could be made of paper or cloth; used to wipe hands or mouth\", \"similar objects\": [\"tissues\", \"paper towels\", \"handkerchiefs\"]}", + 594 + ], + "stone wall": [ + " {\"type\": \"structure\", \"description\": \"made of stones; could be used as a fence or a decoration\", \"similar objects\": [\"brick wall\", \"wooden fence\", \"concrete wall\"]}", + 592 + ], + "sandals": [ + " {\"type\": \"footwear\", \"description\": \"open-toed; could have straps; could be made of leather or rubber\", \"similar objects\": [\"flip-flops\", \"slippers\", \"sneakers\"]}", + 592 + ], + "saucer": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of porcelain; could be used to hold a cup\", \"similar objects\": [\"plate\", \"bowl\", \"cup\"]}", + 591 + ], + "concrete": [ + " {\"type\": \"building material\", \"description\": \"gray; hard; could be used for construction\", \"similar objects\": [\"cement\", \"bricks\", \"stone\"]}", + 591 + ], + "skies": [ + " {\"type\": \"weather\", \"description\": \"blue; could have clouds; could be sunny or rainy\", \"similar objects\": [\"weather\", \"atmosphere\", \"climate\"]}", + 591 + ], + "pine tree": [ + " {\"type\": \"plant\", \"description\": \"evergreen; has needles; could have cones\", \"similar objects\": [\"oak tree\", \"maple tree\", \"spruce tree\"]}", + 591 + ], + "bookshelf": [ + " {\"type\": \"furniture\", \"description\": \"has shelves; could be made of wood or metal; could be used to store books\", \"similar objects\": [\"cabinet\", \"wardrobe\", \"cupboard\"]}", + 590 + ], + "baseball glove": [ + " {\"type\": \"sports equipment\", \"description\": \"leather; has a pocket; could be used to catch a baseball\", \"similar objects\": [\"bat\", \"helmet\", \"cleats\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean", + 589 + ], + "wrist band": [ + " {\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of fabric, metal, or plastic; could be used for decoration or identification\", \"similar objects\": [\"bracelet\", \"watch\", \"anklet\"]}", + 588 + ], + "elbow": [ + " {\"type\": \"body part\", \"description\": \"joint between upper arm and forearm; could be bent\", \"similar objects\": [\"knee\", \"ankle\", \"shoulder\"]}", + 588 + ], + "containers": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of plastic, metal, or glass; could be of different shapes and sizes; could be used to store items\", \"similar objects\": [\"boxes\", \"baskets\", \"jars\"]}", + 585 + ], + "carriage": [ + " {\"type\": \"vehicle\", \"description\": \"horse-drawn; could have four wheels; could have a canopy\", \"similar objects\": [\"wagon\", \"cart\", \"buggy\"]}", + 585 + ], + "airplanes": [ + " {\"type\": \"vehicle\", \"description\": \"long and thin; has wings and a tail; could have multiple engines; could be used for transportation\", \"similar objects\": [\"helicopter\", \"rocket\", \"balloon\"]}", + 585 + ], + "trailer": [ + " {\"type\": \"vehicle\", \"description\": \"long; could be attached to a car; could be used for transporting goods\", \"similar objects\": [\"caravan\", \"truck\", \"motorhome\"]}", + 584 + ], + "handles": [ + " {\"type\": \"hardware\", \"description\": \"used to open and close doors; could be made of metal or plastic; could be round or rectangular\", \"similar objects\": [\"knobs\", \"hinges\", \"locks\"]}", + 584 + ], + "keys": [ + " {\"type\": \"accessory\", \"description\": \"metal; could have a keychain; could have different shapes and sizes\", \"similar objects\": [\"lock\", \"padlock\", \"keycard\"]}", + 583 + ], + "knobs": [ + " {\"type\": \"hardware\", \"description\": \"round; could be used to open or close a door; could be used to adjust the volume of a device\", \"similar objects\": [\"handles\", \"levers\", \"switches\"]}", + 582 + ], + "comforter": [ + " {\"type\": \"bedding item\", \"description\": \"thick, quilted, usually filled with down or synthetic fibers\", \"similar objects\": [\"duvet\", \"blanket\", \"pillow\"]}", + 581 + ], + "mirrors": [ + " {\"type\": \"reflective tool\", \"description\": \"flat; could be made of glass; could be framed\", \"similar objects\": [\"windows\", \"sunglasses\", \"binoculars\"]}", + 581 + ], + "nightstand": [ + " {\"type\": \"furniture\", \"description\": \"small table; could have drawers; could have a lamp on top\", \"similar objects\": [\"dresser\", \"end table\", \"coffee table\"]}", + 581 + ], + "pots": [ + " {\"type\": \"cooking tool\", \"description\": \"round, deep, could have a handle; could be made of metal or ceramic\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 579 + ], + "crate": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of wood or plastic; could be used for storage\", \"similar objects\": [\"box\", \"barrel\", \"basket\"]}", + 575 + ], + "stuffed animal": [ + " {\"type\": \"toy\", \"description\": \"soft, plush, could be shaped like an animal\", \"similar objects\": [\"doll\", \"action figure\", \"plush toy\"]}", + 572 + ], + "tongue": [ + " {\"type\": \"body part\", \"description\": \"pink; could be long and flexible; could be used for tasting and speaking\", \"similar objects\": [\"teeth\", \"nose\", \"ear\"]}", + 572 + ], + "bumper": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the front and rear of a vehicle; made of metal or plastic; designed to absorb impact\", \"similar objects\": [\"grille\", \"headlight\", \"tail light\"]}", + 571 + ], + "tall trees": [ + " {\"type\": \"plant\", \"description\": \"tall; could have leaves; could have branches; could have fruits\", \"similar objects\": [\"palm tree\", \"pine tree\", \"banyan tree\"]}", + 571 + ], + "mound": [ + " {\"type\": \"landform\", \"description\": \"raised area of land; could be made of dirt or rocks\", \"similar objects\": [\"hill\", \"mountain\", \"cliff\"]}", + 569 + ], + "shrubs": [ + " {\"type\": \"plant\", \"description\": \"small, woody plants; could have leaves and flowers; could be evergreen or deciduous\", \"similar objects\": [\"bushes\", \"hedges\", \"trees\"]}", + 568 + ], + "surfboards": [ + " {\"type\": \"sports equipment\", \"description\": \"long and wide; could be made of foam or wood; could have a fin\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 567 + ], + "pack": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cloth; could be used to store items\", \"similar objects\": [\"bag\", \"box\", \"backpack\"]}", + 566 + ], + "side mirror": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the side of a vehicle; used to see the side view of the vehicle\", \"similar objects\": [\"rearview mirror\", \"headlight\", \"windshield\"]}", + 566 + ], + "mask": [ + " {\"type\": \"protective gear\", \"description\": \"could be made of cloth or paper; covers the face; could have straps to secure it\", \"similar objects\": [\"face shield\", \"goggles\", \"respirator\"]}", + 565 + ], + "trunks": [ + " {\"type\": \"clothing item\", \"description\": \"short pants; could be made of cotton or linen; could have pockets\", \"similar objects\": [\"shorts\", \"jeans\", \"capris\"]}", + 563 + ], + "cockpit": [ + " {\"type\": \"aircraft part\", \"description\": \"enclosed space; has control panels; could have multiple seats\", \"similar objects\": [\"cabin\", \"fuselage\", \"wing\"]}", + 562 + ], + "sneaker": [ + " {\"type\": \"footwear\", \"description\": \"lightweight; could be made of fabric or leather; could have laces\", \"similar objects\": [\"running shoes\", \"sandals\", \"boots\"]}", + 561 + ], + "hotdog": [ + " {\"type\": \"food\", \"description\": \"long, cylindrical; could be served in a bun; could be topped with condiments\", \"similar objects\": [\"sausage\", \"burger\", \"sandwich\"]}", + 560 + ], + "vent": [ + " {\"type\": \"ventilation tool\", \"description\": \"rectangular; could be made of metal; could be used to circulate air\", \"similar objects\": [\"fan\", \"air conditioner\", \"heater\"]}", + 560 + ], + "shoulder": [ + " {\"type\": \"body part\", \"description\": \"connects the arm to the torso; could be used to carry heavy objects\", \"similar objects\": [\"elbow\", \"knee\", \"ankle\"]}", + 559 + ], + "ski poles": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, metal poles; used for skiing\", \"similar objects\": [\"hockey stick\", \"golf club\", \"tennis racket\"]}", + 558 + ], + "horn": [ + " {\"type\": \"sound tool\", \"description\": \"could be made of metal; could be used to make loud sound\", \"similar objects\": [\"trumpet\", \"whistle\", \"bell\"]}", + 557 + ], + "sweatshirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have a hood; could be made of cotton\", \"similar objects\": [\"hoodie\", \"jacket\", \"sweater\"]}", + 555 + ], + "airport": [ + " {\"type\": \"location\", \"description\": \"large area with many buildings; could have runways and taxiways; could have many airplanes\", \"similar objects\": [\"train station\", \"bus station\", \"harbor\"]}", + 553 + ], + "picture frame": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; could be made of wood or metal; could have a glass cover\", \"similar objects\": [\"photo album\", \"painting\", \"mirror\"]}", + 551 + ], + "paws": [ + " {\"type\": \"animal body part\", \"description\": \"soft, furry, and have claws\", \"similar objects\": [\"nose\", \"tail\", \"ears\"]}", + 547 + ], + "skin": [ + " {\"type\": \"body part\", \"description\": \"covers the entire body; could be smooth or rough; could be of different colors\", \"similar objects\": [\"hair\", \"nails\", \"teeth\"]}", + 542 + ], + "cardboard box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be used for storage\", \"similar objects\": [\"plastic box\", \"suitcase\", \"basket\"]}", + 541 + ], + "bowls": [ + " {\"type\": \"utensil\", \"description\": \"round; could be made of ceramic, plastic, or metal; could be used for serving food\", \"similar objects\": [\"plates\", \"cups\", \"spoons\"]}", + 539 + ], + "sheets": [ + " {\"type\": \"bedding\", \"description\": \"rectangular; could be made of cotton, silk, or other materials; could be used to cover a bed\", \"similar objects\": [\"pillow\", \"blanket\", \"quilt\"]}", + 539 + ], + "decoration": [ + " {\"type\": \"ornament\", \"description\": \"could be made of paper, fabric, metal, wood, plastic, etc.; could be used to decorate a room, a garden, a tree, etc.\", \"similar objects\": [\"ornament\", \"accessory\", \"figurine\"]}", + 537 + ], + "fabric": [ + " {\"type\": \"material\", \"description\": \"soft; could be made of cotton, silk, or wool; could be woven or knitted\", \"similar objects\": [\"cloth\", \"textile\", \"leather\"]}", + 533 + ], + "tree branches": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, could be curved; could have leaves and fruits\", \"similar objects\": [\"twigs\", \"stems\", \"roots\"]}", + 532 + ], + "computer keyboard": [ + "\n{\"type\": \"input device\", \"description\": \"rectangular; has keys; could be wired or wireless\", \"similar objects\": [\"mouse\", \"trackpad\", \"joystick\"]}", + 529 + ], + "lamp post": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; has a light on top\", \"similar objects\": [\"street light\", \"lantern\", \"torch\"]}", + 528 + ], + "front window": [ + " {\"type\": \"building component\", \"description\": \"transparent; could be made of glass; could be opened and closed\", \"similar objects\": [\"door\", \"balcony\", \"skylight\"]}", + 526 + ], + "patches": [ + " {\"type\": \"clothing accessory\", \"description\": \"small pieces of fabric; could be used to decorate clothes\", \"similar objects\": [\"buttons\", \"zippers\", \"embroidery\"]}", + 526 + ], + "vegetation": [ + " {\"type\": \"plant life\", \"description\": \"all living plants; could include trees, shrubs, grasses, ferns, mosses, and lichens\", \"similar objects\": [\"flora\", \"fauna\", \"herbaceous plants\"]}", + 525 + ], + "square": [ + " {\"type\": \"shape\", \"description\": \"four equal sides; four right angles; could be filled with color\", \"similar objects\": [\"rectangle\", \"triangle\", \"circle\"]}", + 525 + ], + "teddy": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; usually has a soft fur; could be in different shapes and sizes\", \"similar objects\": [\"doll\", \"plush toy\", \"action figure\"]}", + 524 + ], + "train engine": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a locomotive; could have multiple carriages\", \"similar objects\": [\"tram\", \"monorail\", \"subway\"]}", + 521 + ], + "toilet bowl": [ + " {\"type\": \"plumbing fixture\", \"description\": \"round; has a seat; could be connected to a water tank\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 521 + ], + "soap": [ + " {\"type\": \"cleaning product\", \"description\": \"could be solid or liquid; could be used for washing hands or dishes\", \"similar objects\": [\"shampoo\", \"detergent\", \"toothpaste\"]}", + 521 + ], + "plastic container": [ + " {\"type\": \"storage tool\", \"description\": \"transparent; could be sealed; could be used to store food\", \"similar objects\": [\"box\", \"jar\", \"bag\"]}", + 520 + ], + "pizzas": [ + " {\"type\": \"food\", \"description\": \"round; could be topped with cheese, vegetables, and meat; could be cut into slices\", \"similar objects\": [\"pasta\", \"burger\", \"sandwich\"]}", + 518 + ], + "switch": [ + " {\"type\": \"electrical device\", \"description\": \"used to control the flow of electricity; could be a toggle switch or a push button\", \"similar objects\": [\"outlet\", \"plug\", \"socket\"]}", + 516 + ], + "hillside": [ + " {\"type\": \"landscape\", \"description\": \"sloped terrain; could have trees and shrubs; could have a path\", \"similar objects\": [\"mountain\", \"valley\", \"cliff\"]}", + 514 + ], + "lamb": [ + " {\"type\": \"animal\", \"description\": \"white or brown fur; has four legs; could have horns\", \"similar objects\": [\"sheep\", \"goat\", \"calf\"]}", + 513 + ], + "back": [ + " {\"type\": \"body part\", \"description\": \"upper part of the body; could be curved; could be muscular\", \"similar objects\": [\"chest\", \"shoulder\", \"arm\"]}", + 512 + ], + "cross": [ + " {\"type\": \"religious symbol\", \"description\": \"two intersecting lines; could be made of metal or wood; could be used as a decoration\", \"similar objects\": [\"crucifix\", \"star of David\", \"crescent\"]}", + 510 + ], + "doll": [ + " {\"type\": \"toy\", \"description\": \"could be made of plastic or cloth; could have a human-like shape; could have clothes and accessories\", \"similar objects\": [\"action figure\", \"teddy bear\", \"puppet\"]}", + 509 + ], + "stroller": [ + " {\"type\": \"baby transport tool\", \"description\": \"has four wheels; could be folded; could be pushed by an adult\", \"similar objects\": [\"car seat\", \"high chair\", \"baby carrier\"]}", + 507 + ], + "print": [ + " {\"type\": \"action\", \"description\": \"to produce a hard copy of a digital document\", \"similar objects\": [\"copy\", \"scan\", \"fax\"]}", + 507 + ], + "tap": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be used to control water flow\", \"similar objects\": [\"faucet\", \"shower head\", \"hose\"]}", + 506 + ], + "shadow ground": [ + " {\"type\": \"phenomenon\", \"description\": \"dark area created by an object blocking the light\", \"similar objects\": [\"reflection\", \"silhouette\", \"umbra\"]}", + 505 + ], + "baseball field": [ + " {\"type\": \"sports facility\", \"description\": \"large, grassy area; has a diamond-shaped infield; has four bases\", \"similar objects\": [\"soccer field\", \"tennis court\", \"golf course\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber", + 503 + ], + "cushion": [ + " {\"type\": \"furniture\", \"description\": \"soft; could be filled with feathers or foam; could be square or round\", \"similar objects\": [\"pillow\", \"mattress\", \"sofa\"]}", + 503 + ], + "dock": [ + " {\"type\": \"structure\", \"description\": \"a platform built on the edge of a body of water; could be used for loading and unloading ships\", \"similar objects\": [\"pier\", \"wharf\", \"jetty\"]}", + 501 + ], + "plastic bottle": [ + " {\"type\": \"container\", \"description\": \"transparent; could be cylindrical or rectangular; could have a handle\", \"similar objects\": [\"glass bottle\", \"can\", \"jar\"]}", + 501 + ], + "magazine": [ + " {\"type\": \"publication\", \"description\": \"paper-based; could contain articles, stories, images, etc.\", \"similar objects\": [\"newspaper\", \"book\", \"journal\"]}", + 501 + ], + "ketchup": [ + " {\"type\": \"condiment\", \"description\": \"red; could be used as a dip or a sauce\", \"similar objects\": [\"mustard\", \"mayonnaise\", \"barbecue sauce\"]}", + 499 + ], + "visor": [ + " {\"type\": \"headwear\", \"description\": \"worn on the head; could be made of plastic or fabric; could be used to protect from sun or wind\", \"similar objects\": [\"hat\", \"cap\", \"sunglasses\"]}", + 498 + ], + "border": [ + " {\"type\": \"boundary\", \"description\": \"a line that divides two areas; could be physical or imaginary\", \"similar objects\": [\"fence\", \"wall\", \"barrier\"]}", + 496 + ], + "eggs": [ + " {\"type\": \"food\", \"description\": \"oval; could be boiled, fried, scrambled, etc.\", \"similar objects\": [\"yolk\", \"omelette\", \"quiche\"]}", + 494 + ], + "street lights": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lamp post\", \"traffic light\", \"lantern\"]}", + 493 + ], + "posts": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of wood or metal; could be used to support structures\", \"similar objects\": [\"beams\", \"columns\", \"rafters\"]}", + 493 + ], + "sail": [ + " {\"type\": \"nautical tool\", \"description\": \"triangular; used to catch wind; could be attached to a boat\", \"similar objects\": [\"mast\", \"rudder\", \"anchor\"]}", + 492 + ], + "shrub": [ + " {\"type\": \"plant\", \"description\": \"small, woody, evergreen; could have flowers and fruits; could be used for landscaping\", \"similar objects\": [\"bush\", \"hedge\", \"tree\"]}", + 491 + ], + "cords": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, flexible; could be made of plastic or metal; could be used to connect two devices\", \"similar objects\": [\"wires\", \"cables\", \"adapters\"]}", + 491 + ], + "view mirror": [ + " {\"type\": \"accessory\", \"description\": \"rectangular; could be attached to a vehicle; could be used to check the back view\", \"similar objects\": [\"rearview mirror\", \"side mirror\", \"wing mirror\"]}", + 490 + ], + "tape": [ + " {\"type\": \"adhesive tool\", \"description\": \"flexible; could be used to stick two objects together; could be made of plastic or paper\", \"similar objects\": [\"glue\", \"velcro\", \"staples\"]}", + 488 + ], + "headphones": [ + " {\"type\": \"audio device\", \"description\": \"has two earpieces; could be wired or wireless; could be used to listen to music\", \"similar objects\": [\"earphones\", \"speakers\", \"microphone\"]}", + 486 + ], + "dishwasher": [ + " {\"type\": \"appliance\", \"description\": \"large, rectangular; has a door; could be connected to a water source\", \"similar objects\": [\"washing machine\", \"refrigerator\", \"stove\"]}", + 486 + ], + "slope": [ + " {\"type\": \"geometric shape\", \"description\": \"inclined surface; could be curved or straight\", \"similar objects\": [\"hill\", \"mountain\", \"valley\"]}", + 484 + ], + "freezer": [ + " {\"type\": \"appliance\", \"description\": \"large, white, has a door; could be used to store food\", \"similar objects\": [\"refrigerator\", \"microwave\", \"dishwasher\"]}", + 484 + ], + "sausage": [ + " {\"type\": \"food\", \"description\": \"long, cylindrical; could be made of pork, beef, or other meats; could be cooked in a variety of ways\", \"similar objects\": [\"hot dog\", \"bratwurst\", \"kielbasa\"]}", + 484 + ], + "silver spoon": [ + " {\"type\": \"utensil\", \"description\": \"made of silver; has a round bowl; has a long handle\", \"similar objects\": [\"fork\", \"knife\", \"spatula\"]}", + 484 + ], + "bars": [ + " {\"type\": \"structure\", \"description\": \"long, metal, could be used to secure a door or window\", \"similar objects\": [\"grill\", \"fence\", \"gate\"]}", + 483 + ], + "chest": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have drawers; could be used for storage\", \"similar objects\": [\"dresser\", \"cabinet\", \"wardrobe\"]}", + 483 + ], + "necktie": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, usually made of silk; could be tied around the neck\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}", + 483 + ], + "utensils": [ + " {\"type\": \"kitchenware\", \"description\": \"various tools used for cooking and eating; could include forks, spoons, knives, chopsticks, etc.\", \"similar objects\": [\"pots and pans\", \"plates and bowls\", \"serving dishes\"]}", + 482 + ], + "cable": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, flexible; could be made of metal or plastic; could be used to connect two devices\", \"similar objects\": [\"wire\", \"cord\", \"plug\"]}", + 480 + ], + "bacon": [ + " {\"type\": \"food\", \"description\": \"cured, salted, and smoked pork; could be sliced into strips\", \"similar objects\": [\"ham\", \"sausage\", \"pancetta\"]}", + 478 + ], + "wood table": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have four legs; could be rectangular or round\", \"similar objects\": [\"chair\", \"sofa\", \"desk\"]}", + 476 + ], + "knives": [ + " {\"type\": \"utensil\", \"description\": \"sharp; could be made of metal; could be used for cutting\", \"similar objects\": [\"fork\", \"spoon\", \"scissors\"]}", + 474 + ], + "seats": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood, plastic, or metal; could have armrests; could have cushions\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 474 + ], + "tank": [ + " {\"type\": \"vehicle\", \"description\": \"large, heavily armored; could have a turret; could have tracks or wheels\", \"similar objects\": [\"armored car\", \"jeep\", \"helicopter\"]}", + 473 + ], + "land": [ + " {\"type\": \"terrain\", \"description\": \"large area of the Earth's surface; could be flat or hilly; could be covered with vegetation or rocks\", \"similar objects\": [\"ocean\", \"desert\", \"mountain\"]}", + 473 + ], + "guys": [ + "\n{\"type\": \"people\", \"description\": \"group of people; could be male or female; could be of any age\", \"similar objects\": [\"friends\", \"family\", \"classmates\"]}", + 469 + ], + "tusks": [ + " {\"type\": \"animal body part\", \"description\": \"long, curved, ivory-colored; found in elephants and walruses\", \"similar objects\": [\"horns\", \"antlers\", \"claws\"]}", + 469 + ], + "brown table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have four legs\", \"similar objects\": [\"chair\", \"desk\", \"sofa\"]}", + 464 + ], + "fish": [ + " {\"type\": \"animal\", \"description\": \"scaly; could have fins and gills; could be found in water\", \"similar objects\": [\"shark\", \"turtle\", \"dolphin\"]}", + 464 + ], + "pine trees": [ + " {\"type\": \"plant\", \"description\": \"evergreen; has needles; could have cones\", \"similar objects\": [\"spruce trees\", \"fir trees\", \"cedar trees\"]}", + 464 + ], + "beans": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, could be green, yellow, or red; could be cooked or eaten raw\", \"similar objects\": [\"peas\", \"corn\", \"lentils\"]}", + 463 + ], + "wine bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass; could have a cork stopper\", \"similar objects\": [\"water bottle\", \"beer bottle\", \"soda bottle\"]}", + 462 + ], + "telephone pole": [ + " {\"type\": \"utility pole\", \"description\": \"tall, cylindrical; could have wires attached to it; could have a transformer box\", \"similar objects\": [\"light pole\", \"power pole\", \"flag pole\"]}", + 460 + ], + "panel": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could be made of wood or metal; could be used to divide a room\", \"similar objects\": [\"wall\", \"door\", \"window\"]}", + 460 + ], + "bull": [ + " {\"type\": \"animal\", \"description\": \"large, muscular, has horns; could be red or black\", \"similar objects\": [\"cow\", \"bison\", \"buffalo\"]}", + 459 + ], + "front leg": [ + " {\"type\": \"body part\", \"description\": \"long; could be used for walking; could be found in animals\", \"similar objects\": [\"hind leg\", \"arm\", \"wing\"]}", + 456 + ], + "wet suit": [ + " {\"type\": \"clothing\", \"description\": \"tight-fitting; made of neoprene; designed to keep the body warm in cold water\", \"similar objects\": [\"dry suit\", \"wetsuit top\", \"wetsuit bottoms\"]}", + 454 + ], + "meal": [ + " {\"type\": \"food\", \"description\": \"combination of different dishes; could be served with drinks\", \"similar objects\": [\"dinner\", \"lunch\", \"breakfast\"]}", + 452 + ], + "leash": [ + " {\"type\": \"pet accessory\", \"description\": \"long strap; could be made of leather or nylon; could be attached to a collar or harness\", \"similar objects\": [\"collar\", \"harness\", \"leads\"]}", + 452 + ], + "glass bottle": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of glass; could have a lid\", \"similar objects\": [\"jar\", \"can\", \"mug\"]}", + 451 + ], + "tree branch": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, could have leaves and fruits; could be curved\", \"similar objects\": [\"twig\", \"stem\", \"trunk\"]}", + 451 + ], + "chocolate": [ + " {\"type\": \"food\", \"description\": \"brown; could be in bar or chips form; could be sweet or bitter\", \"similar objects\": [\"candy\", \"cookie\", \"cake\"]}", + 450 + ], + "toddler": [ + " {\"type\": \"human\", \"description\": \"small; could be walking or running; could be playing\", \"similar objects\": [\"infant\", \"child\", \"teenager\"]}", + 449 + ], + "markings": [ + " {\"type\": \"patterns\", \"description\": \"could be lines, shapes, symbols, or letters; could be used for decoration or identification\", \"similar objects\": [\"designs\", \"signs\", \"logos\"]}", + 449 + ], + "mitt": [ + " {\"type\": \"protective tool\", \"description\": \"made of fabric; could be used to protect hands from heat\", \"similar objects\": [\"glove\", \"apron\", \"gauntlet\"]}", + 448 + ], + "male": [ + "\n\n{\"type\": \"gender\", \"description\": \"a person who identifies as male; typically has a masculine gender expression\", \"similar objects\": [\"man\", \"boy\", \"gentleman\"]}", + 448 + ], + "skateboards": [ + " {\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could be used for tricks\", \"similar objects\": [\"scooter\", \"rollerblades\", \"bicycle\"]}", + 447 + ], + "trim": [ + " {\"type\": \"tool\", \"description\": \"used to cut or shape something; could be made of metal or plastic\", \"similar objects\": [\"scissors\", \"knife\", \"clippers\"]}", + 447 + ], + "taxi": [ + " {\"type\": \"vehicle\", \"description\": \"yellow; has a meter; could have a sign on the roof\", \"similar objects\": [\"bus\", \"car\", \"ambulance\"]}", + 444 + ], + "foliage": [ + " {\"type\": \"plant\", \"description\": \"green; could be leaves, branches, or stems; could be found in a garden or forest\", \"similar objects\": [\"grass\", \"bush\", \"tree\"]}", + 444 + ], + "trash bin": [ + " {\"type\": \"container\", \"description\": \"rectangular; has a lid; could be made of plastic\", \"similar objects\": [\"garbage can\", \"recycling bin\", \"compost bin\"]}", + 442 + ], + "machine": [ + " {\"type\": \"device\", \"description\": \"could be mechanical or electronic; could be used for various purposes\", \"similar objects\": [\"computer\", \"printer\", \"robot\"]}", + 442 + ], + "lips": [ + " {\"type\": \"body part\", \"description\": \"two curved lines; could be red; could be opened and closed\", \"similar objects\": [\"eyes\", \"nose\", \"ears\"]}", + 441 + ], + "pastry": [ + " {\"type\": \"food\", \"description\": \"sweet; could be filled with cream; could be baked\", \"similar objects\": [\"cake\", \"pie\", \"cookie\"]}", + 440 + ], + "forks": [ + " {\"type\": \"utensil\", \"description\": \"has multiple prongs; could be made of metal or plastic; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 440 + ], + "crosswalk": [ + " {\"type\": \"road marking\", \"description\": \"white stripes on the road; could have a pedestrian sign\", \"similar objects\": [\"traffic light\", \"stop sign\", \"road sign\"]}", + 439 + ], + "stool": [ + " {\"type\": \"furniture\", \"description\": \"has three or four legs; could be made of wood or metal; could be used as a seat\", \"similar objects\": [\"chair\", \"bench\", \"ottoman\"]}", + 439 + ], + "tail light": [ + " {\"type\": \"vehicle part\", \"description\": \"red and round; could be found at the back of a car; could be used to indicate the direction of the car\", \"similar objects\": [\"headlight\", \"brake light\", \"turn signal\"]}", + 438 + ], + "lawn": [ + " {\"type\": \"landscape\", \"description\": \"green grass; could have trees and flowers; could be mowed\", \"similar objects\": [\"garden\", \"park\", \"meadow\"]}", + 438 + ], + "newspaper": [ + " {\"type\": \"publication\", \"description\": \"printed paper; could be folded; could be in black and white or in color\", \"similar objects\": [\"magazine\", \"book\", \"journal\"]}", + 438 + ], + "pillar": [ + " {\"type\": \"architectural structure\", \"description\": \"vertical, cylindrical, could be made of stone or metal; could be used to support a roof\", \"similar objects\": [\"column\", \"obelisk\", \"monument\"]}", + 438 + ], + "ties": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of silk or cotton; could be used to fasten a shirt\", \"similar objects\": [\"belt\", \"scarf\", \"bow tie\"]}", + 437 + ], + "beds": [ + " {\"type\": \"furniture\", \"description\": \"has a mattress; could have a headboard; could have a footboard; could have a frame\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}", + 437 + ], + "holes": [ + " {\"type\": \"shape\", \"description\": \"round or oval; could be empty or filled with something\", \"similar objects\": [\"circles\", \"squares\", \"triangles\"]}", + 436 + ], + "toaster": [ + " {\"type\": \"kitchen appliance\", \"description\": \"rectangular; has slots for bread; could have a timer\", \"similar objects\": [\"coffee maker\", \"blender\", \"microwave\"]}", + 436 + ], + "shower": [ + " {\"type\": \"bathroom fixture\", \"description\": \"has a shower head; could be wall-mounted; could have a curtain\", \"similar objects\": [\"bathtub\", \"sink\", \"toilet\"]}", + 436 + ], + "hooves": [ + " {\"type\": \"animal body part\", \"description\": \"hard, pointed, and curved; found on the feet of horses, cows, and other animals\", \"similar objects\": [\"horns\", \"claws\", \"teeth\"]}", + 433 + ], + "laptops": [ + "\n{\"type\": \"electronic device\", \"description\": \"portable computer; has a keyboard and a screen; could be connected to the internet\", \"similar objects\": [\"tablet\", \"desktop\", \"smartphone\"]}", + 431 + ], + "orange cone": [ + " {\"type\": \"traffic tool\", \"description\": \"orange; cone-shaped; used to block roads\", \"similar objects\": [\"barricade\", \"traffic sign\", \"traffic light\"]}", + 429 + ], + "chin": [ + " {\"type\": \"body part\", \"description\": \"bottom part of the face; could be pointed or round\", \"similar objects\": [\"nose\", \"mouth\", \"forehead\"]}", + 427 + ], + "soda": [ + " {\"type\": \"beverage\", \"description\": \"carbonated; could be flavored; could be in a can or bottle\", \"similar objects\": [\"juice\", \"water\", \"beer\"]}", + 427 + ], + "microphone": [ + " {\"type\": \"audio tool\", \"description\": \"long, thin; could be handheld; could be connected to a stand\", \"similar objects\": [\"speaker\", \"headphone\", \"amplifier\"]}", + 426 + ], + "wooden": [ + " {\"type\": \"material\", \"description\": \"made of wood; could be used to make furniture; could be painted\", \"similar objects\": [\"plastic\", \"metal\", \"glass\"]}", + 425 + ], + "item": [ + "\n{\"type\": \"object\", \"description\": \"could be anything; could be tangible or intangible; could be physical or virtual\", \"similar objects\": [\"thing\", \"entity\", \"objective\"]}", + 423 + ], + "juice": [ + " {\"type\": \"beverage\", \"description\": \"liquid; could be made from fruits or vegetables; could be sweet or sour\", \"similar objects\": [\"smoothie\", \"milkshake\", \"soda\"]}", + 421 + ], + "tennis racquet": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be made of wood or metal\", \"similar objects\": [\"golf club\", \"baseball bat\", \"hockey stick\"]}", + 420 + ], + "railing": [ + " {\"type\": \"structure\", \"description\": \"vertical bars; could be made of metal or wood; could be used as a safety barrier\", \"similar objects\": [\"fence\", \"gate\", \"wall\"]}", + 419 + ], + "column": [ + " {\"type\": \"architectural structure\", \"description\": \"vertical support; could be made of stone, metal, or wood; could be decorated with carvings\", \"similar objects\": [\"pillar\", \"obelisk\", \"monument\"]}", + 419 + ], + "bathroom sink": [ + " {\"type\": \"fixture\", \"description\": \"has a basin; could have a faucet; could have a drain\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 418 + ], + "blankets": [ + " {\"type\": \"bedding item\", \"description\": \"soft; could be made of wool; could be used for warmth\", \"similar objects\": [\"pillows\", \"sheets\", \"quilts\"]}", + 417 + ], + "lamp shade": [ + " {\"type\": \"lighting accessory\", \"description\": \"cylindrical; could be made of fabric; could be used to cover a lamp\", \"similar objects\": [\"lamp finial\", \"lamp harp\", \"lamp base\"]}", + 417 + ], + "shower curtain": [ + " {\"type\": \"bathroom accessory\", \"description\": \"long; could be made of plastic or fabric; could be transparent or opaque\", \"similar objects\": [\"bath mat\", \"towel\", \"bathrobe\"]}", + 417 + ], + "hose": [ + " {\"type\": \"utility tool\", \"description\": \"long, flexible tube; could be used for watering plants or cleaning\", \"similar objects\": [\"pipe\", \"tubing\", \"sprinkler\"]}", + 416 + ], + "light post": [ + " {\"type\": \"street furniture\", \"description\": \"tall; could be made of metal; could have a lamp on top\", \"similar objects\": [\"street sign\", \"traffic light\", \"fire hydrant\"]}", + 416 + ], + "tarmac": [ + " {\"type\": \"pavement\", \"description\": \"black, flat, made of asphalt; could be used for roads and airports\", \"similar objects\": [\"concrete\", \"gravel\", \"asphalt\"]}", + 414 + ], + "paper plate": [ + " {\"type\": \"dining tool\", \"description\": \"round; made of paper; could be used for serving food\", \"similar objects\": [\"plastic plate\", \"bowl\", \"cup\"]}", + 414 + ], + "balloon": [ + " {\"type\": \"toy\", \"description\": \"round; could be filled with air or helium; could be colorful\", \"similar objects\": [\"kite\", \"yo-yo\", \"marble\"]}", + 414 + ], + "cage": [ + " {\"type\": \"enclosure\", \"description\": \"could be made of metal bars; could be used to contain animals\", \"similar objects\": [\"pen\", \"hutch\", \"aviary\"]}", + 412 + ], + "rim": [ + " {\"type\": \"automotive part\", \"description\": \"circular metal part; could be attached to the wheel of a car\", \"similar objects\": [\"tire\", \"hubcap\", \"spoke\"]}", + 412 + ], + "bolt": [ + " {\"type\": \"hardware\", \"description\": \"cylindrical; could be made of metal; could be used to fasten two objects together\", \"similar objects\": [\"screw\", \"nut\", \"washer\"]}", + 412 + ], + "lemon": [ + " {\"type\": \"fruit\", \"description\": \"yellow, round, has a stem\", \"similar objects\": [\"lime\", \"orange\", \"grapefruit\"]}", + 410 + ], + "tile floor": [ + " {\"type\": \"flooring material\", \"description\": \"flat, rectangular, made of ceramic or stone; could be glossy or matte\", \"similar objects\": [\"wood floor\", \"linoleum floor\", \"carpet\"]}", + 408 + ], + "stickers": [ + " {\"type\": \"decoration\", \"description\": \"small, colorful, could be used to decorate surfaces\", \"similar objects\": [\"posters\", \"wallpapers\", \"paintings\"]}", + 408 + ], + "wire fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal wires; could be used to separate areas\", \"similar objects\": [\"chain link fence\", \"barbed wire fence\", \"wooden fence\"]}", + 407 + ], + "puddle": [ + " {\"type\": \"natural phenomenon\", \"description\": \"small pool of water; could be formed by rain or melting snow\", \"similar objects\": [\"lake\", \"river\", \"stream\"]}", + 407 + ], + "sleeve shirt": [ + " {\"type\": \"clothing item\", \"description\": \"long, covers arms; could have buttons or zipper\", \"similar objects\": [\"t-shirt\", \"blouse\", \"jacket\"]}", + 406 + ], + "bow": [ + " {\"type\": \"weapon\", \"description\": \"curved; could be made of wood; could be used to shoot arrows\", \"similar objects\": [\"crossbow\", \"gun\", \"spear\"]}", + 406 + ], + "passenger train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has several compartments; could have a dining car; could have a locomotive\", \"similar objects\": [\"freight train\", \"subway\", \"tram\"]}", + 405 + ], + "calf": [ + " {\"type\": \"animal\", \"description\": \"young cow; has short legs; has a white spot on its forehead\", \"similar objects\": [\"lamb\", \"goat\", \"piglet\"]}", + 405 + ], + "mountain range": [ + " {\"type\": \"landscape\", \"description\": \"a series of mountains; could have snow-capped peaks; could have valleys and rivers\", \"similar objects\": [\"hills\", \"plateau\", \"valley\"]}", + 403 + ], + "cupcake": [ + " {\"type\": \"dessert\", \"description\": \"small, round, could be frosted; could have a topping\", \"similar objects\": [\"muffin\", \"donut\", \"cake\"]}", + 403 + ], + "side walk": [ + " {\"type\": \"structure\", \"description\": \"concrete path; could be used for walking or running; could be found on the side of the road\", \"similar objects\": [\"sidewalk\", \"pathway\", \"trail\"]}", + 402 + ], + "notebook": [ + " {\"type\": \"stationary\", \"description\": \"rectangular; could be spiral bound; could have lined pages\", \"similar objects\": [\"journal\", \"diary\", \"planner\"]}", + 399 + ], + "tennis net": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; made of nylon; has a metal frame\", \"similar objects\": [\"volleyball net\", \"badminton net\", \"soccer goal\"]}", + 399 + ], + "tennis": [ + " {\"type\": \"sport\", \"description\": \"two or four players; use a racket and a ball; played on a court\", \"similar objects\": [\"badminton\", \"squash\", \"table tennis\"]}", + 398 + ], + "mark": [ + " {\"type\": \"marking tool\", \"description\": \"could be a pen, pencil, or crayon; could be used to write or draw\", \"similar objects\": [\"pen\", \"pencil\", \"crayon\"]}", + 397 + ], + "mushroom": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be white, brown, or black; could have a stem\", \"similar objects\": [\"truffle\", \"morel\", \"oyster mushroom\"]}", + 396 + ], + "potato": [ + " {\"type\": \"vegetable\", \"description\": \"oval; could be brown, yellow, or white; could be sliced into pieces; could have green leaves\", \"similar objects\": [\"carrot\", \"onion\", \"sweet potato\"]}", + 392 + ], + "cement": [ + " {\"type\": \"building material\", \"description\": \"gray; powdery; used to make concrete\", \"similar objects\": [\"sand\", \"gravel\", \"mortar\"]}", + 392 + ], + "bookcase": [ + " {\"type\": \"furniture\", \"description\": \"has shelves; could be made of wood or metal; could be used to store books\", \"similar objects\": [\"shelf\", \"cabinet\", \"wardrobe\"]}", + 391 + ], + "moss": [ + " {\"type\": \"plant\", \"description\": \"green; could be found on rocks and trees; could be soft and velvety\", \"similar objects\": [\"lichen\", \"algae\", \"fern\"]}", + 391 + ], + "stop": [ + " {\"type\": \"sign\", \"description\": \"octagonal; red and white; has the word 'STOP' written on it\", \"similar objects\": [\"yield\", \"no parking\", \"no entry\"]}", + 388 + ], + "pasture": [ + " {\"type\": \"landscape\", \"description\": \"large area of land used for grazing animals; could have trees and shrubs; could have a fence\", \"similar objects\": [\"meadow\", \"field\", \"prairie\"]}", + 388 + ], + "windshield wiper": [ + " {\"type\": \"automotive tool\", \"description\": \"long arm with a rubber blade; used to clean windshields\", \"similar objects\": [\"headlight\", \"tire\", \"brake pad\"]}", + 388 + ], + "footprints": [ + " {\"type\": \"evidence\", \"description\": \"imprints left by feet; could be made of mud, snow, or sand\", \"similar objects\": [\"handprints\", \"tire tracks\", \"animal tracks\"]}", + 388 + ], + "zipper": [ + " {\"type\": \"fastener\", \"description\": \"metal or plastic; used to join two edges of fabric together\", \"similar objects\": [\"button\", \"snap\", \"hook and eye\"]}", + 386 + ], + "rackets": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle with a stringed frame; used to hit a ball\", \"similar objects\": [\"tennis racket\", \"badminton racket\", \"squash racket\"]}", + 386 + ], + "cupboard": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular; could have shelves and drawers; could be made of wood\", \"similar objects\": [\"wardrobe\", \"dresser\", \"bookshelf\"]}", + 385 + ], + "strings": [ + " {\"type\": \"object\", \"description\": \"long, thin, flexible; could be made of different materials; could be used for tying or connecting things\", \"similar objects\": [\"threads\", \"ropes\", \"cords\"]}", + 385 + ], + "mushrooms": [ + " {\"type\": \"vegetable\", \"description\": \"various shapes and colors; could be edible or poisonous; could have a stem and a cap\", \"similar objects\": [\"truffles\", \"morels\", \"chanterelles\"]}", + 384 + ], + "floor lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a shade\", \"similar objects\": [\"table lamp\", \"ceiling lamp\", \"wall lamp\"]}", + 384 + ], + "palm": [ + " {\"type\": \"plant\", \"description\": \"has long, green leaves; could have a trunk; could have coconuts\", \"similar objects\": [\"banana tree\", \"date tree\", \"coconut tree\"]}", + 384 + ], + "pink flowers": [ + " {\"type\": \"plant\", \"description\": \"pink petals; could have green leaves; could have a stem\", \"similar objects\": [\"roses\", \"daisies\", \"tulips\"]}", + 383 + ], + "sculpture": [ + " {\"type\": \"artwork\", \"description\": \"three-dimensional artwork; could be made of metal, stone, wood, or other materials; could be abstract or representational\", \"similar objects\": [\"painting\", \"drawing\", \"photograph\"]}", + 382 + ], + "traffic": [ + " {\"type\": \"phenomenon\", \"description\": \"vehicles moving on roads; could be congested; could be regulated by traffic lights\", \"similar objects\": [\"commute\", \"traffic jam\", \"road construction\"]}", + 381 + ], + "spectator": [ + " {\"type\": \"person\", \"description\": \"someone who watches an event; could be wearing a hat\", \"similar objects\": [\"audience\", \"viewer\", \"bystander\"]}", + 381 + ], + "windshield wipers": [ + " {\"type\": \"automotive tool\", \"description\": \"rubber blades; used to clear rain, snow, and debris from windshields\", \"similar objects\": [\"headlights\", \"brake lights\", \"mirrors\"]}", + 380 + ], + "cooler": [ + " {\"type\": \"storage tool\", \"description\": \"box-shaped; could be made of plastic; could have a handle\", \"similar objects\": [\"ice chest\", \"thermos\", \"lunch box\"]}", + 380 + ], + "snout": [ + " {\"type\": \"animal body part\", \"description\": \"long, protruding nose; could be used for smelling\", \"similar objects\": [\"beak\", \"bill\", \"proboscis\"]}", + 380 + ], + "station": [ + " {\"type\": \"location\", \"description\": \"place where people can get on and off trains; could have ticket booths; could have waiting areas\", \"similar objects\": [\"airport\", \"bus station\", \"harbor\"]}", + 377 + ], + "roman numerals": [ + " {\"type\": \"number system\", \"description\": \"uses symbols to represent numbers; symbols include I, V, X, L, C, D, and M\", \"similar objects\": [\"Arabic numerals\", \"binary system\", \"hexadecimal system\"]}", + 377 + ], + "crack": [ + " {\"type\": \"fissure\", \"description\": \"a narrow opening in a surface; could be caused by pressure or heat\", \"similar objects\": [\"crevice\", \"fissure\", \"split\"]}", + 377 + ], + "multi": [ + "\n{\"type\": \"tool\", \"description\": \"multi-purpose tool; could be used for cutting, screwing, and hammering; could have multiple attachments\", \"similar objects\": [\"screwdriver\", \"hammer\", \"pliers\"]}", + 377 + ], + "cluster": [ + " {\"type\": \"group\", \"description\": \"a group of objects or people; could be related or unrelated\", \"similar objects\": [\"bunch\", \"collection\", \"assembly\"]}", + 376 + ], + "fence post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of wood or metal; could be used to support a fence\", \"similar objects\": [\"rail\", \"pillar\", \"stake\"]}", + 375 + ], + "olives": [ + " {\"type\": \"fruit\", \"description\": \"green or black; could be pitted; could be used as a garnish\", \"similar objects\": [\"cherries\", \"grapes\", \"avocado\"]}", + 373 + ], + "blouse": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could have buttons; could have collars; could have long or short sleeves\", \"similar objects\": [\"shirt\", \"dress\", \"jacket\"]}", + 371 + ], + "ottoman": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be used as a footrest; could be used as a seat\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 371 + ], + "silver handle": [ + " {\"type\": \"handle\", \"description\": \"made of silver; could be used for doors, drawers, cabinets, etc.\", \"similar objects\": [\"gold handle\", \"bronze handle\", \"brass handle\"]}", + 370 + ], + "wrinkles": [ + " {\"type\": \"skin condition\", \"description\": \"lines or folds on the skin; could be caused by aging or sun exposure\", \"similar objects\": [\"crow's feet\", \"age spots\", \"freckles\"]}", + 370 + ], + "menu": [ + " {\"type\": \"list of food\", \"description\": \"list of food items and their prices\", \"similar objects\": [\"bill\", \"order\", \"receipt\"]}", + 369 + ], + "spatula": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, long handle; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"ladle\", \"tongs\"]}", + 368 + ], + "blonde hair": [ + " {\"type\": \"hair color\", \"description\": \"light yellowish-brown; could be straight or wavy\", \"similar objects\": [\"brown hair\", \"black hair\", \"red hair\"]}", + 368 + ], + "rows": [ + " {\"type\": \"arrangement\", \"description\": \"a line of objects placed side by side\", \"similar objects\": [\"columns\", \"stacks\", \"piles\"]}", + 368 + ], + "deck": [ + " {\"type\": \"structure\", \"description\": \"wooden platform; could be attached to a house; could be used for outdoor activities\", \"similar objects\": [\"patio\", \"balcony\", \"veranda\"]}", + 368 + ], + "vases": [ + " {\"type\": \"decorative item\", \"description\": \"cylindrical; could be made of glass, ceramic, or metal; could have a wide opening at the top\", \"similar objects\": [\"urns\", \"jars\", \"pots\"]}", + 368 + ], + "baseball players": [ + " {\"type\": \"athletes\", \"description\": \"wearing a uniform; holding a bat; wearing a helmet\", \"similar objects\": [\"soccer players\", \"basketball players\", \"tennis players\"]}", + 366 + ], + "tree trunk": [ + " {\"type\": \"plant part\", \"description\": \"woody; could be straight or curved; could have branches and leaves\", \"similar objects\": [\"branch\", \"root\", \"stump\"]}", + 365 + ], + "bouquet": [ + " {\"type\": \"decoration\", \"description\": \"a bunch of flowers; could be tied with a ribbon\", \"similar objects\": [\"wreath\", \"basket\", \"vase\"]}", + 364 + ], + "sticks": [ + " {\"type\": \"object\", \"description\": \"long, thin, could be made of wood or metal; could be used for walking or playing\", \"similar objects\": [\"rods\", \"poles\", \"batons\"]}", + 362 + ], + "tshirt": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could have short or long sleeves; could have a collar\", \"similar objects\": [\"shirt\", \"blouse\", \"tank top\"]}", + 361 + ], + "entrance": [ + " {\"type\": \"location\", \"description\": \"a place where people can enter; could be a door or a gate\", \"similar objects\": [\"exit\", \"gateway\", \"doorway\"]}", + 361 + ], + "outdoors": [ + "\n{\"type\": \"environment\", \"description\": \"open space; could be surrounded by trees, mountains, rivers, etc.\", \"similar objects\": [\"forest\", \"park\", \"beach\"]}", + 360 + ], + "surfers": [ + " {\"type\": \"people\", \"description\": \"wearing wetsuits; riding on surfboards; in the ocean\", \"similar objects\": [\"swimmers\", \"divers\", \"sailors\"]}", + 360 + ], + "passengers": [ + " {\"type\": \"people\", \"description\": \"traveling in a vehicle; could be sitting or standing\", \"similar objects\": [\"commuters\", \"tourists\", \"pedestrians\"]}", + 360 + ], + "holder": [ + " {\"type\": \"utensil\", \"description\": \"used to hold objects; could be made of metal or plastic\", \"similar objects\": [\"rack\", \"stand\", \"shelf\"]}", + 359 + ], + "door handle": [ + " {\"type\": \"hardware\", \"description\": \"metal; could be round or rectangular; could be used to open a door\", \"similar objects\": [\"knob\", \"lock\", \"hinge\"]}", + 359 + ], + "round plate": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of ceramic, plastic, or metal; could be used for serving food\", \"similar objects\": [\"bowl\", \"cup\", \"mug\"]}", + 357 + ], + "skate board": [ + " {\"type\": \"sports equipment\", \"description\": \"long, flat board; has four wheels\", \"similar objects\": [\"scooter\", \"roller skates\", \"snowboard\"]}", + 356 + ], + "silver knife": [ + "\n{\"type\": \"utensil\", \"description\": \"long, thin, made of silver; could be used for cutting\", \"similar objects\": [\"fork\", \"spoon\", \"spatula\"]}", + 355 + ], + "sets": [ + " {\"type\": \"game\", \"description\": \"two or more players; could involve strategy; could involve luck\", \"similar objects\": [\"cards\", \"board games\", \"dice games\"]}", + 355 + ], + "toppings": [ + " {\"type\": \"food item\", \"description\": \"various ingredients used to decorate food; could be sweet or savory\", \"similar objects\": [\"sauce\", \"dressing\", \"garnish\"]}", + 353 + ], + "zoo": [ + " {\"type\": \"place\", \"description\": \"place where animals are kept for public viewing\", \"similar objects\": [\"aquarium\", \"safari park\", \"wildlife sanctuary\"]}", + 353 + ], + "meter": [ + " {\"type\": \"measuring tool\", \"description\": \"long; could be used to measure length, volume, or weight\", \"similar objects\": [\"ruler\", \"scale\", \"tape measure\"]}", + 353 + ], + "doughnuts": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be glazed or filled with jam\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 353 + ], + "photograph": [ + " {\"type\": \"image\", \"description\": \"captured image; could be printed on paper or stored digitally\", \"similar objects\": [\"painting\", \"drawing\", \"sculpture\"]}", + 352 + ], + "stands": [ + " {\"type\": \"furniture\", \"description\": \"could be made of metal or wood; could be used to hold items; could be adjustable\", \"similar objects\": [\"table\", \"chair\", \"shelf\"]}", + 352 + ], + "computer screen": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be touch-sensitive; could be connected to a computer\", \"similar objects\": [\"monitor\", \"television\", \"tablet\"]}", + 350 + ], + "bell": [ + " {\"type\": \"instrument\", \"description\": \"round; could be made of metal; could produce a ringing sound\", \"similar objects\": [\"cymbal\", \"gong\", \"drum\"]}", + 349 + ], + "glass vase": [ + " {\"type\": \"decorative item\", \"description\": \"transparent; could be made of glass or crystal; could be used to hold flowers\", \"similar objects\": [\"urn\", \"jar\", \"bowl\"]}", + 348 + ], + "pond": [ + " {\"type\": \"water body\", \"description\": \"large, shallow, could have aquatic plants and animals\", \"similar objects\": [\"lake\", \"river\", \"stream\"]}", + 347 + ], + "crane": [ + " {\"type\": \"machine\", \"description\": \"tall; has a long arm; could be used to lift heavy objects\", \"similar objects\": [\"forklift\", \"excavator\", \"bulldozer\"]}", + 347 + ], + "ice": [ + " {\"type\": \"solid\", \"description\": \"transparent; cold to touch; could be in cubes or flakes\", \"similar objects\": [\"snow\", \"frost\", \"hail\"]}", + 346 + ], + "mattress": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be filled with foam; could be covered with fabric\", \"similar objects\": [\"pillow\", \"cushion\", \"sofa\"]}", + 346 + ], + "pineapple": [ + " {\"type\": \"fruit\", \"description\": \"spiky, yellow-green, has a crown\", \"similar objects\": [\"mango\", \"kiwi\", \"coconut\"]}", + 345 + ], + "police officer": [ + " {\"type\": \"occupation\", \"description\": \"uniformed; could carry a gun; could have a badge\", \"similar objects\": [\"firefighter\", \"soldier\", \"doctor\"]}", + 344 + ], + "side view mirror": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the side of a vehicle; used to see the rear view\", \"similar objects\": [\"rear view mirror\", \"headlight\", \"windshield\"]}", + 344 + ], + "grapes": [ + " {\"type\": \"fruit\", \"description\": \"small, round, green or purple; could be clustered together; could be eaten as a snack\", \"similar objects\": [\"blueberries\", \"strawberries\", \"blackberries\"]}", + 344 + ], + "barrier": [ + " {\"type\": \"structure\", \"description\": \"could be made of metal or wood; could be used to block a path\", \"similar objects\": [\"fence\", \"wall\", \"gate\"]}", + 343 + ], + "table cloth": [ + " {\"type\": \"textile\", \"description\": \"rectangular; could be made of cotton, linen, or polyester; could be decorated with patterns\", \"similar objects\": [\"napkin\", \"runner\", \"placemat\"]}", + 343 + ], + "spinach": [ + " {\"type\": \"vegetable\", \"description\": \"dark green leaves; could be cooked or eaten raw; could be used in salads\", \"similar objects\": [\"kale\", \"lettuce\", \"arugula\"]}", + 343 + ], + "wagon": [ + " {\"type\": \"vehicle\", \"description\": \"has four wheels; could be pulled by horses; could be used to transport goods\", \"similar objects\": [\"cart\", \"carriage\", \"truck\"]}", + 343 + ], + "pier": [ + " {\"type\": \"structure\", \"description\": \"a platform built from the shore out over water; could be used for fishing, docking boats, or leisure activities\", \"similar objects\": [\"dock\", \"jetty\", \"wharf\"]}", + 342 + ], + "seagull": [ + " {\"type\": \"bird\", \"description\": \"white; has a long beak; could be seen near the sea\", \"similar objects\": [\"penguin\", \"eagle\", \"swan\"]}", + 342 + ], + "ledge": [ + " {\"type\": \"structure\", \"description\": \"flat surface; could be used as a shelf; could be attached to a wall\", \"similar objects\": [\"shelf\", \"counter\", \"table\"]}", + 340 + ], + "barn": [ + " {\"type\": \"building\", \"description\": \"large, red, wooden; could have a hayloft; could have a silo\", \"similar objects\": [\"shed\", \"stable\", \"garage\"]}", + 340 + ], + "cab": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could be yellow; could have a meter\", \"similar objects\": [\"taxi\", \"car\", \"bus\"]}", + 340 + ], + "thick": [ + "\n{\"type\": \"adjective\", \"description\": \"having a large distance between two opposite sides; having a large diameter; having a large circumference\", \"similar objects\": [\"wide\", \"broad\", \"massive\"]}", + 339 + ], + "fire truck": [ + " {\"type\": \"vehicle\", \"description\": \"red; has a long ladder; could with a water tank\", \"similar objects\": [\"ambulance\", \"police car\", \"garbage truck\"]}", + 339 + ], + "salt": [ + " {\"type\": \"condiment\", \"description\": \"white, granular; could be used to season food\", \"similar objects\": [\"pepper\", \"sugar\", \"garlic powder\"]}", + 338 + ], + "hoof": [ + " {\"type\": \"animal body part\", \"description\": \"hard, curved, and pointed; found on the feet of horses, cows, and other hoofed animals\", \"similar objects\": [\"claw\", \"paw\", \"hoofprint\"]}", + 338 + ], + "handbag": [ + " {\"type\": \"accessory\", \"description\": \"rectangular; could be made of leather; could have straps\", \"similar objects\": [\"purse\", \"backpack\", \"wallet\"]}", + 336 + ], + "dome": [ + " {\"type\": \"architectural structure\", \"description\": \"round; could be made of concrete, steel, or glass; could be used as a roof\", \"similar objects\": [\"cupola\", \"observatory\", \"amphitheater\"]}", + 335 + ], + "figure": [ + " {\"type\": \"artwork\", \"description\": \"could be a sculpture, painting, or drawing; could be abstract or realistic; could be made of different materials\", \"similar objects\": [\"statue\", \"painting\", \"drawing\"]}", + 335 + ], + "pipes": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical; could be made of metal or plastic; could be connected to each other\", \"similar objects\": [\"valves\", \"fittings\", \"tubing\"]}", + 334 + ], + "wood fence": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be used to build a fence\", \"similar objects\": [\"metal fence\", \"brick wall\", \"chain link fence\"]}", + 333 + ], + "mud": [ + " {\"type\": \"substance\", \"description\": \"thick, wet, and sticky; could be found in nature\", \"similar objects\": [\"clay\", \"dirt\", \"soil\"]}", + 333 + ], + "sailboat": [ + " {\"type\": \"vehicle\", \"description\": \"has a sail; could be made of wood; could have a mast\", \"similar objects\": [\"yacht\", \"rowboat\", \"canoe\"]}", + 332 + ], + "cakes": [ + " {\"type\": \"food\", \"description\": \"sweet; could be made of flour, sugar, eggs, butter; could be decorated with cream and fruits\", \"similar objects\": [\"cupcakes\", \"muffins\", \"cookies\"]}", + 330 + ], + "tarp": [ + " {\"type\": \"covering tool\", \"description\": \"waterproof; could be used to cover objects; could be made of plastic or canvas\", \"similar objects\": [\"canopy\", \"awning\", \"tent\"]}", + 330 + ], + "jeep": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheel drive; has a rugged look; could be used for off-road driving\", \"similar objects\": [\"SUV\", \"truck\", \"ATV\"]}", + 330 + ], + "yard": [ + " {\"type\": \"outdoor space\", \"description\": \"open area; could be surrounded by fences; could have grass, trees, and plants\", \"similar objects\": [\"garden\", \"patio\", \"balcony\"]}", + 329 + ], + "silver faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"shiny, metallic; could have a handle; could be attached to a sink\", \"similar objects\": [\"shower head\", \"toilet\", \"bathtub\"]}", + 328 + ], + "straps": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of fabric or leather; could be used to secure items\", \"similar objects\": [\"belts\", \"ties\", \"laces\"]}", + 328 + ], + "balls": [ + "\n{\"type\": \"toy\", \"description\": \"round; could be made of rubber, plastic, or cloth; could be used for playing games\", \"similar objects\": [\"dice\", \"marbles\", \"jacks\"]}", + 326 + ], + "trail": [ + " {\"type\": \"pathway\", \"description\": \"could be a path in the woods; could be a path on a mountain; could be a path on a beach\", \"similar objects\": [\"road\", \"path\", \"track\"]}", + 326 + ], + "train station": [ + " {\"type\": \"location\", \"description\": \"building with multiple tracks; could have a ticket office; could have a waiting area\", \"similar objects\": [\"airport\", \"bus station\", \"subway station\"]}", + 325 + ], + "blue jacket": [ + " {\"type\": \"clothing\", \"description\": \"long sleeve; could be made of cotton; could have a zipper; could have pockets\", \"similar objects\": [\"coat\", \"hoodie\", \"sweater\"]}", + 325 + ], + "ham": [ + " {\"type\": \"food\", \"description\": \"salty, cured, pink meat; could be sliced into thin pieces\", \"similar objects\": [\"bacon\", \"sausage\", \"salami\"]}", + 325 + ], + "speakers": [ + " {\"type\": \"electronic device\", \"description\": \"could be wired or wireless; could be connected to a device; could produce sound\", \"similar objects\": [\"headphones\", \"microphone\", \"amplifier\"]}", + 325 + ], + "key": [ + " {\"type\": \"accessory\", \"description\": \"metal; could have a hole in the middle; could have a pattern on the surface\", \"similar objects\": [\"lock\", \"padlock\", \"keychain\"]}", + 325 + ], + "shirts": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have buttons; could be made of cotton\", \"similar objects\": [\"pants\", \"dresses\", \"jackets\"]}", + 324 + ], + "dots": [ + " {\"type\": \"shape\", \"description\": \"small, round, could be connected to form a line or a pattern\", \"similar objects\": [\"circles\", \"squares\", \"triangles\"]}", + 324 + ], + "dishes": [ + " {\"type\": \"tableware\", \"description\": \"could be made of ceramic, glass, or metal; could be used for eating and drinking\", \"similar objects\": [\"bowls\", \"cups\", \"plates\"]}", + 323 + ], + "rear wheel": [ + " {\"type\": \"automobile part\", \"description\": \"round; could be made of metal; could be connected to the axle\", \"similar objects\": [\"front wheel\", \"tire\", \"hubcap\"]}", + 323 + ], + "spoons": [ + " {\"type\": \"utensil\", \"description\": \"long handle; could be made of metal or plastic; could be used for stirring, scooping, and serving\", \"similar objects\": [\"forks\", \"knives\", \"chopsticks\"]}", + 323 + ], + "mobile phone": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; could have a touchscreen; could have a camera\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 322 + ], + "park bench": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of wood or metal; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"stool\"]}", + 321 + ], + "train cars": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; could be connected to each other; could have multiple compartments\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 320 + ], + "sandwiches": [ + " {\"type\": \"food\", \"description\": \"two slices of bread with fillings in between; could be cut into triangles\", \"similar objects\": [\"burger\", \"wrap\", \"taco\"]}", + 320 + ], + "suitcases": [ + " {\"type\": \"travel item\", \"description\": \"rectangular; could be made of hard materials; could have wheels\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 319 + ], + "platter": [ + " {\"type\": \"serving dish\", \"description\": \"flat, round, could be made of ceramic or metal; could be used to serve food\", \"similar objects\": [\"plate\", \"bowl\", \"tray\"]}", + 318 + ], + "beverage": [ + " {\"type\": \"drink\", \"description\": \"could be hot or cold; could be alcoholic or non-alcoholic; could be in a can, bottle, or glass\", \"similar objects\": [\"juice\", \"tea\", \"coffee\"]}", + 318 + ], + "road sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; could be yellow, red, or blue; could have symbols or words\", \"similar objects\": [\"traffic light\", \"stop sign\", \"yield sign\"]}", + 318 + ], + "salt shaker": [ + " {\"type\": \"kitchen tool\", \"description\": \"cylindrical; has a lid; could be filled with salt\", \"similar objects\": [\"pepper shaker\", \"sugar shaker\", \"spice shaker\"]}", + 317 + ], + "thing": [ + "\n\n{\"type\": \"object\", \"description\": \"can be anything; could be tangible or intangible; could be physical or abstract\", \"similar objects\": [\"item\", \"entity\", \"objective\"]}", + 317 + ], + "guitar": [ + " {\"type\": \"musical instrument\", \"description\": \"long; has strings; could be acoustic or electric\", \"similar objects\": [\"piano\", \"violin\", \"ukulele\"]}", + 317 + ], + "mustard": [ + " {\"type\": \"condiment\", \"description\": \"yellow; could be used as a spread; could be used as a seasoning\", \"similar objects\": [\"ketchup\", \"mayonnaise\", \"hot sauce\"]}", + 316 + ], + "oven": [ + " {\"type\": \"cooking tool\", \"description\": \"box-shaped; could be electric or gas; could have a window to check the food\", \"similar objects\": [\"stove\", \"microwave\", \"toaster\"]}", + 315 + ], + "saddle": [ + " {\"type\": \"riding equipment\", \"description\": \"leather; has a horn; has stirrups\", \"similar objects\": [\"bridle\", \"halter\", \"reins\"]}", + 315 + ], + "strawberries": [ + " {\"type\": \"fruit\", \"description\": \"red, small, has seeds\", \"similar objects\": [\"raspberries\", \"blueberries\", \"blackberries\"]}", + 315 + ], + "tube": [ + " {\"type\": \"object\", \"description\": \"hollow, cylindrical; could be made of metal, plastic, or glass; could be used to transport liquids or gases\", \"similar objects\": [\"pipe\", \"hose\", \"conduit\"]}", + 315 + ], + "veggies": [ + "\n{\"type\": \"food\", \"description\": \"various types of vegetables; could be cooked or eaten raw\", \"similar objects\": [\"fruits\", \"grains\", \"legumes\"]}", + 314 + ], + "emblem": [ + " {\"type\": \"symbol\", \"description\": \"could be a logo or a badge; could be used to represent a group or an organization\", \"similar objects\": [\"logo\", \"crest\", \"banner\"]}", + 314 + ], + "wood chair": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; has four legs; could have armrests\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 313 + ], + "cones": [ + " {\"type\": \"traffic tool\", \"description\": \"orange; could be made of plastic; could be used to block roads\", \"similar objects\": [\"barriers\", \"bollards\", \"signs\"]}", + 313 + ], + "step": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could be made of wood or metal; could be used to climb up or down\", \"similar objects\": [\"ladder\", \"staircase\", \"ramp\"]}", + 313 + ], + "horizon": [ + " {\"type\": \"landscape\", \"description\": \"the line where the sky and the earth seem to meet; could be seen from a distance\", \"similar objects\": [\"skyline\", \"mountain range\", \"sunset\"]}", + 313 + ], + "woods": [ + " {\"type\": \"natural environment\", \"description\": \"trees, plants, and other vegetation; could have animals; could have a path\", \"similar objects\": [\"forest\", \"jungle\", \"meadow\"]}", + 313 + ], + "strawberry": [ + " {\"type\": \"fruit\", \"description\": \"red, small, has seeds\", \"similar objects\": [\"raspberry\", \"blueberry\", \"blackberry\"]}", + 313 + ], + "ladies": [ + " {\"type\": \"people\", \"description\": \"female gender; could be wearing dresses\", \"similar objects\": [\"women\", \"girls\", \"ladies\"]}", + 311 + ], + "beige": [ + " {\"type\": \"color\", \"description\": \"light brown; could be described as a mix of white and brown\", \"similar objects\": [\"tan\", \"ecru\", \"cream\"]}", + 310 + ], + "snow board": [ + " {\"type\": \"sports equipment\", \"description\": \"long, flat board; could have bindings; could be used for snowboarding\", \"similar objects\": [\"skis\", \"surfboard\", \"skateboard\"]}", + 310 + ], + "wicker basket": [ + " {\"type\": \"container\", \"description\": \"made of woven materials; could be used for storage; could be used for carrying items\", \"similar objects\": [\"basket\", \"box\", \"bag\"]}", + 310 + ], + "railroad tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, parallel metal rails; could have wooden ties; could have a signal system\", \"similar objects\": [\"highway\", \"bridge\", \"tunnel\"]}", + 310 + ], + "train track": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, metal rails; could have a bridge or tunnel\", \"similar objects\": [\"road\", \"highway\", \"railway\"]}", + 309 + ], + "grey elephant": [ + "\n{\"type\": \"animal\", \"description\": \"large; has a long trunk; has grey skin; has large ears; has tusks\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}", + 308 + ], + "milk": [ + " {\"type\": \"beverage\", \"description\": \"white; could be served cold or hot; could be flavored\", \"similar objects\": [\"juice\", \"tea\", \"coffee\"]}", + 308 + ], + "jets": [ + " {\"type\": \"aircraft\", \"description\": \"long and narrow; has two or more engines; could be used for military or commercial purposes\", \"similar objects\": [\"helicopter\", \"airplane\", \"glider\"]}", + 308 + ], + "cables": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, could be made of copper or plastic; could be used to connect two devices\", \"similar objects\": [\"wires\", \"cords\", \"connectors\"]}", + 307 + ], + "staircase": [ + " {\"type\": \"structure\", \"description\": \"has multiple steps; could be made of wood or metal; could have a railing\", \"similar objects\": [\"ladder\", \"escalator\", \"elevator\"]}", + 307 + ], + "placemat": [ + " {\"type\": \"tableware\", \"description\": \"rectangular; could be made of fabric, paper, or plastic; used to protect the table from spills and stains\", \"similar objects\": [\"tablecloth\", \"coaster\", \"napkin\"]}", + 307 + ], + "knees": [ + " {\"type\": \"body part\", \"description\": \"joints between the thigh and the lower leg; could be bent\", \"similar objects\": [\"elbows\", \"ankles\", \"shoulders\"]}", + 306 + ], + "drain": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be made of metal; could be used to drain water\", \"similar objects\": [\"sink\", \"pipe\", \"faucet\"]}", + 306 + ], + "end table": [ + " {\"type\": \"furniture\", \"description\": \"small, rectangular, has four legs; could have a drawer\", \"similar objects\": [\"coffee table\", \"nightstand\", \"side table\"]}", + 305 + ], + "dispenser": [ + " {\"type\": \"utility tool\", \"description\": \"could be used to dispense liquids or other materials; could be made of plastic or metal\", \"similar objects\": [\"sprayer\", \"pump\", \"dropper\"]}", + 305 + ], + "picnic table": [ + " {\"type\": \"furniture\", \"description\": \"long, rectangular; could have benches on both sides; could be made of wood or metal\", \"similar objects\": [\"bench\", \"chair\", \"table\"]}", + 305 + ], + "pad": [ + " {\"type\": \"writing tool\", \"description\": \"rectangular; could be made of paper or plastic; could be used for writing or drawing\", \"similar objects\": [\"notebook\", \"journal\", \"sketchbook\"]}", + 303 + ], + "mouse pad": [ + " {\"type\": \"computer accessory\", \"description\": \"flat, rectangular; could be made of rubber or cloth; could have a design\", \"similar objects\": [\"keyboard\", \"mouse\", \"monitor\"]}", + 303 + ], + "objects": [ + "\n{\"type\": \"general object\", \"description\": \"could be anything; could be tangible or intangible; could be physical or virtual\", \"similar objects\": [\"items\", \"things\", \"products\"]}", + 303 + ], + "soap dispenser": [ + " {\"type\": \"cleaning tool\", \"description\": \"could be wall-mounted; could be automatic; could be manual\", \"similar objects\": [\"hand sanitizer dispenser\", \"toilet paper dispenser\", \"paper towel dispenser\"]}", + 303 + ], + "package": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of paper, plastic, or metal; could be sealed with tape\", \"similar objects\": [\"envelope\", \"box\", \"bag\"]}", + 302 + ], + "frisbees": [ + " {\"type\": \"toy\", \"description\": \"round; made of plastic; could be thrown in the air\", \"similar objects\": [\"hula hoop\", \"kite\", \"ball\"]}", + 301 + ], + "bandana": [ + " {\"type\": \"clothing accessory\", \"description\": \"square; could be tied around the head; could be made of cotton\", \"similar objects\": [\"scarf\", \"hat\", \"cap\"]}", + 301 + ], + "engines": [ + " {\"type\": \"machine\", \"description\": \"used to convert energy into motion; could be powered by gasoline, diesel, or electricity\", \"similar objects\": [\"generator\", \"motor\", \"turbine\"]}", + 301 + ], + "wine glasses": [ + " {\"type\": \"drinking tool\", \"description\": \"tall, thin, stemware; could be made of glass or crystal; could have a bowl-shaped top\", \"similar objects\": [\"tumblers\", \"mugs\", \"cups\"]}", + 301 + ], + "grass area": [ + " {\"type\": \"landscape\", \"description\": \"green; could be mowed; could have flowers\", \"similar objects\": [\"lawn\", \"meadow\", \"field\"]}", + 301 + ], + "wood floor": [ + " {\"type\": \"flooring material\", \"description\": \"hard, made of wood; could be stained or painted\", \"similar objects\": [\"tile floor\", \"carpet\", \"linoleum\"]}", + 300 + ], + "wrist watch": [ + " {\"type\": \"accessory\", \"description\": \"worn on the wrist; has a dial; could have a strap\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}", + 299 + ], + "splash": [ + " {\"type\": \"action\", \"description\": \"a sudden, brief burst of liquid or sound; could be accompanied by a spray of water\", \"similar objects\": [\"spray\", \"spurt\", \"gush\"]}", + 299 + ], + "round clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has a face with numbers and hands; could be digital or analog\", \"similar objects\": [\"watch\", \"alarm clock\", \"timer\"]}", + 296 + ], + "mustache": [ + " {\"type\": \"facial hair\", \"description\": \"long, thin, curved; could be groomed\", \"similar objects\": [\"beard\", \"goatee\", \"sideburns\"]}", + 296 + ], + "sugar": [ + " {\"type\": \"ingredient\", \"description\": \"white, granular, sweet; could be used for baking\", \"similar objects\": [\"salt\", \"flour\", \"baking powder\"]}", + 296 + ], + "pasta": [ + " {\"type\": \"food\", \"description\": \"long, thin, cylindrical; could be made of wheat or rice flour; could be boiled\", \"similar objects\": [\"noodles\", \"spaghetti\", \"macaroni\"]}", + 296 + ], + "pink flower": [ + "\n{\"type\": \"plant\", \"description\": \"pink petals; could have green leaves; could have a stem\", \"similar objects\": [\"rose\", \"daisy\", \"tulip\"]}", + 295 + ], + "reflections": [ + " {\"type\": \"phenomenon\", \"description\": \"light bouncing off a surface; could be seen in water or mirrors\", \"similar objects\": [\"refraction\", \"diffraction\", \"interference\"]}", + 295 + ], + "concrete wall": [ + " {\"type\": \"building material\", \"description\": \"gray; hard; could be used to build walls\", \"similar objects\": [\"brick wall\", \"wooden wall\", \"metal wall\"]}", + 295 + ], + "traffic cone": [ + " {\"type\": \"safety tool\", \"description\": \"orange; cone-shaped; could be reflective\", \"similar objects\": [\"barricade\", \"warning sign\", \"road sign\"]}", + 294 + ], + "persons": [ + "\n{\"type\": \"people\", \"description\": \"could be of different genders, ages, and races; could be standing, walking, or running; could be wearing different clothes\", \"similar objects\": [\"crowd\", \"group\", \"family\"]}", + 294 + ], + "rails": [ + " {\"type\": \"transportation tool\", \"description\": \"long, metal bars; used for trains\", \"similar objects\": [\"tracks\", \"ties\", \"sleepers\"]}", + 293 + ], + "bin": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"box\", \"bag\", \"bucket\"]}", + 292 + ], + "claws": [ + " {\"type\": \"animal body part\", \"description\": \"sharp, curved nails; could be found on cats, birds, and other animals\", \"similar objects\": [\"talons\", \"beak\", \"fangs\"]}", + 292 + ], + "crumbs": [ + " {\"type\": \"food\", \"description\": \"small, dry pieces of food; could be made of bread, cake, or cookie\", \"similar objects\": [\"flour\", \"sugar\", \"salt\"]}", + 292 + ], + "center": [ + " {\"type\": \"location\", \"description\": \"middle point; could be used to describe a place\", \"similar objects\": [\"core\", \"heart\", \"focus\"]}", + 292 + ], + "items furniture": [ + "\n{\"type\": \"furniture\", \"description\": \"could be made of wood, metal, plastic, or other materials; could be used for seating, storage, or decoration; could come in various shapes and sizes\", \"similar objects\": [\"chair\", \"table\", \"sofa\"]}", + 291 + ], + "building background": [ + "\n{\"type\": \"background\", \"description\": \"could be a cityscape, a landscape, or a skyline; could have multiple buildings of different shapes and sizes; could have trees, roads, and other elements\", \"similar objects\": [\"cityscape\", \"landscape\", \"skyline\"]}", + 291 + ], + "asphalt": [ + " {\"type\": \"building material\", \"description\": \"black, sticky, used for paving roads\", \"similar objects\": [\"concrete\", \"gravel\", \"tar\"]}", + 290 + ], + "bark": [ + " {\"type\": \"sound\", \"description\": \"sound made by a dog; could be loud and sharp\", \"similar objects\": [\"howl\", \"growl\", \"whimper\"]}", + 289 + ], + "computers": [ + " {\"type\": \"electronic device\", \"description\": \"could be desktop or laptop; could have a monitor, keyboard, and mouse; could be used for various tasks\", \"similar objects\": [\"tablets\", \"smartphones\", \"printers\"]}", + 289 + ], + "wii controller": [ + " {\"type\": \"gaming device\", \"description\": \"rectangular; has buttons and a joystick; could be wireless\", \"similar objects\": [\"xbox controller\", \"playstation controller\", \"joystick\"]}", + 289 + ], + "garbage bin": [ + " {\"type\": \"container\", \"description\": \"rectangular; has a lid; could be made of plastic\", \"similar objects\": [\"trash can\", \"recycling bin\", \"compost bin\"]}", + 288 + ], + "leafy": [ + " {\"type\": \"plant\", \"description\": \"green; could have veins; could be flat or curved; could be attached to a stem\", \"similar objects\": [\"fern\", \"moss\", \"grass\"]}", + 287 + ], + "magazines": [ + " {\"type\": \"publication\", \"description\": \"printed paper; could be glossy; could be bound\", \"similar objects\": [\"newspaper\", \"book\", \"journal\"]}", + 287 + ], + "lift": [ + " {\"type\": \"transportation tool\", \"description\": \"vertical movement; could be used to move people or goods\", \"similar objects\": [\"elevator\", \"escalator\", \"staircase\"]}", + 287 + ], + "pie": [ + " {\"type\": \"food\", \"description\": \"round; could be filled with fruits, cream, or savory ingredients; could be topped with a crust\", \"similar objects\": [\"cake\", \"tart\", \"quiche\"]}", + 287 + ], + "billboard": [ + " {\"type\": \"advertising tool\", \"description\": \"large, rectangular; could be used to display advertisements\", \"similar objects\": [\"poster\", \"signboard\", \"banner\"]}", + 286 + ], + "passenger": [ + " {\"type\": \"person\", \"description\": \"traveling in a vehicle; could be a driver or a passenger\", \"similar objects\": [\"driver\", \"pedestrian\", \"cyclist\"]}", + 286 + ], + "toilet lid": [ + " {\"type\": \"bathroom fixture\", \"description\": \"round; could be made of porcelain; could be attached to a toilet bowl\", \"similar objects\": [\"toilet seat\", \"bathtub\", \"sink\"]}", + 283 + ], + "bedspread": [ + " {\"type\": \"bedding item\", \"description\": \"large piece of fabric; could be quilted; could be used to cover a bed\", \"similar objects\": [\"comforter\", \"duvet\", \"blanket\"]}", + 283 + ], + "blonde woman": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could have fair skin\", \"similar objects\": [\"blonde man\", \"brunette woman\", \"redhead woman\"]}", + 282 + ], + "middle": [ + " {\"type\": \"position\", \"description\": \"in the middle; between two points\", \"similar objects\": [\"center\", \"intermediate\", \"median\"]}", + 282 + ], + "butter": [ + " {\"type\": \"dairy product\", \"description\": \"yellow; soft; could be spread on bread\", \"similar objects\": [\"margarine\", \"yogurt\", \"cream cheese\"]}", + 281 + ], + "printer": [ + " {\"type\": \"electronic device\", \"description\": \"could be connected to a computer; could print documents\", \"similar objects\": [\"scanner\", \"fax machine\", \"copier\"]}", + 280 + ], + "noodles": [ + " {\"type\": \"food\", \"description\": \"long, thin, could be made of wheat, rice, or egg; could be cooked in soup or stir-fried\", \"similar objects\": [\"pasta\", \"ramen\", \"udon\"]}", + 280 + ], + "cleats": [ + " {\"type\": \"footwear\", \"description\": \"has spikes on the bottom; could be made of leather; could be used for sports\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 279 + ], + "cracks": [ + " {\"type\": \"damage\", \"description\": \"lines or fissures on a surface; could be caused by weathering or aging\", \"similar objects\": [\"holes\", \"dents\", \"scratches\"]}", + 279 + ], + "grass field": [ + " {\"type\": \"landscape\", \"description\": \"green; could have flowers; could have trees\", \"similar objects\": [\"meadow\", \"forest\", \"desert\"]}", + 279 + ], + "marks": [ + " {\"type\": \"symbol\", \"description\": \"could be made of lines, circles, or other shapes; could be used to represent ideas or concepts\", \"similar objects\": [\"signs\", \"logos\", \"icons\"]}", + 279 + ], + "kettle": [ + " {\"type\": \"cooking tool\", \"description\": \"round; has a handle; could be made of metal; could be used to boil water\", \"similar objects\": [\"teapot\", \"coffee maker\", \"microwave\"]}", + 278 + ], + "tissue": [ + " {\"type\": \"paper product\", \"description\": \"soft, thin, rectangular; could be used for wiping\", \"similar objects\": [\"paper towel\", \"napkin\", \"toilet paper\"]}", + 278 + ], + "table lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"has a base and a lampshade; could be made of metal or plastic; could be powered by electricity or battery\", \"similar objects\": [\"floor lamp\", \"ceiling lamp\", \"wall lamp\"]}", + 277 + ], + "coffee maker": [ + " {\"type\": \"kitchen appliance\", \"description\": \"machine used to brew coffee; could have a filter; could have a carafe\", \"similar objects\": [\"espresso machine\", \"tea maker\", \"french press\"]}", + 276 + ], + "dugout": [ + " {\"type\": \"boat\", \"description\": \"long, narrow, open boat; could be made of wood; could be propelled by paddles\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 276 + ], + "lighthouse": [ + " {\"type\": \"building\", \"description\": \"tall; has a light on the top; could be made of stones\", \"similar objects\": [\"tower\", \"windmill\", \"observatory\"]}", + 276 + ], + "ponytail": [ + " {\"type\": \"hairstyle\", \"description\": \"hair tied up in a high bun; could be with a hair tie or elastic band\", \"similar objects\": [\"braid\", \"bun\", \"pigtails\"]}", + 275 + ], + "cucumber": [ + " {\"type\": \"vegetable\", \"description\": \"long, green, smooth; could have white stripes; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"eggplant\", \"green bean\"]}", + 275 + ], + "sprinkles": [ + " {\"type\": \"food decoration\", \"description\": \"small, colorful, round; could be made of sugar\", \"similar objects\": [\"jimmies\", \"nonpareils\", \"dragees\"]}", + 275 + ], + "toys": [ + " {\"type\": \"plaything\", \"description\": \"could be made of plastic, wood, or fabric; could be used for educational or recreational purposes\", \"similar objects\": [\"dolls\", \"action figures\", \"building blocks\"]}", + 274 + ], + "paper towels": [ + " {\"type\": \"cleaning tool\", \"description\": \"absorbent; could be used to clean up spills\", \"similar objects\": [\"sponge\", \"cloth\", \"tissue\"]}", + 273 + ], + "boulder": [ + " {\"type\": \"rock\", \"description\": \"large, round, heavy; could be made of granite or limestone\", \"similar objects\": [\"pebble\", \"cobble\", \"gravel\"]}", + 272 + ], + "rock wall": [ + " {\"type\": \"structure\", \"description\": \"made of rocks; could be used for climbing\", \"similar objects\": [\"stone wall\", \"brick wall\", \"wooden fence\"]}", + 271 + ], + "office chair": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could be adjustable; could have armrests\", \"similar objects\": [\"desk chair\", \"sofa\", \"stool\"]}", + 271 + ], + "screen tv": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be connected to a computer; could be used to watch movies\", \"similar objects\": [\"monitor\", \"projector\", \"smartphone\"]}", + 271 + ], + "paddle": [ + " {\"type\": \"sports tool\", \"description\": \"long, thin, has a handle; could be used for rowing\", \"similar objects\": [\"racquet\", \"bat\", \"club\"]}", + 270 + ], + "bald man": [ + " {\"type\": \"person\", \"description\": \"no hair on the head; could have facial hair\", \"similar objects\": [\"bald woman\", \"man with a hat\", \"man with a wig\"]}", + 270 + ], + "lock": [ + " {\"type\": \"security tool\", \"description\": \"has a keyhole; could be made of metal; could be used to secure doors, windows, and other items\", \"similar objects\": [\"padlock\", \"combination lock\", \"deadbolt\"]}", + 270 + ], + "laces": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of cotton or nylon; used to tie shoes\", \"similar objects\": [\"shoelaces\", \"ribbons\", \"strings\"]}", + 269 + ], + "glass door": [ + " {\"type\": \"door\", \"description\": \"transparent; could be made of glass or plastic; could be framed or frameless\", \"similar objects\": [\"sliding door\", \"wooden door\", \"metal door\"]}", + 269 + ], + "block": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of wood, plastic, or metal; could be used for construction\", \"similar objects\": [\"brick\", \"stone\", \"concrete\"]}", + 269 + ], + "ropes": [ + " {\"type\": \"tool\", \"description\": \"long, thin, could be made of different materials; could be used for tying or hanging\", \"similar objects\": [\"chains\", \"cables\", \"strings\"]}", + 269 + ], + "phones": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; could be used to make calls; could be used to access the internet\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 269 + ], + "antenna": [ + " {\"type\": \"electronic device\", \"description\": \"long, thin, could be used to receive signals\", \"similar objects\": [\"transmitter\", \"receiver\", \"satellite dish\"]}", + 269 + ], + "harness": [ + " {\"type\": \"equipment\", \"description\": \"made of straps; used to secure a person or animal; could be used for climbing\", \"similar objects\": [\"rope\", \"belt\", \"strap\"]}", + 268 + ], + "tattoo": [ + " {\"type\": \"body art\", \"description\": \"permanent design on the skin; could be colorful\", \"similar objects\": [\"piercing\", \"henna\", \"scarification\"]}", + 268 + ], + "logs": [ + " {\"type\": \"wood\", \"description\": \"long, cylindrical; could be used as fuel\", \"similar objects\": [\"firewood\", \"timber\", \"branches\"]}", + 268 + ], + "pieces furniture": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood, metal, plastic, or other materials; could be used for seating, storage, or decoration\", \"similar objects\": [\"chair\", \"table\", \"cabinet\"]}", + 267 + ], + "paper bag": [ + " {\"type\": \"container\", \"description\": \"brown; could be folded; could be used to carry items\", \"similar objects\": [\"plastic bag\", \"box\", \"envelope\"]}", + 267 + ], + "eyebrow": [ + " {\"type\": \"body part\", \"description\": \"hair above the eyes; could be shaped differently\", \"similar objects\": [\"eyelash\", \"eyelid\", \"nose\"]}", + 267 + ], + "adult": [ + " {\"type\": \"human\", \"description\": \"over 18 years old; could be male or female\", \"similar objects\": [\"teenager\", \"child\", \"senior\"]}", + 266 + ], + "smile": [ + " {\"type\": \"expression\", \"description\": \"curved lips; could be accompanied by eyes crinkling; could be accompanied by teeth showing\", \"similar objects\": [\"laugh\", \"frown\", \"giggle\"]}", + 266 + ], + "wool": [ + " {\"type\": \"fabric\", \"description\": \"soft, fluffy, made from sheep's fur\", \"similar objects\": [\"cashmere\", \"cotton\", \"silk\"]}", + 265 + ], + "cookie": [ + " {\"type\": \"food\", \"description\": \"round; could be made of flour, sugar, butter; could be decorated with chocolate chips, nuts, etc.\", \"similar objects\": [\"cake\", \"pie\", \"brownie\"]}", + 264 + ], + "panda": [ + " {\"type\": \"animal\", \"description\": \"black and white fur; has a round face; has a short tail\", \"similar objects\": [\"bear\", \"koala\", \"raccoon\"]}", + 263 + ], + "ducks": [ + " {\"type\": \"animal\", \"description\": \"small, webbed feet; quack; could fly; could swim\", \"similar objects\": [\"geese\", \"swans\", \"penguins\"]}", + 263 + ], + "artwork": [ + " {\"type\": \"visual art\", \"description\": \"could be a painting, sculpture, drawing, or other visual representation of an idea or image\", \"similar objects\": [\"painting\", \"sculpture\", \"drawing\"]}", + 262 + ], + "orange cat": [ + "\n{\"type\": \"animal\", \"description\": \"orange fur; could have stripes or spots; could have green or yellow eyes\", \"similar objects\": [\"tiger\", \"leopard\", \"lion\"]}", + 262 + ], + "eye glasses": [ + " {\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be made of metal or plastic; could be tinted\", \"similar objects\": [\"sunglasses\", \"reading glasses\", \"safety glasses\"]}", + 261 + ], + "pen": [ + " {\"type\": \"writing tool\", \"description\": \"cylindrical; could be made of plastic or metal; could have a cap\", \"similar objects\": [\"pencil\", \"marker\", \"highlighter\"]}", + 261 + ], + "tail feathers": [ + " {\"type\": \"bird body part\", \"description\": \"long, thin, colorful; could be used for flight\", \"similar objects\": [\"wings\", \"beak\", \"talons\"]}", + 260 + ], + "trouser": [ + " {\"type\": \"clothing\", \"description\": \"long, loose-fitting, could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 259 + ], + "corn": [ + " {\"type\": \"vegetable\", \"description\": \"yellow, cylindrical; could be eaten as a whole; could be used to make popcorn\", \"similar objects\": [\"peas\", \"beans\", \"carrots\"]}", + 259 + ], + "cabin": [ + " {\"type\": \"structure\", \"description\": \"wooden; could have a chimney; could have a porch\", \"similar objects\": [\"cottage\", \"bungalow\", \"chalet\"]}", + 259 + ], + "columns": [ + " {\"type\": \"architectural structure\", \"description\": \"vertical, cylindrical, could be made of stone or metal; could be used to support a roof\", \"similar objects\": [\"pillars\", \"arches\", \"balustrades\"]}", + 258 + ], + "blade": [ + " {\"type\": \"tool\", \"description\": \"sharp edge; could be used for cutting\", \"similar objects\": [\"knife\", \"axe\", \"scissors\"]}", + 258 + ], + "school bus": [ + " {\"type\": \"vehicle\", \"description\": \"yellow; has a long body; could have multiple doors; could have a stop sign\", \"similar objects\": [\"van\", \"truck\", \"minibus\"]}", + 258 + ], + "game controller": [ + " {\"type\": \"electronic device\", \"description\": \"has buttons and joysticks; could be wireless\", \"similar objects\": [\"console\", \"keyboard\", \"mouse\"]}", + 257 + ], + "metal post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for support\", \"similar objects\": [\"wood post\", \"concrete post\", \"steel beam\"]}", + 257 + ], + "awning": [ + " {\"type\": \"structure\", \"description\": \"can be made of fabric or metal; used to provide shade or shelter from the elements\", \"similar objects\": [\"canopy\", \"umbrella\", \"tent\"]}", + 257 + ], + "blond hair": [ + " {\"type\": \"hair color\", \"description\": \"light yellowish-brown; could be straight or wavy\", \"similar objects\": [\"brown hair\", \"black hair\", \"red hair\"]}", + 256 + ], + "day time picture": [ + "\n{\"type\": \"image\", \"description\": \"bright colors; clear objects; could have shadows; could have people or animals\", \"similar objects\": [\"night time picture\", \"landscape picture\", \"portrait picture\"]}", + 256 + ], + "side table": [ + " {\"type\": \"furniture\", \"description\": \"small, rectangular, has four legs; could have drawers\", \"similar objects\": [\"coffee table\", \"end table\", \"nightstand\"]}", + 256 + ], + "jet engine": [ + " {\"type\": \"machine\", \"description\": \"cylindrical; has a turbine; could be used to power aircrafts\", \"similar objects\": [\"turbine\", \"propeller\", \"rocket engine\"]}", + 256 + ], + "stars": [ + " {\"type\": \"celestial object\", \"description\": \"bright, twinkling points of light in the night sky\", \"similar objects\": [\"planets\", \"moons\", \"comets\"]}", + 256 + ], + "womans": [ + "\n{\"type\": \"clothing\", \"description\": \"designed for female body; could be made of different materials; could have different styles\", \"similar objects\": [\"dress\", \"skirt\", \"blouse\"]}", + 256 + ], + "hardwood floor": [ + " {\"type\": \"flooring material\", \"description\": \"smooth, durable, could be stained; could be made of oak, maple, or walnut\", \"similar objects\": [\"laminate flooring\", \"carpet\", \"tile flooring\"]}", + 255 + ], + "butter knife": [ + " {\"type\": \"utensil\", \"description\": \"flat, short blade; could have a plastic handle\", \"similar objects\": [\"dinner knife\", \"spoon\", \"fork\"]}", + 255 + ], + "front wheels": [ + " {\"type\": \"automobile part\", \"description\": \"round; could be made of metal; could be attached to the axle\", \"similar objects\": [\"back wheels\", \"tires\", \"rims\"]}", + 255 + ], + "stump": [ + " {\"type\": \"wooden object\", \"description\": \"remains of a tree; could be used as a seat\", \"similar objects\": [\"log\", \"firewood\", \"branch\"]}", + 255 + ], + "front windows": [ + " {\"type\": \"building component\", \"description\": \"transparent; could be made of glass; could be opened and closed\", \"similar objects\": [\"doors\", \"shutters\", \"balcony\"]}", + 255 + ], + "utility pole": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could have wires attached to it\", \"similar objects\": [\"street light\", \"traffic light\", \"telephone pole\"]}", + 254 + ], + "bare trees": [ + " {\"type\": \"landscape\", \"description\": \"no leaves; could have branches; could be in a forest\", \"similar objects\": [\"mountains\", \"rivers\", \"deserts\"]}", + 254 + ], + "city street": [ + " {\"type\": \"location\", \"description\": \"urban area; could have buildings, sidewalks, and roads; could have street lights and signs\", \"similar objects\": [\"suburb\", \"countryside\", \"park\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\",", + 254 + ], + "blue wall": [ + "\n{\"type\": \"decoration\", \"description\": \"blue; could be painted or wallpapered; could be plain or patterned\", \"similar objects\": [\"green wall\", \"red wall\", \"white wall\"]}", + 254 + ], + "arrows": [ + " {\"type\": \"weapon\", \"description\": \"pointed, could be made of wood or metal; could be used for hunting or warfare\", \"similar objects\": [\"spear\", \"dart\", \"axe\"]}", + 254 + ], + "leather": [ + " {\"type\": \"material\", \"description\": \"smooth; could be used to make clothes, shoes, bags, etc.\", \"similar objects\": [\"suede\", \"canvas\", \"denim\"]}", + 253 + ], + "dress shirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be buttoned up; could be made of cotton or linen\", \"similar objects\": [\"polo shirt\", \"t-shirt\", \"blouse\"]}", + 253 + ], + "blue stripe": [ + " {\"type\": \"pattern\", \"description\": \"horizontal or vertical lines of different shades of blue\", \"similar objects\": [\"plaid\", \"polka dot\", \"gingham\"]}", + 253 + ], + "round table": [ + " {\"type\": \"furniture\", \"description\": \"circular; could have four legs; could have a glass top\", \"similar objects\": [\"coffee table\", \"dining table\", \"end table\"]}", + 253 + ], + "streetlight": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lamp post\", \"lantern\", \"torch\"]}", + 253 + ], + "boarder": [ + " {\"type\": \"person\", \"description\": \"someone who lives in a house or apartment that is not their own; could be a student living away from home\", \"similar objects\": [\"roommate\", \"tenant\", \"lodger\"]}", + 253 + ], + "dirt road": [ + " {\"type\": \"road\", \"description\": \"uneven; could be dusty; could have potholes\", \"similar objects\": [\"gravel road\", \"dirt path\", \"mud road\"]}", + 253 + ], + "nostril": [ + " {\"type\": \"body part\", \"description\": \"two holes on the nose; could be used for breathing\", \"similar objects\": [\"mouth\", \"ear\", \"eye\"]}", + 252 + ], + "bow tie": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, black, tied around the neck\", \"similar objects\": [\"necktie\", \"cravat\", \"ascot\"]}", + 251 + ], + "opening": [ + " {\"type\": \"action\", \"description\": \"the process of making something accessible; could be a door, window, or other entrance\", \"similar objects\": [\"closing\", \"unlocking\", \"uncovering\"]}", + 251 + ], + "debris": [ + " {\"type\": \"waste\", \"description\": \"pieces of broken objects; could be made of wood, metal, plastic, etc.\", \"similar objects\": [\"rubbish\", \"trash\", \"garbage\"]}", + 250 + ], + "ski tracks": [ + " {\"type\": \"outdoor activity\", \"description\": \"parallel lines on snow; could be made by skis\", \"similar objects\": [\"snowboard tracks\", \"hiking trails\", \"bicycle tracks\"]}", + 250 + ], + "designs": [ + " {\"type\": \"art\", \"description\": \"visual representation of ideas; could be in the form of drawings, paintings, sculptures, etc.\", \"similar objects\": [\"patterns\", \"illustrations\", \"photographs\"]}", + 249 + ], + "enclosure": [ + " {\"type\": \"structure\", \"description\": \"could be made of metal or wood; could be used to contain animals or plants; could have a door or gate\", \"similar objects\": [\"fence\", \"cage\", \"pen\"]}", + 249 + ], + "pickup truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has an open cargo bed; could have four doors\", \"similar objects\": [\"SUV\", \"van\", \"sedan\"]}", + 248 + ], + "silverware": [ + " {\"type\": \"utensil\", \"description\": \"made of metal; could be used for eating; could be a spoon, fork, or knife\", \"similar objects\": [\"plate\", \"bowl\", \"cup\"]}", + 247 + ], + "tunnel": [ + " {\"type\": \"structure\", \"description\": \"long, dark, could be curved; could have two openings\", \"similar objects\": [\"bridge\", \"cave\", \"underpass\"]}", + 247 + ], + "daytime sky": [ + " {\"type\": \"natural phenomenon\", \"description\": \"blue; could have white clouds; could have birds flying\", \"similar objects\": [\"night sky\", \"sunset\", \"sunrise\"]}", + 247 + ], + "coats": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of wool; could have buttons or zippers\", \"similar objects\": [\"jacket\", \"sweater\", \"vest\"]}", + 246 + ], + "toast": [ + " {\"type\": \"food\", \"description\": \"browned bread; could be served with butter and jam\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 246 + ], + "bare tree": [ + " {\"type\": \"plant\", \"description\": \"no leaves; could have branches; could have a trunk\", \"similar objects\": [\"palm tree\", \"pine tree\", \"bamboo\"]}", + 246 + ], + "canoe": [ + " {\"type\": \"watercraft\", \"description\": \"long and narrow; could be made of wood or plastic; could be paddled\", \"similar objects\": [\"kayak\", \"rowboat\", \"sailboat\"]}", + 245 + ], + "shutters": [ + " {\"type\": \"window covering\", \"description\": \"hinged panels; could be made of wood or metal; could be opened and closed\", \"similar objects\": [\"blinds\", \"curtains\", \"drapes\"]}", + 245 + ], + "spray": [ + " {\"type\": \"cleaning tool\", \"description\": \"aerosol; could be used to clean surfaces\", \"similar objects\": [\"mop\", \"broom\", \"duster\"]}", + 245 + ], + "bleachers": [ + " {\"type\": \"seating\", \"description\": \"long, tiered seating; could be made of wood or metal; could be used for outdoor events\", \"similar objects\": [\"benches\", \"stadium seats\", \"grandstands\"]}", + 244 + ], + "cloudy blue sky": [ + "\n{\"type\": \"weather\", \"description\": \"grayish blue; could have white clouds; could be sunny or rainy\", \"similar objects\": [\"sunny sky\", \"rainy sky\", \"snowy sky\"]}", + 243 + ], + "blue hat": [ + " {\"type\": \"clothing item\", \"description\": \"blue; could be made of fabric; could have a brim\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}", + 243 + ], + "racquet": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be used to hit a ball\", \"similar objects\": [\"tennis racket\", \"badminton racket\", \"squash racket\"]}", + 243 + ], + "clothing items": [ + "\n{\"type\": \"clothing\", \"description\": \"could be made of fabric; could be of different colors and patterns; could be of different sizes; could be of different styles\", \"similar objects\": [\"shoes\", \"hats\", \"accessories\"]}", + 243 + ], + "dirty": [ + "\n{\"type\": \"adjective\", \"description\": \"not clean; covered with dirt or dust; not neat or tidy\", \"similar objects\": [\"messy\", \"filthy\", \"unkempt\"]}", + 243 + ], + "wooden bench": [ + " {\"type\": \"furniture\", \"description\": \"long; made of wood; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"table\"]}", + 243 + ], + "chandelier": [ + " {\"type\": \"lighting tool\", \"description\": \"hanging; could have multiple lights; could be made of glass or metal\", \"similar objects\": [\"pendant light\", \"ceiling light\", \"wall sconce\"]}", + 242 + ], + "cans": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could be used to store food or drinks\", \"similar objects\": [\"bottles\", \"jars\", \"boxes\"]}", + 242 + ], + "blue shorts": [ + " {\"type\": \"clothing\", \"description\": \"blue; could be made of cotton; could have pockets; could have a drawstring\", \"similar objects\": [\"jeans\", \"t-shirt\", \"capri pants\"]}", + 241 + ], + "cookies": [ + " {\"type\": \"food\", \"description\": \"round; could be made of flour, sugar, butter; could be decorated with chocolate chips, nuts, etc.\", \"similar objects\": [\"cake\", \"pie\", \"brownies\"]}", + 241 + ], + "toilet brush": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has a brush head; could be made of plastic or metal\", \"similar objects\": [\"mop\", \"duster\", \"scrub brush\"]}", + 241 + ], + "beer bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass; has a cap\", \"similar objects\": [\"wine bottle\", \"soda bottle\", \"water bottle\"]}", + 241 + ], + "flower pot": [ + " {\"type\": \"container\", \"description\": \"round; could be made of clay; could have a drainage hole at the bottom; could have a saucer\", \"similar objects\": [\"vase\", \"urn\", \"planter\"]}", + 241 + ], + "radiator": [ + " {\"type\": \"heating tool\", \"description\": \"long, metal; could be attached to a wall; could be used to heat a room\", \"similar objects\": [\"heater\", \"boiler\", \"air conditioner\"]}", + 240 + ], + "cloudless sky": [ + " {\"type\": \"weather\", \"description\": \"blue; no clouds; could be sunny\", \"similar objects\": [\"clear sky\", \"sunny day\", \"rainy day\"]}", + 240 + ], + "policeman": [ + " {\"type\": \"person\", \"description\": \"wears a uniform; carries a gun; could have a hat\", \"similar objects\": [\"firefighter\", \"soldier\", \"security guard\"]}", + 239 + ], + "signal": [ + " {\"type\": \"communication tool\", \"description\": \"could be a sound, light, or gesture; could be used to indicate a warning or instruction\", \"similar objects\": [\"alarm\", \"bell\", \"whistle\"]}", + 239 + ], + "something": [ + "\n\nObject detection models should focus on identifying the type of object, its description, and any similar objects. For example, for the input \"zucchini\", the model should focus on identifying it as a vegetable, describing it as cylindrical, green, and smooth, and listing similar objects such as cucumber, eggplant, and green bean.", + 238 + ], + "spokes": [ + " {\"type\": \"bicycle part\", \"description\": \"radial rods that connect the hub to the rim of the wheel\", \"similar objects\": [\"rim\", \"tire\", \"hub\"]}", + 238 + ], + "knee pads": [ + " {\"type\": \"protective gear\", \"description\": \"worn around the knee; could be made of foam or plastic; could be used for sports or work\", \"similar objects\": [\"elbow pads\", \"shin guards\", \"helmet\"]}", + 238 + ], + "motorcycle helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"hard shell; could be full-face or open-face; could have a visor\", \"similar objects\": [\"bicycle helmet\", \"skateboard helmet\", \"ski helmet\"]}", + 238 + ], + "knot": [ + " {\"type\": \"fastening tool\", \"description\": \"intertwined loops of rope or thread; could be used to tie two objects together\", \"similar objects\": [\"bow\", \"tie\", \"latch\"]}", + 238 + ], + "couches": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of fabric or leather; could have armrests and backrests\", \"similar objects\": [\"sofa\", \"loveseat\", \"chair\"]}", + 238 + ], + "goats": [ + " {\"type\": \"animal\", \"description\": \"small, four-legged, could have horns; could be white, black, brown, or gray\", \"similar objects\": [\"sheep\", \"cows\", \"llamas\"]}", + 238 + ], + "handlebars": [ + " {\"type\": \"bicycle part\", \"description\": \"two metal bars; could be curved; could be attached to the bicycle frame\", \"similar objects\": [\"saddle\", \"pedals\", \"grips\"]}", + 236 + ], + "baseball helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round, has a face guard; could be made of plastic or metal\", \"similar objects\": [\"hockey helmet\", \"bicycle helmet\", \"motorcycle helmet\"]}", + 235 + ], + "chocolate cake": [ + " {\"type\": \"dessert\", \"description\": \"round; could be layered; could be topped with chocolate frosting\", \"similar objects\": [\"cheesecake\", \"cupcake\", \"brownie\"]}", + 235 + ], + "rear tire": [ + " {\"type\": \"automobile part\", \"description\": \"round; made of rubber; could be inflated\", \"similar objects\": [\"front tire\", \"wheel\", \"spare tire\"]}", + 234 + ], + "neck tie": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, usually made of silk; could be tied around the neck\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}", + 234 + ], + "wallpaper": [ + " {\"type\": \"decoration material\", \"description\": \"paper-like material; could be printed with patterns; could be pasted on walls\", \"similar objects\": [\"paint\", \"fabric\", \"tile\"]}", + 233 + ], + "stomach": [ + " {\"type\": \"organ\", \"description\": \"part of the digestive system; located in the abdomen; could be filled with food\", \"similar objects\": [\"intestines\", \"liver\", \"pancreas\"]}", + 233 + ], + "keyboards": [ + " {\"type\": \"electronic device\", \"description\": \"has multiple keys; could be wired or wireless; could be used for typing\", \"similar objects\": [\"mouse\", \"headphones\", \"game controller\"]}", + 233 + ], + "railroad": [ + " {\"type\": \"transportation system\", \"description\": \"long, straight, has tracks; could have a train running on it\", \"similar objects\": [\"highway\", \"metro\", \"tram\"]}", + 233 + ], + "marker": [ + " {\"type\": \"writing tool\", \"description\": \"has a tip; could be used to write on paper or other surfaces\", \"similar objects\": [\"pen\", \"pencil\", \"crayon\"]}", + 232 + ], + "wooden desk": [ + "\n{\"type\": \"furniture\", \"description\": \"made of wood; could have drawers; could have a flat surface\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 232 + ], + "cowboy hat": [ + " {\"type\": \"headwear\", \"description\": \"wide brim; could be made of straw or felt; could have a band around the crown\", \"similar objects\": [\"fedora\", \"baseball cap\", \"beret\"]}", + 231 + ], + "alarm clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"could have a digital or analog display; could have a snooze button; could have a loud ringing sound\", \"similar objects\": [\"watch\", \"timer\", \"stopwatch\"]}", + 231 + ], + "concrete sidewalk": [ + " {\"type\": \"structure\", \"description\": \"gray; made of concrete; could have cracks; could be used as a walkway\", \"similar objects\": [\"asphalt road\", \"brick wall\", \"gravel path\"]}", + 231 + ], + "bunches": [ + " {\"type\": \"grouping tool\", \"description\": \"used to group items together; could be made of rubber bands, strings, or other materials\", \"similar objects\": [\"ties\", \"clamps\", \"straps\"]}", + 231 + ], + "door knob": [ + " {\"type\": \"hardware\", \"description\": \"round; could be made of metal; could be used to open and close doors\", \"similar objects\": [\"door handle\", \"door latch\", \"door lock\"]}", + 231 + ], + "hook": [ + " {\"type\": \"tool\", \"description\": \"curved metal; could be used to hang things\", \"similar objects\": [\"hanger\", \"clamp\", \"peg\"]}", + 231 + ], + "grassy field": [ + " {\"type\": \"landscape\", \"description\": \"green; could have flowers; could have trees; could have animals\", \"similar objects\": [\"meadow\", \"forest\", \"desert\"]}", + 230 + ], + "sleeves": [ + " {\"type\": \"clothing item\", \"description\": \"attached to the arm of a shirt or dress; could be long or short; could be made of different materials\", \"similar objects\": [\"collar\", \"hem\", \"cuffs\"]}", + 230 + ], + "decorations": [ + " {\"type\": \"ornaments\", \"description\": \"could be made of paper, plastic, metal, fabric, etc.; could be used to decorate a room, a tree, etc.\", \"similar objects\": [\"lights\", \"ornaments\", \"garlands\"]}", + 230 + ], + "pink shirt": [ + " {\"type\": \"clothing\", \"description\": \"light pink; could have buttons; could have a collar\", \"similar objects\": [\"dress\", \"blouse\", \"t-shirt\"]}", + 229 + ], + "material": [ + " {\"type\": \"substance\", \"description\": \"could be solid, liquid, or gas; could be natural or man-made; could be used for making things\", \"similar objects\": [\"fabric\", \"wood\", \"metal\"]}", + 229 + ], + "rear": [ + " {\"type\": \"position\", \"description\": \"opposite of front; could be used to describe a location\", \"similar objects\": [\"back\", \"behind\", \"left\"]}", + 229 + ], + "wood bench": [ + " {\"type\": \"furniture\", \"description\": \"long; made of wood; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 229 + ], + "pillars": [ + " {\"type\": \"architectural structure\", \"description\": \"vertical, cylindrical, could be made of stone or metal; could be used to support a building\", \"similar objects\": [\"columns\", \"arches\", \"arches\"]}", + 228 + ], + "lemons": [ + " {\"type\": \"fruit\", \"description\": \"yellow, oval-shaped; has a sour taste; could be used for cooking\", \"similar objects\": [\"oranges\", \"limes\", \"grapefruits\"]}", + 228 + ], + "leafy tree": [ + " {\"type\": \"plant\", \"description\": \"tall; has many leaves; could have fruits; could have branches\", \"similar objects\": [\"palm tree\", \"pine tree\", \"oak tree\"]}", + 228 + ], + "passenger bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has many seats; could have a luggage compartment\", \"similar objects\": [\"school bus\", \"minibus\", \"coach\"]}", + 228 + ], + "briefcase": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of leather; could have a handle\", \"similar objects\": [\"suitcase\", \"backpack\", \"purse\"]}", + 227 + ], + "steeple": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, pointed, could be found on top of a church\", \"similar objects\": [\"spire\", \"minaret\", \"obelisk\"]}", + 226 + ], + "bit": [ + " {\"type\": \"tool\", \"description\": \"small metal piece; used to drill holes\", \"similar objects\": [\"drill bit\", \"screwdriver bit\", \"router bit\"]}", + 226 + ], + "rust": [ + " {\"type\": \"oxidation\", \"description\": \"a reddish-brown coating on metal surfaces caused by oxidation; could be removed with sandpaper\", \"similar objects\": [\"corrosion\", \"oxidation\", \"tarnish\"]}", + 226 + ], + "nails": [ + " {\"type\": \"hardware\", \"description\": \"small, pointed, metallic; could be used to join two pieces of wood together\", \"similar objects\": [\"screws\", \"bolts\", \"nuts\"]}", + 225 + ], + "stream": [ + " {\"type\": \"natural feature\", \"description\": \"a body of water; could be flowing; could be narrow or wide\", \"similar objects\": [\"river\", \"lake\", \"pond\"]}", + 224 + ], + "pens": [ + " {\"type\": \"writing tool\", \"description\": \"cylindrical; could be made of plastic or metal; could have a cap\", \"similar objects\": [\"pencils\", \"markers\", \"highlighters\"]}", + 224 + ], + "motorcyclist": [ + " {\"type\": \"person\", \"description\": \"wearing a helmet; riding a motorcycle; could be wearing protective gear\", \"similar objects\": [\"biker\", \"cyclist\", \"skater\"]}", + 224 + ], + "drapes": [ + " {\"type\": \"window covering\", \"description\": \"long, hangs from a rod; could be made of fabric\", \"similar objects\": [\"curtains\", \"blinds\", \"shades\"]}", + 223 + ], + "sign post": [ + " {\"type\": \"marker\", \"description\": \"tall; could be made of metal; could have signs on it\", \"similar objects\": [\"traffic light\", \"street light\", \"traffic sign\"]}", + 223 + ], + "tops": [ + " {\"type\": \"clothing\", \"description\": \"short, sleeveless; could be made of cotton or other fabrics; could have different colors and patterns\", \"similar objects\": [\"shirts\", \"blouses\", \"dresses\"]}", + 223 + ], + "city bus": [ + " {\"type\": \"vehicle\", \"description\": \"large; has multiple doors; could be yellow or white\", \"similar objects\": [\"school bus\", \"trolley bus\", \"tour bus\"]}", + 222 + ], + "burger": [ + " {\"type\": \"food\", \"description\": \"round; could be made of beef, chicken, or vegetables; could be served with lettuce, tomato, and onion; could be served with fries\", \"similar objects\": [\"sandwich\", \"hot dog\", \"taco\"]}", + 221 + ], + "pizza crust": [ + " {\"type\": \"food\", \"description\": \"round; could be thin or thick; could be made of wheat or corn flour\", \"similar objects\": [\"tortilla\", \"bread\", \"pie crust\"]}", + 221 + ], + "stainless steel": [ + " {\"type\": \"material\", \"description\": \"shiny, silver-colored metal; resistant to corrosion; used in kitchenware and medical equipment\", \"similar objects\": [\"aluminum\", \"copper\", \"brass\"]}", + 221 + ], + "bath tub": [ + " {\"type\": \"bathroom fixture\", \"description\": \"large, deep, could be made of porcelain; could have a shower head\", \"similar objects\": [\"shower\", \"sink\", \"toilet\"]}", + 221 + ], + "cabinet door": [ + " {\"type\": \"furniture part\", \"description\": \"rectangular; could be made of wood; could be opened and closed\", \"similar objects\": [\"drawer\", \"cupboard\", \"wardrobe\"]}", + 221 + ], + "blue shirt": [ + " {\"type\": \"clothing\", \"description\": \"blue; could have buttons; could have a collar\", \"similar objects\": [\"t-shirt\", \"dress\", \"jacket\"]}", + 220 + ], + "grasses": [ + " {\"type\": \"plant\", \"description\": \"green; could be short or tall; could be in a lawn\", \"similar objects\": [\"trees\", \"shrubs\", \"flowers\"]}", + 220 + ], + "arch": [ + " {\"type\": \"structure\", \"description\": \"curved; could be made of stone; could be used as a bridge\", \"similar objects\": [\"bridge\", \"tunnel\", \"dome\"]}", + 220 + ], + "plaque": [ + " {\"type\": \"decoration\", \"description\": \"flat; could be made of metal or wood; could be engraved with words\", \"similar objects\": [\"trophy\", \"medal\", \"certificate\"]}", + 220 + ], + "beef": [ + " {\"type\": \"meat\", \"description\": \"red; could be cooked in various ways; could be served as steak\", \"similar objects\": [\"pork\", \"lamb\", \"chicken\"]}", + 219 + ], + "jockey": [ + " {\"type\": \"occupation\", \"description\": \"a person who rides horses in races\", \"similar objects\": [\"trainer\", \"rider\", \"stable hand\"]}", + 219 + ], + "blue skies": [ + "\n{\"type\": \"weather phenomenon\", \"description\": \"clear, blue sky; could have white clouds\", \"similar objects\": [\"sunny day\", \"clear night\", \"rainy day\"]}", + 219 + ], + "devices": [ + "\n{\"type\": \"electronic equipment\", \"description\": \"could be used to access the internet; could be used to store data; could be used to communicate with others\", \"similar objects\": [\"laptop\", \"smartphone\", \"tablet\"]}", + 219 + ], + "seeds": [ + " {\"type\": \"planting material\", \"description\": \"small, round, could be of different colors; could be planted to grow plants\", \"similar objects\": [\"bulbs\", \"cuttings\", \"spores\"]}", + 219 + ], + "tea kettle": [ + " {\"type\": \"cooking tool\", \"description\": \"round; has a handle; could be made of metal; could whistle when boiling\", \"similar objects\": [\"teapot\", \"coffee pot\", \"saucepan\"]}", + 219 + ], + "silver pole": [ + " {\"type\": \"utility object\", \"description\": \"long, cylindrical, silver; could be used for support or decoration\", \"similar objects\": [\"flagpole\", \"streetlight pole\", \"fence post\"]}", + 219 + ], + "drinking glass": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be made of glass or plastic; could be cylindrical or conical; could have a handle\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 218 + ], + "hour hand": [ + " {\"type\": \"clock part\", \"description\": \"long, thin, pointed; moves around the clock face\", \"similar objects\": [\"minute hand\", \"second hand\", \"clock face\"]}", + 218 + ], + "plastic fork": [ + " {\"type\": \"utensil\", \"description\": \"long; could be white or colored; could be disposable\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 217 + ], + "figurine": [ + " {\"type\": \"decorative item\", \"description\": \"small, could be made of clay, plastic, or metal; could be in the shape of a person, animal, or object\", \"similar objects\": [\"statue\", \"sculpture\", \"ornament\"]}", + 217 + ], + "metal gate": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could be used to secure an area; could have a lock\", \"similar objects\": [\"fence\", \"wall\", \"barrier\"]}", + 217 + ], + "stop light": [ + " {\"type\": \"traffic signal\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"traffic sign\", \"traffic camera\", \"road barrier\"]}", + 217 + ], + "power line": [ + " {\"type\": \"electrical infrastructure\", \"description\": \"long, thin wires; could be connected to poles; could be seen in the sky\", \"similar objects\": [\"telephone line\", \"cable line\", \"fiber optic line\"]}", + 217 + ], + "photographer": [ + " {\"type\": \"occupation\", \"description\": \"takes pictures; could use a camera\", \"similar objects\": [\"videographer\", \"journalist\", \"artist\"]}", + 217 + ], + "toothpaste": [ + " {\"type\": \"hygiene product\", \"description\": \"white, creamy, comes in a tube; could have a minty flavor\", \"similar objects\": [\"mouthwash\", \"toothbrush\", \"floss\"]}", + 217 + ], + "coffee pot": [ + " {\"type\": \"cooking tool\", \"description\": \"tall, cylindrical; could have a handle; could have a spout\", \"similar objects\": [\"teapot\", \"kettle\", \"thermos\"]}", + 216 + ], + "way sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; could be yellow or white; could have arrows or words\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 216 + ], + "minivan": [ + " {\"type\": \"vehicle\", \"description\": \"longer than a car; has more than four doors; could have a sliding door\", \"similar objects\": [\"SUV\", \"sedan\", \"truck\"]}", + 216 + ], + "baby giraffe": [ + " {\"type\": \"animal\", \"description\": \"long neck; has spots; has long legs; has a short mane\", \"similar objects\": [\"baby elephant\", \"baby zebra\", \"baby horse\"]}", + 216 + ], + "baseball catcher": [ + " {\"type\": \"sports equipment\", \"description\": \"protective gear; has a face mask; has a chest protector; has a glove\", \"similar objects\": [\"baseball bat\", \"baseball glove\", \"baseball cap\"]}", + 216 + ], + "toilet tank": [ + " {\"type\": \"plumbing fixture\", \"description\": \"rectangular; has a lid; could be made of porcelain; could have a flush handle\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 216 + ], + "metal bar": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical, made of metal; could be used for construction\", \"similar objects\": [\"pipe\", \"rod\", \"beam\"]}", + 216 + ], + "bank": [ + " {\"type\": \"financial institution\", \"description\": \"building; could have a vault; could provide financial services\", \"similar objects\": [\"credit union\", \"ATM\", \"investment firm\"]}", + 215 + ], + "skateboard ramp": [ + " {\"type\": \"skateboarding tool\", \"description\": \"sloped surface; could be made of wood or metal; could have a curved shape\", \"similar objects\": [\"half-pipe\", \"quarter-pipe\", \"funbox\"]}", + 214 + ], + "hedge": [ + " {\"type\": \"plant\", \"description\": \"bushy; could be trimmed into shapes; could be used as a boundary\", \"similar objects\": [\"shrub\", \"bush\", \"tree\"]}", + 214 + ], + "table top": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, metal, or glass; could be attached to a base\", \"similar objects\": [\"desk\", \"countertop\", \"shelf\"]}", + 214 + ], + "passenger windows": [ + " {\"type\": \"vehicle part\", \"description\": \"transparent; could be opened and closed; could be tinted\", \"similar objects\": [\"windshield\", \"side mirror\", \"headlight\"]}", + 214 + ], + "remote control": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; has buttons; could be used to control other electronic devices\", \"similar objects\": [\"game controller\", \"keyboard\", \"mouse\"]}", + 214 + ], + "catchers": [ + " {\"type\": \"sports equipment\", \"description\": \"glove; used to catch a ball; could be made of leather\", \"similar objects\": [\"bat\", \"ball\", \"helmet\"]}", + 214 + ], + "baseball hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; has a brim; could have a logo\", \"similar objects\": [\"cap\", \"beanie\", \"sun hat\"]}", + 214 + ], + "pigeon": [ + " {\"type\": \"bird\", \"description\": \"gray; has a white patch on its neck; could fly in flocks\", \"similar objects\": [\"sparrow\", \"duck\", \"seagull\"]}", + 214 + ], + "mannequin": [ + " {\"type\": \"display tool\", \"description\": \"human-like figure; could be made of plastic or wood; could be used for displaying clothes\", \"similar objects\": [\"dummy\", \"dress form\", \"tailor's dummy\"]}", + 214 + ], + "porch": [ + " {\"type\": \"structure\", \"description\": \"an outdoor area attached to a house; could have a roof; could have stairs\", \"similar objects\": [\"deck\", \"balcony\", \"patio\"]}", + 214 + ], + "bracelets": [ + " {\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of metal, plastic, or fabric; could be decorated with jewels or charms\", \"similar objects\": [\"necklace\", \"earrings\", \"rings\"]}", + 213 + ], + "greenery": [ + " {\"type\": \"landscape\", \"description\": \"green plants; could be trees, shrubs, grasses, etc.\", \"similar objects\": [\"flora\", \"vegetation\", \"foliage\"]}", + 213 + ], + "garage door": [ + " {\"type\": \"door\", \"description\": \"large; could be made of metal; could be automated; could be opened and closed with a remote control\", \"similar objects\": [\"front door\", \"back door\", \"sliding door\"]}", + 213 + ], + "minute hand": [ + " {\"type\": \"clock part\", \"description\": \"long, thin, pointed end; moves in a clockwise direction\", \"similar objects\": [\"hour hand\", \"second hand\", \"clock face\"]}", + 213 + ], + "baseball game": [ + " {\"type\": \"sport\", \"description\": \"team sport; involves a bat and a ball; played on a diamond-shaped field\", \"similar objects\": [\"softball\", \"cricket\", \"soccer\"]}", + 212 + ], + "polo shirt": [ + " {\"type\": \"clothing\", \"description\": \"collared shirt; short sleeves; could have buttons; could have a logo\", \"similar objects\": [\"t-shirt\", \"button-down shirt\", \"sweater\"]}", + 212 + ], + "toilets": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a bowl and a seat; could be connected to a water tank; could be flushed\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 212 + ], + "shoreline": [ + " {\"type\": \"landscape\", \"description\": \"the line where the land meets the sea; could have rocks, sand, and waves\", \"similar objects\": [\"beach\", \"coastline\", \"seashore\"]}", + 212 + ], + "clump": [ + " {\"type\": \"object\", \"description\": \"a group of objects that are close together; could be made of different materials\", \"similar objects\": [\"bunch\", \"cluster\", \"collection\"]}", + 212 + ], + "tents": [ + " {\"type\": \"shelter\", \"description\": \"could be made of canvas; could be used for camping\", \"similar objects\": [\"igloo\", \"yurt\", \"teepee\"]}", + 211 + ], + "steam": [ + " {\"type\": \"gas\", \"description\": \"invisible; could be hot; could be created by boiling water\", \"similar objects\": [\"smoke\", \"vapor\", \"fog\"]}", + 210 + ], + "tan building": [ + "\n{\"type\": \"structure\", \"description\": \"rectangular; could be made of bricks; could have windows and doors; could have a roof\", \"similar objects\": [\"house\", \"school\", \"church\"]}", + 210 + ], + "name tag": [ + " {\"type\": \"identification tool\", \"description\": \"could be made of paper or plastic; could have a string to hang around the neck\", \"similar objects\": [\"badge\", \"ID card\", \"lanyard\"]}", + 210 + ], + "stacks": [ + " {\"type\": \"structure\", \"description\": \"a pile of objects; could be made of books, papers, or other materials\", \"similar objects\": [\"piles\", \"heaps\", \"mounds\"]}", + 210 + ], + "apple logo": [ + "\n{\"type\": \"logo\", \"description\": \"white apple with a bite taken out of it; could be in different colors\", \"similar objects\": [\"Microsoft logo\", \"Google logo\", \"Adidas logo\"]}", + 210 + ], + "heads": [ + " {\"type\": \"body part\", \"description\": \"two; located on the top of the body; could be covered with hair\", \"similar objects\": [\"shoulders\", \"arms\", \"legs\"]}", + 210 + ], + "baskets": [ + " {\"type\": \"container\", \"description\": \"could be made of wicker; could be used to store items\", \"similar objects\": [\"boxes\", \"bags\", \"buckets\"]}", + 209 + ], + "glass bowl": [ + " {\"type\": \"kitchenware\", \"description\": \"transparent; could be made of glass or plastic; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"dish\"]}", + 209 + ], + "balloons": [ + " {\"type\": \"decoration\", \"description\": \"round; could be filled with air or helium; could be of different colors\", \"similar objects\": [\"streamers\", \"confetti\", \"banners\"]}", + 209 + ], + "cupcakes": [ + " {\"type\": \"dessert\", \"description\": \"small, round, sweet; could be topped with frosting; could be filled with cream\", \"similar objects\": [\"muffins\", \"cookies\", \"brownies\"]}", + 209 + ], + "intersection": [ + " {\"type\": \"roadway\", \"description\": \"crossing of two or more roads; could have traffic lights\", \"similar objects\": [\"crosswalk\", \"roundabout\", \"traffic circle\"]}", + 208 + ], + "plastic cup": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be disposable; could have a handle\", \"similar objects\": [\"glass cup\", \"mug\", \"bowl\"]}", + 208 + ], + "brick sidewalk": [ + " {\"type\": \"construction material\", \"description\": \"made of bricks; could be used as a walkway\", \"similar objects\": [\"concrete sidewalk\", \"stone sidewalk\", \"wooden sidewalk\"]}", + 207 + ], + "bolts": [ + " {\"type\": \"hardware\", \"description\": \"metal; cylindrical; could have a head and a thread\", \"similar objects\": [\"nuts\", \"screws\", \"washers\"]}", + 207 + ], + "chains": [ + " {\"type\": \"accessory\", \"description\": \"made of metal; could be used to lock things\", \"similar objects\": [\"lock\", \"padlock\", \"cable\"]}", + 207 + ], + "exit sign": [ + " {\"type\": \"signage\", \"description\": \"green; has an arrow pointing to the direction of the exit; could be illuminated\", \"similar objects\": [\"warning sign\", \"stop sign\", \"no smoking sign\"]}", + 207 + ], + "biker": [ + " {\"type\": \"person\", \"description\": \"wears a helmet; rides a bicycle; could have a backpack\", \"similar objects\": [\"cyclist\", \"skater\", \"runner\"]}", + 207 + ], + "blond woman": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could have fair skin\", \"similar objects\": [\"blond man\", \"brunette woman\", \"redhead man\"]}", + 206 + ], + "plug": [ + " {\"type\": \"electrical tool\", \"description\": \"has two or three prongs; could be used to connect electrical appliances\", \"similar objects\": [\"socket\", \"adapter\", \"extension cord\"]}", + 206 + ], + "piano": [ + " {\"type\": \"musical instrument\", \"description\": \"long; has black and white keys; could be acoustic or digital\", \"similar objects\": [\"guitar\", \"violin\", \"drums\"]}", + 206 + ], + "chef": [ + " {\"type\": \"occupation\", \"description\": \"cooks food; wears a hat; could use a knife\", \"similar objects\": [\"waiter\", \"bartender\", \"baker\"]}", + 206 + ], + "wallet": [ + " {\"type\": \"accessory\", \"description\": \"small, rectangular; could be made of leather; could have multiple compartments\", \"similar objects\": [\"purse\", \"bag\", \"backpack\"]}", + 206 + ], + "plaid shirt": [ + " {\"type\": \"clothing\", \"description\": \"has a pattern of different colors; could be long-sleeved or short-sleeved; could be buttoned up or zipped up\", \"similar objects\": [\"striped shirt\", \"checked shirt\", \"floral shirt\"]}", + 206 + ], + "side view": [ + "\n{\"type\": \"perspective\", \"description\": \"view from the side; could be used to show the shape of an object\", \"similar objects\": [\"front view\", \"top view\", \"bottom view\"]}", + 205 + ], + "side window": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be opened; could be tinted\", \"similar objects\": [\"windshield\", \"rear window\", \"sunroof\"]}", + 205 + ], + "bare": [ + " {\"type\": \"adjective\", \"description\": \"without any covering or clothing; naked\", \"similar objects\": [\"unclothed\", \"stripped\", \"unadorned\"]}", + 205 + ], + "metal bench": [ + " {\"type\": \"furniture\", \"description\": \"made of metal; could have a backrest; could be used for seating\", \"similar objects\": [\"chair\", \"sofa\", \"stool\"]}", + 205 + ], + "overcast sky": [ + " {\"type\": \"weather\", \"description\": \"gray; no visible sun or stars; could be raining\", \"similar objects\": [\"cloudy sky\", \"foggy sky\", \"rainy sky\"]}", + 204 + ], + "twigs": [ + " {\"type\": \"natural object\", \"description\": \"small, thin branches; could be used for firewood\", \"similar objects\": [\"sticks\", \"branches\", \"logs\"]}", + 204 + ], + "tomato sauce": [ + " {\"type\": \"condiment\", \"description\": \"red; could be spicy; could be used for cooking\", \"similar objects\": [\"ketchup\", \"mustard\", \"mayonnaise\"]}", + 203 + ], + "left ear": [ + " {\"type\": \"body part\", \"description\": \"part of the head; located on the left side of the face; could be pierced\", \"similar objects\": [\"right ear\", \"nose\", \"eyebrow\"]}", + 203 + ], + "garage": [ + " {\"type\": \"building\", \"description\": \"large, enclosed space; could have a door; could be used for storage\", \"similar objects\": [\"shed\", \"barn\", \"workshop\"]}", + 203 + ], + "brown leaves": [ + " {\"type\": \"plant part\", \"description\": \"dry, thin, could be curved; could be found on the ground\", \"similar objects\": [\"green leaves\", \"twigs\", \"pine needles\"]}", + 203 + ], + "parrot": [ + " {\"type\": \"animal\", \"description\": \"colorful feathers; could talk; could fly\", \"similar objects\": [\"macaw\", \"cockatoo\", \"finch\"]}", + 203 + ], + "pickle": [ + " {\"type\": \"food\", \"description\": \"green; could be cucumber or other vegetables; could be sour or sweet; could be sliced or whole\", \"similar objects\": [\"olive\", \"caper\", \"relish\"]}", + 202 + ], + "backpacks": [ + " {\"type\": \"bag\", \"description\": \"has straps; could be made of fabric or leather; could be used to carry items\", \"similar objects\": [\"suitcase\", \"duffel bag\", \"briefcase\"]}", + 202 + ], + "hamburger": [ + " {\"type\": \"food\", \"description\": \"round; has a bun; could have beef patty, lettuce, tomato, onion, and cheese\", \"similar objects\": [\"hot dog\", \"sandwich\", \"taco\"]}", + 202 + ], + "ski jacket": [ + " {\"type\": \"clothing\", \"description\": \"waterproof; could be insulated; could have a hood\", \"similar objects\": [\"snow pants\", \"ski gloves\", \"ski goggles\"]}", + 201 + ], + "forehead": [ + " {\"type\": \"body part\", \"description\": \"part of the face; located between the eyes and the hairline\", \"similar objects\": [\"cheek\", \"chin\", \"nose\"]}", + 201 + ], + "screw": [ + " {\"type\": \"fastener\", \"description\": \"cylindrical; has a head and a thread\", \"similar objects\": [\"bolt\", \"nail\", \"rivet\"]}", + 201 + ], + "pilot": [ + " {\"type\": \"occupation\", \"description\": \"operates an aircraft; could be a military or commercial pilot\", \"similar objects\": [\"air traffic controller\", \"flight attendant\", \"mechanic\"]}", + 201 + ], + "eyebrows": [ + " {\"type\": \"facial feature\", \"description\": \"two curved lines above the eyes; could be thin or thick; could be arched or straight\", \"similar objects\": [\"eyelashes\", \"eyelids\", \"nose\"]}", + 199 + ], + "burner": [ + " {\"type\": \"cooking tool\", \"description\": \"has a knob to control the flame; could be used to cook food\", \"similar objects\": [\"stove\", \"hot plate\", \"grill\"]}", + 199 + ], + "pepper shaker": [ + " {\"type\": \"kitchen tool\", \"description\": \"cylindrical; has a lid; could be filled with pepper\", \"similar objects\": [\"salt shaker\", \"spice jar\", \"condiment bottle\"]}", + 198 + ], + "orange light": [ + " {\"type\": \"lighting tool\", \"description\": \"round; emits orange light; could be used as a warning signal\", \"similar objects\": [\"lantern\", \"lamp\", \"floodlight\"]}", + 198 + ], + "shrubbery": [ + " {\"type\": \"plant\", \"description\": \"small, woody plants; could have leaves and flowers; could be evergreen or deciduous\", \"similar objects\": [\"bush\", \"hedge\", \"tree\"]}", + 198 + ], + "door frame": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular; could be made of wood or metal; could have a door attached\", \"similar objects\": [\"window frame\", \"wall frame\", \"archway\"]}", + 198 + ], + "grey sky": [ + " {\"type\": \"weather\", \"description\": \"cloudy; could be raining; could be windy\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"stormy sky\"]}", + 198 + ], + "ceiling light": [ + " {\"type\": \"lighting tool\", \"description\": \"fixed to the ceiling; could be a bulb or a lamp\", \"similar objects\": [\"chandelier\", \"wall sconce\", \"pendant light\"]}", + 198 + ], + "pebbles": [ + " {\"type\": \"natural object\", \"description\": \"small, round, could be of different colors; could be found in beaches or rivers\", \"similar objects\": [\"rocks\", \"shells\", \"sand\"]}", + 198 + ], + "blue bus": [ + "\n{\"type\": \"vehicle\", \"description\": \"large, blue, has multiple doors; could have a destination sign\", \"similar objects\": [\"school bus\", \"truck\", \"van\"]}", + 197 + ], + "wristwatch": [ + " {\"type\": \"accessory\", \"description\": \"worn on the wrist; has a dial; could have a strap\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}", + 197 + ], + "paper towel": [ + " {\"type\": \"cleaning tool\", \"description\": \"absorbent; could be used to wipe surfaces\", \"similar objects\": [\"tissue paper\", \"cloth towel\", \"sponge\"]}", + 197 + ], + "slats": [ + " {\"type\": \"building material\", \"description\": \"long, thin pieces of wood; could be used for fencing or shutters\", \"similar objects\": [\"boards\", \"panels\", \"planks\"]}", + 197 + ], + "pastries": [ + " {\"type\": \"food\", \"description\": \"sweet; could be filled with cream; could be in different shapes\", \"similar objects\": [\"cake\", \"pie\", \"cookie\"]}", + 197 + ], + "entertainment center": [ + " {\"type\": \"furniture\", \"description\": \"large; could have shelves and drawers; could have a TV stand\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"armoire\"]}", + 197 + ], + "pedestrians": [ + " {\"type\": \"people\", \"description\": \"walking on the street; could be crossing the street\", \"similar objects\": [\"cyclists\", \"drivers\", \"runners\"]}", + 197 + ], + "radio": [ + " {\"type\": \"electronic device\", \"description\": \"could be portable; could have a speaker; could have a knob to adjust the volume\", \"similar objects\": [\"television\", \"stereo\", \"boombox\"]}", + 196 + ], + "pencil": [ + " {\"type\": \"writing tool\", \"description\": \"long, thin, yellow; has a sharpened tip; could be made of wood\", \"similar objects\": [\"pen\", \"marker\", \"crayon\"]}", + 196 + ], + "highway": [ + " {\"type\": \"road\", \"description\": \"long, wide, with multiple lanes; could have a speed limit sign\", \"similar objects\": [\"freeway\", \"interstate\", \"expressway\"]}", + 196 + ], + "statues": [ + " {\"type\": \"artwork\", \"description\": \"could be made of stone, metal, wood, or other materials; could be of human, animal, or other figures; could be of different sizes\", \"similar objects\": [\"sculptures\", \"paintings\", \"murals\"]}", + 195 + ], + "paper napkin": [ + " {\"type\": \"tableware\", \"description\": \"square; made of paper; used to wipe hands\", \"similar objects\": [\"tissue\", \"paper towel\", \"cloth napkin\"]}", + 194 + ], + "cabbage": [ + " {\"type\": \"vegetable\", \"description\": \"round; green or purple; could be shredded\", \"similar objects\": [\"lettuce\", \"broccoli\", \"cauliflower\"]}", + 194 + ], + "sheeps": [ + " {\"type\": \"animal\", \"description\": \"white, wooly; could have horns; could be found in herds\", \"similar objects\": [\"goat\", \"cow\", \"llama\"]}", + 194 + ], + "televisions": [ + " {\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a cable box; could have a remote control\", \"similar objects\": [\"computer\", \"stereo\", \"gaming console\"]}", + 194 + ], + "barrel": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of wood or metal; could have a lid\", \"similar objects\": [\"bucket\", \"tub\", \"tank\"]}", + 194 + ], + "stems": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, and hollow; could be green or brown; could be attached to leaves and flowers\", \"similar objects\": [\"roots\", \"leaves\", \"petals\"]}", + 193 + ], + "iron fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal; has vertical bars; could be used to enclose a space\", \"similar objects\": [\"wood fence\", \"brick wall\", \"hedge\"]}", + 193 + ], + "caps": [ + " {\"type\": \"clothing accessory\", \"description\": \"headwear; could be made of fabric; could have a logo or design\", \"similar objects\": [\"hat\", \"beanie\", \"visor\"]}", + 193 + ], + "nuts": [ + " {\"type\": \"food\", \"description\": \"small, hard, could be shelled; could be roasted or salted\", \"similar objects\": [\"seeds\", \"dried fruits\", \"legumes\"]}", + 193 + ], + "plastic chair": [ + " {\"type\": \"furniture\", \"description\": \"lightweight; could be stackable; could be foldable; could be colorful\", \"similar objects\": [\"stool\", \"bench\", \"armchair\"]}", + 193 + ], + "article clothing": [ + " {\"type\": \"clothing item\", \"description\": \"could be made of fabric; could have buttons, zippers, or other fasteners; could have pockets; could have a collar or other design elements\", \"similar objects\": [\"shirt\", \"pants\", \"dress\"]}", + 193 + ], + "kickstand": [ + " {\"type\": \"bicycle accessory\", \"description\": \"metal; attaches to the frame of the bike; helps the bike stand upright\", \"similar objects\": [\"pedal\", \"chain\", \"handlebar\"]}", + 193 + ], + "sandal": [ + " {\"type\": \"footwear\", \"description\": \"open-toed; could have straps; could be made of leather or rubber\", \"similar objects\": [\"flip-flop\", \"slipper\", \"clog\"]}", + 192 + ], + "porcelain toilet": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"white; has a bowl; could have a lid; could be connected to a tank\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 192 + ], + "item clothing": [ + " {\"type\": \"clothing\", \"description\": \"could be made of fabric; could be of different colors and patterns; could have buttons, zippers, or other fasteners\", \"similar objects\": [\"shirt\", \"pants\", \"dress\"]}", + 192 + ], + "ice cream": [ + " {\"type\": \"dessert\", \"description\": \"cold; could be made of milk, cream, and sugar; could be served in a cone or cup\", \"similar objects\": [\"sorbet\", \"gelato\", \"frozen yogurt\"]}", + 192 + ], + "mast": [ + " {\"type\": \"nautical tool\", \"description\": \"tall, vertical pole; could be made of metal; could have sails attached to it\", \"similar objects\": [\"boom\", \"spar\", \"yardarm\"]}", + 192 + ], + "cliff": [ + " {\"type\": \"geographical feature\", \"description\": \"steep rock face; could be high and dangerous\", \"similar objects\": [\"mountain\", \"valley\", \"canyon\"]}", + 192 + ], + "fender": [ + " {\"type\": \"automotive part\", \"description\": \"metal; attached to the side of a car; could be painted\", \"similar objects\": [\"bumper\", \"hood\", \"grille\"]}", + 192 + ], + "food item": [ + "\n{\"type\": \"food\", \"description\": \"edible item; could be cooked or raw; could be a single ingredient or a combination of ingredients; could be a meal or a snack\", \"similar objects\": [\"fruit\", \"vegetable\", \"meat\", \"dairy\", \"grain\", \"legume\"]}", + 191 + ], + "writings": [ + " {\"type\": \"literary work\", \"description\": \"could be a book, poem, or article; could be written by hand or typed\", \"similar objects\": [\"essay\", \"novel\", \"journal\"]}", + 191 + ], + "urinals": [ + " {\"type\": \"plumbing fixture\", \"description\": \"long, white, ceramic; could be wall-mounted; could have a flush button\", \"similar objects\": [\"toilet\", \"sink\", \"bathtub\"]}", + 191 + ], + "dinner plate": [ + " {\"type\": \"dining ware\", \"description\": \"round; could be made of ceramic; could be used to serve food\", \"similar objects\": [\"bowl\", \"cup\", \"saucer\"]}", + 190 + ], + "bed sheet": [ + " {\"type\": \"bedding item\", \"description\": \"rectangular; could be made of cotton; could be white or colorful\", \"similar objects\": [\"pillow case\", \"duvet cover\", \"blanket\"]}", + 190 + ], + "wii": [ + " {\"type\": \"gaming console\", \"description\": \"white; has a motion controller; could be connected to a TV\", \"similar objects\": [\"PlayStation\", \"Xbox\", \"Nintendo Switch\"]}", + 190 + ], + "officer": [ + " {\"type\": \"person\", \"description\": \"wears a uniform; could have a badge; could carry a gun\", \"similar objects\": [\"soldier\", \"policeman\", \"security guard\"]}", + 190 + ], + "mousepad": [ + " {\"type\": \"computer accessory\", \"description\": \"flat, rectangular; could be made of rubber or cloth; could have a design\", \"similar objects\": [\"keyboard\", \"mouse\", \"monitor\"]}", + 190 + ], + "brand": [ + " {\"type\": \"mark\", \"description\": \"symbol or design used to identify a product or service\", \"similar objects\": [\"logo\", \"trademark\", \"symbol\"]}", + 190 + ], + "desks": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could have drawers; could be made of wood or metal\", \"similar objects\": [\"table\", \"chair\", \"cabinet\"]}", + 190 + ], + "blue light": [ + " {\"type\": \"lighting tool\", \"description\": \"emits blue light; could be used for signaling\", \"similar objects\": [\"floodlight\", \"strobe light\", \"laser light\"]}", + 190 + ], + "tissue box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could have a lid\", \"similar objects\": [\"box\", \"bag\", \"jar\"]}", + 190 + ], + "cell": [ + " {\"type\": \"microscopic object\", \"description\": \"small, round, contains genetic material\", \"similar objects\": [\"organelle\", \"bacteria\", \"virus\"]}", + 190 + ], + "breads": [ + " {\"type\": \"food\", \"description\": \"loaf-shaped; could be sliced; could be toasted; could be served with butter\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 190 + ], + "dot": [ + " {\"type\": \"shape\", \"description\": \"small, round, could be colored\", \"similar objects\": [\"circle\", \"square\", \"triangle\"]}", + 190 + ], + "thin": [ + "\n{\"type\": \"adjective\", \"description\": \"having little thickness or extent from one surface to its opposite; slender\", \"similar objects\": [\"slender\", \"narrow\", \"slim\"]}", + 189 + ], + "bus driver": [ + " {\"type\": \"occupation\", \"description\": \"operates a bus; responsible for the safety of passengers\", \"similar objects\": [\"taxi driver\", \"truck driver\", \"train conductor\"]}", + 189 + ], + "mailbox": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of metal; could have a flag\", \"similar objects\": [\"letterbox\", \"postbox\", \"drop box\"]}", + 189 + ], + "skyscraper": [ + " {\"type\": \"building\", \"description\": \"tall; could have many floors; could be made of steel and glass\", \"similar objects\": [\"high-rise building\", \"tower\", \"apartment building\"]}", + 189 + ], + "brake light": [ + " {\"type\": \"vehicle part\", \"description\": \"red; usually located at the back of the car; used to indicate braking\", \"similar objects\": [\"headlight\", \"turn signal\", \"reverse light\"]}", + 188 + ], + "tablet": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular, touchscreen; could be used for communication, entertainment, and work\", \"similar objects\": [\"laptop\", \"smartphone\", \"e-reader\"]}", + 188 + ], + "knee pad": [ + " {\"type\": \"protective gear\", \"description\": \"elastic; could be made of foam; could be strapped around the knee\", \"similar objects\": [\"elbow pad\", \"shin guard\", \"helmet\"]}", + 188 + ], + "hinge": [ + " {\"type\": \"hardware\", \"description\": \"metal; used to attach two objects together; could be opened and closed\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 188 + ], + "orange shirt": [ + "\n{\"type\": \"clothing\", \"description\": \"orange; could be long-sleeved or short-sleeved; could have a collar or no collar; could have buttons or no buttons\", \"similar objects\": [\"red shirt\", \"blue shirt\", \"white shirt\"]}", + 188 + ], + "head lights": [ + " {\"type\": \"vehicle accessory\", \"description\": \"attached to the front of a vehicle; emits bright light; could be round or rectangular\", \"similar objects\": [\"tail lights\", \"fog lights\", \"brake lights\"]}", + 188 + ], + "screen television": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be connected to a cable box; could be wall-mounted\", \"similar objects\": [\"computer monitor\", \"projector\", \"smartphone\"]}", + 188 + ], + "lampshade": [ + " {\"type\": \"lighting accessory\", \"description\": \"round or cylindrical; could be made of fabric, paper, or metal; could be used to diffuse light\", \"similar objects\": [\"lamp base\", \"light bulb\", \"ceiling light\"]}", + 188 + ], + "window frame": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of wood or metal; could have glass panes\", \"similar objects\": [\"door frame\", \"wall frame\", \"roof frame\"]}", + 188 + ], + "sedan": [ + " {\"type\": \"vehicle\", \"description\": \"four-door car; has a trunk; could be a hatchback\", \"similar objects\": [\"coupe\", \"SUV\", \"minivan\"]}", + 186 + ], + "zebra grazing": [ + "\n{\"type\": \"animal behavior\", \"description\": \"zebra eating grass or other vegetation; could be in a group or alone\", \"similar objects\": [\"giraffe grazing\", \"elephant grazing\", \"horse grazing\"]}", + 186 + ], + "roadway": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"paved surface for vehicles to travel on; could have lanes and signs\", \"similar objects\": [\"highway\", \"street\", \"bridge\"]}", + 186 + ], + "stuffed": [ + " {\"type\": \"toy\", \"description\": \"soft; could be filled with cotton or other materials; could be shaped like animals or other objects\", \"similar objects\": [\"plush\", \"doll\", \"action figure\"]}", + 186 + ], + "weather": [ + " {\"type\": \"meteorological phenomenon\", \"description\": \"atmospheric conditions; could be sunny, rainy, windy, etc.\", \"similar objects\": [\"climate\", \"temperature\", \"humidity\"]}", + 186 + ], + "stabilizer": [ + " {\"type\": \"electrical tool\", \"description\": \"used to regulate voltage; could be connected to a power outlet\", \"similar objects\": [\"surge protector\", \"voltage regulator\", \"power strip\"]}", + 186 + ], + "chunk": [ + " {\"type\": \"shape\", \"description\": \"irregularly shaped; could be a piece of something\", \"similar objects\": [\"piece\", \"block\", \"cube\"]}", + 186 + ], + "berries": [ + " {\"type\": \"fruit\", \"description\": \"small, round, could be red, blue, or black; could be sour or sweet\", \"similar objects\": [\"grapes\", \"cherries\", \"plums\"]}", + 186 + ], + "pans": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round, has a handle\", \"similar objects\": [\"wok\", \"pot\", \"frying pan\"]}", + 185 + ], + "blonde": [ + " {\"type\": \"hair color\", \"description\": \"light yellowish-brown; could be natural or dyed\", \"similar objects\": [\"brunette\", \"redhead\", \"black\"]}", + 185 + ], + "place mat": [ + " {\"type\": \"tableware\", \"description\": \"rectangular; could be made of cloth; could be used to protect the table\", \"similar objects\": [\"tablecloth\", \"napkin\", \"coaster\"]}", + 185 + ], + "glass jar": [ + " {\"type\": \"container\", \"description\": \"transparent; could be sealed; could be used to store food\", \"similar objects\": [\"bottle\", \"can\", \"box\"]}", + 185 + ], + "tv stand": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have shelves; could be made of wood or metal\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"sideboard\"]}", + 185 + ], + "oar": [ + " {\"type\": \"rowing tool\", \"description\": \"long, thin, has a handle; used to row a boat\", \"similar objects\": [\"paddle\", \"canoe\", \"kayak\"]}", + 185 + ], + "dirt path": [ + " {\"type\": \"landscape\", \"description\": \"uneven, unpaved, could have stones and plants\", \"similar objects\": [\"gravel path\", \"wooden path\", \"grass path\"]}", + 185 + ], + "stuffed animals": [ + " {\"type\": \"toy\", \"description\": \"soft, plush, could be shaped like animals\", \"similar objects\": [\"dolls\", \"action figures\", \"building blocks\"]}", + 184 + ], + "instrument": [ + " {\"type\": \"musical tool\", \"description\": \"could be made of wood, metal, or plastic; could be used to produce sound\", \"similar objects\": [\"guitar\", \"piano\", \"violin\"]}", + 184 + ], + "monkey": [ + " {\"type\": \"animal\", \"description\": \"long tail; could be brown, black, or white; could have a red face\", \"similar objects\": [\"gorilla\", \"chimpanzee\", \"baboon\"]}", + 184 + ], + "stoplight": [ + " {\"type\": \"traffic signal\", \"description\": \"three lights; red, yellow, and green; could be mounted on a pole\", \"similar objects\": [\"traffic sign\", \"traffic cone\", \"road barrier\"]}", + 184 + ], + "quilt": [ + " {\"type\": \"bedding item\", \"description\": \"made of multiple layers of fabric; could be filled with cotton, wool, or synthetic fibers; could be used as a blanket or a decorative item\", \"similar objects\": [\"duvet\", \"comforter\", \"blanket\"]}", + 184 + ], + "trays": [ + " {\"type\": \"serving tool\", \"description\": \"flat, rectangular; could be made of metal or plastic; could be used to carry food\", \"similar objects\": [\"plates\", \"bowls\", \"cups\"]}", + 184 + ], + "blades": [ + " {\"type\": \"tool\", \"description\": \"sharp, could be used for cutting; could be made of metal\", \"similar objects\": [\"knife\", \"scissors\", \"axe\"]}", + 184 + ], + "snowboards": [ + " {\"type\": \"sports equipment\", \"description\": \"long, flat board; could have bindings; could have a curved tip\", \"similar objects\": [\"skis\", \"surfboard\", \"wakeboard\"]}", + 183 + ], + "shopping bag": [ + " {\"type\": \"container\", \"description\": \"made of cloth or plastic; could be reusable; could have handles\", \"similar objects\": [\"tote bag\", \"backpack\", \"suitcase\"]}", + 183 + ], + "towel rack": [ + " {\"type\": \"storage tool\", \"description\": \"long; could be made of metal; could be mounted on the wall\", \"similar objects\": [\"clothes hanger\", \"shelf\", \"hook\"]}", + 183 + ], + "bathroom mirror": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be framed; could be hung on the wall\", \"similar objects\": [\"vanity mirror\", \"wall mirror\", \"dressing mirror\"]}", + 183 + ], + "beach chair": [ + " {\"type\": \"furniture\", \"description\": \"foldable; could be made of plastic or wood; could have armrests\", \"similar objects\": [\"deck chair\", \"lounge chair\", \"chaise longue\"]}", + 183 + ], + "soap dish": [ + " {\"type\": \"bathroom accessory\", \"description\": \"small, flat, usually made of ceramic; could have a draining hole\", \"similar objects\": [\"toothbrush holder\", \"towel rack\", \"shower caddy\"]}", + 183 + ], + "patio": [ + " {\"type\": \"outdoor space\", \"description\": \"open area; could be paved with stones; could have furniture\", \"similar objects\": [\"deck\", \"balcony\", \"veranda\"]}", + 183 + ], + "equipment": [ + " {\"type\": \"tool\", \"description\": \"could be used for various purposes; could be made of metal, plastic, or wood; could be powered by electricity or manual\", \"similar objects\": [\"machine\", \"appliance\", \"instrument\"]}", + 182 + ], + "blue blanket": [ + "\n{\"type\": \"textile\", \"description\": \"blue; could be made of wool; could be used for warmth\", \"similar objects\": [\"quilt\", \"duvet\", \"throw\"]}", + 182 + ], + "triangle": [ + " {\"type\": \"shape\", \"description\": \"three-sided; has three angles\", \"similar objects\": [\"square\", \"rectangle\", \"circle\"]}", + 182 + ], + "toilet paper roll": [ + " {\"type\": \"household item\", \"description\": \"cylindrical; made of paper; could be white or printed\", \"similar objects\": [\"paper towel roll\", \"tissue box\", \"toilet brush\"]}", + 182 + ], + "side windows": [ + " {\"type\": \"building component\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"doors\", \"skylights\", \"shutters\"]}", + 182 + ], + "squash": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be yellow, green, or orange; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"zucchini\", \"pumpkin\", \"cucumber\"]}", + 181 + ], + "leafless tree": [ + " {\"type\": \"plant\", \"description\": \"no leaves; could have branches; could have a trunk\", \"similar objects\": [\"dead tree\", \"bare tree\", \"stump\"]}", + 181 + ], + "tea pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round; has a spout and a handle; could be made of metal or ceramic\", \"similar objects\": [\"coffee pot\", \"kettle\", \"tea infuser\"]}", + 181 + ], + "shades": [ + " {\"type\": \"eyewear\", \"description\": \"dark lenses; could be made of plastic or metal; could be worn on the face\", \"similar objects\": [\"sunglasses\", \"eyeglasses\", \"goggles\"]}", + 181 + ], + "ram": [ + " {\"type\": \"animal\", \"description\": \"large, horned, four-legged mammal; could have thick fur; could have curved horns\", \"similar objects\": [\"sheep\", \"goat\", \"bighorn sheep\"]}", + 181 + ], + "robe": [ + " {\"type\": \"clothing\", \"description\": \"long, loose-fitting garment; could be made of cotton, silk, or other fabrics; could have a belt or sash\", \"similar objects\": [\"dress\", \"tunic\", \"caftan\"]}", + 180 + ], + "kitchen cabinets": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have drawers and shelves; could be painted\", \"similar objects\": [\"cupboard\", \"wardrobe\", \"dresser\"]}", + 180 + ], + "metal fork": [ + " {\"type\": \"utensil\", \"description\": \"has four tines; could be made of metal; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 180 + ], + "cattle": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged, domesticated animals; could have horns; could be used for dairy products\", \"similar objects\": [\"sheep\", \"goat\", \"pig\"]}", + 180 + ], + "foil": [ + " {\"type\": \"kitchen tool\", \"description\": \"thin, metallic, used for wrapping food\", \"similar objects\": [\"plastic wrap\", \"parchment paper\", \"wax paper\"]}", + 180 + ], + "clock hands": [ + " {\"type\": \"timekeeping tool\", \"description\": \"two thin metal rods; could be pointed or round; could be black or white\", \"similar objects\": [\"watch hands\", \"pendulum\", \"hourglass\"]}", + 180 + ], + "bridle": [ + " {\"type\": \"horse equipment\", \"description\": \"leather straps; has a bit; used to control a horse\", \"similar objects\": [\"saddle\", \"halter\", \"reins\"]}", + 179 + ], + "glass cup": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be made of glass or plastic; could have a handle\", \"similar objects\": [\"mug\", \"bowl\", \"plate\"]}", + 179 + ], + "boulders": [ + " {\"type\": \"geological formation\", \"description\": \"large, round rocks; could be found in mountains or rivers\", \"similar objects\": [\"pebbles\", \"gravel\", \"cobbles\"]}", + 179 + ], + "driveway": [ + " {\"type\": \"outdoor area\", \"description\": \"paved area for vehicles to drive on; could be made of concrete or asphalt\", \"similar objects\": [\"sidewalk\", \"patio\", \"porch\"]}", + 179 + ], + "piece clothing": [ + " {\"type\": \"clothing item\", \"description\": \"could be made of fabric; could have buttons, zippers, or other fasteners; could have pockets, collars, or other features\", \"similar objects\": [\"shirt\", \"pants\", \"dress\"]}", + 179 + ], + "front headlight": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the front of a vehicle; emits light; could be round or rectangular\", \"similar objects\": [\"taillight\", \"fog light\", \"turn signal\"]}", + 178 + ], + "pink nose": [ + " {\"type\": \"body part\", \"description\": \"small, round, pink; could be found on the face of some animals\", \"similar objects\": [\"ears\", \"eyes\", \"mouth\"]}", + 178 + ], + "safety helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round; could be made of plastic or metal; has a strap\", \"similar objects\": [\"goggles\", \"gloves\", \"vest\"]}", + 178 + ], + "area rug": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of wool, cotton, or synthetic fibers; could have a pattern\", \"similar objects\": [\"carpet\", \"mat\", \"runner\"]}", + 178 + ], + "steel": [ + " {\"type\": \"material\", \"description\": \"strong, durable, and resistant to corrosion; silver-gray in color\", \"similar objects\": [\"iron\", \"aluminum\", \"titanium\"]}", + 178 + ], + "butterfly": [ + " {\"type\": \"insect\", \"description\": \"has colorful wings; could fly; could have antennae\", \"similar objects\": [\"bee\", \"dragonfly\", \"moth\"]}", + 178 + ], + "desk lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"could be made of metal; has a long arm; could be adjustable; could be connected to a power source\", \"similar objects\": [\"floor lamp\", \"table lamp\", \"ceiling lamp\"]}", + 178 + ], + "cigarette": [ + " {\"type\": \"tobacco product\", \"description\": \"long, thin, cylindrical; could be made of paper; could have a filter\", \"similar objects\": [\"cigar\", \"pipe\", \"hookah\"]}", + 178 + ], + "wine bottles": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass; could have a cork stopper\", \"similar objects\": [\"beer bottles\", \"water bottles\", \"soda bottles\"]}", + 177 + ], + "rod": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical; could be made of metal or wood; could be used for fishing\", \"similar objects\": [\"pole\", \"stick\", \"baton\"]}", + 177 + ], + "deer": [ + " {\"type\": \"animal\", \"description\": \"brown fur; has antlers; could have white spots\", \"similar objects\": [\"elk\", \"moose\", \"reindeer\"]}", + 177 + ], + "cell phones": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; could have a touchscreen; could have a camera\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 176 + ], + "jars": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass or plastic; could have a lid\", \"similar objects\": [\"bottles\", \"cans\", \"boxes\"]}", + 176 + ], + "armchair": [ + " {\"type\": \"furniture\", \"description\": \"has armrests; could be upholstered; could have a reclining back\", \"similar objects\": [\"sofa\", \"loveseat\", \"recliner\"]}", + 176 + ], + "bus stop": [ + " {\"type\": \"transportation facility\", \"description\": \"could have a shelter; could have a signboard; could have a bench\", \"similar objects\": [\"train station\", \"subway station\", \"airport\"]}", + 176 + ], + "brown couch": [ + "\n{\"type\": \"furniture\", \"description\": \"large, rectangular, upholstered; could have armrests and cushions; could be made of wood or metal\", \"similar objects\": [\"sofa\", \"loveseat\", \"armchair\"]}", + 175 + ], + "tea": [ + " {\"type\": \"beverage\", \"description\": \"made from leaves; could be served hot or cold; could be flavored with milk, sugar, or honey\", \"similar objects\": [\"coffee\", \"juice\", \"soda\"]}", + 175 + ], + "earrings": [ + " {\"type\": \"jewelry\", \"description\": \"could be made of metal, plastic, or other materials; could be in different shapes and sizes; could be worn on the earlobes\", \"similar objects\": [\"necklace\", \"bracelet\", \"ring\"]}", + 175 + ], + "computer desk": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; has a flat surface; could have drawers\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 174 + ], + "plain": [ + " {\"type\": \"texture\", \"description\": \"smooth, flat, no patterns\", \"similar objects\": [\"smooth\", \"matte\", \"glossy\"]}", + 174 + ], + "taxi cab": [ + " {\"type\": \"vehicle\", \"description\": \"yellow; has a meter; could have a sign on the roof\", \"similar objects\": [\"bus\", \"limousine\", \"Uber\"]}", + 174 + ], + "brown sand": [ + " {\"type\": \"material\", \"description\": \"dark brown; could be used for construction; could be found in deserts\", \"similar objects\": [\"gravel\", \"dirt\", \"clay\"]}", + 174 + ], + "sinks": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a bowl; could have a faucet; could be made of stainless steel\", \"similar objects\": [\"toilet\", \"bathtub\", \"shower\"]}", + 174 + ], + "remote controls": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; has buttons; could be used to control other devices\", \"similar objects\": [\"game controller\", \"keyboard\", \"mouse\"]}", + 173 + ], + "night stand": [ + " {\"type\": \"furniture\", \"description\": \"small table; could have drawers; could be used to place a lamp\", \"similar objects\": [\"end table\", \"dresser\", \"coffee table\"]}", + 173 + ], + "air conditioner": [ + " {\"type\": \"appliance\", \"description\": \"box-shaped; has a fan; could be wall-mounted\", \"similar objects\": [\"heater\", \"refrigerator\", \"dehumidifier\"]}", + 173 + ], + "tips": [ + " {\"type\": \"advice\", \"description\": \"short pieces of advice; could be related to any topic\", \"similar objects\": [\"hints\", \"suggestions\", \"pointers\"]}", + 173 + ], + "tags": [ + " {\"type\": \"labeling tool\", \"description\": \"small, rectangular; could be made of paper or plastic; could be used to label items\", \"similar objects\": [\"labels\", \"stickers\", \"name tags\"]}", + 173 + ], + "fountain": [ + " {\"type\": \"decorative object\", \"description\": \"could be made of stone; could have water flowing from it\", \"similar objects\": [\"statue\", \"sculpture\", \"waterfall\"]}", + 173 + ], + "paper cup": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of paper; could have a handle\", \"similar objects\": [\"mug\", \"glass\", \"plastic cup\"]}", + 172 + ], + "kayak": [ + " {\"type\": \"watercraft\", \"description\": \"long, narrow, pointed at both ends; could be propelled with a paddle\", \"similar objects\": [\"canoe\", \"rowboat\", \"paddleboard\"]}", + 172 + ], + "twig": [ + " {\"type\": \"plant part\", \"description\": \"thin, small branch; could be used for making a nest\", \"similar objects\": [\"branch\", \"stem\", \"leaf\"]}", + 172 + ], + "waters": [ + " {\"type\": \"liquid\", \"description\": \"clear; could be salty or sweet; could be still or moving\", \"similar objects\": [\"juice\", \"milk\", \"wine\"]}", + 172 + ], + "lap": [ + " {\"type\": \"furniture\", \"description\": \"a piece of furniture for sitting on; could be made of wood or metal; could have armrests\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 172 + ], + "female": [ + "\n\n{\"type\": \"gender\", \"description\": \"refers to a person who is biologically female; could have feminine characteristics\", \"similar objects\": [\"woman\", \"girl\", \"lady\"]}", + 172 + ], + "aprt": [ + " {\"type\": \"building\", \"description\": \"multi-story; could have balconies; could have elevators\", \"similar objects\": [\"condominium\", \"apartment\", \"townhouse\"]}", + 171 + ], + "head light": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the front of a vehicle; could be used to illuminate the road ahead\", \"similar objects\": [\"taillight\", \"fog light\", \"spotlight\"]}", + 171 + ], + "glass windows": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be framed; could be double-paned\", \"similar objects\": [\"doors\", \"shutters\", \"curtains\"]}", + 171 + ], + "unit": [ + " {\"type\": \"measurement\", \"description\": \"a standard quantity used as a basis for comparison\", \"similar objects\": [\"metric\", \"imperial\", \"standard\"]}", + 171 + ], + "parking": [ + " {\"type\": \"location\", \"description\": \"a place for vehicles to park; could be a lot or a street\", \"similar objects\": [\"garage\", \"driveway\", \"parking lot\"]}", + 171 + ], + "magnets": [ + " {\"type\": \"object\", \"description\": \"attracts metal objects; could be in different shapes and sizes\", \"similar objects\": [\"iron filings\", \"electromagnet\", \"magnetic field\"]}", + 171 + ], + "candy": [ + " {\"type\": \"food\", \"description\": \"sweet; could be in different shapes and colors; could be wrapped in paper\", \"similar objects\": [\"chocolate\", \"cookie\", \"ice cream\"]}", + 171 + ], + "mirror wall": [ + " {\"type\": \"decoration\", \"description\": \"wall with multiple mirrors; could be used to create an illusion of space\", \"similar objects\": [\"wallpaper\", \"wall art\", \"wall panel\"]}", + 171 + ], + "shop": [ + " {\"type\": \"building\", \"description\": \"could be a store; could be a mall; could be a market\", \"similar objects\": [\"store\", \"mall\", \"market\"]}", + 170 + ], + "drinks": [ + "\n{\"type\": \"beverage\", \"description\": \"could be alcoholic or non-alcoholic; could be hot or cold; could be in a can, bottle, or glass\", \"similar objects\": [\"juice\", \"soda\", \"tea\"]}", + 170 + ], + "helicopter": [ + " {\"type\": \"vehicle\", \"description\": \"has a rotor; could be used for air rescue\", \"similar objects\": [\"airplane\", \"drone\", \"jet\"]}", + 170 + ], + "fire hydrants": [ + " {\"type\": \"utility tool\", \"description\": \"red; has two outlets; could be used to supply water\", \"similar objects\": [\"water fountain\", \"sprinkler\", \"hose\"]}", + 169 + ], + "toilet paper holder": [ + " {\"type\": \"bathroom accessory\", \"description\": \"could be made of metal or plastic; has a roll holder; could be wall-mounted\", \"similar objects\": [\"towel rack\", \"soap dish\", \"toilet brush holder\"]}", + 169 + ], + "ceiling fan": [ + " {\"type\": \"appliance\", \"description\": \"has blades; could be controlled by a remote; could be used to circulate air\", \"similar objects\": [\"air conditioner\", \"ventilator\", \"exhaust fan\"]}", + 169 + ], + "costume": [ + " {\"type\": \"clothing\", \"description\": \"could be made of fabric; could be used for special occasions; could be used for role-playing\", \"similar objects\": [\"dress\", \"uniform\", \"outfit\"]}", + 168 + ], + "hand towel": [ + " {\"type\": \"cleaning tool\", \"description\": \"small, rectangular; could be made of cotton; could be used to dry hands\", \"similar objects\": [\"bath towel\", \"washcloth\", \"kitchen towel\"]}", + 168 + ], + "nail": [ + " {\"type\": \"tool\", \"description\": \"metal; pointed at one end; used for fastening\", \"similar objects\": [\"screw\", \"bolt\", \"hammer\"]}", + 168 + ], + "weed": [ + " {\"type\": \"plant\", \"description\": \"green; could have small white flowers; could be found in gardens or lawns\", \"similar objects\": [\"grass\", \"clover\", \"dandelion\"]}", + 168 + ], + "gas tank": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could be used to store gas\", \"similar objects\": [\"barrel\", \"tank\", \"drum\"]}", + 167 + ], + "cloudy": [ + " {\"type\": \"weather\", \"description\": \"sky is covered with clouds; could be dark or light; could be accompanied with rain or snow\", \"similar objects\": [\"rainy\", \"sunny\", \"windy\"]}", + 167 + ], + "hoodie": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved, hooded, could be zipped up\", \"similar objects\": [\"sweatshirt\", \"jacket\", \"coat\"]}", + 167 + ], + "circles": [ + " {\"type\": \"shape\", \"description\": \"round; has no angles; could be drawn with a compass\", \"similar objects\": [\"squares\", \"triangles\", \"ovals\"]}", + 167 + ], + "cucumbers": [ + " {\"type\": \"vegetable\", \"description\": \"long, green, smooth; could have white stripes; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"eggplant\", \"green bean\"]}", + 167 + ], + "flock": [ + " {\"type\": \"group of animals\", \"description\": \"group of birds or animals that move together\", \"similar objects\": [\"herd\", \"swarm\", \"school\"]}", + 167 + ], + "pony": [ + " {\"type\": \"animal\", \"description\": \"small horse; has a short mane; could be ridden by children\", \"similar objects\": [\"horse\", \"donkey\", \"mule\"]}", + 167 + ], + "buoy": [ + " {\"type\": \"navigational tool\", \"description\": \"round; could be made of metal; could be painted in red and white stripes; could be used to mark a safe passage\", \"similar objects\": [\"beacon\", \"lighthouse\", \"marker buoy\"]}", + 166 + ], + "muzzle": [ + " {\"type\": \"animal accessory\", \"description\": \"a device that fits over the snout of an animal; could be made of leather or metal; could be used to prevent biting or barking\", \"similar objects\": [\"collar\", \"harness\", \"leash\"]}", + 166 + ], + "orange sign": [ + " {\"type\": \"warning sign\", \"description\": \"round; has an orange background; could have a black symbol\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 166 + ], + "manhole cover": [ + " {\"type\": \"utility object\", \"description\": \"round; made of metal; has a handle\", \"similar objects\": [\"drain cover\", \"grate\", \"vent cover\"]}", + 166 + ], + "feeder": [ + " {\"type\": \"pet tool\", \"description\": \"could be made of plastic or metal; could be used to store pet food; could have a bowl or a tray\", \"similar objects\": [\"water bottle\", \"toy\", \"bed\"]}", + 166 + ], + "roses": [ + " {\"type\": \"flower\", \"description\": \"red; has thorns; could have petals\", \"similar objects\": [\"daisies\", \"tulips\", \"sunflowers\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\",", + 166 + ], + "worker": [ + " {\"type\": \"occupation\", \"description\": \"someone who works for a company or organization; could be a manual laborer or a professional\", \"similar objects\": [\"employee\", \"laborer\", \"professional\"]}", + 166 + ], + "crown": [ + " {\"type\": \"headwear\", \"description\": \"golden; could have jewels; could be worn by royalty\", \"similar objects\": [\"tiara\", \"hat\", \"cap\"]}", + 166 + ], + "baby zebra": [ + " {\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane; smaller than an adult zebra\", \"similar objects\": [\"foal\", \"calf\", \"fawn\"]}", + 166 + ], + "dog collar": [ + " {\"type\": \"pet accessory\", \"description\": \"made of leather or fabric; could have a buckle; could have a tag\", \"similar objects\": [\"leash\", \"harness\", \"muzzle\"]}", + 165 + ], + "beach umbrella": [ + " {\"type\": \"outdoor accessory\", \"description\": \"long pole with a canopy; could be colorful; could be opened and closed\", \"similar objects\": [\"sun hat\", \"sunscreen\", \"beach chair\"]}", + 165 + ], + "evergreen trees": [ + "\n{\"type\": \"plant\", \"description\": \"tall; have needles instead of leaves; could be coniferous or deciduous; could be used for decoration\", \"similar objects\": [\"pine tree\", \"cypress tree\", \"cedar tree\"]}", + 165 + ], + "grape": [ + " {\"type\": \"fruit\", \"description\": \"small, round, could be green, purple, or red; has a stem\", \"similar objects\": [\"blueberry\", \"strawberry\", \"plum\"]}", + 165 + ], + "bright": [ + "\n\n{\"type\": \"adjective\", \"description\": \"having a strong or intense light; having a strong or intense color; having a strong or intense emotion\", \"similar objects\": [\"vivid\", \"luminous\", \"radiant\"]}", + 165 + ], + "shrimp": [ + " {\"type\": \"seafood\", \"description\": \"small, pinkish; could be cooked with garlic and butter\", \"similar objects\": [\"lobster\", \"crab\", \"squid\"]}", + 164 + ], + "parasail": [ + " {\"type\": \"recreational activity\", \"description\": \"uses a parachute attached to a boat or vehicle; person is suspended in the air\", \"similar objects\": [\"paragliding\", \"hang gliding\", \"skydiving\"]}", + 164 + ], + "leaves ground": [ + " {\"type\": \"natural element\", \"description\": \"green; could be scattered on the ground; could be dried\", \"similar objects\": [\"grass\", \"twigs\", \"mulch\"]}", + 164 + ], + "tractor": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a cabin; could have a trailer attached\", \"similar objects\": [\"truck\", \"bulldozer\", \"forklift\"]}", + 164 + ], + "stone building": [ + " {\"type\": \"structure\", \"description\": \"made of stones; could have a roof; could have windows and doors\", \"similar objects\": [\"castle\", \"fortress\", \"monument\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant", + 164 + ], + "petals": [ + " {\"type\": \"flower part\", \"description\": \"thin, colorful, could be arranged in a circle\", \"similar objects\": [\"sepals\", \"stamen\", \"pistil\"]}", + 163 + ], + "propellers": [ + " {\"type\": \"aircraft part\", \"description\": \"long, thin blades; could be attached to an aircraft\", \"similar objects\": [\"engines\", \"wings\", \"fuselage\"]}", + 163 + ], + "sail boat": [ + " {\"type\": \"watercraft\", \"description\": \"has a sail; could be powered by wind; could have a mast\", \"similar objects\": [\"yacht\", \"canoe\", \"rowboat\"]}", + 162 + ], + "wrapper": [ + " {\"type\": \"packaging tool\", \"description\": \"thin, flexible material; could be used to wrap food or other items\", \"similar objects\": [\"bag\", \"box\", \"envelope\"]}", + 162 + ], + "squares": [ + " {\"type\": \"shape\", \"description\": \"four equal sides; four right angles; could be filled with colors\", \"similar objects\": [\"rectangles\", \"triangles\", \"circles\"]}", + 162 + ], + "wake": [ + " {\"type\": \"verb\", \"description\": \"to rouse from sleep; to cause to be alert\", \"similar objects\": [\"arouse\", \"awaken\", \"rouse\"]}", + 162 + ], + "bare feet": [ + " {\"type\": \"body part\", \"description\": \"exposed feet; no shoes or socks\", \"similar objects\": [\"hands\", \"elbows\", \"knees\"]}", + 162 + ], + "audience": [ + " {\"type\": \"group of people\", \"description\": \"gathered together to watch a performance or listen to a speaker\", \"similar objects\": [\"crowd\", \"congregation\", \"spectators\"]}", + 162 + ], + "wet sand": [ + " {\"type\": \"material\", \"description\": \"damp; could be used to make sandcastles; could be found on beaches\", \"similar objects\": [\"dry sand\", \"mud\", \"clay\"]}", + 161 + ], + "basil": [ + " {\"type\": \"herb\", \"description\": \"green; has a strong smell; could be used for cooking\", \"similar objects\": [\"parsley\", \"oregano\", \"thyme\"]}", + 161 + ], + "cement wall": [ + " {\"type\": \"building material\", \"description\": \"gray; hard; could be used to build walls\", \"similar objects\": [\"bricks\", \"concrete blocks\", \"wooden boards\"]}", + 161 + ], + "tail fin": [ + " {\"type\": \"fish body part\", \"description\": \"elongated; could be colorful; could be used for swimming\", \"similar objects\": [\"dorsal fin\", \"pectoral fin\", \"anal fin\"]}", + 161 + ], + "ball cap": [ + " {\"type\": \"headwear\", \"description\": \"curved brim; could have a logo or design; could be adjustable\", \"similar objects\": [\"hat\", \"beanie\", \"visor\"]}", + 160 + ], + "pink umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"pink; could be opened and closed; could be used to protect from rain\", \"similar objects\": [\"hat\", \"sunglasses\", \"scarf\"]}", + 160 + ], + "beanie": [ + " {\"type\": \"clothing item\", \"description\": \"knitted cap; could be made of wool; could have a pom-pom on top\", \"similar objects\": [\"hat\", \"cap\", \"turban\"]}", + 160 + ], + "tail wing": [ + " {\"type\": \"aircraft part\", \"description\": \"attached to the back of an aircraft; helps with stability and control; could be made of metal or composite materials\", \"similar objects\": [\"fuselage\", \"engine\", \"landing gear\"]}", + 160 + ], + "chopsticks": [ + " {\"type\": \"eating utensil\", \"description\": \"two thin sticks; used to pick up food\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}", + 160 + ], + "swan": [ + " {\"type\": \"animal\", \"description\": \"white; long neck; could have a crown\", \"similar objects\": [\"goose\", \"duck\", \"pelican\"]}", + 160 + ], + "pedal": [ + " {\"type\": \"mechanical tool\", \"description\": \"round; could be used to operate a machine\", \"similar objects\": [\"lever\", \"wheel\", \"knob\"]}", + 160 + ], + "cardboard": [ + " {\"type\": \"material\", \"description\": \"lightweight, stiff, and strong; could be recycled\", \"similar objects\": [\"paper\", \"plastic\", \"wood\"]}", + 160 + ], + "shakers": [ + " {\"type\": \"musical instrument\", \"description\": \"two cylindrical containers connected together; could be filled with small objects; could be shaken to make sound\", \"similar objects\": [\"maracas\", \"tambourine\", \"castanets\"]}", + 159 + ], + "life jacket": [ + " {\"type\": \"safety tool\", \"description\": \"orange; could be inflated; could be worn around the body\", \"similar objects\": [\"helmet\", \"vest\", \"floatation device\"]}", + 159 + ], + "tissue paper": [ + " {\"type\": \"paper product\", \"description\": \"thin, soft, and absorbent; could be used for cleaning and wiping\", \"similar objects\": [\"toilet paper\", \"paper towel\", \"napkin\"]}", + 159 + ], + "disc": [ + " {\"type\": \"storage device\", \"description\": \"round; could be made of plastic; could store data\", \"similar objects\": [\"CD\", \"DVD\", \"USB drive\"]}", + 159 + ], + "parsley": [ + " {\"type\": \"herb\", \"description\": \"green; has a long stem; could be used as a garnish\", \"similar objects\": [\"basil\", \"cilantro\", \"mint\"]}", + 159 + ], + "orange carrot": [ + "\n{\"type\": \"vegetable\", \"description\": \"orange, cylindrical, smooth; could have green leaves; could be sliced into round pieces\", \"similar objects\": [\"yellow carrot\", \"red carrot\", \"parsnip\"]}", + 158 + ], + "weather vane": [ + " {\"type\": \"weather tool\", \"description\": \"pointed; could be in the shape of a rooster; could be mounted on a pole\", \"similar objects\": [\"wind sock\", \"anemometer\", \"barometer\"]}", + 158 + ], + "hedges": [ + " {\"type\": \"landscape\", \"description\": \"green; could be trimmed into shapes; could be used to separate gardens\", \"similar objects\": [\"bushes\", \"shrubs\", \"trees\"]}", + 158 + ], + "cd": [ + " {\"type\": \"storage device\", \"description\": \"round; could store music, videos, and other data\", \"similar objects\": [\"dvd\", \"blu-ray\", \"usb drive\"]}", + 157 + ], + "calendar": [ + " {\"type\": \"organizational tool\", \"description\": \"has dates and days; could be used to track events\", \"similar objects\": [\"planner\", \"agenda\", \"diary\"]}", + 157 + ], + "shelter": [ + " {\"type\": \"structure\", \"description\": \"could be made of wood, metal, or plastic; could be used to protect from weather or danger\", \"similar objects\": [\"tent\", \"cabin\", \"hut\"]}", + 157 + ], + "watch man": [ + " {\"type\": \"person\", \"description\": \"dressed in uniform; could be carrying a flashlight; could be standing guard\", \"similar objects\": [\"security guard\", \"police officer\", \"bouncer\"]}", + 157 + ], + "pant": [ + " {\"type\": \"clothing\", \"description\": \"long trousers; could be made of cotton, linen, or wool; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 157 + ], + "power pole": [ + " {\"type\": \"utility pole\", \"description\": \"tall; has wires attached to it; could be made of metal or wood\", \"similar objects\": [\"telephone pole\", \"street light pole\", \"traffic light pole\"]}", + 156 + ], + "brown nose": [ + " {\"type\": \"expression\", \"description\": \"expression used to describe someone who is too eager to please others\", \"similar objects\": [\"yes man\", \"sycophant\", \"toady\"]}", + 156 + ], + "metal bars": [ + " {\"type\": \"building material\", \"description\": \"long, thin, and rigid; could be used to build fences or cages\", \"similar objects\": [\"wooden beams\", \"concrete blocks\", \"steel rods\"]}", + 156 + ], + "pickles": [ + " {\"type\": \"food\", \"description\": \"green; could be cucumbers or other vegetables; could be sour or sweet; could be sliced or whole\", \"similar objects\": [\"olives\", \"relish\", \"sauerkraut\"]}", + 156 + ], + "tongs": [ + " {\"type\": \"cooking tool\", \"description\": \"long, two-pronged; could be made of metal; used for picking up food\", \"similar objects\": [\"spatula\", \"ladle\", \"whisk\"]}", + 156 + ], + "grey car": [ + "\n{\"type\": \"vehicle\", \"description\": \"grey; could have four wheels; could have a steering wheel\", \"similar objects\": [\"truck\", \"van\", \"SUV\"]}", + 156 + ], + "baseball mitt": [ + " {\"type\": \"sports equipment\", \"description\": \"leather glove; used to catch a baseball\", \"similar objects\": [\"bat\", \"ball\", \"helmet\"]}", + 156 + ], + "silver pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round; made of silver; has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 156 + ], + "orange cones": [ + " {\"type\": \"traffic tool\", \"description\": \"orange; cone-shaped; used to block roads\", \"similar objects\": [\"barricades\", \"traffic signs\", \"traffic lights\"]}", + 156 + ], + "panels": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of wood, metal, or plastic; could be used for walls or roofs\", \"similar objects\": [\"boards\", \"sheets\", \"tiles\"]}", + 155 + ], + "head band": [ + " {\"type\": \"accessory\", \"description\": \"worn around the head; could be made of cloth or plastic; could have decorations\", \"similar objects\": [\"hat\", \"cap\", \"scarf\"]}", + 155 + ], + "head board": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could be attached to the wall\", \"similar objects\": [\"bed frame\", \"dresser\", \"nightstand\"]}", + 155 + ], + "fire extinguisher": [ + " {\"type\": \"safety tool\", \"description\": \"red; has a nozzle; could be pressurized\", \"similar objects\": [\"smoke detector\", \"fire alarm\", \"fire blanket\"]}", + 155 + ], + "kitchen sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a basin; could have a faucet; could have a drain\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 155 + ], + "skiier": [ + " {\"type\": \"athlete\", \"description\": \"wears ski boots and skis; could be skiing down a mountain\", \"similar objects\": [\"snowboarder\", \"skater\", \"surfer\"]}", + 155 + ], + "water hose": [ + " {\"type\": \"gardening tool\", \"description\": \"long, flexible tube; could be used to water plants\", \"similar objects\": [\"sprinkler\", \"watering can\", \"garden hose\"]}", + 155 + ], + "owl": [ + " {\"type\": \"animal\", \"description\": \"large eyes; nocturnal; could have feathers of different colors; could have a curved beak\", \"similar objects\": [\"eagle\", \"hawk\", \"falcon\"]}", + 155 + ], + "metal rail": [ + " {\"type\": \"building material\", \"description\": \"long, thin, and rigid; could be used for fencing or support\", \"similar objects\": [\"wooden rail\", \"iron bar\", \"steel beam\"]}", + 154 + ], + "fluffy clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, soft, and billowy; could be seen in the sky\", \"similar objects\": [\"cumulus clouds\", \"stratus clouds\", \"cirrus clouds\"]}", + 154 + ], + "reflector": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of metal; could be used to reflect light\", \"similar objects\": [\"mirror\", \"flashlight\", \"lantern\"]}", + 154 + ], + "pine": [ + " {\"type\": \"tree\", \"description\": \"evergreen; has needles; could have cones\", \"similar objects\": [\"fir\", \"spruce\", \"cedar\"]}", + 154 + ], + "metal poles": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for support\", \"similar objects\": [\"wooden poles\", \"steel beams\", \"concrete pillars\"]}", + 154 + ], + "bikini": [ + " {\"type\": \"clothing\", \"description\": \"two-piece swimsuit; could be made of spandex, nylon, or polyester; could have straps or ties\", \"similar objects\": [\"tankini\", \"monokini\", \"one-piece swimsuit\"]}", + 154 + ], + "bulb": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could be used to light up a room\", \"similar objects\": [\"light bulb\", \"LED bulb\", \"incandescent bulb\"]}", + 154 + ], + "trash bag": [ + " {\"type\": \"container\", \"description\": \"large, black, plastic; could be tied up\", \"similar objects\": [\"garbage can\", \"plastic bag\", \"recycle bin\"]}", + 153 + ], + "surf": [ + " {\"type\": \"activity\", \"description\": \"riding on the waves of the ocean; could use a surfboard\", \"similar objects\": [\"swim\", \"sail\", \"paddleboard\"]}", + 153 + ], + "looks": [ + " {\"type\": \"appearance\", \"description\": \"the way something appears to the eye; could be described as attractive or unattractive\", \"similar objects\": [\"appearance\", \"style\", \"aesthetic\"]}", + 153 + ], + "exhaust pipe": [ + " {\"type\": \"automotive part\", \"description\": \"cylindrical; could be made of metal; could be connected to the engine\", \"similar objects\": [\"muffler\", \"catalytic converter\", \"air filter\"]}", + 153 + ], + "teapot": [ + " {\"type\": \"cooking tool\", \"description\": \"round; has a handle and a spout; could be made of metal or ceramic\", \"similar objects\": [\"kettle\", \"coffee pot\", \"thermos\"]}", + 153 + ], + "wet": [ + " {\"type\": \"adjective\", \"description\": \"describes something that is covered in liquid or has a high moisture content\", \"similar objects\": [\"damp\", \"soaked\", \"saturated\"]}", + 153 + ], + "canister": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could have a lid\", \"similar objects\": [\"jar\", \"box\", \"bottle\"]}", + 152 + ], + "cupboards": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular, has shelves and doors\", \"similar objects\": [\"wardrobe\", \"dresser\", \"bookshelf\"]}", + 152 + ], + "tail lights": [ + " {\"type\": \"vehicle part\", \"description\": \"red and round; could be found at the back of a car; could be used to indicate braking\", \"similar objects\": [\"headlights\", \"turn signals\", \"brake lights\"]}", + 152 + ], + "right eye": [ + " {\"type\": \"body part\", \"description\": \"part of the face; could be closed or open; could be brown, blue, or green\", \"similar objects\": [\"left eye\", \"nose\", \"mouth\"]}", + 152 + ], + "carton": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be used to store items\", \"similar objects\": [\"box\", \"bag\", \"bin\"]}", + 152 + ], + "apartment building": [ + " {\"type\": \"structure\", \"description\": \"multi-story building; could have balconies; could have a lobby\", \"similar objects\": [\"condominium\", \"townhouse\", \"skyscraper\"]}", + 152 + ], + "stuff": [ + "\n{\"type\": \"general object\", \"description\": \"could refer to any physical object; could refer to intangible things such as ideas, feelings, etc.\", \"similar objects\": [\"things\", \"items\", \"belongings\"]}", + 152 + ], + "dirt ground": [ + " {\"type\": \"landscape\", \"description\": \"uneven, brown, could have rocks and stones\", \"similar objects\": [\"grassland\", \"desert\", \"mountain\"]}", + 152 + ], + "foreground": [ + " {\"type\": \"image element\", \"description\": \"the part of an image that is closest to the viewer; could be in focus\", \"similar objects\": [\"background\", \"midground\", \"out-of-focus\"]}", + 152 + ], + "water glass": [ + " {\"type\": \"drinking vessel\", \"description\": \"transparent; could be made of glass or plastic; could have a handle\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 151 + ], + "tennis skirt": [ + " {\"type\": \"clothing\", \"description\": \"short; pleated; could be white or colorful; could have a logo\", \"similar objects\": [\"tennis dress\", \"tennis shorts\", \"tennis shirt\"]}", + 151 + ], + "round mirror": [ + "\n{\"type\": \"decorative item\", \"description\": \"round; could be made of glass; could have a frame\", \"similar objects\": [\"picture frame\", \"clock\", \"vase\"]}", + 151 + ], + "nostrils": [ + " {\"type\": \"body part\", \"description\": \"two holes on the nose; used for breathing\", \"similar objects\": [\"ears\", \"eyes\", \"mouth\"]}", + 151 + ], + "tool": [ + " {\"type\": \"utensil\", \"description\": \"could be used for various purposes; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"hammer\", \"screwdriver\", \"pliers\"]}", + 151 + ], + "scale": [ + " {\"type\": \"measuring tool\", \"description\": \"could be digital or analog; could be used to measure weight or size\", \"similar objects\": [\"ruler\", \"tape measure\", \"calipers\"]}", + 151 + ], + "toothbrushes": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has bristles; could be manual or electric\", \"similar objects\": [\"toothpaste\", \"mouthwash\", \"floss\"]}", + 151 + ], + "baseball uniform": [ + " {\"type\": \"clothing\", \"description\": \"consists of a jersey and pants; could have a cap; could have a team logo\", \"similar objects\": [\"soccer uniform\", \"basketball uniform\", \"hockey uniform\"]}", + 151 + ], + "rolls": [ + " {\"type\": \"food\", \"description\": \"round; could be made of bread; could be filled with different ingredients\", \"similar objects\": [\"buns\", \"bagels\", \"croissants\"]}", + 150 + ], + "spices": [ + " {\"type\": \"food ingredient\", \"description\": \"could be in powder or liquid form; could be used to enhance the flavor of food\", \"similar objects\": [\"herbs\", \"seasonings\", \"condiments\"]}", + 150 + ], + "lion": [ + " {\"type\": \"animal\", \"description\": \"large, tawny-colored; has a mane; could roar\", \"similar objects\": [\"tiger\", \"leopard\", \"cheetah\"]}", + 150 + ], + "crates": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of wood or plastic; could be used for storage\", \"similar objects\": [\"boxes\", \"baskets\", \"barrels\"]}", + 150 + ], + "silver ring": [ + " {\"type\": \"jewelry\", \"description\": \"round; made of silver; could have a gemstone\", \"similar objects\": [\"gold ring\", \"bracelet\", \"necklace\"]}", + 150 + ], + "undershirt": [ + " {\"type\": \"clothing\", \"description\": \"worn under other clothing; usually made of cotton; could have short or long sleeves\", \"similar objects\": [\"tank top\", \"t-shirt\", \"vest\"]}", + 150 + ], + "socket": [ + " {\"type\": \"electrical tool\", \"description\": \"has two or more holes; could be used to plug in electrical devices\", \"similar objects\": [\"plug\", \"outlet\", \"switch\"]}", + 150 + ], + "bubbles": [ + " {\"type\": \"object\", \"description\": \"transparent; could be made of soap; could be filled with air or liquid\", \"similar objects\": [\"balloons\", \"foam\", \"spheres\"]}", + 150 + ], + "burners": [ + " {\"type\": \"cooking tool\", \"description\": \"could be gas or electric; could have multiple flames; could be used to cook food\", \"similar objects\": [\"stove\", \"oven\", \"grill\"]}", + 149 + ], + "ovens": [ + " {\"type\": \"cooking tool\", \"description\": \"box-shaped; could be electric or gas; could have a door\", \"similar objects\": [\"stove\", \"microwave\", \"toaster\"]}", + 149 + ], + "curve": [ + " {\"type\": \"shape\", \"description\": \"smooth, continuous line; could be a circle or an arc\", \"similar objects\": [\"circle\", \"arc\", \"oval\"]}", + 149 + ], + "soil": [ + " {\"type\": \"natural material\", \"description\": \"dark brown; could be wet or dry; could contain organic matter\", \"similar objects\": [\"dirt\", \"clay\", \"sand\"]}", + 149 + ], + "point": [ + " {\"type\": \"geometric shape\", \"description\": \"has no length, width, or depth; could be represented by a dot\", \"similar objects\": [\"line\", \"circle\", \"triangle\"]}", + 149 + ], + "ostrich": [ + " {\"type\": \"animal\", \"description\": \"large, flightless bird; has long legs and neck; has a long tail\", \"similar objects\": [\"emu\", \"cassowary\", \"rhea\"]}", + 149 + ], + "brown basket": [ + "\n{\"type\": \"container\", \"description\": \"brown; could be woven; could be used to store items\", \"similar objects\": [\"box\", \"bag\", \"bin\"]}", + 148 + ], + "shirtless man": [ + "\n{\"type\": \"person\", \"description\": \"no shirt; could have tattoos; could have a muscular body\", \"similar objects\": [\"man with shirt\", \"woman with shirt\", \"man without shirt\"]}", + 148 + ], + "meters": [ + " {\"type\": \"measuring tool\", \"description\": \"long; could be used to measure length, area, volume, etc.\", \"similar objects\": [\"ruler\", \"tape measure\", \"calipers\"]}", + 148 + ], + "dark clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"gray or black; could be accompanied by rain or thunder\", \"similar objects\": [\"fog\", \"hail\", \"snow\"]}", + 148 + ], + "layer": [ + " {\"type\": \"structure\", \"description\": \"consists of multiple layers; could be used to separate different materials\", \"similar objects\": [\"stratum\", \"stack\", \"shelf\"]}", + 148 + ], + "brown box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be sealed with tape\", \"similar objects\": [\"suitcase\", \"briefcase\", \"bag\"]}", + 147 + ], + "range": [ + " {\"type\": \"appliance\", \"description\": \"has a stovetop and an oven; could be gas or electric\", \"similar objects\": [\"stove\", \"cooktop\", \"microwave\"]}", + 147 + ], + "closet": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular; could have shelves and drawers; could be used to store clothes and other items\", \"similar objects\": [\"dresser\", \"wardrobe\", \"armoire\"]}", + 147 + ], + "variety": [ + " {\"type\": \"word\", \"description\": \"a large number of different things or people; could be used to describe a range of different options\", \"similar objects\": [\"diversity\", \"variety\", \"assortment\"]}", + 146 + ], + "chips": [ + " {\"type\": \"food\", \"description\": \"thin, flat, salty; could be made of potatoes\", \"similar objects\": [\"fries\", \"crisps\", \"popcorn\"]}", + 146 + ], + "sweatband": [ + " {\"type\": \"accessory\", \"description\": \"elastic; could be worn around the head or wrist; could be made of cotton or other fabrics\", \"similar objects\": [\"headband\", \"bracelet\", \"hat\"]}", + 146 + ], + "blueberries": [ + " {\"type\": \"fruit\", \"description\": \"small, round, blue; could have a white coating; could be eaten raw or cooked\", \"similar objects\": [\"raspberries\", \"blackberries\", \"strawberries\"]}", + 146 + ], + "cereal": [ + " {\"type\": \"food\", \"description\": \"grain-based; could be served with milk; could be sweet or savory\", \"similar objects\": [\"granola\", \"oatmeal\", \"porridge\"]}", + 146 + ], + "motor": [ + " {\"type\": \"machine\", \"description\": \"has an engine; could be used to power vehicles; could be used to generate electricity\", \"similar objects\": [\"generator\", \"engine\", \"turbine\"]}", + 146 + ], + "throw pillow": [ + " {\"type\": \"decorative item\", \"description\": \"soft; could be square or round; could be filled with feathers or foam\", \"similar objects\": [\"cushion\", \"bolster\", \"tapestry\"]}", + 146 + ], + "blue letters": [ + " {\"type\": \"writing tool\", \"description\": \"blue; could be made of paper or plastic; could be used for writing\", \"similar objects\": [\"pen\", \"pencil\", \"marker\"]}", + 146 + ], + "boards": [ + " {\"type\": \"building material\", \"description\": \"long, flat, could be made of wood or plastic\", \"similar objects\": [\"plywood\", \"sheetrock\", \"paneling\"]}", + 146 + ], + "sponge": [ + " {\"type\": \"cleaning tool\", \"description\": \"soft, absorbent, could be yellow or green\", \"similar objects\": [\"cloth\", \"brush\", \"scrubber\"]}", + 145 + ], + "railings": [ + " {\"type\": \"structure\", \"description\": \"long, metal bars; could be used as a fence\", \"similar objects\": [\"fence\", \"gate\", \"wall\"]}", + 145 + ], + "metal chair": [ + " {\"type\": \"furniture\", \"description\": \"made of metal; has four legs; could have a backrest\", \"similar objects\": [\"wooden chair\", \"plastic chair\", \"stool\"]}", + 145 + ], + "lambs": [ + " {\"type\": \"animal\", \"description\": \"small, white, fluffy; could have black faces; could be found in groups\", \"similar objects\": [\"sheep\", \"goats\", \"calves\"]}", + 145 + ], + "groom": [ + " {\"type\": \"person\", \"description\": \"man wearing a tuxedo; could have a boutonniere; could have a bouquet of flowers\", \"similar objects\": [\"bride\", \"best man\", \"father of the bride\"]}", + 145 + ], + "grassy hill": [ + " {\"type\": \"landscape\", \"description\": \"green; could have a slope; could have wildflowers\", \"similar objects\": [\"meadow\", \"mountain\", \"valley\"]}", + 145 + ], + "cut": [ + " {\"type\": \"action\", \"description\": \"separate an object into two or more parts\", \"similar objects\": [\"slice\", \"chop\", \"dice\"]}", + 144 + ], + "pancakes": [ + " {\"type\": \"food\", \"description\": \"round; could be made of flour, eggs, and milk; could be served with syrup\", \"similar objects\": [\"waffles\", \"crepes\", \"French toast\"]}", + 144 + ], + "vents": [ + " {\"type\": \"ventilation tool\", \"description\": \"could be round or rectangular; could be made of metal or plastic; could be used to circulate air\", \"similar objects\": [\"fans\", \"air conditioners\", \"heaters\"]}", + 144 + ], + "parachute": [ + " {\"type\": \"safety tool\", \"description\": \"large, made of fabric; could be used to slow down the speed of falling objects\", \"similar objects\": [\"life jacket\", \"helmet\", \"seat belt\"]}", + 144 + ], + "hotdogs": [ + " {\"type\": \"food\", \"description\": \"long, cylindrical; could be grilled or boiled; could be served with buns\", \"similar objects\": [\"sausages\", \"hamburgers\", \"sandwiches\"]}", + 143 + ], + "chrome": [ + " {\"type\": \"web browser\", \"description\": \"developed by Google; supports multiple operating systems; has a wide range of extensions and plugins\", \"similar objects\": [\"Firefox\", \"Safari\", \"Edge\"]}", + 143 + ], + "jug": [ + " {\"type\": \"container\", \"description\": \"cylindrical; has a handle; could be made of glass or plastic\", \"similar objects\": [\"pitcher\", \"bottle\", \"jar\"]}", + 143 + ], + "bathroom toilet": [ + " {\"type\": \"furniture\", \"description\": \"white; has a bowl; could have a lid; could be connected to a water tank\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 143 + ], + "bathroom floor": [ + " {\"type\": \"flooring\", \"description\": \"smooth; could be made of tiles; could be slippery when wet\", \"similar objects\": [\"kitchen floor\", \"hallway floor\", \"balcony floor\"]}", + 143 + ], + "rubber tire": [ + " {\"type\": \"automotive part\", \"description\": \"round; made of rubber; used for vehicles\", \"similar objects\": [\"wheel\", \"rim\", \"hubcap\"]}", + 143 + ], + "silver bowl": [ + " {\"type\": \"utensil\", \"description\": \"round; made of silver; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"dish\"]}", + 143 + ], + "olive": [ + " {\"type\": \"fruit\", \"description\": \"green, oval-shaped; could be pitted; could be used for cooking\", \"similar objects\": [\"avocado\", \"fig\", \"date\"]}", + 143 + ], + "leafy trees": [ + " {\"type\": \"plant\", \"description\": \"tall; has many leaves; could have fruits; could have flowers\", \"similar objects\": [\"palm tree\", \"pine tree\", \"oak tree\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant", + 143 + ], + "bus number": [ + " {\"type\": \"transportation\", \"description\": \"large vehicle; could have multiple doors; could have a number printed on the side\", \"similar objects\": [\"train\", \"tram\", \"trolley\"]}", + 143 + ], + "amount": [ + " {\"type\": \"measurement\", \"description\": \"quantity; could be expressed in numbers\", \"similar objects\": [\"volume\", \"weight\", \"length\"]}", + 143 + ], + "foot prints": [ + " {\"type\": \"footprint\", \"description\": \"imprints left by feet; could be made of mud, snow, or sand\", \"similar objects\": [\"shoe prints\", \"animal tracks\", \"tire tracks\"]}", + 143 + ], + "stadium": [ + " {\"type\": \"structure\", \"description\": \"large, open-air; could have a running track; could have a seating area\", \"similar objects\": [\"arena\", \"court\", \"field\"]}", + 142 + ], + "oval": [ + " {\"type\": \"shape\", \"description\": \"elliptical; could be symmetrical; could have curved sides\", \"similar objects\": [\"circle\", \"rectangle\", \"triangle\"]}", + 142 + ], + "sack": [ + " {\"type\": \"container\", \"description\": \"large, made of cloth; could be used to carry things\", \"similar objects\": [\"bag\", \"basket\", \"box\"]}", + 142 + ], + "blue body": [ + " {\"type\": \"clothing item\", \"description\": \"blue color; could be a shirt, dress, or pants\", \"similar objects\": [\"red body\", \"black body\", \"white body\"]}", + 142 + ], + "wh": [ + "\n{\"type\": \"pronoun\", \"description\": \"used to ask questions; could be used to refer to a person, place, thing, or idea\", \"similar objects\": [\"who\", \"what\", \"where\", \"when\", \"why\", \"how\"]}", + 141 + ], + "wipers": [ + " {\"type\": \"cleaning tool\", \"description\": \"long, thin, flexible; could be made of rubber; used to clean surfaces\", \"similar objects\": [\"sponge\", \"brush\", \"mop\"]}", + 141 + ], + "puffy clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, fluffy, could be shaped like animals\", \"similar objects\": [\"fog\", \"haze\", \"smoke\"]}", + 141 + ], + "cheek": [ + " {\"type\": \"body part\", \"description\": \"soft; located on the side of the face; could be pinched\", \"similar objects\": [\"chin\", \"forehead\", \"nose\"]}", + 141 + ], + "gun": [ + " {\"type\": \"weapon\", \"description\": \"long; could be made of metal; could be used to shoot bullets\", \"similar objects\": [\"rifle\", \"pistol\", \"shotgun\"]}", + 141 + ], + "lime": [ + " {\"type\": \"fruit\", \"description\": \"green, round, has a stem\", \"similar objects\": [\"lemon\", \"orange\", \"grapefruit\"]}", + 140 + ], + "male tennis player": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing a white shirt and shorts; holding a racket; playing on a tennis court\", \"similar objects\": [\"female tennis player\", \"golfer\", \"soccer player\"]}", + 140 + ], + "pizza slice": [ + " {\"type\": \"food\", \"description\": \"triangular; could have cheese, tomato sauce, and other toppings\", \"similar objects\": [\"sandwich\", \"burrito\", \"taco\"]}", + 140 + ], + "blue pillow": [ + "\n{\"type\": \"home decor\", \"description\": \"soft; could be square or round; could be made of fabric; could be blue in color\", \"similar objects\": [\"cushion\", \"blanket\", \"rug\"]}", + 140 + ], + "cushions": [ + " {\"type\": \"furniture\", \"description\": \"soft; could be filled with feathers; could be used for sitting or sleeping\", \"similar objects\": [\"pillow\", \"mattress\", \"sofa\"]}", + 140 + ], + "feather": [ + " {\"type\": \"object\", \"description\": \"light and fluffy; could be from a bird; could be used for writing\", \"similar objects\": [\"down\", \"quill\", \"plume\"]}", + 140 + ], + "beam": [ + " {\"type\": \"structure\", \"description\": \"long, straight, could be made of wood or metal; could be used to support a building\", \"similar objects\": [\"column\", \"pillar\", \"girder\"]}", + 140 + ], + "bats": [ + " {\"type\": \"animal\", \"description\": \"winged mammal; nocturnal; could have sharp teeth\", \"similar objects\": [\"birds\", \"insects\", \"rodents\"]}", + 139 + ], + "spire": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, slender, pointed structure; could be made of stone or metal; could be used as a monument or a religious symbol\", \"similar objects\": [\"tower\", \"obelisk\", \"minaret\"]}", + 139 + ], + "blue building": [ + "\n{\"type\": \"structure\", \"description\": \"large, rectangular; could have windows; could be made of bricks; could be painted blue\", \"similar objects\": [\"house\", \"skyscraper\", \"warehouse\"]}", + 139 + ], + "note": [ + " {\"type\": \"paper\", \"description\": \"small; could be written on; could be folded\", \"similar objects\": [\"letter\", \"envelope\", \"postcard\"]}", + 139 + ], + "panda bear": [ + " {\"type\": \"animal\", \"description\": \"black and white fur; has a round face; has a short tail\", \"similar objects\": [\"grizzly bear\", \"koala bear\", \"polar bear\"]}", + 139 + ], + "terrain": [ + " {\"type\": \"landscape\", \"description\": \"natural environment; could be mountainous, hilly, flat, etc.\", \"similar objects\": [\"landscape\", \"scenery\", \"environment\"]}", + 139 + ], + "face mask": [ + " {\"type\": \"protective gear\", \"description\": \"covers the nose and mouth; could be made of cloth or paper; could be disposable or reusable\", \"similar objects\": [\"respirator\", \"goggles\", \"gloves\"]}", + 139 + ], + "desk chair": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could have armrests; could be adjustable; could have a backrest\", \"similar objects\": [\"office chair\", \"dining chair\", \"sofa\"]}", + 139 + ], + "pears": [ + " {\"type\": \"fruit\", \"description\": \"green or yellow; round; has a stem\", \"similar objects\": [\"apple\", \"banana\", \"orange\"]}", + 139 + ], + "strips": [ + " {\"type\": \"fabric\", \"description\": \"long, thin pieces of cloth; could be used for decoration\", \"similar objects\": [\"ribbons\", \"tassels\", \"beads\"]}", + 139 + ], + "magnet": [ + " {\"type\": \"object\", \"description\": \"attracts metal objects; could be in the shape of a horseshoe\", \"similar objects\": [\"iron filings\", \"compass\", \"electromagnet\"]}", + 139 + ], + "flag pole": [ + " {\"type\": \"pole\", \"description\": \"tall, thin, could be made of metal; could be used to hold a flag\", \"similar objects\": [\"flagstaff\", \"mast\", \"flagstaff\"]}", + 139 + ], + "cauliflower": [ + " {\"type\": \"vegetable\", \"description\": \"white, round, has a stem; could have green leaves\", \"similar objects\": [\"broccoli\", \"cabbage\", \"kale\"]}", + 138 + ], + "company logo": [ + "\n{\"type\": \"graphic design\", \"description\": \"unique design; could be a combination of shapes, colors, and words; could be used to represent a company or organization\", \"similar objects\": [\"banner\", \"poster\", \"sign\"]}", + 138 + ], + "blue box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic or metal; could be used for storage\", \"similar objects\": [\"basket\", \"bag\", \"crate\"]}", + 138 + ], + "cleat": [ + " {\"type\": \"footwear\", \"description\": \"has spikes on the bottom; could be made of leather; could be used for sports\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 138 + ], + "vines": [ + " {\"type\": \"plant\", \"description\": \"green; could be climbing plants; could have tendrils; could have flowers\", \"similar objects\": [\"ivy\", \"creepers\", \"climbers\"]}", + 137 + ], + "silver watch": [ + " {\"type\": \"accessory\", \"description\": \"round; made of silver; has a strap\", \"similar objects\": [\"gold watch\", \"bracelet\", \"necklace\"]}", + 137 + ], + "pear": [ + " {\"type\": \"fruit\", \"description\": \"round, green or yellow; has a stem\", \"similar objects\": [\"apple\", \"banana\", \"orange\"]}", + 137 + ], + "dough": [ + " {\"type\": \"food ingredient\", \"description\": \"soft, malleable; could be used to make bread, pizza, and other baked goods\", \"similar objects\": [\"flour\", \"yeast\", \"sugar\"]}", + 137 + ], + "handlebar": [ + " {\"type\": \"bicycle part\", \"description\": \"long, curved, metal; could be used to steer the bicycle\", \"similar objects\": [\"pedal\", \"saddle\", \"chain\"]}", + 137 + ], + "brick chimney": [ + " {\"type\": \"structure\", \"description\": \"rectangular; made of bricks; could have a cap on the top\", \"similar objects\": [\"fireplace\", \"smoke stack\", \"chimney pot\"]}", + 137 + ], + "trolley": [ + " {\"type\": \"transportation tool\", \"description\": \"wheeled; could be used to carry heavy items\", \"similar objects\": [\"cart\", \"hand truck\", \"dolly\"]}", + 137 + ], + "hind legs": [ + " {\"type\": \"body part\", \"description\": \"long, muscular; located at the back of the body; used for jumping and running\", \"similar objects\": [\"forelegs\", \"arms\", \"wings\"]}", + 136 + ], + "police": [ + " {\"type\": \"occupation\", \"description\": \"enforces laws and regulations; could be armed; could wear a uniform\", \"similar objects\": [\"firefighter\", \"soldier\", \"doctor\"]}", + 136 + ], + "mantle": [ + " {\"type\": \"decorative item\", \"description\": \"long, thin, usually made of wood; could be hung on the wall\", \"similar objects\": [\"shelf\", \"picture frame\", \"mirror\"]}", + 136 + ], + "flip flops": [ + " {\"type\": \"footwear\", \"description\": \"flat; could be made of rubber; could have straps\", \"similar objects\": [\"sandals\", \"slippers\", \"sneakers\"]}", + 136 + ], + "refrigerators": [ + " {\"type\": \"appliance\", \"description\": \"large, white, has a door; could have shelves and drawers inside\", \"similar objects\": [\"freezer\", \"microwave\", \"dishwasher\"]}", + 136 + ], + "orange carrots": [ + "\n{\"type\": \"vegetable\", \"description\": \"orange, cylindrical, smooth; could have green leaves; could be sliced into round pieces\", \"similar objects\": [\"yellow carrots\", \"red carrots\", \"purple carrots\"]}", + 136 + ], + "baseball umpire": [ + " {\"type\": \"sports official\", \"description\": \"wears a black and white striped shirt; holds a whistle; stands behind the catcher\", \"similar objects\": [\"referee\", \"linesman\", \"umpire\"]}", + 136 + ], + "stalk": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, and rigid; could be green or brown; could have leaves or flowers\", \"similar objects\": [\"stem\", \"branch\", \"trunk\"]}", + 136 + ], + "neon sign": [ + " {\"type\": \"lighting tool\", \"description\": \"bright, colorful, usually in the shape of letters or symbols; could be used for advertisement\", \"similar objects\": [\"light box\", \"LED sign\", \"billboard\"]}", + 136 + ], + "type": [ + " {\"type\": \"word\", \"description\": \"a word used to describe a class of things; could be a noun, verb, adjective, adverb, etc.\", \"similar objects\": [\"category\", \"classification\", \"label\"]}", + 136 + ], + "charger": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a cable; could be used to charge electronic devices\", \"similar objects\": [\"power bank\", \"adapter\", \"battery\"]}", + 136 + ], + "round pizza": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"flatbread\", \"stuffed crust pizza\"]}", + 136 + ], + "piles": [ + " {\"type\": \"structure\", \"description\": \"a stack of objects; could be made of stones, wood, or other materials\", \"similar objects\": [\"columns\", \"towers\", \"pyramids\"]}", + 136 + ], + "lanyard": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of fabric or plastic; could be used to hold keys or ID cards\", \"similar objects\": [\"keychain\", \"necklace\", \"bracelet\"]}", + 136 + ], + "guard": [ + " {\"type\": \"person\", \"description\": \"wears a uniform; could be armed; could be standing at a gate\", \"similar objects\": [\"security guard\", \"police officer\", \"soldier\"]}", + 136 + ], + "metal train tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, straight, metal rails; could have wooden sleepers; could have electric wires\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 136 + ], + "shape": [ + " {\"type\": \"abstract concept\", \"description\": \"a two-dimensional figure; could be a circle, triangle, square, etc.\", \"similar objects\": [\"form\", \"pattern\", \"geometry\"]}", + 136 + ], + "passenger plane": [ + "\n{\"type\": \"vehicle\", \"description\": \"large; has wings; could have multiple engines; could have multiple floors; could have multiple seats\", \"similar objects\": [\"helicopter\", \"jet\", \"air balloon\"]}", + 136 + ], + "series": [ + " {\"type\": \"collection\", \"description\": \"a set of related objects or events; could be a set of books, movies, or television shows\", \"similar objects\": [\"trilogy\", \"anthology\", \"saga\"]}", + 136 + ], + "binder": [ + " {\"type\": \"office tool\", \"description\": \"has rings to hold papers; could be made of plastic or metal\", \"similar objects\": [\"folder\", \"notebook\", \"clipboard\"]}", + 135 + ], + "sails": [ + " {\"type\": \"nautical tool\", \"description\": \"large, triangular; used to catch wind; could be attached to a mast\", \"similar objects\": [\"masts\", \"rigging\", \"boom\"]}", + 135 + ], + "edges": [ + " {\"type\": \"geometric shape\", \"description\": \"lines that connect two vertices; could be straight or curved\", \"similar objects\": [\"corners\", \"angles\", \"sides\"]}", + 135 + ], + "ad": [ + " {\"type\": \"advertisement\", \"description\": \"visual or audio message to promote a product or service\", \"similar objects\": [\"commercial\", \"promotion\", \"marketing\"]}", + 135 + ], + "purple flower": [ + "\n{\"type\": \"plant\", \"description\": \"purple petals; could have yellow center; could have green stem and leaves\", \"similar objects\": [\"daisy\", \"tulip\", \"sunflower\"]}", + 135 + ], + "w": [ + "\n{\"type\": \"letter\", \"description\": \"the twenty-third letter of the English alphabet; a consonant\", \"similar objects\": [\"v\", \"x\", \"y\"]}", + 135 + ], + "faucets": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be made of metal; could be used to control water flow\", \"similar objects\": [\"shower head\", \"valve\", \"tap\"]}", + 134 + ], + "kind": [ + "\n{\"type\": \"word\", \"description\": \"adjective; describes a person or thing as having a good or benevolent nature or disposition\", \"similar objects\": [\"nice\", \"generous\", \"compassionate\"]}", + 134 + ], + "safety cone": [ + " {\"type\": \"traffic tool\", \"description\": \"orange; has a pointed top; could be reflective\", \"similar objects\": [\"traffic sign\", \"barricade\", \"speed bump\"]}", + 134 + ], + "oil": [ + " {\"type\": \"liquid\", \"description\": \"viscous; could be used for cooking; could be used for lubrication\", \"similar objects\": [\"vinegar\", \"water\", \"juice\"]}", + 134 + ], + "towers": [ + " {\"type\": \"structure\", \"description\": \"tall; could be made of concrete, steel, or wood; could have multiple floors\", \"similar objects\": [\"skyscrapers\", \"bridges\", \"monuments\"]}", + 134 + ], + "pin": [ + " {\"type\": \"fastener\", \"description\": \"small, sharp, metal object; could be used to attach two objects together\", \"similar objects\": [\"needle\", \"paper clip\", \"safety pin\"]}", + 134 + ], + "left eye": [ + " {\"type\": \"body part\", \"description\": \"part of the face; located on the left side; could be closed or open\", \"similar objects\": [\"right eye\", \"nose\", \"mouth\"]}", + 134 + ], + "date": [ + " {\"type\": \"fruit\", \"description\": \"oval; has a hard shell; could be dried; could be eaten fresh\", \"similar objects\": [\"fig\", \"raisin\", \"apricot\"]}", + 134 + ], + "food truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; could be painted with colorful designs; could have a window for serving food\", \"similar objects\": [\"ice cream truck\", \"concession truck\", \"catering truck\"]}", + 134 + ], + "wood door": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of wood; could have a handle\", \"similar objects\": [\"metal door\", \"glass door\", \"plastic door\"]}", + 133 + ], + "lunch": [ + " {\"type\": \"meal\", \"description\": \"a meal eaten in the middle of the day; could include sandwiches, salads, soups, etc.\", \"similar objects\": [\"breakfast\", \"dinner\", \"snack\"]}", + 133 + ], + "brown crust": [ + " {\"type\": \"food\", \"description\": \"hard, crunchy; could be used as a topping or a side dish\", \"similar objects\": [\"crouton\", \"breadcrumb\", \"tortilla chip\"]}", + 133 + ], + "broom": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has bristles; could be made of straw\", \"similar objects\": [\"mop\", \"vacuum cleaner\", \"dustpan\"]}", + 133 + ], + "chunks": [ + " {\"type\": \"food\", \"description\": \"small, irregularly shaped pieces; could be of any food item\", \"similar objects\": [\"bits\", \"pieces\", \"slices\"]}", + 133 + ], + "winter coat": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of wool; could be padded; could have a hood\", \"similar objects\": [\"jacket\", \"parka\", \"sweater\"]}", + 133 + ], + "hairs": [ + " {\"type\": \"body part\", \"description\": \"thin, long, could be of different colors; could be curly or straight\", \"similar objects\": [\"eyelashes\", \"eyebrows\", \"beard\"]}", + 133 + ], + "pumpkin": [ + " {\"type\": \"vegetable\", \"description\": \"round; orange; has a stem; could be carved into a jack-o-lantern\", \"similar objects\": [\"squash\", \"watermelon\", \"cantaloupe\"]}", + 133 + ], + "rings": [ + " {\"type\": \"jewelry\", \"description\": \"circular; could be made of gold, silver, or other metals; could have gemstones\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 132 + ], + "balconies": [ + " {\"type\": \"architectural feature\", \"description\": \"a platform or projection that extends from a wall of a building, often enclosed by a railing; could be used for decoration or for viewing the outdoors\", \"similar objects\": [\"balustrade\", \"terrace\", \"veranda\"]}", + 132 + ], + "tennis players": [ + " {\"type\": \"athletes\", \"description\": \"wearing white clothes; holding a racket; playing on a court\", \"similar objects\": [\"golfers\", \"soccer players\", \"basketball players\"]}", + 132 + ], + "blue train": [ + " {\"type\": \"vehicle\", \"description\": \"long; could have multiple carriages; could be painted blue\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 132 + ], + "signboard": [ + " {\"type\": \"advertisement tool\", \"description\": \"rectangular; could be made of wood or metal; could be used to display messages\", \"similar objects\": [\"billboard\", \"poster\", \"banner\"]}", + 132 + ], + "thigh": [ + " {\"type\": \"body part\", \"description\": \"upper part of the leg; could be muscular; could be covered with skin\", \"similar objects\": [\"calf\", \"knee\", \"ankle\"]}", + 131 + ], + "herbs": [ + " {\"type\": \"plant\", \"description\": \"small, green, could be used for cooking; could be dried\", \"similar objects\": [\"spices\", \"vegetables\", \"flowers\"]}", + 131 + ], + "flower vase": [ + " {\"type\": \"decorative item\", \"description\": \"cylindrical; could be made of glass, ceramic, or metal; could have a wide opening at the top\", \"similar objects\": [\"urn\", \"urns\", \"jar\"]}", + 131 + ], + "pizza box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could have a logo of a pizza shop\", \"similar objects\": [\"takeout box\", \"lunch box\", \"gift box\"]}", + 131 + ], + "fog": [ + " {\"type\": \"weather phenomenon\", \"description\": \"a cloud of tiny water droplets suspended in the air; could reduce visibility\", \"similar objects\": [\"mist\", \"haze\", \"smog\"]}", + 131 + ], + "outline": [ + " {\"type\": \"drawing tool\", \"description\": \"a line that defines the boundary of an object; could be used to draw shapes\", \"similar objects\": [\"sketch\", \"draft\", \"template\"]}", + 131 + ], + "silver metal": [ + " {\"type\": \"material\", \"description\": \"shiny, reflective, malleable\", \"similar objects\": [\"gold\", \"copper\", \"aluminum\"]}", + 130 + ], + "lush": [ + " {\"type\": \"cosmetic brand\", \"description\": \"luxury, natural, handmade cosmetics; could have a variety of products\", \"similar objects\": [\"L'Oreal\", \"Maybelline\", \"MAC\"]}", + 130 + ], + "hand rail": [ + " {\"type\": \"safety tool\", \"description\": \"long, metal bar; could be attached to a wall; could be used to support people\", \"similar objects\": [\"guard rail\", \"balustrade\", \"stair rail\"]}", + 130 + ], + "clean": [ + "\n{\"type\": \"verb\", \"description\": \"to make something free of dirt, dust, or unwanted substances\", \"similar objects\": [\"wash\", \"scrub\", \"polish\"]}", + 130 + ], + "power cord": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, has two ends; could be plugged into a wall outlet\", \"similar objects\": [\"extension cord\", \"USB cable\", \"power strip\"]}", + 130 + ], + "crest": [ + " {\"type\": \"symbol\", \"description\": \"could be a shield; could have a bird or animal on it; could have a motto or slogan\", \"similar objects\": [\"coat of arms\", \"banner\", \"flag\"]}", + 130 + ], + "tissues": [ + " {\"type\": \"cleaning tool\", \"description\": \"soft, thin, rectangular; could be used to wipe nose\", \"similar objects\": [\"paper towels\", \"napkins\", \"wipes\"]}", + 130 + ], + "police car": [ + " {\"type\": \"vehicle\", \"description\": \"blue; has a siren; could with a flashing light\", \"similar objects\": [\"ambulance\", \"taxi\", \"garbage truck\"]}", + 130 + ], + "moped": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a small engine; could be used for short-distance travel\", \"similar objects\": [\"scooter\", \"motorcycle\", \"bicycle\"]}", + 130 + ], + "coach": [ + " {\"type\": \"vehicle\", \"description\": \"long; could have multiple compartments; could have a luggage compartment\", \"similar objects\": [\"bus\", \"train\", \"tram\"]}", + 129 + ], + "wood cabinet": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have drawers and doors; could be used for storage\", \"similar objects\": [\"bookshelf\", \"dresser\", \"armoire\"]}", + 129 + ], + "watermark": [ + " {\"type\": \"image tool\", \"description\": \"transparent logo or text; could be used to protect copyright\", \"similar objects\": [\"logo\", \"stamp\", \"signature\"]}", + 129 + ], + "skateboarders": [ + " {\"type\": \"sport\", \"description\": \"riding on a skateboard; could perform tricks; could wear protective gear\", \"similar objects\": [\"surfers\", \"snowboarders\", \"bikers\"]}", + 129 + ], + "packet": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of paper or plastic; could be sealed\", \"similar objects\": [\"envelope\", \"box\", \"bag\"]}", + 129 + ], + "turn sign": [ + " {\"type\": \"traffic sign\", \"description\": \"triangular; has a red border; could have an arrow pointing left or right\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 129 + ], + "brick house": [ + " {\"type\": \"building\", \"description\": \"made of bricks; could have a chimney; could have a porch\", \"similar objects\": [\"wooden house\", \"stone house\", \"adobe house\"]}", + 129 + ], + "blue tarp": [ + "\n{\"type\": \"protective covering\", \"description\": \"blue; waterproof; could be used to cover objects\", \"similar objects\": [\"canvas\", \"plastic sheeting\", \"tent\"]}", + 129 + ], + "tails": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of fabric; could be attached to the back of a dress or shirt\", \"similar objects\": [\"sashes\", \"belts\", \"scarves\"]}", + 129 + ], + "computer mice": [ + " {\"type\": \"computer accessory\", \"description\": \"small, wireless, has two buttons\", \"similar objects\": [\"keyboard\", \"headset\", \"webcam\"]}", + 129 + ], + "story building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have windows and doors\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"museum\"]}", + 129 + ], + "avocado": [ + " {\"type\": \"fruit\", \"description\": \"oval-shaped; green or black; has a large seed inside\", \"similar objects\": [\"mango\", \"kiwi\", \"papaya\"]}", + 129 + ], + "hind leg": [ + " {\"type\": \"body part\", \"description\": \"long; could be used for jumping; could be found in animals\", \"similar objects\": [\"foreleg\", \"arm\", \"wing\"]}", + 129 + ], + "store sign": [ + " {\"type\": \"advertisement tool\", \"description\": \"could be made of metal or plastic; could be illuminated; could be hung on a wall or a pole\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 128 + ], + "window pane": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of glass; could be opened\", \"similar objects\": [\"door\", \"wall\", \"ceiling\"]}", + 128 + ], + "rectangle": [ + " {\"type\": \"shape\", \"description\": \"four sides; two pairs of parallel sides; four right angles\", \"similar objects\": [\"square\", \"triangle\", \"pentagon\"]}", + 128 + ], + "flour": [ + " {\"type\": \"ingredient\", \"description\": \"white, powdery; used for baking\", \"similar objects\": [\"sugar\", \"yeast\", \"baking powder\"]}", + 128 + ], + "male surfer": [ + "\n{\"type\": \"person\", \"description\": \"wearing a wetsuit; carrying a surfboard; could have a sunhat; could have a sunburn\", \"similar objects\": [\"female surfer\", \"diver\", \"sailor\"]}", + 128 + ], + "bits": [ + " {\"type\": \"tool\", \"description\": \"small, sharp pieces; could be used for drilling\", \"similar objects\": [\"drill bits\", \"screws\", \"nails\"]}", + 127 + ], + "shadow man": [ + " {\"type\": \"figure\", \"description\": \"dark figure; could be seen in the dark; could be a silhouette\", \"similar objects\": [\"ghost\", \"monster\", \"zombie\"]}", + 127 + ], + "calm": [ + " {\"type\": \"emotion\", \"description\": \"peaceful; relaxed; serene\", \"similar objects\": [\"tranquil\", \"placid\", \"serene\"]}", + 127 + ], + "pockets": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, sewn onto clothing; could be used to store items\", \"similar objects\": [\"pouches\", \"bags\", \"purses\"]}", + 127 + ], + "watermelon": [ + " {\"type\": \"fruit\", \"description\": \"large, round, green with dark green stripes; has a hard rind; could be sliced into wedges\", \"similar objects\": [\"cantaloupe\", \"honeydew\", \"papaya\"]}", + 127 + ], + "grey rock": [ + " {\"type\": \"geological object\", \"description\": \"grey; could be smooth or rough; could be of any size\", \"similar objects\": [\"stone\", \"boulder\", \"pebble\"]}", + 127 + ], + "cherry": [ + " {\"type\": \"fruit\", \"description\": \"red, round, has a stem; could have a pit\", \"similar objects\": [\"plum\", \"strawberry\", \"grape\"]}", + 127 + ], + "suit case": [ + " {\"type\": \"travel accessory\", \"description\": \"rectangular; has a handle; could be made of hard materials\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 126 + ], + "light switch": [ + " {\"type\": \"electrical device\", \"description\": \"has a switch to turn on/off the light; could be wall-mounted\", \"similar objects\": [\"outlet\", \"dimmer switch\", \"timer switch\"]}", + 126 + ], + "life vest": [ + " {\"type\": \"safety tool\", \"description\": \"orange; could be inflated; could be worn around the body\", \"similar objects\": [\"helmet\", \"fire extinguisher\", \"safety harness\"]}", + 126 + ], + "screws": [ + " {\"type\": \"hardware\", \"description\": \"small, metal, cylindrical; could have a head and a thread\", \"similar objects\": [\"nuts\", \"bolts\", \"washers\"]}", + 126 + ], + "heels": [ + " {\"type\": \"footwear\", \"description\": \"high-heeled shoes; could be made of leather; could have straps\", \"similar objects\": [\"sandals\", \"boots\", \"flats\"]}", + 126 + ], + "gentleman": [ + " {\"type\": \"person\", \"description\": \"well-mannered; wears a suit; could be carrying a hat\", \"similar objects\": [\"man\", \"businessman\", \"gentlewoman\"]}", + 126 + ], + "ipod": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; has a touch screen; could be used to play music\", \"similar objects\": [\"smartphone\", \"mp3 player\", \"tablet\"]}", + 126 + ], + "envelope": [ + " {\"type\": \"stationery\", \"description\": \"rectangular; could be sealed; could be used to send letters\", \"similar objects\": [\"letter\", \"card\", \"package\"]}", + 126 + ], + "metal structure": [ + " {\"type\": \"building material\", \"description\": \"strong and durable; could be used for construction\", \"similar objects\": [\"wood\", \"concrete\", \"glass\"]}", + 126 + ], + "pathway": [ + " {\"type\": \"structure\", \"description\": \"a route or a track; could be made of stones, bricks, or concrete; could be curved or straight\", \"similar objects\": [\"road\", \"trail\", \"walkway\"]}", + 126 + ], + "microwaves": [ + " {\"type\": \"appliance\", \"description\": \"box-shaped; has a door; could have a timer\", \"similar objects\": [\"refrigerator\", \"oven\", \"toaster\"]}", + 126 + ], + "lit": [ + "\n{\"type\": \"adjective\", \"description\": \"describes something that is illuminated or burning\", \"similar objects\": [\"bright\", \"glowing\", \"shining\"]}", + 126 + ], + "shaker": [ + " {\"type\": \"kitchen tool\", \"description\": \"cylindrical; could have a lid; could be used to mix ingredients\", \"similar objects\": [\"blender\", \"mixer\", \"grinder\"]}", + 125 + ], + "tile wall": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular, could be made of ceramic, stone, or glass; could be used to cover walls or floors\", \"similar objects\": [\"brick wall\", \"wood paneling\", \"vinyl sheet\"]}", + 125 + ], + "suit jacket": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have buttons; could be made of wool or cotton\", \"similar objects\": [\"blazer\", \"sports coat\", \"tuxedo\"]}", + 125 + ], + "rearview mirror": [ + " {\"type\": \"automotive tool\", \"description\": \"attached to the windshield; could be adjusted to different angles; could be used to see the back of the car\", \"similar objects\": [\"side mirror\", \"rearview camera\", \"GPS navigation system\"]}", + 125 + ], + "floor tile": [ + " {\"type\": \"building material\", \"description\": \"square; could be made of ceramic, stone, or wood; could be used to cover floors\", \"similar objects\": [\"wall tile\", \"carpet\", \"linoleum\"]}", + 125 + ], + "homes": [ + " {\"type\": \"structure\", \"description\": \"could be made of wood, brick, or stone; could have multiple rooms; could have a garden\", \"similar objects\": [\"houses\", \"apartments\", \"condos\"]}", + 125 + ], + "seam": [ + " {\"type\": \"sewing tool\", \"description\": \"used to join two pieces of fabric together; could be done by hand or machine\", \"similar objects\": [\"needle\", \"thread\", \"zipper\"]}", + 125 + ], + "front light": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the front of a vehicle; could be used to illuminate the road ahead\", \"similar objects\": [\"headlight\", \"fog light\", \"taillight\"]}", + 125 + ], + "tines": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, pointed; could be made of metal or plastic; could be used for eating or cooking\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}", + 124 + ], + "batting helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round; has a face guard; could be made of plastic or metal\", \"similar objects\": [\"shin guards\", \"shoulder pads\", \"elbow pads\"]}", + 124 + ], + "support": [ + " {\"type\": \"aid\", \"description\": \"helpful; could be physical or emotional; could be given by people or things\", \"similar objects\": [\"assistance\", \"encouragement\", \"comfort\"]}", + 124 + ], + "pizza cutter": [ + " {\"type\": \"kitchen tool\", \"description\": \"round; has a handle; could be used to cut pizza\", \"similar objects\": [\"cheese grater\", \"spatula\", \"whisk\"]}", + 124 + ], + "ski pole": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, has a handle; could be made of metal or plastic\", \"similar objects\": [\"ski boot\", \"ski helmet\", \"ski goggles\"]}", + 124 + ], + "waist": [ + " {\"type\": \"body part\", \"description\": \"area between the rib cage and hips; could be measured with a tape measure\", \"similar objects\": [\"hips\", \"abdomen\", \"torso\"]}", + 124 + ], + "bristles": [ + " {\"type\": \"material\", \"description\": \"stiff, short hairs; could be used for cleaning\", \"similar objects\": [\"bristle brush\", \"toothbrush\", \"scrub brush\"]}", + 124 + ], + "blazer": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be single-breasted; could be made of wool\", \"similar objects\": [\"suit\", \"jacket\", \"coat\"]}", + 124 + ], + "monument": [ + " {\"type\": \"structure\", \"description\": \"could be made of stone; could be a statue; could be a memorial\", \"similar objects\": [\"building\", \"tower\", \"statue\"]}", + 123 + ], + "monitors": [ + " {\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a computer; could be used to display images\", \"similar objects\": [\"television\", \"projector\", \"printer\"]}", + 123 + ], + "control panel": [ + " {\"type\": \"electronic device\", \"description\": \"has multiple buttons and switches; could be used to control other devices\", \"similar objects\": [\"keyboard\", \"joystick\", \"remote control\"]}", + 123 + ], + "skateboard wheels": [ + " {\"type\": \"skateboard accessory\", \"description\": \"round; could be made of plastic or metal; could have bearings\", \"similar objects\": [\"trucks\", \"deck\", \"grip tape\"]}", + 123 + ], + "toes": [ + " {\"type\": \"body part\", \"description\": \"five digits; could be wiggled; could be painted with nail polish\", \"similar objects\": [\"fingers\", \"elbows\", \"knees\"]}", + 123 + ], + "pointy ears": [ + " {\"type\": \"body part\", \"description\": \"elongated, pointed ears; could be found on some animals\", \"similar objects\": [\"whiskers\", \"tail\", \"claws\"]}", + 123 + ], + "shoulder bag": [ + " {\"type\": \"accessory\", \"description\": \"long strap; could be made of leather; could be used to carry items\", \"similar objects\": [\"backpack\", \"purse\", \"tote bag\"]}", + 123 + ], + "zucchini": [ + "\n{\"type\": \"vegetable\", \"description\": \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"cucumber\", \"eggplant\", \"green bean\"]}\n\nObject detection models should focus on the shape, color, texture, and size of the object, as well as any distinguishing features such as stems or leaves. Additionally, the model should be able to recognize similar objects and distinguish them from the input object.", + 123 + ], + "stains": [ + " {\"type\": \"mark\", \"description\": \"discoloration on a surface; could be caused by dirt, oil, or other substances\", \"similar objects\": [\"spots\", \"smudges\", \"dirt\"]}", + 123 + ], + "overpass": [ + " {\"type\": \"structure\", \"description\": \"bridge-like structure; could be made of concrete; could have multiple lanes\", \"similar objects\": [\"viaduct\", \"tunnel\", \"bridge\"]}", + 123 + ], + "wooden shelf": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have multiple shelves\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"cupboard\"]}", + 123 + ], + "metal pipe": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for plumbing\", \"similar objects\": [\"wooden beam\", \"concrete block\", \"steel rod\"]}", + 123 + ], + "planks": [ + " {\"type\": \"building material\", \"description\": \"long, thin, wooden boards; could be used for construction\", \"similar objects\": [\"plywood\", \"timber\", \"lumber\"]}", + 122 + ], + "asphalt road": [ + " {\"type\": \"surface\", \"description\": \"black, smooth, flat; could have yellow lines\", \"similar objects\": [\"concrete road\", \"gravel road\", \"dirt road\"]}", + 122 + ], + "brake lights": [ + " {\"type\": \"vehicle part\", \"description\": \"red; usually found at the back of a car; used to indicate slowing down or stopping\", \"similar objects\": [\"headlights\", \"turn signals\", \"taillights\"]}", + 122 + ], + "dvd player": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a disc tray; could be connected to a TV\", \"similar objects\": [\"Blu-ray player\", \"game console\", \"stereo system\"]}", + 122 + ], + "dinner": [ + "\n{\"type\": \"meal\", \"description\": \"a meal eaten in the evening; could include a variety of dishes\", \"similar objects\": [\"lunch\", \"breakfast\", \"supper\"]}", + 122 + ], + "grip": [ + " {\"type\": \"tool\", \"description\": \"used to hold objects; could be made of rubber or plastic\", \"similar objects\": [\"clamp\", \"vise\", \"tongs\"]}", + 122 + ], + "globe": [ + " {\"type\": \"decoration\", \"description\": \"round; could be made of paper or plastic; could be used to represent the world map\", \"similar objects\": [\"map\", \"ball\", \"terrarium\"]}", + 122 + ], + "turkey": [ + " {\"type\": \"animal\", \"description\": \"large, brown feathers; has a long neck; could have a red wattle\", \"similar objects\": [\"chicken\", \"duck\", \"goose\"]}", + 122 + ], + "steak": [ + " {\"type\": \"food\", \"description\": \"thick, red, could be grilled; could be served with vegetables\", \"similar objects\": [\"roast beef\", \"lamb chop\", \"pork chop\"]}", + 121 + ], + "asparagus": [ + " {\"type\": \"vegetable\", \"description\": \"green, long, thin; could have purple tips; could be steamed or boiled\", \"similar objects\": [\"broccoli\", \"cauliflower\", \"green beans\"]}", + 121 + ], + "leggings": [ + " {\"type\": \"clothing\", \"description\": \"tight-fitting; could be made of cotton, spandex, or polyester; could be long or short\", \"similar objects\": [\"jeans\", \"yoga pants\", \"tights\"]}", + 121 + ], + "greens": [ + " {\"type\": \"vegetable\", \"description\": \"leafy vegetables; could be cooked or eaten raw; could be green, yellow, or purple\", \"similar objects\": [\"spinach\", \"lettuce\", \"kale\"]}", + 121 + ], + "iron": [ + " {\"type\": \"household tool\", \"description\": \"rectangular; has a handle; used to press clothes\", \"similar objects\": [\"steamer\", \"sewing machine\", \"vacuum cleaner\"]}", + 121 + ], + "train door": [ + " {\"type\": \"transportation tool\", \"description\": \"sliding door; could be automatic; could be opened by a handle\", \"similar objects\": [\"elevator door\", \"subway door\", \"airplane door\"]}", + 121 + ], + "orange traffic cone": [ + "\n{\"type\": \"traffic safety tool\", \"description\": \"orange; cone-shaped; could have reflective stripes\", \"similar objects\": [\"traffic barrier\", \"traffic sign\", \"traffic light\"]}", + 121 + ], + "cloud cover": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, fluffy, could be seen in the sky; could block the sunlight\", \"similar objects\": [\"fog\", \"haze\", \"smog\"]}", + 121 + ], + "blurry": [ + "\n{\"type\": \"visual effect\", \"description\": \"lack of clarity; could be caused by camera shake or out-of-focus lens\", \"similar objects\": [\"hazy\", \"fuzzy\", \"distorted\"]}", + 121 + ], + "skiing": [ + " {\"type\": \"sport\", \"description\": \"involves sliding on snow with skis; could be done on a mountain slope\", \"similar objects\": [\"snowboarding\", \"ice skating\", \"sledding\"]}", + 120 + ], + "back legs": [ + " {\"type\": \"body part\", \"description\": \"two legs located at the back of the body; used for walking and running\", \"similar objects\": [\"front legs\", \"arms\", \"feet\"]}", + 120 + ], + "buns": [ + " {\"type\": \"food\", \"description\": \"round; could be steamed or baked; could be filled with different ingredients\", \"similar objects\": [\"dumplings\", \"bread\", \"mantou\"]}", + 119 + ], + "pepperoni pizza": [ + "\n{\"type\": \"food\", \"description\": \"round; has a crust; topped with pepperoni and cheese\", \"similar objects\": [\"margherita pizza\", \"hawaiian pizza\", \"vegetarian pizza\"]}", + 119 + ], + "beach sand": [ + " {\"type\": \"natural material\", \"description\": \"fine, granular, yellowish-brown; could be wet or dry; could be hot or cold\", \"similar objects\": [\"soil\", \"gravel\", \"clay\"]}", + 119 + ], + "front paw": [ + " {\"type\": \"animal body part\", \"description\": \"part of the front leg of an animal; could have claws; could be used for walking, running, and climbing\", \"similar objects\": [\"hind paw\", \"foreleg\", \"hind leg\"]}", + 119 + ], + "muffin": [ + " {\"type\": \"food\", \"description\": \"round; could be sweet or savory; could be topped with fruits or nuts\", \"similar objects\": [\"cupcake\", \"donut\", \"bagel\"]}", + 119 + ], + "fingernail": [ + " {\"type\": \"body part\", \"description\": \"hard, thin, curved; could be painted with nail polish\", \"similar objects\": [\"toenail\", \"cuticle\", \"eyelash\"]}", + 119 + ], + "stairway": [ + " {\"type\": \"structure\", \"description\": \"has steps; could be made of wood or metal; could have a railing\", \"similar objects\": [\"ladder\", \"escalator\", \"elevator\"]}", + 119 + ], + "turf": [ + " {\"type\": \"ground cover\", \"description\": \"green; could be made of synthetic materials; could be used for sports fields\", \"similar objects\": [\"grass\", \"mulch\", \"soil\"]}", + 119 + ], + "neon": [ + " {\"type\": \"element\", \"description\": \"colorless gas; has a low boiling point; could be used in lighting\", \"similar objects\": [\"argon\", \"helium\", \"xenon\"]}", + 119 + ], + "website": [ + " {\"type\": \"digital platform\", \"description\": \"online platform; could contain text, images, videos, etc.\", \"similar objects\": [\"blog\", \"forum\", \"social media\"]}", + 119 + ], + "wooden handle": [ + " {\"type\": \"tool handle\", \"description\": \"made of wood; could be used for tools such as shovels, rakes, and brooms\", \"similar objects\": [\"plastic handle\", \"metal handle\", \"rubber handle\"]}", + 119 + ], + "hallway": [ + " {\"type\": \"space\", \"description\": \"long, narrow, could have doors and windows\", \"similar objects\": [\"corridor\", \"aisle\", \"passageway\"]}", + 119 + ], + "shutter": [ + " {\"type\": \"window covering\", \"description\": \"could be made of wood or metal; could be opened and closed\", \"similar objects\": [\"blinds\", \"curtains\", \"shades\"]}", + 119 + ], + "controllers": [ + " {\"type\": \"electronic device\", \"description\": \"small, handheld device; could have buttons and joysticks; could be used to play video games\", \"similar objects\": [\"gamepads\", \"keyboards\", \"mice\"]}", + 119 + ], + "stretch": [ + " {\"type\": \"exercise\", \"description\": \"involves extending the body to its maximum range of motion; could be done with a partner or alone\", \"similar objects\": [\"yoga\", \"pilates\", \"aerobics\"]}", + 119 + ], + "skate park": [ + " {\"type\": \"recreational facility\", \"description\": \"concrete area with ramps, rails, and other obstacles for skateboarding\", \"similar objects\": [\"playground\", \"basketball court\", \"swimming pool\"]}", + 119 + ], + "brown elephant": [ + "\n{\"type\": \"animal\", \"description\": \"large; has a long trunk; has large ears; has a grayish-brown color\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}", + 119 + ], + "female tennis player": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing a tennis outfit; holding a tennis racket; playing on a tennis court\", \"similar objects\": [\"male tennis player\", \"golfer\", \"soccer player\"]}", + 119 + ], + "dust": [ + " {\"type\": \"particulate matter\", \"description\": \"tiny particles of solid matter; could be made of dirt, pollen, or other materials; could be suspended in air\", \"similar objects\": [\"smoke\", \"fog\", \"haze\"]}", + 119 + ], + "level": [ + " {\"type\": \"measuring tool\", \"description\": \"long; has a bubble in the middle; could be used to measure the flatness of a surface\", \"similar objects\": [\"ruler\", \"tape measure\", \"protractor\"]}", + 119 + ], + "comb": [ + " {\"type\": \"grooming tool\", \"description\": \"long; has teeth; could be made of plastic or metal\", \"similar objects\": [\"brush\", \"scissors\", \"razor\"]}", + 119 + ], + "heater": [ + " {\"type\": \"appliance\", \"description\": \"could be electric or gas; could be wall-mounted or portable; could have a thermostat\", \"similar objects\": [\"air conditioner\", \"humidifier\", \"dehumidifier\"]}", + 118 + ], + "pairs": [ + " {\"type\": \"object\", \"description\": \"two of the same item; could be shoes, gloves, socks, etc.\", \"similar objects\": [\"sets\", \"twins\", \"duos\"]}", + 118 + ], + "stain": [ + " {\"type\": \"mark\", \"description\": \"discoloration on a surface; could be caused by dirt, oil, or other substances\", \"similar objects\": [\"spot\", \"smudge\", \"dirt\"]}", + 118 + ], + "baseball pitcher": [ + " {\"type\": \"athlete\", \"description\": \"throws a baseball from a mound to a catcher; wears a glove\", \"similar objects\": [\"football quarterback\", \"basketball point guard\", \"hockey goalie\"]}", + 118 + ], + "water bottles": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of plastic; could have a lid\", \"similar objects\": [\"mug\", \"cup\", \"thermos\"]}", + 118 + ], + "green": [ + "\n{\"type\": \"color\", \"description\": \"a hue of yellow and blue; could be light or dark; could be associated with nature\", \"similar objects\": [\"blue\", \"yellow\", \"red\"]}", + 118 + ], + "gray clouds": [ + " {\"type\": \"weather\", \"description\": \"dark gray; could be in the form of a cluster; could be accompanied by rain\", \"similar objects\": [\"fog\", \"haze\", \"smog\"]}", + 118 + ], + "arches": [ + " {\"type\": \"architectural structure\", \"description\": \"curved; could be made of stone; could be used as a bridge\", \"similar objects\": [\"columns\", \"domes\", \"vaults\"]}", + 118 + ], + "swimsuit": [ + " {\"type\": \"clothing\", \"description\": \"worn for swimming; could be one-piece or two-piece; could be made of spandex or nylon\", \"similar objects\": [\"bikini\", \"tankini\", \"monokini\"]}", + 118 + ], + "goose": [ + " {\"type\": \"animal\", \"description\": \"gray or white feathers; long neck; webbed feet; honks\", \"similar objects\": [\"duck\", \"swan\", \"turkey\"]}", + 118 + ], + "skull": [ + " {\"type\": \"skeleton\", \"description\": \"white; has two eye sockets; could have teeth\", \"similar objects\": [\"skeleton\", \"skull and crossbones\", \"fossil\"]}", + 117 + ], + "brand name": [ + "\n{\"type\": \"trademark\", \"description\": \"a name, phrase, symbol, or design that identifies and distinguishes the source of goods or services of one party from those of others\", \"similar objects\": [\"logo\", \"slogan\", \"tagline\"]}", + 117 + ], + "rubber": [ + " {\"type\": \"material\", \"description\": \"elastic; could be used for erasing; could be used for making tires\", \"similar objects\": [\"plastic\", \"silicone\", \"latex\"]}", + 117 + ], + "bill": [ + " {\"type\": \"document\", \"description\": \"a written document; could be a financial statement; could be a legal document\", \"similar objects\": [\"invoice\", \"receipt\", \"statement\"]}", + 117 + ], + "jet plane": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has wings; could have two or more engines; could have a tail fin\", \"similar objects\": [\"helicopter\", \"airplane\", \"rocket\"]}", + 117 + ], + "cycle": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could have a basket; could have a bell\", \"similar objects\": [\"bicycle\", \"motorcycle\", \"scooter\"]}", + 117 + ], + "aeroplane": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has wings and a tail; could have multiple engines\", \"similar objects\": [\"helicopter\", \"glider\", \"jet\"]}", + 117 + ], + "skillet": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 116 + ], + "pineapples": [ + " {\"type\": \"fruit\", \"description\": \"spiky, yellow, sweet; has a crown of leaves\", \"similar objects\": [\"mango\", \"kiwi\", \"avocado\"]}", + 116 + ], + "roofs": [ + " {\"type\": \"structure\", \"description\": \"flat or sloped; could be made of tiles, metal, or wood; could be used to protect from rain and snow\", \"similar objects\": [\"walls\", \"ceilings\", \"floors\"]}", + 116 + ], + "grey building": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could have multiple stories; could have windows; could be made of concrete or bricks\", \"similar objects\": [\"house\", \"skyscraper\", \"warehouse\"]}", + 116 + ], + "mugs": [ + " {\"type\": \"drinking tool\", \"description\": \"cylindrical; could have handles; could be made of ceramic, glass, or metal\", \"similar objects\": [\"cups\", \"glasses\", \"tumblers\"]}", + 116 + ], + "winter jacket": [ + " {\"type\": \"clothing\", \"description\": \"thick; could be made of wool; could have a hood; could be waterproof\", \"similar objects\": [\"coat\", \"parka\", \"sweater\"]}", + 116 + ], + "shield": [ + " {\"type\": \"protection tool\", \"description\": \"round; could be made of metal; could have a handle\", \"similar objects\": [\"helmet\", \"armor\", \"sword\"]}", + 116 + ], + "floors": [ + " {\"type\": \"building material\", \"description\": \"flat, hard surface; could be made of wood, tile, or carpet\", \"similar objects\": [\"walls\", \"ceilings\", \"stairs\"]}", + 115 + ], + "price tag": [ + " {\"type\": \"labeling tool\", \"description\": \"small; could be made of paper or plastic; could have a string attached\", \"similar objects\": [\"label\", \"sticker\", \"tag\"]}", + 115 + ], + "wooden wall": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be used to build walls; could be painted\", \"similar objects\": [\"brick wall\", \"concrete wall\", \"plaster wall\"]}", + 115 + ], + "blue umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"blue; has a curved handle; could be opened and closed\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}", + 115 + ], + "sole": [ + " {\"type\": \"fish\", \"description\": \"flat; could be white or brown; could be grilled or fried\", \"similar objects\": [\"cod\", \"halibut\", \"turbot\"]}", + 115 + ], + "clay": [ + " {\"type\": \"material\", \"description\": \"soft, malleable, can be molded into shapes; could be used for pottery\", \"similar objects\": [\"mud\", \"dirt\", \"soil\"]}", + 115 + ], + "sad": [ + "\n{\"type\": \"emotion\", \"description\": \"a feeling of unhappiness, disappointment, or despair; could be accompanied by physical symptoms such as crying or a heavy heart\", \"similar objects\": [\"depressed\", \"lonely\", \"hopeless\"]}", + 115 + ], + "fins": [ + " {\"type\": \"aquatic tool\", \"description\": \"attached to feet; used for swimming\", \"similar objects\": [\"flippers\", \"snorkel\", \"diving mask\"]}", + 115 + ], + "banners": [ + " {\"type\": \"decoration\", \"description\": \"long, thin, could be made of cloth or paper; could be hung up\", \"similar objects\": [\"flags\", \"posters\", \"signs\"]}", + 115 + ], + "chimneys": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could be made of bricks; could be used to release smoke\", \"similar objects\": [\"smokestack\", \"flue\", \"vent\"]}", + 115 + ], + "advertisements": [ + " {\"type\": \"marketing tool\", \"description\": \"visual or audio messages used to promote products or services\", \"similar objects\": [\"commercials\", \"promotions\", \"banners\"]}", + 115 + ], + "air vent": [ + " {\"type\": \"ventilation tool\", \"description\": \"rectangular; could be made of metal; could be used to circulate air\", \"similar objects\": [\"fan\", \"heater\", \"air conditioner\"]}", + 115 + ], + "nut": [ + " {\"type\": \"food\", \"description\": \"hard, round, could be shelled; could be eaten raw or roasted\", \"similar objects\": [\"almond\", \"peanut\", \"cashew\"]}", + 115 + ], + "tuft": [ + " {\"type\": \"textile\", \"description\": \"a bunch of fibers; could be used for decoration\", \"similar objects\": [\"tassel\", \"pompom\", \"fringe\"]}", + 115 + ], + "bib": [ + " {\"type\": \"clothing item\", \"description\": \"worn around the neck; could be made of cloth or plastic; could have pockets\", \"similar objects\": [\"apron\", \"scarf\", \"hat\"]}", + 115 + ], + "pads": [ + " {\"type\": \"hygiene product\", \"description\": \"rectangular; could be disposable; could be used for menstrual cycle\", \"similar objects\": [\"tampons\", \"liners\", \"cup\"]}", + 114 + ], + "grey clouds": [ + " {\"type\": \"weather\", \"description\": \"dark grey; could be raining; could be moving\", \"similar objects\": [\"rain\", \"fog\", \"hail\"]}", + 114 + ], + "book shelf": [ + " {\"type\": \"furniture\", \"description\": \"vertical; could have multiple shelves; could be made of wood or metal\", \"similar objects\": [\"bookcase\", \"cabinet\", \"cupboard\"]}", + 114 + ], + "guard rail": [ + " {\"type\": \"safety tool\", \"description\": \"metal; could be installed along the roadside; could be used to prevent vehicles from going off the road\", \"similar objects\": [\"fence\", \"barrier\", \"bollard\"]}", + 114 + ], + "hotel": [ + " {\"type\": \"building\", \"description\": \"multi-story; could have a lobby; could have a restaurant\", \"similar objects\": [\"motel\", \"inn\", \"hostel\"]}", + 114 + ], + "notepad": [ + " {\"type\": \"stationery\", \"description\": \"rectangular; could be made of paper; could have a cover\", \"similar objects\": [\"notebook\", \"journal\", \"diary\"]}", + 114 + ], + "lip": [ + " {\"type\": \"body part\", \"description\": \"pink; could be curved; could be opened and closed\", \"similar objects\": [\"mouth\", \"nose\", \"eyebrow\"]}", + 114 + ], + "hazy sky": [ + " {\"type\": \"weather condition\", \"description\": \"cloudy; could be with fog; could be with dust\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"snowy sky\"]}", + 114 + ], + "sweat band": [ + " {\"type\": \"accessory\", \"description\": \"elastic; could be worn on the wrist; could be made of cotton\", \"similar objects\": [\"headband\", \"bracelet\", \"anklet\"]}", + 113 + ], + "mixer": [ + " {\"type\": \"kitchen appliance\", \"description\": \"has a handle; could have multiple attachments; could be used to mix ingredients\", \"similar objects\": [\"blender\", \"food processor\", \"juicer\"]}", + 113 + ], + "shingles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of asphalt; could be used for roofing\", \"similar objects\": [\"tiles\", \"slates\", \"siding\"]}", + 113 + ], + "pillow bed": [ + " {\"type\": \"furniture\", \"description\": \"soft; could be filled with feathers; could be used for sleeping\", \"similar objects\": [\"mattress\", \"sofa\", \"couch\"]}", + 113 + ], + "cooker": [ + " {\"type\": \"kitchen appliance\", \"description\": \"could be electric or gas; could have multiple burners; could have a timer\", \"similar objects\": [\"stove\", \"oven\", \"microwave\"]}", + 113 + ], + "peas": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, green; could be eaten raw or cooked; could be found in pods\", \"similar objects\": [\"beans\", \"corn\", \"carrots\"]}", + 113 + ], + "posters": [ + " {\"type\": \"decoration\", \"description\": \"printed paper; could be hung on the wall\", \"similar objects\": [\"paintings\", \"photos\", \"wallpapers\"]}", + 113 + ], + "brown trees": [ + "\n{\"type\": \"plant\", \"description\": \"trunk is dark brown; leaves are green; could have fruits\", \"similar objects\": [\"oak tree\", \"maple tree\", \"pine tree\"]}", + 113 + ], + "harbor": [ + " {\"type\": \"location\", \"description\": \"a sheltered area of water where ships can dock; could have a lighthouse\", \"similar objects\": [\"port\", \"marina\", \"bay\"]}", + 113 + ], + "licence plate": [ + " {\"type\": \"identification tool\", \"description\": \"rectangular; has numbers and letters; could be attached to a vehicle\", \"similar objects\": [\"driver's license\", \"passport\", \"ID card\"]}", + 112 + ], + "purple shirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be made of cotton; could have a collar\", \"similar objects\": [\"dress\", \"jacket\", \"jeans\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"", + 112 + ], + "helmet man": [ + " {\"type\": \"protective gear\", \"description\": \"hard, covers the head; could have a visor\", \"similar objects\": [\"safety glasses\", \"hard hat\", \"ear muffs\"]}", + 112 + ], + "horse grazing": [ + "\n{\"type\": \"animal behavior\", \"description\": \"horse eating grass or other vegetation; could be standing or lying down\", \"similar objects\": [\"cows grazing\", \"sheep grazing\", \"deer grazing\"]}", + 112 + ], + "window blinds": [ + " {\"type\": \"window covering\", \"description\": \"vertical or horizontal slats; could be made of fabric, wood, or metal; could be opened and closed with a cord or remote control\", \"similar objects\": [\"curtains\", \"shades\", \"drapes\"]}", + 112 + ], + "heel": [ + " {\"type\": \"footwear\", \"description\": \"raised back part of a shoe; could be made of leather; could have a pointed toe\", \"similar objects\": [\"sandal\", \"boot\", \"sneaker\"]}", + 112 + ], + "jackson mingus": [ + "\n{\"type\": \"person\", \"description\": \"American jazz bassist, composer, and bandleader\", \"similar objects\": [\"Charles Mingus\", \"John Coltrane\", \"Miles Davis\"]}", + 112 + ], + "bicycle seat": [ + " {\"type\": \"bicycle part\", \"description\": \"attached to the frame; could be padded; could be adjustable\", \"similar objects\": [\"handlebar\", \"pedal\", \"chain\"]}", + 112 + ], + "things": [ + "\n{\"type\": \"general object\", \"description\": \"could be anything; could be tangible or intangible; could be physical or abstract\", \"similar objects\": [\"items\", \"objects\", \"stuff\"]}", + 112 + ], + "brown building": [ + "\n{\"type\": \"structure\", \"description\": \"large, rectangular; could have windows; could be made of bricks; could have a roof\", \"similar objects\": [\"house\", \"skyscraper\", \"warehouse\"]}", + 112 + ], + "adult elephant": [ + "\n{\"type\": \"animal\", \"description\": \"large; has a trunk; has large ears; has tusks; has gray skin\", \"similar objects\": [\"giraffe\", \"rhinoceros\", \"hippopotamus\"]}", + 112 + ], + "baseball jersey": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; has a team logo; could be made of cotton\", \"similar objects\": [\"basketball jersey\", \"soccer jersey\", \"hockey jersey\"]}", + 112 + ], + "riders": [ + " {\"type\": \"people\", \"description\": \"people on horses, bicycles, motorcycles, etc.\", \"similar objects\": [\"cyclists\", \"horseback riders\", \"motorcyclists\"]}", + 112 + ], + "flooring": [ + " {\"type\": \"building material\", \"description\": \"hard surface; could be made of wood, tile, or carpet\", \"similar objects\": [\"wallpaper\", \"ceiling\", \"paint\"]}", + 112 + ], + "wall outlet": [ + " {\"type\": \"electrical device\", \"description\": \"rectangular; has two or more holes; could be used to plug in electrical appliances\", \"similar objects\": [\"power strip\", \"extension cord\", \"surge protector\"]}", + 112 + ], + "denim jeans": [ + " {\"type\": \"clothing\", \"description\": \"blue; could be tight or loose; could have pockets; could have a zipper\", \"similar objects\": [\"jeans\", \"trousers\", \"shorts\"]}", + 111 + ], + "fin": [ + " {\"type\": \"fish body part\", \"description\": \"elongated; could be used for swimming; could be found on the back of a fish\", \"similar objects\": [\"gill\", \"tail\", \"scales\"]}", + 111 + ], + "backside": [ + " {\"type\": \"body part\", \"description\": \"the rear part of the body; could be referred to as the buttocks\", \"similar objects\": [\"buttocks\", \"bottom\", \"bum\"]}", + 111 + ], + "grate": [ + " {\"type\": \"cooking tool\", \"description\": \"metal; has holes; could be used to strain food\", \"similar objects\": [\"strainer\", \"colander\", \"sieve\"]}", + 111 + ], + "hanger": [ + " {\"type\": \"clothing tool\", \"description\": \"long, thin, has a hook\", \"similar objects\": [\"clothes rack\", \"clothespin\", \"clothesline\"]}", + 111 + ], + "metal trash": [ + " {\"type\": \"garbage\", \"description\": \"made of metal; could be cans, bottles, or other metal objects\", \"similar objects\": [\"plastic trash\", \"glass trash\", \"paper trash\"]}", + 111 + ], + "shiny": [ + "\n\nObject detection models should focus on identifying the shape, color, texture, and size of the object. For example, for the input \"zucchini\", the model should focus on identifying the cylindrical shape, green color, smooth texture, and size of the zucchini. For the input \"zebra\", the model should focus on identifying the black and white stripes, long mane, and size of the zebra. For the input \"apple\", the model should focus on identifying the red color, round shape, stem, and size of the apple. For the input \"wok\", the model should focus on", + 111 + ], + "orange frisbee": [ + "\n{\"type\": \"toy\", \"description\": \"round; orange; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"discus\", \"boomerang\", \"football\"]}", + 111 + ], + "chalkboard": [ + " {\"type\": \"writing tool\", \"description\": \"black; could be used to write on; could be erased\", \"similar objects\": [\"whiteboard\", \"blackboard\", \"marker board\"]}", + 111 + ], + "seagulls": [ + " {\"type\": \"bird\", \"description\": \"white; has a long beak; could be seen near the sea\", \"similar objects\": [\"pigeon\", \"duck\", \"crow\"]}", + 111 + ], + "clock building": [ + " {\"type\": \"structure\", \"description\": \"tall; has a clock on the top; could be made of stone or metal\", \"similar objects\": [\"tower\", \"cathedral\", \"monument\"]}", + 110 + ], + "faucet sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a spout and handles; could be made of metal or plastic; could be mounted on the wall or countertop\", \"similar objects\": [\"bathtub\", \"shower\", \"toilet\"]}", + 110 + ], + "kitty": [ + " {\"type\": \"animal\", \"description\": \"small, furry, four legs; could have stripes or spots; could have a long tail\", \"similar objects\": [\"cat\", \"dog\", \"rabbit\"]}", + 110 + ], + "conductor": [ + " {\"type\": \"person\", \"description\": \"wears a hat; holds a baton; leads an orchestra\", \"similar objects\": [\"musician\", \"singer\", \"composer\"]}", + 110 + ], + "loaf": [ + " {\"type\": \"food\", \"description\": \"long, rectangular; could be sliced; could be made of bread\", \"similar objects\": [\"baguette\", \"ciabatta\", \"rye bread\"]}", + 110 + ], + "peach": [ + " {\"type\": \"fruit\", \"description\": \"round, fuzzy, has a pit\", \"similar objects\": [\"plum\", \"apricot\", \"nectarine\"]}", + 110 + ], + "sun glasses": [ + " {\"type\": \"eyewear\", \"description\": \"dark lenses; could be made of plastic or metal; could have a frame\", \"similar objects\": [\"sunglasses\", \"eyeglasses\", \"goggles\"]}", + 110 + ], + "mom": [ + "\n{\"type\": \"person\", \"description\": \"mother; could be caring and loving; could be a source of support and guidance\", \"similar objects\": [\"dad\", \"aunt\", \"grandma\"]}", + 110 + ], + "vine": [ + " {\"type\": \"plant\", \"description\": \"long, thin, flexible; could be used for decoration; could be used for climbing\", \"similar objects\": [\"ivy\", \"creeper\", \"tendril\"]}", + 110 + ], + "business sign": [ + " {\"type\": \"advertisement tool\", \"description\": \"could be made of metal, plastic, or wood; could be illuminated; could be hung on a wall or a pole\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 110 + ], + "fencing": [ + " {\"type\": \"sport\", \"description\": \"two opponents use swords to score points; requires protective gear\", \"similar objects\": [\"archery\", \"judo\", \"taekwondo\"]}", + 110 + ], + "metal container": [ + " {\"type\": \"container\", \"description\": \"made of metal; could be cylindrical or rectangular; could have a lid\", \"similar objects\": [\"jar\", \"box\", \"can\"]}", + 110 + ], + "train platform": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, flat, could have a roof; could have a ticket booth; could have a waiting area\", \"similar objects\": [\"bus station\", \"airport terminal\", \"subway station\"]}", + 110 + ], + "light glare": [ + " {\"type\": \"optical phenomenon\", \"description\": \"bright, intense light; could be caused by reflection of light from a surface\", \"similar objects\": [\"sun glare\", \"reflection\", \"glare from headlights\"]}", + 110 + ], + "cane": [ + " {\"type\": \"walking aid\", \"description\": \"long, thin, wooden or metal stick; could have a curved handle\", \"similar objects\": [\"walker\", \"crutch\", \"wheelchair\"]}", + 110 + ], + "fry": [ + " {\"type\": \"cooking method\", \"description\": \"cooking food in hot oil\", \"similar objects\": [\"saute\", \"roast\", \"grill\"]}", + 109 + ], + "hardwood floors": [ + "\n{\"type\": \"flooring material\", \"description\": \"smooth, durable, and long-lasting; could be made of oak, maple, or walnut; could be stained or painted\", \"similar objects\": [\"laminate flooring\", \"carpet\", \"tile\"]}", + 109 + ], + "dog bun": [ + " {\"type\": \"food\", \"description\": \"round; could be made of wheat flour; could be filled with meat or vegetables\", \"similar objects\": [\"hot dog\", \"hamburger\", \"pizza\"]}", + 109 + ], + "father": [ + " {\"type\": \"person\", \"description\": \"male parent; could be a role model; could be a provider\", \"similar objects\": [\"mother\", \"grandfather\", \"uncle\"]}", + 109 + ], + "outlets": [ + " {\"type\": \"electrical device\", \"description\": \"has two or more holes; could be used to plug in electrical appliances\", \"similar objects\": [\"switches\", \"sockets\", \"plugs\"]}", + 109 + ], + "video game": [ + " {\"type\": \"electronic device\", \"description\": \"interactive; could be played on a console or computer; could have multiple levels\", \"similar objects\": [\"board game\", \"card game\", \"puzzle game\"]}", + 109 + ], + "baseboard": [ + " {\"type\": \"building material\", \"description\": \"long, thin, and flat; usually made of wood or plastic; used to cover the gap between the wall and the floor\", \"similar objects\": [\"molding\", \"trim\", \"skirting board\"]}", + 109 + ], + "cement sidewalk": [ + " {\"type\": \"construction material\", \"description\": \"hard, gray, flat surface; could be used for walkways\", \"similar objects\": [\"asphalt\", \"concrete\", \"gravel\"]}", + 109 + ], + "index finger": [ + " {\"type\": \"body part\", \"description\": \"longest finger; located at the right side of the thumb\", \"similar objects\": [\"middle finger\", \"ring finger\", \"pinky finger\"]}", + 109 + ], + "cloudy skies": [ + " {\"type\": \"weather\", \"description\": \"grayish; could be accompanied by rain; could be accompanied by wind\", \"similar objects\": [\"rainy skies\", \"sunny skies\", \"hazy skies\"]}", + 109 + ], + "antelope": [ + " {\"type\": \"animal\", \"description\": \"long legs; slender body; horns on the head; brown fur\", \"similar objects\": [\"gazelle\", \"deer\", \"wildebeest\"]}", + 109 + ], + "skateboard wheel": [ + " {\"type\": \"skateboard part\", \"description\": \"round; has a hub in the center; could be made of polyurethane\", \"similar objects\": [\"trucks\", \"deck\", \"bearings\"]}", + 109 + ], + "caution sign": [ + " {\"type\": \"warning sign\", \"description\": \"triangular; yellow background; black exclamation mark\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 109 + ], + "cloudless blue sky": [ + "\n{\"type\": \"natural phenomenon\", \"description\": \"clear, blue, no clouds\", \"similar objects\": [\"clear night sky\", \"sunset\", \"sunrise\"]}", + 109 + ], + "cabinet doors": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could be opened and closed\", \"similar objects\": [\"drawers\", \"cupboard\", \"wardrobe\"]}", + 108 + ], + "snowboarders": [ + " {\"type\": \"sport\", \"description\": \"people riding on snowboards; could be wearing protective gear; could be performing tricks\", \"similar objects\": [\"skiers\", \"surfers\", \"skateboarders\"]}", + 108 + ], + "hair dryer": [ + " {\"type\": \"hair styling tool\", \"description\": \"long, cylindrical; has a nozzle; could be corded or cordless\", \"similar objects\": [\"curling iron\", \"straightener\", \"hair clippers\"]}", + 108 + ], + "ambulance": [ + "\n{\"type\": \"vehicle\", \"description\": \"red; has a glaring siren; could with a stretcher\", \"similar objects\": [\"police car\", \"taxi\", \"garbage truck\"]}", + 108 + ], + "cameras": [ + " {\"type\": \"electronic device\", \"description\": \"could be digital or analog; could be used to take pictures or videos; could have a viewfinder\", \"similar objects\": [\"camcorder\", \"smartphone\", \"tablet\"]}", + 108 + ], + "rest": [ + " {\"type\": \"state of being\", \"description\": \"a period of inactivity; a pause from work or activity\", \"similar objects\": [\"sleep\", \"relaxation\", \"break\"]}", + 108 + ], + "grass brown": [ + " {\"type\": \"plant\", \"description\": \"dried, yellowish-brown; could be found in lawns; could be used as a ground cover\", \"similar objects\": [\"moss\", \"mulch\", \"straw\"]}", + 108 + ], + "street lamps": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lantern\", \"lamp\", \"light post\"]}", + 108 + ], + "kitten": [ + " {\"type\": \"animal\", \"description\": \"small, furry, playful; could have stripes or spots; could have short or long fur\", \"similar objects\": [\"puppy\", \"rabbit\", \"hamster\"]}", + 108 + ], + "celery": [ + " {\"type\": \"vegetable\", \"description\": \"long, green, crunchy; could have white or yellowish stems; could be sliced into thin pieces; could has green leaves\", \"similar objects\": [\"carrot\", \"onion\", \"lettuce\"]}", + 108 + ], + "plank": [ + " {\"type\": \"wooden board\", \"description\": \"long, flat, rectangular; could be used for construction\", \"similar objects\": [\"beam\", \"board\", \"timber\"]}", + 108 + ], + "letter t": [ + " {\"type\": \"alphabet\", \"description\": \"straight line with a crossbar; could be in different fonts\", \"similar objects\": [\"letter a\", \"letter b\", \"letter c\"]}", + 108 + ], + "exhaust": [ + " {\"type\": \"pipe\", \"description\": \"long, cylindrical; could be connected to a car engine; could be made of metal\", \"similar objects\": [\"muffler\", \"tailpipe\", \"intake pipe\"]}", + 107 + ], + "aluminum": [ + " {\"type\": \"metal\", \"description\": \"silver-white; malleable; ductile; light weight; non-magnetic; good conductor of electricity and heat\", \"similar objects\": [\"iron\", \"copper\", \"steel\"]}", + 107 + ], + "blue handle": [ + " {\"type\": \"object\", \"description\": \"blue handle; could be attached to a door, drawer, or other object\", \"similar objects\": [\"knob\", \"pull\", \"lever\"]}", + 107 + ], + "kitchen counter": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, stone, or metal; could have cabinets and drawers\", \"similar objects\": [\"table\", \"island\", \"stool\"]}", + 107 + ], + "interior": [ + " {\"type\": \"room\", \"description\": \"could have walls, ceiling, floor, furniture, and decorations\", \"similar objects\": [\"living room\", \"bedroom\", \"kitchen\"]}", + 107 + ], + "condiments": [ + " {\"type\": \"food\", \"description\": \"sauces, spices, and other flavorings used to enhance the taste of food\", \"similar objects\": [\"salt\", \"pepper\", \"vinegar\"]}", + 107 + ], + "scoreboard": [ + " {\"type\": \"sports equipment\", \"description\": \"large; could be digital or manual; could display scores and time\", \"similar objects\": [\"stopwatch\", \"timer\", \"whistle\"]}", + 107 + ], + "bathroom wall": [ + " {\"type\": \"structure\", \"description\": \"flat, vertical surface; could be made of tiles, paint, or wallpaper; could have a mirror\", \"similar objects\": [\"kitchen wall\", \"bedroom wall\", \"living room wall\"]}", + 107 + ], + "armrest": [ + " {\"type\": \"furniture\", \"description\": \"attached to a chair; could be adjustable; could be made of wood or metal\", \"similar objects\": [\"footrest\", \"ottoman\", \"sofa\"]}", + 107 + ], + "parking sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; has a white background; could have a red circle with a line through it\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 107 + ], + "zebra tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, black and white stripes; could be used for defense\", \"similar objects\": [\"horse tail\", \"giraffe tail\", \"elephant trunk\"]}", + 107 + ], + "taillight": [ + " {\"type\": \"vehicle part\", \"description\": \"red or amber; could be found at the back of a car; could be used to indicate direction\", \"similar objects\": [\"headlight\", \"turn signal\", \"brake light\"]}", + 107 + ], + "podium": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular; could have a microphone; could be used for speeches\", \"similar objects\": [\"lectern\", \"desk\", \"table\"]}", + 107 + ], + "paintings": [ + " {\"type\": \"artwork\", \"description\": \"could be made of canvas, paper, or other materials; could be abstract or realistic; could be in various sizes and shapes\", \"similar objects\": [\"drawings\", \"sculptures\", \"photographs\"]}", + 107 + ], + "torso": [ + " {\"type\": \"body part\", \"description\": \"upper body; includes the chest, abdomen, and back; does not include the head, arms, and legs\", \"similar objects\": [\"head\", \"arm\", \"leg\"]}", + 107 + ], + "medium": [ + " {\"type\": \"size\", \"description\": \"somewhere between small and large; could be used to describe objects, people, or events\", \"similar objects\": [\"small\", \"large\", \"extra large\"]}", + 106 + ], + "tabby cat": [ + "\n{\"type\": \"animal\", \"description\": \"striped fur; could have a white belly; could have a short tail; could have green eyes\", \"similar objects\": [\"Siamese cat\", \"Persian cat\", \"Maine Coon cat\"]}", + 106 + ], + "leather jacket": [ + " {\"type\": \"clothing\", \"description\": \"made of leather; could have a zipper; could have pockets\", \"similar objects\": [\"leather pants\", \"leather skirt\", \"leather boots\"]}", + 106 + ], + "types": [ + " {\"type\": \"word\", \"description\": \"a word used to refer to a group of things that have something in common\", \"similar objects\": [\"category\", \"classification\", \"group\"]}", + 106 + ], + "ripple": [ + " {\"type\": \"wave\", \"description\": \"circular; could be caused by a stone thrown into a pond; could be seen on the surface of water\", \"similar objects\": [\"wave\", \"tide\", \"tsunami\"]}", + 106 + ], + "fence posts": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of wood or metal; used to support a fence\", \"similar objects\": [\"rails\", \"poles\", \"stakes\"]}", + 106 + ], + "vans": [ + " {\"type\": \"vehicle\", \"description\": \"boxy; could have sliding doors; could be used for transportation\", \"similar objects\": [\"truck\", \"minivan\", \"SUV\"]}", + 106 + ], + "crackers": [ + " {\"type\": \"food\", \"description\": \"flat, crunchy, could be salty or sweet; could be round or square\", \"similar objects\": [\"chips\", \"cookies\", \"pretzels\"]}", + 106 + ], + "sailboats": [ + " {\"type\": \"watercraft\", \"description\": \"has a sail; could be made of wood or metal; could have one or more masts\", \"similar objects\": [\"yacht\", \"canoe\", \"rowboat\"]}", + 106 + ], + "glass doors": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be framed; could be sliding\", \"similar objects\": [\"windows\", \"shutters\", \"curtains\"]}", + 106 + ], + "frames": [ + " {\"type\": \"decorative item\", \"description\": \"could be made of metal or plastic; could be used to hang pictures or paintings\", \"similar objects\": [\"mirror\", \"photo album\", \"picture frame\"]}", + 105 + ], + "metal chain link fence": [ + "\n{\"type\": \"barrier\", \"description\": \"made of metal; has chain links; could be used to enclose an area\", \"similar objects\": [\"wooden fence\", \"barbed wire fence\", \"brick wall\"]}", + 105 + ], + "shoelaces": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of cotton or nylon; used to tie shoes\", \"similar objects\": [\"belt\", \"tie\", \"scarf\"]}", + 105 + ], + "straw hat": [ + " {\"type\": \"headwear\", \"description\": \"wide brim; could be made of straw; could have a ribbon\", \"similar objects\": [\"sun hat\", \"fedora\", \"baseball cap\"]}", + 105 + ], + "dumpster": [ + " {\"type\": \"container\", \"description\": \"large, rectangular, metal; used for waste disposal\", \"similar objects\": [\"trash can\", \"garbage bin\", \"recycling bin\"]}", + 105 + ], + "glare": [ + " {\"type\": \"visual effect\", \"description\": \"intense brightness; could cause discomfort to eyes\", \"similar objects\": [\"glint\", \"glare\", \"glow\"]}", + 105 + ], + "desert": [ + " {\"type\": \"landscape\", \"description\": \"dry; sandy; could have cacti and other plants\", \"similar objects\": [\"savanna\", \"tundra\", \"grassland\"]}", + 105 + ], + "coaster": [ + " {\"type\": \"tableware\", \"description\": \"round; could be made of wood, plastic, or cork; used to protect surfaces from hot or cold drinks\", \"similar objects\": [\"placemat\", \"tablecloth\", \"napkin\"]}", + 105 + ], + "colorful": [ + "\n\n{\"type\": \"adjective\", \"description\": \"describes something that has multiple colors\", \"similar objects\": [\"vibrant\", \"bright\", \"dazzling\"]}", + 105 + ], + "railway line": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, straight, has tracks\", \"similar objects\": [\"highway\", \"road\", \"bridge\"]}", + 105 + ], + "meadow": [ + " {\"type\": \"landscape\", \"description\": \"large, open area of grass and wildflowers; could have trees and shrubs\", \"similar objects\": [\"field\", \"prairie\", \"pasture\"]}", + 105 + ], + "elbow pad": [ + " {\"type\": \"protective gear\", \"description\": \"padded; could be strapped to the elbow; could be made of foam\", \"similar objects\": [\"knee pad\", \"shin guard\", \"helmet\"]}", + 105 + ], + "reins": [ + " {\"type\": \"horse riding tool\", \"description\": \"leather straps; used to control a horse\", \"similar objects\": [\"bridle\", \"halter\", \"saddle\"]}", + 104 + ], + "tennis balls": [ + " {\"type\": \"sport equipment\", \"description\": \"round; yellow; could be made of rubber\", \"similar objects\": [\"baseball\", \"soccer ball\", \"basketball\"]}", + 104 + ], + "handle bars": [ + " {\"type\": \"bicycle part\", \"description\": \"long, curved metal bars; attached to the front of a bicycle; used to steer the bike\", \"similar objects\": [\"pedals\", \"saddle\", \"chain\"]}", + 104 + ], + "bundle": [ + " {\"type\": \"collection\", \"description\": \"group of items tied together\", \"similar objects\": [\"package\", \"stack\", \"group\"]}", + 104 + ], + "slab": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular, made of concrete or stone\", \"similar objects\": [\"brick\", \"tile\", \"block\"]}", + 104 + ], + "night sky": [ + " {\"type\": \"natural phenomenon\", \"description\": \"dark; stars and planets could be seen; could be cloudy\", \"similar objects\": [\"sunset\", \"aurora\", \"rainbow\"]}", + 104 + ], + "p": [ + "\n{\"type\": \"letter\", \"description\": \"a single letter in the English alphabet; looks like a triangle with a line through the middle\", \"similar objects\": [\"a\", \"b\", \"c\"]}", + 104 + ], + "claw": [ + " {\"type\": \"tool\", \"description\": \"curved; could be used to grab things\", \"similar objects\": [\"tweezers\", \"pliers\", \"scissors\"]}", + 104 + ], + "berry": [ + " {\"type\": \"fruit\", \"description\": \"small, round, could be red, blue, or black; could be sour or sweet\", \"similar objects\": [\"grape\", \"strawberry\", \"blueberry\"]}", + 104 + ], + "divider": [ + " {\"type\": \"organizing tool\", \"description\": \"could be made of wood or plastic; could be used to separate spaces\", \"similar objects\": [\"screen\", \"partition\", \"room divider\"]}", + 104 + ], + "racks": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of metal or wood; could have multiple shelves\", \"similar objects\": [\"cabinet\", \"shelf\", \"drawer\"]}", + 103 + ], + "medicine cabinet": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; has shelves; could be locked\", \"similar objects\": [\"wardrobe\", \"dresser\", \"bookshelf\"]}", + 103 + ], + "ripe": [ + "\n{\"type\": \"adjective\", \"description\": \"describes a fruit or vegetable that is ready to be eaten; could be soft, juicy, and sweet\", \"similar objects\": [\"mature\", \"overripe\", \"fresh\"]}", + 103 + ], + "metal spoon": [ + " {\"type\": \"utensil\", \"description\": \"long handle; made of metal; could be used for stirring\", \"similar objects\": [\"fork\", \"knife\", \"spatula\"]}", + 103 + ], + "jackets": [ + " {\"type\": \"clothing\", \"description\": \"long sleeves; could be made of wool; could be zipped up\", \"similar objects\": [\"coat\", \"sweater\", \"hoodie\"]}", + 103 + ], + "sea foam": [ + " {\"type\": \"natural phenomenon\", \"description\": \"foam created by the agitation of seawater; could be white or greenish in color\", \"similar objects\": [\"tide\", \"wave\", \"surf\"]}", + 103 + ], + "tire tracks": [ + " {\"type\": \"evidence\", \"description\": \"long, curved lines; could be made of rubber; could be found on roads\", \"similar objects\": [\"footprints\", \"fingerprints\", \"vehicle tracks\"]}", + 103 + ], + "rectangular": [ + " {\"type\": \"shape\", \"description\": \"four sides; two pairs of parallel sides; four right angles\", \"similar objects\": [\"square\", \"triangle\", \"circle\"]}", + 102 + ], + "water faucet": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be used to control the flow of water\", \"similar objects\": [\"shower head\", \"hose\", \"sprinkler\"]}", + 102 + ], + "scarves": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of wool or silk; could be used to keep warm\", \"similar objects\": [\"shawl\", \"hat\", \"gloves\"]}", + 102 + ], + "controls": [ + " {\"type\": \"device\", \"description\": \"used to operate a machine or system; could be buttons, knobs, levers, or switches\", \"similar objects\": [\"joystick\", \"keyboard\", \"mouse\"]}", + 102 + ], + "sign board": [ + " {\"type\": \"information tool\", \"description\": \"rectangular; could be made of wood or metal; could have words or symbols printed on it\", \"similar objects\": [\"billboard\", \"placard\", \"poster\"]}", + 102 + ], + "stainless steel refrigerator": [ + "\n{\"type\": \"appliance\", \"description\": \"large, metallic, has a door; could have a freezer compartment\", \"similar objects\": [\"microwave\", \"dishwasher\", \"washing machine\"]}", + 102 + ], + "front paws": [ + " {\"type\": \"animal body part\", \"description\": \"four paws located at the front of the body; could be used for walking and running\", \"similar objects\": [\"back paws\", \"claws\", \"hind legs\"]}", + 102 + ], + "latch": [ + " {\"type\": \"fastening tool\", \"description\": \"metal; could be used to lock doors; could be opened with a key\", \"similar objects\": [\"lock\", \"bolt\", \"hinge\"]}", + 102 + ], + "screens": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be used to display information; could be touch-sensitive\", \"similar objects\": [\"monitors\", \"tablets\", \"smartphones\"]}", + 102 + ], + "rock formation": [ + " {\"type\": \"geological formation\", \"description\": \"natural formation of rocks; could be of various shapes and sizes; could be found in deserts, mountains, and other areas\", \"similar objects\": [\"cave\", \"cliff\", \"mountain\"]}", + 102 + ], + "signpost": [ + " {\"type\": \"directional tool\", \"description\": \"tall; could be made of metal; could have arrows pointing in different directions\", \"similar objects\": [\"road sign\", \"traffic light\", \"street sign\"]}", + 102 + ], + "brown dog": [ + "\n{\"type\": \"animal\", \"description\": \"brown fur; could have short or long hair; could have pointy ears; could have a tail\", \"similar objects\": [\"cat\", \"rabbit\", \"hamster\"]}", + 102 + ], + "cuff": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; could be made of metal or fabric; could be used to fasten sleeves\", \"similar objects\": [\"button\", \"zipper\", \"belt\"]}", + 102 + ], + "doorknob": [ + " {\"type\": \"hardware\", \"description\": \"round; could be made of metal; could be used to open and close doors\", \"similar objects\": [\"lock\", \"hinge\", \"handle\"]}", + 101 + ], + "sign pole": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could have a sign attached to it\", \"similar objects\": [\"flag pole\", \"street light\", \"traffic light\"]}", + 101 + ], + "tools": [ + "\n{\"type\": \"utensils\", \"description\": \"could be made of metal; could be used for various purposes; could be used for cutting, drilling, hammering, etc.\", \"similar objects\": [\"screwdriver\", \"pliers\", \"wrench\"]}", + 101 + ], + "kiwi": [ + " {\"type\": \"fruit\", \"description\": \"brown, oval-shaped; has a fuzzy skin; has a green flesh inside\", \"similar objects\": [\"strawberry\", \"mango\", \"peach\"]}", + 101 + ], + "flip phone": [ + " {\"type\": \"electronic device\", \"description\": \"small; has a hinge in the middle; could be opened and closed\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 101 + ], + "round light": [ + "\n{\"type\": \"lighting tool\", \"description\": \"could be a lamp, flashlight, or candle; could be round or cylindrical; could be powered by electricity or battery\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 101 + ], + "peak": [ + " {\"type\": \"geographical feature\", \"description\": \"highest point of a mountain; could be pointed\", \"similar objects\": [\"summit\", \"ridge\", \"cliff\"]}", + 101 + ], + "referee": [ + " {\"type\": \"person\", \"description\": \"wears a black and white striped shirt; carries a whistle; responsible for enforcing the rules of a game\", \"similar objects\": [\"umpire\", \"coach\", \"linesman\"]}", + 101 + ], + "wood desk": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have drawers; could have a flat surface\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 101 + ], + "clock wall": [ + " {\"type\": \"timekeeping tool\", \"description\": \"mounted on the wall; could have hands or digital display\", \"similar objects\": [\"watch\", \"alarm clock\", \"grandfather clock\"]}", + 101 + ], + "brown eye": [ + " {\"type\": \"body part\", \"description\": \"dark brown; round; could be used to see\", \"similar objects\": [\"blue eye\", \"green eye\", \"hazel eye\"]}", + 101 + ], + "ivy": [ + " {\"type\": \"plant\", \"description\": \"green; could be used for decoration; could be found on walls\", \"similar objects\": [\"fern\", \"moss\", \"honeysuckle\"]}", + 101 + ], + "dragon": [ + " {\"type\": \"mythical creature\", \"description\": \"large, scaly, could have wings and breathe fire\", \"similar objects\": [\"unicorn\", \"phoenix\", \"griffin\"]}", + 101 + ], + "basin": [ + " {\"type\": \"utensil\", \"description\": \"round; could be made of metal or plastic; could be used for washing hands or dishes\", \"similar objects\": [\"sink\", \"tub\", \"bucket\"]}", + 101 + ], + "castle": [ + " {\"type\": \"structure\", \"description\": \"large; could have towers; could have a moat; could have a drawbridge\", \"similar objects\": [\"fortress\", \"palace\", \"manor\"]}", + 101 + ], + "round window": [ + " {\"type\": \"architectural feature\", \"description\": \"circular; could be made of glass; could be framed with wood\", \"similar objects\": [\"oval window\", \"arched window\", \"square window\"]}", + 100 + ], + "snowsuit": [ + " {\"type\": \"clothing\", \"description\": \"insulated; could be waterproof; could have a hood\", \"similar objects\": [\"winter coat\", \"ski jacket\", \"snow boots\"]}", + 100 + ], + "diamond": [ + " {\"type\": \"gemstone\", \"description\": \"transparent; has a unique shape; could be cut into many facets\", \"similar objects\": [\"ruby\", \"sapphire\", \"emerald\"]}", + 100 + ], + "company name": [ + "\n{\"type\": \"business entity\", \"description\": \"a legal entity created by individuals, stockholders, or shareholders to conduct business\", \"similar objects\": [\"corporation\", \"partnership\", \"limited liability company\"]}", + 100 + ], + "earring": [ + " {\"type\": \"jewelry\", \"description\": \"small, round, could be made of metal or plastic; could be pierced through the ear\", \"similar objects\": [\"necklace\", \"bracelet\", \"ring\"]}", + 100 + ], + "ankle": [ + " {\"type\": \"body part\", \"description\": \"connects the foot to the leg; could be sprained\", \"similar objects\": [\"knee\", \"elbow\", \"wrist\"]}", + 100 + ], + "metal handle": [ + " {\"type\": \"hardware\", \"description\": \"made of metal; could be used to open a door\", \"similar objects\": [\"knob\", \"hinge\", \"lock\"]}", + 99 + ], + "metal fencing": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be used to form a barrier; could be used for decoration\", \"similar objects\": [\"wooden fencing\", \"chain link fencing\", \"barbed wire fencing\"]}", + 99 + ], + "bottom half": [ + " {\"type\": \"clothing item\", \"description\": \"covers the lower body; could be pants, skirts, shorts, etc.\", \"similar objects\": [\"top half\", \"dress\", \"jumpsuit\"]}", + 99 + ], + "glass container": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of glass or plastic; could be used to store food or liquids\", \"similar objects\": [\"bottle\", \"jar\", \"can\"]}", + 99 + ], + "remotes": [ + " {\"type\": \"electronic device\", \"description\": \"small, handheld; used to control other electronic devices\", \"similar objects\": [\"game controller\", \"keyboard\", \"mouse\"]}", + 99 + ], + "characters": [ + " {\"type\": \"symbols\", \"description\": \"letters, numbers, punctuation marks, and other symbols used to represent language\", \"similar objects\": [\"words\", \"sentences\", \"paragraphs\"]}", + 99 + ], + "flops": [ + " {\"type\": \"footwear\", \"description\": \"flat; could be made of rubber; could be decorated with straps\", \"similar objects\": [\"sandals\", \"slippers\", \"sneakers\"]}", + 99 + ], + "pigeons": [ + " {\"type\": \"bird\", \"description\": \"gray; could have white patches; could have a long tail; could have a cooing sound\", \"similar objects\": [\"dove\", \"sparrow\", \"crow\"]}", + 99 + ], + "tattoos": [ + " {\"type\": \"body art\", \"description\": \"permanent designs on the skin; could be made with needles and ink\", \"similar objects\": [\"piercings\", \"henna\", \"body paint\"]}", + 99 + ], + "orange flowers": [ + "\n{\"type\": \"plant\", \"description\": \"orange petals; could have yellow centers; could have green leaves\", \"similar objects\": [\"daisy\", \"sunflower\", \"tulip\"]}", + 99 + ], + "left": [ + " {\"type\": \"direction\", \"description\": \"opposite of right; could be used to describe a turn\", \"similar objects\": [\"right\", \"up\", \"down\"]}", + 98 + ], + "baseball pants": [ + " {\"type\": \"clothing\", \"description\": \"long, loose-fitting pants; usually white or gray; could have stripes\", \"similar objects\": [\"baseball cap\", \"baseball jersey\", \"baseball glove\"]}", + 98 + ], + "cds": [ + " {\"type\": \"media storage\", \"description\": \"round; could be made of plastic; could store music, movies, and other data\", \"similar objects\": [\"dvds\", \"vhs tapes\", \"blu-ray discs\"]}", + 98 + ], + "blue bucket": [ + "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic; could have a handle; could be blue in color\", \"similar objects\": [\"pail\", \"tub\", \"barrel\"]}", + 98 + ], + "hearts": [ + " {\"type\": \"symbol\", \"description\": \"red; two overlapping circles; could be used to represent love\", \"similar objects\": [\"diamonds\", \"spades\", \"clubs\"]}", + 98 + ], + "gray wall": [ + " {\"type\": \"building material\", \"description\": \"solid, flat, and gray; could be made of concrete, brick, or stone\", \"similar objects\": [\"white wall\", \"wooden wall\", \"painted wall\"]}", + 98 + ], + "litter": [ + " {\"type\": \"waste\", \"description\": \"unwanted materials; could be plastic bags, bottles, cans, etc.\", \"similar objects\": [\"garbage\", \"trash\", \"rubbish\"]}", + 98 + ], + "fluffy": [ + " {\"type\": \"texture\", \"description\": \"light and airy; could be used to describe fabrics, fur, or feathers\", \"similar objects\": [\"fluffy\", \"soft\", \"smooth\"]}", + 98 + ], + "traffic signals": [ + " {\"type\": \"road safety tool\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"stop sign\", \"yield sign\", \"crosswalk sign\"]}", + 98 + ], + "skyline": [ + " {\"type\": \"landscape\", \"description\": \"buildings and structures of a city; could be seen from a distance\", \"similar objects\": [\"cityscape\", \"mountain range\", \"river\"]}", + 98 + ], + "television screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be connected to a device; could be used to watch movies\", \"similar objects\": [\"computer monitor\", \"tablet\", \"smartphone\"]}", + 98 + ], + "flame": [ + " {\"type\": \"fire\", \"description\": \"orange and yellow; could be seen in a candle or a bonfire\", \"similar objects\": [\"smoke\", \"ember\", \"spark\"]}", + 98 + ], + "metal grate": [ + " {\"type\": \"building material\", \"description\": \"made of metal; has a grid-like structure; could be used as a fence or a floor\", \"similar objects\": [\"chain link fence\", \"metal mesh\", \"expanded metal\"]}", + 98 + ], + "tall tower": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could be made of metal or concrete; could have antennas on top\", \"similar objects\": [\"skyscraper\", \"bridge\", \"monument\"]}", + 97 + ], + "dryer": [ + " {\"type\": \"appliance\", \"description\": \"large; could be used to dry clothes; could be electric or gas powered\", \"similar objects\": [\"washer\", \"iron\", \"vacuum cleaner\"]}", + 97 + ], + "parts": [ + "\n{\"type\": \"object\", \"description\": \"could be made of multiple components; could be used for assembling something\", \"similar objects\": [\"pieces\", \"components\", \"tools\"]}", + 97 + ], + "peaches": [ + " {\"type\": \"fruit\", \"description\": \"round, fuzzy, yellow-orange; has a pit\", \"similar objects\": [\"plums\", \"nectarines\", \"apricots\"]}", + 97 + ], + "picket fence": [ + " {\"type\": \"fence\", \"description\": \"wooden; has pointed tops; could be painted white\", \"similar objects\": [\"chain link fence\", \"barbed wire fence\", \"split rail fence\"]}", + 97 + ], + "athlete": [ + " {\"type\": \"person\", \"description\": \"physically fit; could be wearing sportswear; could be running or jumping\", \"similar objects\": [\"runner\", \"gymnast\", \"swimmer\"]}", + 97 + ], + "lace": [ + " {\"type\": \"fabric\", \"description\": \"delicate, intricate pattern; could be made of cotton, silk, or nylon; could be used for clothing or decoration\", \"similar objects\": [\"tulle\", \"organza\", \"satin\"]}", + 97 + ], + "flower design": [ + " {\"type\": \"decoration\", \"description\": \"could be made of paper, fabric, or other materials; could be in various shapes and colors\", \"similar objects\": [\"painting\", \"sculpture\", \"mosaic\"]}", + 97 + ], + "wineglass": [ + " {\"type\": \"drinking tool\", \"description\": \"tall and thin; could have a stem; could be made of glass or plastic\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 97 + ], + "blind": [ + " {\"type\": \"window covering\", \"description\": \"hangs from a rod; could be made of fabric; could be opened and closed\", \"similar objects\": [\"shade\", \"curtain\", \"drape\"]}", + 97 + ], + "bed cover": [ + " {\"type\": \"bedding item\", \"description\": \"used to cover the bed; could be made of fabric; could be quilted\", \"similar objects\": [\"blanket\", \"comforter\", \"duvet\"]}", + 97 + ], + "trick": [ + " {\"type\": \"action\", \"description\": \"a clever or mischievous act; a clever or mischievous idea\", \"similar objects\": [\"prank\", \"joke\", \"scheme\"]}", + 97 + ], + "beams": [ + " {\"type\": \"building material\", \"description\": \"long, straight, strong; could be made of wood or metal\", \"similar objects\": [\"rafters\", \"joists\", \"studs\"]}", + 97 + ], + "trash cans": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could have a lid\", \"similar objects\": [\"bins\", \"barrels\", \"boxes\"]}", + 97 + ], + "trashcan": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; has a lid\", \"similar objects\": [\"bin\", \"garbage can\", \"dustbin\"]}", + 97 + ], + "dirt brown": [ + " {\"type\": \"color\", \"description\": \"dark brown; could be used to describe soil\", \"similar objects\": [\"muddy brown\", \"burnt umber\", \"chocolate brown\"]}", + 97 + ], + "square window": [ + " {\"type\": \"architectural feature\", \"description\": \"four-sided; could be made of glass; could be opened\", \"similar objects\": [\"rectangular window\", \"door\", \"arch\"]}", + 96 + ], + "orange fruit": [ + "\n{\"type\": \"fruit\", \"description\": \"round; orange in color; has a stem; could be peeled and segmented\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}", + 96 + ], + "donkey": [ + " {\"type\": \"animal\", \"description\": \"gray; has long ears; could be ridden\", \"similar objects\": [\"horse\", \"mule\", \"zebra\"]}", + 96 + ], + "plastic bags": [ + " {\"type\": \"container\", \"description\": \"transparent; could be sealed; could be used for carrying items\", \"similar objects\": [\"paper bags\", \"boxes\", \"envelopes\"]}", + 96 + ], + "traffic signs": [ + " {\"type\": \"road safety tool\", \"description\": \"could be in different shapes and colors; could have symbols or words\", \"similar objects\": [\"road markings\", \"traffic lights\", \"speed bumps\"]}", + 96 + ], + "metal utensil": [ + " {\"type\": \"kitchen tool\", \"description\": \"made of metal; could be used for stirring, scooping, or serving food\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 96 + ], + "leafless trees": [ + " {\"type\": \"plant\", \"description\": \"no leaves; could have branches; could be tall and thin\", \"similar objects\": [\"palm tree\", \"pine tree\", \"bamboo\"]}", + 96 + ], + "lawn chair": [ + " {\"type\": \"furniture\", \"description\": \"foldable; could be made of metal or plastic; could have armrests\", \"similar objects\": [\"deck chair\", \"chaise lounge\", \"recliner\"]}", + 96 + ], + "wood post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used for fencing; could be made of different types of wood\", \"similar objects\": [\"metal post\", \"concrete post\", \"plastic post\"]}", + 96 + ], + "tan sand": [ + " {\"type\": \"material\", \"description\": \"light brown; fine grains; could be used for construction\", \"similar objects\": [\"gravel\", \"soil\", \"clay\"]}", + 96 + ], + "back wheel": [ + " {\"type\": \"vehicle part\", \"description\": \"round; could be made of metal; could be attached to a bicycle or a car\", \"similar objects\": [\"front wheel\", \"tire\", \"rim\"]}", + 96 + ], + "brown rock": [ + " {\"type\": \"geological object\", \"description\": \"brown, solid, could be of various shapes and sizes\", \"similar objects\": [\"stone\", \"boulder\", \"pebble\"]}", + 96 + ], + "roots": [ + " {\"type\": \"plant part\", \"description\": \"underground part of a plant; could be fibrous; could be used for food\", \"similar objects\": [\"bulbs\", \"tubers\", \"rhizomes\"]}", + 96 + ], + "wiper": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; could have a rubber blade; used to clean windows\", \"similar objects\": [\"broom\", \"mop\", \"vacuum cleaner\"]}", + 96 + ], + "pig": [ + " {\"type\": \"animal\", \"description\": \"pink; has a snout; could have curly tail\", \"similar objects\": [\"cow\", \"goat\", \"sheep\"]}", + 96 + ], + "dry": [ + "\n{\"type\": \"adjective\", \"description\": \"lacking moisture; not wet; not juicy\", \"similar objects\": [\"arid\", \"parched\", \"desiccated\"]}", + 96 + ], + "side door": [ + " {\"type\": \"door\", \"description\": \"located on the side of a building; could be opened from the outside\", \"similar objects\": [\"front door\", \"back door\", \"garage door\"]}", + 96 + ], + "spectacle": [ + " {\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be made of metal or plastic; could be used for vision correction\", \"similar objects\": [\"sunglasses\", \"goggles\", \"monocle\"]}", + 95 + ], + "tan wall": [ + "\n{\"type\": \"building material\", \"description\": \"light brown; could be made of bricks, wood, or plaster; could be painted\", \"similar objects\": [\"white wall\", \"concrete wall\", \"stone wall\"]}", + 95 + ], + "water body": [ + " {\"type\": \"natural feature\", \"description\": \"large body of water; could be a lake, river, ocean, etc.\", \"similar objects\": [\"mountain\", \"forest\", \"desert\"]}", + 95 + ], + "bath towel": [ + " {\"type\": \"bathroom accessory\", \"description\": \"large, rectangular; could be made of cotton or other fabrics; could be used to dry body after shower\", \"similar objects\": [\"hand towel\", \"washcloth\", \"bath mat\"]}", + 95 + ], + "shoulders": [ + " {\"type\": \"body part\", \"description\": \"two round parts of the body; could be used to carry heavy objects\", \"similar objects\": [\"arms\", \"elbows\", \"knees\"]}", + 95 + ], + "wall clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has a face with numbers and hands; could be digital\", \"similar objects\": [\"watch\", \"alarm clock\", \"grandfather clock\"]}", + 95 + ], + "street pole": [ + " {\"type\": \"infrastructure\", \"description\": \"tall, cylindrical, metal; could have a sign or a light on top\", \"similar objects\": [\"traffic light\", \"fire hydrant\", \"mailbox\"]}", + 95 + ], + "batters": [ + " {\"type\": \"cooking ingredient\", \"description\": \"thick liquid; could be made of flour, eggs, and milk; could be used for baking\", \"similar objects\": [\"dough\", \"sauce\", \"marinade\"]}", + 95 + ], + "cape": [ + " {\"type\": \"clothing item\", \"description\": \"long, flowing garment; could be made of velvet or other fabrics; could have a hood\", \"similar objects\": [\"cloak\", \"shawl\", \"robe\"]}", + 95 + ], + "lipstick": [ + " {\"type\": \"cosmetic\", \"description\": \"cylindrical; could be in various colors; could be in various shapes\", \"similar objects\": [\"eyeliner\", \"mascara\", \"blush\"]}", + 95 + ], + "metal chain": [ + " {\"type\": \"hardware tool\", \"description\": \"made of metal; could be used to secure items; could be used as a decoration\", \"similar objects\": [\"rope\", \"cable\", \"wire\"]}", + 95 + ], + "cowboy": [ + " {\"type\": \"person\", \"description\": \"wears a hat; wears a belt with a buckle; wears boots; wears a bandana\", \"similar objects\": [\"sheriff\", \"farmer\", \"cowgirl\"]}", + 95 + ], + "wrinkle": [ + " {\"type\": \"skin condition\", \"description\": \"skin folds; could be caused by aging; could be treated with creams\", \"similar objects\": [\"lines\", \"creases\", \"crow's feet\"]}", + 95 + ], + "bushy": [ + " {\"type\": \"plant\", \"description\": \"has many branches; could be evergreen; could have small leaves\", \"similar objects\": [\"tree\", \"shrub\", \"bush\"]}", + 95 + ], + "canal": [ + " {\"type\": \"waterway\", \"description\": \"man-made waterway; could be used for transportation; could be used for irrigation\", \"similar objects\": [\"river\", \"lake\", \"stream\"]}", + 95 + ], + "eyelashes": [ + " {\"type\": \"body part\", \"description\": \"long, thin, curved; could be black or brown; could be curled\", \"similar objects\": [\"eyebrows\", \"eyelids\", \"eyeliner\"]}", + 95 + ], + "rainbow": [ + " {\"type\": \"natural phenomenon\", \"description\": \"an arc of colors in the sky; could be seen after rain\", \"similar objects\": [\"aurora\", \"sunset\", \"clouds\"]}", + 95 + ], + "turn": [ + " {\"type\": \"action\", \"description\": \"to change the direction of something; to rotate something\", \"similar objects\": [\"twist\", \"rotate\", \"revolve\"]}", + 95 + ], + "brown shoes": [ + " {\"type\": \"footwear\", \"description\": \"made of leather; could have laces; could be slip-on\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 95 + ], + "rear window": [ + " {\"type\": \"automobile part\", \"description\": \"transparent; located at the back of the car; could be opened\", \"similar objects\": [\"windshield\", \"side window\", \"sunroof\"]}", + 95 + ], + "knife block": [ + " {\"type\": \"kitchen tool\", \"description\": \"wooden block; could hold multiple knives\", \"similar objects\": [\"cutting board\", \"spoon holder\", \"knife sharpener\"]}", + 94 + ], + "light poles": [ + " {\"type\": \"street furniture\", \"description\": \"tall, cylindrical; could be made of metal; could have a light on top\", \"similar objects\": [\"street signs\", \"traffic lights\", \"mailboxes\"]}", + 94 + ], + "computer speaker": [ + " {\"type\": \"audio device\", \"description\": \"small; could be connected to a computer; could be wireless\", \"similar objects\": [\"headphones\", \"microphone\", \"amplifier\"]}", + 94 + ], + "orange beak": [ + " {\"type\": \"bird\", \"description\": \"orange beak; could have yellow feathers; could have black eyes; could have a long tail\", \"similar objects\": [\"finch\", \"parrot\", \"robin\"]}", + 94 + ], + "orange stripe": [ + " {\"type\": \"pattern\", \"description\": \"alternating bands of orange and other colors; could be used for decoration\", \"similar objects\": [\"plaid\", \"polka dots\", \"chevron\"]}", + 94 + ], + "television set": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could have speakers\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}", + 94 + ], + "drum": [ + " {\"type\": \"musical instrument\", \"description\": \"cylindrical; could be made of wood or metal; could have a skin stretched over one end\", \"similar objects\": [\"guitar\", \"piano\", \"violin\"]}", + 94 + ], + "buckets": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic or metal; could have a handle\", \"similar objects\": [\"pails\", \"barrels\", \"tubs\"]}", + 94 + ], + "arm band": [ + " {\"type\": \"accessory\", \"description\": \"worn around the arm; could be made of fabric or plastic; could be used for decoration or identification\", \"similar objects\": [\"bracelet\", \"anklet\", \"necklace\"]}", + 94 + ], + "blue logo": [ + " {\"type\": \"graphic design\", \"description\": \"could be a combination of shapes, colors, and words; could be used to represent a company or organization\", \"similar objects\": [\"banner\", \"poster\", \"sign\"]}", + 94 + ], + "metal box": [ + " {\"type\": \"container\", \"description\": \"rectangular; made of metal; could have a lid\", \"similar objects\": [\"plastic box\", \"wooden box\", \"cardboard box\"]}", + 94 + ], + "grey shirt": [ + " {\"type\": \"clothing\", \"description\": \"light or dark grey; could have long or short sleeves; could have a collar\", \"similar objects\": [\"t-shirt\", \"sweater\", \"jacket\"]}", + 94 + ], + "bald": [ + " {\"type\": \"hair style\", \"description\": \"no hair on the head; could be shaved\", \"similar objects\": [\"buzz cut\", \"crew cut\", \"shaved head\"]}", + 94 + ], + "toilet paper dispenser": [ + " {\"type\": \"bathroom tool\", \"description\": \"white; could be wall-mounted; could have a slot for inserting paper rolls\", \"similar objects\": [\"towel dispenser\", \"soap dispenser\", \"toilet brush holder\"]}", + 94 + ], + "metal plate": [ + " {\"type\": \"utensil\", \"description\": \"flat, round, made of metal; could be used for serving food\", \"similar objects\": [\"bowl\", \"cup\", \"dish\"]}", + 94 + ], + "seaweed": [ + " {\"type\": \"vegetable\", \"description\": \"green; could be dried; could be used in sushi\", \"similar objects\": [\"kelp\", \"nori\", \"dulse\"]}", + 94 + ], + "cooking utensils": [ + "\n{\"type\": \"kitchen tools\", \"description\": \"various tools used for cooking, such as spatulas, ladles, whisks, tongs, etc.\", \"similar objects\": [\"pots and pans\", \"cutting boards\", \"knives\"]}", + 93 + ], + "tortilla": [ + " {\"type\": \"food\", \"description\": \"flat, round, thin; could be made of wheat or corn flour\", \"similar objects\": [\"taco\", \"burrito\", \"enchilada\"]}", + 93 + ], + "waste basket": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"trash can\", \"garbage can\", \"recycling bin\"]}", + 93 + ], + "power button": [ + " {\"type\": \"electronic device\", \"description\": \"small, round, usually red or green; used to turn on or off a device\", \"similar objects\": [\"volume button\", \"reset button\", \"switch\"]}", + 93 + ], + "pink tongue": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, pink; could be found in some animals\", \"similar objects\": [\"whiskers\", \"claws\", \"horns\"]}", + 93 + ], + "runner": [ + " {\"type\": \"athlete\", \"description\": \"person who runs; could wear running shoes; could have a running watch\", \"similar objects\": [\"jogger\", \"sprinter\", \"marathoner\"]}", + 93 + ], + "babies": [ + " {\"type\": \"human\", \"description\": \"small; could be crying; could be smiling\", \"similar objects\": [\"toddlers\", \"children\", \"adults\"]}", + 93 + ], + "tv screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be connected to a device; could be used to watch movies\", \"similar objects\": [\"computer monitor\", \"tablet\", \"smartphone\"]}", + 93 + ], + "orange bag": [ + "\n{\"type\": \"accessory\", \"description\": \"orange; could be made of fabric; could be used to carry items\", \"similar objects\": [\"backpack\", \"purse\", \"tote bag\"]}", + 93 + ], + "blocks": [ + " {\"type\": \"toy\", \"description\": \"small, square, colorful; could be made of wood or plastic\", \"similar objects\": [\"building blocks\", \"Lego\", \"puzzles\"]}", + 93 + ], + "toothpick": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, pointed; could be made of wood or plastic\", \"similar objects\": [\"skewer\", \"chopstick\", \"fork\"]}", + 93 + ], + "right": [ + " {\"type\": \"direction\", \"description\": \"opposite of left; could be used to describe a turn\", \"similar objects\": [\"forward\", \"backward\", \"upward\"]}", + 93 + ], + "beads": [ + " {\"type\": \"accessory\", \"description\": \"small, round, colorful; could be made of plastic, glass, or metal; could be strung together\", \"similar objects\": [\"buttons\", \"sequins\", \"pearls\"]}", + 93 + ], + "round sign": [ + " {\"type\": \"signage\", \"description\": \"circular; could be made of metal or plastic; could have words or symbols on it\", \"similar objects\": [\"traffic sign\", \"street sign\", \"warning sign\"]}", + 92 + ], + "blue bike": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; could be blue; could have a basket; could have a bell\", \"similar objects\": [\"scooter\", \"motorcycle\", \"tricycle\"]}", + 92 + ], + "dials": [ + " {\"type\": \"control tool\", \"description\": \"round; could be used to adjust settings; could be used to measure something\", \"similar objects\": [\"knobs\", \"switches\", \"levers\"]}", + 92 + ], + "safety vest": [ + " {\"type\": \"protective clothing\", \"description\": \"brightly colored; has reflective stripes; could be worn over other clothing\", \"similar objects\": [\"helmet\", \"gloves\", \"goggles\"]}", + 92 + ], + "vanity": [ + " {\"type\": \"furniture\", \"description\": \"table with a mirror; could have drawers\", \"similar objects\": [\"dresser\", \"desk\", \"nightstand\"]}", + 92 + ], + "hinges": [ + " {\"type\": \"hardware\", \"description\": \"metal; used to attach two objects together; could be opened and closed\", \"similar objects\": [\"screws\", \"bolts\", \"latches\"]}", + 92 + ], + "assortment": [ + " {\"type\": \"collection\", \"description\": \"a group of different items\", \"similar objects\": [\"variety\", \"selection\", \"array\"]}", + 92 + ], + "dinosaur": [ + " {\"type\": \"animal\", \"description\": \"extinct; could have long necks; could have long tails; could have sharp teeth\", \"similar objects\": [\"pterodactyl\", \"triceratops\", \"brontosaurus\"]}", + 92 + ], + "ski pants": [ + " {\"type\": \"clothing\", \"description\": \"long, waterproof, insulated; could have straps and zippers\", \"similar objects\": [\"ski jacket\", \"snow pants\", \"snow boots\"]}", + 92 + ], + "peel": [ + " {\"type\": \"tool\", \"description\": \"long handle; sharp blade; used to remove the skin of fruits and vegetables\", \"similar objects\": [\"knife\", \"spoon\", \"fork\"]}", + 92 + ], + "baby sheep": [ + " {\"type\": \"animal\", \"description\": \"small; white; has a fluffy fur; could have horns\", \"similar objects\": [\"lamb\", \"goat\", \"calf\"]}", + 92 + ], + "butt": [ + " {\"type\": \"body part\", \"description\": \"rounded part of the body; located at the lower back\", \"similar objects\": [\"hips\", \"thighs\", \"waist\"]}", + 92 + ], + "passenger car": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could have four doors; could have a trunk\", \"similar objects\": [\"sedan\", \"SUV\", \"truck\"]}", + 92 + ], + "projector": [ + " {\"type\": \"electronic device\", \"description\": \"used to project images onto a wall or screen; could be connected to a computer or other device\", \"similar objects\": [\"television\", \"monitor\", \"printer\"]}", + 92 + ], + "stapler": [ + " {\"type\": \"office tool\", \"description\": \"rectangular; has a handle; could be used to staple papers together\", \"similar objects\": [\"hole puncher\", \"tape dispenser\", \"paper clip\"]}", + 92 + ], + "story": [ + " {\"type\": \"narrative\", \"description\": \"a sequence of events; could be fictional or non-fictional; could be told orally or written down\", \"similar objects\": [\"tale\", \"myth\", \"legend\"]}", + 92 + ], + "shin guards": [ + " {\"type\": \"protective gear\", \"description\": \"worn on the shins; could be made of plastic or foam; could be strapped on with elastic bands\", \"similar objects\": [\"knee pads\", \"elbow pads\", \"helmet\"]}", + 91 + ], + "kitchen table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal\", \"similar objects\": [\"dining table\", \"coffee table\", \"desk\"]}", + 91 + ], + "elephants trunk": [ + " {\"type\": \"body part\", \"description\": \"long, flexible, muscular; used for grasping and sucking\", \"similar objects\": [\"giraffe's neck\", \"monkey's tail\", \"whale's flipper\"]}", + 91 + ], + "bottle cap": [ + " {\"type\": \"container lid\", \"description\": \"round; could be made of plastic or metal; could have a screw-on or snap-on design\", \"similar objects\": [\"jar lid\", \"can lid\", \"tub lid\"]}", + 91 + ], + "tooth": [ + " {\"type\": \"body part\", \"description\": \"white; has a crown; could be sharp or blunt\", \"similar objects\": [\"teeth\", \"molar\", \"incisor\"]}", + 91 + ], + "gas stove": [ + " {\"type\": \"cooking tool\", \"description\": \"has knobs and burners; could be powered by gas or electricity\", \"similar objects\": [\"electric stove\", \"microwave\", \"oven\"]}", + 91 + ], + "desserts": [ + "\n{\"type\": \"food\", \"description\": \"sweet treats; could be cakes, cookies, ice cream, etc.\", \"similar objects\": [\"pastries\", \"sweets\", \"candy\"]}", + 91 + ], + "bathroom door": [ + " {\"type\": \"door\", \"description\": \"rectangular; could be made of wood or metal; could have a handle and a lock\", \"similar objects\": [\"bedroom door\", \"closet door\", \"front door\"]}", + 91 + ], + "restroom": [ + " {\"type\": \"room\", \"description\": \"has a toilet and sink; could have a mirror\", \"similar objects\": [\"bathroom\", \"lavatory\", \"washroom\"]}", + 91 + ], + "zebra head": [ + "\n{\"type\": \"animal part\", \"description\": \"black and white stripes; has a long mane; could have two ears\", \"similar objects\": [\"horse head\", \"giraffe head\", \"elephant head\"]}", + 91 + ], + "flames": [ + " {\"type\": \"phenomenon\", \"description\": \"orange and red; could be seen in a fire\", \"similar objects\": [\"smoke\", \"fireworks\", \"sparkles\"]}", + 91 + ], + "fringe": [ + " {\"type\": \"textile\", \"description\": \"long, thin strands of fabric; could be used to decorate clothing or other items\", \"similar objects\": [\"tassel\", \"braid\", \"pom-pom\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (c", + 91 + ], + "valley": [ + " {\"type\": \"landscape\", \"description\": \"low-lying area between two mountains or hills; could have a river running through it\", \"similar objects\": [\"canyon\", \"ravine\", \"gorge\"]}", + 91 + ], + "shoe laces": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of cotton or nylon; used to tie shoes\", \"similar objects\": [\"belt\", \"tie\", \"scarf\"]}", + 90 + ], + "bear nose": [ + " {\"type\": \"animal body part\", \"description\": \"black, round, and wet; could be used for smelling\", \"similar objects\": [\"dog nose\", \"cat nose\", \"elephant trunk\"]}", + 90 + ], + "home base": [ + " {\"type\": \"structure\", \"description\": \"a place where people live; could be a house, apartment, or other type of dwelling\", \"similar objects\": [\"apartment building\", \"condominium\", \"townhouse\"]}", + 90 + ], + "chalk": [ + " {\"type\": \"writing tool\", \"description\": \"white; could be used on blackboard; could be used for drawing\", \"similar objects\": [\"pencil\", \"marker\", \"crayon\"]}", + 90 + ], + "metal door": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be painted; could have a handle\", \"similar objects\": [\"wooden door\", \"glass door\", \"plastic door\"]}", + 90 + ], + "wooden post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used for fencing; could be made of wood\", \"similar objects\": [\"metal post\", \"concrete post\", \"plastic post\"]}", + 90 + ], + "concrete curb": [ + " {\"type\": \"construction material\", \"description\": \"gray; could be used to separate roads from sidewalks; could be used to prevent water from flowing onto roads\", \"similar objects\": [\"asphalt\", \"gravel\", \"brick\"]}", + 90 + ], + "tin": [ + " {\"type\": \"container\", \"description\": \"silver; cylindrical; could be used to store food\", \"similar objects\": [\"can\", \"jar\", \"bottle\"]}", + 90 + ], + "water tower": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could be made of metal; could be painted white\", \"similar objects\": [\"windmill\", \"silo\", \"smokestack\"]}", + 90 + ], + "expanse": [ + " {\"type\": \"noun\", \"description\": \"a large area of land or sea; a wide area of something\", \"similar objects\": [\"space\", \"domain\", \"realm\"]}", + 90 + ], + "glass plate": [ + " {\"type\": \"dishware\", \"description\": \"transparent; could be round or square; could be used for serving food\", \"similar objects\": [\"bowl\", \"cup\", \"plate\"]}", + 89 + ], + "sides": [ + " {\"type\": \"food\", \"description\": \"could be served as accompaniment to a main dish; could be made of vegetables, potatoes, or rice; could be fried, boiled, or steamed\", \"similar objects\": [\"salad\", \"soup\", \"stew\"]}", + 89 + ], + "motorcycle tire": [ + " {\"type\": \"vehicle part\", \"description\": \"round; has a tread pattern; could be made of rubber\", \"similar objects\": [\"car tire\", \"bicycle tire\", \"truck tire\"]}", + 89 + ], + "pepper grinder": [ + " {\"type\": \"kitchen tool\", \"description\": \"cylindrical; has a handle; could be made of wood or metal; could be used to grind pepper\", \"similar objects\": [\"salt grinder\", \"mortar and pestle\", \"coffee grinder\"]}", + 89 + ], + "leafs": [ + " {\"type\": \"plant part\", \"description\": \"green; could be shaped differently; could be attached to a stem\", \"similar objects\": [\"petals\", \"flowers\", \"branches\"]}", + 89 + ], + "kitchen cabinet": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have drawers and shelves; could be used to store kitchen items\", \"similar objects\": [\"cupboard\", \"wardrobe\", \"dresser\"]}", + 89 + ], + "yellow writing": [ + " {\"type\": \"stationery\", \"description\": \"yellow color; could be used for writing; could be made of plastic or wood\", \"similar objects\": [\"pen\", \"pencil\", \"marker\"]}", + 89 + ], + "character": [ + " {\"type\": \"symbol\", \"description\": \"could be a letter, number, punctuation mark, or other symbol\", \"similar objects\": [\"letter\", \"number\", \"punctuation mark\"]}", + 89 + ], + "lorry": [ + " {\"type\": \"vehicle\", \"description\": \"large, box-shaped; could have a trailer; could be used for transporting goods\", \"similar objects\": [\"truck\", \"van\", \"bus\"]}", + 89 + ], + "plastic bowl": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be used for serving food; could be round or square\", \"similar objects\": [\"plate\", \"cup\", \"glass\"]}", + 89 + ], + "goatee": [ + " {\"type\": \"facial hair style\", \"description\": \"small patch of hair on the chin; could be trimmed to a point\", \"similar objects\": [\"mustache\", \"beard\", \"sideburns\"]}", + 89 + ], + "grey rocks": [ + " {\"type\": \"geological object\", \"description\": \"grey; could be of different shapes and sizes; could be found in nature\", \"similar objects\": [\"boulders\", \"pebbles\", \"gravel\"]}", + 89 + ], + "front landing gear": [ + " {\"type\": \"aircraft part\", \"description\": \"wheels; could be retracted; could be connected to the fuselage\", \"similar objects\": [\"main landing gear\", \"nose wheel\", \"tail wheel\"]}", + 89 + ], + "pitch": [ + " {\"type\": \"sports tool\", \"description\": \"round; made of rubber; used to throw in baseball\", \"similar objects\": [\"bat\", \"ball\", \"glove\"]}", + 88 + ], + "floor tiles": [ + " {\"type\": \"flooring material\", \"description\": \"square or rectangular; could be made of ceramic, stone, or wood; could be glossy or matte\", \"similar objects\": [\"carpet\", \"linoleum\", \"hardwood\"]}", + 88 + ], + "wii remote": [ + " {\"type\": \"gaming device\", \"description\": \"rectangular; has buttons and a joystick; could be wireless\", \"similar objects\": [\"xbox controller\", \"playstation controller\", \"joycon\"]}", + 88 + ], + "fingernails": [ + " {\"type\": \"body part\", \"description\": \"hard, curved, and pointed; could be painted with nail polish\", \"similar objects\": [\"toenails\", \"eyelashes\", \"hair\"]}", + 88 + ], + "video game controller": [ + "\n{\"type\": \"electronic device\", \"description\": \"has buttons and joysticks; could be wireless; could be connected to a console\", \"similar objects\": [\"keyboard\", \"mouse\", \"gamepad\"]}", + 88 + ], + "horse tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and hairy; could be braided\", \"similar objects\": [\"mane\", \"whiskers\", \"hooves\"]}", + 88 + ], + "pizza pie": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread\"]}", + 88 + ], + "bike seat": [ + " {\"type\": \"bicycle part\", \"description\": \"attached to the frame of the bike; could be padded; could be adjustable\", \"similar objects\": [\"handlebar\", \"pedal\", \"chain\"]}", + 88 + ], + "collars": [ + " {\"type\": \"clothing accessory\", \"description\": \"worn around the neck; could be made of leather or fabric; could have a buckle or a clasp\", \"similar objects\": [\"ties\", \"scarves\", \"belts\"]}", + 87 + ], + "tennis match": [ + " {\"type\": \"sport\", \"description\": \"two players; a net; a court; a ball; a racket\", \"similar objects\": [\"badminton\", \"table tennis\", \"squash\"]}", + 87 + ], + "scooters": [ + " {\"type\": \"transportation tool\", \"description\": \"two-wheeled vehicle; could be powered by electricity or gasoline; could be foldable\", \"similar objects\": [\"bicycle\", \"motorcycle\", \"skateboard\"]}", + 87 + ], + "soda bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic or glass; could have a cap\", \"similar objects\": [\"water bottle\", \"wine bottle\", \"beer bottle\"]}", + 87 + ], + "metal bowl": [ + " {\"type\": \"cooking tool\", \"description\": \"round; made of metal; could have a handle\", \"similar objects\": [\"pot\", \"pan\", \"skillet\"]}", + 87 + ], + "candle holder": [ + " {\"type\": \"decorative item\", \"description\": \"could be made of metal or glass; could hold one or more candles\", \"similar objects\": [\"vase\", \"lantern\", \"tealight holder\"]}", + 87 + ], + "arrow sign": [ + " {\"type\": \"traffic sign\", \"description\": \"triangular; has an arrow pointing in a certain direction\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 87 + ], + "cardboard boxes": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be used for storage; could be made of cardboard\", \"similar objects\": [\"plastic boxes\", \"suitcases\", \"baskets\"]}", + 87 + ], + "metal rack": [ + " {\"type\": \"storage tool\", \"description\": \"made of metal; could have multiple shelves; could be used to store items\", \"similar objects\": [\"shelf\", \"cabinet\", \"bookcase\"]}", + 87 + ], + "metal pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round; made of metal; has a handle\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 87 + ], + "thread": [ + " {\"type\": \"sewing material\", \"description\": \"long, thin, could be made of cotton, silk, or nylon\", \"similar objects\": [\"yarn\", \"ribbon\", \"string\"]}", + 87 + ], + "toiletries": [ + " {\"type\": \"bathroom items\", \"description\": \"items used for personal hygiene; could include soap, shampoo, toothbrush, etc.\", \"similar objects\": [\"towels\", \"bath mat\", \"bathrobe\"]}", + 87 + ], + "desktop": [ + " {\"type\": \"computer\", \"description\": \"flat, rectangular; has a monitor, keyboard, and mouse\", \"similar objects\": [\"laptop\", \"tablet\", \"smartphone\"]}", + 87 + ], + "powder": [ + " {\"type\": \"substance\", \"description\": \"finely divided solid particles; could be in the form of dust or crystals\", \"similar objects\": [\"granules\", \"flakes\", \"pellets\"]}", + 87 + ], + "bowtie": [ + " {\"type\": \"clothing accessory\", \"description\": \"shaped like a bow; could be made of fabric or leather; could be used to tie around the neck\", \"similar objects\": [\"tie\", \"scarf\", \"belt\"]}", + 87 + ], + "silver train": [ + " {\"type\": \"vehicle\", \"description\": \"long; could be made of metal; could have multiple compartments; could have a locomotive\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 87 + ], + "metal sign": [ + " {\"type\": \"signage\", \"description\": \"made of metal; could be rectangular or circular; could have words or symbols printed on it\", \"similar objects\": [\"wooden sign\", \"plastic sign\", \"banner\"]}", + 87 + ], + "beige building": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could be made of bricks; could have windows and doors; could have a roof\", \"similar objects\": [\"house\", \"apartment\", \"office building\"]}", + 86 + ], + "structures": [ + " {\"type\": \"architecture\", \"description\": \"buildings, bridges, monuments, etc.\", \"similar objects\": [\"houses\", \"skyscrapers\", \"monuments\"]}", + 86 + ], + "playground": [ + " {\"type\": \"outdoor area\", \"description\": \"could have swings, slides, and other play equipment; could have a sandbox; could have a fence\", \"similar objects\": [\"park\", \"play area\", \"playground equipment\"]}", + 86 + ], + "display case": [ + " {\"type\": \"furniture\", \"description\": \"glass-enclosed; could be used to showcase items\", \"similar objects\": [\"cabinet\", \"bookshelf\", \"cupboard\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green", + 86 + ], + "pick": [ + " {\"type\": \"tool\", \"description\": \"long handle with a pointed end; could be used for digging\", \"similar objects\": [\"shovel\", \"rake\", \"hoe\"]}", + 86 + ], + "adult giraffe": [ + "\n{\"type\": \"animal\", \"description\": \"tall; has a long neck; has a long tail; has a brown coat with white spots; has a long mane\", \"similar objects\": [\"adult zebra\", \"adult elephant\", \"adult horse\"]}", + 86 + ], + "sun visor": [ + " {\"type\": \"accessory\", \"description\": \"worn on the head; could be made of fabric; could have a peak\", \"similar objects\": [\"hat\", \"cap\", \"headband\"]}", + 86 + ], + "breakfast": [ + "\n{\"type\": \"meal\", \"description\": \"first meal of the day; usually consists of eggs, toast, cereal, and/or fruit\", \"similar objects\": [\"brunch\", \"lunch\", \"dinner\"]}", + 86 + ], + "square tiles": [ + " {\"type\": \"building material\", \"description\": \"flat, square-shaped; could be made of ceramic, stone, or glass; could be used for flooring or wall decoration\", \"similar objects\": [\"hexagonal tiles\", \"mosaic tiles\", \"bricks\"]}", + 86 + ], + "carpeting": [ + " {\"type\": \"floor covering\", \"description\": \"soft; could be made of wool; could be patterned\", \"similar objects\": [\"rug\", \"mat\", \"linoleum\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean).", + 86 + ], + "double-decker bus": [ + " {\"type\": \"vehicle\", \"description\": \"large; two levels; could have an open top deck\", \"similar objects\": [\"trolley bus\", \"school bus\", \"coach bus\"]}", + 86 + ], + "water tank": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could be used to store water\", \"similar objects\": [\"barrel\", \"bucket\", \"tub\"]}", + 86 + ], + "information": [ + " {\"type\": \"abstract concept\", \"description\": \"knowledge; data; facts\", \"similar objects\": [\"data\", \"knowledge\", \"facts\"]}", + 86 + ], + "rag": [ + " {\"type\": \"cleaning tool\", \"description\": \"could be made of cloth; could be used to clean surfaces\", \"similar objects\": [\"sponge\", \"brush\", \"mop\"]}", + 86 + ], + "eagle": [ + " {\"type\": \"bird\", \"description\": \"large; has a hooked beak; has a wingspan of up to 8 feet; could have white head and tail feathers\", \"similar objects\": [\"hawk\", \"osprey\", \"vulture\"]}", + 86 + ], + "awnings": [ + " {\"type\": \"building material\", \"description\": \"fabric or metal; used to provide shade or shelter from the elements; could be attached to the side of a building\", \"similar objects\": [\"canopy\", \"umbrella\", \"shutters\"]}", + 85 + ], + "capital letter": [ + "\n{\"type\": \"alphabet\", \"description\": \"uppercase letter; could be used to start a sentence\", \"similar objects\": [\"lowercase letter\", \"number\", \"symbol\"]}", + 85 + ], + "tablecloths": [ + " {\"type\": \"tableware\", \"description\": \"rectangular; could be made of fabric; could be used to cover tables\", \"similar objects\": [\"napkins\", \"placemats\", \"runners\"]}", + 85 + ], + "signage": [ + " {\"type\": \"visual communication tool\", \"description\": \"could be made of metal, plastic, or paper; could be in the form of a sign, banner, or poster; could be used to convey messages\", \"similar objects\": [\"billboard\", \"flag\", \"placard\"]}", + 85 + ], + "lens": [ + " {\"type\": \"optical tool\", \"description\": \"round; could be used to magnify objects; could be used to focus light\", \"similar objects\": [\"telescope\", \"microscope\", \"binoculars\"]}", + 85 + ], + "badge": [ + " {\"type\": \"accessory\", \"description\": \"could be made of metal; could be pinned on clothes; could have a logo or symbol\", \"similar objects\": [\"medal\", \"pin\", \"ribbon\"]}", + 85 + ], + "bird beak": [ + " {\"type\": \"body part\", \"description\": \"pointed; could be curved; could be long or short; could be hard or soft\", \"similar objects\": [\"tongue\", \"claw\", \"wing\"]}", + 85 + ], + "layers": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, could be made of wool or cotton; could be sleeveless or have long sleeves\", \"similar objects\": [\"cardigan\", \"sweater\", \"coat\"]}", + 85 + ], + "cloths": [ + " {\"type\": \"clothing\", \"description\": \"fabric; could be made of cotton, wool, silk, etc.; could be in different colors and patterns\", \"similar objects\": [\"shirt\", \"dress\", \"pants\"]}", + 85 + ], + "wispy clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"thin, white, and wispy; could be seen in the sky\", \"similar objects\": [\"cumulus clouds\", \"stratus clouds\", \"cirrus clouds\"]}", + 85 + ], + "hotel room": [ + " {\"type\": \"accommodation\", \"description\": \"could have a bed, a desk, a wardrobe, a bathroom; could have a window\", \"similar objects\": [\"apartment\", \"hostel\", \"motel\"]}", + 85 + ], + "states": [ + " {\"type\": \"geographical entity\", \"description\": \"divisions of a country; could have different laws and regulations\", \"similar objects\": [\"provinces\", \"countries\", \"cities\"]}", + 85 + ], + "knit cap": [ + " {\"type\": \"clothing item\", \"description\": \"warm, soft, covers the head and ears; could be made of wool or cotton\", \"similar objects\": [\"beanie\", \"turban\", \"beret\"]}", + 85 + ], + "gutter": [ + " {\"type\": \"building tool\", \"description\": \"long, narrow, made of metal; used to collect rainwater\", \"similar objects\": [\"downspout\", \"drainpipe\", \"rain barrel\"]}", + 85 + ], + "light house": [ + " {\"type\": \"building\", \"description\": \"tall; could be white; could have a beacon light on top\", \"similar objects\": [\"tower\", \"windmill\", \"observatory\"]}", + 85 + ], + "panes": [ + " {\"type\": \"window covering\", \"description\": \"transparent; could be made of glass or plastic; could be opened and closed\", \"similar objects\": [\"curtains\", \"blinds\", \"shutters\"]}", + 84 + ], + "police officers": [ + " {\"type\": \"professionals\", \"description\": \"uniformed; could carry a gun; could have a badge\", \"similar objects\": [\"firefighters\", \"soldiers\", \"doctors\"]}", + 84 + ], + "teal": [ + " {\"type\": \"color\", \"description\": \"a shade of blue-green; could be described as a mix of blue and green\", \"similar objects\": [\"turquoise\", \"aquamarine\", \"cyan\"]}", + 84 + ], + "mozzarella cheese": [ + " {\"type\": \"food\", \"description\": \"white, soft, mild; could be shredded; could be melted\", \"similar objects\": [\"cheddar cheese\", \"parmesan cheese\", \"feta cheese\"]}", + 84 + ], + "gravy": [ + " {\"type\": \"sauce\", \"description\": \"thick, creamy, usually brown; could be used as a topping or a dip\", \"similar objects\": [\"hollandaise sauce\", \"bbq sauce\", \"mayonnaise\"]}", + 84 + ], + "splashes": [ + " {\"type\": \"visual effect\", \"description\": \"water droplets; could be created by throwing stones into a pond\", \"similar objects\": [\"ripples\", \"waves\", \"foam\"]}", + 84 + ], + "soccer": [ + " {\"type\": \"sport\", \"description\": \"team sport; two teams of eleven players; played on a rectangular field; goal is to score by kicking the ball into the other team's goal\", \"similar objects\": [\"football\", \"basketball\", \"baseball\"]}", + 84 + ], + "purple umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"purple; could be opened and closed; could be used to protect from rain\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}", + 84 + ], + "leather couch": [ + " {\"type\": \"furniture\", \"description\": \"made of leather; could be brown or black; could have cushions\", \"similar objects\": [\"sofa\", \"loveseat\", \"sectional\"]}", + 84 + ], + "snow pants": [ + " {\"type\": \"clothing\", \"description\": \"long, insulated, waterproof; could have straps and zippers\", \"similar objects\": [\"ski pants\", \"snowboard pants\", \"snow bibs\"]}", + 84 + ], + "garnish": [ + " {\"type\": \"food decoration\", \"description\": \"decorative food item used to enhance the presentation of a dish; could be edible or inedible\", \"similar objects\": [\"herbs\", \"spices\", \"vegetables\"]}", + 84 + ], + "bulls": [ + " {\"type\": \"animal\", \"description\": \"large, muscular, horned; could be red or black; could have white markings\", \"similar objects\": [\"cows\", \"bison\", \"buffalo\"]}", + 84 + ], + "puddles": [ + " {\"type\": \"water feature\", \"description\": \"small, shallow pools of water; could be formed by rain or melting snow\", \"similar objects\": [\"pools\", \"lakes\", \"streams\"]}", + 84 + ], + "light pole": [ + " {\"type\": \"street furniture\", \"description\": \"tall, cylindrical; could have a light on top; could be made of metal\", \"similar objects\": [\"street sign\", \"traffic light\", \"fire hydrant\"]}", + 84 + ], + "bear cub": [ + " {\"type\": \"animal\", \"description\": \"brown fur; small size; could have a white patch on chest\", \"similar objects\": [\"wolf cub\", \"tiger cub\", \"lion cub\"]}", + 84 + ], + "numerals": [ + " {\"type\": \"mathematical symbols\", \"description\": \"symbols used to represent numbers; could be written in different forms\", \"similar objects\": [\"operators\", \"fractions\", \"decimals\"]}", + 83 + ], + "bed frame": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have a headboard and footboard\", \"similar objects\": [\"mattress\", \"dresser\", \"nightstand\"]}", + 83 + ], + "windowsill": [ + " {\"type\": \"architectural feature\", \"description\": \"horizontal ledge; could be made of wood or stone; could be used to place plants or decorations\", \"similar objects\": [\"balcony\", \"fireplace mantel\", \"staircase\"]}", + 83 + ], + "tea cup": [ + " {\"type\": \"drinking vessel\", \"description\": \"small, round, has a handle; could be made of porcelain\", \"similar objects\": [\"mug\", \"glass\", \"coffee cup\"]}", + 83 + ], + "gold ring": [ + " {\"type\": \"jewelry\", \"description\": \"round; made of gold; could have a gemstone\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 83 + ], + "tree leaves": [ + " {\"type\": \"plant part\", \"description\": \"green; could be oval or round; could have veins; could be attached to a stem\", \"similar objects\": [\"flower petals\", \"grass blades\", \"pine needles\"]}", + 83 + ], + "hut": [ + " {\"type\": \"structure\", \"description\": \"small, made of wood or straw; could have a thatched roof\", \"similar objects\": [\"cabin\", \"igloo\", \"tent\"]}", + 83 + ], + "power outlet": [ + " {\"type\": \"electrical device\", \"description\": \"rectangular; has two or more slots; could be wall-mounted\", \"similar objects\": [\"switch\", \"socket\", \"plug\"]}", + 83 + ], + "blonde girl": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could be wearing a dress\", \"similar objects\": [\"blonde boy\", \"brunette girl\", \"redhead girl\"]}", + 83 + ], + "advertising sign": [ + " {\"type\": \"promotional tool\", \"description\": \"could be made of paper, plastic, or metal; could be illuminated; could be hung on a wall or a pole\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 83 + ], + "orange lights": [ + " {\"type\": \"lighting tool\", \"description\": \"round; emits orange light; could be used as a warning signal\", \"similar objects\": [\"flares\", \"lanterns\", \"floodlights\"]}", + 83 + ], + "skyscrapers": [ + " {\"type\": \"building\", \"description\": \"tall, rectangular, made of steel and glass; could have multiple floors\", \"similar objects\": [\"high-rise buildings\", \"office buildings\", \"apartment buildings\"]}", + 83 + ], + "ripe banana": [ + "\n{\"type\": \"fruit\", \"description\": \"yellow; curved; has a stem; could be mashed\", \"similar objects\": [\"apple\", \"orange\", \"pear\"]}", + 83 + ], + "slide": [ + " {\"type\": \"playground equipment\", \"description\": \"long, curved, could be made of plastic; could be used for sliding down\", \"similar objects\": [\"swing\", \"monkey bars\", \"seesaw\"]}", + 83 + ], + "maroon": [ + " {\"type\": \"color\", \"description\": \"dark red; could be used to describe clothing or objects\", \"similar objects\": [\"burgundy\", \"scarlet\", \"carmine\"]}", + 82 + ], + "dip": [ + " {\"type\": \"food\", \"description\": \"a type of sauce; could be made of yogurt, sour cream, or mayonnaise; could be served with chips, vegetables, or crackers\", \"similar objects\": [\"salsa\", \"guacamole\", \"hummus\"]}", + 82 + ], + "crumb": [ + " {\"type\": \"food item\", \"description\": \"small, dry, and brittle; could be made of bread, cake, or cookie\", \"similar objects\": [\"crust\", \"flake\", \"chip\"]}", + 82 + ], + "wood shelf": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have multiple shelves; could be used to store items\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"wardrobe\"]}", + 82 + ], + "syrup": [ + " {\"type\": \"condiment\", \"description\": \"thick, sweet liquid; could be used as topping for pancakes\", \"similar objects\": [\"honey\", \"jam\", \"jelly\"]}", + 82 + ], + "sink faucet": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be made of metal; could have a sprayer\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"toilet handle\"]}", + 82 + ], + "silver suv": [ + "\n{\"type\": \"vehicle\", \"description\": \"silver; four-wheeled; could have a large cargo space\", \"similar objects\": [\"sedan\", \"minivan\", \"pickup truck\"]}", + 82 + ], + "porcelain": [ + " {\"type\": \"material\", \"description\": \"white, fragile, glossy; could be used to make dishes and sculptures\", \"similar objects\": [\"ceramic\", \"clay\", \"glass\"]}", + 82 + ], + "brick walkway": [ + " {\"type\": \"construction material\", \"description\": \"made of bricks; could be used as a walkway\", \"similar objects\": [\"concrete walkway\", \"stone walkway\", \"wooden walkway\"]}", + 82 + ], + "blue object": [ + "\n{\"type\": \"unknown\", \"description\": \"blue color; could be any shape or size\", \"similar objects\": [\"red object\", \"green object\", \"yellow object\"]}", + 82 + ], + "carts": [ + " {\"type\": \"transportation tool\", \"description\": \"wheeled; could be pushed or pulled; could be used to carry goods\", \"similar objects\": [\"wagon\", \"trolley\", \"hand truck\"]}", + 82 + ], + "airplane wing": [ + " {\"type\": \"aircraft part\", \"description\": \"long, thin, curved; could be made of metal; could have ailerons\", \"similar objects\": [\"fuselage\", \"engine\", \"propeller\"]}", + 82 + ], + "cow grazing": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a long tail; could have horns; could be eating grass\", \"similar objects\": [\"sheep\", \"goat\", \"deer\"]}", + 82 + ], + "support beam": [ + " {\"type\": \"construction tool\", \"description\": \"long, rectangular; could be made of metal or wood; used to support structures\", \"similar objects\": [\"column\", \"pillar\", \"joist\"]}", + 82 + ], + "wooden frame": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; made of wood; could be used to hang pictures\", \"similar objects\": [\"photo frame\", \"mirror frame\", \"canvas frame\"]}", + 82 + ], + "slippers": [ + " {\"type\": \"footwear\", \"description\": \"soft, comfortable; could be made of cloth; could be slip-on\", \"similar objects\": [\"sandals\", \"flip-flops\", \"mules\"]}", + 82 + ], + "rooster": [ + " {\"type\": \"animal\", \"description\": \"red comb and wattles; has a long tail; crows at dawn\", \"similar objects\": [\"chicken\", \"duck\", \"turkey\"]}", + 82 + ], + "coffee machine": [ + " {\"type\": \"kitchen appliance\", \"description\": \"could be electric or manual; could have a water tank; could have a coffee filter\", \"similar objects\": [\"blender\", \"toaster\", \"juicer\"]}", + 81 + ], + "wrap": [ + " {\"type\": \"food item\", \"description\": \"flatbread; could be filled with vegetables, meat, or cheese; could be served cold or hot\", \"similar objects\": [\"taco\", \"burrito\", \"sandwich\"]}", + 81 + ], + "rug floor": [ + " {\"type\": \"floor covering\", \"description\": \"soft; could be made of wool; could be patterned\", \"similar objects\": [\"carpet\", \"mat\", \"linoleum\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean", + 81 + ], + "bridges": [ + " {\"type\": \"structure\", \"description\": \"connects two land masses; could be made of steel, concrete, or wood; could have arches or suspension cables\", \"similar objects\": [\"tunnels\", \"viaducts\", \"causeways\"]}", + 81 + ], + "limb": [ + " {\"type\": \"body part\", \"description\": \"part of the body; could be an arm or a leg\", \"similar objects\": [\"hand\", \"foot\", \"finger\"]}", + 81 + ], + "bean": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, could be green, yellow, or red; could be cooked or eaten raw\", \"similar objects\": [\"pea\", \"corn\", \"lentil\"]}", + 81 + ], + "fire place": [ + " {\"type\": \"heating tool\", \"description\": \"could be made of bricks; has a chimney; could have a fire screen\", \"similar objects\": [\"stove\", \"furnace\", \"wood burning stove\"]}", + 81 + ], + "fishing boat": [ + " {\"type\": \"vessel\", \"description\": \"long; could have a cabin; could have a motor\", \"similar objects\": [\"yacht\", \"sailboat\", \"canoe\"]}", + 81 + ], + "cotton tee shirt": [ + " {\"type\": \"clothing\", \"description\": \"soft, lightweight, breathable fabric; could have short or long sleeves; could have a collar\", \"similar objects\": [\"tank top\", \"polo shirt\", \"hoodie\"]}", + 81 + ], + "cat eyes": [ + " {\"type\": \"animal feature\", \"description\": \"round; could be yellow, green, or blue; could have vertical pupils\", \"similar objects\": [\"dog eyes\", \"bird eyes\", \"fish eyes\"]}", + 81 + ], + "fighter jet": [ + " {\"type\": \"aircraft\", \"description\": \"long and slender; has wings and tail fins; could have a canopy\", \"similar objects\": [\"helicopter\", \"airplane\", \"drone\"]}", + 81 + ], + "bar stool": [ + " {\"type\": \"furniture\", \"description\": \"tall, has a backrest and a footrest; could be made of metal or wood\", \"similar objects\": [\"chair\", \"bench\", \"stool\"]}", + 81 + ], + "exterior": [ + " {\"type\": \"architectural element\", \"description\": \"the outside of a building; could include walls, windows, doors, roof, etc.\", \"similar objects\": [\"interior\", \"facade\", \"landscape\"]}", + 81 + ], + "yacht": [ + " {\"type\": \"vessel\", \"description\": \"large; could have multiple decks; could have a sail\", \"similar objects\": [\"boat\", \"cruise ship\", \"ferry\"]}", + 81 + ], + "pitchers": [ + " {\"type\": \"utensil\", \"description\": \"tall, cylindrical; could be made of glass or metal; could have a handle\", \"similar objects\": [\"mugs\", \"cups\", \"bowls\"]}", + 80 + ], + "wood pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used for construction\", \"similar objects\": [\"metal pole\", \"wood beam\", \"concrete block\"]}", + 80 + ], + "overhang": [ + " {\"type\": \"architectural feature\", \"description\": \"a structure that projects from a wall or building; could be used to provide shade or shelter\", \"similar objects\": [\"awning\", \"balcony\", \"canopy\"]}", + 80 + ], + "tail section": [ + " {\"type\": \"aircraft part\", \"description\": \"the rear part of an aircraft; could have a vertical stabilizer; could have an engine\", \"similar objects\": [\"fuselage\", \"wing\", \"cockpit\"]}", + 80 + ], + "prongs": [ + " {\"type\": \"utensil\", \"description\": \"fork-like; could be used for picking up food\", \"similar objects\": [\"tongs\", \"spatula\", \"ladle\"]}", + 80 + ], + "logos": [ + " {\"type\": \"symbol\", \"description\": \"visual representation of a company, organization, or product; could be a combination of text and images\", \"similar objects\": [\"emblem\", \"mascot\", \"icon\"]}", + 80 + ], + "ketchup bottle": [ + " {\"type\": \"condiment container\", \"description\": \"red; has a long neck; could be made of glass or plastic\", \"similar objects\": [\"mustard bottle\", \"mayonnaise bottle\", \"vinegar bottle\"]}", + 80 + ], + "drawings": [ + " {\"type\": \"artwork\", \"description\": \"could be made with pencils, pens, paints, etc.; could be abstract or realistic; could be on paper or canvas\", \"similar objects\": [\"paintings\", \"sketches\", \"sculptures\"]}", + 80 + ], + "pines": [ + " {\"type\": \"tree\", \"description\": \"tall; has needles; could have cones\", \"similar objects\": [\"fir\", \"spruce\", \"cedar\"]}", + 80 + ], + "silver cell phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could be made of metal; could have a touchscreen; could have a camera\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 80 + ], + "silver device": [ + "\n{\"type\": \"electronic device\", \"description\": \"shiny, metallic; could be a phone, laptop, or other device\", \"similar objects\": [\"cell phone\", \"tablet\", \"computer\"]}", + 80 + ], + "shadow wall": [ + " {\"type\": \"decoration\", \"description\": \"wall with shadows of objects; could be made of paper or fabric\", \"similar objects\": [\"shadow box\", \"shadow puppet\", \"shadow play\"]}", + 80 + ], + "computer monitors": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat, rectangular; could have a stand; could be connected to a computer\", \"similar objects\": [\"television\", \"printer\", \"keyboard\"]}", + 80 + ], + "silver chain": [ + " {\"type\": \"jewelry\", \"description\": \"made of silver; could be in different shapes; could be used to hang pendants\", \"similar objects\": [\"gold chain\", \"bracelet\", \"necklace\"]}", + 80 + ], + "mini van": [ + " {\"type\": \"vehicle\", \"description\": \"smaller than a regular van; could have sliding doors; could have a higher roof\", \"similar objects\": [\"SUV\", \"sedan\", \"truck\"]}", + 80 + ], + "booth": [ + " {\"type\": \"structure\", \"description\": \"enclosed space; could be used for voting or selling goods\", \"similar objects\": [\"kiosk\", \"stall\", \"cabin\"]}", + 80 + ], + "motor bike": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has an engine; could have a sidecar\", \"similar objects\": [\"scooter\", \"moped\", \"bicycle\"]}", + 80 + ], + "analog clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has two hands; could have numbers or symbols\", \"similar objects\": [\"digital clock\", \"stopwatch\", \"timer\"]}", + 80 + ], + "rabbit": [ + " {\"type\": \"animal\", \"description\": \"long ears; white fur; short tail\", \"similar objects\": [\"hare\", \"squirrel\", \"mouse\"]}", + 80 + ], + "wind shield": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; protects the driver from wind and debris; could be made of glass or plastic\", \"similar objects\": [\"headlight\", \"bumper\", \"tire\"]}", + 80 + ], + "drawing": [ + " {\"type\": \"artwork\", \"description\": \"could be made with pencils, pens, paints, etc.; could be abstract or realistic\", \"similar objects\": [\"painting\", \"sketch\", \"sculpture\"]}", + 80 + ], + "tan dog": [ + "\n{\"type\": \"animal\", \"description\": \"tan fur; could have short or long hair; could have pointy ears; could have a long tail\", \"similar objects\": [\"cat\", \"fox\", \"wolf\"]}", + 79 + ], + "relish": [ + " {\"type\": \"condiment\", \"description\": \"a type of sauce; could be sweet or sour; could be made of vegetables or fruits\", \"similar objects\": [\"ketchup\", \"mustard\", \"mayonnaise\"]}", + 79 + ], + "dolls": [ + " {\"type\": \"toy\", \"description\": \"could be made of plastic or fabric; could be of different sizes; could have different facial expressions\", \"similar objects\": [\"action figures\", \"teddy bears\", \"puppets\"]}", + 79 + ], + "t": [ + "\n{\"type\": \"letter\", \"description\": \"straight line with a crossbar; could be capitalized or lowercase\", \"similar objects\": [\"a\", \"b\", \"c\"]}", + 79 + ], + "croissant": [ + " {\"type\": \"food\", \"description\": \"flaky, crescent-shaped pastry; could be filled with chocolate, cheese, or jam\", \"similar objects\": [\"danish pastry\", \"brioche\", \"pain au chocolat\"]}", + 79 + ], + "underside": [ + " {\"type\": \"location\", \"description\": \"the bottom side of something; could be the bottom of a table, chair, or other object\", \"similar objects\": [\"topside\", \"backside\", \"inside\"]}", + 79 + ], + "pencils": [ + " {\"type\": \"writing tool\", \"description\": \"long, thin, made of wood; could have a sharpened tip\", \"similar objects\": [\"pens\", \"markers\", \"crayons\"]}", + 79 + ], + "bandanna": [ + " {\"type\": \"clothing accessory\", \"description\": \"square; could be made of cotton; could be used as a headband or a neckerchief\", \"similar objects\": [\"scarf\", \"hat\", \"cap\"]}", + 79 + ], + "vessel": [ + " {\"type\": \"container\", \"description\": \"could be made of metal, glass, or plastic; could be used to store liquids or other items\", \"similar objects\": [\"jar\", \"bottle\", \"cup\"]}", + 79 + ], + "front grill": [ + " {\"type\": \"automotive part\", \"description\": \"metal; located at the front of a car; has a mesh pattern\", \"similar objects\": [\"bumper\", \"headlight\", \"tail light\"]}", + 79 + ], + "canisters": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could have a lid\", \"similar objects\": [\"jars\", \"bottles\", \"boxes\"]}", + 79 + ], + "notes": [ + " {\"type\": \"writing tool\", \"description\": \"small pieces of paper; could be used to write down information\", \"similar objects\": [\"pen\", \"pencil\", \"notepad\"]}", + 79 + ], + "folding chair": [ + " {\"type\": \"furniture\", \"description\": \"could be made of metal or plastic; could be folded for storage; has a backrest and armrests\", \"similar objects\": [\"stool\", \"bench\", \"rocking chair\"]}", + 79 + ], + "silver sink": [ + "\n{\"type\": \"kitchen tool\", \"description\": \"silver; could be round or rectangular; could have a faucet\", \"similar objects\": [\"bathtub\", \"toilet\", \"dishwasher\"]}", + 79 + ], + "gas station": [ + " {\"type\": \"building\", \"description\": \"has pumps for fuel; could have a convenience store; could have a car wash\", \"similar objects\": [\"convenience store\", \"service station\", \"garage\"]}", + 79 + ], + "side building": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could have windows; could have a door\", \"similar objects\": [\"house\", \"shed\", \"garage\"]}", + 79 + ], + "side plane": [ + " {\"type\": \"aircraft\", \"description\": \"single-engine; has a low wing; could be used for recreational flying\", \"similar objects\": [\"helicopter\", \"glider\", \"jet\"]}", + 79 + ], + "signal light": [ + " {\"type\": \"lighting tool\", \"description\": \"red, yellow, and green; could be used to indicate traffic signals\", \"similar objects\": [\"traffic light\", \"stoplight\", \"streetlight\"]}", + 79 + ], + "sunflower": [ + " {\"type\": \"plant\", \"description\": \"large, yellow flower; has a long stem; has a large center\", \"similar objects\": [\"daisy\", \"dandelion\", \"tulip\"]}", + 79 + ], + "dish washer": [ + " {\"type\": \"appliance\", \"description\": \"electronic; could be built-in or stand-alone; could be white or stainless steel; could have a control panel\", \"similar objects\": [\"refrigerator\", \"washing machine\", \"dryer\"]}", + 78 + ], + "snow goggles": [ + " {\"type\": \"eyewear\", \"description\": \"dark lenses; could be made of plastic; could be worn over eyes to protect from snow glare\", \"similar objects\": [\"sunglasses\", \"safety glasses\", \"ski goggles\"]}", + 78 + ], + "porcelain sink": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"white; could be oval or rectangular; could have a single or double basin; could have a drain\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 78 + ], + "tabletop": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, metal, or plastic; could be used for dining, studying, or working\", \"similar objects\": [\"desk\", \"chair\", \"bench\"]}", + 78 + ], + "buckle": [ + " {\"type\": \"fastening tool\", \"description\": \"metal; could be used to fasten two ends of a belt\", \"similar objects\": [\"clasp\", \"hook\", \"snap\"]}", + 78 + ], + "bunny": [ + " {\"type\": \"animal\", \"description\": \"small, white, fluffy; has long ears; could hop\", \"similar objects\": [\"rabbit\", \"hamster\", \"guinea pig\"]}", + 78 + ], + "metal frame": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could be used to support a structure; could be used to hang items\", \"similar objects\": [\"wood frame\", \"steel frame\", \"aluminum frame\"]}", + 78 + ], + "chalk lines": [ + " {\"type\": \"marking tool\", \"description\": \"white lines; used to mark boundaries; could be used in sports\", \"similar objects\": [\"cones\", \"flags\", \"markers\"]}", + 78 + ], + "walk": [ + " {\"type\": \"action\", \"description\": \"moving one foot in front of the other; could be done with a purpose\", \"similar objects\": [\"run\", \"jog\", \"skip\"]}", + 78 + ], + "penguin": [ + " {\"type\": \"animal\", \"description\": \"black and white feathers; has a short beak; could waddle\", \"similar objects\": [\"seal\", \"otter\", \"albatross\"]}", + 78 + ], + "grout": [ + " {\"type\": \"building material\", \"description\": \"a cement-based paste used to fill gaps between tiles\", \"similar objects\": [\"mortar\", \"cement\", \"concrete\"]}", + 78 + ], + "speed limit sign": [ + " {\"type\": \"traffic sign\", \"description\": \"round; has a number inside; could be yellow or white\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 78 + ], + "moustache": [ + " {\"type\": \"facial hair\", \"description\": \"hairs above the upper lip; could be curved or straight\", \"similar objects\": [\"beard\", \"goatee\", \"sideburns\"]}", + 77 + ], + "teenager": [ + " {\"type\": \"person\", \"description\": \"between 13 and 19 years old; could be in school; could be going through physical and emotional changes\", \"similar objects\": [\"adolescent\", \"youth\", \"child\"]}", + 77 + ], + "hardwood": [ + " {\"type\": \"building material\", \"description\": \"strong and durable; could be used for flooring, furniture, and other construction projects\", \"similar objects\": [\"plywood\", \"laminate\", \"vinyl\"]}", + 77 + ], + "halves": [ + " {\"type\": \"mathematical concept\", \"description\": \"two equal parts of a whole; could be divided into two\", \"similar objects\": [\"fractions\", \"decimals\", \"percentages\"]}", + 77 + ], + "pony tail": [ + " {\"type\": \"hairstyle\", \"description\": \"hair tied up in a high bun; could be with a ribbon\", \"similar objects\": [\"braid\", \"pigtails\", \"bun\"]}", + 77 + ], + "cartoon": [ + " {\"type\": \"art form\", \"description\": \"illustrated images; could be humorous; could be used to convey messages\", \"similar objects\": [\"comic\", \"animation\", \"illustration\"]}", + 77 + ], + "lighting": [ + " {\"type\": \"illumination\", \"description\": \"the process of providing light to an area\", \"similar objects\": [\"lamp\", \"lantern\", \"flashlight\"]}", + 77 + ], + "uniforms": [ + " {\"type\": \"clothing\", \"description\": \"matching clothes; could be used for school, work, or military\", \"similar objects\": [\"suit\", \"dress\", \"overalls\"]}", + 77 + ], + "toilet seat cover": [ + " {\"type\": \"bathroom accessory\", \"description\": \"rectangular; made of plastic or fabric; used to cover the toilet seat\", \"similar objects\": [\"toilet brush\", \"toilet paper holder\", \"toilet plunger\"]}", + 77 + ], + "console": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could have buttons and joysticks; could be used for gaming\", \"similar objects\": [\"television\", \"computer\", \"stereo\"]}", + 77 + ], + "brown jacket": [ + " {\"type\": \"clothing\", \"description\": \"long; could have a hood; could have pockets; could be made of leather\", \"similar objects\": [\"coat\", \"sweater\", \"vest\"]}", + 77 + ], + "drops": [ + " {\"type\": \"liquid\", \"description\": \"small, round, could be of different colors; could be made of water, oil, or other liquids\", \"similar objects\": [\"rain\", \"dew\", \"spray\"]}", + 77 + ], + "macaroni": [ + " {\"type\": \"food\", \"description\": \"small, elbow-shaped pasta; could be cooked with cheese\", \"similar objects\": [\"spaghetti\", \"penne\", \"fusilli\"]}", + 77 + ], + "handrail": [ + " {\"type\": \"support tool\", \"description\": \"long; could be made of metal; could be used to support people\", \"similar objects\": [\"ladder\", \"staircase\", \"balustrade\"]}", + 76 + ], + "plastic spoon": [ + " {\"type\": \"utensil\", \"description\": \"long handle; could be curved; could be transparent\", \"similar objects\": [\"fork\", \"knife\", \"spatula\"]}", + 76 + ], + "silver metal pole": [ + " {\"type\": \"structure\", \"description\": \"long, cylindrical, silver metal; could be used for support\", \"similar objects\": [\"flagpole\", \"fence post\", \"streetlight pole\"]}", + 76 + ], + "arm chair": [ + " {\"type\": \"furniture\", \"description\": \"has armrests; could be upholstered; could have a reclining back\", \"similar objects\": [\"sofa\", \"loveseat\", \"recliner\"]}", + 76 + ], + "orange safety cone": [ + "\n{\"type\": \"safety tool\", \"description\": \"orange; cone-shaped; could have reflective stripes\", \"similar objects\": [\"traffic cone\", \"barricade\", \"warning sign\"]}", + 76 + ], + "gazelle": [ + " {\"type\": \"animal\", \"description\": \"brown and white fur; slender body; long legs; long neck; curved horns\", \"similar objects\": [\"antelope\", \"deer\", \"goat\"]}", + 76 + ], + "stove top": [ + " {\"type\": \"cooking tool\", \"description\": \"flat surface; has burners; could be electric or gas powered\", \"similar objects\": [\"oven\", \"microwave\", \"grill\"]}", + 76 + ], + "lamppost": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; has a light on top; could be made of metal\", \"similar objects\": [\"streetlight\", \"lantern\", \"torch\"]}", + 76 + ], + "ridges": [ + " {\"type\": \"landform\", \"description\": \"long, narrow, raised areas of land; could be found on mountains or the ocean floor\", \"similar objects\": [\"valleys\", \"hills\", \"cliffs\"]}", + 76 + ], + "weapon": [ + " {\"type\": \"tool\", \"description\": \"could be used to cause harm; could be made of metal; could be sharp\", \"similar objects\": [\"gun\", \"knife\", \"sword\"]}", + 76 + ], + "refrigerator door": [ + "\n{\"type\": \"appliance part\", \"description\": \"rectangular; could be made of metal; could have a handle; could have a lock\", \"similar objects\": [\"freezer door\", \"oven door\", \"dishwasher door\"]}", + 76 + ], + "silver container": [ + " {\"type\": \"container\", \"description\": \"shiny; could be made of metal; could be used to store items\", \"similar objects\": [\"box\", \"jar\", \"bag\"]}", + 76 + ], + "soldier": [ + " {\"type\": \"person\", \"description\": \"wears a uniform; carries a gun; could be a part of an army\", \"similar objects\": [\"policeman\", \"firefighter\", \"sailor\"]}", + 76 + ], + "city skyline": [ + " {\"type\": \"landscape\", \"description\": \"buildings of different heights; could have a river or lake; could have a bridge; could have a park\", \"similar objects\": [\"mountain range\", \"desert\", \"forest\"]}", + 76 + ], + "mesh": [ + " {\"type\": \"fabric\", \"description\": \"transparent; could be made of nylon or polyester; could be used for making bags or clothing\", \"similar objects\": [\"net\", \"lace\", \"tulle\"]}", + 75 + ], + "mango": [ + " {\"type\": \"fruit\", \"description\": \"oval; yellow or green; has a stone inside; could be sliced into pieces\", \"similar objects\": [\"papaya\", \"avocado\", \"kiwi\"]}", + 75 + ], + "orange juice": [ + " {\"type\": \"beverage\", \"description\": \"made from oranges; could be sweet or sour; could be served cold or hot\", \"similar objects\": [\"apple juice\", \"lemonade\", \"grape juice\"]}", + 75 + ], + "gray rocks": [ + " {\"type\": \"geological object\", \"description\": \"gray; could be of different shapes and sizes; could be found in nature\", \"similar objects\": [\"pebbles\", \"boulders\", \"gravel\"]}", + 75 + ], + "commuter train": [ + " {\"type\": \"transportation\", \"description\": \"long; could have multiple cars; could have a locomotive\", \"similar objects\": [\"subway\", \"tram\", \"bus\"]}", + 75 + ], + "blue curtain": [ + " {\"type\": \"decoration\", \"description\": \"blue; could be made of fabric; could be hung on a window\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 75 + ], + "purses": [ + " {\"type\": \"accessory\", \"description\": \"small bag; could be made of leather; could have a strap\", \"similar objects\": [\"wallet\", \"backpack\", \"handbag\"]}", + 75 + ], + "baseball batter": [ + " {\"type\": \"sports player\", \"description\": \"holds a bat; wears a helmet; stands in a batter's box\", \"similar objects\": [\"soccer player\", \"golfer\", \"tennis player\"]}", + 75 + ], + "baby stroller": [ + " {\"type\": \"baby product\", \"description\": \"wheeled; could be folded; could have a canopy\", \"similar objects\": [\"car seat\", \"high chair\", \"playpen\"]}", + 75 + ], + "bathroom window": [ + " {\"type\": \"window\", \"description\": \"transparent; could be frosted; could be opened\", \"similar objects\": [\"kitchen window\", \"balcony window\", \"basement window\"]}", + 75 + ], + "spray bottle": [ + " {\"type\": \"cleaning tool\", \"description\": \"has a nozzle; could be filled with liquid; could be used to spray liquid\", \"similar objects\": [\"hose\", \"mop\", \"broom\"]}", + 75 + ], + "manhole": [ + " {\"type\": \"utility structure\", \"description\": \"round; has a cover; could be used for accessing underground utilities\", \"similar objects\": [\"drainage cover\", \"sewer cover\", \"access hatch\"]}", + 75 + ], + "dvds": [ + " {\"type\": \"media storage\", \"description\": \"round; could be made of plastic; could store movies, music, and other data\", \"similar objects\": [\"CDs\", \"Blu-ray discs\", \"USB drives\"]}", + 75 + ], + "wreath": [ + " {\"type\": \"decoration\", \"description\": \"circular; could be made of flowers, leaves, or other materials\", \"similar objects\": [\"garland\", \"bouquet\", \"swag\"]}", + 75 + ], + "building distance": [ + " {\"type\": \"measurement\", \"description\": \"distance between two buildings; could be measured in feet, meters, or other units\", \"similar objects\": [\"road distance\", \"flight distance\", \"water distance\"]}", + 75 + ], + "rear wheels": [ + " {\"type\": \"automobile part\", \"description\": \"round; could be made of metal; could be connected to the axle\", \"similar objects\": [\"front wheels\", \"tires\", \"brakes\"]}", + 75 + ], + "man hole cover": [ + " {\"type\": \"utility object\", \"description\": \"round; made of metal; has a handle\", \"similar objects\": [\"drain cover\", \"vent cover\", \"sewer cover\"]}", + 75 + ], + "brim": [ + " {\"type\": \"clothing accessory\", \"description\": \"wide, circular, could be made of straw; could be worn on the head\", \"similar objects\": [\"hat\", \"cap\", \"visor\"]}", + 75 + ], + "pale": [ + " {\"type\": \"color\", \"description\": \"light, whitish-gray hue; could be described as washed out\", \"similar objects\": [\"ivory\", \"beige\", \"cream\"]}", + 75 + ], + "expression": [ + " {\"type\": \"word\", \"description\": \"a phrase or sentence that conveys a thought or emotion\", \"similar objects\": [\"phrase\", \"sentence\", \"statement\"]}", + 75 + ], + "square plate": [ + " {\"type\": \"dishware\", \"description\": \"flat, four-sided; could be made of ceramic, glass, or metal; could be used for serving food\", \"similar objects\": [\"round plate\", \"bowl\", \"cup\"]}", + 75 + ], + "orange flower": [ + "\n{\"type\": \"plant\", \"description\": \"orange petals; could have yellow center; could have green leaves\", \"similar objects\": [\"sunflower\", \"daisy\", \"tulip\"]}", + 75 + ], + "khaki pants": [ + " {\"type\": \"clothing\", \"description\": \"light brown; could be made of cotton; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"trousers\"]}", + 75 + ], + "life preserver": [ + " {\"type\": \"safety tool\", \"description\": \"round; could be made of foam; could be orange or yellow\", \"similar objects\": [\"floatation device\", \"life jacket\", \"life buoy\"]}", + 75 + ], + "wireless mouse": [ + " {\"type\": \"computer accessory\", \"description\": \"small, rectangular; could be connected to a computer without wires; has two buttons\", \"similar objects\": [\"keyboard\", \"headset\", \"webcam\"]}", + 75 + ], + "dirt area": [ + " {\"type\": \"landscape\", \"description\": \"uneven surface; could be muddy; could have rocks and stones\", \"similar objects\": [\"grassland\", \"desert\", \"forest\"]}", + 75 + ], + "ox": [ + " {\"type\": \"animal\", \"description\": \"large, strong, has horns; could be used for plowing\", \"similar objects\": [\"cow\", \"bull\", \"buffalo\"]}", + 75 + ], + "cherries": [ + " {\"type\": \"fruit\", \"description\": \"red, round, has a stem; could have a green leaf\", \"similar objects\": [\"plums\", \"strawberries\", \"grapes\"]}", + 75 + ], + "tufts": [ + " {\"type\": \"textile\", \"description\": \"soft, fluffy, could be made of wool, cotton, or synthetic fibers\", \"similar objects\": [\"carpet\", \"rug\", \"blanket\"]}", + 75 + ], + "commode": [ + " {\"type\": \"furniture\", \"description\": \"has a toilet bowl; could have a lid; could have a tank\", \"similar objects\": [\"toilet\", \"bathtub\", \"sink\"]}", + 74 + ], + "steering wheel": [ + " {\"type\": \"vehicle part\", \"description\": \"round; has a grip; could be made of leather\", \"similar objects\": [\"gear shift\", \"accelerator\", \"brake pedal\"]}", + 74 + ], + "figurines": [ + " {\"type\": \"decorative item\", \"description\": \"small, could be made of plastic, metal, or ceramic; could be in the shape of animals, people, or objects\", \"similar objects\": [\"statues\", \"sculptures\", \"ornaments\"]}", + 74 + ], + "jean shorts": [ + " {\"type\": \"clothing\", \"description\": \"denim shorts; could have pockets; could have belt loops\", \"similar objects\": [\"jean skirt\", \"cargo shorts\", \"khaki shorts\"]}", + 74 + ], + "brocolli": [ + " {\"type\": \"vegetable\", \"description\": \"green, small florets; could have long stems; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 74 + ], + "crow": [ + " {\"type\": \"bird\", \"description\": \"black; has a loud caw; could have a long beak\", \"similar objects\": [\"raven\", \"robin\", \"pigeon\"]}", + 74 + ], + "glass table": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be made of glass or plastic; could be square or round\", \"similar objects\": [\"coffee table\", \"dining table\", \"end table\"]}", + 74 + ], + "arrangement": [ + " {\"type\": \"arrangement\", \"description\": \"the act of organizing or putting things in order; could be of objects, people, or ideas\", \"similar objects\": [\"organization\", \"structure\", \"system\"]}", + 74 + ], + "destination": [ + " {\"type\": \"concept\", \"description\": \"a place to which one is traveling or a goal to be achieved\", \"similar objects\": [\"journey\", \"voyage\", \"trip\"]}", + 74 + ], + "christmas tree": [ + " {\"type\": \"decoration\", \"description\": \"conical; could be decorated with lights and ornaments; could have a star on the top\", \"similar objects\": [\"wreath\", \"garland\", \"snowman\"]}", + 74 + ], + "prints": [ + " {\"type\": \"artwork\", \"description\": \"could be made of paper or fabric; could be in different colors and shapes; could be framed or unframed\", \"similar objects\": [\"paintings\", \"drawings\", \"photographs\"]}", + 74 + ], + "adults": [ + " {\"type\": \"people\", \"description\": \"over 18 years old; could be male or female\", \"similar objects\": [\"teenagers\", \"seniors\", \"children\"]}", + 74 + ], + "zebra mane": [ + " {\"type\": \"animal feature\", \"description\": \"long, black and white stripes; found on the head of a zebra\", \"similar objects\": [\"horse mane\", \"giraffe neck\", \"elephant trunk\"]}", + 74 + ], + "tennis outfit": [ + " {\"type\": \"clothing\", \"description\": \"white; could have a logo; could be made of breathable fabric\", \"similar objects\": [\"tracksuit\", \"gym clothes\", \"swimsuit\"]}", + 74 + ], + "bracket": [ + " {\"type\": \"hardware\", \"description\": \"L-shaped; could be made of metal or plastic; used to support shelves or other objects\", \"similar objects\": [\"hinge\", \"screw\", \"nail\"]}", + 74 + ], + "store front": [ + " {\"type\": \"building\", \"description\": \"has a large window; could have a sign; could have a door\", \"similar objects\": [\"shop\", \"restaurant\", \"cafe\"]}", + 74 + ], + "shirt collar": [ + " {\"type\": \"clothing accessory\", \"description\": \"attached to the neckline of a shirt; could be folded down; could be made of different materials\", \"similar objects\": [\"tie\", \"bow tie\", \"lapel\"]}", + 74 + ], + "finger nail": [ + " {\"type\": \"body part\", \"description\": \"hard, thin, curved; could be painted with nail polish\", \"similar objects\": [\"toe nail\", \"cuticle\", \"nail clipper\"]}", + 74 + ], + "facade": [ + " {\"type\": \"architectural element\", \"description\": \"the front of a building; could be decorated with sculptures, columns, and windows\", \"similar objects\": [\"balcony\", \"portico\", \"veranda\"]}", + 74 + ], + "wheelchair": [ + " {\"type\": \"mobility aid\", \"description\": \"has two large wheels; could have a motor; could have armrests and footrests\", \"similar objects\": [\"walker\", \"crutches\", \"cane\"]}", + 74 + ], + "dial": [ + " {\"type\": \"tool\", \"description\": \"round; has a knob; could be used to adjust settings\", \"similar objects\": [\"knob\", \"lever\", \"switch\"]}", + 74 + ], + "earphones": [ + " {\"type\": \"audio device\", \"description\": \"small; could be wired or wireless; could be in-ear or over-ear\", \"similar objects\": [\"headphones\", \"speakers\", \"microphone\"]}", + 73 + ], + "timer": [ + " {\"type\": \"measuring tool\", \"description\": \"could be digital or analog; could be used to measure time\", \"similar objects\": [\"stopwatch\", \"clock\", \"thermometer\"]}", + 73 + ], + "motorcycle seat": [ + " {\"type\": \"motorcycle part\", \"description\": \"long, padded, has a backrest\", \"similar objects\": [\"handlebar\", \"exhaust pipe\", \"headlight\"]}", + 73 + ], + "tiger": [ + " {\"type\": \"animal\", \"description\": \"orange with black stripes; has a long tail; could be found in the wild\", \"similar objects\": [\"lion\", \"leopard\", \"jaguar\"]}", + 73 + ], + "wood coffee table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have drawers; could have a glass top\", \"similar objects\": [\"end table\", \"console table\", \"dining table\"]}", + 73 + ], + "icing": [ + " {\"type\": \"food topping\", \"description\": \"sweet; could be used to decorate cakes and cookies; could be made of sugar, butter, and cream\", \"similar objects\": [\"frosting\", \"glaze\", \"ganache\"]}", + 73 + ], + "veins": [ + " {\"type\": \"anatomy\", \"description\": \"blood vessels; could be seen through the skin; could be blue or purple\", \"similar objects\": [\"arteries\", \"capillaries\", \"nerves\"]}", + 73 + ], + "watch woman": [ + " {\"type\": \"accessory\", \"description\": \"worn on the wrist; could be made of metal or leather; could have a digital or analog display\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}", + 73 + ], + "silver bracelet": [ + " {\"type\": \"jewelry\", \"description\": \"made of silver; could be in a shape of a circle; could have decorations\", \"similar objects\": [\"gold necklace\", \"diamond ring\", \"pearl earrings\"]}", + 73 + ], + "melon": [ + " {\"type\": \"fruit\", \"description\": \"round; could be yellow, green, or orange; has a hard rind; could be sliced into pieces\", \"similar objects\": [\"watermelon\", \"honeydew\", \"cantaloupe\"]}", + 73 + ], + "metal faucet": [ + " {\"type\": \"plumbing tool\", \"description\": \"made of metal; has a handle; could be attached to a sink\", \"similar objects\": [\"shower head\", \"hose\", \"valve\"]}", + 73 + ], + "blueberry": [ + " {\"type\": \"fruit\", \"description\": \"small, round, blue; could have a white powdery coating\", \"similar objects\": [\"strawberry\", \"blackberry\", \"raspberry\"]}", + 73 + ], + "storefront": [ + " {\"type\": \"building\", \"description\": \"has a large window; could have a signboard; could have a door\", \"similar objects\": [\"shop\", \"store\", \"boutique\"]}", + 73 + ], + "colour": [ + " {\"type\": \"noun\", \"description\": \"a visual attribute of things that results from the light they reflect, transmit, or emit\", \"similar objects\": [\"hue\", \"shade\", \"tint\"]}", + 73 + ], + "glass bottles": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of glass or plastic; could be used to store liquids\", \"similar objects\": [\"jars\", \"cans\", \"mugs\"]}", + 73 + ], + "pizza sauce": [ + " {\"type\": \"condiment\", \"description\": \"red; could be made of tomatoes; could be spicy\", \"similar objects\": [\"ketchup\", \"mustard\", \"mayonnaise\"]}", + 73 + ], + "bare branches": [ + " {\"type\": \"plant\", \"description\": \"no leaves; could be curved; could be thin and long; could be brown\", \"similar objects\": [\"twigs\", \"branches\", \"stems\"]}", + 73 + ], + "clip": [ + " {\"type\": \"stationery tool\", \"description\": \"small; could be used to hold papers together\", \"similar objects\": [\"binder clip\", \"paper clip\", \"staple\"]}", + 73 + ], + "trough": [ + " {\"type\": \"container\", \"description\": \"long and shallow; could be made of metal or wood; could be used to feed animals\", \"similar objects\": [\"bucket\", \"tub\", \"barrel\"]}", + 73 + ], + "tennis shirt": [ + " {\"type\": \"clothing\", \"description\": \"collared; could have short sleeves; could have a logo of a tennis brand\", \"similar objects\": [\"polo shirt\", \"t-shirt\", \"tank top\"]}", + 73 + ], + "workers": [ + " {\"type\": \"people\", \"description\": \"people who work for a living; could be in different professions\", \"similar objects\": [\"employees\", \"laborers\", \"professionals\"]}", + 73 + ], + "skiis": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, curved; could have bindings\", \"similar objects\": [\"snowboard\", \"skates\", \"sled\"]}", + 73 + ], + "mulch": [ + " {\"type\": \"landscaping material\", \"description\": \"made of organic materials; could be used to cover soil; could be used to retain moisture\", \"similar objects\": [\"compost\", \"peat moss\", \"wood chips\"]}", + 73 + ], + "shoe lace": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of fabric or leather; used to tie shoes\", \"similar objects\": [\"belt\", \"tie\", \"scarf\"]}", + 73 + ], + "wooden fence": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could be used to separate two areas; could be painted\", \"similar objects\": [\"metal fence\", \"brick wall\", \"hedge\"]}", + 73 + ], + "blond": [ + " {\"type\": \"hair color\", \"description\": \"light yellowish-brown; could be natural or dyed\", \"similar objects\": [\"brown\", \"black\", \"red\"]}", + 72 + ], + "plastic lid": [ + " {\"type\": \"container lid\", \"description\": \"round; could be used to cover containers; could be made of plastic\", \"similar objects\": [\"jar lid\", \"bottle cap\", \"can lid\"]}", + 72 + ], + "railroad track": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, straight, parallel lines; could have a rail in the middle; could have a signal light\", \"similar objects\": [\"highway\", \"bridge\", \"tunnel\"]}", + 72 + ], + "pancake": [ + " {\"type\": \"food\", \"description\": \"round; could be made of flour, eggs, and milk; could be served with syrup\", \"similar objects\": [\"crepe\", \"waffle\", \"doughnut\"]}", + 72 + ], + "ribbons": [ + " {\"type\": \"decorative item\", \"description\": \"long, thin, colorful; could be used for tying gifts\", \"similar objects\": [\"bows\", \"wrapping paper\", \"tissue paper\"]}", + 72 + ], + "hand soap": [ + " {\"type\": \"cleaning product\", \"description\": \"liquid; could be scented; could be antibacterial\", \"similar objects\": [\"dish soap\", \"shampoo\", \"laundry detergent\"]}", + 72 + ], + "wood grain": [ + " {\"type\": \"texture\", \"description\": \"has a pattern of lines; could be found on wood surfaces\", \"similar objects\": [\"marble\", \"granite\", \"concrete\"]}", + 72 + ], + "sideview mirror": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the side of a vehicle; used to see the rear view\", \"similar objects\": [\"rearview mirror\", \"headlight\", \"windshield\"]}", + 72 + ], + "tree limb": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, branch of a tree; could have leaves and fruits\", \"similar objects\": [\"branch\", \"twig\", \"trunk\"]}", + 72 + ], + "pajamas": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; usually made of cotton; could have long sleeves and pants\", \"similar objects\": [\"nightgown\", \"robe\", \"slippers\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant", + 72 + ], + "office building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have glass windows; could have a lobby\", \"similar objects\": [\"apartment building\", \"school building\", \"museum\"]}", + 72 + ], + "giraffe neck": [ + " {\"type\": \"body part\", \"description\": \"long, slender, spotted; could be up to 6 feet long\", \"similar objects\": [\"elephant trunk\", \"giraffe legs\", \"giraffe head\"]}", + 72 + ], + "lap top": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; has a keyboard and a screen; could be opened and closed\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 72 + ], + "station wagon": [ + " {\"type\": \"vehicle\", \"description\": \"long; has a large cargo area; could have four doors\", \"similar objects\": [\"sedan\", \"SUV\", \"minivan\"]}", + 72 + ], + "toe": [ + " {\"type\": \"body part\", \"description\": \"five digits; could be painted with nail polish; could be used to kick a ball\", \"similar objects\": [\"finger\", \"hand\", \"foot\"]}", + 72 + ], + "sausages": [ + " {\"type\": \"food\", \"description\": \"long, cylindrical; could be made of pork, beef, or other meats; could be grilled or boiled\", \"similar objects\": [\"hot dogs\", \"bratwurst\", \"kielbasa\"]}", + 72 + ], + "thick clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"large, white, fluffy; could block the sunlight\", \"similar objects\": [\"fog\", \"haze\", \"smog\"]}", + 72 + ], + "tennis shorts": [ + " {\"type\": \"clothing\", \"description\": \"shorts; usually white; could have pockets; could have a drawstring\", \"similar objects\": [\"track shorts\", \"gym shorts\", \"swim shorts\"]}", + 72 + ], + "bangs": [ + " {\"type\": \"hairstyle\", \"description\": \"short, straight hair cut across the forehead\", \"similar objects\": [\"fringe\", \"bob\", \"pixie cut\"]}", + 72 + ], + "dirt bike": [ + " {\"type\": \"vehicle\", \"description\": \"small, off-road motorcycle; has knobby tires; could have a kickstand\", \"similar objects\": [\"motorcycle\", \"ATV\", \"scooter\"]}", + 72 + ], + "story house": [ + " {\"type\": \"building\", \"description\": \"multi-level; could have a balcony; could have a chimney\", \"similar objects\": [\"apartment\", \"mansion\", \"cottage\"]}", + 71 + ], + "tall lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"tall; could have a base; could have a shade; could have a switch\", \"similar objects\": [\"floor lamp\", \"table lamp\", \"desk lamp\"]}", + 71 + ], + "horizon line": [ + " {\"type\": \"landscape feature\", \"description\": \"the line where the sky and the earth meet; could be seen from a distance\", \"similar objects\": [\"mountain\", \"valley\", \"cliff\"]}", + 71 + ], + "potato chips": [ + " {\"type\": \"snack\", \"description\": \"thin, crispy, salty; could be in different flavors\", \"similar objects\": [\"popcorn\", \"pretzels\", \"nuts\"]}", + 71 + ], + "eaten": [ + " {\"type\": \"verb\", \"description\": \"past tense of 'eat'\", \"similar objects\": [\"drink\", \"sleep\", \"run\"]}", + 71 + ], + "orange umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"orange; has a curved handle; could be opened and closed\", \"similar objects\": [\"black umbrella\", \"raincoat\", \"hat\"]}", + 71 + ], + "chalk line": [ + " {\"type\": \"measuring tool\", \"description\": \"long, thin, white line; could be used to mark a straight line on a surface\", \"similar objects\": [\"ruler\", \"tape measure\", \"protractor\"]}", + 71 + ], + "metal ladder": [ + " {\"type\": \"tool\", \"description\": \"made of metal; could be used to reach high places; could be folded\", \"similar objects\": [\"wooden ladder\", \"step ladder\", \"stool\"]}", + 71 + ], + "metal knife": [ + " {\"type\": \"utensil\", \"description\": \"made of metal; has a sharp blade; could have a handle\", \"similar objects\": [\"fork\", \"spoon\", \"scissors\"]}", + 71 + ], + "drivers": [ + " {\"type\": \"tool\", \"description\": \"used to tighten or loosen screws; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"screwdriver\", \"wrench\", \"pliers\"]}", + 71 + ], + "iphone": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a touchscreen; could be used to make calls\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 71 + ], + "dog nose": [ + "\n{\"type\": \"animal body part\", \"description\": \"black, wet, and cold; could be wrinkled; could be pointed\", \"similar objects\": [\"cat nose\", \"horse nose\", \"rabbit nose\"]}", + 71 + ], + "handkerchief": [ + " {\"type\": \"clothing accessory\", \"description\": \"square; could be made of cotton; could be used to wipe sweat or tears\", \"similar objects\": [\"scarf\", \"bandana\", \"towel\"]}", + 71 + ], + "powerlines": [ + " {\"type\": \"utility\", \"description\": \"long, metal wires; could be connected to poles; could be used to transmit electricity\", \"similar objects\": [\"telephone lines\", \"cables\", \"fiber optics\"]}", + 71 + ], + "disk": [ + " {\"type\": \"storage device\", \"description\": \"round; could be made of plastic or metal; could be used to store data\", \"similar objects\": [\"hard drive\", \"USB drive\", \"CD\"]}", + 71 + ], + "roof top": [ + " {\"type\": \"structure\", \"description\": \"flat surface on the top of a building; could be made of tiles or metal sheets\", \"similar objects\": [\"balcony\", \"terrace\", \"veranda\"]}", + 71 + ], + "wood cabinets": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have drawers and doors; could be used for storage\", \"similar objects\": [\"bookshelf\", \"dresser\", \"armoire\"]}", + 71 + ], + "gap": [ + " {\"type\": \"space\", \"description\": \"empty space between two objects; could be a crack or a hole\", \"similar objects\": [\"crack\", \"hole\", \"opening\"]}", + 71 + ], + "drive": [ + " {\"type\": \"verb\", \"description\": \"to operate a vehicle; to move forward\", \"similar objects\": [\"run\", \"walk\", \"ride\"]}", + 71 + ], + "grey sidewalk": [ + "\n{\"type\": \"outdoor structure\", \"description\": \"concrete; could be cracked; could be painted grey\", \"similar objects\": [\"road\", \"driveway\", \"patio\"]}", + 71 + ], + "metal rails": [ + " {\"type\": \"building material\", \"description\": \"long, thin, and rigid; could be used for construction\", \"similar objects\": [\"wooden beams\", \"steel bars\", \"concrete blocks\"]}", + 71 + ], + "bike tire": [ + " {\"type\": \"bicycle part\", \"description\": \"round; has a tube; could be made of rubber\", \"similar objects\": [\"wheel\", \"chain\", \"pedal\"]}", + 70 + ], + "wooden box": [ + " {\"type\": \"container\", \"description\": \"rectangular; made of wood; could have a lid\", \"similar objects\": [\"basket\", \"trunk\", \"drawer\"]}", + 70 + ], + "dining table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal\", \"similar objects\": [\"coffee table\", \"desk\", \"chair\"]}", + 70 + ], + "stainless steel sink": [ + " {\"type\": \"kitchen tool\", \"description\": \"rectangular; made of stainless steel; has a drain\", \"similar objects\": [\"kitchen counter\", \"dishwasher\", \"refrigerator\"]}", + 70 + ], + "stuffed toy": [ + " {\"type\": \"toy\", \"description\": \"soft; could be shaped like an animal; could be filled with cotton\", \"similar objects\": [\"plush toy\", \"doll\", \"action figure\"]}", + 70 + ], + "speck": [ + " {\"type\": \"particle\", \"description\": \"tiny, round, could be seen with a microscope\", \"similar objects\": [\"dust\", \"atom\", \"molecule\"]}", + 70 + ], + "plastic bucket": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic; could have a handle\", \"similar objects\": [\"pail\", \"tub\", \"barrel\"]}", + 70 + ], + "bell pepper": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be red, yellow, or green; has a stem\", \"similar objects\": [\"tomato\", \"cucumber\", \"eggplant\"]}", + 70 + ], + "chainlink fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal; has diamond-shaped openings; could be used to enclose an area\", \"similar objects\": [\"wooden fence\", \"barbed wire fence\", \"brick wall\"]}", + 70 + ], + "profile": [ + " {\"type\": \"image\", \"description\": \"a two-dimensional representation of a person or object; could be a silhouette or a full-body image\", \"similar objects\": [\"portrait\", \"avatar\", \"icon\"]}", + 70 + ], + "metal roof": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be corrugated; could be painted\", \"similar objects\": [\"asphalt shingles\", \"tile roof\", \"wood shingles\"]}", + 70 + ], + "markers": [ + " {\"type\": \"writing tool\", \"description\": \"could be in different colors; could be used to write on paper or other surfaces\", \"similar objects\": [\"pen\", \"pencil\", \"crayon\"]}", + 70 + ], + "advertisement sign": [ + "\n{\"type\": \"promotional tool\", \"description\": \"could be made of paper, plastic, or metal; could be in the form of a billboard, poster, or banner; could have text, images, or both\", \"similar objects\": [\"billboard\", \"poster\", \"banner\"]}", + 70 + ], + "cyclist": [ + " {\"type\": \"person\", \"description\": \"riding a bicycle; wearing a helmet; could be wearing a reflective vest\", \"similar objects\": [\"runner\", \"skater\", \"walker\"]}", + 70 + ], + "blurry background": [ + "\n{\"type\": \"background\", \"description\": \"out of focus; could be a mix of colors; could be a mix of shapes\", \"similar objects\": [\"solid background\", \"gradient background\", \"textured background\"]}", + 70 + ], + "blue house": [ + "\n{\"type\": \"building\", \"description\": \"has a blue exterior; could have a white door; could have a roof\", \"similar objects\": [\"apartment\", \"mansion\", \"cottage\"]}", + 70 + ], + "air plane": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has wings and a tail; could have multiple engines\", \"similar objects\": [\"helicopter\", \"rocket\", \"drone\"]}", + 70 + ], + "streetlights": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lamp post\", \"lantern\", \"light pole\"]}", + 70 + ], + "pink bag": [ + " {\"type\": \"accessory\", \"description\": \"pink; could be made of cloth; could have straps\", \"similar objects\": [\"purse\", \"backpack\", \"wallet\"]}", + 70 + ], + "dark eye": [ + " {\"type\": \"makeup tool\", \"description\": \"black; used to create a smoky eye look; could be in pencil or liquid form\", \"similar objects\": [\"eyeliner\", \"mascara\", \"eyeshadow\"]}", + 70 + ], + "cat tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, flexible; could be fluffy; could be black, white, or other colors\", \"similar objects\": [\"dog tail\", \"rabbit tail\", \"fox tail\"]}", + 70 + ], + "silver tray": [ + " {\"type\": \"serving tool\", \"description\": \"shiny, rectangular; could be used to serve food\", \"similar objects\": [\"platter\", \"dish\", \"bowl\"]}", + 70 + ], + "streamers": [ + " {\"type\": \"decoration\", \"description\": \"long, colorful, made of paper or plastic; could be hung from the ceiling\", \"similar objects\": [\"balloons\", \"confetti\", \"banners\"]}", + 70 + ], + "soccer players": [ + " {\"type\": \"athletes\", \"description\": \"wearing a jersey; running on the field; kicking a ball\", \"similar objects\": [\"basketball players\", \"baseball players\", \"hockey players\"]}", + 70 + ], + "silver knob": [ + " {\"type\": \"hardware\", \"description\": \"round; made of metal; could be used to open a door\", \"similar objects\": [\"handle\", \"lock\", \"hinge\"]}", + 70 + ], + "tan hat": [ + " {\"type\": \"clothing item\", \"description\": \"headwear; could be made of straw; could have a brim; could have a band\", \"similar objects\": [\"cap\", \"fedora\", \"beanie\"]}", + 70 + ], + "student": [ + " {\"type\": \"person\", \"description\": \"could be attending school; could be studying; could be carrying books\", \"similar objects\": [\"teacher\", \"professor\", \"tutor\"]}", + 70 + ], + "gazebo": [ + " {\"type\": \"structure\", \"description\": \"octagonal; could have a roof; could be made of wood or metal\", \"similar objects\": [\"pavilion\", \"pergola\", \"arbor\"]}", + 70 + ], + "tree bark": [ + " {\"type\": \"natural material\", \"description\": \"rough; could be brown or gray; could be peeled off from the tree\", \"similar objects\": [\"wood\", \"stone\", \"leaves\"]}", + 70 + ], + "barbed wire": [ + " {\"type\": \"security tool\", \"description\": \"metal wire with sharp points; could be used to fence off an area\", \"similar objects\": [\"razor wire\", \"chain link fence\", \"electric fence\"]}", + 70 + ], + "raft": [ + " {\"type\": \"watercraft\", \"description\": \"made of logs or inflatable tubes; could be used for floating on water\", \"similar objects\": [\"boat\", \"canoe\", \"kayak\"]}", + 70 + ], + "paper holder": [ + " {\"type\": \"office tool\", \"description\": \"could be made of metal or plastic; could have a clip to hold papers\", \"similar objects\": [\"stapler\", \"paper clip\", \"binder clip\"]}", + 70 + ], + "symbols": [ + " {\"type\": \"visual representation\", \"description\": \"could be shapes, letters, or numbers; could be used to represent ideas or concepts\", \"similar objects\": [\"icons\", \"emojis\", \"logos\"]}", + 70 + ], + "car door": [ + " {\"type\": \"automotive part\", \"description\": \"rectangular; could be opened and closed; could be made of metal\", \"similar objects\": [\"hood\", \"trunk\", \"bumper\"]}", + 69 + ], + "rectangular window": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular shape; could be made of glass; could have a frame\", \"similar objects\": [\"door\", \"curtain\", \"shutter\"]}", + 69 + ], + "picture frames": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; could be made of wood or metal; could have a glass cover\", \"similar objects\": [\"mirror\", \"painting\", \"photo album\"]}", + 69 + ], + "teddy bear": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; usually has a round shape; could be brown, white, or other colors; could have a bowtie\", \"similar objects\": [\"doll\", \"plush toy\", \"stuffed animal\"]}", + 69 + ], + "tennis rackets": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be made of wood or metal\", \"similar objects\": [\"golf clubs\", \"baseball bats\", \"hockey sticks\"]}", + 69 + ], + "hair band": [ + " {\"type\": \"accessory\", \"description\": \"elastic band; could be decorated with beads or flowers; could be used to tie hair\", \"similar objects\": [\"headband\", \"hair tie\", \"scrunchy\"]}", + 69 + ], + "metal tower": [ + " {\"type\": \"structure\", \"description\": \"tall; made of metal; could be used for communication\", \"similar objects\": [\"bridge\", \"building\", \"wind turbine\"]}", + 69 + ], + "stools": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could be made of wood or metal; could be used as a seat\", \"similar objects\": [\"chair\", \"bench\", \"ottoman\"]}", + 69 + ], + "bug": [ + " {\"type\": \"insect\", \"description\": \"small; could have wings; could have multiple legs\", \"similar objects\": [\"spider\", \"ant\", \"bee\"]}", + 69 + ], + "chocolate donut": [ + "\n{\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be filled with chocolate; could be topped with sprinkles\", \"similar objects\": [\"glazed donut\", \"jelly donut\", \"cinnamon roll\"]}", + 69 + ], + "lit candle": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; has a flame; could be made of wax\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 69 + ], + "coin slot": [ + " {\"type\": \"machine part\", \"description\": \"small, round hole; could be used to insert coins\", \"similar objects\": [\"keyhole\", \"card slot\", \"button\"]}", + 69 + ], + "rear legs": [ + " {\"type\": \"body part\", \"description\": \"attached to the back of the body; used for movement; could be four or two\", \"similar objects\": [\"front legs\", \"arms\", \"wings\"]}", + 69 + ], + "nike logo": [ + " {\"type\": \"brand logo\", \"description\": \"swoosh; could be in black, white, or red; could be in a circle\", \"similar objects\": [\"Adidas logo\", \"Puma logo\", \"Reebok logo\"]}", + 69 + ], + "row boat": [ + " {\"type\": \"watercraft\", \"description\": \"long and narrow; could have oars; could be painted in different colors\", \"similar objects\": [\"canoe\", \"kayak\", \"sailboat\"]}", + 69 + ], + "window sill": [ + " {\"type\": \"architectural element\", \"description\": \"horizontal surface that is placed at the bottom of a window; could be made of wood or stone\", \"similar objects\": [\"window frame\", \"window ledge\", \"window seat\"]}", + 69 + ], + "color sky": [ + "\n{\"type\": \"phenomenon\", \"description\": \"blue; could be orange or pink during sunrise or sunset; could be grey during cloudy days\", \"similar objects\": [\"sunset\", \"sunrise\", \"cloudy sky\"]}", + 69 + ], + "pedestal": [ + " {\"type\": \"furniture\", \"description\": \"tall, cylindrical, could be made of wood or metal; could be used to display sculptures or other objects\", \"similar objects\": [\"column\", \"plinth\", \"stand\"]}", + 69 + ], + "seal": [ + " {\"type\": \"animal\", \"description\": \"gray; has a long body; could have whiskers; could be found in the ocean\", \"similar objects\": [\"otter\", \"walrus\", \"penguin\"]}", + 69 + ], + "flamingo": [ + " {\"type\": \"animal\", \"description\": \"pink; long neck; long legs; could stand on one leg\", \"similar objects\": [\"crane\", \"stork\", \"heron\"]}", + 69 + ], + "skinny": [ + "\n{\"type\": \"adjective\", \"description\": \"having a slim or slender figure; having little flesh\", \"similar objects\": [\"slim\", \"slender\", \"lean\"]}", + 68 + ], + "yolk": [ + " {\"type\": \"food ingredient\", \"description\": \"yellow; found in the center of an egg; could be used for baking\", \"similar objects\": [\"egg white\", \"butter\", \"milk\"]}", + 68 + ], + "coke": [ + " {\"type\": \"beverage\", \"description\": \"carbonated; could be in a can or bottle; could be in different flavors\", \"similar objects\": [\"soda\", \"juice\", \"beer\"]}", + 68 + ], + "cargo": [ + " {\"type\": \"transport\", \"description\": \"goods or materials transported in bulk; could be transported by ship, truck, train, or airplane\", \"similar objects\": [\"freight\", \"shipment\", \"parcel\"]}", + 68 + ], + "dark car": [ + "\n{\"type\": \"vehicle\", \"description\": \"dark color; could have four wheels; could have a windshield\", \"similar objects\": [\"truck\", \"SUV\", \"sedan\"]}", + 68 + ], + "silver necklace": [ + " {\"type\": \"jewelry\", \"description\": \"made of silver; could have a pendant; could be in a chain\", \"similar objects\": [\"gold necklace\", \"bracelet\", \"earrings\"]}", + 68 + ], + "silver sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be attached to a sink\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"toilet handle\"]}", + 68 + ], + "wooden railing": [ + " {\"type\": \"building material\", \"description\": \"long, thin pieces of wood; could be used as a fence or a barrier\", \"similar objects\": [\"metal railing\", \"wooden fence\", \"concrete wall\"]}", + 68 + ], + "paper towel dispenser": [ + " {\"type\": \"dispenser\", \"description\": \"usually wall-mounted; has a slot for paper towels\", \"similar objects\": [\"soap dispenser\", \"toilet paper dispenser\", \"hand dryer\"]}", + 68 + ], + "water droplets": [ + " {\"type\": \"liquid\", \"description\": \"small, round, transparent; could be formed on a surface\", \"similar objects\": [\"raindrops\", \"dew drops\", \"tears\"]}", + 68 + ], + "giraffe tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and tufted; could be spotted\", \"similar objects\": [\"elephant trunk\", \"horse mane\", \"monkey tail\"]}", + 68 + ], + "rooftop": [ + " {\"type\": \"structure\", \"description\": \"flat surface on the top of a building; could be used for recreational activities\", \"similar objects\": [\"balcony\", \"terrace\", \"patio\"]}", + 68 + ], + "stalks": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, and hollow; could be green or brown; could be used for decoration\", \"similar objects\": [\"stems\", \"stalks\", \"branches\"]}", + 68 + ], + "marble": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of glass or stone; could be of different colors\", \"similar objects\": [\"ball\", \"bead\", \"dice\"]}", + 68 + ], + "streak": [ + " {\"type\": \"pattern\", \"description\": \"long, thin line; could be in different colors\", \"similar objects\": [\"stripe\", \"band\", \"ribbon\"]}", + 68 + ], + "nobody": [ + " {\"type\": \"pronoun\", \"description\": \"refers to no one; used to emphasize a negative statement\", \"similar objects\": [\"nothing\", \"anybody\", \"anyone\"]}", + 68 + ], + "bicyclist": [ + " {\"type\": \"person\", \"description\": \"riding a bicycle; wearing a helmet; could be wearing a reflective vest\", \"similar objects\": [\"motorcyclist\", \"skateboarder\", \"rollerblader\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"c", + 68 + ], + "ridge": [ + " {\"type\": \"landform\", \"description\": \"long, narrow, elevated area of land; could be formed by erosion or tectonic activity\", \"similar objects\": [\"mountain\", \"valley\", \"hill\"]}", + 68 + ], + "flap": [ + " {\"type\": \"mechanism\", \"description\": \"hinged or sliding piece of material; could be used to cover an opening\", \"similar objects\": [\"door\", \"gate\", \"shutter\"]}", + 68 + ], + "trio": [ + " {\"type\": \"group\", \"description\": \"three people or things together\", \"similar objects\": [\"quartet\", \"quintet\", \"sextet\"]}", + 68 + ], + "metal bolt": [ + " {\"type\": \"hardware\", \"description\": \"cylindrical; has a head and a thread; could be made of metal\", \"similar objects\": [\"screw\", \"nut\", \"washer\"]}", + 68 + ], + "figures": [ + " {\"type\": \"artwork\", \"description\": \"could be made of wood, stone, or metal; could be in the form of human, animal, or abstract shapes\", \"similar objects\": [\"statues\", \"sculptures\", \"carvings\"]}", + 68 + ], + "breast": [ + " {\"type\": \"body part\", \"description\": \"rounded; could be covered with skin; could have nipples\", \"similar objects\": [\"chest\", \"arm\", \"abdomen\"]}", + 68 + ], + "slat": [ + " {\"type\": \"building material\", \"description\": \"long, thin, and flat; could be made of wood or metal\", \"similar objects\": [\"board\", \"plank\", \"beam\"]}", + 68 + ], + "tour bus": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple windows; could have a luggage compartment\", \"similar objects\": [\"school bus\", \"coach bus\", \"minibus\"]}", + 68 + ], + "lounge chair": [ + " {\"type\": \"furniture\", \"description\": \"long, reclining chair; could have armrests; could have a footrest\", \"similar objects\": [\"sofa\", \"loveseat\", \"chaise lounge\"]}", + 68 + ], + "food items": [ + "\n{\"type\": \"food\", \"description\": \"edible items; could be cooked or raw; could be fruits, vegetables, grains, dairy, etc.\", \"similar objects\": [\"meals\", \"snacks\", \"dishes\"]}", + 67 + ], + "grey wall": [ + " {\"type\": \"structure\", \"description\": \"solid, grey, rectangular; could be made of concrete, brick, or wood\", \"similar objects\": [\"door\", \"window\", \"ceiling\"]}", + 67 + ], + "sofas": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of fabric or leather; could have armrests and backrests\", \"similar objects\": [\"couch\", \"loveseat\", \"chair\"]}", + 67 + ], + "grain": [ + " {\"type\": \"food\", \"description\": \"small, hard, could be yellow or brown; could be used to make flour\", \"similar objects\": [\"rice\", \"wheat\", \"barley\"]}", + 67 + ], + "produce": [ + " {\"type\": \"food\", \"description\": \"fruits and vegetables; could be fresh or canned\", \"similar objects\": [\"fruit\", \"vegetable\", \"dairy\"]}", + 67 + ], + "wooden park bench": [ + "\n{\"type\": \"furniture\", \"description\": \"made of wood; has a backrest and armrests; could have a seat cushion\", \"similar objects\": [\"chair\", \"sofa\", \"loveseat\"]}", + 67 + ], + "masts": [ + " {\"type\": \"nautical tool\", \"description\": \"tall, vertical poles; could be made of wood or metal; could be used to hold sails\", \"similar objects\": [\"boom\", \"yardarm\", \"spars\"]}", + 67 + ], + "spice": [ + " {\"type\": \"food ingredient\", \"description\": \"could be in powder or liquid form; could be used to add flavor to food\", \"similar objects\": [\"herb\", \"seasoning\", \"condiment\"]}", + 67 + ], + "bicycle wheel": [ + " {\"type\": \"bicycle part\", \"description\": \"round; has spokes; could be made of metal or plastic\", \"similar objects\": [\"bicycle frame\", \"bicycle tire\", \"bicycle chain\"]}", + 67 + ], + "plant pot": [ + " {\"type\": \"container\", \"description\": \"round; could be made of plastic, clay, or metal; could have drainage holes; could have a saucer\", \"similar objects\": [\"flower pot\", \"vase\", \"urn\"]}", + 67 + ], + "bells": [ + " {\"type\": \"instrument\", \"description\": \"could be made of metal; could be used to make ringing sound\", \"similar objects\": [\"cymbals\", \"xylophone\", \"drum\"]}", + 67 + ], + "brown rocks": [ + " {\"type\": \"geological object\", \"description\": \"brown in color; could be of various shapes and sizes; could be found in nature\", \"similar objects\": [\"boulders\", \"pebbles\", \"gravel\"]}", + 67 + ], + "muffler": [ + " {\"type\": \"automotive part\", \"description\": \"cylindrical; used to reduce engine noise; could be made of metal\", \"similar objects\": [\"exhaust pipe\", \"catalytic converter\", \"air filter\"]}", + 67 + ], + "barrels": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of wood or metal; could be used to store liquids\", \"similar objects\": [\"buckets\", \"drums\", \"tanks\"]}", + 67 + ], + "pane": [ + " {\"type\": \"window\", \"description\": \"transparent; could be made of glass; could be opened and closed\", \"similar objects\": [\"window\", \"door\", \"curtain\"]}", + 67 + ], + "ceiling lights": [ + " {\"type\": \"lighting tool\", \"description\": \"fixed to the ceiling; could be made of metal or plastic; could be round or square\", \"similar objects\": [\"chandelier\", \"pendant light\", \"wall sconce\"]}", + 67 + ], + "farm": [ + " {\"type\": \"location\", \"description\": \"large area of land used for agricultural production; could have animals, crops, and buildings\", \"similar objects\": [\"orchard\", \"ranch\", \"vineyard\"]}", + 67 + ], + "caution tape": [ + " {\"type\": \"safety tool\", \"description\": \"yellow and black stripes; used to warn people of danger\", \"similar objects\": [\"barricade\", \"signs\", \"cones\"]}", + 67 + ], + "city sidewalk": [ + " {\"type\": \"outdoor structure\", \"description\": \"flat, paved surface; could have lines and markings; could have street lights\", \"similar objects\": [\"street\", \"parking lot\", \"driveway\"]}", + 67 + ], + "clumps": [ + " {\"type\": \"aggregate\", \"description\": \"group of objects; could be made of particles; could be held together by a force\", \"similar objects\": [\"clusters\", \"bundles\", \"masses\"]}", + 67 + ], + "broccoli floret": [ + " {\"type\": \"vegetable\", \"description\": \"green, small, tree-like shape; could have small yellow flowers\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 67 + ], + "flight": [ + " {\"type\": \"transportation\", \"description\": \"traveling by air; could be commercial or private\", \"similar objects\": [\"train\", \"bus\", \"boat\"]}", + 67 + ], + "specks": [ + " {\"type\": \"particles\", \"description\": \"tiny, round, could be made of dust, dirt, or other materials\", \"similar objects\": [\"dirt\", \"dust\", \"debris\"]}", + 67 + ], + "dirt patch": [ + " {\"type\": \"landscape\", \"description\": \"uneven surface; could be dry or wet; could have small plants or rocks\", \"similar objects\": [\"mud patch\", \"grass patch\", \"gravel patch\"]}", + 67 + ], + "passenger airplane": [ + "\n{\"type\": \"vehicle\", \"description\": \"large; has wings; could have multiple engines; could have multiple floors; could have multiple seats\", \"similar objects\": [\"helicopter\", \"jet\", \"commercial airliner\"]}", + 67 + ], + "grove": [ + " {\"type\": \"landscape\", \"description\": \"a group of trees; could be surrounded by grass; could have a path\", \"similar objects\": [\"forest\", \"orchard\", \"meadow\"]}", + 66 + ], + "womens": [ + " {\"type\": \"clothing\", \"description\": \"designed for female body shape; could be made of different materials; could have various colors and patterns\", \"similar objects\": [\"dresses\", \"skirts\", \"pants\"]}", + 66 + ], + "man shirt": [ + " {\"type\": \"clothing\", \"description\": \"long sleeves; could have buttons; could have a collar\", \"similar objects\": [\"woman shirt\", \"jacket\", \"sweater\"]}", + 66 + ], + "orange flag": [ + " {\"type\": \"signal flag\", \"description\": \"orange; could be used to signal danger or caution\", \"similar objects\": [\"red flag\", \"yellow flag\", \"green flag\"]}", + 66 + ], + "drape": [ + " {\"type\": \"fabric\", \"description\": \"long, thin, could be made of silk or cotton; could be used to cover windows or furniture\", \"similar objects\": [\"curtain\", \"tapestry\", \"shawl\"]}", + 66 + ], + "silver button": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of metal; could be used to fasten clothes\", \"similar objects\": [\"zipper\", \"hook\", \"snap\"]}", + 66 + ], + "truck tire": [ + " {\"type\": \"vehicle part\", \"description\": \"black; round; has a tread pattern\", \"similar objects\": [\"car tire\", \"motorcycle tire\", \"bicycle tire\"]}", + 66 + ], + "duffle bag": [ + " {\"type\": \"bag\", \"description\": \"cylindrical; has a shoulder strap; could be made of canvas or leather\", \"similar objects\": [\"backpack\", \"suitcase\", \"tote bag\"]}", + 66 + ], + "bike rack": [ + " {\"type\": \"storage tool\", \"description\": \"metal; could be attached to the wall; could hold multiple bikes\", \"similar objects\": [\"bike stand\", \"bike lock\", \"bike basket\"]}", + 66 + ], + "bikers": [ + " {\"type\": \"people\", \"description\": \"people riding bicycles; could be wearing helmets; could be wearing protective gear\", \"similar objects\": [\"cyclists\", \"skaters\", \"runners\"]}", + 66 + ], + "glass windshield": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be curved; could be attached to a car\", \"similar objects\": [\"headlight\", \"bumper\", \"tire\"]}", + 66 + ], + "silver trash": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of metal; has a lid\", \"similar objects\": [\"bin\", \"can\", \"bucket\"]}", + 66 + ], + "street name sign": [ + " {\"type\": \"road sign\", \"description\": \"rectangular; has street name written on it; could be in different colors\", \"similar objects\": [\"stop sign\", \"speed limit sign\", \"no parking sign\"]}", + 66 + ], + "pink towel": [ + " {\"type\": \"cleaning tool\", \"description\": \"pink; could be used to dry hands and body; could be made of cotton\", \"similar objects\": [\"washcloth\", \"bath towel\", \"hand towel\"]}", + 66 + ], + "spool": [ + " {\"type\": \"tool\", \"description\": \"cylindrical; could be used to store thread or wire\", \"similar objects\": [\"bobbin\", \"reel\", \"coil\"]}", + 66 + ], + "polish": [ + " {\"type\": \"cleaning product\", \"description\": \"liquid; could be used to make surfaces shine\", \"similar objects\": [\"wax\", \"cleaner\", \"soap\"]}", + 66 + ], + "boardwalk": [ + " {\"type\": \"structure\", \"description\": \"wooden walkway; could be elevated above the ground; could be used for recreational activities\", \"similar objects\": [\"pier\", \"bridge\", \"trail\"]}", + 66 + ], + "dark sky": [ + " {\"type\": \"weather condition\", \"description\": \"no stars visible; no moonlight; could be cloudy\", \"similar objects\": [\"rainy sky\", \"foggy sky\", \"overcast sky\"]}", + 66 + ], + "leafy plant": [ + " {\"type\": \"plant\", \"description\": \"green; could have multiple leaves; could have a stem\", \"similar objects\": [\"fern\", \"ivy\", \"palm tree\"]}", + 66 + ], + "frosting": [ + " {\"type\": \"food\", \"description\": \"sweet; creamy; could be used to decorate cakes and cupcakes\", \"similar objects\": [\"icing\", \"whipped cream\", \"ganache\"]}", + 66 + ], + "t.v": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could be connected to a remote control\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}", + 66 + ], + "window curtain": [ + " {\"type\": \"decoration\", \"description\": \"long fabric; could be hung on a window; could be opened and closed\", \"similar objects\": [\"blinds\", \"shades\", \"drapes\"]}", + 66 + ], + "cop": [ + " {\"type\": \"occupation\", \"description\": \"law enforcement officer; wears a uniform; carries a gun\", \"similar objects\": [\"police officer\", \"detective\", \"sheriff\"]}", + 66 + ], + "washer": [ + " {\"type\": \"appliance\", \"description\": \"large, rectangular; could be used to clean clothes\", \"similar objects\": [\"dryer\", \"dishwasher\", \"refrigerator\"]}", + 66 + ], + "ski helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard shell; has straps; could have a visor\", \"similar objects\": [\"bicycle helmet\", \"hockey helmet\", \"climbing helmet\"]}", + 66 + ], + "bedding": [ + " {\"type\": \"bedding item\", \"description\": \"soft; could be made of cotton; could be used to cover a bed\", \"similar objects\": [\"pillow\", \"blanket\", \"mattress\"]}", + 66 + ], + "bagel": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be toasted; could be topped with cream cheese\", \"similar objects\": [\"doughnut\", \"pretzel\", \"croissant\"]}", + 66 + ], + "airliner": [ + " {\"type\": \"vehicle\", \"description\": \"large; has wings; could have multiple engines; could have a tail fin\", \"similar objects\": [\"helicopter\", \"private jet\", \"glider\"]}", + 66 + ], + "lane": [ + " {\"type\": \"roadway\", \"description\": \"narrow strip of road; could be divided by lines; could be used for traffic\", \"similar objects\": [\"street\", \"highway\", \"boulevard\"]}", + 66 + ], + "surfboard water": [ + " {\"type\": \"water sport equipment\", \"description\": \"long and narrow; could be made of foam or wood; could have a fin\", \"similar objects\": [\"wakeboard\", \"bodyboard\", \"paddleboard\"]}", + 66 + ], + "subway": [ + " {\"type\": \"transportation\", \"description\": \"underground railway; could be used to travel between cities\", \"similar objects\": [\"train\", \"bus\", \"tram\"]}", + 66 + ], + "jam": [ + " {\"type\": \"food\", \"description\": \"thick, sweet, spreadable; could be made of fruits\", \"similar objects\": [\"jelly\", \"marmalade\", \"honey\"]}", + 66 + ], + "adult zebra": [ + "\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane; could have a saddle on its back; could have a rider\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}", + 66 + ], + "waterfall": [ + " {\"type\": \"natural phenomenon\", \"description\": \"water flowing down from a higher elevation; could be surrounded by rocks and trees\", \"similar objects\": [\"river\", \"lake\", \"geyser\"]}", + 66 + ], + "security camera": [ + " {\"type\": \"surveillance device\", \"description\": \"small, cylindrical; could be mounted on walls or ceilings; could be connected to a monitor\", \"similar objects\": [\"CCTV camera\", \"webcam\", \"motion sensor\"]}", + 65 + ], + "headboard bed": [ + " {\"type\": \"furniture\", \"description\": \"attached to the head of the bed; could be made of wood or metal; could have decorative designs\", \"similar objects\": [\"mattress\", \"pillow\", \"bed frame\"]}", + 65 + ], + "buds": [ + " {\"type\": \"plant\", \"description\": \"small, green, could be found on trees; could be used to make tea\", \"similar objects\": [\"leaves\", \"flowers\", \"berries\"]}", + 65 + ], + "link fence": [ + " {\"type\": \"fencing tool\", \"description\": \"made of metal; has a chain-link pattern; could be used to enclose an area\", \"similar objects\": [\"barbed wire fence\", \"wooden fence\", \"brick wall\"]}", + 65 + ], + "keypad": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has buttons; could be used to enter numbers or letters\", \"similar objects\": [\"keyboard\", \"calculator\", \"remote control\"]}", + 65 + ], + "telephone poles": [ + " {\"type\": \"utility structure\", \"description\": \"tall, cylindrical; could have wires attached to it\", \"similar objects\": [\"street lights\", \"power lines\", \"traffic lights\"]}", + 65 + ], + "shirt man": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have buttons; could be made of cotton\", \"similar objects\": [\"t-shirt\", \"jacket\", \"sweater\"]}", + 65 + ], + "water fountain": [ + " {\"type\": \"fixture\", \"description\": \"tall; could have a spout; could have a basin\", \"similar objects\": [\"bird bath\", \"garden fountain\", \"waterfall\"]}", + 65 + ], + "straws": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, cylindrical; could be made of plastic or paper; could be bent\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 65 + ], + "pillow couch": [ + "\n{\"type\": \"furniture\", \"description\": \"soft; could be used for seating; could be filled with feathers or foam\", \"similar objects\": [\"sofa\", \"armchair\", \"loveseat\"]}", + 65 + ], + "buoys": [ + " {\"type\": \"navigation tool\", \"description\": \"round; could be made of plastic or metal; could be floating on the water\", \"similar objects\": [\"beacons\", \"lighthouses\", \"markers\"]}", + 65 + ], + "mit": [ + " {\"type\": \"institution\", \"description\": \"Massachusetts Institute of Technology; located in Cambridge, Massachusetts; offers undergraduate and graduate degrees in a variety of disciplines\", \"similar objects\": [\"Harvard University\", \"Stanford University\", \"Yale University\"]}", + 65 + ], + "freight train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple cars; could be used to transport goods\", \"similar objects\": [\"passenger train\", \"truck\", \"ship\"]}", + 65 + ], + "labels": [ + " {\"type\": \"stationery\", \"description\": \"small, rectangular; could be printed with words or images\", \"similar objects\": [\"stickers\", \"tags\", \"envelopes\"]}", + 65 + ], + "mist": [ + " {\"type\": \"weather phenomenon\", \"description\": \"a cloud of tiny water droplets suspended in the air; could be seen in the morning or evening\", \"similar objects\": [\"fog\", \"haze\", \"smoke\"]}", + 65 + ], + "partition": [ + " {\"type\": \"furniture\", \"description\": \"divides a room into two or more sections; could be made of wood, glass, or metal\", \"similar objects\": [\"screen\", \"wall\", \"curtain\"]}", + 65 + ], + "refridgerator": [ + " {\"type\": \"appliance\", \"description\": \"large, white, has a door; could have shelves and drawers inside\", \"similar objects\": [\"freezer\", \"microwave\", \"dishwasher\"]}", + 65 + ], + "number plate": [ + " {\"type\": \"identification tool\", \"description\": \"rectangular; has numbers and letters; could be attached to a vehicle\", \"similar objects\": [\"license plate\", \"registration plate\", \"vehicle identification number\"]}", + 65 + ], + "support pole": [ + " {\"type\": \"structural tool\", \"description\": \"long, cylindrical; could be made of metal or wood; could be used to support a structure\", \"similar objects\": [\"column\", \"beam\", \"pillar\"]}", + 65 + ], + "photographers": [ + " {\"type\": \"profession\", \"description\": \"takes pictures; could use a camera\", \"similar objects\": [\"videographer\", \"journalist\", \"artist\"]}", + 65 + ], + "garlic": [ + " {\"type\": \"vegetable\", \"description\": \"small, white, has a strong smell; could be sliced into small pieces; could be used as a seasoning\", \"similar objects\": [\"onion\", \"ginger\", \"shallot\"]}", + 65 + ], + "tall chain link fence": [ + "\n{\"type\": \"barrier\", \"description\": \"made of metal links; could be tall and wide; could have a gate\", \"similar objects\": [\"wooden fence\", \"brick wall\", \"hedge\"]}", + 65 + ], + "lapel": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, flat piece of fabric; could be pinned to a shirt or jacket\", \"similar objects\": [\"tie\", \"pocket square\", \"cufflinks\"]}", + 65 + ], + "archway": [ + " {\"type\": \"architectural structure\", \"description\": \"curved; could be made of stone; could have pillars\", \"similar objects\": [\"doorway\", \"gateway\", \"portal\"]}", + 65 + ], + "suspenders": [ + " {\"type\": \"clothing accessory\", \"description\": \"two straps; could be attached to trousers; could be adjustable\", \"similar objects\": [\"belt\", \"tie\", \"bow tie\"]}", + 65 + ], + "lantern": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of metal or glass; could have a handle; could have a candle or lightbulb inside\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 65 + ], + "fluffy cloud": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white; could be shaped like animals; could be seen in the sky\", \"similar objects\": [\"rain cloud\", \"thundercloud\", \"haze\"]}", + 65 + ], + "brick buildings": [ + " {\"type\": \"structure\", \"description\": \"rectangular; made of bricks; could have windows and doors\", \"similar objects\": [\"houses\", \"apartments\", \"skyscrapers\"]}", + 65 + ], + "silver van": [ + "\n{\"type\": \"vehicle\", \"description\": \"silver; could be a van or minivan; could have sliding doors\", \"similar objects\": [\"SUV\", \"truck\", \"sedan\"]}", + 64 + ], + "sleeveless shirt": [ + " {\"type\": \"clothing\", \"description\": \"no sleeves; could have a collar; could be made of cotton\", \"similar objects\": [\"tank top\", \"vest\", \"t-shirt\"]}", + 64 + ], + "pillowcase": [ + " {\"type\": \"bedding item\", \"description\": \"rectangular; could be made of cotton; could be decorated with patterns\", \"similar objects\": [\"sheet\", \"blanket\", \"duvet cover\"]}", + 64 + ], + "flip flop": [ + " {\"type\": \"footwear\", \"description\": \"flat; could be made of rubber; could have straps\", \"similar objects\": [\"sandals\", \"sneakers\", \"slippers\"]}", + 64 + ], + "limbs": [ + " {\"type\": \"body parts\", \"description\": \"arms and legs; could be used for movement\", \"similar objects\": [\"hands\", \"feet\", \"fingers\"]}", + 64 + ], + "folder": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of paper or plastic; could be used to store documents\", \"similar objects\": [\"envelope\", \"binder\", \"box\"]}", + 64 + ], + "bedside table": [ + " {\"type\": \"furniture\", \"description\": \"small table; could have drawers; could be placed beside a bed\", \"similar objects\": [\"nightstand\", \"dresser\", \"end table\"]}", + 64 + ], + "brown desk": [ + "\n{\"type\": \"furniture\", \"description\": \"wooden; could have drawers; could have a flat surface\", \"similar objects\": [\"table\", \"chair\", \"cabinet\"]}", + 64 + ], + "necks": [ + " {\"type\": \"body part\", \"description\": \"connects the head to the torso; could be long or short; could be flexible\", \"similar objects\": [\"shoulders\", \"arms\", \"legs\"]}", + 64 + ], + "beautiful": [ + "\n{\"type\": \"adjective\", \"description\": \"pleasing to the senses; attractive; having qualities that delight or appeal to the mind or emotions\", \"similar objects\": [\"lovely\", \"gorgeous\", \"stunning\"]}", + 64 + ], + "blue flower": [ + "\n{\"type\": \"plant\", \"description\": \"blue petals; could have yellow center; could have green leaves\", \"similar objects\": [\"daisy\", \"lily\", \"hydrangea\"]}", + 64 + ], + "identification number": [ + "\n{\"type\": \"identification\", \"description\": \"a unique set of numbers or characters used to identify a person or thing\", \"similar objects\": [\"social security number\", \"passport number\", \"driver's license number\"]}", + 64 + ], + "jaw": [ + " {\"type\": \"body part\", \"description\": \"hinged joint connecting the mandible to the skull; used for chewing and speaking\", \"similar objects\": [\"teeth\", \"tongue\", \"lips\"]}", + 64 + ], + "orange truck": [ + "\n{\"type\": \"vehicle\", \"description\": \"orange; could be a pickup truck; could have a large cargo bed\", \"similar objects\": [\"van\", \"SUV\", \"tractor trailer\"]}", + 64 + ], + "furry": [ + "\n{\"type\": \"adjective\", \"description\": \"having a soft, thick coat of fur\", \"similar objects\": [\"fluffy\", \"hairy\", \"shaggy\"]}", + 64 + ], + "electronics": [ + "\n{\"type\": \"electronic device\", \"description\": \"could be a computer, phone, or other electronic device; could have a screen, buttons, and ports\", \"similar objects\": [\"television\", \"printer\", \"stereo\"]}", + 64 + ], + "wig": [ + " {\"type\": \"hair accessory\", \"description\": \"made of synthetic fibers; could be styled in different ways; could be attached to a cap\", \"similar objects\": [\"hat\", \"headband\", \"hair extensions\"]}", + 64 + ], + "blue table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have four legs; could be painted blue\", \"similar objects\": [\"chair\", \"desk\", \"sofa\"]}", + 64 + ], + "e": [ + "\n{\"type\": \"letter\", \"description\": \"the fifth letter of the English alphabet; could be lowercase or uppercase\", \"similar objects\": [\"a\", \"b\", \"c\"]}", + 64 + ], + "toilet sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a bowl and a faucet; could be made of porcelain\", \"similar objects\": [\"bathtub\", \"shower\", \"urinal\"]}", + 64 + ], + "basketball hoop": [ + " {\"type\": \"sports equipment\", \"description\": \"round; has a net; could be attached to a wall or a pole\", \"similar objects\": [\"soccer goal\", \"volleyball net\", \"baseball backstop\"]}", + 64 + ], + "pail": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could have a handle; could be made of metal or plastic\", \"similar objects\": [\"bucket\", \"tub\", \"barrel\"]}", + 64 + ], + "grooves": [ + " {\"type\": \"architectural feature\", \"description\": \"long, narrow indentations; could be found on walls, ceilings, or floors\", \"similar objects\": [\"cornices\", \"mouldings\", \"panels\"]}", + 64 + ], + "polka dots": [ + " {\"type\": \"pattern\", \"description\": \"small, round, and evenly spaced dots\", \"similar objects\": [\"stripes\", \"checks\", \"floral prints\"]}", + 64 + ], + "bulletin board": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of cork; could be used to post notices\", \"similar objects\": [\"whiteboard\", \"chalkboard\", \"pinboard\"]}", + 64 + ], + "crosswalk sign": [ + " {\"type\": \"traffic sign\", \"description\": \"octagonal; has a white background with black symbols; could be red and white\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 64 + ], + "passenger jet": [ + " {\"type\": \"vehicle\", \"description\": \"large, long, has wings; could have multiple engines; could have a tail fin\", \"similar objects\": [\"airplane\", \"helicopter\", \"glider\"]}", + 64 + ], + "front door": [ + " {\"type\": \"entryway\", \"description\": \"wooden; could have a handle; could have a lock\", \"similar objects\": [\"back door\", \"garage door\", \"window\"]}", + 64 + ], + "ocean waves": [ + " {\"type\": \"natural phenomenon\", \"description\": \"constant movement of water; could be caused by wind or tides\", \"similar objects\": [\"tsunami\", \"tidal wave\", \"storm surge\"]}", + 64 + ], + "rugs": [ + " {\"type\": \"floor covering\", \"description\": \"could be made of wool, cotton, or synthetic fibers; could be woven or tufted; could be used to decorate a room\", \"similar objects\": [\"carpet\", \"mat\", \"runner\"]}", + 63 + ], + "bike helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard shell; adjustable straps; could have a visor\", \"similar objects\": [\"skateboard helmet\", \"ski helmet\", \"climbing helmet\"]}", + 63 + ], + "peice": [ + " {\"type\": \"measurement unit\", \"description\": \"a unit of length, area, volume, weight, or time\", \"similar objects\": [\"meter\", \"kilogram\", \"liter\"]}", + 63 + ], + "hand towels": [ + " {\"type\": \"cleaning tool\", \"description\": \"small, rectangular; could be made of cotton or linen; could be used to dry hands\", \"similar objects\": [\"bath towels\", \"washcloths\", \"kitchen towels\"]}", + 63 + ], + "price": [ + " {\"type\": \"monetary value\", \"description\": \"the amount of money required to purchase something\", \"similar objects\": [\"cost\", \"value\", \"fee\"]}", + 63 + ], + "stoves": [ + " {\"type\": \"cooking tool\", \"description\": \"has burners; could be electric or gas; could have an oven\", \"similar objects\": [\"oven\", \"microwave\", \"grill\"]}", + 63 + ], + "dark sunglasses": [ + " {\"type\": \"eyewear\", \"description\": \"dark lenses; could have a frame; could be used to protect eyes from sunlight\", \"similar objects\": [\"eyeglasses\", \"goggles\", \"sunglasses\"]}", + 63 + ], + "computer tower": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; could be made of metal; could have multiple ports\", \"similar objects\": [\"desktop\", \"laptop\", \"monitor\"]}", + 63 + ], + "slopes": [ + " {\"type\": \"landscape\", \"description\": \"inclined surface; could be made of snow, grass, or rocks; could be used for skiing or snowboarding\", \"similar objects\": [\"mountain\", \"hill\", \"valley\"]}", + 63 + ], + "grey pants": [ + " {\"type\": \"clothing\", \"description\": \"light or dark grey; could be made of cotton or polyester; could have pockets; could be long or short\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 63 + ], + "bands": [ + " {\"type\": \"accessory\", \"description\": \"made of metal; could be worn around the wrist; could be used to hold hair\", \"similar objects\": [\"bracelet\", \"hair tie\", \"watch\"]}", + 63 + ], + "draperies": [ + " {\"type\": \"decoration\", \"description\": \"long, thin fabric; could be hung on windows or walls; could be used to block light\", \"similar objects\": [\"curtains\", \"blinds\", \"shades\"]}", + 63 + ], + "brown blanket": [ + "\n{\"type\": \"bedding item\", \"description\": \"brown; could be made of wool; could be used to keep warm\", \"similar objects\": [\"quilt\", \"comforter\", \"duvet\"]}", + 63 + ], + "buggy": [ + " {\"type\": \"vehicle\", \"description\": \"small, open-air vehicle; could be pulled by a horse; could have two or four wheels\", \"similar objects\": [\"carriage\", \"wagon\", \"cart\"]}", + 63 + ], + "icons": [ + " {\"type\": \"visual representation\", \"description\": \"small, graphical images used to represent a concept or idea\", \"similar objects\": [\"emojis\", \"symbols\", \"logos\"]}", + 63 + ], + "brand logo": [ + "\n{\"type\": \"visual representation\", \"description\": \"symbolic representation of a company or organization; could be a combination of colors, shapes, and words\", \"similar objects\": [\"emblem\", \"symbol\", \"mascot\"]}", + 63 + ], + "human": [ + " {\"type\": \"organism\", \"description\": \"bipedal; has two arms and two legs; has a head and torso; could have different skin colors\", \"similar objects\": [\"monkey\", \"ape\", \"gorilla\"]}", + 63 + ], + "motorcycle wheel": [ + " {\"type\": \"motorcycle part\", \"description\": \"round; has spokes; could be made of metal or rubber\", \"similar objects\": [\"motorcycle tire\", \"motorcycle handlebar\", \"motorcycle seat\"]}", + 63 + ], + "arm rest": [ + " {\"type\": \"furniture\", \"description\": \"attached to a chair; could be adjustable; could be made of wood or metal\", \"similar objects\": [\"ottoman\", \"footstool\", \"sofa\"]}", + 63 + ], + "baseball cleats": [ + " {\"type\": \"footwear\", \"description\": \"has spikes on the bottom; could be made of leather; could be black or white\", \"similar objects\": [\"soccer cleats\", \"running shoes\", \"hiking boots\"]}", + 63 + ], + "brown belt": [ + " {\"type\": \"accessory\", \"description\": \"brown; could be made of leather; could be used to hold up pants\", \"similar objects\": [\"black belt\", \"scarf\", \"hat\"]}", + 63 + ], + "thumb nail": [ + " {\"type\": \"body part\", \"description\": \"hard, small, round; located at the end of the thumb\", \"similar objects\": [\"fingernail\", \"toenail\", \"cuticle\"]}", + 63 + ], + "computer chair": [ + " {\"type\": \"furniture\", \"description\": \"has a backrest; could be adjustable; could have wheels\", \"similar objects\": [\"desk chair\", \"office chair\", \"gaming chair\"]}", + 63 + ], + "fork plate": [ + " {\"type\": \"dining tool\", \"description\": \"round; has four prongs; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 63 + ], + "concrete building": [ + " {\"type\": \"structure\", \"description\": \"made of concrete; could have multiple floors; could have windows and doors\", \"similar objects\": [\"skyscraper\", \"bridge\", \"monument\"]}", + 63 + ], + "concrete ground": [ + " {\"type\": \"building material\", \"description\": \"hard, gray, rough surface; could be used for paving roads\", \"similar objects\": [\"asphalt\", \"gravel\", \"brick\"]}", + 63 + ], + "blue boat": [ + "\n{\"type\": \"vehicle\", \"description\": \"blue; could be made of wood or metal; could have a sail or motor; could have a cabin\", \"similar objects\": [\"yacht\", \"canoe\", \"rowboat\"]}", + 63 + ], + "instructions": [ + " {\"type\": \"document\", \"description\": \"written instructions; could be in the form of a book, pamphlet, or online document\", \"similar objects\": [\"manual\", \"guidebook\", \"tutorial\"]}", + 63 + ], + "kite string": [ + " {\"type\": \"toy\", \"description\": \"long, thin, strong; could be made of nylon or cotton\", \"similar objects\": [\"balloon string\", \"frisbee string\", \"yo-yo string\"]}", + 63 + ], + "garment": [ + " {\"type\": \"clothing\", \"description\": \"made of fabric; could be worn on the body\", \"similar objects\": [\"dress\", \"shirt\", \"pants\"]}", + 63 + ], + "passenger window": [ + " {\"type\": \"automobile part\", \"description\": \"transparent; could be opened and closed; could be tinted\", \"similar objects\": [\"windshield\", \"rear window\", \"side window\"]}", + 63 + ], + "drop": [ + " {\"type\": \"liquid\", \"description\": \"small, round, could be of any color; could be of any liquid\", \"similar objects\": [\"drip\", \"splash\", \"puddle\"]}", + 63 + ], + "bench brown": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of wood; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 63 + ], + "creek": [ + " {\"type\": \"water body\", \"description\": \"narrow, shallow, could have a stream\", \"similar objects\": [\"river\", \"lake\", \"pond\"]}", + 63 + ], + "shadow grass": [ + " {\"type\": \"plant\", \"description\": \"low-growing, evergreen grass; has a fine texture; could be used for ground cover\", \"similar objects\": [\"lawn grass\", \"bluegrass\", \"fescue\"]}", + 63 + ], + "mountainside": [ + " {\"type\": \"landscape\", \"description\": \"sloped terrain; could have trees and rocks; could have snow\", \"similar objects\": [\"hillside\", \"valley\", \"cliff\"]}", + 63 + ], + "x": [ + "\n{\"type\": \"letter\", \"description\": \"the 24th letter of the English alphabet; could be used as a variable in mathematics\", \"similar objects\": [\"y\", \"z\", \"a\"]}", + 63 + ], + "pant leg": [ + " {\"type\": \"clothing item\", \"description\": \"long, cylindrical; could be made of fabric; could be attached to a waistband\", \"similar objects\": [\"sleeve\", \"skirt\", \"trouser\"]}", + 63 + ], + "right headlight": [ + "\n{\"type\": \"automotive part\", \"description\": \"attached to the front of a vehicle; emits light; could be round or rectangular\", \"similar objects\": [\"left headlight\", \"taillight\", \"fog light\"]}", + 63 + ], + "page": [ + " {\"type\": \"document\", \"description\": \"rectangular; could be printed or digital; could contain text or images\", \"similar objects\": [\"book\", \"magazine\", \"newspaper\"]}", + 63 + ], + "destination sign": [ + " {\"type\": \"transportation tool\", \"description\": \"rectangular; has a list of destinations; could be found in a bus or train station\", \"similar objects\": [\"timetable\", \"ticket machine\", \"map\"]}", + 63 + ], + "color grass": [ + " {\"type\": \"plant\", \"description\": \"green; could have yellow, pink, purple, or blue flowers; could be found in meadows and fields\", \"similar objects\": [\"daisy\", \"dandelion\", \"clover\"]}", + 62 + ], + "train number": [ + " {\"type\": \"transportation\", \"description\": \"a number assigned to a train; could be used to identify a train\", \"similar objects\": [\"bus number\", \"flight number\", \"ship number\"]}", + 62 + ], + "lump": [ + " {\"type\": \"object\", \"description\": \"irregular shape; could be soft or hard; could be found in the body\", \"similar objects\": [\"cyst\", \"tumor\", \"nodule\"]}", + 62 + ], + "herb": [ + " {\"type\": \"plant\", \"description\": \"small, green, could be used as seasoning\", \"similar objects\": [\"spice\", \"basil\", \"parsley\"]}", + 62 + ], + "seed": [ + " {\"type\": \"planting material\", \"description\": \"small, round, could be of different colors; could be planted to grow plants\", \"similar objects\": [\"bulb\", \"spore\", \"acorn\"]}", + 62 + ], + "door car": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; has two doors; could be a sedan or a coupe\", \"similar objects\": [\"SUV\", \"truck\", \"minivan\"]}", + 62 + ], + "baby cow": [ + "\n{\"type\": \"animal\", \"description\": \"small; has a white and black spotted fur; has a short tail\", \"similar objects\": [\"calf\", \"lamb\", \"goat\"]}", + 62 + ], + "plugs": [ + " {\"type\": \"electrical tool\", \"description\": \"two or three prongs; could be used to connect electrical devices\", \"similar objects\": [\"sockets\", \"adapters\", \"extension cords\"]}", + 62 + ], + "christmas lights": [ + "\n{\"type\": \"decoration\", \"description\": \"string of lights; could be in different colors; could be in different shapes\", \"similar objects\": [\"garland\", \"ornaments\", \"tinsel\"]}", + 62 + ], + "hip": [ + " {\"type\": \"body part\", \"description\": \"part of the pelvis; connects the leg to the torso\", \"similar objects\": [\"thigh\", \"waist\", \"buttocks\"]}", + 62 + ], + "living": [ + "\n{\"type\": \"adjective\", \"description\": \"describes something that is alive\", \"similar objects\": [\"active\", \"awake\", \"breathing\"]}", + 62 + ], + "toilet seat lid": [ + " {\"type\": \"bathroom accessory\", \"description\": \"round; could be made of plastic or wood; could be hinged or removable\", \"similar objects\": [\"toilet brush\", \"toilet paper holder\", \"soap dish\"]}", + 62 + ], + "flower petals": [ + " {\"type\": \"plant part\", \"description\": \"thin, colorful, delicate; could be from a variety of flowers\", \"similar objects\": [\"leaves\", \"stems\", \"seeds\"]}", + 62 + ], + "skate ramp": [ + " {\"type\": \"skateboarding tool\", \"description\": \"sloped; could be made of wood or metal; could have a rail or ledge\", \"similar objects\": [\"half-pipe\", \"quarter-pipe\", \"funbox\"]}", + 62 + ], + "baseball bats": [ + " {\"type\": \"sports equipment\", \"description\": \"long, cylindrical; could be made of wood or metal; used to hit a baseball\", \"similar objects\": [\"golf clubs\", \"tennis rackets\", \"hockey sticks\"]}", + 62 + ], + "wrought iron fence": [ + " {\"type\": \"fencing material\", \"description\": \"made of metal; could be curved; could be painted black\", \"similar objects\": [\"wooden fence\", \"chain link fence\", \"vinyl fence\"]}", + 62 + ], + "inside": [ + " {\"type\": \"location\", \"description\": \"opposite of outside; could be a room or a place\", \"similar objects\": [\"interior\", \"indoors\", \"interior space\"]}", + 62 + ], + "wooden drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have handles; could have multiple compartments\", \"similar objects\": [\"cabinet\", \"dresser\", \"chest of drawers\"]}", + 62 + ], + "flower arrangement": [ + " {\"type\": \"decoration\", \"description\": \"arrangement of flowers in a vase; could be made of different types of flowers\", \"similar objects\": [\"bouquet\", \"centerpiece\", \"wreath\"]}", + 62 + ], + "roman": [ + " {\"type\": \"culture\", \"description\": \"ancient civilization; originated in Italy; known for its art, literature, and architecture\", \"similar objects\": [\"Greek\", \"Egyptian\", \"Mesopotamian\"]}", + 62 + ], + "rectangle sign": [ + " {\"type\": \"sign\", \"description\": \"has four sides; could be made of metal or plastic; could have words or symbols on it\", \"similar objects\": [\"square sign\", \"triangle sign\", \"circle sign\"]}", + 62 + ], + "left headlight": [ + "\n{\"type\": \"automotive part\", \"description\": \"part of a car; located on the left side of the car; emits light\", \"similar objects\": [\"right headlight\", \"taillight\", \"fog light\"]}", + 62 + ], + "grassy": [ + " {\"type\": \"landscape\", \"description\": \"green; could be short or tall; could be soft or hard; could be wet or dry\", \"similar objects\": [\"lawn\", \"meadow\", \"field\"]}", + 62 + ], + "maple leaf": [ + " {\"type\": \"plant\", \"description\": \"green; has five lobes; could be red in autumn\", \"similar objects\": [\"oak leaf\", \"elm leaf\", \"sycamore leaf\"]}", + 62 + ], + "knit hat": [ + " {\"type\": \"clothing item\", \"description\": \"made of wool; could be in different colors; could have a pom-pom on top\", \"similar objects\": [\"scarf\", \"gloves\", \"beanie\"]}", + 61 + ], + "overalls": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could have straps; could have pockets\", \"similar objects\": [\"jeans\", \"jumpsuit\", \"coveralls\"]}", + 61 + ], + "pizza slices": [ + " {\"type\": \"food\", \"description\": \"round; could be cut into triangular pieces; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"sandwich\", \"burger\", \"taco\"]}", + 61 + ], + "cards": [ + " {\"type\": \"game tool\", \"description\": \"rectangular; could be made of paper or plastic; could have numbers or symbols\", \"similar objects\": [\"dice\", \"board game\", \"poker chips\"]}", + 61 + ], + "bookshelves": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have multiple shelves; could be used to store books\", \"similar objects\": [\"cabinet\", \"wardrobe\", \"cupboard\"]}", + 61 + ], + "bite": [ + " {\"type\": \"action\", \"description\": \"to use teeth to cut or tear something; to take a small amount of food into the mouth\", \"similar objects\": [\"chew\", \"gnaw\", \"nibble\"]}", + 61 + ], + "metal fence post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could have pointed tips\", \"similar objects\": [\"wooden fence post\", \"metal railing\", \"concrete post\"]}", + 61 + ], + "round bowl": [ + " {\"type\": \"utensil\", \"description\": \"round; could be made of ceramic, glass, or metal; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 61 + ], + "rear leg": [ + " {\"type\": \"body part\", \"description\": \"part of the hind limb; could be used for walking and running; could be found in animals\", \"similar objects\": [\"front leg\", \"arm\", \"wing\"]}", + 61 + ], + "soap bottle": [ + " {\"type\": \"cleaning tool\", \"description\": \"transparent; could be plastic or glass; could have a pump\", \"similar objects\": [\"shampoo bottle\", \"detergent bottle\", \"hand sanitizer bottle\"]}", + 61 + ], + "marking": [ + " {\"type\": \"writing tool\", \"description\": \"could be a pen, pencil, or marker; could be used to write on paper or other surfaces\", \"similar objects\": [\"pen\", \"pencil\", \"highlighter\"]}", + 61 + ], + "bath mat": [ + " {\"type\": \"bathroom accessory\", \"description\": \"rectangular; made of absorbent material; could have a non-slip backing\", \"similar objects\": [\"bath rug\", \"bathroom rug\", \"shower mat\"]}", + 61 + ], + "childs": [ + "\n{\"type\": \"person\", \"description\": \"young; could be of any gender; could be of any age\", \"similar objects\": [\"teenager\", \"infant\", \"adult\"]}", + 61 + ], + "ships": [ + " {\"type\": \"transportation\", \"description\": \"large; could have sails; could be powered by engines\", \"similar objects\": [\"boats\", \"yachts\", \"submarines\"]}", + 61 + ], + "angel": [ + " {\"type\": \"mythological creature\", \"description\": \"winged humanoid; could have a halo; could be carrying a harp\", \"similar objects\": [\"fairy\", \"mermaid\", \"unicorn\"]}", + 61 + ], + "lettuce sandwich": [ + "\n{\"type\": \"food\", \"description\": \"sandwich with lettuce, mayonnaise, and other ingredients; could be served with other vegetables\", \"similar objects\": [\"hamburger\", \"tuna sandwich\", \"grilled cheese sandwich\"]}", + 61 + ], + "stop signs": [ + " {\"type\": \"traffic sign\", \"description\": \"octagonal; red background with white letters; could be reflective\", \"similar objects\": [\"yield sign\", \"speed limit sign\", \"no parking sign\"]}", + 61 + ], + "plastic water bottle": [ + "\n{\"type\": \"container\", \"description\": \"transparent; cylindrical; has a lid; could be reusable\", \"similar objects\": [\"glass bottle\", \"thermos\", \"mug\"]}", + 61 + ], + "receipt": [ + " {\"type\": \"document\", \"description\": \"paper; could have a barcode; could have a signature\", \"similar objects\": [\"invoice\", \"bill\", \"statement\"]}", + 61 + ], + "canvas": [ + " {\"type\": \"material\", \"description\": \"thick, woven fabric; could be used for painting\", \"similar objects\": [\"linen\", \"burlap\", \"cotton\"]}", + 61 + ], + "bandage": [ + " {\"type\": \"medical tool\", \"description\": \"long strip of cloth; could be used to cover wounds\", \"similar objects\": [\"gauze\", \"tape\", \"plaster\"]}", + 61 + ], + "ruler": [ + " {\"type\": \"measuring tool\", \"description\": \"long; could be made of plastic or metal; has markings\", \"similar objects\": [\"tape measure\", \"protractor\", \"compass\"]}", + 61 + ], + "calculator": [ + " {\"type\": \"electronic device\", \"description\": \"small; has a display screen; could be used for calculations\", \"similar objects\": [\"computer\", \"smartphone\", \"tablet\"]}", + 61 + ], + "gown": [ + " {\"type\": \"clothing\", \"description\": \"long, loose-fitting; could be made of silk or satin; could have a train\", \"similar objects\": [\"dress\", \"robe\", \"tunic\"]}", + 61 + ], + "rams": [ + " {\"type\": \"animal\", \"description\": \"large, horned, four-legged mammal; could have thick fur; could be found in herds\", \"similar objects\": [\"sheep\", \"goats\", \"cows\"]}", + 61 + ], + "thumbs": [ + " {\"type\": \"body part\", \"description\": \"two short, round digits on each hand; used for gripping and pointing\", \"similar objects\": [\"fingers\", \"toes\", \"elbows\"]}", + 60 + ], + "orange slice": [ + " {\"type\": \"food\", \"description\": \"round; orange in color; could be cut into wedges\", \"similar objects\": [\"lemon slice\", \"apple slice\", \"grapefruit slice\"]}", + 60 + ], + "metal pan": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round, made of metal; could have a handle\", \"similar objects\": [\"skillet\", \"frying pan\", \"wok\"]}", + 60 + ], + "stovetop": [ + " {\"type\": \"cooking tool\", \"description\": \"flat surface; has burners; could be electric or gas\", \"similar objects\": [\"oven\", \"microwave\", \"grill\"]}", + 60 + ], + "areas": [ + " {\"type\": \"geographical feature\", \"description\": \"a region or space, typically with homogeneous features; could be divided into smaller areas\", \"similar objects\": [\"regions\", \"districts\", \"neighborhoods\"]}", + 60 + ], + "glass shelf": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be made of glass or plastic; could be used to store items\", \"similar objects\": [\"bookshelf\", \"cupboard\", \"wardrobe\"]}", + 60 + ], + "fence pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of metal or wood; could be used to support a fence\", \"similar objects\": [\"post\", \"stake\", \"pillar\"]}", + 60 + ], + "luggage cart": [ + " {\"type\": \"transportation tool\", \"description\": \"has four wheels; could be folded; could be pushed or pulled\", \"similar objects\": [\"hand truck\", \"dolly\", \"shopping cart\"]}", + 60 + ], + "calm body": [ + "\n{\"type\": \"body of water\", \"description\": \"smooth surface; could be a lake, river, or ocean; could be surrounded by mountains or trees\", \"similar objects\": [\"pond\", \"lagoon\", \"sea\"]}", + 60 + ], + "banana peel": [ + " {\"type\": \"waste\", \"description\": \"yellow; thin and slippery; could be composted\", \"similar objects\": [\"apple peel\", \"orange peel\", \"lemon peel\"]}", + 60 + ], + "yield sign": [ + " {\"type\": \"traffic sign\", \"description\": \"triangle; yellow background; black lettering\", \"similar objects\": [\"stop sign\", \"speed limit sign\", \"no parking sign\"]}", + 60 + ], + "silver pipe": [ + " {\"type\": \"utensil\", \"description\": \"long, cylindrical, made of silver; could be used for smoking\", \"similar objects\": [\"pipe\", \"bong\", \"hookah\"]}", + 60 + ], + "raincoat": [ + " {\"type\": \"clothing\", \"description\": \"waterproof; could be made of plastic or rubber; could be transparent; could have a hood\", \"similar objects\": [\"umbrella\", \"jacket\", \"rain boots\"]}", + 60 + ], + "sea water": [ + " {\"type\": \"liquid\", \"description\": \"salty; could be blue or green; could contain small organisms\", \"similar objects\": [\"river water\", \"lake water\", \"ocean water\"]}", + 60 + ], + "cat whiskers": [ + " {\"type\": \"body part\", \"description\": \"long, thin, and stiff hairs on the face of a cat\", \"similar objects\": [\"cat fur\", \"cat claws\", \"cat eyes\"]}", + 60 + ], + "cloudy gray sky": [ + "\n{\"type\": \"weather\", \"description\": \"gray; could be with some white clouds; could be with some rain\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"foggy sky\"]}", + 60 + ], + "tomato slice": [ + " {\"type\": \"food\", \"description\": \"round; red; could be sliced into pieces\", \"similar objects\": [\"onion slice\", \"apple slice\", \"cheese slice\"]}", + 60 + ], + "ink": [ + " {\"type\": \"writing tool\", \"description\": \"black; could be used for writing or drawing; could be in liquid or solid form\", \"similar objects\": [\"pen\", \"marker\", \"pencil\"]}", + 60 + ], + "giraffe standing": [ + "\n{\"type\": \"animal\", \"description\": \"tall; has a long neck; has a spotted pattern; has long legs; could be standing\", \"similar objects\": [\"elephant\", \"zebra\", \"gazelle\"]}", + 60 + ], + "soap holder": [ + " {\"type\": \"bathroom accessory\", \"description\": \"could be made of plastic or metal; has a hole for the soap bar; could be attached to the wall\", \"similar objects\": [\"toothbrush holder\", \"towel rack\", \"shower caddy\"]}", + 60 + ], + "cloudy day": [ + "\n{\"type\": \"weather\", \"description\": \"overcast sky; could be raining; could be windy; could be cold\", \"similar objects\": [\"rainy day\", \"sunny day\", \"snowy day\"]}", + 60 + ], + "sword": [ + " {\"type\": \"weapon\", \"description\": \"long, sharp blade; could have a handle; could be made of metal\", \"similar objects\": [\"dagger\", \"axe\", \"spear\"]}", + 60 + ], + "motorbikes": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could have an engine; could have a seat for two people\", \"similar objects\": [\"scooter\", \"bicycle\", \"moped\"]}", + 60 + ], + "food tray": [ + " {\"type\": \"serving tool\", \"description\": \"rectangular; could be made of plastic or metal; could have compartments\", \"similar objects\": [\"plate\", \"bowl\", \"dish\"]}", + 60 + ], + "braid": [ + " {\"type\": \"hairstyle\", \"description\": \"interweaving of three or more strands of hair; could be made of synthetic fibers\", \"similar objects\": [\"ponytail\", \"bun\", \"updo\"]}", + 60 + ], + "rubber band": [ + " {\"type\": \"stationery item\", \"description\": \"elastic; could be used to bind things together\", \"similar objects\": [\"paper clip\", \"binder clip\", \"staple\"]}", + 60 + ], + "chain-link fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal; has diamond-shaped openings; could be used to enclose an area\", \"similar objects\": [\"barbed wire fence\", \"wooden fence\", \"brick wall\"]}", + 60 + ], + "wetsuits": [ + " {\"type\": \"clothing\", \"description\": \"tight-fitting; made of neoprene; designed to keep the body warm in cold water\", \"similar objects\": [\"drysuits\", \"swimsuits\", \"diving suits\"]}", + 60 + ], + "dude": [ + " {\"type\": \"slang\", \"description\": \"informal term for a man; could be used to address someone\", \"similar objects\": [\"guy\", \"man\", \"bro\"]}", + 60 + ], + "cash register": [ + " {\"type\": \"machine\", \"description\": \"has a display screen; could have a keyboard; could have a drawer\", \"similar objects\": [\"printer\", \"scanner\", \"calculator\"]}", + 60 + ], + "copyright": [ + " {\"type\": \"legal protection\", \"description\": \"intellectual property protection; could be symbolized by a \u00a9\", \"similar objects\": [\"trademark\", \"patent\", \"trade secret\"]}", + 60 + ], + "fixtures": [ + " {\"type\": \"hardware\", \"description\": \"attached to walls or ceilings; could be used to hold lights or other objects\", \"similar objects\": [\"hooks\", \"brackets\", \"supports\"]}", + 60 + ], + "mini blinds": [ + " {\"type\": \"window covering\", \"description\": \"horizontal slats; could be made of wood, metal, or plastic; could be opened and closed with a cord\", \"similar objects\": [\"shades\", \"curtains\", \"drapes\"]}", + 60 + ], + "onion rings": [ + " {\"type\": \"food\", \"description\": \"round; made of onion slices; could be deep-fried\", \"similar objects\": [\"french fries\", \"potato chips\", \"onion wedges\"]}", + 60 + ], + "bell tower": [ + " {\"type\": \"architecture\", \"description\": \"tall; could have bells; could be made of stone\", \"similar objects\": [\"clock tower\", \"cathedral\", \"observatory\"]}", + 60 + ], + "ocean wave": [ + " {\"type\": \"natural phenomenon\", \"description\": \"repeatedly moving up and down; could be caused by wind or earthquake\", \"similar objects\": [\"tide\", \"tsunami\", \"hurricane\"]}", + 60 + ], + "phone booth": [ + " {\"type\": \"structure\", \"description\": \"enclosed structure; could have a phone inside; could be made of glass or metal\", \"similar objects\": [\"kiosk\", \"bus stop\", \"mailbox\"]}", + 59 + ], + "biscuit": [ + " {\"type\": \"food\", \"description\": \"round; could be sweet or savory; could be served with tea or coffee\", \"similar objects\": [\"cookie\", \"cracker\", \"cake\"]}", + 59 + ], + "orange hat": [ + "\n{\"type\": \"clothing accessory\", \"description\": \"orange; could be made of fabric; could have a brim\", \"similar objects\": [\"baseball cap\", \"beanie\", \"sun hat\"]}", + 59 + ], + "molding": [ + " {\"type\": \"building material\", \"description\": \"used to decorate walls and ceilings; could be made of wood, plaster, or stone\", \"similar objects\": [\"trim\", \"baseboard\", \"crown molding\"]}", + 59 + ], + "raspberry": [ + " {\"type\": \"fruit\", \"description\": \"red, small, has a hollow center\", \"similar objects\": [\"strawberry\", \"blackberry\", \"blueberry\"]}", + 59 + ], + "sort": [ + " {\"type\": \"verb\", \"description\": \"to arrange in order; to classify\", \"similar objects\": [\"arrange\", \"classify\", \"organize\"]}", + 59 + ], + "nightstands": [ + " {\"type\": \"furniture\", \"description\": \"small table; could have drawers; could be made of wood\", \"similar objects\": [\"dresser\", \"end table\", \"coffee table\"]}", + 59 + ], + "jean": [ + " {\"type\": \"clothing\", \"description\": \"blue; could be made of cotton; could have pockets; could be long or short\", \"similar objects\": [\"t-shirt\", \"shorts\", \"skirt\"]}", + 59 + ], + "tourist": [ + " {\"type\": \"person\", \"description\": \"carrying a camera; wearing a hat; carrying a bag\", \"similar objects\": [\"traveler\", \"explorer\", \"adventurer\"]}", + 59 + ], + "ends": [ + "\n{\"type\": \"noun\", \"description\": \"the last part of something; could be used to describe a conclusion\", \"similar objects\": [\"finish\", \"conclusion\", \"termination\"]}", + 59 + ], + "cat paw": [ + " {\"type\": \"animal body part\", \"description\": \"soft, furry, five toes; could have claws\", \"similar objects\": [\"dog paw\", \"bird foot\", \"rabbit paw\"]}", + 59 + ], + "footprint": [ + " {\"type\": \"trace\", \"description\": \"imprint of a foot; could be made of mud or sand\", \"similar objects\": [\"handprint\", \"pawprint\", \"hoofprint\"]}", + 59 + ], + "pizza pan": [ + " {\"type\": \"cooking tool\", \"description\": \"round; has a handle; could be made of metal or ceramic\", \"similar objects\": [\"baking sheet\", \"cake pan\", \"pie pan\"]}", + 59 + ], + "football": [ + " {\"type\": \"sport equipment\", \"description\": \"oval; made of leather; used for playing football\", \"similar objects\": [\"soccer ball\", \"basketball\", \"baseball\"]}", + 59 + ], + "median": [ + " {\"type\": \"statistical measure\", \"description\": \"the middle value of a set of numbers; the value that divides the set into two equal parts\", \"similar objects\": [\"mean\", \"mode\", \"range\"]}", + 59 + ], + "space bar": [ + " {\"type\": \"keyboard key\", \"description\": \"long, rectangular; located between the alphabetic keys and the numeric keys; used to create spaces between words\", \"similar objects\": [\"enter key\", \"shift key\", \"backspace key\"]}", + 59 + ], + "cliffs": [ + " {\"type\": \"geological formation\", \"description\": \"steep rock face; could be made of sedimentary rocks; could be found near the sea\", \"similar objects\": [\"caves\", \"mountains\", \"valleys\"]}", + 59 + ], + "vintage": [ + "\n{\"type\": \"style\", \"description\": \"old-fashioned; could be associated with antiques; could be associated with nostalgia\", \"similar objects\": [\"retro\", \"classic\", \"timeless\"]}", + 59 + ], + "pump": [ + " {\"type\": \"tool\", \"description\": \"cylindrical; could be used to move liquids or air; could have a handle\", \"similar objects\": [\"hose\", \"valve\", \"cylinder\"]}", + 59 + ], + "firetruck": [ + " {\"type\": \"vehicle\", \"description\": \"red; has a long ladder; could with a hose\", \"similar objects\": [\"ambulance\", \"police car\", \"garbage truck\"]}", + 59 + ], + "suits": [ + " {\"type\": \"clothing\", \"description\": \"formal; could be made of wool, cotton, or polyester; could be single-breasted or double-breasted; could have two or three buttons\", \"similar objects\": [\"blazer\", \"tuxedo\", \"vest\"]}", + 59 + ], + "wedding cake": [ + " {\"type\": \"dessert\", \"description\": \"multi-layered; could be decorated with flowers and ribbons; could be topped with figurines\", \"similar objects\": [\"birthday cake\", \"cupcake\", \"cheesecake\"]}", + 59 + ], + "bottoms": [ + " {\"type\": \"clothing\", \"description\": \"pants, shorts, skirts, etc.\", \"similar objects\": [\"jeans\", \"trousers\", \"leggings\"]}", + 59 + ], + "switches": [ + " {\"type\": \"electrical device\", \"description\": \"used to control the flow of electricity; could be manual or automatic\", \"similar objects\": [\"outlets\", \"plugs\", \"circuit breakers\"]}", + 59 + ], + "traffic cones": [ + " {\"type\": \"safety tool\", \"description\": \"orange; cone-shaped; could be reflective\", \"similar objects\": [\"barricades\", \"warning signs\", \"road signs\"]}", + 59 + ], + "decker": [ + " {\"type\": \"vehicle\", \"description\": \"double-decker bus; has two levels; could be red or white\", \"similar objects\": [\"tram\", \"train\", \"monorail\"]}", + 59 + ], + "stair": [ + " {\"type\": \"structure\", \"description\": \"has steps; could be made of wood or metal; could be curved or straight\", \"similar objects\": [\"ladder\", \"escalator\", \"elevator\"]}", + 59 + ], + "fuselage": [ + " {\"type\": \"aircraft part\", \"description\": \"main body of an aircraft; could be made of metal; could be cylindrical or tubular\", \"similar objects\": [\"wing\", \"tail\", \"cockpit\"]}", + 59 + ], + "bay": [ + " {\"type\": \"geographical feature\", \"description\": \"body of water; could be surrounded by land; could be connected to an ocean\", \"similar objects\": [\"lake\", \"river\", \"sea\"]}", + 59 + ], + "spaghetti": [ + " {\"type\": \"food\", \"description\": \"long, thin, cylindrical; could be made of wheat or rice; could be served with sauce\", \"similar objects\": [\"noodles\", \"macaroni\", \"ravioli\"]}\n\nObject detection models should focus on the shape, color, texture, size, and other physical characteristics of the object, as well as any associated objects or features. For example, for the zucchini input, the model should focus on the cylindrical shape, green color, smooth texture, and the presence of brown and rough stems. For the ambulance input, the", + 59 + ], + "muffins": [ + " {\"type\": \"food\", \"description\": \"small, round, sweet; could be made of flour, sugar, butter, eggs\", \"similar objects\": [\"cupcakes\", \"donuts\", \"cookies\"]}", + 59 + ], + "chickens": [ + " {\"type\": \"animal\", \"description\": \"small, feathered, could lay eggs; could have yellow feet and beak\", \"similar objects\": [\"ducks\", \"geese\", \"turkeys\"]}", + 59 + ], + "fork table": [ + " {\"type\": \"furniture\", \"description\": \"long; has four legs; could have a flat surface; could have a patterned top\", \"similar objects\": [\"dining table\", \"coffee table\", \"end table\"]}", + 58 + ], + "square table": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; has a flat surface; could be made of wood or metal\", \"similar objects\": [\"rectangular table\", \"round table\", \"coffee table\"]}", + 58 + ], + "burgundy": [ + " {\"type\": \"color\", \"description\": \"dark red; could be used to describe clothing, furniture, and other items\", \"similar objects\": [\"maroon\", \"scarlet\", \"crimson\"]}", + 58 + ], + "mens": [ + " {\"type\": \"clothing\", \"description\": \"designed for men; could be formal or casual; could be made of different materials\", \"similar objects\": [\"shirt\", \"pants\", \"jacket\"]}", + 58 + ], + "selection": [ + " {\"type\": \"concept\", \"description\": \"the act of choosing from a range of options\", \"similar objects\": [\"choice\", \"decision\", \"preference\"]}", + 58 + ], + "grafitti": [ + " {\"type\": \"art form\", \"description\": \"creative drawings or writings on walls or other surfaces\", \"similar objects\": [\"murals\", \"stencils\", \"street art\"]}", + 58 + ], + "bracelet woman": [ + "\n{\"type\": \"accessory\", \"description\": \"could be made of metal, plastic, or fabric; could be decorated with gems or beads; could be worn around the wrist\", \"similar objects\": [\"necklace\", \"earrings\", \"ring\"]}", + 58 + ], + "hotdog bun": [ + " {\"type\": \"food\", \"description\": \"long, white, could be toasted; could be filled with hotdog\", \"similar objects\": [\"bread roll\", \"baguette\", \"ciabatta\"]}", + 58 + ], + "policemen": [ + " {\"type\": \"profession\", \"description\": \"law enforcement officers; could wear uniforms; could carry guns\", \"similar objects\": [\"firefighter\", \"soldier\", \"security guard\"]}", + 58 + ], + "soccer field": [ + " {\"type\": \"sports field\", \"description\": \"large, rectangular; has two goals; could be grassy or artificial turf\", \"similar objects\": [\"baseball field\", \"football field\", \"tennis court\"]}", + 58 + ], + "wicker chair": [ + " {\"type\": \"furniture\", \"description\": \"made of woven materials; could have a cushion; could be used for outdoor seating\", \"similar objects\": [\"rocking chair\", \"armchair\", \"lounge chair\"]}", + 58 + ], + "desktop computer": [ + "\n{\"type\": \"electronic device\", \"description\": \"has a monitor, a CPU, a keyboard, and a mouse; could be connected to the internet\", \"similar objects\": [\"laptop\", \"tablet\", \"smartphone\"]}", + 58 + ], + "bins": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"boxes\", \"containers\", \"baskets\"]}", + 58 + ], + "kitchen floor": [ + " {\"type\": \"flooring\", \"description\": \"hard surface; could be made of tiles, wood, or linoleum; could be slippery when wet\", \"similar objects\": [\"bathroom floor\", \"hallway floor\", \"balcony floor\"]}", + 58 + ], + "camper": [ + " {\"type\": \"vehicle\", \"description\": \"large; could be used for camping; could have a kitchen and a bedroom\", \"similar objects\": [\"RV\", \"trailer\", \"motorhome\"]}", + 58 + ], + "chord": [ + " {\"type\": \"musical instrument\", \"description\": \"three or more notes played together; could be played on a guitar or piano\", \"similar objects\": [\"arpeggio\", \"scale\", \"melody\"]}", + 58 + ], + "metal sign post": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could have a rectangular shape; could have a sign attached to it\", \"similar objects\": [\"fence\", \"gate\", \"wall\"]}", + 58 + ], + "jumper": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved, usually knitted, could have a hood\", \"similar objects\": [\"sweater\", \"cardigan\", \"hoodie\"]}", + 58 + ], + "kitchen towel": [ + " {\"type\": \"cleaning tool\", \"description\": \"absorbent; could be made of cloth; could be used to dry dishes\", \"similar objects\": [\"sponge\", \"dishcloth\", \"dishrag\"]}", + 58 + ], + "beige carpet": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of wool; could be patterned\", \"similar objects\": [\"rug\", \"mat\", \"linoleum\"]}", + 58 + ], + "covers": [ + " {\"type\": \"accessory\", \"description\": \"used to cover objects; could be made of fabric, plastic, or metal\", \"similar objects\": [\"blankets\", \"sheets\", \"curtains\"]}", + 58 + ], + "jungle": [ + " {\"type\": \"environment\", \"description\": \"dense vegetation; could have animals; could have rivers\", \"similar objects\": [\"forest\", \"rainforest\", \"savanna\"]}", + 58 + ], + "wooden dock": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could be used as a platform for boats; could be used as a walkway\", \"similar objects\": [\"pier\", \"jetty\", \"wharf\"]}", + 58 + ], + "stockings": [ + " {\"type\": \"clothing\", \"description\": \"long, thin, stretchy; could be made of nylon or cotton\", \"similar objects\": [\"tights\", \"leggings\", \"socks\"]}", + 58 + ], + "m": [ + "\n{\"type\": \"letter\", \"description\": \"the thirteenth letter of the English alphabet\", \"similar objects\": [\"n\", \"o\", \"p\"]}", + 58 + ], + "drain pipe": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical; could be made of metal or plastic; could have holes\", \"similar objects\": [\"sink\", \"toilet\", \"shower\"]}", + 58 + ], + "fire engine": [ + " {\"type\": \"vehicle\", \"description\": \"red; has a loud siren; could with a hose\", \"similar objects\": [\"ambulance\", \"police car\", \"garbage truck\"]}", + 58 + ], + "silver chain link fence": [ + "\n{\"type\": \"fencing material\", \"description\": \"made of metal; has a chain link pattern; could be used for security purposes\", \"similar objects\": [\"barbed wire fence\", \"wooden fence\", \"vinyl fence\"]}", + 58 + ], + "throw pillows": [ + " {\"type\": \"decorative item\", \"description\": \"soft, square or round; could be filled with feathers or foam; could be used for decoration or comfort\", \"similar objects\": [\"cushions\", \"blankets\", \"rugs\"]}", + 58 + ], + "wall tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic, stone, or glass; could be used for decoration\", \"similar objects\": [\"floor tile\", \"brick\", \"wood panel\"]}", + 58 + ], + "busses": [ + " {\"type\": \"vehicle\", \"description\": \"long; could have multiple doors; could be yellow or white\", \"similar objects\": [\"truck\", \"van\", \"car\"]}", + 58 + ], + "forearm": [ + " {\"type\": \"body part\", \"description\": \"part of the arm between the elbow and the wrist; could be covered with skin and hair\", \"similar objects\": [\"hand\", \"upper arm\", \"lower arm\"]}", + 58 + ], + "leather seat": [ + " {\"type\": \"furniture\", \"description\": \"made of leather; could be used for sitting; could be used for decoration\", \"similar objects\": [\"sofa\", \"chair\", \"ottoman\"]}", + 58 + ], + "tree limbs": [ + " {\"type\": \"plant part\", \"description\": \"long, thin branches; could be from a variety of trees\", \"similar objects\": [\"twigs\", \"leaves\", \"roots\"]}", + 58 + ], + "metal object": [ + "\n{\"type\": \"object\", \"description\": \"hard, shiny, could be made of iron, steel, aluminum, etc.\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 58 + ], + "shadow snow": [ + " {\"type\": \"weather phenomenon\", \"description\": \"a type of snow that is darker than normal snow; usually found in high altitudes\", \"similar objects\": [\"sleet\", \"hail\", \"freezing rain\"]}", + 58 + ], + "grey cloudy sky": [ + "\n{\"type\": \"weather\", \"description\": \"overcast; could be raining; could be windy; could be dark\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"stormy sky\"]}", + 58 + ], + "sections": [ + " {\"type\": \"geometric shape\", \"description\": \"a shape that is divided into parts; could be a circle, triangle, or square\", \"similar objects\": [\"segments\", \"quadrants\", \"triangles\"]}", + 58 + ], + "automobile": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could have an engine; could have a steering wheel\", \"similar objects\": [\"car\", \"truck\", \"motorcycle\"]}", + 58 + ], + "pottery": [ + " {\"type\": \"artwork\", \"description\": \"handcrafted; could be made of clay; could be glazed\", \"similar objects\": [\"ceramics\", \"sculpture\", \"glassware\"]}", + 58 + ], + "tram": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; runs on tracks\", \"similar objects\": [\"train\", \"subway\", \"trolley\"]}", + 58 + ], + "ivory tusk": [ + " {\"type\": \"animal product\", \"description\": \"long, curved, white; could be used for carving\", \"similar objects\": [\"bone\", \"horn\", \"shell\"]}", + 58 + ], + "pages": [ + " {\"type\": \"document\", \"description\": \"sheets of paper; could be bound together; could be printed or handwritten\", \"similar objects\": [\"book\", \"magazine\", \"newspaper\"]}", + 58 + ], + "broccoli florets": [ + " {\"type\": \"vegetable\", \"description\": \"small, green, tree-like; could be steamed or boiled; could be eaten raw\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 57 + ], + "hooks": [ + " {\"type\": \"hardware\", \"description\": \"metal; could be used to hang items; could be in different shapes\", \"similar objects\": [\"nails\", \"screws\", \"bolts\"]}", + 57 + ], + "smoke stack": [ + " {\"type\": \"industrial tool\", \"description\": \"tall, cylindrical; could be made of metal; could be used to release smoke\", \"similar objects\": [\"chimney\", \"flue\", \"exhaust pipe\"]}", + 57 + ], + "napkin table": [ + " {\"type\": \"furniture\", \"description\": \"small, rectangular; could be made of wood; could have a drawer\", \"similar objects\": [\"end table\", \"coffee table\", \"side table\"]}", + 57 + ], + "shadow person": [ + " {\"type\": \"phenomenon\", \"description\": \"dark figure; could be seen in dark places; could be seen in the corner of the eye\", \"similar objects\": [\"ghost\", \"spirit\", \"apparition\"]}", + 57 + ], + "caboose": [ + " {\"type\": \"train car\", \"description\": \"rectangular; usually at the end of a train; could have a cupola\", \"similar objects\": [\"locomotive\", \"boxcar\", \"passenger car\"]}", + 57 + ], + "reigns": [ + " {\"type\": \"horse riding tool\", \"description\": \"long straps; used to control a horse\", \"similar objects\": [\"bridle\", \"halter\", \"saddle\"]}", + 57 + ], + "wedge": [ + " {\"type\": \"tool\", \"description\": \"triangular; could be used to split objects\", \"similar objects\": [\"axe\", \"hammer\", \"knife\"]}", + 57 + ], + "brown teddy": [ + "\n{\"type\": \"stuffed toy\", \"description\": \"brown; could have a bow; could be soft and cuddly\", \"similar objects\": [\"plush toy\", \"stuffed animal\", \"doll\"]}", + 57 + ], + "silver helmet": [ + " {\"type\": \"protective gear\", \"description\": \"made of metal; has a visor; could be used for sports or military\", \"similar objects\": [\"helmet\", \"goggles\", \"mask\"]}", + 57 + ], + "cake plate": [ + " {\"type\": \"serving tool\", \"description\": \"round; could be made of glass or ceramic; could have a pedestal\", \"similar objects\": [\"platter\", \"tray\", \"bowl\"]}", + 57 + ], + "th": [ + "\n{\"type\": \"letter\", \"description\": \"two letters; the second letter of the English alphabet; used in many words\", \"similar objects\": [\"te\", \"ti\", \"to\"]}", + 57 + ], + "control knobs": [ + " {\"type\": \"control tool\", \"description\": \"round; could be used to adjust settings; could be made of metal or plastic\", \"similar objects\": [\"buttons\", \"switches\", \"dials\"]}", + 57 + ], + "metal table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of metal; could have four legs\", \"similar objects\": [\"wooden table\", \"plastic table\", \"glass table\"]}", + 57 + ], + "foamy": [ + " {\"type\": \"texture\", \"description\": \"light and airy; could be made of bubbles; could be white or colored\", \"similar objects\": [\"fluffy\", \"airy\", \"spongy\"]}", + 57 + ], + "cat ears": [ + " {\"type\": \"accessory\", \"description\": \"pointy; could be made of fabric; could be attached to a headband\", \"similar objects\": [\"rabbit ears\", \"mouse ears\", \"unicorn horn\"]}", + 57 + ], + "video camera": [ + " {\"type\": \"recording device\", \"description\": \"has a lens; could be handheld; could be connected to a tripod\", \"similar objects\": [\"camcorder\", \"digital camera\", \"webcam\"]}", + 57 + ], + "wildflowers": [ + " {\"type\": \"plant\", \"description\": \"various colors; could be found in meadows; could be used for decoration\", \"similar objects\": [\"daisies\", \"sunflowers\", \"tulips\"]}", + 57 + ], + "leather chair": [ + " {\"type\": \"furniture\", \"description\": \"made of leather; could have armrests; could have a cushion\", \"similar objects\": [\"sofa\", \"ottoman\", \"recliner\"]}", + 57 + ], + "orange wall": [ + "\n{\"type\": \"decoration\", \"description\": \"orange-colored wall; could be painted or wallpapered\", \"similar objects\": [\"yellow wall\", \"blue wall\", \"green wall\"]}", + 57 + ], + "sunny sky": [ + " {\"type\": \"weather\", \"description\": \"blue; could have white clouds; could be bright\", \"similar objects\": [\"rainy sky\", \"cloudy sky\", \"snowy sky\"]}", + 57 + ], + "leather bag": [ + " {\"type\": \"accessory\", \"description\": \"made of leather; could be used to store items; could have straps\", \"similar objects\": [\"backpack\", \"purse\", \"wallet\"]}", + 57 + ], + "orange kite": [ + "\n{\"type\": \"toy\", \"description\": \"orange; has a tail; could be flown in the sky\", \"similar objects\": [\"balloon\", \"parachute\", \"paper plane\"]}", + 57 + ], + "light fixtures": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the ceiling; could be made of metal or glass; could have multiple bulbs\", \"similar objects\": [\"chandelier\", \"ceiling fan\", \"pendant light\"]}", + 57 + ], + "chrome faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"shiny, silver, has a handle\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"sink faucet\"]}", + 57 + ], + "grey carpet": [ + " {\"type\": \"floor covering\", \"description\": \"grey; could be made of wool; could be woven\", \"similar objects\": [\"rug\", \"mat\", \"linoleum\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\", and \"", + 57 + ], + "bud": [ + " {\"type\": \"plant part\", \"description\": \"small, green, could be found at the end of a stem; could be a flower bud\", \"similar objects\": [\"leaf\", \"petal\", \"stamen\"]}", + 57 + ], + "articles": [ + " {\"type\": \"writing\", \"description\": \"written pieces of work; could be in the form of essays, stories, reports, etc.\", \"similar objects\": [\"essays\", \"stories\", \"reports\"]}", + 57 + ], + "plastic box": [ + " {\"type\": \"container\", \"description\": \"transparent; could be square or rectangular; could be used to store items\", \"similar objects\": [\"basket\", \"bag\", \"bin\"]}", + 57 + ], + "parasol": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of fabric; could be used to protect from sun\", \"similar objects\": [\"umbrella\", \"hat\", \"sunglasses\"]}", + 57 + ], + "boy skateboard": [ + "\n{\"type\": \"sport equipment\", \"description\": \"long board with four wheels; could be used for skateboarding\", \"similar objects\": [\"scooter\", \"rollerblades\", \"longboard\"]}", + 57 + ], + "granite": [ + " {\"type\": \"rock\", \"description\": \"hard, gray, crystalline; could be used for countertops\", \"similar objects\": [\"marble\", \"limestone\", \"slate\"]}", + 57 + ], + "winter hat": [ + " {\"type\": \"clothing item\", \"description\": \"warm; could be made of wool; could have a pom-pom on top\", \"similar objects\": [\"scarf\", \"gloves\", \"earmuffs\"]}", + 57 + ], + "tail feather": [ + " {\"type\": \"bird body part\", \"description\": \"long, thin, and colorful; could be curved; could be used for flight\", \"similar objects\": [\"wing feather\", \"beak\", \"claw\"]}", + 57 + ], + "bicycle tire": [ + " {\"type\": \"bicycle part\", \"description\": \"round; has a tube; could be made of rubber\", \"similar objects\": [\"bicycle wheel\", \"bicycle chain\", \"bicycle seat\"]}", + 57 + ], + "concrete bench": [ + " {\"type\": \"furniture\", \"description\": \"long, rectangular; made of concrete; could have armrests\", \"similar objects\": [\"wooden bench\", \"stone bench\", \"metal bench\"]}", + 57 + ], + "grease": [ + " {\"type\": \"lubricant\", \"description\": \"oily, slippery, could be used to reduce friction\", \"similar objects\": [\"oil\", \"lubricant\", \"petroleum jelly\"]}", + 57 + ], + "clock roman numerals": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has roman numerals; could have two hands\", \"similar objects\": [\"watch\", \"hourglass\", \"sundial\"]}", + 57 + ], + "mountain side": [ + " {\"type\": \"landscape\", \"description\": \"rugged terrain; could have trees, rocks, and snow; could have a steep incline\", \"similar objects\": [\"cliff\", \"valley\", \"hillside\"]}", + 57 + ], + "terminal": [ + " {\"type\": \"electronic device\", \"description\": \"could be a computer, a phone, or a tablet; could be used to access the internet\", \"similar objects\": [\"laptop\", \"desktop\", \"smartphone\"]}", + 57 + ], + "barren": [ + " {\"type\": \"landscape\", \"description\": \"land with no vegetation; could be sandy or rocky\", \"similar objects\": [\"desert\", \"tundra\", \"savanna\"]}", + 56 + ], + "growth": [ + " {\"type\": \"process\", \"description\": \"increase in size, number, or amount; could be related to development\", \"similar objects\": [\"development\", \"expansion\", \"progress\"]}", + 56 + ], + "sandy ground": [ + " {\"type\": \"landscape\", \"description\": \"dry, loose, yellowish-brown; could have small rocks\", \"similar objects\": [\"desert\", \"beach\", \"dirt\"]}", + 56 + ], + "cruise ship": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple decks; could have a swimming pool; could have a restaurant\", \"similar objects\": [\"ferry\", \"yacht\", \"barge\"]}", + 56 + ], + "wicker": [ + " {\"type\": \"material\", \"description\": \"made of woven plant fibers; could be used to make furniture\", \"similar objects\": [\"bamboo\", \"rattan\", \"willow\"]}", + 56 + ], + "planters": [ + " {\"type\": \"gardening tool\", \"description\": \"could be made of plastic, metal, or wood; could have a handle; could have a drainage hole\", \"similar objects\": [\"pots\", \"containers\", \"flower boxes\"]}", + 56 + ], + "umpires": [ + " {\"type\": \"sports official\", \"description\": \"wears a black and white striped shirt; carries a whistle; makes decisions on the field\", \"similar objects\": [\"referee\", \"linesman\", \"judge\"]}", + 56 + ], + "taxis": [ + " {\"type\": \"vehicle\", \"description\": \"yellow; has a meter; could have a sign on the roof\", \"similar objects\": [\"bus\", \"car\", \"truck\"]}", + 56 + ], + "tights": [ + " {\"type\": \"clothing\", \"description\": \"tight-fitting garment; could be made of nylon or spandex; could be worn by both men and women\", \"similar objects\": [\"leggings\", \"stockings\", \"pantyhose\"]}", + 56 + ], + "car window": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be opened and closed; could be tinted\", \"similar objects\": [\"windshield\", \"door window\", \"rear window\"]}", + 56 + ], + "gray jacket": [ + " {\"type\": \"clothing\", \"description\": \"long sleeve; could be made of wool; could have a zipper\", \"similar objects\": [\"coat\", \"hoodie\", \"sweater\"]}", + 56 + ], + "navy": [ + " {\"type\": \"military force\", \"description\": \"blue uniforms; could have a flag; could have a ship\", \"similar objects\": [\"army\", \"air force\", \"marines\"]}", + 56 + ], + "toy car": [ + " {\"type\": \"toy\", \"description\": \"small, could be made of plastic; could have four wheels\", \"similar objects\": [\"action figure\", \"doll\", \"building blocks\"]}", + 56 + ], + "aluminum foil": [ + " {\"type\": \"kitchen tool\", \"description\": \"thin, silver, shiny; could be used to wrap food\", \"similar objects\": [\"cling wrap\", \"baking paper\", \"parchment paper\"]}", + 56 + ], + "stall": [ + " {\"type\": \"structure\", \"description\": \"could be made of wood or metal; has a roof; could be used for selling goods\", \"similar objects\": [\"kiosk\", \"booth\", \"stand\"]}", + 56 + ], + "flush handle": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be made of metal; used to flush the toilet\", \"similar objects\": [\"toilet handle\", \"shower handle\", \"faucet handle\"]}", + 56 + ], + "wall mirror": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; could be framed; could be hung on the wall\", \"similar objects\": [\"picture frame\", \"wall clock\", \"wall art\"]}", + 56 + ], + "clay tennis court": [ + " {\"type\": \"sports court\", \"description\": \"made of clay; has a net in the middle; could be green or red\", \"similar objects\": [\"grass tennis court\", \"badminton court\", \"basketball court\"]}", + 56 + ], + "lava lamp": [ + " {\"type\": \"decorative item\", \"description\": \"round; has a bulb inside; liquid inside could be colored\", \"similar objects\": [\"glow lamp\", \"neon lamp\", \"fluorescent lamp\"]}", + 56 + ], + "kitchen window": [ + " {\"type\": \"building component\", \"description\": \"rectangular; could be made of glass; could be opened\", \"similar objects\": [\"door\", \"wall\", \"ceiling\"]}", + 56 + ], + "gift": [ + " {\"type\": \"object\", \"description\": \"could be wrapped in paper; could be of any shape or size; could be given to someone as a present\", \"similar objects\": [\"present\", \"token\", \"trophy\"]}", + 56 + ], + "coconut": [ + " {\"type\": \"fruit\", \"description\": \"round, brown, has a hard shell; could have white flesh inside\", \"similar objects\": [\"avocado\", \"mango\", \"papaya\"]}", + 56 + ], + "pink hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; could be made of fabric; could have a brim\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}", + 56 + ], + "classroom": [ + " {\"type\": \"room\", \"description\": \"could have desks and chairs; could have a whiteboard; could have a projector\", \"similar objects\": [\"library\", \"auditorium\", \"gym\"]}", + 56 + ], + "metal basket": [ + " {\"type\": \"container\", \"description\": \"made of metal; could have a handle; could be used for storage\", \"similar objects\": [\"plastic basket\", \"wooden basket\", \"cloth basket\"]}", + 56 + ], + "outdoor": [ + "\n{\"type\": \"environment\", \"description\": \"open air; could be in a park, forest, beach, etc.\", \"similar objects\": [\"indoor\", \"garden\", \"backyard\"]}", + 56 + ], + "bus door": [ + " {\"type\": \"transportation tool\", \"description\": \"large, rectangular; could be opened and closed; could be automatic or manual\", \"similar objects\": [\"car door\", \"elevator door\", \"airplane door\"]}", + 56 + ], + "train window": [ + " {\"type\": \"transportation window\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"airplane window\", \"bus window\", \"boat window\"]}", + 56 + ], + "direction sign": [ + " {\"type\": \"road sign\", \"description\": \"rectangular; could be made of metal; could have arrows or words on it\", \"similar objects\": [\"stop sign\", \"yield sign\", \"warning sign\"]}", + 56 + ], + "cracker": [ + " {\"type\": \"food\", \"description\": \"thin, crispy, could be salty or sweet; could be round or square\", \"similar objects\": [\"cookie\", \"pretzel\", \"biscuit\"]}", + 56 + ], + "telephone wires": [ + " {\"type\": \"communication tool\", \"description\": \"long, thin, black wires; could be connected to poles\", \"similar objects\": [\"cable wires\", \"fiber optic cables\", \"satellite dishes\"]}", + 56 + ], + "palms": [ + " {\"type\": \"plant\", \"description\": \"green; could have long leaves; could be used for decoration\", \"similar objects\": [\"ferns\", \"succulents\", \"cacti\"]}", + 56 + ], + "shadow tree": [ + " {\"type\": \"landscape\", \"description\": \"tree with a shadow; could be a silhouette\", \"similar objects\": [\"mountain\", \"river\", \"lake\"]}", + 56 + ], + "plunger": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has a rubber cup at the end\", \"similar objects\": [\"mop\", \"broom\", \"vacuum cleaner\"]}", + 56 + ], + "stone bench": [ + " {\"type\": \"furniture\", \"description\": \"made of stone; could have a backrest; could be used for sitting\", \"similar objects\": [\"wooden bench\", \"garden bench\", \"park bench\"]}", + 56 + ], + "fist": [ + " {\"type\": \"body part\", \"description\": \"closed hand; could be used to punch\", \"similar objects\": [\"palm\", \"elbow\", \"knee\"]}", + 56 + ], + "donkeys": [ + " {\"type\": \"animal\", \"description\": \"long ears; short tail; gray or brown fur; could be ridden\", \"similar objects\": [\"horses\", \"mules\", \"zebras\"]}", + 56 + ], + "snowman": [ + " {\"type\": \"sculpture\", \"description\": \"made of snow; has a carrot nose; has two coal eyes; has a hat and scarf\", \"similar objects\": [\"snow angel\", \"snow sculpture\", \"snow castle\"]}", + 55 + ], + "apple laptop": [ + "\n{\"type\": \"electronic device\", \"description\": \"portable computer; has a screen; could be connected to other devices\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 55 + ], + "silver handles": [ + " {\"type\": \"hardware\", \"description\": \"shiny, metallic, could be used to open doors or drawers\", \"similar objects\": [\"knobs\", \"hinges\", \"locks\"]}", + 55 + ], + "water pipe": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical; could be made of metal or plastic; could have a valve\", \"similar objects\": [\"hose\", \"valve\", \"faucet\"]}", + 55 + ], + "mozzarella": [ + " {\"type\": \"cheese\", \"description\": \"white; soft; could be shredded; could be melted\", \"similar objects\": [\"parmesan\", \"ricotta\", \"feta\"]}", + 55 + ], + "walk sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; has a walking figure; could be red or green\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 55 + ], + "stores": [ + " {\"type\": \"building\", \"description\": \"could have multiple floors; could have a variety of goods; could have a cashier\", \"similar objects\": [\"mall\", \"supermarket\", \"department store\"]}", + 55 + ], + "cement curb": [ + " {\"type\": \"construction material\", \"description\": \"gray; could be used to form a border; could be used to separate roads\", \"similar objects\": [\"concrete block\", \"asphalt curb\", \"gravel curb\"]}", + 55 + ], + "porcelain plate": [ + "\n{\"type\": \"dishware\", \"description\": \"smooth, white, round; could be decorated with patterns; could be used for serving food\", \"similar objects\": [\"bowl\", \"cup\", \"saucer\"]}", + 55 + ], + "delivery truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have a logo; could have a lift gate\", \"similar objects\": [\"van\", \"pickup truck\", \"box truck\"]}", + 55 + ], + "calm waters": [ + " {\"type\": \"natural phenomenon\", \"description\": \"smooth surface; no waves; could be blue or green\", \"similar objects\": [\"clear sky\", \"sunny day\", \"rainbow\"]}", + 55 + ], + "toy train": [ + " {\"type\": \"toy\", \"description\": \"small, usually made of plastic; could have multiple cars connected together; could have a locomotive\", \"similar objects\": [\"toy car\", \"toy airplane\", \"toy boat\"]}", + 55 + ], + "wooden poles": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of wood; could be used for construction\", \"similar objects\": [\"bricks\", \"concrete blocks\", \"steel beams\"]}", + 55 + ], + "packets": [ + " {\"type\": \"container\", \"description\": \"small, rectangular, could be made of paper or plastic; could be sealed\", \"similar objects\": [\"envelope\", \"box\", \"bag\"]}", + 55 + ], + "blue clock": [ + "\n{\"type\": \"timekeeping tool\", \"description\": \"round; has a blue color; could have numbers or symbols on it\", \"similar objects\": [\"watch\", \"alarm clock\", \"wall clock\"]}", + 55 + ], + "snack": [ + " {\"type\": \"food\", \"description\": \"small, easy to eat; could be salty or sweet\", \"similar objects\": [\"candy\", \"chips\", \"nuts\"]}", + 55 + ], + "outfield": [ + " {\"type\": \"sports area\", \"description\": \"large area outside the infield; used in baseball and cricket\", \"similar objects\": [\"infield\", \"pitch\", \"court\"]}", + 55 + ], + "side road": [ + " {\"type\": \"road\", \"description\": \"smaller than a main road; could be used as a shortcut\", \"similar objects\": [\"highway\", \"street\", \"alley\"]}", + 55 + ], + "food processor": [ + " {\"type\": \"kitchen appliance\", \"description\": \"has a bowl, blades, and a lid; could be used to chop, grind, and mix food\", \"similar objects\": [\"blender\", \"mixer\", \"juicer\"]}", + 55 + ], + "front car": [ + " {\"type\": \"vehicle part\", \"description\": \"the part of a car that is in the front; could have headlights, grille, and bumper\", \"similar objects\": [\"hood\", \"windshield\", \"trunk\"]}", + 55 + ], + "hubcap": [ + " {\"type\": \"automotive part\", \"description\": \"round; covers the wheel of a car; could be made of metal or plastic\", \"similar objects\": [\"wheel cover\", \"wheel trim\", \"wheel hub\"]}", + 55 + ], + "signature": [ + " {\"type\": \"documentation\", \"description\": \"unique handwriting of a person; could be used to verify identity\", \"similar objects\": [\"stamp\", \"seal\", \"autograph\"]}", + 55 + ], + "barriers": [ + " {\"type\": \"structure\", \"description\": \"could be made of metal or wood; could be used to block a path\", \"similar objects\": [\"fences\", \"gates\", \"walls\"]}", + 55 + ], + "sunshine": [ + " {\"type\": \"natural phenomenon\", \"description\": \"bright, yellowish light; could be seen during the day\", \"similar objects\": [\"moonlight\", \"starlight\", \"rainbow\"]}", + 55 + ], + "beach towel": [ + " {\"type\": \"accessory\", \"description\": \"large, rectangular; could be made of cotton or microfiber; could be printed with colorful patterns\", \"similar objects\": [\"bath towel\", \"blanket\", \"bathrobe\"]}", + 55 + ], + "shapes": [ + " {\"type\": \"geometric objects\", \"description\": \"could be circles, squares, triangles, etc.\", \"similar objects\": [\"patterns\", \"figures\", \"forms\"]}", + 55 + ], + "conveyor belt": [ + " {\"type\": \"transportation tool\", \"description\": \"long, continuous loop; could be made of rubber or plastic; could be used to transport items\", \"similar objects\": [\"escalator\", \"elevator\", \"roller coaster\"]}", + 55 + ], + "waterway": [ + " {\"type\": \"natural feature\", \"description\": \"a body of water; could be a river, lake, or stream\", \"similar objects\": [\"ocean\", \"pond\", \"canal\"]}", + 55 + ], + "shack": [ + " {\"type\": \"structure\", \"description\": \"small, wooden, could have a thatched roof\", \"similar objects\": [\"hut\", \"cabin\", \"shed\"]}", + 55 + ], + "license": [ + " {\"type\": \"document\", \"description\": \"official document; could be used to identify a person; could be used to prove a person's legal rights\", \"similar objects\": [\"passport\", \"ID card\", \"certificate\"]}", + 54 + ], + "valve": [ + " {\"type\": \"mechanical device\", \"description\": \"used to control the flow of liquids and gases; could be opened and closed\", \"similar objects\": [\"pump\", \"regulator\", \"actuator\"]}", + 54 + ], + "ribs": [ + " {\"type\": \"food\", \"description\": \"meaty; could be cooked with barbecue sauce; could be served with coleslaw\", \"similar objects\": [\"steak\", \"chicken\", \"pork\"]}", + 54 + ], + "blue jeans": [ + " {\"type\": \"clothing\", \"description\": \"blue, long pants; could have pockets; could have a zipper\", \"similar objects\": [\"jeans\", \"trousers\", \"shorts\"]}", + 54 + ], + "hospital": [ + " {\"type\": \"building\", \"description\": \"large; could have multiple floors; could have a sign with a red cross\", \"similar objects\": [\"clinic\", \"school\", \"library\"]}", + 54 + ], + "roadside": [ + " {\"type\": \"location\", \"description\": \"area near a road; could have trees, buildings, and other structures\", \"similar objects\": [\"highway\", \"street\", \"intersection\"]}", + 54 + ], + "shops": [ + " {\"type\": \"building\", \"description\": \"could have multiple floors; could have a variety of goods; could have a cashier\", \"similar objects\": [\"mall\", \"market\", \"store\"]}", + 54 + ], + "plastic basket": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have handles\", \"similar objects\": [\"box\", \"bag\", \"bin\"]}", + 54 + ], + "supplies": [ + " {\"type\": \"items\", \"description\": \"various items used for a specific purpose; could include paper, pens, pencils, notebooks, etc.\", \"similar objects\": [\"materials\", \"goods\", \"products\"]}", + 54 + ], + "parade": [ + " {\"type\": \"event\", \"description\": \"a procession of people or vehicles, often accompanied by marching bands, floats, and other entertainment\", \"similar objects\": [\"festival\", \"celebration\", \"march\"]}", + 54 + ], + "blue couch": [ + "\n{\"type\": \"furniture\", \"description\": \"large, upholstered, blue; could have armrests and cushions\", \"similar objects\": [\"sofa\", \"loveseat\", \"armchair\"]}", + 54 + ], + "peanut butter": [ + " {\"type\": \"food\", \"description\": \"smooth, creamy, spreadable; could be used as a sandwich spread\", \"similar objects\": [\"jelly\", \"jam\", \"honey\"]}", + 54 + ], + "racer": [ + " {\"type\": \"vehicle\", \"description\": \"fast; could have two or four wheels; could have a driver\", \"similar objects\": [\"motorcycle\", \"car\", \"truck\"]}", + 54 + ], + "silver camera": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; has a lens; could be used to take pictures\", \"similar objects\": [\"video camera\", \"smartphone\", \"digital camera\"]}", + 54 + ], + "porcelain toilet bowl": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"round; made of porcelain; has a flush handle\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 54 + ], + "swim trunks": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting shorts; could be made of nylon or spandex; could have a drawstring waistband\", \"similar objects\": [\"board shorts\", \"swimsuit\", \"bikini\"]}", + 54 + ], + "sandy beach": [ + " {\"type\": \"landscape\", \"description\": \"large area of sand; could have rocks and shells; could have waves and seagulls\", \"similar objects\": [\"desert\", \"mountain\", \"forest\"]}", + 54 + ], + "tide": [ + " {\"type\": \"natural phenomenon\", \"description\": \"the rise and fall of sea levels; could be affected by the moon's gravitational pull\", \"similar objects\": [\"tsunami\", \"storm surge\", \"tidal bore\"]}", + 54 + ], + "d": [ + "\n{\"type\": \"letter\", \"description\": \"fourth letter of the English alphabet; could be capitalized or lowercase\", \"similar objects\": [\"a\", \"b\", \"c\"]}", + 54 + ], + "jean pants": [ + " {\"type\": \"clothing\", \"description\": \"blue; could be made of denim; could have pockets; could have a zipper\", \"similar objects\": [\"jeans shorts\", \"trousers\", \"capri pants\"]}", + 54 + ], + "rear light": [ + " {\"type\": \"vehicle part\", \"description\": \"red; usually located at the back of the car; could be used to indicate the direction of the car\", \"similar objects\": [\"headlight\", \"turn signal\", \"brake light\"]}", + 54 + ], + "jumpsuit": [ + " {\"type\": \"clothing\", \"description\": \"one-piece garment; could be long or short; could be made of cotton, linen, or polyester\", \"similar objects\": [\"romper\", \"overalls\", \"coveralls\"]}", + 54 + ], + "streaks": [ + " {\"type\": \"pattern\", \"description\": \"long, thin lines; could be of different colors; could be in a group or single\", \"similar objects\": [\"stripes\", \"dots\", \"zigzags\"]}", + 54 + ], + "baseline": [ + " {\"type\": \"measurement tool\", \"description\": \"a line used as a reference point for measurements\", \"similar objects\": [\"standard\", \"benchmark\", \"yardstick\"]}", + 54 + ], + "earth": [ + " {\"type\": \"planet\", \"description\": \"third planet from the sun; has one moon; has a blue and green surface\", \"similar objects\": [\"Mars\", \"Venus\", \"Jupiter\"]}", + 54 + ], + "freckles": [ + " {\"type\": \"skin feature\", \"description\": \"small, brown spots on the skin; could be found on the face, arms, and shoulders\", \"similar objects\": [\"moles\", \"birthmarks\", \"scars\"]}", + 54 + ], + "clock side building": [ + " {\"type\": \"architecture\", \"description\": \"tall, rectangular; could have a clock on the side; could have a spire on the top\", \"similar objects\": [\"cathedral\", \"church\", \"tower\"]}", + 54 + ], + "cat ear": [ + " {\"type\": \"accessory\", \"description\": \"pointed; could be made of fur; could be attached to a headband\", \"similar objects\": [\"rabbit ear\", \"fox ear\", \"bear ear\"]}", + 54 + ], + "seat belt": [ + " {\"type\": \"safety device\", \"description\": \"long strap; could be fastened around the waist; could be used in cars\", \"similar objects\": [\"helmet\", \"airbag\", \"child safety seat\"]}", + 54 + ], + "metal hand rail": [ + " {\"type\": \"building tool\", \"description\": \"long, metallic, could be curved; could be used for support\", \"similar objects\": [\"stair rail\", \"balustrade\", \"guard rail\"]}", + 54 + ], + "telephone booth": [ + " {\"type\": \"communication tool\", \"description\": \"small, enclosed, has a phone inside\", \"similar objects\": [\"payphone\", \"cell phone\", \"walkie-talkie\"]}", + 53 + ], + "wii remotes": [ + " {\"type\": \"gaming device\", \"description\": \"wireless controller; could be used with a Wii console\", \"similar objects\": [\"joystick\", \"gamepad\", \"racing wheel\"]}", + 53 + ], + "mantel": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could be decorated with ornaments; could be used to display pictures\", \"similar objects\": [\"shelf\", \"bookcase\", \"cabinet\"]}", + 53 + ], + "pointy": [ + "\n{\"type\": \"shape\", \"description\": \"sharp edges; could be triangular, conical, or pyramidal\", \"similar objects\": [\"spiky\", \"cone-shaped\", \"star-shaped\"]}", + 53 + ], + "metal bridge": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could span a river or a valley; could have a railing\", \"similar objects\": [\"wooden bridge\", \"suspension bridge\", \"viaduct\"]}", + 53 + ], + "bird feeder": [ + " {\"type\": \"bird accessory\", \"description\": \"could be made of wood or plastic; has a tray or container to hold bird food; could have a perch for birds to stand on\", \"similar objects\": [\"birdhouse\", \"birdbath\", \"bird feeder pole\"]}", + 53 + ], + "gas": [ + " {\"type\": \"substance\", \"description\": \"invisible; odorless; flammable; could be compressed into liquid\", \"similar objects\": [\"oil\", \"coal\", \"natural gas\"]}", + 53 + ], + "gray hat": [ + " {\"type\": \"clothing item\", \"description\": \"headwear; could be made of wool; could have a brim\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}", + 53 + ], + "iron gate": [ + " {\"type\": \"fence\", \"description\": \"made of metal; could be decorated with patterns; could be opened and closed\", \"similar objects\": [\"fence\", \"wall\", \"barrier\"]}", + 53 + ], + "grey bird": [ + " {\"type\": \"animal\", \"description\": \"grey feathers; could have a beak; could have wings\", \"similar objects\": [\"pigeon\", \"sparrow\", \"crow\"]}", + 53 + ], + "marina": [ + " {\"type\": \"location\", \"description\": \"a place where boats and ships are moored; could have a harbor\", \"similar objects\": [\"dock\", \"port\", \"harbor\"]}", + 53 + ], + "backs": [ + " {\"type\": \"furniture\", \"description\": \"has a backrest; could be made of wood or metal; could have armrests\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 53 + ], + "handicap sign": [ + " {\"type\": \"sign\", \"description\": \"blue and white; has a wheelchair symbol; could be found near a ramp or parking lot\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 53 + ], + "silver zipper": [ + " {\"type\": \"clothing accessory\", \"description\": \"metallic; could be used to close a garment\", \"similar objects\": [\"button\", \"hook and eye\", \"snap\"]}", + 53 + ], + "baseballs": [ + " {\"type\": \"sport equipment\", \"description\": \"round; made of leather; has a stitched seam\", \"similar objects\": [\"tennis ball\", \"football\", \"golf ball\"]}", + 53 + ], + "garland": [ + " {\"type\": \"decoration\", \"description\": \"string of flowers, leaves, or other materials; could be used for festivals or parties\", \"similar objects\": [\"bunting\", \"streamer\", \"ribbon\"]}", + 53 + ], + "bone": [ + " {\"type\": \"body part\", \"description\": \"hard, white; could be found in the body\", \"similar objects\": [\"joint\", \"cartilage\", \"tendon\"]}", + 53 + ], + "left arm": [ + " {\"type\": \"body part\", \"description\": \"part of the human body; connects to the shoulder and the elbow; could be used to move objects\", \"similar objects\": [\"right arm\", \"leg\", \"head\"]}", + 53 + ], + "door way": [ + " {\"type\": \"architectural feature\", \"description\": \"rectangular; could have a handle; could be made of wood or metal\", \"similar objects\": [\"window\", \"gate\", \"archway\"]}", + 53 + ], + "jet airplane": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has wings; could have two or four engines; could have a tail\", \"similar objects\": [\"helicopter\", \"glider\", \"balloon\"]}", + 53 + ], + "pizza boxes": [ + " {\"type\": \"packaging\", \"description\": \"rectangular; could be made of cardboard; could be printed with logos\", \"similar objects\": [\"takeout boxes\", \"gift boxes\", \"shipping boxes\"]}", + 53 + ], + "silver microwave": [ + "\n{\"type\": \"appliance\", \"description\": \"silver; has a door; could be used to heat food\", \"similar objects\": [\"refrigerator\", \"oven\", \"toaster\"]}", + 53 + ], + "city buildings": [ + " {\"type\": \"architecture\", \"description\": \"tall, rectangular, could have multiple stories; could have windows and balconies\", \"similar objects\": [\"skyscrapers\", \"apartment buildings\", \"office buildings\"]}", + 53 + ], + "dog toy": [ + " {\"type\": \"pet toy\", \"description\": \"could be made of rubber, plastic, or fabric; could be shaped like a bone, ball, or other shapes; could have squeakers or bells\", \"similar objects\": [\"cat toy\", \"bird toy\", \"fish toy\"]}", + 53 + ], + "s": [ + "\n{\"type\": \"letter\", \"description\": \"the 19th letter of the English alphabet; could be lowercase or uppercase\", \"similar objects\": [\"a\", \"b\", \"c\"]}", + 53 + ], + "peoples": [ + "\n{\"type\": \"group of people\", \"description\": \"could be of different ages, genders, and ethnicities; could be in a variety of settings\", \"similar objects\": [\"crowd\", \"audience\", \"family\"]}", + 53 + ], + "worn": [ + " {\"type\": \"adjective\", \"description\": \"showing signs of age or use; not new\", \"similar objects\": [\"old\", \"used\", \"faded\"]}", + 53 + ], + "leaves grass": [ + "\n{\"type\": \"plant\", \"description\": \"green; could be long and thin; could be short and wide; could be in clusters\", \"similar objects\": [\"ferns\", \"moss\", \"shrubs\"]}", + 53 + ], + "insignia": [ + " {\"type\": \"symbol\", \"description\": \"could be a logo, emblem, or badge; could be used to represent a group or organization\", \"similar objects\": [\"crest\", \"banner\", \"flag\"]}", + 53 + ], + "river water": [ + " {\"type\": \"natural element\", \"description\": \"clear; could be flowing; could be deep\", \"similar objects\": [\"lake water\", \"ocean water\", \"rain water\"]}", + 53 + ], + "mole": [ + " {\"type\": \"animal\", \"description\": \"small, dark-colored, burrowing mammal; has a pointed snout and small eyes; could have fur or hairless skin\", \"similar objects\": [\"vole\", \"shrew\", \"hamster\"]}", + 53 + ], + "taillights": [ + " {\"type\": \"vehicle part\", \"description\": \"red and round; could be found at the back of a car; could be used to indicate braking\", \"similar objects\": [\"headlights\", \"turn signals\", \"brake lights\"]}", + 52 + ], + "coffee cups": [ + " {\"type\": \"drinking ware\", \"description\": \"cylindrical; could be made of ceramic, plastic, or glass; could have handles\", \"similar objects\": [\"mugs\", \"teacups\", \"glasses\"]}", + 52 + ], + "jump": [ + " {\"type\": \"action\", \"description\": \"movement of body from one place to another; could be done with legs\", \"similar objects\": [\"hop\", \"leap\", \"skip\"]}", + 52 + ], + "jumping": [ + " {\"type\": \"action\", \"description\": \"moving up and down; could be done by humans or animals\", \"similar objects\": [\"running\", \"hopping\", \"skipping\"]}", + 52 + ], + "safety": [ + " {\"type\": \"concept\", \"description\": \"the state of being protected from harm or danger\", \"similar objects\": [\"security\", \"protection\", \"precaution\"]}", + 52 + ], + "ripe bananas": [ + "\n{\"type\": \"fruit\", \"description\": \"yellow; curved; could have brown spots; could be mashed\", \"similar objects\": [\"apple\", \"orange\", \"pear\"]}", + 52 + ], + "pickup": [ + " {\"type\": \"vehicle\", \"description\": \"truck; has an open cargo bed; could have two or four doors\", \"similar objects\": [\"van\", \"SUV\", \"sedan\"]}", + 52 + ], + "cardboard container": [ + " {\"type\": \"packaging material\", \"description\": \"rectangular; could be folded; could be used to store items\", \"similar objects\": [\"plastic box\", \"paper bag\", \"cardboard box\"]}", + 52 + ], + "car tire": [ + " {\"type\": \"automotive part\", \"description\": \"round; black; has a tread pattern\", \"similar objects\": [\"wheel\", \"rim\", \"hubcap\"]}", + 52 + ], + "smartphone": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a touchscreen; could have multiple cameras\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 52 + ], + "daylight": [ + " {\"type\": \"natural phenomenon\", \"description\": \"natural light from the sun; could be seen during the day\", \"similar objects\": [\"sunlight\", \"moonlight\", \"starlight\"]}", + 52 + ], + "power strip": [ + " {\"type\": \"electrical tool\", \"description\": \"has multiple outlets; could be surge protected; could have a switch\", \"similar objects\": [\"extension cord\", \"power adapter\", \"power bank\"]}", + 52 + ], + "blue ball": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of rubber; could be inflated; could be used for playing catch\", \"similar objects\": [\"red ball\", \"soccer ball\", \"basketball\"]}", + 52 + ], + "transit bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple doors; could be painted in different colors; could have a wheelchair ramp\", \"similar objects\": [\"school bus\", \"trolley bus\", \"coach bus\"]}", + 52 + ], + "dandelions": [ + " {\"type\": \"plant\", \"description\": \"yellow flowers; could have white puffballs; could have long stems\", \"similar objects\": [\"daisies\", \"sunflowers\", \"clover\"]}", + 52 + ], + "television stand": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood or metal; has shelves for storing items; could have a flat surface for placing a television\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"entertainment center\"]}", + 52 + ], + "orange food": [ + " {\"type\": \"food\", \"description\": \"round; orange in color; could be sweet or sour; could be peeled and eaten\", \"similar objects\": [\"apple\", \"grapefruit\", \"tangerine\"]}", + 52 + ], + "tall green": [ + "\n{\"type\": \"plant\", \"description\": \"could be a tree or a bush; could have leaves; could have flowers or fruits\", \"similar objects\": [\"oak tree\", \"pine tree\", \"palm tree\"]}", + 52 + ], + "garbage bag": [ + " {\"type\": \"container\", \"description\": \"black; could be made of plastic; could be tied up\", \"similar objects\": [\"trash can\", \"plastic bag\", \"paper bag\"]}", + 52 + ], + "silver metal fork": [ + "\n{\"type\": \"utensil\", \"description\": \"silver; has four tines; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 52 + ], + "sweat shirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have a hood; could be made of cotton\", \"similar objects\": [\"hoodie\", \"jacket\", \"sweater\"]}", + 52 + ], + "lanes": [ + " {\"type\": \"road feature\", \"description\": \"long, straight, divided into sections; could be used for traffic control\", \"similar objects\": [\"highway\", \"street\", \"intersection\"]}", + 52 + ], + "service bus": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple doors; could have multiple seats\", \"similar objects\": [\"school bus\", \"tour bus\", \"shuttle bus\"]}", + 52 + ], + "money": [ + " {\"type\": \"currency\", \"description\": \"paper or metal; could be coins or notes; could have a country's symbol\", \"similar objects\": [\"cash\", \"credit card\", \"debit card\"]}", + 52 + ], + "elephant tusk": [ + " {\"type\": \"body part\", \"description\": \"long, curved, ivory-colored; could be found on the sides of an elephant's face\", \"similar objects\": [\"hippo tusk\", \"walrus tusk\", \"narwhal tusk\"]}", + 52 + ], + "side wing": [ + " {\"type\": \"aircraft part\", \"description\": \"attached to the fuselage; could be used to control the aircraft's direction\", \"similar objects\": [\"tail fin\", \"aileron\", \"elevator\"]}", + 52 + ], + "storm clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"dark, grey, could be accompanied by thunder and lightning\", \"similar objects\": [\"rain clouds\", \"hail clouds\", \"snow clouds\"]}", + 52 + ], + "flower bouquet": [ + " {\"type\": \"decoration\", \"description\": \"a bunch of flowers tied together; could be in a vase\", \"similar objects\": [\"flower arrangement\", \"bouquet of roses\", \"bouquet of daisies\"]}", + 52 + ], + "blow dryer": [ + " {\"type\": \"hair styling tool\", \"description\": \"long, cylindrical; has a nozzle; could be corded or cordless\", \"similar objects\": [\"curling iron\", \"flat iron\", \"hair straightener\"]}", + 52 + ], + "train train tracks": [ + "\n{\"type\": \"transportation system\", \"description\": \"long metal rails; could have a locomotive; could have multiple carriages\", \"similar objects\": [\"tram\", \"monorail\", \"subway\"]}", + 52 + ], + "metal tray": [ + " {\"type\": \"utensil\", \"description\": \"flat, rectangular; could be made of metal; could be used to carry food\", \"similar objects\": [\"plate\", \"bowl\", \"dish\"]}", + 52 + ], + "mayonnaise": [ + " {\"type\": \"condiment\", \"description\": \"white, creamy, thick; could be used as a spread or dip\", \"similar objects\": [\"mustard\", \"ketchup\", \"aioli\"]}", + 52 + ], + "o": [ + "\n{\"type\": \"letter\", \"description\": \"round; could be used to form words\", \"similar objects\": [\"a\", \"b\", \"c\"]}", + 52 + ], + "landing gear": [ + " {\"type\": \"aircraft part\", \"description\": \"wheels and struts used to support an aircraft on the ground; could be retracted during flight\", \"similar objects\": [\"propeller\", \"wing\", \"fuselage\"]}", + 52 + ], + "chili": [ + " {\"type\": \"spice\", \"description\": \"red; could be dried; could be used to add flavor to dishes\", \"similar objects\": [\"cumin\", \"cayenne pepper\", \"paprika\"]}", + 52 + ], + "sushi": [ + " {\"type\": \"food\", \"description\": \"rice and raw fish; could be wrapped in seaweed\", \"similar objects\": [\"sashimi\", \"tempura\", \"udon\"]}", + 52 + ], + "motor cycle": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"moped\"]}", + 52 + ], + "octagon": [ + " {\"type\": \"shape\", \"description\": \"eight-sided polygon; has eight equal sides and eight equal angles\", \"similar objects\": [\"hexagon\", \"pentagon\", \"square\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean).", + 52 + ], + "side train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could be pulled by a locomotive\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 52 + ], + "gravel road": [ + " {\"type\": \"road surface\", \"description\": \"made of small stones; could be bumpy; could be dusty\", \"similar objects\": [\"dirt road\", \"asphalt road\", \"cobblestone road\"]}", + 51 + ], + "pink jacket": [ + " {\"type\": \"clothing\", \"description\": \"light pink; could have a hood; could have pockets; could be made of cotton\", \"similar objects\": [\"coat\", \"sweater\", \"hoodie\"]}", + 51 + ], + "skate boarder": [ + " {\"type\": \"sportsperson\", \"description\": \"wears protective gear; rides a skateboard; performs stunts\", \"similar objects\": [\"surfer\", \"snowboarder\", \"rollerblader\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant,", + 51 + ], + "liner": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, sleeveless; could be made of silk or cotton\", \"similar objects\": [\"dress\", \"robe\", \"tunic\"]}", + 51 + ], + "cafe": [ + " {\"type\": \"establishment\", \"description\": \"a place to eat and drink; could have outdoor seating; could serve coffee and pastries\", \"similar objects\": [\"restaurant\", \"bar\", \"bakery\"]}", + 51 + ], + "dvd": [ + " {\"type\": \"media device\", \"description\": \"rectangular; could be used to store and play videos\", \"similar objects\": [\"cd\", \"blu-ray\", \"vhs\"]}", + 51 + ], + "purple flowers": [ + "\n{\"type\": \"plant\", \"description\": \"violet petals; could have yellow center; could have green leaves\", \"similar objects\": [\"daisy\", \"tulip\", \"sunflower\"]}", + 51 + ], + "train bridge": [ + " {\"type\": \"structure\", \"description\": \"long, metal; could have two or more pillars; could have a railway track on top\", \"similar objects\": [\"road bridge\", \"viaduct\", \"aqueduct\"]}", + 51 + ], + "silver rim": [ + " {\"type\": \"decorative item\", \"description\": \"shiny, metallic, could be used to decorate plates, cups, etc.\", \"similar objects\": [\"gold rim\", \"plastic rim\", \"glass rim\"]}", + 51 + ], + "pointy ear": [ + " {\"type\": \"body part\", \"description\": \"elongated, pointed ears; could be found on animals such as cats, dogs, and rabbits\", \"similar objects\": [\"whiskers\", \"tail\", \"nose\"]}", + 51 + ], + "soccer goal": [ + " {\"type\": \"sports equipment\", \"description\": \"two posts connected by a crossbar; could be made of metal or plastic; could be used to score points in soccer\", \"similar objects\": [\"basketball hoop\", \"hockey net\", \"volleyball net\"]}", + 51 + ], + "orange bowl": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could be orange in color\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 51 + ], + "footboard": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could be attached to the bed\", \"similar objects\": [\"headboard\", \"nightstand\", \"dresser\"]}", + 51 + ], + "rims": [ + " {\"type\": \"automotive part\", \"description\": \"circular metal rings; used to attach tires to a vehicle\", \"similar objects\": [\"tires\", \"wheels\", \"hubcaps\"]}", + 51 + ], + "cat eye": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of glass; could be attached to a frame\", \"similar objects\": [\"sunglasses\", \"eyeglasses\", \"goggles\"]}", + 51 + ], + "salmon": [ + " {\"type\": \"fish\", \"description\": \"pinkish-orange; has a long body; could have black spots\", \"similar objects\": [\"trout\", \"tuna\", \"cod\"]}", + 51 + ], + "bruise": [ + " {\"type\": \"injury\", \"description\": \"purple or blue mark on the skin; could be painful; could be caused by a blunt force\", \"similar objects\": [\"cut\", \"burn\", \"scratch\"]}", + 51 + ], + "sauce pizza": [ + " {\"type\": \"food\", \"description\": \"tomato-based; could have cheese, vegetables, and meat toppings; could be served in a round shape\", \"similar objects\": [\"pasta\", \"calzone\", \"lasagna\"]}", + 51 + ], + "watch wrist": [ + " {\"type\": \"accessory\", \"description\": \"worn on the wrist; could be digital or analog; could have a strap\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}", + 51 + ], + "toothbrush holder": [ + " {\"type\": \"bathroom accessory\", \"description\": \"could be made of plastic or ceramic; could have multiple slots for toothbrushes; could have a lid\", \"similar objects\": [\"soap dish\", \"toilet brush holder\", \"towel rack\"]}", + 51 + ], + "box truck": [ + " {\"type\": \"vehicle\", \"description\": \"large, rectangular; could have a lift gate; could be used for transporting goods\", \"similar objects\": [\"van\", \"pickup truck\", \"semi-truck\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber", + 51 + ], + "silver laptop": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; rectangular; has a keyboard and a screen; could be used for computing\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 51 + ], + "parking garage": [ + " {\"type\": \"structure\", \"description\": \"multi-level building; has ramps and elevators; could have a ticket machine\", \"similar objects\": [\"parking lot\", \"parking deck\", \"parking tower\"]}", + 51 + ], + "sink basin": [ + " {\"type\": \"plumbing fixture\", \"description\": \"rectangular; could have a faucet; could be made of porcelain\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 51 + ], + "stirrup": [ + " {\"type\": \"equestrian tool\", \"description\": \"metal loop; used to help mount a horse\", \"similar objects\": [\"saddle\", \"bridle\", \"bit\"]}", + 51 + ], + "halter": [ + " {\"type\": \"accessory\", \"description\": \"used to control a horse; made of leather or rope\", \"similar objects\": [\"bridle\", \"reins\", \"saddle\"]}", + 51 + ], + "mcdonald": [ + " {\"type\": \"restaurant\", \"description\": \"fast food chain; could have golden arches logo; could have drive-thru\", \"similar objects\": [\"burger king\", \"kfc\", \"subway\"]}", + 51 + ], + "hand bag": [ + " {\"type\": \"accessory\", \"description\": \"small, rectangular, has straps; could be made of leather\", \"similar objects\": [\"purse\", \"backpack\", \"wallet\"]}", + 51 + ], + "brick tower": [ + " {\"type\": \"structure\", \"description\": \"made of bricks; could be tall and thin; could be used to build walls\", \"similar objects\": [\"building\", \"pyramid\", \"column\"]}", + 51 + ], + "stair case": [ + " {\"type\": \"structure\", \"description\": \"has steps; could be made of wood or metal; could have a railing\", \"similar objects\": [\"ladder\", \"escalator\", \"elevator\"]}", + 51 + ], + "formation": [ + " {\"type\": \"arrangement\", \"description\": \"a group of objects arranged in a particular pattern or structure\", \"similar objects\": [\"pattern\", \"alignment\", \"configuration\"]}", + 51 + ], + "grey concrete": [ + " {\"type\": \"building material\", \"description\": \"light grey; could be used for walls, floors, and other structures; could be mixed with other materials\", \"similar objects\": [\"cement\", \"bricks\", \"stone\"]}", + 51 + ], + "view mirrors": [ + " {\"type\": \"safety tool\", \"description\": \"rectangular; could be attached to the side of a vehicle; could be used to check blind spots\", \"similar objects\": [\"rearview mirror\", \"side mirror\", \"wing mirror\"]}", + 51 + ], + "bedspread bed": [ + " {\"type\": \"furniture\", \"description\": \"large piece of fabric used to cover a bed; could be quilted or embroidered\", \"similar objects\": [\"comforter\", \"duvet\", \"blanket\"]}", + 51 + ], + "grey stone": [ + " {\"type\": \"rock\", \"description\": \"grey; could be smooth or rough; could be of any shape\", \"similar objects\": [\"granite\", \"limestone\", \"basalt\"]}", + 51 + ], + "tier": [ + " {\"type\": \"structure\", \"description\": \"a set of shelves or drawers stacked one above the other; could be made of wood or metal\", \"similar objects\": [\"rack\", \"shelf\", \"cabinet\"]}", + 51 + ], + "blue basket": [ + "\n{\"type\": \"container\", \"description\": \"blue; could be made of plastic or wicker; could have a handle\", \"similar objects\": [\"box\", \"bag\", \"bucket\"]}", + 51 + ], + "foods": [ + "\n{\"type\": \"foods\", \"description\": \"edible items; could be cooked or raw; could be fruits, vegetables, grains, dairy, meat, etc.\", \"similar objects\": [\"meals\", \"snacks\", \"dishes\"]}", + 51 + ], + "chop sticks": [ + " {\"type\": \"eating utensil\", \"description\": \"two thin sticks; used to pick up food\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}", + 51 + ], + "hammer": [ + " {\"type\": \"tool\", \"description\": \"has a handle; has a head with a flat surface and a pointed end\", \"similar objects\": [\"screwdriver\", \"pliers\", \"saw\"]}", + 51 + ], + "flower pattern": [ + " {\"type\": \"design\", \"description\": \"could be colorful; could be symmetrical; could be abstract\", \"similar objects\": [\"geometric pattern\", \"striped pattern\", \"plaid pattern\"]}", + 51 + ], + "pumpkins": [ + " {\"type\": \"vegetable\", \"description\": \"round; orange; has a stem; could be carved into a jack-o-lantern\", \"similar objects\": [\"squash\", \"zucchini\", \"cucumber\"]}", + 51 + ], + "metal rod": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical, made of metal\", \"similar objects\": [\"screwdriver\", \"hammer\", \"pliers\"]}", + 51 + ], + "phone number": [ + " {\"type\": \"contact information\", \"description\": \"a sequence of numbers; could be used to contact someone\", \"similar objects\": [\"email address\", \"postal address\", \"social media account\"]}", + 51 + ], + "train conductor": [ + " {\"type\": \"occupation\", \"description\": \"responsible for the safety of passengers; could be in charge of ticketing; could be in charge of the train schedule\", \"similar objects\": [\"bus driver\", \"pilot\", \"captain\"]}", + 51 + ], + "tote bag": [ + " {\"type\": \"bag\", \"description\": \"rectangular; could be made of canvas; has two handles\", \"similar objects\": [\"backpack\", \"purse\", \"duffel bag\"]}", + 51 + ], + "brown hair": [ + " {\"type\": \"body feature\", \"description\": \"dark, long, could be straight or curly\", \"similar objects\": [\"blonde hair\", \"black hair\", \"red hair\"]}", + 51 + ], + "ocean water": [ + " {\"type\": \"natural element\", \"description\": \"blue; salty; could contain marine life\", \"similar objects\": [\"lake water\", \"river water\", \"sea water\"]}", + 51 + ], + "boogie board": [ + " {\"type\": \"water sport equipment\", \"description\": \"flat, buoyant board; could be used for surfing or bodyboarding\", \"similar objects\": [\"surfboard\", \"skimboard\", \"wakeboard\"]}", + 51 + ], + "giraffe ear": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and pointed; could be brown or black\", \"similar objects\": [\"elephant ear\", \"horse ear\", \"monkey ear\"]}", + 51 + ], + "sprinkle": [ + " {\"type\": \"food topping\", \"description\": \"small, round, could be made of sugar, salt, or other spices\", \"similar objects\": [\"sugar\", \"salt\", \"cinnamon\"]}", + 51 + ], + "paper towel holder": [ + " {\"type\": \"storage tool\", \"description\": \"cylindrical; could be made of metal; could have a handle\", \"similar objects\": [\"toilet paper holder\", \"tissue box\", \"trash can\"]}", + 50 + ], + "tennis court net": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; made of nylon; has a metal frame\", \"similar objects\": [\"volleyball net\", \"badminton net\", \"soccer goal\"]}", + 50 + ], + "fields": [ + " {\"type\": \"landscape\", \"description\": \"large area of land; could be planted with crops; could have trees and grass\", \"similar objects\": [\"meadows\", \"forests\", \"plains\"]}", + 50 + ], + "bulbs": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could be used for lighting\", \"similar objects\": [\"lightbulb\", \"LED bulb\", \"incandescent bulb\"]}", + 50 + ], + "ladle": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle; bowl-shaped; used for serving soup or stew\", \"similar objects\": [\"spoon\", \"fork\", \"tongs\"]}", + 50 + ], + "way street sign": [ + " {\"type\": \"road sign\", \"description\": \"rectangular; has arrows pointing in different directions; could be yellow or white\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 50 + ], + "eggplant": [ + " {\"type\": \"vegetable\", \"description\": \"purple, oval-shaped; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"cucumber\", \"green bean\"]}", + 50 + ], + "sale sign": [ + " {\"type\": \"advertisement tool\", \"description\": \"could be made of paper or plastic; could be in different colors; could have words or images\", \"similar objects\": [\"banner\", \"poster\", \"billboard\"]}", + 50 + ], + "storm": [ + " {\"type\": \"weather phenomenon\", \"description\": \"strong winds; heavy rain; thunder and lightning; could cause flooding\", \"similar objects\": [\"hurricane\", \"typhoon\", \"tornado\"]}", + 50 + ], + "house plant": [ + " {\"type\": \"plant\", \"description\": \"could be potted; could be green; could have leaves; could be flowering\", \"similar objects\": [\"succulent\", \"fern\", \"cactus\"]}", + 50 + ], + "plains": [ + " {\"type\": \"landform\", \"description\": \"large, flat, grassy area; could have small hills and valleys\", \"similar objects\": [\"plateau\", \"valley\", \"mesa\"]}", + 50 + ], + "leather belt": [ + " {\"type\": \"accessory\", \"description\": \"long; could be made of leather; could have a buckle\", \"similar objects\": [\"necklace\", \"bracelet\", \"watch\"]}", + 50 + ], + "metal shelf": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of metal; could have multiple shelves\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"wardrobe\"]}", + 50 + ], + "stuffed bears": [ + " {\"type\": \"toy\", \"description\": \"soft, cuddly, usually has a smiling face; could be of different sizes and colors\", \"similar objects\": [\"stuffed animals\", \"plush toys\", \"dolls\"]}", + 50 + ], + "apartment": [ + " {\"type\": \"building\", \"description\": \"multi-story building; could have balconies; could have a shared lobby\", \"similar objects\": [\"condo\", \"house\", \"townhouse\"]}", + 50 + ], + "kick stand": [ + " {\"type\": \"bicycle accessory\", \"description\": \"metal; attaches to the frame of the bike; used to keep the bike upright when not in use\", \"similar objects\": [\"pedal\", \"chain\", \"handlebar\"]}", + 50 + ], + "yogurt": [ + " {\"type\": \"food\", \"description\": \"smooth, creamy, could be flavored; could be served cold\", \"similar objects\": [\"ice cream\", \"sorbet\", \"smoothie\"]}", + 50 + ], + "thermostat": [ + " {\"type\": \"electronic device\", \"description\": \"used to control temperature; could be digital or analog\", \"similar objects\": [\"humidifier\", \"air conditioner\", \"heater\"]}", + 50 + ], + "nozzle": [ + " {\"type\": \"tool\", \"description\": \"long, thin, could be used to spray water or other liquids\", \"similar objects\": [\"hose\", \"sprinkler\", \"pump\"]}", + 50 + ], + "substance": [ + "\n{\"type\": \"material\", \"description\": \"could be solid, liquid, or gas; could be composed of atoms or molecules; could be natural or man-made\", \"similar objects\": [\"element\", \"compound\", \"mixture\"]}", + 50 + ], + "light bulb": [ + " {\"type\": \"lighting tool\", \"description\": \"round; has a filament; could be made of glass\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 50 + ], + "curtain rod": [ + " {\"type\": \"furnishing tool\", \"description\": \"long; could be made of metal; could be used to hang curtains\", \"similar objects\": [\"curtain rail\", \"curtain track\", \"curtain pole\"]}", + 50 + ], + "paper plates": [ + " {\"type\": \"dining ware\", \"description\": \"round; made of paper; could be disposable\", \"similar objects\": [\"plastic plates\", \"bowls\", \"cups\"]}", + 50 + ], + "skaters": [ + " {\"type\": \"sports\", \"description\": \"people on roller skates or ice skates; could be performing stunts\", \"similar objects\": [\"bicyclists\", \"surfers\", \"snowboarders\"]}", + 50 + ], + "cheesecake": [ + " {\"type\": \"dessert\", \"description\": \"sweet; could be made of cream cheese; could be topped with fruits\", \"similar objects\": [\"pie\", \"tart\", \"cupcake\"]}", + 50 + ], + "sliver": [ + " {\"type\": \"metal\", \"description\": \"shiny; malleable; ductile; has a high electrical and thermal conductivity\", \"similar objects\": [\"gold\", \"copper\", \"aluminum\"]}", + 50 + ], + "kinds": [ + " {\"type\": \"plural noun\", \"description\": \"plural form of the word 'kind'\", \"similar objects\": [\"children\", \"people\", \"animals\"]}", + 50 + ], + "flower bed": [ + " {\"type\": \"landscape feature\", \"description\": \"a bed of flowers; could be surrounded by stones or grass; could have a variety of colors\", \"similar objects\": [\"garden\", \"rock garden\", \"water feature\"]}", + 50 + ], + "shears": [ + " {\"type\": \"tool\", \"description\": \"two blades connected by a pivot; used for cutting\", \"similar objects\": [\"scissors\", \"clippers\", \"pliers\"]}", + 50 + ], + "backdrop": [ + " {\"type\": \"decoration\", \"description\": \"large, flat, usually hung on a wall; could be made of fabric, paper, or vinyl\", \"similar objects\": [\"curtain\", \"wallpaper\", \"tapestry\"]}", + 50 + ], + "glaze": [ + " {\"type\": \"cooking ingredient\", \"description\": \"transparent, glossy coating; could be made of sugar, honey, or syrup\", \"similar objects\": [\"icing\", \"frosting\", \"sauce\"]}", + 50 + ], + "aquarium": [ + " {\"type\": \"container\", \"description\": \"transparent; could be filled with water; could contain fish and other aquatic animals\", \"similar objects\": [\"fish tank\", \"terrarium\", \"vivarium\"]}", + 50 + ], + "bus tire": [ + " {\"type\": \"vehicle part\", \"description\": \"black; round; has a tread pattern\", \"similar objects\": [\"car tire\", \"motorcycle tire\", \"truck tire\"]}", + 50 + ], + "window panes": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be made of glass; could be framed\", \"similar objects\": [\"door\", \"wall\", \"ceiling\"]}", + 50 + ], + "patterns": [ + " {\"type\": \"visual design\", \"description\": \"repeating shapes or designs; could be used for decoration\", \"similar objects\": [\"geometric shapes\", \"textures\", \"motifs\"]}", + 50 + ], + "spine": [ + " {\"type\": \"anatomical structure\", \"description\": \"long, curved; made of bones; connects the head to the pelvis\", \"similar objects\": [\"ribs\", \"skull\", \"vertebrae\"]}", + 50 + ], + "title": [ + " {\"type\": \"document\", \"description\": \"a heading or label for a text or document; could be in bold or italic font\", \"similar objects\": [\"heading\", \"caption\", \"subtitle\"]}", + 50 + ], + "tulips": [ + " {\"type\": \"flower\", \"description\": \"long stem; colorful petals; could have a cup-shaped flower head\", \"similar objects\": [\"roses\", \"daisies\", \"daffodils\"]}", + 50 + ], + "beer bottles": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass or plastic; could have a cap\", \"similar objects\": [\"wine bottles\", \"soda cans\", \"water bottles\"]}", + 50 + ], + "side bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has two doors; could be painted in bright colors\", \"similar objects\": [\"minibus\", \"school bus\", \"tour bus\"]}", + 50 + ], + "stuffed dog": [ + " {\"type\": \"toy\", \"description\": \"soft; could be made of fabric; could have a tail and ears\", \"similar objects\": [\"stuffed bear\", \"stuffed cat\", \"stuffed rabbit\"]}", + 50 + ], + "blue shoes": [ + " {\"type\": \"footwear\", \"description\": \"blue; could be made of leather; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 50 + ], + "raspberries": [ + " {\"type\": \"fruit\", \"description\": \"red, small, round; could have a white or yellow center; could have a tart taste\", \"similar objects\": [\"strawberries\", \"blueberries\", \"blackberries\"]}", + 50 + ], + "grey fence": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be used to separate two areas; could be painted in grey\", \"similar objects\": [\"gate\", \"wall\", \"hedge\"]}", + 49 + ], + "blue cover": [ + " {\"type\": \"accessory\", \"description\": \"blue; could be made of fabric; could be used to cover something\", \"similar objects\": [\"blanket\", \"sheet\", \"pillowcase\"]}", + 49 + ], + "glass dish": [ + " {\"type\": \"cooking tool\", \"description\": \"transparent; could be made of glass or ceramic; could be used for baking\", \"similar objects\": [\"baking dish\", \"pie plate\", \"casserole dish\"]}", + 49 + ], + "orange wheels": [ + " {\"type\": \"toy\", \"description\": \"round, orange, could be made of plastic; could be used for playing\", \"similar objects\": [\"ball\", \"bicycle\", \"tricycle\"]}", + 49 + ], + "scratches": [ + " {\"type\": \"damage\", \"description\": \"marks on the surface of an object; could be caused by sharp objects\", \"similar objects\": [\"dents\", \"gouges\", \"scrapes\"]}", + 49 + ], + "airplane engine": [ + " {\"type\": \"machine part\", \"description\": \"cylindrical; has a propeller; could be powered by jet fuel\", \"similar objects\": [\"turbine\", \"propeller\", \"rocket engine\"]}", + 49 + ], + "daisy": [ + " {\"type\": \"flower\", \"description\": \"white petals with yellow center; could have multiple layers of petals; could have long stems\", \"similar objects\": [\"sunflower\", \"tulip\", \"daffodil\"]}", + 49 + ], + "hair tie": [ + " {\"type\": \"accessory\", \"description\": \"elastic band; could be made of fabric; used to tie hair\", \"similar objects\": [\"hair clip\", \"headband\", \"scrunchy\"]}", + 49 + ], + "wood floors": [ + " {\"type\": \"flooring material\", \"description\": \"hard, durable, could be stained or painted; could be made of different types of wood\", \"similar objects\": [\"tile\", \"carpet\", \"laminate\"]}", + 49 + ], + "blue screen": [ + " {\"type\": \"electronic device\", \"description\": \"a display screen with a blue background; could be used for computer troubleshooting\", \"similar objects\": [\"monitor\", \"television\", \"projector\"]}", + 49 + ], + "chip": [ + " {\"type\": \"food\", \"description\": \"thin, flat, could be salty or sweet; could be made of potatoes, corn, or other grains\", \"similar objects\": [\"crisp\", \"cracker\", \"pretzel\"]}", + 49 + ], + "blue socks": [ + " {\"type\": \"clothing item\", \"description\": \"blue; could be made of cotton; could be ankle-length\", \"similar objects\": [\"white socks\", \"black socks\", \"red socks\"]}", + 49 + ], + "wet sidewalk": [ + " {\"type\": \"environment\", \"description\": \"slippery; could be covered with water; could be dangerous to walk on\", \"similar objects\": [\"icy road\", \"rainy street\", \"muddy path\"]}", + 49 + ], + "ashtray": [ + " {\"type\": \"smoking tool\", \"description\": \"round; could be made of metal or ceramic; has a bowl for ashes\", \"similar objects\": [\"cigarette holder\", \"pipe\", \"cigar cutter\"]}", + 49 + ], + "wire basket": [ + " {\"type\": \"storage tool\", \"description\": \"made of metal wires; could be used to store items\", \"similar objects\": [\"basket\", \"box\", \"container\"]}", + 49 + ], + "tan couch": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of leather; could have cushions\", \"similar objects\": [\"sofa\", \"loveseat\", \"armchair\"]}", + 49 + ], + "decker buses": [ + "\n{\"type\": \"vehicle\", \"description\": \"double-decker buses; could have two levels of seating; could have an open top deck\", \"similar objects\": [\"trolley bus\", \"coach bus\", \"minibus\"]}", + 49 + ], + "baseman": [ + " {\"type\": \"sports position\", \"description\": \"plays in the infield; stands between first and second base\", \"similar objects\": [\"pitcher\", \"catcher\", \"shortstop\"]}", + 49 + ], + "luggage tag": [ + " {\"type\": \"travel accessory\", \"description\": \"small, rectangular; could have a string attached; could have a name written on it\", \"similar objects\": [\"suitcase\", \"backpack\", \"briefcase\"]}", + 49 + ], + "beverages": [ + "\n{\"type\": \"drink\", \"description\": \"liquid; could be hot or cold; could be alcoholic or non-alcoholic\", \"similar objects\": [\"juice\", \"tea\", \"coffee\"]}", + 49 + ], + "blue sticker": [ + " {\"type\": \"decoration item\", \"description\": \"round; could be made of paper; could be used to decorate walls or other surfaces\", \"similar objects\": [\"posters\", \"wall art\", \"wall decals\"]}", + 49 + ], + "hour": [ + " {\"type\": \"time unit\", \"description\": \"60 minutes; 24 hours in a day\", \"similar objects\": [\"minute\", \"second\", \"day\"]}", + 49 + ], + "light bulbs": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could be powered by electricity\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 49 + ], + "indicator": [ + " {\"type\": \"signal device\", \"description\": \"could be a light, sound, or other signal; used to indicate a change in status or condition\", \"similar objects\": [\"alarm\", \"bell\", \"buzzer\"]}", + 49 + ], + "brown bricks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be used to build walls\", \"similar objects\": [\"concrete blocks\", \"cement blocks\", \"stone blocks\"]}", + 49 + ], + "snacks": [ + " {\"type\": \"food\", \"description\": \"small, bite-sized food; could be salty or sweet; could be savory or crunchy\", \"similar objects\": [\"appetizers\", \"finger food\", \"desserts\"]}", + 49 + ], + "luggage bag": [ + " {\"type\": \"travel accessory\", \"description\": \"rectangular; could be made of fabric; could have wheels\", \"similar objects\": [\"suitcase\", \"backpack\", \"duffel bag\"]}", + 49 + ], + "giraffe eye": [ + "\n{\"type\": \"animal body part\", \"description\": \"large, dark brown, almond-shaped; has long eyelashes\", \"similar objects\": [\"elephant eye\", \"horse eye\", \"monkey eye\"]}", + 49 + ], + "orange motorcycle": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has an engine; could be orange in color; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"tricycle\"]}", + 49 + ], + "sheer curtains": [ + " {\"type\": \"window covering\", \"description\": \"transparent; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"blinds\", \"drapes\", \"shades\"]}", + 49 + ], + "graphics": [ + " {\"type\": \"visual representation\", \"description\": \"images, diagrams, charts, etc.\", \"similar objects\": [\"illustrations\", \"photographs\", \"animations\"]}", + 49 + ], + "shark": [ + " {\"type\": \"animal\", \"description\": \"large, gray; has a pointed fin; could have sharp teeth\", \"similar objects\": [\"whale\", \"dolphin\", \"stingray\"]}", + 49 + ], + "compartment": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of wood or plastic; could be divided into several sections; could be used to store items\", \"similar objects\": [\"box\", \"drawer\", \"shelf\"]}", + 49 + ], + "motor boat": [ + " {\"type\": \"watercraft\", \"description\": \"has an engine; could be used for recreational activities; could have a cabin\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}", + 49 + ], + "brown spot": [ + "\n{\"type\": \"marking\", \"description\": \"dark brown; could be round or oval; could be found on skin or fabric\", \"similar objects\": [\"stain\", \"blemish\", \"discoloration\"]}", + 49 + ], + "yarn": [ + " {\"type\": \"craft material\", \"description\": \"long, thin, string-like material; could be made of wool, cotton, or synthetic fibers\", \"similar objects\": [\"thread\", \"ribbon\", \"fabric\"]}", + 49 + ], + "grey road": [ + " {\"type\": \"road\", \"description\": \"asphalt; could have white lines; could have yellow lines\", \"similar objects\": [\"highway\", \"street\", \"freeway\"]}", + 49 + ], + "employee": [ + " {\"type\": \"person\", \"description\": \"works for a company; could have a job title; could have a salary\", \"similar objects\": [\"worker\", \"manager\", \"executive\"]}", + 49 + ], + "streetlamp": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lantern\", \"lamp\", \"light post\"]}", + 49 + ], + "parking meters": [ + " {\"type\": \"parking tool\", \"description\": \"tall, metal, has a coin slot; could be used to pay for parking\", \"similar objects\": [\"parking kiosk\", \"parking app\", \"parking ticket machine\"]}", + 49 + ], + "brown pot": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle; could be made of brown material\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 49 + ], + "necklaces": [ + " {\"type\": \"jewelry\", \"description\": \"could be made of metal, plastic, or beads; could have a pendant; could be worn around the neck\", \"similar objects\": [\"bracelet\", \"earrings\", \"ring\"]}", + 49 + ], + "knife blade": [ + " {\"type\": \"tool\", \"description\": \"sharp, metal, could be used for cutting\", \"similar objects\": [\"scissors\", \"axe\", \"saw\"]}", + 49 + ], + "wedding dress": [ + " {\"type\": \"clothing\", \"description\": \"long, white, could have lace and beading; could have a train\", \"similar objects\": [\"prom dress\", \"evening gown\", \"bridesmaid dress\"]}", + 49 + ], + "diaper": [ + " {\"type\": \"baby product\", \"description\": \"absorbent material; could be disposable or reusable; could have straps\", \"similar objects\": [\"wipes\", \"pacifier\", \"baby bottle\"]}", + 49 + ], + "metal piece": [ + " {\"type\": \"object\", \"description\": \"shiny; could be in different shapes; could be used for construction\", \"similar objects\": [\"screw\", \"nail\", \"bolt\"]}", + 49 + ], + "mannequins": [ + " {\"type\": \"display tool\", \"description\": \"human-like figure; could be made of plastic or cloth; could be used for displaying clothes\", \"similar objects\": [\"dummy\", \"dress form\", \"tailor's dummy\"]}", + 49 + ], + "desk top": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood or metal; could have drawers\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 48 + ], + "speed boat": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has a motor; could be used for racing\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}", + 48 + ], + "bagels": [ + " {\"type\": \"food\", \"description\": \"round; could be boiled and then baked; could be topped with sesame seeds\", \"similar objects\": [\"doughnuts\", \"pretzels\", \"bagel chips\"]}", + 48 + ], + "limes": [ + " {\"type\": \"fruit\", \"description\": \"green, small, round; has a sour taste\", \"similar objects\": [\"lemons\", \"oranges\", \"grapefruits\"]}", + 48 + ], + "burrito": [ + " {\"type\": \"food\", \"description\": \"wrapped in a tortilla; could be filled with meat, beans, cheese, and vegetables\", \"similar objects\": [\"taco\", \"enchilada\", \"quesadilla\"]}", + 48 + ], + "surfboarder": [ + " {\"type\": \"person\", \"description\": \"wearing a wetsuit; riding a surfboard; in the ocean\", \"similar objects\": [\"sailor\", \"swimmer\", \"diver\"]}", + 48 + ], + "khaki shorts": [ + " {\"type\": \"clothing\", \"description\": \"light brown; could be made of cotton; could have pockets; could be knee-length\", \"similar objects\": [\"jeans\", \"capris\", \"cargo shorts\"]}", + 48 + ], + "plantains": [ + " {\"type\": \"fruit\", \"description\": \"long, yellow, has a thick skin; could be cooked\", \"similar objects\": [\"banana\", \"avocado\", \"mango\"]}", + 48 + ], + "patio umbrella": [ + " {\"type\": \"outdoor furniture\", \"description\": \"large, round; could be opened and closed; could be attached to a stand\", \"similar objects\": [\"gazebo\", \"awning\", \"tent\"]}", + 48 + ], + "wood surface": [ + " {\"type\": \"material\", \"description\": \"smooth; could be painted; could be used for furniture\", \"similar objects\": [\"metal surface\", \"plastic surface\", \"glass surface\"]}", + 48 + ], + "tripod": [ + " {\"type\": \"support tool\", \"description\": \"three legs; could be used to support cameras\", \"similar objects\": [\"monopod\", \"selfie stick\", \"camera stand\"]}", + 48 + ], + "teacup": [ + " {\"type\": \"drinking vessel\", \"description\": \"small, round, has a handle; could have a saucer\", \"similar objects\": [\"mug\", \"cup\", \"glass\"]}", + 48 + ], + "book bag": [ + " {\"type\": \"bag\", \"description\": \"rectangular; has straps; could be made of canvas or leather\", \"similar objects\": [\"backpack\", \"duffel bag\", \"tote bag\"]}", + 48 + ], + "bike wheel": [ + " {\"type\": \"bicycle part\", \"description\": \"round; has spokes; could be made of metal or plastic\", \"similar objects\": [\"tire\", \"rim\", \"spoke\"]}", + 48 + ], + "directions": [ + " {\"type\": \"instruction\", \"description\": \"a set of instructions to guide a person from one place to another\", \"similar objects\": [\"map\", \"guidebook\", \"GPS\"]}", + 48 + ], + "rocking chair": [ + " {\"type\": \"furniture\", \"description\": \"has two curved bands connected to a seat and backrest; could be made of wood or metal; could be painted in different colors\", \"similar objects\": [\"armchair\", \"sofa\", \"ottoman\"]}", + 48 + ], + "barricade": [ + " {\"type\": \"barrier\", \"description\": \"could be made of metal or wood; could be used to block a path\", \"similar objects\": [\"fence\", \"gate\", \"wall\"]}", + 48 + ], + "bar stools": [ + " {\"type\": \"furniture\", \"description\": \"tall, has a backrest and a footrest; could be made of wood or metal\", \"similar objects\": [\"chairs\", \"benches\", \"sofas\"]}", + 48 + ], + "antennae": [ + " {\"type\": \"body part\", \"description\": \"long, thin, protruding from the head; could be used for sensing\", \"similar objects\": [\"antlers\", \"horns\", \"whiskers\"]}", + 48 + ], + "door hinge": [ + " {\"type\": \"hardware\", \"description\": \"metal; used to attach a door to a door frame; could be opened and closed\", \"similar objects\": [\"door knob\", \"door latch\", \"door handle\"]}", + 48 + ], + "silver drain": [ + " {\"type\": \"plumbing tool\", \"description\": \"silver; has a hole for water to flow through; could be used to drain water\", \"similar objects\": [\"sink\", \"bathtub\", \"toilet\"]}", + 48 + ], + "grapefruit": [ + " {\"type\": \"fruit\", \"description\": \"round; yellow or pink; has a sour taste\", \"similar objects\": [\"orange\", \"lemon\", \"lime\"]}", + 48 + ], + "photographs": [ + " {\"type\": \"visual media\", \"description\": \"captured images; could be printed on paper or stored digitally\", \"similar objects\": [\"videos\", \"paintings\", \"drawings\"]}", + 48 + ], + "grey roof": [ + " {\"type\": \"building material\", \"description\": \"made of tiles; could be made of metal; could be used to cover a house\", \"similar objects\": [\"shingles\", \"asphalt\", \"slate\"]}", + 48 + ], + "skylight": [ + " {\"type\": \"architectural feature\", \"description\": \"window on the roof; could be made of glass or plastic\", \"similar objects\": [\"dormer window\", \"bay window\", \"awning window\"]}", + 48 + ], + "propellor": [ + " {\"type\": \"mechanical device\", \"description\": \"has blades; could be attached to an engine; could be used to generate thrust\", \"similar objects\": [\"fan\", \"turbine\", \"pump\"]}", + 48 + ], + "silver lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; made of silver; could have a handle\", \"similar objects\": [\"silver lantern\", \"silver flashlight\", \"silver candle\"]}", + 48 + ], + "plaid": [ + " {\"type\": \"pattern\", \"description\": \"intersecting lines of different colors; could be used for clothing\", \"similar objects\": [\"stripes\", \"checks\", \"polka dots\"]}", + 48 + ], + "sauerkraut": [ + " {\"type\": \"food\", \"description\": \"fermented cabbage; could be served with sausages\", \"similar objects\": [\"pickles\", \"kimchi\", \"cucumber salad\"]}", + 48 + ], + "toaster oven": [ + " {\"type\": \"cooking tool\", \"description\": \"box-shaped; has a door; could be used to toast bread\", \"similar objects\": [\"microwave\", \"convection oven\", \"toaster\"]}", + 48 + ], + "poodle": [ + " {\"type\": \"animal\", \"description\": \"small; curly fur; could have a top knot\", \"similar objects\": [\"labrador retriever\", \"golden retriever\", \"schnauzer\"]}", + 48 + ], + "bench seat": [ + " {\"type\": \"furniture\", \"description\": \"long, flat, could have backrest; could be made of wood or metal\", \"similar objects\": [\"sofa\", \"chair\", \"stool\"]}", + 48 + ], + "silver fence": [ + " {\"type\": \"building material\", \"description\": \"metallic; could be used to build a fence; could be used to decorate a garden\", \"similar objects\": [\"iron fence\", \"wood fence\", \"brick wall\"]}", + 48 + ], + "railway": [ + " {\"type\": \"transportation system\", \"description\": \"long, straight tracks; could have multiple tracks; could have a station\", \"similar objects\": [\"subway\", \"tram\", \"bus\"]}", + 48 + ], + "rise building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have windows; could have a roof\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"office building\"]}", + 48 + ], + "bare ground": [ + " {\"type\": \"landscape\", \"description\": \"no vegetation; could be sandy or rocky; could be wet or dry\", \"similar objects\": [\"desert\", \"beach\", \"field\"]}", + 48 + ], + "tale": [ + " {\"type\": \"story\", \"description\": \"narrative; could be fictional or non-fictional; could be told orally or written down\", \"similar objects\": [\"myth\", \"legend\", \"fable\"]}", + 48 + ], + "teen": [ + "\n{\"type\": \"person\", \"description\": \"between 13 and 19 years old; could be in school; could be in the process of discovering their identity\", \"similar objects\": [\"adolescent\", \"youth\", \"child\"]}", + 48 + ], + "kit": [ + " {\"type\": \"collection of items\", \"description\": \"could contain tools, supplies, or other items; could be used for a specific purpose\", \"similar objects\": [\"set\", \"package\", \"bundle\"]}", + 48 + ], + "scoop": [ + " {\"type\": \"utensil\", \"description\": \"long handle; round bowl; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"ladle\", \"tongs\"]}", + 47 + ], + "dashboard": [ + " {\"type\": \"automotive part\", \"description\": \"panel with various gauges and controls; could be digital or analog\", \"similar objects\": [\"steering wheel\", \"gear shift\", \"windshield\"]}", + 47 + ], + "grey metal": [ + " {\"type\": \"material\", \"description\": \"shiny; could be used for construction; could be used for decoration\", \"similar objects\": [\"steel\", \"aluminum\", \"brass\"]}", + 47 + ], + "motorcycle rider": [ + " {\"type\": \"person\", \"description\": \"wearing a helmet; sitting on a motorcycle; could be wearing a leather jacket\", \"similar objects\": [\"bicycle rider\", \"skateboarder\", \"scooter rider\"]}", + 47 + ], + "plastic containers": [ + " {\"type\": \"storage tool\", \"description\": \"transparent; could be of different shapes and sizes; could be used to store food\", \"similar objects\": [\"glass containers\", \"plastic bags\", \"plastic wrap\"]}", + 47 + ], + "smooth": [ + "\n\n{\"type\": \"adjective\", \"description\": \"having a surface that is free from irregularities or bumps; having a texture that is soft and even\", \"similar objects\": [\"silky\", \"velvety\", \"glossy\"]}", + 47 + ], + "tofu": [ + " {\"type\": \"food\", \"description\": \"white, soft, made from soybeans; could be cut into cubes\", \"similar objects\": [\"tempeh\", \"seitan\", \"edamame\"]}", + 47 + ], + "triangular": [ + " {\"type\": \"shape\", \"description\": \"three sides; three angles; could be equilateral, isosceles, or scalene\", \"similar objects\": [\"square\", \"rectangle\", \"circle\"]}", + 47 + ], + "orange building": [ + "\n{\"type\": \"structure\", \"description\": \"orange in color; could be made of brick, stone, or wood; could have windows and doors\", \"similar objects\": [\"house\", \"school\", \"church\"]}", + 47 + ], + "round orange": [ + " {\"type\": \"fruit\", \"description\": \"round, orange, has a stem\", \"similar objects\": [\"apple\", \"peach\", \"plum\"]}", + 47 + ], + "cement floor": [ + " {\"type\": \"flooring material\", \"description\": \"hard, gray, could be polished; could be used for outdoor and indoor\", \"similar objects\": [\"tile floor\", \"wood floor\", \"marble floor\"]}", + 47 + ], + "orange slices": [ + " {\"type\": \"food\", \"description\": \"round, orange, could be cut into pieces; could be sweet or sour\", \"similar objects\": [\"apple slices\", \"lemon slices\", \"grapefruit slices\"]}", + 47 + ], + "plastic plate": [ + " {\"type\": \"dining ware\", \"description\": \"round; could be transparent; could be colorful; could be disposable\", \"similar objects\": [\"ceramic plate\", \"glass plate\", \"wooden plate\"]}", + 47 + ], + "fruit stand": [ + " {\"type\": \"market\", \"description\": \"could be made of wood; could have a roof; could have shelves for fruits\", \"similar objects\": [\"vegetable stand\", \"flower stand\", \"bakery stand\"]}", + 47 + ], + "calico cat": [ + " {\"type\": \"animal\", \"description\": \"medium-sized; has a white, black, and orange fur; has a triangular face\", \"similar objects\": [\"tortoiseshell cat\", \"tabby cat\", \"Siamese cat\"]}", + 47 + ], + "wood paneling": [ + " {\"type\": \"building material\", \"description\": \"wooden boards; could be used for walls and ceilings; could be painted or stained\", \"similar objects\": [\"plywood\", \"drywall\", \"hardboard\"]}", + 47 + ], + "gears": [ + " {\"type\": \"mechanical tool\", \"description\": \"interlocking metal wheels; could be used to transfer motion or power\", \"similar objects\": [\"pulley\", \"cogwheel\", \"chain\"]}", + 47 + ], + "slacks": [ + " {\"type\": \"clothing\", \"description\": \"long trousers; could be made of cotton, wool, or synthetic fibers; could be pleated or flat-fronted\", \"similar objects\": [\"jeans\", \"khakis\", \"trousers\"]}", + 47 + ], + "egg yolk": [ + " {\"type\": \"food ingredient\", \"description\": \"yellow; could be used for baking; could be used for making sauces\", \"similar objects\": [\"egg white\", \"butter\", \"sugar\"]}", + 47 + ], + "narrow": [ + "\n{\"type\": \"adjective\", \"description\": \"having little width; not wide\", \"similar objects\": [\"slim\", \"slender\", \"thin\"]}", + 47 + ], + "duffel bag": [ + " {\"type\": \"bag\", \"description\": \"cylindrical; has a shoulder strap; could be made of canvas or nylon\", \"similar objects\": [\"backpack\", \"suitcase\", \"tote bag\"]}", + 47 + ], + "flamingos": [ + " {\"type\": \"animal\", \"description\": \"pink; long legs; curved neck; could stand on one leg\", \"similar objects\": [\"storks\", \"cranes\", \"herons\"]}", + 47 + ], + "steam engine": [ + " {\"type\": \"machine\", \"description\": \"uses steam to power a piston; could be used to power a locomotive\", \"similar objects\": [\"diesel engine\", \"turbine engine\", \"gasoline engine\"]}", + 47 + ], + "number pad": [ + " {\"type\": \"input device\", \"description\": \"rectangular; has numbers and symbols; could be used for typing\", \"similar objects\": [\"keyboard\", \"mouse\", \"touchpad\"]}", + 47 + ], + "left leg": [ + " {\"type\": \"body part\", \"description\": \"one of the two lower limbs of the human body; could be used for walking and running\", \"similar objects\": [\"right leg\", \"arm\", \"foot\"]}", + 47 + ], + "puffy": [ + " {\"type\": \"clothing item\", \"description\": \"soft, lightweight, could be quilted; could be filled with down or synthetic fibers\", \"similar objects\": [\"jacket\", \"vest\", \"coat\"]}", + 47 + ], + "passenger door": [ + " {\"type\": \"automobile part\", \"description\": \"door located on the side of the car; could be opened from the inside and outside\", \"similar objects\": [\"hood\", \"trunk\", \"bumper\"]}", + 47 + ], + "train doors": [ + " {\"type\": \"transportation tool\", \"description\": \"sliding doors; could be automatic; could be opened by a button\", \"similar objects\": [\"elevator doors\", \"subway doors\", \"airplane doors\"]}", + 47 + ], + "dirt field": [ + " {\"type\": \"landscape\", \"description\": \"uneven ground; could be muddy; could have plants and rocks\", \"similar objects\": [\"grassland\", \"desert\", \"forest\"]}", + 47 + ], + "wood frame": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be used to build walls and furniture; could be made of different types of wood\", \"similar objects\": [\"plywood\", \"particle board\", \"lumber\"]}", + 47 + ], + "orange vegetable": [ + "\n{\"type\": \"vegetable\", \"description\": \"round; orange in color; could be sliced into wedges; could have green leaves\", \"similar objects\": [\"carrot\", \"sweet potato\", \"pumpkin\"]}", + 47 + ], + "cube": [ + " {\"type\": \"geometric shape\", \"description\": \"six equal square faces; eight vertices; twelve edges\", \"similar objects\": [\"rectangle\", \"pyramid\", \"sphere\"]}", + 47 + ], + "headlight bus": [ + "\n{\"type\": \"vehicle\", \"description\": \"large; has a long body; has two headlights; could be used for public transportation\", \"similar objects\": [\"truck\", \"van\", \"school bus\"]}", + 47 + ], + "square sign": [ + " {\"type\": \"traffic sign\", \"description\": \"has four sides; could be yellow, red, or blue; could have symbols or words on it\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 47 + ], + "beach chairs": [ + " {\"type\": \"furniture\", \"description\": \"foldable; could be made of plastic or wood; could have armrests\", \"similar objects\": [\"deck chairs\", \"lounge chairs\", \"sun loungers\"]}", + 47 + ], + "condiment": [ + " {\"type\": \"food item\", \"description\": \"used to enhance the flavor of food; could be in liquid or solid form\", \"similar objects\": [\"sauce\", \"dressing\", \"spice\"]}", + 47 + ], + "tyre": [ + " {\"type\": \"vehicle part\", \"description\": \"round; made of rubber; could be inflated\", \"similar objects\": [\"wheel\", \"rim\", \"hubcap\"]}", + 47 + ], + "frog": [ + " {\"type\": \"animal\", \"description\": \"green; has a wide mouth; could jump high; could croak\", \"similar objects\": [\"toad\", \"salamander\", \"newt\"]}", + 47 + ], + "position": [ + " {\"type\": \"concept\", \"description\": \"location of an object in relation to another object or a reference point\", \"similar objects\": [\"location\", \"place\", \"coordinates\"]}", + 47 + ], + "turbine": [ + " {\"type\": \"machine\", \"description\": \"cylindrical; has blades; used to generate power\", \"similar objects\": [\"generator\", \"engine\", \"compressor\"]}", + 47 + ], + "print style letter": [ + "\n{\"type\": \"document\", \"description\": \"written in a formal style; could be printed on paper; could be sent via mail\", \"similar objects\": [\"memo\", \"letter\", \"invitation\"]}", + 47 + ], + "tubes": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical; could be made of metal or plastic; could be used to transport liquids or gases\", \"similar objects\": [\"pipes\", \"hoses\", \"valves\"]}", + 47 + ], + "tie man": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, usually made of silk; could be worn around the neck\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}", + 47 + ], + "guacamole": [ + " {\"type\": \"food\", \"description\": \"green; made of mashed avocados; could be served with chips\", \"similar objects\": [\"salsa\", \"hummus\", \"dip\"]}", + 47 + ], + "brown frame": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; could be made of wood or metal; could be used to hang pictures\", \"similar objects\": [\"mirror frame\", \"picture frame\", \"photo frame\"]}", + 46 + ], + "glass shower door": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"transparent; could be framed or frameless; could be sliding or hinged\", \"similar objects\": [\"shower curtain\", \"bathtub\", \"bathroom sink\"]}", + 46 + ], + "par": [ + " {\"type\": \"golf score\", \"description\": \"the number of strokes a golfer is expected to make in order to complete a hole, an entire round of golf, or a tournament\", \"similar objects\": [\"birdie\", \"bogey\", \"eagle\"]}", + 46 + ], + "air freshener": [ + " {\"type\": \"household item\", \"description\": \"could be in the form of a spray, plug-in, or gel; could have a pleasant scent\", \"similar objects\": [\"incense\", \"scented candle\", \"room spray\"]}", + 46 + ], + "mustard bottle": [ + " {\"type\": \"condiment container\", \"description\": \"cylindrical; could be yellow or brown; has a lid\", \"similar objects\": [\"ketchup bottle\", \"mayonnaise bottle\", \"vinegar bottle\"]}", + 46 + ], + "pat": [ + " {\"type\": \"action\", \"description\": \"light touch with the hand; could be done with a gentle motion\", \"similar objects\": [\"stroke\", \"caress\", \"rub\"]}", + 46 + ], + "dog ear": [ + " {\"type\": \"body part\", \"description\": \"floppy; could be pointed or rounded; could be covered with fur\", \"similar objects\": [\"cat ear\", \"human ear\", \"rabbit ear\"]}", + 46 + ], + "plastic tray": [ + " {\"type\": \"utensil\", \"description\": \"flat, rectangular; could be used to carry food\", \"similar objects\": [\"plate\", \"bowl\", \"cup\"]}", + 46 + ], + "blue eye": [ + " {\"type\": \"jewelry\", \"description\": \"round; could be made of glass or plastic; could be used as a pendant\", \"similar objects\": [\"earring\", \"necklace\", \"bracelet\"]}", + 46 + ], + "throw": [ + " {\"type\": \"action\", \"description\": \"to propel an object through the air with a motion of the arm\", \"similar objects\": [\"toss\", \"fling\", \"hurl\"]}", + 46 + ], + "pallet": [ + " {\"type\": \"transport tool\", \"description\": \"wooden platform; could be used to move goods\", \"similar objects\": [\"crate\", \"dolly\", \"skid\"]}", + 46 + ], + "kitchen island": [ + " {\"type\": \"furniture\", \"description\": \"large, rectangular, has a countertop; could have drawers and cabinets\", \"similar objects\": [\"table\", \"cabinet\", \"stool\"]}", + 46 + ], + "chocolate doughnut": [ + "\n{\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be filled with chocolate; could be covered with sugar\", \"similar objects\": [\"glazed doughnut\", \"jelly doughnut\", \"cinnamon roll\"]}", + 46 + ], + "bamboo": [ + " {\"type\": \"plant\", \"description\": \"tall, thin, hollow; could be used to make furniture\", \"similar objects\": [\"palm tree\", \"fern\", \"birch tree\"]}", + 46 + ], + "razor": [ + " {\"type\": \"grooming tool\", \"description\": \"sharp blade; could be used for shaving\", \"similar objects\": [\"shaver\", \"scissors\", \"clipper\"]}", + 46 + ], + "mass": [ + " {\"type\": \"measurement unit\", \"description\": \"unit of measure for weight; could be measured in kilograms, pounds, ounces, etc.\", \"similar objects\": [\"volume\", \"length\", \"area\"]}", + 46 + ], + "bus window": [ + " {\"type\": \"transportation window\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"car window\", \"airplane window\", \"train window\"]}", + 46 + ], + "metal legs": [ + " {\"type\": \"furniture part\", \"description\": \"could be made of metal; could be used to support tables, chairs, etc.\", \"similar objects\": [\"wooden legs\", \"casters\", \"wheels\"]}", + 46 + ], + "ramps": [ + " {\"type\": \"accessibility tool\", \"description\": \"inclined surface; could be made of metal or wood; could be used to provide access to wheelchair users\", \"similar objects\": [\"stairs\", \"elevator\", \"escalator\"]}", + 46 + ], + "c": [ + " {\"type\": \"programming language\", \"description\": \"general-purpose, procedural, imperative, compiled language\", \"similar objects\": [\"C++\", \"Java\", \"Python\"]}", + 46 + ], + "courtyard": [ + " {\"type\": \"outdoor space\", \"description\": \"open area surrounded by buildings; could have a fountain or a garden\", \"similar objects\": [\"patio\", \"balcony\", \"terrace\"]}", + 46 + ], + "strands": [ + " {\"type\": \"object\", \"description\": \"long, thin, flexible; could be made of metal, plastic, or other materials\", \"similar objects\": [\"threads\", \"strings\", \"cords\"]}", + 46 + ], + "right leg": [ + " {\"type\": \"body part\", \"description\": \"long; could be bent; could be used for walking\", \"similar objects\": [\"left leg\", \"arm\", \"foot\"]}", + 46 + ], + "mixture": [ + " {\"type\": \"combination\", \"description\": \"combination of two or more substances; could be liquid, solid, or gas\", \"similar objects\": [\"blend\", \"compound\", \"solution\"]}", + 46 + ], + "glass building": [ + " {\"type\": \"structure\", \"description\": \"transparent walls; could be made of steel and glass; could be tall and modern\", \"similar objects\": [\"skyscraper\", \"high-rise building\", \"office building\"]}", + 46 + ], + "language": [ + " {\"type\": \"communication tool\", \"description\": \"a system of symbols, signs, and sounds used to communicate\", \"similar objects\": [\"dialect\", \"slang\", \"vocabulary\"]}", + 46 + ], + "reflectors": [ + " {\"type\": \"safety tool\", \"description\": \"round; could be made of metal; could be used to reflect light\", \"similar objects\": [\"road cones\", \"warning signs\", \"traffic lights\"]}", + 46 + ], + "blur": [ + " {\"type\": \"visual effect\", \"description\": \"lack of focus; could be caused by camera shake or incorrect focus\", \"similar objects\": [\"haze\", \"fog\", \"smoke\"]}", + 46 + ], + "floor mat": [ + " {\"type\": \"household item\", \"description\": \"rectangular; could be made of rubber or fabric; could be used to wipe off dirt from shoes\", \"similar objects\": [\"rug\", \"doormat\", \"carpet\"]}", + 46 + ], + "blue lid": [ + " {\"type\": \"container\", \"description\": \"round; could be made of plastic; could be used to cover a container\", \"similar objects\": [\"cap\", \"cover\", \"lid\"]}", + 46 + ], + "ball player": [ + " {\"type\": \"athlete\", \"description\": \"wears a uniform; could be playing a sport such as basketball, soccer, or baseball; could be using a ball or other equipment\", \"similar objects\": [\"runner\", \"swimmer\", \"cyclist\"]}", + 46 + ], + "blue curtains": [ + " {\"type\": \"decoration\", \"description\": \"blue; could be made of fabric; could be hung on windows\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 46 + ], + "wood wall": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be painted; could be used to build walls\", \"similar objects\": [\"brick wall\", \"concrete wall\", \"plaster wall\"]}", + 46 + ], + "trails": [ + " {\"type\": \"landscape feature\", \"description\": \"paths or tracks made by people or animals; could be made of dirt, rocks, or grass\", \"similar objects\": [\"paths\", \"roads\", \"bridges\"]}", + 46 + ], + "port": [ + " {\"type\": \"harbor\", \"description\": \"a place for ships to dock; could have a lighthouse; could have a bridge\", \"similar objects\": [\"dock\", \"marina\", \"wharf\"]}", + 46 + ], + "support post": [ + " {\"type\": \"structural element\", \"description\": \"vertical; could be made of metal or wood; could be used to support a roof or a bridge\", \"similar objects\": [\"column\", \"pillar\", \"beam\"]}", + 46 + ], + "banana leaf": [ + " {\"type\": \"plant\", \"description\": \"long, green, thin; could be used as a wrapper\", \"similar objects\": [\"coconut leaf\", \"palm leaf\", \"lotus leaf\"]}", + 46 + ], + "freezer door": [ + " {\"type\": \"appliance\", \"description\": \"rectangular; could be made of metal; could have a handle\", \"similar objects\": [\"refrigerator door\", \"oven door\", \"dishwasher door\"]}", + 46 + ], + "pretzel": [ + " {\"type\": \"food\", \"description\": \"twisted; salty; could be made of dough\", \"similar objects\": [\"bagel\", \"croissant\", \"doughnut\"]}", + 46 + ], + "wooden chair": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; has four legs; could have armrests; could have a backrest\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 46 + ], + "coloring": [ + " {\"type\": \"activity\", \"description\": \"using crayons, markers, or paints to create a picture\", \"similar objects\": [\"drawing\", \"painting\", \"sketching\"]}", + 46 + ], + "ornaments": [ + " {\"type\": \"decoration\", \"description\": \"could be made of metal, glass, plastic, wood; could be in various shapes and sizes; could be hung on walls or trees\", \"similar objects\": [\"figurines\", \"sculptures\", \"statues\"]}", + 46 + ], + "r": [ + "\n{\"type\": \"letter\", \"description\": \"the eighteenth letter of the English alphabet; a consonant\", \"similar objects\": [\"s\", \"t\", \"u\"]}", + 46 + ], + "ski marks": [ + " {\"type\": \"outdoor activity\", \"description\": \"long, parallel lines on snow; could be made by skis\", \"similar objects\": [\"snowboard tracks\", \"sled tracks\", \"ice skate marks\"]}", + 46 + ], + "plastic toilet seat": [ + "\n{\"type\": \"bathroom accessory\", \"description\": \"made of plastic; could be round or oval; could be white or colored; could be with or without a lid\", \"similar objects\": [\"toilet brush\", \"toilet paper holder\", \"toilet plunger\"]}", + 46 + ], + "brick clock tower": [ + "\n{\"type\": \"architectural structure\", \"description\": \"tall, rectangular; made of bricks; has a clock on top\", \"similar objects\": [\"bell tower\", \"observatory\", \"windmill\"]}", + 46 + ], + "man hole": [ + " {\"type\": \"utility structure\", \"description\": \"round; has a cover; could be used for accessing underground utilities\", \"similar objects\": [\"drainage hole\", \"sewer hole\", \"ventilation hole\"]}", + 46 + ], + "museum": [ + " {\"type\": \"building\", \"description\": \"large; could have many artifacts; could have many galleries\", \"similar objects\": [\"library\", \"gallery\", \"theater\"]}", + 46 + ], + "roman number": [ + " {\"type\": \"number system\", \"description\": \"uses letters to represent numbers; could be used to represent dates\", \"similar objects\": [\"Arabic numbers\", \"Hindu-Arabic numbers\", \"Chinese numerals\"]}", + 46 + ], + "coffee mugs": [ + " {\"type\": \"drinking tool\", \"description\": \"cylindrical; could have handles; could be made of ceramic, glass, or metal\", \"similar objects\": [\"teacup\", \"glass\", \"cup\"]}", + 45 + ], + "brick fireplace": [ + " {\"type\": \"structure\", \"description\": \"made of bricks; could have a mantel; could have a hearth\", \"similar objects\": [\"wood stove\", \"fire pit\", \"chimney\"]}", + 45 + ], + "road signs": [ + " {\"type\": \"traffic signs\", \"description\": \"could be triangular, rectangular, or circular; could be yellow, red, or blue; could have symbols or words\", \"similar objects\": [\"traffic lights\", \"speed limit signs\", \"road markings\"]}", + 45 + ], + "packages": [ + " {\"type\": \"container\", \"description\": \"could be made of paper, plastic, or metal; could be sealed; could be of different sizes and shapes\", \"similar objects\": [\"boxes\", \"envelopes\", \"bags\"]}", + 45 + ], + "square tile": [ + " {\"type\": \"building material\", \"description\": \"flat, four-sided, could be made of ceramic, stone, or glass\", \"similar objects\": [\"hexagon tile\", \"triangle tile\", \"rectangle tile\"]}", + 45 + ], + "step ladder": [ + " {\"type\": \"tool\", \"description\": \"has two steps; could be folded; could be made of metal or wood\", \"similar objects\": [\"ladder\", \"stool\", \"scaffolding\"]}", + 45 + ], + "metal guard rail": [ + " {\"type\": \"safety tool\", \"description\": \"long, metal, has a top rail; could be used to separate lanes\", \"similar objects\": [\"fence\", \"barrier\", \"wall\"]}", + 45 + ], + "route": [ + " {\"type\": \"path\", \"description\": \"a way from one place to another; could be a road, a river, a railway, etc.\", \"similar objects\": [\"journey\", \"trail\", \"track\"]}", + 45 + ], + "mountain top": [ + " {\"type\": \"landscape\", \"description\": \"high elevation; could have snow; could have a peak\", \"similar objects\": [\"hill\", \"cliff\", \"valley\"]}", + 45 + ], + "goods": [ + " {\"type\": \"products\", \"description\": \"items that are bought and sold; could be tangible or intangible\", \"similar objects\": [\"merchandise\", \"commodities\", \"services\"]}", + 45 + ], + "blood": [ + " {\"type\": \"fluid\", \"description\": \"red; could be found in veins; could be used for medical tests\", \"similar objects\": [\"urine\", \"saliva\", \"cerebrospinal fluid\"]}", + 45 + ], + "vacuum": [ + " {\"type\": \"cleaning tool\", \"description\": \"long tube; has a motor; could be used to suck up dirt and dust\", \"similar objects\": [\"broom\", \"mop\", \"duster\"]}", + 45 + ], + "beige wall": [ + " {\"type\": \"building material\", \"description\": \"light brown; could be made of wood, stone, or plaster; could be painted\", \"similar objects\": [\"white wall\", \"gray wall\", \"brown wall\"]}", + 45 + ], + "giraffe legs": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, slender, and covered in spots; could have hooves\", \"similar objects\": [\"elephant legs\", \"horse legs\", \"zebra legs\"]}", + 45 + ], + "plastic cups": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be used for drinking; could be disposable\", \"similar objects\": [\"plates\", \"bowls\", \"glasses\"]}", + 45 + ], + "plastic handle": [ + " {\"type\": \"handle\", \"description\": \"made of plastic; could be used to open a door or a drawer\", \"similar objects\": [\"metal handle\", \"wooden handle\", \"leather handle\"]}", + 45 + ], + "smokestack": [ + " {\"type\": \"industrial tool\", \"description\": \"tall, cylindrical; could be used to release smoke\", \"similar objects\": [\"chimney\", \"flue\", \"exhaust pipe\"]}", + 45 + ], + "coffee tables": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have a glass top; could have drawers\", \"similar objects\": [\"end table\", \"console table\", \"side table\"]}", + 45 + ], + "orange bus": [ + "\n{\"type\": \"vehicle\", \"description\": \"large, orange, has multiple doors; could have a destination sign\", \"similar objects\": [\"school bus\", \"city bus\", \"shuttle bus\"]}", + 45 + ], + "pink blanket": [ + "\n{\"type\": \"textile\", \"description\": \"soft; could be made of cotton; could be in pink color\", \"similar objects\": [\"pillow\", \"quilt\", \"towel\"]}", + 45 + ], + "spout": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, and curved; used to pour liquids\", \"similar objects\": [\"funnel\", \"ladle\", \"pitcher\"]}", + 45 + ], + "rubber duck": [ + " {\"type\": \"toy\", \"description\": \"yellow; could be made of rubber; could float on water\", \"similar objects\": [\"stuffed animal\", \"ball\", \"action figure\"]}", + 45 + ], + "alligator": [ + " {\"type\": \"animal\", \"description\": \"large, scaly, long snout; could have a long tail; could have sharp teeth\", \"similar objects\": [\"crocodile\", \"turtle\", \"iguana\"]}", + 45 + ], + "dad": [ + " {\"type\": \"person\", \"description\": \"male; could be a father, grandfather, uncle, etc.\", \"similar objects\": [\"mom\", \"grandma\", \"aunt\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\", and \"green", + 45 + ], + "lever": [ + " {\"type\": \"mechanical tool\", \"description\": \"long, thin, metal bar; used to move or lift objects\", \"similar objects\": [\"pulley\", \"crank\", \"screw\"]}", + 45 + ], + "utility poles": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical, made of metal; could have wires attached to it\", \"similar objects\": [\"street lights\", \"traffic lights\", \"telephone poles\"]}", + 45 + ], + "veggie": [ + " {\"type\": \"food\", \"description\": \"could be a variety of vegetables; could be cooked or raw; could be eaten as a side dish or main course\", \"similar objects\": [\"fruit\", \"meat\", \"grain\"]}", + 45 + ], + "sprig": [ + " {\"type\": \"plant\", \"description\": \"small, green, could have flowers; could be used as decoration\", \"similar objects\": [\"branch\", \"stem\", \"leaf\"]}", + 45 + ], + "base line": [ + " {\"type\": \"sports term\", \"description\": \"the line at the back of the court in tennis and other racquet sports\", \"similar objects\": [\"net\", \"service line\", \"center line\"]}", + 45 + ], + "baseball diamond": [ + " {\"type\": \"sports field\", \"description\": \"square; four bases; pitcher's mound; outfield\", \"similar objects\": [\"soccer field\", \"tennis court\", \"basketball court\"]}", + 45 + ], + "jet engines": [ + " {\"type\": \"machine\", \"description\": \"large, powerful engines used to propel aircrafts; could be turbojet, turbofan, or turboprop engines\", \"similar objects\": [\"propellers\", \"rocket engines\", \"turbines\"]}", + 45 + ], + "cat nose": [ + " {\"type\": \"body part\", \"description\": \"small, round, black; could be wet; could be sensitive to touch\", \"similar objects\": [\"dog nose\", \"human nose\", \"rabbit nose\"]}", + 45 + ], + "pink sign": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be made of plastic; could be in pink color\", \"similar objects\": [\"banner\", \"flag\", \"placard\"]}", + 45 + ], + "grizzly": [ + " {\"type\": \"animal\", \"description\": \"brown fur; large size; could have a hump on its back; could have a long snout\", \"similar objects\": [\"polar bear\", \"black bear\", \"brown bear\"]}", + 45 + ], + "fern": [ + " {\"type\": \"plant\", \"description\": \"green; has long, thin leaves; could be found in moist areas\", \"similar objects\": [\"moss\", \"ivy\", \"palm tree\"]}", + 45 + ], + "camera man": [ + " {\"type\": \"occupation\", \"description\": \"person who operates a camera; could be a photographer or videographer\", \"similar objects\": [\"director\", \"producer\", \"editor\"]}", + 45 + ], + "blue helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"blue; could be made of plastic or metal; could have a visor\", \"similar objects\": [\"hard hat\", \"safety goggles\", \"ear muffs\"]}", + 45 + ], + "orange traffic cones": [ + "\n{\"type\": \"traffic safety tool\", \"description\": \"orange; cone-shaped; could be reflective\", \"similar objects\": [\"barricades\", \"traffic signs\", \"warning lights\"]}", + 45 + ], + "stoplights": [ + " {\"type\": \"traffic signal\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"traffic signs\", \"road signs\", \"traffic cones\"]}", + 45 + ], + "hump": [ + " {\"type\": \"geographical feature\", \"description\": \"a rounded protuberance or mound; could be found on the back of a camel\", \"similar objects\": [\"hill\", \"mountain\", \"ridge\"]}", + 45 + ], + "female surfer": [ + "\n{\"type\": \"person\", \"description\": \"wearing a wetsuit; carrying a surfboard; could have long hair\", \"similar objects\": [\"male surfer\", \"diver\", \"swimmer\"]}", + 45 + ], + "openings": [ + " {\"type\": \"architectural feature\", \"description\": \"could be round, rectangular, or other shapes; could be used for windows, doors, or other purposes\", \"similar objects\": [\"windows\", \"doors\", \"arches\"]}", + 45 + ], + "shirt sleeve": [ + " {\"type\": \"clothing item\", \"description\": \"long, cylindrical, attached to a shirt\", \"similar objects\": [\"pants leg\", \"jacket sleeve\", \"skirt\"]}", + 44 + ], + "grey metal pole": [ + " {\"type\": \"structure\", \"description\": \"long, cylindrical, grey; could be used for support\", \"similar objects\": [\"fence post\", \"flagpole\", \"streetlight pole\"]}", + 44 + ], + "toilet roll": [ + " {\"type\": \"household item\", \"description\": \"cylindrical; made of paper; could be used for wiping\", \"similar objects\": [\"tissue paper\", \"paper towel\", \"toilet tissue\"]}", + 44 + ], + "brace": [ + " {\"type\": \"medical tool\", \"description\": \"used to support a body part; could be made of metal or plastic; could be adjustable\", \"similar objects\": [\"splint\", \"cast\", \"crutch\"]}", + 44 + ], + "tan shorts": [ + " {\"type\": \"clothing\", \"description\": \"light brown; could be made of cotton; could have pockets; could have a drawstring\", \"similar objects\": [\"khaki shorts\", \"denim shorts\", \"jean shorts\"]}", + 44 + ], + "cute": [ + "\n\nUnfortunately, object detection models cannot detect abstract concepts such as \"cuteness\". Object detection models focus on physical features of objects, such as shape, color, size, texture, etc.", + 44 + ], + "tee": [ + " {\"type\": \"clothing item\", \"description\": \"short-sleeved; could be plain or patterned; could have a collar\", \"similar objects\": [\"shirt\", \"blouse\", \"tank top\"]}", + 44 + ], + "giant": [ + " {\"type\": \"size\", \"description\": \"larger than normal; could be used to describe objects, animals, or people\", \"similar objects\": [\"huge\", \"enormous\", \"massive\"]}", + 44 + ], + "h": [ + "\n{\"type\": \"letter\", \"description\": \"the eighth letter of the English alphabet; a consonant\", \"similar objects\": [\"g\", \"i\", \"j\"]}", + 44 + ], + "orange chair": [ + "\n{\"type\": \"furniture\", \"description\": \"orange; could have four legs; could have armrests; could have a backrest\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 44 + ], + "cork": [ + " {\"type\": \"material\", \"description\": \"lightweight; could be used to seal bottles; could be made of wood or plastic\", \"similar objects\": [\"foam\", \"rubber\", \"plastic\"]}", + 44 + ], + "bra": [ + " {\"type\": \"clothing item\", \"description\": \"two cups connected by straps; could be made of different materials\", \"similar objects\": [\"tank top\", \"bikini top\", \"sports bra\"]}", + 44 + ], + "flagpole": [ + " {\"type\": \"pole\", \"description\": \"tall, thin, could be made of metal; could be used to hold a flag\", \"similar objects\": [\"flagstaff\", \"mast\", \"light pole\"]}", + 44 + ], + "monitor screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be touch-sensitive; could be connected to a computer\", \"similar objects\": [\"television\", \"tablet\", \"smartphone\"]}", + 44 + ], + "coleslaw": [ + " {\"type\": \"food\", \"description\": \"shredded cabbage and carrots; could be mixed with mayonnaise or vinegar; could be served as a side dish\", \"similar objects\": [\"salad\", \"potato salad\", \"macaroni salad\"]}", + 44 + ], + "water splash": [ + " {\"type\": \"phenomenon\", \"description\": \"a sudden burst of water; could be caused by a stone thrown into a lake\", \"similar objects\": [\"wave\", \"tsunami\", \"tidal wave\"]}", + 44 + ], + "movies": [ + " {\"type\": \"entertainment\", \"description\": \"visual stories; could be in the form of films, television shows, or web series; could be in various genres\", \"similar objects\": [\"books\", \"music\", \"theater\"]}", + 44 + ], + "silver pan": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle; made of silver\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 44 + ], + "dress shoes": [ + " {\"type\": \"footwear\", \"description\": \"leather; could have laces; could have a heel\", \"similar objects\": [\"loafers\", \"sandals\", \"boots\"]}", + 44 + ], + "power": [ + " {\"type\": \"energy source\", \"description\": \"ability to do work; could be generated from natural resources; could be stored in batteries\", \"similar objects\": [\"electricity\", \"fuel\", \"solar energy\"]}", + 44 + ], + "skiiers": [ + " {\"type\": \"sport\", \"description\": \"people skiing on snow; could use ski poles; could wear ski goggles\", \"similar objects\": [\"snowboarders\", \"skaters\", \"surfers\"]}", + 44 + ], + "blurry image": [ + "\n\n{\"type\": \"image\", \"description\": \"blurry; could be out of focus; could be distorted; could be low resolution\", \"similar objects\": [\"photo\", \"picture\", \"snapshot\"]}", + 44 + ], + "ladders": [ + " {\"type\": \"tool\", \"description\": \"long; could be made of metal or wood; could be used to reach high places\", \"similar objects\": [\"step stool\", \"staircase\", \"scaffolding\"]}", + 44 + ], + "love seat": [ + " {\"type\": \"furniture\", \"description\": \"two-seater; could be upholstered; could have armrests\", \"similar objects\": [\"sofa\", \"couch\", \"bench\"]}", + 44 + ], + "windscreen": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; covers the front of a vehicle; could be made of glass or plastic\", \"similar objects\": [\"windshield\", \"headlight\", \"tail light\"]}", + 44 + ], + "tennis raquet": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be made of wood or metal\", \"similar objects\": [\"golf club\", \"baseball bat\", \"hockey stick\"]}", + 44 + ], + "front train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could have a locomotive\", \"similar objects\": [\"subway\", \"tram\", \"monorail\"]}", + 44 + ], + "company": [ + " {\"type\": \"organization\", \"description\": \"a group of people working together to achieve a common goal\", \"similar objects\": [\"corporation\", \"business\", \"enterprise\"]}", + 44 + ], + "skateboard ground": [ + " {\"type\": \"sports equipment\", \"description\": \"flat surface; could be made of wood or concrete; could have ramps and rails\", \"similar objects\": [\"skateboard ramp\", \"half-pipe\", \"skate park\"]}", + 44 + ], + "light fixture": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the ceiling; could be made of metal or glass; could have multiple bulbs\", \"similar objects\": [\"chandelier\", \"ceiling fan\", \"pendant light\"]}", + 44 + ], + "art work": [ + " {\"type\": \"visual art\", \"description\": \"could be a painting, a sculpture, a photograph, or any other form of visual art\", \"similar objects\": [\"painting\", \"sculpture\", \"photograph\"]}", + 44 + ], + "tomatoe": [ + " {\"type\": \"vegetable\", \"description\": \"round, red; could have green stem; could be sliced into pieces\", \"similar objects\": [\"bell pepper\", \"cucumber\", \"eggplant\"]}", + 44 + ], + "bikini top": [ + " {\"type\": \"clothing item\", \"description\": \"two pieces of fabric connected at the shoulder straps; could be made of different materials; could be in different colors and patterns\", \"similar objects\": [\"tank top\", \"bralette\", \"crop top\"]}", + 44 + ], + "city building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have windows; could have a roof\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"office building\"]}", + 44 + ], + "brass": [ + " {\"type\": \"metal\", \"description\": \"yellowish-gold; malleable; could be used to make musical instruments\", \"similar objects\": [\"bronze\", \"copper\", \"aluminum\"]}", + 44 + ], + "leaves trees": [ + " {\"type\": \"plant\", \"description\": \"green; could be oval or long; could have veins; could be attached to branches\", \"similar objects\": [\"grass\", \"ferns\", \"bushes\"]}", + 44 + ], + "sun light": [ + " {\"type\": \"natural light\", \"description\": \"bright, yellowish, warm; could be blocked by clouds\", \"similar objects\": [\"moonlight\", \"starlight\", \"firelight\"]}", + 44 + ], + "scrub brush": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; bristles on one end; could be made of plastic or metal\", \"similar objects\": [\"sponge\", \"mop\", \"broom\"]}", + 44 + ], + "horse rider": [ + " {\"type\": \"person\", \"description\": \"person riding a horse; could be wearing a helmet and protective gear; could be holding a whip\", \"similar objects\": [\"jockey\", \"equestrian\", \"horseback rider\"]}", + 44 + ], + "stone tower": [ + " {\"type\": \"architecture\", \"description\": \"tall, made of stones; could have windows and doors\", \"similar objects\": [\"castle\", \"pyramid\", \"monument\"]}", + 44 + ], + "hoop": [ + " {\"type\": \"toy\", \"description\": \"circular; could be made of plastic or metal; could be used for playing games\", \"similar objects\": [\"ball\", \"frisbee\", \"yo-yo\"]}", + 44 + ], + "wooden bridge": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could have two or more pillars; could have a railing\", \"similar objects\": [\"stone bridge\", \"suspension bridge\", \"viaduct\"]}", + 44 + ], + "beach area": [ + " {\"type\": \"location\", \"description\": \"sandy; could have rocks; could have waves; could have palm trees\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 44 + ], + "orange color": [ + " {\"type\": \"color\", \"description\": \"vibrant, warm, reddish-yellow hue\", \"similar objects\": [\"yellow\", \"red\", \"green\"]}", + 44 + ], + "plane tail": [ + " {\"type\": \"aircraft part\", \"description\": \"vertical, long, has a rudder; could have a logo\", \"similar objects\": [\"fuselage\", \"wing\", \"engine\"]}", + 44 + ], + "engineer": [ + " {\"type\": \"occupation\", \"description\": \"professionals who design, build, and maintain machines, structures, and systems\", \"similar objects\": [\"scientist\", \"architect\", \"programmer\"]}", + 43 + ], + "salad plate": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could be used to serve salad\", \"similar objects\": [\"dinner plate\", \"soup bowl\", \"teacup\"]}", + 43 + ], + "creases": [ + " {\"type\": \"wrinkle\", \"description\": \"lines or folds in fabric or paper; could be caused by folding or crumpling\", \"similar objects\": [\"wrinkles\", \"folds\", \"pleats\"]}", + 43 + ], + "wood trim": [ + " {\"type\": \"building material\", \"description\": \"long, thin strips of wood; used to decorate walls and furniture\", \"similar objects\": [\"molding\", \"baseboard\", \"crown molding\"]}", + 43 + ], + "customer": [ + " {\"type\": \"person\", \"description\": \"someone who purchases goods or services\", \"similar objects\": [\"client\", \"consumer\", \"buyer\"]}", + 43 + ], + "batch": [ + " {\"type\": \"group\", \"description\": \"a collection of items; could be of same type or different types\", \"similar objects\": [\"set\", \"package\", \"bundle\"]}", + 43 + ], + "blue bowl": [ + "\n{\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; has a color of blue\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 43 + ], + "battery": [ + " {\"type\": \"power source\", \"description\": \"rectangular; could be rechargeable; could be used to power electronic devices\", \"similar objects\": [\"solar panel\", \"generator\", \"capacitor\"]}", + 43 + ], + "kittens": [ + " {\"type\": \"animal\", \"description\": \"small, furry, playful; could have stripes or spots; could have short tails\", \"similar objects\": [\"puppies\", \"bunnies\", \"hamsters\"]}", + 43 + ], + "decor": [ + " {\"type\": \"decoration\", \"description\": \"could be made of fabric, paper, wood, metal, etc.; could be used to enhance the appearance of a room\", \"similar objects\": [\"ornament\", \"artwork\", \"furniture\"]}", + 43 + ], + "dog tag": [ + " {\"type\": \"accessory\", \"description\": \"metal; could be engraved with a name; could be hung on a collar\", \"similar objects\": [\"collar\", \"leash\", \"harness\"]}", + 43 + ], + "orange jacket": [ + "\n{\"type\": \"clothing\", \"description\": \"orange; could be made of cotton; could have a zipper; could have pockets\", \"similar objects\": [\"red coat\", \"blue sweater\", \"green hoodie\"]}", + 43 + ], + "hips": [ + " {\"type\": \"body part\", \"description\": \"part of the torso; located between the waist and the thighs; could be used to move the body\", \"similar objects\": [\"shoulders\", \"knees\", \"elbows\"]}", + 43 + ], + "caution": [ + " {\"type\": \"warning sign\", \"description\": \"yellow; has a triangle shape; could have a black exclamation mark\", \"similar objects\": [\"danger\", \"stop\", \"yield\"]}", + 43 + ], + "silver plate": [ + " {\"type\": \"utensil\", \"description\": \"shiny, round, made of metal; could be used for decoration\", \"similar objects\": [\"bowl\", \"cup\", \"tray\"]}", + 43 + ], + "stone pillar": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, cylindrical, made of stone; could have carvings\", \"similar objects\": [\"obelisk\", \"column\", \"monolith\"]}", + 43 + ], + "orange piece": [ + " {\"type\": \"fruit\", \"description\": \"round; has a peel; could be sliced into pieces; could be juicy\", \"similar objects\": [\"apple\", \"lemon\", \"grapefruit\"]}", + 43 + ], + "spectacles": [ + " {\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be made of metal or plastic; could be used for vision correction\", \"similar objects\": [\"sunglasses\", \"goggles\", \"monocle\"]}", + 43 + ], + "pea": [ + " {\"type\": \"vegetable\", \"description\": \"small, green, round; could be eaten raw or cooked; could be found in pods\", \"similar objects\": [\"bean\", \"corn\", \"carrot\"]}", + 43 + ], + "bathroom counter": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could have a sink; could have drawers\", \"similar objects\": [\"kitchen counter\", \"vanity\", \"dresser\"]}", + 43 + ], + "volleyball": [ + " {\"type\": \"sport equipment\", \"description\": \"spherical; has a net; could be played with two teams\", \"similar objects\": [\"basketball\", \"football\", \"tennis ball\"]}", + 43 + ], + "tree stump": [ + " {\"type\": \"plant\", \"description\": \"remains of a tree; could be used as a seat; could be used as a decoration\", \"similar objects\": [\"log\", \"stump\", \"wood\"]}", + 43 + ], + "florets": [ + " {\"type\": \"vegetable\", \"description\": \"small, green, flower-like; could be steamed or boiled; could be used in salads\", \"similar objects\": [\"broccoli\", \"cauliflower\", \"brussels sprouts\"]}", + 43 + ], + "ben": [ + "\n{\"type\": \"name\", \"description\": \"common name; could be a short form of Benjamin\", \"similar objects\": [\"bob\", \"joe\", \"john\"]}", + 43 + ], + "kiosk": [ + " {\"type\": \"structure\", \"description\": \"small, enclosed structure; could have a window or door; could have a roof\", \"similar objects\": [\"booth\", \"hut\", \"shed\"]}", + 43 + ], + "skateboard dude": [ + " {\"type\": \"action figure\", \"description\": \"wearing a helmet and knee pads; has a skateboard; could be in different poses\", \"similar objects\": [\"surfer\", \"biker\", \"rollerblader\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber", + 43 + ], + "plane engine": [ + " {\"type\": \"machine\", \"description\": \"cylindrical; has a propeller; could be powered by gasoline or electricity\", \"similar objects\": [\"helicopter engine\", \"car engine\", \"boat engine\"]}", + 43 + ], + "silver television": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could be connected to a remote control; could be connected to a cable box\", \"similar objects\": [\"computer monitor\", \"stereo system\", \"gaming console\"]}", + 43 + ], + "fridge door": [ + " {\"type\": \"appliance part\", \"description\": \"rectangular; could be made of metal; could have a handle\", \"similar objects\": [\"freezer door\", \"oven door\", \"dishwasher door\"]}", + 43 + ], + "information sign": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be made of metal; could have arrows or symbols\", \"similar objects\": [\"warning sign\", \"street sign\", \"traffic sign\"]}", + 43 + ], + "bent": [ + " {\"type\": \"adjective\", \"description\": \"not straight; curved\", \"similar objects\": [\"crooked\", \"twisted\", \"curved\"]}", + 43 + ], + "cardigan": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be buttoned up; could be knitted\", \"similar objects\": [\"sweater\", \"jacket\", \"coat\"]}", + 43 + ], + "sandwhich": [ + " {\"type\": \"food\", \"description\": \"two slices of bread with filling in between; could be cut into triangles\", \"similar objects\": [\"burger\", \"wrap\", \"taco\"]}", + 43 + ], + "spikes": [ + " {\"type\": \"fastening tool\", \"description\": \"sharp, pointed, metal; could be used to fasten objects together\", \"similar objects\": [\"nails\", \"screws\", \"bolts\"]}", + 43 + ], + "sport": [ + " {\"type\": \"activity\", \"description\": \"involves physical exertion and skill; could be competitive; could be played with a team or individually\", \"similar objects\": [\"exercise\", \"game\", \"competition\"]}", + 43 + ], + "sidecar": [ + " {\"type\": \"vehicle\", \"description\": \"attached to a motorcycle; could be used to transport passengers\", \"similar objects\": [\"tricycle\", \"rickshaw\", \"scooter\"]}", + 43 + ], + "handle drawer": [ + " {\"type\": \"furniture\", \"description\": \"has a handle; could be used to store items\", \"similar objects\": [\"cabinet\", \"wardrobe\", \"chest of drawers\"]}", + 43 + ], + "advertising": [ + " {\"type\": \"marketing tool\", \"description\": \"promoting a product or service to potential customers\", \"similar objects\": [\"branding\", \"public relations\", \"social media marketing\"]}", + 43 + ], + "foal": [ + " {\"type\": \"animal\", \"description\": \"young horse; has a short mane; could be brown or black\", \"similar objects\": [\"calf\", \"puppy\", \"kitten\"]}", + 43 + ], + "alley": [ + " {\"type\": \"location\", \"description\": \"narrow street; could be between two buildings\", \"similar objects\": [\"lane\", \"street\", \"boulevard\"]}", + 43 + ], + "hoofs": [ + " {\"type\": \"animal body part\", \"description\": \"hard, pointed, and curved; found on the feet of horses, cows, and other hoofed animals\", \"similar objects\": [\"horns\", \"claws\", \"teeth\"]}", + 43 + ], + "ornament": [ + " {\"type\": \"decoration\", \"description\": \"could be made of glass, metal, or plastic; could be hung on a tree or wall\", \"similar objects\": [\"figurine\", \"statue\", \"sculpture\"]}", + 43 + ], + "snow jacket": [ + " {\"type\": \"clothing\", \"description\": \"waterproof; could be insulated; could have a hood\", \"similar objects\": [\"ski jacket\", \"raincoat\", \"winter coat\"]}", + 42 + ], + "ferry": [ + " {\"type\": \"transportation\", \"description\": \"large boat; could carry passengers and vehicles; could have a cabin\", \"similar objects\": [\"cruise ship\", \"yacht\", \"barge\"]}", + 42 + ], + "customers": [ + " {\"type\": \"people\", \"description\": \"people who purchase goods or services\", \"similar objects\": [\"clients\", \"consumers\", \"patrons\"]}", + 42 + ], + "rods": [ + " {\"type\": \"tool\", \"description\": \"long, thin, cylindrical; could be made of metal or wood\", \"similar objects\": [\"poles\", \"stakes\", \"bars\"]}", + 42 + ], + "rivets": [ + " {\"type\": \"fastener\", \"description\": \"small, cylindrical, metal; used to join two or more materials together\", \"similar objects\": [\"screws\", \"bolts\", \"nuts\"]}", + 42 + ], + "street scene": [ + "\n{\"type\": \"scene\", \"description\": \"buildings, roads, people, vehicles, trees, etc.\", \"similar objects\": [\"cityscape\", \"landscape\", \"urban scene\"]}", + 42 + ], + "pamphlet": [ + " {\"type\": \"printed material\", \"description\": \"small booklet; could be folded; could contain information\", \"similar objects\": [\"brochure\", \"flyer\", \"leaflet\"]}", + 42 + ], + "porcelain bathroom sink": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"white; could be oval or round; could have a single or double basin; could have a pedestal or wall-mounted design\", \"similar objects\": [\"toilet\", \"bathtub\", \"shower\"]}", + 42 + ], + "supports": [ + " {\"type\": \"structural element\", \"description\": \"could be made of metal, wood, or plastic; could be used to hold up a structure\", \"similar objects\": [\"beams\", \"columns\", \"brackets\"]}", + 42 + ], + "projection screen": [ + " {\"type\": \"display tool\", \"description\": \"large, white, flat surface; could be retractable\", \"similar objects\": [\"whiteboard\", \"television\", \"monitor\"]}", + 42 + ], + "clipboard": [ + " {\"type\": \"office tool\", \"description\": \"rectangular; has a clip on the top; could be made of plastic or metal\", \"similar objects\": [\"stapler\", \"hole puncher\", \"paper clip\"]}", + 42 + ], + "cordless phone": [ + " {\"type\": \"electronic device\", \"description\": \"has a handset; could be used for communication\", \"similar objects\": [\"cell phone\", \"landline phone\", \"walkie talkie\"]}", + 42 + ], + "grey pavement": [ + " {\"type\": \"surface\", \"description\": \"flat, grey, made of concrete or asphalt\", \"similar objects\": [\"sidewalk\", \"road\", \"driveway\"]}", + 42 + ], + "laundry basket": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of plastic or fabric; could have handles\", \"similar objects\": [\"hamper\", \"bin\", \"box\"]}", + 42 + ], + "square pillow": [ + " {\"type\": \"bedding item\", \"description\": \"square-shaped; could be filled with feathers or foam; could have a cover\", \"similar objects\": [\"rectangular pillow\", \"bolster pillow\", \"round pillow\"]}", + 42 + ], + "support beams": [ + " {\"type\": \"construction material\", \"description\": \"long, strong, and sturdy; could be made of metal or wood\", \"similar objects\": [\"rafters\", \"joists\", \"studs\"]}", + 42 + ], + "backsplash": [ + " {\"type\": \"kitchen tool\", \"description\": \"wall covering; could be made of tiles; could be used to protect walls from water and grease\", \"similar objects\": [\"countertop\", \"cabinet\", \"flooring\"]}", + 42 + ], + "stainless steel kitchen sink": [ + "\n{\"type\": \"kitchen tool\", \"description\": \"rectangular; made of stainless steel; has a drain\", \"similar objects\": [\"kitchen faucet\", \"kitchen countertop\", \"kitchen cabinet\"]}", + 42 + ], + "plastic trash": [ + " {\"type\": \"waste\", \"description\": \"non-biodegradable; could be in different shapes and sizes; could be colored\", \"similar objects\": [\"glass bottle\", \"aluminum can\", \"styrofoam\"]}", + 42 + ], + "player number": [ + " {\"type\": \"sports equipment\", \"description\": \"numbers printed on a jersey; could be used to identify a player\", \"similar objects\": [\"jersey\", \"helmet\", \"shoes\"]}", + 42 + ], + "surfer ocean": [ + "\n{\"type\": \"activity\", \"description\": \"person riding a surfboard in the ocean; could be wearing a wetsuit\", \"similar objects\": [\"swimmer\", \"sailor\", \"fisherman\"]}", + 42 + ], + "shadow table": [ + " {\"type\": \"furniture\", \"description\": \"table with a shadow box underneath; could be used for displaying items\", \"similar objects\": [\"display case\", \"curio cabinet\", \"bookshelf\"]}", + 42 + ], + "leather sofa": [ + " {\"type\": \"furniture\", \"description\": \"made of leather; could be brown or black; could have cushions\", \"similar objects\": [\"armchair\", \"loveseat\", \"sectional\"]}", + 42 + ], + "dining room table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood; could have a glass top\", \"similar objects\": [\"coffee table\", \"end table\", \"console table\"]}", + 42 + ], + "nail polish": [ + " {\"type\": \"cosmetic product\", \"description\": \"liquid; could be applied to nails; comes in various colors\", \"similar objects\": [\"lipstick\", \"eyeliner\", \"mascara\"]}", + 42 + ], + "snowy hill": [ + " {\"type\": \"landscape\", \"description\": \"white; could have trees; could have a slope\", \"similar objects\": [\"mountain\", \"valley\", \"glacier\"]}", + 42 + ], + "root": [ + " {\"type\": \"plant part\", \"description\": \"underground part of a plant; could be a tuber or a rhizome; could be edible\", \"similar objects\": [\"bulb\", \"stem\", \"corm\"]}", + 42 + ], + "thermos": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could keep hot or cold\", \"similar objects\": [\"mug\", \"bottle\", \"jar\"]}", + 42 + ], + "paper napkins": [ + " {\"type\": \"tableware\", \"description\": \"square; made of paper; used for wiping hands\", \"similar objects\": [\"paper towels\", \"tissues\", \"cloth napkins\"]}", + 42 + ], + "baby elephants": [ + "\n{\"type\": \"animal\", \"description\": \"smaller than adult elephants; has a trunk; has large ears; has a grayish skin color\", \"similar objects\": [\"baby giraffes\", \"baby rhinos\", \"baby hippos\"]}", + 42 + ], + "concrete ramp": [ + " {\"type\": \"structure\", \"description\": \"sloped surface; made of concrete; used for wheelchair access\", \"similar objects\": [\"stairs\", \"elevator\", \"escalator\"]}", + 42 + ], + "wagon wheel": [ + " {\"type\": \"wheel\", \"description\": \"round; could be made of wood; could be used for a wagon\", \"similar objects\": [\"cart wheel\", \"tire\", \"bicycle wheel\"]}", + 42 + ], + "dark window": [ + "\n{\"type\": \"window\", \"description\": \"black or dark in color; could be made of glass or plastic; could be used to block out light\", \"similar objects\": [\"shutter\", \"blinds\", \"curtain\"]}", + 42 + ], + "waffle": [ + " {\"type\": \"food\", \"description\": \"square; has a honeycomb pattern; could be served with syrup\", \"similar objects\": [\"pancake\", \"crepe\", \"doughnut\"]}", + 42 + ], + "sunflowers": [ + " {\"type\": \"flower\", \"description\": \"yellow; has a long stem; has a large head with many petals\", \"similar objects\": [\"daisy\", \"tulip\", \"rose\"]}", + 42 + ], + "flyer": [ + " {\"type\": \"promotional material\", \"description\": \"printed paper; could be distributed to promote an event or product\", \"similar objects\": [\"poster\", \"brochure\", \"leaflet\"]}", + 42 + ], + "cameraman": [ + " {\"type\": \"occupation\", \"description\": \"person who operates a camera; could be a photographer\", \"similar objects\": [\"director\", \"editor\", \"producer\"]}", + 42 + ], + "cherry tomato": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, red; could be sliced into halves; could have green stems\", \"similar objects\": [\"grape tomato\", \"plum tomato\", \"regular tomato\"]}", + 42 + ], + "skateboard man": [ + " {\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could be used for skateboarding\", \"similar objects\": [\"scooter\", \"rollerblades\", \"longboard\"]}", + 42 + ], + "googles": [ + " {\"type\": \"eyewear\", \"description\": \"round; could be made of plastic or metal; could have lenses\", \"similar objects\": [\"sunglasses\", \"reading glasses\", \"safety glasses\"]}", + 42 + ], + "suit coat": [ + " {\"type\": \"clothing\", \"description\": \"long; has buttons; could be made of wool\", \"similar objects\": [\"blazer\", \"jacket\", \"vest\"]}", + 42 + ], + "kneepads": [ + " {\"type\": \"protective gear\", \"description\": \"worn around the knees; could be made of foam or plastic; could be used for sports or work\", \"similar objects\": [\"elbow pads\", \"shin guards\", \"helmet\"]}", + 42 + ], + "dog paw": [ + " {\"type\": \"animal body part\", \"description\": \"four toes; could have fur; could have claws\", \"similar objects\": [\"cat paw\", \"bird foot\", \"rabbit paw\"]}", + 42 + ], + "wooden bed": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have a headboard; could have four legs\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}", + 42 + ], + "tomato slices": [ + " {\"type\": \"food\", \"description\": \"round; red; could be served with salt and pepper\", \"similar objects\": [\"onion slices\", \"cucumber slices\", \"mushroom slices\"]}", + 42 + ], + "fire alarm": [ + " {\"type\": \"safety device\", \"description\": \"red; has a loud siren; could be wall-mounted\", \"similar objects\": [\"smoke detector\", \"carbon monoxide detector\", \"fire extinguisher\"]}", + 42 + ], + "dolphin": [ + " {\"type\": \"animal\", \"description\": \"gray; has a curved mouth; could be found in the ocean\", \"similar objects\": [\"whale\", \"shark\", \"seal\"]}", + 42 + ], + "turquoise": [ + " {\"type\": \"color\", \"description\": \"blue-green; could be used to describe a gemstone\", \"similar objects\": [\"aquamarine\", \"teal\", \"cyan\"]}", + 42 + ], + "covering": [ + " {\"type\": \"clothing item\", \"description\": \"could be made of fabric; could be used to cover body parts; could be used to keep warm\", \"similar objects\": [\"coat\", \"jacket\", \"scarf\"]}", + 42 + ], + "golf cart": [ + " {\"type\": \"vehicle\", \"description\": \"small, open-air vehicle; has four wheels; could be powered by electricity or gasoline; could be used to transport golfers and their equipment\", \"similar objects\": [\"utility cart\", \"golf buggy\", \"golf trolley\"]}", + 42 + ], + "veil": [ + " {\"type\": \"clothing accessory\", \"description\": \"thin, sheer fabric; could be worn over the head and face\", \"similar objects\": [\"scarf\", \"hat\", \"shawl\"]}", + 42 + ], + "team logo": [ + " {\"type\": \"symbol\", \"description\": \"could be a combination of colors, shapes, and words; could be used to represent a team or organization\", \"similar objects\": [\"flag\", \"emblem\", \"badge\"]}", + 42 + ], + "ski boot": [ + " {\"type\": \"footwear\", \"description\": \"long, black, has a buckle; could be used for skiing\", \"similar objects\": [\"hiking boot\", \"snow boot\", \"snowshoe\"]}", + 42 + ], + "talons": [ + " {\"type\": \"animal body part\", \"description\": \"sharp, curved claws; found on birds of prey\", \"similar objects\": [\"beak\", \"wings\", \"feathers\"]}", + 42 + ], + "leafy bush": [ + " {\"type\": \"plant\", \"description\": \"green; could have multiple leaves; could have small flowers\", \"similar objects\": [\"shrub\", \"hedge\", \"tree\"]}", + 42 + ], + "bar code": [ + " {\"type\": \"identification tool\", \"description\": \"black and white stripes; could be scanned by a scanner\", \"similar objects\": [\"QR code\", \"RFID tag\", \"UPC code\"]}", + 42 + ], + "plane wing": [ + " {\"type\": \"aircraft part\", \"description\": \"long, thin, curved; could be made of metal\", \"similar objects\": [\"fuselage\", \"engine\", \"propeller\"]}", + 42 + ], + "message": [ + " {\"type\": \"communication\", \"description\": \"information sent from one person to another; could be written or spoken\", \"similar objects\": [\"letter\", \"email\", \"phone call\"]}", + 42 + ], + "darker": [ + " {\"type\": \"color\", \"description\": \"a shade of black; could be used to describe objects\", \"similar objects\": [\"black\", \"gray\", \"charcoal\"]}", + 42 + ], + "basketball": [ + " {\"type\": \"sport equipment\", \"description\": \"orange; round; has a net\", \"similar objects\": [\"football\", \"baseball\", \"volleyball\"]}", + 42 + ], + "needles": [ + " {\"type\": \"sewing tool\", \"description\": \"long and thin; could be made of metal or plastic; could be used for sewing or knitting\", \"similar objects\": [\"pins\", \"threads\", \"scissors\"]}", + 42 + ], + "brown bear": [ + " {\"type\": \"animal\", \"description\": \"large; brown fur; could have a snout; could have a hump on its back\", \"similar objects\": [\"grizzly bear\", \"polar bear\", \"black bear\"]}", + 42 + ], + "hawk": [ + " {\"type\": \"bird\", \"description\": \"large wings; sharp beak; could have brown and white feathers\", \"similar objects\": [\"eagle\", \"falcon\", \"osprey\"]}", + 42 + ], + "sink bowl": [ + " {\"type\": \"kitchen tool\", \"description\": \"round; could be made of metal or ceramic; could have a faucet\", \"similar objects\": [\"bathtub\", \"basin\", \"washbasin\"]}", + 41 + ], + "metal stand": [ + " {\"type\": \"furniture\", \"description\": \"made of metal; could be used to support something; could be adjustable\", \"similar objects\": [\"table\", \"chair\", \"shelf\"]}", + 41 + ], + "green tree": [ + "\n{\"type\": \"plant\", \"description\": \"tall; has green leaves; could have fruits; could have branches\", \"similar objects\": [\"bush\", \"shrub\", \"palm tree\"]}", + 41 + ], + "cat paws": [ + " {\"type\": \"animal body part\", \"description\": \"soft, furry, have claws\", \"similar objects\": [\"dog paws\", \"bird feet\", \"monkey hands\"]}", + 41 + ], + "styrofoam cup": [ + " {\"type\": \"container\", \"description\": \"lightweight; white; could be used for hot and cold beverages; could be disposable\", \"similar objects\": [\"plastic cup\", \"paper cup\", \"glass cup\"]}", + 41 + ], + "mickey mouse": [ + "\n{\"type\": \"cartoon character\", \"description\": \"black ears; red shorts; yellow shoes; white gloves; round face\", \"similar objects\": [\"Donald Duck\", \"Goofy\", \"Pluto\"]}", + 41 + ], + "stainless steel microwave": [ + "\n{\"type\": \"appliance\", \"description\": \"silver; has a door; could have a digital display; could have a rotating plate inside\", \"similar objects\": [\"refrigerator\", \"oven\", \"dishwasher\"]}", + 41 + ], + "book case": [ + " {\"type\": \"furniture\", \"description\": \"wooden; has shelves; could be used to store books\", \"similar objects\": [\"cabinet\", \"shelf\", \"wardrobe\"]}", + 41 + ], + "plush": [ + " {\"type\": \"toy\", \"description\": \"soft and cuddly; could be stuffed animals\", \"similar objects\": [\"doll\", \"action figure\", \"building blocks\"]}", + 41 + ], + "grey cat": [ + "\n{\"type\": \"animal\", \"description\": \"grey fur; could have white patches; could have long whiskers; could have a long tail\", \"similar objects\": [\"dog\", \"rabbit\", \"mouse\"]}", + 41 + ], + "dent": [ + " {\"type\": \"imperfection\", \"description\": \"a small hole or depression in a surface; could be caused by a collision or impact\", \"similar objects\": [\"scratch\", \"crack\", \"chip\"]}", + 41 + ], + "poll": [ + " {\"type\": \"survey tool\", \"description\": \"a set of questions used to collect data from a group of people\", \"similar objects\": [\"survey\", \"questionnaire\", \"interview\"]}", + 41 + ], + "horse head": [ + " {\"type\": \"sculpture\", \"description\": \"large, could be made of bronze or stone; has a long mane\", \"similar objects\": [\"horse statue\", \"unicorn head\", \"dragon head\"]}", + 41 + ], + "dish towel": [ + " {\"type\": \"cleaning tool\", \"description\": \"rectangular; made of cloth; used to dry dishes\", \"similar objects\": [\"sponge\", \"dishcloth\", \"scrub brush\"]}", + 41 + ], + "sheer": [ + " {\"type\": \"fabric\", \"description\": \"lightweight; thin; transparent; could be made of silk, nylon, or polyester\", \"similar objects\": [\"chiffon\", \"organza\", \"tulle\"]}", + 41 + ], + "cot": [ + " {\"type\": \"furniture\", \"description\": \"small bed; could be folded; could be made of metal or wood\", \"similar objects\": [\"sofa\", \"mattress\", \"chair\"]}", + 41 + ], + "coca cola": [ + " {\"type\": \"beverage\", \"description\": \"brown, carbonated, sweet; comes in a can or bottle\", \"similar objects\": [\"Pepsi\", \"Sprite\", \"Fanta\"]}", + 41 + ], + "orange object": [ + "\n{\"type\": \"object\", \"description\": \"round; could be orange in color; could be made of plastic, metal, or fabric; could be used for decoration or storage\", \"similar objects\": [\"ball\", \"box\", \"jar\"]}", + 41 + ], + "airplane tail": [ + " {\"type\": \"aircraft part\", \"description\": \"vertical structure at the back of the airplane; could have a logo or symbol\", \"similar objects\": [\"fuselage\", \"wing\", \"engine\"]}", + 41 + ], + "base ball": [ + " {\"type\": \"sport equipment\", \"description\": \"round; made of leather; has a string\", \"similar objects\": [\"tennis ball\", \"soccer ball\", \"golf ball\"]}", + 41 + ], + "restaurant sign": [ + " {\"type\": \"advertisement\", \"description\": \"could be made of metal or plastic; could be illuminated; could have a logo or text\", \"similar objects\": [\"store sign\", \"billboard\", \"street sign\"]}", + 41 + ], + "chain link": [ + " {\"type\": \"connector\", \"description\": \"metal; interlocking loops; could be used to secure objects\", \"similar objects\": [\"padlock\", \"hook\", \"clasp\"]}", + 41 + ], + "carrier": [ + " {\"type\": \"transportation tool\", \"description\": \"could be made of metal; could be used to carry goods\", \"similar objects\": [\"truck\", \"van\", \"wagon\"]}", + 41 + ], + "shadow plate": [ + " {\"type\": \"photography tool\", \"description\": \"flat, black; used to create shadows in photography\", \"similar objects\": [\"reflector\", \"diffuser\", \"softbox\"]}", + 41 + ], + "bookbag": [ + " {\"type\": \"bag\", \"description\": \"rectangular; has straps; could be made of canvas or leather\", \"similar objects\": [\"backpack\", \"duffel bag\", \"tote bag\"]}", + 41 + ], + "zippers": [ + " {\"type\": \"fastener\", \"description\": \"metal or plastic; used to join two pieces of fabric together\", \"similar objects\": [\"buttons\", \"snaps\", \"hooks and eyes\"]}", + 41 + ], + "wiring": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, insulated cables; could be used to connect electrical components\", \"similar objects\": [\"cable\", \"wire\", \"connector\"]}", + 41 + ], + "light switches": [ + " {\"type\": \"electrical tool\", \"description\": \"could be a toggle switch or a dimmer switch; could be used to control the lights\", \"similar objects\": [\"outlet\", \"thermostat\", \"timer\"]}", + 41 + ], + "vcr": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a cassette slot; could be connected to a TV\", \"similar objects\": [\"DVD player\", \"Blu-ray player\", \"video game console\"]}", + 41 + ], + "foilage": [ + " {\"type\": \"plant\", \"description\": \"green; could be in different shapes; could be used for decoration\", \"similar objects\": [\"grass\", \"bush\", \"tree\"]}", + 41 + ], + "placard": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be made of paper or plastic; could be used to display messages\", \"similar objects\": [\"poster\", \"banner\", \"signboard\"]}", + 41 + ], + "cabinet handle": [ + " {\"type\": \"furniture accessory\", \"description\": \"metal or plastic; could be round or rectangular; could be used to open and close a cabinet door\", \"similar objects\": [\"drawer handle\", \"knob\", \"hinge\"]}", + 41 + ], + "lids": [ + " {\"type\": \"container accessory\", \"description\": \"round; could be made of plastic or metal; used to cover containers\", \"similar objects\": [\"caps\", \"covers\", \"tops\"]}", + 41 + ], + "groove": [ + " {\"type\": \"architectural feature\", \"description\": \"long, narrow indentation; could be found in walls, floors, or ceilings\", \"similar objects\": [\"channel\", \"ditch\", \"trench\"]}", + 41 + ], + "back wheels": [ + " {\"type\": \"vehicle part\", \"description\": \"round; could be made of rubber; could be connected to the axle\", \"similar objects\": [\"front wheels\", \"tires\", \"rims\"]}", + 41 + ], + "touchpad": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; used to control a computer or other electronic device\", \"similar objects\": [\"mouse\", \"keyboard\", \"stylus\"]}", + 41 + ], + "door brown": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have a handle\", \"similar objects\": [\"window\", \"cabinet\", \"drawer\"]}", + 41 + ], + "close-up": [ + " {\"type\": \"photography technique\", \"description\": \"taking a picture of a subject from a close distance; could be used to capture details\", \"similar objects\": [\"macro photography\", \"telephoto photography\", \"wide-angle photography\"]}", + 41 + ], + "brake": [ + " {\"type\": \"vehicle part\", \"description\": \"used to slow down or stop a vehicle; could be a pedal or a lever\", \"similar objects\": [\"clutch\", \"accelerator\", \"steering wheel\"]}", + 41 + ], + "sweatpants": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; usually made of cotton; could have pockets\", \"similar objects\": [\"joggers\", \"leggings\", \"track pants\"]}", + 41 + ], + "motor scooter": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a seat\", \"similar objects\": [\"motorcycle\", \"bicycle\", \"skateboard\"]}", + 41 + ], + "pacifier": [ + " {\"type\": \"baby item\", \"description\": \"round; has a handle; could be made of rubber or plastic\", \"similar objects\": [\"bottle\", \"teether\", \"bib\"]}", + 41 + ], + "dining room": [ + " {\"type\": \"room\", \"description\": \"could have a table and chairs; could have a chandelier; could have a sideboard\", \"similar objects\": [\"living room\", \"kitchen\", \"bedroom\"]}", + 41 + ], + "loop": [ + " {\"type\": \"shape\", \"description\": \"circular; could be made of rope or wire\", \"similar objects\": [\"circle\", \"oval\", \"spiral\"]}", + 41 + ], + "toothpicks": [ + " {\"type\": \"utensil\", \"description\": \"small, thin, pointed sticks; could be made of wood or plastic\", \"similar objects\": [\"skewers\", \"chopsticks\", \"forks\"]}", + 41 + ], + "passenger cars": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could be sedan, coupe, hatchback, SUV, etc.; could have two or more doors; could have two or more seats\", \"similar objects\": [\"truck\", \"motorcycle\", \"bus\"]}", + 41 + ], + "hair clip": [ + " {\"type\": \"accessory\", \"description\": \"small; could be made of metal or plastic; used to hold hair in place\", \"similar objects\": [\"hair tie\", \"bobby pin\", \"headband\"]}", + 41 + ], + "pink bow": [ + " {\"type\": \"accessory\", \"description\": \"made of ribbon; could be used to decorate gifts; could be used to tie up hair\", \"similar objects\": [\"ribbon\", \"hair tie\", \"scrunchy\"]}", + 41 + ], + "wood headboard": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have carvings; could be attached to a bed\", \"similar objects\": [\"bed frame\", \"dresser\", \"nightstand\"]}", + 41 + ], + "wall tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic, stone, or glass; could be used to decorate walls\", \"similar objects\": [\"floor tiles\", \"bricks\", \"wood panels\"]}", + 41 + ], + "yellow curb": [ + " {\"type\": \"road marking\", \"description\": \"painted yellow; used to indicate no parking or no stopping\", \"similar objects\": [\"white curb\", \"red curb\", \"blue curb\"]}", + 41 + ], + "petal": [ + " {\"type\": \"flower part\", \"description\": \"thin, colorful, could be curved; could be attached to a stem\", \"similar objects\": [\"stamen\", \"sepal\", \"pistil\"]}", + 41 + ], + "jewelry": [ + " {\"type\": \"accessory\", \"description\": \"could be made of metal, plastic, or other materials; could be decorated with gems or stones; could be worn on the body\", \"similar objects\": [\"watch\", \"bracelet\", \"necklace\"]}", + 41 + ], + "sculptures": [ + " {\"type\": \"artwork\", \"description\": \"three-dimensional artworks made of materials such as stone, metal, wood, clay, or glass\", \"similar objects\": [\"paintings\", \"drawings\", \"photographs\"]}", + 41 + ], + "baseball gloves": [ + " {\"type\": \"sports equipment\", \"description\": \"leather; has a pocket; could be worn on the hand\", \"similar objects\": [\"bat\", \"ball\", \"helmet\"]}", + 41 + ], + "beach scene": [ + " {\"type\": \"landscape\", \"description\": \"sandy shore; blue sky; white clouds; blue sea; palm trees; beach umbrellas; people playing\", \"similar objects\": [\"mountain scene\", \"desert scene\", \"forest scene\"]}", + 41 + ], + "headlight car": [ + "\n{\"type\": \"vehicle part\", \"description\": \"attached to the front of a car; used to provide illumination in the dark\", \"similar objects\": [\"taillight\", \"fog light\", \"turn signal\"]}", + 41 + ], + "glass pitcher": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of glass or plastic; could have a handle and a spout\", \"similar objects\": [\"jug\", \"vase\", \"jar\"]}", + 41 + ], + "teenage boy": [ + "\n{\"type\": \"person\", \"description\": \"young adult; could have facial hair; could wear casual clothes\", \"similar objects\": [\"teenage girl\", \"young adult man\", \"young adult woman\"]}", + 41 + ], + "persons hand": [ + "\n{\"type\": \"body part\", \"description\": \"five fingers; could be used for grasping; could be used for writing\", \"similar objects\": [\"foot\", \"arm\", \"head\"]}", + 41 + ], + "headlight motorcycle": [ + "\n{\"type\": \"vehicle part\", \"description\": \"attached to the front of a motorcycle; used to provide illumination while driving\", \"similar objects\": [\"tail light\", \"turn signal\", \"brake light\"]}", + 41 + ], + "brown tree": [ + "\n{\"type\": \"plant\", \"description\": \"brown bark; could have green leaves; could have fruits or nuts\", \"similar objects\": [\"oak tree\", \"maple tree\", \"pine tree\"]}", + 41 + ], + "brownie": [ + " {\"type\": \"dessert\", \"description\": \"chocolate-flavored; could be served with ice cream; could be cut into squares\", \"similar objects\": [\"cake\", \"cookie\", \"pie\"]}", + 41 + ], + "knife handle": [ + " {\"type\": \"utensil handle\", \"description\": \"could be made of wood, metal, or plastic; could have a curved or straight shape; could have a hole for hanging\", \"similar objects\": [\"fork handle\", \"spoon handle\", \"cleaver handle\"]}", + 41 + ], + "cranes": [ + " {\"type\": \"bird\", \"description\": \"long neck; long legs; could be white or grey; could be seen in groups\", \"similar objects\": [\"herons\", \"storks\", \"egrets\"]}", + 41 + ], + "ossicones": [ + " {\"type\": \"animal feature\", \"description\": \"horn-like structures on the head of giraffes\", \"similar objects\": [\"antlers\", \"horns\", \"tusks\"]}", + 41 + ], + "blond girl": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could be wearing a dress\", \"similar objects\": [\"blond boy\", \"brunette girl\", \"redhead girl\"]}", + 41 + ], + "baby bear": [ + " {\"type\": \"animal\", \"description\": \"small; has a brown fur; could have a white patch on its chest\", \"similar objects\": [\"panda\", \"koala\", \"raccoon\"]}", + 41 + ], + "left wing": [ + " {\"type\": \"political term\", \"description\": \"ideology that supports social equality and progressive policies; typically associated with the Democratic Party in the United States\", \"similar objects\": [\"right wing\", \"centrism\", \"populism\"]}", + 41 + ], + "rhino": [ + " {\"type\": \"animal\", \"description\": \"gray; has a horn; has a thick skin\", \"similar objects\": [\"hippopotamus\", \"elephant\", \"giraffe\"]}", + 41 + ], + "round tire": [ + " {\"type\": \"vehicle part\", \"description\": \"circular; made of rubber; has a metal rim\", \"similar objects\": [\"wheel\", \"hubcap\", \"spare tire\"]}", + 40 + ], + "price sign": [ + " {\"type\": \"retail tool\", \"description\": \"could be made of paper or plastic; could have numbers or symbols; could be hung on a wall or stand on a shelf\", \"similar objects\": [\"label\", \"tag\", \"sign\"]}", + 40 + ], + "zebra ear": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, black and white stripes; could be curved\", \"similar objects\": [\"horse ear\", \"giraffe ear\", \"elephant ear\"]}", + 40 + ], + "wastebasket": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"trash can\", \"garbage can\", \"recycling bin\"]}", + 40 + ], + "metal ring": [ + " {\"type\": \"accessory\", \"description\": \"circular; could be made of gold, silver, or other metals; could be used as a jewelry\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 40 + ], + "hangar": [ + " {\"type\": \"building\", \"description\": \"large, open space; used to store aircrafts\", \"similar objects\": [\"warehouse\", \"garage\", \"shed\"]}", + 40 + ], + "strand": [ + " {\"type\": \"object\", \"description\": \"a group of similar items; could be made of thread, wire, or rope; could be used to hang items\", \"similar objects\": [\"rope\", \"string\", \"chain\"]}", + 40 + ], + "soccer net": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; made of metal or plastic; has a net\", \"similar objects\": [\"basketball hoop\", \"volleyball net\", \"hockey goal\"]}", + 40 + ], + "tan car": [ + "\n{\"type\": \"vehicle\", \"description\": \"tan color; could be a sedan, coupe, hatchback, SUV, etc.\", \"similar objects\": [\"black car\", \"white car\", \"red car\"]}", + 40 + ], + "cook": [ + " {\"type\": \"occupation\", \"description\": \"prepares food; could be a chef; could work in a restaurant\", \"similar objects\": [\"waiter\", \"bartender\", \"baker\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green", + 40 + ], + "erase board": [ + " {\"type\": \"writing tool\", \"description\": \"smooth surface; could be wiped clean; could be used with markers\", \"similar objects\": [\"whiteboard\", \"chalkboard\", \"blackboard\"]}", + 40 + ], + "game controllers": [ + " {\"type\": \"electronic device\", \"description\": \"small handheld device; could have buttons, joysticks, and triggers; could be wireless or wired\", \"similar objects\": [\"keyboard\", \"mouse\", \"game console\"]}", + 40 + ], + "marvin": [ + " {\"type\": \"name\", \"description\": \"unisex name; could be a nickname for Marvin\", \"similar objects\": [\"Marv\", \"Marve\", \"Marvina\"]}", + 40 + ], + "plastic bottles": [ + " {\"type\": \"container\", \"description\": \"transparent; could be used to store liquids; could be recycled\", \"similar objects\": [\"glass bottles\", \"cans\", \"jars\"]}", + 40 + ], + "truck cab": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a cabin; could have a trailer attached\", \"similar objects\": [\"van\", \"SUV\", \"pickup truck\"]}", + 40 + ], + "ball boy": [ + " {\"type\": \"person\", \"description\": \"young; wears a uniform; retrieves balls during a tennis match\", \"similar objects\": [\"umpire\", \"referee\", \"linesman\"]}", + 40 + ], + "santa hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"red and white; has a pom-pom on the top; could be made of fabric or felt\", \"similar objects\": [\"elf hat\", \"reindeer antlers\", \"snowman hat\"]}", + 40 + ], + "hitter": [ + " {\"type\": \"sports tool\", \"description\": \"long, thin, made of wood; used to hit a ball\", \"similar objects\": [\"bat\", \"club\", \"racquet\"]}", + 40 + ], + "mother elephant": [ + "\n{\"type\": \"animal\", \"description\": \"large; has a long trunk; has large ears; has tusks; could have a baby elephant\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}", + 40 + ], + "size": [ + "\n{\"type\": \"measurement\", \"description\": \"the amount of space occupied by an object or the magnitude of a thing\", \"similar objects\": [\"length\", \"width\", \"height\"]}", + 40 + ], + "machines": [ + " {\"type\": \"tools\", \"description\": \"mechanical devices used to perform tasks; could be powered by electricity, gas, or other sources of energy\", \"similar objects\": [\"computers\", \"robots\", \"engines\"]}", + 40 + ], + "wooden building": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could have multiple floors; could have a roof\", \"similar objects\": [\"house\", \"shed\", \"barn\"]}", + 40 + ], + "half sandwich": [ + " {\"type\": \"food\", \"description\": \"half of a sandwich; could be made of two slices of bread with fillings in between; could be cut into two halves\", \"similar objects\": [\"burger\", \"wrap\", \"taco\"]}", + 40 + ], + "metal leg": [ + " {\"type\": \"furniture part\", \"description\": \"long; could be made of metal; could be used to support a table\", \"similar objects\": [\"wooden leg\", \"metal bar\", \"plastic leg\"]}", + 40 + ], + "wooden flooring": [ + " {\"type\": \"flooring material\", \"description\": \"made of wood; could be in planks or tiles; could be stained or painted\", \"similar objects\": [\"laminate flooring\", \"vinyl flooring\", \"carpet\"]}", + 40 + ], + "screen computer monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat, rectangular; could have a stand; could be connected to a computer\", \"similar objects\": [\"television\", \"tablet\", \"smartphone\"]}", + 40 + ], + "orange paint": [ + " {\"type\": \"art material\", \"description\": \"orange color; could be used for painting\", \"similar objects\": [\"red paint\", \"yellow paint\", \"blue paint\"]}", + 40 + ], + "leaf design": [ + " {\"type\": \"decoration\", \"description\": \"could be made of paper; could be painted on walls; could be used to decorate furniture\", \"similar objects\": [\"flower design\", \"geometric design\", \"animal design\"]}", + 40 + ], + "brackets": [ + " {\"type\": \"hardware\", \"description\": \"two pieces of metal or plastic connected by a hinge; used to support shelves or other objects\", \"similar objects\": [\"screws\", \"nails\", \"hinges\"]}", + 40 + ], + "water mark": [ + " {\"type\": \"image effect\", \"description\": \"transparent; could be used to protect images from unauthorized use\", \"similar objects\": [\"copyright symbol\", \"digital signature\", \"watermark logo\"]}", + 40 + ], + "skatepark": [ + " {\"type\": \"recreational facility\", \"description\": \"concrete area with ramps, rails, and other obstacles for skateboarding\", \"similar objects\": [\"playground\", \"basketball court\", \"tennis court\"]}", + 40 + ], + "window shutters": [ + " {\"type\": \"window covering\", \"description\": \"hinged panels; could be opened and closed; could be made of wood or metal\", \"similar objects\": [\"blinds\", \"curtains\", \"shades\"]}", + 40 + ], + "camel": [ + " {\"type\": \"animal\", \"description\": \"humped back; two humps; long neck; long legs; thick fur\", \"similar objects\": [\"llama\", \"alpaca\", \"giraffe\"]}", + 40 + ], + "stone floor": [ + " {\"type\": \"flooring material\", \"description\": \"hard, cold, and rough; could be made of natural stones; could be polished\", \"similar objects\": [\"tile floor\", \"wood floor\", \"concrete floor\"]}", + 40 + ], + "wood dresser": [ + " {\"type\": \"furniture\", \"description\": \"wooden; has drawers; could have a mirror\", \"similar objects\": [\"chest of drawers\", \"wardrobe\", \"armoire\"]}", + 40 + ], + "pomegranate": [ + " {\"type\": \"fruit\", \"description\": \"round; has a hard outer shell; has edible seeds inside\", \"similar objects\": [\"watermelon\", \"cantaloupe\", \"honeydew\"]}", + 40 + ], + "right wing": [ + " {\"type\": \"political ideology\", \"description\": \"believes in limited government, free markets, and individual liberty; opposes government intervention in the economy; supports traditional values and patriotism\", \"similar objects\": [\"libertarianism\", \"conservatism\", \"classical liberalism\"]}", + 40 + ], + "cilantro": [ + " {\"type\": \"herb\", \"description\": \"green; has a strong smell; could be used as a garnish\", \"similar objects\": [\"parsley\", \"basil\", \"mint\"]}", + 40 + ], + "candies": [ + " {\"type\": \"food\", \"description\": \"small, sweet, colorful; could be in different shapes\", \"similar objects\": [\"chocolate\", \"cookies\", \"ice cream\"]}", + 40 + ], + "soldiers": [ + " {\"type\": \"people\", \"description\": \"wearing uniforms; could be carrying weapons; could be marching\", \"similar objects\": [\"police officers\", \"firefighters\", \"marines\"]}", + 40 + ], + "clay pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round; made of clay; could be used for cooking\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 40 + ], + "raisins": [ + " {\"type\": \"dried fruit\", \"description\": \"small, dark brown, wrinkled; could be sweet or sour\", \"similar objects\": [\"currants\", \"dates\", \"prunes\"]}", + 40 + ], + "surf boards": [ + " {\"type\": \"sports equipment\", \"description\": \"long and wide; could be made of foam; could have a fin\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 40 + ], + "extinguisher": [ + " {\"type\": \"safety tool\", \"description\": \"cylindrical; has a handle; could be red or white\", \"similar objects\": [\"fire blanket\", \"fire hose\", \"fire alarm\"]}", + 40 + ], + "orange cap": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; could be made of fabric; could have a logo\", \"similar objects\": [\"hat\", \"beanie\", \"baseball cap\"]}", + 40 + ], + "metal support": [ + " {\"type\": \"structural support\", \"description\": \"made of metal; could be used to support a structure\", \"similar objects\": [\"beam\", \"column\", \"girder\"]}", + 40 + ], + "cement block": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of cement; could be used for construction\", \"similar objects\": [\"bricks\", \"concrete\", \"stone\"]}", + 40 + ], + "silver vehicle": [ + "\n{\"type\": \"vehicle\", \"description\": \"silver; could be a car, truck, or motorcycle\", \"similar objects\": [\"gray vehicle\", \"gold vehicle\", \"black vehicle\"]}", + 40 + ], + "pupil": [ + " {\"type\": \"anatomy\", \"description\": \"black circle in the center of the eye; responsible for adjusting the amount of light entering the eye\", \"similar objects\": [\"iris\", \"cornea\", \"retina\"]}", + 40 + ], + "muddy": [ + " {\"type\": \"adjective\", \"description\": \"dirty; having a lot of mud\", \"similar objects\": [\"mucky\", \"mired\", \"mired\"]}", + 40 + ], + "plastic crate": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be stackable; could be used for storage\", \"similar objects\": [\"box\", \"basket\", \"bin\"]}", + 40 + ], + "mail box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of metal; could have a flag\", \"similar objects\": [\"letter box\", \"post box\", \"drop box\"]}", + 40 + ], + "letter e": [ + " {\"type\": \"alphabet\", \"description\": \"a curved line with a horizontal line across the middle; could be written in different fonts\", \"similar objects\": [\"letter a\", \"letter b\", \"letter c\"]}", + 40 + ], + "dirt trail": [ + " {\"type\": \"landscape\", \"description\": \"uneven, rough, could have rocks and stones; could be muddy\", \"similar objects\": [\"mountain path\", \"forest path\", \"desert trail\"]}", + 40 + ], + "darkness": [ + " {\"type\": \"phenomenon\", \"description\": \"absence of light; could be associated with fear and mystery\", \"similar objects\": [\"night\", \"shadow\", \"gloom\"]}", + 39 + ], + "silver door": [ + " {\"type\": \"building material\", \"description\": \"shiny, metallic, rectangular; could have a handle\", \"similar objects\": [\"gold door\", \"wooden door\", \"glass door\"]}", + 39 + ], + "police motorcycle": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a siren; could be black and white\", \"similar objects\": [\"police car\", \"ambulance\", \"fire truck\"]}", + 39 + ], + "metal grill": [ + " {\"type\": \"cooking tool\", \"description\": \"made of metal; has a grid-like structure; could be used for grilling food\", \"similar objects\": [\"barbecue\", \"griddle\", \"skillet\"]}", + 39 + ], + "factory": [ + " {\"type\": \"building\", \"description\": \"large; could have chimneys; could have many windows\", \"similar objects\": [\"warehouse\", \"office building\", \"shopping mall\"]}", + 39 + ], + "garden hose": [ + " {\"type\": \"gardening tool\", \"description\": \"long, flexible, could be coiled; could be connected to a water source\", \"similar objects\": [\"sprinkler\", \"watering can\", \"rake\"]}", + 39 + ], + "blue cord": [ + " {\"type\": \"utility item\", \"description\": \"long, thin, blue; could be used for electrical wiring\", \"similar objects\": [\"wire\", \"cable\", \"rope\"]}", + 39 + ], + "plastic frisbee": [ + "\n{\"type\": \"toy\", \"description\": \"round; made of plastic; could be thrown in the air\", \"similar objects\": [\"discus\", \"boomerang\", \"kite\"]}", + 39 + ], + "blue pot": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle; could be blue in color\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 39 + ], + "dump truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a large open-box bed; could be used for transporting materials\", \"similar objects\": [\"cement truck\", \"tow truck\", \"garbage truck\"]}", + 39 + ], + "triangle sign": [ + " {\"type\": \"road sign\", \"description\": \"yellow; has three sides; could be a warning sign\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 39 + ], + "thumbnail": [ + " {\"type\": \"image\", \"description\": \"smaller version of an image; could be used as a preview\", \"similar objects\": [\"icon\", \"avatar\", \"profile picture\"]}", + 39 + ], + "newspapers": [ + " {\"type\": \"reading material\", \"description\": \"printed on paper; could be in digital format; could be daily or weekly\", \"similar objects\": [\"magazines\", \"books\", \"journals\"]}", + 39 + ], + "products": [ + "\n{\"type\": \"items\", \"description\": \"could be physical or digital; could be tangible or intangible; could be used for various purposes\", \"similar objects\": [\"goods\", \"merchandise\", \"services\"]}", + 39 + ], + "apple computer": [ + "\n{\"type\": \"electronic device\", \"description\": \"brand of computer; could be a laptop or desktop; could have a mouse and keyboard\", \"similar objects\": [\"PC\", \"Mac\", \"Chromebook\"]}", + 39 + ], + "brown curtain": [ + " {\"type\": \"window covering\", \"description\": \"brown; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"blinds\", \"shades\", \"drapes\"]}", + 39 + ], + "medal": [ + " {\"type\": \"award\", \"description\": \"round; could be made of metal; could have a ribbon\", \"similar objects\": [\"trophy\", \"plaque\", \"certificate\"]}", + 39 + ], + "rays": [ + " {\"type\": \"animal\", \"description\": \"cartilaginous fish; have a flat body; could have a long tail; could have a pointed snout\", \"similar objects\": [\"sharks\", \"skates\", \"stingrays\"]}", + 39 + ], + "habitat": [ + " {\"type\": \"environment\", \"description\": \"place where an organism lives; could be natural or man-made\", \"similar objects\": [\"ecosystem\", \"biome\", \"niche\"]}", + 39 + ], + "alarm": [ + " {\"type\": \"electronic device\", \"description\": \"could beep or ring; could be set to a specific time\", \"similar objects\": [\"clock\", \"timer\", \"watch\"]}", + 39 + ], + "square clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"square; has a face with numbers and hands; could be digital or analog\", \"similar objects\": [\"watch\", \"alarm clock\", \"wall clock\"]}", + 39 + ], + "curves": [ + " {\"type\": \"shape\", \"description\": \"smooth, continuous lines; could be used to describe a path or a surface\", \"similar objects\": [\"lines\", \"circles\", \"squares\"]}", + 39 + ], + "multi story building": [ + "\n{\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have multiple windows; could have multiple entrances\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"office building\"]}", + 39 + ], + "orange fence": [ + "\n{\"type\": \"fencing material\", \"description\": \"orange; could be made of plastic or metal; could be used to separate areas\", \"similar objects\": [\"chain link fence\", \"wooden fence\", \"barbed wire fence\"]}", + 39 + ], + "closet door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could be sliding or hinged; could have a handle\", \"similar objects\": [\"cabinet door\", \"drawer\", \"window\"]}", + 39 + ], + "crust brown": [ + " {\"type\": \"baked goods\", \"description\": \"crispy; could be made of flour, butter, and sugar; could be used as a pie crust\", \"similar objects\": [\"pie crust\", \"cookie crust\", \"tart crust\"]}", + 39 + ], + "siding": [ + " {\"type\": \"building material\", \"description\": \"long, thin pieces of material used to cover the exterior of a house; could be made of wood, vinyl, or aluminum\", \"similar objects\": [\"brick\", \"stone\", \"stucco\"]}", + 39 + ], + "storage": [ + " {\"type\": \"container\", \"description\": \"could be made of plastic, metal, or wood; could be used to store items\", \"similar objects\": [\"box\", \"basket\", \"bin\"]}", + 39 + ], + "swans": [ + " {\"type\": \"animal\", \"description\": \"white; long neck; could be found in water\", \"similar objects\": [\"geese\", \"ducks\", \"cranes\"]}", + 39 + ], + "pedestal sink": [ + " {\"type\": \"bathroom fixture\", \"description\": \"tall, round, has a basin; could have a single or double handle\", \"similar objects\": [\"vanity sink\", \"wall-mounted sink\", \"drop-in sink\"]}", + 39 + ], + "tall plant": [ + " {\"type\": \"plant\", \"description\": \"could be a tree or a shrub; could have leaves or needles; could have flowers or fruits\", \"similar objects\": [\"bush\", \"tree\", \"shrub\"]}", + 39 + ], + "bike rider": [ + " {\"type\": \"person\", \"description\": \"wearing a helmet; riding a bicycle; could have a backpack\", \"similar objects\": [\"skateboarder\", \"rollerblader\", \"runner\"]}", + 39 + ], + "son": [ + " {\"type\": \"person\", \"description\": \"male; could be a child or an adult; could be related to another person\", \"similar objects\": [\"father\", \"brother\", \"grandson\"]}", + 39 + ], + "fireman": [ + " {\"type\": \"occupation\", \"description\": \"person who puts out fires; wears a fire-resistant suit; carries a fire extinguisher\", \"similar objects\": [\"policeman\", \"doctor\", \"firefighter\"]}", + 39 + ], + "cactus": [ + " {\"type\": \"plant\", \"description\": \"spiky; could have flowers; could be green or brown\", \"similar objects\": [\"succulent\", \"aloe vera\", \"yucca\"]}", + 39 + ], + "orange reflector": [ + " {\"type\": \"safety tool\", \"description\": \"round; orange; could be used to reflect light\", \"similar objects\": [\"traffic cone\", \"warning sign\", \"road barrier\"]}", + 39 + ], + "ink pen": [ + " {\"type\": \"writing tool\", \"description\": \"long, thin; could be made of plastic; could have a cap\", \"similar objects\": [\"pencil\", \"marker\", \"highlighter\"]}", + 39 + ], + "orange pumpkin": [ + "\n{\"type\": \"vegetable\", \"description\": \"round; orange; has a stem; could be carved into a jack-o-lantern\", \"similar objects\": [\"acorn squash\", \"butternut squash\", \"cucurbita maxima\"]}", + 39 + ], + "landline phone": [ + " {\"type\": \"communication device\", \"description\": \"wired; could have a dial pad; could have a handset\", \"similar objects\": [\"cell phone\", \"walkie talkie\", \"intercom\"]}", + 39 + ], + "office desk": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have drawers; could have a computer on top\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 39 + ], + "waste bin": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic; has a lid\", \"similar objects\": [\"trash can\", \"garbage can\", \"recycling bin\"]}", + 39 + ], + "brunette woman": [ + "\n{\"type\": \"person\", \"description\": \"dark hair; could have brown eyes; could have a fair complexion\", \"similar objects\": [\"blonde woman\", \"redhead woman\", \"man\"]}", + 39 + ], + "paper towel roll": [ + " {\"type\": \"household item\", \"description\": \"cylindrical; made of paper; could be used to clean up messes\", \"similar objects\": [\"toilet paper roll\", \"paper napkin\", \"paper plate\"]}", + 39 + ], + "bumpers": [ + " {\"type\": \"car part\", \"description\": \"rubber or plastic; used to protect the car from minor collisions\", \"similar objects\": [\"fenders\", \"grille\", \"headlights\"]}", + 39 + ], + "dog bed": [ + " {\"type\": \"pet furniture\", \"description\": \"rectangular; could be made of fabric; could have a cushion\", \"similar objects\": [\"cat bed\", \"pet house\", \"pet carrier\"]}", + 39 + ], + "messenger bag": [ + " {\"type\": \"bag\", \"description\": \"long strap; could be made of leather; could have multiple pockets\", \"similar objects\": [\"backpack\", \"tote bag\", \"duffel bag\"]}", + 39 + ], + "plaid umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"plaid pattern; could be opened and closed; could be used to protect from rain\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}", + 39 + ], + "backwards": [ + " {\"type\": \"direction\", \"description\": \"opposite of forwards; could be used to describe movement\", \"similar objects\": [\"backward\", \"inverse\", \"reverse\"]}", + 39 + ], + "steel fence": [ + " {\"type\": \"barrier\", \"description\": \"made of steel; could be in a form of a fence\", \"similar objects\": [\"wood fence\", \"brick wall\", \"chain link fence\"]}", + 39 + ], + "wooden clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"made of wood; could have a pendulum; could have a face with hands\", \"similar objects\": [\"grandfather clock\", \"alarm clock\", \"cuckoo clock\"]}", + 39 + ], + "dirt floor": [ + " {\"type\": \"flooring material\", \"description\": \"made of soil; could be uneven; could be dusty\", \"similar objects\": [\"concrete floor\", \"wooden floor\", \"tile floor\"]}", + 39 + ], + "signals": [ + " {\"type\": \"communication tool\", \"description\": \"could be visual or audio; could be used to indicate something\", \"similar objects\": [\"alarm\", \"bell\", \"whistle\"]}", + 39 + ], + "antlers": [ + " {\"type\": \"animal body part\", \"description\": \"branch-like structure; could be found on the head of deer, elk, and moose\", \"similar objects\": [\"horns\", \"tusks\", \"claws\"]}", + 39 + ], + "train headlights": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of metal; could be attached to the front of a train\", \"similar objects\": [\"car headlights\", \"airplane headlights\", \"boat headlights\"]}", + 39 + ], + "pink wall": [ + " {\"type\": \"building material\", \"description\": \"solid, flat, and colored pink; could be made of paint, wallpaper, or tiles\", \"similar objects\": [\"white wall\", \"blue wall\", \"green wall\"]}", + 39 + ], + "ivory": [ + " {\"type\": \"material\", \"description\": \"white, smooth, hard; could be used to make sculptures\", \"similar objects\": [\"bone\", \"horn\", \"plastic\"]}", + 39 + ], + "fluorescent": [ + " {\"type\": \"lightbulb\", \"description\": \"long, thin, emits bright light; could be used in ceiling lights\", \"similar objects\": [\"incandescent\", \"halogen\", \"LED\"]}", + 39 + ], + "pink building": [ + "\n{\"type\": \"structure\", \"description\": \"pink; could be made of bricks; could have windows and doors\", \"similar objects\": [\"house\", \"school\", \"office building\"]}", + 39 + ], + "peacock": [ + " {\"type\": \"animal\", \"description\": \"large bird; has colorful feathers; could have a long tail\", \"similar objects\": [\"turkey\", \"duck\", \"goose\"]}", + 39 + ], + "brown cow": [ + "\n{\"type\": \"animal\", \"description\": \"brown fur; has horns; could have white patches\", \"similar objects\": [\"goat\", \"sheep\", \"buffalo\"]}", + 39 + ], + "texture": [ + " {\"type\": \"visual element\", \"description\": \"the look and feel of a surface; could be rough, smooth, bumpy, etc.\", \"similar objects\": [\"pattern\", \"color\", \"shape\"]}", + 39 + ], + "squirrel": [ + " {\"type\": \"animal\", \"description\": \"brown fur; bushy tail; could climb trees\", \"similar objects\": [\"chipmunk\", \"rabbit\", \"rat\"]}", + 39 + ], + "guardrail": [ + " {\"type\": \"safety tool\", \"description\": \"long metal bar; could be painted yellow; could be used to separate lanes\", \"similar objects\": [\"fence\", \"barrier\", \"wall\"]}", + 39 + ], + "photo frame": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; could be made of wood or plastic; could have a stand\", \"similar objects\": [\"picture frame\", \"mirror frame\", \"painting frame\"]}", + 39 + ], + "brown train tracks": [ + "\n{\"type\": \"transportation infrastructure\", \"description\": \"brown; could be made of steel; could be connected to form a railway\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 39 + ], + "clock side tower": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, cylindrical; could have a clock face; could have a bell\", \"similar objects\": [\"cathedral\", \"obelisk\", \"monument\"]}", + 39 + ], + "bunk": [ + " {\"type\": \"furniture\", \"description\": \"two beds stacked on top of each other; could have a ladder\", \"similar objects\": [\"bed\", \"sofa\", \"chair\"]}", + 39 + ], + "girls hair": [ + " {\"type\": \"accessory\", \"description\": \"long, could be braided, could be curled, could be straightened\", \"similar objects\": [\"headband\", \"hat\", \"scarf\"]}", + 39 + ], + "water wave": [ + " {\"type\": \"natural phenomenon\", \"description\": \"ripples on the surface of water; could be caused by wind or other objects\", \"similar objects\": [\"tide\", \"tsunami\", \"seiche\"]}", + 39 + ], + "orange plate": [ + "\n{\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; has an orange color\", \"similar objects\": [\"bowl\", \"cup\", \"mug\"]}", + 39 + ], + "beets": [ + " {\"type\": \"vegetable\", \"description\": \"round, red, has a stem; could be sliced into round pieces; could be boiled or roasted\", \"similar objects\": [\"carrots\", \"radishes\", \"turnips\"]}", + 39 + ], + "sports car": [ + " {\"type\": \"vehicle\", \"description\": \"low-slung; two doors; could be fast; could have a spoiler\", \"similar objects\": [\"sedan\", \"coupe\", \"convertible\"]}", + 39 + ], + "game control": [ + " {\"type\": \"electronic device\", \"description\": \"has buttons and joysticks; could be wireless; could be used to play video games\", \"similar objects\": [\"keyboard\", \"mouse\", \"game console\"]}", + 38 + ], + "storage box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"basket\", \"bin\", \"trunk\"]}", + 38 + ], + "elephants tail": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, thin, and flexible; could be used for communication; could be used for swatting away flies\", \"similar objects\": [\"giraffe neck\", \"monkey arm\", \"whale fin\"]}", + 38 + ], + "officers": [ + " {\"type\": \"people\", \"description\": \"people in uniform; could be police officers, military officers, or other public servants\", \"similar objects\": [\"soldiers\", \"firefighters\", \"paramedics\"]}", + 38 + ], + "blue background": [ + " {\"type\": \"background\", \"description\": \"solid color; could be used as a backdrop\", \"similar objects\": [\"white background\", \"green background\", \"black background\"]}", + 38 + ], + "bath": [ + " {\"type\": \"bathroom fixture\", \"description\": \"large, deep, could have a shower head; could be made of porcelain\", \"similar objects\": [\"shower\", \"sink\", \"toilet\"]}", + 38 + ], + "window curtains": [ + " {\"type\": \"decoration\", \"description\": \"long fabric; could be hung on a window; could be opened and closed\", \"similar objects\": [\"blinds\", \"drapes\", \"shades\"]}", + 38 + ], + "recliner": [ + " {\"type\": \"furniture\", \"description\": \"adjustable; could be made of leather; could have armrests\", \"similar objects\": [\"sofa\", \"chair\", \"ottoman\"]}", + 38 + ], + "blue jersey": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have a logo; could be made of cotton\", \"similar objects\": [\"t-shirt\", \"sweater\", \"hoodie\"]}", + 38 + ], + "green trees": [ + "\n{\"type\": \"plant\", \"description\": \"tall; has green leaves; could have fruits; could have branches\", \"similar objects\": [\"bush\", \"shrub\", \"palm tree\"]}", + 38 + ], + "tall mountain": [ + " {\"type\": \"landscape\", \"description\": \"high; could have snow on the top; could have trees and rocks around\", \"similar objects\": [\"hill\", \"cliff\", \"valley\"]}", + 38 + ], + "street name": [ + " {\"type\": \"location\", \"description\": \"name of a street; could be a road, avenue, boulevard, etc.\", \"similar objects\": [\"city\", \"state\", \"country\"]}", + 38 + ], + "vests": [ + " {\"type\": \"clothing\", \"description\": \"sleeveless; could be made of cotton or wool; could be worn over a shirt\", \"similar objects\": [\"sweater\", \"jacket\", \"coat\"]}", + 38 + ], + "mangoes": [ + " {\"type\": \"fruit\", \"description\": \"oval; yellow or green; has a stone inside; could be sliced into pieces\", \"similar objects\": [\"banana\", \"apple\", \"peach\"]}", + 38 + ], + "float": [ + " {\"type\": \"water toy\", \"description\": \"inflatable; could be in various shapes; could be used in the pool or beach\", \"similar objects\": [\"raft\", \"inner tube\", \"pool noodle\"]}", + 38 + ], + "pug": [ + " {\"type\": \"animal\", \"description\": \"small, short-muzzled, wrinkled face; has a curled tail; could have a black, brown, or fawn coat\", \"similar objects\": [\"bulldog\", \"beagle\", \"chihuahua\"]}", + 38 + ], + "wooden fencing": [ + " {\"type\": \"building material\", \"description\": \"long, thin pieces of wood; could be used to build a fence\", \"similar objects\": [\"metal fencing\", \"bamboo fencing\", \"plastic fencing\"]}", + 38 + ], + "loveseat": [ + " {\"type\": \"furniture\", \"description\": \"two-seater; could be upholstered; could have armrests\", \"similar objects\": [\"sofa\", \"chair\", \"ottoman\"]}", + 38 + ], + "window shade": [ + " {\"type\": \"window covering\", \"description\": \"could be made of fabric; could be rolled up or down; could be manually or electronically operated\", \"similar objects\": [\"blinds\", \"curtains\", \"shutters\"]}", + 38 + ], + "armband": [ + " {\"type\": \"accessory\", \"description\": \"worn around the arm; could be made of fabric or plastic; could be used for decoration or identification\", \"similar objects\": [\"bracelet\", \"watch\", \"anklet\"]}", + 38 + ], + "round food": [ + " {\"type\": \"food\", \"description\": \"could be round in shape; could be fruits, vegetables, or grains\", \"similar objects\": [\"apple\", \"orange\", \"pear\", \"zucchini\", \"rice\", \"quinoa\"]}", + 38 + ], + "droplets": [ + " {\"type\": \"liquid\", \"description\": \"small, round, could be transparent\", \"similar objects\": [\"raindrops\", \"dew drops\", \"water droplets\"]}", + 38 + ], + "bathroom vanity": [ + " {\"type\": \"furniture\", \"description\": \"has a countertop; could have drawers and cabinets; could have a sink\", \"similar objects\": [\"dresser\", \"desk\", \"kitchen cabinet\"]}", + 38 + ], + "top hat": [ + " {\"type\": \"headwear\", \"description\": \"tall, cylindrical, black; could have a band around the base\", \"similar objects\": [\"fedora\", \"bowler hat\", \"baseball cap\"]}", + 38 + ], + "tennis dress": [ + " {\"type\": \"clothing\", \"description\": \"sleeveless; could be white; could have pleats; could have a skirt\", \"similar objects\": [\"tennis skirt\", \"tennis shorts\", \"tennis shirt\"]}", + 38 + ], + "grey sweater": [ + " {\"type\": \"clothing\", \"description\": \"knitted; could be long-sleeved; could have a hood; could have a zipper\", \"similar objects\": [\"jacket\", \"coat\", \"cardigan\"]}", + 38 + ], + "zebra legs": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, black and white striped; could have hooves\", \"similar objects\": [\"horse legs\", \"giraffe legs\", \"elephant legs\"]}", + 38 + ], + "seat cushion": [ + " {\"type\": \"furniture accessory\", \"description\": \"soft; could be filled with foam; could be covered with fabric\", \"similar objects\": [\"pillow\", \"mattress\", \"cushion cover\"]}", + 38 + ], + "guitar case": [ + " {\"type\": \"musical instrument accessory\", \"description\": \"rectangular; could be made of hard material; could be used to store a guitar\", \"similar objects\": [\"drum case\", \"violin case\", \"keyboard case\"]}", + 38 + ], + "cargo shorts": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting shorts; usually made of cotton; has multiple pockets\", \"similar objects\": [\"cargo pants\", \"joggers\", \"khakis\"]}", + 38 + ], + "plastic straw": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, flexible; could be used to drink beverages\", \"similar objects\": [\"straw\", \"spoon\", \"fork\"]}", + 38 + ], + "glass top": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be used as a table top; could be made of glass or plastic\", \"similar objects\": [\"wooden top\", \"metal top\", \"marble top\"]}", + 38 + ], + "lab": [ + " {\"type\": \"building\", \"description\": \"could be used for scientific research; could have multiple rooms; could have a lot of equipment\", \"similar objects\": [\"school\", \"hospital\", \"library\"]}", + 38 + ], + "accents": [ + " {\"type\": \"language feature\", \"description\": \"a way of speaking a language that is different from the standard pronunciation; could be regional or cultural\", \"similar objects\": [\"dialects\", \"intonation\", \"cadence\"]}", + 38 + ], + "speedometer": [ + " {\"type\": \"measuring tool\", \"description\": \"round; has a needle; used to measure speed\", \"similar objects\": [\"odometer\", \"tachometer\", \"fuel gauge\"]}", + 38 + ], + "bare tree branches": [ + " {\"type\": \"plant\", \"description\": \"long, thin, and without leaves; could be curved or straight; could be brown or gray\", \"similar objects\": [\"twig\", \"sapling\", \"branch\"]}", + 38 + ], + "advertisement banner": [ + "\n{\"type\": \"promotional material\", \"description\": \"large, rectangular; could be printed on paper or cloth; could be hung on walls or poles\", \"similar objects\": [\"billboard\", \"poster\", \"flyer\"]}", + 38 + ], + "elbow pads": [ + " {\"type\": \"protective gear\", \"description\": \"padded; could be strapped to the elbow; could be made of foam or plastic\", \"similar objects\": [\"knee pads\", \"shin guards\", \"helmet\"]}", + 38 + ], + "appliances": [ + "\n{\"type\": \"household items\", \"description\": \"electronic devices used for household tasks; could include refrigerators, washing machines, dishwashers, etc.\", \"similar objects\": [\"furniture\", \"electronics\", \"tools\"]}", + 38 + ], + "array": [ + " {\"type\": \"data structure\", \"description\": \"a collection of elements; could be of any data type\", \"similar objects\": [\"list\", \"stack\", \"queue\"]}", + 38 + ], + "stew": [ + " {\"type\": \"food\", \"description\": \"a combination of vegetables and meat cooked in a liquid; could be served with rice or bread\", \"similar objects\": [\"soup\", \"curry\", \"stir-fry\"]}", + 38 + ], + "button shirt": [ + " {\"type\": \"clothing\", \"description\": \"collared; has buttons; could be long or short sleeved\", \"similar objects\": [\"polo shirt\", \"dress shirt\", \"blouse\"]}", + 38 + ], + "pizza tray": [ + " {\"type\": \"cooking tool\", \"description\": \"round; could be made of metal; could have a handle\", \"similar objects\": [\"baking sheet\", \"cake pan\", \"pie dish\"]}", + 38 + ], + "brown grass": [ + " {\"type\": \"plant\", \"description\": \"dried, brown, could be found in the wild\", \"similar objects\": [\"straw\", \"hay\", \"dried leaves\"]}", + 38 + ], + "hurdle": [ + " {\"type\": \"sports equipment\", \"description\": \"a bar that is placed across a track for athletes to jump over\", \"similar objects\": [\"high jump bar\", \"starting block\", \"relay baton\"]}", + 38 + ], + "silver box": [ + " {\"type\": \"container\", \"description\": \"shiny; could be made of metal; could be used to store items\", \"similar objects\": [\"jewelry box\", \"storage box\", \"trunk\"]}", + 38 + ], + "bear eye": [ + " {\"type\": \"animal body part\", \"description\": \"dark brown; round; could be seen in the middle of the face\", \"similar objects\": [\"wolf eye\", \"tiger eye\", \"lion eye\"]}", + 38 + ], + "projector screen": [ + " {\"type\": \"display tool\", \"description\": \"large, white, flat surface; could be retractable\", \"similar objects\": [\"whiteboard\", \"television\", \"monitor\"]}", + 38 + ], + "leafy vegetable": [ + " {\"type\": \"vegetable\", \"description\": \"green; could be in the form of leaves; could be eaten raw or cooked\", \"similar objects\": [\"spinach\", \"kale\", \"lettuce\"]}", + 38 + ], + "fuel tank": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could be used to store fuel\", \"similar objects\": [\"barrel\", \"drum\", \"tank\"]}", + 38 + ], + "b": [ + "\n{\"type\": \"letter\", \"description\": \"second letter of the alphabet; could be capitalized or lowercase\", \"similar objects\": [\"a\", \"c\", \"d\"]}", + 38 + ], + "round cake": [ + " {\"type\": \"food\", \"description\": \"round; could be decorated with cream and fruits; could be served with tea or coffee\", \"similar objects\": [\"pie\", \"cupcake\", \"muffin\"]}", + 38 + ], + "puppy": [ + " {\"type\": \"animal\", \"description\": \"small, furry, four legs; could have floppy ears; could have a tail\", \"similar objects\": [\"kitten\", \"duckling\", \"lamb\"]}", + 38 + ], + "exhibit": [ + " {\"type\": \"display\", \"description\": \"a collection of items presented for public viewing; could be in a museum, gallery, or other public space\", \"similar objects\": [\"showcase\", \"exposition\", \"exhibition\"]}", + 38 + ], + "macbook": [ + " {\"type\": \"electronic device\", \"description\": \"silver; has a keyboard; could be a laptop or a tablet\", \"similar objects\": [\"desktop computer\", \"smartphone\", \"tablet\"]}", + 38 + ], + "drinking cup": [ + " {\"type\": \"utensil\", \"description\": \"cylindrical; could have a handle; could be made of plastic, glass, or ceramic\", \"similar objects\": [\"mug\", \"glass\", \"tumbler\"]}", + 38 + ], + "glass half": [ + " {\"type\": \"container\", \"description\": \"transparent; could be filled with liquid; could be broken into two pieces\", \"similar objects\": [\"cup\", \"bottle\", \"jar\"]}", + 38 + ], + "library": [ + " {\"type\": \"building\", \"description\": \"large; could have many books; could have a reading area\", \"similar objects\": [\"museum\", \"school\", \"theater\"]}", + 38 + ], + "roof building": [ + " {\"type\": \"structure\", \"description\": \"sloped; could be made of tiles or shingles; could have a chimney\", \"similar objects\": [\"shed\", \"garage\", \"barn\"]}", + 38 + ], + "parrots": [ + " {\"type\": \"animal\", \"description\": \"colorful feathers; could talk; could fly\", \"similar objects\": [\"macaw\", \"cockatoo\", \"lovebird\"]}", + 38 + ], + "groups": [ + " {\"type\": \"social unit\", \"description\": \"collection of individuals; could be organized or unorganized; could be based on common interests or goals\", \"similar objects\": [\"teams\", \"clubs\", \"communities\"]}", + 38 + ], + "paper wrapper": [ + " {\"type\": \"packaging material\", \"description\": \"thin, transparent, could be used to wrap food\", \"similar objects\": [\"plastic wrap\", \"foil\", \"paper bag\"]}", + 38 + ], + "slot": [ + " {\"type\": \"machine\", \"description\": \"rectangular; could be used for gambling; could have a lever\", \"similar objects\": [\"vending machine\", \"arcade machine\", \"coin machine\"]}", + 38 + ], + "cloudy blue skies": [ + "\n{\"type\": \"weather\", \"description\": \"grayish blue; could have white clouds; could be sunny or rainy\", \"similar objects\": [\"sunny skies\", \"rainy skies\", \"overcast skies\"]}", + 38 + ], + "metal wire": [ + " {\"type\": \"material\", \"description\": \"flexible; could be used for binding; could be used for electrical wiring\", \"similar objects\": [\"rope\", \"string\", \"cable\"]}", + 38 + ], + "tennis courts": [ + " {\"type\": \"sports facility\", \"description\": \"rectangular; has a net in the middle; could be made of clay, grass, or hard court\", \"similar objects\": [\"basketball court\", \"soccer field\", \"volleyball court\"]}", + 38 + ], + "ski board": [ + " {\"type\": \"sports equipment\", \"description\": \"long, flat board; could have bindings; could be used on snow or water\", \"similar objects\": [\"snowboard\", \"skateboard\", \"surfboard\"]}", + 38 + ], + "bunk bed": [ + " {\"type\": \"furniture\", \"description\": \"two beds stacked on top of each other; could have a ladder for access to the top bed\", \"similar objects\": [\"trundle bed\", \"loft bed\", \"day bed\"]}", + 38 + ], + "cooking pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle; could have a lid\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 38 + ], + "broken": [ + "\n{\"type\": \"adjective\", \"description\": \"not working; not in one piece; not complete\", \"similar objects\": [\"fractured\", \"shattered\", \"cracked\"]}", + 38 + ], + "blackberries": [ + " {\"type\": \"fruit\", \"description\": \"small, round, dark purple; could have a white center; could have a tart taste\", \"similar objects\": [\"blueberries\", \"raspberries\", \"strawberries\"]}", + 38 + ], + "windshields": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be curved; could be made of glass\", \"similar objects\": [\"headlights\", \"tires\", \"bumpers\"]}", + 37 + ], + "motorboat": [ + " {\"type\": \"watercraft\", \"description\": \"has an engine; could have a cabin; could have a canopy\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}", + 37 + ], + "grey trunk": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of wood; could have a lock\", \"similar objects\": [\"chest\", \"box\", \"suitcase\"]}", + 37 + ], + "grater": [ + " {\"type\": \"kitchen tool\", \"description\": \"has a handle; has a flat surface with sharp edges; could be used to grate food\", \"similar objects\": [\"cheese grater\", \"zester\", \"mandoline\"]}", + 37 + ], + "bare legs": [ + " {\"type\": \"body part\", \"description\": \"exposed skin between the waist and feet; could be covered with clothing\", \"similar objects\": [\"arms\", \"shoulders\", \"neck\"]}", + 37 + ], + "bathroom tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic, stone, or glass; could be used to cover walls and floors\", \"similar objects\": [\"flooring\", \"wallpaper\", \"paint\"]}", + 37 + ], + "urn": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or ceramic; could be used to store ashes\", \"similar objects\": [\"vase\", \"jar\", \"pot\"]}", + 37 + ], + "shoulder strap": [ + " {\"type\": \"accessory\", \"description\": \"long, adjustable strap; could be used to carry bags\", \"similar objects\": [\"belt\", \"backpack strap\", \"handbag strap\"]}", + 37 + ], + "plastic glass": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be used for drinking; could be disposable\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 37 + ], + "silver tv": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could be made of metal; could have a remote control\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}", + 37 + ], + "blue tie": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, made of fabric; could be striped or plain; could be made of silk or cotton\", \"similar objects\": [\"shirt\", \"belt\", \"scarf\"]}", + 37 + ], + "valance": [ + " {\"type\": \"decoration\", \"description\": \"long, thin fabric; could be hung on a window\", \"similar objects\": [\"curtains\", \"drapes\", \"blinds\"]}", + 37 + ], + "patio table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have an umbrella\", \"similar objects\": [\"dining table\", \"coffee table\", \"outdoor chair\"]}", + 37 + ], + "locomotive": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a long body; could have multiple compartments; could have a chimney\", \"similar objects\": [\"train\", \"tram\", \"monorail\"]}", + 37 + ], + "orange tag": [ + " {\"type\": \"label\", \"description\": \"round; could be made of paper or plastic; could have a string attached; could have a logo or text printed on it\", \"similar objects\": [\"name tag\", \"price tag\", \"barcode\"]}", + 37 + ], + "wooden pier": [ + " {\"type\": \"structure\", \"description\": \"long, made of wood; could be used as a dock\", \"similar objects\": [\"bridge\", \"dock\", \"jetty\"]}", + 37 + ], + "glass jars": [ + " {\"type\": \"container\", \"description\": \"transparent; could be used to store food; could be sealed with a lid\", \"similar objects\": [\"bottles\", \"cans\", \"tubs\"]}", + 37 + ], + "daisies": [ + " {\"type\": \"flower\", \"description\": \"white petals with yellow center; could have multiple stems\", \"similar objects\": [\"sunflowers\", \"roses\", \"tulips\"]}", + 37 + ], + "pink pillow": [ + " {\"type\": \"home decor\", \"description\": \"soft; could be square or round; could be made of cotton or silk; could be pink or other colors\", \"similar objects\": [\"blanket\", \"cushion\", \"rug\"]}", + 37 + ], + "omelette": [ + " {\"type\": \"food\", \"description\": \"egg-based dish; could be filled with vegetables, cheese, or meat; could be served with toast\", \"similar objects\": [\"scrambled eggs\", \"frittata\", \"quiche\"]}", + 37 + ], + "metal part": [ + " {\"type\": \"manufacturing item\", \"description\": \"could be made of steel, aluminum, or other metals; could be in various shapes and sizes\", \"similar objects\": [\"screw\", \"bolt\", \"washer\"]}", + 37 + ], + "snowy mountains": [ + " {\"type\": \"landscape\", \"description\": \"white; could have snow-covered peaks; could have glaciers; could have snow-covered trees\", \"similar objects\": [\"desert\", \"forest\", \"ocean\"]}", + 37 + ], + "clutter": [ + " {\"type\": \"disorder\", \"description\": \"messy; chaotic; disorganized\", \"similar objects\": [\"chaos\", \"confusion\", \"disarray\"]}", + 37 + ], + "stuffed bear": [ + " {\"type\": \"toy\", \"description\": \"soft, cuddly, usually has a smiling face; could be in different colors\", \"similar objects\": [\"stuffed dog\", \"stuffed cat\", \"stuffed rabbit\"]}", + 37 + ], + "bowl table": [ + " {\"type\": \"furniture\", \"description\": \"round; could be made of wood or metal; could have a stand\", \"similar objects\": [\"coffee table\", \"end table\", \"dining table\"]}", + 37 + ], + "clock hand": [ + " {\"type\": \"timekeeping tool\", \"description\": \"long, thin, pointed; could be made of metal\", \"similar objects\": [\"clock face\", \"watch hands\", \"pendulum\"]}", + 37 + ], + "newspaper box": [ + " {\"type\": \"container\", \"description\": \"rectangular; has a slot for inserting newspapers; could be made of metal\", \"similar objects\": [\"mailbox\", \"trash can\", \"recycling bin\"]}", + 37 + ], + "control tower": [ + " {\"type\": \"structure\", \"description\": \"tall; has a control room; could be found in airports\", \"similar objects\": [\"lighthouse\", \"windmill\", \"water tower\"]}", + 37 + ], + "action figure": [ + " {\"type\": \"toy\", \"description\": \"small, plastic, could be modeled after a character from a movie, TV show, or comic book\", \"similar objects\": [\"doll\", \"building blocks\", \"puzzle\"]}", + 37 + ], + "wristbands": [ + " {\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of fabric, rubber, or metal; could be used for fashion or identification\", \"similar objects\": [\"bracelets\", \"anklets\", \"necklaces\"]}", + 37 + ], + "tall grasses": [ + " {\"type\": \"plant\", \"description\": \"long, thin, green leaves; could be found in fields\", \"similar objects\": [\"weeds\", \"shrubs\", \"bushes\"]}", + 37 + ], + "pepsi": [ + " {\"type\": \"beverage\", \"description\": \"carbonated; could be served in a can or bottle; could be flavored\", \"similar objects\": [\"cola\", \"juice\", \"water\"]}", + 37 + ], + "skateboard park": [ + " {\"type\": \"recreational facility\", \"description\": \"concrete area with ramps, rails, and other obstacles for skateboarding\", \"similar objects\": [\"skate park\", \"skate ramp\", \"skate bowl\"]}", + 37 + ], + "hatch": [ + " {\"type\": \"door\", \"description\": \"rectangular; could be opened from the inside and outside; could be locked\", \"similar objects\": [\"door\", \"gate\", \"window\"]}", + 37 + ], + "hangers": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, metal or plastic; used to hang clothes\", \"similar objects\": [\"clothespins\", \"clothes rack\", \"clothesline\"]}", + 37 + ], + "grey shorts": [ + " {\"type\": \"clothing\", \"description\": \"light grey; could be made of cotton; could have pockets; could have a drawstring\", \"similar objects\": [\"jeans\", \"trousers\", \"shorts\"]}", + 37 + ], + "gates": [ + " {\"type\": \"barrier\", \"description\": \"could be made of metal; could be opened and closed; could be used to control access\", \"similar objects\": [\"fence\", \"wall\", \"door\"]}", + 37 + ], + "shampoo": [ + " {\"type\": \"cleaning product\", \"description\": \"liquid; used for washing hair; could be scented\", \"similar objects\": [\"soap\", \"conditioner\", \"body wash\"]}", + 37 + ], + "step stool": [ + " {\"type\": \"furniture\", \"description\": \"has two or more steps; could be foldable; could be made of wood or plastic\", \"similar objects\": [\"ladder\", \"chair\", \"stool\"]}", + 37 + ], + "warning sign": [ + " {\"type\": \"signage\", \"description\": \"triangular; yellow background; black symbols\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 37 + ], + "merchandise": [ + " {\"type\": \"goods\", \"description\": \"products that are bought and sold; could be tangible or intangible\", \"similar objects\": [\"products\", \"items\", \"goods\"]}", + 37 + ], + "farmer": [ + " {\"type\": \"occupation\", \"description\": \"works in the field; wears a hat; could have a pitchfork\", \"similar objects\": [\"gardener\", \"rancher\", \"agriculturalist\"]}", + 37 + ], + "bell peppers": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be red, yellow, or green; has a stem\", \"similar objects\": [\"tomato\", \"cucumber\", \"eggplant\"]}", + 37 + ], + "pink suitcase": [ + "\n{\"type\": \"luggage\", \"description\": \"rectangular; could be made of hard plastic; could have wheels; could have a handle\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 37 + ], + "handle bar": [ + " {\"type\": \"bicycle part\", \"description\": \"long, curved, metal; could be used to steer the bicycle\", \"similar objects\": [\"pedal\", \"saddle\", \"chain\"]}", + 37 + ], + "spires": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, slender, pointed; could be made of stone or metal; could be part of a church or castle\", \"similar objects\": [\"towers\", \"minarets\", \"obelisks\"]}", + 37 + ], + "number print": [ + " {\"type\": \"printing tool\", \"description\": \"used to print numbers; could be made of plastic or metal\", \"similar objects\": [\"stamp\", \"ink pad\", \"label maker\"]}", + 37 + ], + "roads": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, flat, could be made of asphalt or concrete; could have lines and signs\", \"similar objects\": [\"bridges\", \"tunnels\", \"railways\"]}", + 37 + ], + "pane window": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of glass; could be opened\", \"similar objects\": [\"door\", \"curtain\", \"shutter\"]}", + 37 + ], + "vendor": [ + " {\"type\": \"person\", \"description\": \"sells goods or services; could be found in a market\", \"similar objects\": [\"merchant\", \"shopkeeper\", \"salesperson\"]}", + 37 + ], + "bonnet": [ + " {\"type\": \"clothing accessory\", \"description\": \"headwear; could be made of fabric; could have a brim\", \"similar objects\": [\"hat\", \"cap\", \"beanie\"]}", + 37 + ], + "round object": [ + "\n{\"type\": \"object\", \"description\": \"circular shape; could be made of different materials; could have different sizes\", \"similar objects\": [\"ball\", \"wheel\", \"disc\"]}", + 37 + ], + "trash receptacle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could have a lid\", \"similar objects\": [\"bin\", \"garbage can\", \"trash can\"]}", + 37 + ], + "mechanism": [ + " {\"type\": \"machine\", \"description\": \"a system of parts working together to perform a task; could be powered by electricity, steam, or other sources of energy\", \"similar objects\": [\"machine\", \"engine\", \"device\"]}", + 37 + ], + "pink box": [ + "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be decorated with pink color\", \"similar objects\": [\"bag\", \"basket\", \"suitcase\"]}", + 37 + ], + "ramekin": [ + " {\"type\": \"cooking tool\", \"description\": \"small, round, ceramic bowl; could be used for baking\", \"similar objects\": [\"souffle dish\", \"custard cup\", \"ramequin\"]}", + 37 + ], + "foot board": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could be used to rest feet\", \"similar objects\": [\"ottoman\", \"bench\", \"stool\"]}", + 37 + ], + "topping": [ + " {\"type\": \"food item\", \"description\": \"used to garnish food; could be sweet or savory\", \"similar objects\": [\"sauce\", \"dressing\", \"sprinkle\"]}", + 37 + ], + "snowflakes": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white; could have six points; could be in different shapes and sizes\", \"similar objects\": [\"raindrops\", \"hailstones\", \"fog\"]}", + 37 + ], + "diamonds": [ + " {\"type\": \"gemstone\", \"description\": \"transparent; has a sparkle; could be cut into different shapes\", \"similar objects\": [\"emeralds\", \"rubies\", \"sapphires\"]}", + 37 + ], + "tarmac road": [ + " {\"type\": \"road surface\", \"description\": \"hard, black surface; could be used for airports and highways\", \"similar objects\": [\"asphalt road\", \"concrete road\", \"gravel road\"]}", + 37 + ], + "cloudy grey sky": [ + " {\"type\": \"weather\", \"description\": \"overcast; could be raining; could be windy\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"snowy sky\"]}", + 37 + ], + "grandfather clock": [ + " {\"type\": \"timepiece\", \"description\": \"tall; has a pendulum; could have a chime\", \"similar objects\": [\"wall clock\", \"cuckoo clock\", \"alarm clock\"]}", + 37 + ], + "asphalt surface": [ + " {\"type\": \"road surface\", \"description\": \"black, smooth, made of asphalt\", \"similar objects\": [\"concrete surface\", \"gravel surface\", \"dirt surface\"]}", + 37 + ], + "bartender": [ + " {\"type\": \"occupation\", \"description\": \"prepares and serves alcoholic beverages; could mix drinks\", \"similar objects\": [\"waiter\", \"chef\", \"barista\"]}", + 37 + ], + "bird cage": [ + " {\"type\": \"pet accessory\", \"description\": \"wire mesh; could have a door; could have a perch\", \"similar objects\": [\"aquarium\", \"hamster cage\", \"dog house\"]}", + 37 + ], + "butterflies": [ + " {\"type\": \"insect\", \"description\": \"winged; colorful; could have antennae\", \"similar objects\": [\"bees\", \"dragonflies\", \"moths\"]}", + 37 + ], + "entry": [ + " {\"type\": \"noun\", \"description\": \"a way of entering a place; could be a door, a gate, or a window\", \"similar objects\": [\"access\", \"entrance\", \"opening\"]}", + 37 + ], + "capped mountains": [ + "\n{\"type\": \"landscape\", \"description\": \"mountains with snow-capped peaks; could have glaciers and ice fields\", \"similar objects\": [\"valleys\", \"hills\", \"cliffs\"]}", + 37 + ], + "wire fencing": [ + " {\"type\": \"barrier\", \"description\": \"made of metal wires; could be used to enclose an area\", \"similar objects\": [\"chain link fence\", \"wooden fence\", \"barbed wire fence\"]}", + 37 + ], + "mans hair": [ + "\n{\"type\": \"body part\", \"description\": \"dark; could be short or long; could be curly or straight\", \"similar objects\": [\"eyebrow\", \"beard\", \"eyelash\"]}", + 37 + ], + "stone fence": [ + " {\"type\": \"building material\", \"description\": \"made of stones; could be used to build a fence\", \"similar objects\": [\"wood fence\", \"brick fence\", \"metal fence\"]}", + 37 + ], + "dense": [ + " {\"type\": \"adjective\", \"description\": \"having a high concentration of something; having a lot of something in a small space\", \"similar objects\": [\"thick\", \"compact\", \"packed\"]}", + 37 + ], + "highway sign": [ + " {\"type\": \"road sign\", \"description\": \"rectangular; could be yellow or white; could have symbols or words\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 37 + ], + "support column": [ + " {\"type\": \"structural element\", \"description\": \"vertical; could be made of metal or concrete; could be used to support a building\", \"similar objects\": [\"pillar\", \"beam\", \"girder\"]}", + 37 + ], + "gold frame": [ + " {\"type\": \"decorative item\", \"description\": \"golden; could be used to frame pictures or paintings\", \"similar objects\": [\"silver frame\", \"wooden frame\", \"plastic frame\"]}", + 37 + ], + "water puddle": [ + " {\"type\": \"natural phenomenon\", \"description\": \"a pool of water; could be formed by rain or melting snow; could be shallow or deep\", \"similar objects\": [\"lake\", \"river\", \"stream\"]}", + 37 + ], + "jet ski": [ + " {\"type\": \"watercraft\", \"description\": \"small, lightweight; has a handlebar; could be powered by an engine\", \"similar objects\": [\"speedboat\", \"canoe\", \"kayak\"]}", + 37 + ], + "sea gull": [ + " {\"type\": \"bird\", \"description\": \"white; has a long beak; could be seen near the sea\", \"similar objects\": [\"pigeon\", \"seagull\", \"swan\"]}", + 37 + ], + "brick road": [ + " {\"type\": \"road surface\", \"description\": \"made of bricks; could be red or grey; could be laid in a pattern\", \"similar objects\": [\"asphalt road\", \"gravel road\", \"cobblestone road\"]}", + 37 + ], + "tall mountains": [ + "\n{\"type\": \"landscape\", \"description\": \"high peaks; could have snow-capped peaks; could have steep slopes; could have rocky terrain\", \"similar objects\": [\"hills\", \"valleys\", \"cliffs\"]}", + 37 + ], + "bird legs": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and scaly; could be feathered; could have claws\", \"similar objects\": [\"insect legs\", \"mammal legs\", \"reptile legs\"]}", + 37 + ], + "female skier": [ + " {\"type\": \"athlete\", \"description\": \"wearing ski gear; skiing on snow\", \"similar objects\": [\"male skier\", \"snowboarder\", \"ice skater\"]}", + 37 + ], + "gold hands": [ + " {\"type\": \"jewelry\", \"description\": \"golden; could be in the shape of hands; could be worn as a necklace or bracelet\", \"similar objects\": [\"gold rings\", \"gold earrings\", \"gold pendants\"]}", + 37 + ], + "bald spot": [ + " {\"type\": \"hair loss\", \"description\": \"smooth, round, patch of skin without hair\", \"similar objects\": [\"alopecia\", \"male pattern baldness\", \"receding hairline\"]}", + 36 + ], + "police man": [ + " {\"type\": \"person\", \"description\": \"uniformed; could have a badge; could carry a gun\", \"similar objects\": [\"firefighter\", \"soldier\", \"security guard\"]}", + 36 + ], + "snowy mountain": [ + " {\"type\": \"landscape\", \"description\": \"white; could have snow-covered peaks; could have a river running through it\", \"similar objects\": [\"glacier\", \"valley\", \"desert\"]}", + 36 + ], + "playing tennis": [ + " {\"type\": \"sport\", \"description\": \"two players use rackets to hit a ball over a net; could be played indoors or outdoors\", \"similar objects\": [\"badminton\", \"squash\", \"table tennis\"]}", + 36 + ], + "glow": [ + " {\"type\": \"phenomenon\", \"description\": \"light emitted from a source; could be visible or invisible\", \"similar objects\": [\"glow-in-the-dark\", \"neon light\", \"firefly\"]}", + 36 + ], + "glass window pane": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be framed; could be used to separate rooms\", \"similar objects\": [\"door\", \"wall\", \"curtain\"]}", + 36 + ], + "steel pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of steel; could be used for support\", \"similar objects\": [\"wooden pole\", \"metal pole\", \"concrete pole\"]}", + 36 + ], + "bathroom cabinet": [ + " {\"type\": \"furniture\", \"description\": \"has shelves and drawers; could be made of wood or plastic; could be wall-mounted or floor-standing\", \"similar objects\": [\"dresser\", \"wardrobe\", \"vanity\"]}", + 36 + ], + "blue sock": [ + " {\"type\": \"clothing item\", \"description\": \"blue; could be made of cotton; could have a pattern\", \"similar objects\": [\"glove\", \"hat\", \"scarf\"]}", + 36 + ], + "metal pipes": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for plumbing\", \"similar objects\": [\"wooden beams\", \"concrete blocks\", \"bricks\"]}", + 36 + ], + "brown curtains": [ + " {\"type\": \"window covering\", \"description\": \"brown; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 36 + ], + "retriever": [ + " {\"type\": \"dog breed\", \"description\": \"medium-sized; has a thick, water-resistant coat; has a friendly and gentle temperament\", \"similar objects\": [\"Labrador\", \"Golden Retriever\", \"German Shepherd\"]}", + 36 + ], + "banana bunch": [ + " {\"type\": \"fruit\", \"description\": \"yellow; curved; could be tied together in a bunch\", \"similar objects\": [\"apple\", \"grapes\", \"strawberries\"]}", + 36 + ], + "glass coffee table": [ + "\n{\"type\": \"furniture\", \"description\": \"transparent; could be made of glass or plastic; could have metal legs\", \"similar objects\": [\"end table\", \"dining table\", \"console table\"]}", + 36 + ], + "power line pole": [ + " {\"type\": \"utility pole\", \"description\": \"tall, metal, has wires attached\", \"similar objects\": [\"telephone pole\", \"street light pole\", \"traffic light pole\"]}", + 36 + ], + "towel holder": [ + " {\"type\": \"bathroom accessory\", \"description\": \"could be made of metal or plastic; could be mounted on the wall; could have a bar for hanging towels\", \"similar objects\": [\"soap dish\", \"toilet paper holder\", \"toothbrush holder\"]}", + 36 + ], + "blind window": [ + " {\"type\": \"window covering\", \"description\": \"made of fabric or wood; could be opened and closed; could be used to block light\", \"similar objects\": [\"shades\", \"curtains\", \"drapes\"]}", + 36 + ], + "train lights": [ + " {\"type\": \"lighting tool\", \"description\": \"red and white; could be flashing; could be attached to the train\", \"similar objects\": [\"traffic lights\", \"street lights\", \"lantern\"]}", + 36 + ], + "truck bed": [ + " {\"type\": \"vehicle part\", \"description\": \"flat, rectangular; could be made of metal; could be used to transport goods\", \"similar objects\": [\"trailer\", \"pickup bed\", \"flatbed\"]}", + 36 + ], + "bear paw": [ + " {\"type\": \"animal body part\", \"description\": \"large, furry, five-clawed paw; could be used for walking and digging\", \"similar objects\": [\"wolf paw\", \"tiger paw\", \"lion paw\"]}", + 36 + ], + "marquee": [ + " {\"type\": \"signage\", \"description\": \"large, rectangular; could be lit up with lights; could be used for advertising\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 36 + ], + "pink scarf": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, made of fabric; could be made of wool; could be used to keep warm\", \"similar objects\": [\"hat\", \"gloves\", \"shawl\"]}", + 36 + ], + "orange cup": [ + "\n{\"type\": \"utensil\", \"description\": \"round; could be made of plastic; could be orange in color\", \"similar objects\": [\"mug\", \"glass\", \"bowl\"]}", + 36 + ], + "croissants": [ + " {\"type\": \"food\", \"description\": \"flaky, crescent-shaped pastry; could be filled with chocolate, cheese, or jam\", \"similar objects\": [\"danish pastry\", \"brioche\", \"pain au chocolat\"]}", + 36 + ], + "dog tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, furry; could be wagging\", \"similar objects\": [\"cat tail\", \"horse tail\", \"monkey tail\"]}", + 36 + ], + "silver utensil": [ + " {\"type\": \"utensil\", \"description\": \"made of silver; could be spoon, fork, or knife\", \"similar objects\": [\"gold utensil\", \"plastic utensil\", \"wooden utensil\"]}", + 36 + ], + "neighborhood": [ + " {\"type\": \"location\", \"description\": \"a group of houses and buildings; could have a park or a school\", \"similar objects\": [\"community\", \"suburb\", \"city\"]}", + 36 + ], + "water cooler": [ + " {\"type\": \"appliance\", \"description\": \"tall, cylindrical; has a spout; could have a tap\", \"similar objects\": [\"refrigerator\", \"water dispenser\", \"ice maker\"]}", + 36 + ], + "fedora": [ + " {\"type\": \"hat\", \"description\": \"wide brim; could be made of felt; could have a band around the crown\", \"similar objects\": [\"panama hat\", \"bowler hat\", \"top hat\"]}", + 36 + ], + "kangaroo": [ + " {\"type\": \"animal\", \"description\": \"marsupial; has a pouch; could hop\", \"similar objects\": [\"koala\", \"wallaby\", \"wombat\"]}", + 36 + ], + "pedals": [ + " {\"type\": \"bicycle part\", \"description\": \"attached to the crank arms; could be made of metal or plastic; could be used to propel the bicycle forward\", \"similar objects\": [\"chain\", \"sprocket\", \"tire\"]}", + 36 + ], + "shadow sand": [ + " {\"type\": \"toy\", \"description\": \"black sand; could be used to create 3D shapes; could be used to create shadows\", \"similar objects\": [\"kinetic sand\", \"play dough\", \"modeling clay\"]}", + 36 + ], + "cases": [ + " {\"type\": \"container\", \"description\": \"could be made of plastic, metal, or wood; could be used to store items\", \"similar objects\": [\"boxes\", \"baskets\", \"bags\"]}", + 36 + ], + "cherry tomatoes": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, red; could be sliced into halves; could be used in salads\", \"similar objects\": [\"grape tomatoes\", \"plum tomatoes\", \"regular tomatoes\"]}", + 36 + ], + "mounds": [ + " {\"type\": \"landform\", \"description\": \"raised area of land; could be made of earth, rocks, or other materials; could be natural or man-made\", \"similar objects\": [\"hills\", \"mountains\", \"valleys\"]}", + 36 + ], + "blue seats": [ + " {\"type\": \"furniture\", \"description\": \"blue; could be made of fabric or leather; could be used for sitting\", \"similar objects\": [\"sofa\", \"chair\", \"bench\"]}", + 36 + ], + "khaki": [ + " {\"type\": \"color\", \"description\": \"light brown; could be used to describe clothing\", \"similar objects\": [\"beige\", \"tan\", \"olive\"]}", + 36 + ], + "skier skiing": [ + "\n{\"type\": \"sport\", \"description\": \"person skiing down a slope; could be wearing ski boots and poles; could be wearing a helmet and goggles\", \"similar objects\": [\"snowboarder\", \"skateboarder\", \"surfer\"]}", + 36 + ], + "stone steps": [ + " {\"type\": \"architectural structure\", \"description\": \"made of stones; could be used as stairs\", \"similar objects\": [\"wooden steps\", \"concrete steps\", \"brick steps\"]}", + 36 + ], + "concrete pillar": [ + " {\"type\": \"building material\", \"description\": \"cylindrical; made of concrete; could be used to support a structure\", \"similar objects\": [\"steel beam\", \"wooden post\", \"brick wall\"]}", + 36 + ], + "side boat": [ + " {\"type\": \"watercraft\", \"description\": \"long and narrow; could have a motor; could be used for fishing\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 36 + ], + "airport terminal": [ + " {\"type\": \"building\", \"description\": \"large; has many gates; could have a control tower\", \"similar objects\": [\"train station\", \"bus station\", \"shopping mall\"]}", + 36 + ], + "concrete block": [ + " {\"type\": \"building material\", \"description\": \"rectangular; heavy; could be used for construction\", \"similar objects\": [\"bricks\", \"cement\", \"wooden beams\"]}", + 36 + ], + "wipes": [ + " {\"type\": \"cleaning tool\", \"description\": \"soft, disposable cloths; could be used for cleaning surfaces\", \"similar objects\": [\"tissues\", \"paper towels\", \"sponges\"]}", + 36 + ], + "salad bowl": [ + " {\"type\": \"cooking tool\", \"description\": \"large, round, could be made of plastic or wood; could have a lid\", \"similar objects\": [\"serving bowl\", \"mixing bowl\", \"soup bowl\"]}", + 36 + ], + "concrete barrier": [ + " {\"type\": \"construction tool\", \"description\": \"rectangular; made of concrete; used to block roads\", \"similar objects\": [\"guardrail\", \"traffic cone\", \"bollard\"]}", + 36 + ], + "exit door": [ + " {\"type\": \"door\", \"description\": \"could be marked with an exit sign; could be made of metal; could be opened with a handle\", \"similar objects\": [\"entrance door\", \"fire door\", \"emergency door\"]}", + 36 + ], + "foot rest": [ + " {\"type\": \"furniture\", \"description\": \"small, rectangular, could be made of wood or metal; could be used to rest feet\", \"similar objects\": [\"ottoman\", \"stool\", \"chair\"]}", + 36 + ], + "trolley car": [ + " {\"type\": \"vehicle\", \"description\": \"long; has two or more compartments; could be powered by electricity or diesel\", \"similar objects\": [\"bus\", \"train\", \"tram\"]}", + 36 + ], + "bear ear": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of fur; could be attached to a headband\", \"similar objects\": [\"cat ear\", \"rabbit ear\", \"fox ear\"]}", + 36 + ], + "purple blanket": [ + "\n{\"type\": \"textile\", \"description\": \"soft; could be made of wool; could be of various sizes; could be of various colors\", \"similar objects\": [\"pillow\", \"quilt\", \"towel\"]}", + 36 + ], + "infield": [ + " {\"type\": \"sports field\", \"description\": \"rectangular; could be made of grass; could have a pitcher's mound\", \"similar objects\": [\"soccer field\", \"baseball field\", \"tennis court\"]}", + 36 + ], + "storage container": [ + " {\"type\": \"container\", \"description\": \"could be made of plastic; could be used to store items; could be stackable\", \"similar objects\": [\"box\", \"bin\", \"basket\"]}", + 36 + ], + "square windows": [ + " {\"type\": \"architectural feature\", \"description\": \"four-sided; could be made of glass; could be opened\", \"similar objects\": [\"rectangular windows\", \"arched windows\", \"oval windows\"]}", + 36 + ], + "alcohol": [ + " {\"type\": \"beverage\", \"description\": \"colorless liquid; could be distilled from grains, fruits, or vegetables; could be used for medical and recreational purposes\", \"similar objects\": [\"beer\", \"wine\", \"whiskey\"]}", + 36 + ], + "toilet tissue": [ + " {\"type\": \"hygiene product\", \"description\": \"soft, white, rectangular; could be in rolls\", \"similar objects\": [\"paper towel\", \"toilet paper\", \"wipes\"]}", + 36 + ], + "giraffe grazing": [ + "\n{\"type\": \"animal behavior\", \"description\": \"giraffe eating leaves from trees; could have a long neck; could have a spotted pattern\", \"similar objects\": [\"elephant eating\", \"zebra grazing\", \"horse grazing\"]}", + 36 + ], + "octopus": [ + " {\"type\": \"animal\", \"description\": \"eight tentacles; could be red, brown, or blue; could have suction cups on tentacles\", \"similar objects\": [\"squid\", \"cuttlefish\", \"jellyfish\"]}", + 36 + ], + "orange car": [ + "\n{\"type\": \"vehicle\", \"description\": \"orange; could be a sedan, coupe, hatchback, SUV, etc.\", \"similar objects\": [\"red car\", \"blue car\", \"green car\"]}", + 36 + ], + "grey hair": [ + " {\"type\": \"hair color\", \"description\": \"light to dark shades of grey; could be natural or dyed\", \"similar objects\": [\"blonde hair\", \"brown hair\", \"black hair\"]}", + 36 + ], + "baseball cleat": [ + " {\"type\": \"footwear\", \"description\": \"has spikes on the bottom; could be made of leather; could have laces\", \"similar objects\": [\"soccer cleat\", \"running shoe\", \"hiking boot\"]}", + 36 + ], + "silver mirror": [ + "\n{\"type\": \"decorative item\", \"description\": \"shiny, reflective surface; could be framed; could be used for decoration\", \"similar objects\": [\"picture frame\", \"wall art\", \"vase\"]}", + 36 + ], + "streamer": [ + " {\"type\": \"decoration tool\", \"description\": \"long, colorful, could be made of paper or fabric\", \"similar objects\": [\"balloon\", \"ribbon\", \"confetti\"]}", + 36 + ], + "sub": [ + " {\"type\": \"food\", \"description\": \"long sandwich; could be filled with various ingredients; could be served with sauces\", \"similar objects\": [\"wrap\", \"burrito\", \"panini\"]}", + 36 + ], + "scenery": [ + " {\"type\": \"landscape\", \"description\": \"natural environment; could include mountains, rivers, trees, etc.\", \"similar objects\": [\"landscape\", \"panorama\", \"vista\"]}", + 36 + ], + "wok": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 36 + ], + "zebra eye": [ + "\n{\"type\": \"animal body part\", \"description\": \"black and white; round; has a pupil\", \"similar objects\": [\"horse eye\", \"giraffe eye\", \"elephant eye\"]}", + 36 + ], + "silver metal fence": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be in a variety of shapes; could be painted silver\", \"similar objects\": [\"wood fence\", \"iron fence\", \"brick wall\"]}", + 36 + ], + "glider": [ + " {\"type\": \"aircraft\", \"description\": \"winged; could be powered or unpowered; could be used for recreational purposes\", \"similar objects\": [\"airplane\", \"helicopter\", \"parachute\"]}", + 36 + ], + "everyone": [ + " {\"type\": \"pronoun\", \"description\": \"refers to all people\", \"similar objects\": [\"everybody\", \"nobody\", \"somebody\"]}", + 36 + ], + "cages": [ + " {\"type\": \"container\", \"description\": \"could be made of metal or plastic; could be used to contain animals or objects\", \"similar objects\": [\"enclosures\", \"aquariums\", \"terrariums\"]}", + 36 + ], + "wii controllers": [ + " {\"type\": \"gaming device\", \"description\": \"wireless; could be held in hands; could be used to play games\", \"similar objects\": [\"joystick\", \"gamepad\", \"racing wheel\"]}", + 36 + ], + "thick forest": [ + " {\"type\": \"landscape\", \"description\": \"dense vegetation; trees, shrubs, and other plants; could have a variety of wildlife\", \"similar objects\": [\"jungle\", \"rainforest\", \"woodland\"]}", + 36 + ], + "clock top tower": [ + " {\"type\": \"building\", \"description\": \"tall, cylindrical; could have a clock on top; could have a spire\", \"similar objects\": [\"cathedral\", \"skyscraper\", \"monument\"]}", + 36 + ], + "dress shoe": [ + " {\"type\": \"footwear\", \"description\": \"leather; has a heel; could be lace-up or slip-on\", \"similar objects\": [\"loafer\", \"oxford\", \"sneaker\"]}", + 36 + ], + "car seat": [ + " {\"type\": \"furniture\", \"description\": \"has a back and armrests; could be upholstered; could be adjustable\", \"similar objects\": [\"sofa\", \"chair\", \"bench\"]}", + 36 + ], + "board shorts": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting shorts; could be made of cotton or polyester; could have pockets; could have a drawstring waistband\", \"similar objects\": [\"swim trunks\", \"briefs\", \"boxer shorts\"]}", + 36 + ], + "pine needles": [ + " {\"type\": \"plant material\", \"description\": \"long, thin, sharp; could be green or brown; could be found on the ground\", \"similar objects\": [\"leaves\", \"grass\", \"twigs\"]}", + 36 + ], + "savannah": [ + " {\"type\": \"ecosystem\", \"description\": \"grassland; could have trees and shrubs; could have animals like lions, elephants, and giraffes\", \"similar objects\": [\"desert\", \"rainforest\", \"tundra\"]}", + 36 + ], + "cigar": [ + " {\"type\": \"tobacco product\", \"description\": \"cylindrical; could be made of tobacco leaves; could be lit up\", \"similar objects\": [\"cigarette\", \"pipe\", \"hookah\"]}", + 36 + ], + "color blue": [ + "\n{\"type\": \"color\", \"description\": \"a hue of the visible light spectrum; could be described as a cool color; could be associated with the sky and the ocean\", \"similar objects\": [\"green\", \"purple\", \"yellow\"]}", + 36 + ], + "cigarette butt": [ + " {\"type\": \"waste\", \"description\": \"small, cylindrical, made of paper and tobacco; could have a filter\", \"similar objects\": [\"cigarette pack\", \"cigarette box\", \"cigarette lighter\"]}", + 36 + ], + "entry door": [ + " {\"type\": \"building component\", \"description\": \"rectangular; could be made of wood or metal; could have a handle and a lock\", \"similar objects\": [\"window\", \"garage door\", \"gate\"]}", + 36 + ], + "silver flip phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a flip cover; could be made of metal\", \"similar objects\": [\"smartphone\", \"landline phone\", \"walkie talkie\"]}", + 36 + ], + "skeleton": [ + " {\"type\": \"anatomy\", \"description\": \"human bones; could be used for medical study\", \"similar objects\": [\"skull\", \"rib cage\", \"spine\"]}", + 36 + ], + "scissor": [ + " {\"type\": \"tool\", \"description\": \"two blades connected by a pivot; could be used for cutting\", \"similar objects\": [\"knife\", \"pliers\", \"tweezers\"]}", + 36 + ], + "lemon wedge": [ + " {\"type\": \"food\", \"description\": \"yellow; could be cut into wedges; sour taste\", \"similar objects\": [\"lime wedge\", \"orange wedge\", \"grapefruit wedge\"]}", + 35 + ], + "evergreen": [ + " {\"type\": \"plant\", \"description\": \"tall; has needles; could be coniferous\", \"similar objects\": [\"pine tree\", \"spruce tree\", \"cypress tree\"]}", + 35 + ], + "metal base": [ + " {\"type\": \"building material\", \"description\": \"strong and durable; could be used as a foundation for structures\", \"similar objects\": [\"concrete\", \"wood\", \"steel\"]}", + 35 + ], + "drinking water": [ + " {\"type\": \"beverage\", \"description\": \"clear, odorless, tasteless liquid; could be bottled or from a tap\", \"similar objects\": [\"juice\", \"soda\", \"tea\"]}", + 35 + ], + "boys hair": [ + " {\"type\": \"hairstyle\", \"description\": \"short, spiked, could be gelled; could be combed to one side\", \"similar objects\": [\"buzz cut\", \"crew cut\", \"faux hawk\"]}", + 35 + ], + "snake": [ + " {\"type\": \"animal\", \"description\": \"long, slim body; could be scaly; could be poisonous\", \"similar objects\": [\"lizard\", \"iguana\", \"crocodile\"]}", + 35 + ], + "burnt crust": [ + " {\"type\": \"food\", \"description\": \"dark brown; could be crunchy; could be made of bread\", \"similar objects\": [\"toast\", \"crouton\", \"biscuit\"]}", + 35 + ], + "lamp posts": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a lightbulb on top\", \"similar objects\": [\"streetlight\", \"lantern\", \"torch\"]}", + 35 + ], + "yellow vegetable": [ + "\n{\"type\": \"vegetable\", \"description\": \"could be round or long; could be yellow or orange; could have green leaves\", \"similar objects\": [\"squash\", \"carrot\", \"sweet potato\"]}", + 35 + ], + "metal building": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could be used for industrial or commercial purposes; could have multiple stories\", \"similar objects\": [\"warehouse\", \"factory\", \"shed\"]}", + 35 + ], + "orange container": [ + " {\"type\": \"container\", \"description\": \"orange; could be made of plastic; could have a lid\", \"similar objects\": [\"box\", \"jar\", \"bag\"]}", + 35 + ], + "plenty": [ + " {\"type\": \"abstract concept\", \"description\": \"an abundance of something; could be used to describe a large quantity of something\", \"similar objects\": [\"abundance\", \"surplus\", \"wealth\"]}", + 35 + ], + "ingredients": [ + " {\"type\": \"food items\", \"description\": \"items used to make food; could be raw or processed\", \"similar objects\": [\"spices\", \"vegetables\", \"fruits\"]}", + 35 + ], + "font": [ + " {\"type\": \"typography\", \"description\": \"style of typeface; could be used for printing or display\", \"similar objects\": [\"typeface\", \"type style\", \"type family\"]}", + 35 + ], + "purple jacket": [ + "\n{\"type\": \"clothing\", \"description\": \"long sleeve; could be made of cotton; could have a zipper; could have pockets; could have a hood\", \"similar objects\": [\"coat\", \"sweater\", \"hoodie\"]}", + 35 + ], + "lake water": [ + " {\"type\": \"natural body of water\", \"description\": \"clear; could have aquatic plants; could have fishes\", \"similar objects\": [\"river\", \"ocean\", \"pond\"]}", + 35 + ], + "stove burner": [ + " {\"type\": \"cooking tool\", \"description\": \"round; could be gas or electric; could have multiple burners\", \"similar objects\": [\"oven\", \"grill\", \"microwave\"]}", + 35 + ], + "borders": [ + " {\"type\": \"geographical feature\", \"description\": \"a line that divides two countries or regions; could be physical or political\", \"similar objects\": [\"boundaries\", \"fences\", \"walls\"]}", + 35 + ], + "sconce": [ + " {\"type\": \"lighting tool\", \"description\": \"wall-mounted; could be made of metal; could have a candle or electric bulb\", \"similar objects\": [\"chandelier\", \"lantern\", \"ceiling light\"]}", + 35 + ], + "front tires": [ + " {\"type\": \"automotive part\", \"description\": \"round; made of rubber; used for steering and braking\", \"similar objects\": [\"rear tires\", \"wheels\", \"brakes\"]}", + 35 + ], + "tree ground": [ + " {\"type\": \"landscape\", \"description\": \"large, tall, with leaves and branches; could have roots\", \"similar objects\": [\"forest\", \"bush\", \"grass\"]}", + 35 + ], + "barcode": [ + " {\"type\": \"identification tool\", \"description\": \"black and white stripes; could be scanned by a scanner\", \"similar objects\": [\"QR code\", \"RFID tag\", \"UPC code\"]}", + 35 + ], + "gold handle": [ + " {\"type\": \"accessory\", \"description\": \"shiny, metallic handle; could be used for furniture, doors, etc.\", \"similar objects\": [\"silver handle\", \"bronze handle\", \"brass handle\"]}", + 35 + ], + "team name": [ + " {\"type\": \"group\", \"description\": \"a group of people with a common purpose or goal\", \"similar objects\": [\"club\", \"organization\", \"association\"]}", + 35 + ], + "shells": [ + " {\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be found on the beach; could be used as decoration\", \"similar objects\": [\"rocks\", \"pebbles\", \"seaweed\"]}", + 35 + ], + "store window": [ + " {\"type\": \"display\", \"description\": \"glass window; could have a frame; could be decorated with posters or signs\", \"similar objects\": [\"shop window\", \"showcase\", \"display case\"]}", + 35 + ], + "cross walk": [ + " {\"type\": \"road sign\", \"description\": \"white stripes on the road; could have a red hand sign\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 35 + ], + "sauce plate": [ + " {\"type\": \"dishware\", \"description\": \"small, round, shallow; could be made of porcelain; could have a handle\", \"similar objects\": [\"saucer\", \"soup bowl\", \"teacup\"]}", + 35 + ], + "champagne glass": [ + " {\"type\": \"drinking vessel\", \"description\": \"tall and slender; has a stem; could be made of glass or crystal\", \"similar objects\": [\"wine glass\", \"martini glass\", \"tumbler\"]}", + 35 + ], + "color brown": [ + " {\"type\": \"color\", \"description\": \"dark yellowish-red hue; could be associated with earth tones\", \"similar objects\": [\"beige\", \"tan\", \"taupe\"]}", + 35 + ], + "bomb": [ + " {\"type\": \"explosive device\", \"description\": \"could be cylindrical or spherical; could be made of metal; could be filled with explosives\", \"similar objects\": [\"grenade\", \"missile\", \"rocket\"]}", + 35 + ], + "baseball shoes": [ + " {\"type\": \"footwear\", \"description\": \"made of leather; has cleats; could be white and black\", \"similar objects\": [\"soccer shoes\", \"running shoes\", \"tennis shoes\"]}", + 35 + ], + "crock pot": [ + " {\"type\": \"cooking tool\", \"description\": \"large, slow cooker; could have a lid; could have a timer\", \"similar objects\": [\"pressure cooker\", \"rice cooker\", \"slow cooker\"]}", + 35 + ], + "motorcyclists": [ + " {\"type\": \"transportation\", \"description\": \"riding a motorcycle; wearing a helmet; could have a sidecar\", \"similar objects\": [\"bicyclists\", \"skateboarders\", \"scooter riders\"]}", + 35 + ], + "baseball mit": [ + " {\"type\": \"sports equipment\", \"description\": \"leather; has a pocket; could be used to catch a baseball\", \"similar objects\": [\"baseball bat\", \"glove\", \"helmet\"]}", + 35 + ], + "tile bathroom floor": [ + "\n{\"type\": \"home improvement task\", \"description\": \"installing ceramic, porcelain, or stone tiles on a bathroom floor\", \"similar objects\": [\"paint walls\", \"install sink\", \"lay carpet\"]}", + 35 + ], + "circle sign": [ + " {\"type\": \"road sign\", \"description\": \"round; could be yellow or white; could have a black border\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 35 + ], + "concrete slab": [ + " {\"type\": \"building material\", \"description\": \"flat, heavy, made of cement\", \"similar objects\": [\"brick\", \"stone\", \"tile\"]}", + 35 + ], + "multiple": [ + " {\"type\": \"adjective\", \"description\": \"describes a quantity of more than one\", \"similar objects\": [\"plural\", \"many\", \"various\"]}", + 35 + ], + "whiteboard": [ + " {\"type\": \"writing tool\", \"description\": \"smooth, white surface; could be used with markers\", \"similar objects\": [\"chalkboard\", \"blackboard\", \"dry erase board\"]}", + 35 + ], + "bus destination sign": [ + " {\"type\": \"transportation tool\", \"description\": \"rectangular; has a list of destinations; could be illuminated\", \"similar objects\": [\"train destination sign\", \"airport sign\", \"bus stop sign\"]}", + 35 + ], + "base ball player": [ + " {\"type\": \"athlete\", \"description\": \"wears a uniform; has a bat and a glove; could be running on the field\", \"similar objects\": [\"soccer player\", \"tennis player\", \"golfer\"]}", + 35 + ], + "orange letters": [ + "\n{\"type\": \"alphabet\", \"description\": \"round; could be made of paper; could be in different colors; could be in different sizes\", \"similar objects\": [\"letters\", \"numbers\", \"symbols\"]}", + 35 + ], + "soda cans": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of aluminum; could be sealed with a tab\", \"similar objects\": [\"bottles\", \"jars\", \"cups\"]}", + 35 + ], + "haircut": [ + " {\"type\": \"style\", \"description\": \"a style of cutting hair; could be short, long, layered, etc.\", \"similar objects\": [\"hairstyle\", \"bob cut\", \"pixie cut\"]}", + 35 + ], + "infant": [ + " {\"type\": \"human\", \"description\": \"small; could be crying; could be sleeping\", \"similar objects\": [\"toddler\", \"child\", \"teenager\"]}", + 35 + ], + "wooden dresser": [ + "\n{\"type\": \"furniture\", \"description\": \"wooden; has drawers; could have a mirror\", \"similar objects\": [\"chest of drawers\", \"wardrobe\", \"armoire\"]}", + 35 + ], + "luggage rack": [ + " {\"type\": \"furniture\", \"description\": \"metal frame; could be foldable; could be used to store suitcases\", \"similar objects\": [\"clothes rack\", \"shoe rack\", \"bookshelf\"]}", + 35 + ], + "cement bench": [ + " {\"type\": \"furniture\", \"description\": \"made of cement; could have a backrest; could be used for sitting\", \"similar objects\": [\"concrete table\", \"cement chair\", \"stone bench\"]}", + 35 + ], + "plastic chairs": [ + " {\"type\": \"furniture\", \"description\": \"lightweight; could be stackable; could be foldable; could be colorful\", \"similar objects\": [\"stools\", \"benches\", \"tables\"]}", + 35 + ], + "antique": [ + " {\"type\": \"collectible item\", \"description\": \"old; could be made of wood, metal, or glass; could be valuable\", \"similar objects\": [\"vintage\", \"heirloom\", \"antiquity\"]}", + 35 + ], + "stainless steel fork": [ + "\n{\"type\": \"utensil\", \"description\": \"long handle; four tines; made of stainless steel\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 35 + ], + "work truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have a flatbed; could have a ladder\", \"similar objects\": [\"pickup truck\", \"van\", \"dump truck\"]}", + 35 + ], + "safety line": [ + " {\"type\": \"safety tool\", \"description\": \"rope or cable; used to secure a person or object\", \"similar objects\": [\"harness\", \"helmet\", \"life jacket\"]}", + 35 + ], + "round ball": [ + " {\"type\": \"toy\", \"description\": \"spherical; could be made of rubber, plastic, or metal; could be used for playing catch\", \"similar objects\": [\"basketball\", \"soccer ball\", \"baseball\"]}", + 35 + ], + "wet fur": [ + " {\"type\": \"texture\", \"description\": \"damp; could be soft and slippery; could be clumped together\", \"similar objects\": [\"velvet\", \"suede\", \"leather\"]}", + 35 + ], + "jean jacket": [ + " {\"type\": \"clothing\", \"description\": \"blue; could have pockets; could have buttons; could have a collar\", \"similar objects\": [\"denim jacket\", \"leather jacket\", \"bomber jacket\"]}", + 35 + ], + "headlight train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple headlight; could be powered by electricity or diesel\", \"similar objects\": [\"locomotive\", \"tram\", \"monorail\"]}", + 35 + ], + "orange box": [ + "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be orange in color\", \"similar objects\": [\"suitcase\", \"basket\", \"bag\"]}", + 35 + ], + "heron": [ + " {\"type\": \"bird\", \"description\": \"long legs; long neck; could have a white or grey body; could have a black crest\", \"similar objects\": [\"egret\", \"ibis\", \"stork\"]}", + 35 + ], + "crack sidewalk": [ + " {\"type\": \"ground damage\", \"description\": \"uneven surface; could have a gap; could be caused by tree roots\", \"similar objects\": [\"pothole\", \"uneven pavement\", \"sunken ground\"]}", + 35 + ], + "outdoor light": [ + " {\"type\": \"lighting tool\", \"description\": \"could be wall-mounted; could be powered by electricity or solar energy; could be used for outdoor lighting\", \"similar objects\": [\"street light\", \"flood light\", \"security light\"]}", + 35 + ], + "blue waters": [ + " {\"type\": \"natural phenomenon\", \"description\": \"large body of water; could be deep and clear; could be surrounded by mountains\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 35 + ], + "length": [ + " {\"type\": \"measurement\", \"description\": \"distance between two points; could be measured in inches, feet, meters, etc.\", \"similar objects\": [\"width\", \"height\", \"depth\"]}", + 35 + ], + "rv": [ + " {\"type\": \"vehicle\", \"description\": \"large, box-shaped; could have a kitchen, bedroom, and bathroom; could be used for camping\", \"similar objects\": [\"campervan\", \"motorhome\", \"trailer\"]}", + 35 + ], + "metal posts": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for fencing\", \"similar objects\": [\"wood posts\", \"concrete posts\", \"steel posts\"]}", + 35 + ], + "bus sign": [ + " {\"type\": \"transportation sign\", \"description\": \"rectangular; has a route number; could be made of metal\", \"similar objects\": [\"street sign\", \"traffic sign\", \"stop sign\"]}", + 35 + ], + "silver bolt": [ + " {\"type\": \"hardware\", \"description\": \"metallic; cylindrical; could be used to fasten two objects together\", \"similar objects\": [\"screw\", \"nut\", \"washer\"]}", + 35 + ], + "stone walkway": [ + " {\"type\": \"landscape feature\", \"description\": \"made of stones; could be curved or straight; could be used as a path\", \"similar objects\": [\"gravel path\", \"wooden walkway\", \"brick path\"]}", + 35 + ], + "strainer": [ + " {\"type\": \"kitchen tool\", \"description\": \"has a handle; has a mesh; could be used to strain liquid\", \"similar objects\": [\"colander\", \"sieve\", \"skimmer\"]}", + 35 + ], + "needle": [ + " {\"type\": \"sewing tool\", \"description\": \"long and thin; has a sharp point; could be made of metal or plastic\", \"similar objects\": [\"pin\", \"thimble\", \"thread\"]}", + 35 + ], + "bulldozer": [ + " {\"type\": \"construction vehicle\", \"description\": \"large; has a blade in front; could have a ripper in the back\", \"similar objects\": [\"excavator\", \"loader\", \"tractor\"]}", + 35 + ], + "st": [ + "\n{\"type\": \"abbreviation\", \"description\": \"short form of a word or phrase; could be used to represent a state or country\", \"similar objects\": [\"Dr.\", \"Mr.\", \"Ms.\"]}", + 35 + ], + "shower curtain rod": [ + " {\"type\": \"bathroom accessory\", \"description\": \"long, curved; could be made of metal or plastic; could be mounted on the wall or ceiling\", \"similar objects\": [\"towel bar\", \"soap dish\", \"toilet paper holder\"]}", + 35 + ], + "outdoor clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"could be wall-mounted; could be made of metal; could be waterproof\", \"similar objects\": [\"watch\", \"alarm clock\", \"sundial\"]}", + 35 + ], + "silver plane": [ + " {\"type\": \"vehicle\", \"description\": \"silver; has wings; could have a tail; could have two or more engines\", \"similar objects\": [\"helicopter\", \"rocket\", \"airplane\"]}", + 35 + ], + "shuttle": [ + " {\"type\": \"vehicle\", \"description\": \"aerospace vehicle; could be used for space travel; could be reusable\", \"similar objects\": [\"rocket\", \"spacecraft\", \"satellite\"]}", + 35 + ], + "one": [ + " {\"type\": \"number\", \"description\": \"the smallest natural number; could be written as 1\", \"similar objects\": [\"two\", \"three\", \"four\"]}", + 34 + ], + "grouping": [ + " {\"type\": \"concept\", \"description\": \"the act of arranging objects into groups\", \"similar objects\": [\"classification\", \"categorization\", \"organization\"]}", + 34 + ], + "dinner table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal; could have a glass top\", \"similar objects\": [\"coffee table\", \"dining table\", \"end table\"]}", + 34 + ], + "copyright symbol": [ + " {\"type\": \"symbol\", \"description\": \"circle with a letter C inside; could be used to indicate copyright\", \"similar objects\": [\"trademark symbol\", \"registered symbol\", \"patent symbol\"]}", + 34 + ], + "wooden boat": [ + " {\"type\": \"transportation tool\", \"description\": \"made of wood; could be used for fishing; could be sailed\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 34 + ], + "silver refrigerator": [ + "\n{\"type\": \"appliance\", \"description\": \"silver; has a door; could have a freezer compartment\", \"similar objects\": [\"stove\", \"dishwasher\", \"microwave\"]}", + 34 + ], + "wood drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have handles; could have drawers\", \"similar objects\": [\"dresser\", \"cabinet\", \"chest of drawers\"]}", + 34 + ], + "cable box": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; could have multiple ports; could be used to connect to TV\", \"similar objects\": [\"router\", \"modem\", \"set-top box\"]}", + 34 + ], + "tab": [ + " {\"type\": \"computer tool\", \"description\": \"small rectangular window; could be used to open multiple applications\", \"similar objects\": [\"window\", \"dialog box\", \"menu\"]}", + 34 + ], + "patio chair": [ + " {\"type\": \"furniture\", \"description\": \"outdoor chair; could be made of metal or wood; could have armrests; could have a cushion\", \"similar objects\": [\"lounge chair\", \"deck chair\", \"rocking chair\"]}", + 34 + ], + "grains": [ + " {\"type\": \"food\", \"description\": \"small, edible seeds; could be wheat, rice, oats, etc.\", \"similar objects\": [\"cereal\", \"nuts\", \"legumes\"]}", + 34 + ], + "blue train car": [ + "\n{\"type\": \"transportation vehicle\", \"description\": \"blue; could have multiple cars connected; could have windows\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 34 + ], + "lotion": [ + " {\"type\": \"cosmetic product\", \"description\": \"liquid; could be used for skin care; could be scented\", \"similar objects\": [\"cream\", \"moisturizer\", \"body wash\"]}", + 34 + ], + "beige hat": [ + " {\"type\": \"clothing item\", \"description\": \"round; could be made of wool; could have a brim\", \"similar objects\": [\"cap\", \"fedora\", \"beanie\"]}", + 34 + ], + "adidas logo": [ + "\n{\"type\": \"brand logo\", \"description\": \"three stripes; could be in black and white; could be in different colors\", \"similar objects\": [\"Nike logo\", \"Puma logo\", \"Reebok logo\"]}", + 34 + ], + "dog head": [ + " {\"type\": \"animal body part\", \"description\": \"round; has two ears; could have a snout; could have a tongue\", \"similar objects\": [\"cat head\", \"horse head\", \"rabbit head\"]}", + 34 + ], + "room table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal\", \"similar objects\": [\"chair\", \"sofa\", \"cabinet\"]}", + 34 + ], + "netting": [ + " {\"type\": \"fabric\", \"description\": \"made of thin threads; could be used for fishing or decoration\", \"similar objects\": [\"mesh\", \"lace\", \"tulle\"]}", + 34 + ], + "antennas": [ + " {\"type\": \"electronic device\", \"description\": \"long, thin, could be used to receive signals\", \"similar objects\": [\"transmitter\", \"receiver\", \"satellite dish\"]}", + 34 + ], + "dessert plate": [ + " {\"type\": \"dining ware\", \"description\": \"round; usually smaller than dinner plate; could be decorated\", \"similar objects\": [\"dinner plate\", \"soup bowl\", \"teacup\"]}", + 34 + ], + "computer screens": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be touch-sensitive; could have a monitor\", \"similar objects\": [\"television\", \"tablet\", \"smartphone\"]}", + 34 + ], + "plastic shopping bag": [ + "\n{\"type\": \"container\", \"description\": \"transparent; could be reused; could be folded\", \"similar objects\": [\"paper bag\", \"tote bag\", \"reusable bag\"]}", + 34 + ], + "contrail": [ + " {\"type\": \"atmospheric phenomenon\", \"description\": \"long, white, streaky clouds; formed by aircraft exhaust\", \"similar objects\": [\"cirrus clouds\", \"noctilucent clouds\", \"lenticular clouds\"]}", + 34 + ], + "lone": [ + " {\"type\": \"word\", \"description\": \"adjective; means being alone or isolated\", \"similar objects\": [\"solitary\", \"solo\", \"lonesome\"]}", + 34 + ], + "pink kite": [ + "\n{\"type\": \"toy\", \"description\": \"pink; has a tail; could be flown in the sky\", \"similar objects\": [\"balloon\", \"frisbee\", \"parachute\"]}", + 34 + ], + "metal handrail": [ + " {\"type\": \"building tool\", \"description\": \"long, metallic, could be curved; could be used for support\", \"similar objects\": [\"staircase\", \"balustrade\", \"railing\"]}", + 34 + ], + "paper sign": [ + " {\"type\": \"stationery\", \"description\": \"flat; could be printed with words or images; could be made of paper or plastic\", \"similar objects\": [\"poster\", \"card\", \"banner\"]}", + 34 + ], + "noses": [ + " {\"type\": \"body part\", \"description\": \"two nostrils; could be long or short; could be wide or narrow\", \"similar objects\": [\"mouth\", \"ears\", \"eyes\"]}", + 34 + ], + "peanuts": [ + " {\"type\": \"food\", \"description\": \"small, round, brown; could be roasted or boiled; could be eaten as a snack\", \"similar objects\": [\"cashews\", \"almonds\", \"pistachios\"]}", + 34 + ], + "vanilla": [ + " {\"type\": \"flavor\", \"description\": \"sweet, creamy, and fragrant; used in baking and desserts\", \"similar objects\": [\"chocolate\", \"strawberry\", \"caramel\"]}", + 34 + ], + "orange bottle": [ + "\n{\"type\": \"container\", \"description\": \"round; orange in color; could have a lid\", \"similar objects\": [\"jar\", \"jug\", \"can\"]}", + 34 + ], + "water surface": [ + " {\"type\": \"environment\", \"description\": \"smooth; could be reflecting the sky; could be reflecting the objects on it\", \"similar objects\": [\"lake\", \"ocean\", \"river\"]}", + 34 + ], + "pink ribbon": [ + " {\"type\": \"decoration\", \"description\": \"long, thin, pink; could be used for gift wrapping\", \"similar objects\": [\"bow\", \"string\", \"ribbon\"]}", + 34 + ], + "swimmer": [ + " {\"type\": \"person\", \"description\": \"wearing a swimsuit; could be wearing a swimming cap; could be swimming in a pool or in the ocean\", \"similar objects\": [\"surfer\", \"diver\", \"sailor\"]}", + 34 + ], + "crease": [ + " {\"type\": \"fold\", \"description\": \"a line or mark made by folding or pressing something; could be a wrinkle\", \"similar objects\": [\"wrinkle\", \"pleat\", \"dent\"]}", + 34 + ], + "soaps": [ + " {\"type\": \"cleaning product\", \"description\": \"could be liquid or solid; could be used for washing hands or dishes\", \"similar objects\": [\"detergent\", \"shampoo\", \"toilet cleaner\"]}", + 34 + ], + "mascot": [ + " {\"type\": \"symbol\", \"description\": \"representative figure; could be an animal or a person; could be used to represent a team or organization\", \"similar objects\": [\"logo\", \"emblem\", \"banner\"]}", + 34 + ], + "wood panel": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular; could be made of wood, plastic, or metal; could be used for walls, floors, or ceilings\", \"similar objects\": [\"plywood\", \"drywall\", \"tile\"]}", + 34 + ], + "silver phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could be made of metal; could have a touchscreen\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 34 + ], + "wooden door": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be painted; could have a handle\", \"similar objects\": [\"window\", \"wall\", \"gate\"]}", + 34 + ], + "laptop computer": [ + "\n{\"type\": \"electronic device\", \"description\": \"portable computer; has a keyboard and a screen; could be connected to other devices\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 34 + ], + "airport runway": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, flat, paved surface; could have markings and lights; could have a control tower\", \"similar objects\": [\"highway\", \"railway\", \"seaport\"]}", + 34 + ], + "hummingbird": [ + " {\"type\": \"bird\", \"description\": \"small; has colorful feathers; could hover in the air; could make humming sound\", \"similar objects\": [\"sparrow\", \"finch\", \"pigeon\"]}", + 34 + ], + "identification tag": [ + " {\"type\": \"accessory\", \"description\": \"could be made of plastic or metal; could have a string or clip; could have a name or logo printed on it\", \"similar objects\": [\"name tag\", \"badge\", \"keychain\"]}", + 34 + ], + "soccer jersey": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; has a team logo; could be made of polyester\", \"similar objects\": [\"baseball jersey\", \"basketball jersey\", \"hockey jersey\"]}", + 34 + ], + "skate shoes": [ + " {\"type\": \"footwear\", \"description\": \"low-top; has a flat sole; could have a reinforced toe cap; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 34 + ], + "street sign pole": [ + " {\"type\": \"road infrastructure\", \"description\": \"tall, metal pole; could have a rectangular sign on top\", \"similar objects\": [\"traffic light\", \"guard rail\", \"street light\"]}", + 34 + ], + "toilet cover": [ + " {\"type\": \"bathroom accessory\", \"description\": \"round; could be made of plastic or ceramic; could be white or other colors\", \"similar objects\": [\"toilet seat\", \"toilet brush\", \"toilet paper holder\"]}", + 34 + ], + "orange safety cones": [ + "\n{\"type\": \"safety tool\", \"description\": \"orange; cone-shaped; could be reflective\", \"similar objects\": [\"traffic signs\", \"barricades\", \"warning tape\"]}", + 34 + ], + "brown edge": [ + " {\"type\": \"edge\", \"description\": \"dark brown; could be straight or curved; could be made of wood or metal\", \"similar objects\": [\"border\", \"frame\", \"rim\"]}", + 34 + ], + "male skier": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing ski gear; skiing on snow; could be carrying ski poles\", \"similar objects\": [\"female skier\", \"snowboarder\", \"ice skater\"]}", + 34 + ], + "paint brush": [ + " {\"type\": \"painting tool\", \"description\": \"long handle; bristles at the end; could be made of different materials\", \"similar objects\": [\"paint roller\", \"paint scraper\", \"paint sponge\"]}", + 34 + ], + "statute": [ + " {\"type\": \"sculpture\", \"description\": \"could be made of stone, metal, or wood; could be of a person, animal, or object; could be of any size\", \"similar objects\": [\"monument\", \"fountain\", \"bas-relief\"]}", + 34 + ], + "pieces clothing": [ + " {\"type\": \"clothing\", \"description\": \"various shapes and sizes; could be made of different materials; could be for different occasions\", \"similar objects\": [\"shirt\", \"pants\", \"dress\"]}", + 34 + ], + "wood posts": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used for fencing; could be made of different materials\", \"similar objects\": [\"concrete posts\", \"metal posts\", \"plastic posts\"]}", + 34 + ], + "washing machine": [ + " {\"type\": \"appliance\", \"description\": \"large, rectangular; has a door; could be automatic or manual\", \"similar objects\": [\"dryer\", \"dishwasher\", \"refrigerator\"]}", + 34 + ], + "blue coat": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of wool; could have buttons; could have pockets\", \"similar objects\": [\"jacket\", \"blazer\", \"cardigan\"]}", + 34 + ], + "shoelace": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of fabric or plastic; used to tie shoes\", \"similar objects\": [\"belt\", \"tie\", \"scarf\"]}", + 34 + ], + "archways": [ + " {\"type\": \"architectural structure\", \"description\": \"curved; could be made of stone; could have pillars\", \"similar objects\": [\"doorways\", \"windows\", \"bridges\"]}", + 34 + ], + "tile roof": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be arranged in a pattern\", \"similar objects\": [\"shingles\", \"metal roof\", \"asphalt roof\"]}", + 34 + ], + "pink pants": [ + " {\"type\": \"clothing\", \"description\": \"pink; could be made of cotton; could have pockets; could have a zipper\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 34 + ], + "tan carpet": [ + " {\"type\": \"floor covering\", \"description\": \"light brown; could be made of wool; could be woven\", \"similar objects\": [\"rug\", \"mat\", \"linoleum\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean).", + 34 + ], + "dirty toilet": [ + " {\"type\": \"bathroom fixture\", \"description\": \"white; has a bowl; could have stains; could have a lid\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 34 + ], + "shell": [ + " {\"type\": \"object\", \"description\": \"hard, curved; could be found on the beach; could be used as a decoration\", \"similar objects\": [\"pebble\", \"rock\", \"driftwood\"]}", + 34 + ], + "baseball base": [ + " {\"type\": \"sports equipment\", \"description\": \"white; has three bases; could be made of rubber\", \"similar objects\": [\"bat\", \"glove\", \"ball\"]}", + 34 + ], + "sky scraper": [ + " {\"type\": \"building\", \"description\": \"tall; could be made of steel and glass; could have multiple floors\", \"similar objects\": [\"high-rise building\", \"office building\", \"apartment building\"]}", + 34 + ], + "bedside lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"small; could be placed on the bedside table; could be used for reading\", \"similar objects\": [\"table lamp\", \"floor lamp\", \"ceiling lamp\"]}", + 34 + ], + "metal clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"made of metal; could have a pendulum; could have a face with hands\", \"similar objects\": [\"watch\", \"alarm clock\", \"grandfather clock\"]}", + 34 + ], + "mercedes": [ + " {\"type\": \"vehicle\", \"description\": \"luxury car; has a three-pointed star logo; could be a sedan or an SUV\", \"similar objects\": [\"BMW\", \"Audi\", \"Lexus\"]}", + 34 + ], + "floor rug": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; could be made of wool; could have colorful patterns\", \"similar objects\": [\"carpet\", \"mat\", \"throw rug\"]}", + 34 + ], + "kites air": [ + " {\"type\": \"toy\", \"description\": \"could be made of paper or plastic; has a long string; could be flown in the air\", \"similar objects\": [\"balloons\", \"planes\", \"drones\"]}", + 34 + ], + "mountain peak": [ + " {\"type\": \"landscape\", \"description\": \"tall, pointed, could have snow on top\", \"similar objects\": [\"hill\", \"cliff\", \"valley\"]}", + 34 + ], + "unlit": [ + "\n{\"type\": \"adjective\", \"description\": \"not lit; not illuminated; dark\", \"similar objects\": [\"dim\", \"unilluminated\", \"unenlightened\"]}", + 34 + ], + "grass tennis court": [ + "\n{\"type\": \"sports court\", \"description\": \"flat, green, made of grass; has a net in the middle; could be used for tennis\", \"similar objects\": [\"badminton court\", \"volleyball court\", \"basketball court\"]}", + 34 + ], + "skylights": [ + " {\"type\": \"architectural feature\", \"description\": \"transparent roofing material; could be made of glass or plastic; could be used to bring natural light into a building\", \"similar objects\": [\"windows\", \"doors\", \"roofs\"]}", + 34 + ], + "elephant eye": [ + " {\"type\": \"animal body part\", \"description\": \"large, round, dark; has long eyelashes\", \"similar objects\": [\"giraffe eye\", \"horse eye\", \"dog eye\"]}", + 34 + ], + "pink collar": [ + " {\"type\": \"accessory\", \"description\": \"made of fabric; could be used for pets; could be decorated with rhinestones\", \"similar objects\": [\"leash\", \"harness\", \"bandana\"]}", + 34 + ], + "beach umbrellas": [ + "\n{\"type\": \"outdoor accessory\", \"description\": \"large, colorful, has a pole; could be opened and closed\", \"similar objects\": [\"sunshade\", \"tent\", \"parasol\"]}", + 34 + ], + "horse mane": [ + " {\"type\": \"animal feature\", \"description\": \"long, thick hair on the neck of a horse\", \"similar objects\": [\"horse tail\", \"horse hoof\", \"horse coat\"]}", + 34 + ], + "woven basket": [ + " {\"type\": \"container\", \"description\": \"made of woven materials; could be used for storage\", \"similar objects\": [\"box\", \"bag\", \"trunk\"]}", + 34 + ], + "hub cap": [ + " {\"type\": \"automotive part\", \"description\": \"round; covers the wheel of a car; could be made of metal or plastic\", \"similar objects\": [\"wheel cover\", \"wheel trim\", \"wheel hub\"]}", + 34 + ], + "ferns": [ + " {\"type\": \"plant\", \"description\": \"green; could have fronds; could be found in moist areas\", \"similar objects\": [\"moss\", \"mushroom\", \"lichen\"]}", + 34 + ], + "ocean waters": [ + " {\"type\": \"natural environment\", \"description\": \"blue; could be deep; could have waves; could have sea creatures\", \"similar objects\": [\"lake\", \"river\", \"pond\"]}", + 34 + ], + "clocktower": [ + " {\"type\": \"architecture\", \"description\": \"tall; has a clock face; could have bells\", \"similar objects\": [\"cathedral\", \"obelisk\", \"monument\"]}", + 34 + ], + "space shuttle": [ + " {\"type\": \"spacecraft\", \"description\": \"aerodynamic shape; has wings and a tail; could be used to transport astronauts to space\", \"similar objects\": [\"rocket\", \"satellite\", \"space station\"]}", + 34 + ], + "onion ring": [ + " {\"type\": \"food\", \"description\": \"round; made of onion slices; could be deep-fried\", \"similar objects\": [\"french fries\", \"potato chips\", \"onion slices\"]}", + 34 + ], + "foundation": [ + " {\"type\": \"cosmetic product\", \"description\": \"creamy; could be used to even out skin tone; could be applied with a brush or sponge\", \"similar objects\": [\"concealer\", \"powder\", \"blush\"]}", + 34 + ], + "hiker": [ + " {\"type\": \"person\", \"description\": \"wearing a backpack; carrying a walking stick; wearing a hat; wearing hiking boots\", \"similar objects\": [\"mountaineer\", \"backpacker\", \"trekker\"]}", + 34 + ], + "chocolate cupcake": [ + "\n{\"type\": \"dessert\", \"description\": \"round; has a chocolate flavor; could be topped with frosting\", \"similar objects\": [\"brownie\", \"muffin\", \"cake\"]}", + 34 + ], + "tuxedo": [ + " {\"type\": \"clothing\", \"description\": \"black; has a bow tie; could have a tailcoat\", \"similar objects\": [\"suit\", \"dress\", \"blazer\"]}", + 34 + ], + "elephants eye": [ + " {\"type\": \"animal body part\", \"description\": \"large, round, dark; could be surrounded by wrinkles\", \"similar objects\": [\"giraffe's neck\", \"hippo's mouth\", \"rhino's horn\"]}", + 34 + ], + "puffy cloud": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, fluffy, could be shaped like animals\", \"similar objects\": [\"cumulus cloud\", \"stratus cloud\", \"cirrus cloud\"]}", + 34 + ], + "floret": [ + " {\"type\": \"vegetable part\", \"description\": \"small, round, part of a vegetable; could be part of a cauliflower or broccoli\", \"similar objects\": [\"broccoli floret\", \"cauliflower floret\", \"brussels sprout\"]}", + 33 + ], + "linoleum floor": [ + " {\"type\": \"flooring material\", \"description\": \"smooth, glossy, and durable; could be made of vinyl or cork; could be patterned or plain\", \"similar objects\": [\"tile floor\", \"wood floor\", \"carpet\"]}", + 33 + ], + "church building": [ + " {\"type\": \"structure\", \"description\": \"tall; has a steeple; could have stained glass windows; could have a bell tower\", \"similar objects\": [\"mosque\", \"temple\", \"synagogue\"]}", + 33 + ], + "silver knobs": [ + " {\"type\": \"hardware\", \"description\": \"round; made of metal; could be used to open and close doors\", \"similar objects\": [\"handles\", \"hinges\", \"locks\"]}", + 33 + ], + "circular window": [ + " {\"type\": \"architectural feature\", \"description\": \"round; could be made of glass; could have a frame\", \"similar objects\": [\"door\", \"skylight\", \"bay window\"]}", + 33 + ], + "rusty": [ + "\n{\"type\": \"adjective\", \"description\": \"having a reddish-brown color due to oxidation\", \"similar objects\": [\"corroded\", \"weathered\", \"decayed\"]}", + 33 + ], + "fluffy tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, soft, could be white or grey; could be attached to a cat or a rabbit\", \"similar objects\": [\"whiskers\", \"ears\", \"paws\"]}", + 33 + ], + "baker": [ + " {\"type\": \"occupation\", \"description\": \"person who bakes breads, cakes, and other pastries\", \"similar objects\": [\"chef\", \"cook\", \"pastry chef\"]}", + 33 + ], + "tan dirt": [ + " {\"type\": \"soil\", \"description\": \"light brown; could be dry or wet; could be used for gardening\", \"similar objects\": [\"clay\", \"sand\", \"gravel\"]}", + 33 + ], + "walk way": [ + " {\"type\": \"pathway\", \"description\": \"could be made of concrete, asphalt, or other materials; could have railings; could have lights\", \"similar objects\": [\"sidewalk\", \"trail\", \"path\"]}", + 33 + ], + "silver frame": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; made of silver; could be used to display pictures\", \"similar objects\": [\"gold frame\", \"wooden frame\", \"plastic frame\"]}", + 33 + ], + "glass cups": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be made of glass or plastic; could be used for drinking\", \"similar objects\": [\"mugs\", \"bowls\", \"plates\"]}", + 33 + ], + "camera lens": [ + " {\"type\": \"photography tool\", \"description\": \"cylindrical; could be attached to a camera; could have a zoom function\", \"similar objects\": [\"tripod\", \"filter\", \"flash\"]}", + 33 + ], + "train boarding platform": [ + " {\"type\": \"transportation facility\", \"description\": \"long, flat, has a roof; could have a ticket booth; could have a waiting area\", \"similar objects\": [\"bus station\", \"airport terminal\", \"subway station\"]}", + 33 + ], + "metal bracket": [ + " {\"type\": \"hardware\", \"description\": \"rectangular; could be made of metal; could be used to support shelves\", \"similar objects\": [\"screws\", \"nuts\", \"bolts\"]}", + 33 + ], + "polo": [ + " {\"type\": \"clothing\", \"description\": \"collared shirt; could have short sleeves; could have buttons; could have a logo\", \"similar objects\": [\"t-shirt\", \"button-down shirt\", \"sweater\"]}", + 33 + ], + "blue graffiti": [ + " {\"type\": \"artwork\", \"description\": \"painted on walls; could be in blue color; could be in various shapes and sizes\", \"similar objects\": [\"murals\", \"stencils\", \"posters\"]}", + 33 + ], + "orange tabby cat": [ + "\n{\"type\": \"animal\", \"description\": \"orange fur with black stripes; has a short tail; could have green eyes\", \"similar objects\": [\"calico cat\", \"Siamese cat\", \"Persian cat\"]}", + 33 + ], + "parent": [ + " {\"type\": \"person\", \"description\": \"caregiver of a child; could be a mother or a father\", \"similar objects\": [\"guardian\", \"grandparent\", \"teacher\"]}", + 33 + ], + "cpu": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has ports and slots; could be connected to a monitor\", \"similar objects\": [\"motherboard\", \"graphics card\", \"hard drive\"]}", + 33 + ], + "bird eye": [ + " {\"type\": \"bird\", \"description\": \"small; has a curved beak; could have colorful feathers; could fly\", \"similar objects\": [\"sparrow\", \"pigeon\", \"duck\"]}", + 33 + ], + "smudge": [ + " {\"type\": \"stain\", \"description\": \"dark, smudged mark; could be caused by dirt or oil\", \"similar objects\": [\"blot\", \"streak\", \"smear\"]}", + 33 + ], + "window building": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could have glass panels; could be opened and closed\", \"similar objects\": [\"door\", \"wall\", \"balcony\"]}", + 33 + ], + "slabs": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular, made of stone or concrete\", \"similar objects\": [\"bricks\", \"tiles\", \"pavers\"]}", + 33 + ], + "pink frisbee": [ + "\n{\"type\": \"toy\", \"description\": \"round; pink; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"disc\", \"flying disc\", \"boomerang\"]}", + 33 + ], + "cargo train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has many compartments; could be used to transport goods\", \"similar objects\": [\"freight train\", \"passenger train\", \"tram\"]}", + 33 + ], + "orange towel": [ + "\n{\"type\": \"household item\", \"description\": \"orange; could be made of cotton; could be used for drying\", \"similar objects\": [\"bath towel\", \"hand towel\", \"washcloth\"]}", + 33 + ], + "balck": [ + " {\"type\": \"color\", \"description\": \"dark; could be used to describe objects\", \"similar objects\": [\"white\", \"gray\", \"brown\"]}", + 33 + ], + "houseplant": [ + " {\"type\": \"plant\", \"description\": \"could be potted; could be green; could have leaves; could be flowering\", \"similar objects\": [\"succulent\", \"fern\", \"cactus\"]}", + 33 + ], + "track pad": [ + " {\"type\": \"computer accessory\", \"description\": \"flat, rectangular; used to control the cursor on a computer screen\", \"similar objects\": [\"mouse\", \"keyboard\", \"stylus\"]}", + 33 + ], + "webcam": [ + " {\"type\": \"electronic device\", \"description\": \"small camera; could be connected to a computer; could be used for video conferencing\", \"similar objects\": [\"microphone\", \"speaker\", \"web server\"]}", + 33 + ], + "hoof horse": [ + " {\"type\": \"animal body part\", \"description\": \"hard, curved, and pointed; found on the feet of horses and other hoofed animals\", \"similar objects\": [\"cloven hoof\", \"hoof print\", \"hoof pick\"]}", + 33 + ], + "vein": [ + " {\"type\": \"anatomical structure\", \"description\": \"blood vessels that carry blood from the body to the heart\", \"similar objects\": [\"artery\", \"capillary\", \"lymphatic vessel\"]}", + 33 + ], + "silver towel rack": [ + "\n{\"type\": \"furniture\", \"description\": \"made of silver; could have multiple bars for hanging towels; could be wall-mounted\", \"similar objects\": [\"towel bar\", \"towel holder\", \"towel rail\"]}", + 33 + ], + "clock top building": [ + " {\"type\": \"architecture\", \"description\": \"tall, cylindrical building; has a clock on top; could have a spire\", \"similar objects\": [\"tower\", \"cathedral\", \"observatory\"]}", + 33 + ], + "froth": [ + " {\"type\": \"foam\", \"description\": \"light and airy; could be created by shaking or stirring liquids\", \"similar objects\": [\"foam\", \"bubbles\", \"lather\"]}", + 33 + ], + "capris": [ + " {\"type\": \"clothing\", \"description\": \"ankle-length trousers; could be made of cotton or denim; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"leggings\"]}", + 33 + ], + "rocky hill": [ + " {\"type\": \"landscape\", \"description\": \"uneven terrain; could have rocks and stones; could have plants and trees\", \"similar objects\": [\"mountain\", \"cliff\", \"valley\"]}", + 33 + ], + "wooden ramp": [ + " {\"type\": \"structure\", \"description\": \"sloped; made of wood; could be used to bridge two levels\", \"similar objects\": [\"stairs\", \"ladder\", \"bridge\"]}", + 33 + ], + "water pitcher": [ + " {\"type\": \"utensil\", \"description\": \"tall, cylindrical; could have a handle; could have a spout\", \"similar objects\": [\"teapot\", \"jug\", \"vase\"]}", + 33 + ], + "orange boat": [ + "\n{\"type\": \"watercraft\", \"description\": \"orange; could be made of plastic; could have a motor; could have a sail\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 33 + ], + "tall window": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular; could be made of glass; could be opened\", \"similar objects\": [\"door\", \"balcony\", \"skylight\"]}", + 33 + ], + "spinach pizza": [ + "\n{\"type\": \"food\", \"description\": \"pizza with spinach topping; could have cheese and other toppings\", \"similar objects\": [\"mushroom pizza\", \"pepperoni pizza\", \"vegetable pizza\"]}", + 33 + ], + "teddy bears": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; usually has a round shape; could be of different colors\", \"similar objects\": [\"dolls\", \"plush toys\", \"action figures\"]}", + 33 + ], + "grass lawn": [ + " {\"type\": \"landscape\", \"description\": \"green; could be mowed; could be fertilized\", \"similar objects\": [\"flower bed\", \"hedge\", \"garden\"]}", + 33 + ], + "plaid shorts": [ + " {\"type\": \"clothing\", \"description\": \"patterned shorts; could be made of cotton; could have pockets\", \"similar objects\": [\"plaid shirt\", \"denim shorts\", \"khaki pants\"]}", + 33 + ], + "round lights": [ + "\n{\"type\": \"lighting tool\", \"description\": \"could be made of glass, plastic, or metal; could be used for decoration or illumination; could be powered by electricity or battery\", \"similar objects\": [\"lamp\", \"lantern\", \"chandelier\"]}", + 33 + ], + "bicycle helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard shell; adjustable straps; could be brightly colored\", \"similar objects\": [\"skateboard helmet\", \"ski helmet\", \"motorcycle helmet\"]}", + 33 + ], + "cut grass": [ + " {\"type\": \"landscape material\", \"description\": \"green; could be cut into small pieces; could be used for decoration\", \"similar objects\": [\"mulch\", \"soil\", \"gravel\"]}", + 33 + ], + "manufacturer": [ + " {\"type\": \"business\", \"description\": \"produces goods or services; could be a factory\", \"similar objects\": [\"supplier\", \"distributor\", \"wholesaler\"]}", + 33 + ], + "mall": [ + " {\"type\": \"building\", \"description\": \"large, multiple stories; could have shops, restaurants, and entertainment venues\", \"similar objects\": [\"shopping center\", \"department store\", \"market\"]}", + 33 + ], + "power cables": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, insulated wires; could be used to transfer electricity\", \"similar objects\": [\"extension cords\", \"power strips\", \"surge protectors\"]}", + 33 + ], + "slipper": [ + " {\"type\": \"footwear\", \"description\": \"soft; could be made of cloth; could be slip-on\", \"similar objects\": [\"sandal\", \"flip-flop\", \"mule\"]}", + 33 + ], + "plastic cover": [ + " {\"type\": \"protective tool\", \"description\": \"transparent; could be used to cover objects\", \"similar objects\": [\"tarp\", \"bag\", \"wrap\"]}", + 33 + ], + "hairbrush": [ + " {\"type\": \"grooming tool\", \"description\": \"long handle; has bristles; could be made of plastic or wood\", \"similar objects\": [\"comb\", \"scissors\", \"razor\"]}", + 33 + ], + "eye brow": [ + " {\"type\": \"body part\", \"description\": \"two curved lines above the eyes; could be shaped with a pencil\", \"similar objects\": [\"eyelashes\", \"eyelids\", \"eyeballs\"]}", + 33 + ], + "grey chain link fence": [ + "\n{\"type\": \"barrier\", \"description\": \"made of metal; has a grey color; has a chain link pattern\", \"similar objects\": [\"barbed wire fence\", \"wooden fence\", \"brick wall\"]}", + 33 + ], + "kitchen faucet": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be attached to a sink; could be made of metal or plastic\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"toilet flush\"]}", + 33 + ], + "wet ground": [ + " {\"type\": \"environment\", \"description\": \"ground that is covered with water; could be slippery\", \"similar objects\": [\"flooded area\", \"rainy ground\", \"muddy ground\"]}", + 33 + ], + "deck bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has two levels; could be used for public transportation\", \"similar objects\": [\"tram\", \"trolleybus\", \"metro\"]}", + 33 + ], + "chicken breast": [ + " {\"type\": \"food\", \"description\": \"white, boneless, skinless; could be cooked in various ways\", \"similar objects\": [\"turkey breast\", \"pork chop\", \"salmon fillet\"]}", + 33 + ], + "airplane propeller": [ + " {\"type\": \"aircraft part\", \"description\": \"cylindrical; has blades; could be made of metal\", \"similar objects\": [\"engine\", \"wing\", \"fuselage\"]}", + 33 + ], + "safety jacket": [ + " {\"type\": \"protective clothing\", \"description\": \"brightly colored; could be made of reflective material; could be worn over other clothing\", \"similar objects\": [\"helmet\", \"gloves\", \"boots\"]}", + 33 + ], + "dark windows": [ + " {\"type\": \"building feature\", \"description\": \"windows that are tinted or covered to block out light\", \"similar objects\": [\"shades\", \"curtains\", \"blinds\"]}", + 33 + ], + "skate": [ + " {\"type\": \"sports equipment\", \"description\": \"has four wheels; could be used for skating\", \"similar objects\": [\"rollerblade\", \"scooter\", \"skateboard\"]}", + 33 + ], + "denim pants": [ + " {\"type\": \"clothing\", \"description\": \"blue; made of cotton; could have pockets; could have a zipper\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 33 + ], + "glass wall": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be made of glass or plastic; could be used as a partition\", \"similar objects\": [\"window\", \"door\", \"curtain\"]}", + 33 + ], + "tan tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"ceramic tile\", \"linoleum tile\", \"wood tile\"]}", + 33 + ], + "purple bag": [ + "\n{\"type\": \"accessory\", \"description\": \"purple; could be made of fabric; could have straps\", \"similar objects\": [\"purse\", \"backpack\", \"tote bag\"]}", + 33 + ], + "pillow case": [ + " {\"type\": \"bedding accessory\", \"description\": \"rectangular; could be made of cotton; could be decorated with patterns\", \"similar objects\": [\"sheet\", \"blanket\", \"duvet cover\"]}", + 33 + ], + "drainer": [ + " {\"type\": \"kitchen tool\", \"description\": \"has a bowl-like shape; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"strainer\", \"colander\", \"sieve\"]}", + 33 + ], + "juice box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could contain juice\", \"similar objects\": [\"bottle\", \"can\", \"carton\"]}", + 33 + ], + "receptacle": [ + " {\"type\": \"container\", \"description\": \"could be made of plastic or metal; could be used to store items\", \"similar objects\": [\"bin\", \"box\", \"bag\"]}", + 33 + ], + "brunette": [ + " {\"type\": \"hair color\", \"description\": \"dark brown; could be lighter or darker\", \"similar objects\": [\"blonde\", \"auburn\", \"black\"]}", + 33 + ], + "cabinet brown": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have drawers and doors; could be used for storage\", \"similar objects\": [\"dresser\", \"bookshelf\", \"armoire\"]}", + 33 + ], + "engine car": [ + " {\"type\": \"vehicle\", \"description\": \"has an engine; could be powered by gasoline or diesel; could be used for transportation\", \"similar objects\": [\"truck\", \"motorcycle\", \"bus\"]}", + 33 + ], + "tortillas": [ + " {\"type\": \"food\", \"description\": \"flat, round, thin; could be made of wheat or corn flour\", \"similar objects\": [\"taco shells\", \"tostadas\", \"burrito wraps\"]}", + 33 + ], + "ferris wheel": [ + " {\"type\": \"amusement ride\", \"description\": \"large wheel with gondolas; could be lit up at night\", \"similar objects\": [\"roller coaster\", \"carousel\", \"swing ride\"]}", + 33 + ], + "train signal": [ + " {\"type\": \"signaling tool\", \"description\": \"red and green lights; could be used to indicate the direction of the train\", \"similar objects\": [\"traffic light\", \"stop sign\", \"warning sign\"]}", + 33 + ], + "tree leaf": [ + " {\"type\": \"plant part\", \"description\": \"green; could be oval or round; could have veins; could be attached to a stem\", \"similar objects\": [\"flower petal\", \"pine needle\", \"moss\"]}", + 33 + ], + "sweat pants": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; usually made of cotton; could have pockets\", \"similar objects\": [\"joggers\", \"track pants\", \"leggings\"]}", + 33 + ], + "cheeks": [ + " {\"type\": \"body part\", \"description\": \"round; located on the face; could be blushed\", \"similar objects\": [\"forehead\", \"chin\", \"nose\"]}", + 33 + ], + "sparse": [ + " {\"type\": \"adjective\", \"description\": \"having few parts or elements; not dense\", \"similar objects\": [\"scant\", \"meager\", \"scarce\"]}", + 33 + ], + "oak": [ + " {\"type\": \"tree\", \"description\": \"large, strong, has acorns; could have leaves with lobes\", \"similar objects\": [\"maple\", \"elm\", \"birch\"]}", + 33 + ], + "blinders": [ + " {\"type\": \"horse accessory\", \"description\": \"leather straps that attach to a horse's bridle to limit its vision\", \"similar objects\": [\"halter\", \"bit\", \"saddle\"]}", + 32 + ], + "serving": [ + " {\"type\": \"utensil\", \"description\": \"long handle; could be made of metal or plastic; could be used to serve food\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 32 + ], + "direction": [ + "\n{\"type\": \"concept\", \"description\": \"a way of guiding someone to a certain place; could be indicated by arrows, signs, or words\", \"similar objects\": [\"instruction\", \"guidance\", \"map\"]}", + 32 + ], + "street lamp post": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a lightbulb on top\", \"similar objects\": [\"lamp post\", \"street light\", \"traffic light\"]}", + 32 + ], + "styrofoam container": [ + " {\"type\": \"container\", \"description\": \"lightweight; could be white; could be used to store food\", \"similar objects\": [\"plastic container\", \"paper box\", \"glass jar\"]}", + 32 + ], + "frying pan": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round, has a handle\", \"similar objects\": [\"pan\", \"pot\", \"wok\"]}", + 32 + ], + "gray table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have four legs\", \"similar objects\": [\"chair\", \"desk\", \"sofa\"]}", + 32 + ], + "stainless steel stove": [ + "\n{\"type\": \"cooking tool\", \"description\": \"made of stainless steel; has a flat surface; could have knobs and burners\", \"similar objects\": [\"oven\", \"microwave\", \"grill\"]}", + 32 + ], + "spotlight": [ + " {\"type\": \"lighting tool\", \"description\": \"focused beam of light; could be used for stage lighting\", \"similar objects\": [\"flashlight\", \"lantern\", \"torch\"]}", + 32 + ], + "ice cubes": [ + " {\"type\": \"food item\", \"description\": \"small, solid, cold; could be used to cool drinks\", \"similar objects\": [\"ice cream\", \"frozen yogurt\", \"sorbet\"]}", + 32 + ], + "story window": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular; could be decorated with stained glass; could be used to let in light\", \"similar objects\": [\"bay window\", \"arched window\", \"skylight\"]}", + 32 + ], + "silver cup": [ + " {\"type\": \"utensil\", \"description\": \"round; made of silver; could have a handle\", \"similar objects\": [\"mug\", \"glass\", \"bowl\"]}", + 32 + ], + "wood slats": [ + " {\"type\": \"building material\", \"description\": \"long, thin pieces of wood; could be used for flooring, fencing, or other construction projects\", \"similar objects\": [\"plywood\", \"lumber\", \"timber\"]}", + 32 + ], + "bumps": [ + " {\"type\": \"surface feature\", \"description\": \"raised, irregular protrusions on a surface; could be caused by wear and tear or an accident\", \"similar objects\": [\"dents\", \"scratches\", \"cracks\"]}", + 32 + ], + "outfielder": [ + " {\"type\": \"baseball position\", \"description\": \"plays in the outfield; responsible for catching fly balls and preventing runners from advancing\", \"similar objects\": [\"pitcher\", \"catcher\", \"infielder\"]}", + 32 + ], + "commuter bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple doors; could have a luggage compartment\", \"similar objects\": [\"school bus\", \"city bus\", \"coach bus\"]}", + 32 + ], + "cooking": [ + "\n{\"type\": \"activity\", \"description\": \"the process of preparing food for consumption\", \"similar objects\": [\"baking\", \"grilling\", \"boiling\"]}", + 32 + ], + "brochure": [ + " {\"type\": \"printed material\", \"description\": \"folded paper; could be used for advertising\", \"similar objects\": [\"flyer\", \"pamphlet\", \"catalog\"]}", + 32 + ], + "orange ball": [ + " {\"type\": \"toy\", \"description\": \"round; orange in color; could be made of rubber or plastic\", \"similar objects\": [\"soccer ball\", \"basketball\", \"tennis ball\"]}", + 32 + ], + "iron rod": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical, made of metal; could be used for construction\", \"similar objects\": [\"hammer\", \"screwdriver\", \"pliers\"]}", + 32 + ], + "honey": [ + " {\"type\": \"food\", \"description\": \"sweet; could be in liquid or solid form; could be used as a sweetener\", \"similar objects\": [\"sugar\", \"syrup\", \"molasses\"]}", + 32 + ], + "sedan car": [ + " {\"type\": \"vehicle\", \"description\": \"four-door; has a trunk; could be a hatchback\", \"similar objects\": [\"SUV\", \"coupe\", \"convertible\"]}", + 32 + ], + "stereo": [ + " {\"type\": \"electronic device\", \"description\": \"could have two speakers; could have a CD player; could have a radio\", \"similar objects\": [\"television\", \"boombox\", \"headphones\"]}", + 32 + ], + "advertising banner": [ + "\n{\"type\": \"promotional tool\", \"description\": \"rectangular; could be made of cloth or paper; could be hung on walls or poles\", \"similar objects\": [\"billboard\", \"poster\", \"signboard\"]}", + 32 + ], + "cobblestone street": [ + " {\"type\": \"road surface\", \"description\": \"made of small, rounded stones; could be uneven; could be used for decoration\", \"similar objects\": [\"gravel road\", \"asphalt road\", \"brick road\"]}", + 32 + ], + "tricycle": [ + " {\"type\": \"vehicle\", \"description\": \"three wheels; could have a basket in the back; could have a bell\", \"similar objects\": [\"bicycle\", \"scooter\", \"skateboard\"]}", + 32 + ], + "pilots": [ + " {\"type\": \"profession\", \"description\": \"trained to fly aircrafts; could be military or civilian\", \"similar objects\": [\"air traffic controller\", \"mechanic\", \"airline steward\"]}", + 32 + ], + "orange line": [ + " {\"type\": \"marking\", \"description\": \"a line painted in orange color; could be used to mark a boundary or a warning\", \"similar objects\": [\"yellow line\", \"red line\", \"white line\"]}", + 32 + ], + "gold necklace": [ + "\n{\"type\": \"jewelry\", \"description\": \"chain with a pendant; could be made of gold, silver, or other metals; could be decorated with gems\", \"similar objects\": [\"bracelet\", \"ring\", \"earrings\"]}", + 32 + ], + "pink lips": [ + " {\"type\": \"body part\", \"description\": \"smooth, pink, could be glossy; could be shaped like a bow\", \"similar objects\": [\"eyebrows\", \"nose\", \"cheeks\"]}", + 32 + ], + "blue plate": [ + "\n{\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could be decorated with patterns; could be used for serving food\", \"similar objects\": [\"bowl\", \"cup\", \"mug\"]}", + 32 + ], + "chair cushion": [ + " {\"type\": \"furniture accessory\", \"description\": \"soft; could be filled with foam; could be covered with fabric\", \"similar objects\": [\"pillow\", \"mattress\", \"cushion cover\"]}", + 32 + ], + "stop lights": [ + " {\"type\": \"traffic signal\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"traffic signs\", \"road signs\", \"crosswalk signs\"]}", + 32 + ], + "gold clock": [ + "\n{\"type\": \"decorative item\", \"description\": \"round; made of gold; has hands and numbers\", \"similar objects\": [\"silver clock\", \"antique clock\", \"grandfather clock\"]}", + 32 + ], + "bust": [ + " {\"type\": \"sculpture\", \"description\": \"a sculpture of a person's head and shoulders; could be made of stone, metal, or other materials\", \"similar objects\": [\"statue\", \"monument\", \"fountain\"]}", + 32 + ], + "ocean spray": [ + " {\"type\": \"beverage\", \"description\": \"cranberry juice; could be carbonated; could be sweetened\", \"similar objects\": [\"juice\", \"soda\", \"sports drink\"]}", + 32 + ], + "noodle": [ + " {\"type\": \"food\", \"description\": \"long, thin, could be made of wheat, rice, or other grains; could be cooked in soup or stir-fried\", \"similar objects\": [\"pasta\", \"ramen\", \"udon\"]}", + 32 + ], + "plumbing": [ + " {\"type\": \"construction tool\", \"description\": \"pipes and fixtures used for water supply and drainage\", \"similar objects\": [\"pipe wrench\", \"plunger\", \"pipe cutter\"]}", + 32 + ], + "grey chair": [ + " {\"type\": \"furniture\", \"description\": \"grey; could have armrests; could have a cushion\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 32 + ], + "wireless computer mouse": [ + "\n{\"type\": \"computer accessory\", \"description\": \"small, wireless, has two buttons and a scroll wheel\", \"similar objects\": [\"keyboard\", \"headset\", \"webcam\"]}", + 32 + ], + "green plant": [ + " {\"type\": \"plant\", \"description\": \"green leaves; could have stems; could have flowers\", \"similar objects\": [\"fern\", \"succulent\", \"ivy\"]}", + 32 + ], + "polka": [ + " {\"type\": \"dance\", \"description\": \"a lively dance of Bohemian origin; involves hopping and turning\", \"similar objects\": [\"waltz\", \"tango\", \"foxtrot\"]}", + 32 + ], + "truck door": [ + " {\"type\": \"vehicle part\", \"description\": \"rectangular; could be opened and closed; could be made of metal\", \"similar objects\": [\"car door\", \"van door\", \"SUV door\"]}", + 32 + ], + "paperback book": [ + "\n{\"type\": \"reading material\", \"description\": \"soft cover; could be opened and closed; could be read from left to right\", \"similar objects\": [\"hardcover book\", \"magazine\", \"e-book\"]}", + 32 + ], + "chives": [ + " {\"type\": \"herb\", \"description\": \"long, thin, green; could be chopped into small pieces; has a mild onion flavor\", \"similar objects\": [\"parsley\", \"cilantro\", \"basil\"]}", + 32 + ], + "eyeball": [ + " {\"type\": \"body part\", \"description\": \"round; has a pupil; could be white or blue\", \"similar objects\": [\"iris\", \"eyelid\", \"eyelash\"]}", + 32 + ], + "shadow tennis player": [ + "\n{\"type\": \"sports figure\", \"description\": \"silhouette of a person playing tennis; could be seen on a wall or other surface\", \"similar objects\": [\"shadow boxer\", \"shadow soccer player\", \"shadow basketball player\"]}", + 32 + ], + "doorways": [ + " {\"type\": \"architectural feature\", \"description\": \"rectangular; could have a door; could have a frame\", \"similar objects\": [\"windows\", \"arches\", \"columns\"]}", + 32 + ], + "brief case": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of leather; could have a handle\", \"similar objects\": [\"suitcase\", \"backpack\", \"purse\"]}", + 32 + ], + "headlight front train": [ + "\n{\"type\": \"train part\", \"description\": \"attached to the front of the train; used to light up the track ahead\", \"similar objects\": [\"caboose\", \"engine\", \"coupler\"]}", + 32 + ], + "base board": [ + " {\"type\": \"building material\", \"description\": \"long, thin, flat; could be made of wood or plastic; used to cover the gap between the wall and the floor\", \"similar objects\": [\"molding\", \"trim\", \"crown molding\"]}", + 32 + ], + "silver object": [ + "\n{\"type\": \"metal object\", \"description\": \"shiny, reflective surface; could be jewelry, utensils, or coins\", \"similar objects\": [\"gold\", \"bronze\", \"copper\"]}", + 32 + ], + "detail": [ + " {\"type\": \"concept\", \"description\": \"a small part of something; could be used to describe a task or a process\", \"similar objects\": [\"aspect\", \"element\", \"feature\"]}", + 32 + ], + "metal hinge": [ + " {\"type\": \"hardware\", \"description\": \"used to attach two objects together; could be made of metal; could have two leaves\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 32 + ], + "discoloration": [ + " {\"type\": \"condition\", \"description\": \"change in color; could be caused by a variety of factors\", \"similar objects\": [\"stain\", \"fading\", \"discoloration\"]}", + 32 + ], + "heap": [ + " {\"type\": \"collection\", \"description\": \"a large number of objects piled up together\", \"similar objects\": [\"stack\", \"pile\", \"cluster\"]}", + 32 + ], + "round clock face": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has numbers and hands; could be digital or analog\", \"similar objects\": [\"watch\", \"alarm clock\", \"stopwatch\"]}", + 32 + ], + "line judge": [ + " {\"type\": \"sports official\", \"description\": \"wears a white shirt and shorts; stands on the sidelines of a court; signals when a ball is out of bounds\", \"similar objects\": [\"umpire\", \"referee\", \"scorekeeper\"]}", + 32 + ], + "pink dress": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of silk; could have a bow on the waist; could have a V-neck\", \"similar objects\": [\"skirt\", \"blouse\", \"jumpsuit\"]}", + 32 + ], + "points": [ + " {\"type\": \"measurement unit\", \"description\": \"used to measure length, area, volume, etc.\", \"similar objects\": [\"inches\", \"feet\", \"yards\"]}", + 32 + ], + "cinnamon": [ + " {\"type\": \"spice\", \"description\": \"brown; has a sweet and spicy aroma; could be used as a powder or stick\", \"similar objects\": [\"clove\", \"nutmeg\", \"ginger\"]}", + 32 + ], + "knots": [ + " {\"type\": \"knots\", \"description\": \"interlaced loops of rope or fabric; could be used to tie things together\", \"similar objects\": [\"laces\", \"strings\", \"ropes\"]}", + 32 + ], + "water hole": [ + " {\"type\": \"geographical feature\", \"description\": \"a depression in the ground that is filled with water; could be used as a source of water for animals\", \"similar objects\": [\"lake\", \"river\", \"pond\"]}", + 32 + ], + "leather purse": [ + " {\"type\": \"accessory\", \"description\": \"made of leather; could have a strap; could have a zipper\", \"similar objects\": [\"wallet\", \"backpack\", \"handbag\"]}", + 32 + ], + "armrests": [ + " {\"type\": \"furniture\", \"description\": \"attached to chairs or couches; could be adjustable; could be made of wood or metal\", \"similar objects\": [\"ottoman\", \"footstool\", \"headrest\"]}", + 32 + ], + "spread": [ + " {\"type\": \"condiment\", \"description\": \"smooth; could be made of butter, cream cheese, or peanut butter; could be used on bread, crackers, or toast\", \"similar objects\": [\"jam\", \"jelly\", \"honey\"]}", + 32 + ], + "snowflake": [ + " {\"type\": \"weather phenomenon\", \"description\": \"unique, six-sided, white; could be made of ice crystals\", \"similar objects\": [\"raindrop\", \"hailstone\", \"sleet\"]}", + 32 + ], + "blue water": [ + " {\"type\": \"liquid\", \"description\": \"transparent; could be salty or fresh; could be cold or hot\", \"similar objects\": [\"juice\", \"coffee\", \"tea\"]}", + 32 + ], + "stone structure": [ + " {\"type\": \"architecture\", \"description\": \"made of stones; could be a wall, a bridge, a tower, etc.\", \"similar objects\": [\"wood structure\", \"concrete structure\", \"metal structure\"]}", + 32 + ], + "garbage truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a compactor; could be green or yellow\", \"similar objects\": [\"ambulance\", \"fire truck\", \"tow truck\"]}", + 32 + ], + "chocolate dessert": [ + "\n{\"type\": \"food\", \"description\": \"sweet; could be made of cocoa; could be served with ice cream\", \"similar objects\": [\"cake\", \"pie\", \"tart\"]}", + 32 + ], + "muscles": [ + " {\"type\": \"anatomy\", \"description\": \"tissue that contracts and relaxes to move body parts; could be found in arms, legs, and other parts of the body\", \"similar objects\": [\"tendons\", \"ligaments\", \"bones\"]}", + 32 + ], + "cockpit windows": [ + " {\"type\": \"aircraft part\", \"description\": \"transparent; could be made of glass; could be opened and closed\", \"similar objects\": [\"airplane door\", \"airplane wing\", \"airplane engine\"]}", + 32 + ], + "minute": [ + " {\"type\": \"time unit\", \"description\": \"60 seconds; 1/60 of an hour\", \"similar objects\": [\"second\", \"hour\", \"day\"]}", + 32 + ], + "filter": [ + " {\"type\": \"device\", \"description\": \"used to remove impurities from liquids or gases; could be made of paper or cloth\", \"similar objects\": [\"strainer\", \"sieve\", \"separator\"]}", + 32 + ], + "sideburns": [ + " {\"type\": \"facial hair\", \"description\": \"long, thin hair on the sides of the face\", \"similar objects\": [\"mustache\", \"beard\", \"goatee\"]}", + 32 + ], + "ivory tusks": [ + " {\"type\": \"animal product\", \"description\": \"long, curved, white; from elephants\", \"similar objects\": [\"rhino horns\", \"whale teeth\", \"walrus tusks\"]}", + 32 + ], + "concrete bridge": [ + "\n{\"type\": \"structure\", \"description\": \"made of concrete; could have multiple arches; could have railings\", \"similar objects\": [\"steel bridge\", \"stone bridge\", \"wooden bridge\"]}", + 32 + ], + "glass pane": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be used as a window\", \"similar objects\": [\"window pane\", \"mirror\", \"plexiglass\"]}", + 32 + ], + "rail road tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, parallel metal bars; could have wooden sleepers; could have electric wires\", \"similar objects\": [\"highway\", \"bridge\", \"tunnel\"]}", + 32 + ], + "sill": [ + " {\"type\": \"architectural element\", \"description\": \"horizontal structure; could be made of wood or stone; could be used as a window seat\", \"similar objects\": [\"lintel\", \"lintol\", \"jamb\"]}", + 32 + ], + "wet road": [ + " {\"type\": \"environmental condition\", \"description\": \"road surface is wet; could be slippery; could have puddles\", \"similar objects\": [\"icy road\", \"snowy road\", \"muddy road\"]}", + 32 + ], + "buffalo": [ + " {\"type\": \"animal\", \"description\": \"large, brown, has horns; could have a hump on its back\", \"similar objects\": [\"cow\", \"bison\", \"yak\"]}", + 32 + ], + "plastic bin": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be transparent; could have a lid\", \"similar objects\": [\"box\", \"container\", \"trash can\"]}", + 31 + ], + "number sign": [ + " {\"type\": \"symbol\", \"description\": \"a sign with a hash or pound sign; could be used to denote a number\", \"similar objects\": [\"asterisk\", \"ampersand\", \"dollar sign\"]}", + 31 + ], + "tomatos": [ + " {\"type\": \"vegetable\", \"description\": \"round, red; could have green stems; could be sliced into pieces; could have yellow and orange varieties\", \"similar objects\": [\"potato\", \"bell pepper\", \"eggplant\"]}", + 31 + ], + "ray": [ + " {\"type\": \"fish\", \"description\": \"slender, flat body; could have a long tail; could have a pointed snout; could have a variety of colors\", \"similar objects\": [\"shark\", \"stingray\", \"eel\"]}", + 31 + ], + "silver toaster": [ + "\n{\"type\": \"kitchen appliance\", \"description\": \"silver; has two slots for bread; could have a timer; could have a lever\", \"similar objects\": [\"coffee maker\", \"blender\", \"microwave\"]}", + 31 + ], + "fluffy dog": [ + "\n{\"type\": \"animal\", \"description\": \"soft fur; could have long ears; could have a tail; could be of any color\", \"similar objects\": [\"cat\", \"rabbit\", \"hamster\"]}", + 31 + ], + "beige sofa": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could have armrests; could have cushions; could be made of fabric or leather\", \"similar objects\": [\"armchair\", \"loveseat\", \"ottoman\"]}", + 31 + ], + "baseball team": [ + " {\"type\": \"sports team\", \"description\": \"consists of nine players; has a pitcher, catcher, and other positions; plays on a diamond-shaped field\", \"similar objects\": [\"soccer team\", \"basketball team\", \"hockey team\"]}", + 31 + ], + "marble table": [ + " {\"type\": \"furniture\", \"description\": \"smooth, round, made of marble; could have a glass top\", \"similar objects\": [\"coffee table\", \"dining table\", \"end table\"]}", + 31 + ], + "pop": [ + " {\"type\": \"beverage\", \"description\": \"carbonated; could be flavored; could be served in a can or bottle\", \"similar objects\": [\"soda\", \"juice\", \"beer\"]}", + 31 + ], + "dirty floor": [ + " {\"type\": \"surface\", \"description\": \"could be made of tiles, wood, or carpet; could be covered with dust, dirt, or debris; could be slippery\", \"similar objects\": [\"table\", \"countertop\", \"staircase\"]}", + 31 + ], + "screen monitor": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be connected to a computer; could be touch-sensitive\", \"similar objects\": [\"television\", \"tablet\", \"smartphone\"]}", + 31 + ], + "tin foil": [ + " {\"type\": \"kitchen tool\", \"description\": \"thin, silver, shiny; could be used to wrap food\", \"similar objects\": [\"cling wrap\", \"aluminum foil\", \"baking paper\"]}", + 31 + ], + "silver light": [ + " {\"type\": \"lighting tool\", \"description\": \"metallic; could be used for decoration; could be powered by electricity\", \"similar objects\": [\"chandelier\", \"lamp\", \"lantern\"]}", + 31 + ], + "blackberry": [ + " {\"type\": \"fruit\", \"description\": \"dark purple; small; has a stem\", \"similar objects\": [\"blueberry\", \"strawberry\", \"raspberry\"]}", + 31 + ], + "transportation bus": [ + "\n{\"type\": \"vehicle\", \"description\": \"large, usually yellow; has multiple doors; could have a wheelchair ramp\", \"similar objects\": [\"school bus\", \"trolley bus\", \"minibus\"]}", + 31 + ], + "spacebar": [ + " {\"type\": \"keyboard key\", \"description\": \"long, rectangular; used to insert spaces in text\", \"similar objects\": [\"enter key\", \"shift key\", \"backspace key\"]}", + 31 + ], + "tile backsplash": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; could be made of ceramic, glass, or stone; could be used to cover walls\", \"similar objects\": [\"wallpaper\", \"mosaic\", \"paint\"]}", + 31 + ], + "sponsor": [ + " {\"type\": \"business relationship\", \"description\": \"a company or individual that provides financial or other support to an event, activity, person, or organization\", \"similar objects\": [\"donor\", \"backer\", \"investor\"]}", + 31 + ], + "dining chair": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could have armrests; could have a backrest; could be made of wood or metal\", \"similar objects\": [\"sofa\", \"stool\", \"bench\"]}", + 31 + ], + "lenses": [ + " {\"type\": \"optical tool\", \"description\": \"transparent; could be used to magnify objects; could be used to correct vision\", \"similar objects\": [\"glasses\", \"binoculars\", \"telescope\"]}", + 31 + ], + "oars": [ + " {\"type\": \"rowing tool\", \"description\": \"long, thin, wooden; used to row a boat\", \"similar objects\": [\"paddle\", \"canoe\", \"kayak\"]}", + 31 + ], + "leather glove": [ + " {\"type\": \"clothing item\", \"description\": \"made of leather; could be used to protect hands; could be used for sports\", \"similar objects\": [\"mittens\", \"sleeves\", \"apron\"]}", + 31 + ], + "bed comforter": [ + " {\"type\": \"bedding item\", \"description\": \"quilted; could be filled with down or synthetic material; could be reversible\", \"similar objects\": [\"duvet\", \"blanket\", \"pillow\"]}", + 31 + ], + "cement ground": [ + " {\"type\": \"building material\", \"description\": \"hard, gray, could be used to build walls and floors\", \"similar objects\": [\"concrete\", \"bricks\", \"tiles\"]}", + 31 + ], + "bay window": [ + " {\"type\": \"architectural feature\", \"description\": \"window that projects outward from the exterior wall of a building; could have multiple panels; could be curved or angled\", \"similar objects\": [\"awning window\", \"casement window\", \"picture window\"]}", + 31 + ], + "pinky finger": [ + " {\"type\": \"body part\", \"description\": \"smallest finger; could be used to make a promise\", \"similar objects\": [\"thumb\", \"index finger\", \"ring finger\"]}", + 31 + ], + "swim suit": [ + " {\"type\": \"clothing\", \"description\": \"worn for swimming; could be one-piece or two-piece; could be made of spandex or nylon\", \"similar objects\": [\"bikini\", \"trunks\", \"rash guard\"]}", + 31 + ], + "bathroom light": [ + "\n{\"type\": \"lighting tool\", \"description\": \"could be ceiling-mounted; could be wall-mounted; could be a lamp; could be a fluorescent light\", \"similar objects\": [\"ceiling light\", \"wall light\", \"chandelier\"]}", + 31 + ], + "bale": [ + " {\"type\": \"agricultural tool\", \"description\": \"large, round, made of hay or straw; could be tied with rope\", \"similar objects\": [\"haystack\", \"straw stack\", \"hay bale\"]}", + 31 + ], + "pocketbook": [ + " {\"type\": \"accessory\", \"description\": \"small, rectangular, has a handle; could be made of leather\", \"similar objects\": [\"purse\", \"wallet\", \"clutch\"]}", + 31 + ], + "caution cone": [ + " {\"type\": \"safety tool\", \"description\": \"orange; has a pointed top; could be reflective\", \"similar objects\": [\"barricade\", \"traffic sign\", \"warning light\"]}", + 31 + ], + "glass salt shaker": [ + "\n{\"type\": \"kitchen tool\", \"description\": \"transparent; cylindrical; has a lid; could contain salt\", \"similar objects\": [\"pepper shaker\", \"sugar shaker\", \"spice shaker\"]}", + 31 + ], + "dirty water": [ + " {\"type\": \"liquid\", \"description\": \"cloudy; could contain dirt, debris, and other contaminants\", \"similar objects\": [\"sewage\", \"polluted water\", \"contaminated water\"]}", + 31 + ], + "rice cooker": [ + " {\"type\": \"cooking tool\", \"description\": \"electrical appliance; has a bowl and lid; could have a timer\", \"similar objects\": [\"pressure cooker\", \"slow cooker\", \"microwave\"]}", + 31 + ], + "ring finger": [ + " {\"type\": \"body part\", \"description\": \"third finger from the thumb; could be used to wear a ring\", \"similar objects\": [\"index finger\", \"middle finger\", \"pinky finger\"]}", + 31 + ], + "broth": [ + " {\"type\": \"food\", \"description\": \"liquid; could be made of vegetables, meat, or fish; could be used as a base for soup\", \"similar objects\": [\"stock\", \"sauce\", \"gravy\"]}", + 31 + ], + "shovel": [ + " {\"type\": \"tool\", \"description\": \"long handle; has a flat blade; could be used for digging\", \"similar objects\": [\"rake\", \"hoe\", \"spade\"]}", + 31 + ], + "silver buckle": [ + " {\"type\": \"accessory\", \"description\": \"made of silver; could be used to fasten a belt\", \"similar objects\": [\"belt buckle\", \"shoe buckle\", \"clasp\"]}", + 31 + ], + "clock pole": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could have a clock at the top\", \"similar objects\": [\"flagpole\", \"streetlight pole\", \"telegraph pole\"]}", + 31 + ], + "zebra stripes": [ + " {\"type\": \"pattern\", \"description\": \"black and white stripes; could be found on animals such as zebra, giraffe, and tiger\", \"similar objects\": [\"plaid\", \"polka dots\", \"chevron\"]}", + 31 + ], + "semi": [ + " {\"type\": \"vehicle\", \"description\": \"large truck; has two axles; could be used for long-distance transportation\", \"similar objects\": [\"truck\", \"van\", \"bus\"]}", + 31 + ], + "pocket watch": [ + " {\"type\": \"timekeeping tool\", \"description\": \"small, round, could be attached to a chain; could have a cover\", \"similar objects\": [\"clock\", \"stopwatch\", \"alarm clock\"]}", + 31 + ], + "ice maker": [ + " {\"type\": \"kitchen appliance\", \"description\": \"makes ice cubes; could be built-in or portable; could be manual or automatic\", \"similar objects\": [\"refrigerator\", \"freezer\", \"ice crusher\"]}", + 31 + ], + "packs": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic or paper; could be used to store items\", \"similar objects\": [\"bag\", \"box\", \"basket\"]}", + 31 + ], + "mess": [ + " {\"type\": \"state\", \"description\": \"disorderly; chaotic; untidy\", \"similar objects\": [\"chaos\", \"disarray\", \"disorganization\"]}", + 31 + ], + "grass patch": [ + " {\"type\": \"landscape\", \"description\": \"green; could be in a lawn; could be in a field\", \"similar objects\": [\"flower bed\", \"hedge\", \"bush\"]}", + 31 + ], + "orange train": [ + "\n{\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could be painted orange; could have a locomotive\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 31 + ], + "entry way": [ + " {\"type\": \"architectural feature\", \"description\": \"a passage or doorway that leads into a building or room; could have a door or gate\", \"similar objects\": [\"hallway\", \"staircase\", \"porch\"]}", + 31 + ], + "mini": [ + "\n{\"type\": \"size\", \"description\": \"smaller than the average size\", \"similar objects\": [\"tiny\", \"micro\", \"petite\"]}", + 31 + ], + "temple": [ + " {\"type\": \"building\", \"description\": \"could be made of stone; could have a dome; could have a bell tower\", \"similar objects\": [\"church\", \"mosque\", \"synagogue\"]}", + 31 + ], + "architecture": [ + " {\"type\": \"art form\", \"description\": \"the practice of designing and constructing buildings and other physical structures\", \"similar objects\": [\"sculpture\", \"painting\", \"landscape design\"]}", + 31 + ], + "setting": [ + " {\"type\": \"scene\", \"description\": \"a particular environment or atmosphere; could be a physical or mental state\", \"similar objects\": [\"situation\", \"context\", \"circumstance\"]}", + 31 + ], + "mats": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of fabric, rubber, or plastic; could be used for yoga or exercise\", \"similar objects\": [\"rugs\", \"carpets\", \"towels\"]}", + 31 + ], + "stone statue": [ + " {\"type\": \"sculpture\", \"description\": \"made of stone; could be in the shape of a human or animal; could be used as a decoration\", \"similar objects\": [\"wooden statue\", \"marble statue\", \"bronze statue\"]}", + 31 + ], + "tan bag": [ + " {\"type\": \"accessory\", \"description\": \"tan color; could be made of leather; could have straps\", \"similar objects\": [\"purse\", \"backpack\", \"wallet\"]}", + 31 + ], + "entrance door": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could be made of wood or metal; could have a handle and a lock\", \"similar objects\": [\"window\", \"gate\", \"garage door\"]}", + 31 + ], + "door refrigerator": [ + "\n{\"type\": \"appliance\", \"description\": \"large, rectangular; has a door; could be used to store food\", \"similar objects\": [\"freezer\", \"microwave\", \"dishwasher\"]}", + 31 + ], + "baby boy": [ + " {\"type\": \"human\", \"description\": \"small; could be wearing diapers; could be crying\", \"similar objects\": [\"baby girl\", \"toddler\", \"infant\"]}", + 31 + ], + "silver door knob": [ + "\n{\"type\": \"hardware\", \"description\": \"round; made of silver metal; could have a keyhole\", \"similar objects\": [\"door handle\", \"door latch\", \"door lock\"]}", + 31 + ], + "robot": [ + " {\"type\": \"machine\", \"description\": \"could be humanoid; could be programmed to do certain tasks; could be made of metal\", \"similar objects\": [\"drone\", \"automaton\", \"android\"]}", + 31 + ], + "orange pillow": [ + "\n{\"type\": \"decorative item\", \"description\": \"round; could be made of fabric; could be orange in color\", \"similar objects\": [\"cushion\", \"throw pillow\", \"bolster\"]}", + 31 + ], + "tall light": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a lampshade\", \"similar objects\": [\"floor lamp\", \"table lamp\", \"chandelier\"]}", + 31 + ], + "grey helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"grey; covers the head; could have a visor\", \"similar objects\": [\"hard hat\", \"safety glasses\", \"ear muffs\"]}", + 31 + ], + "rubber tires": [ + " {\"type\": \"automotive part\", \"description\": \"round; made of rubber; used for vehicles\", \"similar objects\": [\"wheels\", \"brakes\", \"shocks\"]}", + 31 + ], + "shadow bench": [ + " {\"type\": \"furniture\", \"description\": \"long, made of metal; could be used for sitting in the shade\", \"similar objects\": [\"garden bench\", \"park bench\", \"picnic table\"]}", + 31 + ], + "break": [ + " {\"type\": \"verb\", \"description\": \"to separate into pieces; to interrupt\", \"similar objects\": [\"crack\", \"shatter\", \"smash\"]}", + 31 + ], + "dish pizza": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread pizza\"]}", + 31 + ], + "chalk board": [ + " {\"type\": \"writing tool\", \"description\": \"black board; could be used to write with chalk\", \"similar objects\": [\"white board\", \"blackboard\", \"marker board\"]}", + 31 + ], + "mitten": [ + " {\"type\": \"clothing item\", \"description\": \"hand-shaped; could be made of wool; could have a string to tie around the wrist\", \"similar objects\": [\"glove\", \"scarf\", \"hat\"]}", + 31 + ], + "water line": [ + " {\"type\": \"utility\", \"description\": \"underground pipe; carries water from a source to a destination\", \"similar objects\": [\"gas line\", \"sewer line\", \"electrical line\"]}", + 31 + ], + "brown fur": [ + " {\"type\": \"material\", \"description\": \"soft; could be used for clothing; could be from animals\", \"similar objects\": [\"wool\", \"leather\", \"cashmere\"]}", + 31 + ], + "carvings": [ + " {\"type\": \"artwork\", \"description\": \"could be made of wood, stone, or metal; could be in the form of sculptures, reliefs, or engravings\", \"similar objects\": [\"paintings\", \"drawings\", \"pottery\"]}", + 31 + ], + "satellite": [ + " {\"type\": \"space object\", \"description\": \"orbiting around the Earth; could be used for communication\", \"similar objects\": [\"space station\", \"rocket\", \"telescope\"]}", + 31 + ], + "raisin": [ + " {\"type\": \"dried fruit\", \"description\": \"small, wrinkled, dark brown; could be sweet or sour\", \"similar objects\": [\"currant\", \"sultana\", \"cranberry\"]}", + 31 + ], + "construction": [ + " {\"type\": \"activity\", \"description\": \"building or repairing structures; could involve heavy machinery\", \"similar objects\": [\"renovation\", \"demolition\", \"excavation\"]}", + 31 + ], + "pointy tip": [ + " {\"type\": \"shape\", \"description\": \"sharp, pointed end\", \"similar objects\": [\"cone\", \"triangle\", \"pyramid\"]}", + 31 + ], + "vulture": [ + " {\"type\": \"bird\", \"description\": \"large; has a bald head; has a hooked beak; has a long wingspan\", \"similar objects\": [\"eagle\", \"hawk\", \"osprey\"]}", + 31 + ], + "toenails": [ + " {\"type\": \"body part\", \"description\": \"hard, curved, and pointed; could be yellowish; could be cut with a nail clipper\", \"similar objects\": [\"fingernails\", \"hair\", \"eyelashes\"]}", + 31 + ], + "kitchen counter top": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, stone, or metal; could have cabinets underneath\", \"similar objects\": [\"table\", \"island\", \"stove\"]}", + 31 + ], + "metal beam": [ + " {\"type\": \"construction material\", \"description\": \"long, rectangular, made of metal; could be used to support a structure\", \"similar objects\": [\"wood beam\", \"steel beam\", \"concrete beam\"]}", + 31 + ], + "water waves": [ + " {\"type\": \"natural phenomenon\", \"description\": \"ripples on the surface of water; could be caused by wind or other objects\", \"similar objects\": [\"tide\", \"tsunami\", \"whirlpool\"]}", + 31 + ], + "sun reflection": [ + " {\"type\": \"optical phenomenon\", \"description\": \"reflection of sunlight on a surface; could be seen on water, glass, or other reflective surfaces\", \"similar objects\": [\"rainbow\", \"glare\", \"mirage\"]}", + 31 + ], + "scratch": [ + " {\"type\": \"action\", \"description\": \"marking a surface with a sharp object; could cause a wound\", \"similar objects\": [\"cut\", \"tear\", \"puncture\"]}", + 31 + ], + "bmw logo": [ + "\n{\"type\": \"logo\", \"description\": \"blue and white circle with the letters 'BMW' in the middle\", \"similar objects\": [\"Mercedes-Benz logo\", \"Audi logo\", \"Volkswagen logo\"]}", + 31 + ], + "swirl": [ + " {\"type\": \"shape\", \"description\": \"curved line; could be in a circular motion\", \"similar objects\": [\"spiral\", \"circle\", \"loop\"]}", + 31 + ], + "orange helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"orange; could be made of plastic or metal; could have a visor\", \"similar objects\": [\"bike helmet\", \"hard hat\", \"ski helmet\"]}", + 31 + ], + "baby girl": [ + "\n{\"type\": \"human\", \"description\": \"small; could be wearing a dress; could have a bow in her hair; could be smiling\", \"similar objects\": [\"toddler\", \"infant\", \"child\"]}", + 31 + ], + "street post": [ + " {\"type\": \"infrastructure\", \"description\": \"tall, cylindrical; could be made of metal; could have a sign on it\", \"similar objects\": [\"traffic light\", \"fire hydrant\", \"telephone pole\"]}", + 31 + ], + "gray elephant": [ + "\n{\"type\": \"animal\", \"description\": \"gray; has a long trunk; has large ears; has a long tail\", \"similar objects\": [\"hippopotamus\", \"rhinoceros\", \"giraffe\"]}", + 31 + ], + "lobster": [ + " {\"type\": \"seafood\", \"description\": \"red; has two large claws; could be boiled or steamed\", \"similar objects\": [\"crab\", \"shrimp\", \"clam\"]}", + 31 + ], + "hull": [ + " {\"type\": \"boat part\", \"description\": \"the outer shell of a boat; could be made of metal or fiberglass; could be curved or flat\", \"similar objects\": [\"keel\", \"deck\", \"mast\"]}", + 31 + ], + "pallets": [ + " {\"type\": \"material handling tool\", \"description\": \"wooden; could be used to store goods; could be stacked\", \"similar objects\": [\"crates\", \"boxes\", \"containers\"]}", + 31 + ], + "steer": [ + " {\"type\": \"animal\", \"description\": \"large, horned, four-legged mammal; could be domesticated\", \"similar objects\": [\"cow\", \"bull\", \"bison\"]}", + 31 + ], + "flash": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; could be used to store data; could be used to take pictures\", \"similar objects\": [\"memory card\", \"USB drive\", \"camera\"]}", + 31 + ], + "cave": [ + " {\"type\": \"geographical feature\", \"description\": \"dark; could have stalactites and stalagmites; could have an underground river\", \"similar objects\": [\"cavern\", \"grotto\", \"abyss\"]}", + 31 + ], + "railway track": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, straight, parallel lines; could have railway sleepers; could have railway signals\", \"similar objects\": [\"highway\", \"road\", \"bridge\"]}", + 31 + ], + "cement blocks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be grey or white; could be used for construction\", \"similar objects\": [\"bricks\", \"concrete\", \"stone\"]}", + 31 + ], + "street corner": [ + " {\"type\": \"location\", \"description\": \"intersection of two streets; could have a traffic light\", \"similar objects\": [\"crosswalk\", \"intersection\", \"traffic circle\"]}", + 30 + ], + "indent": [ + " {\"type\": \"grammatical mark\", \"description\": \"mark used to indicate a new paragraph or a pause in a sentence\", \"similar objects\": [\"comma\", \"semicolon\", \"colon\"]}", + 30 + ], + "dense forest": [ + " {\"type\": \"landscape\", \"description\": \"lots of trees; could be dark; could have animals\", \"similar objects\": [\"jungle\", \"rainforest\", \"woodland\"]}", + 30 + ], + "snowpants": [ + " {\"type\": \"clothing\", \"description\": \"waterproof; could be insulated; could have adjustable straps\", \"similar objects\": [\"ski pants\", \"snowboard pants\", \"snow bibs\"]}", + 30 + ], + "dresses": [ + " {\"type\": \"clothing\", \"description\": \"long or short; could be sleeveless or with sleeves; could be made of different materials\", \"similar objects\": [\"skirts\", \"blouses\", \"pants\"]}", + 30 + ], + "dark hair": [ + " {\"type\": \"hair color\", \"description\": \"black or brown; could be straight or curly\", \"similar objects\": [\"blonde hair\", \"red hair\", \"gray hair\"]}", + 30 + ], + "notebooks": [ + " {\"type\": \"stationery\", \"description\": \"bound paper; could be lined or blank; could be spiral-bound or hardcover\", \"similar objects\": [\"pens\", \"pencils\", \"markers\"]}", + 30 + ], + "building roof": [ + " {\"type\": \"structure\", \"description\": \"flat or sloped; could be made of metal, wood, or tiles; could have gutters\", \"similar objects\": [\"shed\", \"garage\", \"gazebo\"]}", + 30 + ], + "bottle top": [ + " {\"type\": \"container lid\", \"description\": \"round; could be made of plastic or metal; could have a hole in the middle\", \"similar objects\": [\"jar lid\", \"can lid\", \"cap\"]}", + 30 + ], + "crust pizza": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"flatbread pizza\", \"stuffed crust pizza\"]}", + 30 + ], + "zip": [ + " {\"type\": \"fastener\", \"description\": \"metal or plastic; used to close bags or garments\", \"similar objects\": [\"button\", \"hook and eye\", \"velcro\"]}", + 30 + ], + "mouse pads": [ + " {\"type\": \"computer accessory\", \"description\": \"rectangular; could be made of rubber or cloth; could have a design\", \"similar objects\": [\"keyboard\", \"mouse\", \"headset\"]}", + 30 + ], + "tan rock": [ + " {\"type\": \"rock\", \"description\": \"tan in color; could be smooth or rough; could be of any size\", \"similar objects\": [\"pebble\", \"boulder\", \"gravel\"]}", + 30 + ], + "duvet": [ + " {\"type\": \"bedding item\", \"description\": \"soft, quilted, filled with feathers or down; could be used as a comforter\", \"similar objects\": [\"pillow\", \"blanket\", \"mattress\"]}", + 30 + ], + "grey stones": [ + " {\"type\": \"natural object\", \"description\": \"grey, small, round; could be found in the river\", \"similar objects\": [\"pebbles\", \"rocks\", \"boulders\"]}", + 30 + ], + "size bed": [ + " {\"type\": \"furniture\", \"description\": \"large; could have a headboard; could have a footboard; could have a mattress\", \"similar objects\": [\"twin bed\", \"queen bed\", \"king bed\"]}", + 30 + ], + "miniature": [ + " {\"type\": \"size\", \"description\": \"smaller than the original; could be a replica of the original\", \"similar objects\": [\"tiny\", \"mini\", \"petite\"]}", + 30 + ], + "toilet plunger": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has a rubber cup at the end\", \"similar objects\": [\"mop\", \"broom\", \"vacuum cleaner\"]}", + 30 + ], + "tan jacket": [ + " {\"type\": \"clothing\", \"description\": \"light brown; could have a zipper; could have pockets\", \"similar objects\": [\"coat\", \"hoodie\", \"sweater\"]}", + 30 + ], + "mittens": [ + " {\"type\": \"clothing item\", \"description\": \"hand-covering; could be made of wool; could have a string to tie around the wrist\", \"similar objects\": [\"gloves\", \"scarf\", \"hat\"]}", + 30 + ], + "glass mug": [ + " {\"type\": \"drinking vessel\", \"description\": \"transparent; could be made of glass or plastic; could have a handle\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 30 + ], + "flaps": [ + " {\"type\": \"mechanical device\", \"description\": \"hinged panels; could be used to control airflow\", \"similar objects\": [\"doors\", \"gates\", \"shutters\"]}", + 30 + ], + "lifts": [ + " {\"type\": \"elevator\", \"description\": \"vertical transportation device; could be used to move people or goods between floors; could be operated manually or automatically\", \"similar objects\": [\"escalator\", \"staircase\", \"moving walkway\"]}", + 30 + ], + "outcropping": [ + " {\"type\": \"geological feature\", \"description\": \"rock formation protruding from the ground; could be made of sedimentary, igneous, or metamorphic rocks\", \"similar objects\": [\"cliff\", \"cave\", \"mountain\"]}", + 30 + ], + "billboard sign": [ + " {\"type\": \"advertising tool\", \"description\": \"large, rectangular; could be illuminated; could be used to display messages\", \"similar objects\": [\"poster\", \"banner\", \"flyer\"]}", + 30 + ], + "basil leaf": [ + " {\"type\": \"herb\", \"description\": \"green; has a strong smell; could be used for cooking\", \"similar objects\": [\"parsley\", \"oregano\", \"thyme\"]}", + 30 + ], + "baby doll": [ + " {\"type\": \"toy\", \"description\": \"resembles a baby; could be made of plastic or fabric; could have movable limbs\", \"similar objects\": [\"teddy bear\", \"action figure\", \"building blocks\"]}", + 30 + ], + "bronze": [ + " {\"type\": \"metal\", \"description\": \"yellowish-brown; malleable; ductile; could be used to make sculptures\", \"similar objects\": [\"copper\", \"iron\", \"gold\"]}", + 30 + ], + "surfboard man": [ + " {\"type\": \"sports equipment\", \"description\": \"long, narrow, and buoyant; could have a fin; could be used for surfing\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 30 + ], + "blue bag": [ + " {\"type\": \"accessory\", \"description\": \"blue; could be made of cloth; could be used to carry items\", \"similar objects\": [\"purse\", \"backpack\", \"suitcase\"]}", + 30 + ], + "porcelain toilet tank": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"rectangular; made of porcelain; has a lid; could have a flush handle\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 30 + ], + "cargo pants": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting trousers with large pockets; usually made of cotton or nylon\", \"similar objects\": [\"jeans\", \"joggers\", \"overalls\"]}", + 30 + ], + "styrofoam": [ + " {\"type\": \"material\", \"description\": \"lightweight, white, foam-like; could be used for insulation\", \"similar objects\": [\"plastic foam\", \"polystyrene foam\", \"expanded polystyrene foam\"]}", + 30 + ], + "metal lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"made of metal; could have a handle; could have a switch\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 30 + ], + "barefoot man": [ + " {\"type\": \"person\", \"description\": \"not wearing any shoes; could have a hat; could be carrying something\", \"similar objects\": [\"woman\", \"child\", \"elderly person\"]}", + 30 + ], + "advertisment": [ + " {\"type\": \"promotional material\", \"description\": \"could be printed or digital; could be used to promote products or services\", \"similar objects\": [\"flyer\", \"poster\", \"banner\"]}", + 30 + ], + "metal sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"made of metal; could have a faucet; could have a drain\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 30 + ], + "elbows": [ + " {\"type\": \"body part\", \"description\": \"joints of the arms; could be bent\", \"similar objects\": [\"knees\", \"wrists\", \"ankles\"]}", + 30 + ], + "wire mesh": [ + " {\"type\": \"building material\", \"description\": \"made of metal wires; could be used for fencing\", \"similar objects\": [\"chain link fence\", \"welded wire mesh\", \"barbed wire\"]}", + 30 + ], + "swirls": [ + " {\"type\": \"pattern\", \"description\": \"curved lines; could be in different colors; could be in different shapes\", \"similar objects\": [\"circles\", \"squares\", \"triangles\"]}", + 30 + ], + "passenger train car": [ + "\n{\"type\": \"transportation vehicle\", \"description\": \"long; has many compartments; could have a dining car; could have a locomotive\", \"similar objects\": [\"freight train car\", \"bus\", \"airplane\"]}", + 30 + ], + "ear tag": [ + " {\"type\": \"identification tool\", \"description\": \"small, plastic tag; could be attached to the ear of animals; could have a unique number\", \"similar objects\": [\"microchip\", \"collar\", \"tattoo\"]}", + 30 + ], + "backyard": [ + " {\"type\": \"outdoor space\", \"description\": \"open area behind a house; could have a garden, a patio, a pool, etc.\", \"similar objects\": [\"patio\", \"garden\", \"balcony\"]}", + 30 + ], + "dark trees": [ + "\n{\"type\": \"landscape\", \"description\": \"tall, dark trees; could be in a forest; could have leaves or needles; could have a dark sky in the background\", \"similar objects\": [\"mountains\", \"rivers\", \"lakes\"]}", + 30 + ], + "gold door knob": [ + "\n{\"type\": \"hardware\", \"description\": \"round; made of gold; could have a keyhole\", \"similar objects\": [\"brass door knob\", \"silver door knob\", \"bronze door knob\"]}", + 30 + ], + "winter glove": [ + " {\"type\": \"clothing item\", \"description\": \"long; could be made of wool; could be fingerless\", \"similar objects\": [\"mittens\", \"scarf\", \"hat\"]}", + 30 + ], + "cabinet drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have handles; could have multiple drawers\", \"similar objects\": [\"dresser\", \"chest of drawers\", \"sideboard\"]}", + 30 + ], + "crib": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; has four posts; could have a canopy\", \"similar objects\": [\"cot\", \"bassinet\", \"toddler bed\"]}", + 30 + ], + "wall socket": [ + " {\"type\": \"electrical tool\", \"description\": \"rectangular; has two or more holes; could be used to plug in electrical appliances\", \"similar objects\": [\"power strip\", \"extension cord\", \"outlet\"]}", + 30 + ], + "trash container": [ + " {\"type\": \"container\", \"description\": \"large, rectangular; could be made of metal; could have a lid\", \"similar objects\": [\"bin\", \"garbage can\", \"recycling bin\"]}", + 30 + ], + "grey jacket": [ + " {\"type\": \"clothing\", \"description\": \"long sleeve; could be made of wool; could have a zipper\", \"similar objects\": [\"coat\", \"hoodie\", \"sweater\"]}", + 30 + ], + "males": [ + " {\"type\": \"gender\", \"description\": \"male gender; could be used to refer to a group of people\", \"similar objects\": [\"men\", \"boys\", \"gentlemen\"]}", + 30 + ], + "paper dispenser": [ + " {\"type\": \"office tool\", \"description\": \"box-shaped; could be made of plastic or metal; could have a handle\", \"similar objects\": [\"stapler\", \"hole puncher\", \"tape dispenser\"]}", + 30 + ], + "clothe": [ + " {\"type\": \"fabric\", \"description\": \"made of cotton, linen, silk, wool, etc.; could be used to make clothes\", \"similar objects\": [\"fabric\", \"textile\", \"garment\"]}", + 30 + ], + "plaid blanket": [ + " {\"type\": \"textile\", \"description\": \"has a pattern of different colors; could be made of wool or cotton\", \"similar objects\": [\"quilt\", \"throw blanket\", \"rug\"]}", + 30 + ], + "silver forks": [ + " {\"type\": \"utensil\", \"description\": \"silver; has four prongs; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 30 + ], + "soles": [ + " {\"type\": \"footwear\", \"description\": \"flat; could be made of leather; could be slip-on\", \"similar objects\": [\"sandals\", \"flip-flops\", \"sneakers\"]}", + 30 + ], + "orange drink": [ + " {\"type\": \"beverage\", \"description\": \"orange-colored; could be carbonated; could be alcoholic\", \"similar objects\": [\"lemonade\", \"juice\", \"soda\"]}", + 30 + ], + "bathroom rug": [ + " {\"type\": \"floor covering\", \"description\": \"soft; could be made of cotton; could be rectangular or round\", \"similar objects\": [\"bath mat\", \"carpet\", \"area rug\"]}", + 30 + ], + "silver flusher": [ + " {\"type\": \"plumbing tool\", \"description\": \"cylindrical; could be made of metal; used to flush water\", \"similar objects\": [\"toilet brush\", \"plunger\", \"drain snake\"]}", + 30 + ], + "whisk": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle with a loop of wires at the end; used for stirring and mixing\", \"similar objects\": [\"spatula\", \"ladle\", \"wooden spoon\"]}", + 30 + ], + "plate food": [ + " {\"type\": \"dining ware\", \"description\": \"flat, round; could be made of ceramic, plastic, or metal; could be used to serve food\", \"similar objects\": [\"bowl\", \"cup\", \"fork\"]}", + 30 + ], + "grey boulder": [ + "\n{\"type\": \"rock\", \"description\": \"large, grey, round; could have rough surface\", \"similar objects\": [\"stone\", \"pebble\", \"cobble\"]}", + 30 + ], + "paste": [ + " {\"type\": \"adhesive\", \"description\": \"thick, sticky, used to join two surfaces together\", \"similar objects\": [\"glue\", \"tape\", \"epoxy\"]}", + 30 + ], + "blue table cloth": [ + "\n{\"type\": \"tableware\", \"description\": \"blue; rectangular; could be made of cotton or polyester\", \"similar objects\": [\"table runner\", \"placemat\", \"napkin\"]}", + 30 + ], + "anchor": [ + " {\"type\": \"nautical tool\", \"description\": \"heavy metal object; has a hook; could be used to secure a boat\", \"similar objects\": [\"chain\", \"rope\", \"buoy\"]}", + 30 + ], + "adult male": [ + "\n{\"type\": \"human\", \"description\": \"tall; broad shoulders; facial hair; deep voice\", \"similar objects\": [\"teenager\", \"elderly\", \"child\"]}", + 30 + ], + "handle spoon": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, has a handle; could be made of metal or plastic\", \"similar objects\": [\"fork\", \"knife\", \"spatula\"]}", + 30 + ], + "dash": [ + " {\"type\": \"punctuation mark\", \"description\": \"short horizontal line; used to separate words or phrases\", \"similar objects\": [\"comma\", \"semicolon\", \"colon\"]}", + 30 + ], + "hazy": [ + " {\"type\": \"weather condition\", \"description\": \"low visibility; could be caused by dust, smoke, or fog\", \"similar objects\": [\"cloudy\", \"rainy\", \"sunny\"]}", + 30 + ], + "daytime": [ + "\n{\"type\": \"time period\", \"description\": \"the period of time between sunrise and sunset\", \"similar objects\": [\"morning\", \"afternoon\", \"evening\"]}", + 30 + ], + "graphic": [ + " {\"type\": \"visual representation\", \"description\": \"could be a drawing, painting, or photograph; could be used to convey a message or idea\", \"similar objects\": [\"illustration\", \"image\", \"picture\"]}", + 30 + ], + "diner": [ + " {\"type\": \"restaurant\", \"description\": \"could be a small restaurant; could have a counter and booths; could serve classic American food\", \"similar objects\": [\"cafe\", \"dive bar\", \"pub\"]}", + 30 + ], + "baseball shirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; has a baseball logo; could be made of cotton\", \"similar objects\": [\"jersey\", \"t-shirt\", \"hoodie\"]}", + 30 + ], + "lemonade": [ + " {\"type\": \"beverage\", \"description\": \"yellow; sweet and sour; could be served cold\", \"similar objects\": [\"iced tea\", \"juice\", \"soda\"]}", + 30 + ], + "blonde boy": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could be wearing a t-shirt and jeans\", \"similar objects\": [\"blonde girl\", \"brunette boy\", \"brunette girl\"]}", + 30 + ], + "pliers": [ + " {\"type\": \"tool\", \"description\": \"two handles connected by a joint; could be used to grip and twist objects\", \"similar objects\": [\"screwdriver\", \"hammer\", \"wrench\"]}", + 30 + ], + "barefoot": [ + " {\"type\": \"footwear\", \"description\": \"without shoes; could be made of leather or fabric\", \"similar objects\": [\"sandals\", \"flip-flops\", \"slippers\"]}", + 30 + ], + "adult man": [ + "\n{\"type\": \"human\", \"description\": \"tall; could have facial hair; could have wrinkles; could have a bald head\", \"similar objects\": [\"adult woman\", \"teenager\", \"child\"]}", + 30 + ], + "ostriches": [ + " {\"type\": \"animal\", \"description\": \"large, flightless bird; has long legs and neck; could have black and white feathers\", \"similar objects\": [\"emu\", \"cassowary\", \"rhea\"]}", + 30 + ], + "cement slab": [ + " {\"type\": \"building material\", \"description\": \"hard, flat, gray; could be used for flooring or paving\", \"similar objects\": [\"concrete block\", \"bricks\", \"tiles\"]}", + 30 + ], + "orange buoy": [ + "\n{\"type\": \"marine tool\", \"description\": \"round; orange in color; could be used to mark a location\", \"similar objects\": [\"life buoy\", \"life ring\", \"life jacket\"]}", + 30 + ], + "construction crane": [ + " {\"type\": \"construction tool\", \"description\": \"tall; has a long arm; could be used to lift heavy objects\", \"similar objects\": [\"excavator\", \"bulldozer\", \"forklift\"]}", + 30 + ], + "brown tower": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical, made of bricks; could have windows\", \"similar objects\": [\"building\", \"monument\", \"skyscraper\"]}", + 30 + ], + "man arm": [ + "\n{\"type\": \"body part\", \"description\": \"long; could be muscular; could have five fingers\", \"similar objects\": [\"leg\", \"hand\", \"foot\"]}", + 30 + ], + "stone column": [ + " {\"type\": \"architectural structure\", \"description\": \"made of stone; could be cylindrical or rectangular; could be used as a support for a building\", \"similar objects\": [\"pillar\", \"obelisk\", \"monolith\"]}", + 30 + ], + "snowy": [ + " {\"type\": \"weather condition\", \"description\": \"cold; white; could be accompanied by strong wind\", \"similar objects\": [\"rainy\", \"foggy\", \"sunny\"]}", + 30 + ], + "coastline": [ + " {\"type\": \"geographical feature\", \"description\": \"the line where the land meets the sea; could have cliffs, rocks, and beaches\", \"similar objects\": [\"mountain range\", \"river\", \"lake\"]}", + 30 + ], + "l": [ + "\n{\"type\": \"letter\", \"description\": \"straight line; could be used to form words\", \"similar objects\": [\"A\", \"B\", \"C\"]}", + 30 + ], + "mountain goat": [ + " {\"type\": \"animal\", \"description\": \"white fur; curved horns; hooves; lives in high altitudes\", \"similar objects\": [\"bighorn sheep\", \"ibex\", \"mouflon\"]}", + 30 + ], + "crab": [ + " {\"type\": \"animal\", \"description\": \"oval-shaped; has two large claws; could be red or blue\", \"similar objects\": [\"lobster\", \"shrimp\", \"crayfish\"]}", + 30 + ], + "rear landing gear": [ + " {\"type\": \"aircraft part\", \"description\": \"wheels; could be retractable; could be connected to the fuselage\", \"similar objects\": [\"front landing gear\", \"wing\", \"tail\"]}", + 30 + ], + "giraffe horns": [ + " {\"type\": \"animal body part\", \"description\": \"long, curved, brown; could be found on the head of a giraffe\", \"similar objects\": [\"antlers\", \"tusks\", \"horns\"]}", + 30 + ], + "course": [ + " {\"type\": \"academic subject\", \"description\": \"a set of lectures and/or classes on a particular topic; could be part of a degree program\", \"similar objects\": [\"module\", \"program\", \"seminar\"]}", + 29 + ], + "blue surfboard": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long, blue, could have a fin; could be used for surfing\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 29 + ], + "pink meat": [ + " {\"type\": \"food\", \"description\": \"could be pork, beef, or chicken; could be cooked in various ways; could be served with different sauces\", \"similar objects\": [\"red meat\", \"white meat\", \"seafood\"]}", + 29 + ], + "furry dog": [ + "\n{\"type\": \"animal\", \"description\": \"long fur; could be of any color; could have a tail; could have a snout\", \"similar objects\": [\"poodle\", \"husky\", \"labrador\"]}", + 29 + ], + "booklet": [ + " {\"type\": \"publication\", \"description\": \"small, bound collection of papers; could be printed or digital\", \"similar objects\": [\"magazine\", \"book\", \"pamphlet\"]}", + 29 + ], + "lining": [ + " {\"type\": \"fabric\", \"description\": \"smooth; could be made of silk, cotton, or polyester; could be used for clothing or curtains\", \"similar objects\": [\"satin\", \"velvet\", \"chiffon\"]}", + 29 + ], + "footsteps": [ + " {\"type\": \"sound\", \"description\": \"rhythmic sound made by walking\", \"similar objects\": [\"heartbeat\", \"breathing\", \"clapping\"]}", + 29 + ], + "privacy fence": [ + " {\"type\": \"fencing tool\", \"description\": \"wooden or metal panels; could be used to separate yards; could be used for privacy\", \"similar objects\": [\"chain link fence\", \"wooden fence\", \"barbed wire fence\"]}", + 29 + ], + "silver metal spoon": [ + "\n{\"type\": \"utensil\", \"description\": \"shiny, metallic, long handle; could be used for stirring and serving\", \"similar objects\": [\"fork\", \"knife\", \"spatula\"]}", + 29 + ], + "baby carrots": [ + " {\"type\": \"vegetable\", \"description\": \"small, orange, cylindrical; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"carrots\", \"celery\", \"parsnips\"]}", + 29 + ], + "spear": [ + " {\"type\": \"weapon\", \"description\": \"long, sharp, pointed; could be made of metal or wood\", \"similar objects\": [\"sword\", \"dagger\", \"axe\"]}", + 29 + ], + "spice rack": [ + " {\"type\": \"kitchen tool\", \"description\": \"could be made of wood or metal; has several compartments for storing spices; could be wall-mounted or free-standing\", \"similar objects\": [\"utensil holder\", \"knife block\", \"condiment caddy\"]}", + 29 + ], + "bird head": [ + " {\"type\": \"animal body part\", \"description\": \"beak; two eyes; two wings; feathers\", \"similar objects\": [\"duck head\", \"eagle head\", \"parrot head\"]}", + 29 + ], + "woman tennis player": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing a tennis outfit; holding a tennis racket; playing on a tennis court\", \"similar objects\": [\"man tennis player\", \"golfer\", \"soccer player\"]}", + 29 + ], + "lift chair": [ + " {\"type\": \"furniture\", \"description\": \"reclining chair; could be motorized; could be used to help people stand up\", \"similar objects\": [\"recliner\", \"sofa\", \"armchair\"]}", + 29 + ], + "bicycle rack": [ + " {\"type\": \"storage tool\", \"description\": \"metal frame; could be attached to the wall; could hold multiple bicycles\", \"similar objects\": [\"bike stand\", \"bike lock\", \"bike hanger\"]}", + 29 + ], + "neon light": [ + " {\"type\": \"lighting tool\", \"description\": \"bright, colorful, electric light; could be used for signs and decorations\", \"similar objects\": [\"fluorescent light\", \"LED light\", \"incandescent light\"]}", + 29 + ], + "graffitti": [ + " {\"type\": \"art form\", \"description\": \"creative drawings or writings on walls or other surfaces\", \"similar objects\": [\"street art\", \"murals\", \"stencils\"]}", + 29 + ], + "traffic signal light": [ + " {\"type\": \"traffic control device\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"stop sign\", \"yield sign\", \"crosswalk sign\"]}", + 29 + ], + "purse strap": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of leather or fabric; could be adjustable\", \"similar objects\": [\"belt\", \"bag strap\", \"wallet strap\"]}", + 29 + ], + "whisker": [ + " {\"type\": \"kitchen tool\", \"description\": \"long, thin, metal; used for stirring\", \"similar objects\": [\"spatula\", \"ladle\", \"tongs\"]}", + 29 + ], + "spray paint": [ + " {\"type\": \"painting tool\", \"description\": \"aerosol can; could be used to paint on surfaces\", \"similar objects\": [\"brush\", \"roller\", \"airbrush\"]}", + 29 + ], + "apartment buildings": [ + "\n{\"type\": \"structure\", \"description\": \"multi-story buildings; could have balconies; could have multiple units\", \"similar objects\": [\"condominiums\", \"townhouses\", \"row houses\"]}", + 29 + ], + "birthday candles": [ + " {\"type\": \"decoration\", \"description\": \"small, thin, could be in different colors; could be lit up\", \"similar objects\": [\"balloons\", \"streamers\", \"confetti\"]}", + 29 + ], + "picnic": [ + " {\"type\": \"activity\", \"description\": \"outdoor activity; could involve food, drinks, and games; could be done in a park or backyard\", \"similar objects\": [\"barbecue\", \"camping\", \"hiking\"]}", + 29 + ], + "stainless steel faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be attached to a sink\", \"similar objects\": [\"shower head\", \"toilet\", \"bathtub\"]}", + 29 + ], + "drinking": [ + " {\"type\": \"action\", \"description\": \"the act of consuming liquid through the mouth\", \"similar objects\": [\"eating\", \"swallowing\", \"gulping\"]}", + 29 + ], + "mac": [ + " {\"type\": \"computer\", \"description\": \"laptop; has a screen; could be silver or space gray; could have a touch bar\", \"similar objects\": [\"PC\", \"Chromebook\", \"Tablet\"]}", + 29 + ], + "cotton": [ + " {\"type\": \"fabric\", \"description\": \"soft, white, fluffy; could be woven into cloth\", \"similar objects\": [\"linen\", \"silk\", \"wool\"]}", + 29 + ], + "notice": [ + " {\"type\": \"document\", \"description\": \"written information; could be posted on a wall\", \"similar objects\": [\"poster\", \"flyer\", \"sign\"]}", + 29 + ], + "round container": [ + " {\"type\": \"container\", \"description\": \"circular; could be made of plastic, metal, or glass; could have a lid\", \"similar objects\": [\"jar\", \"can\", \"box\"]}", + 29 + ], + "night table": [ + " {\"type\": \"furniture\", \"description\": \"small table; could have drawers; could be used to place a lamp\", \"similar objects\": [\"end table\", \"side table\", \"coffee table\"]}", + 29 + ], + "baseball plate": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; has a white line in the middle; could be made of rubber or plastic\", \"similar objects\": [\"bat\", \"ball\", \"glove\"]}", + 29 + ], + "warehouse": [ + " {\"type\": \"building\", \"description\": \"large, rectangular; could have multiple floors; could have loading docks\", \"similar objects\": [\"factory\", \"storehouse\", \"depot\"]}", + 29 + ], + "metal drain": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be made of metal; could be used to drain water\", \"similar objects\": [\"sink\", \"bathtub\", \"toilet\"]}", + 29 + ], + "ankle strap": [ + " {\"type\": \"footwear accessory\", \"description\": \"a strap that wraps around the ankle; could be made of leather or fabric; could have a buckle or a zipper\", \"similar objects\": [\"shoe laces\", \"velcro straps\", \"elastic bands\"]}", + 29 + ], + "sun hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"wide brim; could be made of straw; could have a chin strap\", \"similar objects\": [\"baseball cap\", \"fedora\", \"beret\"]}", + 29 + ], + "glass case": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of glass or plastic; could be used to store items\", \"similar objects\": [\"display case\", \"cabinet\", \"box\"]}", + 29 + ], + "thick green trees": [ + "\n{\"type\": \"plant\", \"description\": \"large, thick, green leaves; could have a trunk; could have branches\", \"similar objects\": [\"palm tree\", \"pine tree\", \"oak tree\"]}", + 29 + ], + "rubber wheel": [ + " {\"type\": \"wheel\", \"description\": \"made of rubber; could be used for vehicles; could be round or oval\", \"similar objects\": [\"plastic wheel\", \"metal wheel\", \"wooden wheel\"]}", + 29 + ], + "grey van": [ + " {\"type\": \"vehicle\", \"description\": \"grey; could be a minivan; could have sliding doors\", \"similar objects\": [\"SUV\", \"truck\", \"sedan\"]}", + 29 + ], + "stopper": [ + " {\"type\": \"utility tool\", \"description\": \"cylindrical; could be made of rubber; used to plug a hole\", \"similar objects\": [\"plug\", \"cork\", \"cap\"]}", + 29 + ], + "silverware table": [ + " {\"type\": \"furniture\", \"description\": \"long; could have drawers; could have a flat surface\", \"similar objects\": [\"dining table\", \"coffee table\", \"side table\"]}", + 29 + ], + "orange tree": [ + " {\"type\": \"plant\", \"description\": \"tall; has dark green leaves; could have orange fruits\", \"similar objects\": [\"apple tree\", \"lemon tree\", \"olive tree\"]}", + 29 + ], + "overhead": [ + " {\"type\": \"cost\", \"description\": \"expenses that are not directly related to the production of goods or services\", \"similar objects\": [\"fixed cost\", \"variable cost\", \"indirect cost\"]}", + 29 + ], + "wheeler": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could be motorized or non-motorized; could have a basket\", \"similar objects\": [\"bicycle\", \"scooter\", \"tricycle\"]}", + 29 + ], + "china plate": [ + " {\"type\": \"dishware\", \"description\": \"round; could be decorated with patterns; could be made of porcelain\", \"similar objects\": [\"bowl\", \"cup\", \"saucer\"]}", + 29 + ], + "aqua": [ + " {\"type\": \"color\", \"description\": \"light blue; could be used to describe the color of the sea\", \"similar objects\": [\"turquoise\", \"teal\", \"cyan\"]}", + 29 + ], + "helmet batter": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round; could be made of plastic or metal; has a chin strap\", \"similar objects\": [\"safety goggles\", \"knee pads\", \"elbow pads\"]}", + 29 + ], + "fishing pole": [ + " {\"type\": \"fishing tool\", \"description\": \"long, thin, has a hook at the end; could be made of metal or wood\", \"similar objects\": [\"fishing rod\", \"fishing net\", \"fishing line\"]}", + 29 + ], + "sugar packets": [ + " {\"type\": \"food item\", \"description\": \"small, white, rectangular; could have a logo or brand name printed on it\", \"similar objects\": [\"salt packets\", \"tea bags\", \"coffee sachets\"]}", + 29 + ], + "wardrobe": [ + " {\"type\": \"furniture\", \"description\": \"tall; could have doors; could have shelves\", \"similar objects\": [\"dresser\", \"armoire\", \"closet\"]}", + 29 + ], + "thick trees": [ + "\n{\"type\": \"vegetation\", \"description\": \"large, tall, with thick trunks and branches; could have leaves or needles; could have fruits or flowers\", \"similar objects\": [\"bushes\", \"shrubs\", \"palm trees\"]}", + 29 + ], + "fox": [ + " {\"type\": \"animal\", \"description\": \"reddish-brown fur; has a bushy tail; could have white fur on its chest\", \"similar objects\": [\"wolf\", \"dog\", \"rabbit\"]}", + 29 + ], + "tan teddy bear": [ + "\n{\"type\": \"stuffed animal\", \"description\": \"brown; has a soft fur; could have a bowtie\", \"similar objects\": [\"plush toy\", \"stuffed toy\", \"stuffed animal\"]}", + 29 + ], + "ski shoes": [ + " {\"type\": \"footwear\", \"description\": \"long, thick, waterproof; could have spikes on the bottom\", \"similar objects\": [\"hiking boots\", \"snow boots\", \"mountaineering shoes\"]}", + 29 + ], + "race": [ + " {\"type\": \"competition\", \"description\": \"an event in which participants compete against each other to reach a goal in the shortest amount of time\", \"similar objects\": [\"marathon\", \"triathlon\", \"relay race\"]}", + 29 + ], + "ditch": [ + " {\"type\": \"landform\", \"description\": \"long, narrow, and deep; could be filled with water\", \"similar objects\": [\"ravine\", \"valley\", \"gully\"]}", + 29 + ], + "blossoms": [ + " {\"type\": \"plant\", \"description\": \"flowering plants; could be white, pink, yellow, or other colors; could have a pleasant smell\", \"similar objects\": [\"flowers\", \"roses\", \"daisies\"]}", + 29 + ], + "mix": [ + " {\"type\": \"utensil\", \"description\": \"long handle; could have a bowl-shaped head; could be used for stirring\", \"similar objects\": [\"spoon\", \"whisk\", \"ladle\"]}", + 29 + ], + "wooden house": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could have a chimney; could have a porch\", \"similar objects\": [\"brick house\", \"log cabin\", \"igloo\"]}", + 29 + ], + "bulding": [ + " {\"type\": \"structure\", \"description\": \"could be made of concrete, steel, or wood; could have multiple floors; could have windows and doors\", \"similar objects\": [\"house\", \"skyscraper\", \"bridge\"]}", + 29 + ], + "ponies": [ + " {\"type\": \"animal\", \"description\": \"small horses; have short manes; could be ridden by children\", \"similar objects\": [\"horses\", \"donkeys\", \"mules\"]}", + 29 + ], + "round circle": [ + "\n{\"type\": \"shape\", \"description\": \"circular; has no edges; could be drawn with a compass\", \"similar objects\": [\"oval\", \"square\", \"triangle\"]}", + 29 + ], + "metal arm": [ + " {\"type\": \"machine part\", \"description\": \"could be made of metal; could be used to move objects\", \"similar objects\": [\"gear\", \"pulley\", \"lever\"]}", + 29 + ], + "melons": [ + " {\"type\": \"fruit\", \"description\": \"round; could be yellow, green, or orange; has a hard rind; could be sliced into pieces\", \"similar objects\": [\"watermelon\", \"cantaloupe\", \"honeydew\"]}", + 29 + ], + "front windshield": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; located at the front of the car; could be curved\", \"similar objects\": [\"rear windshield\", \"side window\", \"headlight\"]}", + 29 + ], + "dirt hill": [ + " {\"type\": \"landscape\", \"description\": \"mound of soil; could have grass and plants growing on it\", \"similar objects\": [\"sand dune\", \"cliff\", \"mountain\"]}", + 29 + ], + "wooden boards": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular; could be used for construction\", \"similar objects\": [\"plywood\", \"timber\", \"lumber\"]}", + 29 + ], + "tree trunks": [ + " {\"type\": \"plant part\", \"description\": \"thick, cylindrical, could be brown or grey; could have branches and leaves\", \"similar objects\": [\"branches\", \"roots\", \"leaves\"]}", + 29 + ], + "tennis top": [ + " {\"type\": \"clothing\", \"description\": \"short-sleeved; could be white or yellow; could have a logo\", \"similar objects\": [\"t-shirt\", \"polo shirt\", \"tank top\"]}", + 29 + ], + "dandelion": [ + " {\"type\": \"plant\", \"description\": \"has a yellow flower; could have white puffballs; could be found in grassy areas\", \"similar objects\": [\"daisy\", \"sunflower\", \"clover\"]}", + 29 + ], + "tan cow": [ + "\n{\"type\": \"animal\", \"description\": \"brown; has a long tail; could have horns; could have white patches\", \"similar objects\": [\"bull\", \"goat\", \"sheep\"]}", + 29 + ], + "toy bear": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; could be brown or white; could have a bow tie\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"stuffed animal\"]}", + 29 + ], + "pink napkin": [ + "\n{\"type\": \"tableware\", \"description\": \"rectangular; could be made of cloth; could be in pink color\", \"similar objects\": [\"tablecloth\", \"placemat\", \"towel\"]}", + 29 + ], + "patchy grass": [ + " {\"type\": \"plant\", \"description\": \"unevenly distributed; could be green or yellow; could be short or tall\", \"similar objects\": [\"weeds\", \"moss\", \"clover\"]}", + 29 + ], + "digits": [ + " {\"type\": \"numbers\", \"description\": \"numerical symbols used to represent numbers\", \"similar objects\": [\"numerals\", \"arabic numerals\", \"Roman numerals\"]}", + 29 + ], + "ski resort": [ + " {\"type\": \"location\", \"description\": \"place with ski slopes and lifts; could have a ski lodge; could have a ski shop\", \"similar objects\": [\"mountain resort\", \"snow park\", \"ski area\"]}", + 29 + ], + "blue post": [ + " {\"type\": \"object\", \"description\": \"blue; could be made of metal; could be used as a signpost\", \"similar objects\": [\"signpost\", \"streetlight\", \"traffic light\"]}", + 29 + ], + "oatmeal": [ + " {\"type\": \"food\", \"description\": \"porridge-like; could be cooked with milk or water; could be served with fruits and nuts\", \"similar objects\": [\"cereal\", \"granola\", \"porridge\"]}", + 29 + ], + "plastic wrap": [ + " {\"type\": \"packaging material\", \"description\": \"transparent; could be used to cover food; could be stretched\", \"similar objects\": [\"aluminum foil\", \"cling wrap\", \"ziplock bag\"]}", + 29 + ], + "plane wheels": [ + " {\"type\": \"aircraft part\", \"description\": \"round; could be made of metal; could be attached to the landing gear\", \"similar objects\": [\"propeller\", \"engine\", \"wing\"]}", + 29 + ], + "evergreen tree": [ + "\n{\"type\": \"plant\", \"description\": \"tall; has needles; could be coniferous; could be evergreen\", \"similar objects\": [\"pine tree\", \"spruce tree\", \"cypress tree\"]}", + 29 + ], + "crack pavement": [ + " {\"type\": \"surface damage\", \"description\": \"uneven surface; could have small pieces of broken pavement\", \"similar objects\": [\"pothole\", \"uneven sidewalk\", \"cracked road\"]}", + 29 + ], + "smoking sign": [ + " {\"type\": \"warning sign\", \"description\": \"triangular; has a picture of a cigarette; could be red and white\", \"similar objects\": [\"no smoking sign\", \"no littering sign\", \"no parking sign\"]}", + 29 + ], + "trainer": [ + " {\"type\": \"footwear\", \"description\": \"sporty; could have laces; could have a logo\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 29 + ], + "businesses": [ + "\n{\"type\": \"organization\", \"description\": \"groups of people working together to achieve a common goal; could be for-profit or non-profit\", \"similar objects\": [\"corporations\", \"companies\", \"institutions\"]}", + 29 + ], + "street signal": [ + " {\"type\": \"traffic control tool\", \"description\": \"could be red, yellow, or green; could be in the shape of a circle or an arrow; could be used to indicate when to stop or go\", \"similar objects\": [\"traffic light\", \"stop sign\", \"yield sign\"]}", + 29 + ], + "rocket": [ + " {\"type\": \"spacecraft\", \"description\": \"long, cylindrical; could have fins; could have a nose cone\", \"similar objects\": [\"satellite\", \"shuttle\", \"space station\"]}", + 29 + ], + "diamond pattern": [ + " {\"type\": \"pattern\", \"description\": \"geometric shape; could be made of lines or dots; could be used for decoration\", \"similar objects\": [\"plaid\", \"stripes\", \"chevron\"]}", + 29 + ], + "snoopy": [ + " {\"type\": \"cartoon character\", \"description\": \"brown beagle; wears a red and white shirt; has a black nose\", \"similar objects\": [\"Woodstock\", \"Charlie Brown\", \"Linus\"]}", + 29 + ], + "road way": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, flat, paved surface; could have lines and signs; could have two or more lanes\", \"similar objects\": [\"highway\", \"street\", \"bridge\"]}", + 28 + ], + "treat": [ + " {\"type\": \"food\", \"description\": \"could be sweet or savory; could be a snack or a meal\", \"similar objects\": [\"snack\", \"dessert\", \"meal\"]}", + 28 + ], + "ipad": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; touchscreen; could be connected to the internet\", \"similar objects\": [\"laptop\", \"tablet\", \"smartphone\"]}", + 28 + ], + "kettles": [ + " {\"type\": \"cooking tool\", \"description\": \"cylindrical; has a handle; could be made of metal; could have a spout\", \"similar objects\": [\"teapot\", \"coffee pot\", \"thermos\"]}", + 28 + ], + "wii control": [ + " {\"type\": \"gaming device\", \"description\": \"wireless; has buttons and a joystick; could be used with a Wii console\", \"similar objects\": [\"Xbox controller\", \"PlayStation controller\", \"Nintendo Switch controller\"]}", + 28 + ], + "lit lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of metal; emits light\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}", + 28 + ], + "linoleum": [ + " {\"type\": \"flooring material\", \"description\": \"smooth, glossy, and durable; could be made of vinyl or cork\", \"similar objects\": [\"tile\", \"hardwood\", \"carpet\"]}", + 28 + ], + "electricity pole": [ + " {\"type\": \"utility pole\", \"description\": \"tall; has wires and cables; could have a transformer\", \"similar objects\": [\"telephone pole\", \"street light pole\", \"traffic light pole\"]}", + 28 + ], + "bloom": [ + " {\"type\": \"flower\", \"description\": \"could be colorful; could have petals; could have a stem\", \"similar objects\": [\"rose\", \"daisy\", \"tulip\"]}", + 28 + ], + "wrist strap": [ + " {\"type\": \"accessory\", \"description\": \"made of fabric or leather; could be adjustable; could be used to hold a bag or a camera\", \"similar objects\": [\"belt\", \"bracelet\", \"necklace\"]}", + 28 + ], + "dog ears": [ + " {\"type\": \"animal body part\", \"description\": \"pointed; could be floppy or erect; could be black, brown, white, or a mix of colors\", \"similar objects\": [\"cat ears\", \"rabbit ears\", \"mouse ears\"]}", + 28 + ], + "pork": [ + " {\"type\": \"meat\", \"description\": \"white, fatty, could be cooked in various ways\", \"similar objects\": [\"beef\", \"chicken\", \"lamb\"]}", + 28 + ], + "tassels": [ + " {\"type\": \"decorative item\", \"description\": \"hanging threads; could be made of silk, cotton, or other materials; could be used to decorate clothing, curtains, or other items\", \"similar objects\": [\"fringe\", \"beads\", \"pom-poms\"]}", + 28 + ], + "wall light": [ + " {\"type\": \"lighting tool\", \"description\": \"mounted on the wall; could be made of metal; could have a switch\", \"similar objects\": [\"ceiling light\", \"table lamp\", \"floor lamp\"]}", + 28 + ], + "throw blanket": [ + " {\"type\": \"bedding item\", \"description\": \"soft; could be made of wool; could be used for warmth\", \"similar objects\": [\"duvet\", \"comforter\", \"quilt\"]}", + 28 + ], + "wood board": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular; could be used for construction\", \"similar objects\": [\"plywood\", \"timber\", \"sheet metal\"]}", + 28 + ], + "kale": [ + " {\"type\": \"vegetable\", \"description\": \"dark green, curly leaves; could be cooked or eaten raw\", \"similar objects\": [\"spinach\", \"cabbage\", \"lettuce\"]}", + 28 + ], + "mason jar": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass; has a lid\", \"similar objects\": [\"canning jar\", \"bottle\", \"jug\"]}", + 28 + ], + "countertops": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of stone, wood, or other materials; could be used for food preparation\", \"similar objects\": [\"table\", \"desk\", \"shelf\"]}", + 28 + ], + "power poles": [ + " {\"type\": \"utility structure\", \"description\": \"tall, metal poles; could have wires attached to it\", \"similar objects\": [\"street lights\", \"traffic lights\", \"telephone poles\"]}", + 28 + ], + "perosn": [ + "\n{\"type\": \"human\", \"description\": \"two arms, two legs, head, torso; could have different skin colors; could have different hair styles\", \"similar objects\": [\"child\", \"adult\", \"elderly\"]}", + 28 + ], + "triangles": [ + " {\"type\": \"shape\", \"description\": \"three-sided; could be equilateral, isosceles, or scalene\", \"similar objects\": [\"squares\", \"rectangles\", \"circles\"]}", + 28 + ], + "clock front building": [ + "\n{\"type\": \"decoration\", \"description\": \"round; could have numbers and hands; could be made of metal or wood\", \"similar objects\": [\"wall clock\", \"grandfather clock\", \"cuckoo clock\"]}", + 28 + ], + "pink color": [ + " {\"type\": \"color\", \"description\": \"light red; could be associated with femininity\", \"similar objects\": [\"red\", \"magenta\", \"purple\"]}", + 28 + ], + "concrete road": [ + " {\"type\": \"road surface\", \"description\": \"hard, gray, flat; could have lines and signs\", \"similar objects\": [\"asphalt road\", \"gravel road\", \"dirt road\"]}", + 28 + ], + "wash cloth": [ + " {\"type\": \"cleaning tool\", \"description\": \"rectangular; made of cloth; could be used for cleaning\", \"similar objects\": [\"sponge\", \"towel\", \"rag\"]}", + 28 + ], + "carousel": [ + " {\"type\": \"amusement ride\", \"description\": \"round; has horses or other animals; could have colorful lights\", \"similar objects\": [\"ferris wheel\", \"roller coaster\", \"merry-go-round\"]}", + 28 + ], + "wood panels": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular; could be used for walls and floors; could be made of different types of wood\", \"similar objects\": [\"plywood\", \"drywall\", \"hardboard\"]}", + 28 + ], + "linen": [ + " {\"type\": \"fabric\", \"description\": \"smooth; could be made of cotton, flax, or hemp; could be used for bedding, clothing, and other household items\", \"similar objects\": [\"cotton\", \"silk\", \"wool\"]}", + 28 + ], + "shadow floor": [ + " {\"type\": \"flooring material\", \"description\": \"dark, matte finish; could be made of wood, vinyl, or laminate\", \"similar objects\": [\"hardwood floor\", \"tile floor\", \"carpet\"]}", + 28 + ], + "man ground": [ + " {\"type\": \"clothing item\", \"description\": \"long-sleeved; could be made of cotton; could have a hood\", \"similar objects\": [\"hoodie\", \"sweatshirt\", \"jacket\"]}", + 28 + ], + "tool box": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal; could have multiple compartments\", \"similar objects\": [\"tool chest\", \"tool cabinet\", \"tool rack\"]}", + 28 + ], + "brick floor": [ + " {\"type\": \"flooring material\", \"description\": \"hard, rectangular, made of clay; could be red or grey\", \"similar objects\": [\"tile floor\", \"wood floor\", \"concrete floor\"]}", + 28 + ], + "dark glasses": [ + " {\"type\": \"eyewear\", \"description\": \"dark lenses; could be made of plastic or metal; could have a frame\", \"similar objects\": [\"sunglasses\", \"safety glasses\", \"reading glasses\"]}", + 28 + ], + "dishwashers": [ + " {\"type\": \"appliance\", \"description\": \"electronic; could be built-in or portable; could have multiple racks\", \"similar objects\": [\"refrigerator\", \"washing machine\", \"microwave\"]}", + 28 + ], + "room chair": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could have armrests; could be upholstered; could have a backrest\", \"similar objects\": [\"sofa\", \"ottoman\", \"stool\"]}", + 28 + ], + "blue river": [ + " {\"type\": \"natural feature\", \"description\": \"long, blue body of water; could have a current; could have fish\", \"similar objects\": [\"lake\", \"ocean\", \"stream\"]}", + 28 + ], + "address": [ + " {\"type\": \"information\", \"description\": \"a set of information that identifies a location\", \"similar objects\": [\"postal code\", \"phone number\", \"email address\"]}", + 28 + ], + "beige pants": [ + " {\"type\": \"clothing\", \"description\": \"long, beige-colored, could have pockets\", \"similar objects\": [\"jeans\", \"khakis\", \"trousers\"]}", + 28 + ], + "cola": [ + " {\"type\": \"beverage\", \"description\": \"brown, carbonated, sweet; could be served in a can or bottle\", \"similar objects\": [\"soda\", \"juice\", \"beer\"]}", + 28 + ], + "coolers": [ + " {\"type\": \"storage tool\", \"description\": \"box-shaped; could be made of plastic; could have a handle\", \"similar objects\": [\"ice chest\", \"thermos\", \"lunch box\"]}", + 28 + ], + "drapery": [ + " {\"type\": \"fabric\", \"description\": \"long, thin, and lightweight; could be made of silk, cotton, or linen; could be used for curtains or other decorative purposes\", \"similar objects\": [\"curtain\", \"tapestry\", \"upholstery\"]}", + 28 + ], + "smoke detector": [ + " {\"type\": \"safety device\", \"description\": \"round; has a loud alarm; could be battery-powered\", \"similar objects\": [\"fire alarm\", \"carbon monoxide detector\", \"security camera\"]}", + 28 + ], + "waffles": [ + " {\"type\": \"food\", \"description\": \"round; has a grid pattern; could be served with syrup\", \"similar objects\": [\"pancakes\", \"crepes\", \"french toast\"]}", + 28 + ], + "tissue holder": [ + " {\"type\": \"storage tool\", \"description\": \"box-shaped; could be made of metal or plastic; could have a lid\", \"similar objects\": [\"box\", \"container\", \"basket\"]}", + 28 + ], + "bending": [ + " {\"type\": \"action\", \"description\": \"the act of changing the shape of something by applying pressure\", \"similar objects\": [\"twisting\", \"stretching\", \"squeezing\"]}", + 28 + ], + "soccer cleat": [ + " {\"type\": \"footwear\", \"description\": \"has a hard sole; could have spikes; could be made of leather\", \"similar objects\": [\"hiking boot\", \"running shoe\", \"sneaker\"]}", + 28 + ], + "orange tie": [ + "\n{\"type\": \"clothing item\", \"description\": \"orange; could be made of silk; could be used to tie around the neck\", \"similar objects\": [\"red tie\", \"blue tie\", \"black tie\"]}", + 28 + ], + "sandy": [ + " {\"type\": \"texture\", \"description\": \"rough; could be found in beaches; could be yellowish or brownish\", \"similar objects\": [\"granular\", \"gritty\", \"grains\"]}", + 28 + ], + "mixing bowl": [ + " {\"type\": \"cooking tool\", \"description\": \"round; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"pot\", \"pan\", \"measuring cup\"]}", + 28 + ], + "water jug": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could have a handle; could be made of plastic or metal\", \"similar objects\": [\"pitcher\", \"bottle\", \"jar\"]}", + 28 + ], + "wood brown": [ + " {\"type\": \"color\", \"description\": \"dark brown; could be used to describe furniture or other objects\", \"similar objects\": [\"mahogany\", \"walnut\", \"teak\"]}", + 28 + ], + "stadium seats": [ + " {\"type\": \"seating furniture\", \"description\": \"long, connected, could be made of plastic or metal; could have armrests\", \"similar objects\": [\"benches\", \"chairs\", \"sofas\"]}", + 28 + ], + "shaft": [ + " {\"type\": \"mechanical part\", \"description\": \"long, cylindrical; could be made of metal; could be used to transmit power\", \"similar objects\": [\"axle\", \"rod\", \"gear\"]}", + 28 + ], + "entrance way": [ + " {\"type\": \"architectural feature\", \"description\": \"opening in a wall; could have a door; could have a frame\", \"similar objects\": [\"window\", \"gate\", \"doorway\"]}", + 28 + ], + "tress": [ + " {\"type\": \"plant\", \"description\": \"tall; has leaves; could have fruits; could have branches\", \"similar objects\": [\"bush\", \"tree\", \"shrub\"]}", + 28 + ], + "chopstick": [ + " {\"type\": \"eating utensil\", \"description\": \"long; made of wood or bamboo; used to pick up food\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}", + 28 + ], + "connector": [ + " {\"type\": \"electronic device\", \"description\": \"small, cylindrical; used to join two wires together\", \"similar objects\": [\"plug\", \"socket\", \"switch\"]}", + 28 + ], + "broccolli": [ + " {\"type\": \"vegetable\", \"description\": \"green, small florets; could have a long stem; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"kale\"]}", + 28 + ], + "apple slices": [ + " {\"type\": \"food\", \"description\": \"thin, round pieces of apple; could be cooked or eaten raw\", \"similar objects\": [\"orange slices\", \"banana slices\", \"pear slices\"]}", + 28 + ], + "almonds": [ + " {\"type\": \"nut\", \"description\": \"oval-shaped; could be roasted; could be eaten as snacks\", \"similar objects\": [\"walnuts\", \"cashews\", \"peanuts\"]}", + 28 + ], + "thicket": [ + " {\"type\": \"vegetation\", \"description\": \"densely packed shrubs and trees; could be used as a shelter\", \"similar objects\": [\"forest\", \"bush\", \"grove\"]}", + 28 + ], + "tissue papers": [ + " {\"type\": \"paper product\", \"description\": \"soft, thin, rectangular; could be used for wiping\", \"similar objects\": [\"toilet paper\", \"paper towel\", \"napkin\"]}", + 28 + ], + "hair brush": [ + " {\"type\": \"grooming tool\", \"description\": \"long handle; has bristles; could be made of plastic or wood\", \"similar objects\": [\"comb\", \"hair dryer\", \"curling iron\"]}", + 28 + ], + "sweet potatoes": [ + " {\"type\": \"vegetable\", \"description\": \"long, sweet, orange; could have brown skin; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"potatoes\", \"yams\", \"carrots\"]}", + 28 + ], + "satchel": [ + " {\"type\": \"bag\", \"description\": \"rectangular; could be made of leather; could have a strap\", \"similar objects\": [\"backpack\", \"briefcase\", \"purse\"]}", + 28 + ], + "soccer balls": [ + " {\"type\": \"sports equipment\", \"description\": \"round; made of leather; has a pattern of pentagons and hexagons\", \"similar objects\": [\"basketball\", \"baseball\", \"tennis ball\"]}", + 28 + ], + "champagne": [ + " {\"type\": \"beverage\", \"description\": \"sparkling white wine; could be served in a flute glass\", \"similar objects\": [\"sparkling wine\", \"cider\", \"beer\"]}", + 28 + ], + "back leg": [ + " {\"type\": \"body part\", \"description\": \"part of the lower body; connects to the hip; could be used for walking and running\", \"similar objects\": [\"front leg\", \"arm\", \"foot\"]}", + 28 + ], + "motorcyle": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"tricycle\"]}", + 28 + ], + "plane door": [ + " {\"type\": \"aircraft part\", \"description\": \"rectangular; has a handle; could be opened and closed\", \"similar objects\": [\"window\", \"emergency exit\", \"overhead bin\"]}", + 28 + ], + "dark background": [ + " {\"type\": \"background\", \"description\": \"dark color; could be solid or patterned\", \"similar objects\": [\"black background\", \"gray background\", \"white background\"]}", + 28 + ], + "router": [ + " {\"type\": \"electronic device\", \"description\": \"box-shaped; could be connected to a modem; could be used to connect to the internet\", \"similar objects\": [\"modem\", \"switch\", \"hub\"]}", + 28 + ], + "silver clock": [ + "\n{\"type\": \"timekeeping tool\", \"description\": \"round; made of silver; has two hands\", \"similar objects\": [\"watch\", \"alarm clock\", \"grandfather clock\"]}", + 28 + ], + "furry cat": [ + "\n{\"type\": \"animal\", \"description\": \"long fur; could have stripes or spots; could have whiskers; could have a tail\", \"similar objects\": [\"dog\", \"rabbit\", \"mouse\"]}", + 28 + ], + "fancy": [ + "\n{\"type\": \"adjective\", \"description\": \"elegant; luxurious; stylish\", \"similar objects\": [\"opulent\", \"lavish\", \"grand\"]}", + 28 + ], + "mp3 player": [ + " {\"type\": \"electronic device\", \"description\": \"small, portable, digital audio player; could have a screen; could have buttons\", \"similar objects\": [\"iPod\", \"CD player\", \"Walkman\"]}", + 28 + ], + "glass panes": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be used to build windows and doors\", \"similar objects\": [\"wooden boards\", \"bricks\", \"tiles\"]}", + 28 + ], + "feta cheese": [ + " {\"type\": \"food\", \"description\": \"white, crumbly, salty; could be used in salads\", \"similar objects\": [\"goat cheese\", \"parmesan cheese\", \"mozzarella cheese\"]}", + 28 + ], + "handle fork": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, has a handle; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 28 + ], + "cigarettes": [ + " {\"type\": \"tobacco product\", \"description\": \"long, thin, cylindrical; could be wrapped in paper; could have a filter\", \"similar objects\": [\"cigar\", \"pipe\", \"hookah\"]}", + 28 + ], + "brick pavement": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of clay; could be laid in a pattern\", \"similar objects\": [\"concrete\", \"asphalt\", \"stone\"]}", + 28 + ], + "homemade pizza": [ + " {\"type\": \"food\", \"description\": \"round; could have various toppings; could be cooked in an oven\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread pizza\"]}", + 28 + ], + "lighter": [ + " {\"type\": \"lighting tool\", \"description\": \"small; could be made of metal; could be used to light cigarettes\", \"similar objects\": [\"matches\", \"torch\", \"candle\"]}", + 28 + ], + "side mirrors": [ + " {\"type\": \"automotive accessory\", \"description\": \"attached to the side of a vehicle; used to see the sides of the vehicle\", \"similar objects\": [\"rearview mirror\", \"headlights\", \"windshield wipers\"]}", + 28 + ], + "racket handle": [ + " {\"type\": \"sports equipment\", \"description\": \"long, cylindrical; could be made of wood or metal; could have a grip\", \"similar objects\": [\"bat handle\", \"club handle\", \"paddle handle\"]}", + 28 + ], + "route sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; could be yellow or white; could have arrows or numbers\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 28 + ], + "kitchen drawer": [ + " {\"type\": \"furniture\", \"description\": \"has a handle; could be made of wood or metal; could be used to store items\", \"similar objects\": [\"cabinet\", \"shelf\", \"cupboard\"]}", + 28 + ], + "plastic helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"hard, round, could be transparent; could have a chin strap\", \"similar objects\": [\"safety helmet\", \"hard hat\", \"bicycle helmet\"]}", + 28 + ], + "terra": [ + " {\"type\": \"planet\", \"description\": \"third planet from the sun; has one moon; has an atmosphere\", \"similar objects\": [\"Earth\", \"Mars\", \"Venus\"]}", + 28 + ], + "brown snout": [ + " {\"type\": \"animal\", \"description\": \"long, pointed snout; could have a brown fur; could have a long tail\", \"similar objects\": [\"dog\", \"fox\", \"raccoon\"]}", + 28 + ], + "mirror truck": [ + "\n{\"type\": \"vehicle\", \"description\": \"large truck with a large mirror on the back; used for inspecting roads and highways\", \"similar objects\": [\"dump truck\", \"fire truck\", \"tow truck\"]}", + 28 + ], + "beck": [ + " {\"type\": \"musical instrument\", \"description\": \"stringed instrument; has a long neck; has six strings\", \"similar objects\": [\"guitar\", \"violin\", \"ukulele\"]}", + 28 + ], + "crosswalk signal": [ + " {\"type\": \"traffic signal\", \"description\": \"red and green lights; could be in the shape of a hand or a walking figure; could be accompanied by a sound signal\", \"similar objects\": [\"stop sign\", \"traffic light\", \"yield sign\"]}", + 28 + ], + "wood plank": [ + " {\"type\": \"building material\", \"description\": \"long, flat, could be made of wood or plastic\", \"similar objects\": [\"plywood\", \"timber\", \"lumber\"]}", + 28 + ], + "snow shoes": [ + " {\"type\": \"footwear\", \"description\": \"large, flat, made of leather or fabric; could have metal spikes\", \"similar objects\": [\"ski boots\", \"hiking boots\", \"ice skates\"]}", + 28 + ], + "blue sign": [ + " {\"type\": \"signage\", \"description\": \"blue; could be rectangular or square; could have words or symbols\", \"similar objects\": [\"yellow sign\", \"red sign\", \"green sign\"]}", + 28 + ], + "radish": [ + " {\"type\": \"vegetable\", \"description\": \"round; red or white; could have green leaves\", \"similar objects\": [\"carrot\", \"turnip\", \"beetroot\"]}", + 28 + ], + "outfits": [ + " {\"type\": \"clothing\", \"description\": \"could be made of different materials; could be of different colors and styles; could be for different occasions\", \"similar objects\": [\"dress\", \"shirt\", \"pants\"]}", + 28 + ], + "wood planks": [ + " {\"type\": \"building material\", \"description\": \"long, thin, rectangular; could be used for flooring or walls\", \"similar objects\": [\"plywood\", \"hardwood\", \"laminate\"]}", + 28 + ], + "driftwood": [ + " {\"type\": \"natural object\", \"description\": \"wooden pieces found on the beach; could be curved or twisted; could be bleached by the sun\", \"similar objects\": [\"seaweed\", \"shells\", \"rocks\"]}", + 28 + ], + "fuzzy": [ + " {\"type\": \"adjective\", \"description\": \"soft and fluffy; could be used to describe a texture\", \"similar objects\": [\"fluffy\", \"hairy\", \"velvety\"]}", + 28 + ], + "airline": [ + " {\"type\": \"transportation service\", \"description\": \"provides air travel services; could have different classes of services\", \"similar objects\": [\"train\", \"bus\", \"cruise\"]}", + 28 + ], + "garage doors": [ + " {\"type\": \"building component\", \"description\": \"large, rectangular; could be made of metal or wood; could be automated\", \"similar objects\": [\"windows\", \"doors\", \"gates\"]}", + 28 + ], + "aisle": [ + " {\"type\": \"space\", \"description\": \"long, narrow, could be in a store or a church\", \"similar objects\": [\"hallway\", \"corridor\", \"passageway\"]}", + 28 + ], + "tan tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"bricks\", \"wooden planks\", \"marble slabs\"]}", + 27 + ], + "street curb": [ + " {\"type\": \"road feature\", \"description\": \"raised edge of a street; could be made of concrete or metal\", \"similar objects\": [\"sidewalk\", \"guardrail\", \"traffic island\"]}", + 27 + ], + "entertainment stand": [ + " {\"type\": \"furniture\", \"description\": \"could have shelves and drawers; could be made of wood or metal; could have a flat top surface\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"sideboard\"]}", + 27 + ], + "crosswalk light": [ + " {\"type\": \"traffic signal\", \"description\": \"red and green lights; could be mounted on a pole; could be operated by a button\", \"similar objects\": [\"stop sign\", \"traffic light\", \"yield sign\"]}", + 27 + ], + "trash bags": [ + " {\"type\": \"container\", \"description\": \"large, plastic, usually black; could be tied up\", \"similar objects\": [\"garbage can\", \"plastic bag\", \"recycle bin\"]}", + 27 + ], + "horse saddle": [ + " {\"type\": \"equipment\", \"description\": \"leather; has a horn; has stirrups\", \"similar objects\": [\"bridle\", \"halter\", \"reins\"]}", + 27 + ], + "bathroom stall": [ + " {\"type\": \"furniture\", \"description\": \"enclosed; could have a door; could have a toilet\", \"similar objects\": [\"shower stall\", \"changing room\", \"locker room\"]}", + 27 + ], + "bare patch": [ + " {\"type\": \"landscape feature\", \"description\": \"an area of land with no vegetation; could be caused by drought, overgrazing, or other environmental factors\", \"similar objects\": [\"desert\", \"meadow\", \"prairie\"]}", + 27 + ], + "blonde man": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could have fair skin\", \"similar objects\": [\"blonde woman\", \"brunette man\", \"brunette woman\"]}", + 27 + ], + "cardboard sign": [ + " {\"type\": \"advertisement tool\", \"description\": \"rectangular; could be written on; could be held up\", \"similar objects\": [\"poster\", \"banner\", \"billboard\"]}", + 27 + ], + "bronze statue": [ + " {\"type\": \"sculpture\", \"description\": \"made of bronze; could be of a person or an animal; could be standing or sitting\", \"similar objects\": [\"marble statue\", \"wooden statue\", \"iron statue\"]}", + 27 + ], + "blue bed": [ + "\n{\"type\": \"furniture\", \"description\": \"blue; could have a headboard; could have a footboard; could have a mattress\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}", + 27 + ], + "veges": [ + "\n{\"type\": \"food\", \"description\": \"various types of vegetables; could be cooked or eaten raw; could be sliced, diced, or mashed\", \"similar objects\": [\"fruits\", \"grains\", \"legumes\"]}", + 27 + ], + "orange pole": [ + " {\"type\": \"traffic sign\", \"description\": \"orange; could be in a cylindrical shape; could have a sign on it\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 27 + ], + "silver cellphone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a touchscreen; could be made of metal; could have a camera\", \"similar objects\": [\"laptop\", \"tablet\", \"smartwatch\"]}", + 27 + ], + "standing": [ + " {\"type\": \"posture\", \"description\": \"body is upright; feet are on the ground; arms are by the side\", \"similar objects\": [\"sitting\", \"crouching\", \"kneeling\"]}", + 27 + ], + "box brown": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be sealed with tape\", \"similar objects\": [\"bag\", \"envelope\", \"suitcase\"]}", + 27 + ], + "plan": [ + " {\"type\": \"document\", \"description\": \"written document; could be a blueprint; could be a strategy\", \"similar objects\": [\"proposal\", \"report\", \"agenda\"]}", + 27 + ], + "omelet": [ + " {\"type\": \"food\", \"description\": \"egg-based dish; could be filled with vegetables, cheese, or meat; could be served with toast\", \"similar objects\": [\"scrambled eggs\", \"frittata\", \"quiche\"]}", + 27 + ], + "round headlights": [ + " {\"type\": \"automotive part\", \"description\": \"circular; could be attached to the front of a vehicle; could be used to provide illumination\", \"similar objects\": [\"taillights\", \"fog lights\", \"turn signals\"]}", + 27 + ], + "wood boards": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular; could be used for construction\", \"similar objects\": [\"plywood\", \"timber\", \"lumber\"]}", + 27 + ], + "blue words": [ + "\n{\"type\": \"text\", \"description\": \"words with blue color; could be printed on paper or displayed on a screen\", \"similar objects\": [\"red words\", \"green words\", \"black words\"]}", + 27 + ], + "lounge chairs": [ + " {\"type\": \"furniture\", \"description\": \"long chairs; could be made of wood or metal; could have armrests and cushions\", \"similar objects\": [\"sofa\", \"recliner\", \"ottoman\"]}", + 27 + ], + "tree tops": [ + " {\"type\": \"plant part\", \"description\": \"green; could be made of leaves; could be seen from a distance\", \"similar objects\": [\"branches\", \"roots\", \"flowers\"]}", + 27 + ], + "blue ocean": [ + "\n{\"type\": \"natural phenomenon\", \"description\": \"large body of water; could be deep blue or light blue; could have waves\", \"similar objects\": [\"lake\", \"river\", \"sea\"]}", + 27 + ], + "food container": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of plastic or metal; could be sealed; could be used to store food\", \"similar objects\": [\"lunch box\", \"jar\", \"cooler\"]}", + 27 + ], + "furry ear": [ + " {\"type\": \"accessory\", \"description\": \"attached to head; could be made of faux fur; could be of different colors\", \"similar objects\": [\"headband\", \"hat\", \"hair clip\"]}", + 27 + ], + "washcloth": [ + " {\"type\": \"cleaning tool\", \"description\": \"rectangular; made of cloth; could be used for cleaning\", \"similar objects\": [\"towel\", \"sponge\", \"rag\"]}", + 27 + ], + "square mirror": [ + "\n{\"type\": \"decorative item\", \"description\": \"has four sides; could be framed; could be used to reflect light\", \"similar objects\": [\"round mirror\", \"oval mirror\", \"triangle mirror\"]}", + 27 + ], + "train wheels": [ + " {\"type\": \"transportation part\", \"description\": \"round; could be made of metal; could be connected to the train body\", \"similar objects\": [\"car wheels\", \"airplane wings\", \"boat propellers\"]}", + 27 + ], + "pearl necklace": [ + " {\"type\": \"jewelry\", \"description\": \"string of pearls; could be made of different materials; could be of different lengths\", \"similar objects\": [\"bracelet\", \"earrings\", \"ring\"]}", + 27 + ], + "shadow pavement": [ + " {\"type\": \"visual effect\", \"description\": \"darker area on the ground caused by the sun or other light source\", \"similar objects\": [\"reflection\", \"silhouette\", \"highlight\"]}", + 27 + ], + "somebody": [ + "\n{\"type\": \"person\", \"description\": \"could be male or female; could be of any age; could be wearing any type of clothing\", \"similar objects\": [\"man\", \"woman\", \"child\"]}", + 27 + ], + "manes": [ + " {\"type\": \"animal feature\", \"description\": \"long, flowing hair on the neck of horses, lions, and other animals\", \"similar objects\": [\"tails\", \"hooves\", \"horns\"]}", + 27 + ], + "airplane wheels": [ + " {\"type\": \"airplane part\", \"description\": \"round; could be made of metal; could have a tire\", \"similar objects\": [\"propeller\", \"engine\", \"wing\"]}", + 27 + ], + "finger nails": [ + " {\"type\": \"body part\", \"description\": \"hard, thin, curved; could be painted with colors\", \"similar objects\": [\"toenails\", \"eyelashes\", \"hair\"]}", + 27 + ], + "blackboard": [ + " {\"type\": \"teaching tool\", \"description\": \"large, rectangular, black surface; could be used for writing and drawing\", \"similar objects\": [\"whiteboard\", \"chalkboard\", \"projector\"]}", + 27 + ], + "dark jacket": [ + " {\"type\": \"clothing\", \"description\": \"dark color; could be made of leather; could have a hood; could have pockets\", \"similar objects\": [\"coat\", \"hoodie\", \"sweater\"]}", + 27 + ], + "murky": [ + " {\"type\": \"adjective\", \"description\": \"dark, cloudy, unclear\", \"similar objects\": [\"foggy\", \"murky\", \"misty\"]}", + 27 + ], + "sprouts": [ + " {\"type\": \"vegetable\", \"description\": \"small, green, could be cooked or eaten raw; could be served as a side dish\", \"similar objects\": [\"broccoli\", \"cauliflower\", \"green beans\"]}", + 27 + ], + "wicker baskets": [ + " {\"type\": \"container\", \"description\": \"made of woven materials; could be used for storage; could be used for decoration\", \"similar objects\": [\"baskets\", \"boxes\", \"hampers\"]}", + 27 + ], + "crisp": [ + " {\"type\": \"food\", \"description\": \"crunchy; could be salty; could be sweet\", \"similar objects\": [\"chip\", \"cracker\", \"popcorn\"]}", + 27 + ], + "orange suitcase": [ + "\n{\"type\": \"luggage\", \"description\": \"orange; rectangular; has a handle; could have wheels\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 27 + ], + "basketball goal": [ + " {\"type\": \"sports equipment\", \"description\": \"metal hoop with a net; could be attached to a backboard\", \"similar objects\": [\"soccer goal\", \"volleyball net\", \"hockey goal\"]}", + 27 + ], + "junk": [ + " {\"type\": \"waste\", \"description\": \"unwanted items; could be made of metal, plastic, paper, etc.\", \"similar objects\": [\"trash\", \"garbage\", \"rubbish\"]}", + 27 + ], + "travel bag": [ + " {\"type\": \"bag\", \"description\": \"large; could be made of fabric; could have multiple compartments; could have a handle\", \"similar objects\": [\"backpack\", \"duffel bag\", \"suitcase\"]}", + 27 + ], + "cylinder": [ + " {\"type\": \"shape\", \"description\": \"round; has two flat ends; could be made of metal or plastic\", \"similar objects\": [\"cone\", \"sphere\", \"cube\"]}", + 27 + ], + "baseball fans": [ + " {\"type\": \"group of people\", \"description\": \"people who are passionate about baseball; could be wearing baseball caps and jerseys; could be cheering and shouting\", \"similar objects\": [\"soccer fans\", \"basketball fans\", \"hockey fans\"]}", + 27 + ], + "food bowl": [ + " {\"type\": \"utensil\", \"description\": \"round; could be made of plastic or ceramic; used to contain food\", \"similar objects\": [\"plate\", \"dish\", \"cup\"]}", + 27 + ], + "tv set": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could have speakers\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}", + 27 + ], + "silver base": [ + " {\"type\": \"furniture\", \"description\": \"shiny, metallic; could be used as a support for other objects\", \"similar objects\": [\"table\", \"chair\", \"stool\"]}", + 27 + ], + "stainless steel pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round; made of stainless steel; has a handle\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 27 + ], + "curls": [ + " {\"type\": \"hairstyle\", \"description\": \"loose, spiral-shaped locks; could be created with a curling iron\", \"similar objects\": [\"waves\", \"bangs\", \"ponytail\"]}", + 27 + ], + "gathering": [ + " {\"type\": \"event\", \"description\": \"a group of people coming together for a common purpose\", \"similar objects\": [\"meeting\", \"conference\", \"party\"]}", + 27 + ], + "sleeve blue shirt": [ + "\n{\"type\": \"clothing item\", \"description\": \"long sleeve; could be made of cotton; could have buttons; could have a collar\", \"similar objects\": [\"white shirt\", \"black shirt\", \"denim shirt\"]}", + 27 + ], + "cardboard pizza box": [ + "\n{\"type\": \"container\", \"description\": \"rectangular; made of cardboard; could be used to store pizza\", \"similar objects\": [\"paper bag\", \"plastic container\", \"aluminum foil\"]}", + 27 + ], + "cross top building": [ + " {\"type\": \"architecture\", \"description\": \"has a cross-shaped top; could be made of stone or metal; could have a steeple\", \"similar objects\": [\"cathedral\", \"church\", \"mosque\"]}", + 27 + ], + "hunk": [ + " {\"type\": \"slang\", \"description\": \"a physically attractive man\", \"similar objects\": [\"stud\", \"beefcake\", \"hottie\"]}", + 27 + ], + "bed sheets": [ + " {\"type\": \"bedding\", \"description\": \"rectangular; could be made of cotton, linen, or silk; could be plain or patterned\", \"similar objects\": [\"pillow cases\", \"duvet covers\", \"blankets\"]}", + 27 + ], + "pepperonis": [ + " {\"type\": \"food\", \"description\": \"round, red, spicy; could be sliced into small pieces\", \"similar objects\": [\"sausage\", \"bacon\", \"salami\"]}", + 27 + ], + "clasp": [ + " {\"type\": \"fastening tool\", \"description\": \"used to fasten two objects together; could be made of metal or plastic\", \"similar objects\": [\"hook\", \"button\", \"zipper\"]}", + 27 + ], + "kite tail": [ + " {\"type\": \"toy\", \"description\": \"long, colorful, could be made of paper or fabric; could be attached to a kite\", \"similar objects\": [\"balloon\", \"pinwheel\", \"windmill\"]}", + 27 + ], + "surboard": [ + " {\"type\": \"sports equipment\", \"description\": \"long; could be made of wood or fiberglass; could have a fin\", \"similar objects\": [\"skateboard\", \"wakeboard\", \"snowboard\"]}", + 27 + ], + "baby horse": [ + " {\"type\": \"animal\", \"description\": \"smaller than an adult horse; has a short mane; could have a white spot on its forehead\", \"similar objects\": [\"foal\", \"calf\", \"puppy\"]}", + 27 + ], + "laptop screen": [ + " {\"type\": \"computer part\", \"description\": \"flat, rectangular; could be touch-sensitive; could be glossy or matte\", \"similar objects\": [\"keyboard\", \"mouse\", \"monitor\"]}", + 27 + ], + "nike shoes": [ + " {\"type\": \"footwear\", \"description\": \"athletic shoes; could be made of leather; could have a swoosh logo\", \"similar objects\": [\"adidas shoes\", \"converse shoes\", \"puma shoes\"]}", + 27 + ], + "silver motorcycle": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a silver body; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"moped\"]}", + 27 + ], + "certificate": [ + " {\"type\": \"document\", \"description\": \"paper; could be framed; could have a seal\", \"similar objects\": [\"diploma\", \"award\", \"license\"]}", + 27 + ], + "missile": [ + " {\"type\": \"weapon\", \"description\": \"long, cylindrical; could be launched from a rocket\", \"similar objects\": [\"torpedo\", \"bomb\", \"grenade\"]}", + 27 + ], + "tail wings": [ + " {\"type\": \"aircraft part\", \"description\": \"horizontal stabilizers; could be used to control the aircraft's pitch\", \"similar objects\": [\"fuselage\", \"engine\", \"landing gear\"]}", + 27 + ], + "shore line": [ + " {\"type\": \"landscape\", \"description\": \"the line where the land meets the sea; could have rocks, sand, and waves\", \"similar objects\": [\"beach\", \"coast\", \"ocean\"]}", + 27 + ], + "power light": [ + " {\"type\": \"electrical tool\", \"description\": \"could be used to indicate power status; could be used to indicate network status; could be used to indicate system status\", \"similar objects\": [\"power button\", \"reset button\", \"LED indicator\"]}", + 27 + ], + "bump": [ + " {\"type\": \"physical object\", \"description\": \"raised area on a surface; could be caused by an impact\", \"similar objects\": [\"dent\", \"scratch\", \"crack\"]}", + 27 + ], + "cellphones": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; could have a touchscreen; could have a camera\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 27 + ], + "pink house": [ + " {\"type\": \"building\", \"description\": \"pink; could have a roof; could have windows and doors\", \"similar objects\": [\"apartment\", \"mansion\", \"cottage\"]}", + 27 + ], + "light green": [ + " {\"type\": \"color\", \"description\": \"pale green; could be described as yellowish green\", \"similar objects\": [\"mint green\", \"lime green\", \"olive green\"]}", + 27 + ], + "concrete stairs": [ + " {\"type\": \"building material\", \"description\": \"made of concrete; could have a railing; could have multiple steps\", \"similar objects\": [\"wooden stairs\", \"metal stairs\", \"stone stairs\"]}", + 27 + ], + "cloudless": [ + " {\"type\": \"weather condition\", \"description\": \"no clouds in the sky; clear sky\", \"similar objects\": [\"sunny\", \"clear\", \"snowy\"]}", + 27 + ], + "dark shadows": [ + " {\"type\": \"phenomenon\", \"description\": \"dark, mysterious, could be caused by light obstruction\", \"similar objects\": [\"shade\", \"gloom\", \"gloomy atmosphere\"]}", + 27 + ], + "mold": [ + " {\"type\": \"fungus\", \"description\": \"could be green, black, or white; could grow on food or walls; could have a musty smell\", \"similar objects\": [\"mildew\", \"fungus\", \"yeast\"]}", + 27 + ], + "denim jacket": [ + " {\"type\": \"clothing\", \"description\": \"blue; could be made of cotton; could have pockets; could have buttons\", \"similar objects\": [\"jeans\", \"shirt\", \"hoodie\"]}", + 27 + ], + "maroon car": [ + "\n{\"type\": \"vehicle\", \"description\": \"maroon color; could be a sedan, coupe, or SUV; could have four doors\", \"similar objects\": [\"red car\", \"black car\", \"silver car\"]}", + 27 + ], + "cityscape": [ + " {\"type\": \"landscape\", \"description\": \"buildings, roads, trees, and other structures; could be seen from a distance\", \"similar objects\": [\"seascape\", \"mountainscape\", \"countryside\"]}", + 27 + ], + "grates": [ + " {\"type\": \"cooking tool\", \"description\": \"metal; has holes; could be used to drain liquid\", \"similar objects\": [\"strainer\", \"colander\", \"sieve\"]}", + 27 + ], + "train passenger car": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; has many compartments; could have a dining car\", \"similar objects\": [\"bus\", \"airplane\", \"boat\"]}", + 27 + ], + "cinnamon roll": [ + " {\"type\": \"food\", \"description\": \"round; has a swirl of cinnamon and sugar; could be topped with icing\", \"similar objects\": [\"doughnut\", \"croissant\", \"muffin\"]}", + 27 + ], + "air vents": [ + " {\"type\": \"ventilation tool\", \"description\": \"rectangular; could be made of metal; could be used to circulate air\", \"similar objects\": [\"air conditioner\", \"fan\", \"heater\"]}", + 27 + ], + "corners": [ + " {\"type\": \"geometric shape\", \"description\": \"four points that meet at a right angle; could be found in a square or rectangle\", \"similar objects\": [\"edges\", \"vertices\", \"sides\"]}", + 27 + ], + "bedside": [ + " {\"type\": \"furniture\", \"description\": \"small table; could be placed beside a bed; could have drawers\", \"similar objects\": [\"nightstand\", \"dresser\", \"end table\"]}", + 27 + ], + "coin": [ + " {\"type\": \"currency\", \"description\": \"round; could have different values; could be made of metal\", \"similar objects\": [\"dollar bill\", \"credit card\", \"check\"]}", + 27 + ], + "metal bell": [ + " {\"type\": \"instrument\", \"description\": \"made of metal; could be used to make ringing sound\", \"similar objects\": [\"cymbal\", \"gong\", \"xylophone\"]}", + 27 + ], + "baby lamb": [ + " {\"type\": \"animal\", \"description\": \"white; has a small body; could have a curly tail\", \"similar objects\": [\"goat\", \"sheep\", \"calf\"]}", + 27 + ], + "website address": [ + " {\"type\": \"internet resource\", \"description\": \"a string of characters that directs to a web page\", \"similar objects\": [\"URL\", \"domain name\", \"hyperlink\"]}", + 27 + ], + "silver post": [ + " {\"type\": \"ornament\", \"description\": \"shiny; could be made of silver; could be in the shape of a post\", \"similar objects\": [\"necklace\", \"bracelet\", \"ring\"]}", + 27 + ], + "giraffee": [ + " {\"type\": \"animal\", \"description\": \"tall; has a long neck; has a spotted pattern; has a long tail\", \"similar objects\": [\"zebra\", \"elephant\", \"hippopotamus\"]}", + 27 + ], + "cub": [ + " {\"type\": \"animal\", \"description\": \"young animal; could be of a bear, lion, or tiger; could be small and furry\", \"similar objects\": [\"pup\", \"kitten\", \"fawn\"]}", + 27 + ], + "tan house": [ + "\n{\"type\": \"building\", \"description\": \"brown; could have a roof; could have windows and doors\", \"similar objects\": [\"barn\", \"shed\", \"garage\"]}", + 27 + ], + "gold letters": [ + " {\"type\": \"decoration\", \"description\": \"shiny, gold-colored letters; could be used to spell out words\", \"similar objects\": [\"glitter letters\", \"wooden letters\", \"plastic letters\"]}", + 27 + ], + "window ledge": [ + " {\"type\": \"architectural feature\", \"description\": \"horizontal surface that protrudes from a wall; could be used to place decorations or plants\", \"similar objects\": [\"shelf\", \"balcony\", \"fireplace mantel\"]}", + 27 + ], + "gum": [ + " {\"type\": \"food\", \"description\": \"chewy; could be flavored; could be in a pack\", \"similar objects\": [\"candy\", \"chocolate\", \"jelly beans\"]}", + 27 + ], + "blossom": [ + " {\"type\": \"plant\", \"description\": \"flower; could be pink, white, yellow, or other colors; could have petals and pistils\", \"similar objects\": [\"bud\", \"flower\", \"seedling\"]}", + 27 + ], + "bikini bottom": [ + " {\"type\": \"clothing\", \"description\": \"two-piece swimsuit; could be made of spandex or nylon; could have various colors and patterns\", \"similar objects\": [\"tankini\", \"monokini\", \"one-piece swimsuit\"]}", + 27 + ], + "check": [ + " {\"type\": \"document\", \"description\": \"rectangular; could be filled with information; could be signed\", \"similar objects\": [\"invoice\", \"receipt\", \"contract\"]}", + 27 + ], + "metal latch": [ + " {\"type\": \"hardware\", \"description\": \"metal; used to secure a door or window; could have a handle\", \"similar objects\": [\"lock\", \"hinge\", \"door knob\"]}", + 27 + ], + "log ground": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used for construction\", \"similar objects\": [\"bricks\", \"wood\", \"concrete\"]}", + 27 + ], + "jersey number": [ + " {\"type\": \"sports apparel\", \"description\": \"a number printed on a shirt; usually worn by athletes\", \"similar objects\": [\"uniform\", \"helmet\", \"shorts\"]}", + 27 + ], + "adult sheep": [ + " {\"type\": \"animal\", \"description\": \"white or black fur; has horns; could have a beard\", \"similar objects\": [\"goat\", \"cattle\", \"llama\"]}", + 27 + ], + "circus": [ + " {\"type\": \"entertainment\", \"description\": \"various performances; could include clowns, acrobats, animals, etc.\", \"similar objects\": [\"carnival\", \"amusement park\", \"theater\"]}", + 27 + ], + "silver minivan": [ + "\n{\"type\": \"vehicle\", \"description\": \"silver; has sliding doors; could have a third row of seats\", \"similar objects\": [\"SUV\", \"sedan\", \"hatchback\"]}", + 27 + ], + "empire": [ + " {\"type\": \"political entity\", \"description\": \"a large, powerful state or nation; could have a monarchy or a republic form of government; could have a large territory\", \"similar objects\": [\"kingdom\", \"nation\", \"state\"]}", + 27 + ], + "brownies": [ + " {\"type\": \"dessert\", \"description\": \"chocolate-flavored; could be served with ice cream; could be cut into small pieces\", \"similar objects\": [\"cake\", \"pie\", \"cookies\"]}", + 27 + ], + "head rest": [ + " {\"type\": \"furniture\", \"description\": \"attached to the back of a chair; provides support for the head\", \"similar objects\": [\"armrest\", \"footrest\", \"ottoman\"]}", + 27 + ], + "pink helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"pink; could be made of plastic; could have a visor\", \"similar objects\": [\"bike helmet\", \"skateboard helmet\", \"motorcycle helmet\"]}", + 27 + ], + "footwear": [ + " {\"type\": \"clothing item\", \"description\": \"could be made of leather, fabric, or rubber; could be in different shapes and sizes; could be used for protection and decoration\", \"similar objects\": [\"shoes\", \"boots\", \"sandals\"]}", + 27 + ], + "waiter": [ + " {\"type\": \"occupation\", \"description\": \"serves food and drinks; could be wearing a uniform\", \"similar objects\": [\"chef\", \"bartender\", \"hostess\"]}", + 27 + ], + "wood chips": [ + " {\"type\": \"material\", \"description\": \"small pieces of wood; could be used for smoking food\", \"similar objects\": [\"sawdust\", \"bark\", \"shavings\"]}", + 27 + ], + "orange roof": [ + " {\"type\": \"building material\", \"description\": \"orange-colored; could be made of tiles; could be used for roofing\", \"similar objects\": [\"shingles\", \"asphalt\", \"metal roofing\"]}", + 27 + ], + "orange leaves": [ + " {\"type\": \"plant\", \"description\": \"green, oval-shaped; could have a tinge of orange; could be dried and used for decoration\", \"similar objects\": [\"maple leaves\", \"oak leaves\", \"magnolia leaves\"]}", + 27 + ], + "train front": [ + " {\"type\": \"vehicle\", \"description\": \"long; has a locomotive; could have multiple carriages\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 27 + ], + "zebra leg": [ + " {\"type\": \"animal body part\", \"description\": \"long, black and white striped; could be used for walking\", \"similar objects\": [\"horse leg\", \"giraffe leg\", \"elephant leg\"]}", + 27 + ], + "train cart": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; could have multiple compartments; could be connected to other carts\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 27 + ], + "beaks": [ + " {\"type\": \"bird body part\", \"description\": \"sharp, curved, used for pecking and eating\", \"similar objects\": [\"wings\", \"talons\", \"feathers\"]}", + 27 + ], + "brown mane": [ + " {\"type\": \"animal feature\", \"description\": \"long, dark hair on the head of an animal\", \"similar objects\": [\"black mane\", \"white mane\", \"gray mane\"]}", + 27 + ], + "clown": [ + " {\"type\": \"entertainer\", \"description\": \"brightly colored clothes; wears a wig; has a painted face; could have a red nose\", \"similar objects\": [\"magician\", \"juggler\", \"mime\"]}", + 27 + ], + "segment": [ + " {\"type\": \"geometric shape\", \"description\": \"a line with two endpoints; could be curved or straight\", \"similar objects\": [\"line\", \"ray\", \"arc\"]}", + 26 + ], + "spatulas": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, long, and flexible; could be made of metal or plastic; could be used for flipping and stirring food\", \"similar objects\": [\"tongs\", \"spoons\", \"whisks\"]}", + 26 + ], + "computer laptop": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a keyboard and a screen; could be connected to a power source\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 26 + ], + "rocky": [ + " {\"type\": \"landscape\", \"description\": \"uneven surface; could be made of stones; could be found in mountains\", \"similar objects\": [\"cliff\", \"cave\", \"ravine\"]}", + 26 + ], + "beverage bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic or glass; could have a lid\", \"similar objects\": [\"water bottle\", \"thermos\", \"mug\"]}", + 26 + ], + "avocados": [ + " {\"type\": \"fruit\", \"description\": \"oval-shaped; green or black; has a large seed inside\", \"similar objects\": [\"banana\", \"mango\", \"kiwi\"]}", + 26 + ], + "game console": [ + " {\"type\": \"electronic device\", \"description\": \"has buttons and joysticks; could be connected to a TV\", \"similar objects\": [\"computer\", \"smartphone\", \"tablet\"]}", + 26 + ], + "switch plate": [ + " {\"type\": \"electrical tool\", \"description\": \"rectangular; has a switch; could be made of plastic or metal\", \"similar objects\": [\"outlet cover\", \"light switch\", \"dimmer switch\"]}", + 26 + ], + "meats": [ + "\n{\"type\": \"food\", \"description\": \"various types of animal flesh; could be cooked or uncooked; could be fresh or frozen\", \"similar objects\": [\"fish\", \"poultry\", \"seafood\"]}", + 26 + ], + "blue candle": [ + "\n{\"type\": \"lighting tool\", \"description\": \"blue; could be made of wax; could have a wick\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 26 + ], + "silver tea kettle": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round; made of silver; has a handle and a spout\", \"similar objects\": [\"coffee pot\", \"teapot\", \"water boiler\"]}", + 26 + ], + "salsa": [ + " {\"type\": \"condiment\", \"description\": \"spicy; could be made of tomatoes, onions, peppers, and cilantro\", \"similar objects\": [\"guacamole\", \"hot sauce\", \"pesto\"]}", + 26 + ], + "bottom section": [ + " {\"type\": \"clothing item\", \"description\": \"covers the lower part of the body; could be pants, skirts, shorts, etc.\", \"similar objects\": [\"top section\", \"dress\", \"jacket\"]}", + 26 + ], + "silver pipes": [ + " {\"type\": \"building material\", \"description\": \"shiny, metallic, cylindrical; could be used for plumbing\", \"similar objects\": [\"copper pipes\", \"aluminum pipes\", \"plastic pipes\"]}", + 26 + ], + "filing cabinet": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular; has drawers; could be made of metal or wood\", \"similar objects\": [\"desk\", \"bookshelf\", \"chair\"]}", + 26 + ], + "blue kite": [ + "\n{\"type\": \"toy\", \"description\": \"blue; has a tail; could be flown in the sky\", \"similar objects\": [\"balloon\", \"frisbee\", \"parachute\"]}", + 26 + ], + "home plate umpire": [ + "\n{\"type\": \"sports official\", \"description\": \"wears a mask and chest protector; stands behind home plate; makes calls on balls and strikes\", \"similar objects\": [\"baseball umpire\", \"referee\", \"linesman\"]}", + 26 + ], + "kneepad": [ + " {\"type\": \"protective gear\", \"description\": \"worn around the knee; could be made of foam or plastic; could be adjustable\", \"similar objects\": [\"elbow pad\", \"shin guard\", \"helmet\"]}", + 26 + ], + "restaurant menu": [ + " {\"type\": \"list of food items\", \"description\": \"list of food items and their prices; could be printed on paper or displayed on a screen\", \"similar objects\": [\"cafeteria menu\", \"takeout menu\", \"buffet menu\"]}", + 26 + ], + "silver tongs": [ + " {\"type\": \"utensil\", \"description\": \"long; made of metal; used for picking up food\", \"similar objects\": [\"spoon\", \"fork\", \"ladle\"]}", + 26 + ], + "cupboard door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have a handle\", \"similar objects\": [\"drawer\", \"wardrobe\", \"cabinet\"]}", + 26 + ], + "bed spread": [ + " {\"type\": \"bedding item\", \"description\": \"large piece of fabric; could be quilted; could be used to cover a bed\", \"similar objects\": [\"duvet cover\", \"comforter\", \"blanket\"]}", + 26 + ], + "pencil holder": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic or wood; could have multiple compartments\", \"similar objects\": [\"pencil case\", \"pencil box\", \"pencil pouch\"]}", + 26 + ], + "levels": [ + " {\"type\": \"measuring tool\", \"description\": \"has a bubble vial; could be used to measure angles and slopes\", \"similar objects\": [\"ruler\", \"tape measure\", \"protractor\"]}", + 26 + ], + "shopping bags": [ + " {\"type\": \"container\", \"description\": \"made of plastic or paper; could be reusable; could be printed with logos\", \"similar objects\": [\"tote bag\", \"backpack\", \"suitcase\"]}", + 26 + ], + "foot stool": [ + " {\"type\": \"furniture\", \"description\": \"small, rectangular, has legs; could be used as a seat\", \"similar objects\": [\"ottoman\", \"chair\", \"bench\"]}", + 26 + ], + "gold bracelet": [ + " {\"type\": \"jewelry\", \"description\": \"made of gold; could be in a shape of a circle; could have gems\", \"similar objects\": [\"necklace\", \"ring\", \"earrings\"]}", + 26 + ], + "arena": [ + " {\"type\": \"structure\", \"description\": \"large, open space; could be used for sports or entertainment events\", \"similar objects\": [\"stadium\", \"theater\", \"auditorium\"]}", + 26 + ], + "diamond shape": [ + " {\"type\": \"geometric shape\", \"description\": \"four equal sides; four equal angles; two long and two short sides\", \"similar objects\": [\"square\", \"rectangle\", \"triangle\"]}", + 26 + ], + "metal baseball bat": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long, cylindrical; made of metal; used in baseball\", \"similar objects\": [\"wooden baseball bat\", \"tennis racket\", \"golf club\"]}", + 26 + ], + "airline logo": [ + "\n{\"type\": \"logo\", \"description\": \"distinctive design; could be a combination of colors, shapes, and symbols; could be used to represent a company or organization\", \"similar objects\": [\"corporate logo\", \"brand logo\", \"sports logo\"]}", + 26 + ], + "plastic tub": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be used for storage; could be made of plastic\", \"similar objects\": [\"bucket\", \"box\", \"bin\"]}", + 26 + ], + "silver butter knife": [ + "\n{\"type\": \"utensil\", \"description\": \"silver; has a flat blade; could be used for spreading butter\", \"similar objects\": [\"spoon\", \"fork\", \"spatula\"]}", + 26 + ], + "arrow keys": [ + " {\"type\": \"computer input device\", \"description\": \"four keys in a row; used to control the movement of a cursor\", \"similar objects\": [\"mouse\", \"keyboard\", \"joystick\"]}", + 26 + ], + "brick column": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of bricks; could be used to support a structure\", \"similar objects\": [\"concrete column\", \"wood column\", \"steel column\"]}", + 26 + ], + "crashing wave": [ + " {\"type\": \"natural phenomenon\", \"description\": \"large, powerful wave; could be accompanied by foam and bubbles; could be seen in the ocean\", \"similar objects\": [\"tidal wave\", \"tsunami\", \"storm surge\"]}", + 26 + ], + "set train tracks": [ + " {\"type\": \"transportation tool\", \"description\": \"long, metal, parallel lines; could be connected to a train station\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 26 + ], + "wii console": [ + " {\"type\": \"gaming device\", \"description\": \"rectangular; has a controller; could be connected to a TV\", \"similar objects\": [\"PlayStation\", \"Xbox\", \"Nintendo Switch\"]}", + 26 + ], + "handle racket": [ + " {\"type\": \"sports equipment\", \"description\": \"long; has a handle; could be made of wood or metal\", \"similar objects\": [\"tennis racket\", \"badminton racket\", \"squash racket\"]}", + 26 + ], + "chocolate sauce": [ + " {\"type\": \"condiment\", \"description\": \"dark brown; could be used as a topping or dip\", \"similar objects\": [\"caramel sauce\", \"strawberry sauce\", \"whipped cream\"]}", + 26 + ], + "grassy area": [ + " {\"type\": \"landscape\", \"description\": \"green; could have flowers; could have trees; could have a lawnmower\", \"similar objects\": [\"meadow\", \"field\", \"park\"]}", + 26 + ], + "silver mouse": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; has two buttons and a scroll wheel; could be wireless\", \"similar objects\": [\"keyboard\", \"game controller\", \"trackpad\"]}", + 26 + ], + "antelopes": [ + " {\"type\": \"animal\", \"description\": \"long legs; long neck; horns; brown fur; could be found in grasslands\", \"similar objects\": [\"gazelles\", \"deer\", \"wildebeest\"]}", + 26 + ], + "wood bed": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have a headboard and footboard; could have four legs\", \"similar objects\": [\"metal bed\", \"sofa\", \"chair\"]}", + 26 + ], + "sockets": [ + " {\"type\": \"electrical tool\", \"description\": \"has two or more holes; could be used to plug in electrical appliances\", \"similar objects\": [\"plug\", \"switch\", \"outlet\"]}", + 26 + ], + "wet spot": [ + " {\"type\": \"liquid\", \"description\": \"shiny, dark, could be a puddle; could be caused by rain or spilled liquid\", \"similar objects\": [\"damp area\", \"moisture\", \"water\"]}", + 26 + ], + "giraffes neck": [ + " {\"type\": \"body part\", \"description\": \"long; could be spotted; could be brown\", \"similar objects\": [\"elephant's trunk\", \"giraffe's legs\", \"giraffe's head\"]}", + 26 + ], + "utility lines": [ + " {\"type\": \"infrastructure\", \"description\": \"long, thin wires; could be connected to poles; could be used to transmit electricity\", \"similar objects\": [\"telephone lines\", \"cable lines\", \"fiber optic cables\"]}", + 26 + ], + "carpet floor": [ + " {\"type\": \"flooring material\", \"description\": \"soft; could be made of wool; could be patterned\", \"similar objects\": [\"tile floor\", \"wood floor\", \"linoleum floor\"]}", + 26 + ], + "obstacle": [ + " {\"type\": \"barrier\", \"description\": \"something that blocks or hinders progress; could be physical or mental\", \"similar objects\": [\"hurdle\", \"challenge\", \"problem\"]}", + 26 + ], + "orange blanket": [ + "\n{\"type\": \"textile\", \"description\": \"orange; could be made of wool; could be used as a bed cover\", \"similar objects\": [\"quilt\", \"duvet\", \"throw blanket\"]}", + 26 + ], + "fire escape": [ + " {\"type\": \"safety tool\", \"description\": \"metal stairs; could be attached to a building; could be used to escape from fire\", \"similar objects\": [\"emergency exit\", \"fire ladder\", \"fire extinguisher\"]}", + 26 + ], + "futon": [ + " {\"type\": \"furniture\", \"description\": \"a type of bedding; could be folded up for storage; could be used as a couch or a bed\", \"similar objects\": [\"sofa bed\", \"mattress\", \"daybed\"]}", + 26 + ], + "combination": [ + " {\"type\": \"word\", \"description\": \"a group of two or more things combined together\", \"similar objects\": [\"combination lock\", \"combination code\", \"combination key\"]}", + 26 + ], + "wood spoon": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle; made of wood; could be used for stirring\", \"similar objects\": [\"wooden spoon\", \"metal spoon\", \"plastic spoon\"]}", + 26 + ], + "passenger seat": [ + " {\"type\": \"furniture\", \"description\": \"has a backrest; could be reclined; could have armrests; could have a headrest\", \"similar objects\": [\"driver seat\", \"sofa\", \"bench\"]}", + 26 + ], + "orange circle": [ + "\n{\"type\": \"shape\", \"description\": \"round; could be a color of orange; could be a fruit\", \"similar objects\": [\"square\", \"triangle\", \"oval\"]}", + 26 + ], + "cuffs": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of metal; could be used to restrain someone\", \"similar objects\": [\"handcuffs\", \"shackles\", \"chains\"]}", + 26 + ], + "sweet potato": [ + " {\"type\": \"vegetable\", \"description\": \"long, sweet, orange; could have a rough skin; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"potato\", \"yam\", \"carrot\"]}", + 26 + ], + "parka": [ + " {\"type\": \"clothing\", \"description\": \"long coat; could be waterproof; could have a hood\", \"similar objects\": [\"jacket\", \"coat\", \"raincoat\"]}", + 26 + ], + "blue flags": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; could be made of fabric; could be used to indicate a location\", \"similar objects\": [\"banners\", \"signs\", \"posters\"]}", + 26 + ], + "stocking cap": [ + " {\"type\": \"clothing accessory\", \"description\": \"knitted; could be long or short; could have a pom-pom on top\", \"similar objects\": [\"beanie\", \"beret\", \"turban\"]}", + 26 + ], + "bulldog": [ + " {\"type\": \"animal\", \"description\": \"short, stocky; has a wrinkled face; has a short tail\", \"similar objects\": [\"pug\", \"boxer\", \"french bulldog\"]}", + 26 + ], + "cream cheese": [ + " {\"type\": \"dairy product\", \"description\": \"soft, white, spreadable; could be used as a topping or an ingredient\", \"similar objects\": [\"butter\", \"yogurt\", \"sour cream\"]}", + 26 + ], + "softball": [ + " {\"type\": \"sport equipment\", \"description\": \"round; has a leather cover; could be used for playing baseball\", \"similar objects\": [\"baseball\", \"tennis ball\", \"golf ball\"]}", + 26 + ], + "grey tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"wooden flooring\", \"marble tile\", \"granite tile\"]}", + 26 + ], + "power cords": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, flexible; could be made of plastic or rubber; could have multiple plugs\", \"similar objects\": [\"extension cords\", \"adapters\", \"USB cables\"]}", + 26 + ], + "pins": [ + " {\"type\": \"stationery item\", \"description\": \"small, sharp, metal objects; could be used to attach papers together\", \"similar objects\": [\"paper clips\", \"staples\", \"tacks\"]}", + 26 + ], + "rain clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"gray; could be accompanied by rain; could be accompanied by thunder and lightning\", \"similar objects\": [\"hail clouds\", \"snow clouds\", \"fog\"]}", + 26 + ], + "glass display case": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be made of glass or plastic; could be used to display items\", \"similar objects\": [\"cabinet\", \"bookshelf\", \"showcase\"]}", + 26 + ], + "video games": [ + " {\"type\": \"entertainment\", \"description\": \"interactive; could be played on a console or computer; could involve multiple players\", \"similar objects\": [\"board games\", \"card games\", \"puzzles\"]}", + 26 + ], + "orange fire": [ + "\n{\"type\": \"phenomenon\", \"description\": \"bright orange flames; could be caused by a chemical reaction\", \"similar objects\": [\"volcano eruption\", \"meteor shower\", \"lightning storm\"]}", + 26 + ], + "skateboard helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"hard shell; adjustable straps; could have a visor\", \"similar objects\": [\"bicycle helmet\", \"hockey helmet\", \"motorcycle helmet\"]}", + 26 + ], + "metal parking meter": [ + " {\"type\": \"parking tool\", \"description\": \"tall, cylindrical; has a coin slot; could have a display screen\", \"similar objects\": [\"parking kiosk\", \"parking garage\", \"parking lot\"]}", + 26 + ], + "ski goggles": [ + " {\"type\": \"eyewear\", \"description\": \"protective eyewear; could be tinted; could be made of plastic or glass\", \"similar objects\": [\"sunglasses\", \"safety glasses\", \"snow goggles\"]}", + 26 + ], + "pink toothbrush": [ + "\n{\"type\": \"cleaning tool\", \"description\": \"pink; has a handle; could have soft bristles\", \"similar objects\": [\"toothpaste\", \"toothbrush holder\", \"dental floss\"]}", + 26 + ], + "silver cars": [ + "\n{\"type\": \"vehicle\", \"description\": \"metallic color; could be sedan, coupe, or SUV\", \"similar objects\": [\"gray cars\", \"black cars\", \"white cars\"]}", + 26 + ], + "stone bridge": [ + " {\"type\": \"structure\", \"description\": \"made of stones; could span a river or a valley\", \"similar objects\": [\"wooden bridge\", \"suspension bridge\", \"arch bridge\"]}", + 26 + ], + "brunette hair": [ + " {\"type\": \"hair color\", \"description\": \"dark brown; could be straight or wavy; could be long or short\", \"similar objects\": [\"blonde hair\", \"black hair\", \"red hair\"]}", + 26 + ], + "blue square": [ + " {\"type\": \"shape\", \"description\": \"four equal sides; could be filled with blue color\", \"similar objects\": [\"red square\", \"green triangle\", \"yellow circle\"]}", + 26 + ], + "brown bench": [ + "\n{\"type\": \"furniture\", \"description\": \"long, wooden, could have a backrest; could be used for seating\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 26 + ], + "basketball court": [ + " {\"type\": \"sports facility\", \"description\": \"rectangular; has two hoops; has a center line; has three-point lines\", \"similar objects\": [\"soccer field\", \"tennis court\", \"volleyball court\"]}", + 26 + ], + "foot print": [ + " {\"type\": \"evidence\", \"description\": \"mark left by a foot; could be made of mud, snow, or other materials\", \"similar objects\": [\"hand print\", \"tire track\", \"animal track\"]}", + 26 + ], + "silver earring": [ + " {\"type\": \"jewelry\", \"description\": \"round; could be made of silver; could have a gemstone\", \"similar objects\": [\"gold earring\", \"bracelet\", \"necklace\"]}", + 26 + ], + "transformer": [ + " {\"type\": \"electrical device\", \"description\": \"electrical device used to change the voltage of an electric current\", \"similar objects\": [\"circuit breaker\", \"relay\", \"switch\"]}", + 26 + ], + "chrome bathroom": [ + " {\"type\": \"furniture\", \"description\": \"shiny, metallic; could have a sink, a toilet, and a shower\", \"similar objects\": [\"bathtub\", \"vanity\", \"mirror\"]}", + 26 + ], + "crossing": [ + " {\"type\": \"road sign\", \"description\": \"could be a red octagon; could be a white pedestrian sign; could be a yellow diamond\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 26 + ], + "river bank": [ + " {\"type\": \"landscape\", \"description\": \"sloped land near a river; could be made of soil, rocks, or sand; could have trees and plants\", \"similar objects\": [\"lake shore\", \"ocean beach\", \"mountain slope\"]}", + 26 + ], + "video": [ + " {\"type\": \"media\", \"description\": \"digital file; could be a movie, a song, or a game\", \"similar objects\": [\"audio\", \"image\", \"document\"]}", + 26 + ], + "left sneaker": [ + " {\"type\": \"footwear\", \"description\": \"has a sole; could be made of fabric or leather; could have laces\", \"similar objects\": [\"right sneaker\", \"boot\", \"sandal\"]}", + 26 + ], + "trackpad": [ + " {\"type\": \"computer accessory\", \"description\": \"flat, rectangular; used to control the cursor on a computer screen\", \"similar objects\": [\"mouse\", \"keyboard\", \"stylus\"]}", + 26 + ], + "mayo": [ + " {\"type\": \"condiment\", \"description\": \"white, creamy; could be used as a spread\", \"similar objects\": [\"mustard\", \"ketchup\", \"relish\"]}", + 26 + ], + "potato salad": [ + " {\"type\": \"dish\", \"description\": \"made of potatoes, mayonnaise, and other ingredients; could be served cold or hot\", \"similar objects\": [\"coleslaw\", \"macaroni salad\", \"tuna salad\"]}", + 26 + ], + "construction sign": [ + " {\"type\": \"safety sign\", \"description\": \"yellow; has a triangle shape; could have a black symbol\", \"similar objects\": [\"stop sign\", \"yield sign\", \"warning sign\"]}", + 26 + ], + "cup holder": [ + " {\"type\": \"accessory\", \"description\": \"could be made of plastic or metal; could be attached to a car seat or a table; could hold cups or other items\", \"similar objects\": [\"drink holder\", \"cup stand\", \"cup coaster\"]}", + 26 + ], + "round button": [ + " {\"type\": \"accessory\", \"description\": \"small, round, could be made of plastic or metal; could be used for decoration or as a fastener\", \"similar objects\": [\"stud\", \"snap\", \"rivet\"]}", + 26 + ], + "orange part": [ + " {\"type\": \"fruit part\", \"description\": \"orange peel; could be sliced into small pieces; could be used for cooking\", \"similar objects\": [\"lemon peel\", \"apple peel\", \"banana peel\"]}", + 26 + ], + "foggy sky": [ + " {\"type\": \"weather condition\", \"description\": \"low visibility; could be accompanied by mist; could be grey or white\", \"similar objects\": [\"cloudy sky\", \"rainy sky\", \"hazy sky\"]}", + 26 + ], + "round frisbee": [ + "\n{\"type\": \"toy\", \"description\": \"round; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"discus\", \"boomerang\", \"hula hoop\"]}", + 26 + ], + "iron pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for support\", \"similar objects\": [\"steel beam\", \"wooden post\", \"concrete column\"]}", + 26 + ], + "flakes": [ + " {\"type\": \"food\", \"description\": \"light, thin, and crispy; could be made of corn, wheat, or rice; could be used as a cereal\", \"similar objects\": [\"granola\", \"cereal\", \"muesli\"]}", + 26 + ], + "bus doors": [ + " {\"type\": \"transportation tool\", \"description\": \"large, rectangular; could be opened and closed; could be automatic or manual\", \"similar objects\": [\"elevator doors\", \"subway doors\", \"airplane doors\"]}", + 26 + ], + "pet": [ + " {\"type\": \"animal\", \"description\": \"could be a dog, cat, bird, fish, or other small animal; could be kept as a companion\", \"similar objects\": [\"dog\", \"cat\", \"bird\", \"fish\"]}", + 26 + ], + "ten": [ + " {\"type\": \"number\", \"description\": \"a cardinal number; could be written as 10 or X\", \"similar objects\": [\"eleven\", \"twelve\", \"thirteen\"]}", + 26 + ], + "building window": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular; could be made of glass; could be opened\", \"similar objects\": [\"door\", \"balcony\", \"skylight\"]}", + 26 + ], + "flower box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of wood or plastic; could be used to hold flowers\", \"similar objects\": [\"planter\", \"vase\", \"pot\"]}", + 26 + ], + "brown dirt": [ + " {\"type\": \"soil\", \"description\": \"dark brown; could be dry or wet; could be used for gardening\", \"similar objects\": [\"clay\", \"sand\", \"peat moss\"]}", + 26 + ], + "turban": [ + " {\"type\": \"headwear\", \"description\": \"long cloth wrapped around the head; could be decorated with jewels\", \"similar objects\": [\"hat\", \"cap\", \"scarf\"]}", + 26 + ], + "giraffes tail": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, thin, and brown; could have white spots\", \"similar objects\": [\"elephant trunk\", \"horse mane\", \"monkey tail\"]}", + 26 + ], + "window shutter": [ + " {\"type\": \"window covering\", \"description\": \"rectangular; could be made of wood or metal; could be opened and closed\", \"similar objects\": [\"blinds\", \"curtains\", \"shades\"]}", + 26 + ], + "metal bucket": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of metal; could have a handle\", \"similar objects\": [\"plastic bucket\", \"pail\", \"tub\"]}", + 26 + ], + "bidet": [ + " {\"type\": \"bathroom fixture\", \"description\": \"low-level bowl; used for personal hygiene\", \"similar objects\": [\"toilet\", \"sink\", \"shower\"]}", + 26 + ], + "farm house": [ + " {\"type\": \"building\", \"description\": \"large; could have a porch; could have a barn; could have a silo\", \"similar objects\": [\"cottage\", \"barn\", \"bungalow\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cuc", + 26 + ], + "dogs eye": [ + " {\"type\": \"body part\", \"description\": \"round; black; could be surrounded by white fur\", \"similar objects\": [\"cat's eye\", \"rabbit's eye\", \"horse's eye\"]}", + 26 + ], + "landing wheels": [ + " {\"type\": \"aircraft part\", \"description\": \"wheels used for landing; could be retractable\", \"similar objects\": [\"engine\", \"wing\", \"fuselage\"]}", + 26 + ], + "construction worker": [ + " {\"type\": \"occupation\", \"description\": \"wears a hard hat; carries tools; works on construction sites\", \"similar objects\": [\"plumber\", \"electrician\", \"carpenter\"]}", + 26 + ], + "wood box": [ + " {\"type\": \"container\", \"description\": \"rectangular; made of wood; could have a lid\", \"similar objects\": [\"basket\", \"trunk\", \"crate\"]}", + 25 + ], + "glob": [ + " {\"type\": \"decoration\", \"description\": \"round; could be made of glass; could be used to represent the world\", \"similar objects\": [\"sphere\", \"ball\", \"orb\"]}", + 25 + ], + "round windows": [ + " {\"type\": \"architectural feature\", \"description\": \"circular; could be made of glass; could be used for ventilation\", \"similar objects\": [\"arched windows\", \"bay windows\", \"skylights\"]}", + 25 + ], + "web address": [ + " {\"type\": \"internet resource\", \"description\": \"a string of characters that directs to a website\", \"similar objects\": [\"URL\", \"domain name\", \"IP address\"]}", + 25 + ], + "camera strap": [ + " {\"type\": \"accessory\", \"description\": \"long, adjustable strap; could be made of fabric or leather; could be attached to a camera\", \"similar objects\": [\"camera bag\", \"camera case\", \"camera lens\"]}", + 25 + ], + "range hood": [ + " {\"type\": \"kitchen appliance\", \"description\": \"mounted above the stove; has a fan and filter to remove smoke and odors\", \"similar objects\": [\"vent hood\", \"exhaust fan\", \"cooker hood\"]}", + 25 + ], + "mother zebra": [ + " {\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane; has a baby zebra\", \"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}", + 25 + ], + "horse hooves": [ + " {\"type\": \"animal body part\", \"description\": \"hard, round, and pointed; could be black or white; could be used for walking and running\", \"similar objects\": [\"elephant feet\", \"giraffe legs\", \"dog paws\"]}", + 25 + ], + "stitching": [ + " {\"type\": \"sewing technique\", \"description\": \"a method of joining two pieces of fabric together with thread\", \"similar objects\": [\"embroidery\", \"knitting\", \"crocheting\"]}", + 25 + ], + "silver band": [ + " {\"type\": \"jewelry\", \"description\": \"circular; could be made of silver; could be decorated with stones\", \"similar objects\": [\"ring\", \"bracelet\", \"necklace\"]}", + 25 + ], + "seasonings": [ + " {\"type\": \"food ingredient\", \"description\": \"used to enhance the flavor of food; could be in powder or liquid form\", \"similar objects\": [\"spices\", \"herbs\", \"condiments\"]}", + 25 + ], + "bathrobe": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could be made of cotton or terry cloth; could have a belt\", \"similar objects\": [\"pajamas\", \"nightgown\", \"slippers\"]}", + 25 + ], + "round vase": [ + " {\"type\": \"decorative item\", \"description\": \"round; could be made of glass, ceramic, or metal; could have a wide opening at the top\", \"similar objects\": [\"urn\", \"jar\", \"pitcher\"]}", + 25 + ], + "orange vegetables": [ + "\n{\"type\": \"vegetable\", \"description\": \"round; could be orange, yellow, or red; could have a bitter taste; could be cooked or eaten raw\", \"similar objects\": [\"carrot\", \"sweet potato\", \"squash\"]}", + 25 + ], + "orange spot": [ + " {\"type\": \"marking\", \"description\": \"round, orange; could be used to mark a location\", \"similar objects\": [\"flag\", \"sign\", \"marker\"]}", + 25 + ], + "birds eye": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, green; could be frozen; could be cooked in stir-fry dishes\", \"similar objects\": [\"peas\", \"corn\", \"green beans\"]}", + 25 + ], + "chefs": [ + " {\"type\": \"occupation\", \"description\": \"cooks food professionally; could have a hat; could have a white apron\", \"similar objects\": [\"waiter\", \"bartender\", \"barista\"]}", + 25 + ], + "cookie sheet": [ + " {\"type\": \"baking tool\", \"description\": \"flat, rectangular; could be made of metal; could have edges\", \"similar objects\": [\"baking pan\", \"cake pan\", \"pie pan\"]}", + 25 + ], + "business card": [ + " {\"type\": \"stationery\", \"description\": \"small, rectangular; could have contact information printed on it\", \"similar objects\": [\"envelope\", \"letterhead\", \"postcard\"]}", + 25 + ], + "key board": [ + " {\"type\": \"computer accessory\", \"description\": \"rectangular; has keys; could be wired or wireless\", \"similar objects\": [\"mouse\", \"headset\", \"monitor\"]}", + 25 + ], + "cyclists": [ + " {\"type\": \"person\", \"description\": \"riding a bicycle; wearing a helmet; could be wearing a reflective vest\", \"similar objects\": [\"runner\", \"skater\", \"walker\"]}", + 25 + ], + "pink top": [ + " {\"type\": \"clothing\", \"description\": \"short-sleeved; could be made of cotton; could have a round neckline\", \"similar objects\": [\"shirt\", \"blouse\", \"dress\"]}", + 25 + ], + "grassy landscape": [ + " {\"type\": \"environment\", \"description\": \"green; could have trees, flowers, and other plants; could have a river or lake\", \"similar objects\": [\"forest\", \"meadow\", \"desert\"]}", + 25 + ], + "ear buds": [ + " {\"type\": \"audio device\", \"description\": \"small, wireless, could be connected to a device\", \"similar objects\": [\"headphones\", \"earphones\", \"speakers\"]}", + 25 + ], + "contrails": [ + " {\"type\": \"atmospheric phenomenon\", \"description\": \"long, white streaks in the sky; caused by aircrafts\", \"similar objects\": [\"clouds\", \"rainbow\", \"aurora\"]}", + 25 + ], + "control buttons": [ + " {\"type\": \"electronic device\", \"description\": \"could be round, square, or rectangular; could be labeled with numbers or symbols; could be used to control other devices\", \"similar objects\": [\"joystick\", \"keyboard\", \"remote control\"]}", + 25 + ], + "parasailer": [ + " {\"type\": \"recreational activity\", \"description\": \"person is attached to a parachute-like canopy; person is pulled by a boat or vehicle\", \"similar objects\": [\"paragliding\", \"hang gliding\", \"skydiving\"]}", + 25 + ], + "purple coat": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of wool; could have buttons; could have pockets\", \"similar objects\": [\"jacket\", \"cardigan\", \"hoodie\"]}", + 25 + ], + "side car": [ + " {\"type\": \"vehicle\", \"description\": \"attached to a motorcycle; could be used to transport passengers\", \"similar objects\": [\"tricycle\", \"rickshaw\", \"scooter\"]}", + 25 + ], + "swell": [ + " {\"type\": \"verb\", \"description\": \"to increase in size or intensity; to become larger or greater\", \"similar objects\": [\"expand\", \"inflate\", \"intensify\"]}", + 25 + ], + "mouths": [ + "\n{\"type\": \"body part\", \"description\": \"opening of the face; could be used for speaking, eating, and breathing\", \"similar objects\": [\"nose\", \"ears\", \"eyes\"]}", + 25 + ], + "ski slope": [ + " {\"type\": \"terrain\", \"description\": \"sloped surface; could be covered with snow; could have ski lifts\", \"similar objects\": [\"mountain\", \"hill\", \"valley\"]}", + 25 + ], + "bath towels": [ + " {\"type\": \"bathroom accessory\", \"description\": \"soft, absorbent, usually rectangular; could be used to dry body after shower\", \"similar objects\": [\"hand towels\", \"washcloths\", \"bath mats\"]}", + 25 + ], + "bedsheets": [ + " {\"type\": \"bedding\", \"description\": \"rectangular; could be made of cotton; could be printed with patterns\", \"similar objects\": [\"pillowcase\", \"duvet cover\", \"blanket\"]}", + 25 + ], + "toilet bowl brush": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has a brush head; could be made of plastic or metal\", \"similar objects\": [\"toilet plunger\", \"toilet brush\", \"toilet cleaner\"]}", + 25 + ], + "pink ear": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of plastic; could be decorated with rhinestones\", \"similar objects\": [\"earring\", \"bracelet\", \"necklace\"]}", + 25 + ], + "wooden window": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of wood; could have glass panes\", \"similar objects\": [\"door\", \"wall\", \"ceiling\"]}", + 25 + ], + "chart": [ + " {\"type\": \"visual aid\", \"description\": \"graphical representation of data; could be in the form of a table, graph, or diagram\", \"similar objects\": [\"graph\", \"table\", \"diagram\"]}", + 25 + ], + "pink stripe": [ + " {\"type\": \"pattern\", \"description\": \"a combination of pink and white colors; could be vertical or horizontal\", \"similar objects\": [\"polka dot\", \"plaid\", \"gingham\"]}", + 25 + ], + "crosswalk lines": [ + " {\"type\": \"road markings\", \"description\": \"white lines on the road; could be accompanied by pedestrian signs\", \"similar objects\": [\"stop sign\", \"traffic light\", \"road signs\"]}", + 25 + ], + "mirror car": [ + " {\"type\": \"vehicle accessory\", \"description\": \"reflective surface; could be attached to the car\", \"similar objects\": [\"spoiler\", \"bumper guard\", \"headlight protector\"]}", + 25 + ], + "hotel sign": [ + " {\"type\": \"signage\", \"description\": \"could be made of metal or plastic; could be illuminated; could have a logo or text\", \"similar objects\": [\"store sign\", \"street sign\", \"traffic sign\"]}", + 25 + ], + "algae": [ + " {\"type\": \"plant\", \"description\": \"green; could be found in water; could be slimy\", \"similar objects\": [\"seaweed\", \"moss\", \"kelp\"]}", + 25 + ], + "tan chair": [ + " {\"type\": \"furniture\", \"description\": \"brown; has four legs; could have armrests\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 25 + ], + "mattresses": [ + " {\"type\": \"furniture\", \"description\": \"soft; could be made of foam; could be used for sleeping\", \"similar objects\": [\"pillow\", \"bed\", \"sofa\"]}", + 25 + ], + "canoes": [ + " {\"type\": \"watercraft\", \"description\": \"long and narrow; could be made of wood or plastic; could be paddled with oars\", \"similar objects\": [\"kayak\", \"rowboat\", \"paddleboat\"]}", + 25 + ], + "metal spatula": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle; flat and flexible blade; could be made of metal\", \"similar objects\": [\"spoon\", \"ladle\", \"tongs\"]}", + 25 + ], + "silver bar": [ + " {\"type\": \"metal bar\", \"description\": \"shiny, silver-colored; could be used as a form of currency\", \"similar objects\": [\"gold bar\", \"copper bar\", \"platinum bar\"]}", + 25 + ], + "stereo speaker": [ + " {\"type\": \"audio device\", \"description\": \"box-shaped; could have two or more speakers; could be connected to an audio source\", \"similar objects\": [\"headphones\", \"microphone\", \"amplifier\"]}", + 25 + ], + "horse statue": [ + " {\"type\": \"decoration\", \"description\": \"sculpture of a horse; could be made of metal, wood, or stone; could be standing or galloping\", \"similar objects\": [\"dog statue\", \"cat statue\", \"deer statue\"]}", + 25 + ], + "barren trees": [ + "\n{\"type\": \"landscape\", \"description\": \"trees without leaves; could be dead or alive; could be standing in a desert or a dry area\", \"similar objects\": [\"desert\", \"mountain\", \"canyon\"]}", + 25 + ], + "shin": [ + " {\"type\": \"body part\", \"description\": \"part of the leg; could be covered by a sock; could be injured\", \"similar objects\": [\"knee\", \"ankle\", \"foot\"]}", + 25 + ], + "bracelet man": [ + " {\"type\": \"jewelry\", \"description\": \"circular; could be made of metal or plastic; could have charms or beads\", \"similar objects\": [\"necklace\", \"ring\", \"earrings\"]}", + 25 + ], + "load": [ + " {\"type\": \"noun\", \"description\": \"a quantity that can be carried or moved at one time; a burden; a task\", \"similar objects\": [\"weight\", \"burden\", \"responsibility\"]}", + 25 + ], + "blue lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"blue; could be made of glass; could have a switch\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}", + 25 + ], + "ceiling lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"hangs from the ceiling; could be made of metal or glass; could have multiple bulbs\", \"similar objects\": [\"chandelier\", \"pendant light\", \"wall sconce\"]}", + 25 + ], + "metal vent": [ + " {\"type\": \"ventilation tool\", \"description\": \"made of metal; has a grille; could be used for air circulation\", \"similar objects\": [\"air conditioner\", \"fan\", \"heater\"]}", + 25 + ], + "capital": [ + " {\"type\": \"city\", \"description\": \"a city that serves as a political and administrative center of a country or region\", \"similar objects\": [\"metropolis\", \"megacity\", \"conurbation\"]}", + 25 + ], + "spotlights": [ + " {\"type\": \"lighting tool\", \"description\": \"bright, focused light; could be used for stage lighting\", \"similar objects\": [\"floodlights\", \"torches\", \"lanterns\"]}", + 25 + ], + "plaza": [ + " {\"type\": \"public space\", \"description\": \"open area; could have trees, benches, and fountains; could be surrounded by buildings\", \"similar objects\": [\"park\", \"square\", \"courtyard\"]}", + 25 + ], + "tabby": [ + " {\"type\": \"animal\", \"description\": \"striped fur; could be orange, gray, or brown; has a short tail\", \"similar objects\": [\"calico\", \"Siamese\", \"Persian\"]}", + 25 + ], + "gray sky": [ + " {\"type\": \"weather\", \"description\": \"cloudy; could be raining; could be foggy\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"overcast sky\"]}", + 25 + ], + "waste": [ + " {\"type\": \"garbage\", \"description\": \"discarded materials; could be hazardous; could be recycled\", \"similar objects\": [\"trash\", \"rubbish\", \"litter\"]}", + 25 + ], + "ice cube": [ + " {\"type\": \"food item\", \"description\": \"small, solid, cold; could be used to cool drinks\", \"similar objects\": [\"ice cream\", \"sorbet\", \"frozen yogurt\"]}", + 25 + ], + "article": [ + " {\"type\": \"written work\", \"description\": \"a piece of writing; could be published in a newspaper, magazine, or online\", \"similar objects\": [\"essay\", \"report\", \"blog post\"]}", + 25 + ], + "train light": [ + " {\"type\": \"lighting tool\", \"description\": \"long, bright, could be red or green; usually found on the front of a train\", \"similar objects\": [\"headlight\", \"streetlight\", \"lantern\"]}", + 25 + ], + "multiple windows": [ + "\n{\"type\": \"architectural feature\", \"description\": \"multiple glass panes connected together; could be opened and closed; could be framed with wood or metal\", \"similar objects\": [\"doors\", \"shutters\", \"skylights\"]}", + 25 + ], + "blue edge": [ + " {\"type\": \"object\", \"description\": \"has a blue edge; could be a piece of paper, fabric, or other material\", \"similar objects\": [\"red edge\", \"green edge\", \"yellow edge\"]}", + 25 + ], + "element": [ + " {\"type\": \"chemical\", \"description\": \"basic building blocks of matter; could be solid, liquid, or gas; could be found on the periodic table\", \"similar objects\": [\"atom\", \"molecule\", \"compound\"]}", + 25 + ], + "palm fronds": [ + " {\"type\": \"plant\", \"description\": \"long, thin, green leaves; could be used for decoration\", \"similar objects\": [\"ferns\", \"palm leaves\", \"banana leaves\"]}", + 25 + ], + "brushes": [ + " {\"type\": \"tool\", \"description\": \"long handle; could have bristles; could be used for painting or cleaning\", \"similar objects\": [\"sponges\", \"scrubbers\", \"mop\"]}", + 25 + ], + "brick ground": [ + " {\"type\": \"building material\", \"description\": \"hard, rectangular; could be used to build walls\", \"similar objects\": [\"concrete\", \"stone\", \"wood\"]}", + 25 + ], + "drums": [ + " {\"type\": \"musical instrument\", \"description\": \"cylindrical; could be made of wood or metal; could have two or more heads; could be played with sticks\", \"similar objects\": [\"guitar\", \"piano\", \"violin\"]}", + 25 + ], + "tile flooring": [ + " {\"type\": \"flooring material\", \"description\": \"square or rectangular; could be made of ceramic, stone, or vinyl; could be glossy or matte\", \"similar objects\": [\"wood flooring\", \"carpet\", \"linoleum\"]}", + 25 + ], + "utility wires": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, metal wires; could be connected to poles\", \"similar objects\": [\"telephone wires\", \"cable wires\", \"fiber optic cables\"]}", + 25 + ], + "apple slice": [ + " {\"type\": \"food\", \"description\": \"round; could be red or green; could be sprinkled with sugar\", \"similar objects\": [\"orange slice\", \"banana slice\", \"pear slice\"]}", + 25 + ], + "centerpiece": [ + " {\"type\": \"decoration\", \"description\": \"could be made of flowers, candles, or other decorative items; could be placed in the middle of a table\", \"similar objects\": [\"vase\", \"tablecloth\", \"placemat\"]}", + 25 + ], + "refrigerator freezer": [ + "\n{\"type\": \"appliance\", \"description\": \"large, white, has two doors; could have shelves and drawers inside\", \"similar objects\": [\"microwave\", \"dishwasher\", \"washing machine\"]}", + 25 + ], + "lightbulb": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could be powered by electricity\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 25 + ], + "base coach": [ + " {\"type\": \"sports tool\", \"description\": \"stands near the base line; could be holding a bat; could be wearing a cap\", \"similar objects\": [\"umpire\", \"catcher\", \"pitcher\"]}", + 25 + ], + "tall animal": [ + "\n{\"type\": \"animal\", \"description\": \"tall; could have long legs; could have long neck; could have long tail\", \"similar objects\": [\"giraffe\", \"ostrich\", \"llama\"]}", + 25 + ], + "stereo system": [ + " {\"type\": \"electronic device\", \"description\": \"could have speakers, amplifier, and a CD player; could be connected to a TV\", \"similar objects\": [\"home theater system\", \"boombox\", \"turntable\"]}", + 25 + ], + "liquid glass": [ + " {\"type\": \"material\", \"description\": \"transparent; could be used as a sealant; could be used as a coating\", \"similar objects\": [\"epoxy\", \"resin\", \"polyurethane\"]}", + 25 + ], + "jet planes": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has wings; could have two or more engines; could fly at high speed\", \"similar objects\": [\"helicopter\", \"airplane\", \"glider\"]}", + 25 + ], + "shawl": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, could be made of wool; could be used to cover the head and shoulders\", \"similar objects\": [\"scarf\", \"wrap\", \"poncho\"]}", + 25 + ], + "pizza cut": [ + " {\"type\": \"kitchen tool\", \"description\": \"has a wheel-like blade; could be made of metal; could have a handle\", \"similar objects\": [\"cheese grater\", \"spatula\", \"whisk\"]}", + 25 + ], + "danger": [ + " {\"type\": \"concept\", \"description\": \"potential harm or risk; could be associated with warning signs\", \"similar objects\": [\"caution\", \"warning\", \"alert\"]}", + 25 + ], + "belt man": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, made of leather; could have a buckle\", \"similar objects\": [\"tie\", \"scarf\", \"hat\"]}", + 25 + ], + "blue frame": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; could be made of wood or metal; could be used to hang pictures or paintings\", \"similar objects\": [\"picture frame\", \"mirror frame\", \"photo frame\"]}", + 25 + ], + "rain boots": [ + " {\"type\": \"footwear\", \"description\": \"waterproof; could be made of rubber; could be tall and have a handle\", \"similar objects\": [\"wellington boots\", \"galoshes\", \"snow boots\"]}", + 25 + ], + "bear swimming": [ + "\n{\"type\": \"animal\", \"description\": \"large mammal; brown fur; could be swimming in water\", \"similar objects\": [\"otter\", \"seal\", \"walrus\"]}", + 25 + ], + "tassel": [ + " {\"type\": \"decorative item\", \"description\": \"hanging threads; could be made of fabric, yarn, or leather; could be used to decorate clothing, bags, or curtains\", \"similar objects\": [\"fringe\", \"pom-pom\", \"beads\"]}", + 25 + ], + "bald person": [ + " {\"type\": \"person\", \"description\": \"no hair on the head; could have facial hair\", \"similar objects\": [\"shaved head\", \"balding person\", \"buzz cut\"]}", + 25 + ], + "grill marks": [ + " {\"type\": \"cooking technique\", \"description\": \"brown lines on food; usually created by cooking on a hot surface\", \"similar objects\": [\"searing\", \"charring\", \"smoking\"]}", + 25 + ], + "server": [ + " {\"type\": \"computer hardware\", \"description\": \"a computer that provides services to other computers; could be a physical or virtual machine\", \"similar objects\": [\"router\", \"switch\", \"firewall\"]}", + 25 + ], + "purple grapes": [ + "\n{\"type\": \"fruit\", \"description\": \"round, small, dark purple; could have green stems; could be eaten as a snack\", \"similar objects\": [\"blueberries\", \"strawberries\", \"blackberries\"]}", + 25 + ], + "wheat": [ + " {\"type\": \"grain\", \"description\": \"long, yellowish-brown; could be used to make flour\", \"similar objects\": [\"rice\", \"barley\", \"oats\"]}", + 25 + ], + "cheeseburger": [ + " {\"type\": \"food\", \"description\": \"bun, patty, cheese, lettuce, tomato, onion, pickles, condiments\", \"similar objects\": [\"hamburger\", \"hot dog\", \"sandwich\"]}", + 25 + ], + "hand rails": [ + " {\"type\": \"safety tool\", \"description\": \"long, metal bars; could be attached to walls; could be used to support people\", \"similar objects\": [\"guard rails\", \"balustrades\", \"stair rails\"]}", + 25 + ], + "horse trailer": [ + " {\"type\": \"vehicle\", \"description\": \"large, box-shaped; could be towed by a truck; could be used to transport horses\", \"similar objects\": [\"boat trailer\", \"car trailer\", \"motorcycle trailer\"]}", + 25 + ], + "shadow skateboarder": [ + "\n{\"type\": \"sport\", \"description\": \"skateboarding in the dark; could be done with a skateboard; could be done with a longboard; could be done with a penny board\", \"similar objects\": [\"longboarder\", \"penny boarder\", \"skateboarder\"]}", + 25 + ], + "paper bags": [ + " {\"type\": \"container\", \"description\": \"made of paper; could be used to store items; could be folded\", \"similar objects\": [\"plastic bags\", \"boxes\", \"envelopes\"]}", + 25 + ], + "utility box": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal; could be used to store tools\", \"similar objects\": [\"toolbox\", \"locker\", \"cabinet\"]}", + 25 + ], + "metal zipper": [ + " {\"type\": \"clothing accessory\", \"description\": \"silver; has two sides that can be connected and separated; could be used to close a pocket or a garment\", \"similar objects\": [\"button\", \"hook and eye\", \"snap\"]}", + 25 + ], + "water skis": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, curved; could be made of wood or plastic; could be used for skiing on water\", \"similar objects\": [\"wakeboard\", \"surfboard\", \"snowboard\"]}", + 25 + ], + "curtain panel": [ + " {\"type\": \"window covering\", \"description\": \"long, rectangular; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"blinds\", \"shades\", \"drapes\"]}", + 25 + ], + "sand dunes": [ + " {\"type\": \"landscape\", \"description\": \"large mounds of sand; could be found in deserts\", \"similar objects\": [\"mountains\", \"hills\", \"valleys\"]}", + 25 + ], + "quarters": [ + " {\"type\": \"coin\", \"description\": \"round; has a value of 25 cents; could be made of copper, nickel, or silver\", \"similar objects\": [\"dimes\", \"nickels\", \"pennies\"]}", + 25 + ], + "radishes": [ + " {\"type\": \"vegetable\", \"description\": \"round; red or white; could have green leaves; could be sliced into thin pieces\", \"similar objects\": [\"carrots\", \"turnips\", \"beets\"]}", + 25 + ], + "connection": [ + " {\"type\": \"relationship\", \"description\": \"link between two or more things; could be physical or abstract\", \"similar objects\": [\"association\", \"relationship\", \"interaction\"]}", + 25 + ], + "machinery": [ + " {\"type\": \"equipment\", \"description\": \"could be made of metal; could be used for industrial purposes; could be powered by electricity\", \"similar objects\": [\"tools\", \"machines\", \"engines\"]}", + 25 + ], + "brown giraffe": [ + "\n{\"type\": \"animal\", \"description\": \"brown; has a long neck and a long mane; has black and white patches\", \"similar objects\": [\"zebra\", \"elephant\", \"horse\"]}", + 25 + ], + "hatchback": [ + " {\"type\": \"vehicle\", \"description\": \"small car; has a trunk that opens at the back; could have two or four doors\", \"similar objects\": [\"sedan\", \"coupe\", \"SUV\"]}", + 25 + ], + "modem": [ + " {\"type\": \"electronic device\", \"description\": \"box-shaped; used to connect to the internet\", \"similar objects\": [\"router\", \"switch\", \"hub\"]}", + 25 + ], + "easel": [ + " {\"type\": \"painting tool\", \"description\": \"tall, adjustable, has a board for painting\", \"similar objects\": [\"canvas\", \"palette\", \"brush\"]}", + 25 + ], + "tanker": [ + " {\"type\": \"vehicle\", \"description\": \"large; could be used to transport liquids; could have a long hose\", \"similar objects\": [\"truck\", \"trailer\", \"lorry\"]}", + 25 + ], + "brown house": [ + "\n{\"type\": \"building\", \"description\": \"rectangular; could have a porch; could have a chimney; could have windows and doors; could be made of wood or brick\", \"similar objects\": [\"apartment\", \"mansion\", \"cottage\"]}", + 25 + ], + "motorcycle windshield": [ + " {\"type\": \"motorcycle accessory\", \"description\": \"transparent; could be curved; could be attached to the handlebar\", \"similar objects\": [\"motorcycle helmet\", \"motorcycle seat\", \"motorcycle mirror\"]}", + 25 + ], + "lit sign": [ + " {\"type\": \"advertisement tool\", \"description\": \"could be made of neon; could be in the shape of letters; could be in the shape of symbols\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 25 + ], + "metal wall": [ + " {\"type\": \"building material\", \"description\": \"hard, durable, could be painted; could be used for fencing\", \"similar objects\": [\"wood wall\", \"brick wall\", \"concrete wall\"]}", + 25 + ], + "rind": [ + " {\"type\": \"food ingredient\", \"description\": \"outer layer of a fruit or vegetable; could be peeled off; could be used for cooking\", \"similar objects\": [\"peel\", \"skin\", \"husk\"]}", + 25 + ], + "grounds": [ + " {\"type\": \"landscape\", \"description\": \"dirt, soil, or sand; could be used for gardening or landscaping\", \"similar objects\": [\"soil\", \"dirt\", \"sand\"]}", + 25 + ], + "roast beef": [ + " {\"type\": \"food\", \"description\": \"cooked beef; could be served with gravy; could be sliced into thin pieces\", \"similar objects\": [\"steak\", \"roast pork\", \"roast chicken\"]}", + 25 + ], + "wind shield wipers": [ + " {\"type\": \"automotive tool\", \"description\": \"attached to the windshield; used to clear away rain, snow, and debris\", \"similar objects\": [\"headlights\", \"brake lights\", \"mirrors\"]}", + 25 + ], + "man hair": [ + " {\"type\": \"body part\", \"description\": \"dark; could be short or long; could be curly or straight\", \"similar objects\": [\"beard\", \"eyebrow\", \"eyelash\"]}", + 25 + ], + "crosses": [ + " {\"type\": \"religious symbol\", \"description\": \"two intersecting lines; could be made of metal or wood; could be used as a decoration\", \"similar objects\": [\"star of David\", \"crescent\", \"pentagram\"]}", + 25 + ], + "pearls": [ + " {\"type\": \"jewelry\", \"description\": \"round; could be white, pink, or black; could be strung together\", \"similar objects\": [\"diamonds\", \"emeralds\", \"rubies\"]}", + 25 + ], + "riverbank": [ + " {\"type\": \"landscape\", \"description\": \"edge of a river; could be made of soil, rocks, or sand; could have trees and plants\", \"similar objects\": [\"lakebank\", \"seashore\", \"coastline\"]}", + 25 + ], + "cops": [ + " {\"type\": \"law enforcement personnel\", \"description\": \"uniformed; could carry a gun; could drive a police car\", \"similar objects\": [\"security guard\", \"detective\", \"sheriff\"]}", + 25 + ], + "tuna": [ + " {\"type\": \"fish\", \"description\": \"silver; could be canned; could be grilled\", \"similar objects\": [\"salmon\", \"cod\", \"mackerel\"]}", + 25 + ], + "gas pump": [ + " {\"type\": \"utility tool\", \"description\": \"tall; has a nozzle; could be used to fill up a car with fuel\", \"similar objects\": [\"air pump\", \"water pump\", \"fuel pump\"]}", + 25 + ], + "mural": [ + " {\"type\": \"artwork\", \"description\": \"large painting or drawing on a wall or ceiling; could be made of various materials\", \"similar objects\": [\"fresco\", \"graffiti\", \"tapestry\"]}", + 25 + ], + "mans hand": [ + " {\"type\": \"body part\", \"description\": \"five fingers; could be used for grasping; could be used for writing\", \"similar objects\": [\"foot\", \"arm\", \"head\"]}", + 25 + ], + "man water": [ + "\n{\"type\": \"liquid\", \"description\": \"clear; could be salty or fresh; could be used for drinking, cooking, and cleaning\", \"similar objects\": [\"juice\", \"milk\", \"wine\"]}", + 25 + ], + "silver wrist": [ + " {\"type\": \"jewelry\", \"description\": \"shiny; could be made of silver; could be in the shape of a bracelet\", \"similar objects\": [\"gold necklace\", \"diamond ring\", \"platinum earrings\"]}", + 25 + ], + "round piece": [ + " {\"type\": \"shape\", \"description\": \"circular; could be made of any material\", \"similar objects\": [\"circle\", \"disk\", \"sphere\"]}", + 25 + ], + "chocolate chips": [ + " {\"type\": \"food\", \"description\": \"small, round, dark brown; could be used for baking\", \"similar objects\": [\"raisins\", \"nuts\", \"dried fruits\"]}", + 25 + ], + "evergreens": [ + " {\"type\": \"plant\", \"description\": \"coniferous trees; have needles instead of leaves; could be tall and slender\", \"similar objects\": [\"pine tree\", \"spruce tree\", \"cypress tree\"]}", + 25 + ], + "train station platform": [ + " {\"type\": \"transportation facility\", \"description\": \"long, flat, could have a roof; could have a ticket booth; could have a waiting area\", \"similar objects\": [\"bus station\", \"airport terminal\", \"subway station\"]}", + 25 + ], + "towel bar": [ + " {\"type\": \"bathroom accessory\", \"description\": \"long bar; could be made of metal; could be used to hang towels\", \"similar objects\": [\"soap dish\", \"toilet paper holder\", \"shower curtain rod\"]}", + 25 + ], + "names": [ + " {\"type\": \"word\", \"description\": \"words used to identify people; could be composed of letters\", \"similar objects\": [\"titles\", \"labels\", \"monikers\"]}", + 25 + ], + "faint": [ + " {\"type\": \"adjective\", \"description\": \"weak; not strong; not loud\", \"similar objects\": [\"dim\", \"subdued\", \"fuzzy\"]}", + 25 + ], + "surfer water": [ + " {\"type\": \"sport\", \"description\": \"involves riding a surfboard on the surface of a wave; requires a wetsuit; could be done in the ocean or a lake\", \"similar objects\": [\"snowboarding\", \"skateboarding\", \"wakeboarding\"]}", + 25 + ], + "emergency door": [ + " {\"type\": \"door\", \"description\": \"red; has a handle; could be opened with a key or code\", \"similar objects\": [\"fire door\", \"security door\", \"exit door\"]}", + 25 + ], + "metal sign pole": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical, made of metal; could have signs attached to it\", \"similar objects\": [\"fence post\", \"flag pole\", \"street light pole\"]}", + 25 + ], + "mountain peaks": [ + " {\"type\": \"landscape\", \"description\": \"tall, pointed, could be snow-capped; could have a steep slope\", \"similar objects\": [\"hills\", \"valleys\", \"cliffs\"]}", + 25 + ], + "mane zebra": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, black and white hair on the neck of a zebra\", \"similar objects\": [\"tail\", \"hooves\", \"horns\"]}", + 25 + ], + "orange vest": [ + " {\"type\": \"clothing\", \"description\": \"bright orange; could be sleeveless; could have reflective stripes\", \"similar objects\": [\"safety vest\", \"life jacket\", \"raincoat\"]}", + 25 + ], + "water ski": [ + " {\"type\": \"sport equipment\", \"description\": \"long, thin, has two handles\", \"similar objects\": [\"wakeboard\", \"surfboard\", \"snowboard\"]}", + 25 + ], + "ponytail holder": [ + " {\"type\": \"hair accessory\", \"description\": \"elastic band; could be decorated with beads or ribbons\", \"similar objects\": [\"hair tie\", \"scrunchy\", \"headband\"]}", + 25 + ], + "emergency lights": [ + " {\"type\": \"lighting tool\", \"description\": \"flashing red and blue lights; could be used for emergency vehicles\", \"similar objects\": [\"flares\", \"strobe lights\", \"warning lights\"]}", + 25 + ], + "creature": [ + " {\"type\": \"living being\", \"description\": \"could be any living being; could be an animal, a plant, a human, etc.\", \"similar objects\": [\"animal\", \"plant\", \"human\"]}", + 25 + ], + "brick church": [ + "\n{\"type\": \"building\", \"description\": \"made of bricks; has a steeple; could have stained glass windows\", \"similar objects\": [\"cathedral\", \"mosque\", \"synagogue\"]}", + 24 + ], + "mortar": [ + " {\"type\": \"tool\", \"description\": \"cylindrical; used for grinding substances into powder\", \"similar objects\": [\"pestle\", \"grinder\", \"blender\"]}", + 24 + ], + "glass mirror": [ + " {\"type\": \"reflective tool\", \"description\": \"transparent; could be framed; could be used to reflect light\", \"similar objects\": [\"window\", \"sunglasses\", \"telescope\"]}", + 24 + ], + "pink paint": [ + " {\"type\": \"art material\", \"description\": \"pink color; could be used for painting\", \"similar objects\": [\"red paint\", \"blue paint\", \"green paint\"]}", + 24 + ], + "kiwis": [ + " {\"type\": \"fruit\", \"description\": \"green, oval-shaped; has a fuzzy skin; could be sliced into pieces\", \"similar objects\": [\"strawberries\", \"bananas\", \"mangoes\"]}", + 24 + ], + "ice water": [ + " {\"type\": \"beverage\", \"description\": \"cold; could be clear or with bubbles; could be served with ice cubes\", \"similar objects\": [\"juice\", \"soda\", \"tea\"]}", + 24 + ], + "paper cups": [ + " {\"type\": \"utensil\", \"description\": \"disposable; could be made of paper; could be used for drinking\", \"similar objects\": [\"plates\", \"bowls\", \"glasses\"]}", + 24 + ], + "wall lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the wall; could be made of metal; could have a switch\", \"similar objects\": [\"ceiling lamp\", \"floor lamp\", \"table lamp\"]}", + 24 + ], + "flecks": [ + " {\"type\": \"particles\", \"description\": \"tiny, round, could be made of different materials\", \"similar objects\": [\"specks\", \"granules\", \"crumbs\"]}", + 24 + ], + "sun umbrella": [ + " {\"type\": \"accessory\", \"description\": \"large, round; could be made of fabric; could be used to protect from sun\", \"similar objects\": [\"hat\", \"sunglasses\", \"raincoat\"]}", + 24 + ], + "headset": [ + " {\"type\": \"electronic device\", \"description\": \"has two earpieces; could be connected to a device\", \"similar objects\": [\"earphones\", \"headphones\", \"microphone\"]}", + 24 + ], + "collage": [ + " {\"type\": \"artwork\", \"description\": \"a composition of various materials such as photographs, paper, fabric, and other objects; could be framed\", \"similar objects\": [\"mosaic\", \"mural\", \"assemblage\"]}", + 24 + ], + "silver bolts": [ + " {\"type\": \"hardware\", \"description\": \"small, round, metallic; could be used to fasten two objects together\", \"similar objects\": [\"nuts\", \"screws\", \"washers\"]}", + 24 + ], + "sauce bowl": [ + " {\"type\": \"cooking tool\", \"description\": \"round; could be made of ceramic; could be used to hold sauces\", \"similar objects\": [\"soup bowl\", \"rice bowl\", \"salad bowl\"]}", + 24 + ], + "form": [ + " {\"type\": \"document\", \"description\": \"paper or digital; could be filled with information; could be used for applications\", \"similar objects\": [\"application\", \"survey\", \"questionnaire\"]}", + 24 + ], + "grid": [ + " {\"type\": \"pattern\", \"description\": \"rectangular; could be made of lines; could be used to organize data\", \"similar objects\": [\"table\", \"chart\", \"graph\"]}", + 24 + ], + "tin roof": [ + " {\"type\": \"building material\", \"description\": \"silver; could be made of metal; could be used for roofing\", \"similar objects\": [\"asphalt shingles\", \"clay tiles\", \"slate tiles\"]}", + 24 + ], + "bottom drawer": [ + " {\"type\": \"furniture\", \"description\": \"a drawer located at the bottom of a cabinet; could be opened and closed with a handle\", \"similar objects\": [\"top drawer\", \"middle drawer\", \"side drawer\"]}", + 24 + ], + "calves": [ + " {\"type\": \"animal\", \"description\": \"young cows; have short legs; have soft fur\", \"similar objects\": [\"lambs\", \"foals\", \"puppies\"]}", + 24 + ], + "bag strap": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of leather or fabric; used to carry bags\", \"similar objects\": [\"belt\", \"wallet chain\", \"keychain\"]}", + 24 + ], + "calender": [ + " {\"type\": \"organizational tool\", \"description\": \"could be paper or digital; could be used to track dates and events\", \"similar objects\": [\"planner\", \"agenda\", \"diary\"]}", + 24 + ], + "apartments": [ + " {\"type\": \"building\", \"description\": \"multi-story; could have balconies; could have a shared courtyard\", \"similar objects\": [\"condominiums\", \"townhouses\", \"row houses\"]}", + 24 + ], + "tricks": [ + " {\"type\": \"activity\", \"description\": \"something that is done to deceive or surprise someone; could involve physical or mental skills\", \"similar objects\": [\"pranks\", \"stunts\", \"illusions\"]}", + 24 + ], + "shadow trees": [ + " {\"type\": \"landscape\", \"description\": \"trees with shadows; could be silhouettes\", \"similar objects\": [\"mountains\", \"rivers\", \"lakes\"]}", + 24 + ], + "udders": [ + " {\"type\": \"animal body part\", \"description\": \"hanging, fleshy, milk-producing organs of female mammals\", \"similar objects\": [\"teats\", \"mammary glands\", \"nipples\"]}", + 24 + ], + "sacks": [ + " {\"type\": \"container\", \"description\": \"made of cloth or paper; could be used to store items; could be carried on the back\", \"similar objects\": [\"bag\", \"basket\", \"box\"]}", + 24 + ], + "mud flap": [ + " {\"type\": \"automotive accessory\", \"description\": \"attached to the rear of a vehicle; made of rubber or plastic; designed to protect the vehicle from mud and debris\", \"similar objects\": [\"bumper guard\", \"mud guard\", \"splash guard\"]}", + 24 + ], + "burnt piece": [ + " {\"type\": \"object\", \"description\": \"blackened; could be a piece of food, paper, or fabric; could be charred\", \"similar objects\": [\"ash\", \"charcoal\", \"cinder\"]}", + 24 + ], + "side dish": [ + " {\"type\": \"food\", \"description\": \"accompaniment to a main dish; could be a salad, soup, or vegetable dish\", \"similar objects\": [\"main dish\", \"appetizer\", \"dessert\"]}", + 24 + ], + "gold knob": [ + " {\"type\": \"hardware\", \"description\": \"round; made of gold; could be used as a handle\", \"similar objects\": [\"brass knob\", \"silver knob\", \"bronze knob\"]}", + 24 + ], + "towel ring": [ + " {\"type\": \"bathroom accessory\", \"description\": \"round; could be made of metal; used to hang towels\", \"similar objects\": [\"soap dish\", \"toilet paper holder\", \"toothbrush holder\"]}", + 24 + ], + "blond boy": [ + "\n{\"type\": \"person\", \"description\": \"light-colored hair; could have blue eyes; could be wearing a shirt and pants\", \"similar objects\": [\"blond girl\", \"brunette boy\", \"brunette girl\"]}", + 24 + ], + "concrete post": [ + " {\"type\": \"building material\", \"description\": \"gray; cylindrical; could be used to support a structure\", \"similar objects\": [\"wood post\", \"metal post\", \"brick\"]}", + 24 + ], + "tone": [ + " {\"type\": \"sound\", \"description\": \"a sound of a certain pitch and quality; could be musical or non-musical\", \"similar objects\": [\"note\", \"chord\", \"melody\"]}", + 24 + ], + "bent knees": [ + " {\"type\": \"body posture\", \"description\": \"knees bent at an angle; could be used for sitting or squatting\", \"similar objects\": [\"sitting cross-legged\", \"kneeling\", \"lunging\"]}", + 24 + ], + "baby animal": [ + " {\"type\": \"animal\", \"description\": \"small; could be cute; could have soft fur\", \"similar objects\": [\"kitten\", \"puppy\", \"calf\"]}", + 24 + ], + "split": [ + " {\"type\": \"verb\", \"description\": \"to divide into two or more parts; to separate\", \"similar objects\": [\"divide\", \"separate\", \"partition\"]}", + 24 + ], + "lane road": [ + " {\"type\": \"roadway\", \"description\": \"long, straight, has two sides; could have markings and signs\", \"similar objects\": [\"highway\", \"street\", \"freeway\"]}", + 24 + ], + "cloth napkin": [ + " {\"type\": \"tableware\", \"description\": \"square; could be made of cotton; could be used to wipe hands\", \"similar objects\": [\"tablecloth\", \"placemat\", \"towel\"]}", + 24 + ], + "peck": [ + " {\"type\": \"unit of measurement\", \"description\": \"equal to 8 dry quarts or 16 dry pints\", \"similar objects\": [\"bushel\", \"gallon\", \"quart\"]}", + 24 + ], + "silver bell": [ + " {\"type\": \"musical instrument\", \"description\": \"round; made of metal; produces a ringing sound\", \"similar objects\": [\"cymbal\", \"tambourine\", \"triangle\"]}", + 24 + ], + "chain fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal links; could be used to enclose an area\", \"similar objects\": [\"barbed wire\", \"wooden fence\", \"brick wall\"]}", + 24 + ], + "london": [ + " {\"type\": \"city\", \"description\": \"capital of the United Kingdom; located in the south-east of England; has a population of 8.9 million\", \"similar objects\": [\"New York\", \"Paris\", \"Berlin\"]}", + 24 + ], + "pickle slice": [ + " {\"type\": \"food\", \"description\": \"green; could be sliced into round pieces; could be sour or sweet\", \"similar objects\": [\"olive\", \"cucumber\", \"onion\"]}", + 24 + ], + "rail road": [ + " {\"type\": \"transportation system\", \"description\": \"long, straight tracks; could have multiple tracks; could have a train running on it\", \"similar objects\": [\"highway\", \"subway\", \"tram\"]}", + 24 + ], + "bathroom sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"has a handle; could be made of metal; could have a sprayer\", \"similar objects\": [\"bathtub faucet\", \"shower faucet\", \"kitchen sink faucet\"]}", + 24 + ], + "stainless steel bowl": [ + "\n{\"type\": \"cooking tool\", \"description\": \"shiny, metallic, round; could be used for mixing ingredients\", \"similar objects\": [\"pot\", \"pan\", \"skillet\"]}", + 24 + ], + "lilies": [ + " {\"type\": \"flower\", \"description\": \"white, yellow, or pink; has six petals; could have a strong scent\", \"similar objects\": [\"roses\", \"daisies\", \"tulips\"]}", + 24 + ], + "plume": [ + " {\"type\": \"ornament\", \"description\": \"long, feathery; could be used as a headdress\", \"similar objects\": [\"feather\", \"tassel\", \"beads\"]}", + 24 + ], + "elephant tusks": [ + " {\"type\": \"body part\", \"description\": \"long, curved, ivory-colored; found on the face of an elephant\", \"similar objects\": [\"horns\", \"antlers\", \"claws\"]}", + 24 + ], + "reeds": [ + " {\"type\": \"plant\", \"description\": \"tall, thin, hollow stems; could be found in wetlands; could be used to make musical instruments\", \"similar objects\": [\"cattails\", \"rushes\", \"bulrushes\"]}", + 24 + ], + "rock structure": [ + " {\"type\": \"geological formation\", \"description\": \"could be made of stones; could be formed by erosion; could be found in nature\", \"similar objects\": [\"cliff\", \"cave\", \"mountain\"]}", + 24 + ], + "flop": [ + " {\"type\": \"footwear\", \"description\": \"flat; could be made of rubber; could have straps\", \"similar objects\": [\"sandals\", \"slippers\", \"sneakers\"]}", + 24 + ], + "blond man": [ + "\n{\"type\": \"person\", \"description\": \"light-colored hair; could have blue eyes; could have fair skin\", \"similar objects\": [\"blond woman\", \"brunette man\", \"brunette woman\"]}", + 24 + ], + "metal bolts": [ + " {\"type\": \"hardware\", \"description\": \"cylindrical; could be made of steel; could be used to fasten two objects together\", \"similar objects\": [\"nuts\", \"screws\", \"washers\"]}", + 24 + ], + "computer printer": [ + " {\"type\": \"electronic device\", \"description\": \"has a paper tray; could be connected to a computer; could print documents\", \"similar objects\": [\"scanner\", \"fax machine\", \"copier\"]}", + 24 + ], + "overcast skies": [ + " {\"type\": \"weather condition\", \"description\": \"grayish sky; no visible sun; could be raining\", \"similar objects\": [\"rainy skies\", \"cloudy skies\", \"foggy skies\"]}", + 24 + ], + "kitchen chair": [ + " {\"type\": \"furniture\", \"description\": \"has four legs; could have a backrest; could be made of wood or metal\", \"similar objects\": [\"dining chair\", \"stool\", \"armchair\"]}", + 24 + ], + "train windshield": [ + " {\"type\": \"transportation part\", \"description\": \"transparent; could be made of glass; could be curved\", \"similar objects\": [\"car windshield\", \"airplane window\", \"boat window\"]}", + 24 + ], + "toothbrush handle": [ + " {\"type\": \"cleaning tool\", \"description\": \"long, thin, plastic handle; could have bristles at the end\", \"similar objects\": [\"toothpaste tube\", \"razor handle\", \"comb\"]}", + 24 + ], + "tourist bus": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have a luggage compartment; could have a guide\", \"similar objects\": [\"school bus\", \"coach bus\", \"minibus\"]}", + 24 + ], + "silver ladder": [ + " {\"type\": \"tool\", \"description\": \"made of metal; could be used to reach high places; could be folded\", \"similar objects\": [\"step ladder\", \"extension ladder\", \"staircase\"]}", + 24 + ], + "glass flower vase": [ + "\n{\"type\": \"decorative item\", \"description\": \"transparent; could be made of glass or ceramic; could have a wide opening at the top; could have a narrow base\", \"similar objects\": [\"urn\", \"jar\", \"pitcher\"]}", + 24 + ], + "carrot slices": [ + " {\"type\": \"vegetable\", \"description\": \"orange, thin slices; could be cooked or eaten raw\", \"similar objects\": [\"celery\", \"onion\", \"potato\"]}", + 24 + ], + "v": [ + "\n{\"type\": \"letter\", \"description\": \"straight line with two points at the top and bottom; could be used as a Roman numeral\", \"similar objects\": [\"w\", \"x\", \"y\"]}", + 24 + ], + "table clothe": [ + " {\"type\": \"textile\", \"description\": \"rectangular; could be made of cotton, linen, or polyester; could be decorated with patterns\", \"similar objects\": [\"napkin\", \"runner\", \"placemat\"]}", + 24 + ], + "power cable": [ + " {\"type\": \"electrical tool\", \"description\": \"long; has two ends with different plugs; could be used to connect two devices\", \"similar objects\": [\"extension cord\", \"USB cable\", \"HDMI cable\"]}", + 24 + ], + "baby carriage": [ + " {\"type\": \"transportation tool\", \"description\": \"has four wheels; could be folded; could be pushed by adults\", \"similar objects\": [\"stroller\", \"pram\", \"buggy\"]}", + 24 + ], + "cartons": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be used for storing and transporting goods\", \"similar objects\": [\"boxes\", \"crates\", \"barrels\"]}", + 24 + ], + "jetty": [ + " {\"type\": \"structure\", \"description\": \"a structure built on water; could be used for docking boats\", \"similar objects\": [\"pier\", \"dock\", \"wharf\"]}", + 24 + ], + "magazine rack": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal or wood; could have multiple shelves\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"drawer\"]}", + 24 + ], + "bookcases": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have multiple shelves; could be used to store books\", \"similar objects\": [\"cabinet\", \"shelf\", \"wardrobe\"]}", + 24 + ], + "tree shadows": [ + " {\"type\": \"natural phenomenon\", \"description\": \"shadows of trees cast by sunlight; could be long or short; could be dark or light\", \"similar objects\": [\"building shadows\", \"cloud shadows\", \"moon shadows\"]}", + 24 + ], + "dividers": [ + " {\"type\": \"office tool\", \"description\": \"long; could be made of plastic or metal; could have two or more panels\", \"similar objects\": [\"ruler\", \"stapler\", \"scissors\"]}", + 24 + ], + "giraffe mouth": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, black tongue; could reach up to 18 inches; could strip leaves off trees\", \"similar objects\": [\"elephant trunk\", \"hippo mouth\", \"rhino horn\"]}", + 24 + ], + "wrist bands": [ + " {\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of fabric, leather, or metal; could be decorated with beads, stones, or other materials\", \"similar objects\": [\"bracelets\", \"anklets\", \"necklaces\"]}", + 24 + ], + "saddle blanket": [ + " {\"type\": \"horse accessory\", \"description\": \"rectangular; made of wool or cotton; used to protect the horse's back\", \"similar objects\": [\"saddle pad\", \"girth\", \"bridle\"]}", + 24 + ], + "bent leg": [ + " {\"type\": \"body part\", \"description\": \"part of the lower body; could be bent at the knee; could be straightened\", \"similar objects\": [\"arm\", \"foot\", \"hand\"]}", + 24 + ], + "metal scissors": [ + " {\"type\": \"cutting tool\", \"description\": \"two blades connected by a pivot; could be used to cut paper, fabric, etc.\", \"similar objects\": [\"knife\", \"pliers\", \"shears\"]}", + 24 + ], + "center table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have a glass top; could have drawers\", \"similar objects\": [\"coffee table\", \"side table\", \"dining table\"]}", + 24 + ], + "metal beams": [ + " {\"type\": \"building material\", \"description\": \"long, strong, and rigid; could be used to support structures\", \"similar objects\": [\"wood beams\", \"concrete blocks\", \"steel rods\"]}", + 24 + ], + "grey brick": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of concrete; could be used for walls\", \"similar objects\": [\"cement block\", \"stone\", \"wooden plank\"]}", + 24 + ], + "tote": [ + " {\"type\": \"bag\", \"description\": \"rectangular; could be made of canvas; could have handles\", \"similar objects\": [\"purse\", \"backpack\", \"duffel bag\"]}", + 24 + ], + "wrought iron gate": [ + " {\"type\": \"fence\", \"description\": \"made of metal; could be curved; could have intricate designs\", \"similar objects\": [\"chain link fence\", \"wooden fence\", \"brick wall\"]}", + 24 + ], + "metal hook": [ + " {\"type\": \"tool\", \"description\": \"made of metal; could be used to hang things\", \"similar objects\": [\"nail\", \"screw\", \"hanger\"]}", + 24 + ], + "nature": [ + "\n{\"type\": \"environment\", \"description\": \"outdoor environment; could include trees, plants, animals, mountains, rivers, etc.\", \"similar objects\": [\"landscape\", \"wilderness\", \"ecosystem\"]}", + 24 + ], + "dark animal": [ + "\n{\"type\": \"animal\", \"description\": \"dark-colored fur; could be nocturnal; could be a predator\", \"similar objects\": [\"bat\", \"fox\", \"wolf\"]}", + 24 + ], + "brass door knob": [ + "\n{\"type\": \"door hardware\", \"description\": \"round; made of brass; could have a keyhole\", \"similar objects\": [\"door handle\", \"door latch\", \"door lock\"]}", + 24 + ], + "deep": [ + "\n{\"type\": \"adjective\", \"description\": \"having a great extent downward or inward from an outer surface or backward or outward from a center; far-reaching; profound\", \"similar objects\": [\"profound\", \"intense\", \"extreme\"]}", + 24 + ], + "bath rug": [ + " {\"type\": \"bathroom accessory\", \"description\": \"soft, absorbent, usually rectangular; could be made of cotton, wool, or synthetic fibers\", \"similar objects\": [\"bath mat\", \"bath towel\", \"bathroom rug\"]}", + 24 + ], + "shadow boy": [ + " {\"type\": \"figure\", \"description\": \"black silhouette; could be a child; could be standing or running\", \"similar objects\": [\"shadow girl\", \"shadow animal\", \"shadow tree\"]}", + 24 + ], + "collard shirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have a collar; could be buttoned up\", \"similar objects\": [\"polo shirt\", \"t-shirt\", \"dress shirt\"]}", + 24 + ], + "animal grazing": [ + " {\"type\": \"action\", \"description\": \"animals eating grass or other vegetation in a field or pasture\", \"similar objects\": [\"animal drinking\", \"animal running\", \"animal sleeping\"]}", + 24 + ], + "stone path": [ + " {\"type\": \"landscape feature\", \"description\": \"made of stones; could be curved or straight; could be used as a walkway\", \"similar objects\": [\"gravel path\", \"wooden path\", \"brick path\"]}", + 24 + ], + "tissue dispenser": [ + " {\"type\": \"household item\", \"description\": \"box-shaped; could be made of plastic; could have a slot for tissues\", \"similar objects\": [\"toilet paper holder\", \"soap dispenser\", \"paper towel holder\"]}", + 24 + ], + "stone sidewalk": [ + " {\"type\": \"building material\", \"description\": \"made of stones; could be used as a walkway\", \"similar objects\": [\"concrete sidewalk\", \"gravel path\", \"brick path\"]}", + 24 + ], + "pearl": [ + " {\"type\": \"gemstone\", \"description\": \"round; could be white, pink, or black; could be found in oysters\", \"similar objects\": [\"diamond\", \"ruby\", \"sapphire\"]}", + 24 + ], + "mammal": [ + " {\"type\": \"animal\", \"description\": \"warm-blooded; has fur or hair; gives birth to live young; feeds milk to young\", \"similar objects\": [\"dog\", \"cat\", \"monkey\"]}", + 24 + ], + "flusher": [ + " {\"type\": \"plumbing tool\", \"description\": \"cylindrical; has a handle; used to flush out water\", \"similar objects\": [\"toilet brush\", \"plunger\", \"drain snake\"]}", + 24 + ], + "dark grey": [ + " {\"type\": \"color\", \"description\": \"a shade of grey; could be used to describe objects\", \"similar objects\": [\"black\", \"light grey\", \"charcoal\"]}", + 24 + ], + "man hand": [ + "\n{\"type\": \"body part\", \"description\": \"five fingers; could be used for grasping; could be used for writing\", \"similar objects\": [\"foot\", \"arm\", \"head\"]}", + 24 + ], + "lamp pole": [ + " {\"type\": \"street furniture\", \"description\": \"tall, cylindrical; could be made of metal; could have a light on top\", \"similar objects\": [\"street light\", \"traffic light\", \"mailbox\"]}", + 24 + ], + "game system": [ + " {\"type\": \"electronic device\", \"description\": \"could be a console or handheld; could have controllers; could have a screen\", \"similar objects\": [\"television\", \"computer\", \"smartphone\"]}", + 24 + ], + "peaks": [ + " {\"type\": \"landscape\", \"description\": \"mountainous; could have snow-capped peaks; could have steep slopes\", \"similar objects\": [\"valley\", \"cliff\", \"hill\"]}", + 24 + ], + "orange bucket": [ + "\n{\"type\": \"container\", \"description\": \"round; could be made of plastic; could be orange in color\", \"similar objects\": [\"pail\", \"tub\", \"barrel\"]}", + 24 + ], + "hairy": [ + "\n{\"type\": \"adjective\", \"description\": \"having a lot of hair; could be used to describe an animal or a person\", \"similar objects\": [\"furry\", \"fluffy\", \"shaggy\"]}", + 24 + ], + "water stains": [ + " {\"type\": \"stain\", \"description\": \"transparent; could be caused by water; could be found on walls, ceilings, and floors\", \"similar objects\": [\"dirt\", \"grease\", \"mold\"]}", + 24 + ], + "paddles": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, could be made of wood or plastic; used for rowing boats\", \"similar objects\": [\"oars\", \"racquets\", \"bats\"]}", + 24 + ], + "headrest": [ + " {\"type\": \"furniture\", \"description\": \"attached to the back of a chair; provides support for the head and neck\", \"similar objects\": [\"armrest\", \"footrest\", \"ottoman\"]}", + 24 + ], + "mop": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has a head made of cotton or synthetic fibers\", \"similar objects\": [\"broom\", \"vacuum cleaner\", \"duster\"]}", + 24 + ], + "gorilla": [ + " {\"type\": \"animal\", \"description\": \"large, black, has a broad chest; could have silverback; could be found in the jungle\", \"similar objects\": [\"chimpanzee\", \"orangutan\", \"baboon\"]}", + 24 + ], + "metal street lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"tall; made of metal; has a lightbulb; could be found on streets\", \"similar objects\": [\"lantern\", \"lamp\", \"streetlight\"]}", + 24 + ], + "telephone cord": [ + " {\"type\": \"connecting tool\", \"description\": \"long, thin, flexible; could be coiled\", \"similar objects\": [\"USB cable\", \"power cord\", \"Ethernet cable\"]}", + 24 + ], + "york yankees": [ + " {\"type\": \"sports team\", \"description\": \"professional baseball team based in New York City\", \"similar objects\": [\"Boston Red Sox\", \"Chicago Cubs\", \"Los Angeles Dodgers\"]}", + 24 + ], + "garbage cans": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could have a lid\", \"similar objects\": [\"trash can\", \"recycling bin\", \"compost bin\"]}", + 24 + ], + "sports utility vehicle": [ + " {\"type\": \"vehicle\", \"description\": \"large; has four-wheel drive; could have a roof rack\", \"similar objects\": [\"truck\", \"SUV\", \"minivan\"]}", + 24 + ], + "yellow center": [ + " {\"type\": \"flower\", \"description\": \"has yellow petals; could have a green stem; could have a yellow center\", \"similar objects\": [\"daisy\", \"sunflower\", \"daffodil\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber", + 24 + ], + "kitchen drawers": [ + " {\"type\": \"furniture\", \"description\": \"has multiple compartments; could be made of wood or metal; could be opened and closed with a handle\", \"similar objects\": [\"cabinets\", \"shelves\", \"cupboards\"]}", + 24 + ], + "sleeve t-shirt": [ + " {\"type\": \"clothing item\", \"description\": \"long sleeve; could be plain or patterned; could have a collar\", \"similar objects\": [\"long-sleeved shirt\", \"hoodie\", \"sweater\"]}", + 24 + ], + "cloudy overcast sky": [ + "\n{\"type\": \"weather\", \"description\": \"grayish; could have some white clouds; could be raining\", \"similar objects\": [\"rainy sky\", \"sunny sky\", \"snowy sky\"]}", + 24 + ], + "boat dock": [ + " {\"type\": \"structure\", \"description\": \"wooden platform; could be used to tie up boats; could have a ramp\", \"similar objects\": [\"pier\", \"jetty\", \"wharf\"]}", + 24 + ], + "bus windshield": [ + " {\"type\": \"vehicle part\", \"description\": \"transparent; could be curved; could be made of glass\", \"similar objects\": [\"car windshield\", \"motorcycle windshield\", \"truck windshield\"]}", + 24 + ], + "plastic knife": [ + " {\"type\": \"utensil\", \"description\": \"long; could be used for cutting; could be made of plastic\", \"similar objects\": [\"fork\", \"spoon\", \"spatula\"]}", + 24 + ], + "sash": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of fabric; could be tied around the waist\", \"similar objects\": [\"belt\", \"scarf\", \"shawl\"]}", + 24 + ], + "headlight front motorcycle": [ + "\n{\"type\": \"vehicle part\", \"description\": \"attached to the front of a motorcycle; used to provide illumination\", \"similar objects\": [\"taillight\", \"turn signal\", \"horn\"]}", + 24 + ], + "bear fur": [ + " {\"type\": \"animal fur\", \"description\": \"thick, brown, coarse; could be used for clothing\", \"similar objects\": [\"fox fur\", \"wolf fur\", \"rabbit fur\"]}", + 24 + ], + "cobblestone": [ + " {\"type\": \"building material\", \"description\": \"small, round stones; could be used to pave roads\", \"similar objects\": [\"gravel\", \"asphalt\", \"concrete\"]}", + 24 + ], + "attachment": [ + " {\"type\": \"file\", \"description\": \"digital file; could be a document, image, video, etc.\", \"similar objects\": [\"document\", \"image\", \"video\"]}", + 24 + ], + "motorcycle kickstand": [ + " {\"type\": \"motorcycle part\", \"description\": \"metal; used to support the motorcycle when parked; could be adjustable\", \"similar objects\": [\"motorcycle seat\", \"motorcycle handlebar\", \"motorcycle exhaust\"]}", + 24 + ], + "toolbox": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal; could have multiple compartments\", \"similar objects\": [\"tool chest\", \"tool cabinet\", \"tool rack\"]}", + 24 + ], + "windsurfer": [ + " {\"type\": \"sport equipment\", \"description\": \"long board; has a sail; could be used in water\", \"similar objects\": [\"surfboard\", \"kayak\", \"canoe\"]}", + 24 + ], + "luggage carts": [ + " {\"type\": \"transportation tool\", \"description\": \"has two or four wheels; could be folded; could be pushed or pulled\", \"similar objects\": [\"hand truck\", \"dolly\", \"shopping cart\"]}", + 24 + ], + "silver bucket": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of silver; could have a handle\", \"similar objects\": [\"pail\", \"tub\", \"barrel\"]}", + 24 + ], + "dark wall": [ + " {\"type\": \"structure\", \"description\": \"dark color; could be made of wood, stone, or metal; could be used as a background\", \"similar objects\": [\"fence\", \"door\", \"window\"]}", + 24 + ], + "plastic jug": [ + " {\"type\": \"container\", \"description\": \"transparent; could have a handle; could be used to store liquids\", \"similar objects\": [\"bottle\", \"jar\", \"mug\"]}", + 24 + ], + "u": [ + "\n{\"type\": \"letter\", \"description\": \"the 21st letter of the English alphabet; a vowel\", \"similar objects\": [\"v\", \"w\", \"y\"]}", + 24 + ], + "tail-fin": [ + " {\"type\": \"part of a fish\", \"description\": \"elongated, pointed, could be colorful\", \"similar objects\": [\"dorsal fin\", \"pectoral fin\", \"anal fin\"]}", + 24 + ], + "parking signs": [ + " {\"type\": \"traffic signs\", \"description\": \"rectangular; could be yellow or white; could have symbols or words\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 24 + ], + "screwdriver": [ + " {\"type\": \"tool\", \"description\": \"long handle; has a flat or cross-shaped tip\", \"similar objects\": [\"hammer\", \"pliers\", \"wrench\"]}", + 24 + ], + "freeway": [ + " {\"type\": \"roadway\", \"description\": \"multi-lane; has no traffic lights; could have tolls\", \"similar objects\": [\"highway\", \"expressway\", \"interstate\"]}", + 24 + ], + "brakes": [ + " {\"type\": \"automotive part\", \"description\": \"used to slow down or stop a vehicle; could be made of metal or rubber\", \"similar objects\": [\"tires\", \"clutch\", \"steering wheel\"]}", + 24 + ], + "shadow sidewalk": [ + " {\"type\": \"outdoor scene\", \"description\": \"dark area on the sidewalk caused by an object blocking the sunlight\", \"similar objects\": [\"shade\", \"umbrella\", \"building shadow\"]}", + 24 + ], + "beige tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"marble tiles\", \"granite tiles\", \"wooden tiles\"]}", + 24 + ], + "fire trucks": [ + " {\"type\": \"vehicle\", \"description\": \"red; has a long ladder; could with a hose\", \"similar objects\": [\"ambulance\", \"police car\", \"garbage truck\"]}", + 24 + ], + "refection": [ + " {\"type\": \"mirror\", \"description\": \"smooth, flat surface; could be made of glass; could be used to reflect light\", \"similar objects\": [\"window\", \"mirror\", \"sunglasses\"]}", + 24 + ], + "life saver": [ + " {\"type\": \"safety tool\", \"description\": \"round; could be orange or yellow; could be used to float in water\", \"similar objects\": [\"life jacket\", \"floatation device\", \"buoy\"]}", + 24 + ], + "dog eye": [ + " {\"type\": \"animal body part\", \"description\": \"round; black pupil; could be brown, blue, or green; could have a white sclera\", \"similar objects\": [\"cat eye\", \"horse eye\", \"bird eye\"]}", + 24 + ], + "yak": [ + " {\"type\": \"animal\", \"description\": \"large, shaggy, long-haired; could have a hump on its back; could have long horns\", \"similar objects\": [\"cattle\", \"bison\", \"buffalo\"]}", + 24 + ], + "fighter plane": [ + " {\"type\": \"aircraft\", \"description\": \"long and slim; has wings and tail; could have missiles\", \"similar objects\": [\"helicopter\", \"jet\", \"drone\"]}", + 24 + ], + "table leg": [ + " {\"type\": \"furniture part\", \"description\": \"long; could be made of wood or metal; could be attached to a table top\", \"similar objects\": [\"chair leg\", \"cabinet leg\", \"stool leg\"]}", + 23 + ], + "silver buttons": [ + " {\"type\": \"accessory\", \"description\": \"small, round, metallic; could be used to fasten clothes\", \"similar objects\": [\"zippers\", \"hooks\", \"snaps\"]}", + 23 + ], + "bushy trees": [ + " {\"type\": \"plant\", \"description\": \"large; has many branches and leaves; could be evergreen or deciduous\", \"similar objects\": [\"pine tree\", \"oak tree\", \"maple tree\"]}", + 23 + ], + "grey bricks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of concrete; could be used to build walls\", \"similar objects\": [\"cement blocks\", \"wooden planks\", \"stone tiles\"]}", + 23 + ], + "color wall": [ + " {\"type\": \"decoration\", \"description\": \"wall with different colors; could be painted or wallpapers\", \"similar objects\": [\"mural\", \"wall art\", \"wall sticker\"]}", + 23 + ], + "pull": [ + " {\"type\": \"action\", \"description\": \"to move something towards oneself\", \"similar objects\": [\"push\", \"drag\", \"lift\"]}", + 23 + ], + "burn mark": [ + " {\"type\": \"damage\", \"description\": \"dark, discolored area on a surface; could be caused by heat or friction\", \"similar objects\": [\"scratch\", \"tear\", \"stain\"]}", + 23 + ], + "hairdryer": [ + " {\"type\": \"hair styling tool\", \"description\": \"long, cylindrical; has a nozzle; could be corded or cordless\", \"similar objects\": [\"curling iron\", \"straightener\", \"hair clippers\"]}", + 23 + ], + "pylon": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical, made of metal; could be used to support power lines\", \"similar objects\": [\"tower\", \"pole\", \"monument\"]}", + 23 + ], + "telephone lines": [ + " {\"type\": \"communication tool\", \"description\": \"long, thin wires; could be connected to poles; could be used to transmit voice signals\", \"similar objects\": [\"cable lines\", \"fiber optics\", \"satellite dish\"]}", + 23 + ], + "emergency vehicle": [ + " {\"type\": \"vehicle\", \"description\": \"red; has a glaring siren; could with a stretcher\", \"similar objects\": [\"ambulance\", \"police car\", \"fire truck\"]}", + 23 + ], + "prt": [ + " {\"type\": \"transportation tool\", \"description\": \"automated; has a track; could be used for public transportation\", \"similar objects\": [\"monorail\", \"tram\", \"subway\"]}", + 23 + ], + "treeline": [ + " {\"type\": \"landscape\", \"description\": \"a line of trees; could be a forest; could be a park\", \"similar objects\": [\"mountain\", \"river\", \"lake\"]}", + 23 + ], + "brown lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"brown; could be made of metal; could have a switch\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}", + 23 + ], + "shoe strings": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, and flexible; used to tie shoes\", \"similar objects\": [\"laces\", \"elastic bands\", \"velcro straps\"]}", + 23 + ], + "document": [ + " {\"type\": \"paperwork\", \"description\": \"could be printed or digital; could be a form, letter, or report\", \"similar objects\": [\"file\", \"contract\", \"agreement\"]}", + 23 + ], + "art piece": [ + " {\"type\": \"visual art\", \"description\": \"could be a painting, sculpture, or other visual art form; could be abstract or representational; could be made of various materials\", \"similar objects\": [\"photograph\", \"drawing\", \"installation\"]}", + 23 + ], + "wood container": [ + " {\"type\": \"storage tool\", \"description\": \"made of wood; could be used to store items; could be in different shapes and sizes\", \"similar objects\": [\"basket\", \"box\", \"trunk\"]}", + 23 + ], + "sport utility vehicle": [ + "\n{\"type\": \"vehicle\", \"description\": \"large, four-wheel drive; could have a roof rack; could have a third row of seats\", \"similar objects\": [\"SUV\", \"truck\", \"minivan\"]}", + 23 + ], + "display screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be used to show images or videos; could be touch-sensitive\", \"similar objects\": [\"monitor\", \"television\", \"tablet\"]}", + 23 + ], + "brown window": [ + "\n{\"type\": \"building material\", \"description\": \"rectangular; could be made of wood; could be used to let in light\", \"similar objects\": [\"door\", \"shutter\", \"curtain\"]}", + 23 + ], + "cock pit": [ + " {\"type\": \"aircraft component\", \"description\": \"control center of an aircraft; has multiple instruments and controls; could have multiple seats\", \"similar objects\": [\"flight deck\", \"cabin\", \"galley\"]}", + 23 + ], + "trailers": [ + " {\"type\": \"vehicle\", \"description\": \"long; could be attached to a car; could be used for transporting goods\", \"similar objects\": [\"truck\", \"van\", \"caravan\"]}", + 23 + ], + "orange stripes": [ + " {\"type\": \"pattern\", \"description\": \"alternating bands of orange and white; could be used for decoration\", \"similar objects\": [\"black and white stripes\", \"red and blue stripes\", \"green and yellow stripes\"]}", + 23 + ], + "wording": [ + " {\"type\": \"word\", \"description\": \"a group of letters that form a meaningful unit of language\", \"similar objects\": [\"sentence\", \"phrase\", \"clause\"]}", + 23 + ], + "hairy arm": [ + " {\"type\": \"body part\", \"description\": \"covered with hair; could be muscular; could be attached to a hand\", \"similar objects\": [\"leg\", \"torso\", \"face\"]}", + 23 + ], + "score board": [ + " {\"type\": \"sports equipment\", \"description\": \"large board with numbers and letters; could be electronic or manual\", \"similar objects\": [\"stopwatch\", \"whistle\", \"timer\"]}", + 23 + ], + "batting gloves": [ + " {\"type\": \"sports equipment\", \"description\": \"worn on hands; made of leather; could have padding\", \"similar objects\": [\"baseball bat\", \"baseball cap\", \"baseball cleats\"]}", + 23 + ], + "silver garbage": [ + " {\"type\": \"trash bin\", \"description\": \"silver; could have a lid; could be used to store garbage\", \"similar objects\": [\"trash can\", \"recycling bin\", \"compost bin\"]}", + 23 + ], + "route number": [ + "\n{\"type\": \"information\", \"description\": \"a number that identifies a route or a path\", \"similar objects\": [\"street address\", \"zip code\", \"phone number\"]}", + 23 + ], + "fireplace mantle": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have a shelf; could be decorated with ornaments\", \"similar objects\": [\"bookshelf\", \"table\", \"chair\"]}", + 23 + ], + "fold": [ + " {\"type\": \"verb\", \"description\": \"to bend or double over; to make or be made flat by pressing parts together\", \"similar objects\": [\"crease\", \"wrinkle\", \"compress\"]}", + 23 + ], + "window frames": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of wood or metal; could be used to hold glass panes\", \"similar objects\": [\"doors\", \"shutters\", \"awnings\"]}", + 23 + ], + "remote table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have drawers; could be made of wood or metal\", \"similar objects\": [\"desk\", \"chair\", \"cabinet\"]}", + 23 + ], + "toothpaste tube": [ + " {\"type\": \"hygiene product\", \"description\": \"cylindrical; could be squeezed; could have a cap\", \"similar objects\": [\"shampoo bottle\", \"lotion bottle\", \"soap bar\"]}", + 23 + ], + "metal sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"made of metal; has a handle; could have a sprayer\", \"similar objects\": [\"bathtub faucet\", \"shower faucet\", \"kitchen faucet\"]}", + 23 + ], + "shampoo bottle": [ + " {\"type\": \"cleaning product\", \"description\": \"cylindrical; could be plastic; could have a pump\", \"similar objects\": [\"soap\", \"conditioner\", \"lotion\"]}", + 23 + ], + "smiley face": [ + " {\"type\": \"emoticon\", \"description\": \"round; two eyes and a curved mouth; could be yellow\", \"similar objects\": [\"frowny face\", \"heart\", \"thumbs up\"]}", + 23 + ], + "ottomen": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be used as a footrest; could be used as a storage\", \"similar objects\": [\"chair\", \"sofa\", \"table\"]}", + 23 + ], + "food dish": [ + " {\"type\": \"utensil\", \"description\": \"flat, round, could be made of ceramic or metal; could be used to serve food\", \"similar objects\": [\"plate\", \"bowl\", \"tray\"]}", + 23 + ], + "clockface": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has numbers and hands; could be digital or analog\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}", + 23 + ], + "counters": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, metal, or plastic; could have drawers\", \"similar objects\": [\"table\", \"desk\", \"dresser\"]}", + 23 + ], + "silver spokes": [ + " {\"type\": \"bicycle part\", \"description\": \"metallic; used to connect the wheel to the frame\", \"similar objects\": [\"rims\", \"tires\", \"pedals\"]}", + 23 + ], + "flower stem": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, green; could have leaves and flowers\", \"similar objects\": [\"leaf\", \"branch\", \"root\"]}", + 23 + ], + "wave water": [ + " {\"type\": \"natural phenomenon\", \"description\": \"repeated up and down motion of water; could be caused by wind or earthquake\", \"similar objects\": [\"tide\", \"tsunami\", \"surf\"]}", + 23 + ], + "metal watch": [ + " {\"type\": \"accessory\", \"description\": \"made of metal; has a round face; could have a band\", \"similar objects\": [\"bracelet\", \"necklace\", \"ring\"]}", + 23 + ], + "microwave counter": [ + " {\"type\": \"kitchen appliance\", \"description\": \"rectangular; has a door; could have a digital display\", \"similar objects\": [\"refrigerator\", \"stove\", \"dishwasher\"]}", + 23 + ], + "building sign": [ + " {\"type\": \"outdoor signage\", \"description\": \"could be made of metal or plastic; could be illuminated; could be in different shapes and sizes\", \"similar objects\": [\"street sign\", \"billboard\", \"traffic sign\"]}", + 23 + ], + "entertainment": [ + " {\"type\": \"activity\", \"description\": \"something that provides amusement or enjoyment; could involve music, movies, games, etc.\", \"similar objects\": [\"leisure\", \"recreation\", \"pastime\"]}", + 23 + ], + "vinyl": [ + " {\"type\": \"recording medium\", \"description\": \"round, black; could be played on a turntable\", \"similar objects\": [\"CD\", \"cassette tape\", \"digital audio file\"]}", + 23 + ], + "locks": [ + " {\"type\": \"security tool\", \"description\": \"metal; could be used to secure doors, windows, and other items; could be opened with a key\", \"similar objects\": [\"padlock\", \"deadbolt\", \"chain lock\"]}", + 23 + ], + "ripe orange": [ + "\n{\"type\": \"fruit\", \"description\": \"orange; round; has a stem; could be soft and juicy; could have a yellowish-orange color\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}", + 23 + ], + "hall": [ + " {\"type\": \"room\", \"description\": \"large, rectangular; could have multiple doors; could have windows\", \"similar objects\": [\"living room\", \"bedroom\", \"kitchen\"]}", + 23 + ], + "eiffel tower": [ + " {\"type\": \"landmark\", \"description\": \"tall, iron structure; located in Paris, France; has three levels\", \"similar objects\": [\"Statue of Liberty\", \"Big Ben\", \"Leaning Tower of Pisa\"]}", + 23 + ], + "pink balloon": [ + "\n{\"type\": \"decoration item\", \"description\": \"round; could be filled with air or helium; could be made of rubber or plastic; could be in various colors\", \"similar objects\": [\"balloon\", \"streamer\", \"confetti\"]}", + 23 + ], + "violin": [ + " {\"type\": \"musical instrument\", \"description\": \"wooden; has four strings; has a bow\", \"similar objects\": [\"cello\", \"guitar\", \"piano\"]}", + 23 + ], + "starbucks": [ + " {\"type\": \"cafe\", \"description\": \"coffee shop; could have a variety of drinks and snacks; could have a seating area\", \"similar objects\": [\"cafe\", \"diner\", \"restaurant\"]}", + 23 + ], + "blooms": [ + " {\"type\": \"plant\", \"description\": \"flowers; could be colorful; could have petals\", \"similar objects\": [\"flowers\", \"roses\", \"daisies\"]}", + 23 + ], + "upside": [ + " {\"type\": \"direction\", \"description\": \"opposite of down; opposite of right side up\", \"similar objects\": [\"downside\", \"inverted\", \"reverse\"]}", + 23 + ], + "cement stairs": [ + " {\"type\": \"building material\", \"description\": \"made of cement; could be used as stairs\", \"similar objects\": [\"concrete steps\", \"stone steps\", \"wooden steps\"]}", + 23 + ], + "meatballs": [ + " {\"type\": \"food\", \"description\": \"round; could be made of ground beef, pork, or turkey; could be served with sauce\", \"similar objects\": [\"dumplings\", \"falafel\", \"sausages\"]}", + 23 + ], + "dunes": [ + " {\"type\": \"landform\", \"description\": \"mounds of sand; could be found in deserts\", \"similar objects\": [\"hills\", \"mountains\", \"valleys\"]}", + 23 + ], + "metal cart": [ + " {\"type\": \"transportation tool\", \"description\": \"has four wheels; could be made of metal; could be used to carry heavy objects\", \"similar objects\": [\"wheelbarrow\", \"hand truck\", \"dolly\"]}", + 23 + ], + "orange logo": [ + " {\"type\": \"logo\", \"description\": \"round; could be in orange color; could have a symbol or text inside\", \"similar objects\": [\"brand logo\", \"company logo\", \"product logo\"]}", + 23 + ], + "cutting board": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, rectangular; could be made of wood or plastic; could have a handle\", \"similar objects\": [\"chopping board\", \"knife block\", \"rolling pin\"]}", + 23 + ], + "wood chairs": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have armrests; could have a cushion\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 23 + ], + "knifes": [ + " {\"type\": \"utensil\", \"description\": \"sharp; could be made of metal; could have a handle\", \"similar objects\": [\"fork\", \"spoon\", \"chopsticks\"]}", + 23 + ], + "hatchback car": [ + "\n{\"type\": \"vehicle\", \"description\": \"compact car; has a rear door that opens upward; has a sloping roofline\", \"similar objects\": [\"sedan\", \"coupe\", \"SUV\"]}", + 23 + ], + "fronds": [ + " {\"type\": \"plant\", \"description\": \"long, thin, green leaves; could be curved or straight; could be found in tropical regions\", \"similar objects\": [\"palm leaves\", \"ferns\", \"banana leaves\"]}", + 23 + ], + "base plate": [ + " {\"type\": \"building tool\", \"description\": \"flat, rectangular; could be made of metal or plastic; used to support structures\", \"similar objects\": [\"beam\", \"column\", \"joist\"]}", + 23 + ], + "furry animal": [ + "\n{\"type\": \"animal\", \"description\": \"covered with fur; could have a long tail; could have four legs\", \"similar objects\": [\"dog\", \"cat\", \"rabbit\"]}", + 23 + ], + "dollop": [ + " {\"type\": \"measurement\", \"description\": \"a small, rounded amount of a substance, such as cream or butter\", \"similar objects\": [\"spoonful\", \"pinch\", \"dash\"]}", + 23 + ], + "sea shore": [ + " {\"type\": \"landscape\", \"description\": \"sandy beach; could have rocks; could have waves; could have seagulls\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 23 + ], + "dirty ground": [ + " {\"type\": \"environment\", \"description\": \"uneven surface; could be covered with dirt, dust, and debris; could have puddles of water\", \"similar objects\": [\"mud\", \"pavement\", \"grass\"]}", + 23 + ], + "light grey": [ + " {\"type\": \"color\", \"description\": \"a shade of grey; could be described as pale grey\", \"similar objects\": [\"dark grey\", \"silver\", \"charcoal\"]}", + 23 + ], + "fingernail polish": [ + " {\"type\": \"cosmetic product\", \"description\": \"liquid; could be applied to fingernails; comes in various colors\", \"similar objects\": [\"lipstick\", \"eyeliner\", \"mascara\"]}", + 23 + ], + "motorcycle license plate": [ + "\n{\"type\": \"license plate\", \"description\": \"rectangular; has a unique number; could be attached to a motorcycle\", \"similar objects\": [\"car license plate\", \"truck license plate\", \"van license plate\"]}", + 23 + ], + "silver grill": [ + " {\"type\": \"cooking tool\", \"description\": \"silver; has a handle; could be used to grill food\", \"similar objects\": [\"griddle\", \"barbecue\", \"skillet\"]}", + 23 + ], + "grey sweatshirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have a hood; could have a zipper; could have pockets; could be made of cotton\", \"similar objects\": [\"hoodie\", \"sweater\", \"jacket\"]}", + 23 + ], + "steak knife": [ + " {\"type\": \"cutlery\", \"description\": \"long, sharp blade; could have a wooden handle\", \"similar objects\": [\"butter knife\", \"dinner knife\", \"fork\"]}", + 23 + ], + "swivel chair": [ + " {\"type\": \"furniture\", \"description\": \"has a round base; could rotate 360 degrees; could have armrests\", \"similar objects\": [\"office chair\", \"rocking chair\", \"recliner\"]}", + 23 + ], + "escalator": [ + " {\"type\": \"transportation tool\", \"description\": \"long, metal stairs; could move up and down\", \"similar objects\": [\"elevator\", \"staircase\", \"moving walkway\"]}", + 23 + ], + "drum set": [ + " {\"type\": \"musical instrument\", \"description\": \"consists of several drums, cymbals, and other percussion instruments; could be played with sticks\", \"similar objects\": [\"piano\", \"guitar\", \"violin\"]}", + 23 + ], + "compartments": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of wood, plastic, or metal; could have multiple sections; could be used to store items\", \"similar objects\": [\"drawers\", \"boxes\", \"baskets\"]}", + 23 + ], + "ornate": [ + " {\"type\": \"adjective\", \"description\": \"elaborately decorated; intricate; fancy\", \"similar objects\": [\"elaborate\", \"intricate\", \"fancy\"]}", + 23 + ], + "grocery bag": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic or paper; could be reusable\", \"similar objects\": [\"shopping bag\", \"backpack\", \"suitcase\"]}", + 23 + ], + "steeples": [ + " {\"type\": \"architectural structure\", \"description\": \"pointed, tall, could be made of stone or metal; could be part of a church\", \"similar objects\": [\"dome\", \"minaret\", \"obelisk\"]}", + 23 + ], + "pink vase": [ + "\n{\"type\": \"decorative item\", \"description\": \"pink; could be made of ceramic; could have a long neck\", \"similar objects\": [\"urn\", \"urns\", \"flower pot\"]}", + 23 + ], + "wooden cabinets": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have drawers and doors; could be used for storage\", \"similar objects\": [\"dresser\", \"bookshelf\", \"armoire\"]}", + 23 + ], + "rectangles": [ + " {\"type\": \"shape\", \"description\": \"four-sided figure with four right angles; could be of any size\", \"similar objects\": [\"squares\", \"triangles\", \"circles\"]}", + 23 + ], + "dustbin": [ + " {\"type\": \"container\", \"description\": \"rectangular; could have a lid; could be made of plastic\", \"similar objects\": [\"trash can\", \"garbage can\", \"waste bin\"]}", + 23 + ], + "bus windows": [ + " {\"type\": \"transportation window\", \"description\": \"rectangular; could be tinted; could be opened and closed\", \"similar objects\": [\"car window\", \"airplane window\", \"train window\"]}", + 23 + ], + "luggage carrier": [ + " {\"type\": \"transportation tool\", \"description\": \"wheeled; could be made of plastic or metal; could be collapsible\", \"similar objects\": [\"suitcase\", \"backpack\", \"briefcase\"]}", + 23 + ], + "silver water faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be used to control water flow\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"kitchen sink faucet\"]}", + 23 + ], + "pyramid": [ + " {\"type\": \"structure\", \"description\": \"triangular; has four sides; could be made of stones\", \"similar objects\": [\"obelisk\", \"monument\", \"tomb\"]}", + 23 + ], + "bus front windshield": [ + "\n{\"type\": \"automobile part\", \"description\": \"transparent; could be curved; could be made of glass\", \"similar objects\": [\"car windshield\", \"truck windshield\", \"motorcycle windshield\"]}", + 23 + ], + "male skateboarder": [ + "\n{\"type\": \"person\", \"description\": \"wearing a helmet; riding a skateboard; could be wearing a t-shirt and shorts; could have tattoos\", \"similar objects\": [\"female skateboarder\", \"bicyclist\", \"rollerblader\"]}", + 23 + ], + "microphones": [ + " {\"type\": \"audio equipment\", \"description\": \"long, thin; could be wired or wireless; could be used to amplify sound\", \"similar objects\": [\"speakers\", \"headphones\", \"recorders\"]}", + 23 + ], + "purple bowl": [ + "\n{\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; has a color of purple\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 23 + ], + "speedboat": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has a motor; could be used for racing\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}", + 23 + ], + "grey blanket": [ + " {\"type\": \"bedding item\", \"description\": \"soft; could be made of wool; could be used for warmth\", \"similar objects\": [\"pillow\", \"duvet\", \"quilt\"]}", + 23 + ], + "identification": [ + " {\"type\": \"document\", \"description\": \"could be a card or a paper; could contain personal information; could be used to verify identity\", \"similar objects\": [\"passport\", \"driver's license\", \"birth certificate\"]}", + 23 + ], + "pink coat": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of wool; could have buttons; could have pockets\", \"similar objects\": [\"jacket\", \"cardigan\", \"blazer\"]}", + 23 + ], + "rump": [ + " {\"type\": \"cut of meat\", \"description\": \"tender, juicy, and flavorful; usually from the hindquarters of a cow\", \"similar objects\": [\"sirloin\", \"tenderloin\", \"ribeye\"]}", + 23 + ], + "metal garbage": [ + " {\"type\": \"trash\", \"description\": \"made of metal; could be cans, bottles, or other metal objects\", \"similar objects\": [\"plastic garbage\", \"paper garbage\", \"glass garbage\"]}", + 23 + ], + "metal spokes": [ + " {\"type\": \"hardware\", \"description\": \"long, thin, metal rods; could be used to support a wheel\", \"similar objects\": [\"nuts and bolts\", \"screws\", \"washers\"]}", + 23 + ], + "decals": [ + " {\"type\": \"decoration\", \"description\": \"stickers; could be used to decorate walls, windows, and other surfaces\", \"similar objects\": [\"wallpaper\", \"paintings\", \"posters\"]}", + 23 + ], + "tree line": [ + " {\"type\": \"landscape\", \"description\": \"a line of trees; could be a forest; could be a park\", \"similar objects\": [\"mountain range\", \"river\", \"meadow\"]}", + 23 + ], + "backpack strap": [ + " {\"type\": \"accessory\", \"description\": \"long, adjustable, could be made of nylon or leather\", \"similar objects\": [\"belt\", \"shoulder strap\", \"luggage strap\"]}", + 23 + ], + "tangerine": [ + " {\"type\": \"fruit\", \"description\": \"orange, round, has a stem; could be peeled easily\", \"similar objects\": [\"orange\", \"mandarin\", \"clementine\"]}", + 23 + ], + "focus": [ + "\n{\"type\": \"verb\", \"description\": \"to concentrate on a particular object or task\", \"similar objects\": [\"concentrate\", \"attend\", \"pay attention\"]}", + 23 + ], + "sausage links": [ + " {\"type\": \"food\", \"description\": \"cylindrical; could be made of pork, beef, or turkey; could be cooked in a pan or on a grill\", \"similar objects\": [\"hot dog\", \"bratwurst\", \"kielbasa\"]}", + 23 + ], + "kitchen scene": [ + "\n{\"type\": \"scene\", \"description\": \"kitchen; could have a stove, refrigerator, sink, cabinets, countertops, and other kitchen appliances; could have food items such as fruits, vegetables, and utensils; could have people cooking or eating\", \"similar objects\": [\"dining room\", \"living room\", \"bathroom\"]}", + 23 + ], + "wooden edge": [ + " {\"type\": \"tool\", \"description\": \"sharp edge; could be used for cutting; could be made of wood\", \"similar objects\": [\"knife\", \"axe\", \"saw\"]}", + 23 + ], + "grey color": [ + " {\"type\": \"color\", \"description\": \"neutral color; could be light or dark; could be combined with other colors\", \"similar objects\": [\"black\", \"white\", \"silver\"]}", + 23 + ], + "orange shorts": [ + "\n{\"type\": \"clothing\", \"description\": \"orange; could be made of cotton; could have pockets; could have a drawstring\", \"similar objects\": [\"jeans\", \"t-shirt\", \"skirt\"]}", + 23 + ], + "headlamp": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the head; could be battery-powered; could be waterproof\", \"similar objects\": [\"flashlight\", \"lantern\", \"torch\"]}", + 23 + ], + "pendant": [ + " {\"type\": \"jewelry\", \"description\": \"hanging ornament; could be made of metal, glass, or stone; could have a chain or cord\", \"similar objects\": [\"necklace\", \"earrings\", \"bracelet\"]}", + 23 + ], + "metal towel rack": [ + " {\"type\": \"furniture\", \"description\": \"made of metal; has multiple bars for hanging towels\", \"similar objects\": [\"clothes hanger\", \"coat rack\", \"shoe rack\"]}", + 23 + ], + "dark shirt": [ + " {\"type\": \"clothing\", \"description\": \"black or dark-colored; could have long or short sleeves; could have a collar\", \"similar objects\": [\"t-shirt\", \"dress shirt\", \"sweater\"]}", + 23 + ], + "jalapenos": [ + " {\"type\": \"vegetable\", \"description\": \"green, red, or yellow; could be sliced into small pieces; could be spicy\", \"similar objects\": [\"bell pepper\", \"habanero\", \"serrano pepper\"]}", + 23 + ], + "barrette": [ + " {\"type\": \"hair accessory\", \"description\": \"small clip; could be made of metal or plastic; could be decorated with beads or stones\", \"similar objects\": [\"hairpin\", \"hair tie\", \"headband\"]}", + 23 + ], + "piping": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, thin, cylindrical; could be made of metal or plastic; could be used to connect two pipes\", \"similar objects\": [\"elbow\", \"tee\", \"union\"]}", + 23 + ], + "wood handle": [ + " {\"type\": \"tool handle\", \"description\": \"made of wood; could be used for tools such as hammers, axes, and shovels\", \"similar objects\": [\"plastic handle\", \"metal handle\", \"rubber handle\"]}", + 23 + ], + "square container": [ + " {\"type\": \"container\", \"description\": \"has four sides; could be made of plastic or metal; could have a lid\", \"similar objects\": [\"box\", \"jar\", \"basket\"]}", + 23 + ], + "zebra nose": [ + " {\"type\": \"animal body part\", \"description\": \"long, black, and pointed; could be used for smelling\", \"similar objects\": [\"elephant trunk\", \"giraffe tongue\", \"horse hoof\"]}", + 23 + ], + "mail": [ + " {\"type\": \"communication tool\", \"description\": \"could be sent through post office; could be sent electronically\", \"similar objects\": [\"letter\", \"package\", \"email\"]}", + 23 + ], + "lightpost": [ + " {\"type\": \"street furniture\", \"description\": \"tall; could be made of metal; could have a lamp on top\", \"similar objects\": [\"street sign\", \"traffic light\", \"bench\"]}", + 23 + ], + "giraffe face": [ + "\n{\"type\": \"animal face\", \"description\": \"long neck; long eyelashes; two eyes; two ears; two horns\", \"similar objects\": [\"elephant face\", \"horse face\", \"monkey face\"]}", + 23 + ], + "windmill": [ + " {\"type\": \"energy tool\", \"description\": \"tall; has blades; could be used to generate electricity\", \"similar objects\": [\"solar panel\", \"turbine\", \"hydroelectric generator\"]}", + 23 + ], + "bracelet wrist": [ + " {\"type\": \"jewelry\", \"description\": \"worn around the wrist; could be made of metal, plastic, or fabric; could have charms or beads\", \"similar objects\": [\"necklace\", \"ring\", \"earrings\"]}", + 23 + ], + "airport building": [ + " {\"type\": \"structure\", \"description\": \"large; could have multiple floors; could have a control tower; could have multiple gates\", \"similar objects\": [\"train station\", \"shopping mall\", \"stadium\"]}", + 23 + ], + "water drain": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a hole for water to flow through; could be made of metal or plastic\", \"similar objects\": [\"sink\", \"bathtub\", \"toilet\"]}", + 23 + ], + "silver rail": [ + " {\"type\": \"building material\", \"description\": \"long, thin, metallic; could be used for fencing\", \"similar objects\": [\"iron rail\", \"wooden rail\", \"aluminum rail\"]}", + 23 + ], + "airplane door": [ + " {\"type\": \"airplane part\", \"description\": \"rectangular; has a handle; could be opened from inside and outside\", \"similar objects\": [\"window\", \"seat\", \"aisle\"]}", + 23 + ], + "knuckles": [ + " {\"type\": \"body part\", \"description\": \"the joints of the fingers; could be used for self-defense\", \"similar objects\": [\"elbow\", \"knee\", \"ankle\"]}", + 23 + ], + "braces": [ + " {\"type\": \"dental tool\", \"description\": \"metal; used to straighten teeth; could be made of plastic\", \"similar objects\": [\"retainer\", \"headgear\", \"mouthguard\"]}", + 23 + ], + "portions": [ + " {\"type\": \"measurement\", \"description\": \"amount of food; could be divided into smaller parts\", \"similar objects\": [\"servings\", \"portions\", \"pieces\"]}", + 23 + ], + "metal traffic light": [ + "\n{\"type\": \"traffic signal\", \"description\": \"metal; has three lights; could be red, yellow, and green\", \"similar objects\": [\"stop sign\", \"road sign\", \"traffic cone\"]}", + 23 + ], + "paper umbrella": [ + " {\"type\": \"accessory\", \"description\": \"made of paper; could be used to protect from rain\", \"similar objects\": [\"umbrella\", \"raincoat\", \"hat\"]}", + 23 + ], + "pavilion": [ + " {\"type\": \"structure\", \"description\": \"large, open-sided structure; could be made of wood or metal; could have a roof\", \"similar objects\": [\"gazebo\", \"pergola\", \"tent\"]}", + 23 + ], + "grey house": [ + " {\"type\": \"building\", \"description\": \"rectangular; could have a chimney; could have a porch; could have windows and doors\", \"similar objects\": [\"apartment\", \"mansion\", \"bungalow\"]}", + 23 + ], + "barge": [ + " {\"type\": \"watercraft\", \"description\": \"long and wide; could be used to transport goods; could be powered by motor or sail\", \"similar objects\": [\"boat\", \"ship\", \"yacht\"]}", + 23 + ], + "lanterns": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of papers; could be hung in a row\", \"similar objects\": [\"lamps\", \"flashlights\", \"candles\"]}", + 23 + ], + "chair leg": [ + " {\"type\": \"furniture part\", \"description\": \"long, cylindrical; could be made of wood or metal; could have a foot rest\", \"similar objects\": [\"table leg\", \"armrest\", \"backrest\"]}", + 23 + ], + "soccer cleats": [ + " {\"type\": \"footwear\", \"description\": \"has spikes on the bottom; could be made of leather; could be black and white\", \"similar objects\": [\"football boots\", \"running shoes\", \"hiking boots\"]}", + 23 + ], + "quarter": [ + " {\"type\": \"coin\", \"description\": \"round; has a face of a president; has a value of 25 cents\", \"similar objects\": [\"dime\", \"nickel\", \"penny\"]}", + 23 + ], + "flower buds": [ + " {\"type\": \"plant\", \"description\": \"small, round, could be green or yellow; could be attached to a stem\", \"similar objects\": [\"leaves\", \"seeds\", \"berries\"]}", + 23 + ], + "wakeboard": [ + " {\"type\": \"sports equipment\", \"description\": \"a board used for water sports; could have bindings for feet; could have a rope attached\", \"similar objects\": [\"surfboard\", \"skimboard\", \"snowboard\"]}", + 23 + ], + "shadow woman": [ + " {\"type\": \"figure\", \"description\": \"dark silhouette; could be seen in the dark; could be a female figure\", \"similar objects\": [\"shadow man\", \"ghost\", \"phantom\"]}", + 23 + ], + "alien": [ + " {\"type\": \"fictional creature\", \"description\": \"non-human; could have an extraterrestrial origin; could have an unusual appearance\", \"similar objects\": [\"monster\", \"robot\", \"zombie\"]}", + 23 + ], + "cross country": [ + " {\"type\": \"sport\", \"description\": \"long-distance running; could be done in a team or individually; could be done on trails or roads\", \"similar objects\": [\"track and field\", \"marathon\", \"triathlon\"]}", + 23 + ], + "ghost": [ + " {\"type\": \"mythical creature\", \"description\": \"transparent; could be white or grey; could have a scary face\", \"similar objects\": [\"vampire\", \"werewolf\", \"zombie\"]}", + 23 + ], + "radio tower": [ + " {\"type\": \"communication tool\", \"description\": \"tall, metal structure; could have antennas; could have blinking lights\", \"similar objects\": [\"cell tower\", \"satellite dish\", \"television tower\"]}", + 23 + ], + "goblet": [ + " {\"type\": \"drinking vessel\", \"description\": \"tall, stem-like base; could have a wide bowl-like top\", \"similar objects\": [\"cup\", \"mug\", \"glass\"]}", + 23 + ], + "surfer board": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, and flat; could be made of foam or wood; could have a leash\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 23 + ], + "license tag": [ + " {\"type\": \"identification tool\", \"description\": \"rectangular; has a serial number; could be attached to a vehicle\", \"similar objects\": [\"registration plate\", \"license plate\", \"vehicle tag\"]}", + 23 + ], + "blue poles": [ + " {\"type\": \"structural object\", \"description\": \"long, cylindrical, blue; could be used for fencing or decoration\", \"similar objects\": [\"fence posts\", \"flag poles\", \"street lights\"]}", + 23 + ], + "grey elephants": [ + "\n{\"type\": \"animal\", \"description\": \"large; grey in color; has a trunk; has large ears; has tusks\", \"similar objects\": [\"hippopotamus\", \"rhinoceros\", \"giraffe\"]}", + 23 + ], + "cable car": [ + " {\"type\": \"transportation vehicle\", \"description\": \"large, box-shaped; runs on a cable; could have an open-air design\", \"similar objects\": [\"tram\", \"funicular\", \"gondola\"]}", + 23 + ], + "dirty snow": [ + " {\"type\": \"weather phenomenon\", \"description\": \"snow that has been contaminated with dirt, dust, or other debris\", \"similar objects\": [\"slush\", \"black ice\", \"sleet\"]}", + 23 + ], + "units": [ + " {\"type\": \"measurement\", \"description\": \"used to measure physical quantities\", \"similar objects\": [\"meters\", \"grams\", \"liters\"]}", + 23 + ], + "turtle": [ + " {\"type\": \"animal\", \"description\": \"has a shell; could be green, brown, or black; could have yellow stripes\", \"similar objects\": [\"tortoise\", \"snake\", \"iguana\"]}", + 23 + ], + "hazy mountains": [ + " {\"type\": \"landscape\", \"description\": \"mountains covered with fog; could be seen from a distance\", \"similar objects\": [\"misty lake\", \"foggy forest\", \"cloudy sky\"]}", + 23 + ], + "sleeve tee shirt": [ + " {\"type\": \"clothing item\", \"description\": \"long sleeve; could be plain or patterned; could have a collar\", \"similar objects\": [\"long sleeve shirt\", \"hoodie\", \"sweater\"]}", + 23 + ], + "bicycle pedal": [ + " {\"type\": \"bicycle part\", \"description\": \"round; could be made of metal; could be attached to the bicycle frame\", \"similar objects\": [\"bicycle chain\", \"bicycle seat\", \"bicycle handlebar\"]}", + 23 + ], + "cupola": [ + " {\"type\": \"architectural structure\", \"description\": \"dome-shaped; could be used as a lookout point\", \"similar objects\": [\"belvedere\", \"turret\", \"minaret\"]}", + 23 + ], + "knuckle": [ + " {\"type\": \"body part\", \"description\": \"part of the hand; could be used for punching\", \"similar objects\": [\"finger\", \"elbow\", \"knee\"]}", + 23 + ], + "wooden window frame": [ + "\n{\"type\": \"building material\", \"description\": \"rectangular; made of wood; could have glass panes\", \"similar objects\": [\"door frame\", \"wall frame\", \"ceiling frame\"]}", + 23 + ], + "lifeguard": [ + " {\"type\": \"occupation\", \"description\": \"responsible for supervising activities in a swimming pool or beach; wears a red and white striped shirt; carries a whistle\", \"similar objects\": [\"lifeguard trainer\", \"swim instructor\", \"pool manager\"]}", + 23 + ], + "pink tie": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, made of fabric; could be striped or plain; could be made of silk or cotton\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}", + 23 + ], + "parasails": [ + " {\"type\": \"recreational activity\", \"description\": \"uses a parachute-like canopy to glide through the air; could be attached to a boat or vehicle\", \"similar objects\": [\"paragliding\", \"hang gliding\", \"skydiving\"]}", + 23 + ], + "sand area": [ + " {\"type\": \"playground\", \"description\": \"large area filled with sand; could have swings and slides\", \"similar objects\": [\"playground\", \"park\", \"playground equipment\"]}", + 23 + ], + "trophy": [ + " {\"type\": \"award\", \"description\": \"golden; could have a figure on the top; could be made of metal or plastic\", \"similar objects\": [\"medal\", \"plaque\", \"certificate\"]}", + 23 + ], + "orange sauce": [ + " {\"type\": \"condiment\", \"description\": \"orange-colored; could be sweet or sour; could be used as a dip or a topping\", \"similar objects\": [\"barbecue sauce\", \"teriyaki sauce\", \"honey mustard sauce\"]}", + 22 + ], + "lettuce plate": [ + " {\"type\": \"food dish\", \"description\": \"a plate of lettuce; could be served with other vegetables and sauces\", \"similar objects\": [\"salad\", \"fruit plate\", \"vegetable plate\"]}", + 22 + ], + "gray box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of metal or plastic; could have a lid\", \"similar objects\": [\"crate\", \"basket\", \"chest\"]}", + 22 + ], + "shadow elephant": [ + "\n{\"type\": \"image\", \"description\": \"dark silhouette of an elephant; could be seen on a wall or other surface\", \"similar objects\": [\"shadow giraffe\", \"shadow horse\", \"shadow dog\"]}", + 22 + ], + "muddy ground": [ + " {\"type\": \"terrain\", \"description\": \"damp, soft, and sticky; could be brown or gray in color; could have puddles\", \"similar objects\": [\"wet ground\", \"sandy ground\", \"rocky ground\"]}", + 22 + ], + "fence enclosure": [ + " {\"type\": \"structure\", \"description\": \"made of metal or wood; could be used to enclose a space\", \"similar objects\": [\"gate\", \"wall\", \"hedge\"]}", + 22 + ], + "adult elephants": [ + "\n{\"type\": \"animal\", \"description\": \"large; gray; have long trunks; have large ears; have tusks\", \"similar objects\": [\"hippopotamus\", \"rhinoceros\", \"giraffe\"]}", + 22 + ], + "pylons": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical, metal; could be used to support power lines\", \"similar objects\": [\"towers\", \"poles\", \"posts\"]}", + 22 + ], + "pullover": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be knitted; could have a hood\", \"similar objects\": [\"sweater\", \"cardigan\", \"jacket\"]}", + 22 + ], + "tape measure": [ + " {\"type\": \"measuring tool\", \"description\": \"long, flexible; could be made of metal or plastic; could have markings on it\", \"similar objects\": [\"ruler\", \"yardstick\", \"calipers\"]}", + 22 + ], + "freesbee": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could be thrown and caught\", \"similar objects\": [\"ball\", \"frisbee\", \"kite\"]}", + 22 + ], + "taller": [ + " {\"type\": \"measuring tool\", \"description\": \"long, thin, could be made of metal; could have a ruler on it\", \"similar objects\": [\"ruler\", \"measuring tape\", \"yardstick\"]}", + 22 + ], + "gray chain": [ + " {\"type\": \"accessory\", \"description\": \"metal; could be used to hold keys; could be used as a fashion item\", \"similar objects\": [\"bracelet\", \"necklace\", \"keychain\"]}", + 22 + ], + "silver part": [ + " {\"type\": \"metal object\", \"description\": \"shiny; could be used for decoration; could be used for industrial purposes\", \"similar objects\": [\"gold part\", \"bronze part\", \"copper part\"]}", + 22 + ], + "breakfast food": [ + "\n{\"type\": \"food\", \"description\": \"could be savory or sweet; could be served hot or cold; could include eggs, toast, cereal, pancakes, oatmeal, yogurt, etc.\", \"similar objects\": [\"lunch food\", \"dinner food\", \"snack food\"]}", + 22 + ], + "udder": [ + " {\"type\": \"body part\", \"description\": \"soft, spongy organ located on the underside of a cow; produces milk\", \"similar objects\": [\"teat\", \"udder\", \"udder bag\"]}", + 22 + ], + "lightswitch": [ + " {\"type\": \"electrical tool\", \"description\": \"has a switch; could be used to turn on/off the light\", \"similar objects\": [\"outlet\", \"dimmer switch\", \"timer switch\"]}", + 22 + ], + "hanging": [ + " {\"type\": \"action\", \"description\": \"suspending something from a higher point\", \"similar objects\": [\"dangling\", \"swinging\", \"swaying\"]}", + 22 + ], + "blue cell phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could have a touchscreen; could have a camera; could have a microphone\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 22 + ], + "silver lap top": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a keyboard; could be made of metal\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 22 + ], + "rolling pin": [ + " {\"type\": \"cooking tool\", \"description\": \"cylindrical; could be made of wood or metal; has a handle\", \"similar objects\": [\"pastry roller\", \"dough roller\", \"bun roller\"]}", + 22 + ], + "matress": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of foam; could be covered with fabric\", \"similar objects\": [\"pillow\", \"bed\", \"sofa\"]}", + 22 + ], + "water spray": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; could be used to spray water\", \"similar objects\": [\"hose\", \"sprinkler\", \"mister\"]}", + 22 + ], + "cat face": [ + " {\"type\": \"animal face\", \"description\": \"round; two eyes; two ears; whiskers; nose; mouth\", \"similar objects\": [\"dog face\", \"rabbit face\", \"mouse face\"]}", + 22 + ], + "metal park bench": [ + "\n{\"type\": \"furniture\", \"description\": \"made of metal; has a backrest and armrests; could be painted in different colors\", \"similar objects\": [\"wooden bench\", \"garden chair\", \"picnic table\"]}", + 22 + ], + "pink cup": [ + "\n{\"type\": \"utensil\", \"description\": \"pink; could be made of ceramic; could have a handle; could have a lid\", \"similar objects\": [\"mug\", \"teacup\", \"glass\"]}", + 22 + ], + "man hat": [ + "\n{\"type\": \"clothing accessory\", \"description\": \"headwear; could be made of fabric, straw, or felt; could have a brim or a visor\", \"similar objects\": [\"cap\", \"fedora\", \"beanie\"]}", + 22 + ], + "menu board": [ + " {\"type\": \"display tool\", \"description\": \"could be made of wood or plastic; could be hung on the wall; could have multiple sections for different items\", \"similar objects\": [\"chalkboard\", \"whiteboard\", \"notice board\"]}", + 22 + ], + "par tof line": [ + " {\"type\": \"clothing item\", \"description\": \"long, straight, could be made of cotton or other fabrics; could have buttons or zippers\", \"similar objects\": [\"skirt\", \"dress\", \"pants\"]}", + 22 + ], + "feline": [ + " {\"type\": \"animal\", \"description\": \"four legs; could have fur; could have whiskers; could have a tail\", \"similar objects\": [\"cat\", \"tiger\", \"lion\"]}", + 22 + ], + "bird wing": [ + " {\"type\": \"animal body part\", \"description\": \"feathery; could be used for flying; could be of different colors\", \"similar objects\": [\"fish fin\", \"butterfly wing\", \"bat wing\"]}", + 22 + ], + "stainless steel dishwasher": [ + "\n{\"type\": \"appliance\", \"description\": \"large, rectangular; made of stainless steel; has a door; could be used to clean dishes\", \"similar objects\": [\"refrigerator\", \"washing machine\", \"oven\"]}", + 22 + ], + "stainless steel fridge": [ + "\n{\"type\": \"appliance\", \"description\": \"silver; has a door; could have a freezer compartment\", \"similar objects\": [\"stove\", \"dishwasher\", \"microwave\"]}", + 22 + ], + "bent knee": [ + " {\"type\": \"body part\", \"description\": \"flexed joint between thigh and lower leg; could be bent at an angle\", \"similar objects\": [\"elbow\", \"ankle\", \"shoulder\"]}", + 22 + ], + "wilson tennis racket": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be used for tennis\", \"similar objects\": [\"golf club\", \"baseball bat\", \"hockey stick\"]}", + 22 + ], + "toenail": [ + " {\"type\": \"body part\", \"description\": \"hard, curved, and white; grows from the end of the toe\", \"similar objects\": [\"fingernail\", \"hair\", \"skin\"]}", + 22 + ], + "square box": [ + " {\"type\": \"container\", \"description\": \"four equal sides; could be made of cardboard; could have a lid\", \"similar objects\": [\"rectangular box\", \"cylinder\", \"cube\"]}", + 22 + ], + "pink roses": [ + "\n{\"type\": \"flower\", \"description\": \"pink petals; could have thorns; could have green leaves; could have a stem\", \"similar objects\": [\"daisy\", \"tulip\", \"sunflower\"]}", + 22 + ], + "bedsheet": [ + " {\"type\": \"bedding\", \"description\": \"rectangular; could be made of cotton; could be white or colorful\", \"similar objects\": [\"pillowcase\", \"duvet cover\", \"blanket\"]}", + 22 + ], + "clock towers": [ + " {\"type\": \"structure\", \"description\": \"tall; could have a bell; could have a clock face\", \"similar objects\": [\"bell tower\", \"observatory\", \"windmill\"]}", + 22 + ], + "bread roll": [ + " {\"type\": \"food\", \"description\": \"round; could be sliced; could be filled with different ingredients\", \"similar objects\": [\"bun\", \"bagel\", \"croissant\"]}", + 22 + ], + "biscuits": [ + " {\"type\": \"food\", \"description\": \"round; could be sweet or savory; could be served with tea or coffee\", \"similar objects\": [\"cookies\", \"crackers\", \"muffins\"]}", + 22 + ], + "metal top": [ + " {\"type\": \"toy\", \"description\": \"round; could spin on a flat surface; could have colorful patterns\", \"similar objects\": [\"spinning top\", \"yo-yo\", \"marble\"]}", + 22 + ], + "lampposts": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a lightbulb on top\", \"similar objects\": [\"streetlight\", \"lantern\", \"torch\"]}", + 22 + ], + "backpack straps": [ + " {\"type\": \"accessory\", \"description\": \"two straps; could be adjustable; could be made of fabric or leather\", \"similar objects\": [\"shoulder straps\", \"waist straps\", \"luggage straps\"]}", + 22 + ], + "juicy": [ + "\n{\"type\": \"adjective\", \"description\": \"describes something that is full of liquid; could be used to describe fruits or vegetables\", \"similar objects\": [\"moist\", \"succulent\", \"tender\"]}", + 22 + ], + "bird feet": [ + " {\"type\": \"animal body part\", \"description\": \"three toes pointing forward and one toe pointing backward; could have claws\", \"similar objects\": [\"bat wings\", \"fish fins\", \"insect legs\"]}", + 22 + ], + "filling": [ + " {\"type\": \"food ingredient\", \"description\": \"used to stuff food; could be savory or sweet; could be made of meat, vegetables, fruits, or grains\", \"similar objects\": [\"stuffing\", \"dressing\", \"topping\"]}", + 22 + ], + "pies": [ + " {\"type\": \"food\", \"description\": \"round; could be filled with fruits, vegetables, or meat; could be topped with crust\", \"similar objects\": [\"cake\", \"tart\", \"pastry\"]}", + 22 + ], + "food cart": [ + " {\"type\": \"vending tool\", \"description\": \"wheeled; could have a canopy; could have shelves\", \"similar objects\": [\"food truck\", \"kiosk\", \"pushcart\"]}", + 22 + ], + "brown marks": [ + " {\"type\": \"stain\", \"description\": \"dark brown; could be caused by water, oil, or dirt; could be found on clothes, furniture, or walls\", \"similar objects\": [\"dirt\", \"grease\", \"stain\"]}", + 22 + ], + "grey suitcase": [ + "\n{\"type\": \"travel item\", \"description\": \"rectangular; could be made of hard plastic; could have wheels and a handle\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 22 + ], + "stadium lights": [ + " {\"type\": \"lighting tool\", \"description\": \"large, bright, could be mounted on poles; could be used for outdoor events\", \"similar objects\": [\"floodlights\", \"spotlights\", \"street lights\"]}", + 22 + ], + "leafy plants": [ + " {\"type\": \"plant\", \"description\": \"green; could have multiple leaves; could be found in gardens or forests\", \"similar objects\": [\"ferns\", \"shrubs\", \"trees\"]}", + 22 + ], + "cement steps": [ + " {\"type\": \"building material\", \"description\": \"gray; could be used for stairs; could be made of concrete\", \"similar objects\": [\"bricks\", \"tiles\", \"wooden steps\"]}", + 22 + ], + "mouthwash": [ + " {\"type\": \"hygiene product\", \"description\": \"liquid; could be used to rinse the mouth; could be in a bottle\", \"similar objects\": [\"toothpaste\", \"toothbrush\", \"floss\"]}", + 22 + ], + "key chain": [ + " {\"type\": \"accessory\", \"description\": \"small metal rings connected to a metal loop; could have a decorative charm attached\", \"similar objects\": [\"keyring\", \"key fob\", \"key holder\"]}", + 22 + ], + "hilltop": [ + " {\"type\": \"landscape\", \"description\": \"high elevation; could have a view of the surrounding area; could have trees and grass\", \"similar objects\": [\"mountain\", \"cliff\", \"valley\"]}", + 22 + ], + "office phone": [ + " {\"type\": \"communication tool\", \"description\": \"rectangular; has a keypad; could have a headset\", \"similar objects\": [\"cell phone\", \"landline phone\", \"walkie talkie\"]}", + 22 + ], + "block wall": [ + " {\"type\": \"construction material\", \"description\": \"made of concrete blocks; could be used for fencing or retaining walls\", \"similar objects\": [\"brick wall\", \"stone wall\", \"wooden fence\"]}", + 22 + ], + "beige couch": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have armrests; could have cushions; could be made of fabric\", \"similar objects\": [\"sofa\", \"loveseat\", \"armchair\"]}", + 22 + ], + "grey frame": [ + " {\"type\": \"decoration item\", \"description\": \"rectangular; could be made of metal or wood; could be used to hang pictures\", \"similar objects\": [\"mirror frame\", \"picture frame\", \"photo frame\"]}", + 22 + ], + "silver blade": [ + " {\"type\": \"tool\", \"description\": \"shiny, sharp, could be used for cutting\", \"similar objects\": [\"knife\", \"scissors\", \"axe\"]}", + 22 + ], + "orange backpack": [ + "\n{\"type\": \"bag\", \"description\": \"orange; could have straps; could have pockets; could be made of fabric\", \"similar objects\": [\"duffel bag\", \"suitcase\", \"tote bag\"]}", + 22 + ], + "orange number": [ + " {\"type\": \"number\", \"description\": \"a number between 0 and 9; could be written in orange color\", \"similar objects\": [\"blue number\", \"green number\", \"red number\"]}", + 22 + ], + "outhouse": [ + " {\"type\": \"structure\", \"description\": \"small, wooden, has a door; could be used as a toilet\", \"similar objects\": [\"shed\", \"cabin\", \"barn\"]}", + 22 + ], + "charm": [ + " {\"type\": \"accessory\", \"description\": \"small; could be made of metal or plastic; could be in the shape of a heart or a star\", \"similar objects\": [\"pendant\", \"bracelet\", \"necklace\"]}", + 22 + ], + "pink shoes": [ + " {\"type\": \"footwear\", \"description\": \"pink; could be made of leather; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 22 + ], + "riding helmet": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round, has straps; could be made of plastic or foam\", \"similar objects\": [\"bicycle helmet\", \"skateboard helmet\", \"motorcycle helmet\"]}", + 22 + ], + "watermelons": [ + " {\"type\": \"fruit\", \"description\": \"large, round, green; has a hard rind; could be sliced into wedges; could have black seeds\", \"similar objects\": [\"cantaloupe\", \"honeydew\", \"papaya\"]}", + 22 + ], + "metal cage": [ + " {\"type\": \"container\", \"description\": \"made of metal; could be square or rectangular; could have a door\", \"similar objects\": [\"bird cage\", \"dog cage\", \"aquarium\"]}", + 22 + ], + "lounge": [ + " {\"type\": \"furniture\", \"description\": \"long, comfortable; could have armrests; could be upholstered\", \"similar objects\": [\"sofa\", \"couch\", \"chaise\"]}", + 22 + ], + "kitchen door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have a handle\", \"similar objects\": [\"closet door\", \"cabinet door\", \"bathroom door\"]}", + 22 + ], + "mesh fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal wires; could be used to separate areas\", \"similar objects\": [\"chain link fence\", \"barbed wire fence\", \"wooden fence\"]}", + 22 + ], + "zebra hooves": [ + " {\"type\": \"animal body part\", \"description\": \"hard, black, round; could be used for walking\", \"similar objects\": [\"horse hooves\", \"elephant feet\", \"giraffe legs\"]}", + 22 + ], + "code": [ + " {\"type\": \"programming language\", \"description\": \"a set of instructions for a computer to execute; could be written in various languages\", \"similar objects\": [\"JavaScript\", \"Python\", \"C++\"]}", + 22 + ], + "discs": [ + " {\"type\": \"storage device\", \"description\": \"round; could be made of plastic; could store data\", \"similar objects\": [\"CDs\", \"DVDs\", \"USB drives\"]}", + 22 + ], + "crouton": [ + " {\"type\": \"food\", \"description\": \"small, crunchy, usually made of bread; could be used as a topping for salads\", \"similar objects\": [\"breadcrumb\", \"bacon bits\", \"nuts\"]}", + 22 + ], + "bus route": [ + " {\"type\": \"transportation service\", \"description\": \"a route with multiple stops; could be operated by a bus company\", \"similar objects\": [\"train route\", \"airline route\", \"taxi route\"]}", + 22 + ], + "metal cup": [ + " {\"type\": \"utensil\", \"description\": \"made of metal; could be cylindrical or conical; could have a handle\", \"similar objects\": [\"mug\", \"glass\", \"bowl\"]}", + 22 + ], + "dark building": [ + " {\"type\": \"structure\", \"description\": \"tall; could be made of bricks; could have windows; could have a door\", \"similar objects\": [\"house\", \"skyscraper\", \"warehouse\"]}", + 22 + ], + "plate number": [ + " {\"type\": \"identification\", \"description\": \"unique combination of numbers and letters; usually found on the back of a vehicle\", \"similar objects\": [\"license plate\", \"VIN number\", \"registration number\"]}", + 22 + ], + "rain jacket": [ + " {\"type\": \"clothing\", \"description\": \"waterproof; could be made of nylon; could have a hood\", \"similar objects\": [\"raincoat\", \"umbrella\", \"rain boots\"]}", + 22 + ], + "metal wire fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal wires; could be used to separate areas; could be used to protect a property\", \"similar objects\": [\"wooden fence\", \"brick wall\", \"hedge\"]}", + 22 + ], + "orange coat": [ + "\n{\"type\": \"clothing item\", \"description\": \"orange; could be made of wool; could have buttons or zipper; could have pockets\", \"similar objects\": [\"jacket\", \"sweater\", \"vest\"]}", + 22 + ], + "water area": [ + " {\"type\": \"environment\", \"description\": \"large area of water; could be a lake, river, or ocean\", \"similar objects\": [\"swamp\", \"marsh\", \"wetland\"]}", + 22 + ], + "glass panel": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be used as a wall or door; could be made of tempered glass\", \"similar objects\": [\"window\", \"mirror\", \"acrylic sheet\"]}", + 22 + ], + "coca-cola": [ + " {\"type\": \"beverage\", \"description\": \"brown, carbonated, sweet; comes in a can or bottle\", \"similar objects\": [\"Pepsi\", \"Sprite\", \"Fanta\"]}", + 22 + ], + "metal buckle": [ + " {\"type\": \"accessory\", \"description\": \"silver; could be used to fasten two ends of a belt\", \"similar objects\": [\"button\", \"zipper\", \"hook\"]}", + 22 + ], + "metal chairs": [ + " {\"type\": \"furniture\", \"description\": \"made of metal; could have armrests; could have a backrest\", \"similar objects\": [\"plastic chairs\", \"wooden chairs\", \"stools\"]}", + 22 + ], + "folding table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be folded; could be made of wood or plastic\", \"similar objects\": [\"folding chair\", \"folding bed\", \"folding stool\"]}", + 22 + ], + "incline": [ + " {\"type\": \"geographical feature\", \"description\": \"slope that goes up; could be a hill or mountain\", \"similar objects\": [\"decline\", \"ridge\", \"valley\"]}", + 22 + ], + "pink shorts": [ + " {\"type\": \"clothing\", \"description\": \"pink; could be made of cotton; could have pockets; could have a drawstring\", \"similar objects\": [\"jeans\", \"skirt\", \"t-shirt\"]}", + 22 + ], + "purple frisbee": [ + "\n{\"type\": \"toy\", \"description\": \"round; could be made of plastic; has a bright purple color\", \"similar objects\": [\"disc\", \"flying disc\", \"Frisbee\"]}", + 22 + ], + "orange feet": [ + " {\"type\": \"footwear\", \"description\": \"orange; could be made of rubber; could be slip-on\", \"similar objects\": [\"sandals\", \"flip-flops\", \"sneakers\"]}", + 22 + ], + "cranberries": [ + " {\"type\": \"fruit\", \"description\": \"small, red, tart; could be dried; could be used in baking\", \"similar objects\": [\"blueberries\", \"strawberries\", \"raspberries\"]}", + 22 + ], + "round wheel": [ + " {\"type\": \"transportation tool\", \"description\": \"circular; could be made of metal; could have spokes\", \"similar objects\": [\"bicycle wheel\", \"car wheel\", \"tricycle wheel\"]}", + 22 + ], + "gold watch": [ + " {\"type\": \"accessory\", \"description\": \"round; made of gold; could have a strap\", \"similar objects\": [\"bracelet\", \"necklace\", \"ring\"]}", + 22 + ], + "orange banner": [ + "\n{\"type\": \"decoration\", \"description\": \"orange; could be made of fabric; could be hung on walls\", \"similar objects\": [\"flag\", \"bunting\", \"streamer\"]}", + 22 + ], + "surfboard sand": [ + " {\"type\": \"sports equipment\", \"description\": \"long and wide; could be made of foam; could have a leash\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 22 + ], + "countryside": [ + " {\"type\": \"landscape\", \"description\": \"green fields; trees; rivers; mountains; farms; villages\", \"similar objects\": [\"forest\", \"desert\", \"ocean\"]}", + 22 + ], + "kitchen wall": [ + " {\"type\": \"structure\", \"description\": \"flat, vertical surface; could be painted or tiled; could have cabinets or shelves\", \"similar objects\": [\"bathroom wall\", \"bedroom wall\", \"living room wall\"]}", + 22 + ], + "lizard": [ + " {\"type\": \"reptile\", \"description\": \"scaly; could have a long tail; could have a flat body\", \"similar objects\": [\"snake\", \"turtle\", \"crocodile\"]}", + 22 + ], + "bread box": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal; could have a lid\", \"similar objects\": [\"basket\", \"container\", \"jar\"]}", + 22 + ], + "carafe": [ + " {\"type\": \"drinking vessel\", \"description\": \"tall, cylindrical; could have a handle; could have a lid\", \"similar objects\": [\"pitcher\", \"mug\", \"cup\"]}", + 22 + ], + "orange sky": [ + " {\"type\": \"atmospheric phenomenon\", \"description\": \"sky with orange hue; could be caused by dust, smoke, or pollution\", \"similar objects\": [\"red sky\", \"yellow sky\", \"purple sky\"]}", + 22 + ], + "box car": [ + " {\"type\": \"train car\", \"description\": \"rectangular; could be used to transport goods; could be connected to other cars\", \"similar objects\": [\"flat car\", \"tank car\", \"hopper car\"]}", + 22 + ], + "bathmat": [ + " {\"type\": \"bathroom accessory\", \"description\": \"rectangular; could be made of fabric; could be placed outside the bathtub\", \"similar objects\": [\"bath towel\", \"bath rug\", \"bathroom mat\"]}", + 22 + ], + "purple hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; could be made of fabric; could have a brim\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}", + 22 + ], + "globes": [ + " {\"type\": \"decorative item\", \"description\": \"round; could be made of glass; could be used to represent the world\", \"similar objects\": [\"maps\", \"models\", \"globes\"]}", + 22 + ], + "giraffe leg": [ + " {\"type\": \"animal body part\", \"description\": \"long, slender, and covered in spots; has hoof-like feet\", \"similar objects\": [\"elephant leg\", \"horse leg\", \"zebra leg\"]}", + 22 + ], + "tear": [ + " {\"type\": \"liquid\", \"description\": \"clear; could be salty; could be caused by emotions\", \"similar objects\": [\"sweat\", \"rain\", \"dew\"]}", + 22 + ], + "pine cone": [ + " {\"type\": \"plant part\", \"description\": \"brown; has scales; could be found on the ground\", \"similar objects\": [\"acorn\", \"walnut\", \"chestnut\"]}", + 22 + ], + "circle design": [ + " {\"type\": \"shape\", \"description\": \"round; could be made of lines; could be filled with colors\", \"similar objects\": [\"square\", \"triangle\", \"oval\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\", and \"", + 22 + ], + "tall tree": [ + " {\"type\": \"plant\", \"description\": \"tall; could have leaves; could have branches; could have fruits\", \"similar objects\": [\"palm tree\", \"pine tree\", \"banyan tree\"]}", + 22 + ], + "china": [ + " {\"type\": \"country\", \"description\": \"located in East Asia; has a long history; has a large population\", \"similar objects\": [\"Japan\", \"Korea\", \"Vietnam\"]}", + 22 + ], + "water splashes": [ + " {\"type\": \"liquid motion\", \"description\": \"droplets of water flying in the air; could be caused by a stone thrown into a lake\", \"similar objects\": [\"raindrops\", \"wave\", \"fountain\"]}", + 22 + ], + "bicyclists": [ + " {\"type\": \"transportation\", \"description\": \"riding a bicycle; could wear a helmet; could have a basket\", \"similar objects\": [\"motorcyclists\", \"skaters\", \"runners\"]}", + 22 + ], + "label bottle": [ + " {\"type\": \"packaging tool\", \"description\": \"rectangular; could be made of paper or plastic; could be printed with text or logo\", \"similar objects\": [\"box\", \"envelope\", \"bag\"]}", + 22 + ], + "toilet tank lid": [ + " {\"type\": \"bathroom accessory\", \"description\": \"round; could be made of porcelain; could be white or other colors\", \"similar objects\": [\"toilet seat\", \"toilet brush\", \"toilet plunger\"]}", + 22 + ], + "dog paws": [ + " {\"type\": \"animal body part\", \"description\": \"four paws; could have fur; could have claws\", \"similar objects\": [\"cat paws\", \"bear paws\", \"monkey paws\"]}", + 22 + ], + "wire rack": [ + " {\"type\": \"storage tool\", \"description\": \"metal; has multiple shelves; could be used to store items\", \"similar objects\": [\"shelf\", \"cabinet\", \"bookcase\"]}", + 22 + ], + "tread": [ + " {\"type\": \"footwear\", \"description\": \"has a sole and a heel; could be made of rubber or leather; could be used for running or walking\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 22 + ], + "metal train track": [ + " {\"type\": \"transportation tool\", \"description\": \"long, silver, has a rail; could be connected to form a track\", \"similar objects\": [\"tram track\", \"monorail track\", \"subway track\"]}", + 22 + ], + "thick bushes": [ + " {\"type\": \"plant\", \"description\": \"dense, green, could have thorns; could be used as a fence\", \"similar objects\": [\"hedge\", \"shrub\", \"tree\"]}", + 22 + ], + "lattice": [ + " {\"type\": \"structure\", \"description\": \"intersecting strips of wood or metal; could be used as a fence or decoration\", \"similar objects\": [\"grid\", \"mesh\", \"web\"]}", + 22 + ], + "beer glass": [ + " {\"type\": \"drinking vessel\", \"description\": \"tall, thin, with a handle; could have a wide brim\", \"similar objects\": [\"wine glass\", \"mug\", \"tumbler\"]}", + 22 + ], + "shower rod": [ + " {\"type\": \"bathroom accessory\", \"description\": \"long, curved; could be made of metal; could be used to hang a shower curtain\", \"similar objects\": [\"towel bar\", \"toilet paper holder\", \"soap dish\"]}", + 22 + ], + "rock cliff": [ + " {\"type\": \"geological formation\", \"description\": \"large, steep, rocky surface; could have caves and crevices\", \"similar objects\": [\"mountain\", \"canyon\", \"valley\"]}", + 22 + ], + "bed post": [ + " {\"type\": \"furniture\", \"description\": \"vertical, cylindrical; could be made of wood or metal; could have a finial on top\", \"similar objects\": [\"chair leg\", \"table leg\", \"stair post\"]}", + 22 + ], + "chariot": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could be pulled by horses; could be used in wars\", \"similar objects\": [\"wagon\", \"cart\", \"carriage\"]}", + 22 + ], + "tree log": [ + " {\"type\": \"wood\", \"description\": \"long, cylindrical; could be cut into pieces; could be used for firewood\", \"similar objects\": [\"firewood\", \"timber\", \"branch\"]}", + 22 + ], + "business name": [ + "\n{\"type\": \"business entity\", \"description\": \"name of a company, organization, or other commercial entity\", \"similar objects\": [\"brand name\", \"trademark\", \"corporate logo\"]}", + 22 + ], + "waterfront": [ + " {\"type\": \"landscape\", \"description\": \"area near a body of water; could have docks, beaches, and boats\", \"similar objects\": [\"riverbank\", \"lakefront\", \"seaside\"]}", + 22 + ], + "tanks": [ + " {\"type\": \"military vehicle\", \"description\": \"large, heavily armored; could have a turret; could have tracks or wheels\", \"similar objects\": [\"armored personnel carrier\", \"helicopter\", \"fighter jet\"]}", + 22 + ], + "silver kettle": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round; made of silver; has a handle\", \"similar objects\": [\"teapot\", \"coffee pot\", \"water boiler\"]}", + 22 + ], + "helmet boy": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round; could be made of plastic or metal; could have a visor\", \"similar objects\": [\"helmet girl\", \"helmet adult\", \"helmet child\"]}", + 22 + ], + "gravel tracks": [ + " {\"type\": \"road surface\", \"description\": \"made of small stones; could be used for off-road driving\", \"similar objects\": [\"dirt road\", \"asphalt road\", \"cobblestone road\"]}", + 22 + ], + "moose": [ + " {\"type\": \"animal\", \"description\": \"large, brown, has antlers; could have a hump on its back\", \"similar objects\": [\"elk\", \"deer\", \"caribou\"]}", + 22 + ], + "palace": [ + " {\"type\": \"building\", \"description\": \"large, grand, could have a courtyard; could have a throne room\", \"similar objects\": [\"castle\", \"mansion\", \"fortress\"]}", + 22 + ], + "gray train": [ + "\n{\"type\": \"vehicle\", \"description\": \"long; could have multiple compartments; could have a locomotive; could be painted gray\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 22 + ], + "seafoam": [ + " {\"type\": \"color\", \"description\": \"light greenish blue; could be used to describe the color of the sea\", \"similar objects\": [\"turquoise\", \"teal\", \"aquamarine\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant", + 22 + ], + "stone walls": [ + " {\"type\": \"building material\", \"description\": \"made of stones; could be used to build walls or fences\", \"similar objects\": [\"bricks\", \"wooden boards\", \"concrete blocks\"]}", + 22 + ], + "ocean foam": [ + " {\"type\": \"natural phenomenon\", \"description\": \"white, bubbly, salty foam created by the ocean waves\", \"similar objects\": [\"sea spray\", \"surf\", \"tide\"]}", + 22 + ], + "knee brace": [ + " {\"type\": \"medical device\", \"description\": \"elastic bandage; could be made of neoprene; could be used to support the knee joint\", \"similar objects\": [\"ankle brace\", \"elbow brace\", \"wrist brace\"]}", + 22 + ], + "parakeet": [ + " {\"type\": \"bird\", \"description\": \"small; colorful feathers; could be trained to talk\", \"similar objects\": [\"canary\", \"finch\", \"parrot\"]}", + 22 + ], + "nametag": [ + " {\"type\": \"identification tool\", \"description\": \"could be made of paper or plastic; could have a string to hang around the neck\", \"similar objects\": [\"badge\", \"ID card\", \"lanyard\"]}", + 22 + ], + "grassland": [ + " {\"type\": \"ecosystem\", \"description\": \"large area of land covered with grass; could have some trees and shrubs; could have some animals\", \"similar objects\": [\"forest\", \"desert\", \"tundra\"]}", + 22 + ], + "rudder": [ + " {\"type\": \"navigation tool\", \"description\": \"attached to the back of a boat; used to steer the boat\", \"similar objects\": [\"oar\", \"propeller\", \"sail\"]}", + 22 + ], + "purple helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"purple; could be made of plastic or metal; could have a visor\", \"similar objects\": [\"bike helmet\", \"hard hat\", \"ski helmet\"]}", + 22 + ], + "color green": [ + "\n{\"type\": \"color\", \"description\": \"a hue between yellow and blue; could be associated with nature and growth; could be used to represent safety and harmony\", \"similar objects\": [\"blue\", \"yellow\", \"red\"]}", + 22 + ], + "tall clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"tall; could have a pendulum; could have a face with hands\", \"similar objects\": [\"grandfather clock\", \"wall clock\", \"cuckoo clock\"]}", + 21 + ], + "coffee makers": [ + " {\"type\": \"kitchen appliance\", \"description\": \"machine used to brew coffee; could have a filter; could have a carafe\", \"similar objects\": [\"espresso machine\", \"tea maker\", \"french press\"]}", + 21 + ], + "baseball ball": [ + " {\"type\": \"sports equipment\", \"description\": \"round; white with red stitches; could be used for baseball game\", \"similar objects\": [\"tennis ball\", \"soccer ball\", \"basketball\"]}", + 21 + ], + "empty table": [ + "\n{\"type\": \"furniture\", \"description\": \"flat surface; could have four legs; could be made of wood or metal\", \"similar objects\": [\"chair\", \"desk\", \"bench\"]}", + 21 + ], + "coffee pots": [ + " {\"type\": \"cooking tool\", \"description\": \"cylindrical; could have a handle; could have a spout; could have a lid\", \"similar objects\": [\"teapot\", \"kettle\", \"thermos\"]}", + 21 + ], + "speed": [ + " {\"type\": \"measurement\", \"description\": \"rate of change in distance over time; could be measured in mph, kph, m/s\", \"similar objects\": [\"velocity\", \"acceleration\", \"momentum\"]}", + 21 + ], + "hello kitty": [ + " {\"type\": \"character\", \"description\": \"white cat with a red bow; has a cute face\", \"similar objects\": [\"pikachu\", \"stitch\", \"minnie mouse\"]}", + 21 + ], + "copper pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round; made of copper; has a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 21 + ], + "headphone": [ + " {\"type\": \"electronic device\", \"description\": \"has two earpieces; could be connected to a device\", \"similar objects\": [\"earphones\", \"headset\", \"speakers\"]}", + 21 + ], + "judge": [ + " {\"type\": \"occupation\", \"description\": \"person who presides over a court of law; makes decisions on legal matters\", \"similar objects\": [\"lawyer\", \"magistrate\", \"jury\"]}", + 21 + ], + "orange button": [ + "\n{\"type\": \"object\", \"description\": \"round, orange, could be pressed\", \"similar objects\": [\"red button\", \"green button\", \"blue button\"]}", + 21 + ], + "lions": [ + " {\"type\": \"animal\", \"description\": \"large cats; have a mane; could be found in Africa\", \"similar objects\": [\"tigers\", \"leopards\", \"cheetahs\"]}", + 21 + ], + "snowy ground": [ + " {\"type\": \"landscape\", \"description\": \"white; could have trees and plants covered with snow; could have footprints\", \"similar objects\": [\"icy ground\", \"frozen lake\", \"mountain peak\"]}", + 21 + ], + "wood slat": [ + " {\"type\": \"building material\", \"description\": \"long, thin, and flat; could be used for flooring or fencing\", \"similar objects\": [\"plywood\", \"hardboard\", \"particle board\"]}", + 21 + ], + "lodge": [ + " {\"type\": \"building\", \"description\": \"wooden; could have a chimney; could have a porch\", \"similar objects\": [\"cabin\", \"cottage\", \"bungalow\"]}", + 21 + ], + "smoothie": [ + " {\"type\": \"beverage\", \"description\": \"blended fruit and/or vegetable drink; could be served cold\", \"similar objects\": [\"juice\", \"milkshake\", \"slushie\"]}", + 21 + ], + "catsup": [ + " {\"type\": \"condiment\", \"description\": \"red; could be used as a sauce; could be used as a dip\", \"similar objects\": [\"mustard\", \"mayonnaise\", \"soy sauce\"]}", + 21 + ], + "lawn chairs": [ + " {\"type\": \"furniture\", \"description\": \"foldable; could be made of metal or plastic; could have armrests\", \"similar objects\": [\"deck chairs\", \"beach chairs\", \"patio chairs\"]}", + 21 + ], + "boat sailing": [ + " {\"type\": \"watercraft\", \"description\": \"could be made of wood or metal; could have a sail; could have a motor\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}", + 21 + ], + "fingertips": [ + " {\"type\": \"body part\", \"description\": \"end of fingers; could be used to touch and feel\", \"similar objects\": [\"palm\", \"knuckles\", \"nails\"]}", + 21 + ], + "store front window": [ + " {\"type\": \"architectural feature\", \"description\": \"transparent; could be framed; could be decorated with signs or posters\", \"similar objects\": [\"door\", \"balcony\", \"awning\"]}", + 21 + ], + "bushel": [ + " {\"type\": \"measurement unit\", \"description\": \"a unit of volume; usually used to measure dry goods; could be a basket or a box\", \"similar objects\": [\"peck\", \"bushel\", \"barrel\"]}", + 21 + ], + "printers": [ + " {\"type\": \"electronic device\", \"description\": \"used to print documents; could be connected to a computer\", \"similar objects\": [\"scanners\", \"copiers\", \"fax machines\"]}", + 21 + ], + "beach house": [ + " {\"type\": \"building\", \"description\": \"wooden; could have a balcony; could have a view of the beach\", \"similar objects\": [\"cottage\", \"villa\", \"cabin\"]}", + 21 + ], + "denim": [ + " {\"type\": \"fabric\", \"description\": \"blue, thick, durable; could be used to make jeans\", \"similar objects\": [\"cotton\", \"linen\", \"polyester\"]}", + 21 + ], + "metal railroad tracks": [ + "\n{\"type\": \"transportation infrastructure\", \"description\": \"long, straight, made of metal; could have wooden sleepers; could have rail ties\", \"similar objects\": [\"roadway\", \"bridge\", \"tunnel\"]}", + 21 + ], + "blurry people": [ + "\n{\"type\": \"image\", \"description\": \"people with unclear features; could be a group of people; could be in a crowd\", \"similar objects\": [\"crowd\", \"group of people\", \"blurry objects\"]}", + 21 + ], + "wheat bread": [ + " {\"type\": \"food\", \"description\": \"light brown; could be sliced; could be toasted; could be made into sandwiches\", \"similar objects\": [\"rye bread\", \"white bread\", \"whole wheat bread\"]}", + 21 + ], + "burnt spot": [ + " {\"type\": \"damage\", \"description\": \"dark, discolored area; could be caused by fire or heat\", \"similar objects\": [\"scorch mark\", \"charred area\", \"singed area\"]}", + 21 + ], + "dumplings": [ + " {\"type\": \"food\", \"description\": \"round; could be filled with meat, vegetables, or other ingredients; could be boiled, steamed, or fried\", \"similar objects\": [\"potstickers\", \"ravioli\", \"gyoza\"]}", + 21 + ], + "sheet cake": [ + " {\"type\": \"food\", \"description\": \"flat, rectangular; could be decorated with frosting; could be cut into pieces\", \"similar objects\": [\"cupcake\", \"brownie\", \"pie\"]}", + 21 + ], + "baby bottle": [ + " {\"type\": \"feeding tool\", \"description\": \"long, narrow, has a nipple at the end; could be made of plastic or glass\", \"similar objects\": [\"sippy cup\", \"spoon\", \"pacifier\"]}", + 21 + ], + "core": [ + " {\"type\": \"geological feature\", \"description\": \"the innermost layer of the Earth; composed of iron and nickel\", \"similar objects\": [\"mantle\", \"crust\", \"magma\"]}", + 21 + ], + "flip": [ + " {\"type\": \"movement\", \"description\": \"quickly turning over; could be used in gymnastics\", \"similar objects\": [\"twist\", \"spin\", \"roll\"]}", + 21 + ], + "lightpole": [ + " {\"type\": \"street furniture\", \"description\": \"tall, cylindrical; could have a lamp on the top\", \"similar objects\": [\"street sign\", \"traffic light\", \"fire hydrant\"]}", + 21 + ], + "lettuce leaf": [ + " {\"type\": \"vegetable\", \"description\": \"green; could be curly or flat; could be sliced into pieces\", \"similar objects\": [\"spinach\", \"kale\", \"cabbage\"]}", + 21 + ], + "dining room chair": [ + "\n{\"type\": \"furniture\", \"description\": \"has four legs; could have armrests; could have a backrest; could be upholstered\", \"similar objects\": [\"sofa\", \"ottoman\", \"stool\"]}", + 21 + ], + "tapestry": [ + " {\"type\": \"decoration\", \"description\": \"large fabric with colorful patterns; could be hung on walls\", \"similar objects\": [\"rug\", \"wallpaper\", \"painting\"]}", + 21 + ], + "smoke train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has a smoky exhaust; could be used to transport goods\", \"similar objects\": [\"locomotive\", \"freight train\", \"bullet train\"]}", + 21 + ], + "brick walls": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of clay; could be painted\", \"similar objects\": [\"concrete\", \"wood\", \"stone\"]}", + 21 + ], + "denim shorts": [ + " {\"type\": \"clothing\", \"description\": \"blue; could have pockets; could be high-waisted\", \"similar objects\": [\"jeans\", \"jean skirt\", \"jean jacket\"]}", + 21 + ], + "evening sky": [ + " {\"type\": \"natural phenomenon\", \"description\": \"dark blue; stars and moon could be seen; could be cloudy\", \"similar objects\": [\"sunset\", \"sunrise\", \"night sky\"]}", + 21 + ], + "metal lamp post": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; made of metal; could have a lightbulb on top\", \"similar objects\": [\"street light\", \"lantern\", \"torch\"]}", + 21 + ], + "toasters": [ + " {\"type\": \"kitchen appliance\", \"description\": \"small, rectangular; has slots for bread; could be electric or manual\", \"similar objects\": [\"coffee maker\", \"blender\", \"microwave\"]}", + 21 + ], + "frisbee player": [ + " {\"type\": \"sport\", \"description\": \"involves throwing a frisbee; could be played in teams or individually; could involve running and jumping\", \"similar objects\": [\"soccer player\", \"baseball player\", \"basketball player\"]}", + 21 + ], + "letter u": [ + " {\"type\": \"alphabet\", \"description\": \"a curved line with two dots on the top\", \"similar objects\": [\"letter v\", \"letter y\", \"letter x\"]}", + 21 + ], + "orange caution cone": [ + "\n{\"type\": \"safety tool\", \"description\": \"orange; cone-shaped; could have reflective stripes\", \"similar objects\": [\"traffic cone\", \"barricade\", \"warning sign\"]}", + 21 + ], + "shadow road": [ + " {\"type\": \"road\", \"description\": \"dark; could be dangerous; could be a shortcut\", \"similar objects\": [\"back road\", \"dirt road\", \"unpaved road\"]}", + 21 + ], + "stir fry": [ + " {\"type\": \"dish\", \"description\": \"a combination of vegetables and meat cooked in a wok; could be served with rice\", \"similar objects\": [\"fried rice\", \"lo mein\", \"chow mein\"]}", + 21 + ], + "wood tray": [ + " {\"type\": \"serving tool\", \"description\": \"rectangular; made of wood; could have handles\", \"similar objects\": [\"platter\", \"plate\", \"bowl\"]}", + 21 + ], + "blue window": [ + " {\"type\": \"building material\", \"description\": \"glass; could be framed; could be transparent or opaque; could be used to let in light\", \"similar objects\": [\"door\", \"wall\", \"curtain\"]}", + 21 + ], + "sports": [ + "\n{\"type\": \"activity\", \"description\": \"involves physical exertion and skill; could be competitive; could be played in teams or individually\", \"similar objects\": [\"exercise\", \"fitness\", \"athletics\"]}", + 21 + ], + "bike lane": [ + " {\"type\": \"roadway\", \"description\": \"separate lane for bicycles; could be marked with a white line\", \"similar objects\": [\"pedestrian lane\", \"bus lane\", \"truck lane\"]}", + 21 + ], + "shadow building": [ + " {\"type\": \"architecture\", \"description\": \"building with a silhouette of a person or object; could be made of metal or wood\", \"similar objects\": [\"sculpture\", \"statue\", \"monument\"]}", + 21 + ], + "game remote": [ + " {\"type\": \"electronic device\", \"description\": \"has buttons; could be wireless; could be used to control a game console\", \"similar objects\": [\"controller\", \"joystick\", \"keyboard\"]}", + 21 + ], + "front fender": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the front of a vehicle; protects the vehicle from debris and dirt; could be made of metal or plastic\", \"similar objects\": [\"bumper\", \"hood\", \"grille\"]}", + 21 + ], + "fake": [ + " {\"type\": \"adjective\", \"description\": \"not real; not genuine; not authentic\", \"similar objects\": [\"phony\", \"bogus\", \"fraudulent\"]}", + 21 + ], + "hanging plant": [ + " {\"type\": \"decoration\", \"description\": \"could be in a pot; could be hung from the ceiling; could have green leaves\", \"similar objects\": [\"wall plant\", \"flower pot\", \"vase\"]}", + 21 + ], + "computer track pad": [ + " {\"type\": \"input device\", \"description\": \"flat, rectangular; could be used to control the cursor on the screen\", \"similar objects\": [\"mouse\", \"keyboard\", \"touchscreen\"]}", + 21 + ], + "whisker cat": [ + " {\"type\": \"animal\", \"description\": \"long, slender body; long whiskers; pointed ears; short fur\", \"similar objects\": [\"Siamese cat\", \"Persian cat\", \"Maine Coon cat\"]}", + 21 + ], + "movie": [ + " {\"type\": \"entertainment\", \"description\": \"visual storytelling; could be in the form of a film, television show, or video game\", \"similar objects\": [\"television show\", \"video game\", \"play\"]}", + 21 + ], + "brown stain": [ + " {\"type\": \"stain\", \"description\": \"dark brown; could be caused by liquid or food\", \"similar objects\": [\"dirt\", \"grease\", \"oil\"]}", + 21 + ], + "wax paper": [ + " {\"type\": \"kitchen tool\", \"description\": \"translucent; could be used to wrap food; could be used to line baking sheets\", \"similar objects\": [\"parchment paper\", \"aluminum foil\", \"plastic wrap\"]}", + 21 + ], + "chair rail": [ + " {\"type\": \"furniture\", \"description\": \"long, thin, wooden strip; used to divide walls\", \"similar objects\": [\"molding\", \"baseboard\", \"picture rail\"]}", + 21 + ], + "mountain tops": [ + " {\"type\": \"landscape\", \"description\": \"high peaks; could be covered with snow; could have trees and rocks\", \"similar objects\": [\"hills\", \"valleys\", \"cliffs\"]}", + 21 + ], + "fluorescent lights": [ + " {\"type\": \"lighting tool\", \"description\": \"long, thin tubes; emits bright white light; could be connected to a ceiling\", \"similar objects\": [\"LED lights\", \"incandescent lights\", \"halogen lights\"]}", + 21 + ], + "orange cheese": [ + "\n{\"type\": \"food\", \"description\": \"orange, soft, could be sliced; could be used as a topping for pizza\", \"similar objects\": [\"mozzarella cheese\", \"cheddar cheese\", \"parmesan cheese\"]}", + 21 + ], + "emergency light": [ + " {\"type\": \"lighting tool\", \"description\": \"red and white; could be flashing; could be battery-powered\", \"similar objects\": [\"lantern\", \"flashlight\", \"torch\"]}", + 21 + ], + "beak bird": [ + " {\"type\": \"animal\", \"description\": \"pointed beak; could have feathers; could fly\", \"similar objects\": [\"eagle\", \"pigeon\", \"duck\"]}", + 21 + ], + "phone screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be touch-sensitive; could have a camera\", \"similar objects\": [\"tablet\", \"laptop\", \"television\"]}", + 21 + ], + "beige curtains": [ + " {\"type\": \"window covering\", \"description\": \"light-colored fabric; could be hung on a rod; could be pleated\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 21 + ], + "grey trash": [ + " {\"type\": \"garbage\", \"description\": \"grey; could be made of plastic; could be in a bag\", \"similar objects\": [\"recycling\", \"compost\", \"litter\"]}", + 21 + ], + "brown donuts": [ + "\n{\"type\": \"food\", \"description\": \"round; could be filled with jelly or cream; could be covered with sugar or chocolate\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 21 + ], + "earbuds": [ + " {\"type\": \"electronic device\", \"description\": \"small, wireless, could be connected to a device\", \"similar objects\": [\"headphones\", \"speakers\", \"microphone\"]}", + 21 + ], + "buss": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple doors; could be used for public transportation\", \"similar objects\": [\"truck\", \"van\", \"car\"]}", + 21 + ], + "sleigh": [ + " {\"type\": \"transportation tool\", \"description\": \"wooden; has two runners; could be pulled by horses\", \"similar objects\": [\"sled\", \"wagon\", \"carriage\"]}", + 21 + ], + "silver wristwatch": [ + "\n{\"type\": \"accessory\", \"description\": \"round; made of silver; has a strap; could have a clock face\", \"similar objects\": [\"bracelet\", \"necklace\", \"ring\"]}", + 21 + ], + "blinker": [ + " {\"type\": \"electronic device\", \"description\": \"small, rectangular; used to indicate direction; could be attached to a vehicle\", \"similar objects\": [\"turn signal\", \"headlight\", \"taillight\"]}", + 21 + ], + "file cabinet": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular; has drawers; could be made of metal or wood\", \"similar objects\": [\"desk\", \"bookshelf\", \"chair\"]}", + 21 + ], + "bows": [ + " {\"type\": \"accessory\", \"description\": \"could be made of ribbon; could be used to decorate gifts\", \"similar objects\": [\"ribbons\", \"bows\", \"wrapping paper\"]}", + 21 + ], + "chair lift": [ + " {\"type\": \"transportation tool\", \"description\": \"mechanical device used to transport people up and down a mountain; has a seat and a cable\", \"similar objects\": [\"gondola\", \"ski lift\", \"tram\"]}", + 21 + ], + "refrigerator magnet": [ + " {\"type\": \"decorative item\", \"description\": \"small, flat, could be made of plastic or metal; could be shaped like animals or letters\", \"similar objects\": [\"keychain\", \"pin\", \"badge\"]}", + 21 + ], + "safety cones": [ + " {\"type\": \"traffic tool\", \"description\": \"orange; cone-shaped; could be reflective\", \"similar objects\": [\"traffic signs\", \"barricades\", \"warning lights\"]}", + 21 + ], + "pot holder": [ + " {\"type\": \"kitchen tool\", \"description\": \"thick fabric; could be used to hold hot pots and pans\", \"similar objects\": [\"oven mitt\", \"trivet\", \"hot pad\"]}", + 21 + ], + "carriages": [ + " {\"type\": \"transportation tool\", \"description\": \"horse-drawn; could be used to transport people or goods; could have four wheels\", \"similar objects\": [\"wagon\", \"cart\", \"buggy\"]}", + 21 + ], + "drain sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a bowl-shaped basin; could have a stopper; could have a faucet\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 21 + ], + "tile bathroom wall": [ + "\n{\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic, stone, or glass; could be used to cover walls and floors\", \"similar objects\": [\"bricks\", \"wood paneling\", \"vinyl sheeting\"]}", + 21 + ], + "gauge": [ + " {\"type\": \"measuring tool\", \"description\": \"could be used to measure pressure, temperature, or other physical quantities; could be digital or analog\", \"similar objects\": [\"thermometer\", \"barometer\", \"hydrometer\"]}", + 21 + ], + "fire fighter": [ + " {\"type\": \"occupation\", \"description\": \"person who puts out fires; wears a protective suit; carries a hose\", \"similar objects\": [\"police officer\", \"paramedic\", \"doctor\"]}", + 21 + ], + "pug dog": [ + " {\"type\": \"animal\", \"description\": \"small, short-muzzled, wrinkled face; has a curled tail; could have a black, tan, or silver coat\", \"similar objects\": [\"bulldog\", \"beagle\", \"chihuahua\"]}", + 21 + ], + "milkshake": [ + " {\"type\": \"beverage\", \"description\": \"cold, creamy, could be made of ice cream; could be topped with whipped cream\", \"similar objects\": [\"smoothie\", \"shake\", \"float\"]}", + 21 + ], + "swoosh": [ + " {\"type\": \"sound\", \"description\": \"a whooshing sound; could be associated with a fast movement\", \"similar objects\": [\"buzz\", \"hiss\", \"roar\"]}", + 21 + ], + "emergency exit": [ + " {\"type\": \"safety feature\", \"description\": \"red sign with white lettering; could be a door or window\", \"similar objects\": [\"fire extinguisher\", \"smoke detector\", \"emergency alarm\"]}", + 21 + ], + "embroidery": [ + " {\"type\": \"craft\", \"description\": \"decorative stitching on fabric; could be done with a needle and thread\", \"similar objects\": [\"knitting\", \"crocheting\", \"weaving\"]}", + 21 + ], + "hang": [ + " {\"type\": \"verb\", \"description\": \"to suspend something from a higher point\", \"similar objects\": [\"dangle\", \"suspend\", \"attach\"]}", + 21 + ], + "wine rack": [ + " {\"type\": \"storage tool\", \"description\": \"wooden; could be wall-mounted; could hold multiple bottles of wine\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"drawer\"]}", + 21 + ], + "kitchen stove": [ + " {\"type\": \"cooking tool\", \"description\": \"has burners and oven; could be gas or electric; could have a range hood\", \"similar objects\": [\"oven\", \"microwave\", \"toaster\"]}", + 21 + ], + "pink purse": [ + "\n{\"type\": \"accessory\", \"description\": \"small, rectangular, pink; could have a handle or a strap\", \"similar objects\": [\"bag\", \"clutch\", \"wallet\"]}", + 21 + ], + "snow dust": [ + " {\"type\": \"weather phenomenon\", \"description\": \"fine particles of ice; could be seen in the air; could be accompanied by strong winds\", \"similar objects\": [\"hail\", \"sleet\", \"blizzard\"]}", + 21 + ], + "opener": [ + " {\"type\": \"tool\", \"description\": \"used to open bottles, cans, and other containers; could be manual or electric\", \"similar objects\": [\"screwdriver\", \"pliers\", \"hammer\"]}", + 21 + ], + "fridges": [ + " {\"type\": \"appliance\", \"description\": \"large, rectangular, has a door; could be white or silver; could have a freezer compartment\", \"similar objects\": [\"stove\", \"microwave\", \"dishwasher\"]}", + 21 + ], + "advertisement board": [ + " {\"type\": \"promotional tool\", \"description\": \"large; could be made of paper or plastic; could be used to display promotional messages\", \"similar objects\": [\"billboard\", \"poster\", \"signboard\"]}", + 21 + ], + "light clouds": [ + " {\"type\": \"weather\", \"description\": \"white, fluffy, could be seen in the sky\", \"similar objects\": [\"fog\", \"haze\", \"mist\"]}", + 21 + ], + "grazing": [ + " {\"type\": \"action\", \"description\": \"eating grass or other vegetation; usually done by animals\", \"similar objects\": [\"foraging\", \"browsing\", \"ruminating\"]}", + 21 + ], + "burnt edge": [ + " {\"type\": \"cooking technique\", \"description\": \"browning the edges of food; could be done with a pan or oven\", \"similar objects\": [\"searing\", \"roasting\", \"grilling\"]}", + 21 + ], + "foot pedal": [ + " {\"type\": \"mechanical device\", \"description\": \"pedal-shaped; could be used to control a machine\", \"similar objects\": [\"lever\", \"knob\", \"switch\"]}", + 21 + ], + "wooden board": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular; could be used for construction or decoration\", \"similar objects\": [\"plywood\", \"sheetrock\", \"particle board\"]}", + 21 + ], + "dress pants": [ + " {\"type\": \"clothing\", \"description\": \"long, usually made of wool or cotton; could have pleats; could have pockets\", \"similar objects\": [\"slacks\", \"jeans\", \"trousers\"]}", + 21 + ], + "wispy cloud": [ + " {\"type\": \"weather phenomenon\", \"description\": \"thin, white, and fluffy; could be seen in the sky\", \"similar objects\": [\"cumulus cloud\", \"stratus cloud\", \"cirrus cloud\"]}", + 21 + ], + "twin bed": [ + " {\"type\": \"furniture\", \"description\": \"two beds side by side; could have a headboard; could have a footboard\", \"similar objects\": [\"bunk bed\", \"day bed\", \"trundle bed\"]}", + 21 + ], + "k": [ + "\n{\"type\": \"letter\", \"description\": \"the eleventh letter of the English alphabet\", \"similar objects\": [\"j\", \"l\", \"m\"]}", + 21 + ], + "shirt pocket": [ + " {\"type\": \"clothing accessory\", \"description\": \"small pocket on the front of a shirt; could be used to store small items\", \"similar objects\": [\"jacket pocket\", \"pants pocket\", \"vest pocket\"]}", + 21 + ], + "sit": [ + " {\"type\": \"action\", \"description\": \"to be in a position in which the upper body is upright and the legs are supported by some object\", \"similar objects\": [\"stand\", \"lie\", \"kneel\"]}", + 21 + ], + "cardinal": [ + " {\"type\": \"bird\", \"description\": \"red; has a black face; has a crest on its head\", \"similar objects\": [\"robin\", \"blue jay\", \"sparrow\"]}", + 21 + ], + "housing": [ + " {\"type\": \"structure\", \"description\": \"could be made of bricks, wood, or concrete; could have multiple floors; could have a roof\", \"similar objects\": [\"building\", \"apartment\", \"house\"]}", + 21 + ], + "traffic lines": [ + " {\"type\": \"road markings\", \"description\": \"white or yellow lines on the road; could be dashed or solid\", \"similar objects\": [\"road signs\", \"traffic lights\", \"road barriers\"]}", + 21 + ], + "leaves branches": [ + " {\"type\": \"plant parts\", \"description\": \"green; could be long and thin; could be attached to a stem\", \"similar objects\": [\"stems\", \"flowers\", \"roots\"]}", + 21 + ], + "cat collar": [ + " {\"type\": \"pet accessory\", \"description\": \"made of fabric or leather; could have a bell; could have a tag\", \"similar objects\": [\"dog collar\", \"leash\", \"harness\"]}", + 21 + ], + "leather strap": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, flexible; could be used to hold items together\", \"similar objects\": [\"belt\", \"rope\", \"string\"]}", + 21 + ], + "bales": [ + " {\"type\": \"agricultural tool\", \"description\": \"large bundles of hay, straw, or cotton; could be tied with rope\", \"similar objects\": [\"haystack\", \"straw stack\", \"cotton stack\"]}", + 21 + ], + "leather shoes": [ + " {\"type\": \"footwear\", \"description\": \"made of leather; could be black or brown; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 21 + ], + "leather boot": [ + " {\"type\": \"footwear\", \"description\": \"made of leather; could have laces; could be ankle-length\", \"similar objects\": [\"sneakers\", \"sandals\", \"loafers\"]}", + 21 + ], + "zone": [ + " {\"type\": \"area\", \"description\": \"a specific area or region; could be divided into different sections\", \"similar objects\": [\"region\", \"territory\", \"district\"]}", + 21 + ], + "baseball shoe": [ + " {\"type\": \"footwear\", \"description\": \"high-top; has a cleat sole; could be made of leather\", \"similar objects\": [\"soccer cleats\", \"running shoes\", \"hiking boots\"]}", + 21 + ], + "belongings": [ + " {\"type\": \"possessions\", \"description\": \"items owned by a person; could include clothes, books, electronics, etc.\", \"similar objects\": [\"belongings\", \"possessions\", \"property\"]}", + 21 + ], + "tail fins": [ + " {\"type\": \"fish body part\", \"description\": \"elongated, thin, could be colorful; could be used for swimming\", \"similar objects\": [\"gills\", \"scales\", \"dorsal fin\"]}", + 21 + ], + "turned-off": [ + "\n{\"type\": \"state\", \"description\": \"not active; not functioning; not lit up\", \"similar objects\": [\"inactive\", \"off\", \"unpowered\"]}", + 21 + ], + "stone clock tower": [ + "\n{\"type\": \"architectural structure\", \"description\": \"tall, made of stone; could have clock faces; could have bells\", \"similar objects\": [\"cathedral\", \"monument\", \"obelisk\"]}", + 21 + ], + "door entrance": [ + " {\"type\": \"structure\", \"description\": \"has a frame; could be made of wood, metal, or glass; could have a handle; could be opened and closed\", \"similar objects\": [\"window\", \"gate\", \"garage door\"]}", + 21 + ], + "tug boat": [ + " {\"type\": \"vessel\", \"description\": \"small, has a smokestack; could be used to pull larger vessels\", \"similar objects\": [\"ferry\", \"yacht\", \"cruise ship\"]}", + 21 + ], + "soccer socks": [ + " {\"type\": \"clothing item\", \"description\": \"long, usually white; could have stripes or logos; could be made of cotton or polyester\", \"similar objects\": [\"soccer shorts\", \"cleats\", \"jersey\"]}", + 21 + ], + "head gear": [ + " {\"type\": \"accessory\", \"description\": \"worn on the head; could be made of fabric, leather, or metal; could be used for protection or decoration\", \"similar objects\": [\"hat\", \"helmet\", \"cap\"]}", + 21 + ], + "counter tops": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood, stone, or metal; could be used for food preparation\", \"similar objects\": [\"table\", \"desk\", \"shelf\"]}", + 21 + ], + "cement base": [ + " {\"type\": \"building material\", \"description\": \"gray; used to build foundations; could be mixed with water\", \"similar objects\": [\"concrete\", \"mortar\", \"gravel\"]}", + 21 + ], + "orange basket": [ + "\n{\"type\": \"container\", \"description\": \"round; made of woven materials; could be orange in color; could have a handle\", \"similar objects\": [\"bag\", \"box\", \"tote\"]}", + 21 + ], + "bird house": [ + " {\"type\": \"shelter\", \"description\": \"wooden; has a hole for birds to enter; could be hung on a tree\", \"similar objects\": [\"nest box\", \"bird feeder\", \"bird bath\"]}", + 21 + ], + "purple tie": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, made of fabric; could be striped or plain; could be solid color\", \"similar objects\": [\"shirt\", \"belt\", \"scarf\"]}", + 21 + ], + "crepe": [ + " {\"type\": \"food\", \"description\": \"thin, flat, round; could be filled with sweet or savory ingredients\", \"similar objects\": [\"pancake\", \"tortilla\", \"wrap\"]}", + 21 + ], + "jetliner": [ + " {\"type\": \"vehicle\", \"description\": \"large, long, has wings; could have multiple engines; could have a tail fin\", \"similar objects\": [\"airplane\", \"helicopter\", \"glider\"]}", + 21 + ], + "curl": [ + " {\"type\": \"hairstyle\", \"description\": \"a type of hairstyle that involves winding hair around a curling iron to create a spiral shape\", \"similar objects\": [\"braid\", \"ponytail\", \"bun\"]}", + 21 + ], + "pink skirt": [ + " {\"type\": \"clothing\", \"description\": \"long, pink, could be pleated; could have a waistband\", \"similar objects\": [\"dress\", \"jeans\", \"shorts\"]}", + 21 + ], + "silver spoons": [ + " {\"type\": \"utensil\", \"description\": \"shiny; could be used for eating; could be made of silver\", \"similar objects\": [\"forks\", \"knives\", \"chopsticks\"]}", + 21 + ], + "walk signal": [ + " {\"type\": \"traffic signal\", \"description\": \"red and green lights; could be in the shape of a man\", \"similar objects\": [\"stop sign\", \"yield sign\", \"traffic light\"]}", + 21 + ], + "vanity mirror": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be mounted on the wall; could have a frame; could have lights around it\", \"similar objects\": [\"dresser\", \"makeup table\", \"wardrobe\"]}", + 21 + ], + "lead": [ + " {\"type\": \"chemical element\", \"description\": \"a heavy metal; has atomic number 82; is a solid at room temperature\", \"similar objects\": [\"mercury\", \"uranium\", \"plutonium\"]}", + 21 + ], + "monk": [ + " {\"type\": \"person\", \"description\": \"robed; bald; could have a shaved head; could carry a staff\", \"similar objects\": [\"priest\", \"nun\", \"monk\"]}", + 21 + ], + "usb": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a plug-in port; could be used to transfer data\", \"similar objects\": [\"memory card\", \"hard drive\", \"bluetooth device\"]}", + 21 + ], + "rail tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, parallel metal bars; could be used for trains\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 21 + ], + "g": [ + "\n{\"type\": \"letter\", \"description\": \"seventh letter of the English alphabet; could be capitalized or lowercase\", \"similar objects\": [\"h\", \"f\", \"j\"]}", + 21 + ], + "coins": [ + " {\"type\": \"currency\", \"description\": \"round; could be made of metal; could have different values\", \"similar objects\": [\"bills\", \"notes\", \"cash\"]}", + 21 + ], + "hoof giraffe": [ + " {\"type\": \"animal body part\", \"description\": \"hard, pointed, and curved; could be used for walking and running\", \"similar objects\": [\"elephant foot\", \"horse hoof\", \"rhinoceros horn\"]}", + 21 + ], + "reindeer": [ + " {\"type\": \"animal\", \"description\": \"large, brown, has antlers; could have a red nose\", \"similar objects\": [\"elk\", \"moose\", \"deer\"]}", + 21 + ], + "blue sweater": [ + "\n{\"type\": \"clothing\", \"description\": \"blue; could be made of wool; could have a hood; could have long sleeves\", \"similar objects\": [\"jacket\", \"coat\", \"hoodie\"]}", + 21 + ], + "air canada": [ + " {\"type\": \"airline\", \"description\": \"national flag carrier of Canada; provides air transportation services\", \"similar objects\": [\"Air France\", \"British Airways\", \"Lufthansa\"]}", + 21 + ], + "walker": [ + " {\"type\": \"mobility aid\", \"description\": \"has four legs; could have a seat; could have a basket\", \"similar objects\": [\"wheelchair\", \"cane\", \"crutches\"]}", + 21 + ], + "cubs": [ + " {\"type\": \"animal\", \"description\": \"young of certain animals; could be small and furry; could have stripes or spots\", \"similar objects\": [\"kittens\", \"puppies\", \"calves\"]}", + 21 + ], + "round hole": [ + " {\"type\": \"shape\", \"description\": \"circular opening; could be in a wall or a surface\", \"similar objects\": [\"square hole\", \"rectangle hole\", \"oval hole\"]}", + 21 + ], + "church tower": [ + " {\"type\": \"architecture\", \"description\": \"tall; could have a cross on the top; could have bells\", \"similar objects\": [\"cathedral\", \"mosque\", \"temple\"]}", + 21 + ], + "neon lights": [ + " {\"type\": \"lighting tool\", \"description\": \"bright, colorful, electric lights; could be used for decoration\", \"similar objects\": [\"LED lights\", \"fluorescent lights\", \"incandescent lights\"]}", + 21 + ], + "seat cover": [ + " {\"type\": \"furniture accessory\", \"description\": \"made of fabric; could be used to cover chairs and sofas\", \"similar objects\": [\"cushion\", \"pillow\", \"throw blanket\"]}", + 21 + ], + "compass": [ + " {\"type\": \"navigation tool\", \"description\": \"round; has a needle; could be used to determine direction\", \"similar objects\": [\"map\", \"GPS\", \"sextant\"]}", + 21 + ], + "sand beach": [ + " {\"type\": \"landscape\", \"description\": \"yellowish-brown; could have shells and rocks; could have waves\", \"similar objects\": [\"desert\", \"mountain\", \"forest\"]}", + 21 + ], + "car train": [ + " {\"type\": \"transportation\", \"description\": \"long; consists of several cars; could be pulled by a locomotive\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 20 + ], + "city street scene": [ + "\n{\"type\": \"scene\", \"description\": \"buildings, cars, people, street lights, signs, trees, etc.\", \"similar objects\": [\"park\", \"beach\", \"forest\"]}", + 20 + ], + "handler": [ + " {\"type\": \"tool\", \"description\": \"used to hold or carry something; could be made of metal or plastic\", \"similar objects\": [\"hook\", \"clamp\", \"grip\"]}", + 20 + ], + "kitchen appliance": [ + " {\"type\": \"household item\", \"description\": \"used for cooking, baking, or other food preparation; could be electric or manual; could be a stove, oven, blender, mixer, etc.\", \"similar objects\": [\"refrigerator\", \"dishwasher\", \"microwave\"]}", + 20 + ], + "fruit bowl": [ + " {\"type\": \"container\", \"description\": \"round; could be made of ceramic, plastic, or metal; could be used to store fruits\", \"similar objects\": [\"salad bowl\", \"cereal bowl\", \"soup bowl\"]}", + 20 + ], + "trench coat": [ + " {\"type\": \"clothing\", \"description\": \"long, double-breasted, has a belt\", \"similar objects\": [\"overcoat\", \"raincoat\", \"parka\"]}", + 20 + ], + "silver ware": [ + " {\"type\": \"utensil\", \"description\": \"made of silver; could be used for eating\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}", + 20 + ], + "brown drawer": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could have handles; could be made of wood\", \"similar objects\": [\"cabinet\", \"chest of drawers\", \"dresser\"]}", + 20 + ], + "log cabin": [ + " {\"type\": \"building\", \"description\": \"rectangular; made of logs; could have a chimney\", \"similar objects\": [\"cottage\", \"bungalow\", \"chalet\"]}\n\nObject detection models should focus on the shape, color, texture, and size of the object, as well as any distinguishing features that can help to differentiate it from similar objects. For example, for the zucchini, the model should focus on its cylindrical shape, green color, and smooth texture. For the zebra, the model should focus on its black and white stripes and long mane. For the", + 20 + ], + "stirrups": [ + " {\"type\": \"equipment\", \"description\": \"metal loops; used for horse riding\", \"similar objects\": [\"saddle\", \"bridle\", \"bit\"]}", + 20 + ], + "t shirt": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could have short or long sleeves; could have a collar\", \"similar objects\": [\"shirt\", \"tank top\", \"hoodie\"]}", + 20 + ], + "loaves": [ + " {\"type\": \"food\", \"description\": \"long, oval-shaped; could be sliced; could be made of wheat or rye\", \"similar objects\": [\"bread\", \"rolls\", \"bagels\"]}", + 20 + ], + "sail boats": [ + " {\"type\": \"watercraft\", \"description\": \"has a sail; could be propelled by wind; could be used for recreational activities\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}", + 20 + ], + "blue mouse pad": [ + "\n{\"type\": \"accessory\", \"description\": \"rectangular; blue; could be made of rubber or cloth; could have a wrist rest\", \"similar objects\": [\"keyboard pad\", \"mouse mat\", \"gaming pad\"]}", + 20 + ], + "swimming pool": [ + " {\"type\": \"recreational facility\", \"description\": \"large, filled with water; could have a diving board\", \"similar objects\": [\"lake\", \"ocean\", \"river\"]}", + 20 + ], + "wooden bookshelf": [ + "\n{\"type\": \"furniture\", \"description\": \"made of wood; has shelves for books; could have drawers\", \"similar objects\": [\"cabinet\", \"dresser\", \"wardrobe\"]}", + 20 + ], + "leafless branches": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, and dry; could be curved; could be brown or gray\", \"similar objects\": [\"twigs\", \"stems\", \"roots\"]}", + 20 + ], + "silver spatula": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle; made of metal; could be used for flipping food\", \"similar objects\": [\"spoon\", \"ladle\", \"tongs\"]}", + 20 + ], + "storefronts": [ + " {\"type\": \"building\", \"description\": \"has windows and doors; could have signs; could have awnings\", \"similar objects\": [\"shop\", \"store\", \"restaurant\"]}", + 20 + ], + "stone base": [ + " {\"type\": \"building material\", \"description\": \"hard, heavy, could be used as a foundation for a structure\", \"similar objects\": [\"concrete\", \"brick\", \"wood\"]}", + 20 + ], + "apple keyboard": [ + "\n{\"type\": \"computer accessory\", \"description\": \"flat; has keys; could be wireless\", \"similar objects\": [\"mouse\", \"headset\", \"webcam\"]}", + 20 + ], + "mans legs": [ + "\n{\"type\": \"body part\", \"description\": \"two long limbs; could be covered with pants; could have shoes on the feet\", \"similar objects\": [\"arms\", \"hands\", \"feet\"]}", + 20 + ], + "house cat": [ + " {\"type\": \"animal\", \"description\": \"domesticated; usually has fur; could have stripes or spots; could have long or short hair\", \"similar objects\": [\"tiger\", \"lion\", \"leopard\"]}", + 20 + ], + "airplane landing": [ + " {\"type\": \"aircraft maneuver\", \"description\": \"descending to the ground; wings are parallel to the ground; nose is pointing up\", \"similar objects\": [\"takeoff\", \"taxiing\", \"hovering\"]}", + 20 + ], + "sport shoe": [ + " {\"type\": \"footwear\", \"description\": \"made of leather or fabric; has a sole; could be laced up\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 20 + ], + "silver dvd player": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; has a disc tray; could be connected to a TV\", \"similar objects\": [\"blu-ray player\", \"gaming console\", \"stereo system\"]}", + 20 + ], + "foamy waves": [ + " {\"type\": \"natural phenomenon\", \"description\": \"white, bubbly, could be seen in the ocean\", \"similar objects\": [\"tide\", \"surf\", \"tsunami\"]}", + 20 + ], + "flowering plant": [ + " {\"type\": \"plant\", \"description\": \"has colorful flowers; could have leaves and stems; could be potted\", \"similar objects\": [\"succulent\", \"cactus\", \"bonsai\"]}", + 20 + ], + "blue cloudy sky": [ + "\n{\"type\": \"weather\", \"description\": \"blue sky with white clouds; could be sunny or rainy\", \"similar objects\": [\"clear sky\", \"sunny sky\", \"rainy sky\"]}", + 20 + ], + "yellow curtains": [ + " {\"type\": \"decoration\", \"description\": \"yellow; could be made of fabric; could be hung on windows\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 20 + ], + "tent top": [ + " {\"type\": \"shelter\", \"description\": \"cone-shaped; could be made of canvas; could be used for camping\", \"similar objects\": [\"yurt\", \"igloo\", \"teepee\"]}", + 20 + ], + "beige tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"marble tile\", \"granite tile\", \"wooden tile\"]}", + 20 + ], + "antique clock": [ + " {\"type\": \"decorative item\", \"description\": \"round; could have a pendulum; could be made of wood or metal\", \"similar objects\": [\"vintage watch\", \"antique vase\", \"antique mirror\"]}", + 20 + ], + "stall door": [ + " {\"type\": \"door\", \"description\": \"wooden; could be sliding; could be hinged; could be with a latch\", \"similar objects\": [\"barn door\", \"garage door\", \"gate\"]}", + 20 + ], + "watch band": [ + " {\"type\": \"accessory\", \"description\": \"made of metal or leather; could be adjustable; could be decorated with jewels\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 20 + ], + "paper roll": [ + " {\"type\": \"stationery item\", \"description\": \"long, cylindrical; could be used for wrapping gifts\", \"similar objects\": [\"tape roll\", \"ribbon roll\", \"wrapping paper\"]}", + 20 + ], + "holders": [ + " {\"type\": \"utensil\", \"description\": \"used to hold items; could be made of metal, plastic, or wood\", \"similar objects\": [\"hooks\", \"clips\", \"racks\"]}", + 20 + ], + "toilet handle": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical; could be made of metal; could be used to flush the toilet\", \"similar objects\": [\"shower handle\", \"faucet handle\", \"drain plug\"]}", + 20 + ], + "train windows": [ + " {\"type\": \"transportation window\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"car windows\", \"airplane windows\", \"bus windows\"]}", + 20 + ], + "side plate": [ + " {\"type\": \"dining ware\", \"description\": \"round; usually smaller than dinner plate; could be made of ceramic, plastic, or metal\", \"similar objects\": [\"dinner plate\", \"bowl\", \"saucer\"]}", + 20 + ], + "silver bus": [ + "\n{\"type\": \"vehicle\", \"description\": \"large, silver, has multiple doors; could have a wheelchair ramp\", \"similar objects\": [\"school bus\", \"city bus\", \"coach bus\"]}", + 20 + ], + "games": [ + " {\"type\": \"entertainment\", \"description\": \"activities that involve rules and strategies; could be physical or digital; could be played alone or with others\", \"similar objects\": [\"sports\", \"puzzles\", \"board games\"]}", + 20 + ], + "story brick building": [ + "\n{\"type\": \"structure\", \"description\": \"made of bricks; could have multiple stories; could have windows and doors\", \"similar objects\": [\"apartment building\", \"skyscraper\", \"castle\"]}", + 20 + ], + "serviette": [ + " {\"type\": \"tableware\", \"description\": \"square; made of paper; used to wipe hands\", \"similar objects\": [\"napkin\", \"tissue\", \"handkerchief\"]}", + 20 + ], + "crossing sign": [ + " {\"type\": \"traffic sign\", \"description\": \"octagonal; has a white background; has a black silhouette of a person walking\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 20 + ], + "vibrant": [ + " {\"type\": \"adjective\", \"description\": \"bright, lively, energetic\", \"similar objects\": [\"lively\", \"energetic\", \"vivacious\"]}", + 20 + ], + "silver fork utensil": [ + "\n{\"type\": \"utensil\", \"description\": \"silver; has four tines; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 20 + ], + "night time picture": [ + "\n{\"type\": \"image\", \"description\": \"dark; could have stars; could have a moon; could have silhouettes of buildings or trees\", \"similar objects\": [\"daytime picture\", \"landscape picture\", \"portrait picture\"]}", + 20 + ], + "dog eyes": [ + "\n{\"type\": \"animal feature\", \"description\": \"round; could be brown, black, or blue; could have a black pupil\", \"similar objects\": [\"cat eyes\", \"horse eyes\", \"bird eyes\"]}", + 20 + ], + "saddlebag": [ + " {\"type\": \"bag\", \"description\": \"attached to a saddle; could be made of leather; could be used to carry items\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 20 + ], + "ripe fruit": [ + "\n{\"type\": \"fruit\", \"description\": \"soft to the touch; could have a sweet smell; could have a bright color; could have a juicy texture\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}", + 20 + ], + "base ball bat": [ + " {\"type\": \"sports equipment\", \"description\": \"long, cylindrical; could be made of wood or metal; used to hit a ball\", \"similar objects\": [\"golf club\", \"tennis racket\", \"hockey stick\"]}", + 20 + ], + "pantry": [ + " {\"type\": \"storage space\", \"description\": \"room for storing food and kitchen supplies; could have shelves and cabinets\", \"similar objects\": [\"closet\", \"cupboard\", \"drawer\"]}", + 20 + ], + "flip cell phone": [ + " {\"type\": \"electronic device\", \"description\": \"small; has two parts that can be flipped open and closed; has a keypad and a display screen\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 20 + ], + "ash tray": [ + " {\"type\": \"smoking tool\", \"description\": \"round; could be made of metal; could have a lid\", \"similar objects\": [\"cigarette case\", \"cigarette holder\", \"cigarette lighter\"]}", + 20 + ], + "silver legs": [ + " {\"type\": \"furniture\", \"description\": \"metal legs; could be used to support a table or chair\", \"similar objects\": [\"wooden legs\", \"plastic legs\", \"iron legs\"]}", + 20 + ], + "license plates": [ + " {\"type\": \"identification tool\", \"description\": \"rectangular; has numbers and letters; could be attached to a vehicle\", \"similar objects\": [\"passport\", \"ID card\", \"driver's license\"]}", + 20 + ], + "handle refrigerator": [ + "\n{\"type\": \"appliance\", \"description\": \"has a handle; could be opened with a handle; could be used to store food\", \"similar objects\": [\"freezer\", \"microwave\", \"dishwasher\"]}", + 20 + ], + "bears eye": [ + " {\"type\": \"animal body part\", \"description\": \"round; black pupil; white sclera; could be brown, black, or white\", \"similar objects\": [\"cat eye\", \"dog eye\", \"human eye\"]}", + 20 + ], + "owls": [ + " {\"type\": \"bird\", \"description\": \"large eyes; could have feathers of different colors; could have a curved beak; could have a hoot sound\", \"similar objects\": [\"hawks\", \"eagles\", \"crows\"]}", + 20 + ], + "umbrella handle": [ + " {\"type\": \"accessory\", \"description\": \"long, cylindrical; could be made of metal or plastic; could have a curved handle\", \"similar objects\": [\"walking stick\", \"hiking pole\", \"golf club\"]}", + 20 + ], + "orange wheel": [ + " {\"type\": \"fruit snack\", \"description\": \"round; orange-flavored; could be made of dried orange slices\", \"similar objects\": [\"apple wheel\", \"banana wheel\", \"pineapple wheel\"]}", + 20 + ], + "stormy sky": [ + " {\"type\": \"weather\", \"description\": \"dark clouds; strong winds; heavy rain\", \"similar objects\": [\"hurricane\", \"typhoon\", \"blizzard\"]}", + 20 + ], + "mount": [ + " {\"type\": \"geographical feature\", \"description\": \"large landform; could be a mountain, hill, or volcano\", \"similar objects\": [\"cliff\", \"valley\", \"plateau\"]}", + 20 + ], + "right window": [ + " {\"type\": \"window\", \"description\": \"rectangular; could be opened and closed; could be made of glass\", \"similar objects\": [\"left window\", \"door\", \"skylight\"]}", + 20 + ], + "tangle": [ + " {\"type\": \"object\", \"description\": \"a mess of intertwined threads, strings, or cords; could be difficult to untangle\", \"similar objects\": [\"knot\", \"tangle\", \"tangle of wires\"]}", + 20 + ], + "umbrella top": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; could be made of fabric; could be opened and closed\", \"similar objects\": [\"hat\", \"cap\", \"hood\"]}", + 20 + ], + "wooden basket": [ + " {\"type\": \"container\", \"description\": \"made of wood; could be woven; could have a handle\", \"similar objects\": [\"plastic basket\", \"straw basket\", \"metal basket\"]}", + 20 + ], + "side wall": [ + " {\"type\": \"building component\", \"description\": \"vertical structure; could be made of wood, brick, or stone; could be used to divide a room\", \"similar objects\": [\"ceiling\", \"floor\", \"door\"]}", + 20 + ], + "details": [ + "\n{\"type\": \"concept\", \"description\": \"information that is specific and precise; could be used to describe something\", \"similar objects\": [\"information\", \"data\", \"facts\"]}", + 20 + ], + "ruffles": [ + " {\"type\": \"food\", \"description\": \"crinkled, salty chips; could be made of potatoes\", \"similar objects\": [\"crisps\", \"tortilla chips\", \"popcorn\"]}", + 20 + ], + "class": [ + " {\"type\": \"room\", \"description\": \"could have desks and chairs; could have a whiteboard; could have a teacher\", \"similar objects\": [\"lecture hall\", \"auditorium\", \"gym\"]}", + 20 + ], + "pandas": [ + " {\"type\": \"animal\", \"description\": \"black and white fur; has a round face; could be found in bamboo forests\", \"similar objects\": [\"bears\", \"koalas\", \"monkeys\"]}", + 20 + ], + "hamburgers": [ + " {\"type\": \"food\", \"description\": \"ground beef patty; could be served with buns; could be topped with lettuce, tomato, onion, pickles, ketchup, mustard\", \"similar objects\": [\"hot dogs\", \"sandwiches\", \"pizza\"]}", + 20 + ], + "stalls": [ + " {\"type\": \"structure\", \"description\": \"could be made of wood or metal; could be used for selling goods; could have a roof\", \"similar objects\": [\"kiosk\", \"booth\", \"stand\"]}", + 20 + ], + "exhaust fan": [ + " {\"type\": \"ventilation tool\", \"description\": \"round; has blades; could be mounted on the wall or ceiling\", \"similar objects\": [\"air conditioner\", \"air purifier\", \"ventilator\"]}", + 20 + ], + "sesame seeds": [ + " {\"type\": \"ingredient\", \"description\": \"small, round, and have a nutty flavor; could be used as a topping\", \"similar objects\": [\"sunflower seeds\", \"pumpkin seeds\", \"flax seeds\"]}", + 20 + ], + "indentation": [ + " {\"type\": \"marking\", \"description\": \"a depression or hollow in a surface; could be made by pressing or cutting\", \"similar objects\": [\"engraving\", \"etching\", \"scratch\"]}", + 20 + ], + "leaf pattern": [ + " {\"type\": \"pattern\", \"description\": \"veins and shapes of leaves; could be symmetrical or asymmetrical\", \"similar objects\": [\"flower pattern\", \"animal pattern\", \"geometric pattern\"]}", + 20 + ], + "carrot slice": [ + " {\"type\": \"vegetable\", \"description\": \"orange; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"potato\", \"onion\", \"celery\"]}", + 20 + ], + "pavers": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular, made of concrete or stone; used for paving roads and pathways\", \"similar objects\": [\"bricks\", \"tiles\", \"cobblestones\"]}", + 20 + ], + "front hoof": [ + " {\"type\": \"animal body part\", \"description\": \"hard, curved, and pointed; found on the front of the leg of a horse\", \"similar objects\": [\"back hoof\", \"tail\", \"mane\"]}", + 20 + ], + "pink shoe": [ + " {\"type\": \"footwear\", \"description\": \"pink; could be made of leather; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 20 + ], + "midair": [ + " {\"type\": \"location\", \"description\": \"in the air; between two points\", \"similar objects\": [\"sky\", \"space\", \"atmosphere\"]}", + 20 + ], + "peace sign": [ + " {\"type\": \"gesture\", \"description\": \"two fingers pointing up and two fingers pointing down; could be made with one hand\", \"similar objects\": [\"thumbs up\", \"ok sign\", \"heart sign\"]}", + 20 + ], + "mirror frame": [ + " {\"type\": \"decorative item\", \"description\": \"rectangular; could be made of wood or metal; could be decorated with carvings or patterns\", \"similar objects\": [\"picture frame\", \"photo frame\", \"wall art\"]}", + 20 + ], + "purple box": [ + "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be painted in purple\", \"similar objects\": [\"bag\", \"basket\", \"suitcase\"]}", + 20 + ], + "drainage": [ + " {\"type\": \"plumbing system\", \"description\": \"used to remove wastewater from a building; could be made of pipes\", \"similar objects\": [\"sewer\", \"drainpipe\", \"drainage ditch\"]}", + 20 + ], + "concrete ledge": [ + " {\"type\": \"structure\", \"description\": \"hard, flat surface; could be used as a seat or a platform; could be made of concrete\", \"similar objects\": [\"bench\", \"wall\", \"balcony\"]}", + 20 + ], + "crayon": [ + " {\"type\": \"art tool\", \"description\": \"colored; could be used to draw on paper\", \"similar objects\": [\"marker\", \"pencil\", \"paintbrush\"]}", + 20 + ], + "gold car": [ + "\n{\"type\": \"vehicle\", \"description\": \"golden color; could be a sedan, coupe, or SUV; could have four doors\", \"similar objects\": [\"silver car\", \"black car\", \"white car\"]}", + 20 + ], + "faucet handle": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be made of metal; could be used to control water flow\", \"similar objects\": [\"shower handle\", \"knob\", \"valve\"]}", + 20 + ], + "blenders": [ + " {\"type\": \"kitchen appliance\", \"description\": \"cylindrical; has blades; could be used to mix ingredients\", \"similar objects\": [\"food processor\", \"juicer\", \"mixer\"]}", + 20 + ], + "rough": [ + "\n{\"type\": \"adjective\", \"description\": \"uneven surface; not smooth; not level\", \"similar objects\": [\"jagged\", \"bumpy\", \"irregular\"]}", + 20 + ], + "orange handle": [ + "\n{\"type\": \"handle\", \"description\": \"orange; could be made of plastic or metal; could be used for a door, drawer, or tool\", \"similar objects\": [\"knob\", \"pull\", \"lever\"]}", + 20 + ], + "wood log": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be cut into pieces; could be used for burning\", \"similar objects\": [\"timber\", \"plywood\", \"bamboo\"]}", + 20 + ], + "back window": [ + " {\"type\": \"car part\", \"description\": \"located at the back of the car; could be made of glass; could be opened\", \"similar objects\": [\"side window\", \"windshield\", \"rearview mirror\"]}", + 20 + ], + "cake donut": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be topped with icing and sprinkles\", \"similar objects\": [\"glazed donut\", \"jelly donut\", \"cinnamon roll\"]}", + 20 + ], + "cake server": [ + " {\"type\": \"utensil\", \"description\": \"long handle; has a flat blade; could be made of metal or plastic\", \"similar objects\": [\"spatula\", \"tongs\", \"ladle\"]}", + 20 + ], + "colorful flags": [ + " {\"type\": \"decoration\", \"description\": \"could be made of cloth; could be in different shapes and colors; could be hung on poles\", \"similar objects\": [\"bunting\", \"banners\", \"streamers\"]}", + 20 + ], + "powerline": [ + " {\"type\": \"utility\", \"description\": \"long, thin wires; could be connected to poles; could be used to transmit electricity\", \"similar objects\": [\"telephone line\", \"cable line\", \"fiber optic line\"]}", + 20 + ], + "train stop": [ + " {\"type\": \"transportation facility\", \"description\": \"could have a platform; could have a shelter; could have a ticket machine\", \"similar objects\": [\"bus stop\", \"subway station\", \"airport\"]}", + 20 + ], + "half pipe": [ + " {\"type\": \"skateboarding ramp\", \"description\": \"U-shaped; could be made of wood or metal; could be used for skateboarding or snowboarding\", \"similar objects\": [\"quarter pipe\", \"fun box\", \"grind box\"]}", + 20 + ], + "building windows": [ + " {\"type\": \"architectural feature\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"doors\", \"balcony\", \"skylight\"]}", + 20 + ], + "teams": [ + " {\"type\": \"group\", \"description\": \"people working together to achieve a common goal\", \"similar objects\": [\"clubs\", \"organizations\", \"associations\"]}", + 20 + ], + "bundles": [ + " {\"type\": \"collection\", \"description\": \"group of items tied together; could be made of paper, fabric, or rope\", \"similar objects\": [\"packages\", \"bags\", \"boxes\"]}", + 20 + ], + "pizza dough": [ + " {\"type\": \"food ingredient\", \"description\": \"flour-based dough; could be stretched into a round shape; could be topped with various ingredients\", \"similar objects\": [\"bread dough\", \"pie crust\", \"tortilla dough\"]}", + 20 + ], + "orange surfboard": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long, orange, could have a fin; could be used for surfing\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 20 + ], + "hoses": [ + " {\"type\": \"tool\", \"description\": \"long, flexible, could be made of rubber; could be used for watering plants\", \"similar objects\": [\"pipe\", \"tube\", \"sprinkler\"]}", + 20 + ], + "cubicle": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be used as a workspace; could be made of wood or metal\", \"similar objects\": [\"desk\", \"chair\", \"bookshelf\"]}", + 20 + ], + "orange baseball cap": [ + "\n{\"type\": \"clothing accessory\", \"description\": \"orange; has a curved brim; could have a logo or design; could have an adjustable strap\", \"similar objects\": [\"hat\", \"beanie\", \"visor\"]}", + 20 + ], + "chicken sandwich": [ + " {\"type\": \"food\", \"description\": \"bread with chicken, lettuce, tomato, and mayonnaise; could be served with fries\", \"similar objects\": [\"hamburger\", \"taco\", \"hot dog\"]}", + 20 + ], + "pass": [ + " {\"type\": \"verb\", \"description\": \"to move forward; to go beyond a certain point; to allow someone to do something\", \"similar objects\": [\"allow\", \"grant\", \"permit\"]}", + 20 + ], + "grey floor": [ + " {\"type\": \"flooring material\", \"description\": \"light grey; could be made of tiles, wood, or stone\", \"similar objects\": [\"white floor\", \"black floor\", \"brown floor\"]}", + 20 + ], + "leather catcher": [ + " {\"type\": \"sports equipment\", \"description\": \"glove-like; used to catch a ball; could be made of leather\", \"similar objects\": [\"baseball bat\", \"baseball cap\", \"baseball glove\"]}", + 20 + ], + "king": [ + " {\"type\": \"title\", \"description\": \"highest rank in a monarchy; could be a symbol of power\", \"similar objects\": [\"queen\", \"prince\", \"princess\"]}", + 20 + ], + "pouch": [ + " {\"type\": \"bag\", \"description\": \"small, usually made of fabric; could be used to store small items\", \"similar objects\": [\"purse\", \"backpack\", \"wallet\"]}", + 20 + ], + "bird leg": [ + " {\"type\": \"animal body part\", \"description\": \"long and thin; could be feathered; could be used for walking and perching\", \"similar objects\": [\"bat wing\", \"insect leg\", \"fish fin\"]}", + 20 + ], + "donut hole": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be covered with sugar\", \"similar objects\": [\"bagel\", \"doughnut\", \"muffin\"]}", + 20 + ], + "metal fire": [ + " {\"type\": \"tool\", \"description\": \"made of metal; used to start a fire; could have a handle\", \"similar objects\": [\"matches\", \"lighter\", \"flint\"]}", + 20 + ], + "flush": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be used to flush water\", \"similar objects\": [\"toilet\", \"sink\", \"shower\"]}", + 20 + ], + "gold design": [ + " {\"type\": \"decoration\", \"description\": \"shiny; could be made of metal; could be in the form of jewelry\", \"similar objects\": [\"silver design\", \"bronze design\", \"platinum design\"]}", + 20 + ], + "tulip": [ + " {\"type\": \"flower\", \"description\": \"long stem; cup-shaped petals; could be yellow, pink, or red\", \"similar objects\": [\"rose\", \"daisy\", \"sunflower\"]}", + 20 + ], + "concrete pad": [ + " {\"type\": \"building material\", \"description\": \"hard, flat surface; could be used for paving or foundations\", \"similar objects\": [\"asphalt\", \"gravel\", \"brick\"]}", + 20 + ], + "square light": [ + " {\"type\": \"lighting tool\", \"description\": \"has four sides; could be made of glass or plastic; could be used for decoration\", \"similar objects\": [\"lantern\", \"lamp\", \"chandelier\"]}", + 20 + ], + "leather boots": [ + " {\"type\": \"footwear\", \"description\": \"made of leather; could have laces; could be ankle-length\", \"similar objects\": [\"sneakers\", \"sandals\", \"heels\"]}", + 20 + ], + "cloves": [ + " {\"type\": \"spice\", \"description\": \"small, dark brown; has a strong aroma; could be used as a flavoring agent\", \"similar objects\": [\"cinnamon\", \"nutmeg\", \"ginger\"]}", + 20 + ], + "stripe shirt": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; has horizontal stripes; could be made of cotton\", \"similar objects\": [\"polo shirt\", \"t-shirt\", \"sweater\"]}", + 20 + ], + "bed pillow": [ + " {\"type\": \"bedding item\", \"description\": \"rectangular; filled with feathers or foam; could be covered with fabric\", \"similar objects\": [\"mattress\", \"blanket\", \"duvet\"]}", + 20 + ], + "metal sculpture": [ + " {\"type\": \"artwork\", \"description\": \"made of metal; could be abstract or figurative; could be in any size\", \"similar objects\": [\"wood sculpture\", \"ceramic sculpture\", \"glass sculpture\"]}", + 20 + ], + "rusty chain": [ + " {\"type\": \"object\", \"description\": \"metal chain; has a rusty color; could be used for binding\", \"similar objects\": [\"lock\", \"padlock\", \"cable\"]}", + 20 + ], + "blue chair": [ + "\n{\"type\": \"furniture\", \"description\": \"blue; could have armrests; could have a cushion; could have four legs\", \"similar objects\": [\"sofa\", \"stool\", \"bench\"]}", + 20 + ], + "fryer": [ + " {\"type\": \"cooking tool\", \"description\": \"deep; has a basket; could be used to fry food\", \"similar objects\": [\"pan\", \"pot\", \"wok\"]}", + 20 + ], + "brick pavers": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of concrete, stone, or clay; could be used for paving roads or patios\", \"similar objects\": [\"concrete blocks\", \"cobblestones\", \"flagstones\"]}", + 20 + ], + "round tennis ball": [ + "\n{\"type\": \"sports equipment\", \"description\": \"round; yellow and green; made of rubber\", \"similar objects\": [\"soccer ball\", \"basketball\", \"baseball\"]}", + 20 + ], + "pink candle": [ + "\n{\"type\": \"lighting tool\", \"description\": \"pink; could be made of wax; could have a wick\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 20 + ], + "grills": [ + " {\"type\": \"cooking tool\", \"description\": \"metal; has a grate; could be used to cook food\", \"similar objects\": [\"barbecue\", \"smoker\", \"stove\"]}", + 20 + ], + "concrete blocks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be used for construction; could be made of cement\", \"similar objects\": [\"bricks\", \"stones\", \"pavers\"]}", + 20 + ], + "spoon table": [ + " {\"type\": \"furniture\", \"description\": \"long and narrow; could have four legs; could have a flat surface\", \"similar objects\": [\"chair\", \"desk\", \"bench\"]}", + 20 + ], + "cake stand": [ + " {\"type\": \"serving tool\", \"description\": \"round; could be made of metal; could have multiple tiers\", \"similar objects\": [\"cupcake stand\", \"platter\", \"tray\"]}", + 20 + ], + "brown hat": [ + " {\"type\": \"clothing item\", \"description\": \"brown; could be made of wool; could have a brim\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}", + 20 + ], + "rocky wall": [ + " {\"type\": \"landscape\", \"description\": \"rough surface; could be made of stones; could be used for climbing\", \"similar objects\": [\"cliff\", \"mountain\", \"cave\"]}", + 20 + ], + "goalie": [ + " {\"type\": \"sports position\", \"description\": \"protects the goal; wears special protective gear; could be the last line of defense\", \"similar objects\": [\"defender\", \"striker\", \"midfielder\"]}", + 20 + ], + "octopus kite": [ + "\n{\"type\": \"toy\", \"description\": \"eight-legged; could be made of paper or fabric; could be flown in the sky\", \"similar objects\": [\"dragon kite\", \"delta kite\", \"box kite\"]}", + 20 + ], + "shadow skier": [ + " {\"type\": \"sports equipment\", \"description\": \"ski-like device; has a handle; could be used for skiing on flat surfaces\", \"similar objects\": [\"skateboard\", \"rollerblades\", \"snowboard\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cuc", + 20 + ], + "wall paper": [ + " {\"type\": \"decoration material\", \"description\": \"sheets of paper used to cover walls; could be printed with patterns\", \"similar objects\": [\"paint\", \"tile\", \"fabric\"]}", + 20 + ], + "car mirror": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the side of a car; used to see behind the car\", \"similar objects\": [\"headlight\", \"windshield\", \"side window\"]}", + 20 + ], + "bindings": [ + " {\"type\": \"ski equipment\", \"description\": \"attaches ski boots to skis; could be made of plastic or metal\", \"similar objects\": [\"skis\", \"poles\", \"boots\"]}", + 20 + ], + "folders": [ + " {\"type\": \"office supplies\", \"description\": \"used to store documents; could be made of paper or plastic; could be in different colors\", \"similar objects\": [\"envelopes\", \"binders\", \"notebooks\"]}", + 20 + ], + "pinky": [ + " {\"type\": \"finger\", \"description\": \"smallest finger; could be used to make a promise\", \"similar objects\": [\"thumb\", \"index finger\", \"ring finger\"]}", + 20 + ], + "luggages": [ + " {\"type\": \"travel accessory\", \"description\": \"various sizes; could be made of fabric or hard material; could have wheels\", \"similar objects\": [\"suitcase\", \"backpack\", \"duffel bag\"]}", + 20 + ], + "bare spot": [ + " {\"type\": \"landscape feature\", \"description\": \"an area of land with no vegetation; could be caused by drought, overgrazing, or other environmental factors\", \"similar objects\": [\"desert\", \"meadow\", \"prairie\"]}", + 20 + ], + "furry bear": [ + " {\"type\": \"toy\", \"description\": \"soft, cuddly, usually brown; could have a bowtie or a scarf\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"stuffed animal\"]}", + 20 + ], + "woma": [ + " {\"type\": \"reptile\", \"description\": \"small, yellowish-brown; has a long tail; could be found in Australia\", \"similar objects\": [\"python\", \"iguana\", \"tortoise\"]}", + 20 + ], + "credit": [ + " {\"type\": \"financial instrument\", \"description\": \"a form of loan; could be used to purchase goods and services; could be used to pay for services\", \"similar objects\": [\"debit card\", \"loan\", \"cash\"]}", + 20 + ], + "door sedan": [ + " {\"type\": \"vehicle\", \"description\": \"four-door; could have a trunk; could have a roof rack\", \"similar objects\": [\"hatchback\", \"SUV\", \"minivan\"]}", + 20 + ], + "ski glove": [ + " {\"type\": \"clothing item\", \"description\": \"long; could be made of wool; could be waterproof; could have a strap\", \"similar objects\": [\"mittens\", \"snow boots\", \"ski goggles\"]}", + 20 + ], + "metal barrier": [ + " {\"type\": \"barrier\", \"description\": \"made of metal; could be used to block a path\", \"similar objects\": [\"fence\", \"gate\", \"wall\"]}", + 20 + ], + "surge protector": [ + " {\"type\": \"electrical device\", \"description\": \"has multiple outlets; could be plugged into a wall outlet\", \"similar objects\": [\"power strip\", \"extension cord\", \"USB hub\"]}", + 20 + ], + "paneling": [ + " {\"type\": \"building material\", \"description\": \"wooden boards; could be used to cover walls\", \"similar objects\": [\"plywood\", \"drywall\", \"siding\"]}", + 20 + ], + "gray rock": [ + " {\"type\": \"geological object\", \"description\": \"gray in color; could be smooth or rough; could be of any size\", \"similar objects\": [\"stone\", \"boulder\", \"pebble\"]}", + 20 + ], + "colt": [ + " {\"type\": \"animal\", \"description\": \"young horse; has a long mane; could be white or brown\", \"similar objects\": [\"foal\", \"pony\", \"stallion\"]}", + 20 + ], + "walking sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; has a walking figure; could be red or green\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 20 + ], + "window trim": [ + " {\"type\": \"building material\", \"description\": \"wooden; could be painted; could be used to frame windows\", \"similar objects\": [\"door trim\", \"baseboard\", \"crown molding\"]}", + 20 + ], + "color gray": [ + " {\"type\": \"color\", \"description\": \"neutral color; could be light or dark; could be mixed with other colors\", \"similar objects\": [\"black\", \"white\", \"silver\"]}", + 20 + ], + "tower clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"tall; could have a bell; could have a pendulum\", \"similar objects\": [\"grandfather clock\", \"wall clock\", \"alarm clock\"]}", + 20 + ], + "handle umbrella": [ + " {\"type\": \"accessory\", \"description\": \"long handle; could be opened and closed; could be made of fabric\", \"similar objects\": [\"walking stick\", \"parasol\", \"fan\"]}", + 20 + ], + "building structure": [ + " {\"type\": \"architecture\", \"description\": \"could be made of concrete, steel, wood, or other materials; could have multiple floors; could have a roof\", \"similar objects\": [\"house\", \"skyscraper\", \"bridge\"]}", + 20 + ], + "model train": [ + " {\"type\": \"toy\", \"description\": \"small, could be made of plastic or metal; could be powered by electricity or battery; could be used to build a railway system\", \"similar objects\": [\"toy car\", \"action figure\", \"building blocks\"]}", + 20 + ], + "nosecone": [ + " {\"type\": \"aerospace part\", \"description\": \"cone-shaped; could be made of metal; could be used to reduce drag and turbulence\", \"similar objects\": [\"fuselage\", \"wing\", \"tail\"]}", + 20 + ], + "milk crate": [ + " {\"type\": \"container\", \"description\": \"square; could be made of plastic; could be used to store items\", \"similar objects\": [\"box\", \"basket\", \"bin\"]}", + 20 + ], + "bushy tree": [ + " {\"type\": \"plant\", \"description\": \"large; has many branches and leaves; could be evergreen or deciduous\", \"similar objects\": [\"oak tree\", \"pine tree\", \"maple tree\"]}", + 20 + ], + "night light": [ + " {\"type\": \"lighting tool\", \"description\": \"low-wattage light; could be plugged into a wall outlet; could be used to provide a soft light in a dark room\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 20 + ], + "dogs tail": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, thin, furry; could be wagging\", \"similar objects\": [\"cat's tail\", \"horse's mane\", \"bird's beak\"]}", + 20 + ], + "button man": [ + " {\"type\": \"toy\", \"description\": \"small, round, made of plastic; could be pressed to make a sound\", \"similar objects\": [\"stuffed animal\", \"action figure\", \"doll\"]}", + 20 + ], + "wet dog": [ + "\n{\"type\": \"animal\", \"description\": \"fur is wet; could have a strong smell; could be shaking off water\", \"similar objects\": [\"cat\", \"puppy\", \"rabbit\"]}", + 20 + ], + "sandy shore": [ + " {\"type\": \"landscape\", \"description\": \"beach with sand; could have waves; could have rocks; could have shells\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 20 + ], + "power box": [ + " {\"type\": \"electrical device\", \"description\": \"square; could have multiple outlets; could have a switch\", \"similar objects\": [\"extension cord\", \"surge protector\", \"power strip\"]}", + 20 + ], + "shadow giraffe": [ + "\n{\"type\": \"image\", \"description\": \"silhouette of a giraffe; could be black and white; could be abstract\", \"similar objects\": [\"shadow elephant\", \"shadow horse\", \"shadow tree\"]}", + 20 + ], + "silver scissors": [ + "\n{\"type\": \"cutting tool\", \"description\": \"two blades connected by a pivot; could be made of silver; could be used for cutting paper\", \"similar objects\": [\"knife\", \"razor\", \"shears\"]}", + 20 + ], + "gull": [ + " {\"type\": \"bird\", \"description\": \"white; has a long wingspan; could be seen near the sea\", \"similar objects\": [\"pigeon\", \"seagull\", \"eagle\"]}", + 20 + ], + "cement walkway": [ + " {\"type\": \"structure\", \"description\": \"hard, gray surface; could be used as a path\", \"similar objects\": [\"concrete driveway\", \"brick path\", \"gravel walkway\"]}", + 20 + ], + "droppings": [ + " {\"type\": \"animal waste\", \"description\": \"small, round, dark; could be found in the ground\", \"similar objects\": [\"feces\", \"urine\", \"manure\"]}", + 20 + ], + "coca cola sign": [ + " {\"type\": \"advertisement\", \"description\": \"red and white; has the words 'Coca Cola' written on it\", \"similar objects\": [\"billboard\", \"poster\", \"banner\"]}", + 19 + ], + "round sticker": [ + " {\"type\": \"decoration item\", \"description\": \"circular; could be made of paper or plastic; could be used to decorate walls or other surfaces\", \"similar objects\": [\"square sticker\", \"label\", \"decal\"]}", + 19 + ], + "steel fork": [ + " {\"type\": \"utensil\", \"description\": \"long; made of steel; has four prongs\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 19 + ], + "life preservers": [ + " {\"type\": \"safety tool\", \"description\": \"round; made of foam; could be orange or yellow; could be used to float in water\", \"similar objects\": [\"floatation device\", \"life jacket\", \"life buoy\"]}", + 19 + ], + "gift bag": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of paper; could have handles\", \"similar objects\": [\"box\", \"basket\", \"envelope\"]}", + 19 + ], + "brown horse": [ + "\n{\"type\": \"animal\", \"description\": \"brown; has a long mane; could have white spots\", \"similar objects\": [\"zebra\", \"donkey\", \"mule\"]}", + 19 + ], + "silver pots": [ + " {\"type\": \"cooking tool\", \"description\": \"made of silver; could be round or oval; could have a handle\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 19 + ], + "leafy green plant": [ + "\n{\"type\": \"plant\", \"description\": \"green leaves; could have stems; could have flowers; could have fruits\", \"similar objects\": [\"fern\", \"ivy\", \"basil\"]}", + 19 + ], + "cole slaw": [ + " {\"type\": \"food\", \"description\": \"shredded cabbage and carrots; could be mixed with mayonnaise or vinegar; could be served as a side dish\", \"similar objects\": [\"potato salad\", \"macaroni salad\", \"fruit salad\"]}", + 19 + ], + "metal guardrail": [ + " {\"type\": \"safety tool\", \"description\": \"long, metallic, has a rail-like structure; could be used to protect people from falling off a cliff\", \"similar objects\": [\"fence\", \"barrier\", \"wall\"]}", + 19 + ], + "nothing": [ + "\n\nNo output is provided as there is no input.", + 19 + ], + "scar": [ + " {\"type\": \"skin mark\", \"description\": \"raised, discolored mark on the skin; could be caused by injury or disease\", \"similar objects\": [\"bruise\", \"mole\", \"freckle\"]}", + 19 + ], + "orange top": [ + " {\"type\": \"clothing item\", \"description\": \"orange colored; could be sleeveless; could have a round neckline\", \"similar objects\": [\"t-shirt\", \"tank top\", \"sweater\"]}", + 19 + ], + "walkie talkie": [ + " {\"type\": \"communication device\", \"description\": \"two-way radio; could be handheld; could be used for short-distance communication\", \"similar objects\": [\"cell phone\", \"intercom\", \"CB radio\"]}", + 19 + ], + "banner sign": [ + " {\"type\": \"advertising tool\", \"description\": \"long; could be made of cloth or paper; could be hung on a wall or a pole\", \"similar objects\": [\"poster\", \"billboard\", \"flag\"]}", + 19 + ], + "ski hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"warm, knitted, covers the head and ears\", \"similar objects\": [\"beanie\", \"toboggan\", \"balaclava\"]}", + 19 + ], + "sheer curtain": [ + " {\"type\": \"window covering\", \"description\": \"transparent; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"blinds\", \"drapes\", \"shades\"]}", + 19 + ], + "chair seat": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or plastic; could have a cushion\", \"similar objects\": [\"sofa\", \"bench\", \"stool\"]}", + 19 + ], + "contents": [ + " {\"type\": \"collection\", \"description\": \"a group of items; could be a list of items\", \"similar objects\": [\"inventory\", \"catalog\", \"directory\"]}", + 19 + ], + "viewers": [ + " {\"type\": \"optical tool\", \"description\": \"used to magnify objects; could be made of plastic or metal; could have lenses\", \"similar objects\": [\"binoculars\", \"microscope\", \"telescope\"]}", + 19 + ], + "round base": [ + " {\"type\": \"furniture\", \"description\": \"circular; could be used as a support for a table or chair\", \"similar objects\": [\"pedestal\", \"leg\", \"stool\"]}", + 19 + ], + "model airplane": [ + " {\"type\": \"toy\", \"description\": \"small, could be made of plastic or wood; could be powered by a motor or rubber band\", \"similar objects\": [\"drone\", \"helicopter\", \"boat\"]}", + 19 + ], + "sidewalk pedestrians": [ + "\n{\"type\": \"people\", \"description\": \"walking on the sidewalk; could be in groups or alone; could be carrying bags or umbrellas\", \"similar objects\": [\"cyclists\", \"runners\", \"skateboarders\"]}", + 19 + ], + "wireless keyboard": [ + " {\"type\": \"computer accessory\", \"description\": \"flat; could be connected to a computer without wires; could have a touchpad\", \"similar objects\": [\"mouse\", \"headset\", \"USB drive\"]}", + 19 + ], + "note pad": [ + " {\"type\": \"stationery\", \"description\": \"rectangular; could be made of paper; could have lines\", \"similar objects\": [\"notebook\", \"journal\", \"diary\"]}", + 19 + ], + "potatos": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be brown, yellow, or white; could be boiled, mashed, or fried\", \"similar objects\": [\"carrots\", \"onions\", \"sweet potatoes\"]}", + 19 + ], + "toy boat": [ + " {\"type\": \"toy\", \"description\": \"small; could be made of plastic; could float on water\", \"similar objects\": [\"toy car\", \"toy plane\", \"toy train\"]}", + 19 + ], + "bread crumbs": [ + " {\"type\": \"ingredient\", \"description\": \"small, dry pieces of bread; could be used as a coating for food\", \"similar objects\": [\"flour\", \"cornmeal\", \"nuts\"]}", + 19 + ], + "reading": [ + " {\"type\": \"activity\", \"description\": \"involves looking at written words; could involve understanding the meaning of the words; could involve taking notes\", \"similar objects\": [\"studying\", \"writing\", \"listening\"]}", + 19 + ], + "grassy lawn": [ + " {\"type\": \"landscape\", \"description\": \"green; could have flowers; could be mowed\", \"similar objects\": [\"garden\", \"meadow\", \"park\"]}", + 19 + ], + "nike": [ + " {\"type\": \"brand\", \"description\": \"sportswear and equipment company; logo is a swoosh\", \"similar objects\": [\"adidas\", \"puma\", \"reebok\"]}", + 19 + ], + "head scarf": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, rectangular; could be made of silk; could be tied around the head\", \"similar objects\": [\"hat\", \"cap\", \"turban\"]}", + 19 + ], + "metal lid": [ + " {\"type\": \"container lid\", \"description\": \"round; made of metal; could be used to cover a pot or pan\", \"similar objects\": [\"plastic lid\", \"glass lid\", \"wooden lid\"]}", + 19 + ], + "hide": [ + " {\"type\": \"verb\", \"description\": \"to conceal oneself or something from view; to keep out of sight\", \"similar objects\": [\"conceal\", \"disguise\", \"camouflage\"]}", + 19 + ], + "blue hoodie": [ + " {\"type\": \"clothing\", \"description\": \"hooded, long-sleeved, blue; could have a zipper or drawstrings\", \"similar objects\": [\"sweatshirt\", \"jacket\", \"sweater\"]}", + 19 + ], + "mirror motorcycle": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a side mirror; could have a windshield\", \"similar objects\": [\"scooter\", \"moped\", \"bicycle\"]}", + 19 + ], + "chin strap": [ + " {\"type\": \"protective gear\", \"description\": \"strap that goes around the chin; could be made of fabric or plastic; could be used for sports or medical purposes\", \"similar objects\": [\"helmet\", \"face mask\", \"goggles\"]}", + 19 + ], + "gold numbers": [ + " {\"type\": \"decoration\", \"description\": \"shiny, gold-colored numbers; could be used to decorate walls or other surfaces\", \"similar objects\": [\"letters\", \"symbols\", \"shapes\"]}", + 19 + ], + "pink tank top": [ + " {\"type\": \"clothing\", \"description\": \"sleeveless; could have straps; could be made of cotton\", \"similar objects\": [\"t-shirt\", \"blouse\", \"vest\"]}", + 19 + ], + "mail slot": [ + " {\"type\": \"mail receptacle\", \"description\": \"rectangular; could be mounted on a door; could be used to receive mail\", \"similar objects\": [\"letter box\", \"post box\", \"mailbox\"]}", + 19 + ], + "round donut": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be glazed or filled with cream\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 19 + ], + "butterknife": [ + " {\"type\": \"utensil\", \"description\": \"flat, short, has a handle; could be used for spreading butter\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 19 + ], + "fisherman": [ + " {\"type\": \"occupation\", \"description\": \"person who fishes for a living; could use a fishing rod; could wear a hat and boots\", \"similar objects\": [\"hunter\", \"farmer\", \"woodcutter\"]}", + 19 + ], + "corsage": [ + " {\"type\": \"accessory\", \"description\": \"small flower bouquet; could be pinned on clothing\", \"similar objects\": [\"boutonniere\", \"brooch\", \"tiara\"]}", + 19 + ], + "smart phone": [ + " {\"type\": \"electronic device\", \"description\": \"touchscreen; could be used to make calls, send messages, and access the internet\", \"similar objects\": [\"tablet\", \"laptop\", \"smart watch\"]}", + 19 + ], + "blue color": [ + " {\"type\": \"color\", \"description\": \"a hue of the visible spectrum; could be dark or light; could be associated with calmness and serenity\", \"similar objects\": [\"green\", \"purple\", \"yellow\"]}", + 19 + ], + "gadget": [ + " {\"type\": \"electronic device\", \"description\": \"small, portable, could be used for various purposes\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 19 + ], + "canine": [ + " {\"type\": \"animal\", \"description\": \"four-legged; could bark; could have fur\", \"similar objects\": [\"dog\", \"wolf\", \"fox\"]}", + 19 + ], + "mohawk": [ + " {\"type\": \"hairstyle\", \"description\": \"shaved sides with a strip of hair in the middle; could be styled in different ways\", \"similar objects\": [\"faux hawk\", \"pompadour\", \"buzz cut\"]}", + 19 + ], + "grasslands": [ + " {\"type\": \"ecosystem\", \"description\": \"large area of land covered with grasses and other non-woody plants; could have some trees and shrubs; could have some animals\", \"similar objects\": [\"savanna\", \"prairie\", \"desert\"]}", + 19 + ], + "whip cream": [ + " {\"type\": \"dairy product\", \"description\": \"white, creamy, sweet; could be used as a topping\", \"similar objects\": [\"ice cream\", \"yogurt\", \"sour cream\"]}", + 19 + ], + "topper": [ + " {\"type\": \"bedding accessory\", \"description\": \"soft; could be filled with feathers or foam; could be quilted\", \"similar objects\": [\"pillow\", \"mattress\", \"blanket\"]}", + 19 + ], + "plaid pants": [ + " {\"type\": \"clothing\", \"description\": \"trousers with a pattern of squares and lines; could be made of cotton or wool\", \"similar objects\": [\"checked pants\", \"striped pants\", \"denim jeans\"]}", + 19 + ], + "home base plate": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; has a rubber surface; could be used for baseball\", \"similar objects\": [\"bat\", \"glove\", \"ball\"]}", + 19 + ], + "side street": [ + " {\"type\": \"road\", \"description\": \"narrow; could have one-way traffic; could have a dead end\", \"similar objects\": [\"alley\", \"lane\", \"boulevard\"]}", + 19 + ], + "computer speakers": [ + " {\"type\": \"audio device\", \"description\": \"small, rectangular; could be connected to a computer; could have a volume control\", \"similar objects\": [\"headphones\", \"microphone\", \"amplifier\"]}", + 19 + ], + "iron bench": [ + " {\"type\": \"furniture\", \"description\": \"made of metal; could have a backrest; could be used for sitting\", \"similar objects\": [\"wooden bench\", \"sofa\", \"armchair\"]}", + 19 + ], + "grease stain": [ + " {\"type\": \"stain\", \"description\": \"dark, oily, could be found on clothes or furniture\", \"similar objects\": [\"ink stain\", \"coffee stain\", \"blood stain\"]}", + 19 + ], + "brown mud": [ + " {\"type\": \"substance\", \"description\": \"dark brown; could be wet and sticky; could be used for construction\", \"similar objects\": [\"clay\", \"dirt\", \"soil\"]}", + 19 + ], + "wooden panels": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular; could be used for walls and floors; could be painted\", \"similar objects\": [\"plywood\", \"drywall\", \"hardboard\"]}", + 19 + ], + "pink toilet": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"pink; has a bowl and a tank; could have a lid\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 19 + ], + "plastic toy": [ + " {\"type\": \"toy\", \"description\": \"made of plastic; could be colorful; could be in different shapes\", \"similar objects\": [\"stuffed animal\", \"action figure\", \"building blocks\"]}", + 19 + ], + "clock time": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has hands; could have digital display\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}", + 19 + ], + "chrome kitchen faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be mounted on the wall or sink\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"toilet flush\"]}", + 19 + ], + "motorcycle engine": [ + " {\"type\": \"machine part\", \"description\": \"cylindrical; could have multiple cylinders; could have a spark plug; could have a carburetor\", \"similar objects\": [\"car engine\", \"truck engine\", \"boat engine\"]}", + 19 + ], + "description": [ + "\n{\"type\": \"word\", \"description\": \"a word used to describe something; could be a noun, verb, adjective, adverb, etc.\", \"similar objects\": [\"term\", \"expression\", \"phrase\"]}", + 19 + ], + "baggage": [ + " {\"type\": \"travel item\", \"description\": \"large, rectangular; could be made of fabric; could have wheels\", \"similar objects\": [\"suitcase\", \"backpack\", \"duffel bag\"]}", + 19 + ], + "shadow zebra": [ + "\n{\"type\": \"illustration\", \"description\": \"black and white stripes; has a long mane; could be a silhouette\", \"similar objects\": [\"shadow horse\", \"shadow giraffe\", \"shadow elephant\"]}", + 19 + ], + "winter gloves": [ + " {\"type\": \"clothing item\", \"description\": \"long; could be made of wool; could be fingerless\", \"similar objects\": [\"mittens\", \"scarf\", \"hat\"]}", + 19 + ], + "exit": [ + " {\"type\": \"indicator\", \"description\": \"could be a sign or a door; could be marked with an arrow\", \"similar objects\": [\"entrance\", \"door\", \"gate\"]}", + 19 + ], + "bed skirt": [ + " {\"type\": \"bedding accessory\", \"description\": \"long, rectangular; usually made of fabric; covers the space between the mattress and the floor\", \"similar objects\": [\"bed sheet\", \"bedspread\", \"comforter\"]}", + 19 + ], + "shadow dirt": [ + " {\"type\": \"game\", \"description\": \"a two-player game; each player has a set of pieces; pieces are moved around the board to capture the opponent's pieces\", \"similar objects\": [\"checkers\", \"chess\", \"go\"]}", + 19 + ], + "sheet music": [ + " {\"type\": \"musical notation\", \"description\": \"written notes and symbols on a staff; could be in a form of a book\", \"similar objects\": [\"score\", \"tablature\", \"partiture\"]}", + 19 + ], + "binders": [ + " {\"type\": \"office supplies\", \"description\": \"ringed folders; could be made of plastic or metal; could be used to store documents\", \"similar objects\": [\"notebooks\", \"folders\", \"pens\"]}", + 19 + ], + "phone cord": [ + " {\"type\": \"electronic accessory\", \"description\": \"long, thin, flexible; could be coiled; could be connected to a phone\", \"similar objects\": [\"USB cable\", \"power cord\", \"headphone cable\"]}", + 19 + ], + "shadow water": [ + " {\"type\": \"liquid\", \"description\": \"transparent; could be used for cleaning\", \"similar objects\": [\"water\", \"alcohol\", \"bleach\"]}", + 19 + ], + "bruises": [ + " {\"type\": \"injury\", \"description\": \"dark purple or blue marks on the skin; could be painful; could be caused by a blunt force\", \"similar objects\": [\"cuts\", \"burns\", \"scrapes\"]}", + 19 + ], + "text print": [ + " {\"type\": \"printing tool\", \"description\": \"used to print text on paper; could be connected to a computer\", \"similar objects\": [\"printer\", \"scanner\", \"copier\"]}", + 19 + ], + "brown mushroom": [ + " {\"type\": \"fungus\", \"description\": \"brown; could have white spots; could have a stem; could have a cap\", \"similar objects\": [\"white mushroom\", \"portobello mushroom\", \"shiitake mushroom\"]}", + 19 + ], + "cabinet knob": [ + " {\"type\": \"hardware\", \"description\": \"round; could be made of metal or plastic; used to open and close cabinets\", \"similar objects\": [\"door handle\", \"drawer pull\", \"hinge\"]}", + 19 + ], + "brown mountain": [ + " {\"type\": \"landscape\", \"description\": \"large, rocky, could have snow on top; could have trees and plants around\", \"similar objects\": [\"hill\", \"cliff\", \"valley\"]}", + 19 + ], + "chairlift": [ + " {\"type\": \"transportation tool\", \"description\": \"long metal bars; could be used to transport people up a mountain\", \"similar objects\": [\"gondola\", \"ski lift\", \"cable car\"]}", + 19 + ], + "lavender": [ + " {\"type\": \"plant\", \"description\": \"purple flowers; has a strong scent; could be used for essential oils\", \"similar objects\": [\"rosemary\", \"sage\", \"thyme\"]}", + 19 + ], + "chocolate syrup": [ + " {\"type\": \"condiment\", \"description\": \"dark brown; could be used as topping for ice cream\", \"similar objects\": [\"whipped cream\", \"strawberry syrup\", \"caramel sauce\"]}", + 19 + ], + "pigtails": [ + " {\"type\": \"hairstyle\", \"description\": \"two sections of hair tied together at the back of the head; could be braided\", \"similar objects\": [\"bun\", \"ponytail\", \"braid\"]}", + 19 + ], + "flannel shirt": [ + " {\"type\": \"clothing\", \"description\": \"soft, warm, usually plaid; has a collar and buttons\", \"similar objects\": [\"sweater\", \"hoodie\", \"jacket\"]}", + 19 + ], + "gold chain": [ + " {\"type\": \"jewelry\", \"description\": \"made of gold; could be in different shapes; could be with a pendant\", \"similar objects\": [\"bracelet\", \"necklace\", \"ring\"]}", + 19 + ], + "robes": [ + " {\"type\": \"clothing\", \"description\": \"long, loose-fitting garment; could be made of silk or cotton; could have long sleeves\", \"similar objects\": [\"dress\", \"tunic\", \"shawl\"]}", + 19 + ], + "air traffic control tower": [ + "\n{\"type\": \"structure\", \"description\": \"tall, cylindrical; has a control room at the top; could be painted with white and red stripes\", \"similar objects\": [\"radio tower\", \"wind turbine\", \"cell tower\"]}", + 19 + ], + "broccoli pieces": [ + " {\"type\": \"vegetable\", \"description\": \"green, small florets; could be steamed or boiled; could be added to salads\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 19 + ], + "metal tube": [ + " {\"type\": \"object\", \"description\": \"cylindrical; made of metal; could be used for plumbing or other purposes\", \"similar objects\": [\"pipe\", \"hose\", \"conduit\"]}", + 19 + ], + "gren": [ + " {\"type\": \"color\", \"description\": \"dark green; could be a shade of green\", \"similar objects\": [\"emerald\", \"olive\", \"teal\"]}", + 19 + ], + "purple vegetable": [ + "\n{\"type\": \"vegetable\", \"description\": \"could be eggplant, purple cabbage, purple carrots, purple potatoes, purple sweet potatoes, purple peppers, purple onions, purple garlic, purple asparagus, purple artichokes, purple squash, purple tomatoes, purple radishes, purple turnips, purple beets, purple okra, purple beans, purple peas, purple mushrooms, purple brussels sprouts, purple broccoli, purple cauliflower\", \"similar objects\": [\"red vegetable\", \"green vegetable\", \"yellow vegetable\", \"orange vegetable\", \"white vegetable\"]}", + 19 + ], + "round fruit": [ + " {\"type\": \"fruit\", \"description\": \"could be red, yellow, or green; could have a stem; could be sliced into pieces\", \"similar objects\": [\"apple\", \"pear\", \"orange\"]}", + 19 + ], + "yellow table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; has four legs; could be made of wood; has a yellow color\", \"similar objects\": [\"chair\", \"sofa\", \"desk\"]}", + 19 + ], + "florescent light": [ + " {\"type\": \"lighting tool\", \"description\": \"long tube; emits bright white light; could be used in offices and homes\", \"similar objects\": [\"incandescent light\", \"halogen light\", \"LED light\"]}", + 19 + ], + "trash laying": [ + " {\"type\": \"waste\", \"description\": \"could be made of plastic, paper, metal; could be in different shapes and sizes; could be hazardous\", \"similar objects\": [\"garbage\", \"litter\", \"rubbish\"]}", + 19 + ], + "silver rims": [ + " {\"type\": \"automotive accessory\", \"description\": \"shiny, metallic; could be attached to the wheels of a car\", \"similar objects\": [\"tires\", \"hubcaps\", \"spoke covers\"]}", + 19 + ], + "footstool": [ + " {\"type\": \"furniture\", \"description\": \"small, low, has legs; could be used as a seat\", \"similar objects\": [\"ottoman\", \"bench\", \"chair\"]}", + 19 + ], + "cork board": [ + " {\"type\": \"office tool\", \"description\": \"rectangular; has pins; could be used to hang notes\", \"similar objects\": [\"whiteboard\", \"bulletin board\", \"chalkboard\"]}", + 19 + ], + "rowboat": [ + " {\"type\": \"watercraft\", \"description\": \"long and narrow; has oars; could have a sail\", \"similar objects\": [\"canoe\", \"kayak\", \"motorboat\"]}", + 19 + ], + "outdoor table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have four legs\", \"similar objects\": [\"chair\", \"bench\", \"sofa\"]}", + 19 + ], + "serving tray": [ + " {\"type\": \"utensil\", \"description\": \"flat, rectangular; could have handles; could be made of metal or wood\", \"similar objects\": [\"platter\", \"dish\", \"plate\"]}", + 19 + ], + "beige curtain": [ + " {\"type\": \"decoration item\", \"description\": \"long; could be made of fabric; could be hung on a window\", \"similar objects\": [\"blinds\", \"drapes\", \"shades\"]}", + 19 + ], + "adult woman": [ + "\n{\"type\": \"human\", \"description\": \"tall; could have long hair; could wear makeup; could wear clothes\", \"similar objects\": [\"adult man\", \"teenager\", \"child\"]}", + 19 + ], + "clock radio": [ + " {\"type\": \"electronic device\", \"description\": \"combination of a clock and a radio; could have an alarm\", \"similar objects\": [\"alarm clock\", \"stereo\", \"boombox\"]}", + 19 + ], + "wrought iron": [ + " {\"type\": \"material\", \"description\": \"strong, malleable metal; could be used for furniture, gates, and railings\", \"similar objects\": [\"steel\", \"aluminum\", \"brass\"]}", + 19 + ], + "handicap sticker": [ + " {\"type\": \"accessibility tool\", \"description\": \"blue and white; has a wheelchair symbol; could be placed on a car\", \"similar objects\": [\"disabled parking sign\", \"disabled parking permit\", \"disabled parking placard\"]}", + 19 + ], + "base umpire": [ + " {\"type\": \"sports official\", \"description\": \"wears a uniform; stands near the base; makes calls on plays\", \"similar objects\": [\"referee\", \"linesman\", \"umpire\"]}", + 19 + ], + "round building": [ + " {\"type\": \"structure\", \"description\": \"circular; could have multiple floors; could have a dome-shaped roof\", \"similar objects\": [\"tower\", \"dome\", \"pyramid\"]}", + 19 + ], + "dimple": [ + " {\"type\": \"facial feature\", \"description\": \"small indentation on the skin; could be found on the cheeks or chin\", \"similar objects\": [\"wrinkle\", \"freckle\", \"mole\"]}", + 19 + ], + "cell phone screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be touch-sensitive; could be made of glass\", \"similar objects\": [\"tablet\", \"laptop\", \"television\"]}", + 19 + ], + "peanut": [ + " {\"type\": \"nut\", \"description\": \"small, round, brown; could be shelled; could be roasted\", \"similar objects\": [\"almond\", \"cashew\", \"walnut\"]}", + 19 + ], + "blaze": [ + " {\"type\": \"phenomenon\", \"description\": \"intense fire; could be caused by natural or man-made sources\", \"similar objects\": [\"fire\", \"explosion\", \"volcano\"]}", + 19 + ], + "spider": [ + " {\"type\": \"insect\", \"description\": \"eight legs; could have a round body; could have a web\", \"similar objects\": [\"scorpion\", \"ant\", \"bee\"]}", + 19 + ], + "grey metal fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal; could be in grey color; could be in a form of fence\", \"similar objects\": [\"gate\", \"wall\", \"hedge\"]}", + 19 + ], + "police van": [ + " {\"type\": \"vehicle\", \"description\": \"large, white, has a siren; could have a cage in the back\", \"similar objects\": [\"ambulance\", \"fire truck\", \"armored car\"]}", + 19 + ], + "gravels": [ + " {\"type\": \"construction material\", \"description\": \"small stones; could be used for driveways and pathways\", \"similar objects\": [\"rocks\", \"pebbles\", \"boulders\"]}", + 19 + ], + "embankment": [ + " {\"type\": \"structure\", \"description\": \"raised bank of earth; could be used to prevent flooding\", \"similar objects\": [\"dam\", \"levee\", \"dyke\"]}", + 19 + ], + "silver airplane": [ + "\n{\"type\": \"vehicle\", \"description\": \"silver; has wings; could have a tail; could have two or more engines\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}", + 19 + ], + "man pants": [ + " {\"type\": \"clothing\", \"description\": \"long, usually made of cotton or polyester; could have pockets; could have a zipper\", \"similar objects\": [\"jeans\", \"shorts\", \"trousers\"]}", + 19 + ], + "scissor handles": [ + " {\"type\": \"tool\", \"description\": \"two handles connected by a pivot; could be used for cutting\", \"similar objects\": [\"tweezers\", \"pliers\", \"clippers\"]}", + 19 + ], + "persons arm": [ + "\n{\"type\": \"body part\", \"description\": \"long; could be muscular; could have a hand at the end; could have tattoos\", \"similar objects\": [\"leg\", \"torso\", \"head\"]}", + 19 + ], + "tall green plant": [ + "\n{\"type\": \"plant\", \"description\": \"tall; green leaves; could have flowers\", \"similar objects\": [\"fern\", \"palm tree\", \"succulent\"]}", + 19 + ], + "storage compartment": [ + " {\"type\": \"container\", \"description\": \"could be made of plastic or metal; could be used to store items; could have a lid or a lock\", \"similar objects\": [\"box\", \"basket\", \"drawer\"]}", + 19 + ], + "lcd screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be used to display images and videos; could be connected to a computer\", \"similar objects\": [\"monitor\", \"television\", \"projector\"]}", + 19 + ], + "ref": [ + " {\"type\": \"sports official\", \"description\": \"wears a striped shirt; carries a whistle; responsible for enforcing the rules of the game\", \"similar objects\": [\"umpire\", \"linesman\", \"judge\"]}", + 19 + ], + "brown wall": [ + " {\"type\": \"structure\", \"description\": \"brown; could be made of wood, brick, or stone; could have a texture\", \"similar objects\": [\"door\", \"window\", \"ceiling\"]}", + 19 + ], + "silver drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of metal; has a handle\", \"similar objects\": [\"cabinet\", \"chest of drawers\", \"dresser\"]}", + 19 + ], + "courts": [ + " {\"type\": \"sports facility\", \"description\": \"rectangular; has a net in the middle; could be used for playing tennis, badminton, volleyball, etc.\", \"similar objects\": [\"stadium\", \"field\", \"gym\"]}", + 19 + ], + "grey pole": [ + " {\"type\": \"structure\", \"description\": \"long, cylindrical, grey; could be made of metal or wood\", \"similar objects\": [\"fence\", \"flagpole\", \"streetlight\"]}", + 19 + ], + "brown eggs": [ + " {\"type\": \"food\", \"description\": \"oval-shaped; could be boiled, fried, or scrambled; could be used in baking\", \"similar objects\": [\"white eggs\", \"quail eggs\", \"duck eggs\"]}", + 19 + ], + "makeup": [ + " {\"type\": \"cosmetic product\", \"description\": \"used to enhance facial features; could be in the form of powder, cream, or liquid; could come in various colors\", \"similar objects\": [\"eyeliner\", \"lipstick\", \"mascara\"]}", + 19 + ], + "billboards": [ + " {\"type\": \"advertising tool\", \"description\": \"large, rectangular; could be placed on the side of the road; could be used to display advertisements\", \"similar objects\": [\"signs\", \"posters\", \"flyers\"]}", + 19 + ], + "brochures": [ + " {\"type\": \"printed material\", \"description\": \"folded paper; could be used for advertising\", \"similar objects\": [\"flyers\", \"posters\", \"magazines\"]}", + 19 + ], + "females": [ + "\n{\"type\": \"gender\", \"description\": \"female gender; could be identified by physical characteristics such as breasts and hips; could be identified by behavior such as nurturing and caring\", \"similar objects\": [\"women\", \"girls\", \"females\"]}", + 19 + ], + "damage": [ + " {\"type\": \"noun\", \"description\": \"harm or injury caused to something or someone\", \"similar objects\": [\"injury\", \"harm\", \"destruction\"]}", + 19 + ], + "apple stem": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, green; could have leaves and buds; could be attached to an apple\", \"similar objects\": [\"banana stem\", \"orange stem\", \"pear stem\"]}", + 19 + ], + "train rails": [ + " {\"type\": \"transportation tool\", \"description\": \"long, metal, parallel lines; could be connected to a train\", \"similar objects\": [\"tram rails\", \"monorail\", \"subway rails\"]}", + 19 + ], + "toddlers": [ + " {\"type\": \"people\", \"description\": \"young children; usually between 1 and 3 years old\", \"similar objects\": [\"babies\", \"preschoolers\", \"kids\"]}", + 19 + ], + "silver platter": [ + " {\"type\": \"serving dish\", \"description\": \"round; made of silver; could be used to serve food\", \"similar objects\": [\"tray\", \"plate\", \"bowl\"]}", + 19 + ], + "jack": [ + " {\"type\": \"tool\", \"description\": \"small, metal, used to lift heavy objects\", \"similar objects\": [\"wrench\", \"screwdriver\", \"hammer\"]}", + 19 + ], + "bridal": [ + " {\"type\": \"clothing\", \"description\": \"white; could be made of lace; could have a veil\", \"similar objects\": [\"wedding dress\", \"prom dress\", \"evening gown\"]}", + 19 + ], + "speckles": [ + " {\"type\": \"pattern\", \"description\": \"small, round, and irregularly shaped spots; could be of different colors\", \"similar objects\": [\"dots\", \"splotches\", \"blotches\"]}", + 19 + ], + "friend": [ + "\n{\"type\": \"relationship\", \"description\": \"someone who is close to you; someone you can trust and rely on; someone who is supportive and understanding\", \"similar objects\": [\"family\", \"partner\", \"colleague\"]}", + 19 + ], + "silver railing": [ + " {\"type\": \"building material\", \"description\": \"long, thin, metallic; could be used as a fence\", \"similar objects\": [\"iron railing\", \"wooden railing\", \"aluminum railing\"]}", + 19 + ], + "dog tongue": [ + " {\"type\": \"body part\", \"description\": \"pink; long; could be rough; could be wet\", \"similar objects\": [\"cat tongue\", \"human tongue\", \"horse tongue\"]}", + 19 + ], + "clearing": [ + " {\"type\": \"landscape\", \"description\": \"open area with no trees or buildings; could be grassy or sandy\", \"similar objects\": [\"meadow\", \"field\", \"prairie\"]}", + 19 + ], + "dirt track": [ + " {\"type\": \"race track\", \"description\": \"made of dirt; could have obstacles; could be used for racing\", \"similar objects\": [\"oval track\", \"drag strip\", \"motocross track\"]}", + 19 + ], + "duct tape": [ + " {\"type\": \"adhesive tool\", \"description\": \"silver; could be used to fix things; could be torn into pieces\", \"similar objects\": [\"glue\", \"tape\", \"velcro\"]}", + 19 + ], + "poop": [ + " {\"type\": \"waste\", \"description\": \"solid, brown, smelly\", \"similar objects\": [\"urine\", \"feces\", \"manure\"]}", + 19 + ], + "woman hair": [ + " {\"type\": \"body part\", \"description\": \"long, black, curly; could be tied up\", \"similar objects\": [\"man hair\", \"eyebrow\", \"beard\"]}", + 19 + ], + "news paper": [ + " {\"type\": \"reading material\", \"description\": \"printed on paper; could be folded; could be in black and white or in color\", \"similar objects\": [\"magazine\", \"book\", \"journal\"]}", + 19 + ], + "stone tiles": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular, made of stone; could be used for flooring\", \"similar objects\": [\"wooden tiles\", \"ceramic tiles\", \"marble tiles\"]}", + 19 + ], + "giraffe mane": [ + " {\"type\": \"animal feature\", \"description\": \"long, brown, and coarse; could be up to 8 feet long\", \"similar objects\": [\"elephant trunk\", \"horse mane\", \"lion mane\"]}", + 19 + ], + "cross country skier": [ + " {\"type\": \"athlete\", \"description\": \"uses two poles and skis to move across snow; wears special clothing and shoes\", \"similar objects\": [\"downhill skier\", \"snowboarder\", \"ice skater\"]}", + 19 + ], + "breakfast sandwich": [ + " {\"type\": \"food\", \"description\": \"bread with egg, cheese, and other ingredients; could be served hot or cold\", \"similar objects\": [\"burrito\", \"bagel\", \"taco\"]}", + 19 + ], + "motorcycle racer": [ + " {\"type\": \"person\", \"description\": \"wears a helmet; wears a leather suit; rides a motorcycle\", \"similar objects\": [\"race car driver\", \"bicycle racer\", \"skateboarder\"]}", + 19 + ], + "meat sandwich": [ + " {\"type\": \"food\", \"description\": \"bread with meat filling; could have vegetables and sauces\", \"similar objects\": [\"hamburger\", \"hot dog\", \"taco\"]}", + 19 + ], + "purple collar": [ + " {\"type\": \"accessory\", \"description\": \"collar; could be made of fabric; could be decorated with beads; could be used for pets\", \"similar objects\": [\"leash\", \"harness\", \"muzzle\"]}", + 19 + ], + "kitchen knife": [ + " {\"type\": \"cooking tool\", \"description\": \"sharp blade; could have a handle; could be used for cutting\", \"similar objects\": [\"spoon\", \"fork\", \"spatula\"]}", + 19 + ], + "windsheild": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be curved; could be made of glass\", \"similar objects\": [\"headlight\", \"tail light\", \"side mirror\"]}", + 19 + ], + "ornate clock": [ + " {\"type\": \"decorative item\", \"description\": \"round; could have intricate designs; could have a pendulum\", \"similar objects\": [\"grandfather clock\", \"mantel clock\", \"cuckoo clock\"]}", + 19 + ], + "slit": [ + " {\"type\": \"opening\", \"description\": \"long, thin, could be made of paper or fabric\", \"similar objects\": [\"hole\", \"cut\", \"gap\"]}", + 19 + ], + "individuals": [ + " {\"type\": \"people\", \"description\": \"could be a group of people; could be a single person\", \"similar objects\": [\"crowd\", \"family\", \"team\"]}", + 19 + ], + "shaggy dog": [ + " {\"type\": \"animal\", \"description\": \"long fur; could have floppy ears; could have a tail\", \"similar objects\": [\"poodle\", \"collie\", \"sheepdog\"]}", + 19 + ], + "side profile": [ + " {\"type\": \"image\", \"description\": \"image of a person or object from the side; could be a silhouette\", \"similar objects\": [\"front profile\", \"top view\", \"bottom view\"]}", + 19 + ], + "wet nose": [ + " {\"type\": \"animal feature\", \"description\": \"slippery; could be cold; could be used for smelling\", \"similar objects\": [\"paws\", \"tail\", \"fur\"]}", + 19 + ], + "skateboard boy": [ + "\n{\"type\": \"person\", \"description\": \"young person riding a skateboard; could be wearing protective gear; could be performing tricks\", \"similar objects\": [\"bicyclist\", \"rollerblader\", \"surfer\"]}", + 19 + ], + "seashell": [ + " {\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be found on the beach; could be used as decoration\", \"similar objects\": [\"starfish\", \"conch\", \"coral\"]}", + 19 + ], + "brick facade": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be used to build walls\", \"similar objects\": [\"siding\", \"stucco\", \"plaster\"]}", + 19 + ], + "metal statue": [ + " {\"type\": \"decoration\", \"description\": \"made of metal; could be in the shape of a person or an animal\", \"similar objects\": [\"wooden statue\", \"ceramic statue\", \"glass statue\"]}", + 19 + ], + "towel rack wall": [ + " {\"type\": \"storage tool\", \"description\": \"mounted on the wall; could be made of metal; could have multiple bars for hanging towels\", \"similar objects\": [\"coat rack\", \"shelf\", \"hooks\"]}", + 19 + ], + "furry tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, fluffy, could be white, brown, or black; could be attached to a cat, dog, or fox\", \"similar objects\": [\"paws\", \"whiskers\", \"ears\"]}", + 19 + ], + "round dish": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round, could be made of ceramic or metal; could be used for serving food\", \"similar objects\": [\"plate\", \"bowl\", \"tray\"]}", + 19 + ], + "grey door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have a handle\", \"similar objects\": [\"window\", \"cabinet\", \"drawer\"]}", + 19 + ], + "pink object": [ + "\n{\"type\": \"object\", \"description\": \"color is pink; could be any shape or size\", \"similar objects\": [\"red object\", \"blue object\", \"yellow object\"]}", + 19 + ], + "heard": [ + " {\"type\": \"sound\", \"description\": \"vibrations in the air; could be loud or soft; could be high or low pitched\", \"similar objects\": [\"noise\", \"whisper\", \"echo\"]}", + 19 + ], + "snow flakes": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, small, and flaky; could be in different shapes\", \"similar objects\": [\"rain drops\", \"hail stones\", \"sleet\"]}", + 19 + ], + "gold doorknob": [ + "\n{\"type\": \"hardware\", \"description\": \"round; made of gold; could be used to open and close doors\", \"similar objects\": [\"brass doorknob\", \"silver doorknob\", \"bronze doorknob\"]}", + 19 + ], + "lush trees": [ + "\n{\"type\": \"vegetation\", \"description\": \"tall, thick, green; could have leaves and fruits; could have branches and roots\", \"similar objects\": [\"palm trees\", \"pine trees\", \"cedar trees\"]}", + 19 + ], + "time stamp": [ + " {\"type\": \"tool\", \"description\": \"used to mark the time of an event; could be digital or physical\", \"similar objects\": [\"calendar\", \"clock\", \"stopwatch\"]}", + 19 + ], + "brown sticks": [ + " {\"type\": \"object\", \"description\": \"long, thin, brown; could be made of wood or plastic\", \"similar objects\": [\"pencils\", \"popsicle sticks\", \"skewers\"]}", + 19 + ], + "pointer finger": [ + " {\"type\": \"body part\", \"description\": \"longest finger; used for pointing\", \"similar objects\": [\"thumb\", \"middle finger\", \"ring finger\"]}", + 19 + ], + "bathroom scene": [ + "\n{\"type\": \"room\", \"description\": \"could have a sink, toilet, shower, bathtub, mirror, and other fixtures; could have a window; could have a door; could have a rug; could have a towel rack; could have a cabinet; could have a light fixture\", \"similar objects\": [\"bedroom\", \"kitchen\", \"living room\"]}", + 19 + ], + "sidewalks": [ + " {\"type\": \"structure\", \"description\": \"concrete or asphalt; could be used for walking or biking; could have lines or patterns\", \"similar objects\": [\"roads\", \"paths\", \"driveways\"]}", + 19 + ], + "rivet": [ + " {\"type\": \"fastener\", \"description\": \"small, cylindrical, metal; used to join two or more pieces of material together\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 19 + ], + "grey cloudy skies": [ + "\n{\"type\": \"weather\", \"description\": \"overcast; could be raining; could be windy; could be dark\", \"similar objects\": [\"rainy day\", \"foggy day\", \"snowy day\"]}", + 19 + ], + "clay vase": [ + " {\"type\": \"decorative item\", \"description\": \"cylindrical; could be painted; could be glazed\", \"similar objects\": [\"pottery\", \"ceramic\", \"glass vase\"]}", + 19 + ], + "orange peel": [ + " {\"type\": \"food waste\", \"description\": \"orange-colored; thin and dry; could be used as a spice\", \"similar objects\": [\"lemon peel\", \"banana peel\", \"apple peel\"]}", + 19 + ], + "boy shirt": [ + " {\"type\": \"clothing\", \"description\": \"collared; could have buttons; could have short or long sleeves; could be plain or patterned\", \"similar objects\": [\"girl shirt\", \"polo shirt\", \"t-shirt\"]}", + 19 + ], + "hairband": [ + " {\"type\": \"accessory\", \"description\": \"elastic band; could be decorated with beads or flowers\", \"similar objects\": [\"headband\", \"scrunchy\", \"hair tie\"]}", + 19 + ], + "firefighter": [ + " {\"type\": \"occupation\", \"description\": \"wears a protective suit; carries an axe; could have a breathing apparatus\", \"similar objects\": [\"police officer\", \"paramedic\", \"lifeguard\"]}", + 19 + ], + "fire hose": [ + " {\"type\": \"firefighting tool\", \"description\": \"long, flexible, made of rubber; could be connected to a fire hydrant\", \"similar objects\": [\"fire extinguisher\", \"fire blanket\", \"fire axe\"]}", + 19 + ], + "artifact": [ + " {\"type\": \"object\", \"description\": \"an object made by humans; could be a relic or a historical object\", \"similar objects\": [\"antique\", \"relic\", \"artwork\"]}", + 19 + ], + "support pillar": [ + " {\"type\": \"structural element\", \"description\": \"vertical; could be made of concrete, steel, or wood; could be used to support a roof or bridge\", \"similar objects\": [\"column\", \"beam\", \"post\"]}", + 19 + ], + "cement building": [ + " {\"type\": \"structure\", \"description\": \"made of cement; could have multiple floors; could have windows and doors\", \"similar objects\": [\"skyscraper\", \"bridge\", \"monument\"]}", + 19 + ], + "spot lights": [ + " {\"type\": \"lighting tool\", \"description\": \"focused, bright light; could be used for stage lighting\", \"similar objects\": [\"flood lights\", \"LED lights\", \"halogen lights\"]}", + 19 + ], + "caution line": [ + " {\"type\": \"safety tool\", \"description\": \"yellow and black stripes; could be used to mark a dangerous area\", \"similar objects\": [\"barricade\", \"traffic cone\", \"warning sign\"]}", + 19 + ], + "banana tree": [ + " {\"type\": \"plant\", \"description\": \"tall; has long leaves; could have yellow fruits\", \"similar objects\": [\"palm tree\", \"coconut tree\", \"mango tree\"]}", + 19 + ], + "bushy tail": [ + " {\"type\": \"animal feature\", \"description\": \"long, thick, and fluffy tail; could be seen on some animals such as foxes, squirrels, and raccoons\", \"similar objects\": [\"fluffy fur\", \"pointed ears\", \"long whiskers\"]}", + 19 + ], + "semi truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has two axles; could have a trailer attached\", \"similar objects\": [\"truck\", \"van\", \"pickup truck\"]}", + 19 + ], + "canopies": [ + " {\"type\": \"shelter\", \"description\": \"could be made of fabric; could be used to provide shade; could be hung from poles\", \"similar objects\": [\"tent\", \"awning\", \"umbrella\"]}", + 19 + ], + "record": [ + " {\"type\": \"media\", \"description\": \"round; could be made of vinyl; could be used to store music\", \"similar objects\": [\"CD\", \"cassette tape\", \"DVD\"]}", + 19 + ], + "spike": [ + " {\"type\": \"tool\", \"description\": \"sharp, pointed object; could be used for fastening objects\", \"similar objects\": [\"nail\", \"screw\", \"staple\"]}", + 19 + ], + "calm river": [ + " {\"type\": \"natural landscape\", \"description\": \"smooth surface; could have small ripples; could have trees and rocks on the side\", \"similar objects\": [\"lake\", \"ocean\", \"waterfall\"]}", + 19 + ], + "story bus": [ + " {\"type\": \"vehicle\", \"description\": \"large bus; could have colorful decorations; could have stories painted on the side\", \"similar objects\": [\"school bus\", \"tour bus\", \"party bus\"]}", + 19 + ], + "brick walk way": [ + " {\"type\": \"construction material\", \"description\": \"made of bricks; could be used as a walk way\", \"similar objects\": [\"concrete walk way\", \"stone walk way\", \"wooden walk way\"]}", + 19 + ], + "kickstand motorcycle": [ + "\n{\"type\": \"vehicle accessory\", \"description\": \"attached to the side of a motorcycle; used to keep the motorcycle upright when parked\", \"similar objects\": [\"wheel chock\", \"center stand\", \"side stand\"]}", + 19 + ], + "watch person": [ + " {\"type\": \"person\", \"description\": \"wearing a watch; could be looking at the watch\", \"similar objects\": [\"man\", \"woman\", \"child\"]}", + 19 + ], + "parents": [ + "\n{\"type\": \"people\", \"description\": \"caregivers of a child; could be biological or adoptive; could be married or single\", \"similar objects\": [\"guardians\", \"grandparents\", \"teachers\"]}", + 19 + ], + "telephone wire": [ + " {\"type\": \"communication tool\", \"description\": \"long, thin, insulated wire; could be connected to a telephone\", \"similar objects\": [\"cable\", \"fiber optic cable\", \"power line\"]}", + 19 + ], + "dove": [ + " {\"type\": \"bird\", \"description\": \"white; has a long tail; could coo\", \"similar objects\": [\"pigeon\", \"sparrow\", \"seagull\"]}", + 19 + ], + "pink bottle": [ + "\n{\"type\": \"container\", \"description\": \"pink; could be made of plastic; could have a lid\", \"similar objects\": [\"jar\", \"jug\", \"can\"]}", + 19 + ], + "clock clock tower": [ + "\n{\"type\": \"timekeeping tool\", \"description\": \"round; could have hands or digital display; could be a tower with bells\", \"similar objects\": [\"watch\", \"alarm\", \"timer\"]}", + 19 + ], + "water knob": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be used to control the flow of water; could be made of metal\", \"similar objects\": [\"faucet\", \"valve\", \"tap\"]}", + 19 + ], + "gold statue": [ + " {\"type\": \"decorative item\", \"description\": \"made of gold; could be in the shape of a person or an animal\", \"similar objects\": [\"silver statue\", \"bronze statue\", \"marble statue\"]}", + 19 + ], + "seabird": [ + " {\"type\": \"animal\", \"description\": \"could have wings and feathers; could be found near the sea; could have a long beak\", \"similar objects\": [\"penguin\", \"albatross\", \"gull\"]}", + 19 + ], + "porcelain tub": [ + " {\"type\": \"bathroom fixture\", \"description\": \"white; could be oval or rectangular; could have claw feet\", \"similar objects\": [\"bathtub\", \"shower\", \"sink\"]}", + 19 + ], + "shadow skateboard": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could have a graphic design\", \"similar objects\": [\"longboard\", \"skateboard\", \"rollerblades\"]}", + 19 + ], + "hippo": [ + " {\"type\": \"animal\", \"description\": \"large, gray, has a wide mouth; could be found in water\", \"similar objects\": [\"rhinoceros\", \"elephant\", \"crocodile\"]}", + 19 + ], + "motorcycle kick stand": [ + " {\"type\": \"motorcycle part\", \"description\": \"metal stand; used to support the motorcycle when parked\", \"similar objects\": [\"motorcycle handlebar\", \"motorcycle seat\", \"motorcycle exhaust pipe\"]}", + 19 + ], + "dark stain": [ + " {\"type\": \"stain\", \"description\": \"dark color; could be caused by liquid or dirt; could be removed with cleaning products\", \"similar objects\": [\"light stain\", \"dirt\", \"grease\"]}", + 19 + ], + "tire marks": [ + " {\"type\": \"evidence\", \"description\": \"black marks on the ground; could be curved or straight; could be from a car or a bike\", \"similar objects\": [\"footprints\", \"fingerprints\", \"bloodstains\"]}", + 19 + ], + "side engine": [ + " {\"type\": \"vehicle part\", \"description\": \"mounted on the side of a vehicle; used to power the vehicle\", \"similar objects\": [\"engine\", \"transmission\", \"exhaust system\"]}", + 19 + ], + "tennis court surface": [ + " {\"type\": \"sports surface\", \"description\": \"hard, flat, usually made of asphalt or concrete; could be painted with lines\", \"similar objects\": [\"basketball court\", \"volleyball court\", \"badminton court\"]}", + 19 + ], + "typewriter": [ + " {\"type\": \"writing tool\", \"description\": \"has a keyboard; could be mechanical or electronic; could have a carriage return lever\", \"similar objects\": [\"computer\", \"printer\", \"calculator\"]}", + 19 + ], + "church steeple": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, pointed, could be made of stone or wood; could have a cross on top\", \"similar objects\": [\"bell tower\", \"minaret\", \"obelisk\"]}", + 18 + ], + "traffic line": [ + " {\"type\": \"road marking\", \"description\": \"white or yellow lines on the road; could be dashed or solid\", \"similar objects\": [\"road sign\", \"traffic light\", \"road barrier\"]}", + 18 + ], + "order": [ + " {\"type\": \"instruction\", \"description\": \"a request for something to be done; could be verbal or written\", \"similar objects\": [\"command\", \"request\", \"directive\"]}", + 18 + ], + "cement road": [ + " {\"type\": \"road surface\", \"description\": \"hard, gray, and rough; could be used for driving\", \"similar objects\": [\"asphalt road\", \"gravel road\", \"dirt road\"]}", + 18 + ], + "silverware napkin": [ + " {\"type\": \"tableware\", \"description\": \"square; could be made of cloth; used to cover silverware\", \"similar objects\": [\"tablecloth\", \"placemat\", \"coaster\"]}", + 18 + ], + "bottom sign": [ + " {\"type\": \"signage\", \"description\": \"could be made of metal or plastic; could be used to indicate the bottom of a staircase or a ramp\", \"similar objects\": [\"handrail\", \"stair nosing\", \"tactile paving\"]}", + 18 + ], + "metal stove": [ + " {\"type\": \"cooking tool\", \"description\": \"made of metal; has a flat surface; could have multiple burners\", \"similar objects\": [\"gas stove\", \"electric stove\", \"wood stove\"]}", + 18 + ], + "shirtless": [ + " {\"type\": \"clothing\", \"description\": \"without a shirt; could be sleeveless; could be a tank top\", \"similar objects\": [\"t-shirt\", \"tank top\", \"vest\"]}", + 18 + ], + "wood building": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could have a roof; could have windows and doors\", \"similar objects\": [\"shed\", \"cabin\", \"barn\"]}", + 18 + ], + "park benches": [ + " {\"type\": \"furniture\", \"description\": \"long, wooden, could have a backrest; could be placed in a park\", \"similar objects\": [\"chairs\", \"tables\", \"sofas\"]}", + 18 + ], + "automobiles": [ + "\n{\"type\": \"vehicle\", \"description\": \"motorized vehicle; could have four wheels; could have different colors; could have different shapes\", \"similar objects\": [\"car\", \"truck\", \"motorcycle\"]}", + 18 + ], + "paper container": [ + " {\"type\": \"container\", \"description\": \"made of paper; could be used to store food; could be sealed with a lid\", \"similar objects\": [\"plastic container\", \"glass container\", \"metal container\"]}", + 18 + ], + "orange candle": [ + "\n{\"type\": \"lighting tool\", \"description\": \"orange; made of wax; could have a wick\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 18 + ], + "footpath": [ + " {\"type\": \"pathway\", \"description\": \"a path for pedestrians; could be made of concrete, asphalt, or gravel\", \"similar objects\": [\"sidewalk\", \"trail\", \"walkway\"]}", + 18 + ], + "round sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"round; could be made of stainless steel; could have a faucet\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 18 + ], + "grassy ground": [ + " {\"type\": \"landscape\", \"description\": \"green; could have small plants; could have small stones\", \"similar objects\": [\"meadow\", \"field\", \"lawn\"]}", + 18 + ], + "pony tail holder": [ + " {\"type\": \"hair accessory\", \"description\": \"elastic band; could be decorated with beads or ribbons\", \"similar objects\": [\"hair clip\", \"hair tie\", \"headband\"]}", + 18 + ], + "orange sticker": [ + "\n{\"type\": \"decoration item\", \"description\": \"round; orange in color; could be adhesive\", \"similar objects\": [\"label\", \"badge\", \"patch\"]}", + 18 + ], + "break light": [ + " {\"type\": \"vehicle part\", \"description\": \"red; usually found at the back of a car; could be used to indicate braking\", \"similar objects\": [\"headlight\", \"taillight\", \"fog light\"]}", + 18 + ], + "blue belt": [ + " {\"type\": \"accessory\", \"description\": \"made of fabric; could be used to hold up pants; could be used as a fashion statement\", \"similar objects\": [\"black belt\", \"brown belt\", \"red belt\"]}", + 18 + ], + "shower curtains": [ + " {\"type\": \"bathroom accessory\", \"description\": \"long, thin, made of fabric; could be transparent or opaque; could be hung on a rod\", \"similar objects\": [\"towels\", \"bath mats\", \"bath rugs\"]}", + 18 + ], + "baby calf": [ + " {\"type\": \"animal\", \"description\": \"small; has a white and black spotted fur; could have long legs\", \"similar objects\": [\"foal\", \"lamb\", \"piglet\"]}", + 18 + ], + "blurry man": [ + "\n{\"type\": \"image\", \"description\": \"fuzzy, unclear; could be wearing a hat; could have a beard\", \"similar objects\": [\"person\", \"man\", \"woman\"]}", + 18 + ], + "dishcloth": [ + " {\"type\": \"cleaning tool\", \"description\": \"rectangular; made of cloth; could be used to clean dishes\", \"similar objects\": [\"sponge\", \"scrubber\", \"dishrag\"]}", + 18 + ], + "royal": [ + " {\"type\": \"title\", \"description\": \"associated with royalty; could be used to refer to a monarch or a member of a royal family\", \"similar objects\": [\"king\", \"queen\", \"prince\", \"princess\"]}", + 18 + ], + "tea bag": [ + " {\"type\": \"beverage\", \"description\": \"small, paper bag filled with tea leaves; could be steeped in hot water\", \"similar objects\": [\"coffee bag\", \"herbal tea bag\", \"green tea bag\"]}", + 18 + ], + "hairline": [ + " {\"type\": \"hair style\", \"description\": \"a thin line of hair along the forehead; could be straight or curved\", \"similar objects\": [\"buzz cut\", \"fade\", \"undercut\"]}", + 18 + ], + "silver remote": [ + " {\"type\": \"electronic device\", \"description\": \"silver; has buttons; could be used to control other electronic devices\", \"similar objects\": [\"game controller\", \"television remote\", \"keyboard\"]}", + 18 + ], + "dark spot": [ + " {\"type\": \"stain\", \"description\": \"dark, round, could be caused by water or oil\", \"similar objects\": [\"dirt\", \"grease\", \"mold\"]}", + 18 + ], + "teacher": [ + " {\"type\": \"occupation\", \"description\": \"educates students; could be a mentor; could be a role model\", \"similar objects\": [\"professor\", \"instructor\", \"tutor\"]}", + 18 + ], + "bicycle chain": [ + " {\"type\": \"bicycle part\", \"description\": \"metal; has several links; could be connected to the bicycle wheel\", \"similar objects\": [\"bicycle tire\", \"bicycle seat\", \"bicycle handlebar\"]}", + 18 + ], + "dashes": [ + " {\"type\": \"punctuation mark\", \"description\": \"two short lines used to separate words or phrases; could be used to indicate a range of numbers\", \"similar objects\": [\"commas\", \"semicolons\", \"colons\"]}", + 18 + ], + "cat food": [ + " {\"type\": \"pet food\", \"description\": \"dry or wet; could be in cans or bags; could be in different flavors\", \"similar objects\": [\"dog food\", \"bird food\", \"fish food\"]}", + 18 + ], + "rail fence": [ + " {\"type\": \"fence\", \"description\": \"horizontal rails connected by vertical posts; could be made of wood or metal\", \"similar objects\": [\"picket fence\", \"chain link fence\", \"split rail fence\"]}", + 18 + ], + "kitchen cupboards": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have drawers and shelves; could be used to store kitchen items\", \"similar objects\": [\"kitchen cabinets\", \"wardrobe\", \"dresser\"]}", + 18 + ], + "silver engine": [ + " {\"type\": \"machine\", \"description\": \"made of metal; could be used to power a vehicle; could have a combustion engine\", \"similar objects\": [\"diesel engine\", \"electric motor\", \"turbine\"]}", + 18 + ], + "sauces": [ + " {\"type\": \"condiment\", \"description\": \"liquid or semi-solid; could be used to enhance the flavor of food\", \"similar objects\": [\"dressing\", \"marinade\", \"salsa\"]}", + 18 + ], + "keyhole": [ + " {\"type\": \"lock tool\", \"description\": \"small hole; could be used to unlock a door\", \"similar objects\": [\"lock\", \"key\", \"padlock\"]}", + 18 + ], + "staff": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical; could be made of wood or metal; could have a hook at the end\", \"similar objects\": [\"pole\", \"rod\", \"spear\"]}", + 18 + ], + "silver bike": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could be made of silver metal\", \"similar objects\": [\"bicycle\", \"motorcycle\", \"scooter\"]}", + 18 + ], + "dvd players": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a display screen; could be connected to a TV\", \"similar objects\": [\"Blu-ray player\", \"game console\", \"stereo system\"]}", + 18 + ], + "picture window": [ + " {\"type\": \"window\", \"description\": \"large, rectangular; could be made of glass; could be used to view the outside\", \"similar objects\": [\"bay window\", \"casement window\", \"awning window\"]}", + 18 + ], + "records": [ + " {\"type\": \"media\", \"description\": \"round; could be made of vinyl; could be used to play music\", \"similar objects\": [\"CDs\", \"tapes\", \"DVDs\"]}", + 18 + ], + "baby goat": [ + " {\"type\": \"animal\", \"description\": \"small, white or brown fur; has horns; could be playful\", \"similar objects\": [\"lamb\", \"calf\", \"kid\"]}", + 18 + ], + "eye lashes": [ + " {\"type\": \"body part\", \"description\": \"long, thin, curved; could be black or brown; could be curled\", \"similar objects\": [\"eyebrows\", \"eyelids\", \"eyeliner\"]}", + 18 + ], + "cap man": [ + " {\"type\": \"toy\", \"description\": \"small figurine; has a hat; could be made of plastic\", \"similar objects\": [\"action figure\", \"doll\", \"stuffed animal\"]}", + 18 + ], + "brown pillow": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be made of fabric; could be filled with feathers or foam\", \"similar objects\": [\"cushion\", \"mattress\", \"blanket\"]}", + 18 + ], + "stone fireplace": [ + " {\"type\": \"structure\", \"description\": \"made of stones; could have a mantel; could have a hearth\", \"similar objects\": [\"brick fireplace\", \"wood fireplace\", \"outdoor fireplace\"]}", + 18 + ], + "truck wheel": [ + " {\"type\": \"vehicle part\", \"description\": \"round; has a hub; could be made of metal\", \"similar objects\": [\"car wheel\", \"motorcycle wheel\", \"bicycle wheel\"]}", + 18 + ], + "helmet rider": [ + " {\"type\": \"protective gear\", \"description\": \"hard, covers the head; could have a visor\", \"similar objects\": [\"safety glasses\", \"knee pads\", \"elbow pads\"]}", + 18 + ], + "santa": [ + " {\"type\": \"person\", \"description\": \"white beard; wears a red suit; carries a bag of gifts\", \"similar objects\": [\"elf\", \"reindeer\", \"snowman\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant", + 18 + ], + "luggage case": [ + " {\"type\": \"travel accessory\", \"description\": \"rectangular; could be made of hard plastic; could have wheels and a handle\", \"similar objects\": [\"suitcase\", \"backpack\", \"duffel bag\"]}", + 18 + ], + "horizontal": [ + " {\"type\": \"direction\", \"description\": \"parallel to the ground; opposite of vertical\", \"similar objects\": [\"level\", \"flat\", \"straight\"]}", + 18 + ], + "fluorescent light": [ + " {\"type\": \"lighting tool\", \"description\": \"long tube; emits bright white light; could be used in offices and schools\", \"similar objects\": [\"incandescent light\", \"LED light\", \"halogen light\"]}", + 18 + ], + "dell logo": [ + "\n{\"type\": \"logo\", \"description\": \"blue and white circle with a red letter 'D' inside\", \"similar objects\": [\"Apple logo\", \"Microsoft logo\", \"IBM logo\"]}", + 18 + ], + "blonde child": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could be wearing a dress\", \"similar objects\": [\"blonde adult\", \"brunette child\", \"redhead child\"]}", + 18 + ], + "food trucks": [ + " {\"type\": \"vehicle\", \"description\": \"large, mobile, could have a kitchen inside; could be used to sell food\", \"similar objects\": [\"trailer\", \"van\", \"truck\"]}", + 18 + ], + "necklace woman": [ + "\n{\"type\": \"accessory\", \"description\": \"could be made of metal, plastic, or beads; could have a pendant; could be worn around the neck\", \"similar objects\": [\"bracelet\", \"earrings\", \"ring\"]}", + 18 + ], + "stunt": [ + " {\"type\": \"action\", \"description\": \"a dangerous or daring feat; could be performed by a stuntman or stuntwoman\", \"similar objects\": [\"trick\", \"acrobatics\", \"gymnastics\"]}", + 18 + ], + "rain coat": [ + " {\"type\": \"clothing\", \"description\": \"waterproof; could be made of plastic or rubber; could be yellow or other colors\", \"similar objects\": [\"umbrella\", \"jacket\", \"hat\"]}", + 18 + ], + "lampost": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a lightbulb on top\", \"similar objects\": [\"streetlight\", \"lantern\", \"torch\"]}", + 18 + ], + "horseback": [ + " {\"type\": \"activity\", \"description\": \"riding a horse; could be used for leisure or competition\", \"similar objects\": [\"bicycle riding\", \"motorcycle riding\", \"skateboarding\"]}", + 18 + ], + "metal screws": [ + " {\"type\": \"hardware\", \"description\": \"small, cylindrical, metal; could have a head and a thread\", \"similar objects\": [\"nuts\", \"bolts\", \"washers\"]}", + 18 + ], + "leafy vegetables": [ + " {\"type\": \"vegetable\", \"description\": \"green; could be eaten raw or cooked; could be leafy or stem-like\", \"similar objects\": [\"spinach\", \"kale\", \"lettuce\"]}", + 18 + ], + "slits": [ + " {\"type\": \"cut\", \"description\": \"long, thin cuts; could be made on fabric or paper\", \"similar objects\": [\"tears\", \"holes\", \"gashes\"]}", + 18 + ], + "lit light": [ + " {\"type\": \"lighting tool\", \"description\": \"could be a lamp, a flashlight, a candle, or any other lighting tool that is currently lit\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 18 + ], + "knife utensil": [ + " {\"type\": \"kitchen tool\", \"description\": \"sharp blade; could have a handle; could be used for cutting\", \"similar objects\": [\"fork\", \"spoon\", \"spatula\"]}", + 18 + ], + "haze": [ + " {\"type\": \"atmospheric phenomenon\", \"description\": \"a form of air pollution; could be caused by smoke, dust, and other particles; could reduce visibility\", \"similar objects\": [\"smog\", \"fog\", \"dust storm\"]}", + 18 + ], + "wood boat": [ + " {\"type\": \"vessel\", \"description\": \"made of wood; could be used for fishing or sailing\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 18 + ], + "chair arm": [ + " {\"type\": \"furniture part\", \"description\": \"attached to the side of a chair; could be made of wood or metal; could have a cushion\", \"similar objects\": [\"sofa arm\", \"ottoman\", \"footstool\"]}", + 18 + ], + "orange pepper": [ + "\n{\"type\": \"vegetable\", \"description\": \"round; orange in color; could be sliced into pieces; could have green leaves\", \"similar objects\": [\"red pepper\", \"yellow pepper\", \"green pepper\"]}", + 18 + ], + "owner": [ + " {\"type\": \"person\", \"description\": \"person who has possession of something; could be a business owner, property owner, etc.\", \"similar objects\": [\"manager\", \"landlord\", \"proprietor\"]}", + 18 + ], + "brow": [ + " {\"type\": \"body part\", \"description\": \"the area above the eyes; could be arched or straight\", \"similar objects\": [\"eyebrow\", \"forehead\", \"eyelid\"]}", + 18 + ], + "bristle": [ + " {\"type\": \"material\", \"description\": \"stiff, thin, and pointed hairs; could be used for cleaning\", \"similar objects\": [\"bristles\", \"hairs\", \"fibers\"]}", + 18 + ], + "left window": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular; could be opened and closed; could be made of glass\", \"similar objects\": [\"door\", \"balcony\", \"skylight\"]}", + 18 + ], + "bullet train": [ + " {\"type\": \"transportation\", \"description\": \"long, fast, has multiple carriages; could be painted in white and blue\", \"similar objects\": [\"airplane\", \"ferry\", \"subway\"]}", + 18 + ], + "letter c": [ + " {\"type\": \"alphabet\", \"description\": \"third letter of the English alphabet; has a curved shape\", \"similar objects\": [\"letter a\", \"letter b\", \"letter d\"]}", + 18 + ], + "drain hole": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be made of metal; could be used to drain water\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 18 + ], + "upholstery": [ + " {\"type\": \"furnishing material\", \"description\": \"thick fabric used to cover furniture; could be made of cotton, wool, or synthetic fibers\", \"similar objects\": [\"cushion\", \"mattress\", \"rug\"]}", + 18 + ], + "measuring cup": [ + " {\"type\": \"measuring tool\", \"description\": \"has a handle; could be made of plastic or metal; has markings for measuring\", \"similar objects\": [\"measuring spoon\", \"scale\", \"thermometer\"]}", + 18 + ], + "studs": [ + " {\"type\": \"jewelry\", \"description\": \"small, round, metal pieces; could be used to decorate clothing\", \"similar objects\": [\"earrings\", \"rings\", \"bracelets\"]}", + 18 + ], + "brown tracks": [ + " {\"type\": \"footprints\", \"description\": \"could be from animals or humans; could be from different sizes; could be from different shapes; could be from different materials\", \"similar objects\": [\"footprints\", \"tracks\", \"trails\"]}", + 18 + ], + "metal cover": [ + " {\"type\": \"protective tool\", \"description\": \"made of metal; could be used to cover something\", \"similar objects\": [\"shield\", \"helmet\", \"armor\"]}", + 18 + ], + "guard rails": [ + " {\"type\": \"safety tool\", \"description\": \"metal bars; could be installed on the side of the road; could be painted in white and yellow\", \"similar objects\": [\"traffic cones\", \"barriers\", \"signs\"]}", + 18 + ], + "dairy cow": [ + " {\"type\": \"animal\", \"description\": \"large, brown, has horns; produces milk\", \"similar objects\": [\"goat\", \"sheep\", \"buffalo\"]}", + 18 + ], + "clock frame": [ + " {\"type\": \"decoration\", \"description\": \"round; could be made of metal or wood; could have a glass cover\", \"similar objects\": [\"picture frame\", \"mirror frame\", \"photo frame\"]}", + 18 + ], + "brown chair": [ + " {\"type\": \"furniture\", \"description\": \"brown; could have armrests; could have a cushion\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 18 + ], + "wood crate": [ + " {\"type\": \"container\", \"description\": \"rectangular; made of wood; could have a lid\", \"similar objects\": [\"box\", \"basket\", \"barrel\"]}", + 18 + ], + "grey surface": [ + " {\"type\": \"material\", \"description\": \"light to dark grey; could be smooth or rough; could be made of metal, stone, or wood\", \"similar objects\": [\"concrete\", \"asphalt\", \"tile\"]}", + 18 + ], + "bathroom walls": [ + " {\"type\": \"building material\", \"description\": \"smooth; could be painted; could be tiled\", \"similar objects\": [\"floor\", \"ceiling\", \"door\"]}", + 18 + ], + "bird feathers": [ + " {\"type\": \"animal part\", \"description\": \"lightweight; could be colorful; could be used for flying\", \"similar objects\": [\"insect wings\", \"mammal fur\", \"reptile scales\"]}", + 18 + ], + "sink counter": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could have a basin; could be made of marble or granite\", \"similar objects\": [\"kitchen counter\", \"bathroom counter\", \"vanity\"]}", + 18 + ], + "bumper sticker": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; could be printed with words or images; could be stuck on a car\", \"similar objects\": [\"window decal\", \"wall sticker\", \"magnet\"]}", + 18 + ], + "helmet skier": [ + "\n{\"type\": \"protective gear\", \"description\": \"hard, round; could be made of plastic or metal; has a chin strap\", \"similar objects\": [\"bicycle helmet\", \"motorcycle helmet\", \"hockey helmet\"]}", + 18 + ], + "record player": [ + " {\"type\": \"audio device\", \"description\": \"has a turntable; could have a needle; could have a speaker\", \"similar objects\": [\"stereo system\", \"boombox\", \"CD player\"]}", + 18 + ], + "stud": [ + " {\"type\": \"fastener\", \"description\": \"cylindrical; could be made of metal; could have a head\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 18 + ], + "official": [ + " {\"type\": \"person\", \"description\": \"someone with a high rank or position; could be wearing a uniform\", \"similar objects\": [\"manager\", \"leader\", \"supervisor\"]}", + 18 + ], + "bare branch": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, no leaves; could be curved or straight\", \"similar objects\": [\"twig\", \"stem\", \"vine\"]}", + 18 + ], + "mountain bike": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a sturdy frame; could have a suspension system; could have knobby tires\", \"similar objects\": [\"road bike\", \"hybrid bike\", \"cruiser bike\"]}", + 18 + ], + "tennis racket handle": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin handle; could be made of wood or metal; could have strings attached\", \"similar objects\": [\"golf club\", \"baseball bat\", \"hockey stick\"]}", + 18 + ], + "cheeses": [ + " {\"type\": \"food\", \"description\": \"dairy product; could be soft, hard, or semi-soft; could be yellow or white; could be sliced or grated\", \"similar objects\": [\"yogurt\", \"milk\", \"butter\"]}", + 18 + ], + "metal handles": [ + " {\"type\": \"hardware\", \"description\": \"made of metal; could be used to open and close doors; could be attached to furniture\", \"similar objects\": [\"knobs\", \"hinges\", \"latches\"]}", + 18 + ], + "wheelbarrow": [ + " {\"type\": \"transportation tool\", \"description\": \"has two handles and one wheel; could be used to carry heavy objects\", \"similar objects\": [\"hand truck\", \"cart\", \"wagon\"]}", + 18 + ], + "paths": [ + " {\"type\": \"landscape feature\", \"description\": \"a way or track for people or vehicles to follow; could be made of stones, gravel, or asphalt\", \"similar objects\": [\"roads\", \"trails\", \"bridges\"]}", + 18 + ], + "sleeve jacket": [ + " {\"type\": \"clothing item\", \"description\": \"long, covers arms; could be made of wool or cotton\", \"similar objects\": [\"cardigan\", \"blazer\", \"hoodie\"]}", + 18 + ], + "brick pillar": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of concrete or stone; could be used to support a structure\", \"similar objects\": [\"wood beam\", \"steel beam\", \"concrete block\"]}", + 18 + ], + "appetizers": [ + " {\"type\": \"food\", \"description\": \"small, savory dishes served before a meal\", \"similar objects\": [\"hors d'oeuvres\", \"snacks\", \"finger foods\"]}", + 18 + ], + "bottom edge": [ + " {\"type\": \"geometric shape\", \"description\": \"straight line; could be the lowest part of a shape\", \"similar objects\": [\"top edge\", \"left edge\", \"right edge\"]}", + 18 + ], + "toilet bowl cleaner": [ + " {\"type\": \"cleaning product\", \"description\": \"liquid; could be blue; could have a strong smell\", \"similar objects\": [\"dish soap\", \"floor cleaner\", \"all-purpose cleaner\"]}", + 18 + ], + "cowboy boot": [ + " {\"type\": \"footwear\", \"description\": \"high-heeled; could have pointed toes; could have a decorative pattern\", \"similar objects\": [\"hiking boot\", \"sneaker\", \"sandal\"]}", + 18 + ], + "tons": [ + " {\"type\": \"measurement unit\", \"description\": \"unit of weight; equal to 2,000 pounds\", \"similar objects\": [\"kilograms\", \"pounds\", \"ounces\"]}", + 18 + ], + "cat fur": [ + " {\"type\": \"animal fur\", \"description\": \"soft; could be short or long; could be grey, black, white, or other colors\", \"similar objects\": [\"dog fur\", \"rabbit fur\", \"fox fur\"]}", + 18 + ], + "plastic glove": [ + " {\"type\": \"protective tool\", \"description\": \"transparent; could be used for medical or cleaning purposes\", \"similar objects\": [\"latex glove\", \"rubber glove\", \"face mask\"]}", + 18 + ], + "glass ball": [ + " {\"type\": \"decorative item\", \"description\": \"transparent; could be made of glass or crystal; could be used as a paperweight\", \"similar objects\": [\"marble\", \"crystal ball\", \"snow globe\"]}", + 18 + ], + "banana stem": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, green; could be cut into pieces; could be used for cooking\", \"similar objects\": [\"celery stem\", \"cucumber stem\", \"zucchini stem\"]}", + 18 + ], + "rust spot": [ + " {\"type\": \"stain\", \"description\": \"brown or orange; could be found on metal surfaces; could be caused by oxidation\", \"similar objects\": [\"mold\", \"mildew\", \"dirt\"]}", + 18 + ], + "wood beams": [ + " {\"type\": \"building material\", \"description\": \"long, rectangular; could be used for construction\", \"similar objects\": [\"bricks\", \"concrete\", \"steel beams\"]}", + 18 + ], + "briefcases": [ + " {\"type\": \"accessory\", \"description\": \"rectangular; could be made of leather; could have a handle\", \"similar objects\": [\"backpack\", \"suitcase\", \"handbag\"]}", + 18 + ], + "tan curtains": [ + " {\"type\": \"window covering\", \"description\": \"light brown; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 18 + ], + "car lights": [ + " {\"type\": \"vehicle accessory\", \"description\": \"headlights, taillights, fog lights; could be LED or halogen\", \"similar objects\": [\"mirrors\", \"wheels\", \"bumpers\"]}", + 18 + ], + "circular": [ + "\n{\"type\": \"shape\", \"description\": \"round; has no angles; has a circumference\", \"similar objects\": [\"spherical\", \"elliptical\", \"oval\"]}", + 18 + ], + "photo tag": [ + " {\"type\": \"image annotation tool\", \"description\": \"used to identify objects in an image; could be used to add labels, descriptions, and other information to an image\", \"similar objects\": [\"image recognition\", \"image classification\", \"image segmentation\"]}", + 18 + ], + "tissue roll": [ + " {\"type\": \"household item\", \"description\": \"cylindrical; made of paper; could be used to wipe nose\", \"similar objects\": [\"toilet paper\", \"paper towel\", \"napkin\"]}", + 18 + ], + "grey suv": [ + "\n{\"type\": \"vehicle\", \"description\": \"four-wheeled; could be large; could have tinted windows; could have a roof rack\", \"similar objects\": [\"sedan\", \"minivan\", \"pickup truck\"]}", + 18 + ], + "globe light": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could be hung from the ceiling\", \"similar objects\": [\"chandelier\", \"pendant light\", \"ceiling light\"]}", + 18 + ], + "lei": [ + " {\"type\": \"accessory\", \"description\": \"made of flowers or shells; could be worn around the neck\", \"similar objects\": [\"necklace\", \"bracelet\", \"tiara\"]}", + 18 + ], + "blinder": [ + " {\"type\": \"accessory\", \"description\": \"worn on the head; used to cover the eyes; could be made of leather\", \"similar objects\": [\"mask\", \"hat\", \"sunglasses\"]}", + 18 + ], + "lit candles": [ + "\n{\"type\": \"lighting tool\", \"description\": \"small, cylindrical; could be made of wax; could be lit with a match or lighter; could be used for decoration or illumination\", \"similar objects\": [\"lamp\", \"lantern\", \"flashlight\"]}", + 18 + ], + "blurry trees": [ + "\n{\"type\": \"landscape\", \"description\": \"trees with blurred edges; could be in a forest; could be in a park; could be in a garden\", \"similar objects\": [\"mountains\", \"rivers\", \"lakes\"]}", + 18 + ], + "flower print": [ + " {\"type\": \"pattern\", \"description\": \"vibrant colors; could be printed on fabric; could be abstract or realistic\", \"similar objects\": [\"geometric print\", \"animal print\", \"stripes\"]}", + 18 + ], + "handle bike": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has handlebars; could have a basket\", \"similar objects\": [\"scooter\", \"motorcycle\", \"tricycle\"]}", + 18 + ], + "ads": [ + " {\"type\": \"advertisement\", \"description\": \"visual or audio messages used to promote products or services\", \"similar objects\": [\"commercials\", \"promotions\", \"banners\"]}", + 18 + ], + "cement barrier": [ + " {\"type\": \"construction tool\", \"description\": \"rectangular; made of cement; used to block roads\", \"similar objects\": [\"concrete block\", \"barricade\", \"guardrail\"]}", + 18 + ], + "stairwell": [ + " {\"type\": \"structure\", \"description\": \"a set of stairs connecting two or more floors; could have a railing\", \"similar objects\": [\"elevator\", \"escalator\", \"ladder\"]}", + 18 + ], + "scuff marks": [ + " {\"type\": \"markings\", \"description\": \"light or dark marks on the surface; could be caused by friction\", \"similar objects\": [\"scratches\", \"dents\", \"stains\"]}", + 18 + ], + "horse legs": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, slender; could have hooves; could have white socks\", \"similar objects\": [\"elephant legs\", \"giraffe legs\", \"dog legs\"]}", + 18 + ], + "shadow girl": [ + " {\"type\": \"character\", \"description\": \"girl with a shadow-like form; could be seen in the dark\", \"similar objects\": [\"shadow boy\", \"shadow creature\", \"shadow monster\"]}", + 18 + ], + "mitts": [ + " {\"type\": \"clothing accessory\", \"description\": \"worn on hands; could be made of wool; could be fingerless\", \"similar objects\": [\"gloves\", \"scarf\", \"hat\"]}", + 18 + ], + "tag cow": [ + "\n{\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a long tail; could have horns; could be white, black, brown, or spotted\", \"similar objects\": [\"goat\", \"sheep\", \"buffalo\"]}", + 18 + ], + "diamond design": [ + " {\"type\": \"pattern\", \"description\": \"geometric shape; could be made of lines; could be symmetrical\", \"similar objects\": [\"hexagon\", \"triangle\", \"circle\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\",", + 18 + ], + "region": [ + " {\"type\": \"geographical area\", \"description\": \"a part of a country or the world; could be divided into smaller areas\", \"similar objects\": [\"state\", \"province\", \"country\"]}", + 18 + ], + "water skier": [ + " {\"type\": \"sportsperson\", \"description\": \"wearing a life jacket; holding a rope attached to a boat; skiing on the water surface\", \"similar objects\": [\"surfer\", \"sailor\", \"windsurfer\"]}", + 18 + ], + "snowy ski": [ + " {\"type\": \"winter sport equipment\", \"description\": \"long, thin, curved; could have metal edges; could have straps\", \"similar objects\": [\"snowboard\", \"skates\", \"sled\"]}", + 18 + ], + "camera bag": [ + " {\"type\": \"accessory\", \"description\": \"rectangular; could be made of leather; could have multiple compartments; could have a shoulder strap\", \"similar objects\": [\"backpack\", \"laptop bag\", \"briefcase\"]}", + 18 + ], + "groceries": [ + "\n{\"type\": \"items\", \"description\": \"various items that are used for daily needs; could include food, toiletries, and other household items\", \"similar objects\": [\"food\", \"household items\", \"personal items\"]}", + 18 + ], + "plant leaf": [ + " {\"type\": \"plant part\", \"description\": \"green; could be oval or round; could have veins\", \"similar objects\": [\"flower\", \"stem\", \"root\"]}", + 18 + ], + "way traffic sign": [ + " {\"type\": \"road sign\", \"description\": \"triangular; has a white background; could have a red border; could have a black symbol\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 18 + ], + "flash drive": [ + " {\"type\": \"storage device\", \"description\": \"small, rectangular; could be plugged into a computer\", \"similar objects\": [\"hard drive\", \"memory card\", \"external drive\"]}", + 18 + ], + "skateboard mid air": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could be used for tricks; could be used for transportation\", \"similar objects\": [\"scooter\", \"rollerblades\", \"longboard\"]}", + 18 + ], + "water way": [ + " {\"type\": \"natural feature\", \"description\": \"a body of water; could be a river, lake, or ocean\", \"similar objects\": [\"stream\", \"canal\", \"waterfall\"]}", + 18 + ], + "plush toy": [ + " {\"type\": \"toy\", \"description\": \"soft and cuddly; could be shaped like an animal\", \"similar objects\": [\"stuffed animal\", \"doll\", \"action figure\"]}", + 18 + ], + "stone house": [ + " {\"type\": \"building\", \"description\": \"made of stones; could have a chimney; could have a garden\", \"similar objects\": [\"castle\", \"cottage\", \"bungalow\"]}", + 18 + ], + "water bowl": [ + " {\"type\": \"pet accessory\", \"description\": \"round; could be made of plastic or ceramic; could have a stand\", \"similar objects\": [\"food bowl\", \"water bottle\", \"litter box\"]}", + 18 + ], + "cartoon character": [ + " {\"type\": \"illustration\", \"description\": \"two-dimensional; could be hand-drawn or computer-generated; could be funny or serious\", \"similar objects\": [\"comic book character\", \"mascot\", \"logo\"]}", + 18 + ], + "spinach leaves": [ + " {\"type\": \"vegetable\", \"description\": \"dark green, oval-shaped; could be cooked or eaten raw; could be used in salads\", \"similar objects\": [\"kale\", \"lettuce\", \"arugula\"]}", + 18 + ], + "brocoli": [ + " {\"type\": \"vegetable\", \"description\": \"green, small florets; could have a stem; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 18 + ], + "target": [ + " {\"type\": \"object\", \"description\": \"round; has a bullseye in the center; could be used for shooting practice\", \"similar objects\": [\"dartboard\", \"archery target\", \"punching bag\"]}", + 18 + ], + "mirror reflection": [ + " {\"type\": \"optical phenomenon\", \"description\": \"reflection of light off a surface; could be distorted; could be reversed\", \"similar objects\": [\"refraction\", \"diffraction\", \"polarization\"]}", + 18 + ], + "christmas decoration": [ + "\n{\"type\": \"decoration\", \"description\": \"could be made of paper, plastic, or metal; could be in the shape of a star, tree, or snowman; could be lit up with lights\", \"similar objects\": [\"ornaments\", \"garland\", \"tinsel\"]}", + 18 + ], + "newspaper dispenser": [ + " {\"type\": \"container\", \"description\": \"box-shaped; has a slot for coins; could be made of metal or plastic\", \"similar objects\": [\"vending machine\", \"mailbox\", \"kiosk\"]}", + 18 + ], + "airplane hangar": [ + " {\"type\": \"building\", \"description\": \"large, open space; could have a control tower; could have multiple airplane bays\", \"similar objects\": [\"warehouse\", \"garage\", \"factory\"]}", + 18 + ], + "peninsula": [ + " {\"type\": \"geographical feature\", \"description\": \"land surrounded by water on three sides\", \"similar objects\": [\"isthmus\", \"cape\", \"bay\"]}", + 18 + ], + "shadow horse": [ + " {\"type\": \"illusion\", \"description\": \"an image of a horse created by light and shadow\", \"similar objects\": [\"shadow cat\", \"shadow dog\", \"shadow rabbit\"]}", + 18 + ], + "ski sticks": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, curved; used for skiing\", \"similar objects\": [\"hockey sticks\", \"golf clubs\", \"tennis rackets\"]}", + 18 + ], + "tanktop": [ + " {\"type\": \"clothing\", \"description\": \"sleeveless; could be made of cotton; could have a round neckline\", \"similar objects\": [\"t-shirt\", \"vest\", \"camisole\"]}", + 18 + ], + "bull dog": [ + " {\"type\": \"animal\", \"description\": \"short, stocky, muscular; has a short muzzle; has a wrinkled face; has a short tail\", \"similar objects\": [\"pug\", \"boxer\", \"French bulldog\"]}", + 18 + ], + "layer cake": [ + " {\"type\": \"dessert\", \"description\": \"multi-layered cake; could be frosted; could be filled with cream or jam\", \"similar objects\": [\"cupcake\", \"cheesecake\", \"tiramisu\"]}", + 18 + ], + "roast": [ + " {\"type\": \"food\", \"description\": \"cooked meat; could be served with vegetables; could be served with gravy\", \"similar objects\": [\"stew\", \"casserole\", \"soup\"]}", + 18 + ], + "square bowl": [ + " {\"type\": \"dishware\", \"description\": \"has four sides; could be made of ceramic; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 18 + ], + "yellow flower": [ + "\n{\"type\": \"plant\", \"description\": \"bright yellow petals; could have a stem; could have green leaves\", \"similar objects\": [\"daisy\", \"sunflower\", \"daffodil\"]}", + 18 + ], + "airport tarmac": [ + " {\"type\": \"landing area\", \"description\": \"flat, paved surface; used for aircraft to take off and land\", \"similar objects\": [\"runway\", \"taxiway\", \"apron\"]}", + 18 + ], + "brown animal": [ + "\n{\"type\": \"animal\", \"description\": \"could have fur, feathers, or scales; could have a variety of colors including brown; could have a variety of shapes and sizes\", \"similar objects\": [\"dog\", \"cat\", \"rabbit\", \"deer\", \"bear\", \"bird\", \"fish\", \"reptile\"]}", + 18 + ], + "window wiper": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has a rubber blade; used to clean windows\", \"similar objects\": [\"broom\", \"mop\", \"vacuum cleaner\"]}", + 18 + ], + "monkeys": [ + " {\"type\": \"animal\", \"description\": \"long tail; could be brown, black, or white; could be found in groups\", \"similar objects\": [\"apes\", \"gorillas\", \"baboons\"]}", + 18 + ], + "tie knot": [ + " {\"type\": \"clothing accessory\", \"description\": \"a knot used to secure a necktie around the neck\", \"similar objects\": [\"bow tie\", \"scarf knot\", \"belt knot\"]}", + 18 + ], + "orange flags": [ + " {\"type\": \"warning tool\", \"description\": \"orange; could be used to mark a dangerous area; could be used to indicate a construction site\", \"similar objects\": [\"barricades\", \"cones\", \"signs\"]}", + 18 + ], + "turn signal": [ + " {\"type\": \"automotive tool\", \"description\": \"flashing light; could be yellow or orange; could be found on the side of a car\", \"similar objects\": [\"headlight\", \"brake light\", \"fog light\"]}", + 18 + ], + "puppies": [ + " {\"type\": \"animal\", \"description\": \"small, furry, playful; could have different colors; could have short tails\", \"similar objects\": [\"kittens\", \"bunnies\", \"ducks\"]}", + 18 + ], + "paintbrush": [ + " {\"type\": \"painting tool\", \"description\": \"long handle; could have bristles of different sizes; could be used to paint on canvas\", \"similar objects\": [\"paint roller\", \"paint scraper\", \"paint sponge\"]}", + 18 + ], + "walnuts": [ + " {\"type\": \"nut\", \"description\": \"oval-shaped; has a hard shell; could be shelled or unshelled\", \"similar objects\": [\"almonds\", \"cashews\", \"pecans\"]}", + 18 + ], + "cement pavement": [ + " {\"type\": \"building material\", \"description\": \"hard, grey, flat surface; could be used for roads and sidewalks\", \"similar objects\": [\"asphalt\", \"concrete\", \"gravel\"]}", + 18 + ], + "stone bricks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of stones; could be used for walls\", \"similar objects\": [\"concrete blocks\", \"wooden planks\", \"cement blocks\"]}", + 18 + ], + "metal fence pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used to build a fence\", \"similar objects\": [\"wooden fence pole\", \"metal fence panel\", \"metal fence post\"]}", + 18 + ], + "jockeys": [ + " {\"type\": \"sportsperson\", \"description\": \"wears a colorful uniform; rides a horse in a race\", \"similar objects\": [\"cyclists\", \"swimmers\", \"runners\"]}", + 18 + ], + "cottage": [ + " {\"type\": \"building\", \"description\": \"small, usually made of wood; could have a chimney; could have a garden\", \"similar objects\": [\"house\", \"bungalow\", \"cabin\"]}", + 18 + ], + "martini glass": [ + " {\"type\": \"drinking glass\", \"description\": \"cone-shaped; has a stem; could have an olive\", \"similar objects\": [\"wine glass\", \"highball glass\", \"shot glass\"]}", + 18 + ], + "airfield": [ + " {\"type\": \"location\", \"description\": \"large open area; could have runways and hangars; could have control towers\", \"similar objects\": [\"airport\", \"heliport\", \"spaceport\"]}", + 18 + ], + "glass ashtray": [ + " {\"type\": \"ashtray\", \"description\": \"transparent; could be made of glass; could have a lid\", \"similar objects\": [\"ceramic ashtray\", \"metal ashtray\", \"wooden ashtray\"]}", + 18 + ], + "flashlight": [ + " {\"type\": \"lighting tool\", \"description\": \"long; could be made of metal; has a switch\", \"similar objects\": [\"lantern\", \"lamp\", \"candle\"]}", + 18 + ], + "winter scene": [ + " {\"type\": \"landscape\", \"description\": \"snowy; trees covered with snow; could have a frozen lake; could have a snowman\", \"similar objects\": [\"mountain view\", \"forest\", \"desert\"]}", + 18 + ], + "loops": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, round, metal; could be used to fasten clothing\", \"similar objects\": [\"buttons\", \"zippers\", \"snaps\"]}", + 18 + ], + "pizza toppings": [ + " {\"type\": \"food topping\", \"description\": \"various ingredients such as cheese, vegetables, meats, and sauces\", \"similar objects\": [\"burger toppings\", \"salad toppings\", \"taco toppings\"]}", + 18 + ], + "wheel chair": [ + " {\"type\": \"medical device\", \"description\": \"has four wheels; could be motorized; could be foldable; could have armrests\", \"similar objects\": [\"walker\", \"crutches\", \"stretcher\"]}", + 18 + ], + "clock numbers": [ + " {\"type\": \"time indicator\", \"description\": \"numbers from 1 to 12; could be in a circle shape\", \"similar objects\": [\"watch\", \"hourglass\", \"sundial\"]}", + 18 + ], + "hairy leg": [ + "\n{\"type\": \"body part\", \"description\": \"covered with hair; could be human or animal\", \"similar objects\": [\"arm\", \"foot\", \"torso\"]}", + 18 + ], + "orange safety vest": [ + "\n{\"type\": \"clothing\", \"description\": \"bright orange; has reflective stripes; could be worn over other clothing\", \"similar objects\": [\"hard hat\", \"helmet\", \"raincoat\"]}", + 18 + ], + "foot shoe": [ + " {\"type\": \"footwear\", \"description\": \"made of leather or fabric; could have laces; could have a sole\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 18 + ], + "bank sign": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be made of metal; could have a logo of a bank\", \"similar objects\": [\"street sign\", \"store sign\", \"traffic sign\"]}", + 18 + ], + "dragon kite": [ + " {\"type\": \"toy\", \"description\": \"long, colorful, could have wings and tail; could be flown in the sky\", \"similar objects\": [\"airplane kite\", \"delta kite\", \"box kite\"]}", + 18 + ], + "swim shorts": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could be made of nylon or polyester; could have pockets; could have drawstrings\", \"similar objects\": [\"swim trunks\", \"board shorts\", \"bikini\"]}", + 18 + ], + "boat motor": [ + " {\"type\": \"motorized vehicle\", \"description\": \"engine used to propel a boat; could be gasoline or electric powered\", \"similar objects\": [\"outboard motor\", \"inboard motor\", \"jet ski motor\"]}", + 18 + ], + "dark liquid": [ + " {\"type\": \"beverage\", \"description\": \"could be coffee, tea, or cola; could be hot or cold; could be served in a cup or a glass\", \"similar objects\": [\"juice\", \"milk\", \"water\"]}", + 18 + ], + "top building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have a roof\", \"similar objects\": [\"skyscraper\", \"tower\", \"high-rise\"]}", + 18 + ], + "frisby": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"ball\", \"kite\", \"yo-yo\"]}", + 18 + ], + "train headlight": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of metal; could be attached to the front of a train\", \"similar objects\": [\"lantern\", \"flashlight\", \"headlamp\"]}", + 18 + ], + "knee cap": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round; could be made of plastic or metal; could be strapped to the knee\", \"similar objects\": [\"elbow pad\", \"shin guard\", \"helmet\"]}", + 18 + ], + "stucco wall": [ + " {\"type\": \"building material\", \"description\": \"textured, plaster-like wall; could be painted; could be used for interior and exterior walls\", \"similar objects\": [\"brick wall\", \"concrete wall\", \"wood paneling\"]}", + 18 + ], + "multi-story building": [ + "\n{\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have multiple windows; could have multiple entrances\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"office building\"]}", + 18 + ], + "elephants tusk": [ + " {\"type\": \"body part\", \"description\": \"long, curved, ivory-colored; found on the sides of the elephant's mouth\", \"similar objects\": [\"hippopotamus tusk\", \"walrus tusk\", \"narwhal tusk\"]}", + 18 + ], + "stake": [ + " {\"type\": \"tool\", \"description\": \"long, pointed, could be made of metal or wood\", \"similar objects\": [\"hammer\", \"axe\", \"saw\"]}", + 18 + ], + "marque": [ + " {\"type\": \"brand\", \"description\": \"a recognizable symbol or logo associated with a company or product\", \"similar objects\": [\"logo\", \"trademark\", \"brand name\"]}", + 18 + ], + "bird poop": [ + " {\"type\": \"waste\", \"description\": \"white or brown; could be found on the ground or on the surface of objects\", \"similar objects\": [\"insect droppings\", \"bat guano\", \"rodent droppings\"]}", + 18 + ], + "river boat": [ + " {\"type\": \"watercraft\", \"description\": \"long and narrow; could have a motor; could be used for transportation or recreation\", \"similar objects\": [\"canoe\", \"kayak\", \"yacht\"]}", + 18 + ], + "calm water": [ + " {\"type\": \"natural phenomenon\", \"description\": \"smooth surface; no waves; could be blue or green\", \"similar objects\": [\"clear sky\", \"mountain\", \"forest\"]}", + 18 + ], + "fighter jets": [ + " {\"type\": \"aircraft\", \"description\": \"fast; has wings; could have missiles\", \"similar objects\": [\"helicopter\", \"airplane\", \"drone\"]}", + 18 + ], + "silver exhaust pipe": [ + "\n{\"type\": \"automotive part\", \"description\": \"cylindrical; made of metal; used to release exhaust fumes\", \"similar objects\": [\"muffler\", \"catalytic converter\", \"air filter\"]}", + 18 + ], + "rifle": [ + " {\"type\": \"weapon\", \"description\": \"long; has a trigger; could be used for hunting\", \"similar objects\": [\"shotgun\", \"pistol\", \"machine gun\"]}", + 18 + ], + "freight car": [ + " {\"type\": \"vehicle\", \"description\": \"long; could be used to transport goods; could be attached to a locomotive\", \"similar objects\": [\"tanker car\", \"boxcar\", \"flatcar\"]}", + 18 + ], + "oil stains": [ + " {\"type\": \"stain\", \"description\": \"dark, greasy, could be found on clothes or surfaces\", \"similar objects\": [\"grease stains\", \"ink stains\", \"blood stains\"]}", + 18 + ], + "jumbo jet": [ + " {\"type\": \"aircraft\", \"description\": \"large; has two wings; could have multiple engines; could have a tail fin\", \"similar objects\": [\"airplane\", \"helicopter\", \"glider\"]}", + 18 + ], + "mother sheep": [ + " {\"type\": \"animal\", \"description\": \"has a thick wool coat; could have horns; could have a lamb\", \"similar objects\": [\"goat\", \"cow\", \"horse\"]}", + 18 + ], + "ferry boat": [ + " {\"type\": \"transportation vehicle\", \"description\": \"large boat; could have multiple decks; could have a ramp for loading and unloading\", \"similar objects\": [\"cruise ship\", \"yacht\", \"barge\"]}", + 18 + ], + "pamphlets": [ + " {\"type\": \"printed material\", \"description\": \"small, thin, printed paper; could be used for advertisement\", \"similar objects\": [\"flyers\", \"brochures\", \"posters\"]}", + 18 + ], + "hem": [ + " {\"type\": \"sewing tool\", \"description\": \"used to fold fabric edges; could be done by hand or machine\", \"similar objects\": [\"seam\", \"stitch\", \"baste\"]}", + 18 + ], + "competitor": [ + " {\"type\": \"person\", \"description\": \"someone who competes in a race, game, or contest\", \"similar objects\": [\"opponent\", \"rival\", \"adversary\"]}", + 18 + ], + "orange train car": [ + "\n{\"type\": \"vehicle\", \"description\": \"orange; could be a part of a train; could have windows and doors\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 18 + ], + "riddles": [ + " {\"type\": \"puzzle\", \"description\": \"questions with answers that are difficult to guess; could be used for entertainment\", \"similar objects\": [\"brain teasers\", \"crosswords\", \"word searches\"]}", + 18 + ], + "round top": [ + " {\"type\": \"clothing item\", \"description\": \"a type of hat; could be made of straw; has a wide brim\", \"similar objects\": [\"fedora\", \"panama hat\", \"boater hat\"]}", + 18 + ], + "banister": [ + " {\"type\": \"furniture\", \"description\": \"long, vertical, could be made of wood or metal; could have a railing\", \"similar objects\": [\"staircase\", \"balustrade\", \"handrail\"]}", + 18 + ], + "para sail": [ + " {\"type\": \"recreational activity\", \"description\": \"large, colorful parachute; attached to a boat or vehicle; used for gliding in the air\", \"similar objects\": [\"hang gliding\", \"skydiving\", \"bungee jumping\"]}", + 18 + ], + "seatbelt": [ + " {\"type\": \"safety device\", \"description\": \"long strap; could be fastened around the waist; could be used in cars\", \"similar objects\": [\"helmet\", \"airbag\", \"child safety seat\"]}", + 18 + ], + "parked car": [ + " {\"type\": \"vehicle\", \"description\": \"stationary; could be any color; could have four wheels; could have a license plate\", \"similar objects\": [\"truck\", \"motorcycle\", \"bicycle\"]}", + 18 + ], + "land mass": [ + " {\"type\": \"geographical feature\", \"description\": \"large area of land; could be a continent, island, or peninsula\", \"similar objects\": [\"mountain\", \"river\", \"lake\"]}", + 18 + ], + "train engines": [ + " {\"type\": \"transportation vehicle\", \"description\": \"large, long, has multiple compartments; could have a locomotive\", \"similar objects\": [\"tram\", \"subway\", \"bus\"]}", + 18 + ], + "bear snout": [ + " {\"type\": \"animal body part\", \"description\": \"long, pointed, black; could be wet and slimy\", \"similar objects\": [\"dog snout\", \"cat snout\", \"pig snout\"]}", + 18 + ], + "coal car": [ + " {\"type\": \"vehicle\", \"description\": \"long; could be open or closed; could be used to transport coal\", \"similar objects\": [\"freight car\", \"tanker car\", \"hopper car\"]}", + 18 + ], + "shirt cuff": [ + " {\"type\": \"clothing accessory\", \"description\": \"attached to the end of a sleeve; could be folded up or down; could be made of different materials\", \"similar objects\": [\"collar\", \"pocket\", \"button\"]}", + 18 + ], + "wave breaking": [ + " {\"type\": \"natural phenomenon\", \"description\": \"the process of a wave crashing onto the shoreline; could create foam and bubbles\", \"similar objects\": [\"tide\", \"tsunami\", \"surf\"]}", + 18 + ], + "rhinoceros": [ + " {\"type\": \"animal\", \"description\": \"large, gray; has a horn on its nose; has a thick skin\", \"similar objects\": [\"hippopotamus\", \"elephant\", \"giraffe\"]}", + 18 + ], + "motorcycle riders": [ + " {\"type\": \"people\", \"description\": \"people wearing helmets and protective gear; riding motorcycles\", \"similar objects\": [\"bicyclists\", \"skateboarders\", \"scooter riders\"]}", + 18 + ], + "lockers": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal; could have a lock\", \"similar objects\": [\"cabinet\", \"drawer\", \"wardrobe\"]}", + 18 + ], + "metal wires": [ + " {\"type\": \"material\", \"description\": \"thin, flexible, and strong; could be used for electrical wiring\", \"similar objects\": [\"cables\", \"pipes\", \"tubes\"]}", + 18 + ], + "snowy slope": [ + " {\"type\": \"landscape\", \"description\": \"white; could be steep; could have trees and rocks\", \"similar objects\": [\"mountain\", \"valley\", \"glacier\"]}", + 18 + ], + "tides": [ + " {\"type\": \"natural phenomenon\", \"description\": \"the rise and fall of sea levels; caused by the gravitational pull of the moon and sun\", \"similar objects\": [\"tsunami\", \"earthquake\", \"hurricane\"]}", + 18 + ], + "basketball player": [ + "\n{\"type\": \"athlete\", \"description\": \"wears a jersey; has a basketball; could be dribbling or shooting\", \"similar objects\": [\"soccer player\", \"baseball player\", \"tennis player\"]}", + 18 + ], + "beach ball": [ + " {\"type\": \"toy\", \"description\": \"round; usually brightly colored; could be inflated\", \"similar objects\": [\"soccer ball\", \"basketball\", \"football\"]}", + 17 + ], + "basketball net": [ + " {\"type\": \"sports equipment\", \"description\": \"made of metal; has a hoop; could be attached to a pole\", \"similar objects\": [\"soccer goal\", \"volleyball net\", \"tennis net\"]}", + 17 + ], + "pool water": [ + " {\"type\": \"liquid\", \"description\": \"clear; could be chlorinated; could be heated\", \"similar objects\": [\"lake water\", \"ocean water\", \"river water\"]}", + 17 + ], + "plastic table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of plastic; could be foldable\", \"similar objects\": [\"wooden table\", \"plastic chair\", \"metal table\"]}", + 17 + ], + "apple mouse": [ + "\n{\"type\": \"computer accessory\", \"description\": \"shaped like an apple; has two buttons and a scroll wheel; connects to a computer\", \"similar objects\": [\"keyboard\", \"trackpad\", \"joystick\"]}", + 17 + ], + "drink glass": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be made of glass or plastic; could have a handle; could be used to drink water or other beverages\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 17 + ], + "salami": [ + " {\"type\": \"food\", \"description\": \"sliced, cured, fermented meat; could be made of pork, beef, or turkey; could be served as a sandwich topping\", \"similar objects\": [\"ham\", \"bacon\", \"sausage\"]}", + 17 + ], + "country road": [ + " {\"type\": \"road\", \"description\": \"long, winding, could have trees on both sides; could have a single lane\", \"similar objects\": [\"highway\", \"freeway\", \"motorway\"]}", + 17 + ], + "blue wheels": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could have four wheels\", \"similar objects\": [\"tricycle\", \"scooter\", \"skateboard\"]}", + 17 + ], + "grin": [ + " {\"type\": \"facial expression\", \"description\": \"smiling with teeth showing; eyes squinting\", \"similar objects\": [\"smile\", \"laugh\", \"giggle\"]}", + 17 + ], + "giraffe tongue": [ + " {\"type\": \"body part\", \"description\": \"long, black, prehensile; could reach up to 45 cm in length\", \"similar objects\": [\"elephant trunk\", \"monkey tail\", \"grizzly bear paw\"]}", + 17 + ], + "highchair": [ + " {\"type\": \"furniture\", \"description\": \"tall chair with a tray; could be adjustable; could have a safety belt\", \"similar objects\": [\"booster seat\", \"stool\", \"rocking chair\"]}", + 17 + ], + "wrist guard": [ + " {\"type\": \"protective gear\", \"description\": \"made of plastic or metal; could be strapped to the wrist; could be used for sports or other activities\", \"similar objects\": [\"elbow guard\", \"knee guard\", \"helmet\"]}", + 17 + ], + "lamp light": [ + " {\"type\": \"lighting tool\", \"description\": \"could be made of metal; could have a switch; could have a bulb\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}", + 17 + ], + "police motorcycles": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a siren; could be painted in blue and white\", \"similar objects\": [\"police car\", \"ambulance\", \"fire truck\"]}", + 17 + ], + "giraffe head": [ + " {\"type\": \"animal part\", \"description\": \"long neck; large eyes; long ears; long tongue; dark spots on fur\", \"similar objects\": [\"elephant head\", \"horse head\", \"monkey head\"]}", + 17 + ], + "remote controller": [ + " {\"type\": \"electronic device\", \"description\": \"small; has buttons; could be used to control other devices\", \"similar objects\": [\"game controller\", \"keyboard\", \"mouse\"]}", + 17 + ], + "glass lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"transparent; could be made of glass; could have a handle\", \"similar objects\": [\"lantern\", \"lamp\", \"chandelier\"]}", + 17 + ], + "button jacket": [ + " {\"type\": \"clothing item\", \"description\": \"long sleeve; has buttons; could be made of wool\", \"similar objects\": [\"cardigan\", \"blazer\", \"coat\"]}", + 17 + ], + "product": [ + "\n{\"type\": \"item\", \"description\": \"could be physical or digital; could be used for various purposes; could be sold or given away\", \"similar objects\": [\"goods\", \"merchandise\", \"services\"]}", + 17 + ], + "tan blanket": [ + " {\"type\": \"textile\", \"description\": \"light brown; could be made of wool; could be used for warmth\", \"similar objects\": [\"quilt\", \"duvet\", \"throw\"]}", + 17 + ], + "round blue": [ + "\n{\"type\": \"shape\", \"description\": \"circular; could be of any color\", \"similar objects\": [\"circle\", \"oval\", \"sphere\"]}", + 17 + ], + "blonde lady": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could be wearing a dress\", \"similar objects\": [\"brunette woman\", \"blonde man\", \"redhead woman\"]}", + 17 + ], + "taco": [ + " {\"type\": \"food\", \"description\": \"tortilla filled with meat, vegetables, and cheese; could be served with salsa and sour cream\", \"similar objects\": [\"burrito\", \"enchilada\", \"quesadilla\"]}", + 17 + ], + "dark shorts": [ + " {\"type\": \"clothing\", \"description\": \"black or dark-colored; could be made of cotton or denim; could have pockets; could have a zipper or button closure\", \"similar objects\": [\"jeans\", \"trousers\", \"capris\"]}", + 17 + ], + "woman ground": [ + " {\"type\": \"person\", \"description\": \"female; standing on the ground\", \"similar objects\": [\"man\", \"child\", \"elderly\"]}", + 17 + ], + "bowel": [ + " {\"type\": \"utensil\", \"description\": \"round; could be made of ceramic; could be used for serving food\", \"similar objects\": [\"plate\", \"dish\", \"cup\"]}", + 17 + ], + "leaves branch": [ + " {\"type\": \"plant part\", \"description\": \"green; could have multiple leaves; could be curved\", \"similar objects\": [\"stem\", \"flower\", \"root\"]}", + 17 + ], + "baseball player batting": [ + "\n{\"type\": \"sports activity\", \"description\": \"player holding a bat; swinging the bat to hit the ball; wearing a helmet and gloves\", \"similar objects\": [\"golf player\", \"tennis player\", \"soccer player\"]}", + 17 + ], + "silver computer": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; could be a laptop or desktop; could have a monitor, keyboard, and mouse\", \"similar objects\": [\"tablet\", \"smartphone\", \"printer\"]}", + 17 + ], + "silver apple laptop": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; apple logo; laptop shape; could have a touch bar\", \"similar objects\": [\"macbook\", \"chromebook\", \"surface laptop\"]}", + 17 + ], + "party hat": [ + " {\"type\": \"accessory\", \"description\": \"cone-shaped; could be made of paper; could have colorful decorations\", \"similar objects\": [\"crown\", \"tiara\", \"feather boa\"]}", + 17 + ], + "triangle flag": [ + " {\"type\": \"flag\", \"description\": \"three sides; could be made of fabric; could have a pole\", \"similar objects\": [\"rectangle flag\", \"square flag\", \"pentagon flag\"]}", + 17 + ], + "spoon plate": [ + " {\"type\": \"dining tool\", \"description\": \"round; has a bowl-like shape; could be made of metal or plastic\", \"similar objects\": [\"fork\", \"knife\", \"chopsticks\"]}", + 17 + ], + "woman skier": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing ski gear; skiing on snow; could have poles in hands\", \"similar objects\": [\"man skier\", \"snowboarder\", \"ice skater\"]}", + 17 + ], + "adult cow": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a long face; could have horns; could have a long tail; could have a mottled coat\", \"similar objects\": [\"bull\", \"calf\", \"bison\"]}", + 17 + ], + "yellow tags": [ + " {\"type\": \"labeling tool\", \"description\": \"small, yellow, could be attached to items\", \"similar objects\": [\"stickers\", \"labels\", \"tags\"]}", + 17 + ], + "dress tie": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of silk; could be tied around the neck\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}", + 17 + ], + "papaya": [ + " {\"type\": \"fruit\", \"description\": \"oval; yellow-green; has a sweet taste; could be eaten raw\", \"similar objects\": [\"mango\", \"pineapple\", \"avocado\"]}", + 17 + ], + "picnic bench": [ + " {\"type\": \"furniture\", \"description\": \"long, wooden, has a flat surface; could have a backrest\", \"similar objects\": [\"park bench\", \"garden bench\", \"patio bench\"]}", + 17 + ], + "lace curtain": [ + " {\"type\": \"window covering\", \"description\": \"made of lace fabric; could be hung on a window\", \"similar objects\": [\"blinds\", \"shutters\", \"drapes\"]}", + 17 + ], + "track lighting": [ + " {\"type\": \"lighting tool\", \"description\": \"linear; could be mounted on the ceiling; could be adjustable\", \"similar objects\": [\"pendant lighting\", \"wall sconces\", \"ceiling fan\"]}", + 17 + ], + "crop": [ + " {\"type\": \"agricultural product\", \"description\": \"plants grown for food, feed, or other uses; could be harvested\", \"similar objects\": [\"grain\", \"vegetable\", \"fruit\"]}", + 17 + ], + "teenagers": [ + "\n{\"type\": \"demographic group\", \"description\": \"people between the ages of 13 and 19; could be in school or working; could be independent or living with parents\", \"similar objects\": [\"children\", \"adults\", \"seniors\"]}", + 17 + ], + "purple cloth": [ + " {\"type\": \"fabric\", \"description\": \"colorful; could be made of cotton, silk, or other materials; could be used for clothing, curtains, or other decorations\", \"similar objects\": [\"red cloth\", \"blue cloth\", \"green cloth\"]}", + 17 + ], + "streams": [ + " {\"type\": \"natural feature\", \"description\": \"narrow, flowing body of water; could be shallow or deep; could be fresh or salt water\", \"similar objects\": [\"river\", \"lake\", \"ocean\"]}", + 17 + ], + "stadium seat": [ + " {\"type\": \"furniture\", \"description\": \"long, padded, has armrests\", \"similar objects\": [\"theater seat\", \"bench\", \"sofa\"]}", + 17 + ], + "ornate building": [ + " {\"type\": \"architecture\", \"description\": \"elaborate and decorative; could have intricate designs; could have multiple stories\", \"similar objects\": [\"castle\", \"cathedral\", \"palace\"]}", + 17 + ], + "cereal box": [ + " {\"type\": \"packaging\", \"description\": \"rectangular; could be made of cardboard; could have colorful designs\", \"similar objects\": [\"milk carton\", \"juice box\", \"can\"]}", + 17 + ], + "creamy": [ + " {\"type\": \"texture\", \"description\": \"smooth, soft, and thick\", \"similar objects\": [\"velvety\", \"silky\", \"fluffy\"]}", + 17 + ], + "mirror sink": [ + " {\"type\": \"bathroom fixture\", \"description\": \"rectangular; has a mirror on top; could have two faucets\", \"similar objects\": [\"vanity sink\", \"pedestal sink\", \"wall-mounted sink\"]}", + 17 + ], + "sport shoes": [ + " {\"type\": \"footwear\", \"description\": \"lightweight; designed for running; could have laces; could have air cushioning\", \"similar objects\": [\"sneakers\", \"running shoes\", \"trainers\"]}", + 17 + ], + "surfer surfboard": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long board; could have colorful designs; could have a leash\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 17 + ], + "metal chains": [ + " {\"type\": \"accessory\", \"description\": \"made of metal; could be used to lock something; could be used as a decoration\", \"similar objects\": [\"padlock\", \"lock\", \"shackle\"]}", + 17 + ], + "purple wall": [ + "\n{\"type\": \"decoration\", \"description\": \"solid color; could be glossy or matte; could be textured\", \"similar objects\": [\"blue wall\", \"green wall\", \"yellow wall\"]}", + 17 + ], + "penguins": [ + " {\"type\": \"animal\", \"description\": \"black and white; waddles; could have a yellow patch on the head\", \"similar objects\": [\"seals\", \"otters\", \"polar bears\"]}", + 17 + ], + "apple symbol": [ + " {\"type\": \"symbol\", \"description\": \"red, round, with a bite taken out of it\", \"similar objects\": [\"Microsoft logo\", \"Adobe logo\", \"Google logo\"]}", + 17 + ], + "burgers": [ + " {\"type\": \"food\", \"description\": \"round; could be made of beef, chicken, or vegetables; could be served with buns and condiments\", \"similar objects\": [\"hot dogs\", \"sandwiches\", \"tacos\"]}", + 17 + ], + "toilet flush handle": [ + "\n{\"type\": \"plumbing tool\", \"description\": \"round handle; could be made of metal; could be attached to a toilet tank\", \"similar objects\": [\"toilet seat\", \"toilet brush\", \"toilet plunger\"]}", + 17 + ], + "seat lid": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be used to cover a seat; could be made of wood or plastic\", \"similar objects\": [\"table top\", \"chair back\", \"ottoman cover\"]}", + 17 + ], + "wood grain table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of wood; has a grain pattern\", \"similar objects\": [\"wooden chair\", \"wooden bench\", \"wooden shelf\"]}", + 17 + ], + "bead": [ + " {\"type\": \"accessory\", \"description\": \"small, round, could be made of glass, plastic, or metal; could be used for jewelry making\", \"similar objects\": [\"button\", \"sequin\", \"charm\"]}", + 17 + ], + "beige shorts": [ + " {\"type\": \"clothing\", \"description\": \"light-colored; could be made of cotton; could have pockets; could have a drawstring\", \"similar objects\": [\"khaki shorts\", \"denim shorts\", \"linen shorts\"]}", + 17 + ], + "indoor plant": [ + " {\"type\": \"decoration\", \"description\": \"could be a potted plant; could be a hanging plant; could be a succulent; could be a cactus\", \"similar objects\": [\"flower\", \"vase\", \"picture frame\"]}", + 17 + ], + "metal device": [ + "\n{\"type\": \"tool\", \"description\": \"made of metal; could be used for various purposes; could be small or large\", \"similar objects\": [\"screwdriver\", \"hammer\", \"pliers\"]}", + 17 + ], + "blurry person": [ + "\n{\"type\": \"object\", \"description\": \"unclear shape; could be a person; could be wearing clothes; could have facial features\", \"similar objects\": [\"person\", \"animal\", \"vehicle\"]}", + 17 + ], + "gas range": [ + " {\"type\": \"cooking tool\", \"description\": \"has a stovetop and oven; could have knobs and dials; could have a gas line connection\", \"similar objects\": [\"electric range\", \"microwave\", \"toaster oven\"]}", + 17 + ], + "half pizza": [ + " {\"type\": \"food\", \"description\": \"round; could be cut into two pieces; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread pizza\"]}", + 17 + ], + "wood fence post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of wood; could be used to build a fence\", \"similar objects\": [\"metal fence post\", \"wooden beam\", \"concrete post\"]}", + 17 + ], + "plaid pattern": [ + " {\"type\": \"pattern\", \"description\": \"intersecting lines of different colors; could be used for clothing\", \"similar objects\": [\"stripes\", \"checks\", \"floral\"]}", + 17 + ], + "side salad": [ + " {\"type\": \"food\", \"description\": \"a mix of vegetables; could have lettuce, tomatoes, cucumbers, onions, etc.; could be served with dressing\", \"similar objects\": [\"fruit salad\", \"caesar salad\", \"potato salad\"]}", + 17 + ], + "arch way": [ + " {\"type\": \"architectural structure\", \"description\": \"curved; could be made of stone; could have pillars\", \"similar objects\": [\"doorway\", \"gateway\", \"bridge\"]}", + 17 + ], + "metal pizza pan": [ + "\n{\"type\": \"cooking tool\", \"description\": \"flat, round, made of metal; could have holes on the surface\", \"similar objects\": [\"baking sheet\", \"cake pan\", \"pie pan\"]}", + 17 + ], + "front screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be touch-sensitive; could be used to display information\", \"similar objects\": [\"monitor\", \"tablet\", \"smartphone\"]}", + 17 + ], + "railroad ties": [ + " {\"type\": \"construction material\", \"description\": \"wooden; rectangular; used to support railroad tracks\", \"similar objects\": [\"sleepers\", \"railroad spikes\", \"railroad bolts\"]}", + 17 + ], + "plastic forks": [ + " {\"type\": \"utensil\", \"description\": \"long; could be white or colored; could be disposable\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 17 + ], + "wood counter": [ + " {\"type\": \"furniture\", \"description\": \"hard surface; could be made of wood; could be used for kitchen counter\", \"similar objects\": [\"table\", \"chair\", \"cabinet\"]}", + 17 + ], + "cut piece": [ + " {\"type\": \"object\", \"description\": \"a piece of something that has been cut; could be of any shape or size\", \"similar objects\": [\"slice\", \"chunk\", \"shard\"]}", + 17 + ], + "sink drain": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be made of metal; could have a stopper\", \"similar objects\": [\"bathtub drain\", \"toilet drain\", \"shower drain\"]}", + 17 + ], + "sprinkler": [ + " {\"type\": \"irrigation tool\", \"description\": \"has a long arm; could be connected to a hose; could spray water in a circular motion\", \"similar objects\": [\"hose\", \"watering can\", \"drip irrigation system\"]}", + 17 + ], + "crispy": [ + " {\"type\": \"texture\", \"description\": \"crunchy; could be crunchy and light; could be crunchy and hard\", \"similar objects\": [\"crunchy\", \"crispy\", \"crumbly\"]}", + 17 + ], + "hand soap dispenser": [ + " {\"type\": \"cleaning tool\", \"description\": \"could be wall-mounted; could be automatic; could be manual; could be refillable\", \"similar objects\": [\"hand sanitizer dispenser\", \"toilet paper dispenser\", \"paper towel dispenser\"]}", + 17 + ], + "safety rail": [ + " {\"type\": \"safety tool\", \"description\": \"long, metal bar; could be installed on stairs or balconies\", \"similar objects\": [\"guardrail\", \"handrail\", \"balustrade\"]}", + 17 + ], + "side tower": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, slender, could be made of stone or metal; could have windows or balconies\", \"similar objects\": [\"minaret\", \"obelisk\", \"spire\"]}", + 17 + ], + "orange table": [ + "\n{\"type\": \"furniture\", \"description\": \"orange; rectangular; could have four legs; could have a glass top\", \"similar objects\": [\"chair\", \"sofa\", \"desk\"]}", + 17 + ], + "silver metal dinner fork": [ + "\n{\"type\": \"utensil\", \"description\": \"long handle; four tines; made of silver metal\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 17 + ], + "dinner roll": [ + " {\"type\": \"food\", \"description\": \"round; could be made of wheat flour; could be served with butter\", \"similar objects\": [\"bun\", \"bread\", \"bagel\"]}", + 17 + ], + "grassy hills": [ + " {\"type\": \"landscape\", \"description\": \"green; could have rolling hills; could have wildflowers\", \"similar objects\": [\"meadow\", \"field\", \"prairie\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\",", + 17 + ], + "popcorn": [ + " {\"type\": \"food\", \"description\": \"small, white, fluffy; could be popped in a microwave or stove\", \"similar objects\": [\"rice\", \"nuts\", \"cereal\"]}", + 17 + ], + "swimmers": [ + " {\"type\": \"athletes\", \"description\": \"people wearing swimsuits; could be swimming in a pool or in the ocean\", \"similar objects\": [\"runners\", \"cyclists\", \"divers\"]}", + 17 + ], + "claw foot": [ + " {\"type\": \"furniture part\", \"description\": \"curved feet; could be made of wood or metal; could be used to support a chair or table\", \"similar objects\": [\"legs\", \"casters\", \"casters with brakes\"]}", + 17 + ], + "yellow banana": [ + "\n{\"type\": \"fruit\", \"description\": \"yellow, curved, has a stem\", \"similar objects\": [\"green banana\", \"apple\", \"orange\"]}", + 17 + ], + "canopy tent": [ + " {\"type\": \"shelter\", \"description\": \"large, dome-shaped; could be made of fabric; could be used for camping\", \"similar objects\": [\"gazebo\", \"tarp\", \"awning\"]}", + 17 + ], + "railway tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, parallel lines; could be made of steel; could have sleepers\", \"similar objects\": [\"roadway\", \"highway\", \"bridge\"]}", + 17 + ], + "ticket": [ + " {\"type\": \"document\", \"description\": \"could be made of paper or digital; could be used for entrance or transportation\", \"similar objects\": [\"passport\", \"boarding pass\", \"invoice\"]}", + 17 + ], + "towel dispenser": [ + " {\"type\": \"bathroom accessory\", \"description\": \"wall-mounted; could be made of metal; could have a slot for paper towels\", \"similar objects\": [\"soap dispenser\", \"toilet paper holder\", \"hand dryer\"]}", + 17 + ], + "security light": [ + " {\"type\": \"lighting tool\", \"description\": \"bright; could be motion-activated; could be used for security purposes\", \"similar objects\": [\"floodlight\", \"spotlight\", \"lantern\"]}", + 17 + ], + "iron skillet": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round, has a handle; could be made of cast iron\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 17 + ], + "duck swimming": [ + "\n{\"type\": \"animal\", \"description\": \"yellow bill; webbed feet; could be swimming in water; could have a brown body\", \"similar objects\": [\"goose\", \"swan\", \"pelican\"]}", + 17 + ], + "silver speaker": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; could be used to play music; could be connected to other devices\", \"similar objects\": [\"headphones\", \"microphone\", \"stereo\"]}", + 17 + ], + "tan sofa": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; upholstered in tan fabric; could have armrests and cushions\", \"similar objects\": [\"loveseat\", \"armchair\", \"ottoman\"]}", + 17 + ], + "shirt woman": [ + "\n{\"type\": \"clothing\", \"description\": \"long or short sleeves; could have buttons; could be made of cotton, linen, or silk; could have a collar\", \"similar objects\": [\"dress\", \"blouse\", \"jacket\"]}", + 17 + ], + "pepsi logo": [ + "\n{\"type\": \"brand logo\", \"description\": \"red, white and blue; has a circle with a swoosh in the middle; has the word 'Pepsi' written in the circle\", \"similar objects\": [\"Coca-Cola logo\", \"McDonald's logo\", \"Starbucks logo\"]}", + 17 + ], + "slip": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, could be made of silk; could be worn under a dress\", \"similar objects\": [\"skirt\", \"dress\", \"petticoat\"]}", + 17 + ], + "touch pad": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be used to control a computer\", \"similar objects\": [\"mouse\", \"keyboard\", \"stylus\"]}", + 17 + ], + "water tap": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be connected to a pipe; could be used to control the flow of water\", \"similar objects\": [\"shower head\", \"faucet\", \"hose\"]}", + 17 + ], + "purple towel": [ + "\n{\"type\": \"household item\", \"description\": \"soft; could be made of cotton; could be used for drying\", \"similar objects\": [\"blanket\", \"sheet\", \"washcloth\"]}", + 17 + ], + "paddle board": [ + " {\"type\": \"water sport equipment\", \"description\": \"long, wide, flat board; could have a handle; could be used with an oar\", \"similar objects\": [\"surfboard\", \"kayak\", \"canoe\"]}", + 17 + ], + "round slice": [ + " {\"type\": \"food item\", \"description\": \"circular; could be made of bread, cake, pizza, etc.\", \"similar objects\": [\"triangle slice\", \"square slice\", \"rectangle slice\"]}", + 17 + ], + "hoof zebra": [ + " {\"type\": \"animal body part\", \"description\": \"hard, curved, and pointed; found on the feet of horses, zebras, and other hoofed animals\", \"similar objects\": [\"hoof horse\", \"hoof cow\", \"hoof goat\"]}", + 17 + ], + "oxen": [ + " {\"type\": \"animal\", \"description\": \"large, strong, and slow; usually used for farming and transportation; have horns\", \"similar objects\": [\"cattle\", \"buffalo\", \"yak\"]}", + 17 + ], + "round wall clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has a clock face; could have a pendulum\", \"similar objects\": [\"alarm clock\", \"stopwatch\", \"pocket watch\"]}", + 17 + ], + "milk carton": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of paper or plastic; could have a spout\", \"similar objects\": [\"juice box\", \"water bottle\", \"can\"]}", + 17 + ], + "gallon": [ + " {\"type\": \"measurement unit\", \"description\": \"a unit of volume; equal to 3.785 liters\", \"similar objects\": [\"quart\", \"liter\", \"pint\"]}", + 17 + ], + "menus": [ + " {\"type\": \"document\", \"description\": \"list of food items; could be printed on paper or displayed on a screen\", \"similar objects\": [\"flyers\", \"brochures\", \"catalogs\"]}", + 17 + ], + "performer": [ + " {\"type\": \"entertainer\", \"description\": \"could be a singer, dancer, actor, or musician; could be a professional or amateur\", \"similar objects\": [\"artist\", \"comedian\", \"magician\"]}", + 17 + ], + "bones": [ + " {\"type\": \"body part\", \"description\": \"white; could be found in the body; could be used to make soup\", \"similar objects\": [\"teeth\", \"organs\", \"muscles\"]}", + 17 + ], + "bear paws": [ + " {\"type\": \"animal body part\", \"description\": \"large, furry, and have sharp claws\", \"similar objects\": [\"lion paws\", \"tiger paws\", \"wolf paws\"]}", + 17 + ], + "poart": [ + " {\"type\": \"clothing item\", \"description\": \"long, sleeveless, could be made of silk; could have embroidery\", \"similar objects\": [\"dress\", \"robe\", \"tunic\"]}", + 17 + ], + "shuttle bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; could have multiple doors; could have a luggage compartment\", \"similar objects\": [\"school bus\", \"tour bus\", \"minibus\"]}", + 17 + ], + "jump suit": [ + " {\"type\": \"clothing\", \"description\": \"one-piece garment; could be long or short; could be made of cotton, polyester, or other materials\", \"similar objects\": [\"overalls\", \"romper\", \"coveralls\"]}", + 17 + ], + "belt buckle": [ + " {\"type\": \"accessory\", \"description\": \"metal; could be decorated with patterns; could be used to fasten a belt\", \"similar objects\": [\"buckle\", \"clasp\", \"button\"]}", + 17 + ], + "giraffe heads": [ + "\n{\"type\": \"animal part\", \"description\": \"long neck; two eyes; two ears; two horns\", \"similar objects\": [\"elephant head\", \"horse head\", \"zebra head\"]}", + 17 + ], + "police vehicle": [ + " {\"type\": \"vehicle\", \"description\": \"blue; has a siren; could have a flashing light\", \"similar objects\": [\"ambulance\", \"fire truck\", \"taxi\"]}", + 17 + ], + "cucumber slices": [ + " {\"type\": \"food\", \"description\": \"thin, round slices of cucumber; could be eaten raw or cooked\", \"similar objects\": [\"zucchini slices\", \"carrot slices\", \"onion slices\"]}", + 17 + ], + "lots windows": [ + "\n{\"type\": \"architectural feature\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"doors\", \"shutters\", \"blinds\"]}", + 17 + ], + "metal balcony": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could have railings; could be attached to a building\", \"similar objects\": [\"deck\", \"patio\", \"veranda\"]}", + 17 + ], + "blue van": [ + "\n{\"type\": \"vehicle\", \"description\": \"blue; could be a minivan; could have sliding doors\", \"similar objects\": [\"car\", \"truck\", \"SUV\"]}", + 17 + ], + "grey baby elephant": [ + "\n{\"type\": \"animal\", \"description\": \"grey; has a trunk; has four legs; has large ears; has a tail\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}", + 17 + ], + "phone case": [ + " {\"type\": \"accessory\", \"description\": \"protective cover for a phone; could be made of plastic, rubber, or leather; could have a design or pattern\", \"similar objects\": [\"screen protector\", \"phone stand\", \"phone wallet\"]}", + 17 + ], + "pigs": [ + " {\"type\": \"animal\", \"description\": \"pink; has a snout; could have curly tails\", \"similar objects\": [\"goats\", \"sheep\", \"cows\"]}", + 17 + ], + "bedroom door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have a handle; could have a lock\", \"similar objects\": [\"closet door\", \"bathroom door\", \"kitchen door\"]}", + 17 + ], + "blue mailbox": [ + "\n{\"type\": \"mailbox\", \"description\": \"blue; could have a flag; could be rectangular or cylindrical\", \"similar objects\": [\"red mailbox\", \"green mailbox\", \"white mailbox\"]}", + 17 + ], + "pink sweater": [ + " {\"type\": \"clothing\", \"description\": \"knitted; could be long-sleeved; could have a hood; could have a zipper\", \"similar objects\": [\"jacket\", \"coat\", \"cardigan\"]}", + 17 + ], + "trashcans": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could have a lid\", \"similar objects\": [\"bins\", \"barrels\", \"buckets\"]}", + 17 + ], + "toilet cleaner": [ + " {\"type\": \"cleaning product\", \"description\": \"liquid; could be used to clean toilet bowl; could be in a bottle\", \"similar objects\": [\"dishwashing liquid\", \"floor cleaner\", \"all-purpose cleaner\"]}", + 17 + ], + "date stamp": [ + " {\"type\": \"office tool\", \"description\": \"has a handle; could be used to stamp on paper; could be used to mark the date\", \"similar objects\": [\"stamp pad\", \"ink pad\", \"stamp\"]}", + 17 + ], + "guitars": [ + " {\"type\": \"musical instrument\", \"description\": \"long; has strings; could be acoustic or electric\", \"similar objects\": [\"violin\", \"ukulele\", \"piano\"]}", + 17 + ], + "silver poles": [ + " {\"type\": \"structural object\", \"description\": \"long, cylindrical, made of metal; could be used for support\", \"similar objects\": [\"pipes\", \"columns\", \"beams\"]}", + 17 + ], + "toe nails": [ + " {\"type\": \"body part\", \"description\": \"hard, curved, and white; could be cut and shaped\", \"similar objects\": [\"fingernails\", \"hair\", \"eyelashes\"]}", + 17 + ], + "peeler": [ + " {\"type\": \"kitchen tool\", \"description\": \"long handle; sharp blade; used to peel fruits and vegetables\", \"similar objects\": [\"grater\", \"knife\", \"mandoline\"]}", + 17 + ], + "cobblestones": [ + " {\"type\": \"building material\", \"description\": \"small, round stones; could be used to pave roads\", \"similar objects\": [\"bricks\", \"gravel\", \"asphalt\"]}", + 17 + ], + "silver sink drain": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; round; has a stopper; could be connected to a pipe\", \"similar objects\": [\"bathtub drain\", \"toilet drain\", \"shower drain\"]}", + 17 + ], + "grey dog": [ + "\n{\"type\": \"animal\", \"description\": \"grey fur; could have short or long fur; could have pointy ears; could have a tail\", \"similar objects\": [\"cat\", \"wolf\", \"fox\"]}", + 17 + ], + "croutons": [ + " {\"type\": \"food\", \"description\": \"small, crunchy cubes of bread; could be seasoned with herbs and spices\", \"similar objects\": [\"breadcrumbs\", \"nuts\", \"cereal\"]}", + 17 + ], + "post-it": [ + " {\"type\": \"stationery\", \"description\": \"small, sticky, could be used to write notes\", \"similar objects\": [\"sticker\", \"label\", \"notepad\"]}", + 17 + ], + "silver hinge": [ + " {\"type\": \"hardware\", \"description\": \"metallic; used to connect two objects together; could be used to open and close doors\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 17 + ], + "copper": [ + " {\"type\": \"metal\", \"description\": \"shiny, reddish-brown; malleable and ductile; good conductor of electricity and heat\", \"similar objects\": [\"iron\", \"aluminum\", \"gold\"]}", + 17 + ], + "computer cord": [ + " {\"type\": \"electronic accessory\", \"description\": \"long, thin, has a plug at the end\", \"similar objects\": [\"USB cable\", \"power cord\", \"HDMI cable\"]}", + 17 + ], + "cockpit window": [ + " {\"type\": \"aircraft part\", \"description\": \"transparent; could be opened and closed; could be reinforced with metal frames\", \"similar objects\": [\"airplane door\", \"airplane wing\", \"airplane engine\"]}", + 17 + ], + "microphone stand": [ + " {\"type\": \"audio equipment\", \"description\": \"tall, adjustable, has a base\", \"similar objects\": [\"microphone\", \"speaker\", \"headset\"]}", + 17 + ], + "bush background": [ + "\n{\"type\": \"landscape\", \"description\": \"green; could have various shapes; could have flowers and leaves\", \"similar objects\": [\"tree\", \"grass\", \"hedge\"]}", + 17 + ], + "beige house": [ + "\n{\"type\": \"structure\", \"description\": \"rectangular; could have a roof; could have windows and doors; could be made of bricks or wood; could be painted beige\", \"similar objects\": [\"building\", \"shed\", \"garage\"]}", + 17 + ], + "egg roll": [ + " {\"type\": \"food\", \"description\": \"cylindrical; could be filled with vegetables and meat; could be fried or steamed\", \"similar objects\": [\"spring roll\", \"dumpling\", \"potsticker\"]}", + 17 + ], + "materials": [ + " {\"type\": \"substance\", \"description\": \"could be solid, liquid, or gas; could be natural or man-made; could be used for various purposes\", \"similar objects\": [\"elements\", \"compounds\", \"mixtures\"]}", + 17 + ], + "glass shinny": [ + " {\"type\": \"material\", \"description\": \"transparent; could be used to make windows, bottles, and other objects; could be fragile\", \"similar objects\": [\"plastic\", \"wood\", \"metal\"]}", + 17 + ], + "waitress": [ + " {\"type\": \"occupation\", \"description\": \"serves customers in a restaurant; could wear a uniform\", \"similar objects\": [\"waiter\", \"chef\", \"bartender\"]}", + 17 + ], + "jerseys": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could have a team logo; could be made of cotton or polyester\", \"similar objects\": [\"t-shirts\", \"hoodies\", \"sweatshirts\"]}", + 17 + ], + "toboggan": [ + " {\"type\": \"sled\", \"description\": \"long, flat, has a curved front; could be made of wood or plastic\", \"similar objects\": [\"sled\", \"ski\", \"snowboard\"]}", + 17 + ], + "wood doors": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be painted; could be carved; could be solid or hollow\", \"similar objects\": [\"metal doors\", \"glass doors\", \"plastic doors\"]}", + 17 + ], + "iron bars": [ + " {\"type\": \"building material\", \"description\": \"long, metallic, could be used to build fences\", \"similar objects\": [\"steel beams\", \"concrete blocks\", \"wooden planks\"]}", + 17 + ], + "pendulum": [ + " {\"type\": \"mechanical tool\", \"description\": \"hanging object; could be used to measure time; could be made of metal\", \"similar objects\": [\"clock\", \"hourglass\", \"stopwatch\"]}", + 17 + ], + "metal manhole cover": [ + "\n{\"type\": \"utility object\", \"description\": \"round; made of metal; has a hole in the middle\", \"similar objects\": [\"drain cover\", \"grate\", \"vent cover\"]}", + 17 + ], + "fork napkin": [ + " {\"type\": \"tableware\", \"description\": \"long and thin; could be made of metal or plastic; could be used to pick up food\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 17 + ], + "dreadlocks": [ + " {\"type\": \"hairstyle\", \"description\": \"long, matted, twisted locks of hair\", \"similar objects\": [\"braids\", \"cornrows\", \"afro\"]}", + 17 + ], + "ladybug": [ + " {\"type\": \"insect\", \"description\": \"red with black spots; small; could fly\", \"similar objects\": [\"butterfly\", \"bee\", \"dragonfly\"]}", + 17 + ], + "dill pickle": [ + " {\"type\": \"food\", \"description\": \"green; could be sliced into round pieces; has a sour taste\", \"similar objects\": [\"olive\", \"cucumber\", \"pickled pepper\"]}", + 17 + ], + "metal knob": [ + " {\"type\": \"hardware\", \"description\": \"round; could be made of metal; could be used to open and close doors\", \"similar objects\": [\"handle\", \"hinge\", \"lock\"]}", + 17 + ], + "vintage car": [ + " {\"type\": \"vehicle\", \"description\": \"old-fashioned; could have a classic design; could have a long body\", \"similar objects\": [\"antique car\", \"classic car\", \"muscle car\"]}", + 17 + ], + "medium section": [ + " {\"type\": \"media\", \"description\": \"a form of communication; could be written, audio, or video; could be used to share information\", \"similar objects\": [\"newspaper\", \"magazine\", \"radio\"]}", + 17 + ], + "blurry picture": [ + "\n{\"type\": \"image\", \"description\": \"blurred; could be out of focus; could be distorted; could be noisy\", \"similar objects\": [\"photo\", \"picture\", \"snapshot\"]}", + 17 + ], + "orange section": [ + " {\"type\": \"fruit section\", \"description\": \"round; has a peel; could be divided into segments; could be juicy\", \"similar objects\": [\"grapefruit section\", \"lemon section\", \"lime section\"]}", + 17 + ], + "elephant ear": [ + " {\"type\": \"plant\", \"description\": \"large, heart-shaped leaves; could be green or purple; could have white veins\", \"similar objects\": [\"caladium\", \"taro\", \"elephant's foot\"]}", + 17 + ], + "soup spoon": [ + " {\"type\": \"utensil\", \"description\": \"long handle; round bowl; could be made of metal or plastic\", \"similar objects\": [\"teaspoon\", \"fork\", \"ladle\"]}", + 17 + ], + "ports": [ + " {\"type\": \"connectors\", \"description\": \"used to connect two devices; could be USB, HDMI, etc.\", \"similar objects\": [\"cables\", \"adapters\", \"plugs\"]}", + 17 + ], + "gold band": [ + " {\"type\": \"jewelry\", \"description\": \"round; could be made of gold; could be decorated with diamonds\", \"similar objects\": [\"ring\", \"bracelet\", \"necklace\"]}", + 17 + ], + "freight cars": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; could be connected to each other; could be used to transport goods\", \"similar objects\": [\"train\", \"truck\", \"ship\"]}", + 17 + ], + "sand dune": [ + " {\"type\": \"landform\", \"description\": \"mound of sand; could be formed by wind; could be found in deserts\", \"similar objects\": [\"mesa\", \"butte\", \"cliff\"]}", + 17 + ], + "couple people": [ + "\n{\"type\": \"human\", \"description\": \"two people standing together; could be of different genders; could be holding hands\", \"similar objects\": [\"family\", \"group of people\", \"friends\"]}", + 17 + ], + "dresser drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have handles; could be made of wood or metal\", \"similar objects\": [\"cabinet\", \"chest of drawers\", \"wardrobe\"]}", + 17 + ], + "silver color": [ + " {\"type\": \"color\", \"description\": \"shiny, metallic hue; could be used to describe objects\", \"similar objects\": [\"gold\", \"bronze\", \"copper\"]}", + 17 + ], + "car wheel": [ + " {\"type\": \"vehicle part\", \"description\": \"round; has spokes; could be made of metal or rubber\", \"similar objects\": [\"tire\", \"axle\", \"hubcap\"]}", + 17 + ], + "crown molding": [ + " {\"type\": \"decorative item\", \"description\": \"long, thin, decorative strip; could be made of wood or plastic; could be used to cover the gap between walls and ceilings\", \"similar objects\": [\"baseboard\", \"chair rail\", \"picture rail\"]}", + 17 + ], + "sideline": [ + " {\"type\": \"sports term\", \"description\": \"the boundary line of a playing field; could be used to refer to a position outside of the main action\", \"similar objects\": [\"end line\", \"goal line\", \"touchline\"]}", + 17 + ], + "chocolate icing": [ + " {\"type\": \"food topping\", \"description\": \"smooth, sweet, dark brown; could be used to decorate cakes and cupcakes\", \"similar objects\": [\"whipped cream\", \"frosting\", \"caramel sauce\"]}", + 17 + ], + "bright window": [ + "\n{\"type\": \"window\", \"description\": \"large; allows a lot of light to pass through; could be made of glass\", \"similar objects\": [\"door\", \"skylight\", \"curtain\"]}", + 17 + ], + "roman numerials": [ + " {\"type\": \"number system\", \"description\": \"uses letters to represent numbers; could be used to represent dates\", \"similar objects\": [\"Arabic numerals\", \"Greek numerals\", \"Chinese numerals\"]}\n\nObject detection models should focus on the features of the given input, such as color, shape, size, texture, and any other distinguishing characteristics. For example, for the input zucchini, the model should focus on the cylindrical shape, green color, and smooth texture. For the input ambulance, the model should focus on the red color and the glaring siren. For the input roman", + 17 + ], + "tall plants": [ + " {\"type\": \"plant\", \"description\": \"could be trees or shrubs; could have long stems; could have leaves or flowers\", \"similar objects\": [\"bushes\", \"grass\", \"ferns\"]}", + 17 + ], + "sweet": [ + "\n{\"type\": \"food\", \"description\": \"could be sugary; could be savory; could be spicy; could be sour\", \"similar objects\": [\"candy\", \"cake\", \"pie\", \"ice cream\"]}", + 17 + ], + "steel refrigerator": [ + "\n{\"type\": \"appliance\", \"description\": \"large, metallic, has a door; could have shelves and drawers inside\", \"similar objects\": [\"washing machine\", \"dishwasher\", \"microwave\"]}", + 17 + ], + "taps": [ + " {\"type\": \"plumbing tool\", \"description\": \"metal; could be used to control the flow of water; could be attached to a sink or bathtub\", \"similar objects\": [\"faucet\", \"shower head\", \"valve\"]}", + 17 + ], + "paperwork": [ + " {\"type\": \"document\", \"description\": \"could be printed or digital; could be in the form of forms, letters, contracts, etc.\", \"similar objects\": [\"files\", \"documents\", \"reports\"]}", + 17 + ], + "hen": [ + " {\"type\": \"animal\", \"description\": \"brown feathers; could lay eggs; could make clucking sounds\", \"similar objects\": [\"chicken\", \"duck\", \"goose\"]}", + 17 + ], + "power wires": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, insulated wires; could be connected to a power source\", \"similar objects\": [\"cables\", \"wires\", \"connectors\"]}", + 17 + ], + "silver toilet": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"silver; has a bowl and a tank; could have a lid\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 17 + ], + "sidewalk curb": [ + " {\"type\": \"structure\", \"description\": \"raised edge of a sidewalk; could be made of concrete or stone\", \"similar objects\": [\"gutter\", \"drainage ditch\", \"retaining wall\"]}", + 17 + ], + "ravioli": [ + " {\"type\": \"food\", \"description\": \"filled pasta; could be filled with cheese, meat, or vegetables; could be served with sauce\", \"similar objects\": [\"tortellini\", \"gnocchi\", \"lasagna\"]}", + 17 + ], + "grassy hillside": [ + " {\"type\": \"landscape\", \"description\": \"green; could have wildflowers; could have trees; could have a path\", \"similar objects\": [\"meadow\", \"field\", \"forest\"]}", + 17 + ], + "knit": [ + " {\"type\": \"crafting tool\", \"description\": \"needles and yarn; could be used to make clothes\", \"similar objects\": [\"crochet\", \"sewing\", \"weaving\"]}", + 17 + ], + "front building": [ + " {\"type\": \"structure\", \"description\": \"large; could have windows; could have a door; could have a roof\", \"similar objects\": [\"house\", \"apartment\", \"office building\"]}", + 17 + ], + "door lock": [ + " {\"type\": \"security tool\", \"description\": \"could be mechanical or electronic; could be used to secure a door\", \"similar objects\": [\"padlock\", \"deadbolt\", \"keyless entry system\"]}", + 17 + ], + "orange balloon": [ + "\n{\"type\": \"decoration item\", \"description\": \"round; orange in color; could be filled with air or helium\", \"similar objects\": [\"red balloon\", \"yellow balloon\", \"blue balloon\"]}", + 17 + ], + "multistory building": [ + "\n{\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have multiple windows; could have multiple entrances\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"office building\"]}", + 17 + ], + "sparse grass": [ + " {\"type\": \"vegetation\", \"description\": \"low-growing; could be yellowish; could be found in dry areas\", \"similar objects\": [\"weeds\", \"shrubs\", \"moss\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant", + 17 + ], + "smoke air": [ + " {\"type\": \"air pollution\", \"description\": \"grayish, could be seen in the sky; could be caused by burning of fossil fuels\", \"similar objects\": [\"haze\", \"smog\", \"fog\"]}", + 17 + ], + "apple macbook": [ + "\n{\"type\": \"electronic device\", \"description\": \"laptop computer; silver; has an apple logo; could have a touch bar\", \"similar objects\": [\"laptop\", \"desktop\", \"tablet\"]}", + 17 + ], + "tugboat": [ + " {\"type\": \"vessel\", \"description\": \"small; has a tall smokestack; could have a horn\", \"similar objects\": [\"ferry\", \"yacht\", \"cruise ship\"]}", + 17 + ], + "orange construction sign": [ + "\n{\"type\": \"warning sign\", \"description\": \"orange; has a triangular shape; could have a black text or symbol\", \"similar objects\": [\"traffic sign\", \"road sign\", \"stop sign\"]}", + 17 + ], + "wilson": [ + " {\"type\": \"name\", \"description\": \"common English name; could be a surname\", \"similar objects\": [\"john\", \"david\", \"james\"]}", + 17 + ], + "bus headlights": [ + "\n{\"type\": \"vehicle lighting\", \"description\": \"round; could be white or yellow; could be mounted on the front of a bus\", \"similar objects\": [\"car headlights\", \"motorcycle headlights\", \"truck headlights\"]}", + 17 + ], + "sewer": [ + " {\"type\": \"drainage system\", \"description\": \"underground; could be made of concrete; could be connected to a river\", \"similar objects\": [\"drainage pipe\", \"septic tank\", \"culvert\"]}", + 17 + ], + "square building": [ + " {\"type\": \"structure\", \"description\": \"four sides; could have windows; could have multiple floors\", \"similar objects\": [\"cube\", \"rectangle\", \"pyramid\"]}", + 17 + ], + "runways": [ + " {\"type\": \"airport facility\", \"description\": \"long, wide, flat surface; used for aircraft takeoff and landing\", \"similar objects\": [\"taxiway\", \"apron\", \"hangar\"]}", + 17 + ], + "coke bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; has a red label; could be made of glass or plastic\", \"similar objects\": [\"water bottle\", \"wine bottle\", \"beer bottle\"]}", + 17 + ], + "slots": [ + " {\"type\": \"game\", \"description\": \"gambling game; could be played on a machine; could be played with coins\", \"similar objects\": [\"roulette\", \"blackjack\", \"poker\"]}", + 17 + ], + "highlighter": [ + " {\"type\": \"writing tool\", \"description\": \"long, slim; could be yellow, pink, green, blue; could be used to mark important information\", \"similar objects\": [\"marker\", \"pencil\", \"eraser\"]}", + 17 + ], + "sea weed": [ + " {\"type\": \"seaweed\", \"description\": \"green, slimy, could be used as food\", \"similar objects\": [\"kelp\", \"seagrass\", \"algae\"]}", + 17 + ], + "top hill": [ + " {\"type\": \"landscape\", \"description\": \"high elevation; could have a view of the surrounding area; could have trees and plants\", \"similar objects\": [\"mountain\", \"valley\", \"cliff\"]}", + 17 + ], + "wall color": [ + " {\"type\": \"decoration\", \"description\": \"could be painted in different colors; could be textured\", \"similar objects\": [\"floor color\", \"ceiling color\", \"furniture color\"]}", + 17 + ], + "screen door": [ + " {\"type\": \"door\", \"description\": \"has a mesh; could be opened and closed; could be installed on the outside of a house\", \"similar objects\": [\"storm door\", \"patio door\", \"sliding door\"]}", + 17 + ], + "tint": [ + " {\"type\": \"color\", \"description\": \"light hue of a color; could be used to describe a color\", \"similar objects\": [\"shade\", \"tone\", \"tinted\"]}", + 17 + ], + "collar shirt": [ + " {\"type\": \"clothing\", \"description\": \"has a collar; could be long or short sleeve; could be buttoned up or down\", \"similar objects\": [\"polo shirt\", \"t-shirt\", \"dress shirt\"]}", + 17 + ], + "movie poster": [ + " {\"type\": \"advertisement\", \"description\": \"printed paper; could have movie title, actors, and other information; could be hung on a wall\", \"similar objects\": [\"banner\", \"flyer\", \"billboard\"]}", + 17 + ], + "wood bat": [ + " {\"type\": \"sports equipment\", \"description\": \"long, cylindrical; made of wood; used in baseball\", \"similar objects\": [\"baseball glove\", \"baseball cap\", \"baseball bat\"]}", + 17 + ], + "door mat": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of fabric or rubber; could have a pattern or logo\", \"similar objects\": [\"rug\", \"carpet\", \"doormat\"]}", + 17 + ], + "sea bird": [ + " {\"type\": \"animal\", \"description\": \"could have wings and webbed feet; could have a long beak; could be found near the sea\", \"similar objects\": [\"seagull\", \"penguin\", \"albatross\"]}", + 17 + ], + "railway station": [ + " {\"type\": \"building\", \"description\": \"large; could have multiple platforms; could have ticket counters; could have waiting rooms\", \"similar objects\": [\"airport\", \"bus station\", \"subway station\"]}", + 17 + ], + "buckles": [ + " {\"type\": \"accessory\", \"description\": \"metal or plastic; used to fasten two ends of a belt or strap together\", \"similar objects\": [\"buttons\", \"zippers\", \"hooks\"]}", + 17 + ], + "vest man": [ + " {\"type\": \"clothing\", \"description\": \"sleeveless; could be made of wool; could have buttons\", \"similar objects\": [\"jacket\", \"sweater\", \"shirt\"]}", + 17 + ], + "monster": [ + " {\"type\": \"mythical creature\", \"description\": \"could be scary; could have multiple heads; could have wings; could have horns\", \"similar objects\": [\"dragon\", \"gargoyle\", \"unicorn\"]}", + 17 + ], + "plastic top": [ + " {\"type\": \"toy\", \"description\": \"spinning top; could be made of plastic; could have colorful designs\", \"similar objects\": [\"yo-yo\", \"marble\", \"juggling ball\"]}", + 17 + ], + "caramel": [ + " {\"type\": \"food\", \"description\": \"brown, sweet, sticky; could be used as topping or filling\", \"similar objects\": [\"fudge\", \"toffee\", \"marshmallow\"]}", + 17 + ], + "pickle spear": [ + " {\"type\": \"food\", \"description\": \"long, green, sour; could be sliced into round pieces; could be served with hamburgers\", \"similar objects\": [\"olive\", \"cucumber\", \"pickled pepper\"]}", + 17 + ], + "sponsors": [ + " {\"type\": \"business\", \"description\": \"provide financial support to an event or organization\", \"similar objects\": [\"donors\", \"investors\", \"advertisers\"]}", + 17 + ], + "cow statue": [ + " {\"type\": \"decoration\", \"description\": \"could be made of metal or stone; could be standing or sitting; could have horns\", \"similar objects\": [\"horse statue\", \"elephant statue\", \"dog statue\"]}", + 17 + ], + "traveler": [ + " {\"type\": \"person\", \"description\": \"someone who is on a journey; could be carrying a bag\", \"similar objects\": [\"tourist\", \"explorer\", \"adventurer\"]}", + 17 + ], + "skates": [ + " {\"type\": \"sports equipment\", \"description\": \"has two blades; could be used for ice skating or roller skating\", \"similar objects\": [\"rollerblades\", \"ice skates\", \"inline skates\"]}", + 17 + ], + "bison": [ + " {\"type\": \"animal\", \"description\": \"large, brown, has a hump on its back; has a long beard\", \"similar objects\": [\"buffalo\", \"cow\", \"yak\"]}", + 17 + ], + "athletes": [ + " {\"type\": \"people\", \"description\": \"people who are physically active and compete in sports\", \"similar objects\": [\"runners\", \"swimmers\", \"gymnasts\"]}", + 17 + ], + "dalmation": [ + " {\"type\": \"animal\", \"description\": \"white with black spots; has a long tail; has a short muzzle\", \"similar objects\": [\"labrador retriever\", \"boxer\", \"great dane\"]}", + 17 + ], + "vendors": [ + " {\"type\": \"people\", \"description\": \"sellers of goods or services; could be found in markets or festivals\", \"similar objects\": [\"merchants\", \"hawkers\", \"traders\"]}", + 17 + ], + "backboard": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; could be made of wood or metal; used in basketball\", \"similar objects\": [\"hoop\", \"net\", \"ball\"]}", + 17 + ], + "twelve": [ + " {\"type\": \"number\", \"description\": \"the number after eleven and before thirteen\", \"similar objects\": [\"eleven\", \"thirteen\", \"fourteen\"]}", + 17 + ], + "street car": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple doors; could be powered by electricity\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 17 + ], + "attire": [ + " {\"type\": \"clothing\", \"description\": \"could be formal or casual; could be made of different materials; could be of different colors and styles\", \"similar objects\": [\"dress\", \"suit\", \"shirt\"]}", + 17 + ], + "food stand": [ + " {\"type\": \"structure\", \"description\": \"could be made of wood or metal; could have a roof; could have a counter\", \"similar objects\": [\"kiosk\", \"booth\", \"stall\"]}", + 17 + ], + "elk": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged mammal; has antlers; brown fur\", \"similar objects\": [\"deer\", \"moose\", \"caribou\"]}", + 17 + ], + "terrace": [ + " {\"type\": \"structure\", \"description\": \"outdoor space; could be made of wood or stone; could have a railing\", \"similar objects\": [\"balcony\", \"deck\", \"patio\"]}", + 17 + ], + "dream catcher": [ + " {\"type\": \"decoration\", \"description\": \"circular; has a hoop with a woven net; could have feathers and beads\", \"similar objects\": [\"wind chime\", \"wall hanging\", \"tapestry\"]}", + 17 + ], + "whispy clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, wispy, and thin; could be seen in the sky\", \"similar objects\": [\"fog\", \"haze\", \"smoke\"]}", + 17 + ], + "mopeds": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a small engine; could be used for transportation\", \"similar objects\": [\"scooter\", \"motorcycle\", \"bicycle\"]}", + 17 + ], + "toilet base": [ + " {\"type\": \"plumbing fixture\", \"description\": \"rectangular; could be made of porcelain; could have a flush handle\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 17 + ], + "crowds": [ + " {\"type\": \"group of people\", \"description\": \"large group of people gathered together; could be in a public place\", \"similar objects\": [\"crowds of people\", \"gathering\", \"assembly\"]}", + 17 + ], + "vanilla cake": [ + " {\"type\": \"dessert\", \"description\": \"light yellow; could be topped with cream; could be filled with jam\", \"similar objects\": [\"chocolate cake\", \"cheesecake\", \"cupcake\"]}", + 17 + ], + "alot": [ + " {\"type\": \"expression\", \"description\": \"used to emphasize a point; could be used to express surprise or disbelief\", \"similar objects\": [\"really\", \"absolutely\", \"definitely\"]}", + 17 + ], + "delta": [ + " {\"type\": \"geographical feature\", \"description\": \"triangular landform; could be formed by a river\", \"similar objects\": [\"estuary\", \"bay\", \"peninsula\"]}", + 17 + ], + "initials": [ + " {\"type\": \"letter combination\", \"description\": \"two or more letters representing a name or phrase\", \"similar objects\": [\"monogram\", \"acronym\", \"initialism\"]}", + 17 + ], + "stick ground": [ + " {\"type\": \"tool\", \"description\": \"long, thin, and pointed; could be used for poking or stirring\", \"similar objects\": [\"spatula\", \"skewer\", \"chopstick\"]}", + 17 + ], + "straight": [ + " {\"type\": \"adjective\", \"description\": \"opposite of curved; aligned in a line\", \"similar objects\": [\"linear\", \"level\", \"parallel\"]}", + 17 + ], + "pink cell phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"pink; could be a smartphone; could have a touchscreen\", \"similar objects\": [\"laptop\", \"tablet\", \"MP3 player\"]}", + 17 + ], + "girrafe": [ + " {\"type\": \"animal\", \"description\": \"long neck; has spots; has a long mane\", \"similar objects\": [\"zebra\", \"elephant\", \"horse\"]}", + 17 + ], + "onlookers": [ + " {\"type\": \"people\", \"description\": \"people who are watching an event or situation; could be standing or sitting\", \"similar objects\": [\"spectators\", \"audience\", \"bystanders\"]}", + 17 + ], + "lone tree": [ + " {\"type\": \"landscape\", \"description\": \"single tree; could be surrounded by grass or desert\", \"similar objects\": [\"forest\", \"mountain\", \"lake\"]}", + 17 + ], + "rail car": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; could be connected to other rail cars; could be used to transport goods or passengers\", \"similar objects\": [\"train\", \"tram\", \"monorail\"]}", + 17 + ], + "elevator": [ + " {\"type\": \"transportation tool\", \"description\": \"box-shaped; has buttons; could move up and down\", \"similar objects\": [\"escalator\", \"staircase\", \"lift\"]}", + 17 + ], + "blue clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white or greyish clouds with a blue tint; could be seen in the sky\", \"similar objects\": [\"white clouds\", \"rain clouds\", \"cumulus clouds\"]}", + 17 + ], + "soccer shorts": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; usually made of polyester; could have pockets; could have stripes\", \"similar objects\": [\"track shorts\", \"basketball shorts\", \"gym shorts\"]}", + 17 + ], + "pasta salad": [ + " {\"type\": \"dish\", \"description\": \"made of cooked pasta, vegetables, and dressing; could be served cold or hot\", \"similar objects\": [\"macaroni salad\", \"potato salad\", \"coleslaw\"]}", + 17 + ], + "skid marks": [ + " {\"type\": \"road hazard\", \"description\": \"black marks on the road; could be caused by sudden braking\", \"similar objects\": [\"potholes\", \"oil spills\", \"debris\"]}", + 17 + ], + "vehicle tracks": [ + " {\"type\": \"evidence\", \"description\": \"markings left by a vehicle; could be tire tracks, footprints, or other marks\", \"similar objects\": [\"footprints\", \"tire tracks\", \"scuff marks\"]}", + 17 + ], + "branch brown": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, could be curved; could have leaves and fruits\", \"similar objects\": [\"twig\", \"stem\", \"trunk\"]}", + 17 + ], + "metal clip": [ + " {\"type\": \"stationery item\", \"description\": \"small, metallic, has two arms\", \"similar objects\": [\"paper clip\", \"binder clip\", \"bulldog clip\"]}", + 17 + ], + "orange ribbon": [ + " {\"type\": \"decoration item\", \"description\": \"orange color; could be used to tie a gift box; could be used to decorate a room\", \"similar objects\": [\"red ribbon\", \"yellow ribbon\", \"green ribbon\"]}", + 17 + ], + "silver sedan": [ + "\n{\"type\": \"vehicle\", \"description\": \"four-door; could be metallic silver; could have a trunk\", \"similar objects\": [\"SUV\", \"minivan\", \"coupe\"]}", + 17 + ], + "brown tree trunk": [ + "\n{\"type\": \"tree part\", \"description\": \"brown; could be rough; could have branches and leaves\", \"similar objects\": [\"branch\", \"bark\", \"roots\"]}", + 17 + ], + "egret": [ + " {\"type\": \"bird\", \"description\": \"white; long neck; long legs; long beak\", \"similar objects\": [\"heron\", \"ibis\", \"stork\"]}", + 17 + ], + "star design": [ + " {\"type\": \"decoration\", \"description\": \"geometric shape; could be made of paper; could be used for wall decoration\", \"similar objects\": [\"heart design\", \"flower design\", \"circle design\"]}", + 17 + ], + "front pocket": [ + " {\"type\": \"clothing item\", \"description\": \"pocket located at the front of a garment; could be used to store small items\", \"similar objects\": [\"back pocket\", \"side pocket\", \"inner pocket\"]}", + 17 + ], + "gray bricks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of concrete; could be used to build walls\", \"similar objects\": [\"cement blocks\", \"wooden boards\", \"stone tiles\"]}", + 17 + ], + "charcoal": [ + " {\"type\": \"fuel\", \"description\": \"black, powdery; used for grilling\", \"similar objects\": [\"wood\", \"gas\", \"kerosene\"]}", + 17 + ], + "domes": [ + " {\"type\": \"architectural structure\", \"description\": \"round; could be made of concrete, steel, or glass; could be used as a roof\", \"similar objects\": [\"arches\", \"columns\", \"vaults\"]}", + 17 + ], + "beverage cup": [ + " {\"type\": \"drinking tool\", \"description\": \"cylindrical; could have a handle; could be made of plastic, glass, or ceramic\", \"similar objects\": [\"mug\", \"glass\", \"thermos\"]}", + 16 + ], + "rolling suitcase": [ + " {\"type\": \"luggage\", \"description\": \"rectangular; has wheels and a handle; could be made of fabric or hard plastic\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 16 + ], + "model plane": [ + " {\"type\": \"toy\", \"description\": \"small, could be made of plastic or wood; could be remote-controlled\", \"similar objects\": [\"toy car\", \"doll\", \"action figure\"]}", + 16 + ], + "orange cord": [ + " {\"type\": \"electrical tool\", \"description\": \"orange; could be used to connect two devices; could be flexible\", \"similar objects\": [\"extension cord\", \"power cord\", \"USB cable\"]}", + 16 + ], + "gas burner": [ + " {\"type\": \"cooking tool\", \"description\": \"has a knob to control the flame; could be used to cook food\", \"similar objects\": [\"stove\", \"hot plate\", \"electric cooker\"]}", + 16 + ], + "dispensers": [ + " {\"type\": \"utensil\", \"description\": \"used to dispense liquids or other materials; could be manual or automatic\", \"similar objects\": [\"sprayers\", \"pumps\", \"nozzles\"]}", + 16 + ], + "bowl sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"round; could be made of stainless steel; could have a faucet\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 16 + ], + "control knob": [ + " {\"type\": \"control tool\", \"description\": \"round; could be used to adjust the settings of a device\", \"similar objects\": [\"dial\", \"lever\", \"switch\"]}", + 16 + ], + "wet man": [ + " {\"type\": \"person\", \"description\": \"clothes are wet; could be dripping water\", \"similar objects\": [\"swimmer\", \"diver\", \"raincoat person\"]}", + 16 + ], + "head lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"worn on the head; could be rechargeable; could be waterproof\", \"similar objects\": [\"flashlight\", \"lantern\", \"torch\"]}", + 16 + ], + "metal rods": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of metal; could be used for construction\", \"similar objects\": [\"wooden beams\", \"concrete blocks\", \"steel bars\"]}", + 16 + ], + "side tracks": [ + " {\"type\": \"transportation tool\", \"description\": \"long, metal tracks; could be used for trains\", \"similar objects\": [\"railway tracks\", \"monorail tracks\", \"tram tracks\"]}", + 16 + ], + "stuffed monkey": [ + " {\"type\": \"toy\", \"description\": \"soft; could be made of fabric; could have a smiling face\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"doll\"]}", + 16 + ], + "door fridge": [ + " {\"type\": \"appliance\", \"description\": \"large; has a door; could be used to store food\", \"similar objects\": [\"freezer\", \"refrigerator\", \"microwave\"]}", + 16 + ], + "winnie": [ + " {\"type\": \"character\", \"description\": \"a yellow bear; has a red shirt; has a honey pot\", \"similar objects\": [\"tigger\", \"piglet\", \"eyore\"]}", + 16 + ], + "cow tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and hairy; could be used to swat flies\", \"similar objects\": [\"horse tail\", \"goat tail\", \"sheep tail\"]}", + 16 + ], + "grey mouse": [ + "\n{\"type\": \"animal\", \"description\": \"small, grey, has a long tail; could have a pointed nose\", \"similar objects\": [\"rat\", \"hamster\", \"gerbil\"]}", + 16 + ], + "dirt surface": [ + " {\"type\": \"ground surface\", \"description\": \"uneven; could be muddy; could have rocks and pebbles\", \"similar objects\": [\"grass\", \"sand\", \"gravel\"]}", + 16 + ], + "baseboards": [ + " {\"type\": \"building material\", \"description\": \"long, thin, wooden boards; used to cover the gap between the wall and the floor\", \"similar objects\": [\"molding\", \"trim\", \"crown molding\"]}", + 16 + ], + "blue pillows": [ + "\n{\"type\": \"home decor\", \"description\": \"soft, round, blue; could be filled with feathers or foam\", \"similar objects\": [\"cushions\", \"blankets\", \"mattresses\"]}", + 16 + ], + "sandles": [ + " {\"type\": \"footwear\", \"description\": \"open-toed; could be made of leather or rubber; could have straps\", \"similar objects\": [\"flip-flops\", \"slippers\", \"sneakers\"]}", + 16 + ], + "level bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has two levels; could be used for public transportation\", \"similar objects\": [\"tram\", \"metro\", \"train\"]}", + 16 + ], + "folds": [ + " {\"type\": \"geological feature\", \"description\": \"ridges or wrinkles in the Earth's surface; could be caused by tectonic plates shifting\", \"similar objects\": [\"valleys\", \"mountains\", \"hills\"]}", + 16 + ], + "hand sanitizer": [ + " {\"type\": \"cleaning product\", \"description\": \"liquid; could be in a bottle; could be used to clean hands\", \"similar objects\": [\"soap\", \"disinfectant\", \"alcohol wipes\"]}", + 16 + ], + "cabinet drawers": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have handles\", \"similar objects\": [\"dresser\", \"wardrobe\", \"chest of drawers\"]}", + 16 + ], + "gold wedding band": [ + "\n{\"type\": \"jewelry\", \"description\": \"round; made of gold; could have diamonds or other gemstones; could be engraved\", \"similar objects\": [\"engagement ring\", \"bracelet\", \"necklace\"]}", + 16 + ], + "bird bath": [ + " {\"type\": \"garden accessory\", \"description\": \"round; could be made of stone or ceramic; could have a pedestal\", \"similar objects\": [\"bird feeder\", \"bird house\", \"garden fountain\"]}", + 16 + ], + "snow hill": [ + " {\"type\": \"landscape\", \"description\": \"white; could be formed by wind; could be used for skiing\", \"similar objects\": [\"mountain\", \"valley\", \"glacier\"]}", + 16 + ], + "grey parking meter": [ + "\n{\"type\": \"parking tool\", \"description\": \"grey; has a slot for coins; could have a digital display\", \"similar objects\": [\"parking sign\", \"parking ticket machine\", \"parking barrier\"]}", + 16 + ], + "cake table": [ + " {\"type\": \"furniture\", \"description\": \"long, rectangular; could have a cloth cover; could have a cake stand\", \"similar objects\": [\"dining table\", \"coffee table\", \"side table\"]}", + 16 + ], + "mangos": [ + " {\"type\": \"fruit\", \"description\": \"oval; yellow-orange; has a long stem; could have a red blush\", \"similar objects\": [\"peaches\", \"apricots\", \"plums\"]}", + 16 + ], + "clips": [ + " {\"type\": \"stationery\", \"description\": \"small, metal; used to hold papers together\", \"similar objects\": [\"binder clips\", \"paper clips\", \"staples\"]}", + 16 + ], + "orange vase": [ + "\n{\"type\": \"decorative item\", \"description\": \"orange; could be made of ceramic; could have a wide opening\", \"similar objects\": [\"flower pot\", \"urn\", \"jar\"]}", + 16 + ], + "color dog": [ + "\n{\"type\": \"animal\", \"description\": \"can be of any color; has four legs; has a tail; could bark\", \"similar objects\": [\"cat\", \"rabbit\", \"hamster\"]}", + 16 + ], + "wii video game controller": [ + "\n{\"type\": \"gaming device\", \"description\": \"rectangular; has buttons and joysticks; could be wireless\", \"similar objects\": [\"PlayStation controller\", \"Xbox controller\", \"Nintendo Switch controller\"]}", + 16 + ], + "baseball home plate": [ + "\n{\"type\": \"sports equipment\", \"description\": \"rectangular; white; has three holes\", \"similar objects\": [\"baseball bat\", \"baseball glove\", \"baseball cap\"]}", + 16 + ], + "orange design": [ + " {\"type\": \"pattern\", \"description\": \"a combination of orange and other colors; could be abstract or geometric shapes\", \"similar objects\": [\"stripes\", \"floral\", \"plaid\"]}", + 16 + ], + "adidas": [ + " {\"type\": \"brand\", \"description\": \"sportswear and accessories; three stripes logo\", \"similar objects\": [\"Nike\", \"Puma\", \"Reebok\"]}", + 16 + ], + "ski boots": [ + " {\"type\": \"footwear\", \"description\": \"long, thick, waterproof; could be fastened with buckles; could have a high ankle support\", \"similar objects\": [\"hiking boots\", \"snowboard boots\", \"ice skates\"]}", + 16 + ], + "bike frame": [ + " {\"type\": \"bicycle part\", \"description\": \"metal frame; could have two wheels; could have handlebars and pedals\", \"similar objects\": [\"wheels\", \"saddle\", \"chain\"]}", + 16 + ], + "fat": [ + " {\"type\": \"nutrient\", \"description\": \"a type of lipid; essential for energy storage and insulation; could be found in food\", \"similar objects\": [\"carbohydrate\", \"protein\", \"vitamin\"]}", + 16 + ], + "silver vase": [ + " {\"type\": \"decorative item\", \"description\": \"cylindrical; made of silver; could have a wide opening\", \"similar objects\": [\"urn\", \"urns\", \"bowl\"]}", + 16 + ], + "wool hat": [ + " {\"type\": \"clothing item\", \"description\": \"made of wool; could be knitted; could have a pom-pom on top\", \"similar objects\": [\"beanie\", \"scarf\", \"gloves\"]}", + 16 + ], + "silver fridge": [ + "\n{\"type\": \"appliance\", \"description\": \"silver; could be a refrigerator; could have a freezer; could have a water dispenser\", \"similar objects\": [\"stove\", \"dishwasher\", \"microwave\"]}", + 16 + ], + "air conditioner unit": [ + "\n{\"type\": \"cooling device\", \"description\": \"rectangular; has a fan; could be wall-mounted or window-mounted\", \"similar objects\": [\"heater\", \"humidifier\", \"dehumidifier\"]}", + 16 + ], + "comforters": [ + " {\"type\": \"bedding item\", \"description\": \"soft, fluffy, quilted; could be filled with down, feathers, or synthetic materials\", \"similar objects\": [\"duvets\", \"blankets\", \"pillows\"]}", + 16 + ], + "cargo truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a box-shaped body; could have a trailer\", \"similar objects\": [\"pickup truck\", \"semi-truck\", \"van\"]}", + 16 + ], + "thighs": [ + " {\"type\": \"body part\", \"description\": \"upper part of the leg; could be covered with skin; could be muscular\", \"similar objects\": [\"calves\", \"knees\", \"ankles\"]}", + 16 + ], + "tree top": [ + " {\"type\": \"plant part\", \"description\": \"the highest part of a tree; could be made of leaves and branches\", \"similar objects\": [\"trunk\", \"roots\", \"branches\"]}", + 16 + ], + "logotype": [ + " {\"type\": \"graphic design\", \"description\": \"a design or symbol used to identify a company, organization, product, or brand\", \"similar objects\": [\"logo\", \"icon\", \"symbol\"]}", + 16 + ], + "rooftops": [ + " {\"type\": \"architectural feature\", \"description\": \"flat surface on the top of a building; could be made of tiles or metal sheets\", \"similar objects\": [\"balcony\", \"terrace\", \"veranda\"]}", + 16 + ], + "forefront": [ + " {\"type\": \"position\", \"description\": \"the most important or prominent part; the leading position\", \"similar objects\": [\"foreground\", \"vanguard\", \"head\"]}", + 16 + ], + "display window": [ + " {\"type\": \"furniture\", \"description\": \"large glass window; could be used to showcase items\", \"similar objects\": [\"showcase\", \"cabinet\", \"shelf\"]}", + 16 + ], + "ripe tomato": [ + "\n{\"type\": \"vegetable\", \"description\": \"red; soft; could be sliced into pieces; could have green leaves\", \"similar objects\": [\"bell pepper\", \"cucumber\", \"eggplant\"]}", + 16 + ], + "bicycle sign": [ + " {\"type\": \"road sign\", \"description\": \"round; has a bicycle symbol; could be yellow or white\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 16 + ], + "metal tracks": [ + " {\"type\": \"construction material\", \"description\": \"long, thin, metallic; could be used for railway or road construction\", \"similar objects\": [\"concrete slabs\", \"gravel\", \"asphalt\"]}", + 16 + ], + "styrofoam plate": [ + " {\"type\": \"dishware\", \"description\": \"lightweight; white; could be disposable; could be used for food\", \"similar objects\": [\"paper plate\", \"plastic plate\", \"ceramic plate\"]}", + 16 + ], + "onlooker": [ + " {\"type\": \"person\", \"description\": \"someone who watches an event without taking part in it\", \"similar objects\": [\"spectator\", \"bystander\", \"witness\"]}", + 16 + ], + "color yellow": [ + "\n{\"type\": \"color\", \"description\": \"bright, warm hue; could be associated with sunshine and happiness\", \"similar objects\": [\"orange\", \"green\", \"blue\"]}", + 16 + ], + "outdoor bench": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of wood or metal; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"stool\"]}", + 16 + ], + "silver tea pot": [ + "\n{\"type\": \"kitchenware\", \"description\": \"silver; has a spout and a handle; could be used to make tea\", \"similar objects\": [\"coffee pot\", \"kettle\", \"tea kettle\"]}", + 16 + ], + "transformers": [ + " {\"type\": \"toy\", \"description\": \"robot figures; could be changed into vehicles\", \"similar objects\": [\"action figures\", \"building blocks\", \"dolls\"]}", + 16 + ], + "book shelves": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have multiple shelves; could be used to store books\", \"similar objects\": [\"cabinet\", \"wardrobe\", \"cupboard\"]}", + 16 + ], + "brown wood": [ + " {\"type\": \"material\", \"description\": \"dark brown; could be used for furniture; could be carved into shapes\", \"similar objects\": [\"oak\", \"mahogany\", \"teak\"]}", + 16 + ], + "silver lock": [ + " {\"type\": \"security tool\", \"description\": \"made of metal; has a keyhole; could be used to lock doors\", \"similar objects\": [\"padlock\", \"combination lock\", \"deadbolt\"]}", + 16 + ], + "handles cabinet": [ + " {\"type\": \"furniture\", \"description\": \"has handles; could be made of wood; could have drawers\", \"similar objects\": [\"dresser\", \"chest of drawers\", \"wardrobe\"]}", + 16 + ], + "ceiling tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of plastic or metal; could be used to cover the ceiling\", \"similar objects\": [\"drywall\", \"plywood\", \"insulation\"]}", + 16 + ], + "flower pedals": [ + " {\"type\": \"plant part\", \"description\": \"small, colorful, could be petal-shaped; could be scattered on the ground\", \"similar objects\": [\"leaves\", \"seeds\", \"stems\"]}", + 16 + ], + "button nose": [ + " {\"type\": \"facial feature\", \"description\": \"small, round, protruding from the face\", \"similar objects\": [\"eyes\", \"lips\", \"cheeks\"]}", + 16 + ], + "race track": [ + " {\"type\": \"sports facility\", \"description\": \"oval-shaped; has a starting line and a finish line; could have stands for spectators\", \"similar objects\": [\"stadium\", \"arena\", \"court\"]}", + 16 + ], + "standing lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a shade\", \"similar objects\": [\"floor lamp\", \"table lamp\", \"ceiling light\"]}", + 16 + ], + "pit": [ + " {\"type\": \"hole\", \"description\": \"deep; could be dug in the ground; could be filled with water\", \"similar objects\": [\"well\", \"cave\", \"trench\"]}", + 16 + ], + "placemats": [ + " {\"type\": \"tableware\", \"description\": \"rectangular; could be made of cloth or plastic; used to protect the table from spills and scratches\", \"similar objects\": [\"coasters\", \"napkins\", \"tablecloths\"]}", + 16 + ], + "wood logs": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used for firewood\", \"similar objects\": [\"bricks\", \"stones\", \"timber\"]}", + 16 + ], + "wood flooring": [ + " {\"type\": \"flooring material\", \"description\": \"made of wood; could be in planks or tiles; could be stained or painted\", \"similar objects\": [\"laminate flooring\", \"vinyl flooring\", \"carpet\"]}", + 16 + ], + "tooth brush": [ + " {\"type\": \"cleaning tool\", \"description\": \"long handle; has bristles; could be manual or electric\", \"similar objects\": [\"toothpaste\", \"toothpick\", \"mouthwash\"]}", + 16 + ], + "buidling": [ + " {\"type\": \"structure\", \"description\": \"could be made of concrete, steel, wood, or other materials; could have multiple floors; could have windows and doors\", \"similar objects\": [\"house\", \"skyscraper\", \"bridge\"]}", + 16 + ], + "chalkboard sign": [ + " {\"type\": \"writing tool\", \"description\": \"rectangular; could be made of wood or metal; could be written on with chalk\", \"similar objects\": [\"whiteboard\", \"blackboard\", \"marker board\"]}", + 16 + ], + "orange string": [ + " {\"type\": \"craft material\", \"description\": \"orange, thin, flexible; could be used for tying or decorating\", \"similar objects\": [\"yarn\", \"ribbon\", \"twine\"]}", + 16 + ], + "lit screen": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; emits light; could be a monitor or a television\", \"similar objects\": [\"laptop\", \"tablet\", \"smartphone\"]}", + 16 + ], + "metal tongs": [ + " {\"type\": \"cooking tool\", \"description\": \"long; could be made of metal; could be used to pick up food\", \"similar objects\": [\"spatula\", \"ladle\", \"whisk\"]}", + 16 + ], + "hutch": [ + " {\"type\": \"furniture\", \"description\": \"wooden; has shelves and drawers; could be used for storage\", \"similar objects\": [\"cabinet\", \"dresser\", \"armoire\"]}", + 16 + ], + "broccoli piece": [ + " {\"type\": \"vegetable\", \"description\": \"green, florets; could have a stem; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 16 + ], + "linesman": [ + " {\"type\": \"occupation\", \"description\": \"works on power lines; wears safety gear; could be a lineman or a line worker\", \"similar objects\": [\"electrician\", \"cable technician\", \"telecommunications technician\"]}", + 16 + ], + "dozens": [ + " {\"type\": \"number\", \"description\": \"group of twelve; could be used to count items\", \"similar objects\": [\"score\", \"gross\", \"hundred\"]}", + 16 + ], + "palm leaves": [ + " {\"type\": \"plant\", \"description\": \"long, thin, green; could be used for decorations\", \"similar objects\": [\"banana leaves\", \"fern leaves\", \"mango leaves\"]}", + 16 + ], + "bird kite": [ + " {\"type\": \"toy\", \"description\": \"shaped like a bird; could be made of paper or fabric; could be flown in the sky\", \"similar objects\": [\"dragon kite\", \"butterfly kite\", \"delta kite\"]}", + 16 + ], + "play": [ + " {\"type\": \"activity\", \"description\": \"involves physical or mental effort; could be done alone or with others; could be recreational or educational\", \"similar objects\": [\"game\", \"sport\", \"exercise\"]}", + 16 + ], + "sewer grate": [ + " {\"type\": \"infrastructure\", \"description\": \"rectangular; has metal bars; could be found on the ground\", \"similar objects\": [\"manhole cover\", \"drain cover\", \"grill\"]}", + 16 + ], + "colorful box": [ + "\n{\"type\": \"container\", \"description\": \"could be made of cardboard; could have different colors; could be used for storage\", \"similar objects\": [\"bag\", \"basket\", \"bin\"]}", + 16 + ], + "brick structure": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be used to build walls\", \"similar objects\": [\"wood\", \"steel\", \"glass\"]}", + 16 + ], + "script": [ + " {\"type\": \"writing tool\", \"description\": \"written words; could be used for plays, movies, or books\", \"similar objects\": [\"book\", \"pen\", \"pencil\"]}", + 16 + ], + "scroll": [ + " {\"type\": \"document\", \"description\": \"long, thin; could be made of paper or cloth; could be rolled up\", \"similar objects\": [\"book\", \"manuscript\", \"parchment\"]}", + 16 + ], + "ride": [ + " {\"type\": \"transportation\", \"description\": \"could be a car, bus, train, bike, boat, etc.\", \"similar objects\": [\"drive\", \"travel\", \"commute\"]}", + 16 + ], + "hoof prints": [ + " {\"type\": \"animal tracks\", \"description\": \"oval-shaped; could be made by horses, cows, deer, etc.\", \"similar objects\": [\"paw prints\", \"bird footprints\", \"snake tracks\"]}", + 16 + ], + "ice cream truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a jingle; could be painted with ice cream images\", \"similar objects\": [\"food truck\", \"truck\", \"van\"]}", + 16 + ], + "stainless steel knife": [ + "\n{\"type\": \"kitchen tool\", \"description\": \"long; made of stainless steel; could have a handle\", \"similar objects\": [\"fork\", \"spoon\", \"chopping board\"]}", + 16 + ], + "everything": [ + "\n{\"type\": \"abstract concept\", \"description\": \"all things that exist\", \"similar objects\": [\"universe\", \"world\", \"existence\"]}", + 16 + ], + "cake knife": [ + " {\"type\": \"kitchen tool\", \"description\": \"long, thin, sharp blade; could have a handle\", \"similar objects\": [\"butter knife\", \"cheese knife\", \"steak knife\"]}", + 16 + ], + "silver exhaust": [ + " {\"type\": \"automotive part\", \"description\": \"cylindrical; made of metal; used to reduce noise and emissions from the engine\", \"similar objects\": [\"muffler\", \"catalytic converter\", \"air filter\"]}", + 16 + ], + "wooden cupboards": [ + "\n{\"type\": \"furniture\", \"description\": \"made of wood; could have drawers and shelves; could be used for storage\", \"similar objects\": [\"dresser\", \"wardrobe\", \"bookshelf\"]}", + 16 + ], + "metal staircase": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could have multiple steps; could be used to access higher levels\", \"similar objects\": [\"ladder\", \"escalator\", \"elevator\"]}", + 16 + ], + "suit cases": [ + " {\"type\": \"travel accessory\", \"description\": \"rectangular; could be made of hard materials; could have wheels\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 16 + ], + "color orange": [ + "\n{\"type\": \"color\", \"description\": \"vibrant, warm hue; could be associated with energy and enthusiasm\", \"similar objects\": [\"red\", \"yellow\", \"green\"]}", + 16 + ], + "baby blanket": [ + " {\"type\": \"clothing item\", \"description\": \"soft, usually made of cotton; could be decorated with patterns; could be used to wrap a baby\", \"similar objects\": [\"swaddle\", \"burp cloth\", \"receiving blanket\"]}", + 16 + ], + "banana split": [ + " {\"type\": \"dessert\", \"description\": \"ice cream dish; consists of banana, ice cream, and toppings\", \"similar objects\": [\"sundae\", \"milkshake\", \"float\"]}", + 16 + ], + "rubber boots": [ + " {\"type\": \"footwear\", \"description\": \"waterproof; could be made of rubber; could be high or low cut\", \"similar objects\": [\"rain boots\", \"wellington boots\", \"galoshes\"]}", + 16 + ], + "goal post": [ + " {\"type\": \"sports equipment\", \"description\": \"two vertical posts connected by a horizontal crossbar; used in sports such as football and soccer\", \"similar objects\": [\"net\", \"hoop\", \"basket\"]}", + 16 + ], + "gold bell": [ + " {\"type\": \"decorative item\", \"description\": \"round; made of gold; could have a handle\", \"similar objects\": [\"silver bell\", \"copper bell\", \"brass bell\"]}", + 16 + ], + "dell": [ + " {\"type\": \"computer brand\", \"description\": \"manufactures laptops, desktops, and other computer hardware\", \"similar objects\": [\"HP\", \"Lenovo\", \"Apple\"]}", + 16 + ], + "binoculars": [ + " {\"type\": \"optical tool\", \"description\": \"two lenses; could be used to magnify distant objects\", \"similar objects\": [\"telescope\", \"microscope\", \"monocular\"]}", + 16 + ], + "leaves plant": [ + " {\"type\": \"plant\", \"description\": \"green; could have different shapes; could be attached to a stem; could have veins\", \"similar objects\": [\"fern\", \"flower\", \"grass\"]}", + 16 + ], + "toilet flusher": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be manual or automatic; could be connected to a water tank\", \"similar objects\": [\"shower head\", \"faucet\", \"drain\"]}", + 16 + ], + "artichoke": [ + " {\"type\": \"vegetable\", \"description\": \"spiky, green, has a heart; could be boiled or steamed\", \"similar objects\": [\"cauliflower\", \"broccoli\", \"asparagus\"]}", + 16 + ], + "beige pillow": [ + " {\"type\": \"home decor\", \"description\": \"rectangular; could be made of cotton; could have a pattern\", \"similar objects\": [\"blanket\", \"cushion\", \"rug\"]}", + 16 + ], + "leather office chair": [ + "\n{\"type\": \"furniture\", \"description\": \"made of leather; has armrests; could be swiveled; could be adjustable in height\", \"similar objects\": [\"desk chair\", \"sofa\", \"recliner\"]}", + 16 + ], + "bee": [ + " {\"type\": \"insect\", \"description\": \"black and yellow stripes; has wings; could produce honey\", \"similar objects\": [\"wasp\", \"hornet\", \"mosquito\"]}", + 16 + ], + "grinder": [ + " {\"type\": \"tool\", \"description\": \"cylindrical; could be used to grind spices or coffee beans\", \"similar objects\": [\"blender\", \"food processor\", \"mortar and pestle\"]}", + 16 + ], + "grey ground": [ + " {\"type\": \"surface\", \"description\": \"light to dark grey; could be made of concrete, asphalt, or stone\", \"similar objects\": [\"pavement\", \"sidewalk\", \"driveway\"]}", + 16 + ], + "gourd": [ + " {\"type\": \"vegetable\", \"description\": \"hard, green, oval-shaped; could have a long stem; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"squash\", \"pumpkin\", \"cucumber\"]}", + 16 + ], + "metal door knob": [ + "\n{\"type\": \"hardware\", \"description\": \"round; made of metal; could have a keyhole\", \"similar objects\": [\"door handle\", \"lock\", \"hinge\"]}", + 16 + ], + "pink chair": [ + " {\"type\": \"furniture\", \"description\": \"pink; could have armrests; could have a cushion\", \"similar objects\": [\"sofa\", \"bench\", \"stool\"]}", + 16 + ], + "grey bag": [ + " {\"type\": \"accessory\", \"description\": \"made of fabric; could be carried on the shoulder; could be used to store items\", \"similar objects\": [\"backpack\", \"purse\", \"suitcase\"]}", + 16 + ], + "list": [ + " {\"type\": \"data structure\", \"description\": \"ordered collection of items; could be used to store and access data\", \"similar objects\": [\"array\", \"stack\", \"queue\"]}", + 16 + ], + "nintendo wii": [ + " {\"type\": \"gaming console\", \"description\": \"white; has a motion controller; could be connected to a TV\", \"similar objects\": [\"PlayStation\", \"Xbox\", \"GameCube\"]}", + 16 + ], + "street name signs": [ + " {\"type\": \"road signs\", \"description\": \"rectangular; has a street name written on it; could be yellow or white\", \"similar objects\": [\"stop sign\", \"speed limit sign\", \"no parking sign\"]}", + 16 + ], + "sunny day": [ + "\n{\"type\": \"weather\", \"description\": \"bright; clear sky; warm temperature\", \"similar objects\": [\"clear night\", \"rainy day\", \"snowy day\"]}", + 16 + ], + "thick tree": [ + " {\"type\": \"plant\", \"description\": \"large, tall, with thick trunk and branches; could have leaves or needles; could have fruits or flowers\", \"similar objects\": [\"pine tree\", \"oak tree\", \"palm tree\"]}", + 16 + ], + "winter sky": [ + " {\"type\": \"weather phenomenon\", \"description\": \"dark blue; could have stars and snowflakes; could have a full moon\", \"similar objects\": [\"summer sky\", \"autumn sky\", \"spring sky\"]}", + 16 + ], + "nets": [ + " {\"type\": \"fishing tool\", \"description\": \"made of strings; could be used to catch fish\", \"similar objects\": [\"rod\", \"hook\", \"bait\"]}", + 16 + ], + "butter knives": [ + " {\"type\": \"utensil\", \"description\": \"small, flat, has a rounded edge; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 16 + ], + "sausage link": [ + " {\"type\": \"food\", \"description\": \"cylindrical; could be made of pork, beef, or turkey; could be cooked in a pan or grilled\", \"similar objects\": [\"hot dog\", \"bratwurst\", \"kielbasa\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and", + 16 + ], + "leafy bushes": [ + " {\"type\": \"plant\", \"description\": \"green; could have multiple leaves; could be used for decoration\", \"similar objects\": [\"trees\", \"shrubs\", \"flowers\"]}", + 16 + ], + "metal bat": [ + " {\"type\": \"sports equipment\", \"description\": \"long, cylindrical; made of metal; used in baseball\", \"similar objects\": [\"baseball glove\", \"baseball cap\", \"baseball bat\"]}", + 16 + ], + "grey cap": [ + " {\"type\": \"clothing item\", \"description\": \"headwear; could be made of cotton; could have a logo\", \"similar objects\": [\"hat\", \"beanie\", \"baseball cap\"]}", + 16 + ], + "moulding": [ + " {\"type\": \"building material\", \"description\": \"decorative strips used to cover the joints between surfaces; could be made of wood, plastic, or metal\", \"similar objects\": [\"trim\", \"baseboard\", \"crown moulding\"]}", + 16 + ], + "feces": [ + " {\"type\": \"waste\", \"description\": \"solid or semi-solid; could be brown or yellow; could have an unpleasant smell\", \"similar objects\": [\"urine\", \"vomit\", \"blood\"]}", + 16 + ], + "tan shirt": [ + " {\"type\": \"clothing\", \"description\": \"light brown; could have buttons; could have a collar\", \"similar objects\": [\"t-shirt\", \"blouse\", \"dress\"]}", + 16 + ], + "sideways": [ + " {\"type\": \"direction\", \"description\": \"at an angle; not straight\", \"similar objects\": [\"diagonal\", \"oblique\", \"slanted\"]}", + 16 + ], + "tow truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a long arm; could be used to tow other vehicles\", \"similar objects\": [\"dump truck\", \"flatbed truck\", \"wrecker\"]}", + 16 + ], + "silo": [ + " {\"type\": \"storage structure\", \"description\": \"cylindrical; could be made of metal or concrete; could be used to store grain or other materials\", \"similar objects\": [\"barn\", \"warehouse\", \"grain elevator\"]}", + 16 + ], + "metal fan": [ + " {\"type\": \"cooling tool\", \"description\": \"has blades; could be made of metal; could be powered by electricity\", \"similar objects\": [\"air conditioner\", \"ceiling fan\", \"portable fan\"]}", + 16 + ], + "beach front": [ + " {\"type\": \"landscape\", \"description\": \"sand; could have palm trees; could have waves; could have rocks\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 16 + ], + "brick fence": [ + " {\"type\": \"building material\", \"description\": \"made of bricks; could be used to build a fence\", \"similar objects\": [\"wood fence\", \"metal fence\", \"concrete fence\"]}", + 16 + ], + "fall": [ + " {\"type\": \"season\", \"description\": \"cooler temperatures; leaves changing colors; shorter days\", \"similar objects\": [\"winter\", \"spring\", \"summer\"]}", + 16 + ], + "guns": [ + " {\"type\": \"weapon\", \"description\": \"could be made of metal; could be used to shoot bullets\", \"similar objects\": [\"rifle\", \"pistol\", \"shotgun\"]}", + 16 + ], + "chaise lounge": [ + " {\"type\": \"furniture\", \"description\": \"long chair; could be made of wood or metal; could have armrests and a backrest\", \"similar objects\": [\"sofa\", \"loveseat\", \"recliner\"]}", + 16 + ], + "business logo": [ + "\n{\"type\": \"graphic design\", \"description\": \"unique design; could be composed of shapes, colors, and text; could be used to represent a company or organization\", \"similar objects\": [\"banner\", \"poster\", \"sign\"]}", + 16 + ], + "sports shirt": [ + " {\"type\": \"clothing\", \"description\": \"could be made of cotton; could have a logo; could have short sleeves\", \"similar objects\": [\"t-shirt\", \"polo shirt\", \"tank top\"]}", + 16 + ], + "wood table top": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could be polished\", \"similar objects\": [\"wood chair\", \"wood desk\", \"wood shelf\"]}", + 16 + ], + "plane propeller": [ + " {\"type\": \"aircraft part\", \"description\": \"long, thin blades; could be made of metal; could be attached to an engine\", \"similar objects\": [\"jet engine\", \"wing\", \"fuselage\"]}", + 16 + ], + "gold stripe": [ + " {\"type\": \"pattern\", \"description\": \"a stripe with a golden color; could be used for decoration\", \"similar objects\": [\"silver stripe\", \"black stripe\", \"rainbow stripe\"]}", + 16 + ], + "score": [ + " {\"type\": \"measurement\", \"description\": \"a number or mark that shows how well someone has done in a test, game, or other activity\", \"similar objects\": [\"grade\", \"mark\", \"result\"]}", + 16 + ], + "bowl counter": [ + " {\"type\": \"kitchen tool\", \"description\": \"flat, round, has a handle; could be used to mix ingredients\", \"similar objects\": [\"mixing bowl\", \"measuring cup\", \"whisk\"]}", + 16 + ], + "walnut": [ + " {\"type\": \"nut\", \"description\": \"round; has a hard shell; could be brown or black\", \"similar objects\": [\"almond\", \"cashew\", \"pecan\"]}", + 16 + ], + "links": [ + " {\"type\": \"connectors\", \"description\": \"small metal rings; could be used to connect two objects\", \"similar objects\": [\"hooks\", \"clasps\", \"buttons\"]}", + 16 + ], + "barber": [ + " {\"type\": \"occupation\", \"description\": \"cuts and styles hair; could use scissors and razors\", \"similar objects\": [\"hairdresser\", \"stylist\", \"barber shop\"]}", + 16 + ], + "seating": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood, metal, or plastic; could have a backrest and armrest; could be used for sitting\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 16 + ], + "left ski": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, curved; could be used for skiing\", \"similar objects\": [\"right ski\", \"snowboard\", \"skateboard\"]}", + 16 + ], + "crowns": [ + " {\"type\": \"accessory\", \"description\": \"ornamental headgear; could be made of gold or silver; could be decorated with jewels\", \"similar objects\": [\"tiara\", \"hat\", \"headband\"]}", + 16 + ], + "silver wing": [ + " {\"type\": \"bird\", \"description\": \"grayish-white; has a long tail; could have a crest on its head\", \"similar objects\": [\"eagle\", \"hawk\", \"pigeon\"]}", + 16 + ], + "silver lid": [ + " {\"type\": \"utensil\", \"description\": \"round; made of metal; could be used to cover a pot or pan\", \"similar objects\": [\"pot lid\", \"pan lid\", \"jar lid\"]}", + 16 + ], + "tag suitcase": [ + " {\"type\": \"travel accessory\", \"description\": \"rectangular; has a handle; could be locked\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 16 + ], + "outer edge": [ + "\n{\"type\": \"geometric shape\", \"description\": \"a line that forms the outer boundary of a shape\", \"similar objects\": [\"perimeter\", \"circumference\", \"contour\"]}", + 16 + ], + "side part": [ + " {\"type\": \"hairstyle\", \"description\": \"hair is parted to one side; could be combined with other styles\", \"similar objects\": [\"bob cut\", \"pixie cut\", \"mullet\"]}", + 16 + ], + "top road": [ + " {\"type\": \"road\", \"description\": \"long, straight, could have two lanes\", \"similar objects\": [\"highway\", \"freeway\", \"interstate\"]}", + 16 + ], + "color silver": [ + "\n{\"type\": \"color\", \"description\": \"light gray; metallic; shiny\", \"similar objects\": [\"gray\", \"white\", \"black\"]}", + 16 + ], + "baby bird": [ + " {\"type\": \"animal\", \"description\": \"small; has feathers; could have a beak; could be hatched from an egg\", \"similar objects\": [\"chick\", \"duckling\", \"fledgling\"]}", + 16 + ], + "flow": [ + " {\"type\": \"movement\", \"description\": \"continuous movement of something; could be liquid, gas, or particles\", \"similar objects\": [\"stream\", \"current\", \"eddy\"]}", + 16 + ], + "ski trails": [ + " {\"type\": \"outdoor activity\", \"description\": \"long, narrow paths; could be groomed; could be marked with poles\", \"similar objects\": [\"hiking trails\", \"biking trails\", \"snowshoe trails\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects", + 16 + ], + "square pizza": [ + " {\"type\": \"food\", \"description\": \"square shape; could be topped with cheese, vegetables, and meat; could be served with tomato sauce\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"deep dish pizza\"]}", + 16 + ], + "wood sign": [ + " {\"type\": \"decoration\", \"description\": \"could be made of wood; could be painted with words or images\", \"similar objects\": [\"plaque\", \"banner\", \"flag\"]}", + 16 + ], + "squeeze bottle": [ + " {\"type\": \"container\", \"description\": \"long and thin; could be made of plastic; could have a nozzle\", \"similar objects\": [\"water bottle\", \"spray bottle\", \"condiment bottle\"]}", + 16 + ], + "blow": [ + " {\"type\": \"action\", \"description\": \"to move air out of the mouth; could be used to extinguish a flame\", \"similar objects\": [\"breathe\", \"puff\", \"exhale\"]}", + 16 + ], + "gas station sign": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be illuminated; could have a logo of a gas station\", \"similar objects\": [\"road sign\", \"traffic sign\", \"store sign\"]}", + 16 + ], + "blurry tree": [ + "\n{\"type\": \"plant\", \"description\": \"could have multiple branches; could have leaves; could be tall; could be blurry\", \"similar objects\": [\"bush\", \"shrub\", \"palm tree\"]}", + 16 + ], + "giraffe ears": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and pointed; could be brown or black\", \"similar objects\": [\"elephant ears\", \"horse ears\", \"monkey ears\"]}", + 16 + ], + "curvy": [ + "\n{\"type\": \"adjective\", \"description\": \"having curves or bends; not straight\", \"similar objects\": [\"sinuous\", \"winding\", \"meandering\"]}", + 16 + ], + "fir trees": [ + " {\"type\": \"plant\", \"description\": \"evergreen; has needles; could have cones; could be tall and slender\", \"similar objects\": [\"pine tree\", \"spruce tree\", \"cedar tree\"]}", + 16 + ], + "broccoli head": [ + " {\"type\": \"vegetable\", \"description\": \"green, florets; could have a stem; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 16 + ], + "pooh": [ + " {\"type\": \"character\", \"description\": \"yellow bear; has a red shirt; loves honey\", \"similar objects\": [\"tigger\", \"piglet\", \"rabbit\"]}", + 16 + ], + "coal": [ + " {\"type\": \"fuel\", \"description\": \"black, solid; could be used as a fuel source\", \"similar objects\": [\"wood\", \"oil\", \"gas\"]}", + 16 + ], + "orange post": [ + " {\"type\": \"object\", \"description\": \"orange in color; could be made of metal or plastic; could be used as a marker or sign\", \"similar objects\": [\"traffic cone\", \"street sign\", \"road barrier\"]}", + 16 + ], + "coconuts": [ + " {\"type\": \"fruit\", \"description\": \"round, brown, has a hard shell; could have a hairy texture; could have a white flesh inside\", \"similar objects\": [\"avocado\", \"mango\", \"papaya\"]}", + 16 + ], + "broccoli heads": [ + " {\"type\": \"vegetable\", \"description\": \"green, small florets; could have thick stems; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 16 + ], + "glass pepper shaker": [ + "\n{\"type\": \"kitchen tool\", \"description\": \"transparent; cylindrical; has a lid; could be filled with pepper\", \"similar objects\": [\"salt shaker\", \"sugar shaker\", \"spice jar\"]}", + 16 + ], + "clusters": [ + " {\"type\": \"group\", \"description\": \"a group of objects or people; could be related or unrelated\", \"similar objects\": [\"bunch\", \"collection\", \"set\"]}", + 16 + ], + "round eye": [ + " {\"type\": \"eyewear\", \"description\": \"circular frame; could be made of metal or plastic; could be tinted or clear\", \"similar objects\": [\"sunglasses\", \"reading glasses\", \"safety glasses\"]}", + 16 + ], + "gondola": [ + " {\"type\": \"watercraft\", \"description\": \"long, narrow boat; could be used for transportation; could be decorated with colorful ribbons\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 16 + ], + "color shirt": [ + " {\"type\": \"clothing\", \"description\": \"could be any color; could have short or long sleeves; could have a collar\", \"similar objects\": [\"dress\", \"jacket\", \"sweater\"]}", + 16 + ], + "silver bumper": [ + " {\"type\": \"car part\", \"description\": \"metal; could be attached to the front or back of a car; could be used to protect the car from minor collisions\", \"similar objects\": [\"grille\", \"headlight\", \"tail light\"]}", + 16 + ], + "rubber gloves": [ + " {\"type\": \"protective tool\", \"description\": \"long; made of rubber; could be used for cleaning\", \"similar objects\": [\"apron\", \"mask\", \"goggles\"]}", + 16 + ], + "orange pants": [ + "\n{\"type\": \"clothing\", \"description\": \"orange color; could be made of cotton; could be long or short; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 16 + ], + "slender": [ + " {\"type\": \"adjective\", \"description\": \"thin; slim; narrow\", \"similar objects\": [\"slender\", \"slim\", \"lean\"]}", + 16 + ], + "wood poles": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used for construction\", \"similar objects\": [\"bricks\", \"concrete\", \"steel beams\"]}", + 16 + ], + "blue arrow": [ + " {\"type\": \"symbol\", \"description\": \"blue; pointing in a certain direction\", \"similar objects\": [\"red arrow\", \"green arrow\", \"yellow arrow\"]}", + 16 + ], + "traffic camera": [ + " {\"type\": \"surveillance tool\", \"description\": \"mounted on a pole; could be used to detect traffic violations\", \"similar objects\": [\"security camera\", \"speed camera\", \"red light camera\"]}", + 16 + ], + "shot glass": [ + " {\"type\": \"drinking tool\", \"description\": \"small, cylindrical, has a stem; could be made of glass or plastic\", \"similar objects\": [\"wine glass\", \"mug\", \"tumbler\"]}", + 16 + ], + "metal blade": [ + " {\"type\": \"tool\", \"description\": \"sharp; could be used for cutting; could be made of metal\", \"similar objects\": [\"knife\", \"axe\", \"scissors\"]}", + 16 + ], + "wooden sticks": [ + " {\"type\": \"building material\", \"description\": \"long, thin, cylindrical; could be used for construction\", \"similar objects\": [\"bamboo sticks\", \"metal rods\", \"plastic pipes\"]}", + 16 + ], + "plaid jacket": [ + " {\"type\": \"clothing\", \"description\": \"has a pattern of different colors; could be made of wool; could have a zipper\", \"similar objects\": [\"flannel shirt\", \"denim jacket\", \"tweed coat\"]}", + 16 + ], + "warm": [ + "\n{\"type\": \"temperature\", \"description\": \"higher than normal temperature; could be hot or mild\", \"similar objects\": [\"hot\", \"cold\", \"mild\"]}", + 16 + ], + "handwritten sign": [ + " {\"type\": \"written communication\", \"description\": \"could be written with a pen or marker; could be in any language; could be in any font\", \"similar objects\": [\"printed sign\", \"poster\", \"billboard\"]}", + 16 + ], + "sleeveless top": [ + " {\"type\": \"clothing\", \"description\": \"shoulder-baring; could be made of cotton or silk; could have a collar or a V-neck\", \"similar objects\": [\"tank top\", \"halter top\", \"camisole\"]}", + 16 + ], + "toilet paper roll holder": [ + " {\"type\": \"household item\", \"description\": \"cylindrical; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"towel holder\", \"toilet brush holder\", \"soap dish\"]}", + 16 + ], + "soccer shoe": [ + " {\"type\": \"footwear\", \"description\": \"long, flexible, has cleats\", \"similar objects\": [\"running shoe\", \"hiking boot\", \"basketball shoe\"]}", + 16 + ], + "mud puddle": [ + " {\"type\": \"natural phenomenon\", \"description\": \"a pool of water and mud; could be found in a forest or a field\", \"similar objects\": [\"pond\", \"lake\", \"stream\"]}", + 16 + ], + "cement platform": [ + " {\"type\": \"construction material\", \"description\": \"hard, grey, flat surface; could be used to build a foundation\", \"similar objects\": [\"concrete block\", \"bricks\", \"gravel\"]}", + 16 + ], + "wooden structure": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be used to build furniture, houses, etc.\", \"similar objects\": [\"bricks\", \"concrete\", \"steel\"]}", + 16 + ], + "sticker banana": [ + "\n{\"type\": \"decorative item\", \"description\": \"yellow; could be in the shape of a banana; could be used to decorate surfaces\", \"similar objects\": [\"wall decal\", \"window cling\", \"vinyl sticker\"]}", + 16 + ], + "coil": [ + " {\"type\": \"electrical component\", \"description\": \"spiral shape; could be made of copper wire; could be used to store energy\", \"similar objects\": [\"transformer\", \"capacitor\", \"inductor\"]}", + 16 + ], + "pocket knife": [ + " {\"type\": \"tool\", \"description\": \"small, foldable; could have multiple blades\", \"similar objects\": [\"multi-tool\", \"utility knife\", \"scissors\"]}", + 16 + ], + "bicep": [ + " {\"type\": \"muscle\", \"description\": \"flexible; located in the upper arm; could be strengthened by exercise\", \"similar objects\": [\"tricep\", \"quadricep\", \"pectoral\"]}", + 16 + ], + "city scene": [ + "\n{\"type\": \"landscape\", \"description\": \"buildings, roads, trees, people; could have a river or a lake; could have a sky with clouds\", \"similar objects\": [\"countryside\", \"mountain view\", \"beach\"]}", + 16 + ], + "carry": [ + " {\"type\": \"verb\", \"description\": \"to take or transport something from one place to another\", \"similar objects\": [\"transport\", \"lift\", \"move\"]}", + 16 + ], + "armor": [ + " {\"type\": \"protective clothing\", \"description\": \"made of metal; could be worn by knights; could be used for protection\", \"similar objects\": [\"helmet\", \"shield\", \"breastplate\"]}", + 16 + ], + "microwave stove": [ + " {\"type\": \"cooking tool\", \"description\": \"box-shaped; has a door; could be used to heat food\", \"similar objects\": [\"oven\", \"toaster\", \"grill\"]}", + 16 + ], + "display sign": [ + " {\"type\": \"advertising tool\", \"description\": \"could be made of paper, plastic, or metal; could be illuminated; could be hung or placed on a stand\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 16 + ], + "canyon": [ + " {\"type\": \"geographical feature\", \"description\": \"deep, steep-sided valley; could be formed by a river\", \"similar objects\": [\"ravine\", \"gorge\", \"cliff\"]}", + 16 + ], + "storage area": [ + " {\"type\": \"space\", \"description\": \"could be a room or a closet; could be used to store items\", \"similar objects\": [\"garage\", \"basement\", \"attic\"]}", + 16 + ], + "tan bricks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be used to build walls\", \"similar objects\": [\"concrete blocks\", \"cement blocks\", \"stone blocks\"]}", + 16 + ], + "candle holders": [ + " {\"type\": \"decorative item\", \"description\": \"could be made of metal or glass; could hold a candle\", \"similar objects\": [\"vases\", \"lanterns\", \"lamps\"]}", + 16 + ], + "kia logo": [ + " {\"type\": \"logo\", \"description\": \"red and silver; has a stylized letter K\", \"similar objects\": [\"Hyundai logo\", \"Ford logo\", \"Toyota logo\"]}", + 16 + ], + "surfer wave": [ + " {\"type\": \"natural phenomenon\", \"description\": \"large, powerful wave; could be ridden by surfers\", \"similar objects\": [\"tsunami\", \"tidal wave\", \"rogue wave\"]}", + 16 + ], + "gray edge": [ + " {\"type\": \"edge\", \"description\": \"gray; could be sharp or blunt; could be straight or curved\", \"similar objects\": [\"edge\", \"corner\", \"ridge\"]}", + 16 + ], + "motor bikes": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could have an engine; could have a seat for two people\", \"similar objects\": [\"scooter\", \"moped\", \"bicycle\"]}", + 16 + ], + "metal circle": [ + " {\"type\": \"object\", \"description\": \"round; made of metal; could be used for decoration or as a tool\", \"similar objects\": [\"metal ring\", \"metal disc\", \"metal plate\"]}", + 16 + ], + "orange tail": [ + " {\"type\": \"bird\", \"description\": \"orange feathers on the tail; could have black wings; could have a yellow beak\", \"similar objects\": [\"robin\", \"cardinal\", \"blue jay\"]}", + 16 + ], + "bent legs": [ + " {\"type\": \"body part\", \"description\": \"curved legs; could be bent at the knee\", \"similar objects\": [\"arms\", \"shoulders\", \"hips\"]}", + 16 + ], + "rain gutter": [ + " {\"type\": \"building tool\", \"description\": \"long, narrow, installed along the edge of a roof; used to collect and direct rainwater away from the building\", \"similar objects\": [\"downspout\", \"drainpipe\", \"gutter guard\"]}", + 16 + ], + "shepherd": [ + " {\"type\": \"occupation\", \"description\": \"takes care of sheep; could use a stick and a whistle\", \"similar objects\": [\"farmer\", \"shearer\", \"sheepdog\"]}", + 16 + ], + "shin guard": [ + " {\"type\": \"protective gear\", \"description\": \"worn on the shin; could be made of plastic or foam; could be strapped on the leg\", \"similar objects\": [\"helmet\", \"shoulder pads\", \"knee pads\"]}", + 16 + ], + "rip": [ + " {\"type\": \"verb\", \"description\": \"to tear apart; to separate into pieces\", \"similar objects\": [\"tear\", \"shred\", \"split\"]}", + 16 + ], + "horses mane": [ + " {\"type\": \"animal feature\", \"description\": \"long, thick hair on the neck of a horse\", \"similar objects\": [\"horse tail\", \"horse hooves\", \"horse coat\"]}", + 16 + ], + "hairy tail": [ + " {\"type\": \"animal body part\", \"description\": \"long, thick, and covered with fur; could be used for balance and communication\", \"similar objects\": [\"mane\", \"whiskers\", \"horns\"]}", + 16 + ], + "tan wheels": [ + " {\"type\": \"automotive part\", \"description\": \"round; could be made of metal; could be used for cars, trucks, and other vehicles\", \"similar objects\": [\"tires\", \"rims\", \"hubs\"]}", + 16 + ], + "giraffes eye": [ + "\n{\"type\": \"body part\", \"description\": \"large, dark, almond-shaped; has long eyelashes\", \"similar objects\": [\"elephant eye\", \"horse eye\", \"human eye\"]}", + 16 + ], + "vanilla ice cream": [ + "\n{\"type\": \"dessert\", \"description\": \"white; creamy; sweet; could be served with toppings\", \"similar objects\": [\"strawberry ice cream\", \"chocolate ice cream\", \"sorbet\"]}", + 16 + ], + "silver logo": [ + " {\"type\": \"brand symbol\", \"description\": \"could be a shape, a letter, or a combination of both; could be in different colors; could be used to represent a company or a product\", \"similar objects\": [\"logo\", \"emblem\", \"badge\"]}", + 16 + ], + "velcro": [ + " {\"type\": \"fastening tool\", \"description\": \"hook and loop fastener; could be used to attach two surfaces together\", \"similar objects\": [\"zipper\", \"button\", \"snap\"]}", + 16 + ], + "front teeth": [ + " {\"type\": \"body part\", \"description\": \"white; two in the upper jaw; two in the lower jaw; sharp edges\", \"similar objects\": [\"molars\", \"canines\", \"incisors\"]}", + 16 + ], + "baking": [ + " {\"type\": \"cooking technique\", \"description\": \"cooking food in an oven; could involve mixing ingredients\", \"similar objects\": [\"roasting\", \"grilling\", \"frying\"]}", + 16 + ], + "chrome sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be mounted on the wall or countertop\", \"similar objects\": [\"bathroom faucet\", \"kitchen faucet\", \"shower head\"]}", + 16 + ], + "mane hair": [ + " {\"type\": \"hair style\", \"description\": \"long, thick, and curly hair; usually worn by African-American women\", \"similar objects\": [\"afro\", \"cornrows\", \"box braids\"]}", + 16 + ], + "casserole": [ + " {\"type\": \"cooking tool\", \"description\": \"deep, round, has a lid; could be made of ceramic or metal\", \"similar objects\": [\"pot\", \"pan\", \"skillet\"]}", + 16 + ], + "train railroad tracks": [ + "\n{\"type\": \"transportation infrastructure\", \"description\": \"long, parallel metal rails; could have wooden ties; could have electric wires\", \"similar objects\": [\"highway\", \"bridge\", \"tunnel\"]}", + 16 + ], + "style building": [ + " {\"type\": \"architecture\", \"description\": \"tall; could have multiple floors; could have a unique design\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"museum\"]}", + 16 + ], + "plaid design": [ + " {\"type\": \"pattern\", \"description\": \"intersecting lines of different colors; could be used for clothing, furniture, or other items\", \"similar objects\": [\"stripes\", \"checks\", \"floral\"]}", + 16 + ], + "windbreaker": [ + " {\"type\": \"clothing\", \"description\": \"lightweight, water-resistant; could have a hood; could be zipped up\", \"similar objects\": [\"jacket\", \"raincoat\", \"coat\"]}", + 16 + ], + "building clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"large; could be mounted on a building wall; could have a pendulum\", \"similar objects\": [\"grandfather clock\", \"alarm clock\", \"pocket watch\"]}", + 16 + ], + "porcelain bath tub": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"smooth, white, oval-shaped; could have a stand; could have a shower head\", \"similar objects\": [\"shower stall\", \"bathroom sink\", \"toilet\"]}", + 16 + ], + "orange goggles": [ + "\n{\"type\": \"eyewear\", \"description\": \"orange; could be made of plastic; could have lenses\", \"similar objects\": [\"sunglasses\", \"safety glasses\", \"swim goggles\"]}", + 16 + ], + "paper bowl": [ + " {\"type\": \"container\", \"description\": \"round; made of paper; could be used for food\", \"similar objects\": [\"plastic bowl\", \"paper cup\", \"ceramic bowl\"]}", + 16 + ], + "fence rail": [ + " {\"type\": \"building material\", \"description\": \"long, thin, wooden; could be used to build a fence\", \"similar objects\": [\"wooden post\", \"wooden plank\", \"metal rail\"]}", + 16 + ], + "front porch": [ + " {\"type\": \"structure\", \"description\": \"raised platform; could be made of wood; could have a railing; could have a roof\", \"similar objects\": [\"deck\", \"balcony\", \"veranda\"]}", + 16 + ], + "roof house": [ + " {\"type\": \"structure\", \"description\": \"sloped; could be made of tiles, shingles, or metal; could have a chimney\", \"similar objects\": [\"shed\", \"garage\", \"barn\"]}", + 16 + ], + "metal crane": [ + " {\"type\": \"construction tool\", \"description\": \"tall; has a long arm; could be used to lift heavy objects\", \"similar objects\": [\"forklift\", \"excavator\", \"bulldozer\"]}", + 16 + ], + "stories": [ + " {\"type\": \"literature\", \"description\": \"written works of fiction or non-fiction; could be in the form of books, magazines, or online articles\", \"similar objects\": [\"novels\", \"poems\", \"essays\"]}", + 16 + ], + "hamper": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could have a lid; could be made of wicker\", \"similar objects\": [\"basket\", \"bin\", \"box\"]}", + 16 + ], + "gold ball": [ + " {\"type\": \"ornament\", \"description\": \"round; could be made of gold; could be used as a decoration\", \"similar objects\": [\"silver ball\", \"bronze ball\", \"glass ball\"]}", + 16 + ], + "arrow pointing": [ + " {\"type\": \"directional indicator\", \"description\": \"pointing in a certain direction; could be made of metal or wood\", \"similar objects\": [\"sign\", \"pointer\", \"compass\"]}", + 16 + ], + "track ballast": [ + " {\"type\": \"railway material\", \"description\": \"crushed stone; used to support railway tracks\", \"similar objects\": [\"railway sleepers\", \"railway ties\", \"railway spikes\"]}", + 16 + ], + "giraffes legs": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, slender, and covered in fur; could have black spots\", \"similar objects\": [\"elephant's trunk\", \"horse's legs\", \"monkey's tail\"]}", + 16 + ], + "adult bear": [ + " {\"type\": \"animal\", \"description\": \"large; brown fur; could have a snout; could have claws\", \"similar objects\": [\"grizzly bear\", \"polar bear\", \"koala\"]}", + 16 + ], + "line ripples": [ + " {\"type\": \"pattern\", \"description\": \"repeating, curved lines; could be seen on water surfaces\", \"similar objects\": [\"wave ripples\", \"circular ripples\", \"ripples\"]}", + 16 + ], + "gargoyle": [ + " {\"type\": \"sculpture\", \"description\": \"stone figure; could have wings; could have a grotesque face\", \"similar objects\": [\"statue\", \"fountain\", \"monument\"]}", + 16 + ], + "plane landing": [ + " {\"type\": \"aircraft\", \"description\": \"large; has wings; could be seen descending towards the ground\", \"similar objects\": [\"helicopter\", \"glider\", \"drone\"]}", + 16 + ], + "baseball dugout": [ + " {\"type\": \"structure\", \"description\": \"long, rectangular; has benches; could be covered with a roof\", \"similar objects\": [\"soccer dugout\", \"basketball dugout\", \"tennis dugout\"]}", + 16 + ], + "christmas wreath": [ + "\n{\"type\": \"decoration\", \"description\": \"circular; made of evergreen branches; could have red ribbons and ornaments\", \"similar objects\": [\"garland\", \"mistletoe\", \"Christmas tree\"]}", + 16 + ], + "snow suit": [ + " {\"type\": \"clothing\", \"description\": \"thick; could be waterproof; could be insulated; could have a hood\", \"similar objects\": [\"winter coat\", \"ski jacket\", \"snow boots\"]}", + 16 + ], + "stone archway": [ + " {\"type\": \"architectural structure\", \"description\": \"made of stones; could have a curved top; could have pillars\", \"similar objects\": [\"bridge\", \"column\", \"gate\"]}", + 16 + ], + "grey tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"bricks\", \"wooden planks\", \"concrete blocks\"]}", + 16 + ], + "dark roof": [ + " {\"type\": \"building material\", \"description\": \"dark-colored; could be made of asphalt, metal, or tiles; could be used to cover a roof\", \"similar objects\": [\"shingles\", \"tar paper\", \"gutters\"]}", + 16 + ], + "buiding": [ + " {\"type\": \"structure\", \"description\": \"could be made of concrete, steel, wood, or other materials; could have multiple floors; could have windows and doors\", \"similar objects\": [\"house\", \"skyscraper\", \"bridge\"]}", + 16 + ], + "pole light": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be used for outdoor lighting\", \"similar objects\": [\"street light\", \"flood light\", \"lantern\"]}", + 16 + ], + "bird statue": [ + " {\"type\": \"decoration\", \"description\": \"could be made of metal or stone; could be in the shape of a bird\", \"similar objects\": [\"animal statue\", \"flower statue\", \"sculpture\"]}", + 16 + ], + "individual": [ + " {\"type\": \"person\", \"description\": \"a single person; could be a man or a woman; could be of any age\", \"similar objects\": [\"adult\", \"child\", \"teenager\"]}", + 16 + ], + "fire pit": [ + " {\"type\": \"outdoor tool\", \"description\": \"round; could be made of stones; could have a metal grate on top\", \"similar objects\": [\"barbecue grill\", \"fireplace\", \"smoker\"]}", + 16 + ], + "train sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; could be yellow or red; could have a train symbol\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 16 + ], + "dreads": [ + " {\"type\": \"hairstyle\", \"description\": \"long, matted, twisted locks of hair\", \"similar objects\": [\"braids\", \"cornrows\", \"afro\"]}", + 16 + ], + "thick crust": [ + " {\"type\": \"food\", \"description\": \"made of dough; could be topped with cheese, vegetables, and/or meat; could be baked in an oven\", \"similar objects\": [\"pizza\", \"pie\", \"calzone\"]}", + 16 + ], + "shadow fire hydrant": [ + "\n{\"type\": \"fire safety tool\", \"description\": \"red; has a hose connection; could be used to extinguish fires\", \"similar objects\": [\"fire extinguisher\", \"fire hose\", \"fire alarm\"]}", + 16 + ], + "chimney stack": [ + " {\"type\": \"structure\", \"description\": \"tall, cylindrical; could be made of bricks; could have smoke coming out of it\", \"similar objects\": [\"smokestack\", \"windmill\", \"water tower\"]}", + 16 + ], + "horse ears": [ + " {\"type\": \"animal body part\", \"description\": \"long, pointy, could be covered with fur\", \"similar objects\": [\"cat ears\", \"dog ears\", \"rabbit ears\"]}", + 16 + ], + "plantain": [ + " {\"type\": \"fruit\", \"description\": \"long, curved, yellow; could be cooked or eaten raw; could be mashed\", \"similar objects\": [\"banana\", \"avocado\", \"mango\"]}", + 16 + ], + "eraser": [ + " {\"type\": \"stationery\", \"description\": \"rubber; used to erase pencil marks\", \"similar objects\": [\"pencil\", \"pen\", \"marker\"]}", + 16 + ], + "car side mirror": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the side of a car; could be manually adjusted; could be electronically adjusted\", \"similar objects\": [\"headlight\", \"windshield wiper\", \"tail light\"]}", + 16 + ], + "cement pillar": [ + " {\"type\": \"construction material\", \"description\": \"gray; cylindrical; could be used to support a building\", \"similar objects\": [\"concrete block\", \"steel beam\", \"wooden post\"]}", + 16 + ], + "stone tile": [ + " {\"type\": \"building material\", \"description\": \"flat, rectangular, made of stone; could be used for flooring\", \"similar objects\": [\"ceramic tile\", \"wood tile\", \"marble tile\"]}", + 16 + ], + "sidewalk front building": [ + "\n{\"type\": \"structure\", \"description\": \"concrete path; usually located in front of buildings; could have lines or patterns\", \"similar objects\": [\"driveway\", \"patio\", \"porch\"]}", + 16 + ], + "windmills": [ + " {\"type\": \"energy source\", \"description\": \"tall; has blades; could be used to generate electricity\", \"similar objects\": [\"solar panels\", \"hydroelectric dams\", \"nuclear power plants\"]}", + 16 + ], + "brussel sprouts": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, green; could have yellow flowers; could be boiled or steamed\", \"similar objects\": [\"cauliflower\", \"broccoli\", \"kale\"]}", + 15 + ], + "grey rock wall": [ + "\n{\"type\": \"building material\", \"description\": \"grey; made of rocks; could be used to build walls\", \"similar objects\": [\"bricks\", \"concrete\", \"stone\"]}", + 15 + ], + "lego": [ + " {\"type\": \"toy\", \"description\": \"interlocking plastic blocks; could be used to build structures\", \"similar objects\": [\"building blocks\", \"construction sets\", \"puzzles\"]}", + 15 + ], + "toy plane": [ + " {\"type\": \"toy\", \"description\": \"small, could be made of plastic; could have wings and a tail\", \"similar objects\": [\"car\", \"boat\", \"helicopter\"]}", + 15 + ], + "place mats": [ + " {\"type\": \"tableware\", \"description\": \"rectangular; could be made of cloth or paper; used to protect the table from spills and stains\", \"similar objects\": [\"coasters\", \"napkins\", \"tablecloths\"]}", + 15 + ], + "court net": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; used to divide a court into two halves; could be made of nylon\", \"similar objects\": [\"volleyball net\", \"badminton net\", \"tennis net\"]}", + 15 + ], + "bottom corner": [ + " {\"type\": \"geometric shape\", \"description\": \"two intersecting lines; one line is vertical and the other is horizontal; the point of intersection is the bottom corner\", \"similar objects\": [\"top corner\", \"right corner\", \"left corner\"]}", + 15 + ], + "window seal": [ + " {\"type\": \"building material\", \"description\": \"long, thin, flexible; used to seal windows and doors\", \"similar objects\": [\"weather stripping\", \"door seal\", \"caulk\"]}", + 15 + ], + "orange rug": [ + "\n{\"type\": \"furnishing item\", \"description\": \"orange; could be made of wool; could be rectangular or round\", \"similar objects\": [\"carpet\", \"mat\", \"throw rug\"]}", + 15 + ], + "garbage basket": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; has a lid\", \"similar objects\": [\"trash can\", \"recycling bin\", \"compost bin\"]}", + 15 + ], + "broken pieces": [ + "\n{\"type\": \"object\", \"description\": \"irregularly shaped; could be made of different materials; could be of different sizes\", \"similar objects\": [\"fragments\", \"shards\", \"debris\"]}", + 15 + ], + "shine": [ + " {\"type\": \"verb\", \"description\": \"to emit or reflect light; to be bright or brilliant\", \"similar objects\": [\"glow\", \"sparkle\", \"gleam\"]}", + 15 + ], + "tv monitor": [ + " {\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a computer; could be used to watch movies\", \"similar objects\": [\"computer monitor\", \"projector\", \"smartphone\"]}", + 15 + ], + "bouy": [ + " {\"type\": \"navigational tool\", \"description\": \"round; could be made of metal; could be floating on water\", \"similar objects\": [\"buoy\", \"beacon\", \"lighthouse\"]}", + 15 + ], + "capri pants": [ + " {\"type\": \"clothing\", \"description\": \"ankle-length trousers; could be made of cotton or linen; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 15 + ], + "nutella": [ + " {\"type\": \"food\", \"description\": \"chocolate-hazelnut spread; could be used as a topping or dip\", \"similar objects\": [\"peanut butter\", \"jam\", \"marmalade\"]}", + 15 + ], + "desk drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be opened and closed; could have handles\", \"similar objects\": [\"cabinet\", \"wardrobe\", \"chest of drawers\"]}", + 15 + ], + "batteries": [ + " {\"type\": \"power source\", \"description\": \"small, cylindrical; could be rechargeable; could be used to power electronic devices\", \"similar objects\": [\"solar panel\", \"generator\", \"charger\"]}", + 15 + ], + "floor carpet": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of wool; could be colorful\", \"similar objects\": [\"rug\", \"mat\", \"tile\"]}", + 15 + ], + "perch": [ + " {\"type\": \"fish\", \"description\": \"long, slender body; two dorsal fins; could have yellow stripes\", \"similar objects\": [\"bass\", \"trout\", \"catfish\"]}", + 15 + ], + "ranch": [ + " {\"type\": \"building\", \"description\": \"large, sprawling; could have a barn; could have a corral; could have a house\", \"similar objects\": [\"farm\", \"estate\", \"homestead\"]}", + 15 + ], + "poeple": [ + "\n{\"type\": \"living being\", \"description\": \"human; could have different skin colors; could have different genders; could have different ages\", \"similar objects\": [\"animals\", \"plants\", \"insects\"]}", + 15 + ], + "silver bathroom": [ + " {\"type\": \"room\", \"description\": \"could have a sink, toilet, and shower; could have a mirror; could have a tile floor\", \"similar objects\": [\"kitchen\", \"bedroom\", \"living room\"]}", + 15 + ], + "tray table": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be foldable; could be used for serving food\", \"similar objects\": [\"coffee table\", \"end table\", \"dining table\"]}", + 15 + ], + "colorful hat": [ + "\n{\"type\": \"accessory\", \"description\": \"could be made of fabric; could have a brim; could have a feather; could be decorated with colorful patterns\", \"similar objects\": [\"cap\", \"fedora\", \"beanie\"]}", + 15 + ], + "beret": [ + " {\"type\": \"clothing accessory\", \"description\": \"round, flat, brimless hat; could be made of wool or cotton\", \"similar objects\": [\"cap\", \"fedora\", \"turban\"]}", + 15 + ], + "wall heater": [ + " {\"type\": \"heating tool\", \"description\": \"mounted on the wall; could be electric or gas powered; could have a fan\", \"similar objects\": [\"radiator\", \"space heater\", \"fireplace\"]}", + 15 + ], + "round lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of metal or glass; could have a switch\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}", + 15 + ], + "leather saddle": [ + " {\"type\": \"equipment\", \"description\": \"made of leather; used for horse riding; has a horn and stirrups\", \"similar objects\": [\"bridle\", \"halter\", \"reins\"]}", + 15 + ], + "wind sail": [ + " {\"type\": \"sailing tool\", \"description\": \"triangular; could be made of cloth; used to catch wind\", \"similar objects\": [\"sailboat\", \"kite\", \"parasail\"]}", + 15 + ], + "round edge": [ + " {\"type\": \"shape\", \"description\": \"curved edge; could be used to describe objects\", \"similar objects\": [\"square edge\", \"oval edge\", \"triangle edge\"]}", + 15 + ], + "side light": [ + " {\"type\": \"lighting tool\", \"description\": \"small; could be used as a night light; could be wall-mounted\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 15 + ], + "color floor tiles": [ + " {\"type\": \"flooring material\", \"description\": \"square or rectangular; could be made of ceramic, stone, or vinyl; could come in various colors\", \"similar objects\": [\"wood flooring\", \"carpet\", \"linoleum\"]}", + 15 + ], + "doves": [ + " {\"type\": \"bird\", \"description\": \"white; could have a long tail; could coo\", \"similar objects\": [\"pigeons\", \"sparrows\", \"crows\"]}", + 15 + ], + "silver doorknob": [ + "\n{\"type\": \"hardware\", \"description\": \"round; made of metal; could have a keyhole\", \"similar objects\": [\"door handle\", \"door lock\", \"door latch\"]}", + 15 + ], + "wiskers": [ + " {\"type\": \"animal feature\", \"description\": \"long, thin, stiff hairs on the face of some animals\", \"similar objects\": [\"fur\", \"mane\", \"tail\"]}", + 15 + ], + "liquor": [ + " {\"type\": \"beverage\", \"description\": \"alcoholic; could be made from grains, fruits, or vegetables; could be clear or colored\", \"similar objects\": [\"wine\", \"beer\", \"whiskey\"]}", + 15 + ], + "metal kitchen sink": [ + "\n{\"type\": \"kitchen tool\", \"description\": \"made of metal; has a drain; could have two or more basins\", \"similar objects\": [\"bathroom sink\", \"dishwasher\", \"washing machine\"]}", + 15 + ], + "man jacket": [ + " {\"type\": \"clothing\", \"description\": \"long sleeve; could be made of leather; could have a zipper; could have pockets\", \"similar objects\": [\"coat\", \"blazer\", \"hoodie\"]}", + 15 + ], + "plastic wheel": [ + " {\"type\": \"wheel\", \"description\": \"made of plastic; could be used for toys or furniture\", \"similar objects\": [\"rubber wheel\", \"metal wheel\", \"wooden wheel\"]}", + 15 + ], + "silver edge": [ + " {\"type\": \"utensil\", \"description\": \"shiny; could be used for cutting; could be made of metal\", \"similar objects\": [\"knife\", \"scissors\", \"spoon\"]}", + 15 + ], + "book cover": [ + " {\"type\": \"protective item\", \"description\": \"hard; could be made of paper or plastic; could be decorated with pictures or words\", \"similar objects\": [\"folder\", \"envelope\", \"binder\"]}", + 15 + ], + "metal appliance": [ + " {\"type\": \"household item\", \"description\": \"made of metal; could be used for various purposes; could be a refrigerator, stove, or washing machine\", \"similar objects\": [\"microwave\", \"dishwasher\", \"air conditioner\"]}", + 15 + ], + "files": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of paper or plastic; could be stored in folders\", \"similar objects\": [\"documents\", \"books\", \"magazines\"]}", + 15 + ], + "grey tv": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could have a stand; could have a remote control\", \"similar objects\": [\"computer monitor\", \"stereo\", \"game console\"]}", + 15 + ], + "fireplace mantel": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could be decorated with carvings; could have a shelf\", \"similar objects\": [\"bookshelf\", \"table\", \"chair\"]}", + 15 + ], + "cabinetry": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have drawers and shelves; could be used for storage\", \"similar objects\": [\"dresser\", \"bookshelf\", \"armoire\"]}", + 15 + ], + "arc": [ + " {\"type\": \"geometric shape\", \"description\": \"curved line; could be a part of a circle\", \"similar objects\": [\"circle\", \"ellipse\", \"triangle\"]}", + 15 + ], + "orange t-shirt": [ + "\n{\"type\": \"clothing\", \"description\": \"orange; could be short-sleeved or long-sleeved; could have a logo or design\", \"similar objects\": [\"jeans\", \"dress\", \"hoodie\"]}", + 15 + ], + "computer moniter": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could be connected to a computer\", \"similar objects\": [\"television\", \"laptop\", \"tablet\"]}", + 15 + ], + "braids": [ + " {\"type\": \"hairstyle\", \"description\": \"intertwined strands of hair; could be made of synthetic hair\", \"similar objects\": [\"ponytail\", \"bun\", \"bob\"]}", + 15 + ], + "round tray": [ + " {\"type\": \"serving tool\", \"description\": \"round; could be made of metal or plastic; could be used to serve food\", \"similar objects\": [\"plate\", \"bowl\", \"platter\"]}", + 15 + ], + "storage containers": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of plastic, metal, or wood; could be stackable; could have lids\", \"similar objects\": [\"boxes\", \"baskets\", \"jars\"]}", + 15 + ], + "hash browns": [ + " {\"type\": \"food\", \"description\": \"shredded potatoes; could be fried or baked; could be served with breakfast\", \"similar objects\": [\"french fries\", \"tater tots\", \"potato wedges\"]}", + 15 + ], + "dark lines": [ + "\n{\"type\": \"pattern\", \"description\": \"black or dark lines; could be straight or curved; could be used for decoration\", \"similar objects\": [\"stripes\", \"dots\", \"squares\"]}", + 15 + ], + "pink tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"bricks\", \"wooden boards\", \"marble slabs\"]}", + 15 + ], + "gold wedding ring": [ + "\n{\"type\": \"jewelry\", \"description\": \"round; made of gold; could have diamonds or other precious stones; could have engravings\", \"similar objects\": [\"silver wedding ring\", \"bracelet\", \"necklace\"]}", + 15 + ], + "bystander": [ + " {\"type\": \"person\", \"description\": \"someone who is present at an event but not directly involved\", \"similar objects\": [\"spectator\", \"witness\", \"onlooker\"]}", + 15 + ], + "canvas bag": [ + " {\"type\": \"bag\", \"description\": \"made of canvas; could have straps; could be used for carrying items\", \"similar objects\": [\"backpack\", \"tote bag\", \"duffel bag\"]}", + 15 + ], + "passport": [ + " {\"type\": \"document\", \"description\": \"small booklet; has a photo; could be used for international travel\", \"similar objects\": [\"driver's license\", \"ID card\", \"visa\"]}", + 15 + ], + "shadow motorcycle": [ + "\n{\"type\": \"vehicle\", \"description\": \"black; has two wheels; could have a sidecar; could have a loud engine\", \"similar objects\": [\"scooter\", \"moped\", \"tricycle\"]}", + 15 + ], + "food basket": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of wicker; could have a handle\", \"similar objects\": [\"picnic basket\", \"storage basket\", \"laundry basket\"]}", + 15 + ], + "hamburger bun": [ + " {\"type\": \"food item\", \"description\": \"round; could be toasted; could be sliced in half\", \"similar objects\": [\"hot dog bun\", \"bread roll\", \"bagel\"]}", + 15 + ], + "reading lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"small; could be adjustable; could be used for reading\", \"similar objects\": [\"desk lamp\", \"floor lamp\", \"table lamp\"]}", + 15 + ], + "silver hubcap": [ + " {\"type\": \"automotive part\", \"description\": \"round; made of metal; used to cover the wheel of a car\", \"similar objects\": [\"wheel cover\", \"wheel trim\", \"wheel cap\"]}", + 15 + ], + "smoke trail": [ + " {\"type\": \"atmospheric phenomenon\", \"description\": \"a visible trail of smoke left behind by a moving object; could be caused by a rocket, aircraft, or other vehicle\", \"similar objects\": [\"contrail\", \"volcanic ash\", \"smog\"]}", + 15 + ], + "soy sauce": [ + " {\"type\": \"condiment\", \"description\": \"brown; salty; could be used as a dipping sauce\", \"similar objects\": [\"vinegar\", \"teriyaki sauce\", \"hoisin sauce\"]}", + 15 + ], + "kayaks": [ + " {\"type\": \"watercraft\", \"description\": \"long, narrow, pointed at both ends; could be propelled by a paddle\", \"similar objects\": [\"canoe\", \"rowboat\", \"paddleboard\"]}", + 15 + ], + "face guard": [ + " {\"type\": \"protective gear\", \"description\": \"transparent; covers the face; could be made of plastic\", \"similar objects\": [\"helmet\", \"goggles\", \"mask\"]}", + 15 + ], + "ban": [ + " {\"type\": \"object\", \"description\": \"a prohibition or an order to forbid something; could be a law or a rule\", \"similar objects\": [\"restriction\", \"prohibition\", \"regulation\"]}", + 15 + ], + "christmas decorations": [ + "\n{\"type\": \"decoration\", \"description\": \"could be made of paper, plastic, or metal; could be in the shape of stars, bells, or snowflakes; could be in various colors\", \"similar objects\": [\"Easter decorations\", \"Halloween decorations\", \"Valentine's Day decorations\"]}", + 15 + ], + "f": [ + "\n{\"type\": \"letter\", \"description\": \"sixth letter of the English alphabet; could be capitalized or lowercase\", \"similar objects\": [\"e\", \"g\", \"h\"]}", + 15 + ], + "apple computer monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a computer; could be used to display images and videos\", \"similar objects\": [\"television\", \"laptop\", \"tablet\"]}", + 15 + ], + "drift": [ + " {\"type\": \"verb\", \"description\": \"to move slowly and steadily in a particular direction; to be carried along by a current of water, air, or other force\", \"similar objects\": [\"float\", \"drag\", \"sway\"]}", + 15 + ], + "maker": [ + " {\"type\": \"tool\", \"description\": \"could be used to create or assemble something; could be powered by electricity\", \"similar objects\": [\"drill\", \"saw\", \"hammer\"]}", + 15 + ], + "plywood": [ + " {\"type\": \"building material\", \"description\": \"made of thin layers of wood glued together; could be used for furniture and construction\", \"similar objects\": [\"particle board\", \"hardboard\", \"medium-density fiberboard\"]}", + 15 + ], + "fleece jacket": [ + " {\"type\": \"clothing\", \"description\": \"warm; could be made of wool or synthetic materials; could have a hood\", \"similar objects\": [\"sweater\", \"coat\", \"hoodie\"]}", + 15 + ], + "puffy jacket": [ + " {\"type\": \"clothing\", \"description\": \"insulated; could be made of down feathers; could have a hood; could be zipped up\", \"similar objects\": [\"parka\", \"coat\", \"vest\"]}", + 15 + ], + "orange fruits": [ + "\n{\"type\": \"fruit\", \"description\": \"round; orange in color; has a peel; could be segmented into sections\", \"similar objects\": [\"lemon\", \"grapefruit\", \"tangerine\"]}", + 15 + ], + "shirt button": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, round, usually made of plastic or metal; could be used to fasten two pieces of fabric together\", \"similar objects\": [\"zipper\", \"snap button\", \"hook and eye\"]}", + 15 + ], + "silver letters": [ + " {\"type\": \"decoration\", \"description\": \"shiny, metallic letters; could be used to spell out words\", \"similar objects\": [\"gold letters\", \"wooden letters\", \"plastic letters\"]}", + 15 + ], + "mobile": [ + " {\"type\": \"electronic device\", \"description\": \"small, handheld, touchscreen; could have a camera; could be used for communication\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}", + 15 + ], + "pink container": [ + " {\"type\": \"container\", \"description\": \"pink; could be made of plastic; could be used to store items\", \"similar objects\": [\"box\", \"bag\", \"jar\"]}", + 15 + ], + "saucers": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of porcelain; could be used for serving tea\", \"similar objects\": [\"plates\", \"cups\", \"bowls\"]}", + 15 + ], + "sidelines": [ + " {\"type\": \"sports term\", \"description\": \"area outside the playing field; could be used to observe the game\", \"similar objects\": [\"bench\", \"bleachers\", \"dugout\"]}", + 15 + ], + "yellow edge": [ + " {\"type\": \"object\", \"description\": \"yellow line; could be used to separate two areas\", \"similar objects\": [\"fence\", \"barrier\", \"wall\"]}", + 15 + ], + "wall sconce": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the wall; could be made of metal; could have a candle or a light bulb\", \"similar objects\": [\"chandelier\", \"ceiling light\", \"floor lamp\"]}", + 15 + ], + "giraffe hooves": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, thin, and pointed; could have a hard outer covering\", \"similar objects\": [\"elephant feet\", \"horse hooves\", \"rhinoceros horns\"]}", + 15 + ], + "gazelles": [ + " {\"type\": \"animal\", \"description\": \"long legs; slender body; tan fur with white underbelly; horns on head\", \"similar objects\": [\"antelope\", \"deer\", \"goat\"]}", + 15 + ], + "dog house": [ + " {\"type\": \"shelter\", \"description\": \"small, wooden, has a roof; could have a door\", \"similar objects\": [\"cat house\", \"bird house\", \"igloo\"]}", + 15 + ], + "leather shoe": [ + " {\"type\": \"footwear\", \"description\": \"made of leather; could have laces; could have a heel\", \"similar objects\": [\"sneaker\", \"boot\", \"sandal\"]}", + 15 + ], + "pink curtain": [ + " {\"type\": \"decoration\", \"description\": \"pink; could be made of fabric; could be hung on a window\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 15 + ], + "banana bunches": [ + " {\"type\": \"fruit\", \"description\": \"long, curved, yellow; could be grouped together; could have brown spots\", \"similar objects\": [\"apple\", \"grapes\", \"strawberries\"]}", + 15 + ], + "outcrop": [ + " {\"type\": \"geological feature\", \"description\": \"rock formation protruding from the ground; could be made of sedimentary, igneous, or metamorphic rocks\", \"similar objects\": [\"cliff\", \"cave\", \"mountain\"]}", + 15 + ], + "remains": [ + " {\"type\": \"object\", \"description\": \"leftover; could be in the form of bones, ashes, or ruins\", \"similar objects\": [\"debris\", \"rubble\", \"wreckage\"]}", + 15 + ], + "spoon utensil": [ + " {\"type\": \"utensil\", \"description\": \"long handle; bowl-shaped head; used for stirring, scooping, and serving\", \"similar objects\": [\"fork\", \"knife\", \"chopsticks\"]}", + 15 + ], + "phrase": [ + " {\"type\": \"language unit\", \"description\": \"a group of words that express an idea; could be a proverb or an idiom\", \"similar objects\": [\"sentence\", \"clause\", \"idiom\"]}", + 15 + ], + "broccoli stalk": [ + " {\"type\": \"vegetable\", \"description\": \"green; has a thick stem; could be cut into small pieces; could have small florets\", \"similar objects\": [\"cauliflower\", \"asparagus\", \"brussels sprouts\"]}", + 15 + ], + "hand man": [ + " {\"type\": \"sculpture\", \"description\": \"human-like figure; could be made of metal, wood, or stone; could be used for decoration\", \"similar objects\": [\"statue\", \"bust\", \"carving\"]}", + 15 + ], + "bus license plate": [ + "\n{\"type\": \"vehicle identification\", \"description\": \"rectangular; has a unique combination of numbers and letters; could be attached to the back of a bus\", \"similar objects\": [\"car license plate\", \"motorcycle license plate\", \"truck license plate\"]}", + 15 + ], + "base ball cap": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; has a brim; could have a logo\", \"similar objects\": [\"hat\", \"beanie\", \"visor\"]}", + 15 + ], + "watch persons": [ + " {\"type\": \"accessory\", \"description\": \"worn on the wrist; could be digital or analog; could have a strap\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}", + 15 + ], + "utility vehicle": [ + " {\"type\": \"vehicle\", \"description\": \"large; could be used for carrying goods; could have a flatbed\", \"similar objects\": [\"truck\", \"van\", \"pickup truck\"]}", + 15 + ], + "kitchen sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"has a handle; could be made of metal; could have a sprayer\", \"similar objects\": [\"bathroom sink faucet\", \"shower head\", \"toilet flush\"]}", + 15 + ], + "bus seat": [ + " {\"type\": \"furniture\", \"description\": \"long, upholstered, has armrests\", \"similar objects\": [\"airplane seat\", \"theater seat\", \"bench\"]}", + 15 + ], + "pink spot": [ + " {\"type\": \"color spot\", \"description\": \"pink; could be round or oval; could be on a surface\", \"similar objects\": [\"red spot\", \"blue spot\", \"yellow spot\"]}", + 15 + ], + "sweets": [ + " {\"type\": \"food\", \"description\": \"sugary; could be in different shapes and colors; could be made of chocolate, candy, or other ingredients\", \"similar objects\": [\"candy\", \"cake\", \"ice cream\"]}", + 15 + ], + "bikini bottoms": [ + " {\"type\": \"clothing\", \"description\": \"low-rise waist; could be high-waisted; could be string-tied; could be patterned\", \"similar objects\": [\"swimsuit bottoms\", \"shorts\", \"skirt\"]}", + 15 + ], + "pink cap": [ + " {\"type\": \"clothing accessory\", \"description\": \"headwear; could be made of cotton; could have a logo\", \"similar objects\": [\"hat\", \"beanie\", \"baseball cap\"]}", + 15 + ], + "blue wire": [ + " {\"type\": \"electrical tool\", \"description\": \"blue; could be used for electrical wiring\", \"similar objects\": [\"red wire\", \"black wire\", \"green wire\"]}", + 15 + ], + "spot light": [ + " {\"type\": \"lighting tool\", \"description\": \"focused beam of light; could be used for stage lighting\", \"similar objects\": [\"flashlight\", \"lantern\", \"torch\"]}", + 15 + ], + "muscle": [ + " {\"type\": \"anatomy\", \"description\": \"tissue that contracts and relaxes to move parts of the body; could be found in arms, legs, and other parts of the body\", \"similar objects\": [\"tendon\", \"ligament\", \"cartilage\"]}", + 15 + ], + "dressing": [ + " {\"type\": \"condiment\", \"description\": \"liquid; could be made of oil, vinegar, and spices; could be used to season salads\", \"similar objects\": [\"sauce\", \"marinade\", \"dip\"]}", + 15 + ], + "grip tape": [ + " {\"type\": \"skateboarding accessory\", \"description\": \"adhesive tape; used to provide grip on skateboard decks\", \"similar objects\": [\"skateboard wheels\", \"skateboard trucks\", \"skateboard bearings\"]}", + 15 + ], + "stamp": [ + " {\"type\": \"postal tool\", \"description\": \"rectangular; could have a design or text on it\", \"similar objects\": [\"envelope\", \"postcard\", \"seal\"]}", + 15 + ], + "grey umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"grey; has a handle; could be opened and closed\", \"similar objects\": [\"hat\", \"sunglasses\", \"scarf\"]}", + 15 + ], + "hotel bed": [ + " {\"type\": \"furniture\", \"description\": \"large; could have a headboard; could have a mattress; could have a bed frame\", \"similar objects\": [\"sofa\", \"armchair\", \"couch\"]}", + 15 + ], + "mini fridge": [ + " {\"type\": \"appliance\", \"description\": \"small, rectangular, has a door; could be used to store food and drinks\", \"similar objects\": [\"microwave\", \"freezer\", \"refrigerator\"]}", + 15 + ], + "silver hoop": [ + " {\"type\": \"jewelry\", \"description\": \"circular; made of silver; could be worn as an earring or necklace\", \"similar objects\": [\"gold hoop\", \"diamond stud\", \"pearl pendant\"]}", + 15 + ], + "gold faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"shiny, gold-colored; could have a handle; could be used to control water flow\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"sink faucet\"]}", + 15 + ], + "motorcycle jacket": [ + " {\"type\": \"clothing\", \"description\": \"made of leather; has a zipper; could have reflective stripes\", \"similar objects\": [\"biker jacket\", \"racing jacket\", \"motorcycle pants\"]}", + 15 + ], + "wiimote": [ + " {\"type\": \"gaming device\", \"description\": \"wireless controller; has motion sensing capabilities; could be used with a Wii console\", \"similar objects\": [\"joystick\", \"gamepad\", \"racing wheel\"]}", + 15 + ], + "tennis player playing tennis": [ + "\n{\"type\": \"sport\", \"description\": \"two players hitting a ball over a net; using a racket; wearing a tennis outfit\", \"similar objects\": [\"badminton player\", \"table tennis player\", \"squash player\"]}", + 15 + ], + "paddle boat": [ + " {\"type\": \"watercraft\", \"description\": \"has a paddle; could be powered by a motor; could be used for recreational activities\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 15 + ], + "blue straps": [ + " {\"type\": \"accessory\", \"description\": \"blue; could be used to hold items; could be made of fabric or plastic\", \"similar objects\": [\"belts\", \"ties\", \"shoelaces\"]}", + 15 + ], + "post fence": [ + " {\"type\": \"fencing tool\", \"description\": \"wooden posts connected by horizontal boards; could be painted white\", \"similar objects\": [\"chain link fence\", \"barbed wire fence\", \"split rail fence\"]}", + 15 + ], + "cement bridge": [ + " {\"type\": \"structure\", \"description\": \"made of cement; could span a river or a road; could have arches\", \"similar objects\": [\"steel bridge\", \"suspension bridge\", \"viaduct\"]}", + 15 + ], + "round part": [ + " {\"type\": \"object\", \"description\": \"circular shape; could be made of metal, plastic, or wood; could be used for a variety of purposes\", \"similar objects\": [\"disc\", \"wheel\", \"ring\"]}", + 15 + ], + "cotton shirt": [ + " {\"type\": \"clothing\", \"description\": \"soft, lightweight, breathable fabric; could have long or short sleeves; could have a collar\", \"similar objects\": [\"jeans\", \"t-shirt\", \"dress\"]}", + 15 + ], + "seams": [ + " {\"type\": \"sewing tool\", \"description\": \"used to join two pieces of fabric together; could be made of thread or yarn; could be sewn by hand or machine\", \"similar objects\": [\"needle\", \"zipper\", \"button\"]}", + 15 + ], + "girl playing tennis": [ + "\n{\"type\": \"action\", \"description\": \"girl holding a tennis racket; hitting a tennis ball; wearing a tennis outfit\", \"similar objects\": [\"boy playing tennis\", \"woman playing tennis\", \"person playing badminton\"]}", + 15 + ], + "drinking glasses": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be made of glass or plastic; could be used to drink water or other beverages\", \"similar objects\": [\"mug\", \"cup\", \"tumbler\"]}", + 15 + ], + "burn marks": [ + " {\"type\": \"damage\", \"description\": \"dark, charred marks; could be caused by fire or heat\", \"similar objects\": [\"scorch marks\", \"char marks\", \"singe marks\"]}", + 15 + ], + "tree shadow": [ + " {\"type\": \"shadow\", \"description\": \"elongated; could be cast by trees; could be distorted by wind\", \"similar objects\": [\"building shadow\", \"animal shadow\", \"cloud shadow\"]}", + 15 + ], + "grey cell phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could have a touchscreen; could have a camera; could have a headphone jack\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 15 + ], + "extension cord": [ + " {\"type\": \"electrical tool\", \"description\": \"long; has multiple outlets; could be coiled\", \"similar objects\": [\"power strip\", \"surge protector\", \"power adapter\"]}", + 15 + ], + "metal brackets": [ + " {\"type\": \"hardware\", \"description\": \"used to support shelves, cabinets, and other structures; could be made of metal, plastic, or wood; could be in different shapes and sizes\", \"similar objects\": [\"screws\", \"nails\", \"hinges\"]}", + 15 + ], + "womens arm": [ + " {\"type\": \"body part\", \"description\": \"skinny; could have tattoos; could have jewelry\", \"similar objects\": [\"hand\", \"leg\", \"face\"]}", + 15 + ], + "cycles": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could have a basket; could have a bell\", \"similar objects\": [\"bicycle\", \"motorcycle\", \"scooter\"]}", + 15 + ], + "barrier wall": [ + " {\"type\": \"structure\", \"description\": \"concrete; could be used to block roads; could be used to separate areas\", \"similar objects\": [\"fence\", \"gate\", \"hedge\"]}", + 15 + ], + "dense trees": [ + " {\"type\": \"vegetation\", \"description\": \"tall, thick, with many leaves; could have fruits or flowers\", \"similar objects\": [\"bushes\", \"shrubs\", \"grass\"]}", + 15 + ], + "bus stop sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; has a white background; has a red border; has a black lettering\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 15 + ], + "binding": [ + " {\"type\": \"stationery item\", \"description\": \"used to hold papers together; could be made of plastic or metal\", \"similar objects\": [\"stapler\", \"paper clip\", \"tape\"]}", + 15 + ], + "leafy greens": [ + " {\"type\": \"vegetable\", \"description\": \"green; could be spinach, kale, lettuce, etc.; could be eaten raw or cooked\", \"similar objects\": [\"broccoli\", \"cabbage\", \"cauliflower\"]}", + 15 + ], + "juicer": [ + " {\"type\": \"kitchen appliance\", \"description\": \"has a handle; could be electric or manual; could have a strainer\", \"similar objects\": [\"blender\", \"food processor\", \"mixer\"]}", + 15 + ], + "bread basket": [ + " {\"type\": \"container\", \"description\": \"round; could be made of wicker; could have a handle\", \"similar objects\": [\"picnic basket\", \"storage basket\", \"fruit basket\"]}", + 15 + ], + "arrow points": [ + " {\"type\": \"pointing tool\", \"description\": \"long, thin, pointed at one end; could be made of wood or metal\", \"similar objects\": [\"dart\", \"spear\", \"javelin\"]}", + 15 + ], + "airplane landing gear": [ + "\n{\"type\": \"aircraft part\", \"description\": \"wheels and struts; used to support the aircraft on the ground; retractable\", \"similar objects\": [\"engine\", \"wing\", \"fuselage\"]}", + 15 + ], + "turbines": [ + " {\"type\": \"machine\", \"description\": \"large, cylindrical; used to generate electricity; could have blades\", \"similar objects\": [\"generator\", \"engine\", \"motor\"]}", + 15 + ], + "bottom layer": [ + " {\"type\": \"clothing item\", \"description\": \"worn on the lower body; could be pants, skirts, shorts, etc.\", \"similar objects\": [\"top layer\", \"jacket\", \"coat\"]}", + 15 + ], + "bedroom wall": [ + " {\"type\": \"structure\", \"description\": \"flat, vertical surface; could be painted or wallpapered; could have decorations\", \"similar objects\": [\"ceiling\", \"floor\", \"door\"]}", + 15 + ], + "beer mug": [ + " {\"type\": \"drinking vessel\", \"description\": \"cylindrical; could have a handle; could be made of glass or ceramic\", \"similar objects\": [\"wine glass\", \"coffee mug\", \"teacup\"]}", + 15 + ], + "blue wheel": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could have a handle\", \"similar objects\": [\"tricycle\", \"scooter\", \"bicycle\"]}", + 15 + ], + "cuts": [ + " {\"type\": \"injury\", \"description\": \"injury caused by sharp objects; could be bleeding; could be painful\", \"similar objects\": [\"bruises\", \"scrapes\", \"burns\"]}", + 15 + ], + "cutting boards": [ + " {\"type\": \"kitchen tool\", \"description\": \"flat, rectangular; could be made of wood or plastic; could have handles\", \"similar objects\": [\"knives\", \"spatulas\", \"rolling pins\"]}", + 15 + ], + "store name": [ + "\n{\"type\": \"business\", \"description\": \"a place where goods and services are exchanged for money\", \"similar objects\": [\"shop\", \"market\", \"boutique\"]}", + 15 + ], + "bus shelter": [ + " {\"type\": \"structure\", \"description\": \"enclosed structure; could have a roof; could have benches\", \"similar objects\": [\"train station\", \"subway station\", \"bus stop\"]}", + 15 + ], + "pretzels": [ + " {\"type\": \"snack\", \"description\": \"twisted, salty, crunchy; could be in different shapes\", \"similar objects\": [\"chips\", \"popcorn\", \"nuts\"]}", + 15 + ], + "polka dot": [ + " {\"type\": \"pattern\", \"description\": \"small, round, and evenly spaced dots\", \"similar objects\": [\"stripes\", \"plaid\", \"chevron\"]}", + 15 + ], + "menu sign": [ + " {\"type\": \"advertisement tool\", \"description\": \"could be made of paper or plastic; could be hung on the wall; could have a list of items and prices\", \"similar objects\": [\"billboard\", \"poster\", \"flyer\"]}", + 15 + ], + "pita": [ + " {\"type\": \"food\", \"description\": \"flat, round, could be filled with vegetables and meat\", \"similar objects\": [\"tortilla\", \"naan\", \"flatbread\"]}", + 15 + ], + "blue flame": [ + " {\"type\": \"phenomenon\", \"description\": \"blue-colored flame; could be seen in a gas stove\", \"similar objects\": [\"fire\", \"smoke\", \"steam\"]}", + 15 + ], + "padding": [ + " {\"type\": \"material\", \"description\": \"soft, cushiony, used for protection and insulation\", \"similar objects\": [\"cushion\", \"foam\", \"sponge\"]}", + 15 + ], + "chocolate shavings": [ + " {\"type\": \"food\", \"description\": \"small pieces of chocolate; could be used as a topping\", \"similar objects\": [\"cocoa powder\", \"chocolate chips\", \"chocolate bar\"]}", + 15 + ], + "glass shower": [ + " {\"type\": \"bathroom fixture\", \"description\": \"transparent; could be framed or frameless; could be sliding or hinged\", \"similar objects\": [\"bathtub\", \"sink\", \"toilet\"]}", + 15 + ], + "dirty sink": [ + " {\"type\": \"household item\", \"description\": \"has a basin; could have stains; could have a faucet\", \"similar objects\": [\"bathtub\", \"toilet\", \"dishwasher\"]}", + 15 + ], + "sprigs": [ + " {\"type\": \"plant\", \"description\": \"small, green, could be used as garnish; could be used in cooking\", \"similar objects\": [\"sprouts\", \"herbs\", \"greens\"]}", + 15 + ], + "blue windows": [ + " {\"type\": \"building material\", \"description\": \"glass; could be tinted; could be framed\", \"similar objects\": [\"doors\", \"shutters\", \"skylights\"]}", + 15 + ], + "suit top": [ + " {\"type\": \"clothing item\", \"description\": \"long-sleeved; could have buttons; could be made of wool or cotton\", \"similar objects\": [\"shirt\", \"blouse\", \"jacket\"]}", + 15 + ], + "dog bowl": [ + " {\"type\": \"pet accessory\", \"description\": \"round; could be made of plastic or metal; could have a raised edge\", \"similar objects\": [\"cat bowl\", \"bird feeder\", \"fish tank\"]}", + 15 + ], + "blackbird": [ + " {\"type\": \"bird\", \"description\": \"black feathers; could have yellow beak; could have red eyes\", \"similar objects\": [\"crow\", \"raven\", \"starling\"]}", + 15 + ], + "crops": [ + " {\"type\": \"agricultural product\", \"description\": \"plants grown for food, feed, or fuel; could be harvested for consumption\", \"similar objects\": [\"vegetables\", \"grains\", \"fruits\"]}", + 15 + ], + "bent elbow": [ + " {\"type\": \"body part\", \"description\": \"joint between upper arm and lower arm; could be bent\", \"similar objects\": [\"knee\", \"shoulder\", \"ankle\"]}", + 15 + ], + "gym": [ + " {\"type\": \"building\", \"description\": \"large; could have equipment for physical exercise; could have a swimming pool\", \"similar objects\": [\"school\", \"library\", \"museum\"]}", + 15 + ], + "grey vehicle": [ + "\n{\"type\": \"vehicle\", \"description\": \"grey; could be a car, truck, bus, etc.\", \"similar objects\": [\"black vehicle\", \"white vehicle\", \"silver vehicle\"]}", + 15 + ], + "stuffed doll": [ + " {\"type\": \"toy\", \"description\": \"soft; could be filled with cotton; could have a face\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"action figure\"]}", + 15 + ], + "foot strap": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, adjustable strap; could be used to secure feet\", \"similar objects\": [\"ankle strap\", \"shoe strap\", \"toe strap\"]}", + 15 + ], + "plastic toilet lid": [ + "\n{\"type\": \"toilet accessory\", \"description\": \"round; made of plastic; could be white or other colors; could have a handle\", \"similar objects\": [\"toilet seat\", \"toilet brush\", \"toilet plunger\"]}", + 15 + ], + "wood bowl": [ + " {\"type\": \"utensil\", \"description\": \"made of wood; could be used for serving food; could be carved with patterns\", \"similar objects\": [\"ceramic bowl\", \"plastic bowl\", \"metal bowl\"]}", + 15 + ], + "cutter": [ + " {\"type\": \"tool\", \"description\": \"sharp edge; could be used to cut paper or fabric\", \"similar objects\": [\"scissors\", \"knife\", \"razor\"]}", + 15 + ], + "pizza board": [ + " {\"type\": \"cooking tool\", \"description\": \"flat, round; could be made of wood or plastic; used to cut pizza\", \"similar objects\": [\"chopping board\", \"bread board\", \"pastry board\"]}", + 15 + ], + "watch mans": [ + " {\"type\": \"accessory\", \"description\": \"worn on the wrist; could be digital or analog; could have a strap\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}", + 15 + ], + "grass blades": [ + " {\"type\": \"plant\", \"description\": \"thin, long, green; could be in a group\", \"similar objects\": [\"leaves\", \"ferns\", \"moss\"]}", + 15 + ], + "water glasses": [ + " {\"type\": \"drinking tool\", \"description\": \"transparent; could be made of glass or plastic; could be cylindrical or conical\", \"similar objects\": [\"mug\", \"cup\", \"tumbler\"]}", + 15 + ], + "man shoe": [ + " {\"type\": \"footwear\", \"description\": \"leather; could have laces; could have a sole\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 15 + ], + "wooden paddle": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, made of wood; could be used for stirring or serving\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 15 + ], + "sticker apple": [ + " {\"type\": \"decoration\", \"description\": \"round, red, with a stem; could be made of paper or plastic\", \"similar objects\": [\"banana sticker\", \"pear sticker\", \"orange sticker\"]}", + 15 + ], + "cheddar cheese": [ + " {\"type\": \"food\", \"description\": \"yellow; could be sliced; could be melted; could be grated\", \"similar objects\": [\"mozzarella cheese\", \"swiss cheese\", \"parmesan cheese\"]}", + 15 + ], + "pie crust": [ + " {\"type\": \"baking ingredient\", \"description\": \"flaky; could be made of flour, butter, and salt; could be used as a base for pies\", \"similar objects\": [\"pastry dough\", \"tart crust\", \"puff pastry\"]}", + 15 + ], + "crack road": [ + " {\"type\": \"road surface\", \"description\": \"uneven; could have holes; could have cracks\", \"similar objects\": [\"pothole\", \"uneven pavement\", \"gravel road\"]}", + 15 + ], + "motorcycle rear tire": [ + " {\"type\": \"motorcycle part\", \"description\": \"round; has a tread pattern; could be made of rubber\", \"similar objects\": [\"front tire\", \"wheel\", \"brake\"]}", + 15 + ], + "house wall": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could be made of bricks, wood, or concrete; could have windows and doors\", \"similar objects\": [\"fence\", \"building\", \"shed\"]}", + 15 + ], + "locker": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal; could have a lock\", \"similar objects\": [\"cabinet\", \"drawer\", \"wardrobe\"]}", + 15 + ], + "toilet papers": [ + " {\"type\": \"hygiene product\", \"description\": \"rectangular; could be made of paper or fabric; could be used for wiping\", \"similar objects\": [\"tissues\", \"wipes\", \"paper towels\"]}", + 15 + ], + "wood stick": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical, made of wood; could be used for stirring or poking\", \"similar objects\": [\"metal rod\", \"plastic stick\", \"bamboo stick\"]}", + 15 + ], + "purple color": [ + " {\"type\": \"color\", \"description\": \"vibrant, deep hue; could be a mix of blue and red\", \"similar objects\": [\"indigo\", \"violet\", \"magenta\"]}", + 15 + ], + "lit window": [ + " {\"type\": \"architectural feature\", \"description\": \"rectangular; has glass panes; could be illuminated from inside\", \"similar objects\": [\"door\", \"balcony\", \"skylight\"]}", + 15 + ], + "grey phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could have a keypad; could have a touchscreen; could be cordless\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 15 + ], + "replica": [ + " {\"type\": \"object\", \"description\": \"an exact copy of an original object; could be made of different materials\", \"similar objects\": [\"duplicate\", \"copy\", \"facsimile\"]}", + 15 + ], + "silver metal faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be used to control water flow\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"sink faucet\"]}", + 15 + ], + "desktop monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a stand; could be connected to a computer\", \"similar objects\": [\"television\", \"laptop\", \"tablet\"]}", + 15 + ], + "hanging light": [ + " {\"type\": \"lighting tool\", \"description\": \"suspended from the ceiling; could be made of metal or glass; could have multiple bulbs\", \"similar objects\": [\"chandelier\", \"ceiling light\", \"pendant light\"]}", + 15 + ], + "skate shoe": [ + " {\"type\": \"footwear\", \"description\": \"flat sole; could have laces; could have a reinforced toe\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 15 + ], + "volley ball": [ + " {\"type\": \"sport equipment\", \"description\": \"spherical; has a net; could be played with two teams\", \"similar objects\": [\"basketball\", \"football\", \"tennis ball\"]}", + 15 + ], + "pepperoni slices": [ + " {\"type\": \"food\", \"description\": \"round, red, thin slices; could be made of pork or beef\", \"similar objects\": [\"salami\", \"sausage\", \"bacon\"]}", + 15 + ], + "site": [ + " {\"type\": \"location\", \"description\": \"a place or area; could be a physical or virtual location\", \"similar objects\": [\"location\", \"spot\", \"venue\"]}", + 15 + ], + "tailpipe": [ + " {\"type\": \"automotive part\", \"description\": \"cylindrical; located at the end of the exhaust system; could be made of metal\", \"similar objects\": [\"muffler\", \"exhaust manifold\", \"catalytic converter\"]}", + 15 + ], + "mouth area": [ + " {\"type\": \"body part\", \"description\": \"contains lips, teeth, and tongue; could be opened and closed\", \"similar objects\": [\"nose area\", \"eye area\", \"ear area\"]}", + 15 + ], + "blue plane": [ + "\n{\"type\": \"vehicle\", \"description\": \"blue; has wings; could have a tail; could have propellers\", \"similar objects\": [\"helicopter\", \"rocket\", \"airplane\"]}", + 15 + ], + "plastic drinking cup": [ + "\n{\"type\": \"utensil\", \"description\": \"transparent; could have a handle; could have a lid\", \"similar objects\": [\"glass cup\", \"mug\", \"thermos\"]}", + 15 + ], + "circle light": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of metal; could have multiple bulbs\", \"similar objects\": [\"chandelier\", \"ceiling light\", \"pendant light\"]}", + 15 + ], + "shorts man": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting trousers that end above the knee; could be made of cotton, linen, or other materials; could have pockets\", \"similar objects\": [\"capri pants\", \"jeans\", \"skirt\"]}", + 15 + ], + "butterfly kite": [ + "\n{\"type\": \"toy\", \"description\": \"shaped like a butterfly; could be made of paper or fabric; has a long tail\", \"similar objects\": [\"dragon kite\", \"delta kite\", \"box kite\"]}", + 15 + ], + "spinach leaf": [ + " {\"type\": \"vegetable\", \"description\": \"green, oval-shaped; could have a stem; could be cooked or eaten raw\", \"similar objects\": [\"lettuce\", \"kale\", \"chard\"]}", + 15 + ], + "wet umbrella": [ + " {\"type\": \"accessory\", \"description\": \"long handle; could be made of fabric; could be folded; could be wet\", \"similar objects\": [\"umbrella\", \"parasol\", \"raincoat\"]}", + 15 + ], + "chef hat": [ + " {\"type\": \"clothing\", \"description\": \"tall, white, conical shape; could have a black band\", \"similar objects\": [\"apron\", \"gloves\", \"uniform\"]}", + 15 + ], + "bull standing": [ + " {\"type\": \"animal\", \"description\": \"large, muscular, four legs; has horns on its head; could be red or black\", \"similar objects\": [\"cow\", \"bison\", \"buffalo\"]}", + 15 + ], + "oil stain": [ + " {\"type\": \"stain\", \"description\": \"dark, greasy, could be found on clothes or surfaces\", \"similar objects\": [\"grease stain\", \"ink stain\", \"blood stain\"]}", + 15 + ], + "homeplate": [ + " {\"type\": \"sports equipment\", \"description\": \"rectangular; white; used in baseball\", \"similar objects\": [\"baseball bat\", \"baseball glove\", \"catcher's mask\"]}", + 15 + ], + "hardcover book": [ + "\n{\"type\": \"book\", \"description\": \"has a hard cover; could have a dust jacket; could have a spine; could have pages\", \"similar objects\": [\"paperback book\", \"e-book\", \"textbook\"]}", + 15 + ], + "stainless steel spoon": [ + "\n{\"type\": \"utensil\", \"description\": \"silver; has a long handle; could be used for stirring\", \"similar objects\": [\"fork\", \"knife\", \"spatula\"]}", + 15 + ], + "man shorts": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could be knee-length; could have pockets\", \"similar objects\": [\"jeans\", \"shorts\", \"swim trunks\"]}", + 15 + ], + "floor windows": [ + " {\"type\": \"architectural feature\", \"description\": \"large windows that are installed in the floor\", \"similar objects\": [\"skylight\", \"basement window\", \"bay window\"]}", + 15 + ], + "shocks": [ + " {\"type\": \"automotive part\", \"description\": \"suspension components; could be made of metal; could be adjustable\", \"similar objects\": [\"struts\", \"springs\", \"tires\"]}", + 15 + ], + "blue vehicle": [ + " {\"type\": \"vehicle\", \"description\": \"blue; could be a car, truck, bus, motorcycle, etc.\", \"similar objects\": [\"red vehicle\", \"green vehicle\", \"yellow vehicle\"]}", + 15 + ], + "rear wing": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the back of a car; helps to reduce drag and increase downforce\", \"similar objects\": [\"spoiler\", \"diffuser\", \"air dam\"]}", + 15 + ], + "spindle": [ + " {\"type\": \"tool\", \"description\": \"long, thin, cylindrical; could be used for spinning fibers\", \"similar objects\": [\"spinning wheel\", \"bobbin\", \"distaff\"]}", + 15 + ], + "task": [ + " {\"type\": \"activity\", \"description\": \"something that needs to be done; could be a job or a chore\", \"similar objects\": [\"project\", \"assignment\", \"duty\"]}", + 15 + ], + "bookshelf wall": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have multiple shelves; could be used to store books and other items\", \"similar objects\": [\"bookcase\", \"cabinet\", \"wardrobe\"]}", + 15 + ], + "glass panels": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be used for windows or walls; could be made of glass or plastic\", \"similar objects\": [\"windows\", \"doors\", \"shutters\"]}", + 15 + ], + "treetops": [ + " {\"type\": \"landscape\", \"description\": \"the top of a tree; could be green; could have leaves\", \"similar objects\": [\"mountain tops\", \"skyline\", \"cliff tops\"]}", + 15 + ], + "neck scarf": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, could be made of silk; could be tied around the neck\", \"similar objects\": [\"tie\", \"belt\", \"shawl\"]}", + 15 + ], + "brown mountains": [ + "\n{\"type\": \"landscape\", \"description\": \"brown, rocky, could have snow on top; could have trees and plants\", \"similar objects\": [\"hills\", \"valleys\", \"cliffs\"]}", + 15 + ], + "support poles": [ + " {\"type\": \"structural tool\", \"description\": \"long, cylindrical; could be made of metal or wood; used to support structures\", \"similar objects\": [\"columns\", \"beams\", \"posts\"]}", + 15 + ], + "taxi sign": [ + " {\"type\": \"sign\", \"description\": \"yellow; has a black silhouette of a car; could be illuminated\", \"similar objects\": [\"bus sign\", \"street sign\", \"stop sign\"]}", + 15 + ], + "life ring": [ + " {\"type\": \"safety tool\", \"description\": \"round; made of foam; has a rope attached\", \"similar objects\": [\"life jacket\", \"life buoy\", \"life preserver\"]}", + 15 + ], + "silver muffler": [ + " {\"type\": \"car part\", \"description\": \"cylindrical; made of metal; used to reduce engine noise\", \"similar objects\": [\"exhaust pipe\", \"air filter\", \"spark plug\"]}", + 15 + ], + "pink straw": [ + " {\"type\": \"drinking tool\", \"description\": \"long, thin, pink; could be made of plastic\", \"similar objects\": [\"straw\", \"cup\", \"glass\"]}", + 15 + ], + "childrens": [ + "\n{\"type\": \"people\", \"description\": \"young; could be of different ages; could be of different genders\", \"similar objects\": [\"teenagers\", \"babies\", \"adults\"]}", + 15 + ], + "york": [ + " {\"type\": \"city\", \"description\": \"capital of the United Kingdom; located in the north of England; has a population of 8.9 million\", \"similar objects\": [\"London\", \"Manchester\", \"Birmingham\"]}", + 15 + ], + "blue string": [ + " {\"type\": \"utility item\", \"description\": \"long, thin, blue; could be used for tying things together\", \"similar objects\": [\"rope\", \"twine\", \"yarn\"]}", + 15 + ], + "bus wheel": [ + " {\"type\": \"vehicle part\", \"description\": \"round; has a hub; could be made of rubber\", \"similar objects\": [\"car wheel\", \"truck wheel\", \"motorcycle wheel\"]}", + 15 + ], + "silver flush handle": [ + " {\"type\": \"hardware\", \"description\": \"silver, flush, handle-shaped\", \"similar objects\": [\"door knob\", \"door handle\", \"door latch\"]}", + 15 + ], + "head phones": [ + " {\"type\": \"audio device\", \"description\": \"has two ear pieces; could be wired or wireless; could be used to listen to music\", \"similar objects\": [\"earphones\", \"speakers\", \"microphone\"]}", + 15 + ], + "wood beam": [ + " {\"type\": \"building material\", \"description\": \"long, rectangular; could be used for construction\", \"similar objects\": [\"plywood\", \"concrete\", \"steel beam\"]}", + 15 + ], + "grey box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of metal or plastic; could have a lid\", \"similar objects\": [\"crate\", \"basket\", \"trunk\"]}", + 15 + ], + "dell laptop": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a keyboard; could be powered by a battery\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 15 + ], + "pink bike": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could be pink in color\", \"similar objects\": [\"scooter\", \"motorcycle\", \"tricycle\"]}", + 15 + ], + "track marks": [ + " {\"type\": \"injury\", \"description\": \"red or purple lines on the skin; could be caused by drug use\", \"similar objects\": [\"scars\", \"bruises\", \"cuts\"]}", + 15 + ], + "lit building": [ + "\n{\"type\": \"structure\", \"description\": \"has lights on; could be a house, office, or other building\", \"similar objects\": [\"skyscraper\", \"mansion\", \"warehouse\"]}", + 15 + ], + "priest": [ + " {\"type\": \"person\", \"description\": \"wears a robe; could have a cross necklace; could have a bible\", \"similar objects\": [\"monk\", \"pastor\", \"minister\"]}", + 15 + ], + "printing": [ + " {\"type\": \"process\", \"description\": \"process of producing copies of documents or images\", \"similar objects\": [\"copying\", \"duplicating\", \"scanning\"]}", + 15 + ], + "cup board": [ + " {\"type\": \"furniture\", \"description\": \"has shelves; could be made of wood; could be used to store items\", \"similar objects\": [\"dresser\", \"wardrobe\", \"bookshelf\"]}", + 15 + ], + "rectangle window": [ + " {\"type\": \"architectural element\", \"description\": \"has four sides; could be made of glass; could be opened\", \"similar objects\": [\"square window\", \"door\", \"arched window\"]}", + 15 + ], + "beanie hat": [ + " {\"type\": \"clothing item\", \"description\": \"knitted; could be worn on the head; could be in different colors\", \"similar objects\": [\"cap\", \"beret\", \"turban\"]}", + 15 + ], + "wet hair": [ + " {\"type\": \"hair condition\", \"description\": \"shiny; could be clumped together; could be tangled\", \"similar objects\": [\"dry hair\", \"curly hair\", \"straight hair\"]}", + 15 + ], + "ballon": [ + " {\"type\": \"toy\", \"description\": \"round; could be filled with air or helium; could be made of rubber or plastic\", \"similar objects\": [\"kite\", \"yo-yo\", \"juggling balls\"]}", + 15 + ], + "stone pillars": [ + " {\"type\": \"architectural structure\", \"description\": \"large, made of stone; could be used to support a building\", \"similar objects\": [\"columns\", \"arches\", \"domes\"]}", + 15 + ], + "sprayer": [ + " {\"type\": \"tool\", \"description\": \"long handle; could be used to spray liquid\", \"similar objects\": [\"hose\", \"watering can\", \"pressure washer\"]}", + 15 + ], + "dirty plate": [ + "\n{\"type\": \"dishware\", \"description\": \"round; could have food residue; could be made of ceramic, plastic, or metal\", \"similar objects\": [\"bowl\", \"cup\", \"fork\"]}", + 15 + ], + "maple": [ + " {\"type\": \"tree\", \"description\": \"has leaves with five lobes; could have red, yellow, or orange leaves in autumn; could produce maple syrup\", \"similar objects\": [\"oak\", \"birch\", \"elm\"]}", + 15 + ], + "grey post": [ + " {\"type\": \"structure\", \"description\": \"cylindrical; could be made of metal; could be used for support\", \"similar objects\": [\"pole\", \"pillar\", \"column\"]}", + 15 + ], + "arugula": [ + " {\"type\": \"vegetable\", \"description\": \"dark green, long leaves; could be used in salads\", \"similar objects\": [\"spinach\", \"lettuce\", \"kale\"]}", + 15 + ], + "padlock": [ + " {\"type\": \"security tool\", \"description\": \"has a keyhole; could be made of metal; could be used to lock doors or bags\", \"similar objects\": [\"lock\", \"chain\", \"combination lock\"]}", + 15 + ], + "grey bench": [ + " {\"type\": \"furniture\", \"description\": \"long; could be made of wood or metal; could have a backrest\", \"similar objects\": [\"sofa\", \"chair\", \"stool\"]}", + 15 + ], + "luggage compartment": [ + " {\"type\": \"storage space\", \"description\": \"enclosed space; could be found in vehicles; could be used to store items\", \"similar objects\": [\"trunk\", \"cargo area\", \"storage bin\"]}", + 15 + ], + "water board": [ + " {\"type\": \"sports equipment\", \"description\": \"long and wide; could be made of foam; used for surfing\", \"similar objects\": [\"surfboard\", \"skimboard\", \"wakeboard\"]}", + 15 + ], + "grey skies": [ + " {\"type\": \"weather\", \"description\": \"overcast; could be raining; could be cloudy\", \"similar objects\": [\"rainy day\", \"foggy day\", \"snowy day\"]}", + 15 + ], + "sanwich": [ + " {\"type\": \"food\", \"description\": \"two slices of bread with filling in between; could be cut into triangles\", \"similar objects\": [\"burger\", \"wrap\", \"taco\"]}", + 15 + ], + "rise": [ + " {\"type\": \"verb\", \"description\": \"to move upwards; to increase\", \"similar objects\": [\"ascend\", \"climb\", \"soar\"]}", + 15 + ], + "wavy": [ + " {\"type\": \"shape\", \"description\": \"curved; could be used to describe a line or an object\", \"similar objects\": [\"curved\", \"zigzag\", \"straight\"]}", + 15 + ], + "pom pom": [ + " {\"type\": \"decoration\", \"description\": \"fluffy; could be made of yarn; could be in different colors\", \"similar objects\": [\"tassel\", \"feather\", \"beads\"]}", + 15 + ], + "present": [ + " {\"type\": \"gift\", \"description\": \"wrapped in paper; could be in a box; could be with a ribbon\", \"similar objects\": [\"gift box\", \"card\", \"flower bouquet\"]}", + 15 + ], + "silver wedding ring": [ + "\n{\"type\": \"jewelry\", \"description\": \"round; made of silver; could have diamonds or other gemstones; could have engravings\", \"similar objects\": [\"gold wedding ring\", \"engagement ring\", \"bracelet\"]}", + 15 + ], + "puff": [ + " {\"type\": \"pastry\", \"description\": \"light and airy; could be filled with cream; could be round or square\", \"similar objects\": [\"doughnut\", \"croissant\", \"cupcake\"]}", + 15 + ], + "garbage container": [ + " {\"type\": \"container\", \"description\": \"large, rectangular, has a lid; could be made of metal or plastic\", \"similar objects\": [\"trash can\", \"recycling bin\", \"dumpster\"]}", + 15 + ], + "ventilation": [ + " {\"type\": \"ventilation system\", \"description\": \"system of ducts and fans used to circulate air; could be used to regulate temperature and humidity\", \"similar objects\": [\"air conditioning\", \"heating\", \"humidifier\"]}", + 15 + ], + "balance": [ + " {\"type\": \"measuring tool\", \"description\": \"two pans connected by a beam; used to measure weight\", \"similar objects\": [\"scale\", \"ruler\", \"measuring cup\"]}", + 15 + ], + "tan umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"large, tan, has a handle; could be opened and closed\", \"similar objects\": [\"hat\", \"sunglasses\", \"scarf\"]}", + 15 + ], + "asphalt parking lot": [ + "\n{\"type\": \"surface\", \"description\": \"black, flat, made of asphalt; could have white lines and parking spots\", \"similar objects\": [\"concrete pavement\", \"gravel road\", \"dirt road\"]}", + 15 + ], + "parking lines": [ + " {\"type\": \"road markings\", \"description\": \"white or yellow lines painted on the ground; used to indicate parking spaces\", \"similar objects\": [\"stop sign\", \"traffic light\", \"crosswalk\"]}", + 15 + ], + "gold star": [ + " {\"type\": \"decoration\", \"description\": \"golden; five-pointed star shape; could be made of paper or metal\", \"similar objects\": [\"silver star\", \"blue star\", \"red star\"]}", + 15 + ], + "blue glasses": [ + " {\"type\": \"eyewear\", \"description\": \"transparent lenses; could be made of plastic or metal; could have a frame\", \"similar objects\": [\"sunglasses\", \"reading glasses\", \"safety glasses\"]}", + 15 + ], + "sea gulls": [ + " {\"type\": \"bird\", \"description\": \"white; has a long wingspan; could be seen near the sea\", \"similar objects\": [\"pigeon\", \"seagull\", \"eagle\"]}", + 15 + ], + "milk jug": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic or glass; has a handle\", \"similar objects\": [\"pitcher\", \"jar\", \"bottle\"]}", + 15 + ], + "grey stripe": [ + " {\"type\": \"pattern\", \"description\": \"horizontal or vertical lines; could be of different colors\", \"similar objects\": [\"plaid\", \"checkerboard\", \"polka dot\"]}", + 15 + ], + "angle": [ + " {\"type\": \"geometric shape\", \"description\": \"two lines that meet at a point; could be measured in degrees\", \"similar objects\": [\"triangle\", \"rectangle\", \"circle\"]}", + 15 + ], + "costumes": [ + " {\"type\": \"clothing\", \"description\": \"could be made of fabric; could be used for special occasions; could be colorful\", \"similar objects\": [\"dress\", \"uniform\", \"costume\"]}", + 15 + ], + "pilings": [ + " {\"type\": \"construction material\", \"description\": \"long, cylindrical; could be made of wood or metal; used to support structures\", \"similar objects\": [\"posts\", \"columns\", \"beams\"]}", + 15 + ], + "train wheel": [ + " {\"type\": \"transportation tool\", \"description\": \"round; could be made of metal; could have spokes\", \"similar objects\": [\"car wheel\", \"bicycle wheel\", \"truck wheel\"]}", + 15 + ], + "chrome bathroom sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be mounted on the wall or countertop\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"toilet flush valve\"]}", + 15 + ], + "tangerines": [ + " {\"type\": \"fruit\", \"description\": \"small, orange, has a peel; could be segmented\", \"similar objects\": [\"oranges\", \"mandarins\", \"clementines\"]}", + 15 + ], + "police office": [ + " {\"type\": \"building\", \"description\": \"could have a badge logo; could have a jail; could have a desk for police officers\", \"similar objects\": [\"fire station\", \"court house\", \"city hall\"]}", + 15 + ], + "metal stairs": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could have multiple steps; could be used to climb up or down\", \"similar objects\": [\"wooden stairs\", \"ladder\", \"escalator\"]}", + 15 + ], + "giraffes ears": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, thin, and pointed; could be brown or tan in color\", \"similar objects\": [\"elephant's ears\", \"horse's ears\", \"dog's ears\"]}", + 15 + ], + "bicycle frame": [ + " {\"type\": \"bicycle part\", \"description\": \"metal frame; could have two wheels; could have handlebars and pedals\", \"similar objects\": [\"saddle\", \"chain\", \"tire\"]}", + 15 + ], + "side truck": [ + " {\"type\": \"vehicle\", \"description\": \"long; has two axles; could be used to transport goods\", \"similar objects\": [\"semi-truck\", \"pickup truck\", \"van\"]}", + 15 + ], + "bend": [ + " {\"type\": \"verb\", \"description\": \"to curve or angle; to cause to change direction\", \"similar objects\": [\"twist\", \"turn\", \"flex\"]}", + 15 + ], + "span": [ + " {\"type\": \"measuring tool\", \"description\": \"long, thin, metal; used to measure length\", \"similar objects\": [\"ruler\", \"tape measure\", \"yardstick\"]}", + 15 + ], + "stone ground": [ + " {\"type\": \"ground surface\", \"description\": \"made of stones; could be rough and uneven; could be used for pathways\", \"similar objects\": [\"gravel ground\", \"dirt ground\", \"concrete ground\"]}", + 15 + ], + "crucifix": [ + " {\"type\": \"religious symbol\", \"description\": \"cross with a figure of Jesus Christ; could be made of metal or wood\", \"similar objects\": [\"cross\", \"rosary\", \"chalice\"]}", + 15 + ], + "shadow dog": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; has a black and white fur; has a long tail\", \"similar objects\": [\"plush bear\", \"stuffed cat\", \"stuffed rabbit\"]}", + 15 + ], + "wingspan": [ + " {\"type\": \"measurement\", \"description\": \"distance between the tips of the wings of a bird or aircraft\", \"similar objects\": [\"length\", \"height\", \"width\"]}", + 15 + ], + "blue collar": [ + " {\"type\": \"clothing item\", \"description\": \"collarless shirt; usually made of cotton; could be short-sleeved or long-sleeved; could be plain or patterned\", \"similar objects\": [\"white collar\", \"black collar\", \"denim shirt\"]}", + 15 + ], + "stone sculpture": [ + " {\"type\": \"artwork\", \"description\": \"carved from stone; could be in the form of a human or animal figure\", \"similar objects\": [\"wood sculpture\", \"metal sculpture\", \"ceramic sculpture\"]}", + 15 + ], + "shop window": [ + " {\"type\": \"display\", \"description\": \"large glass window; could have a frame; could be decorated with posters or signs\", \"similar objects\": [\"storefront\", \"showcase\", \"display case\"]}", + 15 + ], + "blue bags": [ + " {\"type\": \"accessory\", \"description\": \"could be made of fabric; could be used to carry items; could be of different sizes and colors\", \"similar objects\": [\"purses\", \"backpacks\", \"suitcases\"]}", + 15 + ], + "odd": [ + " {\"type\": \"adjective\", \"description\": \"not even; not divisible by two\", \"similar objects\": [\"uneven\", \"unbalanced\", \"irregular\"]}", + 15 + ], + "dark hat": [ + " {\"type\": \"clothing item\", \"description\": \"black; could have a brim; could have a band\", \"similar objects\": [\"cap\", \"fedora\", \"beanie\"]}", + 15 + ], + "boat engine": [ + " {\"type\": \"machine\", \"description\": \"used to power boats; could be gasoline or diesel powered; could be inboard or outboard\", \"similar objects\": [\"car engine\", \"motorcycle engine\", \"airplane engine\"]}", + 15 + ], + "utility truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have a crane; could have a flatbed\", \"similar objects\": [\"dump truck\", \"tow truck\", \"fire truck\"]}", + 15 + ], + "pews": [ + " {\"type\": \"furniture\", \"description\": \"long, wooden benches; could be found in churches\", \"similar objects\": [\"chairs\", \"sofas\", \"tables\"]}", + 15 + ], + "zebra drinking water": [ + "\n{\"type\": \"animal behavior\", \"description\": \"zebra drinking water from a pond or river; could be standing or kneeling; could be using its mouth or trunk to drink\", \"similar objects\": [\"elephant drinking water\", \"giraffe drinking water\", \"horse drinking water\"]}", + 15 + ], + "bears ears": [ + " {\"type\": \"accessory\", \"description\": \"headband with two furry ears attached; could be used as a costume\", \"similar objects\": [\"cat ears\", \"rabbit ears\", \"fox ears\"]}", + 15 + ], + "stork": [ + " {\"type\": \"bird\", \"description\": \"long legs; long beak; white feathers; could carry a baby in its beak\", \"similar objects\": [\"crane\", \"heron\", \"egret\"]}", + 15 + ], + "speed limit": [ + " {\"type\": \"traffic regulation\", \"description\": \"maximum speed allowed on a road; could be indicated by a sign\", \"similar objects\": [\"traffic light\", \"stop sign\", \"no parking sign\"]}", + 15 + ], + "shreds": [ + " {\"type\": \"food\", \"description\": \"thin, long strips; could be made of vegetables, meat, or cheese\", \"similar objects\": [\"slices\", \"dices\", \"strips\"]}", + 15 + ], + "man tie": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, usually made of silk; could be in different colors and patterns\", \"similar objects\": [\"bow tie\", \"belt\", \"pocket square\"]}", + 15 + ], + "fielder": [ + " {\"type\": \"sports player\", \"description\": \"plays in the outfield; wears a glove; throws and catches the ball\", \"similar objects\": [\"pitcher\", \"catcher\", \"shortstop\"]}", + 15 + ], + "cross sign": [ + " {\"type\": \"traffic sign\", \"description\": \"red and white; has a cross shape; could be used to indicate a stop\", \"similar objects\": [\"yield sign\", \"no entry sign\", \"speed limit sign\"]}", + 15 + ], + "tailfeathers": [ + " {\"type\": \"bird body part\", \"description\": \"long, thin, and curved; could be colorful; could be used for flying\", \"similar objects\": [\"wings\", \"beak\", \"talons\"]}", + 15 + ], + "metal guard": [ + " {\"type\": \"protective tool\", \"description\": \"made of metal; could be used to protect a certain area\", \"similar objects\": [\"fence\", \"gate\", \"barrier\"]}", + 15 + ], + "pita bread": [ + " {\"type\": \"food\", \"description\": \"flat, round, could be stuffed with vegetables and meat\", \"similar objects\": [\"tortilla\", \"naan\", \"bagel\"]}", + 15 + ], + "tan brick": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be used for walls\", \"similar objects\": [\"concrete block\", \"stone\", \"adobe brick\"]}", + 15 + ], + "beet": [ + " {\"type\": \"vegetable\", \"description\": \"round; dark red; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"carrot\", \"radish\", \"turnip\"]}", + 15 + ], + "rock formations": [ + " {\"type\": \"geological feature\", \"description\": \"formed by erosion or deposition; could be made of different types of rocks; could be of different shapes and sizes\", \"similar objects\": [\"caves\", \"cliffs\", \"mountains\"]}", + 15 + ], + "gold clock hands": [ + " {\"type\": \"clock part\", \"description\": \"golden; could be in different shapes; could be used to tell time\", \"similar objects\": [\"clock face\", \"clock mechanism\", \"clock pendulum\"]}", + 15 + ], + "surfboard dude": [ + " {\"type\": \"person\", \"description\": \"wearing a wetsuit; carrying a surfboard; usually found near the beach\", \"similar objects\": [\"surfer\", \"bodyboarder\", \"windsurfer\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber,", + 15 + ], + "batman": [ + " {\"type\": \"fictional character\", \"description\": \"superhero; wears a black cape and cowl; has a utility belt; fights crime\", \"similar objects\": [\"superman\", \"spiderman\", \"wonder woman\"]}", + 14 + ], + "calzone": [ + " {\"type\": \"food\", \"description\": \"folded pizza; could be filled with cheese, vegetables, and/or meat\", \"similar objects\": [\"pizza\", \"stromboli\", \"empanada\"]}", + 14 + ], + "cheese sandwich": [ + " {\"type\": \"food\", \"description\": \"two slices of bread with cheese in between; could be grilled; could be served with other ingredients\", \"similar objects\": [\"hamburger\", \"tuna sandwich\", \"grilled cheese sandwich\"]}", + 14 + ], + "journal": [ + " {\"type\": \"writing tool\", \"description\": \"book with blank pages; could be bound with leather; could have a lock\", \"similar objects\": [\"diary\", \"notebook\", \"scrapbook\"]}", + 14 + ], + "metal shower head": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"made of metal; could be round or square; could have multiple nozzles\", \"similar objects\": [\"faucet\", \"toilet\", \"bathtub\"]}", + 14 + ], + "toasts": [ + " {\"type\": \"food\", \"description\": \"brown; could be made of bread; could be served with butter and jam\", \"similar objects\": [\"sandwich\", \"bagel\", \"croissant\"]}", + 14 + ], + "fuzzy teddy": [ + " {\"type\": \"toy\", \"description\": \"soft, cuddly, usually brown; could have a bow or a ribbon\", \"similar objects\": [\"plush toy\", \"stuffed animal\", \"doll\"]}", + 14 + ], + "brown tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"bricks\", \"concrete blocks\", \"wooden planks\"]}", + 14 + ], + "bottom wall": [ + " {\"type\": \"architectural element\", \"description\": \"the lower part of a wall; could be made of different materials; could be decorated\", \"similar objects\": [\"ceiling\", \"floor\", \"door\"]}", + 14 + ], + "entry doors": [ + " {\"type\": \"building component\", \"description\": \"large, rectangular; could be made of wood or metal; could have a handle\", \"similar objects\": [\"windows\", \"garage doors\", \"gates\"]}", + 14 + ], + "place card": [ + " {\"type\": \"stationery\", \"description\": \"small card; could be decorated with names; could be used to indicate seating arrangements\", \"similar objects\": [\"name tag\", \"table number\", \"menu card\"]}", + 14 + ], + "bill board": [ + " {\"type\": \"advertisement tool\", \"description\": \"large, rectangular; could be used to display ads\", \"similar objects\": [\"signboard\", \"poster\", \"banner\"]}", + 14 + ], + "heel shoes": [ + " {\"type\": \"footwear\", \"description\": \"high heel; could be made of leather; could have straps\", \"similar objects\": [\"pumps\", \"sandals\", \"boots\"]}", + 14 + ], + "brown bed": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could have a headboard; could have a footboard; could have four legs; could have a mattress\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}", + 14 + ], + "window display": [ + " {\"type\": \"visual display\", \"description\": \"could be made of glass; could be used to showcase products; could be used to attract customers\", \"similar objects\": [\"storefront\", \"signage\", \"billboard\"]}", + 14 + ], + "window glass": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be framed; could be double-glazed\", \"similar objects\": [\"mirror\", \"door glass\", \"shower glass\"]}", + 14 + ], + "tire track": [ + " {\"type\": \"evidence\", \"description\": \"long, curved lines; could be made of rubber; could be found on roads\", \"similar objects\": [\"footprint\", \"skid mark\", \"drag mark\"]}", + 14 + ], + "bear statue": [ + " {\"type\": \"decoration\", \"description\": \"could be made of stone, wood, or metal; could be in a sitting or standing position; could have a realistic or cartoonish look\", \"similar objects\": [\"lion statue\", \"elephant statue\", \"dog statue\"]}", + 14 + ], + "side lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"small; could be placed on the side of a desk; could be made of metal\", \"similar objects\": [\"table lamp\", \"floor lamp\", \"ceiling lamp\"]}", + 14 + ], + "nipple": [ + " {\"type\": \"body part\", \"description\": \"small, round, protrudes from the breast\", \"similar objects\": [\"areola\", \"mammary gland\", \"teat\"]}", + 14 + ], + "cubes": [ + " {\"type\": \"shape\", \"description\": \"six-sided, equal-sized squares; could be made of wood, plastic, or metal\", \"similar objects\": [\"spheres\", \"pyramids\", \"cylinders\"]}", + 14 + ], + "pink headband": [ + " {\"type\": \"accessory\", \"description\": \"made of fabric; could be decorated with flowers; could be worn on the head\", \"similar objects\": [\"hat\", \"cap\", \"scarf\"]}", + 14 + ], + "silver rack": [ + " {\"type\": \"furniture\", \"description\": \"made of metal; could have multiple shelves; could be used to store items\", \"similar objects\": [\"shelf\", \"cabinet\", \"bookcase\"]}", + 14 + ], + "purple bus": [ + "\n{\"type\": \"vehicle\", \"description\": \"large, purple, has multiple doors; could have a destination sign\", \"similar objects\": [\"school bus\", \"city bus\", \"tour bus\"]}", + 14 + ], + "oceans water": [ + " {\"type\": \"natural element\", \"description\": \"salty; could be blue or green; could be deep or shallow\", \"similar objects\": [\"lakes\", \"rivers\", \"streams\"]}", + 14 + ], + "blue visor": [ + " {\"type\": \"headwear\", \"description\": \"blue; could be made of cloth; could have a strap to secure it on the head\", \"similar objects\": [\"hat\", \"cap\", \"beanie\"]}", + 14 + ], + "round wood table": [ + "\n{\"type\": \"furniture\", \"description\": \"round; made of wood; could have four legs\", \"similar objects\": [\"square wood table\", \"rectangular wood table\", \"coffee table\"]}", + 14 + ], + "steel post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of steel; could be used for support\", \"similar objects\": [\"wood post\", \"concrete post\", \"metal post\"]}", + 14 + ], + "utensils table": [ + " {\"type\": \"furniture\", \"description\": \"long and narrow; could have drawers; could have a flat surface\", \"similar objects\": [\"desk\", \"dresser\", \"sideboard\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant,", + 14 + ], + "capital letters": [ + "\n{\"type\": \"alphabet\", \"description\": \"uppercase letters; could be used to start a sentence\", \"similar objects\": [\"lowercase letters\", \"numbers\", \"symbols\"]}", + 14 + ], + "turned-on": [ + "\n{\"type\": \"state\", \"description\": \"activated; switched on; running\", \"similar objects\": [\"on\", \"active\", \"powered\"]}", + 14 + ], + "leg pad": [ + " {\"type\": \"protective gear\", \"description\": \"thick, cushiony; could be strapped to the leg; could be used for sports\", \"similar objects\": [\"elbow pad\", \"knee pad\", \"shin guard\"]}", + 14 + ], + "creamer": [ + " {\"type\": \"kitchen tool\", \"description\": \"small container; could be used to add cream to coffee or tea\", \"similar objects\": [\"sugar bowl\", \"milk jug\", \"teapot\"]}", + 14 + ], + "cinder block wall": [ + "\n{\"type\": \"construction material\", \"description\": \"made of concrete blocks; could be used to build walls; could be painted\", \"similar objects\": [\"brick wall\", \"stone wall\", \"wooden fence\"]}", + 14 + ], + "mans pants": [ + " {\"type\": \"clothing\", \"description\": \"long, usually made of cotton or other fabrics; could have pockets; could have a zipper\", \"similar objects\": [\"jeans\", \"shorts\", \"trousers\"]}", + 14 + ], + "winnie pooh": [ + " {\"type\": \"character\", \"description\": \"yellow bear; has a red shirt; has a honey pot\", \"similar objects\": [\"tigger\", \"piglet\", \"eyore\"]}", + 14 + ], + "gray curtain": [ + " {\"type\": \"window covering\", \"description\": \"made of fabric; could be pleated; could be hung on a rod\", \"similar objects\": [\"blinds\", \"shades\", \"drapes\"]}", + 14 + ], + "file": [ + " {\"type\": \"storage tool\", \"description\": \"flat; could be made of metal or plastic; could be used to store documents\", \"similar objects\": [\"folder\", \"envelope\", \"box\"]}", + 14 + ], + "eyeglasses man": [ + "\n{\"type\": \"person\", \"description\": \"wearing eyeglasses; could have a mustache; could be wearing a suit\", \"similar objects\": [\"man with a hat\", \"woman with glasses\", \"man with a beard\"]}", + 14 + ], + "pizza cheese": [ + " {\"type\": \"food ingredient\", \"description\": \"yellow; could be shredded; could be melted\", \"similar objects\": [\"mozzarella cheese\", \"parmesan cheese\", \"cheddar cheese\"]}", + 14 + ], + "leather wallet": [ + "\n{\"type\": \"accessory\", \"description\": \"made of leather; could have multiple compartments; could have a zipper closure\", \"similar objects\": [\"purse\", \"clutch\", \"cardholder\"]}", + 14 + ], + "pink dog tongue": [ + "\n{\"type\": \"body part\", \"description\": \"pink; long and slim; could be wet and sticky\", \"similar objects\": [\"cat tongue\", \"human tongue\", \"horse tongue\"]}", + 14 + ], + "roller": [ + " {\"type\": \"tool\", \"description\": \"cylindrical; could have a handle; could be used to paint walls\", \"similar objects\": [\"paintbrush\", \"paint roller\", \"sponge\"]}", + 14 + ], + "silver hook": [ + " {\"type\": \"tool\", \"description\": \"made of silver; could be used to hang things\", \"similar objects\": [\"nail\", \"screw\", \"hanger\"]}", + 14 + ], + "living room scene": [ + "\n{\"type\": \"scene\", \"description\": \"furniture, such as sofa, chairs, tables; decorations, such as paintings, vases, lamps; other items, such as books, magazines, plants\", \"similar objects\": [\"bedroom scene\", \"kitchen scene\", \"bathroom scene\"]}", + 14 + ], + "cooked": [ + " {\"type\": \"state\", \"description\": \"food that has been heated to a certain temperature\", \"similar objects\": [\"boiled\", \"baked\", \"fried\"]}", + 14 + ], + "cats eye": [ + " {\"type\": \"gemstone\", \"description\": \"translucent; could be yellow, green, or brown; could be cut into cabochon or faceted\", \"similar objects\": [\"tiger's eye\", \"moonstone\", \"hematite\"]}", + 14 + ], + "palm leaf": [ + " {\"type\": \"plant\", \"description\": \"long, thin, green; could be used for weaving\", \"similar objects\": [\"banana leaf\", \"bamboo leaf\", \"coconut leaf\"]}", + 14 + ], + "glass salt": [ + " {\"type\": \"condiment\", \"description\": \"transparent; could be in the form of crystals; could be used for seasoning\", \"similar objects\": [\"pepper\", \"sugar\", \"garlic powder\"]}", + 14 + ], + "roof tiles": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be red or brown in color\", \"similar objects\": [\"shingles\", \"slates\", \"asphalt\"]}", + 14 + ], + "ankle sock": [ + " {\"type\": \"clothing item\", \"description\": \"short; covers the ankle; could be made of cotton\", \"similar objects\": [\"crew sock\", \"knee-high sock\", \"stocking\"]}", + 14 + ], + "grey feathers": [ + " {\"type\": \"bird feature\", \"description\": \"light grey; could be soft; could be used for flying\", \"similar objects\": [\"white feathers\", \"black feathers\", \"brown feathers\"]}", + 14 + ], + "arrow key": [ + " {\"type\": \"computer input device\", \"description\": \"four keys in a cross shape; used to control the movement of a cursor\", \"similar objects\": [\"space bar\", \"enter key\", \"backspace key\"]}", + 14 + ], + "name badge": [ + " {\"type\": \"identification tool\", \"description\": \"could be made of plastic or metal; could have a clip or pin; could have a name printed on it\", \"similar objects\": [\"ID card\", \"key card\", \"security badge\"]}", + 14 + ], + "brick section": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be used to build walls\", \"similar objects\": [\"cement block\", \"wooden beam\", \"tile\"]}", + 14 + ], + "chocolate glaze": [ + " {\"type\": \"food topping\", \"description\": \"dark brown; glossy; could be used to decorate cakes and pastries\", \"similar objects\": [\"icing\", \"frosting\", \"ganache\"]}", + 14 + ], + "door opening": [ + " {\"type\": \"architectural feature\", \"description\": \"rectangular; could be made of wood or metal; could have a handle; could have a lock\", \"similar objects\": [\"window\", \"gate\", \"garage door\"]}", + 14 + ], + "dirty spot": [ + "\n{\"type\": \"stain\", \"description\": \"discolored area on a surface; could be caused by dirt, oil, or other substances; could be difficult to remove\", \"similar objects\": [\"stain\", \"smudge\", \"mark\"]}", + 14 + ], + "blue plaid shirt": [ + "\n{\"type\": \"clothing\", \"description\": \"blue and white pattern; could have buttons; could have a collar\", \"similar objects\": [\"red plaid shirt\", \"striped shirt\", \"denim shirt\"]}", + 14 + ], + "bathroom wall tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic, porcelain, or stone; could be glossy or matte\", \"similar objects\": [\"floor tile\", \"ceiling tile\", \"backsplash tile\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input of zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar", + 14 + ], + "shoppers": [ + " {\"type\": \"people\", \"description\": \"carrying bags; could be in a store\", \"similar objects\": [\"customers\", \"buyers\", \"consumers\"]}", + 14 + ], + "side ear": [ + " {\"type\": \"accessory\", \"description\": \"small, round, worn on the ear; could be made of metal or plastic\", \"similar objects\": [\"earring\", \"stud\", \"hoop\"]}", + 14 + ], + "blacktop": [ + " {\"type\": \"pavement material\", \"description\": \"dark, smooth, asphalt surface; could be used for roads and driveways\", \"similar objects\": [\"concrete\", \"gravel\", \"brick\"]}", + 14 + ], + "santa claus": [ + " {\"type\": \"person\", \"description\": \"white beard; wears a red suit; carries a bag of gifts\", \"similar objects\": [\"elf\", \"reindeer\", \"snowman\"]}", + 14 + ], + "blue animal": [ + "\n{\"type\": \"animal\", \"description\": \"could be any animal with blue color; could have any shape or size\", \"similar objects\": [\"blue bird\", \"blue fish\", \"blue whale\"]}", + 14 + ], + "restaurant table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal; could have a glass top\", \"similar objects\": [\"coffee table\", \"dining table\", \"desk\"]}", + 14 + ], + "silver metal knife": [ + "\n{\"type\": \"utensil\", \"description\": \"silver; metal; has a sharp blade; could be used for cutting\", \"similar objects\": [\"fork\", \"spoon\", \"spatula\"]}", + 14 + ], + "raindrops": [ + " {\"type\": \"weather phenomenon\", \"description\": \"small, round, drops of water; could be seen in the sky\", \"similar objects\": [\"snowflakes\", \"hailstones\", \"sleet\"]}", + 14 + ], + "patchy": [ + " {\"type\": \"adjective\", \"description\": \"unevenly distributed; having spots or patches\", \"similar objects\": [\"spotted\", \"speckled\", \"mottled\"]}", + 14 + ], + "link": [ + " {\"type\": \"connector\", \"description\": \"connects two or more objects; could be physical or virtual\", \"similar objects\": [\"chain\", \"cable\", \"wire\"]}", + 14 + ], + "china cabinet": [ + " {\"type\": \"furniture\", \"description\": \"tall; could have glass doors; could have drawers\", \"similar objects\": [\"bookshelf\", \"dresser\", \"armoire\"]}", + 14 + ], + "hinge door": [ + " {\"type\": \"door\", \"description\": \"hinged on one side; could be opened and closed\", \"similar objects\": [\"sliding door\", \"pocket door\", \"barn door\"]}", + 14 + ], + "paint line": [ + " {\"type\": \"painting tool\", \"description\": \"long, thin, could be made of plastic or metal; used to draw straight lines\", \"similar objects\": [\"paintbrush\", \"paint roller\", \"paint scraper\"]}", + 14 + ], + "apple monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a computer; could be used for gaming or work\", \"similar objects\": [\"television\", \"laptop\", \"tablet\"]}", + 14 + ], + "wooden slat": [ + " {\"type\": \"building material\", \"description\": \"long, thin, rectangular; could be used for flooring or fencing\", \"similar objects\": [\"plywood\", \"lumber\", \"timber\"]}", + 14 + ], + "tub faucet": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be attached to a wall; could have a shower head\", \"similar objects\": [\"shower head\", \"sink faucet\", \"bathtub faucet\"]}", + 14 + ], + "purple scarf": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, could be made of wool; could be in various colors\", \"similar objects\": [\"shawl\", \"hat\", \"gloves\"]}", + 14 + ], + "plums": [ + " {\"type\": \"fruit\", \"description\": \"round; could be red, purple, or yellow; has a stone inside\", \"similar objects\": [\"apricots\", \"peaches\", \"cherries\"]}", + 14 + ], + "petal flower": [ + " {\"type\": \"plant\", \"description\": \"has colorful petals; could have a pistil and stamen; could have a stem and leaves\", \"similar objects\": [\"daisy\", \"sunflower\", \"tulip\"]}", + 14 + ], + "overcoat": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of wool; could have a hood\", \"similar objects\": [\"jacket\", \"coat\", \"parka\"]}", + 14 + ], + "showerhead": [ + " {\"type\": \"bathroom fixture\", \"description\": \"attached to the wall; could have multiple nozzles; could be adjustable\", \"similar objects\": [\"faucet\", \"toilet\", \"bathtub\"]}", + 14 + ], + "donut box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could have a handle\", \"similar objects\": [\"bag\", \"box\", \"basket\"]}", + 14 + ], + "pointy nose": [ + "\n{\"type\": \"facial feature\", \"description\": \"sharp, protruding, could be long or short\", \"similar objects\": [\"pointy chin\", \"arched eyebrows\", \"high cheekbones\"]}", + 14 + ], + "metal street light": [ + "\n{\"type\": \"lighting tool\", \"description\": \"tall; made of metal; has a bulb; could be attached to a pole\", \"similar objects\": [\"lamp post\", \"street lamp\", \"lantern\"]}", + 14 + ], + "storage bin": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"box\", \"basket\", \"trunk\"]}", + 14 + ], + "clover": [ + " {\"type\": \"plant\", \"description\": \"green; has three leaves; could be a symbol of luck\", \"similar objects\": [\"dandelion\", \"daisy\", \"tulip\"]}", + 14 + ], + "toy duck": [ + " {\"type\": \"toy\", \"description\": \"yellow; could have a bill and webbed feet; could quack\", \"similar objects\": [\"stuffed animal\", \"plush toy\", \"action figure\"]}", + 14 + ], + "pink backpack": [ + "\n{\"type\": \"bag\", \"description\": \"pink; could have straps; could be used to carry items\", \"similar objects\": [\"suitcase\", \"duffel bag\", \"tote bag\"]}", + 14 + ], + "front edge": [ + " {\"type\": \"edge\", \"description\": \"the edge that is closest to the front; could be straight or curved\", \"similar objects\": [\"back edge\", \"side edge\", \"top edge\"]}", + 14 + ], + "end button": [ + " {\"type\": \"electronic device\", \"description\": \"small, round, usually red; used to end a process\", \"similar objects\": [\"start button\", \"reset button\", \"power button\"]}", + 14 + ], + "bicycle basket": [ + " {\"type\": \"bicycle accessory\", \"description\": \"attached to the front or back of a bicycle; could be made of metal or plastic; could be used to store items\", \"similar objects\": [\"bike rack\", \"bike bell\", \"bike lock\"]}", + 14 + ], + "bathroom tiles": [ + " {\"type\": \"building material\", \"description\": \"square or rectangular; could be made of ceramic, porcelain, or stone; could be glossy or matte\", \"similar objects\": [\"floor tiles\", \"wall tiles\", \"ceiling tiles\"]}", + 14 + ], + "slivers": [ + " {\"type\": \"food\", \"description\": \"thin, long pieces of food; could be made of vegetables, fruits, or meats\", \"similar objects\": [\"strips\", \"chips\", \"shreds\"]}", + 14 + ], + "knife holder": [ + " {\"type\": \"kitchen tool\", \"description\": \"wooden or metal; could have slots for knives; could be wall-mounted\", \"similar objects\": [\"knife block\", \"knife sharpener\", \"knife rack\"]}", + 14 + ], + "blue frisbee": [ + "\n{\"type\": \"toy\", \"description\": \"round; blue; could be thrown in the air\", \"similar objects\": [\"disc\", \"boomerang\", \"kite\"]}", + 14 + ], + "bent arm": [ + " {\"type\": \"body part\", \"description\": \"upper arm bent at the elbow; could be flexed\", \"similar objects\": [\"forearm\", \"shoulder\", \"hand\"]}", + 14 + ], + "fence gate": [ + " {\"type\": \"barrier\", \"description\": \"wooden or metal; could be opened and closed; could be used to keep animals in or out\", \"similar objects\": [\"fence\", \"wall\", \"hedge\"]}", + 14 + ], + "role": [ + " {\"type\": \"object\", \"description\": \"a character or part played by a performer in a play, film, or other presentation\", \"similar objects\": [\"character\", \"part\", \"performance\"]}", + 14 + ], + "plaster": [ + " {\"type\": \"building material\", \"description\": \"white; could be used to cover walls and ceilings; could be used to make sculptures\", \"similar objects\": [\"cement\", \"concrete\", \"mortar\"]}", + 14 + ], + "window latch": [ + " {\"type\": \"hardware\", \"description\": \"metal; used to secure windows; could be opened and closed with a key\", \"similar objects\": [\"door latch\", \"lock\", \"hinge\"]}", + 14 + ], + "mother giraffe": [ + " {\"type\": \"animal\", \"description\": \"tall; has a long neck; has a brownish-yellow fur; has a long tail; could have a baby giraffe\", \"similar objects\": [\"elephant\", \"horse\", \"zebra\"]}", + 14 + ], + "ont": [ + " {\"type\": \"insect\", \"description\": \"small; has six legs; could be black or brown\", \"similar objects\": [\"spider\", \"ant\", \"bee\"]}", + 14 + ], + "wolf": [ + " {\"type\": \"animal\", \"description\": \"gray; has a bushy tail; could howl\", \"similar objects\": [\"dog\", \"coyote\", \"fox\"]}", + 14 + ], + "orange bike": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; orange in color; could have a basket; could have a bell\", \"similar objects\": [\"bicycle\", \"scooter\", \"motorcycle\"]}", + 14 + ], + "giants": [ + " {\"type\": \"mythical creature\", \"description\": \"large humanoid creatures; could have superhuman strength; could have magical powers\", \"similar objects\": [\"trolls\", \"ogres\", \"dragons\"]}", + 14 + ], + "box spring": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; has a mattress; could be made of metal or wood\", \"similar objects\": [\"mattress\", \"bed frame\", \"headboard\"]}", + 14 + ], + "front bus": [ + " {\"type\": \"vehicle\", \"description\": \"large; has multiple doors; could have a wheelchair ramp\", \"similar objects\": [\"truck\", \"van\", \"school bus\"]}", + 14 + ], + "traffic pole": [ + " {\"type\": \"road safety tool\", \"description\": \"tall, cylindrical; could be painted with white and red stripes; could have a traffic light on top\", \"similar objects\": [\"traffic sign\", \"traffic cone\", \"guardrail\"]}", + 14 + ], + "womens hand": [ + " {\"type\": \"body part\", \"description\": \"five fingers; could have long nails; could be wearing a ring\", \"similar objects\": [\"foot\", \"arm\", \"face\"]}", + 14 + ], + "winter pants": [ + " {\"type\": \"clothing\", \"description\": \"long; could be made of thick fabric; could be insulated; could be waterproof\", \"similar objects\": [\"winter coat\", \"snow boots\", \"scarf\"]}", + 14 + ], + "lace curtains": [ + " {\"type\": \"window covering\", \"description\": \"made of lace fabric; could be hung on a window\", \"similar objects\": [\"sheer curtains\", \"drapes\", \"blinds\"]}", + 14 + ], + "silverware plate": [ + " {\"type\": \"dining tool\", \"description\": \"round; could be made of metal; used to hold silverware\", \"similar objects\": [\"dinner plate\", \"serving plate\", \"platter\"]}", + 14 + ], + "utility van": [ + " {\"type\": \"vehicle\", \"description\": \"box-shaped; could have a sliding door; could be used for transporting goods\", \"similar objects\": [\"truck\", \"minivan\", \"SUV\"]}", + 14 + ], + "turbine engine": [ + " {\"type\": \"machine\", \"description\": \"has a fan-like structure; used to generate power\", \"similar objects\": [\"steam engine\", \"gas turbine\", \"diesel engine\"]}", + 14 + ], + "bodies": [ + "\n{\"type\": \"object\", \"description\": \"could be human or animal; could be alive or dead; could be in different shapes and sizes\", \"similar objects\": [\"corpses\", \"skeletons\", \"remains\"]}", + 14 + ], + "suvs": [ + " {\"type\": \"vehicle\", \"description\": \"large, four-wheeled, could have a high ground clearance; could have a third row of seats\", \"similar objects\": [\"trucks\", \"minivans\", \"sedans\"]}", + 14 + ], + "bo": [ + " {\"type\": \"weapon\", \"description\": \"long wooden staff; could be used for martial arts\", \"similar objects\": [\"sword\", \"spear\", \"nunchaku\"]}", + 14 + ], + "work boots": [ + " {\"type\": \"footwear\", \"description\": \"thick, heavy, and waterproof; could have steel toe caps; could have laces\", \"similar objects\": [\"hiking boots\", \"sneakers\", \"sandals\"]}", + 14 + ], + "backpack person": [ + "\n{\"type\": \"person\", \"description\": \"carrying a backpack; could be wearing a hat; could have a walking stick\", \"similar objects\": [\"hiker\", \"tourist\", \"backpacker\"]}", + 14 + ], + "transportation": [ + "\n{\"type\": \"means of travel\", \"description\": \"any form of movement from one place to another\", \"similar objects\": [\"car\", \"bus\", \"train\", \"plane\", \"boat\"]}", + 14 + ], + "metal steps": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could be used to climb up and down\", \"similar objects\": [\"ladder\", \"staircase\", \"escalator\"]}", + 14 + ], + "plastic cap": [ + " {\"type\": \"container\", \"description\": \"round; could be used to cover bottles; could be made of plastic\", \"similar objects\": [\"lid\", \"stopper\", \"cork\"]}", + 14 + ], + "wooden wheel": [ + " {\"type\": \"tool\", \"description\": \"circular; made of wood; could have spokes\", \"similar objects\": [\"bicycle wheel\", \"wagon wheel\", \"tire\"]}", + 14 + ], + "bathroom area": [ + " {\"type\": \"room\", \"description\": \"could have a toilet, sink, bathtub, shower, and mirror; could have a window; could have a door\", \"similar objects\": [\"bedroom\", \"kitchen\", \"living room\"]}", + 14 + ], + "bathrooms": [ + " {\"type\": \"room\", \"description\": \"has a toilet, sink, and shower; could have a bathtub\", \"similar objects\": [\"kitchen\", \"bedroom\", \"living room\"]}", + 14 + ], + "video game controllers": [ + "\n{\"type\": \"gaming device\", \"description\": \"wireless or wired; could have buttons, joysticks, and triggers; could be used to control video games\", \"similar objects\": [\"keyboard\", \"mouse\", \"gamepad\"]}", + 14 + ], + "business man": [ + " {\"type\": \"person\", \"description\": \"wearing a suit; carrying a briefcase; could have a tie\", \"similar objects\": [\"executive\", \"lawyer\", \"politician\"]}", + 14 + ], + "metal train": [ + " {\"type\": \"toy\", \"description\": \"made of metal; could have multiple cars; could have a locomotive\", \"similar objects\": [\"wooden train\", \"toy car\", \"toy airplane\"]}", + 14 + ], + "metal shelves": [ + " {\"type\": \"storage tool\", \"description\": \"made of metal; could be used to store items; could be adjustable\", \"similar objects\": [\"wooden shelves\", \"bookcase\", \"cabinet\"]}", + 14 + ], + "polar": [ + " {\"type\": \"bear\", \"description\": \"white fur; black eyes; long snout; could be found in the Arctic\", \"similar objects\": [\"grizzly bear\", \"brown bear\", \"black bear\"]}", + 14 + ], + "swimming": [ + " {\"type\": \"activity\", \"description\": \"involves movement in water; could be done for leisure or exercise\", \"similar objects\": [\"running\", \"cycling\", \"hiking\"]}", + 14 + ], + "gold lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of gold; could have a handle\", \"similar objects\": [\"lantern\", \"lamp\", \"chandelier\"]}", + 14 + ], + "see": [ + "\n{\"type\": \"verb\", \"description\": \"to perceive with the eyes; to understand; to recognize\", \"similar objects\": [\"look\", \"observe\", \"watch\"]}", + 14 + ], + "armchairs": [ + " {\"type\": \"furniture\", \"description\": \"has two arms and a back; could be upholstered; could have a cushion\", \"similar objects\": [\"sofa\", \"loveseat\", \"recliner\"]}", + 14 + ], + "metal lock": [ + " {\"type\": \"security tool\", \"description\": \"made of metal; has a keyhole; could be used to lock doors or windows\", \"similar objects\": [\"padlock\", \"combination lock\", \"deadbolt\"]}", + 14 + ], + "striation marks": [ + " {\"type\": \"geological feature\", \"description\": \"long, thin, parallel lines; could be found on rocks and mountains\", \"similar objects\": [\"fault lines\", \"joints\", \"veins\"]}", + 14 + ], + "dirt pitcher": [ + " {\"type\": \"gardening tool\", \"description\": \"long handle; could be made of metal; could have a spout\", \"similar objects\": [\"shovel\", \"rake\", \"hose\"]}", + 14 + ], + "computer monitor screen": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be connected to a computer; could display images and text\", \"similar objects\": [\"television\", \"tablet\", \"smartphone\"]}", + 14 + ], + "rink": [ + " {\"type\": \"sports facility\", \"description\": \"oval-shaped; could be made of ice; could be used for skating or hockey\", \"similar objects\": [\"stadium\", \"court\", \"arena\"]}", + 14 + ], + "fishing net": [ + " {\"type\": \"fishing tool\", \"description\": \"long, wide, made of mesh; could be attached to a pole\", \"similar objects\": [\"fishing rod\", \"fishing line\", \"fishing hook\"]}", + 14 + ], + "stakes": [ + " {\"type\": \"tool\", \"description\": \"long, pointed, made of metal or wood; used to secure something in the ground\", \"similar objects\": [\"nails\", \"screws\", \"anchors\"]}", + 14 + ], + "sweat bands": [ + " {\"type\": \"accessory\", \"description\": \"elastic; could be worn on the wrist; could be made of cotton or other fabrics\", \"similar objects\": [\"headbands\", \"arm bands\", \"ankle bands\"]}", + 14 + ], + "seasoning": [ + " {\"type\": \"food ingredient\", \"description\": \"used to enhance the flavor of food; could be in powder or liquid form\", \"similar objects\": [\"spice\", \"herb\", \"salt\"]}", + 14 + ], + "cupboard doors": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have handles\", \"similar objects\": [\"drawers\", \"wardrobe\", \"cabinet\"]}", + 14 + ], + "horse blanket": [ + " {\"type\": \"clothing item\", \"description\": \"thick fabric; could be made of wool; could be used to cover a horse\", \"similar objects\": [\"saddle blanket\", \"horse halter\", \"horse rug\"]}", + 14 + ], + "seconds": [ + " {\"type\": \"time unit\", \"description\": \"measurement of time; 1 second is equal to 1/60 of a minute\", \"similar objects\": [\"minutes\", \"hours\", \"days\"]}", + 14 + ], + "livestock": [ + " {\"type\": \"animal\", \"description\": \"domesticated animals kept for agricultural purposes; could include cows, sheep, goats, pigs, horses, etc.\", \"similar objects\": [\"poultry\", \"wildlife\", \"aquatic animals\"]}", + 14 + ], + "hotel building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have a lobby; could have a restaurant\", \"similar objects\": [\"apartment building\", \"office building\", \"mall\"]}", + 14 + ], + "condensation": [ + " {\"type\": \"weather phenomenon\", \"description\": \"water droplets on a cold surface; could be seen on windows\", \"similar objects\": [\"fog\", \"rain\", \"snow\"]}", + 14 + ], + "shadow tree grass": [ + "\n{\"type\": \"landscape\", \"description\": \"trees casting shadows on grass\", \"similar objects\": [\"forest\", \"meadow\", \"field\"]}", + 14 + ], + "orange numbers": [ + "\n{\"type\": \"visual representation\", \"description\": \"numbers in orange color; could be used to indicate warnings or caution\", \"similar objects\": [\"yellow numbers\", \"red numbers\", \"green numbers\"]}", + 14 + ], + "grey chain": [ + " {\"type\": \"accessory\", \"description\": \"metal; could be used for decoration or for security purposes\", \"similar objects\": [\"lock\", \"bracelet\", \"necklace\"]}", + 14 + ], + "rungs": [ + " {\"type\": \"ladder part\", \"description\": \"horizontal bars; could be made of metal or wood; could be used to climb up\", \"similar objects\": [\"steps\", \"stairs\", \"handrails\"]}", + 14 + ], + "edging": [ + " {\"type\": \"landscaping tool\", \"description\": \"used to create a border around a garden; could be made of metal or plastic\", \"similar objects\": [\"hedge trimmer\", \"weed whacker\", \"lawn mower\"]}", + 14 + ], + "orange marker": [ + "\n{\"type\": \"writing tool\", \"description\": \"orange; could be used to write on paper or other surfaces; could be erasable\", \"similar objects\": [\"pen\", \"pencil\", \"highlighter\"]}", + 14 + ], + "concrete planter": [ + "\n{\"type\": \"garden tool\", \"description\": \"rectangular; made of concrete; could have a drainage hole\", \"similar objects\": [\"terracotta pot\", \"ceramic pot\", \"wooden planter\"]}", + 14 + ], + "antique car": [ + " {\"type\": \"vehicle\", \"description\": \"old-fashioned; could have a vintage look; could have a classic design\", \"similar objects\": [\"vintage car\", \"classic car\", \"muscle car\"]}", + 14 + ], + "shadow plane": [ + " {\"type\": \"toy\", \"description\": \"flat, paper-like object; could be thrown to create a shadow on the wall\", \"similar objects\": [\"paper airplane\", \"kite\", \"boomerang\"]}", + 14 + ], + "springs": [ + " {\"type\": \"mechanical device\", \"description\": \"coiled metal; used to absorb shock and store energy\", \"similar objects\": [\"shock absorbers\", \"pulleys\", \"gears\"]}", + 14 + ], + "tyres": [ + " {\"type\": \"automotive part\", \"description\": \"round; made of rubber; used to support the weight of a vehicle\", \"similar objects\": [\"wheels\", \"rims\", \"hubs\"]}", + 14 + ], + "wood furniture": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could be a table, chair, or bed\", \"similar objects\": [\"metal furniture\", \"plastic furniture\", \"glass furniture\"]}", + 14 + ], + "wha": [ + "\n{\"type\": \"interjection\", \"description\": \"used to express surprise, disbelief, or confusion\", \"similar objects\": [\"what\", \"huh\", \"why\"]}", + 14 + ], + "color plate": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could have colorful patterns\", \"similar objects\": [\"bowl\", \"cup\", \"plate\"]}", + 14 + ], + "coasters": [ + " {\"type\": \"tableware\", \"description\": \"round; could be made of cork, wood, or plastic; used to protect surfaces from hot or cold drinks\", \"similar objects\": [\"placemats\", \"tablecloths\", \"napkins\"]}", + 14 + ], + "headscarf": [ + " {\"type\": \"clothing accessory\", \"description\": \"square or rectangular; could be made of fabric; could be worn on the head\", \"similar objects\": [\"hat\", \"cap\", \"turban\"]}", + 14 + ], + "plastic trash bag": [ + " {\"type\": \"container\", \"description\": \"transparent; could be tied up; could be used to store garbage\", \"similar objects\": [\"plastic bag\", \"paper bag\", \"garbage can\"]}", + 14 + ], + "truck window": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be made of glass; could be opened and closed\", \"similar objects\": [\"car window\", \"windshield\", \"rearview mirror\"]}", + 14 + ], + "pepsi sign": [ + " {\"type\": \"advertisement\", \"description\": \"red, white, and blue; has the Pepsi logo\", \"similar objects\": [\"Coca-Cola sign\", \"McDonald's sign\", \"Starbucks sign\"]}", + 14 + ], + "bucket hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; brimmed; could be made of cotton or other fabrics\", \"similar objects\": [\"baseball cap\", \"sun hat\", \"beanie\"]}", + 14 + ], + "whit": [ + "\n{\"type\": \"color\", \"description\": \"lightest color; could be described as pure white\", \"similar objects\": [\"ivory\", \"cream\", \"eggshell\"]}", + 14 + ], + "ac": [ + " {\"type\": \"appliance\", \"description\": \"electronic device used to cool a room; could be wall-mounted or window-mounted\", \"similar objects\": [\"heater\", \"fan\", \"refrigerator\"]}", + 14 + ], + "mans arm": [ + "\n{\"type\": \"body part\", \"description\": \"long; could be muscular; could have tattoos; could have a watch\", \"similar objects\": [\"leg\", \"hand\", \"torso\"]}", + 14 + ], + "glass base": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be used to hold flowers; could be used as a table\", \"similar objects\": [\"vase\", \"bowl\", \"cup\"]}", + 14 + ], + "slice pizza": [ + " {\"type\": \"food\", \"description\": \"round; could be cut into triangular pieces; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread pizza\"]}", + 14 + ], + "passenger train cars": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; could have multiple compartments; could have windows\", \"similar objects\": [\"bus\", \"tram\", \"subway\"]}", + 14 + ], + "color bag": [ + " {\"type\": \"accessory\", \"description\": \"could be made of fabric; could have multiple colors; could have a handle\", \"similar objects\": [\"purse\", \"backpack\", \"tote bag\"]}", + 14 + ], + "ground cover": [ + " {\"type\": \"landscape material\", \"description\": \"used to cover the ground; could be made of plastic, fabric, or stone; could be used to prevent weeds\", \"similar objects\": [\"mulch\", \"gravel\", \"soil\"]}", + 14 + ], + "fence line": [ + " {\"type\": \"barrier\", \"description\": \"long line of posts connected by boards or wires; could be used to separate two areas\", \"similar objects\": [\"wall\", \"hedge\", \"gate\"]}", + 14 + ], + "airport worker": [ + " {\"type\": \"occupation\", \"description\": \"could be a pilot, flight attendant, baggage handler, security guard, or other airport staff\", \"similar objects\": [\"airline employee\", \"airport security\", \"air traffic controller\"]}", + 14 + ], + "print shirt": [ + " {\"type\": \"clothing\", \"description\": \"collared; could have long or short sleeves; could have a pattern or design printed on it\", \"similar objects\": [\"t-shirt\", \"polo shirt\", \"button-down shirt\"]}", + 14 + ], + "stadium seating": [ + " {\"type\": \"seating\", \"description\": \"rows of seats; could be made of plastic or metal; could have armrests\", \"similar objects\": [\"theater seating\", \"bleacher seating\", \"bleacher bench\"]}", + 14 + ], + "ball girl": [ + " {\"type\": \"person\", \"description\": \"young; wears a uniform; retrieves balls during a tennis match\", \"similar objects\": [\"umpire\", \"linesman\", \"referee\"]}", + 14 + ], + "program": [ + " {\"type\": \"software\", \"description\": \"a set of instructions that tells a computer what to do; could be written in a programming language\", \"similar objects\": [\"application\", \"script\", \"algorithm\"]}", + 14 + ], + "wooden plank": [ + " {\"type\": \"building material\", \"description\": \"long, flat, made of wood\", \"similar objects\": [\"plywood\", \"timber\", \"lumber\"]}", + 14 + ], + "string beans": [ + " {\"type\": \"vegetable\", \"description\": \"long, green, could be cooked; could be sliced into small pieces\", \"similar objects\": [\"green beans\", \"snap peas\", \"snow peas\"]}", + 14 + ], + "trey": [ + " {\"type\": \"serving tool\", \"description\": \"rectangular; could be made of metal or plastic; could have handles\", \"similar objects\": [\"platter\", \"plate\", \"bowl\"]}", + 14 + ], + "fingernail thumb": [ + " {\"type\": \"body part\", \"description\": \"hard, curved, and pointed; grows from the end of the finger\", \"similar objects\": [\"toenail\", \"eyelash\", \"hair\"]}", + 14 + ], + "skyscraper building": [ + "\n{\"type\": \"structure\", \"description\": \"tall, multi-story building; could have glass windows; could have multiple elevators\", \"similar objects\": [\"office building\", \"apartment building\", \"condominium\"]}", + 14 + ], + "orange orange": [ + "\n{\"type\": \"fruit\", \"description\": \"round; orange in color; has a thick skin; could be peeled and segmented; could be juiced\", \"similar objects\": [\"lemon\", \"grapefruit\", \"tangerine\"]}", + 14 + ], + "napkin holder": [ + " {\"type\": \"tableware\", \"description\": \"could be made of metal or wood; could be round or square; could have a handle\", \"similar objects\": [\"cutlery holder\", \"salt and pepper shaker\", \"sugar bowl\"]}", + 14 + ], + "organizer": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of plastic or wood; could have multiple compartments; could be used to store items\", \"similar objects\": [\"box\", \"basket\", \"drawer\"]}", + 14 + ], + "dragons": [ + " {\"type\": \"mythical creature\", \"description\": \"large, scaly, could breathe fire\", \"similar objects\": [\"unicorn\", \"griffin\", \"phoenix\"]}", + 14 + ], + "gold writing": [ + " {\"type\": \"stationery\", \"description\": \"gold-colored; could be used for writing\", \"similar objects\": [\"pen\", \"marker\", \"pencil\"]}", + 14 + ], + "tile floors": [ + " {\"type\": \"flooring material\", \"description\": \"smooth, flat, and hard surface; could be made of ceramic, stone, or vinyl\", \"similar objects\": [\"hardwood floors\", \"carpet\", \"laminate floors\"]}", + 14 + ], + "cover book": [ + " {\"type\": \"book accessory\", \"description\": \"made of paper or plastic; used to protect the book from dust and dirt\", \"similar objects\": [\"bookmark\", \"book sleeve\", \"book stand\"]}", + 14 + ], + "blond child": [ + "\n{\"type\": \"person\", \"description\": \"light hair color; could have blue eyes; could be wearing a dress\", \"similar objects\": [\"blond adult\", \"brunette child\", \"redhead child\"]}", + 14 + ], + "bare leg": [ + " {\"type\": \"body part\", \"description\": \"exposed skin; could be covered with tattoos; could be shaved\", \"similar objects\": [\"arm\", \"foot\", \"torso\"]}", + 14 + ], + "xbox": [ + " {\"type\": \"gaming console\", \"description\": \"rectangular; has buttons and joysticks; could be connected to a TV\", \"similar objects\": [\"PlayStation\", \"Nintendo Switch\", \"PC\"]}", + 14 + ], + "grey pair": [ + " {\"type\": \"clothing item\", \"description\": \"two pieces of clothing; could be pants, shirt, or dress; could be made of cotton, wool, or other fabrics; could be in different shades of grey\", \"similar objects\": [\"black pair\", \"white pair\", \"blue pair\"]}", + 14 + ], + "digital window": [ + " {\"type\": \"electronic device\", \"description\": \"a window-like display; could be used to show images or videos; could be interactive\", \"similar objects\": [\"touch screen\", \"smart TV\", \"projector\"]}", + 14 + ], + "photography": [ + " {\"type\": \"art form\", \"description\": \"the practice of taking and processing photographs\", \"similar objects\": [\"videography\", \"painting\", \"sculpture\"]}", + 14 + ], + "computer wires": [ + " {\"type\": \"electronic accessory\", \"description\": \"long, thin, could be in different colors; could be connected to a computer\", \"similar objects\": [\"keyboard\", \"mouse\", \"monitor\"]}", + 14 + ], + "poster board": [ + " {\"type\": \"stationery\", \"description\": \"rectangular; could be made of cardboard; could be used for presentations\", \"similar objects\": [\"whiteboard\", \"chalkboard\", \"marker board\"]}", + 14 + ], + "left shoe": [ + " {\"type\": \"footwear\", \"description\": \"could be made of leather; could have laces; could have a sole\", \"similar objects\": [\"right shoe\", \"sneaker\", \"boot\"]}", + 14 + ], + "cement post": [ + " {\"type\": \"building material\", \"description\": \"gray; could be used to build a fence; could be used to support a structure\", \"similar objects\": [\"concrete block\", \"wood post\", \"metal post\"]}", + 14 + ], + "bear ears": [ + " {\"type\": \"accessory\", \"description\": \"furry; could be attached to a headband; could be brown or black\", \"similar objects\": [\"cat ears\", \"rabbit ears\", \"fox ears\"]}", + 14 + ], + "orange tee shirt": [ + "\n{\"type\": \"clothing\", \"description\": \"orange; could be short or long sleeved; could have a collar or a pocket\", \"similar objects\": [\"yellow shirt\", \"blue shirt\", \"white shirt\"]}", + 14 + ], + "blue trash": [ + " {\"type\": \"waste container\", \"description\": \"blue; could be made of plastic; could have a lid\", \"similar objects\": [\"bin\", \"garbage can\", \"recycling bin\"]}", + 14 + ], + "window wipers": [ + " {\"type\": \"cleaning tool\", \"description\": \"long, thin, flexible; could be attached to a car window\", \"similar objects\": [\"squeegee\", \"brush\", \"sponge\"]}", + 14 + ], + "purple paint": [ + " {\"type\": \"art material\", \"description\": \"vibrant color; could be used for painting\", \"similar objects\": [\"blue paint\", \"green paint\", \"yellow paint\"]}", + 14 + ], + "bottom piece": [ + " {\"type\": \"clothing item\", \"description\": \"part of a garment that covers the lower body; could be pants, shorts, skirts, etc.\", \"similar objects\": [\"top piece\", \"dress\", \"jacket\"]}", + 14 + ], + "playing field": [ + " {\"type\": \"outdoor area\", \"description\": \"large, flat, grassy; could have lines and goals\", \"similar objects\": [\"stadium\", \"court\", \"park\"]}", + 14 + ], + "bag woman": [ + " {\"type\": \"accessory\", \"description\": \"could be made of leather; could be used to carry items; could have straps\", \"similar objects\": [\"purse\", \"backpack\", \"tote bag\"]}", + 14 + ], + "teenage girl": [ + "\n{\"type\": \"person\", \"description\": \"young; could have long hair; could wear casual clothes\", \"similar objects\": [\"teenage boy\", \"young adult\", \"child\"]}", + 14 + ], + "apple macbook computer": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; has a keyboard; has a screen; could be connected to the internet\", \"similar objects\": [\"laptop\", \"desktop\", \"tablet\"]}", + 14 + ], + "shallow body": [ + " {\"type\": \"body of water\", \"description\": \"not deep; could be a lake, pond, or river\", \"similar objects\": [\"ocean\", \"sea\", \"stream\"]}", + 14 + ], + "concrete structure": [ + " {\"type\": \"building material\", \"description\": \"hard, durable, and strong; could be used to build walls, foundations, and other structures\", \"similar objects\": [\"steel\", \"wood\", \"brick\"]}", + 14 + ], + "advertising signs": [ + " {\"type\": \"promotional tool\", \"description\": \"could be made of paper, plastic, metal; could be in different shapes and sizes; could be illuminated\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 14 + ], + "cross top church": [ + " {\"type\": \"architecture\", \"description\": \"tall, pointed roof; could have a cross on top; could have stained glass windows\", \"similar objects\": [\"cathedral\", \"temple\", \"mosque\"]}", + 14 + ], + "steam train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has a steam engine; could have multiple carriages\", \"similar objects\": [\"diesel train\", \"tram\", \"monorail\"]}", + 14 + ], + "chocolate donuts": [ + "\n{\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be filled with chocolate; could be topped with sprinkles\", \"similar objects\": [\"doughnuts\", \"bagels\", \"muffins\"]}", + 14 + ], + "wall light switch": [ + " {\"type\": \"electrical tool\", \"description\": \"rectangular; has a switch to turn on/off the light\", \"similar objects\": [\"dimmer switch\", \"outlet\", \"timer switch\"]}", + 14 + ], + "porcelain bathtub": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"white; could be oval or rectangular; could have a glossy finish; could have a drain\", \"similar objects\": [\"shower\", \"sink\", \"toilet\"]}", + 14 + ], + "cap wave": [ + " {\"type\": \"waveform\", \"description\": \"a type of waveform with a flat top and a steep slope; could be used in audio signal processing\", \"similar objects\": [\"square wave\", \"sawtooth wave\", \"triangle wave\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and", + 14 + ], + "pink bowl": [ + "\n{\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; has a color of pink\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 14 + ], + "liquor bottle": [ + " {\"type\": \"container\", \"description\": \"glass; could have a label; could have a cap\", \"similar objects\": [\"wine bottle\", \"beer bottle\", \"soda bottle\"]}", + 14 + ], + "leather bridle": [ + " {\"type\": \"horse equipment\", \"description\": \"made of leather; has a headstall, reins, and bit; used to control a horse\", \"similar objects\": [\"halter\", \"saddle\", \"girth\"]}", + 14 + ], + "moniter": [ + " {\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a computer; could have buttons for adjusting settings\", \"similar objects\": [\"television\", \"laptop\", \"tablet\"]}", + 14 + ], + "hyena": [ + " {\"type\": \"animal\", \"description\": \"spotted; has a loud laugh; scavenger\", \"similar objects\": [\"wolf\", \"jackal\", \"coyote\"]}", + 14 + ], + "wedges": [ + " {\"type\": \"shoe\", \"description\": \"high-heeled; could be made of leather; could have straps\", \"similar objects\": [\"pumps\", \"sandals\", \"flats\"]}", + 14 + ], + "foaming water": [ + " {\"type\": \"liquid\", \"description\": \"bubbles; could be white or colored; could be hot or cold\", \"similar objects\": [\"soda\", \"beer\", \"juice\"]}", + 14 + ], + "pen desk": [ + " {\"type\": \"furniture\", \"description\": \"long, rectangular; could have drawers; could be made of wood or metal\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant", + 14 + ], + "yankees": [ + " {\"type\": \"sports team\", \"description\": \"based in New York City; plays in Major League Baseball\", \"similar objects\": [\"Mets\", \"Red Sox\", \"Cubs\"]}", + 14 + ], + "wood stand": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could be used to display items; could have a flat surface\", \"similar objects\": [\"table\", \"shelf\", \"chair\"]}", + 14 + ], + "chocolate cupcakes": [ + "\n{\"type\": \"dessert\", \"description\": \"round; made of chocolate; could have frosting and sprinkles on top\", \"similar objects\": [\"brownies\", \"cookies\", \"muffins\"]}", + 14 + ], + "floor window": [ + " {\"type\": \"architectural feature\", \"description\": \"large window that is installed in the floor; could be made of glass or metal\", \"similar objects\": [\"skylight\", \"basement window\", \"bay window\"]}", + 14 + ], + "metal hand": [ + " {\"type\": \"tool\", \"description\": \"made of metal; could be used to hold objects; could be used to open doors\", \"similar objects\": [\"pliers\", \"screwdriver\", \"hammer\"]}", + 14 + ], + "wears": [ + " {\"type\": \"clothing\", \"description\": \"covering for the body; could be made of different materials; could be of different colors and styles\", \"similar objects\": [\"shirt\", \"pants\", \"dress\"]}", + 14 + ], + "lacy": [ + " {\"type\": \"fabric\", \"description\": \"lightweight; delicate; intricate patterns; could be made of silk, cotton, or nylon\", \"similar objects\": [\"lace\", \"chiffon\", \"tulle\"]}", + 14 + ], + "seat brown": [ + " {\"type\": \"furniture\", \"description\": \"brown; could be made of wood or leather; could have armrests; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 14 + ], + "shadow boat": [ + " {\"type\": \"toy\", \"description\": \"boat-shaped; could be made of paper; could be used to play shadow puppet show\", \"similar objects\": [\"paper airplane\", \"paper boat\", \"paper kite\"]}", + 14 + ], + "powdery snow": [ + " {\"type\": \"weather phenomenon\", \"description\": \"light, dry, and fluffy; could be blown away by wind\", \"similar objects\": [\"sleet\", \"hail\", \"frost\"]}", + 14 + ], + "baggage cart": [ + " {\"type\": \"transportation tool\", \"description\": \"has four wheels; could be pushed or pulled; could be used to carry luggage\", \"similar objects\": [\"hand truck\", \"dolly\", \"wheelbarrow\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber,", + 14 + ], + "city lights": [ + " {\"type\": \"landscape\", \"description\": \"bright lights from buildings and streets; could be seen from a distance\", \"similar objects\": [\"cityscape\", \"skyscraper\", \"night view\"]}", + 14 + ], + "glass beer bottle": [ + "\n{\"type\": \"container\", \"description\": \"transparent; cylindrical; has a long neck; could be made of glass or plastic; could have a label\", \"similar objects\": [\"wine bottle\", \"water bottle\", \"soda bottle\"]}", + 14 + ], + "emergency": [ + " {\"type\": \"situation\", \"description\": \"urgent; requires immediate attention; could be life-threatening\", \"similar objects\": [\"crisis\", \"disaster\", \"accident\"]}", + 14 + ], + "bathroom mat": [ + " {\"type\": \"household item\", \"description\": \"rectangular; made of absorbent material; could be placed in front of the shower\", \"similar objects\": [\"bath rug\", \"bathroom rug\", \"bath mat\"]}", + 14 + ], + "subway car": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple doors; could be connected to other cars\", \"similar objects\": [\"train\", \"bus\", \"tram\"]}", + 14 + ], + "register": [ + " {\"type\": \"machine\", \"description\": \"electronic device; used to record sales transactions; could have a display screen\", \"similar objects\": [\"cashier machine\", \"scanner\", \"printer\"]}", + 14 + ], + "snow boot": [ + " {\"type\": \"footwear\", \"description\": \"waterproof; has a thick sole; could be insulated\", \"similar objects\": [\"hiking boot\", \"rain boot\", \"ski boot\"]}", + 14 + ], + "broccoli stem": [ + " {\"type\": \"vegetable\", \"description\": \"green; long and thick; could be cut into small pieces; could have small florets\", \"similar objects\": [\"cauliflower stem\", \"asparagus stem\", \"celery stem\"]}", + 14 + ], + "clove": [ + " {\"type\": \"spice\", \"description\": \"small, brown, has a strong aroma; could be used as a flavoring agent\", \"similar objects\": [\"cinnamon\", \"nutmeg\", \"ginger\"]}", + 14 + ], + "bread sandwich": [ + "\n{\"type\": \"food\", \"description\": \"two slices of bread with filling in between; could be toasted; could be served with condiments\", \"similar objects\": [\"burger\", \"wrap\", \"panini\"]}", + 14 + ], + "time display": [ + " {\"type\": \"clock\", \"description\": \"could be digital or analog; could show time, date, and other information; could be wall-mounted or portable\", \"similar objects\": [\"watch\", \"alarm clock\", \"stopwatch\"]}", + 14 + ], + "contraption": [ + " {\"type\": \"machine\", \"description\": \"complex; could be made of multiple parts; could be used for a specific purpose\", \"similar objects\": [\"device\", \"apparatus\", \"mechanism\"]}", + 14 + ], + "stock": [ + " {\"type\": \"financial instrument\", \"description\": \"shares of a company; could be traded in the stock market\", \"similar objects\": [\"bond\", \"mutual fund\", \"derivative\"]}", + 14 + ], + "maze": [ + " {\"type\": \"puzzle\", \"description\": \"a complex network of paths or passages; could be made of walls or hedges\", \"similar objects\": [\"labyrinth\", \"puzzle\", \"riddle\"]}", + 14 + ], + "rags": [ + " {\"type\": \"cleaning tool\", \"description\": \"made of cloth; could be used for cleaning\", \"similar objects\": [\"sponge\", \"towel\", \"brush\"]}", + 14 + ], + "pasta dish": [ + " {\"type\": \"food\", \"description\": \"made of pasta; could be served with sauce; could be topped with cheese\", \"similar objects\": [\"lasagna\", \"ravioli\", \"macaroni and cheese\"]}", + 14 + ], + "desktop computer monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a stand; could be connected to a computer\", \"similar objects\": [\"laptop\", \"television\", \"tablet\"]}", + 14 + ], + "maroon shirt": [ + " {\"type\": \"clothing\", \"description\": \"dark red; could have long or short sleeves; could have a collar\", \"similar objects\": [\"red shirt\", \"black shirt\", \"white shirt\"]}", + 14 + ], + "rung": [ + " {\"type\": \"ladder part\", \"description\": \"horizontal bar; could be made of metal or wood; could be used to climb up\", \"similar objects\": [\"step\", \"stile\", \"tread\"]}", + 14 + ], + "brown ground": [ + " {\"type\": \"surface\", \"description\": \"dark brown; could be made of soil, sand, or gravel; could be uneven\", \"similar objects\": [\"dirt\", \"mud\", \"grass\"]}", + 14 + ], + "wall paint": [ + " {\"type\": \"decoration material\", \"description\": \"liquid; could be used to paint walls; could be in different colors\", \"similar objects\": [\"wallpaper\", \"tile\", \"wood panel\"]}", + 14 + ], + "metal bike rack": [ + " {\"type\": \"storage tool\", \"description\": \"made of metal; could be used to store bikes; could be attached to the ground\", \"similar objects\": [\"bike stand\", \"bike lock\", \"bike hanger\"]}", + 14 + ], + "wrists": [ + " {\"type\": \"body part\", \"description\": \"connects hands to arms; could be thin or thick; could be flexible\", \"similar objects\": [\"ankles\", \"elbows\", \"shoulders\"]}", + 14 + ], + "shrimps": [ + " {\"type\": \"seafood\", \"description\": \"small, pinkish; could be cooked with garlic and butter\", \"similar objects\": [\"lobster\", \"crab\", \"squid\"]}", + 14 + ], + "word police": [ + "\n{\"type\": \"software\", \"description\": \"word processing software; used to create, edit, and format documents\", \"similar objects\": [\"Microsoft Word\", \"Google Docs\", \"Pages\"]}", + 14 + ], + "dish drainer": [ + " {\"type\": \"kitchen tool\", \"description\": \"has a rack for dishes; could be made of plastic or metal; could have a tray for water\", \"similar objects\": [\"dish rack\", \"dish tray\", \"dish mat\"]}", + 14 + ], + "lion statue": [ + " {\"type\": \"decoration\", \"description\": \"sculpture of a lion; could be made of stone, metal, or wood; could have a mane\", \"similar objects\": [\"elephant statue\", \"tiger statue\", \"giraffe statue\"]}", + 14 + ], + "silk tie": [ + " {\"type\": \"clothing accessory\", \"description\": \"long, thin, made of silk; could be patterned\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}", + 14 + ], + "computer cable": [ + " {\"type\": \"electronic accessory\", \"description\": \"long, thin, could be made of plastic or metal; could have multiple connectors\", \"similar objects\": [\"USB cable\", \"power cord\", \"HDMI cable\"]}", + 14 + ], + "decorative": [ + "\n{\"type\": \"decoration\", \"description\": \"could be used to enhance the appearance of a space; could be made of various materials; could be in different shapes and sizes\", \"similar objects\": [\"ornament\", \"accessory\", \"artwork\"]}", + 14 + ], + "belly": [ + " {\"type\": \"body part\", \"description\": \"soft; located in the middle of the body; could be protruding\", \"similar objects\": [\"stomach\", \"abdomen\", \"waist\"]}", + 14 + ], + "silver rings": [ + " {\"type\": \"jewelry\", \"description\": \"round; could be made of silver; could be decorated with stones\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 14 + ], + "puppet": [ + " {\"type\": \"toy\", \"description\": \"could be made of cloth or wood; could be manipulated by strings or rods\", \"similar objects\": [\"doll\", \"action figure\", \"marionette\"]}", + 14 + ], + "grass hill": [ + " {\"type\": \"landscape\", \"description\": \"green; could be sloped; could have wildflowers\", \"similar objects\": [\"meadow\", \"field\", \"prairie\"]}", + 14 + ], + "gold logo": [ + " {\"type\": \"logo\", \"description\": \"golden color; could be a symbol or a text\", \"similar objects\": [\"silver logo\", \"bronze logo\", \"black logo\"]}", + 14 + ], + "bra strap": [ + " {\"type\": \"clothing accessory\", \"description\": \"thin, adjustable straps; could be made of elastic material; could be used to adjust the fit of a bra\", \"similar objects\": [\"belt\", \"shoelace\", \"suspenders\"]}", + 14 + ], + "color snow": [ + " {\"type\": \"art material\", \"description\": \"white, powdery, could be used to create artworks\", \"similar objects\": [\"glitter\", \"paint\", \"clay\"]}", + 14 + ], + "back wall": [ + " {\"type\": \"structure\", \"description\": \"vertical surface; could be made of brick, wood, or other materials; could be painted\", \"similar objects\": [\"ceiling\", \"floor\", \"door\"]}", + 14 + ], + "basket brown": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of wicker; could have a handle\", \"similar objects\": [\"box\", \"bag\", \"bucket\"]}", + 14 + ], + "grey poles": [ + " {\"type\": \"structural object\", \"description\": \"cylindrical; could be made of metal; could be used for support\", \"similar objects\": [\"columns\", \"beams\", \"posts\"]}", + 14 + ], + "mule": [ + " {\"type\": \"animal\", \"description\": \"long ears; has a short mane; could be used as a pack animal\", \"similar objects\": [\"donkey\", \"horse\", \"camel\"]}", + 14 + ], + "blue elephant": [ + "\n{\"type\": \"animal\", \"description\": \"large; has a trunk; has grey or blue skin; has large ears\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}", + 14 + ], + "side bike": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could be motorized or non-motorized; could have a basket\", \"similar objects\": [\"scooter\", \"moped\", \"tricycle\"]}", + 14 + ], + "hikers": [ + " {\"type\": \"people\", \"description\": \"people wearing hiking boots and carrying backpacks\", \"similar objects\": [\"campers\", \"climbers\", \"trekkers\"]}", + 14 + ], + "bath robe": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting; could be made of cotton or terry cloth; could have a hood\", \"similar objects\": [\"towel\", \"pajamas\", \"slippers\"]}", + 14 + ], + "guide": [ + " {\"type\": \"instructional tool\", \"description\": \"could be a book or a person; provides instructions and advice\", \"similar objects\": [\"manual\", \"tutorial\", \"handbook\"]}", + 14 + ], + "depiction": [ + " {\"type\": \"word\", \"description\": \"a representation of something in words or pictures\", \"similar objects\": [\"description\", \"illustration\", \"portrayal\"]}", + 14 + ], + "plat": [ + " {\"type\": \"dishware\", \"description\": \"flat, round; could be made of ceramic, plastic, or metal; could be used to serve food\", \"similar objects\": [\"bowl\", \"plate\", \"cup\"]}", + 14 + ], + "scissor handle": [ + " {\"type\": \"tool\", \"description\": \"two handles connected by a pivot; could be used for cutting\", \"similar objects\": [\"tweezers\", \"pliers\", \"clippers\"]}", + 14 + ], + "hammock": [ + " {\"type\": \"furniture\", \"description\": \"made of fabric; could be hung between two trees; could be used for sleeping\", \"similar objects\": [\"swing\", \"hanging chair\", \"daybed\"]}", + 14 + ], + "brown branch": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, brown; could have leaves and fruits\", \"similar objects\": [\"twig\", \"stem\", \"trunk\"]}", + 14 + ], + "right shoe": [ + " {\"type\": \"footwear\", \"description\": \"could be made of leather; could have laces; could have a sole\", \"similar objects\": [\"left shoe\", \"sneaker\", \"boot\"]}", + 14 + ], + "chair legs": [ + " {\"type\": \"furniture part\", \"description\": \"long, cylindrical; could be made of wood or metal; could have a curved or straight shape\", \"similar objects\": [\"table legs\", \"armrests\", \"footrests\"]}", + 14 + ], + "cross walk sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; has a white background; has a red hand or a red man\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 14 + ], + "button hole": [ + " {\"type\": \"clothing accessory\", \"description\": \"small hole in a garment; used to fasten buttons\", \"similar objects\": [\"zipper\", \"hook and eye\", \"snap\"]}", + 14 + ], + "medals": [ + " {\"type\": \"award\", \"description\": \"could be made of metal; could be in different shapes; could be hung on a ribbon\", \"similar objects\": [\"trophies\", \"certificates\", \"plaques\"]}", + 14 + ], + "orange poles": [ + " {\"type\": \"road safety tool\", \"description\": \"orange; could be used to separate lanes; could be used to indicate a construction area\", \"similar objects\": [\"cones\", \"barricades\", \"traffic signs\"]}", + 14 + ], + "orange band": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of rubber or fabric; could be used for exercise\", \"similar objects\": [\"resistance band\", \"yoga strap\", \"exercise ball\"]}", + 14 + ], + "bus station": [ + " {\"type\": \"location\", \"description\": \"place where buses stop; could have a waiting area; could have ticket booths\", \"similar objects\": [\"train station\", \"airport\", \"bus stop\"]}", + 14 + ], + "water stain": [ + " {\"type\": \"stain\", \"description\": \"transparent; could be caused by water; could be found on walls, floors, and ceilings\", \"similar objects\": [\"dirt\", \"grease\", \"oil\"]}", + 14 + ], + "giraffe nose": [ + " {\"type\": \"body part\", \"description\": \"long, black, protruding; could be up to 18 inches long\", \"similar objects\": [\"elephant trunk\", \"monkey nose\", \"whale snout\"]}", + 14 + ], + "accent": [ + " {\"type\": \"speech pattern\", \"description\": \"a way of speaking that is different from the main language of a region or country; could be characterized by different pronunciation, grammar, and vocabulary\", \"similar objects\": [\"dialect\", \"colloquialism\", \"idiom\"]}", + 14 + ], + "silver digital camera": [ + "\n{\"type\": \"electronic device\", \"description\": \"silver; has a lens; could be used to take pictures\", \"similar objects\": [\"video camera\", \"smartphone\", \"tablet\"]}", + 14 + ], + "brown stick": [ + " {\"type\": \"object\", \"description\": \"long, cylindrical, brown; could be made of wood or plastic\", \"similar objects\": [\"broom\", \"mop\", \"pole\"]}", + 14 + ], + "orange paper": [ + "\n{\"type\": \"material\", \"description\": \"orange color; could be used for writing or drawing; could be used for decoration\", \"similar objects\": [\"yellow paper\", \"red paper\", \"green paper\"]}", + 14 + ], + "crockpot": [ + " {\"type\": \"cooking tool\", \"description\": \"large, slow cooker; could have a lid; could have a timer\", \"similar objects\": [\"pressure cooker\", \"rice cooker\", \"slow cooker\"]}", + 14 + ], + "bare leafless tree": [ + "\n{\"type\": \"plant\", \"description\": \"no leaves; could have branches; could have a trunk\", \"similar objects\": [\"bush\", \"shrub\", \"palm tree\"]}", + 14 + ], + "frizbee": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"ball\", \"kite\", \"yo-yo\"]}", + 14 + ], + "stone ledge": [ + " {\"type\": \"landscape feature\", \"description\": \"flat, rocky surface; could be used as a seat or a platform\", \"similar objects\": [\"cliff\", \"rock formation\", \"rock wall\"]}", + 14 + ], + "luggage handle": [ + " {\"type\": \"travel accessory\", \"description\": \"long, cylindrical; could be made of metal; could be attached to a suitcase\", \"similar objects\": [\"wheels\", \"straps\", \"zipper\"]}", + 14 + ], + "hearse": [ + " {\"type\": \"vehicle\", \"description\": \"black; has a long body; could be used to transport a coffin\", \"similar objects\": [\"ambulance\", \"limousine\", \"truck\"]}", + 14 + ], + "diamond shapes": [ + " {\"type\": \"geometric shape\", \"description\": \"has four sides of equal length; has four angles of equal measure; has two diagonals of equal length\", \"similar objects\": [\"square\", \"rectangle\", \"triangle\"]}", + 14 + ], + "door window": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"window\", \"door\", \"shutter\"]}", + 14 + ], + "tie clip": [ + " {\"type\": \"accessory\", \"description\": \"small metal clip; used to hold a tie in place\", \"similar objects\": [\"cufflinks\", \"lapel pin\", \"tie bar\"]}", + 14 + ], + "construction site": [ + " {\"type\": \"location\", \"description\": \"could have cranes, bulldozers, and other heavy machinery; could have workers in safety gear; could have piles of dirt and gravel\", \"similar objects\": [\"building site\", \"construction zone\", \"construction area\"]}", + 14 + ], + "extension": [ + " {\"type\": \"electrical tool\", \"description\": \"long, flexible; could be used to connect two electrical devices\", \"similar objects\": [\"cable\", \"plug\", \"adapter\"]}", + 14 + ], + "purple eggplant": [ + "\n{\"type\": \"vegetable\", \"description\": \"elongated, purple, smooth; could have green and rough stems; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"cucumber\", \"green bean\"]}", + 14 + ], + "specs": [ + " {\"type\": \"eyewear\", \"description\": \"round or rectangular frames; could have lenses\", \"similar objects\": [\"glasses\", \"sunglasses\", \"goggles\"]}", + 14 + ], + "quiche": [ + " {\"type\": \"food\", \"description\": \"pie-like dish; could be filled with vegetables, cheese, and eggs; could be served hot or cold\", \"similar objects\": [\"tart\", \"frittata\", \"omelette\"]}", + 14 + ], + "curb edge sidewalk": [ + "\n{\"type\": \"infrastructure\", \"description\": \"raised edge of the sidewalk; could be made of concrete or metal; could be painted with yellow or white lines\", \"similar objects\": [\"street corner\", \"crosswalk\", \"traffic island\"]}", + 14 + ], + "plow": [ + " {\"type\": \"agricultural tool\", \"description\": \"long handle; has a blade; used to turn soil\", \"similar objects\": [\"harrow\", \"cultivator\", \"rake\"]}", + 14 + ], + "patio furniture": [ + " {\"type\": \"outdoor furniture\", \"description\": \"could be made of metal, wood, or plastic; could have chairs, tables, and umbrellas\", \"similar objects\": [\"deck furniture\", \"garden furniture\", \"balcony furniture\"]}", + 14 + ], + "lifeguard stand": [ + " {\"type\": \"safety tool\", \"description\": \"tall, red and white stripes; has a flag on top\", \"similar objects\": [\"surfboard\", \"buoy\", \"raft\"]}", + 14 + ], + "yellow hose": [ + "\n{\"type\": \"garden tool\", \"description\": \"long, flexible, yellow; could be used to water plants\", \"similar objects\": [\"sprinkler\", \"watering can\", \"garden hose\"]}", + 14 + ], + "ice cream cone": [ + " {\"type\": \"food\", \"description\": \"cone-shaped; could be filled with ice cream; could be topped with sprinkles\", \"similar objects\": [\"cupcake\", \"donut\", \"pie\"]}", + 14 + ], + "cake topper": [ + " {\"type\": \"decoration\", \"description\": \"small; could be made of plastic; could be used to decorate cakes\", \"similar objects\": [\"cupcake topper\", \"cake stand\", \"cake mold\"]}", + 14 + ], + "shirtless boy": [ + "\n{\"type\": \"person\", \"description\": \"young male; no shirt; could have shorts or pants\", \"similar objects\": [\"girl\", \"man\", \"woman\"]}", + 14 + ], + "multiple train tracks": [ + "\n{\"type\": \"transportation infrastructure\", \"description\": \"parallel metal rails; could have electric wires; could have signals\", \"similar objects\": [\"highway\", \"bridge\", \"tunnel\"]}", + 14 + ], + "cement area": [ + " {\"type\": \"building material\", \"description\": \"gray; could be used to build walls and floors; could be mixed with water\", \"similar objects\": [\"concrete\", \"mortar\", \"tile\"]}", + 14 + ], + "stomach area": [ + " {\"type\": \"body part\", \"description\": \"located in the abdomen; contains digestive organs; could be affected by diseases\", \"similar objects\": [\"liver\", \"intestines\", \"pancreas\"]}", + 14 + ], + "plastic toilet seat lid": [ + "\n{\"type\": \"bathroom accessory\", \"description\": \"round; made of plastic; could be white or other colors; could be used to cover the toilet seat\", \"similar objects\": [\"toilet seat cover\", \"toilet seat cushion\", \"toilet seat riser\"]}", + 14 + ], + "hand dryer": [ + " {\"type\": \"appliance\", \"description\": \"mounted on the wall; has a nozzle; could be used to dry hands\", \"similar objects\": [\"hair dryer\", \"vacuum cleaner\", \"air conditioner\"]}", + 14 + ], + "metal utility pole": [ + " {\"type\": \"utility pole\", \"description\": \"tall, cylindrical, made of metal; could have wires attached to it\", \"similar objects\": [\"wooden utility pole\", \"street light\", \"traffic light\"]}", + 14 + ], + "water mass": [ + " {\"type\": \"body of water\", \"description\": \"large body of water; could be a lake, river, or ocean\", \"similar objects\": [\"pond\", \"stream\", \"sea\"]}", + 14 + ], + "railroad sign": [ + " {\"type\": \"traffic sign\", \"description\": \"rectangular; has a white background; could have a red circle with an X in the middle\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 14 + ], + "grey train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could have a locomotive; could be painted grey\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 14 + ], + "dark ripples": [ + " {\"type\": \"pattern\", \"description\": \"dark, wavy lines; could be seen on water surfaces\", \"similar objects\": [\"light ripples\", \"ripples\", \"waves\"]}", + 14 + ], + "wooden object": [ + " {\"type\": \"material\", \"description\": \"made of wood; could be carved into different shapes; could be painted\", \"similar objects\": [\"plastic\", \"metal\", \"glass\"]}", + 14 + ], + "access door": [ + " {\"type\": \"door\", \"description\": \"could be made of metal; could be locked; could be opened with a key or a code\", \"similar objects\": [\"security door\", \"fire door\", \"garage door\"]}", + 14 + ], + "sit motorcycle": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a seat; could have a handlebar\", \"similar objects\": [\"scooter\", \"bicycle\", \"moped\"]}", + 14 + ], + "tall hill": [ + " {\"type\": \"landscape\", \"description\": \"high elevation; could have trees and grass; could have a winding path\", \"similar objects\": [\"mountain\", \"cliff\", \"valley\"]}", + 14 + ], + "street sign post": [ + " {\"type\": \"road sign\", \"description\": \"tall, metal post; could have a rectangular shape; could have a sign with words or symbols\", \"similar objects\": [\"traffic light\", \"stop sign\", \"yield sign\"]}", + 14 + ], + "blue skys": [ + " {\"type\": \"weather\", \"description\": \"clear, bright blue; could have white clouds\", \"similar objects\": [\"sunny day\", \"rainy day\", \"snowy day\"]}", + 14 + ], + "mane giraffe": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, thick hair on the neck of a giraffe\", \"similar objects\": [\"tail\", \"antlers\", \"horns\"]}", + 14 + ], + "pills": [ + " {\"type\": \"medicine\", \"description\": \"small, round, could be in different colors; could be in a bottle\", \"similar objects\": [\"tablets\", \"capsules\", \"syrup\"]}", + 14 + ], + "biplane": [ + " {\"type\": \"aircraft\", \"description\": \"two wings; two sets of propellers; could have an open cockpit\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}", + 14 + ], + "bench legs": [ + " {\"type\": \"furniture part\", \"description\": \"long, straight, could be made of wood or metal; could have a curved top\", \"similar objects\": [\"table legs\", \"chair legs\", \"stool legs\"]}", + 14 + ], + "none": [ + "\n\nNo output is provided as no input was given.", + 14 + ], + "ben clock tower": [ + " {\"type\": \"architectural structure\", \"description\": \"tall, cylindrical; could have a clock face; could have a bell\", \"similar objects\": [\"cathedral\", \"obelisk\", \"minaret\"]}", + 14 + ], + "car hood": [ + " {\"type\": \"automotive part\", \"description\": \"metal; covers the engine; could be opened\", \"similar objects\": [\"bumper\", \"windshield\", \"door\"]}", + 14 + ], + "fuel truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a tank for fuel; could have a hose\", \"similar objects\": [\"fire truck\", \"cement truck\", \"dump truck\"]}", + 14 + ], + "delta logo": [ + " {\"type\": \"logo\", \"description\": \"triangle with a curved line in the middle; could be blue and white\", \"similar objects\": [\"Air Canada logo\", \"United Airlines logo\", \"American Airlines logo\"]}", + 14 + ], + "pontoon boat": [ + " {\"type\": \"watercraft\", \"description\": \"flat-bottomed boat; could have a canopy; could have a motor\", \"similar objects\": [\"canoe\", \"kayak\", \"rowboat\"]}", + 14 + ], + "pistachio": [ + " {\"type\": \"nut\", \"description\": \"green; has a hard shell; could be eaten as a snack\", \"similar objects\": [\"almond\", \"walnut\", \"cashew\"]}", + 14 + ], + "bear mouth": [ + " {\"type\": \"animal body part\", \"description\": \"round; could be wide open; could have sharp teeth\", \"similar objects\": [\"lion mouth\", \"tiger mouth\", \"wolf mouth\"]}", + 14 + ], + "body suit": [ + " {\"type\": \"clothing\", \"description\": \"full-body garment; could be made of spandex; could be used for swimming or diving\", \"similar objects\": [\"leotard\", \"wetsuit\", \"bodysuit\"]}", + 14 + ], + "stone blocks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of concrete, granite, or marble; could be used for construction\", \"similar objects\": [\"bricks\", \"wooden planks\", \"cement blocks\"]}", + 14 + ], + "lobby": [ + " {\"type\": \"room\", \"description\": \"large, open space; could have a reception desk; could have chairs and couches\", \"similar objects\": [\"hallway\", \"waiting room\", \"foyer\"]}", + 14 + ], + "woodpecker": [ + " {\"type\": \"bird\", \"description\": \"red head; black and white feathers; long beak; sharp claws\", \"similar objects\": [\"sparrow\", \"robin\", \"blue jay\"]}", + 14 + ], + "headlight vehicle": [ + "\n{\"type\": \"vehicle part\", \"description\": \"attached to the front of a vehicle; used to provide illumination in the dark\", \"similar objects\": [\"taillight\", \"fog light\", \"turn signal\"]}", + 13 + ], + "tan curtain": [ + " {\"type\": \"window covering\", \"description\": \"light brown; could be made of fabric; could be hung on a rod\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 13 + ], + "grey ear": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of wool; could be worn on the head\", \"similar objects\": [\"hat\", \"cap\", \"beanie\"]}", + 13 + ], + "center line": [ + " {\"type\": \"road marking\", \"description\": \"yellow; could be dashed or solid; used to separate lanes\", \"similar objects\": [\"road divider\", \"traffic island\", \"guardrail\"]}", + 13 + ], + "toaster counter": [ + " {\"type\": \"kitchen appliance\", \"description\": \"rectangular; has slots for toasting bread; could have a timer\", \"similar objects\": [\"coffee maker\", \"blender\", \"microwave\"]}", + 13 + ], + "stainless steel toaster": [ + "\n{\"type\": \"kitchen appliance\", \"description\": \"rectangular; made of stainless steel; has two slots for bread; could have a timer\", \"similar objects\": [\"coffee maker\", \"blender\", \"microwave\"]}", + 13 + ], + "seals": [ + " {\"type\": \"animal\", \"description\": \"gray; have flippers; could be found in the ocean\", \"similar objects\": [\"sea lions\", \"walruses\", \"otters\"]}", + 13 + ], + "wash basin": [ + " {\"type\": \"bathroom tool\", \"description\": \"round; could be made of porcelain; could have a faucet\", \"similar objects\": [\"sink\", \"bathtub\", \"toilet\"]}", + 13 + ], + "water trough": [ + " {\"type\": \"livestock equipment\", \"description\": \"long, rectangular; could be made of metal or wood; could be used to provide water for animals\", \"similar objects\": [\"feeder\", \"hayrack\", \"salt block\"]}", + 13 + ], + "wooden barn": [ + " {\"type\": \"building\", \"description\": \"large, rectangular; made of wood; could have a hayloft\", \"similar objects\": [\"shed\", \"stable\", \"garage\"]}", + 13 + ], + "coca cola logo": [ + "\n{\"type\": \"brand logo\", \"description\": \"red and white; has the words 'Coca Cola' written in it; has a curved line at the bottom\", \"similar objects\": [\"Pepsi logo\", \"McDonald's logo\", \"Starbucks logo\"]}", + 13 + ], + "liquor bottles": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of glass; could have a label\", \"similar objects\": [\"wine bottles\", \"beer bottles\", \"soda cans\"]}", + 13 + ], + "tennis game": [ + " {\"type\": \"sport\", \"description\": \"two players; a net; a ball; a racket\", \"similar objects\": [\"badminton\", \"table tennis\", \"squash\"]}", + 13 + ], + "pink curtains": [ + " {\"type\": \"decoration\", \"description\": \"pink; could be made of fabric; could be hung on a window\", \"similar objects\": [\"drapes\", \"blinds\", \"shades\"]}", + 13 + ], + "poster bed": [ + " {\"type\": \"furniture\", \"description\": \"four-poster bed; has four tall posts at each corner; could have a canopy\", \"similar objects\": [\"canopy bed\", \"platform bed\", \"daybed\"]}", + 13 + ], + "floater": [ + " {\"type\": \"water toy\", \"description\": \"round; could be made of foam; could be used in the pool\", \"similar objects\": [\"water wings\", \"inner tube\", \"noodle\"]}", + 13 + ], + "heel shoe": [ + " {\"type\": \"footwear\", \"description\": \"high heel; could be made of leather; could have straps\", \"similar objects\": [\"pumps\", \"sandals\", \"wedges\"]}", + 13 + ], + "copyright information": [ + "\n{\"type\": \"legal information\", \"description\": \"information that grants exclusive rights to the creator of a work; could include the name of the creator, the year of creation, and a copyright symbol\", \"similar objects\": [\"trademark information\", \"patent information\", \"license information\"]}", + 13 + ], + "bottom shelf": [ + " {\"type\": \"furniture\", \"description\": \"lowest shelf of a cabinet; could be used to store items\", \"similar objects\": [\"top shelf\", \"middle shelf\", \"drawer\"]}", + 13 + ], + "recliner chair": [ + " {\"type\": \"furniture\", \"description\": \"has a reclining back; could have armrests; could have a footrest\", \"similar objects\": [\"sofa\", \"loveseat\", \"rocking chair\"]}", + 13 + ], + "plastic utensil": [ + " {\"type\": \"utensil\", \"description\": \"made of plastic; could be used for eating\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}", + 13 + ], + "dozen": [ + " {\"type\": \"quantity\", \"description\": \"twelve of something\", \"similar objects\": [\"half-dozen\", \"score\", \"gross\"]}", + 13 + ], + "grass pasture": [ + " {\"type\": \"landscape\", \"description\": \"green; could have some trees; could have some animals\", \"similar objects\": [\"meadow\", \"field\", \"prairie\"]}", + 13 + ], + "tail hair": [ + " {\"type\": \"body part\", \"description\": \"long, thin, and flexible; could be found on animals\", \"similar objects\": [\"fur\", \"feather\", \"scales\"]}", + 13 + ], + "throw rug": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of wool; could be used to decorate a room\", \"similar objects\": [\"carpet\", \"mat\", \"area rug\"]}", + 13 + ], + "tailgate": [ + " {\"type\": \"vehicle part\", \"description\": \"hinged door at the back of a truck; could be opened and closed\", \"similar objects\": [\"trunk\", \"hood\", \"bumper\"]}", + 13 + ], + "grout line": [ + " {\"type\": \"building material\", \"description\": \"narrow line between tiles; could be filled with cement-based material\", \"similar objects\": [\"tile\", \"mortar\", \"concrete\"]}", + 13 + ], + "horse hair": [ + " {\"type\": \"material\", \"description\": \"long, thin, and flexible strands; could be used for making brushes\", \"similar objects\": [\"goat hair\", \"yak hair\", \"camel hair\"]}", + 13 + ], + "brown pillows": [ + "\n{\"type\": \"furniture\", \"description\": \"soft, squishy, usually made of fabric; could be square or round; could be brown or other colors\", \"similar objects\": [\"cushions\", \"mattress\", \"blanket\"]}", + 13 + ], + "rust stains": [ + " {\"type\": \"stain\", \"description\": \"reddish-brown; could be found on metal surfaces; could be removed with a rust remover\", \"similar objects\": [\"oil stains\", \"ink stains\", \"blood stains\"]}", + 13 + ], + "jacket man": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; could be made of cotton, wool, or leather; could have a zipper or buttons\", \"similar objects\": [\"coat\", \"sweater\", \"hoodie\"]}", + 13 + ], + "sugar bowl": [ + " {\"type\": \"kitchenware\", \"description\": \"round; could have a lid; could be made of ceramic or glass\", \"similar objects\": [\"salt shaker\", \"teapot\", \"coffee mug\"]}", + 13 + ], + "silver teapot": [ + "\n{\"type\": \"kitchenware\", \"description\": \"silver; has a handle; could have a spout; could have a lid\", \"similar objects\": [\"coffee pot\", \"kettle\", \"tea kettle\"]}", + 13 + ], + "decorative plant": [ + " {\"type\": \"decoration\", \"description\": \"could be a potted plant; could be artificial; could be used to decorate a room\", \"similar objects\": [\"vase\", \"statue\", \"painting\"]}", + 13 + ], + "television camera": [ + " {\"type\": \"recording device\", \"description\": \"long and cylindrical; has a lens; could be connected to a monitor\", \"similar objects\": [\"video camera\", \"webcam\", \"security camera\"]}", + 13 + ], + "pocket book": [ + " {\"type\": \"accessory\", \"description\": \"small, rectangular; could be made of leather; could be used to store items\", \"similar objects\": [\"wallet\", \"purse\", \"clutch\"]}", + 13 + ], + "silver piping": [ + " {\"type\": \"building material\", \"description\": \"shiny, metallic, cylindrical; could be used for plumbing\", \"similar objects\": [\"copper piping\", \"aluminum piping\", \"plastic piping\"]}", + 13 + ], + "toothy smile": [ + " {\"type\": \"expression\", \"description\": \"showing teeth; could be accompanied with eyes crinkling; could be a sign of happiness\", \"similar objects\": [\"smirk\", \"grin\", \"frown\"]}", + 13 + ], + "wooden bed frame": [ + "\n{\"type\": \"furniture\", \"description\": \"made of wood; could have four legs; could have a headboard and footboard\", \"similar objects\": [\"mattress\", \"sofa\", \"chair\"]}", + 13 + ], + "blue word": [ + " {\"type\": \"color\", \"description\": \"a shade of blue; could be used to describe objects or emotions\", \"similar objects\": [\"green\", \"red\", \"yellow\"]}", + 13 + ], + "wood fencing": [ + " {\"type\": \"building material\", \"description\": \"long, thin pieces of wood; could be used to build a fence\", \"similar objects\": [\"metal fencing\", \"bamboo fencing\", \"vinyl fencing\"]}", + 13 + ], + "metal weather vane": [ + "\n{\"type\": \"weather tool\", \"description\": \"made of metal; has a pointer that points to the direction of the wind\", \"similar objects\": [\"wind sock\", \"wind chime\", \"wind turbine\"]}", + 13 + ], + "brick apartment building": [ + "\n{\"type\": \"structure\", \"description\": \"rectangular; made of bricks; could have multiple stories; could have balconies\", \"similar objects\": [\"condominium\", \"townhouse\", \"row house\"]}", + 13 + ], + "front license plate": [ + " {\"type\": \"vehicle accessory\", \"description\": \"rectangular; has a number or letter printed on it; usually attached to the front of a vehicle\", \"similar objects\": [\"rear license plate\", \"bumper sticker\", \"license plate frame\"]}", + 13 + ], + "sunglasses man": [ + " {\"type\": \"accessory\", \"description\": \"dark glasses; could be worn by a man\", \"similar objects\": [\"hat\", \"scarf\", \"watch\"]}", + 13 + ], + "sauce cup": [ + " {\"type\": \"container\", \"description\": \"small, round, could be made of plastic or paper; could have a lid\", \"similar objects\": [\"bowl\", \"cup\", \"mug\"]}", + 13 + ], + "whip": [ + " {\"type\": \"tool\", \"description\": \"long, thin, flexible; could be made of leather; could be used to hit\", \"similar objects\": [\"rope\", \"chain\", \"stick\"]}", + 13 + ], + "seafood": [ + " {\"type\": \"food\", \"description\": \"various types of sea creatures, such as fish, shellfish, and crustaceans\", \"similar objects\": [\"fish\", \"shrimp\", \"crab\"]}", + 13 + ], + "peopl": [ + "\n{\"type\": \"living beings\", \"description\": \"humans; could be of different genders, ages, and races; could be standing, sitting, or walking\", \"similar objects\": [\"animals\", \"plants\", \"insects\"]}", + 13 + ], + "stone pavement": [ + " {\"type\": \"building material\", \"description\": \"flat, hard surface; could be made of stones, bricks, or concrete; could be used for walkways, patios, or driveways\", \"similar objects\": [\"asphalt\", \"gravel\", \"wooden deck\"]}", + 13 + ], + "porcelain bowl": [ + "\n{\"type\": \"dishware\", \"description\": \"smooth, white, round; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"saucer\"]}", + 13 + ], + "rooms": [ + " {\"type\": \"space\", \"description\": \"enclosed area; could have walls, windows, and doors; could be used for living, working, or storage\", \"similar objects\": [\"house\", \"apartment\", \"garage\"]}", + 13 + ], + "blue ground": [ + " {\"type\": \"landscape\", \"description\": \"blue sky with white clouds; could have green grass and trees\", \"similar objects\": [\"ocean\", \"mountain\", \"desert\"]}", + 13 + ], + "orange bill": [ + " {\"type\": \"bird\", \"description\": \"orange head and bill; black wings and tail; white belly\", \"similar objects\": [\"cardinal\", \"robin\", \"blue jay\"]}", + 13 + ], + "kleenex box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could have a lid\", \"similar objects\": [\"tissue box\", \"storage box\", \"jewelry box\"]}", + 13 + ], + "alcove": [ + " {\"type\": \"architectural feature\", \"description\": \"recessed space in a wall; could be used for storage\", \"similar objects\": [\"niche\", \"bay window\", \"fireplace\"]}", + 13 + ], + "wall painting": [ + " {\"type\": \"decoration\", \"description\": \"painted on walls; could be abstract or realistic; could be made of different materials\", \"similar objects\": [\"mural\", \"sculpture\", \"tapestry\"]}", + 13 + ], + "wooden wagon": [ + " {\"type\": \"toy\", \"description\": \"wooden; four wheels; could be pulled by a handle\", \"similar objects\": [\"tricycle\", \"scooter\", \"bicycle\"]}", + 13 + ], + "note book": [ + " {\"type\": \"stationary\", \"description\": \"bound paper; could be used for writing\", \"similar objects\": [\"journal\", \"diary\", \"sketchbook\"]}", + 13 + ], + "radar": [ + " {\"type\": \"electronic device\", \"description\": \"used to detect objects; emits radio waves; could be used for navigation\", \"similar objects\": [\"sonar\", \"GPS\", \"radio\"]}", + 13 + ], + "window air conditioner": [ + "\n{\"type\": \"cooling device\", \"description\": \"mounted on a window; has a fan and a compressor; could be controlled by a remote\", \"similar objects\": [\"portable air conditioner\", \"split air conditioner\", \"ceiling fan\"]}", + 13 + ], + "gray mouse": [ + "\n{\"type\": \"animal\", \"description\": \"small, gray, has a long tail; could have a white belly\", \"similar objects\": [\"rat\", \"hamster\", \"gerbil\"]}", + 13 + ], + "crocodile": [ + " {\"type\": \"animal\", \"description\": \"green; has a long snout; has a hard shell\", \"similar objects\": [\"alligator\", \"turtle\", \"iguana\"]}", + 13 + ], + "plastic jar": [ + " {\"type\": \"container\", \"description\": \"transparent; could be used to store food; could be sealed with a lid\", \"similar objects\": [\"glass jar\", \"plastic bottle\", \"canister\"]}", + 13 + ], + "metal design": [ + " {\"type\": \"artwork\", \"description\": \"could be made of metal; could be in various shapes and sizes; could be used for decoration\", \"similar objects\": [\"sculpture\", \"jewelry\", \"ornament\"]}", + 13 + ], + "music": [ + " {\"type\": \"art form\", \"description\": \"a form of art that uses sound and silence to create a composition\", \"similar objects\": [\"dance\", \"painting\", \"sculpture\"]}", + 13 + ], + "dell computer monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a stand; could be connected to a computer\", \"similar objects\": [\"laptop\", \"television\", \"printer\"]}", + 13 + ], + "triangle shape": [ + " {\"type\": \"geometric shape\", \"description\": \"three sides; three angles; three vertices\", \"similar objects\": [\"square\", \"rectangle\", \"circle\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\", and \"green bean", + 13 + ], + "asphalt street": [ + " {\"type\": \"road surface\", \"description\": \"black, smooth, made of asphalt; could have yellow lines\", \"similar objects\": [\"concrete street\", \"gravel road\", \"dirt road\"]}", + 13 + ], + "lab coat": [ + " {\"type\": \"clothing\", \"description\": \"long, white, has pockets; could be buttoned up\", \"similar objects\": [\"scrubs\", \"apron\", \"gown\"]}", + 13 + ], + "bike riders": [ + " {\"type\": \"people\", \"description\": \"people riding bicycles; could be wearing helmets; could be wearing protective gear\", \"similar objects\": [\"skateboarders\", \"rollerbladers\", \"scooter riders\"]}", + 13 + ], + "solo cup": [ + " {\"type\": \"drinking vessel\", \"description\": \"red; has a wide rim; could be made of plastic\", \"similar objects\": [\"mug\", \"glass\", \"tumbler\"]}", + 13 + ], + "monitor desk": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could have a stand; could have a drawer\", \"similar objects\": [\"computer desk\", \"writing desk\", \"dressing table\"]}", + 13 + ], + "steel beams": [ + " {\"type\": \"construction material\", \"description\": \"long, metallic, strong; could be used to support buildings\", \"similar objects\": [\"concrete\", \"wood\", \"aluminum\"]}", + 13 + ], + "ford": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could be a car, truck, or SUV; could have a logo of a blue oval\", \"similar objects\": [\"Chevrolet\", \"Toyota\", \"Honda\"]}", + 13 + ], + "hardware": [ + " {\"type\": \"building material\", \"description\": \"nails, screws, bolts, nuts, washers, hinges, locks, etc.\", \"similar objects\": [\"tools\", \"paint\", \"wood\"]}", + 13 + ], + "horse carriage": [ + " {\"type\": \"vehicle\", \"description\": \"has four wheels; could be pulled by horses; could be decorated with flowers\", \"similar objects\": [\"wagon\", \"carriage\", \"buggy\"]}", + 13 + ], + "appetizer": [ + " {\"type\": \"food\", \"description\": \"small, savory dish served before a meal\", \"similar objects\": [\"hors d'oeuvre\", \"amuse-bouche\", \"antipasto\"]}", + 13 + ], + "lunch box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"cooler\", \"thermos\", \"backpack\"]}", + 13 + ], + "metal plaque": [ + " {\"type\": \"decoration\", \"description\": \"flat, made of metal; could be engraved with words or images\", \"similar objects\": [\"wood plaque\", \"plastic plaque\", \"ceramic plaque\"]}", + 13 + ], + "shipping container": [ + " {\"type\": \"transportation tool\", \"description\": \"large, rectangular, made of metal; could be stacked; could be used to transport goods\", \"similar objects\": [\"crate\", \"pallet\", \"barrel\"]}", + 13 + ], + "dogs mouth": [ + "\n{\"type\": \"body part\", \"description\": \"has sharp teeth; could be wet; could be smelly\", \"similar objects\": [\"cat's mouth\", \"horse's mouth\", \"elephant's mouth\"]}", + 13 + ], + "heart shape": [ + " {\"type\": \"shape\", \"description\": \"symmetrical; two curved lines connected at the bottom; could be red\", \"similar objects\": [\"circle\", \"square\", \"triangle\"]}", + 13 + ], + "banana slices": [ + " {\"type\": \"food\", \"description\": \"yellow; could be cut into round pieces; could be used in baking\", \"similar objects\": [\"apple slices\", \"orange slices\", \"strawberry slices\"]}", + 13 + ], + "humans": [ + "\n{\"type\": \"species\", \"description\": \"bipedal; have two arms and two legs; have a head with two eyes, a nose, and a mouth; have a torso and a pelvis\", \"similar objects\": [\"apes\", \"monkeys\", \"gorillas\"]}", + 13 + ], + "char marks": [ + " {\"type\": \"cooking tool\", \"description\": \"black lines on food; could be caused by a grill or a pan\", \"similar objects\": [\"grill marks\", \"sear marks\", \"bar marks\"]}", + 13 + ], + "handle utensil": [ + " {\"type\": \"kitchen tool\", \"description\": \"long, thin, has a handle; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 13 + ], + "water heater": [ + " {\"type\": \"appliance\", \"description\": \"cylindrical; could be electric or gas powered; could be used to heat water\", \"similar objects\": [\"air conditioner\", \"refrigerator\", \"dishwasher\"]}", + 13 + ], + "cat toy": [ + " {\"type\": \"pet toy\", \"description\": \"could be made of feathers, fur, or plastic; could have a bell inside; could be shaped like a mouse\", \"similar objects\": [\"dog toy\", \"bird toy\", \"fish toy\"]}", + 13 + ], + "soup bowl": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could have a handle\", \"similar objects\": [\"plate\", \"cup\", \"mug\"]}", + 13 + ], + "dirt stain": [ + " {\"type\": \"stain\", \"description\": \"brown; could be found on clothes or furniture; could be removed with detergent\", \"similar objects\": [\"grease stain\", \"blood stain\", \"coffee stain\"]}", + 13 + ], + "mirror bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has two rows of windows; could be decorated with colorful patterns\", \"similar objects\": [\"school bus\", \"tour bus\", \"party bus\"]}", + 13 + ], + "wooden crates": [ + " {\"type\": \"container\", \"description\": \"rectangular; made of wood; could be used for storage\", \"similar objects\": [\"boxes\", \"baskets\", \"barrels\"]}", + 13 + ], + "dollar bill": [ + " {\"type\": \"currency\", \"description\": \"green; has a portrait of George Washington; has a serial number\", \"similar objects\": [\"euro\", \"yen\", \"pound\"]}", + 13 + ], + "street lines": [ + " {\"type\": \"road markings\", \"description\": \"yellow or white lines on the road; could be dashed or solid\", \"similar objects\": [\"traffic signs\", \"road signs\", \"road barriers\"]}", + 13 + ], + "bare hand": [ + " {\"type\": \"body part\", \"description\": \"five fingers; could be used for grasping\", \"similar objects\": [\"foot\", \"arm\", \"leg\"]}", + 13 + ], + "soccer shoes": [ + " {\"type\": \"footwear\", \"description\": \"long, flexible; could have spikes on the bottom; could be made of leather\", \"similar objects\": [\"running shoes\", \"cleats\", \"hiking boots\"]}", + 13 + ], + "pizza half": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread\"]}", + 13 + ], + "tomato soup": [ + " {\"type\": \"food\", \"description\": \"red; could be served hot or cold; could be blended with cream\", \"similar objects\": [\"minestrone soup\", \"chicken soup\", \"vegetable soup\"]}", + 13 + ], + "knacks": [ + " {\"type\": \"ornament\", \"description\": \"small, decorative objects; could be made of metal, wood, or glass; could be used for decoration\", \"similar objects\": [\"trinkets\", \"figurines\", \"charms\"]}", + 13 + ], + "arm bands": [ + " {\"type\": \"accessory\", \"description\": \"worn around the arm; could be made of cloth or rubber; could be used for decoration or identification\", \"similar objects\": [\"bracelets\", \"anklets\", \"wristbands\"]}", + 13 + ], + "round logo": [ + " {\"type\": \"graphic design\", \"description\": \"circular shape; could be used for branding\", \"similar objects\": [\"square logo\", \"triangle logo\", \"hexagon logo\"]}", + 13 + ], + "coat rack": [ + " {\"type\": \"furniture\", \"description\": \"tall; has multiple hooks; could be made of wood or metal\", \"similar objects\": [\"hat rack\", \"umbrella stand\", \"shoe rack\"]}", + 13 + ], + "bare arm": [ + " {\"type\": \"body part\", \"description\": \"exposed skin; could be muscular; could have tattoos\", \"similar objects\": [\"leg\", \"hand\", \"torso\"]}", + 13 + ], + "kitchen area": [ + "\n{\"type\": \"room\", \"description\": \"could have a stove, refrigerator, sink, and cabinets; could have a dining table; could have a window\", \"similar objects\": [\"living room\", \"bedroom\", \"bathroom\"]}", + 13 + ], + "glass bowls": [ + " {\"type\": \"kitchenware\", \"description\": \"transparent; could be made of glass or plastic; could be used for serving food\", \"similar objects\": [\"plates\", \"cups\", \"mugs\"]}", + 13 + ], + "dark table": [ + "\n{\"type\": \"furniture\", \"description\": \"dark-colored; could be made of wood or metal; could have a flat surface\", \"similar objects\": [\"chair\", \"desk\", \"cabinet\"]}", + 13 + ], + "sideburn": [ + " {\"type\": \"facial hair\", \"description\": \"long, thick hair growing on the sides of the face\", \"similar objects\": [\"beard\", \"mustache\", \"goatee\"]}", + 13 + ], + "tan pillow": [ + "\n{\"type\": \"home decor\", \"description\": \"rectangular; tan color; could be filled with feathers or foam\", \"similar objects\": [\"cushion\", \"blanket\", \"throw pillow\"]}", + 13 + ], + "storefront window": [ + " {\"type\": \"architectural feature\", \"description\": \"transparent; could be framed; could be decorated with signs\", \"similar objects\": [\"door\", \"balcony\", \"awning\"]}", + 13 + ], + "prices": [ + " {\"type\": \"measurement\", \"description\": \"monetary value of goods or services\", \"similar objects\": [\"costs\", \"fees\", \"expenses\"]}", + 13 + ], + "bear head": [ + " {\"type\": \"animal part\", \"description\": \"round; has two eyes, a nose, and a mouth; could have two ears\", \"similar objects\": [\"wolf head\", \"lion head\", \"tiger head\"]}", + 13 + ], + "flood lights": [ + " {\"type\": \"lighting tool\", \"description\": \"bright, white light; could be used for outdoor lighting\", \"similar objects\": [\"spotlight\", \"halogen light\", \"LED light\"]}", + 13 + ], + "sofa cushion": [ + " {\"type\": \"furniture\", \"description\": \"soft; could be filled with foam; could be covered with fabric\", \"similar objects\": [\"pillow\", \"mattress\", \"chair cushion\"]}", + 13 + ], + "creme": [ + " {\"type\": \"food\", \"description\": \"thick, creamy, sweet; could be used as a topping or filling\", \"similar objects\": [\"custard\", \"mousse\", \"pudding\"]}", + 13 + ], + "wooden spoon": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle; could be made of wood; could be used for stirring\", \"similar objects\": [\"spatula\", \"ladle\", \"whisk\"]}", + 13 + ], + "paper tray": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of plastic or metal; could have multiple compartments\", \"similar objects\": [\"file folder\", \"pencil holder\", \"desk organizer\"]}", + 13 + ], + "half slice": [ + " {\"type\": \"food item\", \"description\": \"half of a round shape; could be a pizza slice, a cake slice, a sandwich slice, etc.\", \"similar objects\": [\"whole slice\", \"quarter slice\", \"triangle slice\"]}", + 13 + ], + "serving plate": [ + " {\"type\": \"dining tool\", \"description\": \"flat; could be made of ceramic; could be decorated with patterns\", \"similar objects\": [\"bowl\", \"cup\", \"saucer\"]}", + 13 + ], + "front grille": [ + " {\"type\": \"automotive part\", \"description\": \"metal mesh; located at the front of a vehicle; used to protect the engine and radiator\", \"similar objects\": [\"bumper\", \"headlight\", \"tail light\"]}", + 13 + ], + "cylinders": [ + " {\"type\": \"shape\", \"description\": \"round; has two parallel, flat faces; has a circular cross-section\", \"similar objects\": [\"cone\", \"sphere\", \"cube\"]}", + 13 + ], + "candlestick": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; has a holder for a candle\", \"similar objects\": [\"lantern\", \"lamp\", \"torch\"]}", + 13 + ], + "glass table top": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be made of glass or plastic; could be round or rectangular\", \"similar objects\": [\"coffee table\", \"dining table\", \"desk\"]}", + 13 + ], + "mirror bathroom wall": [ + "\n{\"type\": \"decorative item\", \"description\": \"rectangular; could be framed; could be hung on the wall\", \"similar objects\": [\"picture frame\", \"painting\", \"clock\"]}", + 13 + ], + "transit": [ + " {\"type\": \"transportation\", \"description\": \"vehicles used for public transportation; could be buses, trains, subways, etc.\", \"similar objects\": [\"taxi\", \"car\", \"bike\"]}", + 13 + ], + "skateboard pavement": [ + " {\"type\": \"surface\", \"description\": \"smooth, flat, and hard; could be made of concrete or asphalt\", \"similar objects\": [\"sidewalk\", \"street\", \"driveway\"]}", + 13 + ], + "tan table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; has four legs; could be made of wood; could be painted tan\", \"similar objects\": [\"chair\", \"sofa\", \"desk\"]}", + 13 + ], + "tan animal": [ + "\n{\"type\": \"animal\", \"description\": \"has a tanned color; could have stripes, spots, or other patterns; could have a long mane or tail\", \"similar objects\": [\"lion\", \"tiger\", \"gazelle\"]}", + 13 + ], + "pesto": [ + " {\"type\": \"sauce\", \"description\": \"green; made of basil, garlic, olive oil, pine nuts, and Parmesan cheese\", \"similar objects\": [\"marinara sauce\", \"alfredo sauce\", \"barbecue sauce\"]}", + 13 + ], + "burnt": [ + " {\"type\": \"state\", \"description\": \"darkened; could be charred; could be smoky\", \"similar objects\": [\"charred\", \"scorched\", \"singed\"]}", + 13 + ], + "baby blue sky": [ + "\n{\"type\": \"natural phenomenon\", \"description\": \"light blue color; could be seen in the sky; could be seen during the day\", \"similar objects\": [\"clear sky\", \"cloudy sky\", \"sunset sky\"]}", + 13 + ], + "trailer truck": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a trailer attached; could be used for transporting goods\", \"similar objects\": [\"semi-truck\", \"tow truck\", \"pickup truck\"]}", + 13 + ], + "stainless steel trash": [ + " {\"type\": \"container\", \"description\": \"silver; cylindrical; has a lid\", \"similar objects\": [\"plastic trash can\", \"recycling bin\", \"garbage can\"]}", + 13 + ], + "refelction": [ + " {\"type\": \"phenomenon\", \"description\": \"the process of light bouncing off a surface\", \"similar objects\": [\"refraction\", \"diffraction\", \"polarization\"]}", + 13 + ], + "home button": [ + " {\"type\": \"electronic device\", \"description\": \"round; could be found on the front of a smartphone\", \"similar objects\": [\"power button\", \"volume button\", \"camera button\"]}", + 13 + ], + "plaid skirt": [ + " {\"type\": \"clothing item\", \"description\": \"has a pattern of different colors; could be pleated; could be knee-length\", \"similar objects\": [\"checked skirt\", \"striped skirt\", \"tartan skirt\"]}", + 13 + ], + "lobe": [ + " {\"type\": \"anatomical structure\", \"description\": \"rounded protrusions of the brain; could be divided into four parts\", \"similar objects\": [\"cerebellum\", \"cerebrum\", \"hippocampus\"]}", + 13 + ], + "sofa chair": [ + " {\"type\": \"furniture\", \"description\": \"long, upholstered seat; could have armrests; could have a backrest\", \"similar objects\": [\"loveseat\", \"armchair\", \"recliner\"]}", + 13 + ], + "hotel bathroom": [ + "\n{\"type\": \"room\", \"description\": \"could have a shower, a sink, a toilet, a mirror, and a bathtub; could have a window; could have a door\", \"similar objects\": [\"bedroom\", \"kitchen\", \"living room\"]}", + 13 + ], + "blanket brown": [ + " {\"type\": \"bedding item\", \"description\": \"brown; could be made of wool; could be used to keep warm\", \"similar objects\": [\"quilt\", \"comforter\", \"duvet\"]}", + 13 + ], + "mic": [ + " {\"type\": \"audio tool\", \"description\": \"long, thin; could be handheld; could be connected to a sound system\", \"similar objects\": [\"headset\", \"speaker\", \"amplifier\"]}", + 13 + ], + "silver circle": [ + " {\"type\": \"object\", \"description\": \"shiny, round, metallic\", \"similar objects\": [\"coin\", \"ring\", \"bracelet\"]}", + 13 + ], + "baseball uniform pants": [ + "\n{\"type\": \"clothing\", \"description\": \"long, loose-fitting pants; usually white with stripes; could have a team logo\", \"similar objects\": [\"baseball cap\", \"baseball jersey\", \"baseball socks\"]}", + 13 + ], + "dragonfly": [ + " {\"type\": \"insect\", \"description\": \"long, slender body; four wings; could be colorful\", \"similar objects\": [\"butterfly\", \"bee\", \"mosquito\"]}", + 13 + ], + "wagon wheels": [ + " {\"type\": \"wheels\", \"description\": \"large, round, made of metal; could be used for wagons\", \"similar objects\": [\"car wheels\", \"bicycle wheels\", \"truck wheels\"]}", + 13 + ], + "pick-up": [ + " {\"type\": \"vehicle\", \"description\": \"has an open cargo bed; could have four doors; could have a cab\", \"similar objects\": [\"truck\", \"van\", \"SUV\"]}", + 13 + ], + "floor boards": [ + " {\"type\": \"building material\", \"description\": \"long, thin, wooden boards; could be painted or stained\", \"similar objects\": [\"plywood\", \"hardwood\", \"laminate\"]}", + 13 + ], + "tennis uniform": [ + " {\"type\": \"clothing\", \"description\": \"white; could have a logo; could have a skirt or shorts; could have a shirt or tank top\", \"similar objects\": [\"golf uniform\", \"soccer uniform\", \"basketball uniform\"]}", + 13 + ], + "cobblestone walkway": [ + " {\"type\": \"pavement\", \"description\": \"made of small, rounded stones; could be used as a path or walkway\", \"similar objects\": [\"gravel path\", \"brick path\", \"concrete path\"]}", + 13 + ], + "propeller plane": [ + " {\"type\": \"aircraft\", \"description\": \"has two or more propellers; could be used for short-distance travel\", \"similar objects\": [\"helicopter\", \"jet plane\", \"glider\"]}", + 13 + ], + "wiener": [ + " {\"type\": \"food\", \"description\": \"long, thin, usually made of pork or beef; could be served with bread or bun\", \"similar objects\": [\"hot dog\", \"sausage\", \"bratwurst\"]}", + 13 + ], + "orange skateboard": [ + "\n{\"type\": \"sports equipment\", \"description\": \"orange; has four wheels; could be used for skateboarding\", \"similar objects\": [\"scooter\", \"rollerblades\", \"longboard\"]}", + 13 + ], + "door knobs": [ + " {\"type\": \"hardware\", \"description\": \"round; could be made of metal or plastic; could be used to open and close doors\", \"similar objects\": [\"locks\", \"hinges\", \"handles\"]}", + 13 + ], + "background wall": [ + " {\"type\": \"decoration\", \"description\": \"could be painted or wallpapered; could be plain or patterned; could be used to create a focal point\", \"similar objects\": [\"wall art\", \"wall mural\", \"wall decal\"]}", + 13 + ], + "rapids": [ + " {\"type\": \"geographical feature\", \"description\": \"fast-moving water; could be found in rivers\", \"similar objects\": [\"waterfall\", \"cascade\", \"whirlpool\"]}", + 13 + ], + "imprint": [ + " {\"type\": \"mark\", \"description\": \"a mark or impression left on a surface; could be made by pressing or stamping\", \"similar objects\": [\"stamp\", \"engraving\", \"etching\"]}", + 13 + ], + "brake handle": [ + " {\"type\": \"vehicle part\", \"description\": \"cylindrical; could be made of metal; could be attached to the steering wheel\", \"similar objects\": [\"accelerator pedal\", \"clutch pedal\", \"gear shift\"]}", + 13 + ], + "balding man": [ + "\n{\"type\": \"person\", \"description\": \"lack of hair on the head; could have gray hair on the sides\", \"similar objects\": [\"bald woman\", \"elderly man\", \"elderly woman\"]}", + 13 + ], + "horse bridle": [ + " {\"type\": \"equipment\", \"description\": \"leather straps; used to control a horse; has a bit\", \"similar objects\": [\"saddle\", \"halter\", \"reins\"]}", + 13 + ], + "bright sky": [ + " {\"type\": \"weather condition\", \"description\": \"clear, blue sky; could be sunny; could have white clouds\", \"similar objects\": [\"sunny day\", \"rainy day\", \"cloudy day\"]}", + 13 + ], + "metal light": [ + " {\"type\": \"lighting tool\", \"description\": \"made of metal; could be round or square; could have a handle\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}", + 13 + ], + "boxcars": [ + " {\"type\": \"train car\", \"description\": \"long, rectangular; could be connected to other cars; could be used to transport goods\", \"similar objects\": [\"flatcar\", \"hopper car\", \"tank car\"]}", + 13 + ], + "frame wall": [ + " {\"type\": \"decoration\", \"description\": \"wooden or metal frames; could be hung on the wall; could contain pictures or other decorations\", \"similar objects\": [\"photo wall\", \"wall art\", \"wall mural\"]}", + 13 + ], + "neck giraffe": [ + " {\"type\": \"animal body part\", \"description\": \"long, slender, and flexible; could have spots\", \"similar objects\": [\"elephant trunk\", \"horse mane\", \"monkey tail\"]}", + 13 + ], + "airplane runway": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, flat, paved surface; could have markings and lights; could have a control tower\", \"similar objects\": [\"airport\", \"helipad\", \"highway\"]}", + 13 + ], + "pepper mill": [ + " {\"type\": \"kitchen tool\", \"description\": \"cylindrical; has a handle; used to grind pepper\", \"similar objects\": [\"salt mill\", \"mortar and pestle\", \"garlic press\"]}", + 13 + ], + "banana skin": [ + " {\"type\": \"waste\", \"description\": \"yellow; thin; could be slippery; could be composted\", \"similar objects\": [\"apple skin\", \"orange peel\", \"melon rind\"]}", + 13 + ], + "cute girl": [ + "\n{\"type\": \"person\", \"description\": \"smiling; wearing cute clothes; could have long hair\", \"similar objects\": [\"boy\", \"woman\", \"man\"]}", + 13 + ], + "bedroom window": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"door\", \"curtains\", \"blinds\"]}", + 13 + ], + "sombrero": [ + " {\"type\": \"headwear\", \"description\": \"wide-brimmed hat; could be made of straw; could have a colorful band\", \"similar objects\": [\"fedora\", \"beret\", \"baseball cap\"]}", + 13 + ], + "plain donut": [ + " {\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be glazed or frosted\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}", + 13 + ], + "joystick": [ + " {\"type\": \"gaming tool\", \"description\": \"has a handle; could have buttons; could be used to control a game\", \"similar objects\": [\"gamepad\", \"racing wheel\", \"arcade stick\"]}", + 13 + ], + "web cam": [ + " {\"type\": \"electronic device\", \"description\": \"small camera; could be connected to a computer; could be used for video conferencing\", \"similar objects\": [\"microphone\", \"speaker\", \"headset\"]}", + 13 + ], + "thick mane": [ + " {\"type\": \"animal feature\", \"description\": \"long, dense hair on the neck and shoulders of certain animals\", \"similar objects\": [\"tail\", \"hooves\", \"horns\"]}", + 13 + ], + "twine": [ + " {\"type\": \"string\", \"description\": \"strong, thin, flexible; could be made of cotton, hemp, or jute\", \"similar objects\": [\"rope\", \"yarn\", \"thread\"]}", + 13 + ], + "downtown": [ + "\n{\"type\": \"location\", \"description\": \"a city center; could have many buildings, shops, and restaurants; could be crowded with people\", \"similar objects\": [\"city center\", \"central business district\", \"urban area\"]}", + 13 + ], + "braclet": [ + " {\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of metal, plastic, or fabric; could be decorated with jewels or beads\", \"similar objects\": [\"necklace\", \"ring\", \"earrings\"]}", + 13 + ], + "gravel train tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"made of gravel; could have two parallel tracks; could have a railway switch\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 13 + ], + "tv remote control": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has buttons; could be used to control a television\", \"similar objects\": [\"game controller\", \"universal remote\", \"air conditioner remote\"]}", + 13 + ], + "metal kickstand": [ + " {\"type\": \"support tool\", \"description\": \"made of metal; used to support a bike\", \"similar objects\": [\"bike rack\", \"bike stand\", \"bike holder\"]}", + 13 + ], + "wrench": [ + " {\"type\": \"tool\", \"description\": \"L-shaped; used to tighten or loosen nuts and bolts\", \"similar objects\": [\"screwdriver\", \"pliers\", \"hammer\"]}", + 13 + ], + "denim blue jeans": [ + "\n{\"type\": \"clothing\", \"description\": \"blue; made of denim fabric; could have pockets; could have a zipper\", \"similar objects\": [\"jeans\", \"trousers\", \"shorts\"]}", + 13 + ], + "city buses": [ + " {\"type\": \"vehicle\", \"description\": \"large; has multiple doors; could be yellow or white\", \"similar objects\": [\"trucks\", \"taxis\", \"school buses\"]}", + 13 + ], + "pottery vase": [ + " {\"type\": \"decorative item\", \"description\": \"round; could be made of clay; could have intricate designs\", \"similar objects\": [\"urn\", \"jar\", \"urn\"]}", + 13 + ], + "wool cap": [ + " {\"type\": \"clothing item\", \"description\": \"knitted; could be in different colors; could have a pom-pom on top\", \"similar objects\": [\"beanie\", \"beret\", \"turban\"]}", + 13 + ], + "award": [ + " {\"type\": \"recognition\", \"description\": \"could be a trophy, medal, certificate, or plaque; could be given to recognize an achievement\", \"similar objects\": [\"prize\", \"medal\", \"plaque\"]}", + 13 + ], + "paper tag": [ + " {\"type\": \"labeling tool\", \"description\": \"rectangular; could be made of paper; could be used to label items\", \"similar objects\": [\"sticker\", \"label\", \"name tag\"]}", + 13 + ], + "passenger side window": [ + " {\"type\": \"automobile part\", \"description\": \"transparent; could be opened and closed; could be tinted\", \"similar objects\": [\"driver side window\", \"windshield\", \"rear window\"]}", + 13 + ], + "cantaloupe": [ + " {\"type\": \"fruit\", \"description\": \"round; has a rough, netted skin; orange flesh; could have a stem\", \"similar objects\": [\"honeydew\", \"watermelon\", \"muskmelon\"]}", + 13 + ], + "carnations": [ + " {\"type\": \"flower\", \"description\": \"pink, white, or red; has a long stem; could be arranged in a bouquet\", \"similar objects\": [\"roses\", \"daisies\", \"tulips\"]}", + 13 + ], + "construction cone": [ + " {\"type\": \"safety tool\", \"description\": \"orange; cone-shaped; could have reflective stripes\", \"similar objects\": [\"barricade\", \"traffic sign\", \"warning sign\"]}", + 13 + ], + "chicken salad": [ + " {\"type\": \"food\", \"description\": \"a combination of chicken, vegetables, and dressing; could be served cold or hot\", \"similar objects\": [\"tuna salad\", \"egg salad\", \"potato salad\"]}", + 13 + ], + "gnome": [ + " {\"type\": \"decoration\", \"description\": \"small, usually made of ceramic; could have a pointed hat; could have a long beard\", \"similar objects\": [\"fairy\", \"elf\", \"troll\"]}", + 13 + ], + "snow ramp": [ + " {\"type\": \"winter sport equipment\", \"description\": \"sloped surface made of snow; used for skiing and snowboarding\", \"similar objects\": [\"ski lift\", \"snowboard ramp\", \"snow tube\"]}", + 13 + ], + "onion slice": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be yellow, white, or red; could be sliced into thin pieces; could have a strong smell\", \"similar objects\": [\"garlic\", \"potato\", \"carrot\"]}", + 13 + ], + "body water": [ + " {\"type\": \"beverage\", \"description\": \"clear liquid; could be flavored; could be carbonated; could be still\", \"similar objects\": [\"juice\", \"soda\", \"tea\"]}", + 13 + ], + "orange collar": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of fabric; could be used for pets\", \"similar objects\": [\"leash\", \"harness\", \"muzzle\"]}", + 13 + ], + "round orange fruit": [ + "\n{\"type\": \"fruit\", \"description\": \"round, orange, has a stem\", \"similar objects\": [\"apple\", \"peach\", \"plum\"]}", + 13 + ], + "marshmallow": [ + " {\"type\": \"food\", \"description\": \"white, fluffy, sweet; could be roasted over fire\", \"similar objects\": [\"gummy bear\", \"jelly bean\", \"cotton candy\"]}", + 13 + ], + "tile walls": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic, stone, or glass; could be used for flooring or walls\", \"similar objects\": [\"bricks\", \"wood panels\", \"concrete blocks\"]}", + 13 + ], + "sunvisor": [ + " {\"type\": \"accessory\", \"description\": \"attached to the car window; could be made of fabric; could be adjustable\", \"similar objects\": [\"sunglasses\", \"hat\", \"umbrella\"]}", + 13 + ], + "scraps": [ + " {\"type\": \"waste\", \"description\": \"small pieces of materials; could be paper, plastic, metal, etc.\", \"similar objects\": [\"garbage\", \"trash\", \"litter\"]}", + 13 + ], + "metal platform": [ + " {\"type\": \"structure\", \"description\": \"flat, made of metal; could be used as a stage\", \"similar objects\": [\"bridge\", \"stairs\", \"ramp\"]}", + 13 + ], + "auditorium": [ + " {\"type\": \"building\", \"description\": \"large room with seating; could have a stage; could have a balcony\", \"similar objects\": [\"theater\", \"concert hall\", \"stadium\"]}", + 13 + ], + "u.s": [ + "\n{\"type\": \"country\", \"description\": \"located in North America; has 50 states; has a federal government\", \"similar objects\": [\"Canada\", \"Mexico\", \"United Kingdom\"]}", + 13 + ], + "plastic batting helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"hard, round, has a face guard; could be colored\", \"similar objects\": [\"baseball helmet\", \"hockey helmet\", \"motorcycle helmet\"]}", + 13 + ], + "motorcycle exhaust pipe": [ + "\n{\"type\": \"motorcycle part\", \"description\": \"cylindrical; could be made of metal; could be attached to the engine\", \"similar objects\": [\"muffler\", \"air filter\", \"spark plug\"]}", + 13 + ], + "switch panel": [ + " {\"type\": \"electrical tool\", \"description\": \"rectangular; has multiple buttons; could be used to control electricity\", \"similar objects\": [\"control panel\", \"circuit breaker\", \"power strip\"]}", + 13 + ], + "airlines logo": [ + "\n{\"type\": \"logo\", \"description\": \"could be a combination of colors, shapes, and symbols; could be a word or phrase; could be a recognizable image\", \"similar objects\": [\"corporate logo\", \"sports team logo\", \"university logo\"]}", + 13 + ], + "laying": [ + " {\"type\": \"action\", \"description\": \"to recline or rest in a horizontal position\", \"similar objects\": [\"lying\", \"sitting\", \"standing\"]}", + 13 + ], + "messy": [ + "\n{\"type\": \"adjective\", \"description\": \"disorganized; chaotic; untidy\", \"similar objects\": [\"chaotic\", \"disorganized\", \"unkempt\"]}", + 13 + ], + "jet airliner": [ + " {\"type\": \"vehicle\", \"description\": \"long, narrow body; has wings and tail; could have multiple engines\", \"similar objects\": [\"helicopter\", \"airplane\", \"glider\"]}", + 13 + ], + "chords": [ + " {\"type\": \"music tool\", \"description\": \"strings; could be made of metal or nylon; could be used to play musical instruments\", \"similar objects\": [\"strings\", \"notes\", \"scales\"]}", + 13 + ], + "tummy": [ + " {\"type\": \"body part\", \"description\": \"abdomen; located between chest and hips; could be bloated\", \"similar objects\": [\"stomach\", \"waist\", \"belly\"]}", + 13 + ], + "highway signs": [ + " {\"type\": \"road signs\", \"description\": \"could be triangular, rectangular, or circular; could be yellow, white, or red; could have words or symbols\", \"similar objects\": [\"traffic lights\", \"road markings\", \"street signs\"]}", + 13 + ], + "water spout": [ + " {\"type\": \"water feature\", \"description\": \"vertical, cylindrical; could be made of metal; could be used to spray water\", \"similar objects\": [\"fountain\", \"waterfall\", \"sprinkler\"]}", + 13 + ], + "chrome bumper": [ + " {\"type\": \"automotive part\", \"description\": \"shiny, metal, attached to the front or rear of a car\", \"similar objects\": [\"grille\", \"headlight\", \"tail light\"]}", + 13 + ], + "tiara": [ + " {\"type\": \"accessory\", \"description\": \"circular; could be made of metal or jewels; could be worn on the head\", \"similar objects\": [\"crown\", \"headband\", \"hat\"]}", + 13 + ], + "sidewalk people": [ + " {\"type\": \"people\", \"description\": \"walking on the sidewalk; could be in groups or alone; could be carrying bags or umbrellas\", \"similar objects\": [\"pedestrians\", \"street vendors\", \"street performers\"]}", + 13 + ], + "orange nose": [ + " {\"type\": \"animal feature\", \"description\": \"round, orange, protruding from the face\", \"similar objects\": [\"black nose\", \"pink nose\", \"white nose\"]}", + 13 + ], + "steel handle": [ + " {\"type\": \"hardware\", \"description\": \"made of steel; could be used to open a door\", \"similar objects\": [\"brass handle\", \"aluminum handle\", \"plastic handle\"]}", + 13 + ], + "man glasses": [ + " {\"type\": \"eyewear\", \"description\": \"rectangular; could be made of metal or plastic; could have lenses\", \"similar objects\": [\"sunglasses\", \"reading glasses\", \"safety glasses\"]}", + 13 + ], + "infielder": [ + " {\"type\": \"sports position\", \"description\": \"plays in the infield; responsible for fielding ground balls and throwing to other players\", \"similar objects\": [\"pitcher\", \"catcher\", \"outfielder\"]}", + 13 + ], + "ground surface": [ + " {\"type\": \"terrain\", \"description\": \"could be grass, soil, sand, concrete, asphalt, etc.\", \"similar objects\": [\"ground\", \"floor\", \"landscape\"]}", + 13 + ], + "cumulus clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, fluffy, and low-lying clouds; could be shaped like cotton balls\", \"similar objects\": [\"stratus clouds\", \"cirrus clouds\", \"nimbus clouds\"]}", + 13 + ], + "labrador": [ + " {\"type\": \"animal\", \"description\": \"medium-sized; has a short, thick coat; could be black, yellow, or chocolate-colored\", \"similar objects\": [\"golden retriever\", \"german shepherd\", \"poodle\"]}", + 13 + ], + "ivory elephant tusk": [ + "\n{\"type\": \"animal product\", \"description\": \"long, curved, white; could be used for decoration or jewelry\", \"similar objects\": [\"hippo tusk\", \"walrus tusk\", \"narwhal tusk\"]}", + 13 + ], + "hoody": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; has a hood; could be zipped up\", \"similar objects\": [\"sweatshirt\", \"jacket\", \"coat\"]}", + 13 + ], + "blue sea": [ + " {\"type\": \"natural phenomenon\", \"description\": \"large body of water; could be deep blue or light blue; could have waves\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 13 + ], + "outlet cover": [ + " {\"type\": \"electrical tool\", \"description\": \"rectangular; could be made of plastic; has two or more holes\", \"similar objects\": [\"switch plate\", \"wall plate\", \"outlet box\"]}", + 13 + ], + "dog food": [ + " {\"type\": \"pet food\", \"description\": \"dry, crunchy, could be in the form of kibble; could be in cans or bags\", \"similar objects\": [\"cat food\", \"bird food\", \"fish food\"]}", + 13 + ], + "color sea water": [ + " {\"type\": \"natural phenomenon\", \"description\": \"varies in shades of blue and green; could be clear or cloudy; could have waves\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 13 + ], + "surfboard leash": [ + " {\"type\": \"surfing accessory\", \"description\": \"long cord; attaches to the surfboard; could be made of nylon\", \"similar objects\": [\"surf wax\", \"surf fins\", \"surfboard bag\"]}", + 13 + ], + "manufacturer logo": [ + "\n{\"type\": \"logo\", \"description\": \"unique design; could be a combination of colors, shapes, and symbols; could be used to represent a company or brand\", \"similar objects\": [\"trademark\", \"emblem\", \"symbol\"]}", + 13 + ], + "side doors": [ + " {\"type\": \"door\", \"description\": \"hinged; could be opened from the side; could be made of wood or metal\", \"similar objects\": [\"front door\", \"back door\", \"garage door\"]}", + 13 + ], + "sub sandwich": [ + " {\"type\": \"food\", \"description\": \"long; could be filled with various ingredients; could be cut into pieces\", \"similar objects\": [\"burger\", \"wrap\", \"pizza\"]}", + 13 + ], + "passenger boat": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple decks; could have a motor\", \"similar objects\": [\"ferry\", \"yacht\", \"cruise ship\"]}", + 13 + ], + "brick pattern": [ + " {\"type\": \"pattern\", \"description\": \"rectangular; could be made of clay; could be used for wall decoration\", \"similar objects\": [\"tile pattern\", \"wood pattern\", \"stone pattern\"]}", + 13 + ], + "sausage sandwich": [ + " {\"type\": \"food\", \"description\": \"bread with sausage inside; could be served with ketchup and mustard\", \"similar objects\": [\"hot dog\", \"burger\", \"taco\"]}", + 13 + ], + "flyers": [ + " {\"type\": \"promotional material\", \"description\": \"printed paper; could be distributed to promote an event or product\", \"similar objects\": [\"posters\", \"banners\", \"brochures\"]}", + 13 + ], + "round blue plate": [ + "\n{\"type\": \"dishware\", \"description\": \"round; blue; could be made of ceramic or plastic; could be used for serving food\", \"similar objects\": [\"bowl\", \"cup\", \"mug\"]}", + 13 + ], + "zebra ground": [ + " {\"type\": \"pattern\", \"description\": \"black and white stripes; could be used for decoration\", \"similar objects\": [\"checkered\", \"plaid\", \"polka dot\"]}", + 13 + ], + "bus lane": [ + " {\"type\": \"traffic lane\", \"description\": \"lane designated for buses; could be painted with yellow lines\", \"similar objects\": [\"bike lane\", \"truck lane\", \"taxi lane\"]}", + 13 + ], + "air freshner": [ + " {\"type\": \"cleaning tool\", \"description\": \"could be in a spray bottle; could be in a plug-in device; could be in a hanging device\", \"similar objects\": [\"air purifier\", \"odor eliminator\", \"scented candle\"]}", + 13 + ], + "fishing boats": [ + " {\"type\": \"vessel\", \"description\": \"long and narrow; could have a motor; could have a fishing net\", \"similar objects\": [\"yacht\", \"canoe\", \"rowboat\"]}", + 13 + ], + "dark object": [ + "\n\n{\"type\": \"unknown\", \"description\": \"dark in color; could be any shape or size\", \"similar objects\": [\"shadow\", \"black object\", \"mystery object\"]}", + 13 + ], + "cereals": [ + " {\"type\": \"food\", \"description\": \"small, crunchy, could be sweet or savory; could be served with milk\", \"similar objects\": [\"granola\", \"muesli\", \"porridge\"]}", + 13 + ], + "air bubbles": [ + " {\"type\": \"phenomenon\", \"description\": \"small, round, transparent; could be seen in water or air\", \"similar objects\": [\"foam\", \"smoke\", \"dust\"]}", + 13 + ], + "boston": [ + " {\"type\": \"city\", \"description\": \"capital of Massachusetts; located in the northeastern United States; home to many universities and colleges\", \"similar objects\": [\"New York City\", \"Chicago\", \"Los Angeles\"]}", + 13 + ], + "barbecue sauce": [ + " {\"type\": \"condiment\", \"description\": \"thick, dark red; has a sweet and smoky flavor\", \"similar objects\": [\"ketchup\", \"mustard\", \"mayonnaise\"]}", + 13 + ], + "silver bicycle": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could be made of silver metal\", \"similar objects\": [\"motorcycle\", \"scooter\", \"tricycle\"]}", + 13 + ], + "homemade": [ + " {\"type\": \"food\", \"description\": \"prepared at home; could be cooked with fresh ingredients; could be served with family\", \"similar objects\": [\"homemade bread\", \"homemade cake\", \"homemade soup\"]}", + 13 + ], + "crack concrete": [ + " {\"type\": \"damage\", \"description\": \"cracks in concrete surfaces; could be caused by weathering, aging, or other external forces\", \"similar objects\": [\"cracked asphalt\", \"cracked brick\", \"cracked stone\"]}", + 13 + ], + "taxicab": [ + " {\"type\": \"vehicle\", \"description\": \"yellow; has a meter; could have a partition between driver and passengers\", \"similar objects\": [\"bus\", \"truck\", \"limousine\"]}", + 13 + ], + "klm": [ + " {\"type\": \"airline\", \"description\": \"Dutch airline; headquartered in Amstelveen, Netherlands; operates flights to over 150 destinations\", \"similar objects\": [\"British Airways\", \"Air France\", \"Lufthansa\"]}", + 13 + ], + "silver chains": [ + " {\"type\": \"jewelry\", \"description\": \"made of silver; could be in different shapes and sizes; could be used as a necklace or bracelet\", \"similar objects\": [\"gold chains\", \"beads\", \"pearls\"]}", + 13 + ], + "multitude": [ + " {\"type\": \"group\", \"description\": \"large number of people or things\", \"similar objects\": [\"crowd\", \"swarm\", \"horde\"]}", + 13 + ], + "pastel": [ + " {\"type\": \"art material\", \"description\": \"soft, crayon-like; could be used to draw on paper\", \"similar objects\": [\"pencil\", \"charcoal\", \"marker\"]}", + 13 + ], + "tennis visor": [ + " {\"type\": \"headwear\", \"description\": \"visor-style; could be made of fabric; could have a logo or design\", \"similar objects\": [\"baseball cap\", \"sun hat\", \"beanie\"]}", + 13 + ], + "silver metal handle": [ + " {\"type\": \"hardware\", \"description\": \"silver in color; made of metal; could be used as a handle\", \"similar objects\": [\"knob\", \"hinge\", \"lock\"]}", + 13 + ], + "school buses": [ + " {\"type\": \"vehicle\", \"description\": \"large, yellow, has multiple doors; could have a stop sign\", \"similar objects\": [\"van\", \"truck\", \"minibus\"]}", + 13 + ], + "cigarette butts": [ + "\n{\"type\": \"waste\", \"description\": \"small, cylindrical, made of paper and tobacco; could have a filter\", \"similar objects\": [\"cigarette packs\", \"cigarette lighters\", \"cigarette ash\"]}", + 13 + ], + "plastic tarp": [ + " {\"type\": \"material\", \"description\": \"waterproof; could be used for covering objects; could be used for camping\", \"similar objects\": [\"canvas tarp\", \"nylon tarp\", \"vinyl tarp\"]}", + 13 + ], + "latches": [ + " {\"type\": \"fastening tool\", \"description\": \"metal; could be used to secure doors or windows; could be opened with a key\", \"similar objects\": [\"locks\", \"hinges\", \"bolts\"]}", + 13 + ], + "gold vase": [ + "\n{\"type\": \"decorative item\", \"description\": \"golden; could be made of metal or ceramic; could have a long neck and a wide base\", \"similar objects\": [\"urn\", \"urns\", \"flower pot\"]}", + 13 + ], + "word bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple doors; could be yellow or white\", \"similar objects\": [\"truck\", \"van\", \"minibus\"]}", + 13 + ], + "train caboose": [ + " {\"type\": \"railway vehicle\", \"description\": \"rectangular; has a red lantern at the back; could be connected to other railway vehicles\", \"similar objects\": [\"locomotive\", \"freight car\", \"passenger car\"]}", + 13 + ], + "drywall": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of gypsum; used to build walls and ceilings\", \"similar objects\": [\"plywood\", \"insulation\", \"concrete\"]}", + 13 + ], + "chocolate birthday": [ + "\n{\"type\": \"food\", \"description\": \"sweet; could be in the form of cake, bar, or chips; could be decorated with cream and fruits\", \"similar objects\": [\"vanilla cake\", \"strawberry tart\", \"caramel ice cream\"]}", + 13 + ], + "way signs": [ + " {\"type\": \"road signs\", \"description\": \"could be triangular, rectangular, or circular; could be yellow, white, or red; could have symbols or words\", \"similar objects\": [\"traffic lights\", \"road barriers\", \"speed limit signs\"]}", + 13 + ], + "square sink": [ + " {\"type\": \"plumbing fixture\", \"description\": \"square; could have a faucet; could be made of stainless steel\", \"similar objects\": [\"bathtub\", \"toilet\", \"basin\"]}", + 13 + ], + "door handles": [ + " {\"type\": \"hardware\", \"description\": \"metal or plastic; could be round or lever-shaped; could be used to open and close doors\", \"similar objects\": [\"knobs\", \"hinges\", \"locks\"]}", + 13 + ], + "kleenex": [ + " {\"type\": \"cleaning tool\", \"description\": \"soft, thin, rectangular; could be used to wipe nose\", \"similar objects\": [\"tissue\", \"paper towel\", \"napkin\"]}", + 13 + ], + "steel train tracks": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, straight, made of steel; could have rail ties\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 13 + ], + "train engineer": [ + " {\"type\": \"occupation\", \"description\": \"operates and maintains a train; responsible for the safety of passengers and cargo\", \"similar objects\": [\"bus driver\", \"pilot\", \"truck driver\"]}", + 13 + ], + "snow boots": [ + " {\"type\": \"footwear\", \"description\": \"waterproof; could be insulated; could have fur lining\", \"similar objects\": [\"hiking boots\", \"rain boots\", \"winter boots\"]}", + 13 + ], + "closet doors": [ + " {\"type\": \"furniture\", \"description\": \"hinged doors; could be sliding; could be made of wood or metal\", \"similar objects\": [\"cabinet doors\", \"drawer doors\", \"wardrobe doors\"]}", + 13 + ], + "stone road": [ + " {\"type\": \"road surface\", \"description\": \"made of stones; could be bumpy; could be slippery when wet\", \"similar objects\": [\"gravel road\", \"asphalt road\", \"dirt road\"]}", + 13 + ], + "seabirds": [ + " {\"type\": \"animal\", \"description\": \"could fly; could be found near the sea; could have colorful feathers\", \"similar objects\": [\"penguin\", \"albatross\", \"gull\"]}", + 13 + ], + "sensor": [ + " {\"type\": \"electronic device\", \"description\": \"small; could detect changes in environment; could be used to measure temperature, humidity, etc.\", \"similar objects\": [\"actuator\", \"transducer\", \"controller\"]}", + 13 + ], + "ski cap": [ + " {\"type\": \"clothing accessory\", \"description\": \"knitted; could be with a pompom; could be with a visor\", \"similar objects\": [\"beanie\", \"beret\", \"turban\"]}", + 13 + ], + "orange wristband": [ + "\n{\"type\": \"accessory\", \"description\": \"orange; could be made of fabric or rubber; could be used as a fashion item or for sports\", \"similar objects\": [\"bracelet\", \"headband\", \"anklet\"]}", + 13 + ], + "captain": [ + " {\"type\": \"title\", \"description\": \"a leader of a group or organization; could have a rank\", \"similar objects\": [\"commander\", \"general\", \"admiral\"]}", + 13 + ], + "wood telephone pole": [ + "\n{\"type\": \"utility pole\", \"description\": \"tall, cylindrical; made of wood; could have wires attached to it\", \"similar objects\": [\"metal telephone pole\", \"street light pole\", \"traffic light pole\"]}", + 13 + ], + "entryway": [ + " {\"type\": \"architectural feature\", \"description\": \"opening in a wall; could have a door; could have a porch\", \"similar objects\": [\"hallway\", \"staircase\", \"balcony\"]}", + 13 + ], + "traffic stop sign": [ + " {\"type\": \"road sign\", \"description\": \"octagonal; red and white; has the word 'STOP' written on it\", \"similar objects\": [\"yield sign\", \"no parking sign\", \"speed limit sign\"]}", + 13 + ], + "garbage pail": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic; has a lid\", \"similar objects\": [\"trash can\", \"bucket\", \"bin\"]}", + 13 + ], + "orange handles": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of plastic; could be used to open doors or drawers\", \"similar objects\": [\"knobs\", \"pulls\", \"handles\"]}", + 13 + ], + "plaques": [ + " {\"type\": \"decoration\", \"description\": \"flat, rectangular; could be made of metal or wood; could be engraved with words or images\", \"similar objects\": [\"trophies\", \"medals\", \"awards\"]}", + 13 + ], + "bubble": [ + " {\"type\": \"object\", \"description\": \"round; could be made of soap; could be filled with air or liquid\", \"similar objects\": [\"balloon\", \"globe\", \"marble\"]}", + 13 + ], + "paper box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of paper; could be used to store items\", \"similar objects\": [\"cardboard box\", \"plastic box\", \"wooden box\"]}", + 13 + ], + "mailboxes": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of metal; could be attached to a wall\", \"similar objects\": [\"lockers\", \"cabinets\", \"drawers\"]}", + 13 + ], + "flats": [ + " {\"type\": \"footwear\", \"description\": \"low-heeled shoes; could be made of leather; could be slip-on\", \"similar objects\": [\"sandals\", \"loafers\", \"mules\"]}", + 13 + ], + "arm bent elbow": [ + "\n{\"type\": \"body part\", \"description\": \"joint between upper arm and forearm; can be bent and straightened\", \"similar objects\": [\"knee\", \"shoulder\", \"wrist\"]}", + 13 + ], + "pointy roof": [ + " {\"type\": \"architectural feature\", \"description\": \"sharp, pointed roof; could be made of tiles or metal sheets\", \"similar objects\": [\"gabled roof\", \"hip roof\", \"flat roof\"]}", + 13 + ], + "handrails": [ + " {\"type\": \"safety tool\", \"description\": \"long, metal bars; could be attached to walls or stairs; could be used for support\", \"similar objects\": [\"guardrails\", \"balustrades\", \"banisters\"]}", + 13 + ], + "refrigerator door handle": [ + "\n{\"type\": \"appliance handle\", \"description\": \"could be made of metal; could be curved or straight; could have a latch\", \"similar objects\": [\"oven door handle\", \"microwave door handle\", \"dishwasher door handle\"]}", + 13 + ], + "soda cup": [ + " {\"type\": \"drinking container\", \"description\": \"transparent; could be made of plastic; could have a straw\", \"similar objects\": [\"water bottle\", \"coffee mug\", \"wine glass\"]}", + 13 + ], + "grease spot": [ + " {\"type\": \"stain\", \"description\": \"dark, oily, could be found on clothes or surfaces\", \"similar objects\": [\"ink stain\", \"coffee stain\", \"blood stain\"]}", + 13 + ], + "bare limbs": [ + " {\"type\": \"body part\", \"description\": \"arms and legs without clothing; could be visible veins and muscles\", \"similar objects\": [\"torso\", \"hands\", \"feet\"]}", + 13 + ], + "color car": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could be of any color; could have a roof\", \"similar objects\": [\"truck\", \"motorcycle\", \"bicycle\"]}", + 13 + ], + "bus front headlight": [ + "\n{\"type\": \"vehicle part\", \"description\": \"round; could be made of glass; could be attached to the front of a bus\", \"similar objects\": [\"car headlight\", \"motorcycle headlight\", \"truck headlight\"]}", + 13 + ], + "distant mountains": [ + "\n{\"type\": \"landscape\", \"description\": \"far away; could be snow-capped; could be in different shapes; could be in different colors\", \"similar objects\": [\"hills\", \"valleys\", \"cliffs\"]}", + 13 + ], + "wooden table leg": [ + "\n{\"type\": \"furniture part\", \"description\": \"long, cylindrical, made of wood; could have a flat base\", \"similar objects\": [\"chair leg\", \"bed leg\", \"stool leg\"]}", + 13 + ], + "chrome pipe": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical, made of chrome; could have a threaded end\", \"similar objects\": [\"copper pipe\", \"PVC pipe\", \"iron pipe\"]}", + 13 + ], + "pets": [ + "\n{\"type\": \"animals\", \"description\": \"domesticated animals kept as companions; could include cats, dogs, birds, fish, reptiles, and small mammals\", \"similar objects\": [\"livestock\", \"wild animals\", \"farm animals\"]}", + 13 + ], + "stone arch": [ + " {\"type\": \"architectural structure\", \"description\": \"made of stones; could be curved or straight; could be used as a bridge or entrance\", \"similar objects\": [\"wooden arch\", \"stone bridge\", \"stone wall\"]}", + 13 + ], + "airlines": [ + " {\"type\": \"transportation service\", \"description\": \"provides air travel services; could have different classes of services\", \"similar objects\": [\"train\", \"bus\", \"cruise\"]}", + 13 + ], + "slides": [ + " {\"type\": \"playground equipment\", \"description\": \"long, curved, could be made of plastic; could have stairs\", \"similar objects\": [\"swing\", \"monkey bars\", \"seesaw\"]}", + 13 + ], + "brick surface": [ + " {\"type\": \"building material\", \"description\": \"hard, rectangular; could be red or grey; could be used for walls or pavements\", \"similar objects\": [\"concrete\", \"stone\", \"tile\"]}", + 13 + ], + "safety barrier": [ + " {\"type\": \"barrier\", \"description\": \"could be made of metal or plastic; could be used to separate traffic or pedestrians; could be used to protect people from danger\", \"similar objects\": [\"fence\", \"guardrail\", \"wall\"]}", + 13 + ], + "bracer": [ + " {\"type\": \"accessory\", \"description\": \"worn around the arm; could be made of leather or metal; could be decorated with jewels\", \"similar objects\": [\"bracelet\", \"armband\", \"cuff\"]}", + 13 + ], + "tennis bag": [ + " {\"type\": \"sports equipment\", \"description\": \"long, rectangular; could have multiple compartments; could have a shoulder strap\", \"similar objects\": [\"golf bag\", \"gym bag\", \"backpack\"]}", + 13 + ], + "silver table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of metal; could have four legs\", \"similar objects\": [\"chair\", \"sofa\", \"desk\"]}", + 13 + ], + "cake pan": [ + " {\"type\": \"baking tool\", \"description\": \"round; has a handle; could be made of metal or silicone\", \"similar objects\": [\"pie pan\", \"bundt pan\", \"muffin tin\"]}", + 13 + ], + "headgear": [ + " {\"type\": \"accessory\", \"description\": \"worn on the head; could be made of fabric, metal, or plastic; could be used for protection or decoration\", \"similar objects\": [\"hat\", \"helmet\", \"cap\"]}", + 13 + ], + "glass wine bottle": [ + "\n{\"type\": \"container\", \"description\": \"transparent; cylindrical; has a long neck; could be sealed with a cork\", \"similar objects\": [\"water bottle\", \"beer bottle\", \"jar\"]}", + 13 + ], + "boy skate board": [ + "\n{\"type\": \"sport equipment\", \"description\": \"long board with four wheels; could be used by a boy\", \"similar objects\": [\"scooter\", \"roller skates\", \"longboard\"]}", + 13 + ], + "wood picnic table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; made of wood; has benches on both sides\", \"similar objects\": [\"patio table\", \"outdoor dining table\", \"deck table\"]}", + 13 + ], + "combo": [ + " {\"type\": \"food\", \"description\": \"a combination of different food items; could include a main dish, side dish, and a drink\", \"similar objects\": [\"meal\", \"platter\", \"plate\"]}", + 13 + ], + "seashells": [ + " {\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be found on the beach; could be used as decorations\", \"similar objects\": [\"rocks\", \"driftwood\", \"seaweed\"]}", + 13 + ], + "times": [ + " {\"type\": \"newspaper\", \"description\": \"daily newspaper; could be printed or digital; could be international or local\", \"similar objects\": [\"The Guardian\", \"The New York Times\", \"The Washington Post\"]}", + 13 + ], + "polygons": [ + " {\"type\": \"geometric shape\", \"description\": \"closed plane figures with three or more sides; could be regular or irregular\", \"similar objects\": [\"triangles\", \"squares\", \"rectangles\"]}", + 13 + ], + "wind sock": [ + " {\"type\": \"weather tool\", \"description\": \"cylindrical; made of fabric; used to measure wind direction and speed\", \"similar objects\": [\"anemometer\", \"weather vane\", \"barometer\"]}", + 13 + ], + "gulls": [ + " {\"type\": \"bird\", \"description\": \"white; has a long wingspan; could be seen near the sea\", \"similar objects\": [\"pigeon\", \"seagull\", \"eagle\"]}", + 13 + ], + "converse": [ + " {\"type\": \"shoe\", \"description\": \"low-top; canvas material; rubber sole; lace-up closure\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 13 + ], + "orange hair": [ + "\n{\"type\": \"hair color\", \"description\": \"bright, vibrant orange color; could be dyed or natural\", \"similar objects\": [\"red hair\", \"blonde hair\", \"brown hair\"]}", + 13 + ], + "luggage carousel": [ + " {\"type\": \"transportation tool\", \"description\": \"round; has a conveyor belt; could be found in airports\", \"similar objects\": [\"escalator\", \"elevator\", \"moving walkway\"]}", + 13 + ], + "hamster": [ + " {\"type\": \"animal\", \"description\": \"small, furry, has a short tail; could have black, brown, white, or grey fur\", \"similar objects\": [\"mouse\", \"gerbil\", \"guinea pig\"]}", + 13 + ], + "rhubarb": [ + " {\"type\": \"vegetable\", \"description\": \"long, red stalks; could have green leaves; could be cooked into pies\", \"similar objects\": [\"celery\", \"asparagus\", \"kale\"]}", + 13 + ], + "yellow chain": [ + " {\"type\": \"accessory\", \"description\": \"long; could be made of metal; could be used as a necklace\", \"similar objects\": [\"bracelet\", \"earrings\", \"anklet\"]}", + 13 + ], + "gym shoe": [ + " {\"type\": \"footwear\", \"description\": \"sporty; could have laces; could have a rubber sole\", \"similar objects\": [\"sneakers\", \"running shoes\", \"tennis shoes\"]}", + 13 + ], + "shingle": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of asphalt; could be used for roofing\", \"similar objects\": [\"tile\", \"siding\", \"brick\"]}", + 13 + ], + "orb": [ + " {\"type\": \"object\", \"description\": \"spherical; could be made of glass or metal; could be used as a decoration\", \"similar objects\": [\"ball\", \"globe\", \"marble\"]}", + 13 + ], + "silver fire": [ + " {\"type\": \"firework\", \"description\": \"sparkling silver color; could be in the shape of stars, circles, or other shapes; could be used for celebrations\", \"similar objects\": [\"firecracker\", \"sparkler\", \"rocket\"]}", + 13 + ], + "steep hill": [ + " {\"type\": \"landscape\", \"description\": \"slope is very steep; could be covered with grass or rocks; could be dangerous to climb\", \"similar objects\": [\"mountain\", \"cliff\", \"valley\"]}", + 13 + ], + "marshmallows": [ + " {\"type\": \"food\", \"description\": \"white, fluffy, sweet; could be melted in hot chocolate\", \"similar objects\": [\"gummy bears\", \"jelly beans\", \"chocolate chips\"]}", + 13 + ], + "dirt patches": [ + " {\"type\": \"ground feature\", \"description\": \"uneven patches of soil; could be dry or wet; could be found in gardens or fields\", \"similar objects\": [\"mud\", \"grass\", \"rocks\"]}", + 13 + ], + "orange sunset": [ + "\n{\"type\": \"natural phenomenon\", \"description\": \"warm, orange-colored sky; could be accompanied by clouds; could be seen in the evening\", \"similar objects\": [\"rainbow\", \"aurora\", \"sunrise\"]}", + 13 + ], + "bottom row": [ + " {\"type\": \"position\", \"description\": \"the row at the bottom of a group of rows\", \"similar objects\": [\"top row\", \"middle row\", \"last row\"]}", + 13 + ], + "round coffee table": [ + "\n{\"type\": \"furniture\", \"description\": \"round; could have a glass top; could have four legs\", \"similar objects\": [\"square coffee table\", \"end table\", \"dining table\"]}", + 13 + ], + "dirty wall": [ + " {\"type\": \"surface\", \"description\": \"could be painted; could be covered with dust; could have stains\", \"similar objects\": [\"floor\", \"ceiling\", \"window\"]}", + 13 + ], + "water buffalo": [ + " {\"type\": \"animal\", \"description\": \"large, gray, has horns; could have a hump on its back\", \"similar objects\": [\"cow\", \"yak\", \"bison\"]}", + 13 + ], + "straw basket": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of straw; could have a handle\", \"similar objects\": [\"basket\", \"bag\", \"box\"]}", + 13 + ], + "block building": [ + " {\"type\": \"structure\", \"description\": \"made of blocks; could be of different shapes and sizes; could be used for play or decoration\", \"similar objects\": [\"lego set\", \"puzzle\", \"model building\"]}", + 13 + ], + "metal bumper": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the front or rear of a vehicle; made of metal; designed to absorb impact\", \"similar objects\": [\"grille\", \"fender\", \"headlight\"]}", + 13 + ], + "wooden walls": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be painted; could be used for interior and exterior walls\", \"similar objects\": [\"brick walls\", \"concrete walls\", \"plaster walls\"]}", + 13 + ], + "girafee": [ + " {\"type\": \"animal\", \"description\": \"long neck; has a long mane; has spots\", \"similar objects\": [\"zebra\", \"elephant\", \"horse\"]}", + 13 + ], + "pedestrian": [ + " {\"type\": \"person\", \"description\": \"walking on the street; could be carrying a bag\", \"similar objects\": [\"cyclist\", \"driver\", \"runner\"]}", + 13 + ], + "fir": [ + " {\"type\": \"tree\", \"description\": \"evergreen; has needles; could have cones\", \"similar objects\": [\"pine\", \"spruce\", \"cedar\"]}", + 13 + ], + "blue berry": [ + "\n{\"type\": \"fruit\", \"description\": \"small, round, blue; could have a white powdery coating; could be sweet or sour\", \"similar objects\": [\"strawberry\", \"blackberry\", \"raspberry\"]}", + 13 + ], + "bird claws": [ + " {\"type\": \"animal body part\", \"description\": \"sharp, curved, used for gripping and climbing\", \"similar objects\": [\"cat claws\", \"monkey hands\", \"bear paws\"]}", + 13 + ], + "stadium light": [ + " {\"type\": \"lighting tool\", \"description\": \"large; could be mounted on a pole; could be used to light up a stadium\", \"similar objects\": [\"floodlight\", \"spotlight\", \"streetlight\"]}", + 13 + ], + "farmhouse": [ + " {\"type\": \"building\", \"description\": \"large; could have a porch; could have a barn; could have a silo\", \"similar objects\": [\"cottage\", \"barn\", \"bungalow\"]}", + 13 + ], + "toilet water": [ + " {\"type\": \"cleaning product\", \"description\": \"blue liquid; used to clean toilets\", \"similar objects\": [\"toilet cleaner\", \"bleach\", \"dishwashing liquid\"]}", + 13 + ], + "door building": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could be made of wood or metal; could have a handle; could have a lock\", \"similar objects\": [\"window\", \"gate\", \"wall\"]}", + 13 + ], + "crochet": [ + " {\"type\": \"crafting tool\", \"description\": \"hook-shaped; could be made of metal or plastic; used to create fabric\", \"similar objects\": [\"knitting needle\", \"sewing needle\", \"yarn\"]}", + 13 + ], + "grease stains": [ + " {\"type\": \"stain\", \"description\": \"dark, oily, could be found on clothes or surfaces\", \"similar objects\": [\"ink stains\", \"coffee stains\", \"blood stains\"]}", + 13 + ], + "bike sign": [ + " {\"type\": \"road sign\", \"description\": \"round; has a bicycle symbol; could be yellow or white\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 13 + ], + "bent neck": [ + " {\"type\": \"body part\", \"description\": \"curved neck; could be caused by poor posture\", \"similar objects\": [\"rounded shoulders\", \"hunchback\", \"slouching\"]}", + 13 + ], + "stumps": [ + " {\"type\": \"wooden object\", \"description\": \"short, thick, could be used as a seat\", \"similar objects\": [\"logs\", \"trees\", \"branches\"]}", + 13 + ], + "farmers": [ + " {\"type\": \"occupation\", \"description\": \"work in the field; grow crops; raise animals\", \"similar objects\": [\"ranchers\", \"gardeners\", \"farmhands\"]}", + 13 + ], + "rafts": [ + " {\"type\": \"watercraft\", \"description\": \"made of logs or inflatable tubes; could be used for recreation or transportation\", \"similar objects\": [\"canoe\", \"kayak\", \"boat\"]}", + 13 + ], + "train depot": [ + " {\"type\": \"transportation facility\", \"description\": \"building with multiple tracks; could have a ticket office; could have a waiting area\", \"similar objects\": [\"bus station\", \"airport\", \"harbor\"]}", + 13 + ], + "amtrak train": [ + " {\"type\": \"transportation\", \"description\": \"long; has multiple cars; could be powered by diesel or electric\", \"similar objects\": [\"subway\", \"bus\", \"airplane\"]}", + 13 + ], + "jalapeno": [ + " {\"type\": \"vegetable\", \"description\": \"green; could be red; has a pointed tip; could be sliced into round pieces; could be spicy\", \"similar objects\": [\"bell pepper\", \"habanero\", \"serrano pepper\"]}", + 13 + ], + "control box": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; could have buttons and switches; could be used to control other devices\", \"similar objects\": [\"remote control\", \"keyboard\", \"joystick\"]}", + 13 + ], + "knight": [ + " {\"type\": \"character\", \"description\": \"armored; could be riding a horse; could have a sword\", \"similar objects\": [\"king\", \"queen\", \"prince\"]}", + 13 + ], + "water pipes": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical; could be made of metal or plastic; could be connected to other pipes\", \"similar objects\": [\"valves\", \"fittings\", \"taps\"]}", + 13 + ], + "instruction sign": [ + " {\"type\": \"signage\", \"description\": \"could be rectangular or square; could have arrows or symbols; could have words or phrases\", \"similar objects\": [\"warning sign\", \"street sign\", \"traffic sign\"]}", + 13 + ], + "presentation": [ + " {\"type\": \"activity\", \"description\": \"a form of communication; could involve slides, videos, or other visuals; could involve speaking or other forms of communication\", \"similar objects\": [\"lecture\", \"speech\", \"workshop\"]}", + 13 + ], + "flatbread": [ + " {\"type\": \"food\", \"description\": \"thin, round, could be made of wheat flour; could be served with various toppings\", \"similar objects\": [\"pita bread\", \"tortilla\", \"naan\"]}", + 13 + ], + "light tower": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a light on the top\", \"similar objects\": [\"street light\", \"lighthouse\", \"floodlight\"]}", + 13 + ], + "shadow sheep": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; has a black and white pattern; could be made of fabric\", \"similar objects\": [\"plush toy\", \"stuffed animal\", \"doll\"]}", + 13 + ], + "dark stripe": [ + " {\"type\": \"pattern\", \"description\": \"long, thin, black lines; could be found on animals or fabrics\", \"similar objects\": [\"plaid\", \"checkerboard\", \"polka dot\"]}", + 13 + ], + "tag ear": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, metal, could be used to hang earrings\", \"similar objects\": [\"earring hook\", \"earring back\", \"earring post\"]}", + 13 + ], + "topless man": [ + "\n{\"type\": \"person\", \"description\": \"male; without a shirt; could have tattoos\", \"similar objects\": [\"woman\", \"child\", \"elderly person\"]}", + 13 + ], + "lumps": [ + " {\"type\": \"solid object\", \"description\": \"irregular shape; could be soft or hard; could be found in different sizes\", \"similar objects\": [\"rocks\", \"pebbles\", \"boulders\"]}", + 13 + ], + "shadow cow": [ + "\n{\"type\": \"optical illusion\", \"description\": \"an image of a cow created by the shadows of other objects\", \"similar objects\": [\"shadow rabbit\", \"shadow cat\", \"shadow dog\"]}", + 13 + ], + "uniform shirt": [ + " {\"type\": \"clothing\", \"description\": \"collared; could have buttons; could have a logo or emblem\", \"similar objects\": [\"dress shirt\", \"polo shirt\", \"t-shirt\"]}", + 13 + ], + "round design": [ + " {\"type\": \"shape\", \"description\": \"circular; could be used for decoration\", \"similar objects\": [\"square\", \"triangle\", \"oval\"]}", + 13 + ], + "keyboard key": [ + " {\"type\": \"computer part\", \"description\": \"rectangular; could have letters or numbers printed on it; could be pressed to type\", \"similar objects\": [\"mouse\", \"monitor\", \"printer\"]}", + 13 + ], + "grandma": [ + " {\"type\": \"person\", \"description\": \"elderly; usually wears glasses; could have wrinkles; could be wearing a dress\", \"similar objects\": [\"grandfather\", \"aunt\", \"uncle\"]}", + 13 + ], + "fireplace tools": [ + " {\"type\": \"household tool\", \"description\": \"used to tend a fire; usually includes a poker, shovel, and brush\", \"similar objects\": [\"firewood\", \"matches\", \"fire extinguisher\"]}", + 12 + ], + "tumbler": [ + " {\"type\": \"drinking vessel\", \"description\": \"cylindrical; could be made of glass or plastic; could have a lid\", \"similar objects\": [\"mug\", \"cup\", \"glass\"]}", + 12 + ], + "lettuce leaves": [ + " {\"type\": \"vegetable\", \"description\": \"green, thin, and long; could be torn into pieces; could be used in salads\", \"similar objects\": [\"spinach\", \"cabbage\", \"kale\"]}", + 12 + ], + "tounge": [ + " {\"type\": \"body part\", \"description\": \"pink; could be used for tasting and speaking; could be long and flexible\", \"similar objects\": [\"teeth\", \"nose\", \"ear\"]}", + 12 + ], + "silver stove": [ + "\n{\"type\": \"cooking tool\", \"description\": \"silver; has a flat top; could have knobs and burners\", \"similar objects\": [\"oven\", \"microwave\", \"toaster\"]}", + 12 + ], + "gas burners": [ + " {\"type\": \"cooking tool\", \"description\": \"has a knob to control the flame; could be used to cook food\", \"similar objects\": [\"stove\", \"oven\", \"grill\"]}", + 12 + ], + "horse jockey": [ + " {\"type\": \"occupation\", \"description\": \"person who rides a horse in a race\", \"similar objects\": [\"race car driver\", \"cyclist\", \"skier\"]}", + 12 + ], + "neat": [ + "\n{\"type\": \"adjective\", \"description\": \"well-organized; tidy; in order\", \"similar objects\": [\"orderly\", \"organized\", \"tidy\"]}", + 12 + ], + "toilet top": [ + " {\"type\": \"bathroom fixture\", \"description\": \"round; could be made of porcelain; could have a lid\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 12 + ], + "tea pots": [ + " {\"type\": \"cooking tool\", \"description\": \"could be made of metal or ceramic; has a spout and a handle; could be used to boil water\", \"similar objects\": [\"kettle\", \"coffee pot\", \"tea cup\"]}", + 12 + ], + "oval window": [ + " {\"type\": \"architectural feature\", \"description\": \"elliptical shape; could be made of glass; could be used for ventilation\", \"similar objects\": [\"round window\", \"arched window\", \"bay window\"]}", + 12 + ], + "chicken leg": [ + " {\"type\": \"food\", \"description\": \"dark meat; could be roasted or fried; could be served with a sauce\", \"similar objects\": [\"turkey leg\", \"duck leg\", \"pork chop\"]}", + 12 + ], + "orange liquid": [ + "\n{\"type\": \"liquid\", \"description\": \"orange in color; could be a juice or a beverage\", \"similar objects\": [\"lemonade\", \"apple juice\", \"cranberry juice\"]}", + 12 + ], + "waste paper basket": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic or metal; has a lid\", \"similar objects\": [\"trash can\", \"recycling bin\", \"garbage can\"]}", + 12 + ], + "coffe cup": [ + " {\"type\": \"drinking vessel\", \"description\": \"cylindrical; could have a handle; could have a lid\", \"similar objects\": [\"mug\", \"teacup\", \"glass\"]}", + 12 + ], + "mountain ridge": [ + " {\"type\": \"landscape\", \"description\": \"long, narrow, elevated landform; could have steep slopes; could have snow-capped peaks\", \"similar objects\": [\"valley\", \"cliff\", \"plateau\"]}\n\nObject detection models should focus on the shape, color, texture, size, and other physical characteristics of the input object. Additionally, they should consider the context of the object, such as its environment, and any similar objects that may be present.", + 12 + ], + "silver latch": [ + " {\"type\": \"hardware\", \"description\": \"metal; could be used to secure a door or window; could be opened with a key\", \"similar objects\": [\"lock\", \"hinge\", \"handle\"]}", + 12 + ], + "eat": [ + "\n{\"type\": \"action\", \"description\": \"the act of consuming food or drink\", \"similar objects\": [\"chew\", \"swallow\", \"bite\"]}", + 12 + ], + "system": [ + " {\"type\": \"abstract concept\", \"description\": \"a set of components that interact to form a complex whole; could be used to describe a process or a structure\", \"similar objects\": [\"network\", \"framework\", \"mechanism\"]}", + 12 + ], + "round pillow": [ + " {\"type\": \"furniture\", \"description\": \"circular; could be filled with feathers or foam; could be used for sleeping or decoration\", \"similar objects\": [\"square pillow\", \"bolster pillow\", \"cushion\"]}", + 12 + ], + "metal brace": [ + " {\"type\": \"medical tool\", \"description\": \"used to support a broken bone; could be made of metal or plastic; could be adjustable\", \"similar objects\": [\"cast\", \"splint\", \"crutch\"]}", + 12 + ], + "tickets": [ + " {\"type\": \"document\", \"description\": \"small pieces of paper; could be used for admission\", \"similar objects\": [\"passport\", \"ID card\", \"boarding pass\"]}", + 12 + ], + "tea kettles": [ + " {\"type\": \"cooking tool\", \"description\": \"round; has a handle; could be made of metal; could have a whistle\", \"similar objects\": [\"pot\", \"pan\", \"coffee maker\"]}", + 12 + ], + "yummy": [ + "\n\nUnfortunately, object detection models cannot detect abstract concepts such as \"yummy\".", + 12 + ], + "thick stripes": [ + " {\"type\": \"pattern\", \"description\": \"wide, bold lines; could be in different colors\", \"similar objects\": [\"dots\", \"checks\", \"plaid\"]}", + 12 + ], + "dogs tongue": [ + "\n{\"type\": \"body part\", \"description\": \"pink; long and flat; could be rough or smooth; could be used for licking\", \"similar objects\": [\"cat's tongue\", \"human's tongue\", \"horse's tongue\"]}", + 12 + ], + "cannister": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could have a lid\", \"similar objects\": [\"jar\", \"box\", \"bottle\"]}", + 12 + ], + "tennis sneakers": [ + " {\"type\": \"footwear\", \"description\": \"lightweight; has a non-slip sole; could be made of mesh fabric\", \"similar objects\": [\"running shoes\", \"basketball shoes\", \"hiking boots\"]}", + 12 + ], + "shoestrings": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, and made of fabric or leather; used to tie shoes\", \"similar objects\": [\"laces\", \"elastic bands\", \"velcro straps\"]}", + 12 + ], + "wall picture": [ + " {\"type\": \"decoration\", \"description\": \"could be framed; could be hung on the wall; could be a painting or a photograph\", \"similar objects\": [\"painting\", \"sculpture\", \"mirror\"]}", + 12 + ], + "tennis court floor": [ + " {\"type\": \"sports court floor\", \"description\": \"flat, green, made of clay or hard court material; could have lines and markings\", \"similar objects\": [\"basketball court floor\", \"badminton court floor\", \"volleyball court floor\"]}", + 12 + ], + "male soccer player": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing a soccer uniform; has a soccer ball; could have a helmet; could have a cleat\", \"similar objects\": [\"female soccer player\", \"basketball player\", \"baseball player\"]}", + 12 + ], + "snowboard boots": [ + " {\"type\": \"footwear\", \"description\": \"high-top; designed for snowboarding; could have laces and straps\", \"similar objects\": [\"ski boots\", \"hiking boots\", \"snowshoes\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber,", + 12 + ], + "snowboard boot": [ + " {\"type\": \"footwear\", \"description\": \"high-top; could be made of leather; could have laces; could have a buckle\", \"similar objects\": [\"ski boot\", \"hiking boot\", \"snowshoe\"]}", + 12 + ], + "silver bars": [ + " {\"type\": \"precious metal\", \"description\": \"shiny, silver-colored; could be in the form of bars or coins\", \"similar objects\": [\"gold bars\", \"platinum bars\", \"copper bars\"]}", + 12 + ], + "horse eye": [ + " {\"type\": \"animal body part\", \"description\": \"large, round, black; could be surrounded by white fur\", \"similar objects\": [\"cow eye\", \"goat eye\", \"sheep eye\"]}", + 12 + ], + "tape dispenser": [ + " {\"type\": \"office tool\", \"description\": \"has a handle; could be made of plastic or metal; could have a blade for cutting tape\", \"similar objects\": [\"stapler\", \"hole puncher\", \"paper clip holder\"]}", + 12 + ], + "paper coffee cup": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of paper; could have a lid\", \"similar objects\": [\"plastic cup\", \"mug\", \"thermos\"]}", + 12 + ], + "silver sign": [ + " {\"type\": \"decoration\", \"description\": \"shiny; could be made of metal; could be in the shape of a circle or a square\", \"similar objects\": [\"gold sign\", \"plaque\", \"trophy\"]}", + 12 + ], + "metal tea kettle": [ + "\n{\"type\": \"cooking tool\", \"description\": \"made of metal; has a handle; could whistle when boiling water\", \"similar objects\": [\"coffee pot\", \"teapot\", \"stovetop kettle\"]}", + 12 + ], + "scanner": [ + " {\"type\": \"electronic device\", \"description\": \"flat; could be used to scan documents\", \"similar objects\": [\"printer\", \"copier\", \"fax machine\"]}", + 12 + ], + "pan pizza": [ + " {\"type\": \"food\", \"description\": \"round; has a thick crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"deep dish pizza\"]}", + 12 + ], + "bicycle rider": [ + " {\"type\": \"person\", \"description\": \"wearing a helmet; riding a bicycle; could be wearing a reflective vest\", \"similar objects\": [\"motorcycle rider\", \"skateboarder\", \"rollerblader\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber", + 12 + ], + "crossbody bag": [ + " {\"type\": \"accessory\", \"description\": \"long strap; could be made of leather; could be worn across the body\", \"similar objects\": [\"backpack\", \"tote bag\", \"clutch\"]}", + 12 + ], + "cool": [ + "\n{\"type\": \"adjective\", \"description\": \"describes something that is not hot; could be used to describe a person or a situation\", \"similar objects\": [\"chill\", \"calm\", \"relaxed\"]}", + 12 + ], + "topped table": [ + " {\"type\": \"furniture\", \"description\": \"has a flat surface; could have four legs; could be made of wood or metal\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 12 + ], + "rectangular table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal\", \"similar objects\": [\"desk\", \"chair\", \"sofa\"]}", + 12 + ], + "pothole": [ + " {\"type\": \"road hazard\", \"description\": \"hole in the road; could be filled with water; could be dangerous for vehicles\", \"similar objects\": [\"crack\", \"bump\", \"manhole\"]}", + 12 + ], + "cd case": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of plastic; could hold multiple CDs\", \"similar objects\": [\"DVD case\", \"jewel case\", \"bookshelf\"]}", + 12 + ], + "mask umpire": [ + " {\"type\": \"sports equipment\", \"description\": \"protective face mask; could be made of metal; has a throat guard\", \"similar objects\": [\"catcher's mask\", \"helmet\", \"shin guard\"]}", + 12 + ], + "cartoons": [ + " {\"type\": \"entertainment\", \"description\": \"animated drawings; could be funny; could be educational\", \"similar objects\": [\"movies\", \"comics\", \"video games\"]}", + 12 + ], + "fence poles": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of wood or metal; could be used to build a fence\", \"similar objects\": [\"posts\", \"stakes\", \"rails\"]}", + 12 + ], + "person water skiing": [ + "\n{\"type\": \"activity\", \"description\": \"person standing on two skis, holding a rope attached to a boat; person is gliding on the water\", \"similar objects\": [\"wakeboarding\", \"surfing\", \"snow skiing\"]}", + 12 + ], + "joint": [ + " {\"type\": \"connector\", \"description\": \"used to connect two objects together; could be made of metal or plastic\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 12 + ], + "sink area": [ + " {\"type\": \"room fixture\", \"description\": \"has a basin; could have a faucet; could have a drain\", \"similar objects\": [\"bathtub\", \"shower\", \"toilet\"]}", + 12 + ], + "flowery": [ + " {\"type\": \"decoration\", \"description\": \"could be made of paper; could be colorful; could be in different shapes\", \"similar objects\": [\"balloon\", \"ribbon\", \"confetti\"]}", + 12 + ], + "platforms": [ + " {\"type\": \"structure\", \"description\": \"flat surface; could be made of wood, metal, or concrete; could be used for standing or walking\", \"similar objects\": [\"stairs\", \"ramps\", \"bridges\"]}", + 12 + ], + "sewer drain": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; has a grate; could be used to drain water\", \"similar objects\": [\"sink drain\", \"toilet\", \"bathtub drain\"]}", + 12 + ], + "arm air": [ + " {\"type\": \"aircraft\", \"description\": \"large; has wings; could have multiple engines; could have a tail\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}", + 12 + ], + "splashing water": [ + " {\"type\": \"action\", \"description\": \"water droplets flying in the air; could be caused by a fountain or a waterfall\", \"similar objects\": [\"raining\", \"spraying\", \"dripping\"]}", + 12 + ], + "metal surface": [ + " {\"type\": \"material\", \"description\": \"smooth, reflective, hard; could be used for construction\", \"similar objects\": [\"wood\", \"plastic\", \"glass\"]}", + 12 + ], + "barn door": [ + " {\"type\": \"door\", \"description\": \"large, wooden, sliding; could have a latch\", \"similar objects\": [\"garage door\", \"barn gate\", \"shed door\"]}", + 12 + ], + "pizza paddle": [ + " {\"type\": \"cooking tool\", \"description\": \"long, flat, wooden handle; used to slide pizzas in and out of the oven\", \"similar objects\": [\"spatula\", \"tongs\", \"whisk\"]}", + 12 + ], + "scroll wheel": [ + " {\"type\": \"computer accessory\", \"description\": \"round; used to scroll up and down on a page\", \"similar objects\": [\"mouse\", \"trackpad\", \"keyboard\"]}", + 12 + ], + "glass display": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be used to showcase items; could be made of glass or plastic\", \"similar objects\": [\"cabinet\", \"shelf\", \"showcase\"]}", + 12 + ], + "transparent": [ + "\n{\"type\": \"adjective\", \"description\": \"able to be seen through; clear; translucent\", \"similar objects\": [\"translucent\", \"see-through\", \"diaphanous\"]}", + 12 + ], + "power switch": [ + " {\"type\": \"electrical device\", \"description\": \"small, rectangular; used to turn on/off electrical appliances\", \"similar objects\": [\"outlet\", \"plug\", \"socket\"]}", + 12 + ], + "toilet wall": [ + " {\"type\": \"bathroom fixture\", \"description\": \"white; could have a flush button; could have a toilet paper holder\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 12 + ], + "wall surface": [ + " {\"type\": \"structure\", \"description\": \"flat, vertical, could be painted or wallpapered\", \"similar objects\": [\"ceiling\", \"floor\", \"door\"]}", + 12 + ], + "stitches": [ + " {\"type\": \"sewing tool\", \"description\": \"threads used to sew fabrics together; could be made of cotton, silk, or nylon\", \"similar objects\": [\"needles\", \"buttons\", \"zippers\"]}", + 12 + ], + "eye ball": [ + " {\"type\": \"body part\", \"description\": \"round; has a pupil; could be blue, brown, or green\", \"similar objects\": [\"iris\", \"eyelid\", \"eyelash\"]}", + 12 + ], + "monitor stand": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of metal; could be adjustable\", \"similar objects\": [\"desk\", \"chair\", \"bookshelf\"]}", + 12 + ], + "blob": [ + " {\"type\": \"shape\", \"description\": \"amorphous; could be any size; could be any color\", \"similar objects\": [\"circle\", \"oval\", \"triangle\"]}", + 12 + ], + "street clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; could be mounted on a wall; could have a pendulum\", \"similar objects\": [\"watch\", \"alarm clock\", \"grandfather clock\"]}", + 12 + ], + "asphalt road surface": [ + " {\"type\": \"road surface\", \"description\": \"black, smooth, made of asphalt\", \"similar objects\": [\"concrete road surface\", \"gravel road surface\", \"dirt road surface\"]}", + 12 + ], + "ankle socks": [ + " {\"type\": \"clothing item\", \"description\": \"short socks that reach up to the ankle; could be made of cotton, wool, or synthetic materials\", \"similar objects\": [\"crew socks\", \"knee-high socks\", \"no-show socks\"]}", + 12 + ], + "gold button": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of metal; could be used to fasten clothes\", \"similar objects\": [\"zipper\", \"snap button\", \"hook and eye\"]}", + 12 + ], + "sink top": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of marble or granite; could have a sink bowl\", \"similar objects\": [\"countertop\", \"tabletop\", \"worktop\"]}", + 12 + ], + "swings": [ + " {\"type\": \"playground equipment\", \"description\": \"has two ropes and a seat; could be hung from a tree\", \"similar objects\": [\"slide\", \"monkey bars\", \"seesaw\"]}", + 12 + ], + "flowering plants": [ + " {\"type\": \"plant\", \"description\": \"has colorful petals; could have leaves and stems; could have a fragrance\", \"similar objects\": [\"roses\", \"tulips\", \"daisies\"]}", + 12 + ], + "cucumber slice": [ + " {\"type\": \"food\", \"description\": \"round; green; could be sliced into thin pieces; could be used in salads\", \"similar objects\": [\"zucchini slice\", \"tomato slice\", \"onion slice\"]}", + 12 + ], + "sun rays": [ + " {\"type\": \"natural phenomenon\", \"description\": \"streams of light from the sun; could be seen in the sky\", \"similar objects\": [\"moonlight\", \"starlight\", \"aurora\"]}", + 12 + ], + "wood nightstand": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have drawers; could have a top surface\", \"similar objects\": [\"dresser\", \"end table\", \"coffee table\"]}", + 12 + ], + "skulls": [ + " {\"type\": \"decoration\", \"description\": \"white; could be made of plastic or ceramic; could have a hollow eye sockets\", \"similar objects\": [\"skeletons\", \"bones\", \"skull masks\"]}", + 12 + ], + "fastener": [ + " {\"type\": \"hardware tool\", \"description\": \"used to join two objects together; could be made of metal or plastic; could be in different shapes\", \"similar objects\": [\"screw\", \"bolt\", \"nail\"]}", + 12 + ], + "dark mountains": [ + " {\"type\": \"landscape\", \"description\": \"rugged, steep, dark; could have snow-capped peaks; could have a foggy atmosphere\", \"similar objects\": [\"valley\", \"cliff\", \"canyon\"]}", + 12 + ], + "bus mirror": [ + " {\"type\": \"vehicle accessory\", \"description\": \"convex; could be attached to the side of a bus; could be used to increase visibility\", \"similar objects\": [\"car mirror\", \"truck mirror\", \"motorcycle mirror\"]}", + 12 + ], + "smoke trails": [ + " {\"type\": \"atmospheric phenomenon\", \"description\": \"long, thin, white trails in the sky; could be caused by aircrafts\", \"similar objects\": [\"contrails\", \"clouds\", \"fog\"]}", + 12 + ], + "handful": [ + " {\"type\": \"measurement\", \"description\": \"amount of something that can be held in one hand\", \"similar objects\": [\"pinch\", \"dash\", \"cupful\"]}", + 12 + ], + "handles cabinets": [ + " {\"type\": \"furniture\", \"description\": \"has two handles; could be made of wood or metal; could be used to store items\", \"similar objects\": [\"drawers\", \"wardrobe\", \"cupboard\"]}", + 12 + ], + "landline telephone": [ + " {\"type\": \"communication tool\", \"description\": \"has a handset; could be connected to a wall; could have a dial pad\", \"similar objects\": [\"cell phone\", \"walkie-talkie\", \"intercom\"]}", + 12 + ], + "remnants": [ + " {\"type\": \"object\", \"description\": \"remains of something; could be broken pieces; could be leftovers\", \"similar objects\": [\"debris\", \"rubble\", \"wreckage\"]}", + 12 + ], + "motorcycle light": [ + " {\"type\": \"vehicle accessory\", \"description\": \"attached to the front of a motorcycle; could be a headlight or a signal light\", \"similar objects\": [\"car light\", \"bicycle light\", \"truck light\"]}", + 12 + ], + "dirty fork": [ + "\n{\"type\": \"utensil\", \"description\": \"metal; has four tines; could be covered with food residue\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 12 + ], + "orange tank top": [ + "\n{\"type\": \"clothing\", \"description\": \"orange; sleeveless; could have a round neckline\", \"similar objects\": [\"t-shirt\", \"dress\", \"sweater\"]}", + 12 + ], + "plastic shower curtain": [ + "\n{\"type\": \"bathroom accessory\", \"description\": \"transparent; could be made of plastic; could be decorated with patterns\", \"similar objects\": [\"bath mat\", \"bath towel\", \"bathrobe\"]}", + 12 + ], + "kickstand bike": [ + " {\"type\": \"bicycle\", \"description\": \"has a kickstand; could have two wheels; could have a basket\", \"similar objects\": [\"mountain bike\", \"road bike\", \"tricycle\"]}", + 12 + ], + "fanny pack": [ + " {\"type\": \"accessory\", \"description\": \"small, waist-worn bag; could be made of fabric or leather; could have a zipper or buckle closure\", \"similar objects\": [\"waist bag\", \"hip bag\", \"belt bag\"]}", + 12 + ], + "girls hand": [ + "\n{\"type\": \"body part\", \"description\": \"five fingers; could have nail polish; could have a bracelet\", \"similar objects\": [\"boys hand\", \"foot\", \"arm\"]}", + 12 + ], + "cats ears": [ + " {\"type\": \"animal body part\", \"description\": \"pointy; could be furry; could be black, white, or other colors\", \"similar objects\": [\"dog ears\", \"rabbit ears\", \"mouse ears\"]}", + 12 + ], + "lap top computer": [ + "\n{\"type\": \"electronic device\", \"description\": \"portable computer; has a keyboard and a screen; could be connected to the internet\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}", + 12 + ], + "caution cones": [ + " {\"type\": \"safety tool\", \"description\": \"orange; cone-shaped; could be reflective\", \"similar objects\": [\"barricade\", \"traffic sign\", \"warning tape\"]}", + 12 + ], + "shift key": [ + " {\"type\": \"keyboard key\", \"description\": \"longer than other keys; used to type capital letters\", \"similar objects\": [\"enter key\", \"backspace key\", \"spacebar\"]}", + 12 + ], + "brick path": [ + " {\"type\": \"landscape feature\", \"description\": \"made of bricks; could be curved or straight; could be used as a walkway\", \"similar objects\": [\"stone path\", \"gravel path\", \"wooden path\"]}", + 12 + ], + "silver basket": [ + " {\"type\": \"container\", \"description\": \"made of silver; could be used to store items; could have a handle\", \"similar objects\": [\"box\", \"bag\", \"jar\"]}", + 12 + ], + "pointy edge": [ + " {\"type\": \"shape\", \"description\": \"sharp edge; could be found on objects such as knives, scissors, and pencils\", \"similar objects\": [\"rounded edge\", \"curved edge\", \"straight edge\"]}", + 12 + ], + "truck driver": [ + " {\"type\": \"occupation\", \"description\": \"operates a truck; responsible for transporting goods; could be a long-distance driver\", \"similar objects\": [\"bus driver\", \"taxi driver\", \"delivery driver\"]}", + 12 + ], + "sea spray": [ + " {\"type\": \"natural phenomenon\", \"description\": \"fine mist of water droplets; could be seen near the shore\", \"similar objects\": [\"ocean waves\", \"tide\", \"surf\"]}", + 12 + ], + "metal decoration": [ + " {\"type\": \"decoration\", \"description\": \"could be made of metal; could be in various shapes; could be used to decorate walls or other objects\", \"similar objects\": [\"ornament\", \"sculpture\", \"statue\"]}", + 12 + ], + "dirty dishes": [ + " {\"type\": \"household item\", \"description\": \"plates, bowls, cups, and cutlery; could be stained with food residue\", \"similar objects\": [\"pots and pans\", \"silverware\", \"glassware\"]}", + 12 + ], + "hair bow": [ + " {\"type\": \"accessory\", \"description\": \"could be made of ribbon; could be attached to the hair with a clip\", \"similar objects\": [\"headband\", \"hair clip\", \"hair tie\"]}", + 12 + ], + "heart design": [ + " {\"type\": \"decoration\", \"description\": \"red; could be made of paper; could be shaped like a heart\", \"similar objects\": [\"flower design\", \"star design\", \"circle design\"]}", + 12 + ], + "train cake": [ + " {\"type\": \"dessert\", \"description\": \"could be made of sponge cake; could be decorated with chocolate and candy; could be shaped like a train\", \"similar objects\": [\"car cake\", \"airplane cake\", \"boat cake\"]}", + 12 + ], + "toy horse": [ + " {\"type\": \"toy\", \"description\": \"could be made of plastic; could be a rocking horse; could have a saddle\", \"similar objects\": [\"doll\", \"action figure\", \"toy car\"]}", + 12 + ], + "loafers": [ + " {\"type\": \"footwear\", \"description\": \"flat, slip-on shoes; could have tassels or buckles\", \"similar objects\": [\"moccasins\", \"oxfords\", \"sneakers\"]}", + 12 + ], + "office telephone": [ + " {\"type\": \"communication tool\", \"description\": \"has a handset; could have a dial pad; could have a speakerphone\", \"similar objects\": [\"cell phone\", \"walkie-talkie\", \"intercom\"]}", + 12 + ], + "water container": [ + " {\"type\": \"container\", \"description\": \"could be made of plastic or metal; could be cylindrical or rectangular; could have a lid or a spout\", \"similar objects\": [\"bottle\", \"jug\", \"pitcher\"]}", + 12 + ], + "gold letter": [ + " {\"type\": \"decoration item\", \"description\": \"made of gold; could be in the shape of a letter; could be used for decoration\", \"similar objects\": [\"silver letter\", \"gold ornament\", \"gold figurine\"]}", + 12 + ], + "beers": [ + " {\"type\": \"beverage\", \"description\": \"alcoholic; could be served in bottles or cans; could be light or dark\", \"similar objects\": [\"wine\", \"whiskey\", \"vodka\"]}", + 12 + ], + "pint": [ + " {\"type\": \"measurement unit\", \"description\": \"equal to 16 fluid ounces; equal to 473.176 milliliters\", \"similar objects\": [\"quart\", \"gallon\", \"liter\"]}", + 12 + ], + "dishtowel": [ + " {\"type\": \"cleaning tool\", \"description\": \"rectangular; made of cloth; used to dry dishes\", \"similar objects\": [\"dishcloth\", \"sponge\", \"scrub brush\"]}", + 12 + ], + "kitchen cabinet door": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have handles or knobs; could be opened and closed\", \"similar objects\": [\"drawer\", \"cupboard\", \"wardrobe\"]}", + 12 + ], + "sun glare": [ + " {\"type\": \"phenomenon\", \"description\": \"bright light from the sun; could cause temporary blindness\", \"similar objects\": [\"sunlight\", \"reflection\", \"glare\"]}", + 12 + ], + "metal street sign": [ + " {\"type\": \"road sign\", \"description\": \"rectangular; made of metal; has words or symbols on it\", \"similar objects\": [\"traffic light\", \"stop sign\", \"warning sign\"]}", + 12 + ], + "chicken plate": [ + " {\"type\": \"dish\", \"description\": \"round; could have a chicken on it; could be made of ceramic\", \"similar objects\": [\"beef plate\", \"pork plate\", \"fish plate\"]}", + 12 + ], + "tall bridge": [ + " {\"type\": \"structure\", \"description\": \"long and tall; could be made of steel or concrete; could have multiple lanes\", \"similar objects\": [\"viaduct\", \"overpass\", \"tunnel\"]}", + 12 + ], + "fishing poles": [ + " {\"type\": \"fishing tool\", \"description\": \"long, thin, has a hook at the end; could be made of metal or wood\", \"similar objects\": [\"fishing rod\", \"fishing net\", \"fishing line\"]}", + 12 + ], + "silver fan": [ + "\n{\"type\": \"decorative item\", \"description\": \"round; could be made of silver; could have blades\", \"similar objects\": [\"wind chime\", \"windmill\", \"windsock\"]}", + 12 + ], + "wooden table top": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have four legs\", \"similar objects\": [\"desk\", \"chair\", \"bench\"]}", + 12 + ], + "silver hand rail": [ + " {\"type\": \"building material\", \"description\": \"long, silver, metal; could be used as a hand rail\", \"similar objects\": [\"stair rail\", \"balustrade\", \"guard rail\"]}", + 12 + ], + "octagon sign": [ + " {\"type\": \"road sign\", \"description\": \"eight-sided; could be yellow or red; could have a black symbol in the middle\", \"similar objects\": [\"stop sign\", \"yield sign\", \"warning sign\"]}", + 12 + ], + "glass front": [ + " {\"type\": \"building material\", \"description\": \"transparent; could be made of plastic or glass; could be used for windows or doors\", \"similar objects\": [\"window pane\", \"door panel\", \"acrylic sheet\"]}", + 12 + ], + "eggplants": [ + " {\"type\": \"vegetable\", \"description\": \"purple, oval-shaped; could have white stripes; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"cucumber\", \"green bean\"]}", + 12 + ], + "blue boxes": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic or cardboard; could be used for storage\", \"similar objects\": [\"bins\", \"crates\", \"trunks\"]}", + 12 + ], + "gummy": [ + " {\"type\": \"candy\", \"description\": \"chewy; could be in different shapes and colors; could be sour or sweet\", \"similar objects\": [\"jelly beans\", \"marshmallows\", \"licorice\"]}", + 12 + ], + "surfer wetsuit": [ + "\n{\"type\": \"clothing\", \"description\": \"full body suit; made of neoprene; designed to keep the body warm in cold water\", \"similar objects\": [\"diving suit\", \"snorkeling suit\", \"swimsuit\"]}", + 12 + ], + "hotels": [ + " {\"type\": \"accommodation\", \"description\": \"building with multiple rooms; could have a restaurant, bar, and other facilities; could provide services such as laundry and room service\", \"similar objects\": [\"motels\", \"hostels\", \"bed and breakfast\"]}", + 12 + ], + "ladles": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle; could have a bowl-shaped head; could be made of metal or plastic\", \"similar objects\": [\"spoon\", \"fork\", \"tongs\"]}", + 12 + ], + "chocolate cookie": [ + " {\"type\": \"food\", \"description\": \"round; could be filled with chocolate chips; could be crunchy or soft\", \"similar objects\": [\"oatmeal cookie\", \"sugar cookie\", \"macaroon\"]}", + 12 + ], + "man racket": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be used to hit a ball\", \"similar objects\": [\"tennis racket\", \"badminton racket\", \"squash racket\"]}", + 12 + ], + "abbreviation": [ + " {\"type\": \"word\", \"description\": \"a shortened form of a word or phrase; usually made up of the first letter of each word in the phrase\", \"similar objects\": [\"acronym\", \"initialism\", \"clipping\"]}", + 12 + ], + "button mouse": [ + " {\"type\": \"computer accessory\", \"description\": \"small, round, has two buttons and a wheel\", \"similar objects\": [\"keyboard\", \"trackpad\", \"joystick\"]}", + 12 + ], + "bathtub faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"has a handle; could be attached to a wall; could have a shower head\", \"similar objects\": [\"shower head\", \"sink faucet\", \"toilet handle\"]}", + 12 + ], + "star logo": [ + " {\"type\": \"symbol\", \"description\": \"five-pointed star; could be in different colors; could be used as a logo\", \"similar objects\": [\"heart logo\", \"circle logo\", \"triangle logo\"]}", + 12 + ], + "number tag": [ + " {\"type\": \"labeling tool\", \"description\": \"small, rectangular; could be made of paper or plastic; could have numbers printed on it\", \"similar objects\": [\"name tag\", \"barcode\", \"price tag\"]}", + 12 + ], + "cow nose": [ + " {\"type\": \"animal body part\", \"description\": \"long, flexible, and pointed; could be used for grazing\", \"similar objects\": [\"horse nose\", \"goat nose\", \"sheep nose\"]}", + 12 + ], + "inset": [ + " {\"type\": \"tool\", \"description\": \"sharp; could be used to cut wood; could be made of metal\", \"similar objects\": [\"chisel\", \"saw\", \"drill\"]}", + 12 + ], + "blue towel": [ + " {\"type\": \"household item\", \"description\": \"blue; could be used for drying hands or body; could be made of cotton or other fabrics\", \"similar objects\": [\"white towel\", \"bathrobe\", \"washcloth\"]}", + 12 + ], + "purple handle": [ + " {\"type\": \"object\", \"description\": \"handle with a purple color; could be made of plastic or metal\", \"similar objects\": [\"knob\", \"lever\", \"pull\"]}", + 12 + ], + "grey body": [ + "\n{\"type\": \"clothing item\", \"description\": \"light or dark grey; could be a shirt, dress, or pants; could be made of cotton, wool, or polyester\", \"similar objects\": [\"black body\", \"white body\", \"blue body\"]}", + 12 + ], + "high-rise building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have elevators\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"office building\"]}", + 12 + ], + "passenger van": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple rows of seats; could have sliding doors\", \"similar objects\": [\"minibus\", \"minivan\", \"SUV\"]}", + 12 + ], + "elder man": [ + " {\"type\": \"person\", \"description\": \"wrinkled skin; gray hair; could be wearing glasses\", \"similar objects\": [\"elder woman\", \"middle-aged man\", \"middle-aged woman\"]}", + 12 + ], + "mosaic": [ + " {\"type\": \"artwork\", \"description\": \"made of small pieces of colored stones, glass, or other materials; could be used to decorate walls or floors\", \"similar objects\": [\"stained glass\", \"tapestry\", \"quilt\"]}", + 12 + ], + "beige sand": [ + " {\"type\": \"material\", \"description\": \"light brown; could be used for construction; could be found in deserts\", \"similar objects\": [\"gravel\", \"dirt\", \"clay\"]}", + 12 + ], + "orange carpet": [ + "\n{\"type\": \"floor covering\", \"description\": \"orange; could be made of wool or synthetic fibers; could be woven or tufted\", \"similar objects\": [\"rug\", \"mat\", \"tapestry\"]}", + 12 + ], + "liquid soap": [ + " {\"type\": \"cleaning product\", \"description\": \"clear; could be used for handwashing; could be in a bottle\", \"similar objects\": [\"dish soap\", \"shampoo\", \"laundry detergent\"]}", + 12 + ], + "pink design": [ + " {\"type\": \"decoration\", \"description\": \"colorful; could be abstract; could be floral; could be geometric\", \"similar objects\": [\"wallpaper\", \"painting\", \"rug\"]}", + 12 + ], + "middle window": [ + " {\"type\": \"architectural element\", \"description\": \"window located in the middle of a wall; could be opened or closed; could be made of glass or wood\", \"similar objects\": [\"side window\", \"bay window\", \"skylight\"]}", + 12 + ], + "tan coat": [ + " {\"type\": \"clothing\", \"description\": \"light brown; could be made of wool; could have buttons\", \"similar objects\": [\"jacket\", \"sweater\", \"vest\"]}", + 12 + ], + "snowy landscape": [ + " {\"type\": \"landscape\", \"description\": \"white; could have trees, mountains, and other features; could have snowflakes\", \"similar objects\": [\"winter scene\", \"arctic landscape\", \"alpine meadow\"]}", + 12 + ], + "button eye": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, round, could be made of plastic or metal; used to fasten clothing\", \"similar objects\": [\"snap\", \"hook and eye\", \"zipper\"]}", + 12 + ], + "armoire": [ + " {\"type\": \"furniture\", \"description\": \"tall, wooden, has doors and shelves\", \"similar objects\": [\"dresser\", \"wardrobe\", \"closet\"]}", + 12 + ], + "head lump": [ + " {\"type\": \"medical condition\", \"description\": \"raised, hard, and painless lump on the head; could be caused by a cyst, tumor, or infection\", \"similar objects\": [\"swelling\", \"bump\", \"lump\"]}", + 12 + ], + "missiles": [ + " {\"type\": \"weapon\", \"description\": \"long, cylindrical; could be launched from a rocket launcher\", \"similar objects\": [\"bombs\", \"rockets\", \"torpedoes\"]}", + 12 + ], + "orange item": [ + "\n{\"type\": \"object\", \"description\": \"round; could be a fruit or a color; could be a ball or a toy\", \"similar objects\": [\"apple\", \"banana\", \"lemon\", \"toy ball\"]}", + 12 + ], + "seawall": [ + " {\"type\": \"structure\", \"description\": \"built to protect the shoreline from erosion; could be made of concrete, rocks, or other materials\", \"similar objects\": [\"breakwater\", \"jetty\", \"dike\"]}", + 12 + ], + "jugs": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could have a handle; could be made of plastic or metal\", \"similar objects\": [\"pitcher\", \"bottle\", \"jar\"]}", + 12 + ], + "building front": [ + " {\"type\": \"structure\", \"description\": \"could have windows, doors, balconies; could be made of bricks, stones, or concrete; could have a roof\", \"similar objects\": [\"house\", \"apartment\", \"office\"]}", + 12 + ], + "coca cola bottle": [ + "\n{\"type\": \"beverage container\", \"description\": \"red and white; has a long neck; could be made of glass or plastic\", \"similar objects\": [\"water bottle\", \"beer bottle\", \"juice bottle\"]}", + 12 + ], + "ceiling tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of plastic or metal; could be used to cover the ceiling\", \"similar objects\": [\"drywall\", \"plywood\", \"insulation board\"]}", + 12 + ], + "street line": [ + " {\"type\": \"road marking\", \"description\": \"painted lines on the road; could be yellow, white, or red; could be dashed or solid\", \"similar objects\": [\"traffic sign\", \"road sign\", \"crosswalk\"]}", + 12 + ], + "brick home": [ + " {\"type\": \"building\", \"description\": \"made of bricks; could have a chimney; could have a porch\", \"similar objects\": [\"wooden home\", \"stone home\", \"adobe home\"]}", + 12 + ], + "stainless steel range hood": [ + "\n{\"type\": \"kitchen appliance\", \"description\": \"rectangular; made of stainless steel; has a fan and a filter; could be mounted on the wall\", \"similar objects\": [\"stove\", \"microwave\", \"dishwasher\"]}", + 12 + ], + "handle scissors": [ + " {\"type\": \"tool\", \"description\": \"two blades connected by a handle; could be used for cutting\", \"similar objects\": [\"tweezers\", \"pliers\", \"knife\"]}", + 12 + ], + "skateboard road": [ + " {\"type\": \"sports equipment\", \"description\": \"long, flat surface; could have ramps and rails; could be made of wood or plastic\", \"similar objects\": [\"rollerblades\", \"scooter\", \"snowboard\"]}", + 12 + ], + "clay dirt": [ + " {\"type\": \"soil\", \"description\": \"dark brown; could be wet or dry; could be used for pottery\", \"similar objects\": [\"sand\", \"gravel\", \"peat moss\"]}", + 12 + ], + "almond": [ + " {\"type\": \"nut\", \"description\": \"oval-shaped; has a brown shell; could be sliced into two halves\", \"similar objects\": [\"walnut\", \"cashew\", \"peanut\"]}", + 12 + ], + "grey sand": [ + " {\"type\": \"material\", \"description\": \"light grey; could be used for construction; could be found in beaches\", \"similar objects\": [\"gravel\", \"cement\", \"concrete\"]}", + 12 + ], + "dirt baseball field": [ + "\n{\"type\": \"sports field\", \"description\": \"large, flat, covered with dirt; could have a pitcher's mound and a home plate\", \"similar objects\": [\"soccer field\", \"tennis court\", \"golf course\"]}", + 12 + ], + "pocket square": [ + " {\"type\": \"accessory\", \"description\": \"small, square-shaped cloth; could be used to decorate a suit pocket\", \"similar objects\": [\"tie\", \"cufflinks\", \"lapel pin\"]}", + 12 + ], + "catch ball": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of rubber; could be thrown and caught\", \"similar objects\": [\"frisbee\", \"hula hoop\", \"juggling balls\"]}", + 12 + ], + "fruit basket": [ + " {\"type\": \"container\", \"description\": \"round; could be made of wicker; could contain various fruits\", \"similar objects\": [\"picnic basket\", \"storage basket\", \"flower basket\"]}", + 12 + ], + "samsung": [ + " {\"type\": \"electronics\", \"description\": \"smartphone; could be a tablet; could be a laptop\", \"similar objects\": [\"iphone\", \"huawei\", \"acer\"]}", + 12 + ], + "paper brown": [ + " {\"type\": \"material\", \"description\": \"lightweight; could be used for writing; could be recycled\", \"similar objects\": [\"cardboard\", \"fabric\", \"plastic\"]}", + 12 + ], + "iris": [ + " {\"type\": \"flower\", \"description\": \"has petals in shades of purple, blue, yellow, white, and pink; has a yellow center\", \"similar objects\": [\"tulip\", \"daffodil\", \"sunflower\"]}", + 12 + ], + "grocery bags": [ + " {\"type\": \"container\", \"description\": \"made of plastic or paper; could be reusable; could be used to carry groceries\", \"similar objects\": [\"backpack\", \"suitcase\", \"tote bag\"]}", + 12 + ], + "shadow pole": [ + " {\"type\": \"measuring tool\", \"description\": \"long pole; used to measure the length of a shadow\", \"similar objects\": [\"sundial\", \"gnomon\", \"astrolabe\"]}", + 12 + ], + "indoor": [ + " {\"type\": \"environment\", \"description\": \"enclosed; could be air-conditioned; could have furniture\", \"similar objects\": [\"outdoor\", \"room\", \"building\"]}", + 12 + ], + "atm machine": [ + " {\"type\": \"banking tool\", \"description\": \"rectangular; has a keypad; could have a card reader\", \"similar objects\": [\"cash machine\", \"debit machine\", \"credit machine\"]}", + 12 + ], + "giraffe laying": [ + " {\"type\": \"animal\", \"description\": \"long neck; has spots; could be laying down\", \"similar objects\": [\"elephant\", \"hippopotamus\", \"rhinoceros\"]}", + 12 + ], + "cannisters": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could have a lid\", \"similar objects\": [\"jars\", \"bottles\", \"boxes\"]}", + 12 + ], + "saloon car": [ + " {\"type\": \"vehicle\", \"description\": \"long; has two rows of seats; could have a trunk\", \"similar objects\": [\"sedan\", \"hatchback\", \"coupe\"]}", + 12 + ], + "car light": [ + " {\"type\": \"vehicle part\", \"description\": \"attached to the front and back of the car; could be used for signaling; could be used for lighting up the road\", \"similar objects\": [\"headlight\", \"taillight\", \"fog light\"]}", + 12 + ], + "dogs leg": [ + "\n{\"type\": \"animal body part\", \"description\": \"four legs; could be furry; could have claws\", \"similar objects\": [\"cat's leg\", \"horse's leg\", \"bird's leg\"]}", + 12 + ], + "metal exhaust pipe": [ + " {\"type\": \"automotive part\", \"description\": \"cylindrical; made of metal; used to exhaust fumes from the engine\", \"similar objects\": [\"muffler\", \"catalytic converter\", \"air filter\"]}", + 12 + ], + "back tires": [ + " {\"type\": \"automotive part\", \"description\": \"round; black; usually made of rubber; located at the back of the car\", \"similar objects\": [\"front tires\", \"wheels\", \"rims\"]}", + 12 + ], + "building side": [ + " {\"type\": \"structure\", \"description\": \"could be made of bricks, concrete, or wood; could have windows, doors, and balconies; could have a roof\", \"similar objects\": [\"house\", \"apartment\", \"warehouse\"]}", + 12 + ], + "silver belt": [ + " {\"type\": \"accessory\", \"description\": \"made of silver; could be used to hold up pants\", \"similar objects\": [\"gold belt\", \"leather belt\", \"fabric belt\"]}", + 12 + ], + "university": [ + " {\"type\": \"institution\", \"description\": \"educational institution; could have multiple buildings; could have a library\", \"similar objects\": [\"college\", \"school\", \"academy\"]}", + 12 + ], + "safety mask": [ + " {\"type\": \"protective gear\", \"description\": \"covers the face; could be made of cloth or plastic; could have a filter\", \"similar objects\": [\"goggles\", \"gloves\", \"helmet\"]}", + 12 + ], + "drip": [ + " {\"type\": \"liquid\", \"description\": \"slowly falling; could be water or oil\", \"similar objects\": [\"stream\", \"flow\", \"trickle\"]}", + 12 + ], + "suspender": [ + " {\"type\": \"clothing accessory\", \"description\": \"elastic straps; used to hold up trousers\", \"similar objects\": [\"belt\", \"tie\", \"bow tie\"]}", + 12 + ], + "sofa pillow": [ + " {\"type\": \"furniture accessory\", \"description\": \"soft; could be square or round; could be filled with feathers or foam\", \"similar objects\": [\"cushion\", \"throw pillow\", \"bolster pillow\"]}", + 12 + ], + "neck area": [ + " {\"type\": \"body part\", \"description\": \"area between the head and the torso; could have a necklace\", \"similar objects\": [\"shoulder\", \"arm\", \"waist\"]}", + 12 + ], + "iron post": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of metal; could be used to support a structure\", \"similar objects\": [\"wood post\", \"concrete post\", \"steel beam\"]}", + 12 + ], + "clay flower pot": [ + "\n{\"type\": \"gardening tool\", \"description\": \"round; could be made of clay; could have a drainage hole at the bottom; could have a saucer\", \"similar objects\": [\"planter\", \"vase\", \"urn\"]}", + 12 + ], + "round face": [ + " {\"type\": \"facial feature\", \"description\": \"circular shape; could have two eyes, a nose, and a mouth\", \"similar objects\": [\"oval face\", \"square face\", \"triangle face\"]}", + 12 + ], + "gray cell phone": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could have a touchscreen; could have a camera; could have a headphone jack\", \"similar objects\": [\"tablet\", \"laptop\", \"smartwatch\"]}", + 12 + ], + "features": [ + "\n{\"type\": \"noun\", \"description\": \"a distinctive attribute or aspect of something; could be physical or abstract\", \"similar objects\": [\"characteristics\", \"qualities\", \"traits\"]}", + 12 + ], + "president": [ + " {\"type\": \"political role\", \"description\": \"head of a country; could be elected by people\", \"similar objects\": [\"prime minister\", \"king\", \"queen\"]}", + 12 + ], + "gel": [ + " {\"type\": \"substance\", \"description\": \"transparent; could be used as a lubricant; could be used as a styling product\", \"similar objects\": [\"jelly\", \"putty\", \"glue\"]}", + 12 + ], + "sticker pole": [ + " {\"type\": \"decoration tool\", \"description\": \"long; could be made of wood or metal; could be used to hang decorations\", \"similar objects\": [\"banner pole\", \"flag pole\", \"hanging rod\"]}", + 12 + ], + "tablets": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular, touchscreen; could be used for communication, entertainment, and work\", \"similar objects\": [\"smartphones\", \"laptops\", \"e-readers\"]}", + 12 + ], + "dates": [ + " {\"type\": \"fruit\", \"description\": \"brown, oval-shaped; has a pit inside; could be dried\", \"similar objects\": [\"figs\", \"raisins\", \"apricots\"]}", + 12 + ], + "mint": [ + " {\"type\": \"herb\", \"description\": \"green; has a strong smell; could be used for cooking\", \"similar objects\": [\"basil\", \"oregano\", \"parsley\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean", + 12 + ], + "iron stand": [ + " {\"type\": \"furniture\", \"description\": \"tall, metal, has a flat top\", \"similar objects\": [\"table\", \"chair\", \"shelf\"]}", + 12 + ], + "threads": [ + " {\"type\": \"textile\", \"description\": \"long, thin, flexible fibers; could be made of cotton, wool, silk, etc.\", \"similar objects\": [\"yarn\", \"fabric\", \"string\"]}", + 12 + ], + "utters": [ + " {\"type\": \"furniture\", \"description\": \"armchair; could have a high back; could have armrests; could have a cushion\", \"similar objects\": [\"sofa\", \"loveseat\", \"recliner\"]}", + 12 + ], + "dark belt": [ + " {\"type\": \"clothing accessory\", \"description\": \"black; could be made of leather; could be used to hold pants up\", \"similar objects\": [\"tie\", \"scarf\", \"suspenders\"]}", + 12 + ], + "blue marker": [ + " {\"type\": \"writing tool\", \"description\": \"cylindrical; could be used to write on paper; could be erasable\", \"similar objects\": [\"pen\", \"pencil\", \"highlighter\"]}", + 12 + ], + "stripe pattern": [ + " {\"type\": \"pattern\", \"description\": \"alternating lines of different colors or shades; could be used for decoration\", \"similar objects\": [\"plaid\", \"checkerboard\", \"polka dot\"]}", + 12 + ], + "print pillow": [ + " {\"type\": \"decorative item\", \"description\": \"square; could have a printed pattern; could be filled with cotton or foam\", \"similar objects\": [\"cushion\", \"throw pillow\", \"bolster\"]}", + 12 + ], + "splatter": [ + " {\"type\": \"artistic technique\", \"description\": \"involves the use of paint or other mediums to create a random, abstract pattern\", \"similar objects\": [\"dripping\", \"spattering\", \"stippling\"]}", + 12 + ], + "sky light": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could be installed on the ceiling\", \"similar objects\": [\"ceiling light\", \"chandelier\", \"pendant light\"]}", + 12 + ], + "leafed": [ + " {\"type\": \"plant part\", \"description\": \"green; could be attached to a stem; could be shaped differently\", \"similar objects\": [\"flower\", \"petal\", \"branch\"]}", + 12 + ], + "glass cover": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of glass or plastic; could be used to cover food\", \"similar objects\": [\"bowl\", \"plate\", \"cup\"]}", + 12 + ], + "water foam": [ + " {\"type\": \"cleaning tool\", \"description\": \"lightweight; could be used to clean surfaces; could be sprayed\", \"similar objects\": [\"sponge\", \"cloth\", \"brush\"]}", + 12 + ], + "marble wall": [ + " {\"type\": \"building material\", \"description\": \"smooth, glossy, made of marble; could be used for decoration\", \"similar objects\": [\"granite wall\", \"stone wall\", \"tile wall\"]}", + 12 + ], + "meter pole": [ + " {\"type\": \"utility pole\", \"description\": \"tall, cylindrical; could have wires attached to it; could have a meter attached to it\", \"similar objects\": [\"telephone pole\", \"street light pole\", \"traffic light pole\"]}", + 12 + ], + "skateboarding helmet": [ + "\n{\"type\": \"protective gear\", \"description\": \"hard shell; has straps; could be padded; could have a visor\", \"similar objects\": [\"bicycle helmet\", \"hockey helmet\", \"motorcycle helmet\"]}", + 12 + ], + "bead necklace": [ + " {\"type\": \"jewelry\", \"description\": \"made of small beads; could be in different colors; could be in different shapes; could be in different lengths\", \"similar objects\": [\"bracelet\", \"earrings\", \"ring\"]}", + 12 + ], + "computer bag": [ + " {\"type\": \"accessory\", \"description\": \"rectangular; could be made of leather; could have a shoulder strap\", \"similar objects\": [\"backpack\", \"briefcase\", \"messenger bag\"]}", + 12 + ], + "fluffy cat": [ + "\n{\"type\": \"animal\", \"description\": \"soft fur; could have different colors; could have long or short fur; could have different eye colors\", \"similar objects\": [\"kitten\", \"dog\", \"rabbit\"]}", + 12 + ], + "tow": [ + " {\"type\": \"vehicle accessory\", \"description\": \"long metal bar; used to pull a vehicle\", \"similar objects\": [\"hook\", \"chain\", \"rope\"]}", + 12 + ], + "suite case": [ + " {\"type\": \"travel item\", \"description\": \"rectangular; has a handle; could be made of hard materials\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 12 + ], + "onion slices": [ + " {\"type\": \"vegetable\", \"description\": \"thin, round, could be yellow or white; could be used for cooking\", \"similar objects\": [\"garlic\", \"potato\", \"carrot\"]}", + 12 + ], + "orange seat": [ + "\n{\"type\": \"furniture\", \"description\": \"orange; could be made of fabric or leather; could have armrests; could have a backrest\", \"similar objects\": [\"sofa\", \"chair\", \"ottoman\"]}", + 12 + ], + "cranberry": [ + " {\"type\": \"fruit\", \"description\": \"red, round, has a stem; sour taste\", \"similar objects\": [\"blueberry\", \"strawberry\", \"raspberry\"]}", + 12 + ], + "brown hill": [ + " {\"type\": \"landscape\", \"description\": \"sloped; could be covered with grass; could have trees\", \"similar objects\": [\"mountain\", \"valley\", \"cliff\"]}", + 12 + ], + "suspension bridge": [ + " {\"type\": \"structure\", \"description\": \"has two towers connected by cables; could be made of steel or concrete; could span a large body of water\", \"similar objects\": [\"viaduct\", \"cantilever bridge\", \"arch bridge\"]}", + 12 + ], + "dvd case": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of plastic; could have a cover\", \"similar objects\": [\"cd case\", \"vhs case\", \"blu-ray case\"]}", + 12 + ], + "headlight front bus": [ + "\n{\"type\": \"vehicle part\", \"description\": \"attached to the front of a bus; used to light up the road ahead\", \"similar objects\": [\"tail light\", \"fog light\", \"turn signal\"]}", + 12 + ], + "blue walls": [ + " {\"type\": \"decoration\", \"description\": \"blue color; could be painted or wallpapered\", \"similar objects\": [\"ceiling\", \"floor\", \"furniture\"]}", + 12 + ], + "kitchen utensil": [ + " {\"type\": \"cooking tool\", \"description\": \"various shapes and sizes; could be made of metal, plastic, or wood; used for stirring, mixing, and serving food\", \"similar objects\": [\"spoon\", \"fork\", \"knife\"]}", + 12 + ], + "jalapeno peppers": [ + " {\"type\": \"vegetable\", \"description\": \"green, red, or yellow; could be sliced into round pieces; could be spicy; could be used as a topping\", \"similar objects\": [\"bell peppers\", \"habanero peppers\", \"chili peppers\"]}", + 12 + ], + "clamp": [ + " {\"type\": \"tool\", \"description\": \"has two arms that can be tightened together; could be used to hold objects in place\", \"similar objects\": [\"clasp\", \"vise\", \"hose clamp\"]}", + 12 + ], + "belt brown": [ + " {\"type\": \"accessory\", \"description\": \"long; could be made of leather; could have a buckle\", \"similar objects\": [\"scarf\", \"tie\", \"hat\"]}", + 12 + ], + "pants woman": [ + "\n{\"type\": \"clothing\", \"description\": \"long trousers; could be made of different materials; could have pockets; could have a zipper or buttons\", \"similar objects\": [\"jeans\", \"skirt\", \"shorts\"]}", + 12 + ], + "sandy hill": [ + " {\"type\": \"landscape\", \"description\": \"a hill made of sand; could have dunes; could be found near a beach\", \"similar objects\": [\"desert\", \"mountain\", \"valley\"]}", + 12 + ], + "whitecaps": [ + " {\"type\": \"ocean phenomenon\", \"description\": \"white, foamy waves on the surface of the ocean\", \"similar objects\": [\"tide\", \"surf\", \"wave\"]}", + 12 + ], + "diploma": [ + " {\"type\": \"document\", \"description\": \"certificate of achievement; could be framed\", \"similar objects\": [\"certificate\", \"award\", \"degree\"]}", + 12 + ], + "coffe table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have a glass top; could have drawers\", \"similar objects\": [\"end table\", \"side table\", \"console table\"]}", + 12 + ], + "digital camera": [ + " {\"type\": \"electronic device\", \"description\": \"small; has a lens; could be connected to a computer\", \"similar objects\": [\"smartphone\", \"camcorder\", \"tablet\"]}", + 12 + ], + "aluminum baseball bat": [ + "\n{\"type\": \"sports equipment\", \"description\": \"silver; long and cylindrical; could have a handle\", \"similar objects\": [\"wooden baseball bat\", \"tennis racket\", \"golf club\"]}", + 12 + ], + "rat": [ + " {\"type\": \"animal\", \"description\": \"small, long tail; could be brown or black; could have a pointed nose\", \"similar objects\": [\"mouse\", \"hamster\", \"squirrel\"]}", + 12 + ], + "frisbie": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"hula hoop\", \"ball\", \"kite\"]}", + 12 + ], + "orange bird beak": [ + "\n{\"type\": \"bird feature\", \"description\": \"orange; curved; pointed tip\", \"similar objects\": [\"parrot beak\", \"finch beak\", \"hummingbird beak\"]}", + 12 + ], + "grips": [ + " {\"type\": \"tool\", \"description\": \"used to hold objects; could be made of rubber or plastic\", \"similar objects\": [\"clamps\", \"pliers\", \"tongs\"]}", + 12 + ], + "pump bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; has a pump on the top; could be made of plastic or glass\", \"similar objects\": [\"spray bottle\", \"jar\", \"bottle\"]}", + 12 + ], + "christmas trees": [ + "\n{\"type\": \"decoration\", \"description\": \"conical; could be decorated with lights, ornaments, and tinsel; could be artificial or real\", \"similar objects\": [\"wreath\", \"garland\", \"snowman\"]}", + 12 + ], + "dirt tennis court": [ + "\n{\"type\": \"sports court\", \"description\": \"flat, made of dirt; could have lines for tennis court\", \"similar objects\": [\"basketball court\", \"volleyball court\", \"badminton court\"]}", + 12 + ], + "rides": [ + " {\"type\": \"amusement park attraction\", \"description\": \"could be a roller coaster, ferris wheel, carousel, etc.\", \"similar objects\": [\"roller coaster\", \"ferris wheel\", \"carousel\"]}", + 12 + ], + "bases": [ + " {\"type\": \"sports equipment\", \"description\": \"three white, round bases; could be used in baseball\", \"similar objects\": [\"bat\", \"glove\", \"ball\"]}", + 12 + ], + "silver desk lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"silver; has a desk stand; could have a switch\", \"similar objects\": [\"floor lamp\", \"table lamp\", \"ceiling lamp\"]}", + 12 + ], + "oval mirror": [ + "\n{\"type\": \"decorative item\", \"description\": \"oval-shaped; could be made of glass; could have a frame\", \"similar objects\": [\"round mirror\", \"rectangular mirror\", \"wall mirror\"]}", + 12 + ], + "airplane hanger": [ + " {\"type\": \"building\", \"description\": \"large, metal structure; used to store airplanes\", \"similar objects\": [\"garage\", \"warehouse\", \"hangar\"]}", + 12 + ], + "police cars": [ + "\n{\"type\": \"vehicle\", \"description\": \"blue and white; has a siren; could have a flashing light\", \"similar objects\": [\"ambulance\", \"taxi\", \"garbage truck\"]}", + 12 + ], + "tall grass": [ + " {\"type\": \"plant\", \"description\": \"long, thin, green blades; could be found in fields\", \"similar objects\": [\"wheat\", \"rye\", \"corn\"]}", + 12 + ], + "flag banner": [ + " {\"type\": \"decoration\", \"description\": \"long; could be made of fabric; could be printed with words or images\", \"similar objects\": [\"bunting\", \"streamer\", \"balloon\"]}", + 12 + ], + "grey scarf": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, made of fabric; could be made of wool; could be patterned\", \"similar objects\": [\"shawl\", \"wrap\", \"blanket\"]}", + 12 + ], + "gray tower": [ + " {\"type\": \"architecture\", \"description\": \"tall, cylindrical, made of concrete or stone; could have windows\", \"similar objects\": [\"building\", \"skyscraper\", \"monument\"]}", + 12 + ], + "brick steps": [ + " {\"type\": \"structure\", \"description\": \"rectangular; could be made of bricks; could be used as stairs\", \"similar objects\": [\"stone steps\", \"wooden steps\", \"concrete steps\"]}", + 12 + ], + "drain floor": [ + " {\"type\": \"plumbing fixture\", \"description\": \"round; could be made of metal; could be used to drain water\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 12 + ], + "dirty bathroom": [ + "\n{\"type\": \"room\", \"description\": \"could have a sink, toilet, and shower; could have a dirty floor; could have a bad smell\", \"similar objects\": [\"kitchen\", \"bedroom\", \"living room\"]}", + 12 + ], + "toddler boy": [ + " {\"type\": \"person\", \"description\": \"small; could be wearing a diaper; could be walking or running; could be playing with toys\", \"similar objects\": [\"baby girl\", \"infant\", \"child\"]}", + 12 + ], + "rash guard": [ + " {\"type\": \"clothing\", \"description\": \"tight-fitting; could be long-sleeved; could be made of spandex\", \"similar objects\": [\"swimsuit\", \"wetsuit\", \"surf shirt\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (c", + 12 + ], + "star pattern": [ + " {\"type\": \"design\", \"description\": \"geometric shape; could be made of lines; could be filled with colors\", \"similar objects\": [\"circle pattern\", \"triangle pattern\", \"square pattern\"]}", + 12 + ], + "plane engines": [ + " {\"type\": \"machine part\", \"description\": \"cylindrical; could be made of metal; could be powered by fuel\", \"similar objects\": [\"turbines\", \"propellers\", \"jet engines\"]}", + 12 + ], + "card board box": [ + "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of paper; could be used for storage\", \"similar objects\": [\"suitcase\", \"basket\", \"bag\"]}", + 12 + ], + "fence wire": [ + " {\"type\": \"building material\", \"description\": \"long, thin, metal wire; could be used to build fences\", \"similar objects\": [\"chain link\", \"barbed wire\", \"wooden posts\"]}", + 12 + ], + "orange legs": [ + "\n{\"type\": \"insect\", \"description\": \"long, orange legs; could have black stripes; could have wings\", \"similar objects\": [\"spider\", \"grasshopper\", \"cricket\"]}", + 12 + ], + "glass wine glass": [ + "\n{\"type\": \"drinking tool\", \"description\": \"tall and thin; could have a stem; could be made of glass or plastic\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 12 + ], + "cement pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be used to support structures\", \"similar objects\": [\"steel pole\", \"wooden pole\", \"concrete block\"]}", + 12 + ], + "unripe": [ + " {\"type\": \"state\", \"description\": \"not yet ripe; not mature; not ready\", \"similar objects\": [\"green\", \"immature\", \"underdeveloped\"]}", + 12 + ], + "mountain slope": [ + " {\"type\": \"landscape\", \"description\": \"steep incline; could have trees and rocks; could be covered with snow\", \"similar objects\": [\"hill\", \"valley\", \"cliff\"]}", + 12 + ], + "baseball coach": [ + " {\"type\": \"person\", \"description\": \"instructs and motivates baseball players; wears a baseball cap; could have a whistle\", \"similar objects\": [\"soccer coach\", \"basketball coach\", \"hockey coach\"]}", + 12 + ], + "seawater": [ + " {\"type\": \"liquid\", \"description\": \"salty; could be blue or green; could contain small organisms\", \"similar objects\": [\"freshwater\", \"lake water\", \"ocean water\"]}", + 12 + ], + "silver mixing bowl": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round; made of silver; could be used for mixing ingredients\", \"similar objects\": [\"pot\", \"pan\", \"baking dish\"]}", + 12 + ], + "bear doll": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; could be brown or black; could have a bow tie\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"stuffed animal\"]}", + 12 + ], + "silver faucets": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"shiny, metallic; could have a handle; could be used to control water flow\", \"similar objects\": [\"taps\", \"shower heads\", \"valves\"]}", + 12 + ], + "price sticker": [ + " {\"type\": \"labeling tool\", \"description\": \"small, adhesive; could be printed with prices\", \"similar objects\": [\"barcode label\", \"name tag\", \"address label\"]}", + 12 + ], + "silver metal bar": [ + " {\"type\": \"metal object\", \"description\": \"long, silver, metallic; could be used for construction\", \"similar objects\": [\"iron bar\", \"aluminum bar\", \"steel bar\"]}", + 12 + ], + "chocolate cookies": [ + " {\"type\": \"food\", \"description\": \"round; could be filled with chocolate chips; could be crunchy or soft\", \"similar objects\": [\"oatmeal cookies\", \"sugar cookies\", \"gingerbread cookies\"]}", + 12 + ], + "shoe brown": [ + " {\"type\": \"footwear\", \"description\": \"brown; could be made of leather; could have laces\", \"similar objects\": [\"sneaker\", \"boot\", \"sandal\"]}", + 12 + ], + "whole": [ + " {\"type\": \"adjective\", \"description\": \"complete; not divided into parts\", \"similar objects\": [\"entire\", \"all\", \"full\"]}", + 12 + ], + "wood seat": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could be curved or straight; could have armrests\", \"similar objects\": [\"chair\", \"bench\", \"sofa\"]}", + 12 + ], + "color wall tiles": [ + " {\"type\": \"decorative item\", \"description\": \"square or rectangular; could be made of ceramic; could be of various colors\", \"similar objects\": [\"floor tiles\", \"wallpaper\", \"paint\"]}", + 12 + ], + "pasta noodles": [ + " {\"type\": \"food\", \"description\": \"long, thin, cylindrical; could be made of wheat, rice, or other grains; could be boiled or fried\", \"similar objects\": [\"spaghetti\", \"macaroni\", \"ramen\"]}\n\nObject detection models should focus on the shape, color, texture, and size of the object, as well as any distinguishing features that can help to differentiate it from similar objects. For example, for the zucchini, the model should focus on its cylindrical shape, green color, smooth texture, and brown and rough stems. For the zebra,", + 12 + ], + "grey plane": [ + "\n{\"type\": \"vehicle\", \"description\": \"grey; has wings; could have two or four engines; could have a tail\", \"similar objects\": [\"helicopter\", \"jet\", \"airplane\"]}", + 12 + ], + "crossing lines": [ + " {\"type\": \"road sign\", \"description\": \"two lines crossing each other; could be yellow or white; could be accompanied by a red light\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 12 + ], + "foot bridge": [ + " {\"type\": \"structure\", \"description\": \"a bridge that is designed for pedestrians to cross over a body of water or a road; could be made of wood, metal, or concrete\", \"similar objects\": [\"suspension bridge\", \"viaduct\", \"trestle bridge\"]}", + 12 + ], + "hooker": [ + " {\"type\": \"tool\", \"description\": \"curved; could be used to hang things\", \"similar objects\": [\"hanger\", \"clamp\", \"peg\"]}", + 12 + ], + "sea shell": [ + " {\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be found on the beach; could be used as decoration\", \"similar objects\": [\"starfish\", \"seaweed\", \"coral\"]}", + 12 + ], + "visor hat": [ + " {\"type\": \"clothing accessory\", \"description\": \"hat with a brim that can be flipped down to cover the eyes; could be made of fabric or straw\", \"similar objects\": [\"baseball cap\", \"sun hat\", \"beret\"]}", + 12 + ], + "grey buttons": [ + " {\"type\": \"accessory\", \"description\": \"small, round, could be made of plastic or metal; could be used to fasten clothes\", \"similar objects\": [\"zippers\", \"snaps\", \"hooks\"]}", + 12 + ], + "squid": [ + " {\"type\": \"animal\", \"description\": \"long, slim body; has eight arms and two tentacles; could be found in the ocean\", \"similar objects\": [\"octopus\", \"cuttlefish\", \"jellyfish\"]}", + 12 + ], + "grassy plain": [ + " {\"type\": \"landscape\", \"description\": \"large, flat area of land covered with grass; could have trees and shrubs\", \"similar objects\": [\"meadow\", \"prairie\", \"savanna\"]}", + 12 + ], + "metal bleachers": [ + " {\"type\": \"seating\", \"description\": \"long, metal benches; could be used for outdoor events\", \"similar objects\": [\"stadium seats\", \"bleacher chairs\", \"bleacher cushions\"]}", + 12 + ], + "forklift": [ + " {\"type\": \"vehicle\", \"description\": \"has a long arm; could be used to lift heavy objects\", \"similar objects\": [\"truck\", \"crane\", \"bulldozer\"]}", + 12 + ], + "shadow bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has a black and white striped pattern; could be used for public transportation\", \"similar objects\": [\"school bus\", \"tour bus\", \"shuttle bus\"]}", + 12 + ], + "water closet": [ + " {\"type\": \"furniture\", \"description\": \"enclosed space; could have a toilet; could have a sink\", \"similar objects\": [\"bathroom\", \"lavatory\", \"washroom\"]}", + 12 + ], + "tube socks": [ + " {\"type\": \"clothing item\", \"description\": \"long, stretchy, could be colorful\", \"similar objects\": [\"ankle socks\", \"knee-high socks\", \"crew socks\"]}", + 12 + ], + "airport vehicle": [ + " {\"type\": \"transportation vehicle\", \"description\": \"large; could be used to transport passengers and cargo; could have a flashing light\", \"similar objects\": [\"bus\", \"train\", \"truck\"]}", + 12 + ], + "cabinet knobs": [ + " {\"type\": \"hardware\", \"description\": \"round; could be made of metal or plastic; used to open and close cabinets\", \"similar objects\": [\"drawer pulls\", \"hinges\", \"door handles\"]}", + 12 + ], + "flower planter": [ + " {\"type\": \"gardening tool\", \"description\": \"container for plants; could be made of plastic, metal, or wood; could have a handle\", \"similar objects\": [\"flower pot\", \"hanging basket\", \"window box\"]}", + 12 + ], + "dogs ears": [ + " {\"type\": \"body part\", \"description\": \"pointed; could be floppy; could be covered with fur\", \"similar objects\": [\"cat ears\", \"rabbit ears\", \"mouse ears\"]}", + 12 + ], + "backing": [ + " {\"type\": \"cooking tool\", \"description\": \"used to mix ingredients; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"whisk\", \"spatula\", \"wooden spoon\"]}", + 12 + ], + "glass shield": [ + " {\"type\": \"protective tool\", \"description\": \"transparent; could be made of glass or plastic; could be used to protect from physical or chemical hazards\", \"similar objects\": [\"helmet\", \"goggles\", \"mask\"]}", + 12 + ], + "highrise building": [ + " {\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have elevators; could have balconies\", \"similar objects\": [\"skyscraper\", \"apartment building\", \"office building\"]}", + 12 + ], + "amplifier": [ + " {\"type\": \"electronic device\", \"description\": \"used to increase the volume of sound; could be connected to speakers\", \"similar objects\": [\"mixer\", \"equalizer\", \"headphone\"]}", + 12 + ], + "tractor trailer": [ + " {\"type\": \"vehicle\", \"description\": \"large; has two parts; could be used for transportation\", \"similar objects\": [\"semi-truck\", \"tanker truck\", \"flatbed truck\"]}", + 12 + ], + "safety net": [ + " {\"type\": \"protective tool\", \"description\": \"made of strong materials; could be used to protect people from falling\", \"similar objects\": [\"helmet\", \"harness\", \"life jacket\"]}", + 12 + ], + "wine cork": [ + " {\"type\": \"bottle stopper\", \"description\": \"cylindrical; made of cork; used to seal wine bottles\", \"similar objects\": [\"beer cap\", \"bottle cap\", \"bottle stopper\"]}", + 12 + ], + "silver containers": [ + " {\"type\": \"storage tool\", \"description\": \"shiny; could be made of metal; could be used to store items\", \"similar objects\": [\"box\", \"jar\", \"bag\"]}", + 12 + ], + "beat": [ + " {\"type\": \"sound\", \"description\": \"rhythmic sound; could be created by drums or other instruments\", \"similar objects\": [\"rhythm\", \"tempo\", \"melody\"]}", + 12 + ], + "pvc pipe": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, white; could be used for plumbing\", \"similar objects\": [\"copper pipe\", \"steel pipe\", \"plastic pipe\"]}", + 12 + ], + "infield grass": [ + " {\"type\": \"landscape\", \"description\": \"green; could be mowed; could be used for sports\", \"similar objects\": [\"lawn\", \"turf\", \"meadow\"]}", + 12 + ], + "gold tag": [ + " {\"type\": \"accessory\", \"description\": \"small, round, made of gold; could be used as a necklace or bracelet\", \"similar objects\": [\"silver tag\", \"diamond tag\", \"platinum tag\"]}", + 12 + ], + "racetrack": [ + " {\"type\": \"sports facility\", \"description\": \"oval-shaped; has a starting line and a finish line; could have stands for spectators\", \"similar objects\": [\"stadium\", \"arena\", \"court\"]}", + 12 + ], + "cloudy grey skies": [ + "\n{\"type\": \"weather\", \"description\": \"overcast; could be accompanied by rain; could be dark and gloomy\", \"similar objects\": [\"rainy day\", \"foggy day\", \"snowy day\"]}", + 12 + ], + "woman arm": [ + "\n{\"type\": \"body part\", \"description\": \"skinny; could have tattoos; could have jewelry\", \"similar objects\": [\"man arm\", \"woman leg\", \"woman hand\"]}", + 12 + ], + "bread sticks": [ + " {\"type\": \"food\", \"description\": \"long, thin, crunchy; could be served with dipping sauces\", \"similar objects\": [\"pretzels\", \"fries\", \"chips\"]}", + 12 + ], + "visitors": [ + " {\"type\": \"people\", \"description\": \"could be in groups; could be carrying bags; could be talking to each other\", \"similar objects\": [\"tourists\", \"travelers\", \"strangers\"]}", + 12 + ], + "thermometer": [ + " {\"type\": \"measuring tool\", \"description\": \"long, thin; could be digital; could measure temperature\", \"similar objects\": [\"barometer\", \"hygrometer\", \"anemometer\"]}", + 12 + ], + "pink bear": [ + " {\"type\": \"stuffed animal\", \"description\": \"pink; could have a bow; could have a heart-shaped nose\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"stuffed rabbit\"]}", + 12 + ], + "plastic wrapper": [ + " {\"type\": \"packaging material\", \"description\": \"transparent; could be used to wrap food items; could be sealed with heat\", \"similar objects\": [\"foil\", \"paper bag\", \"ziplock bag\"]}", + 12 + ], + "blue roof": [ + " {\"type\": \"building material\", \"description\": \"blue; could be made of tiles; could be used for roofing\", \"similar objects\": [\"shingles\", \"asphalt\", \"metal roofing\"]}", + 12 + ], + "banana leaves": [ + " {\"type\": \"plant material\", \"description\": \"long, green, thin; could be used as wrappers\", \"similar objects\": [\"coconut leaves\", \"palm leaves\", \"bamboo leaves\"]}", + 12 + ], + "caution light": [ + " {\"type\": \"warning device\", \"description\": \"flashing yellow or red light; could be used to warn drivers\", \"similar objects\": [\"traffic light\", \"stop sign\", \"road sign\"]}", + 12 + ], + "gauges": [ + " {\"type\": \"measuring tool\", \"description\": \"round; could be used to measure pressure, temperature, and other physical quantities\", \"similar objects\": [\"thermometer\", \"barometer\", \"manometer\"]}", + 12 + ], + "channel": [ + " {\"type\": \"waterway\", \"description\": \"long and narrow; could be man-made or natural; could be used for transportation\", \"similar objects\": [\"river\", \"canal\", \"lake\"]}", + 12 + ], + "man wears": [ + "\n{\"type\": \"clothing\", \"description\": \"could be shirt, pants, shoes, etc.; could be made of different materials; could be of different colors and styles\", \"similar objects\": [\"woman wears\", \"children wears\", \"accessories\"]}", + 12 + ], + "manis": [ + " {\"type\": \"mammal\", \"description\": \"small, furry; has a long snout; could have a long tail; could have claws\", \"similar objects\": [\"squirrel\", \"chipmunk\", \"rabbit\"]}", + 12 + ], + "silver gate": [ + " {\"type\": \"fence\", \"description\": \"made of metal; could be decorated with patterns; could be used to block the entrance\", \"similar objects\": [\"iron gate\", \"wooden gate\", \"chain-link fence\"]}", + 12 + ], + "face clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has a face with hands; could be made of wood or metal\", \"similar objects\": [\"wall clock\", \"alarm clock\", \"pocket watch\"]}", + 12 + ], + "way track": [ + " {\"type\": \"transportation tool\", \"description\": \"long, straight, has two rails; could be used for trains\", \"similar objects\": [\"road\", \"bridge\", \"tunnel\"]}", + 12 + ], + "pink petals": [ + " {\"type\": \"flower part\", \"description\": \"fragile; could be in different shapes; could be in different colors\", \"similar objects\": [\"leaves\", \"stems\", \"seeds\"]}", + 12 + ], + "silver scooter": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could have a handlebar; could have a seat; could be powered by electricity or gasoline\", \"similar objects\": [\"bicycle\", \"motorcycle\", \"skateboard\"]}", + 12 + ], + "silver metal sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver metal; has a handle; could be used to control water flow\", \"similar objects\": [\"shower head\", \"bathtub faucet\", \"toilet handle\"]}", + 12 + ], + "orange door": [ + "\n{\"type\": \"building material\", \"description\": \"orange; could be made of wood or metal; could have a handle\", \"similar objects\": [\"red door\", \"blue door\", \"green door\"]}", + 12 + ], + "grey shoes": [ + " {\"type\": \"footwear\", \"description\": \"grey; could be made of leather; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 12 + ], + "bus front": [ + " {\"type\": \"vehicle\", \"description\": \"large; has a windshield; could have a logo; could have a door\", \"similar objects\": [\"truck\", \"van\", \"car\"]}", + 12 + ], + "brick pathway": [ + " {\"type\": \"landscape feature\", \"description\": \"made of bricks; could be curved or straight; could be used as a walkway\", \"similar objects\": [\"stone pathway\", \"gravel pathway\", \"wooden pathway\"]}", + 12 + ], + "varieties": [ + " {\"type\": \"word\", \"description\": \"a type of something; could be plural\", \"similar objects\": [\"kinds\", \"species\", \"variety\"]}", + 12 + ], + "wood base": [ + " {\"type\": \"building material\", \"description\": \"hard, durable, could be used for flooring or furniture\", \"similar objects\": [\"concrete\", \"brick\", \"stone\"]}", + 12 + ], + "hundreds": [ + " {\"type\": \"number\", \"description\": \"a number between 100 and 999; could be written as '100s'\", \"similar objects\": [\"thousands\", \"millions\", \"billions\"]}", + 12 + ], + "tassle": [ + " {\"type\": \"decorative item\", \"description\": \"long, thin, hangs from the end of a fabric; could be made of fabric, yarn, or metal\", \"similar objects\": [\"fringe\", \"beads\", \"pom-pom\"]}", + 12 + ], + "turret": [ + " {\"type\": \"fortification structure\", \"description\": \"round; could be made of stone; could have a gun mounted on top\", \"similar objects\": [\"bunker\", \"watchtower\", \"wall\"]}", + 12 + ], + "dock water": [ + " {\"type\": \"water body\", \"description\": \"body of water; could be used for docking boats; could be used for swimming\", \"similar objects\": [\"lake\", \"river\", \"ocean\"]}", + 12 + ], + "buffet": [ + " {\"type\": \"furniture\", \"description\": \"long; could have multiple drawers; could have multiple shelves\", \"similar objects\": [\"sideboard\", \"dresser\", \"cabinet\"]}", + 12 + ], + "brown bag": [ + " {\"type\": \"container\", \"description\": \"brown; could be made of paper or cloth; could be used to carry items\", \"similar objects\": [\"backpack\", \"suitcase\", \"briefcase\"]}", + 12 + ], + "smudges": [ + " {\"type\": \"stain\", \"description\": \"dark, blurry, could be caused by dirt or oil\", \"similar objects\": [\"spots\", \"streaks\", \"blotches\"]}", + 12 + ], + "border collie": [ + " {\"type\": \"dog breed\", \"description\": \"medium-sized; has a long, thick coat; has a strong herding instinct\", \"similar objects\": [\"Australian shepherd\", \"German shepherd\", \"Shetland sheepdog\"]}", + 12 + ], + "fly": [ + " {\"type\": \"insect\", \"description\": \"small; has two wings; could be black or yellow\", \"similar objects\": [\"mosquito\", \"bee\", \"butterfly\"]}", + 12 + ], + "orange fur": [ + " {\"type\": \"fabric\", \"description\": \"soft; could be made of synthetic or natural fibers; could be dyed in different colors\", \"similar objects\": [\"velvet\", \"cotton\", \"wool\"]}", + 12 + ], + "train yard": [ + " {\"type\": \"location\", \"description\": \"area where trains are parked and maintained; could have tracks and signals\", \"similar objects\": [\"railway station\", \"railway platform\", \"railway siding\"]}", + 12 + ], + "silver stand": [ + " {\"type\": \"furniture\", \"description\": \"tall, slender, made of metal; could have a round base\", \"similar objects\": [\"table\", \"chair\", \"shelf\"]}", + 12 + ], + "teddybear": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; usually has a round shape; could have a bowtie\", \"similar objects\": [\"doll\", \"plush toy\", \"action figure\"]}", + 12 + ], + "knit sweater": [ + " {\"type\": \"clothing\", \"description\": \"made of wool; could be long-sleeved; could have a pattern\", \"similar objects\": [\"cardigan\", \"hoodie\", \"jacket\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\",", + 12 + ], + "accordion": [ + " {\"type\": \"musical instrument\", \"description\": \"box-shaped; has buttons and keys; could be folded\", \"similar objects\": [\"harmonica\", \"piano\", \"guitar\"]}", + 12 + ], + "indentations": [ + " {\"type\": \"markings\", \"description\": \"depressions or hollows in a surface; could be caused by pressure or impact\", \"similar objects\": [\"scratches\", \"dents\", \"scars\"]}", + 12 + ], + "store signs": [ + " {\"type\": \"advertisement\", \"description\": \"could be made of metal, plastic, or paper; could be in different shapes and sizes; could have words or images\", \"similar objects\": [\"billboards\", \"posters\", \"banners\"]}", + 12 + ], + "hazy mountain": [ + " {\"type\": \"landscape\", \"description\": \"mountain with fog or clouds; could be seen from a distance\", \"similar objects\": [\"foggy valley\", \"misty lake\", \"cloudy sky\"]}", + 12 + ], + "toilet floor": [ + " {\"type\": \"flooring material\", \"description\": \"waterproof; could be made of ceramic, porcelain, or vinyl; could be white or colored\", \"similar objects\": [\"bathroom floor\", \"kitchen floor\", \"basement floor\"]}", + 12 + ], + "brown stains": [ + " {\"type\": \"stain\", \"description\": \"dark brown; could be caused by water, oil, or other liquids; could be found on clothes, furniture, or other surfaces\", \"similar objects\": [\"dirt\", \"dust\", \"grease\"]}", + 12 + ], + "baseball catchers": [ + " {\"type\": \"sports equipment\", \"description\": \"protective gear; has a face mask; has a chest protector; has a glove\", \"similar objects\": [\"baseball bats\", \"baseball gloves\", \"baseball helmets\"]}", + 12 + ], + "leaves water": [ + " {\"type\": \"beverage\", \"description\": \"clear liquid; could be flavored; could be served cold or hot\", \"similar objects\": [\"tea\", \"juice\", \"coffee\"]}", + 12 + ], + "chickpeas": [ + " {\"type\": \"legume\", \"description\": \"small, round, beige; could be cooked or eaten raw; could be mashed into hummus\", \"similar objects\": [\"lentils\", \"beans\", \"peas\"]}", + 12 + ], + "john": [ + "\n{\"type\": \"name\", \"description\": \"common English name; could be a male or female name\", \"similar objects\": [\"jane\", \"james\", \"joseph\"]}", + 12 + ], + "whtie": [ + "\n{\"type\": \"color\", \"description\": \"lightest color; could be described as a shade of gray\", \"similar objects\": [\"black\", \"gray\", \"silver\"]}", + 12 + ], + "life guard": [ + " {\"type\": \"person\", \"description\": \"wears a red swimsuit; has a whistle; could be carrying a rescue tube\", \"similar objects\": [\"swimmer\", \"surfer\", \"coast guard\"]}", + 12 + ], + "yellow wheels": [ + " {\"type\": \"transportation tool\", \"description\": \"round; could be made of rubber; could be attached to a vehicle\", \"similar objects\": [\"tires\", \"wheels\", \"rims\"]}", + 12 + ], + "office supplies": [ + " {\"type\": \"stationary\", \"description\": \"various items used in an office, such as pens, paper, staplers, etc.\", \"similar objects\": [\"school supplies\", \"art supplies\", \"cleaning supplies\"]}", + 12 + ], + "wooden barrel": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of wood; could have metal bands\", \"similar objects\": [\"bucket\", \"tub\", \"tank\"]}", + 12 + ], + "poncho": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting garment; could be made of wool; could have a hood\", \"similar objects\": [\"cape\", \"shawl\", \"raincoat\"]}", + 12 + ], + "wood block": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be used for construction; could be used for crafting\", \"similar objects\": [\"bricks\", \"concrete blocks\", \"plywood\"]}", + 12 + ], + "grey airplane": [ + "\n{\"type\": \"vehicle\", \"description\": \"grey; has wings and a tail; could have two or four engines; could have a cockpit\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}", + 12 + ], + "plaid tie": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, patterned with stripes and checks\", \"similar objects\": [\"striped tie\", \"bow tie\", \"solid tie\"]}", + 12 + ], + "registration number": [ + " {\"type\": \"identification\", \"description\": \"a unique combination of numbers and/or letters; used to identify a vehicle, person, or other entity\", \"similar objects\": [\"license plate\", \"serial number\", \"VIN number\"]}", + 12 + ], + "xii": [ + " {\"type\": \"number\", \"description\": \"Roman numeral for 12\", \"similar objects\": [\"XI\", \"XIII\", \"IX\"]}", + 12 + ], + "passenger side": [ + " {\"type\": \"car part\", \"description\": \"the right side of the car when facing the front; typically has a door and window\", \"similar objects\": [\"driver side\", \"hood\", \"trunk\"]}", + 12 + ], + "purple lettuce": [ + "\n{\"type\": \"vegetable\", \"description\": \"purple leaves; could be round or long; could be crunchy or soft; could have green veins\", \"similar objects\": [\"red lettuce\", \"arugula\", \"spinach\"]}", + 12 + ], + "wood park bench": [ + "\n{\"type\": \"furniture\", \"description\": \"made of wood; has a backrest and armrests; could have a slatted seat\", \"similar objects\": [\"garden bench\", \"picnic table\", \"deck chair\"]}", + 12 + ], + "gold emblem": [ + " {\"type\": \"decoration\", \"description\": \"shiny; could be in the shape of a shield; could be used to represent a symbol or an organization\", \"similar objects\": [\"medal\", \"badge\", \"trophy\"]}", + 12 + ], + "brown leaf": [ + " {\"type\": \"plant part\", \"description\": \"dry, thin, could be curved; could be attached to a stem\", \"similar objects\": [\"green leaf\", \"petal\", \"pine needle\"]}", + 12 + ], + "pole lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could have a lampshade\", \"similar objects\": [\"floor lamp\", \"table lamp\", \"ceiling lamp\"]}", + 12 + ], + "brick work": [ + " {\"type\": \"construction material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be used to build walls\", \"similar objects\": [\"cement\", \"mortar\", \"concrete block\"]}", + 12 + ], + "cash": [ + " {\"type\": \"currency\", \"description\": \"paper money; coins; could be exchanged for goods and services\", \"similar objects\": [\"credit card\", \"debit card\", \"check\"]}", + 12 + ], + "shadow player": [ + " {\"type\": \"game\", \"description\": \"a two-player game; one player is the shadow and the other is the light; the shadow player has to hide from the light player\", \"similar objects\": [\"hide and seek\", \"tag\", \"capture the flag\"]}", + 12 + ], + "silver piece": [ + " {\"type\": \"metal object\", \"description\": \"shiny, reflective, could be used as currency\", \"similar objects\": [\"gold piece\", \"copper piece\", \"bronze piece\"]}", + 12 + ], + "rust stain": [ + " {\"type\": \"stain\", \"description\": \"reddish-brown; could be found on metal surfaces; could be removed with a rust remover\", \"similar objects\": [\"oil stain\", \"ink stain\", \"blood stain\"]}", + 12 + ], + "stone block": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of concrete, granite, or marble; could be used for construction\", \"similar objects\": [\"bricks\", \"cement\", \"wood\"]}", + 12 + ], + "cute dog": [ + "\n{\"type\": \"animal\", \"description\": \"friendly, furry, four-legged; could have a tail; could have different colors and sizes\", \"similar objects\": [\"cat\", \"rabbit\", \"hamster\"]}", + 12 + ], + "cow leg": [ + " {\"type\": \"meat\", \"description\": \"long, cylindrical; could be cooked in a stew\", \"similar objects\": [\"lamb leg\", \"pork leg\", \"chicken leg\"]}", + 12 + ], + "handle fridge": [ + "\n{\"type\": \"appliance\", \"description\": \"has a handle; could be opened and closed; could be used to store food\", \"similar objects\": [\"freezer\", \"microwave\", \"oven\"]}", + 12 + ], + "mushroom pizza": [ + "\n{\"type\": \"food\", \"description\": \"pizza with mushrooms as topping; could have other toppings such as cheese, pepperoni, olives, etc.\", \"similar objects\": [\"vegetable pizza\", \"meat pizza\", \"cheese pizza\"]}", + 12 + ], + "brown train": [ + "\n{\"type\": \"vehicle\", \"description\": \"long; could have multiple compartments; could have a locomotive; could be painted brown\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 12 + ], + "top bed": [ + " {\"type\": \"furniture\", \"description\": \"a bed with a mattress on top; could have a headboard and footboard\", \"similar objects\": [\"bunk bed\", \"day bed\", \"trundle bed\"]}", + 12 + ], + "carnation": [ + " {\"type\": \"flower\", \"description\": \"pink, white, or red; has a long stem; could have multiple petals\", \"similar objects\": [\"rose\", \"daisy\", \"tulip\"]}", + 12 + ], + "brush holder": [ + " {\"type\": \"storage tool\", \"description\": \"cylindrical; could be made of plastic; could have multiple compartments\", \"similar objects\": [\"pen holder\", \"makeup organizer\", \"toothbrush holder\"]}", + 12 + ], + "metal flag pole": [ + " {\"type\": \"structure\", \"description\": \"long, cylindrical, made of metal; could have a flag attached to it\", \"similar objects\": [\"flag pole\", \"flag staff\", \"flag mast\"]}", + 12 + ], + "grey stone building": [ + "\n{\"type\": \"structure\", \"description\": \"made of grey stones; could have a roof; could have windows and doors\", \"similar objects\": [\"castle\", \"church\", \"monument\"]}", + 12 + ], + "rock face": [ + " {\"type\": \"geological formation\", \"description\": \"rough surface; could be made of different types of rocks; could have different shapes and sizes\", \"similar objects\": [\"cliff\", \"mountain\", \"cave\"]}", + 12 + ], + "banana stalk": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, green; could be cut into pieces; could be used for cooking\", \"similar objects\": [\"celery stalk\", \"cucumber stalk\", \"zucchini stalk\"]}", + 12 + ], + "rainbow flag": [ + " {\"type\": \"symbol\", \"description\": \"multi-colored stripes; could have a white background\", \"similar objects\": [\"pride flag\", \"peace flag\", \"American flag\"]}", + 12 + ], + "brick walk": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be laid in a pattern\", \"similar objects\": [\"paving stone\", \"cobblestone\", \"flagstone\"]}", + 12 + ], + "ossicles": [ + " {\"type\": \"anatomical structure\", \"description\": \"small bones in the middle ear; helps in hearing\", \"similar objects\": [\"cochlea\", \"stapes\", \"malleus\"]}", + 12 + ], + "cooktop": [ + " {\"type\": \"cooking tool\", \"description\": \"flat surface; could have burners; could be electric or gas\", \"similar objects\": [\"stove\", \"oven\", \"grill\"]}", + 12 + ], + "checkers": [ + " {\"type\": \"game\", \"description\": \"board game; two players; pieces are black and red\", \"similar objects\": [\"chess\", \"backgammon\", \"go\"]}", + 12 + ], + "purple ribbon": [ + " {\"type\": \"decorative item\", \"description\": \"long, thin, and colorful; could be used for tying gifts\", \"similar objects\": [\"bow\", \"string\", \"ribbon\"]}", + 12 + ], + "elephants foot": [ + " {\"type\": \"plant\", \"description\": \"large, round, succulent; could have thick, fleshy leaves; could have a thick stem\", \"similar objects\": [\"aloe vera\", \"yucca\", \"agave\"]}", + 12 + ], + "city sky line": [ + " {\"type\": \"landscape\", \"description\": \"buildings of different heights; could have a river or lake; could have a bridge; could have a park\", \"similar objects\": [\"mountain range\", \"desert\", \"forest\"]}", + 12 + ], + "court surface": [ + " {\"type\": \"sports surface\", \"description\": \"flat, hard, usually made of concrete or asphalt; could be painted with lines\", \"similar objects\": [\"tennis court\", \"basketball court\", \"volleyball court\"]}", + 12 + ], + "city view": [ + " {\"type\": \"landscape\", \"description\": \"buildings, roads, trees, people; could have a river or a lake; could have a bridge or a park\", \"similar objects\": [\"mountain view\", \"beach view\", \"desert view\"]}", + 12 + ], + "firehydrant": [ + " {\"type\": \"utility tool\", \"description\": \"red; has a hose connection; could be used to put out fires\", \"similar objects\": [\"fire extinguisher\", \"hose\", \"sprinkler\"]}", + 12 + ], + "wound": [ + " {\"type\": \"injury\", \"description\": \"an injury to the skin or tissue; could be caused by a cut, scrape, or burn; could be bleeding\", \"similar objects\": [\"bruise\", \"scratch\", \"laceration\"]}", + 12 + ], + "pant legs": [ + " {\"type\": \"clothing item\", \"description\": \"long, tapered, could be pleated; could be made of different fabrics\", \"similar objects\": [\"jeans\", \"trousers\", \"shorts\"]}", + 12 + ], + "sailboat water": [ + " {\"type\": \"watercraft\", \"description\": \"has a sail; could have a mast; could have a rudder\", \"similar objects\": [\"yacht\", \"canoe\", \"kayak\"]}", + 12 + ], + "plantation": [ + " {\"type\": \"agricultural land\", \"description\": \"large area of land used for growing crops or trees\", \"similar objects\": [\"farm\", \"orchard\", \"vineyard\"]}", + 12 + ], + "grey sign": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be made of metal; could have words or symbols on it\", \"similar objects\": [\"placard\", \"banner\", \"flag\"]}", + 12 + ], + "baby carrot": [ + " {\"type\": \"vegetable\", \"description\": \"small, orange, cylindrical; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"carrot\", \"parsnip\", \"turnip\"]}", + 12 + ], + "backhoe": [ + " {\"type\": \"construction tool\", \"description\": \"large, has a shovel in the front and a bucket in the back; could be used for digging\", \"similar objects\": [\"excavator\", \"bulldozer\", \"tractor\"]}", + 12 + ], + "plaid table cloth": [ + " {\"type\": \"tableware\", \"description\": \"square or rectangular; has a pattern of different colors; could be made of cotton or linen\", \"similar objects\": [\"table runner\", \"napkin\", \"place mat\"]}", + 12 + ], + "airlines plane": [ + " {\"type\": \"vehicle\", \"description\": \"large; has wings; could have multiple engines; could have a tail fin\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}", + 12 + ], + "rock boulder": [ + " {\"type\": \"geological object\", \"description\": \"large, hard, could be found in nature\", \"similar objects\": [\"stone\", \"pebble\", \"gravel\"]}", + 12 + ], + "airline name": [ + "\n{\"type\": \"transportation service\", \"description\": \"provides air travel services\", \"similar objects\": [\"train\", \"bus\", \"ferry\"]}", + 12 + ], + "cast": [ + " {\"type\": \"medical tool\", \"description\": \"hard, white, plaster-like material; used to immobilize broken bones; could be wrapped around a limb\", \"similar objects\": [\"splint\", \"brace\", \"sling\"]}", + 12 + ], + "cats whiskers": [ + " {\"type\": \"body part\", \"description\": \"long, thin, and stiff hairs on the face of cats\", \"similar objects\": [\"dog whiskers\", \"rabbit whiskers\", \"mouse whiskers\"]}", + 12 + ], + "highlights": [ + " {\"type\": \"hair styling\", \"description\": \"lightened strands of hair; could be done with a cap or a brush\", \"similar objects\": [\"ombre\", \"balayage\", \"babylights\"]}", + 12 + ], + "freezer section": [ + " {\"type\": \"storage area\", \"description\": \"cold; could have shelves; could have a door\", \"similar objects\": [\"refrigerator\", \"freezer compartment\", \"icebox\"]}", + 12 + ], + "pastures": [ + " {\"type\": \"landscape\", \"description\": \"large, open fields; could have trees and shrubs; could have animals grazing\", \"similar objects\": [\"meadows\", \"fields\", \"prairies\"]}", + 12 + ], + "buliding": [ + " {\"type\": \"structure\", \"description\": \"could be made of concrete, brick, or wood; could have multiple floors; could have windows and doors\", \"similar objects\": [\"house\", \"skyscraper\", \"bridge\"]}", + 12 + ], + "stone chimney": [ + " {\"type\": \"architectural structure\", \"description\": \"made of stones; could be tall and cylindrical; could have a chimney cap\", \"similar objects\": [\"brick chimney\", \"fireplace\", \"smoke stack\"]}", + 12 + ], + "multiple trees": [ + "\n{\"type\": \"plant\", \"description\": \"could be of different sizes; could have different leaves; could have different colors; could have different shapes; could have different fruits\", \"similar objects\": [\"bush\", \"shrub\", \"grass\"]}", + 12 + ], + "track train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could be powered by electricity or diesel\", \"similar objects\": [\"monorail\", \"tram\", \"subway\"]}", + 12 + ], + "glassware": [ + " {\"type\": \"utensil\", \"description\": \"transparent; could be made of glass or plastic; could be used for drinking or decoration\", \"similar objects\": [\"cup\", \"mug\", \"bowl\"]}", + 12 + ], + "grey button": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, round, could be made of plastic or metal; could be used to fasten clothing\", \"similar objects\": [\"zipper\", \"snap\", \"hook and eye\"]}", + 12 + ], + "cornucopia": [ + " {\"type\": \"ornament\", \"description\": \"horn-shaped; could be made of paper or fabric; could be filled with fruits and vegetables\", \"similar objects\": [\"basket\", \"vase\", \"urn\"]}", + 12 + ], + "propeller blade": [ + " {\"type\": \"aircraft part\", \"description\": \"long, thin, curved; could be made of metal\", \"similar objects\": [\"wing\", \"fuselage\", \"engine\"]}", + 12 + ], + "birds legs": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and flexible; could have scales; could have claws\", \"similar objects\": [\"insect legs\", \"fish fins\", \"mammal paws\"]}", + 12 + ], + "bakery": [ + " {\"type\": \"business\", \"description\": \"sells bread, cakes, and other baked goods\", \"similar objects\": [\"cafe\", \"restaurant\", \"grocery store\"]}", + 12 + ], + "pelicans": [ + " {\"type\": \"bird\", \"description\": \"large; long beak; could have a pouch; could be white and gray\", \"similar objects\": [\"seagulls\", \"cormorants\", \"flamingos\"]}", + 12 + ], + "boxcar": [ + " {\"type\": \"vehicle\", \"description\": \"long, rectangular; could be used to transport goods; could have wheels\", \"similar objects\": [\"train\", \"truck\", \"van\"]}", + 12 + ], + "sucker": [ + " {\"type\": \"candy\", \"description\": \"round; could be on a stick; could be in different colors and flavors\", \"similar objects\": [\"lollipop\", \"jelly bean\", \"gumdrop\"]}", + 12 + ], + "plant life": [ + " {\"type\": \"living organism\", \"description\": \"green; could have leaves; could have roots; could produce oxygen\", \"similar objects\": [\"trees\", \"flowers\", \"grass\"]}", + 12 + ], + "oak tree": [ + " {\"type\": \"plant\", \"description\": \"tall; has a thick trunk; has leaves that are lobed and dark green; could have acorns\", \"similar objects\": [\"maple tree\", \"pine tree\", \"elm tree\"]}", + 12 + ], + "apple core": [ + " {\"type\": \"fruit waste\", \"description\": \"remains of an apple; could be brown and dry; could have seeds inside\", \"similar objects\": [\"orange peel\", \"banana peel\", \"pear core\"]}", + 12 + ], + "side wings": [ + " {\"type\": \"accessory\", \"description\": \"attached to the side of a vehicle; could be made of metal or plastic; could be used for decoration or protection\", \"similar objects\": [\"spoiler\", \"fender flares\", \"mud flaps\"]}", + 12 + ], + "racers": [ + " {\"type\": \"vehicle\", \"description\": \"fast; could have two or four wheels; could have a driver\", \"similar objects\": [\"motorcycle\", \"car\", \"truck\"]}", + 12 + ], + "side panel": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the side of a vehicle; could be made of metal or plastic; could be used to protect the vehicle from external elements\", \"similar objects\": [\"bumper\", \"fender\", \"door\"]}", + 12 + ], + "meet": [ + " {\"type\": \"food\", \"description\": \"red; could be cooked; could be served with sauces\", \"similar objects\": [\"beef\", \"pork\", \"lamb\"]}", + 12 + ], + "bottom teeth": [ + " {\"type\": \"teeth\", \"description\": \"located at the bottom of the mouth; could be sharp or flat; could be white or yellow\", \"similar objects\": [\"top teeth\", \"molars\", \"canines\"]}", + 12 + ], + "pirate hat": [ + " {\"type\": \"accessory\", \"description\": \"black; has a skull and crossbones; could have a feather\", \"similar objects\": [\"eyepatch\", \"bandana\", \"hook\"]}", + 12 + ], + "skii pole": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, metal; could have a handle; could have a strap\", \"similar objects\": [\"hockey stick\", \"golf club\", \"tennis racket\"]}", + 12 + ], + "cargo container": [ + " {\"type\": \"transportation tool\", \"description\": \"large, rectangular, made of metal; could be used to store goods\", \"similar objects\": [\"truck\", \"ship\", \"airplane\"]}", + 12 + ], + "doormat": [ + " {\"type\": \"floor covering\", \"description\": \"rectangular; could be made of rubber or fabric; could have a pattern or logo\", \"similar objects\": [\"rug\", \"carpet\", \"mat\"]}", + 12 + ], + "shadow batter": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, made of wood; used to hit a ball\", \"similar objects\": [\"baseball bat\", \"golf club\", \"tennis racket\"]}", + 12 + ], + "foot tracks": [ + " {\"type\": \"evidence\", \"description\": \"imprints left by feet; could be in different shapes and sizes; could be in different materials\", \"similar objects\": [\"tire tracks\", \"animal tracks\", \"fingerprints\"]}", + 12 + ], + "sandle": [ + " {\"type\": \"footwear\", \"description\": \"open-toed; could have straps; could be made of leather or rubber\", \"similar objects\": [\"flip-flop\", \"slipper\", \"clog\"]}", + 12 + ], + "green hill": [ + " {\"type\": \"landscape\", \"description\": \"green grass; could have trees; could have a river\", \"similar objects\": [\"mountain\", \"valley\", \"meadow\"]}", + 12 + ], + "granite countertop": [ + " {\"type\": \"building material\", \"description\": \"hard, durable, and heat-resistant; could be polished to a glossy finish; could be found in a variety of colors\", \"similar objects\": [\"marble\", \"quartz\", \"concrete\"]}", + 12 + ], + "toilet bowl lid": [ + " {\"type\": \"bathroom accessory\", \"description\": \"round; could be made of plastic; could be white or other colors\", \"similar objects\": [\"toilet seat\", \"toilet brush\", \"toilet paper holder\"]}", + 12 + ], + "pale blue": [ + " {\"type\": \"color\", \"description\": \"light blue; could be described as sky blue\", \"similar objects\": [\"light green\", \"light yellow\", \"light pink\"]}", + 12 + ], + "gold roman numerals": [ + " {\"type\": \"number system\", \"description\": \"numerals written in gold; could be used to represent dates or years\", \"similar objects\": [\"Arabic numerals\", \"Chinese numerals\", \"Hindu-Arabic numerals\"]}", + 12 + ], + "wax": [ + " {\"type\": \"material\", \"description\": \"solid; could be used for making candles; could be melted\", \"similar objects\": [\"soap\", \"clay\", \"plastic\"]}", + 12 + ], + "california license plate": [ + "\n{\"type\": \"vehicle identification\", \"description\": \"blue background with yellow letters and numbers; could have a bear logo\", \"similar objects\": [\"license plate from other states\", \"vehicle registration sticker\", \"vehicle inspection sticker\"]}", + 12 + ], + "rope fence": [ + " {\"type\": \"barrier\", \"description\": \"made of rope; could be used to separate areas\", \"similar objects\": [\"chain link fence\", \"wooden fence\", \"barbed wire fence\"]}", + 12 + ], + "brown branches": [ + " {\"type\": \"plant\", \"description\": \"brown, thin, could be curved; could have leaves\", \"similar objects\": [\"twigs\", \"sticks\", \"branches\"]}", + 12 + ], + "water spots": [ + " {\"type\": \"stain\", \"description\": \"circular; could be caused by water droplets; could be found on walls, windows, and other surfaces\", \"similar objects\": [\"dirt spots\", \"grease spots\", \"mold spots\"]}", + 12 + ], + "wake boat": [ + " {\"type\": \"watercraft\", \"description\": \"long; has a tower; could have a ski pylon; could have a ballast system\", \"similar objects\": [\"speedboat\", \"yacht\", \"canoe\"]}", + 12 + ], + "orange lines": [ + " {\"type\": \"road markings\", \"description\": \"orange lines on the road; could be used to indicate a lane change or a no-passing zone\", \"similar objects\": [\"yellow lines\", \"white lines\", \"dashed lines\"]}", + 12 + ], + "cathedral": [ + " {\"type\": \"building\", \"description\": \"large, tall, has a spire; could have stained glass windows\", \"similar objects\": [\"church\", \"mosque\", \"temple\"]}", + 12 + ], + "pajama pants": [ + " {\"type\": \"clothing\", \"description\": \"loose-fitting trousers; could be made of cotton; could have an elastic waistband\", \"similar objects\": [\"yoga pants\", \"sweatpants\", \"jeans\"]}", + 12 + ], + "silver train cars": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; could be connected together; could be silver in color\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 12 + ], + "shadow toilet": [ + " {\"type\": \"plumbing fixture\", \"description\": \"elongated bowl; has a lid; could be wall-mounted\", \"similar objects\": [\"bidet\", \"urinal\", \"sink\"]}", + 12 + ], + "grey metal post": [ + " {\"type\": \"structure\", \"description\": \"grey; cylindrical; made of metal; could be used as a fence post\", \"similar objects\": [\"fence\", \"gate\", \"barrier\"]}", + 12 + ], + "wood rail": [ + " {\"type\": \"building material\", \"description\": \"long, thin, and rectangular; could be used for fencing or support\", \"similar objects\": [\"metal rail\", \"wood beam\", \"wood plank\"]}", + 12 + ], + "house boat": [ + " {\"type\": \"vessel\", \"description\": \"floating house; could be made of wood; could have a motor\", \"similar objects\": [\"yacht\", \"sailboat\", \"canoe\"]}", + 12 + ], + "turkeys": [ + " {\"type\": \"animal\", \"description\": \"large, brown, have a long neck; could have a red head; could have feathers\", \"similar objects\": [\"chickens\", \"ducks\", \"geese\"]}", + 12 + ], + "dark bag": [ + " {\"type\": \"accessory\", \"description\": \"black; could be made of leather; could be used to store items\", \"similar objects\": [\"backpack\", \"purse\", \"wallet\"]}", + 12 + ], + "nail finger": [ + " {\"type\": \"body part\", \"description\": \"hard, pointed, and curved; could be used to scratch surfaces\", \"similar objects\": [\"toenail\", \"claw\", \"thumb\"]}", + 12 + ], + "cliff face": [ + " {\"type\": \"geological formation\", \"description\": \"steep rock face; could be made of sandstone, limestone, or granite; could have a variety of shapes and sizes\", \"similar objects\": [\"mountain\", \"cave\", \"valley\"]}", + 12 + ], + "grassy meadow": [ + " {\"type\": \"landscape\", \"description\": \"green; could have wildflowers; could have trees; could have a stream\", \"similar objects\": [\"forest\", \"desert\", \"mountain\"]}", + 12 + ], + "paw print": [ + " {\"type\": \"animal track\", \"description\": \"oval-shaped; could have four or five toes; could be from a variety of animals\", \"similar objects\": [\"hoof print\", \"bird footprint\", \"snake track\"]}", + 12 + ], + "iron railing": [ + " {\"type\": \"building material\", \"description\": \"metal; could be used as a fence; could be curved or straight\", \"similar objects\": [\"wooden railing\", \"chain link fence\", \"brick wall\"]}", + 12 + ], + "gas cap": [ + " {\"type\": \"automotive part\", \"description\": \"round; could be made of metal; used to cover the fuel tank\", \"similar objects\": [\"oil cap\", \"radiator cap\", \"air filter\"]}", + 12 + ], + "tvs": [ + " {\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to the internet; could be used to watch movies\", \"similar objects\": [\"computer\", \"smartphone\", \"tablet\"]}", + 12 + ], + "communications": [ + " {\"type\": \"technology\", \"description\": \"the exchange of information between two or more entities\", \"similar objects\": [\"telecommunications\", \"networking\", \"data transmission\"]}", + 12 + ], + "gray building": [ + "\n{\"type\": \"structure\", \"description\": \"gray; could have multiple stories; could have windows and doors\", \"similar objects\": [\"house\", \"apartment\", \"office building\"]}", + 12 + ], + "skii": [ + " {\"type\": \"sport equipment\", \"description\": \"long, thin, has two edges; could be used for skiing\", \"similar objects\": [\"snowboard\", \"skateboard\", \"surfboard\"]}", + 12 + ], + "male baseball player": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing a baseball uniform; holding a baseball bat; wearing a baseball cap\", \"similar objects\": [\"female baseball player\", \"soccer player\", \"basketball player\"]}", + 12 + ], + "orange barrier": [ + " {\"type\": \"safety tool\", \"description\": \"orange; could be made of plastic; could be used to block off an area\", \"similar objects\": [\"fence\", \"barricade\", \"guardrail\"]}", + 12 + ], + "roman numbers": [ + " {\"type\": \"number system\", \"description\": \"numerical system used in ancient Rome; uses combinations of letters to represent numbers\", \"similar objects\": [\"Arabic numbers\", \"Hindu-Arabic numbers\", \"Greek numerals\"]}", + 12 + ], + "wave surfer": [ + " {\"type\": \"sports equipment\", \"description\": \"long board; could have a fin; could be used to ride on waves\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 12 + ], + "tigers": [ + " {\"type\": \"animal\", \"description\": \"orange with black stripes; has a long tail; could be found in the wild\", \"similar objects\": [\"lions\", \"leopards\", \"jaguars\"]}", + 12 + ], + "airplane wings": [ + " {\"type\": \"aircraft part\", \"description\": \"long, thin, curved; could be made of metal; could have engines attached\", \"similar objects\": [\"fuselage\", \"tail\", \"cockpit\"]}", + 12 + ], + "disco ball": [ + " {\"type\": \"decoration\", \"description\": \"round; made of mirrors; reflects light\", \"similar objects\": [\"chandelier\", \"string lights\", \"hanging decorations\"]}", + 12 + ], + "light reflection": [ + " {\"type\": \"optical phenomenon\", \"description\": \"the bouncing of light off a surface; could be seen as a reflection in a mirror\", \"similar objects\": [\"refraction\", \"diffraction\", \"polarization\"]}", + 12 + ], + "conveyor": [ + " {\"type\": \"transportation tool\", \"description\": \"long, continuous belt; could be used to transport items\", \"similar objects\": [\"escalator\", \"elevator\", \"roller coaster\"]}", + 12 + ], + "tall windows": [ + " {\"type\": \"architectural feature\", \"description\": \"long and narrow; could be made of glass; could be opened\", \"similar objects\": [\"doors\", \"shutters\", \"balcony\"]}", + 11 + ], + "giant clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"large; could have a pendulum; could have roman numerals\", \"similar objects\": [\"grandfather clock\", \"wall clock\", \"alarm clock\"]}", + 11 + ], + "cabinet wall": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular; could have shelves and drawers; could be made of wood or metal\", \"similar objects\": [\"bookshelf\", \"armoire\", \"dresser\"]}", + 11 + ], + "pizza slicer": [ + " {\"type\": \"kitchen tool\", \"description\": \"has a handle; has a round blade; could be used to cut pizza into slices\", \"similar objects\": [\"cheese grater\", \"spatula\", \"whisk\"]}", + 11 + ], + "steel knife": [ + " {\"type\": \"kitchen tool\", \"description\": \"long; made of steel; could be sharp\", \"similar objects\": [\"fork\", \"spoon\", \"chopping board\"]}", + 11 + ], + "cockpit area": [ + " {\"type\": \"aircraft area\", \"description\": \"area in the front of the aircraft; contains the controls and instruments for the pilot; could have multiple seats\", \"similar objects\": [\"cabin\", \"galley\", \"lavatory\"]}", + 11 + ], + "color television": [ + " {\"type\": \"electronic device\", \"description\": \"has a screen; could be connected to a cable box; could have multiple inputs\", \"similar objects\": [\"computer monitor\", \"projector\", \"DVD player\"]}", + 11 + ], + "brown roof": [ + " {\"type\": \"building material\", \"description\": \"made of tiles; could be made of wood; could be used to cover a house\", \"similar objects\": [\"shingles\", \"asphalt\", \"metal roof\"]}", + 11 + ], + "shower floor": [ + " {\"type\": \"bathroom fixture\", \"description\": \"slippery; could be made of tiles; could be curved\", \"similar objects\": [\"bathtub\", \"sink\", \"toilet\"]}", + 11 + ], + "price tags": [ + " {\"type\": \"labeling tool\", \"description\": \"small, rectangular; could be made of paper or plastic; could have a string attached\", \"similar objects\": [\"stickers\", \"labels\", \"tags\"]}", + 11 + ], + "half wall": [ + " {\"type\": \"building structure\", \"description\": \"vertical structure; could be made of wood or stone; could be used to separate two spaces\", \"similar objects\": [\"partition wall\", \"room divider\", \"screen\"]}", + 11 + ], + "dull": [ + " {\"type\": \"adjective\", \"description\": \"lacking in sharpness; not bright or clear; not stimulating\", \"similar objects\": [\"boring\", \"tedious\", \"monotonous\"]}", + 11 + ], + "slaw": [ + " {\"type\": \"food\", \"description\": \"shredded cabbage and other vegetables; could be served with dressing\", \"similar objects\": [\"salad\", \"coleslaw\", \"potato salad\"]}", + 11 + ], + "orange tray": [ + "\n{\"type\": \"serving tool\", \"description\": \"round; could be made of plastic; could be orange in color\", \"similar objects\": [\"plate\", \"bowl\", \"cup\"]}", + 11 + ], + "plastic garbage": [ + " {\"type\": \"waste\", \"description\": \"non-biodegradable; could be in different shapes and sizes; could be colored\", \"similar objects\": [\"glass waste\", \"metal waste\", \"paper waste\"]}", + 11 + ], + "rusty train tracks": [ + "\n{\"type\": \"transportation infrastructure\", \"description\": \"metal tracks; could be rusty; could be curved or straight\", \"similar objects\": [\"railway\", \"highway\", \"bridge\"]}", + 11 + ], + "wooden chairs": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could have four legs; could have armrests; could have a backrest\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 11 + ], + "video game console": [ + " {\"type\": \"electronic device\", \"description\": \"has a controller; could be connected to a TV; could be used to play video games\", \"similar objects\": [\"computer\", \"smartphone\", \"tablet\"]}", + 11 + ], + "bottle opener": [ + " {\"type\": \"tool\", \"description\": \"small; could be made of metal; used to open bottles\", \"similar objects\": [\"can opener\", \"screwdriver\", \"hammer\"]}", + 11 + ], + "lunch bag": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be insulated; could have a handle\", \"similar objects\": [\"backpack\", \"tote bag\", \"cooler\"]}", + 11 + ], + "tall table lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"tall; could have a base; could have a shade; could have a switch\", \"similar objects\": [\"floor lamp\", \"desk lamp\", \"ceiling light\"]}", + 11 + ], + "keyboard keys": [ + " {\"type\": \"computer accessory\", \"description\": \"rectangular; could be labeled with letters, numbers, and symbols; could be arranged in rows and columns\", \"similar objects\": [\"mouse\", \"trackpad\", \"joystick\"]}", + 11 + ], + "pizza crumbs": [ + " {\"type\": \"food\", \"description\": \"small, round, crunchy pieces; could be yellowish or brownish; could be sprinkled on top of pizza\", \"similar objects\": [\"bread crumbs\", \"cookie crumbs\", \"cracker crumbs\"]}", + 11 + ], + "messy bed": [ + " {\"type\": \"furniture\", \"description\": \"unmade bed; sheets and blankets are not in place; pillows are scattered\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}", + 11 + ], + "grown": [ + " {\"type\": \"verb\", \"description\": \"past tense of grow; increase in size or number\", \"similar objects\": [\"expand\", \"develop\", \"increase\"]}", + 11 + ], + "advertisement poster": [ + "\n{\"type\": \"promotional material\", \"description\": \"printed paper; could be colorful; could have text and images\", \"similar objects\": [\"flyer\", \"banner\", \"billboard\"]}", + 11 + ], + "oats": [ + " {\"type\": \"cereal\", \"description\": \"small, round, light-brown; could be cooked as porridge\", \"similar objects\": [\"barley\", \"wheat\", \"rye\"]}", + 11 + ], + "food scale": [ + " {\"type\": \"measuring tool\", \"description\": \"has a digital display; could measure weight in grams, ounces, or pounds; could be used to measure food\", \"similar objects\": [\"thermometer\", \"measuring cup\", \"measuring spoon\"]}", + 11 + ], + "hairy ear": [ + " {\"type\": \"body part\", \"description\": \"has a lot of hair; could be found on the outer part of the ear\", \"similar objects\": [\"eyebrow\", \"armpit\", \"nose hair\"]}", + 11 + ], + "wicker table": [ + " {\"type\": \"furniture\", \"description\": \"made of woven materials; could be round or rectangular; could have a glass top\", \"similar objects\": [\"chair\", \"sofa\", \"bench\"]}", + 11 + ], + "husband": [ + " {\"type\": \"person\", \"description\": \"male; married to a wife; could have children\", \"similar objects\": [\"father\", \"brother\", \"uncle\"]}", + 11 + ], + "pointy top": [ + " {\"type\": \"clothing item\", \"description\": \"has a pointed top; could be a shirt, a dress, or a blouse\", \"similar objects\": [\"tunic\", \"blouse\", \"shirt\"]}", + 11 + ], + "apple computer logo": [ + "\n{\"type\": \"logo\", \"description\": \"silver apple with a bite taken out of it; could be surrounded by a rainbow\", \"similar objects\": [\"Microsoft logo\", \"Google logo\", \"Adobe logo\"]}", + 11 + ], + "gray shoes": [ + " {\"type\": \"footwear\", \"description\": \"gray; could be made of leather; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 11 + ], + "cake platter": [ + " {\"type\": \"serving tool\", \"description\": \"flat, round; could be made of metal or glass; could have a handle\", \"similar objects\": [\"plate\", \"tray\", \"bowl\"]}", + 11 + ], + "wood shelves": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could be used to store items; could be wall-mounted\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"wardrobe\"]}", + 11 + ], + "left horn": [ + " {\"type\": \"musical instrument\", \"description\": \"curved; could be made of brass; could be used to produce sound\", \"similar objects\": [\"trumpet\", \"trombone\", \"clarinet\"]}", + 11 + ], + "potatoe": [ + " {\"type\": \"vegetable\", \"description\": \"round; could be brown, yellow, or white; could be sliced into pieces; could have green leaves\", \"similar objects\": [\"carrot\", \"onion\", \"sweet potato\"]}", + 11 + ], + "trellis": [ + " {\"type\": \"garden structure\", \"description\": \"wooden or metal frame; could be used to support plants\", \"similar objects\": [\"arbor\", \"pergola\", \"fence\"]}", + 11 + ], + "chili dog": [ + " {\"type\": \"food\", \"description\": \"hot dog with chili sauce; could be served with mustard and onions\", \"similar objects\": [\"hamburger\", \"taco\", \"fries\"]}", + 11 + ], + "pickle slices": [ + " {\"type\": \"food\", \"description\": \"sliced cucumbers; could be sour or sweet; could be served as a side dish\", \"similar objects\": [\"olives\", \"capers\", \"pickled peppers\"]}", + 11 + ], + "wet rocks": [ + " {\"type\": \"natural object\", \"description\": \"smooth; could be slippery; could be of different colors\", \"similar objects\": [\"stones\", \"pebbles\", \"boulders\"]}", + 11 + ], + "bike basket": [ + " {\"type\": \"bicycle accessory\", \"description\": \"attached to the front or back of a bicycle; could be made of metal or plastic; could be used to store items\", \"similar objects\": [\"bike rack\", \"bike bell\", \"bike lock\"]}", + 11 + ], + "auto": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could be powered by gasoline or electricity; could have a steering wheel\", \"similar objects\": [\"car\", \"truck\", \"motorcycle\"]}", + 11 + ], + "hoof cow": [ + " {\"type\": \"animal body part\", \"description\": \"hard, curved, and pointed; found on the feet of cows\", \"similar objects\": [\"hoof horse\", \"hoof sheep\", \"hoof goat\"]}", + 11 + ], + "rafters": [ + " {\"type\": \"building material\", \"description\": \"long, wooden beams; used to support the roof of a building\", \"similar objects\": [\"joists\", \"studs\", \"trusses\"]}", + 11 + ], + "cake frosting": [ + " {\"type\": \"baking ingredient\", \"description\": \"sweet; creamy; could be used to decorate cakes\", \"similar objects\": [\"icing\", \"whipped cream\", \"ganache\"]}", + 11 + ], + "girls shirt": [ + " {\"type\": \"clothing\", \"description\": \"could be short or long sleeve; could have a collar; could have buttons or zipper; could have prints or patterns\", \"similar objects\": [\"dress\", \"blouse\", \"tank top\"]}", + 11 + ], + "headdress": [ + " {\"type\": \"accessory\", \"description\": \"worn on the head; could be made of feathers, beads, or other materials\", \"similar objects\": [\"hat\", \"crown\", \"tiara\"]}", + 11 + ], + "mud flaps": [ + " {\"type\": \"automotive accessory\", \"description\": \"attached to the rear of a vehicle; made of rubber or plastic; designed to protect the vehicle from mud and debris\", \"similar objects\": [\"spoiler\", \"fender flares\", \"running boards\"]}", + 11 + ], + "hand thumb": [ + " {\"type\": \"body part\", \"description\": \"five fingers; could be used for gripping\", \"similar objects\": [\"fingers\", \"palm\", \"wrist\"]}", + 11 + ], + "tan fur": [ + " {\"type\": \"fabric\", \"description\": \"light brown; soft; could be used for clothing\", \"similar objects\": [\"wool\", \"cashmere\", \"cotton\"]}", + 11 + ], + "paper menu": [ + " {\"type\": \"document\", \"description\": \"printed on paper; could have pictures and descriptions of food items\", \"similar objects\": [\"brochure\", \"flyer\", \"catalog\"]}", + 11 + ], + "handle pot": [ + " {\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 11 + ], + "purple leaves": [ + " {\"type\": \"plant\", \"description\": \"dark purple; could be oval or round; could have veins\", \"similar objects\": [\"green leaves\", \"red leaves\", \"yellow leaves\"]}", + 11 + ], + "beige car": [ + "\n{\"type\": \"vehicle\", \"description\": \"beige color; could have four doors; could have a trunk\", \"similar objects\": [\"sedan\", \"SUV\", \"truck\"]}", + 11 + ], + "vacuum cleaner": [ + " {\"type\": \"cleaning tool\", \"description\": \"long, cylindrical; has a hose and a motor; could be handheld or upright\", \"similar objects\": [\"mop\", \"broom\", \"duster\"]}", + 11 + ], + "slim": [ + " {\"type\": \"adjective\", \"description\": \"thin; slender; narrow\", \"similar objects\": [\"skinny\", \"lean\", \"slender\"]}", + 11 + ], + "horse standing": [ + "\n{\"type\": \"animal\", \"description\": \"large mammal; four legs; long mane; could be standing or running\", \"similar objects\": [\"zebra\", \"donkey\", \"camel\"]}", + 11 + ], + "steel railroad tracks": [ + "\n{\"type\": \"transportation infrastructure\", \"description\": \"long, straight, made of steel; could have wooden sleepers; could have rail ties\", \"similar objects\": [\"highway\", \"bridge\", \"tunnel\"]}", + 11 + ], + "gold number": [ + " {\"type\": \"decorative item\", \"description\": \"shiny, metallic, could be in the shape of a number\", \"similar objects\": [\"silver number\", \"plaque\", \"trophy\"]}", + 11 + ], + "horse nose": [ + " {\"type\": \"animal body part\", \"description\": \"long, flexible; could be black or brown; could be wet\", \"similar objects\": [\"elephant trunk\", \"dog nose\", \"cat nose\"]}", + 11 + ], + "silver trashcan": [ + "\n{\"type\": \"container\", \"description\": \"silver; cylindrical; has a lid; could be used for garbage disposal\", \"similar objects\": [\"bin\", \"bucket\", \"box\"]}", + 11 + ], + "glass shower doors": [ + "\n{\"type\": \"bathroom fixture\", \"description\": \"transparent; could be framed or frameless; could be sliding or hinged\", \"similar objects\": [\"shower curtains\", \"bathtub\", \"bathroom sink\"]}", + 11 + ], + "bento box": [ + " {\"type\": \"food container\", \"description\": \"rectangular; could be made of wood or plastic; could have several compartments\", \"similar objects\": [\"lunch box\", \"picnic basket\", \"thermos\"]}", + 11 + ], + "glass candle holder": [ + "\n{\"type\": \"decorative item\", \"description\": \"transparent; could be made of glass or crystal; could hold a candle\", \"similar objects\": [\"vase\", \"bowl\", \"tealight holder\"]}", + 11 + ], + "camouflage": [ + " {\"type\": \"pattern\", \"description\": \"disruptive coloration; used to hide an object from view\", \"similar objects\": [\"mimicry\", \"disruptive coloration\", \"counter-shading\"]}", + 11 + ], + "metal trashcan": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of metal; has a lid\", \"similar objects\": [\"plastic trashcan\", \"recycling bin\", \"garbage can\"]}", + 11 + ], + "floor surface": [ + " {\"type\": \"flooring material\", \"description\": \"could be made of wood, tile, carpet, vinyl, laminate, etc.\", \"similar objects\": [\"wall surface\", \"ceiling surface\", \"countertop surface\"]}", + 11 + ], + "raquet": [ + " {\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be used to hit a ball\", \"similar objects\": [\"tennis raquet\", \"badminton raquet\", \"squash raquet\"]}", + 11 + ], + "car windshield": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; could be curved; could be made of glass\", \"similar objects\": [\"car window\", \"car mirror\", \"car bumper\"]}", + 11 + ], + "weiner": [ + " {\"type\": \"food\", \"description\": \"long, thin, red; could be served in a bun\", \"similar objects\": [\"hot dog\", \"sausage\", \"bratwurst\"]}", + 11 + ], + "silver foil": [ + " {\"type\": \"material\", \"description\": \"thin, shiny, metallic; could be used for wrapping food\", \"similar objects\": [\"aluminum foil\", \"plastic wrap\", \"parchment paper\"]}", + 11 + ], + "concrete surface": [ + " {\"type\": \"building material\", \"description\": \"hard, gray, rough; could be used for roads and sidewalks\", \"similar objects\": [\"asphalt\", \"brick\", \"stone\"]}", + 11 + ], + "round glass table": [ + "\n{\"type\": \"furniture\", \"description\": \"round; made of glass; could have metal legs\", \"similar objects\": [\"coffee table\", \"dining table\", \"end table\"]}", + 11 + ], + "paw pads": [ + " {\"type\": \"animal body part\", \"description\": \"soft, cushiony, and thick; could be pink or black; could be found on the bottom of the feet of animals\", \"similar objects\": [\"claws\", \"whiskers\", \"fur\"]}", + 11 + ], + "brick archway": [ + " {\"type\": \"architectural structure\", \"description\": \"made of bricks; could have a curved top; could have a door\", \"similar objects\": [\"stone archway\", \"wooden archway\", \"column\"]}", + 11 + ], + "metal wheels": [ + " {\"type\": \"transportation tool\", \"description\": \"round; made of metal; could be attached to a vehicle\", \"similar objects\": [\"tires\", \"castors\", \"rollers\"]}", + 11 + ], + "pizza server": [ + " {\"type\": \"serving tool\", \"description\": \"flat, round; has a handle; could be made of metal\", \"similar objects\": [\"spatula\", \"tongs\", \"ladle\"]}", + 11 + ], + "porch light": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the wall; could be turned on and off; could be made of metal or plastic\", \"similar objects\": [\"ceiling light\", \"chandelier\", \"wall sconce\"]}", + 11 + ], + "carrot sticks": [ + " {\"type\": \"vegetable\", \"description\": \"long, orange, crunchy; could be sliced into thin pieces; could have green leaves\", \"similar objects\": [\"celery\", \"cucumber\", \"zucchini\"]}", + 11 + ], + "handle bag": [ + " {\"type\": \"accessory\", \"description\": \"long strap; could be made of leather; could be used to carry items\", \"similar objects\": [\"backpack\", \"purse\", \"tote bag\"]}", + 11 + ], + "spools": [ + " {\"type\": \"thread holder\", \"description\": \"cylindrical; could be made of plastic or wood; could have a hole in the middle\", \"similar objects\": [\"bobbins\", \"cones\", \"reels\"]}", + 11 + ], + "documents": [ + " {\"type\": \"paperwork\", \"description\": \"could be printed or digital; could be in the form of letters, contracts, or reports\", \"similar objects\": [\"files\", \"records\", \"forms\"]}", + 11 + ], + "colar": [ + " {\"type\": \"clothing accessory\", \"description\": \"worn around the neck; could be made of metal, plastic, or fabric; could have a clasp or buckle\", \"similar objects\": [\"necklace\", \"tie\", \"scarf\"]}", + 11 + ], + "mixers": [ + " {\"type\": \"kitchen tool\", \"description\": \"electronic; could have multiple attachments; could be used for blending, whisking, and kneading\", \"similar objects\": [\"blender\", \"food processor\", \"juicer\"]}", + 11 + ], + "male spectator": [ + " {\"type\": \"person\", \"description\": \"wearing a hat; could be holding a drink; could be cheering\", \"similar objects\": [\"female spectator\", \"referee\", \"coach\"]}", + 11 + ], + "terraces": [ + " {\"type\": \"architecture\", \"description\": \"flat, open-air spaces; could be built on hillsides; could be used for recreational activities\", \"similar objects\": [\"balcony\", \"patio\", \"deck\"]}", + 11 + ], + "sweatbands": [ + " {\"type\": \"accessory\", \"description\": \"elastic; could be worn on the wrist or head; could be made of cotton or other fabrics\", \"similar objects\": [\"headband\", \"bracelet\", \"hat\"]}", + 11 + ], + "catch": [ + " {\"type\": \"verb\", \"description\": \"to take or seize something; to stop something from happening\", \"similar objects\": [\"grab\", \"seize\", \"capture\"]}", + 11 + ], + "football players": [ + " {\"type\": \"athletes\", \"description\": \"wearing a uniform; running on the field; could have a helmet\", \"similar objects\": [\"basketball players\", \"soccer players\", \"baseball players\"]}", + 11 + ], + "wood bookcase": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; has shelves for books; could have drawers\", \"similar objects\": [\"shelf\", \"cabinet\", \"wardrobe\"]}", + 11 + ], + "light sky": [ + " {\"type\": \"weather condition\", \"description\": \"clear sky with few clouds; could be blue or white\", \"similar objects\": [\"sunny sky\", \"cloudy sky\", \"rainy sky\"]}", + 11 + ], + "street cone": [ + " {\"type\": \"traffic tool\", \"description\": \"orange; cone-shaped; could be reflective\", \"similar objects\": [\"traffic sign\", \"barricade\", \"traffic light\"]}", + 11 + ], + "work van": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have a logo; could have a ladder\", \"similar objects\": [\"truck\", \"pickup truck\", \"SUV\"]}", + 11 + ], + "pen table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have drawers; could have a flat surface\", \"similar objects\": [\"desk\", \"chair\", \"bookshelf\"]}", + 11 + ], + "environment": [ + " {\"type\": \"concept\", \"description\": \"the natural world and its resources, including air, water, land, plants, and animals; the physical, chemical, and biological factors that affect an organism or an ecological community\", \"similar objects\": [\"ecosystem\", \"habitat\", \"climate\"]}", + 11 + ], + "donut plate": [ + " {\"type\": \"serving tool\", \"description\": \"round; could have multiple holes for donuts; could have a handle\", \"similar objects\": [\"cake plate\", \"cupcake plate\", \"cookie plate\"]}", + 11 + ], + "packaging": [ + " {\"type\": \"container\", \"description\": \"used to store and protect goods; could be made of paper, plastic, or metal\", \"similar objects\": [\"box\", \"bag\", \"envelope\"]}", + 11 + ], + "storage cabinet": [ + " {\"type\": \"furniture\", \"description\": \"has drawers and shelves; could be made of wood or metal\", \"similar objects\": [\"bookshelf\", \"dresser\", \"wardrobe\"]}", + 11 + ], + "castles": [ + " {\"type\": \"architecture\", \"description\": \"large, made of stone; could have towers and drawbridges; could have a moat\", \"similar objects\": [\"fortress\", \"palace\", \"manor\"]}", + 11 + ], + "center piece": [ + " {\"type\": \"decoration\", \"description\": \"could be made of flowers, candles, or other materials; could be placed in the middle of a table\", \"similar objects\": [\"vase\", \"ornament\", \"figurine\"]}", + 11 + ], + "treads": [ + " {\"type\": \"footwear\", \"description\": \"rubber; could have a pattern; could be used for running\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 11 + ], + "sunlit": [ + " {\"type\": \"atmospheric phenomenon\", \"description\": \"bright, sunny, warm; could be accompanied by blue sky and white clouds\", \"similar objects\": [\"clear sky\", \"rainy day\", \"overcast\"]}", + 11 + ], + "orange cover": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of fabric; could be used to cover objects\", \"similar objects\": [\"bag\", \"sleeve\", \"case\"]}", + 11 + ], + "horse ear": [ + " {\"type\": \"body part\", \"description\": \"long, pointed, could be covered with hair\", \"similar objects\": [\"dog ear\", \"cat ear\", \"cow ear\"]}", + 11 + ], + "soccer game": [ + " {\"type\": \"sport\", \"description\": \"team sport; two teams of 11 players; played on a rectangular field; goal is to score by kicking the ball into the other team's goal\", \"similar objects\": [\"football\", \"basketball\", \"baseball\"]}", + 11 + ], + "pink table cloth": [ + "\n{\"type\": \"tableware\", \"description\": \"pink; rectangular; could be made of fabric\", \"similar objects\": [\"table runner\", \"placemat\", \"napkin\"]}", + 11 + ], + "safety bar": [ + " {\"type\": \"safety tool\", \"description\": \"long, metal bar; could be used to secure a door or window\", \"similar objects\": [\"lock\", \"chain\", \"padlock\"]}", + 11 + ], + "snow gloves": [ + " {\"type\": \"clothing\", \"description\": \"warm; could be waterproof; could be fingerless\", \"similar objects\": [\"mittens\", \"scarf\", \"hat\"]}", + 11 + ], + "space heater": [ + " {\"type\": \"heating tool\", \"description\": \"could be electric or gas powered; could have a fan; could have a thermostat\", \"similar objects\": [\"radiator\", \"fireplace\", \"air conditioner\"]}", + 11 + ], + "litter box": [ + " {\"type\": \"pet accessory\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"cat bed\", \"cat tree\", \"cat scratching post\"]}", + 11 + ], + "ginger": [ + " {\"type\": \"spice\", \"description\": \"brown, knobby root; has a strong aroma; could be grated or chopped\", \"similar objects\": [\"garlic\", \"turmeric\", \"cumin\"]}", + 11 + ], + "brick patio": [ + " {\"type\": \"outdoor structure\", \"description\": \"made of bricks; could be used as a walkway or a seating area\", \"similar objects\": [\"stone patio\", \"wooden deck\", \"concrete patio\"]}", + 11 + ], + "grey walkway": [ + " {\"type\": \"structure\", \"description\": \"concrete; could have a pattern; could be used for walking\", \"similar objects\": [\"sidewalk\", \"pathway\", \"driveway\"]}", + 11 + ], + "icicles": [ + " {\"type\": \"weather phenomenon\", \"description\": \"long, thin, pointed; could be hanging from a roof\", \"similar objects\": [\"hailstones\", \"snowflakes\", \"frost\"]}", + 11 + ], + "pine cones": [ + " {\"type\": \"plant part\", \"description\": \"brown; could be spiky; could be used as decorations\", \"similar objects\": [\"acorns\", \"nuts\", \"seeds\"]}", + 11 + ], + "kneecap": [ + " {\"type\": \"body part\", \"description\": \"round; located at the lower part of the leg; could be injured\", \"similar objects\": [\"elbow\", \"ankle\", \"shoulder\"]}", + 11 + ], + "silver tip": [ + " {\"type\": \"pen\", \"description\": \"has a silver tip; could be used for writing\", \"similar objects\": [\"ballpoint pen\", \"marker\", \"fountain pen\"]}", + 11 + ], + "metal mirror": [ + " {\"type\": \"reflective tool\", \"description\": \"made of metal; could be round or rectangular; could have a frame\", \"similar objects\": [\"glass mirror\", \"window\", \"sunglasses\"]}", + 11 + ], + "ink pens": [ + " {\"type\": \"writing tool\", \"description\": \"long, thin, cylindrical; could have a cap; could be refillable\", \"similar objects\": [\"pencils\", \"markers\", \"highlighters\"]}", + 11 + ], + "wood clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"made of wood; could have a pendulum; could have a face with numbers\", \"similar objects\": [\"grandfather clock\", \"alarm clock\", \"cuckoo clock\"]}", + 11 + ], + "blue scarf": [ + " {\"type\": \"clothing item\", \"description\": \"long, thin, could be made of wool; could be in different colors\", \"similar objects\": [\"shawl\", \"wrap\", \"stole\"]}", + 11 + ], + "lasso": [ + " {\"type\": \"tool\", \"description\": \"long rope; used to capture animals\", \"similar objects\": [\"halter\", \"rein\", \"lead rope\"]}", + 11 + ], + "pink flag": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; has a pink color; could be used for celebration\", \"similar objects\": [\"banner\", \"bunting\", \"streamer\"]}", + 11 + ], + "steel plate": [ + " {\"type\": \"material\", \"description\": \"hard, gray, flat; could be used for construction\", \"similar objects\": [\"aluminum plate\", \"iron plate\", \"wooden board\"]}", + 11 + ], + "teammate": [ + " {\"type\": \"person\", \"description\": \"someone who works together with you to achieve a common goal\", \"similar objects\": [\"colleague\", \"partner\", \"associate\"]}", + 11 + ], + "breakfast foods": [ + "\n{\"type\": \"food\", \"description\": \"foods eaten in the morning; could include eggs, toast, cereal, oatmeal, pancakes, etc.\", \"similar objects\": [\"lunch foods\", \"dinner foods\", \"snack foods\"]}", + 11 + ], + "taller giraffe": [ + "\n{\"type\": \"animal\", \"description\": \"long neck; black and white patches; long legs; could have a tuft of fur on the top of its head\", \"similar objects\": [\"horse\", \"elephant\", \"camel\"]}", + 11 + ], + "camera icon": [ + " {\"type\": \"icon\", \"description\": \"round; has a lens; could be in black and white\", \"similar objects\": [\"video camera icon\", \"microphone icon\", \"television icon\"]}", + 11 + ], + "sharpie marker": [ + " {\"type\": \"writing tool\", \"description\": \"black, cylindrical; could be used to write on paper or other surfaces\", \"similar objects\": [\"pen\", \"pencil\", \"highlighter\"]}", + 11 + ], + "desk phone": [ + " {\"type\": \"communication device\", \"description\": \"has a handset; could have a dial pad; could have a cord\", \"similar objects\": [\"cell phone\", \"landline phone\", \"smartphone\"]}", + 11 + ], + "metal safety": [ + " {\"type\": \"protective gear\", \"description\": \"made of metal; could be used to protect from fire, heat, or radiation; could be used in construction sites\", \"similar objects\": [\"helmet\", \"goggles\", \"gloves\"]}", + 11 + ], + "grey dirt": [ + " {\"type\": \"soil\", \"description\": \"dark grey; could be moist; could be used for gardening\", \"similar objects\": [\"clay\", \"sand\", \"peat\"]}", + 11 + ], + "dirt spot": [ + " {\"type\": \"stain\", \"description\": \"dark, round, could be on a surface\", \"similar objects\": [\"grease spot\", \"ink stain\", \"blood stain\"]}", + 11 + ], + "ceiling vent": [ + " {\"type\": \"ventilation tool\", \"description\": \"rectangular; could be made of metal; could be installed on the ceiling\", \"similar objects\": [\"air conditioner\", \"exhaust fan\", \"heater\"]}", + 11 + ], + "plastic fence": [ + " {\"type\": \"fencing material\", \"description\": \"transparent; could be used to separate areas; could be used to protect plants\", \"similar objects\": [\"wood fence\", \"metal fence\", \"bamboo fence\"]}", + 11 + ], + "train tunnel": [ + " {\"type\": \"structure\", \"description\": \"long, dark, could have two entrances; could have a railway track inside\", \"similar objects\": [\"bridge\", \"tunnel\", \"underpass\"]}", + 11 + ], + "tv camera": [ + " {\"type\": \"recording device\", \"description\": \"long and thin; could be handheld; could be mounted on a tripod\", \"similar objects\": [\"video camera\", \"webcam\", \"action camera\"]}", + 11 + ], + "stainless steel sink faucet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"silver; has a handle; could be connected to a sink\", \"similar objects\": [\"bathroom faucet\", \"kitchen faucet\", \"shower head\"]}", + 11 + ], + "metal hose": [ + " {\"type\": \"tool\", \"description\": \"flexible; made of metal; could be used for water or air\", \"similar objects\": [\"pipe\", \"tube\", \"hose\"]}", + 11 + ], + "orange headlight": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; orange in color; could be used for headlights\", \"similar objects\": [\"yellow headlight\", \"white headlight\", \"blue headlight\"]}", + 11 + ], + "lone sheep": [ + " {\"type\": \"animal\", \"description\": \"white; has a long tail; could have horns; could be seen in a group\", \"similar objects\": [\"goat\", \"cow\", \"pig\"]}", + 11 + ], + "wooden stick": [ + " {\"type\": \"tool\", \"description\": \"long, cylindrical, made of wood\", \"similar objects\": [\"broom\", \"hammer\", \"axe\"]}", + 11 + ], + "dog tags": [ + " {\"type\": \"accessory\", \"description\": \"metal tags; could be engraved with names and other information; could be worn on a chain\", \"similar objects\": [\"keychain\", \"necklace\", \"bracelet\"]}", + 11 + ], + "clcok": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has hands; could have a digital display\", \"similar objects\": [\"watch\", \"alarm clock\", \"timer\"]}", + 11 + ], + "headlamps": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the front of a vehicle; could be used to light up the road\", \"similar objects\": [\"taillights\", \"fog lights\", \"spotlights\"]}", + 11 + ], + "gym bag": [ + " {\"type\": \"bag\", \"description\": \"large; could have multiple compartments; could be made of nylon\", \"similar objects\": [\"duffel bag\", \"backpack\", \"tote bag\"]}", + 11 + ], + "flower basket": [ + " {\"type\": \"container\", \"description\": \"round; could be made of wicker; could be filled with flowers\", \"similar objects\": [\"vase\", \"pot\", \"urn\"]}", + 11 + ], + "siren": [ + " {\"type\": \"alarm device\", \"description\": \"high-pitched sound; could be used for emergency\", \"similar objects\": [\"bell\", \"whistle\", \"horn\"]}", + 11 + ], + "computer tablet": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat, touchscreen, portable\", \"similar objects\": [\"laptop\", \"smartphone\", \"e-reader\"]}", + 11 + ], + "smear": [ + " {\"type\": \"action\", \"description\": \"to spread a substance over a surface; could be done with a brush or a cloth\", \"similar objects\": [\"dab\", \"smudge\", \"blot\"]}", + 11 + ], + "oval table": [ + " {\"type\": \"furniture\", \"description\": \"oval-shaped; could have four legs; could be made of wood or metal\", \"similar objects\": [\"rectangular table\", \"dining table\", \"coffee table\"]}", + 11 + ], + "rock pile": [ + " {\"type\": \"landscape\", \"description\": \"collection of rocks; could be of different sizes and shapes; could be found in nature or man-made\", \"similar objects\": [\"stone wall\", \"gravel\", \"boulder\"]}", + 11 + ], + "womans hair": [ + " {\"type\": \"body part\", \"description\": \"long, could be straight or curly; could be of different colors\", \"similar objects\": [\"eyebrows\", \"eyelashes\", \"beard\"]}", + 11 + ], + "cell phone case": [ + " {\"type\": \"accessory\", \"description\": \"protective cover for a cell phone; could be made of plastic, rubber, or leather; could come in various colors and designs\", \"similar objects\": [\"phone charger\", \"phone stand\", \"phone holder\"]}", + 11 + ], + "scotch tape": [ + " {\"type\": \"adhesive tool\", \"description\": \"transparent; could be used to stick papers together\", \"similar objects\": [\"glue\", \"double-sided tape\", \"velcro\"]}", + 11 + ], + "ski gloves": [ + " {\"type\": \"clothing accessory\", \"description\": \"thick, warm, waterproof; could have straps; could have a pocket\", \"similar objects\": [\"mittens\", \"scarf\", \"hat\"]}", + 11 + ], + "conductors": [ + " {\"type\": \"occupation\", \"description\": \"leads an orchestra or choir; could be a music teacher\", \"similar objects\": [\"musician\", \"composer\", \"singer\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant", + 11 + ], + "knack": [ + " {\"type\": \"skill\", \"description\": \"ability to do something well; could be acquired through practice\", \"similar objects\": [\"talent\", \"aptitude\", \"proficiency\"]}", + 11 + ], + "bathroom stalls": [ + " {\"type\": \"furniture\", \"description\": \"enclosed; could have a door; could have a lock\", \"similar objects\": [\"lockers\", \"cubicles\", \"changing rooms\"]}", + 11 + ], + "marketplace": [ + " {\"type\": \"location\", \"description\": \"a place where people buy and sell goods and services\", \"similar objects\": [\"market\", \"bazaar\", \"shopping mall\"]}", + 11 + ], + "plate pizza": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; could be topped with cheese, vegetables, and meat\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread pizza\"]}", + 11 + ], + "aerosol": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could be used to store liquids or gases\", \"similar objects\": [\"spray can\", \"bottle\", \"jar\"]}", + 11 + ], + "brass lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"made of brass; could have a round or cylindrical shape; could have a handle\", \"similar objects\": [\"lantern\", \"lamp\", \"candle\"]}", + 11 + ], + "dressers": [ + " {\"type\": \"furniture\", \"description\": \"tall, rectangular, has drawers\", \"similar objects\": [\"chest of drawers\", \"wardrobe\", \"armoire\"]}", + 11 + ], + "sky scrapers": [ + "\n{\"type\": \"building\", \"description\": \"tall, multi-story buildings; could be made of steel and glass; could have multiple elevators\", \"similar objects\": [\"office buildings\", \"apartment buildings\", \"condominiums\"]}", + 11 + ], + "strollers": [ + " {\"type\": \"baby transport tool\", \"description\": \"wheeled; could be folded; could be pushed by adults\", \"similar objects\": [\"car seat\", \"high chair\", \"baby carrier\"]}", + 11 + ], + "pointy beak": [ + " {\"type\": \"bird feature\", \"description\": \"sharp, long beak; could be curved; could be used for pecking\", \"similar objects\": [\"curved beak\", \"hooked beak\", \"flat beak\"]}", + 11 + ], + "end tables": [ + " {\"type\": \"furniture\", \"description\": \"small, rectangular, has legs; could be used to place items on top\", \"similar objects\": [\"coffee table\", \"nightstand\", \"side table\"]}", + 11 + ], + "house roof": [ + " {\"type\": \"structure\", \"description\": \"sloped; could be made of tiles, shingles, or metal; could have a chimney\", \"similar objects\": [\"shed roof\", \"gable roof\", \"flat roof\"]}", + 11 + ], + "silver headlight": [ + "\n{\"type\": \"automotive part\", \"description\": \"shiny, metallic; used to provide illumination for vehicles\", \"similar objects\": [\"taillight\", \"fog light\", \"turn signal\"]}", + 11 + ], + "propellar": [ + " {\"type\": \"mechanical tool\", \"description\": \"round; has blades; used to generate thrust\", \"similar objects\": [\"turbine\", \"fan\", \"jet engine\"]}", + 11 + ], + "shaggy": [ + " {\"type\": \"texture\", \"description\": \"long, unkempt, and fluffy\", \"similar objects\": [\"frizzy\", \"curly\", \"wavy\"]}", + 11 + ], + "adult horse": [ + "\n{\"type\": \"animal\", \"description\": \"large; has a long mane; could have white, black, brown, or grey fur; could have four legs\", \"similar objects\": [\"donkey\", \"mule\", \"zebra\"]}", + 11 + ], + "purple sweater": [ + "\n{\"type\": \"clothing\", \"description\": \"long-sleeved; could be made of wool; could have a hood; could have a zipper\", \"similar objects\": [\"jacket\", \"coat\", \"hoodie\"]}", + 11 + ], + "brick street": [ + " {\"type\": \"road surface\", \"description\": \"made of red or grey bricks; could be uneven; could be slippery when wet\", \"similar objects\": [\"cobblestone\", \"asphalt\", \"concrete\"]}", + 11 + ], + "stubby tail": [ + " {\"type\": \"animal feature\", \"description\": \"short, thick tail; could be found in cats, dogs, and other animals\", \"similar objects\": [\"long tail\", \"curly tail\", \"bushy tail\"]}", + 11 + ], + "medium size": [ + "\n{\"type\": \"size\", \"description\": \"somewhere between small and large; could be used to describe objects, clothing, or other items\", \"similar objects\": [\"small\", \"large\", \"extra large\"]}", + 11 + ], + "metal drawer": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of metal; could have multiple drawers\", \"similar objects\": [\"cabinet\", \"dresser\", \"chest of drawers\"]}", + 11 + ], + "computer mouse pad": [ + "\n{\"type\": \"accessory\", \"description\": \"flat, rectangular; could be made of rubber or cloth; used to provide a smooth surface for a computer mouse\", \"similar objects\": [\"keyboard pad\", \"mouse mat\", \"mouse wrist rest\"]}", + 11 + ], + "peices": [ + " {\"type\": \"measurement unit\", \"description\": \"smallest unit of a whole; could be used to measure weight, length, or volume\", \"similar objects\": [\"grams\", \"meters\", \"liters\"]}", + 11 + ], + "batter wears": [ + " {\"type\": \"sports equipment\", \"description\": \"protective gear; could be made of plastic or metal; could have a face guard\", \"similar objects\": [\"helmet\", \"gloves\", \"pads\"]}", + 11 + ], + "dirt pitchers": [ + " {\"type\": \"sports equipment\", \"description\": \"long, thin, made of leather; used to throw a ball\", \"similar objects\": [\"baseball\", \"softball\", \"tennis ball\"]}", + 11 + ], + "trash bins": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic; could have a lid\", \"similar objects\": [\"recycling bins\", \"garbage cans\", \"storage bins\"]}", + 11 + ], + "womens hair": [ + " {\"type\": \"accessory\", \"description\": \"long, could be straight, curly, or wavy; could be dyed in different colors; could be styled in different ways\", \"similar objects\": [\"hat\", \"scarf\", \"sunglasses\"]}", + 11 + ], + "multiple kites": [ + "\n{\"type\": \"toy\", \"description\": \"could be made of paper or plastic; could have a tail; could be flown in the sky\", \"similar objects\": [\"balloon\", \"airplane\", \"parachute\"]}", + 11 + ], + "motorcycle boot": [ + " {\"type\": \"footwear\", \"description\": \"high-top; could be made of leather; could have buckles and straps; could be waterproof\", \"similar objects\": [\"hiking boot\", \"combat boot\", \"work boot\"]}", + 11 + ], + "wick": [ + " {\"type\": \"lighting tool\", \"description\": \"a thin piece of material that is used to draw fuel up to the flame of a candle, lamp, or oil burner\", \"similar objects\": [\"wick holder\", \"wick trimmer\", \"wick snuffer\"]}", + 11 + ], + "casing": [ + " {\"type\": \"protective cover\", \"description\": \"made of metal or plastic; could be used to protect electronic devices\", \"similar objects\": [\"enclosure\", \"shell\", \"housing\"]}", + 11 + ], + "drips": [ + " {\"type\": \"plumbing tool\", \"description\": \"used to control the flow of water; could be made of metal or plastic; could be connected to a faucet\", \"similar objects\": [\"valve\", \"pipe\", \"hose\"]}", + 11 + ], + "cabins": [ + " {\"type\": \"structure\", \"description\": \"wooden; could have a porch; could have multiple rooms\", \"similar objects\": [\"cottage\", \"bungalow\", \"chalet\"]}", + 11 + ], + "blue jet": [ + " {\"type\": \"aircraft\", \"description\": \"large, fast, could have a blue color; could have a tail fin\", \"similar objects\": [\"airplane\", \"helicopter\", \"rocket\"]}", + 11 + ], + "lady tennis player": [ + "\n{\"type\": \"athlete\", \"description\": \"wearing a tennis dress; holding a tennis racket; playing on a tennis court\", \"similar objects\": [\"golfer\", \"soccer player\", \"basketball player\"]}", + 11 + ], + "resort": [ + " {\"type\": \"accommodation\", \"description\": \"a place for leisure and relaxation; could have a pool, spa, and other recreational facilities; could have restaurants and bars\", \"similar objects\": [\"hotel\", \"motel\", \"inn\"]}", + 11 + ], + "car parking": [ + " {\"type\": \"activity\", \"description\": \"parking a car in a designated area; could be in a parking lot or on the street\", \"similar objects\": [\"car driving\", \"car reversing\", \"car stopping\"]}", + 11 + ], + "photographer name": [ + "\n{\"type\": \"person\", \"description\": \"professional photographer; could take pictures for events, weddings, etc.\", \"similar objects\": [\"videographer\", \"photojournalist\", \"photo editor\"]}", + 11 + ], + "sand pit": [ + " {\"type\": \"playground equipment\", \"description\": \"a shallow hole filled with sand; could have toys\", \"similar objects\": [\"swing set\", \"slide\", \"monkey bars\"]}", + 11 + ], + "blue suitcase": [ + "\n{\"type\": \"travel item\", \"description\": \"rectangular; could be made of hard plastic; could have wheels; could have a handle\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 11 + ], + "room door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have a handle and a lock\", \"similar objects\": [\"window\", \"cabinet\", \"drawer\"]}", + 11 + ], + "dog face": [ + " {\"type\": \"animal face\", \"description\": \"round; two eyes; two ears; nose; mouth\", \"similar objects\": [\"cat face\", \"rabbit face\", \"monkey face\"]}", + 11 + ], + "hanging lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"hangs from the ceiling; could be made of metal or glass; could have a chain or cord\", \"similar objects\": [\"ceiling light\", \"chandelier\", \"pendant light\"]}", + 11 + ], + "corkboard": [ + " {\"type\": \"office tool\", \"description\": \"rectangular; could be made of cork; could have pins\", \"similar objects\": [\"whiteboard\", \"bulletin board\", \"chalkboard\"]}", + 11 + ], + "backrest": [ + " {\"type\": \"furniture\", \"description\": \"support for the back; could be made of wood or metal; could be adjustable\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 11 + ], + "handle flush toilet": [ + "\n{\"type\": \"plumbing fixture\", \"description\": \"has a handle to flush; could be round or elongated; could be made of porcelain\", \"similar objects\": [\"urinal\", \"bidet\", \"sink\"]}", + 11 + ], + "car door handle": [ + " {\"type\": \"car part\", \"description\": \"metal; could be round or rectangular; could be pulled to open the door\", \"similar objects\": [\"car window handle\", \"car seat belt\", \"car mirror\"]}", + 11 + ], + "fleece": [ + " {\"type\": \"fabric\", \"description\": \"soft, warm, lightweight; could be made of polyester or wool\", \"similar objects\": [\"velvet\", \"cotton\", \"denim\"]}", + 11 + ], + "snowbank": [ + " {\"type\": \"weather phenomenon\", \"description\": \"large pile of snow; could be found near roads; could be melted by sun\", \"similar objects\": [\"snowdrift\", \"snowdrift\", \"snowdrift\"]}", + 11 + ], + "dudes": [ + " {\"type\": \"slang\", \"description\": \"informal term for a group of people\", \"similar objects\": [\"buddies\", \"friends\", \"mates\"]}", + 11 + ], + "pink feathers": [ + " {\"type\": \"decorative item\", \"description\": \"soft, light, colorful; could be used for crafting\", \"similar objects\": [\"glitter\", \"beads\", \"sequins\"]}", + 11 + ], + "leather baseball glove": [ + "\n{\"type\": \"sports equipment\", \"description\": \"brown; has a pocket; could be used to catch a baseball\", \"similar objects\": [\"baseball bat\", \"catcher's mitt\", \"hockey stick\"]}", + 11 + ], + "eyelash": [ + " {\"type\": \"body part\", \"description\": \"thin, short, curved; could be found on the eyelid\", \"similar objects\": [\"eyebrow\", \"eyelid\", \"eyeliner\"]}", + 11 + ], + "outdoor lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"weatherproof; could be powered by solar energy; could be mounted on a wall or a pole\", \"similar objects\": [\"street light\", \"floodlight\", \"lantern\"]}", + 11 + ], + "bread pizza": [ + " {\"type\": \"food\", \"description\": \"round; made of bread; topped with vegetables and cheese\", \"similar objects\": [\"calzone\", \"stuffed crust pizza\", \"flatbread pizza\"]}", + 11 + ], + "shelve": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of wood or metal; could be used to store items\", \"similar objects\": [\"table\", \"cabinet\", \"bookcase\"]}", + 11 + ], + "doily": [ + " {\"type\": \"decorative item\", \"description\": \"round; made of lace or paper; used to decorate furniture\", \"similar objects\": [\"placemat\", \"tablecloth\", \"coaster\"]}", + 11 + ], + "polka dot umbrella": [ + "\n{\"type\": \"accessory\", \"description\": \"round; has white dots on a colored background; could be opened and closed\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}", + 11 + ], + "cake decoration": [ + " {\"type\": \"baking tool\", \"description\": \"could be made of plastic, metal, or paper; could be used to decorate cakes, cupcakes, and other desserts\", \"similar objects\": [\"icing bag\", \"icing tip\", \"cake topper\"]}", + 11 + ], + "fur coat": [ + " {\"type\": \"clothing\", \"description\": \"made of fur; could be long or short; could be in different colors\", \"similar objects\": [\"leather jacket\", \"wool coat\", \"down coat\"]}", + 11 + ], + "surfaces": [ + " {\"type\": \"material\", \"description\": \"smooth, flat, could be made of wood, metal, plastic, etc.\", \"similar objects\": [\"floor\", \"wall\", \"ceiling\"]}", + 11 + ], + "plastic bat": [ + " {\"type\": \"toy\", \"description\": \"lightweight; could be used for playing baseball; could be made of plastic\", \"similar objects\": [\"ball\", \"frisbee\", \"kite\"]}", + 11 + ], + "square design": [ + " {\"type\": \"shape\", \"description\": \"four equal sides; four right angles; could be filled with colors\", \"similar objects\": [\"rectangle\", \"triangle\", \"circle\"]}", + 11 + ], + "helmet biker": [ + " {\"type\": \"protective gear\", \"description\": \"hard; has a chin strap; could be made of plastic or metal\", \"similar objects\": [\"helmet skier\", \"helmet snowboarder\", \"helmet motorcyclist\"]}", + 11 + ], + "side wheel": [ + " {\"type\": \"vehicle accessory\", \"description\": \"round; could be attached to a vehicle; could be used to steer the vehicle\", \"similar objects\": [\"steering wheel\", \"tire\", \"spare wheel\"]}", + 11 + ], + "surfboard surfer": [ + "\n{\"type\": \"sport equipment\", \"description\": \"long, narrow board; could be used by a surfer\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 11 + ], + "storage unit": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood or metal; could have drawers or shelves; could be used to store items\", \"similar objects\": [\"cabinet\", \"bookshelf\", \"dresser\"]}", + 11 + ], + "clothesline": [ + " {\"type\": \"laundry tool\", \"description\": \"long rope; could be hung between two poles; could be used to hang clothes\", \"similar objects\": [\"clothes hanger\", \"clothespin\", \"clothes rack\"]}", + 11 + ], + "meatball": [ + " {\"type\": \"food\", \"description\": \"round; could be made of ground beef, pork, or turkey; could be served with sauce\", \"similar objects\": [\"dumpling\", \"ravioli\", \"pierogi\"]}", + 11 + ], + "pepper flakes": [ + " {\"type\": \"spice\", \"description\": \"red; small, thin pieces; could be used as a seasoning\", \"similar objects\": [\"chili powder\", \"cumin\", \"curry powder\"]}", + 11 + ], + "silhouettes": [ + " {\"type\": \"art form\", \"description\": \"dark figures against a lighter background; could be used to represent people or objects\", \"similar objects\": [\"shadow puppets\", \"shadow play\", \"shadow art\"]}", + 11 + ], + "silver panel": [ + " {\"type\": \"building material\", \"description\": \"shiny, reflective, metallic surface; could be used for roofing or siding\", \"similar objects\": [\"aluminum panel\", \"copper panel\", \"stainless steel panel\"]}", + 11 + ], + "facemask": [ + " {\"type\": \"protective gear\", \"description\": \"covers the nose and mouth; could be made of cloth or paper\", \"similar objects\": [\"respirator\", \"goggles\", \"gloves\"]}", + 11 + ], + "water dispenser": [ + " {\"type\": \"appliance\", \"description\": \"tall; has a spout; could be connected to a water source\", \"similar objects\": [\"water cooler\", \"water filter\", \"water purifier\"]}", + 11 + ], + "wrought iron fencing": [ + " {\"type\": \"fencing material\", \"description\": \"black; made of metal; could be curved or straight; could have decorative designs\", \"similar objects\": [\"wooden fencing\", \"chain link fencing\", \"vinyl fencing\"]}", + 11 + ], + "front lights": [ + " {\"type\": \"vehicle accessory\", \"description\": \"headlights; could be LED; could be used for night driving\", \"similar objects\": [\"taillights\", \"fog lights\", \"turn signals\"]}", + 11 + ], + "enter key": [ + " {\"type\": \"computer key\", \"description\": \"rectangular; usually located at the right bottom of the keyboard\", \"similar objects\": [\"shift key\", \"backspace key\", \"spacebar\"]}", + 11 + ], + "silver pitcher": [ + "\n{\"type\": \"utensil\", \"description\": \"silver; has a handle; could be used to pour liquids\", \"similar objects\": [\"teapot\", \"jug\", \"vase\"]}", + 11 + ], + "metal canister": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of metal; could have a lid\", \"similar objects\": [\"jar\", \"box\", \"bottle\"]}", + 11 + ], + "leafy lettuce": [ + " {\"type\": \"vegetable\", \"description\": \"green; could be curly or flat; could be used in salads\", \"similar objects\": [\"spinach\", \"kale\", \"arugula\"]}", + 11 + ], + "orange cooler": [ + " {\"type\": \"beverage container\", \"description\": \"orange; could be made of plastic; could have a lid\", \"similar objects\": [\"water bottle\", \"thermos\", \"mug\"]}", + 11 + ], + "spoon handle": [ + " {\"type\": \"utensil\", \"description\": \"long, thin handle; could be made of metal or plastic; could have a bowl-shaped end\", \"similar objects\": [\"fork\", \"knife\", \"chopsticks\"]}", + 11 + ], + "crystal chandelier": [ + "\n{\"type\": \"lighting tool\", \"description\": \"hanging; made of crystal; could have multiple arms\", \"similar objects\": [\"ceiling light\", \"pendant light\", \"wall sconce\"]}", + 11 + ], + "mane horse": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, thick hair on the neck of a horse\", \"similar objects\": [\"tail\", \"hoof\", \"whiskers\"]}", + 11 + ], + "cement path": [ + " {\"type\": \"construction material\", \"description\": \"hard, gray, flat surface; could be used for pathways\", \"similar objects\": [\"asphalt\", \"gravel\", \"concrete\"]}", + 11 + ], + "wood deck": [ + " {\"type\": \"outdoor structure\", \"description\": \"made of wood; could be used as a platform; could be used for outdoor activities\", \"similar objects\": [\"patio\", \"balcony\", \"porch\"]}", + 11 + ], + "car park": [ + " {\"type\": \"structure\", \"description\": \"large area with parking spaces; could have a gate or entrance\", \"similar objects\": [\"garage\", \"parking lot\", \"driveway\"]}", + 11 + ], + "banana slice": [ + " {\"type\": \"food\", \"description\": \"yellow; curved; could be sliced into round pieces\", \"similar objects\": [\"apple slice\", \"orange slice\", \"strawberry slice\"]}", + 11 + ], + "fruit salad": [ + " {\"type\": \"food\", \"description\": \"a mix of different fruits; could be served with yogurt or cream\", \"similar objects\": [\"vegetable salad\", \"fruit platter\", \"fruit bowl\"]}", + 11 + ], + "flower plant": [ + " {\"type\": \"plant\", \"description\": \"has colorful petals; could have leaves and stems; could have a pot\", \"similar objects\": [\"tree\", \"bush\", \"shrub\"]}", + 11 + ], + "gold ribbon": [ + " {\"type\": \"decoration item\", \"description\": \"shiny, gold color; could be used to wrap gifts\", \"similar objects\": [\"silver ribbon\", \"red ribbon\", \"blue ribbon\"]}", + 11 + ], + "members": [ + " {\"type\": \"people\", \"description\": \"group of people; could be related or unrelated\", \"similar objects\": [\"family\", \"friends\", \"colleagues\"]}", + 11 + ], + "time clock": [ + " {\"type\": \"timekeeping tool\", \"description\": \"could be digital or analog; could be used to record the time of arrival and departure of employees\", \"similar objects\": [\"stopwatch\", \"timer\", \"alarm clock\"]}", + 11 + ], + "bicycle lane": [ + " {\"type\": \"roadway\", \"description\": \"a lane on the road designated for bicycles; could be marked with a white line\", \"similar objects\": [\"pedestrian lane\", \"bus lane\", \"truck lane\"]}", + 11 + ], + "wood house": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could have a chimney; could have a porch\", \"similar objects\": [\"brick house\", \"log cabin\", \"igloo\"]}", + 11 + ], + "yellow basket": [ + "\n{\"type\": \"container\", \"description\": \"yellow; could be made of plastic or wicker; could have a handle\", \"similar objects\": [\"bag\", \"box\", \"bin\"]}", + 11 + ], + "metal pitcher": [ + " {\"type\": \"utensil\", \"description\": \"cylindrical; made of metal; has a handle and a spout\", \"similar objects\": [\"teapot\", \"jug\", \"coffee pot\"]}", + 11 + ], + "disks": [ + " {\"type\": \"storage device\", \"description\": \"round; could be made of plastic or metal; could be used to store data\", \"similar objects\": [\"hard drive\", \"USB drive\", \"CD\"]}", + 11 + ], + "television remote control": [ + "\n{\"type\": \"electronic device\", \"description\": \"small, rectangular; has buttons; could be used to control a television\", \"similar objects\": [\"game controller\", \"air conditioner remote\", \"DVD remote\"]}", + 11 + ], + "dice": [ + " {\"type\": \"game tool\", \"description\": \"cube-shaped; has six sides with dots\", \"similar objects\": [\"playing cards\", \"board game pieces\", \"dominoes\"]}", + 11 + ], + "leopard": [ + " {\"type\": \"animal\", \"description\": \"spotted; has a long tail; could be yellow or black\", \"similar objects\": [\"cheetah\", \"jaguar\", \"tiger\"]}", + 11 + ], + "ankles": [ + " {\"type\": \"body part\", \"description\": \"joints between the foot and the leg; could be swollen\", \"similar objects\": [\"knees\", \"elbows\", \"wrists\"]}", + 11 + ], + "decker tour bus": [ + "\n{\"type\": \"vehicle\", \"description\": \"large, double-decker bus; could have a glass roof; could have a luggage compartment\", \"similar objects\": [\"school bus\", \"coach bus\", \"minibus\"]}", + 11 + ], + "lotion bottle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could have a pump; could be made of plastic or glass\", \"similar objects\": [\"shampoo bottle\", \"soap bottle\", \"conditioner bottle\"]}", + 11 + ], + "ski suit": [ + " {\"type\": \"clothing\", \"description\": \"waterproof; could be insulated; could be one-piece or two-piece\", \"similar objects\": [\"snowboard suit\", \"ski jacket\", \"ski pants\"]}", + 11 + ], + "blue tag": [ + " {\"type\": \"accessory\", \"description\": \"small, rectangular, blue; could be used for identification\", \"similar objects\": [\"name tag\", \"keychain\", \"badge\"]}", + 11 + ], + "sausage pizza": [ + "\n{\"type\": \"food\", \"description\": \"round; topped with tomato sauce, cheese, and sausage; could be served with extra toppings\", \"similar objects\": [\"pepperoni pizza\", \"margherita pizza\", \"vegetable pizza\"]}", + 11 + ], + "helmet motorcycle": [ + "\n{\"type\": \"protective gear\", \"description\": \"hard, covers the head; could be made of plastic or metal; could have a visor\", \"similar objects\": [\"bicycle helmet\", \"hockey helmet\", \"construction helmet\"]}", + 11 + ], + "airplane cockpit": [ + " {\"type\": \"aircraft part\", \"description\": \"enclosed space; has control panels; could have multiple seats\", \"similar objects\": [\"fuselage\", \"wing\", \"tail\"]}", + 11 + ], + "metal fire escape": [ + " {\"type\": \"structure\", \"description\": \"metal stairs; could be attached to a building; could be used for emergency evacuation\", \"similar objects\": [\"ladder\", \"escalator\", \"elevator\"]}", + 11 + ], + "christmas hat": [ + " {\"type\": \"accessory\", \"description\": \"red and white; has a pom-pom on the top; could be made of fabric\", \"similar objects\": [\"santa hat\", \"elf hat\", \"reindeer antlers\"]}", + 11 + ], + "grey pipe": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical, grey; could be used for water or gas\", \"similar objects\": [\"hose\", \"valve\", \"faucet\"]}", + 11 + ], + "shade umbrella": [ + " {\"type\": \"outdoor tool\", \"description\": \"large; could be opened and closed; could be used to protect from sun and rain\", \"similar objects\": [\"sun umbrella\", \"gazebo\", \"tent\"]}", + 11 + ], + "board box": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could be used for storage\", \"similar objects\": [\"suitcase\", \"basket\", \"bag\"]}", + 11 + ], + "cheese pizza": [ + " {\"type\": \"food\", \"description\": \"round; has a crust; topped with cheese and other ingredients\", \"similar objects\": [\"pepperoni pizza\", \"vegetable pizza\", \"calzone\"]}", + 11 + ], + "handle window": [ + " {\"type\": \"furniture\", \"description\": \"has a handle to open and close; could be made of wood or metal; could be rectangular or square\", \"similar objects\": [\"door\", \"cabinet\", \"drawer\"]}", + 11 + ], + "shadow vase": [ + " {\"type\": \"decorative item\", \"description\": \"cylindrical; has a silhouette of a person or object; could be made of ceramic or glass\", \"similar objects\": [\"statue\", \"sculpture\", \"figurine\"]}", + 11 + ], + "carriage wheel": [ + " {\"type\": \"transportation tool\", \"description\": \"round; could be made of wood; could have spokes\", \"similar objects\": [\"wagon wheel\", \"bicycle wheel\", \"tricycle wheel\"]}", + 11 + ], + "giraffe drinking water": [ + "\n{\"type\": \"animal behavior\", \"description\": \"giraffe bending its neck to drink water from a pond or river\", \"similar objects\": [\"elephant drinking water\", \"zebra drinking water\", \"hippo drinking water\"]}", + 11 + ], + "plastic soap dispenser": [ + "\n{\"type\": \"household item\", \"description\": \"could be wall-mounted; could be automatic; could be refillable\", \"similar objects\": [\"paper towel dispenser\", \"toilet paper dispenser\", \"hand sanitizer dispenser\"]}", + 11 + ], + "wind chime": [ + " {\"type\": \"decoration\", \"description\": \"made of metal or wood; could have different shapes; could have different colors; could make sound when wind blows\", \"similar objects\": [\"bell\", \"mobile\", \"dream catcher\"]}", + 11 + ], + "cat head": [ + " {\"type\": \"animal body part\", \"description\": \"round; has two ears; has whiskers; has two eyes\", \"similar objects\": [\"dog head\", \"rabbit head\", \"mouse head\"]}", + 11 + ], + "sign posts": [ + " {\"type\": \"marker\", \"description\": \"could be made of metal or wood; could have words or symbols on it; could be used to indicate directions\", \"similar objects\": [\"road signs\", \"traffic signs\", \"warning signs\"]}", + 11 + ], + "bird tail": [ + " {\"type\": \"animal body part\", \"description\": \"feathery; could be colorful; could be long or short\", \"similar objects\": [\"bird wings\", \"bird beak\", \"bird feet\"]}", + 11 + ], + "doorframe": [ + " {\"type\": \"architectural element\", \"description\": \"rectangular; could be made of wood or metal; could have a door\", \"similar objects\": [\"window frame\", \"wall\", \"column\"]}", + 11 + ], + "beige chair": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have armrests; could have a cushion; could be made of wood or metal\", \"similar objects\": [\"sofa\", \"ottoman\", \"bench\"]}", + 11 + ], + "potty": [ + " {\"type\": \"toilet\", \"description\": \"small, round, has a lid\", \"similar objects\": [\"toilet seat\", \"bidet\", \"urinal\"]}", + 11 + ], + "office buildings": [ + "\n{\"type\": \"structure\", \"description\": \"tall; could have multiple floors; could have glass windows; could have a lobby\", \"similar objects\": [\"apartment buildings\", \"schools\", \"hospitals\"]}", + 11 + ], + "tobogan": [ + " {\"type\": \"recreational tool\", \"description\": \"long, curved, could be made of plastic or wood; could be used for sliding down a hill\", \"similar objects\": [\"slide\", \"swing\", \"seesaw\"]}", + 11 + ], + "orange scarf": [ + "\n{\"type\": \"clothing accessory\", \"description\": \"long, thin, orange fabric; could be made of wool or cotton\", \"similar objects\": [\"red scarf\", \"blue scarf\", \"green scarf\"]}", + 11 + ], + "stripe zebra": [ + "\n{\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane; could have a stripe pattern\", \"similar objects\": [\"horse\", \"giraffe\", \"zebra\"]}", + 11 + ], + "flask": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or glass; could have a stopper\", \"similar objects\": [\"bottle\", \"jar\", \"vial\"]}", + 11 + ], + "house brown": [ + " {\"type\": \"insect\", \"description\": \"brown; has six legs; could have wings; could have antennae\", \"similar objects\": [\"ant\", \"bee\", \"spider\"]}", + 11 + ], + "airplane wheel": [ + " {\"type\": \"airplane part\", \"description\": \"round; could be made of metal; could have a tire\", \"similar objects\": [\"engine\", \"wing\", \"propeller\"]}", + 11 + ], + "identification numbers": [ + "\n{\"type\": \"data\", \"description\": \"unique numbers used to identify individuals or objects\", \"similar objects\": [\"passwords\", \"codes\", \"PINs\"]}", + 11 + ], + "ballpoint pen": [ + " {\"type\": \"writing tool\", \"description\": \"long, cylindrical; has a tip for writing; could be refillable\", \"similar objects\": [\"pencil\", \"marker\", \"fountain pen\"]}", + 11 + ], + "farm animals": [ + "\n{\"type\": \"animals\", \"description\": \"livestock; could include cows, chickens, pigs, sheep, goats, horses, etc.\", \"similar objects\": [\"domestic animals\", \"wild animals\", \"pets\"]}", + 11 + ], + "passage": [ + " {\"type\": \"structure\", \"description\": \"a corridor or a hall; could be used to connect two places\", \"similar objects\": [\"hallway\", \"aisle\", \"tunnel\"]}", + 11 + ], + "granny smith apple": [ + "\n{\"type\": \"fruit\", \"description\": \"green, round, has a stem; could be tart\", \"similar objects\": [\"green apple\", \"red apple\", \"golden delicious apple\"]}", + 11 + ], + "train passenger window": [ + "\n{\"type\": \"transportation window\", \"description\": \"rectangular; could be opened and closed; could be made of glass\", \"similar objects\": [\"car window\", \"airplane window\", \"bus window\"]}", + 11 + ], + "life jackets": [ + " {\"type\": \"safety equipment\", \"description\": \"orange; could be inflated; could be worn on the body\", \"similar objects\": [\"helmet\", \"harness\", \"floatation device\"]}", + 11 + ], + "dinosaurs": [ + " {\"type\": \"animal\", \"description\": \"extinct; could be large; could have long tails; could have sharp teeth\", \"similar objects\": [\"pterodactyl\", \"triceratops\", \"brontosaurus\"]}", + 11 + ], + "orange hand": [ + " {\"type\": \"glove\", \"description\": \"orange; could be made of fabric; could have a wrist strap; could have a thumb hole\", \"similar objects\": [\"mittens\", \"gauntlets\", \"sleeves\"]}", + 11 + ], + "construction equipment": [ + " {\"type\": \"tool\", \"description\": \"used for construction; could be a bulldozer, crane, excavator, etc.\", \"similar objects\": [\"truck\", \"drill\", \"hammer\"]}", + 11 + ], + "barb wire fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal wires; could be used to protect a property\", \"similar objects\": [\"chain link fence\", \"wooden fence\", \"brick wall\"]}", + 11 + ], + "frisbey": [ + " {\"type\": \"toy\", \"description\": \"round; could be made of plastic; could be thrown in the air\", \"similar objects\": [\"boomerang\", \"kite\", \"ball\"]}", + 11 + ], + "refrigerator handle": [ + "\n{\"type\": \"appliance handle\", \"description\": \"long, thin, metal; could be curved or straight; could be attached to a refrigerator door\", \"similar objects\": [\"oven handle\", \"microwave handle\", \"dishwasher handle\"]}", + 11 + ], + "nike sign": [ + " {\"type\": \"brand logo\", \"description\": \"swoosh; could be in black and white; could be in different colors\", \"similar objects\": [\"adidas\", \"puma\", \"reebok\"]}", + 11 + ], + "tent tops": [ + " {\"type\": \"outdoor equipment\", \"description\": \"cone-shaped; could be made of fabric; could be used to cover a shelter\", \"similar objects\": [\"canopy\", \"awning\", \"tarp\"]}", + 11 + ], + "army": [ + " {\"type\": \"organization\", \"description\": \"group of people with a common purpose; could be armed forces\", \"similar objects\": [\"navy\", \"air force\", \"marines\"]}", + 11 + ], + "metal clock hands": [ + " {\"type\": \"clock part\", \"description\": \"long, thin, metal pieces; could be pointed or rounded; could be black or silver\", \"similar objects\": [\"clock face\", \"clock mechanism\", \"clock pendulum\"]}", + 11 + ], + "skys": [ + " {\"type\": \"weather phenomenon\", \"description\": \"blue; could have clouds; could have stars; could have rainbows\", \"similar objects\": [\"sunshine\", \"thunderstorm\", \"hail\"]}", + 11 + ], + "tiers": [ + " {\"type\": \"architecture\", \"description\": \"stacked layers; could be made of stones, bricks, or wood; could be used to build walls, towers, or other structures\", \"similar objects\": [\"steps\", \"platforms\", \"balconies\"]}", + 11 + ], + "helmet persons": [ + " {\"type\": \"protective gear\", \"description\": \"hard, covers the head; could have a visor\", \"similar objects\": [\"goggles\", \"gloves\", \"vest\"]}", + 11 + ], + "airplane window": [ + " {\"type\": \"airplane part\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"seat\", \"aisle\", \"overhead bin\"]}", + 11 + ], + "access": [ + " {\"type\": \"noun\", \"description\": \"permission to enter or use; a way of approaching or entering\", \"similar objects\": [\"admittance\", \"entrance\", \"entry\"]}", + 11 + ], + "purple stripe": [ + " {\"type\": \"pattern\", \"description\": \"a line of color; could be of any color; could be of any width\", \"similar objects\": [\"dotted line\", \"zigzag line\", \"diagonal line\"]}", + 11 + ], + "television monitor": [ + "\n{\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to a cable box; could have speakers\", \"similar objects\": [\"computer monitor\", \"projector\", \"smartphone\"]}", + 11 + ], + "silver tines": [ + " {\"type\": \"utensil\", \"description\": \"long, thin, and made of metal; could be used for eating\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}", + 11 + ], + "canvas top": [ + " {\"type\": \"fabric\", \"description\": \"thick, durable, waterproof; could be used to cover vehicles\", \"similar objects\": [\"tarp\", \"awning\", \"tent\"]}", + 11 + ], + "male elephant": [ + "\n{\"type\": \"animal\", \"description\": \"large; has tusks; has large ears; has a long trunk; could have large tusks; could have a large forehead\", \"similar objects\": [\"female elephant\", \"hippopotamus\", \"rhinoceros\"]}", + 11 + ], + "station platform": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, flat surface; could have a roof; could have a ticket booth\", \"similar objects\": [\"train station\", \"bus station\", \"airport terminal\"]}", + 11 + ], + "leather ottoman": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of leather; could be used as a footrest\", \"similar objects\": [\"sofa\", \"chair\", \"bench\"]}", + 11 + ], + "unbrella": [ + " {\"type\": \"protective tool\", \"description\": \"has a long handle; could be opened and closed; could be made of fabric\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}", + 11 + ], + "plastic drink cup": [ + " {\"type\": \"container\", \"description\": \"transparent; could have a lid; could have a straw\", \"similar objects\": [\"glass cup\", \"mug\", \"thermos\"]}", + 11 + ], + "gold knobs": [ + " {\"type\": \"decorative item\", \"description\": \"round; made of gold; could be used to open and close doors\", \"similar objects\": [\"handles\", \"pulls\", \"hinges\"]}", + 11 + ], + "bike pedal": [ + " {\"type\": \"bicycle part\", \"description\": \"round; could be made of metal; could be attached to the bike frame\", \"similar objects\": [\"handlebar\", \"saddle\", \"wheel\"]}", + 11 + ], + "sits": [ + " {\"type\": \"verb\", \"description\": \"to rest on the buttocks\", \"similar objects\": [\"stand\", \"lie\", \"kneel\"]}", + 11 + ], + "shinguard": [ + " {\"type\": \"protective gear\", \"description\": \"worn on the shin; could be made of plastic or foam; could be strapped on the leg\", \"similar objects\": [\"helmet\", \"elbow pad\", \"knee pad\"]}", + 11 + ], + "summer sky": [ + " {\"type\": \"weather\", \"description\": \"blue; could have white clouds; could be sunny\", \"similar objects\": [\"spring sky\", \"autumn sky\", \"winter sky\"]}", + 11 + ], + "pizza plate": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could have a raised edge\", \"similar objects\": [\"bowl\", \"plate\", \"saucer\"]}", + 11 + ], + "wooden table surface": [ + " {\"type\": \"furniture\", \"description\": \"flat, rectangular, made of wood; could have four legs\", \"similar objects\": [\"desk\", \"chair\", \"sofa\"]}", + 11 + ], + "fire plug": [ + " {\"type\": \"firefighting tool\", \"description\": \"cylindrical; could be red; could be connected to a hose\", \"similar objects\": [\"fire hydrant\", \"fire extinguisher\", \"fire hose\"]}", + 11 + ], + "brass door handle": [ + "\n{\"type\": \"hardware\", \"description\": \"made of brass; could be round or rectangular; could have a latch\", \"similar objects\": [\"knob\", \"hinge\", \"lock\"]}", + 11 + ], + "globe lights": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could be hung from the ceiling\", \"similar objects\": [\"chandelier\", \"pendant light\", \"ceiling light\"]}", + 11 + ], + "brunette man": [ + "\n{\"type\": \"person\", \"description\": \"dark hair; could have facial hair; could have brown eyes\", \"similar objects\": [\"blonde man\", \"redhead man\", \"gray-haired man\"]}", + 11 + ], + "cute face": [ + "\n{\"type\": \"expression\", \"description\": \"smiling; could have big eyes; could have rosy cheeks\", \"similar objects\": [\"happy face\", \"sad face\", \"surprised face\"]}", + 11 + ], + "plastic ketchup bottle": [ + "\n{\"type\": \"container\", \"description\": \"transparent; has a red cap; could be squeezed to release ketchup\", \"similar objects\": [\"plastic mustard bottle\", \"plastic mayonnaise bottle\", \"plastic salad dressing bottle\"]}", + 11 + ], + "banner advertisement": [ + "\n{\"type\": \"marketing tool\", \"description\": \"large, colorful, could be printed or digital; could be used to promote products or services\", \"similar objects\": [\"flyer\", \"poster\", \"billboard\"]}", + 11 + ], + "silver suitcase": [ + "\n{\"type\": \"luggage\", \"description\": \"made of silver; rectangular; has a handle; could be locked\", \"similar objects\": [\"briefcase\", \"backpack\", \"duffel bag\"]}", + 11 + ], + "brick bridge": [ + " {\"type\": \"structure\", \"description\": \"made of bricks; could have arches; could span a river or a road\", \"similar objects\": [\"stone bridge\", \"wooden bridge\", \"suspension bridge\"]}", + 11 + ], + "car bumper": [ + " {\"type\": \"automotive part\", \"description\": \"attached to the front or rear of a car; made of metal or plastic; designed to absorb impact\", \"similar objects\": [\"grille\", \"headlight\", \"tail light\"]}", + 11 + ], + "spiky hair": [ + " {\"type\": \"hairstyle\", \"description\": \"short, stands up; could be styled with gel or wax\", \"similar objects\": [\"mohawk\", \"faux hawk\", \"pompadour\"]}", + 11 + ], + "orange sun": [ + " {\"type\": \"astronomical object\", \"description\": \"a star; could be seen in the night sky; could be orange in color\", \"similar objects\": [\"moon\", \"planet\", \"comet\"]}", + 11 + ], + "neckline": [ + " {\"type\": \"clothing feature\", \"description\": \"the line of a garment that frames the neck\", \"similar objects\": [\"collar\", \"hemline\", \"waistline\"]}", + 11 + ], + "gold top": [ + " {\"type\": \"clothing item\", \"description\": \"long-sleeved; could be made of cotton; could have a round neckline\", \"similar objects\": [\"t-shirt\", \"sweater\", \"blouse\"]}", + 11 + ], + "tan stone wall": [ + "\n{\"type\": \"building material\", \"description\": \"made of stones; could be tan in color; could be used to build walls\", \"similar objects\": [\"bricks\", \"concrete blocks\", \"wooden planks\"]}", + 11 + ], + "mets": [ + " {\"type\": \"sports team\", \"description\": \"New York-based baseball team; plays in the National League East division\", \"similar objects\": [\"Yankees\", \"Phillies\", \"Braves\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green", + 11 + ], + "christmas light": [ + "\n{\"type\": \"decoration\", \"description\": \"string of small, colorful lights; could be used to decorate a tree or a house\", \"similar objects\": [\"ornaments\", \"garland\", \"tinsel\"]}", + 11 + ], + "dollar sign": [ + " {\"type\": \"symbol\", \"description\": \"green; has two vertical lines and one horizontal line; could be used to represent money\", \"similar objects\": [\"euro sign\", \"pound sign\", \"yen sign\"]}", + 11 + ], + "metal panel": [ + " {\"type\": \"building material\", \"description\": \"flat, thin, made of metal; could be used for roofing or walling\", \"similar objects\": [\"sheet metal\", \"aluminum panel\", \"steel panel\"]}", + 11 + ], + "sewing machine": [ + " {\"type\": \"tool\", \"description\": \"has a needle; could be used to stitch fabrics\", \"similar objects\": [\"scissors\", \"iron\", \"sewing kit\"]}", + 11 + ], + "wood picket fence": [ + "\n{\"type\": \"fencing material\", \"description\": \"made of wood; has vertical pickets; could be painted white\", \"similar objects\": [\"chain link fence\", \"vinyl fence\", \"wrought iron fence\"]}", + 11 + ], + "member": [ + " {\"type\": \"person\", \"description\": \"part of a group; could have a role or responsibility\", \"similar objects\": [\"associate\", \"assistant\", \"colleague\"]}", + 11 + ], + "wood structure": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be used to build houses, furniture, etc.\", \"similar objects\": [\"bricks\", \"concrete\", \"steel\"]}", + 11 + ], + "protection": [ + " {\"type\": \"concept\", \"description\": \"the act of guarding or shielding from harm or danger; could be physical or psychological\", \"similar objects\": [\"safety\", \"security\", \"defense\"]}", + 11 + ], + "wood stool": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; has four legs; could be used as a seat\", \"similar objects\": [\"chair\", \"bench\", \"ottoman\"]}", + 11 + ], + "plastic pitcher": [ + "\n{\"type\": \"container\", \"description\": \"transparent; could have a handle; could be used to store liquids\", \"similar objects\": [\"jug\", \"jar\", \"bottle\"]}", + 11 + ], + "bald head": [ + " {\"type\": \"body part\", \"description\": \"smooth, no hair; could be shiny\", \"similar objects\": [\"shaved head\", \"bald spot\", \"buzz cut\"]}", + 11 + ], + "rice dish": [ + " {\"type\": \"food\", \"description\": \"made of cooked rice; could be served with vegetables, meat, or seafood; could be served with sauces\", \"similar objects\": [\"noodle dish\", \"stir-fry\", \"sushi\"]}", + 11 + ], + "floor balcony": [ + " {\"type\": \"architectural structure\", \"description\": \"raised platform; could be made of wood or metal; could have railings\", \"similar objects\": [\"balcony\", \"deck\", \"veranda\"]}", + 11 + ], + "pink band": [ + " {\"type\": \"accessory\", \"description\": \"elastic; could be used to tie hair; could be used to decorate clothes\", \"similar objects\": [\"hair tie\", \"ribbon\", \"scrunchie\"]}", + 11 + ], + "metal curtain rod": [ + " {\"type\": \"furnishing tool\", \"description\": \"long, cylindrical, made of metal; could be used to hang curtains\", \"similar objects\": [\"curtain rail\", \"curtain pole\", \"curtain track\"]}", + 11 + ], + "van door": [ + " {\"type\": \"vehicle part\", \"description\": \"sliding door; could be made of metal; could be opened from the inside and outside\", \"similar objects\": [\"car door\", \"truck door\", \"garage door\"]}", + 11 + ], + "flesh": [ + " {\"type\": \"body part\", \"description\": \"soft; could be pinkish; could be covered by skin\", \"similar objects\": [\"muscle\", \"bone\", \"tendon\"]}", + 11 + ], + "flannel": [ + " {\"type\": \"clothing\", \"description\": \"soft, warm, usually made of wool or cotton; could be plaid or plain\", \"similar objects\": [\"sweater\", \"hoodie\", \"cardigan\"]}", + 11 + ], + "round chocolate cake": [ + "\n{\"type\": \"dessert\", \"description\": \"round; made of chocolate; could be decorated with cream and fruits\", \"similar objects\": [\"cupcake\", \"cheesecake\", \"brownie\"]}", + 11 + ], + "roofed building": [ + " {\"type\": \"structure\", \"description\": \"has walls and a roof; could have windows and doors; could be made of brick, wood, or metal\", \"similar objects\": [\"house\", \"shed\", \"garage\"]}", + 11 + ], + "dish soap": [ + " {\"type\": \"cleaning product\", \"description\": \"liquid; could be used to clean dishes; could be scented\", \"similar objects\": [\"dishwasher detergent\", \"all-purpose cleaner\", \"laundry detergent\"]}", + 11 + ], + "foot hill": [ + " {\"type\": \"landscape\", \"description\": \"small hill; could be covered with grass; could have trees\", \"similar objects\": [\"mountain\", \"valley\", \"cliff\"]}", + 11 + ], + "shallow": [ + " {\"type\": \"adjective\", \"description\": \"not deep; not intense; not extreme\", \"similar objects\": [\"superficial\", \"light\", \"mild\"]}", + 11 + ], + "oil lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"round; made of metal; uses oil as fuel\", \"similar objects\": [\"lantern\", \"torch\", \"candle\"]}", + 11 + ], + "shoe string": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, could be made of leather or fabric; used to tie shoes\", \"similar objects\": [\"laces\", \"shoelaces\", \"elastic bands\"]}", + 11 + ], + "silver writing": [ + " {\"type\": \"stationery\", \"description\": \"metallic; could be used for writing; could be used for decoration\", \"similar objects\": [\"pen\", \"marker\", \"paintbrush\"]}", + 11 + ], + "mirror side car": [ + " {\"type\": \"vehicle accessory\", \"description\": \"attached to the side of a vehicle; has a reflective surface\", \"similar objects\": [\"spare tire\", \"bicycle rack\", \"ski rack\"]}", + 11 + ], + "wedding ring": [ + " {\"type\": \"jewelry\", \"description\": \"circular; could be made of gold, silver, or other metals; could have diamonds or other gemstones\", \"similar objects\": [\"engagement ring\", \"bracelet\", \"necklace\"]}", + 11 + ], + "limit sign": [ + " {\"type\": \"traffic sign\", \"description\": \"octagonal; has a red border; has a white background; has a black number\", \"similar objects\": [\"stop sign\", \"yield sign\", \"no parking sign\"]}", + 11 + ], + "pink bicycle": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could be pink in color\", \"similar objects\": [\"motorcycle\", \"scooter\", \"tricycle\"]}", + 11 + ], + "shadow bike": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could be electric; could have a basket\", \"similar objects\": [\"scooter\", \"moped\", \"tricycle\"]}", + 11 + ], + "color building": [ + " {\"type\": \"architecture\", \"description\": \"multi-colored; could be made of bricks, stones, or other materials; could have multiple stories\", \"similar objects\": [\"skyscraper\", \"mansion\", \"castle\"]}", + 11 + ], + "motorcycle mirror": [ + " {\"type\": \"vehicle accessory\", \"description\": \"round; could be attached to the handlebar; could be adjustable\", \"similar objects\": [\"bicycle mirror\", \"car mirror\", \"truck mirror\"]}", + 11 + ], + "motorcycle gas tank": [ + "\n{\"type\": \"motorcycle part\", \"description\": \"cylindrical; could be made of metal; could be painted in different colors; could have a fuel cap\", \"similar objects\": [\"exhaust pipe\", \"engine\", \"handlebar\"]}", + 11 + ], + "road lines": [ + " {\"type\": \"road markings\", \"description\": \"yellow or white lines painted on the road; could be dashed or solid\", \"similar objects\": [\"traffic signs\", \"road signs\", \"road barriers\"]}", + 11 + ], + "team members": [ + "\n{\"type\": \"group\", \"description\": \"people working together to achieve a common goal\", \"similar objects\": [\"colleagues\", \"co-workers\", \"partners\"]}", + 11 + ], + "army knife": [ + " {\"type\": \"tool\", \"description\": \"small, multi-functional; could have a blade, a screwdriver, a can opener, etc.\", \"similar objects\": [\"pocket knife\", \"utility knife\", \"Swiss Army knife\"]}", + 11 + ], + "silver soap dispenser": [ + "\n{\"type\": \"bathroom accessory\", \"description\": \"silver; could be wall-mounted; could be used to dispense soap\", \"similar objects\": [\"toilet paper holder\", \"towel rack\", \"toothbrush holder\"]}", + 11 + ], + "silver blades": [ + " {\"type\": \"utensil\", \"description\": \"sharp, metallic, could be used for cutting\", \"similar objects\": [\"knives\", \"scissors\", \"razors\"]}", + 11 + ], + "police bike": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a siren; could be used by police officers\", \"similar objects\": [\"motorcycle\", \"bicycle\", \"scooter\"]}", + 11 + ], + "gym floor": [ + " {\"type\": \"flooring\", \"description\": \"hard, flat, and non-slip surface; could be made of wood, rubber, or vinyl\", \"similar objects\": [\"basketball court\", \"tennis court\", \"badminton court\"]}", + 11 + ], + "bystanders": [ + " {\"type\": \"people\", \"description\": \"people who are present at an event but not actively involved\", \"similar objects\": [\"spectators\", \"witnesses\", \"onlookers\"]}", + 11 + ], + "hoove": [ + " {\"type\": \"animal body part\", \"description\": \"hard, curved, and pointed; found on the feet of horses, cows, and other animals\", \"similar objects\": [\"claw\", \"paw\", \"hoof\"]}", + 11 + ], + "bronwy": [ + " {\"type\": \"bird\", \"description\": \"brown; has a long beak; could have a crest on its head; could have a white belly\", \"similar objects\": [\"robin\", \"sparrow\", \"woodpecker\"]}", + 11 + ], + "simple": [ + "\n{\"type\": \"adjective\", \"description\": \"having few parts; not complicated; easy to understand\", \"similar objects\": [\"basic\", \"straightforward\", \"uncomplicated\"]}", + 11 + ], + "hummus": [ + " {\"type\": \"food\", \"description\": \"smooth, creamy, made of chickpeas; could be served with pita bread\", \"similar objects\": [\"guacamole\", \"tahini\", \"baba ghanoush\"]}", + 11 + ], + "blue cable": [ + " {\"type\": \"electronic device\", \"description\": \"long, blue, could be used for data transfer\", \"similar objects\": [\"USB cable\", \"HDMI cable\", \"power cable\"]}", + 11 + ], + "wood burning stove": [ + " {\"type\": \"heating tool\", \"description\": \"made of metal; has a chimney; could be used to burn wood\", \"similar objects\": [\"fireplace\", \"furnace\", \"wood stove\"]}", + 11 + ], + "orange base": [ + " {\"type\": \"furniture\", \"description\": \"round; could be made of wood or metal; could have a flat top\", \"similar objects\": [\"table\", \"chair\", \"stool\"]}", + 11 + ], + "bathroom sink basin": [ + " {\"type\": \"plumbing fixture\", \"description\": \"rectangular; could have a faucet; could have a drain\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 11 + ], + "travelers": [ + " {\"type\": \"people\", \"description\": \"people who travel from one place to another; could be on foot, by car, by train, by plane, etc.\", \"similar objects\": [\"tourists\", \"explorers\", \"adventurers\"]}", + 11 + ], + "houseboat": [ + " {\"type\": \"vessel\", \"description\": \"floating house; could be made of wood; could have a motor\", \"similar objects\": [\"yacht\", \"sailboat\", \"canoe\"]}", + 11 + ], + "pink donut": [ + "\n{\"type\": \"food\", \"description\": \"round; pink; has a hole in the middle; could be topped with sprinkles\", \"similar objects\": [\"doughnut\", \"bagel\", \"cupcake\"]}", + 11 + ], + "security cameras": [ + "\n{\"type\": \"surveillance tool\", \"description\": \"small, cylindrical; could be mounted on walls or ceilings; could be connected to a monitor\", \"similar objects\": [\"alarm system\", \"motion sensors\", \"doorbell camera\"]}", + 11 + ], + "post sign": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be made of metal; could have words or symbols on it\", \"similar objects\": [\"traffic sign\", \"street sign\", \"warning sign\"]}", + 11 + ], + "grey metal chain link fence": [ + "\n{\"type\": \"fence\", \"description\": \"made of metal; grey in color; has chain links\", \"similar objects\": [\"barbed wire fence\", \"wooden fence\", \"iron fence\"]}", + 11 + ], + "llama": [ + " {\"type\": \"animal\", \"description\": \"long neck; long fur; could have a hump on its back; could be used as a pack animal\", \"similar objects\": [\"alpaca\", \"camel\", \"goat\"]}", + 11 + ], + "boat reflection": [ + " {\"type\": \"reflection\", \"description\": \"reflection of a boat on the water; could be distorted by the waves\", \"similar objects\": [\"car reflection\", \"building reflection\", \"tree reflection\"]}", + 11 + ], + "display table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have shelves; could be used to display items\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"sideboard\"]}", + 11 + ], + "sunroof": [ + " {\"type\": \"automotive part\", \"description\": \"glass panel on the roof of a car; could be opened to let in light and air\", \"similar objects\": [\"moonroof\", \"convertible top\", \"spoiler\"]}", + 11 + ], + "shopping center": [ + " {\"type\": \"building\", \"description\": \"large; could have multiple stores; could have a parking lot\", \"similar objects\": [\"mall\", \"supermarket\", \"department store\"]}", + 11 + ], + "metal containers": [ + " {\"type\": \"storage tool\", \"description\": \"made of metal; could be cylindrical or rectangular; could have a lid\", \"similar objects\": [\"box\", \"jar\", \"bucket\"]}", + 11 + ], + "silver body": [ + " {\"type\": \"metal\", \"description\": \"shiny, reflective, malleable\", \"similar objects\": [\"gold\", \"copper\", \"aluminum\"]}", + 11 + ], + "metal street": [ + " {\"type\": \"building material\", \"description\": \"strong and durable; could be used for roads and pavements\", \"similar objects\": [\"asphalt\", \"concrete\", \"gravel\"]}", + 11 + ], + "rubber shoe": [ + " {\"type\": \"footwear\", \"description\": \"made of rubber; could be slip-on; could have a sole\", \"similar objects\": [\"sneakers\", \"sandals\", \"flip-flops\"]}", + 11 + ], + "nike tennis shoes": [ + "\n{\"type\": \"footwear\", \"description\": \"athletic shoes; could have a swoosh logo; could have laces; could have a rubber sole\", \"similar objects\": [\"adidas shoes\", \"converse shoes\", \"puma shoes\"]}", + 11 + ], + "orange peels": [ + " {\"type\": \"food waste\", \"description\": \"orange-colored; thin and dry; could be used as a natural cleaner\", \"similar objects\": [\"banana peels\", \"apple cores\", \"lemon peels\"]}", + 11 + ], + "metal doors": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be sliding or hinged; could be painted\", \"similar objects\": [\"wooden doors\", \"glass doors\", \"plastic doors\"]}", + 11 + ], + "boar": [ + " {\"type\": \"animal\", \"description\": \"large, hairy, tusks; could have a long snout; could have a short tail\", \"similar objects\": [\"pig\", \"wild hog\", \"warthog\"]}", + 11 + ], + "brown park bench": [ + "\n{\"type\": \"furniture\", \"description\": \"wooden; could have armrests; could have a backrest; could have a cushion\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 11 + ], + "plaster wall": [ + " {\"type\": \"building material\", \"description\": \"white; could be used to cover walls; could be painted\", \"similar objects\": [\"drywall\", \"tile\", \"wood paneling\"]}", + 11 + ], + "coke machine": [ + " {\"type\": \"vending machine\", \"description\": \"tall; could be red and white; could have a coin slot\", \"similar objects\": [\"snack machine\", \"soda machine\", \"vending machine\"]}", + 11 + ], + "gold curtains": [ + " {\"type\": \"decoration\", \"description\": \"shiny, yellow; could be made of fabric; could be hung on windows\", \"similar objects\": [\"drapes\", \"blinds\", \"curtains\"]}", + 11 + ], + "read": [ + "\n{\"type\": \"verb\", \"description\": \"to look at written or printed words and understand their meaning\", \"similar objects\": [\"write\", \"speak\", \"listen\"]}", + 11 + ], + "shadow train": [ + " {\"type\": \"toy\", \"description\": \"a toy train that casts a shadow on the wall; could be made of plastic\", \"similar objects\": [\"toy car\", \"toy airplane\", \"toy boat\"]}", + 11 + ], + "monks": [ + " {\"type\": \"people\", \"description\": \"robed; could have shaved heads; could be chanting\", \"similar objects\": [\"priests\", \"nuns\", \"monks\"]}", + 11 + ], + "lone skier": [ + " {\"type\": \"person\", \"description\": \"wearing ski gear; skiing on a mountain slope; could be alone or with a group\", \"similar objects\": [\"snowboarder\", \"hiker\", \"climber\"]}", + 11 + ], + "d number": [ + " {\"type\": \"number\", \"description\": \"a number between 0 and 9; could be written as a single digit or two digits\", \"similar objects\": [\"letter\", \"symbol\", \"character\"]}", + 11 + ], + "giraffes mouth": [ + "\n{\"type\": \"body part\", \"description\": \"long, black tongue; could reach leaves from tall trees\", \"similar objects\": [\"elephant's trunk\", \"hippo's mouth\", \"monkey's hands\"]}", + 11 + ], + "silver jet": [ + " {\"type\": \"vehicle\", \"description\": \"long, silver, aerodynamic; could have wings and a tail; could have a cockpit\", \"similar objects\": [\"airplane\", \"helicopter\", \"rocket\"]}", + 11 + ], + "kerchief": [ + " {\"type\": \"clothing accessory\", \"description\": \"square; could be made of cotton; could be used to cover the head\", \"similar objects\": [\"scarf\", \"bandana\", \"hat\"]}", + 11 + ], + "altar": [ + " {\"type\": \"religious structure\", \"description\": \"raised platform; could be decorated with candles and flowers; could be used for religious ceremonies\", \"similar objects\": [\"shrine\", \"temple\", \"church\"]}", + 11 + ], + "cement ramp": [ + " {\"type\": \"construction tool\", \"description\": \"sloped; made of cement; could be used to bridge two levels\", \"similar objects\": [\"stairs\", \"ladder\", \"bridge\"]}", + 11 + ], + "exclamation point": [ + " {\"type\": \"punctuation mark\", \"description\": \"a symbol used to express strong emotion or emphasis; looks like an upside-down 'i' with a period at the bottom\", \"similar objects\": [\"question mark\", \"period\", \"comma\"]}", + 11 + ], + "barefoot woman": [ + "\n{\"type\": \"person\", \"description\": \"not wearing any shoes; could have long hair; could be wearing a dress\", \"similar objects\": [\"man\", \"child\", \"elderly person\"]}", + 11 + ], + "disturbance": [ + " {\"type\": \"event\", \"description\": \"unwanted noise; could be caused by people or machines; could be disruptive\", \"similar objects\": [\"commotion\", \"uproar\", \"ruckus\"]}", + 11 + ], + "fruit market": [ + " {\"type\": \"place\", \"description\": \"a place where fruits are sold; could have a variety of fruits\", \"similar objects\": [\"vegetable market\", \"grocery store\", \"supermarket\"]}", + 11 + ], + "sugar donut": [ + "\n{\"type\": \"food\", \"description\": \"round; has a hole in the middle; covered with sugar\", \"similar objects\": [\"glazed donut\", \"jelly donut\", \"cinnamon donut\"]}", + 11 + ], + "train carts": [ + " {\"type\": \"transportation\", \"description\": \"long, connected, could have multiple compartments\", \"similar objects\": [\"bus\", \"tram\", \"trolley\"]}", + 11 + ], + "camera case": [ + " {\"type\": \"accessory\", \"description\": \"protective case for cameras; could be made of leather or plastic; could have a strap\", \"similar objects\": [\"lens cap\", \"camera strap\", \"battery charger\"]}", + 11 + ], + "copyright notice": [ + "\n{\"type\": \"legal document\", \"description\": \"a statement that gives the copyright holder exclusive rights to reproduce, distribute, and create derivative works of the copyrighted work\", \"similar objects\": [\"trademark\", \"patent\", \"license agreement\"]}", + 11 + ], + "metro train": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could be electric or diesel powered; could have multiple doors\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 11 + ], + "space key": [ + " {\"type\": \"keyboard key\", \"description\": \"rectangular; usually located between the 'alt' and 'ctrl' keys; used to create a space between words\", \"similar objects\": [\"enter key\", \"shift key\", \"backspace key\"]}", + 11 + ], + "playground equipment": [ + " {\"type\": \"recreational tool\", \"description\": \"could be made of metal or plastic; could have slides, swings, seesaws, etc.\", \"similar objects\": [\"playground set\", \"playground structure\", \"playground toy\"]}", + 11 + ], + "vent hood": [ + " {\"type\": \"kitchen appliance\", \"description\": \"mounted above the stove; has a fan and a filter; could be made of stainless steel\", \"similar objects\": [\"range hood\", \"exhaust fan\", \"cooker hood\"]}", + 11 + ], + "door latch": [ + " {\"type\": \"hardware\", \"description\": \"metal; used to secure a door; could be opened with a key or a knob\", \"similar objects\": [\"lock\", \"hinge\", \"handle\"]}", + 11 + ], + "purple shoes": [ + " {\"type\": \"footwear\", \"description\": \"made of fabric or leather; could have laces; could be high-heeled; could be decorated with stones or sequins\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 11 + ], + "cast iron skillet": [ + " {\"type\": \"cooking tool\", \"description\": \"heavy, round, has a handle; could be used for frying, baking, and searing\", \"similar objects\": [\"pan\", \"pot\", \"frying pan\"]}", + 11 + ], + "brown boat": [ + "\n{\"type\": \"watercraft\", \"description\": \"brown; could be made of wood; could have a sail\", \"similar objects\": [\"canoe\", \"kayak\", \"yacht\"]}", + 11 + ], + "toliet": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a bowl; could have a lid; could be connected to a water tank\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 11 + ], + "plastic sunglasses": [ + "\n{\"type\": \"eyewear\", \"description\": \"transparent; could be tinted; could be curved\", \"similar objects\": [\"sunglasses\", \"eyeglasses\", \"goggles\"]}", + 11 + ], + "bottom window": [ + " {\"type\": \"window\", \"description\": \"located at the bottom of a wall; could be opened and closed\", \"similar objects\": [\"top window\", \"side window\", \"skylight\"]}", + 11 + ], + "colander": [ + " {\"type\": \"cooking tool\", \"description\": \"round; has holes; could be made of metal or plastic\", \"similar objects\": [\"strainer\", \"sieve\", \"skimmer\"]}", + 11 + ], + "railway lines": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, straight lines; could be made of steel; could be connected to railway stations\", \"similar objects\": [\"highway\", \"road\", \"bridge\"]}", + 11 + ], + "metal bin": [ + " {\"type\": \"container\", \"description\": \"rectangular; made of metal; could have a lid\", \"similar objects\": [\"trash can\", \"garbage can\", \"storage bin\"]}", + 11 + ], + "motor home": [ + " {\"type\": \"vehicle\", \"description\": \"large, self-contained recreational vehicle; could have a kitchen, bedroom, and bathroom; could be used for camping\", \"similar objects\": [\"campervan\", \"RV\", \"trailer\"]}", + 11 + ], + "train crossing": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"has two metal bars that cross the road; has a warning sign; could have a bell or a siren\", \"similar objects\": [\"traffic light\", \"stop sign\", \"bridge\"]}", + 11 + ], + "fangs": [ + " {\"type\": \"body part\", \"description\": \"sharp, pointed teeth; usually found in animals\", \"similar objects\": [\"claws\", \"horns\", \"scales\"]}", + 11 + ], + "pinwheel": [ + " {\"type\": \"toy\", \"description\": \"round; has colorful blades; could be attached to a stick\", \"similar objects\": [\"kite\", \"top\", \"yo-yo\"]}", + 11 + ], + "cement surface": [ + " {\"type\": \"building material\", \"description\": \"hard, gray, rough; could be used for flooring\", \"similar objects\": [\"concrete\", \"tile\", \"asphalt\"]}", + 11 + ], + "cake tray": [ + " {\"type\": \"baking tool\", \"description\": \"rectangular; could be made of metal; could have a handle\", \"similar objects\": [\"baking pan\", \"cookie sheet\", \"pie dish\"]}", + 11 + ], + "burgundy car": [ + "\n{\"type\": \"vehicle\", \"description\": \"burgundy color; could have four doors; could have a trunk\", \"similar objects\": [\"sedan\", \"SUV\", \"truck\"]}", + 11 + ], + "side head": [ + " {\"type\": \"hairstyle\", \"description\": \"hair is parted to one side; could be styled with curls or waves\", \"similar objects\": [\"bob cut\", \"pixie cut\", \"mohawk\"]}", + 11 + ], + "antler": [ + " {\"type\": \"animal body part\", \"description\": \"branch-like structure; could be found on the head of deer, elk, and moose\", \"similar objects\": [\"horns\", \"tusks\", \"claws\"]}", + 11 + ], + "male player": [ + " {\"type\": \"person\", \"description\": \"male; could be wearing a uniform; could be playing a sport\", \"similar objects\": [\"female player\", \"coach\", \"referee\"]}", + 11 + ], + "frozen": [ + " {\"type\": \"food state\", \"description\": \"solid; cold; could be thawed\", \"similar objects\": [\"refrigerated\", \"chilled\", \"iced\"]}", + 11 + ], + "metal wheel": [ + " {\"type\": \"mechanical tool\", \"description\": \"round; could be made of metal; could be used to move objects\", \"similar objects\": [\"pulley\", \"gear\", \"axle\"]}", + 11 + ], + "grey asphalt": [ + " {\"type\": \"building material\", \"description\": \"dark grey; could be used for roads and pavements; could be mixed with sand and gravel\", \"similar objects\": [\"concrete\", \"cement\", \"tar\"]}", + 11 + ], + "lg logo": [ + " {\"type\": \"logo\", \"description\": \"black and white; has two curved lines; has a circle in the middle\", \"similar objects\": [\"apple logo\", \"nike logo\", \"coca-cola logo\"]}\n\nObject detection models should focus on the shape, color, size, texture, and other visual features of the input object. For example, for the input zucchini, the model should focus on the cylindrical shape, green color, smooth texture, and brown and rough stems. For the input ambulance, the model should focus on the red color, glaring siren, and the", + 11 + ], + "pink hair": [ + " {\"type\": \"accessory\", \"description\": \"colored hair; could be made of synthetic fibers\", \"similar objects\": [\"wig\", \"hair extensions\", \"headband\"]}", + 11 + ], + "hazy clouds": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white or grey; could be thick or thin; could be low or high in the sky\", \"similar objects\": [\"fog\", \"rain\", \"snow\"]}", + 11 + ], + "wall plug": [ + " {\"type\": \"electrical tool\", \"description\": \"rectangular; has two or three pins; could be used to connect electrical appliances\", \"similar objects\": [\"socket\", \"outlet\", \"power strip\"]}", + 11 + ], + "metal chain link": [ + " {\"type\": \"hardware\", \"description\": \"interlocking metal rings; could be used for security purposes\", \"similar objects\": [\"padlock\", \"lock\", \"hinge\"]}", + 11 + ], + "safety fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal; could be used to protect people from danger\", \"similar objects\": [\"guardrail\", \"barbed wire\", \"wall\"]}", + 11 + ], + "silver tap": [ + " {\"type\": \"plumbing tool\", \"description\": \"shiny; could be used to control water flow; could be attached to a sink\", \"similar objects\": [\"faucet\", \"valve\", \"shower head\"]}", + 11 + ], + "scallions": [ + " {\"type\": \"vegetable\", \"description\": \"long, thin, green; could have white roots; could be chopped into small pieces; could have a mild onion flavor\", \"similar objects\": [\"onion\", \"shallot\", \"leek\"]}", + 11 + ], + "payphone": [ + " {\"type\": \"communication tool\", \"description\": \"box-shaped; has a coin slot; could be attached to a wall\", \"similar objects\": [\"telephone booth\", \"cell phone\", \"walkie-talkie\"]}", + 11 + ], + "crosswalk street": [ + " {\"type\": \"road marking\", \"description\": \"white lines on the road; could have pedestrian signs; could have traffic lights\", \"similar objects\": [\"stop sign\", \"yield sign\", \"traffic circle\"]}", + 11 + ], + "pink tag": [ + " {\"type\": \"accessory\", \"description\": \"small, rectangular; could be made of paper or plastic; could have words or symbols printed on it\", \"similar objects\": [\"name tag\", \"label\", \"badge\"]}", + 11 + ], + "color umbrella": [ + " {\"type\": \"accessory\", \"description\": \"could be made of fabric; could be colorful; could be opened and closed\", \"similar objects\": [\"hat\", \"sunglasses\", \"scarf\"]}", + 11 + ], + "icy": [ + " {\"type\": \"weather condition\", \"description\": \"cold; could be slippery; could be covered with snow\", \"similar objects\": [\"frosty\", \"snowy\", \"freezing\"]}", + 11 + ], + "tire rim": [ + " {\"type\": \"automotive part\", \"description\": \"circular; made of metal; used to hold the tire in place\", \"similar objects\": [\"wheel hub\", \"wheel bearing\", \"brake rotor\"]}", + 11 + ], + "wii box": [ + " {\"type\": \"gaming console\", \"description\": \"rectangular; has a disc drive; could be connected to a TV\", \"similar objects\": [\"PlayStation\", \"Xbox\", \"Nintendo Switch\"]}", + 11 + ], + "pink sky": [ + " {\"type\": \"phenomenon\", \"description\": \"a sky with pink color; could be seen during sunrise or sunset\", \"similar objects\": [\"purple sky\", \"orange sky\", \"blue sky\"]}", + 11 + ], + "surfboard ocean": [ + "\n{\"type\": \"water sports equipment\", \"description\": \"long and wide; could be made of foam; could have a fin\", \"similar objects\": [\"paddleboard\", \"kayak\", \"canoe\"]}", + 11 + ], + "axe": [ + " {\"type\": \"tool\", \"description\": \"long handle; sharp blade; could be used for chopping wood\", \"similar objects\": [\"hatchet\", \"hammer\", \"saw\"]}", + 11 + ], + "metal tennis racket": [ + "\n{\"type\": \"sports equipment\", \"description\": \"made of metal; has a handle and strings; could be used to hit a tennis ball\", \"similar objects\": [\"golf club\", \"baseball bat\", \"hockey stick\"]}", + 11 + ], + "entry sign": [ + " {\"type\": \"signage\", \"description\": \"could be made of metal or wood; could have words or symbols; could be hung on a wall or door\", \"similar objects\": [\"exit sign\", \"warning sign\", \"street sign\"]}", + 11 + ], + "knee socks": [ + " {\"type\": \"clothing\", \"description\": \"long socks that reach up to the knee; could be made of cotton, wool, or nylon\", \"similar objects\": [\"ankle socks\", \"stockings\", \"tights\"]}", + 11 + ], + "rock jutting": [ + " {\"type\": \"geological formation\", \"description\": \"a large rock that protrudes from the ground; could be made of different types of rocks; could be found in different shapes and sizes\", \"similar objects\": [\"cliff\", \"boulder\", \"mountain\"]}", + 11 + ], + "cruise boat": [ + " {\"type\": \"vehicle\", \"description\": \"large; could have multiple decks; could have a swimming pool; could have a restaurant\", \"similar objects\": [\"yacht\", \"ferry\", \"sailboat\"]}", + 11 + ], + "bovine": [ + " {\"type\": \"animal\", \"description\": \"large, four-legged mammal; could have horns; could have a long tail; could have a thick coat of fur\", \"similar objects\": [\"cow\", \"buffalo\", \"bull\"]}", + 11 + ], + "bear claws": [ + " {\"type\": \"pastry\", \"description\": \"flaky, sweet, crescent-shaped; could be filled with custard or cream\", \"similar objects\": [\"croissant\", \"danish\", \"donut\"]}", + 11 + ], + "trash barrel": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of plastic; has a lid\", \"similar objects\": [\"bin\", \"garbage can\", \"recycling bin\"]}", + 11 + ], + "wisps": [ + " {\"type\": \"phenomenon\", \"description\": \"thin, light, and wispy; could be seen in the sky; could be made of smoke or dust\", \"similar objects\": [\"clouds\", \"aurora\", \"fog\"]}", + 11 + ], + "door vehicle": [ + "\n{\"type\": \"vehicle accessory\", \"description\": \"hinged; could be opened and closed; could be made of metal or wood\", \"similar objects\": [\"hood\", \"trunk\", \"bumper\"]}", + 11 + ], + "tee-shirt": [ + " {\"type\": \"clothing\", \"description\": \"has a round neck; could be short-sleeved or long-sleeved; could be plain or patterned\", \"similar objects\": [\"tank top\", \"polo shirt\", \"sweater\"]}", + 11 + ], + "ski mask": [ + " {\"type\": \"clothing accessory\", \"description\": \"covers the face; could be made of wool or cotton; could be black or white\", \"similar objects\": [\"balaclava\", \"bandana\", \"scarf\"]}", + 11 + ], + "stabilizers": [ + " {\"type\": \"accessory\", \"description\": \"attached to the wheels of a vehicle; helps to maintain balance\", \"similar objects\": [\"shock absorbers\", \"tyres\", \"wheel rims\"]}", + 11 + ], + "water front": [ + " {\"type\": \"landscape\", \"description\": \"area near a body of water; could have docks, beaches, and other recreational areas\", \"similar objects\": [\"lakefront\", \"riverfront\", \"oceanfront\"]}", + 11 + ], + "dolphins": [ + " {\"type\": \"animal\", \"description\": \"gray; has a curved mouth; could be found in the ocean\", \"similar objects\": [\"whales\", \"sharks\", \"seals\"]}", + 11 + ], + "field grass": [ + " {\"type\": \"plant\", \"description\": \"green; could be tall; could be short; could be in a field\", \"similar objects\": [\"wheat\", \"corn\", \"rye\"]}", + 11 + ], + "wood dock": [ + " {\"type\": \"structure\", \"description\": \"made of wood; could be used as a platform for boats; could be used as a walkway\", \"similar objects\": [\"pier\", \"jetty\", \"wharf\"]}", + 11 + ], + "rear paw": [ + " {\"type\": \"animal body part\", \"description\": \"back paw of a four-legged animal; could have claws\", \"similar objects\": [\"front paw\", \"tail\", \"nose\"]}", + 11 + ], + "machete": [ + " {\"type\": \"tool\", \"description\": \"long, sharp blade; could be used for cutting\", \"similar objects\": [\"axe\", \"hatchet\", \"knife\"]}", + 11 + ], + "wood platform": [ + " {\"type\": \"structure\", \"description\": \"flat surface; could be made of wood; could be used as a stage\", \"similar objects\": [\"deck\", \"porch\", \"balcony\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant,", + 11 + ], + "ave": [ + " {\"type\": \"bird\", \"description\": \"small; could have colorful feathers; could have a curved beak\", \"similar objects\": [\"sparrow\", \"pigeon\", \"robin\"]}", + 11 + ], + "traces": [ + " {\"type\": \"evidence\", \"description\": \"visible marks left by an object or person; could be footprints, tire marks, etc.\", \"similar objects\": [\"clues\", \"signs\", \"indicators\"]}", + 11 + ], + "patrons": [ + " {\"type\": \"people\", \"description\": \"people who support or are customers of a business or organization\", \"similar objects\": [\"clients\", \"customers\", \"supporters\"]}", + 11 + ], + "author": [ + " {\"type\": \"person\", \"description\": \"writes books; could be a poet; could be a journalist\", \"similar objects\": [\"writer\", \"editor\", \"publisher\"]}", + 11 + ], + "lighting fixture": [ + " {\"type\": \"lighting tool\", \"description\": \"attached to the ceiling; could be made of metal or glass; could have multiple bulbs\", \"similar objects\": [\"chandelier\", \"ceiling fan\", \"pendant light\"]}", + 11 + ], + "orange tennis racket": [ + "\n{\"type\": \"sports equipment\", \"description\": \"orange; has a handle and strings; could be used to hit a tennis ball\", \"similar objects\": [\"golf club\", \"baseball bat\", \"hockey stick\"]}", + 11 + ], + "house window": [ + " {\"type\": \"building component\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"door\", \"balcony\", \"skylight\"]}", + 11 + ], + "metro bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; has multiple doors; could be painted in a certain color\", \"similar objects\": [\"school bus\", \"trolley bus\", \"tour bus\"]}", + 11 + ], + "panel door": [ + " {\"type\": \"door\", \"description\": \"rectangular; could be made of wood or metal; could have multiple panels\", \"similar objects\": [\"sliding door\", \"pocket door\", \"barn door\"]}", + 11 + ], + "knee guard": [ + " {\"type\": \"protective gear\", \"description\": \"elastic; could be made of foam; could be strapped around the knee\", \"similar objects\": [\"elbow guard\", \"shin guard\", \"wrist guard\"]}", + 11 + ], + "concrete slabs": [ + " {\"type\": \"building material\", \"description\": \"flat, heavy, gray; could be used for paving roads\", \"similar objects\": [\"bricks\", \"tiles\", \"stones\"]}", + 11 + ], + "ornamentation": [ + " {\"type\": \"decoration\", \"description\": \"could be made of metal, glass, or plastic; could be hung on walls or trees; could be used to decorate a room or garden\", \"similar objects\": [\"figurine\", \"sculpture\", \"painting\"]}", + 11 + ], + "girraffe": [ + " {\"type\": \"animal\", \"description\": \"long neck; has a long mane; has spots\", \"similar objects\": [\"zebra\", \"elephant\", \"horse\"]}", + 11 + ], + "birch tree": [ + " {\"type\": \"plant\", \"description\": \"tall; has white bark; has thin leaves\", \"similar objects\": [\"oak tree\", \"maple tree\", \"pine tree\"]}", + 11 + ], + "gold mirror": [ + "\n{\"type\": \"decorative item\", \"description\": \"round; has a golden frame; could be used to reflect light\", \"similar objects\": [\"silver mirror\", \"picture frame\", \"wall clock\"]}", + 11 + ], + "camp chair": [ + " {\"type\": \"furniture\", \"description\": \"foldable; could be made of metal or fabric; could have armrests\", \"similar objects\": [\"folding chair\", \"stool\", \"bean bag chair\"]}", + 11 + ], + "round knob": [ + " {\"type\": \"hardware\", \"description\": \"round; could be used to open a door; could be made of metal or plastic\", \"similar objects\": [\"handle\", \"lock\", \"hinge\"]}", + 11 + ], + "cobblestone sidewalk": [ + " {\"type\": \"pavement\", \"description\": \"made of small, rounded stones; could be used as a walkway\", \"similar objects\": [\"gravel path\", \"brick path\", \"concrete sidewalk\"]}", + 11 + ], + "bot": [ + " {\"type\": \"software\", \"description\": \"computer program that can perform automated tasks\", \"similar objects\": [\"robot\", \"AI\", \"chatbot\"]}", + 11 + ], + "magenta": [ + " {\"type\": \"color\", \"description\": \"vivid purplish-red; could be used to describe a hue\", \"similar objects\": [\"red\", \"pink\", \"purple\"]}", + 11 + ], + "metal structures": [ + " {\"type\": \"building material\", \"description\": \"strong and durable; could be used for construction; could be made of steel, aluminum, or iron\", \"similar objects\": [\"wood\", \"concrete\", \"glass\"]}", + 11 + ], + "indicators": [ + " {\"type\": \"signal device\", \"description\": \"could be red, yellow, or green; could be used to indicate traffic flow\", \"similar objects\": [\"traffic lights\", \"stop signs\", \"warning signs\"]}", + 11 + ], + "tombstone": [ + " {\"type\": \"memorial object\", \"description\": \"rectangular; could be made of stone or metal; could have inscriptions\", \"similar objects\": [\"monument\", \"plaque\", \"statue\"]}", + 11 + ], + "grey fur": [ + " {\"type\": \"fabric\", \"description\": \"soft; could be used for clothing; could be made of wool or synthetic fibers\", \"similar objects\": [\"velvet\", \"cotton\", \"leather\"]}", + 11 + ], + "train engine car": [ + " {\"type\": \"vehicle\", \"description\": \"long; has a locomotive; could have multiple cars attached\", \"similar objects\": [\"tram\", \"monorail\", \"subway\"]}", + 11 + ], + "subway train": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; has multiple compartments; could be powered by electricity\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 11 + ], + "leafy branch": [ + " {\"type\": \"plant part\", \"description\": \"green; could have multiple leaves; could be curved\", \"similar objects\": [\"stem\", \"twig\", \"flower\"]}", + 11 + ], + "shields": [ + " {\"type\": \"protective tool\", \"description\": \"round; could be made of metal; could be used to protect from weapons\", \"similar objects\": [\"helmet\", \"armor\", \"shields\"]}", + 11 + ], + "toilet paper rolls": [ + " {\"type\": \"household item\", \"description\": \"cylindrical; could be made of paper; could be used for wiping\", \"similar objects\": [\"paper towels\", \"tissues\", \"napkins\"]}", + 11 + ], + "horses ears": [ + "\n{\"type\": \"body part\", \"description\": \"long, pointy, protruding from the sides of the head\", \"similar objects\": [\"donkey ears\", \"cow ears\", \"pig ears\"]}", + 11 + ], + "stance": [ + " {\"type\": \"posture\", \"description\": \"the position of the body when standing; could be upright, leaning, or crouching\", \"similar objects\": [\"pose\", \"position\", \"attitude\"]}", + 11 + ], + "hippos": [ + " {\"type\": \"animal\", \"description\": \"large, gray; has a wide mouth; could be found in water\", \"similar objects\": [\"crocodiles\", \"elephants\", \"rhinoceroses\"]}", + 11 + ], + "bathroom sinks": [ + " {\"type\": \"plumbing fixture\", \"description\": \"could be made of porcelain; could have one or two basins; could have a faucet\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}", + 11 + ], + "hankerchief": [ + " {\"type\": \"clothing accessory\", \"description\": \"square; could be made of cotton; could be used to wipe sweat or tears\", \"similar objects\": [\"scarf\", \"bandana\", \"turban\"]}", + 11 + ], + "engine plane": [ + " {\"type\": \"vehicle\", \"description\": \"large; has wings; has an engine; could fly\", \"similar objects\": [\"helicopter\", \"rocket\", \"airplane\"]}", + 11 + ], + "silver building": [ + " {\"type\": \"structure\", \"description\": \"made of silver material; could have multiple floors; could have windows and doors\", \"similar objects\": [\"skyscraper\", \"mansion\", \"castle\"]}", + 11 + ], + "wall tire": [ + " {\"type\": \"exercise tool\", \"description\": \"round; could be attached to a wall; could be used for stretching and strengthening muscles\", \"similar objects\": [\"resistance band\", \"medicine ball\", \"pull-up bar\"]}", + 11 + ], + "flood light": [ + " {\"type\": \"lighting tool\", \"description\": \"large, bright, directional light; could be used for outdoor lighting\", \"similar objects\": [\"spotlight\", \"lantern\", \"torch\"]}", + 11 + ], + "doubledecker bus": [ + " {\"type\": \"vehicle\", \"description\": \"long; two levels; could have an open top deck\", \"similar objects\": [\"trolley bus\", \"school bus\", \"coach bus\"]}", + 11 + ], + "sands": [ + " {\"type\": \"material\", \"description\": \"fine, granular particles; could be found in beaches; could be used for construction\", \"similar objects\": [\"gravel\", \"soil\", \"clay\"]}", + 11 + ], + "pink flamingo": [ + " {\"type\": \"bird\", \"description\": \"pink; long neck; long legs; could stand on one leg\", \"similar objects\": [\"crane\", \"stork\", \"swan\"]}", + 11 + ], + "eye brows": [ + " {\"type\": \"facial feature\", \"description\": \"two curved lines above the eyes; could be thin or thick; could be arched or straight\", \"similar objects\": [\"eyelashes\", \"eyelids\", \"nose\"]}", + 11 + ], + "bottom lip": [ + "\n{\"type\": \"body part\", \"description\": \"lower part of the face; could be protruded when smiling\", \"similar objects\": [\"upper lip\", \"chin\", \"nose\"]}", + 11 + ], + "apple tree": [ + " {\"type\": \"plant\", \"description\": \"tall; has a trunk; has green leaves; could have red apples\", \"similar objects\": [\"oak tree\", \"pine tree\", \"cherry tree\"]}", + 11 + ], + "gras": [ + " {\"type\": \"plant\", \"description\": \"green; could be found in lawns; could be cut with a lawn mower\", \"similar objects\": [\"weed\", \"clover\", \"dandelion\"]}", + 11 + ], + "end sign": [ + " {\"type\": \"road sign\", \"description\": \"octagonal; red and white; has an arrow pointing downwards\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 11 + ], + "warning signs": [ + " {\"type\": \"safety signs\", \"description\": \"triangular; could be yellow or red; could have symbols or words\", \"similar objects\": [\"stop signs\", \"road signs\", \"traffic signs\"]}", + 11 + ], + "snow glove": [ + " {\"type\": \"clothing item\", \"description\": \"long; made of thick fabric; could be waterproof; could have fur lining\", \"similar objects\": [\"winter coat\", \"ski pants\", \"snow boots\"]}", + 11 + ], + "sport coat": [ + " {\"type\": \"clothing\", \"description\": \"long, tailored, usually made of wool; could have buttons and pockets\", \"similar objects\": [\"blazer\", \"suit jacket\", \"tuxedo\"]}", + 11 + ], + "copse": [ + " {\"type\": \"landscape\", \"description\": \"a group of trees; could be a dense forest\", \"similar objects\": [\"grove\", \"woodland\", \"thicket\"]}", + 11 + ], + "leashes": [ + " {\"type\": \"pet accessory\", \"description\": \"long straps; could be made of leather or nylon; could be used to control pets\", \"similar objects\": [\"collar\", \"harness\", \"muzzle\"]}", + 11 + ], + "eyeglass": [ + " {\"type\": \"eyewear\", \"description\": \"rectangular; could be made of metal or plastic; could have lenses\", \"similar objects\": [\"sunglasses\", \"reading glasses\", \"safety glasses\"]}", + 11 + ], + "left engine": [ + " {\"type\": \"machine part\", \"description\": \"cylindrical; could be made of metal; could be connected to a car\", \"similar objects\": [\"right engine\", \"transmission\", \"exhaust pipe\"]}", + 11 + ], + "rubble": [ + " {\"type\": \"debris\", \"description\": \"broken pieces of rocks, bricks, and other materials; could be scattered on the ground\", \"similar objects\": [\"debris\", \"wreckage\", \"rubbish\"]}", + 11 + ], + "cloud sky": [ + " {\"type\": \"weather phenomenon\", \"description\": \"white, fluffy, could be seen in the sky; could be shaped like animals or objects\", \"similar objects\": [\"rainbow\", \"sunshine\", \"thunderstorm\"]}", + 11 + ], + "stern": [ + " {\"type\": \"nautical term\", \"description\": \"the back of a boat; could be used to describe a person's attitude\", \"similar objects\": [\"bow\", \"hull\", \"keel\"]}", + 11 + ], + "mountaintop": [ + " {\"type\": \"landscape\", \"description\": \"high elevation; could have snow; could have a peak\", \"similar objects\": [\"hill\", \"valley\", \"cliff\"]}", + 11 + ], + "grey legs": [ + " {\"type\": \"furniture\", \"description\": \"long, slender, could be made of metal or wood; could have a cushion on top\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 11 + ], + "birds feathers": [ + " {\"type\": \"animal part\", \"description\": \"lightweight; could be colorful; could be used for flying\", \"similar objects\": [\"insect wings\", \"mammal fur\", \"fish scales\"]}", + 11 + ], + "golf clubs": [ + " {\"type\": \"sports equipment\", \"description\": \"various sizes and shapes; could be made of metal or wood; used to hit a golf ball\", \"similar objects\": [\"tennis racket\", \"baseball bat\", \"hockey stick\"]}", + 11 + ], + "fondant": [ + " {\"type\": \"food\", \"description\": \"sugary paste; could be used to decorate cakes\", \"similar objects\": [\"icing\", \"marzipan\", \"buttercream\"]}", + 11 + ], + "sea waters": [ + " {\"type\": \"natural element\", \"description\": \"blue; could be salty; could be deep\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 11 + ], + "elephants mouth": [ + " {\"type\": \"body part\", \"description\": \"long, curved trunk; used for drinking, breathing, and picking up objects\", \"similar objects\": [\"giraffe's neck\", \"hippo's mouth\", \"rhino's horn\"]}", + 11 + ], + "plane number": [ + " {\"type\": \"aircraft\", \"description\": \"has a unique identification number; could be a commercial or military aircraft\", \"similar objects\": [\"helicopter\", \"drone\", \"balloon\"]}", + 11 + ], + "wire mesh fence": [ + " {\"type\": \"barrier\", \"description\": \"made of metal wires; could be used to separate areas\", \"similar objects\": [\"chain link fence\", \"barbed wire fence\", \"wooden fence\"]}", + 11 + ], + "house number": [ + " {\"type\": \"address marker\", \"description\": \"could be made of metal or plastic; could be attached to a wall or door; could have numbers or letters\", \"similar objects\": [\"mailbox\", \"doorbell\", \"nameplate\"]}", + 11 + ], + "thorns": [ + " {\"type\": \"plant part\", \"description\": \"sharp, pointed, and rigid; could be found on stems and branches of plants\", \"similar objects\": [\"spines\", \"prickles\", \"bristles\"]}", + 11 + ], + "cheetah": [ + " {\"type\": \"animal\", \"description\": \"spotted; has a long tail; could run very fast\", \"similar objects\": [\"leopard\", \"jaguar\", \"lion\"]}", + 11 + ], + "bear ground": [ + " {\"type\": \"animal\", \"description\": \"large; brown fur; could hibernate in winter; could stand on two legs\", \"similar objects\": [\"grizzly bear\", \"polar bear\", \"koala\"]}", + 11 + ], + "bear leg": [ + " {\"type\": \"animal body part\", \"description\": \"hairy; could be brown or black; could have claws\", \"similar objects\": [\"tiger leg\", \"wolf leg\", \"fox leg\"]}", + 11 + ], + "brick area": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be used to build walls\", \"similar objects\": [\"concrete\", \"stone\", \"wood\"]}", + 11 + ], + "calf muscle": [ + " {\"type\": \"body part\", \"description\": \"muscle located in the lower leg; connects the knee to the ankle; helps with movement\", \"similar objects\": [\"quadriceps\", \"hamstrings\", \"glutes\"]}", + 11 + ], + "ballplayer": [ + " {\"type\": \"athlete\", \"description\": \"wears a uniform; could be playing baseball, basketball, soccer, etc.\", \"similar objects\": [\"runner\", \"swimmer\", \"cyclist\"]}", + 11 + ], + "tail plane": [ + " {\"type\": \"aircraft\", \"description\": \"has a long tail; could be used for military purposes\", \"similar objects\": [\"fighter jet\", \"helicopter\", \"bomber\"]}", + 11 + ], + "pink tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring\", \"similar objects\": [\"blue tile\", \"white tile\", \"green tile\"]}", + 11 + ], + "blue ramp": [ + " {\"type\": \"structure\", \"description\": \"sloped surface; could be made of metal or wood; could be painted blue\", \"similar objects\": [\"stairs\", \"ladder\", \"escalator\"]}", + 11 + ], + "cockatoo": [ + " {\"type\": \"bird\", \"description\": \"white or grey feathers; crest on the head; curved beak; could make loud noises\", \"similar objects\": [\"parrot\", \"macaw\", \"pigeon\"]}", + 11 + ], + "round metal": [ + " {\"type\": \"object\", \"description\": \"circular; made of metal; could be used for various purposes\", \"similar objects\": [\"coin\", \"ring\", \"disc\"]}", + 11 + ], + "leather motorcycle seat": [ + "\n{\"type\": \"motorcycle accessory\", \"description\": \"made of leather; designed to fit a motorcycle; could have a backrest\", \"similar objects\": [\"motorcycle helmet\", \"motorcycle gloves\", \"motorcycle boots\"]}", + 11 + ], + "beagle": [ + " {\"type\": \"animal\", \"description\": \"small, short-haired, brown and white; has a long tail; could be a hunting dog\", \"similar objects\": [\"cocker spaniel\", \"dachshund\", \"labrador retriever\"]}", + 11 + ], + "serena": [ + " {\"type\": \"name\", \"description\": \"female name; could be a given name or surname\", \"similar objects\": [\"sophia\", \"olivia\", \"emma\"]}", + 11 + ], + "ewe": [ + " {\"type\": \"animal\", \"description\": \"female sheep; has horns; could have white or black fur\", \"similar objects\": [\"lamb\", \"goat\", \"ram\"]}", + 11 + ], + "wing flaps": [ + " {\"type\": \"aircraft part\", \"description\": \"movable parts of an aircraft; used to control the direction of the aircraft\", \"similar objects\": [\"ailerons\", \"elevators\", \"rudders\"]}", + 11 + ], + "brussel sprout": [ + " {\"type\": \"vegetable\", \"description\": \"small, round, green; could have a stem; could be cooked\", \"similar objects\": [\"cauliflower\", \"broccoli\", \"kale\"]}", + 10 + ], + "burgandy": [ + " {\"type\": \"color\", \"description\": \"deep red; could be used to describe clothing, furniture, and other items\", \"similar objects\": [\"maroon\", \"crimson\", \"scarlet\"]}", + 10 + ], + "condiment cup": [ + " {\"type\": \"container\", \"description\": \"small, round, could be made of plastic; could have a lid\", \"similar objects\": [\"sauce cup\", \"soup bowl\", \"salt shaker\"]}", + 10 + ], + "metal rings": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of metal; could be used as a keychain\", \"similar objects\": [\"bracelet\", \"earrings\", \"necklace\"]}", + 10 + ], + "legos": [ + " {\"type\": \"toy\", \"description\": \"interlocking plastic blocks; could be used to build structures\", \"similar objects\": [\"building blocks\", \"construction sets\", \"action figures\"]}", + 10 + ], + "plane windows": [ + " {\"type\": \"aircraft part\", \"description\": \"transparent; could be round or rectangular; could be opened or closed\", \"similar objects\": [\"cockpit\", \"wings\", \"fuselage\"]}", + 10 + ], + "grey leg": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, could be feathered; could be used for flying\", \"similar objects\": [\"wing\", \"beak\", \"tail\"]}", + 10 + ], + "paper shopping bag": [ + " {\"type\": \"container\", \"description\": \"made of paper; could have handles; could be printed with logos\", \"similar objects\": [\"plastic bag\", \"tote bag\", \"backpack\"]}", + 10 + ], + "telephone handset": [ + " {\"type\": \"communication device\", \"description\": \"long, slim, has a mouthpiece and a receiver\", \"similar objects\": [\"cell phone\", \"walkie-talkie\", \"intercom\"]}", + 10 + ], + "breakfast plate": [ + " {\"type\": \"dishware\", \"description\": \"round; could be made of ceramic; could have a handle\", \"similar objects\": [\"dinner plate\", \"bowl\", \"cup\"]}", + 10 + ], + "circle logo": [ + " {\"type\": \"logo\", \"description\": \"round; could be made of different colors; could have a symbol in the middle\", \"similar objects\": [\"square logo\", \"triangle logo\", \"hexagon logo\"]}", + 10 + ], + "ice chest": [ + " {\"type\": \"storage tool\", \"description\": \"rectangular; could be made of plastic; could have a handle\", \"similar objects\": [\"cooler\", \"box\", \"bag\"]}", + 10 + ], + "zookeeper": [ + " {\"type\": \"occupation\", \"description\": \"takes care of animals in a zoo; could feed animals; could clean cages\", \"similar objects\": [\"veterinarian\", \"animal trainer\", \"animal keeper\"]}", + 10 + ], + "computer case": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; could be made of metal; could have USB ports; could have a fan\", \"similar objects\": [\"laptop\", \"desktop\", \"tablet\"]}", + 10 + ], + "brown mushrooms": [ + " {\"type\": \"vegetable\", \"description\": \"brown, round, could have white spots; could be sliced into pieces; could have a stem\", \"similar objects\": [\"white mushrooms\", \"portobello mushrooms\", \"shiitake mushrooms\"]}", + 10 + ], + "buddha statue": [ + " {\"type\": \"sculpture\", \"description\": \"depicts a seated figure with a serene expression; could be made of stone, metal, or wood; could have a halo around the head\", \"similar objects\": [\"angel statue\", \"goddess statue\", \"saint statue\"]}", + 10 + ], + "dips": [ + " {\"type\": \"food\", \"description\": \"sauce-based condiment; could be served with chips, vegetables, or bread\", \"similar objects\": [\"salsa\", \"guacamole\", \"hummus\"]}", + 10 + ], + "chees": [ + " {\"type\": \"food\", \"description\": \"dairy product; could be soft, hard, or semi-soft; could be yellow or white; could be sliced or grated\", \"similar objects\": [\"yogurt\", \"butter\", \"milk\"]}", + 10 + ], + "business cards": [ + " {\"type\": \"stationery\", \"description\": \"small, rectangular; could be printed with contact information\", \"similar objects\": [\"letterhead\", \"envelope\", \"postcard\"]}", + 10 + ], + "arch window": [ + " {\"type\": \"architectural element\", \"description\": \"semicircular window; could be made of glass or wood\", \"similar objects\": [\"bay window\", \"round window\", \"oval window\"]}", + 10 + ], + "broccoli crown": [ + " {\"type\": \"vegetable\", \"description\": \"green, florets; could have a stem; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"brussels sprouts\", \"asparagus\"]}", + 10 + ], + "hill side": [ + " {\"type\": \"landscape\", \"description\": \"sloped terrain; could have trees and grass; could have a path\", \"similar objects\": [\"mountain\", \"valley\", \"cliff\"]}", + 10 + ], + "side airplane": [ + " {\"type\": \"vehicle\", \"description\": \"long and narrow; has wings and a tail; could have two or four engines\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}", + 10 + ], + "brown poles": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, brown; could be used for construction\", \"similar objects\": [\"wooden beams\", \"steel bars\", \"concrete pillars\"]}", + 10 + ], + "play area": [ + " {\"type\": \"recreational area\", \"description\": \"could have slides, swings, and other play equipment; could be outdoors or indoors\", \"similar objects\": [\"playground\", \"park\", \"amusement park\"]}", + 10 + ], + "ponytail woman": [ + "\n{\"type\": \"hairstyle\", \"description\": \"long hair tied up in a high ponytail; could have bangs\", \"similar objects\": [\"bun\", \"braid\", \"pigtails\"]}", + 10 + ], + "baseball stadium": [ + " {\"type\": \"structure\", \"description\": \"large; has a diamond-shaped field; could have stands for spectators\", \"similar objects\": [\"soccer stadium\", \"basketball court\", \"tennis court\"]}", + 10 + ], + "bat batter": [ + " {\"type\": \"sports equipment\", \"description\": \"wooden; has a handle; used to hit a ball\", \"similar objects\": [\"baseball bat\", \"golf club\", \"tennis racket\"]}", + 10 + ], + "prop": [ + " {\"type\": \"theater tool\", \"description\": \"could be made of wood or metal; used to support a stage set\", \"similar objects\": [\"scenery\", \"backdrop\", \"costume\"]}", + 10 + ], + "pants man": [ + " {\"type\": \"clothing\", \"description\": \"long trousers; could be made of cotton, denim, or other materials; could have pockets; could have a belt\", \"similar objects\": [\"jeans\", \"shorts\", \"skirt\"]}", + 10 + ], + "orange yellow": [ + "\n{\"type\": \"color combination\", \"description\": \"a combination of orange and yellow colors\", \"similar objects\": [\"red green\", \"blue purple\", \"black white\"]}", + 10 + ], + "period": [ + " {\"type\": \"punctuation mark\", \"description\": \"a dot used to end a sentence\", \"similar objects\": [\"comma\", \"exclamation mark\", \"question mark\"]}", + 10 + ], + "kitchen knives": [ + " {\"type\": \"cooking tool\", \"description\": \"sharp; could be made of metal; could have a handle\", \"similar objects\": [\"fork\", \"spoon\", \"chopping board\"]}", + 10 + ], + "silver parking meter": [ + "\n{\"type\": \"parking tool\", \"description\": \"silver; has a slot for coins; could have a digital display\", \"similar objects\": [\"parking kiosk\", \"parking ticket machine\", \"parking pay station\"]}", + 10 + ], + "bench seats": [ + " {\"type\": \"furniture\", \"description\": \"long, wooden, could have backrests; could be used for seating\", \"similar objects\": [\"sofa\", \"chair\", \"stool\"]}", + 10 + ], + "silver wheel": [ + " {\"type\": \"decorative item\", \"description\": \"round; could be made of silver; could be used as a wall decoration\", \"similar objects\": [\"gold wheel\", \"bronze wheel\", \"copper wheel\"]}", + 10 + ], + "jacket hood": [ + " {\"type\": \"clothing accessory\", \"description\": \"attached to the back of a jacket; could be made of fur or other materials; could be adjustable\", \"similar objects\": [\"scarf\", \"hat\", \"gloves\"]}", + 10 + ], + "blue catcher": [ + " {\"type\": \"sports equipment\", \"description\": \"glove-like; used to catch a ball; could be made of leather\", \"similar objects\": [\"baseball bat\", \"baseball glove\", \"batting helmet\"]}", + 10 + ], + "water pond": [ + " {\"type\": \"water body\", \"description\": \"large body of water; could have aquatic plants; could have fish\", \"similar objects\": [\"lake\", \"river\", \"stream\"]}", + 10 + ], + "velvet rope": [ + " {\"type\": \"barrier\", \"description\": \"long, thick, made of velvet; could be used to form a line\", \"similar objects\": [\"chain\", \"barricade\", \"fence\"]}", + 10 + ], + "orange rope": [ + "\n{\"type\": \"utility item\", \"description\": \"orange in color; could be made of nylon or cotton; could be used for tying or hanging\", \"similar objects\": [\"yellow rope\", \"blue rope\", \"red rope\"]}", + 10 + ], + "shopper": [ + " {\"type\": \"person\", \"description\": \"carrying a bag; could be shopping\", \"similar objects\": [\"customer\", \"buyer\", \"shopper\"]}", + 10 + ], + "pink glove": [ + " {\"type\": \"protective gear\", \"description\": \"pink; could be made of fabric; could be used for medical purposes\", \"similar objects\": [\"mask\", \"goggles\", \"apron\"]}", + 10 + ], + "stair rail": [ + " {\"type\": \"safety tool\", \"description\": \"long, metal; could be curved; could be attached to the wall\", \"similar objects\": [\"handrail\", \"guardrail\", \"balustrade\"]}", + 10 + ], + "ziploc bag": [ + " {\"type\": \"storage tool\", \"description\": \"transparent; could be sealed; could be reusable\", \"similar objects\": [\"plastic bag\", \"container\", \"lunch box\"]}", + 10 + ], + "huts": [ + " {\"type\": \"structure\", \"description\": \"small, round, made of wood and straw; could have a thatched roof\", \"similar objects\": [\"cabins\", \"igloos\", \"tents\"]}", + 10 + ], + "community": [ + " {\"type\": \"social group\", \"description\": \"a group of people living in the same area and having common interests\", \"similar objects\": [\"neighborhood\", \"society\", \"village\"]}", + 10 + ], + "stone street": [ + " {\"type\": \"road surface\", \"description\": \"made of stones; could be bumpy; could be slippery when wet\", \"similar objects\": [\"gravel road\", \"asphalt road\", \"dirt road\"]}", + 10 + ], + "bleacher seats": [ + " {\"type\": \"seating\", \"description\": \"long, wooden, with backrests; could be arranged in rows\", \"similar objects\": [\"bench\", \"stadium seats\", \"bleacher chairs\"]}", + 10 + ], + "silver metal fencing": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be in a roll; could be in a panel; could be in a post\", \"similar objects\": [\"iron fencing\", \"aluminum fencing\", \"wooden fencing\"]}", + 10 + ], + "brown stripe": [ + " {\"type\": \"pattern\", \"description\": \"a pattern of alternating light and dark colors; could be used for decoration\", \"similar objects\": [\"plaid\", \"checkerboard\", \"polka dot\"]}", + 10 + ], + "barrier fence": [ + " {\"type\": \"barrier\", \"description\": \"long, metal, has pointed tips; could be used to separate areas\", \"similar objects\": [\"wall\", \"gate\", \"hedge\"]}", + 10 + ], + "orange dirt": [ + " {\"type\": \"soil\", \"description\": \"orange-colored; could be sandy or clay-like; could contain organic matter\", \"similar objects\": [\"red dirt\", \"black dirt\", \"brown dirt\"]}", + 10 + ], + "steel beam": [ + " {\"type\": \"construction material\", \"description\": \"long, rectangular, metallic; could be used for support\", \"similar objects\": [\"wood beam\", \"concrete block\", \"rebar\"]}", + 10 + ], + "car license plate": [ + " {\"type\": \"identification tool\", \"description\": \"rectangular; has numbers and letters; could be attached to a car\", \"similar objects\": [\"driver's license\", \"passport\", \"ID card\"]}", + 10 + ], + "grey handle": [ + " {\"type\": \"handle\", \"description\": \"grey; could be made of metal or plastic; could be used for doors, drawers, cabinets, etc.\", \"similar objects\": [\"knob\", \"pull\", \"hinge\"]}", + 10 + ], + "beaker": [ + " {\"type\": \"laboratory tool\", \"description\": \"cylindrical; could have a spout; could have a handle\", \"similar objects\": [\"flask\", \"test tube\", \"graduated cylinder\"]}", + 10 + ], + "business signs": [ + " {\"type\": \"advertisement tool\", \"description\": \"could be made of metal, plastic, or paper; could be in different shapes and sizes; could be illuminated or non-illuminated\", \"similar objects\": [\"billboards\", \"posters\", \"banners\"]}", + 10 + ], + "breaking wave": [ + " {\"type\": \"natural phenomenon\", \"description\": \"a wave that breaks on the shoreline; could be white and foamy; could be powerful\", \"similar objects\": [\"tide\", \"tsunami\", \"storm surge\"]}", + 10 + ], + "pizza spatula": [ + " {\"type\": \"cooking tool\", \"description\": \"long handle; flat and wide blade; could be made of metal or wood\", \"similar objects\": [\"turner\", \"spoon\", \"tongs\"]}", + 10 + ], + "rectangular box": [ + "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of cardboard; could have a lid\", \"similar objects\": [\"crate\", \"trunk\", \"suitcase\"]}", + 10 + ], + "starfish": [ + " {\"type\": \"animal\", \"description\": \"five-pointed; could be orange, red, or purple; could have spines on its body\", \"similar objects\": [\"sea urchin\", \"crab\", \"jellyfish\"]}", + 10 + ], + "phone pole": [ + " {\"type\": \"utility pole\", \"description\": \"tall, cylindrical; could have wires attached to it\", \"similar objects\": [\"street light pole\", \"traffic light pole\", \"telegraph pole\"]}", + 10 + ], + "umbrella pole": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, metal; could be used to hold an umbrella\", \"similar objects\": [\"walking stick\", \"hiking pole\", \"flagpole\"]}", + 10 + ], + "chocolate chip cookie": [ + "\n{\"type\": \"food\", \"description\": \"round; has chocolate chips; could be soft or crunchy\", \"similar objects\": [\"oatmeal cookie\", \"sugar cookie\", \"peanut butter cookie\"]}", + 10 + ], + "scoreboard wall": [ + " {\"type\": \"sports equipment\", \"description\": \"large wall with numbers and letters; could be used to display scores\", \"similar objects\": [\"scoreboard table\", \"scoreboard board\", \"scoreboard display\"]}", + 10 + ], + "wave ocean": [ + " {\"type\": \"natural phenomenon\", \"description\": \"water movement; could be caused by wind or tide\", \"similar objects\": [\"tsunami\", \"tidal wave\", \"storm surge\"]}", + 10 + ], + "leafy tree branch": [ + "\n{\"type\": \"plant part\", \"description\": \"long, thin, could have leaves; could be curved\", \"similar objects\": [\"twig\", \"stem\", \"vine\"]}", + 10 + ], + "round bush": [ + " {\"type\": \"plant\", \"description\": \"green; could have yellow flowers; could be trimmed into a ball shape\", \"similar objects\": [\"hedge\", \"shrub\", \"topiary\"]}", + 10 + ], + "plastic dish": [ + " {\"type\": \"utensil\", \"description\": \"lightweight; could be transparent; could be used for serving food\", \"similar objects\": [\"plate\", \"bowl\", \"cup\"]}", + 10 + ], + "metal bottle": [ + " {\"type\": \"container\", \"description\": \"made of metal; could be cylindrical or rectangular; could have a lid\", \"similar objects\": [\"thermos\", \"jar\", \"can\"]}", + 10 + ], + "fringes": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, decorative pieces of fabric; could be attached to clothing or other items\", \"similar objects\": [\"tassels\", \"beads\", \"sequins\"]}", + 10 + ], + "purse brown": [ + " {\"type\": \"accessory\", \"description\": \"small, rectangular, could be made of leather; could have a strap\", \"similar objects\": [\"wallet\", \"clutch\", \"handbag\"]}", + 10 + ], + "spiral": [ + " {\"type\": \"shape\", \"description\": \"curved line that winds around a center point; could be clockwise or counterclockwise\", \"similar objects\": [\"circle\", \"helix\", \"ellipse\"]}", + 10 + ], + "yellow pot": [ + "\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle; could be yellow in color\", \"similar objects\": [\"pan\", \"wok\", \"frying pan\"]}", + 10 + ], + "metal grid": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be used as a fence; could be used as a support structure\", \"similar objects\": [\"wire mesh\", \"chain link fence\", \"barbed wire\"]}", + 10 + ], + "hospital bed": [ + " {\"type\": \"furniture\", \"description\": \"long; has wheels; could be adjustable; could have a mattress\", \"similar objects\": [\"wheelchair\", \"stretcher\", \"operating table\"]}", + 10 + ], + "soap dispensers": [ + " {\"type\": \"cleaning tool\", \"description\": \"could be wall-mounted; could be automatic; could be manual\", \"similar objects\": [\"hand sanitizer dispenser\", \"toilet paper dispenser\", \"paper towel dispenser\"]}", + 10 + ], + "boiler": [ + " {\"type\": \"appliance\", \"description\": \"large, cylindrical; used to heat water\", \"similar objects\": [\"furnace\", \"water heater\", \"radiator\"]}", + 10 + ], + "cable lines": [ + " {\"type\": \"utility\", \"description\": \"long, thin wires; could be used for electricity, internet, or phone\", \"similar objects\": [\"power lines\", \"fiber optics\", \"telephone lines\"]}", + 10 + ], + "wooden wheels": [ + " {\"type\": \"object\", \"description\": \"round; made of wood; could be used for toys or furniture\", \"similar objects\": [\"plastic wheels\", \"metal wheels\", \"rubber wheels\"]}", + 10 + ], + "crinkle": [ + " {\"type\": \"sound\", \"description\": \"a sharp, high-pitched sound; could be made by metal objects\", \"similar objects\": [\"squeak\", \"hiss\", \"buzz\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant,", + 10 + ], + "front house": [ + " {\"type\": \"building\", \"description\": \"has a door; could have windows; could have a porch; could have a garden\", \"similar objects\": [\"garage\", \"shed\", \"barn\"]}", + 10 + ], + "kitchen countertop": [ + " {\"type\": \"furniture\", \"description\": \"flat surface; could be made of stone, wood, or other materials; could have cabinets underneath\", \"similar objects\": [\"table\", \"island\", \"cabinet\"]}", + 10 + ], + "crispy piece": [ + " {\"type\": \"food\", \"description\": \"crispy, crunchy; could be made of flour; could be fried or baked\", \"similar objects\": [\"fries\", \"chips\", \"crackers\"]}", + 10 + ], + "candy cane": [ + " {\"type\": \"food\", \"description\": \"long, striped, curved; could be red and white; could be made of sugar\", \"similar objects\": [\"lollipop\", \"chocolate bar\", \"jelly beans\"]}", + 10 + ], + "rickshaw": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; could be pulled by a person; could have a canopy\", \"similar objects\": [\"bicycle\", \"tricycle\", \"scooter\"]}", + 10 + ], + "leather handbag": [ + " {\"type\": \"accessory\", \"description\": \"made of leather; could have straps; could have a zipper\", \"similar objects\": [\"purse\", \"backpack\", \"wallet\"]}", + 10 + ], + "onesie": [ + " {\"type\": \"clothing\", \"description\": \"a one-piece garment for an infant or young child; usually has snaps or zippers at the crotch\", \"similar objects\": [\"romper\", \"bodysuit\", \"jumpsuit\"]}", + 10 + ], + "orange fabric": [ + " {\"type\": \"material\", \"description\": \"orange color; could be made of cotton, silk, or polyester; could be used for clothing, curtains, or upholstery\", \"similar objects\": [\"yellow fabric\", \"green fabric\", \"blue fabric\"]}", + 10 + ], + "orange thing": [ + "\n{\"type\": \"object\", \"description\": \"round; could be orange in color; could be a fruit or other object\", \"similar objects\": [\"apple\", \"lemon\", \"peach\"]}", + 10 + ], + "orange wrist band": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of rubber or fabric; could be used for fashion or sports\", \"similar objects\": [\"bracelet\", \"watch\", \"headband\"]}", + 10 + ], + "metal floor lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"tall; made of metal; could have a round base; could have a lampshade\", \"similar objects\": [\"table lamp\", \"floor lamp\", \"ceiling lamp\"]}", + 10 + ], + "plum": [ + " {\"type\": \"fruit\", \"description\": \"round; purple or red; has a stone inside\", \"similar objects\": [\"apricot\", \"peach\", \"cherry\"]}", + 10 + ], + "washcloths": [ + " {\"type\": \"cleaning tool\", \"description\": \"rectangular; could be made of cotton; could be used for cleaning\", \"similar objects\": [\"towel\", \"sponge\", \"rag\"]}", + 10 + ], + "sports field": [ + " {\"type\": \"outdoor area\", \"description\": \"large, grassy, could have lines and goals\", \"similar objects\": [\"stadium\", \"court\", \"track\"]}", + 10 + ], + "thickets": [ + " {\"type\": \"vegetation\", \"description\": \"densely packed shrubs or small trees; could be used as a natural fence\", \"similar objects\": [\"hedge\", \"bush\", \"bramble\"]}", + 10 + ], + "banks": [ + " {\"type\": \"financial institution\", \"description\": \"provides financial services such as deposits, loans, investments, and insurance; could have branches and ATMs\", \"similar objects\": [\"credit union\", \"savings and loan\", \"investment bank\"]}", + 10 + ], + "banans": [ + " {\"type\": \"fruit\", \"description\": \"long, curved, yellow; has a stem\", \"similar objects\": [\"apple\", \"orange\", \"pear\"]}", + 10 + ], + "ac unit": [ + " {\"type\": \"appliance\", \"description\": \"rectangular; has a fan; could be mounted on a wall or window\", \"similar objects\": [\"air conditioner\", \"heater\", \"humidifier\"]}", + 10 + ], + "security officer": [ + " {\"type\": \"person\", \"description\": \"uniformed; could be armed; could be patrolling\", \"similar objects\": [\"police officer\", \"guard\", \"security guard\"]}", + 10 + ], + "basil leaves": [ + " {\"type\": \"herb\", \"description\": \"green; has a strong smell; could be used as a seasoning\", \"similar objects\": [\"parsley\", \"oregano\", \"thyme\"]}\n\nObject detection models should focus on the shape, color, texture, size, and other physical characteristics of the object. Additionally, they should consider the context of the object, such as its environment, and any associated objects or activities. For example, for the input of zucchini, the model should focus on its cylindrical shape, green color, smooth texture, and the presence of brown and rough stems", + 10 + ], + "pennant": [ + " {\"type\": \"decoration\", \"description\": \"triangular; could be made of fabric; could be hung on a pole\", \"similar objects\": [\"flag\", \"banner\", \"streamer\"]}", + 10 + ], + "round objects": [ + "\n{\"type\": \"shape\", \"description\": \"circular; could be made of different materials; could have different sizes\", \"similar objects\": [\"circle\", \"sphere\", \"ball\"]}", + 10 + ], + "mcdonalds sign": [ + "\n{\"type\": \"brand logo\", \"description\": \"yellow background with red lettering; has an image of a clown\", \"similar objects\": [\"burger king\", \"starbucks\", \"kfc\"]}", + 10 + ], + "dr": [ + "\n{\"type\": \"title\", \"description\": \"abbreviation for Doctor; could be used as a title for a medical professional\", \"similar objects\": [\"professor\", \"mr\", \"mrs\"]}", + 10 + ], + "reflective window": [ + " {\"type\": \"building material\", \"description\": \"transparent; could reflect light; could be used to insulate heat\", \"similar objects\": [\"glass\", \"mirror\", \"plastic\"]}", + 10 + ], + "elevation": [ + " {\"type\": \"geographical term\", \"description\": \"the height of a place above sea level\", \"similar objects\": [\"altitude\", \"height\", \"depth\"]}", + 10 + ], + "tile pattern": [ + " {\"type\": \"decoration\", \"description\": \"geometric shapes; could be made of ceramic, stone, or glass; could be used to decorate walls and floors\", \"similar objects\": [\"mosaic\", \"rug\", \"wallpaper\"]}", + 10 + ], + "glass vases": [ + " {\"type\": \"decorative item\", \"description\": \"transparent; could be made of glass or crystal; could be of various shapes and sizes\", \"similar objects\": [\"urns\", \"bowls\", \"jars\"]}", + 10 + ], + "wall divider": [ + " {\"type\": \"furniture\", \"description\": \"could be made of wood, metal, or fabric; could be used to separate rooms\", \"similar objects\": [\"room divider\", \"screen\", \"partition\"]}", + 10 + ], + "female player": [ + " {\"type\": \"person\", \"description\": \"female; could be playing a sport or a game\", \"similar objects\": [\"male player\", \"coach\", \"referee\"]}", + 10 + ], + "broccoli leaf": [ + " {\"type\": \"vegetable\", \"description\": \"green; could have a stem; could be cooked or eaten raw\", \"similar objects\": [\"cauliflower\", \"kale\", \"spinach\"]}", + 10 + ], + "wood barn": [ + " {\"type\": \"structure\", \"description\": \"large, rectangular; made of wood; could have a door and windows\", \"similar objects\": [\"shed\", \"garage\", \"house\"]}", + 10 + ], + "bakers": [ + " {\"type\": \"profession\", \"description\": \"people who make breads, cakes, and other baked goods\", \"similar objects\": [\"chef\", \"cook\", \"pastry chef\"]}", + 10 + ], + "wood cabinet door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have a handle\", \"similar objects\": [\"drawer\", \"wardrobe\", \"cupboard\"]}", + 10 + ], + "cinder block": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of concrete; could be used for construction\", \"similar objects\": [\"bricks\", \"concrete blocks\", \"pavers\"]}", + 10 + ], + "lampstand": [ + " {\"type\": \"furniture\", \"description\": \"tall; could be made of metal or wood; could have a lampshade\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 10 + ], + "bedskirt": [ + " {\"type\": \"bedding accessory\", \"description\": \"long, rectangular; usually made of fabric; covers the space between the mattress and the floor\", \"similar objects\": [\"bedspread\", \"comforter\", \"duvet cover\"]}", + 10 + ], + "giraffe feeding": [ + "\n{\"type\": \"animal behavior\", \"description\": \"giraffe reaching its neck to feed on leaves from trees\", \"similar objects\": [\"elephant eating\", \"zebra drinking\", \"monkey climbing\"]}", + 10 + ], + "feed": [ + " {\"type\": \"food\", \"description\": \"could be in the form of pellets, powder, or liquid; could be used for animals or plants\", \"similar objects\": [\"fertilizer\", \"grain\", \"seed\"]}", + 10 + ], + "bouquets": [ + " {\"type\": \"decoration\", \"description\": \"arrangement of flowers; could be tied with a ribbon\", \"similar objects\": [\"wreath\", \"basket\", \"vase\"]}", + 10 + ], + "light shirt": [ + " {\"type\": \"clothing\", \"description\": \"lightweight; could be made of cotton; could be short-sleeved or long-sleeved\", \"similar objects\": [\"t-shirt\", \"tank top\", \"blouse\"]}", + 10 + ], + "blue clothing": [ + " {\"type\": \"clothing\", \"description\": \"blue color; could be made of cotton, silk, or other fabrics; could be in different styles\", \"similar objects\": [\"red clothing\", \"green clothing\", \"black clothing\"]}", + 10 + ], + "plastic computer mouse": [ + "\n{\"type\": \"computer accessory\", \"description\": \"rectangular; has two buttons and a wheel; could be wireless\", \"similar objects\": [\"keyboard\", \"headset\", \"webcam\"]}", + 10 + ], + "glass enclosure": [ + " {\"type\": \"structure\", \"description\": \"transparent; could be made of glass or plastic; could be used to contain animals or plants\", \"similar objects\": [\"aquarium\", \"terrarium\", \"greenhouse\"]}", + 10 + ], + "serving platter": [ + " {\"type\": \"dishware\", \"description\": \"flat, round, could be made of ceramic or metal; could have handles\", \"similar objects\": [\"plate\", \"bowl\", \"tray\"]}", + 10 + ], + "metal walkway": [ + " {\"type\": \"structure\", \"description\": \"made of metal; could be used as a bridge; could be used as a pathway\", \"similar objects\": [\"bridge\", \"staircase\", \"escalator\"]}", + 10 + ], + "orange soda": [ + " {\"type\": \"beverage\", \"description\": \"orange-colored; carbonated; sweet\", \"similar objects\": [\"lemonade\", \"cola\", \"root beer\"]}", + 10 + ], + "tank lid": [ + " {\"type\": \"container lid\", \"description\": \"round; could be made of metal; could be used to cover a tank\", \"similar objects\": [\"jar lid\", \"pot lid\", \"bucket lid\"]}", + 10 + ], + "harley davidson logo": [ + "\n{\"type\": \"logo\", \"description\": \"orange and black; has a winged wheel; has the words 'Harley Davidson'\", \"similar objects\": [\"Ford logo\", \"Chevrolet logo\", \"Honda logo\"]}", + 10 + ], + "gifts": [ + " {\"type\": \"item\", \"description\": \"could be wrapped in paper; could be of various shapes and sizes; could be of various materials\", \"similar objects\": [\"presents\", \"toys\", \"souvenirs\"]}", + 10 + ], + "wraps": [ + " {\"type\": \"food\", \"description\": \"flatbread; could be filled with vegetables, meat, and sauces\", \"similar objects\": [\"tacos\", \"burritos\", \"sandwiches\"]}", + 10 + ], + "stove knobs": [ + " {\"type\": \"cooking tool\", \"description\": \"round; could be made of metal; used to control the temperature of the stove\", \"similar objects\": [\"oven knobs\", \"burner knobs\", \"gas knobs\"]}", + 10 + ], + "persons finger": [ + "\n{\"type\": \"body part\", \"description\": \"long, thin, flexible; could have a nail at the end; could be used for pointing\", \"similar objects\": [\"toe\", \"elbow\", \"knee\"]}", + 10 + ], + "tres": [ + " {\"type\": \"musical instrument\", \"description\": \"stringed instrument; has three strings; could be plucked or strummed\", \"similar objects\": [\"guitar\", \"ukulele\", \"banjo\"]}", + 10 + ], + "silver compact car": [ + "\n{\"type\": \"vehicle\", \"description\": \"small, four-wheeled, silver-colored; could have a sunroof; could have four doors\", \"similar objects\": [\"sedan\", \"SUV\", \"hatchback\"]}", + 10 + ], + "restroom sign": [ + " {\"type\": \"signage\", \"description\": \"could be a symbol or a word; could be a male or female figure; could be a combination of both\", \"similar objects\": [\"exit sign\", \"no smoking sign\", \"warning sign\"]}", + 10 + ], + "flowering tree": [ + " {\"type\": \"plant\", \"description\": \"has colorful flowers; could have leaves; could have a trunk\", \"similar objects\": [\"cherry tree\", \"maple tree\", \"oak tree\"]}", + 10 + ], + "diamond sign": [ + " {\"type\": \"symbol\", \"description\": \"rhombus shape; four equal sides; two acute angles and two obtuse angles\", \"similar objects\": [\"square\", \"triangle\", \"circle\"]}", + 10 + ], + "call button": [ + " {\"type\": \"electronic device\", \"description\": \"small, round, has a button; could be used to call for help\", \"similar objects\": [\"doorbell\", \"panic button\", \"emergency button\"]}", + 10 + ], + "gummy bear": [ + " {\"type\": \"candy\", \"description\": \"small, chewy, sweet; could be in different colors and shapes\", \"similar objects\": [\"jelly beans\", \"marshmallows\", \"licorice\"]}", + 10 + ], + "gummy bears": [ + " {\"type\": \"candy\", \"description\": \"small, chewy, sweet; could be in different colors and shapes\", \"similar objects\": [\"jelly beans\", \"marshmallows\", \"licorice\"]}", + 10 + ], + "wall cabinets": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have doors and shelves\", \"similar objects\": [\"bookshelf\", \"dresser\", \"wardrobe\"]}", + 10 + ], + "brick fire place": [ + " {\"type\": \"structure\", \"description\": \"made of bricks; could have a chimney; could have a mantel\", \"similar objects\": [\"wood stove\", \"fire pit\", \"fireplace insert\"]}", + 10 + ], + "skater shoes": [ + " {\"type\": \"footwear\", \"description\": \"low-top; could have laces; could have a rubber sole; could have a logo\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 10 + ], + "half circle": [ + " {\"type\": \"shape\", \"description\": \"180 degree arc; could be drawn with a compass\", \"similar objects\": [\"circle\", \"triangle\", \"square\"]}", + 10 + ], + "change": [ + " {\"type\": \"concept\", \"description\": \"the act of making something different; could be a transformation\", \"similar objects\": [\"transformation\", \"alteration\", \"modification\"]}", + 10 + ], + "shiny nose": [ + " {\"type\": \"body part\", \"description\": \"round; could be made of metal; could be used to detect smells\", \"similar objects\": [\"eyes\", \"ears\", \"mouth\"]}", + 10 + ], + "smoking": [ + " {\"type\": \"activity\", \"description\": \"inhaling and exhaling smoke from a cigarette, cigar, or pipe\", \"similar objects\": [\"vaping\", \"tobacco use\", \"secondhand smoke\"]}", + 10 + ], + "delivery van": [ + " {\"type\": \"vehicle\", \"description\": \"box-shaped; could have a logo of a company; could have a sliding door\", \"similar objects\": [\"truck\", \"pickup truck\", \"minivan\"]}", + 10 + ], + "linens": [ + " {\"type\": \"textile\", \"description\": \"fabric used for bedding, tablecloths, curtains, etc.\", \"similar objects\": [\"towels\", \"blankets\", \"cushions\"]}", + 10 + ], + "silver pickup truck": [ + "\n{\"type\": \"vehicle\", \"description\": \"silver; has a bed in the back; could have four doors\", \"similar objects\": [\"SUV\", \"van\", \"sedan\"]}", + 10 + ], + "sideview": [ + " {\"type\": \"mirror\", \"description\": \"rectangular; could be attached to a vehicle; could be used to see the side of the vehicle\", \"similar objects\": [\"rearview mirror\", \"wing mirror\", \"interior mirror\"]}", + 10 + ], + "candlesticks": [ + " {\"type\": \"decorative item\", \"description\": \"tall, thin, metal; could have a holder for a candle\", \"similar objects\": [\"vases\", \"statues\", \"figurines\"]}", + 10 + ], + "bathroom vanity mirror": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; could be framed; could be illuminated; could be mounted on the wall\", \"similar objects\": [\"dresser mirror\", \"vanity table\", \"makeup mirror\"]}", + 10 + ], + "beverage glass": [ + " {\"type\": \"drinking tool\", \"description\": \"transparent; could be made of glass or plastic; could have a stem\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 10 + ], + "silver vent": [ + " {\"type\": \"ventilation tool\", \"description\": \"silver; could be round or rectangular; could be used to circulate air\", \"similar objects\": [\"fan\", \"air conditioner\", \"heater\"]}", + 10 + ], + "girl skiing": [ + "\n{\"type\": \"activity\", \"description\": \"girl wearing ski gear; skiing on snow-covered slopes; using ski poles\", \"similar objects\": [\"boy skiing\", \"snowboarding\", \"ice skating\"]}", + 10 + ], + "notebook computer": [ + "\n{\"type\": \"electronic device\", \"description\": \"portable; has a keyboard and a screen; could be used for writing and computing\", \"similar objects\": [\"laptop\", \"tablet\", \"desktop computer\"]}", + 10 + ], + "porcelain vase": [ + "\n{\"type\": \"decorative item\", \"description\": \"smooth, white, cylindrical; could have intricate designs; could be used to hold flowers\", \"similar objects\": [\"urn\", \"urns\", \"pottery\"]}", + 10 + ], + "orange curtain": [ + "\n{\"type\": \"decoration item\", \"description\": \"orange; could be made of fabric; could be hung on a window\", \"similar objects\": [\"red curtain\", \"blue curtain\", \"green curtain\"]}", + 10 + ], + "fruit tree": [ + " {\"type\": \"plant\", \"description\": \"tall; could have fruits; could have leaves\", \"similar objects\": [\"apple tree\", \"orange tree\", \"pear tree\"]}", + 10 + ], + "wooden window sill": [ + "\n{\"type\": \"building material\", \"description\": \"long, rectangular, made of wood; could be used as a shelf\", \"similar objects\": [\"wooden door frame\", \"wooden flooring\", \"wooden wall panel\"]}", + 10 + ], + "coffee shop": [ + " {\"type\": \"establishment\", \"description\": \"place where coffee and other beverages are served; could have tables and chairs; could have a counter\", \"similar objects\": [\"cafe\", \"restaurant\", \"bar\"]}", + 10 + ], + "laundry": [ + " {\"type\": \"activity\", \"description\": \"washing clothes; could be done by hand or machine\", \"similar objects\": [\"ironing\", \"drying\", \"folding\"]}", + 10 + ], + "clay court": [ + " {\"type\": \"sports court\", \"description\": \"flat, made of clay; could be used for tennis, squash, and other sports\", \"similar objects\": [\"grass court\", \"hard court\", \"synthetic court\"]}", + 10 + ], + "zebra mouth": [ + "\n{\"type\": \"animal body part\", \"description\": \"long, black and white striped; has two lips\", \"similar objects\": [\"horse mouth\", \"giraffe mouth\", \"elephant mouth\"]}", + 10 + ], + "train traffic signal": [ + " {\"type\": \"traffic signal\", \"description\": \"red, yellow, and green lights; could be placed near railway tracks\", \"similar objects\": [\"road traffic signal\", \"stop sign\", \"yield sign\"]}", + 10 + ], + "flipflops": [ + " {\"type\": \"footwear\", \"description\": \"flat; could be made of rubber; could have straps\", \"similar objects\": [\"sandals\", \"slippers\", \"sneakers\"]}", + 10 + ], + "car tracks": [ + " {\"type\": \"toy\", \"description\": \"long, plastic; could be connected to form a track; could be used with toy cars\", \"similar objects\": [\"train tracks\", \"building blocks\", \"Lego\"]}", + 10 + ], + "diners": [ + " {\"type\": \"restaurant\", \"description\": \"could be a fast food restaurant; could have a drive-thru; could have a counter and tables\", \"similar objects\": [\"cafeteria\", \"food court\", \"buffet\"]}", + 10 + ], + "earlobe": [ + " {\"type\": \"body part\", \"description\": \"soft, fleshy, located on the side of the head; could be pierced\", \"similar objects\": [\"eyebrow\", \"nose\", \"cheek\"]}", + 10 + ], + "adult cat": [ + " {\"type\": \"animal\", \"description\": \"fur; four legs; whiskers; tail; could have stripes or spots; could meow\", \"similar objects\": [\"kitten\", \"dog\", \"rabbit\"]}", + 10 + ], + "street traffic light": [ + "\n{\"type\": \"traffic signal\", \"description\": \"red, yellow, and green lights; could be mounted on a pole; could be automated\", \"similar objects\": [\"stop sign\", \"pedestrian crossing sign\", \"traffic camera\"]}", + 10 + ], + "spigot": [ + " {\"type\": \"plumbing tool\", \"description\": \"has a handle; could be used to control the flow of water\", \"similar objects\": [\"faucet\", \"tap\", \"valve\"]}", + 10 + ], + "canada": [ + " {\"type\": \"country\", \"description\": \"second largest country in the world; has 10 provinces and 3 territories; has a maple leaf as its national symbol\", \"similar objects\": [\"United States\", \"Mexico\", \"Australia\"]}", + 10 + ], + "divider wall": [ + " {\"type\": \"furniture\", \"description\": \"tall, thin, could be made of wood or metal; could be used to separate rooms\", \"similar objects\": [\"room divider\", \"screen\", \"partition wall\"]}", + 10 + ], + "cottage cheese": [ + " {\"type\": \"dairy product\", \"description\": \"soft, white, creamy; could have small curds\", \"similar objects\": [\"ricotta cheese\", \"yogurt\", \"cream cheese\"]}", + 10 + ], + "glass top table": [ + " {\"type\": \"furniture\", \"description\": \"transparent top; could have metal or wooden legs; could be used for dining or working\", \"similar objects\": [\"coffee table\", \"desk\", \"dining table\"]}", + 10 + ], + "ice bucket": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could have a handle; could have a lid\", \"similar objects\": [\"cooler\", \"thermos\", \"jug\"]}", + 10 + ], + "pink drink": [ + " {\"type\": \"beverage\", \"description\": \"pink in color; could be sweet or sour; could be alcoholic or non-alcoholic\", \"similar objects\": [\"soda\", \"juice\", \"smoothie\"]}", + 10 + ], + "wilderness": [ + " {\"type\": \"environment\", \"description\": \"untouched by human; could be filled with trees, mountains, rivers, and animals\", \"similar objects\": [\"forest\", \"desert\", \"jungle\"]}", + 10 + ], + "propellors": [ + " {\"type\": \"mechanical device\", \"description\": \"spinning blades; could be used to generate thrust\", \"similar objects\": [\"turbines\", \"engines\", \"fans\"]}", + 10 + ], + "tile surface": [ + " {\"type\": \"flooring material\", \"description\": \"flat, rectangular, could be made of ceramic, stone, or other materials\", \"similar objects\": [\"wood flooring\", \"carpet\", \"linoleum\"]}", + 10 + ], + "wood kitchen cabinets": [ + "\n{\"type\": \"furniture\", \"description\": \"made of wood; could have drawers and shelves; could be painted in different colors\", \"similar objects\": [\"wardrobe\", \"bookshelf\", \"dresser\"]}", + 10 + ], + "ale": [ + " {\"type\": \"beverage\", \"description\": \"dark, bitter, could be carbonated; could be served in a glass\", \"similar objects\": [\"lager\", \"stout\", \"porter\"]}", + 10 + ], + "corner edge": [ + " {\"type\": \"geometric shape\", \"description\": \"90 degree angle; two intersecting lines\", \"similar objects\": [\"triangle\", \"square\", \"rectangle\"]}", + 10 + ], + "tan strap": [ + " {\"type\": \"accessory\", \"description\": \"long, thin, made of leather; could be used to hold items\", \"similar objects\": [\"belt\", \"bag strap\", \"watch strap\"]}", + 10 + ], + "teaspoon": [ + " {\"type\": \"measuring tool\", \"description\": \"small spoon; could be made of metal or plastic; could be used for measuring ingredients\", \"similar objects\": [\"tablespoon\", \"cup\", \"measuring cup\"]}", + 10 + ], + "marquee display": [ + " {\"type\": \"advertising tool\", \"description\": \"large, bright, scrolling display; could be used to display messages or images\", \"similar objects\": [\"billboard\", \"LED display\", \"neon sign\"]}", + 10 + ], + "candle stick": [ + " {\"type\": \"decorative item\", \"description\": \"tall, thin, could be made of metal or wood; could have a candle on top\", \"similar objects\": [\"vase\", \"statue\", \"lamp\"]}", + 10 + ], + "top table": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood\", \"similar objects\": [\"coffee table\", \"dining table\", \"end table\"]}", + 10 + ], + "giraffe ossicones": [ + "\n{\"type\": \"animal feature\", \"description\": \"horn-like protrusions on the head of a giraffe; could be covered in fur; could be used for defense\", \"similar objects\": [\"antlers\", \"horns\", \"tusks\"]}", + 10 + ], + "chocolate chip cookies": [ + "\n{\"type\": \"food\", \"description\": \"round, sweet, has chocolate chips inside; could be crunchy or soft\", \"similar objects\": [\"brownies\", \"oatmeal cookies\", \"macaroons\"]}", + 10 + ], + "bottle water": [ + " {\"type\": \"container\", \"description\": \"transparent; cylindrical; could be made of plastic; could be sealed\", \"similar objects\": [\"can\", \"jar\", \"jug\"]}", + 10 + ], + "jet fighter": [ + " {\"type\": \"aircraft\", \"description\": \"fast; has wings; could be armed with missiles\", \"similar objects\": [\"helicopter\", \"airliner\", \"bomber\"]}", + 10 + ], + "light sand": [ + " {\"type\": \"material\", \"description\": \"fine, pale yellow; could be used for construction\", \"similar objects\": [\"gravel\", \"clay\", \"soil\"]}", + 10 + ], + "brown pile": [ + " {\"type\": \"object\", \"description\": \"could be made of different materials; could be of different shapes and sizes; could be a pile of leaves, stones, or other objects\", \"similar objects\": [\"heap\", \"stack\", \"cluster\"]}", + 10 + ], + "pillowcases": [ + " {\"type\": \"bedding item\", \"description\": \"rectangular; could be made of cotton; could be decorated with patterns\", \"similar objects\": [\"sheets\", \"blankets\", \"duvet covers\"]}", + 10 + ], + "tennis pitch": [ + " {\"type\": \"sports court\", \"description\": \"rectangular; has a net in the middle; could be made of clay, grass, or hard court\", \"similar objects\": [\"badminton court\", \"volleyball court\", \"basketball court\"]}", + 10 + ], + "gaps": [ + " {\"type\": \"spaces\", \"description\": \"empty spaces between objects; could be physical or conceptual\", \"similar objects\": [\"voids\", \"intervals\", \"interstices\"]}", + 10 + ], + "dark night": [ + "\n{\"type\": \"atmosphere\", \"description\": \"dark sky; stars and moon are visible; could be accompanied by a chill breeze\", \"similar objects\": [\"twilight\", \"dusk\", \"midnight\"]}", + 10 + ], + "wrappers": [ + " {\"type\": \"packaging material\", \"description\": \"thin, flexible, could be made of paper, plastic, or foil\", \"similar objects\": [\"bags\", \"boxes\", \"containers\"]}", + 10 + ], + "piano keys": [ + " {\"type\": \"musical instrument\", \"description\": \"black and white keys; could be made of plastic or wood; could be arranged in a row\", \"similar objects\": [\"organ\", \"synthesizer\", \"accordion\"]}", + 10 + ], + "hedge row": [ + " {\"type\": \"landscape feature\", \"description\": \"a line of shrubs or trees planted close together; could be used as a boundary or for decoration\", \"similar objects\": [\"fence\", \"hedge wall\", \"hedge maze\"]}", + 10 + ], + "plastic skateboard wheel": [ + "\n{\"type\": \"skateboard wheel\", \"description\": \"round; made of plastic; has a bearing in the center\", \"similar objects\": [\"metal skateboard wheel\", \"rubber skateboard wheel\", \"wooden skateboard wheel\"]}", + 10 + ], + "gold buttons": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of metal; could be used to fasten clothes\", \"similar objects\": [\"zippers\", \"snaps\", \"hooks\"]}", + 10 + ], + "hairstyle": [ + " {\"type\": \"style\", \"description\": \"could be braided, curled, straightened, etc.\", \"similar objects\": [\"makeup\", \"clothing\", \"accessories\"]}", + 10 + ], + "handicap symbol": [ + " {\"type\": \"symbol\", \"description\": \"blue; has a wheelchair in the middle; could be seen on the ground or on a sign\", \"similar objects\": [\"no smoking sign\", \"no parking sign\", \"stop sign\"]}", + 10 + ], + "ceiling beams": [ + " {\"type\": \"building material\", \"description\": \"long, straight, wooden beams; used to support the ceiling\", \"similar objects\": [\"wooden planks\", \"rafters\", \"joists\"]}", + 10 + ], + "travel mug": [ + " {\"type\": \"drinking container\", \"description\": \"cylindrical; could be made of metal; has a lid; could be insulated\", \"similar objects\": [\"thermos\", \"water bottle\", \"coffee cup\"]}", + 10 + ], + "hold": [ + " {\"type\": \"verb\", \"description\": \"to keep something in one's hand or grip; to keep something in a particular position or state\", \"similar objects\": [\"grasp\", \"clutch\", \"seize\"]}", + 10 + ], + "steamer": [ + " {\"type\": \"cooking tool\", \"description\": \"has a lid; could be made of metal or bamboo; could be used to steam food\", \"similar objects\": [\"pot\", \"pan\", \"pressure cooker\"]}", + 10 + ], + "dog fur": [ + " {\"type\": \"animal fur\", \"description\": \"soft; could be of different colors; could be curly or straight\", \"similar objects\": [\"cat fur\", \"rabbit fur\", \"fox fur\"]}", + 10 + ], + "grey posts": [ + " {\"type\": \"building material\", \"description\": \"cylindrical; could be made of concrete; could be used for fencing\", \"similar objects\": [\"wooden posts\", \"metal posts\", \"bricks\"]}", + 10 + ], + "tan rocks": [ + " {\"type\": \"geological object\", \"description\": \"brownish-gray; could be smooth or rough; could be of different shapes and sizes\", \"similar objects\": [\"pebbles\", \"boulders\", \"gravel\"]}", + 10 + ], + "sugar doughnut": [ + "\n{\"type\": \"food\", \"description\": \"round; has a hole in the middle; covered with sugar\", \"similar objects\": [\"glazed doughnut\", \"jelly doughnut\", \"custard doughnut\"]}", + 10 + ], + "toddler girl": [ + "\n{\"type\": \"person\", \"description\": \"small; could be wearing a dress; could have long hair; could be playing with toys\", \"similar objects\": [\"baby\", \"child\", \"teenager\"]}", + 10 + ], + "oval plate": [ + " {\"type\": \"dishware\", \"description\": \"oval-shaped; could be made of ceramic; could be used for serving food\", \"similar objects\": [\"bowl\", \"cup\", \"platter\"]}", + 10 + ], + "wooden bowl": [ + " {\"type\": \"utensil\", \"description\": \"made of wood; could be round or oval; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"spoon\"]}", + 10 + ], + "skewers": [ + " {\"type\": \"cooking tool\", \"description\": \"long, thin, metal rods; could be used to hold food for grilling\", \"similar objects\": [\"tongs\", \"spatula\", \"grill brush\"]}", + 10 + ], + "bulky": [ + "\n{\"type\": \"adjective\", \"description\": \"large and heavy; takes up a lot of space\", \"similar objects\": [\"enormous\", \"massive\", \"gigantic\"]}", + 10 + ], + "sports shoes": [ + " {\"type\": \"footwear\", \"description\": \"made of fabric or leather; could have laces; could have a sole\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 10 + ], + "chocolate frosting": [ + " {\"type\": \"food\", \"description\": \"sweet, creamy, dark brown; could be used as a topping or filling\", \"similar objects\": [\"vanilla frosting\", \"whipped cream\", \"icing sugar\"]}", + 10 + ], + "faucet handles": [ + " {\"type\": \"plumbing tool\", \"description\": \"two handles; could be made of metal; could be used to control water flow\", \"similar objects\": [\"shower head\", \"valve\", \"pipe\"]}", + 10 + ], + "curb side road": [ + " {\"type\": \"road feature\", \"description\": \"raised edge of a road; could be made of concrete or asphalt; could be painted with white lines\", \"similar objects\": [\"sidewalk\", \"crosswalk\", \"traffic island\"]}", + 10 + ], + "furry ears": [ + " {\"type\": \"accessory\", \"description\": \"attached to head; could be made of faux fur; could be used for cosplay\", \"similar objects\": [\"tail\", \"horns\", \"wings\"]}", + 10 + ], + "dog buns": [ + " {\"type\": \"food\", \"description\": \"small, round, sweet; could be filled with cream or jam\", \"similar objects\": [\"donuts\", \"cupcakes\", \"cookies\"]}", + 10 + ], + "earpiece": [ + " {\"type\": \"electronic device\", \"description\": \"small; could be worn on the ear; could be connected to a device\", \"similar objects\": [\"headphones\", \"microphone\", \"speaker\"]}", + 10 + ], + "color handle": [ + " {\"type\": \"utensil\", \"description\": \"long handle with a colored tip; could be used for stirring\", \"similar objects\": [\"spoon\", \"fork\", \"ladle\"]}", + 10 + ], + "tan clock tower": [ + "\n{\"type\": \"architectural structure\", \"description\": \"tall, tan-colored; has a clock face; could have a bell\", \"similar objects\": [\"church\", \"monument\", \"obelisk\"]}", + 10 + ], + "emergency exit door": [ + "\n{\"type\": \"door\", \"description\": \"red; has a sign of emergency exit; could be opened from inside and outside\", \"similar objects\": [\"fire exit door\", \"entrance door\", \"exit door\"]}", + 10 + ], + "xbox controller": [ + " {\"type\": \"gaming device\", \"description\": \"rectangular; has buttons and joysticks; could be wireless\", \"similar objects\": [\"playstation controller\", \"nintendo controller\", \"arcade controller\"]}", + 10 + ], + "sweater sleeve": [ + " {\"type\": \"clothing item\", \"description\": \"long, cylindrical, could be made of wool or cotton; could have a cuff at the end\", \"similar objects\": [\"pants leg\", \"shirt sleeve\", \"skirt hem\"]}", + 10 + ], + "peach shirt": [ + " {\"type\": \"clothing item\", \"description\": \"light orange; could have short sleeves; could have a collar\", \"similar objects\": [\"t-shirt\", \"blouse\", \"dress\"]}", + 10 + ], + "silver boat": [ + "\n{\"type\": \"vehicle\", \"description\": \"silver; could be made of metal; could be used for sailing\", \"similar objects\": [\"yacht\", \"canoe\", \"rowboat\"]}", + 10 + ], + "silver bench": [ + "\n{\"type\": \"furniture\", \"description\": \"long, metallic, could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 10 + ], + "lush tree": [ + "\n{\"type\": \"plant\", \"description\": \"large, green, full of leaves; could have fruits or flowers; could have a thick trunk\", \"similar objects\": [\"palm tree\", \"pine tree\", \"oak tree\"]}", + 10 + ], + "round clocks": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; could have numbers or symbols on the face; could have hands or digital display\", \"similar objects\": [\"watch\", \"alarm clock\", \"wall clock\"]}", + 10 + ], + "hindquarters": [ + " {\"type\": \"anatomy\", \"description\": \"the back part of an animal's body; includes the hips, thighs, and tail\", \"similar objects\": [\"forequarters\", \"shoulder\", \"haunch\"]}", + 10 + ], + "savanna": [ + " {\"type\": \"ecosystem\", \"description\": \"grassland with scattered trees and shrubs; could have large mammals such as elephants, giraffes, and lions\", \"similar objects\": [\"desert\", \"rainforest\", \"tundra\"]}", + 10 + ], + "shadow plate table": [ + "\n{\"type\": \"furniture\", \"description\": \"rectangular; has a shadow plate on the top; could be made of wood or metal\", \"similar objects\": [\"coffee table\", \"dining table\", \"console table\"]}", + 10 + ], + "islands": [ + " {\"type\": \"geographical feature\", \"description\": \"land surrounded by water; could be of different sizes and shapes\", \"similar objects\": [\"peninsula\", \"archipelago\", \"atoll\"]}", + 10 + ], + "tile sidewalk": [ + " {\"type\": \"construction material\", \"description\": \"square or rectangular; could be made of stone, ceramic, or concrete; could be used to pave a path\", \"similar objects\": [\"brick sidewalk\", \"gravel path\", \"wooden deck\"]}", + 10 + ], + "glass divider": [ + " {\"type\": \"furniture\", \"description\": \"transparent; could be made of glass or plastic; could be used to separate rooms\", \"similar objects\": [\"room divider\", \"screen\", \"partition wall\"]}", + 10 + ], + "glass wine": [ + " {\"type\": \"drinking vessel\", \"description\": \"transparent; could be made of crystal; could have a stem\", \"similar objects\": [\"cup\", \"mug\", \"tumbler\"]}", + 10 + ], + "wood leg": [ + " {\"type\": \"furniture part\", \"description\": \"long; could be used to support a table or chair\", \"similar objects\": [\"metal leg\", \"plastic leg\", \"wooden beam\"]}", + 10 + ], + "rig": [ + " {\"type\": \"machine\", \"description\": \"large; used for drilling; could be used for oil extraction\", \"similar objects\": [\"drill\", \"crane\", \"truck\"]}", + 10 + ], + "dark brick": [ + " {\"type\": \"building material\", \"description\": \"dark in color; could be rectangular or square; could be used for walls or floors\", \"similar objects\": [\"concrete\", \"stone\", \"tile\"]}", + 10 + ], + "coils": [ + " {\"type\": \"electrical component\", \"description\": \"round; could be made of copper; could be used to store energy\", \"similar objects\": [\"capacitor\", \"transformer\", \"inductor\"]}", + 10 + ], + "pink food": [ + "\n{\"type\": \"food\", \"description\": \"could be any food with pink color; could be sweet or savory\", \"similar objects\": [\"strawberry\", \"raspberry\", \"watermelon\"]}", + 10 + ], + "trees distance": [ + "\n{\"type\": \"measurement\", \"description\": \"distance between two trees; could be measured in feet, meters, or other units\", \"similar objects\": [\"distance between two buildings\", \"distance between two mountains\", \"distance between two rivers\"]}", + 10 + ], + "blue carpet": [ + " {\"type\": \"floor covering\", \"description\": \"blue; could be made of wool; could be woven\", \"similar objects\": [\"rug\", \"mat\", \"tapestry\"]}", + 10 + ], + "spects": [ + " {\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be made of metal or plastic; could have a nose bridge\", \"similar objects\": [\"sunglasses\", \"goggles\", \"monocle\"]}", + 10 + ], + "shelfs": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood or metal; could have multiple levels\", \"similar objects\": [\"bookcase\", \"cabinet\", \"cupboard\"]}", + 10 + ], + "bathroom floor tiles": [ + " {\"type\": \"flooring material\", \"description\": \"square or rectangular; could be made of ceramic, stone, or vinyl; could be glossy or matte\", \"similar objects\": [\"kitchen floor tiles\", \"wood flooring\", \"carpet\"]}", + 10 + ], + "lady bug": [ + " {\"type\": \"insect\", \"description\": \"red with black spots; small; could fly\", \"similar objects\": [\"butterfly\", \"bee\", \"dragonfly\"]}", + 10 + ], + "hula hoop": [ + " {\"type\": \"toy\", \"description\": \"circular; made of plastic; could be used for exercise\", \"similar objects\": [\"jump rope\", \"ball\", \"frisbee\"]}", + 10 + ], + "skirt woman": [ + "\n{\"type\": \"clothing\", \"description\": \"long or short; could be pleated or flared; could be made of different materials\", \"similar objects\": [\"dress\", \"pants\", \"blouse\"]}", + 10 + ], + "accessory": [ + " {\"type\": \"fashion item\", \"description\": \"could be jewelry, bags, hats, scarves, etc.\", \"similar objects\": [\"jewelry\", \"bag\", \"hat\", \"scarf\"]}", + 10 + ], + "deodorant": [ + " {\"type\": \"personal care product\", \"description\": \"could be in spray or stick form; used to reduce body odor\", \"similar objects\": [\"perfume\", \"body wash\", \"soap\"]}", + 10 + ], + "tail pipe": [ + " {\"type\": \"automotive part\", \"description\": \"cylindrical; located at the back of the car; used to expel exhaust gases\", \"similar objects\": [\"exhaust manifold\", \"muffler\", \"catalytic converter\"]}", + 10 + ], + "bird swimming": [ + " {\"type\": \"animal\", \"description\": \"could have feathers; could have wings; could be swimming in water\", \"similar objects\": [\"duck\", \"goose\", \"swan\"]}", + 10 + ], + "pink handle": [ + " {\"type\": \"object\", \"description\": \"handle with pink color; could be made of plastic or metal\", \"similar objects\": [\"knob\", \"lever\", \"pull\"]}", + 10 + ], + "twin beds": [ + " {\"type\": \"furniture\", \"description\": \"two beds side by side; could have a headboard; could have a footboard\", \"similar objects\": [\"bunk beds\", \"daybeds\", \"trundle beds\"]}", + 10 + ], + "grass stain": [ + " {\"type\": \"stain\", \"description\": \"green; could be found on clothes; could be caused by grass\", \"similar objects\": [\"mud stain\", \"blood stain\", \"coffee stain\"]}", + 10 + ], + "scales": [ + " {\"type\": \"measuring tool\", \"description\": \"two plates connected by a lever; could be used to measure weight\", \"similar objects\": [\"ruler\", \"tape measure\", \"thermometer\"]}", + 10 + ], + "wooden pole": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical, made of wood\", \"similar objects\": [\"wooden beam\", \"wooden post\", \"wooden plank\"]}", + 10 + ], + "clock display": [ + " {\"type\": \"timekeeping tool\", \"description\": \"could be digital or analog; could have hands or numbers; could be wall-mounted or portable\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}", + 10 + ], + "shed": [ + " {\"type\": \"structure\", \"description\": \"wooden; could have a door; could have windows; could have a roof\", \"similar objects\": [\"barn\", \"garage\", \"gazebo\"]}", + 10 + ], + "bus lights": [ + " {\"type\": \"lighting tool\", \"description\": \"long, bright, usually white; could be found on the top of a bus\", \"similar objects\": [\"street lights\", \"traffic lights\", \"headlights\"]}", + 10 + ], + "gold door handle": [ + "\n{\"type\": \"hardware\", \"description\": \"golden; could be made of metal; could be used to open a door\", \"similar objects\": [\"knob\", \"lock\", \"hinge\"]}", + 10 + ], + "wooden bucket": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of wood; has a handle\", \"similar objects\": [\"plastic bucket\", \"metal bucket\", \"wooden pail\"]}", + 10 + ], + "scratch marks": [ + " {\"type\": \"markings\", \"description\": \"linear, shallow, could be made by claws\", \"similar objects\": [\"scrapes\", \"gouges\", \"dents\"]}", + 10 + ], + "truck grill": [ + " {\"type\": \"automotive part\", \"description\": \"metal; has a mesh pattern; could be found in the front of a truck\", \"similar objects\": [\"bumper\", \"headlight\", \"tailgate\"]}", + 10 + ], + "blurry lights": [ + "\n{\"type\": \"visual effect\", \"description\": \"lights that appear to be out of focus; could be caused by camera settings or atmospheric conditions\", \"similar objects\": [\"hazy lights\", \"foggy lights\", \"twinkling lights\"]}", + 10 + ], + "air conditioners": [ + " {\"type\": \"appliance\", \"description\": \"large; could be wall-mounted; could have a remote control\", \"similar objects\": [\"refrigerator\", \"heater\", \"fan\"]}", + 10 + ], + "mudflap": [ + " {\"type\": \"automotive accessory\", \"description\": \"attached to the rear of a vehicle; usually made of rubber or plastic; could have a logo or design\", \"similar objects\": [\"bumper guard\", \"spoiler\", \"fender flare\"]}", + 10 + ], + "juice bottle": [ + " {\"type\": \"container\", \"description\": \"transparent; could be made of plastic; could have a lid\", \"similar objects\": [\"water bottle\", \"thermos\", \"mug\"]}", + 10 + ], + "style toilet": [ + " {\"type\": \"plumbing fixture\", \"description\": \"elongated bowl; has a tank; could be wall-mounted\", \"similar objects\": [\"urinal\", \"bidet\", \"sink\"]}", + 10 + ], + "baseball man": [ + " {\"type\": \"sports figure\", \"description\": \"wearing a baseball uniform; holding a baseball bat\", \"similar objects\": [\"soccer player\", \"basketball player\", \"tennis player\"]}", + 10 + ], + "shelving": [ + " {\"type\": \"furniture\", \"description\": \"has multiple shelves; could be made of wood or metal; could be used to store items\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"cupboard\"]}", + 10 + ], + "wood head board": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; made of wood; could have carvings; could have a footboard\", \"similar objects\": [\"bed frame\", \"dresser\", \"nightstand\"]}", + 10 + ], + "toe nail": [ + " {\"type\": \"body part\", \"description\": \"hard, white, curved; grows from the end of the toe\", \"similar objects\": [\"finger nail\", \"hair\", \"eyelash\"]}", + 10 + ], + "pasta sauce": [ + " {\"type\": \"food condiment\", \"description\": \"red; could be made of tomatoes; could be spicy\", \"similar objects\": [\"pesto\", \"alfredo sauce\", \"marinara sauce\"]}", + 10 + ], + "metal pail": [ + " {\"type\": \"container\", \"description\": \"cylindrical; made of metal; has a handle\", \"similar objects\": [\"bucket\", \"barrel\", \"tub\"]}", + 10 + ], + "square pattern": [ + " {\"type\": \"pattern\", \"description\": \"geometric shape; four equal sides; four right angles\", \"similar objects\": [\"triangle pattern\", \"hexagon pattern\", \"circle pattern\"]}", + 10 + ], + "medallion": [ + " {\"type\": \"ornament\", \"description\": \"round; could be made of metal; could have a pattern or engraving\", \"similar objects\": [\"necklace\", \"bracelet\", \"ring\"]}", + 10 + ], + "blue cloth": [ + " {\"type\": \"fabric\", \"description\": \"blue; could be made of cotton, silk, or other materials; could be used for clothing, curtains, or other purposes\", \"similar objects\": [\"red cloth\", \"green cloth\", \"white cloth\"]}", + 10 + ], + "handle mug": [ + " {\"type\": \"drinking tool\", \"description\": \"cylindrical; has a handle; could be made of ceramic, plastic, or metal\", \"similar objects\": [\"cup\", \"glass\", \"thermos\"]}", + 10 + ], + "identification badge": [ + " {\"type\": \"accessory\", \"description\": \"could be made of plastic; could have a photo; could have a barcode\", \"similar objects\": [\"keycard\", \"passport\", \"driver's license\"]}", + 10 + ], + "round rug": [ + " {\"type\": \"floor covering\", \"description\": \"circular; could be made of wool, cotton, or synthetic fibers\", \"similar objects\": [\"carpet\", \"mat\", \"runner\"]}", + 10 + ], + "orange cloth": [ + " {\"type\": \"fabric\", \"description\": \"orange color; could be made of cotton, silk, or other materials; could be used for clothing, curtains, or other purposes\", \"similar objects\": [\"yellow cloth\", \"blue cloth\", \"green cloth\"]}", + 10 + ], + "custom": [ + " {\"type\": \"noun\", \"description\": \"something made or done to order; something not ordinary or usual\", \"similar objects\": [\"unique\", \"individual\", \"special\"]}", + 10 + ], + "underwear": [ + " {\"type\": \"clothing\", \"description\": \"worn close to the body; could be made of cotton, silk, or other fabrics; could be briefs, boxers, or thongs\", \"similar objects\": [\"socks\", \"bra\", \"t-shirt\"]}", + 10 + ], + "foreleg": [ + " {\"type\": \"animal body part\", \"description\": \"long, thin, and muscular; could be used for walking and running; could be found in four-legged animals\", \"similar objects\": [\"hind leg\", \"wing\", \"tail\"]}", + 10 + ], + "portraits": [ + " {\"type\": \"artwork\", \"description\": \"paintings or photographs of people; could be framed\", \"similar objects\": [\"landscape\", \"still life\", \"sculpture\"]}", + 10 + ], + "eyewear": [ + " {\"type\": \"accessory\", \"description\": \"worn on the face; could be used for vision correction; could be made of metal or plastic\", \"similar objects\": [\"glasses\", \"sunglasses\", \"goggles\"]}", + 10 + ], + "pink lamp": [ + "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of plastic; could be pink in color\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}", + 10 + ], + "porcelain toilet tank lid": [ + "\n{\"type\": \"toilet tank lid\", \"description\": \"white; round; could have a handle; could be made of porcelain\", \"similar objects\": [\"toilet seat\", \"toilet bowl\", \"toilet tank\"]}", + 10 + ], + "nike shoe": [ + " {\"type\": \"footwear\", \"description\": \"athletic shoe; could have a swoosh logo; could have a cushion sole\", \"similar objects\": [\"adidas shoe\", \"converse shoe\", \"puma shoe\"]}", + 10 + ], + "sesame seed": [ + " {\"type\": \"food ingredient\", \"description\": \"small, round, brown; could be used as a topping\", \"similar objects\": [\"sunflower seed\", \"pumpkin seed\", \"flax seed\"]}", + 10 + ], + "daffodils": [ + " {\"type\": \"flower\", \"description\": \"yellow; trumpet-shaped; could have multiple petals\", \"similar objects\": [\"tulips\", \"daisies\", \"sunflowers\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant, green bean).", + 10 + ], + "rubber ducks": [ + " {\"type\": \"toy\", \"description\": \"yellow; could be floating in water; could be squeaking\", \"similar objects\": [\"stuffed animals\", \"action figures\", \"building blocks\"]}", + 10 + ], + "chicken wings": [ + " {\"type\": \"food\", \"description\": \"small pieces of chicken; could be fried or grilled; could be served with sauces\", \"similar objects\": [\"drumsticks\", \"nuggets\", \"tenders\"]}", + 10 + ], + "tall poles": [ + " {\"type\": \"structure\", \"description\": \"long, cylindrical, could be made of metal or wood; could be used for support or decoration\", \"similar objects\": [\"fence\", \"flagpole\", \"streetlight\"]}", + 10 + ], + "mammals": [ + "\n{\"type\": \"animal group\", \"description\": \"warm-blooded animals; have fur or hair; give birth to live young; feed their young with milk\", \"similar objects\": [\"reptiles\", \"birds\", \"amphibians\"]}", + 10 + ], + "sailor": [ + " {\"type\": \"occupation\", \"description\": \"wears a navy uniform; could have a hat; could have a rope\", \"similar objects\": [\"soldier\", \"captain\", \"pirate\"]}", + 10 + ], + "country skiers": [ + " {\"type\": \"sport\", \"description\": \"skiing on a cross-country course; could use poles and skis\", \"similar objects\": [\"downhill skiers\", \"snowboarders\", \"ice skaters\"]}", + 10 + ], + "closeup": [ + " {\"type\": \"photography technique\", \"description\": \"taking a picture of a subject from a close distance; could be used to capture details\", \"similar objects\": [\"macro photography\", \"telephoto photography\", \"wide-angle photography\"]}", + 10 + ], + "plastic object": [ + "\n{\"type\": \"material\", \"description\": \"flexible; could be transparent; could be colored; could be molded into different shapes\", \"similar objects\": [\"metal\", \"wood\", \"glass\"]}", + 10 + ], + "shit": [ + "\n\n{\"type\": \"inappropriate language\", \"description\": \"inappropriate language; should not be used in public\", \"similar objects\": [\"crap\", \"damn\", \"hell\"]}", + 10 + ], + "ripe fruits": [ + "\n{\"type\": \"food\", \"description\": \"various colors; soft to touch; sweet smell; could be eaten directly\", \"similar objects\": [\"berries\", \"citrus fruits\", \"stone fruits\"]}", + 10 + ], + "pink stripes": [ + " {\"type\": \"pattern\", \"description\": \"alternating pink and white stripes; could be used for clothing, wallpaper, etc.\", \"similar objects\": [\"polka dots\", \"plaid\", \"chevron\"]}", + 10 + ], + "stuffed penguin": [ + " {\"type\": \"toy\", \"description\": \"plush; black and white; could have a beak and wings\", \"similar objects\": [\"stuffed bear\", \"stuffed dog\", \"stuffed cat\"]}", + 10 + ], + "concrete floor": [ + " {\"type\": \"building material\", \"description\": \"hard, gray, flat surface; could be used for outdoor and indoor\", \"similar objects\": [\"tile floor\", \"wood floor\", \"carpet\"]}", + 10 + ], + "message board": [ + " {\"type\": \"communication tool\", \"description\": \"could be made of wood or plastic; could be used to post messages\", \"similar objects\": [\"bulletin board\", \"whiteboard\", \"chalkboard\"]}", + 10 + ], + "plain wall": [ + "\n{\"type\": \"structure\", \"description\": \"smooth, flat surface; could be painted in different colors; could have decorations\", \"similar objects\": [\"ceiling\", \"floor\", \"door\"]}", + 10 + ], + "camera lense": [ + " {\"type\": \"photography tool\", \"description\": \"cylindrical; could be attached to a camera body; could have different focal lengths\", \"similar objects\": [\"tripod\", \"filter\", \"flash\"]}", + 10 + ], + "wall decor": [ + " {\"type\": \"decoration\", \"description\": \"could be made of wood, metal, or paper; could be hung on the wall; could be in various shapes and sizes\", \"similar objects\": [\"painting\", \"sculpture\", \"mirror\"]}", + 10 + ], + "beach bag": [ + " {\"type\": \"accessory\", \"description\": \"large, usually made of canvas; could have a zipper; could have a strap\", \"similar objects\": [\"backpack\", \"tote bag\", \"purse\"]}", + 10 + ], + "diamond ring": [ + " {\"type\": \"jewelry\", \"description\": \"round; has a diamond in the center; could be made of gold or silver\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 10 + ], + "wedding band": [ + " {\"type\": \"jewelry\", \"description\": \"circular; made of gold or silver; could be engraved with names or symbols\", \"similar objects\": [\"engagement ring\", \"bracelet\", \"necklace\"]}", + 10 + ], + "orange extension cord": [ + "\n{\"type\": \"electrical tool\", \"description\": \"orange; could be used to extend power supply; could be coiled\", \"similar objects\": [\"power strip\", \"extension lead\", \"power cable\"]}", + 10 + ], + "crystals": [ + " {\"type\": \"mineral\", \"description\": \"transparent; could be in various shapes; could be used for decoration\", \"similar objects\": [\"diamonds\", \"gems\", \"rocks\"]}", + 10 + ], + "gas cooker": [ + " {\"type\": \"cooking tool\", \"description\": \"has a gas burner; could have a knob to control the flame; could have a timer\", \"similar objects\": [\"stove\", \"oven\", \"microwave\"]}", + 10 + ], + "flag post": [ + " {\"type\": \"structure\", \"description\": \"tall, thin, could be made of metal; could have a flag on top\", \"similar objects\": [\"flagpole\", \"monument\", \"statue\"]}", + 10 + ], + "plastic eye": [ + " {\"type\": \"accessory\", \"description\": \"round; could be used to replace a missing eye\", \"similar objects\": [\"glass eye\", \"contact lens\", \"sunglasses\"]}", + 10 + ], + "pimple": [ + " {\"type\": \"skin condition\", \"description\": \"small, red, raised bump on the skin; could be filled with pus\", \"similar objects\": [\"acne\", \"blackhead\", \"whitehead\"]}", + 10 + ], + "al": [ + " {\"type\": \"element\", \"description\": \"atomic number 13; silver-white metal; highly reactive\", \"similar objects\": [\"oxygen\", \"carbon\", \"nitrogen\"]}", + 10 + ], + "metal feeder": [ + " {\"type\": \"pet accessory\", \"description\": \"made of metal; could have a bowl; could be used to feed pets\", \"similar objects\": [\"water bottle\", \"pet bed\", \"pet toy\"]}", + 10 + ], + "bushels": [ + " {\"type\": \"measurement unit\", \"description\": \"a unit of volume; usually used to measure dry goods\", \"similar objects\": [\"barrels\", \"bushels\", \"pecks\"]}", + 10 + ], + "volley": [ + " {\"type\": \"sports tool\", \"description\": \"lightweight; could be made of leather; used to hit a ball over a net\", \"similar objects\": [\"racquet\", \"shuttlecock\", \"tennis ball\"]}", + 10 + ], + "skateboard shoes": [ + " {\"type\": \"footwear\", \"description\": \"flat sole; could have laces; could have a reinforced toe cap; could have a padded tongue\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 10 + ], + "broken lines": [ + " {\"type\": \"geometric shape\", \"description\": \"lines that are not connected; could be straight or curved\", \"similar objects\": [\"dotted lines\", \"zigzag lines\", \"squiggly lines\"]}", + 10 + ], + "grafitti wall": [ + " {\"type\": \"artwork\", \"description\": \"painted wall; could be colorful; could have words or symbols\", \"similar objects\": [\"mural\", \"street art\", \"stencil art\"]}", + 10 + ], + "hand fingers": [ + "\n{\"type\": \"body part\", \"description\": \"five digits; could be bent; could be used to grab things\", \"similar objects\": [\"toes\", \"elbow\", \"knee\"]}", + 10 + ], + "conveyer belt": [ + " {\"type\": \"transportation tool\", \"description\": \"long, continuous loop; could be made of rubber or plastic; could be used to transport items\", \"similar objects\": [\"escalator\", \"elevator\", \"roller coaster\"]}", + 10 + ], + "grey speaker": [ + "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could be wireless; could be connected to other devices\", \"similar objects\": [\"headphones\", \"microphone\", \"stereo\"]}", + 10 + ], + "orange license plate": [ + "\n{\"type\": \"vehicle accessory\", \"description\": \"orange background with black letters and numbers; could be rectangular or square\", \"similar objects\": [\"blue license plate\", \"yellow license plate\", \"green license plate\"]}", + 10 + ], + "ruffle": [ + " {\"type\": \"fabric decoration\", \"description\": \"pleated, frilly, could be made of lace or other fabrics\", \"similar objects\": [\"ruffle trim\", \"ruffle ribbon\", \"ruffle edge\"]}", + 10 + ], + "hair woman": [ + "\n{\"type\": \"accessory\", \"description\": \"long, could be straight, curly, or wavy; could be of different colors\", \"similar objects\": [\"wig\", \"hat\", \"headband\"]}", + 10 + ], + "wiper blade": [ + " {\"type\": \"automotive part\", \"description\": \"rubber; used to clean windshields; could be attached to a metal arm\", \"similar objects\": [\"windshield wiper\", \"headlight\", \"brake pad\"]}", + 10 + ], + "gold paint": [ + " {\"type\": \"art material\", \"description\": \"shiny, yellowish color; could be used to paint walls or other surfaces\", \"similar objects\": [\"silver paint\", \"bronze paint\", \"acrylic paint\"]}", + 10 + ], + "girl shirt": [ + " {\"type\": \"clothing\", \"description\": \"could be short or long sleeve; could have a collar; could have buttons or zipper; could have prints or patterns\", \"similar objects\": [\"boy shirt\", \"dress\", \"jacket\"]}", + 10 + ], + "zebra heads": [ + "\n{\"type\": \"animal part\", \"description\": \"black and white stripes; has a long mane; could be the head of a zebra\", \"similar objects\": [\"horse heads\", \"giraffe heads\", \"elephant heads\"]}", + 10 + ], + "horse-drawn carriage": [ + " {\"type\": \"transportation tool\", \"description\": \"pulled by a horse; could have two or four wheels; could have a canopy\", \"similar objects\": [\"wagon\", \"cart\", \"buggy\"]}", + 10 + ], + "flag sticker": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; could be printed with a country's flag\", \"similar objects\": [\"bumper sticker\", \"wall sticker\", \"window sticker\"]}", + 10 + ], + "cutout": [ + " {\"type\": \"craft tool\", \"description\": \"used to cut shapes out of paper; could be made of metal or plastic\", \"similar objects\": [\"scissors\", \"knife\", \"punch\"]}", + 10 + ], + "prunes": [ + " {\"type\": \"fruit\", \"description\": \"dried plums; wrinkled; dark purple\", \"similar objects\": [\"raisins\", \"apricots\", \"dates\"]}", + 10 + ], + "snow poles": [ + " {\"type\": \"outdoor tool\", \"description\": \"long, thin, pointed; used to mark ski trails\", \"similar objects\": [\"ski poles\", \"hiking poles\", \"tent poles\"]}", + 10 + ], + "kernel": [ + " {\"type\": \"seed\", \"description\": \"small, round, hard; could be white, yellow, or black; could be used for planting\", \"similar objects\": [\"bean\", \"corn\", \"wheat\"]}", + 10 + ], + "tailfin": [ + " {\"type\": \"fish body part\", \"description\": \"elongated, pointed, located at the back of the fish\", \"similar objects\": [\"dorsal fin\", \"pectoral fin\", \"anal fin\"]}", + 10 + ], + "brown plant": [ + "\n{\"type\": \"plant\", \"description\": \"brown; could have leaves; could be a shrub or a tree\", \"similar objects\": [\"bush\", \"tree\", \"shrub\"]}", + 10 + ], + "egg carton": [ + " {\"type\": \"container\", \"description\": \"rectangular; has 12 slots for eggs; could be made of plastic or cardboard\", \"similar objects\": [\"milk carton\", \"ice cream container\", \"cereal box\"]}", + 10 + ], + "floor board": [ + " {\"type\": \"building material\", \"description\": \"long, flat, wooden boards; could be used to cover the floor\", \"similar objects\": [\"tile\", \"carpet\", \"linoleum\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant,", + 10 + ], + "indents": [ + " {\"type\": \"marking tool\", \"description\": \"used to make a mark on a surface; could be made of metal or plastic\", \"similar objects\": [\"stamps\", \"punch\", \"engraving tool\"]}", + 10 + ], + "cement structure": [ + " {\"type\": \"building material\", \"description\": \"hard, gray; used to build walls and foundations\", \"similar objects\": [\"concrete\", \"bricks\", \"wood\"]}", + 10 + ], + "metal railings": [ + " {\"type\": \"building material\", \"description\": \"long, thin, metallic; could be used for fencing\", \"similar objects\": [\"iron bars\", \"steel bars\", \"aluminum bars\"]}", + 10 + ], + "walking": [ + " {\"type\": \"action\", \"description\": \"moving forward with one foot in front of the other; could be done with a purpose\", \"similar objects\": [\"running\", \"jogging\", \"hiking\"]}", + 10 + ], + "mansion": [ + " {\"type\": \"building\", \"description\": \"large, luxurious; could have multiple stories; could have a garden\", \"similar objects\": [\"castle\", \"villa\", \"manor house\"]}", + 10 + ], + "cistern": [ + " {\"type\": \"water storage\", \"description\": \"large, cylindrical, made of concrete; could be underground\", \"similar objects\": [\"tank\", \"reservoir\", \"well\"]}", + 10 + ], + "sink cabinet": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could have drawers; could have a countertop\", \"similar objects\": [\"dresser\", \"wardrobe\", \"cupboard\"]}", + 10 + ], + "paisley": [ + " {\"type\": \"pattern\", \"description\": \"teardrop-shaped, abstract, colorful\", \"similar objects\": [\"floral\", \"plaid\", \"geometric\"]}", + 10 + ], + "watch tower": [ + " {\"type\": \"structure\", \"description\": \"tall; could be made of wood or metal; could have a platform on the top\", \"similar objects\": [\"lighthouse\", \"windmill\", \"observatory\"]}", + 10 + ], + "doggy": [ + " {\"type\": \"animal\", \"description\": \"four legs; could have long ears; could have a tail; could be of different colors\", \"similar objects\": [\"cat\", \"rabbit\", \"hamster\"]}", + 10 + ], + "building photo": [ + "\n{\"type\": \"structure\", \"description\": \"could have multiple floors; could have windows; could have a roof; could have a door\", \"similar objects\": [\"house\", \"skyscraper\", \"warehouse\"]}", + 10 + ], + "left paw": [ + " {\"type\": \"animal body part\", \"description\": \"front limb of a four-legged animal; could be used for walking, running, and climbing\", \"similar objects\": [\"right paw\", \"tail\", \"nose\"]}", + 10 + ], + "steel sink": [ + " {\"type\": \"kitchen tool\", \"description\": \"made of steel; has a bowl-like shape; could have a faucet\", \"similar objects\": [\"bathtub\", \"toilet\", \"dishwasher\"]}", + 10 + ], + "jewels": [ + " {\"type\": \"ornament\", \"description\": \"shiny; could be made of precious stones; could be used as a necklace or bracelet\", \"similar objects\": [\"diamonds\", \"pearls\", \"emeralds\"]}", + 10 + ], + "shoulder length": [ + " {\"type\": \"hair style\", \"description\": \"hair that reaches the shoulders; could be layered; could be straight or wavy\", \"similar objects\": [\"bob\", \"pixie cut\", \"bangs\"]}", + 10 + ], + "stuffed rabbit": [ + " {\"type\": \"toy\", \"description\": \"soft, fluffy; could have long ears; could be in different colors\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"stuffed animal\"]}", + 10 + ], + "rail road track": [ + " {\"type\": \"transportation infrastructure\", \"description\": \"long, straight, parallel lines; could have a rail road switch\", \"similar objects\": [\"highway\", \"bridge\", \"tunnel\"]}", + 10 + ], + "wood plate": [ + " {\"type\": \"tableware\", \"description\": \"made of wood; could be round or square; could be used to serve food\", \"similar objects\": [\"ceramic plate\", \"plastic plate\", \"metal plate\"]}", + 10 + ], + "jar lid": [ + " {\"type\": \"container lid\", \"description\": \"round; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"bottle cap\", \"can lid\", \"pot lid\"]}", + 10 + ], + "skinny tree": [ + "\n{\"type\": \"plant\", \"description\": \"tall and thin; could have few leaves; could have a thin trunk\", \"similar objects\": [\"sapling\", \"bamboo\", \"palm tree\"]}", + 10 + ], + "watches": [ + " {\"type\": \"accessory\", \"description\": \"worn on the wrist; could be digital or analog; could be made of metal or plastic\", \"similar objects\": [\"bracelets\", \"rings\", \"necklaces\"]}", + 10 + ], + "honda": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could be a car, motorcycle, or other motorized vehicle; could have a logo of a 'H'\", \"similar objects\": [\"toyota\", \"ford\", \"bmw\"]}", + 10 + ], + "scrunchie": [ + " {\"type\": \"accessory\", \"description\": \"elastic band; could be made of fabric; could be decorated with beads\", \"similar objects\": [\"hair tie\", \"headband\", \"hair clip\"]}", + 10 + ], + "checker pattern": [ + " {\"type\": \"pattern\", \"description\": \"alternating black and white squares; could be used for decoration\", \"similar objects\": [\"plaid\", \"stripes\", \"polka dots\"]}", + 10 + ], + "front windshields": [ + " {\"type\": \"automotive part\", \"description\": \"transparent; located at the front of the car; could be curved\", \"similar objects\": [\"rear windshields\", \"side windows\", \"headlights\"]}", + 10 + ], + "mcdonalds": [ + " {\"type\": \"restaurant\", \"description\": \"fast food chain; has golden arches logo; could have drive-thru\", \"similar objects\": [\"burger king\", \"kfc\", \"taco bell\"]}", + 10 + ], + "metal grates": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be used as a fence or a floor covering; could be used for drainage\", \"similar objects\": [\"wire mesh\", \"chain link fence\", \"expanded metal\"]}", + 10 + ], + "cow horns": [ + " {\"type\": \"animal body part\", \"description\": \"curved; could be long or short; could be brown or black\", \"similar objects\": [\"goat horns\", \"deer antlers\", \"sheep horns\"]}", + 10 + ], + "board sign": [ + " {\"type\": \"signage\", \"description\": \"rectangular; could be made of wood or metal; could be used for advertisement or warning\", \"similar objects\": [\"billboard\", \"placard\", \"poster\"]}", + 10 + ], + "flowerpot": [ + " {\"type\": \"container\", \"description\": \"round; could be made of clay; could have a drainage hole at the bottom\", \"similar objects\": [\"vase\", \"urn\", \"planter\"]}", + 10 + ], + "boy ground": [ + "\n{\"type\": \"person\", \"description\": \"young male; could be playing on the ground\", \"similar objects\": [\"girl\", \"child\", \"teenager\"]}", + 10 + ], + "nike swoosh": [ + " {\"type\": \"logo\", \"description\": \"swoosh shape; could be in black or white\", \"similar objects\": [\"adidas logo\", \"puma logo\", \"reebok logo\"]}", + 10 + ], + "leafed trees": [ + " {\"type\": \"plant\", \"description\": \"has leaves; could be tall; could have different shapes of leaves\", \"similar objects\": [\"palm tree\", \"pine tree\", \"birch tree\"]}", + 10 + ], + "jetway": [ + " {\"type\": \"airport equipment\", \"description\": \"long, narrow, connected to the airplane; could be used to board passengers\", \"similar objects\": [\"escalator\", \"elevator\", \"staircase\"]}", + 10 + ], + "tree stumps": [ + " {\"type\": \"plant\", \"description\": \"remains of a tree; could be cut off; could be used as a seat\", \"similar objects\": [\"logs\", \"roots\", \"branches\"]}", + 10 + ], + "bottom button": [ + " {\"type\": \"clothing accessory\", \"description\": \"small, round, usually made of metal; used to fasten clothing\", \"similar objects\": [\"zipper\", \"snap button\", \"hook and eye\"]}", + 10 + ], + "reader": [ + " {\"type\": \"device\", \"description\": \"electronic device; could be used to read books, magazines, newspapers, etc.\", \"similar objects\": [\"tablet\", \"laptop\", \"smartphone\"]}", + 10 + ], + "water ripples": [ + " {\"type\": \"natural phenomenon\", \"description\": \"circular waves on the surface of water; could be caused by wind or objects\", \"similar objects\": [\"waves\", \"tides\", \"currents\"]}", + 10 + ], + "box fan": [ + " {\"type\": \"electrical appliance\", \"description\": \"rectangular; has blades; could be used to circulate air\", \"similar objects\": [\"ceiling fan\", \"table fan\", \"air conditioner\"]}", + 10 + ], + "swing": [ + " {\"type\": \"playground equipment\", \"description\": \"has a seat; could be hung from a tree branch; could be made of wood or metal\", \"similar objects\": [\"slide\", \"monkey bars\", \"merry-go-round\"]}", + 10 + ], + "bmw motorcycle": [ + "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a BMW logo; could have a sidecar\", \"similar objects\": [\"Harley-Davidson\", \"Honda\", \"Yamaha\"]}", + 10 + ], + "soap pump": [ + " {\"type\": \"cleaning tool\", \"description\": \"cylindrical; could be made of plastic; has a pump\", \"similar objects\": [\"soap dispenser\", \"hand sanitizer dispenser\", \"lotion dispenser\"]}", + 10 + ], + "hash marks": [ + " {\"type\": \"marking tool\", \"description\": \"straight lines; could be used to divide a page into sections; could be used to indicate a list of items\", \"similar objects\": [\"lines\", \"dashes\", \"bullets\"]}", + 10 + ], + "willow tree": [ + " {\"type\": \"plant\", \"description\": \"long, thin leaves; could have a weeping shape; could have a silvery-gray bark\", \"similar objects\": [\"maple tree\", \"birch tree\", \"elm tree\"]}", + 10 + ], + "gas pumps": [ + " {\"type\": \"utility tool\", \"description\": \"cylindrical; could be used to fill up fuel tanks; could have a digital display\", \"similar objects\": [\"air pumps\", \"water pumps\", \"fuel pumps\"]}", + 10 + ], + "hardwood flooring": [ + " {\"type\": \"flooring material\", \"description\": \"smooth; could be made of wood; could be stained in different colors\", \"similar objects\": [\"laminate flooring\", \"tile flooring\", \"carpet flooring\"]}", + 10 + ], + "pink post": [ + " {\"type\": \"object\", \"description\": \"pink; could be made of plastic; could be used for decoration\", \"similar objects\": [\"pink fence\", \"pink mailbox\", \"pink sign\"]}", + 10 + ], + "hunter": [ + " {\"type\": \"person\", \"description\": \"wears camouflage; carries a gun; could be hunting for animals\", \"similar objects\": [\"fisherman\", \"hiker\", \"explorer\"]}", + 10 + ], + "stainless steel hood": [ + " {\"type\": \"kitchen appliance\", \"description\": \"rectangular; made of stainless steel; has a fan and a filter; could be mounted on the wall\", \"similar objects\": [\"stove\", \"oven\", \"microwave\"]}", + 10 + ], + "motorcylce": [ + " {\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"tricycle\"]}", + 10 + ], + "word street": [ + "\n{\"type\": \"location\", \"description\": \"a public thoroughfare; could be lined with buildings\", \"similar objects\": [\"avenue\", \"boulevard\", \"alley\"]}", + 10 + ], + "silver radiator": [ + "\n{\"type\": \"heating tool\", \"description\": \"silver; could be made of metal; could be used to heat a room\", \"similar objects\": [\"heater\", \"boiler\", \"air conditioner\"]}", + 10 + ], + "plastic suitcase": [ + "\n{\"type\": \"luggage\", \"description\": \"hard, rectangular; could be opened with a zipper; could have wheels\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}", + 10 + ], + "grout lines": [ + " {\"type\": \"building material\", \"description\": \"narrow lines between tiles; could be filled with cement-based material\", \"similar objects\": [\"mortar\", \"caulk\", \"sealant\"]}", + 10 + ], + "birthday party": [ + "\n{\"type\": \"event\", \"description\": \"celebration of a person's birthday; could include decorations, food, and activities\", \"similar objects\": [\"wedding\", \"graduation party\", \"baby shower\"]}", + 10 + ], + "pork chop": [ + " {\"type\": \"food\", \"description\": \"thick cut of pork; could be grilled or fried\", \"similar objects\": [\"ribs\", \"bacon\", \"hamburger\"]}", + 10 + ], + "silver wedding band": [ + "\n{\"type\": \"jewelry\", \"description\": \"round; made of silver; could have engravings; could have diamonds\", \"similar objects\": [\"gold wedding band\", \"engagement ring\", \"bracelet\"]}", + 10 + ], + "orange eye": [ + " {\"type\": \"insect\", \"description\": \"orange body; black eyes; long antennae\", \"similar objects\": [\"ladybug\", \"firefly\", \"butterfly\"]}", + 10 + ], + "flower decorations": [ + " {\"type\": \"decoration\", \"description\": \"could be made of paper, fabric, or plastic; could be in various shapes and colors; could be used to decorate walls, tables, or other surfaces\", \"similar objects\": [\"balloons\", \"banners\", \"streamers\"]}", + 10 + ], + "kitchen light": [ + " {\"type\": \"lighting tool\", \"description\": \"could be ceiling-mounted; could be a pendant light; could be a chandelier; could be a wall sconce\", \"similar objects\": [\"lamp\", \"lantern\", \"flashlight\"]}", + 10 + ], + "whale": [ + " {\"type\": \"animal\", \"description\": \"large; could be blue or gray; has a blowhole; could be found in the ocean\", \"similar objects\": [\"dolphin\", \"shark\", \"seal\"]}", + 10 + ], + "flower bush": [ + " {\"type\": \"plant\", \"description\": \"bushy; could have multiple flowers; could have different colors\", \"similar objects\": [\"shrub\", \"hedge\", \"tree\"]}", + 10 + ], + "mass transit bus": [ + " {\"type\": \"vehicle\", \"description\": \"large; has multiple doors; could be painted in a certain color; could have a route number\", \"similar objects\": [\"school bus\", \"trolley bus\", \"minibus\"]}", + 10 + ], + "beef sandwich": [ + " {\"type\": \"food\", \"description\": \"bread with beef, lettuce, tomato, and condiments; could be served hot or cold\", \"similar objects\": [\"hamburger\", \"tuna sandwich\", \"grilled cheese sandwich\"]}", + 10 + ], + "skewer": [ + " {\"type\": \"cooking tool\", \"description\": \"long, thin, pointed metal rod; could be used to hold food together\", \"similar objects\": [\"spatula\", \"tongs\", \"whisk\"]}", + 10 + ], + "microwave kitchen": [ + " {\"type\": \"appliance\", \"description\": \"box-shaped; has a door; could be used to heat food\", \"similar objects\": [\"refrigerator\", \"stove\", \"blender\"]}", + 10 + ], + "guy playing tennis": [ + "\n{\"type\": \"person\", \"description\": \"wearing a white shirt and shorts; holding a tennis racket; playing on a tennis court\", \"similar objects\": [\"person playing badminton\", \"person playing basketball\", \"person playing soccer\"]}", + 10 + ], + "orange life preserver": [ + "\n{\"type\": \"safety tool\", \"description\": \"round; orange in color; could be made of foam; could have straps\", \"similar objects\": [\"life jacket\", \"floatation device\", \"buoy\"]}", + 10 + ], + "butcher block": [ + " {\"type\": \"kitchen tool\", \"description\": \"wooden block; could be used for cutting and chopping\", \"similar objects\": [\"cutting board\", \"chopping board\", \"knife block\"]}", + 10 + ], + "diagram": [ + " {\"type\": \"visual representation\", \"description\": \"graphical representation of a concept; could be a flowchart, a map, or a graph\", \"similar objects\": [\"chart\", \"table\", \"graph\"]}", + 10 + ], + "sea birds": [ + " {\"type\": \"animal\", \"description\": \"could have wings; could have webbed feet; could have long beaks; could be found near the sea\", \"similar objects\": [\"seagulls\", \"penguins\", \"albatrosses\"]}", + 10 + ], + "cordless mouse": [ + " {\"type\": \"computer accessory\", \"description\": \"wireless; has two buttons and a scroll wheel; could be used with a laptop\", \"similar objects\": [\"keyboard\", \"headset\", \"webcam\"]}", + 10 + ], + "tow rope": [ + " {\"type\": \"utility tool\", \"description\": \"long, strong rope; could be used to tow vehicles\", \"similar objects\": [\"chain\", \"cable\", \"strap\"]}", + 10 + ], + "cinder blocks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; made of concrete; could be used for construction\", \"similar objects\": [\"bricks\", \"concrete blocks\", \"pavers\"]}", + 10 + ], + "goofy": [ + " {\"type\": \"cartoon character\", \"description\": \"goofy is a tall, anthropomorphic dog with a Southern drawl, with a distinctively large head\", \"similar objects\": [\"Mickey Mouse\", \"Donald Duck\", \"Pluto\"]}", + 10 + ], + "water outlet": [ + " {\"type\": \"plumbing fixture\", \"description\": \"has a spout and a handle; could be connected to a pipe\", \"similar objects\": [\"faucet\", \"shower head\", \"hose\"]}", + 10 + ], + "mirror side bus": [ + " {\"type\": \"computer architecture\", \"description\": \"a type of computer architecture that uses a shared bus to connect multiple processors to a single memory\", \"similar objects\": [\"hypercube\", \"crossbar switch\", \"multiprocessor system\"]}", + 10 + ], + "glass carafe": [ + " {\"type\": \"container\", \"description\": \"transparent; has a handle; could be used to store liquids\", \"similar objects\": [\"pitcher\", \"jug\", \"thermos\"]}", + 10 + ], + "sharpie": [ + " {\"type\": \"writing tool\", \"description\": \"permanent marker; could be used to write on paper, glass, and other surfaces\", \"similar objects\": [\"marker\", \"pen\", \"pencil\"]}", + 10 + ], + "brocolli plate": [ + " {\"type\": \"dish\", \"description\": \"plate with brocolli as the main ingredient; could be served with other vegetables or meat; could be cooked with sauces\", \"similar objects\": [\"salad\", \"stir-fry\", \"soup\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves", + 10 + ], + "court floor": [ + " {\"type\": \"sports surface\", \"description\": \"hard, flat, usually made of wood or synthetic material; could be painted with lines\", \"similar objects\": [\"tennis court\", \"basketball court\", \"volleyball court\"]}", + 10 + ], + "juvenile giraffe": [ + "\n{\"type\": \"animal\", \"description\": \"tall; has a long neck; has spots; has a tuft of fur on the top of its head; has long legs\", \"similar objects\": [\"adult giraffe\", \"calf\", \"deer\"]}", + 10 + ], + "color cow": [ + "\n{\"type\": \"animal\", \"description\": \"black and white patches; has a long mane; could have horns\", \"similar objects\": [\"horse\", \"goat\", \"sheep\"]}", + 10 + ], + "woman water": [ + "\n{\"type\": \"person\", \"description\": \"female; could be holding a bottle of water\", \"similar objects\": [\"man\", \"child\", \"elderly\"]}", + 10 + ], + "blue tile": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of ceramic; could be used for flooring or wall covering\", \"similar objects\": [\"ceramic tile\", \"glass tile\", \"stone tile\"]}", + 10 + ], + "statue top building": [ + " {\"type\": \"architectural structure\", \"description\": \"could be made of stone, metal, or wood; could be of a person, animal, or object; could be placed on top of a building\", \"similar objects\": [\"sculpture\", \"monument\", \"column\"]}", + 10 + ], + "trash basket": [ + " {\"type\": \"container\", \"description\": \"rectangular; could be made of plastic; could have a lid\", \"similar objects\": [\"bin\", \"garbage can\", \"dustbin\"]}", + 10 + ], + "police horse": [ + " {\"type\": \"animal\", \"description\": \"black and white stripes; has a long mane; could be used by police\", \"similar objects\": [\"horse\", \"zebra\", \"donkey\"]}", + 10 + ], + "kitchen towels": [ + " {\"type\": \"cleaning tool\", \"description\": \"absorbent; could be made of cotton; could be used to dry dishes\", \"similar objects\": [\"sponge\", \"dishcloth\", \"dishrag\"]}", + 10 + ], + "stainless": [ + " {\"type\": \"material\", \"description\": \"shiny, silver-colored, durable; resistant to corrosion and rust\", \"similar objects\": [\"aluminum\", \"brass\", \"bronze\"]}", + 10 + ], + "elephant statue": [ + "\n{\"type\": \"decorative item\", \"description\": \"large, gray, could have a trunk up or down; could have tusks\", \"similar objects\": [\"lion statue\", \"giraffe statue\", \"monkey statue\"]}", + 10 + ], + "grey beak": [ + " {\"type\": \"bird body part\", \"description\": \"hard, curved, and pointed; could be used for pecking and eating\", \"similar objects\": [\"talons\", \"wings\", \"feathers\"]}", + 10 + ], + "power line tower": [ + " {\"type\": \"structure\", \"description\": \"tall, metal, has wires running through it\", \"similar objects\": [\"cell tower\", \"wind turbine\", \"bridge\"]}", + 10 + ], + "shadow bear": [ + " {\"type\": \"toy\", \"description\": \"stuffed animal; has a black and white fur; has a round head and a long body\", \"similar objects\": [\"teddy bear\", \"plush toy\", \"stuffed animal\"]}", + 10 + ], + "metal pieces": [ + " {\"type\": \"material\", \"description\": \"shiny; could be cut into different shapes; could be used for construction\", \"similar objects\": [\"wood\", \"plastic\", \"glass\"]}", + 10 + ], + "orange bench": [ + "\n{\"type\": \"furniture\", \"description\": \"orange; could be made of wood or metal; could have a backrest; could have armrests\", \"similar objects\": [\"chair\", \"sofa\", \"ottoman\"]}", + 10 + ], + "bedcover": [ + " {\"type\": \"bedding item\", \"description\": \"rectangular; could be made of cotton; could be quilted\", \"similar objects\": [\"blanket\", \"comforter\", \"duvet\"]}", + 10 + ], + "wooden ledge": [ + " {\"type\": \"furniture\", \"description\": \"long, flat, made of wood; could be used as a shelf\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}", + 10 + ], + "storm cloud": [ + " {\"type\": \"weather phenomenon\", \"description\": \"dark, grey, could have lightning and thunder\", \"similar objects\": [\"rain cloud\", \"hurricane\", \"tornado\"]}", + 10 + ], + "mama": [ + " {\"type\": \"person\", \"description\": \"female; could be a mother; could be a grandmother\", \"similar objects\": [\"mom\", \"aunt\", \"sister\"]}", + 10 + ], + "dark elephant": [ + "\n{\"type\": \"animal\", \"description\": \"black or dark grey; has a long mane; could have white tusks\", \"similar objects\": [\"zebra\", \"hippopotamus\", \"rhinoceros\"]}", + 10 + ], + "bus driving": [ + " {\"type\": \"transportation\", \"description\": \"large vehicle; could have multiple doors; could have multiple seats; could have a driver\", \"similar objects\": [\"car\", \"truck\", \"train\"]}", + 10 + ], + "pale sky": [ + " {\"type\": \"weather phenomenon\", \"description\": \"light blue; could be cloudy; could be sunny\", \"similar objects\": [\"clear sky\", \"rainy sky\", \"overcast sky\"]}", + 10 + ], + "angels": [ + " {\"type\": \"mythological creature\", \"description\": \"winged; could have a halo; could be depicted with a harp\", \"similar objects\": [\"fairies\", \"demons\", \"unicorns\"]}", + 10 + ], + "advertising billboard": [ + "\n{\"type\": \"outdoor advertisement\", \"description\": \"large, rectangular; could be illuminated; could be placed on the side of the road\", \"similar objects\": [\"street sign\", \"bus stop sign\", \"streetlight\"]}", + 10 + ], + "side burn": [ + " {\"type\": \"hairstyle\", \"description\": \"long hair on the side of the face; could be styled in different ways\", \"similar objects\": [\"mohawk\", \"buzz cut\", \"pompadour\"]}", + 10 + ], + "right sleeve": [ + " {\"type\": \"clothing item\", \"description\": \"attached to the right side of a shirt or dress; could be long or short\", \"similar objects\": [\"left sleeve\", \"collar\", \"hem\"]}", + 10 + ], + "beige walls": [ + " {\"type\": \"building material\", \"description\": \"light brown color; could be made of wood, stone, or plaster; could be painted\", \"similar objects\": [\"white walls\", \"gray walls\", \"brown walls\"]}", + 10 + ], + "thick crust pizza": [ + "\n{\"type\": \"food\", \"description\": \"round; has a thick crust; could be topped with various ingredients\", \"similar objects\": [\"calzone\", \"flatbread pizza\", \"stuffed crust pizza\"]}", + 10 + ], + "turtles": [ + " {\"type\": \"animal\", \"description\": \"have a hard shell; could be green, brown, or black; could be aquatic or land-dwelling\", \"similar objects\": [\"tortoises\", \"snakes\", \"lizards\"]}", + 10 + ], + "blue bridge": [ + "\n{\"type\": \"structure\", \"description\": \"blue; could be made of steel; could span a river or a road\", \"similar objects\": [\"bridge\", \"tunnel\", \"viaduct\"]}", + 10 + ], + "night lamp": [ + " {\"type\": \"lighting tool\", \"description\": \"small; could be made of plastic; emits a soft light\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}", + 10 + ], + "plastic button": [ + " {\"type\": \"accessory\", \"description\": \"round; could be attached to clothes; could be made of plastic\", \"similar objects\": [\"metal button\", \"zipper\", \"snap button\"]}", + 10 + ], + "nike shorts": [ + "\n{\"type\": \"clothing\", \"description\": \"athletic shorts; could be made of polyester; could have a Nike logo\", \"similar objects\": [\"track pants\", \"joggers\", \"sweatpants\"]}", + 10 + ], + "dark marks": [ + " {\"type\": \"stain\", \"description\": \"dark, could be caused by dirt, oil, or other substances; could be removed with cleaning agents\", \"similar objects\": [\"dirt\", \"grease\", \"stain\"]}", + 10 + ], + "wire cage": [ + " {\"type\": \"container\", \"description\": \"made of metal wires; could be used to contain animals or objects\", \"similar objects\": [\"crate\", \"box\", \"basket\"]}", + 10 + ], + "towel hanger": [ + " {\"type\": \"household item\", \"description\": \"has multiple hooks; could be made of metal or plastic; could be wall-mounted\", \"similar objects\": [\"coat hanger\", \"hat rack\", \"key holder\"]}", + 10 + ], + "facet": [ + " {\"type\": \"geometric shape\", \"description\": \"a flat surface of a polyhedron; could be triangular, rectangular, or hexagonal\", \"similar objects\": [\"edge\", \"vertex\", \"corner\"]}", + 10 + ], + "yoke": [ + " {\"type\": \"clothing item\", \"description\": \"a piece of clothing worn around the shoulders and neck; could be made of fabric or leather; could have buttons or laces\", \"similar objects\": [\"vest\", \"cape\", \"shawl\"]}", + 10 + ], + "brick siding": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be used for exterior walls\", \"similar objects\": [\"wood siding\", \"vinyl siding\", \"stucco\"]}", + 10 + ], + "bangle": [ + " {\"type\": \"jewelry\", \"description\": \"round; could be made of metal or plastic; could be decorated with stones or beads\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 10 + ], + "right ear": [ + " {\"type\": \"body part\", \"description\": \"located on the right side of the head; could be pierced\", \"similar objects\": [\"left ear\", \"nose\", \"eyebrow\"]}", + 10 + ], + "turtleneck": [ + " {\"type\": \"clothing\", \"description\": \"high neck; could be long-sleeved; could be made of wool\", \"similar objects\": [\"sweater\", \"cardigan\", \"hoodie\"]}", + 10 + ], + "passenger side mirror": [ + "\n{\"type\": \"automotive part\", \"description\": \"attached to the side of a vehicle; used to see what is behind the vehicle\", \"similar objects\": [\"driver side mirror\", \"rearview mirror\", \"side view mirror\"]}", + 10 + ], + "guests": [ + " {\"type\": \"people\", \"description\": \"people invited to an event; could be family, friends, or strangers\", \"similar objects\": [\"visitors\", \"attendees\", \"participants\"]}", + 10 + ], + "gold decoration": [ + " {\"type\": \"decoration\", \"description\": \"shiny; could be in the form of jewelry; could be in the form of coins\", \"similar objects\": [\"silver decoration\", \"platinum decoration\", \"bronze decoration\"]}", + 10 + ], + "turn arrow": [ + " {\"type\": \"traffic sign\", \"description\": \"triangular; has a yellow background; has a black arrow pointing left or right\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}", + 10 + ], + "frisbee players": [ + " {\"type\": \"sport\", \"description\": \"two teams of players; each team has a frisbee; players throw the frisbee to each other; the goal is to catch the frisbee in the other team's end zone\", \"similar objects\": [\"ultimate frisbee\", \"disc golf\", \"can jam\"]}", + 10 + ], + "pillow cases": [ + " {\"type\": \"bedding item\", \"description\": \"rectangular; could be made of cotton; could be decorated with patterns\", \"similar objects\": [\"sheets\", \"blankets\", \"duvet covers\"]}", + 10 + ], + "wooden slats": [ + " {\"type\": \"building material\", \"description\": \"long, thin pieces of wood; could be used for fencing, flooring, or other construction projects\", \"similar objects\": [\"plywood\", \"lumber\", \"decking\"]}", + 10 + ], + "chili peppers": [ + " {\"type\": \"vegetable\", \"description\": \"small, red, spicy; could be sliced into small pieces; could be dried\", \"similar objects\": [\"bell peppers\", \"jalapenos\", \"habaneros\"]}", + 10 + ], + "blue door": [ + " {\"type\": \"furniture\", \"description\": \"rectangular; could be made of wood; could have a handle; could be painted blue\", \"similar objects\": [\"window\", \"cabinet\", \"drawer\"]}", + 10 + ], + "stove top oven": [ + " {\"type\": \"cooking appliance\", \"description\": \"has a flat top; could have knobs; could have a door\", \"similar objects\": [\"microwave\", \"toaster oven\", \"convection oven\"]}", + 10 + ], + "taxi cabs": [ + " {\"type\": \"vehicle\", \"description\": \"yellow; has a meter; could have a sign on the roof\", \"similar objects\": [\"bus\", \"limousine\", \"Uber\"]}", + 10 + ], + "west": [ + " {\"type\": \"direction\", \"description\": \"opposite of east; could be used to describe a location\", \"similar objects\": [\"north\", \"south\", \"east\"]}", + 10 + ], + "wind vane": [ + " {\"type\": \"weather tool\", \"description\": \"pointed; could be made of metal; could be used to measure wind direction\", \"similar objects\": [\"anemometer\", \"barometer\", \"thermometer\"]}", + 10 + ], + "country skier": [ + " {\"type\": \"athlete\", \"description\": \"uses two poles and skis; wears a helmet and warm clothing; could ski on snow-covered terrain\", \"similar objects\": [\"snowboarder\", \"ice skater\", \"bobsledder\"]}", + 10 + ], + "watch strap": [ + " {\"type\": \"accessory\", \"description\": \"made of leather or metal; could be adjustable; could be decorated with patterns\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}", + 10 + ], + "tail end": [ + " {\"type\": \"body part\", \"description\": \"the end of an animal's body; could be a long, thin structure\", \"similar objects\": [\"head\", \"leg\", \"wing\"]}", + 10 + ], + "stone church": [ + " {\"type\": \"building\", \"description\": \"made of stones; could have a steeple; could have stained glass windows\", \"similar objects\": [\"cathedral\", \"mosque\", \"temple\"]}", + 10 + ], + "sea shells": [ + " {\"type\": \"natural object\", \"description\": \"various shapes and sizes; could be colorful; could be found on the beach\", \"similar objects\": [\"rocks\", \"pebbles\", \"driftwood\"]}", + 10 + ], + "billboard advertisement": [ + "\n{\"type\": \"outdoor advertisement\", \"description\": \"large, printed sign; could be illuminated; could be placed on a roadside\", \"similar objects\": [\"street sign\", \"bus stop sign\", \"streetlight banner\"]}", + 10 + ], + "felt": [ + " {\"type\": \"fabric\", \"description\": \"soft, thick, and fuzzy; could be used for crafting\", \"similar objects\": [\"velvet\", \"flannel\", \"fleece\"]}", + 10 + ], + "floorboard": [ + " {\"type\": \"building material\", \"description\": \"long, flat, wooden boards; could be used to cover the floor\", \"similar objects\": [\"tile\", \"carpet\", \"hardwood\"]}", + 10 + ], + "source": [ + " {\"type\": \"information\", \"description\": \"origin of data; could be a person, document, or website\", \"similar objects\": [\"reference\", \"citation\", \"evidence\"]}", + 10 + ], + "silver wire": [ + " {\"type\": \"material\", \"description\": \"shiny, malleable, ductile\", \"similar objects\": [\"copper wire\", \"aluminum wire\", \"gold wire\"]}", + 10 + ], + "sign boards": [ + " {\"type\": \"information tool\", \"description\": \"rectangular; could be made of wood or metal; could have words or symbols printed on it\", \"similar objects\": [\"billboard\", \"placard\", \"poster\"]}", + 10 + ], + "helmet kid": [ + " {\"type\": \"protective gear\", \"description\": \"hard, round; could be made of plastic or metal; could have a visor\", \"similar objects\": [\"safety goggles\", \"knee pads\", \"elbow pads\"]}", + 10 + ], + "baby gray elephant": [ + "\n{\"type\": \"animal\", \"description\": \"gray; has a trunk; has small ears; has short legs; has a small tail\", \"similar objects\": [\"baby white elephant\", \"baby pink elephant\", \"baby blue elephant\"]}", + 10 + ], + "potholder": [ + " {\"type\": \"kitchen tool\", \"description\": \"square or round; made of fabric or silicone; used to hold hot pots and pans\", \"similar objects\": [\"oven mitt\", \"trivet\", \"hot pad\"]}", + 10 + ], + "lemon slices": [ + " {\"type\": \"food\", \"description\": \"yellow, round, sour; could be used as a garnish\", \"similar objects\": [\"lime slices\", \"orange slices\", \"grapefruit slices\"]}", + 10 + ], + "soap bar": [ + " {\"type\": \"cleaning product\", \"description\": \"rectangular; could be scented; could be in different colors\", \"similar objects\": [\"shampoo\", \"detergent\", \"toothpaste\"]}", + 10 + ], + "green leaves": [ + " {\"type\": \"plant part\", \"description\": \"green; could be oval or lanceolate; could be attached to a stem\", \"similar objects\": [\"petals\", \"seeds\", \"stems\"]}", + 10 + ], + "metal train car": [ + "\n{\"type\": \"transportation vehicle\", \"description\": \"long, rectangular, made of metal; could have windows and doors; could be connected to other cars\", \"similar objects\": [\"bus\", \"truck\", \"airplane\"]}", + 10 + ], + "beige light switch": [ + "\n{\"type\": \"electrical device\", \"description\": \"rectangular; beige in color; has a switch\", \"similar objects\": [\"outlet\", \"dimmer switch\", \"timer switch\"]}", + 10 + ], + "rock ledge": [ + " {\"type\": \"geological formation\", \"description\": \"hard, jagged surface; could be found in mountains or cliffs; could be used as a shelter\", \"similar objects\": [\"cliff\", \"cave\", \"boulder\"]}", + 10 + ], + "colorful wall": [ + " {\"type\": \"decoration\", \"description\": \"could be made of different colors; could be painted or wallpapered\", \"similar objects\": [\"mural\", \"wall art\", \"wall sticker\"]}", + 10 + ], + "color cap": [ + " {\"type\": \"clothing accessory\", \"description\": \"round; could be made of fabric; could have a logo or design\", \"similar objects\": [\"hat\", \"beanie\", \"visor\"]}", + 10 + ], + "leaf lettuce": [ + " {\"type\": \"vegetable\", \"description\": \"green, flat, thin; could be torn into pieces; could be used in salads\", \"similar objects\": [\"romaine lettuce\", \"spinach\", \"arugula\"]}", + 10 + ], + "team number": [ + " {\"type\": \"group identifier\", \"description\": \"a number assigned to a group of people\", \"similar objects\": [\"team name\", \"team logo\", \"team color\"]}", + 10 + ], + "televison": [ + " {\"type\": \"electronic device\", \"description\": \"rectangular; has a screen; could be connected to a remote control\", \"similar objects\": [\"computer\", \"stereo\", \"game console\"]}", + 10 + ], + "dinner knife": [ + " {\"type\": \"utensil\", \"description\": \"long, sharp blade; could have a handle\", \"similar objects\": [\"fork\", \"spoon\", \"butter knife\"]}", + 10 + ], + "wad": [ + " {\"type\": \"object\", \"description\": \"a small lump or bundle of something; could be made of paper, cloth, or other material\", \"similar objects\": [\"ball\", \"clump\", \"bundle\"]}", + 10 + ], + "snowmobile": [ + " {\"type\": \"vehicle\", \"description\": \"long and wide; has a skidoo; could be used in snow\", \"similar objects\": [\"ATV\", \"motorcycle\", \"truck\"]}", + 10 + ], + "lapels": [ + " {\"type\": \"clothing accessory\", \"description\": \"flaps of fabric on the front of a jacket or coat\", \"similar objects\": [\"buttons\", \"pockets\", \"belts\"]}", + 10 + ], + "tall blades": [ + " {\"type\": \"plant\", \"description\": \"long, thin, green leaves; could be arranged in a fan shape; could be used for decoration\", \"similar objects\": [\"ferns\", \"palm leaves\", \"grass\"]}", + 10 + ], + "purple shorts": [ + " {\"type\": \"clothing\", \"description\": \"purple; could be made of cotton; could have pockets; could be knee-length\", \"similar objects\": [\"jeans\", \"skirt\", \"t-shirt\"]}", + 10 + ], + "front tyre": [ + " {\"type\": \"automobile part\", \"description\": \"round; made of rubber; could be inflated\", \"similar objects\": [\"rear tyre\", \"wheel rim\", \"spare tyre\"]}", + 10 + ], + "metal button": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of metal; could have a hole in the middle\", \"similar objects\": [\"zipper\", \"snap\", \"hook\"]}", + 10 + ], + "parapets": [ + " {\"type\": \"architectural structure\", \"description\": \"low walls along the edge of a roof or balcony; could be made of stone or brick\", \"similar objects\": [\"balustrade\", \"railing\", \"fence\"]}", + 10 + ], + "stamen": [ + " {\"type\": \"plant part\", \"description\": \"long, thin, and protrudes from the center of a flower; could be yellow, white, or purple\", \"similar objects\": [\"pistil\", \"petal\", \"sepal\"]}", + 10 + ], + "lashes": [ + " {\"type\": \"cosmetic tool\", \"description\": \"long, thin, curved; could be made of synthetic fibers; could be used to enhance the eyes\", \"similar objects\": [\"mascara\", \"eyeliner\", \"eyebrow pencil\"]}", + 10 + ], + "orange chairs": [ + "\n{\"type\": \"furniture\", \"description\": \"orange; could be made of plastic or wood; could have armrests; could have a cushion\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}", + 10 + ], + "dark nose": [ + " {\"type\": \"body part\", \"description\": \"part of the face; could be black or brown; could be round or pointed\", \"similar objects\": [\"eyes\", \"mouth\", \"ears\"]}", + 10 + ], + "suit pants": [ + " {\"type\": \"clothing\", \"description\": \"long, slim, could be pleated; could have pockets; could be made of wool, cotton, or polyester\", \"similar objects\": [\"trousers\", \"jeans\", \"shorts\"]}", + 10 + ], + "goggle": [ + " {\"type\": \"eyewear\", \"description\": \"protective eyewear; could be tinted; could be used for swimming\", \"similar objects\": [\"sunglasses\", \"safety glasses\", \"reading glasses\"]}", + 10 + ], + "nike tennis shoe": [ + "\n{\"type\": \"footwear\", \"description\": \"athletic shoe; could be white and red; could have a swoosh logo\", \"similar objects\": [\"adidas shoe\", \"converse shoe\", \"puma shoe\"]}", + 10 + ], + "crew": [ + " {\"type\": \"group of people\", \"description\": \"a group of people working together\", \"similar objects\": [\"team\", \"squad\", \"troupe\"]}", + 10 + ], + "parking spaces": [ + " {\"type\": \"parking area\", \"description\": \"rectangular; could be marked with white lines; could be numbered\", \"similar objects\": [\"garage\", \"driveway\", \"parking lot\"]}", + 10 + ], + "dark shoe": [ + " {\"type\": \"footwear\", \"description\": \"dark color; could be made of leather; could have laces\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}", + 10 + ], + "chihuahua": [ + " {\"type\": \"animal\", \"description\": \"small; has a short coat; could have long ears\", \"similar objects\": [\"pug\", \"dachshund\", \"terrier\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input \"zucchini\", the model should focus on the type \"vegetable\", the description \"cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves\", and the similar objects \"cucumber\", \"eggplant\",", + 10 + ], + "satellite dish": [ + " {\"type\": \"communication tool\", \"description\": \"round; could be mounted on a roof; could be used to receive signals from satellites\", \"similar objects\": [\"antenna\", \"radio tower\", \"solar panel\"]}", + 10 + ], + "hoop earrings": [ + " {\"type\": \"accessory\", \"description\": \"circular; could be made of metal or plastic; could be decorated with stones or beads\", \"similar objects\": [\"stud earrings\", \"dangle earrings\", \"drop earrings\"]}", + 10 + ], + "mountain goats": [ + " {\"type\": \"animal\", \"description\": \"white fur; cloven hooves; horns; lives in high altitudes\", \"similar objects\": [\"sheep\", \"bighorn sheep\", \"ibex\"]}", + 10 + ], + "market sign": [ + " {\"type\": \"advertisement tool\", \"description\": \"could be made of metal or plastic; could be in different shapes and sizes; could be illuminated\", \"similar objects\": [\"billboard\", \"banner\", \"poster\"]}", + 10 + ], + "trumpet": [ + " {\"type\": \"musical instrument\", \"description\": \"long, cylindrical; has three valves; could be made of brass\", \"similar objects\": [\"trombone\", \"clarinet\", \"saxophone\"]}", + 10 + ], + "metal garage door": [ + "\n{\"type\": \"building material\", \"description\": \"made of metal; could be rolled up or down; could be automated\", \"similar objects\": [\"wooden garage door\", \"aluminum garage door\", \"steel garage door\"]}", + 10 + ], + "stucco building": [ + "\n{\"type\": \"structure\", \"description\": \"made of stucco; could have a flat roof; could have a porch; could have a chimney\", \"similar objects\": [\"brick building\", \"wooden building\", \"adobe building\"]}", + 10 + ], + "paper hat": [ + " {\"type\": \"headwear\", \"description\": \"made of paper; could be folded into different shapes; could be decorated with colors and patterns\", \"similar objects\": [\"cap\", \"hat\", \"headband\"]}", + 10 + ], + "candelabra": [ + " {\"type\": \"decorative item\", \"description\": \"multiple arms; could be made of metal; could hold candles\", \"similar objects\": [\"chandelier\", \"lamp\", \"lantern\"]}", + 10 + ], + "brickwork": [ + " {\"type\": \"construction material\", \"description\": \"rectangular; could be made of clay, concrete, or stone; could be used to build walls\", \"similar objects\": [\"cement\", \"mortar\", \"concrete block\"]}", + 10 + ], + "shingle roof": [ + " {\"type\": \"roofing material\", \"description\": \"made of asphalt; overlapping pieces; could be rectangular or hexagonal\", \"similar objects\": [\"tile roof\", \"metal roof\", \"solar roof\"]}", + 10 + ], + "scrub": [ + " {\"type\": \"cleaning tool\", \"description\": \"has a handle; could be made of plastic or metal; could have a brush head\", \"similar objects\": [\"brush\", \"sponge\", \"mop\"]}", + 10 + ], + "pink fabric": [ + " {\"type\": \"material\", \"description\": \"light pink; could be made of cotton; could be used for clothing\", \"similar objects\": [\"silk\", \"linen\", \"satin\"]}", + 10 + ], + "skateboard ridden": [ + " {\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could be ridden by standing on it\", \"similar objects\": [\"scooter\", \"rollerblades\", \"bicycle\"]}", + 10 + ], + "tan legs": [ + "\n{\"type\": \"body part\", \"description\": \"skin color between brown and yellow; could be long and slender; could be covered with hair\", \"similar objects\": [\"arms\", \"feet\", \"torso\"]}", + 10 + ], + "yellow green": [ + "\n{\"type\": \"color\", \"description\": \"a combination of yellow and green; could be light or dark; could be bright or dull\", \"similar objects\": [\"olive green\", \"lime green\", \"mustard yellow\"]}", + 10 + ], + "airport control tower": [ + " {\"type\": \"building\", \"description\": \"tall; has a control room; could have a radar\", \"similar objects\": [\"fire station\", \"hospital\", \"train station\"]}", + 10 + ], + "vegitation": [ + " {\"type\": \"plant life\", \"description\": \"green; could be trees, shrubs, grasses, and other plants\", \"similar objects\": [\"flora\", \"fauna\", \"landscape\"]}", + 10 + ], + "tick marks": [ + " {\"type\": \"marking tool\", \"description\": \"small, short lines; could be used to indicate a certain point on a graph or chart\", \"similar objects\": [\"arrows\", \"lines\", \"dots\"]}", + 10 + ], + "toilet water tank": [ + " {\"type\": \"plumbing fixture\", \"description\": \"rectangular; could be made of porcelain; could have a lid; could have a flush handle\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 10 + ], + "eagles": [ + " {\"type\": \"animal\", \"description\": \"large bird; has a hooked beak; has a brown body and white head and tail\", \"similar objects\": [\"hawks\", \"falcons\", \"ospreys\"]}", + 10 + ], + "rock jetty": [ + " {\"type\": \"structure\", \"description\": \"a structure made of rocks that extends into the sea; could be used to protect a harbor or beach from erosion\", \"similar objects\": [\"breakwater\", \"groin\", \"seawall\"]}", + 10 + ], + "wooden bars": [ + " {\"type\": \"building material\", \"description\": \"long, rectangular, made of wood; could be used for fencing\", \"similar objects\": [\"metal bars\", \"concrete blocks\", \"bricks\"]}", + 10 + ], + "underneath": [ + " {\"type\": \"preposition\", \"description\": \"below; beneath; underneath something\", \"similar objects\": [\"above\", \"over\", \"under\"]}", + 10 + ], + "peels": [ + " {\"type\": \"food ingredient\", \"description\": \"thin, edible skin of fruits and vegetables; could be used for cooking\", \"similar objects\": [\"skins\", \"rinds\", \"shells\"]}", + 10 + ], + "drainage grate": [ + " {\"type\": \"infrastructure\", \"description\": \"rectangular; has holes; could be made of metal\", \"similar objects\": [\"manhole cover\", \"sewer cover\", \"drain cover\"]}", + 10 + ], + "gold circle": [ + " {\"type\": \"jewelry\", \"description\": \"round; made of gold; could be a ring or a necklace\", \"similar objects\": [\"silver circle\", \"diamond circle\", \"platinum circle\"]}", + 10 + ], + "orange vehicle": [ + "\n{\"type\": \"vehicle\", \"description\": \"orange; could be a car, truck, or motorcycle; could have four wheels\", \"similar objects\": [\"red vehicle\", \"yellow vehicle\", \"blue vehicle\"]}", + 10 + ], + "stand mixer": [ + " {\"type\": \"kitchen appliance\", \"description\": \"tall; has a bowl and a set of beaters; could be used for mixing ingredients\", \"similar objects\": [\"blender\", \"food processor\", \"hand mixer\"]}", + 10 + ], + "motors": [ + " {\"type\": \"machine\", \"description\": \"used to convert electrical energy into mechanical energy; could be used to power vehicles\", \"similar objects\": [\"engines\", \"generators\", \"turbines\"]}", + 10 + ], + "places": [ + "\n{\"type\": \"location\", \"description\": \"could be a city, town, village, or other geographical area\", \"similar objects\": [\"country\", \"region\", \"state\"]}", + 10 + ], + "elephant tail": [ + " {\"type\": \"body part\", \"description\": \"long, thin, gray; could be used for swatting away flies\", \"similar objects\": [\"giraffe neck\", \"monkey arm\", \"rhinoceros horn\"]}", + 10 + ], + "culvert": [ + " {\"type\": \"engineering structure\", \"description\": \"a tunnel-like structure used to divert water; could be made of concrete or metal\", \"similar objects\": [\"bridge\", \"dam\", \"aqueduct\"]}\n\nObject detection models should focus on the type, description, and similar objects of the given input. For example, for the input zucchini, the model should focus on the type (vegetable), description (cylindrical, green, smooth; could have brown and rough stems; could be sliced into round pieces; could has green leaves), and similar objects (cucumber, eggplant,", + 10 + ], + "service vehicle": [ + " {\"type\": \"vehicle\", \"description\": \"could be used for delivery, maintenance, or repair; could be a truck, van, or car\", \"similar objects\": [\"ambulance\", \"fire truck\", \"garbage truck\"]}", + 10 + ], + "throat": [ + " {\"type\": \"anatomical structure\", \"description\": \"part of the neck; connects the mouth and the esophagus; could be sore\", \"similar objects\": [\"larynx\", \"trachea\", \"pharynx\"]}", + 10 + ], + "shadow snowboarder": [ + "\n{\"type\": \"sports equipment\", \"description\": \"snowboarder silhouette; could be used for snowboarding\", \"similar objects\": [\"skis\", \"snowboard\", \"snowshoes\"]}", + 10 + ], + "blue design": [ + " {\"type\": \"decorative item\", \"description\": \"could be a pattern, a painting, a sculpture, etc.; could be made of different materials; could be of any color\", \"similar objects\": [\"wallpaper\", \"rug\", \"statue\"]}", + 10 + ], + "gutters": [ + " {\"type\": \"building tool\", \"description\": \"long, narrow, metal; used to collect rainwater\", \"similar objects\": [\"downspouts\", \"drainpipes\", \"rain barrels\"]}", + 10 + ], + "thong": [ + " {\"type\": \"clothing item\", \"description\": \"a type of underwear; could be made of lace; could be low-rise\", \"similar objects\": [\"bikini\", \"briefs\", \"boyshorts\"]}", + 10 + ], + "brown clock": [ + "\n{\"type\": \"timekeeping tool\", \"description\": \"brown; could have a round or rectangular shape; could have a pendulum or digital display\", \"similar objects\": [\"watch\", \"alarm clock\", \"grandfather clock\"]}", + 10 + ], + "sky background": [ + " {\"type\": \"background\", \"description\": \"blue; could have clouds; could have birds flying\", \"similar objects\": [\"mountain background\", \"forest background\", \"ocean background\"]}", + 10 + ], + "crack wall": [ + " {\"type\": \"structure\", \"description\": \"has visible cracks; could be made of concrete or brick\", \"similar objects\": [\"broken wall\", \"damaged wall\", \"crumbling wall\"]}", + 10 + ], + "submarine sandwich": [ + " {\"type\": \"food\", \"description\": \"long; usually filled with vegetables, cheese, and meat; could be cut into pieces\", \"similar objects\": [\"burger\", \"hot dog\", \"wrap\"]}", + 10 + ], + "wilson logo": [ + " {\"type\": \"brand logo\", \"description\": \"red and white circle with a black 'W' in the middle\", \"similar objects\": [\"Nike logo\", \"Adidas logo\", \"Puma logo\"]}", + 10 + ], + "photo album": [ + " {\"type\": \"storage tool\", \"description\": \"could be made of leather; could have multiple pages; could have a cover\", \"similar objects\": [\"scrapbook\", \"picture frame\", \"photo box\"]}", + 10 + ], + "ornament tree": [ + " {\"type\": \"decoration\", \"description\": \"could be made of metal or plastic; could be decorated with colorful ornaments; could be used for Christmas or other holidays\", \"similar objects\": [\"Christmas tree\", \"wreath\", \"garland\"]}", + 10 + ], + "grases": [ + " {\"type\": \"plant\", \"description\": \"green; could be found in lawns; could be cut with a lawn mower\", \"similar objects\": [\"weeds\", \"shrubs\", \"trees\"]}", + 10 + ], + "effect": [ + " {\"type\": \"noun\", \"description\": \"a change that is a result or consequence of an action or other cause\", \"similar objects\": [\"consequence\", \"impact\", \"result\"]}", + 10 + ], + "duct": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of metal or plastic; used for ventilation\", \"similar objects\": [\"pipe\", \"hose\", \"tube\"]}", + 10 + ], + "vintage airplane": [ + "\n{\"type\": \"vehicle\", \"description\": \"old-fashioned; could have propellers; could have wings\", \"similar objects\": [\"helicopter\", \"jet\", \"glider\"]}", + 10 + ], + "winglet": [ + " {\"type\": \"aeronautical part\", \"description\": \"small, curved, attached to the wing of an aircraft\", \"similar objects\": [\"aileron\", \"elevator\", \"rudder\"]}", + 10 + ], + "stoop": [ + " {\"type\": \"architectural structure\", \"description\": \"small set of stairs leading to a door; could be made of concrete or wood\", \"similar objects\": [\"porch\", \"veranda\", \"deck\"]}", + 10 + ], + "wooden gate": [ + " {\"type\": \"fence\", \"description\": \"made of wood; could be opened and closed; could be painted\", \"similar objects\": [\"iron gate\", \"chain link fence\", \"wooden fence\"]}", + 10 + ], + "pachyderm": [ + " {\"type\": \"animal\", \"description\": \"large, thick-skinned mammal; includes elephants, rhinoceroses, and hippopotamuses\", \"similar objects\": [\"elephant\", \"rhinoceros\", \"hippopotamus\"]}", + 10 + ], + "dog bone": [ + " {\"type\": \"toy\", \"description\": \"long, white, made of plastic or rubber; could be chewed by dogs\", \"similar objects\": [\"ball\", \"frisbee\", \"plush toy\"]}", + 10 + ], + "water valve": [ + " {\"type\": \"plumbing tool\", \"description\": \"round; could be made of metal; could be used to control the flow of water\", \"similar objects\": [\"faucet\", \"pipe\", \"hose\"]}", + 10 + ], + "orange cut": [ + " {\"type\": \"fruit cut\", \"description\": \"round; has a peel; could be cut into slices\", \"similar objects\": [\"apple cut\", \"lemon cut\", \"grapefruit cut\"]}", + 10 + ], + "vent pipe": [ + " {\"type\": \"plumbing tool\", \"description\": \"long, cylindrical; could be made of metal; could have a cap on one end\", \"similar objects\": [\"drain pipe\", \"exhaust pipe\", \"water pipe\"]}", + 10 + ], + "styrofoam box": [ + " {\"type\": \"packaging material\", \"description\": \"lightweight; could be white or blue; could be used to store food\", \"similar objects\": [\"cardboard box\", \"plastic bag\", \"paper bag\"]}", + 10 + ], + "light cover": [ + " {\"type\": \"lighting accessory\", \"description\": \"could be made of plastic or metal; could be round or square; could be used to cover a light bulb\", \"similar objects\": [\"lamp shade\", \"light bulb\", \"light diffuser\"]}", + 10 + ], + "pink bracelet": [ + " {\"type\": \"accessory\", \"description\": \"round; could be made of plastic or metal; could be decorated with gems\", \"similar objects\": [\"necklace\", \"earrings\", \"ring\"]}", + 10 + ], + "metallic": [ + "\n{\"type\": \"material\", \"description\": \"shiny; could be magnetic; could be malleable; could be ductile\", \"similar objects\": [\"steel\", \"iron\", \"aluminum\"]}", + 10 + ], + "water fall": [ + " {\"type\": \"natural phenomenon\", \"description\": \"water flowing from a higher elevation to a lower elevation; could be accompanied by a loud sound\", \"similar objects\": [\"geyser\", \"rapids\", \"cascade\"]}", + 10 + ], + "orange trim": [ + " {\"type\": \"decorative item\", \"description\": \"orange color; could be used to decorate walls, furniture, etc.\", \"similar objects\": [\"yellow trim\", \"blue trim\", \"green trim\"]}", + 10 + ], + "clock showing time": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has two hands; could have a digital display\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}", + 10 + ], + "thick snow": [ + " {\"type\": \"weather condition\", \"description\": \"white, fluffy, cold; could be heavy and wet; could be slippery\", \"similar objects\": [\"sleet\", \"hail\", \"ice\"]}", + 10 + ], + "glass cockpit": [ + " {\"type\": \"aviation technology\", \"description\": \"electronic instrument panel; replaces traditional analog instruments\", \"similar objects\": [\"electronic flight instrument system\", \"head-up display\", \"synthetic vision system\"]}", + 10 + ], + "macaw": [ + " {\"type\": \"bird\", \"description\": \"large; colorful feathers; long tail; curved beak\", \"similar objects\": [\"parrot\", \"cockatoo\", \"finch\"]}", + 10 + ], + "spray ocean": [ + " {\"type\": \"cleaning product\", \"description\": \"aerosol; could be used to clean surfaces\", \"similar objects\": [\"disinfectant\", \"detergent\", \"all-purpose cleaner\"]}", + 10 + ], + "round ears": [ + " {\"type\": \"animal body part\", \"description\": \"rounded ears; could be furry; could be long or short\", \"similar objects\": [\"whiskers\", \"tail\", \"paws\"]}", + 10 + ], + "skateboard trick": [ + " {\"type\": \"sport activity\", \"description\": \"maneuvering a skateboard in a certain way; could involve jumping, spinning, or flipping\", \"similar objects\": [\"snowboarding trick\", \"surfing trick\", \"BMX trick\"]}", + 10 + ], + "suite": [ + " {\"type\": \"clothing\", \"description\": \"a set of garments; usually includes a jacket and trousers or skirt\", \"similar objects\": [\"tuxedo\", \"dress\", \"blazer\"]}", + 10 + ], + "light globe": [ + " {\"type\": \"lighting tool\", \"description\": \"round; could be made of glass; could contain a light bulb\", \"similar objects\": [\"lamp\", \"light bulb\", \"chandelier\"]}", + 10 + ], + "turbo engine": [ + " {\"type\": \"engine\", \"description\": \"high-performance engine; uses exhaust gases to increase power; could be used in cars, boats, and planes\", \"similar objects\": [\"diesel engine\", \"electric engine\", \"hybrid engine\"]}", + 10 + ], + "beach water": [ + " {\"type\": \"natural environment\", \"description\": \"clear, blue, salty; could have waves; could have sand\", \"similar objects\": [\"ocean\", \"lake\", \"river\"]}", + 10 + ], + "blue tail": [ + " {\"type\": \"animal\", \"description\": \"blue body; long tail; could be a bird or a lizard\", \"similar objects\": [\"blue jay\", \"bluebird\", \"lizard\"]}", + 10 + ], + "start": [ + " {\"type\": \"verb\", \"description\": \"to begin an action; to initiate something\", \"similar objects\": [\"go\", \"run\", \"jump\"]}", + 10 + ], + "waste receptacle": [ + " {\"type\": \"container\", \"description\": \"cylindrical; could be made of metal; could have a lid\", \"similar objects\": [\"trash can\", \"garbage bin\", \"recycling bin\"]}", + 10 + ], + "window opening": [ + " {\"type\": \"architectural feature\", \"description\": \"rectangular; could be made of glass; could be opened and closed\", \"similar objects\": [\"door\", \"balcony\", \"skylight\"]}", + 10 + ], + "metal hardware": [ + " {\"type\": \"building material\", \"description\": \"made of metal; could be screws, nuts, bolts, etc.\", \"similar objects\": [\"wooden hardware\", \"plastic hardware\", \"concrete hardware\"]}", + 10 + ], + "purple train": [ + "\n{\"type\": \"vehicle\", \"description\": \"long; could be painted in purple; could have multiple carriages\", \"similar objects\": [\"bus\", \"tram\", \"monorail\"]}", + 10 + ], + "orange strap": [ + " {\"type\": \"accessory\", \"description\": \"orange color; could be made of fabric or leather; could be used as a belt or a handle\", \"similar objects\": [\"red strap\", \"yellow strap\", \"green strap\"]}", + 10 + ], + "night time sky": [ + " {\"type\": \"natural phenomenon\", \"description\": \"dark blue; stars and moon could be seen; could have clouds\", \"similar objects\": [\"sunset\", \"sunrise\", \"aurora\"]}", + 10 + ], + "orange buoys": [ + " {\"type\": \"safety tool\", \"description\": \"round; orange in color; could be used to mark a safe area in water\", \"similar objects\": [\"lifebuoy\", \"life ring\", \"life jacket\"]}", + 10 + ], + "blue nose": [ + " {\"type\": \"dog breed\", \"description\": \"small; has a short muzzle; has a blue-gray coat\", \"similar objects\": [\"French Bulldog\", \"Pug\", \"Boston Terrier\"]}", + 10 + ], + "car headlights": [ + "\n{\"type\": \"lighting tool\", \"description\": \"attached to the front of a car; could be round or rectangular; could be bright white or yellow\", \"similar objects\": [\"taillights\", \"fog lights\", \"turn signals\"]}", + 10 + ], + "pink strap": [ + " {\"type\": \"accessory\", \"description\": \"pink; could be used to hold items; could be made of fabric or leather\", \"similar objects\": [\"belt\", \"bag strap\", \"watch strap\"]}", + 10 + ], + "firefighters": [ + " {\"type\": \"professionals\", \"description\": \"people who put out fires; wear protective gear; use fire extinguishers\", \"similar objects\": [\"police officers\", \"paramedics\", \"doctors\"]}", + 10 + ], + "toilette": [ + " {\"type\": \"bathroom fixture\", \"description\": \"has a bowl; could have a lid; could be connected to a water tank\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 10 + ], + "mother bear": [ + " {\"type\": \"animal\", \"description\": \"large; brown fur; could have cubs\", \"similar objects\": [\"grizzly bear\", \"polar bear\", \"black bear\"]}", + 10 + ], + "round spot": [ + " {\"type\": \"shape\", \"description\": \"circular; could be of any color; could be of any size\", \"similar objects\": [\"circle\", \"oval\", \"sphere\"]}", + 10 + ], + "plane wings": [ + " {\"type\": \"aircraft part\", \"description\": \"long, thin, curved; could be made of metal\", \"similar objects\": [\"fuselage\", \"engine\", \"landing gear\"]}", + 10 + ], + "orange kitten": [ + "\n{\"type\": \"animal\", \"description\": \"orange fur; small size; could have white spots; could have a long tail\", \"similar objects\": [\"calico kitten\", \"tiger cub\", \"tabby kitten\"]}", + 10 + ], + "lavender flowers": [ + " {\"type\": \"flower\", \"description\": \"purple; has a strong scent; could be used for aromatherapy\", \"similar objects\": [\"rose\", \"sunflower\", \"daisy\"]}", + 10 + ], + "atv": [ + " {\"type\": \"vehicle\", \"description\": \"four-wheeled; could be used for off-road activities; could have a roll cage\", \"similar objects\": [\"dirt bike\", \"go-kart\", \"snowmobile\"]}", + 10 + ], + "soot": [ + " {\"type\": \"particulate matter\", \"description\": \"black, powdery, fine particles; could be produced by burning fossil fuels\", \"similar objects\": [\"smoke\", \"ash\", \"dust\"]}", + 10 + ], + "firetrucks": [ + " {\"type\": \"vehicle\", \"description\": \"red; has a loud siren; could with a hose\", \"similar objects\": [\"ambulance\", \"police car\", \"garbage truck\"]}", + 10 + ], + "eaves": [ + " {\"type\": \"architectural feature\", \"description\": \"overhanging edge of a roof; could be made of wood or metal\", \"similar objects\": [\"gutter\", \"downspout\", \"soffit\"]}", + 10 + ], + "birthday candle": [ + "\n{\"type\": \"decoration\", \"description\": \"long, thin, usually white or colored; could have a number on it\", \"similar objects\": [\"cake topper\", \"balloon\", \"streamer\"]}", + 10 + ], + "cove": [ + " {\"type\": \"geographical feature\", \"description\": \"a small, sheltered bay; could be surrounded by cliffs or rocks\", \"similar objects\": [\"inlet\", \"lagoon\", \"estuary\"]}", + 10 + ], + "highway overpass": [ + " {\"type\": \"structure\", \"description\": \"concrete bridge; could have multiple lanes; could have guardrails\", \"similar objects\": [\"bridge\", \"tunnel\", \"viaduct\"]}", + 10 + ], + "ash": [ + " {\"type\": \"substance\", \"description\": \"grayish-black; powdery; could be from burning wood\", \"similar objects\": [\"soot\", \"charcoal\", \"dust\"]}", + 10 + ], + "relection": [ + " {\"type\": \"optical phenomenon\", \"description\": \"light bouncing off a surface; could be seen in a mirror\", \"similar objects\": [\"refraction\", \"diffraction\", \"interference\"]}", + 10 + ], + "pink pole": [ + " {\"type\": \"object\", \"description\": \"pink; cylindrical; could be used for support\", \"similar objects\": [\"flagpole\", \"streetlight pole\", \"fence post\"]}", + 10 + ], + "drill": [ + " {\"type\": \"tool\", \"description\": \"long handle; has a rotating bit; could be used to make holes\", \"similar objects\": [\"screwdriver\", \"hammer\", \"saw\"]}", + 10 + ], + "horse drinking water": [ + "\n{\"type\": \"animal behavior\", \"description\": \"horse bending down to drink water from a pond or river; could have its mane wet\", \"similar objects\": [\"dog drinking water\", \"cat drinking water\", \"cow drinking water\"]}", + 10 + ], + "trike": [ + " {\"type\": \"vehicle\", \"description\": \"three-wheeled; could be motorized or pedal-powered; could have a basket in the back\", \"similar objects\": [\"bicycle\", \"scooter\", \"motorcycle\"]}", + 10 + ], + "crows": [ + " {\"type\": \"bird\", \"description\": \"black; has a loud cawing sound; could form a flock\", \"similar objects\": [\"ravens\", \"pigeons\", \"seagulls\"]}", + 10 + ], + "competition": [ + " {\"type\": \"event\", \"description\": \"a contest between two or more people or groups to see who is better at a particular activity\", \"similar objects\": [\"tournament\", \"race\", \"contest\"]}", + 10 + ], + "c-kite": [ + " {\"type\": \"toy\", \"description\": \"diamond-shaped; could be flown in the sky; could be made of paper or plastic\", \"similar objects\": [\"kite\", \"balloon\", \"parachute\"]}", + 10 + ], + "cage door": [ + " {\"type\": \"enclosure tool\", \"description\": \"rectangular; could be made of metal bars; could be locked with a key\", \"similar objects\": [\"fence\", \"gate\", \"door\"]}", + 10 + ], + "photo background": [ + " {\"type\": \"photography tool\", \"description\": \"could be a wall, a curtain, a paper, or a cloth; could be plain or patterned; could be of any color\", \"similar objects\": [\"backdrop\", \"scenery\", \"background paper\"]}", + 10 + ], + "safety sign": [ + " {\"type\": \"warning sign\", \"description\": \"triangular; could be yellow and black; could have a symbol or text\", \"similar objects\": [\"traffic sign\", \"road sign\", \"hazard sign\"]}", + 10 + ], + "hedge bush": [ + " {\"type\": \"plant\", \"description\": \"dense, green, could be trimmed into shapes; could have thorns\", \"similar objects\": [\"shrub\", \"ivy\", \"fern\"]}", + 10 + ], + "blue sky background": [ + "\n{\"type\": \"background\", \"description\": \"blue; could have white clouds; could be used as a backdrop for a photo\", \"similar objects\": [\"green grass background\", \"ocean background\", \"mountain background\"]}", + 10 + ], + "whitecap": [ + " {\"type\": \"clothing accessory\", \"description\": \"hat; could be made of cotton; has a brim\", \"similar objects\": [\"baseball cap\", \"sun hat\", \"beanie\"]}", + 10 + ], + "clock roman": [ + " {\"type\": \"timekeeping tool\", \"description\": \"round; has roman numerals; could have two hands\", \"similar objects\": [\"watch\", \"alarm clock\", \"sundial\"]}", + 10 + ], + "race car": [ + " {\"type\": \"vehicle\", \"description\": \"fast; has a sleek design; could have a sponsor logo\", \"similar objects\": [\"sports car\", \"motorcycle\", \"truck\"]}", + 10 + ], + "corn dog": [ + " {\"type\": \"food\", \"description\": \"hot dog covered in cornmeal batter; could be served with ketchup and mustard\", \"similar objects\": [\"taco\", \"burrito\", \"hamburger\"]}", + 10 + ], + "roof line": [ + " {\"type\": \"architectural feature\", \"description\": \"the line formed by the upper edge of a roof; could be straight or curved\", \"similar objects\": [\"gable\", \"eave\", \"ridge\"]}", + 10 + ], + "orange leaf": [ + " {\"type\": \"plant\", \"description\": \"green, oval-shaped; could have a pointy tip; could have a serrated edge; could have a reddish-orange hue\", \"similar objects\": [\"maple leaf\", \"ginkgo leaf\", \"magnolia leaf\"]}", + 9 + ], + "plastic ball": [ + " {\"type\": \"toy\", \"description\": \"round; could be bouncy; could be made of plastic\", \"similar objects\": [\"rubber ball\", \"soccer ball\", \"basketball\"]}", + 9 + ], + "baseball pitch": [ + " {\"type\": \"sports activity\", \"description\": \"throwing a ball with a curved trajectory; could be a fastball, curveball, slider, etc.\", \"similar objects\": [\"bowling\", \"golf\", \"tennis\"]}", + 9 + ], + "macy": [ + " {\"type\": \"store\", \"description\": \"department store; has a variety of products\", \"similar objects\": [\"Target\", \"Walmart\", \"Kohl's\"]}", + 9 + ], + "coaches": [ + " {\"type\": \"transportation\", \"description\": \"long; could have multiple compartments; could be pulled by horses\", \"similar objects\": [\"train\", \"bus\", \"tram\"]}", + 9 + ], + "dinner fork": [ + " {\"type\": \"utensil\", \"description\": \"long handle; four tines; used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 9 + ], + "wooden stand": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could be used to display items; could have a flat surface\", \"similar objects\": [\"table\", \"shelf\", \"chair\"]}", + 9 + ], + "orange carrot slice": [ + "\n{\"type\": \"vegetable\", \"description\": \"orange, round, has a stem; could be sliced into round pieces\", \"similar objects\": [\"zucchini\", \"eggplant\", \"pumpkin\"]}", + 9 + ], + "sparse tree": [ + " {\"type\": \"plant\", \"description\": \"has few leaves; could have a long trunk; could be found in desert areas\", \"similar objects\": [\"cactus\", \"palm tree\", \"bamboo\"]}", + 9 + ], + "mickey": [ + " {\"type\": \"cartoon character\", \"description\": \"black ears; red shorts; yellow shoes; white gloves\", \"similar objects\": [\"minnie\", \"donald duck\", \"goofy\"]}", + 9 + ], + "hashbrowns": [ + " {\"type\": \"food\", \"description\": \"shredded potatoes; could be fried or baked; could be served with breakfast\", \"similar objects\": [\"french fries\", \"tater tots\", \"potato wedges\"]}", + 9 + ], + "sundress": [ + " {\"type\": \"clothing\", \"description\": \"lightweight; usually sleeveless; could be made of cotton or linen; could have floral patterns\", \"similar objects\": [\"maxi dress\", \"shirt dress\", \"shift dress\"]}", + 9 + ], + "clock tower building": [ + "\n{\"type\": \"structure\", \"description\": \"tall, rectangular; could have a clock on the top; could have a bell\", \"similar objects\": [\"church\", \"cathedral\", \"monument\"]}", + 9 + ], + "tennis turf": [ + " {\"type\": \"sports surface\", \"description\": \"green; could be made of artificial grass; could be used for tennis and other sports\", \"similar objects\": [\"football turf\", \"basketball court\", \"running track\"]}", + 9 + ], + "plastic piece": [ + " {\"type\": \"material\", \"description\": \"flexible; could be transparent; could be colored\", \"similar objects\": [\"rubber\", \"metal\", \"wood\"]}", + 9 + ], + "piller": [ + " {\"type\": \"architectural structure\", \"description\": \"vertical, cylindrical, could be made of stone or metal; could be used to support a roof or bridge\", \"similar objects\": [\"column\", \"obelisk\", \"monument\"]}", + 9 + ], + "nectarines": [ + " {\"type\": \"fruit\", \"description\": \"smooth, yellow-orange skin; could have a red blush; has a pit\", \"similar objects\": [\"peaches\", \"plums\", \"apricots\"]}", + 9 + ], + "metal toilet": [ + " {\"type\": \"plumbing fixture\", \"description\": \"made of metal; has a bowl and a tank; could be wall-mounted\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}", + 9 + ], + "beige lamp shade": [ + "\n{\"type\": \"lighting accessory\", \"description\": \"light-colored; could be made of fabric; could be cylindrical or conical\", \"similar objects\": [\"lampshade\", \"light bulb\", \"lantern\"]}", + 9 + ], + "fencepost": [ + " {\"type\": \"building material\", \"description\": \"long, cylindrical; could be made of wood or metal; could be used to build a fence\", \"similar objects\": [\"rail\", \"pillar\", \"stake\"]}", + 9 + ], + "bamboo plant": [ + " {\"type\": \"plant\", \"description\": \"tall, thin, green stalks; could have yellow or white flowers; could have leaves\", \"similar objects\": [\"palm tree\", \"fern\", \"birch tree\"]}", + 9 + ], + "gold color": [ + " {\"type\": \"color\", \"description\": \"shiny yellow; could be used to describe jewelry\", \"similar objects\": [\"silver\", \"bronze\", \"copper\"]}", + 9 + ], + "sun glaring": [ + " {\"type\": \"phenomenon\", \"description\": \"bright light from the sun; could cause discomfort to the eyes\", \"similar objects\": [\"sunlight\", \"glare\", \"reflection\"]}", + 9 + ], + "photo frames": [ + " {\"type\": \"decoration\", \"description\": \"rectangular; could be made of wood, metal, or plastic; could have a picture inside\", \"similar objects\": [\"picture frames\", \"mirrors\", \"paintings\"]}", + 9 + ], + "bread slice": [ + " {\"type\": \"food\", \"description\": \"flat, rectangular; could be toasted; could be served with butter\", \"similar objects\": [\"toast\", \"bagel\", \"croissant\"]}", + 9 + ], + "radiators": [ + " {\"type\": \"heating tool\", \"description\": \"long, metal; could be attached to the wall; could be used to heat up a room\", \"similar objects\": [\"heaters\", \"boilers\", \"air conditioners\"]}", + 9 + ], + "railroad cars": [ + " {\"type\": \"transportation vehicle\", \"description\": \"long; could be connected to each other; could be used to transport goods\", \"similar objects\": [\"train\", \"tram\", \"trolley\"]}", + 9 + ], + "button keyboard": [ + " {\"type\": \"input device\", \"description\": \"rectangular; has multiple buttons; could be connected to a computer\", \"similar objects\": [\"mouse\", \"trackpad\", \"joystick\"]}", + 9 + ], + "surveillance camera": [ + " {\"type\": \"security device\", \"description\": \"small, cylindrical; could be mounted on walls; could be connected to a monitor\", \"similar objects\": [\"motion sensor\", \"alarm system\", \"doorbell camera\"]}", + 9 + ], + "clock minute hand": [ + "\n{\"type\": \"timekeeping tool\", \"description\": \"long, thin, pointed; moves around the clock face\", \"similar objects\": [\"hour hand\", \"second hand\", \"alarm hand\"]}", + 9 + ], + "orange bricks": [ + " {\"type\": \"building material\", \"description\": \"rectangular; could be made of clay; could be used to build walls\", \"similar objects\": [\"concrete blocks\", \"cement blocks\", \"wooden blocks\"]}", + 9 + ], + "bathroom lights": [ + " {\"type\": \"lighting tool\", \"description\": \"could be ceiling lights, wall lights, or vanity lights; could be fluorescent, LED, or incandescent; could be dimmable or non-dimmable\", \"similar objects\": [\"ceiling fan\", \"chandelier\", \"pendant light\"]}", + 9 + ], + "granite counter": [ + " {\"type\": \"building material\", \"description\": \"hard, durable, and heat-resistant; could be polished to a smooth finish; could be used for kitchen countertops\", \"similar objects\": [\"marble\", \"quartz\", \"concrete\"]}", + 9 + ], + "arch doorway": [ + " {\"type\": \"architectural structure\", \"description\": \"curved top; could be made of stone or wood; could have a door\", \"similar objects\": [\"arched window\", \"arched bridge\", \"arched ceiling\"]}", + 9 + ], + "airplane kite": [ + " {\"type\": \"toy\", \"description\": \"long, thin, has a tail; could be made of paper or fabric; could be flown in the sky\", \"similar objects\": [\"dragon kite\", \"delta kite\", \"box kite\"]}", + 9 + ], + "blue strip": [ + " {\"type\": \"object\", \"description\": \"long, thin, blue; could be made of fabric or paper\", \"similar objects\": [\"ribbon\", \"belt\", \"scarf\"]}", + 9 + ], + "blue bench": [ + "\n{\"type\": \"furniture\", \"description\": \"long, blue, made of wood or metal; could have a backrest\", \"similar objects\": [\"chair\", \"sofa\", \"stool\"]}", + 9 + ], + "chest protector": [ + " {\"type\": \"protective gear\", \"description\": \"worn on the chest; could be made of foam or plastic; could have straps\", \"similar objects\": [\"shoulder pads\", \"elbow pads\", \"knee pads\"]}", + 9 + ], + "cream pitcher": [ + " {\"type\": \"kitchenware\", \"description\": \"cylindrical; has a spout; could be made of ceramic or metal\", \"similar objects\": [\"teapot\", \"coffee pot\", \"milk jug\"]}", + 9 + ], + "orange surf board": [ + "\n{\"type\": \"sports equipment\", \"description\": \"long, orange, could have a fin; used for surfing\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}", + 9 + ], + "gray curb": [ + " {\"type\": \"road feature\", \"description\": \"concrete; could be painted gray; could be used to separate lanes\", \"similar objects\": [\"guardrail\", \"traffic island\", \"road divider\"]}", + 9 + ], + "cooks": [ + " {\"type\": \"profession\", \"description\": \"prepares food; could be a chef or a home cook\", \"similar objects\": [\"waiter\", \"bartender\", \"baker\"]}", + 9 + ], + "hole cover": [ + " {\"type\": \"utility tool\", \"description\": \"round; could be made of metal; used to cover holes\", \"similar objects\": [\"manhole cover\", \"drain cover\", \"vent cover\"]}", + 9 + ], + "sizes": [ + " {\"type\": \"measurement\", \"description\": \"measurement of length, width, height, or volume\", \"similar objects\": [\"dimensions\", \"volume\", \"weight\"]}", + 9 + ], + "food menu": [ + " {\"type\": \"document\", \"description\": \"list of food items; could be printed on paper or displayed on a screen\", \"similar objects\": [\"recipe book\", \"shopping list\", \"restaurant menu\"]}", + 9 + ], + "cable wire": [ + " {\"type\": \"electrical tool\", \"description\": \"long, thin, insulated; could be used to connect two devices\", \"similar objects\": [\"power cord\", \"extension cord\", \"USB cable\"]}", + 9 + ], + "checks": [ + " {\"type\": \"financial document\", \"description\": \"rectangular; could be printed with numbers and symbols; could be used to transfer money\", \"similar objects\": [\"cashier's check\", \"money order\", \"debit card\"]}", + 9 + ], + "ground beef": [ + " {\"type\": \"food\", \"description\": \"ground, red, could be used for burgers\", \"similar objects\": [\"ground pork\", \"ground turkey\", \"ground chicken\"]}", + 9 + ], + "colorful jacket": [ + "\n{\"type\": \"clothing\", \"description\": \"multi-colored; could have a hood; could be made of fabric\", \"similar objects\": [\"coat\", \"sweater\", \"hoodie\"]}", + 9 + ], + "light shade": [ + " {\"type\": \"lighting accessory\", \"description\": \"round or cylindrical; could be made of fabric or paper; could be used to diffuse light\", \"similar objects\": [\"lampshade\", \"lantern shade\", \"ceiling shade\"]}", + 9 + ], + "paw prints": [ + " {\"type\": \"animal tracks\", \"description\": \"oval-shaped; could be from cats, dogs, or other animals\", \"similar objects\": [\"hoof prints\", \"bird footprints\", \"human footprints\"]}", + 9 + ], + "wood panel wall": [ + " {\"type\": \"building material\", \"description\": \"made of wood; could be painted; could be used to build walls\", \"similar objects\": [\"drywall\", \"plywood\", \"bricks\"]}", + 9 + ], + "blue emblem": [ + " {\"type\": \"decoration\", \"description\": \"blue; could be a symbol or logo; could be made of metal or plastic\", \"similar objects\": [\"badge\", \"medal\", \"pin\"]}", + 9 + ], + "desktop screen": [ + " {\"type\": \"electronic device\", \"description\": \"flat, rectangular; could be touch-sensitive; could be connected to a computer\", \"similar objects\": [\"laptop screen\", \"tablet screen\", \"television screen\"]}", + 9 + ], + "wooden shelves": [ + " {\"type\": \"furniture\", \"description\": \"made of wood; could be used to store items; could be wall-mounted\", \"similar objects\": [\"bookshelf\", \"cabinet\", \"wardrobe\"]}", + 9 + ], + "orange beverage": [ + " {\"type\": \"drink\", \"description\": \"orange-colored; could be carbonated; could be alcoholic or non-alcoholic\", \"similar objects\": [\"orange juice\", \"soda\", \"beer\"]}", + 9 + ], + "accent pillows": [ + " {\"type\": \"decorative item\", \"description\": \"small, colorful, soft; could be square or round; could have patterns\", \"similar objects\": [\"cushions\", \"throw pillows\", \"blankets\"]}", + 9 + ], + "bread crust": [ + " {\"type\": \"food\", \"description\": \"hard, crunchy; could be brown; could be the outer layer of bread\", \"similar objects\": [\"crouton\", \"toast\", \"biscuit\"]}", + 9 + ], + "headlight front car": [ + "\n{\"type\": \"vehicle part\", \"description\": \"attached to the front of a car; used to provide illumination in the dark\", \"similar objects\": [\"taillight\", \"fog light\", \"turn signal\"]}", + 9 + ], + "bookshelf books": [ + " {\"type\": \"furniture\", \"description\": \"wooden; could have multiple shelves; could be used to store books\", \"similar objects\": [\"cabinet\", \"wardrobe\", \"drawer\"]}", + 9 + ], + "grey hoodie": [ + " {\"type\": \"clothing\", \"description\": \"long-sleeved; has a hood; could be made of cotton or polyester; could have a zipper or drawstrings\", \"similar objects\": [\"sweatshirt\", \"jacket\", \"sweater\"]}", + 9 + ], + "cat drinking water": [ + "\n{\"type\": \"animal behavior\", \"description\": \"cat bending down to drink water from a bowl or other container; could be lapping up the water with its tongue\", \"similar objects\": [\"dog drinking water\", \"bird drinking water\", \"rabbit drinking water\"]}", + 9 + ], + "middle elephant": [ + "\n{\"type\": \"animal\", \"description\": \"large; has a long trunk; has large ears; has a curved back\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}", + 9 + ], + "air condition": [ + " {\"type\": \"appliance\", \"description\": \"has a fan; could be wall-mounted; could be used to cool down a room\", \"similar objects\": [\"heater\", \"humidifier\", \"dehumidifier\"]}", + 9 + ], + "tall hills": [ + " {\"type\": \"landscape\", \"description\": \"high, steep, could have trees and grasses\", \"similar objects\": [\"mountains\", \"valleys\", \"cliffs\"]}", + 9 + ], + "grey trash bin": [ + "\n{\"type\": \"container\", \"description\": \"rectangular; has a lid; could be made of plastic; could be grey in color\", \"similar objects\": [\"recycling bin\", \"garbage can\", \"trash can\"]}", + 9 + ], + "silver leg": [ + " {\"type\": \"jewelry\", \"description\": \"shiny; could be made of silver; could be in the shape of a leg\", \"similar objects\": [\"bracelet\", \"necklace\", \"ring\"]}", + 9 + ], + "pizza oven": [ + " {\"type\": \"cooking tool\", \"description\": \"large, rectangular; could be made of brick; could be heated up to high temperatures\", \"similar objects\": [\"grill\", \"stove\", \"microwave\"]}", + 9 + ], + "guest": [ + " {\"type\": \"person\", \"description\": \"visitor; could be invited to a party or event\", \"similar objects\": [\"stranger\", \"tourist\", \"visitor\"]}", + 9 + ], + "head wrap": [ + " {\"type\": \"accessory\", \"description\": \"long piece of fabric; could be tied around the head\", \"similar objects\": [\"scarf\", \"hat\", \"bandana\"]}", + 9 + ], + "purple fruit": [ + " {\"type\": \"fruit\", \"description\": \"round; could be sweet or sour; could be purple or blue\", \"similar objects\": [\"plum\", \"grape\", \"blueberry\"]}", + 9 + ], + "wooden block": [ + " {\"type\": \"toy\", \"description\": \"square; made of wood; could be painted\", \"similar objects\": [\"building blocks\", \"puzzle\", \"action figures\"]}", + 9 + ], + "front bumper": [ + " {\"type\": \"automobile part\", \"description\": \"attached to the front of a car; could be made of metal or plastic; could have a grille\", \"similar objects\": [\"headlight\", \"hood\", \"fender\"]}", + 9 + ], + "plastic plates": [ + " {\"type\": \"dining ware\", \"description\": \"round; could be transparent; could be colorful; could be disposable\", \"similar objects\": [\"ceramic plates\", \"glass plates\", \"paper plates\"]}", + 9 + ], + "capped": [ + " {\"type\": \"verb\", \"description\": \"to put a cap on something; to limit something\", \"similar objects\": [\"limit\", \"restrict\", \"confine\"]}", + 9 + ], + "gooey cheese": [ + " {\"type\": \"food\", \"description\": \"soft, stretchy, yellow; could be melted\", \"similar objects\": [\"mozzarella cheese\", \"cheddar cheese\", \"feta cheese\"]}", + 9 + ], + "toilet roll holder": [ + " {\"type\": \"bathroom accessory\", \"description\": \"could be made of metal or plastic; has a bar to hold the toilet roll\", \"similar objects\": [\"towel rack\", \"soap dish\", \"toilet brush holder\"]}", + 9 + ], + "silver dinner fork": [ + "\n{\"type\": \"utensil\", \"description\": \"long handle; four tines; made of silver\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}", + 9 + ], + "male child": [ + "\n{\"type\": \"person\", \"description\": \"young; could have short hair; could be wearing a shirt and pants\", \"similar objects\": [\"female child\", \"teenager\", \"adult\"]}", + 9 + ], + "kite handle": [ + " {\"type\": \"toy\", \"description\": \"long, thin, has a string attached to it\", \"similar objects\": [\"frisbee\", \"balloon\", \"yo-yo\"]}", + 9 + ], + "champagne bottle": [ + " {\"type\": \"beverage container\", \"description\": \"tall, slender, has a cork; could be gold or silver\", \"similar objects\": [\"wine bottle\", \"beer bottle\", \"soda bottle\"]}", + 9 + ], + "hospital room": [ + " {\"type\": \"room\", \"description\": \"could have beds, medical equipment, and other medical supplies; could have a window; could have a door\", \"similar objects\": [\"waiting room\", \"operating room\", \"emergency room\"]}", + 9 + ], + "suitcase handle": [ + " {\"type\": \"luggage accessory\", \"description\": \"long, thin, metal; could be attached to a suitcase\", \"similar objects\": [\"luggage wheel\", \"luggage strap\", \"luggage lock\"]}", + 9 + ], + "mauve": [ + " {\"type\": \"color\", \"description\": \"light purple; could be described as pinkish purple\", \"similar objects\": [\"lavender\", \"lilac\", \"periwinkle\"]}", + 9 + ], + "wrist wrap": [ + " {\"type\": \"accessory\", \"description\": \"long strip of fabric; could be used to wrap around the wrist; could be used for decoration\", \"similar objects\": [\"headband\", \"scarf\", \"belt\"]}", + 9 + ], + "computer key board": [ + "\n{\"type\": \"input device\", \"description\": \"rectangular; has keys for typing; could be wired or wireless\", \"similar objects\": [\"mouse\", \"joystick\", \"trackpad\"]}", + 9 + ] +} \ No newline at end of file diff --git a/tools/files/o365.description.v1.json b/tools/files/o365.description.v1.json new file mode 100644 index 0000000000000000000000000000000000000000..0cec5b34a74ab48f7a75b0a4abed3af2f45c9ede --- /dev/null +++ b/tools/files/o365.description.v1.json @@ -0,0 +1 @@ +[{"object": "CD", "object_id": 262, "gpt3_output": "\n{\"type\": \"storage device\", \"description\": \"round; could store music, videos, and other data\", \"similar objects\": [\"DVD\", \"Blu-ray\", \"USB drive\"]}"}, {"object": "air conditioner", "object_id": 5, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"large, rectangular; has a fan and a compressor; could be wall-mounted or window-mounted\", \"similar objects\": [\"heater\", \"refrigerator\", \"dehumidifier\"]}"}, {"object": "airplane", "object_id": 8, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long and narrow; has wings and a tail; could have multiple engines; could be used for transportation\", \"similar objects\": [\"helicopter\", \"rocket\", \"drone\"]}"}, {"object": "ambulance", "object_id": 14, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"red; has a glaring siren; could with a stretcher\", \"similar objects\": [\"police car\", \"taxi\", \"garbage truck\"]}"}, {"object": "american football", "object_id": 527, "gpt3_output": "\n{\"type\": \"sport\", \"description\": \"team sport; two teams of 11 players; played with an oval-shaped ball; goal is to score points by advancing the ball into the opposing team's end zone\", \"similar objects\": [\"soccer\", \"basketball\", \"baseball\"]}"}, {"object": "antelope", "object_id": 18, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"long legs; slender body; horns on the head; brown fur\", \"similar objects\": [\"gazelle\", \"deer\", \"wildebeest\"]}"}, {"object": "apple", "object_id": 22, "gpt3_output": "\n{\"type\": \"fruit\", \n\"description\": \"round, red, has a stem and green leaves\", \n\"similar objects\": [\"orange\", \"banana\", \"pear\"]}"}, {"object": "asparagus", "object_id": 38, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, thin, green stalks; could have purple tips; could be steamed or boiled; could have a nutty flavor\", \"similar objects\": [\"broccoli\", \"cauliflower\", \"green beans\"]}"}, {"object": "avocado", "object_id": 43, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"oval-shaped; green or black; has a large seed inside; could be sliced into pieces\", \"similar objects\": [\"mango\", \"kiwi\", \"papaya\"]}"}, {"object": "backpack", "object_id": 49, "gpt3_output": "\n{\"type\": \"bag\", \"description\": \"rectangular; has straps; could be used to carry items\", \"similar objects\": [\"duffel bag\", \"suitcase\", \"tote bag\"]}"}, {"object": "balloon", "object_id": 60, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"round; made of rubber or plastic; could be filled with air or helium; could be colorful\", \"similar objects\": [\"kite\", \"yo-yo\", \"marble\"]}"}, {"object": "banana", "object_id": 62, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"long, curved, yellow; has a brown peel; could be sliced into pieces\", \"similar objects\": [\"apple\", \"orange\", \"pear\"]}"}, {"object": "baozi", "object_id": 1783, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"steamed bun; could be filled with meat or vegetables; could be served with soy sauce\", \"similar objects\": [\"dumpling\", \"mantou\", \"xiao long bao\"]}"}, {"object": "bar soap", "object_id": 1245, "gpt3_output": "\n{\"type\": \"cleaning product\", \"description\": \"solid; could be in different shapes and sizes; could be scented\", \"similar objects\": [\"liquid soap\", \"shampoo\", \"detergent\"]}"}, {"object": "dumbbell", "object_id": 1699, "gpt3_output": "\n{\"type\": \"exercise equipment\", \"description\": \"two weights connected by a handle; could be used for strength training\", \"similar objects\": [\"barbell\", \"kettlebell\", \"resistance band\"]}"}, {"object": "barrel/bucket", "object_id": 76, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of metal or plastic; could have a handle\", \"similar objects\": [\"tub\", \"pail\", \"bin\"]}"}, {"object": "baseball", "object_id": 80, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"round; made of leather and cork; has a stitching\", \"similar objects\": [\"softball\", \"tennis ball\", \"golf ball\"]}"}, {"object": "baseball bat", "object_id": 81, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, cylindrical; could be made of wood or metal; used to hit a ball\", \"similar objects\": [\"golf club\", \"tennis racket\", \"hockey stick\"]}"}, {"object": "baseball glove", "object_id": 85, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"leather; has a pocket; used to catch a baseball\", \"similar objects\": [\"bat\", \"helmet\", \"cleats\"]}"}, {"object": "basket", "object_id": 94, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"could be made of wicker; could have a handle; could be used for carrying items\", \"similar objects\": [\"bag\", \"box\", \"bucket\"]}"}, {"object": "basketball", "object_id": 1786, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"round; orange; has a net\", \"similar objects\": [\"football\", \"baseball\", \"tennis ball\"]}"}, {"object": "bathtub", "object_id": 101, "gpt3_output": "\n{\"type\": \"bathroom fixture\", \"description\": \"large, deep, usually made of porcelain; could have a shower head\", \"similar objects\": [\"shower\", \"sink\", \"toilet\"]}"}, {"object": "bear", "object_id": 112, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, furry, four legs; could have a snout; could be brown, black, or white\", \"similar objects\": [\"wolf\", \"tiger\", \"lion\"]}"}, {"object": "bed", "object_id": 114, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has a mattress; could have a headboard; could have four legs\", \"similar objects\": [\"sofa\", \"chair\", \"table\"]}"}, {"object": "belt", "object_id": 124, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"long, thin, could be made of leather; could have a buckle\", \"similar objects\": [\"scarf\", \"tie\", \"hat\"]}"}, {"object": "bench", "object_id": 125, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"long, flat, could have backrest; could be made of wood or metal\", \"similar objects\": [\"chair\", \"sofa\", \"table\"]}"}, {"object": "bicycle", "object_id": 129, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"two wheels; has a handlebar; could have a basket\", \"similar objects\": [\"motorcycle\", \"scooter\", \"tricycle\"]}"}, {"object": "billiards", "object_id": 1787, "gpt3_output": "\n{\"type\": \"game\", \"description\": \"table game; involves a cue stick and balls; could be played with multiple players\", \"similar objects\": [\"pool\", \"snooker\", \"bowling\"]}"}, {"object": "binoculars", "object_id": 1640, "gpt3_output": "\n{\"type\": \"optical tool\", \"description\": \"two lenses connected by a bridge; could be used to magnify distant objects\", \"similar objects\": [\"telescope\", \"microscope\", \"monocular\"]}"}, {"object": "blackboard/whiteboard", "object_id": 1553, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"smooth, flat surface; could be used to write or draw; could be magnetic\", \"similar objects\": [\"chalkboard\", \"marker board\", \"dry erase board\"]}"}, {"object": "blender", "object_id": 148, "gpt3_output": "\n{\"type\": \"kitchen appliance\", \"description\": \"electric; has a motor; could be used to blend food\", \"similar objects\": [\"food processor\", \"juicer\", \"mixer\"]}"}, {"object": "board eraser", "object_id": 1788, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"rectangular; made of rubber; used to erase chalkboard writings\", \"similar objects\": [\"sponge\", \"cloth\", \"duster\"]}"}, {"object": "boat", "object_id": 157, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"could be made of wood or metal; could have a sail or motor; could be used for transportation or recreation\", \"similar objects\": [\"ship\", \"yacht\", \"canoe\"]}"}, {"object": "book", "object_id": 161, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"bound paper pages; could have a cover; could have a spine\", \"similar objects\": [\"magazine\", \"newspaper\", \"journal\"]}"}, {"object": "boots", "object_id": 165, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"ankle-high; could be made of leather; could have laces\", \"similar objects\": [\"shoes\", \"sneakers\", \"sandals\"]}"}, {"object": "bottle", "object_id": 168, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"cylindrical; could be made of glass or plastic; could have a cap or a lid\", \"similar objects\": [\"jar\", \"can\", \"jug\"]}"}, {"object": "bow tie", "object_id": 174, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small, black, tied around the neck\", \"similar objects\": [\"necktie\", \"cravat\", \"ascot\"]}"}, {"object": "bowl", "object_id": 175, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round; could be made of ceramic, plastic, or metal; could be used for serving food\", \"similar objects\": [\"plate\", \"cup\", \"dish\"]}"}, {"object": "bracelet", "object_id": 178, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"worn around the wrist; could be made of metal, plastic, or fabric; could have charms or jewels\", \"similar objects\": [\"necklace\", \"earrings\", \"ring\"]}"}, {"object": "bread/bun", "object_id": 182, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; could be sliced; could be made of wheat flour; could be served with butter\", \"similar objects\": [\"bagel\", \"croissant\", \"roll\"]}"}, {"object": "broccoli", "object_id": 192, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"green, florets; could have a stem; could be steamed or boiled\", \"similar objects\": [\"cauliflower\", \"cabbage\", \"brussels sprouts\"]}"}, {"object": "broom", "object_id": 194, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"long handle; has bristles; could be made of straw\", \"similar objects\": [\"mop\", \"vacuum cleaner\", \"dustpan\"]}"}, {"object": "paint brush", "object_id": 196, "gpt3_output": "\n{\"type\": \"painting tool\", \"description\": \"long handle; bristles at the end; could be made of natural or synthetic materials\", \"similar objects\": [\"paint roller\", \"paint scraper\", \"paint sponge\"]}"}, {"object": "bus", "object_id": 212, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, long, has multiple doors; could have multiple levels; could be painted in different colors\", \"similar objects\": [\"truck\", \"van\", \"minibus\"]}"}, {"object": "butterfly", "object_id": 218, "gpt3_output": "\n{\"type\": \"insect\", \"description\": \"wings with colorful patterns; could have antennae; could have long proboscis\", \"similar objects\": [\"moth\", \"dragonfly\", \"bee\"]}"}, {"object": "cabbage", "object_id": 221, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round, green; could have white veins; could be sliced into thin pieces; could have green leaves\", \"similar objects\": [\"lettuce\", \"broccoli\", \"cauliflower\"]}"}, {"object": "cabinet/shelf", "object_id": 223, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"tall, rectangular, has shelves and drawers; could be made of wood or metal\", \"similar objects\": [\"bookcase\", \"wardrobe\", \"dresser\"]}"}, {"object": "cake", "object_id": 230, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; could be decorated with icing and fruits; could be filled with cream\", \"similar objects\": [\"pie\", \"cupcake\", \"muffin\"]}"}, {"object": "calculator", "object_id": 231, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, rectangular; has buttons and a display screen\", \"similar objects\": [\"computer\", \"smartphone\", \"tablet\"]}"}, {"object": "camel", "object_id": 1759, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"humped back; two humps; long legs; thick fur; long eyelashes; thick lips\", \"similar objects\": [\"llama\", \"alpaca\", \"giraffe\"]}"}, {"object": "camera", "object_id": 234, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, rectangular; has a lens; could be digital or analog\", \"similar objects\": [\"camcorder\", \"smartphone\", \"binoculars\"]}"}, {"object": "candle", "object_id": 237, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"cylindrical; could be made of wax; could have a wick\", \"similar objects\": [\"lantern\", \"lamp\", \"flashlight\"]}"}, {"object": "candy", "object_id": 239, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"sweet; could be in different shapes and colors; could be wrapped in paper\", \"similar objects\": [\"chocolate\", \"cookie\", \"ice cream\"]}"}, {"object": "canned", "object_id": 1789, "gpt3_output": "\n{\"type\": \"food item\", \"description\": \"preserved in a sealed container; could be fruits, vegetables, or meat\", \"similar objects\": [\"jarred\", \"bottled\", \"frozen\"]}"}, {"object": "car", "object_id": 245, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"four wheels; could have two or four doors; could be powered by gasoline or electricity; could have a steering wheel\", \"similar objects\": [\"truck\", \"motorcycle\", \"bus\"]}"}, {"object": "carpet", "object_id": 250, "gpt3_output": "\n{\"type\": \"floor covering\", \"description\": \"rectangular; could be made of wool or synthetic fibers; could be used to cover the floor\", \"similar objects\": [\"rug\", \"mat\", \"tapestry\"]}"}, {"object": "carriage", "object_id": 251, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"horse-drawn; could have four wheels; could have a canopy\", \"similar objects\": [\"wagon\", \"cart\", \"buggy\"]}"}, {"object": "carrot", "object_id": 252, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"orange, long, tapered; could have green leaves; could be sliced into round pieces\", \"similar objects\": [\"potato\", \"parsnip\", \"turnip\"]}"}, {"object": "cat", "object_id": 257, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"furry; four legs; whiskers; could meow\", \"similar objects\": [\"dog\", \"rabbit\", \"mouse\"]}"}, {"object": "cell phone", "object_id": 267, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, rectangular; could have a touchscreen; could have a camera\", \"similar objects\": [\"smartphone\", \"tablet\", \"laptop\"]}"}, {"object": "cello", "object_id": 1758, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"large, stringed instrument; has four strings; has a curved body\", \"similar objects\": [\"violin\", \"double bass\", \"guitar\"]}"}, {"object": "chainsaw", "object_id": 1790, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle; has a sharp blade; used for cutting wood\", \"similar objects\": [\"axe\", \"saw\", \"drill\"]}"}, {"object": "chair", "object_id": 272, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has four legs; could have armrests; could have a backrest; could be made of wood or metal\", \"similar objects\": [\"sofa\", \"table\", \"stool\"]}"}, {"object": "cheese", "object_id": 278, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"dairy product; could be soft, hard, or crumbly; could be yellow, white, or orange; could be savory or sweet\", \"similar objects\": [\"yogurt\", \"butter\", \"milk\"]}"}, {"object": "cherry", "object_id": 280, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, red, has a stem\", \"similar objects\": [\"strawberry\", \"plum\", \"grape\"]}"}, {"object": "chicken", "object_id": 282, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"feathered; has a beak; could lay eggs; could have yellow skin\", \"similar objects\": [\"duck\", \"turkey\", \"goose\"]}"}, {"object": "chips", "object_id": 287, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"thin, flat, salty; could be made of potatoes, corn, or other vegetables; could be fried or baked\", \"similar objects\": [\"fries\", \"crisps\", \"crackers\"]}"}, {"object": "chopsticks", "object_id": 289, "gpt3_output": "\n{\"type\": \"eating utensil\", \"description\": \"two thin sticks; used to pick up food\", \"similar objects\": [\"fork\", \"spoon\", \"knife\"]}"}, {"object": "cigar", "object_id": 1791, "gpt3_output": "\n{\"type\": \"tobacco product\", \"description\": \"cylindrical; made of tobacco leaves; could be smoked\", \"similar objects\": [\"cigarette\", \"pipe\", \"hookah\"]}"}, {"object": "cleaning products", "object_id": 1792, "gpt3_output": "\n{\"type\": \"household items\", \"description\": \"products used for cleaning surfaces, such as detergents, soaps, and polishes\", \"similar objects\": [\"sponges\", \"scrubbers\", \"broom\"]}"}, {"object": "clock", "object_id": 301, "gpt3_output": "\n{\"type\": \"timekeeping tool\", \"description\": \"round; has hands; could have a digital display\", \"similar objects\": [\"watch\", \"timer\", \"alarm\"]}"}, {"object": "clutch", "object_id": 1793, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small, handheld bag; could be made of leather; could have a strap\", \"similar objects\": [\"purse\", \"wallet\", \"satchel\"]}"}, {"object": "coconut", "object_id": 314, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, brown, hard shell; white, sweet flesh inside; could have a husk\", \"similar objects\": [\"avocado\", \"mango\", \"papaya\"]}"}, {"object": "coffee machine", "object_id": 317, "gpt3_output": "\n{\"type\": \"kitchen appliance\", \"description\": \"could be electric or manual; could have a water tank; could have a coffee grinder; could have a steam wand\", \"similar objects\": [\"espresso machine\", \"tea maker\", \"blender\"]}"}, {"object": "coffee table", "object_id": 320, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"rectangular; could have a glass top; could have drawers; could have legs\", \"similar objects\": [\"end table\", \"console table\", \"ottoman\"]}"}, {"object": "comb", "object_id": 326, "gpt3_output": "\n{\"type\": \"grooming tool\", \"description\": \"long, thin; has teeth; could be made of plastic or metal\", \"similar objects\": [\"brush\", \"scissors\", \"razor\"]}"}, {"object": "computer box", "object_id": 1794, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"rectangular; could have ports and slots; could have a monitor\", \"similar objects\": [\"laptop\", \"desktop\", \"tablet\"]}"}, {"object": "converter", "object_id": 1795, "gpt3_output": "\n{\"type\": \"electrical device\", \"description\": \"used to convert one type of energy to another; could be plugged into a wall outlet\", \"similar objects\": [\"transformer\", \"inverter\", \"charger\"]}"}, {"object": "cookies", "object_id": 344, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round, sweet, crunchy; could be made of chocolate chips; could be in different shapes\", \"similar objects\": [\"cake\", \"pie\", \"brownies\"]}"}, {"object": "corn", "object_id": 349, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"yellow, cylindrical; could be eaten on the cob; could be ground into flour\", \"similar objects\": [\"peas\", \"beans\", \"potatoes\"]}"}, {"object": "couch", "object_id": 351, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"long, upholstered, has cushions\", \"similar objects\": [\"sofa\", \"loveseat\", \"armchair\"]}"}, {"object": "cow", "object_id": 357, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a long tail; could be black and white or brown; could have horns\", \"similar objects\": [\"goat\", \"sheep\", \"buffalo\"]}"}, {"object": "crab", "object_id": 361, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"oval-shaped; has a hard shell; has eight legs; could be red or blue\", \"similar objects\": [\"lobster\", \"shrimp\", \"crayfish\"]}"}, {"object": "crane", "object_id": 363, "gpt3_output": "\n{\"type\": \"machine\", \"description\": \"tall; has a long arm; could be used to lift heavy objects\", \"similar objects\": [\"forklift\", \"excavator\", \"bulldozer\"]}"}, {"object": "crosswalk sign", "object_id": 1796, "gpt3_output": "\n{\"type\": \"traffic sign\", \"description\": \"octagonal; has a white background with black letters; could be red and white\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}"}, {"object": "cucumber", "object_id": 374, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, green, smooth; could have white stripes; could be sliced into round pieces; could has green leaves\", \"similar objects\": [\"zucchini\", \"eggplant\", \"green bean\"]}"}, {"object": "cue", "object_id": 1797, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, thin, wooden stick; used to hit a ball in billiards\", \"similar objects\": [\"pool stick\", \"snooker cue\", \"billiard cue\"]}"}, {"object": "cup", "object_id": 376, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"round; could have a handle; could be made of ceramic, plastic, or metal; could be used for drinking\", \"similar objects\": [\"mug\", \"glass\", \"bowl\"]}"}, {"object": "cutting/chopping board", "object_id": 383, "gpt3_output": "\n\n{\"type\": \"cooking tool\", \"description\": \"flat, rectangular; could be made of wood or plastic; used for cutting and chopping food\", \"similar objects\": [\"knife\", \"spatula\", \"rolling pin\"]}"}, {"object": "cymbal", "object_id": 1798, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"round; made of metal; produces a loud sound when hit\", \"similar objects\": [\"drum\", \"tambourine\", \"xylophone\"]}"}, {"object": "dates", "object_id": 388, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"small, oval-shaped; could be brown, yellow, or red; has a single seed inside\", \"similar objects\": [\"figs\", \"raisins\", \"apricots\"]}"}, {"object": "deer", "object_id": 393, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"brown fur; has antlers; could have white spots\", \"similar objects\": [\"elk\", \"moose\", \"reindeer\"]}"}, {"object": "desk", "object_id": 396, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"flat surface; could have drawers; could have legs\", \"similar objects\": [\"table\", \"chair\", \"bookshelf\"]}"}, {"object": "dining table", "object_id": 403, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"rectangular; could have four legs; could be made of wood or metal; could have a glass top\", \"similar objects\": [\"coffee table\", \"desk\", \"end table\"]}"}, {"object": "dog", "object_id": 419, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"four legs; fur coat; could bark; could have different breeds\", \"similar objects\": [\"cat\", \"rabbit\", \"hamster\"]}"}, {"object": "dolphin", "object_id": 1781, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"gray; has a curved mouth; could swim in the water\", \"similar objects\": [\"whale\", \"shark\", \"seal\"]}"}, {"object": "donkey", "object_id": 422, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"gray; has long ears; could be used for carrying goods\", \"similar objects\": [\"horse\", \"mule\", \"camel\"]}"}, {"object": "donut", "object_id": 423, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; has a hole in the middle; could be glazed or filled with cream\", \"similar objects\": [\"bagel\", \"croissant\", \"muffin\"]}"}, {"object": "drum", "object_id": 444, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"cylindrical; could be made of wood or metal; could have a skin stretched over one end; could be played with sticks or hands\", \"similar objects\": [\"guitar\", \"piano\", \"violin\"]}"}, {"object": "duck", "object_id": 446, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"brown feathers; webbed feet; quacks\", \"similar objects\": [\"goose\", \"swan\", \"pigeon\"]}"}, {"object": "durian", "object_id": 1799, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"spiky, yellow-green; has a strong smell; could be sweet or bitter\", \"similar objects\": [\"mangosteen\", \"jackfruit\", \"rambutan\"]}"}, {"object": "eagle", "object_id": 451, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large bird of prey; has a hooked beak; has a wingspan of up to 8 feet; has a white head and tail\", \"similar objects\": [\"hawk\", \"osprey\", \"vulture\"]}"}, {"object": "earphone", "object_id": 453, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, portable; could be connected to a device; could be wired or wireless\", \"similar objects\": [\"headphones\", \"speakers\", \"microphone\"]}"}, {"object": "egg", "object_id": 456, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"oval-shaped; white or brown; could be boiled, fried, or scrambled\", \"similar objects\": [\"avocado\", \"tomato\", \"onion\"]}"}, {"object": "egg tart", "object_id": 1630, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; has a flaky crust; could be filled with custard\", \"similar objects\": [\"pie\", \"cake\", \"cookie\"]}"}, {"object": "eggplant", "object_id": 457, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"purple, oval-shaped; could be sliced into round pieces; could have green leaves\", \"similar objects\": [\"zucchini\", \"cucumber\", \"green bean\"]}"}, {"object": "electric drill", "object_id": 1800, "gpt3_output": "\n{\"type\": \"power tool\", \"description\": \"cylindrical; has a trigger; could have a cord or be cordless\", \"similar objects\": [\"screwdriver\", \"sander\", \"circular saw\"]}"}, {"object": "gas stove", "object_id": 1310, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"has a flat top; could have knobs to control the flame; could have a gas line connection\", \"similar objects\": [\"electric stove\", \"microwave\", \"oven\"]}"}, {"object": "elephant", "object_id": 462, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, gray; has a trunk; has large ears; has tusks\", \"similar objects\": [\"giraffe\", \"hippopotamus\", \"rhinoceros\"]}"}, {"object": "eraser", "object_id": 1801, "gpt3_output": "\n{\"type\": \"stationery item\", \"description\": \"rubber; used to erase pencil marks\", \"similar objects\": [\"pencil\", \"pen\", \"marker\"]}"}, {"object": "extension cord", "object_id": 348, "gpt3_output": "\n{\"type\": \"electrical tool\", \"description\": \"long, flexible; could have multiple outlets; could be used to extend power\", \"similar objects\": [\"power strip\", \"surge protector\", \"cable\"]}"}, {"object": "facial cleanser", "object_id": 1802, "gpt3_output": "\n{\"type\": \"cosmetic product\", \"description\": \"used to cleanse the face; could be in liquid or foam form\", \"similar objects\": [\"facial scrub\", \"facial toner\", \"facial mask\"]}"}, {"object": "facial mask", "object_id": 477, "gpt3_output": "\n{\"type\": \"protective gear\", \"description\": \"covers the face; could be made of cloth or paper; could be disposable or reusable\", \"similar objects\": [\"respirator\", \"goggles\", \"gloves\"]}"}, {"object": "fan", "object_id": 480, "gpt3_output": "\n{\"type\": \"electrical appliance\", \"description\": \"has blades; could be used to circulate air\", \"similar objects\": [\"air conditioner\", \"heater\", \"humidifier\"]}"}, {"object": "faucet", "object_id": 483, "gpt3_output": "\n{\"type\": \"plumbing tool\", \"description\": \"has a handle; could be used to control water flow; could be attached to a sink\", \"similar objects\": [\"shower head\", \"hose\", \"valve\"]}"}, {"object": "fire extinguisher", "object_id": 500, "gpt3_output": "\n{\"type\": \"safety tool\", \"description\": \"red; has a nozzle; could be pressurized\", \"similar objects\": [\"smoke detector\", \"fire alarm\", \"fire blanket\"]}"}, {"object": "fire hydrant", "object_id": 501, "gpt3_output": "\n{\"type\": \"utility tool\", \"description\": \"red; has a hose connection; could be used to put out fires\", \"similar objects\": [\"fire extinguisher\", \"fire alarm\", \"fire hose\"]}"}, {"object": "fire truck", "object_id": 502, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"red; has a ladder; could have a water tank\", \"similar objects\": [\"ambulance\", \"police car\", \"garbage truck\"]}"}, {"object": "fish", "object_id": 504, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"scaly; could have fins and gills; could be found in water\", \"similar objects\": [\"shark\", \"turtle\", \"dolphin\"]}"}, {"object": "fishing rod", "object_id": 1803, "gpt3_output": "\n{\"type\": \"fishing tool\", \"description\": \"long, thin, has a reel; could have a line attached\", \"similar objects\": [\"fishing net\", \"fishing hook\", \"fishing line\"]}"}, {"object": "flag", "object_id": 506, "gpt3_output": "\n{\"type\": \"symbol\", \"description\": \"rectangular; could have a pole; could have a design or logo\", \"similar objects\": [\"banner\", \"pennant\", \"standard\"]}"}, {"object": "flashlight", "object_id": 1604, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"long and cylindrical; has a switch; could be powered by batteries\", \"similar objects\": [\"lantern\", \"lamp\", \"candle\"]}"}, {"object": "flower", "object_id": 515, "gpt3_output": "\n{\"type\": \"plant\", \"description\": \"various colors; could have petals; could have a stem; could have leaves\", \"similar objects\": [\"tree\", \"bush\", \"grass\"]}"}, {"object": "flute", "object_id": 1610, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long, thin, has holes; could be made of metal or wood\", \"similar objects\": [\"clarinet\", \"trumpet\", \"saxophone\"]}"}, {"object": "folder", "object_id": 520, "gpt3_output": "\n{\"type\": \"storage tool\", \"description\": \"rectangular; could be made of paper or plastic; could be used to store documents\", \"similar objects\": [\"envelope\", \"binder\", \"file box\"]}"}, {"object": "fork", "object_id": 533, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"has four tines; could be made of metal or plastic; could be used for eating\", \"similar objects\": [\"spoon\", \"knife\", \"chopsticks\"]}"}, {"object": "french fries", "object_id": 537, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"long, thin, golden-brown; could be served with ketchup\", \"similar objects\": [\"potato chips\", \"onion rings\", \"wedges\"]}"}, {"object": "french horn", "object_id": 1804, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long, conical brass instrument; has a flared bell; has three valves\", \"similar objects\": [\"trumpet\", \"trombone\", \"tuba\"]}"}, {"object": "frisbee", "object_id": 539, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"round; made of plastic; could be thrown in the air\", \"similar objects\": [\"discus\", \"boomerang\", \"kite\"]}"}, {"object": "game board", "object_id": 1805, "gpt3_output": "\n{\"type\": \"game tool\", \"description\": \"flat surface; could have grids or squares; could have pieces or tokens\", \"similar objects\": [\"chess board\", \"checkers board\", \"backgammon board\"]}"}, {"object": "garlic", "object_id": 559, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"bulbous, white, has a strong smell; could be sliced into small pieces; could have green leaves\", \"similar objects\": [\"onion\", \"shallot\", \"leek\"]}"}, {"object": "giraffe", "object_id": 570, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"long neck; spotted; long legs; long tail; could have horns\", \"similar objects\": [\"zebra\", \"elephant\", \"gazelle\"]}"}, {"object": "glasses", "object_id": 576, "gpt3_output": "\n{\"type\": \"eyewear\", \"description\": \"two lenses connected by a frame; could be used for vision correction; could be made of plastic or metal\", \"similar objects\": [\"sunglasses\", \"goggles\", \"monocle\"]}"}, {"object": "globe", "object_id": 578, "gpt3_output": "\n{\"type\": \"decorative object\", \"description\": \"round; could be made of paper or plastic; could be used to represent the Earth\", \"similar objects\": [\"map\", \"ball\", \"terrarium\"]}"}, {"object": "glove", "object_id": 579, "gpt3_output": "\n{\"type\": \"clothing item\", \"description\": \"hand-shaped; could be made of leather, cotton, or wool; could have a cuff\", \"similar objects\": [\"mittens\", \"socks\", \"hat\"]}"}, {"object": "goldfish", "object_id": 1722, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"orange; has a long tail; could be kept in an aquarium\", \"similar objects\": [\"guppy\", \"koi\", \"carp\"]}"}, {"object": "golf ball", "object_id": 1670, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"small, round, white; could have dimples\", \"similar objects\": [\"tennis ball\", \"baseball\", \"football\"]}"}, {"object": "golf club", "object_id": 1806, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, metal shaft; could have a wooden or metal head; could have a grip\", \"similar objects\": [\"tennis racket\", \"baseball bat\", \"hockey stick\"]}"}, {"object": "goose", "object_id": 584, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, gray, webbed feet; long neck; honks\", \"similar objects\": [\"duck\", \"swan\", \"turkey\"]}"}, {"object": "grapes", "object_id": 588, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"small, round, green or purple; could be clustered together; could be eaten as a snack\", \"similar objects\": [\"blueberries\", \"strawberries\", \"raisins\"]}"}, {"object": "green beans", "object_id": 1807, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, thin, green; could be sliced into small pieces; could be cooked\", \"similar objects\": [\"zucchini\", \"cucumber\", \"eggplant\"]}"}, {"object": "green onion", "object_id": 1808, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"long, thin, green stalks; white bulb at the bottom; could be chopped into small pieces\", \"similar objects\": [\"scallion\", \"leek\", \"shallot\"]}"}, {"object": "green vegetables", "object_id": 1809, "gpt3_output": "\n{\"type\": \"vegetables\", \"description\": \"various shades of green; could be leafy or cylindrical; could be cooked or eaten raw\", \"similar objects\": [\"broccoli\", \"spinach\", \"cucumber\"]}"}, {"object": "guitar", "object_id": 602, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long body; has strings; could have a pickguard\", \"similar objects\": [\"ukulele\", \"violin\", \"banjo\"]}"}, {"object": "gun", "object_id": 603, "gpt3_output": "\n{\"type\": \"weapon\", \"description\": \"metal; could be long or short; could be used for hunting or self-defense\", \"similar objects\": [\"rifle\", \"pistol\", \"shotgun\"]}"}, {"object": "hair drier", "object_id": 445, "gpt3_output": "\n{\"type\": \"hair styling tool\", \"description\": \"long handle; has a nozzle; could be plugged into an electrical outlet\", \"similar objects\": [\"curling iron\", \"straightener\", \"hair clippers\"]}"}, {"object": "hamburger", "object_id": 611, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"bun, patty, lettuce, tomato, onion, pickles, condiments; could be served with fries\", \"similar objects\": [\"hot dog\", \"sandwich\", \"taco\"]}"}, {"object": "hamimelon", "object_id": 1784, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round; has a green rind; could be yellow, orange, or red inside; has a sweet taste\", \"similar objects\": [\"watermelon\", \"cantaloupe\", \"honeydew\"]}"}, {"object": "hammer", "object_id": 612, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"has a handle and a head; used for pounding or driving nails\", \"similar objects\": [\"screwdriver\", \"pliers\", \"wrench\"]}"}, {"object": "handbag", "object_id": 615, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"rectangular; could be made of leather; could have straps\", \"similar objects\": [\"purse\", \"backpack\", \"tote bag\"]}"}, {"object": "hanger", "object_id": 620, "gpt3_output": "\n{\"type\": \"household item\", \"description\": \"long, thin, with a hook at one end\", \"similar objects\": [\"clothes rack\", \"coat rack\", \"clothes peg\"]}"}, {"object": "hat", "object_id": 623, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"could be made of fabric or straw; could have a brim; could have a band\", \"similar objects\": [\"cap\", \"beanie\", \"fedora\"]}"}, {"object": "head phone", "object_id": 631, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"has two ear pieces; could be connected to a device; could have a microphone\", \"similar objects\": [\"earphones\", \"headset\", \"speakers\"]}"}, {"object": "helicopter", "object_id": 636, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"has a rotor; could be used for air travel; could be used for rescue operations\", \"similar objects\": [\"airplane\", \"drone\", \"blimp\"]}"}, {"object": "helmet", "object_id": 637, "gpt3_output": "\n{\"type\": \"protective gear\", \"description\": \"hard, covers the head; could have a visor\", \"similar objects\": [\"goggles\", \"gloves\", \"knee pads\"]}"}, {"object": "high heels", "object_id": 635, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"elevated heel; could be made of leather; could have straps\", \"similar objects\": [\"pumps\", \"wedges\", \"stilettos\"]}"}, {"object": "hockey", "object_id": 1810, "gpt3_output": "\n{\"type\": \"sport\", \"description\": \"team sport played on ice; two teams of six players use sticks to hit a puck into the opposing team's goal\", \"similar objects\": [\"soccer\", \"basketball\", \"baseball\"]}"}, {"object": "horn", "object_id": 655, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long and curved; could be made of brass; could be played with the mouth\", \"similar objects\": [\"trumpet\", \"trombone\", \"clarinet\"]}"}, {"object": "horse", "object_id": 656, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, four-legged mammal; has a long mane and tail; could be ridden\", \"similar objects\": [\"zebra\", \"donkey\", \"camel\"]}"}, {"object": "hot dog", "object_id": 658, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"long, cylindrical; usually served in a bun; could be topped with condiments\", \"similar objects\": [\"hamburger\", \"sausage\", \"taco\"]}"}, {"object": "hotair balloon", "object_id": 1811, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, round, colorful; could be filled with hot air; could be used for sightseeing\", \"similar objects\": [\"helicopter\", \"airplane\", \"glider\"]}"}, {"object": "hoverboard", "object_id": 1812, "gpt3_output": "\n{\"type\": \"transportation device\", \"description\": \"self-balancing; could be powered by electricity; could be used for recreational purposes\", \"similar objects\": [\"skateboard\", \"segway\", \"electric scooter\"]}"}, {"object": "hurdle", "object_id": 664, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"a bar that is placed across a track or running course for athletes to jump over\", \"similar objects\": [\"high jump bar\", \"steeplechase barrier\", \"pole vault bar\"]}"}, {"object": "ice cream", "object_id": 667, "gpt3_output": "\n{\"type\": \"dessert\", \"description\": \"cold, creamy, sweet; could be served in a cone or cup; could have different flavors\", \"similar objects\": [\"sorbet\", \"gelato\", \"frozen yogurt\"]}"}, {"object": "iron", "object_id": 1813, "gpt3_output": "\n{\"type\": \"household appliance\", \"description\": \"flat, rectangular; used to press clothes; could be plugged into an electrical outlet\", \"similar objects\": [\"steamer\", \"sewing machine\", \"vacuum cleaner\"]}"}, {"object": "jellyfish", "object_id": 1614, "gpt3_output": "\n{\"type\": \"marine creature\", \"description\": \"transparent; has tentacles; could be found in the ocean\", \"similar objects\": [\"octopus\", \"squid\", \"starfish\"]}"}, {"object": "kettle", "object_id": 696, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round; has a handle; could be made of metal; could be used to boil water\", \"similar objects\": [\"teapot\", \"coffee maker\", \"microwave\"]}"}, {"object": "key", "object_id": 697, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"metal; has a hole in the middle; could have a pattern on the surface\", \"similar objects\": [\"lock\", \"padlock\", \"keychain\"]}"}, {"object": "keyboard", "object_id": 698, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has keys; could be wired or wireless\", \"similar objects\": [\"mouse\", \"game controller\", \"microphone\"]}"}, {"object": "kite", "object_id": 704, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"could be made of paper or plastic; has a tail; could be flown in the air\", \"similar objects\": [\"balloon\", \"frisbee\", \"airplane\"]}"}, {"object": "kiwi fruit", "object_id": 707, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"brown, oval-shaped; has a fuzzy skin; has a green flesh inside\", \"similar objects\": [\"strawberry\", \"mango\", \"pineapple\"]}"}, {"object": "knife", "object_id": 711, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"sharp blade; could have a handle; could be used for cutting\", \"similar objects\": [\"fork\", \"spoon\", \"scissors\"]}"}, {"object": "ladder", "object_id": 716, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long; could be made of metal or wood; could have steps\", \"similar objects\": [\"stool\", \"step ladder\", \"scaffolding\"]}"}, {"object": "lamp", "object_id": 721, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"could be made of metal or glass; could have a switch; could be powered by electricity or battery\", \"similar objects\": [\"lantern\", \"flashlight\", \"candle\"]}"}, {"object": "lantern", "object_id": 729, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"round; could be made of metal or glass; could have a handle; could be lit with a candle or a lightbulb; could be hung from a hook or carried\", \"similar objects\": [\"lamp\", \"flashlight\", \"candle\"]}"}, {"object": "laptop", "object_id": 732, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"portable computer; has a keyboard and a screen; could be connected to other devices\", \"similar objects\": [\"desktop computer\", \"tablet\", \"smartphone\"]}"}, {"object": "leather shoes", "object_id": 1814, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"made of leather; could have laces; could have a heel\", \"similar objects\": [\"sneakers\", \"boots\", \"sandals\"]}"}, {"object": "lemon", "object_id": 742, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"yellow, round, has a stem; could be sliced into wedges; has a sour taste\", \"similar objects\": [\"lime\", \"orange\", \"grapefruit\"]}"}, {"object": "lettuce", "object_id": 745, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"green, leafy; could be shredded; could be used in salads\", \"similar objects\": [\"spinach\", \"cabbage\", \"kale\"]}"}, {"object": "life saver", "object_id": 750, "gpt3_output": "\n{\"type\": \"safety tool\", \"description\": \"round; could be orange or red; could be used to save people in danger\", \"similar objects\": [\"life jacket\", \"life buoy\", \"rescue tube\"]}"}, {"object": "lighter", "object_id": 759, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"small, handheld device; could be made of metal; has a flint wheel\", \"similar objects\": [\"matches\", \"torch\", \"candle\"]}"}, {"object": "lion", "object_id": 764, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, tawny-colored; has a mane; could have a tufted tail; could have a black-tipped tail\", \"similar objects\": [\"tiger\", \"leopard\", \"cheetah\"]}"}, {"object": "liquid soap", "object_id": 1246, "gpt3_output": "\n{\"type\": \"cleaning product\", \"description\": \"transparent; could be used for washing hands; could be in a bottle\", \"similar objects\": [\"dish soap\", \"shampoo\", \"hand sanitizer\"]}"}, {"object": "llama", "object_id": 1773, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"long neck; wooly fur; could have a saddle\", \"similar objects\": [\"alpaca\", \"camel\", \"goat\"]}"}, {"object": "lobster", "object_id": 1693, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"red; has two large claws; could be boiled or steamed\", \"similar objects\": [\"crab\", \"shrimp\", \"crayfish\"]}"}, {"object": "luggage", "object_id": 777, "gpt3_output": "\n{\"type\": \"travel accessory\", \"description\": \"large, rectangular, has handles and wheels\", \"similar objects\": [\"suitcase\", \"backpack\", \"duffel bag\"]}"}, {"object": "machinery vehicle", "object_id": 1513, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, heavy, has a motor; could be used for construction or farming\", \"similar objects\": [\"bulldozer\", \"tractor\", \"excavator\"]}"}, {"object": "mango", "object_id": 788, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"oval-shaped; yellow-orange in color; has a fibrous texture; sweet and juicy\", \"similar objects\": [\"papaya\", \"pineapple\", \"avocado\"]}"}, {"object": "mangosteen", "object_id": 1815, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round; has a thick, purple rind; white, juicy flesh inside\", \"similar objects\": [\"lychee\", \"durian\", \"rambutan\"]}"}, {"object": "marker", "object_id": 795, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"has a tip; could be used to write on paper or other surfaces; could be refillable\", \"similar objects\": [\"pen\", \"pencil\", \"highlighter\"]}"}, {"object": "measuring cup", "object_id": 1771, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"has a handle; could be made of plastic or metal; has markings for measuring\", \"similar objects\": [\"measuring spoon\", \"scale\", \"thermometer\"]}"}, {"object": "meat balls", "object_id": 1816, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; made of ground meat; could be served with sauce\", \"similar objects\": [\"dumplings\", \"falafel\", \"meatloaf\"]}"}, {"object": "medal", "object_id": 1817, "gpt3_output": "\n{\"type\": \"award\", \"description\": \"round; could be made of metal; could have a ribbon attached\", \"similar objects\": [\"trophy\", \"plaque\", \"certificate\"]}"}, {"object": "microphone", "object_id": 813, "gpt3_output": "\n{\"type\": \"audio device\", \"description\": \"long, thin; could be handheld; could be connected to a sound system\", \"similar objects\": [\"speaker\", \"headset\", \"amplifier\"]}"}, {"object": "microscope", "object_id": 1818, "gpt3_output": "\n{\"type\": \"scientific tool\", \"description\": \"has an eyepiece and an objective lens; could be used to magnify objects; could be used to observe cells\", \"similar objects\": [\"telescope\", \"binoculars\", \"microscope slides\"]}"}, {"object": "microwave", "object_id": 814, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"box-shaped; has a door; could be used to heat food\", \"similar objects\": [\"oven\", \"toaster\", \"stove\"]}"}, {"object": "mirror", "object_id": 818, "gpt3_output": "\n{\"type\": \"reflective object\", \"description\": \"smooth, flat surface; could be framed; could be hung on the wall\", \"similar objects\": [\"picture frame\", \"window\", \"glass\"]}"}, {"object": "monkey", "object_id": 825, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"long tail; could be brown, black, or white; could have a pink face\", \"similar objects\": [\"gorilla\", \"chimpanzee\", \"baboon\"]}"}, {"object": "mop", "object_id": 1819, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"long handle; could have a sponge or cloth head; could be used to clean floors\", \"similar objects\": [\"broom\", \"vacuum cleaner\", \"duster\"]}"}, {"object": "motorcycle", "object_id": 834, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; has a handlebar; could have a sidecar\", \"similar objects\": [\"scooter\", \"bicycle\", \"tricycle\"]}"}, {"object": "mouse", "object_id": 842, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, furry, has a long tail; could be brown, black, or white\", \"similar objects\": [\"rat\", \"hamster\", \"gerbil\"]}"}, {"object": "mushroom", "object_id": 854, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round, brown, could have white spots; could be sliced into pieces; could have a stem\", \"similar objects\": [\"truffle\", \"oyster mushroom\", \"portobello mushroom\"]}"}, {"object": "necklace", "object_id": 864, "gpt3_output": "\n{\"type\": \"jewelry\", \"description\": \"chain of beads or other decorative items; could be made of metal, plastic, or other materials; could be worn around the neck\", \"similar objects\": [\"bracelet\", \"earrings\", \"ring\"]}"}, {"object": "nightstand", "object_id": 871, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"small table; usually has drawers; could have a lamp on top\", \"similar objects\": [\"dresser\", \"end table\", \"coffee table\"]}"}, {"object": "noodles", "object_id": 872, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"long, thin, could be made of wheat, rice, or egg; could be cooked in boiling water\", \"similar objects\": [\"pasta\", \"ramen\", \"udon\"]}"}, {"object": "notepaper", "object_id": 1820, "gpt3_output": "\n{\"type\": \"stationery\", \"description\": \"rectangular; could be used for writing; could be made of paper\", \"similar objects\": [\"envelope\", \"pen\", \"notebook\"]}"}, {"object": "nuts", "object_id": 879, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"hard, round, could be shelled; could be roasted or salted\", \"similar objects\": [\"seeds\", \"legumes\", \"dried fruits\"]}"}, {"object": "okra", "object_id": 1821, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"green, slimy; could be sliced into round pieces; could have white flowers\", \"similar objects\": [\"eggplant\", \"cucumber\", \"green bean\"]}"}, {"object": "onion", "object_id": 890, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round; has a papery skin; could be sliced into rings; could have a strong smell\", \"similar objects\": [\"garlic\", \"potato\", \"shallot\"]}"}, {"object": "orange", "object_id": 892, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, orange, has a stem; could be peeled and segmented; could be juiced\", \"similar objects\": [\"apple\", \"banana\", \"pear\"]}"}, {"object": "oven", "object_id": 900, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"box-shaped; could be used to bake food; could be electric or gas-powered\", \"similar objects\": [\"stove\", \"microwave\", \"toaster\"]}"}, {"object": "paddle", "object_id": 911, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long, thin, and flat; could be made of wood or plastic; used for propelling a boat through the water\", \"similar objects\": [\"oar\", \"canoe\", \"kayak\"]}"}, {"object": "papaya", "object_id": 1822, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"oval-shaped; yellow-orange in color; has a sweet taste; has black seeds\", \"similar objects\": [\"mango\", \"pineapple\", \"guava\"]}"}, {"object": "paper towel", "object_id": 926, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"absorbent; usually made of paper; could be used to clean surfaces\", \"similar objects\": [\"cloth towel\", \"sponge\", \"rag\"]}"}, {"object": "parking meter", "object_id": 934, "gpt3_output": "\n{\"type\": \"parking tool\", \"description\": \"tall, cylindrical; has a slot for coins; could have a digital display\", \"similar objects\": [\"parking sign\", \"parking lot\", \"parking garage\"]}"}, {"object": "parrot", "object_id": 936, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"colorful feathers; could talk; could fly\", \"similar objects\": [\"macaw\", \"cockatoo\", \"finch\"]}"}, {"object": "pasta", "object_id": 941, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"long, thin, cylindrical; could be made of wheat or rice flour; could be boiled in water\", \"similar objects\": [\"noodles\", \"spaghetti\", \"macaroni\"]}"}, {"object": "peach", "object_id": 951, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, fuzzy, has a pit; could be yellow or pink; could be sliced into wedges\", \"similar objects\": [\"plum\", \"apricot\", \"nectarine\"]}"}, {"object": "pear", "object_id": 954, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, yellow or green; has a stem; could be juicy\", \"similar objects\": [\"apple\", \"banana\", \"orange\"]}"}, {"object": "pen/pencil", "object_id": 959, "gpt3_output": "\n{\"type\": \"writing tool\", \"description\": \"cylindrical; could be made of plastic or wood; could have a cap\", \"similar objects\": [\"marker\", \"highlighter\", \"crayon\"]}"}, {"object": "pencil case", "object_id": 1823, "gpt3_output": "\n{\"type\": \"storage tool\", \"description\": \"rectangular; could be made of fabric or plastic; could have a zipper\", \"similar objects\": [\"pencil pouch\", \"pencil box\", \"pencil holder\"]}"}, {"object": "penguin", "object_id": 961, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"black and white feathers; waddles when walking; has a curved beak\", \"similar objects\": [\"seagull\", \"flamingo\", \"albatross\"]}"}, {"object": "pepper", "object_id": 962, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round; could be red, green, yellow, or orange; could be sliced into small pieces; could have a stem\", \"similar objects\": [\"tomato\", \"onion\", \"cucumber\"]}"}, {"object": "person", "object_id": 965, "gpt3_output": "\n{\"type\": \"living being\", \"description\": \"human; bipedal; could have different skin colors; could have different hair styles\", \"similar objects\": [\"animal\", \"robot\", \"doll\"]}"}, {"object": "piano", "object_id": 971, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"large, rectangular; has black and white keys; could be acoustic or digital\", \"similar objects\": [\"guitar\", \"violin\", \"drums\"]}"}, {"object": "pickup truck", "object_id": 973, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, boxy; has an open bed; could have four doors\", \"similar objects\": [\"SUV\", \"van\", \"sedan\"]}"}, {"object": "picture/frame", "object_id": 975, "gpt3_output": "\n{\"type\": \"decorative item\", \"description\": \"rectangular; could be made of wood or metal; could have a glass cover; could have a picture or painting inside\", \"similar objects\": [\"painting\", \"mirror\", \"photo album\"]}"}, {"object": "pie", "object_id": 977, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; could be filled with fruits, cream, or savory ingredients; could be topped with a crust\", \"similar objects\": [\"cake\", \"tart\", \"quiche\"]}"}, {"object": "pig", "object_id": 980, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"pink; has a snout; four legs; curly tail\", \"similar objects\": [\"cow\", \"goat\", \"sheep\"]}"}, {"object": "pigeon", "object_id": 981, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"grayish-brown; has a white patch on its neck; could fly in flocks\", \"similar objects\": [\"dove\", \"sparrow\", \"crow\"]}"}, {"object": "pillow", "object_id": 984, "gpt3_output": "\n{\"type\": \"bedding item\", \"description\": \"soft; usually rectangular; could be filled with feathers or foam\", \"similar objects\": [\"mattress\", \"blanket\", \"duvet\"]}"}, {"object": "pine apple", "object_id": 990, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"large, round, has a crown of leaves; yellow-green skin; sweet, juicy, yellow flesh\", \"similar objects\": [\"mango\", \"papaya\", \"avocado\"]}"}, {"object": "pitaya", "object_id": 1824, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"red, oval-shaped; has a sweet taste; could be eaten raw or cooked\", \"similar objects\": [\"dragon fruit\", \"strawberry\", \"mango\"]}"}, {"object": "pizza", "object_id": 994, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"round; has a crust; topped with cheese, tomato sauce, and other ingredients\", \"similar objects\": [\"calzone\", \"stromboli\", \"flatbread\"]}"}, {"object": "plate", "object_id": 1006, "gpt3_output": "\n{\"type\": \"dining ware\", \"description\": \"flat, round, could be made of ceramic, plastic, or metal; could be used to serve food\", \"similar objects\": [\"bowl\", \"cup\", \"glass\"]}"}, {"object": "pliers", "object_id": 1785, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"two handles connected by a 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or yellow\", \"similar objects\": [\"watermelon\", \"cantaloupe\", \"honeydew\"]}"}, {"object": "pomelo", "object_id": 1827, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"large, round, yellow-green; has a thick rind; could be segmented into sections; has a sweet-tart flavor\", \"similar objects\": [\"grapefruit\", \"orange\", \"tangerine\"]}"}, {"object": "pot/pan", "object_id": 1030, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round, deep, has a handle; could have a lid\", \"similar objects\": [\"wok\", \"frying pan\", \"skillet\"]}"}, {"object": "potato", "object_id": 1031, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"oval-shaped; could be yellow, white, or brown; could be boiled, mashed, or fried; could have green leaves\", \"similar objects\": [\"carrot\", \"onion\", \"sweet potato\"]}"}, {"object": "potted plant", "object_id": 1595, "gpt3_output": "\n{\"type\": \"decoration\", \"description\": \"could be placed in a pot; could have various types of plants; could have colorful flowers\", \"similar objects\": [\"vase\", \"flower pot\", \"hanging plant\"]}"}, {"object": "power outlet", "object_id": 460, "gpt3_output": "\n{\"type\": \"electrical device\", \"description\": \"rectangular; has two or more slots; could be wall-mounted\", \"similar objects\": [\"switch\", \"socket\", \"plug\"]}"}, {"object": "printer", "object_id": 1039, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"used to print documents; could be connected to a computer; could have a scanner\", \"similar objects\": [\"copier\", \"fax machine\", \"scanner\"]}"}, {"object": "projector", "object_id": 1042, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"used to project images onto a wall or screen; could be connected to a computer or other device\", \"similar objects\": [\"television\", \"monitor\", \"laptop\"]}"}, {"object": "pumpkin", "object_id": 1047, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round; orange; has a stem; could be carved into a jack-o-lantern\", \"similar objects\": [\"squash\", \"zucchini\", \"cucumber\"]}"}, {"object": "rabbit", "object_id": 1051, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"small, furry, long ears; could have white fur; could have a fluffy tail\", \"similar objects\": [\"hare\", \"squirrel\", \"mouse\"]}"}, {"object": "race car", "object_id": 1828, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"fast; has a sleek design; could have a sponsor logo\", \"similar objects\": [\"sports car\", \"motorcycle\", \"truck\"]}"}, {"object": "radiator", "object_id": 1055, "gpt3_output": "\n{\"type\": \"heating tool\", \"description\": \"long, metal, has a fan; could be mounted on the wall\", \"similar objects\": [\"heater\", \"air conditioner\", \"furnace\"]}"}, {"object": "radio", "object_id": 1056, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has a speaker; could be used to listen to music\", \"similar objects\": [\"stereo\", \"boombox\", \"walkman\"]}"}, {"object": "radish", "object_id": 1057, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round, red, has a green stem; could be sliced into thin pieces; could be eaten raw\", \"similar objects\": [\"carrot\", \"turnip\", \"beetroot\"]}"}, {"object": "red cabbage", "object_id": 1829, "gpt3_output": "\n{\"type\": \"vegetable\", \"description\": \"round, red, has a stem; could be sliced into thin pieces; could have green leaves\", \"similar objects\": [\"cauliflower\", \"broccoli\", \"kale\"]}"}, {"object": "refrigerator", "object_id": 1077, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"large, white, has a door; could have shelves and drawers inside\", \"similar objects\": [\"freezer\", \"microwave\", \"dishwasher\"]}"}, {"object": "remote", "object_id": 1079, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"small, handheld; used to control other electronic devices; could have buttons or touchpad\", \"similar objects\": [\"controller\", \"gamepad\", \"keyboard\"]}"}, {"object": "rice", "object_id": 1085, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"small, white grains; could be cooked in boiling water; could be served with other dishes\", \"similar objects\": [\"quinoa\", \"barley\", \"wheat\"]}"}, {"object": "rice cooker", "object_id": 1830, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"electrical appliance; has a lid; could be used to cook rice\", \"similar objects\": [\"pressure cooker\", \"slow cooker\", \"microwave\"]}"}, {"object": "rickshaw", "object_id": 1831, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"two-wheeled; pulled by a person; could have a canopy\", \"similar objects\": [\"bicycle\", \"tricycle\", \"horse-drawn carriage\"]}"}, {"object": "ring", "object_id": 1088, "gpt3_output": "\n{\"type\": \"jewelry\", \"description\": \"circular; could be made of gold, silver, or other metals; could have gemstones\", \"similar objects\": [\"bracelet\", \"necklace\", \"earrings\"]}"}, {"object": "router/modem", "object_id": 1106, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"rectangular; has multiple ports; could be used to connect to the internet\", \"similar objects\": [\"switch\", \"hub\", \"access point\"]}"}, {"object": "sailboat", "object_id": 1114, "gpt3_output": "\n{\"type\": \"watercraft\", \"description\": \"has a mast and sails; could have a cabin; could be powered by wind or motor\", \"similar objects\": [\"yacht\", \"canoe\", \"rowboat\"]}"}, {"object": "sandals", "object_id": 1119, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"open-toed; could have straps; could be made of leather or rubber\", \"similar objects\": [\"flip-flops\", \"slippers\", \"sneakers\"]}"}, {"object": "sandwich", "object_id": 1121, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"two slices of bread with filling in between; could be cut into triangles; could be toasted\", \"similar objects\": [\"burger\", \"wrap\", \"panini\"]}"}, {"object": "sausage", "object_id": 1125, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"cylindrical; could be made of pork, beef, or other meats; could be grilled or boiled\", \"similar objects\": [\"hot dog\", \"bratwurst\", \"kielbasa\"]}"}, {"object": "saw", "object_id": 1832, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long, sharp blade; used for cutting wood\", \"similar objects\": [\"hammer\", \"screwdriver\", \"pliers\"]}"}, {"object": "saxophone", "object_id": 1749, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long, brass; has a curved neck; has keys\", \"similar objects\": [\"clarinet\", \"trumpet\", \"flute\"]}"}, {"object": "scale", "object_id": 1127, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"used to measure weight; could be digital or analog; could have a display\", \"similar objects\": [\"ruler\", \"tape measure\", \"thermometer\"]}"}, {"object": "scallop", "object_id": 1833, "gpt3_output": "\n{\"type\": \"seafood\", \"description\": \"round, white; could be served as sashimi; could be cooked with butter\", \"similar objects\": [\"clam\", \"oyster\", \"mussel\"]}"}, {"object": "scissors", "object_id": 1132, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"two blades connected by a pivot; used for cutting\", \"similar objects\": [\"knife\", \"pliers\", \"tweezers\"]}"}, {"object": "screwdriver", "object_id": 1737, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle with a flat or cross-shaped tip\", \"similar objects\": [\"hammer\", \"pliers\", \"wrench\"]}"}, {"object": "seal", "object_id": 1142, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"black and white; has flippers; could be found in the ocean\", \"similar objects\": [\"otter\", \"walrus\", \"penguin\"]}"}, {"object": "shampoo/shower gel", "object_id": 1154, "gpt3_output": "\n{\"type\": \"personal care product\", \"description\": \"liquid; could be used for hair and body; could be scented\", \"similar objects\": [\"soap\", \"conditioner\", \"body wash\"]}"}, {"object": "shark", "object_id": 1705, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, grayish-blue; has a pointed snout; could have sharp teeth; could have a dorsal fin\", \"similar objects\": [\"whale\", \"dolphin\", \"stingray\"]}"}, {"object": "sheep", "object_id": 1157, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"white, wooly fur; has horns; could be found in herds\", \"similar objects\": [\"goat\", \"cow\", \"llama\"]}"}, {"object": "ship", "object_id": 1165, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large; could have multiple decks; could have sails or engines\", \"similar objects\": [\"boat\", \"yacht\", \"cruise ship\"]}"}, {"object": "shovel", "object_id": 1175, "gpt3_output": "\n{\"type\": \"tool\", \"description\": \"long handle; metal or plastic blade; used for digging\", \"similar objects\": [\"rake\", \"spade\", \"hoe\"]}"}, {"object": "shrimp", "object_id": 1180, "gpt3_output": "\n{\"type\": \"seafood\", \"description\": \"small, pinkish-orange; could be cooked in various ways\", \"similar objects\": [\"lobster\", \"crab\", \"squid\"]}"}, {"object": "sink", "object_id": 1192, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"has a basin; could have a faucet; could be made of stainless steel\", \"similar objects\": [\"bathtub\", \"toilet\", \"shower\"]}"}, {"object": "skateboard", "object_id": 1194, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long board with four wheels; could be used for skateboarding\", \"similar objects\": [\"scooter\", \"rollerblades\", \"longboard\"]}"}, {"object": "skating and skiing shoes", "object_id": 1834, "gpt3_output": "\n\n{\"type\": \"footwear\", \"description\": \"designed for skating and skiing; could have spikes or blades; could be waterproof; could have a thick sole\", \"similar objects\": [\"hiking boots\", \"running shoes\", \"climbing shoes\"]}"}, {"object": "skis", "object_id": 1199, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, thin, made of wood or metal; used for skiing\", \"similar objects\": [\"snowboard\", \"skates\", \"sled\"]}"}, {"object": "slide", "object_id": 1835, "gpt3_output": "\n{\"type\": \"playground equipment\", \"description\": \"long, curved, could be made of plastic; could be used for sliding down\", \"similar objects\": [\"swing\", \"monkey bars\", \"seesaw\"]}"}, {"object": "slippers", "object_id": 1836, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"soft, comfortable; could be made of fabric or rubber; could be slip-on or open-toe\", \"similar objects\": [\"sandals\", \"flip-flops\", \"mules\"]}"}, {"object": "sneakers", "object_id": 1234, "gpt3_output": "\n{\"type\": \"footwear\", \"description\": \"lightweight; could be made of canvas or leather; could have laces\", \"similar objects\": [\"trainers\", \"running shoes\", \"sandals\"]}"}, {"object": "snowboard", "object_id": 1240, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long, flat board; could have bindings; could be used for snowboarding\", \"similar objects\": [\"skis\", \"surfboard\", \"skateboard\"]}"}, {"object": "soccer", "object_id": 1249, "gpt3_output": "\n{\"type\": \"sport\", \"description\": \"team sport; two teams of eleven players; played on a rectangular field; goal is to score by kicking the ball into the opposing team's goal\", \"similar objects\": [\"football\", \"basketball\", \"baseball\"]}"}, {"object": "speaker", "object_id": 1262, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"could be wired or wireless; could be used to amplify sound\", \"similar objects\": [\"headphones\", \"microphone\", \"amplifier\"]}"}, {"object": "spoon", "object_id": 1271, "gpt3_output": "\n{\"type\": \"utensil\", \"description\": \"long handle; round bowl; could be made of metal or plastic\", \"similar objects\": [\"fork\", \"knife\", \"chopsticks\"]}"}, {"object": "sports car", "object_id": 1837, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"sleek, low-slung; usually two-door; could have a powerful engine\", \"similar objects\": [\"sedan\", \"coupe\", \"convertible\"]}"}, {"object": "spring rolls", "object_id": 1838, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"cylindrical; could be filled with vegetables, meat, or seafood; could be fried or steamed; could be served with dipping sauce\", \"similar objects\": [\"egg rolls\", \"dumplings\", \"samosas\"]}"}, {"object": "stapler", "object_id": 1287, "gpt3_output": "\n{\"type\": \"office tool\", \"description\": \"small, rectangular; has a handle; could be used to staple papers together\", \"similar objects\": [\"hole puncher\", \"paper clip\", \"tape dispenser\"]}"}, {"object": "starfish", "object_id": 1692, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"five-pointed; could be orange, red, or purple; could have spines on its body\", \"similar objects\": [\"sea urchin\", \"crab\", \"jellyfish\"]}"}, {"object": "steak", "object_id": 1292, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"thick cut of beef; could be grilled or pan-fried; could be served with sides\", \"similar objects\": [\"chicken\", \"pork\", \"lamb\"]}"}, {"object": "stool", "object_id": 1303, "gpt3_output": "\n{\"type\": \"furniture\", \"description\": \"has three or four legs; could be made of wood or metal; could be used as a seat or a step\", \"similar objects\": [\"chair\", \"bench\", \"ottoman\"]}"}, {"object": "stop sign", "object_id": 1306, "gpt3_output": "\n{\"type\": \"traffic sign\", \"description\": \"octagonal; red background with white letters; could be mounted on a pole\", \"similar objects\": [\"yield sign\", \"speed limit sign\", \"no parking sign\"]}"}, {"object": "storage box", "object_id": 1839, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; could be made of plastic or wood; could have a lid\", \"similar objects\": [\"basket\", \"trunk\", \"drawer\"]}"}, {"object": "strawberry", "object_id": 1314, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"red, small, has a stem and seeds\", \"similar objects\": [\"raspberry\", \"blueberry\", \"blackberry\"]}"}, {"object": "street lights", "object_id": 1319, "gpt3_output": "\n{\"type\": \"lighting tool\", \"description\": \"tall; could be made of metal; could be powered by electricity\", \"similar objects\": [\"lamp post\", \"traffic light\", \"lantern\"]}"}, {"object": "stroller", "object_id": 1324, "gpt3_output": "\n{\"type\": \"baby transport tool\", \"description\": \"has four wheels; could be folded; could be pushed by an adult\", \"similar objects\": [\"car seat\", \"high chair\", \"baby carrier\"]}"}, {"object": "suitcase", "object_id": 1333, "gpt3_output": "\n{\"type\": \"travel item\", \"description\": \"rectangular; has a handle; could be made of hard materials\", \"similar objects\": [\"backpack\", \"duffel bag\", \"briefcase\"]}"}, {"object": "surfboard", "object_id": 1340, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long and narrow; could be made of foam or fiberglass; could have a fin\", \"similar objects\": [\"skateboard\", \"snowboard\", \"wakeboard\"]}"}, {"object": "surveillance camera", "object_id": 1840, "gpt3_output": "\n{\"type\": \"security device\", \"description\": \"small, cylindrical; could be mounted on walls; could be connected to a monitor\", \"similar objects\": [\"security alarm\", \"motion sensor\", \"doorbell camera\"]}"}, {"object": "sushi", "object_id": 1780, "gpt3_output": "\n{\"type\": \"food\", \"description\": \"rice and raw fish; could be wrapped in seaweed\", \"similar objects\": [\"sashimi\", \"tempura\", \"udon\"]}"}, {"object": "suv", "object_id": 1344, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, four-wheeled, typically has a higher ground clearance than a sedan\", \"similar objects\": [\"truck\", \"minivan\", \"sedan\"]}"}, {"object": "swan", "object_id": 1345, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"white; long neck; could have black beak; could have orange feet\", \"similar objects\": [\"goose\", \"duck\", \"pelican\"]}"}, {"object": "swing", "object_id": 1841, "gpt3_output": "\n{\"type\": \"playground equipment\", \"description\": \"has a seat; could be hung from a tree branch; could be made of metal or wood\", \"similar objects\": [\"slide\", \"monkey bars\", \"merry-go-round\"]}"}, {"object": "table tennis paddle", "object_id": 1054, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long handle; has a flat surface; could be made of wood or rubber\", \"similar objects\": [\"tennis racket\", \"badminton racket\", \"squash racket\"]}"}, {"object": "tablet", "object_id": 1363, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"flat, rectangular, touchscreen; could be used for communication, entertainment, and work\", \"similar objects\": [\"laptop\", \"smartphone\", \"e-reader\"]}"}, {"object": "tangerine", "object_id": 1372, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"orange, round, has a stem\", \"similar objects\": [\"orange\", \"mandarin\", \"lemon\"]}"}, {"object": "tape", "object_id": 1376, "gpt3_output": "\n{\"type\": \"adhesive tool\", \"description\": \"long, thin, sticky; could be used to seal packages\", \"similar objects\": [\"glue\", \"velcro\", \"zip ties\"]}"}, {"object": "tape measure/ruler", "object_id": 1673, "gpt3_output": "\n{\"type\": \"measuring tool\", \"description\": \"long, thin, flexible; could have markings on it\", \"similar objects\": [\"yardstick\", \"calipers\", \"protractor\"]}"}, {"object": "target", "object_id": 1842, "gpt3_output": "\n{\"type\": \"object\", \"description\": \"round; has a bullseye in the center; could be used for shooting practice\", \"similar objects\": [\"dartboard\", \"archery target\", \"punching bag\"]}"}, {"object": "tea pot", "object_id": 1384, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"round; could have a spout and handle; could be made of metal or ceramic\", \"similar objects\": [\"kettle\", \"coffee pot\", \"tea infuser\"]}"}, {"object": "teddy bear", "object_id": 1388, "gpt3_output": "\n{\"type\": \"toy\", \"description\": \"stuffed animal; usually has a round face; could have a bow tie; could be brown or other colors\", \"similar objects\": [\"plush toy\", \"stuffed animal\", \"doll\"]}"}, {"object": "telephone", "object_id": 1391, "gpt3_output": "\n{\"type\": \"communication device\", \"description\": \"has a handset; could be cordless; could have a dial pad\", \"similar objects\": [\"cell phone\", \"walkie talkie\", \"intercom\"]}"}, {"object": "tennis ball", "object_id": 1395, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"round; yellow and green; could be made of rubber\", \"similar objects\": [\"soccer ball\", \"basketball\", \"baseball\"]}"}, {"object": "tennis racket", "object_id": 1400, "gpt3_output": "\n{\"type\": \"sports equipment\", \"description\": \"long handle; has strings; could be made of wood or metal\", \"similar objects\": [\"badminton racket\", \"squash racket\", \"table tennis racket\"]}"}, {"object": "tent", "object_id": 1402, "gpt3_output": "\n{\"type\": \"shelter\", \"description\": \"could be made of fabric; could be set up with poles; could be used for camping\", \"similar objects\": [\"yurt\", \"igloo\", \"teepee\"]}"}, {"object": "tie", "object_id": 1407, "gpt3_output": "\n{\"type\": \"clothing accessory\", \"description\": \"long, thin, usually made of silk or polyester; could be worn around the neck\", \"similar objects\": [\"bow tie\", \"scarf\", \"belt\"]}"}, {"object": "tissue", "object_id": 1414, "gpt3_output": "\n{\"type\": \"paper product\", \"description\": \"soft, thin, rectangular; used for wiping and cleaning\", \"similar objects\": [\"paper towel\", \"napkin\", \"toilet paper\"]}"}, {"object": "toaster", "object_id": 1417, "gpt3_output": "\n{\"type\": \"kitchen appliance\", \"description\": \"rectangular; has slots for bread; could be electric or manual\", \"similar objects\": [\"coffee maker\", \"blender\", \"microwave\"]}"}, {"object": "toilet", "object_id": 1422, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"has a bowl and a tank; could be wall-mounted or floor-mounted; could have a lid\", \"similar objects\": [\"sink\", \"bathtub\", \"shower\"]}"}, {"object": "toiletries", "object_id": 1429, "gpt3_output": "\n{\"type\": \"household items\", \"description\": \"items used for personal hygiene and grooming; could include soap, shampoo, toothbrush, etc.\", \"similar objects\": [\"bathroom supplies\", \"cosmetics\", \"cleaning supplies\"]}"}, {"object": "tomato", "object_id": 1430, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"round, red, has a stem; could be sliced into pieces; could be used for salads\", \"similar objects\": [\"apple\", \"pear\", \"cucumber\"]}"}, {"object": "tong", "object_id": 1843, "gpt3_output": "\n{\"type\": \"cooking tool\", \"description\": \"long, metal; used to pick up food\", \"similar objects\": [\"spatula\", \"ladle\", \"fork\"]}"}, {"object": "toothbrush", "object_id": 1436, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"long handle; has bristles; could be manual or electric\", \"similar objects\": [\"toothpaste\", \"dental floss\", \"mouthwash\"]}"}, {"object": "toothpaste", "object_id": 1437, "gpt3_output": "\n{\"type\": \"hygiene product\", \"description\": \"white, paste-like; could be in a tube; could be flavored\", \"similar objects\": [\"mouthwash\", \"toothbrush\", \"floss\"]}"}, {"object": "tortoise/turtle", "object_id": 1733, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"shell; slow-moving; could be aquatic or terrestrial\", \"similar objects\": [\"snake\", \"iguana\", \"lizard\"]}"}, {"object": "towel/napkin", "object_id": 1444, "gpt3_output": "\n{\"type\": \"cleaning tool\", \"description\": \"absorbent fabric; could be used to dry hands or wipe surfaces\", \"similar objects\": [\"cloth\", \"rag\", \"sponge\"]}"}, {"object": "toy", "object_id": 1448, "gpt3_output": "\n{\"type\": \"plaything\", \"description\": \"could be made of plastic, wood, or fabric; could be used for entertainment or educational purposes; could come in various shapes and sizes\", \"similar objects\": [\"doll\", \"action figure\", \"puzzle\"]}"}, {"object": "traffic cone", "object_id": 1452, "gpt3_output": "\n{\"type\": \"safety tool\", \"description\": \"orange; conical shape; could be reflective\", \"similar objects\": [\"barricade\", \"warning sign\", \"road sign\"]}"}, {"object": "traffic light", "object_id": 1453, "gpt3_output": "\n{\"type\": \"traffic signal\", \"description\": \"red, yellow, and green lights; could be mounted on a pole\", \"similar objects\": [\"stop sign\", \"yield sign\", \"crosswalk sign\"]}"}, {"object": "traffic sign", "object_id": 1454, "gpt3_output": "\n{\"type\": \"road sign\", \"description\": \"could be in different shapes and colors; could have words or symbols on it\", \"similar objects\": [\"stop sign\", \"yield sign\", \"speed limit sign\"]}"}, {"object": "train", "object_id": 1458, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"long; has multiple compartments; could be powered by electricity or diesel; could have multiple carriages\", \"similar objects\": [\"tram\", \"subway\", \"monorail\"]}"}, {"object": "trash bin/can", "object_id": 1469, "gpt3_output": "\n{\"type\": \"container\", \"description\": \"rectangular; has a lid; could be made of plastic or metal\", \"similar objects\": [\"recycling bin\", \"garbage can\", \"compost bin\"]}"}, {"object": "treadmill", "object_id": 1723, "gpt3_output": "\n{\"type\": \"exercise equipment\", \"description\": \"long and flat; has a belt; could have handles\", \"similar objects\": [\"elliptical machine\", \"stationary bike\", \"rowing machine\"]}"}, {"object": "tricycle", "object_id": 1844, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"three wheels; could be pedal-powered; could have a basket in the back\", \"similar objects\": [\"bicycle\", \"scooter\", \"skateboard\"]}"}, {"object": "tripod", "object_id": 1480, "gpt3_output": "\n{\"type\": \"support tool\", \"description\": \"three legs; could be used to hold cameras or other objects\", \"similar objects\": [\"monopod\", \"bipod\", \"quadpod\"]}"}, {"object": "trolley", "object_id": 1481, "gpt3_output": "\n{\"type\": \"transportation tool\", \"description\": \"wheeled; could be pushed or pulled; could be used to carry items\", \"similar objects\": [\"cart\", \"wagon\", \"hand truck\"]}"}, {"object": "trombone", "object_id": 1703, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long, brass; has a slide; could be played with a mouthpiece\", \"similar objects\": [\"trumpet\", \"saxophone\", \"clarinet\"]}"}, {"object": "trophy", "object_id": 1845, "gpt3_output": "\n{\"type\": \"award\", \"description\": \"golden; has a figure on the top; could be made of metal or plastic\", \"similar objects\": [\"medal\", \"plaque\", \"ribbon\"]}"}, {"object": "truck", "object_id": 1484, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"large, boxy; could have multiple axles; could have a trailer attached\", \"similar objects\": [\"van\", \"SUV\", \"bus\"]}"}, {"object": "trumpet", "object_id": 1672, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"long, cylindrical; has three valves; could be made of brass\", \"similar objects\": [\"trombone\", \"clarinet\", \"flute\"]}"}, {"object": "tuba", "object_id": 1846, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"large, brass; has a wide bell; produces low-pitched sound\", \"similar objects\": [\"trombone\", \"trumpet\", \"euphonium\"]}"}, {"object": "tv", "object_id": 1393, "gpt3_output": "\n{\"type\": \"electronic device\", \"description\": \"flat screen; could be connected to the internet; could be used to watch movies and shows\", \"similar objects\": [\"computer\", \"smartphone\", \"tablet\"]}"}, {"object": "umbrella", "object_id": 1497, "gpt3_output": "\n{\"type\": \"protective tool\", \"description\": \"has a curved handle; could be opened and closed; could be made of fabric\", \"similar objects\": [\"raincoat\", \"hat\", \"sunglasses\"]}"}, {"object": "urinal", "object_id": 1502, "gpt3_output": "\n{\"type\": \"plumbing fixture\", \"description\": \"rectangular; could be wall-mounted; could be used for urination\", \"similar objects\": [\"toilet\", \"sink\", \"bathtub\"]}"}, {"object": "van", "object_id": 1507, "gpt3_output": "\n{\"type\": \"vehicle\", \"description\": \"box-shaped; could have sliding doors; could be used for transporting goods\", \"similar objects\": [\"truck\", \"minivan\", \"SUV\"]}"}, {"object": "vase", "object_id": 1510, "gpt3_output": "\n{\"type\": \"decorative item\", \"description\": \"cylindrical; could be made of glass, ceramic, or metal; could have a wide opening at the top\", \"similar objects\": [\"urn\", \"jar\", \"jug\"]}"}, {"object": "vent", "object_id": 1515, "gpt3_output": "\n{\"type\": \"ventilation tool\", \"description\": \"rectangular; could be used to circulate air; could be used to exhaust air\", \"similar objects\": [\"fan\", \"air conditioner\", \"heater\"]}"}, {"object": "violin", "object_id": 1663, "gpt3_output": "\n{\"type\": \"musical instrument\", \"description\": \"wooden; has four strings; has a bow\", \"similar objects\": [\"cello\", \"guitar\", \"piano\"]}"}, {"object": "volleyball", "object_id": 1627, "gpt3_output": "\n{\"type\": \"sport equipment\", \"description\": \"spherical; has a net; could be played with two teams\", \"similar objects\": [\"basketball\", \"football\", \"tennis ball\"]}"}, {"object": "washing machine", "object_id": 1532, "gpt3_output": "\n{\"type\": \"appliance\", \"description\": \"large, rectangular; has a door; could be automatic or manual\", \"similar objects\": [\"dryer\", \"dishwasher\", \"refrigerator\"]}"}, {"object": "watch", "object_id": 1534, "gpt3_output": "\n{\"type\": \"accessory\", \"description\": \"small, round, has a strap; could have a digital or analog display\", \"similar objects\": [\"bracelet\", \"ring\", \"necklace\"]}"}, {"object": "watermelon", "object_id": 1540, "gpt3_output": "\n{\"type\": \"fruit\", \"description\": \"large, round, green rind with red flesh; has black seeds\", \"similar objects\": [\"cantaloupe\", \"honeydew\", \"papaya\"]}"}, {"object": "wheelchair", "object_id": 1549, "gpt3_output": "\n{\"type\": \"mobility aid\", \"description\": \"has two large wheels; could be motorized; could have a seat and armrests\", \"similar objects\": [\"walker\", \"crutches\", \"cane\"]}"}, {"object": "wild bird", "object_id": 1847, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"could have colorful feathers; could fly; could have a beak and claws\", \"similar objects\": [\"pigeon\", \"sparrow\", \"eagle\"]}"}, {"object": "wine glass", "object_id": 573, "gpt3_output": "\n{\"type\": \"drinking tool\", \"description\": \"tall and thin; has a stem; could have a round bowl\", \"similar objects\": [\"tumbler\", \"mug\", \"cup\"]}"}, {"object": "yak", "object_id": 1848, "gpt3_output": "\n{\"type\": \"animal\", \"description\": \"large, shaggy, long-haired bovine; could have a long, curved horns; could be found in the Himalayas\", \"similar objects\": [\"cow\", \"buffalo\", \"bison\"]}"}, {"object": "zebra", "object_id": 1591, "gpt3_output": "\n{\"type\": \"animal\", \n\"description\": \"black and white stripes; has a long mane; four legs; hooves; long tail\", \n\"similar objects\": [\"horse\", \"giraffe\", \"elephant\"]}"}, {"object": "__background__", "object_id": 0, "gpt3_output": " {\"type\": \"visual element\", \"description\": \"the area behind the main subject of an image or video; could be a solid color or contain other elements\", \"similar objects\": [\"foreground\", \"frame\", \"scene\"]}"}] \ No newline at end of file diff --git a/tools/files/omnilabel_coco.json b/tools/files/omnilabel_coco.json new file mode 100644 index 0000000000000000000000000000000000000000..6d7140fec37bac21ee9efa1a97e089755c7d93a4 --- /dev/null +++ b/tools/files/omnilabel_coco.json @@ -0,0 +1 @@ +[{"image_id": 1234, "file_name": "./DATASET/omnilabel/coco/000000578922.jpg", "inference_obj_descriptions": ["the flower vase", "The large brown teddy bear in the brown cardboard box.", "The white teddy bear with the red tag on his ear.", "The white teddy bear that is near the foot of the person.", "an educational item that can be read and features red persons on the cover", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2295, 2303, 2304, 2305, 2323, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1236, "file_name": "./DATASET/omnilabel/coco/000000436551.jpg", "inference_obj_descriptions": ["duck with a red and white beak", "The birds that are standing on the grass.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1322, 1412, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1237, "file_name": "./DATASET/omnilabel/coco/000000245311.jpg", "inference_obj_descriptions": ["bowl with the powdered donuts", "sauces in a glass bowl", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1323, 1687, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1239, "file_name": "./DATASET/omnilabel/coco/000000496954.jpg", "inference_obj_descriptions": ["The two cakes closest to the leaf on the fabric.", "The slice of cake is lying on the plate on its side.", "Cup cakes", "the cakes that only have one red rose on the top, no more, no less", "Cakes with visible flower on top", "Cup with water", "The one apple that is red, and is also in the upper right hand corner.", "The container with the alcohol.", "Slice of a frosted dessert, suitable for serving one person.", "a cannister used for holding spicy sauce", "Cup with water", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1260, 1325, 1590, 1669, 1720, 2275, 2365, 2421, 2464, 2466, 2663, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1240, "file_name": "./DATASET/omnilabel/coco/000000000632.jpg", "inference_obj_descriptions": ["The plant is touching the bookcase.", "The plant with the bigger head of brocolli.", "Plant next to the M on the wall", "The black table with the food on it that the baby is sitting at.", "this bathroom device is used to excrete human waste", "Sleeping area with blue quilt", "The thing the kids are standing on.", "The end of the seat where the people are.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1326, 1856, 1899, 2374, 2459, 2468, 2678, 2714, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1839, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1235, 1330, 1808, 1842, 1902, 1972, 2110, 2172, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 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"hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1332, 1402, 1440, 2309, 2374, 2459, 2629, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1335, 1614, 1625, 1638, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 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"motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2295, 2303, 2304, 2305, 2323, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1261, "file_name": "./DATASET/omnilabel/coco/000000475572.jpg", "inference_obj_descriptions": ["Compiled digital seasons of a TV show called MONK", "The large brown teddy bear in the brown cardboard box.", "The white teddy bear with the red tag on his ear.", "an item that contains words that you read", "an educational item that can be read and features red persons on the cover", "The scissors cutting the stack of white papers.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2299, 2303, 2304, 2322, 2323, 2325, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1262, "file_name": "./DATASET/omnilabel/coco/000000060823.jpg", "inference_obj_descriptions": ["cows that are laid down", "each of these cows is all black in color", "The cow that is in the grass", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1340, 1553, 2161, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1264, "file_name": "./DATASET/omnilabel/coco/000000567886.jpg", "inference_obj_descriptions": ["A book titled Cortazar", "The books that are behind the netting.", "The clear glass cut vase with the red flowers in it.", "The white teddy bear with the red tag on his ear.", "The white teddy bear that is near the foot of the person.", "the flower vase", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1403, 1516, 2292, 2304, 2305, 2352, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1267, "file_name": "./DATASET/omnilabel/coco/000000233567.jpg", "inference_obj_descriptions": ["these four sheep look to be the same color but are definitely the four lightest colored", "The sheep that are white in color.", "The sheep that are lying in the grass.", "false question, there is only one sheep in this photo and it asks you to pick two -", "Animal in another animal's mouth", "black and white feline", "The black sheep in the pen with all the others.", "Animals with brown hair", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1574, 1631, 1708, 1721, 2256, 2269, 2307, 2377, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1270, "file_name": "./DATASET/omnilabel/coco/000000364297.jpg", "inference_obj_descriptions": ["Electronic items with display", "Small electronic device used for calls", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2310, 2416, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1271, "file_name": "./DATASET/omnilabel/coco/000000002157.jpg", "inference_obj_descriptions": ["knives not on a plate of food", "visibly held by a human hand", "The knife that is in the black plate.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1343, 1371, 1891, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1272, "file_name": "./DATASET/omnilabel/coco/000000416104.jpg", "inference_obj_descriptions": ["The umbrella in the reflection of the window.", "two umbrellas closer to the sign saying 99 flake", "umbrella closest to the brick wall", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1259, 1344, 1351, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1274, "file_name": "./DATASET/omnilabel/coco/000000181499.jpg", "inference_obj_descriptions": ["The monitor behind the animal.", "the keyboard", "Small black electronic device for calls", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2203, 2314, 2476, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1275, "file_name": "./DATASET/omnilabel/coco/000000115870.jpg", "inference_obj_descriptions": ["the two couches that actually sit opposite of each other", "The two couches that have printed fabric on them instead of the one with only a solid color fabric.", "a piece of furniture that is long and used for sleeping", "The seat with the red runner on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1274, 1607, 2333, 2596, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1277, "file_name": "./DATASET/omnilabel/coco/000000003501.jpg", "inference_obj_descriptions": ["broccoli touching rice", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1345, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1278, "file_name": "./DATASET/omnilabel/coco/000000322163.jpg", "inference_obj_descriptions": ["The part of the oven with the burners on it.", "The highest oven", "Container with water in it", "The green potted plant hung above tables on the wooden wall.", "Round metal utensil", "The fruit in the beige container.", "This is used to put food into your mouth from a container.", "Metal pronged eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2001, 2143, 2258, 2313, 2408, 2541, 2605, 2722, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1279, "file_name": "./DATASET/omnilabel/coco/000000401446.jpg", "inference_obj_descriptions": ["The three bags at the top.", "An item used for carrying smaller items.", "the open umbrella", "The black and white zebra print umbrella.", "The bag that the animal is on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2197, 2316, 2318, 2350, 2456, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1282, "file_name": "./DATASET/omnilabel/coco/000000113403.jpg", "inference_obj_descriptions": ["teddy bears with brownish colored fur", "The stuffed animals that are green.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1348, 1422, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1285, "file_name": "./DATASET/omnilabel/coco/000000304404.jpg", "inference_obj_descriptions": ["each of these umbrellas is multi-colored, red and blue", "The umbrella in the reflection of the window.", "umbrella closest to the brick wall", "the red umbrellas", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1241, 1259, 1351, 1463, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1287, "file_name": "./DATASET/omnilabel/coco/000000060855.jpg", "inference_obj_descriptions": ["A group of green apples towards the top of the pile", "oranges touching the very red apple", "apples that are half way in the bowl", "The oranges that have been cut in half.", "The apple that is more red in color.", "The apple that is closest to the spoon.", "The orange slice with more syrup on it.", "Long yellow fruit", "The one apple that is red, and is also in the upper right hand corner.", "the oranges", "Food with two slices of bread", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1298, 1353, 1380, 1418, 1465, 1795, 2184, 2247, 2365, 2368, 2426, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1288, "file_name": "./DATASET/omnilabel/coco/000000261318.jpg", "inference_obj_descriptions": ["The black and white zebra print umbrella.", "The neatly packed suitcase that is sitting open, and It has camo printed items in it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2350, 2364, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1290, "file_name": "./DATASET/omnilabel/coco/000000223090.jpg", "inference_obj_descriptions": ["carrots touching chopped greens", "The carrot that is touching the fish.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1354, 1922, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1291, "file_name": "./DATASET/omnilabel/coco/000000548506.jpg", "inference_obj_descriptions": ["The apples that are red.", "The bananas that have been cut for the dish.", "The apple that is closest to the spoon.", "The biggest white apple at the bottom.", "The banana closer to the Guinness bear.", "Food with two slices of bread", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1419, 1611, 1795, 1992, 2183, 2433, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1292, "file_name": "./DATASET/omnilabel/coco/000000551822.jpg", "inference_obj_descriptions": ["sandwich touching the small bowl of sauce", "Cylindrical container decorated with a colorful image.", "Deep round dish with broccoli", "The wooden furniture the man can sit on.", "The orange colored utinsil.", "The white utinsil touching the food.", "The reflections of the cups.", "The seat that the man is in.", "Wood and tan sleeping spot", "The surface holding the plate.", "The glass with the clear liquid in it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1356, 2500, 2502, 2517, 2525, 2534, 2555, 2625, 2659, 2666, 2707, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1300, "file_name": "./DATASET/omnilabel/coco/000000005193.jpg", "inference_obj_descriptions": ["all three of these surfboards are the same color as each other", "Pink colored surfboard", "surfboard with yellow, read and orange colors", "The people that are walking on the sidewalk.", "The people with blue shirts on the end of the ramp.", "people that are holding a surf board", "All the people looking at each other.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1246, 1263, 1362, 1881, 1971, 2008, 2070, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1302, "file_name": "./DATASET/omnilabel/coco/000000559956.jpg", "inference_obj_descriptions": ["The sheep are majority white colored.", "Men holding wine glasses", "false question, there is only one sheep in this photo and it asks you to pick two -", "The people kneeling down", "All the people that are playing against each other in the game.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1363, 1663, 1721, 2021, 2135, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1304, "file_name": "./DATASET/omnilabel/coco/000000334399.jpg", "inference_obj_descriptions": ["The people who are wearing red shirts.", "The people wearing white shirts.", "The women that don't wear sleeves.", "Ballplayers wearing shirts with contrasting sleeve color starting at shoulders.", "The clear glass cut vase with the red flowers in it.", "the white teddy bears", "The large brown teddy bear in the brown cardboard box.", "The white teddy bear with the red tag on his ear.", "an item that contains words that you read", "The scissors cutting the stack of white papers.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1851, 1859, 1866, 1901, 2292, 2302, 2303, 2304, 2322, 2325, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1308, "file_name": "./DATASET/omnilabel/coco/000000070774.jpg", "inference_obj_descriptions": ["Bicycle with kid on back", "this motor vehicle can carry more than three passengers", "The mostly white motorcycle parked with the wheel turned.", "the horizontal stacked bikes", "The vehicle with a 22 on the front of it.", "Red vehicle in the road", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1790, 2195, 2326, 2329, 2505, 2578, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1309, "file_name": "./DATASET/omnilabel/coco/000000560178.jpg", "inference_obj_descriptions": ["The broccolis touching other food that isn't broccoli", "The full pieces of brocolli in the dish.", "The food that is made with bread.", "A food item that has been cut in half and includes both sides.", "Plants in the ground", "Slice of a frosted dessert, suitable for serving one person.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1239, 1304, 2225, 2317, 2330, 2464, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1310, "file_name": "./DATASET/omnilabel/coco/000000024567.jpg", "inference_obj_descriptions": ["These two hotdogs are closest to the man in the hat.", "The hot dog that is being held with two hands.", "The hot dog with the green stuff on it.", "person", "bicycle", 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"./DATASET/omnilabel/coco/000000284296.jpg", "inference_obj_descriptions": ["Giraffes in the shade", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": 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{"image_id": 1321, "file_name": "./DATASET/omnilabel/coco/000000416885.jpg", "inference_obj_descriptions": ["yellow fruit that monkeys are known for eating", "Long yellow fruit", "Closest food item", "contain pepperoni as a topping", "Round citrus fruit", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", 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"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1377, 1614, 1650, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1296, 1992, 2199, 2215, 2247, 2317, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1327, "file_name": "./DATASET/omnilabel/coco/000000449432.jpg", "inference_obj_descriptions": ["a long blue transportation device", "Two-seater motor vehicle", "The vehicles with the pedals.", "The vehicle the cat is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2332, 2432, 2563, 2577, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1330, "file_name": "./DATASET/omnilabel/coco/000000400161.jpg", "inference_obj_descriptions": ["partially covered by fur", "dark colored with light buttons", "The remote in the child's left hand.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1333, 1383, 1467, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1332, "file_name": "./DATASET/omnilabel/coco/000000407298.jpg", "inference_obj_descriptions": ["a leather piece of equipment that helps you catch balls", "the long metal object underneath the person with a blue jacket", "Sport item you hit balls with", "Item you stand on with wheels", "A pair of boards you stand on", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2334, 2341, 2344, 2346, 2348, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1333, "file_name": "./DATASET/omnilabel/coco/000000462629.jpg", "inference_obj_descriptions": ["Refrigerator freezer combinations", "refrigerator with more than one magnet", "The refrigerator closest to the woman.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1234, 1350, 1618, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1334, "file_name": "./DATASET/omnilabel/coco/000000577932.jpg", "inference_obj_descriptions": ["Light blue shoulder handbag", "bags resting upon legs that are not crossed", "The handbag that is near the apples", "The bright red handbag.", "Rainbow colored accessory for rain", "The strap on the person in white.", "The large green umbrella over a market cart on the right side on the sidewalk.", "The bag that the animal is on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1235, 1330, 2110, 2172, 2209, 2244, 2360, 2456, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1337, "file_name": "./DATASET/omnilabel/coco/000000485237.jpg", "inference_obj_descriptions": ["The blue plane with the vertical tail stabilizer pointed downwards.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2018, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1338, "file_name": "./DATASET/omnilabel/coco/000000140076.jpg", "inference_obj_descriptions": ["The chairs across from the man.", "The chairs that are inches from the railing of the deck.", "Chairs on the left side of the table", "The food that is between the beer.", "Item you sit on with holes in the back rest", "The food the person will be eating.", "The furniture the dog is resting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1447, 1766, 1780, 2200, 2337, 2610, 2611, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1340, "file_name": "./DATASET/omnilabel/coco/000000356427.jpg", "inference_obj_descriptions": ["The backpack that is on the ground next to the child in the stroller.", "this backpack is actually on the back of a person", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1651, 1912, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1341, "file_name": "./DATASET/omnilabel/coco/000000377814.jpg", "inference_obj_descriptions": ["The donuts that are dark in color.", "The donut that has white frosting on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1238, 1614, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1342, "file_name": "./DATASET/omnilabel/coco/000000460494.jpg", "inference_obj_descriptions": ["The broccolis touching other food that isn't broccoli", "yellow fruit that monkeys are known for eating", "The utinsil with the tines.", "The small red fruit in the plastic bag next to the bottle.", "contain pepperoni as a topping", "Round metal utensil", "The orange veggies on the plate.", "Round citrus fruit", "a cannister used for holding spicy sauce", "Round metal container", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1239, 2220, 2287, 2308, 2379, 2408, 2413, 2439, 2466, 2616, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1345, "file_name": "./DATASET/omnilabel/coco/000000127394.jpg", "inference_obj_descriptions": ["Checkered sofa", "The dark back of the chair that the man in the blue and black shirt is sitting in.", "Sleeping area with blue quilt", "The place where someone would sleep.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2252, 2338, 2468, 2654, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1348, "file_name": "./DATASET/omnilabel/coco/000000349837.jpg", "inference_obj_descriptions": ["refrigerator with smaller freezer section on top", "refrigerator with more than one magnet", "The part of the refrigerator that is open.", "The refrigerator closest to the woman.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1245, 1350, 1529, 1618, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1349, "file_name": "./DATASET/omnilabel/coco/000000274460.jpg", "inference_obj_descriptions": ["all three of these surfboards are the same color as each other", "Pink colored surfboard", "The boards that are yellow.", "surfboard with yellow, read and orange colors", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1246, 1263, 1281, 1362, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1350, "file_name": "./DATASET/omnilabel/coco/000000498747.jpg", "inference_obj_descriptions": ["persons with their faces fully visible", "the two people holding a glass with hands as only visible body part", "The two women in black shirts riding horses.", "children with blond hair", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1247, 1879, 2019, 2114, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1351, "file_name": "./DATASET/omnilabel/coco/000000379842.jpg", "inference_obj_descriptions": ["each one of these books features the mario character", "The books that are sitting on the red table.", "A book titled Cortazar", "the books that are displayed vertically", "The book that has a picture of shoes on the cover.", "The books that are behind the netting.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1248, 1272, 1403, 1407, 1414, 1516, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1352, "file_name": "./DATASET/omnilabel/coco/000000123585.jpg", "inference_obj_descriptions": ["both of these two birds are light grey in color", "The bird with its feet touching the water.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1249, 1483, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1353, "file_name": "./DATASET/omnilabel/coco/000000032334.jpg", "inference_obj_descriptions": ["The wine glasses that the people are holding.", "A wine glass with the word beer on it.", "The two wine glasses sitting near the white dishes.", "each of these glasses has a visible logo on it and words", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1250, 1256, 1261, 1469, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1354, "file_name": "./DATASET/omnilabel/coco/000000078823.jpg", "inference_obj_descriptions": ["The silver car on the roadway.", "The SUV on the road.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1743, 1871, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1355, "file_name": "./DATASET/omnilabel/coco/000000231088.jpg", "inference_obj_descriptions": ["The umbrellas that have patterns on them.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1287, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1356, "file_name": "./DATASET/omnilabel/coco/000000289393.jpg", "inference_obj_descriptions": ["The horses that are standing up.", "the brown dogs", "The small figurine of the brown and white cow that is standing next to a figurine of a giraffe.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2255, 2301, 2340, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1360, "file_name": "./DATASET/omnilabel/coco/000000565391.jpg", "inference_obj_descriptions": ["The light colored van behind the fence.", "the vehicle with a non circular headlight", "The vehicle that is on the roadway.", "motorized scooters", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2263, 2343, 2488, 2530, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1362, "file_name": "./DATASET/omnilabel/coco/000000228942.jpg", "inference_obj_descriptions": ["The cars that are behind the red car.", "A car with both front headlights visible ", "Vehicle made for public transportation", "although it's in the water, this item can fly in the sky", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1443, 1487, 2345, 2458, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1363, "file_name": "./DATASET/omnilabel/coco/000000087476.jpg", "inference_obj_descriptions": ["the long metal object underneath the person with a blue jacket", "Sport item you hit balls with", "Item you stand on with wheels", "A pair of boards you stand on", "Item you put on hand to catch ball", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2341, 2344, 2346, 2348, 2356, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1367, "file_name": "./DATASET/omnilabel/coco/000000176232.jpg", "inference_obj_descriptions": ["the white teddy bears", "The white teddy bear that is near the foot of the person.", "an educational item that can be read and features red persons on the cover", "The scissors cutting the stack of white papers.", "the flower vase", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2302, 2305, 2323, 2325, 2352, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1370, "file_name": "./DATASET/omnilabel/coco/000000068933.jpg", "inference_obj_descriptions": ["The two zebras on the left side.", "Zebra with tail touching rock", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1253, 1995, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1373, "file_name": "./DATASET/omnilabel/coco/000000395343.jpg", "inference_obj_descriptions": ["The vases that have a busy pattern on them.", "The vase that is white.", "The colorful tablecloth covering the table underneath all the vases.", "the colorful table", "Round metal utensil", "cloth piece of furniture used for seating multiple guests together", "The utinsil on the side of the plate.", "The small glass container.", "Furniture with plates of food on it", "The thing the kids are standing on.", "The container with the lettuce in it.", "Metal eating utensil with prongs", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1307, 1758, 2351, 2354, 2408, 2418, 2511, 2591, 2639, 2678, 2706, 2720, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1374, "file_name": "./DATASET/omnilabel/coco/000000533145.jpg", "inference_obj_descriptions": ["this part is used to type and to input data", "a device with more than twenty buttons used for typing", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2394, 2532, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1375, "file_name": "./DATASET/omnilabel/coco/000000507223.jpg", "inference_obj_descriptions": ["the long metal object underneath the person with a blue jacket", "Item you stand on with wheels", "A pair of boards you stand on", "Item you put on hand to catch ball", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2341, 2346, 2348, 2356, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1377, "file_name": "./DATASET/omnilabel/coco/000000263966.jpg", "inference_obj_descriptions": ["canine animal", "Animal lying on the back of another animal", "The black sheep in the pen with all the others.", "Animal with black fur", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2206, 2278, 2307, 2357, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1378, "file_name": "./DATASET/omnilabel/coco/000000087144.jpg", "inference_obj_descriptions": ["The benches with people sitting on them.", "The adults sitting on the benches.", "The women sitting behind the man.", "The people that are waiting for the pitch.", "The two people holding the hands of the person in the white shirt.", "people behind the table", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1257, 1746, 1834, 1867, 2100, 2146, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1379, "file_name": "./DATASET/omnilabel/coco/000000233139.jpg", "inference_obj_descriptions": ["A clock that reads one thirty-two.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1258, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1380, "file_name": "./DATASET/omnilabel/coco/000000322610.jpg", "inference_obj_descriptions": ["The umbrella in the reflection of the window.", "umbrella closest to the brick wall", "The umbrella over the group of people dining.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1259, 1351, 1545, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1383, "file_name": "./DATASET/omnilabel/coco/000000241326.jpg", "inference_obj_descriptions": ["Animal lying on the back of another animal", "The black sheep in the pen with all the others.", "the black dog", "Animals with brown hair", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2278, 2307, 2361, 2377, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1385, "file_name": "./DATASET/omnilabel/coco/000000116589.jpg", "inference_obj_descriptions": ["Zebra with back towards camera", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1262, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1386, "file_name": "./DATASET/omnilabel/coco/000000286458.jpg", "inference_obj_descriptions": ["The black bag the children are sitting on.", "The large green umbrella over a market cart on the right side on the sidewalk.", "The neatly packed suitcase that is sitting open, and It has camo printed items in it.", "The bag on the person's back.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2224, 2360, 2364, 2450, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1387, "file_name": "./DATASET/omnilabel/coco/000000081988.jpg", "inference_obj_descriptions": ["all three of these surfboards are the same color as each other", "these two people each have a pink surfboard", "Person standing next to a table", "The people that are waiting for the pitch.", "Men holding plaques", "The men that are wearing dark blue shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1246, 1524, 1808, 1867, 1902, 1999, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1389, "file_name": "./DATASET/omnilabel/coco/000000001268.jpg", "inference_obj_descriptions": ["Boats with the capability of flying sails.", "The boat with a man and a dog.", "The boat that is bigger.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1295, 1491, 1744, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1391, "file_name": "./DATASET/omnilabel/coco/000000504000.jpg", "inference_obj_descriptions": ["The airplane that is blue and white.", "Airplane with a propeller", "The plane with Q9 on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1266, 1312, 2031, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1392, "file_name": "./DATASET/omnilabel/coco/000000371529.jpg", "inference_obj_descriptions": ["Toilet with brown seat cover", "The toilet the person is touching.", "The open white toilet that the woman is standing in front of and touching.", "the toilets", "The furniture the dog is resting on.", "Sleeping spot", "Wood and tan sleeping spot", "The wooden part of the back of the seat.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1806, 2185, 2366, 2369, 2611, 2618, 2659, 2679, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1394, "file_name": "./DATASET/omnilabel/coco/000000063740.jpg", "inference_obj_descriptions": ["Electronic items with large display", "The device on the sofa near the cat.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2370, 2539, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1395, "file_name": "./DATASET/omnilabel/coco/000000414638.jpg", "inference_obj_descriptions": ["Item you drink out of", "The vegetables in the black container.", "Round blue eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2371, 2612, 2649, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1397, "file_name": "./DATASET/omnilabel/coco/000000042102.jpg", "inference_obj_descriptions": ["Rainbow colored accessory for rain", "Item with wheels and a handle", "The black purse being held by the man in the blue jacket.", "clear objects that keep you dry when it's raining", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2209, 2281, 2373, 2489, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1400, "file_name": "./DATASET/omnilabel/coco/000000492992.jpg", "inference_obj_descriptions": ["canine animal", "The animals with the black and white stripes.", "The gray cat curled up on the plaid couch.", "The small white bird that is sitting on the back of the cow.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2206, 2236, 2358, 2375, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 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2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1405, "file_name": "./DATASET/omnilabel/coco/000000394510.jpg", "inference_obj_descriptions": ["Motorized 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null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1406, "file_name": "./DATASET/omnilabel/coco/000000061171.jpg", "inference_obj_descriptions": ["each of these cows is all black in color", "black and white feline", "Animals with brown spots", "the black dog", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1553, 2269, 2349, 2361, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1407, "file_name": "./DATASET/omnilabel/coco/000000463199.jpg", "inference_obj_descriptions": ["the black luggage bag that does not have a toy doll holding the end of it", "a red object that keeps one dry during rain", "Professional neck accessory", "the open umbrella", "Carrying item with two shoulder straps", "objects that protect you from rain", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2196, 2227, 2284, 2318, 2378, 2467, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1408, "file_name": "./DATASET/omnilabel/coco/000000456015.jpg", "inference_obj_descriptions": ["The horses that are brown and walking in the sand.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1273, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1412, "file_name": "./DATASET/omnilabel/coco/000000550084.jpg", "inference_obj_descriptions": ["these two trucks are each pointed in the same direction", "truck with bomb squad text on the back", "this truck is on the road and can be driven away", "The food truck that is green.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1309, 1358, 1705, 1951, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 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that is yellow, open, and has white and blue-colored items inside", "Person wearing light colored pants", "The people in solid colored dresses.", "All the people sitting behind the person eating pizza.", "A person holding a doughnut cheeseburger.", "something pink that protects from rain", "The container with the stickers on it.", "The black and white zebra print umbrella.", "The red saddlebag purse on the person in the white shirt standing on the right.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1609, 1668, 1810, 1915, 2111, 2140, 2219, 2289, 2350, 2382, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, 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"surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1345, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1421, "file_name": "./DATASET/omnilabel/coco/000000296649.jpg", "inference_obj_descriptions": ["each of these motorcycles is carrying two passengers", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1278, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1428, "file_name": "./DATASET/omnilabel/coco/000000341469.jpg", "inference_obj_descriptions": ["Light blue shoulder handbag", "bags resting upon legs that are not crossed", "Suitcases that are on the top shelf.", "the suitcase that is yellow, open, and has white and blue-colored items inside", "The handbag that is near the apples", "The higher of the handbags", "The handbag being held by the person with the umbrella hat.", "a red object that keeps one dry during rain", "bright green and used for carrying other items", "The red saddlebag purse on the person in the white shirt standing on the right.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1235, 1330, 1601, 1668, 2110, 2150, 2152, 2227, 2380, 2382, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1429, "file_name": "./DATASET/omnilabel/coco/000000310072.jpg", "inference_obj_descriptions": ["The red truck", "truck with bomb squad text on the back", "The food truck that is green.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1282, 1358, 1951, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1430, "file_name": "./DATASET/omnilabel/coco/000000278705.jpg", "inference_obj_descriptions": ["The skateboard being used by the person in the black shirt.", "waiting behind bulls", "The cars that are light in color.", "The silver car on the roadway.", "The car with a visible yellow license plate.", "The blue car.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1283, 1374, 1619, 1743, 2011, 2166, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1431, "file_name": "./DATASET/omnilabel/coco/000000186449.jpg", "inference_obj_descriptions": ["each of these backpacks is being worn and not carried in someone's hand", "The suitcases that are directly on the cart.", "Suitcases that are on the top shelf.", "The backpack that is on the ground next to the child in the stroller.", "The backpack that is closest to the wall.", "The backpack that is closest to the chair.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1284, 1578, 1601, 1651, 1916, 1920, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1434, "file_name": "./DATASET/omnilabel/coco/000000304984.jpg", "inference_obj_descriptions": ["The fruit in the clear bowl.", "Long orange vegetable", "Long yellow fruit", "The liquid in the clear container.", "Plants in the ground", "The utinsil on the side of the plate.", "cannister used for holding cream or other liquids", "Wooden untensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2199, 2215, 2250, 2262, 2330, 2511, 2669, 2687, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1435, "file_name": "./DATASET/omnilabel/coco/000000567740.jpg", "inference_obj_descriptions": ["these skis are red and silver", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1285, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1436, "file_name": "./DATASET/omnilabel/coco/000000346968.jpg", "inference_obj_descriptions": ["The book at the bottom of the stack.", "Boxes with white lettering", "The book that has red as a background color.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1286, 1503, 1838, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1440, "file_name": "./DATASET/omnilabel/coco/000000173008.jpg", "inference_obj_descriptions": ["The appliance built into the top shelfs", "The place where you could wash your hands.", "this appliance has the reflection of the two eletrical outlets in it", "Metal basin for running water", "Deep white tub for running water", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2201, 2390, 2403, 2470, 2494, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1441, "file_name": "./DATASET/omnilabel/coco/000000217948.jpg", "inference_obj_descriptions": ["The two bears with the lighter fur", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1289, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1445, "file_name": "./DATASET/omnilabel/coco/000000527750.jpg", "inference_obj_descriptions": ["The tall white appliance.", "Metal basin used for running water", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2205, 2285, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1446, "file_name": "./DATASET/omnilabel/coco/000000022892.jpg", "inference_obj_descriptions": ["canine animal", "Black and white canine", "The animals with the black and white stripes.", "The dark back of the chair that the man in the blue and black shirt is sitting in.", "The green objects that can grow", "A potted plant with yellowish leaves", "a green object used for sitting", "The places that people can sit.", "The furniture that has a round glass surface.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2206, 2211, 2236, 2338, 2428, 2620, 2676, 2702, 2708, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1451, "file_name": "./DATASET/omnilabel/coco/000000385719.jpg", "inference_obj_descriptions": ["Keyboards with black keys on top of table", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1294, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1454, "file_name": "./DATASET/omnilabel/coco/000000139883.jpg", "inference_obj_descriptions": ["Non-white frisbees", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1297, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1456, "file_name": "./DATASET/omnilabel/coco/000000187144.jpg", "inference_obj_descriptions": ["The buses that are blue and yellow.", "The buses under the covered area.", "The vehicle that is in the air.", "The smaller vehicles on the road.", "The cars with the spoilers.", "The yellow vehicle on the ground.", "The vehicle the cat is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1511, 1629, 2253, 2399, 2520, 2556, 2577, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1463, "file_name": "./DATASET/omnilabel/coco/000000172935.jpg", "inference_obj_descriptions": ["The boats that are closest to the bridge.", "The boat that is bigger.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1264, 1744, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1464, "file_name": "./DATASET/omnilabel/coco/000000115245.jpg", "inference_obj_descriptions": ["these four suitcases are stacked on top of each other", "Green backpacks", "Suitcase that is brown", "The suitcases that are on the bottom of the stack.", "The backpack that is on the ground next to the child in the stroller.", "The backpack on the man leaning over.", "this backpack is actually on the back of a person", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1389, 1400, 1496, 1505, 1651, 1829, 1912, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1465, "file_name": "./DATASET/omnilabel/coco/000000534605.jpg", "inference_obj_descriptions": ["each of these motorcycles is carrying two passengers", "the motorcycles with a grill on the front", "People who are looking at the table", "The people standing in front of the bikes.", "people behind the table", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1278, 1556, 1779, 1949, 2146, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1466, "file_name": "./DATASET/omnilabel/coco/000000173371.jpg", "inference_obj_descriptions": ["The forks on the white dishes.", "The fork that is with the salad.", "Forks propped up inches above table", "The pizzas that have a spatula under them.", "this pizza looks like it has raw meat on it", "All of these are containers that have consumables in them.", "a cannister used for holding spicy sauce", "clear cannisters used for holding alcoholic beverages", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1305, 1308, 1385, 1643, 1979, 2193, 2466, 2670, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1467, "file_name": "./DATASET/omnilabel/coco/000000255401.jpg", "inference_obj_descriptions": ["The toilet with the open lid.", "A toilet seating the child with a hairbrush", "The toilet the person is touching.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1306, 1898, 2185, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1468, "file_name": "./DATASET/omnilabel/coco/000000088040.jpg", "inference_obj_descriptions": ["The fork that is with the salad.", "the spoon in the cup of tea", "A fork that is not on a plate.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1308, 1589, 2045, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1469, "file_name": "./DATASET/omnilabel/coco/000000541291.jpg", "inference_obj_descriptions": ["The sink that is right behind the man.", "The sink next to the stove", "A sink near a basket of washcloths.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1477, 1885, 2181, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1470, "file_name": "./DATASET/omnilabel/coco/000000563702.jpg", "inference_obj_descriptions": ["The trucks that are lighter in color and parked on the street.", "these two trucks are each pointed in the same direction", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1300, 1309, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1471, "file_name": "./DATASET/omnilabel/coco/000000121744.jpg", "inference_obj_descriptions": ["these rackets are each red and blue and are NOT being used by the player", "The racket the woman is holding.", "The tennis racket being held by the man in red.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1310, 1510, 1749, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1472, "file_name": "./DATASET/omnilabel/coco/000000078748.jpg", "inference_obj_descriptions": ["these two bikes are closest to the harley davidson banner", "the motorcycles with a grill on the front", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1311, 1556, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1474, "file_name": "./DATASET/omnilabel/coco/000000109900.jpg", "inference_obj_descriptions": ["Airplane with a propeller", "The airplane that has the word Egyptair on the side.", "The blue plane with the vertical tail stabilizer pointed downwards.", "The vehicle that is in the air.", "a blue mode of transportation designed for water", "a green automobile for multiple passengers", "a brown automobile with a dog on top", "The vehicle meant to run on the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1312, 1581, 2018, 2253, 2465, 2477, 2491, 2586, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1314, 1441, 1476, 1693, 1991, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1479, "file_name": "./DATASET/omnilabel/coco/000000262631.jpg", "inference_obj_descriptions": ["this vase is smaller than the other", "The vase that is red.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1317, 2151, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1487, "file_name": "./DATASET/omnilabel/coco/000000338986.jpg", "inference_obj_descriptions": ["Car next to a tree", "The car that has both headlights visible.", "Vehicle of public transportation", "Red motor vehicle", "The vehicle parked on the number 6.", "Yellow tractor full of people", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1494, 2004, 2235, 2273, 2412, 2493, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 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"./DATASET/omnilabel/coco/000000391144.jpg", "inference_obj_descriptions": ["Elephants by a post", "these two elephants are babies and not as old as the other two", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1425, 1535, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1489, "file_name": "./DATASET/omnilabel/coco/000000398377.jpg", "inference_obj_descriptions": ["Light blue shoulder handbag", "Handbags that have a black color", "The bright red handbag.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1235, 1391, 2172, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2211, 2245, 2256, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1393, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1493, "file_name": "./DATASET/omnilabel/coco/000000066038.jpg", "inference_obj_descriptions": ["Multicolored umbrellas", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1394, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1395, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, 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"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1401, 1641, 1681, 1886, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1316, 1403, 1571, 1838, 2485, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, 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"suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1354, 1922, 2102, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1508, "file_name": "./DATASET/omnilabel/coco/000000125405.jpg", "inference_obj_descriptions": ["The dogs that are in the magazine.", "A dog with a brown spot on the back of its neck.", "The reflection of the dog in the mirror.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop 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dessert, suitable for serving one person.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1288, 2199, 2464, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1515, "file_name": "./DATASET/omnilabel/coco/000000081766.jpg", "inference_obj_descriptions": ["Dog with multi-colored fur", "The reflection of the dog in the mirror.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1408, 2156, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1519, "file_name": "./DATASET/omnilabel/coco/000000229997.jpg", "inference_obj_descriptions": ["both of these two birds are light grey in color", "The bird with its feet touching the water.", "canine animal", "Animal with gray fur", "The animals with the black and white stripes.", "The two birds standing in the back", "Animals with brown spots", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1249, 1483, 2206, 2212, 2236, 2266, 2349, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1520, "file_name": "./DATASET/omnilabel/coco/000000048396.jpg", "inference_obj_descriptions": ["this furniture item is used for sitting", "this item is used for urine and human excrement", "The white area where people could take a nap.", "The place where people would sleep.", "Furniture with people sitting on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2221, 2404, 2454, 2597, 2712, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1524, "file_name": "./DATASET/omnilabel/coco/000000550691.jpg", "inference_obj_descriptions": ["The bus with the number 180 on it.", "A bus that is green", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1729, 1738, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1525, "file_name": "./DATASET/omnilabel/coco/000000231527.jpg", "inference_obj_descriptions": ["The oranges that have been cut in half.", "Oranges touching wrapped food", "Deep round dish with broccoli", "Metal eating utensil", "Cup with water", "The red colored drink.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1418, 1426, 2502, 2580, 2663, 2699, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1527, "file_name": "./DATASET/omnilabel/coco/000000352684.jpg", "inference_obj_descriptions": ["The people that are wearing dresses.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1421, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1528, "file_name": "./DATASET/omnilabel/coco/000000434996.jpg", "inference_obj_descriptions": ["The stuffed animals that are green.", "The bears that are standing.", "The teddy bear sitting on the snow.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1422, 1612, 2059, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1530, "file_name": "./DATASET/omnilabel/coco/000000482719.jpg", "inference_obj_descriptions": ["The chairs across from the man.", "The chairs that are inches from the railing of the deck.", "The chair that is closer to the wall.", "this fruit item is a favorite of monkeys", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1447, 1766, 1782, 2223, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1531, "file_name": "./DATASET/omnilabel/coco/000000189078.jpg", "inference_obj_descriptions": ["Oranges that are on the top of other oranges", "Oranges in front of pear", "The two yellow fruits that are farther away from the camera.", "these two bananas are closest to the red pepper", "Food with pink frosting", "The red round fruits on the right", "Food with two slices of bread", "Small wedge of fruit.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1268, 1386, 1423, 1573, 2213, 2246, 2433, 2498, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1537, "file_name": "./DATASET/omnilabel/coco/000000369370.jpg", "inference_obj_descriptions": ["Oranges that are on the top of other oranges", "The oranges that have been cut in half.", "The sandwiches next to an orange slice.", "The oranges that are dirty.", "The grilled cheese sandwich", "The orange veggies on the plate.", "Slice of a frosted dessert, suitable for serving one person.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1268, 1418, 1452, 1635, 2154, 2413, 2464, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1538, "file_name": "./DATASET/omnilabel/coco/000000322429.jpg", "inference_obj_descriptions": ["The white bowls on the second to bottom shelf.", "Bowl with white food", "The three dark gray, or black pots of Saucy food on the left.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1428, 1485, 1606, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1540, "file_name": "./DATASET/omnilabel/coco/000000076547.jpg", "inference_obj_descriptions": ["Chairs next to a bookcase", "these two benches are the closest to the two women", "motorized scooters", "The living thing with the leaves.", "Green sofa", "The seat with the slats in the back.", "The seat with the fabric on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1548, 1593, 2530, 2628, 2661, 2667, 2704, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1542, "file_name": "./DATASET/omnilabel/coco/000000244181.jpg", "inference_obj_descriptions": ["the sandwiches that are each on the same plate with the other", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1430, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1544, "file_name": "./DATASET/omnilabel/coco/000000191761.jpg", "inference_obj_descriptions": ["oranges touching the very red apple", "Oranges touching wrapped food", "the orange slice furthest to the right", "Food with pink frosting", "The orange veggies on the plate.", "The slices of fruit in the container.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1353, 1426, 1431, 2213, 2413, 2484, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1545, "file_name": "./DATASET/omnilabel/coco/000000179898.jpg", "inference_obj_descriptions": ["The hot dogs that have red toppings.", "the hot dog on the right", "The hot dog that is being held with two hands.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1254, 1432, 1449, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1546, "file_name": "./DATASET/omnilabel/coco/000000308799.jpg", "inference_obj_descriptions": ["an appliance that keeps food cool", "an appliance used for heating food", "White porcelain tub for running water", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2208, 2228, 2469, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1547, "file_name": "./DATASET/omnilabel/coco/000000436617.jpg", "inference_obj_descriptions": ["The two chairs that are at the table that is furthest on the left.", "a piece of furniture that is long and used for sleeping", "The long surface with multiple water glasses on top", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1433, 2333, 2592, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1552, "file_name": "./DATASET/omnilabel/coco/000000224675.jpg", "inference_obj_descriptions": ["These are closest to the upper beam.", "the kite that is pink and yellow with black circles", "The people that are posing for the picture.", "The people who are wearing red shirts.", "The person kneeling on the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1364, 1438, 1818, 1851, 2086, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1556, "file_name": "./DATASET/omnilabel/coco/000000074200.jpg", "inference_obj_descriptions": ["all three of these surfboards are the same color as each other", "Pink colored surfboard", "surfboard with yellow, read and orange colors", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1246, 1263, 1362, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1557, "file_name": "./DATASET/omnilabel/coco/000000563882.jpg", "inference_obj_descriptions": ["the person that is wearing a black helmet", "Chair with no one in it", "The chairs that are empty.", "The people wearing yellow jackets", "The people that are wearing hats.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1655, 1770, 1777, 1840, 1865, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1558, "file_name": "./DATASET/omnilabel/coco/000000449661.jpg", "inference_obj_descriptions": ["the bed that has the patient clearly visible in it", "The beds on the first and second bunk.", "The bed that is closer to the window.", "Blue and wood sofa", "The seat of the man in the jacket.", "The surface with the red covering.", "Blue recliner", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1442, 1445, 1873, 2230, 2261, 2575, 2662, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1559, "file_name": "./DATASET/omnilabel/coco/000000454661.jpg", "inference_obj_descriptions": ["waiting behind bulls", "The cars that are behind the red car.", "The car that has both headlights visible.", "although it's in the water, this item can fly in the sky", "a green automobile for multiple passengers", "Yellow tractor full of people", "The cars with the spoilers.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1374, 1443, 2004, 2458, 2477, 2493, 2520, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1560, "file_name": "./DATASET/omnilabel/coco/000000393569.jpg", "inference_obj_descriptions": ["The beds on the first and second bunk.", "The seat on the right side of the people.", "The furniture the dog is resting on.", "Tan sofa", "Eating surface", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1445, 2553, 2611, 2614, 2660, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1561, "file_name": "./DATASET/omnilabel/coco/000000459396.jpg", "inference_obj_descriptions": ["The cows that have brown fur.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1446, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1563, "file_name": "./DATASET/omnilabel/coco/000000394611.jpg", "inference_obj_descriptions": ["The giraffe on the left side of the bush in the middle.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1448, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1565, "file_name": "./DATASET/omnilabel/coco/000000230993.jpg", "inference_obj_descriptions": ["Light blue shoulder handbag", "The handbag that is near the apples", "Rainbow colored accessory for rain", "Red and checkered accessory used when it is raining", "Professional neck accessory", "The black thing that the woman is digging in.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1235, 2110, 2209, 2232, 2284, 2388, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1567, "file_name": "./DATASET/omnilabel/coco/000000407614.jpg", "inference_obj_descriptions": ["Furniture to sit on", "The seat of the man in the jacket.", "The longest seat in the room.", "The place where people would sleep.", "The food the person will be eating.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2234, 2261, 2453, 2597, 2610, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1569, "file_name": "./DATASET/omnilabel/coco/000000050844.jpg", "inference_obj_descriptions": ["The teddy bear wearing a green hat.", "The teddy bears sitting on the edges of the blanket.", "a side profile of a teddy bear looking to the right", "This teddy bear has a red striped bow and is wearing a pink shirt with a cat on it.", "The teddy bear sitting on the snow.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1451, 1527, 1628, 1716, 2059, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1570, "file_name": "./DATASET/omnilabel/coco/000000268729.jpg", "inference_obj_descriptions": ["The zebras who are facing the right side.", "The zebra with its nose under the bar", "Grey feline", "Animal lying on the back of another animal", "The gray cat curled up on the plaid couch.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1756, 2178, 2216, 2278, 2358, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1577, "file_name": "./DATASET/omnilabel/coco/000000032610.jpg", "inference_obj_descriptions": ["each of these laptops has a screen that is NOT turned on", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1457, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1578, "file_name": "./DATASET/omnilabel/coco/000000309964.jpg", "inference_obj_descriptions": ["the black luggage bag that does not have a toy doll holding the end of it", "The thing providing shade to the people.", "The tie that is being worn by the man in the gray jacket.", "The black purse being held by the man in the blue jacket.", "rainbow object that protects your head from sun and rain", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2196, 2238, 2293, 2373, 2490, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1580, "file_name": "./DATASET/omnilabel/coco/000000186282.jpg", "inference_obj_descriptions": ["The blue cordless phone on the book next to the man.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2312, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1581, "file_name": "./DATASET/omnilabel/coco/000000133778.jpg", "inference_obj_descriptions": ["this cow is dark-brown in color, almost black", "The cows that are black and white spotted.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1458, 1676, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1582, "file_name": "./DATASET/omnilabel/coco/000000340272.jpg", "inference_obj_descriptions": ["The two sheep that are standing together to graze.", "false question, there is only one sheep in this photo and it asks you to pick two -", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1459, 1721, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1583, "file_name": "./DATASET/omnilabel/coco/000000201775.jpg", "inference_obj_descriptions": ["The toilet that is set at a lower level.", "this toilet is to the right of a yellow wall tile", "Toilet with brown seat cover", "The toilet the person is touching.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1460, 1471, 1806, 2185, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1372, 1462, 1629, 2072, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1587, "file_name": "./DATASET/omnilabel/coco/000000530854.jpg", "inference_obj_descriptions": ["The umbrella in the reflection of the window.", "the red umbrellas", "The umbrella over the group of people dining.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1259, 1463, 1545, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1588, "file_name": "./DATASET/omnilabel/coco/000000226147.jpg", "inference_obj_descriptions": ["Light blue shoulder handbag", "The handbag that is near the apples", "The black bag the children are sitting on.", "The strap on the person in white.", "Item with wheels and a handle", "The neatly packed suitcase that is sitting open, and It has camo printed items in it.", "Carrying item with two shoulder straps", "Clear plastic accessory for rain", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1235, 2110, 2224, 2244, 2281, 2364, 2378, 2415, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1590, "file_name": "./DATASET/omnilabel/coco/000000489611.jpg", "inference_obj_descriptions": ["partially covered by fur", "dark colored with light buttons", "The remote in the child's left hand.", "Remote made of legos", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1333, 1383, 1467, 1495, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1591, "file_name": "./DATASET/omnilabel/coco/000000085089.jpg", "inference_obj_descriptions": ["A wine glass with the word beer on it.", "The wine glasses with the clear liquid.", "each of these glasses has a visible logo on it and words", "the people that are young girls that are running", "the two people holding a glass with hands as only visible body part", "The people that are playing a game.", "People whose shirts feature horizontal bands of color.", "The people farther up on the stairs.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1256, 1468, 1469, 1568, 1879, 1894, 1900, 1923, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1592, "file_name": "./DATASET/omnilabel/coco/000000194940.jpg", "inference_obj_descriptions": ["bowl with the powdered donuts", "The white bowls on the second to bottom shelf.", "each one of these bowls does NOT have potatoes in it", "Bowl with white food", "The orange veggie in the bowl.", "Coffee mug", "Round blue eating utensil", "Glass you fill with liquid", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1323, 1428, 1470, 1485, 2265, 2283, 2649, 2719, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1597, "file_name": "./DATASET/omnilabel/coco/000000078266.jpg", "inference_obj_descriptions": ["The place where you would store food to get cooler.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2242, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1606, "file_name": "./DATASET/omnilabel/coco/000000273551.jpg", "inference_obj_descriptions": ["each of these motorcycles is carrying two passengers", "The red motorcycle", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1278, 2162, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1607, "file_name": "./DATASET/omnilabel/coco/000000233771.jpg", "inference_obj_descriptions": ["something pink that protects from rain", "The red, white and blue thing that is blocking the sun.", "Carrying equipment without a shoulder strap", "The tie that is being worn by the man in the gray jacket.", "plaid and can keep you dry from rain", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2219, 2243, 2274, 2293, 2381, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1609, "file_name": "./DATASET/omnilabel/coco/000000458755.jpg", "inference_obj_descriptions": ["Sheep with a white shaved head", "The people are all standing on their feet", "Sheep with a solid black face.", "false question, there is only one sheep in this photo and it asks you to pick two -", "The people that are holding a bat in the air.", "The sheep with the visible white snout that is looking at the camera.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1484, 1532, 1673, 1721, 1883, 2123, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1610, "file_name": "./DATASET/omnilabel/coco/000000221872.jpg", "inference_obj_descriptions": ["bowl with the powdered donuts", "sauces in a glass bowl", "The thing the person is holding to help them eat.", "The container holding the green veggies.", "clear cannisters used for holding alcoholic beverages", "Wooden untensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1323, 1687, 2598, 2668, 2670, 2687, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1611, "file_name": "./DATASET/omnilabel/coco/000000090208.jpg", "inference_obj_descriptions": ["Red colored bus", "The buses that are mostly white.", "The bus with the number 180 on it.", "The blue bus.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1486, 1490, 1729, 2072, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1614, "file_name": "./DATASET/omnilabel/coco/000000572517.jpg", "inference_obj_descriptions": ["Black and white canine", "Grey feline", "The animal walking on four legs.", "Animal lying on the back of another animal", "the brown dogs", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2211, 2216, 2245, 2278, 2301, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1615, "file_name": "./DATASET/omnilabel/coco/000000387098.jpg", "inference_obj_descriptions": ["this keyboard is white in color and actually on the desk", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1554, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1623, "file_name": "./DATASET/omnilabel/coco/000000534664.jpg", "inference_obj_descriptions": ["these four suitcases are stacked on top of each other", "Suitcase that is brown", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1389, 1496, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1624, "file_name": "./DATASET/omnilabel/coco/000000214224.jpg", "inference_obj_descriptions": ["clear water bottles", "the containers that are not white", "The three tallest bottles of the group.", "The bottle near the sink with the green liquid.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1329, 1479, 1497, 1991, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1625, "file_name": "./DATASET/omnilabel/coco/000000568584.jpg", "inference_obj_descriptions": ["Dark striped sofa", "The living thing with the leaves.", "The small tree growing in the corner.", "The thing the kids are standing on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2248, 2628, 2629, 2678, 2725, 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{"image_id": 1626, "file_name": "./DATASET/omnilabel/coco/000000178469.jpg", "inference_obj_descriptions": ["Electronic typing device", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2249, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1627, "file_name": "./DATASET/omnilabel/coco/000000002587.jpg", "inference_obj_descriptions": ["Long yellow fruit", "Plants in the ground", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2250, 2330, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1628, "file_name": "./DATASET/omnilabel/coco/000000235252.jpg", "inference_obj_descriptions": ["The two giraffes that are standing together.", "The giraffe closer to the people.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1498, 2176, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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The pizza has sausage, cheese, and peppers.", "The individual servings of pizza on plates.", "The pizza that is closest to the woman", "The yellow fruit on the right.", "Food with two slices of bread", "Sliced citrus fruit", "Fruit with other food on top.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1380, 1517, 1643, 1717, 1745, 2160, 2397, 2433, 2471, 2496, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2210, 2251, 2403, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1631, "file_name": "./DATASET/omnilabel/coco/000000004495.jpg", "inference_obj_descriptions": ["Greenery container", "The wooden piece where you could put a plate of food.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2445, 2527, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], 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"frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1258, 1502, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1635, "file_name": "./DATASET/omnilabel/coco/000000346703.jpg", "inference_obj_descriptions": ["The slice of cake is lying on the plate on its side.", "Cakes without white stars on it", "Cup cakes", "the cakes that only have one red rose on the top, no more, no less", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1325, 1504, 1590, 1669, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 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"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1506, 1795, 2213, 2406, 2484, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, 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"surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1507, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1641, "file_name": "./DATASET/omnilabel/coco/000000450202.jpg", "inference_obj_descriptions": ["the glasses that are being held by men", "The wine glass being held by the person in the striped shirt.", "Dish with ice in it", "cannister used for holding cream or other liquids", "clear cannisters used for holding alcoholic beverages", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1557, 2153, 2411, 2669, 2692, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1644, "file_name": "./DATASET/omnilabel/coco/000000046378.jpg", "inference_obj_descriptions": ["Black and white canine", "Animal in another animal's mouth", "person", "bicycle", "car", "motorcycle", 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"the white keyboard", "Small electronic device used for calls", "These components of a computer are one piece and not separate.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], 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"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1285, 1866, 1867, 2030, 2080, 2104, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2259, 2285, 2473, 2494, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1650, "file_name": "./DATASET/omnilabel/coco/000000133819.jpg", "inference_obj_descriptions": ["The buses that are mostly white.", "The buses that are blue and yellow.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1490, 1511, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1651, "file_name": "./DATASET/omnilabel/coco/000000203095.jpg", "inference_obj_descriptions": ["carrots touching chopped greens", "The carrot that is touching the fish.", "The red, round fruit.", "the oranges", "Food with two slices of bread", "person", 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2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1654, "file_name": "./DATASET/omnilabel/coco/000000054593.jpg", "inference_obj_descriptions": ["The cars that are behind the red car.", "Mini vans", "people watching the dog jump", "the people that are wearing a grey t-shirt and are in a wheelchair", "The car with a visible yellow license plate.", "The light colored van behind the fence.", "a blue mode of transportation designed for water", "a brown automobile with a dog on top", "The vehicle with the blue body.", "Black vespa", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1443, 1570, 1587, 1980, 2011, 2263, 2465, 2491, 2565, 2567, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, 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"kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2264, 2278, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1656, "file_name": "./DATASET/omnilabel/coco/000000566042.jpg", "inference_obj_descriptions": ["each giraffe we can clearly see both eyes of the animal", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1454, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1657, "file_name": "./DATASET/omnilabel/coco/000000038829.jpg", "inference_obj_descriptions": ["The motorcycles that are on the street.", "the motorcycles with a grill on the front", "Vehicle of public transportation", "motorized scooters", "The yellow vehicle on the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1513, 1556, 2235, 2530, 2556, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1659, "file_name": "./DATASET/omnilabel/coco/000000474881.jpg", "inference_obj_descriptions": ["The sheep that have white faces.", "The sheep that are standing in the grass.", "The sheep that are lying in the grass.", "The sheep that don't have horns.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1303, 1515, 1708, 1724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1661, "file_name": "./DATASET/omnilabel/coco/000000361238.jpg", "inference_obj_descriptions": ["A pizza that is a half circle.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2117, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1345, 1519, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1520, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1665, "file_name": "./DATASET/omnilabel/coco/000000021503.jpg", "inference_obj_descriptions": ["the sandwich that is cut in the middle", "The sandwich that is closer to the wall.", "Electronics with multiple buttons", "Electronic device with display", "The device that can be used to make calls.", "The output device of the computer.", "The device in the child's hand.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1566, 1957, 2277, 2497, 2504, 2509, 2519, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1667, "file_name": "./DATASET/omnilabel/coco/000000028449.jpg", "inference_obj_descriptions": ["Elephants with trunks in the water", "The elephants that are behind the leader.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1390, 1523, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1668, "file_name": "./DATASET/omnilabel/coco/000000305609.jpg", "inference_obj_descriptions": ["The sandwiches next to an orange slice.", "the spoon in the cup of tea", "The spoon near the salt in the bowl with mashed potato", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1452, 1589, 2169, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1670, "file_name": "./DATASET/omnilabel/coco/000000211120.jpg", "inference_obj_descriptions": ["The teddy bear wearing a green hat.", "The teddy bears sitting on the edges of the blanket.", "a side profile of a teddy bear looking to the right", "The teddy bear that is furthest right, and sitting on another teddy bear.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1451, 1527, 1628, 2036, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1671, "file_name": "./DATASET/omnilabel/coco/000000170739.jpg", "inference_obj_descriptions": ["the two smaller elephants that are not facing the camera", "The elephants that are behind the leader.", "The elephants without the yellow tassels", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1437, 1523, 1528, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1674, "file_name": "./DATASET/omnilabel/coco/000000356432.jpg", "inference_obj_descriptions": ["Couch has a black pillow", "The couch that the man is sitting on.", "The part of the couch the person is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1531, 1814, 1882, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1675, "file_name": "./DATASET/omnilabel/coco/000000000776.jpg", "inference_obj_descriptions": ["The stuffed animals that are green.", "these two bears are a little darker than the third bear who has two visible eyes", "The bears that are standing.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1422, 1534, 1612, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1679, "file_name": "./DATASET/omnilabel/coco/000000234660.jpg", "inference_obj_descriptions": ["yellow triangular shape", "Train on the nerarer track", "The train behind the woman with the scarf.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1337, 1647, 1839, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 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[1557, 1800, 1918, 1999, 2111, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1684, "file_name": "./DATASET/omnilabel/coco/000000535253.jpg", "inference_obj_descriptions": ["each one of these books features the mario character", "these books are clustered together and to the right of the furry animal", "The magazines that are on the top shelf.", "The book with a black and yellow cover.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1248, 1316, 1549, 1624, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1689, "file_name": "./DATASET/omnilabel/coco/000000502599.jpg", "inference_obj_descriptions": ["The airplane that has the word Egyptair on the side.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1581, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1690, "file_name": "./DATASET/omnilabel/coco/000000157098.jpg", "inference_obj_descriptions": ["The giraffes that are following the leader.", "The giraffes that are standing together.", "Giraffe standing directly alongside a smaller herbivore of a different breed.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1492, 1639, 1672, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1691, "file_name": "./DATASET/omnilabel/coco/000000081738.jpg", "inference_obj_descriptions": ["Container with red liquid", "Dish that holds food", "The orange colored utinsil.", "The container with the lettuce in it.", "The glass with the clear liquid in it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2271, 2372, 2525, 2706, 2707, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1692, "file_name": "./DATASET/omnilabel/coco/000000280918.jpg", "inference_obj_descriptions": ["The place where you would store food to get cooler.", "Appliance that contains turkey", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2242, 2272, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1693, "file_name": "./DATASET/omnilabel/coco/000000129062.jpg", "inference_obj_descriptions": ["The tables behind the cake.", "the table with the wedding cake", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1440, 1683, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1694, "file_name": "./DATASET/omnilabel/coco/000000296222.jpg", "inference_obj_descriptions": ["The chairs that are on the man's right side.", "The chairs closest to the table near the camera.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1640, 1763, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1695, "file_name": "./DATASET/omnilabel/coco/000000570782.jpg", "inference_obj_descriptions": ["Laptops pressed up to a wall", "this keyboard is white in color and actually on the desk", "The laptop with a black chassis", "the keyboards with trackpad", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1240, 1554, 1558, 1682, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1697, "file_name": "./DATASET/omnilabel/coco/000000155341.jpg", "inference_obj_descriptions": ["Red motor vehicle", "White and red passenger transporter", "Large grey motor vehicle", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2273, 2407, 2414, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1698, "file_name": "./DATASET/omnilabel/coco/000000527784.jpg", "inference_obj_descriptions": ["the sandwiches to the left on the same plate", "The grilled cheese sandwich", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1684, 2154, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1699, "file_name": "./DATASET/omnilabel/coco/000000250901.jpg", "inference_obj_descriptions": ["the sandwich that is cut in the middle", "the person that is wearing a black helmet", "skiers that are standing upright on their skis", "The two women with hair light enough to not be brown.", "People with bare arms sitting on the grass.", "The people who have sleeves past their elbows.", "All the people holding umbrellas", "The grilled cheese sandwich", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1566, 1655, 1685, 1811, 1815, 1988, 2094, 2154, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1701, "file_name": "./DATASET/omnilabel/coco/000000447917.jpg", "inference_obj_descriptions": ["The women that are on bikes.", "The men that are wearing suits.", "The people wearing something on their heads", "All the people with white shirts.", "The person with the headband", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1855, 1869, 2048, 2069, 2186, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1702, "file_name": "./DATASET/omnilabel/coco/000000011699.jpg", "inference_obj_descriptions": ["These all are pieces of luggage that are not being held by a person.", "The black bag the children are sitting on.", "Carrying equipment without a shoulder strap", "The black purse being held by the man in the blue jacket.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2192, 2224, 2274, 2373, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1703, "file_name": "./DATASET/omnilabel/coco/000000191580.jpg", "inference_obj_descriptions": ["Cup with water", "this bathroom device is used to excrete human waste", "Cylindrical container decorated with a colorful image.", "Tall glass with beer", "The surface with the red covering.", "Black and metal seating", "The end of the seat where the people are.", "Deep round dish", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2275, 2459, 2500, 2566, 2575, 2658, 2714, 2721, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1704, "file_name": "./DATASET/omnilabel/coco/000000013659.jpg", "inference_obj_descriptions": ["Laptops pressed up to a wall", "each of these laptops has a screen that is NOT turned on", "The laptop that the man is working on.", "Round table with people around it", "The table with the wine glass on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1240, 1457, 1561, 1822, 1857, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1705, "file_name": "./DATASET/omnilabel/coco/000000428867.jpg", "inference_obj_descriptions": ["The teddy bear wearing a green hat.", "The two teddy bears at the closer end of the table", "The teddy bear sitting on the snow.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1451, 1562, 2059, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1707, "file_name": "./DATASET/omnilabel/coco/000000007818.jpg", "inference_obj_descriptions": ["The two wine glasses sitting near the white dishes.", "each of these glasses still has wine in it", "The two glasses that stand on the closer end of the table", "The wine glass being held by the person in the striped shirt.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1261, 1279, 1563, 2153, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1708, "file_name": "./DATASET/omnilabel/coco/000000012576.jpg", "inference_obj_descriptions": ["The pizza that is near the greens.", "The two pizzas closest to the woman", "The pizzas that have a spatula under them.", "This slice has some burnt crust. The pizza has sausage, cheese, and peppers.", "The pizza that has a knife on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1499, 1621, 1643, 1717, 1731, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1709, "file_name": "./DATASET/omnilabel/coco/000000322895.jpg", "inference_obj_descriptions": ["The pink couch that has a cushion on it.", "The couch that the man is sitting on.", "The part of the couch the person is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1544, 1814, 1882, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1711, "file_name": "./DATASET/omnilabel/coco/000000252294.jpg", "inference_obj_descriptions": ["The nearer bed", "The bed that is closer to the window.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1600, 1873, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1717, "file_name": "./DATASET/omnilabel/coco/000000150417.jpg", "inference_obj_descriptions": ["Blue and wood sofa", "Furniture with food on it", "The surface that is made out of glass.", "The longer seating piece of furniture.", "The thing the kids are standing on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2230, 2279, 2590, 2627, 2678, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1722, "file_name": "./DATASET/omnilabel/coco/000000265108.jpg", "inference_obj_descriptions": ["The three bags at the top.", "The black bag the children are sitting on.", "Item with wheels and a handle", "The container with the stickers on it.", "the open umbrellas", "The bag that the animal is on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2197, 2224, 2281, 2289, 2353, 2456, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1723, "file_name": "./DATASET/omnilabel/coco/000000068765.jpg", "inference_obj_descriptions": ["Electronic device with multiple buttons", "Electronic you set in your lap", "the electronic device that can be used to make a phone call", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2282, 2481, 2523, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1724, "file_name": "./DATASET/omnilabel/coco/000000002592.jpg", "inference_obj_descriptions": ["Coffee mug", "The vegetables in the black container.", "Round metal container", "utensil with tines used for holding food", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2283, 2612, 2616, 2691, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1725, "file_name": "./DATASET/omnilabel/coco/000000459500.jpg", "inference_obj_descriptions": ["duck with a red and white beak", "bird not being obscured by leaves", "The bird with its feet touching the water.", "The bird that has its wings down.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1322, 1346, 1483, 1620, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1518, 2004, 2011, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1729, "file_name": "./DATASET/omnilabel/coco/000000372466.jpg", "inference_obj_descriptions": ["The input device to move the curser on a computer screen", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2288, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 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"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1644, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 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"parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2290, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1736, "file_name": "./DATASET/omnilabel/coco/000000425226.jpg", "inference_obj_descriptions": ["The appliance keeping the food cool.", "Overhead electronic cooking device", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2385, 2427, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 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tables that don't have food on them.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1440, 1660, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 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"./DATASET/omnilabel/coco/000000269196.jpg", "inference_obj_descriptions": ["The sheep that are lying in the grass.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], 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"toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1333, 1467, 1661, 2203, 2394, 2485, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1544, 1814, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2380, 2388, 2446, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 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"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2391, 2413, 2423, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1753, "file_name": "./DATASET/omnilabel/coco/000000410735.jpg", "inference_obj_descriptions": ["both of these bowls are the color white", "Bowl with white food", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1388, 1485, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1754, "file_name": "./DATASET/omnilabel/coco/000000578500.jpg", "inference_obj_descriptions": ["The two couches that have printed fabric on them instead of the one with only a solid color fabric.", "The couches with a gray fabric.", "The couch that the man is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1607, 1622, 1814, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1757, "file_name": "./DATASET/omnilabel/coco/000000365766.jpg", "inference_obj_descriptions": ["an appliance with stickers or magnets that keeps food cool", "The place you can wash dishes by hand", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2218, 2392, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1758, "file_name": "./DATASET/omnilabel/coco/000000221281.jpg", "inference_obj_descriptions": ["each giraffe we can clearly see both eyes of the animal", "The two smaller giraffes", "Giraffe standing directly alongside a smaller herbivore of a different breed.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1454, 1536, 1672, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1761, "file_name": "./DATASET/omnilabel/coco/000000409630.jpg", "inference_obj_descriptions": ["The monitor behind the animal.", "Small black electronic device for calls", "The screen on the shelf.", "The device on the sofa near the cat.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2203, 2476, 2485, 2539, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1762, "file_name": "./DATASET/omnilabel/coco/000000271471.jpg", "inference_obj_descriptions": ["bananas touching an apple", "these two bananas are closest to the red pepper", "The bananas that have been cut for the dish.", "The banana next to the orange.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1355, 1573, 1611, 2171, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1764, "file_name": "./DATASET/omnilabel/coco/000000258883.jpg", "inference_obj_descriptions": ["The pizza that has a knife on it.", "The individual servings of pizza on plates.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1731, 1745, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1765, "file_name": "./DATASET/omnilabel/coco/000000308545.jpg", "inference_obj_descriptions": ["The horses that are facing the camera.", "The horses that have blue fabrics on them.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1632, 1680, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1766, "file_name": "./DATASET/omnilabel/coco/000000437898.jpg", "inference_obj_descriptions": ["this appliance is used for cooking and baking", "Overhead electronic cooking device", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2395, 2427, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1768, "file_name": "./DATASET/omnilabel/coco/000000572388.jpg", "inference_obj_descriptions": ["The donuts with chocolate icing.", "The two Donuts that are behind the back of the yellow toy in front of them.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1625, 1650, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1769, "file_name": "./DATASET/omnilabel/coco/000000253835.jpg", "inference_obj_descriptions": ["each of these backpacks is being worn and not carried in someone's hand", "The backpack that is on the ground next to the child in the stroller.", "The backpack that is closest to the wall.", "The backpack that is closest to the chair.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1284, 1651, 1916, 1920, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1771, "file_name": "./DATASET/omnilabel/coco/000000574315.jpg", "inference_obj_descriptions": ["Controller with multiple buttons", "The area where someone would type.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2355, 2398, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1772, "file_name": "./DATASET/omnilabel/coco/000000167128.jpg", "inference_obj_descriptions": ["Elephants with trunks in the water", "Elephants by a post", "these two elephants are babies and not as old as the other two", "The elephant that is partially on the path.", "The elephants that are adults.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1390, 1425, 1535, 1603, 1634, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1777, "file_name": "./DATASET/omnilabel/coco/000000174371.jpg", "inference_obj_descriptions": ["these two people each have a pink surfboard", "Person with brightly colored shirts", "All the players who are on defense.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1524, 1637, 2106, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1779, "file_name": "./DATASET/omnilabel/coco/000000194875.jpg", "inference_obj_descriptions": ["these two bikes are closest to the harley davidson banner", "the motorcycles with a grill on the front", "Motorcycles with bright colors", "The red motorcycle", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1311, 1556, 1662, 2162, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1781, "file_name": "./DATASET/omnilabel/coco/000000378099.jpg", "inference_obj_descriptions": ["The input device with keys.", "The screen that is lit up.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2400, 2542, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1783, "file_name": "./DATASET/omnilabel/coco/000000329080.jpg", "inference_obj_descriptions": ["the bed that has the patient clearly visible in it", "The beds on the first and second bunk.", "The bed with white sheets.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1442, 1445, 1671, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1787, "file_name": "./DATASET/omnilabel/coco/000000508312.jpg", "inference_obj_descriptions": ["you can use a stream of water to wash your hands in it or fill it with water and wash the dishes", "Metal basin used for running water", "The stove with the open oven door that the man is standing in front of showing what he is cooking in the oven.", "this appliance has the reflection of the two eletrical outlets in it", "White porcelain basin for water", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2240, 2285, 2339, 2403, 2473, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1788, "file_name": "./DATASET/omnilabel/coco/000000166277.jpg", "inference_obj_descriptions": ["The cups that are empty.", "The cups that the cat is not interested in.", "Cup close to plate", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1539, 1540, 1636, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1789, "file_name": "./DATASET/omnilabel/coco/000000557672.jpg", "inference_obj_descriptions": ["The people not holden an umbrella.", "each of these persons face is fully visible", "Two people crouching low to the ground", "The players that are in the game.", "The people who are playing the game.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1541, 1803, 1821, 1893, 1965, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1791, "file_name": "./DATASET/omnilabel/coco/000000537812.jpg", "inference_obj_descriptions": ["this item is used for urine and human excrement", "The area where someone can sit to smoke.", "An electronic device to work the television.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2404, 2448, 2621, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1792, "file_name": "./DATASET/omnilabel/coco/000000054123.jpg", "inference_obj_descriptions": ["you can not see the head of any of these zebras", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1594, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1793, "file_name": "./DATASET/omnilabel/coco/000000528399.jpg", "inference_obj_descriptions": ["The cups that are empty.", "these three cups are actually clear glasses and NOT white in color", "The cup with ice cream on top.", "The vegetables in the black container.", "Round metal eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1539, 1595, 1886, 2612, 2643, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1794, "file_name": "./DATASET/omnilabel/coco/000000290163.jpg", "inference_obj_descriptions": ["the people that are young girls that are running", "People holding wine glasses", "Kids wearing an apron", "A person who is crouching.", "Blue and wood sofa", "The black table with the food on it that the baby is sitting at.", "this item is used to keep warm in colder weather", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1568, 1800, 1809, 2116, 2230, 2374, 2405, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1795, "file_name": "./DATASET/omnilabel/coco/000000099039.jpg", "inference_obj_descriptions": ["bowl with the powdered donuts", "The white bowls on the second to bottom shelf.", "The pizza that has a knife on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1323, 1428, 1731, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1799, "file_name": "./DATASET/omnilabel/coco/000000092053.jpg", "inference_obj_descriptions": ["The white bowls on the second to bottom shelf.", "Bowl with white food", "The plates without utinsels on them", "Long yellow fruit", "The orange veggie in the bowl.", "The small red fruit in the plastic bag next to the bottle.", "The white items holding food", "the food item that looks like a curled up worm", "Round citrus fruit", "Orange sliced vegetable", "Long yellow and brown fruit", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1428, 1485, 1602, 2250, 2265, 2308, 2442, 2460, 2474, 2475, 2492, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1800, "file_name": "./DATASET/omnilabel/coco/000000097230.jpg", "inference_obj_descriptions": ["these two look like babies compared to the third elephant", "The elephant that is partially on the path.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1396, 1603, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1802, "file_name": "./DATASET/omnilabel/coco/000000051309.jpg", "inference_obj_descriptions": ["horses facing the yellow gate", "The horses that are adults and full grown.", "Brown horses walking on the beach.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1361, 1613, 1728, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1804, "file_name": "./DATASET/omnilabel/coco/000000450439.jpg", "inference_obj_descriptions": ["These are closest to the upper beam.", "the kite that is pink and yellow with black circles", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1364, 1438, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1808, "file_name": "./DATASET/omnilabel/coco/000000452321.jpg", "inference_obj_descriptions": ["The buses that are mostly white.", "The buses under the covered area.", "The blue bus.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1490, 1629, 2072, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1809, "file_name": "./DATASET/omnilabel/coco/000000409542.jpg", "inference_obj_descriptions": ["The clock facing away from the building.", "The clock that has a face that is facing the same direction as the statue above it.", "The people that are upright on their boards.", "Ballplayers wearing shirts with contrasting sleeve color starting at shoulders.", "A person who is getting married.", "All the people that are playing against each other in the game.", "A person holding a doughnut cheeseburger.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1630, 1649, 1817, 1901, 2037, 2135, 2140, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1810, "file_name": "./DATASET/omnilabel/coco/000000124659.jpg", "inference_obj_descriptions": ["chair that is pushed up to the table", "Chair on ends of island", "Chairs next to a bookcase", "Chairs on the outsides of the fireplace", "The chairs that are inches from the railing of the deck.", "Furniture with green fabric", "The wooden thing with the dishes on it.", "The thing that is holding the cup.", "a horizontal piece of furniture used for sleeping", "The furniture that has a round glass surface.", "Furniture with dishes on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1352, 1415, 1548, 1694, 1766, 2410, 2551, 2636, 2671, 2708, 2711, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2473, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1814, "file_name": "./DATASET/omnilabel/coco/000000166287.jpg", "inference_obj_descriptions": ["The cows that are black and white.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1579, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1816, "file_name": "./DATASET/omnilabel/coco/000000116208.jpg", "inference_obj_descriptions": ["The slender black bottles.", "The green-tinted bottles", "Slices of pizza on a plate carried by a person", "the pizza without the vegetables on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1476, 1617, 1712, 1945, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1817, "file_name": "./DATASET/omnilabel/coco/000000171298.jpg", "inference_obj_descriptions": ["The bus with the number 52 on it.", "Red colored bus", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1417, 1486, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1820, "file_name": "./DATASET/omnilabel/coco/000000193926.jpg", "inference_obj_descriptions": ["The bananas that have been cut for the dish.", "Bananas in or next to a protective red carry case", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1611, 1627, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1822, "file_name": "./DATASET/omnilabel/coco/000000276921.jpg", "inference_obj_descriptions": ["The stuffed animals that are green.", "The bears wearing black shirts.", "The teddy bear wearing a green hat.", "The bears that are standing.", "a side profile of a teddy bear looking to the right", "The teddy bear that is furthest right, and sitting on another teddy bear.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1422, 1439, 1451, 1612, 1628, 2036, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1823, "file_name": "./DATASET/omnilabel/coco/000000141597.jpg", "inference_obj_descriptions": ["Large grey motor vehicle", "a green automobile for multiple passengers", "motorized scooters", "Red vehicle in the road", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2414, 2477, 2530, 2578, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1824, "file_name": "./DATASET/omnilabel/coco/000000250127.jpg", "inference_obj_descriptions": ["the black luggage bag that does not have a toy doll holding the end of it", "The black bag the children are sitting on.", "Clear plastic accessory for rain", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2196, 2224, 2415, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1825, "file_name": "./DATASET/omnilabel/coco/000000035682.jpg", "inference_obj_descriptions": ["The chairs that no one is sitting in.", "The chairs that are empty.", "The chair closer to the Rolex sign", "The furniture with the red cross on it.", "The colorful tablecloth covering the table underneath all the vases.", "The flower in the container.", "The structure holding the tray of desserts.", "Sleeping area with white sheet", "An electronic device to work the television.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1764, 1777, 2179, 2204, 2351, 2543, 2564, 2595, 2621, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1826, "file_name": "./DATASET/omnilabel/coco/000000574425.jpg", "inference_obj_descriptions": ["The buses that are mostly white.", "The vehicle with a license plate reading TGL552", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1490, 1565, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1827, "file_name": "./DATASET/omnilabel/coco/000000426372.jpg", "inference_obj_descriptions": ["people watching the dog jump", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1587, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1828, "file_name": "./DATASET/omnilabel/coco/000000314182.jpg", "inference_obj_descriptions": ["the bowls that do not contain white dip", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1654, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1829, "file_name": "./DATASET/omnilabel/coco/000000467511.jpg", "inference_obj_descriptions": ["The people operating the cameras.", "People with video game controllers in hands", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1963, 1993, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1830, "file_name": "./DATASET/omnilabel/coco/000000393014.jpg", "inference_obj_descriptions": ["The hot dogs that have red toppings.", "each of these hot dogs are NOT in the middle", "The hot dog that is being held with two hands.", "the sandwich that is closest to the left", "The hot dog with the green stuff on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1254, 1291, 1449, 1656, 1939, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1831, "file_name": "./DATASET/omnilabel/coco/000000561366.jpg", "inference_obj_descriptions": ["Small electronic device used for calls", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2416, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1832, "file_name": "./DATASET/omnilabel/coco/000000320632.jpg", "inference_obj_descriptions": ["The zebras that have their heads down.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1747, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1833, "file_name": "./DATASET/omnilabel/coco/000000518770.jpg", "inference_obj_descriptions": ["Person standing next to a table", "People with bare arms sitting on the grass.", "the little child and the person who appears connected to the child's head", "The people that are wearing hats.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1808, 1815, 1845, 1865, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1834, "file_name": "./DATASET/omnilabel/coco/000000458663.jpg", "inference_obj_descriptions": ["stainless steel object used for cleaning dishes manually", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2417, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1836, "file_name": "./DATASET/omnilabel/coco/000000280891.jpg", "inference_obj_descriptions": ["The people standing near the fruit.", "The men sitting at the table.", "The older people that are playing a game.", "All the people behind the batter", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1560, 1878, 1952, 2095, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1842, "file_name": "./DATASET/omnilabel/coco/000000358525.jpg", "inference_obj_descriptions": ["Suitcase that is brown", "Suitcase on the floor next to white curtain", "the suitcases with the handle extended", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1496, 1598, 1609, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1843, "file_name": "./DATASET/omnilabel/coco/000000438907.jpg", "inference_obj_descriptions": ["The skateboard being used by the person in the black shirt.", "The skateboard in action", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1283, 1658, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1844, "file_name": "./DATASET/omnilabel/coco/000000143556.jpg", "inference_obj_descriptions": ["The motorcycles that don't have a helmet on them.", "the motorcycles with a grill on the front", "Motorcycle with red chassis", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1482, 1556, 1659, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1850, "file_name": "./DATASET/omnilabel/coco/000000286908.jpg", "inference_obj_descriptions": ["The table that the pizza is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1874, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1853, "file_name": "./DATASET/omnilabel/coco/000000397354.jpg", "inference_obj_descriptions": ["the women with long hair", "Black and metal seating", "Blue recliner", "Furniture with electronic equipment on it", "a green object used for sitting", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1608, 2658, 2662, 2664, 2676, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1854, "file_name": "./DATASET/omnilabel/coco/000000339442.jpg", "inference_obj_descriptions": ["The fruit in the clear bowl.", "Long orange vegetable", "yellow fruit that monkeys are known for eating", "Long yellow fruit", "Food with two slices of bread", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2199, 2215, 2220, 2247, 2426, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1857, "file_name": "./DATASET/omnilabel/coco/000000417285.jpg", "inference_obj_descriptions": ["the cakes that are not being cut", "The cake that has a ridged edge.", "Dish with a liquid in it", "Silverware with multiple prongs", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1588, 1652, 2640, 2681, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1859, "file_name": "./DATASET/omnilabel/coco/000000434297.jpg", "inference_obj_descriptions": ["Food with pink frosting", "this fruit item is a favorite of monkeys", "Plants in the ground", "The yellow fruit on the right.", "The orange pieces of food.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2213, 2223, 2330, 2397, 2429, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1862, "file_name": "./DATASET/omnilabel/coco/000000194724.jpg", "inference_obj_descriptions": ["the pizza without the vegetables on it", "A pizza that is a half circle.", "The furniture with the red cross on it.", "Furniture with food on it", "The seat with the red runner on it.", "Furniture with a toddler sitting on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1945, 2117, 2204, 2562, 2596, 2683, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1863, "file_name": "./DATASET/omnilabel/coco/000000125129.jpg", "inference_obj_descriptions": ["The chairs that are not occupied.", "This chair has a small pillow on it. The other chair is facing a black table.", "The chairs that are inches from the railing of the deck.", "The people who are dressed in black.", "The people on the top row.", "The person kneeling on the ground.", "children with blond hair", "Brown furniture to sit on", "The wooden thing with the dishes on it.", "Tan metal furniture with black and red sticker", "Tan sofa", "Round red furniture with paper on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1679, 1718, 1766, 1996, 2058, 2086, 2114, 2425, 2551, 2589, 2614, 2675, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1864, "file_name": "./DATASET/omnilabel/coco/000000360661.jpg", "inference_obj_descriptions": ["horses facing the yellow gate", "The horses that have blue fabrics on them.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1361, 1680, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1867, "file_name": "./DATASET/omnilabel/coco/000000560911.jpg", "inference_obj_descriptions": ["these four suitcases are stacked on top of each other", "The suitcases that are on the bottom of the stack.", "the suitcase that is yellow, open, and has white and blue-colored items inside", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1389, 1505, 1668, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1869, "file_name": "./DATASET/omnilabel/coco/000000128476.jpg", "inference_obj_descriptions": ["The two cakes closest to the leaf on the fabric.", "The slice of cake is lying on the plate on its side.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1260, 1325, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1870, "file_name": "./DATASET/omnilabel/coco/000000293858.jpg", "inference_obj_descriptions": ["Coffee mug", "The utinsil with the tines.", "The chair the person is sitting in.", "Dish containing onions", "Glass with water", "a green object used for sitting", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2283, 2287, 2434, 2579, 2617, 2676, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1872, "file_name": "./DATASET/omnilabel/coco/000000088848.jpg", "inference_obj_descriptions": ["the three people that are wearing sunglasses not regular eyeglasses", "The hydrant with a blue cap.", "the two women that are not wearing a skirt", "The people that are sitting down.", "All the people that are playing against each other in the game.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1670, 1678, 1774, 1930, 2135, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1874, "file_name": "./DATASET/omnilabel/coco/000000427160.jpg", "inference_obj_descriptions": ["The players who have blue shirts.", "All the people who are men.", "All the people sitting behind the person eating pizza.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1677, 2064, 2111, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1875, "file_name": "./DATASET/omnilabel/coco/000000062554.jpg", "inference_obj_descriptions": ["Bowl with white food", "The food that is in the square bowls", "The bowls containing brown-colored food", "white plastic eating utensil with prongs", "The dark colored liquid.", "Metal pronged eating utensil", "Glass with water", "clear cannisters used for holding alcoholic beverages", "Metal pronged eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1485, 1575, 1615, 2536, 2608, 2613, 2617, 2670, 2717, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1877, "file_name": "./DATASET/omnilabel/coco/000000030213.jpg", "inference_obj_descriptions": ["The seat of the man in the jacket.", "Furniture with white surface that holds a pot and a bowl", "White porcelain tub for running water", "Wood and metal place to sit", "The furniture that has a round glass surface.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2261, 2437, 2469, 2644, 2708, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1878, "file_name": "./DATASET/omnilabel/coco/000000484351.jpg", "inference_obj_descriptions": ["each table is round and not with a glass on top", "The table that the pizza is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1455, 1874, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1881, "file_name": "./DATASET/omnilabel/coco/000000234807.jpg", "inference_obj_descriptions": ["Horse being touched by person", "The horses that aren't looking at the camera.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1416, 1665, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1884, "file_name": "./DATASET/omnilabel/coco/000000052996.jpg", "inference_obj_descriptions": ["People riding the same elephant", "Two women standing facing and talking to a person in camo.", "People holding a frisbee", "All the people on the scooters", "the black luggage bag that does not have a toy doll holding the end of it", "Item with wheels and a handle", "Clear plastic accessory for rain", "Backbag carried by blonde woman", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1648, 1816, 1944, 2033, 2196, 2281, 2415, 2440, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1886, "file_name": "./DATASET/omnilabel/coco/000000018575.jpg", "inference_obj_descriptions": ["The slender black bottles.", "Bottles containing light colored sauces", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1476, 1675, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1890, "file_name": "./DATASET/omnilabel/coco/000000435081.jpg", "inference_obj_descriptions": ["Table with birds on it", "the table with the wedding cake", "The table with the wine glass on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1402, 1683, 1857, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1891, "file_name": "./DATASET/omnilabel/coco/000000447313.jpg", "inference_obj_descriptions": ["each one of these zebras has an eye that is visible to the viewer", "The zebras showing their rear ends.", "The zebras who are facing the right side.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1691, 1723, 1756, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1692, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1895, "file_name": "./DATASET/omnilabel/coco/000000206027.jpg", "inference_obj_descriptions": ["clear water bottles", "Sauce containers", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1329, 1695, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1896, "file_name": "./DATASET/omnilabel/coco/000000546011.jpg", "inference_obj_descriptions": ["Two zebras standing side by side", "The zebras that have their heads down.", "The zebras that are facing each other.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1696, 1722, 1732, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1897, "file_name": "./DATASET/omnilabel/coco/000000172571.jpg", "inference_obj_descriptions": ["The red colored drink.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2699, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1898, "file_name": "./DATASET/omnilabel/coco/000000049810.jpg", "inference_obj_descriptions": ["The cat that is sitting at the base of the tree.", "The cat with the fluffy tail.", "The cat that is higher up on the cushion.", "A cat laying down with the tail by it's head.", "A cat who is looking at the camera.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1429, 1698, 1968, 2131, 2191, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1899, "file_name": "./DATASET/omnilabel/coco/000000486040.jpg", "inference_obj_descriptions": ["each of these laptops has a screen that is NOT turned on", "The laptop that is not on.", "Greenery container", "The wooden furniture the man can sit on.", "Porcelain bathroom seat", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1457, 1784, 2445, 2517, 2688, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1902, "file_name": "./DATASET/omnilabel/coco/000000369081.jpg", "inference_obj_descriptions": ["The horses that are brown and walking in the sand.", "horses facing the yellow gate", "Horse being touched by person", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1273, 1361, 1416, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1903, "file_name": "./DATASET/omnilabel/coco/000000023023.jpg", "inference_obj_descriptions": ["The red, white and blue thing that is blocking the sun.", "The black thing that the woman is digging in.", "The bag that the animal is on.", "The colorful thing that the person is holding in front of them.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2243, 2388, 2456, 2487, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1904, "file_name": "./DATASET/omnilabel/coco/000000033707.jpg", "inference_obj_descriptions": ["each of these giraffes has a head that is among the leaves on the trees", "Giraffe standing directly alongside a smaller herbivore of a different breed.", "Smaller giraffe", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1533, 1672, 1703, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1907, "file_name": "./DATASET/omnilabel/coco/000000541773.jpg", "inference_obj_descriptions": ["The people who are squatting near the truck.", "The green potted plant hung above tables on the wooden wall.", "Item you sit on with holes in the back rest", "Wodden furniture holding wine bottles", "The places to sit at the round surface near the window.", "A potted plant with yellowish leaves", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1892, 2313, 2337, 2447, 2601, 2620, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1914, "file_name": "./DATASET/omnilabel/coco/000000000139.jpg", "inference_obj_descriptions": ["the computers without the white boarder", "The monitors that are set up with a keyboard under them.", "TV with bobblehead by it", "The TV sitting on the black and white stand", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1478, 1481, 1547, 1710, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1915, "file_name": "./DATASET/omnilabel/coco/000000512476.jpg", "inference_obj_descriptions": ["The sink that is right behind the man.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1477, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1916, "file_name": "./DATASET/omnilabel/coco/000000345252.jpg", "inference_obj_descriptions": ["The screen the boy is looking at", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2451, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1918, "file_name": "./DATASET/omnilabel/coco/000000069106.jpg", "inference_obj_descriptions": ["the zebras that are looking to the right", "Two zebras that are standing side by side.", "The zebras that are lying down together.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1576, 1714, 1735, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1919, "file_name": "./DATASET/omnilabel/coco/000000577149.jpg", "inference_obj_descriptions": ["The zebras that are standing on the dirt path.", "The zebras that have their heads down.", "The zebras that have their heads down.", "The zebra with its nose under the bar", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1715, 1722, 1753, 2178, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1920, "file_name": "./DATASET/omnilabel/coco/000000094871.jpg", "inference_obj_descriptions": ["these four sheep look to be the same color but are definitely the four lightest colored", "The people that are standing up to play the game.", "The people wearing white shirts.", "The three people who are not using their phone, and wearing a rainbow umbrella hat.", "The women in the black sleeveless shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1574, 1796, 1919, 2017, 2125, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2452, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1927, "file_name": "./DATASET/omnilabel/coco/000000182805.jpg", "inference_obj_descriptions": ["the neck tie", "The thing the person is holding to block the sun.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2296, 2455, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 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"./DATASET/omnilabel/coco/000000435208.jpg", "inference_obj_descriptions": ["furniture intended for people to sit down", "The living thing with the leaves.", "Green sofa", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy 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"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1361, 1728, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1339, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2459, 2468, 2517, 2618, 2710, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1943, "file_name": "./DATASET/omnilabel/coco/000000545129.jpg", "inference_obj_descriptions": ["you can not see the head of any of these zebras", "The zebras that are facing each other.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1594, 1732, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1945, "file_name": "./DATASET/omnilabel/coco/000000240250.jpg", "inference_obj_descriptions": ["The pizza containing pink ham", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1734, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1948, "file_name": "./DATASET/omnilabel/coco/000000376264.jpg", "inference_obj_descriptions": ["The book at the bottom of the stack.", "The book with a black and yellow cover.", "A black notepad with the letter W on it", "Cup with dipping sauce in it", "Dish containing onions", "The clear colored utinsils.", "A mug with coffee in it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1286, 1624, 1737, 2508, 2579, 2609, 2685, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1951, "file_name": "./DATASET/omnilabel/coco/000000366141.jpg", "inference_obj_descriptions": ["Smaller item of upholstered furniture, seating for one person.", "the item used for grooming being held in the child's hands", "The succulent growing in the container.", "Black and metal seating", "The furniture with the floral pattern.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2463, 2522, 2540, 2658, 2695, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1952, "file_name": "./DATASET/omnilabel/coco/000000431727.jpg", "inference_obj_descriptions": ["The bear in the back", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1739, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1955, "file_name": "./DATASET/omnilabel/coco/000000368752.jpg", "inference_obj_descriptions": ["People sitting at the table.", "Person wearing light colored pants", "People with video game controllers in hands", "The players who are on the playing field.", "Container with dipping sauce", "Glass with water", "clear cannisters used for holding alcoholic beverages", "Wooden untensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1742, 1810, 1993, 2005, 2561, 2617, 2670, 2687, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1956, "file_name": "./DATASET/omnilabel/coco/000000243867.jpg", "inference_obj_descriptions": ["The cars that are behind the red car.", "Car next to a tree", "people watching the dog jump", "The silver car on the roadway.", "a green automobile for multiple passengers", "The vehicle with a 22 on the front of it.", "These vehicles run on tracks rather than roads.", "The vehicle that can fit more than ten people.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1443, 1494, 1587, 1743, 2477, 2505, 2512, 2554, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1957, "file_name": "./DATASET/omnilabel/coco/000000322574.jpg", "inference_obj_descriptions": ["Long orange vegetable", "Long yellow fruit", "the individual sandwiches", "Fruit with other food on top.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2215, 2250, 2320, 2496, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1960, "file_name": "./DATASET/omnilabel/coco/000000113720.jpg", "inference_obj_descriptions": ["The pizza that is near the greens.", "The individual servings of pizza on plates.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1499, 1745, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1962, "file_name": "./DATASET/omnilabel/coco/000000356424.jpg", "inference_obj_descriptions": ["The people that are waiting for the pitch.", "the two people that are bent over looking into the mini fridge", "the two people who are seated each with their legs crossed", "people that are seating at a table by the window", "two person stand next to each other while holding wii remote controls", "hands of a person without a face", "The black table with the food on it that the baby is sitting at.", "this bathroom device is used to excrete human waste", "Smaller item of upholstered furniture, seating for one person.", "The flower in the container.", "Tan metal furniture with black and red sticker", "Metal pronged eating utensil", "Orange pronged eating utensil", "The foil container with the food.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1867, 1962, 1975, 2009, 2056, 2141, 2374, 2459, 2463, 2543, 2589, 2613, 2650, 2680, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1964, "file_name": "./DATASET/omnilabel/coco/000000316404.jpg", "inference_obj_descriptions": ["The tennis racket being held by the man in red.", "The racket that is parallel to the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1749, 1958, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1965, "file_name": "./DATASET/omnilabel/coco/000000031269.jpg", "inference_obj_descriptions": ["Zebras that are grazing", "Zebra with tail touching rock", "The zebra with its nose under the bar", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1751, 1995, 2178, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1966, "file_name": "./DATASET/omnilabel/coco/000000070739.jpg", "inference_obj_descriptions": ["each of these glasses has a visible logo on it and words", "The women who are posing together.", "The people that are wearing hats.", "The wine glass held by the woman with the lip piercing", "The women that are on bikes.", "The people wearing dark shirts.", "The wine glass being held by the person in the striped shirt.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1469, 1752, 1792, 1813, 1855, 1872, 2153, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1970, "file_name": "./DATASET/omnilabel/coco/000000441247.jpg", "inference_obj_descriptions": ["Black stool chairs with back rests at the counter", "Chairs on the left side of the table", "the chair that has a person wearing a dark-colored coat sitting in it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1755, 1780, 1974, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1972, "file_name": "./DATASET/omnilabel/coco/000000095786.jpg", "inference_obj_descriptions": ["The vase closest to the TV.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2167, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1973, "file_name": "./DATASET/omnilabel/coco/000000016958.jpg", "inference_obj_descriptions": ["The chairs standing against the wall", "The chairs that are inches from the railing of the deck.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1759, 1766, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1974, "file_name": "./DATASET/omnilabel/coco/000000488075.jpg", "inference_obj_descriptions": ["Chair on ends of island", "each of these chairs has a person sitting in it", "The chairs that have a black armrest.", "Chairs on the left side of the table", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1415, 1706, 1760, 1780, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1977, "file_name": "./DATASET/omnilabel/coco/000000579070.jpg", "inference_obj_descriptions": ["Chair with no one in it", "each of these persons is wearing a short-sleeved shirt", "The people that are holding a bat in the air.", "The people standing up to play a game.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1770, 1802, 1883, 1917, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1979, "file_name": "./DATASET/omnilabel/coco/000000158744.jpg", "inference_obj_descriptions": ["Chair on ends of island", "Chairs on the outsides of the fireplace", "each of these chairs has a person sitting in it", "The chairs that are at the counter.", "Chairs standing on the sidewalk", "white sofa chairs", "Furniture with electronic equipment on it", "The furniture with the floral pattern.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1415, 1694, 1706, 1754, 1765, 2546, 2664, 2695, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1982, "file_name": "./DATASET/omnilabel/coco/000000203294.jpg", "inference_obj_descriptions": ["People facing the bus", "The people that have one leg out the boat.", "The baseball players in gray uniforms and no black top.", "The person with the orange skis on his back", "Large passenger vehicle with cat graffiti on the front", "The vehicle with a 22 on the front of it.", "The yellow vehicle on the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1781, 1890, 2124, 2137, 2472, 2505, 2556, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1984, "file_name": "./DATASET/omnilabel/coco/000000529568.jpg", "inference_obj_descriptions": ["Item with bed sheet on it", "Overhead electronic cooking device", "White porcelain basin for water", "Wooden furniture you sit on", "Blue and white wood eating area", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2309, 2427, 2473, 2568, 2630, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1987, "file_name": "./DATASET/omnilabel/coco/000000325838.jpg", "inference_obj_descriptions": ["Chair with no one in it", "The chairs that are green.", "the chair that has a person wearing a dark-colored coat sitting in it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1770, 1791, 1974, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1989, "file_name": "./DATASET/omnilabel/coco/000000006614.jpg", "inference_obj_descriptions": ["Food with two slices of bread", "The orange pieces of food.", "Food with two slices of bread", "Round citrus fruit", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2426, 2429, 2433, 2474, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1994, "file_name": "./DATASET/omnilabel/coco/000000093437.jpg", "inference_obj_descriptions": ["the pair of chairs next to each other that have the circular base", "Brown chairs", "Checkered sofa", "this item is used for urine and human excrement", "Furniture with plates on it", "Furniture with dishes on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1773, 1776, 2252, 2404, 2653, 2711, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1995, "file_name": "./DATASET/omnilabel/coco/000000479126.jpg", "inference_obj_descriptions": ["Round chairs", "The chairs that are empty.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1719, 1777, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1996, "file_name": "./DATASET/omnilabel/coco/000000088269.jpg", "inference_obj_descriptions": ["The sandwich hanging out over the plate a bit", "The containers with the drinks.", "Tall glass with beer", "A utensil for eating soup", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1778, 2202, 2566, 2686, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1997, "file_name": "./DATASET/omnilabel/coco/000000140640.jpg", "inference_obj_descriptions": ["The people standing near the fruit.", "People who are looking at the table", "Boys wearing long ties", "All the people sitting behind the person eating pizza.", "The people with gloves.", "The place where a person would sleep.", "The place to sit at the desk.", "These are used to sit on while eating.", "The green plant with leaves hanging on the wall", "Breadsticks on a silvery tray", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1560, 1779, 1929, 2111, 2118, 2386, 2599, 2604, 2638, 2684, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1998, "file_name": "./DATASET/omnilabel/coco/000000389381.jpg", "inference_obj_descriptions": ["The yellow fruit on the right.", "a round fruit that is commonly cored and sliced", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2397, 2478, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 1999, "file_name": "./DATASET/omnilabel/coco/000000198641.jpg", "inference_obj_descriptions": ["objects with buttons for typing", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2479, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2000, "file_name": "./DATASET/omnilabel/coco/000000342128.jpg", "inference_obj_descriptions": ["these two chairs are both facing to the right", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1785, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2003, "file_name": "./DATASET/omnilabel/coco/000000421757.jpg", "inference_obj_descriptions": ["The boats that are closest to the bridge.", "Boat with two buoys on the side", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1264, 1789, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2004, "file_name": "./DATASET/omnilabel/coco/000000112378.jpg", "inference_obj_descriptions": ["Plastic furniture in the sun you lounge on", "The seat with the red runner on it.", "The black surface for eating.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2480, 2596, 2693, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2006, "file_name": "./DATASET/omnilabel/coco/000000429623.jpg", "inference_obj_descriptions": ["Furniture you sit on", "the item used for grooming being held in the child's hands", "The small tree growing in the corner.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2482, 2522, 2629, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2007, "file_name": "./DATASET/omnilabel/coco/000000384527.jpg", "inference_obj_descriptions": ["Chair on ends of island", "The chairs across from the man.", "The chairs that have a black armrest.", "The chairs that people are sitting in.", "Item with bed sheet on it", "The seat with the red runner on it.", "Furniture intended to sit on", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1415, 1447, 1760, 1761, 2309, 2596, 2652, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2008, "file_name": "./DATASET/omnilabel/coco/000000300233.jpg", "inference_obj_descriptions": ["the spoon in the cup 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"vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1589, 2026, 2283, 2483, 2566, 2663, 2692, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2009, "file_name": "./DATASET/omnilabel/coco/000000329456.jpg", "inference_obj_descriptions": ["Person with blonde hair", "The people that are wearing hats.", "The people that are playing the sport.", "People holding a frisbee", "The two people in dark shirts, and standing outside of the restaurant/", "All the people sitting behind the person eating pizza.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1788, 1792, 1826, 1944, 2034, 2111, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2010, "file_name": "./DATASET/omnilabel/coco/000000187236.jpg", "inference_obj_descriptions": ["The cat that is sitting at the base of the tree.", "The cat with the darker fur.", "The thing the people are sitting on.", "Tan sofa", "The small tree growing in the corner.", "Tan sofa", "Green sofa", "Blue recliner", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1429, 1793, 2449, 2570, 2629, 2633, 2661, 2662, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2013, "file_name": "./DATASET/omnilabel/coco/000000320642.jpg", "inference_obj_descriptions": ["The people that are standing up to play the game.", "each of these persons is wearing long pants", "The people wearing white shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1796, 1914, 1919, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2014, "file_name": "./DATASET/omnilabel/coco/000000429109.jpg", "inference_obj_descriptions": ["The buses that are parked perpendicular to this street.", "double decker", "The bus with the number 52 on it.", "Bus with dark paint", "The bus with the words MTS on the side of it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1299, 1372, 1417, 1542, 1797, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1799, 1821, 2140, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2017, "file_name": "./DATASET/omnilabel/coco/000000368961.jpg", "inference_obj_descriptions": ["Elephants with trunks in the water", "The elephant on the placard.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1390, 2088, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2019, "file_name": "./DATASET/omnilabel/coco/000000360137.jpg", "inference_obj_descriptions": ["Carrying equipment without a shoulder strap", "the vertical suitcase", "The colorful thing that the person is holding in front of them.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2274, 2319, 2487, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2020, "file_name": "./DATASET/omnilabel/coco/000000357816.jpg", "inference_obj_descriptions": ["the women with long hair", "the people who are each wearing a yellow shirt", "Men sitting at a table", "The people in front of the bus.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1608, 1825, 1852, 1895, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2023, "file_name": "./DATASET/omnilabel/coco/000000579307.jpg", "inference_obj_descriptions": ["the person is wearing a red and grey horizontally-striped shirt", "the people that are wearing a costume or a mask", "people that are holding a surf board", "A person holding a doughnut cheeseburger.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1804, 1947, 2008, 2140, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2024, "file_name": "./DATASET/omnilabel/coco/000000440507.jpg", "inference_obj_descriptions": ["clear objects that keep you dry when it's raining", "rainbow object that protects your head from sun and rain", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2489, 2490, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2026, "file_name": "./DATASET/omnilabel/coco/000000319721.jpg", "inference_obj_descriptions": ["People not next to a horse", "the people who are each wearing a yellow shirt", "The people that are waiting for the pitch.", "Ballplayers wearing shirts with contrasting sleeve color starting at shoulders.", "the people that are wearing a grey t-shirt and are in a wheelchair", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1805, 1825, 1867, 1901, 1980, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2028, "file_name": "./DATASET/omnilabel/coco/000000031817.jpg", "inference_obj_descriptions": ["the three people with no visible earrings", "The people not holding a white towel to open a wine bottle.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1880, 2035, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2030, "file_name": "./DATASET/omnilabel/coco/000000457884.jpg", "inference_obj_descriptions": ["People riding the same elephant", "The two kids playing baseball while standing in the dirt, not grass", "The people who are dressed in black.", "The people wearing blue tops.", "a person stands with their head down, leaning forward while a second person walks next to an approaching bus near a bus stop", "children with blond hair", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1648, 1812, 1996, 2012, 2057, 2114, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2034, "file_name": "./DATASET/omnilabel/coco/000000472046.jpg", "inference_obj_descriptions": ["Metal tub with running water", "Deep white tub for running water", "Kitchen appliance with a pot on top.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2270, 2494, 2495, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2036, "file_name": "./DATASET/omnilabel/coco/000000247917.jpg", "inference_obj_descriptions": ["The two adults", "The people who are customers.", "the two people that are wearing reflective safety vests", "All the people whos face isn't visble.", "The women in the black sleeveless shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1820, 1953, 1981, 2032, 2125, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2037, "file_name": "./DATASET/omnilabel/coco/000000119445.jpg", "inference_obj_descriptions": ["Two people crouching low to the ground", "All the people standing on the court.", "The people in the background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1821, 2112, 2128, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2039, "file_name": "./DATASET/omnilabel/coco/000000318114.jpg", "inference_obj_descriptions": ["Long yellow fruit", "Slice of a frosted dessert, suitable for serving one person.", "Long yellow and brown fruit", "Small wedge of fruit.", "Dish containing onions", "The blue utinsil with the tines.", "Glass with orange juice", "a dish used for soups", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire 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2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2040, "file_name": "./DATASET/omnilabel/coco/000000180101.jpg", "inference_obj_descriptions": ["The men that are standing for the picture.", "the two kids", "The women who are sitting down.", "The dark back of the chair that the man in the blue and black shirt is sitting in.", "The furniture with Winnie the Pooh on it.", "Evil looking little troll dolls", "Breadsticks on a silvery tray", "The seat with the fabric on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1583, 1823, 1828, 2338, 2584, 2619, 2684, 2704, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2041, "file_name": "./DATASET/omnilabel/coco/000000411938.jpg", "inference_obj_descriptions": ["The two man with longer curly hair", "The people in solid colored dresses.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1824, 1915, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2043, "file_name": "./DATASET/omnilabel/coco/000000501005.jpg", "inference_obj_descriptions": ["The people that are playing the sport.", "The people that have one leg out the boat.", "The people operating the cameras.", "All the people sitting at the table.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports 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2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2044, "file_name": "./DATASET/omnilabel/coco/000000492110.jpg", "inference_obj_descriptions": ["the white keyboard", "Electronic device with a hinged lid that houses the screen.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2300, 2499, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2045, "file_name": "./DATASET/omnilabel/coco/000000210388.jpg", "inference_obj_descriptions": ["The people that are sitting down inside.", "The people that are not wearing hats.", "The person kneeling on the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1798, 1827, 2086, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2046, "file_name": "./DATASET/omnilabel/coco/000000561889.jpg", "inference_obj_descriptions": ["The one piece of broccoli closest to the fork", "Metal utensil with prongs", "The red colored drink.", "The container holding the sandwich.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2170, 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null, null, null, null]}, {"image_id": 2047, "file_name": "./DATASET/omnilabel/coco/000000183648.jpg", "inference_obj_descriptions": ["People holding a frisbee", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair 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null, null, null, null, null, null, null, null, null]}, {"image_id": 2051, "file_name": "./DATASET/omnilabel/coco/000000551660.jpg", "inference_obj_descriptions": ["The containers of condiments.", "Deep round dish with broccoli", "Glass with water", "Glass with orange juice", "The container holding the sandwich.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2430, 2502, 2617, 2651, 2715, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2499, 2503, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1832, 1868, 1933, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2056, "file_name": "./DATASET/omnilabel/coco/000000079144.jpg", "inference_obj_descriptions": ["this bear is standing up and has its backside to the camera", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1833, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2058, "file_name": "./DATASET/omnilabel/coco/000000335427.jpg", "inference_obj_descriptions": ["Container with red liquid", "Dish with broccoli in it", "Container with dipping sauce", "Glass container with liquid", "The foil container with the food.", "The container holding the sandwich.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2271, 2507, 2561, 2581, 2680, 2715, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 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the white cover.", "The book that has red as a background color.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1403, 1424, 1838, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2063, "file_name": "./DATASET/omnilabel/coco/000000011511.jpg", "inference_obj_descriptions": ["The two people closest to the flags.", "The men that are wearing suits.", "All the people holding umbrellas", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1830, 1869, 2094, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2065, "file_name": "./DATASET/omnilabel/coco/000000056288.jpg", "inference_obj_descriptions": ["The blue cordless phone on the book next to the man.", "All of the input devices.", "The output device of the computer.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2312, 2393, 2509, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2066, "file_name": "./DATASET/omnilabel/coco/000000017115.jpg", "inference_obj_descriptions": ["each one of these zebras has an eye that is visible to the viewer", "The zebras that are facing each other.", "The zebra on the right", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1691, 1732, 1843, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2067, "file_name": "./DATASET/omnilabel/coco/000000480021.jpg", "inference_obj_descriptions": ["the two people that are on the same motorbike", "the horizontal stacked bikes", "The vehicles that don't need fuel.", "Open air transportation vehicle for two people", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1844, 2329, 2419, 2569, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2069, "file_name": "./DATASET/omnilabel/coco/000000481582.jpg", "inference_obj_descriptions": ["Men wearing blue shirts sitting at a table.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1846, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2071, "file_name": "./DATASET/omnilabel/coco/000000086956.jpg", "inference_obj_descriptions": ["The people wearing yellow jackets", "The people wearing green-colored shirts", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1840, 1850, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2072, "file_name": "./DATASET/omnilabel/coco/000000269316.jpg", "inference_obj_descriptions": ["the people that are young girls that are running", "The men that are standing for the picture.", "The people who are wearing red shirts.", "The children sitting at the table.", "The people that are holding a bat in the air.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1568, 1583, 1851, 1864, 1883, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2075, "file_name": "./DATASET/omnilabel/coco/000000554579.jpg", "inference_obj_descriptions": ["Women with black tops", "All the people looking at each other.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1853, 2070, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2076, "file_name": "./DATASET/omnilabel/coco/000000148783.jpg", "inference_obj_descriptions": ["The zebras that have their heads toward the right.", "Zebra with face near branches", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1466, 1854, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2079, "file_name": "./DATASET/omnilabel/coco/000000355325.jpg", "inference_obj_descriptions": ["The pizza that is near the greens.", "The tables in back of the people.", "The pizza that has a knife on it.", "The table with the wine glass on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1499, 1616, 1731, 1857, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2080, "file_name": "./DATASET/omnilabel/coco/000000451879.jpg", "inference_obj_descriptions": ["The players wearing white shirts.", "The people that have one leg out the boat.", "The people farther up on the stairs.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1858, 1890, 1923, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2081, "file_name": "./DATASET/omnilabel/coco/000000078404.jpg", "inference_obj_descriptions": ["The people that are wearing orange shirts.", "Men wearing blue shirts sitting at a table.", "The people sitting on the edges of the bench.", "The players that are in the game.", "The older people that are playing a game.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1604, 1846, 1860, 1893, 1952, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2083, "file_name": "./DATASET/omnilabel/coco/000000139684.jpg", "inference_obj_descriptions": ["The seating areas made for one person.", "The thing that is holding the cup.", "Blue recliner", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2515, 2636, 2662, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2085, "file_name": "./DATASET/omnilabel/coco/000000347335.jpg", "inference_obj_descriptions": ["The cups that are black.", "the cup with the clear liquid", "The cup with ice cream on top.", "a cannister used for holding spicy sauce", "White plastic eating utensil with prongs", "Metal eating utensil with prongs", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1681, 1863, 1886, 2466, 2631, 2720, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2086, "file_name": "./DATASET/omnilabel/coco/000000123633.jpg", "inference_obj_descriptions": ["The children sitting at the table.", "red seating area", "The dark back of the chair that the man in the blue and black shirt is sitting in.", "The black table with the food on it that the baby is sitting at.", "Sleeping area with white sheet", "Furniture with a toddler sitting on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1864, 2207, 2338, 2374, 2595, 2683, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2088, "file_name": "./DATASET/omnilabel/coco/000000568195.jpg", "inference_obj_descriptions": ["The women that don't wear sleeves.", "the adults that are sitting", "A person who is crouching.", "The people wearing white hats", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1866, 1934, 2116, 2145, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2089, "file_name": "./DATASET/omnilabel/coco/000000002153.jpg", "inference_obj_descriptions": ["The people standing between the goats and the fence.", "All people wearing shorts.", "The person with the headband", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1868, 2129, 2186, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2090, "file_name": "./DATASET/omnilabel/coco/000000008690.jpg", "inference_obj_descriptions": ["Men wearing blue shirts sitting at a table.", "The two people in dark shirts, and standing outside of the restaurant/", "All people wearing shorts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1846, 2034, 2129, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2091, "file_name": "./DATASET/omnilabel/coco/000000379332.jpg", "inference_obj_descriptions": ["The people wearing yellow jackets", "the adults that are sitting", "The people standing in front of the bikes.", "People with video game controllers in hands", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1840, 1934, 1949, 1993, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2092, "file_name": "./DATASET/omnilabel/coco/000000338718.jpg", "inference_obj_descriptions": ["The cars that the dog is not sitting on.", "The SUV on the road.", "The car that has both headlights visible.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1251, 1871, 2004, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2094, "file_name": "./DATASET/omnilabel/coco/000000168974.jpg", "inference_obj_descriptions": ["The device in the child's hand.", "a device with more than twenty buttons used for typing", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2519, 2532, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2095, "file_name": "./DATASET/omnilabel/coco/000000102411.jpg", "inference_obj_descriptions": ["Transportation vehicle that flies", "the motor vehicle with two wheels and carrying two people", "the vehicle that is white in color", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2424, 2521, 2574, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2096, "file_name": "./DATASET/omnilabel/coco/000000284743.jpg", "inference_obj_descriptions": ["the person is wearing a red and grey horizontally-striped shirt", "The people that are sitting down.", "The people visibly wearing a name tag", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1804, 1930, 2053, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2097, "file_name": "./DATASET/omnilabel/coco/000000508730.jpg", "inference_obj_descriptions": ["toilets with a 4 letter word above them", "A toilet seating the child with a hairbrush", "The toilet tank high up.", "The area where someone can sit to smoke.", "The surface where the food is sitting.", "Furniture you put plates on", "Furniture with plates of food on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1366, 1898, 2119, 2448, 2510, 2622, 2639, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2098, "file_name": "./DATASET/omnilabel/coco/000000396729.jpg", "inference_obj_descriptions": ["The area where someone would type.", "The input device with keys.", "objects with buttons for typing", "The screen on the shelf.", "the electronic device that can be used to make a phone call", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2398, 2400, 2479, 2485, 2523, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2099, "file_name": "./DATASET/omnilabel/coco/000000007795.jpg", "inference_obj_descriptions": ["the bed that has the patient clearly visible in it", "The beds on the first and second bunk.", "The bed that is closer to the window.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1442, 1445, 1873, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2100, "file_name": "./DATASET/omnilabel/coco/000000233238.jpg", "inference_obj_descriptions": ["Two women standing facing and talking to a person in camo.", "The people standing on the right side of the table.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1816, 1876, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2101, "file_name": "./DATASET/omnilabel/coco/000000091654.jpg", "inference_obj_descriptions": ["Deep round dish with broccoli", "The orange colored utinsil.", "Container with dipping sauce", "the two items that are identified as flatware", "The dark colored liquid.", "Wooden untensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2502, 2525, 2561, 2573, 2608, 2687, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2103, "file_name": "./DATASET/omnilabel/coco/000000085157.jpg", "inference_obj_descriptions": ["Two people crouching low to the ground", "the little child and the person who appears connected to the child's head", "The people standing between the goats and the fence.", "The men sitting at the table.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1821, 1845, 1868, 1878, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2105, "file_name": "./DATASET/omnilabel/coco/000000263860.jpg", "inference_obj_descriptions": ["Elephants with trunks in the water", "these two look like babies compared to the third elephant", "Elephants by a post", "The elephants that are behind the leader.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1390, 1396, 1425, 1523, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2111, "file_name": "./DATASET/omnilabel/coco/000000077396.jpg", "inference_obj_descriptions": ["Furniture you sit on", "The wooden furniture with the magazine on it", "The seat with the red runner on it.", "Sleeping area with white sheets", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2482, 2528, 2596, 2615, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2114, "file_name": "./DATASET/omnilabel/coco/000000214192.jpg", "inference_obj_descriptions": ["each of these motorcycles is carrying two passengers", "these two bikes are closest to the harley davidson banner", "The motorcycles that don't have a helmet on them.", "The motorcycle of the racer with number 14", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1278, 1311, 1482, 1888, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2115, "file_name": "./DATASET/omnilabel/coco/000000250205.jpg", "inference_obj_descriptions": ["duck with a red and white beak", "The bird that is standing on the grassier area on the left.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1322, 1889, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2116, "file_name": "./DATASET/omnilabel/coco/000000335658.jpg", "inference_obj_descriptions": ["this electronic device can be used to view computer images", "The device in the child's hand.", "a device with more than twenty buttons used for typing", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2222, 2519, 2532, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2117, "file_name": "./DATASET/omnilabel/coco/000000046497.jpg", "inference_obj_descriptions": ["People in the dugout", "The men on the court.", "The people that have one leg out the boat.", "All the people on the scooters", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1801, 1870, 1890, 2033, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2119, "file_name": "./DATASET/omnilabel/coco/000000294162.jpg", "inference_obj_descriptions": ["Small electronic device used for calls", "The input device near the computer.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2416, 2533, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2120, "file_name": "./DATASET/omnilabel/coco/000000116362.jpg", "inference_obj_descriptions": ["The white utinsil touching the food.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2534, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2123, "file_name": "./DATASET/omnilabel/coco/000000218439.jpg", "inference_obj_descriptions": ["The beds on the first and second bunk.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1445, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2124, "file_name": "./DATASET/omnilabel/coco/000000559547.jpg", "inference_obj_descriptions": ["The people wearing yellow jackets", "the baseball players with a mustache", "All the people whos face isn't visble.", "The two people holding the hands of the person in the white shirt.", "All people wearing shorts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1840, 1896, 2032, 2100, 2129, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 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null, null, null, null, null, null, null]}, {"image_id": 2127, "file_name": "./DATASET/omnilabel/coco/000000452084.jpg", "inference_obj_descriptions": ["Cup with water", "white plastic eating utensil with prongs", "Orange pronged eating utensil", "The container holding the green veggies.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2275, 2536, 2650, 2668, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2129, "file_name": "./DATASET/omnilabel/coco/000000546325.jpg", "inference_obj_descriptions": ["The white area where people could take a nap.", "Grey sofa", "The small tree growing in the corner.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2454, 2537, 2629, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2130, "file_name": "./DATASET/omnilabel/coco/000000559513.jpg", "inference_obj_descriptions": ["Plastic eating utensil used to cut food", "Round blue eating utensil", "Metal pronged eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2538, 2649, 2717, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2132, "file_name": "./DATASET/omnilabel/coco/000000271728.jpg", "inference_obj_descriptions": ["The device on the sofa near the cat.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2539, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 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"umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1901, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 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"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1604, 1808, 1903, 2043, 2066, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2140, "file_name": "./DATASET/omnilabel/coco/000000231822.jpg", "inference_obj_descriptions": ["The fruit in the beige container.", "Orange pronged eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2541, 2650, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2141, "file_name": "./DATASET/omnilabel/coco/000000416343.jpg", "inference_obj_descriptions": ["The men that are wearing suits.", "The people that are laughing on the right side of the board.", "A person wearing torn clothing.", "The people with the gray shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1869, 1908, 2038, 2060, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2142, "file_name": "./DATASET/omnilabel/coco/000000248314.jpg", "inference_obj_descriptions": ["The monitor behind the animal.", "Container with water in it", "Empty coke bottle", "Round blue eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2203, 2258, 2635, 2649, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2145, "file_name": "./DATASET/omnilabel/coco/000000231580.jpg", "inference_obj_descriptions": ["All the people sitting behind the person eating pizza.", "The person with the orange skis on his back", "Item with bed sheet on it", "Sleeping area with white sheet", "The closest place to sit near the window.", "This is used to put food and drink on so you can sit and eat in front of it.", "A potted plant with yellowish leaves", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2111, 2137, 2309, 2595, 2600, 2606, 2620, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2146, "file_name": "./DATASET/omnilabel/coco/000000100624.jpg", "inference_obj_descriptions": ["The people that are playing a game.", "The girls that are playing on the court.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1894, 1911, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2149, "file_name": "./DATASET/omnilabel/coco/000000492284.jpg", "inference_obj_descriptions": ["The backpack that is on the ground next to the child in the stroller.", "this backpack is actually on the back of a person", "The backpack that is closest to the chair.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1651, 1912, 1920, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2151, "file_name": "./DATASET/omnilabel/coco/000000252219.jpg", "inference_obj_descriptions": ["each of these persons is wearing long pants", "the two people who are seated each with their legs crossed", "the two people that are wearing reflective safety vests", "people behind the table", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1914, 1975, 1981, 2146, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2152, "file_name": "./DATASET/omnilabel/coco/000000301867.jpg", "inference_obj_descriptions": ["Ballplayers wearing shirts with contrasting sleeve color starting at shoulders.", "The people in solid colored dresses.", "The people who have blue hats.", "All the people who are men.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1901, 1915, 1997, 2103, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2153, "file_name": "./DATASET/omnilabel/coco/000000246968.jpg", "inference_obj_descriptions": ["The wooden piece where you could put a plate of food.", "The round wooden surface that the food is on.", "Furniture with electronic equipment on it", "Porcelain bathroom seat", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2527, 2602, 2664, 2688, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2155, "file_name": "./DATASET/omnilabel/coco/000000506707.jpg", "inference_obj_descriptions": ["People riding the same elephant", "the two women that are not wearing a skirt", "The people who are wearing helmets at this time.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1648, 1774, 1918, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2157, "file_name": "./DATASET/omnilabel/coco/000000045728.jpg", "inference_obj_descriptions": ["The white utinsil touching the food.", "The utinsils that are near the food.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2534, 2545, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2159, "file_name": "./DATASET/omnilabel/coco/000000156372.jpg", "inference_obj_descriptions": ["each of these backpacks is being worn and not carried in someone's hand", "Green backpacks", "The backpack that is on the ground next to the child in the stroller.", "The backpack on the man leaning over.", "The backpack that is closest to the chair.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1284, 1400, 1651, 1829, 1920, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2160, "file_name": "./DATASET/omnilabel/coco/000000404922.jpg", "inference_obj_descriptions": ["each of these persons face is fully visible", "The men sitting at the table.", "The racket that the bigger child is holding.", "The people standing in front of the bikes.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1803, 1878, 1921, 1949, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2162, "file_name": "./DATASET/omnilabel/coco/000000130613.jpg", "inference_obj_descriptions": ["The carrot that is touching the fish.", "The carrot closest to the onions", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1922, 2102, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2166, "file_name": "./DATASET/omnilabel/coco/000000099810.jpg", "inference_obj_descriptions": ["the four food items on the blue plate", "The place where someone would sleep.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2549, 2654, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2167, "file_name": "./DATASET/omnilabel/coco/000000306136.jpg", "inference_obj_descriptions": ["Item you have to paddle around to move", "motorized scooters", "the mode of transportation that has only two wheels", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2336, 2530, 2550, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2169, "file_name": "./DATASET/omnilabel/coco/000000286182.jpg", "inference_obj_descriptions": ["The seat of the man in the jacket.", "Sleeping area with blue quilt", "The wooden thing with the dishes on it.", "The place to sit at the desk.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2261, 2468, 2551, 2599, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2172, "file_name": "./DATASET/omnilabel/coco/000000467176.jpg", "inference_obj_descriptions": ["The seat on the right side of the people.", "A potted plant with yellowish leaves", "The blue jacket the woman is wearing.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2553, 2620, 2626, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2176, "file_name": "./DATASET/omnilabel/coco/000000090891.jpg", "inference_obj_descriptions": ["The people that are wearing tan clothing.", "The people that are sitting down.", "All the players who are in the batters box.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1875, 1930, 2104, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2177, "file_name": "./DATASET/omnilabel/coco/000000291791.jpg", "inference_obj_descriptions": ["The people who are wearing red shirts.", "All the people on the scooters", "people with upper arms showing", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1851, 2033, 2142, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2180, "file_name": "./DATASET/omnilabel/coco/000000272049.jpg", "inference_obj_descriptions": ["Vehicle made for public transportation", "The yellow vehicle on the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2345, 2556, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2182, "file_name": "./DATASET/omnilabel/coco/000000183965.jpg", "inference_obj_descriptions": ["Cup with water", "Round metal utensil", "The containers of 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"inference_obj_description_ids": [2275, 2408, 2430, 2557, 2579, 2720, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2185, "file_name": "./DATASET/omnilabel/coco/000000536073.jpg", "inference_obj_descriptions": ["The white utinsil touching the food.", "Metal pronged eating utensil", "A utensil used to cut vegetables", "A mug with coffee in it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2534, 2558, 2559, 2685, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2186, "file_name": "./DATASET/omnilabel/coco/000000186980.jpg", "inference_obj_descriptions": ["A rectangular Furniture with candles and dishes on it", "The seat with the red runner on it.", "Sleeping area with white sheets", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2560, 2596, 2615, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2191, "file_name": "./DATASET/omnilabel/coco/000000011149.jpg", "inference_obj_descriptions": ["The light colored van behind the fence.", "The vehicle parked on the number 6.", "a green automobile for multiple passengers", "The vehicles with the pedals.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2263, 2412, 2477, 2563, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2193, "file_name": "./DATASET/omnilabel/coco/000000404678.jpg", "inference_obj_descriptions": ["Couch has a black pillow", "this couch has an electronic device sitting on it and not a person", "The couch with multiple pillows on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1531, 1572, 2051, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 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short-sleeved shirt", "The people that are playing the sport.", "The people wearing hats", "The baseball players in gray uniforms and no black top.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1524, 1802, 1826, 2063, 2124, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2198, "file_name": "./DATASET/omnilabel/coco/000000082688.jpg", "inference_obj_descriptions": ["each of these persons is wearing a short-sleeved shirt", "The older people that are playing a game.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1802, 1952, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2200, "file_name": "./DATASET/omnilabel/coco/000000013177.jpg", "inference_obj_descriptions": ["The vehicle with the blue body.", "the vehicle that is white in color", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2565, 2574, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2203, "file_name": "./DATASET/omnilabel/coco/000000036861.jpg", "inference_obj_descriptions": ["The parking meter that is on the right side.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1956, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2204, "file_name": "./DATASET/omnilabel/coco/000000323151.jpg", "inference_obj_descriptions": ["Black container with drinking liquid", "Container with dipping sauce", "Tall glass with beer", "Metal eating utensil", "The silver colored utinsil.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2557, 2561, 2566, 2580, 2703, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2208, "file_name": "./DATASET/omnilabel/coco/000000234779.jpg", "inference_obj_descriptions": ["The sandwiches next to an orange slice.", "the sandwich that has some of its contents spilled onto the saucer", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1452, 1961, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2212, "file_name": "./DATASET/omnilabel/coco/000000546717.jpg", "inference_obj_descriptions": ["Tan sofa", "Furniture with plates on it", "a horizontal piece of furniture used for sleeping", "White furniture for seating", "The thing the kids are standing on.", "White sofa", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2570, 2653, 2671, 2672, 2678, 2718, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2215, "file_name": "./DATASET/omnilabel/coco/000000037740.jpg", "inference_obj_descriptions": ["Sleeping area with blue quilt", "The surface where the food is sitting.", "The succulent growing in the container.", "Greenery container", "Furniture with people sitting on it", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2468, 2510, 2540, 2571, 2712, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2217, "file_name": "./DATASET/omnilabel/coco/000000362682.jpg", "inference_obj_descriptions": ["The mostly white motorcycle parked with the wheel turned.", "The red car that is next to the Hess sign.", "The cars with the spoilers.", "the two vehicles that don't have a visible animated character on the side", "The ground vehicle with two wheels.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2326, 2384, 2520, 2572, 2585, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2219, "file_name": "./DATASET/omnilabel/coco/000000033104.jpg", "inference_obj_descriptions": ["the person that is wearing a black helmet", "People High-Fiving each other", "The people cutting the cake.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1655, 1969, 2078, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2226, "file_name": "./DATASET/omnilabel/coco/000000010363.jpg", "inference_obj_descriptions": ["The vehicle with a 22 on the front of it.", "The vehicle the cat is sitting on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2505, 2577, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2229, "file_name": "./DATASET/omnilabel/coco/000000010707.jpg", "inference_obj_descriptions": ["The people farther up on the stairs.", "The women who are wearing skirts.", "these two people are sitting next to each other", "this bathroom device is used to excrete human waste", "The places that people can sit.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1923, 1972, 1977, 2459, 2702, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2230, "file_name": "./DATASET/omnilabel/coco/000000511999.jpg", "inference_obj_descriptions": ["the person that is wearing a black helmet", "the two people that are wearing reflective safety vests", "People wearing dark blue team shirts", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1655, 1981, 1984, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2234, "file_name": "./DATASET/omnilabel/coco/000000581482.jpg", "inference_obj_descriptions": ["A clock that reads one thirty-two.", "The clock that has a face that is facing the same direction as the statue above it.", "The clock in which all the numbers are visible.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1258, 1649, 1985, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2235, "file_name": "./DATASET/omnilabel/coco/000000005600.jpg", "inference_obj_descriptions": ["Dish with broccoli in it", "Metal pronged eating utensil", "Container with dipping sauce", "Dish containing onions", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2507, 2558, 2561, 2579, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2237, "file_name": "./DATASET/omnilabel/coco/000000322959.jpg", "inference_obj_descriptions": ["Coffee mug", "Deep round dish with broccoli", "Glass with orange juice", "The container holding the sandwich.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2283, 2502, 2651, 2715, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2238, "file_name": "./DATASET/omnilabel/coco/000000397639.jpg", "inference_obj_descriptions": ["false question, there is only one sheep in this photo and it asks you to pick two -", "The bigger sheep that is white.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1721, 1987, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2243, "file_name": "./DATASET/omnilabel/coco/000000274272.jpg", "inference_obj_descriptions": ["The yellow vehicle with the black stripe on it.", "Red coloured large transportation vehicle for multiple passengers", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2544, 2582, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2248, "file_name": "./DATASET/omnilabel/coco/000000100428.jpg", "inference_obj_descriptions": ["A tie hidden by a sweater", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1585, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2249, "file_name": "./DATASET/omnilabel/coco/000000114770.jpg", "inference_obj_descriptions": ["The ground vehicle with two wheels.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2585, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2251, "file_name": "./DATASET/omnilabel/coco/000000492937.jpg", "inference_obj_descriptions": ["Vehicle that transports passengers", "The vehicle with the greatest occupancy.", "Item you have to paddle around to move", "The vehicle with a 22 on the front of it.", "Large metal passenger vehicle on tracks", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2217, 2291, 2336, 2505, 2587, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2252, "file_name": "./DATASET/omnilabel/coco/000000025393.jpg", "inference_obj_descriptions": ["A tie hidden by a sweater", "Tie on person coming out of mirror", "The tie that is a solid color.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1585, 1994, 1998, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2256, "file_name": "./DATASET/omnilabel/coco/000000142971.jpg", "inference_obj_descriptions": ["the two people who are wearing shorts and legs are visible", "The people that have yellow in their uniforms.", "The people who are on the boards.", "All the people standing on the court.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1767, 1837, 2002, 2112, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2263, "file_name": "./DATASET/omnilabel/coco/000000357081.jpg", "inference_obj_descriptions": ["The cows that are sitting under the tree.", "the cow that is not eating grass", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1500, 2010, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2264, "file_name": "./DATASET/omnilabel/coco/000000473821.jpg", "inference_obj_descriptions": ["The seat of the man in the jacket.", "The succulent growing in the container.", "The seat with the red runner on it.", "The furniture the woman is lying down on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2261, 2540, 2596, 2694, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2265, "file_name": "./DATASET/omnilabel/coco/000000396338.jpg", "inference_obj_descriptions": ["The car with a visible yellow license plate.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2011, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2266, "file_name": "./DATASET/omnilabel/coco/000000041872.jpg", "inference_obj_descriptions": ["red seating area", "the four food items on the blue plate", "The place where people would sleep.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2207, 2549, 2597, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2272, "file_name": "./DATASET/omnilabel/coco/000000512657.jpg", "inference_obj_descriptions": ["The people that are wearing hats.", "People with light colored shirts", "People in blue jackets standing together near furry animals.", "The people that are standing up to play a game.", "The people wearing white hats", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1792, 1807, 1848, 2015, 2145, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 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"./DATASET/omnilabel/coco/000000136915.jpg", "inference_obj_descriptions": ["The women who are wearing skirts.", "the people that are wearing a grey t-shirt and are in a wheelchair", "The people who aren't holding a bat", "children with blond hair", "The people with gloves.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1972, 1980, 2025, 2114, 2118, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2029, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2287, "file_name": "./DATASET/omnilabel/coco/000000008021.jpg", "inference_obj_descriptions": ["The people wearing yellow jackets", "A person holding a doughnut cheeseburger.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1840, 2140, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2288, "file_name": "./DATASET/omnilabel/coco/000000292456.jpg", "inference_obj_descriptions": ["The people operating the cameras.", "The people with blue shirts on the end of the ramp.", "All the people on the scooters", "The four people clustered together in a group with their bodies turned to the right of the image.", "people with red hair", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1963, 1971, 2033, 2080, 2115, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2289, "file_name": "./DATASET/omnilabel/coco/000000163640.jpg", "inference_obj_descriptions": ["the people wearing white shirts", "The people who have blue hats.", "The two people in dark shirts, and standing outside of the restaurant/", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1884, 1997, 2034, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2295, "file_name": "./DATASET/omnilabel/coco/000000209142.jpg", "inference_obj_descriptions": ["The clear colored utinsils.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2609, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2296, "file_name": "./DATASET/omnilabel/coco/000000566923.jpg", "inference_obj_descriptions": ["these two people are sitting next to each other", "People wearing dark blue team shirts", "The people who are on the boards.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1977, 1984, 2002, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2297, "file_name": "./DATASET/omnilabel/coco/000000391290.jpg", "inference_obj_descriptions": ["People wearing blue coats", "The people operating the cameras.", "The people who are on the boards.", "A person not wearing a hat.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1903, 1963, 2002, 2039, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2299, "file_name": "./DATASET/omnilabel/coco/000000159458.jpg", "inference_obj_descriptions": ["Item you sit on", "The surface where the food is sitting.", "The furniture the dog is resting on.", "Plant in corner of room", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2347, 2510, 2611, 2710, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2301, "file_name": "./DATASET/omnilabel/coco/000000370208.jpg", "inference_obj_descriptions": ["The meter on the right", "The parking meter with the two on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2042, 2168, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2307, "file_name": "./DATASET/omnilabel/coco/000000125936.jpg", "inference_obj_descriptions": ["The people that are playing the sport.", "The people who are wearing red shirts.", "the two people holding a glass with hands as only visible body part", "The people wearing something on their heads", "The women in the black sleeveless shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1826, 1851, 1879, 2048, 2125, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2309, "file_name": "./DATASET/omnilabel/coco/000000216296.jpg", "inference_obj_descriptions": ["The women who are posing together.", "The people that are laughing on the right side of the board.", "The people who are wearing helmets at this time.", "The people who aren't visibly holding a racket", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1752, 1908, 1918, 2050, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2311, "file_name": "./DATASET/omnilabel/coco/000000434479.jpg", "inference_obj_descriptions": ["cloth piece of furniture used for seating multiple guests together", "The thing the people are sitting on.", "This is used to put food and drink on so you can sit and eat in front of it.", "Orange and red seating area", "The place where someone would sleep.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2418, 2449, 2606, 2632, 2654, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1608, 2022, 2054, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, 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"snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1696, 1751, 2055, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2318, "file_name": "./DATASET/omnilabel/coco/000000495732.jpg", "inference_obj_descriptions": ["People who are looking at the table", "The people in front of the cows.", "The people standing in front of the bikes.", "two person stand next to each other while holding wii remote controls", "All the people with umbrellas over their heads.", "person", "bicycle", "car", "motorcycle", 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"inference_obj_descriptions": ["the two people who are wearing shorts and legs are visible", "People in the dugout", "The men that are posing together.", "people that are holding a surf board", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2325, "file_name": "./DATASET/omnilabel/coco/000000342367.jpg", "inference_obj_descriptions": ["The people wearing hats", "The people with their legs visible.", "The person kneeling on the ground.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", 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2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2327, "file_name": "./DATASET/omnilabel/coco/000000369771.jpg", "inference_obj_descriptions": ["The containers of condiments.", "The clear colored utinsils.", "Glass with water", "The blue utinsil with the tines.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2430, 2609, 2617, 2624, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 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on the bench.", "A person who is crouching.", "All the people without hats.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2067, 2116, 2120, 2725, 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{"image_id": 2333, "file_name": "./DATASET/omnilabel/coco/000000441491.jpg", "inference_obj_descriptions": ["The men on the court.", "All the people behind the man with the blue shirt.", "The people with shades on", "All the people whos eyes are closed.", "The people cutting the cake.", "All the players who are in the batters box.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1870, 2020, 2024, 2068, 2078, 2104, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2334, "file_name": "./DATASET/omnilabel/coco/000000013291.jpg", "inference_obj_descriptions": ["The men that are wearing suits.", "The people that are holding a bat in the air.", "The people who are wearing hats.", "The children who are on the court.", "All the people with white shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1869, 1883, 1954, 1989, 2069, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2335, "file_name": "./DATASET/omnilabel/coco/000000559543.jpg", "inference_obj_descriptions": ["a piece of furniture that is long and used for sleeping", "The place where a person would sleep.", "The purple thing the dog is sitting on.", "The longer seating piece of furniture.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2333, 2524, 2529, 2627, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2337, "file_name": "./DATASET/omnilabel/coco/000000463730.jpg", "inference_obj_descriptions": ["The bus with the number 180 on it.", "The blue bus.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1729, 2072, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2338, "file_name": "./DATASET/omnilabel/coco/000000084674.jpg", "inference_obj_descriptions": ["The men sitting at the table.", "People wearing dark blue team shirts", "The people who aren't babies", "The people who are competing against each other.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1878, 1984, 2074, 2132, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2340, "file_name": "./DATASET/omnilabel/coco/000000439426.jpg", "inference_obj_descriptions": ["The people that are standing up to play a game.", "The people touching the kite's fabric", "Shoes that are on the ground", "The two people who are wearing hats on their head.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2015, 2049, 2076, 2079, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2342, "file_name": "./DATASET/omnilabel/coco/000000092939.jpg", "inference_obj_descriptions": ["The two women with hair light enough to not be brown.", "The girls that are playing on the court.", "All the people behind the man with the blue shirt.", "The people with their legs visible.", "The people cutting the cake.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1811, 1911, 2020, 2077, 2078, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], 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"bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2507, 2631, 2651, 2715, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2345, "file_name": "./DATASET/omnilabel/coco/000000420916.jpg", "inference_obj_descriptions": ["The people that are playing a game.", "Men holding plaques", "The people in the background", "The two people who are wearing hats on their head.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1894, 1902, 2023, 2079, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2347, "file_name": "./DATASET/omnilabel/coco/000000148662.jpg", "inference_obj_descriptions": ["The people farther up on the stairs.", "Boys wearing long ties", "A person wearing torn clothing.", "The people in the background", "A person holding a doughnut cheeseburger.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1923, 1929, 2038, 2082, 2140, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2348, "file_name": "./DATASET/omnilabel/coco/000000371042.jpg", "inference_obj_descriptions": ["The people who are not swinging the bat", "The people who are customers.", "The people who are wearing hats.", "The people wearing dark blue shirt", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1937, 1953, 1954, 2083, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2352, "file_name": "./DATASET/omnilabel/coco/000000138550.jpg", "inference_obj_descriptions": ["The black table with the food on it that the baby is sitting at.", "Tan sofa", "Sleeping area with white sheet", "Furniture you put plates on", "The things that people can sit on.", "The end of the seat where the people are.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2374, 2570, 2595, 2622, 2637, 2714, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2354, "file_name": "./DATASET/omnilabel/coco/000000526103.jpg", "inference_obj_descriptions": ["Elephants shorter than the rest", "The elephant on the placard.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1664, 2088, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2356, "file_name": "./DATASET/omnilabel/coco/000000251140.jpg", "inference_obj_descriptions": ["Bycicles showing handle bars", "Bicycle with kid on back", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1301, 1790, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2357, "file_name": "./DATASET/omnilabel/coco/000000305309.jpg", "inference_obj_descriptions": ["The people whose faces you can't see", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2090, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2358, "file_name": "./DATASET/omnilabel/coco/000000001000.jpg", "inference_obj_descriptions": ["Light blue shoulder handbag", "The bags that the woman with the white shirt is holding.", "handbag worn by the person with orange cap", "The higher of the handbags", "The handbag being held by the person with the umbrella hat.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1235, 1464, 2091, 2150, 2152, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2360, "file_name": "./DATASET/omnilabel/coco/000000355240.jpg", "inference_obj_descriptions": ["The dogs that are in the magazine.", "cannot see full body", "The dog with the visible price tag on it.", "The reflection of the dog in the mirror.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1318, 1341, 2093, 2156, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2361, "file_name": "./DATASET/omnilabel/coco/000000177861.jpg", "inference_obj_descriptions": ["The people that are posing for the picture.", "All the people holding umbrellas", "The baseball players in gray uniforms and no black top.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2363, "file_name": "./DATASET/omnilabel/coco/000000050380.jpg", "inference_obj_descriptions": ["The women who are posing together.", "People who are looking at the table", "The people who are on the bus.", "The people wearing all black.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1752, 1779, 1835, 2087, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2364, "file_name": "./DATASET/omnilabel/coco/000000041990.jpg", "inference_obj_descriptions": ["Men holding plaques", "The people in front of the cows.", "a person stands with their head down, leaning forward while a second person walks next to an approaching bus near a bus stop", "The people without goggles covering their 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"./DATASET/omnilabel/coco/000000042628.jpg", "inference_obj_descriptions": ["The people that are laughing on the right side of the board.", "All the people with umbrellas over their heads.", "All the people wearing dark clothes.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1908, 2071, 2101, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2368, "file_name": "./DATASET/omnilabel/coco/000000002685.jpg", "inference_obj_descriptions": ["The people that are walking on the sidewalk.", "All the people who are men.", "A person who is crouching.", "All the workers on the left side of the conveyor belt.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1881, 2103, 2116, 2121, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2371, "file_name": "./DATASET/omnilabel/coco/000000447187.jpg", "inference_obj_descriptions": ["Men holding wine glasses", "The children who are on the court.", "The people pouring liquid into a cup.", "All the players who are on defense.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1663, 1989, 2022, 2106, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2373, "file_name": "./DATASET/omnilabel/coco/000000375430.jpg", "inference_obj_descriptions": ["Tan sofa", "Wooden place to sit", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2570, 2642, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2375, "file_name": "./DATASET/omnilabel/coco/000000385997.jpg", "inference_obj_descriptions": ["the item used for grooming being held in the child's hands", "Wood and metal place to sit", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2522, 2644, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 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"train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2231, 2620, 2645, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 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"inference_obj_description_ids": [1637, 1821, 2112, 2141, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2379, "file_name": "./DATASET/omnilabel/coco/000000141328.jpg", "inference_obj_descriptions": ["Round metal container", "a utensil used for eating soup and pasta", "A utensil for eating soup", "Glass you fill with liquid", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2616, 2646, 2686, 2719, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2382, "file_name": "./DATASET/omnilabel/coco/000000206579.jpg", "inference_obj_descriptions": ["Orange and red seating area", "Wooden furniture used for seating", "Orange and wood seating", "The furniture the woman is lying down on.", "White sofa", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2632, 2648, 2674, 2694, 2718, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2387, "file_name": "./DATASET/omnilabel/coco/000000531707.jpg", "inference_obj_descriptions": ["a person stands with their head down, leaning forward while a second person walks next to an approaching bus near a bus stop", "All the people without hats.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2057, 2120, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2388, "file_name": "./DATASET/omnilabel/coco/000000032901.jpg", "inference_obj_descriptions": ["The two women in black shirts riding horses.", "A person holding a doughnut cheeseburger.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2019, 2140, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2389, "file_name": "./DATASET/omnilabel/coco/000000271116.jpg", "inference_obj_descriptions": ["Blue and wood sofa", "The white area where people could take a nap.", "Sleeping area with white sheets", "Wood and metal place to sit", "The furniture on which the dishes are sitting.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2230, 2454, 2615, 2644, 2655, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2390, "file_name": "./DATASET/omnilabel/coco/000000384468.jpg", "inference_obj_descriptions": ["The adults sitting on the benches.", "The women that are on bikes.", "The players wearing white shirts.", "The people who have sleeves past their elbows.", "All the people riding the elephant.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1746, 1855, 1858, 1988, 2122, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2391, "file_name": "./DATASET/omnilabel/coco/000000022589.jpg", "inference_obj_descriptions": ["these four sheep look to be the same color but are definitely the four lightest colored", "The sheep with the visible white snout that is looking at the camera.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1574, 2123, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2392, "file_name": "./DATASET/omnilabel/coco/000000254516.jpg", "inference_obj_descriptions": ["The men that are posing together.", "The people who have blue hats.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1973, 1997, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2393, "file_name": "./DATASET/omnilabel/coco/000000145597.jpg", "inference_obj_descriptions": ["the three people that are wearing sunglasses not regular eyeglasses", "People in the dugout", "The women that are on bikes.", "The women in the black sleeveless shirts.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1670, 1801, 1855, 2125, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2394, "file_name": "./DATASET/omnilabel/coco/000000129492.jpg", "inference_obj_descriptions": ["Men holding wine glasses", "People sitting at the table.", "The people that are laughing on the right side of the board.", "All the people sitting behind the person eating pizza.", "The people who aren't babies", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1663, 1742, 1908, 2111, 2126, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2398, "file_name": "./DATASET/omnilabel/coco/000000175364.jpg", "inference_obj_descriptions": ["An oven with a closed drawer next to it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2130, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2399, "file_name": "./DATASET/omnilabel/coco/000000555705.jpg", "inference_obj_descriptions": ["The cat that is sitting at the base of the tree.", "The black and white cat.", "A cat laying down with the tail by it's head.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1429, 1550, 2131, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2400, "file_name": "./DATASET/omnilabel/coco/000000046252.jpg", "inference_obj_descriptions": ["The people operating the cameras.", "A person who is getting married.", "The people who are competing against each other.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1963, 2037, 2132, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2401, "file_name": "./DATASET/omnilabel/coco/000000025986.jpg", "inference_obj_descriptions": ["Cylindrical container decorated with a colorful image.", "a utensil used for eating soup and pasta", "Round metal utensil with a bowl and a handle", "Wooden untensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2500, 2646, 2657, 2687, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2404, "file_name": "./DATASET/omnilabel/coco/000000297084.jpg", "inference_obj_descriptions": ["Dark striped sofa", "Bedroom item that you lay down on", "The small tree growing in the corner.", "Eating surface", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2248, 2311, 2629, 2660, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2405, "file_name": "./DATASET/omnilabel/coco/000000262487.jpg", "inference_obj_descriptions": ["The two people holding the hands of the person in the white shirt.", "All the people that are playing against each other in the game.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2100, 2135, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2408, "file_name": "./DATASET/omnilabel/coco/000000308793.jpg", "inference_obj_descriptions": ["the baseball players with a mustache", "the two people that are wearing reflective safety vests", "The two peopel that are not wearing lanyards.", "The person with the orange skis on his back", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1896, 1981, 2014, 2137, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2409, "file_name": "./DATASET/omnilabel/coco/000000296657.jpg", "inference_obj_descriptions": ["Person with a glass in their hand", "A person with at least one hand between his legs.", "A person wearing light colored pants.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1904, 2044, 2138, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2412, "file_name": "./DATASET/omnilabel/coco/000000546219.jpg", "inference_obj_descriptions": ["The people that are holding a bat in the air.", "The people pouring liquid into a cup.", "A person wearing torn clothing.", "people with upper arms showing", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1883, 2022, 2038, 2142, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2413, "file_name": "./DATASET/omnilabel/coco/000000140556.jpg", "inference_obj_descriptions": ["The people that are walking on the sidewalk.", "All the people with umbrellas over their heads.", "The person with the orange skis on his back", "The people wearing white hats", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1881, 2071, 2137, 2145, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2415, "file_name": "./DATASET/omnilabel/coco/000000420281.jpg", "inference_obj_descriptions": ["the baseball players with a mustache", "The people with long sleeves that are holding devices.", "people not shown eating", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1896, 1955, 2147, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2417, "file_name": "./DATASET/omnilabel/coco/000000477227.jpg", "inference_obj_descriptions": ["The boats that are closest to the bridge.", "The boat with a man and a dog.", "The boat with the number 199 on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1264, 1491, 1990, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2418, "file_name": "./DATASET/omnilabel/coco/000000544052.jpg", "inference_obj_descriptions": ["The skateboard being used by the person in the black shirt.", "skateboards with people doing tricks on them", "The bigger of the two skateboards", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1283, 1360, 2149, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2419, "file_name": "./DATASET/omnilabel/coco/000000513688.jpg", "inference_obj_descriptions": ["A potted plant with yellowish leaves", "Blue recliner", "Furniture with electronic equipment on it", "a horizontal piece of furniture used for sleeping", "The furniture the woman is lying down on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2620, 2662, 2664, 2671, 2694, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2421, "file_name": "./DATASET/omnilabel/coco/000000213224.jpg", "inference_obj_descriptions": ["The vase that is red.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2151, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2425, "file_name": "./DATASET/omnilabel/coco/000000146457.jpg", "inference_obj_descriptions": ["The wooden piece where you could put a plate of food.", "The things growing behind the sofa.", "The thing the kids are standing on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2527, 2607, 2678, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2426, "file_name": "./DATASET/omnilabel/coco/000000165831.jpg", "inference_obj_descriptions": ["The see-through salt or pepper shaker that is next to the White container all the way to the left.", "The container with the rice in it.", "Cup with dipping sauce in it", "Round metal container", "The foil container with the food.", "The red colored drink.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2383, 2483, 2508, 2616, 2680, 2699, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2427, "file_name": "./DATASET/omnilabel/coco/000000558854.jpg", "inference_obj_descriptions": ["The sandwich that is closer to the wall.", "The grilled cheese sandwich", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1957, 2154, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2429, "file_name": "./DATASET/omnilabel/coco/000000458992.jpg", "inference_obj_descriptions": ["a cannister used for holding spicy sauce", "Silverware with multiple prongs", "The red colored drink.", "The red drinking container.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2466, 2681, 2699, 2713, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2408, 2531, 2612, 2649, 2682, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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"surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1951, 2157, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], 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"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2275, 2502, 2538, 2616, 2687, 2717, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2434, "file_name": "./DATASET/omnilabel/coco/000000306733.jpg", "inference_obj_descriptions": ["This is used to put food and drink on so you can sit and eat in front of it.", "Furniture you put plates on", "Furniture not near an island", 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null]}, {"image_id": 2439, "file_name": "./DATASET/omnilabel/coco/000000265777.jpg", "inference_obj_descriptions": ["this pizza looks like it has raw meat on it", "The pizza near the fork", "The pizza that is closest to the woman", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1979, 2155, 2160, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2442, "file_name": "./DATASET/omnilabel/coco/000000578545.jpg", "inference_obj_descriptions": ["Furniture with a toddler sitting on it", "The furniture the woman is lying down on.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2683, 2694, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2443, "file_name": "./DATASET/omnilabel/coco/000000446522.jpg", "inference_obj_descriptions": ["Furniture with plates of food on it", "Furniture with plates on it", "The surface holding the plate.", "The furniture with the floral pattern.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2639, 2653, 2666, 2695, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2446, "file_name": "./DATASET/omnilabel/coco/000000432553.jpg", "inference_obj_descriptions": ["The reflection of the dog in the mirror.", "The dog on the right", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2156, 2164, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2448, "file_name": "./DATASET/omnilabel/coco/000000395701.jpg", "inference_obj_descriptions": ["Grey sofa", "The furniture the woman is lying down on.", "Red and black seating area", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2537, 2694, 2697, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2453, "file_name": "./DATASET/omnilabel/coco/000000532129.jpg", "inference_obj_descriptions": ["Deep round dish with broccoli", "Black container with drinking liquid", "The clear container with the liquid.", "The silver colored utinsil.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2502, 2557, 2701, 2703, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2458, "file_name": "./DATASET/omnilabel/coco/000000529762.jpg", "inference_obj_descriptions": ["The utinsil that is touching the food.", "Container with dipping sauce", "Wooden untensil", "The drinking container with the yellowish liquid.", "Metal pronged eating utensil", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2435, 2561, 2689, 2709, 2717, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2463, "file_name": "./DATASET/omnilabel/coco/000000065455.jpg", "inference_obj_descriptions": ["front legs are straight and not bent", "Giraffes with their mouths open", "The giraffe closer to the people.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1375, 1646, 2176, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2464, "file_name": "./DATASET/omnilabel/coco/000000448448.jpg", "inference_obj_descriptions": ["each giraffe we can clearly see both eyes of the animal", "The giraffe closer to the people.", "The giraffe with the most brown on its nose.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1454, 2176, 2177, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2467, "file_name": "./DATASET/omnilabel/coco/000000382009.jpg", "inference_obj_descriptions": ["The chairs that are at the counter.", "The chairs closest to the table near the camera.", "The chair closer to the Rolex sign", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1754, 1763, 2179, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2468, "file_name": "./DATASET/omnilabel/coco/000000546626.jpg", "inference_obj_descriptions": ["white plastic eating utensil with prongs", "The red colored drink.", "The clear container with the liquid.", "The red drinking container.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2536, 2699, 2701, 2713, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2470, "file_name": "./DATASET/omnilabel/coco/000000479732.jpg", "inference_obj_descriptions": ["Metal pronged eating utensil", "the two items that are identified as flatware", "Wooden untensil", "The glass with the clear liquid in it.", "The container holding the sandwich.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2558, 2573, 2687, 2707, 2715, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2471, "file_name": "./DATASET/omnilabel/coco/000000323571.jpg", "inference_obj_descriptions": ["The trucks that are lighter in color and parked on the street.", "Trucks with openings", "The truck that is white.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1300, 1427, 2180, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2473, "file_name": "./DATASET/omnilabel/coco/000000009772.jpg", "inference_obj_descriptions": ["The sink that is right behind the man.", "The sink next to the stove", "A sink near a basket of washcloths.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1477, 1885, 2181, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2474, "file_name": "./DATASET/omnilabel/coco/000000181796.jpg", "inference_obj_descriptions": ["visibly held by a human hand", "The knife that is in the black plate.", "A knife laying on bread.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1371, 1891, 2182, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2476, "file_name": "./DATASET/omnilabel/coco/000000544565.jpg", "inference_obj_descriptions": ["oranges touching the very red apple", "Oranges touching wrapped food", "The oranges that are dirty.", "The orange slice with more syrup on it.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1353, 1426, 1635, 2184, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2478, "file_name": "./DATASET/omnilabel/coco/000000116206.jpg", "inference_obj_descriptions": ["Container than you drink from", "Deep round dish with broccoli", "Round metal eating utensil", "utensil with tines used for holding food", "Sharp metal utensil for cutting", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2441, 2502, 2643, 2691, 2723, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2480, "file_name": "./DATASET/omnilabel/coco/000000071756.jpg", "inference_obj_descriptions": ["The smaller bear that is on the left side.", "The bear to the left", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1887, 2187, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2481, "file_name": "./DATASET/omnilabel/coco/000000347693.jpg", "inference_obj_descriptions": ["the bed that has the patient clearly visible in it", "The bed with white sheets.", "The bed closest to the window", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1442, 1671, 2188, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2482, "file_name": "./DATASET/omnilabel/coco/000000255747.jpg", "inference_obj_descriptions": ["the sandwiches that are each on the same plate with the other", "these two sandwiches are on the same plate", "A corned beef sandwich.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1430, 1525, 2189, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2483, "file_name": "./DATASET/omnilabel/coco/000000455219.jpg", "inference_obj_descriptions": ["A cow whose tail is curled on its back.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [2190, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {"image_id": 2484, "file_name": "./DATASET/omnilabel/coco/000000063552.jpg", "inference_obj_descriptions": ["The cat with the fluffy tail.", "The cat with the darker fur.", "A cat facing right", "The cat that is higher up on the cushion.", "A cat laying down with the tail by it's head.", "A cat who is looking at the camera.", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"], "inference_obj_description_ids": [1698, 1793, 1966, 1968, 2131, 2191, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804], "tokens_positive": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}] \ No newline at end of file diff --git a/tools/files/omnilabel_noun_phrase.json b/tools/files/omnilabel_noun_phrase.json new file mode 100644 index 0000000000000000000000000000000000000000..eda9a76fd1204fb4c718b31b55b1a8f35a66ae97 --- /dev/null +++ b/tools/files/omnilabel_noun_phrase.json @@ -0,0 +1,1117 @@ +{ + "the flower vase": "The flower vase", + "The large brown teddy bear in the brown cardboard box": "The large brown teddy bear", + "The white teddy bear with the red tag on his ear": "The white teddy bear", + "The white teddy bear that is near the foot of the person": "The white teddy bear", + "an educational item that can be read and features red persons on the cover": "an educational item", + "duck with a red and white beak": "duck", + "The birds that are standing on the grass": "The birds", + "bowl with the powdered donuts": "bowl", + "sauces in a glass bowl": "sauces", + "The two cakes closest to the leaf on the fabric": "The two cakes", + "The slice of cake is lying on the plate on its side": "The slice of cake", + "The plant is touching the bookcase": "The plant", + "The plant with the bigger head of brocolli": "The plant", + "Plant next to the M on the wall": "Plant", + "The black table with the food on it that the baby is sitting at": "The black table", + "this bathroom device is used to excrete human waste": "bathroom device", + "Sleeping area with blue quilt": "Sleeping area", + "The thing the kids are standing on": "The thing", + "The end of the seat where the people are": "The seat", + "this toothbrush is an electric toothbrush": "this toothbrush", + "white and dark blue toothbrush": "toothbrush", + "The train behind the woman with the scarf": "The train", + "Light blue shoulder handbag": "handbag", + "bags resting upon legs that are not crossed": "bags", + "Person standing next to a table": "Person", + "the people that are standing up": "The people", + "Men holding plaques": "Men", + "The women who are wearing skirts": "The women", + "The handbag that is near the apples": "The handbag", + "The bright red handbag": "The bright red handbag", + "The parking meter that is in front of the red wall": "The parking meter", + "the wooden bench": "The wooden bench", + "the white keyboard": "The white keyboard", + "The thing that moves the curser around": "The thing", + "The device on the sofa near the cat": "The device", + "fire hydrants not touching the darker cement": "fire hydrants", + "yellow base and blue lid": "yellow base and blue lid", + "The hydrant with a blue cap": "The hydrant", + "table with food served": "table", + "Table with birds on it": "Table", + "The tables behind the cake": "The tables", + "Item with bed sheet on it": "Item", + "The small tree growing in the corner": "The small tree", + "donuts with chocolate frosting": "donuts", + "The donut that has white frosting on it": "The donut", + "The donuts with chocolate icing": "The donuts", + "Donut with chocolate glaze": "Donut", + "Compiled digital seasons of a TV show called MONK": "digital seasons of a TV show called MONK", + "an item that contains words that you read": "an item", + "The scissors cutting the stack of white papers": "The scissors", + "cows that are laid down": "cows", + "each of these cows is all black in color": "cows", + "The cow that is in the grass": "The cow", + "A book titled Cortazar": "A book", + "The books that are behind the netting": "The books", + "The clear glass cut vase with the red flowers in it": "The clear glass cut vase", + "these four sheep look to be the same color but are definitely the four lightest colored": "four sheep", + "The sheep that are white in color": "The sheep", + "The sheep that are lying in the grass": "The sheep", + "false question, there is only one sheep in this photo and it asks you to pick two -": "Sheep (as there is only one subject in the sentence)", + "Animal in another animal's mouth": "Animal", + "black and white feline": "feline", + "The black sheep in the pen with all the others": "The black sheep", + "Animals with brown hair": "Animals", + "Electronic items with display": "Electronic items", + "Small electronic device used for calls": "Small electronic device", + "knives not on a plate of food": "knives", + "visibly held by a human hand": "a human hand", + "The knife that is in the black plate": "The knife", + "The umbrella in the reflection of the window": "The umbrella", + "two umbrellas closer to the sign saying 99 flake": "two umbrellas", + "umbrella closest to the brick wall": "umbrella", + "The monitor behind the animal": "The monitor", + "the two couches that actually sit opposite of each other": "the two couches", + "The two couches that have printed fabric on them instead of the one with only a solid color fabric": "The two couches", + "a piece of furniture that is long and used for sleeping": "a piece of furniture", + "The seat with the red runner on it": "The seat", + "broccoli touching rice": "broccoli", + "The part of the oven with the burners on it": "The part of the oven", + "The highest oven": "The highest oven", + "Container with water in it": "Container", + "The green potted plant hung above tables on the wooden wall": "The green potted plant", + "Round metal utensil": "Round metal utensil", + "The fruit in the beige container": "The fruit", + "This is used to put food into your mouth from a container": "This", + "Metal pronged eating utensil": "Metal pronged eating utensil", + "The three bags at the top": "The three bags", + "An item used for carrying smaller items": "An item", + "the open umbrella": "the open umbrella", + "The black and white zebra print umbrella": "The black and white zebra print umbrella", + "The bag that the animal is on": "The bag", + "teddy bears with brownish colored fur": "teddy bears", + "The stuffed animals that are green": "The stuffed animals", + "each of these umbrellas is multi-colored, red and blue": "umbrellas", + "the red umbrellas": "The red umbrellas", + "A group of green apples towards the top of the pile": "A group of green apples", + "oranges touching the very red apple": "oranges and apple", + "apples that are half way in the bowl": "apples", + "The oranges that have been cut in half": "The oranges", + "The apple that is more red in color": "The apple", + "The apple that is closest to the spoon": "The apple", + "The orange slice with more syrup on it": "The orange slice", + "Long yellow fruit": "Long yellow fruit", + "The one apple that is red, and is also in the upper right hand corner": "one apple", + "The neatly packed suitcase that is sitting open, and It has camo printed items in it": "The neatly packed suitcase", + "carrots touching chopped greens": "carrots", + "The carrot that is touching the fish": "The carrot", + "The apples that are red": "The apples", + "The bananas that have been cut for the dish": "The bananas", + "The biggest white apple at the bottom": "The biggest white apple", + "The banana closer to the Guinness bear": "The banana", + "Food with two slices of bread": "Food", + "sandwich touching the small bowl of sauce": "sandwich", + "Cylindrical container decorated with a colorful image": "Cylindrical container", + "Deep round dish with broccoli": "Deep round dish", + "The wooden furniture the man can sit on": "The wooden furniture", + "The orange colored utinsil": "The orange colored utinsil", + "The white utinsil touching the food": "The white utensil", + "The reflections of the cups": "The reflections", + "The seat that the man is in": "The seat", + "Wood and tan sleeping spot": "Sleeping spot", + "The surface holding the plate": "The surface", + "The glass with the clear liquid in it": "The glass", + "all three of these surfboards are the same color as each other": "three surfboards", + "Pink colored surfboard": "Pink colored surfboard", + "surfboard with yellow, read and orange colors": "surfboard", + "The people that are walking on the sidewalk": "The people", + "The people with blue shirts on the end of the ramp": "The people", + "people that are holding a surf board": "people", + "All the people looking at each other": "All the people", + "The sheep are majority white colored": "The sheep", + "Men holding wine glasses": "Men", + "The people kneeling down": "The people", + "All the people that are playing against each other in the game": "All the people", + "The people who are wearing red shirts": "The people", + "The people wearing white shirts": "The people", + "The women that don't wear sleeves": "The women", + "Ballplayers wearing shirts with contrasting sleeve color starting at shoulders": "Ballplayers", + "the white teddy bears": "The white teddy bears", + "Bicycle with kid on back": "Bicycle", + "this motor vehicle can carry more than three passengers": "this motor vehicle", + "The mostly white motorcycle parked with the wheel turned": "The mostly white motorcycle", + "the horizontal stacked bikes": "bikes", + "The vehicle with a 22 on the front of it": "The vehicle", + "Red vehicle in the road": "Red vehicle", + "The broccolis touching other food that isn't broccoli": "The broccolis", + "The full pieces of brocolli in the dish": "The full pieces of broccoli", + "The food that is made with bread": "The food", + "A food item that has been cut in half and includes both sides": "A food item", + "Plants in the ground": "Plants", + "Slice of a frosted dessert, suitable for serving one person": "Slice of a frosted dessert", + "These two hotdogs are closest to the man in the hat": "hotdogs", + "The hot dog that is being held with two hands": "The hot dog", + "The hot dog with the green stuff on it": "The hot dog", + "toilets with a 4 letter word above them": "toilets", + "The toilet that is set at a lower level": "The toilet", + "A toilet seating the child with a hairbrush": "A toilet", + "The toilet tank high up": "The toilet tank", + "The toilet the person is touching": "The toilet", + "scissors farthest from the wall": "scissors", + "with beaded chain": "No main subject identified.", + "the spoon in the cup of tea": "The spoon", + "The ladle that is not occluded by another ladle": "The ladle", + "The utinsil that is touching the food": "The utensil", + "The small knife in the round white plate": "The small knife", + "A knife laying on bread": "A knife", + "The innermost banana and lower right hand group of bananas": "bananas", + "The banana next to the orange": "The banana", + "The bananas closer tot he ground": "The bananas", + "dark colored sofa": "dark colored sofa", + "The pink couch that has a cushion on it": "The pink couch", + "The couch that the man is sitting on": "The couch", + "Giraffes in the shade": "Giraffes", + "yellow fruit that monkeys are known for eating": "yellow fruit", + "Closest food item": "food item", + "contain pepperoni as a topping": "pepperoni", + "Round citrus fruit": "citrus fruit", + "chocolate glazed donut that is fully visible": "chocolate glazed donut", + "The two Donuts that are behind the back of the yellow toy in front of them": "The two Donuts", + "An apple with water droplets on it": "An apple", + "The fruit in the clear bowl": "The fruit", + "Long orange vegetable": "Long orange vegetable", + "a long blue transportation device": "a long blue transportation device", + "Two-seater motor vehicle": "Two-seater motor vehicle", + "The vehicles with the pedals": "The vehicles", + "The vehicle the cat is sitting on": "The vehicle", + "partially covered by fur": "Not a complete sentence, cannot extract a main subject.", + "dark colored with light buttons": "N/A (This is not a complete sentence and does not have a clear subject)", + "The remote in the child's left hand": "The remote", + "a leather piece of equipment that helps you catch balls": "a leather piece of equipment", + "the long metal object underneath the person with a blue jacket": "the long metal object", + "Sport item you hit balls with": "Sport item", + "Item you stand on with wheels": "Item", + "A pair of boards you stand on": "A pair of boards", + "Refrigerator freezer combinations": "Refrigerator freezer combinations", + "refrigerator with more than one magnet": "refrigerator", + "The refrigerator closest to the woman": "The refrigerator", + "Rainbow colored accessory for rain": "Rainbow colored accessory", + "The strap on the person in white": "The strap", + "The large green umbrella over a market cart on the right side on the sidewalk": "The large green umbrella", + "The blue plane with the vertical tail stabilizer pointed downwards": "The blue plane", + "The chairs across from the man": "The chairs", + "The chairs that are inches from the railing of the deck": "The chairs", + "Chairs on the left side of the table": "Chairs", + "The food that is between the beer": "The food", + "Item you sit on with holes in the back rest": "Item", + "The food the person will be eating": "The food", + "The furniture the dog is resting on": "The furniture", + "The backpack that is on the ground next to the child in the stroller": "The backpack", + "this backpack is actually on the back of a person": "backpack", + "The donuts that are dark in color": "The donuts", + "The utinsil with the tines": "The utensil", + "The small red fruit in the plastic bag next to the bottle": "The small red fruit", + "The orange veggies on the plate": "The orange veggies", + "a cannister used for holding spicy sauce": "a cannister", + "Round metal container": "Round metal container", + "refrigerator with smaller freezer section on top": "refrigerator", + "The part of the refrigerator that is open": "The part of the refrigerator", + "The boards that are yellow": "The boards", + "persons with their faces fully visible": "persons", + "the two people holding a glass with hands as only visible body part": "the two people", + "The two women in black shirts riding horses": "The two women", + "children with blond hair": "children", + "each one of these books features the mario character": "these books", + "The books that are sitting on the red table": "The books", + "the books that are displayed vertically": "The books", + "The book that has a picture of shoes on the cover": "The book", + "both of these two birds are light grey in color": "two birds", + "The bird with its feet touching the water": "The bird", + "The wine glasses that the people are holding": "The wine glasses", + "A wine glass with the word beer on it": "A wine glass", + "The two wine glasses sitting near the white dishes": "The two wine glasses", + "each of these glasses has a visible logo on it and words": "these glasses", + "The silver car on the roadway": "The silver car", + "The SUV on the road": "The SUV", + "The umbrellas that have patterns on them": "The umbrellas", + "The horses that are standing up": "The horses", + "the brown dogs": "The brown dogs", + "The small figurine of the brown and white cow that is standing next to a figurine of a giraffe": "The small figurine of the brown and white cow", + "The light colored van behind the fence": "The light colored van", + "the vehicle with a non circular headlight": "the vehicle", + "The vehicle that is on the roadway": "The vehicle", + "The cars that are behind the red car": "The cars", + "A car with both front headlights visible ": "A car", + "Vehicle made for public transportation": "Vehicle", + "although it's in the water, this item can fly in the sky": "this item", + "Item you put on hand to catch ball": "Item", + "The two zebras on the left side": "The two zebras", + "Zebra with tail touching rock": "Zebra", + "The vases that have a busy pattern on them": "The vases", + "The vase that is white": "The vase", + "The colorful tablecloth covering the table underneath all the vases": "The colorful tablecloth", + "the colorful table": "The colorful table", + "cloth piece of furniture used for seating multiple guests together": "cloth piece of furniture", + "The utinsil on the side of the plate": "The utensil", + "The small glass container": "The small glass container", + "Furniture with plates of food on it": "Furniture", + "The container with the lettuce in it": "The container", + "Metal eating utensil with prongs": "Metal eating utensil", + "this part is used to type and to input data": "this part", + "a device with more than twenty buttons used for typing": "a device", + "The benches with people sitting on them": "The benches", + "The adults sitting on the benches": "The adults", + "The women sitting behind the man": "The women", + "The people that are waiting for the pitch": "The people", + "The two people holding the hands of the person in the white shirt": "The two people", + "people behind the table": "people", + "A clock that reads one thirty-two": "A clock", + "The umbrella over the group of people dining": "The umbrella", + "Animal lying on the back of another animal": "Animal", + "the black dog": "The black dog", + "Zebra with back towards camera": "Zebra", + "The black bag the children are sitting on": "The black bag", + "The bag on the person's back": "The bag", + "these two people each have a pink surfboard": "these two people", + "The men that are wearing dark blue shirts": "The men", + "Boats with the capability of flying sails": "Boats", + "The boat with a man and a dog": "The boat", + "The boat that is bigger": "The boat", + "The airplane that is blue and white": "The airplane", + "Airplane with a propeller": "Airplane", + "The plane with Q9 on it": "The plane", + "Toilet with brown seat cover": "Toilet", + "The open white toilet that the woman is standing in front of and touching": "The open white toilet", + "Electronic items with large display": "Electronic items", + "Item you drink out of": "Item", + "The vegetables in the black container": "The vegetables", + "Round blue eating utensil": "eating utensil", + "Item with wheels and a handle": "Item", + "The black purse being held by the man in the blue jacket": "The black purse", + "clear objects that keep you dry when it's raining": "clear objects", + "bananas touching an apple": "bananas and apple", + "Animals with brown spots": "Animals", + "the black luggage bag that does not have a toy doll holding the end of it": "The black luggage bag", + "a red object that keeps one dry during rain": "a red object", + "Professional neck accessory": "neck accessory", + "Carrying item with two shoulder straps": "Carrying item", + "objects that protect you from rain": "objects", + "The horses that are brown and walking in the sand": "The horses", + "these two trucks are each pointed in the same direction": "two trucks", + "truck with bomb squad text on the back": "truck", + "this truck is on the road and can be driven away": "this truck", + "The food truck that is green": "The food truck", + "the suitcases with the handle extended": "The suitcases", + "the suitcase that is yellow, open, and has white and blue-colored items inside": "The suitcase", + "Person wearing light colored pants": "Person", + "The people in solid colored dresses": "The people", + "All the people sitting behind the person eating pizza": "All the people", + "A person holding a doughnut cheeseburger": "A person", + "something pink that protects from rain": "something pink", + "The container with the stickers on it": "The container", + "The red saddlebag purse on the person in the white shirt standing on the right": "The red saddlebag purse", + "each of these motorcycles is carrying two passengers": "each of these motorcycles", + "Suitcases that are on the top shelf": "Suitcases", + "The higher of the handbags": "The handbags", + "The handbag being held by the person with the umbrella hat": "The handbag", + "bright green and used for carrying other items": "Not a complete sentence, cannot extract a subject.", + "The red truck": "The red truck", + "The skateboard being used by the person in the black shirt": "The skateboard", + "waiting behind bulls": "None (incomplete sentence)", + "The cars that are light in color": "The cars", + "The car with a visible yellow license plate": "The car", + "The blue car": "The blue car", + "each of these backpacks is being worn and not carried in someone's hand": "backpacks", + "The suitcases that are directly on the cart": "The suitcases", + "The backpack that is closest to the wall": "The backpack", + "The backpack that is closest to the chair": "The backpack", + "The liquid in the clear container": "The liquid", + "cannister used for holding cream or other liquids": "cannister", + "these skis are red and silver": "these skis", + "The book at the bottom of the stack": "The book", + "Boxes with white lettering": "Boxes", + "The book that has red as a background color": "The book", + "The appliance built into the top shelfs": "The appliance", + "The place where you could wash your hands": "The place", + "this appliance has the reflection of the two eletrical outlets in it": "this appliance", + "Metal basin for running water": "Metal basin", + "Deep white tub for running water": "Deep white tub", + "The two bears with the lighter fur": "The two bears", + "The tall white appliance": "The tall white appliance", + "Metal basin used for running water": "Metal basin", + "Keyboards with black keys on top of table": "Keyboards", + "The buses that are blue and yellow": "The buses", + "The buses under the covered area": "The buses", + "The vehicle that is in the air": "The vehicle", + "The smaller vehicles on the road": "The smaller vehicles", + "The cars with the spoilers": "The cars", + "The yellow vehicle on the ground": "The yellow vehicle", + "The boats that are closest to the bridge": "The boats", + "these four suitcases are stacked on top of each other": "four suitcases", + "the motorcycles with a grill on the front": "The motorcycles", + "People who are looking at the table": "People", + "The people standing in front of the bikes": "The people", + "The forks on the white dishes": "The forks", + "The fork that is with the salad": "The fork", + "Forks propped up inches above table": "Forks", + "The pizzas that have a spatula under them": "The pizzas", + "this pizza looks like it has raw meat on it": "pizza", + "All of these are containers that have consumables in them": "containers", + "clear cannisters used for holding alcoholic beverages": "clear cannisters", + "The toilet with the open lid": "The toilet", + "A fork that is not on a plate": "A fork", + "The sink that is right behind the man": "The sink", + "The sink next to the stove": "The sink", + "A sink near a basket of washcloths": "A sink", + "The trucks that are lighter in color and parked on the street": "The trucks", + "these rackets are each red and blue and are NOT being used by the player": "these rackets", + "The racket the woman is holding": "The racket", + "The tennis racket being held by the man in red": "The tennis racket", + "these two bikes are closest to the harley davidson banner": "two bikes", + "The airplane that has the word Egyptair on the side": "The airplane", + "a blue mode of transportation designed for water": "a blue mode of transportation", + "a green automobile for multiple passengers": "a green automobile", + "a brown automobile with a dog on top": "a brown automobile", + "The vehicle meant to run on the ground": "The vehicle", + "Bottles with a white top": "Bottles", + "The bottles of beer on the table": "The bottles of beer", + "The slender black bottles": "The slender black bottles", + "these two bottles of beer are the same brand as each other": "two bottles of beer", + "The bottle near the sink with the green liquid": "The bottle", + "this vase is smaller than the other": "this vase", + "The vase that is red": "The vase", + "Car next to a tree": "Car", + "The car that has both headlights visible": "The car", + "Vehicle of public transportation": "Vehicle", + "Red motor vehicle": "Red motor vehicle", + "The vehicle parked on the number 6": "The vehicle", + "Yellow tractor full of people": "Yellow tractor", + "Elephants by a post": "Elephants", + "these two elephants are babies and not as old as the other two": "two elephants", + "Handbags that have a black color": "Handbags", + "Black and white canine": "Canine", + "The animal walking on four legs": "The animal", + "these giraffes are standing tall and not kneeling to eat grass": "giraffes", + "Animal with gray fur": "Animal", + "The elephants that are carrying things": "The elephants", + "these two look like babies compared to the third elephant": "elephant", + "The elephants that are facing the left side": "The elephants", + "The computer monitors on the desk that are turned on": "The computer monitors", + "The keyboards that are lined up near each other": "The keyboards", + "The tv that is on a black shelf": "The TV", + "TV with bobblehead by it": "TV", + "these books are clustered together and to the right of the furry animal": "books", + "The horizontal book": "The horizontal book", + "The screen on the shelf": "The screen", + "The carrot closest to the onions": "The carrot", + "The dogs that are in the magazine": "The dogs", + "A dog with a brown spot on the back of its neck": "A dog", + "The reflection of the dog in the mirror": "The reflection of the dog", + "The banana stack on the right of the crate": "The banana stack", + "Dog with multi-colored fur": "Dog", + "this furniture item is used for sitting": "furniture item", + "this item is used for urine and human excrement": "this item", + "The white area where people could take a nap": "The white area", + "The place where people would sleep": "The place", + "Furniture with people sitting on it": "Furniture", + "The bus with the number 180 on it": "The bus", + "A bus that is green": "A bus", + "Oranges touching wrapped food": "Oranges", + "Metal eating utensil": "Metal eating utensil", + "Cup with water": "Cup", + "The red colored drink": "The red colored drink", + "The people that are wearing dresses": "The people", + "The bears that are standing": "The bears", + "The teddy bear sitting on the snow": "The teddy bear", + "The chair that is closer to the wall": "The chair", + "this fruit item is a favorite of monkeys": "fruit item", + "Oranges that are on the top of other oranges": "Oranges", + "Oranges in front of pear": "Oranges", + "The two yellow fruits that are farther away from the camera": "Two yellow fruits", + "these two bananas are closest to the red pepper": "two bananas", + "Food with pink frosting": "Food", + "The red round fruits on the right": "The red round fruits", + "Small wedge of fruit": "Small wedge of fruit", + "The sandwiches next to an orange slice": "The sandwiches", + "The oranges that are dirty": "The oranges", + "The grilled cheese sandwich": "The grilled cheese sandwich", + "The white bowls on the second to bottom shelf": "The white bowls", + "Bowl with white food": "Bowl", + "The three dark gray, or black pots of Saucy food on the left": "Three pots of Saucy food", + "Chairs next to a bookcase": "Chairs", + "these two benches are the closest to the two women": "two benches", + "the sandwiches that are each on the same plate with the other": "The sandwiches", + "the orange slice furthest to the right": "the orange slice", + "The slices of fruit in the container": "The slices of fruit", + "The hot dogs that have red toppings": "The hot dogs", + "the hot dog on the right": "The hot dog", + "an appliance that keeps food cool": "an appliance", + "an appliance used for heating food": "an appliance", + "White porcelain tub for running water": "White porcelain tub", + "The two chairs that are at the table that is furthest on the left": "The two chairs", + "The long surface with multiple water glasses on top": "The long surface", + "These are closest to the upper beam": "These", + "the kite that is pink and yellow with black circles": "the kite", + "The people that are posing for the picture": "The people", + "The person kneeling on the ground": "The person", + "the person that is wearing a black helmet": "the person", + "Chair with no one in it": "Chair", + "The chairs that are empty": "The chairs", + "The people wearing yellow jackets": "The people", + "The people that are wearing hats": "The people", + "the bed that has the patient clearly visible in it": "The bed", + "The beds on the first and second bunk": "The beds", + "The bed that is closer to the window": "The bed", + "Blue and wood sofa": "Blue and wood sofa", + "The seat of the man in the jacket": "The seat", + "The surface with the red covering": "The surface", + "The seat on the right side of the people": "The seat", + "The cows that have brown fur": "The cows", + "The giraffe on the left side of the bush in the middle": "The giraffe", + "Red and checkered accessory used when it is raining": "Accessory", + "The black thing that the woman is digging in": "The black thing", + "Furniture to sit on": "Furniture", + "The longest seat in the room": "The longest seat", + "The teddy bear wearing a green hat": "The teddy bear", + "The teddy bears sitting on the edges of the blanket": "The teddy bears", + "a side profile of a teddy bear looking to the right": "a teddy bear", + "This teddy bear has a red striped bow and is wearing a pink shirt with a cat on it": "This teddy bear", + "The zebras who are facing the right side": "The zebras", + "The zebra with its nose under the bar": "The zebra", + "each of these laptops has a screen that is NOT turned on": "laptops", + "The thing providing shade to the people": "The thing", + "The tie that is being worn by the man in the gray jacket": "The tie", + "rainbow object that protects your head from sun and rain": "rainbow object", + "The blue cordless phone on the book next to the man": "The blue cordless phone", + "this cow is dark-brown in color, almost black": "this cow", + "The cows that are black and white spotted": "The cows", + "The two sheep that are standing together to graze": "The two sheep", + "this toilet is to the right of a yellow wall tile": "this toilet", + "Clear plastic accessory for rain": "Clear plastic accessory", + "Remote made of legos": "Remote", + "The wine glasses with the clear liquid": "The wine glasses", + "the people that are young girls that are running": "The people", + "The people that are playing a game": "The people", + "People whose shirts feature horizontal bands of color": "People", + "The people farther up on the stairs": "The people", + "each one of these bowls does NOT have potatoes in it": "bowls", + "The orange veggie in the bowl": "The orange veggie", + "The place where you would store food to get cooler": "The place", + "The red motorcycle": "The red motorcycle", + "The red, white and blue thing that is blocking the sun": "The red, white and blue thing", + "Carrying equipment without a shoulder strap": "Carrying equipment", + "plaid and can keep you dry from rain": "No clear subject can be extracted from this sentence.", + "Sheep with a white shaved head": "Sheep", + "The people are all standing on their feet": "The people", + "Sheep with a solid black face": "Sheep", + "The people that are holding a bat in the air": "The people", + "The sheep with the visible white snout that is looking at the camera": "The sheep", + "The thing the person is holding to help them eat": "The thing", + "The container holding the green veggies": "The container", + "Red colored bus": "Red colored bus", + "The buses that are mostly white": "The buses", + "The blue bus": "The blue bus", + "this keyboard is white in color and actually on the desk": "this keyboard", + "Suitcase that is brown": "Suitcase", + "clear water bottles": "water bottles", + "the containers that are not white": "the containers", + "The three tallest bottles of the group": "The three tallest bottles", + "Dark striped sofa": "Dark striped sofa", + "The living thing with the leaves": "The living thing", + "Electronic typing device": "Electronic typing device", + "The two giraffes that are standing together": "The two giraffes", + "The giraffe closer to the people": "The giraffe", + "The pizza that is on the bottom and right side of the pan": "The pizza", + "This slice has some burnt crust. The pizza has sausage, cheese, and peppers": "The pizza", + "The individual servings of pizza on plates": "The individual servings of pizza", + "The pizza that is closest to the woman": "The pizza", + "The yellow fruit on the right": "The yellow fruit", + "Sliced citrus fruit": "Citrus fruit", + "Fruit with other food on top": "Fruit", + "The clock that is facing the camera": "The clock", + "Cakes without white stars on it": "Cakes", + "The two people closest to the flags": "The two people", + "The people operating the cameras": "The people", + "The people not holding a white towel to open a wine bottle": "The people", + "The suitcases that are on the bottom of the stack": "The suitcases", + "The sheep that are looking toward the camera": "The sheep", + "the glasses that are being held by men": "The glasses", + "The wine glass being held by the person in the striped shirt": "The wine glass", + "Dish with ice in it": "Dish", + "Electronics with a display": "Electronics", + "These components of a computer are one piece and not separate": "These components of a computer", + "the people with shirts being shown": "The people", + "The four people clustered together in a group with their bodies turned to the right of the image": "The four people", + "All the players who are in the batters box": "All the players", + "The tall silver appliance used for cooling food": "The tall silver appliance", + "White porcelain basin for water": "White porcelain basin", + "The red, round fruit": "The red, round fruit", + "The tallest animal": "The tallest animal", + "each giraffe we can clearly see both eyes of the animal": "each giraffe", + "The motorcycles that are on the street": "The motorcycles", + "The sheep that have white faces": "The sheep", + "The sheep that are standing in the grass": "The sheep", + "The sheep that don't have horns": "The sheep", + "A pizza that is a half circle": "A pizza", + "The broccoli closest to the bottom edge of the plate": "The broccoli", + "The horse that has its head down": "The horse", + "the sandwich that is cut in the middle": "The sandwich", + "The sandwich that is closer to the wall": "The sandwich", + "Electronics with multiple buttons": "Electronics", + "Electronic device with display": "Electronic device", + "The device that can be used to make calls": "The device", + "The output device of the computer": "The output device", + "The device in the child's hand": "The device", + "Elephants with trunks in the water": "Elephants", + "The elephants that are behind the leader": "The elephants", + "The spoon near the salt in the bowl with mashed potato": "The spoon", + "The teddy bear that is furthest right, and sitting on another teddy bear": "The teddy bear", + "the two smaller elephants that are not facing the camera": "two smaller elephants", + "The elephants without the yellow tassels": "The elephants", + "Couch has a black pillow": "Couch", + "The part of the couch the person is sitting on": "The part of the couch", + "these two bears are a little darker than the third bear who has two visible eyes": "three bears", + "yellow triangular shape": "yellow triangular shape", + "Train on the nerarer track": "Train", + "People holding wine glasses": "People", + "The people who are wearing helmets at this time": "The people", + "The magazines that are on the top shelf": "The magazines", + "The book with a black and yellow cover": "The book", + "The giraffes that are following the leader": "The giraffes", + "The giraffes that are standing together": "The giraffes", + "Giraffe standing directly alongside a smaller herbivore of a different breed": "Giraffe", + "Container with red liquid": "Container", + "Dish that holds food": "Dish", + "Appliance that contains turkey": "Appliance", + "the table with the wedding cake": "The table", + "The chairs that are on the man's right side": "The chairs", + "The chairs closest to the table near the camera": "The chairs", + "Laptops pressed up to a wall": "Laptops", + "The laptop with a black chassis": "The laptop", + "the keyboards with trackpad": "the keyboards", + "White and red passenger transporter": "Passenger transporter", + "Large grey motor vehicle": "Large grey motor vehicle", + "the sandwiches to the left on the same plate": "The sandwiches", + "skiers that are standing upright on their skis": "skiers", + "The two women with hair light enough to not be brown": "The two women", + "People with bare arms sitting on the grass": "People", + "The people who have sleeves past their elbows": "The people", + "All the people holding umbrellas": "All the people", + "The women that are on bikes": "The women", + "The men that are wearing suits": "The men", + "The people wearing something on their heads": "The people", + "All the people with white shirts": "All the people", + "The person with the headband": "The person", + "These all are pieces of luggage that are not being held by a person": "pieces of luggage", + "Tall glass with beer": "Tall glass", + "Black and metal seating": "Seating", + "Deep round dish": "Deep round dish", + "The laptop that the man is working on": "The laptop", + "Round table with people around it": "Round table", + "The table with the wine glass on it": "The table", + "The two teddy bears at the closer end of the table": "The two teddy bears", + "each of these glasses still has wine in it": "glasses", + "The two glasses that stand on the closer end of the table": "The two glasses", + "The pizza that is near the greens": "The pizza", + "The two pizzas closest to the woman": "The two pizzas", + "The pizza that has a knife on it": "The pizza", + "The nearer bed": "The bed", + "Furniture with food on it": "Furniture", + "The surface that is made out of glass": "The surface", + "The longer seating piece of furniture": "seating piece of furniture", + "the open umbrellas": "The open umbrellas", + "Electronic device with multiple buttons": "Electronic device", + "Electronic you set in your lap": "Electronic", + "the electronic device that can be used to make a phone call": "electronic device", + "bird not being obscured by leaves": "bird", + "The bird that has its wings down": "The bird", + "Kitchen appliance with a pot on top": "Kitchen appliance", + "The cars that are parked near the stop sign": "The cars", + "The input device to move the curser on a computer screen": "The input device", + "The cows with the black fur": "The cows", + "The smallest appliance": "The smallest appliance", + "The appliance keeping the food cool": "The appliance", + "Overhead electronic cooking device": "Electronic cooking device", + "The tables that don't have food on them": "The tables", + "The remote on the top": "The remote", + "Luggage for clothes": "Luggage", + "The fruit sitting on the plate": "The fruit", + "round citrus fruit": "citrus fruit", + "both of these bowls are the color white": "bowls", + "The couches with a gray fabric": "The couches", + "an appliance with stickers or magnets that keeps food cool": "an appliance", + "The place you can wash dishes by hand": "The place", + "The two smaller giraffes": "The two smaller giraffes", + "Small black electronic device for calls": "Small black electronic device", + "The horses that are facing the camera": "The horses", + "The horses that have blue fabrics on them": "The horses", + "this appliance is used for cooking and baking": "this appliance", + "Controller with multiple buttons": "Controller", + "The area where someone would type": "The area", + "The elephant that is partially on the path": "The elephant", + "The elephants that are adults": "The elephants", + "Person with brightly colored shirts": "Person", + "All the players who are on defense": "All the players", + "Motorcycles with bright colors": "Motorcycles", + "The input device with keys": "The input device", + "The screen that is lit up": "The screen", + "The bed with white sheets": "The bed", + "you can use a stream of water to wash your hands in it or fill it with water and wash the dishes": "a stream of water", + "The stove with the open oven door that the man is standing in front of showing what he is cooking in the oven": "The stove", + "The cups that are empty": "The cups", + "The cups that the cat is not interested in": "The cups", + "Cup close to plate": "Cup", + "The people not holden an umbrella": "The people", + "each of these persons face is fully visible": "each of these persons", + "Two people crouching low to the ground": "Two people", + "The players that are in the game": "The players", + "The people who are playing the game": "The people", + "The area where someone can sit to smoke": "The area", + "An electronic device to work the television": "An electronic device", + "you can not see the head of any of these zebras": "zebras", + "these three cups are actually clear glasses and NOT white in color": "three cups", + "The cup with ice cream on top": "The cup", + "Round metal eating utensil": "Round metal eating utensil", + "Kids wearing an apron": "Kids", + "A person who is crouching": "A person", + "this item is used to keep warm in colder weather": "this item", + "The plates without utinsels on them": "The plates", + "The white items holding food": "The white items", + "the food item that looks like a curled up worm": "the food item", + "Orange sliced vegetable": "Orange sliced vegetable", + "Long yellow and brown fruit": "fruit", + "horses facing the yellow gate": "horses", + "The horses that are adults and full grown": "The horses", + "Brown horses walking on the beach": "Brown horses", + "The clock facing away from the building": "The clock", + "The clock that has a face that is facing the same direction as the statue above it": "The clock", + "The people that are upright on their boards": "The people", + "A person who is getting married": "A person", + "chair that is pushed up to the table": "chair", + "Chair on ends of island": "Chair", + "Chairs on the outsides of the fireplace": "Chairs", + "Furniture with green fabric": "Furniture", + "The wooden thing with the dishes on it": "The wooden thing", + "The thing that is holding the cup": "The thing", + "a horizontal piece of furniture used for sleeping": "a horizontal piece of furniture", + "The furniture that has a round glass surface": "The furniture", + "Furniture with dishes on it": "Furniture", + "The cows that are black and white": "The cows", + "The green-tinted bottles": "The green-tinted bottles", + "Slices of pizza on a plate carried by a person": "Slices of pizza", + "the pizza without the vegetables on it": "the pizza", + "The bus with the number 52 on it": "The bus", + "Bananas in or next to a protective red carry case": "Bananas", + "The bears wearing black shirts": "The bears", + "The chairs that no one is sitting in": "The chairs", + "The chair closer to the Rolex sign": "The chair", + "The furniture with the red cross on it": "The furniture", + "The flower in the container": "The flower", + "The structure holding the tray of desserts": "The structure", + "Sleeping area with white sheet": "Sleeping area", + "The vehicle with a license plate reading TGL552": "The vehicle", + "people watching the dog jump": "people", + "the bowls that do not contain white dip": "the bowls", + "People with video game controllers in hands": "People", + "each of these hot dogs are NOT in the middle": "hot dogs", + "the sandwich that is closest to the left": "the sandwich", + "The zebras that have their heads down": "The zebras", + "the little child and the person who appears connected to the child's head": "The little child", + "stainless steel object used for cleaning dishes manually": "stainless steel object", + "The people standing near the fruit": "The people", + "The men sitting at the table": "The men", + "The older people that are playing a game": "The older people", + "All the people behind the batter": "All the people", + "Suitcase on the floor next to white curtain": "Suitcase", + "The skateboard in action": "The skateboard", + "The motorcycles that don't have a helmet on them": "The motorcycles", + "Motorcycle with red chassis": "Motorcycle", + "The table that the pizza is sitting on": "The table", + "the women with long hair": "the women", + "the cakes that are not being cut": "the cakes", + "The cake that has a ridged edge": "The cake", + "Dish with a liquid in it": "Dish", + "Silverware with multiple prongs": "Silverware", + "The orange pieces of food": "The orange pieces of food", + "Furniture with a toddler sitting on it": "Furniture", + "The chairs that are not occupied": "The chairs", + "This chair has a small pillow on it. The other chair is facing a black table": "This chair, The other chair, black table", + "The people who are dressed in black": "The people", + "The people on the top row": "The people", + "Brown furniture to sit on": "Brown furniture", + "Tan metal furniture with black and red sticker": "Tan metal furniture", + "the three people that are wearing sunglasses not regular eyeglasses": "three people", + "the two women that are not wearing a skirt": "the two women", + "The people that are sitting down": "The people", + "The players who have blue shirts": "The players", + "All the people who are men": "All the people", + "The food that is in the square bowls": "The food", + "The bowls containing brown-colored food": "The bowls", + "white plastic eating utensil with prongs": "white plastic eating utensil", + "The dark colored liquid": "The dark colored liquid", + "Glass with water": "Glass", + "Furniture with white surface that holds a pot and a bowl": "Furniture", + "Wood and metal place to sit": "Place to sit", + "each table is round and not with a glass on top": "each table", + "Horse being touched by person": "Horse", + "The horses that aren't looking at the camera": "The horses", + "People riding the same elephant": "People", + "Two women standing facing and talking to a person in camo": "Two women", + "People holding a frisbee": "People", + "All the people on the scooters": "All the people", + "Backbag carried by blonde woman": "Backbag", + "Bottles containing light colored sauces": "Bottles", + "each one of these zebras has an eye that is visible to the viewer": "zebras", + "The zebras showing their rear ends": "The zebras", + "pizza slices with more than just a cheese topping": "pizza slices", + "Two zebras standing side by side": "Two zebras", + "The zebras that are facing each other": "The zebras", + "The cat that is sitting at the base of the tree": "The cat", + "The cat with the fluffy tail": "The cat", + "The cat that is higher up on the cushion": "The cat", + "A cat laying down with the tail by it's head": "A cat", + "A cat who is looking at the camera": "A cat", + "The laptop that is not on": "The laptop", + "The colorful thing that the person is holding in front of them": "The colorful thing", + "each of these giraffes has a head that is among the leaves on the trees": "giraffes", + "The people who are squatting near the truck": "The people", + "Wodden furniture holding wine bottles": "Wodden furniture", + "The places to sit at the round surface near the window": "The places to sit", + "A potted plant with yellowish leaves": "A potted plant", + "the computers without the white boarder": "the computers", + "The monitors that are set up with a keyboard under them": "The monitors", + "The TV sitting on the black and white stand": "The TV", + "The screen the boy is looking at": "The screen", + "the zebras that are looking to the right": "The zebras", + "Two zebras that are standing side by side": "Two zebras", + "The zebras that are lying down together": "The zebras", + "The zebras that are standing on the dirt path": "The zebras", + "The people that are standing up to play the game": "The people", + "The three people who are not using their phone, and wearing a rainbow umbrella hat": "The three people", + "The women in the black sleeveless shirts": "The women", + "The black input device with many keys": "The black input device", + "teddy bears with a black book behind them": "teddy bears", + "the neck tie": "the neck tie", + "The thing the person is holding to block the sun": "The thing", + "Adult wearing colorful clothing": "Adult", + "The players who are on the playing field": "The players", + "A person with at least one hand between his legs": "A person", + "furniture intended for people to sit down": "furniture", + "The pizza containing pink ham": "The pizza", + "A black notepad with the letter W on it": "A black notepad", + "Cup with dipping sauce in it": "Cup", + "Dish containing onions": "Dish", + "The clear colored utinsils": "The clear colored utensils", + "A mug with coffee in it": "A mug", + "Smaller item of upholstered furniture, seating for one person": "Smaller item of upholstered furniture", + "the item used for grooming being held in the child's hands": "the item", + "The succulent growing in the container": "The succulent", + "The furniture with the floral pattern": "The furniture", + "The bear in the back": "The bear", + "People sitting at the table": "People", + "Container with dipping sauce": "Container", + "These vehicles run on tracks rather than roads": "These vehicles", + "The vehicle that can fit more than ten people": "The vehicle", + "the individual sandwiches": "individual sandwiches", + "the two people that are bent over looking into the mini fridge": "the two people", + "the two people who are seated each with their legs crossed": "the two people", + "people that are seating at a table by the window": "people", + "two person stand next to each other while holding wii remote controls": "two person", + "hands of a person without a face": "hands of a person", + "Orange pronged eating utensil": "Orange pronged eating utensil", + "The foil container with the food": "The foil container", + "The racket that is parallel to the ground": "The racket", + "Zebras that are grazing": "Zebras", + "The women who are posing together": "The women", + "The wine glass held by the woman with the lip piercing": "The wine glass", + "The people wearing dark shirts": "The people", + "Black stool chairs with back rests at the counter": "Black stool chairs", + "the chair that has a person wearing a dark-colored coat sitting in it": "The chair", + "The vase closest to the TV": "The vase", + "The chairs standing against the wall": "The chairs", + "each of these chairs has a person sitting in it": "chairs", + "The chairs that have a black armrest": "The chairs", + "each of these persons is wearing a short-sleeved shirt": "each of these persons", + "The people standing up to play a game": "The people", + "The chairs that are at the counter": "The chairs", + "Chairs standing on the sidewalk": "Chairs", + "white sofa chairs": "sofa chairs", + "Furniture with electronic equipment on it": "Furniture", + "People facing the bus": "People", + "The people that have one leg out the boat": "The people", + "The baseball players in gray uniforms and no black top": "The baseball players", + "The person with the orange skis on his back": "The person", + "Large passenger vehicle with cat graffiti on the front": "Large passenger vehicle", + "Wooden furniture you sit on": "Wooden furniture", + "Blue and white wood eating area": "Wood eating area", + "The chairs that are green": "The chairs", + "the pair of chairs next to each other that have the circular base": "The pair of chairs", + "The sandwich hanging out over the plate a bit": "The sandwich", + "The containers with the drinks": "The containers", + "A utensil for eating soup": "A utensil", + "Boys wearing long ties": "Boys", + "The people with gloves": "The people", + "The place where a person would sleep": "The place", + "The place to sit at the desk": "The place", + "These are used to sit on while eating": "These", + "The green plant with leaves hanging on the wall": "The green plant", + "Breadsticks on a silvery tray": "Breadsticks", + "a round fruit that is commonly cored and sliced": "a round fruit", + "objects with buttons for typing": "objects", + "these two chairs are both facing to the right": "two chairs", + "Boat with two buoys on the side": "Boat", + "Plastic furniture in the sun you lounge on": "Plastic furniture", + "The black surface for eating": "The black surface", + "Furniture you sit on": "Furniture", + "The chairs that people are sitting in": "The chairs", + "Furniture intended to sit on": "Furniture", + "The spoon with the crushed garlic": "The spoon", + "Person with blonde hair": "Person", + "The people that are playing the sport": "The people", + "The two people in dark shirts, and standing outside of the restaurant/": "The two people", + "The cat with the darker fur": "The cat", + "The thing the people are sitting on": "The thing", + "each of these persons is wearing long pants": "each of these persons", + "The buses that are parked perpendicular to this street": "The buses", + "The people wearing a white shirt": "The people", + "The elephant on the placard": "The elephant", + "the vertical suitcase": "The vertical suitcase", + "the people who are each wearing a yellow shirt": "The people", + "Men sitting at a table": "Men", + "The people in front of the bus": "The people", + "the person is wearing a red and grey horizontally-striped shirt": "the person", + "the people that are wearing a costume or a mask": "The people", + "People not next to a horse": "People", + "the people that are wearing a grey t-shirt and are in a wheelchair": "The people", + "the three people with no visible earrings": "the three people", + "The two kids playing baseball while standing in the dirt, not grass": "The two kids", + "The people wearing blue tops": "The people", + "a person stands with their head down, leaning forward while a second person walks next to an approaching bus near a bus stop": "Two people", + "Metal tub with running water": "Metal tub", + "The two adults": "The two adults", + "The people who are customers": "The people", + "the two people that are wearing reflective safety vests": "the two people", + "All the people whos face isn't visble": "All the people", + "All the people standing on the court": "All the people", + "The people in the background": "The people", + "The blue utinsil with the tines": "The blue utinsil", + "Glass with orange juice": "Glass", + "a dish used for soups": "a dish", + "The men that are standing for the picture": "The men", + "the two kids": "the two kids", + "The women who are sitting down": "The women", + "The dark back of the chair that the man in the blue and black shirt is sitting in": "The chair", + "The furniture with Winnie the Pooh on it": "The furniture", + "Evil looking little troll dolls": "troll dolls", + "The seat with the fabric on it": "The seat", + "The two man with longer curly hair": "The two man", + "All the people sitting at the table": "All the people", + "Electronic device with a hinged lid that houses the screen": "Electronic device", + "The people that are sitting down inside": "The people", + "The people that are not wearing hats": "The people", + "The one piece of broccoli closest to the fork": "One piece of broccoli", + "Metal utensil with prongs": "Metal utensil", + "The container holding the sandwich": "The container", + "The containers of condiments": "The containers", + "Person wearing shorts": "Person", + "The people standing between the goats and the fence": "The people", + "people that are standing in the picture": "people", + "this bear is standing up and has its backside to the camera": "bear", + "Dish with broccoli in it": "Dish", + "Glass container with liquid": "Glass container", + "The book with the white cover": "The book", + "All of the input devices": "All of the input devices", + "The zebra on the right": "The zebra", + "the two people that are on the same motorbike": "the two people", + "The vehicles that don't need fuel": "The vehicles", + "Open air transportation vehicle for two people": "Open air transportation vehicle", + "Men wearing blue shirts sitting at a table": "Men", + "The people wearing green-colored shirts": "The people", + "The children sitting at the table": "The children", + "Women with black tops": "Women", + "The zebras that have their heads toward the right": "The zebras", + "Zebra with face near branches": "Zebra", + "The tables in back of the people": "The tables", + "The players wearing white shirts": "The players", + "The people that are wearing orange shirts": "The people", + "The people sitting on the edges of the bench": "The people", + "The seating areas made for one person": "The seating areas", + "The cups that are black": "The cups", + "the cup with the clear liquid": "The cup", + "White plastic eating utensil with prongs": "White plastic eating utensil", + "red seating area": "seating area", + "the adults that are sitting": "The adults", + "The people wearing white hats": "The people", + "All people wearing shorts": "All people", + "The cars that the dog is not sitting on": "The cars", + "Transportation vehicle that flies": "Transportation vehicle", + "the motor vehicle with two wheels and carrying two people": "the motor vehicle", + "the vehicle that is white in color": "the vehicle", + "The people visibly wearing a name tag": "The people", + "The surface where the food is sitting": "The surface", + "Furniture you put plates on": "Furniture", + "The people standing on the right side of the table": "The people", + "the two items that are identified as flatware": "two items", + "The wooden furniture with the magazine on it": "The wooden furniture", + "Sleeping area with white sheets": "Sleeping area", + "The motorcycle of the racer with number 14": "The motorcycle", + "The bird that is standing on the grassier area on the left": "The bird", + "this electronic device can be used to view computer images": "electronic device", + "People in the dugout": "People", + "The men on the court": "The men", + "The input device near the computer": "The input device", + "the baseball players with a mustache": "baseball players", + "A person wearing torn clothing": "A person", + "Plastic eating utensil used to cut food": "Plastic eating utensil", + "People wearing blue coats": "People", + "A person who is sitting": "A person", + "both of these people are each wearing a white shirt": "both of these people", + "The people that are laughing on the right side of the board": "The people", + "The people with the gray shirts": "The people", + "Empty coke bottle": "Empty coke bottle", + "The closest place to sit near the window": "The closest place to sit", + "This is used to put food and drink on so you can sit and eat in front of it": "This", + "The girls that are playing on the court": "The girls", + "The people who have blue hats": "The people", + "The wooden piece where you could put a plate of food": "The wooden piece", + "The round wooden surface that the food is on": "The round wooden surface", + "Porcelain bathroom seat": "Porcelain bathroom seat", + "The utinsils that are near the food": "The utinsils", + "The racket that the bigger child is holding": "The racket", + "the four food items on the blue plate": "four food items", + "The place where someone would sleep": "The place", + "Item you have to paddle around to move": "Item", + "The blue jacket the woman is wearing": "The blue jacket", + "The people that are wearing tan clothing": "The people", + "people with upper arms showing": "people", + "Black container with drinking liquid": "Black container", + "A utensil used to cut vegetables": "A utensil", + "A rectangular Furniture with candles and dishes on it": "A rectangular Furniture", + "this couch has an electronic device sitting on it and not a person": "this couch", + "The couch with multiple pillows on it": "The couch", + "The people wearing hats": "The people", + "The vehicle with the blue body": "The vehicle", + "The parking meter that is on the right side": "The parking meter", + "The silver colored utinsil": "The silver colored utinsil", + "the sandwich that has some of its contents spilled onto the saucer": "The sandwich", + "The red car that is next to the Hess sign": "The red car", + "the two vehicles that don't have a visible animated character on the side": "two vehicles", + "The ground vehicle with two wheels": "The ground vehicle", + "People High-Fiving each other": "People", + "The people cutting the cake": "The people", + "these two people are sitting next to each other": "these two people", + "The places that people can sit": "The places", + "People wearing dark blue team shirts": "People", + "The clock in which all the numbers are visible": "The clock", + "The bigger sheep that is white": "The bigger sheep", + "The yellow vehicle with the black stripe on it": "The yellow vehicle", + "Red coloured large transportation vehicle for multiple passengers": "Transportation vehicle", + "A tie hidden by a sweater": "A tie", + "Vehicle that transports passengers": "Vehicle", + "The vehicle with the greatest occupancy": "The vehicle", + "Large metal passenger vehicle on tracks": "Large metal passenger vehicle", + "Tie on person coming out of mirror": "Tie on person", + "The tie that is a solid color": "The tie", + "the two people who are wearing shorts and legs are visible": "the two people", + "The people that have yellow in their uniforms": "The people", + "The people who are on the boards": "The people", + "The cows that are sitting under the tree": "The cows", + "the cow that is not eating grass": "The cow", + "The furniture the woman is lying down on": "The furniture", + "The red drinking container": "The red drinking container", + "People with light colored shirts": "People", + "People in blue jackets standing together near furry animals": "People", + "The people that are standing up to play a game": "The people", + "The people who aren't holding a bat": "The people", + "the giraffe in the front": "The giraffe", + "people with red hair": "people", + "the people wearing white shirts": "The people", + "A person not wearing a hat": "A person", + "Item you sit on": "Item", + "Plant in corner of room": "Plant", + "The meter on the right": "The meter", + "The parking meter with the two on it": "The parking meter", + "The people who aren't visibly holding a racket": "The people", + "Orange and red seating area": "seating area", + "The people pouring liquid into a cup": "The people", + "The people wearing hats that aren't white": "The people", + "a zebra lays on the forest floor with sunlight shining on him": "a zebra", + "The people in front of the cows": "The people", + "All the people with umbrellas over their heads": "All the people", + "The men that are posing together": "The men", + "The people with their legs visible": "The people", + "All the people on the bench": "All the people", + "All the people without hats": "All the people", + "All the people behind the man with the blue shirt": "All the people", + "The people with shades on": "The people", + "All the people whos eyes are closed": "All the people", + "The people who are wearing hats": "The people", + "The children who are on the court": "The children", + "The purple thing the dog is sitting on": "The purple thing", + "The people who aren't babies": "The people", + "The people who are competing against each other": "The people", + "The people touching the kite's fabric": "The people", + "Shoes that are on the ground": "Shoes", + "The two people who are wearing hats on their head": "The two people", + "The people who are not swinging the bat": "The people", + "The people wearing dark blue shirt": "The people", + "Elephants shorter than the rest": "Elephants", + "Bycicles showing handle bars": "Bycicles", + "The people whose faces you can't see": "The people", + "The bags that the woman with the white shirt is holding": "The bags", + "handbag worn by the person with orange cap": "handbag", + "cannot see full body": "N/A (This is not a complete sentence and does not have a main subject)", + "The dog with the visible price tag on it": "The dog", + "The people who are on the bus": "The people", + "The people wearing all black": "The people", + "The people without goggles covering their face": "The people", + "All the people wearing dark clothes": "All the people", + "All the workers on the left side of the conveyor belt": "All the workers", + "Light brown furniture with hot dogs on it": "Light brown furniture", + "a piece of furniture that you sit on": "a piece of furniture", + "a utensil used for eating soup and pasta": "a utensil", + "Glass you fill with liquid": "Glass", + "Wooden furniture used for seating": "Wooden furniture", + "Orange and wood seating": "Orange and wood seating", + "The furniture on which the dishes are sitting": "The furniture", + "All the people riding the elephant": "All the people", + "An oven with a closed drawer next to it": "An oven", + "The black and white cat": "The black and white cat", + "Round metal utensil with a bowl and a handle": "Round metal utensil", + "Bedroom item that you lay down on": "Bedroom item", + "The two peopel that are not wearing lanyards": "The two people", + "Person with a glass in their hand": "Person", + "A person wearing light colored pants": "A person", + "The people with long sleeves that are holding devices": "The people", + "people not shown eating": "people", + "The boat with the number 199 on it": "The boat", + "skateboards with people doing tricks on them": "skateboards", + "The bigger of the two skateboards": "The bigger skateboard", + "The things growing behind the sofa": "The things", + "The see-through salt or pepper shaker that is next to the White container all the way to the left": "The see-through salt or pepper shaker", + "The container with the rice in it": "The container", + "obejcts that hold liquid": "obejcts", + "Container with food in it": "Container", + "The truck with a license plate close to the ground": "The truck", + "Furniture not near an island": "Furniture", + "The pizza near the fork": "The pizza", + "Furniture with plates on it": "Furniture", + "The dog on the right": "The dog", + "The clear container with the liquid": "The clear container", + "front legs are straight and not bent": "front legs", + "Giraffes with their mouths open": "Giraffes", + "The giraffe with the most brown on its nose": "The giraffe", + "Trucks with openings": "Trucks", + "The truck that is white": "The truck", + "Container than you drink from": "Container", + "utensil with tines used for holding food": "utensil", + "Sharp metal utensil for cutting": "Sharp metal utensil", + "The smaller bear that is on the left side": "The smaller bear", + "The bear to the left": "The bear", + "The bed closest to the window": "The bed", + "these two sandwiches are on the same plate": "two sandwiches", + "A corned beef sandwich": "A corned beef sandwich", + "A cow whose tail is curled on its back": "A cow", + "A cat facing right": "A cat" +} diff --git a/tools/pics/.DS_Store b/tools/pics/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..78a6b1fbb1ecb75c96eb07ec86f80bca41e5b773 Binary files /dev/null and b/tools/pics/.DS_Store differ diff --git a/tools/run_demo.py b/tools/run_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..ffc92501e2e5c051771c2a8311f252a439e33d20 --- /dev/null +++ b/tools/run_demo.py @@ -0,0 +1,74 @@ +import matplotlib.pyplot as plt +import matplotlib.pylab as pylab + +import requests +from io import BytesIO +from PIL import Image +import numpy as np +pylab.rcParams['figure.figsize'] = 20, 12 +from maskrcnn_benchmark.config import cfg +from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo +import argparse +import pdb + +def load(url_or_path): + """ + Given an url or a path, this loads the file and + """ + if url_or_path.startswith("http"): + response = requests.get(url_or_path) + pil_image = Image.open(BytesIO(response.content)).convert("RGB") + # convert to BGR format + image = np.array(pil_image)[:, :, [2, 1, 0]] + else: + image = np.array(Image.open(url_or_path).convert("RGB"))[:, :, [2, 1, 0]] + return image + + +parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference") +parser.add_argument("--config", default="configs/pretrain/glip_Swin_T_O365_GoldG.yaml", metavar="FILE", help="path to config file", type=str) +parser.add_argument("--weight", default="OUTPUTS/GLIP_MODEL4/model_0020000.pth", metavar="FILE", help="path to weight file", type=str) +parser.add_argument("--image", default="http://farm4.staticflickr.com/3693/9472793441_b7822c00de_z.jpg", metavar="FILE", help="path to weight file", type=str) +parser.add_argument("--conf", default=0.4, type=float) +parser.add_argument("--caption", default="", type=str) +parser.add_argument("--ground_tokens", default=None, type=str) + +args = parser.parse_args() + +# update the config options with the config file +# manual override some options +cfg.local_rank = 0 +cfg.num_gpus = 1 +cfg.merge_from_file(args.config) +cfg.merge_from_list(["MODEL.WEIGHT", args.weight]) +cfg.merge_from_list(["MODEL.DEVICE", "cuda"]) + +glip_demo = GLIPDemo( + cfg, + min_image_size=800, + show_mask_heatmaps=False +) + +athetics_params = { + "skip_name": False, # whether we overlay the phrase over the box + "override_color": (255, 255, 255), # box color, default is white + "text_size": 1.0, + "text_pixel": 3, + "box_alpha": 1.0, + "box_pixel": 5, + "text_offset_original": 8, # distance between text and box +} + +image = load(args.image) +specified_tokens = args.ground_tokens.split(";") if args.ground_tokens is not None else None + +result, _ = glip_demo.run_on_web_image( + image, + args.caption, + args.conf, + specified_tokens, + **athetics_params) + +plt.imshow(result[:, :, [2, 1, 0]]) +plt.axis("off") +plt.savefig(args.image.replace('.png', "_demo.png").replace('.jpg', "_demo.jpg").replace('.jpeg', "_demo.jpeg"), bbox_inches='tight', pad_inches=0) # save as xxx_demo.xxx diff --git a/tools/test_grounding_net.py b/tools/test_grounding_net.py new file mode 100644 index 0000000000000000000000000000000000000000..cbde6fc4fec7104b3654a83ad567eb0c0c258014 --- /dev/null +++ b/tools/test_grounding_net.py @@ -0,0 +1,292 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# Set up custom environment before nearly anything else is imported +# NOTE: this should be the first import (no not reorder) +from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip + +import argparse +import os + +import torch +from maskrcnn_benchmark.config import cfg +from maskrcnn_benchmark.data import make_data_loader +from maskrcnn_benchmark.modeling.detector import build_detection_model +from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer +from maskrcnn_benchmark.utils.collect_env import collect_env_info +from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process +from maskrcnn_benchmark.utils.logger import setup_logger +from maskrcnn_benchmark.utils.miscellaneous import mkdir +from maskrcnn_benchmark.utils.stats import get_model_complexity_info +import os +import functools +import io +import os +import datetime +import wandb +import torch +import torch.distributed as dist +import pdb +from pprint import pprint + +def init_distributed_mode(args): + """Initialize distributed training, if appropriate""" + if "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + # args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + + dist.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + timeout=datetime.timedelta(0, 72000), + ) + dist.barrier() + setup_for_distributed(args.rank == 0) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def main(): + parser = argparse.ArgumentParser(description="PyTorch Detection to Grounding Inference") + parser.add_argument( + "--config-file", + default="configs/grounding/e2e_dyhead_SwinT_S_FPN_1x_od_grounding_eval.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument( + "--weight", + default=None, + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--local_rank", type=int, default=0) + parser.add_argument( + "opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER + ) + parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes") + parser.add_argument("--dist-url", default="env://", help="url used to set up distributed training") + + parser.add_argument("--task_config", default=None) + parser.add_argument("--eval_negative", action="store_true") + parser.add_argument("--wandb_project_name", default="haroldli/language_det_eval") + args = parser.parse_args() + + num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1 + distributed = num_gpus > 1 + + if distributed: + # torch.cuda.set_device(args.local_rank) + # torch.distributed.init_process_group( + # backend="nccl", init_method="env://" + # ) + init_distributed_mode(args) + print("Passed distributed init") + + cfg.local_rank = args.local_rank + cfg.num_gpus = num_gpus + + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + + log_dir = cfg.OUTPUT_DIR + if args.weight: + log_dir = os.path.join(log_dir, "eval", os.path.splitext(os.path.basename(args.weight))[0]) + if log_dir: + mkdir(log_dir) + + logger = setup_logger("maskrcnn_benchmark", log_dir, get_rank()) + logger.info(args) + logger.info("Using {} GPUs".format(num_gpus)) + logger.info(cfg) + + # logger.info("Collecting env info (might take some time)") + # logger.info("\n" + collect_env_info()) + + model = build_detection_model(cfg) + model.to(cfg.MODEL.DEVICE) + + # we currently disable this + # params, flops = get_model_complexity_info(model, + # (3, cfg.INPUT.MAX_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST), + # input_constructor=lambda x: {'images': [torch.rand(x).cuda()]}) + # print("FLOPs: {}, #Parameter: {}".format(params, flops)) + + checkpointer = DetectronCheckpointer(cfg, model, save_dir=cfg.OUTPUT_DIR) + if args.weight: + _ = checkpointer.load(args.weight, force=True) + else: + _ = checkpointer.load(cfg.MODEL.WEIGHT) + if args.weight: + weight_iter = os.path.splitext(os.path.basename(args.weight))[0].split("_")[-1] + try: + weight_iter = int(weight_iter) + except: + weight_iter = 1 + else: + weight_iter = 1 + + # get the wandb name + train_wandb_name = os.path.basename(cfg.OUTPUT_DIR) + eval_wandb_name = train_wandb_name + "_eval" + "_Fixed{}_Chunk{}".format(not cfg.DATASETS.LVIS_USE_NORMAL_AP, cfg.TEST.CHUNKED_EVALUATION) + + if args.eval_negative: + from maskrcnn_benchmark.engine.inference_contrastive import inference + inference_function = inference + else: + from maskrcnn_benchmark.engine.inference import inference + inference_function = inference + + if is_main_process() and train_wandb_name != "__test__": + api = wandb.Api() + runs = api.runs(args.wandb_project_name) + matched_run = None + history = [] + exclude_keys = ['_runtime', '_timestamp'] + for run in runs: + if run.name == eval_wandb_name and str(run._state) == "finished": + print("run found", run.name) + print(run.summary) + matched_run = run + run_his = matched_run.scan_history() + #print([len(i) for i in run_his]) + + for stat in run_his: + stat_i = {k: v for k, v in stat.items() if k not in exclude_keys and v is not None} + if len(stat_i) > 1: + history.append(stat_i) + #matched_run.delete() + break + wandb_run = wandb.init( + project = 'language_det_eval', + job_type = 'evaluate', + name = eval_wandb_name, + ) + #pprint(history) + # exclude_keys = ['_step', '_runtime', '_timestamp'] + # for stat in history: + # wandb.log( + # {k: v for k, v in stat.items() if k not in exclude_keys}, + # step = stat['_step'], + # ) + else: + wandb_run = None + history = None + print("weight_iter: ", weight_iter) + print("train_wandb_name: ", train_wandb_name) + print("eval_wandb_name: ", eval_wandb_name) + + if args.task_config: + all_task_configs = args.task_config.split(",") + for task_config in all_task_configs: + cfg_ = cfg.clone() + cfg_.defrost() + cfg_.merge_from_file(task_config) + cfg_.merge_from_list(args.opts) + iou_types = ("bbox",) + if cfg_.MODEL.MASK_ON: + iou_types = iou_types + ("segm",) + if cfg_.MODEL.KEYPOINT_ON: + iou_types = iou_types + ("keypoints",) + dataset_names = cfg_.DATASETS.TEST + if isinstance(dataset_names[0], (list, tuple)): + dataset_names = [dataset for group in dataset_names for dataset in group] + output_folders = [None] * len(dataset_names) + if log_dir: + for idx, dataset_name in enumerate(dataset_names): + output_folder = os.path.join(log_dir, "inference", dataset_name) + mkdir(output_folder) + output_folders[idx] = output_folder + data_loaders_val = make_data_loader(cfg_, is_train=False, is_distributed=distributed) + + for output_folder, dataset_name, data_loader_val in zip( + output_folders, dataset_names, data_loaders_val + ): + inference_function( + model, + data_loader_val, + dataset_name=dataset_name, + iou_types=iou_types, + box_only=cfg_.MODEL.RPN_ONLY + and (cfg_.MODEL.RPN_ARCHITECTURE == "RPN" or cfg_.DATASETS.CLASS_AGNOSTIC), + device=cfg_.MODEL.DEVICE, + expected_results=cfg_.TEST.EXPECTED_RESULTS, + expected_results_sigma_tol=cfg_.TEST.EXPECTED_RESULTS_SIGMA_TOL, + output_folder=output_folder, + cfg=cfg_, + wandb_run=wandb_run, + weight_iter=weight_iter, + history=history, + ) + synchronize() + # logger.info("FLOPs: {}, #Parameter: {}".format(params, flops)) + + else: + iou_types = ("bbox",) + if cfg.MODEL.MASK_ON: + iou_types = iou_types + ("segm",) + if cfg.MODEL.KEYPOINT_ON: + iou_types = iou_types + ("keypoints",) + dataset_names = cfg.DATASETS.TEST + if isinstance(dataset_names[0], (list, tuple)): + dataset_names = [dataset for group in dataset_names for dataset in group] + output_folders = [None] * len(dataset_names) + if log_dir: + for idx, dataset_name in enumerate(dataset_names): + output_folder = os.path.join(log_dir, "inference", dataset_name) + mkdir(output_folder) + output_folders[idx] = output_folder + data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed) + + for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val): + inference_function( + model, + data_loader_val, + dataset_name=dataset_name, + iou_types=iou_types, + box_only=cfg.MODEL.RPN_ONLY + and (cfg.MODEL.RPN_ARCHITECTURE == "RPN" or cfg.DATASETS.CLASS_AGNOSTIC), + device=cfg.MODEL.DEVICE, + expected_results=cfg.TEST.EXPECTED_RESULTS, + expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL, + output_folder=output_folder, + cfg=cfg, + wandb_run=wandb_run, + weight_iter=weight_iter, + history=history + ) + synchronize() + # logger.info("FLOPs: {}, #Parameter: {}".format(params, flops)) + + +if __name__ == "__main__": + main() diff --git a/tools/test_net.py b/tools/test_net.py new file mode 100644 index 0000000000000000000000000000000000000000..de4bb47a7883d41457089daf52c9bfbfc3f32be8 --- /dev/null +++ b/tools/test_net.py @@ -0,0 +1,129 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# Set up custom environment before nearly anything else is imported +# NOTE: this should be the first import (no not reorder) +from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip + +import argparse +import os + +import torch +from maskrcnn_benchmark.config import cfg +from maskrcnn_benchmark.data import make_data_loader +from maskrcnn_benchmark.engine.inference import inference +from maskrcnn_benchmark.modeling.detector import build_detection_model +from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer +from maskrcnn_benchmark.utils.collect_env import collect_env_info +from maskrcnn_benchmark.utils.comm import synchronize, get_rank +from maskrcnn_benchmark.utils.logger import setup_logger +from maskrcnn_benchmark.utils.miscellaneous import mkdir +from maskrcnn_benchmark.utils.stats import get_model_complexity_info + + +def run_test(cfg, model, distributed, log_dir): + if distributed and hasattr(model, "module"): + model = model.module + torch.cuda.empty_cache() # TODO check if it helps + iou_types = ("bbox",) + if cfg.MODEL.MASK_ON: + iou_types = iou_types + ("segm",) + if cfg.MODEL.KEYPOINT_ON: + iou_types = iou_types + ("keypoints",) + dataset_names = cfg.DATASETS.TEST + if isinstance(dataset_names[0], (list, tuple)): + dataset_names = [dataset for group in dataset_names for dataset in group] + output_folders = [None] * len(dataset_names) + if log_dir: + for idx, dataset_name in enumerate(dataset_names): + output_folder = os.path.join(log_dir, "inference", dataset_name) + mkdir(output_folder) + output_folders[idx] = output_folder + data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed) + for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val): + inference( + model, + data_loader_val, + dataset_name=dataset_name, + iou_types=iou_types, + box_only=cfg.MODEL.RPN_ONLY and (cfg.MODEL.RPN_ARCHITECTURE == "RPN" or cfg.DATASETS.CLASS_AGNOSTIC), + device=cfg.MODEL.DEVICE, + expected_results=cfg.TEST.EXPECTED_RESULTS, + expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL, + output_folder=output_folder, + cfg=cfg, + ) + synchronize() + + +def main(): + parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference") + parser.add_argument( + "--config-file", + default="/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument( + "--weight", + default=None, + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--local_rank", type=int, default=0) + parser.add_argument( + "opts", + help="Modify config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + + args = parser.parse_args() + + num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1 + distributed = num_gpus > 1 + + if distributed: + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group(backend="nccl", init_method="env://") + + cfg.local_rank = args.local_rank + cfg.num_gpus = num_gpus + + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + + log_dir = cfg.OUTPUT_DIR + if args.weight: + log_dir = os.path.join(log_dir, "eval", os.path.splitext(os.path.basename(args.weight))[0]) + if log_dir: + mkdir(log_dir) + logger = setup_logger("maskrcnn_benchmark", log_dir, get_rank()) + logger.info(args) + logger.info("Using {} GPUs".format(num_gpus)) + logger.info(cfg) + + logger.info("Collecting env info (might take some time)") + logger.info("\n" + collect_env_info()) + + model = build_detection_model(cfg) + model.to(cfg.MODEL.DEVICE) + + params, flops = get_model_complexity_info( + model, + (3, cfg.INPUT.MAX_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST), + input_constructor=lambda x: {"images": [torch.rand(x).cuda()]}, + ) + print("FLOPs: {}, #Parameter: {}".format(params, flops)) + + checkpointer = DetectronCheckpointer(cfg, model, save_dir=cfg.OUTPUT_DIR) + if args.weight: + _ = checkpointer.load(args.weight, force=True) + else: + _ = checkpointer.load(cfg.MODEL.WEIGHT) + + run_test(cfg, model, distributed, log_dir) + logger.info("FLOPs: {}, #Parameter: {}".format(params, flops)) + + +if __name__ == "__main__": + main() diff --git a/tools/test_net_omnilabel.py b/tools/test_net_omnilabel.py new file mode 100644 index 0000000000000000000000000000000000000000..0e75a97dd19f7c8c3ec7c0bf4f9cbc27e92c0647 --- /dev/null +++ b/tools/test_net_omnilabel.py @@ -0,0 +1,656 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +# Set up custom environment before nearly anything else is imported +# NOTE: this should be the first import (no not reorder) +from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip + +import argparse +import os +import functools +import io +import datetime +import itertools +import json +from tqdm import tqdm + +import numpy as np +import torch +import torch.distributed as dist +from collections import defaultdict +from maskrcnn_benchmark.config import cfg +from maskrcnn_benchmark.data import make_data_loader +from maskrcnn_benchmark.engine.inference import inference, create_positive_dict, clean_name +from maskrcnn_benchmark.modeling.detector import build_detection_model +from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer +from maskrcnn_benchmark.utils.collect_env import collect_env_info +from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process, all_gather +from maskrcnn_benchmark.utils.logger import setup_logger +from maskrcnn_benchmark.utils.miscellaneous import mkdir +from maskrcnn_benchmark.utils.stats import get_model_complexity_info +from omnilabeltools import OmniLabel, OmniLabelEval, visualize_image_sample +import time +import json +import tempfile +import matplotlib.pyplot as plt +from transformers import AutoTokenizer, CLIPTokenizerFast +import omnilabeltools as olt +from omnilabeltools import OmniLabel, OmniLabelEval +import pdb +import wandb +from multiprocessing import Pool + +class LLM: + def __init__(self, version, prompt_file = None, temp = 1.0): + self.version = version + self.prompt_file = prompt_file + self.temp = temp + with open(self.prompt_file, "r") as f: + self.prompt = f.read() + + def __call__(self, entity): + time.sleep(0.1) + success = False + fail_count = 0 + + if isinstance(entity, list): + prompt = [self.prompt.replace("PROMPT", e) for e in entity] + else: + if self.version == "chat": + raw_prompt = self.prompt.replace("PROMPT", entity) + try: + prompt = json.loads(raw_prompt) + except: + prompt = [{"role": "user", "content": raw_prompt}] + else: + prompt = self.prompt.replace("PROMPT", entity) + + while not success: + try: + if self.version == "chat": + model = "gpt-3.5-turbo" + response = openai.ChatCompletion.create( + model=model, + messages = prompt, + temperature=self.temp, + ) + else: + if self.version == "curie": + model = "curie" + else: + model = "text-davinci-003" + response = openai.Completion.create( + model=model, + prompt=prompt, + temperature=self.temp, + max_tokens=128, + top_p=1, + frequency_penalty=0.0, + presence_penalty=0.0, + ) + success = True + fail_count = 0 + except Exception as e: + print(f"Exception: {e}") + time.sleep(0.1) + fail_count += 1 + + if fail_count > 10: + print("Too many failures") + return "Too many failures" + if isinstance(entity, list): + if self.version == "chat": + return [r["message"]["content"] for r in response["choices"]] + else: + return [r["text"] for r in response["choices"]] + else: + if self.version == "chat": + return response["choices"][0]["message"]["content"] + else: + return response["choices"][0]["text"] + + +def init_distributed_mode(args): + """Initialize distributed training, if appropriate""" + if "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif "SLURM_PROCID" in os.environ: + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + # args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) + + dist.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + timeout=datetime.timedelta(0, 7200), + ) + dist.barrier() + setup_for_distributed(args.rank == 0) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + +def remove_full_stop(description_list): + ret_list = [] + for descript in description_list: + if descript[-1] == '.': + descript = descript[:-1] # remove '.' + ret_list.append(descript) + return ret_list + +def num_of_words(text): + return len(text.split(' ')) + +def create_queries_and_maps(labels, label_list, tokenizer, additional_labels=None, cfg=None, center_nouns_length = None, override_tokens_positive = None): + + # Clean label list + label_list = [clean_name(i) for i in label_list] + # Form the query and get the mapping + tokens_positive = [] + start_i = 0 + end_i = 0 + objects_query = "Detect: " + #objects_query = "" + + prefix_length = len(objects_query) + # sep between tokens, follow training + separation_tokens = cfg.DATASETS.SEPARATION_TOKENS + + caption_prompt = cfg.DATASETS.CAPTION_PROMPT + use_caption_prompt = cfg.DATASETS.USE_CAPTION_PROMPT and caption_prompt is not None + for _index, label in enumerate(label_list): + if use_caption_prompt: + objects_query += caption_prompt[_index]["prefix"] + + start_i = len(objects_query) + + if use_caption_prompt: + objects_query += caption_prompt[_index]["name"] + else: + objects_query += label + + if "a kind of " in label: + end_i = len(label.split(",")[0]) + start_i + else: + end_i = len(objects_query) + tokens_positive.append([(start_i, end_i)]) # Every label has a [(start, end)] + + if use_caption_prompt: + objects_query += caption_prompt[_index]["suffix"] + + if _index != len(label_list) - 1: + objects_query += separation_tokens + + if additional_labels is not None: + objects_query += separation_tokens + for _index, label in enumerate(additional_labels): + objects_query += label + if _index != len(additional_labels) - 1: + objects_query += separation_tokens + + # print(objects_query) + + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + tokenized = tokenizer(objects_query, return_tensors="pt") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenized = tokenizer(objects_query, return_tensors="pt") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + tokenized = tokenizer( + objects_query, max_length=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN, truncation=True, return_tensors="pt" + ) + else: + raise NotImplementedError + if override_tokens_positive is not None: + new_tokens_positive = [] + for override in override_tokens_positive: + new_tokens_positive.append((override[0] + prefix_length, override[1] + prefix_length)) + tokens_positive = [new_tokens_positive] # this is because we only have one label + + # Create the mapping between tokenized sentence and the original label + # if one_hot: + # positive_map_token_to_label, positive_map_label_to_token = create_one_hot_dict(labels, no_minus_one_for_one_hot=cfg.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT) + # else: + positive_map_token_to_label, positive_map_label_to_token = create_positive_dict( + tokenized, tokens_positive, labels=labels + ) # from token position to original label + return objects_query, positive_map_label_to_token + +def main(): + parser = argparse.ArgumentParser(description="PyTorch Detection to Grounding Inference") + parser.add_argument( + "--config-file", + default="configs/pretrain/glip_Swin_T_O365_GoldG.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument( + "--weight", + default=None, + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--local_rank", type=int, default=0) + parser.add_argument( + "opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER + ) + parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes") + parser.add_argument("--dist-url", default="env://", help="url used to set up distributed training") + + parser.add_argument("--task_config", default=None) + parser.add_argument("--chunk_size", default=20, type=int, help="number of descriptions each time") + parser.add_argument("--threshold", default=None, type=float, help="number of boxes stored in each run") + parser.add_argument("--topk_per_eval", default=None, type=int, help="number of boxes stored in each run") + parser.add_argument("--group_query", action="store_true", help="group query") + parser.add_argument("--noun_phrase_file", default=None, type=str, help="noun phrase file") + + args = parser.parse_args() + + num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1 + distributed = num_gpus > 1 + + if distributed: + # torch.cuda.set_device(args.local_rank) + # torch.distributed.init_process_group( + # backend="nccl", init_method="env://" + # ) + init_distributed_mode(args) + print("Passed distributed init") + + cfg.local_rank = args.local_rank + cfg.num_gpus = num_gpus + + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.freeze() + + log_dir = cfg.OUTPUT_DIR + if args.weight: + log_dir = os.path.join(log_dir, "eval", os.path.splitext(os.path.basename(args.weight))[0]) + if log_dir: + mkdir(log_dir) + + logger = setup_logger("maskrcnn_benchmark", log_dir, get_rank()) + logger.info(args) + logger.info("Using {} GPUs".format(num_gpus)) + logger.info(cfg) + + # logger.info("Collecting env info (might take some time)") + # logger.info("\n" + collect_env_info()) + + device = cfg.MODEL.DEVICE + cpu_device = torch.device("cpu") + + model = build_detection_model(cfg) + model.to(device) + # we currently disable this + # params, flops = get_model_complexity_info(model, + # (3, cfg.INPUT.MAX_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST), + # input_constructor=lambda x: {'images': [torch.rand(x).cuda()]}) + # print("FLOPs: {}, #Parameter: {}".format(params, flops)) + + checkpointer = DetectronCheckpointer(cfg, model, save_dir=cfg.OUTPUT_DIR) + if args.weight: + _ = checkpointer.load(args.weight, force=True) + else: + _ = checkpointer.load(cfg.MODEL.WEIGHT) + + if args.weight: + weight_iter = os.path.splitext(os.path.basename(args.weight))[0].split("_")[-1] + try: + weight_iter = int(weight_iter) + except: + weight_iter = 1 + else: + weight_iter = 1 + + # get the wandb name + train_wandb_name = os.path.basename(cfg.OUTPUT_DIR) + eval_wandb_name = train_wandb_name + "_eval" + "_Fixed{}_Chunk{}".format(not cfg.DATASETS.LVIS_USE_NORMAL_AP, cfg.TEST.CHUNKED_EVALUATION) + if is_main_process() and train_wandb_name != "__test__": + api = wandb.Api() + runs = api.runs('haroldli/language_det_eval') + matched_run = None + history = [] + exclude_keys = ['_runtime', '_timestamp'] + for run in runs: + if run.name == eval_wandb_name and str(run._state) == "finished": + print("run found", run.name) + print(run.summary) + matched_run = run + run_his = matched_run.scan_history() + #print([len(i) for i in run_his]) + + for stat in run_his: + stat_i = {k: v for k, v in stat.items() if k not in exclude_keys and v is not None} + if len(stat_i) > 1: + history.append(stat_i) + #matched_run.delete() + break # only update one + wandb_run = wandb.init( + project = 'language_det_eval', + job_type = 'evaluate', + name = eval_wandb_name, + ) + #pprint(history) + # exclude_keys = ['_step', '_runtime', '_timestamp'] + # for stat in history: + # wandb.log( + # {k: v for k, v in stat.items() if k not in exclude_keys}, + # step = stat['_step'], + # ) + else: + wandb_run = None + history = None + print("weight_iter: ", weight_iter) + print("train_wandb_name: ", train_wandb_name) + print("eval_wandb_name: ", eval_wandb_name) + + # build tokenizer to process data + # tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "bert-base-uncased": + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "roberta-base": + tokenizer = AutoTokenizer.from_pretrained("roberta-base") + elif cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip": + if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS: + tokenizer = CLIPTokenizerFast.from_pretrained( + "openai/clip-vit-base-patch32", from_slow=True, mask_token="ðŁĴij" + ) + else: + tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True) + else: + tokenizer = None + raise NotImplementedError + + ### inference & evaluation + topk_per_eval = args.topk_per_eval + threshold = args.threshold + + model.eval() + + chunk_size = args.chunk_size # num of texts each time + if cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD": + class_plus = 1 + else: + class_plus = 0 + + task_config = args.task_config + assert task_config is not None, "task_config should be assigned" + cfg_ = cfg.clone() + cfg_.defrost() + cfg_.merge_from_file(task_config) + cfg_.merge_from_list(args.opts) + + dataset_name = cfg_.DATASETS.TEST[0] + output_folder = os.path.join(log_dir, "inference", dataset_name) + if not os.path.exists(output_folder): + mkdir(output_folder) + + data_loaders_val = make_data_loader(cfg_, is_train=False, is_distributed=distributed) + _iterator = tqdm(data_loaders_val[0]) # only for the first test set + + predictions = [] + + # adhoclly + # if "coco" in cfg_.DATASETS.TEST[0]: + # gt_json = 'DATASET/omnilabel/dataset_all_val_v0.1.3_coco.json' + # elif "oi_v5" in cfg_.DATASETS.TEST[0]: + # gt_json = 'DATASET/omnilabel/dataset_all_val_v0.1.3_openimagesv5.json' + # elif "oi_v6" in cfg_.DATASETS.TEST[0]: + # gt_json = 'DATASET/omnilabel/dataset_all_val_v0.1.3_openimagesv6.json' + # else: + # assert(0) + + # omni_label = OmniLabel(path_json=gt_json) + if args.noun_phrase_file is not None: + try: + noun_phrase = json.load(open(args.noun_phrase_file)) + except: + noun_phrase = {} + print("No noun phrase file found, will generate one") + llm = LLM(version="chat", prompt_file="tools/data_process/prompts/noun.v1.txt", temp=0.0) + else: + noun_phrase = {} + # stats + pos_rates = [] + query_length = [] + + all_info = [] + + for iidx, batch in enumerate(_iterator): + images, targets, image_ids, *_ = batch + # import ipdb + # ipdb.set_trace() + images = images.to(device) + text_queries = targets[0].get_field('inference_obj_descriptions') + text_queries_ids = targets[0].get_field("inference_obj_description_ids") + image_size = targets[0].size + image_id = image_ids[0] + # pdb.set_trace() + #print(data_loaders_val[0].dataset.dataset_dicts[iidx]) + #all_info.append(data_loaders_val[0].dataset.dataset_dicts[iidx]) + # get the positive label if there is one + try: + positive_info = omni_label.get_image_sample(image_id) + positive_instances = positive_info['instances'] + positive_labels = [] + for i in positive_instances: positive_labels.extend(i['description_ids']) + positive_labels = list(set(positive_labels)) + except: + positive_labels = None + + des_id_start = 0 + # rearrange the queries + query_indexes = [i for i in range(len(text_queries_ids)) if num_of_words(text_queries[i]) > 2] + cat_indexes = [i for i in range(len(text_queries_ids)) if num_of_words(text_queries[i]) <= 2] + # rearrange the queries + if args.group_query: + text_queries_ids = [text_queries_ids[i] for i in query_indexes] + [text_queries_ids[i] for i in cat_indexes] + text_queries = [text_queries[i] for i in query_indexes] + [text_queries[i] for i in cat_indexes] + + + while des_id_start < len(text_queries_ids): + # sinlge descriptions each time + if args.group_query: + if num_of_words(text_queries[des_id_start]) > 2: + description_list = remove_full_stop(text_queries[des_id_start:des_id_start+8]) + description_id_list = text_queries_ids[des_id_start:des_id_start+8] + des_id_start += 8 + else: + description_list = remove_full_stop(text_queries[des_id_start:des_id_start+chunk_size]) + description_id_list = text_queries_ids[des_id_start:des_id_start+chunk_size] + des_id_start += chunk_size + else: + if num_of_words(text_queries[des_id_start]) > 2: + _det_phrase = True + description_list = remove_full_stop([text_queries[des_id_start]]) + description_id_list = [text_queries_ids[des_id_start]] + des_id_start += 1 + else: + _det_phrase = False + description_list = remove_full_stop(text_queries[des_id_start:des_id_start+chunk_size]) + description_id_list = text_queries_ids[des_id_start:des_id_start+chunk_size] + des_id_start += chunk_size + # create postive map, always use continuous labels starting from 1 + continue_labels = np.arange(0, chunk_size) + class_plus + if _det_phrase and args.noun_phrase_file is not None: + # try to find the centern noun phrase + + center_noun = noun_phrase.get(description_list[0], None) + if center_noun is None: + center_noun = llm(description_list[0]) + if len(center_noun) == 0: + center_noun = description_list[0] # failed case + noun_phrase[description_list[0]] = center_noun + start = description_list[0].lower().find(center_noun.lower()) + end = start + len(center_noun) + override_tokens_positive = [(start, end)] + print(description_list[0], center_noun, override_tokens_positive) + cur_queries, positive_map_label_to_token = create_queries_and_maps(continue_labels, description_list, tokenizer, cfg=cfg, override_tokens_positive=override_tokens_positive) + else: + cur_queries, positive_map_label_to_token = create_queries_and_maps(continue_labels, description_list, tokenizer, cfg=cfg) + + set_description_id_list = set(description_id_list) + # intersection between positive labels and current description ids + if positive_labels is not None: + pos_rate = len(set_description_id_list.intersection(set(positive_labels))) / len(set_description_id_list) + pos_rates.append(pos_rate) + query_length.append(len(set_description_id_list)) + + # print(cur_queries) + with torch.no_grad(): + output = model(images, captions=[cur_queries], positive_map=positive_map_label_to_token) + output = output[0].to(cpu_device).convert(mode="xywh") + output = output.resize(image_size) # to the oringinal scale + # print(output) + # import ipdb + # ipdb.set_trace() + # thresolding + if threshold is not None: + scores = output.get_field('scores') + output = output[scores > threshold] + # sorted by scores + if topk_per_eval is not None: + scores = output.get_field('scores') + _, sortIndices = scores.sort(descending=True) + output = output[sortIndices] + # topk + output = output[:topk_per_eval] + + # map continuous id to description id + cont_ids_2_descript_ids = {i:v for i, v in enumerate(description_id_list)} + pred_boxes = output.bbox + pred_labels = output.get_field('labels') - class_plus # continuous ids, starting from 0 + pred_scores = output.get_field('scores') + + # convert continuous id to description id + for box_idx, box in enumerate(pred_boxes): + predictions.append({ + "image_id": image_id, + "bbox": box.cpu().tolist(), + "description_ids": [cont_ids_2_descript_ids[pred_labels[box_idx].item()]], + "scores": [pred_scores[box_idx].item()], + }) + + #print("pos_rate: %.2f"%(np.mean(pos_rates)), pos_rates) + #print("query_length: %.2f"%(np.mean(query_length)), query_length) + # draw a histogram of pos_rate + plt.hist(pos_rates, bins=10) + plt.savefig(os.path.join(output_folder, "pos_rate.png")) + plt.close() + if args.noun_phrase_file is not None: + with open(args.noun_phrase_file, "w") as f: + json.dump(noun_phrase, f, indent=4) + + # collect predictions from all GPUs + synchronize() + all_predictions = all_gather(predictions) + all_predictions = list(itertools.chain(*all_predictions)) + if not is_main_process(): + return + + + result_save_json = "%s_results.json"%(dataset_name) + results_path = os.path.join(output_folder, result_save_json) + print('Saving to', results_path) + json.dump(all_predictions, open(results_path, 'w')) + + from maskrcnn_benchmark.config.paths_catalog import DatasetCatalog + datasetMeta = DatasetCatalog.get(dataset_name) + gt_path_json = datasetMeta['args']['ann_file'] + # import ipdb + # ipdb.set_trace() + # evaluation + gt = OmniLabel(gt_path_json) # load ground truth dataset + dt = gt.load_res(results_path) # load prediction results + ole = OmniLabelEval(gt, dt) + # ole.params.resThrs = ... # set evaluation parameters as desired + ole.evaluate() + ole.accumulate() + score = ole.summarize() + # OUTPUTS/GLIP_MODEL17/eval/model_0270000/inference/omnilabel_val/omnilabel_val_results.json + #with open("tools/files/omnilabel_coco.json", "a") as f: + # json.dump(all_info, f) + + if is_main_process(): + if wandb_run is not None: + # + dataset_name = cfg.DATASETS.TEST[0] + write_to_wandb_log(score, dataset_name, weight_iter, history) + + with open("{}/detailed.json".format(output_folder), "w") as f: + json.dump(score, f) + wandb_run.save("{}/detailed.json".format(output_folder)) + print(score) + +def write_to_wandb_log(score, dataset_name, weight_iter, history): + all_results = defaultdict(dict) + exclude_keys = ['_step', '_runtime', '_timestamp'] + if history is not None: + for stat in history: + all_results[stat['_step']].update({k: v for k, v in stat.items() if k not in exclude_keys}) + + result_dict = {} + for score_i in score: + if score_i["metric"]['metric'] == "AP" and score_i["metric"]['iou'] == "0.50:0.95" and score_i["metric"]['area'] == "all": + result_dict[f"{dataset_name}_AP_{score_i['metric']['description']}"] = score_i['value'] + #wandb.log({f"{dataset_name}_mAP_all": mAP_all, f"{dataset_name}_mAP_rare": mAP_rare, f"{dataset_name}_mAP_common": mAP_common, f"{dataset_name}_mAP_frequent": mAP_frequent}, step = weight_iter) + all_results[weight_iter].update(result_dict) + + # sort all results + max_key = max(all_results.keys()) + for i in range(max_key + 1): + if i in all_results: + wandb.log(all_results[i], step = i) + else: + wandb.log({}, step = i) + # for k in sorted(all_results.keys()): + # # need to do consecutive logging + # wandb.log(all_results[k], step = k) + + +if __name__ == "__main__": + main() + + +''' +from omnilabeltools import OmniLabel, OmniLabelEval + +gt = OmniLabel('DATASET/omnilabel/dataset_all_val_v0.1.3_openimagesv5.json') # load ground truth dataset +dt = gt.load_res("OUTPUTS/GLIP_MODEL17/eval/model_0270000/inference/omnilabel_val/omnilabel_val_results.json") # load prediction results +ole = OmniLabelEval(gt, dt) +ole.evaluate() +ole.accumulate() +ole.summarize() + +gt = OmniLabel('DATASET/omnilabel/dataset_all_val_v0.1.3_coco.json') # load ground truth dataset +dt = gt.load_res("OUTPUTS/GLIP_MODEL17/eval/model_0270000/inference/omnilabel_val/omnilabel_val_results.json") + +gt = OmniLabel('DATASET/omnilabel/dataset_all_val_v0.1.3_object365.json') # load ground truth dataset +dt = gt.load_res("OUTPUTS/GLIP_MODEL17/eval/model_0270000/inference/omnilabel_val/omnilabel_val_results.json") + +''' diff --git a/tools/train_net.py b/tools/train_net.py new file mode 100644 index 0000000000000000000000000000000000000000..296a746fa3f8237b2f756e1f9d96cd2433172fd6 --- /dev/null +++ b/tools/train_net.py @@ -0,0 +1,279 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. +r""" +Basic training script for PyTorch +""" + +# Set up custom environment before nearly anything else is imported +# NOTE: this should be the first import (no not reorder) +from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip + +import argparse +import os + +import torch +from maskrcnn_benchmark.config import cfg, try_to_find +from maskrcnn_benchmark.data import make_data_loader +from maskrcnn_benchmark.solver import make_lr_scheduler +from maskrcnn_benchmark.solver import make_optimizer +from maskrcnn_benchmark.engine.inference import inference +from maskrcnn_benchmark.engine.trainer import do_train +from maskrcnn_benchmark.modeling.detector import build_detection_model +from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer +from maskrcnn_benchmark.utils.collect_env import collect_env_info +from maskrcnn_benchmark.utils.comm import get_world_size, all_gather, is_main_process, broadcast_data, get_rank, synchronize +from maskrcnn_benchmark.utils.imports import import_file +from maskrcnn_benchmark.utils.logger import setup_logger +from maskrcnn_benchmark.utils.metric_logger import MetricLogger, TensorboardLogger +from maskrcnn_benchmark.utils.miscellaneous import mkdir, save_config +import numpy as np +import random +import pdb, wandb +from maskrcnn_benchmark.utils.amp import autocast, GradScaler + + +def train(cfg, local_rank, distributed, use_tensorboard=False, use_wandb=False): + model = build_detection_model(cfg) + device = torch.device(cfg.MODEL.DEVICE) + model.to(device) + + if cfg.MODEL.BACKBONE.RESET_BN: + for name, param in model.named_buffers(): + if "running_mean" in name: + torch.nn.init.constant_(param, 0) + if "running_var" in name: + torch.nn.init.constant_(param, 1) + + if cfg.SOLVER.GRAD_CLIP > 0: + clip_value = cfg.SOLVER.GRAD_CLIP + for p in filter(lambda p: p.grad is not None, model.parameters()): + p.register_hook(lambda grad: torch.clamp(grad, -clip_value, clip_value)) + + data_loader = make_data_loader( + cfg, + is_train=True, + is_distributed=distributed, + start_iter=0, # Sample data from resume is disabled, due to the conflict with max_epoch + ) + + if cfg.TEST.DURING_TRAINING or cfg.SOLVER.USE_AUTOSTEP: + data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed) + data_loaders_val = data_loaders_val[0] + else: + data_loaders_val = None + + if cfg.MODEL.BACKBONE.FREEZE: + for p in model.backbone.body.parameters(): + p.requires_grad = False + + if cfg.MODEL.LANGUAGE_BACKBONE.FREEZE: + print("LANGUAGE_BACKBONE FROZEN.") + for p in model.language_backbone.body.parameters(): + p.requires_grad = False + + if cfg.MODEL.FPN.FREEZE: + for p in model.backbone.fpn.parameters(): + p.requires_grad = False + if cfg.MODEL.RPN.FREEZE: + for p in model.rpn.parameters(): + p.requires_grad = False + + # if cfg.SOLVER.PROMPT_PROBING_LEVEL != -1: + # if cfg.SOLVER.PROMPT_PROBING_LEVEL == 1: + # for p in model.parameters(): + # p.requires_grad = False + + # for p in model.language_backbone.body.parameters(): + # p.requires_grad = True + + # for name, p in model.named_parameters(): + # if p.requires_grad: + # print(name, " : Not Frozen") + # else: + # print(name, " : Frozen") + # else: + # assert(0) + + optimizer = make_optimizer(cfg, model) + scheduler = make_lr_scheduler(cfg, optimizer) + + if distributed: + model = torch.nn.parallel.DistributedDataParallel( + model, + device_ids=[local_rank], + output_device=local_rank, + broadcast_buffers=cfg.MODEL.BACKBONE.USE_BN, + find_unused_parameters=cfg.SOLVER.FIND_UNUSED_PARAMETERS, + ) + + arguments = {} + arguments["iteration"] = 0 + + output_dir = cfg.OUTPUT_DIR + + save_to_disk = get_rank() == 0 + checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler, output_dir, save_to_disk) + extra_checkpoint_data = checkpointer.load(try_to_find(cfg.MODEL.WEIGHT), skip_scheduler = cfg.SOLVER.RESUME_SKIP_SCHEDULE) + arguments.update(extra_checkpoint_data) + + # For full model finetuning + # arguments["iteration"] = 0 + # optimizer = make_optimizer(cfg, model) + # scheduler = make_lr_scheduler(cfg, optimizer) + + checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD + + if use_tensorboard: + meters = TensorboardLogger(log_dir=cfg.OUTPUT_DIR, start_iter=arguments["iteration"], delimiter=" ") + else: + meters = MetricLogger(delimiter=" ") + + do_train( + cfg, + model, + data_loader, + optimizer, + scheduler, + checkpointer, + device, + checkpoint_period, + arguments, + data_loaders_val, + meters, + use_wandb = use_wandb + ) + + return model + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop("force", False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def main(): + parser = argparse.ArgumentParser(description="PyTorch Object Detection Training") + parser.add_argument( + "--config-file", + default="", + metavar="FILE", + help="path to config file", + type=str, + ) + parser.add_argument("--local_rank", type=int, default=0) + parser.add_argument( + "--skip-test", + dest="skip_test", + help="Do not test the final model", + action="store_true", + ) + + parser.add_argument( + "--use-tensorboard", + dest="use_tensorboard", + help="Use tensorboardX logger (Requires tensorboardX installed)", + action="store_true", + default=False, + ) + + parser.add_argument( + "opts", + help="Modify config options using the command-line", + default=None, + nargs=argparse.REMAINDER, + ) + + parser.add_argument("--save_original_config", action="store_true") + parser.add_argument("--disable_output_distributed", action="store_true") + parser.add_argument("--debug_nan_checkpoint", default=None) + parser.add_argument("--override_output_dir", default=None) + parser.add_argument("--wandb_name", default="__test__") + parser.add_argument("--use_wandb", action="store_true") + + args = parser.parse_args() + + num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1 + args.distributed = num_gpus > 1 + + if args.distributed: + import datetime + + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group(backend="nccl", init_method="env://", timeout=datetime.timedelta(0, 7200)) + + if args.disable_output_distributed: + setup_for_distributed(args.local_rank <= 0) + + cfg.local_rank = args.local_rank + cfg.num_gpus = num_gpus + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # specify output dir for models + cfg.OUTPUT_DIR = "OUTPUTS/" + args.wandb_name + if is_main_process(): + mkdir(cfg.OUTPUT_DIR) + + if args.wandb_name != "__test__" and args.use_wandb: + if is_main_process(): + run = wandb.init( + project = 'lang_det', + job_type = 'train_model', + name = args.wandb_name, + ) + with open(os.path.join(cfg.OUTPUT_DIR, 'wandb_run_id.txt'), 'w') as f: + f.write(run.id) + + if args.override_output_dir: + cfg.OUTPUT_DIR = args.override_output_dir + cfg.freeze() + + seed = cfg.SOLVER.SEED + args.local_rank + torch.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + output_dir = cfg.OUTPUT_DIR + if output_dir: + mkdir(output_dir) + + logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank()) + logger.info(args) + logger.info("Using {} GPUs".format(num_gpus)) + + # logger.info("Collecting env info (might take some time)") + # logger.info("\n" + collect_env_info()) + + logger.info("Loaded configuration file {}".format(args.config_file)) + with open(args.config_file, "r") as cf: + config_str = "\n" + cf.read() + logger.info(config_str) + logger.info("Running with config:\n{}".format(cfg)) + + output_config_path = os.path.join(cfg.OUTPUT_DIR, 'config.yml') + logger.info("Saving config into: {}".format(output_config_path)) + # save overloaded model config in the output directory + if args.save_original_config: + import shutil + shutil.copy(args.config_file, os.path.join(cfg.OUTPUT_DIR, "config_original.yml")) + + save_config(cfg, output_config_path) + + + model = train( + cfg=cfg, + local_rank=args.local_rank, + distributed=args.distributed, + use_tensorboard=args.use_tensorboard, + use_wandb=args.wandb_name != "__test__" and args.use_wandb) + +if __name__ == "__main__": + main()